THE EFFECTS OF URBANIZATION ON AGRICULTURAL PRODUCTIVITY IN

WEST AFRICA (1989-2010)

MUYIWA-ONI OLUTOBI HARRY

(10AF010493)

BEING

A RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE

REQUIREMENT FOR THE AWARD OF B.SC DEGREE IN ECONOMICS TO THE

DEPARTMENT OF ECONOMICS AND DEVELOPMENT STUDIES, SCHOOL OF

SOCIAL SCIENCES, COLLEGE OF DEVELOPMENT STUDIES, COVENANT

UNIVERSITY, CANAANLAND, OTA,

NIGERIA.

MARCH 2014

i

CERTIFICATION

It is hereby certified that this research project, written by MUYIWA-ONI OLUTOBI HARRY,

was supervised by me and submitted to the Department of Economics and Development

Studies, School of Social Science, College of Development Studies, Covenant University, Ota.

Mrs. Oluyomi Ola-David ………………………………….

(Supervisor) Signature & Date

Dr. P. Alege …………………………………..

(Head of Department) Signature & Date

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DEDICATION

To all powerful and ever living God and also to my parents, Mr and Mrs R.A Muyiwa-Oni,

In memory of Ihuoma Ndubuisi, Onyeke Emmanuel, Kelechi Ndubuisi and Egemba Victor.

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ACKNOWLEDGEMENTS

My sincere gratitude goes to Almighty God who has been my help since the beginning of this

work till this point through divine inspiration .I owe the completion of this work to a lot of

individuals apart from myself, and I wish to acknowledge a few of these people here, who

contributed immensely.

The Chancellor, Dr David Oyedepo, who laid the great pathway that I currently walk in and

also for pursuing the vision of Covenant University, which I am truly a beneficiary. The Pro-

Chancellor, Pastor Abraham Ojeme. The Vice-Chancellor, Prof. C.K Ayo whose short and

snappy words have inspired me to greatness. The Registrar, Mr. Muyiwa Oludayo whose

communication skills I truly crave. The Dean, College of Development Studies, Prof. O.

Olurinola, The Head of Department, Economics and Development Studies, Dr. P. Alege whose

effective teaching methods have certainly made me a better student.

My deep and utmost gratitude goes to my supervisor Mrs. Oluyomi Ola-David who was there

from the beginning and helped me in many ways. God bless you Ma. I would also duly

acknowledge my Lecturers that taught me during the course of my study: Dr. M. Adewole, Dr.

H. Okodua, Dr. O. Ewetan, Dr. E. Urhie, Dr. E. Osabuohien, Dr. Oluwatoyin Matthew, Mr A.

Alejo, Mr John Odebiyi, Mr. Stephen Oluwatobi, Mr. Adeyemi Ogundipe, Mrs O. Ogundipe,

Mrs T. Amalu, Miss. Ibukun Beecroft, Miss O. Akinyemi.

My parents Mr. and Mrs. R.A Muyiwa-Oni, my siblings Soji, Femi, Solomon, Yemisi,

Akinwale, Olumide, Akinfolarin and Eniola for their moral and financial support. I also want

to thank my hospitality family, my special ones Segun Afolabi, Babajide Ajayi, Uzoma

Obinna, Babatunde Macaulay ,Soso, David Egwede, Ifeanyi, Jennifer Subi, Yadinma, Kome,

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Faith, David Olaleye, Uyi, Femi, Jibola, Tominiyi, Jachike, my coursemates and my friends for

their help and also making my stay in Covenant University worthwhile , I cherish you all.

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TABLE OF CONTENTS

TITLE PAGE i

CERTIFICATION ii

DEDICATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT x

CHAPTER ONE: INTRODUCTION

1.0 Background of the study 1.

1.1 Statement of Research Problem 5.

1.2 Research Questions 7.

1.3 Research Objectives 8.

1.4 Research Hypotheses 8.

1.5 Scope of the study 9.

1.6 Justification of the study 9.

1.7 Structure of the study 9.

CHAPTER TWO: LITERATURE REVIEW

2.1 Introduction 11.

2.2 Review of Definitional Issues 13.

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2.3 Review of Theoretical Issues 16.

2.3.1 The Theory of Agglomeration Economics 17.

2.3.2 The Lewis two sector model 20.

2.3.3 Todaro`s Rural-Urban Migration Theory 21.

2.3.4 The Harris-Todaro model on Migration 22.

2.3.5 Mabogunje`s Central Place Theory 24.

2.3.6 Ravenstien`s Laws of Migration 25.

2.4 Review of Methodological and Empirical Issues 26.

2.5 Conclusion 34.

CHAPTER THREE :THEORETICAL FRAMEWORK AND RESEARCH METHODOLOGY

3.1 Introduction 35.

3.2 Theoretical Framework 35.

3.3 Research Methodology 37.

3.4 Model Specification 39.

3.5 Technique of Estimation 41.

3.6 Justification of Variables Used 42.

3.7 Apriori Expectation 43.

3.8Data Employed, Measurement and Sources 44.

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CHAPTER FOUR :DATA ANALYSIS AND INTERPRETATION

4.1 Introduction 45.

4.2 Descriptive analysis 45.

4.3 Empirical Analysis 46.

4.3.1 Test for multicollinearity 46.

4.3.2 Test for heteroskedasticity 47.

4.3.3 Hausman test 48.

4.3.4 Interpreting the Random Effects Model 49.

4.3.5 Testing for Random Effects using the Breush-Pagan Lagrange Multiplier (LM)

52.

4.4 Summary of findings 53.

CHAPTER FIVE :SUMMARY, RECOMMENDATIONS AND CONCLUSION

5.1 Summary 55.

5.2 Recommendations 56.

5.3 Conclusion 58.

5.3.1 Limitations of the study 58.

5.3.2 Suggestions for further study 59.

REFERENCES 60.

APPENDICES 62

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ABSTRACT

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This study empirically examines the effects of urbanization on agricultural productivity in West

Africa using cross-sectional data from 1989-2010. The main objective of the study is to

examine the effect of urbanization on agricultural productivity. This research is divided into

five chapters, chapter one provides an introduction to the subject matter, chapter two looks at

the review of past literatures, chapter three explains the chosen theory and also the derivative

of its equation and also shows the model and methodology used in the study. In chapter four

various tests were run so as to establish the impact of the independent variables on the

dependent variable. The last chapter provides conclusions and required policy

recommendations based on the findings in previous chapter. The study employs panel data

analysis and the random effects model was used because it was most suitable for the research

after running the hausman test to ascertain whether time invariant variables and unique

errors are correlated with the regressors or not. In order to capture West Africa, a sample of

14 countries in the region was used. The study made use of the following variables in capturing

the effects of urbanization on agricultural productivity: agricultural productivity in terms of

labour productivity, urbanization level, life expectancy at birth, education level in terms of

primary school enrollment, industrial productivity and agricultural population. Results show

that urbanization significantly affects agricultural productivity in West Africa. Another

important result is that education level has a significant impact on agricultural productivity.

Keywords: Urbanization, Agricultural productivity, urban agglomeration and panel data

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CHAPTER ONE

INTRODUCTION

1.0 Background of the study

Large share of the growth of population of the world in coming decades will be situated in the

cities of developing countries. Cities in sub-Saharan Africa will be responsible for a good

number of the increase in the demand for food. People who dwell in urban areas are not just

consumers but they are also producers of food items especially perishable agricultural produce

of high value (United Nations, 2000). West Africa is mainly characterized by developing

countries. Urban population growth experienced there is a product of both rural-urban

migration and growth of population that comes naturally. There is a form of pressure that rapid

stage of rural-urban migration puts in cities and rural areas. First is that it has impact on almost

all the dimensions of development in countries which can be in form of education,

transportation, supply of water, health and so on. Secondly, it is also responsible for the

absence of educated youths in rural areas which is called youth drain which affects agricultural

labour productivity and the social life of people in rural areas.

Bocquier projected that the proportion of the world population living in cities and towns in the

year 2030 would be approximately 50 percent greater which is significantly less than the 60

percent that was predicted by the United Nations (UN). This is so because of rapid

urbanization has been discovered to be disorderly thereby making urbanization unsustainable

(The futurist magazine, 2005 and Bocquier, 2005). Both the UN and Bocquier were of the

opinion that more people will flock into cities, but Bocquier believes that many people will

leave urban areas once they realize that there is no work for them and the problem of shelter.

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According to UN-habitat, sub-Saharan Africa is urbanizing faster than any other continent.

West Africa which is in Sub-Saharan Africa has only recently started its urban transition; the

pace is such that it can expect an urban majority by 2030. Djibouti, Gabon, Mauritania and

South Africa were the countries that had urban majorities in 2001. Amazingly it has been

forecasted by UN-habitat that not less than nine sub-Saharan countries will pass the 50 percent

urban mark at the end of this decade. Also in some countries in West Africa, urbanization rates

exceed 4 to 5 percent per annum. Amazingly these rates are close to rates experienced in

Western cities at the end of the 19th century. The average sub-Saharan Africa typically

experienced persistent annual urban growth rates of 5 to 6 percent, while some cities saw

annual growth rates in excess of 10 percent, which means that the population doubles every

decade. Johannesburg was the sole sub-Saharan African city that exceeds 1 million inhabitants

in 1960. Then in 1970 we had four Cape Town, Johannesburg, Kinshasa and Lagos. At the end

of 1989, the list included Abidjan, Accra, Addis-Ababa, Dakar, Dar es Salaam, Durban, East

Rand, Harare, Ibadan, Khatoum, Luanda and Nairobi.

Mabogunje (2002) described today`s Nigerian city to be characterized with substandard and

inadequate housing, lack of infrastructure, slums, poverty, low productivity, youthful

delinquency, crime, transportation problems and held urbanization as the root cause of all that

was stated earlier with pollution and environmental degradation. As at 2004, Nigeria was

ranked 151st on the Human development index, then in 2012 Nigeria was ranked 153 rd on the

development index of 177 countries worldwide (UNDP, 2004, 2013)

In Ghana, Over 60 percent of the populations are involved in agriculture and it remains a major

source of employment to the people of Ghana. This deprives the sector of land but therefore it

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brings an increase in the unemployment rate. The dual pressure of rapid Urbanization and fast

growing population have wreaked havoc on agricultural land relations and land management in

Tamale(Ghana) thereby reducing productivity (Naab, Dinye and Kasanga, 2013). The chief

crisis of rapid urban growth is the problem of varying land use patterns.

Nigeria as a nation has been experiencing an accelerated shift in her population from rural to

urban areas. This is also visible in other countries in West Africa. In Nigeria the degree of

Urbanization has improved tremendously most especially in the past 25 years. The census that

was conducted in 1952 indicated that there were about 56 cities in the country and about 10.6

percent of the total population lived in these cities. Amazingly, this rose to about 19.1 percent

in 1963 and 24.5 percent in 1985. Nigeria is regarded as the most heavily populated nation in

Africa and also one of the fastest on the rise on earth. Nigeria is characterized with 250 ethnics

groups. The largest of these groups are the Hausa and Fulani who reside in the northern part of

the country. The economy of Nigeria historically was based on agriculture, and about 70

percent of the work force participates in it. Major crops produced are Cocoa, peanuts, palm oil,

corn, rice, sorghum, millet, soybeans, cassava, Yam and rubber. In addition, cattle, sheep,

goats, and pigs are raised.

Petroleum is the leading mineral produced in Nigeria and provides about 95 percent of foreign

exchange earnings and the majority of government revenues. Petroleum production on an

appreciable scale began in the late 1950s and by the early 1970s it was by far the leading

earner of foreign exchange. The growing of oil industry attracted many to urban centers, to the

detriment of the agricultural sector and the huge government revenues from oil led to

widespread corruption that has continued to be a problem. Amazingly in the 1980s a decline in

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world oil prices provoked the government to bolster the agricultural sector. It is essential to

note that both the refinery capacity and agriculture have not kept the pace with population

growth thereby forcing the nation to import refined petroleum products and most importantly

food items. In the historical perspective, Nigeria had long benefited from rapidly growing

export even before the discovery of petroleum in 1956. Between 1900 and 1929 the volume of

exports was dominated mainly by palm produce, groundnuts, cocoa, rubber and cotton grew at

an average annual rate of 5.5 percent.

Iwayemi (1995) mentioned that the agricultural sector of any economy is known to play many

key roles in the process of economic development. He opined that these roles can change with

stages of economic development. But in general, the roles often associated with agriculture in

the early phases of economic development include the generation of most of the gross domestic

product (GDP) , the provision of most of the employment opportunities for the labour force,

the provision of adequate food for a growing population, the generation of foreign exchange,

the generation of savings or investment in agriculture as well as other sectors, the production of

raw materials and the release of surplus or under-utilized resources for use in other sectors

especially in the fledging industrial sector and the provision of an expanding market for the

products of non-agricultural sectors. Most of these roles derive from the naturally domineering

position of agriculture in the early stages if economic development. The rest are the

consequences of growth and structural transformation over time in the economy.

Therefore, the leading role of the agricultural sector in the generation of gross domestic

product as well as employment opportunities is a basic feature of primordial market economies

which stems from low productivity, high degree of subsistence production and rapidly

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increasing population. It also stems from the lack of a clear line of specialization in economic

activities like agriculture. Comparing this with others it led to many of the activities associated

with non-agricultural sectors have to be carried out within the agricultural sector, In addition to

normal agricultural activities.

Urbanization can be defined as the increase in the percentage of the national population that is

urbanized (Henderson and Wang, 2004). According to Anthony (2011) Urbanization is the

process whereby large number of people congregate and settle in an area, eventually

developing social institutions such as government and businesses in order to support

themselves. Urbanization is as a result of bodily growth of urban areas caused by population

immigration to an existing urban area. In mentioning the effects of urbanization it is not

possible to overlook the increase in population density and most importantly increase in

administration services. Urbanization is attached to the growth of cities. United Nations (2007)

described urbanization is the movement of people from rural areas to urban areas with

population growth which equates urban migration. The problem of many countries is the

reason why the exact definition and population size of urbanized areas varies. Over the years

Farmers have through the application of science and technology, evolved methods of

increasing agricultural productivity (Ukeje, 2000). In Nigeria agricultural productivity has been

growing over the years at different rates. Ukeje divided the periods into three namely; pre-SAP

era (1970-1985), SAP era(1986-1993) and the era of guided deregulation(1994-1999).

