supervised by dr. rana ejaz ali khan associate professor

1

DETERMINANTS AND IMPACT OF URBAN

INFORMAL SECTOR’S GROWTH ON DEVELOPMENT

OF SOUTHERN PUNJAB, (PAKISTAN)

By

Durdana Qaiser Gillani

Roll No. 01

Supervised by

Dr. Rana Ejaz Ali Khan

Associate Professor &

Chairman Department of Economics

A thesis

Submitted to

Department of Economics

The Islamia University of Bahawalpur for the partial Fulfillment of

the Degree of Doctorate of Philosophy in Economics

Session: 2008-11


The Islamia University of Bahawalpur

Pakistan

3

CERTIFICATE

It is hereby certified that wok presented by Durdana Qaiser Gillani D/O Syed Qaiser

Abbas Gillani in the thesis entitled “Determinants and Impact of Urban Informal

Sector‟s Growth on Development of Southern Punjab (Pakistan)” has been

successfully defended and is accepted in its present form as satisfying the requirement

for the degree of Doctor of Philosophy in the Economics in the Department and

Faculty the Islamia University of Bahawalpur.

Dr. RANA EJAZ ALI

Associate Professor of Economics

Supervisor


The Islamia University of Bahawalpur

4

Dedication

To the humanity of this universe without any discrimination of

gender, cast, creed and religion. To all those who have chastity of

heart and mind, potential to confront the challenges with

transparency, courage to eradicate hypocrisy, urge to survive

through thick and thin and dignified attitude of paying back, bad

with good.

5

Acknowledgement

I have pearls in my eyes to admire the blessings of compassionate and

omnipotent ALLAH because the words are bound, knowledge is limited and time is

short to express His dignity. It is one of the infinite blessings of ALLAH that He

bestowed me with the potential and ability to complete my research work and explore

a tiny part of ocean of knowledge of world.

I would like to thanks to my supervisor Dr. Rana Ejaz Ali Khan (Associate

Professor and Chairman Department of Economic), The Islamic University,

Bahawalpur (IUB) for his kind guidance during my research work.

I would like to thanks Dr. Karamat Ali (Professor of Economic), Ex

Chairman, Department of Economics in The Islamia University of Bahawalpur for his

valuable guidance. I would like to thanks to Dr. Abid Aman Burki (Professor of

Economics), Lahore University of management Sciences for his kind and humble

cooperation.

I am indebted to Dr. Touseef Azid (Professor of Economics), Bahauddin

Zakariya University Multan for his nice cooperation. I would like to thanks Dr.

Shahnawaz Malik (Professor of Economics), Bahauddin Zakariya University Multan

for his nice encouragement.

I offer my humble thanks from the core of my heart to family members

especially my parents and few friends (my valuable assets) who supported me through

out, motivated and kept involved with their active assistance.

Durdana Qaiser Gillani

6

TABLE OF CONTENTS

Contents Page#

Chapter 1: Introduction

1.1 Statement of the Problem 01

1.2 Objectives of the Study 07

1.3 Material and Methods 08

1.4 Organization of the Study 09

Chapter 2: Urban Informal Sector Growth and Development

2.1 Introduction 11

2.2 Urban Informal Sector: An Overview 12

2.3 An Overview of Pakistan‟s Growth and Development 24

2.4 Population, Labour Force and Employment Pattern in Pakistan 30

Economy

2.4.1 Labour Force Participation Rates in Pakistan 32

2.4.2 The Formal Sector, Informal Sector and 33

Employment Trends or Patterns

2.4.3 Pattern of Employment and Hours of Work 38

2.5 Unemployment Situation 39

2.6 Urbanization, Migration and Pakistan Economy 40

2.7 Poverty and Measures 41

2.8 Women and Urban Informal Sector 42

2.9 Concept of Development 43

2.9.1 Three Core Values of Development 44

2.9.2 The Objectives of Development 45

2.10 Basic Indicators of Development 46

2.10.1 Human Indicators and Development 46

2.10.2 Education Training and Employment Situation 49

2.10.3 Literacy Rates 50

2.11 Socio-Indicators and Development 50

7

2.12 Concluding Remarks 51

Chapter 3: Theoretical Framework

3.1 Introduction 53

3.2 Conceptual Framework 53

3.2.1 Labour Supply and Employment 54

3.3 Neo-Classical theory of Labour Supply Decision 55

3.3.1 Neo-Classical Individual Labour Supply 55

3.3.2 Household Labour Supply 63

3.4 The Basic Theory of Human Capital 65

3.5 Theoretical Approaches Towards Urban Informal Sector 68

3.5.1 Dualistic Labour Market Approach 68

3.5.2 Neo-Liberal Approach 69

3.5.3 Structural Articulation Approach 70

3.6 Conclusion 71

Chapter 4: Literature Review

4.1 Introduction 72

4.2 Informal Employment and Classic Theories of Growth and 72

Development

4.3 Review of Empirical Evidence and Urban Informal Sector 77

4.4 Literature Review of Urban Informal Sector in Pakistan 96


Chapter 5: Measuring Urban Informal Sector: Some Basic Issues

5.1 Introduction 105

5.2 Profile of the Study Areas 105

5.2.1 Bahawalpur Division 106

5.2.2 Multan Division 109

5.2.3 Dera Ghazi Khan Division 112

5.3 Sources of Data and Sampling Design 115

5.4 Survey Limitations 118

8

5.5 Determinants of Informal and Formal Sector Employment in 119

Urban Areas

5.5.1 Age of the Participants 119

5.5.2 Education 120

5.5.3 Gender 122

5.5.4 Marital Status 123

5.5.5 Formal Training 124

5.5.6 Parents‟ Educational Status 125

5.5.7 Household Size 126

5.5.8 Family Setup 127

5.5.9 Dependency Ratio 127

5.5.10 Number of Children 128

5.5.11 Number of Male Adolescents 128

5.5.12 Number of Female Adolescents 129

5.5.13 Spouse Participation in Economic Activities 130

5.5.14 Household Value of Assets 130

5.5.15 Rural-Urban Migration 131

5.5.16 Working Hours 132

5.6 Model and Methodological Issues 132

5.6.1 A Descriptive Data Analysis 132

5.6.2 A Multivariate Analysis of Urban Informal Sector Employment 132

5.6.2.1 A Binary Logit Model 134

5.6.2.2 Earnings Functions 136

5.6.3 Specification of Employment Model 137

5.6.3.1 General Model 137

5.6.3.2 Employment Model with Complete Years 138

of Education

5.6.3.3 Employment Model with Different Levels of 138

Education

5.6.3.4 Earnings Functions 139

5.6.3.5 Earnings Function with Different Levels of

Education 139


Chapter 6: Descriptive Analysis of the Urban Informal and Formal

9

Sector in Southern Punjab, Pakistan


6.2 Pair wise Correlations Matrix 144

6.3 Urban Informal and Formal Sector Employment: An Elementary 146

Analysis

6.3.1 Age Group and Urban Informal and Formal Sector Employment 146

6.3.2 Education and Urban Informal and Formal Sector Employment 147

6.3.3 Marital Status and Urban Informal and Formal Sector 148

Employment

6.3.4 Sex and Urban Informal and Formal Sector Employment 149

6.3.5 Formal Training and Urban Informal and Formal Sector 150

Employment

6.3.6 Father‟s Educational Status and Urban Informal and Formal 150

Sector Employment

6.3.7 Mother‟s Education and Urban Informal and Formal Sector 151

Employment

6.3.8 Size of Household and Urban Informal and Formal Sector 152

Employment

6.3.9 Number of Dependents and Urban Informal and Formal Sector 154

Employment

6.3.10 Family Setup and Urban Informal and Formal Sector 154

Employment

6.3.11 Number of Children and Urban Informal and Formal Sector 155

Employment

6.3.12 Male Adolescents and Urban Informal and Formal Sector 156

Employment

6.3.13 Female Adolescents and Urban Informal and Formal 157

Sector Employment

6.3.14 Spouse Working and Urban Informal and Formal Sector 158

Employment

6.3.15 Rural-Urban Migration and Urban Informal and Formal Sector 159

Employment

6.3.16 Employment Status and Urban Informal and Formal Sector 160

10

Employment

6.3.17 Sector of Employment and Urban Informal Sector Employment 161

6.3.18 Working Hours and Urban Informal Sector Employment 162

6.4 Descriptive Analysis of Urban Male Informal and Formal Sector 163

In Southern Punjab, Pakistan

6.4.1 Age Group and Urban Male Informal and Formal Sector 163

Employment

6.4.2 Education and Urban Male Informal and Formal Sector 165

Employment

6.4.3 Marital Status and Urban Male Informal and Formal Sector 166

Employment

6.4.4 Formal Training and Urban Male Informal and Formal Sector 166

Employment

6.4.5 Father‟s Educational Status and Urban Male Informal and Formal 167

Sector Employment

6.4.6 Mother‟s Education and Urban Male Informal and Formal Sector 168

Employment

6.4.7 Size of Household and Urban Male Informal and Formal Sector 169

Employment

6.4.8 Number of Dependents and Urban Male Informal and Formal 169

Sector Employment

6.4.9 Family Setup and Urban Male Informal and Formal Sector 171

Employment

6.4.10 Number of Children and Urban Male Informal and Formal Sector 171

Employment

6.4.11 Male Adolescents and Urban Male Informal and Formal 172

Sector Employment

6.4.12 Female Adolescents and Urban Male Informal and Formal 173

Sector Employment

6.4.13 Working Spouse and Urban Male Informal and Formal Sector 174

Employment

11

6.4.14 Rural-Urban Migration and Urban Male Informal & Formal 175

Sector Employment

6.4.15 Employment Status and Urban Male Informal Sector 176

Employment

6.4.16 Sector of Employment and Urban Male Informal and Formal 177

Sector Employment

6.4.17 Working Hours and Urban Male Informal Sector Employment 178

6.5 Descriptive Analysis of Urban Female Informal and Formal Sector 179

6.5.1 Age Group and Urban Female Informal and Formal Sector 179

Employment

6.5.2 Education and Urban Female Informal and Formal Sector 180

Employment

6.5.3 Marital Status and Urban Female Informal and Formal Sector 181

Employment

6.5.4 Formal Training and Urban Female Informal and Formal Sector 183

Employment

6.5.5 Father‟s Education Status and Urban Female Informal and 184

Formal Sector Employment

6.5.6 Mother‟s Education and Urban Female Informal and Formal 185

Sector Employment

6.5.7 Size of Household and Urban Female Informal and Formal 186

Sector Employment

6.5.8 Number of Dependents and Urban Female Informal and Formal 187

Sector Employment

6.5.9 Family Setup and Urban Female Informal and Formal Sector 188

Employment

6.5.10 Number of Children and Urban Female Informal and Formal 189

Sector Employment

6.5.11 Male Adolescents and Urban Female Informal and Formal

190

Sector Employment

6.5.12 Female Adolescents and Urban Female Informal and Formal

191

12

Sector Employment

6.5.13 Working Spouse and Urban Female Informal and Formal Sector 192

Employment

6.5.14 Rural-Urban Migration and Urban Female Informal and Formal 193

Sector Employment

6.5.15 Employment Status and Urban Female Informal and Formal 194

Sector Employment

6.5.16 Sector of Employment and Urban Female Informal Sector 195

Employment

6.5.17 Working Hours and Urban Female Informal Sector Employment 195


Chapter 7: Determinants of Urban Informal Sector Employment: An Analysis


7.2 Estimates of Binary Logit Model in Southern Punjab 198

7.3 Estimates of Binary Logit Model in District Bahawalpur 208

7.4 Estimates of Binary Logit Model in District Multan 216

7.5 Estimates of Binary Logit Model in District Dera Ghazi Khan 224


Chapter 8: Earnings Determinants, Development and Urban Informal Sector:

An Analysis


8.2 Estimates of Earning Functions of the Participants in Urban Informal 235

Sector in Southern Punjab

8.2.1 Estimates of Earnings Functions of the Participants in Urban 239

Informal Sector in District Bahawalpur


Informal Sector in District Multan


Informal Sector in District Dera Ghazi Khan

13

8.3 Human Development and Urban Informal Sector 253

8.3.1 Development and Urban Informal Sector in Southern Punjab 254

8.3.2 Development and Urban Informal Sector in District Bahawalpur 260

8.3.3 Development and Urban Informal Sector in District Multan 264

8.3.4 Development and Urban Informal Sector 268

in District Dera Ghazi Khan


Chapter 9: Gender Employment in Urban Informal Sector: A Comparison


9.2 Binary Logit Estimates of Determinants of Gender Employment 276

and Comparison in Urban Informal Sector in Southern Punjab


and Comparison in Urban Informal Sector in District Bahawalpur


and Comparison in Urban Informal Sector in District Multan


and Comparison in Urban Informal Sector in District Dera Ghazi Khan


Chapter 10: Conclusions & Policy Recommendations 324

REFERENCES 339

APPENDIX A 364

14

LIST OF TABLE

Description Page #

2.1 Growth Performance of Key Components of GDP (% Growth at Constant

Factor Cost)

25

2.2 Sectoral Share of the GDP Growth (Percentage Points) 26

2.3 Structure of Savings and Investment (As Percentage of GDP) 27

2.4 Civilian Labour Force, Employed and Unemployed for Pakistan (Million) 31

2.5 Population, Labour Force and Labour Force Participation (LFP) Rates 32

2.6 Employment Percentage in Informal Sector by Regional Gender 35

2.7 Employment Percentages by Major Industry and Gender in Informal Sector 36

2.8 Informal Sector by Major Occupation and Gender in Percentages 37

2.9 Informal Sector by Employment Status and Gender (%) 38

2.10 Hours of Work by Region and Gender (%) 39

2.11 Unemployment in Million by Gender and Region

2.12 Education and Literacy by Gender of Working Age Population (%)

2.13 Literacy Rates in Pakistan and Provinces

39

49

50

5.1 List of Variables Used in the Informal Sector Employment Equations 141

6.1 Pair Wise Correlation Matrix 145

6.2 Distribution of Respondents by Age Groups 146

6.3 Distribution of Respondents by Education 148

6.4 Distribution of Respondents by Marital Status 148

6.5 Distribution of Respondents by Sex 149

6.6 Distribution of Respondents by Formal Training 150

6.7 Distribution of Respondents by Father‟s Educational Status 151

6.8 Distribution of Respondents by Mother‟s Educational Status 151

6.9 Distribution of Respondents by the Size of Household 153

6.10 Distribution of Respondents by Number of Dependents 154

6.11 Distribution of Respondents by Type of Family System 155

6.12 Distribution of Respondents by Number of Children 156

6.13 Distribution of Respondents by Male Adolescents 158

6.14 Distribution of respondents by Female Adolescents 159

15

6.15 Distribution of Respondents by Spouse Participation in Economic Activities. 159 159

6.16 Distribution of Respondents by Rural-urban Migration 159

6.17 Distribution of Respondents by Employment Status 161

6.18 Distribution of Respondents by Sector of Employment 162

6.19 Distribution of Respondents by Working Hours 162

6.20 Distribution of Male Respondents by Age Groups 164

6.21 Distribution of Male Respondents by Levels of Education 165

6.22 Distribution of Male Respondents by Marital Status 166

6.23 Distribution of Male Respondents by Formal Training 167

6.24 Distribution of Male Respondents by Father‟s Educational Status 167

6.25 Distribution of Male Respondents by Mother‟s Educational Status 168

6.26 Distribution of Male Respondents by the Size of Household 169

6.27 Distribution of Male Respondents by Number of Dependents 170

6.28 Distribution of Male Respondents by Type of Family System 171

6.29 Distribution of Male Respondents by Number of Children 172

6.30 Distribution of Male Respondents by Male Adolescents 173

6.31 Distribution of Male Respondents by Female Adolescents 174

6.32 Distribution of Male Respondents by Spouse Participation 175

6.33 Distribution of Male Respondents by Rural-Urban Migration 176

6.34 Distribution of Male Respondents by Employment Status 177

6.35 Distribution of Male Respondents by Sector of Employment 178

6.36 Distribution of Male Respondents by Working Hours 178

6.37 Distribution of Female Respondents by Age Groups 180

6.38 Distribution of Female Respondents by Education 181

6.39 Distribution of Female Respondents by Marital Status 182

6.40 Distribution of Female Respondents by Formal Training 183

6.41 Distribution of Female Respondents by Father‟s Educational Status 184

6.42 Distribution of Female Respondents by Mother‟s Educational Status 185

6.43 Distribution of Female Respondents by the Size of Household 186

6.44 Distribution of Female Respondents by Number of Dependents 187

6.45 Distribution of Female Respondents by Type of Family System 188

6.46 Distribution of Female Respondents by Number of Children 189

6.47 Distribution of Female Respondents by Male Adolescents 190

16

6.48 Distribution of Female Respondents by Female Adolescents 191

6.49 Distribution of Female Respondents by Working Spouse 192

6.50 Distribution of Female Respondents by Rural-Urban Migration 193

6.51 Distribution of Female Respondents by Employment Status 194

6.52 Distribution of Female Respondents by Sector of Employment 195

6.53 Distribution of Female Respondents by Working Hours 195

7.1 Logit Estimates of Determinants of Urban Informal Sector Employment in

Southern Punjab-Probability of Informal Sector Employed (18-64)

206


Southern Punjab with Different Levels of Education -Probability of Informal

Sector Employed(18-64)

207


District Bahawalpur-Probability of Informal Sector Employed(18-64)

214


District Bahawalpur with Different Levels of Education -Probability of Informal

Sector Employed (18-64)

215

7.5 Logit Estimates of determinants of Urban Informal sector employment in

District Multan-Probability of Informal Sector Employed (18-64)

222


District Multan with Different Levels of Education -Probability of Informal


223


District Dera Ghazi Khan-Probability of Informal Sector Employed (18-64)

230


District Multan with Different Levels of Education -Probability of Informal


231

8.1 Earnings Functions of the Participants in Urban Informal Sector in Southern

Punjab

237

8.2 Earnings Functions of the Participants in Urban Informal Sector in Southern

Punjab with Different Levels of Education

238

8.3 Earnings Functions of the Participants in Urban Informal Sector in District

Bahawalpur

242

8.4 Earnings Functions of the Participants in Urban Informal Sector in District 243

17

Bahawalpur with Different Levels of Education


Multan

247


Multan with Different Levels of Education

248


Dera Ghazi Khan

251


Dera Ghazi Khan with Different Levels of Education

252

8.9 Economic Capital and Urban Informal Sector in Southern Punjab 255

8.10 Human Capital and Urban Informal Sector in Southern Punjab 257

8.11 Socio-cultural Activities and Urban Informal Sector in Southern Punjab 259

8.12 Economic Capital and the Urban Informal Sector in District Bahawalpur 261

8.13 Human Capital and the Urban Informal Sector in District Bahawalpur 262

8.14 Socio-Cultural Activities and the Urban Informal Sector in District

Bahawalpur

264

8.15 Economic Capital and the Urban Informal Sector in District Multan 265

8.16 Human Capital and Urban Informal Sector in District Multan 266

8.17 Socio- Cultural Activities and Urban Informal Sector in District Multan 267

8.18 Economic Capital and Urban Informal Sector in District Dera Ghazi Khan 269

8.19 Human Capital and Urban Informal Sector in District Dera Ghazi Khan 270

8.20 Socio-Cultural Activities and Urban Informal Sector in District Dera Ghazi Khan 271

9.1 Logit Estimates of Determinants of Gender Employment in Urban Informal

Sector in Southern Punjab -Probability of Informal Sector Employed (18-64)

286

18


Sector in Southern Punjab with Different Levels of Education -Probability of

Informal Sector Employed (18-64)

287


Sector in District Bahawalpur-Probability of Informal Sector Employed (18-64)

297


Sector in District Bahawalpur -Probability of Informal Sector Employed (18-64)

298


Sector in District Multan-Probability of Informal Sector Employed(18-64)

308


Sector in District Multan with Different Levels of Education -Probability of

Informal Sector Employed (18-64)

309


Sector in District Dera Ghazi Khan- Probability of Informal Sector Employed

(18-64)

318


Sector in District Dera Ghazi Khan with different Levels of Education -

Probability of Informal Sector Employed(18-64)

319

19

LIST OF FIGURES

Fig # Page #

Indifference Curve and Budget Constraints 56

Income and Substitution Effects in Response to Change in Wage Rate 61

ABSTRACT

20

The present study looks at different aspects of the urban informal sector in three

divisions of Southern Punjab, Pakistan. The current study utilizes primary data from

three divisions of Southern Punjab by conducting household survey during 2012.

Theoretically, this research discusses neo-classical theory of labor supply, human

capital theory and approaches towards the urban informal sector. The sample consists

of 1506 participants of the informal and formal sector in the urban areas of three

districts such as Bahawalpur, Multan and Dera Ghazi Khan. The main focus is the

complete analysis of the socio-economic factors of the informal sector participants

who motivate or determine and enhance the growth potential of the urban informal

sector in Southern Punjab, Pakistan. The present study has analyzed the

characteristics of participants of the formal as well as informal sector employed. A

binary Logit model is used in order to estimate the probability of determinants of

urban informal sector employment of total sample along with the gender comparison

in three divisions. In addition, earning functions are estimated including human

capital variables to see the effect on participants‟ earnings. Moreover, the living

standard of the participants of the urban informal sector has been checked from a

Human Development perspective. Human development indicators (i.e. economic,

human and social capital) are used to gauge the relation between poverty and urban

informal sector employment. The study concludes a positive contribution of urban

informal sector in employment creation, income generation and the development of

the participants in Southern Punjab, Pakistan.

Chapter 1

INTRODUCTION

1.1 Statement of the Problem

21

The research conducted in the era of 1950 and 1960 states that the countries

across the world must pass through the process of development which is considered as

successive stages of economic growth. Primarily; it was a theory of economic

development in which the accurate quantity along with saving, investment, and

foreign aid were included. Moreover; these were necessary to make the developing

nations to proceed proactively with an economic growth path which had been

followed by most of the developed countries. Development has thus become

synonymous with rapid, aggregate economic growth.

The linear stages approach was to a great extent supplanted in the 1970s by

two contending schools of thought. The main idea which concentrated on hypotheses

and patterns of structural change used modern economic theory and statistical analysis

trying to depict the interior procedure of structural change that a typical developing

nation must experience in the event that it is to succeed in producing and maintaining

rapid economic growth. The second, the worldwide dependence revolution was more

radical and more political. It saw underdevelopment regarding universal and local

force connections, institutional and structural financial rigidities, and the subsequent

multiplication of dual economies and societies both inside and among the countries of

the world.

Dependence theories had a tendency to stress external and internal institutional

and political constraints on economic development. Accentuation was set on the

requirement for major new policies to eradicate destitution, to give more expanded

employment opportunities, and to reduce income inequalities. These and other

populist purposes were to be achieved inside the setting of developing economy, yet

the economic development essentially was not given grand status agreed to it by their

linear stages and structural change models (Todaro and Smith, 2012).

All through a great part of the 1980s and 1990s, a fourth approach held sway.

This neoclassical (in some cases called neoliberal) counterrevolution in financial

thought accentuated the valuable part of free markets, open economies, and the

privatization of inefficient public enterprises inability to create, as per this theory, is

not because of exploitive external and inward strengths as clarified by dependence

theorists. Maybe, it is essentially the consequence of a lot of government mediation

22

and regulation of the economy. Today's varied approach draws on these points of

view, and the qualities (Todaro and Smith, 2012).

The informal sector represents an imperative part of the economy and suerly

of the labour market in many countries, especially in developing countries. It plays a

pivotal role in creation of employment, production and income generation. The very

sector has the propensity to absorb bulk of the rapidly growing labour force in the

urban areas of countries with high rates of population growth or urbanization.

Informal sector employment provides an essential survival strategy to countries

lacking social safety nets in the form of unemployment insurance or where incomes,

particularly in the public sector, and pensions are low (Hussmanns and Mehran,

2001).

The conventional approach which defines informality rests on a dualistic

model of economy. The major hypothesis of the dualistic model is that surplus labour

can be tranfered from low productive traditional sector to high productive modern

sector to start the development process. Firstly, a theoretical model of development

was presented by Lewis (1954) in a dualistic economy. In this model, transformation

of surplus labour from the traditional sector and and its absorption in the modern

industrial sector indicate the informal sector as a temporary stage or transitory phase.

Be that as it may, the legalist way to deal with informality is based on the legal

instruments which influence informality.The enterpreneures participate in the

informal sector due to government institutions and regulations. Accordingly, informal

sector is referred as a store of financial dynamism refused to achieve its maximum

capacity because of regulations imposed by the government (De Soto 1989). Informal

sector is found as a voluntary phenomenon of firms to avail legal exemption benefits

from a mandated minimum wage policy (Rauch, 1991). The excessive taxes and

regulations by governments having inability to implement compliance increased the

informality (Loayza, 1996).

The trade reforms increase the infomalization. The sectors with the largest

reductions in tariffs experience sharpest increase in the share of skilled workers.

Regarding industry wage, premium diminished more in sectors which face large tariff

reductions and the diminishing premium increase inequality. The increasing size of

23

informal sector is related to the increased foreign competition i.e. sectors which

experienced large tariff reductions and trade exposure face enhanced informality in

prior to the labour market reform (Atanasio et al., 2004).

Contrarily, the growth of employment (formal) has no need to suggest a

compression of casual business if the two are supplements not substitutes. The level

of lower wage informal employment is decreased in spite of having been positively

associated with the business cycle. The informal opportunities are increased due to

home ownership. The majority of the workers are hired by firms in the personal

services sector (Mercilli, 2004). Workers due to economic instability forcefully

participate in the informal sector. Lack of stability and social protection in the formal

sector increses opportunities to work in the informal sector (with low productivity and

poor wages) are become a part of informal sector (Tokman, 2007).

The segmentation of labour market is investigated through a semi-parametric

approach in developing countries. On average, the wages in the formal sector are

higher than wages in informal sector (Paratap and Quintin, 2006). Moreover,

participants‟ earnings in the informal sector are not the lowest in the informal service

employment. In addition the workers earnings are not equal to the wages of unskilled

workers in the formal sector in New Delhi (Dasgupta, 2003).

Education and health are considered as the objectives of development. Health

is important for well-being and education is vital to satisfy and to reward the life.

Both are important for the wider view of extended human capabilities that lie at the

heart of the meaning of development. The role of education is important in the talent

of a developing country to captivate modern technology and to develop the size for

self-sustaining growth and development simultaneously. Furthermore, health is

required to enhance the productivity and effective education also relies on passable

health. Hence, education is viewed as most important components of growth and

development (Todaro and Smith, 2012).

Balanced population growth is crucial for progression of economy in a better

way. The population in a country is crucial in the economic development along with

for the social well-being of the people. Though, social distress and low economic

performance of economy can lead to poor management of human resources.

24

Historically, high population growth rate has been considered as an essential factor in

overall economic development of economy of Pakistan.

The government made commitments for the allocation of funds and measures

on an innovative policy to raise the issue seriously in terms of managing growth in

population and the labour force. Improved health facilities and promoted population

welfare activities through the Ministry of Population Welfare declined the crude birth

and fertility rates significantly, that causes a curtailment in the average growth of the

population accompanied by an increased labour force participation rate. Therefore,

further efforts are needed for development of better human resources.

Govt is facing important challenges to identify the development strategies in

order to generate new employment and income opportunities, and reduce

underemployment and unemployment. The urgent need to create employment

opportunities is underscored due to higher labour force growth rate than population

growth. Moreover, the women‟s share in both (wage and salary employment) has

decreased but still their share is up to a quarter of these jobs. Though, a greater part of

female workers is involved in the urban informal sector to continue existence

(Pakistan Economic Survey, 2011-12).

The formal sector is limited in its capacity to generate employment

opportunities. A greater part of labour force is engaged in informal sector

employment where productivity of labour is at low level and workers are not given

protection against exploitation by the employers. Consequently, wages are very

meagre despite longer working hours in informal sector. Therefore, the informal

sector must be promoted in order to absorb surplus labour. It is an attempt to enhance

the labour productivity in the informal sector and to protect the workers from

exploitation in informal sector (Kemal and Mehmood, 1998).

In Pakistan, the informal sector covers a wide range of labour market activities

and plays an important and sometimes controversial role that makes accessible

number of activities in labour market. It makes possible provision for jobs and

diminishes unemployment but almost all jobs are low paid. Furthermore,

unemployment indicates a situation in which people agreed and eligible to work at the

prevailing wage rate are not sufficiently expert to find jobs. In Pakistan, labour force

25

comprises all persons whose age is ten years and above and who are without work for

the reference period, presently available and looking for work (Labour Force Survey,

2011-12).

There is a complex relationship between informal employment and poverty.

On the one hand, poor people, due to inadequate formal opportunities, work in

informal economic activities as an alternative livelihood strategy. Contrarily, such

informal employment can itself either lead to poverty or contribute to poverty

reduction. These diverse results habitually exist together as restrictive on casual work

sort and particular nation connection and time period (Jutting, Parlevliet and

Xenogioni, 2008).

The significance of fiscal policy can't be overruled as it backings monetary

movement through manageable development and destitution lightening. The powerful

practice of the fiscal activities to assemble assets through taxes and public savings,

can subsidize greatly required public goods and services. It demonstrates supportive

to right financial uneven characters and also to advance venture and development by

ideal designation of investment and growth through making the tax system better.

Rapid economic growth and development need a well structured policy in the

country.1

The inward looking policies with fiscal incentives, mostly to manufacturing,

prevailed at large scale direct resource allocation towards the capital intensive

activities and adopt capital intensive techniques which are responsible for low

employment opportunities. There is a need to redirect policy towards the labour

intensive informal sector which probably uses capital to generate supplementary

employment opportunities with no compromising on economic growth in a better way

(see Kemal and Mehmood, 1993).

The urban informal sector is quite large and expanding rapidly due to growing

urbanization, migration and inadequate formal employment. The informal sector

provides employment to the poor segment and plays an entrepreneurial role in the

development of the economy as well. The informal sector tries to reduce

unemployment by creating more opportunities. The problems and constraints of the

1 see Pakistan Economic survey, 2011-12

26

informal sector must be removed in order to develop it. However, there is a need to

create more formal employment opportunities for the development of the economy.

It is gigantic to study the informal sector comprehensively because the very

sector is hallmark of heterogeneous activities. Inspite of rapid growth of GDP,

employment opportunities have been insufficient to fascinate the labour force which

is growing rapidly in Pakistan, so a large proportion of labour force is persuaded

towards informal sector for employment (Kemal and Mehmood, 1993). Informal

sector growth is very useful in formulating policy concerning employment, human

resource development and growth.

Few studies regarding various aspects of informal sector consisting of earnings

determinants, wage rates, labour productivity, capital intensity, skill development and

constraints on the growth of small units are carried out in Pakistan. While these

studies are very valuable as in they draw out the fundamental attributes of informal

sector exercises and requirements on their development, yet in light of the fact that

they have been done in disengagement from one another and are in view of little

specimen overviews, they regularly think of clashing proof and conflicting

arrangement recommendation which diminish the utility of their discoveries. The

distinctions in the center, technique, review configuration, scope and nature of the

investigation introduced in different studies have given clashing proof (Kemal and

Mehmood, 1993).

This study carries out a survey of the urban informal sector of Southern

Punjab, Pakistan with a view to examine different features of the informal sector.

Taking into consideration the size of the urban informal sector, this research looks at

the pattern of the urban informal sector and employment in three divisions of

Southern Punjab, Pakistan. For sake of analysis, dependent variable is probability of

informal sector employment and logistic techniques are used to analyse the

determinants of urban informal sector employment.

In addition, earnings functions are also estimated including human capital

variables to see effect on earnings of people employed in the informal sector using

regression techniques. A part from this, Human Development Perspective has been

used in order to identify the living standards of participants of the urban informal

27

sector and examine the link between them and poverty. Human development

indicators (i.e. economic, human and social capital) are used to gauge the relation

between poverty and the urban informal sector employment. Indicator of economic

capital is income of participants and the households‟ income which is measured by

using the poverty line to gauge the capacity of the informal sector participants in

meeting basic needs and hence get close to the idea of poverty. Human capital

includes the attainment of education, access to health services and access to housing

facilities which are measured on the basis of high level of access and higher

utilization of these facilities. Social capital covers access to social institution as

indicated by the participation in socio-cultural activities gauged by proportion of

informal sector employed watching television, listening to the radio programmes,

reading the newspapers or participation in local organization activities.

1.2 Objectives of the Study

The study has been conducted in order to examine different aspects of the

informal sector to devise strategy for the growth potential and development of the

urban informal sector in Southern Punjab. Particularly, the study emphasizes on:

1) To assess the nature and size of the urban informal sector employment in

Southern Punjab, Pakistan.

2) To examine the characteristics of people working in the informal and formal

sector employment of Southern Punjab, Pakistan.

3) To analyze the socio-economic and demographic factors of the participants of

the urban informal sector who motivate or determine and enhance growth

potential of the urban informal sector.

4) To empirically estimate the determinants of the urban informal sector

employment by using the binary Logit model.

5) To estimate the earnings structure of the urban informal sector employment by

using the regression techniques.

6) To focus on gender employment in the urban informal sector and comparison

in three divisions of Southern Punjab, Pakistan.

7) To highlight the effects of the urban informal sector‟s employment on

participant‟s development or to what extent these urban informal sector

28

participants possess economic, human and social capital in three divisions (i.e.

Bahawalpur, Multan and Dera Ghazi Khan) of Southern Punjab, Pakistan.

8) To design policies and offer recommendations for the future course of action.

1.3 Material and Methods

The gigantic part of this research is based on the primary source of data. This

study is primarily based on the multi-dimensional field survey that has been

conducted by the author during 2012. Almost 1506 workers are interviewed and

information is recorded for further analysis. Three divisions out of nine divisions have

been selected for survey. From each division, one district and two tehsils have been

selected. Simple random sampling and stratified sampling are undertaken for

collection of the data.

Moreover, the compiled data from the sources like Pakistan Economic Survey

(Annual), Publications of Government of Pakistan, World Bank Publications, ILO

Publications, Pakistan Millennium Developmet Goals Report and Labour Force

Survey (various issues) are used in this research.

In the present study, main focus is the complete analysis of determinants of

participants of the informal sector in urban areas of Southern Punjab, Pakistan. The

present study has analysed the characteristics of participants of the formal as well as

informal sector employment. Furthermore, econometric technique has been adopted

in order to estimate the probability of determinants of the urban informal sector

employment of total sample along with the gender comparison. The study also looks

at the earnings structure of participants of the urban informal sector in Southern

Punjab, Pakistan. Moreover, the living standard of the participants of the urban

informal sector has been checked by Human Development perspective. However, the

data and methodology are interpreted in detail in chapter five.

1.4 Organization of the Study

The organization of the study will be as follows:

The present study consists of ten chapters. After the prelude, Chapter two

deals with an overview of the informal sector, growth and economic development.

29

Chapter three explains Neo-classical Labour Supply theory and Human

Capital theory. Furthermore, the Theoretical Approaches towards the urban informal

sector are highlighted.

Chapter four consists of review of literature about different aspects of the

informal, the urban informal sector and economic development both at national and

international level seperately.

In chapter five, the preliminary analysis of data has been made and

measurement issues concerning it are described. It also explains the detail of study

areas, data source, and explanation of the determinants of the urban informal sector

employment, model, methodological issues and selection of variables.

Chapter six elaborates descriptive analysis of workers in the informal and

formal employment in labour market of Southern Punjab, Pakistan regarding total,

male and female sample.

In chapter seven, we will analyze empirically the socio-economic determinants

of workers who motivate, determine and promote the growth potential of the urban

informal sector of three divisions of Southern Punjab.

Chapter eight describes the earnings determinants of workers engaged in the

urban informal sector and economic development in terms of total surveyed sample in

Southern Punjab and its three districts.

Chapter nine interprets the determinants of employment of both genders and

makes comparison in the urban informal sector of Southern Punjab, Pakistan.

Chapter ten provides conclusions and policy recommendations.

30

Chapter 2

URBAN INFORMAL SECTOR, GROWTH AND

DEVELOPMENT

2.1 Introduction

Inspite of growth of GDP in Pakistan, employment opportunities have been

relatively insufficient to absorb the labour force growing at rapid rate due to promoted

industrialization in Pakistan. Due to trade, investment and public sector policies, the

capital-intensive industries prevailed at large scale however they discouraged the

informal sector or the situation gave way to industrialization (Kemal and Mehmood,

1998).

The growth in the size of the labour force has been increasing at a large scale

than the growth rate of formal sector jobs. In fact, it has been expected that private as

well as informal sector must play the leading role to create employment which, in

turn, changes trends significantly in unemployment, the formal and informal sector

employment. The informal sector is viewed as a very important sector of the economy

because development stratigies are redirected to endorse jobs and equity due to its

presence.

The informal sector certainly generates employment at higher level as

compared to formal sector for any particular investment having relatively high

productivity of capital. The process of the formal sector employment creation depends

on informal sector to a great extent. However, the productivity of workforce in the

informal sector is, most probably, rather low and they are not provided protection

against exploitation by employers who earn skimpy wages at the cost of longer

working hours. Consequently, policy intervention regarding informal sector must

hinge on the truth that informal sector, which is labour-intensive, creates additional

employment. At the same time, public policies are required to induce enhanced

labour productivity in the informal sector with no compromise on growth objective

and on workers‟ protection against exploitation. The policy is also needed to promote

31

and encourage informal economic activities instead of their active discrimination

(Kemal and Mehmood, 1998).

The arrangement of the chapter is as follows.

In section 2.2, we review the urban informal sector and various definitions of

the informal and urban informal sector at national and international level. The section

2.3 describes an overview of Pakistan‟s growth and development in relation to socio-

economic indicators over the time period to understand informal sector employment

in Pakistan. The section 2.4 shows population, labour force and employment pattern

in Pakistan economy. Unemployment situation is explained in section 2.5. Trends in

urbanization and migration in Pakistan economy are described in section 2.6. In

section 2.7, poverty and measures are explained. Section 2.8 highlights the women

and urban informal sector. Section 2.9 explains the concept of development. Human

indicators and development are stated in section 2.10. Section 2.11 shows social

indicators and development. Concluding remarks are presented in section 2.12.

2.2 Urban Informal Sector: An Overview

The informal economy has been oserved as having more of a fixed character

in countries where income and assets are not equally distributed. It was estimated that

informal work accounted for almost 80 % of non-agriculture employment during the

past decade, its share was over 60 % of urban employment and over 90 percent of

new job (see Charmes, 2002).

For women in sub-Saharan Africa, the informal economy denoted 92. 5 % of

the total job opportunities outside of agriculture as compared to men share of 71%.

However, in Asia, informal workers‟ shares ranged from 45 to 85 % of non-

agricultural employment and from 40 percent to 60 % of urban employment (ILO,

2002).2

In the developing countries, the share of self-employment is greater in the

informal employment as compared to wage employment. Specifically, share of self-

employment is 70% of informal employment in Sub-Saharan Africa (if South Africa

2 see Becker (2004).

32

is excluded, the share is 81%) 62% in North Africa, 60% in Latin America and 59%

in Asia (see Becker, 2004).

The bulk of people depend on the informal sector to earn a livlihood due to

lack of employment opportunities in the public as well as in the private formal sector.

Though their earnings remain very meager, that classifies them as poor, yet without

informal sector, it would be even negligible and their poverty would turn even worse.

Resultantly, the productivity and incomes of informal sector workers should be

enhanced outstandingly.3

The developing and developed countries consider informal sector as an issue

of great importance. The informal markets determine the co-operative

entrepreneurship to make economically and politically stronger the poorest people all

over the world. This silent revolution brought changes in societies around the World.

This overwhelmed the societies by extraordinary challenges; increasing opportunities

by setting up institutions and policies to allow their citizens to participate easily in all

sphere of economic, social, and political life (Chickering and Salahuddine,1991).

Urban informl sector contributes to curtail down the costs of urbanization. The

countries having cheaper labour in form of urban informal activities relatively pay

lesser urbanization costs.4

The informal sector has the potential to face sufficiently escalating

unemployment problem in Pakistan. Easy access, low skills and necessary investment

in informal economic activities increase the stock and annual addition to the work

force and the existing financial resources. The very sector has the potential to absorb

large portion of rural and urban workforce and to contribute significantly skill

development of work force (Sabur and Chayur, 1994).

The several definitions have been intricated because of mixed nature of the

informal economy and it can not enfold the presented definitions regarding the

informal sector. Yet, a lot of focal definitions are considered to show the different

view points of the informal economy.

3 see ESCAP, 2006. 4 see Richardson (1987).

33

The concept „Informal‟ is theoretically based on the dichotomy of the urban

economy in underdeveloped countries. Hart used the term “informl sector” in his

famous paper when he attended a conference which was held in Africa on the topic of

“urban unemployment”. It was arranged by the institute of Development Studies at

the University of Sussex. He focused on low income neighbourhood of Nima in Accra

and explained employment in the informal sector depends on new-comers who did not

find employment in the formal sector. He objected traditional outlook to deal with the

informal sector as being remarkably unproductive. In this way, various sorts of

activities, apt to fall in this sector, were neglected by research and policies equally to

a large extent (see Chowdhury, 2006).

International Labour Organization (ILO) introduced the concept of informal

sector in Employment Mission Report on Kenya in 1972 in that migration from the

countryside to the city caused urban unemployment. Along with the incapability of

the formal sector to make available adequate employment to rural migrants as well as

urban dwellers then, they are persuaded for small-scale and micro-level production

and distribution of goods and services. Accordingly, these mostly unrecognized,

unrecorded and unregulated small-scale activities are the informal sector. The

International Labour Organization (ILO) reports presented set of specific

characteristics of the informal sector. These enterprise production unit establishments

are as follows.

easy entry;

dependence on domestic resources;

family possession of enterprises;

labour-intensive, make use of adapted technology;

skills required beyond the scenario of formal school system;

unorganised and contestive markets;

lack of support and acknowledgement from the government;

It was concluded in ILO Kenya report that the informal sector efficiently

creates more jobs alongwith a quick increases the employment than formal sector.

34

Hart (1975) distinguished between income opportunities in wage and self-

employment in his dual model. He regards employment in the formal sector as the

wage employment and in the informal sector as self-employment.

In his conception of the informal and formal sector distinction, Weeks (1975)

emphasized the economic insecurity of operation in the informal sector. All private

sector enterprises which were officially documented, nurtured and regulated by the

state were viewed as in the formal sector. Contrarily, the informal sector comprised

the enterprises and individuals who were devoid of advantages and were not bound by

the government regulations. Moreover, these enterprises did not avail formal credit

and means causing transfer of foreign technology.5 People, without contributing to

social security institutes, were incorporated in informal group, apart from the group 2-

4 persons and domestic workers (Merrick, 1976).

Sethuraman (1976) suggested a list of criteria in order to identify the informal

sector enterprises. A manufacturing enterprise can be enlisted into the informal sector

by satisfying one or more conditions suggested below.

It engages ten persons or less incorporating part time and casual workers;

It functions on an illegal basis, incompatible to govt regulation;

It incorporates household family members of head of the enterprises;

It observes unfixed hours/working days;

Its operations are done in semi-permanent/temporary premises, or it shifts

location;

It does not use any electricity during manufacturing process;

It does not fulfill its credit needs from formal financial institutions;

It usually distributed output provided directly to final consumer;

Almost all workers have less than six years of formal schooling;

Breman (1976) demonstrated the crucial role of personal contacts to determine

the absorption into the informal work process with place and work type. Mazumdar

(1976) defined the informal sector as an “unprotected labour force” not covered by

5 Where numerous measures are seen in the informal sector economic operations such as tariff and

quota protection, import tax rebates, low interest rates selective monetary controls and licensing of

operations.

35

labour legislations. He viewed that the basic difference between informal and formal

sectors showed that formal sector employment was, somehow, protected so that the

wage levels and working conditions in the sector were generally not available to those

who seek job in the market until they enable themselves to cross the barriers of entry.

Peattie (1980) made query on the characteristics defined by ILO (1972). The

author was indifferent to accept the easy entrance in the informal sector. In her words,

these occupations were characterized as in informal sector due to a variety and

complexity of structure rather lack of formal structure. Cavalcanti (1981) explained

that informal sector consisted of small scale units producing and distributing goods

and services most preferably aiming at remarkable employment and income

generation, irrespective of the constraints on capital (i.e. physical and human) and the

technical know-how.

Smith and Koo (1983) identified two measures to distinguish the formal and

informal sector. These were employment type (i.e. all self-employed and unpaid

family workers engaged in the informal sector), and hired workers for domestic

services (such as maids, chauffeurs) or in small family enterprises were included in

the informal sector.

Banergee (1983) included the wage employment in the informal sector. Petty

trade would, approximately fall in informal sector (Okojie, 1980). House (1984)

distinguished the informal sector into two sub-sectors. First was the intermediate

sector which appeared as a reservoir of self-motivated entrepreneurs. The second was

the community of poor comprising large body of left behind and underemployed

labour.

Most important features of the urban informal sector by Fields (1975 and

1988) are given below:

Free entry, in the sense that (all entrants of the sector can get variety of work

which in turn provides with cash earnings);

Income distribution owes to institutional circumstances of production and

sales patterns of that sector;

36

Positive on-the-job search opportunities,because the participants of the

informal sector have a non-zero chance to seek out a job in the formal sector;

An intermediate probability (to search, as the participants have a better chance

to find a formal sector job than agricultural workers but they have a chance

than openly unemployed and unemployed workers);

A lower wage rate in the urban informal sector than in agriculture, which

arises endogenously due to higher search opportunity on-the-job;

Free entry is an important characteristic of the informal sector and other

features characterize the informal sector of typical developing economy;6

Tokman (1986) stated that migrants and newcomers with lack of human and

physical capital entered in the labour market and this induced them to decide to

perform activities avoiding their main requirement of being easy entrant into the

sector. However, the organization of the production seemed the major factor, while

the features of entry were just required to make difference between the units of

production using labour (paid or unpaid), and individual level units.

Others have also emphasized on diverse characteristics of the informal sector

which is of unorganised and non-institutional nature. The unauthorized operations that

did not avail incentive or social security system were incorporated in the informal

sector units.7 The informal sector proved temporary stage in urban areas for rural-

urban migrants expecting for receiving urban income higher than their agricultural

income.8

Generally, formal and informal sectors are distinguished by following:

Formal sector includes difficult entry, large scale, secure employment,

regulated enterprises, corporate ownership, links with international trade, capital

intensive, modern technology, fixed locations, reported/legal activity. Whereas,

informal sector is characterized by ease of entry, small-scale, insecure/seasonal

6 see Fields (1988), where free entry, income sharing, positive on the job training, an intermediate

search probability and lower wage in the urban informal sector than in agriculture. 7 Amin (1987) surveyed of wage workers and self-employed from 230 different informal sector

activities in Bangladesh. 8 The growth of urban sector has seen the establishment and growth of the informal sector, see

Chaudhary (1989).

37

employment, unregulated enterprises, family ownership and self-employment, local

market, labour intensive, traditional technology, transient patterns and

unreported/illicit activity.9

Boyd (1990) characterized the informal sector in terms of employment size,

informal networks, personal and social contacts of self-employed. The author

included self-employed unincorporated business owners in the informal sector. Kozel

and Alderman (1990) declared that labour force activities in household enterprises

engaged and production of goods consumed at home were productive as they

comprised the major part of the day.

Burki and Abbas (1991) measured the informal sector as firm size in Pakistan.

They added those establishments that were unregistered firms and hired 10 or fewer

than 10 workers. The apprentices and entrepreneurs both were used to define urban

informal sector. The informal sector was attributed as ease of entry, flexibility,

employment level (such as petty producers, petty traders, and casual disguised wage

labourers) and the lack of social benefits in Aman Jordan.10

Doan (1992) emphasized

on the stratification contained by the supposed informal sector i.e. part of the

economy that was unregulated by the state.11

The informal sector considered those

establishments which were unregistered with 10 workers or fewer. The concept of

legality was used to define informal sector.

Similarly, Swaminathan (1991) incorporated unregistered and unlicensed

establishments in the informal sector and these enterprises were considered as part of

the informal sector due to their unregulated status. The informal sector was defined as

an enterprise or production units. The authors emphasized that the employment in the

informal sector was not conditioned by regulations (i.e. any contract) and workers did

not access formal employment benefits i.e. fixed wages and employment security.12

9 see World Bank Country Study Report (1989).

10 see Doan (1992) for survey. 11

Doan (1992) showed distribution of workers i.e. petty traders, subcontractors salaried

workers, disguised wage labourers and casual wage labourers. 12 see Kemal and Mehmood (1993).

38

In January 1993 15th

International Conference of Labour Statistics gave the

international statistical definition of the informal sector and defined enterprises in

informal sector depending on following criteria:

There are private unincorporated enterprises, i.e. individuals or households

own these enterprises and are not composed as separate legal entites, as these

are accessible absolute accounts that allows for a financial separation of the

productive activities of the enterprise from the other activities of its owner(s).

All or at least a quantity of goods or services is being produced for sale or

barter, with inclusion of households to produce domestic or personal services

by hiring paid domestic employees.

The employment size of enterprises below a certain threshold must be

determined in keeping with national circumstances and they are unregistered

under particular structure of national legislation.

They are performing activities that come into the category of non-agricultural

along with secondary non-agricultural activities of enterprises in the

agricultural sector.

Paradhan (1995) considered two definitions: The first definition viewed the

size of the enterprise to indicate formality “if it was lower than 6, the work was

grouped as informal, if it was no less than 6, the job was formal”. The 2nd

definition

emphasized on worker‟s status (i.e. self-employed workers) to define informal sector.

Funkhouser (1996) defined informal sector as employment size i.e. self-

employed, domestic workers, family workers, and wage and salary workers in firms

of four or lesser persons excluding professional and technical occupations. Several

authors (Loyaza (1996); Jones and Fortin et al., (1997) measured the informality in

terms of legality. In their words, informal sector employment emerged due to

excessive taxes, regulations and minimum wages. The informal sector was

characterized by ease of entry, small scale, labour intensive and self-employment

(Samith and Metzger, 1998). In Fakuchi‟s (1998) study, the term informal sector was

regarded as those firms which were not formal and covered all small, cottages, and

39

family firms. The informal sector was characterized as sector of migrants, petty

traders and wage earners.13

Those small scale units which are engaged in producing goods and services,

primarily aiming at income and employment generation and not having intention for

tax payment evasion are regarded as informal sector. It has defined the informal sector

employment as labour force in un-incorporated enterprises, owned by own account

workers without considering the enterprise size or by employers who employ fewer

than 10 workers. Thus, informal sector enumerates all household enterprises managed

by own account workers and employers with fewer than ten persons involved in

production of activities, exclusive of agriculture or non-market production.14

Ranis and Stewart (1999) examined the informal sector with regard to the rest

of the economy and divided the very sector into two parts. One part was a

modernizing dynamic and the 2nd

one was a traditional stagnant one. The authors

highlighted that the informal sector was a disadvantaged part of a dualistic labour

market. Moreover, it appeared dualism relating to wages that were exceeded the

market clearing level.

The informal sector included both the family enterprises and industrial

establishments that hired less than ten employers. It also included the non-industrial

enterprises that hired fewer than twenty or at least twenty workers.15

Rosser et al.,

(2000) used the legalist approach to classify the informal sector. Accordingly, the

reasons to work in the informal sector were low tax rates and safety nets.

ILO (2001) describes these appropriate activities as it groups the informal

sector into these major parts:

(a) Owners or employers of microenterprises provide work for small number of

workers.

(b) Own-account workers are those who work without help or with unpaid

employes.

13 Study by Little and Levin et al. (1999). 14 Federal Bureau of Statistics (1998) 15 Malik and Nazli (1999) explained family enterprise, industrial establishments and non-industrial

establishments.

40

(c) Workers who are dependent found in micro-enterprises or serving employers

with no contract and casual workers.

Todaro (2000) argued that the informal sector largely depended on paid work by

women as primary source of employment in most developing countries (see Chen,

2001). It highlighted that the enterprises based on self-employment that availed

assistance of unpaid family members, domestic servants, low educated employees,

hired not more than ten workers. The informal workers did not avail social benefits

and protection and their relationship was not constrained by labour legislation and tax

rules were also included in informal sector.16

Gallaway and Bernasek (2002) found the informal sector as paid workers in a

family business as self-employed. Gray and Tudbal (2002) emphasized family

friendly work participation while defining informal work. The informal sector was

concerned with living condition, security and low benefits. Entrepreneurship was the

fundamental feature of informal sector (Reddy et al., 2003). The non-self-employment

was refered as in informal sector employment.17

The low status and unprofitable work were included in informal sector. 18

Das

(2003) looked upon informal self-employed workers who operated at their own farm

or non-farm enterprises or as own account workers with or without taking help from

partners and helpers, largely by hiring labour and unpaid helpers. This classification

excluded those entrepreneurs employing less than 3.5 workers. The micro-enterprises

were refered to those enterprises that incoporated family labour and hired at least 5

employees. The small scale enterprises provided work for above five and less than

twenty hired workers and medium scale enterprises with 20 employees or above

(Mukras, 2003).

Marshal and Oliver (2005) incorporated the entrepreneurship in informal

sector employment. The informal sector was regarded by two sub-sectors. One was

upper tier informal sector that did not facilitate the employees with benefits from

health, retirement or other benefits, but it was possible for the employees to resort to

16 see ILO report (2002). 17 see survey by Suharto (2002), Florez (2003), Zulu et al., (2002﴿ and Reddy (2003). 18

see studies by Dasgupta (2003), Ozcan et al., (2003), and Guang and Zheng (2005).

41

the law when they need ed it. Second was lower tier informal sector that included the

salaried employers who were, somehow, unprotected concerning the law, without

availing health retirement and other benefits (Bocquier, 2005).

Sandufur (2006) found that the establishments providing employment more

than five employees were included in the informal sector. Henley (2006) defined the

informality in terms of employment contract status, social security protection

according to the nature of the employment and the characteristics of the employer.

Ademu (2006) examined the income generating activities of urban dewellers

as in the informal sector. These worked without the restrictions and legal regulations

imposed by the government. The general characteristics of operators of an informal

sector are defined in the following form:

The factors of production are easily accessed by organizing the family and

friends socially.

Entrepreneurs involve in almost all branches of the economy i.e. productive

activities, general and specialized services.

The constraints on social relations determine more technology.

Operators‟ aspiration towards in the formal sector production as more profit-

oriented.

The operational definition was adopted by Kristic and Sanfy (2006) to define

informal employment. This definition was based on following:

1) Informal employees: wage employees without having payment of social

security contribution (health and pension insurance).

2) Informal self-employed: own-account workers and employers working in non-

agriculture family business without payment of social security contributions;

3) Farmers working on own farm.

4) Family workers who were not paid.

Florez (2003) defined informality in dualistic approach i.e. self-employed

excluding professionals and technicians, unremunerated family workers, domestic

servants, owners and salaried workers in small firms (utilizing 10 or lesser

42

employees). While, the owners and workers having no health insurance were

unprotected (i.e. all unpaid family workers and domestic servants) and added in

structural articulation approach. The term “informal sector” was invoked to refer as

construction work by Li and Peng (2006).

Gunatilaka (2008) defined the informal employment which contained units

involving in economic activities working outside the scope of official statistics. These

activities were done by family workers, employers, employee and temporary and

casual workers in the informal enterprises.

In 2003, 17th

International Conference of Labour Statistics defined the

informal sector employment or households in the following types of jobs:

Own-account workers working at owned informal sector enterprises;

Employers involved in the informal sector enterprises that they owned;

Participating family workers, (whatever be their work domain);

Members of informal producers‟ cooperatives in the informal sector;

Informally employed in the informal or formal sector;

Own-account workers producing goods for personal household use;

The informal sector contains small units responsible for production or services

keeping in view providing employment and incomes to the families engaged in these

activities. Such informal activities have often been characterised by low levels of

capital, skills, access to organized markets and technology; low and unstable incomes

and poor and unpredictable working conditions. In general, these activities are

working outside the scope of official statistics. They also do not avail social

protection.They are highly labour intensive but are based on casual employment

because of kinship. Activities in this sector rely on local and regional demand.19

According to Wamuthenya (2010), all small-scale activities that were

normally semi organized and unregulated and used low and simple technology were

considered informal sector. The sector covered self-employed persons or employers

of a few workers and unpaid family workers. The informal sector was defined by

Jonason (2009) as an unregistered employee, self-employed person, unpaid family

19 Source: Labour Force Survey, 2010-11.

43

worker, or an employer who hired lesser than five employees and did not contribute to

any social security transition.

A lot of research work on the urban informal sector created an ambiguity and

contradiction of definition because of smaller clear empirical basis for the notion. The

informal sector is gigantic in its size under any definition used. In this research, we

have followed the Funkhouser (1996) to define the informal sector i.e. the self-

employed, own-account workers, unpaid family workers, domestic workers, wage and

salaried workers in firms of less than five employees other than professional and

technicians. The employment size increases in urban areas of Southern Punjab,

Pakistan.

2.3 An Overview of Pakistan’s Growth and Development

The growth in per capita income was observed at about 2.2 % in Pakistan

economy during 1950-2000. Accordingly, per capita income has increased three

times. However, a decline was observed in growth rate decade by decade and

performance on social indicators was observed poor owing to this declining trend.

The economy observed a rapid increase in its growth during 2003-2007.

The resilience of Pakistan economy has been observed many times due to

crisis one after the other. The numerous shocks (domestic and external) targeted the

economy from 2007 onwards. The international oil and food inflation, security risks at

domestic level due to operation against extremism and repetitive natural catastrophes

(floods) have buffeted the macro level strategy with shock after shock (Government

of Pakistan Economic Survey, 2011-12).

The campaign against extremism along with associated destruction of physical

infrastructure, the migration of thousands of people from the affected areas with an

increased expenditures to support them have all taken their toll. As a result export

markets slowed down as compared to the last year. Gross Domestic Product (GDP)

growth of 6.5 percent per anum has been trapped at half level of Pakistan‟s long-term

growth potential. This is lesser than growth required for sustainable increase in

employment, income and GDP a reduction in poverty (Government of Pakistan

Economic Survey, 2011-12).

44

The focus has been on maintaining macro level stability, growth, mobilization

of resources at domestic level and greater than ever exports, balanced regional

development and provision of protection for the helpless segments in Pakistan

economy. Despite numerous challenges, the performance of economy is better in

2011-12 as compared to developing and developed countries. There has been a rapid

increase in fuel and commodity prices, recessionary trend globally and weak inflows.

Additionally, the cost of severe rains (in Sindh and part of Balochistan) is estimated at

$ 3.7 billion which struck the economy. The comparative increase in Gross Domestic

Product growth is observed 3.7 percent this year than 3.0 percent last year despite

several challenges (Government of Pakistan Economic Survey, 2011-12).

The GDP growth has been estimated at 3.7 percent with 3.1 percent growth in

agriculture sector and 1.1 percent growth in scale manufacturing (LSM) sector during

2011-12 in the economy. Generally, there is an improved performance of commodity

producing sectors and especially the agriculture sector. There has also been the

services sector‟s growth at 4.0 percent in 2011-12. An increase in per capita income is

estimated at 2.3 percent in 2011-12 as compared to 1.3 percent growth last year. The

important objectives of sustainable high growth, external payment viability and low

inflation can be obtained by eliminating specific structural obstacles in Pakistan

(Government of Pakistan Economic Survey, 2011-12).

Table 2.1: Growth Performance of Key Components of GDP (% growth at

constant factor cost)

Indicators 1950s 1960s 1970s 1980s 1990s 2000s 2010s

Agriculture 1.6 5.1 2.4 5.4 4.2 -0.6 0.62

Manufacturing 7.7 9.9 5.5 8.2 4.8 6.0 5.46

Services - 6.7 6.3 6.7 4.6 5.0 2.63

Real GDP(FC) 3.1 6.8 4.8 6.5 4.6 3.05 3.07

CPI - 3.2 2.5 7.2 9.7 -

Source: i) Khan, and Mahmood (1997), for the growth rates for 1950s.

ii) Govt. of Pakistan, Economic survey 2008, 2009 and 2010, statistical Appendix (2007) and

Consumer Price Index growth rates are the straight averages.

The growth perormance of Pakistan economy is revealed in table 2.1 during

1950s to 2010s. The CPS comprising agriculture and industry has strongly linkages

(i.e. forward and backward) which result into economic development and well-being

45

in the state. The estimates indicate a decline in commodity producing sector during

2010s. The agriculture sector contributed at low level due to structural transformation

process. As a result, the growth of very sector has affected overall economic

performance to a large extent. However, recent calamitous floods weakend its

performance. The manufacturing sector contributed largely to the progress of

economy but it is stressed by shortages of energy, poor law and other situation. The

share of services sector remained at low level.

Table 2.2: Sectoral Share of the GDP Growth (percentage points)

Sectors 200

-01

2001

-02

2002

-03

2003

-04

2004

-05

2005

-06

2006

-07

2007

-08

2008

-09

2009

-10

2010

-11

2011

-12

Agriculture -0.65 0.33 1.0 0.6 1.5 0.4 1.1 0.23 0.86 0.13 0.50 0.66

Manufacturing 1.18 0.75 1.0 3.8 3.1 1.3 1.8 0.92 -0.69 0.10 0.57 0.66

Services 1.92 2.53 2.7 3.1 4.4 4.9 4.2 3.08 0.89 1.37 2.36 2.15

Real GDP (FC) 2.45 3.61 4.7 7.5 9.0 6.6 7.0 3.68 1.72 3.07 3.04 3.67

Source: Govt. of Pakistan, Economic Survey (Various Issues)

Table 2.2 reveals the slow growth performance of economy during the last

five years. The economic growth fluctuations in the fiscal year 2011-2012 expanded

generally due to improved services sector. The commodity producing sector and

services sector contributed in overall growth at 3.67 percent. The estimates indicate

rapid growth of services sector. The Pakistan‟s services sector has been praised as it

has liberalized rights separated regulators from operators for its development. The

Pakistan economy has seen key changes in its economic structure (Govt of Pakistan,


The investment plays role to enhance the productive capacity, to influence the

employment level and promotes technological progress by employing new techniques

in Pakistan. Generally, investment is unpredictable as it depends on various factors

and is responsible largely in GDP fluctuation. In previous some years, the investment

was hit by domestational factors which led to decline in total investment from 22.1

percent of GDP to 12.5 percent from 2007-08 to 2011-12. Fixed investment has

declined to 10.6 percent of GDP from 2007-08 to 2011-12. Private investment has

decreased from 15.0 percent of GDP in 2007-2008 to 7.9 percent in 2011-12. Public

investment as a percent of GDP has also decreased from 5.4 percent in 2007-08 to 3.0

percent in 2011-12. Changes also observed in the composition of investment (private

46

and publc sector) for the duration of the reviewed period (Government of Pakistan


The national savings contribute to domestic investment to indicate indirectly

foreign saving which is essential to fulfil investment demand. The foreign saving is

required to finance the saving investment gap which indicates the current account

deficit in the balance of payments. National savings are estimated about 10.7 percent

of GDP for the year 2011-12. Estimates reveal decline in domestic savings from 11.5

percent of GDP to 8.9 percent of GDP from 2007-08 to 2011-12 and net foreign

resources inflows are used to finance the saving investment gap. Notionally, the

improvement in saving investment gap can be made by increasing savings and

decreasing investment. The Pakistan economy requires gearing up saving and

investment in order to enhance the employment generating ability with expanded

provision of resources (Government of Pakistan Economic Survey, 2011-12).

Investment in public sector catalyzes economic development in economy and

induces spillover effects for private sector investment because development in private

sector can possibly be made by making expenditure predominantly on infrastructure.

Conversely, private sector development is limited by reducing development

expenditures. There is a fall in public sector investment from 5.4 percent to 3.0

percent of GDP from 2007-08 to 2011-12. Table 2.3 explains saving and investment

as percentage of GDP (Government of Pakistan Economic Survey, 2011-12).

Table 2.3: Structure of Savings and Investment (As Percentage of GDP)

Description 2003-

04

2004-

05

2005-

06

2006-

07

2007-

08

2008-

09

2009-

10

2010-

11

2011-

12P

Total

Investment

16.6 19.1 22.1 22.5 22.1 18.2 15.4 13.1 12.5

Gross Fixed

Investment

15.0 17.5 20.5 20.9 20.5 16.6 13.8 11.5 10.9

Public

Investment

4.0 4.3 4.8 5.6 5.4 4.3 3.6 2.9 3.0

Private

Investment

10.9 13.1 15.7 15.4 15.0 12.3 10.2 8.6 7.9

National

Savings

17.9 17.5 18.2 17.4 13.6 12.5 13.2 13.2 10.7

Domestic

Savings

15.7 15.4 16.3 15.6 11.5 9.8 9.3 13.3 8.9

47

The Pakistan economy witnessed growth of per capita real income at 2.3

percent in 2011-12. The growth is observed 1.3 percent for the year 2010. The

increase in per capita income in terms of dollars is observed $114 during 2010-2012.

It is estimated that workers‟ remittances enhanced by 25.8 percent in 2001 which are

higher than the previous year. The diversion of remittances from informal to the

formal channel at government level mostly resulted in resilence of remittances.

A noteworthy financial crisis has been observed in 2007-08 in global

economy. The crises originated in subprime mortgage loan portfolio. This was taken

aback that self-reliance of the international institutions and markets worsened the

economic development and balance of payments all over the world. The sever terms

of trade and slower economic growth which were faced by developing countries,

severly, and resulted in or lead to crises. Furthermore, the consumer, markets and

normally the investment process to produce goods and services were affected by the

financial melt down. This crisis (coincided with the rise in the process of commodities

and oil) decreased in aggregate demand and raised inflation all over the world

(Government of Pakistan Economic Survey, 2011-12).

The economy experienced a decline in inflation for the third consecutive year.

Consumer Price index estimated at a turn down of 14.2 percent during 2008- 2012

and it was observed in a single digit in 2012. The food and non-food inflation, on

average, is recorded at 11.1 percent and at 10.7 percent respectively. Both types of

inflation are higher as compared to previous year inflation.

The world‟s output and trade volume has experienced a turn down due to

inauspicious global environment during year 2011. The world‟s output estimated at

5.3 percent in 2010 and it decelerated to 3.9 percent in 2011. This falling global

economic activity sharply declined the growth of world trade from 13.0 percent in

2010 to 5.8 percent in 2010. An expansion of world output by 3.5 percent and trade

volume by 4.0 percent is anticipated during the period. The commodity prices in

international markets are decreased by global economic slow down which, turned-

down the world trade growth. There was also observed a decline in the prices of non-

fuel commodities from 26.3 percent to 17.8 percent during 2010 to 2011. The increase

in the prices is about at 10.3 percent in 2012 (Government of Pakistan Economic

Survey, 2011-12).

48

During July to April 2012, Pakistan has gone through a growth in export. It

remained buoyant and estimated at close to $ 13 billion, an increase of 16 percent

inspite of the worldwide slowdown. The capital flows to Pakistan were impacted by

worldwide recessionary trend. The sharp rise in oil prices and import of 1.2 million

metric tons of fertilizer also influenced the current account balance. Currently, there

has been observed signs of self-effacing improvement have been observed in the

economy. The current account deficit due to extended trade and services account

deficit was estimated at $ 3,394 during 2011-12.

Yet, continued support of the tranfers of workers‟s remittances expanded the

current account deficit. Trade volume expanded for the most part in result of 14.5

percent growth of imports and the 0.1 percent increased exports; thus increased trade

deficit by 49.2 percent. The trade deficit largely increased due to sharp increase in

import bill for the duration of july-April 2011-12 which boosted up due to higher

prices of crude oil at international level (Government of Pakistan Economic Survey,

2011-12).

It is observed that exports achieved $ 25 billion which is highest and shows 30

percent growth as limits both price and quality effect. Pakistan has also experienced

some diversification geographically regarding exports. 47.2 percent of the exports are

concentrated in five markets (USA, UK, Germany, Hong Kong, and U.A.E) of the

world and all other countires shared in exports at 52.8 percent. The share of exports of

these markets is about 35.7 percent while the share of exports of all other countries is

increased up to 64.3 percent in 2011-12. The increase in imports is noticed at 14.5

percent and continued at $ 33.1 billion for the period 2012. The current account

deficit stood at $ 3.4 billion principally due to high oil prices and import of fertilizers

for the period of 2012. The current account balance contained due to current transfers

in the form of workers‟ remittances (Government of Pakistan Economic Survey,

2011-12).

The public debt of Pakistan stood at Rs. 12024 billion as of March 31, 2012.

Public debt as a percentage of GDP stood at 58.2 percent by end-March 2012. It also

added to the EDL stock for the duration of July-March 2012. Furthermore, public debt

49

servicing stood at Rs. 720.3 billion against the budget amount of Rs.1034.2 billion of

the end of March 2012.20

High and speedy economic growth for large time period is prerequisite for

employment creation to accompany the rapid population growth and high living

statndard. The growth performance of the economy over the last five years can be

appreciated in many ways. Macro economic policies with structural reforms led to the

resurgence and statbility of the economy. However, there has been a slow growth

performance of Pakistan along with a decline in private, public sector investment and

a turn down in domestic savings throughout the last five years (GOP, Economic

Survey 2011-12).

The per capita real income has grown at an average rate of 2.3 percent during

the last year. Unemployment has increased from last three years. The poverty level

has been decreased and the development expenditures as a ratio of the economy

helpfully curtail down levels of unemployment in Pakistan (GOP, Economic Survey

2011-12).

2.4 Population, Labour Force and Employment Pattern in

Pakistan Economy

The population of a country plays an imperative part in economic

development and social well-being of the people. However, a turn down in economic

performance and social distress can be the result of poor human resource

management. The Pakistan economy faced socio-economic crises of food security,

and unemployment due to rapid population growth and lack of well-developed human

resources. However, the situation is being improved by the efforts of government.

Better health amenities and promotion of population welfare activities thorough the

ministry of population helpfully curtails down the crude birth and fertility rates

noticeably which lead to a decline in average growth rate of the population followed

by an increased labour participation rate. Thus, there has not been noticeable

reduction in growth of population and at the same time dependency ratio has

increased. Therefore, further essential efforts are required to make in order to improve

the human resources (Government of Pakistan Economic Survey, 2011-12).

20 GOP, Economic Survey, 2011-12.

50

A high rate of population growth has been exihibted in Pakistan since its

creation. Regarding size of population, Pakistan became sixth largest country of the

world from thirteenth country from 1951 to 2011. The rapidly growing population

demands security and fundamental services provsions. The labour force as being the

economically active part of population supply labour to produce goods and services in

the country. The labour force is very large in result of its large population size in

Pakistan. The government of Pakistan announced six labour policies in 1955, 1959,

1969, 1972, 2002 and 2010. These policies set down parameter for trade unionism

growth, protection of worker‟s rights, the settlement of industrial disputes and the

redress of workers grievances. The policy which reformed the labour law in 1972 was

most progressive one (Government of Pakistan Economic Survey, 2010-11).

The current government prerecognizes the significance of workers‟ and

employers‟ affable association to make them avail benefits without inflicting any set

back on the economy simultaneously. The mutual awareness and understanding of

both the workers‟ and employers‟ rights and obligations made it possible. The

observed labour force of 5.24 million people is 0.91 million more than the previous

year in Pakistan. In the economy, the employed people of 53.84 during 2010-11 were

0.63 million more as compared to the preceding year (Government of Pakistan


Table 2.4: Civilian Labour Force, Employed and Unemployed for Pakistan

(Million)

Year Labour Force Employed Unemployed

2003-04 45.5 42 3.5

2005-06 50.05 46.95 3.1

2006-07 50.33 47.65 2.68

2007-08 51.78 49.09 2.69

2008-09 53.72 50.79 2.93

2009-10 56.33 53.21 3.12

2010-11 57.24 53.84 3.40

Source: Varoius issues of labour force survey (2010-11)

51

2.4.1 Labour Force Participation Rates in Pakistan

The Crude Activity Rate (CAR) and Refined Activity Rate (RAR) are used to

estimate the labour force population rate. The CAR is a measurement of percentage of

the labour force in the total population while RAR as a better measurement and

comprised active labour force that can be achieved by the percentage of the labour

force of persons who are ten years old and obove. The mixed pattern of change in

CAR in rural areas was observed for the periods 2008-09 and 2010-11. Zero net effect

on participation was observed in rural areas, while, the female CAR comparatively

tended to increase more than the males‟ CAR and caused an expansion enhanced the

overall participation rate in urban areas.

