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
Dr. Ashok K. Dutt
______________________________
Department Chair
Dr. Raymond Cox
____________________________
Committee Member
Dr. Peter Leahy
______________________________
Dean of the College
Dr. Charles B. Monroe
_____________________________
Committee Member
Dr. Nancy Grant
______________________________
Dean of Graduate School
Dr. George R. Newkome
_____________________________
Committee Member
Dr. Lathardus Goggins
______________________________
Date
______________________________
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
1
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
2
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
3
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.
4
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
5
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
% of Total Population Living in the
in Urban Areas
90
80
70
60
50
40
30
20
10
0
1950
1960
1970
1980
1990
2000
Years
Africa
Europe
Asia
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
6
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
30
Rate of Change (%) bbb
25
20
15
10
5
0
1950 - 1960
1960 - 1970
1970 - 1980
1980 - 1990
1990 - 2000
-5
Year
Africa
Europe
Northern America
Asia
Latin America & the Caribbean
Oceania
Figure 2.2 Urbanization Rate of Change 1950–60 to 1990–2000
Source of Data: United Nations, 2002
7
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
8
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
9
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,
10
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.
11
Table 2.1 Independence Dates for African Countries
Country
Algeria
1
Angola
2
3 Benin
Botswana
4
5 Burkina Faso
6 Burundi
7 Cameroon
8 Cape Verde
Central African
9 Republic
10 Chad
11 Comoros
Congo,
Democratic
12 Republic of the
Congo, Republic
13 of the
14 Cote d'Ivoire
15 Djibouti
Egypt
16
Equatorial Guinea
17
18 Eritrea
19 Ethiopia
20 Gabon
Gambia, The
21
22 Ghana
23 Guinea
Guinea-Bissau
24
Kenya
25
26 Lesotho
27 Liberia
Independence
Date
July 5, 1962
November 11,
1975
August 1, 1960
September 30,
1966
August 5, 1960
July 1, 1962
January 1, 1960
July 5, 1975
31
32
33
34
35
August 13, 1960
August 11, 1960
July 6, 1975
Namibia
36
37 Niger
38 Nigeria
June 30, 1960
39
Country
Libya
28
Independence Date
December 24, 1951
Madagascar
29
30 Malawi
June 26, 1960
Mali
September 22, 1960
Mauritania
Mauritius
Morocco
Mozambique
November 28, 1960
March 12, 1968
March 2, 1956
June 25, 1975
Rwanda
August 15, 1960
August 7, 1960
June 27, 1977
February 28,
1922
October 12,
1968
May 24, 1993
August 17, 1960
February 18,
1965
March 6, 1957
October 2, 1958
September 24,
1973
December 12,
1963
October 4, 1966
July 26, 1847
12
July 6, 1964
Sao Tome
40 and Principe
41 Senegal
42 Seychelles
Sierra Leone
43
March 21, 1990
August 3, 1958
October 1, 1960
July 1, 1962
July 12, 1975
April 4, 1960
June 29, 1976
April 27, 1961
Somalia
44
45 South Africa
46 Sudan
47 Swaziland
July 1, 1960
Tanzania
48
49 Togo
50 Tunisia
April 26, 1964
51
Uganda
Zambia
52
53 Zimbabwe
May 31, 1910
January 1, 1956
September 6, 1968
April 27, 1960
March 20, 1956
October 9, 1962
October 24, 1964
April 18, 1980
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).
13
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 (KonaduAgyemang, 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
14
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,
15
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.
16
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 nonproductive 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).
17
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
18
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
30
20
10
Stage 1
Stage 3
Stage 2
Stage 4
0
Years
Death Rate
Birth 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.
19
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.
Post Industrial
Mature Industrial
Early Industrial
Urban Population
Traditional Society
Precondition for Takeoff
100%
0%
Pre Industrial
Take off
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
20
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.
21
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
22
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
23
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
24
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.
25
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 (FennoScandinavian 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
26
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
27
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
28
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 nonprimary 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
29
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
30
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
31
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
32
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.
33
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
34
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:
35
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.
36
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).
37
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
38
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:
39
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
40
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
41
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
42
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.
43
Urbanizatio
Developmen
t
Figure 2.5 Relationship between Urbanization and Development
44
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
45
TUN
#
MAR
DZA
LBY
EGY
ESH
MRT
MLI
CPV
NER
GMB
ERI
TCD
SEN
GNB
DJI
SDN
#
BFA
#
GIN
BEN NGA
SLE
CIV
GHA
LBR
ETH
#
CAF
CMR
TGO GNQ
STP
SOM
UGA
#
GAB
ZAR
COG
KEN
RWA
BDI
SYC
TZA
W
MWI
MOZ
AGO
N
ZMB
E
ZWE
NAM
S
MDG
BWA
#
0
1000 Miles
ZAF
#
LSO
Figure 3.1 Countries in Africa
46
COM
SWZ
MUS
Table 3.1 Meaning of Abbreviations for African Countries
Abbreviation
DZA
AGO
BWA
BEN
BDI
TCD
COG
ZAR
CMR
COM
CAF
CPV
DJI
EGY
GNQ
ERI
ETH
GMB
GAB
GHA
GIN
CIV
KEN
LBR
LSO
LBY
MDG
Name
Algeria
Angola
Botswana
Benin
Burundi
Chad
Congo
Zaire
Cameroon
Comoros
Central African Republic
Cape Verde
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gambia
Gabon
Ghana
Guinea
Ivory Coast
Kenya
Liberia
Lesotho
Libya
Madagascar
Abbreviation
MWI
MLI
MAR
MUS
MRT
MOZ
NER
NGA
GNB
RWA
SYC
ZAF
SEN
SLE
SOM
SDN
TGO
STP
TUN
TZA
UGA
BFA
NAM
ESH
SWZ
ZMB
ZWE
47
Name
Malawi
Mali
Morocco
Mauritius
Mauritania
Mozambique
Niger
Nigeria
Guinea-Bissau
Rwanda
Seychelles
South Africa
Senegal
Sierra Leone
Somalia
Sudan
Togo
Sao Tome and Principe
Tunisia
Tanzania
Uganda
Burkina Faso
Namibia
Western Sahara
Swaziland
Zambia
Zimbabwe
N
Number of Projects
0
1-5
6 - 10
11 - 15
16 - 20
W
E
S
0
1000 Miles
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
48
Amount ($ Million)
0
1 - 191.4
191.4 - 377.3
377.3 - 716.6
716.6 - 1523
N
W
E
S
0
1000 Miles
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
49
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
50
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
51
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?
52
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
53
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.
54
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 socioeconomic 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
55
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
56
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
57
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.
58
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
59
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.
60
Table 3.2 Hypotheses and Tests
Hypothesis
Hypothesis 1
The higher the level of
socioeconomic
development
indicators, the higher
the level of
urbanization.
Hypothesis 2
The higher the level of
economic growth
(economic
development) the
higher the level of
urbanization in Africa.
Hypothesis 3
The higher the level of
quality of life (social
development) the
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
Index, Aid per Capita, Public
Expenditure on Education, Adult
Literacy Rate, Combined Gross
Enrolment ratio for primary,
Multiple
secondary and tertiary schools,
Regression
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.
Degree of Urbanization,
Urbanization Index, GDP, GDP per Factor Analysis (to
Capita, GDP at PPP, GDP per
identify economic
Capita at PPP, GDP per Capita
development
Growth Rate, Gini Index, Aid per
variables)
Capita, Telephone mainlines per
1000 people, Employment in the
Multiple
Non-agricultural sector.
Regression
Degree of Urbanization,
Urbanization Index, Gini Index,
Public Expenditure on Education,
Adult Literacy Rate, Combined
Gross Enrolment ratio for primary,
61
Factor Analysis (to
identify social
development
variables)
higher the level of
urbanization.
Table 3.1 Continued
Hypothesis 4
The higher the level of
human development,
the higher the level of
urbanization.
secondary and tertiary schools,
Crude Death Rate, Crude Birth
Rate, Life Expectancy, Public
Expenditure on Health, Access to
improved Sanitation, Access to
Multiple
Regression
improved water sources, Physicians
per million People, Hospital Beds per
1000 people, Radios per 1000 people,
Television sets per 1000 people,
Degree of Urbanization,
Urbanization Index, Human
Development Index, Computed
Human Development Index
Wilcoxon Rank
Sum test,
Multiple
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
Capita, Public Expenditure on
Various
Education, Adult Literacy Rate,
Independent
socioeconomic
sample t-test
development variables Combined Gross Enrolment ratio
for primary, secondary and tertiary
predicting
Regression
urbanization in Africa schools, Crude Death Rate, Crude
Birth Rate, Life Expectancy, Public
Analysis
can vary with
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,
One Way ANOVA,
Various socioeconomic Year of Independence, Surface
development variables area, Average Annual Population
Growth Rate, Population Density,
Multiple
predicting
Regression
urbanization in Africa GDP, GDP per Capita, GDP at
can vary in accordance PPP, GDP per Capita at PPP, GDP
per Capita Growth Rate, Gini
with the European
62
Index, Aid per Capita, Public
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
by regression analysis for
Socioeconomic
development variables hypotheses 1 – 6)
Standard error of the estimate for
can predict
Urbanization Index. (generated by
urbanization index
more accurately than regression analysis for hypotheses
1 – 6)
they would predict
degree of
urbanization (as
measured by the
proportion of total
population living in
urban areas).
countries that
colonized them.
63
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.
64
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
19%
5
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
65
Census Conducted
1
2
3
4
5
6
N
W
0
700 Miles
E
S
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
66
N
Census Conducted after 1999
NO
YES
W
0
Figure 4.3 Censuses Taken by African Countries after 1999
Source of Data: US Census Bureau, 2004
67
700 Miles
E
S
(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 socio- economic 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
68
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.
69
Table 4.1 Variables Derived from Data Sources for the Study
Variable
Colonial Ties
Year of Independence (year)
Surface Area (Sq km).
Average annual population growth rate (%)
Population Density (people per sq. km)
Urbanization (% Urban)
Gross Domestic Product per Capita ($)
Gross national Income ($)
Gross national Income per Capita ($)
Gross Domestic Product (PPP) per Capita ($)
Gross Domestic Product per capita growth rate (%)
Gini Coefficient (Gini Index)
Aid per capita ($)
Adult literacy rate (% of population ages 15 and above)
Combined gross enrolment ratio for primary, secondary
and tertiary schools (%)
Human development index (HDI) (index)
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)
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)
Telephone mainlines (per 1000 people)
Non-agricultural employment (% of employed
population)
70
Category
Colonialization
Area
Population
Urbanization
Income and wealth
Inequality
(income distribution)
Foreign Aid
HDI
Health
Information
Employment
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 wellbeing. 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 nonagricultural 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
71
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
72
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
73
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
74
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 nonlandlocked, 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
75
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).