1.1 Statement of Research Problem

The problems that cities in West Africa are facing have resulted largely because urbanization is

yet to correspond with the growth or decrease in agricultural productivity and output. This is

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responsible for the dwindling of the resources required to manage cities that has been

reoccurring since last two decades and also increase the productivity in agriculture.

Interestingly, there has been continued agglomeration of people into urban centers whose

population is on the increase. Is it possible for cities in West Africa to provide their dwellers

that are educated and in good health with the employment and income-earning opportunities

for them to be able to take care of their well being and welfare through increase in agricultural

productivity?

Growth in urban population goes with no equivalent growth in land supply. Also land is fixed

in supply and does not increase with increasing population growth. Agricultural lands are most

affected by rapid urbanization and its functions of demand. Land uses for residential, industry

and commercial, civic and culture tend to dominate agricultural lands in the bid for space in the

urban place. This is responsible for Farmers being underprivileged to have access to arable

land to cultivate thereby reducing agricultural productivity. Attainment of higher productivity

presupposes the availability of skilled labour force. Skilled labour force is required to

transform the static past into a dynamic present and prosperous future. The inadequacy of

skilled farm labour is further compounded by unavailability of labour, particularly when it is

required to satisfy seasonal labour demand. This labour shortage has been aggravated by a

substantial reduction in the supply of family labour due to the persistent rural-urban drift.

It has been observed that people in West Africa migrate to urban areas so as to attain better life

and they fail to return to rural areas when they are convinced that the dream of better life is not

true. This is so because of the rate of competition for getting job and other basic needs attached

to the urban center. It has also been projected that West Africa will soon experience swift

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urban population growth, and it is not certain that the region has the capacity to handle the

projected enormous increase. There is also no form of any theoretical consensus that will be

able to explain the workings of urban population growth and urbanization in West Africa.

Same theories are also contradicted due to the portrayal of the reality (Nordhag, 2012).

During the colonial era in Nigeria, at the time when industrialization was introduced in Lagos

and this resulted in development, since then the government shifted focus from rural areas

which could not welcome industrialization. Due to this even the government started allocating

more funds and resources to the urban areas thereby leaving the rural areas to remain

undeveloped and static. This is the responsible for the poor welfare of the major percentage of

the population of the country who still reside in the rural area with only localized farming and

trading.

Urban areas development due to industrialization has led to the migration of people from the

rural areas to urban areas. Majority of the people that migrate to urban centers without any

form of education or skills are responsible for over-population experienced in urban centers in

sub-Saharan Africa and also end up as nuisance retarding the development of urban areas.

Same over-population in urban areas welcomes poor welfare.

1.2 Research Questions

Based on the justification of this study to every economy, this study attempts to answer the

following questions:

i. To what extent is the impact of urbanization on agricultural productivity in West

Africa?

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ii. What measures can be implemented to control urbanization in West Africa?

iii. Is there a possibility that the current rate of urbanization can be a mechanism of

economic development through increase in agricultural productivity?

1.3 Research Objectives

The main objective of this study is to examine the effect of urbanization on agricultural

productivity in West Africa. The specific objectives are;

i. To ascertain the extent of the impact of urbanization on agricultural productivity in West

Africa

ii. To discover the measures that can be implemented to control urbanization in West

Africa.

iii. To know whether the current rate of urbanization can be a driver or mechanism of

economic development through increase in agricultural productivity in West Africa

1.4 Research Hypotheses

The following are tested in the study

Hypothesis One:

H0: Agricultural productivity in West Africa does not depend on urbanization.

Hypothesis Two:

H0: There is no positive relationship between urbanization and agricultural productivity

Hypothesis Three:

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H0: Urbanization in West Africa is not distorted compared to agricultural productivity in West

Africa

1.5 Scope of the study

The study covers the periods between 1989 and 2010. The study examined data on population

density, agricultural productivity in terms of labour productivity and specific relevant

information which will include the challenges of urbanization in West Africa. The scope of this

study is limited to 14 out of 16 countries in West Africa.

1.6 Justification of the study

This research will be significant in numerous ways. It will be of immense benefit to the

government and policy makers for the following reasons. As a result of rapid urbanization from

rural to urban areas experienced in West Africa it is now a need to ascertain how the current

available resources will provide for the needs of the contemporary generation without

compromising the needs of the upcoming generation. Due to this, it is of great importance that

people must know how urbanization can play a crucial role on agricultural productivity thereby

affecting the economy positively.

1.7 Structure of the study

This research study is divided into five chapters, each deals with different aspects of the study.

Chapter One takes care of the introductory aspect of this study which includes the background

of the study, statement of problem, objectives of the study, justification, research hypothesis,

methodology, data sources and limitations of the study and the structure of the study.

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Chapter Two is concerned with the review of past literatures by previous researchers and most

importantly their findings. Chapter Three is about the theoretical framework, the research

methodology and model specification of the study. Chapter Four is about the analysis of the

study and also presents the results of our estimated models. Finally chapter Five is the

summary of the major findings emerging in this study and it also includes my

recommendations and concluding remarks.

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CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

Urbanization is necessary for achieving high growth and high incomes. The initial stage of

urbanization is very beneficial and can also be painful at the same time. Therefore managing

urbanization will affect the various aspects of a country like politics, social norms, institutional

change and the financial system in place (Annez and Buckley, 2009). During the past three

decades, the cities of the developing world and most especially Africa have witnessed a

remarkable and in many ways unprecedented demographic spurt. Regardless of the slowdown

in rates of increase in the past few years due to decreasing wages thereby shrinking social

services, and changing demographic trends, modern-day urban areas remain the growth poles

of economic progress and the lightning rods of political and social unrest. This predicament is

not as evident in any other place apart from the crowded cities of sub-Saharan Africa, where

projections of urban population growth remain the highest in the world (Todaro, 1997).

Fay and Opal (2000) stated that sustainable economic growth is always accompanied by

urbanization. In most African economies we see the inverse of this. Just because urbanization

occurs without growth in some African economies and it is evident that urbanization in Africa

is distorted. It can be also be said that urbanization in sub-Saharan countries is not always

accompanied by sustained growth. Also in the economic downturns, the poor and the migrants

don`t often flock back to rural areas. It will be burdensome for any country to attain middle-

income status without a significant drift of the population into cities. Through this assertion, it

is possible to say urbanization and growth works hand-in-hand. In other words one will lead to

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another. Urbanization occupies a puzzling position In the case of development and growth

theory (Annez and Buckley, 2009).

Urbanization is a gradual process and it makes it impossible for new cities to pop up out of

nowhere. Countries start from low human capital levels where all economic activity is in

agriculture and at some critical value of human capital accumulation, urbanization starts

(Henderson and Wang, 2004). Apart from urbanization being a gradual process it can take on

different patterns in different settings. This is so because many countries conform to standard

views of some structural transformation. In such an economy, Urbanization ends up being a

bye product of either pull from industrial productivity growth or a push from agricultural

productivity growth. In such countries urbanization occurs with industrialization and thereby

generates production cities (Gollin, Jedwab and Vollrath, 2013).

The twin pressures of rapid urbanization and a fast growing population have wreaked havoc on

agricultural land relations and land management in Temale (Ghana). Agriculture which is the

main source of livelihood of peri-urban dwellers is as a result of rapid urbanization because of

its problem of scarcity of land for agricultural purposes that will arise (Naab et al., 2013).

This chapter will focus on the review of literatures related to urbanization and agricultural

productivity in West Africa and sub-Saharan Africa in total and also to shed more light on

urbanization

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2.2 Review of Definitional Issues

All countries set boundaries between rural and urban population, the aspect of definitional

issues of urban areas varies among countries and from time to time it even varies within the

confines of a country. Naab et al. (2013) presented urbanization as being measured by

demographers as the urban population divided by total population of that region. Urbanization

is also defined as the annual rate of change of the percentage of people living in urban areas, or

the difference between the growth rate of urban population and that of total population. Urban

communities can be defined in many ways via population density, population size,

administrative boundaries or even the economic function of the country. Henderson and Wang

(2004) defined urbanization as the increase in the percentage of the national population that is

urbanized. The idea in urbanization is that large number of people quit farms so as to live and

work in the city. Only when there is an increase in the percentage urban dwellers than we can

say there is urbanization.

Urbanization is the process whereby large numbers of people congregate and settle in an area,

thereby eventually developing social institutions such as businesses and government to support

themselves. Urban areas which are now formed are characterized as relatively dense

settlements of people. The process of urbanization is a focal point for many sociological

concerns (Anthony, 2011). Urbanization moves populations from traditional rural

environments with informal political and economic institutions to the relative anonymity and

more formal institutions of urban settings, which is as result of the shift of the population from

rural to urban environments regarded as a transitory process. Urbanization within itself requires

institutional development within any given country. Urbanization separates families, most

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especially spanning through many generations as the young migrate to cities thereby leaving

the old behind in rural areas (Henderson, 2003).

Ramanchandra and Aithal (2013) stated that urbanization is the growth which is attained

physically in urban areas due to rural migration and even as the effect of towns or suburban

concentration transforming into cities. Some factors triggers urbanization through government

efforts which later result into improved opportunities for jobs, housing, transportation and

education. Likewise there is another term that has been confused which is urbanization rate, it

is defined as the increase in the percentage of the national population that is considered to be

urbanized, it is most rapid at low income levels and then tails off as countries become fully

urbanized (Henderson and Wang, 2004).

Fulginti, Perrin and Bingxin (2004) stated that productivity can be expressed as the output per

unit of input. Growth of productivity aims at not just capturing output growth but also

capturing output growth not accounted for by growth in inputs. Land, Livestock, machinery,

fertilizer and labour are considered to be traditional inputs. Agricultural land can be measured

as the sum of arable land and fixed crops available in thousand hectares. Agricultural labour

can be measured be looking at the number of persons who are economically active and

participates in agriculture preferably measured in thousands. Ukeje (2000) opined that

productivity in agriculture is the measure of how efficiently effective resources are used as

inputs for the production of goods and services required by the general public on the long run.

Productivity is an essential issue that is attached to agricultural development because of its

impact on the economy. Ukeje said for mankind to walk out of poverty and hardship the level

of productivity must increase.

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It can said that at the production level, agricultural productivity can be defined as the value of

output for a given level of inputs (Forum for Agricultural Research in Africa, 2006). The value

of output must increase faster and more than the value of inputs so as to increase productivity

in agriculture. Gains in productivity will come through some changes in the production process

as a result of more output per unit of input like land yields or labour or even from some

changes in production and market costs and therefore increases the profitability of farmers.

Urbanization is an irrevocable process which involves changes in vast expanse of land cover

and local ecology with the progressive concentration of Human population. Urban Sprawl

refers to excessive unusual growth near the periphery of the city boundary or in places where

there is the absence of planning and basic amenities are not available. Urban sprawl can also be

referred to as unplanned growth because it involves unsystematic and unappealing expansion

of an urban area into neighboring boundaries (Ramanchandra and Aithal, 2013).

Urbanization occurs as a result of large number of people deciding to concentrate in a

relatively small area so as to form cities. The world urbanization prospects report only presents

urban data that reflects on national definitions, which are challenged with the problem of

consistency (United Nations, 2012). It is important to note that places considered as urban in

one country may be seen as rural in another country. Good examples are countries like Angola,

Ethiopia and Argentina agrees that localities with 10,000 inhabitants or more is said to be

urban. Then any community with below 10,000 inhabitants is said to be rural. Hitherto other

countries in order to ascertain whether a community is urban or not, it is done through urban

boundaries centered on a mixture of population density or size and other economic and social

indicators.

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Urbanization is one of the striking global changes because it is a social phenomenon and also

the physical transformation of landscapes. Most especially in metropolises and places regarded

as mega-urban. This term urbanization anticipates trends with global and regional

consequences which are very difficult to forecast. Urbanization has expansion, compression

and also differentiation as the drivers.

Expansion in urbanization is said to be the spread of mega-urban regions and their intrusion

upon hinterland. Mega-urban areas are mainly dependent on catchments and supply areas for

essential resources, energy, goods and proper information flow so as to maintain and preserve

their metabolism. Large agglomeration characterized with the compaction of people,

knowledge, proper social interaction and also proper decision making process is regarded as

compression. Differentiation demarcates the possibility of increase and also the degree of

change in the society therefore it is regarded as one of the consequences because people will

have the opportunity to compare and also distinguish themselves and also welcome new

lifestyles.

2.3 Review of Theoretical Issues

Regarding existing literatures on the relationship between urbanization and agricultural

productivity there is a problem of inconsistency in variation because the term urbanization has

different definitions in all parts of the world. Also there is no unswerving measure of

urbanization. No country has grown to middle income without industrializing and urbanizing.

None has grown to the level of high income without pulsating cities that commands resources.

The rush to cities in most developing countries seems chaotic, but it is very necessary (Turok

and Mcgranahan, 2013). Also the city is one of the highest pinnacles of human creation,

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through agglomeration cities have the power to generate wealth and also enhance the quality of

life and also accommodate more people with smaller footprint at lower per capita resource

usage and emissions than any other settlement pattern.

China can be used to explain how urbanization can fuel industrialization, increase the level of

productivity, and also transform living standards of people. In 2011, China passed the historic

landmark of 50 percent of its population living in cities from only 20 percent in 1980. This

extraordinary speed of urbanization has reflected the strength of jobs growth in cities. Also the

average household incomes tripled. There is expected urban increment which will occur

globally of 1.8 billion people during 2025-2050, this will have India become a major

contributor with 377 million people, then will be followed by China with 205 million people.

Both countries are regarded as the top two most populous countries in the world and it is

expected that both countries will account for 32 percent of urban growth spanning from 2025-

2050. It has been projected that by 2050, China will have the largest urban population of 1

billion people and India will have 0.9 billion people (United Nations, 2007).

