Welfare and environment in Rural Uganda: Results from a small-area estimation approach

This study combines census, survey and bio-physical data to generate spatially disaggregated poverty/biomass information for rural Uganda. It makes a methodological contribution to small area welfare estimation by exploring how the inclusion of bio-physical information improves small area welfare estimates. By combining the generated poverty estimates with national bio-physical data, this study explores the contemporaneous correlation between poverty (welfare) and natural resource degradation at a level of geographic detail that has not been feasible previously. The resulting estimates of poverty measures were improved by the inclusion of bio-physical information and the poverty estimates appear to be more robust, as the standard errors show a decline of up to 40 percent in some cases. The coefficients of variation (i.e., the ratio of the standard error and the point estimate) decline in general as well. Overall, we conclude that the estimates of the poverty measures are more robust when bio-physical information is taken into account. One of the outputs of this study is a series of maps showing poverty and biomass overlays for Uganda. These maps can be used as a planning tool and for targeting purpose

Updating poverty maps with panel data

This paper extends the methodology of Elbers, C., Lanjouw, J. O., & Lanjouw, P. (2003). Micro level estimation of poverty and inequality. Econometrica 71(1), 355-364 and presents a low cost approach to arriving at small area welfare estimates for non-census years. The approach requires panel data and the estimation of a relation between per capita consumption from the year of interest and household characteristics from the census year. The method is illustrated for Uganda. It is shown that with the exception of the North progress in rural poverty reduction was broadly shared during 1992-99. Areas with high initial levels of poverty appear to have benefited less from growth