76
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
77
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
78
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
79
Country
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African
Republic
Chad
Comoros
Congo
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Ivory Coast
Kenya
Lesotho
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tome and Principe
Senegal
Sierra Leone
South Africa
Table 4.2 continued
1- 500000
750000 500000
1000000
750000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.0910
0.2050
0.0000
0.0000
0.1387
0.0000
0.2050
0.0000
0.0910
0.2050
0.0000
0.0000
0.1387
0.0000
0.2050
0.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.1792
0.0932
0.0000
0.2840
0.8576
0.2050
0.0000
0.1375
0.0409
0.4659
0.0000
0.1387
0.1520
0.0000
0.1930
0.0730
0.0000
0.1740
0.0000
0.0463
0.0940
0.2349
0.0000
0.2290
0.0610
0.0000
0.0700
0. 1280
0.0000
0.0000
0.2100
0.1820
0.3895
0.0000
0.0000
0.0000
0.2840
0.0000
0.2050
0.0000
0.0000
0.0409
0.0000
0.0000
0.1387
0.1520
0.0000
0.1930
0.0730
0.0000
0.1740
0.0000
0.0000
0.0940
0.0000
0.0000
0.2290
0.0610
0.0000
0.0700
0.1280
0.0000
0.0000
0.2100
0.1820
0.3895
80
Scale of
1000000
Population
Degree of
and over Concentration Urbanization
(ΣX)
(% urban)
0.0910
1.2730
57.1
0.2050
1.6150
34.2
0.0000
1.0000
42.3
0.0000
1.0000
49.0
0.1387
1.4162
16.5
0.0000
1.0000
9.0
0.2050
1.6150
48.9
0.0000
1.0000
82.2
0.0000
0.0000
0.0000
0.0000
0.0000
0.2050
0.0000
0.0000
0.0409
0.0000
0.0000
0.0997
0.1520
0.0000
0.1930
0.0730
0.0000
0.0000
0.0000
0.0000
0.0940
0.0000
0.0000
0.1700
0.0610
0.0000
0.0000
0.0220
0.0000
0.0000
0.2100
0.0000
0.3475
1.1792
1.0932
1.0000
1.5680
1.8576
1.6150
1.0000
1.1375
1.1227
1.4659
1.0000
1.3771
1.4560
1.0000
1.5790
1.2190
1.0000
1.3480
1.0000
1.0463
1.2820
1.2349
1.0000
1.6280
1.1830
1.0000
1.1400
1.2780
1.0000
1.0000
1.6300
1.3640
2.1264
41.2
23.8
33.2
85.4
84.0
42.7
48.2
18.7
15.5
81.4
30.7
38.1
31.5
27.6
43.8
33.4
28.0
87.6
29.5
14.7
30.2
57.7
41.3
85.5
32.1
30.9
20.6
44.1
6.2
47.0
47.4
36.6
27.5
Country
Sudan
Swaziland
Tanzania
Togo
Tunisia
Uganda
Zaire
Zambia
Zimbabwe
1-500000
500000 –
750000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
1.0000
0.1260
0.0000
0.0610
0.1617
0.1990
0.0470
0.1170
0.1250
0.1100
750000 – 1000000
1000000 and over
0.1260
0.0000
0.0610
0.0000
0.1990
0.0470
0.1170
0.1250
0.1100
0.1260
0.0000
0.0610
0.0000
0.1990
0.0470
0.0980
0.1250
0.1100
Scale of
Degree of
Population Urbanizatio
Concentration
n
(ΣX)
(% Urban)
58.9
1.3780
1.0000
38.1
1.1830
32.3
1.1617
33.4
1.5970
65.6
1.1410
14.2
1.3320
30.3
1.3750
39.8
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 ttest. 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
81
100.0
Degree of Urbanization (% Urban)
Cape Verde
Libya
Morocco
80.0
Djibouti
60.0
40.0
South Africa
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
82
S
SS
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
S
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
SS
S
S
S
Population
S 5,000,000 and over
1,000,000 to 5,000,000
S
S
500,000 to 1,000,000
Countries
N
S
W
E
S
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
Country
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African Republic
Chad
Comoros
Congo
Djibouti
Egypt
Equatorial Guinea
Eritrea
Ethiopia
Gabon
Gambia, The
Ghana
Guinea
Guinea-Bissau
Ivory Coast
Kenya
Lesotho
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Morocco
Mozambique
Namibia
Niger
Nigeria
Rwanda
Sao Tome and Principe
Senegal
Sierra Leone
Degree of
Urbanization
57.1
34.2
42.3
49
16.5
9
48.9
82.2
41.2
23.8
33.2
85.4
84
42.7
48.2
18.7
15.5
81.4
30.7
38.1
27.6
31.5
43.8
33.4
28
87.6
29.5
14.7
30.2
57.7
41.3
85.5
32.1
30.9
20.6
44.1
6.2
47
47.4
36.6
Scale of
Urbanization
Population Factor Score for Urbanizat
Index
Concentration Urbanization ion Index (Percentage)
1.273
0.2528
0.45382
45.38
1.615
0.95336
0.63337
63.34
1
-0.56448
0.24436
24.44
1
-0.67744
0.21541
21.54
1.416
-0.00946
0.38661
38.66
1
-1.30576
0.05438
5.44
1.615
1.22563
0.70315
70.32
1
0.29044
0.46347
46.35
1.179
-0.23082
0.32988
32.99
1.093
-0.74771
0.1974
19.74
1
-0.81029
0.18136
18.14
1.568
2.248
0.96517
96.52
1.858
2.38389
1
100.00
1.615
0.89757
0.61907
61.91
1
-0.77482
0.19045
19.05
1.137
-1.22428
0.07526
7.53
1.123
-1.01623
0.12858
12.86
1.466
1.94461
0.88742
88.74
1
-1.51793
0.01
1.00
1.377
0.21724
0.44471
44.47
1.456
0.00321
0.38985
38.99
1
-0.70028
0.20956
20.96
1.579
0.93685
0.62914
62.91
1.219
-0.27392
0.31883
31.88
1
-0.65946
0.22002
22.00
1.348
0.96103
0.63533
63.53
1
-0.89127
0.16061
16.06
1.046
-0.97396
0.13941
13.94
1.282
-0.18926
0.34053
34.05
1.235
0.46421
0.508
50.80
1
-1.34646
0.04395
4.40
1.628
1.5151
0.77734
77.73
1.183
-0.3353
0.3031
30.31
1
-0.85986
0.16866
16.87
20.30
1.14
-0.72574
0.20303
1.278
0.25219
0.45367
45.37
1
-1.28796
0.05894
5.89
1
-0.43869
0.2766
27.66
1.63
0.72949
0.57599
57.60
1.364
0.48982
0.51457
51.46
Table 4.3 continued
84
Degree of
Urbanization
27.5
58.9
38.1
Country
South Africa
Sudan
Swaziland
Tanzania, United
Republic of
Togo
Tunisia
Uganda
Zaire
Zambia
Zimbabwe
Scale of
Urbanization
Population Factor Score for Urbanizat
Index
Concentration Urbanization ion Index (Percentage)
2.126
2.00457
0.90278
90.28
1.378
0.35513
0.48005
48.01
1
-0.53251
0.25255
25.26
32.3
33.4
65.6
14.2
30.3
39.8
35.3
1.183
1.162
1.597
1.141
1.332
1.375
1.33
-0.4121
-0.30792
1.40288
-1.39163
0.18491
0.5288
-0.0362
0.28341
0.31011
0.74858
0.03237
0.43642
0.52456
0.37975
80.000
100.000
Factor Score for Urbanization
2.0000
1.0000
0.0000
-1.0000
-2.0000
0.000
20.000
40.000
60.000
Urbanization Index (Percentage)
Figure 4.6 Scatter Plot for Urbanization Index and Factor Score for Urbanization
85
28.34
31.01
74.86
3.24
43.64
52.46
37.98
Scale of Population Concentration
1 - 1.046
1.046 - 1.235
1.235 - 1.466
1.466 - 2.126
Not Included in the Study
N
0
1000 Miles W
E
S
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
87
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.
Rank
1
2
3
4
5
6
7
8
9
10
Degree
of
urbanization
Libya
Morocco
Congo
Rank
1
2
3
4
5
6
7
8
9
10
Djibouti
Cape Verde
Gabon
Tunisia
Sudan
Mauritania
Algeria
Population
Concentration
South Africa
Djibouti
Senegal
Morocco
Angola
Cameroon
Egypt
Tunisia
Cote d’Ivoire
Congo
Figure 4.8 Ranking of Degree of Urbanization and Scale of Population Concentration
among the Top 10 Countries of Africa
88
Urbanization Index (%)
1 - 25
25 - 50
50 - 75
75 - 100
Not Included in the Study
N
0
1000 Miles
W
E
S
Figure 4.9 Urbanization Index for African Countries
89
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
Region
Eastern
Middle
Northern
Southern
Western
Number of
Countries among
the Top 10
1
4
3
1
1
Number of
Countries among
the Bottom 10
8
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.