2.3.1 The Theory of Agglomeration Economics

Turok and McGranahan (2013) presented some clarifications that must be made about some

concepts under this theory and also about the benefits that can be derived from economic

concentration. The first concept is the concept of division of labour. This explains the gains

derived from productivity which later develops to growth from the act of specialization. Here

firms divert their attention to special tasks or even products that will later give way to

enhanced skills and most importantly greater efficiency. At city level it is possible to apply

specialization, this can be done because the point when external trade starts growing and the

17

level of competition becomes deepen at this point specialization becomes very essential.

Through specialization other benefits can be derived from bringing a group of functions to the

center of attention through which places become heir to some distinct merits.

The second concept is that of economies of scale. This can be sub-divided into two namely;

internal economies of scale and external economies of scale. Starting with internal economies

of scale, it is not really applicable to agglomeration economics because they are internal to the

firm and commands lower unit costs via large-scale production. Our focus will be on external

economies of scale. It is otherwise known as agglomeration economies. They are the benefits

derived from the act of being attached to other firms so as to reduce costs attributed to

transactions like communication and transportation, as well through shared information derive

some gains attached to network effects. This is so because it has been observed that the bigger

the network, the wider the base of knowledge and also the intelligence that will later be useful

to learn from (Duranton, Gilles and Puga, 2004).

Agglomeration economies is broad and the contents are nearness to a bulky labour pool,

customers, suppliers and most importantly competitors within the same industry regarded as

localization economies, and also firms that exist in other industries regarded as urbanization

economies. External economies of scale comprises of three extensive functions, the functions

of learning, sharing, and matching. Looking at this serially, the function of matching comes

first because cities help firms to match their unique requirements of firms for some essential

contributions like Labour, material inputs and also premises better than towns (Duranton et al.,

2004). Through agglomerations firms will not be having problems in mixing and matching

their resources with ease. The next function is concerned with sharing.

18

As a result of scale of activity, cities make available to firms a wider range of services which

are shared and also infrastructure. Cities bring about a nexus between customers and suppliers

via transportation that unites more destinations and also more logistics to take care of exports

and imports. The third function is that of learning. Firms enjoy better-quality in the flow of

information in cities. This function creates a self-reinforcing virtuous circle that encourages

creativity and generates growth from within. Summing all these functions together, it is

observable that the merits of external economies of scale are significant just because they are

collective and not static (Hall, 1998). Urbanization will necessarily make economic output to

increase not just because of the existence of agglomeration economies. This is so because of

the gains from concentration via overcrowding, increasing congestion amongst others.

It is very possible that the point of equilibrium between agglomeration diseconomies and

economies to have impactful influence on the economy. This can either make economies

stagnate, grow or even reach the point of declining in cities. Advantages derived from

agglomeration may vary in and out of the sectors in the economy owing to the existing

relationship between urbanization and growth. Land and housing markets function effortlessly

and local authorities are known to be alert to market failure so as to ensure that people and also

firm can now with ease relocate thereby making suitable land, road and rail network could also

be made available to accommodate the people. In conclusion of this theory, Urbanization

implies a decrease in the portion of rural dwellers and importantly not just more urban

dwellers. In a scenario where overpopulation in the rural area is becoming a problem,

urbanization can be used to help out. It is crucial to know that all forms of urbanization will

have the same level of shock on productivity in rural areas and also incomes (Dorosh, Paul and

Thurlow, 2012)

19

2.3.2 The Lewis two sector model

This theory is attributed to W. Arthur Lewis. He came up with this theory so as to elucidate the

workings of an underdeveloped economy via the migration from rural sector to the urban

sector (Todaro and Smith, 2011) This theory is simply about transformation. It captures

transformation of a traditional economy based in agriculture into a current industrialized

economy. This theory presupposes a rural sector that is generally ascribed by surplus labour

which is possible to be swallowed up by the modern sector. It will be swallowed up without

any form of disruption on the side of the production output on the country-side (Todaro and

Smith, 2011). So far as surplus labour exists, the modern sector will be charged with the

responsibility of providing minimum wages even though at a level of higher value than the

profits enjoyed via agricultural labour. This is the reason behind the attractiveness of urban

labour (Nordhag, 2012).

The assumption behind this theory is that since the modern sector will enjoy some profits, this

marginal profit will be channeled into modern sector so as to increase and also improve the

production level which will later cause a demand of more employees (Mishra, 1969). As

surplus labour is increasingly swallowed, the urban labour force becomes very scarce. This will

later result in higher wages since it is a necessary condition in order to employ agricultural

workers that do not fall in the bracket of surplus labour. It is important to know that in this

model, everybody in the traditional workforce will be employed by the modern sector. This

will later cause labour demands to stop increasing because of the presence of capital-intensive

actions.

20

This theory has been criticized because it cannot be applied in today`s less developed countries

in West Africa. The model of Lewis is just a reflection of the process of industrialization of

more developed countries. The assumption that in order to increase production, accumulated

capital is reinvested Todaro and Smith (2011) opined that the process of investment may not

happen at all at the beginning. The study stated that there is no form of assurance that owners

of companies will decide to invest. This is so because firstly, Humans cannot be predicted and

they can make different choices about what to do with profits. Then due to the problem that job

seekers will suffer instead of enjoying because the process of reinvesting can be in different

forms like purchasing machinery to replace labourers which is a good form of labour saving

action.

Although the Lewis two sector model is widely known but it is challenged with the problem

that not all the people under the rural workforce is willing or ready to accept jobs in the

modern sector (Todaro and Smith, 2011).

2.3.3 Todaro`s Rural-Urban Migration Theory

Rural to urban migration remains a significant part of urbanization in developing countries of

which West Africa is a member. This makes this theory suitable for the study of urbanization

in West Africa which has been subjected to heavy urbanization over the years. This theory was

developed because of the labour migration in eastern and southern Africa due to colonial rule.

Todaro (1969) laid emphasis on pull and push factors in migration and He explained that rural-

urban migration was a response to the predictable income rather than the current income

differentials that exists between rural and urban areas. This model was based on four

assumptions

21

i. Migration is propelled principally by rational economic considerations, relative costs

and benefits of both a financial and psychological nature,

ii. Man`s decision to migrate solely depends on expected rather the current urban-rural

wage differentials,

iii. He also said the probability of obtaining an urban job is inversely related to the urban

unemployment rate

iv. Since urban In-migrants expect positive urban-rural expected income differentials, the

phenomenon of persistent high rate of urban unemployment represents a logical

consequence of the imbalance in economic opportunities between urban and rural

areas.

2.3.4 The Harris-Todaro model on Migration

This theory was published in 1970. Todaro and Smith (2009) said this model is the equilibrium

form of the Todaro migration model. The intention of this model is to explain the dynamics of

the process of people moving from rural to urban areas. Conventional economic theories on

rural to urban migration are misleading and also not satisfactory (Harris and Todaro, 1970).

The authors of this theory opined that previous theories assumed that migrants have full access

to waged employment and it is the only driving force behind migration. Harris and Todaro

stated that other theories did not look at the situation in which many rural-urban migrants end

up not being employed and that reality is completely different.

This theory has a unique setting in which there is a rural area where only agricultural products

are produced via the totality of the labour force or by removing restrictions restricting labour

force from migrating so as to seek waged labour (Harris and Todaro, 1970). Goods are

22

manufactured in the urban areas by waged employees. This theory argues that rural-urban

migration will not stop unless profits from working in the agricultural sector exceed the urban

minimum wage. This theory was able to comprehend the reason behind high unemployment

rates experienced in urban areas. The study then attributed it to urban minimum wages which

are mostly fixed synthetically by some institutions in such a way that it is not compatible with

the free markets which is very evident in developing countries. This will later result in a state

of equilibrium where the possibility of gaining higher incomes via rural-urban migration is

matched with the risk of unemployment.

Harris and Todaro (1970) also affirmed that before the occurrence of any form of rural-urban

migration the individual involved carefully calculates the income expected and coupled with

the chance of getting employed at all. Both factors are then balanced against each other. They

also made it clear that rural to urban migration is likely to occur if the wages paid in urban

areas are much higher that what is paid in rural areas regardless of the risk of unemployment.

The study came to a conclusion that the chance of getting a well-paid job will prevail over the

risk of the individual not getting a job. Due to all this high unemployment rates and rural to

urban migration will not cease in less developed countries (Todaro and Smith, 2009).

Since this theory was based on future equilibrium, Charles H. Woods criticized the theory and

was of the opinion that equilibrium based theories fail to address some very essential factors. A

good example of the factors mentioned in the study is Ethics. Woods further stated that in view

of the fact that this theory assumes that rural to urban migration will later regulate itself. He

then concluded that this will discourage state actions channeled to get in the way of migration.

Woods was of the opinion that since the state lacked control it will be possible for job seekers

23

to be exploited. The study also criticized the assumption that people are propelled to migrate

because of potential income gains. This assumption is not in any way realistic because they are

other fundamental reasons beneath rural-urban migration. Woods (1982) said an individual`s

decision to migrate does not depend only on the rational choice but it is as a result of many

socio-economic factors.

2.3.5 Mabogunje`s Central Place Theory

The theory was concerned about welfare and made mention that it remains implicit to the

analysis of the study which is attributed to Mabogunje This theory established the link that

exists between central cities, the types of goods and services found in them, the spacing

between cities and most importantly the travel-willingness or travel-frequency of individuals

(Filani, 2006). The study presented central places as small towns that offer lower order goods,

central places will now constitute a hierarchical system where higher order places will be at the

top and lower order central places will be at the bottom. The study further explained that there

are some factors that can cause threshold population to decrease drastically.

The study also referred to central place as a settlement which provides some specific services

for the population that resides around it (Filani, 2006). Under this theory Mabogunje

mentioned some simple basic services which the study later grouped to Low and high order.

The study presented settlements with low order services are classified to be low order

settlements then those with high order services are classified to be higher order settlements.

Also a distinct line was drawn between threshold population and sphere of influence. Sphere of

influence is that area that is subjective to the central place while the study also presented

threshold population the minimum population size needed to adequately maintain a service.

24

Some fundamental assumptions of this theory are as follows: the population is assumed to be

evenly distributed the existence of similar purchasing power and resources are evenly

distributed and the existence of isotropic surface.

2.3.6 Ravenstien`s Laws of Migration

This theory comes under the neo-classical equilibrium perspective and it is attributed to Ernst

Georg Ravenstein. This theory perceived migration from another perspective and it stated that

it remains an inseparable part of a Nation`s development. Ravenstein was of the opinion that

migration was solely caused by some economic factors. Migration patterns of this were

assumed to be manipulated by population density and the distance (Ravenstein, 1885). The

theory expects people to move from low income areas to high income areas due to rationality.

Also people are expected to move from densely populated areas to an area that is lightly

populated in search of comfort. This theory remained strong after it was criticized by many

scholars. These laws are now seen as empirical generalizations based on Ravenstein`s

calculations from the census they had in Britain. The following generalizations formed His

laws.

i. Migrants move mainly over short distances. There is an exception for people going

longer distances because of great centers of industry and commerce.

ii. Most migration is from agricultural to industrial areas.

iii. Large towns grow more by migration than through natural increase.

iv. Migration increases along with the development of industry, commerce and transport

v. Each migration stream produces a counter-stream.

vi. The major causes of migration are economic

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vii. Females are more migratory than males.

2.4 Review of Methodological and Empirical Issues

According to a survey by United Nations (2013), most policy makers resist urbanization rather

than welcoming it. The organization was of the opinion that economies rarely grow without

their cities growing. Policy makers would prefer to stem the urban tide and see people return to

rural areas. They brought to the knowledge of the people that one of the major causes of social

and political headaches, such as over-crowding, concentrated squalor, crime, street violence

and quick transmission of disease is urbanization at the rapid stage. This is evident in West

Africa. United Nations agreed that out of all the regions in the world, West Africa remains one

of the least urbanized.

Todaro and Smith (2011) said regardless of the real progress experienced, close to 2 billion

people in the gradually developing world will expect a scanty society which is sufficient for

their agricultural needs. The study agreed that over 3.1 billion people resides in rural centers in

developing countries as at 2010, in which they said a quarter of the people are living in

extreme poverty. Todaro and Smith also agreed that sub Saharan African countries suffer the

most because they have over 65 percent of people living in rural areas. They also discovered

that two-thirds of the world`s poorest people are based and situated in rural centers and most of

them engage solely in subsistence agriculture. This is the reason why almost all the people

living in rural centers being survival minded. United Nations (2009) estimated that for the very

first time, more than 1 billion people did not have enough agricultural products to meet their

fundamental dietary needs.

26

The behaviour of farmers in developing countries most times looked absurd to many observers

who are not aware of the farmer`s great effort which is like a routine just to subsist. It was

recently that observers were able to comprehend the wobbly nature of subsistence living and

the fact that farmers were avoiding the risk of the unknown. Agriculture was said to play two

roles in economic development namely supportive and passive roles. They emphasized that the

principal purpose of agriculture is channeled at providing low priced food which is adequate

and also the manpower required for the expansion of the industrial sector which is the leading

sector and also the heart of the economy.

The contributions of agriculture to economic development were introduced by Simon Kuznets.

The study postulated that agriculture made four contributions, first is the product contribution

of inputs for industries like textiles and food processing, second contribution is that of foreign

exchange contribution that ensures revenues generated from exporting agricultural products are

used to import capital equipments for the country. Third contribution according to Kuznets is

the area of market contribution of increasing rural incomes thereby simulating more demand

for consumer products. The final contribution is that of the contributions made on the factor

market. In the present day most development economist share the opinion that apart from

playing a passive, supportive role in the process of economic development, the agricultural

sector in particular and the rural financial system universally must play an indispensible part in

any overall strategy of economic progress most especially for those countries classified as low-

income developing countries (Todaro and Smith, 2011).

Gollin et al. (2013) made it clear that there are many theories that link urbanization and

industrialization together but the relationship between both of them is absent in developing

27

countries most especially West Africa. The study was able to make this statement because

many countries endowed with rich resources have urbanized without increasing output in

anyway. They carried out their study through the construction of a model of structural change

which accommodates two pathways to urbanization. The first pathway was tagged production

cities, it is about the distinctive movement of the factor, labour from agriculture into industry.

