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

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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

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

6.5 3.1 5.0 6.0 3.0 4.4 1.8

• 1.7
• 6.3
• 2.7

1.7 3.5 4.4 3.1 3.6 4.4 4.8 1.9 1.9 3.3 2.5 3.4 1.2 2.1 0.9

• 8.0
• 6.0
• 4.0
• 2.0

0.0 2.0 4.0 6.0 8.0

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

• ver 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,

• r not, in SA during the height and aftermath of the global

crisis?

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Evolution of narrow and broad unemployment rates (QLFS), annual averages 2008-2012(%)

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 2008 2009 2010 2011 2012

Narrow and broad unemployment, overall

Narrow Broad 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 2008 2009 2010 2011 2012

Broad unemployment, by gender

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

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

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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 unobservables – False matches

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Transition matrices: NIDS

21/09/2013 9 Labour market status in 2010/11 Labourmarket status in 2008 39.8 6.0 4.7 12.0 5.0 32.5

• Reg. wage

employment Self- employment Casual and

• ther

employment Unemployed, search. Unemployed, disc. NEA

37.1

• Reg. wage

employment

76.4 3.2 3.2 5.3 2.7 9.3

7.4

Self- employment

16.6 34.0 5.3 7.8 2.6 33.8

8.6

Casual and

• ther employ.

24.1 6.4 6.1 12.1 6.1 45.3

18.5

Unemployed, search.

21.7 3.9 6.5 21.6 6.5 39.8

6.3

Unemployed, disc.

18.0 3.2 6.8 18.1 10.8 43.1

22.2

NEA

14.0 3.8 4.4 15.0 6.1 56.8

Transition matrix for labour market status, 2008-2010/11: row proportions (%)

Overall mobility, Mtotal = Mupward + Mdownward + Mwithin non-employment + Mwithin employment 51.4% = 12.6% + 15.1% + 17.1% + 6.6%

Transition matrices: QLFS

21/09/2013 10 Labour market status in quarter t+1 Labourmarket status in quarter t

Formal sector employment Informal sector employment Unemployed, search. Unemployed, disc. NEA

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

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 employment

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

Unemployed, search.

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

Unemployed, disc.

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

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, Mtotal = Mupward + Mdownward + Mwithin non-employment + Mwithin 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% 2012: 18.2% = 3.3% + 3.0% + 9.6% + 2.4%

Transition matrices for labour market status, 2008Q1-2012Q4: row proportions (%)

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

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(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

NIDS probit estimates for regular wage employment transitions, 2008-2010/11 (baseline variables): average marginal effects

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(2a) (2b) (3a) (3b) (4a) (4b) (5a) (5b) (6a) (6b) Male Female Male Female Male Female Male Female Male Female Baseline regressors (not shown) …… …… …… …… …… …… …… …… …… Omitted: elementary occupation Semi-skilled

• 0.0311

0.1014** Manag./professional -0.0495 0.1081** Omitted: agriculture, hunting, forestry, fishing Mining and quarrying

• 0.0899

0.1725*** Manufacturing

• 0.0285
• 0.0869

Utilities 0.1200*** 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 member 0.0548 0.0981*** Written contract 0.0710* 0.0341 Omitted: limited contract duration Unspecified contract duration 0.0499 0.0157 Permanent contract 0.1609** 0.1010 Province dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,096 1,183 995 891 1,092 1,179 1,110 1,192 1,117 1,190

NIDS probit estimates for regular wage employment transitions, 2008-2010/11 (extra job variables): average marginal effects

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(1a) (1b) Male Female 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 Omitted: age 20-25 Age 26-35 0.0153* 0.0174* 0.0305*** 0.0291*** 0.0361*** 0.0563*** 0.0513*** 0.0196* 0.0490*** 0.0292** Age 36-45 0.0329*** 0.0273*** 0.0577*** 0.0393*** 0.0499*** 0.0807*** 0.0612*** 0.0474*** 0.0498*** 0.0492*** Age 46-55 0.0391*** 0.0399*** 0.0572*** 0.0518*** 0.0506*** 0.0901*** 0.0941*** 0.0688*** 0.0527*** 0.0591*** Omitted: no education Primary education 0.0282** 0.0013 0.0237* 0.0084 0.0069 0.0521** 0.0503** 0.0056

