coloureds and indians - michael cole

7/28/2019 Coloureds and Indians - Michael Cole

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Coloureds and Indians: a cohort analysis of

labour market behaviour, 1995-2011

By Michael Cole1

Abstract

1

This paper is dedicated to Jesus Christ. Special thanks to Ingrid Woolard, Nicola Branson and Philip Cole who allprovided me with valuable input and guidance.

South Africas labour force still remains scarred by its former political

regime. Africans were undoubtedly the worst affected by Apartheid

policies. However Coloureds and Indians have had to overcome similar

constraints, yet their story is often eclipsed by that of Africans in the

literature. This paper constructs a synthetic panel using October

Household Surveys (OHSs), Labour Force Surveys (LFSs) andQuarterly Labour Force Surveys (QLFSs) to analyse the labour market

behaviour of Coloureds and Indians relative to their African and White

counterparts between 1995 and 2011. Locally Weighted Scatterplot

Smoothing (LOWESS) is used to produce age profiles by year and birth

cohort. The age profiles show that the labour market experiences of

Coloureds (particularly Coloured males) have worsened since 1995.

Indians appear to be moving towards White levels of lower

unemployment, benefitting significantly from higher levels of education,

employment equity and an increasingly skills based economy.


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1 Introduction

Apartheid systematically discriminated against Africans, Coloureds and Indians subjecting them to

inferior education, constrained mobility and limited employment opportunities relative to Whites. As a

result, South Africas labour force still remains scarred by its former political regime. Africans were

undoubtedly the worst affected by Apartheid policies. Coloureds and Indians have had to overcomesimilar constraints, yet their story is often eclipsed by that of Africans. Numerous authors have

investigated the labour market experiences of Africans from both the supply and demand side (Bhorat,

2004; Bhorat, 2006; Branson, 2006; Dias & Posel, 2006; Dinkelman & Pirouz, 2002; Fedderke, 2012;

Kingdon & Knight, 2004; Kingdon & Knight, 2005; Wittenberg, 2002; & Von Fintel, 2007 amongst

other). Few authors however have drawn significant attention to the experiences of Coloureds and

Indians post-Apartheid.

Coloureds and Indians constitute 9.0% and 2.6% of the population respectively while Africans and

Whites hold 79.5% and 9.0% (Stats SA, 2011). Given that Apartheid inherently benefitted Whites over

the African majority, the literature has focused on contrasting White privilege with African disadvantage.

Moreover, cross-sectional datasets tend to provide small samples of Coloureds and Indians (in particular)that make parametric analysis highly problematic (Von Fintel, 2007:27). Avoiding analysis on Coloureds

and Indians is by no means desirable. Like Africans, Coloureds face exceptionally high levels of

unemployment, reaching 23% in 2011 (QLFS, 2011). Indians appear to have benefitted from higher

average levels of education over time, yet they remain unequal to Whites in terms of employment.

This paper constructs a synthetic panel using October Household Surveys (OHSs), Labour Force

Surveys (LFSs) and Quarterly Labour Force Surveys (QLFSs) to analyse the labour market behaviour of

Coloureds and Indians relative to their African and White counterparts between 1995 and 2011. Synthetic

panels do not provide a study of individual transitions as do regular panel datasets (Von Fintel, 2007;

Duval-Hernandez & Romano, 2009). Instead it is assumed that, on average, the behaviour of individuals

within a group of individuals is well approximated by the behaviour of other individuals of the same age

group or birth cohort (ibid). Applying non-parametric graphical techniques to the dataset provides an

alternative means of analysing employment, unemployment and the non-economically active.

Locally Weighted Scatterplot Smoothing (LOWESS) is used to observe long-run labour market trends for

Coloureds and Indians relative to Africans and Whites. Despite small samples for Indians, LOWESS

appears to produce graphs with consistent trends. This same approach has been used by Wittenberg

(2002), Branson (2006) and Branson & Wittenberg (2007) on Africans to produce age profiles by year and

birth cohort. Age profiles plot the proportion of working, unemployed or non-economically active

individuals in the working age population against age. These profiles are constructed by year and birth

cohort group2

for Coloured and Indian males and females.

The age profiles show that the labour market experiences of Coloureds (particularly Coloured males) have

worsened since 1995. Coloured men and women face increasingly similar unemployment profiles to

Africans, but are still better absorbed into employment. Younger cohorts of Coloured males are revealed

to be working significantly less than older cohorts. Indians appear to be moving towards White levels of

lower unemployment, benefitting significantly from higher levels of education, employment equity and an

increasingly skills based economy. Indian women are seen to have a higher propensity to drop out of the

labour (perhaps to take care of young children) than other races.

2

A birth cohort refers to the group of individuals born within a particular year (e.g 1983). A birth cohort group therefore refersto all the individuals born within a particular period (e.g. 1983-1987).


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The sections in this paper are organised as follows. Section 2 will begin by giving a background of

Coloured and Indian labour market behaviour during and post-Apartheid. The purpose of this section is

to contextualise the analyses in sections 3, 5 and 6. Section 3 will provide an overview of Coloured and

Indian labour market experiences between 1995 and 2003 through various summary statistics. Section 4

will describe the data used to construct the synthetic panel as well as the methodology employed. Section

5 will present the working, unemployed and not economically active age profiles generated usingLOWESS by year and cohort. After presenting the various age profiles, section 6 will provide a brief

discussion before section 7 concludes.

2 Background

Apartheid

The structure of the labour force under Apartheid was largely influenced by discriminatory policies that

conferred rights of mobility, education and employment to individuals based on their race. ThePopulation Registration Act of 1950 formalised racial classification through a defined taxonomy that

divided all South Africans into four major groups (African, Coloured, Indian and White).

The term Coloured was given to people of mixed descent. Their heritage can largely be traced back to

interracial relationships between early Dutch farmers, Khoi-Khoi, Malay and Asians living in the Western

Cape in the late 1600s (Du Pr, 1994:14). Indians, on the other hand, are the direct descendants of Indian

indentured labourers and trades people who immigrated to South Africa in the 1860s (Jithoo, 1991:344).

These immigrants largely worked in the sugar industry in Kwazulu Natal.

Following the Population Registration Act came a wave of Apartheid legislation that enforced racial

segregation (Group Areas Act 1950), prohibited interracial sexual relations (Immorality Act of 1950) andimposed inferior education and job reservation that inherently benefited Whites over Africans, Indians

and Coloureds (Bantu Education Act 1953) (Du Pr, 1994). The Homeland Act of 1958 constrained the

mobility of African, Coloured and Indian South Africans to their respective homeland or land of origin

without appropriate permits. This not only discouraged labour mobility, but sustained large information

asymmetries and increased the cost of job search for Africans, Coloureds and Indians (Kingdon &

Knight, 2005:4). Although not to the same extent as Africans, both Coloureds and Indians were most

notably discriminated against in terms of education and the availability of employment opportunities.

The Apartheid government provided education to Africans, Coloureds and Indian that suited unskilled

and semi-skilled professions. Given the unequal distribution of government resources devoted to

Africans, Coloureds and Indians, the quality of education followed a racial hierarchy. White learnersreceived the best quality education, followed by Indians, Coloureds and lastly Africans (Bunting, 2006).

African education was especially dire; however the situation for Coloureds was by no means far better.

Coloureds were notoriously subject to gutter education, with overly crowded classrooms (in excess of

50 pupils per class) and poorly qualified teachers (Du Pr, 1994:111). Opportunities for tertiary education

were severely limited. Coloureds and Indians could enrol at a number of technical schools (technikons)

and at the University of the Western Cape and University of Durban-Westville respectively (Bunting,

2006: 49). The future benefit of such tertiary education, however, was limited by White job reservation

and disincentivised by wage discrimination in favour of Whites (Du Pr, 1994:109).

Many Coloureds that did acquire tertiary education became teachers as teaching offered more stability andremuneration relative to other Coloured occupations at the time (carpenters, mechanics, electricians and


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nurses) (Du Pr, 1994). Furthermore, the Coloured Labour Preference policy of the Western Cape

ensured preferential access to skilled jobs and training over Africans (Nattrass & Walker, 2005:504).