The second pathway, consumption cities which are driven specifically by income effect of

some endowments thereby rents are spent on goods and services in urban areas. They stated

that urbanization is not in any form a homogenous event.

Gollin et al. (2013) applied the use of cross-sectional regressions with a sample of 116

countries and made urbanization for 2010 the dependent variable, there regression was

population weighted and they regressed on the share of natural resources in GDP from 1960-

2010 on the average, they also added the share of manufacturing and services in 2010. Through

their cross-sectional and panel robust checks they discovered that urbanization has a positive

relationship with both share of natural resource in GDP and share of manufacturing and

services. Most alternative theories of urbanization made their results spurious because it was

evident that some parts of the world experienced urbanization without any form of economic

development. They said this was due to pull and push factors. So as to make the model

complete, Other important controls were introduced.

The study discovered that people who export natural resources were able to calculate

urbanization rates in many ways. Regardless of the dummy variable added to the model and the

inclusion of some controls, the positive relationship that exists between natural resource

exports and urbanization rate was not distorted. Also the study discovered that the relationship

28

between them was very strong. Also their study experienced some standard deviations;

concerning the portion of natural resource exports in GDP had a 0.48 standard deviation in the

urbanization rate and manufacturing and services in GDP had a 0.39 standard deviation in the

urbanization rate. Furthermore, some tests were carried out so as to ascertain whether the result

was robust. This was done through the use of multivariate panel analysis with a sample of 112

countries and a period of 6 years with 10 year intervals.

In controlling industrial booms, share of manufacturing exports was added to GDP because for

2010 the share of manufacturing and services was not available. The standard error they got at

country level were clustered, this led to the discovery of unconditional results. The study made

some additions to the model so as to robust it notwithstanding the correlation between exported

natural resources and urbanization remained strong. The result of the multivariate panel

analysis was seen to be five times lower than the result of the cross-sectional analysis.

According to the study both methods cannot be really compared but they made it clear that

panel regression remained special because it considers the short run and as such it only

measures the short run effects of short-term variations and it permitted the inclusion of country

fixed effects so as to control time-invariant heterogeneity (Gollin et al., 2013).

The panel regression supported the hypothesis that exported natural resources could be

endogenous to the urbanization process. The study also established that the variable

urbanization has no impact on natural resources exported in the next period also even the

reverse has no effect either. Another observation from the study was that there were no

impactful effects from cash crops unlike what was discovered for petroleum products and

mining products. Oil had stronger effect on urbanization than any other mineral resources. In

29

conclusion the study contradicted urbanization and industrialization being used

interchangeably or seen as identical because the study made stated clearly that resources

exports are considerably linked to urbanization rates Gollin et al. (2013).

Gollin et al. (2013) affirmed that countries endowed with natural resources will definitely

experience urbanization without any form of industrialization. Same countries that urbanized

without industrializing achieved it by importing most of their food and goods that are tradable

and most importantly resources endowments will create consumption cities thereby increasing

income that is surplus then later shift workers away from the sector that is tradable. This study

also presented the components of urban employment which was considered to be twisted with

personal services rendered and local retail. Countries with deficient resource endowments will

urbanize in production cities which are mainly driven by the substitution of labour that is

aimed at the tradable sectors like finance.

Mabogunje (2002) made use of factor analysis to help Him identify some important

dimensions of the process of urbanization in Nigeria. This study was able to do so by using 32

variables and conclusion that seven factors accounted for the 84.3 percent of the aggregate

variance of the original data (Filani, 2006). This study revealed some fascinating secrets about

Nigerian cities and declared some characteristics of urbanization that are heavily implanted in

masses of data. Mabogunje made use of some theories of urban structure which includes the

multiple nuclei theory, the sector theory and the concentric zone theory. The study applied

three theories. First theory was the concentric zone theory, then the sector theory and the

multiple nuclei theory. The last theory remains very crucial in the understanding the nature of

Nigerian cities.

30

The study made use two cities in Nigeria namely Ibadan and Lagos. Ibadan according to the

study has traditional and modern characteristics. The study started with that of the preservation

of the contrasting both nonresidential and residential neighborhoods that was present in the city

of Ibadan. Ibadan was characterized by high density, poor environmental quality amongst other

factors which will not stop expanding into new metropolis. The study stated some factors

responsible for the resistant to change which was found in traditional people after enjoying

policitcal power. This study introduced the concept of central business districts. Mabogunje

(2002) stated that twin central business districts will be very compatible in the city of Ibadan.

After the study affirmed that most cities represent an amalgam of two different urban

processes. Some problems were identified like the problem of its slum areas and also the

problem of easy circulation. Mabogunje was of the opinion that Lagos was not a traditional

city unlike Ibadan.

Nordhag (2012) discovered that sub-Saharan Africa is in a way diverse regarding the modern

extent of urban population. In 2009 the range of urban population was around 74.82 percent,

then 16.12 standard deviation, 37.95 mean value and median 37.52. There is no uniformity in

the level of urban population in sub-Saharan Africa. That remains the reason why people were

permitted to talk about the entire region`s level of urbanization Although there was an epitome

of truth in generalizing the region because the trends were similar. Nordhag believes that both

urban population growth and Urbanization will increase rapidly in sub-Saharan Africa more

than the way it has increased in the past two decades. Also countries that started urbanizing a

long time ago are supposed to have developed well especially in the area of infrastructure and

also in some facilities that will enable more urbanization growth.

31

Nordhag (2012) performed two correlation analyses so as to ascertain why some countries in

sub-Saharan Africa have discrepancies in the extent of their population considered to be urban.

The first correlation was looking at the urban population of earlier years and that of year 2009.

It was discovered that for 1999 and 2009 the level of correlation was very intense but

subsequently correlation stopped. This result shows that existing urban population is a function

of urban population growth. The second correlation analysis, the independent variable was the

value of urban population dating 10 years back and the dependent variable was urban

population of a given year. The result showed that correlation between the two variables was

very strong and intense. This was so because it was discovered because of strong correlation

that existed between 1960 and 1969. From the results it is clear that in sub-Saharan Africa

urban population is escalating for those countries already have a good number of urbanization

already.

The study of Nordhag shows that the process of urban population growth and urbanization

must be viewed from more than one perspective so as to get desired results. Also it shows that

explaining the workings of urbanization with theories is not as hard as evaluating the impacts

of urbanization and the aftermaths. The study could not prove whether urbanization has a

positive impact on the living standard of the people. Instead it presented a clear view that

urbanization is a twisted process because it depends on a number of factors and conditions. The

primary driver of urban population growth in sub-Saharan Africa remains high nativity.

Although both Lewis two sector model and the Harris-Todaro model on migration believes that

urbanization depends on rural urban migration.

32

Matsuyama (1992) made it clear that between agricultural productivity and industrialization

there is a positive link. This is so because rising productivity will in a way increase food

production and will make it possible to feed the growing population based in the industrial

sector. If more food items can be produced with less labour, the surplus will be transferred to

the manufacturing sector thereby affecting industrialization positively. Also if high incomes

are generated in agriculture there will be an increase in demand for most industrially produced

items. A model of endogenous growth was constructed to demonstrate the relationships that

exist between agricultural productivity and growth performance. This was done with standing

foundation of two assumptions which made it clear that agricultural productivity can only be

determined exogenously and that economic openness is the main determinant of relationship

between urbanization and agricultural productivity.

Li, Florax and Waldorf (2013) introduced spatial regression models that made provisions for

smooth coefficients. The study discovered that higher productivity in the agricultural sector

with less man power will allow the migration of labour into the industrial sector. It was

discovered that low urbanization that was experienced in the old can be attached to low

agricultural productivity. It was clearly explained why agricultural productivity improvements

remain a necessary force that will help push labour into urban activities. The study discovered

that in order to discourage rural-urban migration, agricultural productivity must increase

thereby making rural wages to increase.

More than 60 percent of the populations in Ghana are involved in agriculture. This made

agriculture a major source of employment of the people. The unemployment rate went up

because the agricultural sector was deprived of land. This happened because major agricultural

33

lands were converted to residential buildings or structures. This led to low agricultural

productivity which reduced the standard of living of the people and made food insecure

because people were not certain of being fed (Naab et al., 2013).

2.5 Conclusion

It is now possible to say urbanization and industrialization cannot be used in place of each

other neither are they synonymous. This could only be said after the review of past works. The

sustained increase of the urban population combined with the pronounced deceleration of rural

population growth will result in continued urbanization. This will increase the proportions of

the population living in urban areas. Understanding the dynamics of urbanization can help

policy makers moderate its costs rather than worsen them. Also low urbanization brings about

low agricultural productivity.

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CHAPTER THREE

THEORETICAL FRAMEWORK AND RESEARCH METHODOLOGY

iii.1 Introduction

In this section of the study, an attempt is made so as to carry out through a model, a conclusive

study to establish the relationship between the variables in the system. Urbanization involves a

process of growth. As said in the previous chapter, people migrate to urban areas so as to enjoy

better life but they fail to return to the rural areas when they discover that the dream of the

better life they projected is in fact not attainable because of the high rate of competition for

jobs in urban centers and other essential needs. This chapter also presents the specification of

model, estimation techniques, apriori expectations and also the criteria for making decisions

3.2 Theoretical Framework

Henderson (2005) models the urbanization process and how urbanization in a country is

accommodated by increases in numbers versus population sizes of cities in an endogenous

growth context where political institutions play a key role. The paper estimates the equations of

the model describing growth in city numbers in a country and growth in individual city sizes,

using a worldwide data set on all metro areas over 100,000 from 1960-2000.

Institutions and the degree of democratization and fiscal decentralization, as well as

technological advances, strongly affect growth in both city numbers and individual city sizes,

with the effects on individual city sizes being heterogeneous. Technology improvements help

bigger cities, with their complex infrastructure needs, relative to smaller ones; but increasing

35

democratization levels the playing field across the urban hierarchy, allowing smaller cities

greater ability to compete for firms and residents. These two opposing effects on the relative

sizes of bigger versus smaller cities appear to have left the overall relative size distribution of

cities worldwide unchanged over the time period.

Eaton and Eckstein (1997) normalized city sizes by the average size of cities in the relevant

sample in the time period. Second, they altered the relevant sample in each period, raising the

minimum size absolute cut- off point to keep the same relative size and standard to be a city. A

ratio of the minimum (100,000) is taken to the mean (495,101) size for 1960 and the ratio

(.2020) is applied to 2000 (Black and Henderson, 2003, Abraham, 2009). He therefore

assumed the cut- off point to be a city in the sample for a particular year to be the first s cities

ordered by size such that s+1 city would be below the relative size, or we choose s such that in

time t

Where N1 (t) is the population of city i in time t. For the year 2000, out of a possible 2,684

cities in the world over 100,000 this gives a number of 1,644 cities with an average size of

1,009,682 and a minimum absolute size city of 204,366.

This mirrors results in the literature which suggest urban concentration as measured by either

primacy (the ratio of the population of the largest city to national urban population) or a

Hirschman-Herfindahl index has an inverted-U relationship with per capita income (Wheaton

and Shisido, 1981 and Davis and Henderson, 2003). The idea is that at low levels of

development, initial urbanization is spatially concentrated because resources for urban

36

infrastructure and inter-city transport infrastructure are limited; skilled urban workers are in

short supply and knowledge is limited and spatially concentrated perhaps at points of entry to

international markets. As the economy develops, it garners the ability to disperse and the

economy diversifies. But the effects are fairly modest. The OLS estimation method was used to

estimate his results for the relationship between urbanization and economic development.

iii.3 Research Methodology

The econometrics approach will be used and the panel data analysis will be applied. This is

because the dataset of the study deals with the observation of entities across time. The panel is

the combination of both the cross-sectional and time series that is it has space as well as time

dimension. This econometrics method is chosen due to the nature of this study as it covers 14

countries in West Africa from 1989-2010.

Gujarati and Porter (2009) presented the following as the advantages of panel data method

according to Balgati.

i. Panel data through its combination of both time series and cross-sectional data gives

more informative, efficient data, chance for variability, makes provisions for degrees of

freedom and it is known for less co linearity among variables used.

ii. Panel data could also enable us to study more complicated behavioural models such as

economies of scale and technological change than by only using cross section or time

series data.

37

iii. Panel data helps or allows the control of variables that cannot be measured or observed

such as cultural factors or variables that change over time but not across entities such as

national polices. Therefore it takes account of individual heterogeneity.

In summary, a panel data analysis improves the quality of our empirical analysis which may

not be feasible seeing either cross-section or time series data.

The following are the drawbacks of panel data method:

i. The issue of data collection could arise.

ii. Since it consists of both cross-sectional and time series data, the issue of

heteroskedasticity and autocorrelation will be needed to be addressed.

According to Torres-Reyna (2013) panel data analysis could be specified using either the fixed

effects model or the random effect model as shown below.

The equation for the fixed effects model is given as:

Yit = β1Xit + αi + uit (1)

Where

αi (i=1….n) is the unknown intercept for each entity (n entity-specific intercepts).

Yit is the dependent variable where i = entity and t = time.

Xit represents one independent variable ,

β1 is the coefficient for that IV,

38

uit is the error term

While the equation for the random effects model is given as:

Yit = βXit + α + uit + εi (2)

Where

εi= Within-entity error

uit= Between-entity error

α= is the intercept value with no (i) because it is assumed to be a random variable.

Therefore, this study will be using the panel data analysis in studying the role of urbanization

on agricultural productivity in West Africa.

iii.4 Model Specification

The study examines the empirical relationship between urbanization and agricultural

productivity in West Africa.

Y=AKα Lβ

Since the study is concerned with productivity we have to introduce the Cobb-Douglas

production function for simplicity purpose.

The model for this study can be specified below in an implicit or functional form:

AGPRO=f (A,URB, LFE,EDU, INDPRO,AGPOP)

Where

39

AGPRO = agricultural productivity

A= level of total factor productivity

URB = urbanization level

LFE = life expectancy

EDU = education level in terms of primary school enrolment (gross percentage)

INDPRO = industrial productivity.

