The South African labour market, 1995-2013

Lyle Festus, Atoko Kasongo, Mariana Moses and Derek Yu

ERSA working paper 493

February 2015

Economic Research Southern Africa (ERSA) is a research programme funded by the National Treasury of South Africa. The views expressed are those of the author(s) and do not necessarily represent those of the funder, ERSA or the author’s affiliated institution(s). ERSA shall not be liable to any person for inaccurate information or opinions contained herein.

The South African labour market, 1995-2013 Lyle Festus,∗ Atoko Kasongo,† Mariana Moses‡ and Derek Yu§ February 2, 2015

Abstract This paper investigates the changes in the South African labour market in the post-apartheid period in 1995-2013 by updating the work by Oosthuizen (2006) and Yu (2008). The three main data sources used are the October Household Survey of 1995, the Labour Force Survey of September 2004 and the Quarterly Labour Force Survey of 2013 Quarter 4. It was found that while unemployment has risen over the period, employment has also increased. Nonetheless, the extent of employment increase was not rapid enough to absorb all net entrants in to the labour force, resulting in increasing unemployment, or an employment absorption rate of below 100 per cent. Unemployment continues to be concentrated in specific demographically and geographically defined groups, most notably blacks, the poorly educated and the youngsters residing in Gauteng. Unemployment is a chronic problem for the youth in particular, as nearly three quarters of them never worked before. Finally, the employment absorption rate was the highest in some less developed provinces like Northern Cape, Mpumalanga and Limpopo, thereby suggesting the possible success of the government’s efforts to promote the development in the poorer provinces. JEL Classification: J40 Keywords: South Africa, labour market trends, labour force, employment, unemployment

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Introduction

The South African labour market has in the past played a very significant role in the country’s economic development. In the pre-democratic regime, prior to 1994, it was used as a mechanism to segregate society. This was achieved through various legislations which segmented the labour market along racial lines, to the disadvantage of non-whites. For example, the Bantu Education Act of 1953 ensured that non-whites received a sub-par quality of education ∗ Postgraduate

student, Department of Economics, University of the Western Cape Department of Economics, University of the Western Cape ‡ Lecturer, Department of Economics, University of the Western Cape § Senior Lecturer, Department of Economics, University of the Western Cape & Part-time Researcher, Development Policy Research Unit, University of Cape Town † Lecturer,

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relative to their white counterparts. This in turn limited their potential for achievement within the labour market and consequently limited their ability to improve their standard of living. Furthermore, legislation such as the Group Areas Act of 1950 and the Black Labour Act of 1964 established segregated areas of residence in urban areas, in the case of the former, as well as strict limitations on the type and conditions of employment available to Blacks for the latter. Furthermore, the Industrial Conciliation Act of 1924 allowed the establishment of Industrial Councils (ICs), which were permanent collective bargaining institutions. These ICs did not recognize African trade unions and prevented African employees from taking part in the collective bargaining process, resulting in the discrimination of non-white workers for the benefit of their white counterparts. In addition, the Mines and Works Act Number 12 of 1911 was the first in a series of laws which effectively limited the occupations available to non-white workers. This was achieved by reserving certain highly-paid and highly-skilled jobs for whites only. All of these laws contributed to the exploitation of nonwhite workers in the apartheid-era labour market. However, it can also be said that the South African labour market contributed toward the abolishment of apartheid, as certain labour market entities, in particular trade unions with their vast membership, were vital to the inception of the democratic process in South Africa. During the post-apartheid period, there are radical changes in the South African labour market in terms of its legislation. Among these include the Basic Conditions of Employment Act of 1997 which stipulates the minimum wages applicable to certain sectors, as well as specifying minimum working conditions for labourers and outlining some of their rights. In addition, the Labour Relations Act of 1995 outlines processes regarding collective bargaining in the labour market and the resolution of labour disputes. Furthermore, the Employment Equity Act of 1998 encourages Affirmative Action, which boils down to the need for employing more non-white workers in order to reduce societal inequalities. The effect of these new laws have had on the labour market, given South Africa’s history, is of particular importance. Moreover, because labour market income remains the main income source for poverty reduction, its reform is essential to addressing inequality and raising the standard of living in South Africa. Currently, the South African labour market continues to play a pivotal role in society. However, the reasons for its prominence have changed somewhat. To this end, the persistent unemployment problems plaguing the labour market have become a focal point for South African government and society. The aforementioned persistent unemployment stems from a range of issues including the low quantity and quality of education of the previously disadvantaged groups (e.g. non-whites). In this regard, recent surveys have shown that South African students on average are among the worst-performing groups when compared to their peers globally. In particular, the Trends in Mathematics and Science Study (TIMSS) 2011 results showed South African was the second worst-performing country in Mathematics and the worst-performing country in Science, out of the 63 participating countries. On the other hand, the Southern and Eastern Africa Consortium for Monitoring and Educational Quality (SACMEQ) 2007 reports 2

have shown that the average reading and mathematics performance of Grade 6 pupils in South Africa (495) was close but marginally below the SACMEQ overall average (512) in reading and (510) in mathematics. In both reading and mathematics, the proportion of learners achieving at the higher SACMEQ levels of competence was significantly low. Hence, the South African education system is producing a continued stream of insufficiently educated new work seekers. This creates a supply of workers who may remain unemployable. Moreover, there has been an emergence of large numbers of unemployed youth in South Africa. This phenomenon is attributed to, amongst others, their lack of experience, which reduces their employability and contributes toward unemployment. This factor is arguably related to the aforementioned low quality and quantity of education in South Africa. In addition, the unemployment problem the South African labour market exhibits also occurs from more direct labour market issues such as employment and wage rigidity. For the former, the labour legislation introduced post-1994, while affording worker’s rights, also served to limit the ability of employers to adjust their consumption of labour. This inhibits the ability of the labour market to function efficiently and thereby increases unemployment. In the case of the latter, wage rigidity due to legislated minimum wages contributes to increased levels of unemployment. This occurs when minimum wages are set above market clearing levels, which limits the ability of employers adjust their consumption of labour efficiently. On this point, both Kingdon and Knight (1999) as well as Bhorat, Kanbur, and Mayet (2012) state that the sector in which the minimum wage is established plays a large role in whether or not is has a negative effect on employment or the number of hours worked. It is also argued that some labour market participants have unrealistically high reservation wages, which prevents them from being employed. However, Kingdon and Knight (2004) point out that there are no reliable data on reservation wages and therefore it is not easy to estimate the proportion individuals who are unemployed because of this problem. Furthermore, skills mismatch is a serious factor contributing to the unemployment problem, and graduate unemployment is an example. This happens because the graduates produced by the education system are either not demanded by the labour market or are already abundant in supply, based on their area of study. This in turn creates unemployable graduates. On this, Bhorat (2004) states that “institutions of higher education are ostensibly not matching their curriculum design effectively enough with the labour demand needs of employers”. In addition, Pauw et al. (2006) attribute the graduate unemployment problem to a skills deficit issue. The aforementioned graduate unemployment problem is also indicative of a serious structural break in the South African labour market. Another serious problem facing the labour market is South Africa’s informal sector, which also plays a role in contributing to unemployment. As the informal sector is not only small in relation to the formal sector, always hovering at around 2 million employed people, but there are also various barriers of entry to this sector, thereby making it difficult for those retrenched or unable to find work in the formal sector to obtain employment in informal sector, thereby worsening 3

the unemployment problem. To this end, Kingdon and Knight (2004) state that “the informal sector is not generally a free-entry sector in South Africa, and that there may be barriers which prevent many of the unemployed from entering much of this sector”. Burger and Woolard (2005) more specifically cite a lack of infrastructure and inadequate access to credit markets as barriers to entering the informal sector in South Africa. Looking at other factors accounting for the unemployment problem, Oosthuizen (2006) states that “economic growth has been unable to provide the necessary employment opportunities required by population growth and rising labour force participation rates, resulting in a rapidly rising rate of unemployment”. Yu (2008) also found rapid increases in both the labour force and labour force participation rates in the late 1990s, which could contribute to the increase levels of unemployment, as the pace of job creation is not rapid enough to absorb all net labour entrants. In the past, many studies were conducted on the South African labour market performance post-apartheid. However, most of these papers focused on the on the first decade after democracy. The general findings of these studies were that employment growth in South Africa was not sufficient to absorb all new entrants to the labour market (e.g., Oosthuizen 2006; Kingdon and Knight 2007; Yu 2008; Hodge 2009). Furthermore, economic growth and subsequent employment growth was not rapid enough to reduce unemployment (Burger and Woolard 2005; Oosthuizen 2006). However, as Bhorat (2009) and Hodge (2009) discussed, the rising unemployment figures post-apartheid are due in small part to substantial labour supply increases. In addition, as Yu (2008) found, the most disadvantaged segments of the population i.e. those who have low levels of education, those who reside in relatively poorer provinces and blacks were most likely to be unemployed. Since 2005, the South African labour market and associated legislation has again changed, with the objective of addressing many of the issues that are now visible from labour market data. To this end, policies such as the revised Basic Conditions of Employment Act were introduced, with the aim of which being reducing the labour market rigidities while still providing certain basic rights to the workers. In addition, 2006 saw the implementation of the Accelerated and Shared Growth Initiative South Africa (ASGISA) which outlined South Africa’s developmental framework and identified key areas and requirements for economic growth. One of ASGISA’s main goals was to reduce the unemployment rate under the narrow definition to 15 per cent by 2014. This plan was subsequently replaced by the New Growth Path (NGP) in 2010 which, among other things, readjusted the timeline for growth targets outlined in ASGISA to be achieved. Importantly, the NGP also set a new target to reduce the unemployment rate to 15 per cent, to be achieved by 2020. More recently, the NGP was replaced by the National Development Plan (NDP) which was introduced in 2012. The labour market goal of the NDP is to reduce the unemployment rate to 6 per cent by 2030. With regard to other labour market legislation introduced since 2005, the Employment Tax Incentives Bill or Youth Wage Subsidy was legislated in 2013 and was officially implemented on 1 January 2014. It pro4

vides a fiscal incentive for employers to hire more youth workers, with the hope of creating employment and providing the youth with essential experience and skills. As a result of these changes to both legislation and strategy regarding the South African labour market and in order to evaluate the success of labour market policy, the need for an updated labour market study is justified. Hence, the main objective of this paper is to provide an 18-year labour market review for the period 1995 to 2013, just before the abovementioned Employment Tax Incentives Bill was launched. The data used for this paper comes from the following sources. From the year 1995 to 1999, the October Household Surveys (OHSs), taking place annually, were used. From the year 2000 to 2007, the Labour Force Surveys (LFSs), which took place twice a year, were used. From the year 2008 to 2013, the Quarterly Labour Force Surveys (QLFSs), taking place four times a year, were used. Furthermore, OHS and LFS data were weighted with Census 2001 weights, while the QLFS data were weighted with Census 2011 weights. Therefore, we expect some unsubstantiated fluctuations of the data between the year 2007 and 2008 as a result adoption of the different weighting method. The remainder of the paper is structured as follows. Section 2 provides an overview of the South African labour from a broad perspective, while Section 3 examines labour market trends specifically between OHS 1995, LFS 2004 and QLFS 2013. This is followed by a multivariate econometric analysis in Section 4 based on the three periods mentioned above. Section 5 concludes the study and provides some policy recommendations. Also, for the rest of the paper, OHSs will be referred to as OHS 1995, OHS 1996, etc., while the LFSs will be referred to as LFS 2000a (for the first round of LFS in 2000), LFS 2000b (second round in 2000), LFS 2001a, LFS 2001b, and so forth. Finally, the QLFSs will be referred to as QLFS 2008Q1 (for the QLFS conducted in the first quarter of 2008), QLFS 2008Q2 (second quarter of 2008), and so forth.

2

The South African labour market: Long-term trends between 1995 and 2013

Figure 1 presents the size of the labour force and the labour participation rate from 1995 to 2013.1 The labour force under the narrow definition increased rapidly during the OHS period and increased steadily during the LFS period. It peaked at nearly 19 million in the first quarter of 2009, before a downward trend was observed due to the impact of the global recession. An upward trend was observed again since 2011 and the LF number reached an all-time high of 20.0 million in QLFS 2013Q4. A similar trend could be observed for the labour force number under the broad definition, and this number reached 22.2 million in the last quarter of 2013. The labour force participation rate (LFPR) under the narrow definition in1 Table A.1 in the appendix shows the number of working-age population, LF, employed, unemployed, as well as LFPRs and unemployment rates in all surveys under study.

