1
OSEVWE LAWRENCE OHWOTEMU
PG/M.SC/07/42672
PG/M. Sc/09/51723
A DISTRIBUTIONAL ANALYSIS OF INCOME IN NIGERIA
ECONOMICS
A THESIS SUBMITTED TO THE DEPARTMENT OF ECONOMICS, FACULTY OF ARTS,
UNIVERSITY OF NIGERIA NSUKKA
Webmaster
Digitally Signed by Webmaster’s Name
DN : CN = Webmaster’s name O= University of Nigeria, Nsukka
OU = Innovation Centre
AUGUST, 2010
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A DISTRIBUTIONAL ANALYSIS OF INCOME IN NIGERIA
BY
OSEVWE LAWRENCE OHWOTEMU
PG/M.SC/07/42672
DEPARTMENT OF ECONOMICS,
UNIVERSITY OF NIGERIA,
NSUKKA
AUGUST, 2010
3
A DISTRIBUTIONAL ANALYSIS OF INCOME IN NIGERIA
BY
OSEVWE LAWRENCE OHWOTEMU
DEPARTMENT OF ECONOIMICS
A PROJECT REPORT SUBMITTED TO THE DEPARTMENT
OF ECONOMICS, UNIVERSITY OF NIGERIA, NSUKKA, IN
PARTIAL FULFILMENT OF TIIE REQUIREMENTS FOR
THE AWARD OF THE DEGREE OF MASTER OF SCIENCE
IN ECONOMICS.
SUPERVISOR: DR. H. E. ICHOKU
AUGUST, 2010
4
APPROVAL PAGE This Project Report has been approved for the award of the Degree of Master of Science
(M.Sc) of the Department of Economics, University of Nigeria, Nsukka.
…………………………… .…………………………
Dr. H. E. Ichoku Prof. C. C. Agu
Supervisor Head of Department
………………………………………..
Prof. E. O. Ezeani ---------------------------
Dean, Faculty of Social Science External Examiner
5
DEDICATION This study is dedicated to the blessed memory of my uncle Sir Osevwe Omonigho for
everything he stood for and making me who I am today, may his soul and the souls of all the
departed rest in perpetual peace, amen.
6
ACKNOWLEDGEMENT
How can one ever acknowledge academic debts satisfactorily? Knowledge is as a result of
a cumulative process spanning over many years and during these periods, the individual
passes through many people, institutions and ideas. It is difficult to categorize these people,
ideas and institutions and where the influence of one stops, that of the other begins. But all
the same, I wish to express my profound gratitude and apparent happiness to my supervisor,
Dr. H. E. Ichoku for the approval of this catchy topic and accepting the responsibility of
supervising this work. I am indebted to him in three reasons.
Firstly as my lecturer, I have been privileged to benefit from his inspiring lectures,
Secondly as my project supervisor; he has shown deep and devoted concern to this research
by making invaluable suggestions and painstaking guidance through reading the manuscript
at all stages, improving the organisation, style and clarity and thirdly, his cordial approach to
my problems and explaining some of the complex aspect of this research work, gave me the
confidence and courage to complete it in spite of odds.
My gratitude goes to the academic staff of the department of Economics whose reactions
in the proposal of this research provided the guidance and support needed in the arrangement
and preparation of this project.
I thank profusely all the non-academic staff of the department for their friendliness and
assistance in one way or the other.
To my mother- Osevwe Mary, my Cousins Bros Cosmas and Onos, my beloved wife-
Mabel, my half brother - Oke, and my dear sisters Rev. Sr. Monica, Franca and Elo. I owe
everything for their encouragement throughout this programme. I acknowledge the effort of
my friends Innocent (SPG), Mmadu Ben, Ayuba, Felix, Madu Ken, Ewubare Innocent and
colleagues Ndidi, Mrs. Oru, Micheal (okafor‘s theory), Ijeoma, Amuche, Arizona, Ify, Lizzy,
Sam, Austin, Tony and Olembe.
Finally, I am grateful to the Lord Almighty for granting me the stamina to withstand the
wears and tears, I Had to go through before completing this research.
Department of Economics,
University of Nigeria, Nsukka.
August, 2010.
Osevwe Ohwotemu Lawrence
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TABLE OF CONTENTS TITLE PAGE
…………………………………………………………………………………...i
APPROVAL PAGE
……………………………………………………………………………ii
DEDICATION
…………………………………………………………………………………iii
ACKNOWLEDGEMENT
……………………………………………………………………..iv
TABLE OF CONTENTS
……………...……………………………………………………….v
ABSTRACT
…………………………...……………………………………………………….vi
C HAPTER ONE – INTRODUCTION
……….……………………………………………....1
1.1 Background of Study
…………………..…………………………………………………1
1.2 Problem Statement
…………………………..……………………………………………3
1.3 Research Objectives
……………………………………….............……………………….5
1.4 Hypotheses
…………………….…………………………………………………………...5
1.5 Significance of the Study
………….……………………………………………………….5
1.6 Scope of the study
…………………….……………………………………………………6
CHAPTER TWO – Nigerian Economy
………….…………………………………………….7
Performance of the Economy
…………………………………………………………………10
CHAPTER THREE – LITERATURE REVIEW
…………………………………………….13
3.1 Introduction
……………………………………………………………………………….13
3.2 Theoretical Literature
…………………………………………………………….……….13
3.3 Empirical Literature
……………………………………………….....................…...........29
CHAPTER FOUR – METHODOLOGY
………………………….…………………………35
4.1 Introduction
…………………………………………………….…………………………35
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4.2 Model Definition
…………………………………………………..……………….……..36
4.3 Techniques of Evaluation
…………………………………………..……………………..37
4.4 Model Derivations
…………………………………………………..………….…………39
4.5 Sources of Data
………………………………………………………..…………….…….41
CHAPTER FIVE - ANALYSIS OF RESULTS
…………………………..…………….........42
5.1 Introduction
……………………………………………………………..……………..….42
5.2 Analyses of Results
………………………………………………………..………….…..42
CHAPTER SIX - SUMMARY RECONMMENDATION AND CONCLUSION
…...…..…63
6.1 Summary
……………………………………………………………………...........…..…63
6.2 Recommendations
…………………………………………………………………...…....64
6.3 Conclusion
…………………………………………………………………………..…....66
REFERENCES
APPENDIX
9
ABSTRACT
Reducing poverty and inequality in the developing world continues to be a major public
policy challenge, and one that is complicated by the lack of a generalized comprehensive
strategy for dealing with it. Putting the combat against poverty to the forefront as the main
objective of the development process has raised the issue of the linkage between inequality
and poverty. There is now a growing agreement that booth the rate and the distributional
impact of income are important in fighting poverty. This paper analyzes the trend in income
distribution in Nigeria and examines the issue of inequality in expenditure among households
as well as urban-rural difference in consumption among households and further examines
geopolitical zone inequality in Nigeria. That is, the decomposition analysis was divided into
two categories. The first category is concerned with the decomposition of households‘
expenditure. This underscores the contributions of these components to overall inequality and
may help in the design of effective economic and social policies to reduce inequality and
poverty in Nigeria. The second category of decomposition analysis dealt with the breakdown
of expenditure into population sub-groups (This approach starts with the division of a sample
into discrete categories; for instance, rural and urban residents, gender, age group, education
level of household heads, household size, occupation, states and geopolitical zones), and then
follows with the estimation of the level of inequality using Gini coefficient while the Lorenz
curve is used to measure changes in the income distribution. The analysis builds on a survey
of 19158 households in Nigeria, which was conducted by the Nigeria living Standard Survey
(NLSS) 2004 of the National Bureau of Statistics.
The results of our analysis indicate that factors such as age, gender, and education level of
the household head are important factors in explaining inequality profile in the country. We
however found that inequality exists in the rural and urban areas but more of the rural areas
10
and inequality is very high in some of the geopolitical zones. This thus suggests policies that
will alleviate poverty in the rural and urban areas as well as policies to reduce inter
geopolitical zone access to opportunities. We further observed from the Lorenz curve that
17.95 percent of the households controlled 46.7 percent of the total expenditure which means
almost quarter of the total of the household controlled almost half of the wealth of the Nation.
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CHAPTER ONE
1.1 BACKGROUND OF STUDY
Poverty and income inequalities are two of the important disturbing factors on the way to
development in developing countries. Rising inequality threatens growth and poverty
reduction targets (Olaniyan and Awoyemi, 2005). A recurring issue in discussions on
development is whether the main focus of development strategies should be placed on growth
or poverty, and/or on inequality (Bourguignon, 2003). Although there is a general consensus
among economists that economic growth is good for the poor, the debate continues on
whether economic growth is sufficient for poverty reduction (Morale, 2006).
In the recent literature, growth and distribution is a lens through which we can investigate
the complex interplay of the factors explaining the nature and causes of the wealth of nations:
population growth; structural change: technological progress; and physical, social, and human
capital accumulation.
Enhancing the well-being of the less well-off and reducing inequalities have become the
principal targets of the development process. The United Nations General Assembly in New
York notably confirmed these aims in September 2000, when some 189 countries approved,
in the context of the Millennium Development Goals (MDGs), that fighting poverty in all its
aspects is the major challenge of the international community. To achieve substantial
progress in poverty reduction, most governments and international organizations now agree
on both the importance of economic growth and on the need for economic growth to be
biased in favor of the poor. Undeniably, absolute poverty is bound to decrease whenever
economic growth positively affects the income distribution of the poor. However, it is
presumably not the only requirement. Policy makers with an interest in poverty reduction are
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also concerned with the distribution of growth rates amongst the population. This is important
because the distribution of any increment to growth will determine the rate at which
economic growth is converted into poverty reduction. More precisely, the larger the share of
any increment to growth captured by the poor, the higher the pro-poorness feature of
economic growth (Sami, 2006).
The relationship between economic growth and poverty reduction is thus of direct
relevance to the challenge of fighting poverty in all its aspects. There is growing evidence
that achieving both high and equitable growth is strengthening the linkage between growth
and poverty reduction. This represents a major departure from the trickle-down development
approach whereby economic growth benefits the more affluent in the first stages of the
development process, followed by the less well-off because of the rise in the expenditures of
the rich. This means that the development process will be accompanied by a rise in the
inequalities since the poor benefit less proportionately from economic growth than do the
non-poor.
Poverty and income inequality are closely related and it has been argued that income
inequality is a manifestation as well as a strong cause of poverty (UNU/WIDER, 2000).
Kolenikov and Shorrocks (2003), found that the high level of poverty in the late 1990‘s in
Russia was due more to the rise in income inequality than to decline in average income.
When economic growth increases, poverty rate decreases, but as income inequality increases,
the incidence of poverty also increases. Because of the linkage between income inequality
and poverty, reducing income inequality has become a major public policy challenge among
development agencies and poverty reduction experts. Yet, in most developing countries,
discussions about poverty reduction strategies often focus almost exclusively on income
growth, neglecting the potential roles of income redistribution and inequality (UNU/WIDER,
13
2000). Most of the discussions often fail to recognize that, to achieve reduction in poverty,
income growth has to be equitably distributed. It is understood that fighting poverty cannot
be continuously relied on redistributive policies in absence of sustainable economic growth.
However, there is plenty of evidence suggesting that high rates of growth accompanied with
progressive distributional changes will be more effective in reducing poverty than growth
patterns that leave the income distribution unchanged. Similarly, even if the poor were to
benefit from growth as much as the non-poor, the initial distribution of income would still
determine the rate of poverty reduction. The higher the level of inequality, the weaker the
linkage between poverty reduction and growth; and the higher the growth rate needed to
reach a given target of poverty reduction.
