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Measuring Vulnerability to Poverty Using Long-Term Panel Data

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  • Katja Landau
  • Stephan Klasen
  • Walter Zucchini

Abstract

We investigate the accuracy of ex ante assessments of vulnerability to income poverty using cross-sectional data and panel data. We use long-term panel data from Germany and apply di erent regression models, based on household covariates and previous-year equivalence income, to classify a household as vulnerable or not. Predictive performance is assessed using the Receiver Operating Characteristics (ROC), which takes account of false positive as well as true positive rates. Estimates based on cross-sectional data are much less accurate than those based on panel data, but for Germany, the accuracy of vulnerability predictions is limited even when panel data are used. In part this low accuracy is due to low poverty incidence and high mobility in and out of poverty.

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Bibliographic Info

Paper provided by DIW Berlin, The German Socio-Economic Panel (SOEP) in its series SOEPpapers on Multidisciplinary Panel Data Research with number 481.

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Length: 42 p.
Date of creation: 2012
Date of revision:
Handle: RePEc:diw:diwsop:diw_sp481

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Keywords: vulnerability; poverty; ROC; German panel data;

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  1. Günther, Isabel & Harttgen, Kenneth, 2009. "Estimating Households Vulnerability to Idiosyncratic and Covariate Shocks: A Novel Method Applied in Madagascar," World Development, Elsevier, vol. 37(7), pages 1222-1234, July.
  2. Selten,Reinhard, . "Properties of a measure of predictive succes," Discussion Paper Serie B 130, University of Bonn, Germany.
  3. Frick, Joachim R. & Grabka, Markus M., 2007. "Item Non-Response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," IZA Discussion Papers 3043, Institute for the Study of Labor (IZA).
  4. Pritchett, Lant & Suryahadi, Asep & Sumarto, Sudarno, 2000. "Quantifying vulnerability to poverty - a proposed measure, applied to Indonesia," Policy Research Working Paper Series 2437, The World Bank.
  5. Ligon, Ethan & Schechter, Laura, 2004. "Evaluating different approaches to estimating vulnerability," Social Protection Discussion Papers 30159, The World Bank.
  6. Joon-Ho Hahm & Douglas G. Steigerwald, 1999. "Consumption Adjustment under Time-Varying Income Uncertainty," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 32-40, February.
  7. Cesar Calvo & Stefan Dercon, 2005. "Measuring Individual Vulnerability," Economics Series Working Papers 229, University of Oxford, Department of Economics.
  8. Shubham Chaudhuri & Jyotsna Jalan & Asep Suryahadi, 2002. "Assessing household vulnerability to poverty from cross-sectional data: A methodology and estimates from Indonesia," Discussion Papers 0102-52, Columbia University, Department of Economics.
  9. Yuan Zhang & Guanghua Wan, 2009. "How Precisely Can We Estimate Vulnerability to Poverty?," Oxford Development Studies, Taylor & Francis Journals, vol. 37(3), pages 277-287.
  10. Hoddinott, John & Quisumbing, Agnes, 2003. "Methods for microeconometric risk and vulnerability assessments," Social Protection Discussion Papers 29138, The World Bank.
  11. Christiaensen, Luc. J. & Subbarao, Kalanidhi, 2004. "Toward an understanding of household vulnerability in rural Kenya," Policy Research Working Paper Series 3326, The World Bank.
  12. Joachim R. Frick & Markus M. Grabka, 2003. "Missing Income Data in the German SOEP: Incidence, Imputation and its Impact on the Income Distribution," Discussion Papers of DIW Berlin 376, DIW Berlin, German Institute for Economic Research.
  13. Asep Suryahadi & Sudarno Sumarto, 2003. "Poverty and Vulnerability in Indonesia Before and After the Economic Crisis," Asian Economic Journal, East Asian Economic Association, vol. 17(1), pages 45-64, 03.
  14. Raghbendra Jha & Tu Dang, 2008. "Vulnerability to poverty in Papua New Guinea," Departmental Working Papers 2008-08, The Australian National University, Arndt-Corden Department of Economics.
  15. Ligon, Ethan & Laura Schechter, 2002. "Measuring Vulnerability," Royal Economic Society Annual Conference 2002 128, Royal Economic Society.
  16. Lusardi, Annamaria, 1996. "Permanent Income, Current Income, and Consumption: Evidence from Two Panel Data Sets," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 81-90, January.
  17. Annelies Debels & Leen Vandecasteele, 2008. "The Time Lag In Annual Household-Based Income Measures: Assessing And Correcting The Bias," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 54(1), pages 71-88, 03.
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