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creased rapidly in the OHSs, before showing a downward trend in general from 2000 to 2004. An upward trend was observed between 2005 and 2008, with the LFPR peaking at 59.4 per cent in QLFS 2008Q2, before it declined due to the impact of recession. The LFPR has been hovering between 55 and 57 per cents since QLFS 2009Q3. The LFPR under the broad definition also increased rapidly in the OHSs, before fluctuating unsteadily between 2000 and 2007. This rate has been stabilized at the 61-63 per cent range since 2008. Note that the abrupt decline of the broad LFPR during the changeover of LFS and QLFS is attributed to the change in the methodology to distinguish the discouraged workseekers (Yu 2009 & 2013). In particular, Yu (2013: 707) highlights the fact that the discouraged workseekers are identified more strictly in the QLFSs, as the respondents’ answers to the question “what was the main reason why you did not try to find work or start a business in the last four weeks?” is considered (this question was not involved at all in the OHS and LFS methodology). Only if the respondents’ answers to the question are ‘no jobs available in the area’, ‘unable to find work requiring his/her skills’ or ‘lost hope of finding any kind of work’, then they could be classified as discouraged workseekers. Hence, this causes the number of discouraged workseekers and subsequently the broad unemployed (which is the sum of narrow unemployed and discouraged workseekers) to drastically decrease between LFS 2007b and QLFS 2008Q1.2 The number of employed is represented in Figure 2.3 In general, an upward trend was observed between OHS 1996 and QLFS 2008Q4, although the abrupt increase of about one million between LFS 2007b and QLFS 2008Q1 is attributed to the change of weighting method by Stats SA, as discussed in Section 1.4 Between QLFS 2008Q4 and QLFS 2009Q4 there was decrease in employment of nearly a million due to recession. Employment showed an upward trend again since QLFS 2010Q2, and reached an all-time high of 15.2 million in the last quarter of 2013. Figure 3 focuses on the unemployment aggregates. First, the number of unemployed under the narrow definition showed an upward trend between OHS 2 Yu (2013: 713-714) attempted to apply the QLFS labour market status derivation methodology to identify the discouraged workseekers in both LFSs and QLFSs. At the end, there was still an abrupt decline (albeit less serious) of his number during the changeover of LFS and QLFS, and Yu (2013: 714) claimed that the possible reasons for the still relatively higher number of discouraged workseekers in the LFSs could either be real, or due to the difference in the questionnaire structure between LFSs and QLFSs. These discussions are beyond the scope of this study. 3 The last two columns of Table A.1 in the appendix show employment by formal and informal sectors. However, Essop and Yu (2008) note that it is not possible to estimate informal sector employment between OHS 1995 and OHS 1996, because employees were not asked to declared the sector of employment. 4 When QLFS 2013Q4 results were released using Census 2011 weights for the first time, Stats SA also released the QLFS 2008Q1-2013Q3 labour market aggregates (which were originally weighted with Census 2001 weights) with Census 2011 weights. This explains the abrupt fluctuations of the aggregates between LFS 2007b (weighted with Census 2001 weights) and QLFS 2008Q1 (re-weighted with Census 2011 weights). Table A.2 in the appendix shows results.

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1995 and LFS 2003a. In fact, the number of employed during the 18-year period under study was the highest in LFS 2003a at 5.1 million. This number stabilized at the 3.9-4.4 million range between LFS 2003b and QLFS 2009Q4, before a slight upward trend was observed during the 2010-2013 period. Looking at the number of unemployed under the broad definition, a similar trend was observed between OHS 1995 and LFS 2003b. In fact, this number also peaked at the latter survey (8.3 million). A downward trend was observed in general between LFS 2003b and LFS 2007b. An abrupt decline of the number of broadly defined unemployed from 7.3 million to 5.6 million was due to the change in methodology to capture the discouraged workseekers, who form part of the broadly defined unemployed (Yu 2009). This number increased steadily between 2008 and 2013. As far as the unemployment rate under the narrow definition is concerned, it increased from 17.6 per cent in OHS 1995 to the peak level of 31.1 per cent in LFS 2003a. This rate showed a downward trend and dropped to as low as 21.5 per cent in the last quarter of 2008. Unfortunately, due to the global recession, an upward trend was observed in 2009, and this rate has been hovering between 24 per cent and 25 per cent in 2010-2013. Finally, the broadly defined unemployment rate increased from 30.8 per cent in OHS 1995 to the highest level of 42.5 per cent in LFS 2003a, before a downward trend was observed until 2007. After the abrupt decline from 35.6 per cent to 27.8 per cent during the changeover of LFS and QLFS, this rate increased steadily to 32.0 percent in QLFS 2010Q1, before hovering in the 31.5-33.5 percent range. As a result of the incomparability of the broad methodology between OHSs/LFSs and QLFSs, for the remainder of the paper, the labour market aggregates under the narrow definition will be the focus of the analyses, unless stated otherwise.

3 3.1

The South African labour market: Snapshots between 1995, 2004 and 2013 Labour force participation

The demographic composition of the labour force under the narrow definition in OHS 1995, LFS 2003b and QLFS 2013Q4 is captured in Table 1. In 2013, the labour force number was more than 20 million, which reflects a cumulative growth of 8.5 million individuals since 1995. The Black racial group has consistently dominated the labour force accounting for 67.9 per cent in 1995, 72.7 per cent in 2004 and rising to 76.0 per cent in 2013. Furthermore, when considering the increase in the size of the labour force, the blacks accounted for a share in excess of 85 per cent in all three surveys being assessed. The male population accounted for the larger proportion of the labour force in all three surveys, while the gender share of the increase in labour force was almost equitably split between the two groups. Table 1 shows that the 24-34 years and 35-44 years cohorts accounted for the greatest increase of labour force during the period under study. Also, the 18-29 year olds, who are age-eligible for the Employment Tax Incentives Bill, 7

experienced a growth of labour force number of 40.6 per cent between 1995 and 2004, and less rapid growth of 13.3 per cent between 2004 and 2013. Considering the spread of the labour force provincially, the Western Cape and Gauteng provinces collectively account for 46.4 per cent of the share in 2013 rising from 39.4 per cent in 2004, with more than 72 per cent of the increase recorded between 2004 and 2013. KwaZulu-Natal, by contrast, while accounting for a labour force in excess of 3 million individuals during 2013, experienced a decline in share of the increase from 18.2 per cent to 5.3 per cent during the respective 1995-2004 and 2004-2013 periods. The labour force size has more than doubled in urban areas, rising from 7.5 million individuals in 1995 to 15.5 million in 2013, reflected by an increase of slightly under 4 million per period. Consequently, the share of the labour force in rural areas has decreased on average by roughly 7 percentage points. As for educational attainment, there was a decline in the share of labour force members who have no schooling and incomplete primary levels, expressed by a cumulative fall of 10.7 percentage points. This implies that more years are spent on acquiring an education which positively impacts upon the labour force share where participants with incomplete secondary schooling and Matric account for 62.6 per cent, 68.4 per cent and 72.8 per cent, respectively for the periods concerned. Furthermore, the share of labour force with post-Matric qualifications, while remaining unchanged at 12 per cent in 1995 and 2004, have risen to slightly under 17 per cent in 2013. This can be attributed to the increasing realisation that a diploma or degree aids in securing better job opportunities. In summary, labour force growth since the transition could be largely attributed to the Black, urban individuals residing in Gauteng, aged 25-44 years at the time of the survey, and having attained at least some secondary schooling. Table 2 shows the LFPRs in the three surveys under investigation. The LFPR of Whites and Coloureds have consistently exceeded 60 per cent, recording levels of 67.4 and 64.1 per cent respectively in 2013. As far as gender is concerned, in 2013, the LFPR gap between male and females have slowly been bridged with males still accounting for the highest rate of 63.4 per cent versus the 50.5 per cent of females, possibly attributable to the difference in retirement age. An excess of 70 per cent of LFPR was recorded in 2013 for the three age group categories ranging between 25 and 54 years. While less than 42 per cent of the 55-65 year old members are active participants, the LFPR has steadily increased over the two decades possibly due to fewer people choosing to retire early. The decline in the LFPR of the youngest cohort (15-24 years), expressed at 25.5 per cent in 2013 from 28.2 per cent in 2004, is linked to higher levels of education being attained and concomitantly years spent studying. As to be expected, Gauteng and the Western Cape have garnered the highest LFPR levels rising from the region of 60 per cent in 1995 to slightly under 70 per cent in 2013, a phenomenon that aligns with the rural-urban dynamics of provinces. By contrast, LFPRs were the lowest in Limpopo (40.4 per cent in 2013) and the Eastern Cape (45.2 per cent in 2013). The LFPR has always been higher for the urban dwellers in all three surveys under study. Finally, 8

the significance of education in securing and retaining employment is noted as labour force members with no schooling recorded the lowest LFPR in all three surveys, with only slightly improved levels for participants with incomplete primary and secondary schooling at just below 45 and 47 per cent respectively in 2013. As expected, the LFPR was the highest for the Degree and post-Matric Certificate/Diploma holders, at 88.8 and 85.5 per cents respectively in 2013. In summary, at the end of 2013, higher LFPRs were recorded for predominantly White males, aged 25-44 years, residing in urban areas in Gauteng or Western Cape, and attaining post-Matric qualifications.

3.2

Employment

The same three surveys would be used to examine the employment aggregates. First of all, there has been a general progressive increase in employment for the period 1995 to 2013, as shown in Table 3. The greatest increase was 3.5 million jobs experienced between 2004 and 2013. The bulk of the increase in employment between 1995 and 2013 took place amongst the blacks, with an increase of about 4.8 million jobs. Furthermore, in 2013 the share of blacks in employment was 73% while the whites only accounted for 13.1% of the total employment. The increase in black employment between 1995 and 2013 can be attributed to the increase of educational attainment of the black workseekers and Affirmative Action policies, which not only encouraged black people to participate in the labour market but also increased their likelihood of employment. Although employment increased in both genders, the female share of employment substantially increased from 39.1% in 1995 to 43.9% in 2013. This is consistent with the increase in the female labour force participation as in discussed in Section 3.1 above. The major increase in employment in the age category was for the age group 25-34 years and 35-44 years. Those between the ages of 15-24 years only experienced a net increase of 200 000 jobs between 1995 and 2013 and a decline in the share of employment from 11.8 per cent to 8.7%. The two age cohorts above the age of 45 years had a combined share of employment from 23.6 per cent in 1995 to 28.8 per cent in 2013. This reveals an increase in the proportion of jobs held by elderly people pointing to an ageing workforce. In addition, the age group 18-29 years (who are eligible for the youth wage subsidy) experienced a decline in the share of employment from 27.7 per cent in 1995 to 26.9 per cent in 2004 and finally to 23.9 per cent in 2013. In addition, the number of youth employment increased from 2.6 million in 1995 to 3.1 million in 2004 and finally to 3.6 million in 2012. The extent of increase of employment for this youth age cohort is slow compared with the middle-age cohorts, and hence validates the rationale of the government intervention through the youth wage subsidy. Gauteng, KwaZulu-Natal, Western Cape and Eastern Cape held the highest share of the total employment. In particular, Gauteng accounted for the largest share of employment expansion of more than 2 million between 1995 and 2013, or 38.5 per cent. Interestingly, Mpumalanga and Limpopo, the two relatively less developed provinces, accounted for 10.0 per cent and 10.3 per cent of the 9

share of increase of employment between 1995 and 2013. As expected, the share of employed with no education declined (from 8.1 per cent in 1995 to 2.4 per cent in 2013). This is a reflection of the increasing demand for skilled labour by the employers. Those with Matric recorded the highest increase in the share of employment (more than 40 per cent). The structural shift towards the hiring of more skilled labour is further consolidated by the increase in the proportion of employed accounted for by those with postMatric qualifications (from 14.1 per cent in 1995 to 19.8 per cent in 2013). As argued by Oosthuizen (2006: 17), merely comparing employment growth between two periods might not necessarily provide a clear picture of employment performance of the South African labour market. Hence, Table 4 presents the target growth rate (TGR), actual growth rate (AGR) and employment absorption rate (EAR) during selected periods. TGR is the rate at which employment must grow to provide employment to all the net entrants to the labour market between two periods of time (from period X to period Y) which need not be consecutive. X T GR = LFYE−LF where LF and E stand for then number of the labour X force and employed respectively (Oosthuizen 2006: 17). On the other hand, AGR is the growth rate of the number of employed from period X to period Y, X . (Oosthuizen 2006: 16), and is calculated as EYE−E X Finally, EAR measures the proportion of the net increase in the labour force from period X to period Y that find employment during the same period. EY −EX EAR = LF = AGR T GR . An EAR of 100 per cent implies that the full net Y −LFX increase in the labour force between two periods were employed (Oosthuizen 2006: 18) Focusing on the 1995-2013 period, for all the net entrants into the labour force to find jobs, employment would need to grow by 89.4 per cent between 1995 and 2013. However, the actual employment growth rate was “only” 60.0 per cent, thereby resulting in the EAR of 67.1 per cent. This implies that employment growth was not rapid enough to absorb all the net entrants to the labour market between 1995 and 2013, as out of the 100 net entrants to the labour force, only 67 of them were able to find employment. Nonetheless, this aggregated view may obscure the varied experiences of groups defined by various demographic and location characteristics (Oosthuizen, 2006: 18). Therefore, Table 4 also shows the TGRs, AGRs and EARs by race, gender, age cohort, province, area type and educational attainment. Once again focusing on the 1995-2013 period and looking at race, the TGR was the highest for blacks (120.4 percent), followed by coloureds (64.7 percent), while this rate was the lowest for the whites (10.9 percent). Similarly, AGR was the highest for blacks (80.8 percent), followed by coloureds (38.0 percent) and this rate was the lowest for whites (6.7 percent). If one only interprets the AGR, it is possible to reach an incorrect conclusion by claiming Affirmative Action is highly successful as the AGR was the highest for the previously most disadvantaged group (i.e. blacks). It is because the AGR (80.8 percent) was actually lower than the TGR (120.4 percent) for blacks. Hence, it means employment growth was not rapid enough to fully absorb the 10

