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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
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What explains regional inequality in Uganda?The role of infrastructure, productive assets, and occupation Isis Gaddis, University ofGoettingen WelfareCongress 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
Introduction Inequality Decomposition (theil-t), 1992/93 - 2005/06
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 bivariateTheil 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)
Results: returns to education Rural Uganda Urban Uganda
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
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.