That growth is good for the poor is debatable. But the real questions is, has economic
growth been pro-poor or pro-rich over the years and what is the distributional trend of
household income and the level of income inequality in Nigeria. Answering these questions is
the principal objective of this research. This research seeks to review the analysis of income
distribution in Nigeria using secondary data covering the period 1993 to 2008.
1.2 PROBLEM STATEMENT
One of the most challenging themes for economists is to explain ―how countries become
rich‖ (Tridico, 2006). Nigeria may be the most challenging and important developing country
in the world today. It has the smallest manufacturing sector of any large economy in the
world, and the greatest concentration of export and government revenue dependence on a
natural resource commodity. It is a country of spectacularly failed economic policies, whose
GDP per capita is no higher than it was forty years ago. It is a country of rising poverty and
increasing income inequality (King, 2003). Achieving equitable distribution of income and
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alleviation of poverty has for some time been a major development objective. Studies have,
therefore, especially in the 1970s, appraised development policies in terms of how far these
objectives are being realized.
In the 1980s many less developed countries (LDCs) introduced SAPs in an effort to
promote growth and redress the negative trends in a number of economic indicators. Studies
have found that adjustment policies have had negative impact on some socioeconomic
groups. Recently the depth and severity of extreme poverty in Nigeria has been alarming.
And over the years, the government undertook some macroeconomic policies with the aim of
reducing, if not totally eradicating poverty. These policies were expected to at least raise the
standard of living of Nigerians. The impact of these policies on alleviating poverty has been
contentious. Some studies in the past have argued that the poor has benefited more from these
policies (Obadan, 1994; Faruquee, 1994); while some found that there was positive real
growth yet poverty and inequality still worsened (Aigbokhan, 2000). There is now a growing
agreement that both the rate and the distributional impact of growth are important in fighting
poverty. This means that pro-poorness of a given growth rate is more important in certain
cases than in others.
The Nigerian problem in the 20th century has been the inability to get the best from her
human resources (World Bank, 2000). The problem goes beyond low income, savings and
growth. It includes high inequality, which includes among others, unequal access to basic
infrastructure and unequal capabilities (education and health status). Incidentally, the
importance of unequal access to opportunities, assets, income and expenditure cannot be
overemphasized as it plays important roles in reducing poverty and spurring the economy to
long-term development. Nigeria has experienced a high incidence of poverty over the last two
decades (Olaniyan and Awoyemi, 2005). The impact of the incidence becomes more
important because of the high inequality associated with even this low level of household
income and expenditure. This is precisely the approach followed in this paper. It is based on
this that this study seeks to answer the following questions;
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What is the distributional impact of income in Nigeria?
What is the level of income inequality in Nigeria?
1.3 RESEARCH OBJECTIVES
The broad objective of this study is to investigate the trend in the income distribution in
Nigeria. Specifically this study seeks to:
To examine the level of income inequality in Nigeria,
To examine the pattern of income distribution in Nigeria,
Estimate the level of poverty in Nigeria,
1.4 HYPOTHESES
The following hypotheses will be tested in this study:
Ho1. There is no significant difference in the impact of income distribution on poverty in
Nigeria.
Ho2. Inequality does not affect poverty.
1.5 SIGNIFICANCE OF THE STUDY
One of the pathetic features of the Nigerian economy today is that a majority of its
populace is living in a state of destitution while the remaining relatively insignificant
minority is living in affluence (Osinubi and Gafaar, 2005). An important objective of this
paper is to carry out analysis of the impact of income and distribution on changes in poverty
in Nigeria and further attempts to provide an update on household expenditure inequality in
Nigeria.
16
This is intended to serve as a tool for explaining the economic performance in Nigeria.
The issue of polarization in income distribution will be specifically investigated and its
influence on poverty will be inferred. This would constitute a major contribution of the study
and further this paper will proffer solutions to findings on the issues above. In this respect,
the study contributes to knowledge on poverty in Nigeria.
The exploration of these factors is expected to raise some policy issues and give policy
directions to policymakers especially concerning the identification of the target groups that
will enhance more equitable distribution of income among Nigerian households. This will not
only reduce inequality but also help the poorest of the poor to contribute to and benefit from
the growth and development process.
1.6 SCOPE OF THE STUDY
The period covered by this research is from, 1993 – 2008. The choice is guided by the
availability of data.
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CHAPTER TWO
Nigerian Economy
The Federal Government on 23 January, 2009 in Abuja reiterated its stance that the
nation‘s economic outlook remained very favourable, in spite of current global financial
crisis.
Former President Umaru Yar‘Adua, gave the re-assurance while declaring open the 3rd
Annual Micro-Finance Conference/Entrepreneurship Awards, organised by the Central Bank
of Nigeria (CBN). He stated that the Federal Government had taken many bold and pragmatic
steps to shield the economy from the negative effects of the global meltdown. ―Our economic
growth is on track, buoyed by the strong performance of the non-oil sector. The provisional
estimate of Nigeria‘s GDP growth rate for the end of 2008 is an impressive 6.8 percent,
compared with a 6.2 percent in 2007‖ he said.
Nigeria‘s economy is struggling to leverage the country‘s vast wealth in fossil fuels in
order to displace the crushing poverty that affects about 57 percent of its population.
Economists refer to the coexistence of vast natural resources wealth and extreme personal
poverty in developing countries like Nigeria as the ―resource curse‖. And according to
Odularu (2008), Nigeria‘s exports of oil and natural gas at a time of peak prices have enabled
the country to post merchandise trade and current account surpluses in recent years.
18
Reportedly, 80 percent of Nigeria‘s energy revenues flow to the government, 16 percent
covers operational costs, and the remaining 4 percent go to investors. However, the World
Bank has estimated that as a result of corruption 80 percent of energy revenues benefit only
one percent of the population. During 2005 Nigeria achieved a milestone agreement with the
Paris Club of lending nations to eliminate all of its bilateral external debt. Under the
agreement, the lenders will forgive most of the debt, and Nigeria will pay off the remainder
with a portion of its energy revenues. Outside the energy sector, Nigeria‘s economy is highly
inefficient. Moreover, human capital is underdeveloped Nigeria ranked 151 out of 177
countries in the United Nations Development Index in 2004 and non-energy-related
infrastructure is inadequate.
During 2003–2007, Nigeria has attempted to implement an economic reform program
called the National Economic Empowerment Development Strategy (NEEDS).
The purpose of NEEDS is to raise the country‘s standard of living through a variety of
reforms, including macroeconomic stability, deregulation, liberalization, privatization,
transparency, and accountability. NEEDS addresses basic deficiencies, such as the lack of
freshwater for household use and irrigation, unreliable power supplies, decaying
infrastructure, impediments to private enterprise, and corruption. The government hoped that
NEEDS would create 7 million new jobs, diversify the economy, boost non-energy exports,
increase industrial capacity utilization, and improve agricultural productivity. A related
initiative on the state level is the State Economic Empowerment Development Strategy
(SEEDS).
A long-term economic development program is the United Nations (UN) sponsored
National Millennium Development Goals for Nigeria. Under the program, which covers the
years from 2000 to 2015, Nigeria is committed to achieve a wide range of ambitious
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objectives involving poverty reduction, education, gender equality, health and environment.
In an update released in 2004, the UN found that Nigeria was making progress toward
achieving several goals but was falling short on others. Specifically, Nigeria had advanced
efforts to provide universal primary education, protect the environment, and develop a global
development partnership. However, the country lagged behind on the goals of eliminating
extreme poverty and hunger, reducing child and maternal mortality, and combating diseases
such as human immunodeficiency virus/acquired immune deficiency syndrome (HIV/AIDS)
and malaria.
A prerequisite for achieving many of these worthwhile objectives is curtailing endemic
corruption, which stymies development and taints Nigeria‘s business environment.
Corruption mostly harms Nigerians themselves, but the country is widely known around the
world for a fraudulent activity known as the "Advance fee fraud" scheme, a.k.a the "419"
scam or the Nigerian scam, which seeks to extort money from foreign recipients of letters and
emails with the promise to transfer a nonexistent windfall sum of money.
The oil boom of the 1970s led Nigeria to neglect its strong agricultural and light
manufacturing bases in favour of an unhealthy dependence on crude oil. In 2000 oil and gas
exports accounted for more than 98 % of export earnings and about 83 % of federal
government revenue. New oil wealth, the concurrent decline of other economic sectors, and a
lurch toward a statistics economic model fueled massive migration to the cities and led to
increasingly widespread poverty, especially in rural areas. A collapse of basic infrastructure
and social services since the early 1980s accompanied this trend. By 2000 Nigeria's per capita
income had plunged to about one-quarter of its mid-1970s high, below the level at
independence. Along with the endemic malaise of Nigeria's non-oil sectors, the economy
continues to witness massive growth of "informal sector" economic activities, estimated by
some to be as high as 75 % of the total economy.
20
The U.S. remains Nigeria's largest customer for crude oil, accounting for 40% of the
country's total oil exports; Nigeria provides about 10% of overall U.S. oil imports and ranks
as the fifth-largest source for U.S. imported oil.
The United States is Nigeria's largest trading partner after the United Kingdom.
Although the trade balance overwhelmingly favors Nigeria, thanks to oil exports, a large
portion of U.S. exports to Nigeria is believed to enter the country outside of the
Nigerian Government's official statistics, due to importers seeking to avoid Nigeria's
excessive tariffs. To counter smuggling and under-invoicing by importers, in May 2001 the
Nigerian Government instituted a 100 % inspection regime for all imports, and enforcement
has been sustained. On the whole, Nigerian high tariffs and non-tariff barriers are gradually
being reduced, but much progress remains to be made. The government also has been
encouraging the expansion of foreign investment, although the country's investment climate
remains daunting to all but the most determined. The stock of U.S. investment is nearly $7
billion, mostly in the energy sector. Exxon Mobil and Chevron are the two largest U.S.
corporate players in offshore oil and gas production. Significant exports of liquefied natural
gas started in late 1999 and are slated to expand as Nigeria seeks to eliminate gas flaring by
2008, as a target which was not achieved.
Oil dependency and the allure it generated of great wealth through government contracts,
spawned other economic distortions. The country's high propensity to import means roughly
80 % of government expenditures is recycled into foreign exchange.
Cheap consumer imports, resulting from a chronically overvalued Naira, coupled with
excessively high domestic production costs due in part to erratic electricity and fuel supply,
have pushed down industrial capacity utilization to less than 30 %. Many more
Nigerian factories would have closed except for relatively low labor costs (10 % - 15 %).
Domestic manufacturers, especially pharmaceuticals and textiles, have lost their ability to
21
compete in traditional regional markets; however, there are signs that some manufacturers
have begun to address their competitiveness.
PERFORMANCE OF THE ECONOMY
The Nigerian economy has had a truncated history. In the period 1960-70, the Gross
Domestic Product (GDP) recorded 3.1 per cent growth annually. During the oil boom era,
roughly 1970-78, GDP grew positively by 6.2 per cent annually - a remarkable growth.
