Post-Apartheid Patterns of Internal Migration in South Africa docx

Internal Migration
Post-Apartheid Patterns of
in South Africa

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PIETER KOK, MICHAEL O’DONOVAN, OUMAR BOUARE AND JOHAN VAN ZYL
Internal Migration
Post-Apartheid Patterns of
in South Africa

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Compiled by the Integrated Rural and Regional
Development Research Programme, Human Sciences Research Council
Executive Director: Mike de Klerk
Published by HSRC Publishers
Private Bag X9182, Cape Town, 8000, South Africa
www.hsrc.ac.za/publishing
©
2003 Human Sciences Research Council
First published 2003
All rights reserved. No part of this book may be reprinted or reproduced
or utilised in any form or by any electronic, mechanical, or other means,
including photocopying and recording, or in any information storage or

retrieval system, without permission in writing from the publishers.
ISBN 0-7969-2004-4
Cover photograph by David Lurie, first published in his book
Life in the Liberated Zone, published in 1994.
Production by comPress
Printed by Print24.com
Distributed in South Africa by Blue Weaver Marketing and Distribution
P.O. Box 30370, Tokai, Cape Town, South Africa, 7966.
Tel/Fax: (021) 701-7302, email:

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Contents
List of tables vii
List of maps ix
List of figures xi
List of graphs xi
About the authors xii
Preface xiv
Overview xvii
1. Introduction 1
Migration data generated by Census ’96 2
Context and scope of the study 2
Purpose of the book 4
Outline of the book 4
2. Literature review 8
Current status of migration research 8
Definitions 8
Data adequacy, reliability and appropriateness 11

Theories and models of the causes of migration 12
Economic factors that cause migration 13
Economic factors that perpetuate migration 18
Non-economic factors that cause migration 19
Non-economic factors that perpetuate migration 26
Evaluation 27
Modified gravity model of migration 28
Problems with ‘migration intervals’ 30
Local/area-specific data: guidelines for research 31
Conclusions 31
3. Population redistribution 33
Urbanisation trends 33
Metropolisation and inter-metropolitan migration 35

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Multivariate statistical techniques used in this study 36
Inter-provincial migration 59
Patterns of inter-provincial migration 38
Multivariate profiles of inter-provincial migrants 38
Inter-district migration 40
Labour migration 41
Migration ‘proper’ 42
Summary of the effect of distance 48
Summarising the interactions between districts 49
Other examples of migration modelling 50
Conclusions 51
4. Migration differentials 52
Proportion of migrants in the population 53

Migration intervals compared: 1975–80 and 1992–96 54
Migration selectivity 55
Age-gender selectivity 55
Employment, education and gender differentials 57
Comparing ‘migrants’ and ‘labour migrants’ 61
Differential migration: a multivariate analysis 63
Conclusions 71
5. The way forward 75
Dealing with migration in future censuses 75
Migration questions in sample surveys 82
Conclusions 82
Appendices
AInter-provincial migration: detailed MNA results 84
B Data and definition issues 92
C Information on the logistic regression 94
DModelling migration: further attempts 96
Index 111

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List of Tables
2.1 A suggested (partial) typology of spatial mobility encom-
passing both circulation and more ‘permanent’ moves, and
incorporating the more flexible approaches to defining
migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
3.1 Migration to, between and from the four main metropoles
and non-metropolitan areas in South Africa (1992–1996) . . . . . . . . . . . 36
3.2 Inter-provincial migration in South Africa (1992–1996):
number of people involved in every migration direction . . . . . . . . . . . . 37

