impact of financial inclusion on economic...
TRANSCRIPT
IMPACT OF FINANCIAL INCLUSION ON ECONOMIC
DEVELOPMENT
THESIS
SUBMITTED TO THE UNIVERSITY OF JAMMU
FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
COMMERCE
Supervisor Submitted by
Prof. Neetu Andotra Ms. Preeti Salathia
POST GRADUATE DEPARTMENT OF COMMERCE
UNIVERSITY OF JAMMU
JAMMU
2014
CONTENTS Page No.
Certificate i
Acknowledgement ii-iii
List of Tables iv-v
List of Figures vi-vii
Abbreviations viii
Preface ix-x
Chapter I An Overview of Financial Inclusion and Economic
Development
1-22
Chapter II Review of Literature 23-64
Chapter III Research Methodology 65-104
Chapter IV Financial Inclusion, Socio-economic Empowerment and
Economic Development
105-159
Chapter V Financial Inclusion and Poverty Reduction 160-186
Chapter VI Financial Inclusion and Area Development 187-209
Chapter VII Conclusion and Strategic Implications 210-228
Bibliography 229-237
Annexures 238-245
i
DEPARTMENT OF COMMERCE
UNIVERSITY OF JAMMU, JAMMU -180006
NEW CAMPUS, BABA SAHEB AMBEDKAR ROAD.
CERTIFICATE
Certified that Ms. Preeti Salathia, who was admitted for the Degree of Ph.D. in
Commerce under my supervision, has completed her work. The title of her thesis is
‘Impact of Financial Inclusion on Economic Development’. She has fulfilled all
the statutory requirements for the submission of thesis for evaluation. It is further
certified that:
i. The title of her thesis has been approved by the Board of Research Studies in
Commerce;
ii. The thesis embodies the work of the candidate herself;
iii. The candidate has worked under me for the period required under rules;
iv. The candidate has got one paper, relevant with research, published in the
national journal;
v. The candidate has put in the required attendance and delivered a seminar in
the department during the period of research; and
vi. The conduct of the scholar remained good during the period of research.
Dated: Prof. Neetu Andotra
(Supervisor)
ii
ACKNOWLEDGEMENT
First of all, I thank Almighty GOD for generating enthusiasm and granting me
spiritual strength to successfully pass through this challenge.
Then, I would like to extend my sincere thanks to my guide Prof. Neetu
Andotra for her mentorship, valuable advice and extensive discussions during my
research. This work would not have been possible without your guidance, support and
encouragement.
I gratefully acknowledge academic staff of our department namely, Prof.
Hardeep Chahal, Dr. Gurjeet Kaur, Dr. Jeevan Jyoti, Sh. Tarsem Lal, Sh. Sunil and
Dr. Bodh Raj for their valuable suggestions.
I also thank Sh. M. P. Raina, Ms. Jyoti, Ms. Pooja, Sh. Ashok besides others
who have not directly contributed in my research work but indirectly helped me out.
I take this opportunity to sincerely acknowledge staff of Rattan Tata Library,
Delhi University and MDI, Gurgaon for letting me avail their library facility to gather
secondary data which buttressed me to perform my work more comfortably.
I extend my sincere thanks to all Business Correspondents for their invaluable
help during survey. My thanks is also due to Mitu, Jagjeet, Neetu Jasrotia, Manjeet
Jamwal, Satvinder, Rajinder, Nikhil besides others for their facilitation during survey.
Without you people, it would not have been possible for me to reach such far-flung
areas for collecting primary data.
My warm appreciation is due to all the Ph.D. scholars of our department
especially, Himani, Ritika, Kamini, Purnima, Jagmeet and Sonia who shared their
knowledge regarding various techniques for testing of hypotheses.
I feel highly indebted to Dr. Kiran Bakshi and other faculty members of Govt.
College for Women, Gandhi Nagar for their constant support, generous care and
homely feeling at my work place.
This list is incomplete without acknowledging my friends specially Pariksha,
Sonal, Aashu and Sushil Choudhary for their genuine friendship and the wonderful
time together. I truly acknowledge all your friendly help that remained the source of
inspiration for me throughout this work.
I convey special thanks to Shruti Gupta. During the inevitable ups and downs
of conducting my research she often reminded me life’s true priorities. This helped
iii
me a lot to work for hours together tirelessly. I doubt that I will ever be able to convey
my appreciation fully, but I owe her my eternal gratitude.
It is hard to express my thanks to my parents Sh. Babishan Singh and Ms.
Sharda Devi in words. Your understanding, faith, advice and indescribable support to
me throughout my whole life are invaluable. I am very grateful for your care, love,
trust, constant interest and positive stimulation. All other family members also
deserve special thanks.
My special thanks is due to my niece Piya and Nephew Ekansh for their
unconditional love and smile which waive off my whole day tiredness. Last but not
least, my thanks is also due to my grandmother Ms. Durgi Devi.
If have forgotten anyone, I apologize. Lastly, my thanks is due to one and all.
Place: Jammu Ms. Preeti Salathia
Date:
iv
LIST OF TABLES
S.No.
Description Page No.
1.1 Definitional Aspects of Financial Inclusion/Exclusion 4
1.2 Financial Inclusion Plan-Summary Progress of all Banks
including RRBs
15
2.1 Review of Literature 36
3.1 Generation of Scale Items 74
3.2 Multicollinearity Analysis 77
4.1 Socio-economic Profile of Respondents 143
4.2 Results showing Factor Loadings and Variance Explained after
Scale Purification (Rotated Component Method)
144
4.3 Reliability & Validity of Latent Constructs 148
4.4 Discriminant Validity of Latent Constructs 148
4.5 Results of CFA Fit Indices 149
4.6 Fitness of the Structural Model 149
4.7 Results of Hypotheses Testing 150
4.8 Output from One-way ANOVA 151
4.9 Mean Difference in the Nature of Financial Inclusion through t-
test
152
4.10 Age-wise Output from One-way ANOVA 152
4.11 Caste-wise Output from One -way ANOVA 153
4.12 Religion-wise Output from One-way ANOVA 153
4.13 Qualification-wise Output from One-way ANOVA 154
4.14 Income-wise Output from One-way ANOVA 155
4.15 Mean Difference in the Nature of Financial Inclusion between
Male & Female through t-test
155
4.16 Mean Difference in the Nature of Financial Inclusion between
Married & Unmarried Beneficiaries through t-test
156
5.1 Trends of Poverty in India 163
5.2 Number & Percentage of Population below Poverty Line by
States (2011-12)
165
5.3 Results showing Factor Loadings and Variance Explained after
Scale Purification (Rotated Component Method) for Poverty
Reduction
178
v
5.4 Result of CFA Fit Indices, Reliability and Validity 179
5.5 Demographic Profile-wise Mean Satisfaction regarding Poverty
Eradication
180
5.6 Demographic Profile-wise Mean Satisfaction regarding Poverty
Reduction through Education
181
5.7 Fitness of the Structural Model 182
5.8 Result of Hypothesis Testing 182
6.1 Results showing Factor Loadings and Variance Explained after
Scale Purification (Rotated Component Method) for Area
Development
202
6.2 Result of CFA Fit Indices, Reliability and Validity 202
6.3 Demographic Profile-wise Mean Satisfaction regarding Area
Development
203
6.4 Fitness of the Structural Model 204
6.5 Result of Hypothesis Testing 204
6.6 Regression Model Summary (with Coefficient) of Access
Dimension as Dependent Variable
204
6.7 ANOVAb
for Measuring Regression Coefficient 204
6.8 Regression Coefficient’s showing the Effect of Barriers of
Financial Inclusion on Access Dimension
205
6.9 Regression Model Summary (with Coefficient) of Usage
Dimension as Dependent Variable
205
6.10 ANOVAb
for Measuring Regression Coefficient 205
6.11 Regression Coefficient’s showing the Effect of Barriers of
Financial Inclusion on Usage Dimension
206
6.12 District and Bank-wise Mean Satisfaction among Beneficiaries
of FID
206
vi
LIST OF FIGURES
S.No. Description Page No.
Figure 1.1 Evolution of Financial Inclusion since 1960’s 2
Figure 1.2 Financial Institutional Products & Services 5
Figure 1.3 Objectives of Financial Inclusion 6
Figure 1.4 Financial Institutions Promoting Financial Inclusion 8
Figure 1.5 Barriers of Financial Inclusion 16
Figure 2.1 Proposed Theoretical Model for Financial Inclusion and
Economic Development
28
Figure 3.1 Normality through Box Plot 90
Figure 3.2 Normality through Q-Q Plot 90
Figure 4.1 Pie Chart for Banks 107
Figure 4.2 Pie Chart for Gender 107
Figure 4.3 Pie Chart for Age 108
Figure 4.4 Pie Chart for Caste 108
Figure 4.5 Pie Chart for Religion 109
Figure 4.6 Pie Chart for Marital Status 109
Figure 4.7 Pie Chart for Qualification 110
Figure 4.8 Pie Chart for Monthly Income 111
Figure 4.9 Pie Chart for Districts 111
Figure 4.10 CFA Model for Access Dimension of FID 127
Figure 4.11 CFA Model for Availability Dimension of FID 128
Figure 4.12 CFA Model for Usage Dimension of FID 129
Figure 4.13 CFA Model for Social Empowerment 130
Figure 4.14 CFA Model for Economic Empowerment 131
Figure 4.15 CFA Model for Economic Development 132
Figure 4.16 Overall Structure Equation Model 136
Figure 4.17 Impact of Financial Inclusion on Economic Development 137
Figure 4.18 Impact of Financial Inclusion on Social Empowerment 137
Figure 4.19 Impact of Social Empowerment on Economic
Development
138
Figure 4.20 Impact of Financial Inclusion on Economic Development
through Social Empowerment
138
vii
Figure 4.21 Impact of Financial Inclusion on Economic
Empowerment
139
Figure 4.22 Impact of Economic Empowerment on Economic
Development
139
Figure 4.23 Impact of Financial Inclusion on Economic Development
through Economic Empowerment
140
Figure 5.1 Relationship between Financial Ecosystem and Poverty 161
Figure 5.2 Trends of Poverty in India 164
Figure 5.3 Poverty Alleviation Programmes 168
Figure 5.4 CFA Model for Poverty Reduction 174
Figure 5.5 SEM Model for Poverty Reduction 177
Figure 6.1 Components of Area Development 187
Figure 6.2 Area Development Programmes in India 189
Figure 6.3 CFA Model for Area Development 194
Figure 6.4 SEM Model for Area Development 198
viii
ABBREVIATIONS
2 Chi Square
AGFI Adjusted Goodness of Fit Index
ANOVA Analysis of Variance
ATM Automatic Teller Machine
AVE Average Variance Extracted
BC Business Correspondents
CFA Confirmatory Factor Analysis
CFI Comparative Fit Index
CR Critical Ratio
ED Economic Development
EE Economic Empowerment
FI Financial Inclusion
FID Financial Inclusion Drive
FI-ED Financial Inclusion-Economic Development
GFI Goodness of Fit Index
GOI Government of India
JKB Jammu & Kashmir Bank
JKGB Jammu & Kashmir Grameen Bank
KYC Know Your Customer
MFI Micro Finance Institutions
NABARD National Bank for Agriculture and Rural Development
NFI Normed Fitness Index
PNB Punjab National Bank
RBI Reserve Bank of India
RMR Root Mean Square Residual
RMSEA Root Mean Square Error of Approximation
RRB Regional Rural Bank
SBI State Bank of India
SE Social Empowerment
SEM Structural Equation Modeling
SRW Standardised Regression Weights
TLI Tucker-Lewis Index
ix
PREFACE
Financial inclusion is the mechanism of ensuring access to financial services along
with timely & adequate credit whenever needed by the vulnerable groups at an
affordable cost. India, a growing economy has special significance of financial
inclusion as it brings large deprived sections of the society under financial ambit. This
access to financial services generates income, decreases social-economic disparities,
creates financial assets, promotes area development and provides new work
opportunities across all sectors and sections of the economy. Thus, financial inclusion
has multiplier effect on the economy as a whole through higher savings pooled from
the vast segment of the bottom of the pyramid (BoP) population. It brings un-banked
people into financial mainstream and results in accelerating the economic
development of the country and is integral to the inclusive growth process. In India,
60.9 million of accounts have been opened under FID through bank branches and BCs
during the year 2013-14. Therefore, financial inclusion has emerged as an important
global agenda for sustainable long-term economic development.
The present research study focuses on evaluating the impact of financial inclusion on
economic development among beneficiaries of five banks namely, JKB, JKGB, SBI
and PNB belonging to five districts i.e., Jammu, Samba, Kathua, Udhampur and Reasi
of Jammu division, J&K state. This study would help RBI along with other financial
intermediaries to redesign financial strategic framework for achieving long term
vision of financial inclusion drive i.e. inclusive growth.
The main body of the present research work comprises of seven chapters along with
tables, figures, charts & annexures to support the analysis and findings of the study.
The scheme and content of each chapter is as follows:
The first chapter entitled, ‘An Overview of Financial Inclusion and Economic
Development’ presents conceptual analysis of financial inclusion, objectives,
significance of financial inclusion, role of financial institutions in promoting financial
inclusion, measures taken by RBI & Govt. of India under financial inclusion
programme, current status of financial inclusion in India and barriers of financial
inclusion.
x
Chapter second, ‘Review of Literature’ outlines the literature review pertaining to
diverse dimensions of financial inclusion and economic development and concluded
with research gap.
The third chapter, ‘Research Methodology’ covers all the relevant issues of the
quantitative approaches being followed in the study. This chapter includes nature &
scope, need, objectives of the study, hypotheses formulation, pretesting, generation of
scale items, sampling techniques, research instrument & analytical tools of
quantitative data, significance and limitations of the study.
Chapter fourth, ‘Financial Inclusion, Socio-economic Empowerment and
Economic Development’ comprises of conceptual analysis of different constructs,
profile of respondents, analysis of impact of financial inclusion on economic
development through socio-economic empowerment sub-divided into scale
purification, CFA & SEM and determination of significant difference in the nature of
financial inclusion across the socio-economic variables. Validity and reliability have
also been measured in the chapter.
Chapter fifth, ‘Financial Inclusion and Poverty Reduction’ embraces conceptual
analysis of poverty, trends of poverty in India, causes of poverty, poverty alleviation
programmes and analysis between financial inclusion & poverty reduction sub-
divided into scale purification, CFA, demographic profile-wise mean satisfaction and
relationship between financial inclusion and poverty reduction through SEM.
Next chapter, ‘Financial Inclusion and Area Development’ discusses the conceptual
analysis of area development, schemes of area development and analysis of financial
inclusion & area development sub-divided into scale purification, CFA, demographic
profile-wise mean satisfaction, relationship between financial inclusion & area
development through SEM and impact of barriers of financial inclusion on access &
usage dimensions.
The last chapter, ‘Conclusion and Strategic Implications’ summarises the overall
findings of the study and provides strategic framework to improve the impact of
financial inclusion.
`
Chapter-I An Overview of Financial Inclusion and Economic
Development
CONTENTS
S.No. Title Page No.
1.1 Introduction 1
1.2 Conceptual Analysis of Financial Inclusion 1
1.3 Objectives of Financial Inclusion 6
1.4 Significance of Financial Inclusion 7
1.5 Role of Financial Institutions in Promoting Financial
Inclusion
8
1.6 Measures Taken by RBI and Government of India
under Financial Inclusion Programmes
10
1.7 Current Status of Financial Inclusion in India 13
1.8 Barriers of Financial Inclusion 16
1.9 Conclusion 18
References 20
1
CHAPTER I
AN OVERVIEW OF FINANCIAL INCLUSION AND
ECONOMIC DEVELOPMENT
1.1 INTRODUCTION
Despite impressive economic growth over the last two decades, many countries in the
world are experiencing inequalities leading to adverse consequences for social
cohesion which in turn, could dampen growth prospects (Zhuang & Hasan, 2008). For
growth to be sustained in the long run, it should be inclusive and broad based across
all sectors and sections of the economy (George, 2011). Inclusive growth promotes
economic growth, increases standard of living, reduces poverty, decreases disparity,
promotes agricultural growth rate and provides new work opportunities (Deutscher &
Jacquet, 2009). Therefore, financial inclusion has emerged as an important topic on
the global agenda for sustainable long-term economic growth. It is a stepping stone
and is integral to the inclusive growth process & sustainable development of the
country (Sadakkadulla, 2007 and Subbarao, 2010).
1.2 CONCEPTUAL ANALYSIS OF FINANCIAL INCLUSION
The concept of financial inclusion was evolved with the initialisation of Co-operative
movement in India during 1904. It got momentum in 1969 when 14 major commercial
banks of the country were nationalised and Lead Bank Scheme was introduced shortly
thereafter in mid 1970’s. Large numbers of bank branches were opened across the
country even in those areas which were neglected earlier. Despite of various
measures, a huge segment of the population of the country was excluded from the
formal banking system (Chattopadhyay, 2011). In 2005, RBI promulgated a drive for
financial inclusion whereby formal financial system promotes the participation of
every household at the district level via. saving accounts for the ‘unbanked’ (Ramji,
2009 and Ramasubbian & Duraiswamy, 2012). Under the chairmanship of
Rangarajan, ‘Committee on Financial Inclusion’ was formulated by the Govt. of India
and it defines financial inclusion as ‘the mechanism of ensuring access to financial
services and timely & adequate credit whenever needed by the vulnerable groups such
as the weaker sections and low income groups at an affordable cost’. Recently, in
2014 to give leverage to financial inclusion drive ‘Pradhan Matri Jan Dhan Yojana’
2
has been introduced. Figure 1.1 summarises the key stages in evolution of financial
inclusion initiated by the government since 1960’s.
FIGURE 1.1: EVOLUTION OF FINANCIAL INCLUSION SINCE 1960’s*
*Source: Naik, Priya (2013). Financial inclusion-key to economic & social development. CSR
Mandate, June-July, 14-17.
Thus, the above figure depicts that India has made a massive contribution to the
economic development by finding innovative ways from time to time to empower the
poor starting with nationalisation of banks, priority sector lending by banks, lead bank
scheme, establishment of regional rural banks, service area approach, self help group-
bank linkage programme, introduction of Pradhan Mantri Jan Dhan Yojana etc.
Multiple steps have been initiated by the RBI over the years to increase credit access
to the poor sections of the society.
Sarma & Pais (2008) defines financial inclusion as, ‘the process that ensure the ease
of access, availability and usage of the formal financial system for all members of an
economy’. Broadly, it means access to finance & financial services for all in a fair,
transparent and equitable manner at an affordable cost (Sarma, 2008 and Solo, 2008).
Murari & Didwania (2010) denotes it as a, ‘delivery of financial services at an
affordable cost to the vast sections of the disadvantaged and low-income groups
including households, enterprises, SMEs, traders. The various financial services
1960s-70s
•Focus on increasing credit to the neglected economy sectors and weaker sections of society.
•Nationalisation of banks.
•Development of the rural banking ecosystem including Regional Rural Banks.
•Lead Bank Scheme launched for rural lending.
1980s-90s
•Establishment of National Bank for Agriculture and Rural Development (NABARD) to provide refinance to banks providing credit to agriculture.
•SHG bank linkage program launched by NABARD.
2000s
•The term 'Financial inclusion' introduced for the first time in RBI's Annual Policy Statement 2005-06.
•Banks asked to offer 'no frills account'.
•Know Your Customer (KYC) norms simplified.
•Banking Correspondent and Banking Facilitator concept introduced.
•100 percent financial inclusion drive launched.
•Electronic Bank Transfer Scheme introduced to transfer social benefits electronically to bank account of beneficiary.
• Introduction of Pradhan Mantri Jan Dhan Yojana.
3
include credit, savings, insurance and payments & remittance facilities’. Kuri & Laha
(2011) defined it as a, ‘process of bringing the weaker and vulnerable sections of
society within the ambit of the organised financial system. It creates conditions for
access to timely & adequate credit and other financial services by vulnerable groups,
such as weaker sections and low income groups at affordable cost’. Bagli & Dutta
(2012) refers it as a, ‘situation where people, in general, have connection with the
formal financial institutions through holding savings bank account, credit account,
insurance policy etc. It may help the person to have affordable access to financial
services like formal savings, credit, payments, insurance and remittance’. It is a
delivery of banking services to masses including privileged and disadvantaged people
at an affordable terms and conditions without any discrimination (Raman, 2012 and
Padma & Gopisetti, 2013). According to Paramasivan & Ganeshkumar (2013), it can
be defined as, ‘a way of easy access to formal financial services or systems and their
usage by all members of the economy’. Financial inclusion is a powerful tool to
achieve inclusive growth. Financial inclusion is the process of ensuring access to
appropriate financial products and services needed by vulnerable groups such as
economically & socially weaker sections and low income groups at an affordable cost
in a fair & transparent manner by formal financial institutions (Uma & Rupa, 2013;
Choithrani, 2013; Sharma & Kukreja, 2013; Srinivas & Upender, 2014; Banerjee &
Francis, 2014 and Shyni & Mavoothu, 2014). Financial inclusion means providing
financial services to all sections of the society which includes the underprivileged, the
poor and the disadvantaged people. It not only means to open savings account of
people but also includes providing financial advice, insurance and credit services
(Garg, 2014). Kalunda (2014) defined financial inclusion as the, ‘process of availing a
range of required financial services, at a fair price, at the right place, form & time,
through formal means and without any form of discrimination to the populace’. Over
the years, several definitions of financial inclusion/exclusion have evolved and are
shown in Table 1.1. The structure of various financial products or services embraced
in financial inclusion and the institutional arrangement is schematically presented in
Figure 1.2.
4
TABLE 1.1 DEFINITIONAL ASPECTS OF FINANCIAL
INCLUSION/EXCLUSION*
Institution/Author Definition Indicators
ADB (2000) Provision of a broad range of financial
services such as deposits, loans,
payment services, money transfers and
insurance to poor and low-income households and their
micro-enterprises.
Deposits, loans, payment
services, money transfer
and insurance.
Stephen P. Sinclair (2001)
Financial exclusion means the inability
to access necessary financial services
in an appropriate form. Exclusion can
come about as a result of problems
with access, conditions, prices,
marketing or self-exclusion in response
to negative experiences or perceptions.
Basic banking services for
money transmission, credit,
insurance, debt and debt
assistance, long-term
savings and financial
literacy.
Chant Link and Associates,
Australia (2004)
Financial exclusion is lack of access by
certain consumers to appropriate low
cost, fair and safe financial products
and services from mainstream providers. Financial exclusion becomes
a concern in the community when it
applies to lower income consumers
and/or those in financial hardship.
Deposit accounts, direct
investments, home loans,
credit cards, personal loans,
building insurance and home insurance.
Treasury Committee, House
of Commons, UK (2004)
Ability of individuals to access
appropriate financial products and
services.
Affordable credit and
savings for all and access to
financial advice.
Scottish Government (2005)
Access for individuals to appropriate
financial products and services. This
includes having the capacity, skills,
knowledge and understanding to make
the best use of those products and
services. Financial exclusion by
contrast, is the converse of this.
Access to products and
services, and/or capacity,
skills, knowledge and
understanding.
United Nations (2006 b)
A financial sector that provides ‘access’ to credit for all ‘bankable’
people and firms, to insurance for all
insurable people and firms and to
savings and payments services for
everyone. Inclusive finance does not
require that everyone who is eligible
use each of the services, but they
should be able to choose to use them if
desired.
Access to credit, insurance, savings, payment services.
Report of the Committee on
Financial Inclusion in India
(Chairman: C.Rangarajan) (2008)
The process of ensuring access to
financial services and timely and
adequate credit where needed by vulnerable groups such as weaker
sections and low income groups at an
affordable cost.
Access to financial services
and timely and adequate
credit.
World Bank (20) Broad access to financial services
implies an absence of price and non-
price barriers in the use of financial
services, it is difficult to define and
measure because access has many
dimensions.
Access to financial services
such as deposit, credit,
payments, insurance.
*Source: Reserve Bank of India. (2009). Financial Inclusion. Retrieved from http://rbidocs.
rbi.org.in/rdocs /Publications/PDFs/86734.pdf, 294-348. Accessed on 27-09-2014.
5
FIGURE 1.2: FINANCIAL INSTITUTIONAL PRODUCTS & SERVICES*
*Source: Reserve Bank of India. (2009). Financial Inclusion. Retrieved from http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/86734.pdf, 294-348. Accessed on 27-09-
2014.
Insurance companies
Insurance
Financial inclusion: Access to
financial products & services
from the formal financial
system
MFIs/NGOs
Small value loans/credit
Post offices
Remittances Postal savings accounts
Payment and
remittance services
Saving accounts
Loan/credit accounts
Financial advice Commercial/Co-operative
bank/Credit unions
6
1.3 OBJECTIVES OF FINANCIAL INCLUSION
Financial inclusion has many objectives (Figure 1.3). A brief description is as under:
i. Economic objectives
Society is divided into different sections on the basis of income, occupation,
caste, etc. Financial inclusion embraces all the sections in the financial ambit.
Equitable growth in all the sections leads to reduction of disparities in terms of
income and savings. Thus, financial inclusion serves as a boom for the
underdeveloped and developing nations.
ii. Mobilisation of savings
Through financial inclusion, weaker sections are provided with the facility of
banking services. This facility mobilises the savings, normally piled up at their
households which can be effectively utilised for the capital formation and
growth of the economy.
FIGURE 1.3: OBJECTIVES OF FINANCIAL INCLUSION*
*Source: Mahajan, Sahil (2014). Financial inclusion & Indian banking sector. The International
Journal of Business & Management, 2(1), 67-73.
iii. Larger market
To serve the requirements and need of the large section of society, there is an
urgent need for the larger market for the financial system which opens up the
Objectives
Economic objectives
Mobilisation of savings
Larger market
Social objectives
Sustainable livelihood
Political ojectives
7
avenue for the new players in the financial sector and can lead to growth of
banking sector also.
iv. Social objectives
Poverty eradication is considered as an important objective of the financial
inclusion scheme since they bridge up the gap between the weaker section of
society and the sources of livelihood and the means of income which can be
generated for them if they get loans and advances.
v. Sustainable livelihood
Once the weaker section of society gets some money in the form of loan, they
start up their own business or support their education through which they can
sustain their livelihood. Thus, financial inclusion is turn out to be a blessing
for the low income households.
vi. Political objectives
There are certain other political objectives which can be achieved with the
wider inclusion of lower strata in the society and an effective direction can be
given to the government programmes.
1.4 SIGNIFICANCE OF FINANCIAL INCLUSION
In majority of the developing countries, access to finance is demanded more for the
bottom up pyramid community and considered as a public good, which is as important
and basic as access to safe water, primary education, etc. The most significant effect
of financial inclusion is that the entire national financial system is benefitted by
greater inclusion, especially when promoted in the wider context of economic
inclusion. India, a growing economy, has special significance of financial inclusion as
it brings large segment of the productive sectors of economy under formal financial
network and could unleash their creative capacities besides augmenting domestic
demand on a sustainable basis driven by income and consumption growth from such
sectors. Financial inclusion efforts do have multiplier effect on the economy as a
whole through higher savings pooled from the vast segment of the bottom of the
pyramid (BoP) population by providing access to formal savings arrangement
resulting in expansion in credit and investment by banks. Deeper engagements of the
8
BoP/under-banked population in the economy through the formal financial system
could lead to improvement of their financial conditions & living standards, alleviation
of the poverty, enabling them to create financial assets, generate income and build
resilience to meet macro-economic & livelihood shocks (Khan, 2012). It encourages
bringing un-banked customers into financial mainstream. All this, results in escalating
the economic development of the country.
1.5 ROLE OF FINANCIAL INSTITUTIONS IN PROMOTING FINANCIAL
INCLUSION
A number of financial institutions exist in financial ecosystem, which promotes
financial inclusion (Figure 1.4). Prominent among them are as under:
FIGURE 1.4: FINANCIAL INSTITUTIONS PROMOTING FINANCIAL
INCLUSION*
*Source: www.nabard.org. Accessed on 16-11-2014
i. Role of commercial banks
Banks play an important role in mobilisation and allocation of resources in any
country. They are the key pillars of India’s financial system. Besides opening
of accounts, GCC, KCC, micro insurance, bank is also doing other activities for
accelerating the growth of financial inclusion. They are encouraging interaction
between financial sector and rural development staff to ensure that financial
Financial institutions promoting
financial inclusion
Commercial banks
Regional rural banks
NABARD
Post offices
Micro finance
institutions
Self help groups
9
sector expertise is included on any rural project that has a finance component.
Besides this, they introduce financial services designed for the poor, providing
improved services to rural clients’ by introducing new technology, offer
flexible grant funding to financial institutions seeking to adapt or introduce new
financial products or to reduce delivery transaction costs or introduce more
diverse & transparent financial services for farmers.
ii. Role of regional rural banks (RRBs)
Regional rural banks came into existence in the Indian financial system since
last four decades. Its inception has improved the rural credit delivery
mechanism in India. Over the years, they are seen as the small man’s bank
taken deep roots and have become a sort of inseparable part of the rural credit
structure. As on March 31, 2013, there were 64 RRBs having a network of
17,867 branches all over the country for extending credit to rural masses. The
growth in the branch network has enabled the RRB’s to expand banking
activities in the unbanked areas and mobilise rural savings. They have emerged
as a strong intermediary for financial inclusion in rural areas by opening large
numbers of ‘No Frills’ accounts and financing under General Credit Card
(GCC). These banks have set up Financial Literacy and Credit Counselling
Centers (FLCC) to create awareness among public.
iii. Role of NABARD
NABARD has been very effective in promoting financial inclusion, capacity
building and assistance for creation of infrastructure. In order to promote
financial education, it has set up literacy centres to educate the masses on
finance, banking and insurance with the help of stakeholders. Based on the
Rangarajan committee’s recommendation, NABARD with the help of
government has set up a Financial Inclusion Promotion and Development Fund
(FIPDF) and Financial Inclusion Technology Fund (FITF) in order to foster
financial inclusion programme.
iv. Role of post offices
In order to speed up financial inclusion process, the Indian Post also plays a
vital role with its wide range network of 1,39,182 rural post office branches and
15,797 urban post office branches. Post offices are offering services like saving
10
account, recurring deposit account, monthly income scheme, public provident
fund, time deposit, senior citizen saving scheme, national saving certificate,
postal life insurance, rural postal life insurance etc. in order to cover more and
more of the rural population under the financial inclusion drive.
v. Role of micro finance institutions
Micro finance has been looked upon as an important means of financial
inclusion in India. Micro finance has to act pro-actively not just as a means to
financial inclusion but also to work towards reducing dependence of poor
borrowers on various informal sources of credit that are often known for the
onerous terms at which they offer credit. It also plays a significant role in
reducing poverty in India. Providing access to micro finance can prove to be an
effective way of reaching the poor and improving their lives. It is an enabling,
empowering and bottom-up tool to poverty alleviation that has provided
economic and non-economic externalities to low-income households in India.
vi. Role of self help group-bank linkage programme
Self help groups have emerged to be the most effective instrument for financial
inclusion. The objectives of self help group programmes are to alleviate
poverty, increase sustainability, improve capacity building and help the weaker
section to build assets. The self help group-bank linkage model is the dominant
channel where commercial banks lend directly to self help groups formed
explicitly for this purpose. This serves as a meaningful linkage between
commercial banks and self help groups. Thus, the micro finance services
provided through self help group-bank linkage programme proved to be the
most successful initiative in financial inclusion. They have been accepted as
effective tools to inclusive growth for extending various financial services to
hitherto excluded categories of poor and rural households.
1.6 MEASURES TAKEN BY RBI AND GOVERNMENT OF INDIA
UNDER FINANCIAL INCLUSION PROGRAMMES
Several measures have been initiated by both the Reserve Bank of India and the
Government to bring the financially excluded people to the fold of the formal banking
services. The important financial inclusion initiatives of RBI are as under:
11
i. Swavalamban
A co-contributory pension scheme launched on September 26, 2010 for
workers of unorganised sector. It is applicable to all citizens in the unorganised
sector who join the National Pension System (NPS) administered by the
Pension Fund Regulatory and Development Authority (PFRDA) Act, 2013.
ii. Swabhiman
In a way to achieve financial inclusion programme, the central government
launched ‘Swabhiman scheme’ on February 10, 2011. It aims to bring banking
services to large rural areas in the country. This campaign is operated by the
Ministry of Finance, Government of India and the Indian Banks' Association
(IBA) to bring banking within the reach of the masses of the Indian population.
iii. Opening of bank branches
Government had issued detailed strategy and guidelines on financial inclusion
in October, 2011. According to the framed strategy, banks were advised to
open branches in all habitations of 5,000 or more population in under-banked
districts and 10,000 or more population in other districts.
iv. No-frills account
In the mid term review of the policy (2005-06), RBI exhorted the banks, with a
view to achieving greater financial inclusion, to make available a basic banking
‘no frills’ account either with ‘NIL’ or very minimum balances as well as
charges that would make such accounts accessible to vast sections of the
population. Banks have been advised to provide small overdrafts in such
accounts.
v. Simplification of know your customer (KYC) norms
In order to ensure that persons belonging to low income group both in urban
and rural areas do not face difficulty in opening the bank accounts due to the
procedural hassles, the KYC procedure for opening accounts has been
simplified to enable those belonging to low income groups without documents
of identity and proof of residence to open banks accounts.
12
vi. Aadhaar - Unique identification authority of India (UIDAI)
The Government of India has embarked an initiative to provide an individual
identification number to every citizen of India and in 2009, it established the
UIDAI to issue these cards on behalf of the GOI. This number provided by
UIDAI serves as a proof of identity and address anywhere in India. The
Aadhaar number also enable people to have access to services such as banking,
mobile phone connections and other government and non-government services
in due course. In addition, the UIDAI has introduced a system in which the
unbanked population will be able to open an account during enrollment with
Aadhaar without going to a bank. The individual will be able to access such
bank accounts through a micro-ATM network with large geographic reach.
vii. Ensuring reasonableness of bank charges
Reserve bank receives several representations from public about unreasonable
service charges being levied by banks. The existing institutional mechanism in
this regard is not adequate and is a basic reason for people reluctance to
opening accounts. Accordingly, to ensure fair practices in banking services, the
RBI has issued instructions to banks making it obligatory for them to display
and continue updating their offices/branches and their websites regarding
various service charges in a prescribed format.
viii. Business correspondent model
With the objective of ensuring greater financial inclusion and increasing the
outreach of the banking sector, banks were permitted by RBI in 2006 to use the
services of intermediaries in providing financial and banking services through
the use of Business Facilitators (BFs) and Business Correspondents (BCs).
Business Correspondents are retail agents engaged by banks for providing
banking services at locations other than a bank branch/ATM. They represent
the bank concerned and enable a bank to expand its outreach and offer limited
range of banking services at low cost, particularly where setting up a brick and
mortar branch is not viable.
ix. Setting up of ultra small branches (UBSs)
Considering the need for close supervision and mentoring of the Business
Correspondent Agents (BCAs) by the respective banks and to ensure that a
13
range of banking services are available to the residents of such villages, Ultra
Small Branches (USBs) are being set up in all villages covered through BCAs
under financial inclusion.
x. Expansion of ATM network
In close consultation with the Department of Financial Services, the Public
Sector Banks (PSBs) worked on a model of area based deployment of
ATMs/Cash dispensers, taking benefit of the power of aggregation, with all
PSBs/RRBs clubbing their requirement and one of the PSB issuing a common
RFP on behalf of all these banks for a geographical cluster. At present, PSBs
have installed around 60,000 ATMs.
xi. General credit card
With a view to help the poor and the disadvantaged with access to easy credit,
banks have been asked to consider introduction of a general purpose credit card
facility up to `25,000 at their rural and semi-urban branches. The objective of
the scheme is to provide hassle-free credit to banks’ customers based on the
assessment of cash flow without insistence on security, purpose or end use of
the credit. This is in the nature of revolving credit entitling the holder to
withdraw up to the limit sanctioned.
xii. Pradhan mantri jan dhan yojana (PMJDY)
It is a national mission for financial inclusion to ensure access to financial
services namely, banking savings & deposit accounts, remittances, credit,
insurance and pension in an affordable manner. It focuses on coverage of
households as against the earlier plan which focussed on coverage of villages.
It focuses on rural as well as on urban areas. Earlier plan targeted only villages
above 2,000 population while under PMJDY whole country is to be covered by
extending banking facilities in each sub-service area consisting 1,000-1,500
households such that facilities are available to all within a reasonable distance.
1.7 CURRENT STATUS OF FINANCIAL INCLUSION IN INDIA
As per census (2011), out of 24.67 crore households in the country, 14.48 crore
(58.7%) households had access to banking services. Of the 16.78 crore rural
14
households, 9.14 crore (54.46%) were availing banking services. Of the 7.89 crore
urban households, 5.34 crore (67.68%) households were using banking services. In
the year 2011, banks covered 74,351 villages, with population more than 2,000 (as per
2001 census), with banking facilities under the ‘Swabhiman’ campaign through
Business Correspondents. However, the program had a very limited reach and impact.
The present banking network of the country (as on 31.03.2014) comprises of a bank
branch network of 1,15,082 and an ATM network of 1,60,055. Of these, 43,962
branches (38.2%) and 23,334 ATMs (14.58%) are in rural areas. Moreover, there are
more than 1.4 lakh Business Correspondents (BCs) of Public Sector Banks and
Regional Rural Banks in the rural areas. BCs are representatives of bank to provide
basic banking services i.e. opening of basic bank accounts, cash deposits, cash
withdrawals, transfer of funds, balance enquiries, mini statements, etc. However,
actual field level experience suggests that many of these BCs are not actually
functional (Keshavamurthy, 2014).
The Reserve bank has encouraged banks to adopt a structured and planned approach
to financial inclusion with commitment at the highest levels through preparation of
board approved FIPs. The first phase of FIPs was implemented over 2010-13. The
Reserve bank has used FIPs to gauge the performance of banks under their FI
initiatives. With the completion of the first phase, a large banking network has been
created and a large number of bank accounts have also been opened. However, it has
been observed that the accounts opened and the banking infrastructure created has not
seen substantial operations in terms of transactions. In order to continue with the
process of ensuring meaningful access to banking services to the excluded ones,
banks were advised to draw up fresh three-year FIPs for 2013-16. Banks were also
advised that the FIPs prepared by them are disaggregated and percolate down to the
branch level so as to ensure the involvement of all the stakeholders in FI efforts and
also to ensure uniformity in the reporting structure under FIPs. The focus under the
new plan is now more on the volume of transactions in the large number of accounts
opened. A brief performance of banks under FIP up to March 31, 2014 (Table 1.2) is
as under:
i. The number of banking outlets has gone up to nearly 3,84,000. Out of these,
1,15,350 banking outlets were opened during 2013-14.
15
ii. Nearly 5,300 rural branches have been opened during the last one year. Out of
these, nearly 4,600 branches fall in unbanked rural centres.
iii. Nearly 33,500 BC outlets have been opened in urban locations during the year
taking the total number of BC outlets in urban locations to 60,730 as at the end
of March 2014.
iv. More than 60 million Basic Savings Bank Deposit Accounts (BSBDAs) are
added during the last year taking the total number of BSBDAs to 243 million.
v. With the addition of 6.2 million Small Farm Sector Credits during 2013-14,
there are 40 million such accounts as on March 31, 2014.
vi. With the addition of 3.8 million Small Non-farm Sector Credits during 2013-
14, there are 7.4 million such accounts as on March 31, 2014.
vii. Nearly 328 million transactions are carried out in BC-ICT accounts during the
last year as compared to 250 million transactions during 2012-13.
TABLE 1.2: FINANCIAL INCLUSION PLAN-SUMMARY PROGRESS OF
ALL BANKS INCLUDING RRBS*
Particulars Year
ended
March 2010
Year
ended
March 2013
Year
ended
March 2014
Period
April 2013-
March 2014
Banking outlets in villages – Branches 33,378 40,837 46,126 5,289
Banking outlets in villages – Branchless
mode
34,316 2,27,617 3,37,678 1,10,061
Banking outlets in villages –Total 67,694 2,68,454 3,83,804 1,15,350
Urban locations covered through BCs 447 27,143 60,730 33,587
Basic Savings Bank Deposit A/c through
branches (no. in million)
60.2 100.8 126.0 25.2
Basic Savings Bank Deposit A/c through
branches (amt. in `billion)
44.3 164.7 273.3 108.6
Basic Savings Bank Deposit A/c through
BCs (No. in million)
13.3 81.3 116.9 35.7
Basic Savings Bank Deposit A/c through
BCs (Amt. in ` billion)
10.7 18.2 39.0 20.7
BSBDA total (No. in million) 73.5 182.1 243.0 60.9
BSBDA total ( Amt. in ` billion) 55.0 182.9 312.3 129.3
ICT A/cs-BC- Transaction - (No. in
million) (During the year)
26.5 250.5 328.6 328.6
ICT A/cs-BC- Transactions - (Amt. in `
billion) (During the year)
6.9 233.9 524.4 524.4
*Source: Reserve Bank of India. (2014). Credit delivery and financial inclusion. Retrieved from http://www.rbi.org.in/scripts/AnnualReportPublications.aspx?Id=1122, 64-71. Accessed on
27-09-2014.
16
1.8 BARRIERS OF FINANCIAL INCLUSION
Over a period of time several measures have been taken by the banks in India to
improve access to affordable financial services through financial education,
leveraging technology, launching of various schemes and generating awareness.
Despite this, access to formal banking system by weaker section of society in India is
affected by several barriers. The lack of awareness, low income & assets, social
exclusion, illiteracy are the barriers from demand side. The distance from bank
branch, branch timings, cumbersome banking procedure & requirements of
documents for opening bank accounts, unsuitable banking products or schemes,
language, high transaction costs and attitudes of bank officials are the barriers from
supply side. Hence, there is a need for financial inclusion to build uniform economic
development, both spatially & temporally and ushering in greater economic & social
equity. Various barriers are shown is Figure 1.5 and a brief description of them is as
under:
FIGURE 1.5: BARRIERS OF FINANCIAL INCLUSION*
*Source: http://rbidocs.rbi.org.in/rdocs/Publications/PDFs/86734.pdf. Accessed on 16-11-2014
High cost
Non price barriers
Behavioural aspects
Geographical barrier
Financial illiteracy
Technological hindrances
Environmental and market factors
Social barriers
Psychological and cultural barriers
Lack of social security payments
Barriers of
financial inclusion
17
i. High cost
Providing and utilising financial services is not available free of cost for both the
service provider and service utiliser.
a. Cost for service provider: Setting up of branches in rural areas are
generally not advantageous due to high cost and low business
b. Cost for service utiliser: It has been observed that the poor living in rural
area are reluctant to utilise these services due to high cost e.g., minimum
balance requirements in saving account, fixed charges in credit cards and
debit cards, loan processing charges, etc.
ii. Non price barriers
Access to formal financial sources requires documents of proof regarding
person’s identity, postal address, income, staff attitude, unsuitable products, etc.
Poor people generally do not have these documents and thus are excluded from
financial services.
iii. Behavioural aspects
As per IDBI Gilts Report (2007) research in behavioral economics has shown
that many people are not comfortable using formal financial services due to
difficulty in understanding the language, reading the document and various
hidden terms & conditions. Poor people also think that financial services and
financial products are meant only for the upper strata of the society.
iv. Geographical barrier
It is concerned with geographical inaccessibility to services in general and
banking outlets in particular. It includes remoteness of residence, insurgency in
a location branch timings, restricted mobility either due to old age or disability
or lack of access to private transport or public transport services.
v. Financial illiteracy
Limited financial literacy, i.e., basic mathematics, business financial skills as
well as lack of understanding often acts as a constraint for accessing financial
services. Literacy requirements inhibits access for those with lower literacy, lack
of awareness and/or English language competency skills.
