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INEQUALITY, INEQUALITY, UNEMPLOYMENT, AND UNEMPLOYMENT, AND
POVERTY IN SOUTH AFRICAPOVERTY IN SOUTH AFRICA
Overview of the projectOverview of the projectp jp j
Theoretical relationship between labour markets and inequalityTheoretical relationship between labour markets and inequalityTheoretical relationship between labour markets and inequalityTheoretical relationship between labour markets and inequality
Insights from the literatureInsights from the literature
Inequality in SAInequality in SA
Unemployment in SAUnemployment in SA
Relationship between employment structure and inequalityRelationship between employment structure and inequalityRelationship between employment structure and inequality Relationship between employment structure and inequality
How much might a minimum wage reduce inequality?How much might a minimum wage reduce inequality?
How much could expanded lowHow much could expanded low--wage employment reduce wage employment reduce
inequality?inequality?inequality?inequality?
Growth, inequality, and poverty reductionGrowth, inequality, and poverty reduction..q y p yq y p y
‘Pen’s parade’ of income distribution‘Pen’s parade’ of income distributionPen s parade of income distributionPen s parade of income distribution
150
120
90m
e ('0
00)
60Inco
m30
0 .2 .4 .6 .8 1Population share
‘P ’ d ’ f i di t ib ti‘P ’ d ’ f i di t ib ti‘Pen’s parade’ of income distribution‘Pen’s parade’ of income distribution
Bottom 95%Bottom 95% Top 5%Top 5%
67
2015
0
45
me
('000
)
901
me
('000
)
12
3In
com
3060In
com
1
0 .2 .4 .6 .8 1Population share
3
.95 .96 .97 .98 .99 1Population share
Earnings Inequality (Gini), 2001Earnings Inequality (Gini), 2001--77Earnings Inequality (Gini), 2001Earnings Inequality (Gini), 2001 77
7.6
7.6
6.6
5in
i4
G.6
4.6
3
2001 2002 2003 2004 2005 2006 2007
Growth incidence curve of earnings (2001Growth incidence curve of earnings (2001--2007)2007)Growth incidence curve of earnings (2001Growth incidence curve of earnings (2001 2007)2007)
64
s (%
)
2n ea
rnin
gs
0
Gro
wth
in
0-2
20 40 60 80 100Percentiles
Halving poverty by 2014Halving poverty by 2014Halving poverty by 2014Halving poverty by 2014
What are the growth and distributional implications of meetingWhat are the growth and distributional implications of meetingWhat are the growth and distributional implications of meeting What are the growth and distributional implications of meeting the AsgiSA target of halving poverty by 2014?the AsgiSA target of halving poverty by 2014?
Framing the AsgiSA targetFraming the AsgiSA targetg g gg g g
poverty line at R450 (March 2006 prices)poverty line at R450 (March 2006 prices)
poverty headcount ratio and poverty gappoverty headcount ratio and poverty gap
Halving poverty by 2014 means cutting poverty headcountHalving poverty by 2014 means cutting poverty headcountHalving poverty by 2014 means cutting poverty headcount Halving poverty by 2014 means cutting poverty headcount ratio to ratio to ++25% and reducing poverty gap to 25% and reducing poverty gap to ++R30 billion.R30 billion.
Can we halve poverty through growth?Can we halve poverty through growth?Can we halve poverty through growth?Can we halve poverty through growth?
3 growth scenarios:3 growth scenarios:
A iSA th t tA iSA th t t 5 43%5 43%AsgiSA growth targetsAsgiSA growth targets 5.43%5.43%
Treasury forecastsTreasury forecasts 4.36%4.36%
Private banks’ forecastsPrivate banks’ forecasts 3.69%3.69%
Poverty in 2014 under current distributionPoverty in 2014 under current distributionyy
0
R)
100
12
capi
ta (R
80
gaps
per
60
of p
over
ty
2040
ive
sum
o2
Cum
ulat
0 .2 .4 .6 .8 1
Cumulative population share
Expenditure Expenditure with AsgiSA targeted growthExpenditure Expenditure with AsgiSA targeted growth
Poverty in 2014 under 3 growth scenariosPoverty in 2014 under 3 growth scenariosPoverty in 2014 under 3 growth scenariosPoverty in 2014 under 3 growth scenarios
Poverty headcount Poverty headcount ratio ratio (%)(%)
Poverty gap Poverty gap (R billion)(R billion)
2006 actual2006 actual 5252 6060
Target: halving povertyTarget: halving poverty 2626 3030Target: halving povertyTarget: halving poverty 2626 3030
Growth scenarios:Growth scenarios:
AsgiSAAsgiSA 3434 3232
TreasuryTreasury 3838 3737TreasuryTreasury 3838 3737
BanksBanks 4040 4040
Si l t d di t ib ti l hSi l t d di t ib ti l hSimulated distributional changeSimulated distributional change
Simulate a range of meanSimulate a range of mean preserving equalising distributionalpreserving equalising distributionalSimulate a range of meanSimulate a range of mean--preserving equalising distributional preserving equalising distributional changes.changes.
ththAround median, 66Around median, 66thth and 75 percentiles.and 75 percentiles.
