What explains regional inequality in Uganda?

The role of infrastructure, productive assets, and occupation

Isis Gaddis, University of Goettingen

Welfare Congress 2011, OECD, Paris

Introduction• While poverty has fallen in Uganda since 1992,

inequality has increased• Analysis in World Bank (2009) show that halting the

trend in increasing inequality while sustaining growth is important if Uganda is to reach its poverty targets

• But what explains high and rising inequality in Uganda?• One of the simplest ways to see what factors are driving

inequality is to perform a between-within decomposition

• Bivariate decomposition (theil-t or theil-l)• This shows that regional inequality is unusually high in

Uganda, and it has been growing over time

IntroductionRegional and International Comparison of

and Within-Group Inequality (theil-t)

  Rural-urban

decompositionRegional

Decomposition

Between Within Between Within Number of groups

East Africa Kenya 2005/06 27 73 24 76 (8)Mozambique 2002 2 98 6 94 (20)Tanzania 2000/01 7 94 5 95 (4)Uganda 2005/06 15 85 16 84 (4) Other countries Benin 2003 14 86 21 79 (12)Brazil 2004 5 9 8 92 (5)Vietnam 1997/98 25 75 (61)Sources: East Africa: World Bank staff estimates. Other countries: World Bank (2003); Ferreira, Leite and Litchfield (2006); Minot, Baulch and Epprecht (2006).

Introduction

1992 2002 20050%

20%

40%

60%

80%

100%

Within urban within regions

Within rural within regions

Within urban between regions

Within rural between regions

Between urban/rural

Inequality Decomposition (theil-t), 1992/93 - 2005/06

Introduction

Poverty by Region, 2005/06 p0 p1 p2

National 0.311 0.087 0.035

Rural Central 0.209 0.047 0.016Eastern 0.375 0.095 0.036Northern 0.642 0.223 0.099

Western 0.214 0.054 0.019

Urban Central 0.055 0.011 0.005Eastern 0.169 0.044 0.015Northern 0.397 0.115 0.045Western 0.093 0.020 0.006

Introduction

• This paper seeks to understand which factors explain inequality between regions (Central, Northern, Western, Eastern)

• Analyze differences between urban regions, and between rural regions (not urban-rural differential)

• The welfare measure is consumption per adult• We focus on the following explaining factors:

– Infrastructure (roads and electricity)– Productive assets (education and land)– Employment structure

Methodology

• Micro-simulation approach based on Bourguignon, Ferreira and Lustig (2005) – adapted to consumption data

• Extension of the traditional Oaxaca-Blinder decomposition• Typically used to explain income-distribution dynamics• Simulates are series of counterfactual distributions to

decompose the differences between actual distributions:– Multivariate (unlike the bivariate Theil decompositions)– Distinguishes between endowment and price effects (like OB)– Can accommodate interdependencies between variables– Simulates full distributions and can thus decompose any

functional indicator (e.g. poverty and inequality indices)

Methodology

• Estimate a model of consumption (at the hh-level) by region (r)

• XCONS,h,r includes:– productive assets: education of all hh members and (rural) size of

land holdings– infrastructure: electricity access and (rural) distance to a trunk road– employment of the head and other hh members– demographic control variables (not used for simulation)

• αc,r are county-specific intercepts

Methodology• Price simulations: equalize returns to (specific) household

endowments across regions (by importing the coefficient vector from the reference region)

• Endowment simulations: use non-parametric and parametric approaches to equalize (specific) endowments across regions– Rank-preserving transformation for continuous or dichotomous variables

(land holding size, years of education, road distance, electricity access)– Multinomial logit for categorical variables (occupation)

– The endowment distribution simulated by importing the coefficients vector of the discrete choice models from the reference region

• Reference: Central Uganda (keeps urban-rural differences)

Methodology

Results: price simulations (p0)Base region: Eastern Northern Western Eastern Northern Western rural urbanObservedBase region 0.372 0.641 0.214 0.173 0.405 0.095Central region 0.21 0.048 Δ% -44% -67% -2% -72% -88% -49%Price simulationselectricity 0.371 0.641 0.214

(see infrastructure below)Δ% 0% 0% 0%rural roads 0.389 0.648 0.228

(not applicable)Δ% 5% 1% 7%education 0.275 0.54 0.186

(see productive assets below)Δ% -26% -16% -13%rural land 0.383 0.646 0.219

(not applicable)Δ% 3% 1% 2%

infrastructure 0.389 0.647 0.228 0.187 0.405 0.097(electricity & rural roads) Δ% 5% 1% 7% 8% 0% 2%productive assets 0.294 0.545 0.194 0.189 0.406 0.102(education & rural land) Δ% -21% -15% -9% 9% 0% 7%occupation 0.404 0.673 0.225 0.262 0.472 0.086

Δ% 9% 5% 5% 51% 17% -9%

Results: returns to education

no educa

tion

some prim

ary

completed

primary

some seco

ndary

completed se

condary

0.00

0.40

0.80

CentralEasternNorthernWestern

no educa

tion

some prim

ary

completed

primary

some seco

ndary

completed

seco

ndary0.00

0.40

0.80

CentralEasternNorthernWestern

Rural Uganda

Urban Uganda

Results: endowment simulations (p0)Base region: Eastern Northern Western Eastern Northern Western rural urbanObservedBase region 0.372 0.641 0.214 0.173 0.405 0.095Central region 0.21 0.048 Δ% -44% -67% -2% -72% -88% -49%Endowment simulations electricity 0.353 0.623 0.204