4.1 Age-specific comparison by province of the proportion of the
population that has ever migrated. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4.2 Former migrants by population group (1975–1980 &
1992–1996) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
4.3 Results of the logistic regression: Comparing ‘labour migration’
and ‘migration’. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4.4 Overall statistical results of the multiple classification
analysis (MCA) of the probability of having ever migrated. . . . . . . . . . . 64
4.5 An MCA-based profile of the probability of having ever
migrated: provincial location of the respondent . . . . . . . . . . . . . . . . . . . 65
4.6 An MCA-based profile of the probability of having ever
migrated: type of locality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.7 An MCA-based profile of the probability of having ever
migrated: population group. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.8 An MCA-based profile of the probability of having ever
migrated: gender . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.9 An MCA-based profile of the probability of having ever
migrated: age . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.10 An MCA-based profile of the probability of having ever
migrated: marital status . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.11 An MCA-based profile of the probability of having ever
migrated: highest educational qualification. . . . . . . . . . . . . . . . . . . . . . . 68
4.12 An MCA-based profile of the probability of having ever
migrated: work status. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
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Page

4.13 An MCA-based profile of the probability of having ever
migrated: annual household income. . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.14 An MCA-based profile of the probability of having ever
migrated: household size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.15 An MCA-based profile of the probability of having ever
migrated: dwelling type . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.16 An MCA-based profile of the probability of having ever
migrated: home-ownership by the household of the dwelling
lived in . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
5.1 Migration questions asked during Census 2001 . . . . . . . . . . . . . . . . . . . 79
5.2 Suggested migration questions for future Censuses . . . . . . . . . . . . . . . . 81
A1 Destinations of inter-provincial migration in South Africa:
Some statistical details of the explanatory variables used
in the multivariate nominal-scale analysis (MNA) . . . . . . . . . . . . . . . . . 84
A2 MNA results on inter-provincial migration destinations
in South Africa: province of origin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
A3 MNA results on inter-provincial migration destinations in
South Africa: type of locality (at destination) . . . . . . . . . . . . . . . . . . . . . 86
A4 MNA results on inter-provincial migration destinations
in South Africa: population group . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
A5 MNA results on inter-provincial migration destinations in
South Africa: migration period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
A6 MNA results on inter-provincial migration destinations in
South Africa: age at (last) migration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
A7 MNA results on inter-provincial migration destinations in
South Africa: migrant worker status. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
A8 MNA results on inter-provincial migration destinations in
South Africa: gender. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
C1 Variables (and their categories) used in the analysis . . . . . . . . . . . . . . . . 95
D1 Estimated coefficients of the model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

D2 Elasticities derived from the model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
D3 Overall distribution of reasons for leaving the previous
place of residence. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
D4 Statistical details of a multivariate nominal-scale analysis
(MNA): reasons for leaving previous place of residence . . . . . . . . . . . . . 104
D5 Reason categories for leaving previous place of residence:
results of the multivariate nominal-scale analysis (MNA). . . . . . . . . . . . 105
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List of maps
1.1 Location of provinces, districts and former homelands . . . . . . . . . . . . . . 1
1.2 Distribution of the population (1996): a district-based perspective . . . . 3
3.1 Usual districts of residence of labour migrants (1996) . . . . . . . . . . . . . . 40
3.2 Ratio of migrant workers to economically active population. . . . . . . . . . 42
3.3 District destinations of migrants (1992–96) . . . . . . . . . . . . . . . . . . . . . . 43
3.4 Origin districts of migrants (1992–96). . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.5 Origin districts of migrants to Gauteng (1992–96) . . . . . . . . . . . . . . . . . 45
3.6 Usual districts of residence of Gauteng labour migrants (1992–96) . . . . 46
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List of figures
2.1 (Extended) Value-expectancy-based model of migration
decision-making . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 De Jong’s general model of migration decision-making. . . . . . . . . . . . . . 27

