HLEG thematic workshop on Measurement of Well Being and Development in Africa, 12-14 November 2015, Durban, South Africa, More information at: www.oecd.org/statistics/measuring-economic-social-progress

1. Unemployment, Informality and
Inequality
Murray Leibbrandt
University of Cape Town
Measurement of Well Being and
Development in Africa
Durban International Convention Center,
South Africa, 12-14 November, 2015

2. Introduction
• Examining unemployment, informality
– prompts reflection on the link between the labour
market and broader wellbeing
– probes what we are measuring and why and what
data we need to do this
• Focus mostly on South Africa knowing that
William follows

3. Composition of household income by
income deciles, South Africa 2012

4. Decomposition of household income
inequality by income source
Income source
Absolute
contribution
Relative
contribution
Wages 0.60 90.65%
Government
grants -0.01 -1.04%
Remittances 0.06 8.53%
Investment 0.01 1.87%
Total 0.66 100.00%

5. Shares of total composition by sector,
2003 and 2012

6. Earnings Inequality and wage share by industry
Industry Gini
Share
of
earners
Wage
share
% Poor
Workers Per
sector
Agriculture 0.506 5.76% 2.25% 9.59%
Mining 0.472 3.12% 3.70% 1.33%
Manufacturing 0.625 13.02% 13.60% 11.93%
Utilities 0.582 0.91% 1.37% 0.53%
Construction 0.608 7.08% 5.45% 8.31%
Trade 0.623 18.24% 15.88% 20.43%
Transport 0.580 6.48% 6.25% 5.73%
Finance 0.622 14.62% 18.07% 12.88%
Services 0.599 24.05% 31.72% 17.36%
Domestic
services 0.412 6.73% 1.72% 11.91%

7. Measuring and valuing informal
enterprises.

8. Earnings volatility and inequality
• Inequality is the distribution of income over a
group of people, generally at a single point in
time.
• Earnings volatility is the variation in income
for an individual person, over time.
• Nonetheless, dynamic measures of inequality
are related to earnings volatility.

9. Unemployment Transitions
Employment Status in
Wave 2
Employment Status in Wave 3
0. Not
Economically
Active
1. Unemployed
Discouraged
2. Unemployed
Strict
3. Employed
0. Not Economically Active 65.6 3.9 14.5 16.0
1. Unemployed Discouraged 43.1 6.2 21.8 29.0
2. Unemployed Strict 29.9 5.7 28.4 36.0
3. Employed 16.2 1.8 10.0 72.0
Employment Status in
Wave 1
Employment Status in Wave 2
0. Not
Economically
Active
1. Unemployed
Discouraged
2. Unemployed
Strict
3. Employed
0. Not Economically Active 76.1 4.8 7.7 11.4
1. Unemployed Discouraged 51.9 9.4 15.5 23.3
2. Unemployed Strict 41.4 7.7 21.0 29.9
3. Employed 29.7 4.0 6.5 59.8

10. 61.0
15.4
56.6
10.6
83.2
22.8
19.5
16.7
15.2
10.3
7.4
5.5
8.0
13.4
9.8
9.5
5.4
19.2
11.5
54.4
18.4
69.5
4.1
52.4
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Youth
unempl.
(73.7%)
Youth
empl.
(26.3%)
prime age,
unempl.
(37%)
prime age,
empl. (63%)
elder
unempl
(53.1%)
elder empl.
(46.9%)
Transition probabilities by age group over W2 & W3
status: By W1 status
EE
EU
UE
UU

11. Youth as a share of the total
population

12. Conclusions
• What measurement for what?
• Data quality matters. Can easily undervalue activities
that are important to most vulnerable at the bottom end
of the distribution because:
• They are hard to measure (informal sector activities and subsistence
agriculture)
• Their monetary value is low but this does not mean their importance
to this group is low
• Static profiles of the labour market, poverty and
inequality do not speak for themselves. We need an
understanding of dynamic trajectories or the lack
thereof.
• Panel data are key to unpacking this situation.
• In Africa there is a case for giving special attention to
youth transitions. Again panel data are necessary.