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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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  • 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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    1. Zachary Parolin, 2019. "The Effect of Benefit Underreporting on Estimates of Poverty in the United States," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 144(2), pages 869-898, July.
    2. Donal O'Neill & Olive Sweetman, 2013. "Estimating Obesity Rates in Europe in the Presence of Self-Reporting Errors," Economics Department Working Paper Series n236-13.pdf, Department of Economics, National University of Ireland - Maynooth.
    3. Battistin, Erich & De Nadai, Michele & Vuri, Daniela, 2017. "Counting rotten apples: Student achievement and score manipulation in Italian elementary Schools," Journal of Econometrics, Elsevier, vol. 200(2), pages 344-362.
    4. Mark Brooks & Rattiya S. Lippe & Hermann Waibel, 2020. "Comprehensive data quality studies as a component of poverty assessments," TVSEP Working Papers wp-019, Leibniz Universitaet Hannover, Institute for Environmental Economics and World Trade, Project TVSEP.
    5. Adrian Chadi, 2019. "Dissatisfied with life or with being interviewed? Happiness and the motivation to participate in a survey," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 53(3), pages 519-553, October.
    6. 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.
    7. 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.
    8. Pudney, Stephen & Diaz, Yadira, 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.
    9. 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.
    10. Zhang, Lixuan & Yencha, Christopher, 2022. "Examining perceptions towards hiring algorithms," Technology in Society, Elsevier, vol. 68(C).
    11. 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.
    12. Fikri Lahafi & Agus Muchsin & Syahriyah Semaun & Zalina Abdul Rahim, 2019. "Development of Creative Industries Training Towards Sharia Economic Empowerment In Bilalangnge Community, Parepare City, South Sulawesi," Malaysian E Commerce Journal (MECJ), Zibeline International Publishing, vol. 3(2), pages 33-35, August.
    13. Jiang Bin & Kim Jun Sung & Li Chuhui & Yang Ou, 2018. "Social Network Structure and Risk Sharing in Villages," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 18(3), pages 1-8, July.
    14. 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.
    15. Margaret M. C. Thomas, 2022. "Longitudinal Patterns of Material Hardship Among US Families," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 163(1), pages 341-370, August.
    16. Maria Michela Dickson & Giuseppe Espa & Lorenzo Fattorini & Flavio Santi, 2022. "Double-calibration estimators accounting for under-coverage and nonresponse in socio-economic surveys," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1273-1288, December.
    17. Firouzeh Noghrehchi & Jakub Stoklosa & Spiridon Penev, 2020. "Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables," Computational Statistics, Springer, vol. 35(3), pages 1291-1317, September.

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    Keywords

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