However, in the 1980s, GDP had negative growth rates. In the period 1988-1997 which
constitutes the period of structural adjustment and economic liberalisation, the GDP
responded to economic adjustment policies and grew at a positive rate of 4.0. In the years
after independence, industry and manufacturing sectors had positive growth rates except for
the period 1980-1988 where industry and manufacturing grew negatively by - 3.2 per cent
and - 2.9 per cent respectively. The growth of agriculture for the periods 1960-70 and 1970-
78 was unsatisfactory. In the early 1960s, the agricultural sector suffered from low
commodity prices while the oil boom contributed to the negative growth of agriculture in the
1970s. The boom in the oil sector lured labour away from the rural sector to urban centres.
The contribution of agriculture to GDP, which was 63 percent in 1960, declined to 34 per
cent in 1988, not because the industrial sector increased its share but due to neglect of the
agricultural sector. It was therefore not surprising that by 1975, the economy had become a
net importer of basic food items. The apparent increase in industry and manufacturing from
1978 to 1988 was due to activities in the mining sub-sector, especially petroleum. Capital
formation in the economy has not been satisfactory. Gross domestic investment as a
percentage of GDP, which was 16.3 percent and 22.8 percent in the periods 1965-73 and
1973-80 respectively, decreased to almost 14 percent in 1980-88 and increased to 18.2
22
percent in 1991 -98. Gross National Saving has been low and consists mostly of public
savings especially during the period 1973-80. The current account balances before official
transfers are negative for 1965-73, 1980-88 and 1991-98.
The table below shows the break down of sectoral contribution to GDP in percentages.
SECTORAL CONTRIBUTION TO GDP %
ACTIVITY SECTOR 1999 2000 2001 2002 2003 2004 2005
1. Agriculture 47.6 35.84 35.58 35.86 34.63 40.99 41.49
(a) Crop Production 37.99 29.89 29.66 29.86 28.98 36.48 36.95
(b) Livestock 6.06 3.48 3.42 3.47 3.28 2.6 2.63
(c) Forestry 1.4 0.78 0.76 0.74 0.68 0.54 0.54
(d) Fishing 2.15 1.69 1.74 1.79 1.69 1.37 1.37
2. Industry 19.77 36.98 37.3 34.67 38.16 29.48 27.72
(a) Crude Petroleum 12.47 32.45 32.65 29.75 33.44 25.72 23.82
(b) Mining & Quarrying 0.37 0.29 0.31 0.31 0.3 0.26 0.27
(c) Manufacturing 6.93 4.24 4.34 4.61 4.42 3.5 3.63
3. Building & Construction 2.46 1.95 2.09 2.11 2.08 1.44 1.53
4. Wholesale & Retail Trade 13.62 13.11 12.85 13.22 12.68 12.9 13.74
5. Services 29.75 12.12 12.17 14.12 12.45 14.56 14.88
(a) Transport 3.64 2.28 2.28 2.59 2.38 2.38 2.41
(b) Communication 0.37 0.11 0.13 0.19 0.21 1.14 1.4
(c) Utilities 0.61 0.44 0.46 0.54 0.52 3.58 3.6
(d) Hotel & restaurant 0.57 0.21 0.21 0.21 0.2 0.37 0.39
(e) Finance & Insurance 11.16 5.2 5.2 6.5 5.34 4.08 3.98
(f) Real Estate & Business Services 0.35 1.9 1.91 1.9 1.78 1.34 1.41
(g) Producers of Govt. Services 11.06 1.25 1.22 1.35 1.24 0.96 0.95
(h) Comm., Social & Pers. Services 1.99 0.73 0.76 0.84 0.78 0.71 0.74
23
Source- Nigeria: Economics Growth Drivers and Financial Challenges by Soludo (2006)
CHAPTER THREE
LITERATURE REVIEW
3.1: Introduction
The related literature has been reviewed under the following sub-headings.
1. Theoretical literature on
Neoclassical Theories of Growth
Endogenous Growth Theories
Pro-poor Growth
Measures of Inequality
2. Empirical literature
3.2: THEORETICAL LITERATURE
24
Economic Growth is not an automatic birthright for an economy. For an economy to
grow, it has to create the right conditions for growth. Growth depends to a significant extent
on the resources a country has. The better the quantity and the quality of the resources the
more potential it has to grow (Moses, 2008).
All economic theories have said something about the relationship between growth and
both income and wealth distribution, which has long been the subject of controversy. These
theories have been used to test whether or not aspects such as foreign trade or human capital
growth contribute to increased inequality and in this way to slowed GDP growth, particularly
in developing countries. In this theoretical review, we situate economic growth within the
framework of main theories of growth namely Neoclassical theories and Endogenous growth
theories, then we look at the concept of pro-poor growth in Nigeria.
3.2:1 Neoclassical Theories of Growth
The basic Neoclassical model of growth developed by Solow (1957) and Swan (1956)
follows the logic of the Keynesian Model, like the Harrod-Domar model, the ultimate aim is
the search for the condition of a stable equilibrium. When using any economic model to
portray a real world problem and to study the effects of various resolutions, the usefulness of
the model is most contingent upon its ability to simulate the real world without excessive
oversimplification. One of the questions this may lead to is whether or not the neoclassical
growth model is a useful tool for economists and policymakers in understanding global
poverty and developing policies to reduce poverty.
The neoclassical growth model emphasizes the role of technological progress and labor
productivity in maintaining a sustained long-run rate of growth. Population growth,
depreciation of capital, and, most notably, technological progress directly affect the dynamics
25
of the growth process. One major idea that encompasses the frameworks of this model
underlines the assumption that over the long run, economic growth is independent of the
savings rate (or equivalently, investment). However, the economy experiences a transitional
state of growth or decline in the capital stock, which could be prolonged over a period of
decades, due to fluctuations in investment generated from savings that is greater or less than
required investment. In steady state, therefore, the growth rate of output is equal to the rate of
population growth and the rate of technological progress. This shows according to David
Stone (2004) that output per worker will grow at the rate of technological progress in a state
of balanced growth over the long run. The neoclassical growth model is achieved by
assuming a diminishing marginal product of capital, in which the economy gradually moves
to a point where savings provides only sufficient enough investment to cover depreciation. In
order to make saving and investment equal, we assume that the economy is closed. This is a
significant and unrealistic assumption to make, yet allows the issues of trades surpluses and
deficits to be overlooked. Taxes and government spending is also ignored in order to put
focus on the behavior of private savings. Lastly, we assume private savings to be proportional
to income.
The first idea we want to explore is whether or not the idea of economic growth is relevant
to developing policies that reduce poverty in developing countries. Indeed, the neoclassical
growth model does effectively highlight an important correlation between economic growth
and poverty reduction. This model theorizes that economic growth is contingent upon the
accumulation of capital-both human and physical-and technological progress. Human capital
refers to the increase in labor productivity due to levels of education, skills and experience,
and the health of people. Physical capital represents the tools used in production. Lastly,
technological progress has a two-fold meaning: it is the ability of larger quantities of output
to be produced with the same quantities of capital and labor. Equivalently, technological
26
progress represents the key ingredient in developing new, better and a larger variety of
products for the public to consume. Studies have shown that "literacy and other indicators of
education remain woefully low across much of the developing world," and a policy that helps
poor people acquire human capital would result in their earning higher wages (Besley and
Burgess, 2003). The neoclassical growth model could be used to argue that a climate that is
more conducive to investment and entrepreneurship would help to reduce poverty. This idea
follows from the premise that heavy regulation of business ownership is not in the public
interest because it results in low capital intensities, low human capital per worker, and low
productivity (Bigsten and Levin, 2000).
Fig 1
Steady-State Growth of Solow-Swan
27
Where Y ……….output
L …………….Labour
k…………….Capital
s…………….. Saving
I ……………Investment
n…………….Population
The above graph is use to explain Solow-Swan steady state growth, which says that the
growth rate of output is equal to the rate of population growth and the rate of technological
progress
3.2:2 Endogenous Growth Theories
The Endogenous growth models emanates from the work of Romer (1986) and Lucas
(1988). Over the last ten years, the mainstream theorists have begun to study more seriously
the relationship between distribution and growth. Although there are facts that explain it, this
reappearance in the academic world has mainly been associated with the emergence and
improvement of endogenous growth theory. Given its particular nature, scholars have usually
used this theory along with an explanation extracted from the theory of endogenous economic
policy. According to Solimano (1998), this literature rests on three methodological
assumptions. First, it reverses the direction of the causality of the Solow model and the
Kuznets curve, so that causality goes from distribution to growth. Second, the new models
28
seem to show investment driven growth. The third methodological assumption Solimano
(1998) mentions is that political mechanisms can be used to show how income distribution
affects growth. Thus, the process is not exclusively economic. For example, if there are two
targets that economic authorities have to deal with, let us say growth and equality, they might
identify the former as the most important. Nevertheless, both the specific grade of social
conflict and the political process that an economy may have will finally determine to what
degree each target is actually important and how each will be achieved. So, whereas the
economic mechanism is based on the outstanding role given to saving as the force that drives
growth, economic policies are not only a result of technocratic discussion but also of a social
and political agreement. Political mechanisms are exemplified through the introduction or
modification of income tax that negatively affects the profitability of either human or
physical capital. According to this argument, it is the relative preference for distributive
policy that determines the new higher tax burden on such inputs and therefore the lower pace
of economic growth. Hence, there is an inverse relationship between the reduction of
inequality and the rate of economic growth, which acts indirectly on growth through
investment decisions made after direct taxes have been paid.
In this theory, the political and social preferences for equality and growth policies are
revealed in the voting process. These sorts of citizen preferences are a function of the
endowments of capital, land, talents, skills, and raw labor. Consequently, in contrast to the
Keynes and Kalecki models, this theory leads to the conclusion that income concentration is
harmful for growth, for three worrisome reasons.
First, the more unequal the income distribution, the higher the income taxes and the
degree of implementation of redistributive policies, thus discouraging private accumulation
of physical and human capital.
29
Second, a high concentration of income and wealth can increase social tension and be a
source of political instability. Investment and economic growth can also be deterred in this
way.
Third, inequality of wealth impedes poor people‘s access to credit and therefore obstructs
their investing in education and other opportunities that may increase their market value as
human capital, thus slowing down investment and growth.
Solimano (1998) says that we should not be so pessimistic. The message of the Kuznets
curve, for example, is that beyond a certain threshold of income per capita, the growth
process will reduce, by itself and in the long run, differences in income distribution.
Furthermore, policies such as a broad education access program will contribute both to
economic growth and to increased income levels for a huge portion of the population. In
addition, it is known that a more equitable distribution of income and economic opportunities
also contributes to the alleviation of social conflict and political instability. However,
Solimano‘s recommendation is not acceptable because most developing countries need to
solve inequality problems immediately or in the medium term, at least. Otherwise, they might
not exist in the long run, which is what mainstream scholars are concerned about.
3.2:3 PRO-POOR GROWTHS
In the past few years, the term ―pro-poor growth‖ has become pervasive in discussions of
development policy. Despite the widespread use of the term, there appears to be much less
consensus as to what exactly pro-poor growth means, let alone what its determinants are.
According to one view, ―pro-poor growth‖ means that poverty falls more than it would
have if all incomes had grown at the same rate (Baulch and McCullock, 2000; Kakwani and
30
Pernia, 2000). This definition focuses on the distributional shifts during the growth process;
roughly speaking, for growth to be deemed ―pro-poor‖ by this definition, the incomes of the
poor should grow at a higher rate than those of the non-poor. A concern with this definition is
that rising inequality during a period of overall economic expansion may come with large
absolute gains to the poor yet this is not deemed to be ―pro-poor growth.‖ (Similarly, a
recession will be deemed pro-poor if poor people lose proportionately less than others, even
though they are in fact worse off.). The second definition, which is a broader and more
intuitive definition, is that growth is pro-poor if the poverty measure of interest falls
(Ravallion and Chen, 2003). This second definition avoids the problem of the first definition
by focusing instead on what happens to poverty. The extent to which growth is pro-poor then
depends on how much a chosen measure of poverty changes. Naturally this will depend in
part on what happens to distribution, but only in part — it will also depend on what happens
to average living standards.