During 2009-2011, it is experienced a marginal decline in CAR in rural areas.

There is a marginal increase in the female RAR and a decrease in male RAR.

However, an aggregate (male and female) increase in RAR abolished diminishing

effect in the RAR and this resulted in no change in overall RAR at the country level.

An observale fact behind this change is that females participated more which is a

good sign of female empowerment in urban areas (Government of Pakistan Economic

Survey, 2011-12).

Table 2.5: Population, Labour Force and Labour Force Participation

(LFP) Rates

Years

Population Labour Force LFP

Rates% Total

(Mn)

Growth

Rate

Working

Age

Total

(Mn)

Increase

(Mn)

1996-97 126.72 2.61 84.65 36.30 1.57 28.6

1997-98 129.97 2.41 88.52 38.20 1.90 29.3

1999-00 136.01 2.23 92.05 39.4 1.20 29.4

2001-02 145.80 2.06 99.60 42.39 2.99 29.6

2003-04 148.72 1.90 103.40 45.23 2.84 30.4

2005-06 155.37 1.90 108.78 50.05 4.82 32.2

Source: Labour Force Survey (various issues).

The table 2.5 explains population and labour force participation trends during

the years of 1996-97 to 2005-2006. The population of Pakistan was apparently

estimated at 32.5 million at the time of independence, which increased 3.06 percent

52

per annum from 1947 to 1981 and decreased by 2.41 percent during 1998 and

expansion has been observed about four and half-fold during sixty years of its

independence. The annual growth rate of population was observed at 1.8 percent in

1947 in Pakistan while it has tended to increase up to 3.06 percent per annum in 1981

and experienced a decline to 2.41 percent in the year 1998. The population growth

rate declined slightly during 2006.

2.4.2 The Formal Sector, Informal Sector and Employment Trends

or Patterns

The excess informalization has been observed in the agriculture sector during

the past years and a self-cultivation trend and decline in share tenancy was also

observed. Both the agriculture and non-agriculture sectors experienced an increased

informalization. The share of the formal sector employment experienced a decline of

7 percent for males and of 6 percent in favour of females during 2000-2008. Trade

and services sector improved informalization in the urban labour market.21

The urban informal sector has been identified as firm size in Pakistan

(Guisinger and Irfan (1980), World Bank (1989a) and Burki (1990). The informal

sector contributed about 69% of employment in Pakistan in 1972-73 and 72.7% in

1985-86. It was observed as 2.878 million persons in 1972-73 and 4.970 million in

1985-86 with an increase of 4.3% annually. The above mentioned estimates indicate

that it is hard to define the informal sector as marginal to the urban economy, provider

of “employment of the last resort” or as a temporary sponge to take in new rural-

urban migrants who migrate with the expectation of getting opportunity in formal

sector in urban labour market. The informal sector largely prevailed in construction

and trade sector and it is autonomous to the formal (Nadvi, 1990).

The women occupied in the urban informal sector of Pakistan represent a

noteworthy part of labour force of economy i-e about 2 million. The women occupied

the urban informal sector of Pakistan represent a noteworthy part of labour force of

economy i.e. about 2 million. As the surplus and unskilled labour is unable to find

formal sector jobs. The informal sector permits entry and access to enterprises that

would otherwise deny them, and it makes offer for conditions well-matched with their

21

Source: GOP, Economic Survey, 2009-2010.

53

cultural constraints.It is estimated that the informal economy accounts for 40 % of

Gross National Product (GNP) of low-income countries.22

The formal sector employment was mainly outstanding in manufacturing

sector in 1972-73 and 35% work force was engaged in manufacturing in informal

sector.23

During 1984-1985, an increase was observed in the remarkable share of the

informal sector to 70.9 % of urban employment in the manufacturing sector in Sindh

and Punjab (World Bank 1989a) and 71% for all Pakistan in 1985-86 (Burki, 1990).

The formal economy comparatively made inroads in the finance, insurance, business,

services, community and social services sector over this period. The informal sector

stood dominant regarding employment in the construction, wholesale and retail

trading, hotels, transport, communications and storage industries within urban

Pakistan (Nadvi, 1990).

Informal sector is defined in terms of non-agriculture sector due to its

difficulty of defining it in the agriculture sector. The share of the informal sector is

64.6 percent of the employment in the main jobs outside agriculture sector and the

informal sector activities accounted for a significant proportion of total employment

and income generation. There are 68.3 percent of employed occupied in the informal

sector in rural areas is comparatively higher than the participants (61.1%) in the urban

areas. There are more formal sector activities in urban areas (38.9%) rather than rural

areas (31.7%). The male participants are relatively higher in the informal sector as

compared to female workers in the rural and urban areas.24

The majority of workforce is engaged in dominant agricultural activities in

rural areas. The increased participation of female workers is noted by 1.4 percent

during 2008-11. However, the male workers‟ involvement is reduced as compared to

female participants.25

The declining trend has been noticed in labour incorporation

sectors, due to growing dependence on capital intensive techniques. The educated and

skilled manpower increasingly are absorbed by the formal sector. Contrarily, a large

22 see World Bank Study (1989). 23 Guisinger and Irfan‟s estimates (1980) 24 Labour Force Survey (2011-2012) 25 Source: Pakistan Economic Survey (2011-12)

54

proportion of the population of uneducated and unskilled labour force invoke towards

informal sector (Labour Force Survey, 2011-12).

Table 2.6: Employment Percentage in the Informal Sector by Regional Gender

Year Pakistan Urban Rural

Total Mae Female Total Male Female Total Male Female

1997-98 67.8 68.1 64.5 63.3 64.0 53.1 73.1 73.0 74.7

1999-00 65.8 65.8 65.7 63.8 64.1 60.7 68.0 67.6 73.1

2001-02 64.6 64.7 63.0 61.1 61.1 60.7 68.3 68.5 65.7

2003-04 70.0 70.4 65.7 67.2 67.8 61.6 72.9 73.3 69.9

2005-06 72.9 74.2 25.2 71.0 71.2 69.1 74.8 74.3 79.4

2009-10 73.3 73.3 73.1 70.4 70.6 68.4 76.3 76.2 77.7

20010-11 73.3 74.1 71.1 71.2 72.4 63.1 76.5 76.2 79.0

Source: Labour Force Survey (Various issues)

Table 2.6 describes employment share in informal and formal sector. It shows

that, the share of informal sector is observed about 73.8 percent of non-agricultural

employment and its share is comparatively higher in rural areas (76.5%) than urban

areas (71.2%) in Pakistan. Females‟ share in the urban formal employment is higher

at (36.99) percent and lower in rural formal employment (21%) as compared to

respective share of men.

55

Table: 2.7: Employment Percentages by Major Industry and Gender in Informal

Sector

Major Industry Division 2009-10 2010-11

Total Male Female Total Male Female

Total 100.0 100.0 100.0 100.0 100.0 100.0

Manufacturing 21.4 17.8 54.6 22.3 18.6 57.4

Construction 15.8 17.4 1.2 16.1 17.7 0.9

Wholesale & Retail Trade 39.2 42.2 11.5 38.9 42.1 9.2

Transport Storage and

Communication

10.8 11.9 0.8 10.7 11.8 0.4

Community Social and

Personal Service

10.8 8.5 31.7 10.0 7.6 31.9

Others(includes mining, &

Quarrying, Electricity, Gas,

and Water and Finance,

Insurance, real estate and

Business Services

2.0 2.2 0.2 2.0 2.2 0.2

Source: Labour Force Survey (2010-11)

The table highlights trends in informal sector employment by major industries

and gender. The estimates show that informal sector accounts for 38.9 % of wholesale

and retail trade, manufacturing (22.3%), construction (16.1%), transport (10.7%) and

10.0% for community, social & personal services. The share of other catagory is 2

about percent.

56

Table: 2.8: Informal Sector by Major Occupation and Gender in Percentages

Major Occupational Group 2009-10 2010-11


Total 100.0 100.0 100.0 100.0 100.0 100.0

Legislators, Senior Officials & Managers 26.0 28.0 8.2 24.3 26.0 7.6

Professional 2.2 2.1 3.4 1.9 1.8 3.3

Technician & Associate Professnals 4.2 3.4 10.9 4.5 3.8 11.8

Clerks 0.2 0.2 0.2 0.2 0.2 0.2

Service Workers and Shop and Market

Sales Workers

9.0 9.6 3.6 8.7 9.4 2.2

Skilled Agriculture Workers 0.1 0.1 0.2 0.1 0.1 0.1

Craft and Related Trade Workers 29.8 27.0 54.9 31.1 28.3 56.9

Plant and Machine Operators and

Assemblers

6.4 7.1 0.4 6.0 6.6 0.2

Elementary(Unskilled) occupations 22.1 22.5 18.2 23.2 23.8 17.7

Source: Labour Force Surveys, 2010-11

Table 2.8 highlights the informal sector employment trends regarding

occupation and gender. The informal sector absorbs the less educated, unskilled and

semi-skilled workforce by generating employment and income for them. The share of

crafts & related trade workers is about 31.1% which is highest.While, the share of

near-half comprises legislator/senior officials & managers and elementary (unskilled)

occupations is seen at 24.3 percent and 23.2 percent respectively. Services

workers/shop & market sales workers account for (8.7%) followed by plant/machine

operators & assemblers (6.0%). The technicians & associate professionals and

professionals account for 4.5% and 1.9 % respectively. The female workforce is

relatively involved at higher rate than male in craft and related trade activities.

57

Table 2.9: Informal Sector by Employment Status and Gender (%)

Major

Occupational

Group

2008-09 2009-11


Total 100.0 100.0 100.0 100.0 100.0 100.0

Employers 2.5 2.7 0.7 2.9 3.1 0.7

Own Account

Workers

42.0 43.1 31.7 42.7 43.4 36.4

Unpaid

Family

Workers

11.2 10.3 20.0 10.4 9.6 18.0

Employees 44.3 43.9 47.6 44.0 43.9 44.9

Workers‟employment status in the informal sector is observed in table 2.9. It

is indicated that the share of employees and own account worker is 44.0 %, 42.7 %

respectively. The former comprises prime share of females (44.9%) and later of males

(43.4%). It is reported that percentage share of contributing family workers and of

employers is 10.4 % and 2.9 % respectively. The males are 9.6 % employees and

female contributing workers are twice the male workers.26

Although informal sector is growing rapidly but it has always been neglected.

Adequate informations are deficient regarding pattern, nature and extent of the

informal sector activites and characteristics of its participants.

2.4.3 Pattern of Employment and Hours of Work

The weekly working hours can be used to indicate the quality of work in the

labour market. The workers who work less than 35 hours in a week are called

underemployed workers. And the workers are called over employed workers who are

engaged in economic activities more than 48 hours.

26 Labour Force Survey, 2011-12.

58

Table 2.10: Hours of Work by Region (%)

Hours Pakistan Urban Rural

Total 100.0 100.0 100.0

<15 1.6 0.7 2.0

15-24 5.4 2.2 6.8

25-34 7.4 4.2 8.7

35-41 20.4 13.6 23.3

42-48 24.5 29.1 22.5

49-55 11.6 12.7 11.1

56 hours

& above 28.4 36.8 24.9

Source: Labour Force Survey, 2010-11

The table 2.10 makes clear that there are 1.6 percent of participants working

less than 15 hours per week and 20.4 percent are found to be working between 35 to

41 hours. On the other hand, 24.5 percent of over employed are involved into work

activities about 48 hours (Labour Force Survey, 2010-11).

2.5 Unemployment Situation

Now we analyse the pattern and trends so far as unemployment. During year 2008-09,

it is estimated that there is 2.93 million unemployed labour forces in the country

which has increased up to 3.40 million in 2010-11(Labour Force Survey, 2011-12).

Table: 2.11 Unemployment in Million by Gender and Region

Years Pakistan Urban Rural

Total Male Female Total Male Female Total Male Female

2008-09 2.93 1.87 1.06 1.17 0.81 0.36 1.76 1.06 0.70

2009-10 3.12 1.91 1.21 1.23 0.79 0.44 1.89 1.12 0.77

2010-11 3.40 2.22 1.18 1.55 1.08 0.47 1.85 1.14 0.71

Table 2.11 indicates that unemployment rate increases gradually in urban as well as in

rural areas in the economy. The unemployment rate among males has increased too

59

over the last three years. However, it indicates significant drop for female rates in year

2010-11. The change for males is comparatively higher than the females.

2.6 Urbanization, Migration and Pakistan Economy

Urbanization is peculiar feature of mega cities and their crucial functionality

can be considered also. Most of the cities are observed as post-industrial production

sites for the leading-industries of present time, finance and specialized services. They

have become national or transnational market places where firms and governments

can purchase financial instruments and specialized services. In this way, they perform

pivotal role for the coordination, control and service of global capital.

Pakistan‟s urban population experienced an expansion over seven-fold which

led to enhanced total population over four-fold. The remarkable changes in social

sector consequently increased the rate of urbanization and the emergence of mega-

cities. Pakistan is seen as mainly urbanized nation in South Asia as city dwellers

account for 36 % of its population (2008). There is a small urbanization rate of 3 %

during 2005-10. The highest urbanization rate is noticed during industrialization

process and in formative years of Pakistan as urban population availed enough

opportunities till even eighties.

The urbanization rate is expected to boost up further because of natural

impetus of past high growth rates. It is also observed that more than half of total urban

population lives in eight urban areas such as Karachi, Lahore, Faisalabad, Rawalpindi,

Multan, Hyderabad, Gujranwala and Peshawar in 2005 in Pakistan. These cities grew

around 3 percent per year during 2000 to 2005. It is further expected that the growth

rates of these cities will exceed in the next decade.

There is a positive association between proportion of migrants and degree of

populous-ness and hence, provinces make a downward sequence of Punjab with

65.8%, Sindh with 24.8%; KPK with 9.0% and Balochistan with 0.4%. Migration

recedes in all provinces but Sindh is an exception in this regard. Moreover, male

migrants are observed in great proportion in all provinces except in Punjab in

coallation with proportion of females (Labour Force Survey, 2011-12).

60

A positive link between Interprovincial migration and level of urbanization is

observed but in decreasing order of Sindh 44.9 %, Punjab 35.0 %, KPK 19.5 % and

Balochistan 0.6 %. Interprovincial migration also decreased except in Sindh. Similar

gender disaggregated proportions are found. The female migrants are proportionally

higher in all provinces except in KP as compared to proportion of male workers. The

positive correlation is seen in the proportion of intra provincial migrants and the

degree of populous-ness. Provinces form expected series in order of Punjab with

74.2%, Sindh with 19.4%, KPK with 6.1% and Balochistan with 0.3% are similar for

both genders. Interprovincial migration recedes in all provinces except in Sindh.

Male migrants are higher in percentage as compared to female migrants in all

provinces except in Punjab (Labour Force Survey, 2011-12).

2.7 Poverty and Measures

Mitigating poverty has been at the top of the agenda of policy makers in most

of the developing economies. The poverty alleviation as one of the major aims of the

millennium development goals was planned by International organizations like the

United Nations Development Programme (UNDP).27

The way wherein people participate in the labour market is key to urban

poverty dynamics. The non-poor get job in the formal employment, whereas chronic

poverty is related with casual labour or with the business activities of the female

workers. Informal work is a mixed blessing dependent on content which offers an

escape inevitably clear that more chances translate into improved working conditions

or remuneration for the poor (Grant, 2008).

Urban poverty is witnessed in Pakistan and as somewhere else in the Third

World, it is probable to increase. The challenge for poverty predictors and policy

makers, thus, is to recognize and make perceptible urban poverty which is frequently

disguised and problematic to identify. To identify urban poverty magnificently, it is

required to recognize poverty in diverse ways, within the services and interstices of

Pakistan‟s fast growing towns, its metropolitan cities and within low income

settlements themselves (see Beall, 1997).

27 see World Bank, 2008

61

The relationship between poverty and growth is not unambiguous in the

context of Pakistan. A high growth did not result in substantial decrease in poverty in

the 1960s. However, incidence of consumption based poverty diminished despite low

growth in the 1970s. A high growth was observed in 1980s and early 2000s which

reduced poverty, which suppoted the poverty-growth nexus.

The official poverty line is calorie based in Pakistan. It is defined as per capita

food and non-food expenditure per month in order to support food consumption which

yields 2,350 calories per adult equivalent per day. The official poverty line was set at

673.54 in Pakistani rupees in 1998/99. Based on this definition of poverty line, the

head count (proportion of people below the poverty line) was 26.1 percent in 1990-91,

the benchmark year for the MDGs. Meanwhie the universal target of halving the

poverty rate by 2015, Pakistan made the goal for 13 percent reduction in absolute

poverty. The evidence indicated that absolute poverty tended to increase to 34.5

percent in the 1990s, and diminished thereafter to 12.4 percent by 2010/11.28

2.8 Women and Urban Informal Sector

Women are dominant among rural-urban migrants and may well consist of the

bulk of the urban population in some regions of the world. A rising number of

women in Latin America, Asia and Africa migrate in search of opportunities. Some of

the migrant women get formal sector jobs while others are forced to work in the

informal sector where they work temporarily with out getting social security benefits.

The single female migrants have also contributed to the increased proportion of urban

household headed by women who become poorer, face constricted resource

constraints, and have relatively high fertility rates. The changing composition of flows

of migration has imperative economic and demographic implications for a lot of urban

areas of the developing world.

Because of the female-headed households are usually bound to work in low-

productivity informal-sector employment and have to face higher dependency

burdens, this makes them more poor and malnourished and they access low formal

28 see Pakistan Millennium Development Goals Report (2013).

62

education, health care or clean water and sanitation, frequently remaining effectively

excluded from government services.

Many women involve themselves in small business ventures or

microenterprises that have need of slight or no initial capital and frequently involve

the marketing of homemade foodstuffs and handicrafts. Nevertheless, women‟s

limited access to capital results in high rates of return on their very small investments,

the extremely low capital labor ratios restrict women to low-productivity activities

(Todaro and Smith, 2012).

2.9 Concept of Development

In pure economic terms, development generally means capacity of a national

economy, whose initial economic condition has been more or less unchanged for a

long time, to generate and maintain an annual increase in its gross national income

(GNI) at rates of 5% to 7% or more. A common alternative economic index of

development has been the use of rates of growth of income per capita by considering

national ability to expand its output at a faster rate than population growth. Levels and

rates of growth of “real” per capita GNI (monetary growth of GNI per capita minus

the rate of inflation) can be used to measure the overall economic well-being of a

population.

In the past, economic development has also been seen in the ways of alteration

of the structure of production and employment. So the development strategies have

been focused on rapid industrialization, at the expense of agriculture and rural

development. With few exceptions, development was nearly always seen as an

economic phenomenon in which rapid gains in overall and per capital GNI growth

would either“trickle down” to the public in the form of jobs and other economic

opportunities or create the necessary conditions to distribute the economic and social

benefits of growth at a wider scale. Problems of poverty, discrimination, and

unemployment and income distribution were less important than “getting the growth

job done” (Todaro and Smith, 2012).

As in 1950s and 1960s, many developing nations did reach their economic

growth targets but the levels of living of the majority were unchanged. This indicates

63

that something was very wrong while defining the development in this traditional

way. In 1970s economic development was redefined in terms of reduction of poverty,

inequality, and unemployment within the context of a growing economy.

Additionally, “Redistribution from growth” became a common signal.29

Development must be conceived as a multidimensional process, which

involves major changes in social structures and national institutions, as well as the

acceleration of economic growth, reduction of inequality, and poverty. In short,

development must show the whole gamut of change by which an entire within that

social system, moves beyond a condition of life which is widely perceived as

unsatisfactory toward a situation which can be better regarded materially and

spiritually (see Todaro and Smith, 2012).

2.9.1 Three Core Values of Development

The main values such as sustenance, self-esteem, and freedom represent

common goals sought by all individuals and societies. They associate with basic

human needs that are found in almost all societies and cultures at all times. These core

values are examined below.30

Sustenance: The Ability to Meet Basic Needs

Evey one has basic needs for life and existence. These lives sustaining basic human

needs include food, shelter, health, and protection. When any of these is absent or in

short supply, a condition of “absolute underdevelopment” exits. A basic function of

all economic activity, therefore of “absolute underdevelopment” exists. A basic

function of all economic activity is to provide people with the means of

overwhelming the helplessness and misery arising from a lack of food, shelter, health

and protection. In this way, economic development is viewed as a necessary condition

for the improvement in the quality of life that is development. The realization of

human potential would not be possible at both (individual and societal level) with-out

the sustained and continuous economic growth.

29 see Todaro and Smith (2012). 30 see in Goulet, Cruel Choice, 1971.

64

Self-Esteem: To Be a Person

A second universal component of the good life is self-esteem which means a sense of

worth and self-respect. In which a person can not be used by others for their own

interests. All people and societies are in search of some basic form of self-esteem. It

can be given the names of authenticity, identity, dignity, respect, honor, or

recognition. The nature and form of this self-esteem may differ from one society to

anothers and from one culture to another.

Freedom from Servitude: To Be Able to Choose

A third and final value is to constitute the meaning of development is the concept of

human freedom. Freedom comprises an expanded variety of choices for societies and

their members together with a minimization of external constraints in the pursuit of

some social goal we call development.

2.9.2 The Objectives of Development

Development is not just a physical reality but also a state of mind in which society has

got the means to obtain better life, through social, economic, and institutional

processes. Whatever are special components of this better life, development in all

societies must possess these objectives:

1. To enhance the accessibility and widen the distribution of basic life sustaining

goods such as food, shelter, health, and protection.

2. To improve living standards with higher incomes, more access to jobs, better

education, and larger concentration on cultural and human values, all these

will provide enhanced material well-being along with greater self-esteem at

individual and national level.

3. To increase economic and social range available to the people and nations.

They should be at liberty from miseries; illiteracy, servitude, dependence and

narrow-mindedness etc not only in relation to other people but also to other

nations (see Todaro and Smith, 2012).

65

2.10 Basic Indicators of Development

The fundamental indictors of three facets of development are real income per

capita used for purchasing power; health which is gauged by life expectancy,

undernourishment, and child mortality, and educational attainment as evaluated by

literacy and schooling (see Todaro and Smith, 2012).

2.10.1 Human Indicators and Development

Human Capital and Development

Human capital has been defined in indifferent ways. As a broad concept, it is

recognized in form of obtainable human characteristics which enhance income. It

generally takes in people‟s knowledge and skills, obtained to some extent through

education along with their strength and vitality, based on their health and nutrition.

Human capital theory views health and education as available inputs for economic

production (Appleton and Teal, 1998).

The concept of human capital comprises knowledge, skills, attitudes, physical

and managerial effort which are needed to maneover capital, technology, and land

among other things, to produce goods and services for human consumption (UNECA,

1990). Human resource development is concerned with double objective of skills

building and provision of productive employment for non-utilized or underutilized

manpower. Equally the above mentioned objectives stem from investment in man in

the form of education and training that plays the role of institutional mechanisms in

order to improve people‟s knowledge, skills and capabilities (see Meire, 1970).

Health care, of course, is greatly related to such other types of basic needs

satisfaction i.e. adequate shelter, water supply and sanitation: however it has its own

contribution to make because, over all, it deals with person at the individual level (see

Ebrahim, 1984). A good health enhances the economic and social development of a

country. Thus, it is requisite to highlight the issue of health status of the people by a

various policies which must consist on short and long term actions to secure better

health outcomes (Pakistan Economic survey, 2011-12).

66

Both the health and education are closely associated in economic

development. From one point of view, greater health capital may increase the return

to investment in education, partly because health is a vital factor in school attendance

and in the formal learning process of a child. A longer life enhances the return to

investments in education; better health during working life may lower the rate of

depreciation of educational capital. From another point of view, high human capital

may increase the return to investments in health since many health programs

regarding health depend on elementary abilities frequently educated at school,

personal health and sanitation are included, not to mention basic literacy and

numeracy; education is also required for the formation and training of health

personnel. An improved productivity of efficiency due to spending on education

increased the return on a life saving investment in health (see Todaro and Smith,

2012).

Education is generally regarded as a key investment in human resources.

Education can be helpful to improve the learners‟ quality of life. The education can

improve too the individuals‟ skills and efficiency to produce useful things (see

Machlup, 1982).

The primary objective of government policy has been to improve the level and

quality of education by increasing enrolements at faster rate than population growth in

the last few years in Pakistan but it has been observed that literacy and primary

schools enrolment rates in Pakistan have shown improvement. Scarcity of resources,

deficient provision of facilities and training are the primary obstacles in imparting and

expanding education (see Mehmood, 1999).

There has been gradually growing educational facilities in Pakistan economy

overtime. A continuing inafficiency and low investment on education lead to low rate

of improvement in educational indicators. The estimated 39 % of population is (50

percent for males and 27 percent for females was the literacy rate, in 1996-97), still

behind most of the regional countries, especially when females‟ education is separated

(see Mehmood, 1999).

It becomes indispensable to evaluate a nation‟s average health and educational

attainments, which imitate core capabilities. Good health positively contributes in

67

economic and social development at country level. The people of Pakistan have

experienced an improved health over the past three decades. The vision of health

sector is based on healthy population having good health, enjoying better life quality

with healthy living standard. The improved measures are adopted to prevent deseases,

promote health, greater coverage of immunization, family planning, and female health

worker service availability to achieve this objective (Government of Pakistan


“Housing” is as multifaceted issue as the societies it works. Per capita income,

its distribution and housing prices set up the amount of housing that family can afford.

The urban growth rate makes the housing problems stronger due to city size. In

developing countries, a large number of cities have been grown faster and such

growth rate is expected to increase further at faster rate (Yeh, 1984).

A high natural population growth rate and growing urbanization coupled with

constrained availability of resources caused a sharp deterioration in the quality of life

gauged by indicator in the urban areas where resources were allocated for the

development of physical and social infrastructure.31

The potable water in its regular supply (basic need of mankind) is essential for

survival. Several health benefits can be achieved and living standards can be

improved by availiabiliy of quantities of water greater than the minimum amount to

maintain life. The secure and convenient sources of clean water will reduce mortality

and morbidity. The poor people of urban areas of developing countries experience

scarce supply of water due to lack of sources to provide the facility and information to

diminish the effects of unhygienic conditions (Kirke and Arthur, 1984).

In developing countries, there is poor potable water supply that is associated

with health hazard and nearly all the fatal diseases among the children.32

Mainly

observed deseaes are water borne in Pakistan. The estimates show that 60 percent of

the infants died caused by infectious and parasitic diseases, nearly all are water-borne.

This shows that drinkable water supply and sewerage facilities would affect

31 see Banuri, Kemal, and Mumtaz, (1997). 32 Study by Ahmad and Abdul Sattar (2007).

68

favourably the death services in Pakistan.33

Health improvement is an essential

ingredient of socio-economic development. The healthy people prove relatively more

productive as compared to unhealthy people (see Blomquist, 1986).

2.10.2 Education and Employment Situation

Education and training are important in employment creation and income generation

in economy of Pakistan. In this way, the technical and vocational competence of

workforce along with its productivity is required to enhance to meet up the emerging

world‟s challenges. As almost workforce without training and semi-skilled felt

reluctance to take value added production assignments due to the negligence on

supply side issues (Labour Force Survey, 2011-12).

Table 2.12: Education and Literacy by Gender of Working Age Population (%)

Education and Literacy 2009-10 2010-11


No formal Education 0.5 0.6 0.5 0.4 0.4 0.4

Below Metric 37.5 44.9 29.5 38.0 45.4 30.0

Matric But Less than

Intermediate

10.7 13.1 8.0 10.8 13.2 8.4

Intermediate But Less

than Degree

4.7 5.6 3.8 4.8 5.7 3.9

Degree and Above 4.3 5.3 3.4 4.5 5.5 3.4

Literate 57.7 69.5 45.2 58.5 70.2 46.3

Illiterate 42.3 30.5 54.8 41.5 29.8 53.9

Total 100.0 100.0 100.0 100.0 100.0 100.0


The table 2.12 highlights the level of education and rate of literacy

percentages by gender of working population for the duration of 2010-11. It is

noticed that the current labour force largely has skills indicated by higher educational

terms. The level of literacy is observed as low as 57.7 percent. The estimates indicate

that under matric are 38 percent, 11 percent have passed matriculation and 5 percent

33 see Human Development Report, 1996.

69

have completed higher secondry education. The small proportion of degree holders is

observed at 4.3 percent. Educational attainment of females is lower as compared to

males in all categories.

2.10. 3 Literacy Rates

Human progress and wellbeing depends on the literacy status or rate of the people.

Literacy can be considered as a fundamental human need and human right. So, there

is a need to enhance the literacy status of the population to progress.

Table 2.13: Literacy Rates (10 years or above) in Pakistan and Provinces

Provinces Total Male Female

Pakistan

Rural

Urban

58.5

50.2

73.7

70.2

64.5

80.5

46.3

35.6

66.4

KP

Rural

Urban

53.2

50.4

66.2

72.0

70.2

79.8

35.1

31.6

52.1

Punjab

Rural

Urban

59.8

53.3

72.8

69.0

64.3

77.9

50.7

42.3

67.5

Sindh

Rural

Urban

60.1

42.3

77.1

72.3

60.3

84.1

46.0

21.1

69.3

Balochistan

Rural

Urban

49.8

44.6

65.7

69.0

64.3

83.5

26.2

20.0

44.4


The above table shows the literacy rates in the provinces. The literacy rate

marks at 58.5% in Pakistan. The literacy rate for males is observed at 70.2 percent

which is greater than the literacy rate of females. Moreover highest literacy rate is

observed in Sindh that is 60.9 percent.

2.11 Social Indicators and Development

The social capital in a society, comprised institutions, the relationships, the

attitudes and values that preside over interactions amongst people and contribute to

economic and social development. The notion that social relations, networks, norms,

70

and values have an important issue in performance as development of society has long

been there in economics, sociology, anthropology, and political science literature.

However, the idea of social capital been put forth as a unifying concept having these

multidisciplinary views only in past 10 years (see Grootaert and Bastelaer, 2001)

Coleman (1990) explained the social capital as a variety of different entities all

of which comprised on some aspect of social structure, and which facilitated specific

actions of actors; whether they are personal or corporate actors. This definition

implicitly viewed the relations amongst the groups, rather than individuals. This

definition enlarged the concept of social capital by including both the associations

(i.e. vertical as well as horizontal) and behavior within and among other entities, such

as firms.

Social capital comprises the stock of active connections amongst people, the

trust, mutual understanding, and shared values and behaviors that connect members of

human networks and communities and make cooperative action possible. Social

capital makes any organization or any cooperative group, more than a collection of

individual‟s intent on achieving their own private purposes (see Cohen and Prusak,

2001).

2.12 Concluding Remarks

In this chapter, we have made an overview of the urban informal sector, keeping in

view the importance of informal sector in Pakistan economy. The urban informal

sector plays an important role in employment creation and in income generation. It is

noticed that the share of urban labour force engaged in the informal activities has also

increased gradually. The performance of Pakistan‟s economy is viewed in different

sectors regarding informal sector since 50s till present. The study further discussed all

the economic, human and social indictors and their performance in the economic

growth and development over the period of time. Evidence indicates that gradually

and constantly, Pakistan has shown an improved performane since the sixties in terms

of macroeconomic indicators. The GDP growth rate, which indicates an overall

economic activity, has increased significantly. The CPI has decreased, and investment

has increased during the years.

71

Moreover, the share of informal sector accounts for above seventy percent of

non-agricultural employment and its share is comparatively higher in rural areas than

urban areas in Pakistan. The impact of urban informal sector on the economic, human

and social development has been explained.

72

Chapter 3

THEORETICAL FRAMEWORK

3.1 Introduction

The decision to do work is ultimately a decision to utilize time in different

ways. One way in which people utlize their available time is pleasureable leisure

activities. On the other hand, people allocate their time more efficiently to work

related activities, most importantly household production. Alternatively, people have

the possibility to work for pay and can utilize their income for purchaseable

necessities (Ehrenberg and Smith, 1994). For maximum fulfillment of wants, the

efficient resource allocation is reqired. Hence, the efficient allocation of labour is

essential for economic development.

The supply of labour is typically defined as an amount of effort offered by a

given size population. In return, this amount is conveniently decomposed into four

factors: (1) the perecentage of population engaged in or seeking gainful employment,

usually called the labour force participation rate; (2) number of hours people are

agreeable to work per day, per week or per year while they are in the labour force; (3)

the effort that people make per hour a day while doing work; and (4) training and skill

levels that workers take to their jobs.34

The arrangement of the chapter is followed as: Section 3.2 discusses and

explains the conceptual framework related to employment and labour supply.

Theoretical framework of Neo-classical labour supply decision is evaluated in section

3.3. Section 3.4 describes the basic human capital theory. Section 3.5 elaborates the

theoretical approaches towards the urban informal sector. The last section 3.6 shows

the concluding remarks.

3.2 Conceptual Framework

A theoretical framework can be developed after problem definition,

completing a literature review and conducting field survey. Developing a good

34 see Berndt (1991)

73

theoretical framework is central to examine the problem under investigation and to

hypothesize and test certain relationships.

The study makes a detailed view of the concepts and issues which are

correlated to supply of labour and informal sector employment such as self-employed

(a person who during the reference period, engages in work activities for profit and

family gain, in cash and kind where the remuneration is directly dependent upon the

profit, the potential profits derived from the goods and services produced) without

getting assistance even from unpaid family members ; own account workers (a person

who is operating his or her own economic enterprise or involving independently in a

profession or trade, and without hiring employees). However, he or she may get

support by unpaid family workers; employers (a person who does work during the

reference period, on own-account or with the help of one or a few partners at a „self-

employment job‟ hiring one or above employees on a continuous basis); owner of

firms and unpaid family helper (a person performing without getting pay by an

economic enterprise being operated by his or her household or other related persons is

regarded as unpaid family member) and employee (a person who performs work to

get pay in kind from a public or private enterprise e.g casual wage worker and

domestic servants) (Labour Force Survey, 2005-06).

3.2.1 Labour Supply and Employment

The employment is defined as all persons who are 10 years old and over who

work minimum for one hour during the reference period and are either paid as

employees or self-employed. Permanent or regular employees who remain away from

work during the reference period, due to some reason but still receive their salary or

wages are also regognized as employed.35

The employed are defined as those individuals who worked for one hour or

more in order to earn wages or salary during the reference week or made use of their

energy for 15 hours or more of unpaid work in a family business or farm. Those

individuals, who did not work due to illness, inclement weather, or strikes and

lockouts are included too as employed but are incorporated in the separate sub-

category with a job but not work during work hours (see Brendt, 1991).

35 Labour Force Survey, 2011-2012.

74

3.3. The Neo-Classical Theory of Labour Supply Decision36

The study explains some theoretical model of labour supply management and

labour force participation developed by different economists. In this research, the

focus remained on growth of the informal sector and policy in urban areas of Southern

Punjab, Pakistan. We examine the individual‟s participation decision and the hours

supplied by the individual, given that participation. In this framework, the further

extensions are made to encompass the household rather than the individual as a

decision unit. Basic human capital theory and approaches to urban informal sector are

also explained.

3.3.1 Neo-Classical Individual Labour Supply37

Neo-Classical theory is an application of theory of consumer behavior. The

assumption is that individuals allocate time to market work and non-marketable

activities (leisure). Maximum utility is achieved by choosing a combination of goods

and leisure hours subject to time, price and income constraints.38

One dimension of labour supply is labour force participation (to be employed

or unemployed) where individuals have options whether to work for leisure or not?

An individual‟s priorities regarding making such decisions are determined by a two

pronged differentiable utility function U = F (Y, L), that depict the utility (U) which is

obtained from consuming alternative quantities of goods (Y) and leisure (L). It is

considered positive both the marginal utilities of Y and of L (MUY ≡ ∂U/∂Y and MUL

≡ ∂dU/dL), and the utility function is concave in Y and L, which implies that ∂2 U /

∂Y2, ∂

2 U / ∂ L

2 < 0, and ∂

2 U / ∂ Y ∂ L > 0.

Same satisfaction level is generated along with an indifference curve by

alternative combination of both L and Y. In Fig. 1.1, four indifference curves showing

greater levels of utility are drawn, I0, I1, I2, and I3. The slope as an important property

of indifference curve is derived in the following. The total differential of the utility

function U (Y, L) is

36 see Berndt (1991) 37 see Berndt (1991) 38 see H.Gregg Lewis (1975) in which labour supply model is presented.

75

dULUdYYU /(/ (3.1)

Along a given indifference curve, dU = 0. Substituting dU = 0 into Eq. (3.1)

and reorganizing yields the slope of indifference curve (dY/dL) in Fig.1. It is known as

the negative of the marginal rate of substitution of leisure for consumer goods. Below

it is denoted as –MRSLY

MRSMU

MU

Y

U

L

U

dL

dY

Y

L

LY (3.2)

Since by assumption, MUL and MUY both are positive, indifference curves

slope downward. The concavity entails the convexity of indifference curves to the

origin. Though it is possible to substitute L for Y and keep utility fixed, the greater the

ratio of L to Y, the greater the marginal amount of L required compensating for giving

up a marginal amount of Y.

Figure: 1.1 Indifference Curves and the Budget Constraint

Since indifference curves that are away from the origin signify successively

higher levels of utility, individuals who want maximum uility will choose the

indifference curve which is highest, considering his or her budget constraint.

76

Prices, non-labour income, and time are the primary three factors that

influence the budget constarint. First, if the unit price of goods be PY, and the

exogenous and constant wage rate be PL, then the individual‟s real wage is PL/ PY.

Second, the individual‟s nonlabour or property income is shown as Q; in case of

consumption goods the real amount of nonlabour income is Q/PY. Third, leisure

(nonmarket) hours L plus hours which is dedicated to market work H must exhaust T,

that is, L+H = T. Further, labour income equals the product PLH, and the real labour

income forgone by choosing one more unit of leisure as an alternate for working

equals PL/PY.

If individual is assumed to spend all of his or her available income, the above

three factors imply the budget constraint

YPQLTPQHPI YLL )( (3.3)

In the equation above, sum of labour and non-labour income is represented by

I, the total money income. There are two ways to write Equation (3.3). As the notion

is that an individual spends his or her full income „C‟ on goods and leisure, Gary

Becker [1965] has included PLL in the both side of Eq. (3.3), obtaining

QTPLPYPYPLPIC LLYYL (3.4)

This formulation indicates that full income budget constraint is comprised of

total amount of available time, T, which is evaluated at the constant wage rate PL plus

non-labour income Q. This full income C is then totally consumed on leisure (PLL)

and on goods (PY Y).

On the other hand, to facilitate graphical analysis, Eq. (3.3) is rewritten in

terms of real income,

LPY

P

Py

QT

P

Py L

y

L ..

(3.5)

When Eq. (3.5) is graphed as in Fig. 1.1, the budget line (MM) which

represents the income constraint, with intercept equal to [(PL/PY). T + Q/PY] and slope

equal to - (PL/PY). Note that even if L = T (while all time is devoted towards leisure),

77

the budget line M'M does not cross the horizontal axis unless nonlabour income Q is

zero.

Utility maximization subject to the budget constraint Eq. (3.5) involves

choosing the set of Y and L that is feasible (on budget line) and is on the highest

indifference curve that touches the budget line M'M. This point is at S, where the

slope of the indifference curve I2 (-MRSLY) equals the slope of the budget line – PL/PY

in Figure.1.1. It is possible for the individual to purchase goods OY', to choose leisure

OH', and the supplies hours of labour H'T to the market at this point.

More formally, the individual obtains solution of his or her maximization

problem by maximizing U = F (Y, L) subject to the budget constraint PYY = PL (T-L)

+ Q. However, the Lagrangian function is applied here

])([),( QLTPYPLYU LY (3.6)

We take first partial derivatives of Ψ with respect to Y and L, set them equal to

zero, and then solve. This yield,

Y

L

LY

Y

L

P

PMRS

MU

MU

YU

LU

/

/ (3.7)

It is shown in Eq. (3.7) that utility is maximized at the point where, MRSLY

(the negative of which, by Eq. (3.2), is equal to the slope of the indifference curve)

and the real wage rate PL/PY (the negative of which, by Eq. (3.5), equals the slope of

the budget line M'M).Collectively they are equal.

Fig. 1.1 indicates that the individual is allowed to maximize his or her utility

at an interior solution S where L < T and H > 0, that, where the individual participates

in the labour force with non-zero H. The case that is mentioned above is critical to

understand the decision of labour force participation. Assume that individual would

earn the lower wage rate by working in market with some non-labur income Q and

preferences as before, he now faces budget constraint PY Y = PL' (T-L) + Q, which is

drawn in Fig.1.1 as the flatter budget line M''M . The individual could attain highest

indifference curve, I1, by showing his or her preferences and the budget constraint

M''M. At point M, the indifference curve I1 touches the budget line MM, where L = T

78

and H = 0, such point shows less partaking of individuals in the labour force by

spending all of his or her time in leisure (nonmarket activities). The individuals are

unable to attain merely any higher indifference curve with such preferences and

budget constraints.

Point M shows a corner solution to the individual‟s utility maximization

problem rather than an interior solution. Note, particularly the slope of the

indifference curve is steeper than that of the budget line, indicating that MRSLY >

PL/PY at the corner solution M, rather than Eq. (3.7) holding, where MRSLY = PL/PY.

This suggest that decision of labour force participation can be seen simply as

complying to whether the individual‟s has any utility maximization problem, given

budget constraints, yields a corner solution or an interior solution. In particular, if at

the solution point, MRSLY = PL/PY, then H > 0 and L < T- an interior solution occurs

and if instead at the solution point, MRSLY > PL/PY, then H= 0 and L= T- a corner

solution is being obtained.

Neo-Classical framework of labour supply puts forward that individual‟s act

rationally for maximization of their utility by willingly opting for jobs administered

by the basic condition that market wage rate exceeds reservation wage. The

reservation wage is actually the amount of extra earnings the individual would be

provided to give up one unit of leisure, the maximum wage where he or she is

willingly to involve in employment, is denoted as w* and indicated by the slope of

indifference curve at point M. 39

Fig. 1.1 shows that the reservation wage w* is greater

than the market wage PL at budget line M''M that is, the extra satisfaction from an

hour of leisure is greater than the wage rate. However, if the wage rate rises then

budget line rotates upward from M''M to M'M, and then at certain point the wage rate

would surpass the reservation wage, which results in a positive labour force supply.

Hence, PL > w* indicates positive labour force participation condition.

A few important imlications of this economic theory of labour force

participaion are noteworthy. Firstly, for individuals having identical reservation

wages those who have higher (potential) wage rates are being participated more in the

39 This reservation wage notion is apparently due to Jacob Mincer (1963). Important extensions and

applications include those of Reuben Granau (1973b) and James Heckman (1974b); also see Gronau

(1986).

79

labour force. Secondly, for individuals having with identical potential wage rates

those with lower reservation wages are being participated more in the labour force. As

“workaholics” have lower reservation wages as compared to, say, avoid hobbyists,

given identical potential wage rates, workaholics are working more. Likewise, women

who have to tackle expensive daycare of their children are likely to have higher

reservation wages as compared to single, career-oriented women having no children;

other things being equal, we would expect the higher labour for participation rates in

the later case. It is noteworthy, that such difference in preferrences among individuals

is indicated by the shape and slope of their indifference curves. Additionally, the

shape of indifference curve may change for a given individual at different points

during his or her life cycle.

The above presented theoretical framework generalizes the cases in which

only several points for hours at work are obtainable to employees. Such situations

state the optimal corner solution i.e. the largest amount of working hours where

market wage exceeds or equals reservation wage.

The changes in non-labour income and in the wage rate affect labour supply.

Suppose that the individual faces an increase in non-labour income from TM to TC in

Fig. 1.1 with constant wage rate at PL and obtains new budget line which is N'N

parallel to the old budget line M'M. If we firstly discuss initial equilibrium at S, the

increased non-labour income makes possible the movement of utility maximizing

individual to a higher indifference curve, I3, tangent to the new budget line N'N at

point S'. This point shows the increases in hours of leisure consumed to OH'', the

decreases to H''T in amount of labour supplied, and increases the amount of goods

consumed upto OY''. The pure income effect is the result of outward movement from

S to S' and increases the L and Y due to increased non-labour income. However, pure

income effect on hours of labour supplied is negative which reflects the implicit

assumption i.e. leisure as a normal good.

When the individuals experience a change in wage rate, the response reflects

both income and substitution effects. Fig. 1.2 illustrates the initial equilibrium point S,

at which the I0 indifference curve is tangent to the original budget line M'M, which

shows OY units of goods and OH hours of leisure consumed and HT hours of labour

supplied. Now, there is an increase in wage rate while goods prices, preferences, and

80

nonlabour income remain unchanged. The increased wage rate results in an upward

rotated new budget line M''M, tangent to the higher, indifference curve I1 at point S'.

The utility maximizing individual faces more goods consumed from OY to OY',

decreases the leisure amount chosen from OH to OH', and increases labour supplied

hours to the market from HT to H'T at this new equilibrium.

The movement from S to S/ can be usefully decomposed into pure income and

compensated substitution effects. The compensated substitution effect (which is the

definite response of the individual in result of change in the wage rate while holding

utility fixed) involves a movement along the individual indifference curve I0.

Graphically, it is shown by notionally reduction in individual‟s non-labour income by

imposing tax on it that the new notional budget line R'R has the same slope as the new

budget line M''M (reflecting the higher wage rate) but is just tangent to the original

indifference curve I0 at point S''. This budget line R'R, concerning utility, just offsets

or compensates for improvement in earning power that is the result of increased wage

rate.

Figure: 3:2 Income and Substitutin Effects in Response to a Change in the Wage Rate

Fig.3.2 demonstrates the movement from S to S'' is the compensated

substitution effect; it directs or leads to reduced leisure OH'' and enhanced labour

supply H''T, reflects the fact that leisure becomes rather expensive due to increased

O

81

wage rate. By isolating this compensated substitution effect, the pure income effect

(without changing the prices) is the residual movement from S'' to S', with addition

leisure from OH'' to OH' and supplied labour to the market falling from H''T to H'T.

Incidentally, the (nomenclature) can be confusing, it is noteworthy that the total

movement from S to S' due to a rise in wage rate is called the uncompensated or gross

substitution effect in copious literature. Hence, the summation of compensated

substitution effect and pure income effect is uncompensated or gross substitution

effect.

The above graphical analysis shows that an increase in the wage rate leads the

utility-maximizing individual to respond in two different ways. Firstly, the

uncompensated substitution effect results in more labour supply and less leisure.

Secondly, pure income effect indicates a reduction of labour supply and more leisure.

Figure 1.2 shows that a positive compensated substitution effect on labour supply lags

behind the negative pure income effect.

Two notable remarks are as follows. First, it is assumed in the above analysis

of income and substitution effects, interior solutions occur before and after increase in

wage rate, that is, it is conditional for positive labour force participation. Although it

is not indicated here, it can be demonstrated if the original solution becomes corner

one with no participation, then a sufficiently large wage increases, ceteris paribus,

could result in positive labour force participation, and reduction in nonlabour income,

ceteris paribus. Thus, participation decisions can also be evaluated for income and

substitution effects.

The way we write the gross, compensated substitution and pure income effects

of change in wage rate are in terms of calculus and in elasticity form 40

and this results

in the well-known Slutsky equation predominantly,

I

HH

P

H

P

H

UUL

gross

L

. (3.8)

40 Follow the theory of consumer demand as, for example, in R.G.D Allen (1938) and Angus Deaton

and John M. Muellbauer (1980).

82

Where, I am money income. The first term on the right-hand side of Eq. (3.8)

is the compensated substitution effect by holding the utility constant, and the second

term indicates the pure income effect. If one multiplies the complete expression in Eq.

(3.1) by PL/H and then multiplies and divides the income effect by I, so Slutsky

relation is obtained in elasticity form,

H

I

I

H

I

HP

H

P

P

H

H

PL

P

H L

UU

L

L

gross

L

....

(3.9)

It can be rewritten as

IHL

c

L

g SLHPHP .. (3.10)

Where SL is the share of labour in total money income and where the g and c

subscripts on substitution elasticities to gross and compensated changes,

correspondingly.41

The Neo-Classical analysis predicts that labour force status of an individual is

therefore determined in a two stage process. Firstly, an individual decides whether or

not to supply labour to the market or not. Secondly, whether he is employed or not, it

is assured by a combination of factors including labour demand (employers

preferences, skills, experience, education, marital status and sex), incentives to look

for employment rapidly and to accecpt any job opportunities. The theory has also its

drawbacks; it does not take into account the family members‟ interdependence and

their decision-making that it doesn‟t succeed or fail to promote constructivity (Van de

Brink, 1994).

3.3.2 Household Labour Supply42

In fact, generally, decision of labour supply is taken in the context of the

decision made by other household or family members. Killings-worth (1983) made a

distinction among three approaches that connect family membership to supply of

labour.

41 The expressions in Eqs (3.8)-(3.10) implicitly assume that interior solutions occur both before and

after the small change in PL. 42 see Berndt (1991).

83

It is assumed in male chauvinist model that wife takes into account her

husband‟s earnings in form of property or nonlabour income while taking labour

supply decisions, however the husband decides to supply his labour exclusively just

taking into account his own wage and actual non-labour income of family.43

The

property income is assumed to incorporate both incomes (labour and non-labour

income) of the husband. In second approach it is assumed that the existence of a

family or household aggregates utility function U = U (Y, L1, L2, -----, Ln), where Li is

the leisure which the ith individual of family consumes. This is the family utility

function and is maximized subject to a family budget constraint.44

In labour supply analysis, family utility approach has become familiar due to

fact that various renowned comparative statics are the consequences of framework of

individual utility maximization goes with little adaptation. The family utility function

approach shows four substitution effects (the response of labour supply of the ith

family member to a change in his or her own wage rate), but two cross-substitution

effects also occur, that involve the ith family member‟s labour supply response due to

a change in the jth family member‟s wage rate and vice versa.

Within the existence of aggregate family utility function, the compensated

cross-substitution effect of a change in the ith individual‟s wage rate on the member‟s

labour supply must be same as the effect of the jth member‟s wage rate on the ith

member‟s labour supply. Moreover, though these effects must be equal in magnitude,

and yet their signs can be either positive (indicative of substitutability) or negative

(complimentarily). However, because the pure income effects on the two family

members need not be the same, the gross (uncompensated) cross-substitution effects

necessarily are not equal. Hence the gross effect of a change in the wife‟s wage rate

on the husband‟s labour supply is not equal to the gross effect of a change in the

husband‟s wage rate on the wife‟s labour supply.

43 This choice of nomenclature is somewhat unfortunate, since while chauvinism is clearly undesirable,

this particular model might or might not be a reasonable description of household decision making. 44 For example the use of the male chauvinist model, see, among others, Bowen and Fineagan (1965,

1969) and Hausman (1981a).

84

The detailed view of a special case of the family utility function framework

was made by authors.45

It occurs when the compensated cross-substitution effects

equal zero for all members in the family. Such an illustration notifies change in the ith

member‟s wage rate which entails a pure income effect on the labour supply of the jth

individual. This signifies that the labour supply function of the jth family member

depends on his or her wage rate and on the sum of labour and non-labour income of

all other family members.46

The family utility function approach is commonly used in studies empirically

done on labour supply because of its analytical easiness. It is note worthy, yet, there

are numeral drawbacks in this framework. Since the aggregate utility function entails

that the family derives utility from consumption collectively, the distribution of goods

consumption does not matter, while this might make sense for family “public goods”.

Furthermore, the approach to family utility function is silent as to the process that

really makes an aggregate utility function in which all members of family are agreed

equally.

Marital status is considered important one in household labour supply models.

In this context, Gary Becker (1974, 1981, and 1988) makes an effort to endognize the

marital status, consumption, and the labour supply decisions of individuals jointly in

his latest work, but, at this level, the author has not developed a model which was

fully empirically implementable.

3.4 The Basic Theory of Human Capital

The human capital theory is a dominant economic theory of wage

determination. The theory was developed by Jocob Mincer (1957, 1958, and 1962),

Theodore Schultz (1960, 1961), Gary Becker (1962, 1964) and Blauge (1969). The

human capital theory in framework of neo-classicals concentrates that individuals

make investment by spending on education and training in order to enhance their

market skills, productivity and earnings. In the theory, the emphasis has been made on

45 see in Malcolm S. Cohen, Samuel A. Rea, and Robert I. Lerman (1970) and Orley Ashenfelter and

James J. Heckman (1974), 46 Applications of this model include Marvin Kosters (1966); Malcolm Cohen, SamRea, and Robert

Lerman (1970); Robert Hall (1973); Orley Ashenfelter and James Heckman (1974); and Jerry Hausman

and Paul Ruud (1984).

85

individual differences in years of education, lenth of the on the job training and those

important factors that induce individual to make comparatively more investment on

human capital than others.

Mincer (1958) presented a theoretical model in which implications for income

distributions of individual differences in investment in human capital have been

derived. He assumed the process of investment was subject to free choice i.e. training.

As the time spent in training found a delay of earnings to a later age, the rational

choice assumption indicated an equalization of present values of life-earnings at the

time the choice was made. Intra-occupational differences increased when human

capital investment included experience on the job. Age measured the obtainable

experience process along with biological growth and decline. The growth of

experience and hence of productivity showed increasing earnings which increased

with age, up to a point when biological decline started to influence productivity badly.

The differences in training caused the differences in earnings levels among

''occupations" in addition to the differences in slopes of life-paths of earnings among

occupations and these differences seemed systematic.

Mincer (1970) presented a theory of lifetime behavior of individuals. It was

stated that the distinction between longitudinal (cohort) analysis and coexistent (cross-

section) analysis would not matter in some special cases of a stationary economy, or

of an economy where the changes were "neutral" regarding categories adding the

human capital model. He argued that modifications which were introduced by secular

change must be considered when the models were used in cross-section.

Becker (1962) analyzed the investment in human capital and found that the

differences in earnings among persons, areas, or time periods were generally due to

differences in physical capital, technological knowledge, ability, or institutions. The

analyses showed that investment in human capital also had a vital influence on

observed earnings because earnings seemed net of investment costs and gross of

investment returns. Certainly, an appreciation of importance of human capital seemed

to resolve many else findings regarding earnings. However, observed earnings were

gross of the return on human capital were influenced by changes in the amount and

86

rate of return. A lot of human capital investment enhanced observed earnings both at

older ages, and at earlier ages.

The attention was paid on the job training (i.e. a specific kind of human

capital) because it usually emphasized on common effects to a general theory.

However, the theory had very vital implications such as earnings were gross of the

return on human capital; some persons may earn more as compared to others because

they spend more on their education. And, since talented persons tended to invest more

than others, the earnings distribution could be very unequal and even skewed, even if

"ability" was symmetrically and not too unequally distributed. The learning, both on

and off the job, and other activities had similar effects on observed earnings as do

education, training, and other traditional human capital investments. Furthermore,

human capital investment did not affect earnings because costs were paid and returns

were gathered by the firms, industries, or countries that used capital. Schultz (1960)

stated that education levels were aggregated, the proportion of total costs attributable

to earnings foregone tended to rise gradually because of more importance of

education at secondary and higher level in current years, a change that offsets the

decrease in the foregone-earnings proportion of high school education alone. He

further stated that spending on education as "investments" based on the behavior of

people looking for investment opportunities would not inconsistent with the

hypothesis i.e. rates of return to education were adequate larger as compared to the

rate of return to investments in physical capital to have "induced" the implicit larger

growth rate of such kind of human capital.

Blaug (1976a) argued that individuals make investment on themselves just for

the future gains, pecuniary and non-pecuniary. He further argued that well educated

population is a productive, ingenious, and inventive resource for both the growth and

wide-ranging growth. This indicated that human capital formation included the formal

and informal education, on-the-job training and „learning by doing‟ because all of

these made a contribution to enhance the people‟s economic capabilities (see

Chattopadhay, 2012).

Becker (1964) emphasized on human capital investments influencing an

individual's potential earnings and cognitive income. The investments in human

87

capital consisted of level of educational, on-the-job skills training, health care,

migration, and regional prices and income issues. The earnings tended to increase

with education and skill level of the individuals. He also discussed the costs and

returns of investments and social and private gains of individuals and compared them

based upon education as well as level of skill.

Schultz (1961) stated that investment in human capital benefits both the

individuals and society. The improvement can be brought out in the quality of human

efforts and its productivity can be increased. Investment in human capital increased

real earnings per worker. The human capital can be estimated by its yield relatively

than by its cost while any capability produced by human investment becomes a part of

the human agents and that‟s it can not be sold; it is however”in touch with the market

place and influences the wages and salaries which the human agents can earn. The

induced increased earnings were the yield on investment. In order to improve human

capabilities, the improvements in activities were necessary. These investments may

lead to improve the human capabilities: (1) health facilities and services incorporating

energies, and the strength and vitality; (2) On-the-job training, incorporating

apprenticeship of old style; (3) formally organized education at the elementary,

secondary and higher level; (4) adults study programmes i.e. protracted programs

particularly in agriculture; (5) individuals and families‟ migration to make adjustment

according to the changes in job chances.

His theorem indicated that on the job training decreased the worker‟s net

earnings at the beginning and enhanced their earnings afterward. In addition,

individuals spend time in searching for job opportunities and gathering information.

3.5 Theoretical ApproachesTowards the Urban Informal Sector

The theoretical literature in terms of the urban informal sector enlightens three

approaches. The precise explanation of these approaches is given below.

3.5.1 Dualistic Labour Market Approach

The dualistic view is that underdeveloping countries are catagorised into two different

sectors: One is modern and dynamic sector which is characterized by capitalist

method of production; and second is a marginal or „subsistence‟ sector overpowerd by

agriculture, characterized by pre-capitalist mothedos of production. The major

88

hypothesis was that the wage determination process in both the sectors was different.

Firstly, a theoretical model of development was presented by Lewis (1954) in a

dualistic economy. However, he based his model on Classical School foundations

having two sectors (i.e. agriculture and non-agriculture) with planned symmetrical

behavior for each one. He discussed the transformation of surplus labour from the

traditional sector and and its absorption in the modern industrial sector in order to

start the development process in an effective way. Fei and Ranis (1964) emphasized

upon the simultaneous growth of agriculture and industrial sectors and product

dualism on their organizational dualism.

Todaro (1969) model stated that people are migrated from rural to urban areas

inspite of unemployment in cities. Moreove, labour force participants, compared their

expected incomes for a given time limit in the urban sector with the prevalent average

rural incomes, and they are migrated if the former surpasses the later. The Harris-

Todaro (1970) model of migration process proposed that all migrants intended to get

employment eventually in urban modern sector employment but did not clarify the

movement of the participants of urban subsistence sector. The authors notioned that

intersectoral wage gap was influenced by reallocation of labour between the sectors

along with the probability of obtaining job in the formal sector. Fields (1975) traced

out that the migrants have three choices: a job in the formal sector, open urban

unemployment and a possibility of getting job in the urban informal sector. But

Banerjee (1983) showed that entrants in informal sector were immersed to Delhi due

to additional chances to find the informal sector work. The new migrants searching

for job in the formal sector took the informal sector as a temporary staging post.

3.5.2 Neo-liberal Approach

The Neo-liberal approach focused on the legal instruments influencing the

providence and existence of informal sector. The enterpreneures were attracted

towards informal sector due to prolonged registration procedures, difficult

administrative steps and the costs incured to make an enterprise legal. The role of

informal sector was looked upon as most advantageous and harmonized response of

economic units towards government-induced distortions i.e minimum wages and

excessive taxation policies (De Soto, 1989).

89

Rauch (1991) found the informal sector as a voluntary phenomenon of firms to

avail legal exemption benefit from a mandated minimum wage policy because it

distorted resources away from first best allocations. Loayza (1996) used an

endogenous growth model and found that informal economy tended to increase in

results of excessive taxes and regulations that were imposed by governments having

inability to implement compliance. Fortin et al. (1997) studied the effects of taxation

and wage controls in an informal sector in developing economy. He found that

significance of the informal sector, unemployment rate and efficiency costs were

increased due to increased tax rate on profits, in the payroll tax, and in the

government set wage rate. Sarte (2000) drew a connection between bureaucratic rent-

seeking, the informal economy, and economic growth. The increased costs of

operating out official laws caused informal sector disappear endogenously

(internally). An increase in minimum wages affected the urban formal sector

employment negatively excluding white-collar workers (Suryahadi, 2003).

3.5.3 Structural Articulation Approach

The focus of this approach is on the neo-liberal school. The urban informal

sector was distinguished into two parts i.e. modernizing dynamic and a traditional

stagnant one. The informal sector was a disadvantaged part of a dualistic labour

market and a dynamic sector tied by subcontract to the urban formal sector and

dualism looked in relation to wages that were exceeded the market clearing level

(Ranis and Stewart, 1999). Florez (2002) defined informality in dualistic approach in

which activities were made for subsistence purpose and for the purpose of drecreasing

costs of labour and attainment of capital accumulation.

Attanasio et al. (2003) worked on trade reforms and wage inequality and

found the influence of drastic tariff reduction on the wage distribution. The skill

premium increased due to skilled biased technological change. However, the sectors

with the largest reductions in tariffs were those with the sharpest increase in the share

of skilled workers. Regarding industry wage, premium diminished more in sectors

which faced large tariff reductions and the diminishing premium increased inequality.

The increasing size of informal sector was related to the increased foreign competition

i.e. sectors which experienced large tariff reductions and trade exposure enhanced

their informality in prior to the labour market reform. Overall, effect of the trade

90

reforms on wage distribution was trivial. The poor people employed in the informal

sector did not earn low incomes in the economy (Dasgupta, 2003). The informal

employment acted in response to the strength of enforcement and, perhaps corporate

tax rates (Ihring and Moe, 2004).

The informal employment was also considered a voluntary phenomenon. The

informal sector was distinguished into two sub-sectors i.e. the intermediate sector

which appeared as a reservoir of self-motivated entrepreneurs and the community of

poor comprised on large body of left behind and underemployed labour (House,1984).

The migrants and newcomers with less human and physical capital participated in the

labour market and started work to avoid their main requirement of being easy entrant

into the sector (Tokman, 1986). Fields (1990) devided the informal sector into two

segments i.e. upper-tier informal sector and „easy entry‟ ones. The mobility from

formal to informal sector and earnings differentials revealed the participants‟

willingness to work in the informal sector (Maloney, 1999).

3.6 Conclusion

We have pointed out different theoretical concepts concerning labour supply

decisions and labour choice in sector of employment. In this chapter, we have

discussed the Neo-classical theory of labour supply decision. The individual‟s labour

force participation decision and the hours supplied by the individual given that

participation are explained. Furthermore, we have described the household

participation decision and hours of labour supplied. We have also explained the basic

human capital theory. The theoretical approaches towards the urban informal sector

are also discussed. In conclusion, it is found that the theoretical approaches towards

urban informal sector and neoclassical theory of labour supply47

are the relevant and

suitable measures to determine the urban informal sector employment in Southern

Punjab, Pakistan. The Human capital theory by Becker also relates regarding earnings

determinants of the participants of urban informal sector. These theories have been

elaborated by various economists in their studies.

47see Gary Becker (1965), Jacob Mincer (1962), Shelly Lundberg (1985).

91

Chapter 4

LITERATURE REVIEW

4.1 Introduction

The informal sector absorbs bulk of such labour and hence reduces the

problem of unemployment or underemployment to a large extent in underdeveloped

countries as indicated or evidenced by theoretical and empirical literature. The effort

to highlight this problem has been made at national and international level and some

strategies are suggested to overcome this emerging issue. Moreover, this issue of

informal sector calls for further research for policy suggestions.

We point out the review of the informal sector and employment at national

and international level considering its importance. The present chapter is planned as

follows: Section 4.2 introduces the classic theories of growth and development and

informal employment. In section 4.3, we review the aspects of the urban informal

sector; its determinants, earnings determinants and impact on poverty or development

at international level. In section 4.4 we present the review of the literature concerning

different aspects at national level. Section 4.5 presents some concluding remarks.

4.2 Informal Employment and Classic Theories of Growth and

Development

In the 2nd

half of the 20th

century, the countries under colonial rule started the

development process. However, it started with the beginning of industrial revolution

in Western countries. Industrial revolution came in Britain between 1760 and 1820.

Capitalistic industrial economy took place of feudal agrarian economy among Britain

and Western coutries by characterizing a laissez-fair economy at intial stage of

industrial capitalism. Other European countries like France, Germany, and the new

World of the United States of America have experienced a similar industrial

revolution as that in Britain.

Adam Smith (1776) presented the theoretical base for such a philosophy of

laissez-fair economy. He argued that invisible hand of providence in the market

transformed the fruits of private entrepreneurs in search of self-interest into

production and supply of goods and services which society requires. The philosophy

92

of laissez-fair took a turn in favour of state intervention based in multi-justification.

Thus the monopoly legislation and its implementation provided the reason for the

emergency of monopolies and oligopolies.The solution of inequalities in form of

progressive taxation and other measures made case for redistribution of income and

wealth and eventually for guiding the welfare state. J. M. Keynes (1936) gave

suggestions for compensatory fiscal measures as a way to enhance income and output

and diminish high unemployment (business cycle which was the result of business

cycle and particularly Great Depression of 1930).

John Kenneth and Galbraith (1979) followed the Pigou‟s approach and

contrasted between private opulence and social squalor. Thus, the policy or

philosophy of state intervention aimed at the optimum use of resources and

maximization of consumer satisfaction. However, Malthus (1978) highlighted the

fundamental problem of imbalance between population growth and production. He

further argued that population control measures could be the way to overcome this

high population growth problem. Schumpeter (1995) focused on inventions and

innovations that were responsible for trade cycle and lead to or directed development

of capitalistic economies.

All these theories were explained by Classical and Neo-classical or even

Modern Economists. However, they did not illustrate theory of development

comprehensively.

The classic post-World II literature, on economic development has been

dominated by four energetic and provocative aspects of thought discusses the linear-

stages-of-growth model, theories and patterns of structural change, the international-

dependence revolution and neoclassical free-market counter-revolution. In current

years, an approach has emerged that is miscellaneous and draws on all of these classic

theories. The wide range of contending theories and approaches are presented in order

to study the economic development.

Walt W. Rostow was an American economist who presented „Stages of

growth‟ model of development.48

The author argued that process of

48 A theory of Economic Development, associated with the American Economic Historian Walt

W.Rostow, according to which a country passes through sequential stages in achieving development.

93

underdevelopment to development, whereby all the developed industrial nations of

the world transformed themselves from backwardness to prosperity, can be explained

in terms of stages of growth.49

In the linear stages model, emphasis is placed on

imperative role of saving and investment to enhance sustainable long-run growth. But

Rostow‟s historical theory did not explain development sufficiently.

Simon Kuznets (1959) explained the contribution of technology in

development so as to make capital productive. Almost underdeveloped countries are

fortunate enough to absorb such kind of productive technology. However, these

underdeveloped countries are endowed with abundant human capital especially

surplus labour and have to adopt labour-intensive technology. He argued that poor

countries should not needlessly take the responsibility in absorbing outdated

technology of the western countries and East European countries. Simultaneously,

underdeveloped countries should not absorb a capital intensive technology which is

viewed modern technology.The idea of labour-intensive technology for productive

use of surplus labour is important one for its significance or concentration among

economists in developed countries. R. Nurkse (1953) argued that labour force, that is

unemployed and underemployed, can be utilized for capital formation process start.

He further emphasized that skilled and unskilled manpower can be productively used

in order to produce capital assets in rural areas. The above illustration of

underdeveloped economies indicates the transformation of surplus labour into capital

assets.

While, capital formation with the use of appropriate technology and skilled,

provoked, dynamic population can lead to development (Guner Myrdal, 1968 and

John Galbraith, 1979). The vicious circle of poverty is considered the major cause of

underdevelopment and the underdeveloped counries are trapped in this demand side

and supply side vicious circle of poverty. The development and growth can be

achieved by eradicating this vicious circle of poverty. Rosenstein-Rodan (1964)

presented the Big Push Theory. The author viewed that big and comprehensive

investment in form of intensive agriculture and promotion proves helpful to bring

economic development. Development can beignified by a joint investment with a

49 see Todaro and Smith (2012).

94

number of backward and farward linkages and benefits of external economies, anemic

efforts towards development will prove worthless or ineffective.

The idea of joint investment in balanced and unbalanced growth theories was

given by Hirshman (1958). According to him, a comprehensive investment caused

unbalanced growth to start with, would eventually cause to investment opprtunties in

the complementary fields of activities and would ultimately excite wide ranging

economic growth and development.

Structural change theory50

emphasizes on the mechanism by which

underdeveloped economies transform their domestic economic structures from a

heavy to a more modern service economy. The Neo-Classical price and resource

allocation theory and modern econometrics is used to show how this transformation

process takes place. The “two-sector surplus labour” theoretical model of W. Arthur

Lewis and the “patterns of development” empirical analysis of Hollis B. Chenery and

his co-authors recognized the illustrations of structural change approach. The Lewis

two-sector model 51

of structural change depicts transfers of resources from low

productivity to high-productivity activities in the process of economic development

by analysing many linkages between traditional agriculture and modern industry and

illuminating recent growth experiences such as that of China ( see Todaro and Smith,

2012).

The Structural-Change model emphasises development as an exclusive

growth process and change with same main features in all countries. Yet, the model

does recognize that differences can be raised amongst countries development pattern,

depending on their particular set of circumstances (see Todaro and Smith, 2012).

In the period of the 1970s, International-Dependence models were supported

in result of growing dissatisfaction with both the stages and Structural-Change models

among intellectuals in developing countries. While this theory to a large degree went

out of favor during the 1980s and 1990s, as only some of its views have been adopted.

50 The hypothesis that underdevelopment is the result of underutilization of resources arising from

structural or institutional factors that has their origins in both domestic and international dualism.

Development therefore requires more than just accelerated capital formation. 51 The process of transforming is in such a way that the contribution to national income by the

manufacturing sector eventually surpasses the contribution by the agricultural sector.

95

The international-dependence theorists recognized the importance of the structure,

mechanism, work of world economy, and decisions made in developed world can

influence the lives of millions of people in third world. The similar applies to

arguments concerning the dualistic structures and the role that ruling elites in the

domestic economies of the developing world. Within this general approach there are

three major streams of thought: the neocolonial dependence model, the false paradigm

model, and the dualistic development thesis (see Todaro and Smith, 2012).

The first major stream entitled as Neo-colonial Dependence Model52

, is an

indirect outgrowth of Marxist. This model argued that the existence and continuance

of underdevelopment was most important to the historical evolution of a highly

unequal international capitalist system of relationship between rich country and poor

country (see Todaro and Smith, 2012).

During 1980s, when the conservative governments were in power in the

United States, Canada, Britain, and the West Germany, the Neo-classical counter-

revolution theory and policy were revitalized. The neoclassical counterrevolution

argued that poor resource allocation due to incorrect policies regarding prices and too

much state intervention by excessively active developing-nation governments caused

underdevelopment. This Neo-classical counter-revolution favoured supply-side macro

economics and privatization of state-owned corporations in developing countires, and

it stressed upon de-thronement of public-ownership, statist planning and govt-

regulations in underdeveploed countries (see Todaro and Smith, 2012).

Thus, the cornerstone of Neo-Classical free market theory is attached with the

liberalization (opening up) of domestic markets which will attract domestic and

foreign investment. Thus, the capital accumulation will enhance. In relation to GNP

growth, this is equalent to elevating domestic saving rates which will enhance capital

labour ratios and per capita incomes in capital poor developing countries. The Solow

Neo-Classical Growth Model53

in particular represented the seminal contribution to

the Neo-classical theory of growth and later received Robert Solow the Nobel Prize in

economics. It distinguishes itself from Harrod-Domar formulation by addition of

52 The main proposition of model is that underdevelopment exists in developing countries due to

continuing exploitative economic, political, and cultural policies of former colonial rulars towards less

developed countries. 53 see Todaro and Smith (2012).

96

labour, and introducing technology to the growth equation. In order to promote

growth along with equity in developing world, the Traditional Neo-Classical Growth

Theory discussed the unquestioning adulation of free markets and open economies

along with the universal disparagement (see Todaro and Smith, 2012).

