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Dennis Essers Institute of Development Management and Policy (IOB) University of Antwerp

South African labour market transitions during the global financial and economic crisis: Micro-level evidence from the NIDS panel. Dennis Essers Institute of Development Management and Policy (IOB) University of Antwerp.

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Dennis Essers Institute of Development Management and Policy (IOB) University of Antwerp

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  1. South African labour market transitions during the global financial and economic crisis:Micro-level evidence from the NIDS panel Dennis Essers Institute of Development Management and Policy (IOB) University of Antwerp Presentation at the Arnoldshain Seminar XI “Migration, Development, and Demographic Change: Problems, Consequences and Solutions” University of Antwerp, 27 June 2013, Session 3B (12:30 – 14:30)

  2. Contents • Introduction • NIDS data description • Empirical model set-up and main results • Further probing • Concluding remarks

  3. Introduction • Many studies have documented 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 not been spectacular and punctuated by renewed global economic slowdown

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

  5. Introduction (2) • Adverse macro-economic trajectory has not been without consequences for South Africans (e.g. Nganduet al. 2010) • Focus here on labour market transitions: • Official Quarterly Labour Force Survey (QLFS) figures indicate net employment loss of about 1 million individuals over 2008Q4-2010Q3 • Labour market status is critical determinant of household and individual well-being (World Bank, 2012), also in SA (Leibbrandtet al. 2012) • (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?

  6. Total number of employed individuals aged 15-64 (in thousands) Net employment loss of +/- 1 million Net employment gain of +/- 650 thousand

  7. Data description • National Income Dynamics Study (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 timid 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.

  8. 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 of NIDS and comparison with QLFS suggests some misclassification between different categories of the non-employed during wave 2 fieldwork (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 (Cichelloet al. 2012)

  9. Transition matrix for employment status 2008-2010/11: row proportions (%) Mobility (%) Overall: 44.8 Upward: 12.6 Downward: 15.1 Within non-empl.: 17.1 • Transition matrix for employment status and type 2008-2010/11: row proportions (%) Mobility (%) Overall: 51.4 Upward: 12.6 Downward: 15.1 Within non-empl.: 17.1 Within empl.: 6.6

  10. Model set-up • Simple (survey-weighted) binary probit model: Pr(y=1|X, Z) = Φ(X’β + Z’δ) • Two kinds of probits: • y equals 1 if individual employed in 2008 and again in 2010/11; 0 if no longer employed in 2010/11 • 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

  11. Probit estimates for employment transitions 2008-2010/11 (baseline and extra household variables): average marginal effects

  12. Probit estimates for regular wage employment transitions 2008-2010/11 (baseline and extra household variables): average marginal effects

  13. Probit estimates for regular wage employment transitions 2008-2010/11 (extra job variables): average marginal effects

  14. Some further probing • Some of the employment transitions may reflect ‘free choices’ rather than influence of external factors (such as economic climate) • NIDS wave 1 and 2 include questions on subjective well-being from which we can construct following variables: • Change in self-reported life satisfaction (-/0/+) • Change in self-reported economic status of household (-/0/+) • Difference between self-reported economic status of household in 2010/11 and economic status anticipated in 2008 (-/0/+) • Do these measures differ between those that remain employed between 2008 and 2010/11 and those that leave employment over the same period?

  15. Changes in subjective well-being, by gender and transition outcome 2010/11: proportions (%)

  16. Conclusions • Main findings: • There was considerable mobility (movements in and out of jobs) in SA labour markets over 2008-2010/11 (cf. other periods, see e.g. Banerjeeet al. 2008; Ranchod & Dinkelman 2008) • Transitions may be, to some extent, 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)

  17. Conclusions (2) • Further analysis indicates that changes in self-perceived life satisfaction and economic status differ significantly between those that remain employed and those that do not • Avenues for future research: • On the NIDS data: • More detailed occupation/sector information (not publicly available) • Incorporating NIDS wave 3 (available soon), to check whether labour market transitions are different between wave 2 and 3 • NIDS data on hours worked and wage earnings is patchy • On the QLFS data: • Using algorithm similar to that of Ranchod & Dinkelman (2008) to match individuals from wave t to wave t+1 for QLFS data 2008Q1-2012Q4 (rotating panel of dwellings); cf. Verick 2012 • Any inference from these matched panels needs to take into account that false matches cannot be ruled out and probability of matching individuals is non-random

  18. Thank you for your attention Mail: dennis.essers@ua.ac.be

  19. Matching algorithm for QLFS (cf. R&D 2008) • Pool all cross-sections/‘waves’ and match households using identifiers • Drop households present in only one wave • Within each wave, drop individuals that belong to the same household and have the same race, gender and age (or age difference of 1 year) • Match remaining individuals across wave t and wave t+1 on household identifier, gender, race and aget = aget+1 • Match also individuals across wave t and wave t+1 on household identifier, gender, race and aget +1 = aget+1 • Take matched individuals of steps 4 and 5 together to form ‘expanded match panels’ • Apply extra consistency checks to ‘expanded match panels’ to form ‘strict match panels’, dropping: • Individuals whose level of education is non-missing and differs between waves • Individuals whose status changes from ‘married’/‘divorced’/‘widowed’ to ‘never married’

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