Indians too occupied a number of semi-skilled professions. The 1990 Manpower Survey found 54% of

technicians to be Indian males and a large number of working Indian women to hold jobs as pharmacist

assistants (Crankshaw, 1996: 645). White men and women held the largest share in skilled professions

which included doctors, lawyers, accountants, head nurses and journalists.

Coloureds and Indians were indeed more advantaged than Africans; however their experiences were still

from being equal with Whites. As a result, the end of Apartheid brought about severe challenges in the

labour market.

Post-Apartheid

South Africas labour market in general has told a dismal story post-Apartheid. With the new South Africa

came increased labour force participation by Africans, Coloureds and Indians. However this has been met

with low labour market absorption and high unemployment as the economy has pursued a skills-based

growth path. South Africas labour market situation has been extensively researched and documentedover the years, looking at both the supply and demand side of the market.

On the supply side, authors have drawn attention to the fact that since 1994 there has been an influx of

largely unskilled workers into the labour force (Banerjee et al, 2008:2). More specifically, there has been

an unprecedented increase in the supply of African women in the labour market (ibid). The end of

Apartheid brought an end to job reservation and segregation which availed more potential working

opportunities for Africans, Coloureds and Indians. This unfortunately took place amidst a demand shift

away from primary employment to tertiary employment (Banerjee et al, 2008:2). Dias & Posel (2006) and

Bhorat (2004) note how economic growth has provided employment disproportionally in favour of more

educated (skilled) individuals, a phenomenon exacerbated by the inherited educational disparities of

Apartheid. What this has meant is that many unskilled workers have struggled to find employment as thedemand for skilled labour has increased.

While the ANC government has been successful in improving access to education for Africans, the

quality of their education remains largely sub-standard (Spaull, 2012:60). Furthermore the high cost of

tertiary education automatically prevents many poorer matriculants from acquiring further education

(Von Fintel, 2007). This is not only the case for Africans, but for Coloureds and Indians too. When the

economy is increasingly biased towards those with higher education, the result is that the youth with

insufficient education and experience are then marginalised (ibid).

South Africa has seen increasing levels of youth unemployment, sitting at over 30% in 2011 while the

regular unemployment rate sat at 24.7% (QLFS, 2011). In an environment of mass unemployment,unskilled individuals may find it rational not to search for employment (Dinkelman & Pirouz, 2002).

Kingdon & Knight (2004) note this as a reason behind high levels of discouragement (particularly

amongst Africans) where the costs of searching for employment far outweigh the benefits. Banerjee

(2008) attributes this high cost to a lack of high density employment centres in South Africa.

Unemployment on the broad definition (including discouraged workers) for Coloured and Indians is fairly

large relative to Whites. Of those who are actively searching for jobs, 50% of Africans and 45% of

Coloureds are actively searching after 6 months, whereas 30% of Indians and Whites remain in this state

(Banerjee et al, 2008: 19). The unemployed in South Africa tend to be unemployed for quite some time.

Different authors cite a number of reasons for such high unemployment rates. One reason relates to theinability of the informal sector to absorb the unemployed while another relates to the high costs that


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labour legislation, union activity and crime bring to businesses (Banerjee et al, 2008). Although the

informal sector absorbs a significant amount of the South African population, low levels of

entrepreneurship prevent the informal sector from fully absorbing the unemployed (ibid).

On the other hand, businesses in the formal sector face considerably high wage bills relative to

productivity. Bhorat (2005) refers to the prescriptions in the Labour Relations Act as being a hindrance toemployment creation while Fedderke (2012) points out that labour is mispriced in the South African

market. Strong union activity has played a role in increasing formal sector wages (Benerjee, 2008).

Consequently, low labour productivity has made hiring irrational at the prevailing wage rates (Fedderke,

2012:15). Crime acts as a further deterrent for new businesses and imposes additional security and theft

costs for existing businesses. Demanding legislation, low productivity and the difficult business

environment has undoubtedly dampened the demand for labour.

Unemployment in post-Apartheid South Africa is clearly far more structural than it is transitional

(Banerjee et al, 2008). After looking at literature, one has a better sense of the South African labour

market and the context in which Coloureds and Indians operate. The following section sheds deeper

insight into the labour market experiences of Coloureds and Indians relative to Africans between 1995and 2011. This is done as a preliminary overview before applying LOWESS graphical techniques to the

synthetic panel.

3 Summary statistics

The previous section gave a clear overall picture of the labour market during and post-Apartheid. This

section presents three different tables that allow one to assess the experiences of Coloureds and Indians

relative to Africans. The tables present percentages (e.g. unemployment) and relative values3. Africans

were used as the benchmark for the relative values. This allows one to see the degree to which Colouredsand Indians have overcome the structural constraints they faced under Apartheid compared to Africans.

Table 1 presents the various labour market indices of Coloureds and Indians relative to Africans for 1995,

2003 and 2011.

Coloureds and Indians have held roughly the same percentage of the population over the period

investigated, 9.0% and 2.6% respectively. Labour force participation has not adjusted much for Coloureds

and Indians, yet Coloureds and Indians consistently participate more so than Africans. Higher levels of

labour force participation by Africans after 1995 however have caused these relative levels to decline

somewhat. Coloured and Indian levels of unemployment (on both the strict and broad definition) are

consistently below that of Africans between 1995 and 2003. This means that Africans are still the worst

off in the labour market.

Broad unemployment includes discouraged workers who have not actively searched for employment over

the last four weeks (Stats SA, 2011). Furthermore, broad unemployment rates better capture the adequacy

of the economy to provide employment (Von Fintel, 2007). What one should then notice is that the

relative levels of broad unemployment have increased slightly for Coloureds (0.64 in 2011) while they

3Relative levels are given by the various labour market rates for Coloureds and Indians divided by the same rate for Africans

(E.g. ).

A relative value of 1.4 for Coloured labour force participation rates, for example, would indicate that a larger proportion ofColoureds are participating the labour market than Africans. A value less than 1 would indicate the opposite.


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have decreased for Indians (0.35 in 2011). Indians have much lower levels of broad unemployment than

Africans and Coloureds.

Absorption rates reflect the proportion of working age individuals (aged 15 to 64) that are employed.

These rates have remained relatively steady for Coloureds while they have slightly increased for Indians

between 1995 and 2011. Both Coloureds and Indians are still being absorbed into employment more sothan their African counterparts, although at a decreasing rate in relative terms. Such trends may be

attributed to higher average levels of education for Coloureds and Indians. However this is less likely to

be the case for Coloureds who have similar education levels as Africans.

Table 1: Coloured and Indian Labour Market Indices relative to Africans for 1995, 2003 and 2011

Coloured

Relativeto

African Indian

Relativeto

African

1995

Percentage of Population 9.1% 2.6%

Labour Force Participation 61.1% 1.40 57.7% 1.32

Unemployment - strict 15.8% 0.73 10.5% 0.49

Unemployment - broad 22.5% 0.60 13.6% 0.36

Absorption rate 51.5% 1.50 51.6% 1.50

Average Education (years)2 8.3 1.04 11.2 1.40

2003Percentage of Population 9.0% 2.6%




Absorption rate 50.4% 1.48 50.0% 1.47

Average Education (years) 9.1 1.06 11.4 1.31

2011

Percentage of Population 9.0% 2.6%




Absorption rate 51.5% 1.27 52.8% 1.30

Average Education (years) 10.2 1.02 12.1 1.21

Source: OHS 1995, LFS 2003(1,2) and QLFS 2011:Q4, own calculations

Overall, levels of education have increased for Coloureds, Indians and Africans since 1995. Mean

Coloured education has increased from 8.3 years in 1995 to 10.2 years in 2011 and remains marginallygreater than Africans post-Apartheid. Yet despite these gains, the average Coloured individual in 2011 still


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does not complete high school. This finding is consistent with Lam et al (2008) and Hofmeyer et al (2011)

who found Coloureds to face high opportunity costs (forgone wages) to remaining in school, resulting in

high dropout rates.