• 0.0077
• 0.0190

Secondary education 0.0741*** 0.0454*** 0.0631*** 0.0398** 0.0483*** 0.1285*** 0.0999*** 0.0533*** 0.0406** 0.0379** Tertiary education 0.1036*** 0.0813*** 0.0891*** 0.0797*** 0.0788*** 0.1770*** 0.1483*** 0.0999*** 0.0859*** 0.0723*** Other education

• 0.0224

0.0526* 0.0537** 0.0106 0.0682*** 0.1837*** 0.1508*** -0.0491 0.0705*

• 0.0726

Omitted: Black/African Coloured 0.0098 0.0152 0.0356*** 0.0034 0.0209** 0.0437*** 0.0272** 0.0281*** 0.0110

• 0.0032

Asian/Indian 0.0068 0.0368*** 0.0034 0.0098 0.0356*** 0.0184 0.0312* 0.0276* 0.0170 0.0131 White 0.0187* 0.0369*** 0.0442*** 0.0243*** 0.0468*** 0.0085 0.0080 0.0293*** 0.0071

• 0.0100

Married 0.0484*** 0.0356*** 0.0402*** 0.0334*** 0.0351*** 0.003

• 0.0006

0.0046 0.0103 0.0013 Household size

• 0.0065*** -0.0048*** -0.0069*** -0.0082*** -0.0038*** -0.0089*** -0.0032**
• 0.0047*** -0.0049*** -0.0051***

Rural

• 0.0100

0.0033

• 0.0103
• 0.0144*
• 0.0232*** -0.0111
• 0.0213**
• 0.0139
• 0.0174*
• 0.0263***

Observations 12,063 12,441 12,438 11,561 12,564 9,100 9,789 9,779 9,358 10,079

QLFS probit estimates for formal sector employment transitions, 2008Q1-2102Q4 (baseline variables): average marginal effects

(2a) (2b) Male Female 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012 Omitted: age 20-25 Age 26-35 0.0115 0.0158 0.0278*** 0.0296*** 0.0350*** 0.0516*** 0.0500*** 0.0173 0.0480*** 0.0275** Age 36-45 0.0250** 0.0231** 0.0538*** 0.0392*** 0.0471*** 0.0714*** 0.0582*** 0.0443*** 0.0473*** 0.0457*** Age 46-55 0.0308*** 0.0342*** 0.0519*** 0.0498*** 0.0460*** 0.0766*** 0.0897*** 0.0644*** 0.0491*** 0.0528*** Omitted: no education Primary education 0.0238* 0.0015 0.0218* 0.0104 0.0100 0.0314 0.0189

• 0.0075
• 0.0144
• 0.0303*

Secondary education 0.0617*** 0.0398*** 0.0556*** 0.0352** 0.0445*** 0.0998*** 0.0610*** 0.0372** 0.0305 0.0210 Tertiary education 0.0874*** 0.0730*** 0.0779*** 0.0725*** 0.0718*** 0.1433*** 0.1111*** 0.0839*** 0.0758*** 0.0525*** Other education

• 0.0242

0.0477* 0.0547*** 0.0056 0.0666*** 0.1546*** 0.1139*** -0.0667 0.0611

• 0.0862

Omitted: Black/African Coloured 0.0088 0.0132 0.0328*** 0.0008 0.0169* 0.0447*** 0.0278*** 0.0257** 0.0126

• 0.0010

Asian/Indian 0.0057 0.0361*** 0.0023 0.0107 0.0357*** 0.0212 0.0278 0.0263 0.0189 0.0148 White 0.0222** 0.0370*** 0.0444*** 0.0265*** 0.0477*** 0.0120 0.0075 0.0310*** 0.0097

• 0.0078

Married 0.0438*** 0.0334*** 0.0375*** 0.0304*** 0.0341*** 0.0010

• 0.0003

0.0045 0.0091 0.0006 Household size

• 0.0063*** -0.0047*** -0.0068*** -0.0081*** -0.0035*** -0.0090*** -0.0032**
• 0.0046*** -0.0050*** -0.0055***

