Magnitudes, Personal Characteristics and Activities of ... · 2 School of Economics, University of...

Magnitudes, Personal Characteristics and Activities of Eastern Cape Migrants: A Comparison with Other Migrants and with Non-migrants using Data from the 1996 and 2001 Censuses

Nalen Naidoo,1 Murray Leibbrandt2 and Rob Dorrington3

AbstractThis paper investigates the changing nature of migration of the African population from the former Transkei, particularly the rural to urban migration to the Cape Metropolitan Area over the period 1991 to 2001 using data from the 1996 and 2001 censuses. The study compares the characteristics of those who migrate from the Transkei to the CMA with those who migrate within the Eastern Cape and those who do not migrate and investigates whether the characteristics of these migrants have changed significantly over this time. Nationally there has been an increase in migration of females and the young and of migration to non-metropolitan areas. Migration flows appear to have stabilised. However this is not uniformly the case with migration from the Eastern Cape to the Western Cape remaining strong. The labour market environment confronting migrants has worsened with significant increases in percentages of migrants who are unemployed and some de-skilling of the occupations in which migrants are finding employment. The dire labour market situation in rural Eastern Cape maintains the flow of migrants despite this hostile environment for migrants.

INTRODUCTIONIn South Africa, as is the case in many countries, internal migration is a social force with profound demographic, political and economic implications. Yet, as in other countries, it is notoriously under-researched (Fix 1999:7). This is

1 Group Financial Reporting, Aviva plc, London, United Kingdom2 School of Economics, University of Cape Town, South Africa (Corresponding author)3 Centre for Actuarial Research, University of Cape Town, South Africa

SADemJ (11)1 3–38

3

4 Southern African Journal of Demography 11(1)

so, not because of a perceived lack of importance but, rather, “the intrinsic difficulty of measuring migration” (Fix 1999:7) and the poor quality and inappropriateness of data available on the topic. During apartheid, much research focused on the movement of the African population under policies designed to channel and encourage this movement to certain areas and stem the flow to others. The result of these activities was the creation of a reservoir of migrant labour, located in such a way as to make their labour accessible, but kept relatively far away from urban and metropolitan areas. This has created a large group of people who, in the post-apartheid era, still reflect the effects of policies of the past but who are prepared to move to redress the imbalances of their circumstances.

Apartheid policies laid the conditions for a migratory population that had to seek out alternative places of residence, however temporary, in an attempt to improve the poor quality of life. In the words of one African worker, “The countryside is pushing you into the cities to survive, and the cities are pushing you into the countryside to die” (Savage 1984:50). It is hardly surprising that a review by Posel (2003) finds that research in the 1970s and 1980s focused on the characteristics of this migrant labour system. More puzzling is the fact that, since 1986 (with the abolition of influx control), empirical research on internal migration has dwindled and, in the 1990s, the focus seems to have moved mainly to immigration issues. Posel (2003) surmises that this may be due to an assumption that temporary and circulatory labour migration has been replaced by permanent migration in post-apartheid South Africa.

The majority of migrants out of the Eastern Cape originate in the former Transkei. The Transkei is one of the largest and poorest of the former home-lands in South Africa. As such, it is of special interest in discerning some of the post-apartheid responses of those who bore the brunt of what Savage (1984:3) has called the “disorganisation and reorganisation” of the African population under apartheid. The region is the area of origin of the majority of the African migrants entering the Western Cape and the profiles of these migrants and the routes they take have been the subjects of many studies (Bekker 2002; Cross and Webb 1999). According to Bekker (2002:10) this stream is currently the “largest and most rapid demographic flow in South Africa”.

Leibbrandt et al. (2002) used the 1996 census micro data to analyse the factors affecting the migratory tendencies of the African population in the former Transkei, focusing particularly on African rural-urban transitions to the Cape Metropolitan Area (CMA). The present study extends this research,

Magnitudes, Characteristics and Activities of EC Migrants 5

using both the 1996 and 2001 censuses to profile migration out of the former Transkei for the periods 1991 to 1996 and 1996 to 2001. Through the development of this profile, a better understanding of the people leaving the Eastern Cape and entering the Western Cape can be gleaned. Based on this understanding, government policies could be designed specifically to meet the needs of these migrants. The analysis makes use of the Census 1996 and Census 2001 Community Profile Databases, and the Census 1996 and Census 2001 10% Samples (Statistics South Africa 1998 and 2004).

The paper proceeds as follows. The following section begins by listing some of the strengths and weaknesses of the census data in addressing migration. The heart of the paper is the presentation of a substantial amount of descriptive information on the migration flows and on the migrants, in the third section. This section starts by comparing the magnitudes of migration out of and within the Eastern Cape with other national migration flows between rural and urban areas and between provinces. It then describes the characteristics of Africans migrating out of the Eastern Cape and compares these characteristics to non-migrants. Profiles are presented by race, age, employment and occupation and education. The concluding section of the paper then uses this descriptive platform as a base from which to draw out some implications for the South African migration literature and for policy towards migrants.

Using Data from the Census to Analyse MigrationThe 1996 and 2001 censuses were conducted in October of those years. New municipal boundaries were implemented in 2000, and new demarcations were made that divided provinces into district councils and metropolitan areas. Despite the complications this introduced, there is a general consistency between the 2001 and 1996 censuses (Statistics Council, 2003). However, there are a number of comparability issues that need to be mentioned.

Most importantly, it is ironic that prior to Census 1996, the censuses gen-erally were not representative of the population in South Africa but contained detailed questions on migration while after that the census became more representative but the questions on migration progressively less detailed. The 1996 census, for example, asked respondents to quantify remittances from migrants, while the 2001 census omitted all remittance questions, as well as questions that related directly to migrant labour. The migration-related ques-tions asked in the census are also problematic. Circulatory and oscillatory migration are impossible to measure, and regionalisation of the household


(Bekker 1999), where family ties are so close that households in rural and urban areas operate as one, serves to confuse questions regarding place of usual residence. Also, migration within a main place could not be analysed. Then, as data are only available as at the time of the census, it is not possible to look at changes to the situation of migrants before and after the migration. For example, changes in employment status or income level due to a move could not be ascertained (Leibbrandt et al. 2002). Finally, as regards the mi-gration variable, throughout this paper a migrant is defined as a person who moved suburb, ward, village, farm or informal settlement at least once during the five-year period prior to the date of the census. This analysis excludes children born during these respective five-year periods. This is because a migrant would have effectively answered “No” to the question: “Were you living in your current residence at the time of the previous census?” Chil-dren born between censuses would, thus, fall outside the scope of this ques-tion and obtaining the details of any move they may have made would add another level of complexity, apart from having to deal with complications of under-enumeration and scanning error (Statistics Council 2003).

There are a few other data issues that are relevant to the analysis of migration. First, there is the definition of urban and rural areas. In the 1996 census, an urban area was defined as an area that fell within a municipality or local authority, and included an ordinary town or city with formal structures, informal dwellings, mining hostels and hospital and prison institutions (Statistics South Africa 2003). Census 2001, while describing an ‘urban settlement’ as structured and organised, with formally planned and maintained roads and the provision of services such as water, electricity and refuse removal, did not classify enumeration areas into urban and rural (Statistics South Africa 2003). However, for the purposes of comparability between the censuses, the 10% sample of the 2001 data was coded to include the classification of urban and rural according to the 1996 definition.