Indians, on the other hand, went from a mean education level of 11.2 in 1995 to 12.1 in 2011. Relative to

Africans, Indians consistently surpass Africans in terms of educational attainment. Table 2 takes thisanalysis further by looking at the highest levels of education obtained for working, unemployed and non-

economically active Coloureds and Indians in 1995 and 2011.

From table 2, one can see that smaller proportions of Coloureds (with primary, incomplete secondary and

secondary education) are working in 2011 than in 1995. Coloureds with more than a primary school

education appear to be better absorbed into the workforce than Africans in 2011. A larger proportion of

Indians across all education levels are working more so now than before.

Looking at unemployed Coloureds with only a primary education, the relative levels have increased from

0.73 in 1995 to 0.96 in 2011 (almost equal to Africans in 2011). Coloured levels of unemployment for

those with incomplete secondary education and secondary education are below those of Africans.Amongst Coloureds with tertiary education there has been a significant decreased in relative

unemployment from 1.84 in 1995 to 0.73 in 2011. This decrease however is more likely a result of lower

labour market activity. The ratio of unemployed Indians to Africans with secondary education decreased

to 0.26 in 2011, while it increased for tertiary education. It appears that the proportion of unemployed

Indians with tertiary education is almost equivalent to that of Africans.

Observing the non-economically active, Africans are still relatively less economically active than

Coloureds in 2011. This is, however, not the case with Indians who reflect lower levels of economic

activity across education levels. This finding can be attributed to two factors: firstly, labour force

participation amongst educated Africans has increased substantially since 1995; and secondly, Indian

women tend remain out of the labour force more so than women of other races. A further point ofinterest is the type of occupations Coloureds and Indians have post-Apartheid. Table 3 presents the

percentages of Coloureds and Indians in 10 different job categories as well as column for all races.

Coloureds have remained largely employed in elementary professions since 1995. Elementary

professions include occupations such as petrol attendants, wine farm workers and taxi assistants.

Coloureds appear to have moved with the SouthAfrican economy towards technical and associate

professionals. Indians went from mostly being employed in craft and related trades in 1995 to

legislators, senior officials and management in 2011. Indians appear to have benefited the most from

higher levels of education, employment equity and the move towards tertiary services in an economy with

severe skills shortages. Africans (not displayed) still remain concentrated in elementary professions, but

there has been a significant move to service, shop and market sales workers.

Coloureds and Indians have had different labour market experiences post-Apartheid. In the sections to

follow, LOWESS graphical techniques are applied to a synthetic panel dataset to construct age profiles by

year and birth cohort. These graphs will reveal Coloureds to be in a worse situation in 2011 than they

were in 1995 and significant improvements for Indians.


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Table 2: Coloured and Indian working, unemployment and non-economically active rates by highest level of education obtained.

Working

1995 2011

Coloured

Relative

toAfrican Indian

Relative

toAfrican Coloured

Relative

toAfrican Indian

Relative

toAfrican

Primary 83.4% 1.08 90.8% 0.91 75.0% 1.01 100.0% 1.35

Incomplete Secondary 80.8% 1.07 88.6% 0.96 73.2% 1.09 86.9% 1.29

Secondary 84.6% 1.17 88.8% 0.92 81.1% 1.18 91.8% 1.34

Tertiary 92.1% 0.96 99.0% 1.00 95.0% 1.02 93.4% 1.00

Unemployed - strict

Primary 16.6% 0.73 9.2% 0.41 25.0% 0.96 0.0% 0.00


Secondary 15.4% 0.55 11.2% 0.40 18.9% 0.60 8.2% 0.26

Tertiary 7.9% 1.84 1.0% 0.23 5.0% 0.73 6.6% 0.97

` Not Economically Active

Primary 45.0% 0.78 56.0% 0.96 45.5% 0.81 74.7% 1.33


Secondary 22.0% 0.48 28.9% 0.63 19.8% 0.83 33.5% 1.41Tertiary 10.5% 0.54 14.9% 0.77 5.7% 1.06 11.6% 2.18

Source: OHS 1995, QLFS 2011: Q4, own calculations


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Table 3: Coloured and Indian Job Occupations Proportions relative to Africans for 1995 and 2011

Coloured Indian All Races

1995 2011 1995 2011 1995 2011

Legislators, senior officials and management 2.4% 5.4% 13.1% *19.5% 5.6% 5.8%

Professionals 1.4% 3.2% 7.5% 9.0% 3.4% 3.7%

Technical and associate professionals 6.8% ***9.9% 12.1% ***12.6% 10.8% ***8.9%

Clerks 10.5% **10.6% *19.9% **18.7% 11.0% 8.1%

Service workers and shop and market sales 11.4% 9.2% ***13.9% 11.3% 10.9% **10.8%

Skilled agricultural and fishery worker 0.8% 0.4% 1.0% 0.0% 1.9% 0.5%

Craft and related trades workers **14.2% 9.0% **14.7% 7.4% ***11.8% 8.6%

Plant and machine operators and assemblers 11.4% 6.5% 11.2% 7.4% 10.5% 6.0%

Elementary occupation *29.2% *18.5% 5.9% 4.3% *22.1% *17.0%

Domestic workers ***12.7% 4.5% 0.8% 0.0% **11.9% 5.8%

Note: * largest proportion; ** second largest proportion; *** third largest proportion

Source: OHS 1005, LFS 2003 and QLFS 2011, own calculations


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4 Data and Methodology

Data

This paper attempts to compare the labour market experiences of Coloureds and Indians relative to their

African and White counterparts from 1995 to 2011. Such an analysis required the construction of asynthetic panel, which is essentially a pooled cross-sectional dataset that covers the period 1995-2011.

Panel datasets allow one to capture the dynamic experiences of individuals by tracking them over time

(Wooldridge, 2009). These panels however often suffer from attrition which can make samples

unrepresentative as more and more individuals fall out of the sample (Deaton, 1985; Romano & Duval-

Hernandez, 2009). Synthetic panels dont provide a study of individual transitions, but rather assume that

on average, the behaviour of individuals within a group of individuals is well approximated by the

behaviour of other individuals of the same cohort or age-group (Von Fintel, 2007; Duval-Hernandez &

Romano, 2009). In other words, it is possible to assess the labour market behaviour of a group, by

assuming that their experiences are the same on average.

The Post-Apartheid Labour Market Survey (PALMS) is an already existing synthetic panel constructed byKerr & Lam (2011) that pools data from the OHS surveys from 1994-1999 and LFS surveys from 2000-

2007. The Standard OHS surveys consisted of 30 000 households in 3000 Primary Sampling Units

(PSUs), covering a range of cross-sectional labour market variables (Stats SA, 2005). A number of

improvements were made each year after their introduction, which brings some results from earlier years

into question (Von Fintel, 2007). The 1994 OHS, for example, utilised 1000 PSUs and is not used in this

paper because of its skewed demography (Branson & Wittenberg, 2007). Branson & Wittenberg (2007)

also express concern over anchoring ones analysis on the 1995 OHS; however most comparative analyses

base themselves on 1995 and so does this paper.

The LFS surveys were provided biannually with 20% of each sample rotated out of the sample every

successive survey; this introduced a small panel dimension to the LFS which a few authors have exploited(Von Fintel, 2007:20). Regarding the comparability of OHS and LFS surveys, Wilson, Woolard & Lee

(2004) caution against comparisons of total employment figures given methodological changes. This

paper however does not work with total employment figures but employment as a proportion of the

working age population.

Together, the OHS and LFS surveys made PALMS. The variables in PALMS have been cleaned to

remain consistent over all the years in question, hence PALMS provides a better alternative to

constructing ones own synthetic panel. In order to cover the period 2007-2011, the QLFS surveys were

appended to PALMS.

