2005
DOI: 10.2139/ssrn.849284
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Welfare and Environment in Rural Uganda: Results from a Small-Area Estimation Approach

Abstract: 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 biophysical data, this study explores the contemporaneous correlation between poverty (welfare) and natural resource degradation at a level of geog… Show more

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Cited by 10 publications
(10 citation statements)
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“…Hence, if it is possible to measure differences in environmental conditions at a fine enough level (such as rainfall, soil fertility, access to markets of each town), it stands to reason that using the information contained in these environmental variables should be relevant for poverty maps. Curiously, even though environmental factors have been identified as contributors to differences in living standards in different areas, there has been little empirical work to ascertain their relationship with poverty rates (although there are exceptions, for example, Gibson et al, 2005 andOkwi et al, 2005). The major problem in performing this type of analysis has been lack of data (and/or the inability to merge environmental data with census data).…”
Section: Introductionmentioning
confidence: 99%
“…Hence, if it is possible to measure differences in environmental conditions at a fine enough level (such as rainfall, soil fertility, access to markets of each town), it stands to reason that using the information contained in these environmental variables should be relevant for poverty maps. Curiously, even though environmental factors have been identified as contributors to differences in living standards in different areas, there has been little empirical work to ascertain their relationship with poverty rates (although there are exceptions, for example, Gibson et al, 2005 andOkwi et al, 2005). The major problem in performing this type of analysis has been lack of data (and/or the inability to merge environmental data with census data).…”
Section: Introductionmentioning
confidence: 99%
“…(1) Recurrent mapping at regular intervals coinciding with census surveys can provide rich databases for future predictions, and poverty estimates for non-census years (see Emwanu et al, 2006;Okwi et al, 2006 for low cost methods on panel data for non-census years).…”
Section: Discussionmentioning
confidence: 99%
“…The eastern Uganda population is predominantly agro-pastoral basically producing at subsistence level (Whyte & Kyaddondo, 2006). Now, most households in the Teso region derive their livelihood on increasingly small land holdings ranging between 0.5 to 4 ha (national average = 0.4 to 3ha) per household (Okwi et al, 2006), hence forcing intensive production systems and/or seeking non-farm income in order to ensure food self sufficiency. Crop production is reported to be declining (NARO, 2002), despite the use of nutrient inputs and pest management strategies (Kayizzi et al, 2007), a limitation thought to arise from water stress conditions.…”
Section: Land Use and Livelihood Strategiesmentioning
confidence: 99%