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A cautionary note on the reliability of the online survey data – the case of Wage Indicator

Author

Listed:
  • Joanna Tyrowicz

    (Institute for Labour Law and Industrial Relations in the European Union IAAEU), Trier University)

  • Magdalena Smyk

    (Warsaw School of Economics)

  • Lucas van der Velde

    (Warsaw School of Economics)

Abstract
We investigate the reliability of data from the Wage Indicator (WI), the largest online survey on earnings and working conditions. Comparing WI to nationally representative data sources for 17 countries reveals that participants of WI are not likely to have been representatively drawn from the respective populations. Previous literature has proposed to utilize weights based on inverse propensity scores, but this procedure was shown to leave reweighted WI samples different from the benchmark nationally representative data. We propose a novel procedure, building on covariate balancing propensity score, which achieves complete reweighting of the WI data, making it able to replicate the structure of nationally representative samples on observable characteristics. While rebalancing assures the match between WI and representative benchmark data sources, we show that the wage schedules remain different for a large group of countries. Using the example of a Mincerian wage regression, we find that in more than a third of the cases, our proposed novel reweighting assures that estimates obtained on WI data are not biased relative to nationally representative data. However, in the remaining 60% of the analyzed 95 datasets systematic differences in the estimated coefficients of the Mincerian wage regression between WI and nationally representative data persists even after reweighting. We provide some intuition about the reasons behind these biases. Notably, objective factors such as access to the Internet or richness appear to matter, but self-selection (on unobservable characteristics) among WI participants appears to constitute an important source of bias

Suggested Citation

  • Joanna Tyrowicz & Magdalena Smyk & Lucas van der Velde, 2018. "A cautionary note on the reliability of the online survey data – the case of Wage Indicator," IAAEU Discussion Papers 201805, Institute of Labour Law and Industrial Relations in the European Union (IAAEU).
    • Handle: RePEc:iaa:dpaper:201805
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    Cited by:

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    4. de Pinto Marco & Goerke Laszlo, 2019. "Efficiency Wages in Cournot-Oligopoly," The B.E. Journal of Economic Analysis & Policy, De Gruyter, vol. 19(4), pages 1-13, October.
    5. Rendtel, Ulrich & Alho, Juha M., 2022. "On the fade-away of an initial bias in longitudinal surveys," Discussion Papers 2022/4, Free University Berlin, School of Business & Economics.
    6. Anastasios Evgenidis & Apostolos Fasianos, 2021. "Unconventional Monetary Policy and Wealth Inequalities in Great Britain," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(1), pages 115-175, February.
    7. Joanna Tyrowicz & Krzysztof Makarski & Marcin Bielecki, 2018. "Inequality in an OLG economy with heterogeneous cohorts and pension systems," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(4), pages 583-606, December.
    8. Brian Fabo, 2020. "The English and Russian Language Proficiency Premium in the post-Maidan Ukraine – an Analysis of Web Survey Data," Discussion Papers 57, Central European Labour Studies Institute (CELSI).
    9. Laszlo Goerke & Michael Neugart, 2020. "Thorstein Veblen, Joan Robinson, and George Stigler (probably) never met: Social Preferences, Monopsony, and Government Intervention," IAAEU Discussion Papers 202001, Institute of Labour Law and Industrial Relations in the European Union (IAAEU).

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    More about this item

    Keywords

    Wage Indicator; online surveys; propensity score matching; weights;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • J30 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - General
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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