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)

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

Introduction Many studies have documented macro-level impacts of 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 Annualised growth of quarterly GDP at constant prices (%)

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

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

Data description (2) Analysis here restricted to ‘balanced panel’ adults aged 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 /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

Employment status in 2010/11 Employment status in Employed Unemployed, search. Unemployed, disc. NEA 53.0Employed Unemployed, search Unemployed, disc NEA Transition matrix for employment status /11: row proportions (%) Transition matrix for employment status and type /11: row proportions (%) Employment status /type in 2010/11 Employment status/type in Reg. wage employment Self-employment Casual and other employment Unemployed, search. Unemployed, disc. NEA 37.1 Reg. wage employment Self-employment Casual and other employment Unemployed, search Unemployed, disc NEA /03/20138

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

(1a)(1b)(2a)(2b)(3a)(3b)(4a)(4b) MaleFemaleMaleFemaleMaleFemaleMaleFemale Omitted: age Age * * * Age ***0.0975*0.0833* **0.1036**0.1198***0.0949* Age Omitted: no education Primary education Secondary education0.1367***0.1620***0.1358***0.1618***0.1455***0.1578***0.0841** Tertiary education0.1881***0.3032***0.1870***0.3075***0.1880***0.2943***0.1153**0.1990*** Omitted: Black/African Coloured0.1071** *** *** ** Asian/Indian0.1467*** *** *** * White0.1149** ** *** Married0.0639* * Household size *** ** ** ** ** ** Rural *** *** *** *** Household head0.1024***0.0806** Omitted: No other workers in household 1 other worker or more other workers *** Household per capita income (log)0.0572***0.0767*** Observations Probit estimates for employment transitions /11 (baseline and extra household variables): average marginal effects 18/03/201310

(1a)(1b)(2a)(2b)(3a)(3b)(4a)(4b) MaleFemaleMaleFemaleMaleFemaleMaleFemale Omitted: age Age Age *0.0827* *0.1423**0.1054**0.1245*0.0816* Age Omitted: no education Primary education ** ** ** ** Secondary education *** *** *** Tertiary education *** *** *** ** Omitted: Black/African Coloured Asian/Indian White Married0.0989** ** ** ** Household size *** *** ** Rural *** *** *** *** Household head0.0865* Omitted: No other regular wage workers in household 1 other regular wage worker or more other regular wage workers *** Household per capita income (log)0.0415*0.1057*** Observations Probit estimates for regular wage employment transitions /11 (baseline and extra household variables): average marginal effects 18/03/201311

(1a)(1b)(2a)(2b)(3a)(3b)(4a)(4b)(5a)(5b)(6a)(6b)(7a)(7b) MaleFemaleMaleFemaleMaleFemaleMaleFemaleMaleFemaleMaleFemaleMaleFemale Baseline regressors (not shown) ……. Omitted: elementary occupation Semi-skilled ** Manag./professional ** Omitted: agriculture, hunting, forestry, fishing Mining and quarrying *** Manufacturing Utilities0.1200*** Construction *** Wholesale and retail trade ** Transport, storage and communication Fin. intermed., real estate and bus. services Community, social and personal services Union member *** Written contract0.0710* Omitted: limited contract duration Unspecified contract duration Permanent contract0.1609** Months in wage employment (log)0.0381***0.0556*** Monthly take-home pay (log)0.0812***0.1011*** Observations Probit estimates for regular wage employment transitions /11 (extra job variables): average marginal effects 18/03/201312

Conclusions Main findings: – There was considerable mobility (movements in and out of jobs) in SA labour markets over /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

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

Thank you for your attention Mail: Tel.: Visit: Office S.S.119, Lange Sint-Annastraat 7, University of Antwerp city campus

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 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