inequality, unemployment, and poverty in south africa - tips

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inequality, unemployment, and poverty in south africa - tips

INEQUALITY,

UNEMPLOYMENT, AND

POVERTY IN SOUTH AFRICA


Overview of the project

• Theoretical relationship between labour markets and inequality

• Insights from the literature

• Inequality in SA

• Unemployment in SA

• Relationship between employment structure and inequality

• How much might a minimum wage reduce inequality?

• How much could expanded low-wage wage employment reduce

inequality?

• Growth, inequality, and poverty reduction.


‘Pen’s parade’ of income distribution

30

Incom me ('000)

60 90

120

150

0 .2 .4 .6 .8 1

Population share


‘Pen’s parade’ of income distribution

ib ti

• Bottom 95% • Top 5%

1 2 3

Incom me ('000)

4 5 6 7

30 60

Incom me ('000)

90 1 20 150

0 .2 .4 .6 .8 1

Population share

.95 .96 .97 .98 .99 1

Population share


Earnings Inequality (Gini), 2001-7

.63

.64

G

ini

.65

.66

.67

2001 2002 2003 2004 2005 2006 2007


Growth incidence curve of earnings (2001-2007)

2007)

-2

0

Growth in

earnings

(%)

2

4

6

20 40 60 80 100

Percentiles


Halving poverty by 2014

• What are the growth and distributional implications of meeting

the AsgiSA target of halving poverty by 2014?

• Framing the AsgiSA target



poverty line at R450 (March 2006 prices)

poverty headcount ratio and poverty gap

Halving poverty by 2014 means cutting poverty headcount

ratio to +25% and reducing poverty gap to +R30 billion.


Can we halve poverty through growth?

• 3 growth scenarios:




AsgiSA growth targets t 5.43%

Treasury forecasts 4.36%

Private banks’ forecasts 3.69%


Poverty in 2014 under current distribution

Cumulat ive sum of poverty

gaps per capita (R R)

20 40

60 80 100 12 0

0 .2 .4 .6 .8 1

Cumulative population share

Expenditure

Expenditure with AsgiSA targeted growth


Poverty in 2014 under 3 growth scenarios

Poverty headcount

ratio (%)

Poverty gap

(R billion)

2006 actual 52 60

Target: halving poverty 26

26 30

Growth scenarios:

AsgiSA 34 32

Treasury 38

38 37

Banks 40 40


Simulated distributional ib ti change

• Simulate a range of mean-preserving equalising distributional

changes.

• Around median, 66 th and 75 percentiles.

• Poorest person R50/R100/R200/R300 per month better off.

• Not transfers, but outcomes of more pro-poor poor growth path.

• Look at poverty outcomes under sixty growth/distributional

scenarios.


2 growth/distributional scenarios in which poverty gap

halved but poverty headcount ratio not halved

of povert ty gaps pe er capita (

R)

ative sum

Cumula

80 1 00 120

20 40

60

0 .2 .4 .6 .8 1

Cumulative population share

Expenditure with high growth, minimal redistribution

Expenditure with low growth, medium-high redistribution


A growth/distributional scenario in which poverty is halved

Cumulat ive sum of poverty

gaps per capita (R R)

20

40

60 80 10 00 120

0 .2 .4 .6 .8 1

Cumulative population share

Expenditure with medium growth, medium-high redistribution


Poverty outcomes under some

growth/distributional scenarios

Distribution

Growth

R300 R200 R100 R50 None

7% H, G H, G H, G - ,G - ,G

6% H, G H, G - ,G - ,G - ,G

5% H, ,G H, ,G - ,G - ,G -, -

4% H, G H ,G - ,G -, - -, -

3% H, G -,G -,G -, - -, -

H = poverty headcount ratio halved; G = poverty gap halved.


Conclusions on meeting AsgiSA poverty targets

• Poverty CAN be halved by 2014.

• But not by growth alone.

• We need a pro-poor poor shift in the growth path.

• Any worsening of inequality will put the AsgiSA poverty

targets even further out of reach.

• Avoid temptation to set poverty line too low.


Inequality & unemployment: International comparison

80

SA

(SSA)

70

60

SA

Gini co oefficient

50

40

30

20

10

0

Consumption/Expenditure Gross income

Disposable income

Gross earnings

0 5 10 15 20 25 30 35 40

Unemployment (%)


Unemployment & labour force earnings inequality, 2001-7

.78

2002-2

La bour For rce Inequ uality (Gin ni)

.76

.74

.72

2007-2

2

2001-1

2005-2

2006-2 2004-2

2005-1

2006-1

2007-1

2004-1

2003-2

2002-1

2001-2

2003-1

.7

22 24 26 28 30 32

Unemployment rate (official)


A very close relationship between unemployment

and earnings inequality over time

24

.63

26

Unemplo oyment (% %)

28

.64

.65

Ineq quality

.66

30

.67

2001 2002 2003 2004 2005 2006 2007

Unemployment rate (official)

i Earnings Inequality (Gini) i)