net entrants into the labour force during the 18-year period for blacks. In fact, the last column of Table 4 shows that the EAR for blacks (67.1 percent) was “only” about 5 percentage points above that of whites (62.2 percent). Given the fact that the majority of the LF consists of blacks (see Table 1), it is important for the government to implement the necessary measures to boost the AGR and EAR of this group further, so as to drastically alleviate the persistent unemployment problem. As far as gender is concerned, the TGR, AGR and EAR were higher for the females between 1995 and 2013. With regard to the five age cohorts, it is interesting that all three rates increased across the elderly cohorts, with the EAR rising from 24.6 per cent for the 15-24 year-old to 89.8 per cent for the 55-65 year-old. Looking those aged 18-29 years (the age-eligible cohort for the Employment Tax Incentives Bill), the TAR was very high at 86.7 per cent, but the AGR was only 37.7 per cent. As a result, only 43.5 per cent of the net entrants to the labour market could successfully find employment. This finding provides another justification for the implementation of the Bill. With regard to the provincial results, it is interesting that the TGR was in excess of 100 per cent in three provinces (Gauteng, Mpumalanga and Limpopo). Also, these three provinces also recorded the highest AGR (83.2 per cent, 97.1 per cent and 101.5 per cent respectively). It is quite surprising that the EAR was the highest in Limpopo (86.4 per cent), followed by KwaZulu-Natal (82.0 per cent) and Western Cape (70.1 per cent). The positive finding in Limpopo could be a reflection of the government’s efforts to promote the development in the poorer provinces. Finally, all three rates were the highest for the Degree holders. In particular, the EAR was 92.7 per cent, which means that more than 9 net entrants to the labour market with Bachelor Degree could successfully find employment, but such likelihood was only as low as 58.0 per cent for those with incomplete secondary education. Table 5 illustrates employment according to the three broad skill categories. There was a consistent increase in the absolute number of employment in all three categories, but such increase was the most rapid for the highly-skilled employment category. Hence, the share of employment accounted for by these people increased from 8.7 per cent in 1995 to 14.0 per cent in 2013. In addition, the share of semi-skilled employment declined by 2 percentage points over the 18-year period. The bulk of the decline can be seen in the skilled agricultural and fishery workers with a loss of 253 000 jobs between 2004 and 2013. This decline can be attributed to the improvement in technology such that labour is replaced by capital. Furthermore, the share of unskilled labour in employment also contracted by 4 percentage points, despite the fact that employment in domestic work and elementary occupations experienced an increase of 0.31 million and nearly 1 million respectively between 1995 and 2013. It can be seen from Table 6 that employment in the primary sector employment contracted from 1.7 million in 1995 to 1.1 million in 2013. As of 2013 the primary sector accounted for only 7.5 per cent of employment, compared with the 17.9 share in 1995. In particular, employment decrease in the agriculture, 11

hunting, forestry and fishing industry amounted to more than 0.5 million between 1995 and 2013. In contrast, secondary sector employment grew by slightly above 1 million from 1995 to 2013, with the bulk of the increase being accounted for by the construction industry. The tertiary sector experienced most of the employment growth holding a share of employment at 72.1 per cent in 2013, rising from 61.0 per cent in 1995. The employment in financial, insurance and business services industry more than trebled between 1995 and 2013 (rising from 0.6 million to 2.0 million), while employment in the wholesale and retail industry nearly doubled (increasing from 1.7 million to 3.2 million). Figure 4 compares the annual average growth of real gross value added (GVA) and employment by each broad industry category between 1995 and 2013. It can be seen that finance and construction industries outperformed other industry categories with an annual employment growth rate of more than 5 per cent and the real GVA growth rate of approximately 5 per cent. In contrast, the two categories in the primary sector (agriculture and mining) experienced a decline in employment growth. This is evidence of the structural shift of the labour market towards an increased demand for highly-skilled workers. Furthermore, mining industry was the only industry category experiencing negative growth rate in both employment and real GVA. This could be attributed to not only structural change of the economy, but also other problems experienced in the mining sector, ranging from minimum wages, continuous strike activities, to stagnant productivity (Bhorat 2004, Burger and Woolard 2005, Oosthuizen 2006). Table 7 presents the ‘simple elasticity’ estimates that describe the relationship between output and employment, and is calculated as: average annual percentage change of employment / average annual percentage change of real GDP.5 First of all, the table shows that the simple real GDP ‘elasticity’ of total employment increased from 0.74 (when comparing OHS 1995 with LFS 2004b) to 0.92 (when comparing LFS 2004b with QLFS 2013Q4). Furthermore, the ‘elasticity’ of formal sector employment increased from 0.86 (1997-2004) to 1.17 (2004-2013). As far as formal and informal sector employment is concerned, Table A.3 in the Appendix shows that the non-agricultural informal sector employment has been hovering around the 2.0-2.5 million ranges since 2005, while nonagricultural formal sector employment increased steadily from 8.0 million to 10.8 million between 2005 and 2013. Kingdon and Knight (2005) explained that there are some constrains to entry into the informal sector such as lack of access to credit, lack of infrastructure and lack of training. The informal sector is also characterised by low remuneration, weak job security, and lack of pension fund as well as other benefit. These constrains impede people from joining the informal sector and hence this resulted in an increase of unemployment. Finally, Table A.2 shows that the number of self-employed has stabilized 5 As mentioned by Oosthuizen (2006: 8), formal modelling is needed to control for different variables that could impact on the relationship between output and employment. Hence, the figures presented in Table 7 are, strictly speaking, not output-employment elasticities.

12

at approximately 2.2 million in recent years, while the number of employees showed an upward trend in general, reaching a peak level of 13.1 million in QLFS 2013Q4. To conclude, there has clearly been a structural change in the South African labour market, as indicated by the more rapid increase of employment in the highly-skilled occupations and tertiary-sector industries, for those with postMatric qualifications.

3.3

Unemployment

This section analyses the demographic characteristics of the unemployed and the unemployment rates between 1995 and 2013. Table 8 depicts the number of unemployed estimated under the narrow definition. This number increased by about 2.8 million over the two decades measuring slightly over 4.8 million in 2013, from 2.0 million in 1995. Blacks accounted for the highest share of unemployed individuals in all three surveys (around 85 per cent). Also, the blacks accounted for the largest share of the increase of unemployed (86.8 per cent between 1995 and 2013). As far as gender is concerned, it is interesting that in 1995 and 2004, the number of male unemployed was lower than the number of female unemployed, but this no longer took place in 2014. This finding could be partly attributed to policy reforms such as the Affirmative Action. At the provincial level, Gauteng accounted for the highest share of unemployment (33.7 per cent in 2013). It is also the province accounting for the highest share of increase of unemployed between 1995 and 2013 (40.6 per cent). In conjunction, the share of unemployment in rural areas fell from 36.5 per cent in 1995 to 21.8 per cent in 2013. This result could be explained by the migration of workseekers from rural areas to urban areas. Individuals with incomplete secondary schooling have consistently recorded the highest share of unemployment levels (averaging 50 per cent) in each of the periods in question. By contrast, the share of unemployed individuals with little or no primary schooling decreased from 25.4 per cent in 1995 to 7.3 per cent in 2013. The benefits that education affords are evident in the lower share of 7 per cent that the unemployed with post-Matric qualifications hold, despite nearly doubling from the 3.1 per cent recorded in 1995. Approximately 1.9 million unemployed individuals were aged between 25 and 34 years at the time of the survey, accounting for the largest share of growth over the two decades (39.3 per cent). For individuals aged 15-24 years, the share of unemployed initially increased then decreased from 33.4 per cent (2004) to 26.4 per cent (2013), coinciding with delayed entry into the labour market due to further studies. During the same period, 35-44 year olds recorded the reverse increasing to 22 per cent. The combined share of those aged between 45 and 65 years account for just above 13 per cent of unemployment growth between 1995 and 2013. Finally, the severity of youth unemployment among 18-29 year olds is evidenced by its share of total unemployed in excess of 49 per cent in the three surveys assessed. These findings once again justify the introduction of the Employment Tax Incentives Bill. 13

In summary, the unemployed in 2013 were more likely to be youngsters below 30 years, Black males, residing in urban areas in the Gauteng province, with incomplete secondary schooling. Table 9 presents the unemployment rates in the three surveys under study. It can be seen that the national unemployment rate increased by 8.6 percentage points during the period from 1995 to 2004, before declining slightly to 24.1 per cent in 2013. These trends are repeated when considering the racial profiles of Blacks and Indians, recording unemployment rates of about 27 and 12 per cent respectively in 2013, while the unemployment rates for the Coloureds and whites increased continuously across the surveys. Nonetheless, the unemployment rate remained the lowest for the whites in all three surveys. In addition, females recorded higher unemployment rates in all three surveys, but the gap between the female and male rates narrowed from 9.1 percentage points in 1995 to only 3.9 percentage points in 2013. In light of the provincial demarcations and urban-rural influences on unemployment, the highest provincial unemployment rates were recorded in the Eastern Cape in 1995 and 2004 at 24.3 per cent and 29.6 per cent respectively, being surpassed by the Free State (33 per cent) in 2013. The latter increased from the lowest recorded rate of 12.4 per cent in 1995, which may be attributed to the shortage of job opportunities in rural areas. The Western Cape is consistently cited among the lowest recorded rates of unemployment, reflecting levels ranging from approximately 14 to 21 per cent between 1995 and 2013. Limpopo (one of the poorest provinces) experienced the lowest unemployment rate of 16.9 per cent in 2013, declining from 27.8 per cent in 2004. However, this result is not that surprising when referring to Table 4, which shows that Limpopo had the highest EAR between 1995 and 2013. The urban-rural dynamic has a minimal effect on the margin of difference observed between the unemployment rates, 2013 a case in point where 24.3 and 23.3 per cent were respectively recorded for urban and rural areas. In 2013, individuals with incomplete secondary schooling and Matric experienced the highest recorded unemployment rates at levels ranging between 26 and 30 per cent, despite declining over the last decade. In conjunction with the declined unemployment levels, those having no and incomplete primary schooling each account for unemployment rates of about 18 per cent in 2013, while individuals with post-Matric qualifications recorded the lowest levels of 6 per cent (degree-holders) and 14 per cent (post-Matric certificate/diploma). The significance of education in gaining access to better job opportunities within the labour market is clearly in evidence, based on the lowest unemployment rate afforded to individuals holding degrees. Furthermore, the unemployment rates declined across the elderly age cohorts in all three surveys. Specifically focusing on the 18-29 year olds, the unemployment rate increased from 29.1 per cent in 1995 to 39.5 per cent in 2013. Finally, with reference to Figure 5, there is a disconcerting upward trend in both the number and proportion of unemployed who never worked during the period under study. Furthermore, this proportion was extremely high at

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71.4 per cent for the 15-24 year-old in QLFS 2013Q4.6 This finding strongly suggests the struggle encountered by most of the unemployed, particularly the youth, in their quest to find their first job. As a consequence, there is need for government support in terms of active labour market policy (i.e. Employment Tax Incentives Bill, Expanded Public Works Program, job-seeking transport subsidy, etc.). In summary, the highest unemployment rates for 2013 were observed among Black females, aged between 15 and 24 years, residing in Free State, with incomplete secondary schooling, and who never worked before.

4

A multivariate analysis of labour force participation and employment

In order to interpret the results of the multivariate regressions performed on the data, we will first outline the methodology used in order to provide comparability, transparency and to aid with understanding the results themselves. That said, the following methodology was used in the regression analysis: Firstly, probit regressions are run on the labour force participation likelihood of the working-age population for OHS 1995, LFS 2004b and QLFS 2013Q4. The marginal fixed effects (MFXs) for these regressions are then calculated. The MFX measures the instantaneous rate of change of a variable. Put differently, the MFX provides a good approximation of the change in the dependent variable for a 1 unit change in the independent variable. For our analysis purposes, theses MFX’s indicate the percentage change in labour force participation likelihood for a particular variable. Secondly, Heckprobit regressions run on employment likelihood of the labour force, conditional on labour force participation. Since not everyone in the working-age population joined the labour force and eventually found employment, the results of a probit regression on employment likelihood of the workingage population would be biased due to sample selection. Hence, the most common technique applied to address this problem is a two-step Heckprobit model. The first step is a probit analysis to identify the factors determining whether someone in the working-age population would join the LF or not. The equation allows the estimation of the inverse Mills ratio (i.e., lambda), which is in turn included in the employment probit (i.e., the second step), making the latter regression conditional on labour force participation. If the inverse Mills ratio variable is statistically significant in this probit, it indicates that the labour force indeed differ from their counterparts who decided not to participate in the labour force, and the two-step Heckman approach is necessary. These Heckprobit regressions are run in the same three surveys as mentioned above. Table 10 shows the results of the labour force participation probit regressions for the three surveys under study. The reference group for these regressions were 6 The proportion of unemployed who never worked before in the other age cohorts in QLFS 2013Q4 were as follows: 25-34 years — 42.2 per cent; 35-44 years — 17.2 per cent; 45-54 years — 6.4 per cent; 55-65 years — 4.7 per cent.