The QLFS surveys were introduced in 2008 with a revised methodology, questionnaire, frequency of dataand survey process systems (Stats SA, 2012). The variables of interest remained largely the same across

LFS and QLFS; however there is a slight discrepancy with the labour market status variable that needs to

be brought to attention. Yu (2009) finds that there is no longer a clear distinction between strict and

broad labour market status in QLFS, making it difficult to derive long-term trends in labour force

participation and unemployment. Changes to the QLFS questionnaire make the QLFS algorithm for

labour market status more complicated than its predecessor (ibid). Section 5, however, reveals that it is

still possible LOWESS is still able to capture distinct trends over time after pooling all the QLFS surveys

to PALMS. QLFS surveys from Q1 2008 to Q4 2011 were all included in the synthetic panel.

An important issue when constructing the synthetic panel was the issue of sample weights. Stats SA

assigns weights to each individual in its surveys that allow one to adjust estimates to conform to the

population distribution at the time. However, since the OHS, LFS and QLFS surveys are cross-sectional,


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their purpose is to produce representative data for the particular year in question (Branson & Wittenberg,

2011). These datasets were not designed to be used as a time series (ibid). Branson & Wittenberg (2011)

employed a cross-entropy approach to produce a set of sample weights that create consistent aggregates

over time. These cross-entropy rates however are only available from 1994 to 2007. Given that generating

such weights between 2008 and 2011 is beyond the scope of this paper, the normal sampling weights are

used. As sections 5 and 6 will reveal, using the normal weights still allows one to examine significanttrends over time.

LOWESS graphing techniques are applied to the synthetic panel to generate age profiles for all four races

by year and Coloureds and Indians by cohort. Similar analysis has been done by Wittenberg (2002),

Branson (2006) and Branson & Wittenberg (2007); however their focus has been almost exclusively on

Africans. By extending this analysis to Coloureds and Indians as well as period in question, it will allow

one to better understand the labour market experience of Coloureds and Indians post-Apartheid relative

to Africans and Whites.

Methodology

Locally Weighted Scatterplot Smoothing (LOWESS) is a graphical approach that uses locally weighted

least squares regressions to smooth a scatterplot of data. Regressions are run within a selected bandwidth

of observations that give the greatest weight to observations closest to the focal observation (Cleveland,1979). The approach is summarized below:

A bandwidth of observations is selected which essentially specifies the proportion of allobservations used to smooth each point.4

Weighted least squares regression of on uses Clevelands (1979) Tri-Cube weights and arecarried out for each observation in the data. The Tri-Cube weight is specified below:

() {( ||) || ||

Where () and is the distance between and its furthest neighbour within the band.Observations that are further away from receive declining weights while if , theweighting allocates a value of 1.

Fitted values () are then used in () , where is white noise with a mean of 0,and plotted on a scatterplot to generate a smooth curve (Cleveland, 1979)

The advantage of employing LOWESS is that it does not require that one specify a restrictive functional

form, allowing for a non-parametric model (Wittenberg, 2002). Attempting to capture age, period and

cohort effects of unemployment, employment and labour market activity becomes problematic when

using a parametric model. These types of parametric models face the identification problem (where

cohort= year-age). Such models need to be specified accordingly to avoid perfect multi-collinearity or

employ advanced techniques such as the use of intrinsic estimators (Black et al, 2010; Yang et al, 2007).

A caution when using LOWESS is to be aware of the trade-off between bias and variance. If the

bandwidth specified is too small, this will result in insufficient data falling into the window of

observations, increasing the variance (Cleveland, 1979). If the span is too large, regressions may over-

4Following Wittenberg (2002), Branson (2006) and Wittenberg & Branson (2007) the bandwidth of 0.3 was selectedfor the age profiles by year. A bandwidth of 0.35 was selected for the age profiles by cohort.


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smooth the data and lead to a large bias (ibid). The balance is then to select the smallest bandwidth that

produces the smoothest fit.

Von Fintel (2007) notes that because sample sizes are small for Coloureds, Whites and Indians, estimates

may be inconsistent and biased. As a rule of thumb, age group sizes and cohort sizes for Coloureds and

Indians should not be lower than 100 observations so as to appeal to the law of large numbers (ibid). ForColoured age-groups and cohorts, observations generally exceed 100; however the same does not apply

for Indians (See Appendix). This is problematic when one wants to compare Indian labour market indices

against Africans.

A sensitivity analysis was conducted to assess the best way of addressing the issue of small Indian samples

(see Appendix A). Possible solutions were to group by age, group by year and group by cohort to increase

the sample sizes. For the age-profiles by year, it was found that neither grouping by age nor year

significantly affected trends, albeit at the end points. Samples of Indians over 45 were incredibly small in

the 1995 OHS (with some samples having only 3 observations). The unemployment profile, in particular,

revealed highly inconsistent trends after age 45. This age profile is therefore restricted to age 45 in 1995.

Wittenberg (2002) applied LOWESS to Indians in two of his figures despite small sample sizes, using onlyindividual cross sectional datasets (OHS 1993, 1994, 1995). One reason for this is that LOWESS is still

able to capture the overall trend of labour market indices, though imperfectly, without producing

controversial results (ibid). Regarding birth cohorts, grouping cohorts together proved to be effective in

generating large sample sizes and meaningful trends.

Section 5 to follow applies the LOWESS approach firstly to construct an age-unemployment profile for

Coloureds and Indians by year. The age profiles for working, unemployed and not economically active

males and females are presented to examine the experiences of Coloured and Indian men and women

against their African and White counterparts post-Apartheid. The situation for Coloureds appears to have

worsened since 1995, but improved for Indians.

5 Age Profiles by Year

By using LOWESS to plot the age profiles of working, unemployed and not economically active South

Africans, it is possible to observe the experiences of different age groups at different periods of time.

Plotting age profiles allows one to examine the lifecycle distribution of a particular labour market status

(e.g. level of unemployment amongst 18 year olds compared to 35 year olds). Looking at age profiles for

1995, 2003 and 2011 will therefore provide a sense of the net flows into a particular labour market status

over the period. This section will look at how labour market status profiles have changed post-Apartheid

and show that Coloured experiences have converged to those of Africans somewhat while Indians haveimproved significantly.

Figure 1 presents the unemployment profile for Coloureds in four year intervals from 1995 to 2011. Note

that this figure shows the unemployed as a percentage of working population, that being individuals

between the ages of 15 and 64 years old. The reason for employing this measure as opposed to the

unemployed as a percentage of the labour force is for two reasons. Firstly, presenting the level of the

unemployed allows one to view the inflow of non-economically active youth into the labour force and see

where the unemployment levels peak. Presenting the unemployment ratewould simply portray a curve

that decreases monotonically, offering little meaningful interpretation. Secondly, Wittenberg (2002),

Branson (2007) and Branson & Wittenberg (2007) apply the same approach making this papers results

directly comparable.


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12

0

.05

.1

.15

.2

.25

10 20 30 40 50 60Age

1995 1999

2003 2007

2011

Year

1995-2011

Age-Unemployment (strict) Profile for Coloureds

Figure 1: The age profile of unemployed Coloureds using the strict definition from 1995 to 2011.

Figure 1 offers a horizontal and vertical interpretation. Looking at the graphs horizontally, it is clear that

unemployment follows the expected trend. Unemployment for Coloureds consistently peaks at around 22

and 23 years of age across the time period. From there, unemployment follows a downward trend before

levelling out briefly in the mid to late 30s/early 40s and declining into the 50s and 60s. Looking at thegraphs vertically, one is able to look at how unemployment levels differ for each age group across the

years. However, one sees a peculiar exogenous shift in the curves prior to and after the year 2000.

Banerjee et al (2008) note how dramatic changes in labour force participation between 1999 and 2000 can

be attributed to changes in the sampling methodology between the OHS and LFS surveys. Wilson,

Woolard & Lee (2004) highlight the incomparability of OHS and LFS unemployment levels given that

some people who would have described themselves as economically inactive in the OHS would be

classified as working (economically active) in the LFS. Consequently this makes it difficult to assess the

true unemployment experiences of the youth. However the graphs still give us a sense of the age profile.

