IDEAS home Printed from https://ideas.repec.org/p/bea/wpaper/0191.html
   My bibliography  Save this paper

The Feasibility of a Quarterly Distribution of Personal Income

Author

Listed:
  • Dennis J. Fixle
  • Marina Gindelsky
  • Robert Kornfeld

    (Bureau of Economic Analysis)

Abstract

The U.S. Bureau of Economic Analysis (BEA) conducted a feasibility study to evaluate whether it is possible to produce a quarterly distribution of personal income and construct inequality metrics that are valid, informative, and transparent. The primary obstacles to producing such estimates are the lack of available quarterly microdata and inability to follow households over time (panel data). Therefore, we cannot account for household behavioral responses to shocks, such as applying for transfers after a wage loss, or participating in the gig economy, and we miss interdependency of these income sources. In this paper, BEA presents estimates of an interpolated quarterly distribution for 2007–2018. The estimates are driven by changes in aggregate income composition, such that the average of the quarterly estimates for each year is equal to the annual estimate. Many sources of data were considered to improve the quarterly estimates. An in-sample forecast exercise using a simplified methodology shows reasonable results during stable growth years but significantly underestimates inequality during periods of economic volatility (Great Recession and recovery). Forecast results can incorrectly show rising (falling) inequality during quarters when it is falling (rising).

Suggested Citation

  • Dennis J. Fixle & Marina Gindelsky & Robert Kornfeld, 2021. "The Feasibility of a Quarterly Distribution of Personal Income," BEA Working Papers 0191, Bureau of Economic Analysis.
    • Handle: RePEc:bea:wpaper:0191
    as

    Download full text from publisher

    File URL: https://www.bea.gov/system/files/papers/BEA-WP2021-8_1.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Emmanuel Saez, 2017. "Taxing the Rich More: Preliminary Evidence from the 2013 Tax Increase," Tax Policy and the Economy, University of Chicago Press, vol. 31(1), pages 71-120.
    2. Gerald Auten & David Splinter, 2019. "Top 1 Percent Income Shares: Comparing Estimates Using Tax Data," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 307-311, May.
    3. Bruce D. Meyer & Wallace K. C. Mok & James X. Sullivan, 2015. "Household Surveys in Crisis," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 199-226, Fall.
    4. Larrimore, Jeff & Mortenson, Jacob & Splinter, David, 2022. "Earnings shocks and stabilization during COVID-19," Journal of Public Economics, Elsevier, vol. 206(C).
    5. 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.
    6. Michael M. Batty & Jesse Bricker & Joseph S. Briggs & Alice Henriques Volz & Elizabeth Ball Holmquist & Susan Hume McIntosh & Kevin B. Moore & Eric R. Nielsen & Sarah Reber & Molly Shatto & Kamila Som, 2019. "Introducing the Distributional Financial Accounts of the United States," Finance and Economics Discussion Series 2019-017, Board of Governors of the Federal Reserve System (U.S.).
    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. Gary Cornwall & Marina Gindelsky, 2024. "Nowcasting Distributional National Accounts for the United States: A Machine Learning Approach," BEA Papers 0130, Bureau of Economic Analysis.

    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. Emmanuel Saez & Gabriel Zucman, 2020. "Trends in US Income and Wealth Inequality: Revising After the Revisionists," Working Papers halshs-03022102, HAL.
    2. Saez, Emmanuel & Stantcheva, Stefanie, 2018. "A simpler theory of optimal capital taxation," Journal of Public Economics, Elsevier, vol. 162(C), pages 120-142.
    3. Advani, Arun & Summers, Andy & Tarrant, Hannah, 2020. "Measuring UK top incomes," CAGE Online Working Paper Series 490, Competitive Advantage in the Global Economy (CAGE).
    4. Wojciech Kopczuk & Eric Zwick, 2020. "Business Incomes at the Top," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 27-51, Fall.
    5. Gallin, Joshua & Molloy, Raven & Nielsen, Eric & Smith, Paul & Sommer, Kamila, 2021. "Measuring aggregate housing wealth: New insights from machine learning ☆," Journal of Housing Economics, Elsevier, vol. 51(C).
    6. 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.
    7. Facundo Alvaredo & François Bourguignon & Francisco Ferreira & Nora Lustig, 2024. "Inequality bands: Seventy-five years of measuring income inequality in latin america," Working Papers halshs-04563817, HAL.
    8. Jesse Bricker & Sarena Goodman & Alice Henriques Volz & Kevin B. Moore, 2021. "A Wealth of Information: Augmenting the Survey of Consumer Finances to Characterize the Full U.S. Wealth Distribution," Finance and Economics Discussion Series 2021-053, Board of Governors of the Federal Reserve System (U.S.).
    9. Advani, Arun & Summers, Andy & Tarrant, Hannah, 2022. "Measuring top income shares in the UK," CAGE Online Working Paper Series 610, Competitive Advantage in the Global Economy (CAGE).
    10. Nishant Yonzan & Branko Milanovic & Salvatore Morelli & Janet Gornick, 2022. "Drawing a Line: Comparing the Estimation of Top Incomes between Tax Data and Household Survey Data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 67-95, March.
    11. Martin Ravallion, 2022. "Missing Top Income Recipients," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 205-222, March.
    12. Jonathan Rothwell, 2019. "The Political Economy of Inequality in Rich Democracies," LIS Working papers 772, LIS Cross-National Data Center in Luxembourg.
    13. Süssmuth, Bernd & Wieschemeyer, Matthias, 2022. "Taxation and the distributional impact of inflation: The U.S. post-war experience," Economic Modelling, Elsevier, vol. 111(C).
    14. Bartels, Charlotte & Waldenström, Daniel, 2021. "Inequality and top incomes," GLO Discussion Paper Series 959, Global Labor Organization (GLO).
    15. Thomas Blanchet & Lucas Chancel & Amory Gethin, 2019. "How Unequal is Europe? Evidence from Distributional National Accounts, 1980-2017," World Inequality Lab Working Papers hal-02877000, HAL.
    16. Piketty, Thomas & Bozio, Antoine & Garbinti, Bertrand & Goupille-Lebret, Jonathan & Guillot, Malka, 2020. "Predistribution vs. Redistribution: Evidence from France and the U.S," CEPR Discussion Papers 15415, C.E.P.R. Discussion Papers.
    17. Alvaredo, Facundo & Bourguignon, François & Ferreira, Francisco H. G. & Lustig, Nora, 2023. "Seventy-five Years of Measuring Income Inequality in Latin America," IDB Publications (Working Papers) 13157, Inter-American Development Bank.
    18. Ranaldi, Marco, 2024. "Global Perspective on Italian Capitalism," SocArXiv 72gwt_v1, Center for Open Science.
    19. Ravallion, Martin, 2019. "Global inequality when unequal countries create unequal people," European Economic Review, Elsevier, vol. 111(C), pages 85-97.
    20. Fix, Blair, 2020. "How the Rich Are Different: Hierarchical Power as the Basis of Income Size and Class," SocArXiv t8muy_v1, Center for Open Science.

    More about this item

    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
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty

    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:bea:wpaper:0191. See general information about how to correct material in RePEc.

    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: Andrea Batch (email available below). General contact details of provider: https://edirc.repec.org/data/beagvus.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.