Pro-Poor Growth and Poverty Reductions
Although economic growth is essential for achieving the MDGs, especially for poverty
reduction, policymakers may wonder if growth alone can improve the welfare of the poor.
For that reason, recent studies have focused on the elements of pro-poor growth. Broadly
speaking, pro-poor growth can be defined as one that enables the poor to actively participate
in and significantly benefit from the economy, economic growth inclusive. And according to
Kakwani and Pernia, 2000, Poverty reduction is about improving human well being (the life
people live, what they can do or cannot do) in particular that of the poor people. It is such that
no person in society is deprived of the minimum basic capabilities. For instance, everyone
should be adequately nourished, no child should be allowed to die prematurely, and populace
should live satisfying lives with long life span.
31
The poor have much lower well-being than the non-poor because they lack the resources
to satisfy the minimum basic necessities of life (Kakwani and Pernia, 2000). The market
forces induced growth process generally benefits the non-poor proportionally more than the
poor. This is because the non-poor have inherent advantages like human and material capital
in a market economy. Moreover, in many countries, government knowingly or unknowingly
adopts policies that are biased in favour of the rich. Consequently, the gap in well-being
between the poor and non-poor tends to persist, if not widen. Thus to foster the overall well-
being of the populace, government needs to pursue policies that will reduce this gap.
Three components can alter poverty levels over time: the rate of economic growth, the
response of poverty to that growth and changes in income distribution. However, studies
show that almost all change comes from just the rate of growth. In describing the impact of
economic growth on poverty Ravallion (2005), it can also be useful to exploit the fact that a
measure of poverty can be written as a function of the mean of the mean of the distribution on
which that measure is based and the Lorenz curve of that distribution. (The Lorenz curve
gives cumulative income shares as a function of the cumulative proportion of the population
ranked by income). So therefore, the government must focus on the types of growth strategies
that will contribute most effective to reducing poverty.
Promoting pro-poor growth requires a strategy that is deliberately biased in favour of the
poor so that the poor benefit proportionally more than the rich. Such an outcome would
rapidly reduce the incidence of poverty so that those at the bottom end of the distribution
curve of consumption would have the resources to meet their minimum basic needs. A pro-
poor growth strategy entails the removal of institutional and policy-induced biases against the
poor as well as the adoption of direct pro-poor policies. For instance, discrimination on
grounds of gender, ethnicity, and religion hurts the poor more than the rich; the same can be
32
said of artificial barriers to entry into certain trades and professions, or into the formal labour
market in general.
Macro policies that tend to constrain pro-poor growth include policies as overvalued
exchange rates, big-city-oriented industrial location policies, and public infrastructure
spending biases toward urban areas and against the welfare of the poor such as monopoly
powers enjoyed by some firms that result in high prices, subsidized public utilities (for
example, low water fees), state universities (low student fees) that benefit primarily the non-
poor, and housing policy (rent control) that limits housing supply.
Direct pro-poor policies are also required. These include adequate public spending for
basic education, health and family planning services, improved access to credit, and the
promotion of small and medium enterprises. A well-administered progressive tax system is
also pro-poor. Typically, this means a heavier reliance on personal income taxation, which is
progressive rather than on indirect taxation, which is regressive. Unfortunately, in many
developing countries revenue generation depends much on indirect than on direct taxes.
However, computing the extent to which growth is pro-poor is a hotly debated subject. For
instance, Ravallion and Chen (2003), and Kraay (2004), describe growth as pro-poor
whenever it decreases the poverty index of interest. Kakwani and Pernia (2000), Son (2004),
and Kakwani and Son (2006), believed that poverty-reducing growth cannot be a sufficient
condition for pro-poorness. The growth process should also benefit the poor proportionately
more than the non-poor. Lopez (2004) and Osmani (2005) find these two definitions of pro-
poorness problematic. They argue that the distributional impact of growth is not all that
matters. For instance, Kakwani and Pernia‘s (2000) definition could conflict with the Pareto
principle as an equitable low growth rate can be judged more pro-poor than an inequitable
33
high growth rate even if the former yields less absolute poverty reduction than the latter.
Osmani (2005), stated that if the nature of growth (that is, its distributional impact) is what
we are after, why bother to coin a new term pro-poorness growth? We already have the
concept of equitable growth, which requires growth to be such as to benefit the poor,
proportionately more than the rich.
3.2:4 MEASURES OF INEQUALITIES
A substantial and growing literature develops various measures or indexes of economic
inequality. Some use the Gini coefficient or other measures or relationships drawn from
Lorenz curves; some prefer different indicators of dispersion, such as an entropy index; some
offer axiomatic derivations of inequality indexes; and still others advocate the use of
normative measures derived from social welfare functions. These measures of inequality that
have been proposed in the literature are grouped into two classes known as:
the descriptive or positive measures and
Normative measures of inequality.
For the purpose of this research, we will discuss the descriptive measure of inequality.
The descriptive or positive measure of inequality is a measure that may be used in
regressions relating inequality and growth — measurement often is necessary. But what
counts as a good measure depends on the economic theory and empirical facts in particular
contexts, not (necessarily) on the properties and axioms that have generally been proposed for
measures of inequality. A few of more important descriptive measures are discussed below.
Range
34
Consider distributions of income over n persons, i=1…,n, let yi be income of person i and
let the average level of income be µ.
The range can be defined as the gap between two observation, it is perhaps based on comparing
the extreme values of distribution, i.e., the highest and the lowest income level if our focus is on
income distribution. It is calculated by subtracting the smallest observation from the greatest.
R = ymax – ymin
Where R is the range and ymax and ymin are, respectively the maximum and minimum
value of income.
It is measured in the same units as the data. Since it only depends on two of the
observations, it is a poor and weak measure of dispersion except when the sample size is
large. The difficulty with the range is evident. It ignores the distribution in between the
extremes. Therefore by concentrating on the extreme values only, the range misses important
features of the distribution.
Relative Mean Deviation
The relative mean deviation is a way of looking at the entire distribution and not merely
at the extreme values, it is to compare the income level of each with the mean income, to sum
the absolute value of all the differences, and then to look at that sum as a proportion of total
income, i.e. the average absolute distance of everyone‘s income from the mean, expressed as
a proportion of the mean.
M = ∑ni=1 │µ - yi│nµ
35
With perfect equality M = 0, and with all income going to one person only, M = 2(n –
1)/n.
The main problem with the relative mean deviation is that it is not at all sensitive to
transfers from a poorer person to a richer person (Pigou-Dalton condition) as long as both lie
on the same side of the mean income. The limitation is that it does not recognise whether the
transfer is at the low income or high income level but it do recognise that there is a transfer.
The Variance and the Coefficient of Variation
The variance is a measure of how spread out a distribution is. It is a measure of
variability.
The variance is computed as the average squared deviation of each number from its mean.
The formula (in summation notation) for the variance in a population is
V = ∑ni=1 (µ - yi)
2/n
It is summation of the square of the distribution, and this would have the result of
accentuating differences further away from the mean, so that a transfer from a relatively rich
person to the relatively poor person would decrease the inequality gap. This enhances Pigou-
Dalton condition as a result of the squaring.
We must understand that the variance depends on the mean income. One distribution A
may show much greater relative variation than B and still A may end up with lower variance
if mean of A around which the variation take place is smaller than mean of B. A measure that
36
does not have this deficiency and concentration on relative variation is the coefficient of
variation, which is simply the square root of the variance divided by the mean income level:
C = V1/2/µ
The coefficient of variation represents the ratio of the standard deviation to the mean, and
it is a useful statistic for comparing the degree of variation from one data series to another,
even if the means are drastically different from each other. While the coefficient of variation
captures the property of being sensitive to income transfers for all income levels and, is
independent of the mean level.
The Standard Deviation of Logarithms
The logarithm recommends income transfers at the lower end which is done by taking
some transformation of incomes that staggers the income levels. Logarithm in contrast with
taking the variance or the standard deviation of actual values, is that it eliminates the
arbitrariness of the units and therefore of absolute levels, since a change of units, which takes
the form of a multiplication of the absolute values, comes out in the logarithmic form as an
addition of a constant, and therefore goes out in the wash when pair wise differences are
being taken.
Logarithm reduces the large differences by rescaling the absolute values into relative.
Note that in the standard statistical literature, the deviation is taken from the geometric mean
rather than the arithmetic mean, but in the income distribution literature using the arithmetic
mean seems more common.
H = [∑ni=1 (logµ - logyi)
2/n ]1/2
37
The fact that a logarithmic transformation staggers the income levels tends to soften the
blow in reflecting inequality since it reduces the deviation, but on the other hand it has
property of highlighting differences at the lower end of the scale.
The logarithm suffers some weaknesses which are:
Firstly, as income levels get higher and higher, they suffer more contraction. This make
the result in the social welfare functions not concave. Secondly, H depends on the arbitrary
squaring of formula, although, this is done after log transformation. Thirdly, it also shares
limitations of the variance and coefficient of variance taking the difference of individual from
the mean.
Lorenz curve
Lorenz curve is propounded by max Lorenz in 1905. Lorenz curve is an important tool for
the analysis of inequality in a variety of situations – inequality in the distribution of income
within a population, inequality in the productivity of scientists in a give population,
inequality in the allocation of research grants to different institutions by a funding agency,
etc. It is a graphical device used to represent distributional inequality. When all the members
of the population receive the same income, the Lorenz curve is equi-distribution or identity
function. The Lorenz curve bends downward to the right, as the distribution of income
becomes more unequal as illustrated below;
38
For a perfectly equal distribution, there would be no area between the 45 degree line and
the Lorenz curve -- a Gini coefficient of zero. For complete inequality, in which only one
person has any income (if that were possible) the Lorenz curve would coincide with the
straight lines at the lower and right boundaries of the curve, so the Gini coefficient would be
one. Real economies have some, but not complete inequality, so the Gini coefficients for real
economic systems are between zero and one.
The Lorenz curve passes all the axiom of inequality. Any other measure of inequality that
has these properties is said to be Lorenz consistent. The properties are Reflexivity,
Transitivity, Anonymity, income homogeneity, Population independent and Pigou-Dalton.
Any other measure that is not consistent with Lorenz curve will give different result. Thus,
an inequality measure I (.) is Lorenz consistent if
Whenever one Lorenz curve I(x) dominates another I(y), then I(x) > I(y)
Whenever two Lorenz curve coincide say I(x) = I(y)
An inequality measure is weakly Lorenz consistent if
39
Whenever one Lorenz curve I(x) dominate another I(y), then I(x) ≥ I(y).
Whenever two Lorenz curve coincide, then I(x) = I(y)
An inequality measure is Lorenz inconsistent, when one Lorenz curve dominate another, then
I(x) < I(y).
