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

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Abstract

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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Bibliographic Info

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

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Postal: CEIS - Centre for Economic and International Studies - Faculty of Economics - University of Rome "Tor Vergata" - Via Columbia, 2 00133 Roma
Phone: +390672595601
Fax: +39062020687
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Web page: http://www.ceistorvergata.it
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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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Web: http://www.ceistorvergata.it

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Keywords: Misclassification error; Survey non-response; Partial identification.;

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References

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  1. Charles F. Manski & John V. Pepper, 2009. "More on monotone instrumental variables," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages S200-S216, 01.
  2. 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.
  3. Molinari, Francesca, 2005. "Partial Identification of Probability Distributions with Misclassified Data," Working Papers 05-10, Cornell University, Center for Analytic Economics.
  4. Cheti Nicoletti, 2004. "Poverty Analysis With Unit And Item Non-Responses: Alternative Estimators Compared," Royal Economic Society Annual Conference 2004 120, Royal Economic Society.
  5. Joel L. Horowitz & Charles F. Manski, 1996. "Censoring of Outcomes and Regressors Due To Survey Nonresponse: Identification and Estimation Using Weights and Imputations," Econometrics 9602007, EconWPA, revised 06 Mar 1996.
  6. Vazquez-Alvarez, R. & Melenberg, B. & Soest, A.H.O. van, 1999. "Bounds on Quantiles in the Presence of Full and Partial Item Nonresponse," Discussion Paper 1999-38, Tilburg University, Center for Economic Research.
  7. Andrew Chesher & Christian Schluter, 2001. "Welfare measurement and measurement error," CeMMAP working papers CWP03/01, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  8. Biewen, Martin, 2002. "Bootstrap inference for inequality, mobility and poverty measurement," Journal of Econometrics, Elsevier, vol. 108(2), pages 317-342, June.
  9. Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
  10. 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.
  11. 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.
  12. 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.
  13. Kreider, Brent & Pepper, John V., 2007. "Disability and Employment: Reevaluating the Evidence in Light of Reporting Errors," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 432-441, June.
  14. 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.
  15. Charles F. Manski & John V. Pepper, 1998. "Monotone Instrumental Variables: With an Application to the Returns to Schooling," Virginia Economics Online Papers 308, University of Virginia, Department of Economics.
  16. Vazquez-Alvarez, R. & Melenberg, B. & Soest, A.H.O. van, 2001. "Nonparametric Bounds in the Presence of Item Nonresponse, Unfolding Brackets and Anchoring," Discussion Paper 2001-67, Tilburg University, Center for Economic Research.
  17. Ravallion, Martin, 1994. "Poverty rankings using noisy data on living standards," Economics Letters, Elsevier, vol. 45(4), pages 481-485, August.
  18. 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.
  19. Juan Carlos Chavez-Martin del Campo, 2004. "Partial Identification of Poverty Measures with Contaminated Data," Econometric Society 2004 Latin American Meetings 221, Econometric Society.
  20. 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.
  21. 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.
  22. 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.
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Citations

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Cited by:
  1. Donal O'Neill & Olive Sweetman, 2013. "Estimating Obesity Rates in Europe in the Presence of Self-Reporting Errors," Economics, Finance and Accounting Department Working Paper Series n236-13.pdf, Department of Economics, Finance and Accounting, National University of Ireland - Maynooth.
  2. Adrian Chadi, 2014. "Dissatisfied with Life or with Being Interviewed? Happiness and Motivation to Participate in a Survey," IAAEU Discussion Papers 201403, Institute of Labour Law and Industrial Relations in the European Union (IAAEU).
  3. O'Neill, Donal & Sweetman, Olive, 2013. "Estimating Obesity Rates in the Presence of Measurement Error," IZA Discussion Papers 7288, Institute for the Study of Labor (IZA).
  4. Bruno Arpino & Elisabetta De Cao & Franco Peracchi, 2011. "Using panel data to partially identify HIV prevalence When HIV status is not missing at random," Working Papers 048, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
  5. Diaz, Yadira & Pudney, Stephen, 2013. "Measuring poverty persistence with missing data with an application to Peruvian panel data," ISER Working Paper Series 2013-22, Institute for Social and Economic Research.

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