1. South African labour market transitions during the global financial and economic crisis: Preliminary micro-level evidence from the NIDS panel Dennis Essers (IOB) Presentation at the 2013 CSAE Conference on Economic Development in Africa 18 March 2013, Labour 2 (11:00 – 13:00)

2. Contents Introduction NIDS data description Empirical model set-up and results Concluding remarks 18/03/20132

3. Introduction Many studies have documented macro-level impacts of 2008-2009 global crisis on developing and EM countries: private capital flows, trade, remittances, etc. South Africa was well-integrated into the world economy and did not escape the crisis; entered recession in 2008Q4, driven by decline in manufacturing, mining, wholesale/retail trade and financial/real estate/business services Recovery has not been spectacular and punctuated by renewed global economic slowdown 18/03/20133

4. 4 Annualised growth of quarterly GDP at constant prices (%)

5. Introduction (2) Adverse macro-economic trajectory has not been without consequences for South Africans (Ngandu et al. 2010) Focus here on labour market transitions: – Official Quarterly Labour Force Survey (QLFS) figures indicate net employment loss of almost 1 million individuals over 2008Q4-2010Q4 – Labour market status is critical determinant of household and individual well-being in SA – (Pre-crisis) high and structural unemployment and segmented labour markets described as SA’s “Achilles’ heel” (Kingdon & Knight 2009) – Complement to earlier crisis impact studies, which use repeated cross- sections of QLFS (Leung et al. 2009; Verick 2010, 2012) Research question: which household-level, individual and job-specific characteristics are associated with staying employed, or not, in SA during the global crisis? 18/03/20135

6. Data description National Income Dynamics Survey (NIDS) is SA’s first nationally representative panel data survey So far 2 NIDS ‘waves’ have been conducted, resulting in panel of 21,098 individuals appearing both in wave 1 (Jan-Dec2008) and wave 2 (May2010-Sep2011) NIDS combines household and individual questionnaires on various topics: expenditure, demographics, health, education, labour market participation etc. Analysis of NIDS is a useful complement to existing studies on SA labour markets during the crisis: – Convenient timing: before height of the global crisis and during recovery – Longitudinal character enables analysis of gross changes/transitions in labour market participation – Labour market section contains detailed information on job history, occupation/industry, hours worked, earnings and benefits, contract types, unionisation, job search strategies, labour market expectations, etc. 18/03/20136

7. Data description (2) Analysis here restricted to ‘balanced panel’ adults aged 20-55 in 2008 Four mutually exclusive groups/labour market statuses: – Employed (regular wage/self-/casual/subsistence agriculture/assistance with others’ business) – Searching unemployed – Discouraged unemployed – Not economically active (NEA) Cross-sectional analysis indicates that aggregate proportion of employed individuals fell with 1.7% points over 2008-2010/11; but also unexpected decline in proportion of searching employed (6.5% points) → employment proportions more reliable than unemployment proportions due to misclassification by fieldworkers in wave 2 (SALDRU 2012) NIDS data best-suited for longitudinal study of individual labour market transitions; simplest representation by means of transition matrix for different labour market statuses (Cichello et al. 2012) 18/03/20137

8. Employment status in 2010/11 Employment status in 2008 50.612.05.032.4 Employed Unemployed, search. Unemployed, disc. NEA 53.0Employed71.66.73.218.5 Unemployed, search. 32.321.66.539.7 6.3 Unemployed, disc. 28.018.110.843.1 22.1NEA22.115.06.156.8 Transition matrix for employment status 2008-2010/11: row proportions (%) Transition matrix for employment status and type 2008-2010/11: row proportions (%) Employment status /type in 2010/11 Employment status/type in 2008 39.86.04.712.05.032.5 Reg. wage employment Self-employment Casual and other employment Unemployed, search. Unemployed, disc. NEA 37.1 Reg. wage employment 76.43.2 5.32.79.3 7.4 Self-employment 16.634.05.37.82.633.8 8.6 Casual and other employment 24.16.46.112.16.145.3 18.5 Unemployed, search. 21.73.96.521.66.539.8 6.3 Unemployed, disc. 18.03.26.818.110.843.1 22.2 NEA 14.03.84.415.06.156.8 18/03/20138