90
DEGREE OF
URBANIZATION
Algeria
Cape Verde
Mauritania
Sudan
Gabon
Libya
Congo
Djibouti
Morocco
Tunisia
Angola
Cameroon
Cote d’Ivoire
South Africa
URBANIZATION
INDEX
Egypt
Senegal
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
91
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
92
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)
93
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
94
Table 4.5 Computed Human Development Index for African Countries
Country
Algeria
Angola
Benin
Botswana
Burkina Faso
Burundi
Cameroon
Cape Verde
Central African
Republic
Chad
Comoros
Congo
Congo, Dem. Re
p. of the
Côte d'Ivoire
Djibouti
Egypt
Equatorial
Guinea
Eritrea
Ethiopia
Gabon
Gambia
Ghana
Guinea
Guinea-Bissau
Kenya
Lesotho
Libya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Morocco
Mozambique
Namibia
Niger
Nigeria
Life
Expectancy
Index
Combined gross
enrolment ratio
Adult
(for primary,
GDP
Literacy
secondary and Index
Index
tertiary schools)
index
Education
Index
HDI
HDI
(computed)
0.920
0.185
0.450
0.218
0.328
0.203
0.353
0.933
0.727
0.378
0.350
0.856
0.000
0.487
0.714
0.815
0.654
0.141
0.423
0.654
0.038
0.179
0.474
0.692
0.592
0.347
0.178
0.679
0.185
0.047
0.332
0.558
0.702
0.299
0.374
0.789
0.013
0.385
0.634
0.774
0.704
0.381
0.421
0.589
0.302
0.339
0.501
0.717
0.738
0.277
0.334
0.562
0.175
0.211
0.439
0.755
0.178
0.300
0.698
0.390
0.464
0.427
0.562
0.907
0.154
0.205
0.333
0.372
0.200
0.166
0.290
0.156
0.360
0.353
0.486
0.728
0.361
0.379
0.530
0.494
0.246
0.273
0.491
0.425
0.218
0.213
0.328
0.898
0.646
0.478
0.683
0.554
0.103
0.295
0.064
0.731
0.055
0.264
0.331
0.491
0.465
0.417
0.476
0.613
0.365
0.399
0.454
0.653
0.246
0.298
0.378
0.667
0.410
0.500
0.320
0.598
0.530
0.628
0.405
0.313
0.313
0.090
0.998
0.518
0.128
0.395
0.490
0.980
0.895
0.145
0.315
0.333
0.473
0.925
0.569
0.372
0.754
0.324
0.790
0.365
0.347
0.926
0.889
0.892
0.706
0.635
0.080
0.368
0.926
0.491
0.437
0.913
0.056
0.699
0.500
0.179
0.192
0.705
0.333
0.346
0.128
0.231
0.436
0.590
1.000
0.333
0.705
0.090
0.321
0.641
0.487
0.282
0.667
0.000
0.333
1.000
0.132
0.100
0.626
0.290
0.347
0.344
0.077
0.166
0.379
0.660
0.087
0.027
0.143
0.358
0.747
0.491
0.173
0.611
0.106
0.124
0.783
0.439
0.312
0.738
0.327
0.642
0.286
0.308
0.763
0.789
0.928
0.582
0.658
0.083
0.352
0.831
0.490
0.385
0.831
0.037
0.577
0.703
0.439
0.359
0.648
0.452
0.568
0.425
0.350
0.488
0.493
0.794
0.469
0.388
0.326
0.465
0.785
0.620
0.354
0.607
0.292
0.466
0.731
0.357
0.244
0.654
0.382
0.539
0.345
0.233
0.414
0.419
0.862
0.395
0.271
0.207
0.400
0.853
0.625
0.234
0.586
0.159
0.391
95
Table 4.5
Continued
Rwanda
Sao Tome and
Principe
Senegal
Sierra Leone
South Africa
Sudan
Swaziland
Tanzania
Togo
Tunisia
Uganda
Zambia
Zimbabwe
0.155
0.731
0.436
0.220
0.632
0.431
0.336
0.925
0.500
0.040
0.403
0.570
0.075
0.270
0.430
1.000
0.325
0.000
0.030
0.911
0.343
0.301
0.948
0.610
0.882
0.833
0.606
0.782
0.727
0.869
1.000
0.551
0.244
0.333
0.744
0.218
0.538
0.154
0.615
0.718
0.667
0.333
0.500
0.229
0.274
0.000
0.730
0.309
0.534
0.027
0.258
0.632
0.242
0.118
0.377
0.791
0.310
0.311
0.880
0.479
0.768
0.607
0.609
0.761
0.707
0.691
0.833
0.645
0.437
0.273
0.666
0.505
0.519
0.407
0.495
0.745
0.493
0.389
0.491
0.648
0.361
0.117
0.671
0.453
0.459
0.301
0.432
0.798
0.425
0.270
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
96
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.
97
Human Development Index (Computed)
0.1 - 0.25
0.25 - 0.5
0.5 - 0.75
0
0.75 - 1
No Data
N
1000 Miles
W
E
S
Figure 4.11 Computed Human Development Index for Africa by Countries
98
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
99
Location
Coastland
Landlocked
N
0
1000 Miles
W
E
S
Figure 4.12 Location of African Countries in Relation to the Sea
100
Number of Colonies
Portugal
10%
Spain Belgium
2%
6%
Italy
6%
Not Colonized
2%
Britain
36%
France
38%
Figure 4.13 Distribution of European Colonies in Africa after World War I
101
Colonial Ties
Belgium
Britain
France
Not Colonized
Italy
Portugal
Spain
Not Included in the Study
N
0
1000 Miles
W
E
S
Figure 4.14 African Colonial Ties with Europe after World War I
Source of Information: Aryeetey- Attoh, 2001, White, 1997
102
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)
103
35
30
30
Number of Countries
25
20
15
10
9
10
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
104
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.
105
Period of Independence
Not Colonized
Before 1960
1960 - 1969
After 1969
Not Included in the Study
N
0
1000 Miles
W
E
S
Figure 4.16 Periods in which Independence was Attained by Countries in Africa
Source of Data: Wikipedia, 2003
106
GDP Per Capita (US$)
95 - 706
707 - 2066
2067 - 3935
3936 - 6453
Not Included in the Study
N
0
1000 Miles W
E
S
Figure 4.17 GDP per Capita for African Countries in 2001
Source: World Development Indicators, 2002
107
With GDP at PPP exchange rate, Equatorial Guinea emerges the richest country
with a GDP per capita of US$ 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
AT PPP
EXCHANGE RATE
GDP PER CAPITA
1 Libya
2 Eritrea
3 Mauritius
4 Gambia, The
5 Botswana
6 South Africa
7 Tunisia
8 Algeria
9 Namibia
10 Equatorial Guinea
11 Cape Verde
12 Swaziland
13 Morocco
Equatorial Guinea
South Africa
Mauritius
Botswana
Libya
Namibia
Tunisia
Algeria
Gabon
Cape Verde
Swaziland
Morocco
Egypt
Figure 4.18 The Richest Thirteen African countries in 2001
108
GDP per Capita at PPP (US$)
470 - 890
890 - 1870
1870 - 4330
4330 - 15080
Not Included in the Study
N
0
W
E
1000 Miles
S
Figure 4.19 GDP per Capita at PPP Exchange rate for African countries in 2001
Source of Data: World Development Indicators, 2002
109
In terms of the five poorest countries, Sierra Leone became the poorest country
with a GPD per capita at PPP exchange rate of US$ 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
EXCHANGE
RATE
GDP
PER
CAPITA
1 Gabon
2 Burundi
3 Zaire
4 Sierra Leone
5 Ivory Coast
1 Sierra Leone
2 Tanzania
3 Malawi
4 Zaire
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
110
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
Regions
East
Middle
North
South
West
Number of Countries above Percentage of Countries
Average
in the Region
7
0
3
3
3
44
0
60
60
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
HIV/AIDS POSSIBLE
COUNTRY
LIFE
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
112
Degree of Urbanization
0 - 25
25 - 50
50 - 75
75 - 100
Not Included in the Study
N
0
1000 Miles W
E
S
Figure 4.21 Degree of Urbanization by Countries in Africa, 2001
Source of Data: World Urbanization Prospects, 2002
113
Life Expectancy (Years)
1 - 43.6
43.6 - 48.97
48.97 - 58.02
58.02 - 75.52
Not Included in the Study
N
0
1000 Miles
W
E
S
Figure 4.22 Life Expectancy (2001) in Africa by Countries
Source: World Development Indicators, 2002
114
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.
115
Human Development Index
0.1 - 0.25
0.25 - 0.5
0.5 - 0.75
0.75 - 1
Not Included in the Study
N
0
1000 Miles
W
E
S
Figure 4.23 Human Development Indicators for African Countries, 2001
Source of Data: Human Development Report, 2002
116
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
Region
Eastern Africa
Middle Africa
Northern Africa
Southern Africa
Number of Countries
above average
%
9
4
4
5
56
44
80
100
117
Western Africa
8
53
Access to W ater
(% of Population)
1 - 25
25 - 50
50 - 75
75 - 100
Not Included in the Study
N
0
1000 Miles
W
E
S
Figure 4.24 Access to Improved Sources of Water by Countries in Africa, 2001
Source of Data: Human Development Report, 2002
118
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.
119
Access to Sanitation
(% of Population)
1 - 25
25 - 50
50 - 75
75 - 100
Not Included in the Study
N
0
1000 Miles
W
E
S
Figure 4.25 Access to Improved Sanitation by Countries in Africa, 2001
Source of Data: World Development Report, 2002
120
Physicians per Million
Population
1 - 100
100 - 300
300 - 1000
1000 - 1600
Not Included in the Study
N
0
1000 Miles W
E
S
Figure 4.26 Physicians per Million Population in Africa by Countries, 2001
Data Source: World Development Report, 2002
121
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.
122
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.
123
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
125
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 socioeconomic dimensions. In other words, socioeconomic development variables predict
126
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
R
R Square
Standard Error
of the Estimate
F
Significance
.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
R
R Square
Standard Error
of the Estimate
F
Significance
.803
.644
12.22142
3.117
.003
127
Residuals for Urbanization Index
Residuals
N
-23.221 - -8.635
-8.635 - -0.96
-0.96 - 8.201
8.201 - 32.041
No Data
W
0
E
S
1000 Miles
Figure 5.1 Residuals for Urbanization Index Using the Full Model (Enter Method)
128
Estimation for Urbanization Index
N
W
Estimation
Over estimated
Under Estimated
No Data
E
S
0
Figure 5.2 Estimation for Urbanization Index
129
1000 Miles
Residuals for Degree of Urbanization
N
Residuals
-32 - -15.47
-15.47 - -1.41
-1.41 - 11.09
11.09 - 51.37
No Data
W
E
S
0
1000 Miles
Figure 5.3 Residuals for Degree of Urbanization Using the Full Model (Enter Method)
130
Estimation for Degree of Urbanization
N
W
Estimation
Over Estimated
Under Estimated
No Data
E
S
0
Figure 5.4 Estimation for Degree of Urbanization
131
1000 Miles
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
132
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
R
R Square
Standard Error
of the Estimate
F
Significance
.723
.522
10.98401
10.395
.000
Coefficients
B
Standardized
Coefficients
Beta
t
Significa
nce
2.352
.024
(Constant)
15.442
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.
133
Table 5.4 Model Summary and Coefficient Table for Degree of Urbanization using
Stepwise Method with Socioeconomic Variables
R
R Square
.604
.365
Standard Error
of the Estimate
20.2545
F
Significance
11.478
.000
Coefficients
B
(Constant)
Proportion of Labor in Non-agric
Average annual population Growth
Rate 1980- 2001
Standardized
Coefficients
Beta
t
Significa
nce
-11.364
.588
.638
4.763
.000
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
134
derivation of socioeconomic development variables predicting urbanization is an
indication that socioeconomic variables can predict urbanization. Hence hypothesis 1 is
supported.
