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)
Contents
•
•
•
•
•
Introduction
NIDS data description
Empirical model set-up and main results
Further probing
Concluding remarks
27/06/2013
2
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
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3
Annualised growth of (seasonally-adjusted) quarterly GDP
at constant prices (%)
8.0
6.5
6.0
6.0
4.4
4.4
5.0
4.0
4.4
4.8
3.5
3.4
3.3
3.6
3.1
2.0
3.0
3.1
2.1
1.8
2.5
1.7
1.9
0.9
1.9
1.2
0.0
-1.7
-2.0
-2.7
-4.0
-6.0
-6.3
-8.0
27/06/2013
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 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 crosssections 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?
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5
Total number of employed individuals aged 15-64
(in thousands)
14,250
14,000
13,750
14,027
Net employment loss
of +/- 1 million
13,621
13,500
13,250
Net employment gain
of +/- 650 thousand
13,000
12,975
12,750
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6
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.
27/06/2013
7
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 (Cichello et al. 2012)
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8
Transition matrix for employment status 2008-2010/11: row proportions (%)
Employment status in 2010/11
12.0
5.0
Mobility (%)
Overall: 44.8
Upward: 12.6
Downward: 15.1
Within non-empl.: 17.1
Employment status in 2008
50.6
32.4
Employed
Unemployed,
search.
Unemployed,
disc.
NEA
53.0
Employed
71.6
6.7
3.2
18.5
18.5
Unemployed,
search.
32.3
21.6
6.5
39.7
6.3
Unemployed,
disc.
28.0
18.1
10.8
43.1
22.1
NEA
22.1
15.0
6.1
56.8
Transition matrix for employment status and type 2008-2010/11: row proportions (%)
Mobility (%)
Employment status/type in 2008
39.8
6.0
5.0
32.5
Self-employment
Casual and other
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
Casual and other
employment
24.1
6.4
6.1
12.1
6.1
45.3
Unemployed,
search.
21.7
3.9
6.5
21.6
6.5
39.8
Unemployed,
disc.
18.0
3.2
6.8
18.1
10.8
43.1
NEA
14.0
3.8
4.4
15.0
6.1
56.8
9
Overall: 51.4
8.6
Upward: 12.6
Downward: 15.1
18.5
Within non-empl.: 17.1
Within empl.: 6.6
6.3
27/06/2013
Reg. wage
employment
Employment status /type in 2010/11
4.7
12.0
22.2
Model set-up
• Simple (survey-weighted) binary probit model:
Pr(y=1|X, Z) = Φ(X’β + Z’δ)
• Two kinds of probits:
1)
2)
•
•
•
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
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10
Probit estimates for employment transitions 2008-2010/11 (baseline and extra
household variables): average marginal effects
(1a)
(1b)
(2a)
(2b)
(3a)
(3b)
(4a)
(4b)
Male
Female
Male
Female
Male
Female
Male
Female
0.0414
0.0833*
0.0221
0.0356
0.0678
0.0124
0.0652*
0.1123**
0.0703
0.0558
0.1036**
0.0465
0.0723*
0.0502
0.1198*** 0.0949*
0.0630
0.0363
Omitted: age 20-25
Age 26-35
0.0751*
0.0494
Age 36-45
0.1298*** 0.0975*
Age 46-55
0.0777
0.0494
Omitted: no education
Primary education
0.0217
0.0481
Secondary education
0.1367*** 0.1620***
Tertiary education
0.1881*** 0.3032***
Omitted: Black/African
Coloured
0.1071** -0.0461
Asian/Indian
0.1467*** 0.0984
White
0.1149** 0.0548
Married
0.0639*
-0.0273
Household size
-0.0170*** -0.0123**
Rural
0.0246
-0.1367***
Household head
Omitted: No other workers in household
1 other worker
2 or more other workers
Household per capita income (log)
Observations
27/06/2013
1576
1933
0.0227
0.0521
0.0328
0.0403
-0.0110
0.1358*** 0.1618*** 0.1455*** 0.1578*** 0.0841**
0.1870*** 0.3075*** 0.1880*** 0.2943*** 0.1153**
0.0110
0.0785
0.1990***
0.1182***
0.1613***
0.1141**
0.0437
-0.0102**
0.0239
0.1024***
-0.0732
-0.0122
-0.0363
-0.0488
-0.0061
-0.1151***
-0.0443
0.1054
0.0622
0.0065
-0.0081
-0.1326***
0.0806**
0.1057***
0.1673***
0.1282***
0.0632*
-0.0105**
0.0225
-0.0425
0.0963
0.0584
-0.0210
-0.0156**
-0.1415***
0.1039**
0.1151*
0.0668