The Gini coefficient
The Gini coefficient is a measure of inequality developed by the Italian statistician
Corrado Gini and published in his 1912 paper "Variabilità e mutabilità". The Gini coefficient
is widely used as a measure of income inequality, and there have been many attempts to find
an intuitive meaning to it. To mention a few examples, Yitzhaki (1979), Hey and Lambert
(1980) and Berrebi and Silber (1985) showed that the Gini coefficient represents the degree
of relative deprivation in a society, Lerman and Yitzhaki (1984) and Shalit (1985) related the
Gini coefficient to the covariance between a household's income and its income rank, and
Milanovic (1994) expressed the Gini coefficient as the weighted average of differences
between each household's importance as a member of a society and its importance as an
income-receiving unit. The Gini coefficient is a numerical measure of inequality based on the
Lorenz curve. These measures can be used to represent any sort of distributional inequity. It
is usually used to measure income inequality, but can be used to measure any form of uneven
distribution. The Gini coefficient is a number between 0 and 1, where 0 corresponds with
perfect equality (where everyone has the same income) and 1 corresponds with perfect
inequality (where one person has all the income, and everyone else has zero income). The
Gini index is the Gini coefficient expressed in percentage form, and is equal to the Gini
coefficient multiplied by 100.
40
Table 3.1: Gini Ratio From Various Studies
1960 1963 1965 1970 2005
Adelman and
Morris
0.45
Vielrose 0.474
Aboyade 0.5-0.6
Vielrose 0.492
Olaniyan and
Owoyemi
0.54
Source: Olaniyan & Owoyemi (2005)
World Bank (2003) shows that in 1996/97, Gini index for Nigeria was 0.506.
Table 3.1 presents some of the earlier estimates of inequality in Nigeria using Gini ratio.
We observed that inequality was low in the early 1960s, increased in 1965 and drop little in
1970 and again increased in 2005. The lowest inequality observed from the table is in 1960
when the Gini coefficient is 0.45 and the highest was in 1965 when the Gini Coefficient was
between 0.5-0.6 and also in 2005 with the Gini Coefficient of 0.54.
3.3: EMPIRICAL LITERATURE
During the past 20 years, there has been a tremendous focus on achieving growth in
developing countries in an effort to reduce poverty and boost living standards. For
policymakers around the world this is their top priority. Economists tend to advise them that
disciplined macroeconomic policies, structural policies that promote competition and
41
flexibility and strong institutions provide a framework in which entrepreneurship and growth
should flourish and according to Ricardo H., D. Rodrik and A. Velasco (2005) many
countries have adopted the policies known as Washington Consensus, that is the enforcement
of property rights, maintenance of macroeconomic stability, integration with the world
economy, and creation of a sound business environment, to help them achieve this goal.
Results have been extraordinarily varied, according to their findings that policies that work
wonders in one place may have weak, unintended or negative effects in other places. Roberto
Zagha, Gobind nankani and Indermit Gill, (2005) also found that the most important lesson
from this period is that our knowledge of economic growth is extremely incomplete. Ricardo
H., D. Rodrik and A. Velasco (2005) proposed that, countries need to figure out the one or
two most binding constraints on their economies and then focus on lifting them.
One study on income distribution and growth based upon both endogenous growth and a
new approach to reform that is much more contingent on the economic environment
endogenous policy theories was done by Perotti (1992). He applied an overlapping-
generations model to describe economic structure where growth is the result of private
investment in education and there is no capital market. He explained the political mechanism
by an endogenous median voter process that reveals individual preferences toward
distribution in the form of higher taxes. So, the lower the pretax income on the median voter
relative to the average, the higher her or his preferred tax rate, and, consequently, her or his
share of government expenditure in the GDP.
Using data from over 100 countries, Dollar and Kraay (2002) reject the trickle-down
approach to poverty reduction. They find that the per capita income of the poorest quintile of
the population grew one-for-one with the growth rate of the whole economy over the last four
decades; leaving the income distribution unchanged. Some studies in Nigeria have argued to
42
the contrary, that the poor has benefited more from these macroeconomic policies (Obadan,
1994; Faruquee, 1994; Canagarajah, et al., 1997). But Aigbokhan (2000) found that there was
positive real growth throughout the period of his study, 1980 to 1997, yet poverty and
inequality still worsened. However, other authors, Li and Zou, (1998), Barro, (2000) and
Lundberg and Squire, (2003), have found that greater inequality may promote economic
growth. The principal implications of their findings are that growth is good for poverty
reduction, irrespective of the nature of economic growth, and that pro-poor growth policies
are the best poverty reduction strategy.
Sami Bibi (2006) found that economic growth has led to a two-edge impact on poverty:
increasing mean income of the poor and reducing income inequality; thus reducing both
distributional-insensitive and distributional sensitive poverty indices when he used household
surveys from Mexico and Tunisia. According to him, for policy makers concerned with
poverty reduction, the aim should certainly be to sustain high growth, but with the poorest
capturing a proportionately larger share of the increment to growth. It is obvious that any
improvement in distribution achieved at the expense of growth would have adverse
implications for poverty reduction. Thus, there is no need for governments to implement
specific strategies to achieve the MDGs. They should instead maximise sustainable economic
growth by promoting competitive markets and adopting rigorous monetary and fiscal
policies.
Fosu(2008) study that explored the extent to which inequality influences the impact of
growth on poverty reduction, based on a global sample of 1977–2004 unbalanced panel data
for SSA and non-SSA countries. Several models are estimated with growths of the
headcount, gap, and squared gap poverty ratios as respective dependent variables, and
growths of the Gini and PPP-adjusted incomes as explanatory variables, the paper finds the
43
impact of GDP growth on poverty reduction as a decreasing function of initial inequality. The
study additionally observes that higher rates of increases in inequality tend to exacerbate
poverty, with the magnitude of this effect rising with initial income. The income–growth
elasticity, moreover, tends to increase with mean income relative to the poverty line. The
above estimated impacts are similar between the SSA and non-SSA samples with respect to
direction, so that within either sample there are considerable disparities in terms of the
responsiveness of poverty to changes in growth and inequality. This finding suggests that the
marginal benefit in terms of poverty reduction in the SSA region would require larger
reductions in inequality or accelerations in growth than elsewhere in the developing world.
Furthermore, the findings of the study suggest that the growth impact is likely to differ by
country in SSA, depending primarily on the inequality attributes of countries. For example,
the poverty-reduction efficacy of a given rate of growth acceleration in Ethiopia would be
more than twice that in Namibia, thanks to the much higher level of inequality in the latter
country. Similarly, the degree of responsiveness of Botswana‘s poverty rate is estimated to be
only slightly higher than that in Namibia, which might explain the minimal rate of poverty
reduction in Botswana, with the headcount poverty rate for instance falling by only 5
percentage points in a decade, despite the tremendous growth in that country. In contrast, in
Ghana where the income–growth elasticity is about twice that of Namibia, the headcount
poverty rate for example declined substantially, by about 10 percentage points within a
decade, in spite of the relatively modest growth. Thus, understanding the inequality-
generating characteristics of individual countries could help in designing most effective
poverty-reducing strategies for this region of the world where the challenge seems so great.
Meanwhile, a number of studies find that inequality plays an important role in the
income–growth–poverty relationship (e.g., Adams 2004; Kalwij and Verschoor 2007;
44
Ravallion 1997). Thus, meeting the poverty targets of the MDGs, for instance, may require
special attention predicated on a better understanding of the poverty–growth–inequality
relationship, particularly in SSA. Based on cross-country African data, Ali and Thorbecke
(2000) find that poverty responds more to income distribution than to growth. More recent
studies have focused on the role of initial inequality in the impact of growth on poverty. For
example, Ravallion (1997) and Easterly (2000) estimate the income–growth elasticity of
poverty as a decreasing function of inequality. Similarly, using the rather limited sample of
32 paired rural and urban sectors for 16 SSA countries employed in Ali and Thorbecke
(2000), Fosu (2008) arrives at a similar conclusion about the inequality impact on the income
elasticity of poverty. Adams (2004) also finds that a sub-sample of countries with a higher
level of inequality exhibits a smaller growth elasticity of poverty, on the assumption of a
lognormal distribution of income.
Bourguignon (2003) and Epaulard (2003) estimate equations that assume that the
income–growth elasticity, for instance, depends on the ratio of the poverty line to mean
income as well as on initial inequality. Based on similar specifications as in Bourguignon
(2003), Kalwij and Verschoor (2007) reach similar conclusions as in Bourguignon (2003) and
Epaulard (2003), and emphasize regional diversity in poverty responsiveness to growth and
inequality.
Adams (2004) used a new data set of 126 intervals from 60 developing countries to
analyze the growth elasticity of poverty and found that economic growth does indeed reduce
poverty (as measured by the international standard of $1.00/person/ day), the actual extent of
poverty reduction depends very much on how economic growth is defined. When economic
growth is measured by changes in survey mean income (consumption), there is a strong,
negative, statistical link between growth and poverty; however, when economic growth is
45
measured by changes in GDP per capita, the statistical relationship between growth and
poverty reduction is much weaker. However measured, economic growth reduces poverty in
this study because growth has little impact on income inequality. Income distributions do not
generally change much over time. Analysis of the 126 intervals included in the data set shows
that income inequality rises on average less than 1.0% per year. Moreover, econometric
analysis shows that economic growth—as measured by changes in the survey mean or GDP
per capita— has no statistical effect on income distribution. Since income distributions are
relatively stable over time, economic growth has the general effect of raising incomes for all
members of society, including the poor. In many developing countries poverty, as measured
by the $1 per person per day standard, tends to be ‗‗shallow‘‘ in the sense that many people
are clustered right below (and above) the poverty line. Thus, even a modest rate of economic
growth has the effect of ‗‗lifting‘‘ people out of poverty. Poor people are capable of using
economic growth—especially labor-intensive economic growth which provides more jobs—
to ‗‗work‘‘ themselves out of poverty. As noted above, however, the number of poor people
who are able to use economic growth to ‗‗work‘‘ themselves out of poverty depends very
much on how economic growth is defined. Adams (2004) further noted that the growth
elasticity of poverty is higher—not lower—when growth is defined using survey mean
figures as opposed to those coming from national accounts (the source of GDP per capita
data).
46
CHAPTER FOUR
METHODOLOGY
4.1: INTRODUCTION
This chapter clearly states the methods, techniques and approaches adopted in this
research. It further specifies the conditionality of the model and the sources of data used. In
order to achieve the objective of this study, we proceed by first identifying the unit of
analysis and then determining the definition of welfare.
In defining the unit of analysis, the we consider household as against individual members
of the household. This is dictated by the data we have on our disposal. Our unit of analysis is
thus the household and the extent of inequality is that between households. There are many
indicators of welfare that can be used as the basis for measuring inequality. The most
common ones are the income and the expenditure of the households. However, it has been
argued that income is problematic in the sense that the reported household incomes do not
always reflect the true position of household welfare. In this vein, this study uses household
per capita expenditure as the measure of welfare on which inequality index is computed.
The method adopted in this study is the Gini coefficient. The choice of this method is
made because it is best suited for calculating inequality. The Gini coefficient is one of the
commonly used measures of a welfare improvement indicator while the Lorenz curve is used
to measure changes in the income distribution.
