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South African labour market transitions during the global financial and economic crisis: Micro-level evidence from the NIDS panel Dennis Essers Institute.

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Presentation on theme: "South African labour market transitions during the global financial and economic crisis: Micro-level evidence from the NIDS panel Dennis Essers Institute."— Presentation transcript:

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 27/06/20132

3 Introduction Many studies have documented macro-level impacts of 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 27/06/20133

4 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. Ngandu et 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 (Leibbrandt et 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? 27/06/20135

6 6 Total number of employed individuals aged (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. 27/06/20137

8 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 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 (Cichello et al. 2012) 27/06/20138

9 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 Mobility (%) Overall: 51.4 Upward: 12.6 Downward: 15.1 Within non-empl.: 17.1 Within empl.: 6.6 Mobility (%) Overall: 44.8 Upward: 12.6 Downward: 15.1 Within non-empl.: /06/20139

10 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 27/06/201310

11 (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 27/06/201311

12 (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 27/06/201312

13 (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 27/06/201313

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? 27/06/201314

15 Changes in subjective well-being, by gender and transition outcome 2010/11: proportions (%) MaleFemaleMaleFemale Not empl. Empl.F-stat. Not empl. Empl.F-stat. Not wage empl. Wage empl. F-stat. Not wage empl. Wage empl. F-stat. Change in life satisfaction * * Change in economic status * *** Difference between actual and anticipated economic status *** *** /06/201315

16 Conclusions Main findings: – There was considerable mobility (movements in and out of jobs) in SA labour markets over /11 (cf. other periods, see e.g. Banerjee et 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) 27/06/201316

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 27/06/201317

18 Thank you for your attention Mail:

19 Matching algorithm for QLFS (cf. R&D 2008) 1)Pool all cross-sections/‘waves’ and match households using identifiers 2)Drop households present in only one wave 3)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) 4)Match remaining individuals across wave t and wave t+1 on household identifier, gender, race and age t = age t+1 5)Match also individuals across wave t and wave t+1 on household identifier, gender, race and age t +1 = age t+1 6)Take matched individuals of steps 4 and 5 together to form ‘expanded match panels’ 7)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’ 27/06/201319


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