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List of graphs
3.1 Urbanisation levels (1904–1996) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.2 Net inter-provincial migration (1992–1996) . . . . . . . . . . . . . . . . . . . . . . 39
3.3 Proportions of the various migrant categories . . . . . . . . . . . . . . . . . . . . . 41
3.4 ‘Contribution rate’ and migration distance. . . . . . . . . . . . . . . . . . . . . . . . 46
3.5 Transformed ‘rate of contribution’ and migration distance:
areas that were not part of former homelands . . . . . . . . . . . . . . . . . . . . . 47
3.6 Transformed ‘rate of contribution’ and migration distance:
former homeland areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.1 (a) Age-specific migration rates (1975–1980) based on the
official 5 per cent sample of the 1980 census . . . . . . . . . . . . . . . . . . . 56
4.1 (b) Age-specific migration rates (1992–1996) based on the
migration community profile of Census ’96. . . . . . . . . . . . . . . . . . . . 56
4.2 District-based higher education and unemployment rates. . . . . . . . . . . . 58
4.3 District-based in-migration and unemployment rates . . . . . . . . . . . . . . . 58
4.4 District-based out-migration and unemployment rates . . . . . . . . . . . . . . 59
4.5 District-based out-migration and unemployment rates
(in terms of the ratio of females to males in the district). . . . . . . . . . . . . 60
4.6 Relative probability of having migrated (by income). . . . . . . . . . . . . . . . 62
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About the authors

Pieter Kok
Pieter Kok is a chief research specialist in the Integrated Rural and Regional Development
research programme. He holds a Ph.D. in urban and regional planning from the University
of Pretoria. Prior to joining the HSRC, Dr Kok worked as a town planner at the Borough of
Newcastle (KwaZulu-Natal), where he was responsible for research and master planning.
Pieter’s areas of research specialisation are internal migration and urbanisation, and he
has been the project leader for two large multidisciplinary studies on migration (since 1999)
and urbanisation (1987–1994). He was the leader of two provincial studies, one on the
socio-economic profile of Gauteng (1996–1998) and the other on development indicators
for KwaZulu-Natal (1995–1997). Pieter has been the author or co-author of a number of
research reports (including a two-volume book on urbanisation), journal articles, formal
papers and book reviews.
Dr Kok also taught part-time in demography at the University of Pretoria and held the
position of Professor Extraordinary from 1991 to 2001. In 1996 he received the HSRC Award
for Research Excellence.
Michael O’Donovan
Michael O’Donovan is a research specialist in the Surveys, Analyses, Modelling and Mapping
research programme. He holds a Master’s degree in sociology from the University of the
Witwatersrand. Prior to joining the HSRC, Mr O’Donovan established the Township
Databank, which aimed at the creation of an up-to-date demographic profile of Gauteng
townships and settlements.
Michael’s area of research specialisation is the analysis, interpretation and management
of data, with a particular emphasis on large datasets, and he has recently been the project
leader for a study to develop a minimum data set on ageing in South Africa.
Mr O’Donovan was the co-recipient of the United Nations Development Programme
(UNDP) Best Practice Award for a best practice in empowering urban communities, and
between 1998 and 2000 he was a board member of Agrèment South Africa.
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Oumar Bouare
Oumar Bouare is a chief research specialist in the Social Aspects of HIV/AIDS and Health
research programme of the HSRC. He holds a Ph.D. in economics (New School for Social
Research, New York), a Ph.D. in mathematics (University of Paris VI), and an MA in
philosophy (University of Paris I, Sorbonne).
Oumar’s area of research specialisation is economic analysis. One of his outstanding
achievements is the development of a new theory of international trade, ‘Profit, and profit
and externalities as a basis for international trade’ (Instituto di Economia Internazionale, vol.
LI, no 3 in 1998). In this article, he developed a theory that profit, and profit and
externalities are a basis for international trade.
Dr Bouare has recently been selected for inclusion in the 2002 edition of Great Minds of
the 21st Century, compiled by the Governing Board of Editors of the American Biographical
Institute (ABI). According to the ABI his selection is due to ‘significant accomplishments
within, and mastery of economics’. Earlier he was also included in Who’s Who in Economics
in the 21st Century, compiled by the International Biographical Centre, Cambridge, England.
Johan van Zyl
Johan van Zyl is a research specialist in the Integrated Rural and Regional Development
research programme. He holds a BA (Hons) degree in geography from the University of
Pretoria. Prior to joining the HSRC, Johan was employed by the Bureau for Market Research
at the University of South Africa (UNISA), and served in an advisory capacity for a number
of local and international organisations. These include the SAARF Demographic Research
Division of the Bureau of Market Research at UNISA, Statistics South Africa (Census 1991
and 1996 and October Household Surveys from 1995), the 1998 South African
Demographic Survey and the Demographic Surveillance System 1999–2000 (Wellcome Trust
and the Africa Centre for Population Studies and Reproductive Health based at Hlabisa,
KwaZulu-Natal).
Johan’s areas of research expertise include analytical demography (adjustment of census
data for smaller geographical areas, population projections, estimation of fertility and

