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Spatial Patterns and Geographic Determinants of Welfare and Poverty in Tunisia

Listed author(s):
  • Mohamed Ayadi

    ()

    (Université de Tunis)

  • Mohamed Amara

Previous poverty analysis in Tunisia concluded that the poor population is concentrated in interior areas, especially in the northwest and center west. Thus more information on the spatial dimension of welfare and poverty may be of interest for any poverty alleviation programs as poverty may be associated to geographic locations. However, the analysis of the spatial dimension cannot be limited to the addition of some variables to our econometric model. We have to consider the neighborhood effects and the heterogeneity of households’ behaviors in more disaggregated geographic units using specific tools of spatial and geographical analysis. First, we conduct an exploratory spatial data analysis (ESDA) based on a geographical information system (GIS), to visualize the “local” spatial structure of poverty. Second — to deal with spatial autocorrelations and unobserved spatial heterogeneity of the households’ behaviors — we use a spatial autoregressive model (SAR) and a geographical weighted regression model (GWR) respectively. Spatial and non-spatial models are compared according to their prediction performances. SAR and GWR spatial models are found superior to the traditional non-spatial regression model, and give a better approximation of the Tunisian poverty map.

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Paper provided by Economic Research Forum in its series Working Papers with number 478.

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Length: 25 pages
Date of creation: Mar 2009
Date of revision: Mar 2009
Publication status: Published by The Economic Research Forum (ERF)
Handle: RePEc:erg:wpaper:478
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  1. Elbers, Chris & Fujii, Tomoki & Lanjouw, Peter & Ozler, Berk & Yin, Wesley, 2007. "Poverty alleviation through geographic targeting: How much does disaggregation help?," Journal of Development Economics, Elsevier, vol. 83(1), pages 198-213, May.
  2. Farrow, Andrew & Larrea, Carlos & Hyman, Glenn & Lema, German, 2005. "Exploring the spatial variation of food poverty in Ecuador," Food Policy, Elsevier, vol. 30(5-6), pages 510-531.
  3. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2005. "Imputed welfare estimates in regression analysis," Journal of Economic Geography, Oxford University Press, vol. 5(1), pages 101-118, January.
  4. Minot, Nicholas & Baulch, Bob, 2005. "Spatial patterns of poverty in Vietnam and their implications for policy," Food Policy, Elsevier, vol. 30(5-6), pages 461-475.
  5. Anselin, Luc & Getis, Arthur, 1992. "Spatial Statistical Analysis and Geographic Information Systems," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 26(1), pages 19-33, April.
  6. Takeshi Daimon, 2001. "The Spatial Dimension of Welfare and Poverty: Lessons from a Regional Targeting Program in Indonesia," Working Papers EMS_2001_04, Research Institute, International University of Japan.
  7. Amarasinghe, Upali & Samad, Madar & Anputhas, Markandu, 2005. "Spatial clustering of rural poverty and food insecurity in Sri Lanka," Food Policy, Elsevier, vol. 30(5-6), pages 493-509.
  8. Rupasingha, Anil & Goetz, Stephan J., 2007. "Social and political forces as determinants of poverty: A spatial analysis," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 36(4), pages 650-671, August.
  9. Benson, Todd & Chamberlin, Jordan & Rhinehart, Ingrid, 2005. "An investigation of the spatial determinants of the local prevalence of poverty in rural Malawi," Food Policy, Elsevier, vol. 30(5-6), pages 532-550.
  10. Yee Leung & Chang-Lin Mei & Wen-Xiu Zhang, 2000. "Statistical tests for spatial nonstationarity based on the geographically weighted regression model," Environment and Planning A, Pion Ltd, London, vol. 32(1), pages 9-32, January.
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