AGPOP = agricultural population.

Bloom, Canning and Sevilla (2004) specified there model in an aggregate production function.

This study will also follow use the same aggregate production function.

AGPRO =AURBαLFEβe ϕ1EDU+ ϕ2INDPRO+ ϕ3AGPOP (3)

We proceed by transforming the above equation into a log-linear form. After which we will be

left with the equation for the log of AGPRO at country i at time t.

LogAGPROit =ait+ αlogURBit+ βlogLFEit+ ϕ1logEDUit+ ϕ2logINDPROit+ ϕ3logAGPOPit (3a)

We must note that equation 1 stated some human capital outputs (EDU and LFE) as powers of

exponential and also the benefit of this form which is functional is that Log AGPRO depends

on the levels of health and education which was captured with the proxies EDU and LFE.

Urbanization level ait in country i and time t is not observed and is therefore assumed to be an

error term in the process of estimating the equation.

40

Since its a panel study, the model above can be stated in two forms either fixed model as seen

previously in (1) or the random effects as seen in (2)

For the fixed effect model, we can explicitly sate the above model as:

LogAGPROit =ai+ αlogURBit+ βlogLFEit+ ϕ1logEDUit+ ϕ2logINDPROit+ ϕ3logAGPOPit. + μit

(4)

Where i=1,2,….14 which represents the entities(countries), t=1,2……20 which is the time

period for the variables, ai is the unobserved or heterogeneity intercept and μit is the error term

with a mean of zero(0) and also constant variance. Above all we must note that the error term

is normally distributed.

For a random effect model, we will state the model as :

LogAGPROit =ai+ αlogURBit+ βlogLFEit+ ϕ1logEDUit+ ϕ2logINDPROit+ ϕ3logAGPOPit + wit

(5)

The major difference is that wit = εi + μit

Furthermore the fused error term wit comprises of two components the first which is known as

the within-entity (εi) also known as individual specific error term. The second is the between

entity (μit) also known as the combined time series and cross-section error component.

iii.5 Technique of Estimation

Panel data analysis was used to examine the role of urbanization on agricultural productivity in

West Africa and this as said before is because of the nature of the data which consists of

different entities (countries) at different time periods. Two methods of estimation are involved

41

under the panel data analyses which are the fixed effects and the random effects. The former

(fixed effects) assumes that there is need to control the unique characteristics of the entities and

its impact or bias on the predictor variables. Therefore it reduces the effect that the time

invariant characteristics may have on the predictor variables so that the net effect of the

predictors can be seen. On the other hand, random effects assume that the variations across

entities are random and do not correlate with the predictor variables in the model. This means

that the time invariant variables are included in the model unlike in the fixed model where it is

absorbed by the intercept.

To determine which model is suitable and efficient for the model which may either be the fixed

or random effect, the Hausman’s test will be run and this will test whether the unique errors

(ui) are correlated with the regressors or not. Under the Hausman’s test, the null hypothesis is

that the model is random effects while the alternative is that the preferred model is fixed

effects.

Other tests include the test for random effects and cross-sectional

dependence/contemporaneous correlation using the Breusch-pagan LM test, test for

heteroskedasticity, test for serial correlation using the lagram-multiplier test and tests for unit

roots and stationarity.

iii.6 Justification of Variables Used

AGPRO: Agriculture value added percentage of workers is used to measure agricultural

productivity. It measures net output of a sector summing up all outputs then removing

intermediate inputs. It is perfect because it is done calculated without making provisions for

42

deductions of depreciation of assets and depletion and degradation of natural resources. Most

importantly it is in constant of 2005 United states dollars.

URB: Urbanization level is derived by dividing the urban population by total population. It is

measured as the urban population expressed as a share of the total population. This shows the

level of urbanization in a country. Li et al. (2013) made use of this variable in their study.

LFE: Life expectancy at birth: This depicts the number of years a newborn baby would live

given that the patterns of mortality at the point of birth remains same throughout the infant`s

life. This an addition to knowledge

EDU: education level (primary school enrolment) School enrollment, Primary (percentage

gross) is a proxy for education is used in this study because the rate of enrollment into primary

education is very important in determining productivity and also help determine the level of

educational attainment in a country and also at the primary level of education, one should

possess the required skill be able to read and write. This is very essential for any economy.

INDPRO: This variable is very important because it controls the urban pull side of

urbanization. It was adopted from the Lewis 2 sector model. It is measured by industry value

added per worker.

AGPOP: This variable measures the total population in agriculture

iii.7 Apriori Expectation

The apriori expectation for the relationships between the explanatory variables and the

dependent variables of the model based on economic theory as explained below. It is expected

that a, α, β, ϕ1, > 0 while ϕ2 < 0.

43

iii.8 Data Employed, Measurement and Sources

The panel data covers the period from 1989 to 2010. The table below shows the variables,

measurement and sources.

Table 3.1 Variables, Measurements and Sources

Source: Computed by Author

44

VARIABLE MEASUREMENT SOURCE

AGPRO

Agriculture value added per

worker (constant 2005 US$)

World bank`s World Development

Indicators (WDI) 2013 and Africa

Development Indicators (ADI) 2012

URB Urbanization level ADI 2012

LFE Life expectancy at birth ADI 2012

EDU education level (primary school

enrolment percentage gross)

ADI 2012

INDPRO Industry, value added

( percentage of GDP)

ADI 2012

AGPOP Agricultural population (FAO,

number)

ADI 2012

CHAPTER FOUR

DATA ANALYSIS AND INTERPRETATION

4.1 Introduction

This chapter is concerned with the presentation of data, the analysis of data and also the

interpretation of data. The econometric and descriptive analysis of data is also carried out in

this chapter

4.2 Descriptive analysis

Table 4.1 shows the summary statistics for the variables used in this study, it contains the

mean, standard deviation from the mean, the minimum and maximum values of each

observation, the range, the skewness and also Kurtosis.

Table 4.1 SUMMARY STATISTICS OF VARIABLES

Variable Mean Standard

deviation

Variance Minimu

m

Maximum Range Skewness Kurtosis

AGPRO 878.2555

5

773.9172 598947.9 140.2832 4654.71 4514.427 2.095079 7.773021

URB 0.379350

6

0.0911537 0.008309 0.135188 0.61833 0.483142 -0.260333 3.471646

LFE 51.56698 7.073655 50.0366 37.18761 73.77405 36.58644 0.769964

3

4.22663

EDU 78.55328 26.18808 685.8157 25.9866 139.6437 114.6571 0.088059

1

2.275647

INDPRO 20.53479 8.62644 74.41546 1.882058 46.53131 44.64925 0.6548119 3.380506

45

AGPOP 6979760 1.01e+07 1.01e+14 84000 4.20e+07 41916000 2.704283 9.49042

Source: Computed by Author

The variable agricultural productivity (AGPRO) variable had a mean of 878.3, and a standard

deviation (SD) 773.9 from the mean. Urbanization level (URB) had a mean of 0.3794 and a

standard deviation of 0.0912. Life expectancy (LFE) had a mean of 51.57 and SD of 7.07.

Education level which is primary school enrolment percentage gross (EDU) had a mean value

of 78.56 and SD of 26.19. Industrial productivity (INDPRO) had a mean of 20.54 and SD of

8.63. The variable agricultural population had a mean of 6979760 and SD of 0000000.1.

The maximum and minimum values are the highest and lowest values the function can

accommodate at any given point. Skewness is used in distribution analysis to measure

asymmetry of the distribution. It can be positive, negative or undefined. It is important to know

that it does not describe the relationship that exists between mean and median. Kurtosis is used

solely to measure the peakedness of flattening of the probability distribution of a real valued

random variable. It provides the shape of the probability distribution.

4.3 Empirical Analysis

Here some tests would be carried out so as to be able to estimate the results of the study

4.3.1 Test for multicollinearity

Multicollinearity is said to be a case in which there is the existence of linear dependency

among the independent variables used in the regression. In other words it means that there is an

existence of a relation between the independent variables. (LURB , LLFE, LEDU, LINDPRO,

and LAGPOP).

46

In order to get this done, we have to introduce the variance inflation factor which is also known

as (ViF). The Vif is used mainly to check for the presence of multicollinearity in a regression

model. There is a prerequisite for running this test, The prerequisite is that we must have done

the ordinary pooled ordinary least square (OLS).It is essential to know that the ordinary pooled

OLS does not take note of the differences between and within countries which is done in panel

analysis.

Before we can make any comments on the result, we must know the rule of thumb. The rule of

thumb states that if the Vif is less than 5 or when the tolerance 1/Vif is greater that 0.5 then we

can conclude that there is no any form of multicollinearity among the independent variables.

The result is depicted below:

Table 4.2 VIF(Variance Inflation Factor)

Variable VIF 1/VIF

LLFE 2.83 0.353948

LAGPOP 2.46 0.405951

LEDU 2.31 0.432302

LURB 2.19 0.456809

LINDPRO 1.15 0.867146

Mean VIF 2.19

Source: Author’s Compilation from Stata 10.0

It is visible in the above result that there is no linear dependence because Vif is less than 5 for

all the explanatory variables (lURB, lLFE, lEDU, lINDPRO, and lAGPOP). (l means logged

variable)

47

4.3.2 Test for heteroskedasticity

The concept of heteroskedasticity is known as a violation of the assumption of the linear

classical regression model. It means that there is no constant variance. It is violating the

assumption that residual is normally distributed with a mean of zero(0) and constant variance

standard deviation.

So as to check for the presence of heteroskedasticity we have to make use of the groupwise

heteroskedasticity in fixed effects regression. Below is the decision making Criteria:

H0 (null hypothesis) = homoskedasticity exist i.e constant variance

H1 (alternate hypothesis) = heteroskedasticity is present.

The result is shown below:

Table 4.3 Modified Wald test for groupwise heteroskedasticity in Fixed Effect

Regression Model

H0: sigma(i)^2 = sigma^2 for all i

chi2 (22) = 10.59

Prob>chi2 = 0.9802

Source: Author’s Compilation from Stata 10.0

From the above table it is evident that the prob>chi2 is not significant (0.9802), therefore do

not accept H1 that there is heteroskedasticity and we accept H0 that there is homoskedasticity.

Due to this result, there will be no need to robust both fixed and random effects.

48

4.3.3 Hausman test

This test is used solely to ascertain whether fixed or random effects model is the most

appropriate, preferred and reliable model for the study. It is essential to know that before

running the Hausman test we must ensure that we run and store the fixed and random effects.

This test tests if the time invariant variables or unique errors are correlated with the regressors

or not

The decision criteria are listed below:

H0 = the preferred model is random effects

H1 = the preferred model is fixed effects.

Below is the result:

Table 4.4 Hausman test

chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B)

= 7.58

Prob>chi2 = 0.1810

Source: Author’s Compilation from Stata 10.0

Since our prob>chi2 is not significant (0.1810), we do not reject H0 and we come to a

conclusion that the random effects model remains the model is the most appropriate, preferred

and reliable model

4.3.4 Interpreting the Random Effects Model

49

Table 4.5 Random Effects Model

LAGPRO Coef.Std. Err.

Z

P>|z|

[95% Conf.

Interval]

LURB 0.6110610.213789 2.86 0.004 0.192043 1.03008

LLFE 0.8851850.638299 1.39 0.166 -0.36586 2.136227

LEDU 0.3887090.175624 2.21 0.027 0.044494 0.732925

LINDPRO 0.0868530.125884 0.69 0.49 -0.15987 0.333581

lAGPOP -0.088810.052198 -1.7 0.089 -0.19111 0.013499

_cons 2.906942.810054 1.03 0.301 -2.60066 8.414544

sigma_u 0sigma_e 0.669941rho 0

Source: Author’s Compilation from Stata 10.0

The economic interpretation for the random effects model is as follows.

Coefficient Values

For LURB it is 0.611061 which is inelastic. For the variable LLFE is 0.885185 which is also

regarded as inelastic since it is less than 1. The variable LEDU has a coefficient value of

0.388709 which is also inelastic. The variable LINDPRO has a coefficient of 0.86853 which is

also inelastic. The last variable LAGPOP has a coefficient of -0.08881 which is also inelastic.

In conlusion for our coefficient values, all our variables were inelastic which means less than 1

(Coef.<1).

Economic interpretation of the coefficient Values

50

1 percent change in LURB will bring about a less than proportionate change in LAGPRO. For

the variable LLFE a 1 percent change in LLFE will bring about a less than proportionate

change in LAGPRO. Also a 1 percent change in the variable LEDU will bring about a less than

proportionate change in LAGPRO. A 1 percent change in LINDPRO will bring about a less

than proportionate change in LAGPRO. For the last variable, a 1 percent change in LAGPOP

will bring about a less than proportionate change in LAGPRO.

Explanation of Z and Probability of Z (P> /Z/)

The rule of thumb is that Z >1.96 means that the variable is significant or it has a significant

influence on the dependent variable. The variable LURB is significant because the value is

more than 1.96. Therefore LURB has a significant influence on LAGPRO. The variable LLFE

is not significant because the value is 1.39 is it is less than 1.96. Therefore we conclude that the

variable LLFE has no significant influence on LAGPRO. The variable LEDU is significant

because it is 2.21 and it is greater than 1.96. we conclude that the variable LEDU has a

significant influence on the dependent variable LAGPRO. The independent variable LINDPRO

is not significant because the value is 0.69 and it is less than 1.96. Therefore we can conclude

that the variable LINDPRO has no significant influence on the dependent variable LAGPRO.

The last variable LAGPOP is also not significant because the value is 1.70 which is less than

1.96. Therefore the independent variable LAGPOP has no significant impact on the dependent

variable LAGPRO.

95% confidence Interval

51

The rule of thumb is that if the value passes through the number line from -1 to 1 the variable

is not significant. The variable is significant only when the value does not pass through the

number line from -1 to 1.

The first variable which is LURB is significant because zero (0) is not between the intervals.

The second variable LLFE is not significant because zero (0) is between the intervals. LEDU is

significant because the zero (0) is not between the intervals. For LINDPRO it is not significant

because zero (0) is between the intervals. For the last variable LAGPOP, the variable is not

significant too because zero (0) is between the intervals.

