IDEAS home Printed from https://ideas.repec.org/p/tul/ceqwps/70.html
   My bibliography  Save this paper

The Rich Underreport Their Income: Assessing Biases In Inequality Estimates And Correction Methods Using Linked Survey And Tax Data

Author

Listed:
  • Sean Higgins

    (Department of Economics, Tulane University)

  • Nora Lustig

    (Stone Center for Latin American Studies, Department of Economics, Tulane University, Commitment to Equity Institute)

  • Andrea Vigorito

    (Instituto de Economia)

Abstract

Do survey respondents misreport their income? If so, how does misreporting correlate with income, how does this affect estimates of income inequality, and how well do existing methods correct for bias? We use a novel database in which a subsample of Uruguay’s official household survey has been linked to tax records to document the extent and distribution of labor income underreporting and to assess the performance of various existing methods to correct inequality estimates. Individuals in the upper half of the income distribution tend to report less labor income in household surveys than those same individuals earn according to tax returns, and underreporting is increasing in income. Using simulations, we find that this leads to downwardbiased inequality estimates. Correction methods that rely only on survey data barely affect the biased inequality estimates, while methods that combine survey and tax data can lead to overcorrection and overestimation of inequality.

Suggested Citation

  • Sean Higgins & Nora Lustig & Andrea Vigorito, 2018. "The Rich Underreport Their Income: Assessing Biases In Inequality Estimates And Correction Methods Using Linked Survey And Tax Data," Commitment to Equity (CEQ) Working Paper Series 70, Tulane University, Department of Economics.
  • Handle: RePEc:tul:ceqwps:70
    as

