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Later one knows better: the over-reporting of short-time work in firm surveys

Author

Listed:
  • Christian Kagerl

    (Institute for Employment Research (IAB) and FAU Erlangen-Nürnberg)

  • Malte Schierholz

    (Ludwig-Maximilians-Universität München)

  • Bernd Fitzenberger

    (Institute for Employment Research (IAB) and FAU Erlangen-Nürnberg
    IAB, FAU, IFS, CESifo, IZA, and ROA)

Abstract

Short-time work (STW) in Germany allows for a lot of flexibility in actual usage. Ex ante, firms notify the Employment Agency about the total number of employees eligible, and, up to the total granted, firms can flexibly choose how many employees actually use STW. In firm-level surveys, which provide timely information on STW in Germany, over-reporting of the number of employees on STW is prevalent. This study explores reasons for STW over-reporting based on a high-frequency and low-cost survey initiated during the Covid-19-pandemic (BeCovid) and a low-frequency and high-cost long-running survey (BP). Merging administrative records on actual use of STW, firms that use STW prove more likely to participate in the BeCovid survey. Multi-establishment firms over-report STW because they tend to report STW for all subfirms. The BP uses more interview time and confirms the over-reporting of STW use in the survey month, while—crucially—the over-reporting drops sharply with a few months of retrospection.

Suggested Citation

  • Christian Kagerl & Malte Schierholz & Bernd Fitzenberger, 2022. "Later one knows better: the over-reporting of short-time work in firm surveys," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-19, December.
    • Handle: RePEc:spr:jlabrs:v:56:y:2022:i:1:d:10.1186_s12651-022-00312-9
    DOI: 10.1186/s12651-022-00312-9
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    References listed on IDEAS

    as
    1. Tito Boeri & Herbert Bruecker, 2011. "Short-time work benefits revisited: some lessons from the Great Recession [‘Reversed roles? Wage and employment effects of the current crisis’]," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 26(68), pages 697-765.
    2. 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.
    3. repec:oup:ecpoli:v:26:y:2011:i:68:p:697-765 is not listed on IDEAS
    4. Fitzenberger, Bernd & Kagerl, Christian & Schierholz, Malte & Stegmaier, Jens, 2021. "Zeitnahe Daten in der Corona-Krise: Von der schwierigen Vermessung der Kurzarbeit (Realtime Economic Data in the Covid19-Crisis: On the Difficulty of Measuring Short-Time Work)," IAB-Kurzbericht 202124, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Benjamin Küfner & Joseph W. Sakshaug & Stefan Zins, 2022. "Establishment survey participation during the COVID-19 pandemic," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 56(1), pages 1-18, December.

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    More about this item

    Keywords

    Short-time work; Survey evidence; Establishment surveys; Non-response bias; Measurement error;
    All these keywords.

    JEL classification:

    • J63 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Turnover; Vacancies; Layoffs
    • J65 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment Insurance; Severance Pay; Plant Closings
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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