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Improved Spatially Disaggregated Livestock Measures for Uganda

Carlo Azzarri () and Elizabeth Cross
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Elizabeth Cross: United States Bureau of Labor Statistics (BLS), Washington, DC

The Review of Regional Studies, 2016, vol. 46, issue 1, 37-73

Abstract: The objective of our study is twofold: on one side, to complement earlier analyses that estimate the spatial density of livestock holdings using different methods; on the other, to show that by combining different data sources—the 2009/10 Uganda National Panel Survey (UNPS) and the 2008 Uganda National Livestock Census (UNLC)—and applying the Small Area Estimation (SAE) technique, it is possible to provide a finer spatial disaggregation and representation of missing livestock measures in the census. First, we combine our livestock population and density figures with those from the UNLC. Second, we fit an estimation model of livestock income and share on the UNPS to generate an out-of-sample prediction of the missing information in the UNLC, mapping livestock income and share at the local level. Our results suggest that the integrated use of multiple data sources, such as household surveys, censuses, and administrative data, together with spatial analysis techniques, such as SAE, can provide reliable, coherent, and location-specific insights to guide policy and investment. This work shows a useful method that allows for a reliable spatial livestock analysis, whenever sectorial databases offer greater coverage of the population of interest, but more limited information than specialized surveys. This method can be applied in all countries where there is a similar livestock information system, and common support between livestock census and household surveys with detailed agricultural/livestock modules. Cross-validation across data sources provides clearer insights into livestock-related policy and a better springboard for effective poverty-reduction strategies.

Keywords: mapping; livestock; Uganda; Small Area Estimation (search for similar items in EconPapers)
JEL-codes: Q12 R12 (search for similar items in EconPapers)
Date: 2016
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