4.3 Review of Empirical Evidence and Urban Informal Sector

This section reviews the empirical evidence on aspects of the informal sector

and the urban informal sector employment determinants. The literature on the

informal sector is characterized by terminological confusion. Theoretical literature on

the informal sector is commonly taken to show dualistic as well as Neo-liberal and

legalist approach in this research. We also review the literature regarding earnings

determinants in the urban informal sector. The present study also reviews empirical

evidence on the subject of the informal sector development and socio-economic

factors in support of the informal sector development at the national as well as at

international level. In the next paragraph, we will review some studies associated with

our topic. There is a diversity of literature on topic of the urban informal sector

development though we are reviewing a few imperative studies in order to support

this research.

Mazumdar (1976) examined the urban informal sector. The results showed

that there were disproportionally young and old participants in the informal sector.

Majority of the participants were female workers. Moreover, the participants of the

informal sector were possessed with formal education. The informal sector did not

prove the entry point to the rural migrants. The workers earned at very low level in

the informal sector. The participants were not the primary earners. The employment,

productivity and earnings trends indicated that migration function implicit in

probabilistic job search models exaggerated the rate of migration to the urban market,

and underplayed the role of actual economic performance in controling migration

over time.

Koo and Smith (1983) discussed the urban informal sector and migration to

city based on data from National Demographic Survey conducted in 1968. The

various factors were explored like migrants‟ participation, personal income, sex and

level of education which influenced the informal sector. The authors used sectoral

97

distribution and regression techniques. The results indicated that age, years of

education, and hours of work were positively associated with the personal earnings of

the participants. The sex and earnings of the informal sector workers were positively

associated. Findings also showed that rural migrants‟ earnings were lower in the

informal sector and this sector was tertiary. It was the informal sector which enabled

the rural migrants to participate in the informal occupations. The informal economies

provided continuous income opportunities predominantly to migrant women. Finally,

results concluded a great deal of overlap between participants‟ income in both the

(formal and informal) sector.

By using primary data, Tielhet-Waldorf and Waldorf (1983) explored the

earnings of the self-employed in an informal sector in Bangkok. The authors

examined the impact of years of experience in periods, previous experience, migrants‟

status, sex and region on the earnings of self-employed. OLS regression techniques

were used in the study. The results showed that self-employed workers noticeably

earned relatively higher average earnings than unskilled workers in the formal sector.

The result of the study also showed that level of education of participats was low

(primary). Analysis indicated that there were higher earnings of the self-employed

with primary level education. In conclusion, the recent migrants, almost all self-

employed were inclined to earn a smaller amount as compared to city born for that

time period.

Hill (1983) studied female labour force participation in developing and

developed countries based on Japanese data. The author used the multinomial logit

model. It was found that women did not regard the decision to work as identical to an

employee‟s decision to work. The increase in husband‟s wage certainly raised the

wife‟s inclination to work in the informal sector as family worker. It was concluded

that the option to work in family business along with the choice not to take part can

not be combined.

Okojie (984) studied female migrants in the urban labour market by collecting

data in 1980 in the survey of female Benin City of Nigeria. The explanatory variables

such as sex, marital status, religion, and education levels were included in the study.

The author used the regression estimates in the study. The results pointed out that

migrants took the urban informal sector as refuge. The evidence indicated that both

98

the migrant and non-migrant women, with their low education, were occupied in low

income jobs in the informal and formal sector. The results of study suggested that

employment opportunities should be increased in the urban areas and rural-urban

migration must be decreased on behalf of both female and male workers possessing

higher education.

House (1984) conducted a survey of the informal sector enterprises in mid

1977 in Nairobi. The author of study used the percentage distribution and ordinary

least square method. The results indicated that participants having low skill level

entered in the urban informal sector easily and the required amount of money to start

business was insignificant. Moreover, there was an influx of migrants in informal

sector in urban areas. The study concluded that the informal sector offered a

permanent way to urban existence, even at a bare survival. The study has given policy

suggestions about the maximization of development potential of the intermediate

sector and about lessening the size of the community of the poor at similar time.

Terrell (1989) analyzed the determinants of wages by using data from the

Labour Force Survey in Guatemala. The regression results indicate that there were

low and positive returns to education and experience. Positive relationship was also

found between education and earnings with large differentials. The result also found

the interindustry wage differential were limited to large groups (i.e. modern and

traditional). The returns to hours worked were found to be negative. Results found the

sex discrimination in the formal sector occupation among the wages of street vendors

and shop assistance. The variable years of schooling affected more wages of shop

assistants as compared to the earnings of street vendors. It was also found that

earnings of the street vendors were increased with the experience. Moreover, job

tenure did not determine the wages in either of these occupations. Finally, a negative

association was found between working hours and earnings of the street vendors.

Boyd (1990) examined the Black and Asian self-employment in large

metropolitan areas in United States by using micro data sample, U.S census Volumes,

and published sources. The explanatory variables were individual and personal

characteristics. The study used a logistic regression model. A positive relationship

was found between age, education and self-employment of Asians. The findings

99

indicated that Asians also used informal networks for guidance in business. However,

Blacks did not get benefit in business ownership due to lack of informal network

support.

Banergee (1993) examined the role of informal sector in migration process.

The data was collected by survey method from October 1975 to April 1976. The

author used earnings functions logit model and logit estimations of mobility between

sectors. It was found that mobility was very low from informal to formal sector. It

was also found that return to education and experience was equally rewarded in both

the sectors. The return to education and experience were high in the informal sector.

The main conclusion was that education of the workers determined mobility between

the sectors.

Aly and Quisi (1996) estimated socio-economic determinants influencing

women‟s decisions in labor market. The non-linear maximum likelihood function was

used in the study. The results found that women‟s monthly wage rate and their

education affected their working decisions in the labour force. However, variables

such as marital status, number of children under five years of age and their age

influenced their decisions negatively.

Funkhouser (1996) examined the employment patterns and earnings structures

by conducting household surveys in five Spanish-speaking countries of Central

America. Factors regarding age, years of education, marital status, sex, children less

than 10 and adults were used by author. The author of the study adopted the probit

model to estimate the informal sector employment. The results highlighted that there

were higher returns to a year of education in the informal sector in each country as

compared to most developed countries and gender differential was to a great extent in

informal sector. The results also showed that participants enjoyed the higher returns

due to labour market experience. Furthermore, it was found that heads were being

employed more in informal sector employment. As far as number of children is

concerned, workers were being employed more or less in informal sector employment

across countries in Central America. The workers having male children less than 10

years of age decreased their involvement in informal employment in some of the

countries. Married workers participated less in the informal sector employment.

100

The findings also indicated that informal sector employment increased due to

having female children less than 10 years and it decreased in Nicaroga in 1993 and in

Costa Rica in 1980, 1985 and 1991. The study concluded that the important

determinant of informal sector employment was level of development.

Borjas (1996) worked on self-employment experience of immigrants by using

data from U.S censuses of 1970 and 1980. The author used maximum likelihood logit

regressions in this study. The result showed that self-employment proved a vital

aspect of experience of the immigrants of the labour market. It was also found that

self-employment rates of immigrants were higher as compared to self-employment

rates of native-born male workers. The assimilation influenced positively the self-

employment rates. More immigrants adopt self-employment more often.

Loayza (1996) examined the determinants and effects of the informal sector

by using data from Latin American countries in the early 1990s. The author used an

endogenous growth model. Results showed that the informal economy tended to

increase in results of excessive taxes and regulations that were imposed by

governments having inability to implement compliance.

Fortin et al. (1997) studied the effects of taxation and wage controls in an

informal sector in developing economy. The authors applied a simple general

equilibrium model consistent with different parts in labour market. Results indicated

that the significance of the informal sector, unemployment rate and efficiency costs

were increased because of an expansion in the tax rate on profits, in the payroll tax,

and in the government set wage rate.

Samith and Metzger (1998) discussed the return to education among street

vendors. The authors used data drawn in 1994, survey of street vendors in Mexico.

The explanatory variables consisted of capital, capital association, hours worked per

year, hours worked per day, secondary education, beyond primary education,

experience, experience-squared, family worker, gender, merchandise and prepared

food. The study was based on the specification of earning function. Findings

indicated that there were significant positive returns to formal education among street

vendors. A positive relationship was found between capital investment and

educational attainment. The results also indicated an inverse association between

101

earnings and working hours per year. It was also found that earnings of the vendors

seemed to increase gradually.

Maloney (1999) worked on informality segmentation in urban labour market

in Mexico based on data from National urban employment survey. The author

estimated the earnings differentials and multinomial logit model was used in the

study. The author found a relationship between the formal and the informal sector.

The result showed that informal sector workers forcefully worked in the sector due to

inefficiencies and low productivity levels of the labour in developing countries. The

major conclusion was that mobility from formal to informal and earnings differentials

showed the participants‟ willingness to detain or engage them selves in the informal

sector.

Hout and Rosen (2000) studied the influence of family background and race

on self-employment in United States. Data were drawn from the General Social

Survey (GSC) made from1500 English speaking adults. By using a logistic regression

analysis, it was found that the self-employment rate among African, Americans and

Latinos whose fathers were self-employed was lower as compared to a European

ancestory average man whose father was not self-employed. Self-employment rates

were influenced by ancestry and immigration. The findings concluded that

individual‟s self employment was primarily influenced by the self-employment status

of his or her father or family background.

Sarte (2000) introduced a simple economic model and drew a connection

between bureaucratic rent-seeking, the informal economy, and economic growth. The

results showed that increased costs in respect of operating out official laws caused

informal sector disappear endogenously (internally).

Rosser et al. (2000) worked on income inequality and the informal economy

based on data which was collected from sixteen transition economies. The authors of

the study used the bivariate OLS regressions. Results highlighted that the

progressively larger informal economy tended to create more inequality because of

falling tax revenues. In conclusion, the social safety nets were weak due to

progressively informal economy and inequality was enhanced due to further informal

activity as social solidarity and trust.

102

Smith (2001) estimated the effects of human capital variables on earnings by

using 1991 population survey of United States. The personal characteristics, socio-

economic and demographic variables affected the earnings. The author estimated

three sets of regression equations. Findings indicated that wages were positive

function of education and training. It was also found that training was of great

importance to determine females‟ wages. The analysis estimated that the training and

earnings were positively correlated. A negative relationship was found between all

levels of education and earnings. The results highlighted that age and experience were

positively correlated with earnings. However, experience- squared and age-squared

were negatively associated with earnings. The results suggested that sex, age and

experience remarkably determined the earnings.

The determinants and entrepreneurial intention of immigrants were examined

by Raijman (2001). The household survey was conducted during 1994 by using

random clustered sample of villagers‟ households in Chicago. Socio-economic

characteristics affected the entrepreneurial intention of immigrants. Logistic

regression model of potential entrepreneurship was applied. Findings proved that self-

employment was relatively high in the community and social ties played a central role

in self-employment process. They emphasized having business on behalf of

individuals amongst family members to serve as role models. The people with meager

sources availed business ownership due to assistance. In addition, study results

confirmed that the household economic resources (financial investments) aspired the

entrepreneurs to launch business. The results suggested that policy to develop

community business must mull over both (financial and nonfinancial) factors.

The informal sector and rural-urban migration was analyzed by Meng (2001)

based on survey data set of 1504 rural-urban migrants of Chinese city in 1995. The

multinomial logit model was used to estimate the influence of individual‟s marital

status, sex, human capital variables, financial resources and occupation before

migration and family background on formal and informal sector employment. Results

revealed that individuals with higher labour market quality preferred to become self-

employed rather to seek other work. Male workers participated in wage as well as in

self-employment. Results concluded that the wage workers and self-employed were

103

comparatively better-off than those in the informal sector with regard to earnings

advantages.

Roberts (2001) investigated the factors that determined job choice of rural-

urban migrants. Data was collected in 1993 from individuals in the fifth sampling

survey of the floating population of Shanghai. The author of study attempted to show

the impact of socio-economic factors, region of origin and village-based networks.

The author used multinomial regression model to analyse the determinants. Results

highlighted that personal characteristics (age, gender, marital status, education and

region or origion) and village based networks motivated migrants into particular

occupations and destinations because nearly all young workers participated regularly

in menial work of construction and manual labour. The results also showed that

illiterate migrants participated more in the occupations of farming while their

participation was less in construction sector. Furthermore, returns to migrants‟

education were higher. Hence, study concluded that education and province of origin

determind the job choice signficantly.

Fan (2001) analysed the migration and returns in urban labour market of

China.The demographic variables, residential status, experience, ownership sector and

occupations were used as explanatory variables in the present study. The author used

the regression techniques. The results found that labour market was segmented as

indicated by the benefits. The results also found that permanent migrants availd

benefits from work in the labour market as compared to the recent or temporary

migrants. The major conclusion was that residential status was found as the most

important determinant of income and benefits of the workers. Moreover, income and

benefits were remarkably determined the returns in the labour market of urban China.

Returns to education and earnings differentials at different levels were

analysed by Wahba (2002) based on data taken from Egyptian Labour Force Sample

Survey (LFSS) conducted in October 1988. The author found that human capital

variables influenced the earnings in labour market. The extended earning function

method was used to estimate the returns to different levels of education. Results also

showed that there were higher returns to incremental level of education and however,

this was different from the pattern that commonly prevailed in most countries.

104

Gallaway and Bernasek (2002) identified the determinants of labour force

participation in formal or informal (wage and self-employment) sector for men and

women based on data from Family Life Survey for 1993 (IFLS) in Indonesia. The

authors used socio-ecomonic and demographic variables to determine sector of

employment. In this study, the multinomial logit model was used. The results

estimated that female informal sector employment was decreased with age. The

results revealed that education and child care helped the women to make decisions

with regard to time and place of work. Findings pointed out that persons with the

highest education levels had a preference to work in the wage or formal sector

employment while those possessing low human capital were forced to work in

informal sector. The results revealed that informal sector employment (both at home

without pay and with pay in labour market) was positively associated with number of

male adults. However, a negative relationship was found between number of male

adults and male female informal sector employment. The females work participation

at home increased due to an additional toddler and having an infant. The findings

suggested that women decided distinctly to occupy in the formal or informal sector.

Results confirmed that informal sector seemed inferior relative to formal sector.

Maternity leave policies, paid maternity leave policies to take care of infants and child

care arrangements to take care of infants and toddlers and women‟s and girls‟ access

to education were essential policy implications to increase women‟s participation in

paid-work.

Suharto (2002) examined the human development and the urban informal

sector among the street traders by using surveys at micro level in 1999 in Bandung,

Indonesia. Results illustrated that human development was estimated by economic

(i.e. household income and trading revenues), human capital (education, health and

housing facilities) and social capital (i.e. socio-cultural activities). These indicators

were all qualitative. Percentage distribution was used as the study was exploratory.

The results illustrated that overall, the street traders were seemed not poor as their

incomes were frequently higher than the official poverty line. The income of the few

street traders was relatively higher than low skilled workers in the formal and

unskilled construction sector. The results also revealed that many street traders lived

in a vulnerable condition unable to meet the costs of basic necessities. In addition,

105

these street traders did not seem to be poor who had sufficient formal education and

access to health services and housing facilities.

The relationship between urban poverty and labour force participation in

informal sector was examined by Odhiambo and Manda (2003). The data was

collected from various Welfare Monitoring Surveys in Kenya. Authors used logit

model in this study. Results observed that variables such as age, gender, family size,

education and the sector of employment influenced the poverty level or risk of being

poor. The study results showed that household heads without having initial formal

education were seemed to be poorer. The results also pointed out that there was a

positive relationship between poverty and labour force participation. However, the

households seemed to be poor. There was need to plan a strategy to improve the

productivity and incomes of the majority of poor informal sector workers. It was

suggested that the wide gender disparities should be reduced in labour force

participation.

Reddy (2003) examined the aspects of urban informal sector by utilizing

primary data from three urban areas in 2001 in Fiji. The author used the percentage

distribution and factor analysis technique. The workers, on average, had Primary level

education. The informal employment was high in urban area than some big cities of

underdeveloped countries. Results also found that informal enterprises incorporated

instantaneous family members and the informal sector activities required relatively

long working days and weeks. Moreover, workers had no access to credit facilities.

They had no contact with the national and municipal laws and regulations which

governed the conduct of business patterns in the state. The study suggested for an

urgent national level survey of informal sector to devise policy in a better way.

Odhiambo and Manda (2003) analysed the effect of human capital on earnings

by using data from Welfare Monitoring Survey (WMS) undertaken by the

Government of Kenya in 1994. The determinants of earnings such as education at

different levels, experience, sex and region were included. The Mincer (1974)

methodology was followed to estimate semi-logrithemic equations. Results found that

the earnings increased with education levels. The results also showed that human

capital externality increased the earnings and all the workers benefitted in the form of

higher earnings due to increased level of education. Furthermore, men were relatively

106

more advantaged than women participants in labour market. A positive relationship

was also found between household size and earnings.

Ozcan et al. (2003) discussed the issue of wage differences by gender, wage

and self-employment by using data from the 1994 income survey in Turkey. The

education, experience, age, marital status and sector of activity affected the earnings

of wage workers and self-employed. The authors adopted the OLS techniques. The

results were based on two- step Heckman procedure. The results also indicated

differences in both the employment (wage and self-employment) as well. The results

found that the returns to education were higher in the self-employment. In addition,

job experience, working hours per week and marital status contributed positively to

men employment. In contrast, job experience increased women‟s wages and working

hours reduced the women wages in urban Turkey.

Suryahadi (2003) evaluated the minimum wage policy and its impact on

employment in the urban formal sector by using data from the national labour force

surveys conducted annually by BPS in Indonesia. OLS estimation results showed that

an increase in minimum wages influenced the urban formal sector employment

negatively excluding white-collar workers.

Attanasio et al. (2003) worked on trade reforms and wage inequality by using

data of the household National survey from 1984, 1986, 1988, 1990, 1992, 1994,

1996 and 1998. The author found the influence of drastic tariff reduction of the 1980s

and 1990s on the wage distribution. The results showed that increase in skill premium

was primarily driven by skilled biased technological change. However, the sectors

with the largest reductions in tariffs were those with the sharpest increase in the share

of skilled workers.

The study results found that regarding industry wage, premium diminished

more in those sectors which faced large tariff cuts and this decreased premium caused

to inequality. It was also found that increasing size of informal sector was associated

to the enlarged foreign competition. Yet, increasing returns to education and

variations in industry premiums and informality alone did not entirely clarify the

increased observed wage inequality at that period. On the whole, influence of the

trade reforms on wage distribution was small.

107

The structural and behavioural characteristics of informal service employment

were discussed by Dasgupta (2003). The primary data source was used and survey

was conducted in New Delhi. Investment, earnings, returns to human capital, and

socio-economic characteristics were included as the explanatory variables. Regression

results indicated that employment in informal services sector did not require complex

skills yet experienced people were invoked to this employment. The result also

showed that workers entered into the informal service employment because of lack of

education. Furthermore, poor people strained in the informal service activities due to

lesser credit facilities. The study results concluded that lack of education and credit

facility determined workers towards the informal service employment while they did

not earn low incomes in the economy.

Ihrig and Moe (2004) examined a relationship between the tax policy and the

informal employment in Asia by developing a simple dynamic model. The model

reproduced an inverse and convex association between the informal employment and

a country‟s living standard. The study results made clear that informal employment

gave response to tax policies consistent with cross country data. Results suggested

that the informal employment acted in response to the strength of enforcement and,

perhaps corporate tax rates.

Blanchflower (2004) focused on self-employment by using world values

survey, 1981-1983 (ICPSR no. 9309) and world values survey, 1981-1984 and 1990-

1993 (ICPSR no 6160), along with data collected during 1995-1997. The probit

model techniques were applied in the study. The proportion of self-employment

across the OECD was remarkably higher for men and for older workers as compared

to younger workers. Results indicated that the probabilities of induction in the

informal employment were lower in Europe than United States. The main conclusion

of the study was that rates of self-employments were decreasing across the OECD

from 80 countries with the exception of UK and Newzealand.

Calves et al. (2004) assessed the changing pattern of youth employment in the

labour market based on National Representative Survey Data collected in 2000 in

Barkina Faso. The authors used descriptive techniques. Findings portrayed that people

with initial formal education participated in informal economy. The results showed

that urban informal sector provided exceedingly higher employment opportunities to

108

the young people with low formal education. The young age migrants entered the

labour market because of urbanization and unemployment and exacerbated the

competition in labour market. The policy implication was that the training

opportunities should be given especially school going young people to recreate the

education and employment relationship and to meet up the labour market demand for

creative and vibrant operators in informal labour market. In addition, the provision of

scholarships to encourage female school enrolment, vocational and technical training

facilities and loan facilities to prop up female informal entrepreneurship were required

to endorse equal access to young urban workers.

Bulutay and Tasti (2004) studied the informal sector in the Turkish labour

market by utilizing data from household labour force survey and SIS sources.

Analysis was based on the descriptive techniques. The result showed that informal

sector was overpowering.The results also indicated that earnings and wages of the

workers in the informal sector were low and self-employment rate was dominantly

high in the informal sector.

Guang and Zeng (2005) analyzed the migration as second best option based on

data from the Chinese Life History survey in China and United States in 1996.

Individual and household characteristics were included as explanatory variables. The

authors used the logistic regression model. Results indicated that workers were more

likely to be employed as non-farmer, migrant workers, wage workers and

entrepreneurs with age. Result showed that male workers participated more in non-

farm job and wage work. Result also found that migrant workers and those who had

non-farm job were married. Furthermore, migrant workers normally earned higher

income but they suffered from inferior work and living conditions. Finally, a large

number of Chinese migrated to the cities to avoid low status and unprofitable work in

grain cultivation.

Matya et al. (2005) analyzed socio-economic factors influencing people to

become fisherman around Lake Malombe. Primary as well as secondary sources were

used to collect the relevant data. The authors explored the relationship between socio-

economic factors like gender, age, marital status, family size, literacy level, land

holdings, access to credit and other income generating activity and decision to

become fishermen. The logistic regression model was used in this study. The study

109

results concluded that most important variables i.e. gender, access to credit, land

holding size and no other income generating activity influenced people to engage in

fishing industries. Results suggested that a multi-sectoral approach was required to

establish adult literacy institutions, family planning clinics to reduce the population

growth rate, to increase agricultural productivity and to enhance business

entrepreneurship to reduce effort in the lake.

Gindling and Terrell (2005) analyzed the effect of minimum wage on actual

wages in formal and informal sectors in Costa Rica. Using 12 years of micro data,

results demonstrated that legal minimum wages significantly affected the wages of

employees in very small ( five or lesser employees) and large firms in both the urban

and rural areas.

Pisani and Voskowitz (2005) studied the labour market for gardeners in South

Texas. The authors used the logistic regression model. Result indicated that gardeners

were Mexican by birth and nationality. The middle aged gardeners, having Middle

level education worked full time in gardening. The variables such as previous work

experience as a gardener, medical insurance and year around work as gardener were

most important variables that determined their employment. Results highlighted that

they were occupied in the gardening trade and their hourly wage was better as

compared to skilled occupations in the region. The study concluded that it was the

profession of gardening which materialized the dream of these workers to be able to

make their earnings through their favourite occupation.

Kim (2005) used a household survey to analyze the effect of poverty on

informal economy participation in Romania. The author used a simple theoretical

model. The results revealed that low income and a gap between desired and actual

income level forced the participants towards informal sector. Furthermore, individuals

persuaded for informal sector due to poverty.

Krstic and Sanfey (2006) investigated level of poverty and well-being among

the participants in the informal sector based on panel data from the living standards

measurement studies in Bosnia and Herzegovina. A Probit model was estimated to

determine individual and labour-force characteristics that were related with successful

transitions out of poverty. Results found that the workers in informal sector suffered

110

more from poverty than formal sector workers. Furthermore, there was found an

earning inequality in the informal sector. The study results indicated that the

participants in the informal sector were less satisfied as compared to all other groups

in the labour market. Generally, results concluded that informal sector represented

itself as an imperative coping strategy for the bulk of people yet the formal sector

provided much better opportunities for the uplift of their living standards.

Valodia et al. (2006) studied the low wage and informal employment based on

data taken from Labour Force Survey 2000 and 2004 in South Africa. The authors

used the descriptive technique. The participants in informal emloyment were black,

married and young in great number. Results found that men earned comparatively

higher income than women. The study results indicated that it was possible for the

participants with high education to obtain highly paid job. The study concluded that

workers with low human capital obtained more precarious employment. On the

whole, they were not a part of trade unions.

Paratap and Quintin (2006) investigated about the segmentation of labour

market in developing countries through a semi-parametric approach. The data were

drawn from Argentina‟s urban household survey from 1993 to 1995. The parametric

tests were adopted to examine the relationship between individual characteristics like

education level, gender, age and job characteristics i.e. establishment size, hours

worked and the informal sector. The results pointed out that on average 60% in the

establishments that hired 5 or lesser employees. The results showed that the female

informal sector employees were in high proportion. The results also indicated that on

average, the wages in the formal sector were higher than wages in the informal sector

and output in informal sector was observed about 10 or 15% of GDP in informal

sector in most developed economies. It was found that the tax burden, weak rule of

law, government corruption, and heavy bureaucracy alongwith registration, weak

security of property rights and the quality of the legal system changed the size of

the informal sector in the countries who have similar economic development

levels.

Mitra (2007) focused on the role of networks in getting jobs in urban labour

market. The research was based on a primary survey of 200 households in Delhi

slums in 2004-2005. The explanatory variables such as age, social capital in terms of

111

contact with relatives, villages and neighbors, members of the same caste group,

friends, colleagues, religious organization, educational categories, household size,

caste groups, gender differences, and access to political contact and availability of

property at the place of origin affected the participation of the informal sector

workers. The author of study used the binomial logit model. The results highlighted

that bulk of workers was engaged in the urban informal employment through various

informal channels or networks of information flows.

Reddy (2007) utilized primary data in order to examine the contribution of the

urban informal sector to create employment opportunities and to alleviate poverty in

Fiji‟s developing economy. Study results showed that education level of operators,

family labour involvement and experience played a central role to alleviate poverty in

developing economy. By using probit model, results found that workers in the

informal sector were educated at primary level and mostly family members were

involved in enterprises. It was also found that the participants experienced a

tremendous increase in incomes and assets in informal sector as compared to pre-

informal sector days. Findings also suggested that informal sector contributed

significantly to reduce poverty and to generate employment.

Jampaklay et al. (2007) examined the residential settings of migrants in

Thiland. The data were drawn from a survey of migrants from rural Nang Rong

district, Bangkok metropolitan area and the eastern Seaboard. The individual

characteristics, education, type of work, work place context, and migrant experience

were used as independent variables in the analysis. The logistic regression techniques

were made in the study. The regression results showed that migrants were culturally

different, socially unified, and organized around certain occupations and work place

settings. It was found that human capital along with employment situation

significantly influenced neighborhood co-region concentration. The result of co-

regression cluster showed no influence of duration on residence. Findings indicated

that high socio-economic status was related with migrants‟ residential integration with

majority group populations located in more rich and desirable communities. It was

also found that both the perspectives were supported around Isan clustering among

migrants. Finally, the results found a relationship between education and residence

along with a relationship between employment and residential settings.

112

Gunatilaka (2008) examined the nature, probability of employment and

determinants of wages by drawing data from the QLFS of 2006 in Sri Lanka. The

author found a relationship between demographic and occupational variables and

informal sector employment. The author used multinomial logit model to find out the

determinants of the informal sector employment. The results revealed that the

contributing family workers were more likely to be female and married. The results

also indicated that small business informal employees were facilitated in formal

enterprises by family ties and family labour. Furthermore, better educated attainment

decreased the probability of being employed in the informal sector employment. The

informal sector employment decreased with different education levels. As far as sex is

concerned, probability of being employed in informal sector tended to increase. The

study confirmed that precariousness was major characteristic of the informal

employment which favoured male rather female workers. Policy required dealing with

the issue of job creation by improving microenterprises employing half of informal

employees. The research required that emphasis should be laid on the costs and

benefits of formalization. Research and policy were required to deal with the factors

that restrict formal employment expansion in formal enterprises simultanously. The

restrictions on infrastructure and labour regulations were required to be noticed.

Baudar (2008) focused on immigrants‟ decisions to be self-employed based on

primary data from the survey of 509 Vancouver residents of predominantly Chinese

immigrants‟ neighborhood and South Asian immigrants during 2003 in Canada. The

author found a relationship between gender, language, place of residence, labour

status, occupation, class, school, continuous variables (i.e. age, household size and

years of education) and self-employment by using the Ordinal Logistic Regression.

The results indicated that origin and background of immigrants positively affected the

desire to become self-employed. The results also indicated that there was consistent

relationship between urban background and lower desire to be self-employed as

compared to rural background. Furthermore, females experienced lower opportunities

to be self-employed. It was concluded that urban or rural background was further

leading variable that determined entrepreneurship.

Based on British household survey data (BHSD) data, Mentzakis et al. (2008)

examined the determinants of co-residential informal care. The authors used

113

regression techniques. The result showed that the probability of the informal care

decision was lower with age. Result also revealed that probability of female care

given decision increased with household size. The main conclusion was that

participation in the labour market decreased the probability of care given decision.

Heckman et al. (2008) estimated the earnings functions and marginal internal

rates of returns for different levels of schooling to access the need to increase or

decrease expenditures on education by using data for born men taken from household

surveys in 1940-2000. The authors applied a general non-parametric approach to

estimate the rates of returns. The variables such as tuition costs, income taxes and

non-linearities in the earnings schooling, experience relationship were used. The

results showed that returns to college level education were also increasing. Result

also indicated that there were comparatively larger returns to graduation level

education than high school level education. However, both were observed increasing

with the passage of time.

Frost and Jones (2008) analyzed the returns to qualifications among urban

youth in Egypt by using data carried out from Egyptian labor market survey (ELMPS)

2006. The authors used explanatory variables such as human capital characteristics,

current experience, non-productivity related variables to measure the earnings. Study

was based on the standard Oxaca-Blinder model of wage decomposition with

mincerian human capital and experience term was used. The returns to skills training

in the informal sector employment were higher. Moreover, there were similar returns

to informal and formal work experience across employment groups.

Attia and Moawad (2009) examined the informal economy as an engine for

poverty reduction and development. Informal sector was characterized by low

productivity, low wages, poor working conditions and long working hours. Results

showed that people got many opportunities of jobs in the informal sector in society,

including the formal sector, with the goods and services. Some showed their

willingness to enter in this sector. The study concluded that bulk of the poor

participated in the informal sector in Egypt.

Mundalmen and Montes Rojas (2009) emphasized on self-employment and

micro-entrepreneurship as desired outcome by using data from urban household

114

surveys in 1995-2003. The authors analyzed the effect of explanatory variables which

included education, age, gender, a variable identifying household heads, firm size, a

public sector employment dummy, last period‟s wage (log hourly wage) and a

variable identifying those individuals who became unemployed in the survey in

between on self-employment and micro entreprenuership. Three sets of estimates

were made by using descriptive statistics, probit estimation and regression equation.

The result showed that the earnings of the workers in the informal sector were much

lower as compared to formal sector employees. Additionlly those job holders who at

present earned higher salaries desired for individuals having high human capital to

transit themselves as enterprenuers at a higher rate.

Schiitte (2009) aimed to highlight that the poverty and insecurity were

intricately intertwined with conditions of informality in Afghan cities. Data was

collected from fourty poor households in Kabul in relation to the informal sector.

Results revealed that the urban poor survived with livelihood risks all the way through

a range of the informal arrangements. The informal settlements were still not supplied

through basic amenities such as electricity, safe water supply and adequate sanitation

systems. Additionally, the people deprived from basic services had to face health

risks. Owing to high competition, they could not avail the opportunities in the

informal sector.

Wamuthenya (2010) examined the determinants of formal and informal sector

employment by using data from Labour Force Survey cross sectional data of 1986 and

1998. A multinomial Logit model was applied to indicate the relationship between

personal characteristics like age, level of education or years of schooling, marital

status and household headship and wages in the market and household characteristics

(such as child care responsibilities: number of young children below school-age, the

size of the household, and the presence of female relatives in a household) as well as

the socio-economic background, and formal and the informal sector employment. The

findings indicated that the urban formal sector employment, the informal sector

employment and unemployment increased with age in 1986. The study results also

found that male and female workers with low level of education were forced to work

in the informal sector while these possessing low level of education prefered to join

formal labour market. Furthermore, the old age people were more likely to be

115

employed in the formal and informal sector employment. It was also pointed out that

married people and men were inclined to informal sector. The results concluded that

working hours were unified and it was more flexible to combine care and productive

work in the informal sector.

Chunquize (2010) studied the contribution of the informal sector in

unemployment situations of Nigerian third world cities. The author adopted

methodological approach for reviews of multiple documents including published

books and journal articles. The different studies on aspects of the informal sector

around the world revealed the incapability of the formal (modern) sector employment

to absorb such an influx of job seekers. Findings illustrated that the small enterprises

contributed impressively in the informal sector of the Third World with regard to

employment in developing countries. Awareness about the potential of the sector was

important to decrease the problem of unemployment. Without the informal sector, the

unemployment situation would be worse. Consequently, the informal sector deserved

acknowledgement from politico-economic planners. The informal sector was taken as

a neglected sector and it got very little attention and assistance from the government.

Qui and Hudson (2010) explored the private returns to education by using data

from China Health and Nutrition Surveys (CHNS) in 1989, 1993, 1997 and 2000.

Factors that affected the returns to education were number of years in formal

education, potential experience, gender, sector of employment and regions. By using

the OLS technique, results highlighted that there were perceptible increase in rates of

return to education especially from 1997 to 2000. In addition, the returns to education

depended mainly on gender and sectors of employment. Moreover, the education

decreased the earnings gap. The results suggested higher productivity and marginal

productivity of labour productivity itself in Beijing and specifically Shanghai.

4.4 Literature Review of the Urban Informal Sector in Pakistan

In this section we have presented the review of literature in Pakistan. The

review is concerned with different aspects of urban informal sector in Pakistan. The

studies regarding urban informal sector, poverty and development are also presented

which give an important insight in order to understand the informal sector.

116

Guisinger et al. (1984) analysed the earnings, rates of return to education of

the participants in the informal sector in Pakistan by collecting data from a survey of

1000 households in Rawalpindi in Pakistan. The authors of study used the regression

model to analyze the influence of schooling and sector of employment on earnings.

The results indicated that there were low rates of return to schooling regarding rate of

return on physical capital. The rates of return to schooling were also low in other

developing countries.

Kazi (1987) observed the urbanization due to rural to urban migration, and

low rates of employment expansion in the modern sector and suggested the beginning

of the informal sector. The author used primary data in Rawalpindi and Lahore in

Pakistan. Percentage or mean average of age, schooling, source of acquisition and

monthly income have been shown. The results described that eighty nine percent of

the self-employed earned more than Rs.1500 as against Rs. 1100 earned by formal

sector employee. Findings showed that the earnings of the skilled self-employed in

the informal sector were greater than the earnings of skilled workers in the formal

sector. Moreover, the study suggested that the informal sector played a great role in

skill learning process in the economy by introducing a system of informal

apprenticeship as it was advantagous for both employer and apprentice.

Kozel and Alderman (1990) estimated the factors which determined work

participation and decisions of labour supply in urban areas of Pakistan economy. The

data was collected in 1986 under the auspices of international Food Policy Research

institute (IFPR) and Pakistan Institute of Development Economics (PIDE). Age, age-

square, dummy variables for the highest level of education achieved (primary, middle

school, secondary, university) and family structure were used as explanatory

variables. The authors made use of tobit and probit estimation technique. The

empirical results revealed that wage tended to increase and reached at the highest

level of 46 years. The results also found a negative relationship between age-squared

and total wages of the participants. Furthermore, the educated young males or almost

informal workers increased the job search for the extended family structure along with

the availability of remittances. The study results concluded that labour force

participation increased with an expansion in expected earnings and remuneration

117

definitely revealed discrepancies mainly caused by differences in human resource

capacity due to education and work experience.

Ali (1990) focused on problem of rural women in the informal sector and its

contribution to the rural non-formal sector. The study conducted a survey in six

villages of Multan district that were selected randomly. The results illustrated that the

social and cultural factors were not the hindrances to partake in economic activities

for prosperity and well being. The study results revealed that the economic factors

were more responsible for miserable condition of women engaged in rural areas and

were great hurdle for better utilization of their skills, efforts and time.

The earnings functions in Pakistan‟s urban informal sector were determined by

Burki and Abbas (1991) based on data from the survey of male self-employed in the

skill-intensive urban informal sector of Pakistan, conducted by the department of

Economics, Quaid-e-Azam university, Islamabad in 1989. The human capital

variables like schooling, experience and vocational training influenced the earnings.

The authors estimated the pure human capital earnings functions. The evidence

indicated that reward to human capital investment was extraordinarily analogous to

the existing reward in the formal sector of Pakistan. The important findings of study

were that education, vocational training and experience were positively related with

the earnings of those who stick to the urban informal sector of Pakistan.

Burki and Ubaidullah (1992) examined the earnings, training and urban

informal sector employment in Pakistan based on the survey data of Gujranwala city.

The study was based on earnings functions of workers. The authors used mean

average regarding earnings, training and employment. The evidence indicated that

earnings or wages of the workers in the informal sector were significantly higher as

compared to the earnings of government employees in the formal sector. Furthermore,

bulk of workers engaged in the informal sector was not recent migrants and informal

sector enhanced presence of its participants. The results also found that both groups

were equally advantageous by skill generation under ustad-shagird system. The study

suggested that there was a need to introduce some planned measures by the

government in order to promote this very important sector of the economy.

118

Ashraf and Ashraf (1993) used household income and expenditure survey data

in 1979 and 1985-86 to analyze the male-female earnings differentials in Pakistan.

Factors that affected the earnings were age, province of residence, education level and

industry. The study was based on the methodology of Oaxaca (1973) and Cotton

(1988). The results indicated that the gender earnings gap between 1979 and 1985-

1986, declined significantly. The results of the study also showed that age

significantly affected the earnings of both male and female in both the years. It was

very small earnings differential in inter-province in the case of NWFP-Sindh. The

coefficient estimate for Balochistan was statistically insignificant in 1979 but was

highly significant in 1985-86. The findings showed that workers earned at a lower rate

in manufacturing, electricity and construction sectors as compared to their agricultural

counterparts. The earnings increased with different levels of education and earnings

tended to increase with age but decreased with age-squared in Pakistan. Finally,

earnings and sector of employment or occupation were negatively as well as

positively associated.

Burki and Afaqi (1996) studied informal sector in Pakistan. This study

reviewed the existing literature on the informal sector of Pakistan. Results illustrated

that informal sector gained powerful importance due to its noteworthy growth and

positive effects on employment especially in urban areas during the last two decades.

Malik (1996) discussed urban poverty alleviation through development of the

informal sector. The data sources were ESCAP, State of Urbanization in Asia and the

pacific, 1993(ST/ESCAP/1300), 1993. The author analyzed the trends in the

incidence of urban poverty in selected developing countries of ESCAP region.

Findings demonstrated that the role of the informal sector was remarkable to alleviate

poverty, and to create employment and to generate income. Moreover, he emphasized

on the growth of workers‟ earnings and productivity in the informal sector. The crux

is that the informal sector expanded due to growing urbanization, insufficient jobs in

the formal private sector and curtailment of jobs in the public sector jobs. The author

emphasized that govt must solve the problems and constraints and formulate

strategies on the development of informal sector. The author suggested a holistic

approach to cope up with the multidimensional problems of the informal sector.

119

Kemal and Mehmood (1998) analyzed the characteristics of workers in the

urban informal sector of Pakistan. The study was based on the survey of urban

informal sector in four provinces of Pakistan conducted in 1992. The percentage

distribution of self-employment by their characteristics was shown. Results also

showed that some young persons joined the informal sector and stayed there till they

got employed in the formal sector. The self-employed of sizeable proportion took it as

a permanent activity. In addition, the urban informal sector was comprised above half

of workers with least secondary education. Findings indicated that only one third

entrepreneurs possessed similar education levels as that of their fathers. The study

also illustrated that a large proportion of the entrepreneurs possessed experience in

another field. In conclusion, the informal sector was characterized as permanent

activity of the educated and skilled people in the urban areas of Pakistan.

Sargana (1998) emphasized on the urban informal sector by conducting a

survey in Rawalpindi and Islamabad in Pakistan. The author demonstrated the impact

of factors such as years of schooling, experience and on the job training on earnings

of the workers in informal sector. The Mincerian model was estimated in this study.

The results of study indicated that income expanded due to increase in education and

experience. The regression results indicated that schooling payed extra or

supplementary to the self-employed than wage earners. The results also indicated that

earnings of informal sector workers increased due to an increase on the job training.

The results suggested the importance of human capital variable to enhance earnings of

the participants. Policy implication was that investment should be ensured on both

human and physical capital. The informal sector can be promoted to assist the poor

without any risk to rich and redistribution of the income. The results suggested that

more investment should be made to middle level education and there is a need to

launch schools in urban areas where the bulk of workers can avail the facility to much

extent. Moreover, measures should be taken to protect the workers from the whim of

police and other authorities in neighbourhood. Yet, the emterprises must be

responsible to pay the taxes.

Siddiqui and Siddiqui (1998) studied the decomposition of male-female

earnings differentials by using data from Household Income and Expenditure Survey

1993-94. The authors examined the impact of variables like age, age-squared, area

120

(urban / rural), number of days worked, dummy variables for provinces, and dummy

variables for different occupational categories, dummy variables for different

industrial categories and dummy variables for employment status on earnings. The

methodology of Oaxaca (1973) and Cotton (1988) was adopted. The results of

earnings functions showed that returns to schooling for male workers were higher and

female returns to schooling were also higher. The findings indicated that age, area,

provinces and employment status determined the earnings for both the gender. Results

revealed a high level of market disregard against female workers. The results also

indicated that old age females gained income more than their male counterparts.

Nasir (1998) discussed the determinants of personal earnings by utilizing the

National Labour Force Survey 1993-94 data in Pakistan. In this study, variables

consisting of educational categories, age, and its square terms, job training, regional

location, gender groups, occupational catagories and size of establishments

determined the earnings. The author of the study estimated the wage function by the

maximum likelihood method using probit estimates of wage participation. The results

demonstrated that returns to education were higher as consistent with the human

capital theory. Results also indicated that the age and education were crucial factors

that determined earnings and productivity. The author suggested that professions i.e.

agro-based industries and cottage industries shoud be encouraged to reduce the

regional earnings differential in rural areas. More incentives were recommended to

labour-intensive industries in order to absorb unemployed youth so far. The author

suggested policies to enhance human capital to ensure females‟ more participation

and enhanced employment opportunities. The low mark-up rate formal credit given to

females who launch businesses at micro level to facilitate the attenuate the burden of

domestic labour can make certain substantial increase in their earnings.

Nasir (2002) discussed human capital in Pakistan. The author used PIHS data,

in 1995 for gender disaggregated analysis. The important factors influencing the

earnings were education, experience, technical training, and numeracy skills. The

analysis based on mincerian model. The results also indicated that there was a positive

association between higher earnings and higher levels of education. The results also

indicated that literacy, numeracy and technical training made substantial increase in

males‟ earnings. The education played its part too in development process of country.

121

It also improved the productivity of workers which was considered an obligatory

component of growth. The study further concluded that earnings of male and female

workers were influenced noticeably by human capital. Policy implication in terms of

an urgent need to enhance the literacy and numeracy skills by providing education

(formal and informal) was suggested.

Naqvi and Shahnaz (2002) examined the women‟s participation decision in

economic activities by using data from Pakistan Integrated Household Survey (PIHS)

in 1998-99. The authors adopted probit and multinomial logit model in the study. The

study results indicated that education and age positively affected the women‟s

decision to take a part in economic activities. It was also found that married women

participated less in economic activities. Finally, results showed that economic

difficulties, number of children and household size influenced negatively the

women‟s decision.

Arif and Hamid (2009) recognised urbanization, city growth and quality of life

of women by utililizing both, the 2001 Pakistan Socio-Economic Survey (PSES) and

Pakistan Rural Household Survey PRHS undertaken by PIDE. Authors evaluated

quality of life regarding food security, health, wealth, housing, children‟s education

women‟s employment, and security. Results illustrated that migrant women due to

poverty preferred to be household informal sector workers and were satisfied. They

had better job opportunity, communication and facilities and the quality of life was

much improved than village. In addition, in urban areas, social services were easily

accessed in urban areas. These services were equally beneficial for both migrant and

non-migrant population. Employment opportunities along with access to social

services helped migrants to steadily improve their standards of living. The migrant

women lived in poor houses with little access to basic necessities and worked for low

wages in poor working condition. They became more empowered to manage their

household affairs and to feed their children.

Malik et al. (2010) investigated the relationship between socio-economic

variables and casual employment by using primary data. The authors used the logistic

regression model. The results revealed that age, education, closed relative‟s education,

assets and family setup reduced the probability of casual employment. The results

also pointed out that married and rural-dwellers participated more in casual

122

employment. The study suggested that govt should facilitate the participants with

tertiary education.

There are very few studies about the urban informal sector and characteristics

in Pakistan but still there is no research mode of the urban informal sector

employment at national level especially in Southern Punjab, Pakistan. Further,

estimation is different due to the areas of study, definition of the informal sector

employment and methodology. There is a need of national level survey using

scientific technique to have the fixed figures of the urban informal sector employment

in Pakistan.


In this chapter, we have explained literature on some classic theories of

growth and development. We have also made a detailed view of some empirical

studies on urban informal sector and employment at national and international level.

The studies regarding earnings determinants in the urban informal sector are also

reviewed. Furthermore, the issues regarding development, poverty and the urban

informal sector are also taken into account. This review of literature highlights that

the urban informal sector employment is an emerging issues of research in labour

economics. We have observed that major findings of the studies are characteristics,

size, nature, determinants and earnings determinants of the urban informal sector.

These aspects and motivating factors are relevant with literature prevailing in Pakistan

economy. It can be concluded from the literature that however much research has

been done on the urban informal sector regarding earnings but still it requires more

research in some areas in the informal sector. It is noteworthy that there is a

deficiency in research on the probability of the informal sector employment in

Pakistan and especially in Southern Punjab.

The present study not only describes the characteristics, nature and size of the

informal sector but also analyzes the determinants for indulging in this sector.

Consequently, this study differentiates from previous studies as it is endowed with a

logical and comprehensive analysis of factors which influence the probability of

workers being employed in urban informal sector in detail. The present study not only

finds out the earnings determinants of the workers in the informal sector but also

123

reveals how much the participants possess economic, human and social capital. The

informal sector employment is higher in Pakistan and the urban informal sector is

playing an imperative role in employment creation, income generation and in the

development of the country.

124

Chapter 5

MEASURING URBAN INFORMAL SECTOR: SOME

BASIC ISSUES

5.1 Introduction

Urban informal sector has significance in employment creation, income

generation and economic development. Numerous researches have been made on

informal, urban informal sector and economic development issues at global level.

Many studies have been conducted on earnings determinants of the participants in the

urban informal sector in Pakistan. The present study assesses nature, size and the

probability of urban informal sector employment in Southern Punjab by using primary

data through household survey from Southern Punjab, Pakistan.

The present study of the urban informal sector generally depends on the

primary source of data collected from three divisions of southern Punjab by author

during July-December 2012. In the present study, we have used some qualitative and

quantitative techniques for preliminary and empirical analysis of the determinants of

urban informal sector employment in Southern Punjab, Pakistan.

This chapter is arranged as follows: In section 5.2 we explain a profile of the

selected study areas (i.e. Bahawalpur, Multan and Dera Ghazi Khan Divisions) of

Southern Punjab in detail. In section 5.3, we explain the issues relevant to sampling

design and questionnaires. The limitations during field survey are pointed out in

section 5.4. The determinants of the urban informal sector employment are explained

in detail in section 5.5. In section 5.6 the model and methodological issues concerning

descriptive data analysis and econometric specifications are shown. Finally, section

5.7 shows some concluding remarks.

5.2 Profile of the Study Areas

Urban informal sector is most important for employment creation, income

generation and poverty reduction in Pakistan. It contributes 71% to GDP of the

economy. A high proportion of labour force (rural-urban migrants and urban dwellers)

turns into the urban informal sector. There are 34 districts and nine divisions in

125

Punjab province. Three divisions such as Bahawalpur, Multan and Dera Ghazi Khan

are important and form integral part of Punjab Province which are chosen for research

purpose because the bulk of the rural-urban migrants are inhabited. These districts

enhance the growth potential of the informal sector and are developing. From each

three divisions, one district has been chosen. Further, two Tehsils have been selected

randomly from each district.

5.2.1 Bahawalpur Division

The district Bahawalpur forms one of the southern parts of the province of

Punjab. Ahmadpur East, Bahawalpur, Hasilpur, Khairpur Tamewali and Yazman are

the five sub divisions of the district. The total area of district Bahawalpur is about

24,830 sq. km. The very district is bounded on north by Multan, Lodhran and Vehari

districts, on east by Bahawalpur district and India, on the south also by India and on

the west by Rahim yar Khan and Muzaffargarh districts. From all of its sides, the

district is land locked. In the South and South-East, the Cholistan reaches to the

Indian border while in the north it turns parallel to the Southern part of Punjab plains

and river Setluj makes a common border with the Lodhran and Muzaffargarh districts.

There are three parts the riverain area, the plain area and the desert area of the district

Bahawalpur.

The census report of 1998 indicates that the climate of district Bahawalpur is

tremendously hot and dry in summer while it is cold and dry in winter. The annual

rain precipitation of 125 to 200 millimeters generally occurs during the monsoon

season in July and August. On average, there occurs 10-25 centimeters rainfall

because the district is at the tail of the Monsoon region. The Setluj River with its

length 176 kilometers from Head Islam to Head Panjnad flows in the North side of the

District.

Culturally, the life style of people is very simple. The women wear generally

Ghagra, Cotton suit and Chunni in desert areas. The villagers usually wear Chadar

and Kurta and Turban on their heads. The males use shalwar kameez and almost all

young men wear trousers and shirts in urban areas of the district. Nearly all the

people of district eat good and simple food.

126

The district has a population of 2.433 million or 2433091 as indicated by annual

average census report of district Bahawalpur that is issued in October 1999. On

average, rate of population growth is 3.07 percent in the district. The total area of the

district is 24830 sq. km with a population density of 98 persons per sq. km. The

average household size is about 6.8. Rural dwellers are almost 72.7 % of the total

population while those persons who live in urban areas are 665304 or 27.3% of the

total population of the district.

One Municipal Corporation, two Municipal Committees, one Cantonement

and four Town Committees are present. Total number of Mauzas is 1216. The

Muslims are 98.1 percent of total population. While the proportion of the Hindus and

the Christians is 0.9 and 0.6 percent points respectively. Siraiki is the major language

that represents 64.3 percent of the population, followed by Punjabi language spoken

by 28.4 percent of population. The proportion of the infants less than one year,

children under 5 years, children under 10 years, and those who are 15 years old is

observed 2.3 percent, 14.9 percent, 31.4 percent and 44.4 percent respectively of the

total population. The 50.7 percent population is above 18 years and 42.9 percent are

above 21 years, of the total population. The working age group i.e. 15-64 is

proportionately 52.3 percent and those who are over 65 year are 3.3 percent resulting

age dependency ratio of 91.2 percent. The proportion of never married is lower at 28

percent than the married of 66.2 percent of the population. The widowed and divorced

are 5.5 percent and 0.4 percent respectively.

The literacy rate of district Bahawalpur is observed 35%. The literacy rate is

higher at 57 percent in urban areas as compared to low literacy rate of 26 percent in

rural areas. The literacy rate for males is 44.9% while literacy rate for females is 24%.

Considering education level, 20% are below Primary, 32% have passed Primary

education, 20.8% percent have completed Middle level education, Matriculates are

16.5 %, and Intermediates are 5.2 percent. Those who have accomplished Graduation

and Master‟s level education are 3.1% and 1.4% respectively out of the total educated

population. A large number of populations are engaged in agriculture. The percentage

share of participants is 72 percent in agriculture, forestry, hunting and fishing

industries. The percentage share of workers in construction industries and community,

social and personal services industries and agriculture, forestry, hunting and fishing

127

industries is 26.4 percent and 22.0 percent respectively. Most important crops of

district are pulses, rice, sugarcane and grain.

The district census report of 1998, explains that the 49.3% is economically

active out of the male population, children less than 10 years are 30 percent, students

are 10.1 percent, share of domestic workers are 1.6 percent while landlord, property

owners, retired and disabled persons are 8.2 percent. There is higher absorption rate in

urban areas as compared to rural areas. The unemployment rate is 19.8 % (20 %

among male workers and 6.1 % among female workers).

While, the underemployment rate (19.3%) is low in rural areas than urban

areas which is 21.1%. The share of workers in agriculture and fishry is 44.7 percent.

While the share of elementary occupations and service workers, shop and market sale

workers and craft and related trade workers is 34.8 percent, 9.2 percent and 3.5

percent of the employed workers. The proportion of self-employed, private employed

and Government employed is 68 percent, 18 percent and 6.5 percent respectively out

of economically active population. 18.3 % are private employees and 6.5% are

Government employees. The unpaid family helpers are recorded as 6.3% of the

employed population. The district is well-known for its trade production, trade centre

and Bahawalpur chamber of commerce.

As far as, educational and health institutions are concerned, there are 797

Primary schools for males and 756 Primary schools are for females while private

registered schools are 138. For males and females, 80 and 85 Middle schools are

providing educational facilities respectively. There are 120 private registered Middle

schools. Females are given education by 13 community model schools and males by

100 Tanzeem schools. There are 273 masjid Maktab schools and 60 Arabic schools

are educating male students. The number of high schools for males is 77 and for

females are 45. There are 80 private registered high schools which provide education

to the students. The higher secondary schools for males and females are 4 and 3

respectively. Moreover, it is noted that there are two inter-colleges for females, 6

degree colleges for males and 3 for females and one commerce college in the district

Bahawalpur.

128

As regards professional and higher education, there are 3 commercial

institutes, 3 vocational institutes, one polytechnic institute, 3 elementary colleges, 2

medical colleges, 1 paramedical school, 1 nursing school and one university which are

facilitating the people with education and training in district Bahawalpur. Major

health institutions of this district are Bahawalpur Victoria Hospital, Juble Female

Hospital, Mission Hospital, Tehsil Headquarters Hospital Hailpur, Ahmad pur East,

Yazman and Khairpur Tamewali, Quaid-e-azam Medical College Bahawalpur, Tibia

College Bahawalpur, Homeopathy College Bahawalpur, Pera Medical School

Bahawalpur, and Nursing School Bahawalpur are medical teaching institutions.54

5.2.2 Multan Division

Multan division comprises four subdivisions such as Multan Contonment,

Multan Sadar, Shujabad, and Jalalpur Pirwala. The district Multan lies in a bend made

in the course of five confluent rivers. The very district is bounded in the east by

Lodhran and Khanewal districts, in the North by Khanewal district, in the South by

Bahawalpur district dividing the two districts by Setluj River between and on the

West by Chenab across Muzaffargarh district is situated. The total area of the district

is reported as 3,720 sq. km. The district comprises four tehsils such as Multan Saddar,

Multan city, Shujabad and Jalalpur Pirwala. The district can be divided in the riverine

(which is high barren area) and Utar that shows low water level.

The climate is dry and hot in summer and cold in winter. The annual rainfall is

normally recorded about 186 millimeters. The rain fall during monsoons is high while

rain is very rare in winter.

Culturally, man in rural areas wears a pag (turban) on his head and Kulla or

cap inside. It is observed that the waist coat or Majhal, a Chola or shirt and Chaddar

or plaid worn over the shoulders as a normal dress. However, the western dress is

used by educated class normally in towns. The women commonly wear Shalwar or

Pajama or Chola or Kurta in urban and in rural areas. Mostly women observe pardah.

The Multani people eat delecious food.

54 See District Census Report of Bahawalpur, Government of Pakistan, 1999, Islamabad.

129

The total population of the district Multan in 1998 is 3,116,851 as specified in

March, 1998. The annual average growth rate is 2.7 percent during this period in the

district. The area of district gives population density of 838 persons per square

kilometer. The urban population is 1,314,748 or 42.2 percent of the total population

which showed growth at an average rate of 2.9 percent for years 1981-98. There is a

one Municipal Corporation, one Municipal Committee, one Cantonment and three

town Committees. As it appears from 1998 Census, the population of Muslims is

dominant at 99.12 percent. The next higher percentage is of Christians with 0.62

percent, followed by Ahmadis 0.09 percent. There is a small number of other

minorities like Hindu (jati), scheduled castes etc. The majority of the population of

60.67 percent speaks Siraiki language and Punjabi language is spoken by 21.64

percent of the population. Urdu and Pushto speaking are 15.86 percent and 0.62

percent respectively. While other languages Sindhi, Balochi, Bravi and Dari etc are

also spoken.

According to the census report, sex ratio is recorded at 110 percent with 108

percent in rural area and 113 in urban areas in 1998. The District Census Report 1998

points out that the proportion of the infants less than one year is 2.3 percent, children

under 5 are 14.3 percent, children less than 10 years is 30.3 percent, under 15 years

are 43.6 percent of the total population. Those who are above 18 years observed 51.3

percent while those eligible for casting vote are 43.1 (21 years and above) percent of

the total population. The working age group i.e. 15 to 64 years makes share of 53.2

percent. The percentage share of the people who are over 65 years is 3.2 percent that

shows high dependency ratio of 87.9 percent. It is recorded that the percentage share

of never married and married is 30.7 percent and 63.4 percent respectively. While,

widowed and divorced are 5.6 percent and 0.4 percent accordingly.

According to Census report of 1998, there was small-scale industry in Multan

before independence. Since 1947 Multan has become an important center of industry

such as textile industry and other industries linked to the agricultural production of the

district like cotton-ginning and vegetable oil etc. There are 243 registered factories

having less than 100 employees in each while 17 registered factories having more

than 100 employees in each during the year 1996.

130

The district census report describes that the literacy ratio is 43.4 percent in

1998. The literacy ratio of males is observed 53.3 percent and of females is 32.3

percent. The literacy ratio is comparatively higher in urban areas than rural areas for

both males and for females. The census report measures enrolment ratio which is 34.9

percent with significant rural-urban differential for both gender. 18.6 percent are

below Primary, those persons who have passed Primary level education are 30.7

percent, 21.6 percent have completed Middle level education, those who have

accomplished Martriculation, Intermediae level education and Graduation level

education are 16.2 percent, 6.3 percents 4.1 percent respectively. The Post graduates

are 1.5 percent while 0.4 percent is diploma / certificate holders out of the total

educated population. As regards sex differential, males are comparatively more

educated and have higher education than females. The life time migrants are 5.0

percent of population of the district.

Taking into consideration the employment structure, the economically active

population is observed at 24.3 percent in district Multan. Of the total male population,

45.1 percent are economically active, while the ratio of percentage persons who are

not active is 54.9 percent, children under 10 years are 30.3 percent, students are 7.4

percent, and domestic workers are 32.6 percent. There are 5.3 percent land lords,

property owners, retired and disabled persons etc. The employment is at higher rate in

urban areas as compared to rural areas. The unemployment rate is 20.5 percent due to

high unemployment rate amongst males i.e. 21.0 percent.

In 1998 census report, 39.5 percent has elementary occupation; skilled

agricultural and fishery workers are 25.5 percent. While, service workers, shop and

market sales workers and craft related trade workers account for 17.6 percent and 5.1

percent respectively of the total employed persons. In 1998 census report, 33.6

percent are engaged in agriculture and forestry occupation of the district Multan. The

workers involved in hunting and fishing industries are 23.8 percent where 18.3 % are

working in construction industries and community, social and personal services

industries. In the district, the employed population is registered at 79.5 percent of total

economically active population. There are about 58 percent people working as self-

employed, 27.6 percent are employed as private employees and 6.8 percent are

government employees. While, unpaid family helpers are recorded as 5.3 percent. The

131

difference in proportions of employed is significant between the genders and the

urban and rural residences.

The census report highlights that there are 2,173 educational institutions

performing in district Multan, reporting education from the level of mosque / primary

upto post graduate level. In district Multan, there are 590 Primary schools for males

and 800 Primary schools are providing education to females. The Middle schools

giving education to males are 101 and 77 to females. There are 173 secondary schools

providing education to males while 38 secondary schools are giving education to

females. There are 8 higher secondary schools out of which 6 are for males and 2 are

for females. There are 7 intermediate and degree colleges for males and 3 for females.

In the district, only 376 mosque schools are educating males. Regarding health

institutes, Multan district is having a Civil Hospital and Nishtar Hospital attached

with Nishtar Medical College is also situated in Multan city. Two Tehsil headquarters

Hospitals are present; one in Shujabaed Tehsil and other is in Jalalpur Pirwala

Tehsil.55

5.2.3 Dera Ghazi Khan Division

Dera Ghazi Khan is also one district of Punjab Province. The district Dera

Ghazi Khan is bounded on the North by Dera Ismail Khan District of NWFP and its

adjoining Tribal Area, on the West by Musa Khel and Barkhan districts of

Baluchistan province, on the South by Rajanpur and on the east by Muzaffargarh and

Layyah separating the later two districts by river. The total area of the district is about

11,922 sq. km. The very district consists of two tehsils Dera Ghazi Khan and Taunsa

and one De-Excluded area with an area of 3,814, 3,769 and 5.339 sq. km

correspondingly. Its rural area comprises 826 mauzas. Regarding area, district Dera

Ghazi Khan is divided into mountain area which is in the west and the plain area is in

the east. The hills of the Suleman Mountains cover the western half of the district.

The source of cultivation is the spill of the river Indus.

In district Dera Ghazi Khan, climate is extremely dry in the same way in the

hills and plains in summer and winter. Generally, food and life style of people of the

55 See District Census report of Multan, Government of Pakistan, 1999, Islamabad.

132

district is very simple. In the Western parts, the male dress consists of white Pagri,

Masons, a loose Kurta and big Shalwar. The dress of women comprises Dopatta,

Kurta and big Shalwar. Cultivation and livestock breeding are sources of livelihood of

population.

According to the Census report of the District in 1999, the total population of

the district is 1,643,118 as detailed in March, 1998 with an intercensal percentage

increase of 74.0 since March, 1981 when it was 943,665 sq. km which gives

population density of 138 persons per sq.km. The urban population is 228,839 or 13.9

percent of the total population with speedily average growth rate of 3.8 percent for the

period of 1981-98. There is one Municipal Committee and one Town Committee in

Dera Ghazi Khan. The higher proportion of population is generally Muslims i.e 99.56

percent.

Moreover, Ahmadis are with 0.20 percentage points. The scheduled Castes

with 0.10 percent come after Ahmadis. The major language is Siraiki being spoken in

the district by 80.3 percent of the population. The Balochi language spoken by 14.3

percent comes after Siraiki, followed by Urdu 3.2 percent and Punjabi 1.3 percent

while others speak Sindhi, Pushto, Bravo, Dari etc.

The census report shows that sex ratio is 108 percent with high ratio of 108

percent in rural areas as compared to 107 percent in urban areas. In 1998 census

report, the proportion of the infants under one year are 2.5 percent, children less than

5 years are 17.5 percent, children less than 10 years are 35.6 percent, less than 15

years are 49.1 percent of the total population. The proportion of over 18 years

corresponds to 46.5 percent while those who are 21 years or above are 38.7 percent of

the total population. The proportion of working age group i.e. 15 to 64 years is traced

as 47.6 percent and over 65 years 3.3 percent which results in ratio of 110.1 percent.

The never married are 23.1 percent, 72.1 percent are married, 4.6 percent are

widowed and 0.2 percent are divorced as detailed in census report of 1998.

As far as literacy ratio is concerned, it is noted 30.6 percent (42.1 percent for

males and 18.1 percent for females) in district census report of 1998. The noted ratio

is higher in urban areas as compared to rural areas for both males and females.

133

The enrolment ratio is 23.0 percent as measured from 1998 Census data with

significant rural urban differential for both males and for females. The person whose

level of education is below Primary is 22.9 percent, 30.1 percent has passed Primary

level education, 20.6 percent have completed Middle level education, 15.0 percent

have accomplished Matriculation, 5.7 percent have passed Intermediate. While, those

who are Graduates and Post graduates are 3.2 percent and 1.6 percent rsespectively.

About 0.9 percent is diploma / certificate holders out of the educated population. As

regards sex differential males are more educated and have higher education than

females. The life time-migrants are recorded 23,921 or 1.5 percent of population of

the district.

According to the district census report of Dera Ghazi Khan 1998, the

economically active population is 23.8 percent of the total population or 37.0 percent

of the population is 10 years and over. Of the total male population, 45.1 percent are

economically active, while 54.9 percent are those who are not economically active.

The children under 10 years, students, domestic workers, land lords, property owners,

retired and disabled persons are 35.3 %, 9.6 %, 1.2 %, 8.9 % respectively.

The participation rate is enlarging in rural areas as compared to urban

dwellers. The unemployment rate in the district is 24.8 percent which is due to 25.2

percent of unemployment among males and 4.8 percent of unemployment among

female workers. The share of employed in agriculture, in forestry, in hunting and in

fishing industries, followed by construction and wholesale, retail trade, restaurant and

hotel industries and construction industries are observed i.e. 67.4 %, 11.3 %,11.1 %

respectively. The community social and personal services industries workers,

wholesale retail trade, restaurant and hotel industries participants and workers of

construction industries are 40.5%, 24.8% and 17.5 % respectively in urban areas.

As far as, employment status is concerned, 75.2 percent are registered as

employed out of the total population. More or less three-fourths as 72.6 percent are

self employed, 9.9 percent are working as private employees and 6.3 percent of the

working population is government employees. 10.2 percent of the workers are unpaid

family workers. A larger number of people are employed in government jobs, Semi

government and private concerns. The skilled labour consists of masons, carpenters,

134

blacksmiths and gold-smiths in district Dera Ghazi Khan. It is noted that 9 industrial

units at large scale and the district is famous for lacquered articles.

Regarding educational and health institutes, 1,405 primary schools, 142

middle schools, 99 high schools and 8 higher secondary schools are providing

educational facilities to the people. There is only 1 intermediate college and 4 degree

colleges are established for both males and females in Dera Ghazi Khan Division. As

for professional and higher education: 2 technology and polytechnic institutes, 12

commercial training institutes, 12 vocational institutes and one university sub-campus

facilitate the people of district Dera Ghazi Khan. The district census report 1998

illustrates that there are 6 hospitals with 305 beds, 35 dispensaries with 14 beds, 9

Rural Health Centers with 180 beds, 52 Basic Health Units with 8 beds along with

one T. B. Clinics, 34 Sub-health Centers and 6 Mother Care Health Centers medically

facilitate people of the district.56

5.3 Sources of Data and Sampling Design

Usually, data can be gathered for analysis testing hypothesis and answering

the research questions to conduct the research. The data can be obtained from three

sources such as primary, secondary and tertiary sources keeping research in view.

The primary data, firstly, makes available the information by the researcher for

variables which are required for the specific purpose of the study. Accordingly, we

collect primary data for the present research.

For current study work, data has been gathered through a survey conducted in

urban regions of Southern Punjab that cover three divisions: Bahawalpur, Multan and

Dera Ghazi Khan and urban areas of these districts. Further, from each division, (i.e.

Bahawalpur, Multan and Dera Ghazi Khan) two districts (i.e. Bahawalpur and

Hasilpur, Multan and Shujaabad and Dera Ghazi Khan and Tounsa Sharif) are

preferably chosen to get precise information. A large number of people are occupied

there in the urban informal sector to earn their livelihood.

The data is gathered or obtained randomly about household from the areas

selectecd for sample purpose. Consequently, random or stratified sampling is applied

56 District Census Report of Dera Ghazi Khan, Government of Pakistan, 1999, Islamabad.

135

within each stratum which is a tactic to improve representativeness of the sample and

to reduce sample error. We have developed a comprehensive questionnaire for this

research. Furthermore, interviews are taken according to the requirement.

The questionnaire for this survey incorporated 6 groups of questions (location,

household characteristics, characteristics of participants of the informal sector, sector

of employment, personal and other socio-economic factors, household and

geographical factors and development indicators) in which questions is binary or

qualitative in nature. Ordinary least square method (OLS), Logit model estimations

and HDI are constructed on the basis of this data. This survey is conducted in 2012 by

the author who interviewed 1506 urban households (i.e. 506 households from

Bahawalpur division, 513 from Multan division, and 487 from Dera Ghazi Khan) in

the urban areas of Southern Punjab. Location for the survey is extended crosswise the

colonies, blocks and mohalas.

We have considererd representative sample of formal and informal sector

employed in Southern Punjab. The types of sectors covered are trade, services,

manufacturing, transport and construction in case of males and trade, services and

manufacturing for females. The sample includes all working persons aged 18-64 in

the survey year. For this research, we use samples of both married and unmarried

urban male and female workers. The information is obtained by workers and their

households with the help of questionnaire based on multiple choices and open ended

questions. The questions are of qualitative and quantitative nature. The interviews are

taken in mother language of respondents.

Out of 1506, 934 respondents are males and 572 are females in each division.

Out of the total 1506 respondents 986 are from informal sector while the 520 are from

formal sector. The sample size in current study is comparatively large. Further, out of

total 986 respondents, 23.32 % belong to the trade sector, 40.67 % belong to services

sector, in the sample 26.98 % belong to the manufacturing sector, 5.37 % belong to

the transport sector and 3.65 % are construction workers. Almost 27.48 % of the

respondents are wage-earners and 53.7 percent are the informal self-employed, 6.7

percent are salaried workers, family workers are 4.6 percent. The domestic workers

are 1.2 % percent.

136

Further, income of the participants of the informal sector is taken in rupees on

weekly and monthly basis. Income in the form of kind is converted into cash at

market price.

The sample urban area in district Bahawalpur consists of geographic areas of

Tehsil headquarter Bahawalpur (Riaz Colony, Satellite town, Maila gali, Farid gate,

Model Town B block, Model Town C block, Islamia Colony, Bindra basti, Quaid-e-

Azam Colony, Cheema Town, Chirimar Mohala), Tehsil Headquarter Hasil Pur

(Ghareeb Mohala, Satellite town, Madina town, Darbar road, Jadid Colony and

Mehmood Colony).

The sample urban area in district Multan consists of geographic areas of Tehsil

headquarter Multan ( Rashid Abad, Wahdat Colony, Dolat Gate, Bohar Gate,

Gulgasht Colony, Shamsabad Colony), Tehsil Head Quarter Shujabad (Murad

Colony, Bodla Colony, Rajpoy Colony, Jinah Colony, Multani Darwaza, Islamia

Colony.

The sample urban areas in district Dera Ghazi Khan consists geographic areas

of Tehsil headquarter Dera Ghazi Khan (Satellite town, 46 Block, Kachi Abadi,Y

Block, and Khayaban-e-Sarwar), Tehsil headquarter Tounsa Sharif (Muhala Khawaja,

Muhala Khosa, Muhala Qaisrani, Muhala Shamsabad, Mahala Sikhani and Muhala

Lashari). The interviews are applied from main colonies and towns of the selected

urban areas. Furthermore, the sample is randomly drawn for each stratified location.

The dependent variable is “urban informal sector” which implies small-scale

non-agricultural activities producing and distributing goods and services consisting on

self-employed workers, own-account workers57

, unpaid family workers, domestic

workers, and wage58

and salaried workers in small firms employing less than five

workers except technical and professional occupations in urban areas of Southern

Punjab. The informal sector employment indicates an engagement in economic

activities that are unregulated and unrecorded. These economic activities are done by

both (male and female participants) in trade, services, manufacturing, transport and

construction sectors of informal sector.

57 The survey also includes those self-employed who work on own-account basis in their profession

with unpaid family worker. 58 The survey also includes part-time and casual workers.

137

The urban informal sector is major provider of employment and income to all

these categories. Thus, study identified the personal, household, demographic and

socio-economic factors of these groups that manage, promote, and determine this

sector in urban areas of Southern Punjab. These identifying variables are the age in

complete years, complete years of education, marital status, sex, formal training,

father‟s educational status, mother‟s educational status, family setup, household size,

dependency ratio, number of children, number of male adolescents, number of female

adolescents, household‟s value of assets, spouse participation in economic activities,

and rural-urban migrant. Several variables make obvious policy proposals to enhance

the growth potential of the urban informal sector employment in the economy.

