Urbanization in Africa in Relation to Socio-Economic

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URBANIZATION IN AFRICA IN RELATION TO SOCIO-ECONOMIC

DEVELOPMENT: A MULTIFACETED QUANTITATIVE ANALYSIS

A Dissertation

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

Christian Tettey

August, 2005 URBANIZATION IN AFRICA IN RELATION TO SOCIO-ECONOMIC

DEVELOPMENT: A MULTIFACETED QUANTITATIVE ANALYSIS

Christian Tettey

Dissertation

Approved: Accepted:

______Advisor Department Chair Dr. Ashok K. Dutt Dr. Raymond Cox

______Committee Member Dean of the College Dr. Peter Leahy Dr. Charles B. Monroe

______Committee Member Dean of Graduate School Dr. Nancy Grant Dr. George R. Newkome

______Committee Member Date Dr. Lathardus Goggins

______Committee Member Dr. Helen Liggett

______Committee Member Dr. Carolyn Behrman

ii ABSTRACT

Developing countries are fast urbanizing and those in Africa are among the fastest when compared to Asia and Latin America. The process of urbanization is believed to be connected with levels of development and some assert that, for a country to develop, there is the need for an increased level of industrialization because according to the modernization school of thought, there cannot be urbanization without economic growth.

The developed countries passed through this process and according to this approach, developing countries must do the same. This situation, however, is believed to be different in the developing countries in general and in Africa in particular. Modernization theory of urbanization does not apply to developing countries which have not attained the economic growth of the developed countries before reaching high levels of urbanization.

This then raises the question about how developing countries, to which all African countries belong, become urbanized and still continue to urbanize. In other words, is

modernization theory of urbanization applicable to African urbanization?

A standard measure, urbanization index, was developed for measuring

urbanization in Africa since the traditional measure for urbanization depends on what

each country defines as urban. This was then compared with the traditional measure of

urbanization to note any differences in the prediction ability of urbanization in Africa. It

was found that social indicators of development tend to predict urbanization more than

the traditional economic variables on which modernization theory is based. Also,

iii socioeconomic development variables tend to predict urbanization index more precisely than degree of urbanization, which is the traditional measure for urbanization. Though the applicability of modernization theory is validated for urbanization in Africa, modification is recommended for the theory.

iv ACKNOWLEDGEMENT

I wish to express my sincere gratitude and appreciation to my dissertation committee for guiding this research and providing the opportunities toward its completion. The members include Dr. Ashok K. Dutt (Advisor), Dr. Peter J. Leahy, Dr.

Nancy K. Grant, Dr. Lathardus Goggins, Dr. Helen Liggett and Dr. Carolyn Behrman.

I am greatly indebted to Dr. Dutt through whose fatherly direction and encouragement this research became a reality and delaying his retirement for this course.

To his wife Dr. Hiran M. Dutta, I say thank you for your motherly love and support.

I would like to express my gratitude to Dr. Kwadwo Konadu-Agyemang for his invaluable assistance, resourcefulness and determination in helping me complete my dissertation. I would also like to thank Dr. Frank J. Costa for editing the final manuscript.

I am equally grateful to the faculty and staff of the Department of Geography and

Planning, for providing me office space and teaching opportunities.

Finally, my sincere appreciation goes to my wife Josie Batson and my children

Jordan and Jayda Tettey for their support and inspiration. The same goes to my mother

Elizabeth Ntibrey and my brothers, Charles and Phanuel Nani, not forgetting Phanuela and Priscilla Nani. To my mother-in-law, Mrs. Mary Batson, I say thank you for all your help in the course of my study.

I dedicate this research to the memory of Togbe Adase IV (Phanuel Kofi Dzadey),

Asafofia of Ho Ahoe, in return for his love, advice and support.

v TABLE OF CONTENTS

Page

LIST OF TABLES………………………………………………………………………ix

LIST OF FIGURES……………………………………………………………………...xii

CHAPTER

I INTRODUCTION…………………………………...…………………………..1

II LITERATURE REVIEW……………………………………...... ……………5

Introduction……………………………………………………………………5

Global Urbanization……………………………………………………….…..5

History of Urbanization in Africa……………………………………………..9

The Arrival of the Europeans to Post Colonial Era………………...……….…9

Post Colonial Urbanization in Africa……………………………………..….13

Theories of Urbanization……...…………………………………...………...15

Modernization Theory…………………………………….…...... 15

Dependency Theory……………………………….…………...... 22

Urban Bias Theory…………………………….……………………….23

Pre-colonial Urbanization ignored by the Three Theories…………...... 24

Conceptual Framework…………………..…………………………………..25

Urbanization…………………………………………………………...25

vi Defining Development………………………………………………...29

Urbanization and Development……………….……..………...………34

Summary…..…………………………………………………………………42

III STATEMENT OF THE PROBLEM………………………...……………….45

Justification for the Study……………………………………………………50

Research Significance and Hypotheses……………………...………………52

Research Purpose and Research Question…………………………….52

Research Significance…………………………………………………53

Research Hypotheses…………………………………………………..54

IV DATA AND METHODOLOGY…………………...………………………..64

Data Sources…..…………..…………………………………………………64

Methodology……..…………………………………………………………..72

Factor Analysis……………………………..………………………………..76

Developing Indices……………………..……………………………………77

Urbanization Index…………………….…………….……………………….77

Human Development Index for Africa………………..……….…………….92

Descriptive Analysis and Spatial Presentation of Data…………….………..99

Limitations of the Study…………………………………………………….122

Summary……………………………………………………………………123

V RESULTS……………….……………………………...……………..…..…124

Hypothesis Testing……………………………………..…………………...124

Multiple Regression…...………………………………..…………………..124

vii Variables Predicting Urbanization……….…………………..……………..153

Urbanization Index……………………..…………………………………...154

Degree of Urbanization…………………………………..…………………158

VI CONCLUSION…...…………………………………………………………162

Summary of Findings……………………………..………………………...162

Measure for Urbanization and Human Development Index…………..……167

Implications of Findings for Urban Studies and Policy…..…………..….....169

Future Research………………………..…………………………………...175

BIBLIOGRAPHY……………...…………………………………………………176

APPENDICES………...………………………………………………………….191

APPENDIX A. REGIONS OF AFRICA…..……..……..………………….192

Appendix A1. Table showing Regions of Africa...……...…………..192

Appendix A2. Map showing Regions of Africa…………….……...193

APPENDIX B. FACTOR ANALYSIS – ROTATED COMPONENT MATRIX……….………………………………………..…….………….194

APPENDIX C. REGRESSION COEFFICIENT AND COLLINEARITY TEST………………………………………………………...….……...195

Appendix C1. Regression Coefficients with Collinearity Test for Degree of Urbanization…………………………………………...195

Appendix C2. Regression Coefficient with Collinearity Test for Urbanization Index………………………………………………..196

APPENDIX D. CORRELATION COEFFICIENT TABLE…………….....197

viii LIST OF TABLES

Table Page

2.1 Independence Dates for African Countries………………………………………12

3.1 Meaning of Abbreviations for African Countries.……………………………….47

3.2 Hypotheses and Tests…………………………………………………………….61

4.1 Variables Derived from Data Sources for the Study…………………...………..70

4.2 Scale of Population Concentration by Countries in Africa ...………….………...80

4.3 Urbanization Index for African Countries ………...………………….…………84

4.4 Number of Countries among the Top 10 and Bottom 10 in Terms of Urbanization Index…………………………………………………….…………90

4.4 Computed Human Development Index for African Countries...……….………..95

4.5 Gini Index above Continental Average by Regions of Africa….………………111

4.6 Countries with the Worst Rate of Life Expectancy…………………….………112

4.7 Number of Countries with 61% or more of the Total Population having Access to Improved Sources of Water Supply in Africa by Region …. .117

5.1 Model Summary - Degree of Urbanization using Enter Method with Socioeconomic Variables……………………………………. ………………...127

5.2 Model Summary - Urbanization Index using Enter Method with Socioeconomic Variables………………………………………………...……………………...127

5.3 Model Summary and Coefficient Table for Urbanization Index using Stepwise Method with Socioeconomic Variables ….……………………133

5.4 Model Summary and Coefficient Table for Degree of Urbanization using Stepwise Method with Socioeconomic Variables .………………………134

ix 5.5 Variable Grouping based on Factor Analysis…………………………………..135

5.6 Model Summary for Urbanization Index, using Enter Method, on Economic Variables………………………………………………………….…137

5.7 Model Summary and Coefficient Table for Urbanization Index, using stepwise method, on Economic Variables……...... ………………………..138

5.8 Model Summary for Degree of Urbanization, using Enter Method, on Economic Variables…………………………………………………………….138

5.9 Model Summary and Coefficient Table for Degree of Urbanization, using Stepwise Method, on Economic Variables ……..…………………….…139

5.10 Model Summary for Social Indicators, using Enter Method, on Urbanization Index ……………………………………………………………..140

5.11 Model Summary and Coefficient Table for Social Indicators, using Stepwise Method, on Urbanization Index ………………………………...……143

5.12 Model Summary for Social Indicators, using Enter Method, on Degree of Urbanization………………………………………………..….………………..141

5.13 Model Summary and Coefficient Table for Social Indicators, using Stepwise Method, on Degree of Urbanization……………..……..…………….141

5.14 Model Summary and Coefficient Table for Human Development Index on Degree of Urbanization ……………………………………………………..143

5.15 Model Summary and Coefficient Table for Human Development Index on Urbanization Index…………………………………………...…………………143

5.16 Model Summary and Coefficient Table for Computed Human Development Index on Degree of Urbanization ...………………………….….144

5.17 Model Summary Model Summary and Coefficient Table for Computed Human Development Index on Urbanization Index…………………...…….…144

5.18 Model Summary for Urbanization Index with Coastline Countries ……….…...146

5.19 Model Summary for Degree of Urbanization with Coastline Countries.….…...147

5.20 Model Summary for Urbanization Index with Countries without Coastline.…...147

x 5.21 Model Summary for Degree of Urbanization with Countries without Coastline ……………………………………………………………………….147

5.22 Variables Predicting Urbanization based on Geographical Location..………....148

5.23 ANOVA Table and Post Hoc Analysis for Degree of Urbanization and Colonial Ties………………………………………………………………..…149

5.24 ANOVA Table and Post Hoc Analysis for Urbanization Index and Colonial Ties……………………………………………………………………150

5.25 Paired Sample Test for Standard Error of the Estimates……..…….………..…152

5.26 Paired Sample Statistics for the Standard Error of the Estimates…………...…152

5.27 Variables Predicting Urbanization……………………………………………..155

5.28 Summary of Results…………………………………………………………….160

xi LIST OF FIGURES

Figure Page

2.1 Level of Urbanization 1950 – 2000… …………………………………………..6

2.2 Urbanization Rate of Change 1950-60 to 1990-2000……………………………7

2.3 Demographic Transition…………………………………………………………19

2.4 Rostow's Economic Stages in Relation to Levels of Urbanization………………20

2.5 Relationship between Urbanization and Development…………………………..44

3.1 Countries in Africa……………………………………………………………….46

3.2 Total Number of Urban Projects Approved and/or Funded for Africa by the World Bank between 1960 and 2004…………………………………………….48

3.3 Total Amount Approved and/or Spent by the World Bank on Urban Projects in Africa between 1960 and 2004………………………………………49

4.1 Number of Census Conducted by African Countries Over a Period of Six Decades……………………………………………………………...…………...65

4.2 Number of Census Taken by African Countries in the Past Six Decades (1945-54 to 1995-2004)………………………………………………………….66

4.3 Censuses Taken by African Countries after 1999.………………………………67

4.4 Scatter Plot for Degree of Urbanization and Scale of Population Concentration for African countries….……………………………………….…82

4.5 Urban Areas in Africa with Population of 500,000 or more in 2000…..………..83

4.6 Scatter Plot for Urbanization Index and Factor Score for Urbanization…………85

4.7 Scale of Population Concentration for Africa by countries..………………….…86

xii 4.8 Ranking of Degree of Urbanization and Scale of Population Concentration among the Top 10 Countries of Africa…………………………...88

4.9 Urbanization Index for African Countries…………………………………….…89

4.10 Venn Diagram showing the Top 10 Ranking Countries in Africa in terms of Scale of Population Concentration, Urbanization Index and Degree of Urbanization………………………………………………………………...……91

4.11 Computed Human Development Index for Africa by Countries……………...…98

4.12 Location of African Countries in Relation to the Sea..…………………..……..100

4.13 Distribution of European Colonies in Africa after World War I………………101

4.14 African Colonial Ties with Europe after World War I.…………….…………..102

4.15 Periods when African Countries became Independent..………………………..104

4.16 Periods in which Independence was Attained by Countries in Africa.…… …..106

4.17 GDP per Capita for African Countries in 2001………………………………...107

4.18 The Richest Thirteen African Countries in 2001...………….………………….108

4.19 GDP per Capita at PPP Exchange rate for African countries in 2001.....………109

4.20 Five Poorest African Countries in terms of Income, 2001.………………...…..110

4.21 Degree of Urbanization by Countries in Africa, 2001……………………….....113

4.22 Life Expectancy (2001) in Africa by Countries..…………………………….....114

4.23 Human Development Indicators for African Countries, 2001..………………...116

4.24 Access to Improved Sources of Water by Countries in Africa, 2001…..…...….118

4.25 Access to Improved Sanitation by Countries in Africa, 2001………………….120

4.26 Physicians per Million Population in Africa by Countries, 2001…………...….121

5.1 Residuals for Urbanization Index Using the Full Model (Enter Method)..…….128

5.2 Estimation for Urbanization Index……………………………………………...129

xiii 5.3 Residuals for Degree of Urbanization Using the Full Model (Enter Method)…130

5.4 Estimation for Degree of Urbanization…………………………………………131

5.5 Venn Diagram of Socio-Economic Variables Predicting Urbanization………...135

xiv

CHAPTER I

INTRODUCTION

Urbanization is fast occurring in developing countries especially those in Africa and Asia and with African countries experiencing the most rapid urbanization. (United

Nations, 2004; Ziegler, Brunn & Williams, 2003). Urban researchers indicate that urbanization in developing countries is due largely to rural – urban migration and this movement is often explained by various theories such as modernization, dependency and urban bias theories. These theories are elaborated upon in the literature review. Of these three theories, modernization theory is the one that is most often referred to as the cause of urbanization (Kasarda & Crenshaw, 1991). This theory holds that there is a positive relationship between urbanization and development. Various researchers have confirmed this assertion but none of these studies have used socioeconomic data to study this relationship with Africa as a continent.

Urbanization is associated with problems such as inadequate infrastructure, waste management and inadequate housing and these problems are difficult to eradicate or control. The developed countries continue to battle these problems, but they are worse in the developing countries where the dearth of necessary resources tends to hinder attempts to solve urban problems. In order to overcome these problems, the developing countries

turn to international donor agencies for assistance. However, their efforts do not seem to yield any meaningful result because urban problems continue to escalate. This is an indication that some important points might have been missed in their efforts to solve these urban problems. The donor agencies, which happen to be either the developed countries or based in the developed countries, tend to have one cure for all ailments regarding urbanization and this is the provision or expansion of infrastructural facilities in the urban areas.

The rate of urbanization in Africa and other developing countries is quite different from what happened in the presently developed countries, at the time they were developing (Butler & Crooke, 1973, Palen, 1997, Okpala, 1987). Dutt and Parai (1994) report that, demographically, migration is a result of urban pull which was the chief cause of urbanization in Europe and the United States. Urbanization rates were also gradual in the developed countries. In Africa and other developing countries on the other hand, both migration and natural increase were the main cause of urbanization and migration is attributed to rural-push. The rate of urbanization is also rapid. Thus the factors that contributed to urbanization in the developed countries were different from what Africa has experienced and continue to experience. This divergence calls for a different approach in the attempt to solve urban problems. Moreover, little effort has been made to find the root causes of the problems and also the problem solving–approaches seem to have disintegrated instead of tackling the problem comprehensively.

It is very difficult to define what is known as urban. As can be inferred from the literature review, there is no specific definition for the term urban; rather it has been defined differently in various countries and by various disciplines. To effectively study

urbanization and also find solutions to the problems of urbanization, there is the need to derive a standard measure for urbanization and proceed from there.

Critical to this study is an attempt to derive a standard measure for urbanization and also to identify the various socio-economic development variables that tend to predict urbanization in Africa. This study holds that its research findings might help, in the long run, to understand the factors that tend to predict urbanization and to serve as a basis for efforts to find suitable solutions to urban problems particularly in Africa and the developing world in general.

Though the study considered urban bias and dependency theories of urbanization, this research is based on the modernization theory of urbanization which is the most applicable to the African case. This study is the continuation of previous research and will contribute to advancement of knowledge in the following ways:

i) Identifying the socioeconomic variables that tend to predict

urbanization in Africa and

ii) Creating a way of measuring urbanization with a standardized measure.

iii) Exploring the possibility of advancing an atlernative theory of

urbanization.

The study is divided into a total of six chapters. The first chapter is the introduction, which is the prelude to the study. The second chapter is the literature review, where the author tries to provide highlights on the history of urbanization in

Africa, the main study area. It also tries to provide the various definitions for urbanization and development and also reviews work done by various researchers in the establishment of relationship between urbanization and development. The third chapter deals with

statement of the problem and the various hypothesis the study attempts to test. The fourth chapter is concerned with the various data sources and the statistical techniques used in

the study. The fifth chapter is the presentation of the statistical analysis and the final

chapter provides about the summary of the findings and the implication of the findings

for urban studies and policies as well as suggested areas for future research.

CHAPTER II

LITERATURE REVIEW

Introduction

This section reviews literature on urbanization in Africa primarily during the

colonial and post-colonial era. It also looks into the factors that various urban researchers

indicate were the causes of urbanization and theories developed to explain the

urbanization process. Concepts of urbanization, development and then the relationship between urbanization and development are also discussed. Applicable theories of

urbanization pertaining to Africa are also discussed.

Global Urbanization

Urbanization is not a modern phenomenon; it has been occurring since about

5000 B.C. (Sjoberg, 1960). The level of urbanization, measured by the proportion of

urban population to total population, has been increasing over the years. After the

Second World War, urbanization took place rapidly around the globe. Urbanization

levels were high in developed countries – Europe, North America and Oceania, with

more than 50% of the population living in the urban areas (United Nations, 2002). A

relatively high level of urbanization was also true in Latin America and the Caribbean

region, with more than 40% of the population living in the urban areas. Africa and Asia

were the least urbanized; in 2000, about 40% of the population of Africa and Asia lived

in the urban areas. (See figure 2.1). Literature has it that, urbanization curve has the

shape of an attenuated “S”, where the initial period of urbanization is characterized by gradual urban population growth, followed by a steep rise indicating a large share of the

100

in the 70

60 50

in Urban Areas 30

% of Total Population Living 10

0 1950 1960 1970 1980 1990 2000 Years

Africa Asia Europe Latin America & the Caribbean Northern America Oceania

Figure 2.1 Level of Urbanization 1950 – 2000

Source of Data: United Nations, 2002.

total population living in the urban areas. Once a greater share of the population

becomes urban (about 80% of the total population) the curve flattens (Northam, 1979;

Knox, 1994). As can be seen from Figure 2.1, apart from Africa and Asia, all the others

are approaching the 80% mark indicating urbanization would be leveling off in those areas while Africa and Asia continue to experience increasing urbanization.

On the other hand, the rate of urban change has been higher in Asia and Africa than the developed countries, Latin America and the Caribbean region since 1950.

Africa had between 25% and 17 % rates of change between 1950-60 and 1990-2000 respectively while Asia had between 19% and 16% rates of change during the same period. The developed countries however had a rate of change below 11% (See figure

2.2). According to Amis (1990) Africa is the least urbanized but most rapidly urbanizing

20 bbb

5 Rate of Change (%)

0 1950 - 1960 1960 - 1970 1970 - 1980 1980 - 1990 1990 - 2000 -5 Year

Africa Asia Europe Latin America & the Caribbean Northern America Oceania

Figure 2.2 Urbanization Rate of Change 1950–60 to 1990–2000

Source of Data: United Nations, 2002

region of the world. He went on to indicate that in 1960, there were 7 cities in Africa with over 500,000 population and by 1980, it had increased to 14 (p. 9).

It is evident from Figure 2.2 that the urbanization growth rate for the developed countries had been falling faster than the developing countries. Moreover, the population growth rate tends to be slower or stabilized hence the effect on urbanization growth rate.

The developing countries, particularly in Africa and Asia, had urbanization levels less than 40% in 2000; hence higher rates of urbanization were experienced and would continue to be experienced until they reach the level of about 80% urban where the rate of urbanization tends to decline. It can be seen from Figure 2.1 that Africa is fast urbanizing and literature sources indicate that it is faster than what happened in the developed countries during the Industrial Revolution era (Butler & Crooke, 1973, Palen,

1997, Okpala, 1987). With regard to particular cities, rates of population growth range from less than 1 percent per annum in places like New York, to more than 6 percent per annum in many African cities like Nairobi, Lagos, and Lusaka. This is another indication that Africa is fast urbanizing. In Asia and Latin America, many cities are growing at rates of about 5 percent per annum.

The Industrial Revolution in Europe during the 18th Century and the

industrialization of America beginning in the mid 19th Century brought about rapid

urbanization in these areas. Factories needed labor and a rise in commercial activities

created the needed opportunities in the urban areas. Population then moved from the rural

areas to the urban areas for employment which was a stepping stone for better life.

Economic growth, which is the increase in the value of goods and services produced by

an economy (country) and urbanization (an increase in the proportion of total population

living in urban areas) go hand in hand. The increase and the globalization of the world economy has encouraged greater international trade, providing urban areas with greater roles since they have become the hub for the various global economic activities resulting

in migration to these urban centers hence increased global urbanization.

History of Urbanization in Africa

Urbanization in Africa has been widely misconceived as having been the result of

colonialization. This misconception assumed that the Africans did not have the political

sophistication and the organizational ability to build towns but rather lived in isolated

settlements (Hull, 1976). The assumption was that town living existed as a result of alien

inspiration. Urbanization in Africa started long before the arrival of the Europeans in the

1400s. According to Chandler (1994), urbanization appeared in northern Africa as early

as 3200 BC and later extended to the rest of the continent. These urban centers were

located along the trade routes used by the Arab traders who brought wares from the

Middle and Far East to trade with Africans, mostly from the forest regions (Becker,

Hamer & Morrison, 1994). Some of these urban centers include Cairo (Kahira) and

Alexandria in present day Egypt, Tripoli in Libya, Fez in Morocco, Timbuktu in Mali,

Kumasi in Ghana and Kano in Nigeria.

The Arrival of the Europeans to Post Colonial Era

The arrival of the Europeans in the 1400s brought with it a new wave of urban development in Africa, resulting from the establishment of transportation networks, ports,

administrative headquarters and mining facilities. The Europeans arrived first along the coast of West Africa in an effort to break the trade monopoly of the Arabs with the West

African coast. On arrival, they established trading posts along the coast for their business

activities, and for the easy transportation of commodities to their mother countries, small ports were established. Transportation networks were then developed from these port centers into the interior for the exploitation of the commodities. Accra in the Gold Coast

(presently Ghana), Dakar in Senegal, and Freetown in Sierra Leone were some of these ports. Others were Cape Town and Durban in South Africa, Beira in Mozambique,

Mombassa in Kenya and Tunis in Tunisia. Alexandria in Egypt (though developed as a

port city by the Greeks over 2300 years ago), became the trading outposts during colonial

times. All these port centers grew to become urban centers of today.

Administration was another need that led to the development of urban areas in

Africa. The Europeans, in order to control the interior of their colonies, established

centers for the political control of the colonies. Colonial administrative centers generally

created peaceful conditions for the surrounding areas, which led to the movement of the

indigenous population to settle in these areas, acting as magnets for the rural population.

A case in point is Accra, Ghana, where the removal of the capital from Cape Coast in

1447 to Accra led to the development of Accra as an urban center. (Konadu-Agyemang,

2001). Colonial administrative centers were located at the coast and they grew to become capitals for the various countries.

The third category of urban development linked to colonialization was related to mining. Mining opportunities attracted expatriates as well as indigenes, who were later employed as mine workers, to the mining areas, resulting in urban development. Cases in point were Obuasi, Tarkwa, and Dunkwa in Ghana, Jos in Nigeria and Kimberly in South

Africa. Further, for easy transportation of mining equipment to the mining centers and the

transportation of the extracted minerals and other exportable commodities for export,

railway lines were laid from the coast to the mining centers (Gould, 1960). Urban settlements developed along these lines and grew into urban areas as a result of other economic activities other than mining.

In addition to all these, policies by the colonial administrations in one way or another led to urbanization. In order to generate enough revenue, head taxes were introduced. Every household was required to pay this tax annually. With the subsistence economy in existence by then, able-bodied individuals had to travel to the urban areas to seek employment to raise enough money to fulfill this obligation. Urban residence at this point was considered temporary (Oliver & Atmore, 1994) because the migrants returned to their villages after earning the money they needed for the payment of their taxes and other needs requiring cash.

Despite the policy which tended to force the able bodied individuals to find their way to the urban centers to work and earn money for payment of their head taxes, this movement had not been all that easy. There were strict laws that discouraged the indigenous population from dwelling in the urban areas. These laws include strict building codes requiring the use of expensive building materials and direct control of population movement into the urban areas. By means of direct control, the indigenous population needed permits in order to live in the urban centers. The police were used to enforce these laws.

