IDEAS home Printed from https://ideas.repec.org/p/ehl/lserod/119351.html
   My bibliography  Save this paper

Income source confusion using the SILC

Author

Listed:
  • Bollinger, Christopher R.
  • Tasseva, Iva

Abstract

We use a unique panel of household survey data—the Austrian version of the European Union Statistics on Income and Living Conditions (SILC) for 2008–2011—which have been linked to individual administrative records on both state unemployment benefits and earnings. We assess the extent and structure of misreporting across similar benefits and between benefits and earnings. We document that many respondents fail to report participation in one or more of the unemployment programs. Moreover, they inflate earnings for periods when they are unemployed but receiving unemployment compensation. To demonstrate the impact of income source confusion on estimators, we estimate standard Mincer wage equations. Since unemployment is associated with lower education, the reports of unemployment benefits as earnings bias downward the returns to education. Failure to report unemployment benefits also leads to substantial sample bias when selecting on these benefits, as one might in estimating the returns to job training.

Suggested Citation

  • Bollinger, Christopher R. & Tasseva, Iva, 2023. "Income source confusion using the SILC," LSE Research Online Documents on Economics 119351, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:119351
    as

    Download full text from publisher

    File URL: http://eprints.lse.ac.uk/119351/
    File Function: Open access version.
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bruce D. Meyer & Nikolas Mittag, 2019. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness, and Holes in the Safety Net," American Economic Journal: Applied Economics, American Economic Association, vol. 11(2), pages 176-204, April.
    2. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2021. "Errors in Reporting and Imputation of Government Benefits and Their Implications," IZA Discussion Papers 14396, Institute of Labor Economics (IZA).
    3. Peter Lynn & Annette Jäckle & Stephen P. Jenkins & Emanuela Sala, 2012. "The impact of questioning method on measurement error in panel survey measures of benefit receipt: evidence from a validation study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 289-308, January.
    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. Ha Trong Nguyen & Huong Thu Le & Luke Connelly & Francis Mitrou, 2023. "Accuracy of self‐reported private health insurance coverage," Health Economics, John Wiley & Sons, Ltd., vol. 32(12), pages 2709-2729, December.

    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. R. Bollinger, Christopher & Valentinova Tasseva, Iva, 2022. "Income source confusion using the SILC," ISER Working Paper Series 2022-04, Institute for Social and Economic Research.
    2. Colleen Heflin & Michah W. Rothbart & Mattie Mackenzie-Liu, 2022. "Below the Tip of the Iceberg: Examining Early Childhood Participation in SNAP and TANF from Birth to Age Six," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(2), pages 729-755, April.
    3. Paul Fisher & Omar Hussein, 2023. "Understanding Society: the income data," Fiscal Studies, John Wiley & Sons, vol. 44(4), pages 377-397, December.
    4. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2022. "Stigma in Welfare Programs," IZA Discussion Papers 15431, Institute of Labor Economics (IZA).
    5. Hicks, Jeffrey & Simard-Duplain, Gaëlle & Green, David A. & Warburton, William, 2022. "The effect of reducing welfare access on employment, health, and children's long-run outcomes," CLEF Working Paper Series 51, Canadian Labour Economics Forum (CLEF), University of Waterloo.
    6. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    7. Jonathan Fisher & Bradley L. Hardy, 2023. "Money matters: consumption variability across the income distribution," Fiscal Studies, John Wiley & Sons, vol. 44(3), pages 275-298, September.
    8. Bruckmeier, Kerstin & Riphahn, Regina T. & Wiemers, Jürgen, 2019. "Benefit underreporting in survey data and its consequences for measuring non-take-up: new evidence from linked administrative and survey data," IAB-Discussion Paper 201906, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    9. Breen, Casey & Goldstein, Joshua R., 2022. "Berkeley Unified Numident Mortality Database: Public Administrative Records for Individual-Level Mortality Research," SocArXiv pc294, Center for Open Science.
    10. Jeff Larrimore & Jacob Mortenson & David Splinter, 2023. "Unemployment Insurance In Survey And Administrative Data," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 42(2), pages 571-579, March.
    11. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    12. Liu, Xueyue & Zuo, Sharon Xuejing, 2023. "From equality to polarization: Changes in urban China’s gender earnings gap from 1988 to 2016," Journal of Economic Behavior & Organization, Elsevier, vol. 205(C), pages 303-337.
    13. Davillas, Apostolos & Pudney, Stephen, 2020. "Using biomarkers to predict healthcare costs: Evidence from a UK household panel," Journal of Health Economics, Elsevier, vol. 73(C).
    14. Elira Kuka & Bryan A. Stuart, 2021. "Racial Inequality in Unemployment Insurance Receipt and Take-Up," NBER Working Papers 29595, National Bureau of Economic Research, Inc.
    15. Bronchetti, Erin T. & Christensen, Garret & Hoynes, Hilary W., 2019. "Local food prices, SNAP purchasing power, and child health," Journal of Health Economics, Elsevier, vol. 68(C).
    16. 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.
    17. 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".
    18. Lidia Ceriani & Vladimir Hlasny & Paolo Verme, 2021. "Bottom Incomes and the Measurement of Poverty: A Brief Assessment of the Literature," Working Papers 589, ECINEQ, Society for the Study of Economic Inequality.
    19. Jeehoon Han & Bruce D. Meyer & James X. Sullivan, 2022. "Real-Time Poverty, Material Well-Being, and the Child Tax Credit," National Tax Journal, University of Chicago Press, vol. 75(4), pages 817-846.
    20. Paul Fisher, 2019. "Does Repeated Measurement Improve Income Data Quality?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 989-1011, October.

    More about this item

    Keywords

    VP1-2017-010to C.R.B.].; OUP deal;

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns
    • J01 - Labor and Demographic Economics - - General - - - Labor Economics: General

    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:ehl:lserod:119351. 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: LSERO Manager (email available below). General contact details of provider: https://edirc.repec.org/data/lsepsuk.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.