Population
Density
Average
Annual
Population
Growth
Rate
Proportion of
Labor in Non
Agricultural
Sector
Degree of
Urbanization
Hospital Beds
per 1,000
people
Health
Expenditure
per capita
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
135
Table 5.5 Variable Grouping based on Factor Analysis
Variable
Grouping
Variable
Landlocked
Colonial Ties
Year of Independence
Gross national Income ($)
Gross national Income per Capita ($)
PPP Gross National income ($)
PPP Gross National Income per Capita ($)
Gross Domestic Product per capita growth Rate (%)
Telephone mainlines (per 1000 people)
Non-agricultural employment (% of employed population)
Public Expenditure on Education (% of GDP)
Adult literacy rate (% of population ages 15 and above)
Combined gross enrolment ratio for primary, secondary and tertiary
schools (%)
Crude Death Rate ((per 1000 people))
Crude Birth Rate (per 1000 people)
Life expectancy at birth (years)
Public Expenditure on Health as percentage of GDP
Access to improved sanitation (% of Total Population)
Sustainable access to an improved water source (% of Total
Population)
Physicians (per 1000 people)
Hospital Bed (per 1000 people)
Radios (per 1000 people)
Television Sets (per 1000 people)
Urbanization (% Urban)
Urbanization Index (Index (%))
Surface Area Sq km.
Average annual population growth rate (%)
Population Density (people per sq. km)
Gini Coefficient (Gini Index)
Aid per capita ($)
Human development index (HDI) (Index)
136
Economic
Social
Urbanization
Other
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
R
R Square
Standard Error
of the Estimate
F
Sig.
.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
137
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
R
R Square
Standard Error
of the Estimate
F
Sig.
.340
.116
14.34736
6.290
.016
Coefficients
(Constant)
Proportion of Labor in Non-agric
Telephone mainlines (per 1,000
people)
B
15.084
.927
Standardized
Coefficients
Beta
-.288
.766
t
2.195
4.294
Sig.
.034
.000
-.546
-3.061
.004
Table 5.8 Model Summary for Degree of Urbanization, using Enter Method, on
Economic Variables
R
R Square
Standard Error
of the Estimate
F
Sig.
.571
.326
23.4515
6.494
.017
138
Table 5.9 Model Summary and Coefficient Table for Degree of Urbanization, using
Stepwise Method, on Economic Variables
R
R Square
Standard Error
of the Estimate
F
Sig.
.493
.243
20.0981
15.449
.000
Coefficients
(Constant)
Proportion of Labor in Non-agric
B
23.140
t
4.663
Sig.
.000
.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.
139
Table 5.10 Model Summary for Social Indicators, using Enter Method, on Urbanization
Index
R
R Square
Standard Error
of the Estimate
F
Significance
.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
R
.562
R Square
.316
Standard Error
F
Significance
9.238
.001
B
24.714
t
3.731
Significance
.001
10.595
2.508
.016
of the Estimate
13.14619
Coefficients
(Constant)
Hospital Beds per 1,000 people
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
140
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
R
R Square
Standard Error
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
R
R Square
Standard Error
of the Estimate
F
Sig.
.543
.295
21.9161
17.127
.000
Coefficients
(Constant)
Hospital Beds per 1,000 people
141
B
24.135
t
4.902
12.342
3.931
Sig.
.000
.000
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.
142
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
R
.364
R Square
Standard Error
of the Estimate
F
Sig.
.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
R Square
R
.018
.000
Std. Error of
the Estimate
F
19.89002
Sig.
.016
.900
Coefficients
(Constant)
Human Development Index
143
B
40.632
t
2.856
Sig.
.006
-3.506
-.126
.900
Table 5.16 Model Summary and Coefficient Table for Computed Human Development
Index on Degree of Urbanization
R
R Square
.563
.318
Std. Error of
the Estimate
25.1899
F
Sig.
22.330
.000
Coefficients
(Constant)
Computed Human Development Index
B
14.535
t
2.406
Sig.
.020
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
.242
R Square
.058
Standard Error of the Estimate
17.12667
F
2.978
Coefficients
(Constant)
Computed Human Development Index
144
B
24.957
t
2.827
Sig.
.007
30.235
1.726
.091
Sig.
.091
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
145
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
R
R Square
Standard Error
of the Estimate
F
Sig.
.647
.419
17.4858
11.538
.000
146
Table 5.19 Model Summary for Degree of Urbanization with Coastline countries
R
R Square
Standard Error
of the Estimate
F
Sig.
.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
R
R Square
Standard Error
of the Estimate
F
Sig.
.843
.711
7.5768
14.758
.001
Table 5.21 Model Summary for Degree of Urbanization with Countries without Coastline
R
R Square
Standard Error
of the Estimate
F
Sig.
.764
.583
9.7947
8.394
.005
147
Table 5.22 Variables Predicting Urbanization based on Geographical Location
Measure of
Urbanization
Degree of
Urbanization
Urbanization
Index
Variables Predicting Urbanization
Non-Landlocked Countries
Proportion of labor in
1
Non-agricultural sector
Proportion of labor in
1
Non-agricultural sector
Telephone mainlines per
2
1000 population
Landlocked Countries
1
1
2
GDP
Hospital Beds per 1000
population
Health expenditure per
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.
148
Table 5.23 ANOVA Table and Post Hoc Analysis for Degree of Urbanization and
Colonial Ties
DEGREE
Between Groups
Within Groups
Total
Colonial
Ties
SUM OF
OF
SQUARES FREEDOM
7388.057
5
13394.018
44
20782.075
49
Colonial Ties
Significance
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
149
MEAN
SQUARE
1477.611
304.410
F
SIGNIFICANCE
4.854
.001
Table 5.24 ANOVA Table and Post Hoc Analysis for Urbanization Index and Colonial
Ties
Between Groups
Within Groups
Total
Sum of Squares
6022.023
26162.636
32184.659
Degree of
Freedom
5
44
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
150
Mean Square
1204.405
594.605
F
2.026
Sig.
.094
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.
151
Table 5.25 Paired Sample Test for Standard Error of the Estimates
Urbanization Index - Degree of urbanization
t
Significance
-8.726
.000
Table 5.26 Paired Samples Statistics for Standard Error of the Estimates
Urbanization Index
Degree of urbanization
Mean
14.569
20.893
Standard. Deviation
3.712
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 nonagricultural sector and telephone mainlines per 1000 population were among the
variables predicting urbanization.
152
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
153
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
154
Variables Predicting Urbanization
Grouping
Socioeconomic
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
Growth rate
Proportion of Labor employed
3
in non-Agricultural sector
4 Population Density
Proportion of Labor employed
Proportion of Labor employed
1
in non-Agricultural sector
in non-Agricultural sector
Telephone Mainlines per 1000
2
population
Hospital beds per 1000
Hospital beds per 1000
1
1
population
population
1
Economic
Social
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 nonagricultural 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
155
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
156
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 nonagricultural 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
157
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
158
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
Hypotheses
Hypothesis 1
Variables predicting/Proportion
Explained
Degree of
Urbanization
Urbanization
Index
Proportion of labor
Hospital beds per
employed in non1000 population
agricultural sector
Average annual
Health expenditure
population growth
per capita
rate
159
Result
Supported for
Degree of
urbanization
Supported for
Proportion of labor
employed in nonagricultural sector
Proportion
explained
Hypothesis 2
36.5
Proportion of labor
employed in nonagricultural sector
Proportion
explained
Hypothesis 3
Hospital beds per
1000 population
Hospital beds per
1000 population
31.6%
Human
Development Index
Human
Development Index
13.2%
0.00%
Human
Development Index
(Computed)
Human
Development Index
(Computed)
5.8%
Proportion
explained
Supported for
Degree of
Urbanization
Supported for
Urbanization Index
11.6%
29.5%
Proportion
explained
Hypothesis 4
Proportion of labor
employed in nonagricultural sector
Telephone
mainlines per 1000
population
24.3%
Proportion
explained
Hypothesis 4
52.2%
31.8%
Supported for
Degree of
Urbanization Index
Supported for
Urbanization Index
Supported for
Degree of
Urbanization
Not Supported for
Urbanization Index
Supported for
Degree of
Urbanization
Not Supported for
Urbanization Index
Table 5.28. (Continued)
Hypothesis 5
Countries with
Coastline
Proportion
explained
Proportion of labor
in Non-agricultural
sector
Proportion of labor
in Non-agricultural
sector
Telephone
mainlines per 1000
population
25.0%
41.9%
160
Supported for
Degree of
Urbanization
Supported for
Urbanization Index
Hypothesis 5
Hospital Beds per
1000 population
GDP
Landlocked
Countries
Proportion
explained
Hypothesis 6
Health expenditure
per capita
58.3
No variable
included
71.1%
No variable
included
Hypothesis 7
161
Supported for
Degree of
Urbanization
Supported for
Urbanization Index
Not Supported for
either Degree of
Urbanization or
Urbanization Index
Supported
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
162
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
163
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.
164
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
165
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
166
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
167
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 socioeconomic 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
168
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
169
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
170
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.
171
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 nonagricultural 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
172
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
173
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.
175
BIBLIOGRAPHY
Abiodun, Josephine O. 1997. The challenge of growth and development in
metropolitan Lagos. In Rakodi Carolee (Ed.), The urban challenge in Africa:
growth and management of its large cities. New York: United Nations University
Press. Pp 192 – 222.
Abramovitz, M. 1959. The allocation of economic resources. Stanford: Stanford
University Press.
Abu-Lughod, J. 1965. Urbanization in Egypt: Present state and future prospects.
Economic Development and Cultural Change. 13, 313 – 343.
Abu-Lughod, J. 1971. Cairo. Princeton: Princeton University Press.
Abu-Lughod, J. 1976. Development in North African urbanism: The process of
decolonization. In Brian J. L. Berry (Ed.), Urbanization and counter urbanization.
New Bury Park: Sage. Pp. 191 – 211.
Abu-Lughod, Janet & Hay, Richard. 1977. Third World urbanization. Chicago:
Maaroufa Press.
Adams, Robert M. 1966. The evolution of urban society: Early Mesopotamian and
prehistoric Mexico. Chicago: Aldine Publishing Company.
Allison, Paul D. 1999. Multiple regression. Thousand Oaks, CA: Pine Forge Press.
Amis, Philip. 1990. Key themes in contemporary African urbanization. In Amis Philip
and Peter Lloyd (Eds.), Housing Africa’s urban poor. New York: Manchester
University Press. Pp. 1 – 31.
Andrews, Anthony, P. 1995. First cities. Montreal: St. Remy Press.
Andrews, M. Frank & Withey, Stephen B. 1976. Social indicators of well-being:
Americans’ perceptions of life quality. New York: Plenum
176
Press.
