Report no. 03-04-02 (2006) Statistics South Africa
Pali Lehohla
Published by Statistics South Africa, Private Bag X44, Pretoria 001
© Statistics South Africa, 2006
Users may apply or process this data, provided Statistics South Africa (Stats SA) is acknowledged as the original source
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any form whatsoever without prior permission from Stats SA.
Authors: Pieter Kok (Human Sciences Research Council) and
Mark Collinson (Agincourt MRC/Wits University Rural Public Health & Health Transition Research Unit)
with contributions from: Louis van Tonder (Stats SA), Niël Roux (Dept. of Social Development) and
Michel Garenne (IRD & Pasteur Institute, Paris, France)
Suggested citation:
Kok, P. and Collinson, M. 2006: Migration and urbanization in South Africa. Report 03-04-02, Pretoria:
Statistics South Africa.
Stats SA Library Cataloguing-in-Publication (CIP) Data
Migration and Urbanisation in South Africa / Statistics South Africa, Pretoria: Statistics South Africa, 2006. 38p.
ISBN 0-621-36509-2
1. Migration
2. Urbanisation trends
3. Internal migration
4. Urban growth
I. Statistics South Africa
II. Census 2001
III. Pieter Kok
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Table of contents
INTRODUCTION ................................................................................................................................. 1
ONTEXTUALISATION ................................................................................................................ 1
RIEF OVERVIEW OF THE THEORETICAL LITERATURE................................................................. 2
VERVIEW OF THE REPORT......................................................................................................... 3
INTERNAL MIGRATION IN SOUTH AFRICA OVER TIME ...................................................... 4
IGRATION DEFINITIONS AND OTHER DATA ISSUES ................................................................... 4
Origin and destination................................................................................................. 4
International versus internal migration ...................................................................... 4
Temporary circular migration..................................................................................... 4
Defining migration...................................................................................................... 5
IGRATION LEVELS AND VOLUMES............................................................................................ 7
EASURES AND SPATIAL PATTERNS OF INTERNAL MIGRATION .................................................. 9
IGRATION CAUSES AND CONSEQUENCES.................................................................................. 9
The causes of migration ............................................................................................ 10
Reasons for migration (derived from the Agincourt 1999–2003 study).................... 10
Motives for migration (derived from the 2001–02 HSRC Migration Survey)........... 13
The consequences of migration................................................................................. 14
OLICY AND PLANNING IMPLICATIONS ..................................................................................... 16
URBANISATION IN SOUTH AFRICA............................................................................................ 16
EFINING URBANISATION......................................................................................................... 16
ETERMINING THE 2001 URBANISATION LEVEL ....................................................................... 18
OMPONENTS OF URBAN POPULATION GROWTH....................................................................... 20
ISTORICAL AND EXPECTED FUTURE URBANISATION TRENDS .................................................. 21
LANNING ISSUES ..................................................................................................................... 24
SUMMARY AND CONCLUSIONS .................................................................................................. 25
REFERENCES..................................................................................................................................... 28
APPENDIX........................................................................................................................................... 34
Methods used in the Agincourt Health and Demographic Surveillance System ................................ 34
Definition of a permanent migrant....................................................................................................... 34
Definition of a temporary migrant ....................................................................................................... 34
Definition of a household ..................................................................................................................... 34
Migration typology................................................................................................................................35
List of figures
Figure 1: South Africa’s historical urbanization trends (1904-2001)………………. 22
Figure 2: Urbanization levels per province and for South Africa (2001)…………... 23
List of Tables
Table 1: Former migrants by population group (1975–1980, 1992–1996 and
Table 2: Causes of migration, by migration type, age and sex……………………
Table 3: Agreement/disagreement between 1996 and 2001 locality types………. 19
Table 4: Adjusted 2001 urbanisation levels……………………………………… 19
Table 5: Source of uncertainty for the unknown 2001 usual residence locality
Table 6: Source of uncertainty for the unknown 1996 locality types (at origin) ....
Table 7: Cross tabulation of locality types before and after migration between
1996 and 2001…………………………………………………………..
Table 8: Population dynamics in the Agincourt subdistrict, 1995–2003… 24
In policy debates and in the popular press, migration and urbanisation are often viewed in a
negative light, almost as if they were undesirable problems that need to be rectified or threats
that must be avoided. Looked at from this angle, being sedentary and immobile seems to be
regarded as the ‘right thing to do’. Often governments in urbanising countries want to slow
down or reverse rural-urban migration, not taking into account the fact that migration is often
central to households’ livelihoods (De Haan 2000: 24). What is not understood is the various
forms that migration takes in different settings and that each form may have different
outcomes in terms of health or socio-economic status. A case that will be brought out in
another Stats SA publication is a consideration of temporary labour migration versus
definitive migration, a classification that has unique contours in the southern African
Migration ‘is often seen as the consequence of ruptures, of environmental disaster, economic
exploitation, or political or civil tensions and violence. And it is often perceived to be a cause
of problems, like environmental degradation, health problems, “brain drain”, political or
social instability, declining law and order, and unravelling social fabric and support systems’
(De Haan, 2000: 1). Viewed from these perspectives, it is no wonder that migration tends to
be associated only with problems. What may not always be understood and appreciated is the
fact that migration and urbanisation are processes that offer hope for the future – at least from
the point of view of the individual or household concerned.1 Recent work on the Agincourt
Health and Demographic Surveillance System has shown a positive correlation of household
asset ownership in a rural household if there is a temporary migrant linked to the household
(Collinson et al, 2005).
Migration and urbanisation are therefore processes surrounded by a great deal of controversy,
and in this report an attempt is made to dispel some misconceptions about these two inter-
related processes. The aim of the report will be to describe the different forms of migration
and relate them to urbanisation, examining causes and consequences of migration and
urbanisation and drawing some conclusions from the research for the purposes of policy-
making and planning.
While this report does not deal directly with international and cross-border migration, some
reference will be made to these processes as well. Urbanisation is affected not only by
internal migration but also by migratory moves from across the country’s borders.
Current migration and urbanisation trends need to be placed in a proper historical context.
The legacy of apartheid in South Africa will linger on for some decades, and we need to
understand that the inequities of the past, through discriminatory migration and urbanisation
controls, cannot be driven out with the wave of a magic wand. As shown by Wentzel and
Tlabela (2004), ‘South Africa has a sad history of racially based government interventions in
the movement and settlement patterns of its own people and those from other countries in the
region, with grave effects on the well-being of most of its population. The dramatic political
1 Sometimes migration is also regarded as positive from a broader perspective, as when governments implicitly
or explicitly encourage emigration, such as Turkey, the Philippines, Bangladesh and Jamaica (De Haan 2000:
changes that took place in the early 1990s did remove the cause of this pain for most but not
necessarily the lasting effects. Very poor rural people, trapped in the legacy of the apartheid
homeland policy, have probably found it difficult to escape from their situation.’ They (2004)
indicate that this helps to explain the lack of any significant change in South Africa’s
migration levels between the periods 1975–80 and 1992–96 found by Kok, O’Donovan,
Bouare and Van Zyl (2003). Temporary labour migration, or the capability of a household to
send a migrant to find employment, is a critical factor here. Households that can send a
temporary migrant, or possess livestock assets, are the households that survive the legacy of
the former ‘homeland’ system (Collinson et al, 2005).
There are a few misconceptions regarding migration and urbanisation on the one hand and
economic development and unemployment on the other that need to be refuted. Firstly, based
on the extensive study by the Urban Institute of the United States on the economic
consequences of high levels of migration into California by predominantly low-skilled,
uneducated Mexicans, Gelderblom and Kok (1994: 181–184) show that large-scale in-
migration does not necessarily have a negative impact on the receiving area, provided that it
has a relatively strong economy (as in the case of South Africa’s major cities). In fact, it was
proved by the Urban Institute that California’s high levels of economic activity could actually
be ascribed to its large inflows of migrants (see Gelderblom & Kok, 1994: 184). It is the
same in the case of urbanisation in South Africa, where the urban sectors have developed
from the influx of labour from the hinterlands to dig the mines and work as industrial
labourers, drivers, security guards, etc.
Secondly, and related to the above, is the commonly held misconception that rural-urban
migration causes unemployment. What the migration of (unemployed) persons does, at most,
is merely to displace unemployment. Rural-urban migration can only cause unemployment if
employed rural persons migrate to urban areas and stay there while remaining unemployed.
Such a scenario is most unlikely in view of the high unemployment rates in South Africa’s
rural areas and the relatively lower levels of urban unemployment. The fact that migration
and urbanisation can lead to a displacement of unemployment should therefore not be
confused with a notion that these processes are the causes of unemployment. With temporary
labour migration this negative association with unemployment is unlikely because migration
can be seen as a job-hunting strategy, which is the means of lessening unemployment in the
rural population; formerly this was coerced through the labour migrant system but presently it
is the most promising job-hunting strategy for young people in rural areas.
Despite what has been said above, it is necessary, thirdly, to also mention another
misconception that may be expected to flow from the argument presented above. This relates
to the assumption that areas of high rates of unemployment also have higher out-migration
rates than areas with low unemployment levels. As was shown by Kok et al, (2003: 59), the
1996 census data show that the (predominantly rural) South African districts with high levels
of unemployment are in fact significantly associated with low out-migration rates. This
confirms, amongst other things, the suggestion by Gelderblom (1999) that members of poor
households trapped in impoverished areas do not have access to social (and specifically
migrant) networks that would have assisted them in escaping from their current
Brief overview of the theoretical literature
Migration models or theories can probably be classified into three categories: (1) spatial
models, (2) economic theories and models, and (3) social theories or models. This does not
mean that there were no historical, psychological or development models or theories, for
example, but these other models/theories have arguably been dwarfed in the migration
literature by the three main categories mentioned. The early theories and models of migration
attempted merely to describe the characteristics of migration without a concomitant attempt
to explain the phenomenon. Ernest-George Ravenstein’s (1885, 1889) ‘laws of migration’ are
examples of these approaches.
During the 1940s the spatial models of migration, which had their origins in the so-called
gravity models, were introduced by Steward (1941) and Zipf (1946). Other analysts later
added more variables in an attempt to improve the original gravity models and together these
became known as the so-called spatial interaction models (c.f. Zietsman, 1984). An important
problem with most of these models was that they tried to describe migration patterns with no
or very little underlying migration theory. As Shaw (1975: 49) stated correctly, ‘seeking a
theory to fit the model is indeed an awkward strategy’.
Microlevel migration modelling in the context of economic theory became prominent in the
1960s with the human capital model of Larry Sjaastad (1962) and Michael Todaro’s (1969)
emphasis on expected rather than real income differentials as explanations for rural-urban
migration in the Third World. Very useful reviews of the economic theories and models of
migration are given by Massey et al (1993, 1994). As indicated by DaVanzo (1981) and
Goodman (1981), these so-called microeconomic models brought to the fore the important
issues of uncertainty and imperfect information, and these highlighted the potential
importance of psychological factors (notably risk-taking ability) as another set of dimensions
in migration processes.
At about the same time Julian Wolpert (1965) introduced the notion of ‘place utility’ and
Everett Lee (1966) formulated his ‘general theory of migration’. These contributions,
together with the important earlier work of Peter Rossi (1950), paved the way for the social
theories and models of migration that came to be developed by Alden Speare (see Speare
1974; Speare, Kobrin & Kingkade, 1982), who built further on Rossi’s work to introduce the
concept of ‘residential satisfaction’, and by Gordon De Jong, who developed and later refined
the so-called value-expectancy model of migration decision-making (see De Jong & Fawcett,
1981; De Jong, 2000).
In a related report published by Stats SA (Migration and changing settlement patterns) a
discussion of the literature that covers the economic and sociological explanations of the
links between temporary migrants and rural households is given, and explains the high
presence of temporary migration in the former ‘homeland’ communities of South Africa.
Although no testing of the abovementioned theories and models are envisaged for this report,
it is important to note that migration has many dimensions and therefore cannot be properly
analysed without taking into account spatial, economic and social factors – to name but a
Overview of the report
The main components of the remainder of this chapter are (1) internal migration in South
Africa, where appropriate with reference to three periods in the country’s history (1975–
1980, 1992–1996 and 1996–2001) although the main emphasis here falls on the period 1996–
2001, (2) South Africa’s urbanisation processes and history, and (3) a summary of the
findings. Wherever possible and appropriate, attention will be given to the policy and
planning implications of the observed patterns and trends.
While this report and the one on migration and changing settlement patterns have some
common features, the emphasis in this report is on the broad migration and urbanisation
processes in South Africa. In contrast, the report on migration and changing settlement
patterns deals more extensively with the detailed patterns of migration and urbanisation in
this country. These two reports, although different in approach, provide a logical and
coherent perspective on the phenomena of internal migration and urbanisation in this part of
the world.