For the first time in South Africa, the 2001 census used imputation to re-place unavailable, unknown, incorrect or inconsistent responses. This proce-dure involves replacing a response (non-blank) or non-response (blank) with a value determined either logically from other responses, or, where this is not possible, from a ‘hot deck’. (Hot decking entails using the response from the most recently processed response from a person or household that was simi-lar to the person/household with the missing response.) Table 1 shows the extent to which variables used in this paper were subjected to imputation.


Table 1 Percentage of each type of imputation undertaken for each variable of interest

Variables No imputationLogical

imputation(from blank)

Logical imputation(non blank)

Hot deck imputation

(from blank)

Hot deck imputation(non blank)

Figures shown as percentages

Age 77 1 22 0 0

Gender 99 1 0 0 none

Marital status 94 3 1 2 0

Previous residence 94 2 4 None none

Year moved 99 1 0 None none

Province of previous residence 97 2 1 None none

Main place of previous 99 0 1 0 0

Level of education 88 0 7 4 1

Source Derived from the Census 2001 10% Sample, Statistics South Africa (2004)

Table 1 shows that levels of imputation ranged from a high of over 23 per cent for Age to a low of little over 1 per cent for Year moved and Gender. In most cases it was under 6 per cent. The two variables with the highest levels of imputation were:

Level of education, which had the highest level of imputation, with a relatively high proportion being hot-deck from blank and logical from non-blank. Age, in which just under a quarter of all observations were corrected. The majority of the corrections were logical imputations from non-blank, which were mostly corrections to age based on date of birth.While the level of imputation is generally higher than the 2 per cent level

targeted,4 the intention of this process was to improve the overall quality of the data. Inspection suggests that there is little difference between the distributions of each variable including or excluding the imputed data and thus the imputed data are used.

Two final points about the data are worth noting. First, the unemployment levels that are used in the analysis in the paper were calculated from the census. It is known that these data produce higher unemployment levels than the official statistics for that time (Statistics South Africa 2004:53).

4 Personal communication with Professor Jacky Galpin, Chair of the 2001 Census Sub-Committee of the South African Statistics Council.

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•


Second, this study will not include any analysis of the income of migrants. This is largely due to the problematic nature of the income variable in the census, with widespread under-reporting, and the high level of imputation undertaken for the variable (Cronje and Budlender 2004).

A PROFILE OF MIGRANTS: 1991–1996 and 1996–2001This section presents a general description of the characteristics of migrants in South Africa, based on the 1996 and 2001 Census Community Profile databases, and the 10% Samples (Statistics South Africa 1998 and 2004). All results using the 10% Samples are calculated using the person sample weights supplied by Statistics South Africa. After considering migration at the national level, we examine the characteristics of African migrants compared to African non-migrants and non-African migrants from the Eastern Cape to the Western Cape, from the Eastern Cape to the rest of South Africa, and migrants remaining within the Eastern Cape. The results from the 2001 census are contrasted with those from the previous census. This comparison assumes that the characteristics of Eastern Cape migrants approximate those of migrants from the former Transkei.

Figure 1 Proportion of the South African population in urban areas

Source HSRC (1996): Socio-economic Atlas for South Africa, African; Savage (1984:20); Census 1996 and 2001 10% Sample, Statistics South Africa (1998 and 2004)5

5 The data for ‘Total South African’ were sourced from Socio-economic Atlas for South Africa and the Census 2001 10% Sample. The data for ‘Total African’ were sourced from Savage (1984), and the Census 1996 and Census 2001 10% Samples.

0%

10%

20%

30%

40%

50%

60%

70%

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010

Y ear

Prop

ortio

n To ta lSouthA f r ic anTota lA f r ic an


UrbanisationWe begin with an analysis of movements to urban and metropolitan areas.

As Figure 1 shows, there has been a strong trend of urbanisation in South Africa with just over half of the South African population residing in urban areas by 2001. This trend accords with Kok et al. (2003) who noted a similar strong trend to 1996. However, this level of urbanisation still falls short of the types of levels predicted by Todaro (1976) under the mobility transition hypothesis. According to Esau et al. (2004), this is largely due to:

the existence of influx control laws that slowed urbanisation until about 1986, circular migration, that has been found to feature in migration patterns, and a rising incidence of migration between informal settlements (intra-urban migration).In addition, the stagnation in growth of employment opportunities in

urban areas has probably played a part, too.

Table 2 Percentages of the population in urban areas

Percentage

Popu

latio

n co

untry

wide

Afric

an p

opul

ation

co

untry

wide

Non-

Afric

an

popu

latio

n in

the

West

ern

Cape

Afric

an p

opul

ation

in

the

West

ern

Cape

Non-

Afric

an

popu

latio

n in

the

East

ern

Cape

Afric

an p

opul

ation

in

the

East

ern

Cape

As at Census 1996 54 43 87 95 87 29

As at Census 2001 56 47 88 95 89 31

% increase 5 9 1 0 2 8

Notes The 1996 definition of an urban area is used for the calculations in this table.

Source Census 1996 10% Sample, Statistics South Africa (1998) and Census 2001 10% Sample, Statistics South Africa (2004)

As can be seen from Table 2 and Figure 1, despite the lower proportions relative to the whole population, there has been steady urbanisation of the African population with nearly half living in urban areas by 2001 (an increase of 9 per cent from 1996). In contrast, only 31 per cent of Africans in the Eastern Cape were living in urban areas in 2001, with most still living in

•

•

•


the former ‘homelands’ in this province. This is despite the fact that the increase in urbanisation amongst this group (8 per cent) was greater than for the general population (5 per cent). On the other hand nearly 95 per cent of Africans in the Western Cape were living in urban areas, although most African adults in these areas are rural-born (Bekker 2002).

Part of this trend is the strong flow of migrants to South Africa’s major metropolitan areas (metros). These areas are generally large economic hubs and migrants have been found to be willing to travel long distances to these large centres due to the range of products, services and opportunities on offer.

Table 3 shows the proportions of migrants whose destinations were various metropolitan and non-metropolitan areas (including those who moved from one metropolitan area to another, or one non-metropolitan area to another). Kok et al. (2003) defined two different types of ‘metropolisation’; primary metropolisation, which involves rural to metropolitan movement, and secondary metropolisation, which is either a town to metro or a city to metro movement.

Tables 2 and 3 do not distinguish between these two types of metropoli-sation. In the 2001 census, three Gauteng metropolitan areas were defined, whereas in the 1996 census, all were combined into one. The six metropolitan areas in Census 2001 (as demarcated by Statistics South Africa) are the destina-tion areas of 48 per cent of all migrants, with the remaining 52 per cent moving to the more numerous non-metropolitan areas. There has been an increase in

Table 3 Percentages of migrants who moved to metropolitan or non-metropolitan areas in the previous five years

1991–1996 1996–2001

All African Non-African All African Non-African

Cape Town 9 4 18 10 5 21

Durban 3 2 5 7 7 8

Gauteng 28 28 27 28 29 27

East Rand 8 9 7

Johannesburg 12 13 11

Pretoria 8 8 9

Port Elizabeth 3 2 3 3 2 5

Non-metropolitan 57 64 47 52 57 39

Source Census 1996 10% Sample, Statistics South Africa (1998) and Census 2001 10% Sample, Statistics South Africa (2004)


in metropolisation6 from the 43 per cent who had moved to metropolitan areas by the time of Census 1996.7 However, the strong non-metropolitan migration provides some support for the claim by Cross and Webb (1999) that there has been a strong trend towards peri-urban settlement and a densification around small towns and cities. They suggest that this may be due to the difficulties of travelling to metropolitan areas, with these small urban centres offering the only feasible contact for the rural population with the urban economy.