Angotti, T. 1993. Metropolis 2000: Planning, poverty and politics. New York:
Routeledge.
Ankerl, Guy. 1986. Urbanization overspeed in Tropical Africa, 1970 – 2000: Facts,
social problems and policy. Geneva: INU Press.
Aryeetey-Attoh, Samuel. 1997. Urban geography of Sub-Saharan Africa. In Samuel
Aryeetey-Attoh (Ed.), Geography of Sub-Saharan Africa Upper Saddle River,
N.J.: Prentice Hall. Pp. 182 – 222.
Aryeetey-Attoh, Samuel. 2001. Africa south of the Sahara. In David L. Clawson (Ed.),
World regional geography: A developmental approach. Upper Saddle River:
Prentice Hall. Pp. 477 – 551.
Ashmawy, Alaa K. 2004, January, 21. The city of tents. Retrieved February 15, 2004,
from http://ce.eng.usf.edu/pharos/cairo/history/arab.html
Atkinson, Tony. 2002. Social indicators: The EU and social inclusion. New York:
Oxford University Press.
Becker, Charles M., Hamer, Andrew A. & Morrison, Andrew R. 1994. Beyond urban
bias in Africa: Urbanization in the era of structural adjustment. Portsmouth:
Heinemann.
Baker, Thelma, S. 1972. The urbanization of man: A social science perspective.
Berkeley, Ca: McCutchan Publishing Corporation.
Berk, Richard A. 2003. Regression analysis: A constructive critique. Thousand Oaks,
CA: Sage Publications.
Belsley, David A., Kuh, Edwin & Welsch, Roy E. 1980. Regression diagnostics:
Identifying influential data and sources of collinearity. New York: John Wiley &
Sons.
Berliner, Joseph. 1977. Internal migration: A comparative disciplinary view. In Alan
Brown & EgonNeuberger (Eds.), Internal migration: A comparative perspective.
New York: Academic Press. Pp. 443 – 461.
Bertinelli, L. & Black, D. 2004. “Urbanization and growth.” Journal of Urban
Economics, 56, 80 – 96.
Berry, William D. 1993. Understanding regression assumptions. Thousand Oaks, CA:
Sage Publications.
177
Bjeren, Gunilla. 1971. Some theoretical and methodological aspects of the study of
African urbanization. Uppsala: Nordiska Afrikainstitutet.
Boahen, A. A. 1966. Topics in West African history. London: Longmans.
Bonine, M. E. 1993. Cities of the Middle East and North Africa. In Brunn and
Williams (Eds.), Cities of the world: World regional urban development. New
York: HarperCollins College Publishers. Pp. 225 – 266.
Boyer, Richard E. & Davies, Keith A. 1973. Urbanization in 19th century Latin
America: Statistics and sources. Los Angeles: Latin American Center, University
of California.
Bradshaw, York W. 1987. Urbanization and underdevelopment: A global study of
modernization, urban bias, and economic dependency. American Sociological
Review, 52 (2), 224-239.
Bradshaw, York W. & Fraser, E. 1989. City size, economic development and quality of
life in China: New empirical evidence. American Sociological Review, 54 (6), 986 –
1003.
Brinkhoff, Thomas. 2003, April, 16. The principal cities and agglomerations of
countries in Africa. Retrieved January 10, 2004, from
http://www.citypopulation.de/Africa.html
Brown, Alan A. & Neuberger, Egon. Internal migration: A comparative
perspective. New York: Academic Press.
Buenker, John D., Greenfield, Gerald Michael & Murin, William J. 1981. Urban history:
A guide to information sources. Detroit: Gale Research Co.
Butler, J and Crooke, Patrick. 1973. Urbanisation. London: Angus and Robertson
Limited.
Chandler, Tertius. 1994 Urbanization in ancient Africa. In Tranver J. D. (Ed.),
Urbanization in Africa. Westport, Conn.: Greenwood Press. Pp. 3-14.
Chandler, Tertius. 1987. Four thousand years of urban growth. Lewiston: St. David’s
University Press.
Chandler, Tertius and Fox, Gerald. 1974. 3000 years of urban growth. New York:
Academic Press.
Chenery, H et al. 1974. Redistribution with growth. Oxford: Oxford University Press.
Cherunilam, Francis. 1984. Urbanisation in developing countries: A socio-economic
178
and demographic analysis. Bombay: Himalaya Publishing House.
Chesnais, Jean-Claude. 1992. The demographic transition: stages, patterns, and
economic implications: A longitudinal study of sixty-seven countries covering the
period 1720-1984. New York: Oxford University Press.
Childe, Gordon V. 1942. What happened in history. London: Max Parrish.
Childe, Gordon V. 1950. The urban revolution. Town Planning Review. XXI (April),
3-7.
Christopher, Garland. 1977. Circulation de la main d'oeuvre et le milieu
socioéconomique de la zone dense de Korhogo. Abidjan: Centre Ivoirien de
Recherche Économique et Sociale, Ministère de la Recherche Scientifique,
Université Nationale de Côte d'Ivoire.
Clark, Colin G. 1960. The conditions of economic progress. London: Macmillan.
Cohen, J. & Cohen, P. 1983. Applied multiple regression/correlation analysis for the
behavioral sciences. (2nd Ed). Hillsdale: Lawrence Erlbaum Associates.
Costello, Vincent F. 1977. Urbanization in the Middle East. New York: Cambridge
University Press.
Davidson, Basil. 1977. A history of West Africa, 1000 – 1800. Harlow: Longman Group
Limited.
Davis, James C. & Henderson, J. Vernon. 2003. Evidence on the political economy of
the urbanization process. Journal of Urban Economics, 53 (1), 98–125.
Davis, Kingsley. 1965. Human society. New York: Macmillan.
Davis, Kingsley. 1976. The origin and growth of urbanization in the world. In Baker,
Thelma S (Ed.), The urbanization of man: A social science perspective. Berkeley,
Ca: McCutchan Publishing Corporation.
Davis, Kingsley. 1976. World urbanization. 1950 – 1970. Westport, Conn: Greenwood
Press.
Drakakis-Smith, David. 1987. The Third World city. New York: Methuen.
Drakakis-Smith, David. 2000. Third World cities. New York: Routledge.
179
Dutt, A. K. 2001. Global urbanization: Trends, form and density gradients. Allahabad:
Professor R. N. Dubey Foundation.
Dutt, A. K. & Parai, A. 1994. Perspectives on Asian urbanization: An east-west
Comparison. In Dutt et al (Eds.), The Asian city: Process of development,
characteristics and planning. Dordrecht: Kluwer Academic Publishers.
Easterly, William. 1999. Life during growth. Journal of Economic Growth, 4 (3), 239
– 275.
Eicher, C. et al. 1970. Employment generation in African agriculture. Institute of
International Agriculture Research Report. Number 9. East Lansing: Michigan
State University.
El-Shakhs, Salah & Balau, Ademola. 1979. Modernization and the planning of cities in
Africa: Implications for internal structure. African Urban Studies, 4, 17 - 36
Evans, Peter & Timberlake, Michael. 1980. “Dependence, inequality, and the growth of the
tertiary: A Comparative Analysis of Less Developed Countries”. American Sociological
Review, 45 (4), 531 – 552.
Fair, T. J. D., 1990 Rural and urban balance: Policy and practice in ten African
countries. Pretoria, Republic of South Africa: Africa Institute of South Africa.
Ferguson, James. 2002. The constitution of the object of "development": Lesotho as a
"less developed country". In Schech, Susanne & Haggis, Jane (Eds.),
Development: a cultural studies reader. Malden: Blackwell. Pp. 93 – 103.
Ferguson, James. 1997. Development and bureaucratic power in Lesotho. In Rahnema,
Majid with Bawtree, Victoria (Eds.), The post-development reader. Atlantic
Highlands: Zed Books. Pp. 223 - 233
Firebaugh, G. 1999. Empirics of world income inequality. American Journal of
Sociology, 104 (6), 1597-1630.
Firebaugh, Glenn. 1979. Structural determinants of urbanization in Asia and Latin
America, 1950 – 1960. American Sociological Review, 44, 199 – 215.
Fischer, Claude. 1975. Toward a subcultural Theory of Urbanism. American Journal of
Sociology, 80(6), 1319-1351.
180
Fox, John (1991). Regression diagnostics. Thousand Oaks, CA: Sage Publications.
Fox, John. 1997. Applied regression analysis, linear models and related methods.
Thousand Oaks: Sage Publications.
Frey, William H & Zimmer, Zachary. 2001. Defining the city. In R. Paddison (Ed.),
Handbook of urban studies. Thousand Oaks: Sage Publications. Pp. 14 – 35.
Garson, David G. 2004. Factor analysis Retrieved November 23, 2004, from
http://www2.chass.ncsu.edu/garson/pa765/factor.htm
Garlake, Peter S. 1973. Great Zimbabwe. London: Thames and Hudson
Gellar, Sheldon. 1995. The colonial era. In Phyllis M. Martin & Patrick O'Meara
(Eds.), Africa. Bloomington: Indiana University Press. Pp. 135 – 155.
Ghai, Dharam. P. 1987. Successes and failures in growth in Sub-Saharan Africa: 1962 –
82. Geneva: International Labor Office.
Ghosh, Pradip K. 1984. Urban development in the Third World. Westport: Greenwood
Press.
Gibbs, J.P. 1966. Measures of urbanization. Social Forces, 45, 170 – 177.
Giventer, Lawrence L. 1996. Statistical analysis for Public Administration. Belmont:
Wadsworth Publishing Company.
Gould, Peter R. 1960. The development of the transportation pattern in Ghana.
Evanston, Ill.: Dept. of Geography, Northwestern University.
Graaff, Johann. 2003. Poverty and development. Oxford: Oxford University Press.
Greer, Scott. 1968. The new urbanization. New York: St Martin’s Press.
Gross, B. 1966. The state of the nation: Social systems accounting. In R. A. Bauer
(Ed.), Social indicators. Cambridge, Mass.: MIT Press.
Grove, David & Huszar, Laszlo. 1964. The towns of Ghana: The role of service
centres in regional planning. Accra: Ghana University Press.
Gugler, Josef. 1970. Urban growth in Sub- Saharan Africa. Kampala: Makerere Institute
of Social Research.
Gugler, J & Flanagan, W. 1978. Urbanization and social change in West Africa. New
York: Cambridge University Press.
Haining, Robert P. 1990. Spatial data analysis in the social and environmental
181
sciences. New York: Cambridge University Press.
Hance, William Adams. 1970. Population, migration and urbanization in Africa. New
York: Columbia University Press.
Hansen, Karen Tranberg. 1997. Keeping house in Lusaka. New York: Columbia
University Press.