Table 2.1 Independence Dates for African Countries

Independence Country Date Country Independence Date 1 Algeria July 5, 1962 28 Libya December 24, 1951 Angola November 11, Madagascar June 26, 1960 2 1975 29 3 Benin August 1, 1960 30 Malawi July 6, 1964 Botswana September 30, Mali September 22, 1960 4 1966 31 5 Burkina Faso August 5, 1960 32 Mauritania November 28, 1960 6 Burundi July 1, 1962 33 Mauritius March 12, 1968 7 Cameroon January 1, 1960 34 Morocco March 2, 1956 8 Cape Verde July 5, 1975 35 Mozambique June 25, 1975 Central African Namibia March 21, 1990 9 Republic August 13, 1960 36 10 Chad August 11, 1960 37 Niger August 3, 1958 11 Comoros July 6, 1975 38 Nigeria October 1, 1960 Congo, Democratic Rwanda July 1, 1962 12 Republic of the June 30, 1960 39 Congo, Republic Sao Tome 13 of the August 15, 1960 40 and Principe July 12, 1975 14 Cote d'Ivoire August 7, 1960 41 Senegal April 4, 1960 15 Djibouti June 27, 1977 42 Seychelles June 29, 1976 Egypt February 28, Sierra Leone 16 1922 43 April 27, 1961 Equatorial Guinea October 12, Somalia July 1, 1960 17 1968 44 18 Eritrea May 24, 1993 45 South Africa May 31, 1910 19 Ethiopia 46 Sudan January 1, 1956 20 Gabon August 17, 1960 47 Swaziland September 6, 1968 Gambia, The February 18, Tanzania April 26, 1964 21 1965 48 22 Ghana March 6, 1957 49 Togo April 27, 1960 23 Guinea October 2, 1958 50 Tunisia March 20, 1956 Guinea-Bissau September 24, Uganda October 9, 1962 24 1973 51 Kenya December 12, Zambia October 24, 1964 25 1963 52 26 Lesotho October 4, 1966 53 Zimbabwe April 18, 1980 27 Liberia July 26, 1847

Post Colonial Urbanization in Africa

The 1960s and early 1970s were often referred to as the beginning of the post

colonial era although some countries gained independence long before the 1960s. Liberia

for instance was established as an independent country by the United States in 1847 after

the abolition of the slave trade. Egypt became independent in 1922, Libya in 1951,

Morocco, Sudan and Tunisia in 1956, Ghana in 1957 and Guinea in 1958. Despite this,

more than fifty percent of the African countries gained their independence during 1960s

and early 1970s hence the period is often referred to as the commencement of the post

colonial era (See table 2.1).

Urbanization during the post-colonial era had been rapid. By 1960, about 18.5% of the

population in Africa lived in urban areas and by the year 2000, it increased to 37.2%, an increase of nearly 100% (United Nations, 2002). Rapid urbanization was taking place in eastern Africa with an increase of over 200% while the least urban growth region was southern Africa with less than 30% increase between the years 1960 and 2000.

Post colonial urbanization had been attributed largely to rural – urban migration by Zacharia and Conde (1981). Their study revealed that rural–urban migration accounted for about 50% of urbanization in West Africa. Gugler and Flanagan (1978) agreed that migration contributed largely to urbanization in Africa although it is not the only cause.Rural–urban migration was explained by migration theory, which suggested

that the volume of migration was related to income differentials between the rural and

urban areas (Eicher, et al., 1970; Rakodi, 1997, Dutt, 2001; Todaro, 1977). This theory

has been expanded by not limiting the reasons for rural–urban migration to income

differentials but also to the probability of obtaining formal sector work (Rakodi, 1997).

Apart from income differences, the urban bias nature of investment and policies by various governments (Lipton, 1977) serve as pull factor for migration into the urban areas. Environmental deterioration, coupled with increased agricultural density, as a result of population growth, put pressure on land for cultivation. Agricultural densities most often become so high in the rural areas that land owners could not afford to subdivide the land to accommodate additional farmers. Ideally, new lands are needed for cultivation but such lands are most often not available hence the excess farm labor migrates to the urban centers (Dutt, 2001; Firebaugh, 1979).

Another cause of change in urban population during the post independence era is natural increase (Konadu- Agyemang, 2001; Arteetey-Attoh, 1997). It has been estimated that 40 – 50% of population growth in cities in the developing world is due to natural increase (Konadu-Agyemang, 2001). As a result of improved medical technology, mortality rates have fallen resulting in increased life expectancy rates while fertility and birth rates continue to be high. According to Wertz (1973), urban centers have been the main recipients of the new improvements in mortality rate because they are the places where the medical facilities, scientific techniques as well as expert personnel are located and where the largest number of people can be reached at the least cost.

Alteration in city boundaries is another component of urban growth (Konadu-

Agyemang, 2001; Arteetey-Attoh, 1997; Firebaugh, 1979). City boundaries are altered and as a result the outlying suburbs are being incorporated into the city boundaries, resulting in growth in terms of population and city size. Examples from my experience are the cities of Accra and Tema. The city of Accra before 1980 did not include Madina, and the city of Tema before 1980 did not include Ashiamang. (Both Madina and

Ashiamang served as “dormitory towns” for Accra and Tema respectively). In 1984, the boundaries of both cities were redrawn to include their suburbs, thus increasing the urban population.

Theories of Urbanization

Various theories are used to explain urban growth and these theories include (i) modernization, (ii) dependency and (iii) urban bias theories. These are explained below.

Modernization Theory

Modernization theory was developed in the mid 20th Century. Modernization is

the term used for the transition from the traditional society of the past to modern society

as found in the west. Modernization theory presents the idea that by introducing modern

methods of production like the use of advanced technology for industry the

underdeveloped countries will experience a strengthening in their economies and this will

lead them to development. This theory holds that the modernization of states through

economic development encourages other forms of development like social and political

development. This theory focuses on individual countries for analysis and it is examined

mainly with economic development as operationalized variables such as GDP per capita.

According to the modernization school, which is the view shared by the classical

economists, there cannot be urbanization without industrialization (Berliner, 1977). In

other words, the more industrialized a society is, the more urbanized it is and this is

believed to be as a result of agriculture releasing surplus rural labor for industries located

in the cities (Dutt, 2001). Urban researchers adopted an analytical tool based on

evolutionary and functionalist perspectives in explaining this theory. The evolutionary

perspective consists of a framework in which the social changes are unidirectional,

progressive and gradual. The evolution is irreversible as the rural primitive stage advances to high level of advanced urban-based society. The functionalist perspective recognizes that as society proceeds towards modernization, systematic and transformative changes take place; giving rise to change from traditional values to modern ones.

Technology and industrialization-based economic growth become engines of growth

(Kasarda & Crenshaw, 1991). Thus there is the need for a country to experience migration from rural to urban areas in order to become an industrial (modern) society

(Bradshaw, 1987). This is based on the assumption that the development process and urbanization move along a continuum.

One of the key proponents of modernization theory is Walter Rostow. The theory is often tied with his (Rostow’s) concept of the evolutionary ladder of development, which he entitled ‘The Stages of Economic Growth’ (1977). This has a connection with the Demographic transition model, based on an interpretation which began in 1929 by the

American demographer Warren Thompson (Chesnais, 1992) with the only difference being the number of stages. The evolutionary ladder of development consists of five

stages while the demographic transition model consists of four stages. The stages are as follows:

Evolutionary ladder of development Demographic transition

1. Traditional society 1. Pre Modern 2. Pre Takeoff 2. Industrializing/Transitional 3. Take-Off Stage 3. Mature Industrial/Industrial 4. Stage of Maturity 4. Post Industrial 5. High mass consumption.

There is an implied relationship between Rostow’s stages of economic growth and

Thompson’s demographic transition. The first stage Traditional Society is characterized

by high level of subsistence economic activities where production is consumed rather

than traded. Most workers are in agricultural production where they have limited savings

and use labor-intensive techniques in their production which is usually referred to as the traditional method of production. Society at this stage is rural and wealth is spent on non-

productive activities, largely on military and religious activities. Demographic conditions at this stage are characterized by a high birth rates and high death rates. Hygiene at this

stage is at the lowest level since there is no potable water and modern medical care is not

available. The high death rates cancel out the high birth rate hence population growth at

this stage is slow. See diagram in Figures 2.4 and 2.5.

The second stage Preconditions for Take-Off is characterized by the beginning of

specialization where production increases, generating surplus for external trade. Trade is concentrated on primary products. Income and savings begin to increase. At this stage, the population is internally awakening to a desire for a high standard of living and changes in attitude occur. The demographic transition associated with this stage is characterized by a rapid decline in death rate with birth rate remaining high. Death rate falls at this stage as a result of improved sanitation and health care. Population growth is rapid. This tends to put pressure on farmlands since there is limited room for expansion.

Redundant labor begins to grow at this stage. As a result of surplus production, urbanization begins to slowly occur as a condition indicated by various researchers

(Palen, 1997; Angotti, 1993; Childe, 1950; Sjoberg, 1960; Dutt, 2001).

Take-Off Stage, which is the third stage, is characterized by increased industrialization where new technologies and capital are applied to increase production.

Manufacturing becomes important. This growth however is concentrated in a few regions. New political and social institutions that support industrialization such as

education, where technical education is emphasized and highly valued (Pomeroy, 2003),

and banks, which serve the purpose of capital mobilization evolve. Saving rates at this

stage are high as a result of increasing surplus production and this savings as well as

profits are mostly invested. Demographically, this stage is associated with increased

population growth. The death rate at this stage continues to fall while birth rates still

remain high as a result of continued improvement in health related facilities. In terms of

urbanization, however, this is the period where a large proportion of the population

migrates to areas where manufacturing activities are concentrated, for employment. This movement is necessitated by two factors related to agriculture. In the first place, the increased population puts pressure on land for cultivation, calling for the “thinning out”

of the excess farm labor, which ends up finding their way into the manufacturing regions

for employment. Secondly, improvement in agricultural practices by means of

mechanization result in lesser need for farm hands resulting in excess farm labor, which

ends up finding their way into manufacturing employment in the urban centers.

Drive to Maturity, the fourth stage has the characteristics of technological

diffusion into all parts of the economy, where a wide range of goods and services are

produced. Workers become specialized at this stage and all forms of infrastructure needs are established. Consumer goods become the main bulk of production, while services are on the rise. The demographic transition associated with this stage sees declining death

rates while birth rates begin to drop at a faster rate than death rates. As a result of increasing urbanization at this stage, families begin to realize that children are expensive

Rate per 1000 40

Stage 1 Stage 2 Stage 3 Stage 4 0 Years

Birth Rate Death Rate

Figure 2.3 Demographic Transition

Source: Getis, Getis and Fellman, 2004 p. 204.

to raise and that having too many children hinder them from taking advantage of job opportunities, since most families have become two income earners. In the rural areas, where birth rates tend to be higher, continued decline in infant mortality means parents realizing they do not require so many children to be born to ensure a comfortable old age.

Life expectancy is also improved. Urbanization at this point continues to progress since more and more people move to the urban centers where the jobs are.

100% Early Industrial Tr adi ti onal Socie ty Post Industrial Post Precondition Takeoff for Urban Population Mature Industrial

0% Pre Industrial Take of f Mass Consumption

Phases of Economic Growth

Figure 2.4 Rostow’s Economic Stages in Relation to Levels of Urbanization

Source: Dutt & Noble, 1996 p. 8.

The final stage is known as High Mass Consumption and is characterized by the

economy focusing on durable consumer goods like cars instead of production for heavy

industries like heavy machines. Personal incomes are high and individuals do not worry

about securing basic necessities of life but spend more of their energies on non-economic

activities. Per capita income continues to increase while the service sector becomes more

important than manufacturing industries in terms of employment. The final stage of the

demographic transition is associated with this stage, where death rates continue to decline

while birth rates also decline to the extent of equaling or even falling below death rate

producing zero to negative population growth. At this point, urbanization begins to level off because countries experiencing this stage of development have reached the 80% urban population mark.

Rostow’s concept of ‘stages of economic growth’ has been criticized by many development economists who hold that it is culturally biased. As a result, they doubt its application to developing countries like those in Africa. Despite the criticisms, the model remains the valid description of the development path trod by nearly all developed countries and all other countries are required to tread the same path.

According to modernization theory, urban areas contain modernizing institutions such as schools, factories, entertainment centers and the mass media, as well as advanced medical care (Bradshaw, 1987). These institutions then serve as a pull factor for the rural dwellers (urban pull), encouraging them to migrate into the urban areas. Examples of such attractions are there in both developed and developing countries. Factories in

England attracted a large number of migrants from rural areas to settle in cities with the advent of the Industrial Revolution which began in the second half of the 18th Century.

The development of fuel powered tractors in the early 20th century led to the migration of

cotton plantation workers from the south of the United States (rural-push) to take up jobs

in places located in the North East and the Midwest. Moreover “rural push” has caused a

large scale rural to urban migration in the recent years in the developing countries.

Dependency Theory

In view of the flaws of modernization theory and its inability to account for Third World

underdevelopment, an alternative theory was devised by a group of scholars known

collectively as the dependency school, which originated in Latin America. This school

holds that development in the developing countries is conditioned by the growth and

expansion of Europe. This school addresses certain issues not considered by

modernization theory. It lays importance on historical processes in explaining the

changes which have occurred in the structure of cities as a result of the switch from the

pre-capitalist to capitalist mode of production. It also lays emphasis on the dependent

nature of capitalist development in the Third World which places emphasis on external

economic forces in the study of cities. The dependency school argues that the developed

countries use the developing countries as a source of input (raw material supplier) for

their factories. This results in foreign investment in large-scale agricultural production

which displaces peasant farmers in the rural areas. The displaced farmers then move to

the urban areas to seek employment (Firebaugh, 1979; Walton, 1977; Bradshaw, 1987).

Also large foreign investments in capital-intensive manufacturing in the urban

areas resulted in increased output and industrialization in the urban areas. This then does

have a multiplier effect since businesses spring up to provide services that are linked

either directly or indirectly to the manufacturing activities in the urban areas. This creates

the false impression for the rural dwellers that there is high-paying employment

opportunities for them in the urban areas hence their migration to the urban areas. On

their arrival in the urban areas and to their dismay they cannot get the high paying

employment; they end up in the informal sector. The informal sector workers are the least

paid among the urban labor force. This theory argues that the core, consisting of industrialized nations, dominate over the periphery which consists of the Third World.

The Third World urban development is, thus, conditioned by the developed world.

The recent economic globalization trends have restructured the labor-capital relationship between the developed and developing worlds. In the new structure of

economic globalization it is not only the less skilled jobs related to garment, shoe and

handbag making but also upscale jobs such as chip design, engineering, basic research

and financial analysis that are out-sourced by the multinational corporations to

developing countries. The labor costs are much cheaper in the developing countries.

These semi-skilled and upscale jobs are being created increasingly in the developing

countries. This in turn causes growth in supporting service sector employment leading to

labor moving into the urban centers to fill up these jobs hence growth in urban population

(Kentor, 1981; Dutt & Noble, 2003).

Urban Bias Theory

Another approach to understanding urban development in developing countries is

through the application of urban bias theory. This theory shifts the emphasis of urban

development from the economic perspective to political perspective. This perspective,

spearheaded by Lipton (1977), argues that policies favor the urban areas to the detriment

of the rural areas, hence the concentration of facilities and the creation of favorable

conditions in the urban areas. State policies allegedly overtax the rural citizens with

similar incomes. The production of the rural areas, notably agricultural products, are

overtaxed due to price twists. Overtaxing works in the following way. State controlled

marketing boards buy agricultural products from the local farmers at an artificially low price and then resell these products to the consumers at the prevailing higher market price; the difference is often used to provide facilities in the urban areas.

In addition, governments in the developing countries tend to invest domestic capital on the provision of development facilities. These facilities are largely located in the urban areas while a larger proportion of the population is found in the rural areas. The facilities include hospitals, schools, libraries and other government/semi-government facilities. Investable resource in favor of the rural dwellers, who are basically farmers, in the form of roads, small-scale irrigation facilities, agricultural machinery and storage facilities are often downplayed by the policy makers. Higher standards of living are created in the urban areas resulting in the creation of disparity between the urban and the rural areas. As a result, the rural dwellers tend to migrate to the urban areas to take advantage of the favorable policies.

Pre-colonial Urbanization ignored by the Three Theories

Other underlying factors contributing to urbanization do exist, which have not

been covered by modernization theory or urban bias theory. Dependency theory is not

considered here because it is based on the continuation of colonial-based dependence.

The urbanization process which took place before Western colonialization can not be

explained by either modernization or urban bias theories because there had not been any

drastic innovations in terms of technology when it comes to production. Moreover,

according to Becker, Hamer and Morrison (1994), no urban area in pre-colonial Africa

rose to be a manufacturing center. Rather, according to Miner (1967), urbanization during

the pre-colonial era was as a result of politics. According to the modernization school of thought, political development is a product of economic development. According to

Miner’s (1967) analysis, economic development did not occur before the political development. It was rather the opposite; thus the presence of defense. Urban areas developed not only because of establishment of administrative centers, but trading, port activities, religious activities and defense needs also caused town and cities to originate and grow. Rural surpluses as well as growth of exchange economy resulted from the provision of defense and the creation of transshipment posts. This led to the establishment and growth of urban areas. The above factors were used to explain the development of Audoghast, Kumbi Saleh, Gao and Timbuktu as urban areas during the pre-colonial era.

Conceptual Framework

For the study to proceed there is the need to define the terms “urban” and

“development”. As these two words lack any clear-cut definition there is a need to define

each of them in the context of this research.

Urbanization

The term urban lacks a very specific definition. Urban is defined in terms of

political status, demographic attributes, economic variables and socio-cultural behaviors

(Gibbs, 1966). According to Macura (1961) about thirty definitions of urban are in use

but none of them is very succinct and this makes it difficult for international

comparisons. Despite the problems, there is a need to study urbanization and also a need

to make comparisons. Various researchers define the term urban from various

perspectives, mostly based on the discipline.

Geographers are space-oriented and hence tend to link urban with the space and the people that occupy the space. To geographers, an area is defined as urban based upon a certain concentration of population. Urban to them, is a settlement agglomeration with a certain density of population and/or a minimum required population threshold. This concentration is usually within a specified area hence density is also used sometimes by geographers to define urban. Since there is no standard minimum threshold for determining an area as urban, every country has its own definition for urban when using demography as a means of definition. In Denmark, Sweden and Finland (Fenno-

Scandinavian Countries) a settlement with a population of 200 or more constitutes an

urban area. A thousand inhabitants constitute an urban area in Canada and Venezuela

while 2,500 inhabitants constitute urban in the United States of America. In Ghana and

India, a settlement becomes urban after attaining a population size of 5,000. In Greece

and Sierra Leone on the other hand, the minimum population threshold for designating an

area as urban is 10,000 (Jones, 1966, Ziegler, Brunn & Williams, 2003, Hartshorn, 1992).

In addition to population threshold, some countries add population density to define a

place as urban. In India for example, in addition to having a population of 5,000 or more,

there is the need for a density of over 1,000 per square mile for a place to be termed

urban (Jones, 1966). Frey and Zimmer (2001) classify this as the ecological element of

defining urban area.

Sociologists and anthropologists link urban with human behavior and relations.

Wirth (1938) argued that population alone does not make a place urban but the influence

that the urban areas exert on the social life of the people is more important. According to

Wirth, it would be difficult to use population to designate a place as urban. Especially

where population density is used as suggested by Wilcox (1926), population density

would be meaningless in defining urban since censuses enumerate night time population

and not the day time population. The population of the city center is low during the night

hence may not adequately define the area as urban. Rather, this could be combined with

the behavior of the dwellers. Wirth went on further to say that being urban is a mode of

life instead of agglomerating at a particular spot or area. It is a mode of life in the sense

that lifestyles change in the direction of the mode of life dominant in the urban areas.

Wirth was of this view because demography as a means of defining urban means that

there are clear-cut boundaries for the urban areas but the surrounding areas and the

immediate periphery are also a part of the urban area as the population there exhibits a

similar mode of life. Sociologists, therefore, define urban as a relatively large, dense and

permanent settlement of socially heterogeneous individuals.

Wirth further explained the heterogeneity by indicating that the urban area cannot

reproduce itself demographically, hence the need to recruit migrants from somewhere

else. Thus it then becomes a place of mixture of various races, people and cultures and

where individual differences are tolerated and rewarded. The relationships in the urban

areas therefore were formal since urban dwellers know very little about the people with

whom they interact. The contacts are more secondary than primary. Based on an

argument like this, Mayer (1964) speaks of the urbanized individual as one who is

committed to and involved in an urban way of life.

Fischer (1975), on the other hand, differed with Wirth’s idea of urban areas

creating formal relationships. Fischer sided with Wirth that urban areas have their

densities, have large sizes and also that the inhabitants are heterogeneous. The

heterogeneity of the people makes urban dwellers resort to the formation of subcultures within the urban areas where people interact within the subcultures informally.

According to Frey and Zimmer (2001), both Fischer and Wirth use the social element for the definition of urban.

Another element of consideration for an area to be identified as urban, according to Frey and Zimmer (2001) is the functional aspect of the urban area. Apart from demography, the activities of the area can differentiate an urban area from a rural area.

These activities could be either economic or political or both. Economically, the activities taking place in the urban areas include manufacturing (secondary) and services (tertiary) and of late, research and development (quaternary) activities. Another dimension to this is employment, where more than fifty percent of the employed population is outside the primary (mainly agricultural) sector. Politically, the urban area functions as an administrative center. The major activities in the rural areas, according to the political economy argument, are primary economic activities, part of which are activities concerned with gathering and extraction of raw materials for the industrial sector. The economic function, where this research would specifically add the administrative function, as defining the urban area is a bit problematic in developing countries, especially in Africa. In the developing countries, according to the dependency theory of urbanization, the Western countries used their colonies as the suppliers of commodities for their factories. As a result, plantations were established for the production of cocoa, coffee, tea, rubber and oil palm and other inputs for the industries located in the western countries – Cadbury located in Britain depended on West African cocoa, Firestone in the

United States on tropical. This activity then draws a lot of population from other places to

the centers where the plantations are located for employment. At the end, the population threshold tends to meet the minimum threshold for designating the area as urban. Arguing from the point of Wirth and Fischer, subcultures tend to be formed where the interactions become more informal. Looking at this situation from the political economy point of view, the place does not qualify to be designated urban. This is because the main function of the place is primary economic activity and the employed population is mainly in agricultural activities while the political economy argument requires a population of fifty percent or less in order to qualify for the term urban.

Putting these arguments together, this research perceives urbanization from the geographer’s point of view, by means of the minimum required threshold population, higher density of population and greater proportion of population depending on non- primary occupations. For the purpose of this study, urbanization is defined according to the United Nations definition which is the proportion of total population living in urban areas. However, since the United Nations could not come up with any standard threshold for the term urban, the study further developed an urbanization index (which would be discussed later) for the study alongside the United Nations definition in order to come out with the best measure for studying urbanization in Africa.

Defining Development

The term development, just as the term urban, lacks specific definition.

Traditionally, the term development has been viewed from the economic standpoint and

this is tied to either Gross Domestic Product (GDP) or Gross National Product (GNP)

hence usually seen as a function of economic growth. By this traditional definition, a

country must be able to achieve and sustain an annual increase of 5% GDP growth or

more and, at the same time, this rate should be greater than the population growth rate in order to be considered as developing. This concept is tied to industrialization since this is the only way higher economic growth could be achieved. GDP is the total value of output produced by an economy by both residents and nonresidents. GNP, on the other hand, is made up of GDP and the difference between the income residents receive from abroad for services in the form of labor and capital and what was paid out to nonresidents who contribute to the domestic economy. GDP and GNP do not measure the well being of the overall population.

To the classical economists, the best means of measuring the relative well being of the people is the use of GNP per capita. GNP per capita is the dollar value of a country’s final output of goods and services in a year, divided by its population. It reflects the average income of a country’s citizens. Economists tend to measure the well being of people in different countries by means of GNP per capita. The belief here is the higher the GNP per capita, the better the well being of the people within that country hence the higher the level of development.

Since countries do not use the same currency, for the purpose of comparison, official exchange rates are used to standardize the currencies. Sometimes, this standardization is overestimated when converting the currencies especially from the developing countries into US dollars. To rectify this, Purchasing Power Parity (PPP) is used instead of the exchange rate conversion. PPP is the rate of currency conversion which eliminates the differences in price levels between countries. Thus, when the GDP for different countries are converted to a common currency by means of PPP, they are

expressed at the same set of prices so that comparisons between countries reflect only differences in the volume of goods and services purchased.

Another measure of development is the structure of production and employment.

For development to prevail, according to this concept, agriculture should be downplayed in favor of industrialization. This means a concerted effort to reduce agriculture’s share of production and a subsequent increase in manufacturing and services as well as quaternary activities. According to Clark (1960), during economic growth, labor moves away from the primary sector, primarily agriculture, to the manufacturing and service sectors. Clark indicated that agricultural sector employment shrinks because as income rises, the share of income spent on food declines. Hence resources shift from agriculture to manufacturing and service sectors.

Development may also be viewed from the political perspective. For development to occur, the country should have democracy in the form of a multiparty political system.

Political freedom as a result of democracy is believed to provide an enabling environment for promoting material welfare through increased competition (Ingham, 1993). The

World Bank argues that there is a causal relationship between democracy and sustained economic growth, and gives examples as countries like Mauritius and Botswana as having sustained economic development because of their multiparty system (World

Bank, 1989).

Development as economic growth was the view held by economists until the late

1960s but from the 1970s new ideas about development surfaced in which non-economic, social indicators are used to supplement economic indicators (Todaro, 2000). Questions started flowing about what has been happening to income distribution, basic needs and

poverty, after the growth in national income and experiences of structural change and industrialization. Concerns started growing for more equitable income distribution. The belief by the economists was that after attaining economic growth, all other things would be taken care of and that growth would trickle down to the poor (Chenery et al, 1974).

Chenery and others argued that though some countries experience higher economic growth, the low-income group become worse off. Closely related to income distribution

is basic need. Growth without the basic needs being met is not development. According

to Dudley Seers (1972), people never spend all their income on basic needs – food,

clothing and shelter. Where these are not met there is a problem of income distribution.

Hence there is no meaningful development:

if our definition of development assumes that a more equal

income distribution is an integral part of an acceptable

development strategy, we need to take account of the fact

that economic growth of itself may generate increased

poverty. (Ingham, 1993, p 1813).

The United Nations Development Program (UNDP) came up with human

development as a new indicator for development in addition to economic indicators and

income distribution. This proposed people-oriented development. The UNDP came up

with a Human Development Index (HDI), which it uses to rank countries. The variables

used in the ranking include:

1. Higher life expectancy as indication of health care in terms of delivery and

quality

2. Literacy represents the ability to communicate, to obtain and keep up jobs and

also to appreciate culture

3. Purchasing power demonstrates the ability of the population to meet basic

needs – an indication of income distribution

The score of the HDI tells the degree to which funds are shared to meet the basic needs of the population and the choices open to them as well as the priorities of the government. Unfortunately, this computation is based on the amount of money spent on the various variables such health and education. Ingham (1993) argues that it should rather be based on the ‘human development’ itself: ‘how many people can read and write’ instead of how much was spent on education. Are people living longer? What is happening to malnutrition instead of how much was spent on health care.

