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Optimising Anti-Poverty Transfers With Quantile Regressions

  • Christophe Muller

    ()

    (Universidad de Alicante)

Anti-poverty transfer schemes are one of the main way of fightingpoverty. Under perfect observation of incomes, designing such scheme boilsdown to solving an optimisation program under constraints, which can beachieved with well-defined methods. In contrast, when incomes cannot beperfectly observed, the schemes are usually based on predictions of livingstandards using ancillary regressions and household survey data to predict theunobserved living standards of households. In this paper, we study the povertyminimisation program under imperfect information. We show why usingpredictions of living standards helps to deal approximately with an otherwiseintractable problem. Then, we propose a new approach to the practicaloptimisation procedure based on improved predictions of living standards interms of the targeting problem to be solved. Our new empirical methodology totarget direct transfers against poverty is based on observable correlates and onestimation methods that can focus on the poor: the quantile regressions. Weillustrate our results using data from Tunisia.

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Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2006-07.

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Length: 29 pages
Date of creation: Jun 2006
Date of revision:
Publication status: Published by Ivie
Handle: RePEc:ivi:wpasad:2006-07
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  1. Park, Albert & Wang, Sangui & Wu, Guobao, 2002. "Regional poverty targeting in China," Journal of Public Economics, Elsevier, vol. 86(1), pages 123-153, October.
  2. 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.
  3. Chakravarty, Satya R. & Mukherjee, Diganta, 1998. "Optimal subsidy for the poor," Economics Letters, Elsevier, vol. 61(3), pages 313-319, December.
  4. Christophe Muller & Sami Bibi, 2007. "Focused Targeting against Poverty Evidence from Tunisia," Working Papers DT/2007/07, DIAL (Développement, Institutions et Mondialisation).
  5. Schady, Norbert R., 2000. "Picking the poor : indicators for geographic targeting in Peru," Policy Research Working Paper Series 2477, The World Bank.
  6. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-66, May.
  7. 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.
  8. Glewwe, P. & Kanaan, O., 1989. "Targeting Assistance to the Poor: A Multivariate Approach Using Household Survey Data," Papers 94, Warwick - Development Economics Research Centre.
  9. Moshe Buchinsky & Jinyong Hahn, 1998. "An Alternative Estimator for the Censored Quantile Regression Model," Econometrica, Econometric Society, vol. 66(3), pages 653-672, May.
  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. 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.
  12. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
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