5.4 Survey Limitations

A lot of problems are faced while conducting survey. The respondents were

reluctant to provide pertinent information regarding age, personal income, family

income, consumption, working hours, household‟s value of assets, and education

level. As regards females, they were reluctant to give information about the household

head‟s income, assets, household‟s value of assets and working hours. Even the

females had no accurate information about their ages. The respondents forcefully gave

complete informations of the family members regarding working hours and wages.

The present study endeavours to diminish the quantity of sampling error. The efforts

are also made to lessen the non-sampling errors taking into account circumstances and

financial conditions.

Few other variables which have an effect on the urban informal sector

employment are not incorporated in the current study due to some constraints. These

excluded variables want more research.

138

5.5 Determinants of the Formal and Informal Sectors Employment

in Urban Areas

The present study discusses the explanatory variables in order to determine the

probability of workers being employed in the urban informal sector. The explanatory

variables affecting the probability to participate in the informal sector include age

profile of the workers joining the informal sector employment, education level of the

participants of informal sector, gender, marital status, formal training of the workers,

education level of the parents, household size, family size, dependency ratio, number

of children, number of male adolescents, number of female adolescents, spouse

participation in economic activities, household‟s value of household assets, and rural-

urban migrants.

5.5.1 Age of the Participants

Age of the participants enhances the growth potential of employment in labour

market. Life-cycle hypothesis postulates that the persons allocate their time more to

work in younger and older age. Age of the people who participate in the informal

sector employment underlies some of the factors that are considered important to

influence their decision to join the informal sector. Two views can be presented about

age of workers involved in the informal sector. Firstly, if the relative participation of

young people is greater in informal sector, the very sector may probably be

considered a transition stage before opting for formal sector. Secondly, informal

sector may be considered as a desirable constant choice if there is a large participation

ratio of older persons in the informal sector (see Kemal and Mehmood, 1993).

Following studies have indicated that the decision of informal sector employment is

positively as well as negatively associated with age.

With age, the workers earned more in the informal sector (Boyed, 1990).

Wages were increased with the increase of age of the worker (Kozel and Alderman,

1990). Funkhouser (1996) examined that old age and young workers were more likely

to be employed in the informal sector employment. It was argued that some persons

participated in the informal sector at a quite early age and stayed there till their

induction in the formal sector (Kemal and Mahmood, 1998). An inverse relationship

139

between age and women informal sector employment was observed.59

The age was

negatively related with informal total and formal sector employment under

subsistence informal sector approach and positive with male employment (Florez,

2003). Guang and Zheng (2005) highlighted that non-farmers, migrant job workers,

wage workers and entrepreneurs were more likely to be employed with age. The

workers were more likely to be employed in the formal and informal sector

employment with age in 1986.60

The middle aged gardeners engaged themselves in

the profession of gardening (Pisani and Voskovitz, 2005). Baudar (2008) found a

positive association of self-employment and age.

The age of the workers can be incorporated as explanatory variable in order to

find out the relationship between age and informal sector employment. Here, we have

used the complete years of age to see the informal sector employment response. It is

predictable that the informal sector employment is positively as well as negatively

related with age.

5.5.2 Education

Schooling assures the quality of opportunity and increased social

responsibility. Education is a critical input in economic development. The

externalities which arise due to high literacy rate benefit the individuals having better

education. Consequently, improved human capabilities getting high education and

training are significant not only in its own right and also owe in the overall economic

growth of the economy, which in return diminishes poverty and enhances

empowerment of disadvantaged groups (Behrman, 1995).

Theoretically, there is a mixed effect of education on participation decision

and time allocation decision in labour market. Human capital theory hypothesized a

positive association of high income and output. Education level of the participants in

the labour market can work in two contradictory ways. For example, better educated

workers have a tendency to be more productive as their level of education implies to

improve the skills in the course of training however low education indicates increased

likelihood to participate in a business start-up as self-employed or as wage labourers

59 see Gallaway and Bernasek (2002). 60 see Wamuthenya (2010) study.

140

in informal urban sector. It is expected that there is a negative relationship between

level of education and informal sector employment. An increase in education is

related with a decrease in the probability of the informal sector employment.

The education influences the labour force and duration of work by income and

subsititution effects. Education level and work involvement are positively related.

Labor force participation and substitution effects are negatively related. By and large,

the net effect of education on work force involvement is based on which effect is

dominant. Almost studies showed strong subsititution effect in the following

studies.61

The returns to education were higher in the informal sector (Banergee, 1983).

Evidences demonstrated a positive association between years of education and self-

employment (Boyd, 1990 and Blaunchflower, 2004). Kemal and Mahmood (1998)

has characterized that the entrepreneurs and self-employed were educated people in

Pakistan.62

Funkhouser (1996) found that the low educated workers were more

involved in informal sector employment in Central America. The street vendors

gained positive returns to education (Samith and Metzger, 1998).

The educational attainment and capital investment were positively correlated

(Kemal and Mehmood, 1998). The workers were being employed more in the

informal sector with increasing years of schooling.63

The well-educated decided to

work as self-employed workers (Meng, 2001). Gallaway and Bernasek (2002) found a

positive link between primary education and males as well as females (both working

at home without pay and with pay in labour market) and negative relationship for

females in labour market. The authors further indicated that relationship between

junior high school education and male female informal sector employment (i.e. home

as well as market) but negative for males working in labour market. The negative

association was observed between informal sector employment (both working at

home and in labour market) for males and females and senior high school education

but positive relationship for female working at home. The study showed that there

61 see chapter 4 62 see Kemal and Mahmood (1998). 63 see Meng (2001).

141

was an inverse association between male female informal sector employment and

post-secondary education.

The majority of the entrepreneurs manning the small and medium enterprises

have passed primary and secondary school education (Mukras, 2003). It was found

that education and earnings were positively related in the informal sector (Smith,

2001). Low educated were forced to work in the informal service employment

(Dasgupta, 2003). Pisani and Voskovitz (2003) indicated that informal workers were

educated at middle level. Calves et al. (2004) illustrated that people having low

education participated more in the informal sector employment.

Evidence indicated that self-employed were more likely to be employed in the

informal sector (Marshal and Oliver, 2005; Avirgin, 2005). Mitra (2007) found the

low educated workers in the informal sector. Gunatilaka (2008) estimated that

workers with primary and secondary education participated more in the informal and

the formal sector employment in Sri Lanka. Mentzakis et al. (2008) sowed that

probability of care given decision decreased with the education. Wamuthenya (2010)

highlighted that urban informal sector employment increased with low education

levels.

Above mentioned studies have mixed effect of education on informal sector

employment, however, many studies showed strong substitution effects. It is expected

that informal sector employment is negatively related with the level of education. To

analyse a significant impact of education on informal sector employment in the

present study, we have used complete years of education. The influence of education

is also observed with the help of five dummies such as Middle, Middle, Intermediate,

Graduation and Master‟s or higher education. In the previous literature, the education

has shown contradicting effects on worker‟s participation in urban informal sector.

5.5.3 Gender

Sex or gender is one more factor which influences the decision to work in the

informal sector. Labour supply theory suggested that male workers are expected to be

involved more in labour activities in order to meet up the economic necessities of the

142

family as compared to female workers and this shows strong income effect. Hence, it

is suggested that, male workers may be hypothesized as positively related to informal

labour market. We have included the binary variable to find out the effects of sex or

gender on the informal sector employment. On the other hand, it is expected that the

probability of females working in the informal labour market was low because of

child care and household responsibilities.

Tokman (1989) argued that the females were less than the male participants in

low productivity jobs. Several studies explained that males were being engaged more

as self-employed (Boyd, 1990; Roberts, 2001and Guang and Zheng, 2005). Levin et

al. (1999) pointed out that women were less likely to be employed as wage earners as

compared to men. Pagnatario (2001) explained the informal sector as male dominant.

Meng (2001) found that males were being employed more as wage earners and were

less employed in self-employment.

Florez (2003) examined that sex and urban informal sector (dynamic) were

negatively correlated. Men were found more in the informal sector employment

(Ozcan et al., 2003). Calves et al. (2004) illustrated that people having low education

participated more in the informal sector employment. Pisani (2005) found the workers

into the profession of gardening. Gunatilaka (2008) showed a positive relationship

between sex and informal sector employment. Baudar (2008) has revealed that the

females were less likely to be self-employed.

There is found mixed literature or results of gender influence on informal

sector employment. So, we incorporate this explanatory variable as a binary choice in

this analysis.

5.5.4 Marital Status

Theoretically married people prefer to invoke more in economic activities.

Neo-Classical framework of labour supply puts forward that individual‟s act

rationally for maximization of their utility by willingly opting for jobs administered

by the basic condition that market wage rate exceeds reservation wage. Here, income

effect is greater than the substitution effect in their decision. Theoretically, there is a

mixed effect of marital status on workers‟ participation in labour market. As far as the

informal sector employment is concerned, the male informal employment is expected

143

to be positively as well as negatively associated with marital status. Generally,

married people prefer to work in the secure formal labour market. The married people

face additional responsibilities and if donot find job in formal sector ultimately get

invoked to informal sector. It is hypothesized that married workers, participate in the

informal sector more than other groups to accomplish family needs and to give bright

future to their family. The probability of male workers‟ participation in informal

sector is expected to be positively correlated. Concerning female participants, it is

expected that they have a less likeliness to enter into the informal labour market

because of child care at home and other responsibilities. However, married females

without children are more likely to participate in the informal sector.

Several studies demonstrate a positive relationship between marital status and

informal sector employment in urban areas (Funkhouser, 1996; Meng, 2001, and

Gunatilaka, 2008). Roberts (2001) found that sector of choice of rural labour migrants

to Shanghai was positively associated with marital status. Gaung and Zheng (2005)

analyzed that non-farm job and migrants were positively correlated with marital status

but there was a negative relationship between wage workers alongwith entrepreneurs

and marital status. Gunatilaka (2008) showed that the households were more likely to

be married in the informal sector. Wamuthenya (2010) examined that the married

participants found work in the informal sector.

We have included this variable as binary in our model in order to analyse its

effects on the informal sector employment.

5.5.5 Formal Training

People having some kind of formal training have more chances to join the labour

market with higher gains as predicted by human capital theory. The argument which

has been given to encourage the informal and formal sector activities is that normally

workers are unskilled or having low level of formal or informal training and informal

sector is considered as a refuge for people having low formal skills. This literature

consists of the contradictory views on skill level and urban informal sector. House

(1984) found that low skill level motivated the informal sector workers to work in

informal sector. Kemal and Mahmood (1998) found that a large number of self-

employed gained some kind of training in urban areas of Pakistan.

144

Meng (2001) found a positive relationship between total training days before

migration and wage as well as self-employment. Achary (2001) showed that migrants

were low / medium to the unskilled category workers. Smith (2001) found that

training and earnings of the workers were positively related. Self-employed

entrepreneurs possessed the skills (Meng, 2001). Calves et al. (2004) illustrated that

people having low education participated more in informal employment.

Marshall and Oliver (2005) defined that the skill was definitely indispensable

to the success of entrepreneurs in Indiana. Etherington and Simon (2005) found that

poor people were more often forced to work in the informal economy because of lack

of skills. Tornaroli (2007) examined that unskilled young people were engaged in

labour market as wage-earners in Latin America and the Caribbean. The returns to

skills training were high in the informal sector employment (Frost and Jones, 2008).

Our analysis is concerned with the negative association between formal

training and the informal sector employment in urban areas of Southern Punjab. To

analyze the impact of formal training on urban informal sector employment, the study

has included formal training as binary variable in our analysis.

5.5.6 Parents’ Educational Status

The Neo-classical labour supply theory explains that the family labour supply

decisions are interdependent. Family background of the informal workers, especially

father‟s education and mother‟s education proves helpful to determine the

intergenerational occupational mobility and growth potential of the informal sector.

Probability of informal sector employment is affected by the educational status of the

parents. Parents‟ education level has been used as dummy variables. Theoretically, it

is expected that informal sector employment is negatively related with parental

education. Assad et al. (2001) found a negative relationship between females

employed in casual and self-employment and husband‟s basic education.

145

5.5.7 Household Size

The variable “household size” is an essential factor in determining the urban

informal sector employment decision. Household size64

is, in general, taken into

account to be an indicator of dependents on the heads of household. As far as males

are concerned, it is expected that household size affects positively the informal sector

employment. Theoretically, two varying hypotheses can be formulated regarding the

effect of household size on informal sector involvement. Firstly, it signifies the

promotion of the informal sector due to manifold increase in labour supply. Secondly,

the more of making the family financially sound compels the head of large household

to opt informal sector. Household size is found to be positively influencing male

workers to involve in the informal sector employment.

For females‟ employment, the household size is expected to be inversely

related with the informal sector participation. Those females who belong to the large

family size, have more children and old people, allocate their more time for child care

and household responsibilities and have relatively less participation in work related

activities. It is hypothesized that the females have a less likelihood to engage in the

informal sector employment due to large household size.

Hayami (2006) shows a positive as well as a negative relationship between

household size and urban informal sector employment. The majority of both pickers

and collectors have the average family size of larger than five. Kumar and Aggarwal

(2003) argued that most of the self-employed of slum-population were with average

size of 5 members. Menzakis et al. (2008) found that probability of care given

decision increased with household size. The probability of female care given decision

increased with the increased household size. Wamuthenya (2010) found that

probability of the informal sector employment increased with household size in

Kenya in 1986 and 1998.

We have used the household size as a continuous variable in our models.

64 Children, females, male labourers and old age people.

146

5.5.8 Family Setup

Family setup (both joint and nuclear) plays a central role to join sector of

employment in urban labour market. It is expected that labour supply theory assumed

that the workers living in a joint family system have the possibility to participate in

the informal sector, to support and fulfill household necessities. On behalf of men, it

is expected that there is a negative relationship between male participants in informal

sector and joint family setup. Generally, male workers in joint family structure are

less likely to work because some other family members are also busy in earnings

activities and it is the strong substitution effect of extra income of other family

members which forces the head not to work anymore. Other argument is that in joint

family structure, male informal workers are more likely to participate because of

family labour supply involvement.

Conversely, female informal sector employment and joint family setup are

expected to be positively correlated. Almost female responsibilities are shared by

other family members and the strong income effect forces them to involve in earnings

activities at home or in the labour market. A negative relationship between extended

family and self-employment was found in all three models (see Boyd, 1999).

To evaluate the effects of family setup in determining the informal sector

employment, we use the binary variable in our models in the analysis.

5.5.9 Dependency Ratio

The dependency ratio has a substantial effect on participants of informal

labour market. Here, dependents are well thought-out persons who are below 15 years

of age and above 64 years. Dependency ratio is attained by dividing the number of

dependents by household size. Theoretically, it is expected that the dependency ratio

and participation in the informal sector employment are positively as well as

negatively correlated. The male informal sector participants have the tendency to join

informal sector because of high dependency ratio. However, in case of females, it is

assumed that there is an inverse relationship between female informal sector

employment and dependency ratio. Generally, females are less likely to engage in

employment in informal sector because of child care and household responsibilities of

147

the dependents. One more argument is that, unmarried females in the household are

expected to be positively correlated with the informal sector employment.

The present study has incorporated this variable as continuous variable to see

its impact on urban informal sector.

5.5.10 Number of Children (6-14)

The labour supply theory predicts that the number of children have a great

impact on the decision of household regarding choice of sector of employment or in

labour market. Theoretically, there are mixed effects. As far as males are concerned,

the presence of children of this age group can affect their decision to participate in the

informal sector. Some of the children in the family are pursuaded to informal work at

home as family helpers or in labour market or as child labourers. In this way, the male

heads will participate less due to strong substitution effect of extra income. This

indicates an inverse relationship of children and male informal sector employment.

Theoretically, it is argued that females having children (6-14) compratively

pay less care to them. Thus, this shows a positive relationship between sector of

employment and having children. It appears that the female informal sector workers

having more children above 6 years and below 15 years have more participation rate

in the informal sector employment. We have used this variable as continuous to find

the relationship. Having male children less than 10 years old, workers participated

less in the informal sector except in El Salvador in 1985 and in Hunduras in 1989.

However, they participated more in the informal sector employment with female

children less than 10 years age in Nicaroga in 1993 and in Costa Rica in 1980, 1985

and 1991.

5.5.11 Male Adolescents

Theoretically, it is argued that those households having male adolescents

dissuade from informal sector employment because the family labour supply

decisions are interdependent. Male adolescents can affect worker‟s decision

concerning participation in urban informal sector employment because some male

adolescent family members have chances to join the informal or formal sector. It is

148

expected that mostly household heads (males or females) spend less time at work

because there has been a ceteris paribus increase in household income, and because an

hour of male adolescent‟s labour now earns more income than before (relatively to an

hour of heads own time). In other words, there is a „cross-income effect‟ and a cross-

substitution effect on head‟s labour supply. The consequent twin- effects enable the

head to work less firstly because now the family will enjoy more leisure due to the

alleged increase in its income. Secondly, the heads will be inclined to substitute

better-paid labour time with low-paid toil. We include number of male adolescents as

a continuous variable.

Funkhouser (1996) found that workers participated more or less in the

informal sector with male and female children across countries. Gallaway and

Bernasek (2002) have revealed a positive link between number of male adults and

male and female informal sector employment (such as both at home without pay and

with pay in labour market) and negative relationship between male and female

informal sector employment (such as both with pay at home in labour market and with

out pay at home for females) except women in labour market.

Male adolescents inversely affect the urban informal sector employment

decision. It indicates that participants of the informal sector having more male

adolescents have a less participation rate in the informal urban sector employment.

5.5.12 Female Adolescents

Neoclassical labour supply theory argued that household males / females

having female adolescents rather participated more to join the economic activity. The

labour supply theory explains that the family labour supply decisions are

interdependent. However, female adolescents can affect workers‟ decision concerning

participation in the urban informal sector employment. It is argued that female

adolescents have the low opportunity to work and especially in the formal sector job,

so household heads have to fulfill female adolescent‟s requirements. In this way, they

join the informal labour market. Study presents a positive correlation between female

adolescents and the urban informal sector employment decision. It appears that the

informal workers having more female adolescets are more likely to join the urban

informal sector.

149

5.5.13 Spouse Participation in Economic Activities

It is assumed in Neo-classical labour supply theory that the family labour

supply decisions are interdependent. The spouse participation in economic activities

affects the urban informal sector employment. If male spouse involves himself in

earning activity, the resultant enhancement in his wage will cast both income and

substitution effect on his spouse‟ labour supply. It is forecasted that female spouse

will work less firstly due to an increase in her family income and secondly because of

per hour increase in her husband‟s income. In other words, there is a „cross-income

effect‟ and a cross-substitution effect on the wife‟s labour supply. The consequent

twin- effects enable her to work less firstly because now the family will enjoy more

leisure due to the alleged increase in its income and secondly, the husbands will be

inclined to substitute better-paid labour time with low-paid toil.

Consistent with the added worker effect, when male labourers lose their jobs

because of recession, female paticipants of the labour market might work in order to

offset the loss in the income of the family. Accordingly, this effect shows a positive

effect on employment in economic and business activities in result of loss on spouse

job. Theoretically, it is expected that spouse participation in economic activities is

positively associated with informal employment of added worker effect (see Brendt,

1991).

5.5.14 Household’s Value of Assets

The household‟s value of assets is an imperative economic factor that has

much influence on sector of choice in the labour market. All types of assets of

household such as tangible (i.e. land, shops, homes, plots, livestock population, etc)

physical (i.e. homes, machinery, vehicle and electrical goods) and financial (i.e. gold,

bank deposits, securities etc) are considered important and secure source of earnings.

Theoretically, it is argued that with an increase in the value of assets and property,

people are less willing to participate in economic activities because of strong

unearned income effect. Theoretically, ownership of assets makes the individuals

financially stronger through the un-earned income and workers prefer to leisure and

do not work anymore. There are mixed effects. We have used household‟s value of

150

assets as continuous variable to trace out the effects of value of assets on informal

urban sector employment.

Theoretically, it is expected that there is a positive or negative relationship

between household‟s value of assets and informal sector employment or value of

assets decreases the demand for labour supply. Raijman (2001) examined a positive

impact of economic resources (i.e. financial investments) on decision to start a

business. Assaad et al. (2000) found that probability of being employed as casual

worker increased with the household assets in Egypt. The self-employment was found

as an entrepreneurial strategy by people who have access to productive assets (Assad

et al., 2003).

5.5.15 Rural-Urban Migration

It is theorized that the rural-urban migration is the major determinant towards

growth of the urban informal activities and this sector is considered as a refuge for

rural migrants. Informal economies provide a wide range of income opportunities.

The informal sector is viewd as temporary opportunity for migrants as predicted by

most classical migration models or informal sector may play an important role in

economic development.65

The urban informal sector required low skills and

insufficient finance to establish the business. This suggested that the sector absorbed

mainly the recent rural migrants to the city (House, 1984).

The studies found a positive association of rural urban migrants and self-

employed in urban areas (Mukhopadhyay, 1998; Reddy et al. 2003 and Baudar,

2008). Little (1999) pointed out that the large number of migrants enhanced the

growth of petty trade in the sector. The author estimated that the rural-migrants and

informal sector employment were negatively associated with particular occupations

(Roberts, 2001). Study explored that the street vending was increasing with increasing

rural to urban migration in Khatmandu (Timalsina, 2007). Jampaklay (2007) has

examined the residential patterns of rural-urban migrants that were involved in factory

jobs and construction in Thailand. The authors demonstrated that the proportion of

female migrants was higher in the construction sector in India (Bhattacharyya and

Korinek (2007).

65see Todaro, (1969), Fields, (1975) and Mazumdar, (1976, 1977).

151

This explanatory variable is included as a binary variable such as rural-urban

migrants in our analysis.

5.5.16 Working Hours

It is theorized that the workers with more working hours can earn more in the

labour market. Almost workers in the informal sector allocate their time for work

more in order to earn more income and are willing to work more in the informal

economic activities. The strong income effect forces them to work more as their

earnings will increase. The working hours and earnings are expected to be positively

associated in urban informal sector.

5.6 Model and Methodological Issues

After the accomplishment of data collection, the analytical techniques are used to find

out results and conclusions. The informal sector employment can be determined

quantitatively and qualitatively. Though, present study made a qualitative and

quantitative analysis in order to determine the probability of the urban informal sector

employment in Southern Punjab, Pakistan.

5.6.1 A Descriptive Data Analysis

Conducting a household survey in urban areas of Southern Punjab, we analyse

the informal sctor employment discriptively in order to construct the quantitative

models to formulate null hypotheses against alternative hypothesis to test the

analytical work. The percentages of the participants in the informal and formal sector

employment are shown in descriptive or bivariate analysis.

5.6.2 A Multivariate Analysis of Urban Informal Sector Employment

We make a multivariate analysis of the factors that influence the probability of

participation in the urban informal sector employment. The diverse macroeconomic

and micro-economic factors are used to determine whether an individual engages into

diverse economic activities or not.66

A few of several factors influence labour supply

66 see Blundel, 1987

152

decisions. We have used the econometric methods to investigate the determinants of

urban informal sector employment in Southern Punjab. The regression analysis are

applied to see the influence on informal sector employment decision of a specific

household or individual‟s characteristics while keeping fixed all other characteristics.

A multivariate regression analysis is made in this econometric analysis. A

general function is as

Yi= f (X1, X2, X3…Xn) (5.1)

The dependent variable “Yi” shows probability of the informal sector

employment in the model. It is dichotomous in nature which takes value “1” or “0”

i.e. a qualitative variable and is incorporated in regression model as a dummy

variable. In this case the value “1” signifies the probability of being employed in the

urban informal sector. Contrarily, value “0” shows probability of not being employed

in the urban informal sector. The binary logit technique is used here. The variables,

for instance, X1, X2….Xn represent diverse socio-economic and demographic

variables.

We consider the simple model in the following.

Yi= β0+ β1X1i+ β2X2i+ β3X3i+ … ΒkXj+ ui (5.2)

Xj= is a set of independent variables where j= 1, 2….k.

These independent variables are age in complete years, complete years of

education, formal training, sex, marital status, father‟s education, mother‟s education,

family setup, household size, dependency ratio, number of children, number of male

adolescents, number of female adolescents, spouse participation in economic

activities, value of household‟s assets and rural-urban migrant variable.

ui = error term

The above model expresses the dichotomous Yi as a linear function of the

explanatory variable Xj, is called linear probability model. Though, linear probability

model which is used in binary choice dependent variable face a problem to generate

153

predicted values which may fall outside 0, 1 interval and may violate the basic tenets

of probability. The other problems are faced in the form of heteroscedacity and

generally lower R2 values.

Hence, it has been suggested for non linear probability models, (probit and

logit) models to outweigh the problems by using linear probability model (LPM).

These models make use of Maximum Likelihood Estimation (MLE) procedures. The

logit model, which is based on cumulative logistic probability function, is relatively

similar to the cumulative normal function and can be used easily for computation.

Both logit and probit are transformation such that a cumulative distribution is

estimated with LPM. Generally, the non-probability model is used for non-linear

maximum likelihood estimation. Both Logit and Probit models commonly provide

similar results for the same data among the non linear probability models.67

The effect

of one or more independent variables on dicotomus dependant variable can be

estimated by using logit model. 68

The present study is based on the binary logit model and (OLS) regression

analysis to estimate the probability of employment in the urban informal sector.

5.6.2.1 A Binary Logit Model

We analyse the determinants of urban informal sector employment by using

the binary logit model technique. However, similar existing models have been applied

for the informal sector and employment in developing and developed countries.

Funkhouser (1996) used probit model. The logistic regression model was used by

[Boyd (1990), Rosen (2000), Raijman (2001), Florez (2003), Reddy et al. (2003),

Guang & Zheng (2005)]. The logit model was used by [(Matiya et al. (2005); Istrate

(2007)]. Mentzakis et al. (2008) and Gunatilaka (2008) used the regression

techniques. All of these studies are concerned with work decision of informal sector

employment having binary variable or limited variables.

The Methodology of logistic model which we adopt in present research is

followed by Maddala (1983).

67 see Gujrati (1995). 68 see detail in Gujrati (1995), Kumenta (1986) and Green (1992).

154

The variable Y* is defined in a regression relationship

Yi* = β /

Xi + ui (5.3)

Where

β / = [β1, β2………………….. βk] and

ui is normally distributed with zero mean

In practice, Yi* is unobservable. What we observe is a dummy variable Y is defined

as

Y = 1 if Yi* > 0 (5.4)

Y = 0 otherwise

Here, β /

Xi is not E (Yi / Xi) as in the linear probability model; it is E(Yi*

/ Xi ).

W can find the expression to find the probability from the expressions (5.3)

and (5.4)

Prob (Yi=1) =Prob (ui > - β /

Xi)

= 1- F (β /

Xi) (5.5)

Where, F is the cumulative distribution function for ui.

The observed values of Yi are just the realizations of a binominal process

denoting probabilities in equation (5.5), which varies with Xi. Then a likelihood

function can be written as:

L = ℿ F (– β/xi) ℿ [l – F (– β/xi)] (5.6) yi = 0 yi = 1

155

The functional form which is imposed on F in equation (5.6) is based on the

assumptions made about ui in equation (5.3). If the cumulative distribution of ui is the

logistic, we have the logit model. In this case

Hence

(5.7)

The equation basically shows the probability of being employed [pr (Yi =1)]

Here, is a closed form expression for F, because it does not contain integrals

explicitly. Not all distributions permit such a closed form expression.

Let Xik for the kth element of the vector of explanatory variables Xi, and let βk

be kth element of β. Then the derivatives for the probabilities given by the logit model

are

(5.8)

These derivatives are used to predict the effects of changes in one of the

independent variables on the probability of being employed.

5.6.2.2 Earnings Functions

The human capital theory has been presented in the economic literature with

the seminal work which Becker (1962) and Blaug (1969) done. Before they search

even, in 1957 and 1972, Jacob Mincer presented a theoretical model and emphasized

on human capital as central explanatory variable. In literature, it is also known as the

“Earnings Function” and has been applied in this study to measure the earnings

differentials in the urban informal sector of Southern Punjab, Pakistan. In case of

Pakistan, the authors like Burki and Abbas (1991); Burki and Ubaidullah (1992),

F (– β / Xi) =

exp (− 𝛽 / Xi)

1+exp ( − 𝛽/ Xi) =

1

1+exp ( 𝛽/ Xi)

1 – F (– β / Xi) =

exp ( 𝛽 / Xi)

1+exp ( 𝛽/ Xi)

𝜕

𝜕𝑋𝑖𝑘 L(𝑋𝑖

/β) =

exp ( 𝑋𝑖 /𝛽)

[1+exp ( 𝑋𝑖/ 𝛽)]2

βk

156

Sargana (1998) and Nasir (2002)] have used the same model to estimate the rates of

returns of human capital variables in the urban informal sector with small surveys. In

case of other countries, Manda et al. (2002) have used the same model. Whereas,

Kozel and Alderman (1990), Ashraf and Ashraf (1993), Wahba (2002), and Hudson

(2010) have extended the Mincerian earnings model (1974) to estimate the effect of

other variables on earnings in the urban informal sector by conducting small surveys.

Where

Yi= monthly earnings of the ith informal sector employed, Xi are ith explanatory

variables for ith informal sector employed and ui as random disturbance for ith

informal sector employed.

5.6.3 Specification of Employment Model

The specification of informal sector employment is given as follows.

5.6.3.1 General Model

Based on the general model which we specified in the previous section, we

estimate the logit model to evaluate the effects of socio-economic and demographic

variables on the urban informal sector employment.

UISE = f [AGY, EDY, SEX, MRS, FTD, FEDU, MEDU, FSP, HSIZ, DPNR,

NCHL, NMAD, NFAD, HVAT, SPN, RMGT]

In the above logit model, the dependent variable is urban informal sector

employment. The independent variables are age in complete years, complete years of

education, sex, marital status, formal training, father‟s education, mother‟s education,

household size, family setup, dependency ratio, number of children, number of male

adolescents, and number of female adolescents, household‟s value of assets, spouse

participation in economic activities and rural-urban migrant worker.

The model specified for urban informal sector employment is given as follows.

Ln Yi = β0 + 𝛴𝑖=0𝑘 𝑋𝑖 + 𝑢𝑖

157

5.6.3.2 Employment Model with Complete Years of Education

We estimate the logit model to evaluate the effects of socio-economic,

demographic variables on being employed in the urban informal sector employment

with complete years of education.

α0 + α1 AGY i + α2 EDY i + α3 SEX i + α4 MRSi + α5FTD i + α6 FEDU i + α7

MEDUi + α8 HSIZ i + α8FSPi + α10DPNRi + α11NCHLi + α12NMADi + α13

NFADi + α14 HVATi +α15SPNi + α16RMGTi + ui

In the above equation of probability of being employed in the urban informal

sector of the Model, the independent variables are age, complete years of education,

sex, marital status, formal training, father‟s education, mother‟s education, household

size, family setup, dependency ratio, number of children, number of male adolescents,

number of female adolescents, household‟s value of assets, spouse participation in

economic activities and rural-urban migrant worker.

5.6.3.3 Employment Model with Different Levels of Education

In second model of urban informal sector employment, we have introduced

five categorical educational dummies to capture the influence of different level of

education on the urban informal sector employment while EDU I has been taken as

base outcome.

β0 + β1 AGYi + β2EDU IIi + β3EDU IIIi + β4EDU IVi + β5 EDU Vi + β6

EDU VIi + β7 SEXi + β8MRSi + β9FTDi + β10FEDUi + β11 MEDUi +

β12HSIZi + β13 FSPi + β14 DPNR i + β15 NCHL i + β16 NMADi + β17 NFADi

+ β18 HVATi + β19 SPNi + β20 RMGTi +ui

In the above equation of probability of being employed in the informal sector

of the model, the independent variables are age, Middle level education, Matric level

education, Intermediate level education , Graduation level education and Master‟s or

higher level education, sex, marital status, formal training, father‟s education,

mother‟s education, household size, family setup, dependency ratio, number of

children, number of male adolescents, number of female adolescents, household

UISEi =

UISEi =

158

household value of assets, spouse participation in economic activities and rural-urban

migrant variable.

5.6.3.4 Earning Function

The present study tests with a survey data that has been conducted in three

divisions of Southern Punjab, Pakistan with focus on the subsectors of the urban

informal sector. The following model has been specified as follows.

ln Yi= α0 + β1 AGYi +β2 EDY2i+β3TRNi+β4SEXi+β5 MRSi+ β6FSPi +β7HVATi

+β8WHRi+ iu

The model is specification of earning determinants of participants in urban informal

sector. Where, ln Yi (dependent variable) is log of the monthly earnings of the

participants and explanatory variables are age in complete years, complete years of

education, training, sex, marital status, family setup, household‟s value of assets and

weekly working hours.

5.6.3.5 Earning Function with Different Levels of Education

The present study tests with a survey data that has been conducted in three

divisions of Southern Punjab, Pakistan with focus on subsectors of the urban

informal sector. The model has been specified as follows.

ln Yi= α0 + β1 AGYi +β2 EDUIIi + β3EDUIIIi+ β4 EDUIVi+ β5 EDUVi+ β6 EDUVIi

+β7TRNi+β8SEXi+β9MRSi+ β10FSPi +β11HVATi +β12WHRi + µi

The model is specification of earnings determinants of participants in the urban

informal sector. Where, ln Yi (dependent variable) is log of the monthly earnings of

the participants and explanatory variables are age of the workers, Middle level

education, Matric level education, Intermediate level education, Graduation level

education and Master‟s level education, training, sex, marital status, family setup,

household‟s value of assets and weekly working hours.

The list of the variables for logit estimates of the determinants of urban

informal sector employment is explained in the table 5.2. The theoretical expected

159

signs of described variables in table are also explained. The expected relationship

between urban informal sector employment and complete years of age, marital status,

number of children, and household‟s value of assets can be positive as well as

negative. It is hypothesized that probability of being employed in the informal sector

and formal training, education, parent‟s education, number of male adolescents and

rural-migrant worker is negative. Theoretically, the expected relationship between

informal sector employment, household size, and family setup, spouse participation in

economic activities, dependency ratio and female adolescents are positively

correlated.

160

Table 5.1: List of variables used in the informal sector employment equations.

Variables Description of variables

Dependent variable

UISE =1 if the participant is being employed in the urban informal sector

=0 otherwise

Explanatory variables

AGY = Age of the participant of the labour market (in years).

EDY = A continuous variable defined as the complete years of education.

SEX 1 = if the participant of the labour market is male

0 = otherwise

MRS =1 if the participant of the labour market is married

=0 otherwise

FTD = 1 if the individual working in labour market has some formal

training

=0 otherwise

EDU II = 1 if the participant‟s education level is up to Middle (8 years of

education)

=0 otherwise

EDU III = 1 if the participant‟s education level is Matric (10 year of

education

=0 otherwise

EDU IIV =1 if the participant‟s education level is Intermediate (12 years of

education)

=0 otherwise

EDU V =1 if the participant‟s education level is B.A/B.S.C/B.Com (14

years of education)

=0 otherwise

EDU VI =1 if the participant‟s education level is M.A/M.S.C/M.Com or

higher (16 years of education)

=0 otherwise

MEDU =1 if participant‟s mother is educated

=0 otherwise

FEDU =1 if the participant‟s father is educated

FSP =1 if the participant of labour market belong to joint family system

=0 otherwise

HSIZ =Size of the household or total member of the family

DPNR = Dependency ratio

NCHL Total number of children (6-14) in the family

NMAD Number of male adolescents (15-18)

NFAD Number of female adolescents (15-18)

SPN =1 if the worker‟s spouse participates in economic activities

=0 otherwise

HVAT = Household‟s value of assets in rupees

=0 otherwise

RMGT =1 if the worker is rural-urban migrant

=0 otherwise

161


In this chapter, an attempt has been made to explain the data collection

methods. The methodological issues are also discussed. We have conducted the

survey in urban areas of three divisions of Southern Punjab, Pakistan. Simple random

and stratified sampling techniques have been used to collect data from three districts

because these districts are generating more the informal employment opportunities in

Southern Punjab. Furthermore, we have convoluted the determinants regarding urban

informal sector employment in southern Punjab, Pakistan. It has also given the

complete construction of analytical techniques for studying the determinants of

informal sector employment in urban areas. In conclusion, the present study makes a

descriptive analysis of determinants of the urban informal sector employment by

using binary logit models.

162

Chapter 6

DESCRIPTIVE ANALYSIS OF URBAN INFORMAL AND

FORMAL SECTORS IN SOUTHERN PUNJAB,

PAKISTAN

6.1 Introduction

The informal sector is comparatively larger due to its role in creating

employment in the developing countries. Lesser employment opportunities in the

formal sector invoke people to adhere to the informal sector. A dominant proportion

of the urban population is working in the informal sector because the sector has the

potential to absorb the growing labour force. Therefore, informal sector plays its role

to tackle the problem of urban poverty and to enhance the income and productivity.

The development-oriented and job-promoting feature of the informal sector

can be understood by taking a detailed account of descriptive analysis of participants

of the informal urban sector. A descriptive study of the working people in the

informal sector assists in recognizing factors influencing workers‟ choice to indulge

into urban informal sector employment. Moreover, the knowledge of these factors

would enable the government in turn, to design policy programme to enhance the

growth potential of the urban informal sector.

In this section we present an overview of the informal and formal sector

employment in Southern Punjab in terms of its extent, nature and characteristics. In

the current study, a descriptive analysis of the determinants of employment in the

urban informal and formal sector of Southern Punjab, Pakistan is made. Especially,

pair-wise correlation matrix in section 6.2 of the present chapter is arranged. The

descriptive analysis of workers of both genders in the urban informal sector has also

been explained separately in section 6.3. The descriptive analysis of the determinants

of the urban informal sector employment for male workers is explained in section 6.4

and female participants in section 6.5. Finally, concluding remarks are given in

section 6.6.

163

6.2 Pair-wise Correlation Matrix

The above table indicates the pairwise correlation matrix of the explanatory

variables. This shows a relationship among explanatory variables. The correlation

matrix indicates that there is no multicolinearity among the explanatory variables.

164

Table No. 6.1 Pair Wise Correlation Matrix

AGY EDY MRS SEX FTD FEDU MEDU HSIZ DPNR FSP NFAD NMAD NCHL SPN VAT RMGT

AGY 1.00

EDY -0.20 1.00

MRS 0.37 -0.09 1.00

SEX 0.04 0.10 0.01 1.00

FTD -0.12 0.24 -0.06 -0.03 1.00

FEDU -0.20 0.34 -0.04 -0.02 0.17 1.00

MEDU -0.16 0.33 -0.07 -0.01 0.19 0.44 1.00

HSIZ 0.10 -0.16 0.00 0.08 -0.11 -0.12 -0.17 1.00

DPNR -0.08 -0.03 -0.04 0.06 0.00- -0.02 0.00 0.13 1.00

FSP 0.01 -0.11 -0.12 0.05 0.06 -0.09 -0.08 0.32 0.14 1.00

NFAD 0.22 -0.17 0.09 0.03 -0.18 -0.17 -0.17 0.32 -0.17 0.07 1.00

NMAD 0.12 -0.03 0.04 -0.02 -0.03 0.02 -0.05 0.19 -0.25 0.02 0.21 1.00

NCHL 0.02 -0.13 0.08 -0.01 -0.04 -0.09 -0.11 0.36 0.33 0.10 -0.04 -0.14 1.00

SPN 0.01 0.07 0.30 -0.21 0.06 0.08 0.13 -0.15 0.00 -0.16 -0.08 -0.02 -0.01 1.00

HVAT 0.09 0.10 0.08 -0.03 0.02 0.07 0.05 0.03 -0.03 -0.04 0.03 0.10 -0.09 0.06 1.00

RMGT -0.10 -0.03 -0.06 0.04 0.02 -0.06 -0.06 0.03 0.06 0.03 -0.01 -0.03 0.05 -0.06 -0.07 1.00

6.3 The Urban Informal Sector Employment: An Elementary

Analysis

In this section we look at the descriptive or elementary analysis of informal and

formal workers in order to strenghthen the analysis of determinants in chapter 7, 8 and 9.

This section comprises an elementary analysis of the data. The data is derived from

primary source through a field survey by the author. The total data emanates on 1506

observations. Descriptive data that can be done by averages, ratios and percentages.

Moreover, elementary data analysis is very useful for null hypothesis.

6.3.1 Age Groups and Urban Informal and Formal Sector Employment

Two views can be presented about age of workers involved in the informal sector.

Firstly, if the relative participation of young people is greater in the informal sector, the

very sector may probably be considered a transition stage before opting formal sector.

Secondly, the informal sector may be considered as a desirably constant choice if there is

a large participation ratio of older persons in the informal sector. Table 6.2 illustrates the

distribution of informal and formal sector employment in terms of age.

Table 6.2: Distribution of Respondents by Age Groups

Age Groups Urban Informal

Sector Employed

Urban Formal Sector

Employed

Total

AGE I

15-24

97

(9.84)

[50]

97

(18.65)

[50]

194

(12.88)

[100]

AGE II

25-34

155

(15.72)

[54.96]

127

(24.42)

[45.41]

282

(19.52)

[100]

AGEIII

35-44

213

(21.60)

[59.33]

146

(28.08)

[40.67]

359

(23.83)

[100]

AGEIV

45-54

404

(40.97)

[77.25]

119

(22.88)

[22.75]

523

(34.73)

[100]

AGEV

55-64

117

(11.87)

[79.05]

31

(5.96)

[20.95]

148

(9.83)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]

Source: Survey by the author.

Note: Values in round brackets are percnetages of total columns, while the values in square brackets are

percentages of total rows.

clxvi

Table (6.2) depicts an analysis of age of participants of the urban informal and

formal sector employment. It is shown that urban informal sector employment in the age

group 15-24 is 9.84% which is the lowest. However, the same is on the rise with 15.72 %

in the age group 25-34. Urban informal sector employment is observed 21.60 percent in

the age group 35-44. The results indicate that the age group 45-54 reserves the highest

participation rate of 40.97 %. Informal sector appears to favour older persons with nearly

half of the workers falling into this age group. Therefore, age group (55-64) of informal

sector employment is relatively low with 11.87 percent. Table shows a positive

relationship between age factor and informal sector employment up to the age group (45-

54). However, the age is inversely related with informal sector employment after the

noted age group. In general, the younger and older people are less likely to join urban

informal sector employment in Southern Punjab, Pakistan and this worth noticing fact

matches the life cycle hypothesis.69

6.3.2 Education Level and Urban Informal and Formal Sector Employment

It demonstrates the distribution of the informal as well as formal sector

employment respondents with education level in the table 6.3. The table shows that the

Primary level education of informal sector workers is 13.94 percent. The workers being

in the informal sector possess 23.27 percent Middle level education. The data shows that

a worker whose education level is up to Matric, their absorption rate is the highest at

32.71 percent in the informal economic activities. Results point out that informal sector is

particularly favoured by the workers having Matric level education. It also shows that

share of Graduates in the urban informal sector is 9.66 percent. The participation rate is

the lowest for persons having Master‟s level and higher degree 5.49 percent. The data

shows that there is a negative relationship between level of education and the participants

of the informal sector in the urban areas of Southern Punjab.

69 see Ando and Modigliani (1963)

clxvii

Table 6.3: Distribution of Respondents by Education Level

Level of Education Urban Informal

Sector Employed

Urban Formal Sector

Employ

Total

EDU I

Primary

127

(13.94)

[ 85.81]

21

(4.14)

[14.19]

148

(10.44)

[100]

EDU II

Middle

212

(23.27)

[89.08 ]

26

(5.13)

[10.92]

238

(16.78)

[100]

EDU III

Matric

298

(32.71)

[73.58]

107

(21.10)

[24.42]

405

(28.56)

[100]

EDU IV

Intermediate

136

(14.93)

[58.87]

95

(18.74)

[41.13]

231

(16.29)

[100]

EDU V

Graduation

88

(9.66)

[41.71]

123

(24.26)

[58.30]

211

(14.88)

[100]

EDU VI

Master’s or highr

50

(5.49)

[27.03]

135

(26.62)

[72.97]

185

(13.05)

[100]

Total

911

(100)

[64.25]

507

(100)

[35.75]

1418

(100)

[100]




6.3.3 Marital Status and Urban Informal and Formal Sector Employment

Marital status has been observed as the most significant factor in the favour of

joining the urban informalsector. It is argued that married people prefer to work more in

the formal or informal sector because of their financial responsibilities. Distribution of

both the formal and informal sector workers by marital status is revealed in table 6.4.

Table 6.4: Distribution of Respondents by Marital Status

Marital Status Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Married

732

(74.24)

[66.30]

372

(71.54)

[33.70]

1104

(73.31)

[100]

Unmarried

254

(25.76)

[63.18]

148

(28.46)

[36.82]

402

(26.69)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]


clxviii



Table 6.4 reports the distribution of the urban informal and formal sector

participants regarding marital status. It predicts that the married participants relatively

have the maximum proportion of 74.24 percent in the informal sector while there are

25.76 percent unmarried workers in the informal sector. Moreover, engagement in

informal sector employment comprises those who are married (71.5 percent). Results

indicate that formal sector is favoured by the married people. The data trend highlights a

positive relationship between the urban informal sector employment and married

participants in Southern Punjab.

6.3.4 Sex and Urban Informal and Formal Sector Employment

Table 6.5 portrays the relationship between informal sector and sex of participant.

Theoretically, it is notioned that most male workers are employed in the urban informal

sector as compared to female workers in Southern Punjab.

Table 6.5: Distribution of Respondents by Sex

Sex Urban Informal

Sector Employed

Urban Formal Sector

Employed

Total

Male

615

(62.37)

[65.71]

321

(61.73)

[34.29]

936

(62.15)

[100]

Female

371

(37.63)

[65.09]

199

(38.27)

[34.91]

570

(37.85)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]


Note: Values in round brackets are perrcnetages of total columns, while the values in square brackets are

percentages of total row.

Table 6.5 shows the distribution of respondents in the urban informal and formal

sector by sex. The table reveals that 65 percent people are engaged in the informal sector

as compared to 35 percent who are participating in the formal sector while, 62.37 percent

males are involved in the urban informal sector. Contrarily, 37.63 percent female

clxix

participants are involved in the informal sector. These noticeable facts demonstrate that

males are more likely to be persuaded in the urban informal sector. There are

proportionally more males than females in the urban informal sector of Southern Punjab.

6.3.5 Formal Training and Urban Informal and Formal Sector Employment

Table 6.6 highlights the percentage distribution of respondents by formal training.

Results show that the informal sector employment among those workers who have some

kind of the formal training in the urban informal sector of Southern Punjab. The urban

informal sector employment rate is 13.18 percent for those who are formally trained and

86.82 percent for those who are untrained. Findings demonstrate that the urban informal

sector employment and formal training are negatively correlated.

Table 6.6: Distribution of Respondents by Formal Training

Level of Training Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Formal Training

130

(13.18)

[38.58]

207

(39.81)

[61.42]

337

(22.38)

[100]

Untrained

856

(86.82)

[73.23]

313

(60.19)

[26.78]

1169

(77.62)

[100]

Total

986

(100)

[100]

520

(100)

[100]

1506

(100)

[100]




6.3.6 Father’s Educational Status and Urban Informal and Fromal Sector

Employment

Father‟s education also influences the decision to particiapate in the labour

market. It is hypothesized that there is a negative effect of father‟s education on informal

sector employment. Table 6.7 shows a negative effect of father‟s educational status on

the decision of participation in the urban informal sector of Southern Punjab.

clxx

Table 6.7: Distribution of Respondents by Father’s Educational Status

Father’s Educational

Status

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Educated Father

345

(34.99)

[40.05]

373

(71.73)

[51.95]

718

(47.68)

[100]

Uneducated Father

641

(65.01)

[81.35]

147

(28.27)

[18.65]

788

(52.32)

[100]

Total

986

(100)

[100]

520

(100)

[100]

1506

(100)

[100]



percentages of totl rows.

Results reflect that there are 65.01 percent of the informal sector workers whose

fathers are uneducated as compared to 34.99 percent of those participants whose fathers

are educated. The analysis concludes that father‟s education exerts a great impact on the

decision to work in the urban formal sector. Moreover, the workers are less likely to

participate in the urban informal sector of Southern Punjab whose fathers are educated.

6.3.7 Mother’s Educational Status and Urban Informal and Formal Sector

Employment

Mother‟s education is an important factor to determine the growth potential of the

urban informal and formal sectors of Sourhen Punjab. It is expected that those workers

whose mothers are educated are less likely to be engaged in the informal sector

employment. Our results confirm it.

Table 6.8: Distribution of Respondents by Mother’s Educational Status

Mother’s Educational

Status

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Educated Mothers

179

(18.15)

[73.70]

288

(55.38)

[26.30]

467

(31.01)

[100]

Uneducated Mothers

807

(81.85)

[38.33]

232

(44.61)

[22.33]

1039

(68.99)

[100]

Total

986

(100)

[100]

520

(100)

[100]

1506

(100)

[100]


clxxi



Table 6.8 depicts a decreasing trend of those who get involved into the urban

informal labour market and their mothers are uneducated. It is seen that 18.15 percent

workers are engaged in the urban informal sector whose mothers are educated and

participation rate of those workers whose mothers are uneducated is 81.85 percent. The

study results conclude that there is a negative association between informal sector

employment and mother‟s education in the urban areas of Southern Punjab.

6.3.8 Size of Household and Urban Informal and Formal Sector

The household size is also a major factor which affects the probability of working

in the informal sector. Table 6.9 predicts the relationship between the size of household

and the urban informal sector employment decision. The informal sector employment

decision and household size is positively correlated. Urban informal sector employment

rate is 51.42 percent for family size of 5 to 8 members. The urban informal sector

employment rate is 7.30 percent for the family size of 1 to 4 members. The urban

informal sector employment rate is 27.38 percent for the family size of 1to 12 embers.

However, the rate of informal sector employment is 2.64 percent for the family size of 13

to 16 members which is the lowest. Table presents that mostly workers participate in the

urban informal economic activities with the increasing size of household. Economic

theory justifies this relationship because people work more in order to meet up basic

requirements of family members.

This table illustrates the household size and employment in the informal and

formal sector. The workers who belong to family size comprising 5 to 8 family members

are more likely to participate in the urban informal sector of Southern Punjab.

clxxii

Table 6.9: Distribution of Respondents by the Size of Household

Size of Household Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

1-4

72

(7.30)

[39.34]

115

(22.12)

[61.50]

187

(12.42)

[100]

5-8

607

(61.56)

[61.07]

337

(64.81)

[39.93]

944

(6.57)

[100]

9-12

281

(61.56)

[80.98]

66

(12.69)

[19.02]

347

(80.98)

[100]

13-16

26

(2.64)

[92.86]

2

(0.38)

[7.14]

28

(1.86)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]




clxxiii

6.3.9 Number of Dependents and Urban Informal and Formal Sector

Employment

Table 6.10 illustrates the distribution of the informal and formal sector

participants and the dependency ratio. The informal workers are indulging into economic

activities due to increase in number of dependents.

Table 6.10: Distribution of Respondents by Number of Dependents

Number of

Dependents

Urban Informal Sector

Employed

Urban Formal Sector

Employed Total

1

68

(6.90)

[51.13]

65

(12.5)

[48.87]

133

(8.33)

[100]

2

172

(17.44)

[57.33]

128

(24.62)

[42.67]

300

(19.92)

[100]

3

205

(20.79)

[65.29]

109

(20.96)

[34.71]

314

(20.85)

[100]

4

176

(17.85)

[72.13]

68

(13.08)

[27.87]

244

(16.20)

[100]

5

128

(12.98)

[76.19]

40

(7.69)

[23.81]

168

(11.16)

[100]

6

86

(8.72)

[77.47]

25

(4.81)

[22.52)

111

(7.37)

[100]

7

47

(4.77)

[83.93]

9

(1.73)

[16.07]

56

(3.72)

[100]

8 and above

49

(4.97)

[84.48]

9

(1.73)

[15.51]

58

(3.85)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]




6.3.10 Family Setup and Urban Informal and Fromal Sector Employment

Family setup, nuclear or joint, also plays an important role in determining the

growth potential of the urban informal sector. Table 6.11 describes the distribution of the

formal and the informal sector employment by family setup. The table expresses a

clxxiv

positive relationship between joint family setup and the urban informal sector

employment. The data indicates a high informal sector employment rate of 64.40 percent

in the joint family setup and the low rate of 35.60 percent in the nuclear family set up. It

is observed that those who belong to the joint family setup are more likely to join the

informal activities or there are proportionately more participants with joint family setup

in the urban informal sector of Southern Punjab.

Table 6.11: Distribution of Respondents by Type of Family System

Family Setup Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Joint Family

635

(64.40)

[73.92]

224

(43.08)

[26.07]

859

(57.08)

[100]

Nuclear Family

351

(35.60)

[54.25]

296

(56.92)

[45.75]

647

(42.96)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]




6.3.11 Number of Children and Urban Informal and Formal Sector

Employment

Another significant factor, the number of children, plays a vital role regarding

decision to engage in the informal activities. Table 6.12 depicts a relationship between

urban informal sector employment and number of children. A positive relationship is

found between number of children and the urban informal sector employment decision. It

is shown that family heads having more adult members are more likely to indulge into the

urban informal sector employment in Southern Punjab. Economic theory justifies this

positive association.

clxxv

Table 6.12 Distribution of Respondents by Number of Children

Number of Children Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

0

252

(25.56)

[56.76]

192

(36.92)

[43.24]

444

(29.48)

[100]

1

122

(12.37)

[59.22]

84

(16.15)

[40.78]

206

(13.68)

[100]

2

228

(23.12)

[64.59]

125

(24.04)

[35.41]

353

(23.44)

[100]

3

166

(16.84)

[68.88]

75

(14.42)

[31.12]

241

(16.00)

[100]

4

126

(12.78)

[80.77]

30

(5.77)

[19.23]

156

(10.36)

[100]

5

71

(7.20)

[89.87]

8

(1.54)

[10.13]

79

(5.25)

[100]

6

17

(1.72)

[73.91]

6

(1.15)

[26.09]

23

(1.53)

[100]

7

4

(0.41)

[100]

0

(0)

[0]

4

(0.27)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]




6.3.12 Male Adolescents and Urban Informal and Formal Sector

Employment

Male adolescents also influence the decision to participate in the urban informal

sector employment. Table 6.13 shows a relationship between urban informal sector

employment and male adolescents. Data indicates a negative association between number

of male adolescents and the urban informal sector employment decision. It is observed

that the informal sector workers having more male adolescents are less likely to join the

urban informal sector in Southern Punjab.

clxxvi

clxxvii

Table 6.13: Distribution of Respondents byNumber of Male Adolescents

Number of Male

Adolescents

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

0

582

(59.03)

[66.29]

296

(56.92)

[100]

878

(58.30)

[100]

1

239

(24.24)

[68.29]

111

(21.35)

[31.71]

350

(23.24)

[100]

2

127

(12.88)

[60.77]

82

(15.77)

[39.23]

209

(13.88)

[100]

3

29

(2.94)

[58]

21

(4.04)

[42]

50

(3.32)

[100]

4

6

(0.61)

[37.5]

10

(1.92)

[62.5]

16

(1.06)

[100]

5

3

(0.30)

[100]

0

(0)

[0]

3

(0.20)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.52]

1506

(100)

[100]




6.3.13 Female Adolescents and Urban Informal and Fromal Sector

Employment

Presence of female adolescents is also an imperative factor that has an effect on

worker‟s decision concerning the urban informal sector. Table 6.14 illustrates a

relationship between the urban informal and formal sector employment and female

adolescents. Data shows that there is a negative relationship between female adolescents

and the urban informal sector employment decision. It is pointed out that people with

more female adolescents have more chances to participate in the urban informal sector of

Southern Punjab.

clxxviii

Table 6.14: Distribution of Respondents by Number of Female Adolescents

Number of Female

Adolescents

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

0

404

(40.97)

[55.12]

329

(63.27)

[44.88]

733

(48.67)

[100]

1

172

(17.44)

[61.87]

106

(20.38)

[38.13]

278

(18.46)

[100]

2

246

(24.95)

[78.10]

69

(13.27)

[21.90]

315

(20.92)

[100]

3

129

(13.08)

[90.21]

14

(2.69)

[9.79]

143

(9.50)

[100]

4

32

(3.25)

[94.12]

2

(0.38)

[5.88]

34

(2.26)

[100]

Above 4

3

(0.30)

[100]

0

(0)

[0]

3

(0.20)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]




6.3.14 Spouse Working and Urban Informal and Formal Sector Employment

Spouse participation in economic activities negatively influences the choice of

participation in the urban informal sector employment. It is hypothesized if the spouses

are working in the informal sector economic activities, then their counterparts are less

likely to participate in the urban informal sector employment. The findings of the study

support the theory.

clxxix

Table 6.15: Distribution of Respondents by their Working Spouse

Working spouse Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Working Spouse

308

(31.23)

[54.32]

259

(49.81)

[45.68]

567

(37.65)

[100]

Non-Working Spouse

678

(68.76)

[72.20]

261

(50.19)

[27.80]

939

(62.35)

[100]

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]




Table 6.15 illustrates a negative relationship between the informal sector

employment decision and spouse‟s participation in economic activities. It is found that

68.76 percent of the informal sector workers‟ spouses are not working and 31.23 percent

workers‟ spouses are engaged in economic activites. Conversely, in formal sector

workers‟ spouses are participating more than urban informal sector. Hypothetically, it is

expected that there is an inverse association between the urban informal sector

employment decision and spouse participation via economic activities.

6.3.15 Rural-Urban Migration and Urban Informal and Fromal Sector

Employment

Theoretically, the urban informal sector absorbs an influx of rural-urban migrants

and urban dwellers and creates employment opportunities. The study result corroborates

the hypothesis.

Table 6.16: Distribution of Respondents by Rural-Urban Migration

Rural-Urban

Migration

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

RMGT

327

(33.16)

[73.81]

116

(22.31)

[26.19]

443

(29.42)

[100]

NTV

659

(66.84)

[61.99]

404

(77.69)

[38.01]

1063

(70.58)

[100]

clxxx

Total

986

(100)

[65.47]

520

(100)

[34.53]

1506

(100)

[100]




clxxxi

Table 6.16 presents the distribution of respondents by rural-urban migration.

Table indicates that urban dwellers account for 66.18 percent of informal labour force

while rural migrants account for 33.16 percent of the informal sector in Southern Punjab.

It is observed that urban dwellers are more likely to participate in the informal sector as

compared to the rural–urban migrants. The share of rural migrants is slightly lower. In

contrast, the greater proportion of the urban dwellers is observed in the urban informal

sector of Southern Punjab. It is concluded that the urban informal sector is creating more

employment opportunities for rural-urban migrants and urban-dwellers in Southern

Punjab.

6.3.16 Employment Status and Urban Informal and Formal Sector

Employment

Table 6.17 indicates the employment status of participants of the urban informal

sector. 1506 participants of labour market have been included in the survey. Out of them,

986 workers are participating in the informal sector and 520 are participating in the

formal labour market. Out of the 986 workers, 1.2 percent are domestic workers, 27.48

percent are wage workers. Self-employed workers account for 53.7 percent of the

informal sector employment. In contrast, salaried workers are 6.7 percent, 5.8 percent are

own-account workers currently working as self-employed and 4.6 percent are unpaid

family workers.

clxxxii

Table 6.17 Distribution of Respondents by Sector of Employment Status

Employment Status Participants

Formal Sector Employed 520

(34.53)

Informal Sector Employed

986

(65.47)

Domestic Workers

18

(1.2)

Wage Workers

271

(27.48)

Self-employed

529

(53.7)

Salaried workers

66

(6.7)

Own account Workers

57

(5.8)

Unpaid Family Workers

45

(4.6)

Total 1506

Source: Field Survey by the author.

Note: Values are percnetages of total columns.

6.3.17 Sector of Employment and Urban Informal Sector Emloyment

Table 6.18 portrays the distribution of workers in terms of sector of employment.

Out of the 986 participants of the informal sector, 23.32 percent are working in the trade

sector, 40.67 percent are working in the services sector. Service providers make up bulk

of the urban informal sector. The highest proportion of the participants is found in the

services sector. The 2nd

highest percentage of workers is found in the manufacturing

sector. Transport workers account for 5.37 percent of the informal sector employed. The

contribution of construction sector is 3.65 percent which is the lowest in the survey.

clxxxiii

Table 6.18 Distribution of Respndents by Sector of Employment

Sector of Employment Participants

Trade 230

(23.32)

Services 401

(40.67)

Manufacturing 266

(26.98)

Transport 53

(5.37)

Construction 36

(3.65)

Total 986

Source: Survey by the author

Note: Values are percnetages of total columns.

6.3.18 Working Hours and Urban Informal Sector Employment

Table 6.19 presents the working hours of those who are involved in the urban

informal sector. The proportion of the workers who work less than 15 hours in a week is

about 1.3 %. The 11.6 % male informal workers are engaged in 15-24 hours. It is also

observed that 27.4 % participants of the urban informal sector work 48 hours in a week

and 19.8 % of the workers work more than 56 hours in a week.

Table 6.19: Distribution of Respondents by Working Hours

Working Hours Participants

Less than 15 13

(1.3)

15-24 Hours 114

(11.6)

25-34 Hours 139

(14.1)

35-41 Hours 119

(12.1)

42-48 Hours 270

(27.4)

49-55 Hours 136

(13.8)

56 Hours and above 195

(19.8)

Total 986

Source: Survey by the author

Note: Values in round brackets are percnetages of total columns.

clxxxiv

clxxxv

6.4 Descriptive Analysis of Urban Male Informal and Formal

Sector in Southern Punjab, Pakistan

In this section, we present the descriptive analysis of determinants of male

informal and formal sector employment. Mostly males, being household heads, have to

contribute more in informal labour market because they have to fulfill the household

responsibilities. In this section, we will discuss the various socio-economic and

demographic factors of male participants of the urban informal and formal sector

employment in Southern Punjab, Pakistan. It is essential to take a detailed account of the

determinants of urban male informal sector participants descriptively to understand the

development-oriented and job-promoting character of the informal sector. A study of the

descriptive analysis of male urban informal sector workers assists to know the factors

which influence male individual‟s choice to engage in this sector. The analysis will make

it possible for policy makers to formulate or design policies regarding participants in

informal sector.

6.4.1 Age Groups and Urban Male Informal and Formal Sector

Employment

Age plays a positive role in determining the growth potential of informal and

formal sector employment. Data reflects that employment rate for male informal sector

workers are very low at early and higher age groups. Male informal sector employment is

observed to be diminishing in early and old age.

clxxxvi

Table 6.20 Distribution of Male Respondents by Age Groups


Sector Employed

Urban Formal Sector

Employed Total

AGE I

15-24

67

(10.93)

[54.03]

57

(17.76)

[45.97]

124

(13.28)

[100]

AGE II

25-34

95

(15.50)

[57.58]

70

(21.81)

[42.42]

165

(17.67)

[100]

AGE III

35-44

128

(20.88)

[58.18]

92

(28.66)

[41.82]

220

(23.55)

[100]

AGE IV

45-54

245

(39.97)

[75.15]

81

(25.23)

[24.85]

326

(34.90)

[100]

AGE V

55-64

78

(12.72)

[78.79]

21

(6.54)

[21.21]

99

(10.60)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




Table 6.20 shows the distribution of female participants in the urban informal and

formal sector employment by age profile. It is shown that the urban informal sector

employment in the age group 15-24 is 10.9 % which is the lowest for male. However, the

same which is on the rise is 15.50 % in the age group 25-34. Urban male informal sector

employment is observed 20.88 percent in the age group 35-44. The results indicate that

the age group 45-54 reserves the highest participation rate of 39.97 %. Informal sector

appears to favour mature males with nearly half of the workers falling into this age group.

Therefore, the age group 55-64, urban male informal sector employment is relatively low

with 12.72 %. Table shows a positive relationship between age factor and urban informal

sector employment up to the age group 45-54. On the other hand, age is inversely related

to male informal sector employment after the noted age group. The results conclude that

workers at their early and old age are less likely to join the informal sector employment

in the urban areas of Southern Punjab, Pakistan. This vital fact matches the life cycle

hypothesis and indicates inverse U-shaped phenomenon.

clxxxvii

6.4.2 Education and Urban Male Informal and Formal Sector Employment

Education is an imperative factor in determining the participation in sector of

employment. It is postulated that education and the urban informal sector employment

decision are negatively correlated. The study results confirm the hypothesis and show the

distribution of male participants of the informal and formal sector by education level in

given table 6.21.

Table 6.21 Distribution of Male Respondents by Level of Education


Sector Employed

Urban Formal Sector

Employed Total

EDU I

Primary

57

(9.66)

[79.17]

15

(4.77)

[20.83]

72

(7.96)

[100]

EDU II

Middle

137

(23.22)

[88.96]

17

(5.41)

[11.04]

154

(17.04)

[100]

EDU III

Matric

209

(35.42)

[74.91]

70

(22.29)

[25.09]

279

(30.86)

[100]

EDU IV

Intermediate

98

(16.61)

[60.87]

63

(20.06)

[39.13]

161

(17.81)

[100]

EDU V

Graduation

61

(10.34)

[43.57]

79

(25.16)

[56.43]

140

(15.49)

[100]

EDU VI

Mastr’s or higher

Education

28

(4.75)

[28.57]

70

(22.29)

[71.43]

98

(10.84)

[100]

Total

590

(100)

[65.27]

314

(100)

[34.73]

904

(100)

[100]




The table 6.21 demonstrates a negative relationship between male informal and

formal sector employment and education level. It is observed that the informal sector

employment decreases with an increase in the education level of the participants. There is

highest proportion of the informal sector participants who have Matric level education

which is 35 percent. Result also indicates the 2nd

highest level of education is the Middle

level. As the educational level increases, the percentages of male informal sector

employment decreases. Our findings indicate that the lowest level of education is

clxxxviii

Master‟s level. Results conclude that males possessing higher education level are inclined

to the urban formal labour market of Southern Punjab.

6.4.3 Marital Status and Male Informal and Formal Sector Employment

A further factor, marital status also determines the decision of participation in

sector of employment. It is expected that married people contribute more in economic

activities due to financial responsibility. A positive relationship is established between

male informal sector employment and marital status in urban areas of Southern Punjab in

the following table.

Table 6.22: Distribution of Male Respondents by Marital Status

Marital Status Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Married

454

(74.06)

[65.89]

235

(73.21)

[34.11]

689

(73.77)

[100]

Unmarried

159

(25.94)

[64.90]

86

(26.79)

[35.10]

245

(26.23)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




In fact, there is highest proportion of 74.06 percent of married male workers who

joined the informal sector indicated by table 6.22. Contrarily, 25.94 percent unmarried

are observed working in the informal sector. Study portrays a positive relationship

between marital status and male informal sector employment rate. It is concluded that

married people are more likely to involve themselves in employment as compared to

unmarried persons in the urban informal sector of Southern Punjab.

clxxxix

6.4.4 Formal Training and Urban Male Informal and Formal Sector

Employment

Participants of the informal sector possess some kind of formal and informal

training. It is clear from the table 6.23 that informal sector employment is negatively

correlated with some kind of formal training in urban areas of Southern Punjab. The 13.5

% males engaged in urban informal sector employment have formal training. The share is

very low. On the other hand, those who are untrained their participation rate is 86.95

percent in the urban informal sector of Southern Punjab.

Table 6.23: Distribution of Male Respondents by Formal Training

Level of Training Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Formal Training

80

(13.05)

[40.20]

119

(37.07)

[59.80]

199

(21.31)

[100]

Untrained

533

(86.95)

[72.52]

202

(62.93)

[27.48]

735

(78.69)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




6.4.5 Father’s Educational Status and Male Urban Informal and Formal

Sector Employment

Father‟s educational status exerts a great impact on male workers‟ decision to join

the labour market. It is expected that father‟s educational status seems to affect negatively

the participation in the informal sector. The data in the table demonstrates a negative

relationship between father‟s education and male workers‟ participation in the urban

informal sector of Southern Punjab.

Table 6.24: Distribution of Male Respondents by Father’s Educational Status


Status

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Educated Father

225

(36.70)

[51.37]

213

(66.36)

[48.63]

438

(46.90)

[100]

Uneducated Father 388 108 496

cxc

(63.30)

[78.23]

(33.64)

[21.77]

(53.10)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




Table 6.24 portrays that male participants‟ account for 36.70 percent of the

informal labour market having educated fathers. Even as, there are 63.30 percent of the

informal sector workers whose fathers are uneducated. The results conclude that workers

whose fathers are educated are less likely to take part in the urban informal sector of

Southern Punjab, Pakistan.

6.4.6 Mother’s Educational Status and Urban Male Informal and Formal

Sector Employment

Mother‟s educational status also helps in growth potential of the informal

employment or occupation for male workers. It is hypothesized that there is a negative

relationship between participants‟ choice of involvement in the informal sector and their

mother‟s educational status.

Table 6.25: Distribution of Male Respondents by Mother’s Educational Status


Status

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Educated Mothers

122

(19.90)

[42.81]

163

(50.78)

[57.19]

285

(30.51)

[100]

Uneducated Mothers

491

(80.10)

[75.65]

158

(49.22)

[24.35]

649

(100)

[69.49]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]


Note: Values in round brackets are percnetages of total columns totals, while the values in square brackets

are percentages of total rows.

It is observed from the table 6.25 that the male‟s involvement in the informal

sector has decreasing trend regarding mother‟s education. There are 19.90 percent of the

participants in the informal sector whose mothers are educated compared to 80.10 percent

of those whose mothers are uneducated. The results conclude that higher the mother‟s

education level, the lower the male informal employment rate in Southern Punjab.

cxcii

6.4.7 Household Size and Urban Male Informal and Formal Sector

Employment

One more factor, size of household, can have a substantial influence on males‟

participation in the urban informal sector employment. It is hypothesized that males‟

employment in the urban informal sector is positively correlated with household size.

The table 6.26 shows a positive relationship between male workers‟ employment in the

urban informal sector and household size.

Table 6.26: Distribution of Male Respondents by the Size of Household

Size of Household Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

1-4

35

(5.71)

[35.71]

63

(19.63)

[64.29]

98

(10.49)

[100]

5-8

366

(59.71)

[63.10]

214

(66.67)

[36.90]

580

(62.10)

[100]

9-12

194

(31.65)

[81.51]

44

(13.71)

[18.86]

238

(25.48)

[100]

13-16

18

(2.94)

[100]

0

(0)

[0]

18

(1.93)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




The data indicates an increasing tendency of male informal sector employment

and size of the household. The urban informal sector employment rate is highest when

the household size lies between 5 to 8 persons in the house. The present study concludes

a positive relationship between household size and male worker‟s participation in the

urban informal sector of Southern Punjab.

6.4.8 Number of Dependents and Urban Male Informal and Formal Sector

Employment

It is postulated that dependency ratio has a positive impact on the males‟ decision

to work in the informal sector employment. Table indicates a positive relationship

cxciii

between male informal sector employment and number of dependents in urban areas of

Southern Punjab, Pakistan. An analysis of the urban informal sector employment with

number of dependents is given in the table 6.27.

Table 6.27: Distribution of Male Respondents by Number of Dependents

Number of

Dependents

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

1

37

(6.04)

[49.33]

38

(11.83)

[50.67]

75

(8.03)

[100]

2

98

(15.99)

[54.44]

82

(25.55)

[45.56]

180

(19.27)

[100]

3

113

(18.43)

[60.11]

75

(23.36)

[39.89]

188

(20.13)

[100]

4

99

(16.15)

[70.21]

42

(13.08)

[29.79]

141

(15.10)

[100]

5

87

(14.19)

[77.68]

25

(7.79)

[22.32]

112

(11.99)

[100]

6

70

(11.42)

[87.5]

10

(3.12)

[12.5]

80

(8.57)

[100]

7

41

(6.69)

[89.13]

5

(1.56)

[10.87]

46

(4.93)

[100]

8 and above

39

(6.36)

[88.64]

5

(1.56)

[11.36]

44

(4.71)

[100]

Total

613

(100)

[100]

321

(100)

[34.37]

934

(100)

[100]



percentagesof total rows.

A positive relationship is found between number of dependents and share of the

informal sector employment for male workers. Results indicate that informal sector

employment of male increases with an increase in the number of dependents in Southern

Punjab. The higher the number of dependents, the higher the male workers‟ involvement

in the urban informal sector.

cxciv

6.4.9 Family Setup and Urban Male Informal and Formal Sector

Employment

It is hypothesized that family setup influences the males‟ share in urban labour

market. The relationship between males‟ working in urban informal sector and their

family setup is portrayed in table 6.28. It has been reported that male informal sector

employment increases with the joint family system in urban areas of Southern Punjab.