Hicks and Streeten, (1973) hold that social indicators should be used instead of economic indicators when it comes to comparative studies because they avoid the exchange and valuation problem. For the purpose of the study, development is defined as a process of an increase in income (economic growth), improvement in quality of life and transformation in economic structure (Nnadozie, 2003). The goal of development is to improve the standard of living (Takyi & Addai, 2003) but then, growth is necessary for improved standard of living to occur. This calls for the expansion of the definition for development to encompass both the economic and social aspects of development.

Urbanization and Development

The literature suggests that there is a relationship between urbanization and

national income. In this study urbanization is referred to as a process in which an

increasing proportion of an entire population lives in cities Butler and Crooke, (1973) in a

study of world urbanization hold:

The wealth of nations does seem to have some bearing on the degree of urbanization that can be supported” p 9. “…. there is some evidence to support the premise that there is a correlation between income and urbanization. Certainly the income of under $US200 a head countries do not appear able to support any very significant degree of urbanization. Conversely, at incomes of over $US1000 a head, urbanization to an extent above 50 per cent is the norm. (p 12-13).

Preston (1988) also indicated in his research that:

All things being equal, nations with higher levels of GDP per capita and with fast rates of economic growth have faster growing cities (p.22).

These statements confirm a relationship between urbanization and development,

which is often seen from the economic perspective with the main measure being Gross

Domestic Product (GDP) or Gross National Product (GNP) per capita.

Other researchers indicate a relationship between urbanization and other

demographic factors. Dutt (2001) for instance shows that areas experiencing a higher

level of urbanization have a lower crude birth rate, improved life expectancy and a higher

level of female participation in economic activities. Rostow (1990) indicated in a study

that birth and death rates are negatively correlated with per capita GNP. The argument

here was that as countries become developed, they tend to invest more in modern health

care facilities to take care of the health needs. The improvement in death rate, especially infant mortality rate means the chances of children surviving are higher hence no need for many children. Moreover, technological advancement, especially in agricultural

production, means less farm labor was needed and most often it was children that were

needed to work the land. Hondroyannis and Papapetrou (2002) in a study in Greece found

that the higher the per capita GNP growth, the higher the fertility rate implying higher

population where a large proportion ends up in the urban areas. It can therefore be

assumed from this research that there is a relationship between growth in income per

capita and urbanization.

Studies on urbanization and development so far have focused on areas other than

Africa; either the world, the developed world, the developing world or South or Southeast

Asia, but none specifically on Africa as a continent. As a result, this research is meant to

find out the relationship between the level of urbanization and levels of development in

Africa.

Davis and Henderson (2003) conducted a study to establish a relationship among urbanization, development and agriculture. In this study, development was seen as Gross

Domestic Product per Capita and they were able to establish a positive correlation between the logarithms of GDP per capita and level of urbanization expressed as percentage of the total population living in urban areas (p.100). They also established a negative correlation between level of urbanization and agriculture value added as a percentage of GDP (p.100). This indicates that as development takes place, the contribution of agriculture to the GDP decreases. They further found that:

urbanization is driven by the shift from agriculture to industry and modern services. Development advances technology in agriculture, releasing labor from agriculture to work in services and manufacturing. This sectoral shift in labor leads to urbanization as firms and workers cluster in cities (p. 99).

They therefore concluded by establishing a positive relationship between urbanization and development.

Henderson (2003) again in another study indicated that urbanization and development seem to be interconnected. He (Henderson, 2003) found a positive correlation coefficient of about 0.85 between urbanization and the log of GDP (p.47).

This is an indication that there is a relationship between urbanization and development if we take GDP as one of the measures for development. In these studies, Davis and

Henderson (2003), and Henderson (2003) limit their definition of development to Gross

Domestic Product while the modern definition of development goes beyond that.

Moreover, the study was on urbanization of the world as a whole but not to any particular region especially Africa, where the interest of the current research lies.

Bertinelli and Black (2004) in a study found that the process of urbanization and

the process of development are linked but the causal link is not clear-cut. They conducted

their study based on migration as the cause of urbanization. “Urban migration will have

the dynamic benefits due to the investment in human capital by urban migrants” (p.82).

This is an indication that the high human capital converging in the urban areas will have

something to do with overall development. They however found a negative correlation between urbanization and health in terms of infant mortality.

Bradshaw and Fraser (1989) conducted research in China to establish the relationship between urbanization and development. They established a positive relationship between level of urbanization and income on one hand, and quality of life on the other. They went on further to indicate, using modernization theory that urbanization facilitates the development of certain attitudes and values that are deemed necessary for economic development (p. 988). They measured quality of life made up of infant mortality, death rate and illiteracy. This study contradicts the findings of Bertinelli and

Black (2004) that there is negative relationship between level of urbanization and health

(infant mortality), but reaffirms the findings of Davis and Henderson (2003) and

Henderson (2003) that there is a positive relationship between level of urbanization and

income. Amis (1990) agrees with a link between urbanization and economic development

(growth) but sees urbanization as being dependent on economic growth. In discussing the

projection of urbanization, Amis (1990) was of the view that the projections are made

independently from the processes of economic change (p. 10; Hardoy & Sattethwaite,

1986). Amis went on further by saying:

Given the present economic recession and the previous discussion there do seem to be very clear reason why we might expect the rate of urbanization to decline. To suggest otherwise is to implicitly admit that urbanization is totally unrelated to economic change (p 11).

Firebaugh (1999) indicated that numerous cross-national studies found a positive relationship between urbanization and economic development. He further stated that economic development is the most important determinant of urbanization because industrialization produces rapid expansion of urban employment opportunities (202,

213).

Brockerhoff (1999) on the other hand, deviates from the use of economic indicators of development to determine the relationship with urbanization. He rather used a demographic indicator, proportion of population aged 65 or older to establish a link with urbanization. He found out that there is a negative relationship between proportion of total population aged 65 or older and level of urbanization.

… countries with percentages of persons aged 65 or older exceeding 4.2 experienced urban growth rates of 0.8 percent lower than average. Moreover a yearly increase of one percent in the proportion of population aged 65 or older …….. reduces the urban growth rate by 0.41 percent points….. (pp. 772-773).

Studies establishing relationships between urbanization and development have been based on economic indicators especially growth as the measure for development.

Lisa Peattie (1996) indicated that the approach to urban research should be changed from the norm, by introducing new dimensions. Based on her argument, this dissertation has taken a new dimension by introducing the socioeconomic aspect of development into the study of urbanization in Africa instead of the usual economic indicators (growth) as the measure. In 1970, the Organization of Economic Co-operation and Development (OECD) held that economic growth is not an end in itself but rather an instrument for creating better conditions of life (Verwayen, 1980 p. 237). Kuznets, (1953) indicated that:

It does seem to me, however, that as customary national income estimates and analysis are extended, and as their coverage includes more and more countries that differ markedly in their industrial structure and form of social organization, investigators interested in quantitative comparisons will have to take greater cognizance of the aspects of economic and social life that do not now enter national income measurement; and that national income concepts will have to be either modified or partly abandoned, in favor of more inclusive measures, less

dependent upon appraisals of the market system.(pp. 172 – 173) The eventual solution would obviously lie in devising a single yardstick that could then be applied to both types of economies – a yardstick that would perhaps lie outside the different economic and social institutions and be grounded in experimental science (of nutrition, warmth, health shelter, etc.). p. 178

Economic indicators of development are more straightforward and easily identifiable than social indicators. Economic indicators of development are concerned with variables associated with total wealth of the country. According to Streeten and Javed (1978) social aspect of development has to do with raising the standard of living of the population, especially the poor. White (1987) also indicated that the social aspect of development concerns the improvement of the quality of life of the people including improvements in the well being of the poor. Social indicators of development therefore have to be arrived

at in line with indicators for better quality of life for the study. The concept ‘Quality of

life’ as social indicator for development came up as a result of debates in the 1960s on economically developed societies (Zapf, 1980; Gross, 1960). The concept, instead of

stressing quantitative growth, stresses qualitative growth, which is preferred by modern

development economists as indicators of development. Socioeconomic development

therefore can be said to be concerned with an increase in total wealth of a nation and at

the same time, improving the quality of life of the people especially the poor.

Various researches have been undertaken to arrive at acceptable variables for

social indicators of development. Verwayen (1980) in his study viewed socioeconomic

development from eleven broad areas and these according to him has got to do with the

measure of quality of life. The broad areas considered by Verwayen include:

1. Healthfulness of life

2. Measurement of learning

3. Employment

4. Quality of working life

5. Time and leisure

6. Income, Wealth and material deprivation

7. Housing condition

8. Quality of the natural environment

9. Measurement of victimization

10. Inequality

11. Economic accessibility

Zapf (1980) on the other hand, viewed social indicators of development as the well-being of individuals and households. In his study of Germany, he identified ten areas for socioeconomic development indicators and these are as follows:

1. Population

2. Social Status/Mobility

3. Employment/working conditions

4. Income and income distribution

5. Consumption

6. Transportation

7. Housing

8. Health

9. Education

10. Participation

Easterly (1999) in his study of socioeconomic indicators of development identified seven broad areas and these are:

1. Individual Rights and Democracy

2. Political stability and war

3. Education

4. Health

5. Transport and Communication

6. Inequality across class and gender

7. ‘Bads’ (unwanted byproducts of higher income)

Esping-Anderson (2000) said social indicators should be viewed in terms of risk across the population and proposed three units at which these risks can be measured and these he identified as individual, household and societal units. The international organizations on the other hand differed from Esping-Anderson by redefining the issues from risks to needs. The international organizations however have countries as their levels of measure.

For the purpose of this study, socioeconomic variables considered are from the following broad areas:

1. Population

2. Income and wealth/distribution

3. Education

4. Health

5. Information

6. Employment

Summary

From the review, it becomes apparent that urbanization in Africa started earlier

than the arrival of the Europeans hence could not be seen as a product of colonialism.

Urbanization appeared as far back as 3200 B.C in Egypt and continued to exist before the

arrival of the Europeans on the coast of western Africa in the 15th Century. The arrival of the Europeans, however, brought with it new waves of urbanization which began to occur along the coast.

Africa is experiencing a high rate of urbanization, which could be explained with the help of demographic transition on one hand, and Rostow’s stages of growth on the other. In terms of demographic transition, Africa can be said to be somewhere between stages two and three due to the relatively high fertility and high birth rates and the

corresponding falling death rate despite the HIV/AIDS menace on the continent. At this

stage of the demographic transition, there is rapid population growth as a result of natural

increase.

In terms of stages of growth, it can be comfortably said to be between the

Precondition to take off and Takeoff stage. This is because the trading activities of the

continent correspond to economic activities associated with these stages – largely trading

in agricultural and other primary economic activities. The arable lands cannot support the

increased population growth hence surplus labor is created which find their way to the

urban centers, a phenomenon known as ‘rural-push’.

Post colonial African urbanization can be explained by all the three theories

discussed earlier – modernization, dependency and urban bias but dependency seems to

better explain urbanization than the others. The establishment of plantations by the

foreign investors tends to take arable lands away from the natives who are already facing shortage of adequate plots of arable lands for cultivation. Moreover, the introduction of capital intensive methods of production on the plantations reduce the amount of labor that

could be employed hence the creation of redundant labor, which tends to find their way to

the urban centers.

The term urban varies in definition by discipline but tying urban to space seem

reasonable and also the data available for such studies are spatially based in nature hence

population threshold is used to define urban and the measures for urbanization developed

from there. Development on the other hand, is viewed largely from the perspective of

improvement in the quality of life.

The relationship between urbanization and development is so intricate that,

development fosters urbanization and urbanization fosters development. The relationship

can therefore be said to be circular in nature, as shown in figure 2.6.

Urbanizatio

Developmen t

Figure 2.5 Relationship between Urbanization and Development

CHAPTER III

STATEMENT OF THE PROBLEM

The relationship between urbanization and development is both positive and circular in nature. (See Figure 2.6). Urbanization has given rise to many problems over

the years in the developing countries, a category to which most African nations belong.

Urbanization has also resulted in efforts by government to address these problems.

Paramount among these problems are inadequacy of infrastructural facilities, waste management and housing. Research has been undertaken to identify possible solutions but so far not much has been done to establish the possible predictors of urbanization,

with reference to development in Africa.

The World Bank and other donor agencies have been helping African countries with financial and managerial resources to help solve their urban problems. Between

1960 and June 2004, the World Bank approved and/or funded 220 projects in 41 countries in Africa at a total cost of about nine billion US dollars (World Bank, 2004).

Details of these are shown in the maps in Figures 3.3 and 3.3. Despite this, the problems

have persisted indicating that the possible root predictors have not been taken into

account. Since most urbanization theories are Western in origin and since the rate and

TUN

# MAR

DZA LBY EGY

ESH MRT

MLI NER ERI CPV SEN TCD

# DJI GMB BFA SDN GNB # GIN BEN NGA SLE ETH CIV # LBR GHA CAF CMR SOM

# TGO GNQ UGA KEN STP GAB RWA COG ZAR BDI TZA SYC

COM MWI N AGO MOZ ZMB

W E ZWE MDG MUS BWA S NAM

0 1000 Miles ZAF # SWZ

LSO

Figure 3.1 Countries in Africa

Table 3.1 Meaning of Abbreviations for African Countries

Abbreviation Name Abbreviation Name DZA Algeria MWI Malawi AGO Angola MLI Mali BWA Botswana MAR Morocco BEN Benin MUS Mauritius BDI Burundi MRT Mauritania TCD Chad MOZ Mozambique COG Congo NER Niger ZAR Zaire NGA Nigeria CMR Cameroon GNB Guinea-Bissau COM Comoros RWA Rwanda CAF Central African Republic SYC Seychelles CPV Cape Verde ZAF South Africa DJI Djibouti SEN Senegal EGY Egypt SLE Sierra Leone GNQ Equatorial Guinea SOM Somalia ERI Eritrea SDN Sudan ETH Ethiopia TGO Togo GMB Gambia STP Sao Tome and Principe GAB Gabon TUN Tunisia GHA Ghana TZA Tanzania GIN Guinea UGA Uganda CIV Ivory Coast BFA Burkina Faso KEN Kenya NAM Namibia LBR Liberia ESH Western Sahara LSO Lesotho SWZ Swaziland LBY Libya ZMB Zambia MDG Madagascar ZWE Zimbabwe

Number of Projects W E 0 1 - 5 S 6 - 10 11 - 15 16 - 20 01000Miles

Figure 3.2 Total Number of Urban Projects Approved and/or Funded for Africa by the World Bank between 1960 and 2004

Source of Data: World Bank, 2004

Amount ($ Million) N 0 1 - 191.4 W E 191.4 - 377.3 377.3 - 716.6 S 716.6 - 1523 01000Miles

Figure 3.3 Total Amount Approved and/or Spent by the World Bank on Urban Projects in Africa between 1960 and 2004

Source of Data: World Bank, 2004

nature of urbanization in Africa is quite different from that experienced in the western countries, the relationship between urbanization and development, specifically in Africa, might be different. This may account for the lack of success of efforts made by the World

Bank and other donor agencies in achieving intended outcomes. Thus, in order to find possible solutions to problems of urbanization in Africa, there is the need to determine the socioeconomic development variables that best predict urbanization in Africa and that

is what this study seeks to do.

Justification for the Study

Various research studies have been conducted to establish the relationship

between urbanization and development and various results have been summarized. To

date, none of these studies have used comprehensive socioeconomic development data to

identify this relationship for Africa as a continent. (See Figure 3.1 for details).

Abu-Lughod (1965, 1976) conducted significant research on African urbanization

but none of her research linked urbanization with development. She traced the growth of

urbanization mainly in the Northern Africa Region (1965, 1976), providing a vivid

description of urban growth and development in the region from prehistoric to current

times. Since urbanization in Africa started mainly in the north, her research was and still

continues to be a useful source of information for the study of urbanization in Africa. Her

work, however, does not deal with the influence of development on urbanization in

Africa.

Hope has examined African experiences in both urbanization and development,

did not establish any link between the two. In his work on urbanization, he traces the

trend of urbanization in Africa and discussed the causes of urbanization. He mentions two

factors as the primary contributors to African urbanization; i) natural population increase and ii) rural-urban migration (Hope, 1998 pp. 348–353). For natural increase, he identifies social and economic development as contributors as well as education which is regarded by UNDP and the World Bank as a development indicator, and as a cause of rural–urban migration. However, he has not established any empirical link between the development indicators and urbanization.

The study closest in approach to this dissertation is ‘Urbanization and

Development in Sub-Saharan Africa’ by Njoh (2003). Njoh tries to establish the link between urbanization and development. However, he limits his study to sub-Saharan

Africa. He also employs a limited measure for urbanization, which is the proportion of total population living in the urban areas and Human Development Index (HDI), which is defined by the United Nations Development Program (UNDP) as embodying three dimensions of human development; health, knowledge and a decent standard of living

(Njoh, 2003, UNDP, 2000). In his research, he uses HDI as the dependent variable and urbanization as the independent variable to establish a positive relationship.

Since the definition for urbanization is not the same for all countries in Africa, this research will compute an urbanization index, using a combination of the scale developed by Gibbs (1966) and a formula offered by UNDP for computing HDI in order to compare urbanization with development. We shall also exhibit the areal variations of development and urbanization in Africa in the form of maps. The dissertation attempts to answer the question ‘What socio-economic development variables best predict urbanization in Africa’? The research takes into account the continent as a whole, location in relation to the sea and political affiliation based on colonialization. Finally it

attempts to identify the best measure for urbanization for Africa by means of most accurate prediction of urbanization by the socioeconomic variables.

Research Purpose and Hypotheses

Research Purpose and Research Question

As can be seen from Figure 2.1, Africa is fast urbanizing and has experienced efforts by various donor agencies to address and to solve problems associated with urbanization on the continent. Basing their arguments on modernization theory, many

urban researchers agree that there is a relationship between urbanization and development

and this is manifested in the level of urbanization experienced in the developed countries

(Berliner, 1977; Dutt, 2001; Kasarda & Crenshaw, 1991; Bradshaw, 1987; Davis and

Henderson, 2003; Henderson, 2003; Bertinelli and Black, 2004; Firebaugh, 1999). The

research studies tend to single out GDP (wealth) as the development variable best predicting urbanization. None of the research has used socioeconomic data to predict

urbanization in Africa. This dissertation, therefore, attempts to establish the relationship

between urbanization and socioeconomic development and also to identify the

socioeconomic variables that tend to predict urbanization in Africa. By so doing, the

dissertation tries to answer the following research questions:

1 What socio-economic development variables predict urbanization in Africa?

2 What economic development variables predict urbanization in Africa?

3. What social development variables tend to predict urbanization in Africa?

4. What Human Development Indicators predict urbanization in Africa?

5. Do development variables that predict urbanization in Africa vary with

location especially with respect to their coastal connection?

6. Do development variables that predict urbanization in Africa vary with past

colonial experience?

7. Do the development variables that predict urbanization in Africa

predict

Urbanization Index more precisely than the degree of urbanization?

The study is a continuation of efforts made by earlier researchers to establish relationship between urbanization and development but is limited to Africa. This research, however, use comprehensive socio-economic development data and other related data. It also explores the possibility of a better measure for urbanization for

Africa, which is different from what others have done so far.

Research Significance

Urbanization has contributed to many problems over the years in the developing

countries of which Africa belongs. Some of these problems include inadequacy of

infrastructural facilities, waste management and housing. As indicated earlier, efforts

have been made by donor agencies to solve the urbanization problems being faced by

African countries but the problems continue to persist. From the observed outcomes of

the policies and programs being implemented by the donor agencies, it appears that

efforts being made to solve these problems use the “fire fighting technique”, where the

problems are allowed to occur before efforts are made to solve them. In Nouakchott

(Mauritania) for instance, the World Bank approved a ten-year slum upgrading program

in the sum of US$ 99 million (World Bank 2001). Ideally, the necessary programs such

as site and service facilities should have been put in place before the massive

urbanization began. Identifying the socioeconomic variables that predict urbanization, which this dissertation seeks to do, could be a basis for anticipating urban problems and the necessary policies and programs put in place before they even occur. By so doing, negative effects of urbanization could be minimized and the donor agencies may in turn maximize the value for their intervention efforts.

Research Hypotheses

Reviews of urbanization theories coupled with my personal observation with the

distribution of socioeconomic facilities reveal that urban bias theory would be the most

appropriate framework for this study. This is because most of the socioeconomic

development facilities are found in urban areas. Unfortunately, this theoretical

framework cannot be applied due to lack of the necessary data. In order to undertake the

study based on urban bias theory, the socioeconomic data ought to be on urban – rural

comparison but this is not available and hence could not be applied to the study. The

available data can most appropriately be examined in light of modernization theory. As

a result, the applicability of modernization theory in analyzing African urbanization has

been tested in this study.

Hypothesis 1. Urban researchers and development economists posit a relationship

between urbanization and development. However, most often researchers refer to

development as economic growth. As indicated earlier, various research studies have

established a link between urbanization and development. Firebaugh, (1979) in a study on

the determinants of urbanization in Asia and Latin America reports that development

(economic) is the most important determinant of urbanization (p. 213). Njoh (2003)

established a relationship between urbanization and development in Sub Saharan Africa.

Since these relationships exist for Asia, Latin America and Sub Saharan Africa, there is the likelihood that a relationship would exist between urbanization and development in

Africa as a continent. Modernization theory supports this view in the sense that

industrialization has been identified to be the engine of urbanization (Bradshaw, 1987,

Firebaugh, 1979). This research is based on the belief that identifying the variables that predict urbanization in Africa could help solve or minimize the problems of urbanization

in Africa. In order to answer research question 1, hypothesis 1 was developed.

Hypothesis 1 therefore reads: The higher the level of socioeconomic development indicators, the higher the level of urbanization.

Hypothesis 2. Hypothesis 1 the way it is requires the agglomeration of all socio-

economic development indicators used in this study. The concept of development does

not have only one measurement such as per capita income or wealth, but a number of

them. These measures ought to include improvement in the quality of life (social

development), wealth and economic growth (economic development) and human

development (Human Development Index).

Modernization Theory of urbanization, based on classical economic thinking

states that:

industrialization and manufacturing employment growth has been the engine of growth and will continue to be so in the future (Kelley and Williamson, 1984. p.179).

Industrialization is associated with economic growth. Bradshaw (1987) indicates that

industrialization is conducive for economic development and Todaro (2000) supportes this assertion by stating that modern (industrial) sector productivity is higher than that of

the traditional (agricultural) sector resulting in increased national wealth (Gross Domestic product (GDP) and Gross National Product (GNP)). The agricultural sector can release labor to the industrial sector without having much effect on its production. Most of the redundant labor in this sector usually find its way into the urban areas for employment in non-agricultural sectors (manufacturing and service sectors). The release of labor from agricultural to non-agricultural sector coupled with GDP and GNP growth associated with the secondary and tertiary sectors are signs of development. Growth in wealth

(GDP, GNP and income per capita) are some of the measures for economic development.

This then leads to the search for an answer to research question 2 by attempting to test hypothesis 2, which is as follows:

Higher level of economic growth (economic development) fosters higher level of urbanization in Africa.

Hypothesis 3. In terms of quality of life as a measure of development, the urban bias theory, attributed greatly to Lipton (1977, 1984) has it that developing nations implement policies favorable to the urban areas. According to this theory, the urbanized countries as well as urban areas in developing countries have higher standards of living in terms of better health facilities (hospitals, doctors, access to improved water sources, access to improved sanitation) and better education facilities (schools, libraries). These facilities bring about improved life expectancy, improved infant and maternal mortality rates and at the end of it all, improved quality of life. According to the World Bank, the challenge to development is to improve the quality of life. To the modernization theorists, the provision of these modernization facilities is related to the wealth of the country. The argument arising from this, therefore, is that improvement in the quality of life fosters

more urbanization since the social service facilities, mostly located in the urban centers serve as pull factors for people in the rural areas to migrate to the urban areas and make use of these facilities (Dutt, 2001; Steinbacher & Benson, 1997). The hypothesis is:

The higher the level of quality of life (social development) the higher the level of urbanization.

Hypothesis 4. Human development is the third approach to measure development.

According to the United Nations Development Program (UNDP), the end result of development must be human well-being. Human development, therefore, is defined by

UNDP as the process of enlarging people’s choices to lead lives that they value (UNDP,

2001 p. 9). Fundamental to enlarging these choices is building human capabilities – thus the range of things people can do in life. Human development is measured by longevity, knowledge base and decent living standards.

Human development is slightly different from quality of life (hypothesis 3) because quality of life is concerned with human welfare and the satisfaction of basic needs. Human development on the other hand extends beyond the satisfaction of basic needs approach by offering choices to people. The more choices people have in terms of longer life and education, the more would be their desire to move to the urban areas. The hypothesis then is: The higher the level of human development, the greater the level of urbanization.

Hypotheses 1 to 4 are intended to provide comprehensive prediction for urbanization in Africa, based on socioeconomic variables and hence, assist in identifying of possible remedies to urbanization problems in Africa. Finally, the hypotheses are

supposed to provide answers to research questions 1 to 4 as well as test the applicability of modernization theory of urbanization on Africa.

Hypothesis 5. Literature suggests that most urban areas owe their growth to commercial and industrial activities (Weber, 1963, p.173). Navigable waters have a large role to play in this regard because water transportation moves bulky goods more easily and cheaply than rail and road transportation. As a result coastal locations can undertake large-scale commercial and industrial activities which can lead to higher level of coastal urbanization. Those African countries which do not have access to the sea, transport goods for commercial and industrial activities by rail and road transportation. This limits the amount of goods that could be carried hence the limited level of aforesaid activities.

With this in mind, there can be differences in the level of urbanization based on the location of countries in relation to the sea.

According to the modernization approach to urban growth, there cannot be urbanization without industrialization (Berliner, 1977). Since the landlocked countries cannot have direct access to the sea which limits their ability to industrialize, there is the belief that landlocked countries would be less urbanized than coastal countries and the variables predicting their urbanization are likely to be different. Before testing this hypothesis, the study would verify if there were any differences between the levels of urbanization for landlocked and non-landlocked countries. If differences do exist, then there is the likelihood that the variables predicting urbanization would differ with location.

The research question 5 seeks to find out if there is any difference in the variables predicting urbanization based on geographical location and the hypothesis to test this therefore is:

Various socioeconomic development variables predicting urbanization in Africa can vary with geographical location (in relation to the sea).