Poorest person R50/R100/R200/R300 per month better off.Poorest person R50/R100/R200/R300 per month better off.p pp p
Not transfers, but outcomes of more proNot transfers, but outcomes of more pro--poor growth path.poor growth path.
Look at poverty outcomes under sixty growth/distributional Look at poverty outcomes under sixty growth/distributional scenarios. scenarios.
2 growth/distributional scenarios in which poverty gap 2 growth/distributional scenarios in which poverty gap halved but poverty headcount ratio not halvedhalved but poverty headcount ratio not halvedhalved but poverty headcount ratio not halvedhalved but poverty headcount ratio not halved
R)
0012
0
er c
apita
(80
1
ty g
aps
pe0
60
of p
over
t20
40
ativ
e su
mC
umul
a
0 .2 .4 .6 .8 1
Cumulative population share
Expenditure with high growth, minimal redistributionExpenditure with low growth medium high redistributionExpenditure with low growth, medium-high redistribution
A growth/distributional scenario in which poverty is halvedA growth/distributional scenario in which poverty is halvedg p yg p y
R)
0012
0
capi
ta (R
8010
gaps
per
60
of p
over
ty
040
ive
sum
o20
Cum
ulat
0 .2 .4 .6 .8 1
Cumulative population share
Expenditure with medium growth medium high redistributionExpenditure with medium growth, medium-high redistribution
Poverty outcomes under some Poverty outcomes under some growth/distributional scenariosgrowth/distributional scenarios
Distribution
R300 R200 R100 R50 NoneGrowthGrowth
7% H, G H, G H, G - ,G - ,G6% H, G H, G - ,G - ,G - ,G5% H, G H, G - ,G - ,G -, -5% , G , G ,G ,G ,4% H, G H ,G - ,G -, - -, -3% H G G G3% H, G - ,G - ,G -, - -, -
H = poverty headcount ratio halved; G = poverty gap halved.H = poverty headcount ratio halved; G = poverty gap halved.
Conclusions on meeting AsgiSA poverty targetsConclusions on meeting AsgiSA poverty targetsg g p y gg g p y g
Poverty CAN be halved by 2014.Poverty CAN be halved by 2014.
But not by growth alone.But not by growth alone.
We need a proWe need a pro--poor shift in the growth path.poor shift in the growth path.
A i f i lit ill t th A iSA tA i f i lit ill t th A iSA tAny worsening of inequality will put the AsgiSA poverty Any worsening of inequality will put the AsgiSA poverty targets even further out of reach.targets even further out of reach.
Avoid temptation to set poverty line too low.Avoid temptation to set poverty line too low.
Inequality & unemployment: International comparisonInequality & unemployment: International comparisonq y p y pq y p y p
70
80SA
(SSA)
60SA
40
50
oeffi
cien
t
30Gin
i co
10
20
00 5 10 15 20 25 30 35 40
Consumption/Expenditure Gross incomeDisposable income Gross earnings
Unemployment (%)
Unemployment & labour force earnings inequality, 2001Unemployment & labour force earnings inequality, 2001--77p y g q y,p y g q y,
8
2002-22002-2.78
ni)
2001-12001-1
2003-22003-22002-12002-1
2003-12003-1.76
ualit
y (G
in
2006-22006-2 2004-22004-22005-12005-1
2005-22005-2
2004-12004-1
2001-22001-2
.74
rce
Ineq
u
2007 22007 2
2007-12007-1
2006-12006-1
.72
bour
For
2007-22007-2
7
La.7
22 24 26 28 30 32
Unemployment rate (official)p y ( )
A very close relationship between unemployment A very close relationship between unemployment and earnings inequality over timeand earnings inequality over time
.67
.66
30
%)
.65
qual
ity28
oym
ent (
%
.64
Ineq
26
Une
mpl
o
.63
224
2001 2002 2003 2004 2005 2006 2007
U l t t ( ffi i l) E i I lit (Gi i)Unemployment rate (official) Earnings Inequality (Gini)
.632 6
42
.58
ity2830
ent (
%) 5.