Table 6.28: Distribution of Male Respondents by Type of Family System


Sector employed

Urban Formal Sector

Employed Total

Joint Family

368

(60.03)

[71.46]

147

(45.79)

[28.54]

515

(55.14)

[100]

Nuclear Family

245

(39.97)

[58.47]

174

(54.21)

[41.53]

419

(44.86)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]


Note: Values in round brackets are percentages of thotal columns, while the values in square brackets are


The distribution of male informal sector employment by family setup indicates

that the males who belong to joint family setup are more likely to participate in the urban

informal sector having 60.03 percent as compared to those who live in nuclear families

having 39.97 percentages. The results conclude a positive relationship between male

informal sector employment and joint family system in Southern Punjab.

6.4.10 Number of Children and Urban Male Informal and Formal Sector

Employment

Number of children has also an effect on the males‟ participation decision

regarding informal sector employment in Southern Punjab. A positive relationship is

found between males‟ participation in the informal sector and number of children.

Findings demonstrate a negative correlation between number of children and males‟

urban informal sector employment decision. It has been observed that the workers having

more children, having more children have a high propensity to work in urban informal

sector of Southern Punjab.

cxcv

Table 6.29: Distribution of Male Respondents by Number of Children


Sector Employed

Urban Formal Sector

Employed Total

0

161

(26.26)

[60.98]

103

(32.09)

[39.01]

264

(28.27)

[100]

1

84

(13.70)

[58.74]

59

(18.38)

[41.26]

143

(15.31)

[100]

2

147

(23.98)

[64.47]

81

(25.23)

[35.53]

228

(24.41)

[100]

3

93

(15.17)

[64.58]

51

(15.89)

[35.42]

144

(15.42)

[100]

4

73

(11.91)

[78.49]

20

(6.23)

[21.51]

93

(9.96)

[100]

5

40

(6.53)

[90.91]

4

(1.25)

[9.09]

44

(4.71)

[100]

6

12

(1.96)

[80]

3

(0.93)

[20]

15

(1.61)

[100]

7

3

(0.49)

[100]

0

(0)

[0]

3

(0.32)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




6.4.11 Male Adolescents and Male Informal and Formal Sector Employment

Having male adolescents is another factor which helps regarding participation

decision in relation to the urban informal sector of Southern Punjab. Table 6.30 expresses

the relationship between males‟ contribution in the urban informal sector and the

presence of male adolescents. Study presents a negative association between male

adolescents and the informal sector employment decision. The study concludes that the

informal sector workers having more male adolescents have a less likelihood in the urban

informal sector of Southern Punjab.

cxcvi

Table 6.30: Distribution of Male Respondents by Number of Male Adolescents

Number of Male

Adolescents

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

0

369

(60.20)

[67.09]

181

(56.39)

[32.91]

550

(58.89)

[100]

1

142

(23.16)

[65.74]

74

(23.05)

[34.26]

216

(23.13)

[100]

2

79

(12.89)

[61.24]

50

(15.58)

[38.76]

129

(13.81)

[100]

3

18

(2.94)

[60]

12

(3.740

[40]

30

(3.21)

[100]

4

3

(0.49)

[42.86]

4

(1.25)

[57.14]

7

(0.75)

[100]

5

2

(0.33)

[100]

0

(0)

[0]

2

(0.21)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




6.4.12 Female Adolesents and Urban Male Informal and Formal Sector

Employment

Presence of female adolescents is also an important factor that affects labours‟

decision to indulge into the urban informal sector employment. Table 6.31 illustrates a

relationship between working in the informal sector and female adolescents. The data in

the table shows a positive correlation between female adolescents and male informal

sector absorption. Results make clear that male workers having more female adolescents

are more likely to invoke the urban informal sector employment of Southern Punjab,

Pakistan.

cxcvii

cxcviii

Table 6.31: Distribution of Male Respondents by Female Adolescents

Female Adolescents Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

0

259

(42.25)

[57.05]

195

(60.75)

[42.95]

454

(48.61)

[100]

1

104

(16.97)

[61.18]

66

(20.56)

[38.82]

170

(18.20)

[100]

2

136

(22.19)

[74.32]

47

(14.64)

[25.68]

183

(19.59)

[100]

3

86

(14.03)

[88.66]

11

(3.43

[11.34]

97

(10.39)

[100]

4

26

(4.24)

[92.86]

2

(0.62)

[7.14]

28

(3.0)

[100]

5

2

(0.33)

[100]

0

(0)

[0]

3

(0.33)

[100]

Above5

1

(0.16)

[100]

0

(0)

[0]

1

(0.11)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




6.4.13 Working Spouse and Urban Male Informal Sector Employment

Theoretically, it is hypothesized that there is a negative relationship between

working spouse and participation in the labour market (formal and informal). Our results

confirm the hypothesis. Table 6.32 indicates a negative relationship between working

spouses and male participants of the urban informal sector.

cxcix

Table 6.32: Distribution of Male Respondents by Spouse Participation

Spouse Participation Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Working Spouse

146

(23.82)

[52.52]

132

(41.12)

[47.48]

278

(29.76)

[100]

Non-Working Spouse

467

(76.18)

[71.19]

189

(58.88)

[28.81]

656

(70.24)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]


Note: Values in round brackets are percentages of total columns, while the values in square brackets are


Table 6.32 presents a negative association between male informal sector

employment and their spouse‟s participation in economic activities. The results reflect

that there is 76.18 percent males are engaged in the urban informal sector whose spouses

are not participating in economic activities. Contrarily, the percentage of working

spouses of the male participants of urban informal sector is 23.82 percent which is very

low. It is concluded that males‟ share in the urban informal sector and their spouse‟s

participation in economic activities are negatively correlated.

6.4.14 Rural-Urban Migration and Male Informal and Formal Sector

Employment

It is argued that the urban informal sector is highly influenced by the influx of

rural-urban migrants as well as urban dwellers. The urban informal sector provides more

employment opportunities to both groups. It is hypothesized that rural-urban migrant

males are positively associated with the informal sector employment in the urban areas of

Southern Punjab. An analysis of rural-urban migrants and the urban informal sector

employment is given in the following table.

cc

Table 6.33: Distribution of Male Respondents by Rural-Urban Migration

Rural-Urban

Migration

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

RMGT

216

(35.24)

[75]

72

(22.43)

[25]

288

(30.84)

[100]

NTV

397

(64.76)

[61.46]

249

(77.57)

[38.54]

646

(69.16)

[100]

Total

613

(100)

[65.63]

321

(100)

[34.37]

934

(100)

[100]




Table 6.34 shows a relationship between rural-urban migrants and participation

rate in the informal and formal sector. Results explain that 35. 24 percent rural-urban

migrant males are working in the urban informal sector whereas contribution of urban

dwellers in the informal sector is 76.1 percent. Data trend indicates that urban dwellers

are more likely to participate in informal sector employment.

6.4.15 Employment Status and Urban Male Informal and Formal Sector

Employment

Table 6.34 indicates the participation of the informal and formal sector males

according to employment status. In terms of employment status, 934 male participants

have been included in the survey. Out of them, 613 workers are participating in urban

informal sector and 321 are participating in formal labour market of Southern Punjab.

Out of the 613 male workers, 0.8 percent males are domestic workers. In contrast, 19.4

percent males are engaged in wage work. Note that 50.4 percent male participants are

working as self-employed, 8.0 percent are salaried workers, 7.3 percent males are own

account workers, and 3.9 percent males are unpaid family workers.

cci

Table 6.34 Distribution of Male Respondents by Employment Status



Informal Sector Employed 613

Domestic Workers 5

(0.8)

Wage Workers 180

(29.36)

Self-employed 310

(50.4)

Salaried workers 49

(8.0)

Own account Workers 45

(7.3)

Unpaid Family Workers 24

(3.9)

Total 613

Source: Field Survey by the author.

Note: Values in round brackets are percentages of total columns.

6.4.16 Sector of Employment and Urban Male Informal Sector Employment

Table 6.35 reveals the distribution of male participants in the informal and formal

sector. Out of 613 participants of the informal sector, 28.38 percent males are working in

the trade sector. The highest proportion of the male participants is 33.77 percent which is

found in the services sector. The contribution of male workers is 23.65 percent in

manufacturing sector. Generally, 8.48 percent of males are employed in the transport

sector. The contribution of male workers in construction sector is 5.87 percent which is

lowest in the survey.

ccii

Table 6.35 Distribution of Urban Male Respondents by Sector of Employment


Trade 173

(28.22)

Services 207

(33.77)

Manufacturing 145

(23.65)

Transport 52

(8.48)

Construction 36

(5.87)

Total 613


Note: Values in round brackets are percnetages of total columns.

6.4.17 Working Hours and Urban Informal Sector Employment

The table 6.36 highlights that about 1.5 % males employed in the informal sector

work less than 15 hours. The 8.3 % male participants of the urban informal sector work

more than 15-24 hours. Another fact is that 27.73 % male workers work 48 hours in a

week and 19.2% of the workers comprises the group who worked more than 56 hours in a

week.

Table 6.36: Distribution of Urban Male Respondents by Working Hours.


Less than 15 9

(1.5)

15-24 Hours 51

(8.3)

25-34 Hours 94

(15.3)

35-41 Hours 68

(11.09)

42-48 Hours 170

(27.73)

49-55 Hours 102

(16.6)


(19.2)

Total 613



cciii

6.5 Descriptive Analysis of Urban Female Informal and Formal

Sector Employment in Southern Punjab, Pakistan.

This section decribes the descriptive analysis of female workers involved in the

urban informal sector of Southern Punjab, Pakistan. The data for the current study are

derived from primary source which is collected from urban areas of Southern Punjab. The

household survey is conducted and information is obtained from 514 females

amalgamating the informal sector. A descriptive analysis is made to discern the share of

female workers in the urban informal sector of Southern Punjab, Pakistan. In order to

understand the development-oriented and job-promoting character of informal sector, it is

imperative to take a detailed account of the characteristics of participants of the urban

informal sector who manage this sector. A study of the characteristics of the female

participants of the informal sector assists in order to identify the factors which influence

their choice to employ in this sector.

6.5.1 Age Groups and Urban Female Informal and Formal Sector

Employment

It is expected that age groups and females‟ employment in the urban informal

sector are highly associated. It is argued that the people are engaged highly in the urban

informal activities in young and old age groups. The present study ascertains a

relationship between age group and females‟ participation in the urban informal sector of

Southern Punjab.

Table 6.37 reveals an analysis of females by age group distribution of urban

informal and formal sector employment in southern Punjab. It is shown that female

participants of the informal sector in the age group 15-24 are 8.04 % which is the lowest.

However, the same is on the rise with 10.09% in the age group 25-34. Female informal

sector employment is observed 22.79 percent in the age group 35-44. The results indicate

that the age group 45-54 reserves the highest participation rate of 42.64 %. The urban

informal sector appears to favour older female with nearly half of the workers falling into

this age group. As far as the age group 55-64 is concerned, females‟ participation is

relatively low with percentage of 10.46. Table shows a positive relationship between age

cciv

factor and female informal sector employment up to the age group (45-54). However, age

is inversely related with female informal sector employment after the noted age group.

Results conclude that females have a less participation in young and old age in the urban

informal sector of Southern Punjab, Pakistan. The result supports the life cycle

hypothesis.

Table 6.37: Distribution of Female Respondents by Age Groups


Sector Employed

Urban Formal Sector

Employed Total

AGE I

15-24

30

(8.04)

[42.86]

40

(20.10)

[57.14]

70

(12.24)

[100]

AGE II

25-34

60

(16.o9)

[50.83]

57

(28.04)

[48.42]

117

(20.4)

[100]

AGE III

35-44

85

(22.79)

[61.15]

54

(27.14)

[38.85]

139

(24.30)

[100]

AGE IV

45-54

159

(42.63)

[80.71]

38

(19.10)

[19.29]

197

(34.44)

[100]

AGE V

55-64

39

(10.46)

[79.59]

10

(5.03)

[20.41]

49

(8.57)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




6.5.2 Education and Urban Female Informal and Formal Sector

Employment

It is hypothesized that workers involved in the urban informal sector are also

influenced by their educational level. In theory, education negatively influences the

females to work in the urban informal sector. A negative relationship is established

between level of education and female informal sector employment. The given table 6.38

evaluates an association between sector of employment and education level.

ccv

Table 6.38: Distribution of Female Respondents by Education


Sector Employed

Urban Formal Sector

Employed Total

EDUI

Primary

70

(21.81)

[92.11]

6

(3.31)

[7.89]

76

(14.79)

[100]

EDUII

Middle

75

(23.36)

[89.29]

9

(4.97)

[10.71]

84

(16.34)

[100]

EDUIII

Matric

89

(27.73)

[70.63]

37

(20.44)

[29.37]

126

(24.51)

[100]

EDUIV

Intermediate

38

(11.83)

[54.29]

32

(17.68)

[45.71]

70

(13.62)

[100]

EDUV

Graduation

27

(8.41)

[38.03]

44

(24.31)

[61.97]

71

(13.81)

[100]

EDUVI

Master’s or higher

Education

22

(6.85)

[25.29]

65

(35.91)

[74.71]

87

(16.93)

[100]

Total

321

(100)

[62.45]

181

(100)

[35.21]

514

(100)

[100]




Table 6.38 implies the distribution of female participants regarding formal and

informal sector employment and their education level. The study analysis indicates that

the female participants of the informal sector and education levels are negatively

correlated. Results show a decreasing trend of females in the urban informal sector with

the increasing education level.The results also point out that there is highest proportion of

female participants whose education level is up to Matric, which is 27.73 percent. The

share of those workers whose level of education is up to Primary is 21.81 perecnt.

ccvi

6.5.3 Marital Status and Urban Female Informal and Formal Sector

Employment

The marital status is most significant factor in determining the informal sector

employment for female workers. The females‟ share in the informal labour market is

influenced by marital status. Labour supply theory indicates that married females are less

likely to involve in economic activities in the informal sector. The certain facts indicate a

negative relationship between females‟ employment choice in urban informal sector and

their marital status.

Table 6.39: Distribution of Female Respondents by Marital Status

Marital Status Urban Informal Sector

Employed

Urban Formal Sector

Employed Total

Married

278

(74.53)

[66.99]

137

(68.84)

[33.01]

415

(72.55)

[100]

Unmarried

95

(25.47)

[60.51]

62

(31.16)

[39.49]

157

(27.45)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]


Note: Values in round brackets are percentages of total columns totals, while the values in square brackets

are percentages of total rows.

The distribution of females‟ engagement in urban informal and formal sector

regarding marital status is presented in table 6.39. The study results display that share of

the informal sector employment is high among married females. This indicates that there

is a positive effect of marital status on female informal sector participants. Married

female workers account for 74.53 % of the urban informal labour market at the same time

as unmarried females‟ contribute about 25.47%. The results conclude that married

females are more likely to join urban informal sector of Southern Punjab, Pakistan.

ccvii

6.5.4 Formal Training and Urban Female Informal and Fromal Sector

Employment

Formal training plays an important role to make females join the sector of

employment in urban areas of Southern Punjab. Formal training is helpful for generating

more income. It is expected that formally trained females have high propensity to invoke

the formal labour market. We establish the relationship between females working in the

urban informal sector and formal training in the following table.

Table 6.40: Distribution of Female Respondents by Formal Training

Level of Training Urban Informal Sector

Employed

Urban Formal Sector

Employed Total

Formal Training

50

(13.40)

[36.23]

88

(44.22)

[63.77]

138

(24.13)

[100]

Untrained

323

(86.60)

[74.42]

111

(55.78)

[25.58]

434

(75.87)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




It is clear from the table 6.40 that females‟ absorption in the urban informal sector

has decreased because of low formal training. Results explain that females who are

formally trained account for 13.40 percent of the informal sector employment and the

employment rate of untrained is 86.60 percent. In conclusion, share of female workers in

the urban informal sector and formal training are negatively correlated.

ccviii

6.5.5 Father’s Educational Status and Urban Female Informal and


Education level of father helps in determining the decision to adhere to sector of

employment for female workers. Theoretically, father‟s educational status and females‟

contribution in the urban informal sector are negatively associated. The following table

describes a relationship between the females working in urban informal sector and their

educated fathers.

Table 6.41: Distribution of Female Respondents by Father’s Education Status


Status

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Educated Father

120

(32.17)

[42.86]

160

(80.40)

[57.14]

280

(48.95)

[100]

Uneducated Father

253

(67.83)

[86.64]

39

(19.60)

[13.36]

292

(51.05)

[100]

Total

373

(100

[65.21]

199

(100)

[34.79]

572

(100)

[100]




Table 6.41 presents distribution of female‟s informal and formal sector

employment by the presence of fathers‟ education. It is observed that education of father

is negatively correlated to the females‟ participation in the informal sector. Results

confirm that there are 32.17 % of female participants of the urban informal sector whose

fathers are educated as compared to 67.83 % of those female participants whose fathers

are uneducated. This trend indicates that the females whose fathers are uneducated are

more likely to participate in the urban informal sector of Southern Punjab. Results

conclude a negative association between father‟s education and choice of the urban

informal sector employment in Southern Punjab.

ccix

6.5.6 Mother’s Educational Status and Female Urban Informal and


It is hypothesized that mother‟s educational status influences the decision of

female workers to participate in labour market. It is suggested that mothers‟ education

and females‟ participation in the urban informal sector are negatively associated. The

present study ascertains a relationship between female informal sector absorption and

mother‟s education in the following table.

Table 6.42: Distribution of Female Respondents by Mother’s Educational Status


Status

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Educated Mothers

57

(15.28)

[31.32]

125

(62.81)

[68.68]

182

(31.82)

[100]

Uneducated Mothers

316

(84.72)

[81.03]

74

(37.19)

[18.97]

390

(68.18)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




Table 6.42 describes the distribution of female respondents in formal and informal

sector employment by the existence of mother‟s education. The data trend renders the

share of female participants of the informal sector whose mothers are educated is 15.28

%. Contrarily, those females whose mothers are uneducated are 81.03 percent involved in

the urban informal sector. Results found that the mother‟s education and female informal

sector employment is negatively related.

ccx

6.5.7 Size of Household and Female Informal and Formal Sector

Employment

Household size has a vital influence on female informal sector employment. Size

of household can influence females both working at home or in labour market activities

for generating income. Hypothetically, it is anticipated that large household size affects

inversely the females‟ employment choice. The present study analyzes a negative

relationship between the urban informal sector choice and household size in the table

below.

Table 6.43: Distribution of Female Respondents by the Size of Household

Size of Household Urban Informal Sector

Employed

Urban Formal Sector

Employed Total

1-4

37

(9.92)

[41.57]

52

(26.13)

[58.43]

89

(15.56)

[100]

5-8

241

(64.61)

[66.21]

123

(61.81)

[33.79]

364

(63.64)

[100]

9-12

87

(23.32)

[76.58]

22

(11.06)

[20.18]

109

(19.06)

[100]

13-16

8

(2.14)

[80]

2

(1.01)

[20]

10

(1.75)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




Table 6.43 particularizes the distribution of female respondents in the formal and

informal sector according to the household size. The household size exerts a negative

effect on females‟ working in the urban informal sector. The data indicates that female

employment rate decreases with an increase in the size of household. The females whose

size of family is 5 to 8 persons, their absorption rate is about 64.6 % in urban informal


ccxi

6.5.8 Number of Dependents and Urban Female Informal and Formal

Sector Employment

Number of dependents and the urban informal sector employment is negatively

correlated on part of female employment. The probability of working in the urban

informal sector increases among females with increasing number of dependents. The data

describes a relationship between females‟ participation in the urban informal sector and

number of dependents in the following table.

Table 6.44: Distribution of Female Respondents by Number of Dependents

Number of

Dependents

Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

1

31

(8.31)

[53.45]

27

(13.57)

[46.55]

58

(10.14)

[100]

2

74

(19.84)

[61.67]

46

(23.12)

[38.33]

120

(20.98)

[100]

3

92

(24.66)

[73.02]

34

(17.09)

[26.98]

126

(22.03)

[100]

4

77

(20.64)

[74.76]

26

(13.07)

[25.24]

103

(18.00)

[100]

5

41

(10.99)

[73.21]

15

(7.54)

[26.79]

56

(9.79)

[100]

6

16

(4.29)

[51.61]

15

(7.54)

[48.39]

31

(5.42)

[100]

7

6

(1.61)

[60]

4

(2.01)

[40]

10

(1.75)

[100]

8 and Above

10

(2.68)

[71.43]

4

(2.01)

[28.57]

14

(2.45)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




Table 6.44 shows the distribution of informal and formal sector female

employment regarding number of dependents. The data represents a negative association

ccxii

of female informal sector employment and number of dependents in the urban areas of

Southern Punjab.

6.5.9 Family Setup and Urban Female Informal and Formal sector

Employment

Family setup is an important factor which determines females‟ participation in

sector of employment. It is supposed that family labour involvement also determines the

sector of choice. The study indicates a positive association of female participants of urban

informal sector and joint family system. Table 6.45 reveals a positive association between

female participants in the informal sector and family setup below.

Table 6.45: Distribution of Female Respondents by Type of Family System


Sector Employed

Urban Formal Sector

Employed Total

Joint Family

267

(71.58)

[77.62]

77

(38.69)

[22.38]

344

(60.14)

[100]

Nuclear Family

106

(28.42)

[46.49]

122

(61.31)

[53.52]

228

(39.86)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




The distribution of female absorption in informal and formal sector with the

family setup is given in table 6.45. Female working in the urban informal sector is

positively correlated with joint family system. Results indicate that the majority of

females engaged in the urban informal sector 71.58 % belong to joint family setup and

low rate of 28.42 % of females belongs to nuclear families. It is concluded that the

females who belong to joint families are more likely to indulge into the urban informal

sector of Southern Punjab, Pakistan.

ccxiii

6.5.10 Number of Children and Urban Female Informal and Formal Sector

Employment

The Neo-classical labour supply theory postulates that children can have a greater

influence on females‟ decision regarding participation in urban informal sector.

Generally, it is assumed that females having more children have greater chances to work

in urban informal sector. Table 6.46 presents a relationship between females‟ informal

sector employment and number of children. The data shows a negative correlation

between number of adults and the urban informal sector employment decision. It appears

that the females having more children participate more in informal sector employment.

Table 6.46: Distribution of Female Respondents by Number of Children


Sector Employed

Urban Formal Sector

Employed Total

0

91

(24.40)

[50.55]

89

(44.72)

[49.44]

180

(31.47)

[100]

1

38

(10.19)

[60.32]

25

(12.56)

[39.68]

63

(11.01)

[100]

2

81

(21.72)

[64.8]

44

(22.11)

[35.2]

125

(21.85)

[100]

3

73

(19.57)

[75.26]

24

(12.06)

[24.74]

97

(16.96)

[100]

4

53

(14.21)

[84.13]

10

(5.03)

[15.87]

63

(11.01)

[100]

5

31

(8.31)

[88.57]

4

(2.01)

[11.43]

35

(6.12)

[100]

6

5

(1.34)

[62.5]

3

(1.51)

[37.5]

8

(1.40)

[100]

7

1

(0.27)

[100]

0

(0)

[0]

1

(0.17)

[100]

Total

373

(65.21)

[100]

199

(34.79)

[100]

572

(100)

[100]




ccxiv

6.5.11 Male Adolescents and Urban Female Informal and Formal Sector

Employment

Male adolescents affect females‟ participation in the urban informal sector. Table

6.47 depicts the relationship between female participants in the informal sector and male

adolescents. Here, we analyse a relationship of male adolescents and female informal

sector employment in the table below.

Table 6.47: Distribution of Female Respondents by Male Adolescents

Male Adolescents Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

0

213

(57.10)

[64.94]

115

(57.79)

[35.06]

328

(57.34)

[100]

1

97

(26.0)

[72.39]

37

(18.59)

[27.61]

134

(23.43)

[100]

2

48

(12.87)

[60]

32

(16.08)

[40]

80

(13.99)

[100]

3

11

(2.94)

[55]

9

(4.52)

[45]

20

(3.50)

[100]

4

3

(0.80)

[33.33]

6

(3.01)

[66.67]

9

(1.57)

[100]

5

1

(0.27)

[100]

0

(0)

[0]

1

(0.17)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




Table 6.47 indicates a negative inclination of female workers in the urban

informal sector with increasing male adolescents. It comes into view that the females are

proportionately participating less in the urban informal economic activities in the

presence of male adolescents.

ccxv

6.5.12 Female Adolescents and Urban Female Informal and Formal Sector

Employment

It is put forward in labour supply theory that female adolescents affect females‟

decision in the urban informal sector. We setup a relationship between informal sector

involvement among female workers and female adolescents. Study presents a positive

correlation between female adolescents and urban informal sector employment decision

in Southern Punjab. It is clear from the table that female workers having more female

adolescents are more likely to involve in the urban informal sector to contribute in

household expenses.

Table 6.48: Distribution of Female Respondents by Female Adolescents

Female Adolescents Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

0

145

(38.87)

[51.97]

134

(67.34)

[40.03]

279

(48.78)

[100]

1

68

(18.23)

[62.96]

40

(20.10)

[37.04]

108

(18.88)

[100]

2

110

(29.49)

[83.33]

22

(11.06)

[16.67]

132

(23.08)

[100]

3

43

(11.53)

[93.48]

3

(1.51)

[6.52]

46

(8.04)

[100]

4

6

(1.61)

[100]

0

(0)

[0]

6

(1.05)

[100]

5 and Above

1

(0.27)

[100]

0

(0)

[0]

1

(0.17)

[100]

Total

373

(100)

[65.73]

199

(1000

[34.79]

572

(100)

[100]




ccxvi

6.5.13 Working Spouse and Urban Female Informal and Formal Sector

Employment

The Neo-classical labour supply theory hypothesizes that females are less likely

to join the urban informal sector if their spouses are involved in income generating

activities. Our study results confirm the hypothesis. Hence, data describes an inverse

relationship between working spouse and females‟ participation regarding urban informal

sector of Sothern Punjab.

Table 6.49: Distribution of Female Respondents by Working Spouse

Working Spouse Urban Informal

Sector Employed

Urban Formal Sector

Employed Total

Working Spouse

162

(43.43)

[56.06]

127

(63.82)

[43.94]

289

(50.52)

[100]

Non-Working Spouse

211

(56.57)

[74.56]

72

(36.18)

[25.44]

283

(49.48)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




Table 6.49 describes that participation rate of females in the informl sector and

working husbands is lower than those female workers whose spouses are not participating

in economic activities. Data shows that share of females whose husbands are working in

the urban informal sector is 43.43 percent while the contribution rate is 56.57 percent of

those females whose husbands are not working. The data results demonstrate a negative

association between spouse participation in economic activities and females‟

participation in the urban informal sector employment.

ccxvii

6.5.14 Rural-Urban Migration and Female Informal and Formal Sector

Employment

Rural-urban migration influences the participation in the urban informal sector. It

is expected that rural-urban migration also affects the females‟ share in informal sector

employment. Study establishes relationship of female working in the informal sector

regarding rural-urban migration. It is expected that rural-urban migrant females

participate more in the informal employment.The following table explains the

interdependence between females‟ share in the urban informal sector and rural-urban

migration.

Table 6.50: Distribution of Female Respondents by Rural-Urban Migration

Rural-Urban Migration

Urban

Informal

Sector

Employment

Urban Formal

Sector

Employment

Total

RMGT

111

(29.76)

[71.61]

44

(22.11)

[28.39]

155

(27.10)

[100]

NTV

262

(70.24)

[62.83]

155

(77.89)

[37.17]

417

(72.90)

[100]

Total

373

(100)

[65.21]

199

(100)

[34.79]

572

(100)

[100]




ccxviii

6.5.15 Employment Status and Urban Female Informal Sector Employment

The females‟ participation in the urban informal and formal sector according to

employment status is described in table 6.51. It is important to note that 572 female

participants have been included in the survey and 373 female workers are participating in

the urban informal sector. In contrast, 199 are participating in the formal labour market.

Out of the 373 female participants of the informal sector, 2.3 percent females are

domestic workers. Thus roughly, 24.40 percent females are wage workers. The

percentage of females is 59 percent in self-employment, 4.6 percent females are salaried

workers, and 3.2 percent are own account workers. Only 5.7 percent females are

described as unpaid family workers.

Table 6.51 Distribution of Female Respondents by Employment Status



Informal Sector Employed 373

Domestic Workers 13

(2.3)

Wage Workers 91

(24.40)

Self-employed 219

(59.0)

Salaried Workers 17

(4.6)

Own-account Workers 12

(3.2)

Unpaid Family Workers 21

(5.7)

Total 373

Soruce: Field Survey by the author.


ccxix

6.5.16 Sector of Employment and Urban Female Informal Sector

Employment

Table 6.52 reveals the distribution of female workers into different employment

sectors. Results indicate that 15.01 percent females are working in the trade sector out of

total 373 informal sector participants. The highest proportion of the female participants is

52.01 percent observed in the services sector. The 32.43 percent female workers are

involved in the manufacturing sector. But very lower percentage of the informal sector

female participants is found in the transport sector which is 0.27.

Table 6.52 Distribution of Female Respondents by Sector of Employment


Trade 57

(15.28)

Services 194

(52.01)

Manufacturing 121

(32.43)

Transport 1

(0.27)

Construction 0

Total 373



6.4.17 Working Hours and Urban Female Informal Sector Employment

Table 6.53: Distribution of Female Respondents by Working Hours.


Less than 15 4

(1.1)

15-24 Hours 63

(17.0)

25-34 Hours 45

(12.1)

35-41 Hours 51

(13.67)

42-48 Hours 100

(27.0)

49-55 Hours 34

(9.2)


(23.6)

Total 373

ccxx



The results in table 6.53 point out that the about 1.1 % of females about of the

employed in informal sector are the workers who work less than 15 hours. The 17.0 %

female informal sector workers work 15-24 hours. It is noteworthy that 27.0 % female

participants worked 48 hours in a week and 23.6% of the female constitutes the group

who work more than 56 hours in a week.


The present study is based on primary source of data, comprising 1506 workers.

The stratified random sampling technique is used to collect the data. We have described

the different socio-economic and demographic variables concerning the urban informal

sector employment. We have explained the determinants of the informal sector

participants descriptively in urban areas of Southern Punjab, Pakistan in the present

chapter. We have also made an effort to study the descriptive analysis of male workers‟

partaking in the formal and informal sector. A relationship is built up between various

socio-economic and demographic factors (descriptively) and female participants in urban

informal and formal sector in Southern Punjab. The data trends broach the same

relationship which is explained in theory.

ccxxi

Chapter 7

DETERMINANTS OF URBAN INFORMAL SECTOR

EMPLOYMENT: AN ANALYSIS

7.1 Introduction

The informal sector plays a pivotal role in employment creation, production and

income generation in the economy of Pakistan. This sector absorbs rapidly growing urban

labour force with high population growth and urbanization. The objectives of Pakistan

economy are economic growth, increase in per capita income, economic stability,

elimination of poverty and decreasing unemployment etc. Inspite of rapid growth of

GDP, it is requisite to enhance more adequate employment opportunities in order to

decrease unemployment and poverty which are the key challenges being faced by the

people. Moreover, there is a need for an allocation of resources towards the capital

intensive activities and adoption of highly capital intensive techniques which generate

relatively less employment opportunities.

We discuss the motives regarding decision to indulge into informal sector

particularly in the urban areas of Southern Punjab, Pakistan. The existing study is an

endeavour to highlight the socio-economic factors for determining the role of informal

sector for providing employment jobs, income growth and poverty reduction and

development in urban areas of Southern Punjab, Pakistan.

Particularly, the present chapter is arranged as followed. The econometric analysis

of the determinants of the urban informal sector employment in Southern Punjab is

explained in section 7.2 which takes into consideration total sample. We have also

analyzed the determinants of the urban informal sector employment in district

Bahawalpur in section 7.3. The determinants of the urban informal sector employment in

district Multan have also been explained in section 7.4. We also describe determinants of

the urban informal sector employment in district Dera Ghazi Khan in section 7.5. Finally,

we explain concluding remarks in the section 7.6.

ccxxii

7.2 Estimates of Binary Logit Model in Southern Punjab

We estimate binary logit models of determinants of the informal sector

employment in the urban areas of Southern Punjab, Pakistan. After that we have split the

sample into urban areas of three districts such as Bahawalpur, Multan and Dera Ghazi

Khan in order to see the impact of explanatory variables on the urban informal sector

employment with complete yet with different levels of education. We have made an

overall analysis of determinants of the urban informal sector employment in Southern

Punjab with complete years of education. We have also checked the influence of different

education levels on the urban informal sector employment in Southern Punjab.

Tables 7.1 and 7.2 present the binary logit estimates of urban informal sector

employment considering the total sample in Southern Punjab, Pakistan. Each table

contains four columns which illuminate explanatory variables, the estimated parameters,

their asymptotic z-statistic and marginal effects correspondingly. The intercept term in

the binary logit equation is positive and statistically significant. In many of the cases,

intercept term has no economic meaning or interpretation; it just highlights the average

effect of all other variables on explained variables that are omitted. Marginal effects show

the probability derivatives at the mean of independent variables. The probability

derivative indicates the change in probability due to one unit change in a given

explanatory variable after keeping all other variables as constant.

Two views can be presented about the age of workers involved in the informal

sector. Firstly, if the relative participation of young people is greater in the informal

sector, the very sector may probably be considered as a transition stage before opting

formal sector. Secondly, informal sector may be taken as a desirable constant choice if

there is a large participation ratio of older persons in the informal sector. Age is

considered an imperative factor which affects the workers‟ choice of participation in the

urban informal sector employment in Southern Punjab. In the binary logit models of the

urban informal sector employment, age in complete years is used as an explanatory

variable. The coefficient of complete years of age (AGY) is found to be positive and

ccxxiii

statistically significant. The probability of participation in the urban informal sector

increases by 0.4 percentage points respectively as a result of

one year increase in age of the worker. Results indicate that older people have a higher

likelihood of being engaged in the urban informal sector. It can be accounted for that the

workers are apt to work in the urban informal sector due to insufficiency of jobs in the

formal sector in Southern Punjab. Moreover, lack of higher education for govt jobs is one

of major reasons for relatively increased employment in the urban informal sector

because private sector employment is more operational than strategic level. The results

conclude that the urban informal sector absorbs people with increasing age in Southern

Punjab. Our results are similar to Funchouser‟s (1996) findings.

The probability of working in the urban informal sector employment diminishes

by about 3.7 percent points because of an increase in one year of education. Findings

demonstrate that education of the worker decreases the probability of being employed in

the urban informal sector. The negative marginal effect may indicate that participants

possessing higher education level do not commensurate in the urban informal sector in

Southern Punjab. Similar results are found in Funkhouser‟s (1996) findings.

Education is a critical input in economic development (Behrman, 1995).

Theoretically, education level of the participants in the labour market can play two

different roles. For instance, more educated workers tend to be more fertile as their

education serves as an impetus in enhancing their skill via training. On the other hand,

low education increases the probability of involvement in the informal sector. Education

of workers (EDY) is included as a categorical variable adding five categories (non-formal

education is taken as base category) in model II. Result reveals that the coefficient of

Middle level education is positive and statistically insignificant. Those who are working

in the urban informal sector encompassing initial formal education (EDU II) are unable to

find formal employment in Southern Punjab. The coefficient of Matric level education

(EDU III) is negative and statistically insignificant. The probability of being employed in

the informal sector of those with Matric level education (EDU III) is 1.7 percentage

points more than the excluded category. The coefficient of Intermediate level education

(EDU IV) has a negative sign. The probability of being employed in the informal sector

ccxxiv

of those with Graduation and Master‟s level education or higher is 23.6 and about 37

percentage points respectively less than the excluded category. Results conclude that

participants with high level of education in the labour market are less likely to join the

urban informal sector and are inclined to the formal sector. It also owes to that other

factors such as human capital and informal skills are not more essential for the urban

informal sector employment than such a high education level. Results conclude that level

of education and the informal sector employment are negatively associated in urban areas

of Southern Punjab. The findings are consistent as studies by Funkhouser (1996) and

Florez (2003).

Marital status (MRS) plays a pivotal role in determining work participation in the

urban informal sector. The coefficient of marital status (MRS) is positive and has

statistically insignificant influence on choice of participation in the urban informal sector

employment. Married persons are more likely to be employed in urban informal sector as

compared to single persons. It also owes to that generally married persons are household

heads and they are leaned to switch over the code from the informal to formal labour

market which is secure and lucrative source of livelihood. Moreover, formal sector can

not absorb the influx of people in urban areas of Southern Punjab, Pakistan. Ultimately, a

large number of participants with their low formal education are forced to opt for

relatively accessible informal sector employment. Furthermore, early marriages hinder

education and ultimately, they are forced to opt for relatively accessible urban informal

sector employment. The positive marginal effect in the informal sector may mean that

married persons easily find work in the informal sector with unfixed working hours

where it is more flexible to perform care and productive work.

The sex (SEX) is another significant factor which compels people to join the

labour market. The sex of the participants in the urban informal sector has significant

impacts on the informal employment in both logit models. The probability of indulging

into informal sector employment decreases by about 5.7 and 8.2 percentage points

respectively in result of one additional informal male worker. The argument is that male

participants‟ harldy accept the urban informal sector employment because it does not

commensurate with their level of human capital. Moreover, the level of jobs relates more

ccxxv

to less educated participants who are more available as female workers in Southern

Punjab. Findings are similar with Florez‟s (2003) study results.

The initial education level is considered as a poor indicator of the skills of the

informal sector worker. To acquire occupation-specific knowledge is important to

enhance productivity in the form of on the job training. Majority of the informal sector

workers has insufficiency with regard to formal training skills. The coefficient of formal

training (FTD) is significantly negative in both logit models. The probability of being

employed in the urban informal sector employment decreases by 23.1 and 24.4

percentage points respectively due to an increase of one unit in formal training. The

possible outcome of the fact is that formal training increases their efficiency. The

negative marginal effect shows that the participants hardly accept the informal sector

employment with formal training because it does not commensurate with their high

formal skills. Additionally, formal training is appropriate for the formal sector job. It is

concluded that informal sector does not require the formal training. The study results

conclude that the urban informal sector absorbs those participants having less formal

training.

One more crucial factor is the family background of workers in the urban

informal sector, particularly father‟s education and mother‟s education which influences

negatively the decision to work in the informal sector. Theoretically, it is hypothesized

that the workers are unlikely to join the urban informal sector whose parents are

educated. The study results corroborate the hypothesis in the urban informal sector of

Southern Punjab, Pakistan. Two dichotomous variables (i.e. educated father and educated

mother) are used to observe how the probability of the urban informal sector employment

is influenced by these variables. The coefficients of variables father‟s education (FEDU)

and mother‟s education (MEDU) are negative and highly significant. The probability of

being employed in the urban informal sector decreases by 14.9 and 15.6 percentage

points respectively due to one unit increase in father‟s education (FEDU). The probability

of workers being involved in the informal sector decreses by 20.1 and about 20.4

percentage points respectively due to one unit increase in mothers‟ education. The

economic cause of this inverse relationship is that educated parents can afford higher

ccxxvi

educational facilities to their children which result in the development of formal labour

market of Southern Punjab. There is a negative association between parents‟ education

and employment in the urban informal sector. The results conclude that the participants

whose parents are uneducated are more likely to be employed in the urban informal


In addition, size of the household (HSIZ) is, in general, taken into account as an

indicator of dependents on the head of household and it also exerts an effect on

employment in urban labour market. Theoretically, two varying hypotheses can be

formulated regarding the effect of household size on the informal sector involvement.

Firstly, it signifies the promotion of informal sector due to manifold increase in labour

supply. Secondly, the motive of making the family financially sound compels the head of

large household to opt for informal sector. Household size is found to be positively

influencing people for their involvement in the informal sector employment. The results

highlight that coefficients of (HSIZ) are positive and have significant effect on the

informal sector employment models. When the household size increases by one, the

participants are more likely to be included in the urban informal sector by about 2.9 and

3.1 percentage points respectively in Southern Punjab. For certain reason a family head

has to work more to earn more to support a large family. The study concludes that there is

a positive association between household size and the urban informal sector of Southern

Punjab. The large household size motivates people for the informal sector employment

for their better livelihood.

Labour supply theory postulates that the family labour supply decisions are

interdependent. The coefficient of dependency ratio (DPNR) is positive and has

statistically significant effects. The probability of participation in the urban informal

sector employment increases by 12.3 and 12.2 percentage points respectively due to one

unit increase in dependency ratio. This can also be reason that most of the time, family

head (himself/herself) assumes the responsibility to support his/her family. In other cases,

family members motivate or force him/her to participate in the informal economic

activities. The results conclude that growth of the urban informal sector of Southern

Punjab increases with an increase in dependency ratio.

ccxxvii

It is expected that workers having joint family systems join urban informal labour

market to make the family financially sound. Results found that the family setup (FSP)

exerts a positive and significant influence on the urban informal sector employment.

Workers living in joint family setup are about 11.1 and 11.7 percentage points more

likely to be employed in the informal sector. The possible reason exists that majority of

the family members lack quality skill or higher education due to financial pressures, so

they have more inclination towards the urban informal sector. The urban informal sector

employment enhances joint family participation because of the financial attraction that it

renders to them. The results conclude that urban informal sector increases with joint

family setup in Southern Punjab.

Number of Children (NCHL) also affects the decision regarding choice of sector

of employment. So, there is a positive relationship between presence of adult children

and the informal sector employment in both logit models. Findings demonstrate that an

addition of one child decreases the probability of workers being employed in the informal

sector by 2.4 and about 2.1 percentage points respectively as compared to formal sector

employment. The fact is that hoseholds pay compartively lessened care to adult family

members and to household responsibilities. Our results are supported by Funkhouser‟s

(1996) study results.

The next variable is about having male adolescents (NMAD). The probability of

workers being employed in the urban informal sector employment decreases by 7.9 and

about 8.5 percentage points respectively due to an addition of one male adolescent at

home. The coefficient indicates negative and significant influences. Theoretically, it is

argued that those households who have male adolescents don‟t get involved into urban

informal employment because their family labour supply decisions are interdependent.

Male adolescents can affect worker‟s decisions regarding participation in the urban

informal sector because some male adolescents have the chances to earn. It is also argued

that strong substitution effect of better-paid labour time for that of male adolescent

induces workers to have less participation in the urban informal sector. In this society,

parents think that it is time to reap the benefits of the toil they did for their children.

Moreover, working adolescents force parents to stop working any more in old age and the

ccxxviii

parents are too old to work for livelihood. Results conclude that presence of male

adolescents reduces the probability of workers for being employed in the urban informal


Analysis indicates that the coefficients of the variable female adolescents (NFAD)

are positive and statistically significant in logit models. An addition of one female

adolescent in family increases the probability of worker‟s participation in the urban

informal sector about 9.5 and 9 percentage points respectively as compared to formal

sector. Households having female adolescents are more inclined to the urban informal

sector because family labour supply decisions are interdependent. The positive marginal

effect may indicate that female adolescents heve less likelihood in economic activities

due to some social and religious constraints and household heads participate more in

economic activities in informal labour market where jobs are more open according to

their skills to fulfill their female adolescents necessities. Having more female adolscents

is yet another determinant which influences the parents‟ inclusion in the urban informal

sector employment. Same results are found in Funkhouser‟s (1996) findings.

The Neo-classical labour supply theory postulates that the family labour supply

decisions are interdependent. There is a negative association between informal sector

employment and spouse‟s participation in economic activities. The coefficient of spouse

participation (SPN) in economic activities is negative and highly significant at 1 percent

level of significance. Due to one unit increase in spouse participation in economic

activities diminishes the probability of the informal sector workers by 12.5 percentage

points as compared to the formal sector workers. The participants, whose counterparts

indulge in income generating activities, are less likely to partake in the urban informal

sector in Southern Punjab. The possible outcome of the fact is that spouses of the

participants in labour market are self-contented and strong substitution effect (of leisure

and not to work) dissuades them not to work in the urban informal sector anymore. In this

way the partners of the working spouses are reluctant to work anymore. It is concluded

that higher the spouse participation in economic activities, the lower the absorption in the


ccxxix

Theoretically, it is expected that value of assets makes workers more affluent and

economically stable all the way through the un-earned income. Findings indicate that the

coefficients of the value of household‟s assets (HVAT) are negative and highly

significant. It is argued that strong substitution effect of unearned income forces the

participants to stop working and they do so due to increase in the value of financial

assets. Another argument may be that some families having enough financial resources

temporarily stop working to enjoy the benefits of those extra pennies. Illiteracy may be

one of the factors for not investing those extra financial resources inside business (in

portfolio or otherwise) to have a back-up plan against rainy days.

Rural-urban migration plays a decisive role in determining the employment

decision in urban labour market. It is argued that urban informal sector provides more

earnings opportunities to both the rural-urban migrants and urban dwellers. Study results

highlight a positive association between informal sector employment and rural-urban

migration. The rural-urban migration has a large impact on the probability of being

employed in the urban informal sector as presented in dualistic approach for informality.

The probability of being employed in the urban informal sector increases by about 12.7

and 12 percentage points respectively because of one additional rural-urban migrant

worker in the urban areas of Southern Punjab. These results of the study conclude that

urban informal sector of Southern Punjab creates more employment opportunities to the

rural-urban migrants who with low formal education can not find job in the formal sector.

The urge of employment in the urban informal sector is high due to influx of rural-urban

migrants who come to earn high wages or incomes and low jobs in the formal sector in

urban areas of southern Punjab, Pakistan.

ccxxx

Table 7.1: Logit Estimates of Determinants of the Urban Informal Sector

Employment in Southern Punjab-Probability of the Informal Sector Employed (18-

64)

Explanatory Variables Coefficients Z-Statistic Marginal Effects

CONSTANT 1.2692 2.7283

AGY 0.0183** 2.4308 0.0042

EDY -0.1621*** -7.4069 -0.0369

MRS 0.0093 0.0514 0.0021

SEX -0.2483* -1.6474 -0.0565

FTD -1.0168*** -6.3500 -0.2313

FEDU -0.6557*** -4.3035 -0.1492

MEDU -0.8819*** -5.6646 -0.2006

HSIZ 0.1267*** 3.5886 0.0288

DPNR 0.5417* 1.7048 0.1232

FSP 0.4867*** 3.2866 0.1107

NFAD 0.4171*** 5.4991 0.0949

NMAD -0.3478*** -4.2320 -0.0791

ccxxxi

NCHL 0.1069* 2.0807 0.0243

SPN -0.5339*** -3.4687 -0.1215

HVAT -0.0000* -1.8083 -0.0000

RMGT 0.5575*** 3.5211 0.1268

Sample Size (N) =1506 Mcfadden R2 = 0.33

Logliklihood = -650.2725 P-value = 0.000

LR statistics (16df) =640.6265

Source: Author estimated by using Eviews statistical software.

Note: The Z- statistic is that of associated coefficients from the logit model, where the formal

sector employment is taken base outcome. Non-formal education is taken as base category.

*** Significant at 1% level of Significance

** Significant at 5% level of Significance

* Significant at 10% level of Significance

ccxxxii


Employment in Southern Punjab with Different Levels of Education-Probability of

the Informal Sector Employed (18-64)

Explanatory Variables Coefficients Z-Statistics Marginal Effects

CONSTANT -0.0402 -0.0897

AGY 0.0188** 2.4571 0.0043

EDU II 0.7718** 2.4415 0.1756

EDU III -0.0761 -0.3037 -0.0173

EDU IV -0.4552* -1.7002 -0.1036

EDU V -1.0376*** -3.8059 0.2361

EDU VI -1.6246*** -5.6148 -0.3696

MRS 0.0364 0.1944 0.0083

SEX -0.3614** -2.3239 -0.0822

FTD -1.0747*** -6.5494 -0.2445

FEDU -0.6854*** -4.4085 -0.1559

MEDU -0.8957*** -5.6399 -0.2038

HSIZ 0.1372*** 3.8037 0.0312

DPNR 0.5376* 1.6486 0.1223

FSP 0.5125*** 3.4057 0.1166

NFAD 0.3959*** 5.1346 0.0901

NMAD -0.3717*** -4.4608 -0.0846

NCHL 0.0902* 1.7165 0.0205

SPN -0.5510*** -3.5156 -0.1254

HVAT -0.0000* -1.7298 -0.0000

RMGT 0.5280*** 3.2628 0.1201

Sample Size (N) =1506 Mcfadden R2= 0.35

Log Likelihood = -634.2498 LR statistics (20df) = 672.6718

P-value =0.000

Source: Auther estimated by using Eviews statistical software.

Note: The Z- statistic is that of associated coefficients from the logit model, where the formal sector

eployment is taken as base outcome. Non-formal education is taken as the base category.



ccxxxiii


7.3 Estimates of Binary Logit Model in Bahawalpur District

Firstly, we have made an overall analysis of determinants of the urban informal

sector employment with complete years of education in district Bahawalpur.

Furthermore, the influence of different levels of education on the urban informal sector

employment is analyzed in district Bahawalpur.

The binary logit estimates of the informal sector employment in district

Bahawalpur are presented in the tables 7.3 and 7.4. The intercept terms in both of the

tables indicate positive sign and are insignificant. The explanatory variables, the

estimated parameters, their asymptotic z-statistic and marginal effects are demonstrated

in each table respectively. The insignificant impact of the intercept term on the urban

informal sector employment decision describes that the explanatory variables existing in

the model are adequate to determine the informal sector employment. The marginal

effects imply the change in the urban informal sector employment for a unit change in the

formal sector employment.

Age of workers motivates them to participate in the informal sector. In this way, it

is important for their choice of employment sector. Two views can be presented about

age of participants in the informal sector. Firstly, if the relative sharing of young people

is greater in the informal sector, the very sector may be considered a transition stage

before opting formal sector. Secondly, informal sector may be considered as a desirable

constant choice if there is a large involvement ratio of older persons in the informal

sector (see Kemal and Mehmood, 1993).

We have included this explanatory variable in complete years. The results indicate

that age exerts a positive and statistically significant effect on the probability of being

employed in the urban informal sector (about 0.1 and 0.1 %) percentage points

respectively which is cause of an increase in one year age of worker. The reason is that

formal sector cannot absorb all the persons with their low formal education. Generally,

ccxxxiv

participants with their low human capital take this sector as permanent activity and

engage themselves in this informal sector in their old age. It is concluded that mature

people with their initial basic education are more likely to pursuade an accessible

informal sector employment which best suits their skills. So, the urban informal sector is

a permanent source of earnings not the temporary or refuge for the unemployed or

underemployed in district Bahawalpur. Our results are similar to Funkhouser‟s (1996)

findings.

Education is an essential factor in determining the sector of employment. In the

logit model I, education steadily reduces the probability of the urban informal sector

employment in district Bahawalpur. The study has included complete years of education

(EDY) as an explanatory variable. The coefficient of the complete years of education

(EDY) is found to be negative and highly significant. The probability of inclination in the

informal sector diminishes by about 3.1 percentage points due to an increase in one year

of education of the worker. The results highlight that highly educated people are less

likely to contribute in the urban informal sector employment because it is not appropriate

with their high human capital. This result coincides with the facts and findings from other

studies such as Funkhouser (1996) and Florez (2003) in relation to education.

Education level of the participants in the labour market can play two different

roles: For instance, more educated workers tend to be more fertile as their education

serves as an impetus to enhance their skill via training. On the other hand, low education

increases the probability of involvement in the informal sector. Education reduces the

probability of participation in the urban informal sector of district Bahawalpur.

Participants‟ level of education is also incorporated as a categorical variable through five

categories (considering non-formal education as reference category). In relation to effect

of education on the urban informal sector employment, the co-efficient of Middle level

(EDU II) education is observed to be positive and has significant impact on work

participation in the urban informal sector. Results indicate that participants with low

formal education are incapable to get formal sector job. The coefficients of Matric level

education (EDU III), Intermediate (EDU IV) level education and Graduation level

ccxxxv

education (EDU V) have insignificant positive effect on probability of being included in

the urban informal sector. The coefficient of Master‟s level education or higher (EDU VI)

is found to be negative and has significant effect. The Graduation (EDU V) and Master‟s

or high level education (EDU VI) exert a zero effect, while both the levels of education

are unimportant for the informal sector employment. The result coincides with the

evidence from studies by Florez (2003) in relation to level of education. The economic

interpretation of this negative influence of higher education on the informal sector

employment may be that the workers with higher education invoke to more formal sector

employment. The results in relation to education reflect the classical theory of production

and have close similarity with law of diminishing returns. It means that when the level of

education increases, the marginal urban informal sector employment gets down.

Generally, marital status (MRS) affects participation decision in sector of

employment. The present study found a positive and statistically insignificant

relationship of urban informal sector employment and marital status. The possible reason

can be that, by and large, less educated couple is inclined to join the urban formal sector

employment which is nearby for their survival in this society or to fulfill requirements.

The sex (SEX) is an added important factor which determines the participation in

labour market. The sex of workers has negative and significant impacts on probabilities

of being employed in urban informal sector. The probability of being inducted in the

urban informal sector decreases by about 11.6 and 12.11 percentage points respectively

because of an increase of one additional male worker. Male workers hardly engage

themselves in the urban informal sector because of the informal work commensuration

with their level of education. So, the male workers are switching out from the informal

sector and moving towards the formal sector which is an important and lucrative source

of earning. In addition, the level of jobs more relates to less educated participants who are

more available as female workers in district Bahawalpur. Findings are consistent with

Florez‟s (2003) results.

It is expected that persons take part in the informal sector whose formal skill level

(FTD) is low. The findings indicate a negative relationship between formal training and

ccxxxvi

probability of the informal sector employment. The coefficient of formal training

signifies a negative and highly significant impact on workers‟ inclusion in informal sector

of district Bahawalpur. The individuals with 21.8 and about 23 percentage points are less

likely to be employed in the urban informal sector as compared to those in the formal

sector. This observable fact owes to higher earnings in the formal labour market as

formal skill enhances the efficiency to utilize the skills and provides opportunities to

work in a better way. Moreover, according to dualistic labour market approach, the

informal sector doesn‟t require high skills.

Parents‟education helps in determining the growth potential of informal labour

market. Findings indicate that the workers are less likely to participate in the informal

sector whose parents are literate. The coefficients of (FEDU) are negative and

statistically significant. The probability of being employed in the urban informal sector

decreases by 16.5 and 16.8 percentage points respectively due to an increase of one unit

in the (FEDU). The probability of being employed in the urban informal sector falls by

about 18.6 and 18.4 percentage points due to an increase of one unit in (MEDU)

respectively. It can be justified that the educated parents guide their children in attaining

higher education and better career counseling to move towards appropriate, secure and

well-paid formal labour market for better utilization of their skills. Hence, possibility of

joining the urban informal sector has increased in district Bahawalpur.

Theoretically, two varying hypotheses can be formulated regarding the effect of

household size on the informal sector involvement. Firstly, it signifies the promotion of

the informal sector due to manifold increase in labour supply. Secondly, the motive of

making the family financially sound compels the head of large household to opt informal

sector. It has been recognized that household size is found to be positively influencing

people to invoke into urban informal sector. The coefficient of (HSIZ) is found to be

positive and statistically significant. When the family size increases by one, workers are

by 5.8 and about 5.7 percentage points respectively more likely to be employed in the

informal sector as compared to the formal sector employment. The economic rationale of

this positive trend is that household heads with their low human capabilities in result of

financial pressure are leaned to work in urban informal sector.

ccxxxvii

Labour supply theory indicates that the family labour supply decisions are

mutually dependent. The coefficients of dependency ratio (DPNR) are positive and

statistically insignificant. The results conclude that persons having more dependents

being employed more into the formal sector in order to support the household increasing

expenditures.

Influence of joint family structure (FSP) on participation decision can not be

neglected. It is noted that urge to work is low in the joint family system because of strong

substitution effect of leisure. Whereas, in the present study, the case is reverse because of

family labour involvement in the informal setup. The study results show that the joint

family setup has positive and significant impact on the probability of being employed in

the urban informal sector. The probability of being employed in urban informal sector

increases by about 12.3 and 13.6 percentage points respectively due to one unit increase

in joint family set up. Approximately family members with lack of quality education opt

to work in the urban informal sector in order to make the family financially sound.

Participants in the labour market also consider the number of children in their

decisions. Theoretically, persons having children up to 9 to 14 years of age join the

urban informal sector. Findings bring to light that the number of children (NCHL) has

positive and insignificant impacts on the urban informal sector employment in both

tables.

The presence of male adolescents (NMAD) has an effect on working participants

in urban labour market. The coefficients of number of adolescents are found to be

significantly negative. The probability of finding urban informal sector employment

dwindles by 13.7 and 13.5 percentage points respectively due to an addition of one male

adolescent at home. Some male adolescents have chances to earn in formal or informal

sector. Hence, this strong substitution effect of better-paid labour time for that of male

adolescents stimulates workers to have a less eagerness in urban informal sector. There

are similar results found with Funkhouser‟s (1996) findings.

ccxxxviii

In labour supply theory, it is argued that households having female adolescents

(NFAD) have a preference to work in the informal activity due to interdependence of

family members‟ decisions. Findings highlight that the coefficients of female adolescents

are positive and significant. The probability of working in the informal sector increases

by 12.9 and about 12.5 percentage points due to an increase of one additional female

adolescent in the family. The female adolscents have low enthusiasm to work due to

social and religious constraints. As a result, parents have to fulfill female adolescents‟

requirements. This phenomenon encourages the parents to join the urban informal sector

in district Bahawalpur. Our findings are consistent with Funkhouser‟s (1996) results.

Hypothetically, the spouse participation in economic activities (SPN) is

negatively related with the choice of work in the labour market. Hence, it reduces the

probability of finding work in the urban informal sector. The coefficients bring to bear

negative and significant effect on informal sector eployment. It also owes to the strong

substitution effect of not to work and prefer leisure. Generally, the spouses of the

participants in the urban informal sector allocate more time to leisure in district

Bahawalpur.

An increase in the value of assets has an enlarge effect on the sector of

employment choice. It is argued that people prefer leisure and stop working on an

increase in value of financial assets. Findings indicate that the coefficients of the

household value of assets (HVAT) are negative and highly significant at 1 percent level

of significance. The probability of the urban informal sector decreases with an increase in

(HVAT). The strong substitution effect is greater than the low income effect. In this way,

participants are reluctant to work or invest more in the urban informal sector in district

Bahawalpur.

The rural-urban migration is one more significant variable that has a large impact

on the probability of being employed in the urban informal sector. The probability of

workers‟ participation increases by about 14.2 and 13.8 percentage points respectively

due to one additional rural-urban migrant worker. These results of the study indicate that

the probability of insertion in the informal sector is high in urban areas where surplus

ccxxxix

labour is absorbed. The fact is that the rural to urban migrants and urban dwellers

increase their insertion more rapidly in the urban informal sector of district Bahawalpur.

So the workers prefer to connect to the urban informal sector instead of formal sector.


Employment in District Bahawalpur. Probability of the Informal Sector Employed

(18-64).

Explanatory

Variables Coefficients Z-Statistic Marginal Effects

CONSTANT 1.0286 1.2116

AGY 0.0048 0.3276 0.0012

EDY -0.1349*** -3.4014 -0.0307

MRS 0.3877 1.1236 0.0882

SEX -0.5089* -1.8235 -0.1158

FTD -0.9604*** -3.4954 -0.2185

FEDU -0.7275*** -2.7250 -0.1655

MEDU -0.8165*** -2.8939 -0.1858

HSIZ 0.2549*** 3.1279 0.0580

DPNR 0.3458 0.5591 0.0787

FSP 0.5393* 1.9350 0.1227

NFAD 0.5665*** 3.5570 0.1288

NMAD -0.6026*** -3.4902 -0.1371

NCHL 0.0106 0.1157 0.0024

SPN -0.5961** -1.9901 -0.1356

ccxl

HVAT -0.0000** -2.2502 -0.0000

RMGT 0.6225** 2.0012 0.1416

Sample Size (N) = 506 Mcfadden R2 = 0.35

Log Liklihood = -213.8608 LR statistics (16df) = 232.1911

P-value =0.000



employment is taken base outcome. Non-formal education is taken as the base category.




ccxli


Employment in District Bahawalpur with Different Levels of Education. Probability

of the Informal Sector Employed (18-64).


CONSTANT 0.2324 0.2812

AGY 0.0029 0.1963 0.0007

EDU II 0.3417 0.5885 0.0777

EDU III -0.6562 -1.3638 -0.1493

EDU IV -0.6603 -1.3222 -0.1502

EDU V -1.0305** -1.9834 -0.2344

EDU VI -1.5707*** -2.9928 -0.3573

MRS 0.5154 1.4468 0.1173

SEX -0.5325* -1.8555 -0.1211

FTD -1.0130*** -3.5858 -0.2305

FEDU -0.7393*** -2.7203 -0.1682

MEDU -0.8090*** -2.8247 -0.1840

HSIZ 0.2502*** 3.0345 0.0569

DPNR 0.3229 0.5161 0.0735

FSP 0.5961** 2.0914 0.1356

NFAD 0.5487*** 3.4449 0.1248

NMAD -0.5934*** -3.4273 -0.1350

NCHL 0.0105 0.1132 0.0024

SPN -0.6335** -2.0905 -0.1441

HVAT -0.0000** -2.0670 -0.0000

RMGT 0.6043* 1.9115 0.1375

Sample Size (N) = 506 Mcfadden R2 =0.36


P-value =0.000



employment is taken as base category. Non-formal education is taken as base category.


ccxlii



7.4 Estimates of Binary Logit Model in Multan District

In this section, Firstly, we estimate the determinants of the urban informal sector

employment in district Multan with complete years of education followed by the

influence of various education levels on the urban informal sector employment.

Tables 7.5 and 7.6 indicate the binary logit estimates of probability of urban

informal sector employment which takes into account the total sample in urban areas of

Multan district. Each table consists of four columns such as explanatory variables, the

estimated parameters, and their asymptotic z-statistic and marginal effects

correspondingly. In table 7.5, the intercept term is positive and statistically significant

while it is negative and insignificant in table 7.6. The marginal effects denote the effect

of a unit change in each variable on the probability of being employed in the urban

informal sector based on formal sector employment.

Age of the workers is an important factor which motivates them to indulge in

informal sector especially in urban areas of district Multan. Two views can be presented

about age of workers engaged in informal sector. Firstly, if the relative participation of

young people is greater in informal sector, the very sector may probably be considered a

transition stage before opting formal sector. Secondly, informal sector may be considered

as a desirable constant choice if there is a large participation ratio of older persons in the

informal sector. Age in complete years is taken as an explanatory variable in the binary

logit model I. The coefficients of age in years (AGY) are positive and have statistically

significant effects on informal employment. The probability of finding employment in the

urban informal sector shows an increasing trend by 0.7 and 0.8 percentage points

respectively which is due to an increase in one year age of the worker. The positive

coefficients of the age variable mean that participants easily attain employment in the

urban informal sector. Owing to the insufficient jobs in the formal sector and better

compensations in the informal sector in district Multan, the workes are forced to indulge

ccxliii

in accessible urban informal sector as compared to formal sector. Our findings are similar

with Funkhouser (1996) findings.

Education is the most important factor in determining an employment decision.

Education increases the efficiency and productivity. The complete years of education

(EDY) decreases the probability of urban informal sector employment. The probability of

the informal sector employment diminishes by 4.1 percentage points due to one year

increase in education of the worker. It may also be due to that most fortunate workers

with high human capital easily find a job in the formal sector. The reason of it is that the

urban informal sector absorbs less educated people in district Multan. This confirms

similar findings by Funkhouser (1996) in relation to complete years of education.


roles. For instance, more educated workers tend to be more fertile as their education

serves as an impetus in enhancing their skill via training. On the other hand, low

education increases the probability of involvement in the urban informal sector. In order

to see the effect of education on urban informal sector employment, level of the

education of workers (EDY) is incorporated as a categorical variable using five

categories (non-formal education is taken as base category) in binary logit model II.

The coefficient of Middle level education (EDU II) is positive and statistically

significant. The probability of being employed in the informal sector of those with

Middle level education is 28.20 percentage points more than the formal sector

employment. Participants with low formal education are unable to get formal

employment in public as well as in private sector. So, they are occupied in the urban

informal sector. The coefficients of Matric level education (EDU III) and Intermediate

level education (EDU IV) are positive but the results are statistically insignificant. The

coefficient of Master‟s or higher level education (EDU VI) is found to be negative and

statistically significant. The probability of being employed in the informal sector of those

with Master‟s level education is less by about 30.8 percentage points as compared to the

formal sector.

ccxliv

This phenomenon indicates that educated people comparatively are more prone to

the formal sector. Results in relation to impact of different education levels on informal

sector employment replicate the classical theory of production and have close similarity

with law of diminishing returns. The trend indicates that the level of education increases,

the marginal informal sector employment gets down.

The findings highlight that the coefficient of marital status (MRS) is negative and

statistically significant. The probability of involvement in the informal sector reduces by

13 percentage points due to an addition of one married worker. The reason behind this

decline is that mostly married workers possessing high human capital are inclined to join

lucrative formal labour market in order to meet their needs and to secure better future of

their children. Our findings are consistent with Funkhouser‟s (1996) findings.

The sex (SEX) is another important factor which compels people to join the

labour market. The coefficient of sex variable is negative in both the tables. However, the

results are insignificant. The possible outcome of the fact is that male workers easily

obtain employment in the modern formal economy in stead of female workers.

Almost informal sector workers have less formal training but have some kind of

informal training. The coefficients of the formal training (FTD) are negative and highly

significant in binary logit models. One unit increase in formal training increases the

probability of the informal sector participation by 21.4 and 24.4 percentage points

respectively. This can be accounted for that with formal skill workers lean to switch over

the code from the informal to the formal labour market because psychologically a person

works with a better motivation when he gets to know the way of doing it in best way with

enhanced efficiency. Results conclude that formal training has less influence on the urban

informal sector of Multan.

The family background of the participants of the urban informal sector, especially

parents‟ education negatively affects the urban informal sector employment. In theory,

the notion is that workers are more likely to join the labour market whose parents are

educated. The study results are different in this analysis. The coefficients of father‟s

ccxlv

education (FEDU) are negative and statistically significant in the urban informal sector

employment models. The workers whose fathers are educated are less likely to be

employed in the informal sector by14.6 and about 16.2 percentage points respectively as

compared to the formal sector. The coefficients of (MEDU) are negative and highly

significant. The participants, whose mothers are educated, are less likely to be employed

in the informal sector by about 20.4 and 22.4 percentage points respectively as compared

to the formal sector. The decreasing trends indicate that the educated parents can afford

higher educational facilities to their children which result in the development of formal

sector.

Theoretically, two varying hypotheses can be formulated in relation to the effect

of household size on the informal sector involvement. Firstly, it signifies the promotion

of the informal sector due to manifold increase in labour supply. Secondly, the motive of

making the family financially sound motivates the head of large household to opt

informal sector. The household size is found to be positively influencing people for their

engagement in urban informal sector. The probability of being employed in the urban

informal sector increases by 27.7 percentage points respectively in table 7.6 as a result of

one member increase in HSIZ. The possible outcome of the fact is that workers have a

higher likeliness of working in informal sector in order to improve the overall living

standard of family and to make the family financially sound.

Regarding family setup (FSP), it is argued that in joint family structure the

workers participate in the informal sector to support financially and fulfill family

requirements. Family setup has positive and significant impact on work participation in

urban informal sector. The participants who belong to joint family setup are 10.2 and

about 11.5 percentage points more likely to be employed in the urban informal sector.

This positive impact is due to high family labour involvement and financial

responsibility. The results point out that the urban informal sector increases the joint

family participation because of the financial attraction that it renders to them in district

Multan. The family members and supply of labour in the form of family helpers also

determine and promote the growth potential of the urban informal sector in district

ccxlvi

Multan. Moreover, workers who lack quality education intend to opt for the urban

informal sector employment.

Labour supply theory explains the interdependent family labour supply decisions.

The results show that the coefficients of (DPNR) are positive and have insignificant

effect on informal sector employment in the urban areas of district Multan.

According to Neo-classical labour supply theory, people having children from the

age group of 6 to 14 do not need to look after them, and they participate more in informal

economic activities. The probability of involvement in the informal sector shows an

increasing trend by 35.3 and 29.6 percentage points respectively for an addition of one

child at home. Results conclude that the informal sector and number of children are

positively correlated. The positive marginal effects may indicate that parents of these

children have to do more informal work to meet up high educational expenses of their

children. Results are consistent with Funkhouser‟s (1996) study results.

The estimates reveal that the coefficients of number of male adolescents are

negative and have significant effect on employment in both models. The probability of

individual‟s participation in the urban informal sector decreases by about 5.1 and about

5.8 percentage points respectively due to an addition of male adolescent at home.

Theoretically, it is argued that the households having male adolescent don‟t get involved

into economic activities because family labour supply decisions are interdependent. The

possible reason may exist that some male adolescents have chances to earn and the family

labour income increases. It is the strong substitution effect of better-paid labour time for

that of male adolescents which dissuades workers from more work in informal economic

employment. Moreover, the urban informal sector increases the joint family participation

because of the financial attraction that it rendered to them. Similar results are found in

Funkhouser‟s (1996) study.

Results show that the coefficient of female adolescents (NMAD) is positive and

highly significant. An addition of one female adolescent increases the probability of

being employed by 7.5 and about 7.6 percentage points respectively. Hypothetically, it is

ccxlvii

argued that household heads having female adolescents engage them in urban informal

sector because family labour supply decisions are interdependent. It is also argued that

parents are generally less educated that‟s why they are unable to provide better education

to female adolescents and heads pursuade to join the informal labour market to meet up

female adolescents‟s requirements and expenses. It also owes to that these female

adolsecents who dissuade to work in income generating activities because of social,

religious and economic constraints. This encourages parents to stick to the urban informal

sector in order to fulfill female adolescents‟requirements. Having more female

adolescents is yet another determinant which influences the parents‟ inclusion in the

urban informal sector employment. Our findings are consistent with Funkhouser‟s (1996)

study findings.

The Neo-classical theory of labour supply postulates that the family labour supply

decisions are interdependent. Results found that the spouse participation in economic

activities (SPN) shows a decreasing trend in the probabilities of finding urban informal

sector employment in district Multan. But the coefficients are negative and results are

statistically insignificant in the analysis.

The estimates in table 7.5 highlight that the household value of assets (HVAT)

also affects the informal sector employment decision. Theoretically, it has been argued

that increase in the value of assets will increase the unearned income. This increased

income stabilizes the workers financially and discourages them for unnecessary

investment or work. Findings demonstrate that the coefficients of the value of

household‟s assets (HVAT) are positive and prove to be insignificant determinant.

Rural-urban migration (RMGT) is another significant variable influencing the

informal sector employment in district Multan. The probability of employment in the

informal sector rises by about 14.7 and 15.2 percentage points respectively in support of

an addition of one migrant worker. Results conclude that workers induced by higher

wages in the formal or urban informal sector have to join the informal sector of district

Multan. So, the probability of workers in the urban informal sector increases instead of

formal sector. It is concluded that the urban informal sector is also the sector of migrants

ccxlviii

or the urban informal sector absorbs rural-urban migrants and enables rural-urban

migrants for productive work in district Multan.

ccxlix


Employment in District Multan-Probability of the Informal Sector Employed (18-

64).

Explanatory


CONSTANT 1.2450 1.6301

AGY 0.0313*** 2.4757 0.0072

EDY -0.1799*** -4.5753 -0.0414

MRS -0.5001 -1.5923 -0.1152

SEX -0.0460 -0.1793 -0.0106

FTD -0.9288*** -3.5022 -0.2140

FEDU -0.6350** -2.4049 -0.1463

MEDU -0.8833*** -3.4989 -0.2035

HSIZ 0.0961 1.6171 0.0221

DPNR 0.3884 0.6498 0.0895

FSP 0.4437** 1.8485 0.1022

NFAD 0.3262*** 2.6675 0.0752

NMAD -0.2205* -1.7808 -0.0508

NCHL 0.1532* 1.7530 0.0353

SPN -0.3165 -1.3294 -0.0729

HVAT -0.0000 -0.3141 -0.0000

RMGT 0.6361** 2.4332 0.1466

Sample Size (N) = 512 Mcfadden R2 =

0.30

Log Liklihood = -235.5585 LR Statistics (16df) =199.8925

P-value = 0.000



employment is taken as base category. Non-formal education year is taken as base category.




ccl


Employment in district Multan with Different Levels of Education - Probability of

the Informal Sector Employed (18-64).


