South African labour market transitions during the global financial - PowerPoint PPT Presentation

South African labour market transitions during the global financial and economic crisis: Micro-level evidence from the NIDS panel and matched QLFS cross-sections Dennis Essers Institute of Development Management and Policy (IOB) University of Antwerp Presentation at the UNU-WIDER Conference on Inclusive Growth in Africa: Measurement, Causes and Consequences Helsinki, 21 September 2013, Parallel Session 4.1: Labour Mobility

Contents • Introduction • Data description: NIDS and matched QLFS • Transition matrices and mobility measures • Empirical model set-up • Results and discussion • Concluding remarks 21/09/2013 2

Introduction • Macro-level impacts of 2008-2009 global crisis on developing and EM economies: private capital flows, trade, remittances, etc. (IMF 2009, 2010; ODI 2010; World Bank 2009) • 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 been anaemic and punctuated by renewed global economic slowdown 21/09/2013 3

Annualised growth of (seasonally-adjusted) quarterly GDP at constant prices (%) 8.0 6.5 6.0 6.0 4.8 4.4 4.4 4.4 5.0 4.0 3.4 3.5 3.3 3.6 3.1 3.1 2.1 3.0 2.0 1.8 2.5 1.7 0.9 1.9 1.9 1.2 0.0 -1.7 -2.0 -2.7 -4.0 -6.0 -6.3 -8.0 21/09/2013 4

Introduction (2) • Adverse macro-economic trajectory has not been without consequences for South Africans (e.g., Ngandu et al. 2010) • Focus here on labour market transitions: – Official (QLFS) figures indicate net employment loss of 1 million individuals over 2008Q4-2010Q3 and rise in unemployment rates over 2008-2012 – Labour market status is critical determinant of household and individual well- being (World Bank 2012), also in SA (Leibbrandt et al. 2012) – (Pre-crisis) high and structural unemployment and segmented labour markets described as SA’s “Achilles’ heel” (Kingdon & Knight 2009) – Economic recessions tend to have heterogeneous impacts on workers (e.g., Kydland 1984; Cho & Newhouse 2011; Hoynes et al. 2012) – Complement to earlier crisis impact studies, which use repeated cross- sections of QLFS (Leung et al. 2009; Verick 2010, 2012) • Research question: which individual, household-level and job- specific characteristics are associated with staying employed, or not, in SA during the height and aftermath of the global crisis? 21/09/2013 5

Evolution of narrow and broad unemployment rates (QLFS), annual averages 2008-2012(%) Narrow and broad unemployment, overall Broad unemployment, by gender 40.0 40.0 35.0 35.0 30.0 30.0 25.0 25.0 20.0 20.0 15.0 15.0 10.0 10.0 5.0 5.0 0.0 0.0 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 Narrow Broad Male Female • Cross-sectional data only provide a netted-out picture of changes in SA labour markets • To evaluate gross changes we need longitudinal datasets 21/09/2013 6

Data description: NIDS National Income Dynamics Study (NIDS) is SA’s first nationally • representative, multi-purpose, individual-level panel data survey 2 NIDS ‘waves’: panel of 21,098 individuals appearing both in wave 1 • (Jan2008-Dec2008) and wave 2 (May2010-Sep2011) Analysis restricted to adults aged 20-55 in 2008 (cf. Cichello et al. 2012) • 6 mutually exclusive labour market statuses: • Regular wage employment – Self-employed – Casual and other employment – Searching unemployed – Discouraged unemployed – Not economically active (NEA) – Problems with NIDS (SALDRU 2012): • Some misclassification between different categories of the non-employed during wave 2 – fieldwork Between-waves attrition rates are especially high for better-off Whites, which reduces – reliability of estimates for this group 21/09/2013 7

Data description: QLFS Quarterly Labour Force Survey (QLFS) is SA’s official, nationally • representative survey on labour market activity since 2008Q1 Designed as rotating panel of dwellings (+/- 30,000); each quarter 25% of • dwellings is replaced; only household identifiers are generally maintained Matching of individuals from quarter t to quarter t+1 using household ID, • age, gender, race, education, marital status (Ranchod & Dinkelman 2008): 760,847 matched obs over 2008Q1-2012Q4, average matching of 68.8% IPW techniques to correct for non-random matching on observables • Analysis restricted to adults aged 20-55 in quarter t • 5 mutually exclusive labour market statuses: • Formal sector employment – Informal sector employment – Searching unemployed – Discouraged unemployed – Not economically active (NEA) – Problems with matched QLFS: • Non-random matching on un observables – False matches – 21/09/2013 8

