1. What explains regional inequality in Uganda? The role of infrastructure, productive assets, and occupation Isis Gaddis, University of Goettingen Welfare Congress 2011, OECD, Paris

2. 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

3. Introduction Regional and International Comparison of and Within-Group Inequality (theil-t) Rural-urban decomposition Regional Decomposition BetweenWithinBetweenWithin Number of groups East Africa Kenya2005/0627732476(8) Mozambique2002298694(20) Tanzania2000/01794595(4) Uganda2005/0615851684(4) Other countries Benin200314862179(12) Brazil200459892(5) Vietnam1997/98 2575(61) Sources: East Africa: World Bank staff estimates. Other countries: World Bank (2003); Ferreira, Leite and Litchfield (2006); Minot, Baulch and Epprecht (2006).

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

5. Introduction Poverty by Region, 2005/06 p0p1p2 National0.3110.0870.035 Rural Central0.2090.0470.016 Eastern0.3750.0950.036 Northern0.6420.2230.099 Western0.2140.0540.019 Urban Central0.0550.0110.005 Eastern0.1690.0440.015 Northern0.3970.1150.045 Western0.0930.0200.006

6. 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

7. 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)

8. Methodology Estimate a model of consumption (at the hh-level) by region (r) X CONS,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) α c,r are county-specific intercepts

9. 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)

10. Methodology

11. Results: price simulations (p0) Base region: EasternNorthernWesternEasternNorthernWestern ruralurban Observed Base region 0.3720.6410.2140.1730.4050.095 Central region 0.210.048 Δ% -44%-67%-2%-72%-88%-49% Price simulations electricity 0.3710.6410.214 (see infrastructure below) Δ% 0% rural roads 0.3890.6480.228 (not applicable) Δ% 5%1%7% education 0.2750.540.186 (see productive assets below) Δ% -26%-16%-13% rural land 0.3830.6460.219 (not applicable) Δ% 3%1%2% infrastructure 0.3890.6470.2280.1870.4050.097 (electricity & rural roads)Δ% 5%1%7%8%0%2% productive assets 0.2940.5450.1940.1890.4060.102 (education & rural land)Δ% -21%-15%-9%9%0%7% occupation 0.4040.6730.2250.2620.4720.086 Δ% 9%5% 51%17%-9%

12. Results: returns to education Rural Uganda Urban Uganda

13. Results: endowment simulations (p0) Base region: EasternNorthernWesternEasternNorthernWestern ruralurban Observed Base region 0.3720.6410.2140.1730.4050.095 Central region 0.210.048 Δ% -44%-67%-2%-72%-88%-49% Endowment simulations electricity 0.3530.6230.204 (see infrastructure below) Δ% -5%-3%-5% rural roads 0.3720.6380.213 (not applicable) Δ% 0% education 0.3450.5920.198 (see productive assets below) Δ% -7%-8%-7% rural land 0.3870.6460.224 (not applicable) Δ% 4%1%5% infrastructure 0.3530.620.2030.1150.2470.066 (electricity & rural roads)Δ% -5%-3%-5%-34%-39%-31% productive assets 0.3620.5980.2060.1360.3150.069 (education & rural land)Δ% -3%-7%-4%-21%-22%-27% occupation 0.3840.640.2160.1490.3960.103 Δ% 3%0%1%-14%-2%8%

14. Results: combined simulations

15. 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)

16. 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

17. Thank you!

18. 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.

19. Results: simulated education Rural Urban CentralEasternNorthernWestern CentralEasternNorthernWestern Actual educational attainment (%) no formal educ.12.217.12422.73.69.814.99.4 some primary46.249.553.147.324.233.541.129.2 compl. primary16.214.812.515.118.718.516.919.9 some secondary15.812.26.69.324.320.214.516.5 compl. secondary9.56.43.85.629.21812.825 Total100 Average years5.754.24.5 8.06.75.77.1 Simulated educational attainment (%, rank-preserving transformation) no formal educ.12.212.1 3.6 3.73.6 some primary46.246.346.4 24.2 24.324.2 compl. primary16.2 18.718.818.718.9 some secondary15.8 24.3 24.2 compl. secondary9.5 29.229.1 Total100 Average years5.7 8.0

20. Results: simulated electricity RuralUrban CentralEasternNorthernWestern CentralEasternNorthernWestern Actual electricity access percent10.82.70.22.0 55.128.29.226.8 Simulated electricity access (rank-preserving transformation) percent10.811.211.411.3 55.158.056.7

21. (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 2005/060.32115%25%4%48%8%27% Price simulations: infrastructure 0.32215%24%4%49%8%27% productive assets 0.30114%26%4%51%6%24% occupation 0.34216%25%5%46%8%28% Endowment simulations: infrastructure 0.32018%26%2%47%7%27% productive assets 0.32016%25%3%50%6%24% occupation 0.32216%25%4%47%8%28% Combined simulations: infrastructure, productive assets and occupation 0.30215%25%3%53%5%22% all (incl. county FE and demographic prices) 0.27214%27%0%59%0%14%