CONSTANT -0.7155 -0.9692

AGY 0.0351*** 2.7124 0.0081

EDU II 1.2240** 2.1922 0.2820

EDU III 0.3388 0.7962 0.0781

EDU IV -0.1961 -0.4165 -0.2756

EDU V -0.7055 -1.5237 -0.6125

EDU VI -1.3364** -2.7359 -0.3079

MRS -0.5660* -1.7358 -0.1304

SEX -0.1380 -0.5210 -0.0318

FTD -1.0595*** -3.8234 -0.2441

FED -0.7023** -2.5746 -0.1618

MED -0.9735*** -3.7110 -0.2243

HSIZ 0.1201** 1.9373 0.0277

DPNR 0.3637 0.5907 0.0838

FSP 0.4987** 2.0279 0.1149

NFAD 0.3294*** 2.6353 0.0759

NMAD -0.2529** -2.0083 -0.0583

NCHLD 0.1285 1.4345 0.0296

SPN -0.2945 -1.2095 -0.0679

HVAT -0.0000 -0.3595 -0.0000

RMGT 0.6592** 2.4715 0.1519

Sample Size (N) =512 Mcfadden R2 =

0.32

Log Likelihood = -229.2439 LR statistics (20 df) = 212.5218

P-value = 0.000


ccli

Note: The Z-statistic is that of associated coefficients from the logit model, where formal employment is

taken as taken as base outcome. Non-formal education is taken as base category.




cclii

7.5 Estimates of Binary Logit Model in Dera Ghazi Khan District

In this section we analyze the determinants of the urban informal sector

employment in district Dera Ghazi Khan with complete year of education and different

levels of education to estimate their impact on the urban informal sector employment.

In this study, tables 7.7 and 7.8 highlight the binary logit estimates of informal

sector employment by considering total sample in urban areas in district Dera Ghazi

Khan. Four columns of the tables make explanatory variables clear, the estimated

parameters, their asymptotic Z-Statistic and marginal effects correspondingly. The

intercept term is positive and statistically significant in table 7.7 while it is positive and

insignificant in table 7.8. The marginal effects indicate the effect of a unit change in each

variable on the probability of being in urban informal sector employment relative to the

base category (formal employment).


Firstly, if the relative participation of young people is greater in informal sector, the very

sector may probably be considered a transition stage before opting formal sector.

Secondly, informal sector may be considered as a desirable constant choice if there is a

large participation ratio of older persons in the informal sector. In the urban informal

employment model of Dera Ghazi Khan, complete years of age is taken as an explanatory

variable. The coefficient of complete years (AGY) is found to be positive and statistically

significant in both models. In table 7.7, the probability of being exerted in the urban

informal sector shows an increasing trend by 0.4 percent points due to one year increase

of age of the worker. On the other hand, the coefficient of complete year of age is

positive but statistically insignificant in table 7.8. It owes to the people possessing low

formal education easily find employment in urban informal sector in their older age

instead of involving in the formal sector of district Dera Ghazi Khan. Moreover, due to

insufficiency of the formal sector jobs, they stick to the informal sector employment and

stay there for longer period of time.

ccliii

Education level of workers is yet another variable which determines the job

choice in labour market. We have used complete years of education as an explanatory

variable. Results indicate that education slowly but surely diminish the probability of

working in the urban informal sector. The coefficient of the complete years of education

(EDY) is negative and highly significant. An increase in one year of education diminishes

the contribution in the informal sector employment by about 3.8 percentage points. The

reason for this decreasing participation is that workers switch out from informal sector

towards the formal labour market that is more profitable. The results may be related to

the expected role of the informal sector under approaches of the informal sector, it is

expected that the sector is a disadvantaged sector with low human capital. The result

coincides with the evidence from Funkhouser‟s (1996) in relation to education.




education increases the probability of involvement in the informal sector. In binary logit

model II, the education level of workers (EDY) is incorportaed as a categorical variable

with five categories (non-formal education being the base category). The probability of

being employed in the informal sector of those with Matric level education is 16.9

percent more than the excluded category. The coefficient of Middle level education

(EDU III) is positive and statistically significant. The coefficient of Intermediate level

education (EDU IV) is negative and has insignificant effect on employment in urban

informal sector. The probability of Graduation level education (EDU V) decreases by

about 31.8 percent for an increase of one unit change in the Graduation level. The

coefficient of Master‟s or higher level education (EDU VI) is found to be negative and


Master‟s or higher level education is less by 58.6 percentage points as compared to

formal sector employment. This declining trend highlights the importance of the formal

sector in the urban areas of district Dera Ghazi Khan. Infact, human capital and or simple

skills (or none) are more essential for urban informal sector employment than high

education level. Informal sector employment results based on education reflect the

ccliv

classical theory of production and they are similar with law of diminishing returns. It

means that the level of education increases and the marginal urban informal sector

employment gets down. The findings match with Florez‟s (2003) study results.

The study results indicate that the coefficient of marital status (MRS) is positive

and has insignificant effects on employment in urban informal sector in both models. The

economic justification is that the formal sector is not capable to absorb the workers with

formal education and ultimately mostly couples are apt to join an accessible informal

sector employment in order to meet up their needs. Additionally, geographical

immobility may also be the reason to detain in this sector.

The sex (SEX) is another important factor which compels people to work in the

labour market. The results found the negative and insignificant coefficients of the

variable sex in urban informal sector of district Dera Ghazi Khan. This can be accounted

for that the male workers may not accept low-paid informal sector employment because it

does not commensurate with their human capital. On the other hand, the informal sector

may prefer female workers because the nature of work requires low formal education.

This confirms the similar findings by Florez‟s (2003) study results.

Nearly informal sector workers have less formal training but possess some kind of

informal training. The coefficient of formal training (FTD) is found to be negative and

highly significant. The probability of finding the urban informal sector employment

decreases by about 27.8 and 27.5 percentage points respectively due to an increase of one

unit in formal training of the workers. The reason can be that formal diploma and degree

holders preferably invoke formal labour market for proper utilization of their skills in

district Dera Ghazi Khan.

The labour supply theory predicts that the workers are more likely to join the

labour market whose parents are educated. The findings of the present study are different.

The estimates show that the coefficients of father‟s education (FEDU) are negative and

statistically significant. The probability of being employed in the informal sector falls by

about 18.1 and 17.2 percentage points for an increase of one unit in (FEDU) respectively.

cclv

The coefficients of mother‟s education (MEDU) variable are found to be negative and

highly significant. The probability of contribution in the urban informal sector decreases

by about 17.6 and about 16.4 percent respectively for an addition of one educated mother.

The results show that the educated parents provide higher education to their children

which result in the development of lucrative formal sector. Results conclude that

participants‟, whose parents are educated, are less likely to join the urban informal sector

employment in district Dera Ghazi Khan.

In theory, it is argued that those workers who belong to joint family setup (FSP)

join the urban informal employment to fulfill family requirements and to stablize the

family financially. Family setup has a positive and an insignificant impact on probabilty

of insertion in the urban informal sector.



informal sector due to manifold increase in labour supply. Secondly, the motive of

making the family financially strong compels the head of large household to invoke to

informal sector. The results describe that the coefficient of HSIZ is positive and has

significant effect on work participation in the informal sector in both models. When the

household size increases by one, the workers are about 2.3 and 2.5 percentage points

respectively more likely to be employed in the informal sector in district Dera Ghazi

Khan. Results conclude that workers are more likely to participate in the urban informal

sector with large household size in order to improve the overall living standard of the

family or to nourish the every child in house.

Dependency ratio (DPNR) is also important one which is deemed to participate in

the informal sector. The notion is that family labour supply decisions are interdependent.

The results reveal that the coefficients of dependency ratio (DPNR) are positive and have

insignificant influence on joining the urban informal sector in district Dera Ghazi Khan.

According to labour supply theory, people having number of children from 9 to

14 are more likely to participate in formal or informal labour market because of low

cclvi

household responsibilities. Findings show that the number of children (NCHL) has a

positive and insignificant impact on informal sector employment decision. The results

indicate the phenomenon of child labour.

The variable „number of male adolescents‟ (NMAD) is also expected to influence

the urban informal sector employment decision. The coefficient of male adolescent

(NMAD) is found to be negative and has statistically significant influence. The informal

sector employment diminishes by about 8.3 and 9 percentage points respectively due to

one additional male adolscent. The reason behind this low participation is that the strong

substitution effect of better-earnings of male adolescents deters heads to participate in the

urban informal sector. Furthermore, to reap the benefits of toil they did for their male

adolscents, the parents stop working. Results make clear that presence of male adolescent

decreases the probability of workers being employed in the urban informal sector in Dera

Ghazi Khan.

A female adolescent (NFAD) is an important factor which determines the

participation in labour market. The coefficients of number of female adolescents are

found to be positive and statistically significant. The participants are more likely to be

employed in the informal sector by about 9.1 and 7.7 percentage points respectively due

to an increase of one additional female adolescent. Theoretically, it is argued that

households having female adolescent prefer to work more in the urban informal sector

because some female adolescents have to look after the household responsibilities and

cannot involve in productive work. Approximately females are less likely to participate in

work related activities due to low formal education, social and economic constrictions. In

this way, the heads have to fulfill female adolescents‟ basic requirements. Having more

female adolscents is yet another determinant which influences the parents‟ inclusion in

the urban informal sector employment in district Dera Ghazi Khan.

The family labour supply decisions are interdependent as predicted by the labour

supply theory. Spouse‟s participation in economic activities (SPN) reduces the

probability of urban informal sector employment. The probability of being inducted in

the informal sector decreases by 20.4 and about 20.2 percentage point respectively due to

cclvii

one unit increase in spouse participation in economic activities. The reason can be that

spouses of the participants of labour market allocate their time to leisure and participate

less in economic activities because of increase of family income. It is concluded that

spouse participation in economic activities may decrease the probability of working in the

urban informal sector. Moreover, informal sector in the urban areas of Dera Ghazi Khan

shows the retrenchment with the increase of spouse participation in economic activities.

The labour supply theory predicts that the household‟s value of assets (HVAT)

affects the participation decision regarding sector of employment. Findings point out that

the coefficients of the household‟s value of assets (HVAT) are negative and statically

insignificant. The strong substitution effect of unearned income induces the workers less

likely to allocate less time to the urban informal sector.

It is assumed that rural-urban migration (RMGT) affects the urban informal sector

employment. The study results confirm the hypothesis. The probability of the urban

informal sector involvement rises by 7.5 percentage points due to an addition of one

rural-urban migrant worker in the urban areas, the coefficient of rural-urban migrant

variable (RMGT) is found to be positive although there are insignificant effects on urban

informal sector in district Dera Ghazi Khan. Thus, the sector enables workers for

productive work. The study results conclude that informal employment is creating more

employment in urban areas of Dera Ghazi Khan due to high rural-urban migration rate

and wage differential and getting employment opportunities in the formal sector is

probable.

cclviii


Employment in District Dera Ghazi Khan - Probability of the Informal Sector

Employed (18-64).

Explanatory


CONSTANT 1.3670 1.5145

AGY 0.0197 1.4304 0.0043

EDY -0.1737*** -4.2389 -0.0378

MRS 0.2537 0.7677 0.0552

SEX -0.1781 -0.6155 -0.0388

FTD -1.2773*** -3.8918 -0.2779

FED -0.8321*** -2.8314 -0.1811

MED -0.8085*** -2.6908 -0.1759

HSIZ 0.1036* 1.7769 0.0225

DPNR 0.7958 1.4363 0.1732

FSP 0.3613 1.2669 0.0786

NFAD 0.4174*** 3.1602 0.0908

NMAD -0.3808*** -2.5490 -0.0829

NCHL 0.1557 1.5135 0.0339

SPN -0.9390*** -3.2085 -0.2043

HVAT -0.0000 -0.5607 -0.0000

RMGT 0.3458 1.2176 0.0752

Sample Size (N) = 487 Mcfadden R2 =

0.38

Log Liklihood = -188.0803 LR statistic (16df) = 230.0525

P-value = 0.000


Note: The Z- statistic is that of associated coefficients from the logit model, where formal sector

employment is taken as base outcome. Non-formal education is taken as the base category.



* Significant at 10% level of Significanc

cclix


Employment in Dera Ghazi Khan with Different Levels of Education - Probability

of the Informal Sector Employed (18-64)


CONSTANT 0.2286 0.2547

AGY 0.0170 1.1814 0.0037

EDU II 0.7775 1.4579 0.1692

EDU III 0.1833 0.4142 0.0399

EDU IV -0.5508 -1.1819 -0.1199

EDU V -1.4594*** -2.9185 -0.3176

EDU VI -2.6932*** -3.7697 -0.5860

MRS 0.2654 0.7644 0.0578

SEX -0.3537 -1.1364 -0.0770

FTD -1.2651*** -3.6418 -0.2753

FEDU -0.7894** -2.5693 -0.1718

MEDU -0.7419** -2.3363 -0.1614

HSIZ 0.1161* 1.9465 0.0253

DPNR 0.7154 1.1670 0.1557

FSP 0.2421 0.8117 0.0527

NFAD 0.3555** 2.5527 0.0774

NMAD -0.4116** -2.5822 -0.0896

NCHL 0.1709 1.5556 0.0372

SPN -0.9271*** -3.0902 -0.2017

HVAT -0.0000 -0.7318 -0.0000

RMGT 0.2720 0.9093 0.0592

Sample Size (N) = 487 Mcfadden R2

= 0.42


P-value= 0.0000


cclx

Note: The Z- statistic is that of associated coefficients from the logit model, where formal sector

employment is taken base outcome. Non-formal education is taken as the base category.





We have used econometric analysis to investigate the determinants of the urban

informal sector employment in this chapter. A binary logit model is applied in the present

study. The analysis of current study is based on stratified random sample of 1506

informal and formal sector participants in urban areas of Southern Punjab. The analysis

has also been made in Southern Punjab as well as separately in each division with

different sample sizes. Most of the explanatory variables produce different results at

various levels of analysis in this study. In Southern Punjab, all of the variables i.e. age of

worker (AGE), their complete years of education (EDY), gender (SEX), formal training

(FTD), parental education (FEDU), (MEDU), household size (HSIZ), dependency ratio

(DPNR), family setup (FSP), number of female adolescents (NFAD), number of male

adolescents (NMAD), number of children (NCHL), spouse participation in economic

activities (SPN), household‟s value of assets (HVAT), and rural-urban migrat variable

(RMGT) are highly significant factors except the variable marital status (MRS) which is

positive but highly insignificant. Furthermore, level of education i.e. education up to

Middle (EDU II), Intermediate (EDU IV), Graduation (EDU V), and Master‟s or higher

levels (EDU VI) appear significant except Matric level education (EDU III) which is

found insignificant. All these significant variables of the informal sector employment in

the urban areas of Southern Punjab have correct sign and cope up with the theoretical

foundation.

The study results of district Bahawalpur are somewhat different. Age of the

respondents (AGY), the household dependency ratio (DRN) and number of children

(NCHLD) are found to be insignificant in logit models. Regarding level of education,

Middle (EDU II), Matric (EDU II) and Intermediate (EDU IV) are insignificant in district

Bahawalpur.

cclxi

In Multan district, the study results of urban informal employment model I

highlight that male sex (SEX), the marital status(MRS), household size (HSIZ) the

household dependency ratio (DPNR), spouse participation in economic activities (SPN)

and household‟s value of assets (VAT) are found to be insignificant. While number of

children (NCHL) affects insignificantly the informal employment decision in model II.

Whereas, Matric level education (EDU III) Intermediate level education (EDU IV) and

Graduation level education (EDU V) are the insignificant factors in determining informal

employment work in district Multan.

In model I of urban informal sector employment of district Dera Ghazi Khan,

marital status (MRS) is positive and insignificant. The sex (SEX) of the workers has

insignificant effects. The household dependency ratio (DPNR) and family setup (FSP),

the household‟s value of assets (HVAT) and rural-urban migrant variable (RMGT) are

found to be insignificant in urban informal sector employment of district Dera Ghazi

Khan. The result of model II demonstrates the insignificant effect of age of the workers

(AGY) on their informal work decision. The results of Matric (EDU III) and Intermediate

level education (EDU IV) are also insignificant. Again marital status (MRS) is found to

be positive and has insignificant effects. Moreover, number of children (NCHL) also

affects insignificantly the informal employment decision in model II.

cclxii

Chapter 8

EARNINGS DERTERMINANTS, DEVELOPMENT AND

URBAN INFORMAL SECTOR: AN ANALYSIS

8.1 Introduction

The bulk of work force invoke informal sector for employment due to limited

capacity of formal sector to generate employment opportunities. However, labour gets

relatively low productivity in the informal sector. Informal sector requires to be promoted

to absorb surplus labour, while an attempt needs to be made to enhance the productivity

of labour in the informal sector to protect the workers against exploitation (Kemal and

Mehmood, 1993).

There is an association between poverty and informality. The pessimistic viewed

informal sector as marginal and subsistence activities, where the productivity and

earnings of workers are low. Additionally the participants of the informal sector access

low social protection and their working conditions are low (ESCAP, 2006).

In Pakistan, poverty remains prevalent inspite of impressive economic growth.

This prevalent is due to low growth in the productivity, macro-economic instability, and

structural adjustment not taking care for poor and external shocks. Though some

programmes are in process to diminish poverty, however these programmes are

unaccessable to the poor. More efforts can be made in this area which has a high scope to

explore more completely the potential for social safety net programmes. The profitable

employment opportunities must be created for destitute classes both in rural areas and

urban slums. However, there is a need to implement policies to promote an effective and

equitable growth pattern (Mahmood, 1999).

In this chapter, we examine the earnings determinants of participants in the urban

informal sector of three divisions of Southern Punjab such as Bahawalpur, Multan and

Dera Ghazi Khan in section 8.2. The Human Development and the urban informal sector

cclxiii

in three divisions of Southern Punjab, Pakistan is described in section 8.3. Finally,

concluding remarks are given in section 8.4.

cclxiv

8.2 Estimates of Earnings Functions of the Participants in Urban Informal

Sector in Southern Punjab

In this section we look at the earnings determinants for the participants of the

urban informal sector in Southern Punjab. We also split the analysis into three districts

such as Bahawalpur, Multan and Dera Ghazi Khan.

Tables 8.1 and 8.2 present the results of determinants of workers‟ earnings in the

urban informal sector of Southern Punjab. Each table contains three columns that indicate

explanatory variables, coefficients and t-statistics. Generally impact of age, years of

education, training facilility, sex, marital status, and family structure, household‟s value

of assets and weekly working hours are checked by using simple Ordinary Least Square

regressions on monthly earnings of the urban informal sector workers.

Age is an important factor which determines the earnings potential of workers of

the urban informal sector. Theoretically, it is expected that earnings increase with age and

indicate a positive relationship. The results show that income is an increasing function of

age of participants of the informal sector. Our study shows that coefficient of

participant‟s age is positive and statistically significant at 5 % level of significance. The

study results conclude that age directs or guides for higher returns.

Education is a critical input into economic development (Behrman, 1995).

Education is assumed to be a passport to good jobs and it icreases the efficiency. The

coefficient of complete years of education (EDY) is highly significant at 1 percent level

of significance. A positive relationhip is found between years of education and earnings

of the participants in the urban informal sector of Southern Punjab. This shows that

attainment of education leads to higher earnings of the participants of the urban informal

sector in Southern Punjab. The results support studies by Burki and Abbas (1991),

Funkhouser (1996) and Sargana (1998).

Human capital theory hypothesizes that better-educated workers have a

propensity to be more productive and able to perform functionally more sophisticated

jobs as compared to those workers who have less formal education. By using the binary

cclxv

variables for different levels of education, results support our previous findings which

indicate that education has greater impact on earnings.The coefficient of the Middle level

education is positive and significant at 5% level of significance. The coefficient of Matric

level education (EDU II) is positive and statistically significant at 5% level of

significance. Likewise, coefficients of Intermediate level education (EDU IV),

Graduation level education (EDU V) and Master‟s or higher level education (EDU VI)

are positive and highly significant at 1 percent level. The results make clear that returns

to education for different levels are highly significant for those working in the urban

informal sector of Southern Punjab, Pakistan. This trend indicates that earnings are

positively associated with incremental educational levels. Overall, earnings tend to

increase with increasing education levels. The results conclude that higher levels of

education indicate higher earnings for the participants of urban informal sector in

Southern Punjab. This confirms the results by Sargana (1998).

In terms of gender (SEX), the probabilities of participants‟ earnings increase and

are statistically significant at 1 percent level of significance. The result indicates that

gender goes towards higher earnings in the urban informal sector of Southern Punjab.

Human Capital theory shows that earnings and some kind of training (informal

and formal) are positively correlated. The coefficients of skill training (TRN) are positive

but have insignificant effect on participants‟ earnings in both regression equations. High

skill also results in higher earnings of the participants in the urban informal sector of

Southern Punjab.

Human capital theory argues that there is a positive relationship between marital

status and workers‟ earnings. The estimated coefficients of marital status (MRS) are

positive but statistically insignificant.

Family setup is also an important factor in determining the income of the

participants of the informal sector. The results point out that the coefficients of joint

family setup (FSP) are found to be negative and have statistically insignificant impact on

earnings of the workers engaged in the urban informal sector of Southern Punjab,

cclxvi

Pakistan. It indicates that earnings returns decrease due to an increase in joint family

setup. The possible reason may be the increasing family expenditures.

It is postulated that there is a positive association between working hours and

earnings of the participants of labour market. The findings indicate that coefficients of

the variable working hours (WHR) are also positive and highly significant at 1 percent

level of significance. It indicates that an increase in working hours directs the higher

earnings of the workers in urban informal sector employment of Southern Punjab. The

study results support studies by House (1984), Smith and Metzger (1998) and Dasgupta

(2003).

Theoretically, there is a negative relationship between earnings and household‟s

value of assets in the labour market. However, our results are positive. The variable value

of assets is included by obtaining from all types of financial and physical assets. We have

used all types of assets. In both the regression models, the results highlight that the

coefficients of the variable household value of assets (HVAT) are also positive and

highly significant at one percent level. The study results also show a positive relationship

between increase in value of assets and earnings of the participants of urban informal

sector.

Table 8.1: Earnings Functions of the Participants in the Urban Informal Sector in

Southern Punjab

Variables Coefficients t-statistics

C 3.0136 45.3219

AGY 0.00272** 2.3803

EDY 0.0275*** 9.1022

TRN 0.0102 0.4438

SEX 0.3738*** 14.4282

MRS 0.0317 1.1599

FSP -0.0017 -0.0706

HVAT 0.0000*** 11.2816

WHR 0.0041*** 5.3059

cclxvii

R2 = 0.41 Adj. R

2 = 0.4057

F- statistics = 85.0620 Size of sample = 986

P -value =0.000

Note: Values are calculated by collected data by the author from Southern Punjab

***significant at 1% level

**significant at 5% level

*significant at 10% level

cclxviii

Table 8.2: Earnings Functions of Participants of Urban Informal Sector in Southern

Punjab with Different Levels of Education


C 3.1147 48.8349

AGY 0.0026** 2.2819

EDU II 0.0777** 2.2331

EDU III 0.1811*** 5.5062

EDU IV 0.1601*** 4.0451

EDU V 0.3092*** 6.6726

EDU VI 0.4149*** 7.4817

TRN 0.0136 0.5899

SEX 0.3816*** 14.5922

MRS 0.0316 1.1512

FSP -0.0048 -0.2026

HVAT 0.0000*** 11.1010

WHR 0.0043*** 5.5736

R

2 = 0.4148 Adj.R

2 = 0.4075

F-statistics = 57.47 Size of sample =986

P 0.000

Note: Values are calculated by collected data by the author from Southern Punjab.




cclxix

8.2.1 Estimates of Earnings Functions of Participants in Urban Informal

Sector in District Bahawalpur

We present results of earnings determinants of participants of the urban informal

sector regarding district Bahawalpur with complete years of education as well as with

different levels of education in this section.

The tables 8.3 and 8.4 incorporate the results of earnings determinants of informal

sector employed workers. Each table contains three columns that indicate explanatory

variables, coefficients and t-statistics. Results in tables 8.3 and 8.4 reveal that all human

capital variables are highly significant.

Age is an imperative factor for the income of workers of the urban informal

sector. Theoretically, there is found a positive correlation between age and earnings. The

results show that income is an increasing function of age in both the regression models.

Findings indicate that the coefficients of age (AGY) are positive and have statistically

insignificant impact on the earnings of those persons engaged in the urban informal

sector of district Bahawalpur.

On the other hand, coefficient of complete years of education (EDY) is positive

and statistically significant. Higher education of the informal sector participants directs to

higher returns of the workers engaged in the urban informal sector of Southern Punjab,

Pakistan. Our findings are supported by Burki and Abbas (1991), Funkhouser (1996),

Sargana (1998).

We have also used dummy variables for different level of education in order to

check the impact on earnings. The coefficient of Matric (EDU II) level education is

positive but statistically significant at 1 % level of significance. While the coefficients of

Intermediate level education (EDU III) and Graduation level education (EDU IV) are

positively significant at 5 % level of significance. The coefficient of Master‟s or higher

level education (EDU V) is positive and highly significant at 1 % percent level of

significance in the analysis. Our study results in table 8.4 seem to suggest that informal

sector workers gain higher earnings with different level of education or there is positive

cclxx

association between earnings and different levels of education of the participants in the

urban informal sector of district Bahawalpur. This trend indicates the positive association

of earnings and incremental levels of education. Our findings are similar with results by

Sargana (1998).

Human capital theory highlights that there is a positive association between

training and earnings of the workers in labour market. The coefficients regarding training

skills (TRN) are positive but have statistiacally insignificant impact on income of

workers in both regression models.

The variable “SEX” is pivotal in determining the earnings potential. Results

indicate that the coefficients of sex are highly significant at 1 % percent level of

significance. There is found a positive relationship between earnings and workers‟

participation in the urban informal sector employment in district Bahawalpur. However,

the relatonship is highly significant at 1 percent level of significance.

It is expected that married workers‟ participation rate is high in labour market.

The estimated coefficients of marital status (MRS) imply 0.10 percent increase in

earnings for each additional married person in regression model II. Result in table 8.4

shows that dummy variable for on the marital status (MRS) is significant at 5 percent

level of significance. Comparatively, married workers have higher returns in urban

informal sector of Bahawalpur. Results conclude that earnings increase with more

inclusion of married workers in urban informal sector in district Bahawalpur.

While the coefficients of family setup (FSP) are insignificant in both the

regression equations. Theoretically, it is supposed that working hours have positive

influence on earnings in the labour market. Our results indicate that coefficients of the

variable working hours (WHR) are also positive and have highly significant impact on

income of the participants of the urban informal sector of district Bahawalpur. This

shows that earnings of the informal sector workers increase with an increase of working

hours. This confirms the similar results by House (1984), Smith and Metzger (1998) and

Dasgupta (2003).

cclxxi

Assets are considered as a financial security. There is a positive relartionship

between household‟s value of assets and earnings of the participants in the urban

informal sector. Results indicate that coefficient of household‟s assets (HVAT) are also

positive and highly significant. The earnings of the workers increase with an increase in

their value of assets in the urban informal sector of Bahawalpur.

In conclusion, education of the participants is an essential and strong source of

variation in their earnings. A positive association with education level and earnings

confirm the hypothesis that education is an investment that has good returns in urban

informal sector of district Bahawalpur. By and large, it can be concluded that all human

capital variables and other socio-economic variables like sex, marital status, household‟s

value of assets and working hours are significant determinants of earnings and growth

potential of the urban informal sector in district Bahawalpur.

cclxxii

Table 8.3: Earnings Functions of Participants in the Urban Informal Sector in

District Bahawalpur


C 2.8526 22.5094

AGY 0.0015 0.6731

EDY 0.0255*** 4.8501

TRN 0.0450 1.1116

SEX 0.5672*** 12.3242

MRS 0.1318** 2.6420

FSP 0.0663 1.4920

HVAT 0.0000*** 6.6716

WHR 0.0030*** 2.6931

R 2= 0.5356 Adj.R

2 = 0.5238

F-statistics =45.69 Size of sample =326

P-value =0.000

Note: Values are calculated using the data collected from district Bahawalpur

*** Significant at 1% level



cclxxiii

Table 8.4: Earnings Functions of Participants in Urban informal Sector in District

Bahawalpur with Different Levels of Education


C 2.9871 24.9430

AGY 0.0015 0.6869

EDU II -0.0076 -0.1177

EDU III 0.1830*** 2.9674

EDU IV 0.1392** 2.1148

EDU V 0.2417*** 3.1808

EDU VI 0.3282*** 3.9027

TRN 0.0562 1.3764

SEX 0.5626*** 12.0269

MRS 0.1095** 2.1450

FSP 0.0515 1.1436

HVAT 0.0000*** 6.6659

WHR 0.0032*** 2.8881

R2=0.5428 Adj. R

2 = 0.5252

F-statistics =30.9673 Size of sample =326

P-value = 0.0000

Note: Values are calculated by using the data collected from district Bahawalpur




cclxxiv

8.2.2 Estimates of Earnings Functions of Participants in Urban

Informal Sector in District Multan

In this section, we present results of earnings determinants of the informal sector

participants in district Multan. The analysis is concluded with complete years of

education and with different levels of education.

The tables 8.5 and 8.6 indicate the results of earnings determinants of the informal

sector employed workers. Each table contains three columns that indicate explanatory

variables, coefficients and t-statistics. The relationship of explanatory variables is

checked by using simple Ordinary Least Square regression on monthly earnings of

participants of the urban informal sector.

It is theorized that age and earnings of workers in labour market are positively

correlated. Our findings confirm the theory. The study results show that there is a positive

relationship between age and income of those workers who are involved in the urban

informal sector. Findings indicate that the impact of additional year of age (AGY) on

earnings is highly significant at 1 percent. The result highlights that there is a positive

relationship between age and earnings returns of the workers in the urban informal sector

of district Multan. Old age participants of the urban informal sector have higher returns.

Human capital theory hypothesizes that better-educated workers are more

productive and carry out sophisticated jobs as compared to those workers who have less

formal training. Study results highlight that higher returns owe to seniority of the

participants. The coefficient of completed years of education (EDY) is highly significant

at 1 percent level of significance for workers in urban informal sector. Our findings are

consistent with results by Burki and Abbas (1991), Funkhouser (1996) and Sargana

(1998).

Results in table 8.6 indicate that all human capital variables are highly significant. The

effects of different education levels on earnings are described in table 8.6. The results

point out that returns to education for different levels are positive and highly significant

cclxxv

at 1 % and 5% level of significance for the informal sector workers in district Multan.

This trend indicates that earnings are positively associated with incremental educational

levels of the participants of urban informal sector. Earnings tend to increase as the level

of educational attainment increases. Our findings are similar with results by Sargana

(1998).

Human capital theory hypothesizes that earnings and training (TRN) are

positively correlated. Our findings confirm the theory. The coefficients of the variable

(TRN) are positive and significant at 5% level of significance. Findings support Burki

and Abbas‟s (1991), Burki and Ubaidullah‟s (1992) and Nasir‟s (1998) study results.

In table 8.5 and 8.6, the coefficients of sex are highly significant at 1 % percent

level of significance. Findings demonstrate that male gender earns higher returns in the

urban informal sector of district Multan.

It is expected that married persons (MRS) are more likely to work in order to

increase their earnings. However, the estimated coefficients of the variable marital status

are negative and statistically insignificant.

Whereas, the coefficients of family setup (FSP) are negative and insignificant.

Results indicate that joint family system causes lower earnings of the participants of the

urban informal sector. Results show that coefficients of the variable working hours

(WHR) are also positive and highly significant at 1 percent level of significance. Higher

earnings are the result of higher working hours of the workers involving into the urban

informal sector of district Multan. Our results are corroborated by Smith and Metzger

(1998) and Dasgupta (2003).

The study indicates that household‟s value of assets guides to lower earnings in

the labour market. However, our results demonstrate the positive and highly significant

coefficients of the variable household‟s value of assets (HVAT). An increase in value of

assets leads to higher earnings for the workers engaged in the urban informal sector of

cclxxvi

district Multan. The results reveal that returns of informal sector participants increase

with the increase in working hours and value of assets.

The findings conclude that education of the participants is an essential and strong

source of variation for earnings. The positive association of human capital varibles and

earnings confirm the hypothesis that human capital variables have good returns in urban

informal labour market. Overall, it can be concluded that all human capital variables and

other socio-economic variables such as sex, skill training, household‟s value of assets and

working hours are positive and significantly affect the earnings of the participants of the

informal sector in urban areas of district Multan.

cclxxvii

Table 8.5: Earnings Functions of Urban Informal Sector Participants in District

Multan


C 2.8975 30.8614

AGY 0.0080*** 4.2197

EDY 0.0287*** 5.8504

TRN 0.1066*** 2.8366

SEX 0.2953*** 7.1083

MRS -0.0697 -1.5557

FSP -0.0540 -1.4578

HVAT 0.0000*** 5.0658

WHR 0.0047*** 3.4551

R2 = 0.4826 Adj. R

2= 0.4695

F-statistics = 36.96 Size of Sample =326

P-value = 0.000

Note: Values are calculated using the data collected from district Multan




cclxxviii

Table 8.6: Earnings Functions of the Participants of Urban Informal Sector in

District Multan with Different Levels of Education


C 2.9771 31.8250

AGY 0.0075*** 3.8406

EDU II 0.1516*** 2.6865

EDU III 0.1972*** 3.6190

EDU IV 0.1628** 2.4463

EDU V 0.3235*** 4.4982

EDU VI 0.3856*** 4.4848

TRN 0.1017*** 2.6510

SEX 0.3036*** 7.0848

MRS -0.0605 -1.3313

FSP -0.0546 -1.4535

HVAT 0.0000*** 5.0102

WHR 0.0053*** 3.8358

R2 =0.4790 Adj.R

2 =0.4590

F-statistics =23.9788 Size of Sample =326

P -value =0.000

Note: Values are calculated using the data collected from district Multan




cclxxix

8.2.3 Estimates of Earnings Functions of Participants in Urban Informal

Sector in District Dera Ghazi Khan

We show the earnings functions of participants in the urban informal sector in

district Dera Ghazi Khan in this section. We have used complete years of education and

different levels of education to see the influence on monthly earnings of the workers.

Tables 8.7 and 8.8 represent the results of earnings functions of informal sector workers.

Each table contains explanatory variables, coefficients and t-statistics. The study used

Ordinary Least Square regressions on the monthly earnings of the participants of the

urban informal sector. Results reveal that all human capital variables are highly

significant.

Age factor also determines the growth potential of earnings of urban informal

sector workers. Results highlight that the coefficients of age (AGY) are positive and have

insignificant influence on earnings of the urban informal sector participants in district

Dera Ghazi Khan.

Human capital theory argues that education and income of workers in labour

market are positively correlated. The study results also predict that income is an

increasing function of education. Education is an important investment in human capital

for development. The coefficient of complete years of education (EDY) is highly

significant at 1 percent level of significance in regression model I. Result suggests that

higher earnings are found to be positively associated with higher level of education. Our

findings support the results by Burki and Abbas (1991), Funkhouser (1996), and Sargana

(1998).

The effects of different education levels on earnings are described in Table 8.8.

Results in equation table 8.8 indicate that all human capital variables are highly

significant. The results reveal that returns to education for different levels are positive

and highly significant at 10 % while 1 % level of significance for those working in the

urban informal sector of district Dera Ghazi Khan. This shows that earnings are tended to

cclxxx

increase as the level of educational attainment increases. Our results are supported by

Funkhouser (1996) and Sargana (1998).

The results indicate that the coefficients of training skill are negative and are

statistically insignificant in both the regression equations.

The results point out that coefficients of sex (SEX) are highly significant at 1 %

percent level of significance in regression equations. Gender (SEX) has higher returns in

the urban informal sector of district Dera Ghazi Khan.

Furthermore, the estimated coefficients of dummy variable on the marital status

(MRS) are positive and insignificant for each additional married worker in both the

models. The results reveal that the coefficients of family setup variable (FSP) are found

to be positive and have insignificant influence on earnings of the participants of the urban

informal sector in regression models.

The working hours (WHR) is an increasing function of earnings of participants in

the urban informal sector. Results show that variable „working hours‟ has significantly

positive effects on earnings of the urban informal sector participants. The earnings

increase with the working hours as indicated by the positive and significant coefficients

at 5 percent level of significance. This study findings support the results by House

(1984), Smith and Metzger (1998) and Dasgupta (2003).

The household‟s value of assets (HVAT) is an increasing function of earnings in

the urban informal sector in both models. The study results conclude that coefficients of

household‟s value of assets are positive and have highly significant effect on earnings of

the workers. Findings demonstrate that there is a positive correlation between

household‟s value of assets and earnings of those working in urban informal sector.

The study results conclude that education of the participants is an essential and

strong source of variation for earnings. This positive association of education and

earnings confirm the hypothesis that education is investment that has good returns in

labour market. Overall, results conclude that all human capital variables and other socio

cclxxxi

economic variables such as sex, household‟s value of assets and working hours are found

to be significantly positive in earnings determinants of urban informal sector in district

Dera Ghazi Khan.

Table 8.7: Earnings Functions of Participants in Urban Informal Sector in District

Dera Ghazi Khan


C 3.3278 26.1824

AGY 0.0002 0.0835

EDY 0.0211*** 3.7962

TRN -0.1164*** -2.9895

SEX 0.2599*** 5.6453

MRS 0.0224 0.4997

FSP 0.0163 0.4284

HVAT 0.0000*** 6.3860

WHR 0.0036** 2.1667

R2 = 0.3124 Adj. R

2 =0.30

F-statistics = 18.46 Size of Sample = 334

P -value =0.000

Note: Values are calculated by using the data collected from district Dera Ghazi Khan




cclxxxii

Table 8.8: Estimates of Earnings Functions of Participants of Urban Informal

Sector in District Dera Ghazi Khan with Different Levels of Education


C 3.3834 27.8714

AGY 0.0002 0.1084

EDU II 0.0974* 1.6829

EDU III 0.1468*** 2.6802

EDU IV 0.1915*** 2.6876

EDU V 0.2667*** 2.8282

EDU VI 0.5381*** 3.4263

TRN -0.1185*** -3.0410

SEX 0.2627*** 5.6987

MRS 0.0185 0.4113

FSP 0.0147 0.3857

HVAT 0.0000*** 6.4603

WHR 0.0036** 2.1393

R2 = 0.32 Adj. R

2= 0.2988

F-Statistics =12.82 Size of sample =334

P-value = 0.000

Note: Values are calculated by using the data collected from distric Dera Ghazi Khan




cclxxxiii

8.3 Human Development and Urban Informal Sector

The informal sector reduces unemployment by creating more employment

opportunities and generating incomes. However, it it is essential to enhance more the

income and productivity of the informal sector workers.

The living standards of participants of urban informal sector examine the

association between them and poverty rests on human development perspective. The

United Nations Development Programme adopted human development approach in 1990

which basically argues that people are the real assets of a nation and thus the

development should center to expand their choices by creating “an enabling environment

to enjoy long, healthy, and creative lives”( see UNDP,1990). The approach then makes a

detailed view in order to define and develop the Human Development Index (HDI).

The definition of human development is very broad (see UNDP, 1990; Ranis and

Stewart, 1999; Suharto, 2002). The Human Development Index (HDI) is defined and

developed based on this approach. This idex goes to measure interrelated and unweighted

aspects of economic (e.g income), education (e.g literacy rate), health (e.g life

expectancy), and socio-political (e.g social participation, freedom of speech, multiparty

system) determinants of development in order to make cross-country comparisons.70

The measures mentioned above are narrowed empirically in order to explore the

informal sector‟s participants‟ level of trrhuman development. So, present research takes

into account the indicators of human development of workers (engaged in the urban

informal sector) that comprise economic, human and social capital. The participants of

the urban informal sector are categorized as “poor” or “not poor” depending on

economic, human and social capital.

In this study, we explain the economic, human and social capital of participants of

the urban informal sector in Souhern Punjab, Pakistan and separately in three districts

such as Bahawalpur, Multan and Dera Ghazi khan in Southern Punjab, Pakistan.

70 see Suharto (2002).

cclxxxiv

8.3.1 Development and Urban Informal Sector in Southern Punjab

In this section, we explain economic, human and social capital as indicators of

development of workers involved in the urban informal sector in Southern Punjab,

Pakistan.

8.3.1. I. Economic Capital and Urban Informal Sector

Economic capital consists of participants and households incomes. Household

income also shows a standard of living of inmates in the urban informal sector. We have

used poverty line to estimate the ability of informal sector workers in fulfilling basic

necessities and thus get close to the idea of absolute or extreme poverty. The participants

in the informal sector are classified as poor and non-poor. 1st group is classified as poor if

the monthly income is below the basic poverty line. 2nd

group is classified as non-poor

because they earn monthly higher than the poverty line. Here, monthly income of

workers and households‟ income are used to gauge the poverty line. The poverty and

informal employment are considerably associated by classifying it into two groups on the

basis of characterization of poverty line.

Group one: The poor workers are defined as those workers whose monthly

income is below poverty line of Rs.3750. They are poor as their income is below the

poverty line. Based on participant‟s income, 16.9 percent of workers in urban informal

sector are in this category in Southern Punjab. Based on households‟ income, 72.7

percent of the households are in this this category in Southern Punjab. They are poor as

their financial condition is below the poverty line.

Group two: The non-poor informal sector workers are are defined as those

workers whose monthly income is higher than Rs.3750. Depending on the criteria of

workers income, the number of participants is grouped as the „non-poor” workers having

percentage of 83.1 total specified population. The number of households is grouped as

the non-poor having percentage of 27.3.

cclxxxv

Table 8.9: Economic Capital and Urban Informal Sector in Southern Punjab

Monthly Income Workers’

Monthly Income

Households’ Per Capita

Income Per Month

Poor if

Less than Rs.3750

Rs. 167

(16.9)

Rs. 717

(72.7)

Non-Poor if

Above Rs.3750

Rs. 819

(83.1)

Rs. 269

(27.3)

Total 986

(100)

986

(100) Sample Size (986)

Source: International Poverty Line (2012).

In the present study, findings indicate that monthly income of 16.9 percent

workers of urban informal sector is below poverty line and 83.1 percent are non-poor

workers. Result also indicates that 72.7 percent households‟ are poor because their per

capita income per month is below poverty line.

8.3.1.2: Human Capital and Urban Informal Sector

As far as aspect of development is concerned, basic requirements absorb not only

appropriate economic capital but also human and social capital (see Suhartu, 2002). The

informal sector workers‟ education and skills as well as good health and housing

condition are used to measure their level of poverty and development. Hence, it is argued

that the relationship between the urban informal sector employment and poverty can not

be estimated just on the basis of low incomes. Human capital incorporates

accomplishment of education, access to health services and attainment of housing

facilities. It is argued that economic amelioration cannot be achieved without being

educated, healthy and well-aboded population.

The indicator in relation to attainment of education can be evaluated on the basis

of some formal education (i.e. Primary, Middle, Matric, Intermediate, Graduation and

Post graduation or higher level education).

cclxxxvi

The indicators in terms of health accessibility can be measured by availing health

services or medical facilities. These facilities can be ranged between high quality

treatment to low quality treatment such as doctor, hospital, health centre, and others

(traditional healer or self-treatment). Higher utilization of these facilities signifies a high

living standard which in turn indicates development. The indicators about access to

health can be gauged by the utilization of health services or medical facilities.

Theoretically, high utilization of these medical facilities specifies the standardized access

to health facilities enabling participants to treat health-related problems at nominal cost of

medical treatment available or approachable there. This, therefore, decreases the inclines

to decrease life expectancy. In this way, higher level of utilization of these facilities

reveals high development (see Suhartu, 2002).

Another important aspect of human capital is availability of passable housing

facilities, such as clean water supply, toilet facilities with a septic tank, electricity, and a

constructed floor. These facilities are significant ingredients of human well-being. An

access to these facilities indicates high living standard and human development in the


In the present study, education, health and housing facilities have been taken into

account as essential human capital for further economic and social development of the

participants of the urban informal sector of Southern Punjab. The table highlights the

level of human capital of participants with respect to access to education, health services

and housing facilities. Generally, findings highlight that informal sector workers have

sufficient access to such human capital.

cclxxxvii

Table 8.10: Human Capital and Urban Informal Sector in Southern Punjab

Level of Education Total (%)

Primary Education 12.9

Middle Education 21.5

Matric Education 30.2

Intermediate Level Education 13.8

Graduation Level Education 8.9

Post Graduate Education or Higher

Education 5.1

Illiteracy 8.0

Access to Health Services

Doctor 70.5

Hospital 67.1

Health center 40.0

Others 33.0

Average 52.7

Access to Housing Facilities

Water Facility 49.5

Toilet Facility 94.8

Electricity Facility 97.7

Floor Facility 73.1

Average 79

Sample Size (986)

The data in the table 8.10 shows that 12.9 percent of the urban informal sector

participants have accomplished Primary education, 21.5 percent informal workers are

educated at Middle level of education, 30.2 percent are Matriculate, and 13.8 percent are

intermediate.Those who have completed Graduation and Post graduation or higher

education are 8.9 and 5.1 percent respectively. While, the illiteracy level is found to be at

cclxxxviii

8.0 percent among the participants of the urban informal sector in Southern Punjab.The

study results are corroborated by Suharto (2002).

It is concluded that the highest level of education attained ranges such as 30.2

percent for Matriculates, 21.5 percent for Middle level education and 13.8 percent for

higher secondary level education, 8.9 percent for Intermediate and 5.1 percent for

Master‟s level education or higher education for participants in urban informal sector in

Southern Punjab. Higher level education highlights the development of the workers who

are indebted in urban informal sector. Results are corroborated by Suharto (2002).

The data illustrates that 70.5 percent of the urban informal workers get medical

treatment from doctors when they suffer illness, 67.1 percent approach to hospital and 40

percent avail a health centre. The later indicates a miscellaneous option of procurement of

sickness from traditional healer or self medication (using medicine not prescribed by a

doctor). Those having self-medication imply 33.2 percent.

The study results demonstrate that the higher level of utilization of doctors and

hospitals shows higher human capital. Theoretically, high utilization of these medical

facilities reveals the optimal access of health facilities which enable people to cure

health-related illness in improved way. Results conclude that 52.7 percent of participants

of urban informal sector have availed health facilities in Southern Punjab. The results are

corroborated by Suharto (2002).

Another essential aspect of human capital is the availability of adequate housing

facilities, such as clean water supply, toilet facilities with a septic tank, electricity and a

constructed floor. These facilities are considerable starts of human well-being. Better

housing facilities decrease vulnerability to diseases (see Suhartu, 2002).

This study found that the proportion of informal workers‟ shelter is provided with

housing facilities showing inadequacy. The results estimate that overall 79 percent of the

informal sector participants‟ houses are furnished with each of the facilities mentioned

above. 49.5 percent informal sector workers have access to wholesome drinking water,

94.8 percent have access the toilet facility and 97.7 percent are electrified. There are still

cclxxxix

73.1 percent of the houses with cemented floor. On average workers in urban informal

sector are provided with better accomodation facilities. Results are corroborated by

Suharto (2002).

8.3.1.3 Social Capital and Urban Informal Sector

Standard of living of society can also be reflected by using leisure time for social

and cultural activities (see Suhartu, 2002).

In this study, the involvement of socio-cultural chores is gauged by the

participants of the urban informal sector of watching television, listening to the radio

programmes, reading the news papers, or participation in local organization activities

activities such as welfare organizations, youth organization, cooperatives and religious

groups. Higher proportion of social-cultural activities indicates higher living standard and

social development of the urban informal sector workers in Southern Punjab.

While using human and social capital indicators, informal sector workers can

possibly be categorized as “poor” or “not poor”, because if they have or haven‟t adequate

basic education and access to health services and housing facilities and social-cultural

activities.

Table 8.11: Socio-Cultural Activities and Urban Informal Sector in Southern

Punjab

Socio-Cultural Activities Total (%)

Television 85.8

Radio Programmes 40.6

Newspaper 25.3

Local organizations 25.6

Average 44.25

Sample Size (986)

The data in the table 8.11 demonstrates that in total, social capital of participants

in urban informal sector is very low at 44.25 percent. The proportion of the urban

ccxc

informal sector participants having access to social cultural activities is relatively better.

The results found that 85.8 percent of the informal sector workers watch television, 40.6

percent listen to the radio programmes. Results make clear that 25.3 percent workers read

newspapers and 25.6 percent of them participate to the local organizations.

8.3.2 Development and Urban informal Sector in District

Bahawalpur

In this section, we describe the economic, human and social capital of participants

of the urban informal sector in district Bahawalpur.

8.3.2.1 Economic Capital and Urban Informal Sector

Using the survey data of district Bahawalpur, we measure the monthly income of

the workers involved in the urban informal sector. In order to gauge the relationship

between poverty and the urban informal sector employment, we categorize the

participants into two groups: poor and non-poor on the basis of characterization of

poverty line.

Group one: The poor workers are refered as those, who earn below Rs. 3750

monthly. They are poor because their income is below the poverty line. Based on

workers‟ income, 21.2 percent of the workers are found in this category. Based on

households‟ income, 67.2 percent of the households are found in this category.

Group two: The non-poor informal sector workers are defined as those, whose

monthly earnings are higher than Rs. 3750. The data indicates that “non-poor” workers

account for 78.8 percent of the total sampled population. Result also shows that non-poor

households account for 32.8 percent.

The study findings seem to show that 21.2 percent of workers in the urban

informal sector are living below poverty line in district Bahawalpur. It is also found that

67.2 percents of the households‟ are living below poverty line in Southern Punjab

Pakistan.

ccxci

Table 8.12: Economic Capital and Urban Informal Sector in District Bahawalpur

Monthly Income

Workers’

Income per

Month

Households’ Per

Capita Income Per

Month

Poor if

Less than Rs.3750

Rs. 69

(21.2)

Rs. 219

(67.2)

Non-Poor if

Above Rs.3750

Rs. 257

(78.8)

Rs. 107

(32.8)

Total 326

(100)

326

(100)

Smple Size (326)


8.3.2.2 Human Capital and Urban Informal Sector

Education, health and housing have been taken as fundamental measures. These

facilities can be ranged between high quality treatment to low quality treatment such as

doctor, hospital, health centre, and others (traditional healer or self-treatment). Higher

utilization of these facilities signifies a high living standard which in return indicates

development. The indicators about access to health can be gauged by the utilization of

health services or medical facilities. The human capital of the participants can also be

measured by the availability of sufficient housing facilities, such as clean water supply,

toilet facilities with a septic tank, electricity, and a constructed floor. In this way, higher

level of utilization of these facilities denotes high development of those who contribute in

the urban informal sector.

ccxcii

Table 8.13: Human Capital and Urban Informal Sector in District Bahawalpur


Primary Education 16.9

Middle Education 17.8



Graduation 11.3

Post graduation or higher education 8.0

Illiteracy 6.4

Access to Health Services

Doctor 66.3

Hospital 60.4

Health Center 37.7

Others 30.4

Aveage 48.7


Water Facility 56.7



Floor Facility 75.8

Average 82.37

Sample Size (326)

The data shows that 16.9 percent workers have completed Primary education,

17.8 percent of the workers have passed Middle level education, and 22.7 percent of

workers are educated at Matric level. Those who have completed Intermediate level

education are 16.9 percent of the participants. Findings also indicate that the workers who

are educated at Graduation level are 11.3 and those who have accomplished Post

graduation and higher education is 8 percent respectively. The illiteracy rate is lower at

ccxciii

6.4 percent amongst the participants of the urban informal sector of district Bahawalpur.

The results are similar with Suharto‟s (2002) findings.

In terms of health facilities, the results highlight that 66.3 percent of the informal

workers visit a doctor for medical treatment, 60.4 percent workers approach the hospital

and 37.7 percent workers avail a health centre when they suffer illness. Data shows that

those who cure their sickness themselves or by traditional healer are 30.4 percent

respectively.

Results conclude that highest percent of the sampled population contacts the

doctors and 2nd

highest percent is of the hospital visitors in this district. This trend shows

that the participants of the urban informal sector of Bahawalpur are not poor. Overall,

48.7 percent participants avail the health facilities. The higher access to doctors

highlights higher human capital development.

Findings show that the proportion of workers‟ shelter is provided with housing

facilities. 56.7 percent of the workers‟ houses are furnished with each of the above

mentioned facilities. 56.7 percent workers have an access to wholesome drinking water.

Those who have access to housing facilities are 98.8 percent of the participants and 98.5

percent of the participants in urban informal sector are electrified. There are still, 75.8

percent of the workers with cemented floor. Results conclude that, on average, 82.4

percent of informal sector workers are more likely to have better housing facilities in

district Bahawalpur.


Standards of living in a society can be imitated too by leisure time for social and

cultural activities. In this study, an access to socio-cultural activities is estimated

approximately by the proportion of the informal workers watching television, listening to

the radio programmes, reading newspapers, or by the proportion of workers participating

in local organizational activities i.e. welfare organization, youth organizations,

cooperatives and religious groups.

ccxciv

Table 8.14: Socio-Cultural Activities and Urban Informal Sector in District

Bahawalpur

Socio-cultural Activities Total (%)

Television 83.1


Newspaper 21.8


Average 41.02

Sample Size (326)

The table (8.15) shows that overall, social capital of participants of the informal

sector is very low. The results reveal that 83.1 percent of urban informal sector workers

watch the television, 41.4 percent listen to the radio programmes. Those who read

newspaper are 21.8 percent and 17.8 percent of workers participate in local organization

in urban informal sector of district Bahawalpur.

8.3.3 Development and Urban informal Sector in District Multan

In this section we made descriptive analysis of economic, human and social

capital of urban informal sector workers of district Multan.


We also use poverty line in order to estimate the monthly income of the workers

of urban informal sector and households in district Multan. It is argued that the

relationship between poverty and the informal sector employment can be established if it

is grouped into two categories on the basis of characterization of poverty line.

Group one: the poor workers are defined as those workers whose monthly

income is lower than Rs.3750. They are classified as poor because their economic

situation is less than basic poverty line of Rs. 3750 per month. This group comprises 17.8

ccxcv

percent of the workers respectively. Result also indicates that 73.9 percent households are

poor in district Multan because their per capita income per month is below the poverty

line of Rs.3750.

Group two: The non-poor workers are defined as those workers whose monthly

earnings exceed Rs.3750. Based on participant‟s income “non-poor” participants are 82.2

percent of the total sampled population in district Multan. The non-poor households are

about 26.1 percent in district Multan.

Table 8.15: Economic Capital and Urban Informal Sector in District Multan

Monthly Income

Workers

Monthly

Income

Houesolds’ Per

Capita Income Per

Month

Poor if

Less than Rs.3750

Rs. 58

(17.8)

Rs. 241

(73.9)

Non-poor if

Above Rs.3750

Rs. 267

(82.2)

Rs. 85

(26.1)

Total 326

(100)

326

(100)

Sample Size (326)


On the basis of such an official poverty line, it is concluded that 17.8 percent of

workers‟ monthly income is below the poverty line. It is also found that 73.9 percent of

the households‟ per capita income per month is below poverty line.

8.3.3.2: Human Capital and Urban Informal Sector

The level of education, health accessibility and housing facilities has been

considered as crucial human capital for economic and social development. The indicators

regarding attainment of education is evaluated on the basis of some formal education (i.e.

Primary, Middle, Matric, Intermediate, Graduation and Post graduation or higher

ccxcvi

education). The indicators in terms of health accessibility are measured by availing

facilities ranged between high quality treatment to low quality treatment such as doctor,

hospital, health centre, and others (traditional healer or self-treatment). Higher utilization

of these facilities shows a high living standard which in return indicates development.

The indicators concerning access to health are gauged by the utilization of health services

or medical facilities such as clean water supply, toilet facilities with a septic tank,

electricity, and a constructed floor. Such facilities are substantial constitutes of human

well being. Better housing facilities improve resistance to disease.

Table 8.16: Human Capital and Urban Informal Sector in District Multan


Primary Education 11

Middle level Education 20.9



Graduation Education 9.8

Post Graduation or Higher Education 5.8

Illiteracy 11

Access to health services

Doctor 60.1

Hospital 63.8

Health Center 46

Others 30.4

Average 50.07


Water Facility 50.6



Floor Facility 75.5

Average 80.7

ccxcvii

Sample Size (326)

The results illustrate that 11 percent of the workers have achieved Primary level

education, 20.9 percent informal workers have accomplished Middle level education, and

31.9 percent are Matriculate. 12.9 percent are Intermediate. It also indicates that

GraduateS and Post graduates or highly educated participants of urban informl sector are

9.8 and 5.8 percent correspondingly. 11 percent are illiterate among the participants of

the urban informal sector in district Multan.

Regarding access to health services, the findings show that 60.1 percent of the

workers get medical treatment from doctor in case of illness, 63.8 percent avail the

hospital and 46 percent approach a health centre. Those who prefer self treatment or

consult traditional healer are 30.4 percent. In the present study, higher utilization of

hospitals shows higher expenditures by the government on health.

Results reveal that the proportion of workers provided with housing facilities

appears to be adequate. Overall, 80.7 percent of the participants are provided with each of

these health-related facilities. 50 percent have access to wholesome drinking water, 98.5

percent have access to toilet facility, and 98.2 percent are electrified. There are still, 75.5

percent of the houses with cemented floor. On average, informal sector workers are more

likely to have better housing facilities in disrict Multan.


The leisure time for social and cultural activities can be used to gauge the living

standard of a society. We measure the access to socio-cultural activities approximately by

the participants watching television, listening to the radio programmes, reading

newspapers and by the proportion of workers participating in local organization activities

i.e. welfare organization, youth organizations, cooperatives and religious groups.

Table 8.17: Socio-Cultural Activities and Urban Informal Sector in District Multan

Socio-Cultural Activities Total (%)

ccxcviii

Television 81.9


Newspaper 27.3

Local Organizations 21.8

Average 43.9

Sample Size (986)

The result in table 8.17 shows that the social capital of participants of urban

informal sector is very low. The proportion of those workers who have availed socio-

cultural activities is relatively better. The results highlight that 81.9 percent informal

workers watch television, 44.5 percent listen to the radio programmes. Results also show

that 27.3 percent read newspapers and 21.8 percent of participants involve in local

organization in the urban informal sector of district Multan.

8.3.4 Development and Urban informal Sector in District Dera Ghazi

Khan

In this section we describe the economic, human and social capital involved in

urban informal sector of district Dera Ghazi Khan.


Participants‟ and households‟ income is used to measure their living standard in

urban informal sector in district Dera Ghazi Khan. Poverty line is also used to measure

the economic capital of the urban informal sector participants. Here, monthly earnings of

urban informal sector workers and households‟ per capita income per month are used to

check the relationship between poverty and the urban informal sector employment. The

relationship between poverty and urban informal sector employment is adequaltly

established by grouping the participants into two groups.

Group one: The poor are regarded as those workers whose monthly income is

below Rs. 3750. They are poor because their monthly income is less than the poverty line

ccxcix

(Rs.3750). Based on participants‟ income, 11.9 percent of the informal sector workers are

poor. Result also indicates that 76.9 percent of the households are poor in district Dera

Ghazi Khan.

Group two: The non-poor informal sector workers are defined as those having

monthly income above poverty line of Rs. 3750. The “non-poor” workers are 88.1

percent of the total sampled population in district Dera Ghazi Khan. The “non-poor”

households are 23.1 percent in district Dera Ghazi Khan.

ccc

Table 8.18 Economic Capital and Urban Informal Sector in District Dera Ghazi

Khan

Monthly Income

Workers’

Monthly

Income

Households’ Per

Capita Income Per

Month

Poor if

Less than Rs. 3750

Rs. 40

(11.9)

Rs. 257

(76.9)

Non-poor if

Above Rs. 3750

Rs. 295

(88.1)

Rs. 77

(23.1)

Total 334

(100)

334

(100)

Sample Size (334)


The estimate shows that 11.9 percent of the workers‟ monthly income is below

poverty line. It is concluded that economically, the participants are not poor in Dera

Ghazi Khan district. However, 76.9 percent of the households are poor in the district.

8.3.4.2 Human Capital and Urban Informal Sector

Human capital includes education accomplishment, access to health services and

access to housing facilities. Education, health and housing have been observed as an

indispensable human capital for added economic and social development. As regards

development, economic well-being or poverty reduction can be achieved with an

educated, healthy and well-aboded population. The indicators regarding attainment of

education can be evaluated on the basis of some formal education (i.e. Primary, Middle,

Matric, Intermediate, Graduation and Post graduation or higher education). The

indicators in terms of health accessibility can be measured by availing facilities ranged

between high quality treatment to low quality treatment such as doctor, hospital, health

centre, and others (traditional healer or self-treatment). The availability of sufficient

housing facilities such as clean water supply, toilet facilities with a septic tank,

electricity, and a constructed floor is one more important feature of human capital.These

facilities increase the human well-being. Better housing facilities improve resistance to

disease. The high level of human capital indicates high development. The evidence in

ccci

table shows that workers have an adequate access to such human capital in district Dera

Ghazi Khan.

cccii

Table8.19: Human Capital and Urban Informal Sector in District Dera Ghazi Khan


Primary Level Education 10.8

Middle Level Education 25.7

Matric Level Education 35.9

Intermediate Level Education 11.

Graduation 5.7

Post Graduation or Higher Education 1.5

Illiteracy 9.3

Access to Health Facility

Doctor 84.7

Hospital 76.9

Health Center 36.3

Other 38.7

Average 59.15


Water Facility 41.3

Toilet facility 87.7


Floor Facility 68.3

Average 73.42 Sample Size (334)

Results in table 8.19 point out that 10.8 percent of participants in urban informal

sector have completed Primary level education, the proportion of workers who have

passed Middle level education is 25.7 percent. 35.9 percent are Matriculate, and 11.7

percent have achieved Intermediate level education. The Graduates and Postgraduates or

above are 11.7 and 1.5 percent respectively. The result indicates that 9.3 percent are

illiterate among the urban informal sector workers in district Dera Ghazi Khan.

The result also shows that 84.7 percent of the urban informal workers visit a

doctor for medical treatment to cure illness, 76.9 percent approach hospital and 36.3

percent avails health centre. The ratio of those who do self-treatment or go to traditional

healers is 38.7 percent in urban informal sector in district Dera Ghazi Khan.

The estimates indicate that the proportion of the informal sector‟s participants is

provided with adequate housing facilities. Overall 73 percent of the workers are provided

ccciii

with each of these facilities. 41.3 percent have access to wholesome drinking water, 87.7

percent have an access to toilet facility, and 96.4 percent are electrified and 68.3 percent

of the workers are furnished with constructed floor. On average, informal sector workers

are more likely to have better housing facilities.


Social capital covers access to social institutions as indicated by the participation

in socio-cultural activities. Standard of living of workers can also be revealed by using

their time for social and cultural activities.

Here, the access to socio-cultural activities is measured approximately by the

proportion of urban informal workers watching television, listening to the radio or radio

programmes, reading the news papers and participation in local organization activities.

Higher proportion of socio-cultural activities specifies high living standard and social

development of informal sector workers.

Table 8.20: Socio-Cultural Activities and Urban Informal Sector in District Dera

Ghazi Khan

Socio-cultural Activities Total (%)

Television 92.2


Newspaper 26.6


Average 47.9

Sample Size (334)

Table 8.20 illustrates that the social capital of participants of urban informal

sector is very low at 47.9 percent in Dera Ghazi Khazi Khan. The proportion of those

participating in the informal sector having access to social cultural activities is relatively

better. The estimates highlight that 92.2 percent informal sector participants watch the

television. The findings indicate that 35.9 percent workers listen to the radio

programmes, 26.6 percent read newspapers and 36.9 percent of them participate to the

local organizations.

ccciv


Results regarding earnings determinants of total sample size and separately in

each division are also elaborated. In Southern Punjab, age of the informal sector workers

(AGY), complete years of education (EDY), sex (SEX), working hours (WHR) and

household‟s value of assets (HVAT) are found to be significantly positive factors in

determining the earnings of the informal sector workers. The returns to earnings from all

levels of education are also found to be positive and significant in the regression

equations.

Results of earnings determinants of the participants in the urban informal sector of

district Bahawalpur are also presented. The coefficients of complete years of education

(EDY), marital status (MRS), working hours (WHR) and household‟s value of assets

(HVAT) are found to be significant and increasing functions of earnings. The Matric

level education (EDU III), Intermediate level education (EDU IV), Graduation (EDUV)

and Master‟s or higher level education (EDU VI) valriables are found to be positively

significant.

The estimate indicates that the coefficients of complete years of age (AGY),

complete year of education (EDY), skill training (TRA), working hours (WHR) and

household‟s value of assets (HVAT) are observed to be significant and positive in

determining the earnings in Multan district. These variables are increasing functions of

earnings in the urban informal sector. The Matric level education (EDU III), Intermediate

level education (EDU IV), Graduation (EDU V) and Master‟s or higher level educations

(EDU VI) are positively significant. The results of all levels of education are positive and

significant. This indicates that earnings of participants of the urban informal sector

increase with increasing level of education.

In district Dera Ghazi Khan, coefficients of complete years of education (EDY),

sex of the workers (SEX), training facility (TRN), working hours (WHR) and

household‟s value of assets (HVAT) are statistically significant and positive. The Middle

cccv

level education (EDU II), Matric level education (EDU III), Intermediate level education

(EDU IV), Graduation (EDU V) and Master‟s or higher level education (EDU VI) makes

clear the positive significant effects. The results of all levels of education are positive and

significant. These variables are the increasing functions of informal workers‟ earnings.

In Southern Punjab, the group of poor workers is on average 16.9 percent and

83.11 percent of the sample consists of “non-poor” workers engaged in urban informal

sector. High economic capital shows development of the participants of the urban

informal sector. However, 72.7 percent of the households are living below poverty line in

Southern Punjab. An adequate level of human capital is observed among the informal

sector workers of Southern Punjab, Pakistan. On average, 52.7 percent of the workers

have access to health facilities and 79 percent of the workers have availed some housing

facilities. This higher utilization of human capital indicates high living standard of

employed in the urban informal sector. As regards social capital, 44.25 percent of the

workers are participating in socio-cultural activities in Southern Punjab. This low human

capital indicates low development regarding socio-cultural activities. Collectively,

development indicators show high development in urban informal sector of Southern

Punjab, Pakistan.

In district Bahawalpur, on average, the poor workers group is observed 21.2

percent and 78.8 percent of the sample comprises “non-poor” workers working in urban

informal sector. 67.2 percent housholds are poor in district Bahawalpur. An adequate

level of human capital is also observed among the informal sector workers. On average,

48.7 percent of the workers have access to health facilities and 82.37 percent of the

workers in the urban informal sector have availed some housing facilities in district

Bahawalpur. This higher utilization of human capital demonstrates high living standard

of the participants of the informal sector. In terms of social capital, 41.02 percent of the

workers are taking part in socio-cultural activities. This low social capital indicates low

development regarding socio-cultural activities. On the whole, economic, human and

social development indicators show development of the participants of urban informal

sector in district Bahawalpur.

cccvi

The estimates reveal that 17.8 percent of workers are observed poor, while 82.2

percent are “non-poor” in the urban informal sector of district Multan. The estimate

shows that 73.9 percent households are poor in district Multan. Among the urban

informal workers, an adequate level of human capital is also observed. On average, 50.7

percent of the workers have access to health facilities and 80.7 percent of the workers in

the urban informal sector of Multan have availed some housing facilities. This higher

utilization of human capital reveals high living standard of the participants of urban

informal sector. As far as social capital is concerned, 43.9 percent of the informal sector

participants are partaking in socio-cultural activities. This low social capital indicates low

development regarding socio-cultural activities. By and large, development indictors

refer to economic, human and social indicators show development of those who

contribute in the urban informal sector of Bahawalpur district.

Findings show that 11.9 percent of workers are observed to be poor in the urban

informal sector of district Dera Ghazi Khan. The proportion of “non-poor” is 88.1 percent

in the urban informal sector. The estimate indicates that 76.9 percent of the households

are included as poor in district Dera Ghazi Khan. The participants have an adequate level

of education in the urban informal sector. The participants of urban informal sector who

have access to health facilities are 59.15 percent of the sample and 73.49 percent of the

workers have availed housing facilities. The result indicates higher utilization of human

capital that reveals high living standard of the participants involved in the urban informal

sector. Results conclude that low social capital such as 47.9 percent among the informal

sector participants, which indicates low development in terms of socio-cultural activities.

The development indictors such as economic, human and social indicators show high

level of development in the urban informal sector of district Dera Ghazi Khan.

cccvii

cccviii

Chapter 9

GENDER EMPLOYMENT IN URBAN INFORMAL

SECTOR: A COMPARISON

9.1 Introduction

Informal sector essentially creates employment for participants of both genders

(i.e. males and females) in the labour market. In this chapter, we investigate the

determinants of urban informal sector employment for both (males and females) and

gender comparison in three divisions of Southern Punjab. Informal sector plays a pivotal

role in the development of Pakistan economy. The informal sector employment

essentially absorbs 73.8 percent of Pakistan‟s total labour force. In both rural (76.3

percent to 76.5 percent) and urban areas (from 70.4 percent to 71.2 percent) the

percentage of the informal sector participants has increased. The share of women is

disproportionally high in the urban informal sector employment in Pakistan economy

(Govt. of Pakistan Economic Survey, 2011-12).

Females are extensively increased in informal sector employment rather than male

workers in Southern Punjab, Pakistan. In the present chapter, we discuss the various

socio-economic and demographic factors that motivate the male and female workers to

participate and promote the growth potential of urban informal sector of Southern Punjab

The chapter is organized as: In section 9.2, we analyse gender employment in

urban informal sector and comparison by using econometric techniques in Southern

Punjab, Pakistan. In section 9.3, we estimate the urban informal sector employment of

both genders and make comparison in district Bahawalpur. The section 9.4 describes the

informal sector employment for both genders and comparison in district Multan. Section

9.5 elaborates urban informal sector employment of (both genders) and comparison in

district Dera Ghazi Khan. Finally, concluding remarks are presented in section 9.6.

cccix

9.2 Binary Logit Estimates of Determinants of Gender Employment and

Comparison in Urban Informal Sector in Southern Punjab

In this section, an analysis of the determinants of gender employment in urban

informal sector is shown in Southern Punjab, Pakistan. Howerever, we split the analysis

in three Districts (i.e. Bahawalpur, Multan and Dera Ghazi Khan) in next sections. The

study has used a binary logit model in order to analyze the determinants of gender

employment in the informal sector and comparison with complete years of education and

with different levels of education in urban areas of Southern Punjab.

Tables 9.1 and 9.2 give the logit estimates of the determinants of urban male and

female informal sector employment. The estimated parameters, their asymptotic z-

statistic and marginal effects for both male and for female workers are given in three

columns of the tables. The intercept terms in the urban informal sector employment

equation are positive and statistically insignificant in table 9.1 of male sample while it is

significant and negative in table 9.2. In female sample, the intercept terms are positively

significant. The marginal effect represents the effect of a unit change in each variable on

the probability of being employed in the urban informal sector employment relative to the

base category that is formal employment.

Age of the people motivates some of the factors influencing their decision of

participation towards urban informal labour market. Two views can be presented about

age of workers involved in the informal sector. Firstly, if the relative participation of

young people is greater in informal sector, the very sector may probably be considered as

a transition stage before opting formal sector. Secondly, informal sector may be

considered as a desirably constant choice if there is a large participation ratio of older

persons in the informal sector (see Kemal and Mehmood, 1993). Age of the workers is

used in completed years as an explanatory variable in the informal sector employment

models. Males‟ estimates show that the coefficients of complete years of age (AGY) are

found to be positive and have statistically significant influence on informal employment.

The probability of male workers being employed in the urban informal sector increases

by 0.5 percentage points due to one year increase of age of male worker. This owes to

cccx

that the formal sector can not absorb these male workers with low formal education and

ultimately they have to join an accessible urban informal sector employment in order to

earn their livelihood. The results conclude that mature male workers with low formal

education and high experience have a higher likelihood to determine the urban informal

sector employment in Southern Punjab.

