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The Accuracy of Tax Imputations: Estimating Tax Liabilities and Credits Using Linked Survey and Administrative Data

In: Measuring Distribution and Mobility of Income and Wealth

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  • Bruce D. Meyer
  • Derek Wu
  • Grace Finley
  • Patrick Langetieg
  • Carla Medalia
  • Mark Payne
  • Alan Plumley

Abstract

This paper calculates accurate estimates of income and payroll taxes using a groundbreaking set of linked survey and administrative tax data that are part of the Comprehensive Income Dataset (CID). We compare our estimates to survey imputations produced by the Census Bureau and those generated using the TAXSIM calculator from the National Bureau of Economic Research. The administrative data include two sets of Internal Revenue Service (IRS) data: (1) a limited set of tax information for the population of individual income tax returns covering selected line items from Forms 1040, W-2, and 1099-R; and (2) an extensive set of population tax records processed by the IRS in 2011, covering nearly every line item on Form 1040 and most lines on a series of third-party information returns. We link these IRS records to the Current Population Survey Annual Social and Economic Supplement (CPS) for reference year 2010. We describe how we form tax units and estimate various types of tax liabilities and credits using these linked data, providing a roadmap for constructing accurate measures of taxes while preserving the survey family as the sharing unit for distributional analyses. We find that aggregate estimates of various tax components using the limited and extensive tax data estimates are close to each other and much closer to public IRS tabulations than either of the imputations using survey data alone. At the individual level, the absolute errors of survey-only imputations of federal income taxes and total taxes are on average 10% and 13%, respectively, of adjusted gross income. In contrast, the limited tax data imputations yield mean absolute errors for federal income taxes and total taxes that are about 2% and 3% of adjusted gross income, respectively. For the Earned Income Tax Credit, the limited tax data imputation is off by less than $20 on average for a typical family (compared to more than $500 using either of the survey-only imputations).
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Suggested Citation

  • 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, pages 459-498, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:14441
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    References listed on IDEAS

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    1. Bruce D. Meyer & James X. Sullivan, 2003. "Measuring the Well-Being of the Poor Using Income and Consumption," NBER Working Papers 9760, National Bureau of Economic Research, Inc.
    2. Thomas Piketty & Emmanuel Saez, 2007. "How Progressive is the U.S. Federal Tax System? A Historical and International Perspective," Journal of Economic Perspectives, American Economic Association, vol. 21(1), pages 3-24, Winter.
    3. Gordon B. Dahl & Lance Lochner, 2012. "The Impact of Family Income on Child Achievement: Evidence from the Earned Income Tax Credit," American Economic Review, American Economic Association, vol. 102(5), pages 1927-1956, August.
    4. 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.
    5. Maggie R. Jones & Amy B. O’Hara, 2016. "Do Doubled-Up Families Minimize Household-Level Tax Burden?," National Tax Journal, National Tax Association;National Tax Journal, vol. 69(3), pages 613-640, September.
    6. Duncan, Greg J & Hill, Daniel H, 1985. "An Investigation of the Extent and Consequences of Measurement Error in Labor-Economic Survey Data," Journal of Labor Economics, University of Chicago Press, vol. 3(4), pages 508-532, October.
    7. Daniel Feenberg & Elisabeth Coutts, 1993. "An introduction to the TAXSIM model," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 12(1), pages 189-194.
    8. Giertz, Seth H., 2007. "The Elasticity of Taxable Income Over the 1980s and 1990s," National Tax Journal, National Tax Association;National Tax Journal, vol. 60(4), pages 743-768, December.
    9. 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.
    10. Hoynes, Hilary W. & Luttmer, Erzo F.P., 2011. "The insurance value of state tax-and-transfer programs," Journal of Public Economics, Elsevier, vol. 95(11), pages 1466-1484.
    11. Gruber, Jon & Saez, Emmanuel, 2002. "The elasticity of taxable income: evidence and implications," Journal of Public Economics, Elsevier, vol. 84(1), pages 1-32, April.
    12. Burkhauser, Richard V. & Corinth, Kevin & Elwell, James & Larrimore, Jeff, 2019. "Evaluating the Success of President Johnson's War on Poverty: Revisiting the Historical Record Using a Full-Income Poverty Measure," IZA Discussion Papers 12855, Institute of Labor Economics (IZA).
    13. Richard V. Burkhauser & Kevin Corinth & James Elwell & Jeff Larrimore, 2019. "Evaluating the Success of President Johnson’s War on Poverty: Revisiting the Historical Record Using an Absolute Full-Income Poverty Measure," NBER Working Papers 26532, National Bureau of Economic Research, Inc.
    14. Bruce D. Meyer & James X. Sullivan, 2012. "Winning the War: Poverty from the Great Society to the Great Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(2 (Fall)), pages 133-200.
    15. Congressional Budget Office, 2018. "The Distribution of Household Income, 2015," Reports 54646, Congressional Budget Office.
    16. Congressional Budget Office, 2018. "The Distribution of Household Income, 2014," Reports 53597, Congressional Budget Office.
    17. David Splinter & Jeff Larrimore & Jacob Mortenson, 2017. "Whose Child Is This? Shifting of Dependents among EITC Claimants within the Same Household," National Tax Journal, National Tax Association;National Tax Journal, vol. 70(4), pages 737-758, December.
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    Cited by:

    1. Jeehoon Han & Bruce D. Meyer & James X. Sullivan, 2020. "Income and Poverty in the COVID-19 Pandemic," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(2 (Summer), pages 85-118.
    2. Larrimore, Jeff & Mortenson, Jacob & Splinter, David, 2023. "Earnings business cycles: The Covid recession, recovery, and policy response," Journal of Public Economics, Elsevier, vol. 225(C).
    3. Corinth, Kevin & Larrimore, Jeff, 2024. "Has Intergenerational Progress Stalled? Income Growth over Five Generations of Americans," IZA Discussion Papers 16807, Institute of Labor Economics (IZA).
    4. Maggie R. Jones & Adam Bee & Amanda Eng & Kendall Houghton & Nikolas Pharris-Ciurej & Sonya R. Porter & Jonathan Rothbaum & John Voorheis, 2024. "Mobility, Opportunity, and Volatility Statistics (MOVS): Infrastructure Files and Public Use Data," Working Papers 24-23, Center for Economic Studies, U.S. Census Bureau.
    5. Kevin C. Corinth & Jeff Larrimore, 2024. "Has Intergenerational Progress Stalled? Income Growth Over Five Generations of Americans," Finance and Economics Discussion Series 2024-007, Board of Governors of the Federal Reserve System (U.S.).
    6. Iselin, John & Mackay, Taylor & Unrath, Matthew, 2023. "Measuring take-up of the California EITC with state administrative data," Journal of Public Economics, Elsevier, vol. 227(C).

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    More about this item

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • H20 - Public Economics - - Taxation, Subsidies, and Revenue - - - General
    • H24 - Public Economics - - Taxation, Subsidies, and Revenue - - - Personal Income and Other Nonbusiness Taxes and Subsidies
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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