Johannesburg, East Rand and Pretoria can be grouped into the ‘Gauteng metropolitan areas’ due to their proximity and to ensure consistency of comparison with the 1996 census. This is the largest receiving metropolitan area, receiving a full 28 per cent of all migrants. Cape Town is second, receiving 9 per cent to 10 per cent of all migrants. It is interesting to note that the majority of non-African migrants move to metropolitan areas (53 per cent in the 1996 census and 61 per cent in the 2001 census) while the majority of African migrants (64 per cent in Census 1996 and 57 per cent in Census 2001) move to non-metropolitan areas. This may be due to the fact that non-Africans are more likely to have the necessary social networks in place in metropolitan areas, making the transition to these areas easier. However, the increase in the proportion of Africans migrating to metropolitan areas may indicate that urban migration is becoming easier for potential African migrants.

Inter-provincial MigrationTable 4 shows inter-provincial migration in South Africa for the period 1991 to 1996 and Table 5 shows this migration for the period 1996 to 2001. During the former period, 2.6 per cent of the total population born at least five years prior to the censuses was involved in these migratory flows, while the corresponding proportion for the latter period was 2.8 per cent. Gauteng was clearly the main destination province for both periods (440 156 and 605 452, respectively), followed by the Western Cape (173 965 and 135 514). The Eastern Cape was by far the largest sending area (224 314 and 350 761), followed by Gauteng (196 966 and 262 992) and Limpopo (161 202 and 246 074).

6 For these purposes, metropolisation is defined as a process where a migrant chooses a metropolitan area over a non-metropolitan area as destination.

7 It should be noted that the equivalent figure according to Kok et al. (2003) is 75 per cent. It is not clear how they arrived at this figure but they must have used a different definition of metropolitan area.


The Eastern Cape has the Western Cape as its major receiving area, with 41 per cent of its migrants moving there over the period 1996–2001. This is slightly less than the 45 per cent for the previous five years. Of the small flow of people leaving the Western Cape, 40 per cent moved to the Eastern Cape over the period 1996–2001. It is probable that much of this is return migra-tion of people previously from the Eastern Cape. This flow has increased since the 1991–1996 period, when only 25 per cent of migrants out of the

Table 5 Inter-provincial migration in South Africa 1996–2001

Province of originProvince of destination

EC FS GT KZN LP

Eastern Cape (EC) – 16,810 90,032 59,729 6,368

Free State (FS) 8,761 – 60,031 8,556 4,380

Gauteng (GT) 29,166 25,205 – 45,003 39,652

KwaZulu-Natal (KZN) 18,233 8,948 132,948 – 7,065

Limpopo (LP) 2,679 4,133 171,142 5,094 –

Mpumalanga (MP) 3,187 5,720 88,950 11,249 18,143

Northern Cape (NC) 2,954 7,635 11,060 1,850 1,719

North West (NW) 4,302 10,327 108,719 4,352 11,602

Western Cape (WC) 26,688 5,235 32,602 9,314 2,491

Total (SA) 95,970 67,203 605,452 85,418 85,052

Source Census 2001 Migration Community Profile, Statistics South Africa (2003)

Table 4 Inter-provincial migration in South Africa 1991–1996

Province of origin Province of destination

EC FS GT KZN LP

Eastern Cape (EC) – 16,375 54,474 31,868 1,193

Free State (FS) 3,337 – 31,922 3,526 829

Gauteng (GT) 11,729 24,000 – 24,634 18,081

KwaZulu-Natal (KZN) 5,813 6,671 73,974 – 949

Limpopo (LP) 489 2,102 109,670 1,502 –

Mpumalanga (MP) 878 3,301 55,781 4,763 8,829

Northern Cape (NC) 1,674 5,162 6,157 806 192

North West (NW) 785 8,445 94,437 1,304 4,685

Western Cape (WC) 10,100 3,233 13,741 4,007 555

Total (SA) 34,805 69,289 440,156 72,410 35,313



Western Cape moved to the Eastern Cape. It is also interesting to note that, over both five-year periods, Gauteng was both a major receiving and a major sending area.

It seems that major flows emanate from provinces where the former homelands were located (Eastern Cape, Kwa Zulu-Natal, Limpopo and North West), towards provinces that are economically relatively better off, and have large metros (Gauteng and the Western Cape). It can, thus, be assumed that

Province of destination Province of origin

MP NC NW WC Total

6,222 1,746 10,564 101,872 224,314 Eastern Cape (EC)

4,912 4,905 15,556 6,218 71,205 Free State (FS)

38,711 3,777 40,782 35,252 196,966 Gauteng (GT)

11,361 691 2,490 10,833 112,782 KwaZulu-Natal (KZN)

29,853 367 15,969 1,250 161,202 Limpopo (LP)

– 554 5,553 1,963 81,622 Mpumalanga (MP)

980 – 5,184 14,172 34,327 Northern Cape (NC)

4,270 9,238 – 2,405 125,569 North West (NW)

2,011 5,465 1,434 – 40,546 Western Cape (WC)

98,320 26,743 97,532 173,965 1,048,533 Total (SA)

Province of destination Province of origin

MP NC NW WC Total

10,087 4,142 21,227 142,366 350,761 Eastern Cape (EC)

6,991 6,417 20,119 13,017 119,511 Free State (FS)

34,721 6,829 53,413 58,169 262,992 Gauteng (GT)

18,852 1,893 7,910 24,631 202,247 KwaZulu-Natal (KZN)

37,739 1,385 21,374 5,207 246,074 Limpopo (LP)

– 1,486 11,560 6,003 143,111 Mpumalanga (MP)

1,429 – 7,529 21,430 52,652 Northern Cape (NC)

6,354 16,360 – 7,057 164,771 North West (NW)

3,133 9,649 3,769 – 66,193 Western Cape (WC)

109,219 44,019 125,674 135,514 1,257,551 Total (SA)


the processes of urbanisation and metropolisation mentioned above are driv-ing these flows.

Of course these proportions are not the same for all ages and, as will be shown below, the bulk of the urbanisation and metropolisation occurs in the young adult working ages, whereas the other migration streams will be concentrated at different ages, including some return migration from urban areas at around retirement ages.

Figure 2 and the Tables A.1 and A.2 in the Appendix record the net inter-provincial in- migration. It can be seen that Gauteng and the Western Cape have received the largest overall numbers due to migration, over both periods. Every other province (except Mpumalanga) experienced a net loss during both periods with Limpopo and the Eastern Cape showing the highest net losses. Aside from a small net loss to Mpumalanga over the period 1991–1996, the Western Cape is also the only province with a net gain from each of the other provinces during both periods. The Eastern Cape is the only province with a net loss to each of the other provinces, over both periods. Gauteng, though dominating the migration flows, has net outflows to the Western Cape over both periods (25 567 for 1996–2001 and 21 511 for 1991–1996). The next section considers the profile of the migrants themselves.