Hypothesis 6. Various Europeans countries colonized Africa and administered their territories differently in terms of investment strategy, management of resources,

government styles and instituting modern transport systems. This can lead to differences

in levels of development. Studies have demonstrated that foreign investment increases

urbanization and at the same time expands the service and informal sectors (Bradshaw,

1987, Evans & Timberlake, 1980, Kentor, 1981, Timberlake & Kentor, 1983). This is in

line of thought according to the dependency approach to development. This approach

holds that urbanization in the developing countries, and for that matter Africa, is

conditioned by the growth and expansion strategy of Europe. According to Kileff and

Pendleton (1975), the German colonial policies on urbanization led to the growth of

urban centers namely Windhoek, Tsumeb, Walvis Bay and others in Namibia by 1921.

According to Garland Christopher (1977), the French economic development policies in

Ivory Coast led to the high level of urbanization in the south to the detriment of the

north. Simone (1998) indicates that the nature of urbanization in Africa is attributed to

the roles of cities as apparatuses of colonial control and extraction. In Kenya for

instance, there was a colonial policy restricting Africans from residing in urban areas

(Obudho, 1979 p. 248). These assertions imply that the influence of colonialization

might have some effect on the level of urbanization in the various countries and the

various development variables predicting urbanization in Africa based on colonial

background affiliation might be different hence development of research question 6.

For this hypothesis to be tested there is the need to establish the possible differences

among the levels of urbanization based on colonial ties. The existence of differences

could mean the possible differences in the variables predicting urbanization. The

hypothesis to test for an answer for this assertion is:

Various socioeconomic development variables predicting urbanization in Africa can vary in accordance with the European countries that colonized them.

Hypothesis 7. The study holds that there can be a better way to measure

urbanization by means of standardizing the definition. As can be inferred from the

literature, the measure of urbanization by means of degree of urbanization has no

standard threshold. Rather each country has its own threshold for defining a place as

urban. The study as a result has developed a measure referred to as urbanization index

as an alternative measure for urbanization and holds that socioeconomic variables would

predict urbanization index more precisely than they would degree of urbanization. The

acceptance or rejection of this hypothesis would provide an answer to research question

7. The hypothesis to test this assertion reads: Socioeconomic development variables

can predict urbanization index more accurately than they would predict degree of

urbanization (as measured by the proportion of total population living in urban

areas).

All the hypotheses are meant to provide a comprehensive explanation for

urbanization in Africa, based on socioeconomic variables and hence assist in the

formulation of policies to serve as possible solutions to urbanization problems in Africa.

Table 3.2 Hypotheses and Tests

Hypothesis Variables Test(s) Degree of Urbanization, Urbanization Index, Surface area, Average Annual Population Growth Rate, Population Density, GDP, GDP per Capita, GDP at PPP, GDP per Capita at PPP, GDP per Capita Growth Rate, Gini Hypothesis 1 Index, Aid per Capita, Public Expenditure on Education, Adult The higher the level of Literacy Rate, Combined Gross socioeconomic Enrolment ratio for primary, Multiple development secondary and tertiary schools, Regression indicators, the higher Crude Death Rate, Crude Birth the level of Rate, Life Expectancy, Public urbanization. Expenditure on Health, Access to improved Sanitation, Access to improved water sources, Physicians per million People, Hospital Beds per 1000 people, Radios per 1000 people, Television sets per 1000 people, Telephone mainlines per 1000 people, Employment in the Non-agricultural sector. Hypothesis 2 Degree of Urbanization, Urbanization Index, GDP, GDP per Factor Analysis (to The higher the level of Capita, GDP at PPP, GDP per identify economic economic growth Capita at PPP, GDP per Capita development (economic Growth Rate, Gini Index, Aid per variables) development) the Capita, Telephone mainlines per higher the level of 1000 people, Employment in the Multiple urbanization in Africa. Non-agricultural sector. Regression

Hypothesis 3 Degree of Urbanization, Factor Analysis (to Urbanization Index, Gini Index, identify social The higher the level of Public Expenditure on Education, development quality of life (social Adult Literacy Rate, Combined variables) development) the Gross Enrolment ratio for primary,

higher the level of secondary and tertiary schools, urbanization. Crude Death Rate, Crude Birth Multiple Rate, Life Expectancy, Public Regression Expenditure on Health, Access to improved Sanitation, Access to Table 3.1 Continued improved water sources, Physicians per million People, Hospital Beds per 1000 people, Radios per 1000 people, Television sets per 1000 people, Hypothesis 4 Degree of Urbanization, Wilcoxon Rank The higher the level of Urbanization Index, Human Sum test, human development, Development Index, Computed

the higher the level of Human Development Index Multiple urbanization. Regression.

Degree of Urbanization, Urbanization Index, Landlocked, Surface area, Average Annual Population Growth Rate, Population Density, GDP, GDP per Capita, GDP at PPP, GDP per Hypothesis 5 Capita at PPP, GDP per Capita Growth Rate, Gini Index, Aid per Various Capita, Public Expenditure on socioeconomic Education, Adult Literacy Rate, Independent development variables Combined Gross Enrolment ratio sample t-test predicting for primary, secondary and tertiary urbanization in Africa schools, Crude Death Rate, Crude Regression can vary with Birth Rate, Life Expectancy, Public Analysis geographical location Expenditure on Health, Access to (in relation to the sea). improved Sanitation, Access to improved water sources, Physicians per million People, Hospital Beds per 1000 people, Radios per 1000 people, Television sets per 1000 people, Telephone mainlines per 1000 people, Employment in the Non-agricultural sector. Hypothesis 6 Degree of Urbanization, Urbanization Index, Colonial Ties, Various socioeconomic Year of Independence, Surface One Way ANOVA, development variables area, Average Annual Population predicting Growth Rate, Population Density, Multiple urbanization in Africa GDP, GDP per Capita, GDP at Regression can vary in accordance PPP, GDP per Capita at PPP, GDP with the European per Capita Growth Rate, Gini 62

countries that Index, Aid per Capita, Public colonized them. Expenditure on Education, Adult Literacy Rate, Combined Gross Enrolment ratio for primary, secondary and tertiary schools, Table 3.1 Continued Crude Death Rate, Crude Birth Rate, Life Expectancy, Public Expenditure on Health, Access to improved Sanitation, Access to improved water sources, Physicians per million People, Hospital Beds per 1000 people, Radios per 1000 people, Television sets per 1000 people, Telephone mainlines per 1000 people, Employment in the Non-agricultural sector. Hypothesis 7 Standard error of the estimate for Independent Degree of Urbanization (generated sample t-test Socioeconomic by regression analysis for development variables hypotheses 1 – 6) can predict Standard error of the estimate for urbanization index Urbanization Index. (generated by more accurately than regression analysis for hypotheses they would predict 1 – 6) degree of urbanization (as measured by the proportion of total population living in urban areas).

CHAPTER IV

DATA AND METHODOLOGY

To analyze the possible relationship between urbanization and socioeconomic development, this research relies on the most current socioeconomic data available for

African countries. One time data, for the year 2001, were used for the study and various statistical methods, which are discussed later, were used in testing the various

hypotheses. This section also touches upon socioeconomic conditions in Africa with the

help of descriptive analysis.

Data Sources

Various sources of data were used in a study of urbanization in Africa. The source

that would have been most appropriate for this study would have been the population

censuses of the various countries. Unfortunately, there was a limitation to the availability

and use of these data. Out of the 54 countries in Africa, only 28% were relatively regular

in conducting their population censuses but the rest were not. In the last decade (1995 –

2004), one in four African countries did not conduct any population census at all. The

detail is shown in Figures 4.1 to 4.3. Moreover, not all the countries collect data on all the

types of information needed for this study.

Based on these inadequacies, data provided by the United Nations and the World

Bank were the most reliable source of information for this study. These agencies have been collecting data over the years on member countries to inform and guide them on

decision-making for their programs and projects. The available data cover population,

economic, environment, housing and social issues. These data sources have become the

data bank for most research on Africa and less frequently also on Asia. Njoh

1 5% 2 10%

6 28%

3 14%

4 5 19% 24%

Figure 4.1 Number of Censuses Conducted by African Countries over a Period of Six Decades

Source of Data: US Census Bureau; International Data Base, 2004

Census Conducted 1 2 3 4 N 5 W E 6 0700MilesS

Figure 4.2 Number of Censuses Taken by African Countries in the Past Six Decades (1945-54 to 1995-2004)

Source of Data: US Census Bureau, 2004

N Census Conducted after 1999 NO W E YES 0 700 Miles S

Figure 4.3 Censuses Taken by African Countries after 1999

Source of Data: US Census Bureau, 2004

(2003) for instance in his study of the relationship between urbanization and Human

Development Indicators in Sub-Saharan Africa used the United Nations data publications as his source. Morris (1979) in his study to measure the physical quality of life used data from United Nations Demographic Year Book. This implies that UN sources have been reliable for socioeconomic studies in providing standardized information for the purpose of international comparison. The specific sources of data for this study include reports by the World Bank (World Development Indicators, World Development Report and African Development Indicators), United Nations (World Urbanization Prospects,

Habitat), Population Reference Bureau (database) and United Nations Development

Program (UNDP) (Human Development Indicators). Some of these sources contain the same data from different sources hence were used as triangulation for the data sets to look for consistencies and reliability, which they actually did provide. They tend to have the same definition and measure the same thing, and hence are reliable and valid sources of information.

It can be inferred from the literature review that studies establishing relationships between urbanization and development have been primarily based on economic development indicators especially growth, in the form of GDP per capita, as the measure for development. Since Peattie (1996) argued that the approach to urban research should introduce new dimensions, this study has taken a new approach by introducing the socioeconomic aspect of development into the study of urbanization in Africa instead of the usual economic indicators (growth) only.

The current study, therefore, used a combination of social and economic

(socioeconomic) indicators of development as variables. Economic indicators for

development seem to be more straightforward and easily identifiable than social indicators that promote better quality of life.

Previous research has not provided a consistent classification for grouping social indicators of development. The classification obviously is more subjective than objective.

Researches on social indicators used both subjective and objective definition for quality of life. Subjective social indicators are concerned with well being and being satisfied with things in general. This is based on reports made by individuals about how they feel.

Objective social indicators on the other hand are concerned with fulfilling societal and cultural demands for material wealth, social status and physical well-being (Rossi &

Galmartin, 1980; Andrews & Withey, 1976; Atkinson et al. 2002). According to previous researches, objective social indicators are more reliable than attitude data and also attitude data tend to be ordinal in nature. In this study, based on the available data, the objective definition of social indicators of development was used and this covers broad areas like education, health and information. (See Table 4.1)

The unit of analysis for this study is the country. As indicated earlier, most studies concerning urbanization in Africa is about sub-Saharan Africa and not the entire continent. This study took continental Africa, made up of 54 countries and each country was treated as a case. Unfortunately, all desired data do not exist for four countries:

Liberia, Somalia, Seychelles and Western Sahara. These were dropped from the study leaving 50 countries as the cases. The ideal study should have been on the various urban centers in Africa but there were no sufficient data at this level for the study hence the use of country level data.

Table 4.1 Variables Derived from Data Sources for the Study

Variable Category Colonial Ties Colonialization Year of Independence (year) Surface Area (Sq km). Area Average annual population growth rate (%) Population Population Density (people per sq. km) Urbanization (% Urban) Urbanization Gross Domestic Product per Capita ($) Gross national Income ($) Gross national Income per Capita ($) Income and wealth Gross Domestic Product (PPP) per Capita ($) Gross Domestic Product per capita growth rate (%) Inequality Gini Coefficient (Gini Index) (income distribution) Aid per capita ($) Foreign Aid Adult literacy rate (% of population ages 15 and above) Combined gross enrolment ratio for primary, secondary and tertiary schools (%) Human development index (HDI) (index) HDI Crude Death Rate (per 1000 people) Crude Birth Rate (per 1000 people) Life expectancy at birth (years) Health Expenditure per Capita Access to improved sanitation (% of Total Population) Health Sustainable access to an improved water source (% of Total Population) Physicians (per 1,000, 000 people) Hospital Bed (per 1000 people) Radios (per 1000 people) Television Sets (per 1000 people) Information Telephone mainlines (per 1000 people) Non-agricultural employment (% of employed Employment population)

The variables included in this study are 29 and grouped into categories as shown

in Table 4.1. Data for wealth and income include data on GDP, GNP, GDP per capita,

and GDP growth rate. In terms of quality of life as a measure of development, the data

for this include those related to health, education and communication as well as

inequality in terms of income distribution. Quality of life is equated with social well-

being. These data were extracted from the World Bank’s World Development Indicators

and UNDP Human Development Report as well as from African Development Indicators,

also published by the World Bank. In order to avoid the arbitrary grouping of the

variables under the various development indicators, factor analysis was employed to

group all the variables into economic development, social development, urbanization and

other variables as shown in Table 5.16.

For the purpose of this study, economic transformation was added as a measure of

development. Development researchers argue that as a country develops, employment in

the primary economic sector tends to decrease in favor of non-primary economic sector.

In the same vein, the contribution of the primary sector to the GDP tends to decrease with increasing levels of development. For this purpose, data for employment in the non- agricultural sector were extracted from the International Labor Organization’s World

Employment Report and African Development Indicators.

For measuring urbanization, I use degree of urbanization, which is defined as

proportion of the total population living in urban areas. These data are published by the

United Nations’ World Urbanization Prospects. In addition to the proportion of the total

population living in urban areas, data for population growth rate and population density

for the various countries were considered in the analysis and they were also from the same source.

Data to differentiate the landlocked countries from countries with coastlines was

taken from an African map from Philip’s Atlas of the World (2003) and the political

affiliations (colonial ties) were extracted from a map prepared by Aryeetey-Attoh (2001,

p487), and also by White (1997). Two sources were used because Aryeetey-Attoh’s map

was only for Sub-Saharan Africa and White’s map covers the entire continent. Both were

used to check for consistency.

Methodology

Various statistical techniques were used for analyzing the data and this ranged

from spatial descriptive, difference of means tests to regression analysis. Geographic

Information System (GIS) was employed to produce maps showing the spatial

distribution of various variables across Africa. Factor analysis was used to unearth

underlying assumptions for urbanization in Africa and also for grouping the variables into

factors to simplify the analysis. Indexes – urbanization Index and Human Development

Index were developed by me to further enhance accuracy of the study.

Hypothesis 1

Multiple regression was used to test hypothesis 1. Previous studies on the

relationship between urbanization and development statistically analyzed the data quantitatively. The technique researchers used most was Pearson’s correlation with which they were able to establish the relationships between the pairs of variables. They were able to get results because they use one predictor variable against a criterion variable.

Njoh (2003) for instance uses Human Development Index and Urbanization, defined as

the proportion of the urban population living in urban areas. Davis and Henderson (2003) and Henderson (2003) also use correlation to establish the relationship. This indicates that correlation can be used to study relationship between urbanization and development indicators when using univariate data. This study however differs from the earlier methods because it uses multivariate data. Correlation can equally be used in this analysis but the limitation of correlation would not permit it to be used. Some of the limitations of correlation such as the following suggested it was inappropriate for use here:

1. It cannot describe the nature of relationship, should one exist, in the form of

mathematical equation

2. It cannot assess the degree of accuracy of description or prediction achieved

and

3. It cannot assess the relative importance of the various predictor variables in

their contribution to the criterion variable (Kachigan, 1991; Tacq, 1997;

Lewis-Beck, 1980).

Since the study aims at establishing the relationship in the form of mathematical equation, assess the degree of accuracy of the prediction and also assess the relative importance of the various predictor variables in their contribution to the criterion variable, the suitable methodology for this analysis is multiple regression. (See Table 3.1 for variables.)

Hypothesis 2

Factor Analysis was used to group the variables into economic, social and other development indicators for the analysis (Kim and Mueller, 1978; Kachigan, 1991;

Kleibaum, Krupper and Muller, 2000). Ideally, factor scores for the group identified by

the factor analysis as the economic development variables should have been used to run regression analysis on degree of urbanization and urbanization index. This would have reduced the predictor variables for the analysis, but then, one of the aims of this dissertation is to identify the variables that tend to predict urbanization hence the factor scores could not be used. The set of variables identified as economic development variables were used to run regression analysis on degree of urbanization and urbanization index to get the economic development variables that predict urbanization in Africa.

Hypothesis 3

Factor Analysis once again identified variables social development variables and these variables were used to run regression analysis on degree of urbanization and urbanization index in order to get the social development variables that tend to predict urbanization in Africa.

Hypothesis 4

Human Development Index for African countries (HDI computed) was computed for the study and this was used alongside the Human Development Index computed by the United Nations Development Program (UNDP). The study anticipated that, the ranking of HDI scores would be the same for scores of HDI computed. In order to establish this, a non-parametric test, Wilcoxon Signed Ranks Test, was used to compare the scores of the HDI and HDI computed. Although the values in each data set are interval in nature, the scores of HDI Computed are likely to be higher than that for HDI computed by UNDP since the UNDP used 174 countries and the maximum scores are for the developed countries. These differences would be dealt with by means of ranking the scores and these rankings would be on the same continuum. The existence of differences

is an indication for testing the hypothesis using both HDIs – HDI and HDI computed

using regression analysis. Regression analysis would therefore determine which of the

HDIs best predicts urbanization. On the other hand, if there is no difference, then either of the HDIs could be used to run the regression to see if HDI predicts urbanization in

Africa.

Hypothesis 5

For hypothesis 5 to be tested, there was the need to know if differences exist between landlocked and non landlocked countries in terms of degree of urbanization and urbanization index. Since the data for measuring degree of urbanization and urbanization index are continuous and we were comparing two variables, landlocked and non- landlocked, the best testing methodology was independent sample t-test (McGrew &

Monroe, 2000; Tacq, 1997, Giventer, 1996). This was done by comparing the mean value for each group, to see if significant differences in urbanization exist between the two locations. Differences in the level of urbanization between the two locations would be a basis for running multiple regression to finally test the hypothesis to know if the variables predicting urbanization in Africa differ in terms of location.

Hypothesis 6

Just as hypothesis 5, there was the need to know if differences exist among the various countries in terms of degree of urbanization and urbanization index, based on their colonial ties with European countries and the periods within which they attained independence. In order to ascertain the possible differences, One Way ANOVA technique is the most appropriate for this comparison because the data is continuous

(interval) and more than two groups are involved in this analysis and t-test technique

cannot be employed (McGrew & Monroe, 2000; Tacq, 1997, Giventer, 1996). African countries were colonized by 6 different European countries and these are Britain, France,

Spain, Italy, Belgium and Portugal. This technique is indirectly related to the testing of

Hypothesis 6 in the sense that, the existence of differences in levels of urbanization would be the basis for testing hypothesis 6 using multiple regression.

Hypothesis 7

To test hypothesis 7, Paired Sample T Test was used to compare the standard error of the estimate for urbanization index and degree of urbanization. This method was deemed appropriate because two variables, urbanization index and degree of urbanization, are being compared for possible significant differences in their standard errors of the estimate.

Factor Analysis

Factor analysis was employed in the study as a variable grouping technique on

one hand (Kim and Mueller, 1978; Kachigan, 1991; Kleibaum, Krupper and Muller,

2000) and as a means of deriving indexes. By means of factor analysis, it was possible to

identify the socioeconomic variables that group under social development and economic

development indicators for the study. (See Appendix for factor groupings). It was also

used to develop an index for urbanization, an additional measure developed by this study

for comparison with the traditional measure, proportion of total population living in

urban areas (degree of urbanization).

Developing Indices

As indicated, the study computed Human Development Index and urbanization

index specifically for Africa. This was necessary because the HDI computed by the

UNDP was developed for 147 countries with wide range of values used in the

computation. With urbanization, there was no standard population threshold to indicate

urban status for Africa hence the index is an attempt to create a standard yardstick for measuring urbanization for this study.

Urbanization Index

Urbanization data reported by the United Nations and the World Bank need to be

treated with caution. This is because these agencies report what each country perceives

and defines as the population living in the urban areas. Meanwhile in the literature

review, there is no standard definition or population threshold for identifying a place as

urban.

To deal with this problem, Gibbs (1966) came up with three measures for

studying urbanization and these are degree of urbanization, scale of urbanization and

scale of population concentration. Degree of urbanization was defined as the proportion

of the total population living in urban areas, which is what the World Bank and United

Nation Agencies report. Scale of urbanization considers the distribution of urban

population among the various class sizes of urban units and the same distribution with

each class considered as a proportion of the total population of the country. Scale of

population concentration is Gibbs’ third measure for urbanization. This method considers

all points of population concentration. Gibbs’ finding was that the three methods measure

the same thing since there were no significant differences among these three measures in his study signifying that any of them could be used to measure urbanization.

Based on the scarcity of detailed population data for the various countries, the study was able to improve upon scale of population concentration, to develop an urbanization index, and to use it as an alternative measure for urbanization. The computed scale of population concentration was compared with degree of urbanization and there was a significant difference between these two measures for Africa indicating that one cannot be substituted for the other. Moreover, since there is no standard threshold to measure urbanization, an urbanization index therefore needs to be computed for the countries in Africa, as a “standard” measure for urbanization. As mentioned earlier, factor analysis was employed in the derivation of urbanization index. The computed scale of population concentration was used as a variable and added to the other socioeconomic variables (in Table 4.1) to run factor analysis. That variable (computed scale of population concentration) was grouped with degree of urbanization under the same factor. Based on this information, the factor score for that group was used to develop an urbanization index for African countries, by means of employing the formula for computing Human Development Index. (Formula discussed later). This index was used to run regression analysis and the results compared with the results for degree of urbanization in order to determine the best measure for urbanization for the study.

Ideally, indices are usually measured between 0 and 1 but in order to favorably compare the results of urbanization index with the results of degree of urbanization, the units of measurement must be the same. Since degree of urbanization was measured in

percentages, the scale of urbanization was also converted into percentages by multiplying the index by 100.

Computing Urbanization index

A process was developed to compute urbanization index for Africa. The computation started with the development of scale of population concentration for the continent. Scale of population concentration was developed by Gibbs (1969) and this considered all points of population aggregation, reported by the World Bank and other

agencies for 2001. This computation used the formula:

SPC = ΣX where:

SPC is the measure for Scale of Population Concentration and

X is the proportion of the total population in each class size and over.

Based on the data available, the class sizes for the study were: 1 – 500,000 500,000 – 750,000 750,000 – 1,000,000 1,000,000 and over The computation yielded the data shown in Table 4.2.

Table 4.2 Scale of Population Concentration by Countries in Africa

Scale of 1- 500000 750000 - 1000000 Population Country 500000 - Degree of 1000000 and over Concentration 750000 Urbanization (ΣX) (% urban) Algeria 1.0000 0.0910 0.0910 0.0910 1.2730 57.1 Angola 1.0000 0.2050 0.2050 0.2050 1.6150 34.2 Benin 1.0000 0.0000 0.0000 0.0000 1.0000 42.3 Botswana 1.0000 0.0000 0.0000 0.0000 1.0000 49.0 Burkina Faso 1.0000 0.1387 0.1387 0.1387 1.4162 16.5 Burundi 1.0000 0.0000 0.0000 0.0000 1.0000 9.0 Cameroon 1.0000 0.2050 0.2050 0.2050 1.6150 48.9 Cape Verde 1.0000 0.0000 0.0000 0.0000 1.0000 82.2 Central African Republic 1.0000 0.1792 0.0000 0.0000 1.1792 41.2 Chad 1.0000 0.0932 0.0000 0.0000 1.0932 23.8 Comoros 1.0000 0.0000 0.0000 0.0000 1.0000 33.2 Congo 1.0000 0.2840 0.2840 0.0000 1.5680 85.4 Djibouti 1.0000 0.8576 0.0000 0.0000 1.8576 84.0 Egypt 1.0000 0.2050 0.2050 0.2050 1.6150 42.7 Equatorial Guinea 1.0000 0.0000 0.0000 0.0000 1.0000 48.2 Eritrea 1.0000 0.1375 0.0000 0.0000 1.1375 18.7 Ethiopia 1.0000 0.0409 0.0409 0.0409 1.1227 15.5 Gabon 1.0000 0.4659 0.0000 0.0000 1.4659 81.4 Gambia 1.0000 0.0000 0.0000 0.0000 1.0000 30.7 Ghana 1.0000 0.1387 0.1387 0.0997 1.3771 38.1 Guinea 1.0000 0.1520 0.1520 0.1520 1.4560 31.5 Guinea-Bissau 1.0000 0.0000 0.0000 0.0000 1.0000 27.6 Ivory Coast 1.0000 0.1930 0.1930 0.1930 1.5790 43.8 Kenya 1.0000 0.0730 0.0730 0.0730 1.2190 33.4 Lesotho 1.0000 0.0000 0.0000 0.0000 1.0000 28.0 Libya 1.0000 0.1740 0.1740 0.0000 1.3480 87.6 Madagascar 1.0000 0.0000 0.0000 0.0000 1.0000 29.5 Malawi 1.0000 0.0463 0.0000 0.0000 1.0463 14.7 Mali 1.0000 0.0940 0.0940 0.0940 1.2820 30.2 Mauritania 1.0000 0.2349 0.0000 0.0000 1.2349 57.7 Mauritius 1.0000 0.0000 0.0000 0.0000 1.0000 41.3 Morocco 1.0000 0.2290 0.2290 0.1700 1.6280 85.5 Mozambique 1.0000 0.0610 0.0610 0.0610 1.1830 32.1 Namibia 1.0000 0.0000 0.0000 0.0000 1.0000 30.9 Niger 1.0000 0.0700 0.0700 0.0000 1.1400 20.6 Nigeria 1.0000 0. 1280 0.1280 0.0220 1.2780 44.1 Rwanda 1.0000 0.0000 0.0000 0.0000 1.0000 6.2 Sao Tome and Principe 1.0000 0.0000 0.0000 0.0000 1.0000 47.0 Senegal 1.0000 0.2100 0.2100 0.2100 1.6300 47.4 Sierra Leone 1.0000 0.1820 0.1820 0.0000 1.3640 36.6 South Africa 1.0000 0.3895 0.3895 0.3475 2.1264 27.5 Table 4.2 continued

Country 1-500000 500000 – 750000 – 1000000 Scale of Degree of 750000 1000000 and over Population Urbanizatio Concentration n (ΣX) (% Urban) Sudan 1.0000 0.1260 0.1260 0.1260 1.3780 58.9 Swaziland 1.0000 0.0000 0.0000 0.0000 1.0000 38.1 Tanzania 1.0000 0.0610 0.0610 0.0610 1.1830 32.3 Togo 1.0000 0.1617 0.0000 0.0000 1.1617 33.4 Tunisia 1.0000 0.1990 0.1990 0.1990 1.5970 65.6 Uganda 1.0000 0.0470 0.0470 0.0470 1.1410 14.2 Zaire 1.0000 0.1170 0.1170 0.0980 1.3320 30.3 Zambia 1.0000 0.1250 0.1250 0.1250 1.3750 39.8 Zimbabwe 1.0000 0.1100 0.1100 0.1100 1.3300 35.3

This study tests his (Gibbs’) theory by comparing the degree of urbanization, defined as

the proportion of total population living in urban areas with the Scale of urbanization,

computed in table 4.2, and the difference was significant with a p value of 0.00 for the t-

test. Moreover, there is a weak correlation (p value of 0.383) between the two variables.

