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Development Southern Africa Volume 35, 2018 - Issue 4
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Articles

Examining employment discrimination in South Africa, 1997–2016

Fadwah FredericksDepartment of Economics, University of the Western Cape, Bellville, South Africa
&
Derek YuDepartment of Economics, University of the Western Cape, Bellville, South AfricaCorrespondence[email protected]
Pages 527-553 | Published online: 19 Jun 2018

Figures & data

Figure 1. Labour force participation rates by population group and gender, 1995–2016: (a) by population group; (b) by gender. Source: Own calculations using OHS 1995–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 1. Labour force participation rates by population group and gender, 1995–2016: (a) by population group; (b) by gender. Source: Own calculations using OHS 1995–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 2. Unemployment rates by population group and gender, 1995–2016: (a) by population group; (b) by gender. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 2. Unemployment rates by population group and gender, 1995–2016: (a) by population group; (b) by gender. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 3. Probability of finding employment in formal non-agricultural sector, 1997–2016: (a) by population group; (b) by gender. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 3. Probability of finding employment in formal non-agricultural sector, 1997–2016: (a) by population group; (b) by gender. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 4. Probability of finding employment in high-skilled occupations, 1997–2016: (a) by population group; (b) by gender. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 4. Probability of finding employment in high-skilled occupations, 1997–2016: (a) by population group; (b) by gender. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Table 1. Probit regressions on employment likelihood of labour force by population group, selected surveys.

Table 2. Probit regressions on employment likelihood of labour force by gender, selected surveys.

Figure 5. Decomposition of average white–African employment probability gap, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 5. Decomposition of average white–African employment probability gap, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 6. Decomposition of average white–coloured employment probability gap, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 6. Decomposition of average white–coloured employment probability gap, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 7. Decomposition of average male–female employment probability gap, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 7. Decomposition of average male–female employment probability gap, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Table 3. Probit regressions on likelihood of employed involved in high-skilled occupations by population group, selected surveys.

Table 4. Probit regressions on likelihood of employed involved in high-skilled occupations by gender, selected surveys.

Figure 8. Decomposition of average white–African high-skilled occupational attainment differential, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 8. Decomposition of average white–African high-skilled occupational attainment differential, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 9. Decomposition of average white–coloured high-skilled occupational attainment differential, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 9. Decomposition of average white–coloured high-skilled occupational attainment differential, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 10. Decomposition of average male–female high-skilled occupational attainment differential, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Figure 10. Decomposition of average male–female high-skilled occupational attainment differential, 1997–2016. Source: Own calculations using OHS 1997–1999, LFS 2000–2007 September, QLFS 2008–2015 Q4 and QLFS 2016 Q3 data.

Table A1. Characteristics of employed, selected surveys.

Table A2. Characteristics of highly-skilled employed, selected surveys.

Table A3. Probit regressions on labour force participation likelihood of working-age population by race, selected surveys.

Table A4. Probit regressions on labour force participation likelihood of working-age population by gender, selected surveys.

Table A5. Decomposition of average white–African, white–coloured and male–female employment probability gap.

Table A6. Decomposition of average white–African, white–coloured and male–female high-skilled occupational attainment differential.

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