mortality rates from survey data, applied demography and field surveys), interpreting the
needs of clients in order to design a survey (questionnaire design, sample specifications, and
management of fieldwork) and a detailed knowledge of the principles of data analysis.
Mr Van Zyl has delivered various papers at national and international conferences, and
published or edited several books, chapters or client reports in areas related to demography
as well as other survey-based studies.
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Preface
Aims
This book seeks to achieve the following objectives:
 provide clear definitions of migration concepts and present a literature-based foundation
for the study of internal migration in South Africa;
 provide a comprehensive overview of internal migration (based on recent census and
other secondary data, mostly those provided by Statistics South Africa);
 introduce techniques and approaches that can be used to analyse data on internal
migration;
 provide guidelines for questions on internal migration in future censuses and other
surveys; and
 help to pave the way for the questionnaire survey to be undertaken as part of the larger
project on the causes of migration.
As a work it is rather technical so it is aimed at analysts (e.g. academics, students and researchers)
and decision makers dealing with migration issues rather than at the general public. It should
appeal to those with a ‘feel’ for figures and, preferably, some basic knowledge of statistics.
Overview
What is immediately clear from the research undertaken for this book is that despite political
and economic changes, migration patterns are essentially a continuation of patterns that pre-

date the abolition of apartheid in South Africa. A more substantive finding derived from the
analyses is that relationships and patterns of migration are complex.
At times the relationships demonstrate great continuity, for example the apparent
continuation of migration rates set 20 years ago by segregationist policies. At others,
established trends are discontinued, for example in the unexpected prominence of migration
from metropoles to non-urban areas. There is discontinuity also in the role played by
distance, with one pattern evident in the population of the former homelands and another
in the non-homeland population. One source of misunderstanding is the divergence in
absolute numbers and rates. Rates of migration appear to be contradictory. How the
population is distributed by location, area type and political heritage (setting the social
dimension aside) has to be borne in mind when examining the data.
In the past, South Africa lacked suitable data on internal migration. This meant that
historical trends could not be analysed to the extent required in a country that underwent
such notable political, social and economic changes during the final decade of the 20
th
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century. The only available historical data on internal migration were those for the period
1975–80, but these were flawed by the exclusion of data in respect of the former homelands
of Transkei, Bophuthatswana and Venda.
Census ’96 provided a welcome change by making available, for the first time, data on
internal migration for the entire country and population. This new source of data has invited
the undertaking of appropriate analyses. However, the prior absence of suitable data has
obstructed the development of experience in the analysis of migration data over time. This
calls for some guidance on techniques for using national data on internal migration and for
examples of the kinds of analyses that can be undertaken. This book aims to address these
needs.