Figure 2 depicts the very same age profile of unemployment, but for Indians. Indians reflect a similarpeak level of unemployment for 22/23 year olds to Coloureds. This also closely coincides with the peak

age of unemployment for Africans which sits at around 25 years of age (Branson, 2006).


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0

.1

.2

.3

10 20 30 40 50 60Age

1995 1999

2003 2007

2011

Year

1995-2011

Age-Unemployment (strict) Profile for Indians

Figure 2: The age profile of unemployed Indians using the strict definition.

From figures 1 and 2, one can see that the lifecycle of unemployment peaks between 20 and 25. Indian

unemployment levels out strongly from the mid-30s. Figures 3 and 4 now separate Coloureds and Indians

by gender to examine their working, unemployment and labour inactivity experiences relative to Africans

and Whites. These figures show that Indians are converging at a fast pace to White levels of

unemployment, while the Coloured experience increasingly resembles that of Africans.

Figure 3 presents the working, unemployed and not economically active age profiles for males in 1995,

2003 and 2008. These graphs paint a negative picture for Coloured males. In 1995, Coloured males

between the ages of 18 and 25 were absorbed fairly quickly into employment relative to Africans. By

2011, their rate of absorption was almost identical to Africans5. This was not as a result of improvements

among Africans. Note that by 2011, absorption rates had declined for all races. Furthermore, 76% of 25

year old Coloured males in 1995 were working, while by 2011, the figure had dropped to 47%.

The 1995 OHS severely downplayed the level of unemployment for Africans, Coloureds and Indians.

Along with an increase in economic activity, the Coloured unemployment profile has converged closer to

that of Africans. This is particularly the case after age 25. Although not displayed, this trend in

unemployment is clearly evident when one looks at the years in between 2003 and 2011. These trends

contrast strongly against Indian males, who appear to be moving closer to lower White levels of

unemployment, although not converging.

5The rate of absorption is given by the slope of the working curve.


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14

Figure 3: Working, unemployed and non-economically active males for 1995, 2003 and 2011 across race

Source: OHS 1995, LFS 2003, QLFS 2011, own calculations

As highlighted in section 1, Indians have higher average levels of education than both Africans and

Coloureds, but lower than Whites. This order is reflected in the working profiles of Indians that are

consistently above Africans, distanced away from Coloureds and remained fairly close to Whites. Indian

unemployment peaks at age 26 at 13% in 2011, down from 26% at age 22 in 2003. Such levels should be

treated with caution as Indian sample sizes are still very small and open to bias. What can be said is that

Indian unemployment appears to have declined post-Apartheid and moved closer to that of Whites.

Section 2 saw Indian unemployment to have decreased from 2003 to 2011. Economic activity has

remained relatively constant between 1995 and 2003 for Indian males.

Looking at women, all trends have occurredwithin the context of a rapid feminisation of the labour

force that has taken place in South Africa since the 1995 (Casale & Posel, 2002: 163). Women of all raceshave traditionally occupied the role of homemakers and caregivers, remaining out of the labour force to

0

.1

.2

.3

10 20 30 40 50 60Age

Unemployed Males 2003

0

.1

.2

.3

10 20 30 40 50 60Age


0

.2

.4

.6

.8

1

10 20 30 40 50 60Age

Working Males 2011

0

.2

.4

.6

.8

1

10 20 30 40 50 60Age

Working Males 1995

0

.2

.4

.6

.8

1

10 20 30 40 50 60Age

Working Males 2003

0

.1

.2

.3

10 20 30 40 50 60Age


0

.2

.4

.6

.8

1

10 20 30 40 50 60Age

Non Economically Active Males 1995

0

.2

.4

.6

.8

1

10 20 30 40 50 60Age


0

.2

.4

.6

.8

1

10 20 30 40 50 60Age


African Coloured

Indian White

Race


16/30

15

take care of children and attend to domestic duties. In line with a global shift, South African women have

become increasingly more active in the labour market (Casale & Posel, 2002). Higher levels of

participation can be attributed to a number of factors.

Firstly, rising levels of education among women have increased their employment prospects as well as

their opportunity cost of having children (Casale & Posel, 2002:172). Additionally, employment equitypolicy in South Africa favours women over men in employment selection. This presents a further

incentive to pursue employment. Secondly, higher levels of male unemployment have led more women to

look for work (Casale & Posel, 2002: 175). When women are living with unemployed men (or are living

by themselves) there is increased pressure for women to earn an income (ibid). Other factors affecting

female labour force participation include: the age that women are getting married, the age they decide to

have children and the number of children they choose to have. Such factors can lead women to stop

working for a period of time, before re-entering the labour force at a later stage. These trends are

reflected in figure 4.

Figure 4: Working, unemployed and non-economically active females for 1995, 2003 and 2011 across race

0

.2

.4

.6

.8

10 20 30 40 50 60Age

Working Females 1995

0

.2

.4

.6

.8

10 20 30 40 50 60Age


0

.2

.4

.6

.8

10 20 30 40 50 60Age


0

.1

.2

.3

.4

10 20 30 40 50 60Age

Unemployed Females 1995

0

.1

.2

.3

.4

10 20 30 40 50 60Age


0

.1

.2

.3

.4

10 20 30 40 50 60Age


.2

.4

.6

.8

1

10 20 30 40 50 60Age

Non Economically Active Females 1995

.2

.4

.6

.8

1

10 20 30 40 50 60Age


.2

.4

.6

.8

1

10 20 30 40 50 60Age


African Coloured

Indian White

Race

Source: OHS 1995, LFS 2003, QLFS 2011, own calculations


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16

Similarly to figure 3, figure 4 displays the age profiles by year of the different labour market states for

women. Looking at Coloured women, the working profiles have remained fairly similar in relation to

other races, levelling out at around 60% after age 30. Comparing the 1995 and 2003 working curves, one

can see the trend amongst Coloured and White women to remain working for a longer period of time.

This is illustrated by the steep inflow of Coloured and White women into work between about 18 and 29,

before plateauing in the 30s and declining in the 40s. These trends are reflected in the non-economicallyactive curves, which (for Coloured women) have decreased from a minimum of 31% in 1995 to 20% in

2011.

Compared to Africans, Coloured women are working more until about the late 40s. In terms of its

position in relation to Africans, the Coloured unemployment profiles are consistently to the left over the

period, peaking at around age 21. What this means is that Coloured women have continued to transition

into the labour force sooner than African women. This can be linked to higher levels of education

amongst Coloured women who then transition into the labour force after leaving school. By 2011,

Coloured and Indian women appear below the age of 40 have a working profile that is almost identical.

Indian women have tended to transition into work at a similar rate as Coloured women. However lookingat the working curves for each of the three periods, it is clear that Indian women tend to transition out of

the labour force at a younger age than women of any other race. This is evident by the fact that the peak

levels of work and minimum levels of non-economic activity correspond roughly to the same ages, at

around 25, 28 and 40 for 1995, 2003 and 2011 respectively. These findings suggest that, despite higher

labour market participation amongst females, Indian women are more likely to drop out of the labour

force (perhaps to care for young children) than women of other races. One should notice that in 1995 the

working curve peaked at age 25 while in 2011, it peaked at age 40. This implies that Indian women are

working for longer periods before dropping out of the labour force.

By 2011, the unemployment profile for Indian women had moved considerably closer to that of White

women however not quite converging. Posel & Dias (2006) found that Indian women (as well as

Coloured women) with tertiary education to have benefited the most since the end of Apartheid with

much lower probabilities of unemployment. This is most certainly related to employment equity policy

that has favoured Indian females in job selection (CEE, 2011:22).

From looking at the age profiles of men and women by year, one can see that the situation for Coloured

males has become worse while remaining roughly the same for Coloured women post-Apartheid.

Coloured unemployment experiences appear to be progressively similar to Africans. Indians on the other

hand, appear to have benefitted significantly since 1995. Indians have lower unemployment levels and

consistently high employment levels relative to Africans and Coloureds. The next section will plot the

age-profiles by birth cohort, allowing one to assess the extent to which Coloured and Indian experiences

have differed across generations.