Transition matrices: NIDS Transition matrix for labour market status, 2008-2010/11: row proportions (%) Labour market status in 2010/11 39.8 6.0 4.7 12.0 5.0 32.5 Casual and Reg. wage Self- Unemployed, Unemployed, other NEA employment employment search. disc. employment Labourmarket status in 2008 Reg. wage 76.4 3.2 3.2 5.3 2.7 9.3 37.1 employment Self- 16.6 34.0 5.3 7.8 2.6 33.8 7.4 employment Casual and 24.1 6.4 6.1 12.1 6.1 45.3 8.6 other employ. Unemployed, 21.7 3.9 6.5 21.6 6.5 39.8 18.5 search. Unemployed, 6.3 18.0 3.2 6.8 18.1 10.8 43.1 disc. 14.0 3.8 4.4 15.0 6.1 56.8 22.2 NEA Overall mobility, M total = M upward + M downward + M within non-employment + M within employment 51.4% = 12.6% + 15.1% + 17.1% + 6.6% 21/09/2013 9

Transition matrices: QLFS Transition matrices for labour market status, 2008Q1-2012Q4: row proportions (%) Labour market status in quarter t+1 Formal sector Informal sector Unemployed, search. Unemployed, disc. NEA employment employment 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 Labourmarket status in quarter t Formal sector employment 91.0 92.0 92.5 92.7 92.7 3.9 3.3 3.2 3.1 3.1 2.8 2.9 2.3 2.4 2.3 0.5 0.5 0.6 0.7 0.7 1.8 1.3 1.4 1.2 1.2 Informal sector 12.2 10.3 10.0 9.5 9.8 74.4 76.9 79.4 80.1 79.0 6.3 5.5 4.5 4.8 4.8 1.7 2.5 2.3 2.2 2.7 5.5 4.8 3.8 3.3 3.8 employment Unemployed, 9.9 7.2 5.6 5.6 6.3 6.8 5.0 5.1 4.1 4.3 62.2 65.5 68.0 69.5 70.1 5.5 7.1 8.4 7.9 7.2 15.6 15.2 13.0 13.0 12.2 search. Unemployed, 6.4 4.1 3.3 3.6 3.3 6.8 5.0 5.3 3.9 4.1 18.6 17.7 16.1 15.8 14.7 43.9 52.0 55.8 58.5 60.9 24.4 21.3 19.5 18.3 17.0 disc. NEA 2.7 1.8 1.8 1.8 1.8 3.4 2.6 2.0 1.7 1.9 10.3 9.6 9.0 8.8 8.5 4.2 5.3 6.3 6.7 6.2 79.5 80.8 80.9 80.9 81.6 Overall mobility, M total = M upward + M downward + M within non-employment + M within employment 2008: 21.0% = 4.8% + 4.0% + 8.9% + 3.3% 2009: 19.4% = 3.6% + 3.5% + 9.6% + 2.7% 2010: 19.0% = 3.4% + 3.0% + 10.2% + 2.4% 2011: 18.7% = 3.2% + 2.9% + 10.3% + 2.3% 21/09/2013 10 2012: 18.2% = 3.3% + 3.0% + 9.6% + 2.4%

Model set-up • Simple (survey-weighted) binary probit models: Pr(y=1| X , Z ) = Φ ( X ’ β + Z ’ δ ) • Two kinds of probits: 1) NIDS: 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 2) QLFS: y equals 1 if individual in formal sector employment in quarter t and again in quarter t+1 ; 0 if no longer in formal sector employment in quarter t+1 ; quarter-to-quarter transitions are pooled per year over 2008-2012 X is vector of individual and household-level demographic and • location variables: age cohort, education, race, household size, rural/urban, province dummies, etc. Z is vector of job-specific variables: occupation and industry • types, union membership, contract type/duration, etc. All estimations separate for men and women • 21/09/2013 11

NIDS probit estimates for regular wage employment transitions, 2008-2010/11 (baseline variables): average marginal effects (1a) (1b) Male Female Omitted: age 20-25 Age 26-35 0.0550 0.0467 Age 36-45 0.1335* 0.0827* Age 46-55 0.0855 0.0414 Omitted: no education Primary education -0.0976** 0.0050 Secondary education 0.0084 0.1621*** Tertiary education 0.0228 0.2621*** Omitted: Black/African Coloured 0.0352 -0.0389 Asian/Indian -0.0311 0.0450 White -0.0367 0.0489 Married 0.0989** 0.0510 Household size -0.0154*** -0.0106 Rural -0.0471 -0.1486*** Province dummies Yes Yes Observations 1,122 1,199 21/09/2013 12