18
vi. Technological hindrances
Customers sometimes from fear or lack of familiarity hesitates to conduct their
banking activities through technological advancements. Some of those groups
affected by restricted mobility may also be vulnerable to technological
exclusion.
vii. Environmental and market factors
Environmental & market factors includes the broader socio-economic and
demographic trends such as changing market structure, political trends such as
transfer of risk & responsibilities from state and employer to individuals.
viii. Social barrier
It comprises of two major factors i.e., gender and age.
a. Gender issues: Access to credit is often limited for women who either do
not have or cannot hold title to assets such as land and property or must seek
male guarantees to borrow.
b. Age factor: Financial service providers usually target the middle of the
economically active population, often overlooking the design of appropriate
products for older or younger potential customers.
ix. Psychological and cultural barriers
The feeling that banks are not interested to look into their cause has led to self-
exclusion for many of the low income groups. However, cultural and religious
barriers to banking have also been observed in some of the countries.
x. Lack of social security payments
The countries where the social security payment system is not linked to the
banking system, banking exclusion has been higher.
1.9 CONCLUSION
To sum up, financial inclusion is considered to be critical for achieving inclusive
growth, a prerequisite for economic development. Recognising the importance of
economic development in India, efforts are being taken to make the financial system
19
more inclusive. RBI is furthering financial inclusion in a mission mode through a
combination of strategies ranging from relaxation of regulatory guidelines, provision
of innovative products, encouraging use of technology and other supportive measures
for achieving sustainable and scalable financial inclusion. Despite the laudable
achievements in the field of rural banking, issues such as slow progress in increasing
the share of institutional credit, high dependence of small & marginal farmers on non-
institutional sources, skewed nature of access to credit between developed regions &
less developed regions appear larger than ever before. Therefore, the key issue now is
to ensure that rural credit from institutional sources achieves wider coverage and
expands financial inclusion drive.
20
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`
Chapter-II Review of Literature
CONTENTS
S.No. Title Page No.
2.1 Introduction 23
2.2 Literature Review 23
2.3 Research Gap 29
References 30
23
CHAPTER II
REVIEW OF LITERATURE
2.1 INTRODUCTION
The relevant literature has been reviewed to explore the theoretical foundation behind
financial inclusion and various aspects pertaining to it. This attempt also focus on
analysing and understanding the extent of financial inclusion throughout India,
relationship between financial inclusion & poverty reduction, financial inclusion &
rural development, impact of financial inclusion on socio-economic empowerment
and various barriers of financial inclusion. The chapter concluded with research gap
and proposed model on the basis of reviewed literature.
2.2 LITERATURE REVIEW
Financial inclusion is a timely delivery of banking services at affordable cost to vast
sections of vulnerable groups such as weaker sections, disadvantaged and low income
groups (Ramji, 2009; Murari & Didwania, 2010; Arputhamani & Prasannakumari,
2011; Mishra, 2012; Divya, 2013; Padma & Gopisetti, 2013; Sharma & Kukreja,
2013; Banerjee & Francis, 2014; Srinivas & Upender, 2014; Shyni & Mavoothu,
2014; Garg, 2014; Verma & Aggarwal, 2014; Kalunda, 2014 and Joseph, 2014). It
does not mean opening of saving accounts only but also includes providing insurance
(Bagli, 2012; Padma & Gopisetti, 2013 and Garg, 2014), credit services (Bagli, 2012;
Padma & Gopisetti, 2013 and Garg, 2014) and financial advice (Garg, 2014). Swamy
(2011) identified exclusion of large number of households from the ambit of financial
services in India. Over the years, situation has improved as in 2007, 41% of the
population had no bank account (Cnaan et al., 2011), in 2009 about 36% of the
sample remained excluded from any kind of formal or informal saving accounts
(Ramji, 2009), in 2010, only 23% were not having bank accounts (Cnaan et al., 2011)
and in 2011, merely 17% of the population were excluded from access to financial
services (Rachna, 2011). Real rate of financial inclusion in India is very low due to
lack of interest in opening bank account, reluctance in opening bank branches in rural
areas, poor connectivity, no issuance of smart card and lack of trust on business
correspondents (Choithrani, 2013; Sharma & Kukreja, 2013 and Srinivas & Upender,
24
2014). Widespread disparities exist among various regions in the spread and progress
of banking services in the country (Pokhriyal & Ghildiyal, 2011). It showed that
banking network & services have grown better in Southern, Northern & Western
regions compared to North-eastern regions where banks need to put in more
concentrated efforts for eliminating the disparities (Pokhriyal & Ghildiyal, 2011).
Region-wise, exclusion is most acute in Central, Eastern and North-eastern regions
(Kr. & Sahoo, 2011). Financial inclusion through micro finance in the country is not
uniform and unevenly distributed in six regions of the country viz. Northern, North-
eastern, Eastern, Central, Western and Southern (Swamy, 2011 and Shankar, 2013).
Southern and Western regions are characterised by widespread availability, while the
Central region has low availability of MFI & banking services (Shankar, 2013).
Further, it is revealed that Eastern and North-eastern regions showed high availability
of micro finance but not banking services, while the Northern region showed high
availability of banking but not micro finance services (Shankar, 2013). Among
different states in India, Chandigarh is at the top and Manipur is at the bottom in
terms of level of financial inclusion (Kuri & Laha, 2011). Another study by Chithra &
Selvam (2013) revealed that only two states - Maharashtra and Uttar Pradesh have
high financial inclusion, whereas other states namely, Kerala, Tamil Nadu, Punjab
and West Bengal are falling under the medium financial inclusion category and states
particularly, Karnataka, Uttarakhand, Himachal Pradesh, Andhra Pradesh, Haryana,
J&K, Gujarat, Orissa, Bihar, Assam, Madhya Pradesh & Rajasthan are forming the
group of low financial inclusion. Majority of poor are excluded from financial
services and suffer from higher incidence of poverty, unemployment and inequality in
income distribution which can be reduced through financial inclusion, micro finance
and micro-credit plus services of micro finance (Barik, 2009; Rautela et al., 2010;
Goel et al., 2011; Kr. & Sahoo, 2011; Das, 2012 and Srinivas & Upender, 2014).
Financial inclusion plays a crucial role in creating employment, improving access to
credit for consumption & productive purpose, increasing household’s expenditure,
preventing exploitation by the informal moneylenders, increasing income & asset,
developing human capital and improving standard of living which in turn can lead to
poverty reduction, rural development and social & economic development (Rautela et
al., 2010; Ellis et al., 2010; Arputhamani & Prasannakumari, 2011; Kumar & Sharma,
2011; Das, 2012; Raman, 2012; Sajeev & Thangavel, 2012; Sharma & Kukreja, 2013;
Padma & Gopisetti, 2013; Verma & Aggarwal, 2014; Banerjee & Francis, 2014;
25
Satpathy et al., 2014; Kapoor & Singh, 2014; Shyni & Mavoothu, 2014; Mutai &
Achieno, 2014 and Srinivas & Upender, 2014). Access to micro finance also brings
positive change in income which leads to socio-economic empowerment through
increasing saving habits, lessening family violence, raising capabilities to deal with
social evils, day to day problems, enhanced asset ownership, creation of employment,
improved purchasing power, buying of new clothes, boosting confidence of rural
masses, declining income inequality, greater ability to meet unforeseen circumstances,
improved standard of living and change in life style (Sathpathy, 2014; Verma &
Aggarwal, 2014; Mutai & Achieno, 2014 and Saidu et al., 2014). Micro finance
institutions play a significant role in facilitating inclusion of excluded population
(Verma & Aggarwal, 2014). MFIs break down many barriers of access to financial
inclusion (Shankar, 2013). If barriers of financial inclusion i.e., high charges,
minimum balance requirement, lack of financial literacy are reduced, then it can
stimulate household investment, thereby contributing to growth in developing
countries (Ellis et al., 2010). There is positive relationship between financial inclusion
and growth of banking system, more growth of banking system leads to more of
financial inclusion (Raman, 2012). Financial inclusion leads to inclusive development
of rural areas and bring their quality of life at par with the people of urban areas
(Padma & Gopisetti, 2013). Factors such as economic status of the household, non-
farm employment, rural & area development, social security, level of education, asset
holding, level of income, accessibility, culture and occupation status are the
significant contributors to financial inclusion (Kuri & Laha, 2011; Ghatak, 2013 and
Joseph, 2014). Ghatak (2013) revealed that accessibility to financial services is the
most important and asset possessed by a person is the least important driver of
financial inclusion. Kapoor & Singh (2014) highlighted that well functioning financial
system enables economically & socially excluded people to actively contribute to
development and protect themselves against economic shocks. Inclusion in the
financial services brings positive and significant changes in general and economic
condition of people (Uma & Rupa, 2013) and brings economic prosperity to region or
a state (Gupta et al., 2014). There is positive correlation between sustainability of
financial inclusion and rural dwellers which are the mainstream for economic growth
in any country (Nwankwo & Nwankwo, 2014). Several studies found financial
development and monetary benefits influence economic growth and are considered
critical in achieving inclusive growth (Nagadevra, 2009; Barik, 2009 and Das, 2012)
26
which ultimately leads to economic development (Srinivas & Upender, 2014).
Financial inclusion enables rural people to channelise their savings in such ways that
lead to increase in GDP of the country (Garg, 2014). Singh & Kodan (2011) identified
existence of significant relationship between financial inclusion & economic
development. Positive correlation exists between IFI & HDI i.e., higher the level of
financial inclusion higher will be the level of human development (Sarma & Pais,
2011; Arputhamani & Prasannakumari, 2011; Kuri & Laha, 2011; Banerjee &
Francis, 2014 and Gupta et al., 2014). States with high financial inclusion have high
GDP per capita and good human development index and vice versa (Gupta et al.,
2014). Sarma (2010) identified penetration, availability and usage as the indicators of
financial inclusion. Most of the states have significant and positive relationship
between deposit & credit penetration which has increased in the current decade
(Kumar, 2009). Number of households with bank accounts doubled over the duration
of the financial inclusion drive or from past five years to receive government
assistance but have low usage & awareness about the account (Ramji, 2009 and
Ramasubbian & Duraiswamy, 2012). Joseph (2014) revealed that level of financial
inclusion has increased due to rising awareness about financial products and services
among respondents. There is negative influence of population density on deposit
penetration (Kumar, 2009). Most accessible financial services are saving accounts and
loans (Cnaan et al., 2011). Access to affordable financial services, especially credit &
insurance, enlarges livelihood opportunities through adoption of different economic
activities. No significant difference exists among respondents belonging to different
age group (Dias, 2013 and Kalunda, 2014), gender (Dias, 2013 and Kalunda, 2014),
income group (Dias, 2013) and occupation (Dias, 2013) with regard to demand and
use of financial services under financial inclusion scheme. Whereas, on other
demographic variables such as, marital status, level of education, level of income
(Divya, 2013), gender (Anjuman, 2011) and caste (Swamy, 2014) significant
difference exist. The NGOs, MFIs, Cooperatives, NBFCs and the SHGs are regularly
connecting the poor with NABARD, SIDBI, RRBs and other commercial banks to
make resources available to them for their social & economic upliftment (Pandey &
Kumar, 2011). SHGs have positive impact on financial inclusion especially on access
to financial services as number of bank accounts, credit availed and repayment of
credit has increased among SHGs members (Anjuman, 2011 and Uma & Rupa, 2013).
Other studies found out SHG-bank linkage programme increased the degree of
27
financial inclusion and served only the large & medium farmers but completely
neglected or no linkage can be inferred to marginal & small farm size group
(Anjuman, 2011; Swamy, 2011 and Sajeev & Thangavel, 2012). SHG through
financial inclusion ensures social and economic empowerment of its members (Uma
& Rupa, 2013). There exists significant difference in the family income of the
respondents before and after joining the SHG (Arputhamani & Prasannakumari,
2011). Gupte et al. (2012) found that the outreach (penetration & accessibility) is
directly proportional to financial inclusion and added another dimension i.e., ease of
transaction variable which includes number of location to open deposit/loan account
& submit loan application. Kalunda (2014) revealed high level of inclusion leads to
high usage in terms of credit access. In rural and semi-urban location, people were not
satisfied with the services provided by the banks & NGOs as only nationalised banks
had been entrusted the task of implementing financial inclusion and they hesitate in
operating in rural areas for lower income-earning classes (Rachana, 2011; Pokhriyal
& Ghildiyal, 2011 and Ramassibbian & Duraiswamy, 2012). Socio-economic
variables such as, per capita GDP, high income level, literacy, ownership of house,
urbanisation, infrastructure variables such as, network of paved road, telephone &
internet subscription shows positive and significant relationship with financial
inclusion (Arputhamani & Prasannakumari, 2009; Sarma & Pais, 2011; Singh &
Kodan, 2011 and Chithra & Selvam, 2013). Whereas other socio-economic variables
such as, income inequality, rural population, unemployment, infrastructural variable
like radio, newspaper, cable T.V. & computer, sex ratio, urban population as the
percent of total population and banking variable i.e., NPA, CAR, foreign ownership
interest rate shows negative relationship with financial inclusion (Arputhamani &
Prasannakumari, 2009; Sarma & Pais, 2011; Singh & Kodan, 2011 and Chithra &
Selvam, 2013). Das (2012) identified gap exist between demand and supply side
factor responsible for low inclusion. Various studies revealed demand side factors i.e.,
lack of awareness, unsuitability of the financial products, unfriendly & unempathetic
attitude, exorbitant & non-transaction fees and age group influence financial inclusion
(Kumari, 2009; Nagadevra, 2009; Bihari, 2011; Venkataramaraju & Ramesh, 2011;
Rachna, 2011; Cnaan et al., 2011; Goel et al., 2011; Mishra, 2012 and Jain et al.,
2012). Supply-side factors which act as barriers to financial inclusion are high
transaction cost, typology (rural/urban), gender and marital status (Nagdevra, 2009;
Bihari, 2011; Gupte et al., 2012 and Jain et al., 2012). Other factors such as lower
28
level of job, legal identity, affordability, minimum amount required to open account,
number of days to process loan application, complicated procedure, psychological &
cultural barriers, spatial distribution of banking services and annual fee charged for
ATM’s have inverse relationship with financial inclusion (Rachana, 2011;
Venkataramaraju & Ramesh, 2011; Jain et al., 2012 and Gupte et al., 2012). Literacy
is a pre-requisite for creating investment awareness (Paramasivan & Ganeshkumar,
2013) as inadequate financial education results into lower financial literacy (Kalunda,
2014). But it is not always true, such as Kerala state has a very low value of usage
dimension of financial inclusion despite having high literacy rate, while, Karnataka
state has higher value of usage dimension than literacy rate. Therefore, literacy alone
cannot guarantee high financial inclusion (Paramasivan & Ganeshkumar, 2013), other
factors such as branch density (Paramasivan & Ganeshkumar, 2013), behavioural
factors etc. be emphasised rather than bringing mere improvement in literacy rate
(Gupta & Singh, 2013). Various RBI efforts such as, opening of no frill account,
issuance of general credit card, opening of rural bank branches should be properly
implemented for making financial inclusion a success and bringing prosperity to the
aspiring poor through financial inclusion (Garg, 2014).
The integrated review of literature, based on secondary literature on financial
inclusion and its determinants is summarised in tabular form (Table 2.1).
On the basis of aforesaid literature, a proposed theoretical model (Figure 2.1) is as
under:
FIGURE 2.1: PROPOSED THEORETICAL MODEL FOR FINANCIAL
INCLUSION & ECONOMIC DEVELOPMENT
Access
Availability
Usage
Economic-empowerment
Social-empowerment
Economic
development Financial
inclusion
29
2.3 RESEARCH GAP
The aforesaid reviewed literature revealed that various conceptual studies have been
conducted on financial inclusion but few empirical studies that too with limited
geographical coverage, have touched varied aspects of financial inclusion. Further,
there is paucity of empirically tested relation between financial inclusion & poverty,
financial inclusion & area development and financial inclusion & economic
development (Rautela et al., 2010; Murari & Didwania, 2010; Sharma et al., 2011;
Kumar & Sharma, 2011; Arputhamani & Prasannakumari, 2011; Singh & Kodan,
2011; Sharma et al., 2011; Das, 2012; Jain et al., 2012; Mishra, 2012; Sharma &
Kukreja, 2013; Padma & Gopisetti, 2013; Banerjee & Francis, 2014; Srinivas &
Upender, 2014; Verma & Aggarwal, 2014; Satpathy et al., 2014; Kapoor & Singh,
2014 and Garg, 2014). Impact of no-frill account on financial inclusion (Barik, 2009),
socio-economic impact (Rautela, 2010; Murari & Didwania, 2010; Cnaan et al., 2011;
Raman, 2012; Das, 2012; Sharma & Kukreja, 2013; Banerjee & Francis, 2014;
Srinivas & Upender, 2014; Verma & Aggarwal, 2014; Garg, 2014; Sathpathy, 2014
and Kapoor & Singh, 2014), urban-rural & gender aspect of financial inclusion
(Sarma, 2010), social inclusion through financial inclusion (Kumar & Sharma, 2011
and Cnaan et al., 2011) and financial inclusion role in empowerment (Kr & Sahoo,
2011; Kumar & Sharma, 2011; Uma & Rupa, 2013; Mutai & Achieno, 2014 and
Sathpathy, 2014) have been integrated into one scale i.e., access, availability, usage,
social empowerment, economic empowerment, economic development, poverty
reduction & area development and the extent of its impact on economic development
have been measured among beneficiaries residing in five districts namely, Jammu,
Samba, Kathua, Reasi & Udhampur of Jammu division of Jammu & Kashmir state.
30
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36
TABLE 2.1: REVIEW OF LITERATURE
S.
No.
Author (Year) Objectives Methodology Findings Limitation/
Future research
1
Kumar (2009) Attempted to
understand the
behaviour and
determinants of
financial inclusion
in India.
Annual data from varied
sources such as hand book
of statistics on Indian
economy were explored to
collect the data. Standard
econometric techniques
were employed for state-
wise panel data spanning
over a period of 1995-
2008.
Most of the states have significant & positive
relationship between the deposit & credit
penetration. High credit penetration has high
deposit penetration. There is continuous
improvement of credit & deposit penetration
in the current decade. There is negative
influence of population density on deposit
penetration. Level of economic condition is a
vital determinant of financial inclusion.
Primary study need
to be conducted.
2 Ramji (2009) Explored extent of
financial inclusion
and find out the
indicators of
financial depth and
financial inclusion
in India.
Survey, in-depth
interviews and in-situ
observation method were
conducted for data
collection. Structured
questionnaire was used
and administered on 999
respondents, spread over
50 villages of Gulbarga
district in Karnataka state.
Mean and percentage
were used to analyse the
data.
Number of households with bank account
doubled over the duration of the financial
inclusion drive. 36% of the sample remained
excluded from any kind of formal or semi-
formal savings accounts. Access to credit and
usage are the indicators of financial inclusion.
Bank accounts mainly opened to receive
government assistance. Usage & awareness
among accountholder’s remain low.
The study is limited
to Gulbarga district
in Karnataka and
further be
conducted in other
regions to validate
its result.
37
3 Barik (2009) Analysed the role
of financial
inclusion in
empowering rural
households in
India.
Secondary sources of
information were used to
gather the data.
Found that financially excluded population
suffer from higher incidence of poverty.
Financial inclusion is the key to
empowerment of poor, underprivileged and
low skilled rural households. It lifts the
financial condition & improves the standard
of lives of people & the disadvantages.
Access to affordable financial services,
especially credit & insurance, enlarges
livelihood opportunities through adoption of
different economic activities. Better financial
inclusion would lead to increasing economic
activities and self/wage employment
opportunities for rural households. Financial
inclusion provides monetary fuel for
economic growth and is considered critical
for achieving inclusive growth.
Impact of ‘no-frill
accounts’ on
empowerment of
rural households
need to be study in
the country.
4 Nagadevra
(2009)
Identified supply
& demand side
factors that
influences
financial inclusion.
Data were collected from
national data survey on
saving patterns. The
sample covered both rural
& urban areas of the
country.
The study found that financial development is
a major factor influencing economic growth.
Factors such as annual income, age group &
ownership of occupied house influence
financial inclusion from demand side.
Similarly, the factors that are specific to
supply side are media exposure, typology
(rural/urban), gender, exposure to radio &
marital status.
Other factors like
living standard,
qualification,
occupation etc. also
need to be explored
in future.
38
5 Rautela et al.,
(2010)
Examined the role
of micro-finance in
rural development.
Various secondary sources
such as, national &
international journals,
websites of various MFI’s
etc. were explored to
gather the relevant
information.
Micro-finance is a powerful tool for poverty
alleviation and rural development.
Socio-economic
impact of micro
finance needs to be
examined more
closely through
empirical study.
6 Sarma (2010) Measured the
extent of financial
inclusion across
different
economies and
monitored
progress with
respect to financial
inclusion over
time.
Secondary sources of
information were used to
collect the data.
Comparisons were done to
examine the data.
Identified penetration, availability and usage
as the indicators of financial inclusion. Across
49 economies, Austria leads with the highest
IFI value of 0.953 and Madagascar ranks
lowest with an IFI value of 0.009. Inspite of
low density of bank branches, the usage of the
banking system in terms of volume of credit
and deposit seems to be moderately high in
India.
Urban-rural and
gender aspect of
financial inclusion
are not covered.
Other aspects like
affordability,
timeliness and
quality of the
financial services
are also missing.
7 Ellis (2010)
To examine the
impact of access to
financial services
on household
investment.
Finscope survey data from
Kenya and Tanzania were
utilised for analysis.
The study found out that access to financial
services enable households to invest in
activities which contribute to high future
income, therefore growth. Further, it was
revealed that if barriers of financial inclusion
i.e., high charges, minimum balance
requirement, lack of financial literacy are
reduced, then it can stimulate household
investment, thereby contribute to growth and
The study need to
be extended to
other parts of the
country and in other
countries as well.
39
poverty reduction in developing countries.
8 Murari &
Didwania (2010)
To assess the
impact of financial
inclusion on
poverty.
Secondary sources were
explored for data
collection.
The study revealed that financial inclusion
along with micro finance is an effective
instrument for lifting the poor above the level
of poverty as it provides increased self
employment opportunities and make them
credit worthy.
Empirical study
need to be
conducted.
9 Cnaan et al.
(2011)
Aimed to study the
breadth & depth of
financial inclusion
in rural south India
and also validate
the claims of
financial reach &
breadth.
A schedule was used to
collect the relevant
information from residents
of four villages of south
India i.e., Karnataka,
Andhra Pradesh, Tamil
Nadu & Kerala. Mean,
Chi-square & logistic
regression, ANOVA and
scheffe test were used to
explore the data.
Majority of households have access to banks
as only 23% do not have bank account in
2011 as compare to 41% in 2007. Most
accessible financial services are saving
accounts & loans. Most poverty-stricken
villages are composed of Hindus, Schedule
castes are more financially excluded. Middle
caste people having informal loans &
households with low level of education are
more likely to be financially excluded.
Long-term
consequences such
as poverty
alleviation, social
inclusion need to be
evaluated in future.
10 Anjuman (2011) Analysed the
impact of SHG-
bank linkage
program on
promotion of
financial inclusion,
assessed
association
Multi-stage purposive
sampling technique was
followed to select
districts, taluks and
villages in Tamil Nadu.
Then, random sampling
was used to collect data
from 30 households with
SHG program had significant impact on the
access to financial services in terms of
borrowings by households. Marginal farmers
tend to avail only institutional borrowings &
shun non-institutional borrowings. There exist
inequity between men & women in terms of
access to financial services. Also found out
SHG-bank linkage program increased the
The study is limited
to few districts of
Tamil Nadu state
only.
40
between the degree
of financial
inclusion &
participation in
SHGs and also
determined the
gender equity in
financial inclusion.
or without SHG’s each. T-
test and correlation were
used to evaluate the data.
degree of financial inclusion among landless
household but no linkage can be inferred with
respect to marginal & small farm size groups.
The study also revealed that SHG-bank
linkage had increased the flow of institutional
credit to landless & marginal farm households
& discouraged non-institutional borrowing.
11 Sarma & Pais
(2011)
Examined the
relationship
between financial
inclusion &
development and
investigated the
factors associated
with financial
inclusion.
Secondary sources of
information were explored
to collect the data.
Regression & correlation
were applied to derive the
results.
Correlation exists between IFI & HDI.
Countries with high level of human
development also have high level of financial
inclusion. Socio-economic variables such as,
GDP per capita, higher income level, literacy,
urbanisation and infrastructure variables such
as, network of paved road, telephone &
internet subscription shows significant &
positive relation with financial inclusion.
Whereas, other socio-economic variables i.e.,
income inequality, rural population &
unemployment; infrastructural related
variable i.e., radios, newspaper, cable T.V. &
computer and banking variable i.e., NPA,
CAR, foreign ownership interest rate shows
no significant connection with financial
inclusion.
Effect of public
policy initiatives
aimed at improving
financial inclusion
need to be covered
in future.
41
12 Kodan &
Chhikara (2011)
Analysed the
status of financial
inclusion in
Haryana and also
compared it with
aggregate India.
Various secondary sources
such as, basic statistical
returns of scheduled
commercial banks in
India, reports of trends &
progress of banking in
India, annual report of
RBI, national income
statistics, centre for
monitoring Indian
economy were explored to
collect the data. Statistical
techniques i.e., average,
ACGP, kruska-lwallis test
& t-test were applied to
analyse the data.
No significant difference exist between the
number of deposit accounts & credit accounts
per 1000 population in Haryana & India but
status of deposit account in Haryana is
somewhat better as compared to India &
status of India is better than Haryana w.r.t.
credit account per 1000 population. Exist no
difference between Haryana & India
regarding availability of banking services in
terms of population per bank office, moreover
status of Haryana regarding availability of
banking services is superior to aggregate
India. Also indicated that there is no
difference between the usage ratio of Haryana
& India, but the usage ratio of Haryana is low
as compared to aggregate India. Regression
equation clearly shows there is positive &
strong association between usages ratio &
growth of NSDP/GDP.
The study covers
only limited time
i.e., between
2000/01 to 2008/09.
Covered only some
selective variables.
The study used
comparative
measure of the
status of financial
inclusion in
Haryana to India
which is not a
standard measure.
Based purely on
secondary data.
13 Rachana (2011) Studied financial
inclusion in rural
areas, determined
the reasons for low
inclusion,
satisfaction level
of the rural people
Structured questionnaire
was used to collect the
data from 200 people
residing in Ambasan,
Jotana and Khadalpur
villages of Gujarat.
Secondary sources i.e.,
83% of the sample had bank accounts & 17%
don’t have account. Lower level of jobs,
lower education qualification and lower
annual income of rural public were some of
the reasons for low inclusion. Rural people
were not strongly satisfied with the services
provided by the bank & NGOs and govt.
The study is limited
to only three
villages of Gujarat
state.
42
towards banking
services and
assessed the
performance of the
banks working in
rural areas.
internet, articles of RBI &
bank circulars were also
used to collect the data.
Chi-square, ANOVA and
tabulation were used to
analyse the data and for
hypotheses testing.
effort for financial inclusion. Though banks in
rural areas had good coverage but most of
them are running into losses.
14 Pandey &
Kumar (2011)
Focussed upon the
achievement of the
micro finance
services towards
financial inclusion.
Secondary sources were
explored to collect the
data. Additions and
comparisons of the current
year with previous year
were done to analyse the
data.
Micro finance institutions helps general
public to have access to all the available
financial services in the economy. The NGOs,
MFI, cooperatives, NBFCs and the SHGs are
regularly connecting the poor with NABARD,
SIDBI, RRBs and other commercial banks to
make them available the resources for their
social & economic upliftment. The retail
banking services in the micro finance sector
result in fulfilment of the financial inclusion
objective.
-
15 Kr. & Sahoo
(2011)
Examined the role
of micro finance in
empowering
people and
realisation of
financial inclusion
in India.
Data were collected from
reports of national sample
survey organisation and
reports on status of micro
finance in India.
Percentage and sum were
used to analyse the data.
54% of farmers are financially excluded. 73%
of farmers have no access to formal sources
of credit. Exclusion is most acute in central,
eastern & north-eastern regions of India.
Marginal farmer constitutes 66% of total farm
households. Only 45% of these households
are indebted to either formal or non-formal
sources of finance. Credit plus services of
Primary data need
to be collected to
verify the results.
43
micro finance positively correlated with the
improvement in household’s expenditure,
income, assets and employment. Micro-credit
plus services of micro finance bring out the
poor from below poverty line and reduces
poverty.
16 Pokhriyal &
Ghildiyal (2011)
Critically analysed
the spread of SCBs
and progress of the
SHG-bank linkage
program.
Secondary sources
comprising of various
issues of banking statistics
review, handbook of
statistics on Indian
economy, report on trends
& progress of banking in
India, publications of
NABARD and other
published work were used
to collect the data. Sum
and percentages were
utilised to analyse the
data.
Network of commercial banks inclined more
towards metropolitan cities. Credit extention
has reduced in all areas i.e., rural, semi-urban
& urban except metropolitan. Goals of
financial inclusion are not achieved as SCBs
hesitate in operating in the rural areas & for
low income-earning classes. The study also
revealed widespread disparities exist among
various regions in the spread & progress of
banking. It showed that banking network &
services have grown better in southern,
northern & western regions and other regions
are lagging behind particularly north eastern
region where banks need to put in more
concentrated efforts for eliminating the
disparities. Rural areas & rural people are still
deprived & discriminated in the context of
financial inclusion & growth. SHG movement
has remained successful in status where an
incidence of poverty is comparatively less.
The study is based
on secondary
sources only.
44
`17 Sharma et al.
(2011)
Focussed on the
need of financial
inclusion for
poverty alleviation
and GDP growth.
Various reports such as
report of world bank,
IARI-FAO/RAP study,
UNDP human
development report and
CSO were used to gather
the information.
Correlation and
percentage were
calculated to analyse the
data.
Poverty in rural areas decreases with the
increase in farm size. Irrigation has positive
impact on poverty alleviation. Literacy has a
very high impact on poverty alleviation & on
hunger reduction. Majority of illiterate
people, whether urban or rural are the most
poor & malnourished. There exists negative
relationship between levels of poverty per
capita SDP (State Development Product)
across the 14 major states in India.
Empirical study
need to be
conducted.
18 Venkataramaraju
& Ramesh (2011)
Focussed on
various aspects of
financial inclusion
in India, UK & US
in conjunction
with an analysis of
its outcome in
India and factors
influencing it.
Secondary sources such as
report of RBI and report
of promoting financial
inclusion (UK) were used
to collect the data.
Percentages were
calculated to analyse the
data.
80% of the population is without life, health
& non-life insurance cover. India has around
403 million mobile users, about 46% of them
did not have bank account. Only 5.2% of
India’s 650000 villages have bank branches.
Legal identity, limited literacy, level of
income, terms & conditions, complicated
procedures, psychological & cultural barriers,
place of living and lack of awareness are the
factors that affect access to financial services.
Primary data need
to be collected.
19 Goel et al.
(2011)
Aimed to show
how the banking
correspondent
model can create
financial
RBI basic statistical return
& population census,
report of world bank and
Rangarajan committee
report on financial
The study shows that lower degree of
financial inclusion is leading to higher
poverty, unemployment and inequality in
income distribution. The study on impact of
BC model indicates lack of financial literacy
Empirical research
need to be
conducted in the
future.
45
inclusivity and
help in
empowering
people. Also
evaluated the
impact of the BC
model in India so
far.
inclusion were used to
collect the data.
among the people in the rural areas is the
major hindrance in its successful
implementation.
20 Kumar &
Sharma (2011)
Studied the extent
of financial
inclusion in India
through micro
finance and
discussed the
required means for
people
empowerment &
financial inclusion.
Secondary sources such as
reports of government of
India committee on
financial inclusion, other
reports & various
published or unpublished
journals were used to
gather the relevant
information.
Access to financial services by the poor &
vulnerable groups is a prerequisite for poverty
reduction, social cohesion, people
empowerment and financial inclusion.
Empirical study
need to be
conducted in the
future.
21 Swamy (2011) Evaluated the
coverage, progress
& trends of
financial inclusion
in India.
Various secondary sources
such as, websites, reports
of world bank, NABARD,
RBI, national sample
survey organisation and
national accounts statistics
of central statistical
organisation were
Coverage through number of bank is not
adequate for the large population living in
rural areas. Banking services in six regions of
the country viz., northern, north-eastern,
eastern, central, western & southern is
unevenly distributed. Agri-credit as a ratio of
total credit is still below the level of 1970’s.
Continuous downslide in the contribution of
Empirical research
need to be
conducted in the
future.
46
explored to collect the
data. Analysis had been
done using tables &
graphs.
agriculture to the GDP. Large number
households are excluded from financial
services. Financial inclusion among the
farmer’s households has so far been able to
save only the large & medium farmers and
has completely neglected the marginal &
small farmers.
22 Arputhamani &
Prasannakumari
(2011)
Examined the role
of financial
inclusion on rural
development
through micro
finance
beneficiaries in
Rajapalayam block
of Virudhnagar
district of Tamil
Nadu. Also to
study the
relationship
between financial
inclusion & socio-
economic
variables.
Both primary & secondary
sources of data were used
to collect the information.
Primary data were
collected from 250
members of 25 SHGs by
using simple random
sampling technique.
Various statistical tools
like, percentages, mean, z-
test, correlation,
regression, dummy
variable model and
Garett’s ranking technique
were used to analyse the
collected data.
The study found out that states having high
level of human development index will have a
high level of financial inclusion. PSNDP and
literacy rate are positively associated with
financial inclusion. Income inequality is
negatively associated with financial inclusion.
There exists significant difference in the
family income of the respondents before and
after joining the group. Financial inclusion
through micro finance laid the seeds for rural
development, because all round economic
development depends on rural development.
The study is limited
to Rajapalayam
block of
Virudhnagar district
of Tamil Nadu.
47
23 Bihari (2011) Introduced the
term financial
inclusion and
highlighted its
global & national
scenario. Also
presented the
supply-side &
demand-side
barriers of
financial inclusion.
Secondary sources i.e.,
RBI websites, speeches,
RBI bulletin were used to
collect the relevant
information.
The study found out demand side factor i.e.,
lack of awareness, unsuitability of the
financial products, unfriendly & un-
empathetic attitude and exorbitant & often
times non-transparent fees whereas supply
side factors i.e., high transaction costs, lack of
communication, lack of infrastructure and low
literacy levels are barriers to access of
financial services.
Empirical study
need to be
conducted.
24 Latif et al.
(2011)
Investigated the
sustainability of
micro-credit
system in Pakistan
and determined its
impact on poverty
alleviation.
Data were collected
through structured
interview method from
200 respondents who used
micro credit such as,
farmers/growers, officers
of micro credit etc. by
using simple random
sampling technique and
were analysed using SPSS
18 version.
The study shows micro credit has positive
impact on poverty alleviation. Micro credit
results in increasing productivity by creating
employment & developing human capital
which can lead to reduction of poverty.
The study is limited
to 200 respondents
only which cannot
represent whole of
the Pakistan, so
more of the sample
should be collected
to verify and
validate the results.
25 Singh & Kodan
(2011)
Examined the
relationship
between financial
inclusion &
Data were collected from
secondary sources i.e.,
annual report of RBI,
NABARD, report of trend
Positive & significant relationship exists
between financial inclusion & economic
development. No significant difference
subsists among Indian states with regard to
The study is limited
to 15 states of India
while other states
were excluded
48
economic
development and
also determined
the factors
associated with
financial inclusion.
& progress of banking in
India, economic survey of
India, etc. Various
statistical techniques such
as, mean, standard
deviation, C.V., t-test,
multiple regression (OLS)
and IFI were used to
analyse the data.
financial inclusion. Per capita NSDP
significantly & positively predicts financial
inclusion while employment rate do not.
Among social development indicators,
urbanisation positively & significantly
explore financial inclusion while literacy rate,
urban population as per cent of total
population & sex ratio do not.
including J&K.
26 Kuri & Laha
(2011)
To measure the
inter-state
variations in the
access to finance
using a composite
index of financial
inclusion and to
identify factors
that are
responsible for
creating obstacles
in the process of
financial inclusion
in rural West
Bengal.
Secondary data were
collected from basic
statistical return,
economic survey,
economic review of West
Bengal, census, etc.
Whereas, primary data
were collected on various
indicators of financial
inclusion i.e., penetration,
availability and usage of
banking services from
beneficiaries of the three
districts namely, Birbhum,
Bankuria and North
Pargana. Regression is
used to examine the
The results revealed that among different
states of India, Chandigarh is at the top and
Manipur is at the bottom in terms of the level
of financial inclusion. District-wise variation
is not so prominent as most of the districts
belong to lower financial inclusion category.
It is also revealed that economic status of the
household, level of education, assets holding,
non-farm employment, rural development &
social security are the significant factors of
financial inclusion.
The study is limited
to West Bengal
only and need to be
further extended to
other states of the
country as well.
49
determinants of financial
inclusion.
27 Kuri & Laha
(2011)
To establish the
relationship
between financial
inclusion and HDI.
Secondary data on
financial inclusion
indicators (penetration,
availability & usage of
banking services) and
HDI indicators (decent
standard of living, long &
healthy life and
knowledge) were
collected from statistical
return (RBI), census
(2001), economic survey
(2009-10), national family
health survey.
The results of the study revealed that there is
positive correlation between financial
inclusion and human development i.e., higher
the level of financial inclusion higher will be
the level of HDI.
Empirical research
need to be
conducted in the
future.
28 Raman (2012) To assess the
relationship
between financial
inclusion and
growing of Indian
banking system.
Secondary data were used. The study revealed that there is positive
relationship between financial inclusion and
growth of banking system, more growth of
banking system leads to more of financial
inclusion. The study further highlighted that
financial inclusion plays a crucial role in
reducing poverty in the country. Additionally,
it exposed that financial inclusion leads to
economic growth, raising standard of living ,
equality, etc.
The study is limited
to secondary data
only.
50
29 Ramasubbian &
Duraiswamy
(2012)
Analysed the
issues pertaining to
implementation of
financial inclusion
in economically
down trodden
district of Tamil
Nadu.
Interview based
questionnaire method was
used to collect the data.
Graphs were used to
analyse the data.
The study revealed that no-frill saving bank
accounts & general purpose credit cards had
been issued to ensure the implementation of
financial inclusion in India but other steps
such as, granting overdraft facilities in saving
bank accounts & providing banking services
at the door step of villages through smart card
had not been implemented. The study found
that nationalised bank had been entrusted the
task of implementing financial inclusion in
rural & semi-urban locations. Gradual
increase in opening of saving accounts had
been identified from past 5 years.
The study is limited
to a district of
Tamil Nadu only.
30 Sajeev &
Thangavel
(2012)
Analysed the
impact of SHG
bank linkage
programme in the
promotion of
financial inclusion
in rural areas and
role of bank in the
upliftment of
landless SHG
members.
Structured questionnaire
related to socio-economic
status was used to collect
the data administered on
3500 SHG members of 9
district of Kerala on 51
attributes/parameters.
Cluster techniques such as
K-means and Fuzzy C-
means algorithm were
used to examine the data.
The study revealed that financial inclusion is
effectively working in a small group of SHG
members. Majority of SHG members (33.9%)
have savings in post office/insurance, whereas
only 18.15% members have savings in banks.
Institutional credit to landless members is
available only through SHG. The percentage
of inclusion is relatively more among the
households with SHG. Bank play important
role in the upliftment of landless SHG
members as they are availing maximum
benefits from bank.
The study is limited
to only one state i.e.
Kerela only, so
need to be extended
to other parts of the
country as well.
51
31 Das (2012) Analysed the
status & concerns
of access to
financial services.
Secondary sources like,
RBI website, NABARD
website, newspapers,
different issues of Journal
of Indian Institutes of
Banking & Finance were
used to gather the
information.
The study revealed that there exist gap
between demand & supply and majority of
poor are excluded from financial services.
Inclusive growth cannot happen without
ensuring banking services at affordable cost
to the weaker section of society who do not
have any access to the formal financial
system. Financial inclusion is a great step to
alleviate poverty.
Empirical study
need to be
conducted in future.
32 Gupte et al.
(2012)
Aimed to study the
determinants that
measure the extent
of financial
inclusion and
focussed on the
computation of an
index that would
comprehensively
capture the impact
of multi-
dimensional
variables with
specific reference
to India.
Secondary sources such
as, reports of CGAP the
world bank group,
NABARD and published
papers of past reviewers
of financial inclusion were
explored to collect the
relevant data.
The study found out that outreach
(penetration & accessibility) is directly
proportional to the financial inclusion index.
Further, it was revealed that there is direct
relation between the usage & the other
variable i.e., volume of deposits & loans as a
percentage of GDP. In case of ease of
transactions, two variables i.e., number of
location to open deposits/loan account &
submit loan application are directly related to
dimension whereas other variable i.e.,
affordability, minimum amount to open
account, documents required, number of days
to process loan application are inversely
related to the dimension. High value of
variable i.e., annual fee charged for ATMs,
cost of international transfer of money
Empirical research
need to be
conducted in the
future.
52
reduces the level of financial inclusion.
33 Sharma (2012) Focussed on
concept,
importance,
measures and path
of financial
inclusion.
Secondary source i.e.,
report of NSSO was used
to collect the data. Mean
was calculated to analyse
the data.
Out of 89.3 million, only 45.9 million have
access to credit from institutional or non-
institutional sources. 32.8% borrowed from
institutional source & 67.2% from non-
institutional sources.
To measure the
efficiency and
effectiveness of
financial inclusion,
primary source
need to be
explored.
34 Mishra (2012) Determined the
level of awareness
among people
about various
financial products
& services and
studied the impact
of SHG-bank
linkage program &
low income people
on promotion of
financial inclusion.
The data were collected
from various publications
and different government
and non-government
sources.
The study found out people are not aware
about various financial products & services.
Government should encourage banks to adopt
financial inclusion by means of financial
assistance, advertisements & awareness
programmes etc. to achieve inclusive growth.
Primary data need
to be collected to
validate the results.
35 Jain et al. (2012) Examined the role
of financial
inclusion in
reduction of
poverty and to find
out the challenges
Various secondary data
sources such as, reports on
trends & progress of
banking sector in India,
websites of RBI &
NABARD were explored
The study found out financial inclusion is
seen as an intensification & continuation of
poverty alleviation efforts. Further, the study
revealed various challenges such as, high
cost, spatial distribution of banking services,
non-price barriers, behavioural aspect in the
To find out
relationship
between financial
inclusion & poverty
reduction, empirical
study need to be
53
in the area of
financial inclusion
in India.
to collect the relevant
information.
area of financial inclusion exits. conducted.
36 Sharma &
Kukreja (2013)
To explore the role
of financial
inclusion for
economic & social
development of
society and to
analyse current
status of financial
inclusion.
Secondary data were
collected from books,
magazines, newspapers,
research articles, research
journals, e-journals, RBI
report, report of
NABARD etc.
The study found that financial inclusion plays
a catalytic role for the economic & social
development of society. Further, it
highlighted developing countries like India
are not showing keen interest in opening bank
account and in providing basic facility of
opening of number of bank branches in the
rural areas.
Primary study need
to be conducted.
37 Shankar (2013) To analyse
whether micro
finance institutions
break down
barriers to
financial service
access in India.
Both primary and
secondary sources were
explored. Primary data
were collected through
interview administered on
103 MFI field officers of
Grama Vidiyal
microfinance limited in
state of Tamil Nadu.
It is found that MFIs break down many
barriers to access to financial inclusion. The
study revealed that micro finance penetration
in the country is not uniform. Southern and
western regions were characterised by
widespread availability of MFI & banking
services, while the central region had low
availability of both kinds of services. Further,
it was revealed that eastern and north eastern
regions showed high availability of micro
finance but not banking services, while the
northern region showed high availability of
banking but not microfinance services.
The study is limited
to Tamil Nadu state
only, so need to be
conducted in other
parts of the country
too.
54
38 Gupta & Singh
(2013)
To explore the
relationship
between financial
inclusion index
and literacy rate.
Secondary data were used
for analysis. Karl pearson
coefficient of correlation
was used for analysis.
The study revealed that although literacy has
positive impact on financial inclusion. But it
is not always true, for instant Kerala has a
very low value of the usage dimension of
financial inclusion despite of high literacy
rate. While, Karnataka has higher value of
usage dimension than literacy rate. It is
further revealed that behaviour factors be
emphasised along with bringing improvement
in literacy rate.
