IDEAS home Printed from https://ideas.repec.org/p/mod/depeco/0170.html
   My bibliography  Save this paper

Nonresponse and measurement errors in income: matching individual survey data with administrative tax data

Author

Listed:
  • Michele Lalla
  • Maddalena Cavicchioli

Abstract

A (local) survey on income carried out in the city of Modena in 2002 generated four categories of units: interviewees, refusals, noncontacts, and sometimes unused reserves. In this study, all units were matched with their corresponding records in the Ministry of Finance 2002 databases for fiscal incomes of 2001 and the 2001 Census. Considering all four categories, participation increased by education level and activity status, while it decreased among low or high incomes. Considering interviewees only, over- and underreporting, as well as measurement errors, were investigated by comparing the surveyed income with fiscal income. Age and level of income were the main covariates affecting the behaviours of taxpayers.

Suggested Citation

  • 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".
    • Handle: RePEc:mod:depeco:0170
    as

    Download full text from publisher

    File URL: http://155.185.68.2/wpdemb/0170.pdf
    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. Richard T. Curtin & Thomas Juster & James N. Morgan, 1989. "Survey Estimates of Wealth: An Assessment of Quality," NBER Chapters, in: The Measurement of Saving, Investment, and Wealth, pages 473-552, National Bureau of Economic Research, Inc.
    3. 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," Upjohn Working Papers 15-242, W.E. Upjohn Institute for Employment Research.
    4. Christopher R. Bollinger & Barry T. Hirsch, 2013. "Is Earnings Nonresponse Ignorable?," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 407-416, May.
    5. 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.
    6. Durrant, Gabriele B. & Steele, Fiona, 2009. "Multilevel modelling of refusal and non-contact in household surveys: evidence from six UK Government surveys," LSE Research Online Documents on Economics 50112, London School of Economics and Political Science, LSE Library.
    7. Gabriele B. Durrant & Fiona Steele, 2009. "Multilevel modelling of refusal and non‐contact in household surveys: evidence from six UK Government surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 361-381, April.
    8. Paul Bingley & Alessandro Martinello, 2017. "Measurement Error in Income and Schooling and the Bias of Linear Estimators," Journal of Labor Economics, University of Chicago Press, vol. 35(4), pages 1117-1148.
    9. Kristen Olson, 2013. "Do non-response follow-ups improve or reduce data quality?: a review of the existing literature," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(1), pages 129-145, January.
    10. Erik Meijer & Jelmer Ypma, 2008. "A Simple Identification Proof for a Mixture of Two Univariate Normal Distributions," Journal of Classification, Springer;The Classification Society, vol. 25(1), pages 113-123, June.
    11. Daniel Wilhelm, 2018. "Testing for the presence of measurement error," CeMMAP working papers CWP45/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    12. 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.
    13. Korinek, Anton & Mistiaen, Johan A. & Ravallion, Martin, 2007. "An econometric method of correcting for unit nonresponse bias in surveys," Journal of Econometrics, Elsevier, vol. 136(1), pages 213-235, January.
    14. James Alm, 2012. "Measuring, explaining, and controlling tax evasion: lessons from theory, experiments, and field studies," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 19(1), pages 54-77, February.
    15. Michael Hurd & F. Thomas Juster & James P. Smith, 2003. "Enhancing the Quality of Data on Income: Recent Innovations from the HRS," Journal of Human Resources, University of Wisconsin Press, vol. 38(3).
    16. 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.
    17. Franco Peracchi, 2002. "The European Community Household Panel: A review," Empirical Economics, Springer, vol. 27(1), pages 63-90.
    18. 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.
    19. Pamela Campanelli & Colm O'Muircheartaigh, 1999. "Interviewers, Interviewer Continuity, and Panel Survey Nonresponse," Quality & Quantity: International Journal of Methodology, Springer, vol. 33(1), pages 59-76, February.
    20. 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.
    21. repec:taf:jnlbes:v:30:y:2012:i:2:p:191-201 is not listed on IDEAS
    22. Robert E. Lipsey & Helen Stone Tice, 1989. "The Measurement of Saving, Investment, and Wealth," NBER Books, National Bureau of Economic Research, Inc, number lips89-1.
    23. Massimo Baldini & Paolo Bosi & Michele Lalla, 2009. "Tax evasion and misreporting in income tax returns and household income surveys," Politica economica, Società editrice il Mulino, issue 3, pages 333-348.
    24. Philip Franses & Irma Geluk & Paul Van Homelen, 1999. "Modeling Item Nonresponse in Questionnaires," Quality & Quantity: International Journal of Methodology, Springer, vol. 33(2), pages 203-213, May.
    25. Pickhardt, Michael & Prinz, Aloys, 2014. "Behavioral dynamics of tax evasion – A survey," Journal of Economic Psychology, Elsevier, vol. 40(C), pages 1-19.
    26. 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.
    27. Matsaganis, Manos & Flevotomou, Maria, 2010. "Distributional implications of tax evasion in Greece," LSE Research Online Documents on Economics 26074, London School of Economics and Political Science, LSE Library.
    28. Michele Lalla & Davide Ferrari & Patrizio Frederic, 2012. "Unit nonresponse errors in income surveys: a case study," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(6), pages 1769-1794, October.
    29. Burton, Jonathan & Crossley, Thomas F. & Fisher, Paul & Gaia, Alessandra & Jäckle, Annette, 2020. "Understanding and reducing income reporting error in household surveys," Understanding Society Working Paper Series 2020-01, Understanding Society at the Institute for Social and Economic Research.
    30. 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.
    31. Anton Korinek & Johan Mistiaen & Martin Ravallion, 2006. "Survey nonresponse and the distribution of income," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 4(1), pages 33-55, April.
    32. Manos Matsaganis & Maria Flevotomou, 2010. "Distributional Implications of Tax Evasion in Greece," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 31, Hellenic Observatory, LSE.
    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. Maddalena Cavicchioli & Michele Lalla, 2022. "Evidences from survey data and fiscal data: nonresponse and measurement errors in annual incomes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 587-615, September.

    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. Maddalena Cavicchioli & Michele Lalla, 2022. "Evidences from survey data and fiscal data: nonresponse and measurement errors in annual incomes," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 587-615, September.
    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. 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.
    4. 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 (SJES), Swiss Society of Economics and Statistics (SSES), vol. 152(II), pages 125-177, June.
    5. 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.
    6. 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.
    7. 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).
    8. Crossley, Thomas F. & Fisher, Paul & Hussein, Omar, 2023. "Assessing data from summary questions about earnings and income," Labour Economics, Elsevier, vol. 81(C).
    9. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, Institute of Labor Economics (IZA).
    10. 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," Upjohn Working Papers 15-242, W.E. Upjohn Institute for Employment Research.
    11. 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.
    12. 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.
    13. 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).
    14. Bruce D. Meyer & Nikolas Mittag, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," NBER Working Papers 25738, National Bureau of Economic Research, Inc.
    15. ChangHwan Kim & Christopher R. Tamborini, 2014. "Response Error in Earnings," Sociological Methods & Research, , vol. 43(1), pages 39-72, February.
    16. 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).
    17. 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.
    18. 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.
    19. 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.
    20. 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.

    More about this item

    Keywords

    Fiscal income; surveyed income; unit nonresponse; response bias; fiscal under-reporting;
    All these keywords.

    JEL classification:

    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution

    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:mod:depeco:0170. 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: Sara Colombini (email available below). General contact details of provider: https://edirc.repec.org/data/demodit.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.