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Poverty Mapping in Rural Syria for Enhanced Targeting

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  • Szonyi, Judit A.
  • de Pauw, Eddy
  • La Rovere, Roberto
  • Aw-Hassan, Aden

Abstract

Poverty maps allow assessing the well-being of rural population in a spatial context and identifying poverty hotspots. The maps can be used for regional policy analysis as they help in identifying areas where the rural poor live and where rural poverty is determined by the endowment and quality of natural resources and by population pressure. Natural resource endowment was assessed in the study by calculating an Agricultural Resource Index based on the availability of different major agricultural resources. Income per-capita was calculated by using census data, adjusted by the rural population density. The results show that the better income areas of Syria are located in the irrigated or higher-rainfall areas, but lower-income pockets exist due to the presence of various ecological and topographic factors. The study provides the elements of a 'top-down' approach for poverty mapping to disaggregate income from census sources to the pixel level based on agroecological data. It is an important advancement on methodologies to link micro and macro economic analysis to successfully map poverty in data scarce regions. However, we also critically suggest a number of practical improvements for the approach. Poverty mapping can indeed become more effective and cost-efficient if classical bottom-up approaches, based on household survey data are integrated with top-down approaches such as the one presented.

Suggested Citation

  • Szonyi, Judit A. & de Pauw, Eddy & La Rovere, Roberto & Aw-Hassan, Aden, 2006. "Poverty Mapping in Rural Syria for Enhanced Targeting," 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia 25564, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae06:25564
    DOI: 10.22004/ag.econ.25564
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    References listed on IDEAS

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    1. Baulch, Bob & Minot, Nicholas, 2002. "Poverty mapping with aggregate census data: what is the loss in precision?," MSSD discussion papers 49, International Food Policy Research Institute (IFPRI).
    2. Nicholas Minot & Bob Baulch, 2005. "Poverty Mapping with Aggregate Census Data: What is the Loss in Precision?," Review of Development Economics, Wiley Blackwell, vol. 9(1), pages 5-24, February.
    3. Sullivan, Caroline, 2002. "Calculating a Water Poverty Index," World Development, Elsevier, vol. 30(7), pages 1195-1210, July.
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    Keywords

    Food Security and Poverty;

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