IDEAS home Printed from https://ideas.repec.org/p/zbw/glodps/575.html
   My bibliography  Save this paper

What accounts for the rising share of women in the top 1%?

Author

Listed:
  • Burkhauser, Richard V.
  • Hérault, Nicolas
  • Jenkins, Stephen P.
  • Wilkins, Roger

Abstract

The share of women in the top 1% of the UK’s income distribution has been growing over the last two decades (as in several other countries). Our first contribution is to account for this secular change using regressions of the probability of being in the top 1%, fitted separately for men and women, in order to contrast between the sexes the role of changes in characteristics and changes in returns to characteristics. We show that the rise of women in the top 1% is primarily accounted for by their greater increases (relative to men) in the number of years spent in full-time education. Although most top income analysis uses tax return data, we derive our findings taking advantage of the much more extensive information about personal characteristics that is available in survey data. Our use of survey data requires justification given survey under-coverage of top incomes. Providing this justification is our second contribution.

Suggested Citation

  • Burkhauser, Richard V. & Hérault, Nicolas & Jenkins, Stephen P. & Wilkins, Roger, 2020. "What accounts for the rising share of women in the top 1%?," GLO Discussion Paper Series 575, Global Labor Organization (GLO).
  • Handle: RePEc:zbw:glodps:575
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/219288/1/GLO-DP-0575.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Anthony B. Atkinson & Thomas Piketty & Emmanuel Saez, 2011. "Top Incomes in the Long Run of History," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 3-71, March.
    2. Anthony B. Atkinson & Alessandra Casarico & Sarah Voitchovsky, 2018. "Top incomes and the gender divide," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(2), pages 225-256, June.
    3. 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.
    4. Brian Bell & John Reenen, 2014. "Bankers and Their Bonuses," Economic Journal, Royal Economic Society, vol. 124(574), pages 1-21, February.
    5. Chris Belfield & Richard Blundell & Jonathan Cribb & Andrew Hood & Robert Joyce, 2017. "Two Decades of Income Inequality in Britain: The Role of Wages, Household Earnings and Redistribution," Economica, London School of Economics and Political Science, vol. 84(334), pages 157-179, April.
    6. Mark B. Stewart, 2011. "The Changing Picture of Earnings Inequality in Britain and the Role of Regional and Sectoral Differences," National Institute Economic Review, National Institute of Economic and Social Research, vol. 218(1), pages 20-32, October.
    7. Roman Bobilev & Anne Boschini & Jesper Roine, 2020. "Women in the Top of the Income Distribution: What Can We Learn From LIS-Data?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(1), pages 63-107, March.
    8. Richard V. Burkhauser & Nicolas Hérault & Stephen P. Jenkins & Roger Wilkins, 2018. "Survey Under‐Coverage of Top Incomes and Estimation of Inequality: What is the Role of the UK's SPI Adjustment?," Fiscal Studies, John Wiley & Sons, vol. 39(2), pages 213-240, June.
    9. Yun, Myeong-Su, 2004. "Decomposing differences in the first moment," Economics Letters, Elsevier, vol. 82(2), pages 275-280, February.
    10. Mike Brewer & Ben Etheridge & Cormac O’Dea, 2017. "Why are Households that Report the Lowest Incomes So Well‐off?," Economic Journal, Royal Economic Society, vol. 127(605), pages 24-49, October.
    11. Mike Brewer & Ben Etheridge & Cormac O’Dea, 2017. "Why are Households that Report the Lowest Incomes So Well‐off?," Economic Journal, Royal Economic Society, vol. 127(605), pages 24-49, October.
    12. Richard V Burkhauser & Nicolas Hérault & Stephen P Jenkins & Roger Wilkins, 2018. "Top incomes and inequality in the UK: reconciling estimates from household survey and tax return data," Oxford Economic Papers, Oxford University Press, vol. 70(2), pages 301-326.
    13. Boschini, Anne & Gunnarsson, Kristin & Roine, Jesper, 2020. "Women in top incomes – Evidence from Sweden 1971–2017," Journal of Public Economics, Elsevier, vol. 181(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. What Accounts for the Rising Share of Women in the Top 1%?
      by maximorossi in NEP-LTV blog on 2020-07-01 14:43:12
    2. What Accounts for the Rising Share of Women in the Top 1%?
      by maximorossi in NEP-LTV blog on 2020-07-21 17:46:52

