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Optimising Anti-Poverty Transfers With Quantile Regressions

Under perfect observation of incomes, designing such scheme boils down to solving an optimisation program under constraints, which can be achieved with well-defined methods. In contrast, when incomes cannot be perfectly observed, the schemes are usually based on predictions of living standards using ancillary regressions and household survey data to predict the unobserved living standards of households. In this paper, we study the poverty minimisation program under imperfect information. We show why using predictions of living standards helps to deal approximately with an otherwise intractable problem. Then, we propose a new approach to the practical optimisation procedure based on improved predictions of living standards in terms of the targeting problem to be solved. Our new empirical methodology to target direct transfers against poverty is based on observable correlates and on estimation methods that can focus on the poor: the quantile regressions. We illustrate our results using data from Tunisia.

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Paper provided by Institut d'economie publique (IDEP), Marseille, France in its series IDEP Working Papers with number 0601.

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Length: 30 pages
Date of creation: Feb 2006
Date of revision: Feb 2006
Handle: RePEc:iep:wpidep:0601
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  1. Schady, Norbert R, 2002. "Picking the Poor: Indicators for Geographic Targeting in Peru," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 48(3), pages 417-33, September.
  2. Christophe Muller & Sami Bibi, 2006. "Focused Targeting against Poverty Evidence from Tunisia," IDEP Working Papers 0602, Institut d'economie publique (IDEP), Marseille, France, revised Apr 2006.
  3. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
  4. Glewwe, Paul, 1992. "Targeting assistance to the poor : Efficient allocation of transfers when household income is not observed," Journal of Development Economics, Elsevier, vol. 38(2), pages 297-321, April.
  5. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-66, May.
  6. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
  7. Chakravarty, Satya R. & Mukherjee, Diganta, 1998. "Optimal subsidy for the poor," Economics Letters, Elsevier, vol. 61(3), pages 313-319, December.
  8. Ravallion, Martin & Chao, Kalvin, 1989. "Targeted policies for poverty alleviation under imperfect information: Algorithms and applications," Journal of Policy Modeling, Elsevier, vol. 11(2), pages 213-224.
  9. Glewwe, P. & Kanaan, O., 1989. "Targeting Assistance to the Poor: A Multivariate Approach Using Household Survey Data," Papers 94, Warwick - Development Economics Research Centre.
  10. Besley, Timothy J & Kanbur, S M Ravi, 1988. "Food Subsidies and Poverty Alleviation," Economic Journal, Royal Economic Society, vol. 98(392), pages 701-19, September.
  11. Park, Albert & Wang, Sangui & Wu, Guobao, 2002. "Regional poverty targeting in China," Journal of Public Economics, Elsevier, vol. 86(1), pages 123-153, October.
  12. Bigman, David & Srinivasan, P. V., 2002. "Geographical targeting of poverty alleviation programs: methodology and applications in rural India," Journal of Policy Modeling, Elsevier, vol. 24(3), pages 237-255, June.
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