The study is
confined to
secondary data
only.
39 Ghatak (2013) To identify factors
influencing
demand of
financial inclusion
and establishes a
relationship
between various
factors and
financial inclusion.
Primary data were
collected by administering
on 500 respondents.
Sample was chosen using
simple random sampling
technique. Various
statistical tool such as,
factor analysis, multiple
regression, correlation
were used for analysing
data.
The study purported that factors i.e.,
accessibility, culture, assets, literacy and
income influences the demand of financial
inclusion. Further, it is revealed that
accessibility to financial services is the most
important factor that derives the demand for
financial inclusion and assets possessed by a
person is the least important driver of
financial inclusion.
-
40 Paramasivan &
Ganeshkumar
(2013)
To discuss the
overview of
financial inclusion
in India.
Secondary data were used. The study revealed that literacy is a pre-
requisite for creating investment awareness in
financial inclusion. Further, it is showed that
literacy alone cannot guarantee high level
financial inclusion in a state. Branch density
The study is limited
to secondary data
only.
55
has significant impact on financial inclusion.
It is further revealed that creating investment
awareness and improving investment
opportunities are key tool for accomplishing
financial inclusion.
41 Uma & Rupa
(2013)
To highlight the
role of SHGs in
financial inclusion.
Primary data were
collected using structured
questionnaire
administered on 300
members in Hunsur taluk
of Mysore district of
Karnataka state. Random
sampling method was
adopted to select the
sample. Percentage and
paired sample t-test were
used for checking results
of the collected data.
The results revealed that SHGs have positive
impact on financial inclusion as number of
bank accounts, credit availed and repayment
of credit has increased among SHGs
members. It is further found that SHGs
through financial inclusion enables social and
economic empowerment of its members.
The study is limited
to one district of
Karnataka only
which hinders its
generalisability, so
need to be
conducted in other
part of the country.
42 Choithrani
(2013)
To analyse
financial inclusion
status in India and
to find problems &
challenges to
financial inclusion.
Secondary sources such
as, RBI bulletin, referred
journals, internet and
newspaper were explored
for data collection.
The result revealed that commercial banks
are forcibly opening 25% of their branches in
rural areas and taking no interest in this
scheme resulted into non-operational
accounts, poor connectivity, no issuance of
smart card and lack of trust on BCs.
Empirical research
need to be
conducted.
56
43 Chithra &
Selvam (2013)
To measure the
index of financial
inclusion across
states in India and
to identify the
determinants of
financial inclusion.
Secondary data from RBI
annual reports, websites,
journals, books, etc. were
used.
The study revealed that only two states
(Maharashtra & Uttar Pradesh) have high
financial inclusion, states namely, Kerela,
Tamil Nadu, Punjab & West Bengal are
falling under the medium financial inclusion
category, whereas others that is, Karnataka,
Uttarakhand, Himachal Pradesh, Andhra
Pradesh, Haryana, J&K, Gujarat, Orissa,
Bihar, Assam, Madhya Pradesh & Rajasthan
are forming the group of low financial
inclusion. Further, the results showed GDP,
income, literacy, internet, phone facilities &
road deposit penetration have positive
association with financial inclusion. Whereas,
unemployment, newspaper, credit penetration,
credit-deposit ratio and investment ratio have
negative association with financial inclusion.
Primary study need
to be conducted.
44 Dias (2013) To measure the
relationship
between
demographic
variables and
financial inclusion.
Data were collected using
simple random sampling
from 70 respondents of
Kotekar area of
Mangalore district.
Correlation was used to
analyse the data.
The study revealed that no significant and
positive relationship exists between financial
inclusion and different demographic variables
i.e., age, gender, income & occupation
Other variables
such as, geography,
psychography can
be studied in future.
45 Padma &
Gopisetti (2013)
To examine the
relationship
Primary data were
collected through personal
The study revealed that there is positive
relationship between financial inclusion and
The study is limited
to Nizamabad
57
between financial
inclusion and rural
development.
interview administered on
beneficiaries of
Nizamabad district of
Andhra Pradesh.
rural development. Further, it is revealed that
financial inclusion leads to inclusive
development of rural areas and bring their
quality of life at par with the people of urban
areas.
district of Andhra
Pradesh which
restricts its
generalisability.
46 Uma (2013) To examine the
impact of
financial inclusion
on economic
condition of Saral
savings account
holders and to
assess change in
general conditions
of beneficiaries
before and after
financial inclusion.
Primary data were
collected using structured
questionnaire
administered on 100
respondents of Hunsur
taluk in Mysore district.
Random sampling method
was adopted for sample
selection. Analysis was
done using paired sample
t-test.
The study showed that there is positive
change in economic condition of financially
included people. Further, it is revealed that
positive and significant changes have occur in
general condition of Saral saving account
holders after being covered under financial
inclusion scheme.
The study is limited
to Mysore district
only, so need to be
extended.
47 Divya (2013) To assess the
impact of financial
inclusion on daily
wage earners and
to find whether
financial services
are reaching to low
income groups or
Questionnaire was used to
collect data from 210
daily wage earners of
Tenali town in Guntur
district of Andhra
Pradesh. Random
sampling was done to
choose the respondents.
The finding exhibited that male, married &
illiterate respondents are more interesting in
availing financial inclusion services than
female, unmarried & literate respondents
respectively. It is further revealed that daily
wagers with more income are more inclined
towards financial inclusion.
The study can
further be
conducted for other
categories of
respondents i.e.,
farmers, local
businessmen, etc.
58
not. Mean and frequency were
calculated for analysis.
48 Kalunda (2014) To assess the
current level of
financial inclusion
& credit
accessibility by
Small Scale Tea
Farmers (SSTF) in
Kenya and to find
the relationship
between gender
and age on
demand and use of
financial product.
Data were collected
through simple random
sampling using structured
questionnaire from 133
farmers. Frequencies and
Chi-square test were used
to analyse the data.
The study revealed that the level of inclusion
is high and usage in terms of credit access is
also high. It is also exhibited that inadequate
financial education results into lower financial
literacy. Further, it was found that male and
female do not differ with regard to demand &
use of financial services. There is no
difference in the demand and usage pattern of
farmers belonging to different age group.
The study should
further be
conducted with
larger sample size.
49 Gupta & Chotia
(2014)
To analyse the
extent of financial
inclusion across 28
states and 6
regions of India
and to explore the
relationship
between financial
inclusion and
human
development.
Secondary data from RBI
report, report of state
government, journals,
websites were used for
analysis.
The results showed that states with high
financial inclusion have high GDP per capita
and good human development index.
Whereas, states with low financial inclusion
have low GDP per capita, social
backwardness and slow economic progress.
Further, results revealed that there is positive
relation between financial inclusion index and
human development index. It was also
highlighted that there is positive relation of
financial inclusion with economic prosperity
Due to inadequacy
of data, present
study has not
quantified various
initiatives taken by
RBI & GOI.
Various other
parameters such as,
affordability,
timeliness &
quality of banking
59
of a region or a state. services has not
taken under
consideration.
50 Banerjee &
Francis (2014)
To investigate the
impact of financial
inclusion on the
social
development.
Secondary data from
various journal, websites,
reports, census were used
for analysis.
The results revealed that there is direct
correlation between financial inclusion and
human development index. Financial
inclusion leads to poverty reduction.
Empirical research
need to be
conducted in the
future.
51 Saidu et al.
(2014)
To examine the
change in income
of farmers due to
participation in
micro finance
schemes.
A multi-stage cluster
sampling technique was
used to collect data from
364 beneficiaries of Kano
state, Nigeria. Structured
questionnaire was used for
data collection. Paired
sample t-test was used to
analyse the data.
The study revealed that participation in micro
finance scheme brings positive change in
income and standard of living of the
beneficiaries. Further, the study showed that
after participating in micro finance scheme
average annual income of beneficiaries has
extensively increased. Micro finance scheme
leads to economic empowerment which
ultimately results into improved purchasing
power, buying of new cloths, domestic
appliances, enable them for unforeseen
circumstances, improvement in standard of
living and change in life style.
The study is limited
in the geographical
area of Kano state
in Nigeria.
52 Srinivas &
Upender (2014)
To investigate the
role of Indian
banking sector &
RBI in economic
development
Data were collected from
the published reports of
RBI, NABARD, GOI,
journals and internet
search engines like google
The study highlighted that more than half of
Indian population is financially excluded.
Most of the account holders do not use their
account even once in a month. Real rate of
financial inclusion in India is very low.
Empirical study
need to be
conducted.
60
through financial
inclusion.
etc. Financial inclusion process help in bringing
people out of miserable poverty condition.
Further, it is revealed that financial inclusion
leads to the economic growth which
ultimately leads to economic development.
53 Verma &
Aggarwal (2014)
To determine the
impact of micro
finance institutions
on financial
inclusion with
special focus on
poverty alleviation
and women
empowerment.
Various secondary sources
such as, journals, articles,
various research based
websites and reports on
micro finance were used
to gather the data.
The study revealed that micro finance
institutions play a significant role in
facilitating inclusion of excluded population.
It is further revealed that micro finance is the
most effective tool for reducing poverty and
enhancing socio-economic position of women
in the society.
Empirical research
need to be
conducted in the
future.
54 Mutai &
Achieno (2014)
To investigate the
impact of micro
finance on
economic
empowerment of
MFIs women
clients belonging
to Narok town of
Kenya.
Data were collected
through questionnaire
administered on 107
women clients and 10
MFIs staff in Narok town
using non probability
snowball sampling
technique. Data so
collected were analysed
using descriptive
techniques such as,
frequencies & percentages
The study purported that access to micro
finance has positive impact on economic
empowerment of women as it has improved
their income, asset ownership and created
employment. Additionally, the study revealed
that access to micro finance leads to
improved standard of living of women.
Future research
should be
conducted in other
regions in order to
assess the nation
wide impact of
micro finance
programmes on the
economic
empowerment of
women. Also need
to be extended in
61
and results were presented
in tables, bar & pie charts.
other country’s as
well.
55 Garg (2014) To evaluate need
and find the level
of financial
inclusion
compliance with
the RBI guidelines
and to examine the
role of different
financial
institutions
towards it.
Secondary sources of
information were explored
to collect the data.
The study revealed that financial inclusion
enables rural people to channelise their
savings in such ways that leads to increase in
GDP of the country. Various RBI efforts such
as, opening of no frill account, issuance of
general credit card are properly implemented.
Different financial institutions are doing
tremendous job by increasing rural bank
branches and bringing prosperity to the
aspiring poor through financial inclusion.
Empirical research
need to be
conducted to
validate the results.
56 Swamy (2014) To examine the
significance of
financial inclusion
through micro
finance in the
economic
upliftment of poor
households in
Indian economy.
Both secondary and
primary data were
explored to collect the
data. Secondary data was
collected from RBI
publications, NABARD
publication, status report
of micro finance in India,
etc. whereas, primary data
were collected using
questionnaire
administered on 1052
respondents across
The study revealed that there is significant
impact of financial inclusion on income of the
poor particularly, women. Further, study
highlighted that general category women are
having favourable impact of financial
inclusion programs because of their
awareness levels and access of instruments of
economic progress. Additionally, women
belonging to SC/ST categories have large
impact of financial inclusion on standard of
living.
Sample size need to
be increase as it is
not truly
representing the
country.
62
different regions of India.
Stratified random
sampling approach was
used for sample selection.
57 Shyni &
Mavoothu
(2014)
To explore means
towards inclusive
growth of the vast
excluded
population through
financial inclusion.
Secondary sources were
explored to collect the
data.
The study revealed that financial inclusion
assist banks in penetrating into unbanked
areas and thereby attaining profit. Further, it
is disclosed that low income & weaker section
of the society avail various financial services
through financial inclusion which ultimately
prevent their exploitation by the informal
moneylenders. Additionally, it is concluded
that financial inclusion is an important step
and pillar of inclusive growth.
Empirical research
need to be
conducted.
58 Pavithran &
Raihanath (2014)
To investigate the
role of commercial
banks in the
financial inclusion
programme.
Secondary sources such
as, reports of GOI,
journals, websites, etc.
were explored to collect
the data.
The study revealed that commercial banks
play a very crucial role in making financial
inclusion a success. Many financial inclusion
programme such as, financial literacy, credit
counselling, BC/BF model, KYC norms, no
frill accounts, branch expansion, mobile
banking, etc. have been undertaken by
commercial banks for success of financial
inclusion.
Primary sources
need to be
explored.
59 Nwankwo &
Nwankwo
(2014)
To examine the
sustainability of
financial inclusion
Primary data were
collected using well
structured and multiple
The study revealed that there is positive
correlation between sustainability of financial
inclusion and rural dwellers. Sustainability of
The study is
conducted in
Nigeria, need to be
63
to rural dwellers in
Nigeria.
choice questionnaire
administered on bank
officials and rural
dwellers. Mean and
correlation were used to
analyse the data.
financial inclusion among rural dwellers
remains the mainstream for economic growth
in any country.
validated in other
countries as well.
60 Satpathy (2014) To identify
variables of micro
finance initiatives
that contributes to
the economic
development of
rural area in
Jagatsinghpur
district of Odisha.
Secondary data were used
for the study.
The study highlighted that micro finance
initiatives have increased the household
income significantly which resulted into
reducing poverty, declining income
inequality, increasing saving habits &
borrowings, enhance employment
performance, boost confidence of rural
masses, lessen family violence, increases
capabilities to deal with social evils & day to
day problems, helps in empowering
economically & socially.
Primary study need
to be conducted to
validate the results.
61 Joseph (2014) To measure the
intensity of
financial inclusion
and financial
awareness among
the people.
Data were collected from
both secondary and
primary sources.
Published books,
periodicals, journals, etc.
were explored to gather
secondary data. Whereas,
primary data were
collected using structured
The study revealed that level of income, level
of education and occupation status is
dependent on financial inclusion. It is further
revealed that respondents were having above
average awareness about financial products
and services of banking system.
The study has
limited coverage, so
needs to be
extended to other
parts as well.
64
questionnaire
administered on 100
respondents belonging to
different occupational
groups of Piravom
panchayat in Ernakulum
district of Kerala state.
62 Kapoor & Singh
(2014)
To evaluate &
analyse the
contributions of
well functioning of
financial system in
the economic and
social
development of the
nation.
Secondary data were
gathered from books,
magazines, newspaper,
research articles, research
journals, e-journals, RBI
report and report of
NABARD, etc.
The study revealed that well functioning
financial system enables economically &
socially excluded people to actively
contribute to development and protect
themselves against economic shocks. Access
to finance is an essential poverty alleviation
tool. Financial inclusion is playing a catalytic
role for the economic and social development
of society
Empirical research
need to be
conducted in the
future.
`
Chapter-III Research Methodology
CONTENTS
S.No. Title Page No.
3.1 Introduction 65
3.2 Steps in Research Methodology 65
3.2.1 Nature and Scope of the Study 66
3.2.2 Need of the Study 66
3.2.3 Objectives of the Study 67
3.2.4 Hypotheses Formulation 67
3.2.5 Scale Pretesting and Purification 73
3.2.6 Significance of the Study 88
3.2.7 Limitations of the Study 88
References 91
65
CHAPTER III
RESEARCH METHODOLOGY
3.1 INTRODUCTION
Research is a scientific & systematic search for pertinent information on a specified
area and to solve the research problem. It involves gathering, recording and analysing
critically relevant facts about any problem along with logic behind them. It is the
manipulation of things, concepts or symbols for the purpose of generalising to extend,
correct, verify knowledge, whether that knowledge aids in construction of theory or in
practice of an art (Kothari, 2005). It is directed towards development of an organised
body of knowledge & discovery of new insights into unsolved problems.
3.2 STEPS IN RESEARCH METHODOLOGY
To assess the impact of financial inclusion on economic development, following
sequential steps have been followed:
3.2.1 Nature and scope of the study
3.2.2 Need of the study
3.2.3 Objectives of the study
3.2.4 Hypotheses formulation
3.2.5 Scale pretesting & purification
a. Nature & sources of information
b. Generation of scale items and data collection form
c. Pretesting
d. Sampling techniques and data collection
e. Outliers
f. Normality
g. Multicollinearity
h. Statistical tools applied
66
i. Goodness of fit indices
j. Reliability and validity
3.2.6 Significance of the study
3.2.7 Limitations of the study
A brief description of these aforesaid steps is as under:
3.2.1 Nature and Scope of the Study
The study is both descriptive and evaluative in nature. It examines the impact of
financial inclusion on economic development. The study is limited to five districts
i.e., Jammu, Samba, Kathua, Udhampur and Reasi of Jammu division of J&K state.
Data are collected from beneficiaries of financial inclusion belonging to four banks
viz., Jammu & Kashmir Bank (JKB), Jammu & Kashmir Grameen Bank (JKGB),
State Bank of India (SBI) and Punjab National Bank (PNB). The proposed study shall
provide useful insight to researchers, financial analysts, RBI & commercial bank
officials and shall assist them to assess the impact of financial inclusion on economic
development along with testing other important relationships i.e., financial inclusion
& socio-economic empowerment, financial inclusion & poverty reduction and
financial inclusion & area development.
3.2.2 Need of the Study
Since 2005, financial inclusion is considered as a fast emerging concept and a
mechanism for inclusive growth. It is an innovative notion which helps in promoting
the banking habits among people. Access to a well-functioning financial system,
enables economically and socially excluded people to integrate better and actively
contribute to development of the economy and protect themselves against economic
shocks. Therefore, financial inclusion plays a very crucial role in the process of
economic growth by channelising all resources from bottom to top. Literature which
is available so far highlights the various dimensions of financial inclusion and
theoretically establishes its relationship with economic development, poverty
reduction and area development, though empirical research on stated relationship is
scanty. Also, the mediating role played by social and economic empowerment in FI-
ED link has not been addressed anywhere in the literature. So, need arises to
empirically test the relationships, overcome the barriers to financial inclusion and
67
bringing improvement in the process of financial inclusion which is focus of the
study. This study will help the central bank to contribute substantially towards
inclusive growth.
3.2.3 Objectives of the Study
Financial inclusion has gained increasing prominence in the past few years as national
policy initiative for balanced regional & area development, policy guidelines of RBI
to banking institutions and others in the development field. Accordingly, the present
study has been undertaken with the following objectives:
i. To identify significant predictors of financial inclusion.
ii. To analyse the direct impact of financial inclusion on social empowerment,
economic empowerment and economic development.
iii. To examine the mediating relationship between financial inclusion and
economic development through socio-economic empowerment.
iv. To assess the impact of financial inclusion on poverty reduction and area
development across socio-economic profile of the respondents.
v. To unearth the barriers of financial inclusion on the access and usage
dimensions.
vi. To suggest measures to bring more people within the ambit of financial
inclusion and ensuring their empowerment & overall economic development.
3.2.4 Hypotheses Formulation
The proposed study would examine and verify the following hypotheses formulated
on the basis of review of literature:
Financial inclusion is a process that ensures the ease of access, availability and usage
of the formal financial system for all members of an economy (Sarma & Pais, 2008).
This definition underlines numerous dimensions of financial inclusion, viz.,
accessibility, availability and usage of the financial system assists in building
inclusive financial system (Commonwealth Secretariat & La Francophonie, 2011;
Arputhamani & Prasannakumari, 2011 and Rao & Bhatnagar, 2012). Alliance for
financial inclusion (2011) provided guidelines on financial inclusion measurement,
including a more robust catalogue of indicators covering the access and usage
68
dimensions of financial inclusion. Financial inclusion encompasses primary
dimensions i.e., access to a range of formal financial services and usage (Ramji,
2009). Three basic dimensions of an inclusive financial system include banking
penetration, availability of the banking services and usage of the banking system
(Chattopadhyay, 2011; Kuri & Laha, 2011; Yorulmaz, 2013; Gupta & Singh, 2013;
Padmanbhan & Sumam 2014; Chibango, 2014; Malik & Yadav, 2014 and Banerjee &
Francis, 2014). Further, Gupta (2014) identified three dimensions viz., banking
penetration to measure accessibility, availability and usage of banking services for
evaluating the extent of financial inclusion. Thus, it is hypothesised that,
H1a: Access significantly predicts the financial inclusion.
H1b: Availability significantly predicts the financial inclusion.
H1c: Usage significantly predicts the financial inclusion.
Financial institutions act as a catalyst in the economic and social growth of the
stakeholders (Banerjee & Francis, 2014). Financial inclusion through these
institutions is a tool for empowering financial users (Reyes et al., 2011). It lays impact
on achieving economic and social empowerment (Jha, 2008 and Barik, 2009).
Financial inclusion increases the economic opportunities for the poor & low income
people, which lead towards positive result in social progress, economic development,
economic empowerment and social/political/legal empowerment (Ali & Hatta, 2012
and Mishra, 2012). Financial inclusion is the key to empowerment of poor,
underprivileged and low skilled rural households (Jha, 2008; Barik, 2009 and
Ranganath & Rao, 2011). To improve the financial condition and living standard of
the poor & disadvantaged classes, efforts on financial inclusion need to be stressed
leading a thrust on empowerment of the common person and marginal income groups
in the lower strata of the society (Jha, 2008; Barik, 2009 and Reyes et al., 2011).
Providing access to financial services promotes social inclusion, builds confidence
and helps in empowering vulnerable groups (Banerjee & Francis, 2014 and
Tamilarasu, 2014). Financial inclusion is one of the building blocks of empowerment
(CYFI National Implementation Workshop, 2014). Credit facility in financial
inclusion plays a critical role in social and economic empowerment of the poor
(Savagaon, 2012; Singh & Yadav, 2012 and Shetty, 2008). Financial inclusion
through micro finance generates habits of economic independence and self reliance
69
(Singh & Yadav, 2012) and is considered as one of the best mechanisms in
empowering stakeholders (Devi et al., 2012 and Singh & Yadav, 2012). Financial
inclusion provides monetary fuel for economic development and is considered critical
for achieving inclusive growth (Klapper et al., 2004 and Barik, 2009).
Macroeconomic evidence indicates that well developed financial systems have a
strong positive impact on economic development over long time period (Thorsten,
2007 and Cull, 2012). Financial inclusion is a path way and prerequisite for
sustainable economic development of the country (Bihari, 2011; Raman, 2012;
Memdani & Rajyalakshmi, 2013; Ghatak, 2013; Chithra & Selvam, 2013; Uma &
Rupa, 2013; Gupta et al., 2014; Srinivas & Upender, 2014 and Shyni & Mavoothu,
2014). It helps in bringing people in the ambit of financial services which leads to
greater economic and social equity resulting in accelerating economic development of
the country (Bihari, 2011; Roy, 2012; Porkodi & Aravazhi, 2013; Uma, 2013 and
Srikanth, 2013). Therefore, it is hypothesised that,
H2a: Financial inclusion has direct impact on social empowerment.
H2b: Financial inclusion has direct impact on economic empowerment.
H2c: Financial inclusion has direct impact on economic development.
Economic empowerment is an essential element for the inclusive growth of an
economy (Jarrett, 2013). It emerged as an important idea for development in 1980’s
(Tucker & Ludi, 2012). Economic empowerment is a key prerequisite for pro-poor
growth (Tucker & Ludi, 2012) which accelerate the overall rate of growth and leads
to the economic development (Oxaal & Baden, 1997 and Duflo, 2012). Jarnett (2013)
emphasised on clear economic link between empowerment and economic
development. Social and economic empowerment has a significant positive impact on
economic development of the country (Kalyani & Seena, 2012 and Prakash &
Chandarsekar, 2012). Enhancing the economic empowerment among financially
excluded is a crucial mechanism of improvement, which boosts economic growth of
the country and promotes economic development (Ajani et al., 2013). Empowerment
is a key measure to foster economic development (Tertilt, 2010). Hence, it is
hypothesised that,
H3a: Social empowerment has direct impact on economic development.
H3b: Economic empowerment has direct impact on economic development.
70
Financial inclusion results in achieving social & economic empowerment which leads
to significant positive impact on economic development of the country (Barik, 2009
and Prakash & Chandarsekar, 2012). The potential for social empowerment over the
state and the economy is enhanced when concentration of economic power are
eliminated (Noya & Clarence, 2007). Economic empowerment combined with similar
advances in social empowerment make economic development much more effective.
Financial inclusion is a powerful agent which empowers individuals and their families
by providing economic opportunities for strong and inclusive growth (Largarde,
2014). Thus, the next hypothesis formulated as,
H4a: Social empowerment mediates the relationship between financial inclusion
& economic development.
H4b: Economic empowerment mediates the relationship between financial
inclusion & economic development.
Poverty alleviation has all along been the priority goal of Indian polity and financial
inclusion is considered a pre-requisite for poverty reduction (Murari & Didwania,
2010; Cnaan et al., 2011; Kumar et al., 2012 and Jain et al., 2012). Well developed
financial system can effectively alleviate poverty (Beck, 2005). Access to financial
services enables the poor to fight with various dimensions of poverty, make
improvement in their lives and provides impetus for the growth & development
(Rautela et al., 2010 and Gupte et al., 2012). Mishra (2012) identified close
connection between poverty and financial inclusion, which can lead to estrangement,
disaffection and reduced participation in society by low-income families. Banerjee &
Newman (1993) have observed that a critical factor that enables people to exit poverty
by enhancing productivity is access to finance. According to Swamy (2011) financial
inclusion has far reaching positive consequences, which can facilitate many people to
come out of the abject poverty conditions. Financial inclusion has the potential to
reduce poverty and promote pro-poor growth (Hassein & Kirkpatrick, 2005;
Setboonsarng & Parpiev, 2008 and Chibba, 2009). It is a tool for combating and
bringing people out of awful poverty condition (Nalini & Mariappan, 2012;
Krishnakumar & Vijaykumar, 2013; Kapoor & Singh, 2014; Verma & Aggarwal,
2014 and Srinivas & Upender, 2014). An inclusive financial system results into better
employment opportunities, economic upliftment and poverty alleviation of the weaker
groups of the society (Rahman, 2008 and Gupta et al., 2014). Access to safe, easy &
affordable credit and other financial services is a prerequisite for poverty reduction as
71
it helps poor and socially & economically vulnerable groups to raise their income,
acquire capital and break the chain of poverty (Beck et al., 2007; Jack & Suri, 2009;
Ranparia, 2013; Krishnakumar & Vijaykumar, 2013; Gandhi, 2013; Kapoor & Singh,
2014; Raihanath & Pavithran, 2014; Gupta et al., 2014 and Banerjee & Francis,
2014). Kuri & Laha (2011) and Satpathy et al. (2014) highlights negative correlation
exists between financial inclusion and poverty. Financial inclusion has positive
impact on the lives of rural Indians and helps in bringing them out of the clutches of
poverty (Shaffer, 2008 and Bansal, 2012). It is critical tool that has positive impact on
poverty reduction (Shaffer, 2008; Uma & Rupa, 2013 and Nwankwo & Nwankwo,
2014). Policies resulting in reduction of barriers to financial services boosts household
investment, thereby results into poverty reduction (Ellis, 2010). Financial inclusion
plays major role in assisting people in improving their lives, reducing inequalities and
thereby driving away poverty from the country (Raman, 2012; Shivani, 2013 and
Kapoor & Singh, 2014). Thus, the fourth hypothesis set as,
H5: Financial inclusion is positively related to poverty reduction.
Finance is the lubricant, which oils the wheels of development and financial inclusion
is identified as a key factor in shaping the growth process of the economy
(Arputhamani & Prasannakumari, 2011 and Christabell & Vimal, 2012). Financial
inclusion enables the downtrodden in the rural areas to become self reliant and obtain
financial independence and freedom so that they can play an active role in the process
of development. Financial inclusion through micro finance, laid the seeds for area
development because, the all round economic development depends upon area
development (Das et al., 2008). Banking the ‘unbankable’ through financial inclusion
is a valuable contribution to the development planning as it presents an alternative
way to development (Arputhamani & Prasannakumari, 2011). Kalpana (2008) pointed
that access to bank credit leads to the area development. Micro finance as a means of
financial inclusion has been widely recognised as the modern tool for achieving area
development (Sultana, 2014). Financial inclusion has great potential in area
development particularly of rural areas (Roy, 2011). Padma & Gopisetti (2013) and
Garg (2014) highlight close relationship between financial inclusion and area
development. Hence, it is hypothesised that,
H6: Financial inclusion is positively related to area development.
72
Banking industry has shown tremendous growth in volume and complexity over the
last few decades (Anjugam, 2011). But despite making significant improvements in
all areas relating to financial viability, profitability & competitiveness, there are
serious concern that banks have been unable to include vast segments of the
population into the fold of basic banking services, especially the under-privileged
section of society (Thorat, 2007). The literature on financial inclusion has identified
financial exclusion as reflection of a broader problem of ‘social exclusion’. Several
studies have shown that the exclusion from the financial system occur to person who
belong to low income group, the ethnic minorities, immigrants and the aged
(Kempson & Whyley, 1998; Connolly & Hajaj, 2001 and Barr, 2004). Geographical
factors such as people living in rural areas and in location that is remote from urban
financial centres are more likely to be financially excluded (Leyshon & Thrift, 1995
and Kempson & Whyley, 1998). Low level of income inequalities tends to have
relatively high level of financial inclusion (Kempson & Whyley, 1998; Buckland et
al., 2005 and Kempson, 2006). Another factor that has been associated with financial
inclusion is employment (Goodwin et al., 2000). The unemployed or those with
irregular and insecure employment are less likely to participate in the financial system
(Sarma & Pais, 2011). Thus, it is hypothesised that,
H7: Nature of financial inclusion differs across socio-economic profile of
respondents.
Barriers limit the participation by certain section of the society to access financial
services and remain fragmented & incomplete (Reyes et al., 2010). Barriers like
geographical barriers, cultural barriers, trust issues or inadequate products & services
for a specific environment prevent customers accessing existing financial institutions
but would enable its access and usage in case such barriers are lifted (Reyes et al.,
2010). Poor education or lack of literacy represents a significant barrier to accessing
and properly using formal financial services (Ellis et al., 2010). Qualifying
requirements such as minimum account balance, availability of collateral or
guarantor, proper documentation, fees and others are main barriers to abstain the
reach of poor people to financial services (Ellis et al., 2010). Agrawal (2008)
underpinned low income, ignorance, low levels of financial literacy, cultural &
psychological barriers such as, language, perceived/actual racism, suspicion or fear of
73
financial institutions result in lack of access and result in financial exclusion. Hence,
it is hypothesised that,
H8: Barriers to financial inclusion have significant impact on the access and
usage dimensions.
3.2.5 Scale Pretesting and Purification
The present study tries to explore the impact of financial inclusion on economic
development as well as on social empowerment & economic empowerment,
association between financial inclusion and area development & poverty reduction.
The following steps are taken to make it more effective and accurate.
a. Nature and sources of information
The study used both primary and secondary sources for collecting required
information pertaining to research problem. Primary data are obtained personally
from the beneficiaries of financial inclusion drive of RBI belonging to five districts
i.e., Jammu, Samba, Kathua, Udhampur and Reasi of Jammu division through self
developed schedule. Questions are put in Dogri dialect from beneficiaries belonging
to four banks i.e., Jammu & Kashmir Bank Ltd., Jammu & Kashmir Grameen Bank
Ltd., State Bank of India and Punjab National Bank. Secondary information has been
collected from journals viz., Asian Economic Review, International Journal of
Advanced Research and Innovations, International Journal of Economics and Finance,
International Journal of Innovative Research & Development, International Research
Journal of Commerce & Behaviour Science, IOSR Journal of Economics and Finance,
Journal of Accounting and Finance, Journal of Economics, Business and
Management, Journal of Global Business and Economics, Journal of International
Business and Economics, Journal of International Development, Journal of Rural
Development, Journal of Social Policy etc., published information from internet, RBI
reports and magazines.
b. Generation of scale items and data collection form
Extensive relevant literature has been reviewed to generate items pertaining to
different dimensions of financial inclusion, social & economic empowerment,
economic development, poverty reduction and area development. Since no paper has
been found with well established scale, so the research papers mentioned in Table 3.1
are reviewed to get an idea to frame a self developed schedule.
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TABLE 3.1: GENERATION OF SCALE ITEMS
Dimensions No. of
items
Sources
Access 17 Sarma & Pais, 2008; Kumar, 2011 and Gupte
et al., 2012
Availability 18 Sarma & Pais, 2008; Kumar, 2011 and Gupte
et al., 2012
Usage 9 Sarma & Pais, 2008; Kumar, 2011 and Gupte
et al., 2012
Social empowerment 25 Barik, 2009; Kumar & Sharma, 2011;
Arputhamani & Prasannakumari, 2011 and
Cnaan et al., 2011
Economic empowerment 11 Barik, 2009; Kumar & Sharma, 2011;
Arputhamani & Prasannakumari, 2011 and
Cnaan et al., 2011
Economic development 11 Agrawal, 2007 and Das, 2011
Poverty reduction 10 Rautela et al., 2010; Latif et al., 2011 and
Mishra, 2012
Area development 8 Rautela et al., 2010 and Arputhamani &
Prasannakumari, 2011
The scale items are finalised after reviewing the above mentioned literature, detailed
discussions with the subject experts and academicians. Schedule is thereafter used for
collecting the requisite information from the financial inclusion beneficiaries.
Schedule consisted of two sections, one general and other to elicit information about
eight dimensions of financial inclusion namely, access, availability, usage, social
empowerment, economic empowerment, economic development, poverty reduction
and area development. Schedule comprised of total 127 items, out of which 11 p
ertained to general information, 44 items related to financial inclusion (17 of access,
18 of availability & 9 of usage), 25 items of social empowerment, 11 items of
economic empowerment, 11 items of economic development, 10 items of poverty
reduction, 8 items of area development and remaining 7 items pertained to reasons for
not having a bank account. The data are collected on five point Likert scale (5<----1>)
where 5 denotes strongly agree and 1 denotes strongly disagree. Suggestions are kept
in open ended form.
c. Pretesting
The initial schedule was prepared in the year 2012. To assess its comprehension
among beneficiaries and calculate final sample size, pretesting is done on 100
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beneficiaries covered under the financial inclusion drive of RBI. The respondents are
selected on judgement basis, selecting two beneficiaries from ten villages each of five
districts of Jammu division i.e., Jammu, Samba, Kathua, Reasi and Udhampur. The
schedule comprised questions in dichotomous form, open ended and ordinal form of 5
point Likert scale, where rank ‘5’ denotes ‘strongly agree’ and rank ‘1’ denotes
‘strongly disagree’. The initial schedule contained 141 items on various dimensions
i.e., access, availability, usage, social empowerment, economic empowerment,
economic development, poverty reduction and area development. In order to collect
more clear and satisfactory responses from beneficiaries, some items are modified &
few items deleted and ultimately 127 items are retained for final survey. Due to the
non-availability of authentic records with BCs, the final sample size is arrived at 884
using following formula (Malhotra, 2002).
n = σ
2 * z
2/D
2
The final sample size is round off to 900. Thereafter, the subsequent chapters on
access, availability, usage, social empowerment, economic empowerment, economic
development, poverty reduction and area development are completed with such a
refined schedule.
d. Sampling techniques and data collection
The study is confined to villages belonging to five districts namely, Jammu, Samba,
Kathua, Reasi and Udhampur of Jammu division. Business correspondents of all
eighty three villages are contacted, out of which twenty eight either out rightly
rejected to cooperate or refused by saying nothing has been done on financial
inclusion till date. Of the remaining fifty five villages, primary data are collected from
523 beneficiaries on judgement sampling, criteria adopted is availability and
willingness to respond. The survey is carried on during February-July, 2013 and the
effective response rate came out to be 58.11%.
e. Outliers
An outlier is an observation which is numerically away from rest of the data (Barnett
& Lewis, 1994). According to Grubbs, ‘an outlying observation is one which appears
deviated from the other members of the sample’. There are number of methods
provided in the statistics for identifying and deleting outliers. Box plot is considered
as the most objective and quantitative approach to look out outliers (Mendenhall et
76
al., 1993). In the present study, outliers are identified through box plot by calculating
Z-scores of all the dimensions individually with the help of SPSS (17.0 version). The
outlier observations which are occurring for 3 or 4 times are deleted. Thereafter,
overall Z score of all dimensions is calculated. Again outliers are identified and
deleted with the help of box plot. In box plot, those points which are outside the end
of the whiskers are outliers. There are 23 outlier observations, which are deleted from
the data sheet (Figure 3.1). Further to check normalcy, Kolmogorov-Smirnov and
Shapiro-Wilk test are performed which came out to be insignificant and proved that
data is normal.
f. Normality
Normality is a test which is used to determine whether a set of data is well defined by
normal distribution or not. As we all know that assessment of normality is prerequisite
before applying any parametric statistical tests. Normality can be assessed by two
ways: Graphically and Numerically (Park, 2008).
Graphically method
The output of Q-Q plot (quantile-quantile plot) is used to determine normality.
In Q-Q plot, if the data are normally distributed then the data points fall
approximately on a diagonal straight line (Figure 3.2), which indicates high
correlation and if the data points strayed away from the diagonal line then data
are not normally distributed (Field, 2009). All the data points are closer to the
straight diagonal line and no point is strayed outside, which indicates that data
are normally distributed.
Numeric method
While testing normality numerically in SPSS, Skewness and Kurtosis are
some of the easiest tests (Mardia, 1970) and as per rule of thumb, the data
become normal when its Skewness and Kurtosis have value between -1 and +1
or closer to zero (Gao et al., 2008).
With the help of SPSS (17.0 version), Skewness and Kurtosis tests are
performed and the value of Skewness is .270 and Kurtosis is -.241, which is as
per rule of thumb between -1 to +1. This shows that data is normally
distributed.
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g. Multicollinearity analysis
Multicollinearity is also checked between all the latent constructs viz., financial
inclusion, social empowerment, economic empowerment, economic development,
poverty reduction and area development to eliminate high inter-construct correlation.
Table 3.2 predicted that tolerance is greater than .10 and the variance inflation factor
(VIF) is less than 10 in all the cases, suggesting that multicollinearity is not an issue.
TABLE 3.2: MULTICOLLINEARITY ANALYSIS
Dependent variables Independent variables Collinearity statistics
Tolerance VIF
Financial inclusion
Social empowerment .353 2.834
Economic empowerment .727 1.375
Economic development .330 3.030
Poverty reduction .350 2.859
Area development .690 1.449
Social empowerment Economic empowerment .716 1.397
Economic development .322 3.109
Poverty reduction .445 2.245
Area development .688 1.453
Financial inclusion .468 2.138
Economic
empowerment
Economic development .322 3.110
Poverty reduction .349 2.866
Area development .691 1.448
Financial inclusion .418 2.394
Social empowerment .310 3.222
Economic development
Poverty reduction .386 2.591
Area development .770 1.298
Financial inclusion .438 2.281
Social empowerment .322 3.101
Economic empowerment .744 1.345
Poverty reduction Area development .687 1.456
Financial inclusion .412 2.429
Social empowerment .396 2.527
Economic empowerment .715 1.398
Economic development .342 2.925
Area development Financial inclusion .413 2.422
Social empowerment .311 3.217
Economic empowerment .719 1.390
Economic development .347 2.883
Poverty reduction .349 2.865
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h. Statistical tool and techniques applied
The data collected with the help of survey method have been analysed in order to
bring out relevant results with the help of appropriate statistical tools. The descriptive
analysis of access, availability, usage, social & economic empowerment, economic
development, poverty reduction and area development have been carried out with the
help of mean and standard deviation. Mean has been used in order to know the value
of each observation. Further, to know the amount of deviation in the respondents’
view, standard deviation has been analysed (Beri, 2005). Multivariate techniques used
in the study are discussed as under:
Exploratory factor analysis
Exploratory factor analysis is used to summarises the data for further analysis.
The multivariate data reduction techniques of factor analysis has been used
with the help of 17.0 version of SPSS which is the most appropriate for the
present study as it involves the examination of interrelationships among
variables and reduces large number of variables into few manageable sets
(Stewart, 1981). EFA has two primary functions i.e., data summarisation and
data reduction. In data summarisation, factor analysis derives underlying
dimensions that when interpreted and understood, describes the data in much
smaller numbers of concepts than the original individual variables. Data
reduction can be achieved by calculating scores for each underlying dimension
and substituting them for original variables. This is done through factor score.
The study used principal component analysis with a varimax rotation (Kakati
& Dhar, 2002), as it is the best and the most commonly used orthogonal
rotation procedure (Gorsuch, 1974; Stewart, 1981 and Malhotra, 2002).
Therefore, the present study focuses on high communalities and on high
reliabilities to reduce the number of variables (Fabrigar et al., 1995). The
Eigen value equal to or more than one criterion has been used to determine the
number of components to be extracted for further analysis (Stewart, 1981 and
Alfansi & Sergeant, 2000). KMO measure of sampling adequacy has been
used to verify the appropriateness of a factor loading, where the value greater
than .50 is acceptable, values between .50 & .70 are mediocre, .70 & .80 are
good, .80 & .90 great and above .90 superb (Malhotra, 2002). Further, Bartlett
test of spherecity, which is also called zero identity matrix, has also been used
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to determine correlation among the variables (Hair et al., 1995 and Field,
2000).
The statement with factor loading less than .50 are ignored for subsequent
analysis (Hair et al., 2007). The data reduction is performed in four steps, first
inter-item correlation is checked, the items with value less than 3 are removed,
in the second step, anti-image correlation matrix, the items with value less
than .50 on the diagonal axis are deleted. In the third step, the extracted
communalities are checked and items with values less than .50 are ignored for
further analysis. In the fourth & the final step, rotated component matrix,
statement with cross or multiple loading and values below .50 are deleted.
Regression
Regression term is first used by Francis Galton in the end of nineteenth
century. Regression analysis is a powerful technique for analysing the
associative relationship between a dependent variable and one or more
independent variables (Malhotra, 2002). It is a measure of the average
relationship between two or more variables in terms of the original units of the
data. The objective of regression analysis is to use the independent variable,
whose values are known to predict the single dependent variable. When the
problem involves two or more independent variables, it is termed as multiple
regression.
One way ANOVA and t-test
Analysis of variance (abbreviated One Way ANOVA) is a statistical technique
used to determine whether samples from two or more groups come from
population with equal means (i.e., Do the group means differ significantly?). It
is used to compare means of two or more samples. ANOVA examines one
dependent variable. It is used to test the differences among at least three
groups.
The term ‘t-test’ is introduced by William Sealy Gosset in 1908. T-test
assesses the statistical significance of the difference between means of two
samples for a single dependent variable. The t-test is a special case of
ANOVA for two groups or levels of treatment variables.
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Confirmatory factor analysis
CFA is used to provide a confirmatory test to the measurement theory. It is a
way of testing how well measured variables represent a construct. To assess
fitness, reliability and validity of latent constructs, CFA is performed. In CFA,
there is no distinction between exogenous and endogenous constructs, hence it
is an interdependence techniques. CFA is different from EFA as in EFA all
measured variables are related to every factor by a factor loading estimate,
whereas in CFA researcher has to assign variables to each factor on the basis
of preconceived theory. Thus, CFA statistics tells us how our specification of
the factor matches the reality i.e., the actual data (Hair et al., 2009). In CFA,
measurement model can be of two types viz., reflective or formative factor
models. Reflective measurement theory is based on the idea that latent
constructs is reflected through the measured variables and that the error results
is an inability to fully explain these measure. The reflective model are more
common in social science studies (Hair et al., 2007).
Structural equation modeling
Structural equation modeling (SEM) often involves both a measurement
theory and a structural theory. SEM has become one of the most widely
applied data analysis techniques in the business research. The reason being its
ability to assess simultaneously the fitness of the measurement models and the
structural model, where measurement models tests relationship (i.e. paths)
between the measured (manifest) variables and the construct, i.e., latent
variables, structural model specifies relationships between latent variables of
interest (composite measures). Maximum likelihood estimation is used in
estimating the structural model. It is most commonly and widely used
approach. Researchers compared maximum likelihood estimation (MLE) with
other technique and found that it produce reliable results under many
circumstances (Marsh et al., 1998). One of the major benefits of using SEM
techniques is that it allows for concurrent assignment of both reliability and
validity by applying CFA. Moreover, it can handle various kind of relationship
became a dependent model in other relationship. Thus, it can be concluded
that SEM is a more appropriate technique as compared to multiple regression.
