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Improving the Measure of the Distribution of Personal Income

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  • Dennis Fixler
  • Marina Gindelsky
  • David Johnson

Abstract

With releases of GDP in the U.S., there are typically stories about the impact on inequality and the distribution of growth. The Financial Times stated: "What’s the matter with GDP?" suggesting that GDP is missing information about who gets the increase (Smith, July 2018). Interest has grown regarding the relationship between the distribution of aggregate growth and increase in inequality. This disconnect has been amplified during the past few years, fueled by the Great Recession. The recent rise in inequality, especially at the top of the distribution, has reinvigorated the effort to produce distributional measures. Along with the creation of the World Inequality Database and Piketty, Saez and Zucman (PSZ) (2018), new consistent measures of the distribution of the national accounts have been developed (see also Auten and Splinter (2018) and Zwijneneburg (2019)). As Kuznets (1955) stressed, a distribution of the national accounts is necessary to completely examine how economic growth, whose measures rely on national account statistics, is distributed. In earlier work at the Bureau of Economic Analysis (Fixler and Johnson (2014) and Fixler et al. (2017)), tried to develop a distribution of personal income using survey data. This paper uses survey data, tax records, and administrative data for 2007 and 2012 to improve the measures of the distribution. Supplementary data sources are particularly important for measuring the top income categories and accordingly, we adjust the Current Population Survey (CPS) data to reflect higher income households and estimate alternative measures of inequality. Though reducing the 90/10 ratio, the tail adjustment and inclusion of incomes from supplementary sources significantly raises top income shares and mean income compared to measures calculated using the internal CPS data alone.

Suggested Citation

  • Dennis Fixler & Marina Gindelsky & David Johnson, 2019. "Improving the Measure of the Distribution of Personal Income," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-14, Economic Statistics Centre of Excellence (ESCoE).
    • Handle: RePEc:nsr:escoed:escoe-dp-2019-14
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    References listed on IDEAS

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    1. Dennis Fixler & Marina Gindelsky & David Johnson, 2019. "Improving the Measure of the Distribution of Personal Income," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 302-306, May.
    2. 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.
    3. Thomas Piketty & Emmanuel Saez & Gabriel Zucman, 2018. "Distributional National Accounts: Methods and Estimates for the United States," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 553-609.
    4. Dennis Fixler & David S. Johnson, 2014. "Accounting for the Distribution of Income in the U.S. National Accounts," NBER Chapters, in: Measuring Economic Sustainability and Progress, pages 213-244, National Bureau of Economic Research, Inc.
    5. Facundo Alvaredo & Lucas Chancel & Thomas Piketty & Gabriel Zucman, 2018. "Distributional National Accounts," Post-Print halshs-03342488, HAL.
    6. Christopher R. Bollinger & Barry T. Hirsch & Charles M. Hokayem & James P. Ziliak, 2019. "Trouble in the Tails? What We Know about Earnings Nonresponse 30 Years after Lillard, Smith, and Welch," Journal of Political Economy, University of Chicago Press, vol. 127(5), pages 2143-2185.
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    Cited by:

    1. Marina Gindelsky, 2022. "Do transfers lower inequality between households? Demographic evidence from Distributional National Accounts," Economic Inquiry, Western Economic Association International, vol. 60(3), pages 1233-1257, July.
    2. Dennis Fixler & Marina Gindelsky & David S. Johnson, 2020. "Distributing Personal Income: Trends over Time," NBER Chapters, in: Measuring Distribution and Mobility of Income and Wealth, pages 589-603, National Bureau of Economic Research, Inc.
    3. Dennis Fixler & Marina Gindelsky & David Johnson, 2020. "Measuring Inequality in the National Accounts," BEA Working Papers 0175, Bureau of Economic Analysis.
    4. Mark C. Long, 2022. "Seattle's local minimum wage and earnings inequality," Economic Inquiry, Western Economic Association International, vol. 60(2), pages 528-542, April.
    5. Dennis Fixler & Marina Gindelsky & David Johnson, 2019. "Improving the Measure of the Distribution of Personal Income," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 302-306, May.
    6. Stephen P. Jenkins, 2022. "Top-income adjustments and official statistics on income distribution: the case of the UK," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 151-168, March.
    7. Süssmuth, Bernd & Wieschemeyer, Matthias, 2022. "Taxation and the distributional impact of inflation: The U.S. post-war experience," Economic Modelling, Elsevier, vol. 111(C).
    8. Wojciech Kopczuk & Eric Zwick, 2020. "Business Incomes at the Top," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 27-51, Fall.
    9. Jagjit S. Chadha & Richard Barwell, 2019. "Renewing our Monetary Vows: Open Letters to the Governor of the Bank of England," National Institute of Economic and Social Research (NIESR) Occasional Papers 58, National Institute of Economic and Social Research.

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    More about this item

    Keywords

    Income distribution; National Income and Product Accounts;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts

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