Chapter 2 deals with the theories and models of migration. Although the underlying
(root) causes of migration are predominantly economic in nature, there are some very
important non-economic (mainly social) reasons why migration is often perpetuated and
sometimes becomes systemic. Non-migration is seen as the result of constraints to migration
caused, among other things, by the costs associated with moving (especially over longer
distances) and by personality characteristics (such as aversion to risk-taking). Very often,
non-migration is caused by – and almost inevitably leads to – an in situ adjustment to the
current situation, irrespective of how unattractive that may be.
In Chapter 3 the patterns of internal migration are analysed in relation to urbanisation
and metropolisation trends in their historical context and with reference to the role of
apartheid. The concentration of people and poverty in the former homeland areas and the
resultant high population densities in these ‘rural’ areas were discussed. The economic
anomaly of high unemployment and low out-migration rates was highlighted. The
importance of regional (spatial) planning and development for the provision of economic
opportunities and services in these hitherto deprived areas should again be emphasised.
The two main types of migration that have been identified are ‘migration’ and ‘labour
migration’. The reason for separating these two lies more in the data constraints than in any
inherent conceptual or definitional differences. In fact, the general migration typology
discussed in Chapter 2 treats these two migration types as very much the same thing. The
data are not that tolerant though. In the analyses in Chapter 4 that deals with ‘migration’,
the main question addressed is why some people migrate and others not. The analyses on
‘labour migration’ in this chapter deal mainly with a similar question, namely why some
people become labour migrants and others not. Various explanations for migration and non-
migration are offered, but in the end the questions remain largely unanswered, mainly
owing to the absence of suitable data.
Despite the data problem, an attempt is made in Chapter 4 to compare the basic migration
patterns for the two five-year periods (1975–1980 and 1992–1996) for which relatively
comparable information is available. The only analyses that are viable in view of the data
constraints are those dealing with the different levels of migration among the various age and
population groups. The findings indicate that despite dramatic political, social and economic

changes in South Africa (including the abolition of apartheid’s migration-related measures
such as influx control and group area demarcations), there was an insignificant change in the
overall level of migration between the late 1970s and the early 1990s.
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A ‘modified’ gravity model is applied in Appendix D, and it is shown that
unemployment, income and racial differentials seem to play a lesser role in inter-provincial
migration than crime levels. An analysis of the 1997 October Household Survey data (see
Appendix D) shows that a slight majority of moves were caused by economic factors
(especially work-related reasons), but that the remaining moves had been caused by non-
economic factors. The results of the modelling attempts reported in Appendix D are
inconclusive and even confusing. This highlights, once again, the need for ‘purpose-made’
data for the analysis of migration causes and patterns. This need should be addressed to a
large extent by the HSRC-funded questionnaire survey that will be undertaken as part of the
larger project.
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Overview
As in many other countries, internal migration in South Africa is an under-researched topic.
This lack of research attention does not necessarily stem from perceived unimportance or a
lack of interest but rather from the historical absence of appropriate census data. Problems
with census-based migration data are usually derived from limits to the number of
questions, difficulties with the coding of origin data, and memory lapse (affecting mainly
data on time of the move). The main aim of this book is to describe migration in South

Africa, based on recent South African censuses and other national surveys, and to provide an
analytical evaluation of the migration data that they generated.
Before Census ’96, South Africa lacked suitable census data on internal migration. The only
partly useful data were those for the period 1975–80 from the 1980 census, but these were
flawed by the exclusion of the former Transkei, Bophuthatswana and Venda homeland areas.
Census ’96 therefore provided a welcome change, with a battery of questions that covered the
last move, irrespective of the time of the move, for the entire population and for the country as
a whole. Using age-specific migration rates as a criterion for judging the validity of migration
data, it was shown that the Census ’96 data conformed, in all the important respects, to the
expected pattern. Other findings pointed to the same conclusion. Our study therefore indicates
that Census ’96 provided, for the first time ever, an invaluable source of data on South Africa’s
internal migration.
Census 2001 has built nicely on the baseline data provided by Census ’96 by
concentrating on migration since the 1996 census, and it will also improve on Census ’96 by
not only generating data on country but also province of birth.
Although the emphasis in this book is more on census-based migration data, we also
looked at other secondary national data (such as the October Household Surveys) that might
help to obtain a clearer understanding of the internal migration process in South Africa.
However, from an analytical point of view, these other data sources proved to be somewhat
disappointing. It should nevertheless be understood that migration is a highly complex
component of population dynamics.
While ‘purpose-made’ sample surveys can generally deal effectively with the processes
and causes of migration, census-based migration data are essential for providing the context
within which migration takes place in a country. Although there clearly are limits to the
scope of the migration data that censuses can provide, sample surveys cannot be successful
in providing the necessary insight into migration processes without reliable census data on
internal migration patterns and trends.
Census ’96 opened up many opportunities for meaningful analyses of internal migration
patterns, and Census 2001 built further upon that solid foundation. These two censuses promise
to provide very useful data for comparative migration analyses. It is now up to the migration