Hardoy, Jorge E & Satterthwaite, David. 1986. Small and intermediate urban
centres: Their role in regional and national development in the Third World.
Boulder: Westview Press.
Hardoy, Jorge E. 1975. Two thousand years of Latin American urbanization. In Jorge
E. Hardoy (Ed), Urbanization in Latin America: Approaches and issues. Garden
City: Anchor Books. Pp. 3–56.
Hartshorn, Truman A. 1992. Interpreting the city: An urban geography. New York:
Wiley.
Henderson, J. Vernon. 2003. The urbanization process and economic growth: The so
what question. Journal of Economic Growth, 8 (1), 47 – 71.
Henderson, J. Vernon. 2000. The effects of urban concentration on economic growth.
Cambridge, MA: National Bureau of Economic Research.
Hicks, Norman & Streeton, Paul. 1973. Indicators of development: The search for a
basic yardstick”. World Development, 7 (6), 567 – 580.
Hondroyannis, G. & Papapetrou, E. 2002. Demographic transition and economic
growth: Empirical evidence from Greece. Journal of Population Economics, 15,
221- 242.
Hope, Kempe R. 1984. The dynamics of development and development administration
Westport: Greenwood Press
Hope, Kempe R. 1996. Development in the Third World: From Policy Failure to Policy
Reform. Armonk, NY: M. E. Sharpe.
Hope, Kempe R. 1997. African political economy: Contemporary issues in
development. Armonk, NY: M.E. Sharpe
Hope, Kempe, R. 1998. Urbanization and urban growth in Africa. Journal of Asian &
African Studies, 33 (4), 345 – 358.
Hope, Kempe Ronald. 2002. From crisis to renewal: Development policy and
management in Africa. Boston: Brill
182
Richard Hooker. 1999, June, 6. Civilization in Africa. Retrieved May, 14, 2004
http://www.wsu.edu:8080/~dee/CIVAFRCA/AXUM.HTM
Hull, R. W. 1976. African cities and towns before the European conquest. New York:
W. W. Norton & Company Inc.
Ingham, Barbara. 1993. The meaning of development: Interactions between “new” and
“old” ideas. World Development, 21 (11), 1803 – 1821.
Jefferson, Mark. 1939. The law of the primate city. The Geographical Review, 29,
226-232.
Johnson, Richard Arnold & Wichern, D. W. 2002. Applied multivariate statistical
analysis. Upper Saddle River, N.J.: Prentice Hall.
Jones, Emry. 1966. Towns and cities. New York: Oxford University Press.
Kachigan, Sam K. 1986. Statistical analysis: An interdisciplinary introduction to
univariate and multivariate methods. New York: Radius Press.
Kahane, Leo H. 2001. Regression basics. Thousand Oaks, CA: Sage Publications
Kasarda, J.D. & Crenshaw, E. M. 1991. Third World urbanization: Dimensions, theories
and determinants. Annual Review of Sociology, 17 (1), 467 – 501.
Keller, Edumond J. 1995. “Decolonization, Independence, and the Failure of Politics” in
Phyllis M. Martin and Patrick O'Meara (Eds.), Africa. Bloomington: Indiana
University Press. Pp. 156 – 174.
Kelley, Allen & Williamson, Jeffery. 1982. The limits of urban growth: Suggestions
for micromodeling Third World economies. Economic Development and Cultural
Change, 30, 595 – 623.
Kemp, Barry J. 1977. The early development of towns in Egypt. Antiquity, 51, 185 –
200.
Kentor, Jeffrey. 1981. Structural determinants of peripheral urbanization: The effects
of international dependence. American Sociological Review, 44(2), 201 – 211.
Kileff, Clive & Pendleton, Wade C. 1975. Urban man in Southern Africa Gwelo
[Rhodesia]: Mambo Press,
King, Anthony D. 1976. Colonial urban development: Culture, social power and
environment. Boston: Routledge & K. Paul.
183
Kleinbaum, David G., Kupper, Lawrence L. Muller, Keith E. & Nizam, Azhar, 1998.
Applied regression analysis and other multivariable methods. (3rd Ed.) Pacific
Grove: Duxbury Press.
Knox Paul L. 1994. Urbanization: An introduction to urban geography. Englewood
Cliffs, N.J.: Prentice-Hall
Konadu-Agyemang, K. 2001. The political economy of housing and urban
development in Africa: Ghana’s experience from colonial times to 1998.
Westport, Conn: Praeger.
Kuznets, Simon. 1953. Economic change. New York: W.W. Norton.
Lewis, Paul G. 1996. Shaping suburbia: How political institutions organize urban
development. Pittsburgh: University of Pittsburgh Press.
Lewis-Beck, Michael S. 1980. Applied regression: An introduction. Newbury Park: Sage
Publications.
Leys, Colin. 1996. The rise & fall of development theory. Bloomington: Indiana
University Press.
Linn, Johannes F. 1983. Cities in the Developing World: Policies for their equitable
and efficient growth. New York: World Bank.
Lipton, M. 1977. Why poor people stay poor: A study of urban bias in world
development. Cambridge: Harvard University Press.
Lowder, Stella. 1986. The geography of Third World cities. Totowa, N.J.: Barnes &
Noble Books.
Lugard, Frederick D. 1965. The dual mandate in British Tropical Africa. Hamden,
Conn.: Archon Books. (5th Ed)
Macionis, John, J. & Parrillo, Vincent, N. 2004. Cities and urban life. Upper Saddle
River, New Jersey: Pearson Education.
Macura, Milo. 1961. Basic statistics on the Yugoslav communes. International Social
Science Journal, 13 (3), 427 – 433.
Marshall, J. U. 1989. The structure of urban systems. Toronto: University of Toronto
Press.
Mayer, P. 1964. Labour migracy and the social network. In J. F. Holleman et al (Eds.),
Problems of transition. Natal: Natal University Press.
McGee, T. G. 1971.The urbanization process in the Third World: Explorations in
184
search of a theory. London: Bell.
McGrew, Chapman J & Monroe, Charles B. 2000. An introduction to statistical problem
solving in geography. Boston: McGraw-Hill.
Menard, Scott 1995. Applied logistic regression analysis. Thousand Oaks, CA: Sage
Publications.
Miles, Jeremy & Shevlin, Mark 2001. Applying regression and correlation: A guide for
students and researchers. Thousand Oaks, CA: Sage Publications.
Miner, H. 1967. The city in modern Africa. New York: Praeger.
Morris, David Morris. 1979. Measuring the condition of the world's poor: The physical
quality of life index. New York: Pergamon Press.
Nash, Gary B. 1997. “The Social Evolution of Preindustrial American Cities, 1700 –
1820”. In Raymond A. Mohl (Ed.), The making of urban America. Wilmington:
Scholarly Resources.
Njoh, Ambe J. 1999. Urban planning, housing and spatial structures in Sub-Saharan
Africa: Nature, impact and development implications of exogenous forces
Aldershot: Ashgate.
Njoh, Ambe J. 2003. Planning in contemporary Africa: The state, town planning and
society in Cameroon. Aldershot: Ashgate.
Njoh, Ambe J. 2003. Urbanization and Development in Sub-Saharan Africa. Cities, 20
(3), 167 – 174.
Nnadozie, Emmanuel. 2003. Definition and measurement of growth and development.
In Emmanuel Nnadozie (Ed.), African economic development. New York:
Academic Press.
Northam, Ray M. 1979. Urban geography New York: Wiley
Obudho, R. A & El-Shakhs, Salah. 1979. Development of urban systems in Africa. New
York: Praeger.
Ogaki, Masao. 1992. Engel's Law and Cointegration. Journal of Political Economy, 100
(5), 1027-46.
Okpala, Don C. I. 1987. Received concepts and theories in African urbanization studies
and urban management strategies: A critique. Urban Studies, 24(2), 137-150.
185
Oliver, Roland Anthony & Atmore, Anthony. 1994. Africa since 1800. Cambridge:
Cambridge University Press.
Oliver, Roland Anthony. 1993. The African experience London: Pimlico.
Omuta, Gideon E. D & Onokerhoraye, Andrew G. 1986. Regional development and
planning for Africa. Benin City, (Nigeria): University of Benin.
Pakenham, Thomas. 1992. The scramble for Africa: White man's conquest of the
dark continent from 1876 To 1912. New York: Avon Books.
Palen, John J. 1997. The urban world. New York: McGrew-Hill Book Company.
Peil, M with Sada, P. O. 1984. African urban society. New York: John Wiley and Sons.
Peil, Margaret. 1984. African urbanization in comparative perspective. Birmingham:
University of Birmingham.
Pirenne, Henri. 1925. Medieval cities, their origins and the revival of trade.
(Translated from French by Frank D. Halsey) Princeton: Princeton University
Press.
Pomeroy, George M. 2003. A spatio-temporal, functional classification of Indian
Cities. In Dutt et al. (Eds.), Challenges to Asian urbanization in the 21st Century.
Dordrecht: Kluwer Academic Publishers. Pp. 137 - 162
Population Reference Bureau. 2004. Population data sheet. Washington D.C.: The
Bureau
Preston, Samuel H. 1988. Urban growth in developing countries: A demographic
Appraisal. In Joseph Gugler (Ed.), The urbanization of the Third World. New
York: Oxford University Press.
Rakodi, Carole. 1997. Global forces, urban change, and urban management in Africa. In
Rakodi Carolee (Ed.), The urban challenge in Africa: Growth and management of
its large cities. New York: United Nations University Press. Pp. 17 – 73.
Rencher, Alvin C. 2002. Methods of multivariate analysis. New York: J. Wiley.
Robert Lafrance & Schembri, Lawrence. 2002. “Purchasing-power parity: Definition,
measurement and interpretation” Bank of Canada Review, (Autumn), 27 – 33.
Rondinelli, Dennis A. & Ruddle, Kenneth. 1978. Urbanization and rural development:
A spatial policy for equitable growth. New York: Praeger,
186
Rosser, Colin. 1972. Urbanization in Tropical Africa: A demographic introduction. New
York: International Urbanization Survey.
Rossi, J. Robert & Gilmartin, J. Kevin. 1980. The handbook of social indicators:
sources, characteristics and analysis. New York: Garland STPM Press.
Rostow, W. W. 1971. Stages of economic growth. New York: Cambridge University
Press.
Rostow, W.W. 1990. Theorists of economic growth from David Hume to the present.
New York: Oxford University Press.
Rummel, R. J. 1988 Applied factor analysis. Evanston: Northwestern University
Press, 1988
Schnore, Leo F. 1961. The statistical measurement of urbanization and economic
development. Land Economics, 37 (3),229-245.