47
4.2: MODEL DEFINITIONS
The Gini coefficient is widely used as a measure of income inequality, and there have been many
attempts to find an intuitive meaning to it according to Cheong (1999). To mention a few examples,
Yitzhaki (1979), Hey and Lambert (1980) and Berrebi and Silber (1985) showed that the Gini
coefficient represents the degree of relative deprivation in a society, Lerman and Yitzhaki (1984) and
Shalit (1985) related the Gini coefficient to the covariance between a household's income and its
income rank, and Milanovic (1994) expressed the Gini coefficient as the weighted average of
differences between each household's importance as a member of a society and its importance as an
income-receiving unit. The Gini coefficient is a numerical measure of inequality based on the Lorenz
curve. These measures can be used to represent any sort of distributional inequity. It is usually used to
measure income inequality, but can be used to measure any form of uneven distribution. The Gini
coefficient is a number between 0 and 1, 0 corresponds with perfect equality (where everyone has the
same income) where the Lorenz curve coincides with the 45 degree straight line and 1 corresponds
with perfect inequality (where one person has all the income, and everyone else has zero income). The
Gini index is the Gini coefficient expressed in percentage form, and is equal to the Gini coefficient
multiplied by 100.
Lorenz curve is an important tool for the analysis of inequality in a variety of situations –
inequality in the distribution of income within a population and many others. It is a graphical device
used to represent distributional inequality. When all the members of the population receive the same
income, the Lorenz curve is equi-distribution or identity function. The Lorenz curve bends downward
to the right, as the distribution of income becomes more unequal as illustrated below;
48
For a perfectly equal distribution, there would be no area between the 45 degree line and
the Lorenz curve -- a Gini coefficient of zero. For complete inequality, in which only one
person has all the income (if that were possible) the Lorenz curve would coincide with the
straight lines at the lower and right boundaries of the curve, so the Gini coefficient would be
one. Real economies have some, but not complete inequality, so the Gini coefficients for real
economic systems are between zero and one. The Lorenz curve illustrates the cumulative
income share on the vertical axis against the cumulative share of population on the horizontal
axis as in the figure above. If each individual had the same income, the income distribution
curve would be a straight line and the more bowed downward the Lorenz curve is, the more
unequal is the distribution of income in the graph.
4.3: TECHNIQUES OF EVALUATION
There are many ways to express and calculate the Gini coefficient according to
Milanovic (1997). Following the formula derived in Pyatt et al. (1980)), and used more
recently by Lerman and Yitzhaki (1984) and Yitzhaki (1994)) which will be adopted in work
is:
49
xN
rxarG
xi ),(cov2
…………………………………… (1)
Where covar (xi, rx) is the covariance between income (x) and ranks of all individuals"
according to their income (rx) ranging from the poorest individual (rank= I) to the richest
(rank=N).
N is total number of individuals or observation, and
x = mean income.
The Gini coefficient can also be calculated as the ratio of the area between the Lorenz Curve and the Equality line (or the 45 degree line) to the area below the equality line.
BA
AG
………………………………………………………… (2)
If A = 0, the Gini coefficient becomes 0 which means perfect equality whereas if B = 0,
the Gini coefficient becomes 1 which means completed inequality.
It is important to note, however, that the Gini coefficient represent less information than the
full Lorenz curve, different Lorenz curves may possess the same Gini coefficient but with
different interpretation. One would conclude, solely on the basis of two Lorenz curves having
the same Gini coefficients, that the two income distribution are equally unequal, whereas, one
Lorenz curve may show relatively more equal distribution among the low-income households
while the other Lorenz curve shows a relatively more equal distribution among the high-
income households. That is why it is important to augment the Gini coefficient with Lorenz
curve in its interpretation.
50
It is also important to note, however, that when the figure of the Gini Coefficient is high,
that is close to one, it means that income inequality is high and further depict that few
individuals are benefiting from the Economic growth and low Gini Coefficient means less
income inequality which further depict that majority of the individuals are benefiting from
Economic growth.
4.4: MODEL DERIVATIONS
Using equation (1), Gini coefficient can be state as:
xN
rxarG
xi ),(cov2
From equation (1) above;
covar(xi, rx,) = σxσrxp(x, rx,)……………………………………………………….(3)
Where σx = standard deviation of income,
σrx=standard deviation of individuals' ranks, and
p(x,rx) = correlation coefficient between x and rx.
Now, writing out the standard deviation of ranks
51
N
ii
rx
Ni
N 2
12
1
…………………………………. (4)
After some straightforward but tedious manipulation,
N
ii
Ni
2
12
=
N
ii
NNii
4
1)1(
22
=
=
N
ii
N
i
N
iNiNi
1 1
22 )1(4
1)1(
= 426
)1()1()12)(1(22
NNNNNNN
= 12
)1(3)12)(1(22
NNNNN
= 12
3632642323
NNNNNN
= 12
3
NN =
12
)1(2
NN,
Replacing the last expression into (4):
σrx=12
12N ……………………………………………………………………(5)
Using equation (5), we can write:
52
G = xN
rxar x),(cov2
G=
xN
rx x
12
),( 1N2 2x
G=NN
r x
x xx
1),(
3
12
………………………………………………………………(6)
For a sufficiently large N, the last term in (6) will be equal to 1. We thus obtain the final
formula for the Gini coefficient:
G = ),(3
1r x
x xx
This clearly shows that the Gini coefficient is the product of
(i) A constant,
(ii) Coefficient of variation of income, and
(iii) Correlation coefficient between income and rank.
4.5: SOURCES OF DATA
Data collection will essentially be from secondary source. The data for the study will be
collected from the Nigeria living Standard Survey (NLSS) 2004 of the National Bureau of
Statistics in collaboration with the European Union (EU), World Bank, United Nations (UN),
53
Department for international Development (DFID) and the UNDP in November, 2004. The
survey covered all the States of the federation and the Federal Capital Territory (FCT),
Abuja.
CHAPTER FIVE
ANALYSIS OF RESULTS
5.1: Introduction
Inequality in this paper is conceptualised as the dispersion of the distribution of the
attributes of the welfare indicators of the population, like income and consumption. As
revealed in the last chapter our welfare indicator is per capita expenditure of the household.
The results of the inequality using Gini Coefficient and Mean status of the household as well
as the decomposition analysis are presented in this chapter. Total inequality is decomposed
into within group components according to several socio-economic variables taken at a time.
The variables include the age, gender, and education of the head of the household. Others are
the economic activity of the household head, household size, rural, urban as well as the states
and geopolitical zone that the household head belongs.
5.2: Analyses of Results
The estimation results using Gini Coefficient and Mean is presented in table 5.1 below
Table 5.1: Estimation result
Areas
Analyzed
Percentage Share of
Households
Mean Gini
General 33795.37 0.44068
54
Rural 27.6 44626.98 0.44436
Urban 72.4 30928.02 0.43016
Source: Computed by the Author
We start by presenting the context of rural inequality in Nigeria in relation to urban and
national inequality. While, the mean per capita expenditure of households in the rural areas is
N44626.98 compared to N30928.02 among urban households. Although inequality among
urban households as reflected by the Gini index of 0.430 is very high, it is lower than both
rural and national inequality index. While the Gini index for the rural households is 0.444, it
is 0.441 among all household both urban and rural. Generally, all inequality indices reveal
that inequality is higher among urban households than rural households but the case is
different here because of the data in our disposal, because the observed household are not
equal, while 4010 households were observed in the rural area as compare to 15148
households observed in urban area respectively. The differential inequality reveals that since
most of the rural households are poorer, their per capita expenditure is dispersed compared to
what obtains among urban households. However, it is important to investigate the prevalence
of inequality among rural households as this will inform policy options of alleviating poverty
in the sector without worsening the inequality in the sector of the economy.
World Bank (2003) shows that in 1996/97, Gini Coefficient for Nigeria was 0.506. And
from our result presented in Table 5.1 above, we observed that the general Gini Coefficient is
0.441. This shows that there is a reduction in inequality in Nigeria from what we have in
Table 3.1 in literature review to 0.441. This significant reduction could be attributed to the
effort of government to reduce poverty in Nigeria through poverty alleviation programmes.
Nigeria is a federation with three tiers of governance at the national, state and the local
government levels. There are 36 states and 774 local governments in the country. However,
55
for geographical and tribal conveniences, the nation is often is subdivided into six
geopolitical zones. We therefore assess the impact of geopolitical zones on aggregate living
standards, as well as welfare differences between households. We find that location and
climate could have large effects on income levels and income distribution, through their
effects on transport costs, disease burdens, and agricultural productivity, among other
channels.
Table 5.2: Percentage share and Mean Expenditure of Households by
Geopolitical Zone
Areas
Analyzed
Percentage Share
of households
Mean Gini
General 33795.37 0.44068
South South 20.8 46649.26 0.392
South East 20.1 48256.52 0.3864
South West 20.5 43394.63 0.4024
North Central 14.1 26264.64 0.4524
North East 11.8 23711.36 0.3973
North West 12.7 21558.88 0.3870
Total 100
Source: Computed by the Author
Table 5.2 shows that South East zone has the highest mean expenditure of N48256.52
with 20.1 percentage share of the Households expenditure while North West zone accounts
for the least mean expenditure of N21558.88 with 12.7 percentage share of the households
expenditure. However, in spite of differential value of average expenditure across these
zones, the inequality index is high in all the geopolitical zones with the North central being
56
the zone with the highest level of inequality and the South East with the lowest level of
inequality. This means that some policies to reduce inter-geopolitical zones might reduce
some inequality in the country.
Table 5.3: Percentage share, Mean Expenditure Gini Coefficient of
Households by State
States Mean Sum Percentage
proportion of HH
expenditure
Gini
Abia 53235.62 30557246.65 4.72 0.361
Adamawa 23181.56 11706685.67 1.81 0.398
Akwa Ibom 44264.035 22574657.68 3.49 0.372
Anambra 55890.173 28448097.97 4.40 0.342
Bauchi 20537.247 11767842.44 1.82 0.441
Bayelsa 57480.017 30119528.62 4.65 0.351
Benue 37519.452 19435076.06 3 0.443
Borno 28606.511 14532107.59 2.25 0.367
Cross River 41825.09 20954367.35 3.24 0.419
Delta 36730.14 15279739.39 2.36 0.339
Ebonyi 33762.38 19210794.69 2.97 0.360
Edo 47057.15 26163774.09 4.04 0.411
Ekiti 41034.11 19655336.57 3.04 0.350
Enugu 43617.79 23553604.23 3.64 0.372
Gombe 22010.39 10873131.02 1.68 0.371
Imo 56194.23 28378084.98 4.38 0.427
Jigawa 12292.53 6871522.05 1.06 0.325
Kaduna 33101.44 18934024.38 2.92 0.384
Kano 27571.21 16101584.26 2.49 0.394
Katsina 23540.98 12688589.48 1.96 0.382
Kebbi 16621.14 8825826.88 1.36 0.284
Kogi 15526.96 9207489.12 1.42 0.460
Kwara 15862.74 8883136.01 0.14 0.463
Lagos 37941.90 18325936.89 2.83 0.488
Nassarawa 28377.88 13820018.54 2.14 0.362
Niger 27681.35 15418512.86 2.38 0.383
Ogun 50751.79 26796947.25 4.14 0.406
Ondo 38012.69 19994673.15 3.09 0.342
Osun 44700.42 23735925.24 3.67 0.358
Oyo 47365.66 24061755.33 3.72 0.328
Plateau 27175.21 13669128.98 2.11 0.394
Rivers 51456.72 19605008.79 3.03 0.400
57
Sokoto 18330.60 8542061.14 1.32 0.325
Taraba 29943.31 16319103.38 2.52 0.372
Yobe 18691.74 11009433.39 1.70 0.352
Zamfara 18302.50 10542236.94 1.63 0.326
FCT 42041.66 10888788.9 1.68 0.432
Source: Computed by the Author
58
Fig 2
59
Fig 3
lx
Table 5.3 shows the analysis of state inequality decomposition in Nigeria. We observed
average Household per capita expenditure of the Country was estimated to be 33795.37. State
average with highest figure was Bayelsa with 57480.017 as the mean expenditure and has the
highest percentage proportion of per capita expenditure of 4.65%. We further observed Jigawa to
have the lowest mean of 12292.53 and also with low Gini coefficient of 0.325, although Kebbi
has the lowest Gini coefficient of 0.284 and also we observed Lagos with the highest inequality
of Gini coefficient of 0.488.
