IDEAS home Printed from https://ideas.repec.org/h/nbr/nberch/12663.html

Measuring the Accuracy of Survey Responses Using Administrative Register Data: Evidence from Denmark

In: Improving the Measurement of Consumer Expenditures

Author

Listed:
  • Claus Thustrup Kreiner
  • David Dreyer Lassen
  • Søren Leth-Petersen

Abstract

This paper shows how Danish administrative register data can be combined with survey data at the person level and be used to validate information collected in the survey. Register data are collected by automatic third party reporting and the potential errors associated with the two data sources are therefore plausibly orthogonal. Two examples are given to illustrate the potential of combining survey and register data. In the first example expenditure survey records with information about total expenditure are merged with income tax records holding information about income and wealth. Income and wealth data are used to impute total expenditure which is then compared to the survey measure. Results suggest that the two measures match each other well on average. In the second example we compare responses to a one-shot recall question about total gross personal income (collected in another survey) with tax records. Tax records hold detailed information about different types of income and this makes it possible to test if errors in the survey response are related to the reporting of particular types of income. Results show bias in the mean and that the survey error has substantial variance. Results also show that the errors are correlated with conventional covariates suggesting that the errors are not of the classical type. The latter example illustrates how Denmark can be used as a "laboratory" for future validation studies. Tax records with detailed information about different types of income are available for the entire Danish population and can be readily merged to survey data. This makes it possible to test the ability of respondents to accurately report different types of income using different interviewing techniques and questions. The examples presented in this paper are based on cross section data. However, the possibility to issue surveys repeatedly to the same persons and linking up to longitudinal tax records provides an opportunity to learn more abo
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Claus Thustrup Kreiner & David Dreyer Lassen & Søren Leth-Petersen, 2014. "Measuring the Accuracy of Survey Responses Using Administrative Register Data: Evidence from Denmark," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 289-307, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberch:12663
    as

    Download full text from publisher

    File URL: http://www.nber.org/chapters/c12663.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. Wojciech Kopczuk & Emmanuel Saez & Jae Song, 2010. "Earnings Inequality and Mobility in the United States: Evidence from Social Security Data Since 1937," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(1), pages 91-128.
    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. Fagereng, Andreas & Onshuus, Helene & Torstensen, Kjersti N., 2024. "The consumption expenditure response to unemployment: Evidence from Norwegian households," Journal of Monetary Economics, Elsevier, vol. 146(C).
    2. Francesco D’Acunto & Daniel Hoang & Michael Weber, 2022. "Managing Households’ Expectations with Unconventional Policies," The Review of Financial Studies, Society for Financial Studies, vol. 35(4), pages 1597-1642.
    3. Elinder, Mikael & Erixson, Oscar & Waldenström, Daniel, 2018. "Inheritance and wealth inequality: Evidence from population registers," Journal of Public Economics, Elsevier, vol. 165(C), pages 17-30.
    4. Buda, Gergely & Carvalho, Vasco & Hansen, Stephen & Ortiz, Alvaro & Rodrigo, Tomasa & Rodríguez Mora, José V, 2022. "National Accounts in a World of Naturally Occurring Data: A Proof of Concept for Consumption," CEPR Discussion Papers 17519, Centre for Economic Policy Research.
    5. Kolsrud, Jonas & Landais, Camille & Spinnewijn, Johannes, 2020. "The value of registry data for consumption analysis: An application to health shocks," Journal of Public Economics, Elsevier, vol. 189(C).
    6. Li, Feng & Wang, Xintao, 2024. "Misreporting in household income and expenditure: Evidence from the Chinese Household Income Project," Economics Letters, Elsevier, vol. 237(C).
    7. repec:cam:camjip:2220 is not listed on IDEAS
    8. Andreas Fagereng & Elin Halvorsen, 2017. "Imputing consumption from Norwegian income and wealth registry data," Journal of Economic and Social Measurement, IOS Press, issue 1, pages 67-100.
    9. Tomas Lichard & Jan Hanousek & Randall K. Filer, 2012. "Measuring the Shadow Economy: Endogenous Switching Regression with Unobserved Separation," Economics Working Paper Archive at Hunter College 438, Hunter College Department of Economics.
    10. Burt S. Barnow & David Greenberg, 2015. "Do Estimated Impacts on Earnings Depend on the Source of the Data Used to Measure Them? Evidence From Previous Social Experiments," Evaluation Review, , vol. 39(2), pages 179-228, April.
    11. Janet Hua Jiang & Rupal Kamdar & Kelin Lu & Daniela Puzzello, 2024. "How Do Households Respond to Expected Inflation? An Investigation of Transmission Mechanisms," CAEPR Working Papers 2024-004 Classification-D, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    12. Alain Galli & Rina Rosenblatt-Wisch, 2022. "Analysing households' consumption and saving patterns using tax data," Working Papers 2022-03, Swiss National Bank.
    13. Landais, Camille & Kolsrud, Jonas & Spinnewijn, Johannes, 2017. "Studying Consumption Patterns using Registry Data: Lessons From Swedish Administrative Data," CEPR Discussion Papers 12402, Centre for Economic Policy Research.
    14. Hanousek, Jan & Lichard, Tomáš & Torosyan, Karine, 2016. "‘Flattening’ the Tax Evasion: Evidence from the Post-Communist Natural Experiment," CEPR Discussion Papers 11229, Centre for Economic Policy Research.
    15. Martin Browning & Thomas F. Crossley & Joachim Winter, 2014. "The Measurement of Household Consumption Expenditures," Annual Review of Economics, Annual Reviews, vol. 6(1), pages 475-501, August.
    16. Fink Simonsen, Nicolai & Kjær, Trine, 2021. "New Evidence of Health State Dependent Utility of Consumption: A combined survey and register study," DaCHE discussion papers 2021:2, University of Southern Denmark, Dache - Danish Centre for Health Economics.
    17. 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.
    18. Tomáš Lichard & Jan Hanousek & Randall K. Filer, 2021. "Hidden in plain sight: using household data to measure the shadow economy," Empirical Economics, Springer, vol. 60(3), pages 1449-1476, March.
    19. 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.
    20. Christopher D. Carroll, 2014. "Representing Consumption and Saving without a Representative Consumer," NBER Chapters, in: Measuring Economic Sustainability and Progress, pages 115-134, National Bureau of Economic Research, Inc.
    21. Randall K. Filer & Jan Hanousek & Tomáš Lichard & Karine Torosyan, 2019. "‘Flattening’ tax evasion? : Evidence from the post‐communist natural experiment," The Economics of Transition, The European Bank for Reconstruction and Development, vol. 27(1), pages 223-246, January.
    22. Yvonne McCarthy & Kieran McQuinn, 2016. "Attenuation Bias, Recall Error and the Housing Wealth Effect," Kyklos, Wiley Blackwell, vol. 69(3), pages 492-517, August.
    23. Cabral, Ana Cinta G. & Gemmell, Norman, 2018. "Estimating Self-Employment Income-Gaps from Register and Survey Data: Evidence for New Zealand," Working Paper Series 7625, Victoria University of Wellington, Chair in Public Finance.