The scatter plot in Figure 4.4, showing a weak pattern, further confirmed the weak

correlation.

This implies that Gibbs’ theory does not apply to these measures of urbanization

in Africa, necessitating a further search for measurement for urbanization in Africa and

this called for the next step, development of an urbanization index. The literature reports

that factor analysis can be used to develop indices (Garson, 2004; Rummel, 1988). The

computed Scale of Population Concentration was added to a set of variables (listed in

Table 4.1) to run factor analysis. Degree of urbanization and Scale of Population

Concentration were loaded on the same factor indicating that they have the same dimension (structure). According to Rummel (1988) factor scores are scales or indices

100.0

Cape Verde Libya Morocco Djibouti 80.0

60.0

40.0

South Africa

Degree of Urbanization (% Urban) 20.0

Burkina Faso

Rwanda 0.0

1.000 1.200 1.400 1.600 1.800 2.000 2.200 Scale of Population Concentration

Figure 4.4 Scatter Plot for Degree of Urbanization and Scale of Population Concentration for African Countries

S S S SS S S S S S S SS

S S S S S S S S S S S S S S S S S SS S S S S S S S

S S S S S S S

S S S S S S

SS S

S S N

Population W E S 5,000,000 and over S 1,000,000 to 5,000,000 S S 500,000 to 1,000,000 Countries 0 800 Miles

Figure 4.5 Urban Areas in Africa with Population of 500,000 or more in 2000

Sources of Data: Brinkoff, Thomas, 2003; World Bank, 2003

Table 4.3 Urbanization Index for African Countries 83

Scale of Urbanization Degree of Population Factor Score for Urbanizat Index Country Urbanization Concentration Urbanization ion Index (Percentage) Algeria 57.1 1.273 0.2528 0.45382 45.38 Angola 34.2 1.615 0.95336 0.63337 63.34 Benin 42.3 1 -0.56448 0.24436 24.44 Botswana 49 1 -0.67744 0.21541 21.54 Burkina Faso 16.5 1.416 -0.00946 0.38661 38.66 Burundi 9 1 -1.30576 0.05438 5.44 Cameroon 48.9 1.615 1.22563 0.70315 70.32 Cape Verde 82.2 1 0.29044 0.46347 46.35 Central African Republic 41.2 1.179 -0.23082 0.32988 32.99 Chad 23.8 1.093 -0.74771 0.1974 19.74 Comoros 33.2 1 -0.81029 0.18136 18.14 Congo 85.4 1.568 2.248 0.96517 96.52 Djibouti 84 1.858 2.38389 1 100.00 Egypt 42.7 1.615 0.89757 0.61907 61.91 Equatorial Guinea 48.2 1 -0.77482 0.19045 19.05 Eritrea 18.7 1.137 -1.22428 0.07526 7.53 Ethiopia 15.5 1.123 -1.01623 0.12858 12.86 Gabon 81.4 1.466 1.94461 0.88742 88.74 Gambia, The 30.7 1 -1.51793 0.01 1.00 Ghana 38.1 1.377 0.21724 0.44471 44.47 Guinea 27.6 1.456 0.00321 0.38985 38.99 Guinea-Bissau 31.5 1 -0.70028 0.20956 20.96 Ivory Coast 43.8 1.579 0.93685 0.62914 62.91 Kenya 33.4 1.219 -0.27392 0.31883 31.88 Lesotho 28 1 -0.65946 0.22002 22.00 Libya 87.6 1.348 0.96103 0.63533 63.53 Madagascar 29.5 1 -0.89127 0.16061 16.06 Malawi 14.7 1.046 -0.97396 0.13941 13.94 Mali 30.2 1.282 -0.18926 0.34053 34.05 Mauritania 57.7 1.235 0.46421 0.508 50.80 Mauritius 41.3 1 -1.34646 0.04395 4.40 Morocco 85.5 1.628 1.5151 0.77734 77.73 Mozambique 32.1 1.183 -0.3353 0.3031 30.31 Namibia 30.9 1 -0.85986 0.16866 16.87 Niger 20.6 1.14 -0.72574 0.20303 20.30 Nigeria 44.1 1.278 0.25219 0.45367 45.37 Rwanda 6.2 1 -1.28796 0.05894 5.89 Sao Tome and Principe 47 1 -0.43869 0.2766 27.66 Senegal 47.4 1.63 0.72949 0.57599 57.60 Sierra Leone 36.6 1.364 0.48982 0.51457 51.46

Table 4.3 continued

Scale of Urbanization Degree of Population Factor Score for Urbanizat Index Country Urbanization Concentration Urbanization ion Index (Percentage) South Africa 27.5 2.126 2.00457 0.90278 90.28 Sudan 58.9 1.378 0.35513 0.48005 48.01 Swaziland 38.1 1 -0.53251 0.25255 25.26 Tanzania, United Republic of 32.3 1.183 -0.4121 0.28341 28.34 Togo 33.4 1.162 -0.30792 0.31011 31.01 Tunisia 65.6 1.597 1.40288 0.74858 74.86 Uganda 14.2 1.141 -1.39163 0.03237 3.24 Zaire 30.3 1.332 0.18491 0.43642 43.64 Zambia 39.8 1.375 0.5288 0.52456 52.46 Zimbabwe 35.3 1.33 -0.0362 0.37975 37.98

2.0000

1.0000

0.0000

Factor Score for Urbanization Score for Factor -1.0000

-2.0000

0.000 20.000 40.000 60.000 80.000 100.000 Urbanization Index (Percentage)

Figure 4.6 Scatter Plot for Urbanization Index and Factor Score for Urbanization

Scale of Population Concentration 1 - 1.046 1.046 - 1.235 1.235 - 1.466 N 1.466 - 2.126 Not Included in the Study 01000MilesW E

Figure 4.7 Scale of Population Concentration for Africa by Countries

which are weighed hence the factor scores for urbanization could be taken as an index for urbanization. Scores for the various countries are shown in Table 5.5. As can be inferred 86

from the table some of the scores are negative making it difficult to apply it as a reasonable index. Hence adjustments were made to the factor scores, by applying the formula for computing HDI. The formula for computing Human Development Index, developed by Haq (UNDP, 1990) is as follows:

Index = (x – y) / (z – y) where:

x is the actual value for the country

y is the minimum value for the data set

z is the maximum value for the dataset

This formula was applied to remove the negative element of the factor score for urbanization in order for the scores to be converted into urbanization index, also shown in

Table 4.3. There was a perfect correlation between factor scores and the urbanization

index developed from the factor scores with a correlation coefficient of 1.0 (see scatter

plot in Figure 4.6) indicating that they measure the same thing.

The scale of population concentration and the urbanization index generated were

used to prepare maps showing the spatial distribution of the variables. When the map for

the scale of population concentration was compared with the map for degree of

urbanization, only four of the countries that ranked among the top 10 in terms of degree

of urbanization were ranked among the top 10 in terms of scale of urbanization. The

countries that remained in the ranking were Djibouti, Morocco, Tunisia and Congo, indicating that they have higher levels of degree of urbanization and at the same time, higher levels of population concentration. Libya, which had the highest degree of urbanization, did not have a high concentration of population hence fell out of the

ranking for scale of population concentration. Other countries in this group include

Gabon, Algeria, Cape Verde, Sudan and Mauritania. On the other hand, some countries which ranked low and were not among the top 10 in terms of degree of urbanization ranked high in terms of scale of population concentration. These countries include Cote d’Ivoire, South Africa, Senegal, Cameroon, Angola and Egypt. Figure 4.8 presents the details.

Degree of Population Rank Rank urbanization Concentration 1 Libya 1 South Africa 2 Morocco 2 Djibouti 3 Congo 3 Senegal 4 Djibouti 4 Morocco 5 Cape Verde 5 Angola 6 Gabon 6 Cameroon 7 Tunisia 7 Egypt 8 Sudan 8 Tunisia 9 Mauritania 9 Cote d’Ivoire 10 Algeria 10 Congo

Figure 4.8 Ranking of Degree of Urbanization and Scale of Population Concentration among the Top 10 Countries of Africa

Urbanization Index (%) 1 - 25 25 - 50 50 - 75 75 - 100 N Not Included in the Study

0 1000 Miles W E

Figure 4.9 Urbanization Index for African Countries

When it comes to urbanization index, the ten top ranking countries include Djibouti,

Congo, South Africa, Gabon, Morocco and Tunisia. The rest are Cameroon, Libya,

Angola and Cote d’Ivoire in that order. Middle Africa and Northern Africa as regions are

more urbanized when it comes to urbanization index and Eastern region is the least. (See

Appendix 1A and 1B for details). As can be seen from Table 4.4, as many as eight

countries from the Eastern region were among the lowest ten countries, while the middle

and the Northern regions had none in this category.

Table 4.4 Number of Countries among the Top 10 and Bottom 10 in Terms of Urbanization Index

Number of Number of Region Countries among Countries among the Top 10 the Bottom 10 Eastern 1 8 Middle 4 - Northern 3 - Southern 1 1 Western 1 1

In ranking of the top 10 countries in terms of the three measures of urbanization identified in this study, some countries were found to be highly urbanized when viewed

from all three aspects. In all, four countries, two from the Northern and one each from the

Middle and Eastern Regions of Africa ranked high in all the three measures of

urbanization. The details are shown in the Venn diagram in Figure 4.10.

DEGREE OF URBANIZATION

Algeria Cape Verde Mauritania Sudan

Gabon Libya Congo Djibouti Morocco Tunisia

Angola Egypt Cameroon Senegal Cote d’Ivoire South Africa

URBANIZATION INDEX SCALE OF POPULATION CONCENTRATION

Figure 4.10 Venn Diagram showing the Top 10 Ranking Countries in Africa in terms of Scale of Population Concentration, Urbanization Index and Degree of Urbanization

Human Development Index for Africa

Gross Domestic Product (GNP) is often used as the measure for development and

welfare but Abramovitz (1959) has observed that GNP is not a satisfactory measure for

welfare. Morris (1979) developed Physical Quality of Life Index (PQLI) which he used

to measure welfare. His Physical Quality of Life Index (PQLI) summarizes infant

mortality, life expectancy at age one and basic literacy on a zero to 100 scale. The index

enabled researchers to rank countries, not by incomes but by changes in real life chances.

In 1989, search for new composite index started again and a Pakistani economist,

Mahbub ul-Haq developed a new index, Human Development Index (HDI), which has

been in use by the United Nations Development Program (UNDP) since 1990 (UNDP,

1990). This index measures poverty, knowledge and long and healthy life. Poverty is

measured by GDP per capita at Purchasing Power Parity Exchange (PPP) rate,

knowledge by adult literacy rate and combined primary, secondary and tertiary gross

enrollment ratio and long and healthy life measured by life expectancy. This index is

used to rank countries in terms of well-being. Unlike PQLI, which uses unweighted

scores, HDI uses weighted scores for knowledge, where adult literacy carries two-thirds

of the weight while combined primary, secondary and tertiary gross enrollment ratio

carries a third of the weight.

UNDP computes HDI, for the United Nations member countries, which have the

necessary data, and uses the maximum and minimum values, of each variable used, for

the various indices. The maximum values are found in the developed countries while the

minimum in the developing countries. This in the end ranks African countries very low

on the HDI ladder. For the purpose of this study, HDI was computed using only the

African countries in the study. This brings out the true standing of African countries in terms of HDI against each other. The computation however uses the formula which the

UNDP uses in its computation. This result was used to run regression analysis to determine the best predictor of urbanization by the two HDIs.

Computation of Human Development Index for Africa

As indicated earlier, the United Nations Development Program (UNDP) computes

Human Development Index (HDI) for 174 countries that have the necessary reliable data for the computation. HDI is a measure for the average achievement in a country in terms of decent standard of living, long and healthy life and knowledge. Long and healthy life

is measured by life expectancy, decent standard of living by Gross Domestic Product

(GDP) per capita at Purchasing Power Parity (PPP) exchange rate and knowledge by a

combination of adult literacy rate and gross enrollment ratio. To compute HDI, indices

are computed for life expectancy, adult literacy, gross enrolment, GDP and education.

Adult literacy index, gross enrolment index and life expectancy index are computed using

the following formula:

Index = (x – y) / (z – y) where:

x is the actual value for the country

y is the minimum value for the data set

z is the maximum value for the dataset

Education Index is then computed by the formula:

2/3 (adult literacy index) + 1/3 (gross enrollment index)

GDP Index is computed by the formula:

(Log (x) – log (y)) / (log (z) – log (y))

where x, y and z remain the same as above.

HDI therefore is computed by finding the average for the indices thus: HDI = (life

expectancy index + education index + GDP index)/3

This method, developed by Haq (UNDP, 1990), is used to compute HDI annually by the UNDP but concerns were raised about the adequacy of the application of HDI in terms of the variables for measuring human development. This study amends the Human

Development Index computed by the UNDP. UNDP as a world agency considers the standing of all countries of the world, with reliable data, against each other in terms of human development. Since this study is concerned with only Africa as a continent, the computation of the HDI was limited to African countries.

The indices range from a minimum of 0 and a maximum of 1 with 1 being the best, 19 countries have HDI score above the mean of 0.43. All the northern and almost all the southern African countries have scores above the average. The Northern African and southern African countries score highly because these are relatively rich countries and GDP per capita at PPP exchange rate featured prominently in the computation of

HDI. GDP per capita explains about 72.1% of the variance in HDI. A large proportion of the countries that performed poorly in terms of HDI index are located in the Sahel region

(on the fringe of Sahara desert), where droughts might have affected their economic

Table 4.5 Computed Human Development Index for African Countries

Combined gross enrolment ratio Life Adult (for primary, GDP Education HDI Country Expectancy Literacy HDI secondary and Index Index (computed) Index Index tertiary schools) index Algeria 0.920 0.727 0.654 0.592 0.702 0.704 0.738 Angola 0.185 0.378 0.141 0.347 0.299 0.381 0.277 Benin 0.450 0.350 0.423 0.178 0.374 0.421 0.334 Botswana 0.218 0.856 0.654 0.679 0.789 0.589 0.562 Burkina Faso 0.328 0.000 0.038 0.185 0.013 0.302 0.175 Burundi 0.203 0.487 0.179 0.047 0.385 0.339 0.211 Cameroon 0.353 0.714 0.474 0.332 0.634 0.501 0.439 Cape Verde 0.933 0.815 0.692 0.558 0.774 0.717 0.755 Central African Republic 0.178 0.464 0.154 0.200 0.360 0.361 0.246 Chad 0.300 0.427 0.205 0.166 0.353 0.379 0.273 Comoros 0.698 0.562 0.333 0.290 0.486 0.530 0.491 Congo 0.390 0.907 0.372 0.156 0.728 0.494 0.425 Congo, Dem. Re p. of the 0.218 0.646 0.103 0.055 0.465 0.365 0.246 Côte d'Ivoire 0.213 0.478 0.295 0.264 0.417 0.399 0.298 Djibouti 0.328 0.683 0.064 0.331 0.476 0.454 0.378 Egypt 0.898 0.554 0.731 0.491 0.613 0.653 0.667 Equatorial Guinea 0.410 0.925 0.500 1.000 0.783 0.703 0.731 Eritrea 0.500 0.569 0.179 0.132 0.439 0.439 0.357 Ethiopia 0.320 0.372 0.192 0.100 0.312 0.359 0.244 Gabon 0.598 0.754 0.705 0.626 0.738 0.648 0.654 Gambia 0.530 0.324 0.333 0.290 0.327 0.452 0.382 Ghana 0.628 0.790 0.346 0.347 0.642 0.568 0.539 Guinea 0.405 0.365 0.128 0.344 0.286 0.425 0.345 Guinea-Bissau 0.313 0.347 0.231 0.077 0.308 0.350 0.233 Kenya 0.313 0.926 0.436 0.166 0.763 0.488 0.414 Lesotho 0.090 0.889 0.590 0.379 0.789 0.493 0.419 Libya 0.998 0.892 1.000 0.660 0.928 0.794 0.862 Madagascar 0.518 0.706 0.333 0.087 0.582 0.469 0.395 Malawi 0.128 0.635 0.705 0.027 0.658 0.388 0.271 Mali 0.395 0.080 0.090 0.143 0.083 0.326 0.207 Mauritania 0.490 0.368 0.321 0.358 0.352 0.465 0.400 Mauritius 0.980 0.926 0.641 0.747 0.831 0.785 0.853 Morocco 0.895 0.491 0.487 0.491 0.490 0.620 0.625 Mozambique 0.145 0.437 0.282 0.173 0.385 0.354 0.234 Namibia 0.315 0.913 0.667 0.611 0.831 0.607 0.586 Niger 0.333 0.056 0.000 0.106 0.037 0.292 0.159 Nigeria 0.473 0.699 0.333 0.124 0.577 0.466 0.391 95

Table 4.5 Continued Rwanda 0.155 0.731 0.436 0.220 0.632 0.431 0.336 Sao Tome and Principe 0.925 0.911 0.551 0.229 0.791 0.645 0.648 Senegal 0.500 0.343 0.244 0.274 0.310 0.437 0.361 Sierra Leone 0.040 0.301 0.333 0.000 0.311 0.273 0.117 South Africa 0.403 0.948 0.744 0.730 0.880 0.666 0.671 Sudan 0.570 0.610 0.218 0.309 0.479 0.505 0.453 Swaziland 0.075 0.882 0.538 0.534 0.768 0.519 0.459 Tanzania 0.270 0.833 0.154 0.027 0.607 0.407 0.301 Togo 0.430 0.606 0.615 0.258 0.609 0.495 0.432 Tunisia 1.000 0.782 0.718 0.632 0.761 0.745 0.798 Uganda 0.325 0.727 0.667 0.242 0.707 0.493 0.425 Zambia 0.000 0.869 0.333 0.118 0.691 0.389 0.270 Zimbabwe 0.030 1.000 0.500 0.377 0.833 0.491 0.413

activities. These countries are also landlocked making it difficult for them to have access

to effective and efficient international trade. Examples include Niger, Burkina Faso, Mali and Chad. The others are politically-related conflict areas. The political turmoil in these

countries could be the cause of their inability to provide the service facilities necessary for improving human development. Examples are Sierra Leone, Democratic Republic of

Congo and Burundi. The rankings for Zambia and Tanzania can be explained by the prevalence of HIV/AIDS. Life expectancy is the second largest contributor to the explanation of the variance for HDI and HIV/AIDS reduce life expectancy. See Figure

5.12.

Correlation between the HDI computed for Africa and that computed by the

UNDP using data for all the 174 countries is very high about .999 but there was a significant difference between the two variables, HDI computed by UNDP and HDI computed for the dissertation, (p-value of 0.00) using Wilcoxon signed ranks test. This

then implies that the two variables are different justifying the need for a computed HDI for Africa to be included in the analysis in order to come up with the best, out of the two, for studies concerning urbanization in Africa.

Human Development Index (Computed) 0.1 - 0.25 N 0.25 - 0.5 0.5 - 0.75 0.75 - 1 01000MilesW E No Data S

Figure 4.11 Computed Human Development Index for Africa by Countries

Descriptive Analysis and Spatial Presentation of Data

Geographic Information System (GIS) mapping technique is employed to spatially display some variables, which are not highly correlated, on maps. This provides vivid pictures of what the data are saying with regards to the variables.

Location was important to the study because the study assumes that there is a

relationship between urbanization and the location of the country with respect to access

to the sea. Out of the fifty countries in the study, 30% (15) have no access to the sea as

can be seen from Figure 5.1. Countries without access to the sea usually have problems

with their economic activities because bulky goods and machinery cannot be delivered easily since they have to be transported by land, either by road or rail. From experience, countries like Burkina Faso, Niger and Mali import and export some of their goods through the roads of Ghana, where they have to be transported through the length of

Ghana, to their destinations. This might have affected their development activities and eventually their levels of urbanization.

Europeans colonized countries in Africa since their arrival on the continent in the

15th Century. The only country, among those included in the study, not colonized was

Ethiopia. The Ethiopians resisted the takeover by the Italians after they succeeded in taking Djibouti and the present-day Eritrea. European countries colonized all the other countries with the exception of Liberia, (not included in the study) which was created by the United States in 1822 to settle freed American Slaves. This analysis dwells on colonization after the First World War, when the Germans were driven out, as a result of their defeat in the war, and their colonies taken over and shared by the allied forces, mainly the British and the French, among themselves. Examples of this colony include

Location Coastland Landlocked

01000MilesW E

Figure 4.12 Location of African Countries in Relation to the Sea

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Number of Colonies

Spain Belgium Portugal 2% 6% 10%

Italy 6% Not Colonized 2% Britain 36%

France 38%

Figure 4.13 Distribution of European Colonies in Africa after World War I

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Colonial Ties Belgium Britain France Not Colonized Italy Portugal Spain N Not Included in the Study

W E 0 1000 Miles

Figure 4.14 African Colonial Ties with Europe after World War I

Source of Information: Aryeetey- Attoh, 2001, White, 1997

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the present-day Togo and part of Ghana (the Volta Region) which were German colonies.

Togo became a French colony and the Volta Region was added to the then Gold Coast under the British. Other German colonies lost were Cameroon, South West Africa now

Namibia and Tanganyika, now Tanzania. In all, the French had the largest number of colonies, a total of 19 (38%), followed by the British with 18 (36%) colonies. Figures

4.13 and 4.14 show details of the distribution.

In the Twentieth Century, colonized African countries started agitating for political independence. The first country to have independence was South Africa and this was in 1910. After the First World War, by 1919, the remaining African countries started talking about gaining independence from their colonial masters. The only country able to do this before the Second World War was Egypt and this was in 1922. After the Second

World War in 1945, many colonial powers such as Great Britain and France were

weakened and African countries continued to press for political independence. As can be

seen in Figure 4.15, by the end of 1959, a total of nine countries had gained their political

independence. Between 1960 and 1969, 30 countries gained independence and 10

countries after 1969. (Figure 4.15)

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30 30

10 Number of Countries of Number 10 9

5 1 0 Not Colonized Before 1960 1960 - 1969 After 1969 Period

Figure 4.15 Periods when African Countries became Independent

Source of Data: Wikipedia, 2003

In terms of economic variables for measuring development, using Gross Domestic

Product (GDP) per capita, most of the countries in Africa fall below the average of

US$932.00 for the continent. The country with the highest GDP per capita in 2001 was

Libya with US$ 6,453.00 and the lowest was Gabon with US$ 95.00. Both countries produce oil. Gabon is the fifth producer and exporter of oil in Africa thus it is surprising to see Gabon with such a low income per capita. Only 13 countries, out of 50 being studied, had GDP per capita greater than the continental average and these countries in descending order were Libya, Eritrea, Mauritius, The Gambia, Botswana, South Africa,

Tunisia, Algeria, Namibia, Equatorial Guinea, Cape Verde, Swaziland and Morocco. The

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lowest five countries were Gabon, Democratic Republic of Congo (Zaire), Burundi,

Sierra Leone and Cote d’Ivoire (Ivory Coast). Here comes the surprise with Gabon again

because the country has enjoyed political stability since her political independence from

France in 1960. The rest of the low ranking countries have been afflicted by internal

political conflict in one way or the other. Democratic Republic of Congo, Sierra Leone

and Cote d’Ivoire had and continue to have political turmoil while Burundi had ethnic

related conflict. These could be reasons for their dismal economic performance hence

their low income in terms of GDP per capita.

Using GDP per capita at Purchasing Power Parity exchange rates tells quite a

different story. Purchasing Power Parity (PPP) exchange rate is a method used to

calculate exchange rates between the currencies of different countries (Lafrance and

Schembri, 2002). It is used to compare standard of living internationally because by using this method, the differences in national price levels are minimized and a comparable measure for purchasing power is obtained.

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Period of Independence Not Colonized Before 1960 1960 - 1969 After 1969 N Not Included in the Study 0 1000 Miles W E

Figure 4.16 Periods in which Independence was Attained by Countries in Africa

Source of Data: Wikipedia, 2003

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GDP Per Capita (US$) 95 - 706 707 - 2066 2067 - 3935 3936 - 6453 Not Included in the Study N

0 1000 Miles W E

Figure 4.17 GDP per Capita for African Countries in 2001 Source: World Development Indicators, 2002

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With GDP at PPP exchange rate, Equatorial Guinea emerges the richest country with a GDP per capita of US$ 15073.00 and Sierra Leone the poorest with US$ 470.00.