15

black males residing in the rural areas of the Eastern Cape, with no formal education and aged between 15 and 24 years old. From these probit regression results we see the following findings: Firstly, gender plays a major role in employment likelihood, as females remain significantly less likely to participate in the labour force than males, across all three surveys. The trend in this fact is not yet clear, as from OHS 1995 to LFS 2004b it decreases from females being 16.34 per cent less likely than males, to 12.78 per cent less likely to participate in the labour force. However, QLFS 2013Q4 again shows an increase at 13.44 per cent. Thus, no definitive conclusion with regard to gender equity in the labour force can be inferred from the data. With regard to race, Coloureds are significantly more likely to enter labour force across all three surveys when compared to Blacks. However this effect appears to be diminishing as its decreases from 11.47 per cent in OHS 1995 to 2.01 per cent in QLFS 2013Q4. On the other hand, the opposite occurs with Whites, as the labour force participation likelihood is negative and significant across all three surveys, again when compared to Blacks. This trend seems to become increasingly negative from -1.82 per cent in OHS 1995 to -7.52 per cent in QLFS 2013Q4. Similarly, Indians show increasingly negative labour force participation likelihood versus the reference Black category, but to a lesser degree, while the OHS 1995 result is statistically insignificant. Therefore, race still remains a significant factor when determining the labour force participation likelihood of an individual. When examining the age characteristics, we see that across all the surveys and for all age cohorts, except for those between the age of 55 and 65 years in OHS 1995 and LFS 2004b, labour force participation likelihood is significantly greater than the reference group (15-24 years). In addition to this fact, when examining the endpoints (OHS 1995 and QLFS 2013Q4) each age category shows an increasing trend in labour market participation likelihood over the reference 15 to 24 age category. Thus, the data could reflect an increasing trend in discouraged workers among the 15 to 24 age category. Furthermore, those residing in urban areas exhibit a significantly greater probability of entering the labour force, as opposed to their rurally residing counterparts. This trend is also increasing across all three survey years, from 2.53 per cent in OHS 1995 to 10.97 per cent in the QLFS 2013Q4 period. Thus, as one would expect, residing in an urban area plays a significant role in labour force participation likelihood, most likely due to the increased employment opportunities available in these areas. When looking at the province of residence, the data shows three distinct categories. Firstly, those residing in Western Cape, Free State, Gauteng and Mpumalanga are significantly more likely to participate in the labour force than those residing in Eastern Cape, across all three surveys. The trend among these provinces, with the exception of Mpumalanga tends to be erratic as they decline in the LFS 2004b and proceed to increase in QLFS 2013Q4. The aforementioned Mpumalanga province shows a continual increase in labour force participation likelihood from 5.92 per cent in OHS 1995 to 10.00 per cent in QLF S2013Q4. Secondly, the Northern Cape, KwaZulu-Natal and North West provinces ex16

hibit no definite trend when compared to the Eastern Cape across the survey periods. In addition, they either have statistically less significant or entirely insignificant MFXs for any one particular survey period. Lastly, compared to Eastern Cape residents, Limpopo residents are less likely to enter the labour force in all surveys. The data therefore shows that the province of residence plays a significant role in the labour force participation likelihood of an individual. In particular, provinces known for being economic centres show positive, significant labour force participation likelihoods most likely due to the larger number of employment opportunities on offer. In terms of education, in general, as educational attainment increases there is a significantly greater likelihood of entering the labour force. One exception for this rule occurs in OHS 1995 and is characterised by the unusual negative MFX value for the incomplete secondary education variable. The other is shown by the statistically insignificant MFX values for the degree education spline in both OHS 1995 and LFS 2004b. Consequently, the degree variable is only statistically significant in the QLFS 2013Q4 survey. Thus, only during this survey period did an individual possessing a bachelor’s degree have a significantly better chance of participating in the labour force, as opposed to someone having a post-Matric certificate/diploma. This point is indicative of a structural change in the labour market or a possible increase in the demand for relatively more educated, skilled people in the labour force in 2013. The data therefore shows that increased levels of education do significantly increase the labour force participation likelihood of an individual. With regard to the household head variable, those who are household heads are significantly more likely to enter labour force across all the survey periods. However, a diminishing trend is noticeable across the periods as well. The significance of this variable can be attributed the need for the head of the household to be the breadwinner in the family. In addition, the psychological factors such as improved self-esteem may also play a role in the household head’s decision to participate in the labour force. Thus, the head of the household is more likely to participate in the labour force as there is an extra incentive to do so. Similarly, those who are married or are living with a partner are significantly more likely to participate in the labour force. The reason for this may again be psychological as the need to take care of their partner may provide an extra incentive to labour force participation. The number of children in the household variable shows a significant and increasingly negative trend over the survey periods, as the MFX decreases from -1.83 per cent in OHS 1995 to -2.66 per cent in QLFS 2013Q4. When taking into account the fact that the reference group is male, this is somewhat counterintuitive, as one would expect a female reference group to become more likely to stay home, as the number of children increases in order to serve as primary caregivers. However, legislative changes toward gender equality may be able to explain why men may now also be more likely to stay home and take care of children. On the other hand, a more practical reason for this would be that the greater the number of children in a household, the greater the amount of income that can be claimed through social grants. We would expect that this 17

substitution of labour market income for social grant income would be more prevalent in lower income households, where the income derived from labour force participation is similar to social grant income. Thus, the labour force participation likelihood of individuals will decrease as the number of children in the household increases for few reasons. In contrast to the number of children in a household, the presence of more male members in the household leads to a significantly greater likelihood of entering the labour force in the LFS 2004b and QLFS 2013Q4 survey periods, while the OHS 1995 result is shown to be statistically insignificant. The reason for this emerging behaviour could once again be psychological, as an improved self-esteem or even peer pressure may provide an extra incentive to these individuals’ participation in the labour force. Similarly, the greater the number of female members in the household, the greater the probability of an individual entering the labour force, and this finding is statistically significant in all three surveys. Finally, the probit regressions show that the presence of more elderly members in the household leads to a significantly lower likelihood of entering the labour force across all three survey periods. In addition, the negative MFX in absolute terms is increasing over the survey periods. This could again be due to the substitution of social grant income for labour market income. Thus, individuals in households with elderly members are less likely to participate in the labour force as there is less incentive to do so. Next, as detailed in the second part of the methodology outlined previously, Table 11 below shows the results of the labour force participation Heckprobit regressions for the three periods. The reference group for these regressions were again black males residing in the rural areas of the Eastern Cape, with no formal education and aged between 15 and 24 years old. From these Heckprobit regression results we see the following results: Firstly, females are more likely than males to be employed, across all three survey periods. This is in stark contrast to the results obtained on the first probit regression. However, the explanation for this trend is somewhat logical, as an increased focus on gender equity in recent years has made it more attractive for females to enter the labour force. This as both the wages and employment opportunities afforded to females has shown more parity with their male counterparts. Thus, the employment likelihood of females has been positive across all survey periods, when compared to males. With reference to the race variables, Whites are shown to be the group with the greatest likelihood of being employed, followed by Indians and Coloureds. This again, is at odds with the declining employment likelihood for Whites shown by the probit regression. The important inference that must be drawn from this point is that despite Affirmative Action (AA) and Black Economic Empowerment (BEE) initiatives being legislated, labour force trends still show persistent disparities between Blacks and other races. On this point and with the aid of the Oaxaca-Blinder decomposition, Burger and Jafta (2006) conclude that “affirmative action policies have therefore not been successful in its aim to redress the disadvantages in employment experienced by designated groups 18

[or] to ensure their equitable representation in all occupational categories and levels in the workforce”. Therefore, as the data shows, Whites, Indians and Coloureds are still significantly more likely than Blacks to be employed, despite the fact that the MFXs in all three groups declined between LFS 2004b and QLFS 2013Q4. When examining the age cohort variables, we see the following results, compared to the 15 to 24 year reference age category: those aged 35 to 44 years are only significantly more likely to be employed in QLFS 2013Q4, while those 45 to 54 years do so in LFS 2004b. In addition, more importantly, those aged 55 to 65 years are most the likely to be employed across all three survey periods. This may be due to a lack of experience prevalent amongst younger members of the labour force, making older workers much more valuable to their respective fields, or because older workers are less likely to resign as they are closer to their retirement age and stand to lose more financially. Thus, for either reason, those in the 55 to 65 age category are most likely to employed. In the case of provincial variables, the results were mostly mixed. The exception to this was Mpumalanga and the North West province which showed increasingly negative labour force participation likelihoods when compared to the reference Eastern Cape province. The aforementioned mixed results can be seen in the seemingly random and in some case, insignificant MFX values for the Western Cape, Northern Cape, Free State, KwaZulu-Natal and Gauteng. On the other hand, the Limpopo province shows a positive and significant MFX value in comparison to the Eastern Cape and taking into account the previous probit regression results. However, when one takes into account the fact that the EAR between 1995 and 2013 was the highest in Limpopo (Table 4) and that unemployment rate in this province was the lowest in QLFS 2013Q4 (Table 9), the negative MFXs of this province in the Heckprobit regressions might not be too surprising, and the results reflect the possible success of the government’s efforts to promote the less developed provinces. In terms of the education splines in the Heckprobit regression, the results seem more logical than the probit regression results obtained earlier. To this end, an individual possessing an education either up to or including Matric is either just as likely, or less likely to find employment as someone with no formal education. Furthermore, both the Matric and Certificate or Diploma and Degree splines show a statistically significant and positive MFX value. For the latter this applies to just LFS 2004b and QLFS 2013Q4. However, this shows that an individual possessing a degree or higher is significantly more likely to be employed, meaning the demand trend for more highly educated workers has been prevalent since much earlier than the probit regression in Table 10 implies. This also implies that in order to survive in the labour market an individual needs to pursue educational attainment beyond Matric level. Thus, as one would expect, the relatively more educated an individual is, the more likely they are to be employed, especially with regard to qualifications higher than Matric. Lastly, the results show that lambda is statistically significant in the Heckprobit regressions. This is of vital importance as it justifies the need for the two-step Heckprobit regression. Furthermore, implies that if we were to run the 19

employment probit as a one-step probit, the sample would be biased and would lead to incorrect results an interpretation. Therefore, it is essential to run the employment probit as a two-step Heckprobit regression, to control for labour force participation and provide an accurate snapshot of employment likelihood over the years.

5

Conclusion

This paper looked at the South African labour market trends for the past 18 years (1995-2013). It found that the labour force and LFPR increased since the end of apartheid in 1994. This partly played a role in the persistent and high levels of unemployment still seen today as some of these workers will remain forever unemployable due to various reasons (e.g. skills mismatch). On top of this, the TGR far exceeded the AGR for most demographic categories, which suggests that the extent of employment growth was not rapid enough to absorb the net entrants into the labour force. This fact in conjunction with barriers to entry in the informal sector and an overall shift towards relatively more skilled occupations and slow economic growth contributed to the unemployment figures seen currently. In addition, based on the results obtained from the multivariate analysis, the following worrying results can be seen: In terms of labour force participation likelihood, race still plays a major role in labour force participation and consequently employment likelihood. Given that equality is very important in the South African context, this area must be revisited. In addition, the regression results show that urban areas are increasingly not able to provide sufficient employment opportunities to their inhabitants. On this point, provincial data confirms that employment creation is a significant problem even in the major provinces. In this regard, measures to encourage employment creation outside of major cities need to be implemented. Furthermore, the results show that experienced older workers are still more likely to be employed. This has a two-fold implication for South Africa. In the short-term, younger generations will have to deal with poverty, while in the long-term, problems surrounding generating economic growth even social unrest may arise. Thus, correcting this problem may avert future economic, social and political stability issues in South Africa. In terms of the positive trends seen in the data, we see that over time the labour force in South Africa has become relatively more educated on the back of secondary and tertiary employment growing significantly. Furthermore, formal sector employment has also shown significant increases over the survey period which is indicative of an improved standard of living at least for some workers, since formal sector employment conditions are much better than informal sector ones. In addition, the Heckprobit results showed that, gender equality legislation in the workplace seems to have had an impact on the number of females being employed. Since developmental economic theory advocates that women are vital to improving the general standard of living in society, this trend should be fostered. In addition, results obtained through the education splines show 20

that educational attainment post Matric is becoming increasingly important in the context of the South African labour market. Given that studies have shown an inverse correlation between the level of education and social issues within a country, the flourishing demand for relatively more educated labour market participants may prove beneficial. Thus, particular focus should be placed on introducing an effective education system coupled with adequate resources, in order to satiate this labour market demand trend. In conclusion, if the 6 per cent unemployment rate as set out in the National Development Plan (NDP) is to be achieved, it is clear that difficult decisions regarding South African labour market legislation and policy, as outlined above, need to be addressed. At the same time, any such decisions taken should ensure that future economic growth benefits all facets of society so as to address unemployment holistically. Based on the issues identified in this paper, some policy changes need to occur in order to address certain problems in the labour market. Firstly, policies such as Affirmative Action (AA), Black Economic Empowerment (BEE) and Broad Based Black Economic Empowerment (BBBEE) need to be revisited, since Black individuals are still least likely to participate in the labour force. Moreover, Van der Berg (2011) points to a growing intra-racial inequality gap in terms of income, illustrating the need for policies aimed at benefiting all South African citizens. Perhaps, the emergence of the trend toward demanding relatively more educated workers provides some guidance on where employment equity policy should go. Specifically, as employing individuals on the basis of educational attainment alone can be beneficial by simply improving efficiency, which is essential for a free market system to be effective. However, given South Africa’s history and the need for reparations this outcome is unlikely. Thus, when revising the policies at hand it is necessary to consider many factors, not just economic ones and such a discussion would be beyond the scope of this paper. What can be inferred from the data is that a multifaceted approach is required to solve South Africa’s labour market problems, as these stem problems from various factors. With this point in mind, education can play a pivotal role in turning around the unemployment problem South Africa is dealing with. This view is also reflected by Burger and Woolard (2005) and Van der Berg (2011). In order to achieve this goal and provide the solution to the unemployment problem, educational policy needs to be decisive and have long-term benefits and economic growth goals in mind. The need for these reforms can be shown empirically as recent global studies have shown South African students to be of relatively poor quality in relation to their peers. It is therefore clear that policy reform in education is required. Besides improving global competitiveness, providing a higher standard and level of education does have other benefits. This, as a more educated population is associated with lower levels of crime and generally higher standards of living as well. It may also go some way to improving social cohesion and stability. Furthermore, educational reforms should also include an integrated skills transfer aspect, performing a role similar to that of Sectorial Education and Training Authorities (SETAs). This would provide some mechanism for transferring skills from the older generation to the younger ones, in 21

order to ensure that essential skills are not lost when the older generation leaves the labour force. As with the previously mentioned employment equity legislations, the specifics of the necessary reforms in education are beyond the scope of this paper. However, looking at the example set by neighbouring African countries with arguably fewer resources, reforming South Africa’s educational policy is both possible and can be beneficial to economic growth and the creation of employment. The next issue identified in the results is that of broad unemployment, which seems to be prevalent in non-urban areas. To address this issue, Bhorat (2012) advocates a transport subsidy for unemployed youth, in addition to the already introduced youth wage subsidy. The aforementioned transport subsidy will go some way to addressing the issue of economic opportunities being limited to urban areas and only benefiting those residing in or adjacent to urban areas as individual outside these areas will then also be able to access these employment opportunities. These youth transport subsidies would also, by their nature provide a certain amount of disincentive to urbanisation, which given its associated problems, would provide both budgetary and social benefits. Furthermore, policies and incentives to decentralisation may be beneficial in the long run, but empirical and practical evidence, given the developmental state South Africa is in is non-existent. Lastly, while not directly discernible from the results of the data analysis, labour unrest in South Africa needs to be addressed. This, as the effect of labour unrest in recent years has become undisputable. Furthermore, this effect has impacted on both private and public enterprise in South Africa. In this regard, Bhorat (2012) associates highly regulated labour markets with relative low-income countries. Furthermore, Kingdon and Knight (2007) cite legal and procedural requirements as a source of problems for employers. In addition, Yu (2013) points to collective bargaining as exacerbating rather than alleviating the unemployment problem in South Africa. Thus, a shift toward a less regulated labour market environment may bring long-term employment benefits at the cost of short-term earnings. However, given the relative power of unions and labour market entities in South Africa, this outcome is may be less than likely.

