Inequality trends and determinants in SSA,1990-2010: a preliminary - - PowerPoint PPT Presentation

Inequality trends and determinants in SSA,1990-2010: a preliminary assessment

Giovanni Andrea Cornia

University of Florence

• Helsinki, Conference on ‘Inclusive Growth in SSA’ 21-09-2013

Structure of presentation

• 1. Growing focus on within-country inequality
• 2. Several deteriorations but also some improvements

(L.A., SEA)

• 3. Situation of SSA – which has a highly heterogeneous

economic structure – remains unexplored

• 4. In this paper we attempt a first overall assessment of

inequality trends and its drivers using L.A. as a benchmark.

Pinkowsky and Sala-I-Martin (2010) argue that in SSA inequality (and poverty) fell over 1995-2006 ….

…but their results depend on (i) only 118 surveys for 48 countries and (ii) ‘heroic’ assumptions about the lognormal shape of all distributions

Washington Consensus and Lost Decade Augmented Washington Consensus New Policy Approach

P and S-I-M wrong conclusions abt SSA ineq are similar to the LA Gini trend …… but drivers are different

Source: author’s elaboration

Empirical approach: data

• Compile database of Gini’s for countries with at least 4-5 well spaced
• bs from WIID, POVCAL, WYD. Will check trends against 64 new

‘harmonized cons. Gini’ computed by WB

• Eliminate 6-7 ‘obvious outliers’ (20 points changes from a year to the

next)

• Retained 28 countries (on 48) = to more 90% SSA’s pop/gdp
• About 220-240 observed-checked data for 1990-2010
• Different country trends due to SSA’s heterogeneity
• Grouped the 28 countries according to their inequality trends
• [Need to further probe quality and meaning of data]

Heterogeneous Gini trends in SSA

2. What could explain these inequality changes over 1990-2010 ?

Methodology

• Distinguish between:
• (i) immediate (statistical) causes of inequality

fall (using decomposition a la Lerman-Ytzaki, Milanovic, etc) which separate effects of changes in Gini into changes in: (i) income shares (labor, rents, transfers, etc.) and (ii) their concentration coefficients (C). Doable at country level

• (ii) underlying factors of ∆ income shares and

(economic theory + macro panel regression analysis) doable on aggregate (panel) data

table

chart chart

Trend in HIV prevalence in countries with rates greater than 5 %

1990 1995 2000 2005 2011 Southern Africa 3.3 14.3 21.1 21.1 19.7 West Africa 2.4 5.7 5.8 4.6 4.3 East Africa 6.1 8.9 8.5 8.0 7.5 Average SSA 3.9 9.6 11.8 11.2 10.5

Pattern of growth: at least 18 countries depend on (generally un-equalizing)oil/min rents Evolution of ‘natural resource rents/GDP’ ratio, 1990, 2000, 2010

Country 1990 2000 2010 Country 1990 200 201 Country 199 200 201 (a) % share > 20% (b) % share btw 10-20 % (c) % share btw 5-10% Angola 30.5 42.3 46.9 B.Faso 3.5 3.3 10.5 C.Ivoire 3.0 4.5 6.4 Chad 4.5 5.9 38.4 Burundi 9.5 9.3 10.9 Ethiopia 6.5 10.1 6.4 Congo DR 16.0 21.1 31.8 Camerun 11.3 12.7 9.0 Ghana 4.4 5.4 8.9 Congo Rep 46.0 75.6 66.4 G Bissau 10.1 11.2 4.8 Malawi 6.7 5.9 3.9

• Eq. Guinea

12.6 67.0 46.0 Guinea 18.3 10.0 18.2 Mozamb. 8.6 4.5 8.7 Gabon 34.7 50.7 50.0 Liberia … 16.7 11.0 S.Leone 12.6 7.7 3.5 Mauritania 11.6 12.3 51.8 Mali 2.4 2.9 12.3 Tanzania 8.3 2.7 7.9 Nigeria 47.5 46.9 27.7 S.Africa 6.3 2.2 9.9 Uganda 9.7 6.7 5.8 Zambia 19.3 4.4 25.8 Sudan … 12.8 17.6 Zimbab. 3.2 2.4 9.9 Average 24.7 36.2 42.7 Average 7.7 9.0 11.6 Average 7.0 5.5 6.8