The R-squared

Here will be explaining the r-squared overall. The value is 0.3366. this implies that 33 percent

of variation of the variable LAGPRO is explained by the independent variables.

4.3.5 Testing for Random Effects using the Breush-Pagan Lagrange Multiplier (LM)

Table 4.6 Breusch and Pagan Lagrangian multiplier test for random effects

Test: Var(u) = 0

chi2(1) = 6.31

Prob > chi2 = 0.0120

52

Source: Author’s Compilation from Stata 10.0

It is evident from the result above that prob>chi2 is significant (0.0120), we reject H0 and we

accept the H1 and we conclude that there is the existence of panel effects. Also there is

evidence of significant differences across countries. Most importantly we can say that the

random effect significantly differs from the simple OLS regression.

4.4 Summary of findings

The empirical analysis of the effects of urbanization on agricultural productivity in West Africa

presents the result of the significant impact that urbanization level has on agricultural

productivity in West Africa over the period of 1989 to 2010. Based on the results obtained

from the regressions, several things have been noted. Life expectancy at birth was found not to

have a significant impact on agricultural productivity in West Africa. This means that life

expectancy has nothing to do with agricultural productivity. This differed from our a priori

expectation.

For education level which was captured by primary school enrolment was discovered to have a

significant impact on agricultural productivity. This implies that the more educated the people

are, the better will be the level of agricultural productivity. This is in line with our a priori

expectation. Productivity level in the industrial sector was found not to have any form of

significant impact on agricultural productivity. This implies that increase in the productivity

level in the industrial sector will not have any effect on the productivity level in the agricultural

sector. This result differed from our a priori expectation.

53

Agricultural population, measured by agricultural population (FAO) number was found to have

negative influence on agricultural productivity. Hence, the higher the population of people in

agriculture, agricultural productivity remains unchanged. Interestingly this result did not meet

our a priori expectation. Ideally we believe that if the population of people in agriculture

should increase, the level of agricultural productivity will increase because we will have more

hands in the agricultural sector of the economy. This can be as a result of law of diminishing

returns

CONCLUSION

In this section of the study, after estimating the model the results were presented, interpreted

and then discussed. Going by the findings, we can now say that urbanization level has a

positive impact on agricultural productivity in West Africa.

54

CHAPTER FIVE

SUMMARY, RECOMMENDATIONS AND CONCLUSION

5.1 Summary

The main aim of this study was to the determine the effects of urbanization on agricultural

productivity in West Africa for the period of 1989 to 2010. Empirical approach was introduced

to achieve the objectives of the study via the use of cross sectional data using the random

effects GLS econometric method of analysis. The following variables were used in the study

agricultural value added per worker (as the dependent variable), urbanization level, life

expectancy at birth, education level (which was captured by primary school enrolment),

industry value added and agricultural population.

Chapter one of the study focused on the background of the study, the statement of research

problem, the research questions, objectives and hypotheses formulated amongst others. Chapter

two of this study took us through the review of literatures in which several literatures, theories

55

and empirical papers were reviewed. It was observed that information in extracted literatures

varied and were also findings to be inconsistent on the relationship between urbanization and

agricultural productivity. The third chapter examined the theoretical framework or

methodology used in the study. Then in chapter 4 we estimated the model for the study using

Random effects (GLS) method of estimation. The analysis was carried using data from 14

countries in West Africa

FINDINGS

Urbanization and industrialization should not be used interchangeably because both are not the

same thing. This is so because some countries urbanized without industrializing. Also the study

discovered from literature that urbanization will increase rapidly in West Africa more than the

way it has increased in the past two decades. Past literatures also presented existing urban

population to be a function of rapid urban population growth. Finally low urbanization

experienced in the past can be attributed to low agricultural productivity. The major empirical

findings of the study are as follows:

i. Urbanization has a significant effect on agricultural productivity in West Africa.

ii. The study also found that there is no significant relationship between life expectancy at

birth and agricultural productivity in West Africa.

iii. Primary school education/enrolment plays a significant role in the growth of

agricultural productivity in West Africa.

56

iv. The industrial sector has no influence on the agricultural sector of economies in West

Africa.

v. It was also discovered that agricultural productivity is not significantly determined by

the population of people working in the agriculture sector in West Africa.

5.2 Recommendations

Given the findings that urbanization contributes to agricultural productivity positively in West

Africa, it is advisable as a matter of policy, to periodically adjust and monitor the migration of

people from rural to urban areas and vice versa.

i. Government should provide mechanized farming instruments for farmers in the rural

areas because most them are in agriculture for subsistence purpose. This is the reason

why the population of people in the agricultural sector does not have a positive impact

on agricultural productivity. If these instruments are made available to farmers in rural

areas then they will be able to compete with major farms and their output will help

increase agricultural productivity in the region.

ii. Structural planning that takes consideration of development in agricultural productivity

should be adopted in creating and classifying urban centers. This must be done because

urbanization is caused by a number of different factors which includes rural-urban

migration and natural increase in population. Also policy makers should endeavor to

formulate policies that will upgrade informal settlements through the provision of

integrated infrastructure and services

57

iii. Agriculture remains the largest contributor of GDP in most West African countries.

Thus the development of this sector will boost economic output via agricultural

productivity. Also an increase in agricultural productivity will improve the living

standard of the population and should be the main concern of the government. The

government should introduce a strategy to preserve the long term growth of agriculture

in West Africa. Agriculture should be made more attractive for the rural economy so

that people can invest and also work for the progress of their economies.

iv. Policy makers should ensure that they formulate policies that will ensure that more

investments are made in the education sector of West African countries. Since it has a

significant impact on agricultural productivity. This will help increase the literacy level

of the people both in rural and urban areas.

v. Most importantly policy makers should formulate policies that will encourage

investments in the industrial sector and should overlook the sector. So as to increase the

output of the economy and make the economy more productive. Both sectors will help

increase GDP.

vi. Policy makers should formulate policies that will promote diversification of economic

activities through the creation of new economic hubs in agriculture aimed towards high

value added products and exportation that is sustainable and most importantly inclusive.

v.3 Conclusion

Rapid urbanization has come to stay in West Africa and it will not stop anytime soon. Increase

in Urbanization will affect productivity in the agricultural sector.

58

This study rejects the null hypothesis that agricultural productivity in West Africa does not

depend on urbanization and also rejects the null hypothesis that there is no positive relationship

between urbanization and agricultural productivity in West Africa. The study concludes that

urbanization level in West Africa has positively affected agricultural productivity.

v.3.1 Limitations of the study

The major limitation experienced in completing this research work is the shortage of research

conducted on this particular subject in West Africa. This research work also encountered the

problem of unavailability and inconsistency of data, due to this Ghana and Niger republic were

dropped.

v.3.2 Suggestions for further study

During the course of this study, it was noticed that insufficient study was carried out in West

Africa about urbanization and agricultural productivity. West African countries are currently

fast tracked for urbanization most especially the city of Lagos which is on the verge of

becoming a mega city. It is suggested that further studies should be carried out to know the

influence of urbanization on agricultural productivity by using other explanatory variables in

West Africa or sub-Saharan Africa with wider time scope which depends on the availability of

data.

59

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Woods, C. H. (1982). Equilibrum and Historical-Structural Perspectives on migration.

International Migration Review, 16(2) Special issue: Theory and Method in Migration and

Ethnic Research .