References [1] Bhorat, H. (2004). Labour market challenges in the post-apartheid South Africa. South African Journal of Economics 72(5): 940-977. [2] Bhorat, H. (2009). Unemployment in South Africa: Descriptors and determinants. Proceedings of the IZA (Institute for the Study of Labour)/World Bank Conference on Employment and Development. [3] Bhorat, H. (2012). A nation in search of jobs: Six possible policy suggestions for employment creation in South Africa. DPRU Working Paper 12/150. Cape Town: Development Policy Research Unit, University of Cape Town. 22

[4] Bhorat, H. Kanbur, R. & Mayet, N. (2012). The Impact of Sectoral Minimum Wage Laws on Employment, Wages and Hours of Work in South Africa. DPRU Working Paper 12/155. Cape Town: Development Policy Research Unit, University of Cape Town. [5] Burger, R. & Jafta, R. (2006). Returns to race: Labour market discrimination in post-apartheid South Africa. Stellenbosch University Working Paper 4/2006. Stellenbosch: Department of Economics, University of Stellenbosch. [6] Burger, R. & Woolard, I. (2005). The state of the labour market in South Africa after the first decade of democracy. Journal of Vocational Education and Training. 57(4): 453-476. [7] Hodge, D. (2009). Growth, employment and unemployment in South Africa. South African Journal of Economics. 77(4): 488-504. [8] Kingdon, G.G & Knight, J. (1999). Unemployment and wages in South Africa: A spatial approach. Centre for the Study of African Economies. Oxford: Institute of Economics and Statistics, University of Oxford. [9] Kingdon, G.G. & Knight, J. (2004). Unemployment in South Africa: The nature of the beast. World Development: 32(3): 391-408. [10] Kingdon, G.G. & Knight, J. (2007). Unemployment in South Africa, 19952003: Causes, problems and policies. Journal of African Economies. 16(5): 813-848. [11] Oosthuizen, M. (2006). The post-apartheid labour market: 1995-2004. DPRU Working Paper 06/103. Cape Town: Development Policy Research Unit, University of Cape Town. [12] Pauw, K. Bhorat, H. Goga, S. Ncube, L. & Van der Westhuizen, C. (2006). Graduate unemployment in the context of skills shortages, education and training: Findings from a firm survey. DPRU Working Paper 06/115. Cape Town: Development Policy Research Unit, University of Cape Town. [13] Van der Berg, S. (2011). Current poverty and income distribution in the context of South African history. Economic History of Developing Regions. 26(1): 120-140. [14] Yu, D. (2008). The South African labour market 1995-2006. Stellenbosch Economic Working Paper 05/2008. Stellenbosch: Stellenbosch University. [15] Yu, D. (2009). The comparability of Labour Force Survey (LFS) and Quarterly Labour Force Survey (QLFS). Stellenbosch Economic Working Paper 08/2009. Stellenbosch: Stellenbosch University. [16] Yu, D. (2013). Revisiting unemployment levels and trends in South Africa since transition. Development Southern Africa. 30(6): 701-723.

23

Figure 1: Labour force number and labour force participation rates, 1995-2013

Figure 2: Number of employed, 1995-2013

24

Figure 3: Number of unemployed and unemployment rates, 1995-2013

Figure 4: Annual percentage growth of employment versus annual percentage growth of real gross value added (2005 prices) by industry, 1995-2013

25

Figure 5: Number and proportion of unemployed who never worked before, selected surveys

26

Table 1: Labour force under the narrow definition: OHS 1995, LFS 2004b and QLFS 2013Q4

Total Race

Gender

Age

Province

Area type

Education

Total Black Coloured Indian White Male Female 15-24 years 25-34 years 35-44 years 45-54 years 55-65 years 18-29 years WC EC NC FS KZN NW GAU MPU LIM Urban Rural None Incomplete primary Incomplete secondary Matric Matric + Cert/Dip Degree Other/Not specified

OHS 1995 11 527 589 7 829 299 1 361 640 401 060 1 935 590 6 712 969 4 814 620 1 769 981 4 096 707 3 224 181 1 739 518 697 202 3 716 161 1 569 818 1 211 519 267 445 858 356 2 160 459 904 977 3 129 719 699 106 726 190 7 597 108 3 930 481 911 537 1 913 406 4 681 861 2 532 532 939 439 456 321 92 493

LF Number LFS 2004b 15 761 080 11 453 770 1 657 357 483 742 2 130 253 8 791 142 6 961 048 2 668 275 5 615 409 3 824 276 2 572 292 1 080 828 5 435 531 2 075 382 1 812 095 301 685 1 087 130 2 929 125 1 158 148 4 129 582 1 047 320 1 220 613 11 576 677 4 184 403 845 697 2 094 435 6 438 994 4 340 699 1 111 537 776 868 152 850

QLFS 2013Q4 20 022 751 15 217 215 2 102 630 565 416 2 137 490 10 971 891 9 050 860 2 602 218 6 789 997 5 684 393 3 470 778 1 475 365 6 000 263 2 829 872 1 845 068 439 290 1 111 115 3 156 293 1 193 925 6 459 675 1 579 344 1 408 169 15 513 215 4 509 536 452 599 1 460 425 8 209 848 6 366 755 1 830 142 1 522 666 180 316

OHS 1995 100.0% 67.9% 11.8% 3.5% 16.8% 58.2% 41.8% 15.4% 35.5% 28.0% 15.1% 6.0% 32.2% 13.6% 10.5% 2.3% 7.4% 18.7% 7.9% 27.1% 6.1% 6.3% 65.9% 34.1% 7.9% 16.6% 40.6% 22.0% 8.1% 4.0% 0.8%

27

Share LFS 2004b 100.0% 72.7% 10.5% 3.1% 13.5% 55.8% 44.2% 16.9% 35.6% 24.3% 16.3% 6.9% 34.5% 13.2% 11.5% 1.9% 6.9% 18.6% 7.3% 26.2% 6.6% 7.7% 73.5% 26.5% 5.4% 13.3% 40.9% 27.5% 7.1% 4.9% 1.0%

1995 vs. 2004 QLFS 2013Q4 100.0% 76.0% 10.5% 2.8% 10.7% 54.8% 45.2% 13.0% 33.9% 28.4% 17.3% 7.4% 30.0% 14.1% 9.2% 2.2% 5.5% 15.8% 6.0% 32.3% 7.9% 7.0% 77.5% 22.5% 2.3% 7.3% 41.0% 31.8% 9.1% 7.6% 0.9%

Number 4 233 491 3 624 471 295 717 82 682 194 663 2 078 173 2 146 428 898 294 1 518 702 600 095 832 774 383 626 1 719 370 505 564 600 576 34 240 228 774 768 666 253 171 999 863 348 214 494 423 3 979 569 253 922 -65 840 181 029 1 757 133 1 808 167 172 098 320 547 60 357

Share 100.0% 85.6% 7.0% 2.0% 4.6% 49.1% 50.7% 21.2% 35.9% 14.2% 19.7% 9.1% 40.6% 11.9% 14.2% 0.8% 5.4% 18.2% 6.0% 23.6% 8.2% 11.7% 94.0% 6.0% -1.6% 4.3% 41.5% 42.7% 4.1% 7.6% 1.4%

2004 vs. 2013 Number 4 261 671 3 763 445 445 273 81 674 7 237 2 180 749 2 089 812 -66 057 1 174 588 1 860 117 898 486 394 537 564 732 754 490 32 973 137 605 23 985 227 168 35 777 2 330 093 532 024 187 556 3 936 538 325 133 -393 098 -634 010 1 770 854 2 026 056 718 605 745 798 27 466

Share 100.0% 88.3% 10.4% 1.9% 0.2% 51.2% 49.0% -1.6% 27.6% 43.6% 21.1% 9.3% 13.3% 17.7% 0.8% 3.2% 0.6% 5.3% 0.8% 54.7% 12.5% 4.4% 92.4% 7.6% -9.2% -14.9% 41.6% 47.5% 16.9% 17.5% 0.6%

1995 vs. 2013 Number 8 495 162 7 387 916 740 990 164 356 201 900 4 258 922 4 236 240 832 237 2 693 290 2 460 212 1 731 260 778 163 2 284 102 1 260 054 633 549 171 845 252 759 995 834 288 948 3 329 956 880 238 681 979 7 916 107 579 055 -458 938 -452 981 3 527 987 3 834 223 890 703 1 066 345 87 823

Share 100.0% 87.0% 8.7% 1.9% 2.4% 50.1% 49.9% 9.8% 31.7% 29.0% 20.4% 9.2% 26.9% 14.8% 7.5% 2.0% 3.0% 11.7% 3.4% 39.2% 10.4% 8.0% 93.2% 6.8% -5.4% -5.3% 41.5% 45.1% 10.5% 12.6% 1.0%

Table 2: Labour force participation rates under the narrow definition: OHS 1995, LFS 2004b and QLFS 2013Q4

Total Race

Gender

Age

Province

Area type

Education

OHS 1995 47.7% 43.1% 60.1% 56.3% 63.3% 58.2% 38.0% 21.7% 63.9% 69.6% 62.7% 31.7% 41.3% 61.7% 35.8% 52.9% 52.3% 44.0% 45.1% 61.9% 43.0% 28.8% 56.4% 36.7% 40.1% 46.8% 40.0% 61.4% 78.1% 82.0% 41.6%

Total Black Coloured Indian White Male Female 15-24 years 25-34 years 35-44 years 45-54 years 55-65 years 18-29 years WC EC NC FS KZN NW GAU MPU LIM Urban Rural None Incomplete primary Incomplete secondary Matric Matric + Cert/Dip Degree Other/Not specified

28

LFPR LFS 2004b 53.8% 50.6% 61.8% 58.8% 68.8% 62.0% 46.2% 28.2% 71.3% 73.3% 66.3% 37.9% 50.3% 66.0% 45.3% 53.4% 57.0% 49.5% 48.3% 65.9% 54.0% 39.0% 62.2% 39.3% 41.4% 46.8% 45.6% 69.8% 86.6% 86.7% 66.2%

QLFS 2013Q4 56.8% 54.7% 64.1% 58.4% 67.4% 63.4% 50.5% 25.5% 73.7% 77.8% 70.0% 41.7% 50.3% 68.1% 45.2% 58.0% 59.7% 47.9% 50.4% 69.9% 58.9% 40.4% 64.9% 39.9% 36.1% 44.8% 46.8% 70.7% 85.5% 88.8% 60.0%

Table 3: Number of employed: OHS 1995, LFS 2004b and QLFS 2013Q4

Total Race

Gender

Age

Province

Area type

Education

Total Black Coloured Indian White Male Female 15-24 years 25-34 years 35-44 years 45-54 years 55-65 years 18-29 years WC EC NC FS KZN NW GAU MPU LIM Urban Rural None Incomplete primary Incomplete secondary Matric Matric + Cert/Dip Degree Other/Not specified

Number of employed OHS LFS QLFS 1995 2004b 2013Q4 9 499 347 11 630 196 15 195 491 6 136 137 7 866 030 11 095 766 1 144 836 1 296 317 1 619 629 358 589 418 797 494 820 1 859 785 2 014 698 1 985 276 5 789 311 6 764 751 8 519 684 3 710 036 4 860 273 6 675 807 1 124 324 1 287 063 1 329 385 3 275 749 3 944 374 4 869 104 2 858 183 3 129 906 4 622 237 1 586 764 2 266 227 3 021 899 654 327 1 002 626 1 352 866 2 633 843 3 128 797 3 628 091 1 353 355 1 689 152 2 236 564 917 098 1 276 170 1 332 779 212 901 227 910 330 045 752 051 776 099 744 876 1 712 758 2 089 722 2 529 716 749 330 833 907 868 444 2 637 048 3 067 735 4 831 651 583 856 787 662 1 150 933 580 950 881 839 1 170 483 6 309 317 8 637 002 11 738 367 3 190 030 2 993 194 3 457 124 770 646 720 256 370 633 1 538 685 1 564 795 1 189 140 3 682 335 4 320 886 5 729 298 2 093 433 3 138 018 4 739 794 888 596 1 001 154 1 578 569 444 862 752 183 1 433 366 80 790 132 904 154 691

OHS 1995 100.0% 64.6% 12.1% 3.8% 19.6% 60.9% 39.1% 11.8% 34.5% 30.1% 16.7% 6.9% 27.7% 14.2% 9.7% 2.2% 7.9% 18.0% 7.9% 27.8% 6.1% 6.1% 66.4% 33.6% 8.1% 16.2% 38.8% 22.0% 9.4% 4.7% 0.9%

29

Share LFS 2004b 100.0% 67.6% 11.1% 3.6% 17.3% 58.2% 41.8% 11.1% 33.9% 26.9% 19.5% 8.6% 26.9% 14.5% 11.0% 2.0% 6.7% 18.0% 7.2% 26.4% 6.8% 7.6% 74.3% 25.7% 6.2% 13.5% 37.2% 27.0% 8.6% 6.5% 1.1%

1995 vs. 2004 QLFS 2013Q4 100.0% 73.0% 10.7% 3.3% 13.1% 56.1% 43.9% 8.7% 32.0% 30.4% 19.9% 8.9% 23.9% 14.7% 8.8% 2.2% 4.9% 16.6% 5.7% 31.8% 7.6% 7.7% 77.2% 22.8% 2.4% 7.8% 37.7% 31.2% 10.4% 9.4% 1.0%