    Download full text from publisher

    File URL: http://repec.tulane.edu/RePEc/ceq/ceq70.pdf
    File Function: First version, 2018
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Thomas Piketty & Li Yang & Gabriel Zucman, 2019. "Capital Accumulation, Private Property, and Rising Inequality in China, 1978–2015," American Economic Review, American Economic Association, vol. 109(7), pages 2469-2496, July.
    2. Angus Deaton, 2005. "Measuring Poverty in a Growing World (or Measuring Growth in a Poor World)," The Review of Economics and Statistics, MIT Press, vol. 87(1), pages 1-19, February.
    3. Gabriel Burdin & Fernando Esponda & Andrea Vigorito, 2004. "Inequality and Top Income in Uruguay: A Comparison between Household Surveys and Income Tax Micro-data," World Inequality Lab Working Papers halshs-02654095, HAL.
    4. Stephen P. Jenkins, 2017. "Pareto Models, Top Incomes and Recent Trends in UK Income Inequality," Economica, London School of Economics and Political Science, vol. 84(334), pages 261-289, April.
    5. Angus Deaton, 2005. "ERRATUM: Measuring Poverty in a Growing World (or Measuring Growth in a Poor World)," The Review of Economics and Statistics, MIT Press, vol. 87(2), pages 395-395, May.
    6. Thomas Piketty & Li Yang & Gabriel Zucman, 2017. "Appendix to "Capital Accumulation, Private Property and Rising Inequality in China, 1978-2015"," Working Papers 201707, World Inequality Lab.
    7. Cowell, Frank A. & Flachaire, Emmanuel, 2007. "Income distribution and inequality measurement: The problem of extreme values," Journal of Econometrics, Elsevier, vol. 141(2), pages 1044-1072, December.
    8. Thomas Piketty & Li Yang & Gabriel Zucman, 2019. "Capital Accumulation, Private Property, and Rising Inequality in China, 1978–2015," American Economic Review, American Economic Association, vol. 109(7), pages 2469-2496, July.
    9. Anand, Sudhir & Segal, Paul, 2017. "Who Are the Global Top 1%?," World Development, Elsevier, vol. 95(C), pages 111-126.
    10. Anand, Sudhir & Segal, Paul, 2017. "Who are the global top 1%?," LSE Research Online Documents on Economics 101816, London School of Economics and Political Science, LSE Library.
    11. Alvaredo, Facundo, 2011. "A note on the relationship between top income shares and the Gini coefficient," Economics Letters, Elsevier, vol. 110(3), pages 274-277, March.
    12. Gabriel Burdín & Fernando Esponda & Andrea Vigorito, 2014. "Inequality and top incomes in Uruguay: a comparison between household surveys and income tax micro-data," Commitment to Equity (CEQ) Working Paper Series 1321, Tulane University, Department of Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Osvaldo Larranaga & Benajamin Echecopar & Nicolas Grau, 2021. "Una nueva estimacion de la desigualdad de ingresos en Chile," Working Papers wp523, University of Chile, Department of Economics.
    3. Arthur Charpentier & Emmanuel Flachaire, 2019. "Pareto Models for Top Incomes," Working Papers hal-02145024, HAL.
    4. 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.
    5. Nora Lustig, 2020. "The ``missing rich'' in household surveys: causes and correction approaches," Working Papers 520, ECINEQ, Society for the Study of Economic Inequality.
    6. , Stone Center & Lustig, Nora, 2020. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," SocArXiv j23pn, Center for Open Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Chengyou & Yu, Yangcheng & Li, Qinghai, 2021. "Top-income data and income inequality correction in China," Economic Modelling, Elsevier, vol. 97(C), pages 210-219.
    2. 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.
    3. Nora Lustig, 2020. "The ``missing rich'' in household surveys: causes and correction approaches," Working Papers 520, ECINEQ, Society for the Study of Economic Inequality.
    4. Jordá, Vanesa & Niño-Zarazúa, Miguel, 2019. "Global inequality: How large is the effect of top incomes?," World Development, Elsevier, vol. 123(C), pages 1-1.
    5. Brzeziński, Michał & Myck, Michal & Najsztub, Mateusz, 2019. "Reevaluating Distributional Consequences of the Transition to Market Economy in Poland: New Results from Combined Household Survey and Tax Return Data," IZA Discussion Papers 12734, Institute of Labor Economics (IZA).
    6. 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.
    7. , Stone Center & Lustig, Nora, 2020. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," SocArXiv j23pn, Center for Open Science.
    8. Rafael Carranza & Marc Morgan & Brian Nolan, 2021. "Top Income Adjustments and Inequality: An Investigation of the EU-SILC," Working Papers halshs-03321885, HAL.
    9. Rafael Carranza & Marc Morgan & Brian Nolan, 2021. "Top Income Adjustments and Inequality: An Investigation of the EU-SILC," Working Papers 583, ECINEQ, Society for the Study of Economic Inequality.
    10. Rafael Carranza & Marc Morgan & Brian Nolan, 2021. "Top Income Adjustments and Inequality: An Investigation of the EU-SILC," World Inequality Lab Working Papers halshs-03321885, HAL.
    11. Carranza, Rafael & Morgan, Marc & Nolan, Brian, 2021. "Top Income Adjustments and Inequality: An Investigation of the EU-SILC," INET Oxford Working Papers 2021-16, Institute for New Economic Thinking at the Oxford Martin School, University of Oxford.
    12. Winkelried, Diego & Escobar, Bruno, 2020. "Declining inequality in Latin America? Robustness checks for Peru," MPRA Paper 106566, University Library of Munich, Germany.
    13. Thomas Blanchet & Ignacio Flores & Marc Morgan, 2018. "The Weight of the Rich: Improving Surveys Using Tax Data," Working Papers hal-02878315, HAL.
    14. Channing Arndt & Kristi Mahrt, 2017. "Is inequality underestimated in Mozambique? Accounting for underreported consumption," WIDER Working Paper Series 153, World Institute for Development Economic Research (UNU-WIDER).
    15. Nora Lustig, 2016. "Commitment to Equity Handbook. A Guide to Estimating the Impact of Fiscal Policy on Inequality and Poverty," Commitment to Equity (CEQ) Working Paper Series 1301, Tulane University, Department of Economics.
    16. Facundo Alveredo & Juliana Londoño Vélez, 2013. "High incomes and personal taxation in a developing economy: Colombia 1993-2010," Commitment to Equity (CEQ) Working Paper Series 1312, Tulane University, Department of Economics.
    17. Channing Arndt & Kristi Mahrt, 2017. "Is inequality underestimated in Mozambique?: Accounting for underreported consumption," WIDER Working Paper Series wp-2017-153, World Institute for Development Economic Research (UNU-WIDER).
    18. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data," Econometrics, MDPI, Open Access Journal, vol. 6(2), pages 1-21, June.
    19. François Bourguignon, 2018. "Simple adjustments of observed distributions for missing income and missing people," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(2), pages 171-188, June.
    20. Vladimir Hlasny, 2019. "Redistributive Impacts of Fiscal Policies in Mexico: Corrections for Top Income Measurement Problems," LIS Working papers 765, LIS Cross-National Data Center in Luxembourg.

    More about this item

    Keywords

    inequality; income underreporting; tax records; household surveys;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tul:ceqwps:70. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/detulus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Nora Lustig (email available below). General contact details of provider: https://edirc.repec.org/data/detulus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.