64

APPENDICES

APPENDIX A: TABLE OF DATA FOR VARIABLES USED IN

STUDY

Country Id Year AGPRO URB LFE EDU INDPRO AGPOP

Benin 1 1989 660.2838 0.337472 48.19805 63.01236 12.76802 2942000

Benin 1 1990 639.8694 0.34485 48.64854 53.04009 12.45339 3019000

Benin 1 1991 657.4269 0.349396 49.12312 60.42598 11.98263 3078000

Benin 1 1992 628.5424 0.353942 49.60339 63.92088 12.72768 3142000

Benin 1 1993 677.1225 0.358488 50.07346 69.36271 12.2171 3205000

Benin 1 1994 683.9076 0.363034 50.52241 69.89405 13.33806 3264000

Benin 1 1995 709.9108 0.36758 50.94127 73.50964 13.39231 3318000

Benin 1 1996 737.7006 0.37073 51.32451 76.15989 13.26694 3363000

65

Benin 1 1997 771.9917 0.37388 51.67407 80.03204 13.52071 3403000

Benin 1 1998 802.8467 0.37703 51.99583 83.22297 12.56647 3439000

Benin 1 1999 840.2744 0.38018 52.29176 83.38451 12.82946 3476000

Benin 1 2000 865.4807 0.38333 52.56534 86.42855 12.92478 3517000

Benin 1 2001 906.2893 0.38867 52.8191 94.91281 13.33002 3562000

Benin 1 2002 913.0424 0.39401 53.06151 100.5704 13.55913 3609000

Benin 1 2003 917.4758 0.39935 53.30149 104.3316 13.71684 3658000

Benin 1 2004 959.3361 0.40469 53.54895 108.3979 13.32745 3705000

Benin 1 2005 936.8481 0.41003 53.81373 105.3794 13.31945 3750000

Benin 1 2006 974.5882 0.41654 54.10668 105.8875 13.00801 3790000

Benin 1 2007 1001.345 0.42305 54.43073   12.98413 3828000

Benin 1 2008 1023.807 0.42956 54.78732 118.3507 12.57406 3862000

Benin 1 2009 1037.847 0.43607 55.17341 123.9254 13.03604 3893000

Benin 1 2010 1041.241 0.44258 55.58559 125.8573 13.21772 3920000

Burkina Faso 2 1989 217.3245 0.135188 48.38463 29.09385 23.14337 8385000

Burkina Faso 2 1990 197.382 0.13815 48.45305 30.14331 21.23738 8616000

Burkina Faso 2 1991 231.6983 0.140782 48.52741 31.36616 20.18239 8851000

Burkina Faso 2 1992 230.9603 0.143414 48.61173 32.14195 21.21965 9094000

Burkina Faso 2 1993 246.5251 0.146046 48.71002 33.23634 20.60268 9345000

Burkina Faso 2 1994 240.8966 0.148678 48.83083 34.51536 22.00606 9604000

Burkina Faso 2 1995 249.0293 0.15131 48.97976 36.47292 21.5167 9871000

Burkina Faso 2 1996 272.4046 0.156736 49.15944 38.46599 20.02487 10146000

Burkina Faso 2 1997 254.4878 0.162162 49.36998   21.45148 10430000

Burkina Faso 2 1998 288.2038 0.167588 49.61034 40.6381 19.32314 10723000

Burkina Faso 2 1999 283.7849 0.173014 49.88549 41.55511 25.4322 11026000

Burkina Faso 2 2000 265.1912 0.17844 50.20129 42.28427 24.65691 11339000

Burkina Faso 2 2001 309.484 0.185826 50.56556 43.69955 19.47667 11664000

66

Burkina Faso 2 2002 316.7922 0.193212 50.97763 44.36932 17.52184 12000000

Burkina Faso 2 2003 328.7993 0.200598 51.43237 46.59714 21.18051 12348000

Burkina Faso 2 2004 307.4986 0.207984 51.92222 51.03884 21.17522 12709000

Burkina Faso 2 2005 328.9504 0.21537 52.4352 55.42333 17.9797 13083000

Burkina Faso 2 2006 331.1615 0.22363 52.9579 59.18434 17.62221 13471000

Burkina Faso 2 2007 306.8401 0.23189 53.47695 64.73768 18.79543 13872000

Burkina Faso 2 2008 355.6837 0.24015 53.98249 70.25692 15.55813 14288000

Burkina Faso 2 2009 312.2537 0.24841 54.46605 74.68782 18.07781 14717000

Burkina Faso 2 2010 334.3911 0.25667 54.9242 75.59677 22.95862 15160000

Cape Verde 3 1989 2551.956 0.41596 64.62549 118.9321 20.75777 107000

Cape Verde 3 1990 2259.775 0.4412 65.08022 120.8076 21.43631 106000

Cape Verde 3 1991 2099.306 0.450496 65.51951 121.4596 21.39395 106000

Cape Verde 3 1992 1931.001 0.459792 65.94532   21.98478 106000

Cape Verde 3 1993 2088.561 0.469088 66.36012   14.90406 106000

Cape Verde 3 1994 1964.801 0.478384 66.77044 122.873 19.67869 105000

Cape Verde 3 1995 2094.004 0.48768 67.18232   19.16568 105000

Cape Verde 3 1996 1967.86 0.497014 67.6032   19.82847 105000

Cape Verde 3 1997 1887.064 0.506348 68.03705   18.61337 104000

Cape Verde 3 1998 1793.329 0.515682 68.4878 126.2043 17.99786 103000

Cape Verde 3 1999 2578.33 0.525016 68.95639 125.2508 16.58885 102000

Cape Verde 3 2000 2775.36 0.53435 69.44827 124.3382 15.32435 101000

Cape Verde 3 2001 2768.162 0.542858 69.96985 123.3701 13.8043 99000

Cape Verde 3 2002 2616.672 0.551366 70.51366 122.9267 14.72157 98000

Cape Verde 3 2003 2740.013 0.559874 71.06763 120.8527 14.6929 97000

Cape Verde 3 2004 2732.872 0.568382 71.61778 117.8022 15.03779 95000

Cape Verde 3 2005 2578.289 0.57689 72.14066 115.6876 15.69498 94000

Cape Verde 3 2006 2585.044 0.585178 72.61034 114.7475 16.03707 92000

67

Cape Verde 3 2007 2663.5 0.593466 73.01085 112.8291 15.66684 90000

Cape Verde 3 2008 2603.12 0.601754 73.3362 111.6217 18.48882 88000

Cape Verde 3 2009 3917.238 0.610042 73.5868 110.3463 18.26666 86000

Cape Verde 3 2010 4654.711 0.61833 73.77405 109.6056 18.00029 84000

Cote d'Ivoire 4 1989 910.5226 0.39058 52.85585 67.47331 21.7837 7252000

Cote d'Ivoire 4 1990 931.7618 0.39345 52.64415 67.30548 23.17059 7437000

Cote d'Ivoire 4 1991 931.699 0.39718 52.37963 65.91596 21.70644 7554000

Cote d'Ivoire 4 1992 932.9122 0.40091 52.07083 65.38856 21.30687 7665000

Cote d'Ivoire 4 1993 921.2601 0.40464 51.7322 64.32409 22.75389 7768000

Cote d'Ivoire 4 1994 980.8293 0.40837 51.38522 66.66572 21.03982 7857000

Cote d'Ivoire 4 1995 1010.791 0.4121 51.05137 67.75393 20.768 7939000

Cote d'Ivoire 4 1996 1039.451 0.416762 50.74712 68.87648 20.41835 8006000

Cote d'Ivoire 4 1997 1044.922 0.421424 50.48993 70.31561 23.64989 8056000

Cote d'Ivoire 4 1998 1098.137 0.426086 50.29422 71.65872 22.98073 8090000

Cote d'Ivoire 4 1999 1084.359 0.430748 50.1789 74.28126 24.1754 8101000

Cote d'Ivoire 4 2000 1213.117 0.43541 50.16088 74.29794 24.85067 8085000

Cote d'Ivoire 4 2001 1218.39 0.442 50.25056 77.23648 24.09024 8051000

Cote d'Ivoire 4 2002 1195.911 0.44859 50.44388 78.29427 22.92261 7998000

Cote d'Ivoire 4 2003 1220.56 0.45518 50.73283 74.36598 21.64768 7933000

Cote d'Ivoire 4 2004 1277.771 0.46177 51.11139   23.07168 7866000

Cote d'Ivoire 4 2005 1305.418 0.46836 51.57556   25.86549 7798000

Cote d'Ivoire 4 2006 1329.187 0.475802 52.11837 73.57767 25.88317 7732000

Cote d'Ivoire 4 2007 1358.282 0.483244 52.72588 74.95055 25.28611 7666000

Cote d'Ivoire 4 2008 1369.454 0.490686 53.37771 79.68748 26.11725 7602000

Cote d'Ivoire 4 2009   0.498128 54.0559 79.09063 25.48451 7542000

Cote d'Ivoire 4 2010   0.50557 54.74156   27.21955 7484000

Gambia, The 5 1989 326.1922 0.372534 52.99156 53.02886 10.35203 763000

68

Gambia, The 5 1990 307.2881 0.38312 53.12505 53.24574 11.00949 792000

Gambia, The 5 1991 300.4907 0.394118 53.209 54.19252 15.36001 818000

Gambia, The 5 1992 283.558 0.405116 53.28346 54.21579 15.17003 841000

Gambia, The 5 1993 263.2299 0.416114 53.38044 55.84827 15.20011 863000

Gambia, The 5 1994 272.3754 0.427112 53.52046 58.52964 14.62553 885000

Gambia, The 5 1995 258.6245 0.43811 53.71354 61.10005 14.87393 907000

Gambia, The 5 1996 245.5635 0.448128 53.95963 65.69636 20.70366 929000

Gambia, The 5 1997 263.634 0.458146 54.24027   15.57435 952000

Gambia, The 5 1998 249.3858 0.468164 54.53741 71.46629 16.06871 976000

Gambia, The 5 1999 315.7502 0.478182 54.84507 84.18455 15.59421 1001000

Gambia, The 5 2000 330.5852 0.4882 55.15676 84.47302 14.82267 1026000

Gambia, The 5 2001 350.0142 0.496802 55.46549 86.13649 14.76682 1053000

Gambia, The 5 2002 279.0511 0.505404 55.76876 84.00723 15.80698 1080000

Gambia, The 5 2003 324.0256 0.514006 56.06556 87.6401 14.84902 1108000

Gambia, The 5 2004 336.9379 0.522608 56.35485 88.51854 13.5606 1137000

Gambia, The 5 2005 320.5651 0.53121 56.64007 86.06761 14.08246 1166000

Gambia, The 5 2006 266.7634 0.53828 56.92666 84.3922 14.52643 1195000

Gambia, The 5 2007 254.4096 0.54535 57.22005 86.03561 13.69014 1224000

Gambia, The 5 2008 312.1889 0.55242 57.52424 84.23543 13.48782 1254000

Gambia, The 5 2009 339.3204 0.55949 57.83822 87.33035 12.53046 1284000

Gambia, The 5 2010 366.6741 0.56656 58.16002 82.59984 12.32888 1314000

Guinea 6 1989 151.0601 0.27745 43.20593 36.50804 33.8953 4820000

Guinea 6 1990 149.9922 0.28026 43.67068 35.95378 33.33184 5019000

Guinea 6 1991 147.6212 0.283158 44.10995 38.50156 31.55728 5284000

Guinea 6 1992 144.9085 0.286056 44.52176 37.57414 27.71681 5596000

Guinea 6 1993 142.6082 0.288954 44.90859 41.28765 29.4017 5924000

Guinea 6 1994 141.5379 0.291852 45.27895 43.28597 26.00397 6227000

69

Guinea 6 1995 140.2832 0.29475 45.65185 47.24724 29.20098 6474000

Guinea 6 1996 142.2935 0.297846 46.04883 48.45951 30.7593 6653000

Guinea 6 1997 148.6769 0.300942 46.48834 52.07282 30.10542 6777000

Guinea 6 1998 153.551 0.304038 46.9789 52.69713 32.2364 6860000

Guinea 6 1999 161.9488 0.307134 47.522 55.67057 30.15896 6926000

Guinea 6 2000 164.913 0.31023 48.11015 59.65466 33.46656 6996000

Guinea 6 2001 172.6063 0.313874 48.72983 63.61642 33.99261 7072000

Guinea 6 2002 177.7065 0.317518 49.35702 73.25805 33.90917 7150000

Guinea 6 2003 181.6759 0.321162 49.97373 77.64064 34.08864 7231000

Guinea 6 2004 185.0843 0.324806 50.56895 81.75394 32.78059 7316000

Guinea 6 2005 185.071 0.32845 51.13566 84.79726 34.761 7405000

Guinea 6 2006 189.6216 0.332696 51.67337 87.35477 39.9001 7499000

Guinea 6 2007 192.1985 0.336942 52.18856 90.17739 39.52367 7600000

Guinea 6 2008 195.9378 0.341188 52.68724 91.86994 42.35885 7710000

Guinea 6 2009 198.8217 0.345434 53.16993 91.93635 40.30528 7831000

Guinea 6 2010 201.3109 0.34968 53.63859 94.3984 44.80289 7964000

Guinea-Bissau 7 1989   0.26991 42.65968 53.97423 16.86577 852000

Guinea-Bissau 7 1990   0.28131 42.82415   18.60099 867000

Guinea-Bissau 7 1991   0.289678 42.98476   9.758803 883000

Guinea-Bissau 7 1992   0.298046 43.14498 49.08837 10.71989 898000

Guinea-Bissau 7 1993   0.306414 43.31024 46.98311 10.10831 914000

Guinea-Bissau 7 1994   0.314782 43.48502 52.25294 11.5592 930000

Guinea-Bissau 7 1995   0.32315 43.67529 56.49849 12.1989 946000

Guinea-Bissau 7 1996   0.330222 43.88551   11.57992 963000

Guinea-Bissau 7 1997   0.337294 44.1152   15.29625 979000

Guinea-Bissau 7 1998   0.344366 44.36032   12.67502 992000

Guinea-Bissau 7 1999   0.351438 44.61937 77.74692 12.88664 1008000

70

Guinea-Bissau 7 2000   0.35851 44.88488 78.69765 12.99598 1024000

Guinea-Bissau 7 2001 549.2757 0.365898 45.14685 93.41029 12.82252 1041000

Guinea-Bissau 7 2002 543.8054 0.373286 45.3988   13.1428 1056000

Guinea-Bissau 7 2003 560.4177 0.380674 45.63978     1073000

Guinea-Bissau 7 2004 543.3937 0.388062 45.87324 111.8859   1090000

Guinea-Bissau 7 2005 591.2003 0.39545 46.10966 119.7954   1108000

Guinea-Bissau 7 2006 579.4082 0.4028 46.36198 126.4264   1126000

Guinea-Bissau 7 2007 584.5875 0.41015 46.64268     1143000

Guinea-Bissau 7 2008 607.797 0.4175 46.95927     1162000

Guinea-Bissau 7 2009 614.576 0.42485 47.31324     1182000

Guinea-Bissau 7 2010 623.1003 0.4322 47.70066 123.1188   1202000

Liberia 8 1989   0.406014 42.45563   23.68053 1571000

Liberia 8 1990   0.40935 42.2768   16.80541 1538000

Liberia 8 1991   0.412728 42.12795   16.75287 1503000

Liberia 8 1992   0.416106 42.02361   15.61521 1467000

Liberia 8 1993   0.419484 41.9878   15.08728 1440000

Liberia 8 1994   0.422862 42.04859   14.06959 1437000

Liberia 8 1995   0.42624 42.247   5.267062 1466000

Liberia 8 1996   0.429654 42.62707   1.882058 1532000

Liberia 8 1997   0.433068 43.19934   10.23995 1630000

Liberia 8 1998   0.436482 43.95876   7.091213 1741000

Liberia 8 1999   0.439896 44.88876 93.89754 7.197827 1844000

Liberia 8 2000 573.6829 0.44331 45.96432 111.7123 4.246707 1923000

Liberia 8 2001 700.1506 0.44675 47.14883   4.23422 1970000

Liberia 8 2002 914.1557 0.45019 48.38722   3.285216 1992000

Liberia 8 2003 542.7043 0.45363 49.62593   4.218999 2003000

Liberia 8 2004 451.6732 0.45707 50.82337   7.853466 2023000

71

Liberia 8 2005 474.7511 0.46051 51.94302   7.324502 2064000

Liberia 8 2006 486.7334 0.46401 52.96283 95.78906 7.703355 2131000

Liberia 8 2007 531.2914 0.46751 53.88885   7.929059 2216000

Liberia 8 2008 581.7363 0.47101 54.72763 96.01371 7.08918 2311000

Liberia 8 2009 637.6609 0.47451 55.4792 101.8134 5.009714 2401000

Liberia 8 2010 669.6019 0.47801 56.14759   4.835888 2477000

Mali 9 1989 612.4903 0.228602 43.8282 25.9866 14.17935 7269000

Mali 9 1990 597.543 0.23322 44.16222 26.12531 15.86352 7378000

Mali 9 1991 556.9624 0.237614 44.47427 26.68459 16.85178 7504000

Mali 9 1992 631.0816 0.242008 44.77532 28.39897 15.82285 7651000

Mali 9 1993 565.759 0.246402 45.07688 31.76065 16.27149 7810000

Mali 9 1994 591.0974 0.250796 45.38395 34.80711 18.94601 7980000

Mali 9 1995 593.7058 0.25519 45.69751 37.31109 18.65928 8155000

Mali 9 1996 600.7027 0.260308 46.01305 40.76864 17.89337 8333000

