IDEAS home Printed from https://ideas.repec.org/p/cen/wpaper/23-19.html
   My bibliography  Save this paper

Self-Employment Income Reporting on Surveys

Author

Listed:
  • Christian Imboden
  • John Voorheis
  • Caroline Weber

Abstract

We examine the relation between administrative income data and survey reports for self-employed and wage-earning respondents from 2000 - 2015. The self-employed report 40 percent more wages and self-employment income in the survey than in tax administrative records; this estimate nets out differences between these two sources that are also shared by wage-earners. We provide evidence that differential reporting incentives are an important explanation of the larger self-employed gap by exploiting a well-known artifact – self-employed respondents exhibit substantial bunching at the first EITC kink in their administrative records. We do not observe the same behavior in their survey responses even after accounting for survey measurement concerns.

Suggested Citation

  • Christian Imboden & John Voorheis & Caroline Weber, 2023. "Self-Employment Income Reporting on Surveys," Working Papers 23-19, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:23-19
    as

    Download full text from publisher

    File URL: https://www2.census.gov/library/working-papers/2023/adrm/ces/CES-WP-23-19.pdf
    File Function: First version, 2023
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    2. Erik Hurst & Geng Li & Benjamin Pugsley, 2014. "Are Household Surveys Like Tax Forms? Evidence from Income Underreporting of the Self-Employed," The Review of Economics and Statistics, MIT Press, vol. 96(1), pages 19-33, March.
    3. Raj Chetty & John N. Friedman & Emmanuel Saez, 2013. "Using Differences in Knowledge across Neighborhoods to Uncover the Impacts of the EITC on Earnings," American Economic Review, American Economic Association, vol. 103(7), pages 2683-2721, December.
    4. Pischke, Jorn-Steffen, 1995. "Measurement Error and Earnings Dynamics: Some Estimates from the PSID Validation Study," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 305-314, July.
    5. Emmanuel Saez, 2010. "Do Taxpayers Bunch at Kink Points?," American Economic Journal: Economic Policy, American Economic Association, vol. 2(3), pages 180-212, August.
    6. Arie Kapteyn & Jelmer Y. Ypma, 2007. "Measurement Error and Misclassification: A Comparison of Survey and Administrative Data," Journal of Labor Economics, University of Chicago Press, vol. 25(3), pages 513-551.
    7. William Gale & Samuel Brown, 2013. "Small Business, Innovation, and Tax Policy: A Review," National Tax Journal, National Tax Association;National Tax Journal, vol. 66(4), pages 871-892, December.
    8. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    9. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    10. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    11. Joel Slemrod & Caroline Weber, 2012. "Evidence of the invisible: toward a credibility revolution in the empirical analysis of tax evasion and the informal economy," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 19(1), pages 25-53, February.
    12. Mary Layne & Deborah Wagner & Cynthia Rothhaas, 2014. "Estimating Record Linkage False Match Rate for the Person Identification Validation System," CARRA Working Papers 2014-02, Center for Economic Studies, U.S. Census Bureau.
    13. Charles L. Ballard & Sanjay Gupta, 2018. "Perceptions and Realities of Average Tax Rates in the Federal Income Tax: Evidence from Michigan," National Tax Journal, National Tax Association;National Tax Journal, vol. 71(2), pages 263-294, June.
    14. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    Full references (including those not matched with items on IDEAS)