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i. Goodness of fit indices
Goodness of fit refers to the ability of a model to reproduce the data (i.e., usually the
variance-covariance matrix). A good fitting model is one that is reasonably consistent
with the data and so does not require re-specification. In addition, a good fit
measurement model is required before interpreting the casual paths of the structural
model.
Parameter estimates must be carefully examined to determine if one has a good model
estimates, it should have good fitting model too. Also, it is important to rely that one
might obtain a good fitting model, yet it is still possible to improve the model and
remove specification error. Measurement model’s validity depends on the goodness of
fit (GOF) of the model. GOF indicates how well the specified model reproduces the
covariance matrix among the indicators items (i.e., similarity of observed and
estimated covariance matrix). The model fit compares theory to reality as represented
by the data. If the proposed theory is perfect, the estimated covariance matrix would
be same, thus the closer the values of these two matrices, the better the model is said
to fit. GOF can be measured in following ways:
Absolute fit indices
Absolute fit indices determine how well a priori model fits the sample data
(McDonald & Ho, 2002) and demonstrate which proposed model has the most
superior fit. These measures provide the most fundamental indication of how
well the proposed theory fits the data. Unlike incremental fit indices, their
calculation does not rely on comparison with a baseline model but is instead a
measure of how well the model fits in comparison to no model at all (Joreskog
& Sorbom, 1993). Included in this category are the Chi-square test, GFI,
AGFI, RMR, SRMR and RMSEA.
Chi-square statistics (χ2)
The Chi-square value is the traditional measure for evaluating overall model
fit and assesses the magnitude of discrepancy between the sample and fitted
co-variances matrices (Hu & Bentler, 1999). A good model fit would provide
an insignificant result at a 0.05 threshold (Barrett, 2007), thus the Chi-square
statistic is often referred to as either a ‘badness of fit’ (Kline, 2005) or a ‘lack
of fit’ (Mulaik et al., 1989) measure. Chi-square statistics is the fundamental
82
measure used in SEM to quantify the differences between the observed and the
estimated covariance matrices. A large value of Chi-square relative to the
degree of freedom signifies that the observed and estimated matrices differ
considerably. Statistical significance level indicates the probability that these
differences are solely due to sampling variations. Thus, the p-value of Chi-
square test should be large, indicating no statistical difference between the
matrices. While the Chi-square statistics retains its popularity as a fit statistic,
there exist a number of severe limitations in its use. Firstly, this test assumes
multivariate normality and severe deviations from normality may result in
model rejections even when the model is properly specified (McIntosh, 2006).
Second, because the Chi-square statistic is in essence a statistical significance
test it is sensitive to sample size which means that the Chi-square statistic
nearly always rejects the model when large samples are used (Bentler &
Bonnet, 1980 and Joreskog & Sorbom, 1993). On the other hand, where small
samples are used, the Chi-square statistic lacks power and because of this may
not discriminate between good fitting models and poor fitting models (Kenny
& McCoach, 2003). Now a days, researchers are using Chi-square and degree
of freedom ratio, a value less than 5 is deemed appropriate for model to be fit.
Goodness-of-fit statistics (GFI)
The Goodness-of-fit statistic (GFI) is created by Joreskog & Sorbom in 1993
as an alternative to the Chi-square test and calculates the proportion of
variance that is accounted for by the estimated population covariance
(Tabachnick & Fidell, 2007). By looking at the variances and co-variances
accounted for by the model, it shows how closely the model comes to
replicating the observed covariance matrix (Diamantopoulos & Siguaw, 2000).
This statistic ranges from 0 to 1. When there are a large number of degrees of
freedom in comparison to sample size, the GFI has a downward bias (Sharma
et al., 2005). In addition, it has also been found that the GFI increases as the
number of parameters increases (MacCallum & Hong, 1997) and also has an
upward bias with large samples (Bollen, 1990 and Miles & Shevlin, 1998).
Traditionally, an omnibus cut-off point of 0.90 has been recommended for the
GFI. However, simulation studies have shown that when factor loadings and
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sample sizes are low a higher cut-off of 0.95 is more appropriate (Miles &
Shevlin, 1998).
Adjusted GFI (AGFI)
Related to the GFI is the AGFI which adjusts the GFI based upon degrees of
freedom, with more saturated models reducing fit (Tabachnick & Fidell,
2007). In addition to this, AGFI tends to increase with sample size. As with
the GFI, values for the AGFI also range between 0 & 1 and it is generally
accepted that values of 0.90 or greater indicate well fitting models.
Root mean square residual (RMR) and standardised root mean square
residual (SRMR)
The RMR and the SRMR are the square root of the difference between the
residuals of the sample covariance matrix and the hypothesised covariance
model. The range of the RMR is calculated based upon the scales of each
indicator, therefore, if a questionnaire contains items with varying levels
(some items may range from 1-5 while others range from 1-7), the RMR
becomes difficult to interpret (Kline, 2005). The standardised RMR (SRMR)
resolves this problem and is therefore much more meaningful to interpret.
Values for the SRMR range from zero to 1.0 with well fitting models
obtaining values less than .05 (Byrne, 1998 and Diamantopoulos & Siguaw,
2000), however values as high as 0.08 are deemed acceptable (Hu and Bentler,
1999). A SRMR of 0 indicates perfect fit but it must be noted that SRMR will
be lower when there are high number of parameters in the model and models
are based on large sample sizes.
Root mean square error of approximation (RMSEA)
The RMSEA is first developed by Steiger in 1990. The RMSEA tells us how
well the model with unknown but optimally chosen parameter estimates would
fit the populations’ covariance matrix (Byrne, 1998). In recent years, it is
regarded as ‘one of the most informative fit indices’ (Diamantopoulos &
Siguaw, 2000). Recommendations for RMSEA cut-off points have been
reduced considerably in the last fifteen years. Up till the early nineties, a
RMSEA in the range of 0.05 to 0.10 is considered an indication of fair fit and
values above 0.10 indicated poor fit (MacCallum et al., 1999). It is then
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thought that an RMSEA of between 0.08 to 0.10 provides a mediocre fit and
below 0.08 shows a good fit (MacCallum et al., 1999). However, more
recently, a cut-off value close to .06 (Hu & Bentler, 1999) or a stringent upper
limit of 0.07 (Steiger, 2007) seems to be the general consensus amongst
authorities in this area. One of the greatest advantages of the RMSEA is its
ability for a confidence interval to be calculated around its value (MacCallum
et al, 1999). This is possible due to the known distribution values of the
statistic and subsequently allows for the null hypothesis (poor fit) to be tested
more precisely (McQuitty, 2004). In a well-fitting model, the lower limit is
close to 0 while the upper limit should be less than 0.08.
Incremental fit indices
Incremental fit indices, also known as comparative fit indices (Miles &
Shevlin, 2007) or relative fit indices (McDonald and Ho, 2002). These are a
group of indices that do not use the Chi-square in its raw form but compare the
Chi-square value to a baseline model.
Normed fit index (NFI)
NFI statistic assesses the model by comparing the χ2 value of the model to the
χ2 of the null model. The null/independence model is the worst case scenario
as it specifies that all measured variables are uncorrelated. Values for this
statistic range between 0 and 1 with Bentler & Bonnet (1980) recommending
values greater than 0.90 indicating a good fit. More recent suggestions state
that the cut-off criteria should be NFI ≥ .95 (Hu & Bentler, 1999). A major
drawback to this index is that it is sensitive to sample size, underestimating fit
for samples less than 200 (Mulaik et al., 1989 and Bentler, 1990) and is thus
not recommended to be solely relied on (Kline, 2005). This problem is
rectified by the Non Normed Fit Index (NNFI, also known as the Tucker-
Lewis index), an index that prefers simpler models. However in situations
where small samples are used, the value of the NNFI can indicate poor fit
despite other statistics pointing towards good fit (Bentler, 1990; Kline, 2005
and Tabachnick & Fidell, 2007). However, Hu & Bentler (1999) have
suggested NNFI ≥ 0.95 as the threshold.
85
Comparative fit index (CFI)
The Comparative fit index (Bentler, 1990) is a revised form of the NFI, which
takes into account sample size (Byrne, 1998) that performs well even when
sample size is small (Tabachnick & Fidell, 2007). This index is first
introduced by Bentler in 1990. Like the NFI, this statistic assumes that all
latent variables are uncorrelated (null/independent model) and compares the
sample covariance matrix with this null model. As with the NFI, values for
this statistic range between 0.0 and 1.0 with values closer to 1.0 indicating
good fit. A cut-off criterion of CFI ≥ 0.90 is initially advanced. However,
recent studies have shown that a value greater than 0.90 is needed in order to
ensure that mis-specified models are not accepted (Hu & Bentler, 1999). From
this, a value of CFI ≥ 0.95 is presently recognised as indicative of good fit (Hu
and Bentler, 1999). Today this index is included in all SEM programs and is
one of the most popularly reported fit indices due to being one of the measures
least effected by sample size (Fan et al., 1999).
j. Reliability and validity
Reliability
Reliability is defined as the extent to which a questionnaire, test, observation or any
measurement procedure produces the same results on repeated trials. In short, it is the
stability or consistency of scores over time or across raters. Without the agreement of
independent observers able to replicate research procedures, or the ability to use
research tools and procedures that yield consistent measurements, researchers would
be unable to satisfactory draw conclusions, formulate theories, or make claims about
the generalisability of their research. In addition to its important role in research,
reliability is critical for many parts of our lives, including manufacturing, medicine
and sports. Joppe (2000) defined reliability as, ‘the extent to which results are
consistent over time and an accurate representation of the total population under study
is referred to as reliability and if the results of a study can be reproduced under a
similar methodology, then the research instrument is considered to be reliable’. It is
measured in following ways:
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i. Cronbach’s alpha
It refers to the extent to which items inter-correlate with one another. Internal
consistency implies that multiple items measure the same construct and inter-
correlate with one another. In contrast, low inter-item correlation indicates that
some items are not drawn from the appropriate domain and are unreliable
(Churchill, 1979). The commonly accepted measure of internal consistency is
Cronbach’s alpha. The value of an alpha is .70 is the minimum acceptable
standard for demonstrating internal consistency (Kennedy et al., 2002).
ii. Construct/Composite reliability
It is the measurement of reliability and internal consistency of the measured
variables representing latent construct. It is easily computed from the squared
sum of factor loadings for constructs and the sum of the error terms for a
construct (Hair et al., 2001).
CR = (Sum of standardised loading)2 / (Sum of standardised loading)
2 + Sum
of error terms
The rule of thumb for composite reliability is 0.70 or higher (Fornell &
Larcker, 1981).
Validity
Validity refers to the degree to which a study accurately reflects or assesses the
specific concept that the researcher is attempting to measure. While reliability is
concerned with the accuracy of the actual measuring instrument or procedure, validity
is concerned with the study’s success in measuring, what the researchers set out to
measure. Validity determines whether the research truly measures, which it is
intended to measure or how truthful are the research results. There are different types
of validity criteria namely, content, construct and divergent validity.
i. Content validity
It is the extent to which the content of the items is consistent with the
construct definition (Hinkin, 1995). It can be established through existing
literature on the subject or discussions with subject experts.
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ii. Construct validity
It is the extent to which a set of measured items actually reflects the theoretical
latent construct. It deals with the accuracy of measurement (Joppe, 2000). It
can be established through convergent, discriminant and nomological validity.
Convergent validity
Convergent validity tests the extent to which the covariance between the
two measures is uniquely explained by the trait factor. Thus, items that are
indicators of a specific construct should converge or share a high
proportion of variance in common. It involves the extent to which a
measure correlates highly with other measures designed to measure the
same construct.
It can be established in following ways:
a) Factor loading: High factor loading i.e., above 0.50 or ideally 0.70 or
higher indicate level of convergence.
b) Average variance extracted: In CFA, the average percentage of
variance extracted (VE) is a summary indicator of convergence. AVE
is calculated by using standardised loadings, which is as under:
AVE = Sum of squared standardised factor loadings / Number of items
If AVE is above 0.50, convergent validity gets established.
Discriminant validity
Discriminant validity refers to the extent to which the measure differs
from other measures designed to measure different concepts. It can be
examined through the evaluation of the average variance extracted (AVE).
Fornell & Larcker (1981) highlighted the importance of evaluating the
discriminant validity of the construct used in the research. They suggested
that average variance extracted for each construct should be greater than
the squared correlation between constructs.
Nomological validity
It is a type of validity that assesses the relationship between theoretical
constructs. It seeks to confirm significant correlations between the
88
constructs as predicted by theory. It is a form of construct validity. A
nomological net is built in which several construct are systematically
interrelated (Hair et al., 2007). It gets established by proving the already
existing theoretical relations.
3.2.6 Significance of the Study
Heading fast towards the status of ‘Economic Super Power’, the Indian economy still
bears the stigma of financial exclusion, as 50% of its population lives in poverty &
69% of its masses is disadvantaged & deprived of any sort of financial access.
Financial inclusion needs to reach the unreached section of people and to bring them
to the mainstream economy. In fact, an access to savings and credit can initiate or
strengthen a series of interlinked and mutually reinforcing ‘virtuous spirals’ of
empowerment in society. In the era of financial globalisation, financial inclusion has
been considered as a major requirement which protects against risks and shocks by
using finance facility. Thus, leading to increase in income earning opportunities. The
significance of the study lies in finding predictors of financial inclusion, its
relationship with social & economic empowerment, economic development, poverty
reduction and area development. The study ends with certain suggestions and if the
global & national policy makers adhere to those suggestions, it will actually proof to
be a boon for the country in particular and world in general.
3.2.7 Limitations of the Study
All feasible efforts are made to make the study more reliable, valid and exhaustive,
yet certain limitations could not be ruled out and are required to keep in the mind
whenever its findings are considered for implementation. These limitations are as
under:
i. The scope of the study is limited to five districts of Jammu division only because
of restricted resources and time availability. Comparison of the extent of
financial inclusion between districts, divisions and states can be undertaken in
future.
ii. The study is based on cross-sectional data and further be extended on
longitudinal data.
iii. The information obtained from the respondents may not be free from
subjectivity.
89
iv. The study is limited to financial inclusion beneficiaries’ perception only and
could be carried further on the perception of other stakeholders such as bank
officials, business correspondents and Sarpanchs’.
v. The study has covered financial inclusion beneficiaries through commercial
banks only. Other institutes like, post office, cooperative banks, regional rural
banks, cooperative societies, SHG’s besides other are excluded from the study.
vi. Comparative study between those who availed the financial inclusion scheme
and those who have not availed, has not been done.
90
FIGURE 3.1: NORMALITY THROUGH BOX PLOT
FIGURE 3.2: NORMALITY THROUGH Q-Q PLOT
91
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Tucker, Josephine, & Ludi, Eva (2012). Empowerment and equity, Retrieved from
books.google.co.in/books?isbn=9264168354. Accessed on 5-10-2014.
Uma, H.R., & Rupa, K.N. (2013). The role of SHGS in financial inclusion: a case
study. International Journal of Scientific and Research Publications, 3(6), 1-5.
Verma, Sakshi & Aggarwal, Khushboo (2014). Financial inclusion through
microfinance institutions in India. International Journal of Innovative Research &
Development, 3(1), 178-183.
Yorulmaz, Recep (2013). Construction of a regional financial inclusion index in
Turkey. Retrieved from www.bddk.org.tr/websites/turkce/raporlar/bddkdergi/122
52makale4.pdf. Accessed on 5-10-2014.
`
Chapter-IV Financial Inclusion, Socio-economic Empowerment and
Economic Development
CONTENTS
S.No. Title Page No.
4.1 Introduction 105
4.2 Dimensions of Financial Inclusion and Economic
Development
105
4.3 Profile of Respondents 107
4.4 Financial Inclusion and Economic Development
through Socio-economic Empowerment
112
4.4.1 Scale Purification 112
4.4.2 Confirmatory Factor Analysis 123
4.4.2.1 CFA and Construct Validity 123
4.4.2.2 Fitness of CFA Models 125
4.4.2.3 CFA Models 126
4.4.3 Structural Equation Modeling 132
4.4.3.1 Structural Model 133
4.4.3.2 Hypotheses Testing 134
4.4.3.3 Mediation of SE in FI-ED Link 136
4.4.3.4 Mediation of EE in FI-ED Link 139
4.4.4 Output from One-way ANOVA 140
References
157
105
CHAPTER IV
FINANCIAL INCLUSION, SOCIO-ECONOMIC
EMPOWERMENT AND ECONOMIC DEVELOPMENT
4.1 INTRODUCTION
Financial inclusion is the process of ensuring access to financial services by
vulnerable groups such as the weaker sections and low income groups at an affordable
cost (Rangarajan Committee Report, 2008). It provides access to various banking
products & services like deposit account, credit products, micro insurance, transfer &
payment of money which helps in getting the facilities like secured savings,
unexploited credit, safe transfer of money and getting direct benefits from government
(Uma et al., 2013). Access to financial services is provided by the financial
institutions which act as the catalyst in the economic & social growth of an individual
and progress of an economy (Banerjee & Francis, 2014). This access helps in gaining
financial empowerment, promoting social inclusion, building self-confidence and thus
leading to social & economic empowerment of rural folk especially women which
ultimately leads to economic development (Kelkar, 2010; Uma et al., 2013;
Paramasivan & Ganeshkumar, 2013 and Banerjee & Francis, 2014). Financial
inclusion is a key driver for economic empowerment at an individual level and
economic development at national level (Lewis, 2007). Therefore, financial inclusion
is a fundamental cornerstone of economic & social empowerment and both cause &
effect of economic development (Uma & Rupa, 2013).
4.2 DIMENSIONS OF FINANCIAL INCLUSION AND ECONOMIC
DEVELOPMENT
Financial inclusion is a broader concept which embraces three key dimensions
namely, access, availability and usage. Other development aspects undertaken in the
study are social empowerment, economic empowerment and economic development.
A brief overview of these dimensions is as under:
i. Access
It refers to the ability to use available financial products & services from formal
106
institutions. It provides an insight and analysis of potential barriers to opening &
using of bank account such as cost, physical proximity of bank branches, etc.
ii. Availability
It is described as services which bank is providing. It includes services such as loan,
overdraft, insurance, pass book, debit card, etc.
iii. Usage
It is related to regularity, frequency and length of time used. It focuses on the depth
and extent of financial service or product.
iv. Social empowerment
Empowerment in general refers to the process of change that gives an individual the
ability to gain access to the resources they need while also gaining the ability to
influence the wider policy, regulatory and institutional environment that shapes their
livelihoods and lives. Social empowerment in particular means an improvement of the
social status and living standard of the beneficiaries. It reflects the income security,
housing security, improved housing conditions & hygiene, security of health and
social reputation.
v. Economic empowerment
It refers to the sustained, concerted efforts of policy makers & community to promote
the standard of living & economic health in a specific area. It is the process which
increases beneficiaries’ real power over economic decisions that influence their lives
and priorities in society.
vi. Economic development
Economic development is a continuous process which is extended over a long period
of time so as to break the vicious circle of poverty and lead a country to a stage of
self-sustaining growth or to self-generating economy. In the words of Meier &
Baldwin, ‘economic development is a process whereby an economy's real national
income increases over a long period of time’. According to Okun & Richard, ‘it is a
sustained secular improvement in well being, which may be considered to be reflected
in an increasing flow of goods &services’. Baran refers it as, ‘as an increase over time
in per capital output of material goods’. Clark defined as, ‘an improvement in
economic welfare’.
107
4.3 PROFILE OF RESPONDENTS
A brief profile of respondents (Table 4.1) contacted during the study is as under:
i. Name of the bank
Table 4.1 connotes that out of 500 respondents, 47.2% respondents are covered by
JKB, 25% belongs to JKGB, 18% belongs to SBI and 9.8% belongs to PNB. Thus, it
is found that JKB has the largest share and PNB has the smallest share (Figure 4.1).
FIGURE 4.1: PIE CHART FOR BANKS*
*Source: Survey
ii. Gender
Figure 4.2 shows 87.6% respondents are male and 12.4% are female. It is found that
male are more interested in opening bank accounts and female are still reluctant to
open an account in bank even if it is a no frill account.
FIGURE 4.2: PIE CHART FOR GENDER*
*Source: Survey
JKB
JKGB
SBI
PNB
Male
Female
108
iii. Age
Figure 4.3 reveals age-wise distribution of respondents. It shows that majority of the
respondents are in the age group of 40-50 years which constitute 36.4% of the total
respondents. Respondents in the age group of upto 30 years consist of only 10.2% and
35.8% i.e., 179 belongs to 30-40 years age group. The age group above 50 years
comprises of 88 respondents that contributes 17.6% to the total respondents. Thus, it
reveals that middle age people are more interested in opening accounts and availing
the benefits of government schemes.
FIGURE 4.3: PIE CHART FOR AGE*
*Source: Survey
iv. Caste
As far as caste is concerned, it is found out that 57% respondents belongs to general
caste, 35% belongs to SC, 2.6% belongs to ST and 5.4% belongs to OBC (Figure 4.4).
Thus, it can be inferred that general caste people are more aware about the
government programmes and lower castes & tribes are still unaware.
FIGURE 4.4: PIE CHART FOR CASTE*
*Source: Survey
Above 50 Years
40-50 Years
30-40 Years
Upto 30 Years
General
SC
ST
OBC
109
v. Religion
Religion-wise, respondents are Hindu (97.2%), Muslims (2.2%) and Sikhs (0.6). As
evident from Figure 4.5, Hindu respondents are majorly covered under the financial
inclusion drive in the five districts i.e., Jammu, Udhampur, Reasi, Samba and Kathua.
FIGURE 4.5: PIE CHART FOR RELIGION*
*Source: Survey
vi. Marital status
It is found that 90% (450) respondents are married and 10% (50) respondents are
unmarried (Figure 4.6). Thus, it can be concluded that married respondents are more
family & saving oriented and unmarried want to spend money.
FIGURE 4.6: PIE CHART FOR MARITAL STATUS*
*Source: Survey
Hindu
Muslim
Sikhs
Married
Unmarried
110
vii. Qualification
Figure 4.7 highlights that 5.8% respondents are illiterate, whereas 2.8% respondents
are having qualification below primary. Another group of respondents who are
qualified upto primary & middle are 13% & 31% respectively. 172 respondents are
just secondary pass constituting 34.4% of the total respondents. 11.4% respondents
are having higher secondary qualification and 1.4% are graduates. Thus, it becomes
clear that financial inclusion drive is covering everyone whether they are illiterate or
highly qualified.
FIGURE 4.7: PIE CHART FOR QUALIFICATION*
*Source: Survey
viii. Monthly income
As far as income of respondents is concerned, it is found that 51.6% of the
respondents are having income upto `5,000. 39.8% respondents are having monthly
income of `5,000-10,000. Respondents with `10,000-20,000 income per month are
7.8% or 39 in number. Whereas only 0.8% respondents are having income above
`20,000 (Figure 4.8). Thus, it is found that maximum respondents are having income
upto `5,000.
Illiterate
Below primary
Upto Primary
Upto Middle
Upto Secondary
Upto Higher Secondary
Upto Graduate
Anyother
111
FIGURE 4.8: PIE CHART FOR MONTHLY INCOME*
*Source: Survey
ix. District
District-wise analysis (Figure 4.9) shows 204 respondents are from Jammu district
which comprises of 40.8% of the total respondents. Kathua district has 32.6%
contribution in the total respondents’ percentage. Only 3% respondents belong to
Reasi district. 7.8% and 15.8% respondents contacted are from Samba and Udhampur
district respectively. Thus, it is found out that Jammu is the largest district among all
and has majority of respondents covered under FID.
FIGURE 4.9: PIE CHART FOR DISTRICTS*
*Source: Survey
Income upto Rs 5,000
Rs 5,000-10,000
Rs 10,000-20,000
Above Rs 20,000
Jammu
Kathua
Reasi
Samba
Udhampur
112
4.4 FINANCIAL INCLUSION AND ECONOMIC DEVELOPMENT
THROUGH SOCIO-ECONOMIC EMPOWERMENT
The relationship between financial inclusion and economic development through
socio-economic empowerment is examined under the following sub-heads:
4.4.1 Scale purification
4.4.2 Confirmatory factor analysis
4.4.2.1 CFA and construct validity
4.4.2.2 Fitness of CFA models
4.4.2.3 CFA models
4.4.3 Structural equation modeling
4.4.3.1 Structural model
4.4.3.2 Hypotheses testing
4.4.3.3 Mediation of SE in FI-ED link
4.4.3.4 Mediation of EE in FI-ED link
4.4.4 Output from One-way ANOVA
A brief description of each aforesaid sub heads is as under:
4.4.1 Scale Purification
Purification of constructs administered on beneficiaries of financial inclusion drive of
RBI is separately carried using SPSS (version 17.00) and the emergent results are
shown in Table 4.2. Constructs under consideration are access, availability, usage,
social empowerment, economic empowerment and economic development.
Beneficiaries’ perception regarding access
The suitability of raw data for factor analysis obtained from bank customers is
examined through KMO value, Bartlett test of sphercity and p-value = 0.000,
indicating sufficient common variance and correlation matrix (Dess et al., 1997 and
Field, 2000). The process of R-mode principal component analysis (PCA) with
Varimax rotation brought the construct to the level of 12 statements out of 17
statements originally kept in the domain of access. The KMO value (0.906) and
Bartlett test of sphercity (2986.617) indicates acceptable and significant values.
113
Therefore, factor loading in the final factorial design are consistent with conservative
criteria, thereby resulting into three-factor solution using Kaiser criteria (i.e. eigen
value ≥ 1) with 67.60% of the total variance explained. The communality for 12 items
ranges from 0.558 to 0.806 indicating moderate to high degree of linear association
among the variables. The factor loading ranges from 0.633 to 0.845 and the
cumulative variance extracted ranges from 35.17 to 67.60 percent. The communalities
and percentage of variance explained by each factor is displayed in the Table 4.2. A
brief description of factors emerged are as under:
Factor 1 (Information accessibility)
This factor consists of seven items namely, ‘The bank is conveniently located’, ‘The
employees are easily accessible when needed’, ‘Banking institution or its substitute is
easily approachable’, ‘Bank is easily approachable in case of emergencies’, ‘You
have easy access to the information that is useful’, ‘Employees’ of bank are
cooperative, friendly and knowledgeable’ and ‘Employees’ possess sufficient banking
information’. The mean values varied between 3.44 - 3.84, factor loading between
.650 - .816 and communalities from .569 - .806. This factor highlights that
accessibility of bank representatives & officials, information, cooperative behaviour is
must for success of financial inclusion.
Factor 2 (Physical accessibility)
This factor envisages three items i.e., ‘Bank have sufficient staff to meet its
customers’ requirements’, ‘The bank manager promptly redress your problems’ and
‘Banking officials respond well’. The mean values for the aforesaid items ranges
between 2.42 - 3.34. The factor loading ranges between .633 - .742 and communality
from .558 - .721. This factor connotes that access in terms of location is must for
financial inclusion.
Factor 3 (Approachability)
The items, ‘This is the only bank in your area’ and ‘As compared to other banks, this
bank is nearest to you’ are taken into consideration by this factor which supports the
items with significant mean values 4.12 & 4.29, high factor loading values .845 &
.811 and communalities with values .741 & .710 respectively. On the whole, all items
significantly contribute towards this factor.
114
Reliability
Three factors are obtained after scale purification falling within the domain of access
dimension in financial inclusion. As evident from the Table 4.2, the Cronbach’s
reliability coefficients for all 12 scale items underlying three factor ranges from .580 -
.896. The alpha reliability coefficients for F1 i.e., Information accessibility (.896) is
higher than the criteria of .77 obtained by Gordon & Narayanan (1984) indicating
high consistency. F2 namely, Physical accessibility (.700) and F3 i.e., approachability
(.580) are also at minimum acceptable level of 0.50 as recommended by Brown et al.
(2001) and Kakati & Dhar (2002) thereby obtaining satisfactory internal consistency.
However, overall alpha reliability score for all factors is very much satisfactory at
0.877. Adequacy and reliability of sample size to yield distinct and reliable factors is
further demonstrated through Kaiser-Meyer-Olkin measure of sampling adequacy that
is 0.906 and all factor loadings are greater than .50.
Validity
The three factors obtained alpha reliability higher or equal to 0.50 and satisfactory
KMO value at .906, indicating significant construct validity of the construct (Hair et
al., 1995).
Beneficiaries’ perception regarding availability
The suitability of raw data for factor analysis obtained from bank customers is
examined through Anti-image, KMO value, Bartlett test of sphercity and p-value =
0.000, indicating sufficient variance and correlation matrix (Dess et al., 1997 and
Feild, 2000). The process of R-mode principal component analysis (PCA) with
Varimax rotation extracted 9 statements out of 18 statements which are actually kept
in the construct of availability. The KMO value (.770) and Bartlett test of sphercity
(2170.576) indicates highly acceptable and significant values. Therefore, factor
loadings in the final factorial design are consistent with conservative criteria, thereby
resulting into three-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with
72.56% of the total variance explained. The communalities for 9 statements range
from .546 to .918, indicating high degree of linear association among the variables.
The factor loading ranges from .616 to 0.944 and the cumulative variance extracted
ranges from 29.516 to 72.562 percent. The communalities and percentage of variance
115
explained by each factor is displayed in the Table 4.2. A brief description of factors
emerged are as under:
Factor 1 (Loan availability)
It contained three items, ‘Loan is easily available’, ‘Loan is available within time
limit’ and ‘Procedure involved in getting loan is easy’. The mean values for all the
items ranges from 2.37 to 2.42, factor loadings from .885 to .944 and communalities
from .826 to .918. The statement ‘Loan is easily available’ adjudged to be most
important and strongest among all with highest factor loading (.944) & communality
(.918). The beneficiaries perceives that easy procedure for availing loan that too
within time frame is must for achieving efficiency.
Factor 2 (Support & assistance)
This factor comprised of four items specifically, ‘Help desk/assisting staff is available
for filling withdrawal/deposit form’, ‘Fieldworkers promotes various schemes of
bank’, ‘Infrastructure is as per the requirements of the customers’ and ‘Bank follows
quick problem solving approach’. The mean values for the items fluctuate between
2.87 to 3.52 representing moderate position. The factor loadings ranges between .616
- .757 and communalities from .546 - .589. The factor depicts that bank should not
only focus on making various products & services available but due consideration be
given for lending helping hand to the customers.
Factor 3 (Promotion)
The two items falling under this factor consisted of ‘Employee’s are helpful in
making information available regarding new schemes’ and ‘New bank schemes are
advertised frequently’. The two variables factor loading values are .890 & .880 and
communalities .812 & .792 respectively which reveals that the variables significantly
and positively contributes to the factor. Beneficiaries strongly perceive that
information about the new schemes be advertised frequently.
Reliability
Three factors are obtained after scale purification falling within the domain of
availability in financial inclusion. As evident from the Table 4.2, the Cronbach’s
reliability for all 9 scale items underlying in three factors ranges from .700 to .933.
The alpha reliability coefficients for F1 (Loan availability) & F3 (Promotion) is .933
116
& .782 respectively which is higher than the criteria of .77 obtained by Gordon &
Narayanan (1984) indicating high internal consistency. For F2 namely, Support &
assistance, calculated alpha value arrived at .700 which is also at a minimum
acceptable level of 0.50 as recommended by Brown et al. (2001) and Kakati & Dhar
(2002) thereby obtaining satisfactory internal consistency. However, overall alpha
reliability score for all factors is very much satisfactory at 0.806. Adequacy and
reliability of sample size to yield distinct and reliable factors is further demonstrated
through Kaiser-Meyer-Olkin measure of sampling adequacy that is 0.770 and all
factor loadings are greater than .50.
Validity
The three factors obtained alpha reliability higher or equal to 0.50 and satisfactory
KMO value at .770, indicating significant construct validity of the construct (Hair et
al., 1995).
Beneficiaries’ perception regarding usage
The suitability of raw data for factor analysis obtained from bank customers is
examined through Anti-image, KMO value, Bartlett test of sphercity and (p-value =
0.000), indicating sufficient variance and correlation matrix (Dess et al., 1997 and
Feild, 2000). The process of R-mode principal component analysis (PCA) with
Varimax rotation extracted 6 statements out of 9 statements which are actually kept in
the construct of usage. The KMO value (.677) and Bartlett test of sphercity (708.037)
indicates acceptable and significant values. Therefore, factor loadings in the final
factorial design are consistent with conservative criteria, thereby resulting into two-
factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with 67.78% of the total
variance explained. The communalities for 6 statements range from .522 to .739,
indicating high degree of linear association among the variables. The factor loading
ranges from .652 to .856 and the cumulative variance extracted ranges from 35.96 to
65.78 percent. The communalities and percentage of variance explained by each
factor is displayed in the Table 4.2. A brief description of factors emerged are as
under:
Factor 1 (General usage)
This factor envisages three items focussing upon ‘You frequently use credit facilities
of the bank’, ‘Advance schemes of bank are frequently used by you’ and ‘You are
117
using bank for the repayment of loan’. The mean values for the aforesaid items ranges
between 1.66 - 2.03. The factor loadings and communalities exhibited significant
values (Table 4.2). This factor emphasises on not merely opening of account but on its
usage as well. Accounts opening under this scheme must be operated, used for
availing credit facilities, repayment of loans, etc.
Factor 2 (Specific usage)
The three variables included in this factor are ‘You are using bank for depositing
money’, ‘You are using banking services, because interest charged by the bank on
advance is economical than charged by the moneylender’ and ‘You are a regular
visitor of the bank’ signifying mean values between (3.46 - 3.99), factor loadings
(.652 - .831) and communalities (.522 - .692). Beneficiaries recognise that frequent
visits are necessary for effective implementation of financial inclusion scheme.
Reliability
Two factors are obtained after scale purification falling within the domain of usage in
financial inclusion. As evident from the Table 4.2, the Cronbach’s reliability for all 6
scale items underlying two factors ranges from .601 to .780. The alpha reliability
coefficient for F1 i.e., General usage (.780) is higher than the criteria of .77 obtained
by Gordon & Narayanan (1984) indicating high internal consistency. F2 namely,
Specific usage (.601) is also at a minimum acceptable level of 0.50 as recommended
by Brown et al. (2001) and Kakati & Dhar (2002) thereby obtaining satisfactory
internal consistency. However, overall alpha reliability score for all factors is very
much satisfactory at 0.662. Adequacy and reliability of sample size to yield distinct
and reliable factors is further demonstrated through Kaiser-Meyer-Olkin measure of
sampling adequacy that is 0.677 and all factor loadings are greater than .50.
Validity
The two factors obtained alpha reliability higher or equal to 0.50 and satisfactory
KMO value at .677, indicating significant construct validity of the construct (Hair et
al., 1995).
Beneficiaries’ perception regarding social empowerment
The suitability of raw data for factor analysis obtained from bank customers is
examined through Anti-image, KMO value, Bartlett test of sphercity and (p-value =
118
0.000), indicating sufficient variance and correlation matrix (Dess et al., 1997 and
Feild, 2000). The process of R-mode principal component analysis (PCA) with
Varimax rotation extracted 14 statements out of 25 statements which are actually kept
in the construct of social empowerment. The KMO value (.803) and Bartlett test of
sphercity (3406.412) indicates acceptable and significant values. Therefore, factor
loadings in the final factorial design are consistent with conservative criteria, thereby
resulting into four-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with
69.54% of the total variance explained. The communalities for 14 statements range
from .526 to .870, indicating high degree of linear association among the variables.
The factor loading ranges from .509 to 0.925 and the cumulative variance extracted
ranges from 29.425 to 69.539 percent. The communalities and percentage of variance
explained by each factor is displayed in the Table 4.2. A brief description of factors
emerged are as under:
Factor 1 (Creditworthiness)
It contained seven variables namely, ‘FI has changed your personality & life style’,
‘You are socially more developed after being covered under FI drive’, ‘FI has made
you socially more reputed’, ‘FI has led to improved hygiene & education’, ‘FI
improved your health & household hygiene’, ‘FI has increased your confidence level’
and ‘FI enhanced your confidence level, business relation & reduced family crisis &
social violence’ represents average mean values fluctuating between 3.29 - 3.76 but
identifies significant factor loadings (.602 - .837) and communalities (.526 - .730).
The factor accentuates that positive change in personality & lifestyle, individual
development, improved hygiene and building confidence is necessary for social
empowerment.
Factor 2 (Liberation)
This factor encompasses of only two items namely, ‘You are free to move to any
NGO or any other for any kind of help & support’ and ‘You are free to move to any
SHG or any other for any kind of help & support’ with low mean values 1.54 & 1.62,
high factor loadings .925 & .922 and communalities .881 & .870 respectively. This
factor stresses on free movement of beneficiaries to anywhere for availing any sort of
financial help.
119
Factor 3 (Awareness)
This factor includes two items that is, ‘You avail those special schemes that are
offered by the government’ and ‘You are aware about all special schemes that are
offered by the government’ which exhibits mean values 2.06 & 2.95, factor loadings
.849 & .800 and communalities .816 & .784 respectively. This factor believes that
awareness about various available schemes is imperative for social empowerment.
Factor 4 (Grievances)
The final factor envisages three items, ‘You made complaints to authorities regarding
the delivery of financial services’, ‘Your response and feedback is always appreciated
regarding any financial issue’ and ‘You can bring any change in the society easily’
with low mean values (2.37 - 3.37), factor loadings (.509 - .866) and communalities
(.535 - .802). This factor underlines that all items moderately to significantly
contribute to the construct.
Reliability
Four factors are obtained after scale purification falling within the domain of social
empowerment. As evident from the Table 4.2, the Cronbach’s reliability for all 14
scale items underlying four factors ranges from .637 to .930. The alpha reliability
coefficient for creditworthiness (.770) and liberation (.930) is higher than the criteria
of .77 obtained by Gordon & Narayanan (1984) indicating high internal consistency.
Alpha value for awareness (.709) and grievances (.637) is also at a minimum
acceptable level of 0.50 as recommended by Brown et al. (2001) and Kakati & Dhar
(2002) thereby obtaining satisfactory internal consistency. However, overall alpha
reliability score for all factors is very much satisfactory at 0.830. Adequacy and
reliability of sample size to yield distinct and reliable factors is further demonstrated
through Kaiser-Meyer-Olkin measure of sampling adequacy that is 0.803 and all
factor loadings are greater than .50.
Validity
The four factors obtained alpha reliability higher or equal to 0.50 and satisfactory
KMO value at .803, indicating significant construct validity of the construct (Hair et
al., 1995).
120
Beneficiaries’ perception regarding economic empowerment
The suitability of raw data for factor analysis obtained from bank customers is
examined through Anti-image, KMO value, Bartlett test of sphercity and p-value =
0.000, indicating sufficient variance and correlation matrix (Dess et al., 1997 and
Feild, 2000). The process of R-mode principal component analysis (PCA) with
Varimax rotation extracted 9 statements out of 11 statements which are actually kept
in the construct of economic empowerment. The KMO value (.901) and Bartlett test
of sphercity (2534.429) indicates acceptable and significant values. Therefore, factor
loadings in the final factorial design are consistent with conservative criteria, thereby
resulting into two-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with
67.87% of the total variance explained. The communalities for 9 statements range
from .529 to .779, indicating moderate degree of linear association among the
variables. The factor loading ranges from .639 to 0.846 and the cumulative variance
extracted ranges from 46.768 to 67.873 percent. The communalities and percentage of
variance explained by each factor is displayed in the Table 4.2. A brief description of
factors emerged are as under:
Factor 1 (Stability)
It comprises of seven items, ‘FI has prepared you for emergencies’, ‘FI has increased
your purchasing power’, ‘You have enough savings to meet any contingent situation’,
‘FI has raised your living standard’, ‘FI enabled your children to get better education’,
‘FI has reduced your need to borrow money or goods’ and ‘FI enhanced your source
of income’. The items attained mean values between 3.24 - 3.79, significant factor
loadings .639 - .846 and communalities .529 - .779. This factor indicates that
preparedness for emergencies, enhanced purchasing power, raised living standard,
ability to face contingent situation are main components of economic empowerment.
Factor 2 (Employability)
Two items included in this factor are ‘FI created new employment opportunities’ and
‘FI directly effects capital formations & technological investment’. The mean values
identified are 2.63 & 2.27, factor loadings as .830 & .751 and communalities .702 &
.598 respectively. This factor believes that new employment opportunities and
increased technological investment make beneficiaries financially sound and
empowered.
121
Reliability
Two factors are obtained after scale purification falling within the domain of
economic empowerment. As evident from the Table 4.2, the Cronbach’s reliability for
all 9 scale items underlying two factors ranges from .568 to .912. The alpha reliability
coefficient for stability (.912) is higher than the criteria of .77 obtained by Gordon &
Narayanan (1984) indicating high internal consistency whereas for employability
(.568) is also at a minimum acceptable level of 0.50 as recommended by Brown et al.
(2001) and Kakati & Dhar (2002) thereby obtaining satisfactory internal consistency.
However, overall alpha reliability score for all factors is very much satisfactory at
0.890. Adequacy and reliability of sample size to yield distinct and reliable factors is
further demonstrated through Kaiser-Meyer-Olkin measure of sampling adequacy that
is 0.901 and all factor loadings are greater than .50.
Validity
The two factors obtained alpha reliability higher or equal to 0.50 and satisfactory
KMO value at .901, indicating significant construct validity of the construct (Hair et
al., 1995).
Beneficiaries’ perception regarding economic development
The suitability of raw data for factor analysis obtained from bank customers is
examined through Anti-image, KMO value, Bartlett test of sphercity and p-value =
0.000, indicating sufficient variance and correlation matrix (Dess et al., 1997 and
Feild, 2000). The process of R-mode principal component analysis (PCA) with
Varimax rotation extracted 8 statements out of 11 statements which are actually kept
in the construct of economic development. The KMO value (.875) and Bartlett test of
sphercity (2109.914) indicates acceptable and significant values. Therefore, factor
loadings in the final factorial design are consistent with conservative criteria, thereby
resulting into two-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with
69.96% of the total variance explained. The communalities for 8 statements range
from .591 to .811, indicating moderate to high degree of linear association among the
variables. The factor loading ranges from .546 to .875 and the cumulative variance
extracted ranges from 40.266 to 69.96 percent. The communalities and percentage of
variance explained by each factor is displayed in the Table 4.2. A brief description of
factors emerged are as under:
122
Factor 1 (Growth)
This factor contained five items namely, ‘FI has increased per capita income of your
family’, ‘FI has increased life expectancy of your family members’, ‘FI has increased
access to education of the society’, ‘FI has led to increase in value of your assets’ and
‘FI has reduced level of stress in your life’. The mean values for the factor ranges
between 3.22 - 3.70, factor loadings from .546 - .846 and communalities between .591
- .759. This factor acknowledges the importance of increased per capita income,
enhanced life expectancy, improved access to education, enlargement in value of
assets and reduces stress level for economic development.
Factor 2 (Development)
Three items included in this factor are ‘FI has led to progress of the village’, ‘FI has
made the village sustainable for further progress’ and ‘FI has empowered the
members of the society’. The mean scores identified are 3.83, 3.62 & 3.68, factor
loadings as .875, .854 & .601 and communalities as .811, .761 & .678 respectively.
This factor underlines continuous progress is must for development.
Reliability
Two factors are obtained after scale purification falling within the domain of
economic development. As evident from the Table 4.2, the Cronbach’s reliability for
all 8 scale items underlying two factors ranges from .864 to .803. The alpha reliability
coefficient for growth (.864) and development (.803) is higher than the criteria of .77
obtained by Gordon & Narayanan (1984) indicating high internal consistency.
However, overall alpha reliability score for all factors is very much satisfactory at
0.888. Adequacy and reliability of sample size to yield distinct and reliable factors is
further demonstrated through Kaiser-Meyer-Olkin measure of sampling adequacy that
is 0.875 and all factor loadings are greater than .50.
