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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.

Suggested Citation

  • Cheti Nicoletti & Franco Peracchi & Francesca Foliano, 2009. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," CEIS Research Paper 145, Tor Vergata University, CEIS, revised 30 Sep 2009.
  • Handle: RePEc:rtv:ceisrp:145
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    References listed on IDEAS

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    1. Juan Carlos Chavez-Martin del Campo, 2004. "Partial Identification of Poverty Measures with Contaminated Data," Econometric Society 2004 Latin American Meetings 221, Econometric Society.
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    17. 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.
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    Cited by:

    1. Nic Baigrie & Katherine Eyal, 2014. "An Evaluation of the Determinants and Implications of Panel Attrition in the National Income Dynamics Survey (2008-2010)," South African Journal of Economics, Economic Society of South Africa, vol. 82(1), pages 39-65, March.
    2. Ayllón, Sara & Fusco, Alessio, 2017. "Are income poverty and perceptions of financial difficulties dynamically interrelated?," Journal of Economic Psychology, Elsevier, vol. 61(C), pages 103-114.
    3. repec:eee:econom:v:200:y:2017:i:2:p:344-362 is not listed on IDEAS
    4. 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.
    5. 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.
    6. 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.
    7. Erich Battistin & Michele De Nadai & Daniela Vuri, 2014. "Counting Rotten Apples: Student Achievement and Score Manipulation in Italian Elementary Schools," CEIS Research Paper 329, Tor Vergata University, CEIS, revised 08 Sep 2014.
    8. Srini Vasan & Adelamar Alcantara, 2016. "GIS-based Methods for Estimating Missing Poverty Rates & Projecting Future Rates in Census Tracts," Review of Economics & Finance, Better Advances Press, Canada, vol. 6, pages 1-13, August.
    9. Chadi, Adrian, 2014. "Dissatisfied with Life or with Being Interviewed? Happiness and Motivation to Participate in a Survey," Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100505, Verein für Socialpolitik / German Economic Association.
    10. Donal O’Neill & Olive Sweetman, 2016. "Bounding obesity rates in the presence of self-reporting errors," Empirical Economics, Springer, vol. 50(3), pages 857-871, May.

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    Keywords

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

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