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Small area estimation-based prediction methods to track poverty : validation and applications

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  • Christiaensen, Luc
  • Lanjouw, Peter
  • Luoto, Jill
  • Stifel, David

Abstract

Tracking poverty is predicated on the availability of comparable consumption data and reliable price deflators. However, regular series of strictly comparable data are only rarely available. Price deflators are also often missing or disputed. In response, poverty prediction methods that track consumption correlates as opposed to consumption itself have been developed. These methods typically assume that the estimated relation between consumption and its predictors is stable over time -- an assumption that cannot usually be tested directly. This study analyzes the performance of poverty prediction models based on small area estimation techniques. Predicted poverty estimates are compared with directly observed levels in two country settings where data comparability over time is not a problem. Prediction models that employ either non-staple food or non-food expenditures or a full set of assets as predictors are found to yield poverty estimates that match observed poverty well. This offers some support to the use of such methods to approximate the evolution of poverty. Two further country examples illustrate how an application of the method employing models based on household assets can help to adjudicate between alternative price deflators.

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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 5683.

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Date of creation: 01 Jun 2011
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Handle: RePEc:wbk:wbrwps:5683

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Keywords: Rural Poverty Reduction; Regional Economic Development; Debt Markets; Achieving Shared Growth;

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References

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  1. Carlo Azzarri & Gero Carletto & Benjamin Davis & Alberto Zezza, 2006. "Monitoring Poverty Without Consumption Data : An Application Using the Albania Panel Survey," Eastern European Economics, M.E. Sharpe, Inc., M.E. Sharpe, Inc., vol. 44(1), pages 59-82, February.
  2. John Gibson & Steven Stillman, 2004. "CPI Bias and Real Living Standards in Russia During The Transition," Econometric Society 2004 North American Summer Meetings, Econometric Society 504, Econometric Society.
  3. Steven Stillman & Duncan Thomas, 2008. "Nutritional Status During an Economic Crisis: Evidence from Russia," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 118(531), pages 1385-1417, 08.
  4. Angus Deaton, 2008. "Price trends in India and their implications for measuring poverty," Working Papers, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies. 1008, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
  5. Gibson, John & Huang, Jikun & Rozelle, Scott, 2001. "Improving Estimates Of Inequality And Poverty From Urban China'S Household Income And Expenditure Survey," Working Papers, University of California, Davis, Department of Agricultural and Resource Economics 11989, University of California, Davis, Department of Agricultural and Resource Economics.
  6. Sarah Harrower & John Hoddinott, 2005. "Consumption Smoothing in the Zone Lacustre, Mali," Journal of African Economies, Centre for the Study of African Economies (CSAE), Centre for the Study of African Economies (CSAE), vol. 14(4), pages 489-519, December.
  7. Tarozzi, Alessandro, 2007. "Calculating Comparable Statistics From Incomparable Surveys, With an Application to Poverty in India," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 25, pages 314-336, July.
  8. Demombynes, Gabriel & Elbers, Chris & Lanjouw, Jean O. & Lanjouw, Peter, 2007. "How good a map ? Putting small area estimation to the test," Policy Research Working Paper Series 4155, The World Bank.
  9. Lanjouw, Jean Olson & Lanjouw, Peter, 2001. "How to Compare Apples and Oranges: Poverty Measurement Based on Different Definitions of Consumption," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 47(1), pages 25-42, March.
  10. Alessandro Tarozzi & Angus Deaton, 2009. "Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 773-792, November.
  11. Tarozzi, Alessandro, 2011. "Can census data alone signal heterogeneity in the estimation of poverty maps?," Journal of Development Economics, Elsevier, Elsevier, vol. 95(2), pages 170-185, July.
  12. Martin Wall & Deborah Johnston, 2008. "Counting Heads or Counting Televisions: Can Asset-based Measures of Welfare Assist Policy-makers in Russia?," Journal of Human Development and Capabilities, Taylor & Francis Journals, Taylor & Francis Journals, vol. 9(1), pages 131-147.
  13. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, Econometric Society, vol. 71(1), pages 355-364, January.
  14. Angus Deaton, 2003. "Adjusted Indian Poverty Estimates for 1999-2000," Working Papers, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies. 200, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
  15. Astrid Mathiassen, 2009. "A model based approach for predicting annual poverty rates without expenditure data," Journal of Economic Inequality, Springer, Springer, vol. 7(2), pages 117-135, June.
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Cited by:
  1. Douidich, Mohamed & Ezzrari, Abdeljaouad & Van der Weide, Roy & Verme, Paolo, 2013. "Estimating quarterly poverty rates using labor force surveys : a primer," Policy Research Working Paper Series 6466, The World Bank.
  2. 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.
  3. Hassine, Nadia Belhaj, 2014. "Economic inequality in the Arab region," Policy Research Working Paper Series 6911, The World Bank.
  4. 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.

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