Unemployment (%)

22 24

.54

.56

26

Inequal

28 30

.58

ity

.6

32

Unemployment (%)

36

38

5.2 5.4 5.6

Inequal

ity

40

5.8

42

6

2001 2002 2003 2004 2005 2006 2007

Unemployment (official) Labour Force Inequality (RMD)

2001 2002 2003 2004 2005 2006 2007

Unemployment (expanded) Labour Force Inequality (MLD)

Unemployment (%

)

24

.75

26

.8

28

.85

Inequality

30

.9 .955

Unemployment (%

)

24

1.6

26

28

1.8

2

Inequality

30

2.22

2001 2002 2003 2004 2005 2006 2007

Unemployment rate (official) Earnings Inequality (Theil)

2001 2002 2003 2004 2005 2006 2007

Unemployment rate (official) Earnings Inequality (MLD)


Relationship between earnings inequality & unemployment

• Possible explanations for this close relationship:


Direct causality from unemployment rate to earnings

inequality, through effects of unemployment on the

composition of the employed.



Indirect causality from unemployment to earnings inequality,

through ‘reserve army’ type effects.

Common underlying factors, relating to distributional character

of the growth path.

• Suggests no strong trade-off between reducing unemployment

and reducing inequality.


How much do earnings contribute to overall inequality?

• Households receiving no income from work are mostly female-

headed, overwhelmingly African, and much worse off than

households receiving any income from work.

• 74% of all income comes from work.

• Income from work contributes 79% to total income inequality.


How does labour market structure explain earnings

inequality?

• Decompose labour force and working age adult earnings

inequality by employment status

Rate of unemployment and wage dispersion amongst the

employed both contribute significantly.

• Unemployed/informally y employed/formally y employed

Rate of unemployment, wage dispersion among each of

the informal and informal sectors, and wage gap between

formal and informal sectors all contribute significantly to

inequality.


How do changes in labour market structure changes in

explain earnings inequality?

• Dynamic decomposition of labour force and working age adult

earnings inequality by employment status

Changes in unemployment rate explain most of initial increase

and later fall in inequality, changes in wage dispersion explain

some.

• Unemployed/informally employed/formally employed

Changes in rate of unemployment & in formal/informal

proportions of employment explain most of changes in

inequality;

Changes in wage dispersion among each of the informal and

informal sectors contribute less to changes in inequality.


Conclusions (i)

• Unemployment explains a lot of earnings inequality amongst the

labour force and amongst working-age adults.

• Also a close relationship between unemployment and earnings

inequality amongst the employed.

• Suggests no strong trade-off between addressing unemployment and

inequality.

• Rather, reducing unemployment is central to reducing inequality.

• Earnings dispersion amongst employed also contributes to inequality.

• Gap between formal and informal sector earnings raises inequality.


Conclusions (ii)

• Generating low-wage wage jobs on a mass scale would reduce inequality,

but not dramatically relative to scale.

• Minimum wage would generally reduce inequality, but net effect

depends on any associated job losses.

• Emphasise mass creation of decent jobs.

• Continuation of inappropriate growth path unlikely to address either

unemployment or inequality.

• Aggressive policies needed to deal with legacy of mass

unemployment of young people who have seldom or never worked.

• Scale of unemployment goes far beyond ‘labour market’ issue.


Conclusions (iii)

• By international standards, poverty in SA associated more with

distribution ib ti than with total t resources.

• AsgiSA target of halving poverty is achievable…

• But not realistically with growth alone.

• Need a pro-poor poor shift in growth path.

• Considerable scope for progressive distributional change.

• But unlikely l to happen endogenously.

• Internationally, ‘downward stickiness’ of inequality.

• Reduction of inequality as explicit policy objective.


Additional slides for reference


Effects of a R1000 minimum wage under 5 scenarios

# raised to

Gini

%

min. wage # indirectly affected

wage

(‘000) (‘000) employed

labour force

bill

1 3 885 0.567 0.666 4.5

2 2 660 0.600 0.692 2.2

3 1 640 0.604 0.695 19 1.9

4 773 867 lose jobs,

5 773 867 lose jobs,

616 benefit from ripple

0.626 0.712 0.2

0.625 0.711 0.3


Expanded low-wage wage employment scenarios

Gini

% total

earnings

Benchmark: current levels 071 0.71 -

Employing ⅓ unemployed at median informal wage 0.69 2.0

Employing ⅓ unemployed at average informal wage 0.68 3.7

Employing ½ unemployed at median informal wage 0.68 3.1

Employing ½ unemployed at average informal wage 0.66 5.6

Employing ⅔ unemployed at median informal wage 0.67 4.1

Employing ⅔ unemployed at average informal wage 0.64 7.5


Inequality with expanded low-wage wage employment

0

.2

Cumulati

.4

ve income

share

.6

.8

1

0 .2 .4 .6 .8 1

Cumulative population share

Earnings

Earnings with expanded low-wage employment

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