Figure 2 Net inter-provincial migration in South Africa

Source Census 1996 and Census 2001 Migration Community Profiles, Statistics South Africa (1998 and 2003)

-3 0 0 0 0 0

-2 0 0 0 0 0

-1 0 0 0 0 0

0

1 0 0 0 0 0

2 0 0 0 0 0

3 0 0 0 0 0

4 0 0 0 0 0

Gaute

ng

Wes

tern

Cape

Mpum

alang

a

Free

Stat

e

North

ern

Cape

North

Wes

t

KwaZ

ulu-

Natal

Limpo

po

Easte

rnCa

pe

P ro vin ce

Migr

atio

n ga

ins o

r los

ses (

num

ber o

f pe

ople)

1991-1996

1996-2001


The Characteristics of MigrantsRacial compositionTable 6 shows that 12 per cent of the South African population moved from one main place to at least one other place in the five years prior to Census 2001.8 Compared with the five years prior to the 1996 census, it seems that the population is becoming more settled in its residential areas, as about 20 per cent of people were involved in moves between 1991 and 1996. However, given the increase in inter-provincial migration, it seems that those who do move were more likely to change provinces.

The White population seems much more mobile than any other group with 41 per cent in 1996 and 27 per cent in 2001 moving between main places. Bekker (2002) surmises that this may be a result of Whites having superior access to information, a housing market and financial resources, and a lower reliance on family support networks. This migration includes a tendency for Whites to move to the Western Cape when they retire. The decrease in proportions migrating could indicate that the White population is becoming more settled, or that the job and resettlement opportunities for this group have become more limited.

Table 6 Migrants between main places by population group

Population Group

1996

Pop

ulat

ion

(com

mun

ity

prof

ile)

Migr

ants

(1

991–

1996

)

Migr

ants

as a

pr

opor

tion

of th

e 19

96 C

ensu

s po

pula

tion

(%)

2001

Pop

ulat

ion

(com

mun

ity

prof

ile)

Migr

ants

(1

996–

2001

)

Migr

ants

as a

pr

opor

tion

of th

e 20

01 C

ensu

s po

pula

tion

(%)

Black African 31 127 631 5 294 080 17 35 416 165 3 771 878 11

Coloured 3 600 445 864 754 24 3 994 509 506 842 13

Indian 1 045 595 284 183 27 1 115 466 151 556 14

White 4 434 695 1 799 444 41 4 293 641 1 152 542 27

Total 40 583 569 8 242 461 20 44 819 781 5 582 818 12

Source Census 1996 and Census 2001 Migration Community Profiles, Statistics South Africa (1998 and 2003)

The general level of movement amongst Africans is proportionately much lower than other groups (17 per cent and 11 per cent in each period). These lower relative proportions and their decrease over time may indicate that

8 Movement within main places is not considered in this calculation.


movement amongst the African population is stabilising at its current levels, after the volatility of the pre-1990s. Bekker (2002) also noticed a fall off in migration amongst Africans (to the Western Cape in particular).

Table 7 Percentages of migrants split by population group

Population Group (%)

Non-

migr

atin

g co

untry

wide

Migr

atin

g co

untry

wide

Non-

migr

atin

g in

the

EC

Migr

atin

g fro

m

the

EC to

the

WC

Migr

atin

g fro

m

the

EC to

the

rest

of

SA

Migr

atin

g with

in

the

EC

1991–1996Black African 81 65 89 85 87 71Coloured 9 10 7 6 2 14Indian or Asian 2 3 0 0 1 1White 8 22 4 9 10 141996–2001Black African 80 67 89 87 79 75Coloured 9 9 7 6 8 11Indian or Asian 3 3 0 0 1 1White 8 21 4 7 12 13

Source Census 1996 and Census 2001 10% Sample, Statistics South Africa (1998 and 2004)

Table 7 offers a further analysis of the ethnic composition of these migrants over the 1991–1996 and 1996–2001 periods. It shows that, whereas only 68 per cent of the countrywide migratory population is African in the latter period (up from 64 per cent in the 1996 period), 86 per cent of the stream from the Eastern Cape to the Western Cape, and 79 per cent of the stream from the Eastern Cape to the rest of South Africa are African (down from 85 per cent and 88 per cent, respectively, in the 1996 period). This accords with the largely African composition of the Eastern Cape. Of note, is the decrease in the proportion of Africans migrating from the Eastern Cape to the rest of South Africa excluding the Western Cape (88 per cent in the 1996 period to 79 per cent in the 2001 period), and the large increase in the proportion of Coloureds in this stream (2 per cent to 8 per cent).

AgeFigure 3 shows the age distribution of migrants. Additional detail is provided in Figures A.1 and A.2 in the Appendix. We see that about 59 per cent of all people moving from the Eastern Cape to the Western Cape between 1996


and 2001 fall into the 20 to 39 age band. The equivalent proportion for 1991–1996 was 56 per cent. The 20 to 24 age group represents the largest proportion moving from the Eastern Cape during both periods. With respect to non-migrating Africans in the Eastern Cape, about 34 per cent and 32 per cent fall into the 5 to 14 year age band for 1991–1996 and 1996–2001 respectively, while 16 per cent fall into the 50 year and above age band over both periods. This effectively means that, since the early 1990s, almost half of the population remaining in the Eastern Cape falls outside of the prime working ages.

Of the streams originating from the Eastern Cape, it appears from Figures 4 and 5 that the stream flowing to the Western Cape is somewhat more youthful than the stream flowing to other parts of the country. Also noteworthy is that about 20 per cent of migrants within the Eastern Cape (for both five-year periods) are in the 5 to 14 year age band. While this situation may reflect the age structure of the Eastern Cape population and movement in these ages could be connected to changes in schooling, it is also possible that these may be children having to move between different support bases while they wait to follow their highly mobile parents, who may have already left the province in search of work. The migrants within the Eastern Cape also appear older than those leaving the province.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

1991-1996

1996-2001

1991-1996

1996-2001

1991-1996

1996-2001

1991-1996

1996-2001

1991-1996

1996-2001

1991-1996

1996-2001

1991-1996

1996-2001

Non-migratingA f r ic an

Country w ide

MigratingNon-A f r ic anCountry w ide

MigratingA f r ic an

Country w ide

Non-migratingA f r ic an in the

EC

MigratingA f r ic an f romthe EC to the

W C


res t o f SA


w ith in the EC

Stream

Prop

ortio

n

50 y ears and above45 to 49 y ears40 to 44 y ears35 to 39 y ears30 to 34 y ears25 to 29 y ears20 to 24 y ears15 to 19 y ears5 to 14 y ears

Figure 3 Age distribution of migrants

Source Census 1996 and Census 2001 10% Samples (Statistics South Africa, 1998 and 2004)


0%

5%

10%

15%

20%

25%

30%

35%

40%5-

14

15-1

9

20-2

4

25-2

9

30-3

4

35-3

9

40-4

4

45-4

9

50+

A ge Grouping (y ears )

Prop

ortio

n

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Figure 4 Age profile of African migrants originating in the Eastern Cape, 1991–1996

Source Census 1996 10% Sample, Statistics South Africa (1998)

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Figure 5 Age profile of African migrants originating in the Eastern Cape, 1996–2001


Considering migration into or within the provinces, Figure 6 suggests that the majority of migrants fall into the 20–39 year age band in both five-year periods, with Gauteng and the Western Cape showing the highest proportions. The second most mobile age range over the two periods is the 5–19 year age band, probably as a result of children following parents who have moved earlier or possibly, for those over 16, migrating in seek of work. Those aged 60 and above were the least mobile.