Schroeder, Larry D., Sjoquist, David L., and Stephan, Paula E. 1986. Understanding
regression analysis: An introductory guide. Thousand Oaks, CA: Sage
Publications.
Schwab, William A. 1992. The sociology of cities. Englewood Cliffs: Prentice Hall.
Seers, Dudley. 1972. “What are we trying to measure?” Annals of Development Studies,
8, 21 – 36.
Simone, Abdou Malique. 1998. Urban processes and change in Africa. Dakar:
Publication Unit, CODESRIA.
Sjoberg, G. 1960. The pre-industrial city: Past and present. New York: The Free Press.
Spybey, Tony. 1992. Social change, development, and dependency: modernity,
colonialism and the development of the West. Cambridge: Polity Press.
Stavrianos, Leften Stavros. 1983. A global history: The human heritage. Englewood
Cliffs: Prentice-Hall.
Smith, David A. 1996. Third World cities in global perspective: The political economy
of uneven urbanization. Boulder: Westview Press.
Steinbacher, Roberta and Benson, Virginia O. 1997. Introduction to urban studies.
Dubuque: Kendall/Hunt Publishing Company.
187
Streeten, P. & Burki. Javed S. (1978). Basic needs: Some issues. World Development,
6 (3), 411 – 421
Stren, R. E. 1982. Underdevelopment, urban squatting and the state of bureaucracy: A
case study of Tanzania. Canadian Journal of African Studies, 16 (1), 20-35.
Stren, R. E. 1972. Urban policy in Africa: A political analysis. African Studies Review,
15, 494 - 512.
Tabachnick, Barbara G. & Fidell, Linda S. 2001. Using multivariate statistics. (10th Ed)
Boston: Allyn and Bacon.
Tacq, J. A. 1997. Multivariate analysis techniques in social science research: From
problem to analysis. London: Thousand Oaks: Sage.
Takyi, Baffour K. & Addai, Isaac. 2003. Population growth and socioeconomic
developments in Ghana. In Wisdom J. Tettey, K. P. Puplampu, and B. J. Berman
(Eds.), Critical perspectives on politics and socio-economic developments in
Ghana. Leiden, Netherlands & Boston, MA: Brill Academic Press. Pp. 223-237.
Tarver, James D. 1994. Urbanization in Africa: A handbook. Westport, Conn:
Greenwood Press.
Tarver, James D. 1996. The demography of Africa. Westport, Conn: Praeger.
Timberlake, Michael & Kentor, Jeffrey. 1983. Economic dependence, overurbanization
and economic growth: A study of less developed countries. Sociological
Quarterly, 24, 489 – 507.
Tipple, Graham, A. (1994). The need for new urban housing in Sub-Saharan Africa:
Problems or opportunity. African Affairs, 93, 587 – 608.
Todaro, Michael P. 1977. Economic development in the Third World. New York:
Longman Press.
Todaro, Michael P. 2000. Economic development (7th Ed). Reading, Mass.: Addison
Wesley
United Nations Center for Human Development. 2001. Cities in a globalizing world:
Global report on human settlements 2001. London: Earthscan Publications.
United Nations Development Program. 2000. Human Development Report. New York:
Oxford University Press.
188
United Nations Development Program. 2001. Human Development Report. New York:
Oxford University Press.
United Nations. 2002. World urbanization prospects: The 2001 revision. New York:
Upper Saddle River.
United Nations. 2004. World urbanization prospects: The 2003 revision. New York:
Upper Saddle River.
US Census Bureau. 2004. Census Dates for Countries and Areas of Africa:
1945 to 2014. Retrieved on May, 25, 2004, from
http://www.census.gov/ipc/www/cendates/cenafric.html
Verwayen, Henri. 1980. The specifications and measurement of the quality of life in
OECD countries. In Alexander Szalai and Frank M. Andrews (Eds.), The quality
of Life: Comparative studies. Beverly Hills: Sage Publications Ltd. Pp. 235 –
247.
Wallerstein, I. 1998. The present state of the debate on world inequality. In Seligson,
Mitchell A. & Passé-Smith, John T. (Eds.), Development and underdevelopment:
The political economy of inequality. Boulder: L. Rienner Publishers.
Walton, J. (1977) Accumulation and comparative urban systems: Theory and some
tentative contrasts of Latin America and Africa. Comparative Urban Research,
5(1), 5-18.
Waterston, Albert. 1965. Development planning: Lessons of experience. Baltimore:
Johns Hopkins Press.
Wertz, Raanan. 1973. Urbanization and the developing countries. Report on the
Sixth Rehovot Conference. New York: Praeger.
White, L. G. (1987). Creating opportunities for change: Approaches to managing
development programs. Boulder, CO: Lynne Reinner Publishing.
White, Matthew. 1997, November. Africa in the Early Twentieth Century. Retrieved
2004, May, 25 from http://users.erols.com/mwhite28/afri1914.htm
Wickins, Peter L. 1986. Africa, 1880 – 1980: An economic history. Cape Town: Oxford
University Press.
Wikipedia, 2003, July, 6. List Of Countries By Date Of Nationhood. Retrieved on 2004,
May, 25, from
http://en.wikipedia.org/wiki/List_of_countries_by_date_of_nationhood
189
Wilcox, Walter F. 1926. A redefinition of city in terms of density of population. In
Ernest Burgess (Ed.), The urban community: Selected papers from the
proceedings of American Sociological Society. Chicago: University of Chicago
Press.
Wirth, Louis. 1938. Urbanism as a way of life. The American Journal of Sociology,
44 (1), 1-24.
World Bank. 1989. Sub-Sahara Africa: From crisis to sustainable growth. Washington
DC: The World Bank.
World Bank. 2001. Mauritania – Urban Development Project (Project Report) Report
No. PID9248.
World Bank. 2002. African development indicators. Washington, D.C.: The World Bank
World Bank. 2002. World development indicators. Washington, D.C.: The World Bank
World Bank. 2004. Projects and Programs. Retrieved 2004, July, 24, from
http://web.worldbank.org/WBSITE/EXTERNAL/PROJECTS/0,,menuPK:115635
~pagePK:64020917~piPK:64021009~theSitePK:40941,00.html
Zacharia, K. C. & Conde, J. 1981. Migration in West Africa: Demographic aspects.
Oxford: Oxford University Press
Zapf, Wolfgang. 1980. The SPES social indicators system in comparative perspective. In
Alexander Szalai & Frank M. Andrews (Eds.), The quality of life: Comparative studies.
Beverly Hills: Sage Publications Ltd. Pp. 250 – 269
Zeigler, Donald J., Brunn, Stanley D. and Williams, Jack F. 2003. World urban
development. In Brunn, Stanley D., Jack F. Williams and Donald J. Zeigler (Eds.), Cities
of the world: World regional development. Lanham: Rowman & Littlefield. Pp. 1 – 46
190
APPENDICES
191
APPENDIX A. REGIONS OF AFRICA
Appendix A1. Table Showing Regions of Africa
Eastern
Africa
Burundi
Comoros
Djibouti
Eritrea
Ethiopia
Kenya
Madagascar
Malawi
Mauritius
Mozambique
Rwanda
Somalia
Uganda
Tanzania
Zambia
Zimbabwe
Middle Africa
Angola
Cameroon
Central African
Republic
Chad
Congo
Democratic
Republic of
Congo
Equatorial
Guinea
Gabon
Sao Tome and
Principe
Northern
Africa
Algeria
Egypt
Libya
Morocco
Sudan
Tunisia
Western Sahara
Source of Data: United Nations, 2002
192
Southern
Africa
Botswana
Lesotho
Namibia
South Africa
Swaziland
Western
Africa
Benin
Burkina Faso
Cape Verde
Cote d’Ivoire
Gambia
Ghana
Guinea
Liberia
Mali
Mauritania
Niger
Nigeria
Senegal
Sierra Leone
Togo
Region
Eastern Africa
Middle Africa
Northern Africa
Southern Africa
Western Africa
N
0
1000 Miles
W
E
S
Appendix A2. Map Showing Regions of Africa.
Source of Data: United Nations, 2002
193
APPENDIX B. FACTOR ANALYSIS - ROTATED COMPONENT MATRIX
Component
Rotated Component Matrix(a)
Crude Birth Rate per 1000 people
Crude Death Rate per 1000 people
Hospital Bed per 1000 people 1990 2001
Life expectancy at birth (years) 2001
Adult literacy rate
Physicians per Million people 1990 2001
Radios per 1000 people
Television sets per 1000 people
Access to improved sanitation facilities
% of population
Access to Improved Water Source % of
population
Combined gross enrolment ratio for
primary, secondary and tertiary schools
Gross national Income $ billion
Gross national Income Per Capita $
GDP per capita annual growth rate (%)
1990 - 2001
GDP per capita PPP
Telephone mainlines (per 1,000 people)
2001
Proportion of Labor in Non-agric 1990
Human development index (HDI) value
2001
Percentage Urban 2001
Urbanization Index (Percentage)
Aid per capita $
Average annual population Growth
Rate 1990 - 2001
Population density (people per sq km)
Gini index
1
Social
-0.7846
-0.8243
2
Economic
-0.4133
-0.1941
3
Urbanization
-0.0428
-0.2508
4
Other
-0.1317
-0.1645
0.4868
0.8126
0.7814
0.2287
0.2802
-0.0595
0.4451
0.1795
0.0589
-0.1898
0.0929
0.1128
0.6871
0.5301
0.7337
0.4072
-0.0548
0.2178
0.2671
-0.0199
0.0770
0.1133
0.0027
-0.1317
0.3929
0.2872
0.2770
0.0949
0.4362
0.4191
0.2775
0.0384
0.8437
-0.0048
0.4599
0.1391
0.5566
0.5262
-0.0473
0.2808
0.0710
0.1498
0.1690
-0.0314
0.0342
0.1496
0.6634
0.6057
-0.2147
-0.3394
0.0682
-0.0246
0.1638
0.2942
0.8283
0.6980
0.3195
0.4803
0.0230
0.0361
0.2633
0.3518
-0.0079
-0.2835
0.6197
-0.0220
-0.2516
0.0807
0.4289
0.7589
0.8223
-0.2321
0.1777
-0.0097
-0.0723
-0.3152
-0.0659
0.1985
0.2168
0.0014
-0.2388
-0.0205
0.0313
-0.2515
-0.2504
-0.7180
0.6001
-0.6188
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser
Normalization.
194
APPENDIX C. REGRESSION COEFFICIENTS AND COLLINEARITY TEST
Appendix C1. Regression Coefficients with Collinearity Test for Degree of
Urbanization
B
(Constant)
GDP per capita
Standardized
Coefficients
Beta
30.6533
(US$)
-0.0025
-0.1483
t
Sig.
