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Estimating the Impact of Sectoral Minimum Wages in South Africa

Estimating the Impact of Sectoral Minimum Wages in South Africa. Haroon Bhorat and Benjamin Stanwix Development Policy Research Unit School of Economics, University of Cape Town Presentation to Portfolio Committee on Labour Wednesday 20 th August 2014 Parliament of South Africa.

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Estimating the Impact of Sectoral Minimum Wages in South Africa

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  1. Estimating the Impact of Sectoral Minimum Wages in South Africa Haroon Bhorat and Benjamin Stanwix Development Policy Research Unit School of Economics, University of Cape Town Presentation to Portfolio Committee on Labour Wednesday 20th August 2014 Parliament of South Africa

  2. Outline • Employment & Wage shifts: What is the evidence of changes across sectors? • Sectoral Minimum Wages: A Tale of Two Policy Shocks • Enforcement: Weak enforcement with partial compliance

  3. Where Have All the Jobs Gone? Source: SARB & StatsSA (LFS 2001-2007 and QLFS 2008-2012), Author’s Calculations

  4. What Should Have Happened…But Didn’t Source: SARB , Quarterly Bulletin, Various issues and Authors’ Calculations

  5. Wanted:Labour Intensive Growth…

  6. Relatives & Absolutes DO Matter…

  7. The Winners and Losersfrom Employment… Source: StatsSA (LFS 2001 and QLFS 2012), Author’s Calculations

  8. Wither: Employment Since Democracy • Employment driven by 2001-2008 growth • Primary sector employment collapse • Agriculture (Impact of Wm) and Mining together losing over 700 000 jobs • Both employers of least-skilled workers • Lacklustre employment growth in Manufacturing • Growth within tertiary sectors such as financial services and community services • Public sector as a growing source of employment • Financial Services & Temporary Empl. Service Providers • Employment gains in high- and medium-skilled occupations

  9. Real Average Wages By Sector Source: Wittenberg (2014), ‘Analysis of employment, real wage, and productivity trends in South Africa since 1994’, ILO.

  10. Real Average Wages By Sector. cont’d Source: Wittenberg (2014), ‘Analysis of employment, real wage, and productivity trends in South Africa since 1994’, ILO.

  11. Mainly Good, and One Bad Story:Sectoral Minimum Wages Five sectors non-agricultural sectors (2.2 million individuals in 2007, 17.2 % of employment). Area types A, B, C, etc., with A areas generally urban, B semi-urban, and C rural. Mapping of workers to minimum wages using sector, occupation, and area codes.

  12. Data The dataset used is a pooled dataset consisting of 15 waves of the South African Labour Force Survey (LFS), conducted between September 2000 and 2007. The LFS is a biannual national household survey conducted by Statistics South Africa. This is the last available dataset containing useable information on income in South Africa. Data pooled and treated as repeated cross sections across time.

  13. Method Is Important:Identification of Control Groups Overall Control Group sample restricted to non-unionsed, non-Wm employed with less than Grade 12 schooling. For each Wm, sector and occupation codes used to identify similar occupations. Domestic workers: African and Coloured females; unskilled. Forestry: African and Coloured; unskilled. Retail: Individuals in semi-skilled occupations. Security and Taxi: African and Coloured males; medium-skilled. Agriculture: Unskilled individuals or in elementary occupations.

  14. So is Specification1 Yijkt = β0 + β1POSTt + β2Treatmentk + β3POSTt *Treatmentk + j + ijkt (1) • where Yijkt is the outcome variable of interest (employment, wages, hours of work) for individual i in group k in period t. • POSTtrefers to the time dummy, capturing the period before (0) and after (1) the minimum wage is introduced. • Treatmentk captures whether the individual is in the treatment (1) or control group (2). • POSTt *Treatmentkis the difference-in-difference term, which captures the impact of the minimum wage as a consequence of being in the treatment group, during the treatment period. • jare district council effects.

  15. Just To Be Sure: Specification 11 A difference-in-differences model as well, but tests to see whether wages increased more in areas where workers wages were lower in the pre-law period (Gapjk ). Yijkt =0+  1Postt + 2Gapjk + 3Postt *Gapjk +ijkt +  ijkt (II) Where • Gapjkis a constructed variable measuring the proportional increase in the pre-law wage wjk(t-1)necessary to meet the initial introduced minimum wmjk(t) (real terms). Gapjk=[wmjk(t) -wjk(t-1)]/wjk(t-1) where Gapjk≥0 •  ijkt are controls for various worker characteristics. • Control group comparison, rules out changes in outcome variable due to non-minimum wage shifts over sample period.

