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How Well Can Method Substitute for Data? Five Experiments in Poverty Analysis

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  • Ravallion, Martin

Abstract

No one doubts the good data are essential to sound policymaking. Alas, data are invariably faulty. Methodological solutions to data inadequacies have often been proposed and implemented, but they have been tested only rarely. Yet the methods that are used may well determine the direction of policy. For example, the particular survey method used--and the way nonsurvey data are interpreted--may be critical in assessing whether a country's strategy for reducing poverty is working. This article shows how counterfactual experiments can help test the reliability of various methods of dealing with common data problems. Well-designed methods--and they need not be very complicated--can help get around the problem, although it appears that substituting method for data is a long way from being perfect. Copyright 1996 by Oxford University Press.

Suggested Citation

  • Ravallion, Martin, 1996. "How Well Can Method Substitute for Data? Five Experiments in Poverty Analysis," World Bank Research Observer, World Bank Group, vol. 11(2), pages 199-221, August.
  • Handle: RePEc:oup:wbrobs:v:11:y:1996:i:2:p:199-221
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    Cited by:

    1. Bidani, Benu & Ravallion, Martin, 1997. "Decomposing social indicators using distributional data," Journal of Econometrics, Elsevier, vol. 77(1), pages 125-139, March.
    2. Gaurav Datt & Martin Ravallion, 2002. "Is India's Economic Growth Leaving the Poor Behind?," Journal of Economic Perspectives, American Economic Association, vol. 16(3), pages 89-108, Summer.
    3. Quang Tran, Tuyen & Hong Nguyen, Son & Van Vu, Huong & Quoc Nguyen, Viet, 2014. "Determinants of poverty among ethnic minorities in the Northwest region, Vietnam," MPRA Paper 59144, University Library of Munich, Germany, revised 01 Oct 2014.
    4. Carlo Azzarri & Gero Carletto & Benjamin Davis & Alberto Zezza, 2006. "Monitoring Poverty Without Consumption Data : An Application Using the Albania Panel Survey," Eastern European Economics, Taylor & Francis Journals, vol. 44(1), pages 59-82, February.
    5. Fambon, Samuel & McKay, Andy & Timnou, Joseph-Pierre & Kouakep, Olive Stephanie & Dzossa, Anaclet, 2014. "Growth, poverty, and inequality: The case study of Cameroon," WIDER Working Paper Series 154, World Institute for Development Economic Research (UNU-WIDER).
    6. Ahmed, Faizuddin & Dorji, Cheku & Takamatsu, Shinya & Yoshida, Nobuo, 2014. "Hybrid survey to improve the reliability of poverty statistics in a cost-effective manner," Policy Research Working Paper Series 6909, The World Bank.
    7. Dang, Hai-Anh H. & Lanjouw, Peter F. & Serajuddin, Umar, 2014. "Updating poverty estimates at frequent intervals in the absence of consumption data : methods and illustration with reference to a middle-income country," Policy Research Working Paper Series 7043, The World Bank.
    8. Howard White, 2005. "Combining the Quantitative and Qualitative Approaches to Poverty Measurement and Analysis," Development and Comp Systems 0505003, EconWPA.
    9. Haughton, Jonathan & Khandker, Shahidur R., 2014. "The Surprising Effects of the Great Recession: Losers and Winners in Thailand in 2008–09," World Development, Elsevier, vol. 56(C), pages 77-92.
    10. World Bank, 2011. "Georgia - Poverty dynamics, 2003-2010," World Bank Other Operational Studies 2806, The World Bank.
    11. Sanjeev Gupta & Marijn Verhoeven & Erwin R. Tiongson, 2003. "Public spending on health care and the poor," Health Economics, John Wiley & Sons, Ltd., vol. 12(8), pages 685-696.
    12. Newhouse, D. & Shivakumaran, S. & Takamatsu, S. & Yoshida, N., 2014. "How survey-to-survey imputation can fail," Policy Research Working Paper Series 6961, The World Bank.
    13. Tilman Bruck, "undated". "Determinants of Rural Poverty in Post-War Mozambique: Evidence from a Household Survey and Implications for Government and Donor Policy," QEH Working Papers qehwps67, Queen Elizabeth House, University of Oxford.

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