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Improved Spatially Disaggregated Livestock Measures for Uganda

Author

Listed:
  • Carlo Azzarri

    (International Food Policy Research Insitutte (IFPRI), Washington, DC)

  • Elizabeth Cross

    (United States Bureau of Labor Statistics (BLS), Washington, DC)

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.

Suggested Citation

  • Carlo Azzarri & Elizabeth Cross, 2016. "Improved Spatially Disaggregated Livestock Measures for Uganda," The Review of Regional Studies, Southern Regional Science Association, vol. 46(1), pages 37-73, Winter.
  • Handle: RePEc:rre:publsh:v46:y:2016:i:1:p:37-73
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    References listed on IDEAS

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    1. Harold Alderman & Miriam Babita & Gabriel Demombynes & Nthabiseng Makhatha & Berk Özler, 2002. "How Low Can You Go? Combining Census and Survey Data for Mapping Poverty in South Africa," Journal of African Economies, Centre for the Study of African Economies (CSAE), vol. 11(2), pages 169-200, June.
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    3. Luc Christiaensen & Peter Lanjouw & Jill Luoto & David Stifel, 2012. "Small area estimation-based prediction methods to track poverty: validation and applications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 10(2), pages 267-297, June.
    4. A. Colin Cameron & Pravin K. Trivedi, 2010. "Microeconometrics Using Stata, Revised Edition," Stata Press books, StataCorp LP, number musr, December.
    5. Hentschel, Jesko, et al, 2000. "Combining Census and Survey Data to Trace the Spatial Dimensions of Poverty: A Case Study of Ecuador," World Bank Economic Review, World Bank Group, vol. 14(1), pages 147-165, January.
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    7. Burke, William J. & Jayne, Thomas S. & Freeman, H. Ade & Kristjanson, Patricia, 2007. "Factors Associated with Farm Households’ Movement Into and Out of Poverty in Kenya: The Rising Importance of Livestock," Food Security International Development Working Papers 54563, Michigan State University, Department of Agricultural, Food, and Resource Economics.
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    More about this item

    Keywords

    mapping; livestock; Uganda; Small Area Estimation;
    All these keywords.

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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