More generally, in the 1996–2001 period migrants who have moved into or within Gauteng make up almost 21 per cent of the population. This is a decline from the five years prior to 1996, where they contributed 33 per cent. In the latter period, migrants who have settled in the Western Cape make up 18 per cent of the population. This also represents a decline from the 1991–1996 period, when they made up 30 per cent. The residents of Gauteng, the Western Cape and Free State appear to be much more settled in 2001 than they were in 1996, with the number of migrants decreasing by at least 12 per cent of the total in absolute terms.

However, perhaps the key point to note is that, in nearly all the provinces, the majority of the population is non-migratory. A notable exception is the

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Figure 6 Age distribution of migrants into or within a province as a proportion of the respective provincial population

Source Census 1996 and Census 2001 Migration Community Profiles (Statistics South Africa, 1998 and 2003)


migration stream of Africans from the Eastern Cape to the Western Cape, which still accounts for a significant proportion of the expanding African population in the Western Cape (Bekker 2002).

Employment and occupationFigure 7 and Table A.3 in the Appendix analyse the employment status of migrants using the expanded definition of unemployment from the census in 2001, and a consistent definition in 1996. This analysis only looks at the economically active people as defined in the censuses (i.e. between the ages of 15 and 65). Additional detail is provided in Tables A.4 and A.5 in the Appendix. The most striking observation is the general increase in unemployment rates of migrants between the 1991–1996 period and the 1996–2001 period.9 More specifically, these tables show that it is likely that an African migrant out of the Eastern Cape is moving in search of work. Of the people moving into the Western Cape in the five years prior to Census 2001, 38 per cent were unemployed, while 36 per cent of those moving to the rest of South Africa fell into this category. The corresponding 1996 census data give rates of 29 per cent and 32 per cent respectively. The changes in rates between the two censuses seem to point to a worsening of economic status of those moving to the Western Cape, relative to those moving to the rest of South Africa.

Figure 8 goes on to show the distribution of unemployed migrants by how soon they could start work. This figure looks at the unemployed from the 2001 census alone as this question was not asked in the 1996 Census. Almost two thirds of the African migrants leaving the Eastern Cape are willing to start work within one week. This far exceeds the proportion associated with non-migrants in, and migrants within, the Eastern Cape, where the respective percentages are 33 per cent and 45 per cent respectively. This indicates the keenness for work of those leaving the province.

Three quarters of the non-migrants in the Eastern Cape fall outside of working age and, therefore, are not economically active. Only 9 per cent are employed. The higher levels of employment of African migrants both out of, and within, the Eastern Cape would seem to indicate that those with jobs are willing to undertake the risks of migrating in search of potentially better jobs. Such an explanation is in line with trends at

9 It is possible that part of the explanation lies in the slightly different questions asked in each census and the resultant regrouping of 1996 data to get responses consistent with the 2001 data.


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Figure 7 Employment status of economically active migrants originating in the Eastern Cape



Figure 8 Distribution of 1996–2001 unemployed migrants by how soon they could start work


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Figure 9 Occupational status of employed migrants, 1991–1996

Source Census 1996 10% Samples, Statistics South Africa (1998)

Figure 10 Occupational status of employed migrants, 1996–2001

Source Census 2001 10% Samples, Statistics South Africa (2004)


the national level as shown in Figure 7 and Table A.3, where migrating Africans during both five-year periods are generally more likely to be economically active than their non-migrating counterparts, and seem to be moving either in search of employment, or in search of better jobs.

Interestingly, Table 8 shows that, in 1996, Gauteng seemed to be the most popular province for people residing in the Eastern Cape but working in another province (with 35 per cent working there), followed by the Western Cape (19 per cent) and KwaZulu-Natal (17 per cent). However, by 2001, the order changed, with the majority of those Eastern Cape residents working in another province being in KwaZulu-Natal (31 per cent), followed by Gauteng (21 per cent) and the Western Cape (19 per cent). It has to be noted, though, that the overwhelming majority of the employed population of the Eastern Cape work in that province (almost 95 per cent).

Table 8 Distribution of the Eastern Cape population, working elsewhere, by the province where they work

Province1996 2001

Number Percentage Number Percentage

Western Cape 2 879 19 3 594 19

Northern Cape 266 2 722 4

Free State 1 668 11 1 431 8

KwaZulu-Natal 2 585 17 5 915 31

North West 1 465 10 1 840 10

Gauteng 5 219 35 4 038 21

Mpumalanga 278 2 745 4

Limpopo 658 4 658 3

Total 15 018 100 18 943 100

Source Census 1996 and 2001 10% Samples, Statistics South Africa (1998 and 2004)

Figures 9 and 10 show that migrants in both five-year periods are employed in similar proportions in the same occupations. Interestingly, the majority of the working African migrants from the Eastern Cape to the Western Cape (54 per cent in the 1991–1996 period and 52 per cent in the 1996–2001 period) are employed in elementary occupations such as street vendors, domestic workers, building caretakers, agricultural and fishery labourers, and construction, manufacturing and transport labourers. This far surpasses


any other migration stream; where typically between 33 per cent and 39 per cent are employed in these occupations. Imbalances in skills between the population groups are also apparent. For each five-year period, respectively, 23 per cent and 31 per cent of non-African migrants are legislators, senior officials, managers, or professionals, while only 8 per cent of African migrants fall into these two groups.

Notable changes in proportions occurred mainly amongst the Eastern Cape population. The proportion of African migrants within the Eastern Cape, who were professionals, decreased from 12 per cent in the five years prior to Census 1996 to 7 per cent in the five years prior to Census 2001. The corresponding figures for the African non-migrating population of that province showed a similar fall from 13 per cent to 5 per cent. These decreases were accompanied by increases in the proportions of technicians and associate professionals. For this occupation group, the proportion of Africans migrating within the Eastern Cape increased from 4 per cent to 11 per cent, while the proportion of non-migrants increased from 4 per cent to 13 per cent.

Of those moving out of the Eastern Cape, large changes were noted amongst ‘craft and related trades’ workers. The proportion of Africans involved here, moving to the Western Cape, fell from 15 per cent to 10 per cent, while the proportions moving to the rest of South Africa fell from 21 per cent to 14 per cent.

Figure 11 examines whether those migrants who are employed are employees, employers, self-employed or family workers. The y-axis has been truncated to aid with the scale of the figure with the full results being presented in Tables A.6 and A.7 in the Appendix. From this it can be seen that at least 84 per cent of any group of working migrants are paid employees. Moreover, these proportions have increased on the whole between the censuses, and are accompanied by decreases in the proportion who are employers. Also, the proportions of the self-employed have largely fallen between censuses, and are more variable in 2001 than 1996, except for migrating non-Africans (increased from 7 per cent to 13 per cent). This corresponds with a large decrease in the proportion of employers in this group (7 per cent to 2 per cent), which probably reflects the change in employment status of non-Africans due to transformation of the work force. The very high reliance on others as providers of work amongst the African migrants from the Eastern Cape to the Western Cape is evident.