Just as with GDP per capita, 13 countries have GDP at PPP exchange rate above the mean of US$ 2653.00, but some new countries found their way into the top 13 while others dropped off. The thirteen countries in descending order are Equatorial Guinea,

South Africa, Mauritius, Botswana, Libya, Namibia, Tunisia, Algeria, Gabon, Cape

Verde, Swaziland, Morocco and Egypt. Gabon, which was among the poorest in terms of

GDP, is among the top thirteen rich counties when it comes to GDP at PPP Rate and

Egypt became a new member of the group as well. Eritrea and The Gambia lost their positions from the group of the thirteen richest countries when it comes to GDP at PPP exchange rate. The details are shown in Figures 4.18 and 4.19

GDP PER CAPITA GDP PER CAPITA AT PPP EXCHANGE RATE 1 Libya Equatorial Guinea 2 Eritrea South Africa 3 Mauritius Mauritius 4 Gambia, The Botswana 5 Botswana Libya 6 South Africa Namibia 7 Tunisia Tunisia 8 Algeria Algeria 9 Namibia Gabon 10 Equatorial Guinea Cape Verde 11 Cape Verde Swaziland 12 Swaziland Morocco 13 Morocco Egypt

Figure 4.18 The Richest Thirteen African countries in 2001

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GDP per Capita at PPP (US$) 470 - 890 890 - 1870 N 1870 - 4330 4330 - 15080 W E Not Included in the Study 0 1000 Miles S

Figure 4.19 GDP per Capita at PPP Exchange rate for African countries in 2001 Source of Data: World Development Indicators, 2002

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In terms of the five poorest countries, Sierra Leone became the poorest country with a GPD per capita at PPP exchange rate of US$ 470.00, followed by Tanzania,

Malawi Zaire and Burundi in that order. As can be seen in Figure 4.18, Gabon, which used to be the country with the least GDP moved up to join the top 13 countries in terms of GDP per capita at PPP exchange rate. Tanzania and Malawi joined the group while

Gabon and Cote d’Ivoire moved up. This is shown in Figure 4.20

GDP PER CAPITA AT PPP GDP PER EXCHANGE CAPITA RATE 1 Gabon 1 Sierra Leone 2 Burundi 2 Tanzania 3 Zaire 3 Malawi 4 Sierra Leone 4 Zaire 5 Ivory Coast 5 Burundi

Figure 4.20 Five Poorest African Countries in terms of Income, 2001

In terms of urbanization, measured as the proportion of the total population living

in urban areas, on the average, Africa had about 40.7% of the total population living in

urban areas in 2001. Libya was the highest urbanized country with 87.6% while Rwanda

was the least with 6.2%. Twenty-one (42%) countries had urban population higher than

the average for the continent. (See Figure 4.21)

After the income analysis was made, there was the need to know how the income

was shared among the population and this was measured by Gini Index. This index

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measures the extent to which income distribution deviates from equal distribution. Gini

Index is measured from between 0 and 100. The value of 0 indicates perfect equality and

100, perfect inequality. In Africa, the country with the best income distribution in 2001 was South Africa with 19.5% and the worst was Central African Republic with 61.3%

The average Gini Index for the continent was 39.8 and 16 countries had income distribution better than the average as shown in Table 4.5. Comparing this with income in terms of GDP per Capita at PPP Exchange rate, South Africa was the only high income country with income distribution better than the continental average. All the rest were below the average and the distribution was not better in the relatively lower income countries either.

Table 4.6 Gini Index above Continental Average by Regions of Africa

African Number of Countries above Percentage of Countries Regions Average in the Region

East 7 44 Middle 0 0 North 3 60 South 3 60 West 3 20

(Refer to Appendix A for details on Regional Grouping)

Apart from GDP been used as determinant of development, other variables are used as

well and are usually termed socio-economic indicators. One of such indicators is life

expectancy. Africa as a continent has an average life expectancy of 50.94 years in 2001 with Mauritius having the highest life expectancy of 75.52 years and Zambia the least 111

with 38.43 years. Seventeen countries had life expectancy higher than the average for the continent. All the countries in North Africa with the exception of Tunisia fall in this

category. Nine countries experienced the worst life expectancy and these include

Zambia, Botswana, Malawi, Zimbabwe, Rwanda, Sierra Leone, Burundi, Ethiopia and

Mali. The cause of the low level of life expectancy for the four worst countries can be explained by the prevalence of HIV/AIDS in those countries. These countries have adult

HIV/AIDS prevalence rates ranging from 16.50% to 37.30%. The next set of countries, three in number, is saddled with conflicts. Sierra Leone has a political conflict while

Burundi and Rwanda both have ethnic related conflicts. Ethiopia and Mali on the other hand have problems with famine hence their possible low level of life expectancy. (See

Table 4.6 and Figure 4.22).

Table 4.7 Countries with the Worst Rate of Life Expectancy

ADULT COUNTRY LIFE HIV/AIDS POSSIBLE NAME EXPECTANCY PREVALENCE CAUSE RATE (%) Zambia 38.43 16.50 HIV/AIDS Botswana 38.83 37.30 HIV/AIDS Malawi 39.08 14.20 HIV/AIDS Zimbabwe 39.50 33.70 HIV/AIDS Rwanda 40.43 5.10 Conflict Sierra Leone 40.52 7.00 Conflict Burundi 42.66 6.00 Conflict Ethiopia 43.27 4.40 Famine Mali 43.53 1.90 Famine

Source of HIV/AIDS Data: Population Reference Bureau, 2004

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Degree of Urbanization 0 - 25 N 25 - 50 50 - 75 75 - 100 01000MilesW E Not Included in the Study S

Figure 4.21 Degree of Urbanization by Countries in Africa, 2001

Source of Data: World Urbanization Prospects, 2002

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Life Expectancy (Years) 1 - 43.6 43.6 - 48.97 N 48.97 - 58.02 58.02 - 75.52 Not Included in the Study 0 1000 Miles W E

Figure 4.22 Life Expectancy (2001) in Africa by Countries

Source: World Development Indicators, 2002

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Human Development Index (HDI) is another measure used by the United Nations

Development Program (UNDP) to measure development from the human welfare perspective rather than the traditional economic figures. HDI is designed to reflect the average achievements in three aspects of human development and these are leading a long life, being knowledgeable, and enjoying a decent standard of living. Longevity is measured by life expectancy at birth; knowledge is measured by a combination of the adult literacy rate and the combined gross primary, secondary, and tertiary enrollment ratio; and standard of living is measured by GDP per capita (PPP US$). This index is computed for 174 countries instead of all member countries (191) of the United Nations because only the 174 countries have data sufficient enough for computing HDI. It is measured on a scale from zero to one.

Fortunately HDI has been computed for all countries used in this study. The

highest HDI score for the fifty countries under study is 0.78 and this is for Libya while

the least score is 0.28 for Sierra Leone. A total of twenty-one countries had HDI scores

above the mean of 0.49. All the Southern African countries, defined by the United

Nations (see Appendix), have HDI scores higher than the average. The same applied to the Northern African countries with the exception of Egypt. Out of the five countries with the least HDI score, four were from Western Africa. See Figure 4.23. Five countries with the lowest HDI in 2001 in descending order are Sierra Leone, Niger, Burkina Faso,

Burundi and Mali. Apart from Burundi, which is from Eastern Africa, the rest are from

Western Africa.

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Human Development Index 0.1 - 0.25 0.25 - 0.5 0.5 - 0.75 N 0.75 - 1 Not Included in the Study W E 01000Miles

Figure 4.23 Human Development Indicators for African Countries, 2001 Source of Data: Human Development Report, 2002

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Certain variables such as potable water and good sanitation come into play when we talk about the health of the people. African countries try to provide these facilities for their various populations despite their low level of income. Access to improved sources of water is defined as household connections, public standpipes, boreholes, protected dug wells, protected springs and rainwater collection within one kilometer of the user’s dwelling. In terms of access to improved sources of water, African countries did not fair badly. Mauritius provides potable water for all (100%) of its total population while

Ethiopia, provides the least amount of potable water, serving only about 25% of its population. Just like the Human Development Index, the Northern and Southern regions of Africa provides potable for a larger proportion of the population in their respective

regions. On the average, about 61% of the African population have access to improved

sources of water supply.

In all, 30 countries provide 61% or more of their total population with access to

improved water sources. All the Northern African countries with the exception of Tunisia

and all countries of Southern Africa with the exception of South Africa belong to this group of countries. (See Table 4.7 and Figure 4.24).

Table 4.8 Number of Countries with 61% or more of the Total Population having Access to Improved Sources of Water Supply in Africa by Region

Number of Countries Region % above average Eastern Africa 9 56 Middle Africa 4 44 Northern Africa 4 80 Southern Africa 5 100 117

Western Africa 8 53

Access to Water (% of Population) 1 - 25 25 - 50 50 - 75 75 - 100 Not Included in the Study

01000Miles W E

Figure 4.24 Access to Improved Sources of Water by Countries in Africa, 2001 Source of Data: Human Development Report, 2002

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When it comes to sanitation, Africa as a whole as compared to the average for developing countries did not fair badly. On the average, about 54% of the African population have access to improved sanitation as against 43% for developing countries.

Just as access to improved water sources, Mauritius had the highest coverage of 99% of

the population while Rwanda took over from Ethiopia as the least with only 8%

coverage. Nineteen countries were above the continental average in terms of access of

their population to improved sanitation facilities. Improved sanitation facility is defined

as adequate excreta disposal facilities, such as a connection to a sewer or septic tank

system, a pour-flush latrine, a simple pit latrine or a ventilated improved pit latrine.

Physicians per capita is another measure for health. For this study, this was

measured as physicians per 1,000,000 population. Egypt had the highest physician per

capita ration of 1600 physicians to a million population while Malawi had the lowest of about 28 physicians per million. Only eleven countries were able to exceed the continental average of 226 physicians per one million population. All the Northern

African countries with the exception of Tunisia were above average. In the other regions, two countries each in the Eastern, Middle and Southern African countries were above average but none from Western Africa. It appears emphasis is laid on the preventive aspect of public health than curative in West Africa.

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Access to Sanitation (% of Population) 1 - 25 25 - 50 50 - 75 75 - 100 Not Included in the Study

N 0 1000 Miles W E

Figure 4.25 Access to Improved Sanitation by Countries in Africa, 2001 Source of Data: World Development Report, 2002

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Physicians per Million Population 1 - 100 100 - 300 N 300 - 1000 1000 - 1600 0 1000 Miles W E Not Included in the Study S

Figure 4.26 Physicians per Million Population in Africa by Countries, 2001

Data Source: World Development Report, 2002

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Limitations of the Study

Development is viewed from many perspectives, which include social, economic

and political perspectives but the study is limited to social and economic perspectives of

development. Based on the data analysis techniques used for this study, data on the

political perspective of development could not be included because the data is categorical

in nature.

Limitations exist with the socioeconomic data used for the study. The first

limitation is the data on percentage of total population living in urban areas (degree of

urbanization). The data sources used what each country defines as urban and this can

pose a problem for purposes of comparison. This study however tries to deal with this problem by introducing a new measure for urbanization – urbanization index – which attempts to standardize urbanization for possible comparisons.

Data on economic development include telephones, which is measured as telephones per 1000 population. Ideally, the study should have included data on cell phone subscribers as well but data is not available for all countries in Africa. The same problem exists for Internet usage hence these were not included either.

Development is always associated with ‘bad’ outcomes. Easterly (1999) refers to

‘bad’ as unwanted byproducts of development and these include crime and pollution. The various data sources used for this research do not have adequate data on these variables for Africa hence these were not included in the study.

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Summary

In summary, various quantitative methods are used in the study in order to test the

various hypotheses either directly or indirectly. Techniques used directly in testing the

hypotheses include regression analysis for testing hypotheses 1 through 6 and Paired

Samples T Test for testing hypothesis 7. Regression analysis is also used to generate data for testing hypothesis 7. The other quantitative techniques that are used indirectly include

Independent sample T test for creating the basis for testing hypothesis 6. Factor analysis is employed to group the variables into economic, social and urban variables, for testing hypothesis 2 and 3 as well as to develop an index of urbanization for testing hypothesis 7.

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CHAPTER V

RESULTS

A number of statistical techniques were employed in the analysis. This chapter presents the results of the statistical applications.

Hypothesis Testing

For testing the hypotheses various statistical techniques such as paired sample t

test, independent sample t test, Wilcoxon sign rank test and ANOVA were used but

regression analysis was widely used.

Multiple Regression

So far, the study derived a new measure for urbanization – urbanization index in

addition to the traditional measure of urbanization – degree of urbanization, which is

defined as the proportion of total population living in urban areas. This study establishes

the set of independent variables that predict urbanization in Africa and the most suitable

technique for this analysis is multiple regression. Multiple regression was chosen over

Pearson’s correlation because the latter analysis provides only the extent to which two

variables are related and does not go beyond that. Regression analysis on the other hand,

can provide an equation describing the nature of the relationship between the independent

variable(s) and the dependent variable (Kachigan, 1991 p.160) and at the same time

establish the predictive importance of the independent variables. Statistical literature

specifies multiple regression equation as: 124

y = a + b1x1+ b2x2 + …+ bnxn + e

where:

y = Dependent variable (in this case Degree of Urbanization and Urbanization Index)

a = Constant or the y intercept

b1, b2 …bn = regression coefficients

x1, x2, … xn = Independent Variables (GDP, etc)

e = Error term

(Achen, 1982; Allison, 1999; Berk, 2003; Kachigan, 1991; McGrew & Monroe, 2000).

For the analysis multiple regression is used to determine the statistical

significance of the independent variables on the dependent variable and also to find how the independent variables associate with the dependent variables. The ANOVA Table generated by the analysis is an indicator as to whether or not the model is significant based on the F Statistic. A significant F is an indication that the hypothesis is supported.

The method again helps identify the individual variables that are significant in the model

by means of the significance level of the t. This is an indication that the variables with

significant t predict urbanization. Further, both enter and stepwise methods are used, to

test hypotheses 1 through 4, in order to generate enough data for the standard error of the

estimate for degree of urbanization and urbanization index, for the purpose of comparison

in order to determine the most precisely predicted measure of urbanization by the various

development variables, for Africa as a test for hypothesis 7. Regression analysis is used

in testing all the hypotheses. A significant F statistic (p-value of less than 0.05) is an

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indication that the model is significant and the variable predicting the dependent variable are identified by variables with significant t (p-value less than 0.05).

A correlation coefficient of 0.9 or greater among independent variables for multiple regression is an indication of intercorrelation and could result in problems with collinearity (Belsley, Kuh & Welsch, 1980, Berk, 2003, Cohen & Cohen, 1983, Fox,

1991, Kahane, 2001, Kleinbaum et al, 1998, Lewis-Beck, 1980). Based on this information, a correlation matrix was prepared and inspected. (See Appendix D for correlation matrix). This study used a lower limit (0.75 or greater) rather than the limit suggested by literature (0.9 or greater) in order to take care of any possible collinearity.

As a double check, collinearity diagnostics for both tolerance and value inflation factor

(VIF) were used in the regression analysis in order to detect any possible collinearity. If tolerance is less than .20 or VIF is greater than 5.0 then multicollinearity is a problem

(Belsley, Kuh & Welsch, 1980, Berk, 2003, Cohen & Cohen, 1983, Fox, 1991, Kahane,

2001, Kleinbaum et al, 1998, Lewis-Beck, 1980). To ensure that the problem of

multicollinearity is taken care off, all these standards were checked for the regression

analysis. (See Appendix C).

Urbanization and Socioeconomic Development. Hypothesis 1 was tested on both

degree of urbanization and urbanization index with the socioeconomic variables. As

indicated earlier both ‘enter’ and ‘stepwise’ methods were used in the analysis.

Using ‘enter’ method for Degree of urbanization, the regression model was

significant at 0.009 (see Table 5.1) making it obvious that it is a function of socio-

economic dimensions. In other words, socioeconomic development variables predict

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degree of urbanization. The variables explained 60.5 % of urbanization for the degree of urbanization. (See Table 5.1 for the details)

Table 5.1 Model Summary - Degree of Urbanization using Enter Method with Socioeconomic Variables

Standard Error R R Square F Significance of the Estimate

.778 .605 19.2726 2.638 .009

Using the same model for urbanization index, the model was significant at 0.003 (see

Table 5.2) again an indication of the function of socio – economic dimensions. With urbanization index, the variables explain 64.4% of the variance in the urbanization index, a little higher than they explained for degree of urbanization.

Table 5.2 Model Summary - Urbanization Index using Enter Method with Socioeconomic Variables

Standard Error R R Square F Significance of the Estimate

.803 .644 12.22142 3.117 .003

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Residuals for Urbanization Index

Residuals N -23.221 - -8.635 -8.635 - -0.96 W E -0.96 - 8.201 8.201 - 32.041 S No Data 01000Miles

Figure 5.1 Residuals for Urbanization Index Using the Full Model (Enter Method)

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Estimation for Urbanization Index

W E

Estimation S Over estimated Under Estimated No Data 0 1000 Miles

Figure 5.2 Estimation for Urbanization Index

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Residuals for Degree of Urbanization

N Residuals -32 - -15.47 -15.47 - -1.41 W E -1.41 - 11.09 11.09 - 51.37 S No Data 0 1000 Miles

Figure 5.3 Residuals for Degree of Urbanization Using the Full Model (Enter Method)

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Estimation for Degree of Urbanization

W E

Estimation S Over Estimated 0 1000 Miles Under Estimated No Data

Figure 5.4 Estimation for Degree of Urbanization

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Looking further at the table summary, it appears that the model is a more precise predictor for urbanization using urbanization index than for degree of urbanization. This can be inferred from the Standard Error of the Estimate. Standard Error of the Estimate is a measure of the accuracy of predictions made with a regression line. Degree of urbanization had a higher error margin (19.3) than urbanization index (12.2). This

inference cannot be confirmed at this point until all the standard errors generated are

subjected to statistical scrutiny to determine any possible difference. The model

overestimated the degree of urbanization for 30 countries while 20 were underestimated.

With urbanization index, 26 countries were overestimated and 24 countries

underestimated. Looking at the range of the residuals, degree of urbanization had a wider

range. (See figures 5.2 and 5.4)

Unfortunately, all the variables were not significant in the model using the entire

set of variables. (See Appendix C) This calls for another method that would include only

the significant variables in the regression analysis. The suitable method is the stepwise

method. This method selects the independent variable that is the best predictor of the

dependent variable to be included in the model in stages until no other independent

variable could be added again. The end product is the choice of smaller set of predictor

variables from among a larger set (Kachigan, 1991). This method, according to Menard

(1995) is used either in the exploratory phase of a research or for predictive purposes and

this study sets to explore the variables that predict urbanization. Hence the method fits

well.

Using the stepwise method, only four socio-economic variables were valid in the

model for predicting Urbanization index. The four variables explained 52.2% of the

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variance in the criterion variable and these are hospital beds per 1,000 people, health expenditure per capita, proportion of labor employed in non agricultural sector and population density. (See Table 5.3).

Table 5.3 Model Summary and Coefficient Table for Urbanization Index using Stepwise Method with Socioeconomic Variables

Standard Error R R Square of the Estimate F Significance

.723 .522 10.98401 10.395 .000

Coefficients

Standardized Significa B Coefficients t nce Beta (Constant) 15.442 2.352 .024 Hospital Beds per 1,000 people 2001 17.489 0.525 -3.167 .000 Health Expenditure per Capita -0.309 -0.518 3.587 .000 Proportion of Labor in Non-agric 0.627 .497 3.962 .001 Population Density -0.138 -0.444 -4.384 .003

Degree of urbanization on the other hand, was predicted by two socioeconomic variables

and they together explained 36.5% of the variance in Degree of urbanization as shown in

Tables 5.4. Once again, the error terms of the estimates indicate that the model tends to

predict urbanization index better than it does for degree of urbanization.

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Table 5.4 Model Summary and Coefficient Table for Degree of Urbanization using Stepwise Method with Socioeconomic Variables

Standard Error R R Square of the Estimate F Significance

.604 .365 20.2545 11.478 .000

Coefficients

Standardized Coefficients Significa B Beta t nce (Constant) -11.364 Proportion of Labor in Non-agric .588 .638 4.763 .000 Average annual population Growth Rate 1980- 2001 11.963 .281 2.099 .042

From the variables that were significant in the reduced model, it was observed

that Proportion of labor in non agricultural sector was common to both urbanization

index and degree of urbanization. Variables related to population were common to both

of them but the difference had to do with the specific variables. The population variable

that predicted degree of urbanization was average annual population growth rate.

Urbanization index on the other hand, had population density as the population variable

that predicted urbanization index. The other two variables were connected to health and

these were hospital beds per 1,000 population and health expenditure per capita.

Using both degree of urbanization and urbanization index as the measure for

urbanization, the model was significant at p-values of less than 0.05. Moreover, the

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derivation of socioeconomic development variables predicting urbanization is an indication that socioeconomic variables can predict urbanization. Hence hypothesis 1 is

supported.

Population Density

Average Hospital Beds

Annual Proportion of per 1,000

Population Labor in Non people Growth Agricultural Rate Sector Health Expenditure per capita

Degree of Urbanization Urbanization Index

Figure 5.5 Venn Diagram of Socio-Economic Variables Predicting Urbanization

Urbanization and Economic Development. Just as the socioeconomic variables

were regressed against the urban variables, economic variables were regressed as well,

using both enter and stepwise methods, for testing hypothesis 2. Economic variables were

arrived at as a result of factor analysis. Factor analysis grouped the socioeconomic

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Table 5.5 Variable Grouping based on Factor Analysis

Variable Variable Grouping Landlocked Colonial Ties Year of Independence Gross national Income ($) Gross national Income per Capita ($) PPP Gross National income ($) PPP Gross National Income per Capita ($) Economic Gross Domestic Product per capita growth Rate (%) Telephone mainlines (per 1000 people) Non-agricultural employment (% of employed population) Public Expenditure on Education (% of GDP) Adult literacy rate (% of population ages 15 and above) Combined gross enrolment ratio for primary, secondary and tertiary schools (%) Crude Death Rate ((per 1000 people)) Crude Birth Rate (per 1000 people) Life expectancy at birth (years) Public Expenditure on Health as percentage of GDP Social Access to improved sanitation (% of Total Population) Sustainable access to an improved water source (% of Total Population) Physicians (per 1000 people) Hospital Bed (per 1000 people) Radios (per 1000 people) Television Sets (per 1000 people) Urbanization (% Urban) Urbanization Urbanization Index (Index (%)) Surface Area Sq km. Average annual population growth rate (%) Population Density (people per sq. km) Other Gini Coefficient (Gini Index) Aid per capita ($) Human development index (HDI) (Index) 136

variables into four factors (groups) and these were labeled economic, social, urban and others. The factor analysis grouped variables concerned with economic growth and national wealth together. (See Table 5.5).

Regressing the economic variables on urbanization index using the enter method,

the economic variables explained 28.7% of the variance in urbanization index and the

model was significant at 0.029 at 95 percent probability. Once again, not all the economic

variables were significant in the model using enter; thus calling for stepwise method where only variables that are significant in predicting urbanization are included in the analysis. With the stepwise method, two variables were included in the model and they explained 11.6% of the variance at 95% probability. The variables explaining the variance in urbanization were proportion of labor in non-agricultural sector and telephone mainlines per 1000 population.

Table 5.6 Model Summary for Urbanization Index, using Enter Method, on Economic Variables

Standard Error R R Square F Sig. of the Estimate

.536 .287 17.90667 3.240 .029

When it comes to economic indicators of development, using enter method, degree of

urbanization did not do any better than urbanization index. Though the model was

significant at 0.017, the variance explained by all the economic variables was less than

what they explain for urbanization index. The variance explained was 32.6% as against

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28.7% for urbanization index and just as in urbanization index, not all the variables were significant in the model. By means of the stepwise method, only one variable, proportion of labor employed in non-agricultural sector was significant and it explained 24.3% of the variance. Significant F statistic and the derivation of some economic development variables as predictors of urbanization is an indication that economic development variables can predict urbanization in Africa. Hence hypothesis 2 is supported.

Table 5.7 Model Summary and Coefficient Table for Urbanization Index, using Stepwise Method, on Economic Variables

Standard Error R R Square F Sig. of the Estimate

.340 .116 14.34736 6.290 .016

Coefficients

Standardized Coefficients B Beta t Sig. (Constant) 15.084 2.195 .034 Proportion of Labor in Non-agric .927 .766 4.294 .000 Telephone mainlines (per 1,000 -.288 -.546 -3.061 .004 people)

Table 5.8 Model Summary for Degree of Urbanization, using Enter Method, on Economic Variables

Standard Error R R Square F Sig. of the Estimate .571 .326 23.4515 6.494 .017

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Table 5.9 Model Summary and Coefficient Table for Degree of Urbanization, using Stepwise Method, on Economic Variables

Standard Error R R Square F Sig. of the Estimate

.493 .243 20.0981 15.449 .000

Coefficients

B t Sig. (Constant) 23.140 4.663 .000 Proportion of Labor in Non-agric .500 4.139 .000

Urbanization and Social Development. To test hypothesis 3: urbanization in

Africa can be predicted by the level of quality of life – (social development indicators),

social variables were used to run regression on the criterion variable – urbanization

indicators. These variables (social variables), like economic variables, were identified

by means of factor analysis.

Social variables of development are associated with variables that contribute to

improvement in the quality of life (See Table 5.5). Using enter method to run regression

on urbanization index, the social variables were able to explain about 37.2% of the

variance and the model was significant at 0.029. As usual, all the variables were not

significant in the model hence stepwise method was used to extract the social variables

predicting urbanization.

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Table 5.10 Model Summary for Social Indicators, using Enter Method, on Urbanization Index

Standard Error R R Square F Significance of the Estimate

.610 .372 15.01235 2.520 .029

Using the stepwise method however, hospital beds per 1000 population was the only

variable included in the model and it explained 31.6% of the variance with an error term of 13.14.

Table 5.11 Model Summary and Coefficient Table for Social Indicators, using Stepwise Method, on Urbanization Index

Standard Error R R Square F Significance of the Estimate 13.14619 .562 .316 9.238 .001

Coefficients

B t Significance (Constant) 24.714 3.731 .001 Hospital Beds per 1,000 people 10.595 2.508 .016

Regressing social indicators of development on degree of urbanization using enter

method, the model was not significant in predicting degree of urbanization either. The

model explained 35.9% of the variance in degree of urbanization, which was lower than

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it explained for urbanization index and the F statistic from the ANOVA table, as can be seen from Table 5.12, was .037 making the model significant since it was less than 0.05.

Table 5.12 Model Summary for Social Indicators, using Enter Method, on Degree of Urbanization

Standard Error R R Square of the Estimate F Sig.

.599 .359 22.7072 2.381 .037

Using the stepwise method however, the only social variable included in the model was

Hospital Beds per 1,000 population just as it was with urbanization index. Although the

model explained a higher variance of 29.5% in degree of urbanization than it did for

urbanization index, the Standard Error of the Estimate was higher – 21.9161 as against

13.1462. (See Tables 5.13 and 5.11). Derivation of hospital beds per 1000 population as the social variable predicting urbanization implies that social variables can predict urbanization in Africa; hence hypothesis 3 is also supported.

Table 5.13 Model Summary and Coefficient Table for Social Indicators, using Stepwise Method, on Degree of Urbanization

Standard Error R R Square F Sig. of the Estimate

.543 .295 21.9161 17.127 .000

Coefficients

B t Sig. (Constant) 24.135 4.902 .000 Hospital Beds per 1,000 people 12.342 3.931 .000

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Human Development and Urbanization. Hypothesis 4, the level of human development

can predict the level of urbanization in Africa was tested in the following way. Human

Development index (HDI), both the one computed for the 147 countries by the UNDP

and the one computed for this study, using only countries in Africa, were used to run

regression analysis on the urban variables. For differences to exist there is the need for

the t-value of the Z score to be less than 0.05. The study first tested for the possibility of

differences existing between the two HDIs. The absence of any difference could be

taken to mean that both HDIs are the same and only one could be used to test hypothesis

4. Although the data for the HDIs are continuous (interval data), non-parametric method

of comparing means was used to test for possible differences. This is because the HDI

computed by UNDP had lower HDI scores for African countries than the one computed

specifically for African countries by this study and this could influence the mean

difference. This study asserts that despite the computations, the ranking of the countries

would be the same for both HDIs. As a result, Wilcoxon Sign Rank Test was used to

test for possible differences. When the Wilcoxon Sign Rank Test was conducted the p

value for the Z Score was 0.021, which is less than 0.05 giving an indication that

differences exist in the ranking of the two Human Development Indicators. Hence the

testing of Hypothesis 4 was conducted using both measures for urbanization.