9. Model set-up Simple (survey-weighted) binary probit model: Pr(y=1|X, Z) = Φ(X’β + Z’δ) Two kinds of probits: 1)y equals 1 if individual employed in 2008 and again in 2010/11; 0 if no longer employed in 2010/11 2)y equals 1 if individual in regular wage employment in 2008 and again in 2010/11; 0 if no longer in regular wage employment in 2010/11 X is vector of individual and household-level demographic and location variables for 2008: age cohort, education, race, household size, rural/urban, province dummies, etc. Z is vector of job-specific variables for 2008: occupation and industry types, union membership, contract type/duration, months in wage employment, take-home pay Estimation separate for men and women 18/03/20139

10. (1a)(1b)(2a)(2b)(3a)(3b)(4a)(4b) MaleFemaleMaleFemaleMaleFemaleMaleFemale Omitted: age 20-25 Age 26-350.0751*0.04940.04140.03560.0652*0.05580.0723*0.0502 Age 36-450.1298***0.0975*0.0833*0.06780.1123**0.1036**0.1198***0.0949* Age 46-550.07770.04940.02210.01240.07030.04650.06300.0363 Omitted: no education Primary education0.02170.04810.02270.05210.03280.0403-0.01100.0110 Secondary education0.1367***0.1620***0.1358***0.1618***0.1455***0.1578***0.0841**0.0785 Tertiary education0.1881***0.3032***0.1870***0.3075***0.1880***0.2943***0.1153**0.1990*** Omitted: Black/African Coloured0.1071**-0.04610.1182***-0.04430.1057***-0.04250.1039**-0.0732 Asian/Indian0.1467***0.09840.1613***0.10540.1673***0.09630.1151*-0.0122 White0.1149**0.05480.1141**0.06220.1282***0.05840.0668-0.0363 Married0.0639*-0.02730.04370.00650.0632*-0.02100.0488-0.0488 Household size-0.0170***-0.0123**-0.0102**-0.0081-0.0105**-0.0156**-0.0092**-0.0061 Rural0.0246-0.1367***0.0239-0.1326***0.0225-0.1415***0.0429-0.1151*** Household head0.1024***0.0806** Omitted: No other workers in household 1 other worker-0.0016-0.0356 2 or more other workers-0.1562***0.0670 Household per capita income (log)0.0572***0.0767*** Observations15761933157219181576193315761933 Probit estimates for employment transitions 2008-2010/11 (baseline and extra household variables): average marginal effects 18/03/201310

11. (1a)(1b)(2a)(2b)(3a)(3b)(4a)(4b) MaleFemaleMaleFemaleMaleFemaleMaleFemale Omitted: age 20-25 Age 26-35 0.0550 0.04670.02580.06080.06270.06430.04880.0510 Age 36-450.1335*0.0827*0.09850.0989*0.1423**0.1054**0.1245*0.0816* Age 46-550.08550.04140.04390.04180.09350.05670.07180.0267 Omitted: no education Primary education-0.0976**0.0050-0.0940**0.0147-0.0980**-0.0036-0.1035**-0.0433 Secondary education0.00840.1621***0.00930.1588***0.00950.1544***-0.01560.0544 Tertiary education0.02280.2621***0.02720.2634***0.02410.2549***-0.01990.1246** Omitted: Black/African Coloured0.0352-0.03890.0467-0.04230.0386-0.03210.0401-0.0694 Asian/Indian-0.03110.0450-0.02020.0399-0.04080.0445-0.0615-0.1140 White-0.03670.0489-0.03970.0392-0.04000.0436-0.0741-0.0647 Married0.0989**0.05100.0807**0.05220.1012**0.04070.0903**0.0142 Household size-0.0154***-0.0106-0.0093-0.0082-0.0176***-0.0155**-0.0085-0.0018 Rural-0.0471-0.1486***-0.0485-0.1483***-0.0487-0.1483***-0.0275-0.1194*** Household head0.0865*0.0247 Omitted: No other regular wage workers in household 1 other regular wage worker-0.00670.0260 2 or more other regular wage workers0.06490.1159*** Household per capita income (log)0.0415*0.1057*** Observations11221199111811891122119911221199 Probit estimates for regular wage employment transitions 2008-2010/11 (baseline and extra household variables): average marginal effects 18/03/201311