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Figure 12 Educational classification of migrants originating in the Eastern Cape, 1991–1996



EducationFigures 12 and 13 present the distributions of the educational classification for various types of Eastern Cape migrants This presentation excludes all children under age five. These distributions are seen to have remained largely the same over the two five-year periods. Both figures show that the largest proportion of migrants out of the Eastern Cape has some secondary education, with the second largest having some primary education. These proportions have remained stable. However, there are some changes in the proportions of the other groups. The third largest category in the 1991–1996 period was ‘No Schooling’, while this was ‘Grade 12/Std 10’ in the 1996–2001 period. Overall there seems to be a slight improvement in educational attainment amongst migrants.

In contrast to the relative stability of the distribution of the educational classification of the migrants, the corresponding distribution of non-migrating Africans has changed markedly. In the 1996 census it was skewed to the left. In the 2001 census, the proportion of those with no schooling fell relative to 1996, while the ‘some primary’ and ‘some secondary’

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categories increased. Most simply, this could point to an increase in education amongst non-migrants in the Eastern Cape. Alternately, it could relate to the selective effect of migration, in that many people with ‘no schooling’ have, in fact, left the province. This seems unlikely given that, overall, it appears that migrating Africans of Eastern Cape origin have a higher level of education than non-migrating Africans of this province.

These findings seem broadly in line with the national results shown in Figure 14. Here, too, non-migrating Africans are, on the whole, less educated than migrating Africans. With 47 per cent of migrants having some secondary education or higher in 1996 and 60 per cent in 2001, it also seems that African migrants out of the Eastern Cape to the Western Cape are slightly more educated than African migrants from the Eastern Cape to the rest of South Africa where the respective percentages are 44 per cent and 58 per cent.

Of particular concern is that, over both five-year periods, at least 60 per cent of the non-migrating African population of the Eastern Cape has no schooling, or only some primary schooling. This highlights the low level of education amongst the population of this province.

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Tables A.8 and A.9 in the Appendix present some detail on the qualifications of migrants and non-migrants with post-school qualifications. Such a comparison provides an opportunity to see where highly educated migrants and non-migrants perceive their skills could best be put to use. However, it should be noted that direct comparison is difficult because there werechanges in the choices available to respondents in the relevant questions of the 1996 and 2001 censuses.

The major share (40 per cent) of Africans in the Eastern Cape who did not migrate, or who migrated within the province, in the period prior to Census 2001, were involved in education, training and development. This proportion was even greater in the period prior to Census 1996 (52 per cent). Also, of those choosing to move out of the Eastern Cape in the period prior to Census 2001, 21 per cent were involved in business, commerce or the management sciences – a greater proportion than those choosing to stay behind. Of those who chose to move (out of, or within the province) in the period 1996–2001, there was a greater proportion involved in engineering and engineering technologies than those who never moved at all. For African migrants to the Western Cape, it is interesting to note that the proportion involved in the computer science and data processing, or computing fields, was greater than other flows in both five-year periods. Similar comments apply to the portions of this stream involved in technical, and administrative and clerical work, in the period prior to Census 1996.

CONCLUSIONSUsing an empirical picture drawn from South Africa’s two most recent national population censuses, this paper has provided much empirical detail on South African internal migration patterns and recent changes to these patterns. This is not a policy exercise per se in that it does not rigorously assess the causes of migration or the impact of a specific policy on migration flows. Nonetheless the findings are relevant to policy. The budgeting of and planning for service delivery and social welfare uses information on the present distribution of the population and likely changes to this distribution. This final section of paper pulls out some key findings that seem particularly germane in this regard.

Countrywide, migration patterns reveal strong trends of urbanisation within the South African population. Africans in the Eastern Cape are still largely rural-dwellers. However, this group is urbanising at a faster rate than


the general population. A large portion of the national urbanisation trend is more appropriately regarded as metropolisation in the sense that the strongest migratory flows are to South Africa’s metropolitan areas. The three Gauteng metropolitan areas account for the largest migratory flows in the country. However, it is interesting to note that the majority of non-African migrants settle in metropolitan areas (with this increasing over time), while the majority of African migrants settle in non-metropolitan areas (with this decreasing over time). Major flows seem to be emanating from provinces where the former homelands were located (Eastern Cape, KwaZulu-Natal, Limpopo and North West), towards provinces that are relatively economically better off, and have large metropolitan areas (Gauteng and the Western Cape).

The context for these migratory flows seems to be one in which the overall population is becoming more settled in its residential areas, with less of the population being involved in moves between 1996 and 2001, than between 1991 and 1996. Also, the proportions of migrants who have moved into or within Gauteng, the Western Cape and Free State have decreased greatly between the censuses, meaning that the residents of these provinces appear to be much more settled in 2001 than they were in 1996. The White population remains more mobile than any other population group but even this group has become less mobile in recent years.

Generally the level of movement amongst Africans is proportionately lower than other groups. Documenting such movements is particularly important given that the share of the population that is African is so much larger than that of other groups and is increasing. This group contains almost all of the population that is especially in need of targeted government assistance. The trends with regard to migration in this group seem to indicate that movement amongst the African population is stabilising at its current levels, after the volatility of the decade from the late 1980s onwards immediately following the abolition of influx control. Such volatility has greatly complicated the work of provincial and municipal planners over the post-apartheid period. If this stabilisation trend can be confirmed using more recent data this would be important.

The majority of all people moving from the Eastern Cape to the Western Cape (and countrywide as well) between 1991 and 1996, and 1996 and 2001, falls into the 20 to 39 age band. The largest proportion moving from the Eastern Cape during both periods was in the 20 to 24 year age group, and it appears that the stream flowing to the Western Cape is somewhat


more youthful than the stream flowing to other parts of the country. This age profile of migrants makes it very likely that an African migrant out of the Eastern Cape is moving in search of work.

Given the history of migrant labour in South Africa, this empirical finding is hardly surprising. Yet, with the Eastern Cape being South Africa’s poorest province such confirmation is important. Unfortunately the paper goes on to establish that there is little evidence of quick labour market benefits from this migration in search of work. Indeed, perhaps the most striking finding in the paper is the general increase in unemployment rates of migrants from the 1991–1996 period to the 1996–2001 period. While there may be some contention about the way that the census measures unemployment, many of these unemployed migrants are recorded as being willing to take up a job in the next week should they be offered one. Thus, it is evident that these working age migrants do want to work but are not working.

A recent review of the evidence from national household surveys reports that over the post-apartheid period “an increasing proportion of African rural households report non-resident members who are labour migrants” (Posel and Casale 2006:261). This study also notes that these migrant households tend to be poorer than non-migrant rural households and that remittances from the migrants are important to the livelihoods of these households. Thus, even when viewed from the rural end, it appears that there are many households that are driven by the need to send out migrants into non-rural labour markets even if the expected returns are uncertain and low.

The rising level of unemployment of migrants documented in this paper implies a lowering of the expected returns to rural households from such migration. It is possible that the early evidence of a stabilisation of migrant flows that was mentioned earlier in the conclusion is a response to such a change in returns. However, modelling such behaviour and behaviour change is a methodologically demanding exercise that is well beyond the scope of this paper.