2004 vs. 2013

1995 vs. 2013

Number

Share

Number

Share

Number

Share

2 130 849 1 729 893 151 481 60 208 154 913 975 440 1 150 237 162 739 668 625 271 723 679 463 348 299 494 954 335 797 359 072 15 009 24 048 376 964 84 577 430 687 203 806 300 889 2 327 685 -196 836 -50 390 26 110 638 551 1 044 585 112 558 307 321 52 114

100.0% 81.2% 7.1% 2.8% 7.3% 45.8% 54.0% 7.6% 31.4% 12.8% 31.9% 16.3% 23.2% 15.8% 16.9% 0.7% 1.1% 17.7% 4.0% 20.2% 9.6% 14.1% 109.2% -9.2% -2.4% 1.2% 30.0% 49.0% 5.3% 14.4% 2.4%

3 565 295 3 229 736 323 312 76 023 -29 422 1 754 933 1 815 534 42 322 924 730 1 492 331 755 672 350 240 499 294 547 412 56 609 102 135 -31 223 439 994 34 537 1 763 916 363 271 288 644 3 101 365 463 930 -349 623 -375 655 1 408 412 1 601 776 577 415 681 183 21 787

100.0% 90.6% 9.1% 2.1% -0.8% 49.2% 50.9% 1.2% 25.9% 41.9% 21.2% 9.8% 14.0% 15.4% 1.6% 2.9% -0.9% 12.3% 1.0% 49.5% 10.2% 8.1% 87.0% 13.0% -9.8% -10.5% 39.5% 44.9% 16.2% 19.1% 0.6%

5 696 144 4 959 629 474 793 136 231 125 491 2 730 373 2 965 771 205 061 1 593 355 1 764 054 1 435 135 698 539 994 248 883 209 415 681 117 144 -7 175 816 958 119 114 2 194 603 567 077 589 533 5 429 050 267 094 -400 013 -349 545 2 046 963 2 646 361 689 973 988 504 73 901

100.0% 87.1% 8.3% 2.4% 2.2% 47.9% 52.1% 3.6% 28.0% 31.0% 25.2% 12.3% 17.5% 15.5% 7.3% 2.1% -0.1% 14.3% 2.1% 38.5% 10.0% 10.3% 95.3% 4.7% -7.0% -6.1% 35.9% 46.5% 12.1% 17.4% 1.3%

Table 4: Target growth rates, actual growth rates and employment absorption rates

Total Race

Gender

Age

Province

Area

Education

Total Black Coloured Indian White Male Female 15-24 years 25-34 years 35-44 years 45-54 years 55-65 years 18-29 years WC EC NC FS KZN NW GAU MPU LIM Urban Rural None Incomplete primary Incomplete secondary Matric Matric + Cert/Dip Degree

OHS 1995 vs. LFS 2004b TGR AGR EAR 44.6% 22.4% 50.3% 59.1% 28.2% 47.7% 25.8% 13.2% 51.2% 23.1% 16.8% 72.8% 10.5% 8.3% 79.6% 35.9% 16.8% 46.9% 57.9% 31.0% 53.6% 79.9% 14.5% 18.1% 46.4% 20.4% 44.0% 21.0% 9.5% 45.3% 52.5% 42.8% 81.6% 58.6% 53.2% 90.8% 65.3% 18.8% 28.8% 37.4% 24.8% 66.4% 65.5% 39.2% 59.8% 16.1% 7.0% 43.8% 30.4% 3.2% 10.5% 44.9% 22.0% 49.0% 33.8% 11.3% 33.4% 37.9% 16.3% 43.1% 59.6% 34.9% 58.5% 85.1% 51.8% 60.9% 63.1% 36.9% 58.5% 8.0% -6.2% -77.5% -8.5% -6.5% 76.5% 11.8% 1.7% 14.4% 47.7% 17.3% 36.3% 86.4% 49.9% 57.8% 19.4% 12.7% 65.4% 72.1% 69.1% 95.9%

LFS 2004b vs. QLFS 2013Q4 TGR AGR EAR 36.6% 22.4% 61.2% 47.8% 28.2% 58.9% 34.3% 13.2% 38.5% 19.5% 16.8% 86.1% 0.4% 8.3% 2318.9% 32.2% 16.8% 52.3% 43.0% 31.0% 72.1% -5.1% 14.5% -282.0% 29.8% 20.4% 68.5% 59.4% 9.5% 16.0% 39.6% 42.8% 108.0% 39.4% 53.2% 135.3% 18.0% 18.8% 104.1% 44.7% 24.8% 55.5% 2.6% 39.2% 1515.4% 60.4% 7.0% 11.7% 3.1% 3.2% 103.5% 10.9% 22.0% 202.5% 4.3% 11.3% 263.1% 76.0% 16.3% 21.5% 67.5% 34.9% 51.7% 21.3% 51.8% 243.5% 45.6% 36.9% 80.9% 10.9% -6.2% -56.8% -54.6% -6.5% 12.0% -40.5% 1.7% -4.2% 41.0% 17.3% 42.3% 64.6% 49.9% 77.3% 71.8% 12.7% 17.6% 99.2% 69.1% 69.7%

OHS 1995 vs. QLFS 2013Q4 TGR AGR EAR 89.4% 60.0% 67.1% 120.4% 80.8% 67.1% 64.7% 41.5% 64.1% 45.8% 38.0% 82.9% 10.9% 6.7% 62.2% 73.6% 47.2% 64.1% 114.2% 79.9% 70.0% 74.0% 18.2% 24.6% 82.2% 48.6% 59.2% 86.1% 61.7% 71.7% 109.1% 90.4% 82.9% 118.9% 106.8% 89.8% 86.7% 37.7% 43.5% 93.1% 65.3% 70.1% 69.1% 45.3% 65.6% 80.7% 55.0% 68.2% 33.6% -1.0% -2.8% 58.1% 47.7% 82.0% 38.6% 15.9% 41.2% 126.3% 83.2% 65.9% 150.8% 97.1% 64.4% 117.4% 101.5% 86.4% 125.5% 86.0% 68.6% 18.2% 8.4% 46.1% -59.6% -51.9% 87.2% -29.4% -22.7% 77.2% 95.8% 55.6% 58.0% 183.2% 126.4% 69.0% 100.2% 77.6% 77.5% 239.7% 222.2% 92.7%

Table 5: Employment by broad occupation category: OHS 1995, LFS 2004b and QLFS 2013Q4

Highly-skilled Legislators, senior officials and managers Professionals Semi-skilled Technicians and associate professionals Clerks Service workers and shop and market sales Skilled agricultural and fishery worker Craft and related trade workers Plant and machinery operators and assemblers Unskilled Elementary occupations Domestic workers Others/Unspecified Others/Unspecified Highly-skilled Semi-skilled Unskilled

OHS 1995 Number 825 097 499 595 325 502 5 611 697 1 058 897 1 133 818 1 080 787 114 486 1 117 053 1 106 656 3 044 666 2 349 250 695 416 17 887 17 887 Share 8.7% 59.2% 32.1% 100.0%

30

LFS 2004b

QLFS2013Q4

1 366 808 908 768 458 040 6 744 465 1 148 089 1 168 175 1 451 746 328 213 1 536 315 1 111 927 3 496 091 2 616 024 880 067 22 832 22 832

2 127 286 1 233 013 894 273 8 739 481 1 639 907 1 625 488 2 298 237 75 295 1 848 480 1 252 074 4 328 724 3 309 300 1 019 424 0 0

11.8% 58.1% 30.1% 100.0%

14.0% 57.5% 28.5% 100.0%

Table 6: Employment by broad industry category

Primary sector Agriculture, hunting, forestry and fishing Mining and quarrying Secondary sector Manufacturing Electricity, gas and water supply Construction Tertiary Wholesale and retail Transport, storage and communication Financial, insurance and business services Community/social/personal services Private households Other / Unspecified Other / Unspecified

OHS 1995 1 673 951 1 233 552 440 399 1 964 091 1 434 815 84 432 444 844 5 690 739 1 665 345 476 005 579 879 2 171 561 797 949 170 566 170 566

LFS 2004b 1 465 181 1 060 893 404 288 2 634 449 1 712 449 99 266 822 734 7 504 906 2 539 864 562 628 1 146 395 2 182 449 1 073 570 25 660 25 660

QLFS 2013Q4 1 140 807 714 851 425 956 3 099 788 1 768 479 126 918 1 204 391 10 952 113 3 233 002 963 023 2 039 180 3 471 732 1 245 176 2 783 2 783

17.9% 21.1% 61.0% 100.0%

12.6% 22.7% 64.7% 100.0%

7.5% 20.4% 72.1% 100.0%

Share Primary sector Secondary sector Tertiary

Table 7: Employment elasticity to economic growth Employment elasticity to economic growth OHS 1995 LFS 2004b Employment 9 499 347 11 630 196 Real GDP (2005 prices, R million) 1 134 582 1 492 330 1995 vs. 2004 2004 vs. 2013 Annual percentage change of employment 2.3% 3.0% Annual percentage change of real GDP 3.1% 3.3% ∆ Employment / ∆ real GDP 0.74 0.92 Formal sector employment elasticity to economic growth OHS 1997 LFS 2004b Formal sector employment 6 436 017 7 684 843 Real GDP (2005 prices, R million) 1 214 768 1 492 330 1997 vs. 2004 2004 vs. 2013 Annual percentage change of formal sector employment 2.6% 3.8% Annual percentage change of real GDP 3.0% 3.3% ∆ Formal sector employment / ∆ real GDP 0.86 1.17

31

QLFS 2013Q4 15 195 491 1 993 433 1995 vs. 2013 2.6% 3.2% 0.83 QLFS 2013Q4 10 780 187 1 993 433 1997 vs. 2013 3.3% 3.2% 1.03

Table 8: Number of unemployed under the narrow definition

Total Race

Gender

Age

Province

Area

Education

Total Black Coloured Indian White Male Female 15-24 years 25-34 years 35-44 years 45-54 years 55-65 years 18-29 years WC EC NC FS KZN NW GAU MPU LIM Urban Rural None Incomplete primary Incomplete secondary Matric Matric + Cert/Dip Degree Other/Not specified

Number of unemployed OHS LFS QLFS 1995 2004b 2013Q4 2 028 242 4 130 884 4 827 260 1 693 162 3 587 740 4 121 449 216 804 361 040 483 001 42 471 64 945 70 596 75 805 115 555 152 214 923 658 2 026 391 2 452 207 1 104 584 2 100 775 2 375 053 645 657 1 381 212 1 272 833 820 958 1 671 035 1 920 893 365 998 694 370 1 062 156 152 754 306 065 448 879 42 875 78 202 122 499 1 082 318 2 306 734 2 372 172 216 463 386 230 593 308 294 421 535 925 512 289 54 544 73 775 109 245 106 305 311 031 366 239 447 701 839 403 626 577 155 647 324 241 325 481 492 671 1 061 847 1 628 024 115 250 259 658 428 411 145 240 338 774 237 686 1 287 791 2 939 675 3 774 848 740 451 1 191 209 1 052 412 140 891 125 441 81 966 374 721 529 640 271 285 999 526 2 118 108 2 480 550 439 099 1 202 681 1 626 961 50 843 110 383 251 573 11 459 24 685 89 300 11 703 19 946 25 625

Share LFS 2004b 100.0% 86.9% 8.7% 1.6% 2.8% 49.1% 50.9% 33.4% 40.5% 16.8% 7.4% 1.9% 55.8% 9.3% 13.0% 1.8% 7.5% 20.3% 7.8% 25.7% 6.3% 8.2% 71.2% 28.8% 3.0% 12.8% 51.3% 29.1% 2.7% 0.6% 0.5%

OHS 1995 100.0% 83.5% 10.7% 2.1% 3.7% 45.5% 54.5% 31.8% 40.5% 18.0% 7.5% 2.1% 53.4% 10.7% 14.5% 2.7% 5.2% 22.1% 7.7% 24.3% 5.7% 7.2% 63.5% 36.5% 6.9% 18.5% 49.3% 21.6% 2.5% 0.6% 0.6%

32

1995 vs. 2004 QLFS 2013Q4 100.0% 85.4% 10.0% 1.5% 3.2% 50.8% 49.2% 26.4% 39.8% 22.0% 9.3% 2.5% 49.1% 12.3% 10.6% 2.3% 7.6% 13.0% 6.7% 33.7% 8.9% 4.9% 78.2% 21.8% 1.7% 5.6% 51.4% 33.7% 5.2% 1.8% 0.5%

2004 vs. 2013

1995 vs. 2013

Number

Share

Number

Share

Number

Share

2 102 642 1 894 578 144 236 22 474 39 750 1 102 733 996 191 735 555 850 077 328 372 153 311 35 327 1 224 416 169 767 241 504 19 231 204 726 391 702 168 594 569 176 144 408 193 534 1 651 884 450 758 -15 450 154 919 1 118 582 763 582 59 540 13 226 8 243

100.0% 90.1% 6.9% 1.1% 1.9% 52.4% 47.4% 35.0% 40.4% 15.6% 7.3% 1.7% 58.2% 8.1% 11.5% 0.9% 9.7% 18.6% 8.0% 27.1% 6.9% 9.2% 78.6% 21.4% -0.7% 7.4% 53.2% 36.3% 2.8% 0.6% 0.4%

696 376 533 709 121 961 5 651 36 659 425 816 274 278 -108 379 249 858 367 786 142 814 44 297 65 438 207 078 -23 636 35 470 55 208 -212 826 1 240 566 177 168 753 -101 088 835 173 -138 797 -43 475 -258 355 362 442 424 280 141 190 64 615 5 679

100.0% 76.6% 17.5% 0.8% 5.3% 61.1% 39.4% -15.6% 35.9% 52.8% 20.5% 6.4% 9.4% 29.7% -3.4% 5.1% 7.9% -30.6% 0.2% 81.3% 24.2% -14.5% 119.9% -19.9% -6.2% -37.1% 52.0% 60.9% 20.3% 9.3% 0.8%