Female informal sector employment is also affected by age. The coefficients of

the variable age (AGY) are again positive but the study results are statistically

insignificant. The positive marginal effects may indicate that almost females have to pay

relatively lessened child care and household responsibilities in old age and participate

more in the urban informal sector employment. This may also argue that young females

have the potential or spirit to involve more in economic activities especially in the formal

sector. The results conclude that male workers with increasing age easily persuade urban


Education is a critical input in economic development (Behrman, 1995).

Theoretically, education level of the labour market participants can play two different


serves as an impetus in enhancing their skills via training. On the other hand, low

education increases the probability of involvement in the informal sector. Results

highlight that education diminishes the probability of the urban informal sector

employment for both male and female samples. In model I of informal sector

employment, we have used complete years of education as an explanatory variable.

Between two models, the coefficient of years of education (EDY) is negative and highly

significant. The male and female workers are less likely to be employed in the informal

sector by 3.3 and about 4.6 percentage points respectively due to one year increase in

their education. The results indicate that probability of female workers being employed in

the informal sector comparatively falls at faster rate by 1.3 percentage points than the

male workers due to one year increase of education. We have observed in the analysis

that probability of female employment in informal sector declines at faster rate rather

than male participants. Economically, it can be justified that highly educated workers

especially females are inclined to urban formal labour market of Southern Punjab which

cccxi

is more lucrative and permanent source of income. This confirms similar findings by

Florez‟s (2003).

cccxii

In table 9.2 of male sample, education level of participants (EDY) is included as a

categorical variable with five categories (non-formal education is taken as base category)

to see its influence on the urban informal sector employment. Results highlight that the

coefficient of Middle level education (EDU II) is observed to be positive and statistically

significant. The probability of male workers being employed in the urban informal sector

of those with Middle level education (EDU II) is 4.5 percentage points more than the

non-formal education. The probability of male workers being employed in the informal

sector of those with Matric leve education is 9.6 percent more than the excluded catagory.

The reason can be that more male workers with initial basic education are engaged in the

urban informal sector. The coefficients of Intermediate (EDU IV), Graduation (EDU V)

and Master‟s or higher level education (EDU VI) are found to be negative and have

significant impact on males‟ urban informal sector employment. The economic

interpretation of this negative influence of education on the informal sector employment

can be that the high education of the male workers dissuades them to work in the urban

informal sector and they move towards profitable formal sector employment in Southern

Punjab.

Concerning females‟ employment, the coefficient of Matric level education (EDU

III) is negative but has insignificant effect on probability of employment in the urban

informal sector. The coefficients of Intermediate (EDU IV), Graduation (EDU V) and

Master‟s or higher education (EDU VI) for female workers are observed to be negative

and highly significant. Findings show that female workers with more (22.2 and 27.1)

percentage points are less likely to be involved in the informal sector as compred to male

workers. The results points out that participants especially females of Southern Punjab

are relatively less likely to join the urban informal sector having high level of education.

It can be justified that highly professional educated females have comparatively more

opportunities to get formal employment than less educated females. The results reflect

the classical theory of production and are also closely related with law of diminishing

returns. The level of education increases, the marginal informal sector employment falls.

The study results may relate to the expected role of the informal sector with low capital

accumulation.

cccxiii

The married male persons are more likely to be employed in the urban informal

sector comparatively unmarried male workers. The estimates reveal that marital status

(MRS) has also an effect on choice of sector of employment. The coefficients of marital

status (MRS) are found to be positive and have statistically insignificant impact on male

informal sector employment. Infact, male participants hardly accept informal

employment as it does not commensurate with their high level of education or quality

skill. Furthermore, mostly males are risk averser and are inclined to switch over the code

from the informal to formal labour market.

The female estimates in table 9.1 indicate a positive but insignificant impact on

their participation in the urban informal sector employment. The possible outcome of the

fact is that female workers are moving towards formal sector to earn better. However, the

coefficient of marital status is negative and has insignificant influence on female informal

sector employment in table 9.2. The reason can be that it is not easy for married female

workers to engage themselves in child care along with household responsibilities and

informal work involvement in Southern Punjab. Overall, marital status does not

significantly influence the informal sector employment.

The results indicate that the urban informal sector workers have less formal

training but have some kind of informal training. The coefficients of formal training

(FTD) for males are inverse and highly significant. The probability of being employed in

the urban informal sector decreases by about 22.3 and 22.2 percentage points

respectively. An increase of one unit in the formal training decreases the probability of

female workers being employed in informal sector by 26.8 and 29.5 percentage points

respectively. Results also trace out that there is a decrease of more 4.5 and 7.3 percentage

points in the probability of female workers engaged in the informal sector as compared to

male participants due to one unit increase in formal training. It also owes to those male

and female participants who possess formal training have a preference to stick to formal

labour market to explore their capabilities in a better way. It is concluded that higher the

formal training, lower the informal sector employment in Southern Punjab.

cccxiv

Family background of the people joining urban informal sector (i.e. father‟s

education and mother‟s education) helps in growth potential of urban informal sector in

Southern Punjab. Theoretically, it is predictable that the workers are less likely to

participate in the urban informal sector, whose parents are educated. The results of the

study confirm the hypothesis. In the current study, we have included two dichotomous

variables (i.e. educated father and educated mother) to observe the influence of parents‟

education on male as well as on female workers in the urban informal sector. In tables 9.1

and 9.2, the coefficients of father‟s education (FEDU) are negative and statistically

highly significant. The probability of male workers being employed in urban informal

sector shows a declining trend by about 14.3 and 14.8 percentage points respectively due

to one unit increase in father‟s education (FEDU).

It is also expected that the female workers whose parents are educated are less

likely to incline to the unskilled informal sector jobs. The study results are similar as

hypothesized. The coefficients of father‟s education for female workers are negative and

highly significant. One unit increase in father‟s education decreases the probability of

female informal workers by 17.5 and 18.3 percentage points respectively. Findings

indicate that female participation in the urban informal sector relatively drops more about

3.2 and 4.5 percent as compared to male workers in both models.

The coefficient of mother‟s education (MEDU) is negative and highly significan.

Male workers whose mothers are uneducated are less likely to be employed in the

informal sector by about 14.9 and 14.7 percentage points as compared to the formal

sector. The results also indicate that probability of informal sector employment

diminishes by 31.3 and about 33 percentage points for female informal sector workers

respectively in the urban informal sector employment in Southern Punjab.

There is high decrease of 16.4 and 18.3 percent in the probability of female

workers being employed in the informal sector from an educated mother. This can also be

the reason that the educated parents provide higher educational facilities and better career

counseling to their male and especially female children. In this society, educated parents

feel importance of female education more and the formal labour market is more secure

cccxv

and riskless for female absorption as compared to the urban informal sector. The findings

highlight that more female workers with increasing education of their parents are less

likely to participate in the urban informal sector of Southern Punjab.




making the family financially sound compels the head of large household to opt urban

informal sector. The analyses highlight that the coefficients of household size variable

(HSIZ) are positive andstatistically significant. The probability of male workers

employed in urban informal sector increases by 5.5 and about 5.10 percentage points

respectively as a result of one additional member in the house (HSIZ). The male

participants with large household size are more likely to join the urban informal sector in

Southern Punjab. The economic reason being that male household heads have no choice

except to work more in urban informal sector.The study results conclude that the informal

sector employment increases with an increase of household size in urban areas of

Southern Punjab.

Contrarily, the coefficients of the variable household size (HSIZ) are negative.

However, the study results are statistically insignificant. The noticeable fact may also be

that the females have to look after the children and household responsibilities and they

have a less likeliness to engage in the urban informal sector with the responsibility of

large household size. Findings conclude that urban informal sector is a sector with

females having low household size.

The dependency ratio (DPNR) is another factor affecting the decision to work in

urban formal and informal sector employment. The coefficients of (DPNR) are positive

and highly significant for urban male informal sector employment. Results conclude that

dependency ratio is the main reason for male workers to persuade urban informal sector

in Southern Punjab. It can be accounted for that mostly male family heads assume the

responsibility themselves to prop up the family. In other cases, family members motivate

cccxvi

or force the male head to indulge into an employment in the urban informal sector which

is accessible according to their human capital.

Contrarily, the coefficients of dependency ratio (DPNR) for females are negative

and have insignificant effect on the urban informal sector employment in Southern

Punjab. It may be argued that females are less expected to contribute in the urban

informal sector due to more dependency burden of child care and household

responsibilities. However, female workers mange both the domestic work along with

informal sector works in Southern Punjab.

The results demonstrate that family setup (FSP) has a positive and insignificant

impact on males‟ participation in the urban informal sector. It can be justified that the

males living in joint family system do not work and prefer leisure and it is the strong

substitution effect which forces them. Another argument is that high family labour

supply or involvement in form of family helpers determines the employment in the urban

informal sector.

For female employment, joint family setup (FSP) is highly significant and affects

positively the decision of participation in the urban informal sector. The coefficients of

family setup (FSP) for females are positive and highly significant. The workers who

belong to joint family setup are more likely to be employed by about 30.5 and about 30.1

percentage points respectively as compared to the formal sector. The noteworthy fact is

that females living in joint family system have more extra working hours to carry out

informal activities because domestic issues are shared by other family members. Family

labour supply is also one of the determinants for this high participation. Consequently,

females are more likely to be employed in urban informal sector with joint family setup

in Southern Punjab.

The result points out that the coefficients of number of children (NCHL) variable

are negative. The results are statistically insignificant in both the equations. Theoretically,

the male workers, having more children, are less likely to participate in the urban

informal economic activities due to strong substitution effect of extra earnings of the

cccxvii

children. The phenomenon of child labour can also be the determinant of this decreasing

males‟ employment trend. Moreover, workers have to work in accessible informal sector

to fulfill children‟ requirements.

cccxviii

Results show that having children (NCHL) significantly increases the probability

of urban female informal sector employment. The probability of females‟ participation in

informal sector increases by about 7.9 and 8.0 percentages points respectively due to an

increase of one additional child in the family. Theory shows that the female workers

having more children participate less in the informal employment. However, the results

show that workers having children in the age group 6-14 particpate more in the urban

informal sector employment either because to fulfill their requirements and expenses or

mothers of the children pay comparatively lessened care to adult family members. Our

findings are similar with Funkhouser‟s (1996) study results.

Next variable is having male adolescents (NMAD). The participants are about 8.8

and 10.1 percentage points less likely to be engaged in the informal sector respectively

because of an addition of one male adolescent at home. The coefficients of male

adolescent are negative and highly significant. Results also indicate that the coefficients

of male adolescents are negative and statistically significant for female analysis. The

probability of female informal sector employment decreases by about 7.1 and 6.9

percentage points respectively by one additional male adolescent in the family.

However, probability of female informal sector employment is comparatively

lower by 1.8 and 3.2 percentage points due to an addition of one male adolescent or male

worker‟s participation decreases more by 1.8 and 3.2 percent due to an additional male

adolescent. This can be the reason that the male adolescents have more chances to find

out formal or informal sector employment to fulfill family needs to secure their future.

The male heads as well as especially mothers participate less in economic activities due

to a strong substitution effect of extra earnings of the male adolescents in the house. The

results conclude that male as well as female household heads are less likely to be engaged

in the urban informal sector with male adolescent in Southern Punjab.

The results highlight that male participants of informal sector are more likely to

be employed by about 6.9 and 6.0 percentage points respectively because of an addition

of one more female adolscent (NFAD) at home. The coefficients of female adolescent

variable are found to be positive. The results are highly significant in Southern Punjab.

cccxix

Likewise, the coefficients of female adolescents are positive and significant for urban

female informal sector employment in both equations. The probability of female workers

increases by about 16 percentage points respectively due to an increase of one female

adolescent at home. The probability of female participants of the informal sector

increases by 10.1 and 10 percentage points respectively more than male participants.

Results highlight that female adolescents are engaged less in market as well as the formal

sector employment due to socio-economic constraints. In this way, the participation rate

of urban male and female informal sector employment increases in order to contribute to

family expenses. The results conclude that workers participate more in economic

activities in the presence of female adolescents. Having more female adolescents is yet

another determinant which influences the parents‟ inclusion in the urban informal sector

and increases the growth potential of the urban informal sector employment in Southern

Punjab.

It has been noticed in male sample that the spouse participation (SPN) in

economic activities decreases the probability of working in the informal sector by 13.9

and 14.4 percentage points respectively due to one unit increase in spouse participation in

earning activity. The reason can be that the male workers allocate less time to work due

to the extra income earned by spouses.

Relativity of female spouse participation in economic activities (SPN), results

reveal that the spouse‟ participation in economic activities reduces the probability of

female urban informal employment. However, the results are insignificant.

The household‟s value of assets can also affect the choice to work or not to work

in the urban informal labour market. It is expected that an increase in value of assets may

decrease the participation in labour market. The study results confirm the hypothesis. It

has been observed that the coefficients of value of household‟s assets (HVAT) are

negative and statistically insignificant. Results also demonstrate that the coefficients of

value of assets (HVAT) for females are negative and significant at 5 percent level of

significance. The probability of female working in urban informal sector decreases with

an increase in (HVAT). The reason possibly exists that the female workers dissuade to

cccxx

work due to increase in unearned income. Another argument is that, illiteracy may be one

of the factors in this society for not investing those extra financial resources in side

business (in portfolio or otherwise) to have a back-up plan against rainy days.

The notion is that rural-urban migration (RMGT) also determines the probability

of participation in the urban informal sector in Southern Punjab. An increase of one rural-

urban worker increases the probability of male workers being employed in the informal

sector by 13.4 and 12.7 percentage points due to an addition of one rural-urban migrant

worker in urban areas of Southern Punjab. Female estimates show that the probability of

being employed in the urban informal sector increases by about 11.3 percentage points

due to one additional rural-urban migrant worker in the urban areas of Southern Punjab.

Our results reveal that there is a small increase of 2.1 and 2.4 percentage points in the

probability of males being employed in the informal sector due to an additional rural-

urban migrant as compared to female workers. The study results conclude that the

probability of participation in the urban informal sector is high in Southern Punjab,

Pakistan and the rural to urban migration increases rapidly due to rural urban wage

differential but the probability of getting employment opportunities in the formal sector is

low as compared to migration rate. Consequently, the workers especially males are lean

to work in the urban informal labour market rather than to work in the urban formal

sector. Results conclude that urban informal sector create more employment

opportunities for the rural-urban migrants with low education and indicates high

absorption and growth potential in Southern Punjab.

cccxxi

Table 9.1: Logit Estimates of Determinants of Gender Employment in Urban

Informal Sector in Southern Punjab. Probability of Informal Sector Employed (18-

64)

Male Female

Explanatory

Variables Coefficients Z-statistic

Marginal

Effects Coefficients Z-statistics

Marginal

Effects

CONSTANT 0.2603 0.4729 3.1290 3.5422

AGY 0.0213** 2.2992 0.0048 0.0043 0.3082 0.0010

EDY -0.1458*** -5.0741 -0.0332 -0.2016*** -5.3921 -0.0459

MRS -0.0201 -0.0899 -0.0046 0.0246 0.0744 0.0055

FTD -0.9782*** -4.8386 -0.2225 -1.1798*** -4.1246 -0.2684

FEDU -0.6271*** -3.4121 -0.1427 -0.7698*** -2.6307 -0.1751

MEDU -0.6544*** -3.4034 -0.1489 -1.3739*** -4.7526 -0.3126

HSIZ 0.2419*** 5.2913 0.0550 -0.0757 -1.2285 -0.0172

DPNR 0.7842** 2.0490 0.1784 -0.1140 -0.1832 -0.0259

FSP 0.0245 0.1338 0.0056 1.3416*** 4.7238 0.3052

NFAD 0.3018*** 3.4285 0.0687 0.7027*** 4.3610 0.1599

NMAD -0.3844*** -3.6324 -0.0875 -0.3114** -2.1271 -0.0708

NCHL -0.0245 -0.3864 -0.0056 0.3468*** 3.5111 0.0789

SPN -0.6120*** -3.2040 -0.1392 -0.2869 -1.0613 -0.0653

HVAT -0.0000 -0.7384 -0.0000 -0.0000** -2.1034 -0.0000

RMGT 0.5892*** 3.0316 0.1340 0.4962* 1.6632 0.1129

Sample Size (N) = 934 Sample Size (N)= 572

Log Liklihood = -433.3446 Logliklihood = -194.3233

LR Statistics (15df) = 335.2722 LR Statistis (15df) = 350.5357

Mcfadden R2 = 0.28 Mcfadden R

2 = 0.47

P-value = 0.000 P-value = 0.000


Note: The z- statistic is that of the associated coefficients from the logit model, where formal sector employment is

taken as base outcome. Non-formal education is taken as base category.




cccxxii


Informal Sector in Southern Punjab with Different Levels of Education-Probability

of Informal Sector Employed (18-64).

Male Female

Explanatory


Marginal


Marginal

Effects

CONSTANT -1.4266 -2.6169 1.9201 2.2820

AGY 0.0215** 2.2812 0.0049 0.0048 0.3317 0.0011

EDU II 1.1988*** 3.0290 0.0452 0.3624 0.6391 0.0824

EDU III 0.4228 1.3438 0.0962 -0.5001 -1.0978 -0.1138

EDU IV -0.0176 -0.0531 -0.0040 -1.0345** -2.0756 -0.2353

EDU V -0.6379* -1.8703 -0.1451 -1.6142*** -3.2302 -0.3672

EDU VI -1.1465*** -2.9756 -0.2608 -2.3369*** -4.8072 -0.5316

MRS 0.0561 0.2426 0.0128 -0.0538 -0.1582 -0.0122

FTD -0.9768*** -4.7297 -0.2222 -1.2991*** -4.3701 -0.2955

FEDU -0.6522*** -3.4463 -0.1484 -0.8058*** -2.6873 -0.1833

MEDU -0.6480*** -3.2874 -0.1474 -1.4488*** -4.8569 -0.3296

HSIZ 0.2628*** 5.6260 0.0598 -0.0758 -1.1963 -0.0172

DPNR 0.7588* 1.9374 0.1726 -0.0660 -0.1009 -0.0150

FSP 0.0911 o.4866 0.0207 1.3222*** 4.5605 0.3008

NFAD 0.2647*** 2.9497 0.0602 0.7017*** 4.2917 0.1596

NMAD -0.4439*** -4.0757 -0.1010 -0.3027** -2.0563 -0.0689

NCHL -0.0652 -0.9944 -0.0148 0.3534*** 3.484 0.0804

SPN -0.6346*** -3.2470 -0.1444 -0.2964 -1.0712 -0.0674

HVAT -0.0000 -0.8022 -0.0000 -0.0000** -1.8650 -0.0000

RMGT 0.5563*** 2.8093 0.1266 0.4549 1.4719 0.1035

Sample Size (N) = 934 Sample Size (N) = 572


LR Statistic (19df) = 362.92 LR Statistic (19df) = 361.95


2 = 0.49

P-value = 0.000 P-value =0000

Source: Author estimated by usingEviews statistical software.

Note: The Z- statistic is that of associated coefficients from the logit model, where formal sector employment is

considered as base outcome. Non-formal education is taken as base category.




cccxxiii

9.3 Binary Logit Estimates of Determinants of Gender Employment

and Comparison in Urban Informal Sector in

District Bahawalpur

In this section, we made an analysis of determinants of employment of both the

genders in comparison with respect to district Bahawalpur. The study utilizes the binary

logit model analysis. The impact of complete years of education as well as different

levels of education is checked on urban informal sector employment.

The logit estimates of the determinants of gender employment in the urban

informal sector are shown in the tables 9.3 and 9.4 with three sets of numbers such as

estimated parameters, their asymptotic z-statistic and marginal effects for both male and

female participants in both tables. The intercept term in the informal sector employment

equation is negative and statistically insignificant in model I while it is statistically

significant in model II. The very term is positively significant in both logit models of

female analysis. The marginal effects show the effect of a one unit change in each

variable on the probability of participation in the urban informal sector employment

relative to the base category of formal sector employment.



sector may probably be considered a transition stage before opting formal sector.

Secondly, the informal sector may be considered as a desirable constant choice if there is

a large participation ratio of older persons in the informal sector. Age of the people

motivates some of the factors influencing their decision to enter in the urban informal

sector. Age of household in complete years is used as an explanatory variable in both

analyses. The estimates show that the coefficients of complete years of age (AGY) for

male workers are positive and statistically insignificant. The analysis indicates that

persons having high education or human capital determine the formal sector employment

in district Bahawalpur.

cccxxiv

Concerning with female employment, age also influences the decision to enter

urban informal sector. The coefficients of age (AGY) for females‟ employment are

negative and found to be insignificant. The possible outcome of this fact is that females

hardly involve into the urban formal sector employment and almost young females with

quality education are forced to work in the urban informal sector employment due to

social constraints. The study results conclude that the urban informal sector is a sector

with low human capital accumulation in district Bahawalpur.




education increases the probability of involvement in the urban informal sector.

Our study results highlight that education reduces the probability of being

inducted in the urban informal sector of district Bahawalpur. The results point out that the

coefficient of complete years of education (EDY) is negative and statistically

insignificant. The reason can be that male participants with low education are forced to

work in the informal sector of district Bahawalpur.

The study results specify that education steadily reduces the probability of

females being engaged in the informal sector. The coefficient of complete years of

education (EDY) for female employment is negative and highly significant. The

probability of female participants being inducted in the urban informal sector

employment falls by 4 percentage points due to an increase of one year of education. The

comparison between the genders points out that probability of 2.1 percent of female

participants of the informal sector drop which is more than the male workers in model I

due to one year increase in education of the worker. The possible reason may be that

highly educated female workers aspire for well-paid formal sector employment as

compared to male workers. The study results highlight that the informal sector absorbs

the low educated people in district Bahawalpur. Our results are consistent with Florez‟s

(2003) findings.

cccxxv

In table 9.4, education level of the participants (EDY) is incorporated as a

categorical variable with five categories (non-formal education is considered as base

category) to check the influence of education on urban informal sector employment. The

coefficient of Middle level education (EDU II) is positive and statistically insignificant.

While the coefficients of Matric level education (EDU III), Intermediate level education

(EDU IV), Graduation level education (EDUV) and Master‟s or higher level education

(EDU VI) are found to be negative. However, these results are insignificant. The

economic interpretation for this fact is that the workers with high education have more

opportunities to get formal sector employment and the informal sector involves workers

having basic education. The results are associated with the classical theory of production

and they have similarity with law of diminishing returns. The results indicate that as the

level of education increases, the marginal urban informal sector employment falls in

district Bahawalpur.

The females estimate indicates that the coefficient of Middle level education

(EDU II) is positive and insignificant. Results show that there is a negative impact of

Matric level education (EDUIII), Intermediate level education (EDUIV) but these results

are insignificant. However, coefficient of Graduation level education (EDUV) and

Master‟s level education (EDUVI) are found to be negative and statistically significant.

The probability of female workers being employed in informal sector with Graduation

and Master‟s level or high education is 34.6 and 40 percentage points less than the

excluded category. The economic interpretation of this negative influence of education on

informal sector employment can be that female workers with initial basic education

engage themselves in earning activities domestically or on pay in the informal sector in

Bahawalpur. The study results conclude that the informal sector absorbs the workers with

low capital accumulation.

Marital status (MRS) also affects the choice to work in labour market. The

married people are more likely to join the urban informal sector than rather unmarried

male workers. The coefficients of marital status (MRS) for male worker are positive and

statistically insignificant. Infact, workers move towards the the formal sector which is

more important source of earnings. The coefficient of marital status for female worker

cccxxvi

(MRS) is positive and statistically significant at 5 percent level of significance. The

positive marginal effect (ME) may signify that married females with basic education

easily attach to informal sector work with unfixed working hours and to fulfill productive

work and child care responsibility. Another argument is that some social and economic

constraints compel these females to work in urban informal sector.

The findings reveal that the coefficients of the formal training (FTD) for male

employment are found to be negative and highly significant. The male workers are being

employed in urban informal sector by 21.6 and 22.1 percentage points respectively due to

one unit increase in the formal training. Similarly, the coefficients of formal training

(FTD) for female participants are negative and highly significant. The female workers

with formal training are less likely to be employed in the informal sector by about 27.5

and 28.4 percent respectively as compared to the formal sector. The participants (male

and female) having degrees and formal diplomas are lean to formal labour market. Our

results point out that there is a high decrease of 5.9 and 6.3 percent points in the

probability of females‟ induction in the urban informal sector because of an addition of

one unit in formal training as compared to male workers. This negative influence

specifies that the formally trained workers especially females are really interested to get

the certain jobs done in a certain way from those people trained for the purpose in the

formal sector.

Parents‟ educational status also affects the decision to work in labour market. The

notion is that those workers whose parents are educated are less likely to partake into

urban informal sector employment. In the current study, we have included binary

variables to observe the influence of parent‟s education on the informal sector

employment. The coefficients of father‟s education (FEDU) for male workers are

negative and highly significant. The probability of males‟ being employed in the urban

informal sector diminishes by about 21.9 and 22.6 percentage points respectively due to

one unit increase in (FEDU). The fact is that whatever information parents especially

fathers get to know about brilliant careers, they enforce it upon their children considering

their interests. Hence, the probability of formal sector employment increases. For female

cccxxvii

sample, the coefficients of father‟s education (FEDU) are negative and statistically

insignificant.

cccxxviii

The analysis points out that the coefficients of mother‟s education (MEDU) for

male workers are also negative and statistically insignificant. For female analysis,

coefficients of mother‟s education (MEDU) are negative and have significant effects on

probability of being employed in the urban informal sector. The probability of female‟s

participation in urban informal sector decreases by 37.4 and 38.4 percentage points

respectively for one unit increase in mother‟s education. In this society, educated mothers

think about better career of their children and suggest that the formal sector employment

is more worthwhile as compared to the urban informal sector employment. Thus, higher

the parents‟ education, lower the male and female informal sector participation in district

Bahawalpur.


household size on informal sector involvement. Firstly, it signifies the promotion of the



sector. The results point out that the coefficients of household size (HSIZ) are positive

and have statistically significant effect on the urban informal sector employment. The

participation of male informal sector employed increases by 11 and 10.8 percentage

points respectively as a result of one additional worker in the house. The positive

marginal effects may indicate that large household size makes it obligatory for household

heads to work more in accessible urban informal sector to improve the overall living

standard of family. Hence, workers are forced to indulge in easily available employment

in the urban informal sector.

Household size is also important variable that determine the decision to

participate in the labour market. The results reveal that the coefficients of HSIZ are

negative and have statistically insignificant impact on females being employed in the

urban informal sector. Results conclude that male participants having large household

size are being employed more in urban informal sector.

Dependency ratio (DPNR) is another factor which influences the male workers‟

participation in the urban informal sector of district Bahawalpur. The coefficients of

cccxxix

dependency ratio (DPNR) for males‟ employment are found to be positive and

insignificant. The coefficient of dependency ratio (DPNR) for females‟ employment is

positive and insignificant in table 9.3, while, the coefficient of (DPNR) is negative and

insignificant for females‟ employment in table 9.4. The results may conclude that

dependency ratio has no influence on males and females urban informal sector

employment in district Bahawalpur.

In addition, family setup (FSP) variable has a positive and insignificant impact on

males‟ participation in the urban informal sector. However, an increase of one unit in

joint family setup increases the probability of females being incorporated in the urban

informal sector by about 22.6 and about 22 percentage points respectively. Our results

point out that those females who belong to joint family setup contribute more in the urban

informal sector to share family financial burden by involving in child care and productive

work at a same time. Consequently, the contribution of female workers in the urban

informal sector increases in district Bahawalpur.

Another variable i.e. number of children is also important in determining the

participation decision in the sector of employment. The results highlight that the number

of children (NCHL) are inversely associated with informal sector employment for male

workers. However, the study results are insignificant. Theoretically, the male workers

having more children do not participate more in informal economic activities due to

strong substitution effect of leisure. However, to fulfill their children‟s needs and

requirements, they have to work in the informal sctor. Results also indicate that

coefficients of number of children (6-14) variable are positive and insignificant.

Theoretically, female workers having more children of this age group work more in

informal economic activities to fulfill their requirements and expenses. Moreover, the

parents of these children pay comparatively lessened care to these children in the family.

However, some of the female workers with children in the house do not work in the

informal sector.

Results reveal that having male adolescents (NMAD) decreases the probability of

male participants in the informal sector employment by about 21.4 and 21.7 percentge

cccxxx

points. The coefficients of male adolescents are negative and statistically highly

significant. The possible outcome of the fact is that male adolescents increase household

income by working for pay in the formal and informal sector and male heads dissuade to

occupy in urban informal sector. It is the strong substitution effect, which forces the

parents to work due to extra money which is earned by their male adolescents. Results

point out that the coefficients of female adolescents (NFAD) are negative and

insignificant. The result indicates that informal employment for male decreases the

probability of being employed in the urban informal sector from an additional male

adolescent. The results conclude that both male and female participants participate less in

the urban informal sector in the presence of male adolescents.

Number of female adolescents (NFAD) also influences the decision to participate

in the urban informal sector in district Bahawalpur. The results highlight that male

workers employment in the urban informal sector increases by 12.5 and about 12.2

percentage points respectively because of one additional female adolescent at home. The

coefficients of number of female adolescents are positive and statistically significant in

the equation of male informal sector employment. The females with low education are

devoid of getting employment in the formal sector due to certain limitations. Another

argument is that they have to perform household responsibilities, so parents‟ especially

male heads are occupied there in accessible urban informal sector employment for their

better living standard. The coefficients of number of female adolescent variable (NFAD)

are positive and significant for female employment analysis. The probability of females‟

participation tends to increase by 20.3 and about 19.8 percentage points respectively

because of one additional female adolescent. The result makes clear that female

participants are 7.8 and 7.6 percent more inducted in urban informal sector in the

presence of female adolescents as compared to male workers. The reason of positive

partiality may be that female adolescents participants with low human capital cannot

access to formal sector employment and the female heads are forced to determine the

informal sector employment for their better living standard of female adolescents. Results

conclude that higher the female adolescents, the higher the probability of both genders in

the urban informal sector of district Bahawalpur.

cccxxxi

Spouse‟s participation (SPN) in economic activities has also an effect on the

urban informal sector employment. The male workers have less participation in informal

sector by 21.0 and about 20.7 percentage points respectively because of one unit increase

in spouse participation in earnings activities. While, females estimate reveal negative

influence of spouse‟s participation in economic activities. However, the results are found

to be insignificant. Our study results reveal that probability of males in informal

employment comparatively drops more likely than female informal sector employment

due to one unit increase in spouse participation in economic activities. The reason can be

that male workers‟ allocate less time to informal work due to strong income effect.

The coefficients of the household‟s value of assets (HVAT) are negative and

insignificant in males‟ employment analysis. Likewise, the coefficients of household‟s

value of assets (HVAT) are negative. The results are highly significant. An increase in

the value of assets diminishes the probability of females‟ induction in urban informal

sector employment. The reason possibly exists that the female workers due to the strong

substitution effect of not working and prefer leisure are less likely to involve in the urban

informal earnings activities with an increase in household‟s value of assets. Results show

that an increase in value of assets reduces the chances of females‟ participation in the

urban informal sector. It is argued that participants stop working on an increase in value

of financial assets. The reason can be that majority of females include poor households

whose life remains hand to mouth most of the time. This situation makes them habitual to

a non-progressive mentality. If they get enough financial resources, they temporarily stop

working to enjoy the benefits of those extra pennies. It may also be the reason that with

lack of basic education, they are reluctant to invest anymore.

The rural-urban migration (RMGT) is significant factor which has an influence on

sector of employment. Rural-urban migrant (RMGT) workers are occupied in the urban

informal sector. The coefficient of variable rural-urban migrant is positive and

statistically significant in male employment analysis. The probability of workers being

employed more in the informal sector increases by about 25 percentage points due to an

increase of one aditional rural-urban migrant worker as compared to formal sector

employed workers. The coefficients of migrant female informal sector workers are

cccxxxii

positive though the results are insignificant. The reason can be that migrant female

workers can find job in the formal sector. Findings indicate that urban informal sector

absorbs more male migrant workers as compared to female workers. Results conclude

that the urge of urban male informal employment to saturate the labour is high in district

Bahawalpur.

This is due to the fact that the male rural to urban migration increases due to rural-

urban wage differential but the probability of getting employment opportunities in formal

sector is low as compared to migration rate.

cccxxxiii


Informal Sector in District Bahawalpur. Probability of Informal sector Employed

(18-64).

Male Female

Explanatory


Marginal


Marginal

Effects

CONSTANT -1.1517 -1.0552 4.3972 2.6005

AGY 0.0073 0.3965 0.0017 -0.0391 -1.2864 -0.0086

EDY -0.0830 -1.5035 -0.0193 -0.1815*** -2.6783 -0.0401

MRS 0.4216 0.9352 0.0983 1.2070** 1.8169 0.2669

FTD -0.9274*** -2.4965 -0.2162 -1.2435*** -2.5411 -0.2749

FEDU -0.9399*** -2.7902 -0.2191 -0.5959 -1.1366 -0.1318

MEDU -0.4940 -1.3395 -0.1152 -1.6926*** -3.1129 -0.3742

HSIZ 0.4733*** 4.0311 0.1103 -0.0239 -0.1763 -0.0053

DPNR 0.2444 0.3144 0.0570 0.4298 0.3505 0.0950

FSP 0.1245 0.3443 0.0290 1.0214** 1.7440 0.2258

NFAD 0.5355*** 2.7617 0.1248 0.9171*** 2.6494 0.2028

NMAD -0.9200*** -3.7337 -0.2145 -0.3892 -1.1406 -0.0860

NCHL -0.0959 -0.8339 -0.0224 0.2421 1.2914 0.0535

SPN -0.9024** -2.2893 -0.2103 -0.2442 -0.4432 -0.0540

HVAT -0.0000 -1.1251 -0.0000 -0.0000*** -2.9129 -0.0000

RMGT 1.0709** 2.4887 0.2497 0.2197 0.3941 0.0486



LR Statistics (15df) =142.53 LR Statistics (15df) =119.79

Mecfadden R2=

0.35 Mecfadden R2= 0.48

P-value = 0.0000 P-value = 0.000


Note: The Z- statistic is that of associated coefficients from the logit model, where the formal sector employment is

taken as base outcome. Non-formal education is taken as the base category.




cccxxxiv


Informal Sector in District Bahawalpur with Different Levels of Education-

Probability of Informal Sector Employed (18-64).

Male Female

Explanatory


Marginal


Marginal

Effects

CONSTANT -1.8852 -1.7674 3.3121 1.9541

AGY 0.0064 0.3418 0.0015 -0.0424 -1.3516 -0.0094

EDU II 0.5847 0.7564 0.1363 0.1684 0.1657 0.0372

EDU III -0.0506 -0.0800 -0.0118 -0.7765 -0.8007 -0.1717

EDU IV -0.1507 -0.2251 -0.0351 -1.0008 -1.1359 -0.2213

EDU V -0.4486 -0.6398 -0.1046 -1.5654* -1.6443 -0.3461

EDU VI -1.0462 -1.3729 -0.2439 -1.8266** -2.0524 -0.4039

MRS 0.5305 1.1536 0.1237 1.3520** 1.9309 0.2989

FTD -0.9486** -2.5084 -0.2211 -1.2832*** -2.5045 0.2837

FEDU -0.9688*** -2.7986 -0.2258 -0.6132 -1.1148 -0.1356

MEDU -0.4317 -1.1498 -0.1006 -1.7376*** -3.1340 -0.3842

HSIZ 0.4637*** 3.8951 0.1081 -0.0116 -0.0842 0.0025

DPNR 0.2048 0.2585 0.0477 0.4203 0.3359 0.0929

FSP 0.2439 0.6547 0.0569 0.9931* 1.6573 0.2196

NFAD 0.5221*** 2.7109 0.1217 0.8948** 2.5770 0.1978

NMAD -0.9292*** -3.7240 -0.2166 -0.3402 -1.0133 -0.0752

NCHL -0.1043 -0.8938 -0.0243 0.2566 1.3509 0.0567

SPN -0.8857** -2.2196 -0.2065 -0.3714 -0.6621 -0.0821

HVAT -0.0000 -1.0319 -0.0000 -0.0000** -2.7475 -0.0000

RMGT 1.0828** 2.4677 0.2524 0.1676 0.2972 0.0371

Sample Size (N) = 310 Sample Size (N) =196




2 =0.48

P-value =0.0000 P-value = 0.000


Note: The Z- statistic is that of the associated coefficients from the logit model, where the formal sector

employment is taken as base outcome. Non-formal education year is taken as the base category.




cccxxxv


and Comparison in Urban Informal Sector in District

Multan

In this section, an analysis of determinants of gender employment in the urban informal

sector and comparison is examined in district Multan with complete years of education.

The impact of different education levels on gender employment in the urban informal

sector is also estimated in district Multan. Here, a binary logit model is used to evaluate

the determinants of gender employment in the urban informal sector and comparison

regarding different levels of education in district Multan.

Tables 9.5 and 9.6 give the binary logit estimates of determinants of gender

employment in the urban informal sector and comparison with three sets of numbers as

estimated parameters, their asymptotic z-statistic and marginal effects for male as well as

for female analysis. The intercept terms are positive and statistically significant in tables

9.5 and 9.6 in male analysis while it is positively significant in females‟ employment in

table 9.5 and insignificant in table 9.6. The marginal effects indicate the effect of a unit

change in each variable on the probability of participation in the urban informal sector

employment relative to the base category of formal sector employment.

Age of the participants of labour market influences their decision. Two views can

be presented about age of workers involved in the urban informal sector. Firstly, if the

relative participation of young people is greater in the informal sector, the very sector

may probably be considered a transition stage before opting formal sector. Secondly,

informal sector may be considered as a desirable constant choice if there is a large

participation ratio of older persons in urban informal sector. We have used complete

years of age as an explanatory variable in informal sector employment model for both

male and female workers in district Multan. The coefficients of variable age (AGY) are

positive and statistically significant. The probability of male workers being engaged in

the informal employment increases by 0.8 and about 1 percentage points for one year

cccxxxvi

increase of age of the informal sector worker. The reason can be that people enter into

urban informal sector at early age and stay there

for long time period in district Multan. The lack of higher education for formal sector job

is one of the reasons for relatively increased employment in urban informal sector of

district Multan.

Resutls found that the coefficients of complete years of age (AGY) for female

employment are observed to be positive and statistically insignificant. The positive

marginal effects draw attention to that probability of female participants being employed

in the urban informal sector increases. It is because that the female workers in their old

age have low dependency of household responsibilities. Furthermore, lack of higher

education compels these females to occupy the urban informal sector. Overall, it is

concluded that female workers with high education are moving towards the urban formal

sector which is very important and lucrative source of earnings in district Multan.

Another factor “education” is an important one as it makes a way to enter into

sector of employment. We have used complete years of education as an explanatory

variable for the urban informal sector employment in table 9.5. For males‟ employment

analysis, the coefficient of complete years of education (EDY) seems to be negative and

statistically significant. The probability of male workers being employed in the informal

sector falls by 3.3 percentage points as a result of one year increase in education of the

worker. The possible outcome of the fact is that male workers having high education

prefer to work in the formal sector.

For females estimate, it is observed that education reduces the probability of

informal sector employment. The result indicates that coefficient of the complete years of

education (EDY) is negative and highly significant. One year increase in education

reduces the probability of females working in urban informal sector by 6.6 percentage

points. Our results indicate that probability of females‟ employment in urban informal

sector drops by 3.3 percent at faster rate than male workers due to one year increase in

education. Results possibly conclude that labourious and diligent females with high

cccxxxvii

education enter into more profitable formal labour market in district Multan. The similar

results are found in Florez‟s (2003) study findings.

cccxxxviii

In table 9.6 of the informal sector employment, education level of workers (EDY)

is included as a categorical variable with five categories (non-formal education as a base

category) in order to check the relationship between education levels and informal sector

employment. The results highlight that the coefficients of Middle level education (EDU

II) and Matric level education (EDU III) for males‟ employment are significantly

positive. The reason of this positive impact is that male workers with basic education

seek refuge in urban informal sector. The coefficient of Intermediate level education

(EDU IV) is positive but the result is statistically insignificant. While the coefficients of

Graduation level education (EDU V) and Master‟s or higher level education (EDU VI) is

found to be negative and insignificant. The economic interpretation of this negative

influence of higher education level on informal sector employment can be that the male

participants of labour market are switching over the code from the informal to the formal

sector due to rising high level education. Since this sector is viewed as permanent and

secure source of living.

Concerning female employment, the coefficient of Middle level education (EDU

II) is found to be positive. However, the result is insignificant. The result points out that

the negative impact of Matric level (EDU III) education and Intermediate level education

(EDU IV) are insignificant. While the coefficients of Graduation level education (EDU

V) and Master‟s or higher level education (EDU VI) are negative and statistically


Graduation and Master‟s level education decreases by about 42.4 and 60.7 percentage

points respectively as compared to the non-formal education. Furthermore, females‟

incorporation with high education increases the formal sector employment. Females are

reluctant to employ themselves in the urban informal sector due to lack of economic and

social capital. The study results may relate to the expected role of informal sector with

low capital accumulation.

Marital status (MRS) is an important factor which affects the participation

decision in labour market. The analyses shows that married male workers are less likely

to join the urban informal sector as compared to unmarried male workers. The

coefficients of marital status (MRS) are negative and statistically insignificant. It can be

cccxxxix

argued that married are working in the informal sector to fulfill their basic needs and to

make the family financially sound in district Multan.

The results point out that the coefficients of marital status (MRS) for female

workers are negative. The probability of female participants in informal sector decreases

by 24.2 percentage point respectively in table 9.6 in result of an increase of one married

female worker. The evidence reveals that married females are comparatively 10.3 percent

more employed in the urban informal sector of district Multan. The possible outcome of

the fact is that working in the urban informal sector is thought to be risky and the higher

preference is given to the more secure and riskless formal labour market. The study

results conclude that urban informal sector is not the sector of married female workers in

district Multan. Our findings are similar to Funkhouser‟s (1996) results.

Findings highlight that the urban informal sector participants have less formal

training (FTD). Most participants have some kind of informal training. The coefficients

of formal training (FTD) for male workers are negative and highly significant. The

probability of males‟ working in urban informal sector falls by about 21.6 and 24.3

percent because of one unit increase in formal training. However, the coefficients of

formal training are negative and insignificant for females‟ employment. Our results

conclude that male workers are comparatively more likely to be employed in the formal

sector having formal training. The urban informal sector employment does not require the

formal skills. To utilize the capabilities and skills with better match, male workers having

formal training and skills tend to incline towards the formal labour market in district

Multan.

Presence of parent‟s educational status influences the probability of participation

in urban informal sector in district Multan. Theoretically, it is predictable that those

workers, whose parents are well-educated, are, reluctant to participate towards menial

jobs in the urban informal sector. Findings confirm the hypothesis. On the part of male

employment analysis, the coefficients of variable father‟s education (FED) are negative

and statistically insignificant. Some of the male workers have to work in the informal

sector.

cccxl

In addition to another reason, parents‟ educational status also affects the females

being employed in the urban informal sector. In theory, it is expected that the female

workers whose parents are educated are less likely to incline towards the urban informal

sector. The probability of working in the urban informal sector diminishes by 24.3 and

27.2 percentage points respectively due to one unit increase in father‟s education

(FEDU).

The coefficients of mother‟s education (MEDU) are significantly negative. The

male workers‟ participation in the informal sector decreases about 15.2 and 15.1

percentage points respectively as a result of one unit increase in mothers‟ education. The

female workers whose mothers are uneducated are about 36.4 and about 40.3 percentage

points less likely to be engaged in the urban informal sector as compared to those whose

mothers are educated. Findings also point out that more females 21.2 and 25.2 percent

points are less likely to be engaged in the urban informal sector of district Multan as

compared to male workers. The reason probably exists that the educated parents

especially mothers provide higher educational opportunities to their children which lead

to development of the formal sector employment in district Multan. The results conclude

that higher the parents‟ education, lower the female employment in informal sector in

district Multan.

The results show that the coefficients of household size (HSIZ) are positive and

have significant effect on males‟ participation in the urban informal sector. When

household size increases by one, the male participants are more likely to be employed in

the urban informal sector by 5.5 and 6.5 percentage points respectively as compared to

the formal sector employment. In order to improve the overall living standard of the

family members, heads decide on to work more in accessible urban informal sector jobs.

The results conclude that male workers with large household size are more likely to be

employed in the urban informal sector in district Multan.

Concerning females‟ employment, household size has different effect on the

decision of working in urban informal sector. The coefficients of (HSIZ) are negative and

statistically insignificant. Theoreically, it is argued that females dissuade from economic

cccxli

activities in the presence of large household size or it becomes difficult for females to

engage in productive work instead of care work for large household size. The results

conclude that some of the females having more potential are working in the informal

sector with increased household size. Our findings reveal that male participants with

large household size are more likely to be engaged in the informal sector of district

Multan.

Dependency ratio (DPNR) is one important factor which motivates the

participants in the urban informal sector employment in district Multan. The coefficients

of dependency ratio (DPNR) for male workers are found to be positive and statistically

insignificant.

Though, the coefficients of (DPNR) are negative and statistically insignificant for

female urban informal sector employment. The dependency ratio has not significant

influence on growth potential of the urban informal sector in district Multan. The

insignificant results conclude that dependency ratio exerts no influence on males and

females urban informal sector employment in district Multan.

Family setup (FSP) helps to increase the growth potential of urban informal sector

employment. Results demonstrate that the coefficients of family setup (FSP) are found to

be negative and statistically insignificant in male analyses. On the part of females‟

employment, the results are different. The coefficients of family setup are positive and

highly significant for female informal employment.

The probability of female informal workers increases by 37.8 and 34.8

percentage points respectively as a result of one unit increase in joint family setup. The

possible outcome of the fact is that the females belonging to joint family system have

more additional working hours for informal activities (both market as well as home-

based) because domestic issues are contributed by other family members. Thus, urban

female informal sector employment increases due to joint family setup in district Multan.

The variable number of children of 6 -14 years old is also important to decide to

work in the sector of employment. Number of children (NCHL) also affects the urban

cccxlii

informal sector employment. The results point towards the fact that the number of

children variable (NCHL) has a negative and insignificant impact on males‟ working in

the urban informal sector employment. Theoretically, the workers having more these

children participate less in economic activities due to expected unearned income from

these children. The argument is that there is an increasing tendency of child labour in

district Multan.

It is hypothesized that the number of children variable (NCHL) has a positive and

significant impact on female workers‟ participation in urban informal sector. The

probability of female workers being engaged in the urban informal sector is curtailed by

10.4 and about 10.1 percentage points respectively by an increase of one additional child.

Theory shows that the female workers having more children from 6 to 14 participate

more in economic activities to accomplish their basic needs and to contribute family

expenses. Moreover, the mothers of children pay comparatively lessened care to these

children because they are free from household responsibilities in district Multan. Results

support Funkhouser‟s (1996) findings.

The variable number of male adolescents is important in determining the informal

sector employment. The male participants are less likely to be engaged in informal sector

by 7.4 percent respectively in table 9.6 due to an addition of one child in the family. The

coefficients of number of male adolescents (NMAD) are negative. On the other hand, the

coefficients of male adolescents‟ variable are negative and statistically insignificant for

female employment in the urban informal sector of district Multan.

Results seem to suggest that male participants are less likely to be involved in the

informal work as compared to female workers from an additional male adolescent. The

reason possibly exists that mostly male adolescents allocate their time in economic

activities to increase family income. Male household heads are less likely to participate

in the informal sector due to strong substitution effect of increased income of the male

adolescent.

cccxliii

The estimates for males make obvious that the probability of working in urban

informal sector increases because of an addition of one female adolescent (NFAD) at

home. The coefficient of female adolescent is positive but statistically insignificant in

table 9.5. Female adolescents (NFAD) variable influences the decision to work in labour

market. The coefficient of number of female adolescent is positive and significant in male

informal workers‟ analysis in table 9.6.

However the results reveal that the coefficients of number of female adolescent

(NFAD) for females‟ employment are positive and statistically significant. The females

are being inducted into informal sector by 12.4 and 13.2 percentage points as a result of

one additional female adolescent. It also owes to that female adolescents having low

formal education are faced with social constraints. It becomes obligatory for the parents

to work in the urban informal sector employment for the better living standard of their

female adolescents.

It has been noted that the spouse‟s participation in economic activities (SPN)

decreases the probability of urban male workers participation in the informal sector. The

study results are negative and have insignificant effects on employment. Relativity of

spouse participation in economic activities (SPN), the coefficients of spouse participation

in economic activities are positive and statistically insignificant. The insignificant results

point out that influence of spouse participation in economic activities on informal

employment is worthless in district Multan.

Household‟s value of assets (HVAT) has an important effect on the sector of

employment. The coefficients of the household‟s value of assets (HVAT) are negative

and statistically insignificant. The probable reason exists that some low educated workers

with their non-progressive mentality are unenthusiastic to invest extra financial resources

due to uncertainty. Results also indicate that the coefficients of the value of household‟s

assets (HVAT) for females‟ employment are positive and statistically insignificant.

Rural-urban migration (RMGT) also affects urban informal sector employment

decision in district Multan. An addition of one male rural-urban migrant worker in

cccxliv

informal sector increases the probability of male informal workers by 17.3 and 18.7

percentage points. The coefficients of rural to urban migration for female workers are

found to be positive. However, results exert insignificant effects.

These results conclude that the male informal employment is high or expands in

urban areas of district Multan Pakistan. It is because that informal sector is expanding

due to rural-urban migration, rural urban wage differential and low chance of

employment opportunities in formal sector.

cccxlv

The results may correlate with the expected role of the urban informal sector that

the sector is the refuge for rural migrants in the urban areas in district Multan.

cccxlvi


Informal Sector in District Multan-Probability of Informal Sector Employed (18-

64).

Male Female

Explanatory


Marginal


Marginal

Effects

CONSTANT 0.2307 0.2692 3.8762 2.1672

AGY 0.0352** 2.2579 0.0080 0.0361 1.3561 0.0085

EDY -0.1453*** -2.9611 -0.0331 -0.2791*** -3.3163 -0.0658

MRS -0.5794 -1.4011 -0.1318 -0.8905 -1.5183 -0.2098

FTD -0.9505*** -2.8797 -0.2162 -0.6165 -1.1496 -0.1452

FEDU -0.4905 -1.5244 -0.1116 -1.0318** -1.8469 -0.2431

MEDU -0.6683** -2.0930 -0.1520 -1.5456*** -3.1272 -0.3641

HSIZ 0.2422*** 3.0192 0.0551 -0.1793 -1.5151 -0.0422

DPNR 1.0273 1.3719 0.2337 -1.2889 -1.0250 -0.3037

FSP -0.1639 -0.5434 -0.0373 1.6048*** 3.1830 0.3781

NFAD 0.1619 1.1196 0.0368 0.5274** 1.9081 0.1243

NMAD -0.2411 -1.4492 -0.0548 -0.0877 -0.4001 -0.0207

NCHL -0.0511 -0.4434 -0.0116 0.4398** 2.5394 0.1036

SPN -0.3960 -1.2830 -0.0901 0.1340 0.3342 0.0316

HVAT -0.0000 -0.5707 -0.0000 0.0000 0.4294 0.0000

RMGT 0.7624** 2.3724 0.1734 0.5583 0.9720 0.1315



LR Statistic (15df) = 98.33 LR Statistic (15df) =132.91

Mcfadden R2 =0.24 Mcfadden R

2 = 0.51

P-value =0.0000 P-value = 0.000


Note: The Z- statistic is that of associated coefficients from the logit model, where the formal sector employment

is taken as base outcome. Non-formal education years are taken as the base category.




cccxlvii


Informal Sector in District Multan with Different Levels of Education- Probability

of Informal sector Employed (18-64).

Male Female

Explanatory

Variables

Coefficients Z-statistic Marginal

Effects

Coefficients Z-statistics Marginal

Effects

CONSTANT -2.1355 -2.3658 1.9734 1.1941

AGY 0.0425*** 2.5705 0.0097 0.0352 1.3120 0.0083

EDU II 1.7606*** 2.6285 0.4005 0.5112 0.4029 0.1204

EDU III 1.0030** 1.9328 0.2284 -0.6085 -0.6049 -0.1434

EDU IV 0.4261 0.7743 0.0969 -1.7383 -1.4549 -0.4095

EDU V -0.3128 -0.5573 -0.0712 -1.8002* -1.6369 -0.4241

EDU VI -0.7184 -1.1647 -0.1634 -2.5783** -2.4164 -0.6074

MRS -0.6088 -1.3935 -0.1385 -1.0290* -1.6819 -0.2424

FTD -1.0701*** -3.1063 -0.2434 -0.7037 -1.2772 -0.1658

FEDU -0.5446 -1.6111 -0.1239 -1.1549** -1.9585 -0.2721

MEDU -0.6655** -2.0155 -0.1514 -1.7093*** -3.2821 -0.4027

HSIZ 0.2874*** 3.3629 0.0654 -0.1774 -1.4499 -0.0180

DPNR -0.8155 -1.0497 -0.1855 -1.0510 -0.8068 -0.2476

FSP -0.0654 -0.2090 -0.0149 1.4781*** 2.8193 0.3482

NFAD 0.1593 1.0603 0.0362 0.5620** 2.0118 0.1324

NMAD -0.3229** -1.8984 -0.0735 -0.0669 -0.2924 -0.0158

NCHL -0.1193 -0.9786 -0.0271 0.4281** 2.4157 0.1009

SPN -0.4037 -1.2630 -0.0918 0.1786 0.4283 0.0421

HVAT -0.0000 -0.4864 -0.0000 0.0000 0.6389 0.0000

RMGT 0.8234** 2.4946 0.1873 0.3887 0.6538 0.0916





2 = 0.52

P-value = 0.000 P-value = 0.000



employment is taken as the base outcome. Non-formal education is taken as base category.




cccxlviii


and Comparison in Urban informal Sector in District Dera Ghazi

Khan

In this section, an analysis of the determinants of gender employment in the urban

informal sector and comparison is examined in district Dera Ghazi Khan. We have made

a choice of binary logit model in order to analyze the influence of explanatory variables

on gender employment with complete years of education and with different levels of

education.

Tables 9.7 and 9.8 present the logit estimates of the determinants of male and

female informal sector employment. Three sets of numbers are shown in three columns

which are estimated parameters, their asymptotic z-statistic and marginal effects for both

the models (male and female). The intercept term in the urban informal sector

employment equation for males is positive and statistically insignificant but negative and

insignificant in table 9.8. In female employment analyses, the intercept terms are

positively significant. The marginal effects indicate that the effect of a unit change in

each variable on the urban informal sector employment relative to the base category that

is formal sector employment.

Two views are presented about age of workers involved in the informal sector.


sector may probably be considered a transition stage before opting for formal sector.

Secondly, the informal sector may be considered as a desirable constant choice if there is

a large participation ratio of older persons in the informal sector. Age is another

important matter which affects participants‟ choice in labour market. The coefficients of

complete years of age (AGY) for male workers are found to be positive. However, study

results are statistically insignificant. In old age, increased experience of male workers‟

with initial basic education motivates them to engage in informal sector employment.

cccxlix

Female participants of urban informal sector are also affected by their age (AGY).

The results indicate that the coefficients of years of age (AGY) appear to be positive and

statistically insignificant. The possible reason can be that female workers with high

formal education and high experience easily find out work in the formal sector in district

Dera Ghazi Khan.

Theoretically, education level of the participants in the labour market can play

two different roles: For instance, more educated workers tend to be more fertile because

their education serves as an impetus in enhancing their skill via training. On the other

hand, low education increases the probability of involvement in the urban informal

sector. We have used complete years of education as an explanatory variable in this

study. Results found that the coefficient of the complete years of education for both the

genders is (EDY) significantly negative. The probability of males‟ participation in the

urban informal sector falls by about 4.2 percentage points as a result of an increase of one

year in education of the worker. The trend shows that the level of education increases, the

marginal informal sector employment falls. The probability of female workers being

employed in the urban informal sector drops by 63.4 percentage points as a result of an

increase of one year in education. Findings indicate that females‟ employment decreases

more by 58.2 percentage points due to an increase of one year age of the worker.

Consequently, male participants with higher education are employmed more in the formal

sector employment which is well-paid.

In model II, education level of workers (EDY) is added as a categorical variable

with five categories (non-formal education is taken as base category). The results point

out that the coefficient of Middle level education (EDU II) is positive and has significant

effect on participation in the urban informal sector. The coefficient of Matric level

education (EDU III) is positive and statistically insignificant. The coefficient of

Intermediate level education (EDU IV) exerts a negative effect. The coefficient for

Graduation level education (EDU V) is negative and significantly affects the male

participants in the urban informal sector. The coefficient of Master‟s or higher level

education (EDU VI) appears to be negative and statistically significant.

cccl

For female informal sector employment, the coefficients of Middle level

education (EDU II) and Matric levels education (EDU III) are positive. However, the

study results are statistically insignificant. The results also reveal that coefficient of

Intermediate level education (EDU IV) is negative and insignificant. The coefficient of

Graduation level education (EDU V) is negative. The coefficient of Master‟s or higher

level education (EDU VI) is found to be negative and statistically significant. The results

conclude that the urban informal sector absorbs the low educated workers (male and

female) in district Dera Ghazi Khan.

The results demonstrate that the coefficients of marital status (MRS) for male

participants are positive. However, the study results are statistically insignificant in the

analysis. The bulk of married male workers possibly having high formal education join

the formal sector in order to meet up their family necessities. Female estimates show that

the coefficients of marital status (MRS) are negative and statistically insignificant. The

married females are less willing to employ themselves into the formal economic activities

due to performance of household obligations and responsibilities in district Dera Ghazi

Khan.

Formal training (FTD) has a greater impact on the urban informal sector in district

Dera Ghazi Khan. The coefficients of formal training prove to be negative and highly

significant. The male participants are less likely to be employed in the urban informal

sector by 23.2 and 19 percentage points respectively because of an increase of one unit in

formal training. Equally, the coefficient of formal training is negative and highly

significant for female informal sector employment. The probability of female workers

diminishes by about 46.7 and 54.1 percent due to an increase of one unit in formal

training. The study findings indicate that females‟ participation relatively decreases at

faster rate by 23.5 and 35 percentage points respectively in informal sector as compared

to male participants. It owes to that many workers especially female desire to occupy the

formal sector for proper utilization of their formal skills and where they are satisfied to

perform their responsibility in a better way. Results conclude that higher the formal

training, lower the urban male and female informal sector employment in district Dera

Ghazi Khan.

cccli

In addition to another reason, parents‟ education helps to determine the growth

potential of the workers in the urban informal sector. It is predictable that the workers are

less likely to engage themselves in urban informal sector, whose parents are educated.

The results highlight that coefficients of father‟s education (FED) seem to be negative

and statistically significant for male workers. The probability of males‟ working in urban

informal sector diminishes by about 13.8 and 14 percentage points respectively due to

one unit increase in father‟s education (FEDU). Parental education also affects the urban

female informal sector employment decision. The coefficients of father‟s education

(FEDU) are negative and statistically significant in table 9.7. The probability diminishes

by about 24 percentage points respectively in table 9.8 due to one unit increase in father‟s

education (FEDU) in female analysis. Findings show that 14.5 percent more female

workers are less being inducted into the urban informal sector because of one unit

increase in father‟s education.

Results also indicate that coefficients of mother‟s education (MEDU) are found to

be negative and statistically significant. Results show that one unit increase in mother‟s

education reduces the probability of male workers‟ involvement in urban informal sector

by 15.8 and about 15.6 percent respectively due to one unit increase in (MEDU). The

results also point out that coefficient of mother‟s education (MEDU) for male participants

is negative and highly significant in model II.

The coefficients of mother‟s education (MEDU) are found to be negative and

significant in model I. The probability of females‟ employment decreases by 29

percentage points due to one unit increase in mother‟s education. However, the

probability of female workers being employed in the urban informal sector falls about

13.9 percentage points more than male participation rate due to one unit increase in

mothers‟ education. Infact, the educated parents or mothers provide higher education to

their children and suggest for beneficial formal sector jobs to secure their future. The

results conclude that urban male and female informal sector employment decreases in the

presence of educated parents in district Dera Ghazi Khan.

ccclii


household size on informal sector involvement. Firstly, it signifies the promotion of the



sector. The results indicate that household size (HSIZ) positively affects the decision to

work informally. The coefficients of (HSIZ) exert positive and significant effect on

males‟ involvment in the urban informal sector. One additional member in the house

increases the male work participation by 3.2 and about 4.1 percentage points respectively.

It also owes to that the basic necessities of family members force the male heads to call

upon more in the urban informal sector employment. Results conclude that male urban

informal sector is a sector with large household size in district Dera Ghazi Khan.

Different from males‟ results, the household size has a negative impact on

females‟ contribution in the urban informal sector. The results indicate that the

coefficients of varible HSIZ are negative and have insignificant effect on females‟

informal sector employment. It may also be due to that females with large household size

also participate in the informal employment as they have to fulfill the family members‟s

needs in district Dera Ghazi Khan.

Dependency ratio (DPNR) affects positively the male worker‟s participation

decision in the urban informal sector. The coefficients of dependency ratio (DPNR) for

male paricipants are positive and statistically insignificant. The estimates point out that

the coefficients of dependency ratio (DPNR) seem to be negative and are statistically

insignificant.

Results show that coefficients of family setup (FSP) are negative and statistically

insignificant. Male workers living in joint family setup are less likely to participate in the

informal earning activities. This trend is due to strong substitution effect of more leisure

and less work.

However, coefficient of family setup has a positive and highly significant

influence on females‟ informal sector employment. The workers who belong to joint

cccliii

family setup are being employed in the informal sector by about 41.6 and 42.7 percentage

points respectively as compared to the formal sector. The possible outcome of the fact is

that females living in joint family system have more extra working hours to work in the

urban informal activities because there are other family members to share the household

responsibilities. As a result, females are occupied more in the urban informal sector

employment. The results conclude that higher the joint family set up, higher the female

employment in the informal sector in district Dera Ghazi Khan.

cccliv

The result highlights that the number of children variable (NCHL) has a positive

and insignificant impact on males‟ participation in the urban informal sector employment.

Results also indicate that number of children (NCHL) have a positive and significant

impact on females informal sector participation decision. The females are being invoked

more in the informal sector by 10.2 and 14 percentage points for an increase of one

additional child. The results make clear that female participants are more likely to be

engaged in the urban informal sector in the presence of children as compared to male

workers. The results indicate that the females pay relatively lessened care or look after

the children and they indulge in the urban informal economic activities to cope up their

basic needs. Findings support the Funkhouser‟s (1996) results.

Next variable is about having male adolescents (NMAD). The male workers‟

participation in the informal sector diminishes by about 7 and 9.7 percentage points due

to an addition of one more male adolescent. The coefficients of the variable number of

male adolescents (NMAD) are negative and statistically significant. The females are

being employed less in the informal sector by 16.6 percentage points for an increase of

one male adolescent in both models. Our study results reveal that there is 9.6 and 6.9

percent more decline in female participation for an additional male adolescent. The

participants or female participants dissuade to work to any further extent due to increased

unearned income of the male adolescent. The results conclude that contribution in the

urban informal sector and numberof male adolescents are inversely correlated in Dera

Ghazi Khan. Results are similar with Funkhouer‟s (1996) findings.

The noteworthy result in table 9.7 point out that probability of males‟ induction in

the urban informal sector increases by 6.8 percentage points because of an addition of

one female adolescent (NFAD) at home. The result indicates that the coefficient of

number of female adolescent is positive and statistically insignificant in table 9.8. For

females‟ employment, the probability increases because of an addition of one female

adolescent at home. The results also indicate that the coefficients of female adolescents

are positive and highly significant. The female workers are being employed in the urban

informal sector by about 22.9 and 26.2 percentage points respectively due to an addition

of one female adolescent. The study findings highlight that females are 16.1 and 3.9

ccclv

percent more likely to be involved in the urban informal sector because of one additional

female adolescent as compared to male workers. In order to support family and to bear

the expenditures of female adolescents, the parents especially females are enthusiastic to

work in an accessible urban informal sector. The results conclude that presence of female

adolescent increases the growth potential of urban informal sector employment in district

Dera Ghazi Khan. In this way the prospectus of the urban informal sector employment

increases.

The result in table 9.7 highlights that the coefficient of spouse participation in

economic activities (SPN) is found to be negative and statistically significant at 5 percent

level of significance. The result in table 9.8 indicates that spouse‟s participation in

economic activities (SPN) decreases the urban male informal sector employment by 15.9

percentage points. It has been observed that the spouse‟s participation in economic

activities decreases the likelihood of urban male informal sector employment by 16.5

percentage points. Relativity of spouse participation in economic activities, results also

indicate that the spouse‟s participation in economic activities reduces the probability of

urban female participants of the informal sector by 22 and 24.5 percentage points. The

evidences show the 5.5 and 8.6 percent more falls in female employment due to one unit

increase in spouse involvement in economic activities as compared to male workers. If

the spouses are working in earnings activities, the female workers are less probable to

work due to less awareness, insufficient jobs according to the level of education in the

formal labour market. There are the social or religious constrictions that expect the

female spouses to provide more care to children and to perform household

responsibilities.

Household‟s value of assets (HVAT) has a positive and insignificant impact on

male informal sector employment as indicated by the positive coefficients in both

equations. However, the coefficients of the household‟s value of assets (HVAT) are

observed inverse and insignificant for female workers. It is argued that the females invest

more the extra financial resources due to uncertainty.

ccclvi

In terms of effect of rural-urban migration (RMGT), the coefficients of rural-

urban migrant variable are found to be positive and statistically insignificant for males

working in the urban informal sector. These results conclude that the strength of informal

employment is not high in urban areas of district Dera Ghazi Khan. While the

coefficients for female migrant workers are positive and statistically insignificant. These

results conclude that the informal sector is can not absorb more migrants due to high

rural-urban migration, high rural urban wage differential and low probability of getting

employment opportunities in the formal sector.

ccclvii


Informal Sector in District Dera Ghazi Khan-Probability of Informal Sector

Employed (18-64).

Male Female

Explanatory

Variables


Effects


Effects

CONSTANT 1.0659 0.9658 3.3787 1.8999

AGY 0.0214 1.2406 0.0045 0.0074 0.2831 0.0016

EDY -0.1980*** -3.5284 -0.0416 -0.1539** -2.3870 -0.6340

MRS 0.2579 0.6411 0.0542 -0.1672 -0.2436 -0.0370

FTD -1.1047** -2.6305 -0.2320 -2.1106*** -3.1486 -0.4667

FEDU -0.6549** -1.8585 -0.1375 -1.2814** -2.0523 -0.2833

MEDU -0.7518** -2.1050 -0.1579 -1.3131* -1.9520 -0.2903

HSIZ 0.1537** 2.1551 0.0323 -0.1094 -0.9275 -0.0242

DPNR 0.9504 1.4313 0.1996 -0.1691 -0.1341 -0.0374

FSP -0.0841 -0.2408 -0.0177 1.8798*** 2.9803 0.4156

NFAD 0.3255** 2.2096 0.0684 1.0362*** 2.7770 0.2291

NMAD -0.3317* -1.7993 -0.0697 -0.7513** -2.1388 -0.1661

NCHL 0.0791 0.6514 0.0166 0.4617* 1.9021 0.1021

SPN -0.7868** -2.1951 -0.1652 -0.9990* -1.8179 -0.2209

HVAT 0.0000 0.3000 0.0000 -0.0000 -0.9920 -0.0000

RMGT 0.1627 0.4821 0.0342 0.8805 1.4229 0.1947

Sample Size (N) = 306 Sample Size (N) =181



Mcfadden R2

= 0.32 Mcfadden R2 =

0.56

P-value = 0.000 P-value = 0.000


Note: The Z- statistic is that of associated coefficients from the logit model, where the formal sector employment is

taken as base outcome. Non-formal education is taken as base category.




ccclviii


Informal Sector in District Dera Ghazi Khan with Different Levels of Education-

Probability of Informal Sector Employed (18-64).

Male Female

Explanatory

Variables


Effects


Effects

CONSTANT -0.8764 -0.8062 3.4590 1.8086

AGY 0.0164 0.9049 0.0034 0.0008 0.0283 0.0002

EDU II 1.2907* 1.7825 0.2710 0.6289 0.6393 0.1390

EDU III 0.5474 0.9274 0.1150 0.3093 0.3678 0.0684

EDU IV -0.2976 -0.5043 -0.0625 -0.6224 -0.6508 -0.1376

EDU V -1.0698* -1.7138 -0.2247 -1.8437** -1.8605 -0.4076

EDU VI -2.3481*** -2.4860 -0.4931 -3.9582*** -2.8184 -0.8751

MRS 0.4001 0.9368 0.0840 -0.4542 -0.5942 -0.1004

FTD -0.9069** -2.0297 -0.1904 -2.4460*** -3.2222 -0.5408

FEDU -0.6721* -1.7980 -0.1411 -1.0800 -1.5912 -0.2388

MEDU -0.7415** -1.9526 -0.1557 -1.2398 -1.6216 -0.2741

HSIZ 0.1933*** 2.6449 0.0406 -0.1909 -1.4948 -0.0422

DPNR 0.9172 1.2745 0.1926 -0.7662 -0.5232 -0.1694

FSP -0.0818 -0.2262 -0.0172 1.9337*** 2.7240 0.4275

NFAD 0.1951 1.2411 0.0410 1.1860*** 2.8333 0.2622

NMAD -0.4645** -2.2982 -0.0975 -0.7515** -1.9808 -0.1662

NCHL 0.0428 0.3293 0.0090 0.6440** 2.2877 0.1424

SPN -0.7573** -2.0886 -0.1590 -0.1118* -1.8720 -0.2458

HVAT 0.0000 0.0377 0.0000 -0.000 -0.9280 -0.0000

RMGT 0.0615 0.1745 0.0129 0.6857 1.0026 0.1516


Log Liklihood = -120.6264 Logloklihood = -44.007


Mcfadden R2

=0.36 Mcfadden R2

= 0.62

P-value = 0.000 P-value = 0.000



employment is taken as base outcome. Non-formal education is taken as base category.




ccclix


The gist of this chapter is that we have done an econometric analysis of gender

employment and comparison in the urban informal sector of Southern Punjab, Pakistan.

We have used the binary logit model for this analysis. The analysis of present study is

based on stratified random sample of 1506 participants of labour market. Out of which,

934 are male and 572 are female workers in the informal and formal sector. The study

has found that that most of the explanatory variables produce different results at different

levels of analysis.

In the analysis of Southern Punjab, age of the male workers has a positive trend in

participating in the urban informal sector. The education (EDY) appears to be negative

and highly significant factor in determining the growth potential of informal sector

employment. The Middle level education (EDUC II) is significant and positively

correlated with the participation in urban informal sector employment. Graduation (EDU

V) and Master‟s or higher education (EDU VI), formal training (FTD), mother‟s

education (MEDU), father‟s education (FEDU), number of male adolescents (NMAD),

and spouse participation in economic activities (SPN) negatively affect the male informal

sector participants. The household size (HSIZ), dependency ratio (DPNR), number of

female adolescents (NFAD) and rural-urban migrants (RMGT) have significantly

positive influence on the decision of males‟ employment in the urban informal sector of

Southern Punjab. The presence of these variables increases the growth potential of the

urban informal sector.

On the part of female employment, the age (AGY) has a positive but insignificant

effect on urban informal sector employment. The complete years of education (EDY)

affect negatively the female worker‟s involvement regarding urban informal sector. The

Intermediate level education (EDU IV), Graduation level education (EDU V) and

Master‟s or higher level education (EDU VI) variables are found to be negative and

highly significant. The effect of marital status is observed to be negative and statistically

insignificant. Formal training (FTD), father‟s education (FEDU) and mother‟s education

(MEDU) have a negative and significant impact on the probability of being employed in

ccclx

the urban informal sector. Family setup (FSP) and number of female adolescents (NFAD)

variables are highly significant factors in determining the female informal sector

employment. The influence of household‟s value of assets (HVAT) on female informal

sector employment is found to be negative and statistically significant. The rural-urban

migrant (RMGT) variable is also positively correlated with urban female informal sector

employment and enhances the growth potential of the urban informal sector in Southern

Punjab, Pakistan.