After documenting rising unemployment for migrants, the paper goes on to establish that there has been a lowering of the expected returns from migration even for those migrants who are employed. Almost the whole stream of migrants who are employed are paid employees, pointing to a very high reliance on others as providers of work. Moreover, the interrogation of the occupations of those African migrants from the Eastern Cape to the Western Cape who are employed suggests that the majority are involved in elementary occupations


(street vendors, domestic workers, building caretakers, agricultural and fishery labourers, and construction, manufacturing and transport labourers).

This sombre labour market situation occurs despite the fact that migrating Africans of Eastern Cape origin have a higher level of education than non-migrating Africans of this province. It also seems that African migrants out of the Eastern Cape to the Western Cape are slightly more educated than African migrants from the Eastern Cape to the rest of South Africa. This could suggest that the Western Cape offers more opportunities for those migrants from the Eastern Cape with a higher level of education. However, given that the data for migrants to the Western Cape and elsewhere show high unemployment rates and a crowding of the employed into elementary occupations at all levels of education, they do not substantiate this possibility.

Finally, the paper provides another view of the changing returns structure for migrants by providing a close look at who does not migrate from the Eastern Cape. Since the early 1990s at least, the vast majority of the non-migrating African population of the Eastern Cape falls outside of the prime working ages or are not economically active, and very few are actually employed. In addition, the majority has low levels of schooling. Such a population profile implies a very low capacity for self-initiated or sustainable livelihood generation. Indeed the implied demands on the state are daunting. When such a situation is assessed through the eyes of a potential migrant of working age it is not hard to see why they continually migrate, even if this is into a hostile labour market elsewhere.


ReferencesBekker, S. 2002. Migration Study in the Western Cape 2001 – Main Report.

Retrieved 11 May 2004 from the World Wide Web: http://www.westerncape.gov.za/environmental_affairs_development_planning/development_planning/migrationstudy.asp.

Bekker, S. 1999. Circulatory Migration Linking Cape Town to the Eastern Cape: Some Reflections. Unpublished mimeo, Stellenbosch.

Cronje, M. and Budlender, D. 2004. “Comparing Census 1996 and Census 2001: An Operational Perspective”, Southern African Journal of Demography 9(1):67–89.

Cross, C. and Webb, M. 1999. Population migration in South Africa’s coastal provinces: An overview of trends. Unpublished mimeo, Durban.

Esau, F., Horner, D. and Ndegw, D. 2004. The Links between Migration, Poverty and Health: Evidence from Khayelitsha Mitchell’s Plain. Southern Africa Labour and Development Research Unit: Cape Town.

Fix, A. 1999. “The Study of Migration”. Chapter 1 in Fix A (ed.). Migration and Colonization in Human Microevolution. Cambridge, Cambridge University Press.

Kok, P., O’Donovan, M., Bouare, O. and Van Zyl, J. 2003. Post-Apartheid Patterns of Internal Migration in South Africa. Cape Town, HSRC Publishers.

Leibbrandt, M., Van der Berg, S., Burger, R. and Mlatsheni, C. 2002. Migration and the changing rural-urban interface in South Africa: What can we learn from census and survey data? Retrieved 12 December 2003 from the World Wide Web: http://www.csae.ox.ac.uk/conferences/2002-UPaGiSSA/papers/Leibbrandt-csae2002.pdf.

Posel, D. 2003. Have Migration Patterns in Post-Apartheid South Africa Changed? Retrieved 5 February 2004 from the World Wide Web: http://pum.princeton.edu/pumconference/papers/1-Posel.pdf.

Posel, D. and Casale, D. 2006. “Internal Labour Migration and Household Poverty in Post-Apartheid South Africa”. Chapter 10 in Bhorat, H. and Kanbur, R. (eds). Poverty and Policy in Post-Apartheid South Africa. Cape Town: HSRC Press.

Savage, M. 1984. Pass laws and the disorganisation and reorganisation of the African population in South Africa. Unpublished mimeo, Cape Town.

Statistics Council. 2003. Statement by the South African Statistics Council on the 2001 Census. http://www.statssa.gov.za/census01/HTML/Stats_Council_Statement.pdf accessed on 12 March 2007.

Statistics South Africa. 2003. Investigation into appropriate definitions of urban and rural areas for South Africa. Pretoria, Statistics South Africa.

Statistics South Africa. 2004. Census 2001 10% Sample Metadata. Pretoria, Statistics South Africa. (Multi-media CD-ROM).

Statistics South Africa. 1998. Census 1996 10% Sample Metadata. Pretoria, Statistics South Africa. (Multi-media CD-ROM).

Todaro, M. 1976. Internal Migration in Developing Countries, A Review of Theory, Methodology, Evidence and Research Priorities. Geneva: The International Labour Office.


APPENDIX

Table A.1 Net inter-provincial migration in South Africa 1991–1996

Province of origin

Province of destination

EC FS GT KZN LP MP NC NW WCEastern Cape (EC) – 13,038 42,745 26,055 704 5,344 72 9,779 91,772

Free State (FS) –13,038 – 7,922 –3,145 –1,273 1,611 –257 7,111 2,985

Gauteng (GT) –42,745 –7,922 – –49,340 –91,589 –17,070 –2,380 –53,655 21,511

KwaZulu–Natal (KZN) –26,055 3,145 49,340 – –553 6,598 –115 1,186 6,826

Limpopo (LP) –704 1,273 91,589 553 – 21,024 175 11,284 695

Mpumalanga (MP) –5,344 –1,611 17,070 –6,598 –21,024 – –426 1,283 –48

Northern Cape (NC) –72 257 2,380 115 –175 426 – –4,054 8,707

North West (NW) –9,779 –7,111 53,655 –1,186 –11,284 –1,283 4,054 – 971

Western Cape (WC) –91,772 –2,985 –21,511 –6,826 –695 48 –8,707 –971 –

Total (SA) –189,509 –1,916 243,190 –40,372 –125,889 16,698 –7,584 –28,037 133,419


Figure A.1 Age distribution of migrants from the Eastern Cape to the Western Cape


1991-1996

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Table A.2 Net inter-provincial migration in South Africa 1996–2001

Province of origin

Province of destination

EC FS GT KZN LP MP NC NW WC

Eastern Cape (EC) – 8,049 60,866 41,496 3,689 6,900 1,188 16,925 115,678

Free State (FS) –8,049 – 34,826 –392 247 1,271 –1,218 9,792 7,782

Gauteng (GT) –60,866 –34,826 – –87,945 –131,490 –54,229 –4,231 –55,306 25,567

KwaZulu–Natal (KZN) –41,496 392 87,945 – 1,971 7,603 43 3,558 15,317

Limpopo (LP) –3,689 –247 131,490 –1,971 – 19,596 –334 9,772 2,716

Mpumalanga (MP) –6,900 –1,271 54,229 –7,603 –19,596 – 57 5,206 2,870

Northern Cape (NC) –1,188 1,218 4,231 –43 334 –57 – –8,831 11,781

North West (NW) –16,925 –9,792 55,306 –3,558 –9,772 –5,206 8,831 – 3,288

Western Cape (WC) –115,678 –7,782 –25,567 –15,317 –2,716 –2,870 –11,781 –3,288 –

Total (SA) –254,791 –52,308 342,460 –116,829 –161,022 –33,892 –8,633 –39,097 69,321