Validity
The two factors obtained alpha reliability higher or equal to 0.50 and satisfactory
KMO value at .875, indicating significant construct validity of the construct (Hair et
al., 1995).
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4.4.2 Confirmatory Factor Analysis
CFA is a statistical tool that enables researchers to either confirm or reject
preconceived theory. It is a deductive approach and multivariate statistical technique
that is used to test how well the measured variables represent the construct and model
building. To perform CFA, it is essential to specify both the number of factors that
fall within a set of variables and which factor of each variable will load highly on
before results can be computed. CFA is of great use in improving quantitative
measurement in social sciences. It is generally based on a strong theoretical and
empirical foundation that allows the analyst to specify an accurate factor structure in
advance.
CFA is conducted with the objective of verifying the fitness of each latent construct.
In the present study, it is performed to assess the fitness, reliability and validity of five
measured constructs, viz., financial inclusion (FI) consists of three main dimensions
i.e., access, availability & usage; social empowerment (SE); economic empowerment
(EE) and economic development (ED). CFA is a way of testing how well measured
variables represent a smaller number of constructs. Once baseline models are
identified and measures are validated for discriminant and convergent validity
(Fornell & Larchel, 1981), reliability is assessed through the computation of
Cronbach’s alpha, composite reliability and average variance extracted (Hair et al.,
2009).
CFA is carried out construct-wise to restrict the number of indicators. During CFA,
19 items from the latent constructs having SRW below .50 got deleted (Hair et al.,
2009). All the CFA models fulfilled the necessary condition of identification,
according to which there must be at least three manifest variables for each construct
so that it can have enough degrees of freedom to estimate all free parameters. The
constructs have been found to be both uni-dimensional as well as multi-dimensional.
Most of the indices like GFI, AGFI, NFI, TLI and CFI are above .90 whereas badness
of fit indices i.e., RMSEA of all the constructs is below .08 and Chi-square statistics
(CMIN/DF) is less than recommended 5.0 level (Bagazzi & Yi, 1988).
4.4.2.1 CFA and construct validity
CFA is a unique type of factor analysis and is the first component of a comprehensive
test of a structure model. It is applied to analyse construct validity of each construct,
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as construct validity has immediate influence on the substantive models under testing
(Bagozzi & Edwards, 1998). One of the biggest merits of CFA/SEM is its potential to
assess the construct validity of a proposed model (Hair et al., 2009) and also to enable
the concurrent assessment of both reliability and validity (Landis et al., 2009).
Construct validity is the extent to which a set of manifest variables actually represent
the theoretical latent construct that is intended to measure. Thus, it is concerned with
the accuracy of the measurement. Proof of the construct validity helps in ensuring that
manifest variables have been taken from a sample representing the actual true score
that exists in the population. Construct validity comprises of two components, i.e.,
convergent validity and discriminant validity and the present study also examines
construct validity through convergent and discriminant validity.
i. Convergent validity
It is the extent to which the manifest variables of a specific construct share a high
proportion of variance in common. Thus, it is the test in which the covariance
between the two measures is uniquely explained by trait factor (Lim & Ployhart,
2006). It can be measured as:
(a) Factor loading/SRW
The size of the factor loading/SRW is one of the important considerations.
High factor loading, i.e., above .50 or ideally .70 or higher indicates level of
convergence. Convergent validity gets established in the present study as
majority of standardised loading are .50.
(b) Average variance extracted (AVE)
It is used to ascertain average percentage of variation explained among the
manifest variables. By using standardised loadings, we calculated variance
extracted. It is computed as the total of all squared standardised factor
loadings divided by the number of items. AVE should be 0.50 or greater to
suggest adequate convergent validity. In the present study, the value of AVE
for all the constructs came above 0.50 (Table 4.3).
ii. Reliability
Reliability is concerned with testing the consistency of the measures and is also an
indicator of convergent validity. Cronbach’s alpha co-efficient is one of the most
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prominent estimate of reliability and in the present study, overall value of Cronbach’s
alpha stands above .70 for all the constructs. In this regard, Hatcher (1994) suggested
that reliability reaching 0.70 or exceeding 0.70 satisfy the general requirement of
reliability for the study. Alternative way of testing reliability is through composite
reliability (CR), which is generally computed through structural model. It is computed
as the square of sum of standardised loadings divided by square of sum of
standardised loadings plus sum of error variance term. The rule of thumb for
composite reliability is .70 or higher (Fornell & Larchel, 1981). In the present study,
the value of composite reliability of all the latent constructs is above .90, which
indicates internal consistency of the data (Table 4.3).
iii. Discriminant validity
It is the extent to which a construct is truly distinct from other constructs. It has been
assessed by comparing AVE with the squared correlation between constructs (Fornell
& Larcker, 1981). The squared correlation between pair of constructs came out to be
less than AVE in almost all the cases (Table 4.4), thereby suggesting discriminant
validity (Ok et al.,2005).
4.4.2.2 Fitness of CFA models
A good fitting model is one that is reasonably consistent with the data. The model fit
compares the theory to the reality as represented by the data. In case the proposed
theory is perfect, the estimated covariance matrix and the actual observed covariance
matrix would be the same. Thus, closer the value of these two matrices, better the
model is said to be fit. CFA model validity depends on the goodness-of-fit (GOF) and
its measure can be classified into three groups.
i. Absolute fit measures/indices
It is a direct measure of how well the model specified by the research reproduces the
observed data (Kenny & McCoach, 2003). Some of the prominent absolute fit indices
used in the present study are (a) Chi-square statistics (b) Goodness-of-fit (GFI) (c)
Adjusted GFI (AGFI) (d) Root measure squared error of approximation (RMSEA).
ii. Incremental fit indices
It studies to what extent a specified model fits relative to some alternative baseline
model. The most common baseline model is called a null model, on that presumes all
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observed variables are uncorrelated. Some of the important incremental fit indices
used in the study are (a) Normed fit index (NFI) (b) Tucker-lewis index (TLI) and (c)
Comparative fit index (CFI).
iii. Parsimony fit indices
It provides information about which model among a set of competing models is best,
considering its fit relative to its complexity. Since these indices are somewhat
controversial, they have not been considered in the present study.
4.4.2.3 CFA models
CFA is applied to assess the fitness, reliability and validity of six constructs, viz.,
financial inclusion (FI) consists of three main dimensions i.e., access, availability &
usage; social empowerment (SE); economic empowerment (EE) and economic
development (ED). The various resulting models are as under:
CFA model for access
First order CFA (Figure 4.10) is performed on access dimension, which constituted of
twelve items. Among twelve items, three items got deleted as they are not meeting the
criteria i.e. SRW’s > .50. After deleting, CFA produced good fit as CMIN/DF =
4.735, GFI = .955, AGFI = .912, NFI = .960, TLI = .950, CFI = .968 and RMSEA =
.087 (Table 4.5).The model has been found to be valid and reliable. The alpha value is
.884 whereas composite reliability came out to be .991 thereby indicating that all
items are reliable. Model has been proved to valid, as AVE came out to be .533
(Table 4.3). The construct validity also stands established as all the indicators have
factor loading above .50. Out of the twelve items, ‘Employee’s are helpful in making
information available regarding new schemes’ emerged to be strongest contributor
towards access dimension, as its regression weight is .90 .
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FIGURE 4.10: CFA MODEL FOR ACCESS DIMENSION OF FID
AC1= The bank is conveniently located; AC2 = The employees are easily accessible when needed;
AC5 = Banking institution or its substitute is easily approachable; AC6 = Banking institutes response
well; AC8 = The bank manager promptly redress your problem; AC13 = Bank is easily approachable
in case of emergencies; AC15 = You have easy access to the information that is useful; AC16 =
Employees’ of bank are cooperative, friendly and knowledgeable and AC17 = Employees’ possess
sufficient banking information.
CFA model for availability
First order CFA (Figure 4.11) is executed on the latent construct i.e. availability
which consisted of nine items. The different fit indices evaluated the fitness of the
model and the results shows that the model fits the data well as CMIN/DF = 1.852,
GFI = .967, AGFI = .941, NFI = .963, TLI = .965, CFI = .975 and RMSEA = .065
(Table 4.5). Four items got deleted as their regression weights are below .50.
Access
AC17 e1
.76
AC16 e2
.81
AC15 e3 .90
AC13 e4 .62
AC8 e5 .56
AC6 e6
.77
AC5 e7
.77
AC2 e8
.81
AC1 e9
.51
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Remaining five items are having above .50 regression weights, thus it becomes clear
that all remaining measured items are the significant contributors of this constructs.
In addition to the model fit, reliability and validity are also examined. The scale
exceeded the recommended cut off value of .70 as composite reliability = .981. So, it
is reasonable to conclude that the scale is reliable. Reliability is also confirmed
through Cronbach’s alpha value which is measured to be .806. As far as AVE is
concerned, the value is greater than 0.50 (AVE = .602). Also, each of the item loading
is greater than .50 (Table 4.3), which provides empirical support for the convergent
validity of construct. Among five items, ‘Loan is made available within time limit’
contributes highest to the main constructs with SRW .95.
FIGURE 4.11: CFA MODEL FOR AVAILABILITY DIMENSION OF FID
AV1 = Loan is easily available; AV9 = Loan is made available within time limit; AV11 = Procedure
involved in getting loan is quite lengthy; AV13 = New bank schemes are advertised frequently and
AV16 = Help desk/assisting staff is available for filling withdrawal/deposit form
CFA model for usage
First order CFA (Figure 4.12) is performed on usage dimension. It constituted of six
items. The model has been found valid and reliable after deleting three items having
regression weights below .50. The result of CFA shows the model fully fits the data,
CMIN/DF = 4.563, GFI = .988, AGFI = .965, NFI = .983, TLI = .979, CFI = .975 and
RMSEA = .085 (Table 4.5). The model found to be valid and reliable which is
Availability
AV1 e1 .93
AV9 e2 .95
AV11 e3 .82
AV13 e4
.52
AV16 e5
.53
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confirmed through Cronbach’s alpha (.628), composite reliability (.965) and AVE =
.546 (Table 4.3). Out of five items, item ‘You frequently use credit facilities of the
bank’ contributes highest with regression weight .84.
FIGURE 4.12: CFA MODEL FOR USAGE DIMENSION OF FID
US3 = You frequently use credit facilities of the bank; US6 = You are using bank for repayment of
loan and US8 = Advance schemes of bank are frequently used by you
CFA model for social empowerment
Figure 4.13 depicts first order CFA is performed on social empowerment construct
which consisted of fourteen indicators. Each indicator of the construct is measured
using five point scale. While running CFA, seven items got deleted as they are not
meeting the criteria. The results reveals that the model fit statistics are within
recommended levels i.e., CMIN/DF = 3.286, GFI = .984, AGFI = .950, NFI = .979,
TLI = .965, CFI = .985 and RMSEA = .068 (Table 4.5). Additionally, this model has
been found to be valid and reliable, as AVE is .608, composite reliability equals to
.989. The value of Cronbach’s alpha is .806 and all items loading above .50 (Table
4.3). Thus, validity and reliability got established. Among seven items, ‘FI has
changed your personality & life style’ has the highest factor loading of .84, thus
contributes maximum to social empowerment.
Usage
US3 e1 .84
US6 e2
.67
US8 e3
.70
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FIGURE 4.13: CFA MODEL FOR SOCIAL EMPOWERMENT
Social-emp = Social empowerment; SE2 = FI has led to improved hygiene and education; SE3 = FI has
changed your personality & life style; SE5 = FI has increased your confidence level; SE10 = FI enhanced
your confidence level, business relations and reduced family crisis & social violence; SE11 = FI improved
your health and household hygiene; SE16 = You can bring any change in the society easily and SE26 =
You are socially more developed after being covered under FI drive.
CFA model for economic empowerment
Figure 4.14 reveals first order CFA is performed on economic empowerment
construct which consisted of nine items. Responses are measured using five point
Likert scale. As evident from Table 4.5, CFA model yields good model fit results,
CMIN/DF =2.597, GFI = .981, AGFI = .959, NFI = .985, TLI = .985, CFI = .984 and
RMSEA = .057. The model has been found to be valid and reliable after deleting two
items. Reliability is also examined by calculating composite reliability (.992) (Table
4.3). AVE reflects the overall variance in the indicators accounted for by the latent
constructs. In this model, AVE exceeds the critical level of .50 (.595). This establishes
the reliability and convergent validity of measurement scale in the study. Also all
regression weights are above .50, thus it becomes clear that all measured variables are
Social-
emp
SE2 e1
.75
SE3 e2 .84
SE5 e3 .62
SE10 e4
.55
SE11 e5
.69
SE16 e6
.50
SE26 e7
.71
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significant contributor of this construct. Scale reliability is established through
Cronbach’s alpha (.890). Among seven items, ‘FI has increased your purchasing
power’ adjudged as the strongest contributor as it is having highest SRW.
FIGURE 4.14: CFA MODEL FOR ECONOMIC EMPOWERMENT
Eco-emp = Economic empowerment; EE1 = FI has reduced your need to borrow money or goods; EE2 = FI has raised your living standard; EE3 = FI has prepared you for emergencies; EE4 = You have
enough savings to meet any contingent situation; EE7 = FI has increased your purchasing power; EE8 =
FI enabled your children to get better education and EE11 = FI enhanced your source of income.
CFA model for economic development
Figure 4.15 portrays first order CFA is performed on economic development
construct, which comprised of eight items. The results of CFA indicated that model fit
the data, CMIN/DF = 4.601, GFI = .966, AGFI = .919, NFI = .968, TLI = .952, CFI =
.974 and RMSEA = .085 (Table 4.5). In addition to model fit data, composite
reliability and validity are examined. The overall composite reliability and validity
came out to be .992. Scale reliability is established through Cronbach’s alpha .888.
The value of AVE is greater than .50. (.512). Furthermore, it is found that each factor
Eco-emp
EE11 e1
.75
EE8 e2 .83
EE7 e3 .88
EE4 e4 .69
EE3 e5
.74
EE2 e6
.86
EE1 e7
.67
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loading is greater than .50 (Table 4.3), which provides empirical evidence for the
convergent validity of the construct. Item ‘FI has increased access to education of the
society’ emerged as the strongest contributor (.79) of the main construct.
FIGURE 4.15: CFA MODEL FOR ECONOMIC DEVELOPMENT
Eco-dvt = Economic development; ED1 = FI has increased life expectancy of your family members;
ED2 = FI has increased access to education of the society; ED3 = FI has empowered the members of the
society; ED4 = FI has led to progress of the village; ED5 = FI has made the village sustainable for
further progress; ED8 = FI has reduced level of stress in your life; ED10 = FI has increased per capita
income of your family and ED11 = FI has led to increase in value of your assets
.
4.4.3 Structural Equation Modeling
Structural equation modeling (SEM) is introduced to overcome the main weakness of
mostly used research techniques, viz., multiple regression, factor analysis,
multivariate analysis of variance and discriminant analysis that examine only a single
relationship at a time. It allows researcher to test theoretical propositions regarding
Eco-dvt
ED1 e1
.74
ED2 e2 .79
ED3 e3 .78
ED4 e4 .63
ED5 e5
.59
ED8 e6
.71
ED10 e7
.77
ED11 e8
.68
133
how constructs are theoretically linked and the direction of the significant
relationships. In other words, it is a multivariate technique combining aspects of
factor analysis and multiple regression that enables the researcher to simultaneously
examine a series of interrelated dependence relationships among the manifest
variables and latent constructs (Hair et al., 2009).
SEM represents the theory with a set of structural equations and is usually depicted
through a visual diagram. It is a procedure for accommodating measurement error
directly in the estimation of a series of dependence relationships. SEM is the best
multivariate procedure for testing both construct validity and theoretical relationships
among a set of concepts represented by multiple measured variables. It is the only
multivariate technique that allows the simultaneous estimation of multiple equations.
These equations represent the way constructs relate to manifest variables as well as
the way constructs are related to one other. Thus, when SEM techniques are used to
test a structural theory, it is equivalent to performing factor analysis and regression
analysis in one step (Hair et al., 2009)
4.4.3.1 Structural model
After applying CFA and checking for reliability and validity, SEM is conducted by
using AMOS (version 16.00) to assess fitness of structural model. The SEM technique
is used as the main statistical tool to test the main hypotheses proposed in the study.
As suggested by Hair et al. (2009), the proposed theoretical model is modeled in a
recursive manner to avoid problems associated with statistical identification. There
are a total of 25 indicators contained in the final structural model. Each indicator is
connected to the underlying theoretical construct in a reflective manner. The structural
relationship between latent constructs represented by single headed straight arrows are
specified according to the hypotheses established. In summary, the present structural
model includes (a) path from FI to SE (b) path from FI to EE (c) path from FI to ED
(d) path from SE to ED and (e) path from EE to ED. The proposed model strived to
identify the financial inclusion impact on economic development. By using reliable
and validated four constructs measures, the proposed original model is tested and
assessed in this section to identify the best fitted model.
Prior to model testing, the standardised loading estimates for the proposed structural
model are examined to ensure problems associated with interpretational confounding
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are not existed (Hair et al. 2009). It is found that all loading estimates for the
structural model did not change substantially compared to loading estimates of the
final modified model. This further support the validity of modified model specified.
Full SEM model including all indicators are tested. The fit indices for the proposed
model are presented in the Table 4.6. The proposed model fitness results revealed that
the proposed model has not met the best-fitting model criteria.
The initial proposed structural model fit (CMIN/DF = 9.963, GFI = .874, AGFI =
.811, TLI = .827, NFI =.849, CFI = .862, RMSEA = .134) demonstrated inadequate
fit with the observed data, indicating the model could be further improved by
modifications. This involves adjusting the estimated model by freeing (estimating) or
setting (not estimating) parameters. Several modifications are made to the
hypothesised model based on the modification index (Byrne 2001). In this case, error
terms for items of same construct are allowed to covary. The reviews of the MIs for
the regression weights revealed four parameters with relatively large scores. (a) a2 &
a3, (b) d3 & d4, (d) c4 & c5 and (d) availability & access. The modifications are
given within the same constructs.
After incorporating these modifications to the model, the modified model
demonstrated a better model fit and is used as the final model (Figure 4.16) for
hypothesis testing (CMIN/DF= 4.924, GFI = .937, AGFI = .896, NFI = .932, TLI =
.924, CFI =.945, RMSEA = .089) (Table 4.6)
4.4.3.2 Hypotheses testing
On the basis of SEM results, the framed hypotheses have been tested (Table 4.7) and
the results are as under:
H1a: Access significantly predicts the financial inclusion.
H1b: Availability significantly predicts the financial inclusion.
H1c: Usage significantly predicts the financial inclusion.
It becomes evident from the SEM results (Figure 4.16 & Table 4.7) that Access (ß =
.75, p = .000), Availability (ß = .46, p = .000) and Usage (ß = .54, p = .000)
significantly predicts financial inclusion. Thus, hypotheses H1a, H1b and H1c stands
accepted.
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H2a: Financial inclusion has direct impact on social empowerment.
H2b: Financial inclusion has direct impact on economic empowerment.
H2c: Financial inclusion has direct impact on economic development.
SEM results indicate that financial inclusion has positive significant and direct impact
on social empowerment (ß = .74, p = .000), economic empowerment (ß = .79, p =
.000) and economic development (ß = .47, p = .000). Therefore, hypotheses H2a, H2b
and H2c are accepted.
H3a: Social empowerment has direct impact on economic development.
H3b: Economic empowerment has direct impact on economic development.
It is inferred from SEM results that social empowerment has direct impact on
economic development (ß = .42, p = .029) and economic empowerment also has
significant impact on economic development (ß = .68, p = .020). Hence, H3a & H3b
stands accepted.
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FIGURE 4.16: OVERALL STRUCTURE EQUATION MODEL
AC= Access; AV= Availability; US= Usage; FI= Financial inclusion; EE= Economic empowerment;
SE= Social empowerment; ED= Economic development; SE2= FI has led to improved hygiene and
education; SE3= FI has changed your personality & life style; SE5= FI has increased your confidence
level; SE10= FI enhanced your confidence level, business relations and reduced family crisis & social
violence; SE11= FI improved your health and household hygiene; SE16= You can bring any change in the
society easily; SE26= You are socially more developed after being covered under FI drive; EE1= FI has
reduced your need to borrow money or goods; EE2= FI has raised your living standard; EE3= FI has prepared you for emergencies; EE4= You have enough savings to meet any contingent situation; EE7= FI
has increased your purchasing power; EE8= FI enabled your children to get better education; EE11= FI
enhanced your source of income; ED1= FI has increased life expectancy of your family members; ED2=
FI has increased access to education of the society; ED3= FI has empowered the members of the society;
ED4= FI has led to progress of the village; ED5= FI has made the village sustainable for further progress;
ED8= FI has reduced level of stress in your life; ED10= FI has increased per capita income of your family
and ED11= FI has led to increase in value of your assets.
4.4.3.3 Mediation of SE in FI-ED link
Structural equation modeling (SEM) has been used to check various propsed relations,
it is a multivariate technique that seeks to explain the relationship among multiple
variables (Kaplan, 2000). In the present study, the relationship between financial
inclusion, social empowerment and economic development have been assessed. In
order to test the mediating effect, all the conditions described by Baron & Kenny
(1986) are first satisfied. These conditions are (a) the relationship between
137
independent variable and dependent variable should be significant (b) the relationship
between independent and third variable i.e. mediator should be significant (c) the
relationship between the mediator or third variable and outcome should also be
significant (d) when the mediator is entered into the equation, relationship between
independent and dependent variables becomes insignificant.
We used four step procedure through structural analysis in which we first assessed the
impact of financial inclusion on economic development (Figure 4.17), which is
significant (SRW= 0.52, p<0.01). Hence, satisfying first condition for mediation.
FIGURE 4.17: IMPACT OF FINANCIAL INCLUSION ON ECONOMIC
DEVELOPMENT
In the second step, we analysed the impact of financial inclusion on social
empowerment (Figure 4.18), which is significant (SRW= 0.56, p<0.01). Therefore,
second condition for mediation fulfilled.
FIGURE 4.18: IMPACT OF FINANCIAL INCLUSION ON SOCIAL
EMPOWERMENT
In the next step, we assessed the impact of social empowerment on economic
development (Figure 4.19), which is significant (SRW= 0.48, p<0.01). So, the first
three conditions of mediation given by Baron & Kenny (1986) are satisfied.
138
FIGURE 4.19: IMPACT OF SOCIAL EMPOWERMENT ON ECONOMIC
DEVELOPMENT
In order to test the mediating effect, in the last step we added the mediating variable
i.e., social empowerment between financial inclusion and economic development. The
result revealed that when the mediator i.e. social empowerment is entered into the
equation of financial inclusion and economic development, the relationship between
financial inclusion and economic development became insignificant (SRW= 0.37,
p>0.05) and relationship between financial inclusion & social empowerment and
social empowerment & economic development remained significant (p<.05), (Figure
4.20) which satisfies the conditions of mediation effect as suggested by Baron &
Kenny (1986). This indicates that hypothesis, ‘Social empowerment mediates the
relationship between financial inclusion and economic development’ stands
accepted.
FIGURE 4.20: IMPACT OF FINANCIAL INCLUSION ON ECONOMIC
DEVELOPMENT THROUGH SOCIAL EMPOWERMENT
139
4.4.3.4 Mediation of EE in FI-ED link
In order to test the mediating effect all the conditions described by Baron & Kenny
(1986) are first satisfied. We used four step procedure through structure equation
modeling in which we first assessed the impact of predictor i.e. financial inclusion on
dependent variable i.e. economic development, which is significant (SRW= 0.52, P <
.01). Hence, first condition of mediation is accepted as financial inclusion is
positively affecting economic development (Figure 4.17).
In the second step, we analysed the impact of financial inclusion on economic
empowerment. The results (Figure 4.21) revealed that financial inclusion is a
significant predictor of economic empowerment (SRW= 0.52, P< .01). Hence, second
condition of mediation is also fulfilled.
FIGURE 4.21: IMPACT OF FINANCIAL INCLUSION ON ECONOMIC
EMPOWERMENT
In the next step, we evaluated the impact of economic empowerment (mediator) on
economic development (criterion). The results (Figure 4.22) revealed that economic
empowerment is significantly affecting economic development. Hence third condition
of mediation also gets fulfilled (SRW= 0.75, P< .01).
FIGURE 4.22: IMPACT OF ECONOMIC EMPOWERMENT ON ECONOMIC
DEVELOPMENT
140
In order to test the mediating effect, in the last step the mediating variable i.e.
economic empowerment between financial inclusion and economic development is
added. It is found that with the introduction of economic empowerment as mediator in
financial inclusion and economic development equation, the relationship between
financial inclusion and economic development become insignificant (SRW=0.40,
p>0.05) and relationship between financial inclusion & economic empowerment and
economic empowerment & economic development remained significant (p< .05)
Figure 4.23 satisfy the conditions of mediation effects as suggested by Baron &
Kenny, 1986. This shows that hypothesis, ‘Economic empowerment mediates the
relationship between financial inclusion and economic development’ stands
accepted.
FIGURE 4.23: IMPACT OF FINANCIAL INCLUSION ON ECONOMIC
DEVELOPMENT THROUGH ECONOMIC EMPOWERMENT
4.4.4 Output from One-way ANOVA
Table 4.8 shows output from One-way ANOVA using different socio-economic
variables subdivided into age, caste, religion, qualification and income on nature of
financial inclusion. Socio-economic variable wise, variance of groups is not same as
the value of p is less than 0.05, indicating significant mean difference exist in the
nature of financial inclusion with regard to religion, qualification and income whereas
for age and caste, p value is more than 0.05 indicating no significant different exists.
Table 4.9 depicts the output from independent t-test measuring significance of mean
difference on the basis gender and marital status. As evident from the table, there exist
141
no significant difference between male & female and married & unmarried
respondents, as value of p>0.05 level of significance.
So, on the basis of Table 4.8 & 4.9, we can say that the hypothesis ‘Nature of
financial inclusion differs across the socio-economic variable’ is accepted for
religion, qualification & income and rejected for age, caste, gender & marital status.
Table 4.10 depicts age-wise output from One-way ANOVA using different
dimensions of financial inclusion subdivided into access, availability and usage. In
case of access, variance of group is same as the value of p is more than 0.05,
indicating insignificant mean difference exist among respondents of different age
groups. Whereas in case of availability and usage, variance of group is not same as
the value of p is less than 0.05, indicating significant mean difference exist among
different age groups. With regard to availability & usage dimensions of financial
inclusion, beneficiaries belonging to above 50 years of age (2.81 & 2.93) are highly
satisfied followed by 40-50 years (2.70 & 2.88), 30-40 years (2.61 & 2.76) and upto
30 years (2.44 & 2.67).
Table 4.11 shows caste-wise output from One-way ANOVA using different
dimensions of financial inclusion i.e. access, availability and usage. For access &
usage dimensions, variance of group is not same as the value of p is less than 0.05
indicating significant mean difference exist among respondents belonging to different
caste. Whereas no significant mean difference exist among respondents of different
caste with respect to availability dimension as variance of group is same as the value
of p is more than 0.05. Caste-wise analysis shows that with regard to access
dimension general caste (3.54) beneficiaries are highly contended followed by SC
(3.53), ST (3.31) and OBC (3.19) beneficiaries. As far as usage dimension is
concerned, general caste (2.88) beneficiaries are more satisfied than SC (2.78), OBC
(2.64) and ST (2.46) beneficiaries.
Table 4.12 indicates religion-wise output from One-way ANOVA using different
dimensions of financial inclusion i.e. access, availability and usage. Insignificant
mean difference exists among respondents belonging to different caste as value of p is
more than 0.05 in case of access and availability dimensions. As far as usage
dimension is concerned, variance of group is not same as the value of p is less than
0.05 indicating significant mean difference exist among respondents belonging to
142
different castes. Religion-wise analysis depicts that Sikh respondents (3.83) are more
satisfied with usage dimension followed by Hindu (2.83) and Muslim (2.39)
beneficiaries.
Table 4.13 depicts qualification-wise output from One-way ANOVA for different
dimensions of financial inclusion. For all the dimensions of financial inclusion i.e.
access, availability and usage variance of group is not same as the value of p is less
than 0.05, indicating significant mean difference exist among beneficiaries under
different qualification categories. For all the dimensions i.e., access, availability &
usage, beneficiaries with upto higher secondary qualification are maximally satisfied
with mean value (3.79, 2.85 & 3.00) and beneficiaries with below primary
qualification are minimally satisfied with mean value (2.89, 2.10 & 2.33)
respectively.
Table 4.14 shows income-wise output from One-way ANOVA. Variance of group is
not same as the value of p is less than 0.05, indicating significant mean difference
exist among respondents belonging to different monthly income categories with
regard to different dimensions of financial inclusion i.e., access, availability and
usage. Beneficiaries with above `20,000 income are highly satisfied with regard to
access & availability dimensions followed by beneficiaries with `10,000-20,000;
`5,000-10,000 and upto `5,000 monthly income. On the dimension of usage,
beneficiaries with income between `10,000-20,000 are more satisfied than
beneficiaries with `5,000-10,000, above `20,000 and upto `5,000 monthly income.
Table 4.15 shows output from independent t-test measuring significance of mean
difference among male & female. As evident from the table, significant difference
exists with regard to usage as value of p is less than 0.05. Whereas no significant
mean difference exist between male & female with regard to access & availability as
the p value is greater than 0.05. With regard to usage dimension, male are found to be
more contended with mean score 2.84 than female with mean value of 2.70.
Table 4.16 reveals output from independent t-test measuring significance of mean
difference among married & unmarried respondents. As evident from the table, value
of p is less than 0.05 indicating significant mean difference exists between married &
unmarried with regard to availability & usage. But insignificant mean difference
exists on the dimension of access as p value is more than 0.05. Married beneficiaries
143
are more satisfied (2.69 & 2.84) than unmarried beneficiaries (2.41 & 2.66) with
regard to availability & usage dimension of financial inclusion respectively.
TABLE 4.1: SOCIO-ECONOMIC PROFILE OF RESPONDENTS*
S.No. Variable Classification Number Percentage 1 Name of bank JKB 236 47.20
JKGB 125 25.00
SBI 90 18.00
PNB 49 9.80
Sub Total 500 100
2 Gender Male 438 87.60
Female 62 12.40
Sub Total 500 100
3 Age Upto 30 years 51 10.20
30-40 years 179 35.80
40-50 years 182 36.40 Above 50 years 88 17.60
Sub Total 500 100
4 Caste General 285 57.00
SC 175 35.00 ST 13 2.60
OBC 27 5.40
Sub Total 500 100
5 Religion Hindu 486 97.20 Muslim 11 2.20
Sikh 3 0.60
Sub Total 500 100
6 Marital Status Married 450 90.00 Unmarried 50 10.00
Sub Total 500 100
7 Qualification Literate but below Primary 14 2.80
Upto primary 65 13.00 Upto middle 155 31.00
Upto secondary 172 34.40
Upto higher secondary 57 11.40
Upto graduate or higher 7 1.40 Any other 1 0.20
Illiterate 29 5.80
Sub Total 500 100
8 Income Upto ` 5,000 258 51.60
(Monthly) ` 5,000-10,000 199 39.80
` 10,000-20,000 39 7.80
Above ` 20,000 4 0.80
Sub Total 500 100
9 District Jammu 204 40.80
Kathua 163 32.60
Reasi 15 3.00 Samba 39 7.80
Udhampur 79 15.80
Sub Total 500 100
*Source: Survey
144
TABLE 4.2: RESULTS SHOWING FACTOR LOADINGS AND VARIANCE EXPLAINED AFTER SCALE PURIFICATION
(ROTATED COMPONENT METHOD)*
Factor-wise dimension Mean Standard
deviation
Factor
loading
Eigen
value
Variance
explained
%
Cumulative
explained
%
Communality Alpha
(α)
ACCESS
Factor 1: Information accessibility 5.386 40.764 40.764 .904
You have easy access to the information which is useful 3.44 1.05 .857 .810
Banking officials respond well 3.34 1.24 .848 .719
Employees’ of bank are cooperative, friendly and
knowledgeable
3.50 1.02 .816 .699
Banking institution or its substitute is easily approachable 3.57 1.06 .785 .650
Employees’ possess sufficient banking information 3.62 .89 .771 .643
The employees are easily accessible when needed 3.66 1.02 .753 .721
The bank manager promptly redress your problems 2.70 1.41 .733 .628
Factor 2: Physical accessibility 1.507 14.585 55.348 .593
Bank is easily approachable in case of emergencies 3.54 1.10 .547 .604
ATM service is nearby from your place 2.51 1.48 .802 .651
The bank is conveniently located 3.83 1.13 .639 .589
Factor 3: Approachability 1.244 12.456 67.805 .580
This is the only bank in your area 4.12 .55 .835 .713
As compared to other banks, this bank is nearest to you 4.29 .51 .822 .712
AVAILABILITY
Factor 1: Loan availability 3.562 29.516 29.516 .933
Loan is easily available 2.42 1.53 .944 .918
Loan is available within time limit 2.37 1.50 .934 .903
Procedure involved in getting loan is easy 2.42 1.46 .885 .826
145
Factor 2: Support and assistance 1.765 22.743 52.260 .700
Help desk/assisting staff is available for filling withdrawal/
deposit form
3.52 1.02 .757 .589
Field workers promotes various schemes of bank 3.24 1.26 .699 .546
Infrastructure is as per the requirements of the customers 3.49 .99 .704 .557
Bank follows quick problem solving approach 2.87 1.32 .616 .585
Factor 3: Promotion 1.203 20.302 72.562 .782
Employees’ are helpful in making information available
regarding new schemes
1.94 1.25 .890 .812
New bank schemes are advertised frequently 1.69 1.03 .880 .794
USAGE
Factor 1: General usage 2.246 35.956 35.956 .780
You frequently use credit facilities of the bank 1.92 1.20 .856 .739
Advance schemes of bank are frequently used by you 2.03 1.26 .845 .716
You are using bank for the repayment of loan 1.66 1.20 .778 .605
Factor 2: Specific usage 1.701 29.821 65.777 .601
You are using bank for depositing money 3.87 .75 .831 .692
You are using banking services, because interest charged
by the bank on advance is economical than charged by the
moneylender
3.99 .49 .815 .672
You are a regular visitor of the bank 3.46 1.01 .652 .522
SOCIAL EMPOWERMENT
Factor 1: Creditworthiness 4.972 25.346 25.346 .864
FI has changed your personality & life style 3.45 .99 .834 .725
FI has made you socially more reputed 3.52 .94 .723 .696
FI has led to improved hygiene & education 3.29 1.02 .744 .614
You are socially more developed after being covered under
FI drive
3.76 .58 .764 .619
FI improved your health & household hygiene 3.45 .93 .731 .580
146
FI has increased your confidence level 3.54 .95 .673 .524
FI enhanced your confidence level, business relations &
reduced family crisis & social violence
3.56 .86 .607 .546
Factor 2: Liberation 2.683 15.896 41.242 .860
You are free to move to any NGO or any other for any
kind of help or support
1.54 .92 .922 .868
You are free to move to any SHG or any other for any kind
of help or support
1.62 .99 .888 .809
Without FI, you feel difficult in socialising with people of
different social groups
1.96 1.04 .786 .629
Factor 3: Awareness 1.446 8.893 50.135 .637
You avail those special schemes that are offered by the
govt.
2.06 1.21 .844 .805
You are aware about all special schemes that are offered
by the govt.
2.95 1.32 .789 .785
Factor 4: Grievances 1.159 8.662 58.797 .503
You made complaints to the authorities regarding the
delivery of financial services
2.59 1.32 .759 .687
You can bring any change in the society easily 3.37 1.02 .703 .696
ECONOMIC EMPOWERMENT
Factor 1: Stability 4.993 46.768 46.768 .912
FI has prepared you for emergencies 3.51 .97 .846 .719
FI has increased your purchasing power 3.33 1.06 .824 .779
You have enough savings to meet any contingent situation 3.28 1.08 .812 .660
FI has raised your living standard 3.46 .99 .808 .736
FI enabled your children to get better education 3.27 1.05 .762 .703
FI has reduced your need to borrow money or goods 3.79 .81 .683 .529
FI enhanced your source of income 3.24 1.04 .639 .683
Factor 2: Employability 1.116 21.106 67.873 .568
FI created new employment opportunities 2.63 1.13 .830 .702
147
FI directly effects capital formation & investment in
technology
2.27 1.08 .751 .598
ECONOMIC DEVELOPMENT
Factor 1: Growth 4.558 40.266 40.266 .864
FI has increased per capita income of your family 3.23 1.05 .846 .759
FI has increased life expectancy of your family members 3.33 .94 .798 .683
FI has increased access to education of the society 3.47 .89 .768 .693
FI has led to increase in value of your assets 3.22 1.05 .765 .620
FI has reduced level of stress in your life 3.70 .82 .546 .591
Factor 2: Development 1.039 29.694 69.960 .803
FI has led to progress of the village 3.83 .69 .875 .811
FI has made the village sustainable for further progress 3.62 .88 .854 .761
FI has empowered the members of the society 3.68 .78 .601 .678
*Source: Survey
148
TABLE 4.3: RELIABILITY & VALIDITY OF LATENT CONSTRUCTS*
Constructs AVE Composite
reliability
Cronbach’s alpha
(α)
Access .533 .991 .884
Availability .602 .981 .806
Usage .546 .965 .628
Social empowerment .608 .989 .806
Economic empowerment .595 .992 .890
Economic development .512 .992 .888
*Source: Survey
TABLE 4.4: DISCRIMINANT VALIDITY OF LATENT CONSTRUCTS*
AC AV US SE EE ED POV AD
AC (.533)
AV .28 (.602)
US .19 .48 (.546)
SE .38 .28 .15 (.608)
EE .36 .30 .21 .50 (.595)
ED .38 .25 .22 .48 .51 (.512)
POV .32 .23 .13 .44 .42 .52 (.634)
AD .22 .08 .07 .12 .11 .17 .09 (.491)
AC = Access, AV = Availability, US = Usage, SE = Social empowerment, EE = Economic empowerment, ED
= Economic development, POV = Poverty and AD = Area Development
*Source: Survey
149
TABLE 4.5: RESULTS OF CFA FIT INDICES*
Constructs CMIN/DF GFI AGFI CFI NFI TLI RMSEA
Access 4.735 .955 .912 .968 .960 .950 .087
Availability 1.852 .967 .941 .963 .965 .975 .065
Usage 4.563 .988 .965 .983 .979 .975 .085
Social empowerment 3.286 .984 .950 .985 .979 .965 .068
Economic empowerment 2.597 .981 .959 .984 .985 .985 .057
Economic development 4.601 .966 .919 .974 .968 .952 .085
*Source: Survey
TABLE 4.6: FITNESS OF THE STRUCTURAL MODEL*
Model CMIN/DF GFI AGFI CFI NFI TLI RMSEA
Modified model 4.924 .937 .896 .945 .932 .924 .089
Proposed model 9.963 .874 .811 .862 .849 .827 .134
*Source: Survey
150
TABLE 4.7: RESULTS OF HYPOTHESES TESTING*
Hypotheses CR SRW P-
value
Accepted/
Rejected
H1a Access significantly predicts the financial
inclusion.
10.966 .75 .000 Accepted
H1b Availability significantly predicts the
financial inclusion.
14.997 .46 .000 Accepted
H1c Usage significantly predicts the financial
inclusion.
17.868 .54 .000 Accepted
H2a Financial inclusion has direct impact on
social empowerment.
12.463 .74 .000 Accepted
H2b Financial inclusion has direct impact on
economic empowerment.
8.702 .79 .000 Accepted
H2c Financial inclusion has direct impact on
economic development.
3.187 .47 .000 Accepted
H3a Social empowerment has direct impact on
economic development
2.273 .42 .029 Accepted
H3b Economic empowerment has direct impact
on economic development
2.333 .68 .020 Accepted
H4a Social empowerment mediates the
relationship between financial inclusion and
economic development
Accepted
H4b Economic empowerment mediates the
relationship between financial inclusion and
economic development
Accepted
H7 Nature of financial inclusion differs across
the socio-economic variables
Partially
accepted
*Source: Survey
151
TABLE 4.8: OUTPUT FROM ONE-WAY ANOVA*
Particular Description of variable Mean Nature of
variable
Sum of
square
Df Mean
square
F Sig. Remarks
Financial inclusion Age Upto 30 years 2.95 Between group 2.273 3 .758 2.139 .094 Insignificant
30-40 years 3.03 Within group 175.729 496 .353
40-50 years 3.10 Total 178.003 499
Above 50 years 3.18
Financial inclusion Caste General 3.11 Between group 2.670 3 .890 2.517 .057 Insignificant
SC 3.07 Within group 175.333 496 .353
ST 2.87 Total 178.003 499
OBC 2.82
Financial inclusion Religion Hindu 3.08 Between group 2.731 2 1.366 3.872 .021 Significant
Muslim 2.80 Within group 175.272 497 .353
Sikh 3.88 Total 178.003 499
Financial inclusion Qualification Below primary 2.51 Between group 15.708 7 2.244 6.803 .000 Significant
Upto primary 2.82 Within group 162.295 492 .330
Upto middle 3.01 Total 178.003 499
Upto secondary 3.22
Upto higher
secondary
3.28
Upto graduate or
higher
3.08
Any other 3.19
Illiterate 2.98
Financial inclusion Income Upto ` 5,000 2.89 Between group 19.713 3 6.571 20.590 .000 Significant
(Monthly) ` 5,000-10,000 3.25 Within group 158.290 496 .319
` 10,000-20,000 3.39 Total 178.003 499
Above ` 20,000 3.56
*Source: Survey
152
TABLE 4.9: MEAN DIFFERENCE IN THE NATURE OF FINANCIAL INCLUSION THROUGH T-TEST*
Particular Nature of variable Mean Standard deviation t-value Df Sig. Remarks
Financial inclusion Gender Male 3.08 .61 .501 91.597 .617 Insignificant
Female 3.04 .48
Financial inclusion Marital status Married 3.09 .60 1.719 498 .086 Insignificant
Unmarried 2.94 .52
*Source: Survey
TABLE 4.10: AGE-WISE OUTPUT FROM ONE-WAY ANOVA*
Dimensions of
financial inclusion
Description of
variables
Mean Nature of
variable
Sum of
square
Df Mean
square
F Sig. Remarks
Access Upto 30 years 3.47 Between group .697 3 .232 .518 .670 Insignificant
30-40 years 3.48 Within group 222.397 496 .448
40-50 years 3.51 Total 223.094 499
Above 50 years 3.59
Availability Upto 30 years 2.44 Between group 5.198 3 1.733 2.739 .043 Significant
30-40 years 2.61 Within group 313.726 496 .633
40-50 years 2.70 Total 318.924 499
Above 50 years 2.81
Usage Upto 30 years 2.67 Between group 3.642 3 1.214 3.152 .025 Significant
30-40 years 2.76 Within group 190.999 496 .384
40-50 years 2.88 Total 194.641 499
Above 50 years 2.93
*Source: Survey
153
TABLE 4.11: CASTE-WISE OUTPUT FROM ONE-WAY ANOVA*
Dimensions of
financial inclusion
Description
of variables
Mean Nature of
variable
Sum of
square
DF Mean
square
F Sig. Remarks
Access General 3.54 Between group 3.497 3 1.166 2.633 .049 Significant
SC 3.53 Within group 219.597 496 .443
ST 3.31 Total 223.094 499
OBC 3.19
Availability General 2.70 Between group 1.824 3 .608 .951 .416 Insignificant
SC 2.64 Within group 317.100 496 .639
ST 2.55 Total 318.924 499
OBC 2.46
Usage General 2.88 Between group 3.948 3 1.316 3.423 .017 Significant
SC 2.78 Within group 190.693 496 .384
ST 2.46 Total 194.641 499
OBC 2.64
*Source: Survey
TABLE 4.12: RELIGION-WISE OUTPUT FROM ONE-WAY ANOVA*
Dimensions
of financial
inclusion
Description of
variables
Mean Nature of
variable
Sum of
square
Df Mean
square
F Sig. Remarks
Access Hindu 3.51 Between group 1.704 2 .852 1.912 .149 Insignificant
Muslim 3.21 Within group 221.390 497 .445
Sikh 4.00 Total 223.094 499
Availability Hindu 2.66 Between group 3.677 2 1.839 2.898 .056 Insignificant
Muslim 2.54 Within group 315.247 497 .634
Sikh 3.74 Total 318.924 499
Usage Hindu 2.83 Between group 5.093 2 2.547 6.677 .001 Significant
Muslim 2.39 Within group 189.548 497 .381
Sikh 3.83 Total 194.641 499
*Source: Survey
154
TABLE 4.13: QUALIFICATION-WISE OUTPUT FROM ONE-WAY ANOVA*
Dimensions of
financial inclusion
Description of variables Mean Nature of
variable
Sum of
square
Df Mean
square
F Sig. Remarks
Access Below primary 2.89 Between group 20.354 7 2.908 7.056 .000 Significant
Upto primary 3.21 Within group 202.740 492 .412
Upto middle 3.45 Total 223.094 499
Upto secondary 3.65
Upto higher secondary 3.79
Upto graduate or higher 3.40
Any other 3.75
Illiterate 3.41
Availability Below primary 2.10 Between group 15.055 7 2.151 3.482 .001 Significant
Upto primary 2.42 Within group 303.869 492 .618
Upto middle 2.58 Total 318.924 499
Upto secondary 2.81
Upto higher secondary 2.85
Upto graduate or higher 2.71
Any other 2.55
Illiterate 2.64
Usage Below primary 2.33 Between group 11.122 7 1.589 4.259 .000 Significant
Upto primary 2.67 Within group 183.519 492 .373
Upto middle 2.75 Total 194.641 499
Upto secondary 2.98
Upto higher secondary 2.91
Upto graduate or higher 3.00
Any other 3.00
Illiterate 2.65
*Source: Survey
155
TABLE 4.14: INCOME-WISE OUTPUT FROM ONE-WAY ANOVA*
Dimensions of
financial inclusion
Description of
variables
Mean Nature of
variable
Sum of
square
Df Mean
square
F Sig. Remarks
Access Upto ` 5,000 3.36 Between group 13.606 3 4.535 10.738 .000 Significant
` 5,000-10,000 3.64 Within group 209.489 496 .422
` 10,000-20,000 3.82 Total 223.094 499
Above ` 20,000 3.92
Availability Upto ` 5,000 2.43 Between group 29.330 3 9.777 16.745 .000 Significant
` 5,000-10,000 2.88 Within group 289.594 496 .584
` 10,000-20,000 2.96 Total 318.924 499
Above ` 20,000 3.44
Usage Upto ` 5,000 2.63 Between group 21.472 3 7.157 20.500 .000 Significant
` 5,000-10,000 3.01 Within group 173.169 496 .349
` 10,000-20,000 3.16 Total 194.641 499
Above ` 20,000 3.00
*Source: Survey
TABLE 4.15: MEAN DIFFERENCE IN THE NATURE OF FINANCIAL INCLUSION BETWEEN MALE & FEMALE THROUGH
T-TEST*
Dimensions of
financial inclusion
Nature of
variable
Mean Standard
deviation
t-value Df Level of
significance
Remarks
Access Male 3.50 .69 -1.259 95.863 .211 Insignificant
Female 3.59 .51
Availability Male 2.68 .81 1.227 498 .220 Insignificant
Female 2.54 .75
Usage Male 2.84 .65 2.139 103.399 .035 Significant
Female 2.70 .44
*Source: Survey
156
TABLE 4.16: MEAN DIFFERENCE IN THE NATURE OF FINANCIAL INCLUSION BETWEEN MARRIED & UNMARRIED
BENEFICIARIES THROUGH T-TEST*
Dimensions of
financial
inclusion
Nature of
variable
Mean Std. dev. t-value Df Level of
significance
Remarks
Access Married 3.51 .68 .466 498 .641 Insignificant
Unmarried 3.47 .60
Availability Married 2.69 .80 2.322 498 .021 Significant
Unmarried 2.41 .72
Usage Married 2.84 .62 1.95 498 .052 Significant
Unmarried 2.66 .65
*Source: Survey
157
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`
Chapter-V Financial Inclusion and
Poverty Reduction
CONTENTS
S.No. Title Page No.