analysts to utilise this opportunity to delve deeper into the complex set of factors associated with
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South African migration patterns and trends. The HSRC has shown its commitment to help fill
the existing gaps by funding empirical research on the causes of migration in this country. It is
trusted that other organisations and migration scholars will do the same. If this book contributes
to making it possible, we will have achieved most of our objectives.
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CHAPTER ONE
Introduction
I
N SOUTH AFRICA the analysis of migration trends has been hampered by the absence of
comprehensive and detailed data on human movement. Until recently migration analysts
relied on sample surveys that are neither detailed nor comprehensive enough for an
understanding of this dynamic phenomenon. Only censuses potentially offer information
with the required breadth of detail.
1
Unfortunately, before 1996 South African censuses
generally failed to record migration data such as the place and timing of migratory moves
within (and to) South Africa. The 1996 version, officially referred to as Census ’96, was a
welcome exception.
Although South African censuses routinely provide data on the time and country of
birth, only two previous censuses provided data on migration within the country’s borders.

In 1980 respondents were asked where they had lived five years prior to the census, while in
1991 a question on the duration of residence at the current address was included.
1
Map 1.1: Location of provinces, districts and former homelands
LEGEND
Magisterial districts
Former ‘homelands’
Provincial boundaries

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When used in isolation both questions have inherent limitations when it comes to analysing
migration.
More problematic, though, is that in both those censuses only part of the country’s
population was covered. The 1980 census excluded the former nominally independent states
of Transkei, Venda and Bophuthatswana, and migration data for these former homelands are
not available despite constituting a substantial source of migrants, in particular labour
migrants (see Map 1.1). In 1991, another nominally independent territory, the former
Ciskei, which also constituted a significant reservoir of migrants, was excluded from the
census. Only Census ’96 covered migration data for the entire country and provided useful
data on movement patterns.
It is hoped that the detailed analyses of some of the 1996 census data on migration
presented here will contribute to a collective understanding of migration while making
migration studies more accessible by showing how the information, coupled with the use of
statistical techniques, can enhance understanding of the phenomenon.
Migration data generated by Census ’96
The box on pp. 6–7 lists all the questions included in the 1996 census that are related to
migration. It shows that five fields of migration analyses were covered in Census ’96. These are
(a) ‘lifetime migration’ (see questions 1.1 and 1.2), (b) ‘migrant labour’ (see questions 2 and

5), (c) ‘place of usual residence’ (questions 3.1 and 3.2), (d) ‘duration of residence’ (question
4.1), and (e) ‘origin of the most recent move’ (question 4.2). Taken together these questions
provide, for the first time, a potentially powerful source of information on spatial mobility
leading to a thorough understanding of the dynamics of migration.
Despite the fact that Census ’96 has been severely criticised in various quarters for not
being altogether reliable, it provides an important opportunity for undertaking the sort of
migration analyses required to understand its dynamics and this book explores how the data
can be used in migration research.
Context and scope of the study
In 1996 South Africa’s overall population density was approximately 33 persons per square
kilometre (km
2
), which made it the 66th least densely populated of the 196 countries listed
by the Population Reference Bureau (1997). However, the population was very unevenly
distributed, with densities varying significantly between and within provinces (see Map 1.2).
For example, the Northern Cape is the largest province (in terms of land area) and the
smallest in terms of resident population. In 1996 it had a population density of just more
than 2 persons per km
2
. At that time, Gauteng, the smallest province in terms of area,
accommodated 432 persons per km
2
.
2
Population densities are usually related to the extent of urbanisation but in South Africa
some of the least developed rural districts exhibit high population densities. Particularly
noticeable on any population density map are the high densities in the former homeland
areas.
3
These high densities can be ascribed, among other things, to past segregationist