12. (1a)(1b)(2a)(2b)(3a)(3b)(4a)(4b)(5a)(5b)(6a)(6b)(7a)(7b) MaleFemaleMaleFemaleMaleFemaleMaleFemaleMaleFemaleMaleFemaleMaleFemale Baseline regressors (not shown) ……. Omitted: elementary occupation Semi-skilled-0.03110.1014** Manag./professional-0.04950.1081** Omitted: agriculture, hunting, forestry, fishing Mining and quarrying-0.08990.1725*** Manufacturing-0.0285-0.0869 Utilities0.1200*** Construction-0.2723***-0.0392 Wholesale and retail trade-0.1678**-0.0181 Transport, storage and communication-0.0814-0.1041 Fin. intermed., real estate and bus. services-0.0854-0.0146 Community, social and personal services-0.0491-0.0225 Union member0.05480.0981*** Written contract0.0710*0.0341 Omitted: limited contract duration Unspecified contract duration0.04990.0157 Permanent contract0.1609**0.1010 Months in wage employment (log)0.0381***0.0556*** Monthly take-home pay (log)0.0812***0.1011*** Observations10961183995891109211791110119211171190954102311221199 Probit estimates for regular wage employment transitions 2008-2010/11 (extra job variables): average marginal effects 18/03/201312

13. Conclusions Main findings: – There was considerable mobility (movements in and out of jobs) in SA labour markets over 2008-2010/11 (cf. Banerjee et al. 2008) – Transitions may be to a large degree explained by ‘individual choice’, but there seem to be certain types of workers with a significantly lower probability of retaining (broadly defined) employment: Young (20-35) and older (46-55) workers Workers with less than secondary education … and a significantly lower probability of retaining regular wage employment: Female wage workers with less than secondary education Female wage workers in elementary occupations Male wage workers in construction and wholesale/retail trade Male wage workers with a non-permanent contract (Wage workers with a shorter job history or a lower take-home pay) 18/03/201313

14. Conclusions (2) Preliminary analysis indicates that changes in self-perceived life satisfaction and economic status are significantly different for various employment transitions Caveats/avenues for future research: – Further robustness testing of initial findings is needed: Other regressors, interaction terms Variations on outcome variable (who finds employment during crisis?) More detailed occupation/sector information Third wave – So far only employment status under consideration; no analysis of NIDS data on: Hours worked (and changes therein) Labour earnings (and changes therein) Contribution to household income Interaction with social grants (e.g. State pension, Child support grant, Unemployment insurance, etc.) 18/03/201314

15. Thank you for your attention Mail: dennis.essers@ua.ac.bedennis.essers@ua.ac.be Tel.: +32 3 265 59 35 Visit: Office S.S.119, Lange Sint-Annastraat 7, University of Antwerp city campus

16. Related literature Paper is most closely related to 3 recent empirical studies on SA: – Leung et al. (2009) Probit of 6 QLFS cross-sections pooled over 2008-2009 with crisis variable and interactions Conclusions: human capital reduces negative impact of crisis on employment; race did not compound crisis effect – Verick (2010) Multinomial logits of 3 QLFS cross-sections (2008Q2, 2009Q2, 2009Q3) Conclusions: increase in probability of discouraged unemployment for African men and males with lower educational attainment – Verick (2012) Multinomial logits of 4 pre-crisis QLFS cross-sections (2008Q1-Q4) and 8 crisis quarters (2009Q1-2010Q4) + matching approach to create QLFS panel Conclusions: corroborates Verick (2010); mobility between employment statuses lower during than before the crisis Both Leung et al. and Verick explicitly acknowledge the need for detailed panel data analysis 18/03/201316