5.1 Introduction 160
5.2 Conceptual Analysis of Poverty & Poverty
Reduction
161
5.3 Trends of Poverty in India 162
5.4 Causes of Poverty in India 166
5.5 Poverty Alleviation Programmes 167
5.6 Poverty Reduction through Financial Inclusion 171
5.6.1 Scale Purification 172
5.6.2 Confirmatory Factor Analysis 173
5.6.3 Demographic Profile-wise Mean Satisfaction
regarding Poverty Reduction
175
5.6.4 Relationship between Financial Inclusion and
Poverty Reduction
177
References 183
160
CHAPTER V
FINANCIAL INCLUSION AND POVERTY REDUCTION
5.1 INTRODUCTION
Financial inclusion is an important tool for combating the multi-dimensional aspects
of poverty (Nalini & Mariappan, 2012). It enhances financial access which leads to
several benefits such as overcoming problem of poverty, unemployment, inequality
and deteriorating welfare (Nirmala & Yepthomi, 2014). Access to basic financial
services such as savings, loans, insurance, credit etc. through financial inclusion has
generated a positive impact on the lives of the poor (Caskey et al., 2006 and Dupas &
Robinson, 2009) and help them to come out of the clutches of poverty (Bhandari,
2009 and Bansal, 2012). Financial inclusion provides access to a well-functioning
financial system which limits the risks, enables economically & socially excluded
people to integrate and actively contributes in the development of the economy and
shields themselves from shocks of drought, illness and death (Swamy &
Vijayalakshmi, 2009; Collins et al., 2009; Napier et al., 2013; Chibango, 2014 and
Shyni & Mavoothu, 2014). There is a bidirectional cause and effect relationship
between poverty and financial inclusion (Bhandari, 2009). Access to financial
services through financial inclusion provides opportunities to save, avail credit, make
investment and meet emergency situations and enables the poor to smoothen
consumption pattern, properly manage risk, increase assets gradually, enhance earning
capacity and providing good standard of living. Financial inclusion is a win-win
opportunity for the poor, for the banks and for the nation (Subbarao, 2009). It
contributes in employment generation, income generation, proper utilisation of
resources, mobilisation of savings etc. which help in poverty alleviation and thus lead
to GDP growth in any economy (Shyni & Mavoothu, 2014). The linkage between a
developed financial system or ecosystem and improved wealth of the poor is depicted
in Figure 5.1 (Selvarajah et al., 2012).
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FIGURE 5.1: RELATIONSHIP BETWEEN FINANCIAL ECOSYSTEM AND
POVERTY*
*Source: Selvarajah, Jeyanthi, Nair, Mahendhiran, Vaithilingam, Santha, & Ng, Jason (2012).
Financial inclusion and the blueprint financial ecosystem in poverty reduction: an
exploratory analysis. Retrieved from www.ibrarian.net/navon/mismachKarov.jsp?Ppid=
21309790. Accessed on 27-09-2014. Accessed on 27-09-2014.
5.2 CONCEPTUAL ANALYSIS OF POVERTY & POVERTY
REDUCTION
The word ‘poverty’ derives from French word ‘pauvre’ meaning poor (Khanna,
2012). Poverty means not having enough money for basic needs such as food, water,
shelter or toilets. It means the state of lacking material possessions of having little or
no means to support oneself.
India is the first country in the world to define poverty as the total per-capita
expenditure of the lowest expenditure class. Rowntree (1910) defines poverty as a
‘situation where the total earnings of a family are insufficient to obtain the minimum
162
necessities for the maintenance of merely physical efficiency’. The World Bank
(1990) refers poverty as, ‘the inability to attain a minimal standard of living’. Further,
‘Poverty is hunger. Poverty is lack of shelter. Poverty is being sick and not being able
to see a doctor. Poverty is not having access to school and not knowing how to read.
Poverty is not having a job, is fear for the future, living one day at a time. Poverty is
losing a child to illness brought about by unclean water. Poverty is powerlessness,
lack of representation and freedom’ (World Bank, 1990). Amartya Sen (2000) states
that poverty refers to, ‘insufficient income along with absence of wide range of
capabilities, including security and ability to participate in economic and political
systems’. According to World Bank (2001), poverty refers to, ‘deprivation of well-
being related to lack of material income or consumption, low levels of education &
health, vulnerability & exposure to risk, no opportunity to be heard and
powerlessness’. Poverty is lack of or the inability to achieve a socially acceptable
standard of living (Bellu, 2005). European Commission (2007) defines poverty as,
‘when resources of people, families and groups of persons are so limited and they are
unable to achieve minimum acceptable way of life in the state to which they belong’.
Selvarajah et al., (2012) & Mel et al., (2009) describe poverty as, ‘lacking of basic
needs such as food, shelter, employment, healthcare and education’. Murari &
Didwania (2010) defined poverty as ‘when a person faces difficulties in meeting the
minimum requirement of acceptable living standards’. Poverty refers to deprivation
from income, basic needs and human capabilities (Chibango, 2014). According to
Government of India reports, a person who consumes 2400 kcal/day in rural and 2100
kcal/day in urban areas are not poor or below the poverty line category (Sharma et al.,
2011). But recent report claims that those spending `32 and `47 in towns and cities
respectively should not be considered as poor (Singh, 2014). Poverty reduction is
defined as successfully lessening deprivation of well-being (Sunderlin, 2004). Poverty
reduction refers to reduction of number or percentage of people living in poverty or
the severity of the impact of poverty on the lives of poor people (Kraai, 2012).
5.3 TRENDS OF POVERTY IN INDIA
According to Planning Commission of India, poverty has shown a declining trend as
evident from Table 5.1 and Figure 5.2. In 1993-94, about 403.7 million of people
(45.3 percent of the total population) lived below poverty line. In 2004-05, as many as
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407.1 million of people (37.2 percent of population) were living below poverty line.
During 2009-10, the level of poverty has come down to 29.8 percent. Whereas,
poverty has further declined to a record of 22 percent in 2011-12. The total number
of poor estimated at 269.3 million, out of which 216.5 million reside in rural India.
For the year 2013-14, estimated value highlights further decline in poverty ratio to
18.7. However, total estimated number of poor arrived at 230.8 million, where 183.6
million belongs to rural India.
TABLE 5.1: TRENDS OF POVERTY IN INDIA*
Year Poverty ratio (%) Number of poor (million)
Rural Urban Total Trend Rural Urban Total Trend
1993-94 50.1 31.8 45.3 - 328.6 74.5 403.7 -
2004-05 41.8 25.7 37.2 326.3 80.8 407.1
2009-10 33.8 20.9 29.8 278.2 76.5 354.7
2011-12 25.7 13.7 21.9 216.7 53.1 269.8
2013-14 20.6** 11.3** 18.7** 183.6** 47.2** 230.8**
*Source: Planning Commission, Government of India. (2014). Review of methodology for
measurement of poverty. Retrieved from planningcommission.gov.in/reports/genrep/pov_rep
0707.pdf. Accessed on 5-10-2014.
**Estimated figures
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FIGURE 5.2: TRENDS OF POVERTY IN INDIA*
*Source: Retrieved from http://www.livemint.com/r/LiveMint/Period1/2013/07/25/ Photos/g-
charticle (poverty).jpg. Accessed on 5-10-2014.
Table 5.2 shows state-wise number and percentage of below poverty line population
for the year 2011-12. The percentage of population below poverty line is different in
different states of India. Overall, Chhattisgarh (39.9%), Jharkhand (37.0%) and
Manipur (36.9) are reported to have largest population below poverty line. Whereas in
Goa, only 5.1% of population is below poverty line, which is lowest among all states.
Likewise in Kerala, 7.1% of the population is living below the poverty line. Urban
area-wise, Manipur (32.6) & Bihar (31.2) are considered to have largest below
poverty line population and Sikkim (3.7), Goa (4.1), Himachal Pradesh (4.3) & Kerala
165
(5.0) are regarded as states with least below poverty population. Rural area-wise,
poverty is accounted to be largest in Chhattisgarh (44.6%) & Jharkhand (40.8) and
least measured in Goa (6.8%), Punjab (7.7%) & Himachal Pradesh (8.5%).
TABLE 5.2: NUMBER & PERCENTAGE OF POPULATION BELOW
POVERTY LINE BY STATES 2011-12*
S.No. States Rural Urban Total
No. of
persons
(lakhs)
%age of
persons
No. of
persons
(lakhs)
%age
of
persons
No. of
persons
(lakhs)
%age
of
persons
1 Andhra Pradesh 61.8 11.0 17.0 5.8 78.8 9.2
2 Arunachal Pradesh 4.2 38.9 0.7 20.3 4.9 34.7
3 Assam 92.1 33.9 9.2 20.5 101.3 32.0
4 Bihar 320.4 34.1 37.8 31.2 358.2 33.7
5 Chhattisgarh 88.9 44.6 15.2 24.8 104.1 39.9
6 Delhi 0.5 12.9 16.5 9.8 17.0 9.9
7 Goa 0.4 6.8 0.4 4.1 0.8 5.1
8 Gujarat 75.4 21.5 26.9 10.1 102.2 16.6
9 Haryana 19.4 11.6 9.4 10.3 28.8 11.2
10 Himachal Pradesh 5.3 8.5 0.3 4.3 5.6 8.1
11 Jammu & Kashmir 10.7 11.5 2.5 7.2 13.3 10.3
12 Jharkhand 104.1 40.8 20.2 24.8 124.3 37.0
13 Karnataka 92.8 24.5 37.0 15.3 129.8 20.9
14 Kerala 15.5 9.1 8.5 5.0 23.9 7.1
15 Madhya Pradesh 191.0 35.7 43.1 21.0 234.1 31.6
16 Maharashtra 150.6 24.2 47.4 9.1 197.9 17.4
17 Manipur 7.4 38.8 2.8 32.6 10.2 36.9
18 Meghalaya 3.0 12.5 0.6 9.3 3.6 11.9
19 Mizoram 1.9 35.4 0.4 6.4 2.3 20.4
20 Nagaland 2.8 19.9 1.0 16.5 3.8 18.9
21 Orissa 126.1 35.7 12.4 17.3 138.5 32.6
22 Punjab 13.4 7.7 9.8 9.2 23.2 8.3
23 Rajasthan 84.2 16.1 18.7 10.7 102.9 14.7
24 Sikkim 0.4 9.9 0.1 3.7 0.5 8.2
25 Tamil Nadu 59.2 15.8 23.4 6.5 82.6 11.3
26 Tripura 4.5 16.5 0.8 7.4 5.2 14.0
27 Uttar Pradesh 479.4 30.4 118.8 26.1 598.2 29.4
28 Uttarakhand 8.2 11.6 3.4 10.5 11.6 11.3
29 West Bengal 141.1 22.5 43.8 14.7 185.0 20.0
Population with high below poverty line population
Population with low below poverty line population
*Source: Planning Commission, Government of India. (2014). Review of methodology for measurement
of poverty. Retrieved from planningcommission.gov.in/reports/genrep/pov_rep0707.pdf. Accessed on 5-10-2014.
166
5.4 CAUSES OF POVERTY IN INDIA
There are many reasons for poverty in India. Some of them are as follows:
i. Rapidly rising population
Population has been rising in India at a rapid pace. On an average 17 million people
are added every year to its population which raises the demand for consumption
goods considerably. This rise is mainly due to fall in the death rate and more or stable
birth rate, for the last many decades. Heavy pressure of population adds to
dependency burden, implying greater poverty over time.
ii. Low productivity in agriculture
Low level of agricultural productivity due to subdivided & fragmented holdings, lack
of capital, use of traditional methods of cultivation, underutilisation of human
resources, illiteracy etc. is the main cause of poverty in the country. This brings them
down in their standard of living.
iii. Low rate of growth
Rate of growth of economy has been quite low during five year plans in India. During
the period of planning, growth rate of GDP has been nearly 4 percent. But owing to
nearly 2 percent growth rate of population, per capita income grew by 2.4 percent
only. Low growth rate of per capita income has tended to sustain poverty.
iv. Inflationary pressures
Due to low rate of production and high rate of population growth, less developed
economies like India are often caught in the cobweb of inflationary pressures.
Inflation has persisted almost as a permanent feature of the Indian economy. It means
a situation of continual rise in prises. This continuous and steep rise in prices has
added to the miseries of poor.
v. Chronic unemployment and underemployment
India is a country sustaining chronic unemployment and underemployment. Number
of job seekers is increasing at higher rate than the expansion in employment
opportunities. This expanding problem of unemployment is another cause of poverty.
167
vi. Paucity of able and efficiency entrepreneurship
In the initial stages of industrial development of a country, there is a need for efficient
and skilled entrepreneurs possessing initiative, imagination and risk-taking ability.
Unfortunately, there is a great shortage of such entrepreneurs in India. Consequently,
production activity has remained at a very low level. Low level of production implies
low level of employment and high level of poverty.
vii. Outdated social institutions
The social structure of our country is full of outdated traditions and institutions like
caste system, joint family system, laws of inheritance & succession, etc. All these
obstruct dynamic changes in the economy. This backwardness is not conducive to
faster development. Due to this, growth rate is hampered and have aggravate the
problem of poverty.
viii. Lack of infrastructure
Energy, transport and communication - the vital components of economic
infrastructure as well as education, health and housing services - the principal
components of social infrastructure are in a very bad shape. These serve as the
foundation of any programme of growth and development. But unfortunately, this
foundation is still in its infancy stage despite more than 63 years of planning. Slow
multiplication of growth trends and persistence of poverty are the obvious
consequences.
5.5 POVERTY ALLEVIATION PROGRAMMES
Many poverty reduction programmes have been initiated in most developing
countries. Since the inception of planning in India, poverty reduction has become
important goal of developmental programmes. These programmes have been broadly
categorised into self-employment & wage employment programmes, food safety
programme, social security programmes and housing programmes (Figure 5.3).
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FIGURE 5.3: POVERTY ALLEVIATION PROGRAMMES*
*Source: http://planningcommission.nic.in, Accessed on 16-11-2014
A. Self-employment and Wage Programme
i. Swarnajayanti gram swarozgar yojana (SGSY)
SGSY came into existence in April, 1999. It replaces earlier poverty eradication
programmes like integrated rural development programme (IRDP), training for
rural youth for self-employment (TRYSEM), etc. It is a holistic programme for
developing micro enterprises in rural areas. The micro enterprises are organised
as individual enterprises as well as on collective basis as self help groups
(SHGs). The SHG may consist of 10 to 20 members. It laid focus on lending
credit, organising the rural poor into self-help groups, capacity-building,
training, participatory approach to planning of self-employment ventures,
infrastructure support, technology and credit & marketing linkages. Thus, SGSY
is a credit-cum-subsidy programme, where credit is the major component and
Self-employment and wage programmes
• Swarnjayanti gram swarozgaryojana
• Mgnrega
• Wage emplyment programme
• Jwahar rozgar yojana
• Sampoorna gramin rozgar yojana
Food safety programmes
• Annapurna
• National food for work programme
Social security programmes
• National old age pension scheme
• National family benefit scheme
• National maternity benefit scheme
Housing programmes
• Indira awaas yojana
• Valmiki ambedkar awaas yojana
169
subsidy is the minor component (Yesudian, 2007 and Murari & Didwania,
2010).
ii. Mahatama Gandhi national rural employment guarantee act (MGNREGA)
The NREGA bill was notified in 2005 and came into force in 2006. It was
further modified as Mahatma Gandhi national rural employment guarantee act
(MGNREGA) in 2008. Under this scheme, 100 days of paid work is guaranteed
to people in the rural areas. Now these 100 days work is raised to 150 days
work.
iii. Wage employment programme
Wage employment programme is an important component of the anti-poverty
strategy. In this programme, employment opportunities are provided during lean
agricultural seasons, in times of floods, droughts and other natural calamities.
They create rural infrastructure which supports further economic activity. These
programmes also put an upward pressure on market wage rates by attracting
people to public works programmes, thereby reducing labour supply and
pushing up demand for labour. While public works programmes to provide
employment in times of distress have a long history, major thrust to wage
employment programmes in the country was provided only after the attainment
of self-sufficiency in food grains in the 1970’s.
iv. Jawahar rozgar yojana (JRY)
The JRY came into existence to generate employment opportunities for
unemployed and underemployed people in rural areas through creating
economic infrastructure and community & social assets.
v. Sampoorna gramin rozgar yojana (SGRY)
SGRY was launched on September 1, 2001. The basic objectives of this scheme
are to generate employment opportunities to the surplus workers, development
of regional economic & social condition, establish durable economic
infrastructure in rural areas and provision of food & nutrition security to the
poor. Work taken under this programme is labour-intensive and the workers are
paid the minimum wages notified by the states. Workers are paid partly in cash
and partly in kind.
170
B. Food Safety Programmes
i. Annapurna
Government of India started this scheme in 1999-2000. The main aim of this
scheme is to provide food to senior citizens who cannot take care of themselves
and are not covered under the national old age pension scheme (NOAPS) and
who have no one to take care of them in their village.
ii. National food for work programme (NFFWP)
To generate additional supplementary wage employment with food security,
NFFWP was launched in November 2004 in the 150 most backward districts.
Free of cost food grains were received by states under NFFWP. The programme
focuses on works relating to water conservation, drought proofing (including
aforestation), land development, flood-control/protection (including drainage in
waterlogged areas) and rural connectivity in terms of all weather roads.
C. Social Security Programmes
i. National old age pension scheme
Under this scheme, pension is provided to old people who were above the age of
65 years (now reduced to 60 years) who could neither feed themselves nor have
any means of subsistence. This pension is given by the central government.
According to Budget (2011-12), the amount of old age pension is `200 & `500
per month for applicants aged 60-79 years & above 80 years respectively.
ii. National family benefit scheme
Government of India started this scheme in August 1995. This scheme is
sponsored by the state government under community & rural development
department. It was transferred to the state sector scheme after 2002-03. A sum of
`10,000 is given to a person who becomes the head of the family after the death
of its primary breadwinner. The breadwinner is defined as a person who is
above 18 who earns the most for the family and on whose earnings the family
survives. It is for families below the poverty line.
171
iii. National maternity benefit scheme (NMBS)
Under this scheme, a sum of `500 is given to a pregnant mother for the first two
live births. The women should be older than 19 years of age. It is given normally
12-8 weeks before the birth and in case of the death of the child the women can
still avail it. The NMBS is implemented by states and union territories with the
help of panchayats and municipalities.
D. Housing Programmes
i. Indira awaas yojana (IAY)
The scheme came into existence since 1985-86. The main aim of this scheme is
providing dwelling units free of cost to below poverty line (BPL) families in
rural areas. In 1996, this scheme gains a boost when central government
identified ‘Housing’ as one of the seven components under the basic minimum
services (BMS) agenda to provide housing to the shelterless poor in rural areas
in a time bound manner.
ii. Valmiki Ambedkar awaas yojana (VAMBAY)
VAMBAY was launched in December, 2001 to facilitate the construction and
up-gradation of dwelling units for the slum dwellers and to provide a healthy &
enabling urban environment through community toilets under Nirmal Bharat
Abhiyan. The Central government provides a subsidy of 50 per cent, with the
balance provided by the state government.
5.6 POVERTY REDUCTION THROUGH FINANCIAL INCLUSION
The relationship between poverty reduction and financial inclusion is examined under
following sub-heads:
5.6.1 Scale purification
5.6.2 Confirmatory factor analysis
5.6.3 Demographic profile-wise mean satisfaction regarding poverty reduction
5.6.4 Relationship between financial inclusion and poverty reduction
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A brief description of each aforesaid sub heads is as under:
5.6.1 Scale Purification
Purification of construct administered on beneficiaries of financial inclusion drive of
RBI is separately carried using SPSS (version 17.00) and the results are evident from
the Table 5.3.
Beneficiaries’ perception regarding poverty reduction
The suitability of raw data for factor analysis obtained from bank customers is
examined through Anti-image, KMO value, Bartlett test of sphercity and (p-value =
0.000), indicating sufficient variance and correlation matrix (Dess et al., 1997 and
Feild, 2000). The process of R-mode principal component analysis (PCA) with
Varimax rotation extracted 8 statements out of 10 statements which are actually kept
in the construct of poverty reduction. The KMO value (.904) and Bartlett test of
sphercity (3225.782) indicates acceptable and significant values. Therefore, factor
loadings in the final factorial design are consistent with conservative criteria, thereby
resulting into two-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with
79.40% of the total variance explained. The communalities for 8 statements range
from .663 to .872, indicating high degree of linear association among the variables.
The factor loading ranges from .768 to 0.908 and the cumulative variance extracted
ranges from 57.52 to 79.40 percent. The communalities and percentage of variance
explained by each factor is displayed in the Table 5.3. A brief description of factors
emerged are as under:
Factor 1 (Poverty eradication)
Six items included in this factor are, ‘Family crisis are reduced through better living
standards’, ‘Health has improved by having qualitative food’, ‘Your consumption
level has increased’, ‘You consume more qualitative food than before’, ‘Your
expenditure on clothing has increased’ and ‘Your expenditure on luxuries has
increased’. The mean values for all items lie between 3.13 - 3.52 exhibiting moderate
scores, but significant factor loading between .768 - .908 and communalities .663 -
.872. The factor acknowledges the importance of financial inclusion which assists
beneficiaries in raising living standard, obtaining quality food, increasing
consumption level and acquiring cloths & luxuries.
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Factor 2 (Poverty reduction through education)
Two items identified by this factor are, ‘Head of the family is educated enough to
guide other members to move on right track’ and ‘Most of the members are educated
in your family’. The items have high factor loading of .881 & .845 and significant
communalities as .803 & .767 respectively. This factor clearly demonstrates that
proper guidance and education level of family member help in proper implementation
of financial inclusion scheme.
Reliability
Two factors are obtained after scale purification falling within the domain of poverty
reduction. As evident from the Table 5.3, the Cronbach’s alpha for all 8 scale items
underlying two factors ranges from .720 to .946. The alpha reliability coefficient for
F1 i.e., poverty eradication (.946) is higher than the criteria of .77 obtained by Gordon
and Narayanan (1984) indicating high internal consistency. F2 i.e., poverty reduction
through education (.720) is also at a minimum acceptable level of 0.50 as
recommended by Brown et al. (2001) and Kakati & Dhar (2002) thereby obtaining
satisfactory internal consistency. However, overall alpha reliability score for all
factors is very much satisfactory at 0.904. Adequacy and reliability of sample size to
yield distinct and reliable factors is further demonstrated through Kaiser-Meyer-Olkin
measure of sampling adequacy that is 0.904 and all factor loadings are greater than
.50.
Validity
The two factors obtained alpha reliability higher & equal to 0.50 and satisfactory
KMO value at .904, indicating significant construct validity of the construct (Hair et
al., 1995).
5.6.2 Confirmatory Factor Analysis
CFA is applied to assess the fitness, reliability and validity of poverty reduction
dimension using AMOS (version 16.00).
Poverty reduction dimension comprises of total 8 items. First order CFA (Figure 5.4)
is performed to assess the model fitness. After applying CFA, two items namely,
‘Head of the family is educated enough to guide other members to move on right
track’ and ‘Most of the members are educated in your family’ got deleted as their
174
standard regression weight (SRW) is below the acceptable criteria of .50. Rest 6 items
have regression weight above .50, thus it become clear that all remaining measured
variables are the significant contributors of the construct. This model has been found
to have a good fit (CMIN/DF = 3.927, GFI = .982, AGFI = .947, CFI = .993, NFI =
.991, TLI = .985 and RMSEA = .077) (Table 5.4). Convergent validity also got
established as AVE arrived at .745 and Cronbach’s alpha is .946 and composite
reliability equals to .996 (Table 5.4). Thus, the model has been proved to be valid and
reliable. In our study, variable ‘Family crisis are reduced through better living
standard’ contributes highest towards poverty reduction, as its regression weight is
.943 (Table 5.4). Financial inclusion enables rural population to avail the financial
services and increases their income earning capabilities which results into raising of
standard of living and reducing family crisis. Los Cobos declaration on financial
inclusion (2012) also recognised that financial inclusion is a key component in the
development of healthy, vibrant and stable financial systems which contribute to
sustainable economic growth. Access to safe, secure and reliable financial system is
important for improving standard of living.
FIGURE 5.4: CFA MODEL FOR POVERTY REDUCTION
POVRED = Poverty Reduction; P5 = Your expenditure on clothing has increased; P6 = You consume more qualitative food than before; P7 = Your expenditure on luxuries has increased; P8 = Health has
improved by having qualitative food; P9 = Your consumption level has increased and P10 = Family
crisis are reduced through better living standard.
Pov-red
P5 e1
.84
P6 e2 .86
P7 e3 .74
P8 e4
.88
P9 e5
.90
P10 e6
.94
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5.6.3 Demographic Profile-wise Mean Satisfaction regarding Poverty
Reduction
Table 5.5 shows demographic profile-wise mean satisfaction regarding poverty
eradication. Demographic profile is sub-categorised into age, gender, district, caste,
religion, marital status and qualification.
Age-wise analysis depicts that age is sub-divided into four categories i.e., upto 30
years, 30-40 years, 40-50 years and above 50 years. Respondents are highly satisfied
with the statement ‘Health has improved by having qualitative food’ with mean
response of 3.53 and least satisfied with the statement ‘Your consumption level has
increased’ with mean response of 3.15. Age-wise, respondents belonging to 30-40
years (3.45) category are maximally satisfied followed by above 50 years (3.41), upto
30 years (3.34) and 40-50 years (3.32) age category.
On the basis of gender, statement-wise maximum mean level of satisfaction is
observed for the statement ‘Health has improved by having qualitative food’ at 3.58
and least at 3.21 for the statement ‘Your consumption level has increased’. Gender-
wise, female respondents are found to be more satisfied with mean value of 3.55
compared to male with mean value of 3.36.
On the basis of districts, statement-wise mean satisfaction varies from lowest 3.13 for
‘Your consumption level has increased’ to highest upto 3.52 for ‘Health has improved
by having qualitative food’. On the whole, district-wise mean satisfaction among
beneficiaries in descending order is found to be Reasi (3.61), Jammu (3.58),
Udhampur (3.26), Samba (3.24) and Kathua (3.21).
Caste-wise, maximum mean score is found to be at 3.39 for ‘Health has improved by
having qualitative food’ and minimum at 2.98 for ‘Your consumption level has
increased’. Caste-wise, mean level of satisfaction in ascending order is OBC (3.06),
ST (3.07), SC (3.24) and general (3.52).
On religion-wise analysis, statement ‘Your consumption level has increased’ has
lowest mean satisfaction at 3.57 and statement ‘Health has improved by having
qualitative food’ has highest mean satisfaction at 3.29. Religion-wise, mean level of
satisfaction in descending order is Sikh (4.00), Hindu (3.39) and Muslim (2.96).
Marital status-wise analysis depicts that maximum mean satisfaction is noticed for the
statement ‘Health has improved by having qualitative food’ at 3.48 and minimum for
176
the statement ‘Your consumption level has increased’ at 3.15. Marital status-wise,
married respondents are more satisfied (3.40) in contrast to unmarried beneficiaries
(3.29).
Qualification-wise analysis shows that statement ‘Health has improved by having
qualitative food’ has highest mean value at 3.38 and lowest at 3.09 for the statement
‘Your consumption level has increased’. Qualification-wise, mean satisfaction in
increasing order is upto primary (2.61), literate below primary (2.72), illiterate (2.90),
upto graduate or higher (3.05), upto middle (3.41), upto secondary (3.63), upto higher
secondary (3.84) and any other qualification (4.00).
Table 5.6 shows demographic-wise mean satisfaction regarding poverty reduction
through education.
Statement-wise analysis across the demographic profile i.e., age, gender, district,
caste, religion, marital status & qualification depicts that beneficiaries are maximally
satisfied with the same statement ‘Health has improved by having qualitative food’
with mean value 3.54, 3.52, 3.46, 3.32, 3.62, 3.51 & 3.52 and minimally satisfied with
the same statement ‘Your consumption level has increased’ with mean response of
3.28, 3.31, 3.26, 3.06, 3.46, 3.29 & 3.36 respectively.
Age-wise analysis shows that beneficiaries under the age group of above 50 years
(3.46) are highly satisfied followed by upto 30 years (3.55), 30-40 years (3.33) and
40-50 years (3.29).
Gender-wise analysis depicts that female beneficiaries (3.49) are more satisfied in
comparison to male beneficiaries (3.34).
District-wise analysis exhibits satisfaction among beneficiaries of different districts in
ascending order, Kathua (2.96), Udhampur (3.13), Samba (3.43), Reasi (3.54) and
Jammu (3.73).
Caste-wise, mean satisfaction in descending order is found to be at 3.53 (General),
3.39 (ST), 3.19 (SC) and 2.67 (OBC).
Religion-wise, maximum mean satisfaction is found to be 4.00 (Sikh), followed by
3.35 (Hindu) and least 3.27 (Muslim).
Marital status-wise, unmarried beneficiaries are more satisfied (3.45) than married
beneficiaries (3.35).
177
Qualification-wise, mean satisfaction in descending order is found to be 5.00 (Any
other qualification), 4.09 (upto higher secondary), 3.93 (upto graduate or higher), 3.64
(upto secondary), 3.25 (upto middle), 2.83 (Illiterate), 2.58 (upto primary) and 2.36
(Literate below primary).
5.6.4 Relationship between Financial Inclusion and Poverty Reduction
Figure 5.5 highlights the relationship between financial inclusion and poverty
reduction. The results indicated that model fit the data excellently (CMIN/DF = 4.899,
GFI = .946, AGFI = .907, CFI = .972, NFI = .965, TLI = .961 and RMSEA = .088)
(Table 5.7). SEM results in Table 5.8 indicates that financial inclusion has positive
and significant relation with poverty reduction (β = .553, p = .000). Therefore,
hypothesis ‘Financial inclusion is positively related to poverty reduction’ stands
accepted. The result of the study is in line with the previous study Murari &
Didwania (2010) which has the same observation and found that scheme of financial
inclusion is an effective instrument which can lift poor above the level of poverty by
providing them increased self employment opportunities and making them credit
worthy. Another study by Rahman (2013) highlights financial inclusion combat
poverty by making advanced opportunities available for the disadvantaged poor,
thereby promoting social inclusion and inclusive socio-economic growth.
FIGURE 5.5: SEM MODEL FOR POVERTY REDUCTION*
AC = Access; AV = Availability; US = Usage; P5 = Your expenditure on clothing has increased; P6 =
You consume more qualitative food than before; P7 = Your expenditure on luxuries has increased; P8
= Health has improved by having qualitative food; P9 = Your consumption level has increased and
P10 = Family crisis are reduced through better living standard.
*Source: Survey
Poverty
reduction
P5 e5 .85
P6 e6 .87
P7 e7 .76
P8 e8 .89
P9 e9
.89
P10 e10
.93
Financial
Inclusion .76
US e4
AV e3 .90
AC e2 .61
.55
e1
178
TABLE 5.3: RESULTS SHOWING FACTOR LOADINGS AND VARIANCE EXPLAINED AFTER SCALE PURIFICATION
(ROTATED COMPONENT METHOD) FOR POVERTY REDUCTION*
Factor-wise dimension Mean Standard
deviation
Factor
loading
Eigen
value
Variance
explained
%
Cumulative
explained
%
Communality Alpha
(α)
POVERTY REDUCTION
Factor 1: Poverty eradication 5.162 57.523 57.523 .946
Family crisis are reduced through better living
standard
3.45 .90 .908 .872
Health has improved by having qualitative
food
3.43 .91 .897 .830
Your consumption level has increased 3.44 .91 .888 .819
You consume more qualitative food than
before
3.52 .89 .885 .804
Your expenditure on clothing has increased 3.34 .98 .856 .793
Your expenditure on luxuries has increased 3.13 1.03 .768 .663
Factor 2: Poverty reduction through education 1.190 21.875 79.399 .720
Head of the family is educated enough to guide
other members to move on right track
3.22 1.26 .881 .803
Most of the members are educated in your
family
3.49 1.04 .845 .767
*Source: Survey
179
TABLE 5.4: RESULT OF CFA FIT INDICES, RELIABILITY AND VALIDITY*
Construct Statements SRW Fit indices Validity Reliability
Poverty
reduction
P5 Your expenditure on clothing
has increased
.843 CMIN/DF 3.927
AVE = .7455
Cronbach’s alpha = .946
Composite reliability = .996 P6 You consume more qualitative
food than before
.858 GFI .982
P7 Your expenditure on luxuries
has increased
.742 AGFI .947
P8 Health has improved by having
qualitative food
.880 CFI .993
P9 Your consumption level has
increased
.902 NFI .991
P10 Family crisis are reduced
through better living standard
.943 TLI .985
RMSEA .077
*Source: Survey
180
TABLE 5.5: DEMOGRAPHIC PROFILE-WISE MEAN SATISFACTION
REGARDING POVERTY ERADICATION*
Demographic variables Statements** Overall
mean 1 2 3 4 5 6
Age Upto 30 years 3.33 3.49 3.22 3.25 3.33 3.39 3.34
30-40 years 3.38 3.55 3.19 3.51 3.54 3.50 3.45
40-50 years 3.31 3.46 3.01 3.40 3.35 3.40 3.32
Above 50 years 3.34 3.63 3.18 3.34 3.50 3.47 3.41
Overall Mean 3.34 3.53 3.15 3.38 3.43 3.44
Gender Male 3.31 3.51 3.10 3.41 3.2 3.43 3.36
Female 3.55 3.65 3.32 3.60 3.61 3.57 3.55
Overall Mean 3.43 3.58 3.21 3.51 3.52 3.50
District Jammu 3.55 3.69 3.39 3.65 3.62 3.60 3.58
Kathua 3.15 3.38 2.89 3.25 3.28 3.31 3.21
Reasi 3.47 3.73 3.47 3.53 3.67 3.80 3.61
Samba 3.13 3.36 2.95 3.31 3.38 3.33 3.24
Udhampur 3.28 3.43 2.94 3.32 3.30 3.33 3.26
Overall Mean 3.32 3.52 3.13 3.41 3.45 3.47
Caste General 3.50 3.65 3.26 3.56 3.57 3.57 3.52
SC 3.14 3.39 2.98 3.31 3.33 3.31 3.24
ST 2.92 3.31 2.85 2.92 3.08 3.31 3.07
OBC 3.15 3.19 2.81 3.07 3.00 3.11 3.06
Overall Mean 3.18 3.39 2.98 3.22 3.25 3.33
Religion Hindu 3.35 3.53 3.13 3.44 3.45 3.45 3.39
Muslim 2.82 3.18 2.73 2.82 3.00 3.18 2.96
Sikh 4.00 4.00 4.00 4.00 4.00 4.00 4.00
Overall Mean 3.39 3.57 3.29 3.42 3.48 3.54
Marital
status
Married 3.35 3.54 3.12 3.46 3.46 3.46 3.40
Unmarried 3.28 3.42 3.18 3.24 3.32 3.33 3.29
Overall Mean 3.32 3.48 3.15 3.35 3.39 3.40
Qualification Literate below
primary
2.43 2.93 2.57 2.86 2.79 2.71 2.72
Upto primary 2.63 2.86 2.32 2.75 2.77 2.78 2.69
Upto middle 3.32 3.61 3.12 3.48 3.45 3.46 3.41
Upto secondary 3.65 3.73 3.37 3.65 3.67 3.70 3.63
Upto higher
secondary
3.84 3.86 3.75 3.84 3.88 3.88 3.84
Upto graduate
or higher
3.00 3.00 3.00 3.00 3.14 3.14 3.05
Anyother 4.00 4.00 4.00 4.00 4.00 4.00 4.00
Illiterate 2.76 3.03 2.59 2.96 3.07 2.97 2.90
Overall Mean 3.20 3.38 3.09 3.32 3.35 3.33
*Source: Survey ** 1. stands for Family crisis are reduced through better living standard; 2. Health has improved by
having qualitative food; 3. Your consumption level has increased; 4. You consume more qualitative
food than before ; 5. Your expenditure on clothing has increased and 6. Your expenditure on
luxuries has increased.
181
TABLE 5.6: DEMOGRAPHIC PROFILE-WISE MEAN SATISFACTION
REGARDING POVERTY REDUCTION THROUGH EDUCATION*
Demographic variables Statements** Overall
mean 1 2
Age Upto 30 years 3.65 3.45 3.55
30-40 years 3.45 3.20 3.33
40-50 years 3.45 3.13 3.29
Above 50 years 3.59 3.32 3.46
Overall Mean 3.54 3.28
Gender Male 3.48 3.19 3.34
Female 3.56 3.42 3.49
Overall Mean 3.52 3.31
District Jammu 3.79 3.67 3.73
Kathua 3.24 2.68 2.96
Reasi 3.60 3.47 3.54
Samba 3.36 3.49 3.43
Udhampur 3.29 2.97 3.13
Overall Mean 3.46 3.26
Caste General 3.73 3.32 3.53
SC 3.21 3.16 3.19
ST 3.46 3.31 3.39
OBC 2.89 2.44 2.67
Overall Mean 3.32 3.06
Religion Hindu 3.49 3.21 3.35
Muslim 3.36 3.18 3.27
Sikh 4.00 4.00 4.00
Overall Mean 3.62 3.46
Marital
Status
Married 3.49 3.20 3.35
Unmarried 3.52 3.38 3.45
Overall Mean 3.51 3.29
Qualification Literate below
primary
2.50 2.21 2.36
Upto primary 2.75 2.40 2.58
Upto middle 3.44 3.06 3.25
Upto secondary 3.81 3.47 3.64
Upto higher
secondary
4.11 4.07 4.09
Upto graduate or
higher
4.29 3.57 3.93
Anyother 5.00 5.00 5.00
Illiterate 2.59 3.07 2.83
Overall Mean 3.56 3.36
*Source: Survey
**1 stands for Head of the family is educated enough to guide other members to move on right track and
2. Most of the members are educated in your family.
182
TABLE 5.7: FITNESS OF THE STRUCTURAL MODEL*
Model CMIN/DF GFI AGFI CFI NFI TLI RMSEA
Final
model
4.899 .946 .907 .972 .965 .961 .088
*Source: Survey
TABLE 5.8: RESULT OF HYPOTHESIS TESTING*
Hypothesis CR SRW P-value Accepted/
Rejected
Financial inclusion is positively
related to poverty reduction
9.777 .553 .000 Accepted
*Source: Survey
183
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`
Chapter-VI Financial Inclusion and
Area Development
CONTENTS
S.No. Title Page No.
6.1 Introduction 187
6.2 Conceptual Analysis of Area Development 188
6.3 Schemes for Area Development 189
6.4 Area Development through Financial Inclusion
192
6.4.1 Scale Purification 192
6.4.2 Confirmatory Factor Analysis 194
6.4.3 Demographic Profile-wise Mean Satisfaction
regarding Area Development
195
6.4.4 Relationship Between Financial Inclusion
and Area Development
197
6.5 Barriers of Financial Inclusion 198
6.6 Impact of Barriers on Access and Usage Dimension
of Financial Inclusion
199
6.6.1 Impact of Barriers to Financial Inclusion on
Access Dimension
199
6.6.2 Impact of Barriers to Financial Inclusion on
Usage Dimension
200
6.7 Comparative Analysis of Banks 201
References 207
187
Threats
Opportunities
Commitment
Basics
Programmes
Technology
Cooperation
Contents
Benefits Cooperation
Purchasing
Production
Marketing
Selling
New work
cultures
Enthusiasm
Networking
Implementation
Skills
Will
Modernisation
Networking
CHAPTER VI
FINANCIAL INCLUSION AND AREA DEVELOPMENT
6.1 INTRODUCTION
In today’s decade of liberalisation, privatisation and globalisation, the term ‘inclusive
growth of an economy’ has become very popular all over the globe. It implies
participation and sharing benefits from the growth process. The Indian economy is
considered as the fastest growing economy of the world. Demographic dividend,
greater domestic & international competition, enhancement in factor productivity,
increase in entrepreneurial activities and globalisation are the major drivers of the
growth acceleration (Kelkar, 2010). This is the bright side of the coin. But, the other
part is dark - where we find increasing inequalities in distribution of income and
wealth. Vast disparity exists between urban and rural areas as urban regions are
growing rapidly but the growth of rural regions is stagnant (Saruparia, 2010). Finance
acts as the lubricant, which oils the wheels of development (Christabell & Vimal,
2012). Whereas, financial inclusion assists millions of poor households who are out
of reach of financial services through micro credit, micro savings, money transfers,
micro insurance, etc. (Levine, 1997). These services have become fulcrum for
development initiatives in the third world countries. Financial inclusion is important
for area development. Financial intermediation in the rural areas will help to lubricate
rural economic activities for the stimulation of area development (Matunhu & Mago,
2013). Components of area development are shown in Figure 6.1.