2
Post-apartheid patterns of internal migration in South Africa

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policies such as the Group Areas Act and influx control. These instruments prevented the
African people from migrating to urban areas (see, for example, Kok 1986), and gave rise to
large-scale forced resettlements (mainly to the former homeland areas). This resulted in the
formation of densely populated areas with some of the lowest levels of service delivery,
infrastructure and employment in the country. Despite changes since the abolition of the
segregationist legislation (including the re-incorporation of the nominally independent
homelands), after 1994, the backlog is formidable. These previously neglected areas
continue to be marked by high levels of poverty and vulnerability and thus continue to
influence migration patterns.
Migration, as usually reflected by people moving to better-serviced places with more
promising job prospects, has the potential to help redress these inequities. However, if
people are denied the opportunity to move (for whatever reason), past inequities can even
be exacerbated. It is therefore important not to look only at the causes and consequences of
migration as a social phenomenon, but also at the causes and consequences of non-
migration.
As mentioned earlier, until 1996 no South African census provided the data required for
comprehensive migration analyses. The project that led to these analyses being undertaken
was called ‘Causes of internal migration in South Africa’. It dealt with migration patterns
within the country, and with the causes of both migration and non-migration. This book was
the first in a series of products arising from the project, which included an empirical survey.
Introduction
3
Map 1.2: Distribution of the population (1996):
A district-based perspective

Kilometres
500 people per dot

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Purpose of the book
This book attempts to place migration (and non-migration) in perspective. For that purpose,
use is made predominantly of the data contained in two census data sets. The first is the
spatially highly detailed Census ’96: Migration Community Profile, and the second is the 10 per
cent sample of the 1996 census. The first data set offers great detail on the spatial location
of the population (and thus of migrants). The second data set lacks the spatial detail but
presents the full census results for 10 per cent of the population, making possible an
exposition of the social profiles of migrants and non-migrants. Emphasis is placed in the
analyses on obtaining a better understanding of the complex migration/non-migration
differential, among other things. In the process, migration/non-migration is analysed mainly
from a spatial perspective for provinces and smaller spatial entities (by utilising the data of
the Migration Community Profile), and from the perspective of the individual resident
(through analyses of the 10 per cent sample).
The issue of appropriately defining migration is dealt with extensively in Chapter 2, but
it is necessary to point out here that the term ‘migration’, as used in this book, includes all
migrants, while ‘labour migration’ refers to a specific subset of migrants.
Outline of the book
Chapter 2 provides a theoretical exposition of migration research and serves as a backdrop
against which the remainder of this book should be viewed. It explores the depth of our
collective misunderstanding of internal migration in South Africa. As indicated above, this
misunderstanding can be attributed, at least in part, to the historical absence of suitable
migration data for the country as a whole. The definition of ‘migration’ receives some
attention, and it is shown that in this book ‘migration’ refers to those movements that
involve a change in the usual place of residence from one magisterial district to another. The