In order to compare migration levels and trends over different time periods it is necessary to
adopt a common definition. The problems associated with migration definitions are therefore
discussed briefly in the next section. Following this is a description of the levels and volumes
of migration, followed by an analysis of the measures and spatial processes of migration in
South Africa. A brief discussion of the causes and consequences of internal migration in this
country then follows, and some policy and planning implications of the observed internal
migration and urbanisation patterns and trends are considered at the end of the report.
Migration definitions and other data issues
There are some basic concepts used in migration studies that need to be briefly clarified here
before the definition of migration is discussed in more detail. These relate to migration
‘origins’ and ‘destinations’ and ‘internal’ versus ‘international’ migration.
Origin and destination
Every residential move has an origin (which is the place from where the person moves) and a
destination (i.e. the place where the specific move ends). The origin and destination of a
residential move can be in the same country/area or in different countries/areas.
International versus internal migration
If the migratory move involves the crossing of a national boundary, it is known as
international migration. The person involved in such a move is simultaneously called an
emigrant (from the perspective of his/her country of origin) and an immigrant (when viewed
from the country of destination).
If both the origin and destination of a specific migratory move are in the same country, the
move constitutes internal migration. If the origin and destination are in the same country, the
person who migrates from a particular place is called an out-migrant from that area, and at
the same time he/she is an in-migrant into the area of destination.
Temporary circular migration
The characteristic pattern of labour migration in southern Africa, which arose through policy
and cultural adaptation over many generations, laid the foundation for the definition of a
temporary circular migrant. A household based in a rural or peri-urban setting can have one
or more linked temporary migrants remitting money back from another, usually urban, place
of work. Circular migration represents a large proportion of the movement among the black
African population. A migration is circular when the usual place of residence (de jure)
remains in the rural or peri-urban setting, but a person migrates usually for employment or
education purposes, and stays connected to the ‘sending’ household through communication,
regular return visits and with a high likelihood of cash or non-monetary remittance.
Defining migration
As Skeldon (1990) correctly points out, migration analyses should preferably not be restricted
by what the available data have to offer, but the reality is that an analysis of census-based
migration data inevitably has to fit in with the available data. Other migration survey data
may be less restrictive. Therefore, defining ‘migration’ for a particular study is not a mere
pedantic exercise but a crucial component of migration research. Users of the research
findings need to know what criteria were used to distinguish migrants from non-migrants.
Although it is important for researchers to develop and apply conceptually sound definitions
of migration and not data-driven conceptualisations, this does not always seem to be possible.
For example, a key question in migration analysis is: What constitutes the areas (spatial units)
to or from which moves can be classified as migration? Standing (1984) points out that the
limits often placed on the concept ‘area’ can be largely arbitrary or become a mere
expediency. In his experience these are usually determined by the administrative unit
identified in censuses or surveys – to the detriment of scientific inquiry: ‘Somewhat
remarkably, most demographers and other social scientists have let statisticians and survey
administrators determine the areas between which moves are classified as “migration”. In
principle, this surely cannot be generally acceptable. Indeed, it has been said that areas
between which moves count as migration are first defined by bureaucrats and later
rationalised by social scientist researchers’ (Standing, 1984: 32).
Peter Morrison, while echoing Guy Standing’s sentiments, acknowledges the many practical
problems experienced by migration researchers, because they hardly ever have the luxury of
dealing with large, disaggregate data sets. ‘Instead they must content themselves with data
that only partly satisfy their conceptual requirements’ (Morrison, c.1980: 8). He warns,
though, that any statistical limitations of that nature would not only inhibit the development
of theory but also distort observation. In fact, the relationship between concept and
measurement can become quite perverse if analysts start manipulating the concept to fit the
data available. It is necessary to adapt the available data to the conceptual requirements
(Morrison, c.1980: 8). In this way theory or ‘concepts of the study’ remains based on reality.
However, with each means of collecting migration data we encounter a limitation in the data-
collection instrument that constrains the data that a system can manage. One should therefore
interrogate the accuracy and reliability of each instrument and also recognise the conceptual
limitations within any study. It is shown in the report on migration and changing settlement
patterns that in reality a large proportion of the rural-to-urban migration is of a temporary
nature. However, in the 1996 and 2001 censuses this is concealed in the national-level
migration data. The census data report migration in terms of origin and destination, with the
limitations described above, but are unable to identify temporary circular migration or
migration with multiple destinations. Nevertheless, each data set has a critical role to provide
empirical evidence on migration, and can be interpreted in the light of these limitations.
The analyses described here are based on the migration community profile data for the
different local/metropolitan governments that were provided by Statistics South Africa (Stats
SA) in respect of the 1996 and 2001 censuses.2 The 1992–1996 analyses were undertaken for
data originally provided by Stats SA from Census 1996 at an enumerator area (EA) level of
enumeration and then aggregated by the researchers to the magisterial district level. For the
1996–2001 analyses Stats SA made the Census 2001 data available at various spatial levels
(including the ‘magisterial district’ and ‘main place’ levels).
While it seems fair to suggest, as was done by Kok et al (2003), that one should, ideally,
define migration formally as the crossing of the boundary of a predefined spatial unit by
persons involved in a change of residence, this is not always possible. For example, if one
wishes to analyse South African census-based migration data over different intercensal
periods it should be understood that the 1980 and 1996 migration data were restricted to
moves between different places of ‘usual residence’. In the case of Census 2001 Statistics
South Africa did away with this restriction and reported migration-origin data without
reference to ‘usual residence’ in respect of the place of previous residence.3 Strictly speaking,
comparisons between different periods are not justified, but it is believed that the impact of
the differences in respect of these de jure (1980 and 1996 censuses) and de facto (2001
census) migration levels and volumes will be negligible.
The 1980 census data relate to migration over a fixed-period, five-year interval between
magisterial districts of usual residence. In the 1996 census migration data were collected for
all individuals in respect of the last move that had been made from one magisterial district of
usual residence to another. The year of that move was also recorded. As mentioned before, in
the case of the 2001 migration data the origin of the last move was not required to have been
the ‘place of usual residence’ before the move. The authors therefore decided to use, with a
view to ensuring internal consistency, the place of enumeration at the time of Census 2001
instead of the 2001 ‘place of usual residence’. The important point to be made in this regard
is therefore that while the 1975–1980 and 1992–1996 migration analyses were done on a de
jure (place of usual residence) basis, the migration analyses for 1996–2001 were done on a de
facto (place of enumeration) basis in as far as place of origin is concerned.
The migration data from the 2001 census were based on the place of residence at a fixed
previous date (Census Day in 1996). One problem with this approach is that persons who had
lived in the same electoral ward at the time of Census 1996 as the one in which they were
enumerated at the time of Census 2001 were regarded as non-migrants irrespective of where
they might have migrated in the five years in between and then returned. Another problem
(especially important from a demographic perspective) is that no migration data are available
in respect of children born between the two censuses. This means that children aged 0–4
years at the time of Census 2001 could not be included in most of the migration analyses
presented here.
The data structure as it relates to migration also poses a problem regarding the place of
destination, i.e. the place of census enumeration or de facto household. In Census 2001 there
was no way of knowing which households had linked temporary migrants, or which
households had de jure household members ‘temporarily’ living somewhere else.
2 The kind assistance of Mr Piet Alberts and Ms Lana Evtimova of Stats SA in providing the requested data is
gratefully acknowledged.
3 Information on the current place of ‘usual residence’ was, however, also made available to make it possible to
conduct de jure analyses of the current distribution of the population.
All censuses are plagued by problems of under-enumeration. These under-enumerations are
not spread evenly geographically and their effects thus become more pronounced as one
moves down from the national level to lower spatial levels (e.g. down to magisterial district
level, as needed to be done here). While the use of weights (provided by Statistics SA) in all
the analyses reported here might have reduced the overall impact of such an under-
enumeration, they could not overcome any spatially selective under-enumeration problems.
The three periods under scrutiny here are firstly from 6 May 1975 to 5 May 1980, secondly
from 1 January 1992 to 10 October 1996 and thirdly from 11 October 1996 to 10 October
2001. While the first and third periods are exactly five years, the second period is somewhat
shorter because the census collected migration data only in respect of years (i.e. not years and
months as is often the case in sample-based migration surveys). Strictly speaking, therefore,
comparisons between the three periods should take this slight time difference into account,
but the effect is expected to be small and will thus not be accounted for here.
Migration levels and volumes
It was suggested earlier that migration should be defined both theoretically and operationally
as the crossing of the boundary of a predefined spatial unit by persons involved in a change
of residence. In Census 1980 and Census 1996 the spatial unit of analysis for both place of
origin of the last move and place of enumeration was the ‘magisterial district of usual
In the case of Census 2001 the issue is, however, somewhat more complex. The migration
data were made available in respect of current ‘main place’ of ‘usual’ residence or the place
of enumeration, while the place of migration origin was not required to be the ‘usual main
place of residence’. (Fortunately, only 1,5% of the population, being enumerated in a place
different from where they ‘usually lived’, was affected4). The migration community profile
data sets that were requested from Stats SA covered only area of enumeration though, and
can therefore be regarded as internally consistent.
The proportion of the population that migrated over a defined time period reflects the level of
migration in the district, province or country. Table 1 gives the proportion, for each
population group, of migration levels during the period 11 October 1996 to 10 October 2001.
The corresponding figures for the periods 6 May 1975 to 5 May 1980 and 1 January 1992 to
10 October 1996 are also given. During all these three five-year periods, only about one in
eight (11–13 per cent) of South Africans migrated. The implications of these surprisingly
consistent migration levels are described in some detail by Kok et al (2003).
The largest population migration stream by race is the black population with 3 754 379 who
were migrants over the period 1996–2001. In the section on urbanisation below, we can see
that approximately half the black population is urban and the other half rural. South Africa’s
white population has consistently been far more migratory than the other three groups. This
can probably be ascribed to whites’ historically widespread distribution over all nine
provinces, possibly giving them access to social (migrant) networks in many parts of the
country, and possibly also better access to the economic and other resources needed for
longer-distance migration.
4 In an attempt to identify any potential bias, additional analyses were undertaken to look for demographic
differences. These analyses showed that the effects of these differences are probably negligible.
Table 1: Former migrants by population group (1975–1980, 1992–1996 and 1996–2001)
Number and percentage migrants (having moved during the five-year period concerned)
1975–1980 1992–1996 1996–2001
group 1980
(based on the
5% sample)
(full census)
(1992–1996) Proportion
(full census)
(1996–2001) Proportion
Black African* 9 916 560* 894 000* 9%* 31 127 630 2 909 948 9% 35 416 070 3 754 379 11%
Coloured 2 251 480 228 980 10% 3 600 447 331 321 9% 3 994 570 500 460 13%
Indian/Asian 706 600 63 720 9% 1 045 595 125 664 12% 1 115 540 150 087 13%
White 4 041 220 1 023 420 25% 4 434 695 921 514 21% 4 293 597 1 136 722 26%
Total* 16 915 860* 2 210 120* 13%* 40 208 367 4 288 447 11% 44 819 777 5 541 649 12%
* The 1980 census excluded the (mainly black African) population of the former Transkei, Bophuthatswana and Venda.
Sources: (a) 1975–1980: The 5 per cent sample of the 1980 population census, as provided by the then Department of Statistics. (Please note: Both current
and previous place of residence refer to ‘place of usual residence’. ‘Magisterial district’ is the spatial unit of analysis here).
(b) 1992–1996: The migration community profile from Census 1996, as provided by Statistics South Africa. (Please note: Only persons with a
valid and a current place of usual South African residence were included here. The migration origin is also the previous place of
usual residence. Again the spatial unit of analysis here is ‘magisterial district’).
(c) 1996–2001: The 10 per cent sample from Census 2001, as provided by Statistics South Africa, was used to calculate the number of migrants
per population group. (Please note: There was no requirement here that the last move must have taken place from the previous
area of ‘usual residence’, but the destination was defined as being the current ‘place of usual residence’. ‘Main place’ is the
spatial unit of analysis here).
The Agincourt data given in the report on migration and changing settlement patterns show
that for every permanent migration there were three temporary migrants in the population of
the subdistrict under demographic surveillance. Therefore, for this migration area alone more
than a million migrations in the period could have been of a circular temporary type. It is
important to highlight this migration stream type since it is a direct legacy of the pre-
democratic apartheid system, and it is also strongly tied to transformation and livelihood
strategies of the rural and peri-urban population. These are the most impoverished people in
the country. Also, as mentioned above, the migration definition tends to obscure the presence
of labour migration in de facto population data sets. This all the more increases the chance of
overlooking the challenges of this component of the population.