6 Age Profiles by Birth Cohort

The following graphs reflect the age profiles (by birth cohort) of Coloured and Indian males and females.

The intuition behind these graphs is that one is able to track the average labour market experiences of a

particular birth cohort over time and compare them to that of younger cohorts. One can plot a single

curve that say tracks the average level of unemployment of individuals born in 1988. These individuals

would be 15 in 2004, 16 in 2005 and so forth. This then allows one to examine the lifecycle distribution

of unemployment for different birth cohorts. To combat the recurring problem of small Indian sample

sizes, cohorts are divided into 5 year groups (See Appendix A). These groups are defined by their ages in


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17

2003 (i.e. 16-20, 21-25, 26-30 and 31-35 year olds)6. Figure 5 below presents the age profiles by birth

cohort for Coloured and Indian males.

Figure 5: Working, unemployed and non-economically active Coloured and Indian males by birth cohort.

Source: OHS 1995-1999, LFS 2003-2007, QLFS 2008-2011, own calculations

6

More explicitly, 16-20 year olds were born between 1983 and 1987; 21-25 year olds between 1978 and 1982; 26-30 year oldsbetween 1973 and 1977; and 31-35 year olds between 1968 and 1972.

0

.2

.4

.6

.8

1

10 20 30 40 50Age

Working Indian Males by Birth Cohort

0

.2

.4

.6

.8

1

10 20 30 40 50Age

Working Coloured Males by Birth Cohort

0

.1

.2

.3

10 20 30 40 50Age

Unemployed Coloured Males by Birth Cohort

0

.1

.2

.3

10 20 30 40 50Age

Unemployed Indian Males by Birth Cohort

0

.2

.4

.6

.8

1

10 20 30 40 50Age

Non Economically Active Coloured Males by Birth Cohort

16-20 21-25

26-30 31-35Note: age of cohort group defined by age in 2003

0

.2

.4

.6

.8

1

10 20 30 40 50Age

Non Economically Active Indian Males by Birth Cohort


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18

Looking at the working and unemployment curves reveals a deeply discouraging story for Coloured

males. The working curves show that younger Coloured cohorts have experienced lower levels of

employment than their older counterparts. This is evident by the fact that none of the curves cross after

the age of 24, nor do their trends reveal any propensity to do so. The proportion of working 24 year olds

in the 16-20 cohorts peaked at just over 60% while the proportion of working 31-35 cohorts peaked at

around 75%. One should also notice that the orders of the curves, from top to bottom, are perfectlysequential, with the 16-20 cohorts, followed by the 21-25 cohorts and so on.

The unemployment curves illustrate a similar picture, with no interaction in the curves after around 23

years of age. Coloured males are still more likely to find employment as they get older; however younger

cohorts are finding this employment in declining proportions. What this picture implies is that there has

been barely any improvement for Coloured males (as a group) post-Apartheid. In fact younger Coloured

males are worse off in terms of employment than their older counterparts.

The working age population comprises of the unemployed, employed and non-economically active.

Therefore one may anticipate that higher levels of economic participation amongst younger cohorts of

Coloured males are driving the higher unemployment levels. However, the proportion of non-economically active Coloured males appears relatively constant across cohorts. This is in contrast to that

of Africans where labour force participation has steadily increased for younger cohorts (See Appendix C).

This means that the increase in the proportion of unemployed Coloured males is largely offset by the

decrease in the proportion of those who are working.

Looking at Indians males one sees a different picture from that of Coloured males. Careful inspection of

the curves reveals a positive story for the 26-30 cohorts. These cohorts experienced higher levels of

participation and a faster flow into employment compared to their older and younger counterparts. This

is given by the steep gradient of the curve between 18 and 22 and working levels consistently above that

of the 31-35 cohorts. The 21-25 cohorts show a peculiar trend with working levels peaking at age 26

before swiftly declining. This same trend is reflected in the unemployment and non-economically active

curve. Possible explanations for this may be an improved QLFS survey design or LOWESS capturing an

inaccurate trend at the end points.

Figure 6 below looks at the same labour market experiences for Coloured and Indian women. The

working curves for Coloured women have remained relatively constant across cohorts. The flows into

unemployment after 18 are particularly steep for all cohort groups and unemployment appears to have

become progressively worse for younger cohorts. The 21-25 cohort peaks at an unemployment level of

27% (7% more than that of the 26-30 cohorts). The 31-35 cohorts appear to be abnormally high in the

early 20s. Younger cohorts of Coloured women are also more economically active than older cohorts.

Indian women reveal an interesting trend. Looking at the working curves one can see a similar profileacross all birth cohorts where after reaching its peak, the proportion of women working experiences a dip

for a few years before increasing again. The level of unemployment and non-participation compensate the

dip in employment. This may confirm the finding in section 4 where Indian women display a higher

propensity to stop working and take care of young children than do women of other races. More

specifically, looking at the 31-35 cohorts, one sees the level of working Indian women to peak locally at

33, before dipping for 3 years and increasing again. This is reflected in the non-economically active curve.

It appears to be very common for Indian women to stop working for a period of 3 to 4 years before

working again. Given that the drop out period is consistently 3 to 4 years, one would expect this to be

related to Indian women dropping out of the labour force to take care of young children.


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19

Figure 6: Working, unemployed and non-economically active Coloured and Indian Females by birth cohort.

Source: OHS 1995-1999, LFS 2003-2007, QLFS 2008-2011, own calculations

Younger cohorts of Indian women appear to be leaving their working professions at a younger age than

older cohorts. This must be interpreted with caution given that at the end points LOWESS only has

observations to the left that fall within the bandwidth. What one should notice is that employment for the

16-20 cohorts at age 25 is above that of all the older cohorts. Additionally, between age 20 and 25 the rate

of absorption into employment for the 16-20 cohorts exceed that of its older counterparts. Looking

carefully at the horizontal differences between the curves at 20%, one can see that younger cohorts of

Indian women are working at age 20 while older cohorts are working at 19 and 18 respectively. This canbe attributed to higher levels of education for younger cohorts of Indian women.

0

.2

.4

.6

.8

10 20 30 40 50Age

Working Indian Females by Birth Cohort

0

.2

.4

.6

.8

10 20 30 40 50Age

Working Coloured Females by Birth Cohort

0

.1

.2

.3

10 20 30 40 50Age

Unemployed Coloured Females by Birth Cohort

0

.1

.2

.3

10 20 30 40 50Age

Unemployed Indian Females by Birth Cohort

.2

.4

.6

.8

1

10 20 30 40 50Age

Non Economically Active Coloured Females by Birth Cohort

.2

.4

.6

.8

1

10 20 30 40 50Age

Non Economically Active Indian Females by Birth Cohort

16-20 21-25

26-30 31-35Note: age of cohort group defined by age in 2003


21/30

20

6 Discussion

After examining the various age profiles, it is clear that Coloured and Indian experiences are still centred

between the two extremes of White privilege and African disadvantage. Coloured labour market

experiences have (in some respects) closely come to resemble Africans while Indian experiences have

moved towards that of Whites. The case for Coloureds is an area of serious concern that should notdisregarded by policymakers.

Although second to Africans, Coloured unemployment is particularly high, reaching 23% in 2011 (QLFS,

2011). For both Coloured males and females, age profiles of unemployment have come to closely

resemble that of Africans. Both Coloureds and Africans face severe youth unemployment as direct

consequence of low levels of education relative to Indians and Whites; increased labour force

participation; a labour market that favours skilled workers and an informal sector that is unable to absorb

the unemployed. Working age profiles by year and cohort have remained fairly steady for Coloured

women. However, increased labour force participation by Coloured women appears to have translated

into higher levels of unemployment.

On the other hand, labour force participation for Coloured males has remained relatively constant since

1995, indicating that increased unemployment is largely offset by decreases in the proportion of those

who are working. There are a number of possible reasons for this. One reason may be that in 1995,

21.1% of Coloured males in the Western Cape were employed in the agricultural sector.7 By 2011, this

proportion had dropped to 2.72%. The Western Cape agricultural industry includes farming in viticulture

and various fruit (Van Burg et al, 2005). According to Van Burg et al (2005:4), the impact of open

markets and a more flexible labour market has led to an increase in mechanisation and an increase in

casualization of labour. Casualization of labour refers mainly to the use of labour brokers where workers

are contracted on a temporary basis to avoid compliance with labour market legislation (ibid).

