Chapter 3 Employment and Public Works
3.2 Labour markets in Gauteng, KwaZulu Natal and Northern Province
The problem of high unemployment was reflected in the surveys we conducted in KwaZulu Natal, the Northern Province and Gauteng. Unemployment is relatively high in Northern Province, reflecting this province’s large rural population and lim-ited formal sector job opportunities (figure 3.2). However, the lower unemployment rate in Gauteng and KwaZulu Natal does not only suggest a higher formal employ-ment rate in these two provinces. There are also far more underemployed people in Gauteng and KwaZulu Natal. Figure 3.2 shows that these labour markets offer far more opportunities for odd jobs, temporary and casual employment and part-time jobs.
Africans constitute a large proportion of the unemployed compared to other racial groups. A substantial portion of African and Coloured respondents were under-employed in odd and casual jobs and part-time employment. Figure 3.3 re-flects underemployment and unemployment along racial lines.
Women form the majority of the unemployed. Figure 3.4 indicates a strong correlation between gender and unemployment in our three provinces. There is also a higher proportion of women in odd and casual jobs.
While there are substantially higher numbers of unemployed women than men in all the three provinces, this is especially true in the Northern Province (see figure 3.5).
Figure 3.2 Labour force in Gauteng, KwaZulu Natal and Northern Province (n 2619)4
Gauteng KwaZulu Natal Northern Province
Working fulltime Odd jobs, casual and parttime Unemployed
0 10 20 30 40 50 60 70 80 90 100
Percent
4 These findings correspond relatively well with the 1996 Population Census results which in-dicated that Northern province had an unemployment rate of 46%, KwaZulu Natal 39% and Gauteng 28%. The differences may be accounted for by the formulation of the questions. The Census asked whether people had been involved in any economy activities during the past 7 days, while we simply asked whether people considered themselves unemployed, employed, etc.
Figure 3.4 Labour force by gender (n 2619)
Female Male
Working fulltime Odd jobs, casual and parttime Unemployed
0 10 20 30 40 50 60 70 80 90 100
Percent Figure 3.3 Labour force and population group break-down (n 2619)*
African Coloured Indian White
Working fulltime Odd jobs, casual and parttime Unemployed
0 10 20 30 40 50 60 70 80 90 100
Percent
* While these three provinces do not compose a logical unit, we have run the analysis also for the three provinces separately. The different degrees of connections to the labour mar-ket do not vary substantially between the three provinces in terms of gender breakdown. We have therefore here merged the provincial data in our analysis.
More than four out of five of the unemployed had been without work for two years or more; short-term unemployment seems less common. Figure 3.6 indicates the length of unemployment within the provinces. Short-term unemployment is more of a problem in Gauteng, while longer-term unemployment of a year or more is more common in KwaZulu Natal and the Northern Province. This reflects the point made earlier about casual job opportunities being more common in the more in-dustrialised economy of Gauteng.
Education and skills levels
The low and uneven education and skills levels in South Africa are well document-ed (May et al 1997). Although the unemploydocument-ed are generally expectdocument-ed to be illiter-ate, studies indicate that the unemployed also include those who have finished pri-mary schooling. The October Household Survey 1995 (StatsSA) shows, for example,
Figure 3.5 Labour Force by Gender in Gauteng, KwaZulu Natal and the Northern Province, percentages (n 2619)
Gauteng KwaZulu Natal Northern Province
Working fulltime Odd jobs, casual and parttime Unemployed
0 10 20 30 40 50 60 70 80 90 100
Percent Male
Female
Male Female
Male Female
Figure 3.6 Length of unemployment by province (percentages)
0 10 20 30 40 50 60 70 80 90 100
Percent Gauteng
KwaZulu Natal Northern Province
2 years
or more More than 1 year but less than 2 years
About a year More than 6 months but less than 1 year
Six months or less
that 38% of the unemployed had had six to nine years of schooling and 18% had matric (Erasmus 1999). A strong correlation between unemployment and low levels of education emerged from our surveys in three provinces. This is reflected in fig-ure 3.7.
Labour markets offer different job opportunities for the various educational groups in the three provinces. Amongst the lower educated groups, ie those with no for-mal education or only primary education, a large proportion are unemployed or in odd, casual and part-time jobs. Amongst those with higher educational qualifica-tions (secondary, diploma and university education) in contrast, the large majority is working full-time. South Africa has an oversupply of unskilled workers. In our sample, 20 % of the full-time workers were unskilled. Close to 50% of the part-time workers were unskilled, reconfirming the vulnerability caused by lack of edu-cation.
Income levels
Income disparities have characterised labour markets and social relations in South Africa for decades. In the three provinces, a substantial number of respondents in the labour force indicated their individual income to be less than R800 a month (figure 3.8). The majority of these financially vulnerable groups are either unem-ployed6 (33%), or are doing ‘odd jobs’ or casual or part-time jobs (49%). A com-parison of our three provinces indicates that, in Gauteng, only 12% of those in fulltime employment earn less than R800, while in KwaZulu Natal 17% and in the
Figure 3.7 Labour force according to education (n 2619)
L/Primary education H/Primary education Secondary education Diploma University education No formal education
Working fulltime Odd jobs, casual and parttime Unemployed
0 10 20 30 40 50 60 70 80 90 100
Percent
6 Some of the unemployed may be involved in marginal employment activities or others may generate incomes from other members of their family. Others may be involved in informal sector or have casual jobs, but still regard themselves as unemployed due to the marginal
Northern Province as many as 26% fall into this low-income category. Fifty one percent of the labour force in Gauteng and as much as 65% in the Northern Prov-ince struggle to survive on less than R800 a month. The majority are African, and women predominate. Marginalisation of definable groups in the labour market reinforces the need for policy interventions to bring relief to these groups.