Although, the first ten States average with high figure were Bayelsa (57480.017), Imo
(56194.23), Anambra (55890.17), Abia (53235.62), Rivers (51456.72), Ogun (50751.79), Oyo
(47365.66), Edo (47057.15), Osun (44700.42) and Akwa Ibon (44264.035). And the last ten
State average with low figure are Jigawa (12292.53), Kogi (15526.96), Kwara (15862.74), Kebbi
(16621.14), Zamfara (18302.5), Sokoto (18330.6), Yobe (18691.74), Bauchi (20537.25), Gombe
(22010.39) and Adamawa (23181.56). The analysis further revealed the inequalities of the
Households in the different States of Nigeria. From the observation, the first ten States with high
inequality from their Gini Coefficient are Lagos (0.488), Kwara (0.463), Kogi (0.460), Benue
(0.443), Bauchi (0.441), FCT (0.432), Imo (0.427), Cross River (0.419), Edo (0.411) and Ogun
(0.406). And the last ten State with low inequality from their Gini Coefficient are Kebbi (0.284),
Jigawa (0.325), Sokoto (0.325), Zamfara (0.326), Oyo (0.328), Delta (0.339), Anambra (0.342),
Ondo (0.342), Ekiti (0.350) and Bayelsa (0.351). Then, our chart shows the percentage
contribution of each States with Kwara having the lowest percentage share of 0.14 percent while
Abia with the highest percentage share of 4.72 percent.
lxi
Table 5.4: Mean Expenditure, Percentage Share of Households and
Household Expenditure by Gender of Household Heads
Gender Percentage
Share of
Households
Mean
Expenditure
Percentage
Share of HH
Expenditure
Gini
Male 85 32056.4 81 0.443
Female 15 43981.8 19 0.399
Source: Computed by the Author
Fig 4
lxii
Table 5.4 reveals that 85 percent of households in Nigeria are headed by male while only 15
percent are headed by the female. However, the mean expenditure of female-headed households
is richer as their mean expenditure of 43981.8 is higher than the mean expenditure of male-
headed households with 32056.4.
However, inequality index is a little low no matter the gender of the household head as the
Gini Coefficient for both sexes is 0.441. This is further revealed in the decomposition analysis as
revealed by Table 5.4; which indicate that gender inequality is not a prominent factor in overall
expenditure inequality. However, the Gini index indices suggest higher inequality among male
headed households of 0.443 than female headed households of 0.399, but there is no much
significant. This means that elimination of gender inequality will not reduce total expenditure
inequality significantly. We further observed that 85 percent of the male Household head
controlled 81 percent of the nation‘s wealth from their expenditure and 15 percent of the female
Household head controlled just 17 percent of the Nation‘s wealth.
lxiii
Table 5.5: Mean Expenditure and Proportion of Households by Age of
Household Head
Age Group
Class (In
Years)
Percentage
Share of
Households
Mean
Expenditure
Percentage
Share of HH
Expenditure
Gini
0-4 0 0 0 0
5-9 0 0 0 0
10-14 0.01 0 0.002 0
15-19 0.23 48879.92 0.33 0.405
20-24 2.07 48880.78 3 0.414
25-29 6.57 43132.56 8.38 0.428
30-34 10.40 34404.33 10.60 0.431
35-39 12.18 31701.18 11.43 0.437
40-44 13.16 30849.97 12.01 0.453
45-49 12.67 29546.98 11.08 0.417
lxiv
50-54 11.75 30140.13 10.48 0.429
55-59 7.96 31089.22 7.32 0.419
60-64 8.45 34230.94 8.55 0.440
65-69 5.53 37236.77 6.10 0.439
70 and above 9.04 40178.81 10.74 0.463
Source: Computed by the Author
There is a close link between the age structure and the distribution of income among the
people, because the size and composition of personal incomes from work, property and transfer
vary during the lifecycle, as well as for the fact that individual experiences reflect the different
historical periods in which people live. And according to the Life-Cycle hypothesis, that
household income usually increases gradually with age of the household head until a certain age.
After reaching a peak, it starts to decline. This is however not the case for Nigeria. Rather there
is U shaped relationship between the age groups and mean expenditure with two spikes at age
below 24 years and age 70 years and above categories. Table 5.6 reveals that the relative mean
expenditure of age groups15-19 years, 20-24 years, 30-34 years, 60-64 years, 65-69 years and 70
years and above are above the average mean expenditure of all the households while the mean
expenditure of other age groups are less than the average national mean expenditure. Incidentally
age group 0-4 years, 5-9 years and 10-14 years have the zero mean per capita expenditure. The
age group between 15 -19 years, 20-24 years and 25-29 years which correspond to a period when
most of the household head are single and unmarried accounted for the high mean expenditure.
We further observed that Households within age group 30-59 years were in their primes and the
crisis negatively affected their income status as at the period of reference. Further, the age-group
lxv
corresponds to the period when most individuals in Nigeria start their own families and start
having children which further reduced the per-capita expenditure and the current economic crises
left so many youths unemployed.
Table 5.5 further revealed that inequality is high among the households whose household
head‘s age falls within the age groups with the highest mean expenditures. The Gini Coefficient
for the age group of 70 years and above has the highest figure of 0.463 and approximately 9
percent of the Households have 10.74 percent of per-capita expenditure while the age group with
the lowest inequality is 15 – 19 age group with a Gini Coefficient of 0.405 and though age group
of 0-4, 5-9 and 10-15 have zero inequality because of no observation.
Our decomposition reveals that the disparity between the age - group is not significant in
overall inequality. This means that age is not important determinant factors in explaining
inequality among the households.
Table 5.6: Mean Expenditure and Proportion of Households by Education of
Households Head
Education
Level
Percentage
Share of
Households
Mean
Expenditure
Percentage
Share of HH
Expenditure
Gini
No Education 46 27086.50 37 0.427
Elementary 1 30175.92 1 0.426
Primary 4 37820.64 4 0.410
lxvi
Secondary 34 39971.49 40 0.417
Tertiary 6 58159.89 11 0.441
Others 9 27109.13 7 0.408
Source: Computed by the Author
In most developing countries the level of education is low and Nigeria is not an exception.
Table 5.6 shows that 46 percent of the Household had no education, 1 percent had elementary, 4
percent had primary school, 34 percent had secondary education, 6 percent had tertiary education
while 9 percent had other education. Human capital theory suggests positive correlation between
educational level and job opportunities and capacity to earn high income. Hence, employment
opportunities tend to vary between individuals depending on the level of educational attainment.
This is because one‘s labour productivity is affected by the amount of knowledge, information
and skills acquired and education can be a major determinant of inequality.
Table 5.6 shows a positive relationship between educational attainment of the household head
and the per capita mean expenditure. We found that the higher the educational attainment of the
head of the household, the higher the mean expenditure of the household. Hence, mean
expenditure is 27086.50 for households whose head has no formal education as the lowest while
households whose head had tertiary education with mean expenditure of 58159.89 as the highest.
Our findings in Table 5.6 revealed that inequality is highest in the Households of those with
tertiary education with Gini Coefficient of 0.441 as the overall Gini Coefficient while Household
with primary school education with the lowest inequality with Gini Coefficient of 0.410.
lxvii
Decomposing this inequality, we observed that in addition to inequality within each
educational level of household heads, differences in educational level attained by the household
head also account for inequality among Nigerian households. The implication is that although,
household heads may have attained the same educational level, their incomes are largely
determined by their employment activities which further determine the structure of earnings
which cause differences in earnings and thus mean average expenditure in educational level
attained by the household head also account for inequality among Nigerian households.
Table 5.7: Mean Expenditure and Proportion of Households by Economic
Activity of Household Head
Occupational
level
Percentage
Share of
Households
Mean
Expenditure
Percentage Share
of HH
Expenditure
Gini
Students, Retired,
Unemployed or
Inactive
5.25 50916.16 7.90 0.470
Professional 6.38 46898.13 8.86 0.445
lxviii
and Technical
Administration 0.21 50651.05 0.31 0.507
Clerical 4.93 42481.93 6.19 0.427
Sales and
Related
10.20 42133.48 12.20 0.426
Services and
related
3.56 40143.79 4.22 0.423
Agricultural
and Forestry
61.97 27787.29 50.96 0.418
Production and
Transport
2.51 41015.76 3.04 0.407
Manufacturing
and Processing
1.53 38876.43 1.76 0.404
Others 3.47 39296.36 4.04 0.410
Source: Computed by the Author
It should be of interest to identify the degree to which differences in the type of primary
occupation contribute to overall income inequality and the role it has played in the widening of
income disparity. Table 5.7 shows that farming is still the main stay of employment in Nigeria.
61.97 percent of households in Nigeria engage in farming activities. Our results revealed that
mean expenditure for households that are Students, Retired, unemployed or inactive is 50916.16
as the highest Mean expenditure and agricultural and forestry activity with the lowest mean
expenditure of 27787.29. This suggest that farming activities bring low income, that even with
61.97 percent of the Households engaged in farming we still have low mean expenditure. The
decomposition of the inequality measures are illustrated in Table 5.7, which reveals
lxix
administration with the highest inequality of 0.507 as the Gini Coefficient while manufacturing
and processing has the lowest inequality of 0.404 as the Gini Coefficient.
Table 5.8: Mean Expenditure and Proportion of Households by Household
Size
HH size class Percentage
Share of
Households
Mean
Expenditure
Percentage
Share of HH
Expenditure
Gini
1 person 11.46 20268.35 23.83 0.376
2-4 persons 39.76 35718.44 42.02 0.393
5-9 persons 42.10 24246.14 30.21 0.391
10-19 persons 6.61 19997.41 3.91 0.453
20 persons
and above
0.06 14086.91 0.03 0.303
Source: Computed by the Author
It has been hypothesised that although larger households tend to have higher level of
expenditure, per capita household expenditure decreases as the household size decreases. This is
not entirely true of the Nigerian case. The mean expenditure of the household with 2-4 persons
household size is the highest as observed from table 5.8 while mean expenditure of the
household with 20 persons and above is smallest. We further observed that household size of 5-9
persons is more common, being 42.10 percent of the total household observed.
Table 4.8 presents the inequality decomposition by household size and finds that Inequality is
highest within 10-19 persons households with the Gini Coefficient of 0.453 and lowest within
lxx
20-persons and above households size with a Gini Coefficient of 0.303. In other words, making
household size equal will not have significant bearing on the overall inequality in Nigeria.