Regressing the HDI computed by UNDP on urbanization index, the model was not significant (0.900) and the variable did not explain any variance (R2=0.00). However,

with UNDP on Degree of urbanization, the model was significant at 0.009 and the

variable explained 13.2% of the variance.

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Using the computed Human Development Index (computed HDI), the analysis gives a better story. Regressing the computed HDI on degree of urbanization, the variable explained 31.8% of the variance in degree of urbanization, as against 13.2% using the

HDI computed by UNDP and the model for the computed HDI was significant at the p value of 0.000.

Table 5.14 Model Summary and Coefficient Table for Human Development Index on Degree of Urbanization

Standard Error R Square F Sig. R of the Estimate

.364 .132 25.3839 7.310 .009

Coefficients

B t Sig. (Constant) 12.833 1.205 .234 Human Development Index 56.452 2.704 .009

Table 5.15 Model Summary and Coefficient Table for Human Development Index on Urbanization Index

Std. Error of R Square F Sig. R the Estimate

.018 .000 19.89002 .016 .900

Coefficients

B t Sig. (Constant) 40.632 2.856 .006 Human Development Index -3.506 -.126 .900

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Table 5.16 Model Summary and Coefficient Table for Computed Human Development Index on Degree of Urbanization

Std. Error of R R Square the Estimate F Sig.

.563 .318 25.1899 22.330 .000

Coefficients

B t Sig. (Constant) 14.535 2.406 .020 Computed Human Development Index 56.645 4.726 .000

Although the model was not significant (.091) for urbanization index using computed

HDI as the predictor variable, it explained 5.8% of the variance. This is an indication that

the HDI computed for Africa predicts degree of urbanization more than the one computed

by UNDP.

Table 5.17 Model Summary and Coefficient Table for Computed Human Development Index on Urbanization Index

R R Square Standard Error of the Estimate F Sig. .242 .058 17.12667 2.978 .091

Coefficients

B t Sig. (Constant) 24.957 2.827 .007 Computed Human Development Index 30.235 1.726 .091

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Testing hypothesis 4; the level of human development can predict the level of urbanization in Africa, yielded positive results for degree of urbanization and negative

results for urbanization index. For degree of urbanization, human development indicators

as a measure for development had significant Fs of less than 0.05 indicating that Human

Development can predict urbanization. Only part of this hypothesis is supported based on

the above findings. If as researcher wants to use human development as an indicator to

study urbanization, it should be used with degree of urbanization. Moreover, HDI

computed for Africa explained more variance (about 2.5 times) in degree of urbanization

than HDI computed by UNDP. Nevertheless, the study found Hypothesis 4 to be

supported.

Urbanization, Socioeconomic Development and Geographical Location. Countries with

access to the sea tend to be more industrialized since they get easy access to the delivery of bulky inputs for industrial activities and also the ability to export their products in bulk. This in the end translates into rapid urbanization since population in the rural areas flock to the industrial areas for employment. In order to test hypothesis 5 which reads various socioeconomic development variables predicting urbanization in Africa can vary with geographical location (in relation to the sea), as an input, the study compared the level of urbanization, using both degree of urbanization (percentage of the total population living in urban areas) and the urbanization index computed by the study. This was undertaken to find out if differences do exist between the two types of location in terms of levels of urbanization.

Since the scale of measurement for the data was interval and we were comparing the mean scores of two groups on the same variable, the suitable statistical

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technique was independent sample t – Test. Tests for differences in degree of urbanization and urbanization index between landlocked countries and countries with access to the sea show that there were significant differences in levels of urbanization, in terms of both degree of urbanization and urbanization index, between landlocked countries and countries with access to the sea. Using both measures for urbanization, countries with access to the sea tend to be more urbanized than landlocked countries (p

value of 0.00). This is an indication that the socioeconomic variables explaining

urbanization in Africa are likely to differ based on geographical location. Hence the need

for hypothesis 5.

Analyzing the data based on geographical location, using regression analysis, the

study found out that different variables explain urbanization based on location. The

socioeconomic variables explain 25% of the variance in degree of urbanization while

they explain 42% of the variance in urbanization index. (See Tables 5.18 and 5.19) for

countries with coastline. The variables predicting urbanization for countries with

coastline include proportion of labor employed in the non agricultural sector for degree of

urbanization and proportion of labor employed in the non agricultural sector and

telephone mainlines per 1000 population for urbanization index. This implies that

economic variables tend to predict urbanization for countries with access to the sea.

Table 5.18 Model Summary for Urbanization Index with Coastline Countries

Standard Error R R Square F Sig. of the Estimate

.647 .419 17.4858 11.538 .000

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Table 5.19 Model Summary for Degree of Urbanization with Coastline countries

Standard Error R R Square F Sig. of the Estimate

.500 .250 20.8636 11.003 .002

For landlocked countries, socioeconomic variables explained 71% of the variance

for urbanization index and 58% for degree of urbanization. (See Tables 5.20 and 5.21).

GDP as an economic variable featured in the prediction of urbanization for landlocked countries using degree of urbanization while social variables related to health – Hospital

Beds per 1,000 population and Health Expenditure per Capita predicted urbanization

index for landlocked countries.

Table 5.20 Model Summary for Urbanization Index with Countries without Coastline

Standard Error R R Square F Sig. of the Estimate

.843 .711 7.5768 14.758 .001

Table 5.21 Model Summary for Degree of Urbanization with Countries without Coastline

Standard Error R R Square F Sig. of the Estimate

.764 .583 9.7947 8.394 .005

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Table 5.22 Variables Predicting Urbanization based on Geographical Location

Measure of Variables Predicting Urbanization Urbanization Non-Landlocked Countries Landlocked Countries Degree of Proportion of labor in 1 1 GDP Urbanization Non-agricultural sector Proportion of labor in Hospital Beds per 1000 1 1 Urbanization Non-agricultural sector population Index Telephone mainlines per Health expenditure per 2 2 1000 population capita

Examining the variables predicting urbanization based on geographical location

(in relation to the sea), the variables predicting urbanization for each location is different.

Two different sets of variables tend to predict urbanization for each measure for

urbanization. Based on this observation, the study concludes that hypothesis 5 has been

supported.

Socioeconomic Development, Urbanization and Colonial Ties. The hypothesis

‘various socioeconomic development variables predicting urbanization in Africa vary in

accordance with the European countries that colonized them’ has been tested with

ANOVA and multiple regression. Earlier studies have indicated that the rate of

urbanization varies with colonialism (King, 1976, Stren, 1972, El-Shakhs & Balau,

1979). This was looked at in terms of colonial attachments the various countries had with

European countries and their various years of independence. The years of independence

were grouped into three – those before the 1960s, those in the 1960s and those after the

1960s. This study tested these assertions using ANOVA. ANOVA was used because

more than two groups were being compared for differences on urbanization variables

(degree of urbanization and Urbanization Index) and the data is continuous.

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Table 5.23 ANOVA Table and Post Hoc Analysis for Degree of Urbanization and Colonial Ties

DEGREE SUM OF OF MEAN SQUARES FREEDOM SQUARE F SIGNIFICANCE Between Groups 7388.057 5 1477.611 4.854 .001 Within Groups 13394.018 44 304.410 Total 20782.075 49

Colonial Colonial Ties Significance Ties France 0.073 Portugal 0.259 Britain Belgium 0.071 Italy-Spain 0.001* Independent 0.168 Britain 0.073 Portugal 0.960 France Belgium 0.007* Italy-Spain 0.015 Independent 0.032* Britain 0.259 France 0.960 Portugal Belgium 0.022* Italy-Spain 0.034 Independent 0.059 Britain 0.071 France 0.007* Belgium Portugal 0.022* Italy-Spain 0.00* Independent 0.904 Britain 0.001* France 0.015 Italy-Spain Portugal 0.034* Belgium 0.00* Independent 0.001* Britain 0.168 France 0.032* Independent Portugal 0.059 Belgium 0.904 Italy-Spain 0.001* * The mean difference is significant at the .05 level

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Table 5.24 ANOVA Table and Post Hoc Analysis for Urbanization Index and Colonial Ties

Degree of Sum of Squares Freedom Mean Square F Sig. Between Groups 6022.023 5 1204.405 2.026 .094 Within Groups 26162.636 44 594.605 Total 32184.659 49

Colonial Ties Colonial Ties Significance France 0.110 Portugal 0.518 Britain Belgium 0.330 Italy-Spain 0.077 Independent 0.210 Britain 0.110 Portugal 0.682 France Belgium 0.071 Italy-Spain 0.344 Independent 0.052 Britain 0.518 France 0.682 Portugal Belgium 0.203 Italy-Spain 0.279 Independent 0.134 Britain 0.330 France 0.071 Belgium Portugal 0.203 Italy-Spain 0.038* Independent 0.717 Britain 0.077 France 0.344 Italy-Spain Portugal 0.279 Belgium 0.038* Independent 0.028* Britain 0.210 France 0.052 Independent Portugal 0.134 Belgium 0.717 Italy-Spain 0.028

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With political affiliations, the regression coefficient and the F were not significant for either degree of urbanization or urbanization index and hence could not include any variable explaining urbanization using either measure. As a result, hypothesis 6 is not supported in any way hence it cannot concluded that the variables predicting urbanization vary with political affiliation.

Predicting Urbanization Index and Degree of Urbanization. The assertion that,

‘Socioeconomic variables can predict urbanization index more precisely than they

predict degree of urbanization’, was tested by comparing the Standard Error of the

Estimates, generated by the regression analysis for urbanization index and degree of

urbanization. Looking through all the model summaries, the Standard Error of the

Estimates for urbanization index was always lower than that for degree of urbanization.

When the Standard Errors of the Estimates for both measures were tested statistically,

using paired sample t test, significant differences were found indicating that the

prediction for urbanization index is a little more precise than the prediction for degree of

urbanization. A significance level of less than .05 is an indication of the existence of

differences and the computed significance level for the test was .000 and this confirms

the assertion made earlier that the mean standard error for urbanization index is less than

that of degree of urbanization as can be seen in Table 5.26. It also means that the

prediction of urbanization by socioeconomic variables is closer to the regression line

than that for degree of urbanization. Hypothesis 7 is therefore supported by the study.

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Table 5.25 Paired Sample Test for Standard Error of the Estimates

t Significance Urbanization Index - Degree of urbanization -8.726 .000

Table 5.26 Paired Samples Statistics for Standard Error of the Estimates

Mean Standard. Deviation Urbanization Index 14.569 3.712 Degree of urbanization 20.893 4.426

Applicability of Modernization Theory. Testing hypotheses 1 through 4 gave

indications that there is a positive relationship between urbanization and development

and elements of modernization come into play. According to modernization theory, industrialization and manufacturing employment are the engine of growth and this

growth is manifested by increased national wealth. The end result of this growth is

urbanization since it tends to draw population from the rural areas to the industrial centers

for employment.

However, economic growth, measured by GDP, on which the classical economist based their argument for urbanization does not seem to have any direct impact in this study. None of the elements of national wealth, GDP, GDP per Capita, GDP Growth Rate or GDP at PPP exchange rate were included in the variables predicting urbanization.

Rather other economic variables, namely proportion of labor employed in non- agricultural sector and telephone mainlines per 1000 population were among the variables predicting urbanization.

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The regression analysis indicates that social variables were more important in the prediction of urbanization in Africa than economic variables. They predicted a larger proportion of urbanization (37.2%) than economic variables (28.7%). (See Tables 5.6 and

5.10). These social variables are related to modernization and are often referred to as modernization facilities. The variables hospital beds per 1000 population and health expenditure per capita are related to the improvements in health. Incidentally, from my personal experience, these facilities are mostly located in the urban areas, luring rural population to the urban areas. This phenomenon is explained by urban bias theory.

Nevertheless, modernization theory is applicable to urbanization in Africa but since social development indicators, in the form of social service facilities, are more important in the prediction of urbanization than economic variables, the study suggests some degree of modification to modernization theory.

Variables Predicting Urbanization

Regression analysis is an important method for gaining knowledge about the effects of each of the independent variables on each of the measures for urbanization under consideration (i.e. urbanization index and degree of urbanization). Various variables were selected for inclusion in the model for each urban indicator, using all the socioeconomic variables and this is shown in Table 5.27. The issue here is to identify their relative importance in predicting urbanization. One procedure for doing this is to compare the slopes of the partial regression coefficient (b coefficient) in each model. According to

Lewis-Beck (1980), this method would be ideal only when the variables are measured in the same unit. Other than that the standardized partial regression coefficient should be used. The independent variables identified as predicting urbanization, both degree of

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urbanization and urbanization index, in the dependent variables were not measured in the same units. For example, Hospital beds are measured in per 1,000 while proportion of labor employed in non-agricultural activities is measured in percent. As a result, the

standardized partial regression coefficient was used in determining the variables of

relative importance in predicting the urban variables.

Urbanization Index

Using all the independent socio-economic variables on urbanization index, a close

examination of the standardized partial regression reveals that each variable impacts

urbanization index differently with values ranging from 0.525 and 0.444 and these values

were either negative or positive. The variables are explained below:

Population density. The standardized partial regression value for this variable is –0.444

signifying that as population density increases urbanization index decreases. This could be as a result of the low level of urbanization because much of the population is in the rural areas, with lower level of population concentration and at the same time lower proportion of the population living in areas designated as urban. This helps to confirm that Africa is the least urbanized continent.

Proportion of Labor Employed in Non-agricultural Sector. This variable had a standardized partial regression value of 0.497, which signifies that an increase in the proportion of labor employed in non-agricultural sector increases the urbanization index.

This is an indication of development because literature has it that for a country to be regarded as developed, agricultural employment must give way to non-agricultural

Table 5.27 Variables Predicting Urbanization

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Variables Predicting Urbanization Grouping Degree of Urbanization Urbanization Index Proportion of Labor employed Hospital beds per 1000 1 1 in non-Agricultural sector population Average Annual Population 2 2 Health Expenditure per capita Socioeconomic Growth rate Proportion of Labor employed 3 in non-Agricultural sector 4 Population Density Proportion of Labor employed Proportion of Labor employed 1 1 in non-Agricultural sector in non-Agricultural sector Economic Telephone Mainlines per 1000 2 population Hospital beds per 1000 Hospital beds per 1000 Social 1 1 population population

employment. Ziegler, Brunn and Williams (2003) hold that as level of urbanization

increases, the population employed in the agricultural sector decreases. The classical

economists’ view of development has to do with economic growth and they argue that there is a positive relationship between economic growth and urbanization. Engel’s law also has it that as incomes rise, the share of expenditures for food products declines

(Ogaki, 1992). It is therefore not surprising that proportion of labor employed in the non- agricultural sector is an important variable in predicting urbanization.

Hospital Beds per 1,000 population. The standardized regression coefficient for

Hospital Beds per 1,000 population was 0.525 and the relationship is positive, indicating that the higher the number of hospital beds per 1,000 population, the higher the level of urbanization. This takes the concept of development away from strictly economic perspective toward a social–economic perspective and again establishes the relationship

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between urbanization and socioeconomic development. As countries develop, they tend

to improve upon health service facilities for the growing population, which tends to find their way especially to the urban areas mostly as a result of pressure on land for agricultural activities.

Health Expenditure per Capita. This is another indicator of the socio–economic aspect of development explaining urbanization. With a standardized regression coefficient of – 0.518 the variable indicates that as the health expenditure per capita increases, level of urbanization decreases. This could be attributed to the “bright light” theory which asserts that rural–urban migration occurs because people migrate to the urban areas to enjoy certain facilities including health related facilities in the urban centers. The conclusion here could be that increases in per capita expenditure on health could mean the provision of basic health facilities in the rural areas and this in turn reduces the rate of migration from the rural areas to the urban centers.

When the variables grouped under social indicators of development were used to run regression on urbanization index, the only variable included in the model was hospital beds per 1,000 population and the standardized regression coefficient was 0.493.

This was a positive regression indicating that the regression of the social variables on

urbanization index is not very different from the regression for the model using all the

socio-economic variables. The only difference here was the variable health expenditure

per capita was not included in the model.

Using the economic variables to run the regression on urbanization index, two

variables were included in the model and these were the proportion of labor employed in

non-agricultural sector and telephone mainlines per 1000 population. The proportion of

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labor employed in non-agricultural sector, just as in the model using all the socioeconomic variables, regressed positively on urbanization index. The standardized regression coefficient however was higher (0.766) than it was in the model using all the socio-economic variables (0.518).

Telephone mainlines per 1000 population. This is an economic variable

explaining variances in urbanization index. This variable regressed negatively, with a

standardized regression coefficient value of -0.546, indicating that as telephone mainlines

per 1000 population increases, the level of urbanization decreases. With the current

globalization of all sectors of the economy, communication plays a very vital role. The

pull theory of migration might also be at work here, where the presence of telephone

facilities among other things in the urban areas might be a cause for almost all non-

agricultural sector economic activities being located in the urban centers. The end result

is migration from the rural areas to the urban areas for employment. The negative

regression could be a sign that as telephone mainlines per 1000 population increase, these

increases might find their way to the rural areas where other economic activities could be

located to offer employment to the rural residents and end up reducing rural-urban

migration.

Degree of Urbanization

Using all the socio-economic variables to run regression on degree of

urbanization, the standardized regression coefficient reveals values ranging from 0.281 to

0.638. However, unlike urbanization index where four variables were included in the model, only two variables were included in the model for degree of urbanization and the

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standardized regression coefficient were both positive. The variables included in the model were the proportion of labor employed in the non-agricultural sector and average annual population growth rate. Compared to urbanization index, the standardized regression coefficient for the proportion of labor employed in the non-agricultural sector was higher (0.638 for degree of urbanization as compared to 0.518 for urbanization index). The explanation however remains the same for both degree of urbanization and urbanization index.

Average Annual Population Growth Rate. As indicated earlier, this variable regressed positively on degree of urbanization with a standardized regression coefficient value of 0.281. This is an indication that as the average annual population growth rate

increases, degree of urbanization increases. This situation could be explained by push

factors. Research has it that population growth rates are higher in the rural areas and since

agriculture is the main economic activity in the rural areas, the increased population tends

to put pressure on the land available for farming. Hence the excess population finds its

way to the urban areas to look for formal employment (Dutt, 2001; Firebaugh, 1979)

Using the social variables, the only significant variable for inclusion in the model

was Hospital beds per 1,000 population and just as in urbanization index, the

standardized regression coefficient was positive. The only difference was that the value

of the standardized regression coefficient was higher in the model for degree of

urbanization (0.493) than in the model for urbanization index (0.340). The explanation

however remains the same.

While two economic variables were included in the model explaining the variance

in urbanization index, only one economic variable was significant in explaining degree of

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urbanization. The proportion of labor employed in the non-agricultural sector was the variable common in the model for both degree of urbanization and urbanization index.

(See Venn diagram in Figure 5.5). The only difference here however has got to do with the standardized regression coefficients. The standardized regression coefficient for the proportion of labor employed in the non-agricultural sector was lower (0.543) in the model explaining the variance in degree of urbanization than it did for urbanization index

(0.766). The explanation however remains the same.

Table 5.28 Summary of Results

Variables predicting/Proportion Explained Hypotheses Result Degree of Urbanization Urbanization Index Hypothesis 1 Proportion of labor Supported for Hospital beds per employed in non- Degree of 1000 population agricultural sector urbanization Average annual Health expenditure population growth per capita rate Supported for

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Proportion of labor employed in non- agricultural sector Proportion 36.5 52.2% explained Proportion of labor Proportion of labor Supported for employed in non- employed in non- Degree of agricultural sector agricultural sector Urbanization Hypothesis 2 Telephone mainlines per 1000 Supported for population Urbanization Index Proportion 24.3% 11.6% explained Supported for Hospital beds per Hospital beds per Hypothesis 3 Degree of 1000 population 1000 population Urbanization Index Proportion Supported for 29.5% 31.6% explained Urbanization Index Supported for Human Human Hypothesis 4 Degree of Development Index Development Index Urbanization Proportion Not Supported for 13.2% 0.00% explained Urbanization Index Human Human Supported for Hypothesis 4 Development Index Development Index Degree of (Computed) (Computed) Urbanization 5.8% Not Supported for Proportion Urbanization Index 31.8% explained

Table 5.28. (Continued)

Hypothesis 5 Proportion of labor Proportion of labor Supported for in Non-agricultural in Non-agricultural Degree of Countries with sector sector Urbanization Coastline Telephone mainlines per 1000 population Proportion Supported for explained 25.0% 41.9% Urbanization Index

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Hypothesis 5 Supported for Hospital Beds per GDP Degree of 1000 population Landlocked Urbanization Countries Health expenditure per capita Supported for Proportion Urbanization Index 58.3 71.1% explained Hypothesis 6 No variable No variable Not Supported for included included either Degree of Urbanization or Urbanization Index Hypothesis 7 Supported

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CHAPTER VI

CONCLUSION

Summary of Findings

Africa is fast urbanizing and the rate of urbanization will continue to increase.

According to projections by the United Nations, by 2030, the degree of urbanization for

Africa will be about 53.5%, (United Nations, 2004. p 161) which is less that the percentage required (about 80% according to Dutt, 2001) for the rate of urbanization to start slowing down. Since Africa is already beset with problems connected to urbanization, there is the need for measures to be taken in order to adequately contain the increasing absolute size and the rate of urbanization in Africa.

When landlocked countries and countries with access to the sea were compared, the analysis indicats that the degree of urbanization differs with the location of the country in relation to the sea. Countries with access to the sea tend to be more urbanized than those without access. According to the analysis, in terms of degree of urbanization, countries with access to the coast have an average of about 45% of their total population living in areas defined as urban while countries that are landlocked have an average of about 27% of their total population living in areas defined as urban. Defining urbanization from the perspective of urbanization index, which was computed for this study, the findings are not too different. Here again, countries with access to the sea tend

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to have higher rates of urbanization (an average of about 44%) than countries that are landlocked (an average of about 27%). These findings provided an input for Hypothesis 5 which indicates that the variables predicting urbanization in Africa could differ with the geographical location of countries in relation to the sea.

The degree of urbanization again differs with colonial ties. On the average, the countries colonized by Italy and Spain have the highest degree of urbanization, an average of 73% while those colonized by Belgium are the least urbanized, with an average of 15%. Unfortunately all the countries colonized by Belgium namely Rwanda,

Burundi and Democratic Republic of Congo (Zaire) are war torn and this could have influenced their low level of degree of urbanization. Degree of urbanization however does not differ with the period of political independence. With urbanization index, the trend is similar to that of degree of urbanization with Italy and Spain again having the highest urbanization index of 61% and Belgium again the least with 18%. Again, this analysis serves as an input for hypothesis 6 which indicates that variables predicting urbanization could differ with political affiliations based on colonial ties.

Regression analysis however unearthed the variables that tend to predict urbanization, in terms of degree of urbanization and urbanization index, and ends up answering all the research questions posed by the study. The variable common in predicting both methods of measuring urbanization is proportion of labor employed in the non-agricultural sector, as shown in the Venn diagram in Figure 5.5. However, in addition to this common variable for both measures for urbanization, the measure, degree of urbanization has one other variable that tends to predict urbanization in Africa and this was average annual population growth rate. Urbanization index as a measure for

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urbanization on the other hand has three more variables predicting urbanization and these

are population density, hospital beds per 1,000 population and health expenditure per

capita. When the variables were broken down into social and economic factors, different

variables come into play.

Using economic variables, the variables that emerge as those predicting degree of

urbanization include proportion of labor employed in non-agricultural sector, while the

same variable in addition to telephone mainlines per 1000 population tend to predict

urbanization index. When only social variables were used, health related variables are

important in predicting urbanization. Both degree of urbanization and urbanization index

had hospital beds per thousand population as common variable.

This study also confirmed the study by Njoh (2003) that there is a positive

relationship between urbanization and development, defined as human development.

Using both measures for urbanization on one hand and the HDI computed by UNDP and

the one computed specifically for the study, there was a positive relationship between all

the combinations, except the relationship between the HDI computed by UNDP and

Urbanization index which had a negative relationship. The study, therefore, concludes

that: 1) HDI can be used to study degree of urbanization but not urbanization index and

2) in cases where the study involves countries of the same continent, HDI should be

computed for that continent and the data used for the analysis. This is because the HDI

computed using only African countries explains more variance in degree of urbanization

more than the one computed by UNDP.

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Results of Hypotheses Testing

The study tested a total of seven hypotheses out of which six are supported. The summary is shown below:

Hypothesis 1. As can be inferred from the dissertation Hypothesis 1 which reads

“Urbanization in Africa can be predicted by socio-economic development variables” can be supported. The socioeconomic variables explain 52% of the variance in urbanization index and 36.5% of the variance in Degree of urbanization (See pages 132 and 133).

When the socioeconomic development variables were broken down into economic variables, social variables and Human Development Index separately, these are supported as well.

Hypothesis 2. This hypothesis which is concerned with economic variables and reads: “Urbanization in Africa can be predicted by the level of growth (economic development)” is supported by the study. In this case, the economic variables explained

29% of the variance in urbanization index and 33% for degree of urbanization (see pages

135 and 136).

Hypothesis 3. The hypothesis “Urbanization in Africa can be predicted by the

level of quality of life – (social development indicators)” and dealing with Social

variables is also supported and explains larger proportions of the variance in

urbanization than do economic variables. They explain 37% for urbanization index and

35%for degree of urbanization (see pages 137 and 138).

Hypothesis 4. The hypothesis “The level of human development can predict the

level of urbanization in Africa” dealing with Human Development Index (HDI) – is also

supported. As indicated, HDI has always been computed by UNDP but as could be

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inferred from the dissertation, the study computed HDI specifically for Africa. The HDI computed for Africa explains more variances in degree of urbanization than the one computed by UNDP; 32% of the variance in degree of urbanization as against 13% for

HDI computed by UNDP (See pages 140 – 142). The acceptance of hypotheses 1 to 4 is an indication that there is a positive relationship between urbanization and socioeconomic variables and the variables predict urbanization.

