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Missing Top Income Recipients

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  • Martin Ravallion

    (Georgetown University, and the NBER)

Abstract

Low response rates among rich households are thought to be a serious problem in many applications using household surveys. The paper discusses the various ways the problem can be dealt with, and makes some recommendations for practice, including in developing countries. Under certain conditions, income-selective non-compliance with an initially randomized assignment can be corrected by reweighting the data. This requires that the surveys pick up at least some top incomes. If not, then income tax records can help, including in estimating distributional national accounts. However, tax data come with their own concerns including tax avoidance/evasion, weak coverage of informal sectors and illicit incomes, and concerns about construct validity, given the limitations of taxable income as a basis for inter-personal comparisons of economic welfare. An appropriately weighted survey-based distribution of an acceptable measure of economic welfare need not be less reliable for most purposes of distributional analysis than income-tax records, including in combination with surveys. The choice will depend on the question to be addressed, and country-specific circumstances. These measurement issues warrant further research across multiple settings.

Suggested Citation

  • Martin Ravallion, 2022. "Missing Top Income Recipients," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 205-222, March.
  • Handle: RePEc:spr:joecin:v:20:y:2022:i:1:d:10.1007_s10888-022-09530-0
    DOI: 10.1007/s10888-022-09530-0
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    Cited by:

    1. Ravallion, Martin & Chen, Shaohua, 2022. "Fleshing out the olive? Observations on income polarization in China since 1981," China Economic Review, Elsevier, vol. 76(C).
    2. Karina Doorley & Jan Gromadzki & Piotr Lewandowski & Dora Tuda & Philippe Van Kerm, 2023. "Automation and income inequality in Europe," IBS Working Papers 06/2023, Instytut Badan Strukturalnych.
    3. Haiyuan Wan & Yangcheng Yu, 2023. "Correction of China's income inequality for missing top incomes," Review of Development Economics, Wiley Blackwell, vol. 27(3), pages 1769-1791, August.
    4. Luis Ayala & Ana Pérez & Mercedes Prieto-Alaiz, 2022. "The impact of different data sources on the level and structure of income inequality," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(3), pages 583-611, September.
    5. Kristian S. Nielsen & Kimberly A. Nicholas & Felix Creutzig & Thomas Dietz & Paul C. Stern, 2021. "The role of high-socioeconomic-status people in locking in or rapidly reducing energy-driven greenhouse gas emissions," Nature Energy, Nature, vol. 6(11), pages 1011-1016, November.
    6. Di Caro, Paolo & Figari, Francesco & Fiorio, Carlo & Manzo, Marco & Riganti, Andrea, 2022. "One step forward and three steps back: pros and cons of a flat tax reform," MPRA Paper 113684, University Library of Munich, Germany.
    7. Chancel, Lucas & Cogneau, Denis & Gethin, Amory & Myczkowski, Alix & Robilliard, Anne-Sophie, 2023. "Income inequality in Africa, 1990–2019: Measurement, patterns, determinants," World Development, Elsevier, vol. 163(C).
    8. Martin Ravallion & Shaohua Chen, 2022. "Is that really a Kuznets curve? Turning points for income inequality in China," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(4), pages 749-776, December.

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    More about this item

    Keywords

    Inequality; Top incomes; Surveys; Nonresponse; National accounts;
    All these keywords.

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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