Table 5.9: Mean Expenditure and Percentage Share of Households
Component Expenditure
Components of HH
Expenditure
Mean Expenditure Percentage share of
Expenditure
Food 48163.47 37
Own Produced 22412.13 17
Education 6883.55 5
Health 17874.85 14
Rent 10682.47 8
Non Food Frequent 13618.69 11
Non Food Infrequent 10481.07 8
Source: Computed by the Author
We observed in table 5.9 the components of Households expenditure and found that the bulk
of Household expenditure goes for food with the highest mean expenditure and 37 percent share
of total expenditure. Education has the lowest mean expenditure with 5 percent share of the total
expenditure, this suggest a great implication on the human capital development in Nigeria. Table
5.9 was better buttress with the help of pie chart above.
lxxi
Fig 5
Table 5.10: Cumulative Relative Frequencies of Households per Capita
Expenditure and Households
Per Capita
Expenditure
Percentage
Share of
Households
Cumulative
share of
Households
Deciles share
of
Cumulative
share of
lxxii
Class (N)
Expenditure Expenditure Households Households
Under 10000 2.73 2.73 1.31 1.31
10001-20000 12.48 15.21 2.83 4.14
20001-30000 14.85 30.06 2.03 6.17
30001-40000 12.60 42.66 1.23 7.4
40001-50000 10.65 53.31 0.81 8.21
50001-60000 8.24 61.55 0.51 8.72
60001-70000 7.085 68.635 0.37 9.09
70001-80000 5.410 74.045 0.24 9.33
80001-90000 4.148 78.193 0.17 9.5
90001-100000 3.421 81.614 0.123 9.623
100001-110000 2.329 83.943 0.075 9.698
110001-120000 2.345 86.288 0.069 9.767
120001-130000 1.656 87.944 0.045 9.812
130001-140000 1.479 89.423 0.037 9.849
140001-150000 1.160 90.583 0.028 9.877
150001-160000 1.027 91.61 0.023 9.9
160001-170000 0.920 92.53 0.018 9.918
170001-180000 0.542 93.072 0.011 9.929
180000-190000 0.569 93.641 0.010 9.939
lxxiii
190001-200000 0.572 94.213 0.010 9.949
200001-210000 0.472 94.685 0.008 9.957
210001-220000 0.363 95.048 0.006 9.963
220001-230000 0.208 95.256 0.003 9.966
230001-240000 0.438 95.694 0.006 9.972
240001-250000 0.076 95.77 0.001 9.973
250001-260000 0.274 96.044 0.004 9.977
260001-270000 0.124 96.168 0.002 9.979
270001-280000 0.298 96.466 0.004 9.983
280001-290000 0.264 96.73 0.003 9.986
290001-300000 0.046 96.776 0 9.986
300001-310000 0.057 96.833 0 9.986
310001-320000 0.293 97.126 0.003 9.989
320001-330000 0.050 97.176 0 9.989
330001-340000 0.361 97.537 0.004 9.993
340001-350000 0 97.537 0 9.993
350001-360000 0.054 97.591 0 9.993
360001-370000 0.113 97.704 0.001 9.994
370001-380000 0 97.704 0 9.994
380001-390000 0.059 97.763 0 9.994
lxxiv
390001-400000 0.061 97.824 0 9.994
400001-410000 0 97.824 0 9.994
410001-420000 0.127 97.951 0.001 9.995
420001-430000 0.131 98.082 0.001 9.996
430001-440000 0 98.082 0 9.996
440001-450000 0.069 98.151 0 9.996
450001-460000 0 98.151 0 9.996
460001-470000 0,217 98.368 0.002 9.998
470001-480000 0 98368 0 9.998
480001-490000 0.075 98.443 0 9.998
490001-500000 0.077 98.52 0 9.998
500001-510000 0.078 98.598 0 9.998
510001-530000 0 98.598 0 9.998
530001-540000 0.083 98.681 0 9.998
540001-660000 0 98.681 0 9.998
660001-670000 0.103 98.784 0 9.998
670001-970000 0 98.784 0 9.998
970001-980000 0.150 98.934 0 9.998
980001-1010000 0 98.934 0 9.998
1010001-1020000 0.157 99.091 0 9.998
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1020000-1730000 0 99.091 0 9.998
1730001-1740000 0.268 99.359 0 9.998
1740001-1940000 0 99.359 0 9.998
1940001-1950000 0.301 99.66 0 9.998
1950001-2290000 0 99.66 0 10
2290001-2300000 0.355 100 0 10
Source: Computed by the Author
Table 5.10 show the per capita expenditure distribution of the households. It can be shown in
the above that 82.05 percent of the households controlled 53.30 percent of the total expenditure
lxxvi
earned less that N50000 annually while the remaining 17.95 percent of the households controlled
46.7 percent of the total expenditure which means that quarter of the total of the household
controlled almost half of the wealth of the Nation. This shows that the distribution of wealth is
not fairly equal in the Country. This is confirmed by the Gini coefficient of 0.441. According to
the ratio got from Gini coefficient, the disparity in the distribution of wealth was not great but it
is quite obvious that it exists. For there is not to be any disparity in the distribution of wealth,
that is, perfectly distributed, the Gini index should be zero. Therefore, what we have is high
enough to cause some problems.
CHAPTER SIX
SUMMARY, RECOMMENDATION AND CONCLUSION
6.1: Summary
Increasing income inequality and poverty continue to be the most challenging economic
problem facing most developing countries. This study attempted to investigate the distributional
trend of income in Nigeria and examine the issue of inequality in expenditure among households
as well as urban-rural difference in consumption among households and further examine
geopolitical zone inequality in Nigeria. That is, the decomposition analysis was divided into two
categories. The first category is concerned with the decomposition of households‘ expenditure.
This underscores the contributions of these components to overall inequality and may help in the
design of effective economic and social policies to reduce inequality and poverty in Nigeria. The
second category of decomposition analysis dealt with the breakdown of expenditure into
population sub-groups (This approach starts with the division of a sample into discrete
categories; for instance, rural and urban residents, gender, age group, education level of
household heads, household size, occupation, states and geopolitical zones), and then follows
with the calculation of the level of inequality using Gini coefficient. The results of our analysis
lxxvii
indicate that factors such as age, gender, and education level of the household head are important
factors in explaining inequality profile in the country. We however found that inequality exists in
the rural and urban areas but more of the rural areas and inequality is very high in some of the
geopolitical zones. This thus suggests policies that will alleviate poverty in the rural and urban
areas as well as policies to reduce inter geopolitical zone access to opportunities.
It has been reported that the scourge of poverty in Nigeria is tilted towards the rural sector.
Rural areas in Nigeria, according to the World Bank (1996), accounted for 66 per cent of the
incidence of poverty, 72 per cent of the depth of poverty and 69 per cent of the extreme poor.
Several studies have revealed that the majority of the rural dwellers are engaged in farming. We
also observed that inequality in the rural areas is higher than the urban areas, and even higher
than the general inequality.
Again, the gap between the rich and the poor in Nigeria continued to widen everyday and this
has created a serious threat to both the socio-economic and political stability in the country.
Meanwhile, this study has shown that consumption inequality is increasing in the rural and urban
areas and this can be linked to the growing dimensions of poverty.
In spite of the various economic reforms and policies of the government to eliminate
inequality and reduce poverty to the bearable state in the country, inequality still persists and
poverty keeps increasing everyday. One, therefore, is inclined to wonder if these policies are
actually implemented or are simply touted.
6.2: Recommendations
lxxviii
Based on the findings from the analysis carried out, an encompassing policy framework
would be necessary to reduce national inequality and poverty. Some of the policies might include
the following:
i. Agricultural activities should be promoted among rural households in Nigeria. This is
because, apart from being an inequality decreasing income source, it remains a major
income source for the rural households. Policy makers, therefore, must concentrate on
measures to increase agricultural productivity through targeted efforts such as
distribution of improved seed varieties and better extension services delivery. Despite
the concern that agricultural growth may not provide the exit way out of poverty, the
results of this paper seem to suggest that there is scope for poverty reduction through
growth in agricultural income.
ii. Direct targeting of the poor households for income transfer, should be considered to help
reduce the high level income inequality in rural Nigeria.
iii. There should be policies targeted in Redistribution of wealth.
iv. Measures should be taken by the government to pursue MDG‘s goals seriously and there
should be programs to track the Virtual Poverty Fund in MDG‘s office.
v. Measures should be taken to improve human development level in the country to energise
the real sector and also to develop social expenditure programs targeting the poor and
vulnerable communities in Nigeria.
vi. Government should intensify efforts towards the achievement of her poverty reduction
strategies; the realisation of the objectives of the NEEDS living document should
remain the paramount concern of the government.
lxxix
vii. Measures should be taken to design programme that are geared towards capacity building
for Nigerians so as to minimise the ugly trend of unemployable jobs seekers.
viii. Massive industrialisation of the rural areas should be embarked upon in order to develop
those areas and so stop the rural urban drift.
ix. There is need to intensify the crusade on population control since large households have
low per capita income.
x. The Government Poverty Alleviation Programme should be restructured if not re-
designed and should be centred on the ‗basic needs‘ approach. This approach
emphasizes the importance of separating generalized increase in income from the
more significant attainment of the requirements for a permanent reduction of poverty
through the provision of health services, education, housing sanitation, water supply
and adequate nutrition.
xi. Government value for money audit or due process mechanism in public procurement
should be intensify since government tackle corruption and improve transparency in
public expenditure through this.
xii. The two government commissions; Economic and Financial Crime Commission as well
as Independent Corrupt Practices Commission meant to tackle corruption in the
domestic business environment should intensify their effort in pursuing cases of
corrupt practices especially corruption in public offices.
6.3: Conclusion
Poverty has the consequence of breeding social disillusionment with respect to what the
societal objectives are and member‘s responsibilities towards attainment of these objectives. Just
lxxx
as ignorance maintains poverty, so also can poverty perpetuate ignorance, since the victims
cannot think and plan beyond where the next meal is coming from. Moreover, in a country or
locality where the concentration of the bulk of wealth is in few hands, there is serious
implication. A society where majority spend almost 90% of their income on consumption and
having little or nothing for saving, which could be ploughed back into the economy for re-
investment, economic growth would be slow and impeded, since the rate of economic growth is
a function of investment through multiplier effects. This means that the group of people affected
would not participate effectively in the process of development of that nation. In other words,
poverty is a vicious cycle reproducing itself in perpetuity. For the poor to back out of this vicious
cycle in which they are presently enmeshed, government must make reaching the poor a priority
in its own right.
Despite the commitments already shown by the government of the Country towards the
achievement of the goal of reducing inequality, it is crystal clear that Nigeria has had various
programs targeted at reducing inequality and poverty, either in consumption of income but the
problem has been in the implementation and sustenance of the programs.
Therefore, efforts to reduce poverty are unlikely to succeed in the long run unless there is
greater investment in the human capital of the poor. Improvement in education, health and
nutrition directly address the worst consequences of being poor. There is ample evidence that
investing in human capital, especially in education, shelter and social services increases the
poor‘s productivity and also attacks some of the most important causes of poverty. Improving the
social services of the poor will be an essential part of any long-term strategy for reducing poverty
in Nigeria as a whole.
lxxxi
Conclusively, any policy designed to ameliorate the plight of the poor must among other
things recognize housing, provision of potable water, improved health care facilities, improved
transportation in terms of good roads and provision of more mass transit buses and train, sound
education for the wards of the poor and employment opportunities. No societies can surely
flourish and be happy, when by far the greater part of the numbers are poor and miserable.
However, we would like to acknowledge that all government economic reforms would be
inadequate unless they are able to result in tangible improvements in the welfare of the ordinary
Nigerians. As Amartya Sen (okonjo-Iweala, 2006) once said, the goal of development must be
viewed as enhancing the capabilities of (poor) people. Therefore, the need to alleviate poverty in
Nigeria as a whole should be the highest priority of the government and the citizenry.
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