One question still remains to be answered fully, though: Has there been an increase in the
migration propensity of the South African population in recent years? This question cannot
be answered entirely on the basis of the evidence provided in Table 1, because the 1996–2001
figures presented there are not entirely comparable to those for the period 1992–1996. The
1992–1996 figures (as for the period 1975–1980) were based on migration between
‘magisterial districts’, while those for 1996–2001 were based on residential moves between
‘main places’ and were also derived from only a sample of the population. A separate
analysis, based on one of the sets of migration community profile data from Census 2001
provided by Stats SA, dealt with moves between magisterial districts by the total population
(i.e. not differentiated in terms of population group, age or gender), and showed that
3 544 784 persons (South Africans and foreigners) moved between 1996 and 2001 (7,9%)5 as
compared to the 3 382 026 who had migrated between 1992 and 1996 (8,9%). When
interpreting these figures it should be borne in mind that the latter period was notably shorter
than five years, but at the same time it should also be remembered that the 1996–2001 figures
exclude children in the age group 0–4 years. These problems make it difficult to draw a more
direct comparison between migration levels in the two periods. However, when all the
limitations are taken into account, it seems that there probably was no significant difference
in overall migration levels between these two periods. Nevertheless, this would not preclude
a change in the proportions of moves in the different migration categories, such as permanent
and temporary.
Measures and spatial patterns of internal migration
One of the key measures of migration is the family of so-called migration rates. Rates can be
calculated to express in-migration, out-migration and net migration in terms of population
size. For a more detailed description of migration rates see Kok (forthcoming).
Migration causes and consequences
There is clearly a need for a properly nuanced evaluation of the causes and consequences of
migration. Census data do not provide a suitable basis for determining the causes of
migration. Purpose-made migration surveys and interviews are needed to get an
understanding of why people move, and why some people from the same area do not.
5 When this figure is compared with the estimated migration volume of 5 541 649 during the period 1996–2001
as derived from the 10% sample of Census 2001, huge differences in the two volumes become evident. These
can be explained by the different geographical sizes of the ‘migration-defining’ spatial units (i.e. between ‘main
places’ and ‘magisterial districts’).
The causes of migration
The causes of migration are theoretically complex, multilevel in nature, difficult to
determine, and not easily generalisable. A number of studies have been undertaken on this
topic in different parts of the world, but because of the inherent intricacy of the required
research, the theory needed for determining the causes of migration has only come of age
during the last two decades or so, following the important contributions of De Jong and
Gardner (1981), Massey et al (1993, 1994) and De Jong (2000). Although the glib view is
that migration is caused simply by economic factors and considerations, it is clear from the
migration literature that the causal and perpetuation factors in migration are both economic
and non-economic in nature (see, for example, Kok et al, 2003: 13–28).
Reasons for migration (derived from the Agincourt 1999–2003 study)
Before we can discuss the reasons for migration as identified by the Agincourt Health and
Demographic Surveillance System, it may be necessary to take a quick look at the migration
research done by Agincourt research. This background information and some cautionary
notes are provided in the Appendix.
Migration rates can be obtained from Table 2 by dividing the total number of migrations in
each of the migration categories by the total population of 68 500. This gives an in-migration
rate for the whole population of 6.5 per 1 000 population per year and an out-migration rate
of 7.3 per 1000 per year, but the most impressive rate is temporary migration at 17.7 per
1 000 population per year. Because of internal migration (within-site mobility) 4.4 per 1 000
are counted in both the permanent in-migration rate and out-migration rate. These can be
removed from either rate to get the actual rate of (permanent) in-migration into the study site
from outside the site perimeter and the out-migration rate from within the study area to a
destination beyond the perimeter of the surveillance operation.
Reasons for moving can be analysed by age and sex to see the primary reasons for move in
each age-sex combination. Table 2 shows reasons for move (by age and sex) for each
migration type to facilitate comparability.
The first column gives the totals of each migration stream and the breakdown by reason as
percentages of the total in each migration category. Here it can be seen that the primary
reasons for permanent adult migration were the start or end of a marital union, a person
moving to stay with a spouse or other family member, or a new dwelling for the whole
household. Temporary migration was seldom engaged for these reasons. On the other hand,
‘work’ was a primary driving force behind temporary migration. Other important reasons for
temporary migration were schooling, studying, or looking for work. These were seldom cited
reasons for permanent migration. In this way the data on causes of migration support the use
of the migration typology because it indicates that the migration flows by reason are
reasonably mutually exclusive.
For children of both sexes there were large numbers of all kinds of moves: (a) permanent
moves took place to accompany parents or for other family-related reasons, and (b)
temporary moves were made to live with another family member, whether for schooling or
Table 2: Causes of migration, by migration type, age and sex
Total Age: 0–14 years Age: 15–34 years Age: 35–54 years Age: 55 +years Migration type and reason category Total-
sexes F M F M F M F M F M
In-migration (2002)
Number of cases 4 473 2 727 1 746 1 009 894 1 370 640 241 164 107 48
Marriage (start/end) 17% 27% 2% 1% 0% 47% 1% 35% 7% 4% 21%
Moving to live with another 12% 10% 16% 15% 16% 9% 19% 1% 4% 0% 0%
New dwelling for household 14% 13% 16% 0% 0% 17% 27% 39% 56% 23% 35%
Work 2% 1% 2% 0% 0% 2% 3% 2% 11% 1% 8%
Looking for work 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Health 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
School/study 0% 0% 0% 0% 0% 1% 1% 0% 0% 0% 0%
Child accompanies parent move 36% 31% 43% 66% 68% 12% 21% 7% 2% 8% 2%
Other/unknown 18% 17% 21% 18% 15% 12% 28% 15% 20% 64% 33%
Out-migration (2002)
Number of cases 5 152 3 001 2 151 1 129 1 071 1 485 797 269 223 118 60
Marriage (start/end) 17% 27% 2% 1% 0% 49% 2% 28% 9% 9% 18%
Moving to live with another 14% 12% 17% 18% 17% 9% 22% 2% 3% 0% 0%
New dwelling for household 20% 18% 23% 3% 3% 22% 35% 50% 61% 38% 47%
Work 1% 0% 1% 0% 0% 1% 1% 1% 5% 1% 0%
Looking for work 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Health 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
School/study 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Child accompanies parent move 34% 30% 40% 65% 65% 11% 20% 4% 3% 3% 2%
Other/unknown 14% 13% 17% 14% 14% 9% 19% 14% 18% 48% 33%
Table 2 (continued)
Total Age: 0–14 years Age: 15–34 years Age: 35–54 years Age: 55 +years Migration type and reason category Total-
sexes F M F M F M F M F M
Temporary migration (2002)
Number of cases 12 136 4 139 7 997 699 691 2 144 4 232 1 187 2 608 109 466
Marriage (start/end) 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Moving to live with another 6% 15% 2% 5% 5% 18% 2% 15% 1% 12% 5%
New dwelling for household 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%
Work 67% 51% 75% 0% 1% 50% 73% 81% 95% 83% 92%
Looking for work 6% 4% 6% 0% 0% 7% 10% 2% 3% 1% 2%
Health 0% 0% 0% 0% 0% 0% 0% 0% 0% 2% 1%
School/study 12% 18% 9% 38% 32% 22% 11% 1% 0% 0% 0%
Child accompanies parent move 7% 10% 6% 56% 60% 2% 1% 0% 0% 0% 0%
Other/unknown 2% 1% 2% 1% 1% 2% 3% 1% 1% 2% 1%
Source: Original data based on the Agincourt Demographic Surveillance System
Young adults were the age category most likely to produce a migration of one kind or
another. Women aged 15–34 years were very likely to conduct permanent migration for
marriage reasons. Culturally, this is usually associated with a dowry or bride wealth
transferred from the destination household to the household of origin. Household moves were
another important reason for people in this age group. Male and female young adults were
both engaged in temporary migration for reasons of employment and schooling. For female
temporary migrants a higher percentage of the reasons was for schooling, and for males
working was the reason provided at a higher percentage.
For older adult age groups the likelihood of movement was less. Marriage-related reasons
were still important for women and increasingly so for men, and work-related temporary
migration was relevant for both sexes.
To summarise: with regard to ‘reason for migration’ in Agincourt, employment was the
driving force behind temporary migration, while households moving to better situations, and
moving for reasons of union formation or dissolution were primary drivers of permanent
migration among young women. Children were mobile in all categories (with or without
Motives for migration (derived from the 2001–02 HSRC Migration Survey)
At the national level some work has been done by the HSRC with a view to determining the
causes and consequences of migration in South Africa. Kok (2004b) describes a 2001–02
national sample survey that, together with the important census data, provides evidence that
almost one-quarter (24%) of the 3 618 respondents in the survey were planning to migrate
(permanently) during the next five years, while an additional four percent indicated that they
intended to move temporarily. Of course not all these intentions will be converted into actual
migration, though, with unanticipated external obstacles or even personality constraints (e.g.
low levels of risk-taking ability and efficacy)6 preventing many of these planned moves.
The motives for planned ‘permanent’ moves recorded in the survey are analysed by Kok and
Aliber (2005) in respect of moves from the Eastern Cape, Northern Cape and Limpopo to the
nine major cities in South Africa, while Cross et al (2005) analyse the motives for intentions
to migrate to Gauteng. Both these sets of analyses use place-related expectations, weighted
by the values attached to the underlying goals, as the primary determinants of migration
These two studies show that people intend to migrate (‘permanently’): (a) when their
expectations for the current area become lower than those in respect of an alternative place of
residence, (b) which are often influenced by the information received about the alternative
place of abode from relatives and friends living there, (c) if they have reason to believe that
these networks at the possible destination will provide assistance and support during and after
the move, and (d) when they become sufficiently dissatisfied with their lives in the current
area of residence. (e) Although most people do not necessarily prefer to move to the
6Kok (2004a: Table 6) shows, for example, that efficacy (the ability to ‘get things done’) has a significant (p
0,1%), direct and positive effect on risk-taking ability. Risk-taking ability, in turn, has a significant (p 1%),
direct and positive effect on a person’s income, which in turn has a significant (p 0,1%), direct and positive
effect on the person’s level of satisfaction with life on the whole. In turn, life satisfaction, has a significant (p
0,1%), direct and negative effect on a person’s intention to move permanently to another area. The ‘meaning’ of
this somewhat technical account is that areas with high out-migration rates tend to shed those residents they can
least afford to lose, and these migrants tend to leave because they are dissatisfied with their lives.
metropolitan cities, they frequently end up there because of the factors described above. (f)
High poverty levels in the (local government) area where people reside are an inhibiting
factor in the decision to move away permanently, indicating that a significant proportion of
people in very poor areas may be trapped there. (g) People with a higher score on the scale
for risk-taking ability are more likely to plan a migratory move than their more risk-averse
counterparts, while (h) younger, unmarried adults (especially in the age category 18–29
years) will be more inclined to migrate than their older, married counterparts, and (i) persons
who have migrated before are more likely to consider migrating again. Other factors
associated with an intention to migrate are: (j) a higher educational attainment, (k) being a
black African person, and (l) not currently living in a metropolitan city.
The consequences of migration
As far as the consequences of migration are concerned, the issues involved may perhaps be
less complex to determine than for the causes of migration, yet they are also frequently
misunderstood. Research into migration consequences should aim to contribute to the debate
by studying the following four perspectives as suggested by, amongst others, the Population
Information Program (1983):
(a) The migrants and non-migrants themselves (to be determined mainly by means of
longitudinal surveys that deal specifically with the impact of migration and urbanisa-
tion for the individuals concerned and their families, also covering related issues, such
as non-family cohabitation (described by, for example, Katz, 2001)
(b) The areas of origin, covering issues such as ‘brain drain’ (see, for example, Crush,
2000; Ushkalov & Malakha, 2001; Brown, Kaplan & Meyer, 2001). Circular labour
migration may have positive economic and health effects on the ‘sending’ household
(Collinson, 2005a; Kuhn, 2003), however there can also be negative effects associated
with this type of move, including increased exposure to HIV/AIDS and other sexually
transmitted diseases (Lurie, 2001; Collinson, 2005b)
(c) The areas of destination, not only looking at economic effects and the impact on
infrastructure and service delivery (see, for example, CDE 1997), but also taking
cognisance of issues around citizenship, xenophobic sentiments and related
discriminatory actions (see for example Reitzes, 1995; Mattes et al, 2000b) and more
indirect effects, such as the impact of migration on education (as described by, for
example, Potgieter & Bredenkamp, 2002)
(d) The subcontinental/national perspective (e.g. for the entire southern African region and
the individual countries concerned), including the implications of the ‘brain
gain/circulation’ in the region (described by, for example, Mattes, Crush & Richmond,
The consequences of migration on population size can at least partly be derived from census
data. Censuses provide an excellent opportunity to draw conclusions regarding the effects of
migration from at least two spatial perspectives, namely that of the area of origin and the area
of destination. In the subsections below these consequences are described briefly, where
appropriate in the context of migration from the rural province of Eastern Cape to the South
African CitiesNetwork’s nine major cities (Buffalo City, Cape Town, Ekurhuleni, eThekwini,
Johannesburg, Mangaung, Msunduzi, Nelson Mandela and Tshwane). The Eastern Cape can
perhaps be viewed as a case study for the purpose of highlighting the consequences of
migration, also in respect of migration from remote or rural areas to the major
urban/metropolitan centres in the country.