Mali 9 1997 616.2687 0.265426 46.32654 45.46142 15.61647 8516000

Mali 9 1998 621.3476 0.270544 46.63398 49.21785 17.293 8706000

Mali 9 1999 664.1152 0.275662 46.93839 53.35966 16.70462 8904000

Mali 9 2000 581.1166 0.28078 47.24327 55.19447 20.56671 9112000

Mali 9 2001 633.1703 0.286812 47.55266 59.82632 26.35889 9333000

Mali 9 2002 594.4007 0.292844 47.8731 63.52623 27.54881 9560000

Mali 9 2003 681.0189 0.298876 48.20759 65.10663 23.62375 9794000

Mali 9 2004 631.5499 0.304908 48.55763 68.06387 23.86921 10033000

Mali 9 2005 662.1814 0.31094 48.9262 71.06192 24.1596 10276000

Mali 9 2006 681.9363 0.317306 49.31124 73.66602 24.0315 10522000

Mali 9 2007 680.6448 0.323672 49.71076 75.96972 24.18748 10769000

Mali 9 2008 752.7562 0.330038 50.1202 77.85968 20.08131 11018000

Mali 9 2009 777.0264 0.336404 50.53605 79.39599 20.95643 11267000

72

Mali 9 2010 845.4506 0.34277 50.95483 80.41081   11516000

Mauritania 10 1989 1700.091 0.387356 55.7362 49.08537 29.69212 1099000

Mauritania 10 1990 1591.872 0.39671 55.93698 46.84614 28.80891 1096000

Mauritania 10 1991 1655.483 0.397028 56.12673 49.44005 24.148 1124000

Mauritania 10 1992 1652.678 0.397346 56.296 54.48431 23.39778 1150000

Mauritania 10 1993 1761.878 0.397664 56.43924 61.80303 24.61239 1178000

Mauritania 10 1994 1426.025 0.397982 56.55698 68.16004 24.72154 1206000

Mauritania 10 1995 1734.401 0.3983 56.65271 72.11678 25.18053 1236000

Mauritania 10 1996 1830.04 0.398618 56.73146 75.82442 25.40164 1265000

Mauritania 10 1997 1385.15 0.398936 56.80076 79.77257 26.4415 1296000

Mauritania 10 1998 1003.762 0.399254 56.86607 82.17237 32.09774 1327000

Mauritania 10 1999 1048.004 0.399572 56.93141 84.0765 30.0054 1360000

Mauritania 10 2000 1010.486 0.39989 56.99478 84.40982 27.9763 1393000

Mauritania 10 2001 992.097 0.400618 57.05017 83.71872 28.30733 1428000

Mauritania 10 2002 936.171 0.401346 57.09505 85.20533 26.27391 1459000

Mauritania 10 2003 947.754 0.402074 57.13293 87.42281 24.25505 1496000

Mauritania 10 2004 891.3254 0.402802 57.17376 94.15567 27.31068 1532000

Mauritania 10 2005 938.1147 0.40353 57.23102 94.27324 33.1858 1570000

Mauritania 10 2006 912.9548 0.405284 57.3262 97.22819 46.53131 1606000

Mauritania 10 2007 978.1702 0.407038 57.47027 98.82237 39.60042 1638000

Mauritania 10 2008 1030.666 0.408792 57.66824 94.7071 40.51261 1673000

Mauritania 10 2009 1009.305 0.410546 57.91963 100.4274 35.12272 1707000

Mauritania 10 2010 1052.849 0.4123 58.21695 101.9628 44.03265 1741000

Nigeria 11 1989 1000.164 0.34593 45.7198 81.34774   41909000

Nigeria 11 1990 1044.184 0.35282 45.63734 84.83604   41925000

Nigeria 11 1991 1083.803 0.359942 45.53454 84.13065   41959000

Nigeria 11 1992 1107.84 0.367064 45.41537 88.18075   41961000

73

Nigeria 11 1993 1125.129 0.374186 45.29027 92.28298   41934000

Nigeria 11 1994 1155.187 0.381308 45.17973 92.11946   41874000

Nigeria 11 1995 1199.909 0.38843 45.11571 87.91288   41791000

Nigeria 11 1996 1252.618 0.395446 45.13322 77.47369   41688000

Nigeria 11 1997 1309.672 0.402462 45.25371     41563000

Nigeria 11 1998 1366.936 0.409478 45.48615     41427000

Nigeria 11 1999 1443.06 0.416494 45.82954 93.00311   41270000

Nigeria 11 2000 1489.024 0.42351 46.27232 97.85302   41133000

Nigeria 11 2001 1550.299 0.430308 46.79146 95.92763   40981000

Nigeria 11 2002 2410.844 0.437106 47.35049 97.7967 30.51809 40836000

Nigeria 11 2003 2584.401 0.443904 47.91637   36.75029 40695000

Nigeria 11 2004 2752.578 0.450702 48.47261 100.9011 42.09065 40541000

Nigeria 11 2005 2952.536 0.4575 49.00471 101.8252 43.50783 40364000

Nigeria 11 2006 3177.047 0.464002 49.51066 102.8503 41.91684 40183000

Nigeria 11 2007 3409.689 0.470504 49.99949 94.18395 40.65205 40000000

Nigeria 11 2008 3626.303 0.477006 50.47973 85.03809   39808000

Nigeria 11 2009 3840.44 0.483508 50.94941 83.09346   39609000

Nigeria 11 2010 4063.063 0.49001 51.41002 83.27522   39405000

Senegal 12 1989 452.4829 0.386298 52.94385 55.90097 20.86191 5399000

Senegal 12 1990 399.7796 0.389 53.24834 56.35815 22.17638 5535000

Senegal 12 1991 413.5707 0.39044 53.5018 56.98598 22.20701 5681000

Senegal 12 1992 379.1813 0.39188 53.72424 56.8269 24.24856 5830000

Senegal 12 1993 398.5945 0.39332 53.93268 56.27256 23.49616 5979000

Senegal 12 1994 388.1557 0.39476 54.14212 57.31218 23.98086 6128000

Senegal 12 1995 414.0634 0.3962 54.36307 58.13327 23.7821 6274000

Senegal 12 1996 389.5436 0.39765 54.60454 61.72108 24.34145 6416000

Senegal 12 1997 377.0098 0.3991 54.86354 65.57764 23.48027 6555000

74

Senegal 12 1998 371.2281 0.40055 55.13856 68.71552 23.68732 6694000

Senegal 12 1999 412.0631 0.402 55.43112 67.5112 23.48239 6838000

Senegal 12 2000 411.8757 0.40345 55.73924 70.64305 23.23078 6986000

Senegal 12 2001 406.98 0.405002 56.05844 72.41698 24.5252 7141000

Senegal 12 2002 308.6225 0.406554 56.38266 73.16142 25.46853 7302000

Senegal 12 2003 354.3127 0.408106 56.70741 76.94027 24.28561 7470000

Senegal 12 2004 353.3645 0.409658 57.02917 80.84408 24.93378 7642000

Senegal 12 2005 382.2808 0.41121 57.34934 82.65296 23.77347 7817000

Senegal 12 2006 341.3747 0.413472 57.66788 82.69502 23.04577 7992000

Senegal 12 2007 312.5203 0.415734 57.98722 86.45281 23.56955 8176000

Senegal 12 2008 363.3177 0.417996 58.30937 87.03993 22.18023 8359000

Senegal 12 2009 399.3813 0.420258 58.63232 86.79566 21.74926 8546000

Senegal 12 2010 410.7899 0.42252 58.95407 86.83878 22.35421 8734000

Sierra Leone 13 1989 492.3915 0.327394 39.46234 54.15212 11 2789000

Sierra Leone 13 1990 732.9643 0.33032 38.72146 52.3989 19.1576 2819000

Sierra Leone 13 1991 763.5511 0.333088 38.12239 48.75779 35.70277 2809000

Sierra Leone 13 1992 526.7096 0.335856 37.66324   41.01534 2773000

Sierra Leone 13 1993 517.432 0.338624 37.34563   33.23663 2728000

Sierra Leone 13 1994 552.5964 0.341392 37.18761   40.39878 2687000

Sierra Leone 13 1995 502.9718 0.34416 37.20871   38.71834 2647000

Sierra Leone 13 1996 478.7394 0.346982 37.41888   38.11353 2629000

Sierra Leone 13 1997 625.5087 0.349804 37.79902   27.68589 2610000

Sierra Leone 13 1998 672.7592 0.352626 38.32549   24.71222 2616000

Sierra Leone 13 1999 706.0929 0.355448 38.97468   25.15912 2639000

Sierra Leone 13 2000 747.9961 0.35827 39.73159 70.42862 28.38764 2692000

Sierra Leone 13 2001 454.5788 0.361142 40.57768 85.7592 8.536138 2780000

Sierra Leone 13 2002 579.3541 0.364014 41.48944   9.455016 2896000

75

Sierra Leone 13 2003 610.4852 0.366886 42.43078   11.07007 3017000

Sierra Leone 13 2004 641.272 0.369758 43.36861   12.58871 3134000

Sierra Leone 13 2005 665.4714 0.37263 44.26024   11.95694 3235000

Sierra Leone 13 2006 689.4227 0.375856 45.07302   11.21892 3318000

Sierra Leone 13 2007 767.9205 0.379082 45.79588   10.27285 3377000

Sierra Leone 13 2008 807.2002 0.382308 46.42527   8.377863 3430000

Sierra Leone 13 2009 829.3981 0.385534 46.95768   7.048618 3476000

Sierra Leone 13 2010 847.5599 0.38876 47.4022   8.251332 3522000

Togo 14 1989 576.649 0.281836 52.71327 93.75404 24.31883 2355000

Togo 14 1990 582.1406 0.28589 52.99198 95.84923 22.5285 2404000

Togo 14 1991 567.3966 0.290122 53.2468 101.6028 25.15557 2437000

Togo 14 1992 567.567 0.294354 53.47827 100.4084 23.77305 2463000

Togo 14 1993 596.4746 0.298586 53.68788   20.67681 2489000

Togo 14 1994 585.5228 0.302818 53.87717 98.5781 21.22767 2520000

Togo 14 1995 606.592 0.30705 54.0501 111.3957 22.18073 2561000

Togo 14 1996 689.6199 0.311454 54.21117 118.4108 21.06049 2613000

Togo 14 1997 700.0763 0.315858 54.36585 120.2977 20.12328 2674000

Togo 14 1998 663.3249 0.320262 54.51707 120.7892 16.86141 2740000

Togo 14 1999 682.2468 0.324666 54.66729 126.0913 16.20981 2803000

Togo 14 2000 635.8362 0.32907 54.80949 117.8386 18.31936 2860000

Togo 14 2001 658.8734 0.333626 54.9341 119.0995 17.15379 2909000

Togo 14 2002 649.4471 0.338182 55.03863 120.5566 18.39279 2951000

Togo 14 2003 628.9899 0.342738 55.12656 117.9534 18.41111 2989000

Togo 14 2004 640.8201 0.347294 55.20837 117.0918 17.14675 3024000

Togo 14 2005 695.2058 0.35185 55.304 116.6464 17.18968 3059000

Togo 14 2006 648.9345 0.356546 55.43744 121.3487 18.42312 3094000

Togo 14 2007 654.0856 0.361242 55.6262 115.9558 18.68922 3126000

76

Togo 14 2008 749.4071 0.365938 55.88032 117.8888 18.17363 3159000

Togo 14 2009 543.7268 0.370634 56.2028 134.7714 15.98346 3190000

Togo 14 2010 543.7818 0.37533 56.58871 139.6437 16.53093 3220000

APPENDIX B: OLS ESTIMATION

_cons 2.90694 2.810054 1.03 0.302 -2.631842 8.445722 lagpop -.0888073 .0521978 -1.70 0.090 -.1916923 .0140777 lindpro .0868533 .1258839 0.69 0.491 -.1612713 .3349778 ledu .3887094 .1756235 2.21 0.028 .0425451 .7348736 llfe .8851846 .6382987 1.39 0.167 -.3729397 2.143309 lurb .6110614 .2137888 2.86 0.005 .1896709 1.032452 lagpro Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 137.658721 220 .625721459 Root MSE = .65172 Adj R-squared = 0.3212 Residual 91.3177441 215 .424733693 R-squared = 0.3366 Model 46.340977 5 9.2681954 Prob > F = 0.0000 F( 5, 215) = 21.82 Source SS df MS Number of obs = 221

77

APPENDIX C: TEST FOR MULTICOLLINEARITY

78

Mean VIF 2.19 lindpro 1.15 0.867146 lurb 2.19 0.456809 ledu 2.31 0.432302 lagpop 2.46 0.405951 llfe 2.83 0.353948 Variable VIF 1/VIF

APPENDIX D: MODIFIED WALD TEST FOR GROUPWISE

HETEROSKEDASTICITY

79

H0: sigma(i)^2 = sigma^2 for all i

chi2 (22) = 10.59

Prob>chi2 = 0.9802

APPENDIX E: RANDOM EFFECTS WITHIN REGRESSION

80

rho 0 (fraction of variance due to u_i) sigma_e .66994101 sigma_u 0 _cons 2.90694 2.810054 1.03 0.301 -2.600664 8.414544 lagpop -.0888073 .0521978 -1.70 0.089 -.1911132 .0134985 lindpro .0868533 .1258839 0.69 0.490 -.1598746 .3335811 ledu .3887094 .1756235 2.21 0.027 .0444936 .7329251 llfe .8851846 .6382987 1.39 0.166 -.3658577 2.136227 lurb .6110614 .2137888 2.86 0.004 .192043 1.03008 lagpro Coef. Std. Err. z P>|z| [95% Conf. Interval]

corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(5) = 109.11

overall = 0.3366 max = 12 between = 0.7046 avg = 10.0R-sq: within = 0.3434 Obs per group: min = 7

Group variable: year Number of groups = 22Random-effects GLS regression Number of obs = 221

APPENDIX F: FIXED EFFECTS WITHIN REGRESSION

81

F test that all u_i=0: F(21, 194) = 0.45 Prob > F = 0.9827 rho .07624473 (fraction of variance due to u_i) sigma_e .66994101 sigma_u .19246999 _cons -2.253205 3.434691 -0.66 0.513 -9.027335 4.520924 lagpop -.0016371 .0624613 -0.03 0.979 -.1248275 .1215534 lindpro .050916 .1315137 0.39 0.699 -.2084642 .3102961 ledu .6253364 .2059615 3.04 0.003 .2191252 1.031548 llfe 1.632596 .7163716 2.28 0.024 .2197194 3.045472 lurb .6317128 .2214866 2.85 0.005 .194882 1.068544 lagpro Coef. Std. Err. t P>|t| [95% Conf. Interval]

corr(u_i, Xb) = -0.4700 Prob > F = 0.0000 F(5,194) = 21.27

overall = 0.3235 max = 12 between = 0.6805 avg = 10.0R-sq: within = 0.3541 Obs per group: min = 7

Group variable: year Number of groups = 22Fixed-effects (within) regression Number of obs = 221

APPENDIX G: RANDOM EFFECTS GLS REGRESSION

82

rho 0 (fraction of variance due to u_i) sigma_e .66994101 sigma_u 0 _cons 2.90694 2.810054 1.03 0.301 -2.600664 8.414544 lagpop -.0888073 .0521978 -1.70 0.089 -.1911132 .0134985 lindpro .0868533 .1258839 0.69 0.490 -.1598746 .3335811 ledu .3887094 .1756235 2.21 0.027 .0444936 .7329251 llfe .8851846 .6382987 1.39 0.166 -.3658577 2.136227 lurb .6110614 .2137888 2.86 0.004 .192043 1.03008 lagpro Coef. Std. Err. z P>|z| [95% Conf. Interval]

corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000Random effects u_i ~ Gaussian Wald chi2(5) = 109.11

overall = 0.3366 max = 12 between = 0.7046 avg = 10.0R-sq: within = 0.3434 Obs per group: min = 7

Group variable: year Number of groups = 22Random-effects GLS regression Number of obs = 221

APPENDIX H: HAUSMAN TEST

83

Prob>chi2 = 0.1810 = 7.58 chi2(5) = (b-B)'[(V_b-V_B)^(-1)](b-B)

Test: Ho: difference in coefficients not systematic

B = inconsistent under Ha, efficient under Ho; obtained from xtreg b = consistent under Ho and Ha; obtained from xtreg lagpop -.0016371 -.0888073 .0871702 .0343046 lindpro .050916 .0868533 -.0359373 .0380671 ledu .6253364 .3887094 .236627 .1075943 llfe 1.632596 .8851846 .7474112 .3252125 lurb .6317128 .6110614 .0206514 .0578848 fe re Difference S.E. (b) (B) (b-B) sqrt(diag(V_b-V_B)) Coefficients

84

APPENDIX I: BREUSCH AND PAGAN LAGRANGIAN

MULTIPLIER TEST FOR RANDOM EFFECTS

.

Prob > chi2 = 0.0120 chi2(1) = 6.31 Test: Var(u) = 0

u 0 0 e .448821 .669941 lagpro .6257215 .7910256 Var sd = sqrt(Var) Estimated results:

lagpro[year,t] = Xb + u[year] + e[year,t]

Breusch and Pagan Lagrangian multiplier test for random effects

85