In district Bahawalpur, the males‟ estimate demonstrates that complete years of

age affects positively although the results are insignificant. Contrarily, the education

seems negative and highly significant factor in determining the males‟ insertion in urban

informal sector. Master‟s or higher level education (EDUVI), formal training (FTD),

father‟s education (FEDU), number of male adolescents (NMAD) and spouse

participation in economic activities (SPN) negatively affects the male workers‟

absorption in the urban informal sector. Furthermore, the household size (HSIZ) strongly

and positively affects the male participants in urban informal sector employment. The

number of female adolescents (NFAD) and rural-urban migrants (RMGT) variable

positively and significantly influence the decision of males‟ participation in urban

informal sector in district Bahawalpur.

While the female results indicate that, age (AGY) has a negative and insignificant

impact on urban informal sector employment. The complete years of education (EDY)

affect negatively the female worker‟s participation in the urban informal sector. In

addition, Graduation level education (EDU V) and Master‟s or higher level education

(EDU VI) variables are found to be negative and highly significant for female informal

sector workers. The effect of marital status is positive and insignificant at 5 % level.

Formal training (FTD), mother‟s education (MEDU) and household‟s value of assets

(HVAT) have a negative and highly significant impact on probability of female being

employed in the urban informal sector employment. Family setup (FSP) and number of

female adolesents (NFAD) affects positively the females‟ participation decision in the

urban informal sector in district Bahawalpur.

ccclxi

In district Multan, the complete years of age (AGY) has a positive but

insignificant influence on male worker‟s participation in urban informal sector. The

coefficient of education in complete years (EDY) gives the negative impression. Middle

(EDU II) level education and Matric level education (EDU III) are positively correlated

with participation decision in the urban informal sector. The effect of marital status

(MRS) on informal sector employment is negative. Formal training (FTD), mother‟s

education (MED), number of male adolescents (NMAD) negatively affect the males‟

incorporation in the urban informal sector. Moreover, household size (HSIZ) strongly

positively affects male participants to work in the urban informal sector.The rural-urban

migrant (RMGT) variable positively and significantly influences the urban informal

labour market of district Multan.

The results also indicate that the complete years of education (EDY) has a

negative and highly significant effect on the female workers‟ participation concerning

urban informal sector. Results show that Graduation level education (EDU V) and

Master‟s or higher level education (EDU VI) variables are found to be significantly

negative for the female employment in the urban informal sector. The effect of marital

status is negative and insignificant at 10 % level of significance. Father‟s education

(FEDU) and mother‟s education (MEDU) have negative and highly significant impact on

females‟ inclusion in the urban informal sector. On the contrary, family setup (FSP) and

number of female adolescents (NFAD) affects positively the urban female informal

sector employment in district Bahawalpur.

The empirical results of district Dera Ghazi Khan highlight that the influence of

education (EDY) is negative and highly significant factor for the urban male informal

sector employment. The Middle level education (EDU II) is positively associated with

the urban informal sector employment. The effect of marital status (MRS) on the

probability of inclusion in the urban male informal sector is positive and insignificant.

Formal training (FTD), father‟s education (FEDU), mother‟s education (MEDU), number

of male adolescents (NMAD) and spouse participation in economic activities (SPN)

negatively affect the males‟ involvement in the urban informal sector. Moreover, the

ccclxii

household size (HSIZ) is positively associated with the urban male informal sector

employment.

The effect of education (EDY) on females‟ insertion in urban informal sector

seems to be negative. In terms of level of education, Graduation (EDU V) and Master‟s

or higher level education (EDU VI) are negative and significant for urban female

informal sector participants. The coefficient of formal training (FTD) is observed

negative and highly significant. Father‟s education (FEDU), mother‟s education (MEDU)

and household size (HSIZ) and spouse participation in economic activities (SPN) have a

negative and significant impact on females‟ employment in the urban informal sector in

model I. However, family setup (FSP) and number of female adolescents (NFAD)

variables affects positively the females‟ contribution in the urban informal sector of

district Dera Ghazi Khan. The results are highly significant.

ccclxiii

Chapter 10

CONCLUSIONS AND POLICY RECOMMENDATIONS

In this research, the employment patterns and the earnings structure and

development are recognized by conducting a survey of three districts of Southern Punjab,

Pakistan. This is one of the first studies of cross districts comparisons in order to examine

contending views on the urban informal sector, determinants of the urban informal sector

employment, earnings determinants as well as impact on the development of participants.

The urban informal sector is a noteworthy part of the labour force in each of three

districts. The clear results of our findings of the informal sector confirm the previous

findings which have been made by conducting smaller surveys. Our findings indicate that

the urban informal sector is disproportionally youngest, the oldest, the least educated, the

sector of female workers and migrants as well. Additionally, it is observed that the urban

informal sector employment is closely associated with household composition for both

the genders (males and females). Each district has noteworthy returns to human capital

variables in the urban informal sector. Additionally, development of participants of urban

informal sector is gauged by economic, human and social capital. By and large, the

workers are not the poorest in the urban informal sector of Southern Punjab, Pakistan.

However, they are economically poor at household level. The participants have enhanced

the growth potential of urban informal sector employment in Southern Punjab.

The urban informal sector employment has been viewed for an empirical

investigation by using descriptive statistics and binary logit model techniques in Southern

Punjab and separately in three divisions of Southern Punjab. We have also viewed

earnings determinants empirically by employing ordinary least square estimation. For

this, the primary data is collected by the author through stratified random sampling

technique. In the past, studies on urban informal sector employment have been largely

narrowed in their scope in investigating some of the aspects of urban informal sector and

determinants of urban informal sector employment. The conclusion has been given

comprehensively at the end of each chapter previously.

ccclxiv

In chapter 6, we have made a descriptive analysis of factors of workers

considering total, male and female participants of the urban informal and formal sector.

The analysis of the total sample of Southern Punjab and of different districts separately

has been enlarged by examining the determinants of probability of finding employment in

the urban informal sector in chapter 7. In chapter 8, we have examined the earnings

determinants of participants of the urban informal sector and their development. In

chapter 9, logit model is used to analyse the socio-economic and demographic factors of

males and females involved in the urban informal sector employment and their

comparison is also made.

We have explained the determinants of workers‟ participation in the urban

informal sector in Southern Punjab, Pakistan in chapter 6. The determinants of urban

informal sector employment have been examined by employing a descriptive data

analysis. In the descriptive data analysis, a relationship is built up between various socio-

economic and demographic factors and male informal sector employment in the urban

areas of Southern Punjab. Furthermore, an analysis of the determinants regarding

females‟ working in the informal sector has been made by employing descriptive data

analysis. Here, a relationship is established between various factors and urban informal

sector employment in Southern Punjab, Pakistan.

In chapter 7, we have used econometric analysis to investigate the determinants of

urban informal sector employment. A binary logit model is applied. The analysis of

current study is based on simple random and stratified random sampling of 1506 informal

sector participants in the urban areas of Southern Punjab. The analysis has also been

made separately in each division with different sample size. In chapter 7, the results

indicate that all of the variables such as age of participants (AGY), their complete years

of education (EDY), sex (SEX), formal training (FTD), parental education (FEDU),

(MEDU), household size (HSIZ), dependency ratio (DPNR), family setup (FSP), number

of female adolescents (NFAD), number of male adolescents (NMAD), number of

children (NCHL), spouse participation (SPN), household‟s value of assets (HVAT), and

rural-urban migrants (RMGT) are highly significant factors except the variable marital

status (MRS) which is positive but highly insignificant in model I. In addition, level of

ccclxv

education such as education up to Middle (EDUC II), Intermediate (EDU IV), Graduation

(EDUC V), and Master‟s or higher level education (EDU VI) appear to be significant

except Matric level education (EDU III) which is found positive and insignificant. All

these significant variables of the urban informal sector employment in Southern Punjab

have correct sign that match the theoretical foundation.

The study results suggest that age of the respondents (AGY), their marital status

(MRS), the household dependency ratio (DPNR) and number of children (NCHL) are

found insignificant factors in model I in district Bahawalpur. While marital status affects

positively and significantly the informal sector employment decision in model II.

Considering the level of education, Middle level education (EDU II), Matric level

education (EDU II) and Intermediate level education (EDU IV) are insignificant factors

in district Bahawalpur. The results conclude that urban informal sector of district

Bahawalpur is a sector of people having low formal education and low formal training.

Moreover, it is sector of female workers and the workers of uneducated parents. The

informal sector is also sector of those workers who have large family size, who belong to

the joint family setup and it absorbs the migrant workers also. Moreover, the workers

participate less in the informal sector with spouse participation in economic activities,

number of male adolescents and with an increase in household value of assets. The

results are similar as hypothesized by the Neo-classical theory and approaches of the

urban informal sector.

In district Multan, the study results of urban informal employment model I

highlight that sex (SEX), the household dependency ratio (DPNR), spouse participation

in economic activities (SPN) and the household‟s value of assets (HVAT) are found to be

statistically insignificant variables. While, variable number of children (NCHL) affects

insignificantly the urban informal employment decision in model II. Whereas, Matric

level education (EDU III) and Intermediate level education (EDU IV) are observed to be

insignificant factors in urban informal employment in district Multan. The findings

conclude that the urban informal sector of district Multan is sector of aged people with

low education. The workers having low formal training are involved in the urban

informal sector employment. The workers of uneducated parents are engaged in the urban

ccclxvi

informal sector. The urban informal sector is also observed as sector of those workers

who have large family size and who belong to the joint family setup. Additionally, the

workers have a lower likelihood of participating in the informal sector with spouse

participation and male adolescents. The parents of children and number of female

adolescents are also engaged in this sector and finally, it is the sector of migrant workers

as well.

In informal sector employment model I, marital status is seemed as positive and

insignificant. The sex (SEX), dependency ratio (DPNR), family setup (FSP), the

household‟s value of assets (HVAT) and rural-urban migrants (RMGT) demonstrate the

insignificant effect on the urban informal sector employment in district Dera Ghazi Khan.

The result in model II makes obvious the insignificant influence of age of the workers

(AGY) on their induction in urban informal sector. The result of Matric (EDUIII) and

Intermediate level education (EDU IV) are also insignificant. Again marital status (MRS)

is positive and statistically insignificant. The results of sex (SEX) of the workers have an

insignificant effect on employment. The dependency ratio (DPNR) family setup (FSP),

the household‟s value of assets (HVAT) and rural-urban migrants‟ variable (RMGT) are

also insignificant in total analysis. Whereas, Matric level education (EDU III) and

Intermediate level education (EDU IV) are found to be insignificant factors in urban

informal employment in district Multan. In the light of findings in urban informal sector

of district Dera Ghazi Khan, it is concluded that urban informal sector is comprises

educated workers with low formal training and it is the sector of the workers of

uneducated parents. The urban informal sector is found as a sector of those workers who

have large family size and who belong to the joint family setup. The parents of children

and female adolescents are also engaged in this sector. In addition, the workers are found

to be less likely incorporated in urban informal sector with spouse participation and male

adolescents. The results are similar as theorized by the neo-classical theory and

approaches of the urban informal sector.

In chapter 8, age of the informal sector workers (AGY), the education in complete

years (EDY), sex (SEX), working hours (WHR) and household‟s value of assets (HVAT)

are found to be positive and significant factors that increase the earnings or returns of

ccclxvii

workers of urban informal sector in Southern Punjab. The returns to earnings from all

levels of education are also found to be positive and significant.

Results of earnings determinants of urban informal sector participants in district

Bahawalpur are also presented. The coefficients of complete year of education (EDY),

marital status (MRS), family setup (FSY), working hours (WHR) and household‟s value

of assets (HVAT) are observed to be significant and are increasing functions of earnings.

The variables such as Matric level education (EDU III), Intermediate level education

(EDU IV), Graduation (EDU V) and Master‟s or higher level education (EDU VI) are the

important positive significant factors.

In district Multan, the coefficients of age (AGY), complete year of education

(EDY), training (TRN), working hours (WHR) and value of assets (VAT) are seemed to

be significant and positive. These variables are increasing functions of earnings. The

variables such as Matric level education (EDU III), Intermediate level education (EDU

IV), Graduation (EDU V) and Master‟s or higher level education (EDU VI) are positively

significant. The results of all education levels are positive and significant. This indicates

that earnings increase with increasing levels of education of the participants of the urban

informal sector in district Multan.

In district Dera Ghazi Khan, coefficients of complete years of education (EDY),

sex of the worker (SEX), working hours (WHR) and household‟s value of assets (HVAT)

are found to be significant and positive factors. These variables are indicating increasing

returns in urban informal sector employment. The Middle level education (EDU II),

Matric level education (EDU III), Intermediate level education (EDU IV), Graduation

(EDU V) and Master‟s or higher level education (EDU VI) make clear the positive

significant effects. The results of all education levels are positively significant. These

variables are positively associated with earnings of participants of urban informal sector.

In Southern Punjab, the workers who are included as poor or their income is

below poverty line are 16.9 percent. The “non-poor” group is on average 83.1 percent in

urban informal sector. High economic capital shows development of the participants in

ccclxviii

urban informal sector. However, 72.7 percent of the households are poor in Southern

Punjab because their per capita income is below poverty line. A sufficient level of human

capital is observed among the urban informal sector participants in Southern Punjab,

Pakistan. On average, 52.7 percent of the workers have an access to health facilities and

79 percent of the workers have availed some housing facilities. This higher utilization of

human capital indicate high living standard of those who are the part of urban informal

sector. As regards social capital, 44.3 percent of the workers are participating in socio-

cultural activities in Southern Punjab and this indicates low development regarding socio-

cultural activities of the participants. By and large, development indicators show

development of participants of urban informal sector in Southern Punjab.

In district Bahawalpur, the workers who are included as poor are 21.2 percent

because their income is below poverty line. On average, the “non-poor” group is

estimated about 78.8 percent are in urban informal sector. This high economic capital

shows development of the urban informal sector workers. On average, poor households

are 67.2 percent in district Bahawalpur. Results indicate an adequate level of human

capital among the informal sector workers. On average, 48.7 percent of the workers have

an access to health facilities and 82.4 percent of the workers have availed some housing

facilities in district Bahawalpur. This higher utilization of human capital makes clear high

living standard of the workers of urban informal sector. In terms of social capital, 41.02

percent of the workers are engaging in socio-cultural activities. This low social capital

shows low development in terms of socio-cultural activities. The study concludes that on

the whole economic, human and social development indicators highlight development of

participants of urban informal sector of district Bahawalpur.

The study results demonstrate that 17.8 percent of workers are observed as poor

and 82.2 percent are “non-poor” in the urban informal sector of district Multan. The

results indicate that high economic capital highlights the high development level of the

participants in urban informal sector. However, result highlights that 73.9 percent

households are poor in district Multan. Among the urban informal workers, an adequate

level of human capital is also noted. Findings reveal that on average, 50.7 percent of the

workers have access to health facilities and 80.7 percent of the participants have availed

ccclxix

some housing facilities in district Multan. This higher utilization of human capital reveals

high living standard of the participants of urban informal sector. Regarding social capital,

43.9 percent of the urban informal sector participants are observed to be engaged in

socio-cultural activities. This low social capital indicates low development regarding

socio-cultural activities. On the whole, development indictors such as economic, human

and social indicators show development in the urban informal sector of district

Bahawalpur.

The study results reflect that 11.9 percent of the urban informal sector participants

are observed as poor in district Dera Ghazi Khan. The share of “non-poor” group is 88.1

percent. The results indicate high economic capital and development among urban

informal sector participants. It is found that 76.9 percent of the households are poor in

district Dera Ghazi Khan.The results also show that participants have an adequate level of

education in urban informal sector employment. The urban informal sector workers with

access to health facilities are observed at 59.2 percent of the sample and 73.49 percent of

the workers have availed housing facilities. The result indicates high living standard in

terms of higher utilization of human capital of participants in urban informal sector.

Results reveal low social capital such as 47.9 percent among the informal sector

participants, which shows low development in terms of socio-cultural activities. The

development indictors such as economic, human and social indicators show a high level

of development of the urban informal sector in Dera Ghazi Khan.

In chapter 9, we have done an econometric analysis for male and female

participants of urban areas of informal sector in Southern Punjab, Pakistan and separately

in each division. We have used the binary logit model for the said analysis. The analysis

of present study is based on simple random and stratified random sample of 1506

workers. Out of which, 934 are male workers and 572 are female participants of urban

informal and formal sector. The study has revealed that most of the explanatory variables

produce different results at different levels of analysis.

In an analysis of Southern Punjab, age of male participants has a positive trend in

joining the urban informal sector. While, education has a negative influence and is a

ccclxx

highly significant factor in determining the urban informal sector employment. Middle

level education (EDU II) and Matric level education (EDU III) variables are also

significant and positively correlated with the participation in urban informal sector.

Graduation level education (EDU V) and Master‟s or higher level education (EDU VI),

formal training (FTD), mother‟s education (MEDU), father‟s education (FEDU), number

of male adolescents (NMAD), and spouse participation in economic activities (SPN)

negatively affect male participants in the urban informal sector. The workers with high

education have a lower likelihood of working in the urban informal sector and those

participants who are formally trained are working more in the formal sector. Thus, urban

informal sector is a sector of the low educated with low formal training. The household

size (HSIZ), dependency ratio (DPNR), number of female adolescents (NFADS) and

rural-urban migrants (RMGT) variable positively and significantly influence the decision

of males‟ insertion into urban informal sector employment. Moreover, informal sector is

absorbing those who are rural-urban migrants and who have a large household size

especially with number of female adolescents.

Concerning females‟ employment, age has a positive but insignificant effect on

the urban informal sector employment participation. The complete years of education

(EDY) affects negatively the females‟ participation in urban informal sector. Whereas,

variables such as Intermediate (EDU IV), Graduation (EDU V) and Master‟s or higher

level education (EDU VI) are found negatively influencing the decision of participation

in the urban informal sector employment. The study results are highly significant. The

results conclude that females with low formal education are engaged in urban informal

sector of Southern Punjab. The highly educated are more likely to be engaged in the

formal labour maret. The effect of marital status (MRS) is negative and insignificant.

Formal training (FTD), father‟s education (FEDU) and mother‟s education (MEDU)

variables influence negatively the female employment. The results conclude that the

workers having formal training are less likely to be engaged in the informal sector while,

the proportion is higher for those having educated parents. Family setup (FSP) and

number of female adolescents (NFAD) is highly significant factor in determining the

females‟ participation in urban informal sector. The influence of household‟s value of

ccclxxi

assets (HVAT) on probability of the urban informal sector employment is found to be

negative and statistically significant. The rural-urban migratns (RMGT) variable is also

positively correlated with urban female informal sector employment in Southern Punjab.

The female migrants are being employed more in urban informal sector of Southern

Punjab, Pakistan.

By comparing the results of both the genders, results show that educated females

involved in the urban informal sector comparatively face more declines in their

absorption as compared to males‟ employment. Female workers‟ inclusion in informal

sector falls relatively more than male workers due to high formal training. Those female

workers, whose mothers are educated, are relatively less likely to be inducted in informal

sector as compared to male participants. Having male adolescents reduces the female

workers‟ participation more than the male workers in the urban informal sector of

Southern Punjab. The number of female adolescents leads to increase the females‟

absorption in the informal sector more than the male participants. The female workers

whose spouses are also working relatively reduces partaking in the informal sector more

than the male workers. Male migrants are being employed more in the urban informal

sector as compared to female workers.

In district Bahawalpur, the males‟ employment analysis demonstrates that

complete years of age affects positively although the result is insignificant. Contrarily,

education seems to be a negative and a highly significant factor in determining the urban

male informal sector employment. Master‟s or higher level education (EDU VI), formal

training (FTD), father‟s education (FEDU), male adolescents (NMAD), and spouse

participation in economic activities (SPN) negatively affect the urban male informal

sector participants. In the light of results, it is argued that higher education, parental

education and presence of male adolescents dissuade workers to enter into the urban

informal sector. Additionally, the household size (HSIZ) strongly and positively increases

involvement in the urban informal sector. The number of female adolescents (NFAD) and

rural-urban migrants (RMGT) variable positively and significantly affect the decision of

males‟ participation in urban informal sector.

ccclxxii

For female sample, results indicate that age has a negative and insignificant effect

on the urban informal sector employment. The complete years of education (EDY)

negatively affects the females‟ participation pertaining to the urban informal sector.

Considering the effect of level of education, Graduation (EDU V) and Master‟s or higher

level education (EDU VI) are found to be negatively correlated and statistically highly

significant. The effect of marital status (MRS) is positive and insignificant at 5 % level.

Formal training (FTD), mother‟s education (MEDU) and household‟s value of assets

(HVAT) variables are negative and have highly significant impact on female urban

informal sector employment. Family setup (FSP) and number of female adolescents

(NFAD) positively affects the urban female informal sector employment in district

Bahawalpur.

The comparison makes clear that the female workers with rising age have less

participation in the urban informal sector as compared to male participants. Our results

point out that those female wokers who belong to joint family setup are working more in

the urban informal sector of Southern Punjab as compared to male workers. Formally

trained female participants have comparatively less participation in informal sector than

male participants. Having adult members‟ increases rather more the probability of female

informal employment as compared to male participants. Male workers of the informal

sector show a decreasing trend of being employed in the urban informal sector from an

additional male adolescent. The results reflect that female participants are being inducted

more in urban informal sector in the presence of female adolescents as compared to male

workers. Our study results reveal that male informal sector employment is quite more

likely to be decreased than female employment. The comparison is that urban informal

sector absorbs more male migrants as compared to female workers.

In district Multan, the complete years of age has a positive significant impact on

urban male informal sector employment. The complete years of education gives a

negative impression and is highly significant factor in determining the male informal

sector employment. Middle level education (EDU II) and Matric level education (EDU

III) are positively correlated to urban informal sector employment. The effect of marital

status (MRS) on the probability of working in urban informal sector is negative. In

ccclxxiii

conclusion, married couples switch from informal sector towards formal sector. Formal

training (FTD), father‟s education (FEDU), mother‟s education (MEDU) and number of

male adolescents (NMAD) negatively affect the urban male informal sector workers. In

addition, the household size (HSIZ) strongly affects the participation decision. The

results conclude that the urban informal sector absorbs those who have large household

size. The rural-urban migrant (RMGT) variable positively and significantly affects the

decision of male participants in the urban informal sector of district Multan. The results

show that potential of urban informal sector to absorb the rural-urban migrants is high.

The estimates reveal that age has a negative and highly significant impact on the

female worker‟s participation in relation to urban informal sector. Whereas, Intermediate

level education (EDU IV), Graduation level education (EDU V) and Master‟s or higher

level education (EDU VI) variables are negative and significantly determined factors in

female informal employment. The effect of marital status is negative and insignificant at

10 % level of significance. Father‟s education (FEDU), mother‟s education (MEDU) and

household size (HSIZ) have a negative and a highly significant impact on female

employment in the urban informal sector. Contrarily, family setup (FSP) and number of

female adolescents (NFAD) affect positively the urban female informal sector

employment in district Bahawalpur. The results conclude that urban informal sector is

endowed with male and female workers possessing low formal education. In addition,

female workers with a joint family setup are also working more in the urban informal

sector of district Multan.

By comparing the results of both the genders, it is found that educated females

contribute less in the informal sector as compared to male workers. The married females

comparatively have less contribution in the urban informal sector of district Multan. Our

results conclude that male workers are rather more likely to be employed in formal sector

employment. It is also found that female participants having educated parents have less

participation and more involvement in the formal sector as compared to male participants

of the informal sector. Our findings reveal that male workers are more likely to be

engaged in the informal sector having large household size as compared to female

workers. It seems that male participants are somewhat less likely to be involved in

ccclxxiv

informal work as compared to female workers from additional male adolescents. Our

results conclude that spouse participation in economic activities has not significant

influence on male as well as female informal sector employment in district Multan. The

study results point out that male workers have a relatively lower participation in the urban

informal sector due to increase in household‟s value of assets as compared to female

workers.

In district Dera Ghazi Khan, the empirical results highlight that the influence of

education is negative and a highly significant factor for male analysis. For male

employment, Middle level education (EDU II) is positively correlated with urban male

informal sector employment. The male workers with initial basic education can work

easily in the urban informal sector of district Dera Ghazi khan. The effect of marital

status (MRS) on the probability of inclusion in the urban informal sector is positive and

insignificant in males‟ employment. The results indicate a negative and a highly

significant effect of formal training (FTD) on males‟ partaking in the urban informal

sector. Father‟s education (FEDU), mother‟s education (MEDU), number of male

adolescents (NMAD) and spouse participation in economic activities (SPN) negatively

affect the urban male informal sector employment decision. The findings of the study

conclude that urban informal sector is a sector of workers, whose parents are uneducated,

have low formal training and whose spouses are working less in economic activities.

Moreover, the household size (HSIZ) is positively associated with the urban male

informal sector workers. The sector also absorbs the workers having large household size.

Results also conclude that education (EDY) negatively influences the female

participants in the urban informal sector. Graduation (EDU V) and Master‟s or higher

level education (EDU VI) variables are found to be negative and significant. The results

reveal that females with basic formal education are compelled to work in the urban

informal sector and participants who possess high human capital stimulate to work in the

formal labour market. Formal training (FTD), father‟s education (FEDU), mother‟s

education (MEDU) household size (HSIZ) and spouse participation in economic

activities (SPN) have a negative and statistically significant impact on probability of

females being incorporated in the urban informal sector. Those female participants whose

ccclxxv

parents are educated are less likely to be employed in the urban informal sector.

Moreover, the females with large household size and with contribution of the

counterparts have a less likelihood of participation in the urban informal sector. However,

family setup (FSP) and number of female adolescent (NFAD) variables affects positively

the females‟ partaking in the urban informal sector employment in district Bahawalpur.

The results are highly significant.

The comparison makes clear that more females are moving towards the urban

formal sector with their increased age than male workers in Dera Ghazi Khan. The study

findings indicate that females‟ participation in informal sector reduces relatively more as

compared to male participants. As compared to male workers, the female whose fathers‟

are educated show less participation in the informal sector employment. Having children

rather increases the contribution of female workers in urban informal sector. An

additional male adolescent also reduces the females‟ involvement in the urban informal

sector as compared to male workers. However, presence of female adolescents relatively

increases the working probability. The findings show that there is a comparatively higher

decline in females‟ employment due to one unit increase in spouse involvement in

economic activities in the urban informal sector of district Dera Ghazi Khan.

Policy Recommendations

In Pakistan, the informal sector is not only large but also growing rapidly. It

requires to be promoted by generating employment opportunities and removing

constraints on them due to its potential to create opportunities. The participants are

without protection against exploitation in the informal sector in terms of low wages and

longer hours of work coresspondingly. The urban infromal sector seems to encourage the

child labour and thus confines human capital accumulation in Pakistan‟s economy.

The study results demonstrate the mixed effect of complete years of age,

education, and marital status on the urban informal sector participants as well. The study

results indicate that mostly workers are being employed more in the urban informal

sector employment with increasing age. The possible reason may be that insufficient jobs

ccclxxvi

in the formal sector and low human capital compel them to work in urban informal

sector. So, Govt should provide more jobs in the formal sector provide more educational

facilities to these workers with their low formal education.

The study results highlight the negative role of education in the urban informal

sector in Southern Punjab. Highly educated are moving towards the formal sector

whereas, for those workers with their initial basic education, it becomes obligatory to

participate in the urban informal sector. There is a need of strong mobilization and

convincing policies regarding higher education to the informal sector participants.

Regarding sex, majority of the male workers having high education hardly invoke

urban informal sector. There is a high presence of females in the urban informal sector

employment with their basic education.

Taking into account the above discussion, the subsequent policy implications are

going over the main points as followed:

1 There is a need to direct efforts to enhance literacy status of participants in the

urban informal sector and to make them legal literate.

2 Government should provide more jobs in the urban formal sector and provide

more education to these workers with their low formal education.

3 More vocational and technical training institutes should be established in rural as

well as urban areas as an enduring solution towards poverty alleviation in

Southern Punjab.

4 There is a serious need of more tertiary and higher education especially in urban

areas of Southern Punjab.

5 There is a serious necessity of a well-planned/organized planning and policy

making to urge participants in the urban informal sector on elevating their

education level and also to encourage their children for better education.

6 Higher education should be made accessible for all and sundry.

ccclxxvii

7 Females are more prone to working in the informal sector than the formal sector,

thus Public policy should favour women in this preference by enhancing their

opportunities in the urban informal sector in Southern Punjab.

8 More labour-intensive and small industries should be established in both rural and

urban areas of Southern Punjab.

9 Strong policies should be devised to ensure more investment in order to increase

the growth potential of the urban informal sector in Southern Punjab.

10 In order to decrease instability, Govt should decrease the factors that contribute to

instbality to encourage the business.

11 Besides introducing family planning programmes, the families consisting of more

members should be encouraged and persuaded to work.

12 Rural-urban wage differentials must be eliminated. In rural areas facilities should

be increased to avoid the influx of migrants to urban areas and deterioration of


13 Research and policy need to address the factor that confines the expansion of

formal employment in formal enterprises. Furthermore, infrastructure constraints

and labour regulations impact merit attention.

14 Maximum possible efforts should be directed to ensure health, housing and other

maximum facilities at work place and efforts must ensure sufficient social

security safety-nets in the from of supply of credit, medical facilities and other

benefits to formal sector participants.

15 The concerning authorities should keenly exert attention to improve the policy for

minimum wage laws and regulation for labour.

Thus, the present study is an endeavour to elucidate the various socio-economic

and demographic factors of participants in order to determine the employment, earnings

and development in the urban informal sector and enhance the growth potential of urban

informal sector employment in Southern Punjab. Although, many copious aspects are to

be explored yet a few critical points are mentioned in this research. More attention is

required to improve the growth potential of the urban informal sector. There is a serious

ccclxxviii

necessity of nationwide survey to formulate comprehensive a policy on urban informal

sector.

ccclxxix

REFERENCES

Ademu, W. A. (2006), “The Informal Sector and Employment Generation in Nigeria: The

Role of Credit”, Paper Presented at the Annual Conference of Nigerian Economic

Society (NES), Calabar, Nigeria.

Ahmad, E and Abdul Sattar (2007), “Awareness and Demand of Safe Drinking Water

Practices”, Working Paper No. 21, Pakistan Institute of Development Economics,

Islamabad.

Alen, R.G.D. (1938), Mathematical Analysis of Economists, London: Macmillan.

Ali, K. (2001), Pakistan: The Political Economy of Human Resource Development,

Vanguard Books (PVT) Ltd, Lahore.

Ali, K. (1990), “Problems of Working Women in the Rural Informal Sector of Multan

District”, Pakistan Economic and Social Review, 28(2), 89-104.

Aly, Y. H and I, A. Quisi (1996), “Determinants of Women Labour Force Participation in

Kuwait: A Logit Analysis”, Middle East Business and Economic Review, 8(2), 1-

9.

Avirgan, T., Bivens, L.J. and S. Gammage (eds.) (2005), “Good Jobs, Bad Jobs, No Jobs

– Labor Markets and Informal Work in Egypt, El Salvador, India, Russia, and

South Africa”, Economic Policy Institute, Washington.

Amemiya, T. (1981), “Qualitative Response Models: A Survey”, Journal of Economic

Literature, 19(4), 1483-1536.

ccclxxx

Amin, N. A. T. M. (1987), “The Role of the Informal Sector in Economic Development:

Some Evidence from Dhaka, Bangladesh”, International Labour Review, 126(5),

611-625.

Appleton, S and Teal, F. (1998), “Human Capital and Economic Development: A

Background Paper Prepared for the African Development Report 1998.

Arif, G. M and Hamid, S. (2009), “Urbanization, City Growth, and Quality Of Life in

Pakistan”, European Journal of Social Sciences, 10(2), 196-215.

Arimah, B.C. (2001), “Nature and Determinants of the Linkages between Informal and

Formal Sector Enterprises in Nigeria”, African Development Review, 13(1), 114–

144.

Ashenfelter, O and James, J. H. (1974), “The Estimation of Income and Subsititution

Effects in a Model of Family Labour Supply”, Econometrica, 42(1), 73-86.

Ashraf, J. and Ashraf, B. (1993), “An Analysis of the Male-female Earnings Differentials

in Pakistan”, Pakistan Development Review, 32(4), 895-904,

Assad, R; Ei-Hamidi, F and Ahmed, A. U. (2003), “The Determinants of Employment

Status in Egypt”, FCND Discussion Paper No. 88, International Food Policy

Research Institute, Egypt.

Attanasio, O, Goldberg, P. K & Pavcnik, N. (2003), “Trade Reforms and Wage

Inequality in Colombia”, Journal of Development Economics, 74(2), 331-366.

Attia, Moawad. S. (2009), “The Informal Economy as an Engine for Poverty Reduction

and Development in Egypt”, MRPA Paper No. 13034, 1-31.

ccclxxxi

Avirgan, T; Bivens, J and Gammage, S. (2005), “Good Jobs, Bad Jobs, No Jobs: Labour

Markets and Informal Work in Egypt, El Salvador, India Russia and South

Africa”, Economic policy Institute. Available at: www.epinet.org.

Banerjee, B. (1983), “The Role of Informal Sector in the Migration Process: A Test of

Probabilistic Migration Models and Labour Market Segmentation for India”,

Oxford Economic Papers, 35(3), 399-422.

Banuri, T., Kemal, A. R and Mumtaz, K. (Eds) (1997), “Just Development: Beyond

Adjustment with a Human Face”, Oxford University Press, Karachi.

Bauder, H. (2008), “Explaining Attitudes towards Self-Employment among Immigrants”,

A Canadian Case Study Journal Compilation, IOM International Migration,

46(2), 109-133.

Beal, J. (1997), “Assessing and Responding to Urban Poverty: Lessons from Pakistan”,

IDS Bulletin, 28(2), 58-67.

Becker, G. S. (1962), “Investment in Human Capital: A Theoretical Analysis”, Journal of

Political Economy, 70(5), 9-49.

Becker, G. S. (1965), “A Theory of Allocation of Time”, Economic Journal, 75(299),

493-517.

Becker, G. S. (1974), “A Theory of Marriage in Theodore W.Schultz, ed., Economics of

Family”, Chicago: University of Chicago Press, 293-344.

Becker, Gary. S. (1988), “Family Economics and Macro Behaviour”, American

Economic Review, 78(1), 1-13.

http://www.epinet.org/

ccclxxxii

Becker, Gary. S. (1964), Human Capital, A Theoretical and Empirical Analysis with

Special Reference to Education, New York: National Bureau of Economic

Research.

Becker, G. S. (1981), A Treatise on the Family, Cambridge, Mass.: Harvard University

Press.

Becker, K. F. (2004), “Facts Finding Study: The Informal Economy”, Swedish

International Development Agency.

Behrman, J. R. (1995), “Pakistan: Human Resource Development and Economics Growth

into the Next Century”, Background Paper for Pakistan 2010, Washington: The

World Bank.

Berndt, E. R. (1991), The Practice of Econometrics: Classic and Contemporary,

Reading. Addition-Wesley Publishing Company.

Bhattacharyya, S. K and Korinek, K. (2007), “Opportunities and Vulnerabilities of

Female Migrants in Construction Work in India”, Asian and Pacific Migration

Journal, 16 (4), 511-531.

Blanchflower, D. (2004), “Self-Employment. More May Not Be Better”, Swedish

Economic Policy Review, 11(2), 15-74.

Blaug, M. (1969), Economics of Education 2, London: Richard Clay Ltd.

Blaug, Mark (ed) (1976a), “The Empirical Status of Human Capital Theory: A Slightly of

Human Capital Theory: A Slightly Jaundiced Survey”, Journal of Economic

Literature, 14(3), 827-55.

Blomqvist, A. G. (1978), “Urban Job Creation and Unemployment in LDCs: Todaro vs.

Harris and Todaro”, Journal of Development Economics, 5(1), 3-18.

ccclxxxiii

Blundell, R (ed.), (1987), “Specification Testing in Limited and Discrete Dependent

Variable Models”, Journal of Econometrics, 34(1/2), 1-4.

Boquier, Phillipe. (2005), “Informal Sector Verses Informal Contracts in Nairobi,

Kenya”, IRD Document d travail D, A. Available at: www.dial.prd.fr.

Borjas, G. (1986), “The Self-employment Experience of Immigrants”, Journal of Human

Resource, 21, 485-456.

Bowen, W. G. and T, Alderich, F. (1965), “Labour Force Participation and

Unemployment in Arthur M. Ross, ed., Employment Policy and Labour Market”,

Berkeley, Calif.: University of California Press, pp.115-161.

Bowen, William. G. and Finegan, T. A. (1969), The Economics of Labour Force

Participation, Princeton, N.J.: Princeton University Press.

Boyd, R. L. (1990), “Black and Asian Self-Employment in Large Metropolitan Areas: A

Comparative Analysis,” Society for the Study of Social Problems”, 37(2), 258-

274.

Breman, J. (1976), “A Dualistic Labour System: A Critique of the „Informal Sector

Concept”, Economic and Plitical Weekly, 11 (48), 1870-1876.

Bulutay, T and Tasti, E. (2004), “Informal Sector in the Turkish Labour Market”,

Discussion Paper No. 22, Turkish Economic Association.

Burki, A. A and Abbas, Q. (1991), “Earnings Functions in Pakistan‟s Urban Informal

Sector: A Case Study”, The Pakistan Development Review, 30 (4), 695-706.

Burki, A. A. and Ubaidullah (1992), “Earnings, Training, and Employment in

Gujranwala‟s Urban Informal Sector: Evolution or involution?”, Pakistan

Economic and Social Review, 30(1), 49-66.

ccclxxxiv

Burki, A.A and Afaqi, U. (1996), “Pakistan‟s Informal Sector: Review of Evidence and

Policies Issues”, Pakistan Journal of Applied Economics, 12(1), 1-30.

Burki, A. A. (1990a), “Urban Informal Sector in Pakistan: Some Selected Issues”, Sixth

Annual Generally Meeting of the Pakistan Society of Development Economist.

Islamabad. Pakistan Institute of Development Economics.

Calves, A. and Schoumaker, B. (2004), “Deteriorating Economic Context and Changing

Patterns of Youth Employment in Urban Burkina Faso”, World Development,

32(8), 1341-1354.

Cavalcanti, C. (1981), “Employment, Production and Income Distribution in the Informal

Urban Sector of the Northeast: The Case of Salvador”, Bahia Luso-Brazilian

Review, 18(1), 139-154.

Chattopadhay, S. (2012), Education and Economics: Disciplinary Evolution and Policy

Discourse, Oxford University Press.

Chen, M. A. (2001), “Women in the Informal Sector: A Global Picture, the Global

Movement”, SAIS Review, 21(1), 1-10.

Chenery, H. B et al. (1974), Redistribution with Growth, London: Oxford University

Press.

Chickering, A. L and Salahdine, M. (1991), “The Silent Revolution: The Informal Sector

in Five Asian and Near Eastern Countries”, International Center for Economic

Growth.

Chaudhuri, T. D. (1989), “A Theoretical Analysis of the Informal Sector, World

Development, 17(3), 351-355.

ccclxxxv

Chowdhury, U. (2006), Informal Sector in a Developing Economy, Anmol Publication

PVT, LTD, India.

Chukuezi, C. O. (2010), “Urban Informal Sector and Unemployment in Third World

Cities: The Situation in Nigeria”, Asian Social Science, 6(8), 1-131.

Cohen, D and Prusak, L. (2001), “In Good Company: How Social Capital Makes

Organizations Work”, Cambridge: Harvard Business Press.

Cohen, M. S., Samuel A. Rea, and Robert I. Lerman (1970), “A Micro Model of Labour

Supply”, Washington, D.C.: U.S. Bureau of Labour Statistics, BLS Staff Paper

No.4.

Cole, W. E and Sander, R. D. (1985), “Internal Migration and Urban Employment in the

Third World”, American Economic Review, 75(3), 481-494.

Coleman, J. (1990), „Foundations of Social Theory’. Cambridge Mass: Harward

University Press.

Dasgupta, S. (2003), “Structural and Behavioral Characteristics of Informal Service

Employment: Evidence from a Survey in New Delhi”, Journal of Development

Studies, 39 (3), 51-80.

Desoto, H. (1989), The Other Path: The Invisible Resolution in the Third World, New

York: Harper and Row.

Denis, Goulet. (1971), The Cruel Choice: A new Concept in the Theory of Development,

New York: Atheneum.

Doan, R. M. (1992), “Differentiation and the Informal Sector in Amman, Jordan”,

International Journal of Middle East Studies, 24(1), 27-38.

ccclxxxvi

Ebrahim, G.J. (1984), “Health Care and the Urban Poor”, in Richards, P.J and Thomson,

A. M (1984), Basic Needs and Urban Poor: The Provision of Communal

Services, International Labor Organization.

Economic and Social Commission for Asia and the Pacific (ESCAP), 2006, “Poverty and

the Informal Sector: Role of the Informal Sector in Poverty Reduction”, Third

session. Available at: www.unescap.org.

Etherington, K and Simon, D. (1996), “Paratransit and Employment in Phnom Penh: the

Dynamics and Development Potential of Cyclo Riding)”, Journal of Transport

Geography, 4(1), 37-53.

Ehrenberg, R. G and Smith, R. S. (1985), Modern Labor Economics: Theory and Public

Policy, Fifth Edition Harper Collins College Publishers.

Fakuchi, T. (1998), “A Simulation Analysis of the Urban Informal Sector”, Developing

Economies, Xxxvi, 225-56.

Fan, C. C. (2001), “Migration and Labour Market Returns in Urban China: Results from

a Recent Survey in Guangzhou”, Environment and Planning, 33, 479-508.

Fie, J. C.H and Ranis, G. (1964), Development of the Labour Surplus Economy, Theory

and Policy, Homewood, III: Irwin.

Fields, G. S. (1975), “Rural-Urban Migration, Urban Unemployment and

Underemployment, and Job-Search Activity in LDCs”, Journal of Development

Economics, 2 (1975), 165-187.

Fields, G. S. (1990), “Labour Market Modelling and the Urban Informal Sector: Theory

and Evidence in D.Tumham, B.Salome, and A.Schwarz, eds, The Informal Sector

Revisted (Paris: Organization for Economic Cooperation and Development

(OECD).

http://www.unescap.org/

ccclxxxvii

Fields, G. (1988), “Employment and Economic Growth in Costa Rica”, World

Development, 16(2), 1493-1509.

Freire, P. (1990), Pedagogy of the Oppressed, New York: Continum.

Florez, E.C. (2003), “Migration and Urban Informal Sector in Colombia”, Paper Prepared

for Conference on African Migration in Comparative Perspective, Johannesburg,

South Africa.

Fortin, B; Marceau, N and Savard, L. (1997), “Taxation, Wage Controls and the Informal

Sector”, Journal of Public Economics, 66(2), 293-312.

Frost and Jon (2008), “Returns to Qualification in Informal Employment: A Study of

Urban Youth and in Egypt. MPRA Paper No. 12599.

Funkhouser, E. (1996), “The Urban Informal Sector in Central America: Household

Survey Evidence”, World Development, 24(11), 1737-1751.

Galbraith, J. k. (1979), The Nature of Mass Poverty, Cambridge: Harvard University

Press.

Gallaway, T. H. and Bernasek, A. (2002), “Gender and Informal Sector Employment in

Indonesia”, Journal of Economic Issues, 36(2), 313-321.

Grootaert, C and Bastelaer, T. (2001), “Understading and Measuring Social Capital”,

Initiative Working Paper No .24.The World Bank, Social Development Family

Environmentally and Socially Sustainable Development.

Ghayur, S. (1993), “Employment Pattern and Extent of Underutilization of Manpower in

Burani Areas of Pakistan”, Pakistan Development Review, 30(4), 1225-1233.

ccclxxxviii

Ghosh, P.K ed. (1984), Third World Development: A Basic Needs Approach, Westport,

Conn: Green-Wood Press.

Gindling, T. H and Terrell, K. (2005), “The Effect of Minimum Wages on Actual Wages

in Formal and Informal Sectors in Costa Rica”, World Development, 33(11),

1905-1921.

Goulet, D. (1971), The Cruel Choice: A New Concept in Theory of Development, New

York: Atheneum, 1971.

Government of Pakistan (1998), “District Census Report of Bahawalpur”, Population

Census Organization, Statistics Division, Government of Pakistan, Islamabad.

Government of Pakistan, (1998), “District Census Report of Dera Ghazi Khan,”

Population Census Organization, Statistics Division, Government of Pakistan,

Islamabad.

Government of Pakistan, (1998), “District Census Report of Multan”, Population Census

Organization, Statistics Division, Government of Pakistan, Islamabad.

Government of Pakistan (2007), “Pakistan Economic Survey”, Ministry of Finance,

Government of Pakistan, Islamabad.



Government of Pakistan (2010), Pakistan Economic Survey, Ministry of Finance,




ccclxxxix

Government of Pakistan (2012-2013), “Pakistan Economic Survey”, Ministry of Finance,


Government of Pakistan (2005-2006), “Labour Force Survey 2005-2006”, Federal

Bureau of Statistics, Statistical Division, Islamabad.

Government of Pakistan (2011-2012), “Labour Force Survey 2011-12”, Federal Bureau

of Statistics, Statistical Division, Islamabad.

Grant, U. (2008), “Opportunity and Exploitation in Urban Labour Markets‟, ODI Briefing

Paper No 44: London, UK.

Gronau, R. (1973b), “Human Production: A Forgotten Industry”, Review of Economics

and Statistics, 62(5), 408-415.

Gronau, R. (1986), “Home Production: A Survey Chapter 4 in Orley Ahsenfelter and

Richard Layard, eds., Handbook of Labour Economics,” Vol. 1, New York:

Elsvier Sciences BV, pp. 273-304.

Guang, L and Zheng, L. (2005), “Migration as the Second Best Option: Local Power and

Off-farm Employment”, China Quarterly, 181, 22-45.

Gujrati, D.N. (1995), Basic Econometrics, Third Edition, New York: Mcgraw Hill, Inc.

Gunatilaka, R. (2008), “Informal Employment in Sri Lanka: Nature, Probability of

Employment and Determinants of Wages”, Prepared for ILO Colombo Version,

ILO Asia-Pacific Working Series.

Hall, Robert E. (1973), “Wages, Income and Hours of Work in the U.S. Labour Force”,

“in Glen C. Cain and Harold W. Watts, eds., Income Maintenance and Labour

Supply, Chicago: Rand Mcmally.102-162.

cccxc

Harris, J. R. and Todaro, M. P (1970), “Migration, Unemployment and Development: A

Theoretical Analysis”, American Economic Review, 60(1), 126–142.

Hart, K. (1971), “Informal Income Opportunities and Urban Employment in Ghana‟, in

Jolly, R et al. (eds), Third World Employment: Problems and Strategy, Penguin,

Harmondsworth.

Hart, j. K. (1973), “Informal Income Opportunities and Urban Employment in Ghana”,

Journal of Modern African Studies, 11(1), 61-89.

Hausman, J. A. (1981a), “Labour Supply, “in Henry J. Aaron and Joseph A. Peechman,

eds., How Taxes Affect Economic Behaviour”, Washington, D.C.: The Brookings

Institution, pp. 27-72.

Hayami, Y; Dikshit, A. K. and Mishra, S. N. (2006), “Waste Pickers and Collectors in

Delhi Poverty and Environment in an Urban Informal Sector”, Journal of

Department Studies, 42(1), 41-69.

Heckman, J. J. (1979), “Sample Selection Bias as a Specification Error,” Econometrica,

47(1), 153-162.

Heckman, J. J; Lane J. Lachner, L. J and Todd, P. E. (2008), “Earnings Functions and

Rates of Return”, Working Paper No. 13780, National Bureau of Economic

Research.

Heckman, J. J. (1974b), “Shadow Prices, Market Wages and Labour Supply”,

Econometricica, 42(4), 679-694.

Henley, Anrew, Arabsheibani. G. Reza and Carneiro, G. Franciso (2006), “On Defining

and Measuring the Informal Sector”, IZA Disccussion Paper No. 2473.

cccxci

Hill, A. M. (1983), “Female Labour Force Participation in Developing and Developed

Countries--Consideration of Informal Sector,” Review of Economics and

Statistics, 65(3), 459-468.

Hirshman, A. O. (1958), The Strategy of Economic Development, Yales University Press.

House, W. J. (1984), “Nairobi‟s Informal Sector: Dynamic Entrepreneurs or Surplus

Labour?”, Economic Development and Cultural Change, 32(2), 277–302.

Hout, M. and Rosen, H. (2000), “Self-employment, Family Background, and Race”,

Journal of Human Resources, 35(4), 670-692.

Husman, J. A. and Paul, R. (1984), “Family Labour Supply With Taxes”, American

Economic Review, 74 (2), 242-248.

Hussmanns, R. and Farhad, M. (2001), Statistical Definition of the Informal Sector-

International Standards and National Practices. Available at: www.gdrc.org.

Ihrig, J. and Moe, K. (2004), “Lurking in the Shadows: The Informal Sector and

Government Policy”, Journal of Development Economics, 73(2), 541–557.

International Labour Organization (1972), Employment, Incomes and Equality: A

Strategy for Increasing Productive Employment in Kenya. ILO, Geneva.

International Labour Organization (2000), Very Child Counts, International Labour

Office, Geneva.

cccxcii

International Labour Organization (2002a), Decent Work in the Informal Economy

International Labour Office, Geneva.

International Labour Organization (2002), Women and Men in the Informal Economy, A

Statistical picture, International Labour Office, Geneva.

International Labour Organization (2005), Facts on Child Labour. Available at:

www.ilo.org.

Charmes, J. (2002), “Estimations and Survey Methods for the Informal Sector, University

of Versailles-St Quentin en Yvelines, Centre of Economics and Ethics for

Environment and Development.

Jampaklay, A; Korinek, k and Entwisle, B. (2007), “Residential Clustering among Nang

Rong Migrants in Urban Settings of Thailand”, Asian and Pacific Migration

journal, 16(4), 485-510.

Jones, P (1997), “The Impact of Minimum Wage Legislation in Developing Countries

Where Coverage is Incomplete”, WPS/98-2, Centre for the Study of African

Economies, Institute of Economics and Statistics, University of Oxford.

Jutting, J; J, Perlevliet and T, Xeniogiani (2008), “Informal Employment Re-loaded”,

Institute of Development Studies, (IDS) Bulletin, 39(2), 28-37.

Kazi, S. (1987), “Skill Formation, Employment and Earnings in the Urban Informal

Sector”, The Pakistan Development Review, XXVI (4), 711-719.

Kemal, A. R and Mahmood, Z. (1998), “Characteristics of the Workers In the Urban

Informal Sector of Pakistan”, Research Report No 160, Pakistan Institute of

Development Economics, Islamabad, pp. 1-25.

cccxciii

Kemal, A. R and Mehmood, Z. (1993), “The Urban Informal Sector of Pakistan: Some

Stylized Facts”, Research Report No.161, Pakistan Institute of Development

Economics, Islamabad.

Kemal, A. R and Mehmood, Z. (1993), “Labour Absorption in the Informal Sector and

Economic Growth in Pakistan”, Informal Sector Study No. 2, Pakistan Institute of

Development Economics, Islamabad.

Kementa, J. (1971), “Elements of Econometrics”, London: Macmillan Publishing

Company.

Keynes, J. M. (1936), The General Theory of Employment, Interest and Money,

Macmillan Cambridge University Press.U.S. A.

Killingworth, M. R. (1983), “Labour Supply”, New York: Cambridge University Press.

Kim, B. (2005), “Poverty and Informal Economy Participation”, Economics of

Transition, 13(1), 163-185.

Kirke, J and Arther, J. (1984), “Water Supply Issues”, in Richards, P. J and Thomson, A.

M (1984), Basic Needs and Urban Poor: The Provision of Communal Services,

International Labor Organization.

Koo, H and Smith, P. C. (1983), “Migration, the Urban Informal Sector, and Earnings in

the Philppines”, Socialogical Quarterly, 24(2), 219-232.

Kosters, Marvin (1966), “Income and Substitution Effects in a Family Labour Supply

Model,” Santa Monica, Calif.: Rand Corporation, Report No. 3339.

Kozel, V and Alderman, H. (1990), “Factors Determining Work Participation and Labour

Supply Decisions in Pakistan‟s Urban Areas”, Pakistan Development Review,

29(1), 1-18.

cccxciv

Krstic, G and Sanfey P. (2006), “Mobility, Poverty and Well–Being among the

Informally Employed in Bosnia and Herzegovina”, Economic Systems, 31(3),

311-335.

Kumar, N and Aggarwal, S. C. (2003), “Patterns of Consumption and Poverty in Delhi

Slums”, Economic and Political Weekly, 38(50), 5294-5300.

Kuznets, S. (1959), Six Lectures on Economic Growth. Glencoe, Free Press.

Levin, Carol. E., Marie, T. Ruel, Saul, S. Morris, Daniel, G. Maxwell, Margaret, Armar-

Klemesu and Clement, A. (1999), “Working Women in an Urban Setting:

Traders, Vendors and Food Security in Accra”, World Development, 27(11), 33-

47.

Lewis, Arthur. W. (1954), “Economic Development with Unlimited Supplies of Labour,

Manchester School”, 22(154), 139-191.

Lewis, H. Gregg (1957), “Hours of Work and Hours of Leisure”, Proceedings of the

Industrial Relations Research Association, pp.196-206.

Lewis, W. Arthur (1995), Theory of Economic Growth, Homeland: Irwin.

Loayza, Norman. V. (1996), “The Economics of the Informal Sector: A Simple Model

and Some Empirical Evidence from Latin America”, Carnegie-Rochester

Conference Series on Public Policy, 45, 129-162.

Lundberg, S. (1985), “The Added Worker Effect”, Journal of Labour Economics, 3(1),

Supplement, Part 1, 11-37.

cccxcv

Machlup, F. (1982), “Issues in the Theory of Human Capital: Education as Investment”,

Pakistan Development Review, 21(1), 1-9.

Mandelman, F.S and Montes-Rojas, G.V. (2009), “Is Self-Employment and Micro –

Entrepreneurship a Desired Outcome?” World Development, 20(10), 1-9.

Mahmood, Z. (1998), “A Social Sector Strategy for Pakistan”, Research Report No.163,

Pakistan Institute of Development Economics, Islamabad.

Malik, M. H. (1996), “Urban Poverty Alleviation through Development of the Informal

Sector”, Asia Pacific Development Journal, 3(2), 31-48.

Malik, S. J. and Nazli, H. (1999), “Population, Employment and the State of Human

Resources, Oxford University Press, Karachi,” pp. 328-373.

Malik, S., Faridi, M. Z, Hussain, S. and Shoukat, M (2010), “Socio-Economic Factors

Affecting Casual-Employment in Pakistan: An Empirical Analysis”, European

Journal of Economics, Finance and Administrative Sciences, 20, 127-138.

Maloney, W. (1999), “Does Informality Imply Segmentation in urban Labour Markets?

Evidence from Sectoral Transitions in Mexico”, World Bank Economic Review,

13(2), 275-302.

Manda, D. K; Mwabu, G; Kimenyi, M. S. (2002), “Human Capital Externalities and

Returns to Education in Kenya”, Kipra Discussion Paper No.13, Social Sector

Division, Kenya Institute for Public Policy Research and Analysis.

Mentzakis, E; mcmee, P and Ryan, M. (2008), “Who Cares and How Much: Exploring

the Determinants of Co-residential Informal Care”, Review of the Economics of

Household, 7(3), 283-303.

cccxcvi

Marcelli, E. A. (2004), “Unauthorized Mexican Immigration, Day Labour and other

Lower-Wage Informal Employment in California”, Regional Studies, 38, 1-13.

Marshall, M. I and Oliver, W. N. (2005), “The Effects of Human, Financial, and Social

Capital on the Entrepreneurial Process for Entrepreneurs for Indiana”, Paper

Prepared for presentation at the Allied Social Science Associations Annual

Meeting Philade Phia, Pennsylavama.

Matiya, G; Wakabayashi, Y; Ng‟on‟Ola, D and Takenounchi, N. (2005), “A Logit

Analysis of Socio-economic Factors Influencing People to Become Fisherman

around Lake Malombe in Malawi”, Journal of Applied Science Research, 1(1),

18-23.

Mazumdar, D. (1976), “The Urban Informal Sector”, World Development, 4(8), 655-679.

McGee, T. G. (1973), “Peasants in the Cities: A Paradox, A Most Ingenious Paradox”,

Human Organization, 32(2), 135–142.

Mddala, G. S. (1983), Limited Dependent and Qualitative Variables in Econometrics,

Canbridge, England: Cambridge University Press”, Econometric Society

Monographs No. 3.

Meier, G. M. (1970), Leading Issues in Economic Development, 2nd

Ed. Oxford: Oxford

University Press.

Meng, X. (2001), “The Informal Sector and Rural –Urban Migration: A Chinese Case

Study”, Asian Economic journal, 15(5), 71-89.

Merrick, T. W. (1976), “Employment and Earnings in the Informal Sector in Brazil: The

Case of Belo Horizonte”, Journal of Development Areas, 10(3), 337-354.

cccxcvii

Mincer, J. (1958), “Investment in Human Capital and Personal Investment in Human

Capital and Personal Income Distribution”, Journal of Political Economy, 66(4),

281-302.

Mincer, J. (1963), “Market Prices, Opportunity Costs, and Income Effects”, in Carl

Christ, ed., Measurement in Economics: Essays in Honor of Yehuda Grun-field,”

Stanford, Calif.: Stanford University Press, pp. 67-82.

Mincer, J. (1962), “Labour Force Participation of Married Women: A Study of Labour

Supply,” in H. Gregg Lewis, ed., Aspects of Labour Economics, Princeton, N.J.:

Princeton University Press, pp.63-97.

Mincer, J. (1970), “The Distribution of Labour Incomes: A Survey with Special

Reference to the Human Capital Approach”, Journal of Economic Literature,

8(1), 1-26.

Mitra, A. (2008), “Social Capital, Livelihood and Upward Mobility”, Habitat

International,” 32(2), 261-269.

Mukhopadhy, I. (1998), “Calcutta‟s Informal Sector: Changing Pattern of Labour Use”,

Economic and Political Weekly, 33(47/48), 3075-3080.

Mukras, M. S. (2003), “Poverty Reduction through Small and Medium Enterprises”,

Botswana Journal of Studies, 17(2), 58-69.

Nadvi, K. M. (1990), “Employment Creation in Urban Microenterprises in the

Manufacturing Sector in Pakistan”, Pakistan Employment and Manpower

Strategies and Policies Project (Pak/88/007).

Nasir, Z. M. (1998), “Determinants of Personal Earnings in Pakistan: Findings from the

Labour Force Survey 1993-94”, Pakistan Development Review, 37(3), 251-274.

cccxcviii

Nasir, Z.M. (2002), “Returns to Human Capital in Pakistan: A Gender Disaggregated

Analysis”, Pakistan Development Review, 41(1), 1-28.

Naqvi, Z. F and Lubna, S. (2001), “How Do Women Decide to Work in Pakistan”?

Pakistan Development Review, 41(4), part II: 495-513.

Nurkse, R. (1953), Problems of Capital Formation in underdeveloped countries, Oxford

University Press, New York.

Odhiambo, W. and Manda, D. K. (2003), “Urban Poverty and Labour Force Participation

in Kenya”, Kenya Institute for Public Policy Research and Analysis (KIPRA),

Paper to be presented at the World Bank Research Symposium Washington D.C.

Okojie, E, E. C. (1984), “Female Migrants in the Urban Labour Market Benin City

Nigeria”, Canadian Association of African Studies, 18(3), 547-562.

Ozcan, Y; Ucdogruk, S and Ozcan, M. K. (2003), “Wage differences by Gender, Wage

and Self-employment in Urban Turkey”, Journal of Economic Cooperation,

24(1), 1-24.

Pagnataro, M. B. O. (2001), “The Informal Sector in Venezuela: Catalyst or Hindrance

for Poverty Reduction”, Poverty Project Papers Vol. 2, Association Civil Para la

Promocion de Estodios Sociales

Pagés, C and Stampini, M. (2007), “No Education, No Good Jobs? Evidence on the

Relationship Between Education and Labour Market Segmentation”, Discussion

Paper No. 3187, Institute for the Study of Labour.

Pakistan Millinnium Development Goals Report (2013), Ministry of Planning

Development of Pakistan Reforms, Government of Pakistan.

cccxcix

Peattie, L. R. (1980), “Anthropological Perspective on the Concepts of Dualism: The

Informal Sector and Marginalities in Developing Urban Economies”,

International Regional Science Review, 5, 1-31.

Little, Peter. O. (1999), “Selling to Eat: Petty Trade and Traders in Peri-urban Areas of

Sub-Saharan Africa”, A Research Paper Presented for the Broadening Access and

Strengthening Input Market Systems- Collaborative Research Support Programme

(BASIS-CRSP), Institute for Development Anthropology.

Pisani, Mi. J, and Yoskowitz, D. W. (2005), “Grass, Sweat, and Sun: An Exploratory

Study of the Labour Market for Gardeners in South Texas”, Social Sience

Quarterly, 86(1), 229-251.

Pradhan, M and Soest, V. A. (1995), “Formal and Informal Sector Employment in Urban

Areas of Bolivia”, Labour Economics, 2, 275-297.

Pratap, S and Quintin, E. (2006), “Are Labour Markets Segmented in Developing

Countries? A Semi Parametric Approach”, European Economic Review, 50, 1817-

1841.

Putnam, R. (1993), Making Democracy Work: Civic Traditions in Modern Italy.

Princeton: Princeton University Press.

Qiu, T and Hudson, J. (2010), “Private Returns to Education in Urban China”, Econ

Change Restruct, 43, 131-150.

Quijano, A. (1974), “The Marginal Pole of the Economy and the Marginalized Labour

Force”, Economy and Society, 3(4), 393–428.

Raijman, R. (2001), “Determinants of Entrepreneurial Intentions: Mexican Immigrants

in Chicago”, Journal of Socio- Economics, 30(5), 393-411.

cd

Rama, M. (1994), “Flexibility in Sri Lanka's Labour Market”, Policy Research Working

Paper No.1262, Washington DC: The World Bank.

Ranis, G and Stewart, F. (1995), “V-Goods and the Role of the Urban Informal Sector in

Development”, Working Paper No. 85.

Rauch, J. (1991), “Modellng the Informal Sector Formally”, Journal of Development

Economics, 35(1), 33-47.

Reddy, M. (2007), “Modeling Poverty Dimensions of Urban Informal Sector Operators in

a Developing Economy”, European Journal of Development Research, 19(3),

459-479.

Reddy, M; Naidu, V. and Mohanty, M. (2003), “The Urban Informal Sector in Fiji”, Fiji

Institute of Applied Studies, 1(1), 127-154.

Richardson (1984), “The Role of Informal Sector in Developing Countries: An

Overview”, RDD, 5, 3-55.

Roberts, D. K. (2001), “The Determinants of Job Choice by Rural Labor Migrants in

Shanghais”, China Economic Review, 12 (1), 15-39.

Rosenstein-Rodan, P. N. (1964), Capital Formation and Economic Development,

London: Aleen and Unwin.

Rosser, J. B; Jo, Marina V. R and Ahmad, E. (1999), “Income, Inequality and Informal

Economy in Transition Economies”, Journal of Comparative Economics, 28(1),

156-171.

Richardson, H. W. (1987), “The Costs of Urbanization in Developing Countries”,

Economic Development and Cultural Change,” 30(3), 625-648.

cdi

Rostow, W. W. (1960), The Stages of Economic Growth: A Non-communist Manifesto,

London: Cambridge University Press.

Sandefur, J. (2006), “Explaining the Trend towards Informal Employment in Africa:

Evidence from Manufacturing Sector”, Center for the Studies of African

Economies, Oxford University.

Sargana, M. A. (1998), “The Urban Informal Sector in an Adjusting Economy: The Case

of Pakistan”, Pakistan Development Review, 37(4), 11-27.

Sarte, P. D. G. (2000), “Informality and Rent-seeking Bureaucracies in a Model of Long-

run Growth,” Journal of Monetary Economics, 46(1), 173-197.

Sawaminanthan, M. (1991), “Understanding the Informal Sector: A Survey”, Working

Paper No. 95, World Institute for Development Economics Research of the

United Nations.

Schiitte, S. ﴾2009﴿, “Informal Insecurity in Urban Afghanistan”, Iranian Studies, 42(3),

465-491.

Schmpeter, J. A. (1995), Theory of Economic Development: An Inquiry into Profit,

Credit, Interest, and Capital the Business Cycle, Social Science Classic Series.

Schultz, T. W. (1960), “Capital Formation by Education”, Journal of Political

Economy, 68(6), 571-583.

Schultz, T.W. (1961), “Investment in Human Capital. American Economic Review,”

51(1), 1-17.

Sethuraman, S. V. (1976), “The Urban Informal Sector, Concept, Measurement and

Policy”, International Labour Review, 114(1), 69-81.

cdii

Siddiqui, R and Siddiqui, R. (1998), “A Decomposition of Male-female Differentials”,

Pakistan Development Review, 37(4), 885-898.

Smith, A. (1776), The Wealth of Nations, London: J. M. Dent and Sons, Reprinted in

1937 as the Cannan edition by The Modern Library, Random House, New York.

Smith, P.A. & Metzger, M.R. (1998), “The Returns to Education: Street Vendors in

Mexico”, World Development, 26(2), 289-296.

Solow, R. M. (1956), “A Contribution to the Theory of Economic Growth”, Quarterly

Journal of Economics, 70 (1), 65-94.

Suharto, EDI (2002), “Human Development and the Urban Informal Sector in Bandung,

Indonesia: The Poverty Issue”, New Zealand Journal of Asian Studies, 4(2), 115-

133.

Suryahadi, A; Widyanti, W; Perwira, D and Sumarto, S. (2003), “Minimum Wage Policy

and its Act on Employment in the Urban Formal Sector”, Bulletin of Indonesian

Economic Studies, 39(1), 29-50.

Tielhet-Waldorf, S. and Waldorf, W. H. (1983), “Earnings of the Self-employed in an

Informal Sector: A Case Study of Bangkok”, Economic Development and

Cultural Change, 31(3), 587-60.

Terrell, K. (1989), “An Analysis of the Wage Structure in Guanatimala City”, Journal of

Developing Areas, 23(3), 405-423.

Todaro, Michael. P. (1969), “A Model of Labor Migration and Urban Unemployment in

Less Developed Countries”, American Economic Review, 59(1), 138-148.

Todaro, M. P and Smith, S. C. (2012), Economic Development, 11th

Edition

Manufactured in the United States of America.

cdiii

Tokman, V. E. (1989), “Policies for Heterogeneous Informal Sector in Latin America”,

World Development, 17(7), 1067-1076.

Tokman, V. E. (1986), “The Informal Sector: Fifteen Years Later”, Paper Presented at

Conference. The Comparative Study of the Informal Sector: Harper‟s Ferry, West

Virginia.

Tokman, V. M. (2007), “The Informal Economy, Insecurity and Social Cohesion in Latin

America, International Labour Review, 146, 81-107.

Tornarolli, L and Gasparini, L. (2007), “Labour Informality in Latin America and the

Caribbean: Patterns and Trends from Household Survey Microdata”, Documento

de Trabajo Nro.46. CEDLAS, Universidad Nacional de La Plata.

United Nations Development Programme (1996), Human Development Report, New

York: Oxford University Press.

United Nations Development Programme (1990), Human Development Report, New

York: Oxford University Press.

United Nations Economic Commission for Africa (1990), Handbook of Manpower

Planning in Africa, Addis Ababa, and UNECA.

UNDP (United Nations Development Programme) (1994), Human Development Report,

New York: Oxford University Press.

United Nations Economic Commission for Africa (1990), Handbook of Manpower

United Nations, Report on the World Social Situation.

cdiv

Valodia, I; Lebani, L and Skinner, C. (2005), “Low-waged and Informal Employment in

South Africa Transformation”, Critical Perspectives in Southern Africa, 60, 90-

126.

Yeh, S. (1984), “Urban Low Income Housing in South East Asia”, in Richards, P.J and

Thomson, A. M (1984), Basic Needs and Urban Poor: The Provision of

Communal Services, International Labor Organization.

Yue, S. (2004), “The Challenge of the Informal Work in the Philippines, ILO”. Sub

Regional Office South-East Asia and the Pacific Manila, Philippines.

Wahba, J. (2002), “The Returns to Education and Regional Earnings Differentials in

Egypt”, Discussion Papers in Economics and Econometrics. Available at:

http://www/soton.ac.uk.

Wamuthenya, R. W. (2010), “Determinants of Employment in Formal and Informal

Sector of the Urban Areas of Kenya”, AERC Research Paper No. 194, African

Economic Research Consortium, Nairobi.

Weeks, J. (1975), “Policies for Expanding Employment in the Informal Urban Sector of

Developing Countries”, International Labour Review, 3(1), 1-13.

http://www/soton.ac.uk

cdv

APPENDIX A

FORMAT OF QUESTIONNAIRE

A: Location Southern Punjab:

Tehsil Colony/ Mohala /Ward

Date District

Name

Place of birth: 1: Urban 2: Rural

Name of the household head:

1: When did you migrate from rural to urban area?

2: How much time have you spent in urban area?

B: Characteristics of Formal and Informal Sectors

1: Single: 2: Ownership of premises

3: Age of the firm

4: Working hours

How many hours did you actually work last week? (in one or more jobs or own business)

/how many hours do you usually work per week?

5: Working conditions

C: Household Characteristics

Sr.# Name Sex Age Education Occupation Weekly/Monthly

Income

1

2

3

4

5

6

D: Characteristics of Respondents:

1: Name

2: Sex

Male (1) Female (2)

cdvi

3: Age 4: Education

5: Marital Status a) Married b) Unmarried

6: Sector of Employment: a) Formal b) Informal

Indicate the category of occupation:

b1) Domestic worker b2) Self-employed

b3) Unpaid family worker b4) Own account worker

b5) Wage worker

b6) Salaried worker

E: Spouse Characteristics

1: Age: 2: Education:

3: Occupation: 4: Weekly/ Monthly Income:

5: Hours of Work/day

6: Benefits

7: Hours spent on children

8: Hours Spent on domestic activities

F: Economic condition

1: Farm Size

1) What is the size of land you own? (in Acres)

2) Of the total how much is your own land? (in Acres)

3) How much of the total land is jointly owned (in Acres)

4: Household properties

1) Ownership of land

No. of acreage Value

Other assets

1) No. of shops Value

2) No.of houses Value

3) No. of plots Value

4) No.of animals Value

5) Other assets Value

6) Do you have financial asseets?

cdvii

7) Value of the financial assets

cdviii

5. Do you have some kind of skill training?

If yes?

1. Formal

a) Vocational

b) Technical

c) Politechnique

d) Other

2. Informal

6. How many years have you been working as informal

worker?_______________________

7. How did you join the urban informal sector?

1. Referrals by friends 2. Referrals by relatives 3. Own choice

8. Do you have access to credit?

1. Shopkeeper 2. NGO 3. Family member 4. Bank

9: Location of the place of work

Where is your current or last place of work located?

10: Are you affected by major disease?

If yes

1. Sugar 2. Blood pressure 3. Heart disease 4. Eyesight problem

5. T.B Lings 6. Any other (specify)

11. Do you pay tax?

If Yes?

1. Income tax 2.Professional tax 3. Transportation fee

12. Distance_______________________

I: Human Development Indicators

1. Economic Capital

1.1 Worker‟s Income per month_____________________

1.2 Households‟ income per month_____________________________

2. Human Capital

1. Education Facilities:

cdix

(1) No schooling (2) Primary (3) Middle (4) Matric (5) Intermediate (6) Graduation

(7) Master‟s or higher

3. Health Facilities

Do you visit a doctor to cure illness? Yes/No

Do you approach the hospital when feel sick? Yes/No

Do you avail the health center in case of illness? Yes/No

Do you use traditional healer or self-treatment to cure

sickness? Yes/No

4. Access to Housing Facilities:

Do you avail the facility of clean water supply? Yes/No

Do you have a toilet with a septic tank? Yes/No

Are you electrified? Yes/No

Do you have constructed floor? Yes/No

5. Socio-cultural Activities

Do you have access to socio-cultural activities?

Do you have the benefit of watching Television? Yes/No

Do you listen to the Radio Programmes? Yes/ No

Do you read the newspaper? Yes/No

Do you partake in local organizations? Yes/No

supervised by dr. rana ejaz ali khan associate professor

Documents