Consequences for the area of origin
Kok and Aliber (2005) show the dramatic effects of migration from the Eastern Cape7 to
Cape Town among younger adults (especially those aged about 15–34 years) and their
children. More specifically, on average more than 20 000 persons in the age group 20–24
years were part of this particular migration stream in each of the two five-year periods, as
compared to the fewer than about 5 000 moving to the other cities over the same two periods.
It may be necessary to state the obvious though: provinces losing migrants might have been
worse off if these people had been prevented from moving away. Many of these young
people migrate in search of better employment, education and life-style opportunities in the
cities. Preventing them from moving would create higher levels of dissatisfaction and
therefore an even far greater desire to move. South Africa’s sad experience with influx
control should be a case in point.
Collinson et al showed a strong correlation between temporary circular migration and socio-
economic status as measured by the household’s possession of modern assets or consumer
durables (Collinson, 2005a). This relationship was supported by household head education-
status data, which correlated positively with both circular migration status and ownership of
modern assets. In addition, cash or non-monetary transfers was (on the aggregate) a
significant income stream for the rural households.
There is a growing literature on the relationship between temporary migration and the
HIV/AIDS epidemic (Jochelson et al, 1991; Lurie, 2001; IOM, 2002; IOM & UNAIDS,
2003; IOM & Care International, 2003; IOM & SAMP, 2005). A study of the zero-
prevalence of HIV in rural KwaZulu-Natal found a threefold higher risk of HIV infection
associated with a recent migration (Abdool Karim et al, 1992). The mechanism underlying
this risk is that migrants are more likely than non-migrants to practise unsafe sex with
multiple sexual partners (Lurie et al, 1997). However, recent studies show that the link
between migration and HIV transmission may be more complex than first suggested, and that
both communities of origin and communities of destination are affected by the high levels of
migration (Lurie et al, 1997; Dladla et al, 2001). Evidence from the Agincourt Health and
Demographic Surveillance System also strongly suggests that increasing numbers of circular
labour migrants of prime working age are becoming ill in the urban areas where they work
before coming home to be cared for and eventually to die in the rural areas where their
families live (Clark et al, 2005).
Consequences for the area of destination
The opposite probably applies to areas that experience a net gain of these permanent
migrants. To a large extent their gain is the loss of the areas shedding migrants. In Cross et al
(2005) some of the spatial poverty consequences of migration to Gauteng, South Africa’s
main migration destination, are discussed. This follows on the report on migration to Gauteng
by Oosthuizen, Peberdy et al (2004), which shows that, while Gauteng has been successful in
attracting many highly educated persons from other provinces, in-migrants tend to be
employed in less skills-intensive sectors – notably women migrants in domestic employment.
7 It should perhaps be pointed out that, for migration from the ‘Eastern Cape’ to Buffalo City and Nelson
Mandela Metro, migrants from within the destination city concerned are excluded. However, migrants from the
other city in the Eastern Cape (e.g. Buffalo City) are included in the stream to that city (e.g. Nelson Mandela
These migrants, being predominantly young adults in their reproductive years, can be
expected to contribute significantly to the natural increase in these cities. These migrants also
add to the receiving cities’ pool of young entrepreneurs with the required personality
characteristics to contribute to economic growth in the destination cities (such as the risk-
taking and efficacy aptitudes mentioned earlier).
Policy and planning implications
There are various policy options in respect of migration, and a number of potential planning
responses are available to governments, be it internationally, nationally or locally. It is safe to say,
however, that no universally viable policy response has so far been found. As is shown by Kok,
Gelderblom and Van Zyl (2006), periodic changes in the views on migration-related problems
were reported by the United Nations (2003) for the governments of South Africa and four of
its immediate neighbours.
Even though internal migration in a country is sometimes regarded as unacceptable by policy
makers or planners, the reality is that people’s spatial movements cannot easily be stopped or
even redirected (see, for example, Kok, 1986; Kok & Aliber, 2005). Migration also has a
direct and important impact on urbanisation, and this impact is usually the one of most
concern to those who must plan for meeting future needs for infrastructure, facilities and
services. The next session deals with the process of urbanisation.
The greater propensity among developing countries to intervene in the processes of
population redistribution than their industrialised counterparts can probably be ascribed to the
perceived detrimental consequences of the rapid urban population growth that is taking place
in many of these countries (United Nations 2003: Highlights, p. 14).
In this section an attempt will firstly be made to define urbanisation. This will be followed by
an attempt to determine the 2001 urbanisation level in South Africa. Then follows an analysis
of the components of urban population growth and thereafter a description of historical and
possible future urbanisation trends is given. That is followed by a brief look at the policy and
planning implications of these patterns and trends.
Defining urbanisation
Urbanisation is probably best defined in terms of the so-called balancing equation in respect
of urban population growth, which can be written as follows to describe the process of
Ut = U0 + (Bu – Du) + (Iu – Ou) + r8
8 The so-called balancing equation in demography in terms of which a country’s population growth can be
described, is as follows (in a somewhat simplified form):
Pt = P0 + (B – D) + (I – E) + e
where: Pt = Total population at time t (i.e. at the end of a time interval of length t);
P0 = Total population at the beginning of the time interval;
B = Number of births that have taken place during the time interval;
D = Number of deaths that occurred during the time interval;
I = Immigration volume during the time interval;
E = Emigration volume during the time interval, and
e = error term (or the so-called element of closure).
where: Ut = Urban population at time t (i.e. at the end of a time interval of length t);
U0 = Urban population at the beginning of the time interval;
Bu = Number of urban births that have taken place during the time interval;
Du = Number of urban deaths that occurred during the time interval;
Iu = Urban in-migration volume during the time interval;
Ou = Urban out-migration volume during the time interval; and
r = Population reclassified from ‘rural’ to ‘urban’ during the time interval.
Similarly, the (Bu–Du) in the above equation is the natural increase of the urban population
during the time interval t, while the (Iu–Ou) is the urban net migration over period t.
Urbanisation can therefore be defined formally as the increase9 in the urban population of a
country or area due to the following components of urban population growth: (a) urban
natural increase, (b) urban net migration, and (c) the reclassification of parts of the rural
population into the category ‘urban’ (due to the sprawl of existing urban areas into their rural
surroundings or the development of new towns in former rural areas).
The level of urbanisation can be given in terms of the following equation:
PU =
where: PU = Proportion of the population living in urban areas (i.e. the urbanisation level)
at a particular point in time (and for the purposes of the analyses that follow,
Census Day in 1996 and 2001, i.e. 10 October, is used);
U = Urban population;
P = Total population; and
k = multiplication factor (usually 100 to obtain a percentage value).
The above equation is deceptively simple. Although the indicator ‘level of urbanisation’ is
frequently used, it should be pointed out that there are serious scholarly debates regarding the
appropriateness of such indicators for denoting the urbanisation level. The main problem is
not with the equation as such, but lies with the definition of ‘urban areas’ (as distinct from
‘rural areas’). There is a body of academic literature criticising the use of the dichotomy
‘urban’ versus ‘rural’, when there are so many areas that cannot unambiguously be defined as
belonging to either the one or the other category. There is consequently an influential school
of thought that insists on the use of a rural-urban continuum instead of the rural/urban
dichotomy. Although this issue will not be discussed here, the reader is referred to Kok
(forthcoming) for a summary of the debate in South Africa.
Moreover, as indicated by, amongst others, United Nations (2001), urban and rural parts of
countries are becoming increasingly integrated as a result of better transport and
communications, rural-urban and return migration, urban economic activities spreading to
rural areas (rural industrialisation) and rural economic activities pursued in urban areas
(urban agriculture). The distinction between urban and rural areas has thus become quite
Defining ‘urban’ and ‘rural’ is therefore not as simple as may appear at first sight. If the
general practice of defining the rural population simply as ‘the residual population after the
9 Since urban natural increase and urban net migration in a country are virtually always positive, the urban
population ‘change’ is almost invariably an increase.
urban population is distinguished’ (Shryock, Siegel & Associates, 1976: 83) emphasises the
necessity to have a good definition of what the term ‘urban area’ entails. Shryock, Siegel and
Associates (1976: 83–84) deal extensively with the practices in various countries in the
world, and describe a six-category classification previously suggested by the United Nations.
In the South African censuses between 1904 and 1946 and again between 1980 and 1996 the
only criterion for an area to be classified as ‘urban’ was that it had to have been governed by
some form of (urban) local government.10 What is important now is that it has, since 2000,
become very difficult, if not outright impossible, to define urban areas in terms of their local
government status. The criterion of a ‘local government area’ has thus become almost
meaningless in present-day South Africa. Statistics SA have considered the idea of using
settlement density as a criterion (Statistics South Africa 2003), but this has proved not to be a
solution to the problem, partly because a single criterion cannot sufficiently distinguish
between rural and urban areas.11
The best way to define ‘urban’ and ‘rural’ is therefore to do so in terms of a number of
factors instead of using one single criterion only. Furthermore, the classification is probably
done best after the census at the census office concerned (when appropriate and up-to-date
data have become available). This is probably done best at the enumerator area (EA) level,
every time dealing with a particular EA as well as the majority of directly abutting EAs). This
will ensure that only contiguous EAs are classified in this way, thereby avoiding the
classification of isolated EAs. Factors to be considered should preferably include (1)
economic criteria, e.g. majority of the labour force of the area (e.g. the EA concerned)
engaged in non-agricultural pursuits (for urban and vice versa for rural), and (2) demographic
indicators, e.g. minimum population density, and (3) urban characteristics, e.g. residential
areas with formally aligned (but not necessarily tarred) streets close to commercial
enterprises and educational, health and other services. The latter is clearly difficult to
standardise effectively, but while it may be easier to use only the first two criteria (as was
done by Graaff 1986) the problem is that these do not deal effectively with higher-density
settlements in the former homelands of South Africa that lack the important characteristics to
justify their classification as ‘urban’. This problem brings us back to the need for a threefold
(such as ‘rural’, ‘semi-urban’ and ‘urban’) or fourfold (e.g. Graaff’s 1986 ‘rural’, ‘semi-
urban’, ‘peri-urban’ and ‘urban’) classification that ‘would describe the situation better than a
dichotomy’ (Shryock, Siegel & Associates, 1976: 84). In the Stats SA report on migration
and changing settlement patterns a fivefold settlement classification is used to investigate
changes in settlement patterns due to migration.
Determining the 2001 urbanisation level
In the absence of more appropriate data to allow a classification on the basis discussed above,
the following procedure has been adopted by the authors to arrive at a suitable urbanisation
level for 2001. By inspecting the differences between the 1996 and 2001 enumerator area
(EA) types for the total population, and assuming that all 2001 ‘tribal areas’ were rural in
nature [as is suggested by Statistics South Africa (2004:Appendix B)], it was possible to
10 In the censuses of 1951, 1960 and 1970 additional criteria, such as ‘urban characteristics’, were used to
classify a particular area as ‘urban’.
11 Merely using the 1996 urban/rural classification for 2001 EAs is probably an attractive option to deal with
this problem. It has the important advantage that spatial comparisons over time can be made, but unfortunately
the changes in the character of EAs are not taken into account. It is therefore not a good idea to rely on a
classification made earlier, even when incorrect earlier classifications have been identified and rectified (as
discussed in Statistics South Africa 2003).
determine the ‘true’ urbanisation levels at the time of the 2001 census. Table 3 gives a
summary of the findings.
Table 3: Agreement/disagreement between 1996 and 2001 locality types
type Agreement/
disagreement Population
(2001) Proportion of
subgroup Proportion of the
total population
Concordant 19 171 375 97,87% 42,77%
Discordant* 417 892 2,13% 0,93%
Subtotal 19 589 267 100,00% 43,71%
Concordant 25 217 571 99,95% 56,26%
Discordant** 12 939 0,05% 0,03%
Subtotal 25 230 510 100,00% 56,29%
Concordant 44 388 946 99,04% 99,04%
Discordant 430 831 0,96% 0,96%
Total 44 819 777 100,00% 100,00%
* This is probably equivalent to a reclassification from the category ‘rural’ to ‘urban’
during the period 1996–2001.
** This can possibly be ascribed to an incorrect classification of a few rural areas as ‘urban’
for the purposes of the 1996 census.