2 799 018 2 428 287 266 197 28 125 76 409 1 528 549 1 270 469 627 176 1 099 935 696 158 296 125 79 624 1 289 854 376 845 217 868 54 701 259 934 178 876 169 834 1 135 353 313 161 92 446 2 487 057 311 961 -58 925 -103 436 1 481 024 1 187 862 200 730 77 841 13 922

100.0% 86.8% 9.5% 1.0% 2.7% 54.6% 45.4% 22.4% 39.3% 24.9% 10.6% 2.8% 46.1% 13.5% 7.8% 2.0% 9.3% 6.4% 6.1% 40.6% 11.2% 3.3% 88.9% 11.1% -2.1% -3.7% 52.9% 42.4% 7.2% 2.8% 0.5%

Table 9: Unemployment rates under the narrow definition Unemployment rate Total Race

Gender

Age

Province

Area

Education

Total Black Coloured Indian White Male Female 15-24 years 25-34 years 35-44 years 45-54 years 55-65 years 18-29 years WC EC NC FS KZN NW GAU MPU LIM Urban Rural None Incomplete primary Incomplete secondary Matric Matric + Cert/Dip Degree Other/Not specified

OHS 1995 17.6% 21.6% 15.9% 10.6% 3.9% 13.8% 22.9% 36.5% 20.0% 11.4% 8.8% 6.1% 29.1% 13.8% 24.3% 20.4% 12.4% 20.7% 17.2% 15.7% 16.5% 20.0% 17.0% 18.8% 15.5% 19.6% 21.3% 17.3% 5.4% 2.5% 12.7%

33

LFS 2004b 26.2% 31.3% 21.8% 13.4% 5.4% 23.1% 30.2% 51.8% 29.8% 18.2% 11.9% 7.2% 42.4% 18.6% 29.6% 24.5% 28.6% 28.7% 28.0% 25.7% 24.8% 27.8% 25.4% 28.5% 14.8% 25.3% 32.9% 27.7% 9.9% 3.2% 13.0%

QLFS 2013Q4 24.1% 27.1% 23.0% 12.5% 7.1% 22.3% 26.2% 48.9% 28.3% 18.7% 12.9% 8.3% 39.5% 21.0% 27.8% 24.9% 33.0% 19.9% 27.3% 25.2% 27.1% 16.9% 24.3% 23.3% 18.1% 18.6% 30.2% 25.6% 13.7% 5.9% 14.2%

Table 10: Probit regression on labour force participation (under the narrow definition) likelihood

Female Coloured Indian White 25-34 years 35-44 years 45-54 years 55-65 years Urban WC NC FS KZN NW GAU MPU LIM Education spline: None to incomplete primary Education spline: Incomplete secondary Education spline: Matric Education spline: Matric + Cert/Dip Education spline: Degree Household head Married or living with a partner Number of children 0-15 years in the household Number of male members 16-59 years in the household Number of female members 16-59 years in the households Number of elderly 60+ years in the household

OHS1995 MFX X-bar *** -0.1634 0.5394 *** 0.1147 0.1360 0.0007 0.0389 ** -0.0182 0.1306 *** 0.3487 0.2425 *** 0.3473 0.1918 *** 0.2332 0.1302 *** -0.0797 0.1080 *** 0.0253 0.5380 *** 0.1428 0.1029 *** 0.0706 0.0417 *** 0.1036 0.0955 *** 0.0774 0.1988 *** 0.0611 0.0804 *** 0.1629 0.1092 *** 0.0592 0.0963 *** -0.0672 0.0949 *** 0.0052 5.1369 *** -0.0076 2.6215 *** 0.2150 0.2315 *** 0.1224 0.0714 -0.0052 0.0229 *** 0.2990 0.3115 *** 0.0950 0.4502 *** -0.0183 1.8791 -0.0021 1.5624 *** 0.0128 1.8099 *** -0.0397 0.3619

LFS2004b MFX X-bar *** -0.1278 0.5397 *** 0.0427 0.1374 *** -0.0675 0.0205 *** -0.0769 0.0766 *** 0.3354 0.2314 *** 0.3337 0.1869 *** 0.2328 0.1406 0.0042 0.1019 *** 0.0474 0.5594 *** 0.0678 0.1123 * -0.0184 0.0653 *** 0.0292 0.0741 -0.0027 0.2573 *** -0.0426 0.0867 *** 0.0659 0.1094 *** 0.0633 0.0733 *** -0.0721 0.0928 *** 0.0079 5.1920 *** 0.0128 2.6840 *** 0.2009 0.2370 *** 0.1803 0.0585 -0.0337 0.0196 *** 0.2187 0.3556 *** 0.0878 0.3787 *** -0.0235 1.7772 *** 0.0094 1.4016 *** 0.0120 1.6413 *** -0.0527 0.3208

QLFS2013Q4 MFX X-bar *** -0.1344 0.5357 ** 0.0201 0.1198 *** -0.1053 0.0213 *** -0.0752 0.0719 *** 0.3952 0.2339 *** 0.3948 0.1808 *** 0.3292 0.1556 *** 0.0929 0.1269 *** 0.1097 0.6297 *** 0.1116 0.1282 *** 0.0473 0.0550 *** 0.0525 0.0862 ** -0.0244 0.1718 0.0036 0.0748 *** 0.1105 0.1611 *** 0.1000 0.0951 *** -0.0388 0.1124 * 0.0040 5.5549 *** 0.0269 3.4034 *** 0.1589 0.3317 *** 0.1368 0.0983 ** 0.0481 0.0426 *** 0.1753 0.3622 *** 0.0633 0.3466 *** -0.0266 1.5321 ** 0.0056 1.2873 *** 0.0259 1.5078 *** -0.0614 0.3427

80 387 0.4765 0.4599 28 510 0.0000 0.2569

67 871 0.5385 0.5042 21 132 0.0000 0.2246

54 568 0.5683 0.5424 20 244 0.0000 0.2683

Number of observations Observed probability Predicted probability at x-bar Chi-squared statistic Prob > Chi-squared Pseudo R-squared *** Significant at 1%

** Significant at 5%

34

* Significant at 1%

Table 11: Heckprobit regression on employment likelihood, conditional on labour force participation

Female Coloured Indian White 25-34 years 35-44 years 45-54 years 55-65 years Urban WC NC FS KZN NW GAU MPU LIM Education spline: None to incomplete primary Education spline: Incomplete secondary Education spline: Matric Education spline: Matric + Cert/Dip Education spline: Degree Lambda Number of observations Observed probability Predicted probability at x-bar Chi-squared statistic Prob > Chi-squared Pseudo R-squared *** Significant at 1%

OHS1995 MFX X-bar *** 0.0419 0.4306 *** 0.0204 0.1668 *** 0.0763 0.0461 *** 0.1166 0.1742 *** -0.1148 0.3260 *** -0.0667 0.2829 -0.0093 0.1726 *** 0.1092 0.0735 *** -0.0583 0.6091 * 0.0119 0.1350 -0.0147 0.0469 *** 0.0378 0.1061 0.0030 0.1911 *** 0.0299 0.0772 -0.0035 0.1445 *** 0.0276 0.0891 *** 0.0371 0.0665 *** -0.0040 5.2012 0.0018 2.8317 *** -0.0647 0.3310 *** 0.0674 0.1222 0.0234 0.0408 *** -0.3095 0.6255

LFS2004b MFX X-bar *** 0.0742 0.4705 *** 0.0740 0.1560 *** 0.1677 0.0237 *** 0.2063 0.1015 *** -0.1831 0.3160 *** -0.0931 0.2643 *** 0.0214 0.1745 * 0.2035 0.0718 *** -0.0919 0.6464 *** 0.0332 0.1388 *** 0.0145 0.0674 -0.0294 0.0809 *** -0.0013 0.2265 -0.0003 0.0773 -0.0370 0.1424 *** -0.0187 0.0787 * 0.0404 0.0714 *** -0.0136 5.3131 *** -0.0139 3.0248 *** -0.1069 0.3486 *** 0.0681 0.1018 *** 0.0872 0.0345 *** -0.5297 0.6103

QLFS2013Q4 MFX X-bar *** 0.0283 0.4842 *** 0.0482 0.1387 *** 0.1293 0.0231 *** 0.1685 0.0946 *** -0.0821 0.3188 *** 0.0002 0.2564 0.0793 0.1986 *** 0.1935 0.0881 *** -0.0802 0.7388 *** -0.0249 0.1619 * -0.0072 0.0559 -0.0716 0.0940 *** 0.0801 0.1435 *** -0.0021 0.0672 -0.0609 0.2081 *** -0.0411 0.0965 *** 0.0939 0.0786 -0.0044 5.7026 *** -0.0195 3.8356 -0.0042 0.4652 *** 0.0471 0.1594 *** 0.0743 0.0709 *** -0.3345 0.5376

37 189 0.8241 0.8837 6 611 0.0000 0.1971

33 784 0.7379 0.7817 7 229 0.0000 0.1845

28 930 0.7589 0.7879 4 253 0.0000 0.1314

** Significant at 5%

35

* Significant at 1%

Appendix Table A.1: Labour market trends, 1995-2013 Survey OHS1995 OHS1996 OHS1997 OHS1998 OHS1999 LFS2000a LFS2000b LFS2001a LFS2001b LFS2002a LFS2002b LFS2003a LFS2003b LFS2004a LFS2004b LFS2005a LFS2005b LFS2006a LFS2006b LFS2007a LFS2007b QLFS2008Q1 QLFS2008Q2 QLFS2008Q3 QLFS2008Q4 QLFS2009Q1 QLFS2009Q2 QLFS2009Q3 QLFS2009Q4 QLFS2010Q1 QLFS2010Q2 QLFS2010Q3 QLFS2010Q4 QLFS2011Q1 QLFS2011Q2 QLFS2011Q3 QLFS2011Q4 QLFS2012Q1 QLFS2012Q2 QLFS2012Q3 QLFS2012Q4 QFLS2013Q1 QLFS2013Q2 QLFS2013Q3 QLFS2013Q4

15-65yrs 24 190 583 24 909 065 25 506 089 25 665 233 26 246 545 26 465 110 27 836 456 28 062 004 28 084 327 28 298 255 28 495 088 28 724 521 28 906 230 29 099 787 29 270 821 29 489 763 29 663 379 29 817 824 29 972 571 30 160 997 30 387 402 31 700 031 31 859 272 31 987 108 32 141 290 32 293 255 32 452 436 32 590 099 32 733 898 32 917 597 33 062 618 33 246 836 33 365 615 33 508 825 33 672 970 33 818 983 33 974 114 34 128 626 34 301 187 34 456 238 34 616 851 34 756 987 34 908 625 35 077 845 35 231 309

Labour force Narrow Broad 11 527 589 13 731 073 11 190 599 13 532 623 11 544 385 14 295 597 12 528 080 14 996 600 13 509 926 16 231 269 16 205 643 18 424 127 16 381 316 18 596 239 16 668 067 19 361 231 15 817 377 18 807 980 16 494 331 19 535 489 16 214 594 19 404 685 16 409 029 19 642 235 15 840 687 19 609 716 15 787 749 19 549 788 15 761 080 19 704 344 16 172 520 19 991 966 16 770 161 20 078 497 16 707 953 20 386 846 17 173 402 20 386 338 16 965 854 20 464 900 17 194 198 20 632 876 18 819 077 20 021 290 18 871 451 19 971 536 18 859 224 19 951 529 18 830 810 20 019 823 18 994 790 20 227 098 18 713 390 20 248 100 18 315 304 19 960 259 18 408 899 20 133 886 18 430 474 20 319 214 18 452 797 20 411 247 18 321 525 20 399 505 18 281 358 20 456 705 18 512 827 20 754 386 18 712 021 20 924 463 18 827 682 21 038 831 18 813 971 21 152 923 19 063 136 21 441 264 19 065 829 21 426 195 19 481 358 21 695 856 19 249 776 21 550 431 19 430 812 21 830 242 19 677 242 22 100 329 19 939 574 22 234 844 20 022 751 22 221 218

Employed 9 499 347 8 966 307 9 093 647 9 370 130 10 356 143 11 874 409 12 224 406 12 260 207 11 167 541 11 603 398 11 283 924 11 297 621 11 411 351 11 378 217 11 630 196 11 894 320 12 287 798 12 437 963 12 787 285 12 634 896 13 293 327 14 450 646 14 604 053 14 561 398 14 784 916 14 631 692 14 374 908 13 841 980 13 982 850 13 820 568 13 834 144 13 668 819 13 915 884 13 917 447 13 933 454 14 131 609 14 349 931 14 297 605 14 348 370 14 583 192 14 541 707 14 569 906 14 706 731 15 061 904 15 195 491

Narrow 2 028 242 2 224 292 2 450 738 3 157 950 3 153 783 4 331 234 4 156 910 4 407 860 4 649 836 4 890 933 4 930 670 5 111 408 4 429 336 4 409 532 4 130 884 4 278 200 4 482 363 4 269 990 4 386 117 4 330 958 3 900 871 4 368 431 4 267 398 4 297 826 4 045 894 4 363 098 4 338 482 4 473 324 4 426 049 4 609 906 4 618 653 4 652 706 4 365 474 4 595 380 4 778 567 4 696 073 4 464 040 4 765 531 4 717 459 4 898 166 4 708 069 4 860 906 4 970 511 4 877 670 4 827 260

Unemployed Broad 4 231 726 4 566 316 5 201 950 5 626 470 5 875 126 6 549 718 6 371 833 7 101 024 7 640 439 7 932 091 8 120 761 8 344 614 8 198 365 8 171 571 8 074 148 8 097 646 7 790 699 7 948 883 7 599 053 7 830 004 7 339 549 5 570 644 5 367 483 5 390 131 5 234 907 5 595 406 5 873 192 6 118 279 6 151 036 6 498 646 6 577 103 6 730 686 6 540 821 6 836 939 6 991 009 6 907 222 6 802 992 7 143 659 7 077 825 7 112 664 7 008 724 7 260 336 7 393 598 7 172 940 7 025 727

