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Estimating Income Poverty in the Presence of Missing Data and Measurement Error

Reliable measures of poverty are an essential statistical tool for public policies aimed at reducing poverty. In this paper we consider the reliability of income poverty measures based on survey data which are typically plagued by missing data and measurement error. Neglecting these problems can bias the estimated poverty rates. We show how to derive upper and lower bounds for the population poverty rate using the sample evidence, an upper bound on the probability of misclassifying people into poor and non-poor, and instrumental or monotone instrumental variable assumptions. By using the European Community Household Panel, we compute bounds for the poverty rate in ten European countries and study the sensitivity of poverty comparisons across countries to missing data and measurement error problems. Supplemental materials for this article may be downloaded from the JBES website.

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Paper provided by Tor Vergata University, CEIS in its series CEIS Research Paper with number 145.

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Length: 32 pages
Date of creation: 30 Sep 2009
Date of revision: 30 Sep 2009
Handle: RePEc:rtv:ceisrp:145
Contact details of provider: Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
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  1. John Bound & Alan B. Krueger, 1989. "The Extent of Measurement Error In Longitudinal Earnings Data: Do Two Wrongs Make A Right?," NBER Working Papers 2885, National Bureau of Economic Research, Inc.
  2. Horowitz, J.L. & Manski, C.F., 1995. "Censoring of Outcomes and Regressors Due to Survey Nonresponse: Identification and Estimation Using Weights and Imputations," Working papers 9525, Wisconsin Madison - Social Systems.
  3. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
  4. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
  5. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Oxford University Press, vol. 69(2), pages 357-378.
  6. Ravallion, Martin, 1994. "Poverty rankings using noisy data on living standards," Economics Letters, Elsevier, vol. 45(4), pages 481-485, August.
  7. Nicoletti, Cheti, 2003. "Poverty analysis with unit and item nonresponses: alternative estimators compared," ISER Working Paper Series 2003-20, Institute for Social and Economic Research.
  8. Kreider, Brent & Pepper, John V., 2003. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Staff General Research Papers 10229, Iowa State University, Department of Economics.
  9. Nicoletti, Cheti & Peracchi, Franco, 2002. "A cross-country comparison of survey nonparticipation in the ECHP -ISER working paper-," ISER Working Paper Series 2002-32, Institute for Social and Economic Research.
  10. Pudney, Stephen & Francavilla, Francesca, 2006. "Income mis-measurement and the estimation of poverty rates: an analysis of income poverty in Albania," ISER Working Paper Series 2006-35, Institute for Social and Economic Research.
  11. Charles F. Manski & John V. Pepper, 2009. "More on monotone instrumental variables," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages S200-S216, 01.
  12. Bound, John, et al, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-68, July.
  13. van Praag, Bernard M S & Hagenaars, Aldi J M & van Eck, Wim, 1983. "The Influence of Classification and Observation Errors on the Measurement of Income Inequality," Econometrica, Econometric Society, vol. 51(4), pages 1093-108, July.
  14. Juan Carlos Chavez-Martin del Campo, 2004. "Partial Identification of Poverty Measures with Contaminated Data," Econometric Society 2004 Latin American Meetings 221, Econometric Society.
  15. Molinari, Francesca, 2005. "Partial Identification of Probability Distributions with Misclassified Data," Working Papers 05-10, Cornell University, Center for Analytic Economics.
  16. Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
  17. Cheti Nicoletti & Franco Peracchi, 2006. "The effects of income imputation on microanalyses: evidence from the European Community Household Panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 625-646.
  18. Biewen, Martin, 2002. "Bootstrap inference for inequality, mobility and poverty measurement," Journal of Econometrics, Elsevier, vol. 108(2), pages 317-342, June.
  19. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843 Elsevier.
  20. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
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