From Table 3 it can be concluded that South Africa had an overall urbanisation level of
56,26% in 2001 (if the 12 939 persons in the ‘urban discordant’ category, who represented
0,03% of the total population, are now reclassified as ‘rural’). For the individual groups these
12 939 persons were added to the rural population on a pro-rata basis and therefore in effect
subtracted from the urban population. The 417 892 persons (0,93%) in the ‘rural discordant’
category can possibly be explained in terms of a rural-urban reclassification between 1996
and 2001.
The 2001 urbanisation figures as adjusted in terms of the procedure described above are
given in Table 4. The table shows that still only a minority (47%) of the African population
was urbanised in 2001, while more than 85% of the other groups were living in urban areas.
The Indian/Asian population was almost fully urbanised at 97%.
Table 4: Adjusted 2001 urbanisation levels
group Total
population Urban
population Rural
population Proportion
Black African 35 433 492 16 820 234 18 613 258 47,47%
Coloured 3 987 419 3 460 376 527 043 86,78%
Indian/Asian 1 113 183 1 085 279 27 904 97,49%
White 4 285 683 3 851 681 434 002 89,87%
Total 44 819 777 25 217 571 19 602 206 56,26%
In the next section we take a brief look at the components of the urban population growth
during the period 1996–2001.
Components of urban population growth
In order to determine the components of urban population growth over the five-year period
1996–2001 it is necessary to be able to unambiguously classify the place of residence in both
1996 and 2001. Following a classification of localities (main places) as either rural or urban,
the locality types of individuals in 2001 were cross-tabulated with their locations in 1996,
assuming the same locality type as in 2001. Not unexpectedly the previous type of locality
could not be established in all cases, resulting in about 6 per cent of the population not being
classified as either rural or urban in 1996. As can be seen in Table 5 this digression is largely
attributable to the fact that we only have information on the current province of residence in
some cases (54%) and because the current place of usual residence could not be determined
at all (38%).
Table 5: Source of uncertainty for the unknown 2001 usual residence locality types
Source of uncertainty Number %
Only province of usual residence known 77 138 54%
Current place of usual residence could not be determined 53 705 38%
Current place of usual residence outside South Africa 10 910 8%
Total number with unknown place of usual residence in 2001 141 753 100%
Table 6 shows that our inability to determine the type of previous location for individuals in
the age group 0–4 years was the main reason for the 1996 locality type being unknown
Table 6: Source of uncertainty for the unknown 1996 locality types (at origin)
Source of uncertainty Number %
Only the previous province of residence is known 228 454 9%
Previous place not determined 141 498 5%
Previous place outside South Africa 175 251 7%
Previous place of 0–4 year olds not known 2 072 454 79%
Total number with uncertain place of residence in 1996 2 617 657 100%
These unknowns left us with 2 759 410 persons that could not be correctly classified as urban
or rural at the time of either the 1996 or 2001 census. Only the 42 million individuals with
known locality types in 1996 and 2001 were therefore included in the analyses presented in
Table 7.
Table 7: Cross tabulation of locality types before and after migration between 1996 and
Locality type at place of usual residence in 2001
Urban Rural Total
Type of residence in 1996 Number % Number % Number %
Urban 23 601 359 56% 677 696 2% 24 279 055 58%
Rural 684 137 2% 17 097 175 41% 17 781 312 42%
Total 24 285 496 58% 17 774 871 42% 42 060 367 100%
It is clear from the percentages in Table 7 that the majority of the population (56%) lived in
urban areas at the time of both censuses, while a minority (41%) lived in rural areas. A mere
two per cent of the rural population in 1996 ended up living in urban areas in 2001 and the
same proportion applied to the opposite trend (urban-to-rural changes). These results give one
grounds to question the assumption of rapid rural-to-urban net migration by showing that,
between 1996 and 2001, a comparable net exchange of people took place from rural to urban
areas and vice versa.
The purpose of this analysis is to determine the contribution of migration vis-à-vis natural
increase to urban population growth. The magnitude of this contribution can be estimated by
subtracting migration from the net rural and urban growth. In theory this difference should
provide the magnitude of the urban population growth that is attributable to natural increase
and urban reclassification.12 It was, however, not possible to undertake these indirect esti-
mates on the basis of the 1996 and 2001 census data alone. The data from the next census
will hopefully provide the necessary basis for such calculations.
Historical and expected future urbanisation trends
International trends and prospects
Whereas 39 per cent of Africa’s total population lived in urban areas in 2003, by 2030 the
continent’s urban areas are likely to accommodate the majority (54%) of its people (United
Nations 2004). As far as the internal population redistribution in the subregion is concerned,
the United Nations (2004) report shows that the rural populations of Botswana, Lesotho and
South African are purported to be currently experiencing negative growth, while Botswana,
Lesotho and Swaziland experienced rapid urbanisation between 1976 and 2003. Botswana
was particularly affected by urbanisation processes, with the level of urbanisation increasing
from 13 per cent in 1976 to 50 per cent in 2003.13
In contrast to the industrial countries, only 4 per cent of the developing countries regarded the
spatial distribution of their populations as satisfactory in 1976 (compared to the 32% of the
industrial countries), but this proportion has since increased to 18% in 2003, perhaps as the
realisation set in that very little can be done to influence population distribution:
The spatial distribution of population has been remarkably intractable with regard to
policy initiatives. Governments in the past have attempted to modify population
distribution in a variety of ways, including building new capitals; encouraging growth in
small and medium-sized cities rather than in large ones; creating regional development
zones; and controlling the movement of people to cities. Most of these attempts have
failed to achieve their objectives (United Nations 2003: Highlights, p. 14).
Governments of developing countries still tend to be particularly concerned about internal
migration into metropolitan areas, though:
In developed countries, the share of Governments with policies to modify this flow fell
from approximately one-half to one-quarter of countries between 1996 and 2003. In
contrast, developing countries are now more likely to intervene than in the past. Between
1996 and 2003, the share of developing countries with policies to influence internal
migration rose from 53 per cent to almost three-fourths (United Nations 2003:
Highlights, p. 14).
12 The effect of urban reclassification of areas has been calculated earlier at 417 892 persons, which represents a
negligible proportion of less than one per cent the 2001 population (0,93%).
13 The United Nations (2004) indicate that Africa’s urban population grew at the very high rate of 4,4% per year
between 1950 and 2000, and although this growth should slow down, it is expected to continue growing quite
rapidly, at 3,1% per annum, between 2000 and 2030, compared to the 0,9% annual population growth expected
in the rural areas of the continent.
Historical trends: Africans and minority groups
Figure 1 shows the historical urbanisation trends for the four main population groups and the
total population in South Africa for the censuses between 1904 and 2001. It is clear all groups
except black Africans have consistently experienced urbanisation levels above the national
figure. Although the urbanisation of black Africans lost momentum between the censuses of
1951 and 1985 (probably due mainly to the effects of the draconic influx control measures
applied during the height of apartheid), it has in recent years again been increasing at an
accelerated pace.
Figure 1
South Africa's historical urbanisation trends (1904-2001*)
1904 1911 1921 1936 1946 1951 1960 1970 1980* 1985* 1991* 1996 2001*
Census year
Proportion urban (%)
Black African Coloured Indian/Asian White Total
* The urbanisation figures for 1980, 1985 and 1991 were not derived from the censuses themselves but are
interpolations. This was necessary because these censuses excluded those parts of the country that were
covered by the former homelands of Transkei, Bophuthatswana and Venda (1980, 1985 and 1991) and also
Ciskei (1985 and 1991).
The figures for 2001 are estimates (based on the procedure described earlier).
In Figure 2 the provincial urbanisation levels are shown. Gauteng (96%), Western Cape
(90%), Northern Cape (80%) and Free State (75%) all have levels of urbanisation higher than
the national figure of 56 per cent. The least urbanised province is Limpopo (10%), followed
by the Eastern Cape (38%), Mpumalanga (39%), North West (41%) and KwaZulu-Natal
Expected and possible future trends, including a possible urban-rural population turnaround
What are the likely future urbanisation trends in South Africa? As is clear from Figure 1, the
urbanisation levels of the Indian/Asian and white population groups have tapered off as they
approach saturation level. That of the coloured population is expected to taper off soon as
well. As far as the black African population is concerned, it can probably be expected that the
increase in its urbanisation level will continue for the foreseeable future.
Figure 2
Urbanisation levels per province and for South Africa (2001)
Eastern Cape Northern
Free State KwaZulu-
North West Gauteng Mpumalanga Limpopo SOUTH
Proportion urban (%)
What are the chances of a future urban-rural turnaround, where people move to rural parts of
the country in large numbers? As pointed out by Kok (1990), the (short-lived?) urban-rural
turnaround experienced in most industrial countries ‘seems to have been selective of the more
affluent population in the countries where it has been observed’ (p. 123). He then went on to
analyse his migration data for whites (which he regarded as a suitable group for investigating
the possibility of a similar trend in this country) and concluded that ‘the prospects of a
significant urban-rural turnaround among white South Africans are very slim’ (p. 124). No
evidence that has since been produced suggests anything different.
Rural depopulation in South Africa: A realistic possibility?
A recent United Nations (2004) report suggests that the rural populations of Botswana,
Lesotho and South Africa can be expected to be experiencing negative growth by now (see
also Kok, Gelderblom & Van Zyl, 2006). But how realistic is this assumption? In the report
on migration and changing settlement patterns it is shown that, based mainly on an analysis
of migration there is limited evidence of this purported new phenomenon. Does this also
apply nationally? This question has to remain unanswered for now because it has not been
possible to do the required calculations on the data from the 1996 and 2001 censuses alone.
Some evidence is, however, provided from the Agincourt Health and Demographic
Surveillance System, covering three periods of three years each, namely 1995–1997, 1998–
2000 and 2001–2003, to examine trends of change in the key elements of population
dynamics. These are given in Table 8. The average population given is the sum of years
contributed by members of the population over the three calendar years reported, divided by
three (to get the average). The rates are also reported over three-year periods. The population
dynamics are recorded using the following variables: (i) the rate of natural increase, i.e. the
crude birth rate minus the crude death rate, (ii) the rate of net migration, in this case the in-
migration rate minus the out-migration rate, and (iii) the rate of population growth, i.e. the
rate of natural increase plus the rate of net migration. The final variable included is (iv) the
rate of temporary migration. This is the average, over the three-year period, of the proportion
of temporary migrants in the population at the time of each census round. Since they remain
on the household roster, this extra dimension of the population dynamics is available due to
the de jure household definition in the Health and Demographic Surveillance System.
Table 8: Population dynamics in the Agincourt subdistrict, 1995–2003
Agincourt 1995–97 1998–2000 2001–03
Average population 66 265 68 044 68 509
Rate of natural increase 20,2 19,0 12,6
Rate of net migration -9,2 -10,6 -11,4
Rate of population growth 10,9 8,4 1,3
NB. Rates are per 1 000 persons-years
Table 8 shows that after 1995 the natural increase in the population of the Agincourt
subdistrict tended to decline, primarily due to a reduction in fertility and also to a mortality
increase associated with the emergence of HIV/AIDS (Garenne et al, 2006). The migration
flows became increasingly negative over the period. By 2001–2002, population growth had
become negative, by excess of net out-migration and natural increase, though became again
positive in 2003. The population of the HDSS went to a maximum of approximately 69 100
persons in year 2001 and has since been slowly declining (Collinson et al, 2006). The
slowdown in momentum of population growth is primarily driven by fertility decline and
rising mortality in some age groups (Garenne et al, 2006). While out-migration is gaining
over in-migration in time, the key migration phenomenon to note is the high and increasing
levels of temporary migration. Less population growth and more temporary mobility suggests
that there is indeed a worrying population decline in rural areas, but much less pronounced
than would be evident if the permanent and temporary migration flows were classified
together as ‘rural-to-urban’ migration. Without classifying migration types, the rates of
urbanisation would look much higher than they really are.
Planning issues
In this subsection we look at some international practices and then we briefly outline policy
issues in South Africa.
As pointed out by United Nations (2003), in contrast to the industrial countries, only 4% of
the developing countries regarded the spatial distribution of their populations as satisfactory
in 1976 (compared to the 32% of the industrial countries), but this proportion has since
increased to 18% in 2003, perhaps as the realisation set in that very little can be done to
influence population distribution. In the United Nations’ (2003) report on population policies,
it is pointed out that ‘the spatial distribution of population has been remarkably intractable
with regard to policy initiatives’ (Highlights, p. 14). According to this report, the proportion
of governments of industrial countries that attempted to modify this flow fell from
approximately one-half to one-quarter of countries between 1996 and 2003.