Collinearity
Statistics
Tolerance
0.2324
0.8187
-0.5370
0.5975
0.2845
VIF
4.9197
GDP per capita annual growth rate (%)
1.4776
0.1144
0.5731
0.5733
0.3530
2.8329
Gross national Income ($ 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)
Average annual population Growth Rate
(%)
-0.0018
-0.0507
-0.2182
0.8296
0.2607
3.8353
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 ($)
-0.3228
-0.5725
-1.7366
0.0986
0.2295
3.7209
Hospital Bed (per 1000 people)
6.1821
0.2559
0.9626
0.3479
0.2992
4.0199
Life expectancy at birth (years)
-0.4441
-0.1999
-0.2502
0.8051
0.4221
4.3368
Adult literacy rate
Combined gross enrolment ratio for
primary, secondary and tertiary schools
0.4830
0.4644
2.1171
0.0477
0.2925
3.4192
-0.3438
-0.2929
-1.4359
0.1673
0.3383
2.9562
GDP per capita at PPP ($)
-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
Access to improved sanitation facilities (%
of population)
Access to Improved Water source (% of
population)
Proportion of Labor in Non-agric (% of
employed population)
0.0626
0.2303
0.9735
0.3425
0.2516
3.9749
-0.1413
-0.1537
-0.8082
0.4290
0.3893
2.5690
-0.0567
-0.0427
-0.2000
0.8436
0.3081
3.2453
0.4663
0.5096
1.9366
0.0678
0.2033
4.9188
Dependent Variable: Degree of Urbanization
195
Appendix C2. Regression Coefficient with Collinearity Test for Urbanization Index
B
(Constant)
GDP per capita
Standardized
Coefficients
Beta
-116.3214
US$ 2001
t
Sig.
Collinearity
Statistics
Tolerance
-0.6762
0.5071
VIF
-0.0020
-0.0984
-0.3407
0.7370
0.2845
4.4197
-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
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
3.9494
0.6863
0.7912
0.4386
0.3205
4.8539
GDP per capita annual growth rate (%)
Average annual population Growth Rate
(%)
Crude Death Rate (per 1000 people)
Gini index (Index)
-0.7785
-0.2418
-1.3138
0.2046
0.4548
2.1987
Health Expenditure per capita ($)
-0.4241
-0.6031
-1.7490
0.0964
0.2295
3.7209
Hospital Bed (per 1000 people)
13.7987
0.4580
1.6471
0.1160
0.2992
4.0199
Life expectancy at birth (years)
2.3238
0.8387
1.0037
0.3281
0.4221
4.3368
Adult literacy rate
-0.0397
-0.0306
-0.1335
0.8952
0.2925
3.4192
Combined gross enrolment ratio for
primary, secondary and tertiary schools
-0.3532
-0.2413
-1.1308
0.2722
0.3383
2.9562
0.0004
0.0788
0.3943
0.6978
0.3855
2.5939
Physicians per Million people
-0.0479
-0.4889
-0.9607
0.3488
0.2595
3.8133
Radios per 1000 people
GDP per capita at PPP ($)
-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
0.1875
0.1133
0.5069
0.6180
0.3081
3.2453
0.6479
0.5678
2.0629
0.0531
0.2033
4.9188
Access to Improved Water source (% of
population)
Proportion of Labor in Non-agric (% of
employed Population)
Dependent Variable: Urbanization Index
196
APPENDIX D. CORRELATION COEFFICIENT TABLE
GDP
per
capita
($)
GDP per capita annual growth
rate (%)
Gross national Income ($)
Gross national Income
Capita ($)
GDP
per
capita
annual
growth
rate
(%)
Gross
national
Income
($)
Gross
national
Income
Per
Capita
($)
Aid per
capita
($)
Surface
Area
(sq km)
Average
annual
population
Growth
Rate (%)
Population
density
(people
per sq km)
0.1705
0.0867
-0.0755
Per
0.2739
0.4556
0.2126
-0.1009
0.1092
-0.3604
-0.1592
0.0868
-0.1742
0.4224
0.0044
-0.2790
-0.1147
-0.4754
-0.1454
-0.3031
0.0294
0.1047
0.1597
0.1682
-0.0559
0.2105
-0.0760
-0.3754
-0.4034
-0.3258
-0.3465
-0.4445
-0.6285
0.1625
0.0258
0.3559
-0.2570
-0.4593
-0.0899
-0.5339
-0.3599
0.2580
-0.1998
0.1689
-0.1000
-0.0048
0.0487
-0.0163
-0.0877
0.1784
-0.1602
0.0510
0.1640
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
0.0215
-0.0211
0.0625
0.1359
-0.1423
0.0826
0.0732
-0.1551
Aid per capita ($)
Surface Area (sq km)
Average annual population
Growth Rate (%)
Population density (people per
sq km)
Crude Birth Rate (per 1000
people)
Crude Death Rate (per 1000
people)
Gini index (Index)
Health Expenditure per capita
($)
Adult literacy rate (% of
Population 15 years and above)
Combined gross enrolment ratio
for primary, secondary and
tertiary schools
GDP per capita at PPP ($)
0.1629
0.2324
0.1191
0.1710
-0.1044
0.0989
-0.0404
-0.1429
-0.0247
0.2545
0.1892
0.1274
-0.2134
0.1352
-0.0541
-0.0614
Physicians per Million people
0.4380
0.2658
0.5406
0.5059
-0.1973
0.2756
-0.3408
0.1345
Radios per 1000 people
Telephone mainlines (per 1,000
people)
0.1797
0.0492
0.0534
0.1781
-0.1933
0.0563
0.1565
0.1525
0.6648
0.4154
0.1519
0.4517
-0.1464
0.0178
-0.3978
0.4691
Television sets per 1000 people
0.1634
0.2313
0.2797
0.5994
-0.1105
0.1398
-0.3052
0.1946
Access to improved sanitation
facilities (% of population)
0.1977
0.0063
0.3180
0.2541
-0.1682
0.0813
-0.3095
0.1227
0.2544
0.2171
0.3518
0.5192
-0.1358
0.0456
-0.3208
0.1875
0.5709
0.1660
0.4029
0.3955
-0.2639
0.2476
-0.2871
0.0836
Access to Improved Water
source (% of population)
Proportion of Labor in Nonagric
(%
of
employed
population)
197
Appendix D. (Continued)
Crude
Birth
Rate
(per
1000
people)
Crude
Death
Rate
(per
1000
people)
Gini index
(Index)
Health
Expenditure
per capita
($)
Hospital
Bed per
1000
people
Life
expectancy
at
birth
(years)
Adult
literacy
rate (% of
population
15years
and over)
Combined
gross
enrolment
ratio for
primary,
secondary
and
tertiary
schools
GDP per capita annual growth rate (%)
Gross national Income ($)
Gross national Income Per Capita ($)
Aid per capita ($)
Surface Area (sq km)
Average annual population Growth
Rate (%)
Population density (people per sq km)
Crude Birth Rate (per 1000 people)
Crude Death Rate (per 1000 people)
0.6442
Gini index (Index)
0.0090
0.1048
-0.5577
-0.2924
Health Expenditure per capita ($)
-0.1442
Hospital Bed per 1000 people
-0.4289
-0.4352
0.1225
0.3501
Life expectancy at birth (years)
Adult literacy rate (% of Population 15
years and above)
Combined gross enrolment ratio for
primary, secondary and tertiary schools
-0.6910
-0.6520
-0.0341
0.2927
0.4743
-0.2361
-0.1023
-0.3696
0.1650
0.0909
-0.2513
-0.2879
-0.3847
0.1537
-0.0054
0.2390
0.6466
GDP per capita at PPP ($)
-0.1456
-0.0690
-0.1065
0.1205
-0.1691
0.0778
0.4033
Physicians per Million people
-0.6987
-0.6030
-0.1200
0.3929
0.5788
0.6464
0.1008
0.1606
Radios per 1000 people
-0.3077
-0.3339
-0.0920
0.2014
0.1785
0.2992
0.0906
0.1403
Telephone mainlines (per 1,000 people)
-0.3777
-0.3639
-0.0324
0.3027
0.3593
0.4180
-0.0048
0.1139
Television sets per 1000 people
Access to improved sanitation facilities
(% of population)
Access to Improved Water source (% of
population)
Proportion of Labor in Non-agric (% of
employed population)
-0.6437
-0.5833
-0.0231
0.5742
0.4591
0.6286
0.0647
0.0032
-0.5527
-0.3853
-0.1369
0.2598
0.3457
0.3221
0.2447
0.0494
-0.6842
-0.4932
-0.1387
0.4867
0.2523
0.4496
0.1987
0.1316
-0.4211
-0.5206
-0.1523
0.3456
0.5074
0.5154
-0.0044
0.0707
198
0.0344
0.4279
Appendix D. (Continued)
Crude
Birth
Rate
(per
1000
people)
Crude
Death
Rate
(per
1000
people)
Gini index
(Index)
Health
Expenditure
per capita
($)
Hospital
Bed per
1000
people
Life
expectancy
at
birth
(years)
Adult
literacy
rate (% of
population
15years
and over)
Combined
gross
enrolment
ratio for
primary,
secondary
and
tertiary
schools
GDP per capita annual growth rate (%)
Gross national Income ($)
Gross national Income Per Capita ($)
Aid per capita ($)
Surface Area (sq km)
Average annual population Growth
Rate (%)
Population density (people per sq km)
Crude Birth Rate (per 1000 people)
Crude Death Rate (per 1000 people)
0.6442
Gini index (Index)
0.0090
0.1048
-0.5577
-0.2924
Health Expenditure per capita ($)
-0.1442
Hospital Bed per 1000 people
-0.4289
-0.4352
0.1225
0.3501
Life expectancy at birth (years)
Adult literacy rate (% of Population 15
years and above)
Combined gross enrolment ratio for
primary, secondary and tertiary schools
-0.6910
-0.6520
-0.0341
0.2927
0.4743
-0.2361
-0.1023
-0.3696
0.1650
0.0909
-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
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
Access to improved sanitation facilities
(% of population)
Access to Improved Water source (% of
population)
Proportion of Labor in Non-agric (% of
employed population)
-0.6437
-0.5833
-0.0231
0.5742
0.4591
0.6286
0.0647
0.0032
-0.5527
-0.3853
-0.1369
0.2598
0.3457
0.3221
0.2447
0.0494
-0.6842
-0.4932
-0.1387
0.4867
0.2523
0.4496
0.1987
0.1316
-0.4211
-0.5206
-0.1523
0.3456
0.5074
0.5154
-0.0044
0.0707
199
0.0344
0.4279