    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. Richard V. Burkhauser & Nicolas Hérault & Stephen P. Jenkins & Roger Wilkins, 2018. "Survey Under‐Coverage of Top Incomes and Estimation of Inequality: What is the Role of the UK's SPI Adjustment?," Fiscal Studies, John Wiley & Sons, vol. 39(2), pages 213-240, June.
    2. Terhi Ravaska, 2020. "Gender-specific top incomes: are they Pareto distributed?," Economics Bulletin, AccessEcon, vol. 40(3), pages 1994-2004.
    3. Roman Bobilev & Anne Boschini & Jesper Roine, 2020. "Women in the Top of the Income Distribution: What Can We Learn From LIS-Data?," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(1), pages 63-107, March.
    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. , Stone Center & Lustig, Nora, 2020. "The “Missing Rich” in Household Surveys: Causes and Correction Approaches," SocArXiv j23pn, Center for Open Science.
    6. Burkhauser, Richard V. & Hérault, Nicolas & Jenkins, Stephen P. & Wilkins, Roger, 2017. "Survey under-coverage of top incomes and estimation of inequality: what is the role of the UK’s SPI adjustment?," ISER Working Paper Series 2017-08, Institute for Social and Economic Research.
    7. A. B. Atkinson, 2017. "Pareto and the Upper Tail of the Income Distribution in the UK: 1799 to the Present," Economica, London School of Economics and Political Science, vol. 84(334), pages 129-156, April.
    8. Thomas Blanchet & Ignacio Flores & Marc Morgan, 2018. "The Weight of the Rich: Improving Surveys Using Tax Data," Working Papers hal-02878315, HAL.
    9. Vladimir Hlasny, 2020. "Parametric Representation of the Top of Income Distributions: Options, Historical Evidence and Model Selection," Working Papers 547, ECINEQ, Society for the Study of Economic Inequality.
    10. Callealta Barroso, Francisco Javier & García-Pérez, Carmelo & Prieto-Alaiz, Mercedes, 2020. "Modelling income distribution using the log Student’s t distribution: New evidence for European Union countries," Economic Modelling, Elsevier, vol. 89(C), pages 512-522.
    11. Bruce D. Meyer & Derek Wu & Grace Finley & Patrick Langetieg & Carla Medalia & Mark Payne & Alan Plumley, 2020. "The Accuracy of Tax Imputations: Estimating Tax Liabilities and Credits Using Linked Survey and Administrative Data," NBER Chapters, in: Measuring Distribution and Mobility of Income and Wealth, National Bureau of Economic Research, Inc.
    12. A. B. Atkinson & Stephen P. Jenkins, 2020. "A Different Perspective on the Evolution of UK Income Inequality," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(2), pages 253-266, June.
    13. 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.
    14. Blundell, Richard & Joyce, Robert & Norris Keiller, Agnes & Ziliak, James P., 2018. "Income inequality and the labour market in Britain and the US," Journal of Public Economics, Elsevier, vol. 162(C), pages 48-62.
    15. Boschini, Anne & Gunnarsson, Kristin & Roine, Jesper, 2017. "Women in Top Incomes – Evidence from Sweden 1974-2013," Working Paper Series 5/2017, Stockholm University, Swedish Institute for Social Research.
    16. Advani, Arun & Koenig, Felix & Pessina, Lorenzo & Summers, Andy, 2020. "Importing Inequality: Immigration and the Top 1 Percent," IZA Discussion Papers 13731, Institute of Labor Economics (IZA).
    17. Dominic Webber & Richard P. Tonkin & Martin Shine, 2020. "Using Tax Data to Better Capture Top Incomes in Official UK Income Inequality Statistics," NBER Chapters, in: Measuring Distribution and Mobility of Income and Wealth, National Bureau of Economic Research, Inc.
    18. Thomas Piketty & Emmanuel Saez & Gabriel Zucman, 2018. "Distributional National Accounts: Methods and Estimates for the United States," The Quarterly Journal of Economics, Oxford University Press, vol. 133(2), pages 553-609.
    19. Nora Lustig, 2020. "The ``missing rich'' in household surveys: causes and correction approaches," Working Papers 520, ECINEQ, Society for the Study of Economic Inequality.
    20. 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.

    More about this item

    Keywords

    Top 1%; top incomes; inequality; gender differences; survey under-coverage;
    All these keywords.

    JEL classification:

    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • 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:zbw:glodps:575. 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: (ZBW - Leibniz Information Centre for Economics). General contact details of provider: https://edirc.repec.org/data/glabode.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 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.

    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.