Governments of developing countries, on the other hand, still tend to be particularly
concerned about internal migration into metropolitan areas, and developing countries now
appear to be more likely to intervene than in the past: ‘Between 1996 and 2003, the share of
developing countries with policies to influence internal migration rose from 53 per cent to
almost three-fourths’ (United Nations 2003: Highlights, p. 14). The increased desire among
countries in the developing world to intervene can probably be ascribed to the perceived
negative consequences of rapid urban population growth they are experiencing (United
Nations 2003: Highlights, p. 14).
There are several planning issues to be considered. The first set of options would be to try
and modify the migration processes in the country, which might include attempts to curb the
flow of migrants to the cities by adopting migration-control measures to prevent such
migratory moves. A second option would be to encourage growth in small and medium-sized
towns to divert migration away from the larger cities. A third option could be to build new
regional capitals. However, as the United Nations’ (2003) report correctly points out, and as
the past apartheid experience in this country proved, ‘most of these attempts have failed to
achieve their objectives’ (Highlights, p. 14).
A more pragmatic approach may be to try and accommodate the spontaneous urbanisation
processes in the country (c.f. Kok & Aliber, 2005). Such an approach will have to involve the
Departments of Housing, Health, Transport, Social Development, Education, Provincial and
Local Government, the National Treasury and many others.
The aim of this report was to describe the different forms of migration and relate them to
urbanisation, by examining the levels, causes and consequences of migration and
urbanisation. These exercises made it possible to draw some conclusions for the purposes of
policy making and planning.
We have shown that migration is reasonably constant at around 12 per cent of the population
in each five-year period investigated, namely, 1975–1980, 1992–1996 and 1996–2001.
However, a large proportion of these moves were temporary migrations. The ratio of
permanent to temporary migrations in 2002 in the Agincourt subdistrict population was 1 to
2, i.e. two-thirds (67%) of migratory moves in the rural South African northeast were
temporary or circular in nature. Data from the Agincourt Demographic Surveillance System
have enabled us to see the reasons for migration by age and sex in a rural subdistrict
population and the most evident message was that permanent and temporary migration types
can be identified as mutually exclusive population flows. The national census data in 2001
mixed these two types of migration together and the population group for which it is crucial
to discriminate these migration types is the black African population.
Urbanisation levels and patterns were investigated in this report. There is a steady increase in
the proportion of urbanisation over time by race, with the black African population being the
least urbanised subpopulation, although this rate is shifting steadily upwards. In the report
some challenges around the use of census data to inform urbanisation trends were explored,
in particular the classification of enumerator areas into ‘rural’ and ‘urban’ types, which tends
to oversimplify the contours of settlement patterns in these areas. Moreover, changes of the
definitions used in census-data collection over time to some extent limit the accurate
portrayal of trends.
The findings on migration and urbanisation presented in this report should be examined in the
context of the urban transition on the continent. It was mentioned above that most countries
in the developing world have declared their dissatisfaction with the spatial distribution of
their populations. This attitude towards population movements is the result of a common
view by which migration in the developing world is seen as a major factor contributing to
urban unemployment, the uncontrolled expansion of urban areas and consequent urban
Despite being the continent with the lowest level of urbanisation, sub-Saharan Africa’s urban
population is growing at a higher rate than any other region in the world. The scale of urban
transition is unprecedented, as well as the rates of urban population growth (Montgomery,
Stren et al, 2003). The urban population in Africa was 15% in 1950, 32% in 1990, and is
projected to 54–60% by 2030 (United Nations, 1998; UNEP, 2005). However, this rapid
growth of urban areas is occurring in the context of generally declining economic
performance (World Bank, 2000). This fact, combined with poor planning and governance,
increases the visibility of urban poverty whereby a significant proportion of urban
populations live below the poverty line in over-crowded slums and sprawling townships. This
is the case in most African countries. Indeed, it is estimated that about 72% of all urban
residents in sub-Saharan Africa live in informal settlements [UN-Habitat (United Nations
Human Settlement Programme) 2003].
In the African setting migration is often employed to maximise family and household
livelihoods by diversifying sources of household income and risks (Stark and Bloom, 1985;
Adepoju, 1995). The South African data have shown that much of the rural-to-urban
migration is temporary, in other words the migrants stay in touch with their rural household
and usually remit money or consumables back to the rural household and are likely to return
and live in the rural area upon retirement or retrenchment. The benefits of temporary
migration as a strategy have been widely documented as providing opportunities for
improved living conditions not only for migrants but also for families and origin communities
(Mendosa, 1982; Goldscheider, 1984; Lockwood, 1990; Collinson, Tollman et al, 2005). In
South Africa, the historical impact of segregationist policies like influx control, pass laws and
the ‘homelands’ were the cornerstones of the labour migration system, which set the stage for
high levels of temporary migration. These levels remain intractably high (Posel and Casale,
2002; Collinson, Tollman et al, 2005). An important contributory factor may be the relatively
low levels of income per capita at a national level, compared with the economies that
underpinned the urban transition in more developed countries in the previous century
(Montgomery, Stren et al, 2003). Socio-cultural links with rural areas may also be a key part
of the explanation, for example, migrants retain special links with their home areas that go
way beyond economic benefits (De Bruijn, Van Dijk et al, 2001).
The temporary nature of rural-to-urban migration in South Africa may add insight into the
persistence of the overcrowding and poor conditions in urban townships. Migrants from rural
or peri-urban areas may employ a calculated strategy to maximise the benefits to their
household of origin, rather than their own benefit or the residential units in the urban setting.
This may result in the need for low-budget rental accommodation, for example backyard
shacks, or rooms in the dwelling of a family member. In Nairobi attempts to move squatter
residents to better and more expensive housing had very limited success because many of
them prefer to live in relatively cheap housing that is only found in informal settlements in
order to make enough savings to allow them to build decent housing in their home
communities, pay for school fees and other expenses for their family (Johnston, 1974).
Aside from rural-to-urban migration another key component of urban growth is natural
increase (births minus deaths) within the urban population. For this report we were unable to
compute the relative contributions towards urban growth of the three components: urban
‘natural increase’, ‘reclassification of areas’ and ‘rural-to-urban migration’. This requires one
more round of the South African census and it will be important to conduct this work. Other
work done in developing world settings has estimated that the average contribution of the
components of urban growth has shown that ‘natural increase’ contributed around 60% of
urban growth (United Nations 1980; Chen, Valente et al, 1998). The data presented in this
report on the Agincourt subdistrict shows a declining trend in natural increase in this rural
subdistrict population, primarily due to a reduction in fertility rates. This may imply that the
rates of urban natural increase are also declining in urban South Africa, which would
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Methods used in the Agincourt Health and Demographic Surveillance System
This is the first of several sections in this report and the one on migration and changing
settlement patterns that use data from the Agincourt Health and Demographic Surveillance
System. The aim of the section is to categorise migration in a rural subdistrict population and
look at the reasons for migration in each migration type.
The Medical Research Council/Wits University Agincourt Unit in Rural Public Health and
Health Transitions Research, based in Bushbuck Ridge, conducts health and demographic
surveillance on a rural subdistrict population in the former homeland district of Bushbuck
Ridge some 500 kilometres north-east of Johannesburg. A baseline census in the twenty
villages of the Agincourt subdistrict was conducted in 1992. Since then, an annual field
operation has been conducted to collect information on all births, deaths and in- and out-
migrations in the surveillance population. The annual update involves visiting every
household, where a fieldworker verifies existing records, adds new individual and household
data, and records the demographic events that have occurred since the preceding year’s
census update. A verbal autopsy is conducted for every death. (Kahn et al, 1999; Tollman,
1999; Tollman et al, 1999). In 2001 the population under surveillance was 68 500. Migration
and household definitions are used daily in demographic surveillance data collection. The
following are the key migration and household definitions:
Definition of a permanent migrant
The Agincourt definition of a permanent migrant is a person who enters or leaves a
household with a permanent intention of entering or leaving. This definition follows the
classic definition that migrants are people who experience a change in residence (Bilsborrow,
1993). This includes people who leave the index household and establish a household or join
a household elsewhere. A key feature is that the destination household becomes the new
home base for the migrant.
Definition of a temporary migrant
A temporary migrant is a household member who is away most of the time, but retains a
significant link to their base household. A six-month-per-year cut-off point was chosen to
differentiate ‘temporary migrants’ from ‘local residents’. Thus, people who are referred to as
temporary migrants were absent from the household for more than six months of the year
preceding observation, but who considered the index household to be their home base.
Definition of a household
The definition of a household in the Agincourt Health and Demographic Surveillance System
is a group who reside and eat together, plus the linked temporary migrants who would eat
with them on return. This is a de jure household definition because it is more closely related
to links of responsibility within the household, as opposed to a de facto household definition
which more closely matches the co-residential household, as used in the national census. One
implication of the Agincourt definition in data collection is that when a field worker
encounters a permanent out-migrant, this person becomes removed from the household
roster, whereas a temporary migrant is retained on the household roster.
Migration typology
The migration categories used in this section are as follows:
1. In-migration into a rural household
2. Out-migration from a rural household
3. Temporary migration out of a rural village
This typology discriminates ‘permanent’ migration (in and out) from ‘temporary’ migration.
The Health and Demographic Surveillance System enables this classification due to the de
jure household definition, and the frequent and regular monitoring of migration events.
Households in the study population are regularly visited, movements in and out are
monitored, and the household respondent reports whether or not the move has a permanent
intention. Intention of return is an effective means of discerning these categories, since
permanent intention is rarely the case for the circular, oscillating migrants who are a highly
prevalent feature of Southern Africa demography (Wilson, 2001). There are grey areas,
where a ‘permanent’ migrant returns to the original household (for example after a divorce),
or a ‘temporary’ migrant deserts and never returns (for example a desertion scenario), but
these are changes from the original intention and can be managed in a health and
demographic surveillance system. Despite some blurring at the edges it is critical for
migration scholars to differentiate a complex phenomenon like migration in some way, and
the data on migration reasons given below lend support to this classification.
The data presented in Table 2 are shown according to the categories listed above. A caution
to note when interpreting the data is that a large portion of the permanent migrations occurred
within the study site and therefore had both a household of origin and a household of
destination under demographic surveillance. In this way two migrations were recorded for
each move, both the out-migration and in-migration, one at each pole of the move. There
were approximately 3 000 such moves internal to the site in 2002 (4,4 per 1 000 persons per
year). A mathematical implication is that there would be an over-count of 3 000 if one
summed up all the moves shown in Table 2, and these ‘duplicated’ moves (i.e. the 3 000)
should be subtracted from the total. The benefit of leaving the internal migrants in the table is
that each migration stream is complete and the rates can be calculated according to the
categories listed above.
‘Reason for move’ is recorded for each permanent migration captured in the Health and
Demographic Surveillance System. Generally, ‘reason for move’ of temporary migrants is
not recorded, but a special module was conducted in 2002 in which temporary migrants’
‘reason for move’ was obtained. For this analysis the permanent migration categories were
also restricted to that year, i.e. 2002, to enable a more direct comparison and give a
reasonable ‘snap-shot’ of the movement types by reason. For both permanent and temporary
migrations ‘reason for move’ was captured using a pre-defined ‘reason’ code, with the option
of ‘other’, where the reason was recorded in text if a respondent’s explanation did not match
one of the pre-coded categories. Reasons were re-coded from the pre-defined categories and
specified text, to establish nine comparable categories of ‘reason for move’ across all
migration types.
The nine ‘reason for move’ categories were as follows:
1. ‘Union formation or dissolution’. This is where the migration occurs as the formation of a
new social union between two people, either as formal marriage or as ‘living-together’, or
the breakdown of such a union.
2. ‘To live with another’. This is when a person leaves the original de jure household and
moves into another de jure household with the reported reason that there is a family link
between the two households. Multiple underlying reasons prevail in this type of move,
but the characteristic is that the locus of residence for a person changes to another
household in the family network. The ‘reason for move’ category ‘school/study’ is
excluded here, although it is actually a subset of this migration type, to show migration
for education as a discrete stream (see Reason 7 below).
3. ‘New dwelling for the household’ is where a whole household leaves a dwelling place
and relocates to another dwelling place.
4. ‘Work’ is where the migration is expressly for the purpose of employment.
5. ‘Looking for work’ is where the migration is in search of an opportunity for employment.
6. ‘Health’ is where some form of healing is expressed as a goal of the migration. Primarily,
it involves access to services, both modern and traditional.
7. ‘School/study’ is a special case of living with somebody else, where educational reasons
were expressed by the respondent, or a child of school-going age moved to ‘live with
another’ in the family network.
8. ‘Child accompanies adult’ is a category for children who move in the company of an
adult, who in turn is moving for one of the reasons given above. If the child moves
without accompanying an adult, for example ‘to live with another’ then it is not recorded
in this category.
