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

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Author Info

  • Nicoletti, Cheti
  • Peracchi, Franco
  • Foliano, Francesca

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.

(This abstract was borrowed from another version of this item.)

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File URL: http://pubs.amstat.org/doi/abs/10.1198/jbes.2010.07185
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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 29 (2011)
Issue (Month): 1 ()
Pages: 61-72

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Handle: RePEc:bes:jnlbes:v:29:i:1:y:2011:p:61-72

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References

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  1. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
  2. 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.
  3. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," Review of Economic Studies, Oxford University Press, vol. 69(2), pages 357-378.
  4. 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.
  5. repec:att:wimass:9525 is not listed on IDEAS
  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. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May.
  8. 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.
  9. Biewen, Martin, 2002. "Bootstrap inference for inequality, mobility and poverty measurement," Journal of Econometrics, Elsevier, vol. 108(2), pages 317-342, June.
  10. Charles F. Manski & John V. Pepper, 2009. "More on monotone instrumental variables," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages S200-S216, 01.
  11. Ravallion, Martin, 1994. "Poverty rankings using noisy data on living standards," Economics Letters, Elsevier, vol. 45(4), pages 481-485, August.
  12. 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.
  13. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
  14. 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.
  15. 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.
  16. Juan Carlos Chavez-Martin del Campo, 2004. "Partial Identification of Poverty Measures with Contaminated Data," Econometric Society 2004 Latin American Meetings 221, Econometric Society.
  17. Cheti Nicoletti, 2004. "Poverty Analysis With Unit And Item Non-Responses: Alternative Estimators Compared," Royal Economic Society Annual Conference 2004 120, Royal Economic Society.
  18. 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.
  19. repec:ese:iserwp:2006-35 is not listed on IDEAS
  20. 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.
  21. repec:ese:iserwp:2002-32 is not listed on IDEAS
  22. 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.
  23. Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January.
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Citations

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Cited by:
  1. repec:ese:iserwp:2013-22 is not listed on IDEAS
  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. Bruno Arpino & Elisabetta De Cao & Franco Peracchi, 2011. "Using panel data to partially identify HIV prevalence when HIV status is not missing at random," EIEF Working Papers Series 1113, Einaudi Institute for Economics and Finance (EIEF), revised Aug 2011.
  4. 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).
  5. 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.

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