Rural

• 0.0141
• 0.0024
• 0.0134*
• 0.0217*** -0.0319*** -0.006
• 0.0088
• 0.0091
• 0.0139
• 0.0226**

Omitted: agriculture, hunting, forestry, fishing Mining and quarrying 0.0509*** 0.0254** 0.0364** 0.0169 0.0098 0.0966*** 0.1333*** 0.0833*** 0.0846*** Manufacturing 0.0129

• 0.0070

0.0040

• 0.0038
• 0.0153

0.0476** 0.0716*** 0.0525*** 0.0220 0.0338 Utilities 0.0053 0.0065 0.0015 0.0175

• 0.0108
• 0.0861

0.0546 0.0629**

• 0.0092

0.0960*** Construction

• 0.0750*** -0.0658*** -0.0757*** -0.0639*** -0.0826*** 0.0005
• 0.0190
• 0.0392

0.0148 0.0204 Wholesale/retail trade

• 0.0197
• 0.0250*
• 0.0122
• 0.0285*** -0.0336*** 0.0155

0.0562*** 0.0182 0.0240 0.0270 Transport et al.

• 0.0314*
• 0.0320**
• 0.0374**
• 0.0523*** -0.0518*** 0.0351

0.0771*** 0.0302 0.0314 0.0605**

• Fin. intermed. et al.
• 0.0214
• 0.0175
• 0.0066
• 0.0082
• 0.0182

0.0347* 0.0795*** 0.0200 0.0237 0.0280

• Comm. et al. services

0.0267** 0.0035 0.0092 0.0056

• 0.0030

0.0577*** 0.0607*** 0.0260 0.0306* 0.0458** Observations 12,062 12,436 12,436 11,557 12,560 9,097 9,786 9,774 9,260 10,078

QLFS probit estimates for formal sector employment transitions, 2008Q1-2102Q4 (extra job variables): average marginal effects

Main findings

• Considerable mobility in SA labour markets over 2008-2012 (cf.
• ther periods: Banerjee et al. 2008; Ranchod & Dinkelman 2008)
• NIDS and QLFS suggest that likelihood of continued employment

differs significantly between particular types of workers in SA:

– Lower for younger workers, workers with less than secondary education and males employed in construction and trade – Higher for trade union members and those with written and/or permanent contracts

• Evolution of transitions between labour market states over

2008Q1-2012Q4:

– Overall mobility gradually decreased; rise in unemployment rates mostly due to reduced inflows into employment (cf. Verick 2012) – Time variation in economic significance of some demographic and job-specific exploratory variables; e.g., diminishing strength of buffering effect of higher education – Not straightforward to distinguish ‘trends’ that can be connected to the broader evolution of SA’s economy over the course of the crisis

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Avenues for further research

• A study of other labour market transitions:

e.g., factors that help or hinder the unemployed in SA in finding a job during the difficult economic climate of 2008-2012 (cf. Posel et al. 2012 for NIDS)

• Use of more detailed information on
• ccupations/job tasks and specific subsectors
• f employment
• Extension of the NIDS panel with a third wave

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Thank you for your attention

Mail: dennis.essers@uantwerpen.be

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

Matching algorithm for QLFS (cf. Ranchod & Dinkelman 2008)

1) Append all QLFS cross-sections (quarters), sort on household identifier and quarter, and drop households that appear only once 2) For each quarter and within the same household, drop individuals that have same race, gender and ages differing by at most 1 year 3) Match remaining individuals across quarter t and quarter t+1 using household identifier, gender, race and aget = aget+1 4) Match also individuals across quarter t and quarter t+1 using household identifier, gender, race and aget +1 = aget+1 5) Keep only the individuals matched in Steps 3 and 4 to form ‘expanded match’ panel 6) Impose extra consistency requirements on ‘expanded match’ panel to form ‘strict match’ panels, by dropping:

– Individuals whose level of education differs between quarter t and quarter t+1 – Individuals whose marital status changes from ‘married’/‘divorced’/‘widowed’ in quarter t to ‘never married’ in quarter t+1

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