9. ‘Other unknown’ is where data is missing or inconclusive in the HDSS records, or when a
person is in prison, since this was not frequent enough to warrant a category of its own.
For ‘permanent’ in- and out-migration the number of moves are given, pooled for the 1999–
2003 period, with the distribution of ‘reason for move’ given within age/sex groups. For
temporary migration the data are given for one year only, namely 2002. Pooling the data on
temporary migration and presenting it by age and sex is complicated, because a person may
be a temporary migrant for the whole observation period and thus change age categories in
the process. The problem is somewhat resolved by taking only the temporary migrant
subpopulation in one year, but care should be taken to multiply the numbers by five if one
wants to compare the scale of the different migration streams in this population. Permanent
migration is easier to handle as an event in time.
... The province of KZN, which includes numerous rural areas [21], experiences various barriers in service provision. There are lengthy waiting lists for admission to the only two available public in-patient treatment centers (ITCs) [22], both located in urban areas. ...
... The study was conducted at a rural district, one of eleven districts in KZN with a total population of 11,3 million people, equating to approximately 19,2% of the South African population [15]. KZN has more than 55% of the population domiciled in rural areas [21], with 25% of disadvantaged South Africans (57% below the poverty line) residing in the province [15]. The province has the triple burden of high poverty levels, HIV and Tuberculosis (TB) [32], complicating service provision. ...
Full-text available
Background Provision of aftercare services for persons with substance use disorders (PWSUD) within a rural context is typically met with various intersecting challenges, including unclear policy implications and lack of resources. In the South African context, service providers are expected to provide aftercare services that should successfully reintegrate persons with PWSUD into society, the workforce, family and community life as mandated by Act No. 70 of 2008, despite population diversity. Little has been established on the provision of aftercare services in South Africa and specifically within a rural context. This article explores service providers’ perspectives in aftercare service provision for PWSUD in a rural district. Methods A qualitative exploratory study design was conducted in a rural district in South Africa using semi-structured interviews and focus group discussions with forty-six service providers from governmental and non-governmental institutions, ranging from implementation to policy level of service provision. Data were analyzed thematically using a deductive approach. Codes were predetermined from the questions and the aims and objectives of the study used Beer’s Viable Systems Model as a theoretical framework. NVivo Pro 12 qualitative data analysis software guided the organization and further analysis of the data. Results Four themes emanated from the data sets. Theme 1 on reflections of the interactional state of aftercare services and program content identified the successes and inadequacies of aftercare interventions including relevant recommendations for aftercare services. Themes 2, 3, and 4 demonstrate reflections of service provision from implementation to policy level, namely, identifying existing barriers to aftercare service provision, situating systemic enablers to aftercare service provision, and associated aftercare system recommendations. Conclusions The intersecting systemic complexities of providing aftercare services in a rural context in South Africa was evident. There existed minimal enablers for service provision in this rural district. Service providers are confronted with numerous systemic barriers at all levels of service provision. To strengthen the aftercare system, policies with enforcement of aftercare services are required. Moreover, a model of aftercare that is integrated into the existing services, family centered, sensitive to the rural context and one that encourages the collaboration of stakeholders could also strengthen and sustain the aftercare system and service provision.
... "Since people are illegally connecting pipes, I would say that the result of this practice is illness because they drink unsafe water and are re-connected by people who do not have water connection certificates, which eventually causes germs to enter the water pipes and even the main supplier people. This puts us all at risk of contracting waterborne diseases" (Zinhle, 2021 Kok and Collinson (2006) state that water theft leads to poor water quality as illegal water pipe connections may cause water to be highly polluted. Individuals who live in dwellings that are illegally connected to water sources often contract waterborne diseases. ...
... The literature is also in agreement with the above statements. Kok and Collinson (2006) argue that water theft causes licensed users of water to experience shortages even though they pay exorbitant amounts for water. Furthermore, Gowlland-Gualtieri (2007) argues that water cut-offs are partly caused by increased water consumption by illegal means and this threatens the livelihood of many households and individuals. ...
Full-text available
This study focused on illegal water connections and water meter tampering (referred to as water theft) as core factors that lead to water shortages, with specific reference to a township context. The goal of this study was to evaluate the causes of water theft and the effectiveness of existing strategies to combat this crime. The objectives of the study were to: (i) assess the nature and extent of water inaccessibility in Folweni Township; (ii) determine the causes of water theft in this township; (iii) explore the effects of water theft on the community and the eThekwini Municipality; and (iv) assess measures and strategies that may be effective in curbing water theft in the study area. The study utilized social disorganisation theory, strain theory, and rational choice theory. The study area for data collection is Folweni Township, KwaZulu-Natal province. Telephonic semi-structured interviews using open-ended questions were used to collect data. The findings of this study suggest that the causes of water theft are unemployment, ineffective communication between the community and the municipality, and a lack of social control. The study further determined that no strategies had been put into place to curb water theft. Participants argued that the municipality introduced only inconsistent mechanisms to deal with this crime and residents were expected to use these to cope with water shortages.
... In addition, urbanisation has been increasing steadily in South Africa in the past five decades, rising from 24% in 1904; to 38% in 1946; 48% in 1980; 53% in 1991; 57% in 2001; to over 66% of the population residing in urban and peri-urban settings in 2019 (Kok & Collinson, 2006;The World Bank, 2020). This has been accompanied by a rising prevalence of hypertension, as urbanisation is commonly associated with increased prevalence of risk factors for hypertension such as increasing age, rising salt intake, lack of exercise, diminishing fruit and vegetable intake, and increasing rates of obesity, diabetes and social stress. ...
... However, it is challenging to determine the duration of exposure to urban environments, since South Africa is characterised by temporary migration of people between rural and urban areas as they seek work Table 4 Final regression results 1 for hypertension, and systolic and diastolic blood pressure outcomes in the African sub-sample (N = 5315). (Kok & Collinson, 2006). Furthermore, the length of stay in urban or rural settlement types is complicated by the fact that many South Africans do not even have street addresses for their homesparticularly in rural areas and urban informal settings. ...
Full-text available
Background: Hypertension is the leading cardiovascular disease in Africa. It is increasing in prevalence due partly to the epidemiological transition that African countries, including South Africa, are undergoing. This epidemiological transition is characterised by a nutrition transition andurbanisation; resulting in behavioural, environmental and stress changes that are subject to racial and geographic divides. The South African National Health and Nutrition Examination Survey (SANHANES) examined the association of traditional risk factors; and less traditional risk factors such as race, geographical location, social stressors and psychological distress with hypertension in a national population-based sample of South Africans. Methods: Data were analysed on individuals ≥15 years who underwent a physical examination in the SANHANES (n = 7443). Hypertension was defined by blood pressure ≥140/90 mmHg or self-reported hypertension medication usage. Stepwise regression examined the association of demographic, socioeconomic, life stressors, and health risk factors with systolic blood pressure, diastolic blood pressure, and hypertension. Secondly, the risk factor associations and geographical location effects were investigated separately for the African race group. Results: Increasing age (AOR = 1.069, p < 0.001); male gender (AOR = 1.413, p = 0.037); diabetes (AOR = 1.66, p = 0.002); family history of high blood pressure (AOR = 1.721, p < 0.001); and normal weight, overweight and obesity (relative to underweight: AOR = 1.782, p = 0.008; AOR = 2.232, p < 0.001; AOR = 3.874, p < 0.001 respectively) were associated with hypertension. Amongst African participants (n = 5315) age (AOR = 1.068, p < 0.001); male gender (AOR = 1.556, p = 0.001); diabetes (AOR = 1.717, p = 0.002); normal weight, overweight and obesity (relative to underweight: AOR = 1.958, p = 0.006; AOR = 2.118, p = 0.002; AOR = 3.931, p < 0.001); family history of high blood pressure (AOR = 1.485, p = 0.005); and household crowding (AOR = 0.745, p = 0.037) were associated with hypertension. There was a significantly lower prevalence of hypertension in rural informal compared to urban formal settings amongst African participants (AOR = 0.611, p = 0.005). Other social stressors and psychological distress were not significantly associated with hypertension. Conclusion: There was no significant association between social stressors or psychological distress and hypertension. However, the study provides evidence of high-risk groups for whom hypertension screening and management should be prioritised, including older ages, males, people with diabetes or with family history of hypertension, and Africans who live in urban formal localities.
... The province of KZN, which includes numerous rural areas [22], experiences various barriers in service provision. There are lengthy waiting lists for admission to the only two available public in-patient treatment centers (ITCs) [23], both located in urban areas. ...
... The study was conducted at a rural district, one of eleven districts in KZN with a total population of 11,3 million people, equating to approximately 19,2% of the South African population [16]. KZN has more than 55% of the population domiciled in rural areas [22], with 25% of disadvantaged South Africans (57% below the breadline) residing in the province [16]. The province has the triple bind of high poverty levels, HIV and Tuberculosis (TB) [33], complicating service provision. ...
Full-text available
Background: Provision of aftercare services for persons with substance use disorders (PWSUD) within a rural context is typically met with various intersecting challenges, including unclear policy implications and lack of resources. In the South African context, service providers are expected to provide aftercare services that should achieve successful reintegration of persons with PWSUD into society, the workforce, family and community life as mandated by Act No. 70 of 2008, despite population diversity. Little has been established on the provision of aftercare services in South Africa and specifically within a rural context. This article explores the perspectives of service providers in aftercare service provision for PWSUD in a rural district. Methods: A qualitative exploratory study design was conducted in a rural district in South Africa using semi-structured interviews and focus group discussions with forty-six service providers from governmental and non-governmental institutions, ranging from implementation to policy level of service provision. Data were analyzed thematically using a deductive approach. Codes were predetermined from the questions and the aims and objectives of the study used Beer’s Viable Systems Model as a theoretical framework. NVivo Pro 12 qualitative data analysis software guided the organization and further analysis of the data. Results: Four themes emanated from the data sets. Theme 1 on reflections of the interactional state of aftercare services and program content identified the successes and inadequacies of aftercare interventions including relevant recommendations for aftercare services. Themes 2, 3, and 4 demonstrate reflections of service provision from implementation to policy level, namely, identifying existing barriers to aftercare service provision, situating systemic enablers to aftercare service provision, and associated aftercare system recommendations. Conclusions: The intersecting systemic complexities of providing aftercare services in a rural context in South Africa was evident. There existed minimal enablers for service provision in this rural district. Service providers are confronted with numerous systemic barriers at all levels of service provision. To strengthen the aftercare system, policies with enforcement of aftercare services are required. Moreover, a model of aftercare that is integrated into the existing services, family centered, sensitive to the rural context and one that encourages the collaboration of stakeholders could also strengthen and sustain the aftercare system and service provision.
... In South Africa, using population density, the Limpopo Province is considered the most rural with the highest percentage of the rural population (90%). In contrast, Gauteng Province is the least rural [18]. ...
Full-text available
Evidence is unequivocal that rural and urban areas in South Africa are vulnerable to the impacts of climate change; however, impacts are felt disproportionately. This difference in vulnerability between rural and urban areas is presently unclear to guide context-based climate policies and frameworks to enhance adaptation processes. A clear understanding of the differences in vulnerability to climate change between rural and urban areas is pertinent. This systematic review aimed to explore how vulnerability to climate change varies between rural and urban areas and what explains these variations. The approach was guided by the Intergovernmental Panel on Climate Change vulnerability framework incorporating exposure, sensitivity, and adaptive capacity dimensions integrated into the Sustainable Livelihood Framework. The review used 30 articles based on the search criteria developed. The findings show differences in vulnerability to climate change between rural and urban areas owing to several factors that distinguish rural from urban areas, such as differences in climate change drivers, infrastructure orientation, typical livelihood, and income-generating activities. We conclude that vulnerability varies with location and requires place-based analyses. Instead of blanket policy recommendations, localized interventions that enhance adaptation in specific rural and urban areas should be promoted.
... Despite removing the Apartheid era movement restrictions (Bouare 2001), ruralurban migration has remained circulatory due to many migrants preferring to retain ties with their households of origin (Collinson et al. 2003;Posel 2004Posel , 2010. The nature and patterns of rural-urban migration in South Africa is a relatively well-researched issue (Kok & Collinson 2006). However, studies on the consequences of this rapidly increasing migration are more limited in South African literature, and these studies focus on the impact of migration on the migrant itself. ...
