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Lining Up : Survey and Administrative Data Estimates of Wealth Concentration

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  • Arthur B. Kennickell

Abstract

The Survey of Consumer Finances (SCF) has a dual-frame sample design that supplements a standard area-probability frame with a sample of observations drawn from statistical records derived from tax returns. The tax-based frame is stratified on the basis of a \"wealth index\" constructed largely from observed income flows, with the intent of heavily oversampling wealthy households. Although the SCF is not specifically designed to estimate wealth concentration, the design arguably provides sufficient support to enable such analysis with a reasonable level of credibility. Similar estimates may also be made by using tax-based data directly, as in [1], by using a construct very close to a key part of the SCF wealth index. Such an approach has appeal as a way of tapping a much larger set of information to improve SCF estimates. Not surprisingly, there are differences in the two approaches, largely as a result of conceptual differences or complications in the survey implementation. This paper focuses on the top 1 percent of the wealth distribution, the group most intensively covered by the SCF list sample and it explores the stability of the relationship between the patterns of concentration in the survey data and parallel patterns in tax-based estimates and considers how those patterns differ across survey participants, the full sample and the entire survey frame. In addition, the paper makes as series of recommendation for further research on the technical support of the survey.

Suggested Citation

  • Arthur B. Kennickell, 2017. "Lining Up : Survey and Administrative Data Estimates of Wealth Concentration," Finance and Economics Discussion Series 2017-017, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2017-17
    DOI: 10.17016/FEDS.2017.017
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    References listed on IDEAS

    as
    1. Jesse Bricker & Alice Henriques & Jacob Krimmel & John Sabelhaus, 2016. "Measuring Income and Wealth at the Top Using Administrative and Survey Data," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(1 (Spring), pages 261-331.
    2. Arthur B. Kennickell, 2012. "The other, other half: changes in the finances of the least wealthy 50 percent, 2007-2009," Finance and Economics Discussion Series 2012-40, Board of Governors of the Federal Reserve System (U.S.).
    3. Daphne Greenwood, 1983. "An Estimation Of U.S. Family Wealth And Its Distribution From Microdata, 1973," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 29(1), pages 23-44, March.
    4. Dale W. Jorgenson & J. Steven Landefeld & Paul Schreyer, 2014. "Measuring Economic Sustainability and Progress," NBER Books, National Bureau of Economic Research, Inc, number jorg12-1.
    5. Alice M. Henriques & Joanne W. Hsu, 2014. "Analysis of Wealth Using Micro- and Macrodata: A Comparison of the Survey of Consumer Finances and Flow of Funds Accounts," NBER Chapters, in: Measuring Economic Sustainability and Progress, pages 245-274, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Emmanuel Saez & Gabriel Zucman, 2019. "Progressive Wealth Taxation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 50(2 (Fall)), pages 437-533.
    2. Arthur B. Kennickell, 2019. "The tail that wags: differences in effective right tail coverage and estimates of wealth inequality," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(4), pages 443-459, December.
    3. repec:hal:pseptp:halshs-03231244 is not listed on IDEAS
    4. Bertrand Garbinti & Jonathan Goupille-Lebret & Thomas Piketty, 2021. "Accounting for Wealth-Inequality Dynamics: Methods, Estimates, and Simulations for France," Journal of the European Economic Association, European Economic Association, vol. 19(1), pages 620-663.
    5. repec:hal:journl:halshs-03231244 is not listed on IDEAS

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    More about this item

    Keywords

    Nonresponse; Oversampling; Sampling; Skewed distributions; Wealth measurement;
    All these keywords.

    JEL classification:

    • 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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