Towards understanding the impact of the international financial crisis on child poverty in South Africa: The child poverty context

Ramos Mabugu*, Debra Shepherd** and Servaas van der Berg**with

Margaret Chitiga°, Bernard Decaluwé°°, Hélène Maisonnave*, Véronique Robichaud°°, Judith Streak** and Dieter von Fintel**

* Financial and Fiscal Commission, South Africa** University of Stellenbosch° University of Pretoria °° Laval University (Québec) and PEP network

Draft paper to Unicef/ODI conference, London, November 2009

Introduction

• Macro-micro study: – CGE model uses micro-econometrically

determined elasticities – Micro-simulations use CGE outputs

• Issues covered here:– Child poverty profile– Child Support Grant as major instrument to

combat child poverty– CGE model and its results– Possible implications for poverty

Poverty profile• Stochastic poverty dominance for most dimensions • Adult equivalence scale does not much affect poverty

profile• Thus use per capita income at 40th percentile as

poverty line• 52.9% of population poor: 65.5% of all children (11.8

million)• Differentials larger for P1 and P2 – thus lower poverty

line would increase child-adult poverty differential• Poverty largest amongst youngest, blacks, rural areas,

poorer provinces• Reported hunger strongly declined – probably

because of Child Support Grants (CSGs) and other grants (employment growth played only a small role)

Poverty profile for children and adults (poverty line at 40th percentile of household per capita income)

Child poverty (0-17 years)

P0 Poverty headcount rate P1 Poverty depth measure

P2 Poverty severity measure

Rate (%) Number

Age 0-4 66.1 3 066 509 0.336 0.213

5-14 65.7 6 681 507 0.343 0.202

15-17 63.8 2 067 609 0.332 0.203

0-17 (all children) 65.5 11 822 544 0.328 0.205

18+ (all adults) 45.2 0.213 0.126

Racial groupBlack 72.5 11 100 826 0.375 0.232

Coloured 41.3 623 412 0.167 0.093Asian 24.2 76 137 0.093 0.052White 2.0 18 081 0.012 0.008

Urban/Rural location

Rural 82.8 7 376 451 0.446 0.28Urban 48.6 4 442 491 0.226 0.133

Households that reported that children went hungry in the past year

Child Support Grants

• Introduced 1998, expanded rapidly– Growing coverage within age-eligible groups– Age-eligibility increased from 7 to 15 years

• Quite good targeting – but puzzlingly large errors of exclusion

• Reduced poverty in period of good growth• Hypothesis: CSF acts as a form of income

diversification that mitigates impact of economic shocks on vulnerable children

Contribution of spending on each type of social grant to total social grant spending

CSG: Eligibility and coverageGHS Eligible

age

Recipients of CSG Pop. of eligible

age[D]

Coverage rate

[A]/[D]Of eligible age [A]

Not of eligible age [B]

Total[C]

2003 0-8 years 2 241 760 321 534 2 563 294 8 299 039 27.0%

2004 0-10 years 4 201 481 175 526 4 377 007 11 100 241 37.9%

2005 0-13 years 5 702 793 139 043 5 841 836 14 052 170 40.6%

2006 0-13 years 6 459 760 265 579 6 725 339 14 152 509 45.6%

CSG roll-out: Progression over time in CSG coverage rates in households by earnings decile of the employed

0%

10%

20%

30%

40%

50%

60%

70%

Decile1 Decile2 Decile3 Decile4 Decile5 Decile6 Decile7 Decile8 Decile9 Decile10 GHS2002 GHS2003 GHS2004 GHS2005 GHS2006

Economic crisis and modelling its impact

• SA’s first recession in 17 years• Cumulative job losses 1 million• Two scenarios modelled in CGE model:

– Moderate (growth picks up from end 2009)– Severe (protracted global slowdown)

IMF Growth Projections for South Africa (estimates after 2008)

Potential effect of CSG and job losses on FGT poverty measures (2008 = base year)

(provisional results)P0

Headcount Poverty rate

P1

Poverty Gap ratio

P2

Squared Poverty

Gap ratioBaseline 2008 Child with CSG 0.686 0.417 0.304

Child without CSG 0.721 0.509 0.422

2009: 1.2 million job losses

Child with CSG 0.706 0.445 0.331

Child without CSG 0.736 0.538 0.454

Note: CSG has larger effect than employment

In economic boom, similar results held: employment contributed little to poverty decline

Static effect of CSG on child poverty headcount

0.2

.4.6

.8

0 500 1000 1500

Total Income 08 Total Income 08 no CSG

Effect of 1.2 million job loss on child poverty headcount

0.2

.4.6

.8

0 500 1000 1500

Total Income 08 Total Income 09

Elasticities and effect of prices

• Prices derived in modelling NIDS data• Urban-rural poverty differential narrows when

using these prices• Generally high price elasticities mean much

substitution following price rises• However, low price elasticity for staple

(maize) impacts on consumption volumes

Poverty adjusted for price differentials: Early estimates

Headcount poverty rate Urban Rural Total

Households 0.395 0.480 0.434

All individual level 0.463 0.513 0.489

• Children 0.495 0.531 0.516

• Adults 0.434 0.498 0.465

Provisional conclusion• Economic crisis may not have completely

reversed progress in child poverty that resulted from expansion of CSG in recent growth period

• Yet the crisis ended the period of declining child poverty and hunger

• Non-money metric impact probably more limited, due to structure of SA service provision (e.g. education free for poor, public health largely free)