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

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Author Info
Cheti Nicoletti (ISER, University of Essex Colchester)
Franco Peracchi () (Faculty of Economics, University of Rome "Tor Vergata")
Francesca Foliano (Faculty of Economics, University of Rome "Tor Vergata")

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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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File URL: ftp://www.ceistorvergata.it/repec/rpaper/RP145.pdf
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Publisher 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
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Related research
Keywords: Misclassification error; Survey non-response; Partial identification.;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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    Other versions:
  2. 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. [Downloadable!] (restricted)
    Other versions:
  3. Cowell, Frank A & Victoria-Feser, Maria-Pia, 1996. "Robustness Properties of Inequality Measures," Econometrica, Econometric Society, vol. 64(1), pages 77-101, January. [Downloadable!] (restricted)
  4. Biewen, Martin, 2002. "Bootstrap inference for inequality, mobility and poverty measurement," Journal of Econometrics, Elsevier, vol. 108(2), pages 317-342, June. [Downloadable!] (restricted)
  5. 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. [Downloadable!] (restricted)
  6. 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. [Downloadable!] (restricted)
  7. 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. [Downloadable!] (restricted)
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  8. Molinari, Francesca, 2008. "Partial identification of probability distributions with misclassified data," Journal of Econometrics, Elsevier, vol. 144(1), pages 81-117, May. [Downloadable!] (restricted)
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  9. 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. [Downloadable!] (restricted)
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  10. Vazques Alvarez, R. & Melenberg, B. & Soest, A. van, 2001. "Nonparametric bounds in the presence of item nonresponse, unfolding brackets, and anchoring," Discussion Paper 67, Tilburg University, Center for Economic Research. [Downloadable!]
  11. 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. [Downloadable!] (restricted)
  12. 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.
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  13. Ravallion, Martin, 1994. "Poverty rankings using noisy data on living standards," Economics Letters, Elsevier, vol. 45(4), pages 481-485, August. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
  15. Charles F. Manski & John V. Pepper, 2009. "More on monotone instrumental variables," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages S200-S216, 01. [Downloadable!] (restricted)
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