FIGURE 6.1: COMPONENTS OF AREA DEVELOPMENT*
*Source: http://www.google.co.in. Accessed on 5-10-2014.
188
6.2 CONCEPTUAL ANALYSIS OF AREA DEVELOPMENT
Area development generally refers to the process of improving the quality of life and
economic well-being of people living in relatively isolated and sparsely populated
areas. Area development refers to, ‘the process by which efforts of the people and
governmental authorities are united together to improve the economic, social and
cultural conditions in the life of the nation and to relate them to contribute fully to
national programme (Government of India, 1966). According to World Bank (1975),
‘it is a well designed strategy to improve the socio-economic life of rural poor by
extending the benefits of development to poorest in rural areas i.e. small farmers,
tenants, landless and other disadvantage group’. Crops (1972) defined area
development as, ‘a process which aims at improving the well being and self-
realisation of people living outside the urbanised area through collective efforts’. Area
development is, ‘a process of increasing the level of per capita income in the rural
areas along with the improvement in the quality of life of the rural population
measured by food & nutritional level, health, education, recreation and security
(Diejemaoh, 1973). Mishra & Sunderam (1979) refer area development as,
‘development of rural areas along with the development of quality of life of the rural
masses into self-reliant and sustaining modern little communities. It is, therefore,
development of rural areas in such a way that each component of rural life changes in
a desired direction’. Area development is the process whereby those who are living in
rural areas have a good standard of living (Lele, 1975). Chambers (1983) defined area
development as, ‘a strategy of enabling a specific group of people, poor rural women
and men to gain for themselves and their children more of what they want and need’.
Area development refers to ‘a process of developing and utilising natural & human
resources, technologies, infrastructural activities, institutions, organisations &
government policies & programmes to encourage & speed up economic growth in
rural areas, to provide jobs and to improve the quality of rural life towards self-
sustenance (Singh, 1986)’. According to Agarwal (1989), ‘area development is a
strategy designed to improve the economic and social life of rural poor’. Area
development refers to, ‘overall sustainable development of rural areas with a view to
improve the quality of life of rural people’ (Singh, 1999). Acharya (2008) defined
area development as, ‘a multidisciplinary process of development which seeks
transformation of the society from traditional to modern nature’. Further he connotes
189
that, ‘area development is a process of development and change to improve rural
social life by developing infrastructure, commercialising agriculture, proper utilisation
& mobilisation of resources and inclusive social development’. The United Nations
defines ‘area development as a process of change in which the efforts of people are
united with those of Government authorities to improve their economic, social and
cultural conditions and to enable them to contribute fully to national programme. It is
a process of bringing change among rural community from the traditional way of
living to progressive way of living’.
6.3 SCHEMES FOR AREA DEVELOPMENT
Schemes undertaken by centre and state government for rural development are also
the schemes for area development. Some of them are as follows (Figure 6.2).
i. Pradhan mantri gram sadak yojana (PMGSY)
Pradhan mantri gram sadak yojana (PMGSY) was launched on December 25, 2000 as
a fully funded centrally sponsored scheme. The basic objective of PMGSY is to
provide all weather road connectivity to rural areas of the country.
FIGURE 6.2: AREA DEVELOPMENT PROGRAMMES IN INDIA*
*Source: rural.nic.in, Accessed on 16-11-2014
Area development programmes
Pradhan mantri gram
sadak yojana
Council for advancement of people's action &
rural technology
Aajeevika : national rural
livelihood missin
Provision for urban
amenities in rural areas
Member of parliament local area
development scheme
Rural sanitation:
total sanitation campaign
Drought prone area programme
Desert developmen
t programme
Rural drinking
water suppply
programme
190
ii. Council for advancement of people’s action & rural technology
(CAPART)
Government of India set up CAPART in September, 1986. It is a registered society.
The basic objective of CAPART is to strengthen & promote voluntary efforts in rural
development by injecting new technological inputs, to encourage, promote & assist
voluntary action for the implementation of projects intending enhancement of rural
prosperity and to act as a catalyst for the development of technology appropriate for
rural areas.
iii. Aajeevika national rural livelihoods mission (NRLM)
Govt. of India has launched NRLM in June, 2011. The basic aim of NRLM is to
create efficient and effective institutional platforms of the rural poor enabling them to
increase household income through sustainable livelihood enhancements and
improved access to financial services.
iv. Provision for urban amenities in rural areas (PURA)
The mission of the PURA scheme is holistic and accelerated development of compact
areas around a potential growth centre in a gram panchayat through public private
partnership (PPP) framework for providing urban amenities and livelihood
opportunities to improve the quality of life in rural areas. The scheme aims to provide
urban amenities and livelihood opportunities in rural areas to bridge the rural urban
divide.
v. Member of parliament local area development scheme (MPLADS)
MPLADS was launched in 1993 and allocated 1 crore rupees per annum to take up
developmental works in the constituency. In response to the programme, the
allocation of funds was gradually enhanced to `2 crores per annum. The physical
achievements registered under this programme were not satisfactory due to some
regional problems in execution of works. To make the program a success, the hurdles
have to be identified and policies amended suitably in an environmental perspective.
vi. Rural sanitation- total sanitation campaign (TSC)
The centrally sponsored scheme of Central Rural Sanitation Programme (CRSP),
remodeled as the Total Sanitation Campaign (TSC). It has the main objectives of
bringing improvement in the general quality of life in rural areas, accelerate sanitation
191
coverage, generate demand through awareness & health education, cover all schools
& Anganwadi’s in rural areas with sanitation facilities, promote hygiene behaviour
among students & teachers, encourage cost effective & appropriate technology
development and contribute to reduce water & sanitation related diseases.
vii. Drought prone area programme (DPAP)
This national programme was launched in 1973-74 in some selected drought prone
areas of the country. The main objective of this plan was to re-establish the
environmental balance in these areas by promoting the balanced development of land,
water and other natural resources. This programme is being carried on by the rural
development department.
viii. Desert development programme (DDP)
The desert development programme was started in 1977-78 in some selected districts
to check the formation of deserts, to end the drought effects in the deserts, to re-
establish the ecological balance in the affected areas and to increase the land
productivity and water resources in these areas. This programme is being
implemented totally on the basis of union support but the division of the funds in the
hot arid areas is done between the union and the states.
ix. Rural drinking water supply programme
National drinking water mission was launched in 1986, which subsequently was
renamed Rajiv Gandhi National Drinking Water Mission (RGNDWM) in 1991. The
main objective of this programme is providing safe drinking water to all villages,
assisting local communities to maintain sources of safe drinking water in good
conditions and giving special attention for water supply to schedule caste and
schedule tribe.
x. Sansad adarsh gram yojana (SAGY)
This scheme was launched on October 11, 2014 on the birth anniversary of Jai
Prakash Narayan. Under this scheme, each Member of Parliament needs to choose
one village from the constituency that they represent, fix parameters such as piped
drinking water, connectivity to the main road, electricity supply to all households,
library, telecom & broadband connectivity, etc. and make it a model village by 2016.
192
The main purpose of adoption is to remove poverty from the adopted villages and
thus, ultimately promotes area development.
6.4 AREA DEVELOPMENT THROUGH FINANCIAL INCLUSION
The relationship between area development and financial inclusion is examined under
the following sub-heads:
6.4.1 Scale purification
6.4.2 Confirmatory factor analysis
6.4.3 Demographic profile-wise mean satisfaction regarding area development
6.4.4 Relationship between financial inclusion and area development
A brief description of each aforesaid sub heads is as under:
6.4.1 Scale Purification
Purification of construct administered on beneficiaries of financial inclusion drive of
RBI is separately carried using SPSS (version 17.00) and the results are evident from
the Table 6.1.
Beneficiaries’ perception regarding area development
The suitability of raw data for factor analysis obtained from bank customers is
examined through Anti-image, KMO value, Bartlett test of sphercity and (p-value =
0.000), indicating sufficient variance and correlation matrix (Dess et al., 1997 and
Feild, 2000). The process of R-mode principal component analysis (PCA) with
Varimax rotation extracted 5 statements out of 8 statements which are actually kept in
the construct of area development. The KMO value (.598) and Bartlett test of
sphercity (1534.307) indicates acceptable and significant values. Therefore, factor
loadings in the final factorial design are consistent with conservative criteria, thereby
resulting into two factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with
83.09% of the total variance explained. The communalities for 5 statements range
from .623 to .950, indicating moderate to high degree of linear association among the
variables. The factor loading ranges from .787 to 0.975 and the cumulative variance
extracted ranges from 44.58 to 83.09 percent. The communalities and percentage of
193
variance explained by each factor is displayed in the Table 6.1. A brief description of
factors emerged are as under:
Factor 1 (Balanced development)
The variables contributing towards this factor include, ‘Your area is regionally
balanced’, ‘Income is equally distributed’ and ‘Good road connectivity exists’. The
variable ‘Your area is regionally balanced’ contributes significantly to this factor with
highest factor loading (.906) and communality (.830). The other variables ‘Income is
equally distributed’ and ‘Good road connectivity exists’ also have significant factor
loadings (.882 & .787) and communalities (.801 & .623) respectively which states
their significant contribution in this factor. This factor reveals that financial inclusion
scheme is helping in reducing income gaps, providing good road connectivity and
equal regional balance.
Factor 2 (Health indicators)
Two variables identified by this factor are ‘Good health services are available’ and ‘A
regular doctor (govt./private) visits in the village’. The mean scores are normal (3.51
& 3.40). The two variables scored factor loadings .975 & .969 and communalities
.950 & .950 respectively which discloses that the variables significantly and
positively contributes to this factor. This factor perceives that good medical facilities
and availability of doctors are important components for area development.
Reliability
Two factors are obtained after scale purification falling within the domain of area
development. As evident from the Table 6.1, the Cronbach’s reliability for all 5 scale
items underlying two factors ranges from .824 to .948. The alpha reliability
coefficient for F1 i.e. balanced development (.824) and F2 i.e. health indicators (.948)
is higher than the criteria of .77 obtained by Gordon & Narayanan (1984) indicating
high internal consistency. However, overall alpha reliability score for all factors is
very much satisfactory at 0.707. Adequacy and reliability of sample size to yield
distinct and reliable factors is further demonstrated through Kaiser-Meyer-Olkin
measure of sampling adequacy that is 0.598 and all factor loadings are greater than
.50.
194
Validity
The two factors obtained alpha reliability higher & equal to 0.50 and satisfactory
KMO value at .598, indicating significant construct validity of the construct (Hair et
al., 1995).
6.4.2 Confirmatory Factor Analysis
CFA is applied to assess the fitness, reliability and validity of area development
dimension using AMOS (version 16.00).
Area development dimension comprises of total 5 items. First order CFA (Figure 6.3)
is performed to assess the model fitness. After applying CFA, two items namely,
‘Good health services are available’ and ‘A regular doctor (Govt./private) visits in the
village’ got deleted as their standard regression weight (SRW) is below than the
acceptable criteria of .50. Rest 3 items have regression weight above .50, thus it
become clear that all measured variables are the significant contributors of the
construct. This model has been found to have a good fit (CMIN/DF = 4.671, GFI =
.988, AGFI = .965, CFI = .989, NFI = .986, TLI = .986 and RMSEA = .086) (Table
6.2). Convergent validity also got established as AVE arrived at .64 and Cronbach
alpha at .824 and composite reliability equals to .954 (Table 6.2). Thus, the model has
been proved to be valid and reliable. In our study, variable ‘Your area is regionally
balanced’ contributes highest towards area development, as its regression weight is
.963 (Table 6.2).
FIGURE 6.3: CFA MODEL FOR AREA DEVELOPMENT
Area-dvt = Area development; AD2 = Your area is regionally balanced; AD3 = Income is
equally distributed and AD8 = Good road connectivity exists.
Area-dvt
AD2 e1 .96
AD3 e2 .79
AD4 e3
.61
195
6.4.3 Demographic Profile-wise Mean Satisfaction regarding Area
Development
On the basis of demographic profile, mean satisfaction is shown in Table 6.3.
Demographic profile is sub-categorised into age, gender, district, caste, religion,
marital status and qualification.
Age-wise mean satisfaction reveals that beneficiaries under the age group of upto 30
years & above 50 years are highly satisfied with the statement ‘Your area is regionally
balanced’. Whereas, beneficiaries under the age group of 30-40 years and 40-50 years
are highly satisfied with the statement ‘Good road connectivity exists’. Beneficiaries
of all age group are least satisfied with the same statement ‘A regular doctor
(Govt./private) visits in the village’. On the whole, statement-wise satisfaction ranges
between 3.70 for ‘Your area is regionally balanced’ to 3.36 for ‘A regular doctor
(Govt./private) visits in the village’. Beneficiaries under the age group of above 50
years are highly satisfied with mean value (3.67), followed by 40-50 years with mean
value (3.57) and least satisfied beneficiaries belong to upto 30 years age group with
mean value (3.47).
On the basis of gender, mean satisfaction among male fluctuates between lowest from
3.40 for the statement ‘A regular doctor (Govt./private) visits in the village’ to the
highest upto 3.74 for ‘Good road connectivity exists’. In case of female beneficiaries,
maximum mean satisfaction value at 3.60 is observed for the statement ‘Your area is
regionally balanced’ and minimum at 3.32 for ‘Income is equally distributed’. On an
average, highest mean level of satisfaction is observed for the statement ‘Your area is
regionally balanced’ at 3.64 and lowest at 3.40 for the statement ‘A regular doctor
(Govt./private) visits in the village’. Gender-wise, male respondents are found to be
more satisfied with mean value of 3.57 compared to female with mean value of 3.46.
District-wise, highest mean satisfaction value among beneficiaries of Jammu &
Kathua is observed for the same statement ‘Good road connectivity exists’ at 3.73 &
3.70 respectively. But lowest mean satisfaction value at 3.19 is computed for ‘A
regular doctor (Govt./private) visits in the village’ by beneficiaries of Jammu district
& at 3.27 is observed for the statement ‘Income is equally distributed’ by respondents
of Kathua district. Among the beneficiaries of Reasi district, maximum mean
satisfaction computed at 4.20 for the statement ‘A regular doctor (Govt./private) visits
in the village’ and minimum for ‘Income is equally distributed’ at 3.99. In case of
196
Samba district, the highest satisfaction arrived at 3.69 for ‘Your area is regionally
balanced’ and lowest at 3.23 for ‘A regular doctor (Govt./private) visits in the
village’. Beneficiaries of Udhampur opines that the statement ‘Good health services
are available’ has maximum mean satisfaction value at 4.07 as compared to ‘Good
road connectivity exists’ with minimum mean satisfaction at 3.67. Overall,
respondents have highest mean satisfaction (3.77) for the statement ‘Your area is
regionally balanced’ and lowest mean satisfaction (3.59). District-wise, beneficiaries
of Reasi are highly satisfied (4.08), followed by Udhampur (3.89), Samba (3.52),
Jammu (3.50) and Kathua (3.44).
On the basis of caste, beneficiaries of general caste are highly contended with the
statement ‘Your area is regionally balanced’ with the mean value 3.68 and least
contended with ‘A regular doctor (Govt./private) visits in the village’ with mean value
3.41. Mean level satisfaction of SCs & OBCs fluctuates minimum from 3.39 & 3.22
for the same statement ‘A regular doctor (Govt./private) visits in the village’ to 3.78
& 3.81 for the statement ‘Good road connectivity exists’ respectively. Beneficiaries
belonging to ST category found highest mean value (3.85) for ‘Good health services
are available’ and lowest mean value (3.06) for ‘Income is equally distributed’. In
general, maximum mean score arrived at 3.59 for three statements ‘Your area is
regionally balanced’, ‘Good health services are available’ & ‘Good road connectivity
exists’ and minimum at 3.37 for ‘A regular doctor (Govt./private) visits in the
village’. Caste-wise, mean perception about satisfaction in descending order is SC
(3.58), OBC (3.57), general (3.55) and ST (3.34).
On the basis of religion, mean satisfaction found to be maximum at 3.72 for ‘Good
road connectivity exists’ and minimum at 3.42 for ‘A regular doctor (Govt./private)
visits in the village’ among Hindu beneficiaries. In case of Muslim beneficiaries,
highest mean value computed is 3.73 for the statement ‘Good health services are
available’ and lowest at 2.91 for ‘Good road connectivity exists’. The mean
satisfaction among Sikh beneficiaries score maximum at 4.00 for two statements
‘Your area is regionally balanced’ & ‘Income is equally distributed’ and minimum at
1.00 for two statements i.e., ‘Good health services are available’ & ‘A regular doctor
(Govt./private) visits in the village’. In nutshell, statement ‘Your area is regionally
balanced’ gives highest mean satisfaction (3.59) whereas statement ‘A regular doctor
(Govt./private) visits in the village’ gives least mean satisfaction (2.53). On the other
197
hand, religion-wise maximum mean satisfaction is found to be 3.57 (Hindu), followed
by 3.18 (Muslim) & 2.73 (Sikh).
Marital status-wise, maximum mean satisfaction among married & unmarried
beneficiaries is measured at 3.71 & 3.72 for the statement ‘Good road connectivity
exists’ & ‘Your area is regionally balanced’ respectively. Whereas, both married &
unmarried beneficiaries found to be least satisfied with the same statement ‘A regular
doctor (Govt./private) visits in the village’ with mean value 3.44 & 2.98 respectively.
Overall, the statement ‘Good road connectivity exists’ has highest mean score of 3.71
and the statement ‘A regular doctor (Govt./private) visits in the village’ has lowest
mean score of 3.21. On an average, unmarried beneficiaries are more satisfied in
contrast to married beneficiaries with mean value of 3.57 & 3.46 respectively.
Qualification-wise, mean score highlights that beneficiaries who are literate below
primary & having graduation degree are having highest level of satisfaction for the
same statement ‘Good road connectivity exists’ with mean value 3.64 & 4.00 and
lowest satisfaction for the same statement ‘Income is equally distributed’ with mean
value 3.00 & 3.14 respectively. The statement ‘Your area is regionally balanced’ has
maximum mean score (3.48) and the statement ‘Income is equally distributed’ has
minimum mean score (3.17) among the beneficiaries who are having only primary
qualification. Beneficiaries with middle & secondary qualification have utmost mean
score (3.75 & 3.79) for the same statement ‘Good road connectivity exists’ and least
mean score (3.40 & 3.44) for the same statement ‘A regular doctor (Govt./private)
visits in the village’. Beneficiaries who are having qualification till higher secondary
and who are illiterate are more contended with the statement ‘Your area is regionally
balanced’ with mean score 3.81 & 3.59 and least satisfied with ‘A regular doctor
(Govt./private) visits in the village’ with mean score 3.51 & 2.97 respectively.
Overall, beneficiaries are highly satisfied with ‘Good road connectivity exists’ with
mean score computed (3.73) and least satisfied with ‘Income is equally distributed’
with mean score 3.44.
6.4.4 Relationship between Financial Inclusion and Area Development
Figure 6.4 underlines the relationship between financial inclusion and area
development. The results indicated that model fit the data outstandingly (CMIN/DF =
4.291, GFI = .981, AGFI = .982, CFI = .982, NFI = .976, TLI = .961 and RMSEA =
198
.081) (Table 5.7). SEM results in Table 6.5 indicates that financial inclusion has
positive and significant relation with area development (β = .392, p = .000).
Therefore, hypothesis ‘Financial inclusion is positively related to area development’
stands accepted. A study by Padma & Gopisetti (2013) also stated that there is close
relationship between financial inclusion & area development and identified that 100%
financial inclusion is important for overall development including rural areas for
bringing their quality of life at par with the people of urban areas.
FIGURE 6.4: SEM MODEL FOR AREA DEVELOPMENT
AC = Access; AV = Availability; US = Usage; AD2 = Your area is regionally balanced; AD3 =
Income is equally distributed and AD8 = Good road connectivity exists.
6.5 BARRIERS OF FINANCIAL INCLUSION
Banking for the poor is a viable option in India as major chunk of the population is at
the bottom of the pyramid. If financial institutions successfully tap this potential, then
it would be a ‘win-win’ situation for financial institutions, people and country as well.
But this ‘win-win’ situation cannot be achieved because of certain barriers of financial
inclusion. These barriers are divided into two categories i.e. demand-side and supply-
side barriers. Demand-side barriers include lack of awareness regarding financial
services & products, limited literacy especially financial literacy, financial capability,
self-exclusion, low income/assets, psychological & cultural barriers which stem from
mistrust of banks due to negative experiences or negative perceptions and social
exclusion. Whereas, supply-side barriers consist of geographical barriers, branch
timings, cumbersome documentation & procedures, marketing exclusion, unsuitable
products, negative staff attitudes, product design, language barriers, high transaction
costs, lack of communication, lack of a proper framework or infrastructure, terms &
conditions of banks are not suitable and easy availability of informal credits
Area-dvt
AD2 e1 .90
AD3 e2 .87
AD4 e3
.60
Financial
inclusion .78
US e4
AV e5 .87
AC e6 .61
.39
e7
199
(Kempson, 2006; Thorat, 2007; Mitton, 2008; Isern & De Koker, 2009 and Goel &
Nayak, 2012).
6.6 IMPACT OF BARRIERS ON ACCESS AND USAGE DIMENSION OF
FINANCIAL INCLUSION
Financial inclusion comprises of three basic dimensions i.e., access, availability and
usage. But impact of barriers to financial inclusion is examined only on access and
usage dimension. Availability dimension remain out of this ambit because availability
is judged from bank’s point of view. The impact of barriers to financial inclusion is
examined under following sub-head:
6.6.1 Impact of barriers to financial inclusion on access dimension
6.6.2 Impact of barriers to financial inclusion on usage dimension
6.6.1 Impact of Barriers to Financial Inclusion on Access Dimension
Table 6.6 shows output from multiple regression analysis using 7 barriers of financial
inclusion to know their impact on dependent variable i.e. ‘access’. The result of linear
regression enticed four independent barriers have significant impact on the dependent
variable. These are ‘Don’t like dealing with bank’, ‘High fees & service charges’,
‘Inconvenient hours/location’ and ‘Don’t need an account’. The correlation between
predictor and outcome is .368, which signifies positive but low correlation exists
between predictor and the outcome. In model summary, R is .368 which indicates
37% association exists between dependent and independent variables. R-square for
this model is .136 which means 14% of variation in access can be explained from the
four independent variables. Further, adjusted R-square for the model is .123 which
indicates that if anytime another independent variable is added to model, the R-square
will increase by 12%.
The regression model in the ANOVA (Table 6.7) exhibits that the overall model is
significantly different. Since, the calculated value of ‘F’ is 11.000 (p = .000), it
indicates that there is significant difference between four barriers of financial
inclusion. Hence, it can be concluded that four barriers are not equally significant
towards ‘access’.
Further, information provided in coefficient table is examined to determine which
independent variable has significant impact on access (Table 6.8). The standardised
200
coefficient beta value reveals that ‘Don’t like dealing with bank’, ‘Inconvenient
hours/location’ and ‘Don’t need account’ have beta coefficient of -.117, -.116 and -
.127 (t = -2.085, -1.996 and -2.453) respectively whereas p = .038, .047 and .014
respectively which shows they have significant and negative but mediocre impact on
access. Whereas, another barrier ‘High fees & service charges’ has beta value of .102
(t= 2.358, p =.019) which indicates that it also has significant and positive but
middling impact. To assess multi-collinearity, tolerance and VIF values were
examined. Tolerance level between four barriers is above .517 and the VIF is below
1.934. Hence, it can be safely concluded that multi-collinearity among the
independent variable is not a problem.
Therefore, on the basis of above analysis, we can conclude that the hypothesis,
‘Barriers to financial inclusion have significant impact on the access dimension’ is
accepted for four barriers namely, ‘Don’t like dealing with bank’, ‘High fees &
service charges’, ‘Inconvenient hours/location’ & ‘Don’t need account’ and rejected
for three barriers i.e., ‘High minimum balance’, ‘Insufficient money’ and ‘No bank
open account’.
6.6.2 Impact of Barriers to Financial Inclusion on Usage Dimension
Table 6.9 shows output from multiple regression analysis using 7 barriers of financial
inclusion to know their impact on dependent variable i.e. ‘usage’. The result of linear
regression enticed two independent barriers has significant impact on the dependent
variable. These are ‘Insufficient money’ and ‘Don’t need account’. The correlation
between predictor and outcome is .185, which signifies positive but low correlation
exists between predictor and the outcome. In model summary, R is .185 which
indicates 19% association exists between dependent and independent variables. R-
square for this model is .034 which means 3% of variation in usage can be explained
from the two independent variables. Further, adjusted R-square for the model is .20
which indicates that if anytime another independent variable is added to model, the R-
square will increase by 20%.
The regression model in the ANOVA (Table 6.10) reflects that the overall model is
significantly different. Since, the calculated value of ‘F’ is 2.474 (p = .017), it
indicates that there is significant difference between two barriers of financial
201
inclusion. Hence, it can be concluded that two barriers are not equally significant
towards ‘usage’.
Further, information provided in coefficient table is examined to determine which
independent variable has significant impact on usage (Table 6.11). The standardised
coefficient beta value reveals that ‘Insufficient money’ has beta value of .124 (t =
2.529, p = .012) which reveals positive and significant but average impact on usage.
Whereas, ‘Don’t need account’ has beta value -.172 (t = -3.156, p = .002) which
reveals negative and significant but average impact on usage. To assess multi-
collinearity, tolerance and VIF values were examined. Tolerance level between two
barriers is above .662 and the VIF is below 1.510. Hence, it can be safely concluded
that multi-collinearity among the independent variable is not a problem.
Therefore, on the basis of above analysis, we can conclude that the hypothesis,
‘Barriers to financial inclusion have significant impact on the usage dimension’ is
accepted for two barriers i.e., ‘Insufficient money’ & ‘Don’t need account’ and
rejected for five barriers ‘Don’t like dealing with bank’, ‘High fees & service
charges’, ‘Inconvenient hours/location’, ‘High minimum balance’ and ‘No bank open
account’.
6.7 COMPARATIVE ANALYSIS OF DISTRICTS AND BANKS
District and bank-wise mean level of satisfaction among beneficiaries is shown in
Table 6.12. District-wise analysis shows that beneficiaries of Jammu district are
highly satisfied with the services of SBI (3.36) followed by JKGB (3.34), JKB (3.24)
and PNB (2.91). In district Samba, mean satisfaction is maximum among
beneficiaries of SBI (3.31) and least among beneficiaries of JKB (2.96). For district
Reasi, JKB adjudged as best with mean score of 3.26 among beneficiaries. In case of
district Kathua, JKB is performing better with high mean values of 2.95 among
beneficiaries followed by SBI (2.78), JKGB (2.47) and PNB (2.32). In Udhampur
district, beneficiaries are more satisfied with mean score of 3.48 with JKB than SBI
with mean score of 2.74. Overall, mean values highlights that JKGB is performing
better among all banks with highest mean score of 3.14 followed by JKB (3.09), SBI
(2.77) and PNB (2.49).
202
TABLE 6.1: RESULTS SHOWING FACTOR LOADINGS AND VARIANCE EXPLAINED AFTER SCALE PURIFICATION
(ROTATED COMPONENT METHOD) FOR AREA DEVELOPMENT*
Factor-wise dimension Mean Standard
deviation
Factor
loading
Eigen
value
Variance
explained
%
Cumulative
explained
%
Communality Alpha
(α)
AREA DEVELOPMENT
Factor 1: Balanced development 2.409 44.577 44.577 .824
Your area is regionally balanced 3.68 .78 .906 .830
Income is equally distributed 3.51 .89 .882 .801
Good road connectivity exists 3.71 .80 .787 .623
Factor 2: Health indicators 1.745 38.515 83.092 .948
Good health services are available 3.51 1.27 .975 .950
A regular doctor (Govt./private) visits in the
village
3.40 1.34 .969 .950
*Source: Survey
TABLE 6.2: RESULT OF CFA FIT INDICES, RELIABILITY AND VALIDITY*
Construct Statements SRW Fit indices Validity Reliability
Area
development
AD2 Your area is regionally
balanced
.963 CMIN/df 4.671
AVE = .640
Cronbach’s alpha = .824
Composite reliability = .954 AD3 Income is equally distributed .788 GFI .988
AD8 Good road connectivity exists .611 AGFI .965
CFI .989
NFI .986
TLI .986
RMSEA .086
*Source: Survey
203
TABLE 6.3: DEMOGRAPHIC PROFILE-WISE MEAN SATISFACTION
REGARDING AREA DEVELOPMENT*
Demographic variables Statements** Overall
mean 1 2 3 4 5
Age Upto 30 years 3.69 3.53 3.41 3.04 3.67 3.47
30-40 years 3.64 3.46 3.43 3.37 3.69 3.52
40-50 years 3.62 3.49 3.54 3.45 3.75 3.57
Above 50 years 3.85 3.63 3.67 3.56 3.66 3.67
Overall Mean 3.70 3.52 3.51 3.36 3.69
Gender Male 3.69 3.53 3.51 3.40 3.74 3.57
Female 3.60 3.32 3.53 3.40 3.47 3.46
Overall Mean 3.64 3.43 3.52 3.40 3.61
District Jammu 3.67 3.58 3.32 3.19 3.73 3.50
Kathua 3.55 3.27 3.38 3.29 3.70 3.44
Reasi 4.07 3.99 4.13 4.20 4.00 4.08
Samba 3.69 3.44 3.67 3.23 3.59 3.52
Udhampur 3.87 3.76 4.07 4.06 3.67 3.89
Overall Mean 3.77 3.61 3.71 3.59 3.74
Caste General 3.68 3.51 3.49 3.41 3.68 3.55
SC 3.70 3.53 3.51 3.39 3.78 3.58
ST 3.23 3.06 3.85 3.46 3.08 3.34
OBC 3.74 3.54 3.52 3.22 3.81 3.57
Overall Mean 3.59 3.41 3.59 3.37 3.59
Religion Hindu 3.69 3.52 3.52 3.42 3.72 3.57
Muslim 3.09 3.00 3.73 3.18 2.91 3.18
Sikh 4.00 4.00 1.00 1.00 3.67 2.73
Overall Mean 3.59 3.51 2.75 2.53 3.43
Marital
Status
Married 3.67 3.50 3.53 3.44 3.71 3.57
Unmarried 3.72 3.54 3.36 2.98 3.70 3.46
Overall Mean 3.70 3.52 3.45 3.21 3.71
Qualification Literate below
primary
3.43 3.00 3.50 3.50 3.64 3.41
Upto primary 3.48 3.17 3.46 3.31 3.42 3.37
Upto middle 3.63 3.50 3.48 3.40 3.75 3.55
Upto secondary 3.78 3.66 3.53 3.44 3.79 3.64
Upto higher
secondary
3.81 3.67 3.63 3.51 3.74 3.67
Upto graduate
or higher
3.86 3.14 3.71 3.71 4.00 3.68
Anyother 4.00 4.00 4.00 4.00 4.00 4.00
Illiterate 3.59 3.41 3.34 2.97 3.52 3.37
Overall Mean 3.70 3.44 3.58 3.48 3.73
*Source: Survey
**1 stands for ‘Your area is regionally balanced’, 2 Income is equally distributed’, 3 Good road
connectivity exists’, 4 Good health services are available’, 5 A regular doctor (Govt./private)
visits in the village.
204
TABLE 6.4: FITNESS OF THE STRUCTURAL MODEL*
Model CMIN/DF GFI AGFI CFI NFI TLI RMSEA
Final
model
4.291 .981 .942 .982 .976 .961 .081
*Source: Survey
TABLE 6.5: RESULT OF HYPOTHESIS TESTING*
Hypothesis CR SRW P-value Accepted/
Rejected
Financial inclusion is positively
related to area development
7.559 .392 .000 Accepted
*Source: Survey
TABLE 6.6: REGRESSION MODEL SUMMARY (WITH COEFFICIENT) OF
ACCESS DIMENSION AS DEPENDENT VARIABLE*
Model R R-square Adjusted R
square
Standard error of
the estimate
1 .368
a .136 .123 .62625
a. Predictors: (Constant), Don’t need an account; High minimum balance; High fees & service
charges; Insufficient money; Inconvenient hours/location; Don’t like dealing with banks, No bank open account.
b. Dependent variable: Access
*Source: Survey
TABLE 6.7: ANOVAb
FOR MEASURING REGRESSION COEFFICIENT*
Model Sum of
squares
DF Mean
square
F Significance
1. Regression 30.199 7 4.314 11.000 .000
a
Residual 192.566 491 .392
Total 222.765 498
a. Predictors: (Constant), Don’t need an account; High minimum balance; High fees & service
charges; Insufficient money; Inconvenient hours/location; Don’t like dealing with banks, No bank
open account.
b. Dependent variable: Access
*Source: Survey
205
TABLE 6.8: REGRESSION COEFFICIENT’S SHOWING THE EFFECT OF
BARRIERS OF FINANCIAL INCLUSION ON ACCESS DIMENSION*
Model Standardised coefficient Sig. Collinearity statistics
Beta t Tolerance VIF
1. (Constant) 9.697 .000
Don’t like dealing
with banks
-.117 -2.085 .038 .555 1.803
High fees & service
charges
.102 2.358 .019 .945 1.058
Inconvenient hours/
location
-.116 -1.996 .047 .517 1.934
Don’t need an
account
-.127 -2.453 .014 .662 1.510
*Source: Survey
TABLE 6.9: REGRESSION MODEL SUMMARY (WITH COEFFICIENT) OF
USAGE DIMENSION AS DEPENDENT VARIABLE*
Model R R-square Adjusted R
square
Standard error
of the estimate
1 .185
a .034 .020 .61767
a. Predictors: (Constant), Don’t need an account; High minimum balance; High fees & service charges; Insufficient money; Inconvenient hours/location; Don’t like dealing with banks, No bank
open account.
b. Dependent variable: Usage
*Source: Survey
TABLE 6.10: ANOVAb
FOR MEASURING REGRESSION COEFFICIENT *
Model Sum of
squares
DF Mean
square
F Significance
1. Regression 6.606 7 .944 2.474 .017a
Residual 187.322 491 .382
Total 193.928 498
a. Predictors: (Constant), Don’t need an account; High minimum balance; High fees & service
charges; Insufficient money; Inconvenient hours/location; Don’t like dealing with banks, No bank
open account.
b. Dependent variable: Usage
*Source: Survey
206
TABLE 6.11: REGRESSION COEFFICIENT’S SHOWING THE EFFECT OF
BARRIERS OF FINANCIAL INCLUSION ON USAGE DIMENSION*
Model Standardised coefficient Sig. Collinearity statistics
Beta t Tolerance VIF
1. (Constant) 5.896 .000
Insufficient money .124 2.529 .012 .816 1.225
Don’t need an
account
-.172 -3.156 .002 .662 1.510
*Source: Survey
TABLE 6.12: DISTRICT AND BANK-WISE MEAN SATISFACTION AMONG
BENEFICIARIES OF FID*
District Name of the bank Mean value
Jammu JKB 3.24
JKGB 3.34
SBI 3.36
PNB 2.91
Samba JKB 2.96
JKGB -
SBI 3.31
PNB -
Reasi JKB 3.26
JKGB -
SBI -
PNB -
Kathua JKB 2.95
JKGB 2.47
SBI 2.78
PNB 2.32
Udhampur JKB 3.48
JKGB -
SBI 2.74
PNB -
Overall mean
sarifaction
JKB 3.09
JKGB 3.14
SBI 2.77
PNB 2.49
*Source: Survey
207
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`
Chapter-VII Conclusion and Strategic
Implications
CONTENTS
S.No. Title Page No.
7.1 Financial Inclusion and Economic Development 210
7.2 Research Methodology 210
7.3 Data Analysis and Interpretation 212
7.3.1 Financial Inclusion, Socio-economic
Empowerment and Economic Development
212
7.3.2 Financial Inclusion and Poverty Reduction 219
7.3.3 Financial Inclusion and Area Development 221
7.4 Strategic Implications 224
7.5 Conclusion 228
210
CHAPTER VII
CONCLUSION AND STRATEGIC IMPLICATIONS
7.1 FINANCIAL INCLUSION AND ECONOMIC DEVELOPMENT
Financial inclusion is the mechanism of ensuring access to financial services and
timely & adequate credit whenever needed by the vulnerable groups at an affordable
cost. This access to financial services generates income, increases standard of living,
reduces poverty, decreases disparity, creates financial assets, promotes area
development, escalates agricultural growth rate and provide new work opportunities
across all sectors & sections of the economy. This shows financial inclusion has
multiplier effect on the economy as a whole through higher savings pooled from the
vast segment of the bottom of the pyramid (BoP) population. It brings un-banked
people into financial mainstream and results in escalating the economic development
of the country. Thus, it is a stepping stone and is integral to the inclusive growth
process & sustainable development of the country.
7.2 RESEARCH METHODOLOGY
The present study assesses the impact of financial inclusion on the beneficiaries of
four banks namely, Jammu & Kashmir Bank (JKB), Jammu & Kashmir Grameen
Bank (JKGB), State Bank of India (SBI) and Punjab National Bank (PNB) belonging
to five districts i.e., Jammu, Samba, Kathua, Udhampur and Reasi of Jammu division
of J&K State. Data were collected from both primary and secondary sources. Primary
data for the study were gathered personally in Dogri dialect through self-developed
schedule consisted of two sections, one general and other to elicit information about
eight dimensions of financial inclusion namely, access, availability, usage, social
empowerment, economic empowerment, economic development, poverty reduction
and area development. Information relating to these dimensions were collected on five
point Likert scale (5<----1>) where 5 denotes strongly agree and 1 denotes strongly
disagree. Suggestions were kept in open ended form to get specific response. On the
basis of pretesting on a sample of 10 beneficiaries belonging to 10 villages, selecting
2 from each of five districts of Jammu division, the final sample 884 arrived at was
rounded off to 900 respondents. Judgment sampling was followed for collecting data
211
from 900 beneficiaries, criteria adopted was availability and willingness to respond.
Business correspondents of all eighty three villages were contacted, out of which
twenty eight either out rightly rejected to cooperate or refused by saying that nothing
has been done on financial inclusion till date. Of the remaining fifty five villages,
primary data were collected from 523 beneficiaries only, representing a response rate
of 58.11%. The items of the construct consisted of access dimension incorporating 17
items (Sarma & Pais, 2008; Kumar, 2011 and Gupte et al., 2012), availability
containing 18 items (Sarma & Pais, 2008; Kumar, 2011 and Gupte et al., 2012),
usage including 9 items (Sarma & Pais, 2008; Kumar, 2011 and Gupte et al., 2012),
social empowerment encompassing 25 items (Barik, 2009; Kumar & Sharma, 2011;
Arputhamani & Prasannakumari, 2011 and Cnaan et al., 2011), economic
empowerment comprising 11 items (Barik, 2009; Kumar & Sharma, 2011;
Arputhamani & Prasannakumari, 2011 and Cnaan et al., 2011), economic
development consisting 11 items (Agrawal, 2007 and Das, 2011), poverty reduction
embracing 10 items (Rautela et al., 2010; Latif et al., 2011 and Mishra, 2012) and area
development including 8 items (Rautela et al., 2010 and Arputhamani &
Prasannakumari, 2011). The survey was carried on during February-July, 2013. After
pretesting, normalcy of the data was checked which lead to deletion of 23 outlier
observations. Subsequently, the data so collected was purified with the help of factor
analysis using SPSS package (17.0 version). Thereafter, various statistical tools such
as mean, standard deviation, multiple regression, one-way ANOVA, t-test etc., were
used to derive meaningful results. Confirmatory factor analysis and SEM were also
used through AMOS (16.0 version) for further analysis. Secondary information was
collected from various journals viz., Asian Economic Review, International Journal of
Advanced Research and Innovations, International Journal of Economics and Finance,
International Journal of Innovative Research & Development, International Research
Journal of Commerce & Behaviour Science, IOSR Journal of Economics and Finance,
Journal of Accounting and Finance, Journal of Economics, Business and
Management, Journal of Global Business and Economics, Journal of International
Business and Economics, Journal of International Development, Journal of Rural
Development, Journal of Social Policy etc., published information from internet, RBI
reports and magazines.
212
7.3 DATA ANALYSIS AND INTERPRETATION
The data for the study was analysed under following three sub-heads:
7.3.1 Financial inclusion, socio-economic empowerment and economic
development
7.3.2 Financial inclusion and poverty reduction
7.3.3 Financial inclusion and area development
A brief description of each sub-head is as under:
7.3.1 Financial Inclusion, Socio-economic Empowerment and Economic
Development
Financial inclusion is the process of ensuring access to financial services by
vulnerable groups such as the weaker sections and low income groups at an affordable
cost. It provides access to various banking products & services which act as the
catalyst in the economic & social growth of an individual and progress of an
economy. This access helps in gaining financial empowerment, promoting social
inclusion, building self-confidence and thus leading to social & economic
empowerment of rural folk. Therefore, financial inclusion is a key driver for social &
economic empowerment at an individual level and economic development at national
level. Perception about various aspects of financial inclusion, socio-economic
empowerment and economic development among the beneficiaries of four banks
belonging to five districts was examined under the following sub-heads:
a. Scale purification
b. Confirmatory factor analysis
c. Structural equation modeling
d. Mediation between financial inclusion and economic development through socio-
economic empowerment
e. One-way ANOVA
f. t-test
g. Demographic profile-wise analysis
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Purification of constructs administered on beneficiaries’ of financial inclusion drive of
RBI was separately carried using SPSS (17.0 version). Beneficiaries’ perception
regarding access dimension was analysed through factor analysis and the process of
R-mode principal component analysis (PCA) with Varimax rotation brought the
construct to the level of 12 statements out of 17 statements. The KMO value (0.906)
and Bartlett test of sphercity (2986.617) indicated acceptable and significant values,
resulted into three-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with
67.60% of the total variance explained. The communality for 12 items ranged from
0.558 to 0.806 which indicated moderate to high degree of linear association among
the variables. The factor loading ranged from 0.633 to 0.845 and the cumulative
variance extracted varied from 35.17 to 67.60 percent.
Beneficiaries’ perception regarding availability dimension of financial inclusion
divulged that the process of R-mode principal component analysis (PCA) with
Varimax rotation extracted 9 statements out of 18 statements which were actually kept
in the construct. The KMO value (.770) and Bartlett test of sphercity (2170.576)
indicated highly acceptable and significant values. Factor loadings in the final
factorial design were consistent with conservative criteria, thereby resulted into three-
factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with 72.56% of the total
variance explained. The communalities for 9 statements ranged from .546 to .918,
indicated high degree of linear association among the variables. The factor loading
ranged from .616 to 0.944 and the cumulative variance extracted from 29.516 to
72.562 percent.
The scale measuring beneficiaries’ perception regarding usage dimension of financial
inclusion revealed the KMO value of .677 and Bartlett test of sphercity at 708.037
which indicated high acceptable & significant values and resulted into two-factor
solution with 67.78% total variance explained. The communality for 6 retained items
ranged from .522 to .739, factor loadings from .652 to .856 and the cumulative
variance extracted from 35.96 to 65.78 percent.
The scale measuring beneficiaries’ perception regarding social empowerment
revealed the KMO value of .803 and Bartlett test of Sphercity 3406.412 (p-value =
0.000). 14 statements extracted out of 25 statements which were actually kept in the
construct of social empowerment, resulted into four-factor solution with 69.54% of
the total variance explained. The communalities for 14 statements ranged from .526 to
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.870, indicated high degree of linear association among the variables. The factor
loading ranged from .509 to 0.925 and the cumulative variance extracted ranged from
29.425 to 69.539 percent.