Some of these resources could be redistributed via the budget … but political economy…

y = -0.1222x + 0.0026 R2 = 0.0474

• 0.06
• 0.04
• 0.02

0.02 0.04 0.06 0.08

• 0.15
• 0.1
• 0.05

0.05 0.1 0.15

% changes in GDP growth (x-axis) and Gini coefficient (y-axis

• ver 1990-2007, in L.America (top) and SSA (bottom)

chart table chart

Skilled/unskilled vs rural population (%)

Ratio of skilled workers (2ary/3ary education) to unskilled workers (1ary or none), 1990, 2000, 2009

Unweighted Regional Tax/GDP ratios, early 1970s to 2008 (137 countries)

1980 1990 2000 2008 ∆ 1980- 2000 ∆ 2000- 2008

• N. of countries where tax/GDP rose
• ver 2000-8 on total number of

countries SSA 19.3 18.1 17.9 19.9

• 1.4

+2.0 28 (50)

• L. America

15.5 13.3 15.3 18.9

• 0.2

+3.6 17 (18)

Revenue collection data (% of GDP) by type of tax, 15 SSA countries

Country Year Indirect Taxes Direct Taxes Trade Taxes Total83 Benin 2008 5.5 2.3 9.3 17.1 Botswana 2007/08 3.8 11.5 10.5 25.8 Burundi 2008 8.8 5.1 2.9 16.8 Ethiopia 2008/09 1.5 3.2 1.9 6.6 Ghana 2008 8.7 7.1 4.1 19.9 Kenya 2007/08 5.7 8.4 4.9 19.0 Malawi 2008/09 8.9 7.8 2.1 18.8 Mauritius 2007/08 12.1 4.2 1.1 17.4 Rwanda 2008 6.6 5.1 1.8 13.5 Senegal 2008 4.6 10.3 3.4 18.3 Sierra Leone 2008 2.6 3.4 4.8 10.8 South Africa 2008/09 8.7 16.4 1.2 26.3 Tanzania 2008/09 7.3 6.2 1.3 14.8 Uganda 2008/09 7.1 3.6 1.2 11.9 Zambia 2008 6.6 8.5 2.5 17.6

3 6 9 12 15 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Right Centre Left

Trends in political regimes (right, centre, left), 1990-2009

Initial Regression:Gini coeff..of distribution of income, 1985-09, LSDV estimator with country fixed effects)

VARIABLES 1 2 3 4 5

GDPpc growth rate * Δ Agricultural production index

• 0.0039**
• 0.0040**
• 0.0040**
• .0041**
• 0.0051**

Share of workers with tertiary education 0.3871 0.464 0.464 0.4187

• 0.7522

[ Share of workers without education ] 0.1333*** 0.1149** 0.1022* 0.1079** 0.2616*** Δ external debt (% of GDP) ???? 0.0212 0.0171 0.0171 0.0159 0.0237 Revenue (% of GDP)

• 0.0001
• 0.0004*
• 0.0004*
• 0.0003*
• 0.0006**

Terms of trade 0.0369 Terms of trade*Mineral_rich 0.0507** 0.0507** 0.0512** 0.0571* Remittances (% of GDP) 0.0019

• 0.0128
• 0.0128
• 0.0102
• 0.4454

AID (% of GDP) ????

• 0.0083
• 0.008
• 0.008
• 0.008
• 0.0064

FDI 0.1811*** 0.1834*** 0.1834** * 0.1833** *

• 0.0729

Δ polity2 0.008 0.0372 Conflicts 0.4188 2.2327* Quality of public administration

• 9.1753**

Constant 43.90*** 47.25*** 47.25*** 47.52*** 56.83***

Observations 262 262 262 262 194 R-squared 0.828 0.828 0.828 0.828 0.868

Challenges to reduce inequality in SSA

• Reduce rural-urban income gap through increase in rural incomes
• Green Revolution and agric. Support policies
• Liberalization of sector
• RER devaluation
• Rural non agricultural activities
• Luck (better world prices)
• Avoid ri-polarization of distribution of land
• Intensify efforts on education (lagged effects on skill premium in urban areas)
• efforts at progressive tax collec.(+ aid) to finance ‘public goods’ in non inflationary way
• Tax rents
• Roads to reduce ‘spatial inequality’
• Education (see above)
• Transfers
• Some transfers
• Democratization (along non-ethnic lines) seems to favor adoption of progressive

policies

• In the long term avoid re-primarization of export with ‘open economy industrial policy’