8ity
40
ent (
%)
.56
Ineq
ual
26
Une
mpl
oym
e
5.4
5.6
Ineq
ual
38Une
mpl
oym
e
.54
2224
2001 2002 2003 2004 2005 2006 2007
5.2
5
36
2001 2002 2003 2004 2005 2006 2007
Unemployment (official) Labour Force Inequality (RMD)
5
Unemployment (expanded) Labour Force Inequality (MLD)
2
.9.9
5
30
)
2.2
30
)
.85
Ineq
ualit
y
28
empl
oym
ent (
%
82
Ineq
ualit
y
28
empl
oym
ent (
%
.826
Un e 1.8
26Un e
.75
24
2001 2002 2003 2004 2005 2006 2007
Unemployment rate (official) Earnings Inequality (Theil)
1.6
24
2001 2002 2003 2004 2005 2006 2007
Unemployment rate (official) Earnings Inequality (MLD)
Relationship between earnings inequality & unemploymentRelationship between earnings inequality & unemploymentp g q y p yp g q y p y
Possible explanations for this close relationship:Possible explanations for this close relationship:Possible explanations for this close relationship:Possible explanations for this close relationship:Direct causality from unemployment rate to earnings Direct causality from unemployment rate to earnings inequality through effects of unemployment on theinequality through effects of unemployment on theinequality, through effects of unemployment on the inequality, through effects of unemployment on the composition of the employed.composition of the employed.
Indirect causality from unemployment to earnings inequality, Indirect causality from unemployment to earnings inequality, through ‘reserve army’ type effects.through ‘reserve army’ type effects.
Common underlying factors, relating to distributional character Common underlying factors, relating to distributional character of the growth path.of the growth path.
Suggests no strong tradeSuggests no strong trade--off between reducing unemployment off between reducing unemployment and reducing inequality.and reducing inequality.
How much do earnings contribute to overall inequality?How much do earnings contribute to overall inequality?How much do earnings contribute to overall inequality?How much do earnings contribute to overall inequality?
Households receiving no income from work are mostly femaleHouseholds receiving no income from work are mostly female--headed, overwhelmingly African, and much worse off than headed, overwhelmingly African, and much worse off than households receiving any income from work.households receiving any income from work.
74% of all income comes from work.74% of all income comes from work.
Income from work contributes 79% to total income inequality.Income from work contributes 79% to total income inequality.
How does labour market structure explain earnings How does labour market structure explain earnings inequality?inequality?
Decompose labour force and working age adult earnings Decompose labour force and working age adult earnings inequality by employment status inequality by employment status
Rate of unemployment and wage dispersion amongst the Rate of unemployment and wage dispersion amongst the employed both contribute significantly.employed both contribute significantly.
Unemployed/informally employed/formally employedUnemployed/informally employed/formally employedp y y p y y p yp y y p y y p yRate of unemployment, wage dispersion among each of Rate of unemployment, wage dispersion among each of the informal and informal sectors, and wage gap betweenthe informal and informal sectors, and wage gap betweenthe informal and informal sectors, and wage gap between the informal and informal sectors, and wage gap between formal and informal sectors all contribute significantly to formal and informal sectors all contribute significantly to inequality.inequality.q yq y
How do How do changes inchanges in labour market structure labour market structure changes inchanges inexplain earnings inequality?explain earnings inequality?
Dynamic decomposition of labour force and working age adult Dynamic decomposition of labour force and working age adult earnings inequality by employment status earnings inequality by employment status g q y y p yg q y y p y
Changes in unemployment rate explain most of initial increase Changes in unemployment rate explain most of initial increase and later fall in inequality, changes in wage dispersion explain and later fall in inequality, changes in wage dispersion explain some.some.
Unemployed/informally employed/formally employedUnemployed/informally employed/formally employedChanges in rate of unemployment & in formal/informal Changes in rate of unemployment & in formal/informal g p yg p yproportions of employment explain most of changes in proportions of employment explain most of changes in inequality; inequality; Changes in wage dispersion among each of the informal and Changes in wage dispersion among each of the informal and informal sectors contribute less to changes in inequality.informal sectors contribute less to changes in inequality.
Conclusions (i)Conclusions (i)Conclusions (i)Conclusions (i)Unemployment explains a lot of earnings inequality amongst the Unemployment explains a lot of earnings inequality amongst the l b f d t kil b f d t ki d ltd ltlabour force and amongst workinglabour force and amongst working--age adults.age adults.
Also a close relationship between unemployment and earningsAlso a close relationship between unemployment and earningsAlso a close relationship between unemployment and earnings Also a close relationship between unemployment and earnings inequality amongst the employed.inequality amongst the employed.