problems with migration intervals are discussed, and a rationale is provided for the adoption
of the period 1992–96 as the most appropriate migration period for the majority of analyses
contained in the book. Chapter 2 also covers the need for more localised surveys to capture
patterns of residential mobility in the context of such area-specific studies.
Chapter 3 discusses population redistribution patterns and trends. In South Africa and
elsewhere, urbanisation levels increased steadily during the 20
th
century and, by 1996,
almost 54 per cent of the country’s population lived in urban areas (defined as those places
with an urban form of local government). However, great variations were observed among
the nine provinces, with urbanisation levels ranging from a low of 11 per cent in Limpopo
(previously known as the Northern Province) to 97 per cent in Gauteng. The latter was also
by far the most preferred destination in inter-provincial migration between 1992 and 1996,
followed by the Western Cape, Mpumalanga and North West.
Chapter 4 provides general profiles of migrants (and, by default, non-migrants). It is
shown that only about one quarter of the total population has ever migrated, and that there
have been surprisingly few changes in the level of migration between 1975–80 and 1992–96.
Migration in South Africa is clearly selective in terms of age, and there are some important
differences in migration levels among the various employment and educational categories in
Post-apartheid patterns of internal migration in South Africa
4

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the population. A multivariate profile of migrants (versus non-migrants) shows that
Mpumalanga, Gauteng and the Western Cape have the highest proportions of former
migrants when the effects of other variables are eliminated. The multivariate profile also
shows that people living in shacks and the homeless were generally much more migratory
than those living in traditional dwellings and formal houses and flats.

Chapter 5 provides guidelines for future censuses and sample surveys dealing with
migration.
Notes
1Like almost all countries exhibiting a similar level of development, South Africa does not maintain a rigorous
population register of where people currently reside and have moved from.
2 The three magisterial districts with the highest densities were predominantly ‘township’ areas situated in each of
the three main metropoles: Soweto in Gauteng (10 625 persons per square kilometre), Umlazi in KwaZulu-
Natal (7 426 per km
2
), and Mitchells Plain in the Western Cape (6 657 per km
2
).
3 These former homeland areas are spread over seven provinces, namely the Eastern Cape, KwaZulu-Natal, Free
State, Limpopo (formerly known as the Northern Province), Mpumalanga, North West and the Northern Cape
(see Map 1.1).
Introduction
5

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Box 1
Migration questions asked during Census ’96
Section A: In respect of each household member:
1.1 Was (the person) born in South Africa? (Include the former TBVC states –
Transkei, Bophuthatswana, Venda, Ciskei)
1 = Yes
2 = No
1.2 (If ‘No’) In what country was the person born?
Write in the name of the country.

3 Is (the person) a migrant worker? (Someone who is absent from home FOR
MORE THAN A MONTH each year to work or to seek work.)
1 = Yes
2 = No
3.1 Is this DWELLING (e.g. house, room, shack, flat) the place where (the person)
usually lives, i.e. where (the person) spends at least four nights per week?
1 = Yes
2 = No
3.2 (If ‘No’) Where does (this person) usually live?
Name of suburb/village/settlement:
Name of city/town/farm/tribal authority:
Name of magisterial district:
If not South Africa, please state name of country:
If no usual address, circle ‘3’ 3
4.1 In which year did (the person) move to the DWELLING (e.g. house, room,
shack, flat) where he/she usually lives?
Write in the year that he/she moved 19
OR
The person has never moved, circle ‘1’ 1
(Lived in the dwelling since birth)
4.2 (For the person who has moved)
From where did (the person) move? (Before moving into the dwelling where
he/she usually lives)
Name of suburb/village/settlement:
Name of city/town/farm/tribal authority:
Name of magisterial district: …… ……
If not South Africa, please state name of country: …… … …
Post-apartheid patterns of internal migration in South Africa
6

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Section B: In respect of the entire household:
5. Are there any persons who are usually members of this household, but who
are away for a month or more because they are migrant workers? (A migrant
worker is someone who is absent from home for more than a month each year
to work or to seek work.)
1 = Yes
2 = No
(If ‘Yes’) Indicate the person’s particulars:
Age in years:
Gender:
Relationship to head of household:
Where is (the person) living:
Name of suburb/village/settlement:
Name of city/town/farm/tribal authority:
Name of magisterial district:
If not South Africa, state name of country:
Introduction
7

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