Furthermore, many small farms in the Western Cape have been consolidated into fewer larger farms

(Aliber et al, 2007:135). As a result, the Western Cape agricultural sector has shed thousands of jobs since

1995, many of which have been Coloured males (ibid).

Another reason may be related to high levels of gang activity amongst Coloured males in the Western

Cape. Predominantly Coloured areas such as Mitchells Plain in Cape Town are notorious for burglary and

drug dealing associated mostly with Coloured gangs (Legget, 2012:67). Such activities may present a

viable alternative for Coloured males living in a climate of high unemployment. More research, however,

is needed to verify to what extent this is an issue.

Looking at Indians, one sees a more positive story. Indian unemployment is consistently lower than that

of Africans and Coloureds, yet more so than Whites. Indian mean levels of education are only marginally

lower than that of Whites at 12.1 and 12.9 years respectively. Moreover, South African firms particularlydesperate for skilled candidates from designated groups (Africans, Coloureds, Indians, Women and the

disabled) (CEE, 2011). Consequently, the Commission for Employment Equity in 2011 found employers

more likely to hire Indians than Africans and Coloureds (ibid). Such opportunities have prompted many

Indian women to reject their traditional role as homemakers, seek employment and remain working for

longer.

The age profiles by year revealed Indian women to have a greater propensity to dropping out of the

labour force than women of other races. Such trends reflect the cultural role typically played by Indian

women as homemakers and caregivers. The persistence of these trends may be partially explained by the

7This figure comes from the authors own calculation using the synthetic panel


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21

Indian family setting. Many Indian families still live in joint (3 generation) households, pooling resources

and income from working family members (Singh, 2005:32). Such a situation may reduce the opportunity

costs for women to drop out of the labour force and take care of children. Although Indian women still

have a greater propensity to drop out of the labour force (often for 3 or 4 years before re-entering),

section 5 found that more Indian women are working for longer.

7 Conclusion

From this paper, one can see that Apartheid policies enforced a structural hierarchy on the South African

labour market that still persists today. Whites remain in the most favourable position, followed by Indians

and then Coloureds. Africans are unambiguously the worst off in the labour market, with lower relative

levels of education, poor labour market absorption and high unemployment. Applying LOWESS

graphing techniques to the synthetic panel has proved to provide meaningful analysis. Since 1995

Coloured and Indian labour market experiences appear to have moved closer towards that of Africans

and Whites respectively.

Coloureds have obtained more education on average, although only marginally more than Africans by

2011. Higher education levels amongst Coloured women have seen higher levels of labour force

participation by younger cohorts. This has unfortunately not translated into greater labour market

absorption, but rather high youth unemployment. Consequently, the age profile of Coloured women has

come to closer resemble that of Africans. Like Coloured women, the age profile of Coloured males has

converged closely to that of Africans since 1995. Younger cohorts of Coloured males are working

significantly less than their older counterparts, indicating that the labour market has not favoured young

Coloured males. These trends are very clear and may be at least partially explained by massive job

shedding in the Western Cape agricultural industry since 1995. Other possible explanations include: lower

levels of education (relative to Indians and Whites); an increasingly skills biased labour market; aninformal sector that does not absorb the unemployed; and the monetary incentives of illegal gang activity

within Coloured areas in the Western Cape.

For both Indian males and females, higher levels of education have translated into greater employment.

Employment equity has clearly favoured Indians who now hold a large number of jobs as legislators,

senior officials and management. Indian unemployment profiles have moved progressively closer to lower

White levels of unemployment. However, Indians are not quite on equal terms with Whites in terms of

employment. Across all education levels, Indian women display a stronger tendency to drop out of the

labour force to take care of children than do women of other races. This finding is consistent across birth

cohorts and may be explained by the tendency among Indians to live in joint households and pool

together resources. Such a setting may reduce the opportunity cost of dropping out of the labour force totake care of children.

Coloureds and Indians have clearly had different labour market experiences post-Apartheid. While

employment equity has been successful in providing opportunities for Indians, Coloureds have not been

so fortunate. Coloured unemployment, amongst the youth in particular, is an area of serious concern and

highlights the broader issue of youth unemployment in South Africa. Policymakers should ensure that

Coloureds are not marginalised in any efforts to curb youth unemployment. If the appropriate policies

are put in place, then perhaps one will see decreases in unemployment for not only Africans, but

Coloureds as well.


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References

Aliber, M., Baiphethi, M. & Jacobs, P. 2007. Agricultural Employment Scenarios. Centre for poverty,employment and growth. Human Sciences Research Council (HSRC). :112-144.

Banerjee, A. and others. 2008. Why Has Unemployment Increased in the New South Africa.Economics ofTransition. 16(4):715-740.

Bhorat, H. 2004. Labour Market Challenges in the Post-Apartheid South Africa. South African Journal ofEconomics. 72(5):940-977.

Bhorat, H. & Kanbur, R. 2006. Poverty and Well-being in Post-ApartheidSouth Africa:An Overview of Data, Outcomes and Policy. HSRC press. :1-18.

Black, D., Tseng, Y. & Wilkins, R. 2010. The Decline in Male Employment in Australia: a CohortAnalysis.Australian Economic papers , Blackwell publishing Ltd/University of Adelaide and Flinders University.

Branson, N. 2006. SALDRU Working paper 06/07. The South African Labour Market 1995 -2004: ACohort Analysis. 18-20 October 2006.

Branson, N. & Wittenberg, M. 2007. The Measurement of Employment Status in South Africa UsingCohort Analysis, 1994-2004. South African Journal of Economics. 75(2):313-326.

Branson, N. & Wittenberg, M. 2011. Re-weighting South African National Household Survey Data tocreate a consistent series over time: A cross entropy estimationapproach.A Southern Africa Labour and Development Research unit working paper number 54. Cape Town:SALDRU, University of Cape Town.

Bunting, I. 2006. The Higher Education Landscape under Apartheid. In Transformation in Higher Education:Global Pressures and Local Realities. N. Cloete, and others, Eds. 10th ed. Dordrecht: Springer. 35-52.

Casale, D. & Posel, D. 2002.The Continued Feminisation of the Labour Force in South Africa: AnAnalysis of recent data and trends.The South African Journal of Economics. 70(1): 156-184.

CEE. 2011. 11th Commission for Employment Equity Annual Report, 2010-2011. Pretoria: Department ofLabour Republic of South Africa.

Cleveland, S. 1979. Robust Locally Weighted Regression and Smoothing Scatterplots.Journal of theAmerican Statistical Association. 74(368):829-836.

Crankshaw, O. 1996. Changes in the Racial Division of Labour During the Apartheid Era.Journal ofSouthern African Studies. 22(4):633-656.

Deaton, A. 1985. Panel Data from Time Series of Cross-Sections.Journal of Econometrics. 30(1):109-126.

Dias, R. & Posel, D. 2006. Unemployment, Education and Skills Constraints in Post-Apartheid SouthAfrica. DPRU/TIPS Conferance on Accelerated and Shared Growth in South Africa: Determinants, constraintsand opportunities. Birchwood hotel. Johannesburg.

Dinkelman, T. & Pirouz, F. 2002. Individual, Household and Regional Determinants of Labour ForceAttachment in South Africa: Evidence from the 1997 October Household Survey. South African

Journal of Economics. 70(5):865-891.


24/30

23

Du Pre, R. 1994. The 'Coloured' People of South Africa - A Political History.Johannesburg: Jonathan BallPublishers (Pty) Ltd.

Duval-Hernndez, R. & Orraca Romano, P. 2009. A Cohort Analysis of Labor Participation inMexico, 1987-2009. [Online]. Available: http://www.politiquessociales.net/IMG/pdf/dp4371.pdf.