    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. Estelle Dauchy & Francisco Navarro-Sanchez & Nathan Seegert, 2021. "Taxation and Inequality: Active and Passive Channels," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 42, pages 156-177, October.
    2. 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 1-008.
    3. Gorodnichenko, Yuriy & Song, Jae & Stolyarov, Dmitriy, 2013. "Macroeconomic Determinants of Retirement Timing," IZA Discussion Papers 7744, IZA Network @ LISER.
    4. Mehtabul Azam, 2022. "Household income mobility in India, 1993–2011," Review of Development Economics, Wiley Blackwell, vol. 26(4), pages 1902-1943, November.
    5. Molly Dahl & Thomas DeLeire & Jonathan A. Schwabish, 2011. "Estimates of Year-to-Year Volatility in Earnings and in Household Incomes from Administrative, Survey, and Matched Data," Journal of Human Resources, University of Wisconsin Press, vol. 46(4), pages 750-774.
    6. Yonatan Berman, 2022. "Absolute intragenerational mobility in the United States, 1962–2014," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(3), pages 587-609, September.
    7. Yuriy Gorodnichenko & John Laitner & Jae Song & Dmitriy Stolyarov, 2013. "Technological Progress and the Earnings of Older Workers," Working Papers wp280, University of Michigan, Michigan Retirement Research Center.
    8. Laisney, François & Pohlmeier, Winfried & Staat, Matthias, 1991. "Estimation of labour supply functions using panel data: a survey," ZEW Discussion Papers 91-05, ZEW - Leibniz Centre for European Economic Research.
    9. John Abowd & Martha Stinson, 2011. "Estimating Measurement Error in SIPP Annual Job Earnings: A Comparison of Census Bureau Survey and SSA Administrative Data," Working Papers 11-20, Center for Economic Studies, U.S. Census Bureau.
    10. Till Treeck, 2014. "Did Inequality Cause The U.S. Financial Crisis?," Journal of Economic Surveys, Wiley Blackwell, vol. 28(3), pages 421-448, July.
    11. Serdar Ozkan & Jae Song & Fatih Karahan & Fatih Guvenen, 2013. "What Do Data on Millions of U.S. Workers Say About Labor Income Risk?," 2013 Meeting Papers 1271, Society for Economic Dynamics.
    12. Stella Martin & Kevin Stabenow & Mark Trede, 2024. "Measurement Error in Earnings," CQE Working Papers 10824, Center for Quantitative Economics (CQE), University of Muenster.
    13. repec:osf:socarx:6vmws_v1 is not listed on IDEAS
    14. Michael A. Clemens & Claudio Montenegro & Lant Pritchett, 2016. "Bounding the Price Equivalent of Migration Barriers," Growth Lab Working Papers 67, Harvard's Growth Lab.
    15. Muthitacharoen, Athiphat & Burong, Trongwut, 2023. "Climbing the economic ladder: Earnings inequality and intragenerational mobility among Thai formal workers," Journal of Asian Economics, Elsevier, vol. 89(C).
    16. Raj Chetty & Nathaniel Hendren & Patrick Kline & Emmanuel Saez, 2014. "Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1553-1623.
    17. Smith, Jennifer C., 2002. "Pay Cuts And Morale : A Test Of Downward Nominal Rigidity," The Warwick Economics Research Paper Series (TWERPS) 649, University of Warwick, Department of Economics.
    18. Anthony B. Atkinson & Thomas Piketty & Emmanuel Saez, 2011. "Top Incomes in the Long Run of History," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 3-71, March.
    19. Frank A Cowell & Christian Schluter, 1998. "Measuring Income Mobility with Dirty Data (published in Ethnic and Racial Studies, 22(3), May 1999)," CASE Papers 016, Centre for Analysis of Social Exclusion, LSE.
    20. Robert I. Lerman, 2012. "Can the United States Expand Apprenticeship? Lessons from Experience," Working Papers 2012-18, American University, Department of Economics.
    21. Meyer, Bruce D. & Mittag, Nikolas, 2019. "Combining Administrative and Survey Data to Improve Income Measurement," IZA Discussion Papers 12266, IZA Network @ LISER.

    More about this item

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey 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:nbr:nberch:12663. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.