Figure A.2 Age distribution of non-migrants in the Eastern Cape


1991-1996

34%

14%9%

7%

6%

6%

4%

4%

16%5-1415-1920-2425-2930-3435-3940-4445-4950+

1996-2001

34%

14%8%

6%

6%

6%

5%

4%

17%5-1415-1920-2425-2930-3435-3940-4445-4950+


Table A.3 Percentage distributions of migrants by employment status

% Non-migrating African countrywide

Migrating Non-Africancountrywide

Migrating Africancountrywide

1991–1996 1996–2001 1991–1996 1996–2001 1991–1996 1996–2001

Not applicable 31 32 19 22 20 19

Employed 17 17 50 48 37 33

Unemployed 18 24 6 7 21 27

Not economically 34 27 25 23 22 21


Table A.4 Percentage distribution of migrants by employment status, 1991–1996

%

Non-

migr

atin

g Af

rican

co

untry

wide

Migr

atin

g no

n-Af

rican

co

untry

wide

Migr

atin

g Afri

can

coun

trywi

de

Non-

migr

atin

g Af

rican

in th

e EC

Migr

atin

g Afri

can

from

the

EC to

th

e W

C

Migr

atin

g Afri

can

from

the

EC to

the

rest

of S

A

Migr

atin

g Afri

can

with

in th

e EC

Not applicable 31 19 20 35 15 11 22

Employed 17 50 37 9 34 41 29

Unemployed 18 6 21 18 28 32 20

Not economically 34 25 22 38 23 16 29

Total 100 100 100 100 100 100 100


Table A.5 Percentage distribution of migrants by employment status,1996–2001

%

Non-

migr

atin

g Af

rican

co

untry

wide

Migr

atin

g no

n-Af

rican

co

untry

wide

Migr

atin

g Afri

can

coun

trywi

de

Non-

migr

atin

g Af

rican

in

Migr

atin

g Afri

can

from

the

EC to

the

Migr

atin

g Afri

can

from

the

EC to

the

Migr

atin

g Afri

can

with

in th

e EC

Not applicable 32 22 19 38 15 12 22

Employed 17 48 33 9 28 32 25

Unemployed 24 7 27 21 38 36 26

Not economically 27 23 21 32 19 20 27

Total 100 100 100 100 100 100 100



Table A.6 Percentage distribution of employed migrants by employment status, 1991–1996

%No

n-m

igrat

ing

Afric

an c

ount

rywi

de

Migr

atin

g non

-Af

rican

cou

ntry

wide

Migr

atin

g Afri

can

coun

trywi

de

Non-

migr

atin

g Af

rican

in th

e EC

Migr

atin

g Afri

can

from

the

EC to

th

e W

C

Migr

atin

g Afri

can

from

the

EC to

the

rest

of S

A

Migr

atin

g Afri

can

with

in th

e EC

Employee 90 84 91 88 94 93 90

Family worker 1 2 1 2 1 1 2

Self-employed 6 7 5 7 4 4 5

Employer 3 7 3 3 1 2 3

Total 100 100 100 100 100 100 100


Table A.7 Percentage distribution of employed migrants by employment status, 1996–2001

%

Non-

migr

atin

g Af

rican

cou

ntry

wide

Migr

atin

g non

-Af

rican

cou

ntry

wide

Migr

atin

g Afri

can

coun

trywi

de

Non-

migr

atin

g Af

rican

in th

e Ec

Migr

atin

g Afri

can

from

the

ECto

the

WC

Migr

atin

g Afri

can

from

the

EC to

the

rest

of S

A

Migr

atin

g Afri

can

with

in th

e EC

Paid employee 92 84 93 90 96 93 93

Paid family worker 1 1 1 2 1 1 2

Self-employed 5 13 5 5 2 5 4

Employer 1 2 1 2 1 1 1

Unpaid family worker 1 0 0 1 0 0 0

Total 100 100 100 100 100 100 100



Table A.8 Percentage distribution of 1991–1996 migrants with post-secondary education by qualification

%No

n-m

igrat

ing

Afric

an c

ount

rywi

de

Migr

atin

g non

-Af

rican

cou

ntry

wide

Migr

atin

g Afri

can

coun

trywi

de

Non-

migr

atin

g Af

rican

in th

e EC

Migr

atin

g Afri

can

from

the

EC to

th

e W

C

Migr

atin

g Afri

can

from

the

EC to

the

rest

of S

A

Migr

atin

g Afri

can

with

in th

e EC

Arts 8 11 9 8 9 10 8

Science 2 6 2 3 3 2 3

Law 1 2 1 1 3 2 2

Theology 1 1 1 0 2 3 1

Economics and management 4 11 6 4 6 10 5

Education 39 11 31 53 13 19 50

Medical 7 6 7 8 5 8 9

Engineering 1 3 2 1 0 1 1

Administration 3 5 4 3 10 7 2

Protection 2 3 3 2 1 2 2

Building 0 1 0 0 0 1 0

Technical 5 13 6 3 14 7 4

Computing 2 2 2 1 7 2 2

Veterinary 0 0 0 0 0 0 0

Other 25 25 26 13 27 26 11

Total 100 100 100 100 100 100 100



Table A.9 Percentage distribution of 1996–2001 migrants with post-secondary education by qualification

%

Non-

migr

atin

g Af

rican

Migr

atin

g non

-Af

rican

cou

ntry

wide

Migr

atin

g Afri

can

coun

trywi

de

Non-

migr

atin

g Af

rican

in E

C

Migr

atin

g Afri

can

from

the

EC to

th

e W

C

Migr

atin

g Afri

can

from

the

EC to

the

rest

of S

A

Migr

atin

g Afri

can

with

in th

e EC

Agriculture or renewable energy resources 2 2 2 4 2 2 3

Architecture or environmental design 1 1 1 1 1 1 1

Arts 2 4 2 2 2 2 2Business, commerce or management sciences 14 23 19 11 21 21 16

Communication 1 2 2 1 3 2 1

Computer science or data processing 9 7 8 6 12 8 7

Education, training or development 35 12 21 45 15 16 33Engineering or engineering technology 8 12 10 5 12 10 7

Health care or health sciences 8 9 8 6 4 9 8

Home economics 1 1 1 1 1 1 0

Industrial arts, trades or technology 1 2 1 1 1 1 1

Languages, linguistics or literature 1 1 1 1 1 1 1

Law 2 4 3 2 1 3 2

Libraries or museums 0 0 0 0 2 1 0

Life sciences or physical sciences 1 2 1 1 1 1 1

Mathematical sciences 1 1 1 1 1 1 1

Military sciences 1 0 1 1 1 1 0

Philosophy, religion or theology 1 1 1 1 0 1 1

Physical education or leisure 0 1 0 0 0 0 0

Psychology 1 2 1 1 0 1 0Public administration or social services 4 2 4 3 3 3 3

Social science or social studies 2 2 3 2 2 3 3

Other 6 9 9 4 15 11 8

Total 100 100 100 100 100 100 100


Magnitudes, Personal Characteristics and Activities of ... · 2 School of Economics, University of...

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