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

Using geospatial information to connect ecosystem services and human well-being in Kenya

The application of geospatial information in the analysis of ecosystem services would help decision makers to develop programs for poverty reduction in Kenya that would improve the targeting of social expenditures and ecosystem interventions so that they reach areas of greatest need

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

Food

This chapter examines the principal domestic sources of food in Kenya, including crop production, livestock, fishing, and hunting-gathering. A detailed livelihood map gives an overview of how Kenyan households use natural resources, wage labor, and other urban employment to make a living. Maps of cropping intensities show that Kenya’s rainfed agriculture reflects the country’s rainfall patterns, with a significant proportion of farmers being exposed to the risks of unreliable rainfall or prolonged drought. A detailed view of central and western Kenya, where more than 90 percent of croplands are located shows that farmers dedicate large shares of their cropland to food crops in selected high-potential Districts such as Trans Nzoia, Uasin Gishu, Lugari, upper Nandi, and Nakuru (maize and other cereals), Narok (wheat), and lower Kirinyaga (rice). Food crop shares are also high in the more marginal cropping areas—but here agriculture is dominated by lower-yielding maize—for example, along Lake Victoria and large parts of Laikipia, Machakos, Mwingi, Kitui, Makueni, Taita Taveta, Kwale, Kilifi, and Malindi Districts. Livestock production in Kenya also displays distinct spatial patterns: high dairy output and surpluses primarily in central Kenya; milk deficits in large parts of Nyanza and Western Provinces; and pastoral and agropastoral livestock rearing in the arid and semi-arid lands. The chapter concludes with a set of maps on fishing and hunting-gathering of wild animals and plants