    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. Paulus, Alari, 2015. "Tax evasion and measurement error: An econometric analysis of survey data linked with tax records," ISER Working Paper Series 2015-10, Institute for Social and Economic Research.
    2. Michele Lalla & Patrizio Frederic & Daniela Mantovani, 2022. "The inextricable association of measurement errors and tax evasion as examined through a microanalysis of survey data matched with fiscal data: a case study," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(5), pages 1375-1401, December.
    3. Adam Bee & Joshua Mitchell & Nikolas Mittag & Jonathan Rothbaum & Carl Sanders & Lawrence Schmidt & Matthew Unrath, 2023. "National Experimental Wellbeing Statistics - Version 1," Working Papers 23-04, Center for Economic Studies, U.S. Census Bureau.
    4. Quinn Moore & Irma Perez-Johnson & Robert Santillano, 2018. "Decomposing Differences in Impacts on Survey- and Administrative-Measured Earnings From a Job Training Voucher Experiment," Evaluation Review, , vol. 42(5-6), pages 515-549, October.
    5. Michele Lalla & Maddalena Cavicchioli, 2020. "Nonresponse and measurement errors in income: matching individual survey data with administrative tax data," Department of Economics 0170, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    6. Bruce D. Meyer & Nikolas Mittag, 2015. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," NBER Working Papers 21676, National Bureau of Economic Research, Inc.
    7. Dean R. Hyslop & Wilbur Townsend, 2020. "Earnings Dynamics and Measurement Error in Matched Survey and Administrative Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 457-469, April.
    8. Stefan Angel & Richard Heuberger & Nadja Lamei, 2018. "Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 575-603, July.
    9. Hyslop, Dean R. & Townsend, Wilbur, 2017. "Employment misclassification in survey and administrative reports," Economics Letters, Elsevier, vol. 155(C), pages 19-23.
    10. Meyer, Bruce D. & Mittag, Nikolas, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," IZA Discussion Papers 10943, Institute of Labor Economics (IZA).
    11. ChangHwan Kim & Christopher R. Tamborini, 2014. "Response Error in Earnings," Sociological Methods & Research, , vol. 43(1), pages 39-72, February.
    12. Bollinger, Christopher R. & Hirsch, Barry & Hokayem, Charles M. & Ziliak, James P., 2018. "Trouble in the Tails? What We Know about Earnings Nonresponse Thirty Years after Lillard, Smith, and Welch," IZA Discussion Papers 11710, Institute of Labor Economics (IZA).
    13. Bruce Meyer & Nikolas Mittag, 2017. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness and Holes in the Safety Net," Working Papers 2017-075, Human Capital and Economic Opportunity Working Group.
    14. Peter Gottschalk & Minh Huynh, 2010. "Are Earnings Inequality and Mobility Overstated? The Impact of Nonclassical Measurement Error," The Review of Economics and Statistics, MIT Press, vol. 92(2), pages 302-315, May.
    15. Ana Cinta G. Cabral & Norman Gemmell & Nazila Alinaghi, 2021. "Are survey-based self-employment income underreporting estimates biased? New evidence from matched register and survey data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 28(2), pages 284-322, April.
    16. Stüber, Heiko & Grabka, Markus M. & Schnitzlein, Daniel D., 2023. "A tale of two data sets: comparing German administrative and survey data using wage inequality as an example," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 57, pages 1-8.
    17. García-Suaza, A & Lobo, J & Montoya, S & Ordóñez, J & Oviedo, J. D, 2022. "Impact of the collection mode on labor income data. A study in the times of COVID19," Documentos de Trabajo 20396, Universidad del Rosario.
    18. Jenkins, Stephen P. & Rios-Avila, Fernando, 2021. "Reconciling Reports: Modelling Employment Earnings and Measurement Errors Using Linked Survey and Administrative Data," IZA Discussion Papers 14405, Institute of Labor Economics (IZA).
    19. Felix Schmutz, 2016. "Measuring the Invisible: An Overview of and Outlook for Tax Non-Compliance Estimates and Measurement Methods for Switzerland," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 152(2), pages 125-177, April.
    20. Whalley, Alexander, 2011. "Education and labor market risk: Understanding the role of data cleaning," Economics of Education Review, Elsevier, vol. 30(3), pages 528-545, June.

    More about this item

    Keywords

    Income Reporting; Survey Accuracy; Measurement Error; Tax Evasion; Tax Avoidance;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • H24 - Public Economics - - Taxation, Subsidies, and Revenue - - - Personal Income and Other Nonbusiness Taxes and Subsidies
    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance

    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:cen:wpaper:23-19. 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: Dawn Anderson (email available below). General contact details of provider: https://edirc.repec.org/data/cesgvus.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.