S dS d ff b dd i l dff b dd i l dSuggests no strong tradeSuggests no strong trade--off between addressing unemployment and off between addressing unemployment and inequality.inequality.
Rather, reducing unemployment is central to reducing inequality.Rather, reducing unemployment is central to reducing inequality.
Earnings dispersion amongst employed also contributes to inequality.Earnings dispersion amongst employed also contributes to inequality.
Gap between formal and informal sector earnings raises inequalityGap between formal and informal sector earnings raises inequalityGap between formal and informal sector earnings raises inequality.Gap between formal and informal sector earnings raises inequality.
Conclusions (ii)Conclusions (ii)Conclusions (ii)Conclusions (ii)Generating lowGenerating low--wage jobs on a mass scale would reduce inequality, wage jobs on a mass scale would reduce inequality, b t t d ti ll l ti t lb t t d ti ll l ti t lbut not dramatically relative to scale.but not dramatically relative to scale.
Minimum wage would generally reduce inequality, but net effectMinimum wage would generally reduce inequality, but net effectMinimum wage would generally reduce inequality, but net effect Minimum wage would generally reduce inequality, but net effect depends on any associated job losses.depends on any associated job losses.
E h i i f d j bE h i i f d j bEmphasise mass creation of decent jobs.Emphasise mass creation of decent jobs.
Continuation of inappropriate growth path unlikely to address eitherContinuation of inappropriate growth path unlikely to address eitherContinuation of inappropriate growth path unlikely to address either Continuation of inappropriate growth path unlikely to address either unemployment or inequality.unemployment or inequality.
Aggressive policies needed to deal with legacy of mass Aggressive policies needed to deal with legacy of mass unemployment of young people who have seldom or never worked.unemployment of young people who have seldom or never worked.
Scale of unemployment goes far beyond ‘labour market’ issue.Scale of unemployment goes far beyond ‘labour market’ issue.
Conclusions (iii)Conclusions (iii)Conclusions (iii)Conclusions (iii)By international standards, poverty in SA associated more with By international standards, poverty in SA associated more with di t ib ti th ith t t ldi t ib ti th ith t t ldistribution than with total resources.distribution than with total resources.
AsgiSA target of halving poverty is achievable…AsgiSA target of halving poverty is achievable…AsgiSA target of halving poverty is achievable…AsgiSA target of halving poverty is achievable…
But not realistically with growth alone.But not realistically with growth alone.
Need a proNeed a pro--poor shift in growth path.poor shift in growth path.
Considerable scope for progressive distributional change.Considerable scope for progressive distributional change.
B t lik l t h d lB t lik l t h d lBut unlikely to happen endogenously.But unlikely to happen endogenously.
Internationally, ‘downward stickiness’ of inequality.Internationally, ‘downward stickiness’ of inequality.y, q yy, q y
Reduction of inequality as explicit policy objective.Reduction of inequality as explicit policy objective.
Additi l lid f fAdditi l lid f fAdditional slides for referenceAdditional slides for reference
Effects of a R1000 minimum wage under 5 scenariosEffects of a R1000 minimum wage under 5 scenariosEffects of a R1000 minimum wage under 5 scenariosEffects of a R1000 minimum wage under 5 scenarios
# raised tomin. wage
(‘000)# indirectly affected
(‘000)
Gini % wage
billl d l b f(‘000) (‘000) billemployed labour force1 3 885 0.567 0.666 4.5
2 2 660 0.600 0.692 2.2
3 1 640 0 604 0 695 1 93 1 640 0.604 0.695 1.9
4 773 867 lose jobs, 0.626 0.712 0.2
5 773 867 lose jobs, 616 benefit from ripple
0.625 0.711 0.3
Expanded lowExpanded low--wage employment scenarioswage employment scenariospp g p yg p y
Gini % totalGini % total earnings
Benchmark: current levels 0 71Benchmark: current levels 0.71 -Employing ⅓ unemployed at median informal wage 0.69 2.0Employing ⅓ unemployed at average informal wage 0.68 3.7Employing ½ unemployed at median informal wage 0.68 3.1Employing ½ unemployed at average informal wage 0.66 5.6Employing ⅔ unemployed at median informal wage 0.67 4.1Employing ⅔ unemployed at average informal wage 0.64 7.5
Inequality with expanded lowInequality with expanded low--wage employmentwage employmentq y pq y p g p yg p y
18
16
.8e
shar
e.6
ve in
com
e.4
Cum
ulat
i.2
0
0 .2 .4 .6 .8 1Cumulative population share
Earnings Earnings with expanded low-wage employment