Beneficiaries’ perception regarding economic empowerment was analysed through
factor analysis and the process of R-mode principal component analysis (PCA) with
Varimax rotation brought the construct to the level of 9 statements out of 11
statements of economic empowerment. The KMO value (.901) and Bartlett test of
sphercity (2534.429) indicated acceptable and significant values, thereby resulted into
two-factor solution using Kaiser criteria (i.e. eigen value ≥ 1) with 67.87% of the total
variance explained. The communalities for 9 statements ranged from .529 to .779,
factor loading from .639 to 0.846 and the cumulative variance from 46.768 to 67.873
percent.
The scale measuring beneficiaries’ perception regarding economic development
revealed that KMO value of .875 and Bartlett test of sphercity at 2109.914 which
indicated high acceptable & significant values and resulted into two-factor with
69.96% of the total variance explained. The process of R-mode principal component
analysis (PCA) with Varimax rotation extracted 8 statements out of 11 statements
which were actually kept in the construct of economic development. The
communalities for 8 statements ranged from .591 to .811, indicated moderate to high
degree of linear association among the variables. The factor loading ranged from .546
to .875 and the cumulative variance extracted from 40.266 to 69.96 percent.
CFA was conducted with the objective of verifying the fitness of each latent
construct. In the present study, it was performed to assess the fitness, reliability and
validity of six measured constructs, viz., financial inclusion (FI) consists of three
main dimensions i.e., access, availability & usage; social empowerment (SE);
economic empowerment (EE) and economic development (ED).
First order CFA was performed on access dimension, which constituted of twelve
items. Among twelve items, three items got deleted as they were not meeting the
criteria i.e., SRW’s > .50. After deleting, CFA produced good fit as CMIN/DF =
4.735, GFI = .955, AGFI = .912, NFI = .960, TLI = .950, CFI = .968 and RMSEA =
.087. The model had been found to be valid and reliable. The alpha value was arrived
at .884 whereas composite reliability came out to be .991, which indicated that all
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items were reliable. Model was proved to be valid, as AVE came out to be .533. The
construct validity also stands established as all the indicators had factor loading above
.50. Out of the twelve items, ‘Employee’s are helpful in making information available
regarding new schemes’ emerged to be strongest contributor towards access
dimension, as its regression weight was .90 .
First order CFA was executed on the latent construct ‘availability’ which consisted of
nine items. The different fit indices evaluated the fitness of the model and the results
showed that the model fitted the data well as CMIN/DF = 1.852, GFI = .967, AGFI =
.941, NFI = .963, TLI = .965, CFI = .975 and RMSEA = .065. Four items got deleted
as their regression weights were below .50. In addition to the model fitness, reliability
and validity were also examined. The scale exceeded the recommended cut off value
of .70 as composite reliability = .981. So, it was reasonable to conclude that the scale
was reliable. Reliability was also confirmed through Cronbach’s alpha value which
was measured to be .806. As far as AVE was concerned, the value was greater than
0.50 (AVE = .602). Also, each of the item loading was greater than .50, which
provided empirical support for the convergent validity of construct. Among five
items, ‘Loan is made available within time limit’ contributed highest to the main
construct with SRW .95.
First order CFA was performed on usage dimension. It constituted of six items. After
deleting three items having regression weights below .50, the result of CFA showed
the model fully fits the data, CMIN/DF = 4.563, GFI = .988, AGFI = .965, NFI =
.983, TLI = .979, CFI = .975 and RMSEA = .085. The model found to be valid and
reliable which was confirmed through Cronbach’s alpha (.628), composite reliability
(.965) and AVE = .546. Out of five items, item ‘You frequently use credit facilities of
the bank’ contributed highest with regression weight .84.
First order CFA was performed on social empowerment construct which consisted of
fourteen indicators. While running CFA, seven items got deleted as they were not
meeting the criteria. The result revealed that the model fit statistics were within
recommended levels i.e., CMIN/DF = 3.286, GFI = .984, AGFI = .950, NFI = .979,
TLI = .965, CFI = .985 and RMSEA = .068. Additionally, this model had also been
found to be valid and reliable, as AVE was .608, composite reliability equals to .989.
The value of Cronbach’s alpha was .806 and all items loading above .50. Thus,
validity and reliability got established. Among seven items, ‘FI has changed your
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personality & life style’ had the highest factor loading of .84, thus contributed
maximum to social empowerment.
First order CFA was performed on economic empowerment construct which consisted
of nine items. CFA model yielded good model fit results, CMIN/DF =2.597, GFI =
.981, AGFI = .959, NFI = .985, TLI = .985, CFI = .984 and RMSEA = .057. The
model found to be valid and reliable after deleting two items. Reliability was also
examined by calculating composite reliability (.992). In this model, AVE exceeds the
critical level of .50 (.595). This established the reliability and convergent validity of
measurement scale in the study. All regression weights were also above .50 which
indicated that all measured variables were significant contributors of this construct.
Scale reliability was established through Cronbach’s alpha (.890). Among seven
items, ‘FI has increased your purchasing power’ was adjudged as the strongest
contributor having highest SRW.
First order CFA was performed on economic development construct, which
comprised of eight items. The result of CFA indicated that model fit the data,
CMIN/DF = 4.601, GFI = .966, AGFI = .919, NFI = .968, TLI = .952, CFI = .974 and
RMSEA = .085. The composite reliability came out to be .992. Scale reliability was
established through Cronbach’s alpha .888. The value of AVE was greater than .50.
(.512). Furthermore, it was found that each factor loading was greater than .50, which
provides empirical evidence for the convergent validity of the construct. Item ‘FI has
increased access to education of the society’ emerged as the strongest contributor
(.79) of the main construct.
After applying CFA and checking reliability & validity, SEM was conducted by using
AMOS (version 16.0) to assess fitness of structural model. The SEM technique was
used to test the main hypotheses proposed in the study. There were a total of 25
indicators in the final structural model. The proposed model strived to identify the
financial inclusion impact on economic development. Final SEM model including all
indicators were tested. The final model fitness results revealed CMIN/DF= 4.924, GFI
= .937, AGFI = .896, NFI = .932, TLI = .924, CFI =.945 and RMSEA = .089. The
result of SEM depicted that access, availability and usage significantly predicts
financial inclusion where access emerged as the strongest predictor with highest beta
value (.75). Therefore, the hypothesis ‘Access, availability and usage significantly
predicts the financial inclusion’ holds true. It was also inferred that financial
217
inclusion had direct impact on social empowerment, economic empowerment and
economic development leading to the acceptance of next hypothesis i.e., ‘Financial
inclusion has direct impact on social empowerment, economic empowerment and
economic development’. SEM results also revealed that social empowerment and
economic empowerment has direct impact on economic development. Thus, the next
hypothesis ‘Social empowerment and economic empowerment has direct impact on
economic development’ stands accepted.
Mediation between financial inclusion and economic development through social &
economic empowerment was also assessed. To test the mediation effect, all the
conditions described by Baron & Kenny (1986) were satisfied first. Then mediator
i.e., social empowerment and economic empowerment were entered separately into
the equation of financial inclusion and economic development, the relationship
between financial inclusion & economic development became insignificant and
relationship between other variables i.e., financial inclusion-social empowerment &
social empowerment-economic development and financial inclusion-economic
empowerment & economic empowerment-economic development remained
significant. Therefore, the aforesaid analysis supported the hypothesis ‘Social
empowerment and economic empowerment mediates the relationship between
financial inclusion and economic development’.
The result of One-way ANOVA depicted that among five socio-economic variables
i.e., age, caste, religion, qualification & income, significant mean difference exist in
the nature of financial inclusion for all the variables except for age (.094) and caste
(.057) variables.
Output from independent t-test measuring significance of mean difference on the
basis of gender and marital status divulged that no significant difference exist between
male & female and married & unmarried beneficiaries with regard to nature of
financial inclusion.
The findings from One-way ANOVA and t-test supported the hypothesis ‘Nature of
financial inclusion differs across socio-economic profile of respondents’ for three
and rejected for four variables.
Demographic profile-wise analysis disclosed that JKB had the largest share and PNB
had the smallest share under FID. It was also found that male were more interested in
218
opening bank accounts and female were still reluctant to open an account in bank
even if it was a no frill account. It revealed that middle age people were more
interested in opening accounts and availing the benefits of government schemes.
Caste-wise, general category beneficiaries were more awared about the FID compared
to lower castes & tribes. Hindu beneficiaries were majorly covered under the financial
inclusion drive in the five districts i.e., Jammu, Samba, Kathua, Udhampur and Reasi.
It was found that married respondents were more family oriented and saving
conscious whereas unmarried want to spend money. Financial inclusion drive
uniformly covered illiterate or highly qualified. It was found that maximum
respondents were having monthly income upto `5,000 revealing that lower income
people were also covered under this drive. Among all districts, district Jammu topped
in the list of beneficiaries of financial inclusion drive. With regard to availability &
usage dimensions of financial inclusion, beneficiaries belonging to above 50 years of
age (2.81 & 2.93) were highly satisfied followed by 40-50 years (2.70 & 2.88), 30-40
years (2.61 & 2.76) and upto 30 years (2.44 & 2.67). Caste-wise analysis showed that
with regard to access dimension, general caste (3.54) beneficiaries were highly
contended followed by SC (3.53), ST (3.31) and OBC (3.19) beneficiaries. As far as
usage dimension was concerned, general caste (2.88) beneficiaries were more
satisfied than SC (2.78), OBC (2.64) and ST (2.46) beneficiaries. Religion-wise
analysis depicted that Sikh respondents (3.83) were more satisfied with usage
dimension followed by Hindu (2.83) and Muslim (2.39) beneficiaries. For all the
dimensions i.e., access, availability & usage, beneficiaries with upto higher secondary
qualification were maximally satisfied with mean value (3.79, 2.85 & 3.00) and
minimally satisfied with mean value (2.89, 2.10 & 2.33) respectively. Beneficiaries
with above `20,000 monthly income were highly satisfied with regard to access &
availability dimensions followed by beneficiaries with `10,000-20,000; `5,000-
10,000 and upto `5,000 income. On usage dimension, beneficiaries with income
between `10,000-20,000 were more satisfied than beneficiaries with `5,000-10,000,
above `20,000 and upto `5,000 income. With regard to usage dimension, male were
found to be more contended with mean score 2.84 than female with mean value of
2.70. Married beneficiaries were more satisfied (2.69) than unmarried beneficiaries
(2.41) with regard to availability dimension of financial inclusion.
219
7.3.2 Financial Inclusion and Poverty Reduction
Financial inclusion is an important tool for combating the multi-dimensional aspects
of poverty. It enhances financial access which leads to several benefits such as
overcoming problem of poverty, unemployment, inequality, deteriorating welfare etc.
and has generated a positive impact on the lives of the poor. There is a bi-directional
cause and effect relationship between poverty and financial inclusion. It is a win-win
opportunity for the poor, for the banks and for the nation. It contributes in
employment generation, income generation, proper utilisation of resources,
mobilisation of savings etc. which help in poverty alleviation and thus lead to GDP
growth in any economy. Perception about financial inclusion and poverty reduction
among the beneficiaries of four banks belonging to five districts was examined under
the following sub-heads:
a. Scale purification
b. Confirmatory factor analysis
c. Demographic profile-wise mean satisfaction regarding poverty eradication
d. Demographic profile-wise mean satisfaction regarding poverty reduction through
education
e. Relationship between financial inclusion and poverty reduction
Purification of construct administered on beneficiaries of financial inclusion drive of
RBI was separately carried using SPSS (version 17.00). The scale measuring
beneficiaries’ perception regarding poverty reduction revealed the KMO value of .904
and Bartlett test of sphercity of 3225.782 which indicated acceptable and significant
values. The process of R-mode principal component analysis (PCA) with Varimax
rotation brought the construct to the level of 8 statements out of 10 statements thereby
resulted into two-factor with 79.40% of the total variance explained. The
communalities for 8 statements ranged from .663 to .872, indicated high degree of
linear association among the variables. The factor loading varied from .768 to .908
and the cumulative variance extracted ranged from 57.52 to 79.40 percent.
First order CFA was performed to assess the model fitness. While applying CFA, two
items got deleted as their standard regression weight (SRW) was below the acceptable
criteria of .50. Rest 6 items had regression weight above .50. This model had good fit
220
(CMIN/DF = 3.927, GFI = .982, AGFI = .947, CFI = .993, NFI = .991, TLI = .985
and RMSEA = .077). Convergent validity also got established as AVE arrived at .745
and Cronbach’s alpha was .946 and composite reliability equals to .996. Thus, the
model had proved to be valid and reliable. Variable ‘Family crisis are reduced through
better living standard’ contributed highest towards poverty reduction, as its regression
weight was .943. Financial inclusion enabled rural population to avail the financial
services and increases their income earning capabilities which resulted into raising of
standard of living and reducing family crisis. Los Cobos declaration on financial
inclusion (2012) also recognised that financial inclusion is a key component in the
development of healthy, vibrant and stable financial systems which contribute to
sustainable economic growth. Access to safe, secure and reliable financial system is
important for improving standard of living.
Demographic profile-wise mean satisfaction analysis regarding poverty eradication
factor revealed that age-wise, respondents belonging to 30-40 years (3.45) category
were maximally satisfied followed by above 50 years (3.41), upto 30 years (3.34) and
40-50 years (3.32) age category. Gender-wise, female respondents were found to be
more satisfied with mean value of 3.55 compared to male with mean value of 3.36.
District-wise mean satisfaction among beneficiaries in descending order was found to
be Reasi (3.61), Jammu (3.58), Udhampur (3.26), Samba (3.24) and Kathua (3.21).
Caste-wise, mean level of satisfaction in ascending order was OBC (3.06), ST (3.07),
SC (3.24) and general (3.52). Religion-wise, mean level of satisfaction in descending
order was Sikh (4.00), Hindu (3.39) and Muslim (2.96). Marital status-wise, married
respondents were more satisfied (3.40) in contrast to unmarried beneficiaries (3.29).
Qualification-wise, mean satisfaction in increasing order was upto primary (2.61),
literate below primary (2.72), illiterate (2.90), upto graduate or higher (3.05), upto
middle (3.41), upto secondary (3.63), upto higher secondary (3.84) and any other
qualification (4.00).
Demographic profile-wise mean satisfaction analysis regarding poverty reduction
through education disclosed age-wise beneficiaries under the age group of above 50
years (3.46) were highly satisfied followed by upto 30 years (3.55), 30-40 years (3.33)
and 40-50 years (3.29). Gender-wise analysis depicted that female beneficiaries (3.49)
were more satisfied in comparison to male beneficiaries (3.34). District-wise analysis
exhibited satisfaction among beneficiaries of different districts in ascending order,
221
Kathua (2.96), Udhampur (3.13), Samba (3.43), Reasi (3.54) and Jammu (3.73).
Caste-wise, mean satisfaction in descending order was found to be at 3.53 (General),
3.39 (ST), 3.19 (SC) and 2.67 (OBC). Religion-wise, maximum mean satisfaction was
found to be 4.00 (Sikh), followed by 3.35 (Hindu) and least 3.27 (Muslim). Marital
status-wise, unmarried beneficiaries were more satisfied (3.45) than married
beneficiaries (3.35). Qualification-wise, mean satisfaction in descending order was
found to be 5.00 (Any other qualification), 4.09 (upto higher secondary), 3.93 (upto
graduate or higher), 3.64 (upto secondary), 3.25 (upto middle), 2.83 (Illiterate), 2.58
(upto primary) and 2.36 (Literate below primary).
Relationship between financial inclusion and poverty reduction was checked through
SEM (AMOS, version 16.0). The results showed that model fit the data excellently
(CMIN/DF = 4.899, GFI = .946, AGFI = .907, CFI = .972, NFI = .965, TLI = .961
and RMSEA = .088). SEM results indicated financial inclusion has positive and
significant relation with poverty reduction (β = .553, p = .000). Therefore, the
hypothesis ‘Financial inclusion is positively related to poverty reduction’ stands
accepted. The result of the study was in line with the previous study Murari &
Didwania (2010) which had the same observation and found that scheme of financial
inclusion is an effective instrument which can lift poor above the level of poverty by
providing them increased self employment opportunities and making them credit
worthy. Another study by Rahman (2013) highlighted financial inclusion combat
poverty by making advanced opportunities available for the disadvantaged poor,
thereby promoting social inclusion and inclusive socio-economic growth.
7.3.3 Financial Inclusion and Area Development
In today’s decade of liberalisation, privatisation and globalisation, the term ‘inclusive
growth of an economy’ has become very popular all over the globe. It implies
participation and sharing benefits from the growth process. The Indian economy is
considered as the fastest growing economy of the world. Finance acts as the lubricant,
which oils the wheels of development. Whereas, financial inclusion assists millions
of poor households who are out of reach of financial services through micro credit,
micro savings, money transfers, micro insurance, etc. These services have become
fulcrum for development initiatives in the third world countries. Financial inclusion is
important for area development. Financial intermediation in the rural areas will help
to lubricate rural economic activities for the stimulation of area development.
222
Perception about financial inclusion and area development among the beneficiaries of
four banks belonging to five districts was examined under the following sub-heads:
a. Scale purification
b. Confirmatory factor analysis
c. Demographic profile-wise mean satisfaction regarding area development
d. Relationship between financial inclusion and area development
e. Impact of financial inclusion on access & usage dimension through multiple
regression
f. Comparative analysis of banks
Purification of construct administered on beneficiaries of financial inclusion drive of
RBI was separately carried using SPSS (version 17.0). The process of R-mode
principal component analysis (PCA) with Varimax rotation extracted 5 statements out
of 8 statements which were actually kept in the construct of area development. The
KMO value (.598) and Bartlett test of sphercity (1534.307) indicated acceptable and
significant values. Therefore, factor loadings in the final factorial design were
consistent with conservative criteria, thereby resulting into two factor solution using
Kaiser criteria (i.e. eigen value ≥ 1) with 83.09% of the total variance explained. The
communalities for 5 statements ranged from .623 to .950 and thus indicated moderate
to high degree of linear association among the variables. The factor loading ranged
from .787 to 0.975 and the cumulative variance ranged from 44.58 to 83.09 percent.
First order CFA was performed to assess the model fitness. After applying CFA, two
items got deleted as their standard regression weight (SRW) was below than the
acceptable criteria of .50. This model had been found to have a good fit (CMIN/DF =
4.671, GFI = .988, AGFI = .965, CFI = .989, NFI = .986, TLI = .986 and RMSEA =
.086). Convergent validity also got established as AVE arrived at .64 and Cronbach
alpha at .824 and composite reliability equals to .954. Thus, the model proved to be
valid and reliable. In the study, variable ‘Your area is regionally balanced’ contributed
highest towards area development, as its regression weight was .963.
Demographic profile-wise analysis exhibited age-wise beneficiaries under the age
group of above 50 years were highly satisfied with mean value (3.67), followed by
40-50 years with mean value (3.57) and upto 30 years age group with mean value
223
(3.47). District-wise, beneficiaries of Reasi were highly satisfied (4.08), followed by
Udhampur (3.89), Samba (3.52), Jammu (3.50) and Kathua (3.44). Caste-wise mean
perception regarding area development in descending order was SC (3.58), OBC
(3.57), general (3.55) and ST (3.34). Religion-wise, maximum mean satisfaction was
found to be 3.57 (Hindu), followed by 3.18 (Muslim) and 2.73 (Sikh). Marital status-
wise, unmarried beneficiaries were more satisfied in contrast to married beneficiaries
with the mean value of 3.57 & 3.46 respectively. Gender-wise, male respondents were
found to be more contended with mean value of 3.57 compared to female with mean
value of 3.46.
The relationship between financial inclusion and area development was checked
through SEM (AMOS, version 16.0). The results indicated that model fit the data
outstandingly (CMIN/DF = 4.291, GFI = .981, AGFI = .982, CFI = .982, NFI = .976,
TLI = .961 and RMSEA = .081). SEM results indicated that financial inclusion has
positive and significant relation with area development (β = .392, p = .000).
Therefore, hypothesis ‘Financial inclusion is positively related to area development’
holds true. A study by Padma & Gopisetti (2013) also stated that there is close
relationship between financial inclusion & area development and identified that 100%
financial inclusion is important for overall development including rural areas for
bringing their quality of life at par with the people of urban areas.
Multiple regression was used to elicit the impact of barriers of financial inclusion on
access dimension. The result of analysis enticed that four independent factors i.e.,
‘Don’t like dealing with bank’, ‘High fees & service charges’, ‘Inconvenient
hours/location’ and ‘Don’t need an account’ were significant in predicting the
dependent variable from the beneficiaries perspective whereas other three barriers i.e.,
‘High minimum balance’, ‘Insufficient money’ and ‘No bank open account’ were not
predicting the dependent variable. Therefore, the aforesaid finding supported the
hypothesis ‘Barriers to financial inclusion have significant impact on the access
dimension’ for four barriers and rejected for three barriers.
Multiple regression was also used to bring out the impact of barriers of financial
inclusion on usage dimension. The result highlighted two independent barriers
namely, ‘Insufficient money’ has positive and ‘Don’t need account’ had negative but
significant impact on the dependent variable i.e., usage. Other barriers such as, ‘Don’t
like dealing with bank’, ‘High fees & service charges’, ‘Inconvenient hours/location’,
224
‘High minimum balance’ and ‘No bank open account’ had no impact on usage
dimension. Therefore, the finding supported the hypothesis ‘Barriers to financial
inclusion have significant impact on the usage dimension’ for two barriers and
rejected for five barriers.
Comparative mean satisfaction of beneficiaries belonging to four banks i.e., JKGB,
JKB, SBI and PNB. On the basis of mean values, it is concluded that JKGB was
performing better among all banks with highest mean score of 3.14 followed by JKB
(3.09), SBI (2.77) and PNB (2.49.)
7.4 STRATEGIC IMPLICATIONS
Strategic implications of the study are divided into two categories, one study specific
implications and other general implications based on the observation and existing
literature. To enhance the level of financial inclusion and its impact, implications are
as under:
Specific Implications
i. In the present study, it was found that there exists a communication gap between
bank managers and customers which is manifested from the mean score given to
the statement ‘The bank manager promptly redress your problem’. So, it is
suggested that a suitable mechanism must exist for receiving and redressing
customer grievances courteously, promptly and satisfactorily. In order to collect
grievances promptly from the customers, banks should implement a mechanism
like Centralised Complaint Management System, where all the complaints
received from various channels particularly from business correspondents are
processed and solved immediately.
ii. The mean response was low for ‘ATM service is nearby from your place’. Thus,
banks should enhance their ATM network in rural & unbanked areas to serve
poor villagers. While doing so, adequate care should be taken regarding
safety/security issues. Further, to enable beneficiaries to withdraw cash from
ATM anywhere in the country, banks need to convert FINO card to electronic
credit card. In addition to this, banks should explore the possibility of issuing
multi-purpose cards which could function as debit cards, KCCs, GCCs as per
the requirement of the rural masses.
225
iii. The beneficiaries have responded low for ‘Loan is not easily & timely
available’, which leads to the conclusion that there is cumbersome process of
getting or obtaining loan. Thus, policy makers should simplify the procedure of
granting credit to the customers. Moreover, banks must ensure adequate and
timely credit to the vulnerable section of the society. Extending timely credit or
loan would alleviate poverty and thus contribute towards economic development
which is an outcome of financial inclusion.
iv. The mean score for the item ‘Employees are helpful in making information
available regarding new schemes’ was arrived low, so it could be concluded that
BC’s themselves are not equipped with adequate information which is needed
by the customers. Thus, there is a need to establish better standards and capacity
of BC’s. Further, common programmes should be instituted by the regulators or
banks frequently for providing minimum required knowledge and skills to BCs.
Web portal be maintained and upgraded with new schemes and its access should
be provided to BCs in order to acquaint themselves with latest information.
v. Below average response was scored for the statement ‘New bank schemes are
advertised frequently’. The most important aspect of financial inclusion is
financial literacy but it was found that there is lack of awareness among rural
masses about various schemes of financial inclusion. To increase awareness and
interest in financial products offered under various schemes of financial
inclusion, it is recommended to enhance promotion through electronic or print
media in local language with local icons and artists as brand ambassador of the
campaign.
vi. The statement, ‘You frequently use credit facilities of the bank’ scored very low
which deduced that customers were not using credit facilities because of hidden
terms & conditions and high rate of interest along with hidden transaction costs.
So, it is suggested that terms & conditions for availing loan facility should be
mentioned in clear and lucid language (preferably in Hindi or in local language).
Beside this, the most important terms & conditions termed as standard set of
conditions should be highlighted and sent separately to the prospective
customers at all the stages so that customer do not remain in doubt.
226
vii. Beneficiaries gave below average score to a statement, ‘You are free to move to
any SHG or NGO for any kind of help or support’ which revealed that they were
reluctant to move to any NGO’s, SHG’s for any financial help & support. But
after probing into the reason, it was found that no such social groups were
existing in their respective villages. Thus, it was concluded that no serious
attempts were made to leverage the SHG/NGO-bank linkage programmes to
achieve the financial inclusion goals. Therefore, it is suggested that SHG’s
should exist in each and every village as they seek to reach out to the extended
category of population from banking systems.
viii. Since independence, the government has launched various social schemes to
make the villages socially empowered. But the fundamental drawback of these
social schemes is that people are unaware of the programmes designed. The
same affirmation holds true for the present study as well which is manifested
from the lower mean score to the factor ‘Awareness’. It was found that
beneficiaries were not fully availing the benefits of social schemes because of
lack of awareness among them. The probable reason could be that recipients are
never consulted or there is lack of people’s participation in the social
development schemes. The developmental programmes are sometimes thrusted
on the people even when it is not needed by family or the community. Some
awareness generation projects should be implemented under which the camps
should be organised. The voluntary organisation should come forward and
activities like role play, puppet shows, road shows, nukad nataks,
documentaries, drama skills should be included in the camp for creating
awareness among the rural masses. Furthermore, media can play an important
role in changing lives of the people through disseminating relevant information
regarding various social schemes in order to make them socially empowered.
ix. The study revealed that beneficiaries feel reluctant in complaining to the
authorities regarding delivery of financial services. Though RBI has advised
banks to constitute grievance redressal mechanism within the bank for redressal
of complaints about the services rendered by BCs. But still beneficiaries are
unaware of this redressal mechanism. Beneficiaries of BCs services are
generally illiterate and are prone to misguidance. Many a times customers tend
to perceive the BCs as bank staff & not as agents functioning on behalf of the
227
banks. Thus, the reputation and standing of bank, in the eyes of beneficiaries
will be at stake if BCs either not functioning as per the guidelines or not been
extending banking services expected of him. Thus, banks should give wide
publicity to the grievances redressal mechanism through electronic & print
media. Moreover, direct feedback system should be established whereby, bank
employees should personally visit the beneficiaries in order to look into the
quality & adequacy of customer services being extended by BCs appointed in
financially included villages. This would make beneficiaries to lodge their
complaints freely and without any hassles.
x. To ensure further employability, it is suggested that banks should move beyond
deposit products and should introduce credit products, insurance, mutual funds,
pension plans to people living in financial seclusion. Establishment of rural
infrastructure is a pre-requisite for financial inclusion and all stakeholders
including banks have to contribute towards setting up connectivity and ensuring
power supply among other things. Bank should also endeavour to bond with the
rural community by initiating programmes to adopt schools, conduct vocational
courses for the rural youth and so on.
General Implications
i. There should not be any limit on depositing and withdrawal amount.
ii. To address the issue of credibility of BCs, banks should provide banking
counters through wide network of post offices and fair price shops.
iii. Even multi-language ATMs with audio-video services could be considered.
iv. To address the high attrition among BCs because of low commission/earnings,
banks should nominate housewives, owners of fair price shops, retired people
and people with limited disabilities to become BCs to supplement their regular
income. To attract and retain people in the business of BCs, provision for higher
commission by routing more financial services through BCs can be considered
as well as providing them with respectable designation and identification cards,
with incentivised structured benefits in terms of bonus or promotion or
absorption in mainstream banking.
228
v. The RBI and commercial banks should plan a coordinated campaign in
partnership with the trainers and professional to educate customers about the
basic financial products, services and offerings.
vi. For the success of financial inclusion program, the govt. should make
subscription to financial services mandatory. At the same time, they should also
realise that simplification of procedures will encourage more people to use
banking services.
vii. There should be proper implementation of national programme of ‘Pradhan
Mantri Jan Dhan Yojana’ for wider coverage.
viii. Under financial literacy programme, RBI is issuing a guide which answers basic
questions related to managing money. Responses to certain basic queries such as
why to save, how to save, why save in banks, when to borrow, from whom to
borrow, etc. have been provided in very simple and lucid language, through
pictorial representation. Therefore, to educate, motivate and encourage people
this guide should be widely distributed.
7.5 CONCLUSION
In the wake of inclusive growth for the overall development of the economy, central
bank along with other financial intermediaries must realise the importance of financial
inclusion in promoting the banking habits among people. For enhancing financial
inclusion, suitable mechanism must exist for receiving & redressing customer
grievances courteously, promptly & satisfactorily; ATM network in rural & unbanked
areas be enhanced to serve poor villagers; FINO card be converted into electronic
credit card; single card with multi usage be issued; procedure for granting adequate &
timely credit to the customers be simplified by using clear & lucid language; for
generating awareness some common promotional programmes be instituted by the
regulators or banks through electronic & print media and direct feedback system be
established. In addition to these, the existing literature also enticed some strategies to
enhance the level of financial inclusion such as, extension to banking counters
through wide network of post offices & fair price shops be provided; multi-language
ATMs with audio-video services could be considered; higher commission provision to
BCs be proposed; subscription to financial services be made mandatory and national
programme of ‘Pradhan Mantri Jan Dhan Yojana’ be properly implemented .
`
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229
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`
Annexures
238
ANNEXURE – I
SCHEDULE FOR COLLECTING INFORMATION PERTAINING TO
‘IMPACT OF FINANCIAL INCLUSION ON ECONOMIC DEVELOPMENT’
Note: You are requested to notify the right information as per your knowledge. The information so
collected would be kept secret and used only for research purpose.
Research Scholar:
Preeti Salathia
Phd. Scholar
University of Jammu
General Information
1. Ordinal Information Instruction: Please share correct information on 5-point likert scale ranging from 1 to 5 on the basis of
your knowledge regarding Access, Availability, Usage, Social empowerment, Economic
empowerment, Economic development, Poverty reduction and Area development (Where, 1 stands
for strongly disagree, 2 stands for disagree, 3 stands for neither agree nor disagree, 4 for agree
and 5 stands for strongly agree)
Name
Age Upto 30 Yrs
30-40 Yrs
40-50 Yrs
Above 50 Yrs
Gender Male
Female
District Tehsil Block Village
Caste General
SC
ST
OBC
Religion
Marital Status Married
Unmarried
Qualification
Literate Literate but below
primary
Primary
Middle
Secondary
Higher secondary
Graduate or higher
Any other
Illiterate
Occupation
Income (Monthly) Upto `5,000
`5,000-10,000
`10,000-20,000
Above `20,000
Name of Prime Bank
239
1 Access to Banking Services
1.1 The bank is conveniently located 1 2 3 4 5
1.2 The employees are easily accessible when needed 1 2 3 4 5
1.3 ATM service is nearby from your place 1 2 3 4 5
1.4 Mobile ATM Van visits frequently 1 2 3 4 5
1.5 Banking institution or its substitute is easily approachable 1 2 3 4 5
1.6 Banking officials respond well 1 2 3 4 5
1.7 Financial services are accessible to disabled customers 1 2 3 4 5
1.8 The bank manager promptly redress your problems 1 2 3 4 5
1.9 Transaction timings are convenient 1 2 3 4 5
1.10 Account opening formalities are easy 1 2 3 4 5
1.11 This is the only bank in your area 1 2 3 4 5
1.12 As compared to other banks, this bank is nearest to you 1 2 3 4 5
1.13 Bank is easily approachable in case of emergencies 1 2 3 4 5
1.14 Bank have sufficient staff to meet its customers’
requirements 1 2 3 4 5
1.15 You have easy access to the information which is useful 1 2 3 4 5
1.16 Employees’ of bank are cooperative, friendly and
knowledgeable 1 2 3 4 5
1.17 Employees’ possess sufficient banking information 1 2 3 4 5
1.18 Overall, you are satisfied with your access to banking services
1 2 3 4 5
2 Availability of Banking Services
2.1 Loan is easily available 1 2 3 4 5
2.2 Attractive saving schemes are available 1 2 3 4 5
2.3 Bank provide overdraft facility 1 2 3 4 5
2.4 Bank provide insurance services 1 2 3 4 5
2.5 Debit card facility is available 1 2 3 4 5
2.6 Locker facility is available 1 2 3 4 5
2.7 For any service, hidden charges are collected 1 2 3 4 5
2.8 No frill account service is available 1 2 3 4 5
2.9 Loan is available within time limit 1 2 3 4 5
2.10 New cheque or passbook are issued as & when asked for 1 2 3 4 5
2.11 Procedure involved in getting loan is easy 1 2 3 4 5
2.12 Information regarding new interest rates is provided in time 1 2 3 4 5
2.13 New bank schemes are advertised frequently 1 2 3 4 5
2.14 Fieldworkers promotes various schemes of bank 1 2 3 4 5
2.15 Employee’s are helpful in making information available regarding new schemes
1 2 3 4 5
2.16 Help desk/assisting staff is available for filling
withdrawal/deposit form 1 2 3 4 5
2.17 Bank follows quick problem solving approach 1 2 3 4 5
2.18 Infrastructure is as per the requirements of the customers 1 2 3 4 5
2.19 Overall, you are satisfied with the banking services made
available to you 1 2 3 4 5
3 Usage of Banking Services
3.1 You save money frequently 1 2 3 4 5
3.2 You withdraw money frequently 1 2 3 4 5
3.3 You frequently use credit facilities of the bank 1 2 3 4 5
3.4 You are a regular visitor of the bank 1 2 3 4 5
3.5 You are using bank for the payment of insurance premium 1 2 3 4 5
240
3.6 You are using bank for the repayment of loan 1 2 3 4 5
3.7 You are using bank for depositing money 1 2 3 4 5
3.8 Advance schemes of bank are frequently used by you 1 2 3 4 5
3.9 You are using banking services, because interest charged
by the bank on advance is economical than charged by the
money lenders
1 2 3 4 5
3.10 Overall, you visit regularly to bank for saving, withdrawing, borrowing etc.
1 2 3 4 5
4 Social Empowerment
4.1 FI has made you independent in decision making 1 2 3 4 5
4.2 FI has led to improved hygiene and education 1 2 3 4 5
4.3 FI has changed your personality & life style 1 2 3 4 5
4.4 FI has made you socially more reputed 1 2 3 4 5
4.5 FI has increased your confidence level 1 2 3 4 5
4.6 FI has improved your technical skills 1 2 3 4 5
4.7 Your family supports your business decisions 1 2 3 4 5
4.8 FI positively affects your social status (purchase of car,
land, tour, travels, T.V., refrigerator, A.C.) 1 2 3 4 5
4.9 New house constructed after covering under FI drive 1 2 3 4 5
4.10 FI enhanced your confidence level, business relations and
reduced family crisis & social violence 1 2 3 4 5
4.11 FI improved your health and household hygiene 1 2 3 4 5
4.12 You often meet with & talked to people from other social groups outside your home regarding financial inclusion
1 2 3 4 5
4.13 Without FI, you feel difficult in socialising with people of
different social groups 1 2 3 4 5
4.14 You like to change your lifestyle 1 2 3 4 5
4.15 You participate in any community activity 1 2 3 4 5
4.16 You can bring any change in the society easily 1 2 3 4 5
4.17 You actively participate in the general assembly voting 1 2 3 4 5
4.18 You are influenced by others when choosing candidate to
support in election 1 2 3 4 5
4.19 You made complaints to the authorities regarding the
delivery of financial services 1 2 3 4 5
4.20 FI has empowered you to move to all public places freely 1 2 3 4 5
4.21 You are aware about all special schemes that are offered by the govt.
1 2 3 4 5
4.22 You avail those special schemes that are offered by the
govt. 1 2 3 4 5
4.23 You are free to move to any SHG or any other for any kind of help or support
1 2 3 4 5
4.24 You are free to move to any NGO or any other for any
kind of help or support 1 2 3 4 5
4.25 Your response and feedback is always appreciated regarding any financial issue
1 2 3 4 5
4.26 You are socially more developed after being covered under
FI drive 1 2 3 4 5
5 Economic Empowerment
5.1 FI has reduced your need to borrow money or goods 1 2 3 4 5
5.2 FI has raised your living standard 1 2 3 4 5
5.3 FI has prepared you for emergencies 1 2 3 4 5
241
5.4 You have enough savings to meet any contingent situation 1 2 3 4 5
5.5 You are assisted while deciding where savings are to be
used 1 2 3 4 5
5.6 FI has made you independent regarding spending of your
savings 1 2 3 4 5
5.7 FI has increased your purchasing power 1 2 3 4 5
5.8 FI enabled your children to get better education 1 2 3 4 5
5.9 FI created new employment opportunities 1 2 3 4 5
5.10 FI directly effects capital formation & investment in
technology 1 2 3 4 5
5.11 FI enhanced your source of income 1 2 3 4 5
5.12 Overall, FI enables you & your family to enjoy better
economic status 1 2 3 4 5
6 Economic Development
6.1 FI has increased life expectancy of your family members 1 2 3 4 5
6.2 FI has increased access to education of the society 1 2 3 4 5
6.3 FI has empowered the members of the society 1 2 3 4 5
6.4 FI has led to progress of the village 1 2 3 4 5
6.5 FI has made the village sustainable for further progress 1 2 3 4 5
6.6 FI has led to increase in the production of goods & services
in the area 1 2 3 4 5
6.7 FI has increased economic activities in each sector 1 2 3 4 5
6.8 FI has reduced level of stress in your life 1 2 3 4 5
6.9 FI has increased productivity in agriculture sector 1 2 3 4 5
6.10 FI has increased per capita income of your family 1 2 3 4 5
6.11 FI has led to increase in value of your assets 1 2 3 4 5
6.12 Overall, FI has led to the economic development 1 2 3 4 5
7 Poverty
7.1 Most of the members are educated in your family 1 2 3 4 5
7.2 Most of the members are wage laborers or earners 1 2 3 4 5
7.3 Head of the family is educated enough to guide other
members to move on right track 1 2 3 4 5
7.4 FI has increased your value of dwelling 1 2 3 4 5
7.5 Your expenditure on clothing has increased 1 2 3 4 5
7.6 You consume more qualitative food than before 1 2 3 4 5
7.7 Your expenditure on luxuries has increased 1 2 3 4 5
7.8 Health has improved by having qualitative food 1 2 3 4 5
7.9 Your consumption level has increased 1 2 3 4 5
7.10 Family crisis are reduced through better living standard 1 2 3 4 5
7.11 Overall, FI has reduced the level of poverty 1 2 3 4 5
8 Area Development
8.1 New jobs are created in your area 1 2 3 4 5
8.2 Your area is regionally balanced 1 2 3 4 5
8.3 Income is equally distributed 1 2 3 4 5
8.4 Good health services are available 1 2 3 4 5
8.5 A regular doctor (govt./pvt.) visits in the village 1 2 3 4 5
8.6 Accredited social health activists like ASHA is available 1 2 3 4 5
8.7 A school like facility for educating the adults in the village
is available 1 2 3 4 5
8.8 Good road connectivity exists 1 2 3 4 5
242
8.9 Overall, you are satisfied with the development in your area 1 2 3 4 5
9 Reason for Not Having Bank Account
9.1 You don’t like dealing with banks Yes No
9.2 The fees and service charges are too high Yes No
9.3 No bank will open an account Yes No
9.4 The minimum balance is too high Yes No
9.5 No bank has convenient hours or location Yes No
9.6 Do not have enough money Yes No
9.7 Do not need/want an account Yes No
SUGGESTIONS:
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ANNEXURE – II
SAMPLE POPULATION FOR THE STUDY
S. No. District Tehsil Block Name of
the
bank
Village Population
1. Jammu Akhnoor Akhnoor SBI Sungal Lower 6148
SBI Palwan 2393
PNB Ambaran 3048
JKGB - -
JKB Sungal Lower 6148
JKB Gurah Jagir 6097
Khour SBI Bakore 3713
PNB - -
JKGB Samwan 3805
JKGB Bhalwal Mullo 3525
JKB Pallanwal Lower 3821
JKB Bakore 3713
Jammu Marh SBI Gango Chak 2011
PNB - -
JKGB Akalpur 2158
JKGB Kanah Chak 2005
JKB - -
Satwari SBI Rakh Gadigarh 2066
SBI Rattno Chak 4861
PNB - -
JKGB Hakal 3618
JKGB Bhour (East) 2829
JKB - -
Bhalwal SBI Kote 9022
PNB Jandiyal 5284
PNB Bhagani 3216
JKGB Baran 5272
JKGB Amb 4277
JKB - -
Dansal SBI Badsoo 3208
SBI Katal Batal 2308
PNB Jagti 3239
PNB Bhatyari 2519
JKG Kanyalla 3278
JKGB Tarah 2940
JKB - -
R.S.Pura R.S.Pura SBI - -
PNB Kharian 2595
JKGB - -
JKB Chohalla 3800
JKB B.Qazian 2802
Bishnah Bishnah SBI - -
PNB Harsa Dabber 2597
PNB Ratnal 2074
JKGB Laswara 2966
JKGB Sehoura 2334
244
JKB Raipur 5200
JKB Allah 3240
2. Samba Samba Samba SBI Chilla Danga 2142
PNB - -
JKGB - -
JKB - -
Vijaypur SBI - -
PNB - -
JKGB - -
JKB Palie 8274
Ghagwal SBI - -
PNB - -
JKGB - -
JKB Sarara 3097
Parmandal SBI - -
PNB - -
JKB Patti 1536
JKB Jhak 1604
3. Kathua Basohli Basohli SBI Plakh 2461
PNB Dhar Jhankar 2347
JKGB Danbrah 2532
JKGB Draman 2521
JKB Hati 854
Billawar Billawar SBI Makwal 2145
PNB - -
JKGB Tehr 2704
JKGB Dungara 2629
JKB Dhar Duggan 3085
JKB Kahuag 2814
Barnoti Barnoti SBI Muthi Hardu 2280
PNB Forlain 5389
PNB Bhortian 2140
JKGB - -
JKB Falote 2793
JKB Barnoti 1351
Kathua Hiranagar SBI Chelak 3763
SBI Mangloor 3045
PNB - -
JKGB - -
JKB Kharote 3146
JKB Chak Desa
Singh
2226
Banni SBI Surjan 2569
PNB - -
JKGB - -
JKB - -
Lohai Malhar
SBI - -
PNB - -
JKGB Lahari 2167
JKB - -
4. Udhampur Udhampur Udhampur SBI - -
245
PNB - -
JKGB - -
JKB Chapper 2778
JKB Hartarya 2688
Pancheri SBI Ladda 4250
SBI Chulna 2757
PNB - -
JKGB - -
JKB - -
Chennani Chennani SBI Mada 3786
SBI Balli 2748
PNB Satyalta 2335
PNB Ladha 2098
JKGB - -
JKB Nagulta 2533
JKB Sira 2482
Ramnagar Ghordi SBI - -
PNB Jandrore 2959
PNB Nalaghorian 2661
JKGB - -
JKB - -
Ramnagar SBI Marta 2827
PNB - -
JKGB - -
JKB Kirnoo 2270
JKB Bhatyari 2252
Majalta Majalta SBI - -
PNB - -
JKGB - -
JKB Thalora 2439
JKB S.K.Bair 2059
5 Reasi Reasi Reasi SBI - -
PNB - -
JKGB - -
JKB Kotli
Manotrian
2396
JKB Bahaga 2070
Arnas SBI - -
PNB - -
JKGB - -
JKB Salalkote 2844