Fedderke, J. 2012. The Cost of Rigidity: The Case of the South African Labour Market.Economic ResearchSouthern Africa.Working Paper 290:1-26.

Jithoo, S. 1991. Indians in South Africa: Tradition vs Westernization.Journal of Comparative Family Studies.22(3):343-357.

Kerr, A. & Lam, D. 2011. Post-Apartheid Labour Market Series 1994-2007. [dataset]. Version 1.0.7. ofthe harmonised dataset based on Statistics SA's OHS and LFS surveys. Cape Town: DataFirst [dataproducer and distributor], 2011.

Kingdon, G. & Knight, J. 2004. Unemployment in South Africa: The Nature of the Beast. World

Development. 32(3):391-408.

Kingdon, G. & Knight, J. 2005. Unemployment in South Africa, 1995-2003: Causes, Problems andPolicies. Global Poverty Research Group.

Leggett, T. 2012. Still Marginal: Crime in the Coloured Community. In Being Coloured. M. Peach, Ed. CapeTown: WB Peach Media and Communication. 59-67.

Natrass, N. & Walker, R. 2005. Unemployment and Reservation Wages in Working Class Cape Town.South African Journal of Economics. 73(3):498-509.

Singh, A. 2005. Indians in Post-Apartheid South Africa. New Delhi: Concept Publishing.

Smith, K. 2005. The Status of Cape Town: Development Overview. Cape Town: Isandla Institute.

Spaull, N. 2012. Equity & Efficiency in South African Primary Schools - A Preliminary Analysis ofSACMEQ III South Africa. Department of Economics. University of Stellenbosch.

Stats SA, 2011.Mid-year Population Estimates. Pretoria: Statistics South Africa.

Stats SA, 2012.Quarterly Labour Force Survey - Quarter 1, 2012. Pretoria: Statistics South Africa.

Van Burg, A., Lindoor, E. & Messina, S. 2009. Going for Broke: A Case Study of Labour Brokerage on Fruit

Farms in Grabouw. Stellenbosch: Centre for Rural Legal Studies.

Von Fintel, D. 2007. Rising Unemployment in South Africa: An Intertemporal Analysis using a BirthCohort Panel. Masters of Commerce. University of Stellenbosch.

Wilson, R., Woolard, I. & Lee, D. 2004. Developing a National Skills Forecasting Tool for South Africa.Warwick Institute for Employment Research & Human Sciences Research Council. :24.

Wittenberg, M. 2002. Job Search in South Africa: a Non-Parametric Analysis. South African Journal ofEconomics. 70(8):1163-1197.

Wooldridge, J. 2009. Introductory Econometrics a Modern Approach. Canada: South-Western Cenage.506-609.


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Yang, Y., Schulhofer-Wohl, S. & Land, C. 2007. A Simulation Study of the Intrinsic Estimatorfor Age-Period-Cohort Analysis. Department of sociology, university of chicago.

Yu, D. 2009. The Comparability of Labour Force Survey (LFS) and Quarterly Labour Force Survey(QLFS). Stellenbosch working paper series no. WP08/2009.

Appendices

A. Investigating the sample sizes & Sensitivity Analysis

Table A1 below gives a picture of the sample sizes of African, Coloured, Indian and White of a particular

age, year and gender. For example there will be a sample of 18 year old African females for each year

between 1995 and 2011. There will be similar samples for each race, gender and age for every year in

question.

Table A1: Summary statistics of age sample sizes by race, gender and year

Males

Populationgroup

Meansample

sizeStandarddeviation Minimum Maximum

Numberof agegroups


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There are a number of possible ways to mitigate the problem of small sample sizes in the Indian

population. By exploring these different methods, one can assess the sensitivity of such specifications and

decide on a preferred approach.

One can work with 5 year age groups so as to increase the number of sample observations in excess of

100. Doing so, however, produces a singular plot at each five year interval as opposed to a plot at eachage. As a result, employing LOWESS produces jagged edged curves with a profile that differs

significantly from those obtained using ungrouped ages. Working with 2 year age intervals improves the

curves substantially; however it differs very little from using singular ages.

Another option is to look at more than one year at a time, effectively working out a 2 or 3 year average

unemployment rate. Such an approach secures larger sample sizes, resulting in means that are more

precise; however one loses out on assessing the transition from one year to another by calculating an

average rate over 2 or 3 years.

The strict unemployment age profiles for 1995 are displayed below using the approaches described. The

1995 year was selected as it is the most problematic with Indian sub-samples that are particularly small.LOWESS was surprisingly able to produce consistent trends for the working and not economically active

curves in 1995.

Figure A1: Graphical sensitivity analysis using age categories and year groups

Using 5 year age intervals Using 2 year age intervals

Using 3 years of data Using 2 years of data

From these figures, it is clear that using age intervals does little to improve the appearance of the graphs.

In fact, using 5 year age intervals makes the graphs look even more irregular. A wider LOWESS

bandwidth could be used to make the graphs appear more regular, however doing so does not take away

from the biasness reflected at the endpoints. Using 2 and 3 years of data improves the distribution at the

end points, but it is more the unemployment of younger individuals which is of more interest. The peak

0

.05

.1

.1

5

.2

10 20 30 40 50 60Age

Unemployed (strict) Males 1995

0

.05

.1

.15

10 20 30 40 50 60Age

Unemployed (strict) Males 1995

0

.05

.1

.15

10 20 30 40 50 60Age

Unemployed (strict) Males 1995-1997

0

.05

.1

.15

10 20 30 40 50 60Age

Unemployed (strict) Males 1995 & 1996


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of unemployment level in fact remains similar to the one year case at around 10%. For these reasons, the

author has decided not to group by age nor by year. The unemployment profile for 1995 however will be

restricted to age 45 to correct for its irregular trend.

Similarly to Table A1, Table A2 presents the same summary statistics only by cohort instead of year.

Cohort sizes appear to be inherently larger. Cohorts were divided into 5 year groups covering the period1968 to 1988. Cohort groups were defined by their ages in 2003.

The Indian male and female age sub-samples only have 8 and 9 groups with less than 100 observations

respectively. Constructing the age-profiles by cohort group was therefore not particularly problematic.

Table A2: Summary statistics of birth cohort sample sizes by race, gender and age

Males

Populationgroup

Meansample

sizeStandarddeviation Minimum Maximum

Numberof agegroups


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0

.2

.4

.6

.8

10 20 30 40 50 60Age

1995 1999

2003 2007

2011

Year

1995-2011Age-Working Profile for Coloureds

0

.2

.4

.6

.8

10 20 30 40 50 60Age

1995 1999

2003 2007

2011

Year

1995-2011

Age-Working Profile for Indians

B. Additional Age Profiles for Coloureds and Indians


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.2

.4

.6

.8

1

10 20 30 40 50 60Age

1995 1999

2003 2007

2011

Year

1995-2011

Age Non-Economically Active Profile for Coloureds

.2

.4

.6

.8

1

10 20 30 40 50 60Age

1995 1999

2003 2007

2011

Year

1995-2011

Age Non-Economically Active Profile for Indians


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C. Age Profiles by Birth Cohort for African and White males and females

0

.2

.4

.6

.8

1

10 20 30 40 50Age

Working African Males by Birth Cohort

0

.2

.4

.6

.8

10 20 30 40 50Age

Working African Females by Birth Cohort

0

.1

.2

.3

10 20 30 40 50Age

Unemployed (strict) African Males by Birth Cohort

0

.1

.2

.3

10 20 30 40 50

Age

Unemployed (strict) African Females by Birth Cohort

0

.2

.4

.6

.8

1

10 20 30 40 50Age

Working White Males by Birth Cohort

0

.2

.4

.6

.8

10 20 30 40 50Age

Working White Females by Birth Cohort

0

.1

.2

.3

10 20 30 40 50Age

Unemployed (strict) White Males by Birth Cohort

0

.1

.2

.3

10 20 30 40 50

Age

Unemployed (strict) White Females by Birth Cohort

coloureds and indians - michael cole

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