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The “Missing Rich” in Household Surveys: Causes and Correction Approaches

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  • Nora Lustig

    (Stone Center for Latin American Studies, Department of Economics, Tulane University, CEQ Institute)

Abstract

This paper presents a survey of causes and correction approaches to address the “missing rich” problem in household surveys. “Missing rich” here is a catch-all term for the issues that affect the upper tail of the distribution of income: undercoverage, sparseness, unit and item nonresponse, underreporting and top coding. Upper tail issues can result in serious biases and imprecision of survey-based inequality measures. A number of correction approaches have been proposed. A main distinction is between those that rely on within-survey methods and those that combine survey data with information from external sources such as tax records, National Accounts, rich lists or other external information. Within each category, the methods can correct by replacing top incomes or increasing their weight (reweighting). Correction methods can be nonparametric and parametric. This survey aims to help researchers choose appropriate correction strategies and design robustness tests.

Suggested Citation

  • Nora Lustig, 2019. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," Commitment to Equity (CEQ) Working Paper Series 75, Tulane University, Department of Economics.
  • Handle: RePEc:tul:ceqwps:75
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    Cited by:

    1. Gabriel Burdín & Mauricio de Rosa & Andrea Vigorito & Joan Vilá, 2019. "Was falling inequality in all Latin American countries a data-driven illusion? Income distribution and mobility patterns in Uruguay 2009-2016," Documentos de Trabajo (working papers) 19-30, Instituto de Economia - IECON.
    2. Advani, Arun, 2021. "Missing Incomes in the UK : Evidence and Policy Implications," The Warwick Economics Research Paper Series (TWERPS) 1364, University of Warwick, Department of Economics.
    3. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    4. Pablo Gutiérrez Cubillos, 2022. "Gini and undercoverage at the upper tail: a simple approximation," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 29(2), pages 443-471, April.
    5. Bartels, Charlotte & Waldenström, Daniel, 2021. "Inequality and top incomes," GLO Discussion Paper Series 959, Global Labor Organization (GLO).
    6. Mauricio de Rosa & Joan Vilá, 2020. "Distributing the missing third: growth and falling inequality in Uruguay 2009-2016," Documentos de Trabajo (working papers) 20-05, Instituto de Economia - IECON.
    7. Ignacio Flores, 2021. "The capital share and income inequality: Increasing gaps between micro and macro-data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 685-706, December.
    8. Ooms, Tahnee, 2021. "Correcting the underestimation of capital incomes in inequality indicators: with an application to the UK, 1997–2016," LSE Research Online Documents on Economics 108900, London School of Economics and Political Science, LSE Library.

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    More about this item

    Keywords

    top incomes; inequality measures; nonresponse; underreporting; replacing and reweighting methods; imputation; poststratification; Pareto distribution; tax records;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

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