0.0488
-0.0092**
0.0429
-0.0016
-0.0356
-0.1562*** 0.0670
0.0572*** 0.0767***
1572
1918
1576
1933
1576
1933
11
Probit estimates for regular wage employment transitions 2008-2010/11 (baseline
and extra household variables): average marginal effects
(1a)
(1b)
(2a)
(2b)
(3a)
(3b)
(4a)
(4b)
Male
Female
Male
Female
Male
Female
Male
Female
0.0608
0.0989*
0.0418
0.0627
0.1423**
0.0935
0.0643
0.1054**
0.0567
0.0488
0.1245*
0.0718
0.0510
0.0816*
0.0267
Omitted: age 20-25
0.0550
Age 26-35
0.0467
0.0258
Age 36-45
0.1335*
0.0827*
0.0985
Age 46-55
0.0855
0.0414
0.0439
Omitted: no education
Primary education
-0.0976** 0.0050
-0.0940**
Secondary education
0.0084
0.1621*** 0.0093
Tertiary education
0.0228
0.2621*** 0.0272
Omitted: Black/African
Coloured
0.0352
-0.0389
0.0467
Asian/Indian
-0.0311
0.0450
-0.0202
White
-0.0367
0.0489
-0.0397
Married
0.0989** 0.0510
0.0807**
Household size
-0.0154*** -0.0106
-0.0093
Rural
-0.0471
-0.1486*** -0.0485
Household head
0.0865*
Omitted: No other regular wage workers in household
1 other regular wage worker
2 or more other regular wage workers
Household per capita income (log)
Observations
27/06/2013
1122
1199
1118
0.0147
-0.0980**
0.1588*** 0.0095
0.2634*** 0.0241
-0.0036
-0.1035**
0.1544*** -0.0156
0.2549*** -0.0199
-0.0433
0.0544
0.1246**
-0.0423
0.0399
0.0392
0.0522
-0.0082
-0.1483***
0.0247
0.0386
-0.0408
-0.0400
0.1012**
-0.0176***
-0.0487
-0.0321
0.0445
0.0436
0.0407
-0.0155**
-0.1483***
0.0401
-0.0615
-0.0741
0.0903**
-0.0085
-0.0275
-0.0694
-0.1140
-0.0647
0.0142
-0.0018
-0.1194***
-0.0067
0.0649
0.0260
0.1159***
0.0415*
0.1057***
1122
1199
1189
1122
1199
12
Probit estimates for regular wage employment transitions 2008-2010/11 (extra job variables):
average marginal effects
(1a)
(1b)
(2a)
(2b)
(3a)
(3b)
(4a)
(4b)
(5a)
(5b)
(6a)
(6b)
(7a)
(7b)
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
Male
Female
0.1609**
0.1010
……. …….
Permanent contract
……. …….
0.0157
……. …….
……. …….
0.0499
……. …….
……. …….
……. …….
……. …….
……. …….
……. …….
……. …….
……. …….
……. …….
Unspecified contract duration
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
Months in wage employment (log)
0.0381*** 0.0556***
Monthly take-home pay (log)
Observations
1096
27/06/2013
0.0812*** 0.1011***
1183
995
891
1092
1179
1110
1192
1117
1190
954
1023
1122
1199
13
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?
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14
Changes in subjective well-being, by gender and transition outcome 2010/11:
proportions (%)
Difference between
actual and anticipated
economic status
Change in economic
status
Change in life satisfaction
Male
Female
Male
Not
wage
empl.
Wage
empl.
0.5441
0.5273
0.1531
0.1330
0.3516
0.3027
0.3625
0.3298
0.2753
0.3406
0.3830
0.3622
0.7132
0.5899
0
0.1487
0.2676
+
0.1380
0.1425
Not
empl.
Empl.
-
0.5939
0.5335
0
0.1141
0.1244
+
0.2920
-
Not
wage
empl.
Wage
empl.
0.5909
0.4819
0.1440
0.1715
0.3398
0.2651
0.3467
0.3942
0.2775
0.3527
0.3229
0.3613
0.3330
0.3185
0.3469
0.3296
0.2446
0.3895
0.3288
0.3301
0.6468
0.6390
0.7801
0.5645
0.6932
0.6461
0.2102
0.2050
0.1112
0.2749
0.1770
0.2180
0.1430
0.1560
0.1087
0.1606
0.1298
0.1359
Not
empl.
Empl.
0.5300
0.4638
0.1201
0.1846
0.3421
0.3498
0.3638
0.2830
0
0.3389
0.3340
+
0.2974
-
27/06/2013
F-stat.
0.94
2.60
*
6.07
***
Female
F-stat.
2.86
*
1.71
0.11
F-stat.
0.28
4.73
***
11.17
***
F-stat.
2.63
*
0.26
0.70
15
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.
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:
•
•
•
•
•
27/06/2013
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)
16
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 nonrandom
27/06/2013
17
Thank you for your attention
Mail: [email protected]
Matching algorithm
for QLFS (cf. R&D 2008)
1)
2)
3)
4)
5)
6)
7)
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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