Hypothesis 5. ‘Various socioeconomic development variables predicting

urbanization in Africa can vary with geographical location (in relation to the sea)’ as a

hypothesis is supported. This is an indication that the socioeconomic variables

predicting urbanization differ with geographical location. The study found that mainly

economic variables tend to predict urbanization in countries with a coastline while

social variables tend to predict urbanization in landlocked countries. (See pages 143 –

145).

Hypothesis 6. The hypothesis ‘various socioeconomic development variables predicting urbanization in Africa can vary in accordance with the European countries that colonized them’ could not be supported by the study. This could mean that there are no differences in the socioeconomic variables predicting urbanization based on political affiliations.

Hypothesis 7. ‘Socioeconomic variables can predict urbanization index more accurately than they would predict degree of urbanization’ as a hypothesis was supported by the study. The study is able to prove that socioeconomic variables tend to predict urbanization index more precisely than they predict degree of urbanization hence supporting hypothesis 7. From the data derived from the regression analysis, there was a

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statistically significant difference between the standard error of the estimates for urbanization index and that for degree of urbanization. This is an indication that the prediction for urbanization index seems to be closer to the regression line than that for degree of urbanization.

Measure for Urbanization and Human Development Index

Though the results were too close to determine the differences easily, the question “Do the various development variables predicting urbanization in Africa predict degree of urbanization more accurately than urbanization index?” was answered after statistically testing the output of the regression analysis. The study used both degree of urbanization

and urbanization index as measures for urbanization after which the measure most

precisely predicted by the various development variables was selected as the best

measure. The criterion used to select the best possible was the Standard Error of the

Estimate from regression analysis. Literature has it that the closer the standard error of

the estimate is to zero, the better the prediction. The results from the regression analysis were used for the selection.

With all the regression analysis using degree of urbanization and urbanization index as the dependent variables, the Standard Errors of the Estimate for urbanization index were closer to zero than they were for degree of urbanization. When the Standard

Errors of the Estimates were statistically compared a significant difference was found between the results for degree of urbanization and urbanization Index. The Standard

Errors of the Estimates for urbanization index were found to be less than that for degree of urbanization. Thus, urbanization index is the best measure for urbanization. Moreover,

as could be inferred from the computation of urbanization index, the method used scaling

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to arrive at the index. Thus this measure for urbanization took care of the proportion of the total population living in areas defined as urban, which has no specific standard threshold, and the concentration of the total population in the various class sizes of settlement. As a result, the arbitrary nature of defining urban was taken care of by making urbanization index a more reliable measure for urbanization. Moreover, the various variables under consideration predicted higher proportions of variance in urbanization

index than in degree of urbanization. Hence urbanization index as the best possible

measure of urbanization.

In the case of Human Development Index (HDI), the study found that it was better to compute HDI specifically for the African countries. By this method, the information on the other 124 countries of the world was eliminated. Using the regression analysis output criterion for selection, the Standard Error of the Estimate for HDI computed by UNDP was slightly higher (19.8) than that for the HDI computed for the

study (17.1). Moreover, the variance explained by the various variables for HDI

computed was higher than that for the HDI computed by the UNDP justifying the

computation of HDI specifically for the study. The HDI computed for Africa tends to

predict urbanization in Africa more precisely than the one computed by the UNDP for

174 countries. Moreover, the HDI computed for the study tend to explain more variance

in degree of urbanization (31.8%) than the one computed by UNDP (13.2%).

In conclusion, this study found that there is a positive relationship between socio-

economic development and urbanization and further identified the socioeconomic

variables that tend to influence urbanization in Africa. Surprisingly, GDP, which used to

be the prominent measure for development did not feature in the prediction of

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urbanization in Africa. Previous research on urbanization in relation to development come to the conclusion that GDP and other economic development variables tend to predict urbanization. This is not the case with this study on Africa. Social indicators of development in the form of service facilities tend to predict urbanization more than economic development variables in this context. Based on this, the study proposed an amendment to modernization theory of development. It proposes that, the modified theory to be neo-modernization theory.

It further concludes that socioeconomic variables tend to predict urbanization index (as a measure for urbanization) more precisely than degree of urbanization. Hence the study proposed the adoption of urbanization index as a measure for urbanization.

Finally, there is the need to compute HDI for countries of interest (in this case, Africa) in a study rather than using the HDI as computed by UNDP.

Implications of Findings for Urban Studies and Policy Findings of the study indicated that there is the need for both researchers and

practitioners to expand their views for measuring urbanization and the necessary policies

to solve urbanization problems. In measuring urbanization, the study found urbanization

index to be the best measure since it takes many factors into consideration. When it

comes to policies and programs, there is the need for multi–sectoral, multidimensional

and regional policies and programs in dealing with urbanization problems.

In this study for instance, analysis of the research data indicated that certain

variables predict urbanization in Africa and for problems associated with urbanization to

be ameliorated, there is the need to pay attention to the issues that arise from the data

analysis. Efforts by donor agencies such as the World Bank to address the urbanization

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problems in Africa could be equated with ‘spot improvement’ programs, where specific problems are identified for solution. From my personal experience, urban improvement programs are more of an end than means to an end. One problem is picked up for solution without consideration for the others. Hence the projects do not seem to address what this study has identified as contributors to urbanization. For instance, Urban IV projects financed by the World Bank, identified sanitation as a problem facing all the urban areas in Ghana. Hence logistics, in the form of waste removing equipment, were provided to take care of that problem.

To ameliorate effectively urbanization problems, a planning approach, different from the approach currently in use is suggested by this study. There is the need for regional approach and at the same time a comprehensive approach to planning needs to be adopted in dealing with urbanization problems. This means that urbanization should

be geared towards distribution over space instead of the continuous concentration in a

few urbanized centers. Projects geared towards solving urbanization problems and funded

mainly by the donor agencies, were focused in limited areas – urban centers like Accra in

Ghana, Abidjan in La Cote d’Ivoire, Blantyre in Malawi and Antananarivo in

Madagascar among others (World Bank, 2004). Though this study does not deny the fact

that this approach is an effort in the right direction, the approach is inadequate. Efforts to

solve problems in the existing urban areas without much effort to direct resources to

improve the other areas that serve as the possible source of supply of urban population is

not sufficient.

The study finds that alongside the efforts to solve urban problems such as efforts

to address potable water supply, sanitation and transportation problems in the identified

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urban areas, there is the need to direct the urban growth to other areas and this is where the regional nature of planning comes in. The urban population is concentrated in fewer settlements making the problems overwhelming. Hence the concentration of the efforts of the donor agencies in these areas. While efforts are being made to solve these problems, more and more population tends to migrate to these few urban centers making the problems unmanageable. Grove and Huszar (1964) in a study identified hierarchies of settlements namely higher, medium and lower hierarchies. The medium and lower hierarchies could be explored as part of the solution to the urban problems. Various countries can plan in a regional fashion such that while efforts are made to take care of

the inadequacies of facilities in the existing urban areas, attempts could be made to

upgrade places of lower hierarchy to become attractive to would–be migrants and

investors so as to reduce the pressure on the already high-populated settlements. This

approach is termed the growth pole concept.

Policies could be put in place by the various governments to develop the lower

ranking settlements with the variables identified by the study (see page 171). On the

whole, issues related to health were found to be the variables predicting urbanization and

these include health expenditure per capita and hospital beds per 1,000 population. The

beta coefficient of the regression indicated a negative relationship between urbanization

and health expenditure per capita. This might mean that increase in health expenditure

when extended to the rural areas could cause the rural dwellers to get the necessary

satisfaction for their health needs hence lowering their motive to move to the urban areas.

This might mean expenditure on preventive health in the form of potable water supply

and improvement in sanitation in other areas.

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Hospital beds per 1,000 population had a positive beta coefficient for the regression. This implies that in order to develop the settlements at the lower hierarchy into urban centers and reduce the population pressure on the few large urban centers, there is the need to provide and upgrade the health facilities in the form of increases in the number of hospital beds, to go along with the other necessary resources, both human and equipment. This is where the comprehensive nature of the policy and planning comes in to play because policy to tackle issues like this cut across sectors and the policies ought to be implemented simultaneously.

Proportion of the labor employed in the non-agricultural sector and telephone mainlines per 1000 population are the issues identified as economic variables that predict urbanization. Thus they could be used for solving urbanization problems from the economic perspective. The study found a positive relationship between urbanization and proportion of labor employed in non-agricultural sector. This affirms the notion of defining urbanization from the political economy point of view, which indicates that urbanization has to go along with higher proportion of the population to be employed in the non-agricultural sector. On the other hand, a negative relationship exist between telephone mainlines and urbanization. This is an indication that with improved communication, production activities do not necessarily have to be located in the existing urban areas and it is also a sign that this could be coupled with the establishment of non- agricultural production activities in the rural areas to reduce population pressure on the urban areas.

When it comes to theory, the results of the study seem to confirm modernization theory as the theory which explains only a part of urbanization levels in Africa. Before

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the analysis of data, the study anticipated GDP to be the variable that would feature in the prediction of urbanization in Africa as it did in previous research of this kind.

Surprisingly this is not the case with Africa. In Africa, it is rather social variables that featured in predicting urbanization. The important economic variables include proportion of labor employed in non-agricultural sector. This might be taken to mean that industrial

development might have a role to play as was purported by modernization school but

research has it that most of the labor employed in the non-agricultural sector in the developing world is found in the informal sector (Dutt, 2001). The informal sector includes household industry which is labor intensive. This is mostly urban based. Though the study did not include data on the informal sector, my personal experience in a developing country in Africa makes me believe that a large number of non-agricultural employment is in the informal sector. Though modernization theory seems to be applicable in explaining urbanization in Africa, a combination of modernization and urban bias theory seem to be more applicable. Thus the modernization theory needs a modification with compliments from urban bias theory. A change to the traditional modernization theory is therefore proposed and this is referred to as neo-modernization theory, where emphasis is shifted from the strictly economic explanation for urbanization and development relationship to socioeconomic explanations.

In summary, the study concludes that urbanization problems in Africa can be addressed by looking comprehensively at the socioeconomic development variables that predict urbanization. The improvement of facilities, related to the variables identified as predictors of urbanization, in the rural areas would in the long run entice the establishment of non-agricultural economic activities in the rural areas, where redundant

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farm labor exists and finds its way to the urban areas. The rural areas could be improved by means of the expansion of health facilities as well as the extension and improvement of communication facilities in this case telephones since this facility was found to predict

urbanization in Africa. This could be coupled with policies for the extension and

improvement of transportation facilities to the rural areas.

The study found urbanization index to be the best measure for urbanization and

HDI computed specifically for Africa or the continent concerned to be a better measure

than the one computed by the UNDP. This is because urbanization index used standard

for determining urbanization while degree of urbanization has no specific standards for

determining urbanization. Moreover, it is statistically supported by this study that the

variables predicting urbanization tend to be more precise in predicting urbanization index than they are for degree of urbanization.

Based on the finding that the urbanization index is the best measure of urbanization for Africa, the study asserts that the variables predicting urbanization for

Africa are those that predict urbanization index. The study therefore concludes that the socioeconomic variables predicting urbanization in Africa include:

• Proportion of Labor employed in non-Agricultural sector

• Hospital beds per 1000 population

• Health Expenditure per capita

• Telephone Mainlines per 1000 population

Future Research Future research should employ the urbanization index approach to defining

urbanization in other regions of the world especially in developing countries to see if it 174

could be applicable to other areas and, thus become a new measure for urbanization. In addition, similar data could be used to answer the question “What socio-economic variables tend to predict urbanization” in Asia or other places of interest. Another area that might be of interest for exploration to find out whether the shifting of capitals from one urban center to the other, as happened in Nigeria and Cote d’Ivoire, do have any influence on the distribution of urban population in Africa. Also, based on availability of data, the other two theories; urban bias theory and dependency theory need to be tested by future researchers in order to predict urbanization.

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APPENDICES

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APPENDIX A. REGIONS OF AFRICA

Appendix A1. Table Showing Regions of Africa

Eastern Middle Africa Northern Southern Western Africa Africa Africa Africa Burundi Angola Algeria Botswana Benin Comoros Cameroon Egypt Lesotho Burkina Faso Djibouti Central African Libya Namibia Cape Verde Eritrea Republic Morocco South Africa Cote d’Ivoire Ethiopia Chad Sudan Swaziland Gambia Kenya Congo Tunisia Ghana Madagascar Democratic Western Sahara Guinea Malawi Republic of Liberia Mauritius Congo Mali Mozambique Equatorial Mauritania Rwanda Guinea Niger Somalia Gabon Nigeria Uganda Sao Tome and Senegal Tanzania Principe Sierra Leone Zambia Togo Zimbabwe

Source of Data: United Nations, 2002

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Region Eastern Africa Middle Africa N Northern Africa 0 1000 Miles W E Southern Africa Western Africa S

Appendix A2. Map Showing Regions of Africa.

Source of Data: United Nations, 2002

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APPENDIX B. FACTOR ANALYSIS - ROTATED COMPONENT MATRIX

Component 1 2 3 4 Rotated Component Matrix(a) Social Economic Urbanization Other Crude Birth Rate per 1000 people -0.7846 -0.4133 -0.0428 -0.1317 Crude Death Rate per 1000 people -0.8243 -0.1941 -0.2508 -0.1645 Hospital Bed per 1000 people 1990 - 2001 0.4868 0.2287 0.4451 -0.1898 Life expectancy at birth (years) 2001 0.8126 0.2802 0.1795 0.0929 Adult literacy rate 0.7814 -0.0595 0.0589 0.1128 Physicians per Million people 1990 - 2001 0.6871 0.4072 0.2671 0.1133 Radios per 1000 people 0.5301 -0.0548 -0.0199 0.0027 Television sets per 1000 people 0.7337 0.2178 0.0770 -0.1317 Access to improved sanitation facilities % of population 0.3929 0.2872 0.2770 0.0949 Access to Improved Water Source % of population 0.4362 0.4191 0.2775 0.0384 Combined gross enrolment ratio for primary, secondary and tertiary schools 0.8437 0.1391 -0.0473 0.1498 Gross national Income $ billion -0.0048 0.5566 0.2808 0.1690 Gross national Income Per Capita $ 0.4599 0.5262 0.0710 -0.0314 GDP per capita annual growth rate (%) 1990 - 2001 0.0342 0.6634 -0.2147 0.0682 GDP per capita PPP 0.1496 0.6057 -0.3394 -0.0246 Telephone mainlines (per 1,000 people) 2001 0.1638 0.8283 0.3195 0.0230 Proportion of Labor in Non-agric 1990 0.2942 0.6980 0.4803 0.0361 Human development index (HDI) value 2001 0.2633 0.6197 0.4289 0.1777 Percentage Urban 2001 0.3518 -0.0220 0.7589 -0.0097 Urbanization Index (Percentage) -0.0079 -0.2516 0.8223 -0.0723 Aid per capita $ -0.2835 0.0807 -0.2321 -0.3152 Average annual population Growth Rate 1990 - 2001 -0.0659 0.0014 0.0313 -0.7180 Population density (people per sq km) 0.1985 -0.2388 -0.2515 0.6001 Gini index 0.2168 -0.0205 -0.2504 -0.6188 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

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APPENDIX C. REGRESSION COEFFICIENTS AND COLLINEARITY TEST

Appendix C1. Regression Coefficients with Collinearity Test for Degree of Urbanization

Collinearity Standardized Statistics B Coefficients t Sig. Beta Tolerance VIF

(Constant) 30.6533 0.2324 0.8187 GDP per capita (US$) -0.0025 -0.1483 -0.5370 0.5975 0.2845 4.9197

GDP per capita annual growth rate (%) 1.4776 0.1144 0.5731 0.5733 0.3530 2.8329 Gross national Income ($ billion) 0.3569 0.1633 0.7588 0.4573 0.3040 3.2892 Gross national Income Per Capita ($) 0.0088 0.3532 1.0693 0.2983 0.3290 3.7508 Aid per capita ($) 0.0681 0.0612 0.3765 0.7107 0.5330 1.8761 Surface Area (sq km) -0.0018 -0.0507 -0.2182 0.8296 0.2607 3.8353 Average annual population Growth Rate (%) 7.7591 0.1717 0.7472 0.4641 0.2665 3.7526 Population density (people per sq km) -0.0835 -0.3939 -1.8815 0.0753 0.3211 3.1142 Crude Birth Rate (per 1000 people) 0.3034 0.1080 0.2477 0.8071 0.2740 4.5222 Crude Death Rate (per 1000 people) -2.3541 -0.5101 -0.6151 0.5458 0.3205 4.8539 Gini index (Index) 0.4618 0.1788 1.0164 0.3222 0.4548 2.1987

Health Expenditure per capita ($) -0.3228 -0.5725 -1.7366 0.0986 0.2295 3.7209 Hospital Bed (per 1000 people) 6.1821 0.2559 0.9626 0.3479 0.2992 4.0199 Life expectancy at birth (years) -0.4441 -0.1999 -0.2502 0.8051 0.4221 4.3368 Adult literacy rate 0.4830 0.4644 2.1171 0.0477 0.2925 3.4192 Combined gross enrolment ratio for primary, secondary and tertiary schools -0.3438 -0.2929 -1.4359 0.1673 0.3383 2.9562 GDP per capita at PPP ($) -0.0007 -0.1693 -0.8862 0.3866 0.3855 2.5939 Physicians per Million people -0.0090 -0.1140 -0.2343 0.8173 0.2595 3.8133 Radios per 1000 people -0.0098 -0.0688 -0.3687 0.7164 0.4042 2.4738

Telephone mainlines (per 1,000 people) 0.1291 0.3112 1.0205 0.3203 0.2514 4.6068 Television sets per 1000 people 0.0626 0.2303 0.9735 0.3425 0.2516 3.9749 Access to improved sanitation facilities (% of population) -0.1413 -0.1537 -0.8082 0.4290 0.3893 2.5690 Access to Improved Water source (% of population) -0.0567 -0.0427 -0.2000 0.8436 0.3081 3.2453 Proportion of Labor in Non-agric (% of employed population) 0.4663 0.5096 1.9366 0.0678 0.2033 4.9188

Dependent Variable: Degree of Urbanization

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Appendix C2. Regression Coefficient with Collinearity Test for Urbanization Index

Collinearity Standardized Statistics B Coefficients t Sig. Beta Tolerance VIF

(Constant) -116.3214 -0.6762 0.5071 GDP per capita US$ 2001 -0.0020 -0.0984 -0.3407 0.7370 0.2845 4.4197

GDP per capita annual growth rate (%) -2.0734 -0.1288 -0.6165 0.5449 0.3530 2.8329 Gross national Income $ billion 0.4249 0.1559 0.6926 0.4969 0.3040 3.2892 Gross national Income Per Capita $ 0.0066 0.2132 0.6172 0.5444 0.3290 3.7508 Aid per capita $ -0.0274 -0.0198 -0.1162 0.9087 0.5330 1.8761 Surface Area (sq km) -0.0127 -0.2823 -1.1615 0.2598 0.2607 3.8353 Average annual population Growth Rate (%) 7.0922 0.1259 0.5236 0.6066 0.2665 3.7526 Population density (people per sq km) -0.1580 -0.5981 -2.7310 0.0133 0.3211 3.1142 Crude Birth Rate (per 1000 people) -0.1860 -0.0531 -0.1164 0.9086 0.2740 3.5222 Crude Death Rate (per 1000 people) 3.9494 0.6863 0.7912 0.4386 0.3205 4.8539 Gini index (Index) -0.7785 -0.2418 -1.3138 0.2046 0.4548 2.1987 Health Expenditure per capita ($) -0.4241 -0.6031 -1.7490 0.0964 0.2295 3.7209 Hospital Bed (per 1000 people) 13.7987 0.4580 1.6471 0.1160 0.2992 4.0199 Life expectancy at birth (years) 2.3238 0.8387 1.0037 0.3281 0.4221 4.3368 Adult literacy rate -0.0397 -0.0306 -0.1335 0.8952 0.2925 3.4192 Combined gross enrolment ratio for primary, secondary and tertiary schools -0.3532 -0.2413 -1.1308 0.2722 0.3383 2.9562 GDP per capita at PPP ($) 0.0004 0.0788 0.3943 0.6978 0.3855 2.5939 Physicians per Million people -0.0479 -0.4889 -0.9607 0.3488 0.2595 3.8133 Radios per 1000 people -0.0490 -0.2751 -1.4091 0.1750 0.4042 2.4738 Telephone mainlines (per 1,000 people) 0.0457 0.0883 0.2767 0.7850 0.2514 4.6068 Television sets (per 1000 people) 0.1081 0.3189 1.2888 0.2129 0.2516 3.9749 Access to improved sanitation facilities (% of population) 0.0488 0.0426 0.2140 0.8328 0.3893 2.5690 Access to Improved Water source (% of population) 0.1875 0.1133 0.5069 0.6180 0.3081 3.2453 Proportion of Labor in Non-agric (% of employed Population) 0.6479 0.5678 2.0629 0.0531 0.2033 4.9188

Dependent Variable: Urbanization Index

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APPENDIX D. CORRELATION COEFFICIENT TABLE

GDP per Gross capita national Average GDP annual Gross Income annual Population per growth national Per Aid per Surface population density capita rate Income Capita capita Area Growth (people ($) (%) ($) ($) ($) (sq km) Rate (%) per sq km) GDP per capita annual growth rate (%) 0.1705 Gross national Income ($) 0.0867 -0.0755 Gross national Income Per Capita ($) 0.2739 0.4556 0.2126 Aid per capita ($) -0.1009 0.1092 -0.3604 -0.1592

Surface Area (sq km) 0.0868 -0.1742 0.4224 0.0044 -0.2790 Average annual population Growth Rate (%) -0.1147 -0.4754 -0.1454 -0.3031 0.0294 0.1047 Population density (people per sq km) 0.1597 0.1682 -0.0559 0.2105 -0.0760 -0.3754 -0.4034 Crude Birth Rate (per 1000 people) -0.3258 -0.3465 -0.4445 -0.6285 0.1625 0.0258 0.3559 -0.2570 Crude Death Rate (per 1000 people) -0.4593 -0.0899 -0.5339 -0.3599 0.2580 -0.1998 0.1689 -0.1000 Gini index (Index) -0.0048 0.0487 -0.0163 -0.0877 0.1784 -0.1602 0.0510 0.1640 Health Expenditure per capita ($) 0.2281 0.3815 0.2355 0.6456 -0.0942 0.1980 -0.2696 0.0395 Hospital Bed per 1000 people 0.4627 0.0417 0.1810 0.4463 -0.2148 0.0507 -0.1503 0.1881 Life expectancy at birth (years) 0.4635 0.1658 0.5133 0.3860 -0.2154 0.1958 -0.3099 0.1874 Adult literacy rate (% of Population 15 years and above) 0.0215 -0.0211 0.0625 0.1359 -0.1423 0.0826 0.0732 -0.1551 Combined gross enrolment ratio for primary, secondary and tertiary schools 0.1629 0.2324 0.1191 0.1710 -0.1044 0.0989 -0.0404 -0.1429 GDP per capita at PPP ($) -0.0247 0.2545 0.1892 0.1274 -0.2134 0.1352 -0.0541 -0.0614 Physicians per Million people 0.4380 0.2658 0.5406 0.5059 -0.1973 0.2756 -0.3408 0.1345 Radios per 1000 people 0.1797 0.0492 0.0534 0.1781 -0.1933 0.0563 0.1565 0.1525 Telephone mainlines (per 1,000 people) 0.6648 0.4154 0.1519 0.4517 -0.1464 0.0178 -0.3978 0.4691 Television sets per 1000 people 0.1634 0.2313 0.2797 0.5994 -0.1105 0.1398 -0.3052 0.1946 Access to improved sanitation facilities (% of population) 0.1977 0.0063 0.3180 0.2541 -0.1682 0.0813 -0.3095 0.1227 Access to Improved Water source (% of population) 0.2544 0.2171 0.3518 0.5192 -0.1358 0.0456 -0.3208 0.1875 Proportion of Labor in Non- agric (% of employed population) 0.5709 0.1660 0.4029 0.3955 -0.2639 0.2476 -0.2871 0.0836

197

Appendix D. (Continued)

Combined gross enrolment Crude Crude Adult ratio for Birth Death literacy primary, Rate Rate Health Hospital Life rate (% of secondary (per (per Expenditure Bed per expectancy population and 1000 1000 Gini index per capita 1000 at birth 15years tertiary people) people) (Index) ($) people (years) and over) schools GDP per capita annual growth rate (%) Gross national Income ($) Gross national Income Per Capita ($) Aid per capita ($) Surface Area (sq km) Average annual population Growth Rate (%) Population density (people per sq km) Crude Birth Rate (per 1000 people) Crude Death Rate (per 1000 people) 0.6442 Gini index (Index) 0.0090 0.1048 Health Expenditure per capita ($) -0.5577 -0.2924 -0.1442

Hospital Bed per 1000 people -0.4289 -0.4352 0.1225 0.3501 Life expectancy at birth (years) -0.6910 -0.6520 -0.0341 0.2927 0.4743 Adult literacy rate (% of Population 15 years and above) -0.2361 -0.1023 -0.3696 0.1650 0.0909 0.0344 Combined gross enrolment ratio for primary, secondary and tertiary schools -0.2513 -0.2879 -0.3847 0.1537 -0.0054 0.2390 0.6466 GDP per capita at PPP ($) -0.1456 -0.0690 -0.1065 0.1205 -0.1691 0.0778 0.4033 0.4279 Physicians per Million people -0.6987 -0.6030 -0.1200 0.3929 0.5788 0.6464 0.1008 0.1606 Radios per 1000 people -0.3077 -0.3339 -0.0920 0.2014 0.1785 0.2992 0.0906 0.1403 Telephone mainlines (per 1,000 people) -0.3777 -0.3639 -0.0324 0.3027 0.3593 0.4180 -0.0048 0.1139 Television sets per 1000 people -0.6437 -0.5833 -0.0231 0.5742 0.4591 0.6286 0.0647 0.0032 Access to improved sanitation facilities (% of population) -0.5527 -0.3853 -0.1369 0.2598 0.3457 0.3221 0.2447 0.0494 Access to Improved Water source (% of population) -0.6842 -0.4932 -0.1387 0.4867 0.2523 0.4496 0.1987 0.1316 Proportion of Labor in Non-agric (% of employed population) -0.4211 -0.5206 -0.1523 0.3456 0.5074 0.5154 -0.0044 0.0707

198

Appendix D. (Continued)

199