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What did you really earn last year?: explaining measurement error in survey income data

Author

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  • Stefan Angel
  • Franziska Disslbacher
  • Stefan Humer
  • Matthias Schnetzer

Abstract

The paper analyses the sources of income measurement error in surveys with a unique data set. We use the Austrian 2008–2011 waves of the European Union ‘Statistics on income and living conditions' survey which provide individual information on wages, pensions and unemployment benefits from survey interviews and officially linked administrative records. Thus, we do not have to fall back on complex two‐sample matching procedures like related studies. We empirically investigate four sources of measurement error, namely social desirability, sociodemographic characteristics of the respondent, the survey design and the presence of learning effects. We find strong evidence for a social desirability bias in income reporting, whereas the presence of learning effects is mixed and depends on the type of income under consideration. An Owen value decomposition reveals that social desirability is a major explanation of misreporting in wages and pensions, whereas sociodemographic characteristics are most relevant for mismatches in unemployment benefits.

Suggested Citation

  • Stefan Angel & Franziska Disslbacher & Stefan Humer & Matthias Schnetzer, 2019. "What did you really earn last year?: explaining measurement error in survey income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1411-1437, October.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1411-1437
    DOI: 10.1111/rssa.12463
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    Cited by:

    1. Mathias Silva & Michel Lubrano, 2023. "Bayesian correction for missing rich using a Pareto II tail with unknown threshold: Combining EU-SILC and WID data," AMSE Working Papers 2320, Aix-Marseille School of Economics, France.
    2. Christina Siegert, 2021. "Erwerbsarmut in Österreich aus Geschlechterperspektive," Wirtschaft und Gesellschaft - WuG, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik, vol. 47(4), pages 511-535.
    3. Sonja Spitzer & Daniela Weber, 2019. "Reporting biases in self-assessed physical and cognitive health status of older Europeans," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-22, October.
    4. Ozan Bakis & Sezgin Polat, 2023. "Wage inequality dynamics in Turkey," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 50(3), pages 657-694, August.
    5. Antonio Calcagnì & Luigi Lombardi, 2022. "Modeling random and non-random decision uncertainty in ratings data: a fuzzy beta model," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 145-173, March.
    6. Jana Emmenegger & Ralf Münnich & Jannik Schaller, 2022. "Evaluating Data Fusion Methods to Improve Income Modelling," Research Papers in Economics 2022-03, University of Trier, Department of Economics.
    7. Stefan Jestl & Emanuel List, 2020. "Distributional National Accounts (DINA) for Austria, 2004-2016," wiiw Working Papers 175, The Vienna Institute for International Economic Studies, wiiw.
    8. 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.
    9. Stephan Klasen & Maria C. Lo Bue & Vincenzo Prete, 2020. "What's behind pro-poor growth?: The role of shocks and measurement error," WIDER Working Paper Series wp-2020-16, World Institute for Development Economic Research (UNU-WIDER).
    10. Glenn Abela & William Gatt, "undated". "Who are the (dis)savers? A look at household saving patters and wealth composition in Malta," CBM Policy Papers PP/01/2022, Central Bank of Malta.
    11. Katariina Mueller-Gastell, 2023. "Poach or Promote? Job Sorting and Gender Earnings Inequality across U.S. Industries," Working Papers 23-23, Center for Economic Studies, U.S. Census Bureau.
    12. Stefan Jestl & Emanuel List, 2020. "Distributional National Accounts (DINA) for Austria, 2004-2016," Working Papers halshs-03022077, HAL.
    13. Emmenegger Jana & Münnich Ralf, 2023. "Localising the Upper Tail: How Top Income Corrections Affect Measures of Regional Inequality," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 243(3-4), pages 285-317, June.
    14. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," AMSE Working Papers 2311, Aix-Marseille School of Economics, France.
    15. 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.
    16. Stefan Jestl & Emanuel List, 2023. "Inequality, Redistribution, and the Financial Crisis: Evidence from Distributional National Accounts for Austria," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(1), pages 195-227, March.
    17. 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).
    18. Stefan Jestl & Emanuel List, 2020. "Distributional National Accounts (DINA) for Austria, 2004-2016," World Inequality Lab Working Papers halshs-03022077, HAL.
    19. Stefan Jestl & Emanuel List, 2020. "Distributional national accounts (DINA) for Austria 2004-2016," Working Paper Reihe der AK Wien - Materialien zu Wirtschaft und Gesellschaft 197, Kammer für Arbeiter und Angestellte für Wien, Abteilung Wirtschaftswissenschaft und Statistik.

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