  16. The Good News In Non-Agric Sectors: Employment Notes: Robust standard errors used adjusting for clustering at the district council level (not shown). ***p<0.01, ** p<0.05, * p<0.1.

  17. Good News… Notes: Robust standard errors clustered by district council (not shown here). ***p<0.01, ** p<0.05, *p<0.1

  18. But the Market Does Respond: Hours of Work Notes: Robust standard errors clustered by district council (not shown here). ***p<0.01, ** p<0.05, *p<0.1

  19. The Bad Story:The Minimum Wage in Agriculture Average Characteristics of the Treatment Group (2000-2007)

  20. The Bad News In Agriculture:Employment Notes: Robust standard errors in parentheses. All regressions are weighted. *** p<0.01, ** p<0.05, * p<0.1. The dependent variable is whether the individual is employed as a farmworker (1) or not (0). The sample includes individuals of working age who are unemployed or searching for work who have no more than 12 years of education. POST = 1 after March 2003 and 0 otherwise. The Wage Gap is the district level difference between the log of median farmworker wages and the log of median wages for the control group.

  21. Good News for the FewFarmworkers Left: Hourly Wages Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All regressions are weighted. Regression 1 is run on the sample of farmworkers and the control group. Regressions 2 and 3 include only farmworkers. Regressions have the 'Log of Hourly Wages' as dependent variables. POST = 1 after March 2003 and 0 otherwise. The Wage Gap is the district level difference between the log of median farmworker wages and the log of median wages for the control group.

  22. One Minor Positive Result:Contract Coverage Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All regressions are weighted. Regression 1 is run on the sample of farmworkers and the control group. Regressions 2 and 3 include only farmworkers. The dependent variable is whether the individual has a written employment contract (1) or not (0). POST = 1 after March 2003 and 0 otherwise. The Wage Gap is the district level difference between the log of median farmworker wages and the log of median wages for the control group.

  23. Hours Worked: Move away from Non-Permanent workers Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. All regressions are weighted. Regression 1 is run on the sample of farmworkers and the control group. Regressions 2 and 3 include only farmworkers. The dependent variable is hours worked per week. POST = 1 after March 2003 and 0 otherwise. The Wage Gap is the district level difference between the log of median farmworker wages and the log of median wages for the control group.

  24. The Good and the Bad Stories:Revisited… Non-Agriculture Minimum Wages • Some evidence of significant increase in real hourly wages in the post-law period as a result of the introduction of a minimum wage in four out of the five Non-Agriculture sectors examined (notably the Retail, Domestic worker, Taxi and Security sectors). • Nosignificant employment effects of the new law in any of the sectors assessed. • Some indication that for sectors where employment continued to rise in the post-law period, notably the Retail and Security sectors, the introduction of minimum wages may have been associated with a reduction in the usual number of weekly hours worked. Agriculture Minimum Wage • Minimum wage law in Agriculture in South Africa has had significant labour market effects: • Farmworker Wages rose by approximately 17% as a result of the law. • Increased contract coverage , as number of workers with a written employment contract increased to reach 57% in 2007. • Adjustments at the intensive margin were observed as part-time workers lost jobs. • Employment fell significantly in response to the law.

  25. Enforcement of the Minimum Wage • A key consideration for minimum wage legislation. • ECC oversees Sectoral Determinations but not Enforcement. • DoL responsible for enforcement through the Inspection and Enforcement Service (IES) which is a provincial competence. • 45% of covered workers paid wages below the legislated minimum in 2007 • Recent administrative data still suggest significant non-compliance • Probability of inspection low. • IES under-resourced.

  26. Fines for Violation of Sectoral Determinations Source: Schedule 2 of the BCEA, (1997)

  27. Partial Compliance in Agriculture: 2001-2005 Notes: The vertical red line represents the (‘urban’) Minimum Wage in 2003.

  28. Partial Compliance in Agriculture, By Province: 2000-07 Notes: The horizontal red line represents the (‘urban’) Minimum Wage in 2003. The vertical black line represents the timing of the law. Provinces coded as: 1-WC, 2-EC, 3-NC, 4-FS, 5-KZN, 6-NW, 7-GTG, 8-MPM, and 9-LMP.

  29. Towards A National Minimum Wage Some issues to consider: Choosing the level of the Wage is Crucial Deciding on single or split wage (e.g. Youth; Agriculture and Non-Agriculture) Enforcement Efforts A Phase-In Period? ECC as governing structure or a new Minimum Wage Commission?

  30. Thank you

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