Full-text available
Background: Rural-urban migration is largely depicted as a household survival strategy, yet rigorous quantitative studies to uncover its impact on the sending households is rare. Aim: The study aims to assess the causal impacts of rural-urban migration on sending households’ economic and subjective well-being (SWB). Setting: The context of the analysis is South African rural-urban migration using the National Income Dynamics Study panel data. Methods: A range of methods are used to increase the consistency and precision of estimates, namely: Ordinary Least Squares, Fixed Effects, Difference in Differences, Difference in Differences with Propensity Score Matching and Difference in Differences with instrumental variables, controlling for pertinent issues such as fixed effects, self-selection and endogeneity. Results: Our econometric analysis reveals a positive correlation between migration and the SWB of the sending household. This effect can be attributed to a range of factors discussed in the study, one of which is the positive association observed between the migration of a household member and the origin household’s economic well-being. This upswing in economic well-being is captured by increasing the sending household’s monthly income per capita and increased remittance inflows. Conclusion: From our analysis, we can infer that the improvement in economic well-being offsets the psychological effects of separation, thus leading to the enhanced SWB of the migrant-sending households in South Africa.
... Similar to migrants escaping from challenging natural environments and economic circumstances in African and Asian countries (Kleist and Thorsen 2017;Kok and Collinson 2006;Hossain 2001), migrants from rural areas to UB in Mongolia are taking on an increased risk (Mayer 2016). For these migrants, the allure of a higher expected urban wage would be the dominant incentive, balancing risks that include high unemployment rates in both peri-and central UB and lack of sufficient skill levels. ...
Full-text available
With the expansion of pure forest planting area and the increase in the number of rotations used, soil activity and plant productivity have significantly reduced. The functional diversity of soil microorganisms plays a vital role in forest health and the long-term maintenance of productivity. Though the optimization of forest cutting and regeneration methodologies is necessary to improve the functional diversity of soil microorganisms, the effects of harvest residual treatment on the functional diversity of soil microorganisms remain unclear. During the period 2018–2020, we designed four harvest residual treatments—reference (RF), residual burning (RB), crushing and mulching (MT), and no residuals (NR)—to determine soil physical and chemical properties. We also used microbial biomass (MB) to evaluate the diversity in carbon source metabolism of soil microorganisms through Biolog microplate technology, and discussed the response mechanism of microbial functional diversity to the different forest cutting and regeneration methodologies used in Chinese fir plantations. The results indicated that RB significantly increased the carbon metabolic capacity of the microbial community, the community richness, and its dominance compared to RF, MT, and NR; however, they also showed that it decreased the uniformity of the soil microbial community. NR showed a poor carbon utilization capacity for microorganisms compared to RF and MT, while MT significantly increased the utilization capacity of carbohydrate and amino acid carbon compared with RF. Soil nutrients were the main driving factors of soil microbial carbon metabolic activity, and the different responses of microbial functional diversity to various forest cutting and regeneration methodologies were mainly due to the variation in the nutrient inputs of harvest residues. This study provides a practical basis for enhancing the functional diversity of soil microorganisms in plantations through the management of harvest residues.
... Similar to migrants escaping from challenging natural environments and economic circumstances in African and Asian countries (Kleist and Thorsen 2017;Kok and Collinson 2006;Hossain 2001), migrants from rural areas to UB in Mongolia are taking on an increased risk (Mayer 2016). For these migrants, the allure of a higher expected urban wage would be the dominant incentive, balancing risks that include high unemployment rates in both peri-and central UB and lack of sufficient skill levels. ...
Full-text available
During the urbanization process in Mongolia, Ulaanbaatar (UB) became the most dominant metro-city in the nation and the most popular migration destination. We examined the changes in migration to UB during the economic transition of 2002–2017 and explored driving forces using the dataset from the Mongolia National Labor Survey. The results confirmed that relatively higher wages and lower unemployment positively influenced migration toward UB. Using the multinomial logistic regression, we find a 1% increase in the expected wage increases the likelihood of migration to core UB by 6.75%; a 1% increase in relative income increases the odds of migration to core UB by 3.17%; and a 1% increase in the expected unemployment rate decreases the odds of migration to core UB and peri-UB by 1.14% and 1.64%, respectively. We also confirmed the impact of environmental change and climate extremes on migration destination choice, as well as the influence of several demographic variables.
... Communal lands are areas that are under traditional African tenure arrangements, where land is allocated by local Zulu chiefs and councils. Most of these areas are used for subsistence agriculture, and the inhabitants of these areas are heavily dependent on government grants and remittances from family members working in the cities (Kok andCollinson 2006, StatsSA 2018). The state-owned conservation area, Hluhluwe-iMfolozi Game Reserve, covers 98,000 ha and aims to ensure conservation and the sustainable use of the biodiversity under its jurisdiction. ...
Anticipating, avoiding, and managing disruptive environmental change such as regime shifts and the impacts it has on human well-being is a key sustainability challenge. Woody encroachment is a globally important example of a regime shift that occurs in savanna systems, where a large fraction of the world's poor live. Woody encroachment is known to negatively impact a variety of ecosystem services, but few studies have investigated the impact of woody encroachment on local land users and their livelihoods. In this study, we conducted semi-structured interviews to determine how different land users - local subsistence communities and managers of conservation tourism areas - perceive woody encroachment in the Hluhluwe region of South Africa, how it affects the ecosystem services they rely upon, and what costs they incur in undertaking activities to reverse woody encroachment. Most interviewees perceived trees to be increasing in the landscape (83%). However, perceptions about the causes of woody encroachment differed: community members cited the reduced usage of trees as the reason for woody encroachment, whereas conservation managers mostly attributed the change to increased CO2. Most community members felt woody encroachment was harmful to their household and general well-being, citing loss of grazing for livestock, and fear of attacks by wild animals and criminals as the main impacts. In contrast, conservation managers perceived woody encroachment to have both harmful and beneficial impacts, with the main negative impacts being loss of grazing for wildlife and impacts on tourism through reduced visibility for game viewing. All the conservation areas invested in tree clearing compared to only 20% of respondents in the community areas, where an average of ZAR367 (US$25) was spent per year on clearing, compared to ZAR293,751 (US$20,000) and ZAR163,000 (US$11,000) spent in private game reserves and government reserves, respectively. Our findings highlight the negative impacts of ongoing woody encroachment, the differentiated impacts it has on different land users, and differences in capacity to combat encroachment. These findings highlight the need for state-funded management interventions to support clearing of trees in encroached areas, particularly in communal areas.
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This chapter provides an introduction and overview of the book. It discusses the concept of informal mining in Johannesburg and places this within a historical context of gold mining and labour migration in Southern Africa. The main conceptual framework of informality and exclusion are discussed, and an overview of the research methods presented. Finally, some key contemporary socio-economic indicators that provide context to informal and precariously livelihoods such as informal mining are shared.
This article analyzes the causes, patterns and results of involvement in international wage labor migration for Nazaré, a Portuguese fishing village and tourist resort. Some migrate permanently, others return to invest locally, and yet others only work abroad part of each year. Data indicate that international migration is a successful strategy for coping with a restrictive local opportunity structure. International migrants have more income, savings, capital goods, consumer goods and automobiles than non-migrants or those who only migrate within Portugal.
The World Development Report 1999/2000, the 22nd in this annual series, addresses the changing development landscape of the early 21st century. Development thinking has evolved into a broad pragmatism, realizing that development must move beyond economic growth to encompass important social goals - reduced poverty, improved quality of life, enhanced opportunities for better education and health, and more. Experience has also taught that sustainable progress toward these goals requires integrated implementation and must be firmly anchored in processes that are open, participatory, and inclusive. In the absence of a strong institutional foundation, the outcomes of good policy initiatives tend to dissipate. These lessons and insights are incorporated into the Comprehensive Development Framework, recently initiated by the World Bank to address the challenges of development in a more holistic, integrated way by bringing in aspects such as governance, legal institutions, and financial institutions, which were too often given short shrift earlier. Looking ahead, this report explores the environment in which the major issues of the 21st century - poverty, population growth, food security, water scarcity, climate change, cultural preservation - will be faced. Many powerful forces, both glacial and fast-paced, are reshaping the development landscape. These include innovations in technology, the spread of information and knowledge, the aging of populations, the financial interconnections of the world, and the rising demands for political and human rights. The report focuses in particular on two clusters of change - globalization and localization - because of their immense potential impact. They open up unprecedented opportunities for growth and development, but they also carry with them the threats of economic and political instability that can erode years of hard-earned gains.
Home as a concept has several dimensions and aspects of theoretical importance. From a family and individual perspective “being at home” can mean a variety of different statuses and experiences. In the global society the question arises as to how families and their members are able to negotiate establishing a home and having a place as well as group identity. Understanding of mobility includes decision making as both an individual and a family activity that moves one from being a potential migrant or immigrant into an actual one, and from a temporary worker to a long term resident. Clashes of interest and different perceptions of opportunity are important at different points in the process. The globalization of the economic system and the growth of world political and non-governmental agencies and movements have challenged the way macro-systems are understood. These macroprocesses are mediated and interpreted at the micro level by individuals and their families. Two aspects at this micro level are newly developed in this model for family mobility and migration decisions in this model: -The relative attachment of families to place and culture and how their worldviews influence their negotiation skills and their understanding of mobility or stability options. -The many inputs from family and social networks at different points in the mobility decision making process and its implementation. Home and family are altered in the dynamic process of decision-making and the resulting new situation may be evaluated by families and society around the critical social values. In this context assimilation, integration, or continued multiple loyalties are not simple policy and program plans, but familial choices in their search for a secure and satisfactory survival of family and home.
This chapter reviews the empirical and theoretical literature on migration to analyze the extent and manner in which "motivation for migration" and related concepts have been employed proposes a new theoretical approach (the value-expectancy model) and presents a general causal framework in which the value-expectancy model is integrated with individual-level household-level and societal-level determinants of migration. To understand the motivation for migration it is necessary to adopt the perspective of the individual. A complete model of migration shows linkages between macro and microlevels of behavior. Motivation is defined as a function of the value placed on certain goals and the perceived likelihood that a behavior will lead to those goals. Motivation for migration has been employed in volume distance and direction models ecological theory and migration systems approaches to migration the mobility transition hypothesis proposed by Zelinsky and economic maximization theory. Different motives are described for migration: economic motives in household decision models the social mobility/social status migration motive the residential satisfaction motive the family and friend influences as motives and the motive of life style preferences. A decision making framework is proposed as a model for understanding migration behavior. The value-expectancy model specifies the personally valued goals that might be met by moving and an assessment of the perceived linkage between migration behavior and the attainment of goals in alternative locations. Values and goals consist of wealth status comfort stimulation autonomy affiliation and morality. Microlevel migration theory can suggest ways in which policy interventions may alter the expectation that potential migrants have about obtaining their goals in alternative locations.
The purpose of this discussion is to develop the concepts and tools with which to determine the influence of migration as an equilibrating mechanism in a changing economy. Some of the important costs and returns to migration--both public and private--are identified and to a limited extent methods for estimating them are devised. This treatment places migration in a resource allocation framework because it deals with migration as a means to promoting efficient resource allocation and because migration is an activity which requires resources. Within this framework the goal is to determine the return to investment in migration rather than to relate rates of migration to income differentials. The studies of net migration conducted thus far partially reveal the functioning of the labor market yet they provide little more than the fact that net migration is in the "right" direction. The estimated response magnitude of net migration to gaps in earnings is of little value in gauging the effectiveness of migration as an equilibrator. There are several alternative approaches. 1 simple approach is to compare rates of (gross) migration with changes in earnings over time. Numerous compositional corrections would be required and this approach would still have to answer the difficult question of how much equalization of earnings should be brought about by a given amount of migration. A better alternative at least analytically is to cast the problem strictly as one of resource allocation. To do this migration is treated as an investment increasing the productivity of human resources an investment which has costs and which also renders returns. The private costs can be broken down into money and nonmoney costs. The money costs include out of pocket expenses of movement and the nonmoney costs include foregone earnings and the psychic costs of changing ones environment. For any particular indivdual the money returns to migration will consist of a positive or negative increment to his real earnings streams to be obtained by moving to another place. This increment will arise from a change in nominal earnings a change in costs of employment a change in prices or a combination of these three. It was found that psychic costs of migration can be ignored since they involve no resource cost. Likewise nonmoney returns arising from locational preferences should be ignored to the extent that they represent consumption which has a zero cost of production. In sum migration cannot be viewed in isolation. Complementary investments in the human agent are probably as important or more important than the migration process itself.
This study applies to interstate migration the notion that dissatisfaction with residence or community presages movement. The other side of the coin, the view that disposition to move is inhibited by satisfaction with job and place of residence, and by social bonds, is tested using data from a panel study in Rhode Island for the period 1969–79. Results of a multivariate analysis show that noneconomic factors influence decisions to move. Strong social bonds inhibit migration, but employment (except for two-job families) did not. Measures of satisfaction with community and job failed to reveal the expected strong relationship. The effects of three background variables, age, sex-marital status, and place of birth were mediated by level of satisfaction and social bonds. But duration of residence and education of household head had significant direct effects on migration. Possible reasons for these findings are explored.