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

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  • 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 about the time series properties of measurement errors, a subject about which little evidence exist, in the future.

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  • Claus Thustrup Kreiner & David Dreyer Lassen & Søren Leth-Petersen, 2013. "Measuring the Accuracy of Survey Responses using Administrative Register Data: Evidence from Denmark," NBER Working Papers 19539, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:19539
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    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.
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    Cited by:

    1. 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.
    2. 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.
    3. Fagereng, Andreas & Halvorsen, Elin, 2017. "Imputing consumption from Norwegian income and wealth registry data," Journal of Economic and Social Measurement, IOS Press, issue 1, pages 67-100.
    4. Rodríguez Mora, José V & Buda, Gergely & Carvalho, Vasco & Hansen, Stephen & Ortiz, Alvaro & Rodrigo, Tomasa, 2022. "National Accounts in a World of Naturally Occurring Data: A Proof of Concept for Consumption," CEPR Discussion Papers 17519, C.E.P.R. Discussion Papers.
    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. 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.
    7. 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.
    8. 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.
    9. Dr. Alain Galli & Dr. Rina Rosenblatt-Wisch, 2022. "Analysing households' consumption and saving patterns using tax data," Working Papers 2022-03, Swiss National Bank.
    10. Landais, Camille & Kolsrud, Jonas & Spinnewijn, Johannes, 2017. "Studying Consumption Patterns using Registry Data: Lessons From Swedish Administrative Data," CEPR Discussion Papers 12402, C.E.P.R. Discussion Papers.
    11. Hanousek, Jan & Lichard, Tomáš & Torosyan, Karine, 2016. "‘Flattening’ the Tax Evasion: Evidence from the Post-Communist Natural Experiment," CEPR Discussion Papers 11229, C.E.P.R. Discussion Papers.
    12. Yvonne McCarthy & Kieran McQuinn, 2016. "Attenuation Bias, Recall Error and the Housing Wealth Effect," Kyklos, Wiley Blackwell, vol. 69(3), pages 492-517, August.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.

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    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

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