36

Discouraged 2 203 484 2 342 024 2 751 212 2 468 520 2 721 343 2 218 484 2 214 923 2 693 164 2 990 603 3 041 158 3 190 091 3 233 206 3 769 029 3 762 039 3 943 264 3 819 446 3 308 336 3 678 893 3 212 936 3 499 046 3 438 678 1 202 213 1 100 085 1 092 305 1 189 013 1 232 308 1 534 710 1 644 955 1 724 987 1 888 740 1 958 450 2 077 980 2 175 347 2 241 559 2 212 442 2 211 149 2 338 952 2 378 128 2 360 366 2 214 498 2 300 655 2 399 430 2 423 087 2 295 270 2 198 467

LFPR Narrow 47.7% 44.9% 45.3% 48.8% 51.5% 61.2% 58.8% 59.4% 56.3% 58.3% 56.9% 57.1% 54.8% 54.3% 53.8% 54.8% 56.5% 56.0% 57.3% 56.3% 56.6% 59.4% 59.2% 59.0% 58.6% 58.8% 57.7% 56.2% 56.2% 56.0% 55.8% 55.1% 54.8% 55.2% 55.6% 55.7% 55.4% 55.9% 55.6% 56.5% 55.6% 55.9% 56.4% 56.8% 56.8%

Broad 56.8% 54.3% 56.0% 58.4% 61.8% 69.6% 66.8% 69.0% 67.0% 69.0% 68.1% 68.4% 67.8% 67.2% 67.3% 67.8% 67.7% 68.4% 68.0% 67.9% 67.9% 63.2% 62.7% 62.4% 62.3% 62.6% 62.4% 61.2% 61.5% 61.7% 61.7% 61.4% 61.3% 61.9% 62.1% 62.2% 62.3% 62.8% 62.5% 63.0% 62.3% 62.8% 63.3% 63.4% 63.1%

Unemployment rate Narrow Broad 17.6% 30.8% 19.9% 33.7% 21.2% 36.4% 25.2% 37.5% 23.3% 36.2% 26.7% 35.5% 25.4% 34.3% 26.4% 36.7% 29.4% 40.6% 29.7% 40.6% 30.4% 41.8% 31.1% 42.5% 28.0% 41.8% 27.9% 41.8% 26.2% 41.0% 26.5% 40.5% 26.7% 38.8% 25.6% 39.0% 25.5% 37.3% 25.5% 38.3% 22.7% 35.6% 23.2% 27.8% 22.6% 26.9% 22.8% 27.0% 21.5% 26.1% 23.0% 27.7% 23.2% 29.0% 24.4% 30.7% 24.0% 30.6% 25.0% 32.0% 25.0% 32.2% 25.4% 33.0% 23.9% 32.0% 24.8% 32.9% 25.5% 33.4% 24.9% 32.8% 23.7% 32.2% 25.0% 33.3% 24.7% 33.0% 25.1% 32.8% 24.5% 32.5% 25.0% 33.3% 25.3% 33.5% 24.5% 32.3% 24.1% 31.6%

Sector Formal

Informal

Not available 6 436 017 6 508 097 6 796 008 6 672 951 7 077 307 6 798 257 7 019 158 7 089 163 7 173 080 7 223 138 7 364 616 7 473 638 7 684 843 7 741 991 7 979 587 8 051 532 8 376 441 8 414 719 9 034 135 9 935 320 10 073 656 10 121 062 10 232 739 10 168 230 10 087 548 9 794 028 9 853 327 9 711 038 9 624 375 9 496 044 9 729 983 9 792 343 9 779 940 10 006 894 10 220 128 10 126 825 10 202 637 10 319 781 10 277 582 10 246 079 10 383 033 10 722 499 10 780 187

1 043 347 1 077 141 1 571 646 1 819 556 2 026 065 2 836 182 1 964 763 1 821 426 1 778 542 1 827 711 1 901 131 1 764 630 1 944 236 2 068 479 2 459 690 2 187 940 2 376 338 2 129 164 2 083 855 2 438 758 2 451 627 2 281 786 2 369 114 2 291 205 2 247 096 2 110 248 2 250 218 2 153 671 2 301 425 2 281 915 2 322 247 2 281 844 2 308 797 2 266 346 2 234 273 2 216 096 2 211 243 2 333 346 2 352 891 2 337 302 2 364 705 2 332 517 2 455 277

Table A.2: Labour market aggregates under the narrow definition in QLFS 2008Q1-2013Q3, using the Census 2001 weights and 2011 weights

LFS2007b (using Census 2001 weights) QLFS2008Q1 QLFS2008Q2 QLFS2008Q3 QLFS2008Q4 QLFS2009Q1 QLFS2009Q2 QLFS2009Q3 QLFS2009Q4 QLFS2010Q1 QLFS2010Q2 QLFS2010Q3 QLFS2010Q4 QLFS2011Q1 QLFS2011Q2 QLFS2011Q3 QLFS2011Q4 QLFS2012Q1 QLFS2012Q2 QLFS2012Q3 QLFS2012Q4 QFLS2013Q1 QLFS2013Q2 QLFS2013Q3

Using Census 2001 weights Labour force Employed Unemployed 17 194 198 13 293 327 3 900 871 17 826 085 13 636 995 4 189 090 17 863 803 13 749 288 4 114 515 17 788 621 13 668 530 4 120 091 17 732 760 13 861 822 3 870 938 17 833 149 13 652 530 4 180 619 17 510 797 13 388 133 4 122 664 17 086 407 12 896 820 4 189 587 17 146 008 12 983 951 4 162 057 17 133 544 12 825 578 4 307 966 17 075 327 12 766 534 4 308 793 17 392 161 12 998 660 4 393 501 17 285 752 13 151 352 4 134 400 17 497 435 13 135 337 4 362 098 17 673 452 13 138 658 4 534 794 17 772 580 13 333 561 4 439 019 17 755 077 13 514 108 4 240 969 17 959 039 13 436 149 4 522 890 17 933 020 13 466 866 4 466 154 18 332 020 13 667 835 4 664 185 18 097 084 13 597 355 4 499 729 18 234 352 13 634 013 4 600 339 18 460 637 13 738 998 4 721 639 18 664 637 14 057 925 4 606 712

37

Using Census 2011 weights Labour force Employed Unemployed 17 194 198 13 293 327 3 900 871 18 819 077 14 450 646 4 368 431 18 871 451 14 604 053 4 267 398 18 859 224 14 561 398 4 297 826 18 830 810 14 784 916 4 045 894 18 994 790 14 631 692 4 363 098 18 713 390 14 374 908 4 338 482 18 315 304 13 841 980 4 473 324 18 408 899 13 982 850 4 426 049 18 430 474 13 820 568 4 609 906 18 452 797 13 834 144 4 618 653 18 321 525 13 668 819 4 652 706 18 281 358 13 915 884 4 365 474 18 512 827 13 917 447 4 595 380 18 712 021 13 933 454 4 778 567 18 827 682 14 131 609 4 696 073 18 813 971 14 349 931 4 464 040 19 063 136 14 297 605 4 765 531 19 065 829 14 348 370 4 717 459 19 481 358 14 583 192 4 898 166 19 249 776 14 541 707 4 708 069 19 430 812 14 569 906 4 860 906 19 677 242 14 706 731 4 970 511 19 939 574 15 061 904 4 877 670

Table A.3: Formal and informal sector employment, and number of employees and self-employed, 1995-2013 Employment status Survey OHS1995 OHS1996 OHS1997 OHS1998 OHS1999 LFS2000a LFS2000b LFS2001a LFS2001b LFS2002a LFS2002b LFS2003a LFS2003b LFS2004a LFS2004b LFS2005a LFS2005b LFS2006a LFS2006b LFS2007a LFS2007b QLFS2008Q1 QLFS2008Q2 QLFS2008Q3 QLFS2008Q4 QLFS2009Q1 QLFS2009Q2 QLFS2009Q3 QLFS2009Q4 QLFS2010Q1 QLFS2010Q2 QLFS2010Q3 QLFS2010Q4 QLFS2011Q1 QLFS2011Q2 QLFS2011Q3 QLFS2011Q4 QLFS2012Q1 QLFS2012Q2 QLFS2012Q3 QLFS2012Q4 QFLS2013Q1 QLFS2013Q2 QLFS2013Q3 QLFS2013Q4

Employees 8 123 412 8 313 240 8 167 479 8 339 925 8 844 574 8 787 145 9 370 733 9 024 720 9 011 975 9 081 627 9 081 716 9 194 238 9 276 158 9 356 332 9 414 391 9 535 624 9 846 100 9 771 856 10 184 406 10 253 063 10 936 220 12 214 626 12 320 340 12 287 147 12 462 637 12 322 116 12 142 870 11 840 232 11 837 762 11 737 057 11 688 146 11 509 780 11 698 410 11 730 987 11 743 862 11 959 646 12 193 700 12 158 459 12 181 817 12 328 466 12 321 392 12 331 144 12 494 517 13 003 373 13 047 005

Self-employed 1 375 935 611 045 926 168 1 025 748 1 505 706 3 073 630 2 825 474 3 218 407 2 144 102 2 508 940 2 190 994 2 099 251 2 131 304 2 018 613 2 206 814 2 340 253 2 422 542 2 658 832 2 592 531 2 365 182 2 322 623 2 236 020 2 283 713 2 274 251 2 322 279 2 309 576 2 232 038 2 001 748 2 145 088 2 083 511 2 145 998 2 159 039 2 217 474 2 186 460 2 189 592 2 171 963 2 156 231 2 139 146 2 166 553 2 254 726 2 220 315 2 238 762 2 212 214 2 058 531 2 148 486

Unspecified 0 42 022 0 4 457 5 863 13 634 28 199 17 080 11 464 12 831 11 214 4 132 3 889 3 272 8 991 18 443 19 156 7 275 10 348 16 651 34 484 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Domestic workers

Informal (nonagricultural)

Formal (nonagricultural)

Employment by sector Informal Formal (agricultural) (agricultural)

Don't know

Not specified

Formal employment as % total employment

Not available 828 254 747 281 812 465 1 002 719 941 463 844 135 881 168 875 172 843 019 885 322 894 626 845 965 880 067 848 914 858 199 849 085 884 898 935 642 1 024 039 1 234 278 1 257 121 1 348 845 1 376 368 1 393 306 1 287 201 1 255 181 1 232 144 1 272 107 1 253 274 1 216 336 1 214 133 1 215 173 1 218 949 1 205 165 1 224 363 1 260 774 1 258 693 1 230 036 1 191 798 1 221 452 1 216 960 1 265 543 1 245 176

1 043 347 1 077 141 1 571 646 1 819 556 2 026 065 2 836 182 1 964 763 1 821 426 1 778 542 1 827 711 1 901 131 1 764 630 1 944 236 2 068 479 2 459 690 2 187 940 2 376 338 2 129 164 2 083 855 2 438 758 2 451 627 2 281 786 2 369 114 2 291 205 2 247 096 2 110 248 2 250 218 2 153 671 2 301 425 2 281 915 2 322 247 2 281 844 2 308 797 2 266 346 2 234 273 2 216 096 2 211 243 2 333 346 2 352 891 2 337 302 2 364 705 2 332 517 2 455 277

6 436 017 6 508 097 6 796 008 6 672 951 7 077 307 6 798 257 7 019 158 7 089 163 7 173 080 7 223 138 7 364 616 7 473 638 7 684 843 7 741 991 7 979 587 8 051 532 8 376 441 8 414 719 9 034 135 9 935 320 10 073 656 10 121 062 10 232 739 10 168 230 10 087 548 9 794 028 9 853 327 9 711 038 9 624 375 9 496 044 9 729 983 9 792 343 9 779 940 10 006 894 10 220 128 10 126 825 10 202 637 10 319 781 10 277 582 10 246 079 10 383 033 10 722 499 10 780 187

38

187 486 202 082 284 336 1 507 625 1 074 413 742 404 382 241 862 747 550 068 443 426 365 378 340 515 425 083 513 022 337 884 702 881 472 697 459 509 368 256 165 943 124 201 116 232 128 083 132 558 99 732 77 876 108 987 91 303 88 978 88 634 93 919 98 535 87 590 84 513 86 640 89 354 89 690 86 844 84 709 86 996 95 614 112 218 100 699

525 618 725 474 798 905 756 510 766 917 784 712 764 521 864 576 851 897 841 440 831 526 912 831 624 358 647 448 578 059 605 795 605 129 602 942 666 533 676 347 697 448 693 473 678 612 646 393 653 331 604 647 538 174 592 449 566 092 585 890 555 602 529 552 538 178 568 691 584 527 604 556 586 107 613 185 634 727 678 077 646 419 629 127 614 152

0 0 0 86 472 108 318 214 235 127 023 74 868 61 643 57 332 36 403 25 704 52 970 27 756 33 783 14 098 46 935 52 537 47 251 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

72 925 110 055 92 783 28 576 229 923 40 282 28 667 15 446 25 675 19 252 17 671 14 934 18 639 46 710 40 596 26 632 24 847 40 383 69 258 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

76.6% 77.2% 73.3% 62.6% 64.2% 61.9% 69.7% 68.5% 71.1% 71.4% 71.8% 73.7% 71.4% 70.5% 69.6% 69.6% 70.2% 71.4% 73.0% 73.4% 73.8% 74.3% 73.8% 73.9% 74.7% 75.1% 74.3% 74.6% 73.7% 73.8% 73.9% 74.2% 74.1% 74.8% 75.3% 75.1% 75.2% 75.0% 75.0% 75.0% 75.0% 75.4% 75.0%