IDEAS home Printed from https://ideas.repec.org/p/iae/iaewps/wp2017n16.html
   My bibliography  Save this paper

Survey Under-Coverage of Top Incomes and Estimation of Inequality: What Is the Role of the UK’s SPI Adjustment?

Author

Listed:
  • Richard V. Burkhauser

    () (Lyndon B. Johnson School of Public Affairs, University of Texas-Austin; Department of Policy Analysis and Management, Cornell University; Melbourne Institute: Applied Economic and Social Research, The University of Melbourne)

  • Nicolas Hérault

    () (Melbourne Institute: Applied Economic and Social Research, The University of Melbourne)

  • Stephen P. Jenkins

    (London School of Economics; Institute for Social and Economic Research, University of Essex; and Institute for the Study of Labor (IZA); Melbourne Institute: Applied Economic and Social Research, The University of Melbourne)

  • Roger Wilkins

    () (Melbourne Institute: Applied Economic and Social Research, The University of Melbourne)

Abstract

Survey under-coverage of top incomes leads to bias in survey-based estimates of overall income inequality. Using income tax record data in combination with survey data is a potential approach to address the problem; we consider here the UK’s pioneering ‘SPI adjustment’ method that implements this idea. Since 1992, the principal income distribution series (reported annually in Households Below Average Income) has been based on household survey data in which the incomes of a small number of ‘very rich’ individuals are adjusted using information from ‘very rich’ individuals in personal income tax return data. We explain what the procedure involves, reveal the extent to which it addresses survey under-coverage of top incomes, and show how it affects estimates of overall income inequality. More generally, we assess whether the SPI adjustment is fit for purpose and consider whether variants of it could be employed by other countries.

Suggested Citation

  • Richard V. Burkhauser & Nicolas Hérault & Stephen P. Jenkins & Roger Wilkins, 2017. "Survey Under-Coverage of Top Incomes and Estimation of Inequality: What Is the Role of the UK’s SPI Adjustment?," Melbourne Institute Working Paper Series wp2017n16, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  • Handle: RePEc:iae:iaewps:wp2017n16
    as

    Download full text from publisher

    File URL: http://melbourneinstitute.unimelb.edu.au/__data/assets/pdf_file/0005/2394797/wp2017n16.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Richard Burkhauser & Shuaizhang Feng & Stephen Jenkins & Jeff Larrimore, 2009. "Recent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data," Working Papers 09-26, Center for Economic Studies, U.S. Census Bureau.
    2. 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.
    3. Brewer, Mike & Wren-Lewis, Liam, 2012. "Accounting for changes in income inequality: decomposition analyses for Great Britain, 1968-2009," ISER Working Paper Series 2012-17, Institute for Social and Economic Research.
    4. Richard V. Burkhauser & Shuaizhang Feng & Stephen P. Jenkins & Jeff Larrimore, 2012. "Recent Trends in Top Income Shares in the United States: Reconciling Estimates from March CPS and IRS Tax Return Data," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 371-388, May.
    5. repec:bla:econom:v:84:y:2017:i:334:p:157-179 is not listed on IDEAS
    6. 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.
    7. Stefan Bach & Giacomo Corneo & Viktor Steiner, 2009. "From Bottom To Top: The Entire Income Distribution In Germany, 1992-2003," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 55(2), pages 303-330, June.
    8. Jeff Larrimore & Richard V. Burkhauser & Gerald Auten & Philip Armour, 2016. "Recent Trends in U.S. Top Income Shares in Tax Record Data Using More Comprehensive Measures of Income Including Accrued Capital Gains," NBER Working Papers 23007, National Bureau of Economic Research, Inc.
    9. Andreas Alfons & Matthias Templ & Peter Filzmoser, 2013. "Robust estimation of economic indicators from survey samples based on Pareto tail modelling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(2), pages 271-286, March.
    10. Richard V. Burkhauser & Markus H. Hahn & Roger Wilkins, 2016. "Top Incomes and Inequality in Australia: Reconciling Recent Estimates from Household Survey and Tax Return Data," Melbourne Institute Working Paper Series wp2016n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
    11. Brewer, M & Etheridge, B & O'Dea, C, 2013. "Why are households that report the lowest incomes so well-off," Economics Discussion Papers 8993, University of Essex, Department of Economics.
    12. Mike Brewer & Liam Wren-Lewis, 2016. "Accounting for Changes in Income Inequality: Decomposition Analyses for the UK, 1978–2008," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 78(3), pages 289-322, June.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Inequality; income inequality; survey under-coverage; SPI adjustment; top incomes; tax return data; survey data;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:iae:iaewps:wp2017n16. 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: (Sheri Carnegie). General contact details of provider: http://edirc.repec.org/data/mimelau.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.