(see infrastructure below)Δ% -5% -3% -5%rural roads 0.372 0.638 0.213

(not applicable)Δ% 0% 0% 0%education 0.345 0.592 0.198

(see productive assets below)Δ% -7% -8% -7%rural land 0.387 0.646 0.224

(not applicable)Δ% 4% 1% 5%

infrastructure 0.353 0.62 0.203 0.115 0.247 0.066(electricity & rural roads) Δ% -5% -3% -5% -34% -39% -31%productive assets 0.362 0.598 0.206 0.136 0.315 0.069(education & rural land) Δ% -3% -7% -4% -21% -22% -27%occupation 0.384 0.64 0.216 0.149 0.396 0.103

Δ% 3% 0% 1% -14% -2% 8%

Results: combined simulations0

2040

60

perc

ent

0 20 40 60 80 100percentile

difference to Central ruralinfr., assets, occup. all

Combined simulations Eastern rural

050

100

150

200

250

perc

ent

0 20 40 60 80 100percentile

difference to Central ruralinfr., assets, occup. all

Combined simulations Northern rural

020

4060

perc

ent

0 20 40 60 80 100percentile

difference to Central ruralinfr., assets, occup. all

Combined simulations Western rural

-20

020

4060

80

perc

ent

0 20 40 60 80 100percentile

difference to Central urbaninfr., assets, occup. all

Combined simulations Eastern urban

-50

050

100

150

perc

ent

0 20 40 60 80 100percentile

difference to Central urbaninfr., assets, occup. all

Combined simulations Northern urban

-20

020

40

perc

ent

0 20 40 60 80 100percentile

difference to Central urbaninfr., assets, occup. all

Combined simulations Western urban

Some caveats• No a causal model, no clear identification of effects• Potential endogeneity problems (esp. for electricity access)• Accounting exercise• No general equilibrium effects• No standard errors/confidence intervals• County-effects (unobservables) play a huge role• Not all simulations have a clear policy implication (e.g.

equalizing land holding sizes)• Simulations do not necessarily reduce total regional inequality

(because the urban-rural gap may even get larger)

Conclusion• The simulations show that the following factors come out as

determinants of regional inequality in Uganda– Educational attainment (urban and rural)– Access to electricity (urban and rural)– Returns to education (rural)– Returns to non-agricultural activities (urban and rural)

• This suggests policies to invest in education and electricity and increase profitability of non-agricultural employment in lagging areas

• However, inequality considerations need to be balanced with overall growth considerations

Thank you!

References• Bourguignon, François, Francisco H. G. Ferreira and Phillippe G. Leite (2008).

“Beyond Oaxaca-Blinder: Accounting for Differences in Household Income Distributions.” Journal of Economic Inequality Vol. 6: 117-148.

• Bourguignon, François, Francisco H. G. Ferreira and Nora Lustig (eds.) (2005). The Microeconomics of Income Distribution Dynamics in East Asia and Latin America. Washington DC: World Bank and Oxford University Press.

• Ferreira, Francisco H. G. (2010). “Distributions in Motion: Economic Growth, Inequality and Poverty Dynamics.” World Bank Policy Research Working Paper No. 5424, Washington DC: World Bank.

• Leite, Phillippe G., Alan Sanchez and Caterina R. Laderchi (2009). “The Evolution of Urban Inequality in Ethiopia.” Draft version March 2009, World Bank HDNSP and AFTP2.

Results: simulated education Rural Urban Central Eastern Northern Western Central Eastern Northern WesternActual educational attainment (%)

no formal educ. 12.2 17.1 24 22.7 3.6 9.8 14.9 9.4some primary 46.2 49.5 53.1 47.3 24.2 33.5 41.1 29.2compl. primary 16.2 14.8 12.5 15.1 18.7 18.5 16.9 19.9some secondary 15.8 12.2 6.6 9.3 24.3 20.2 14.5 16.5compl. secondary 9.5 6.4 3.8 5.6 29.2 18 12.8 25Total 100 100 100 100 100 100 100 100

Average years 5.7 5 4.2 4.5 8.0 6.7 5.7 7.1Simulated educational attainment (%, rank-preserving transformation)

no formal educ. 12.2 12.1 12.1 12.1 3.6 3.6 3.7 3.6some primary 46.2 46.3 46.4 46.4 24.2 24.2 24.3 24.2compl. primary 16.2 16.2 16.2 16.2 18.7 18.8 18.7 18.9some secondary 15.8 15.8 15.8 15.8 24.3 24.3 24.3 24.2compl. secondary 9.5 9.5 9.5 9.5 29.2 29.1 29.1 29.1Total 100 100 100 100 100 100 100 100

Average years 5.7 5.7 5.7 5.7 8.0 8.0 8.0 8.0

Results: simulated electricityRural Urban

Central Eastern Northern Western Central Eastern Northern Western

Actual electricity access percent 10.8 2.7 0.2 2.0 55.1 28.2 9.2 26.8

Simulated electricity access (rank-preserving transformation) percent 10.8 11.2 11.4 11.3 55.1 58.0 56.7 56.7

(I) (II) (III) (IV) (V) (VI) (VII)

theil-t

Share of inequality … total inequality between

regions =(II)+(IV)+(VI)

between urban

and rural

within urban within regions

within urban

between regions

within rural

within regions

within rural

between regions

Actual 2005/06 0.321 15% 25% 4% 48% 8% 27%Price simulations:infrastructure 0.322 15% 24% 4% 49% 8% 27%productive assets 0.301 14% 26% 4% 51% 6% 24%occupation 0.342 16% 25% 5% 46% 8% 28%Endowment simulations: infrastructure 0.320 18% 26% 2% 47% 7% 27%productive assets 0.320 16% 25% 3% 50% 6% 24%occupation 0.322 16% 25% 4% 47% 8% 28%Combined simulations: infrastructure, productive assets and occupation 0.302 15% 25% 3% 53% 5% 22%

all (incl. county FE and demographic prices) 0.272 14% 27% 0% 59% 0% 14%