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Measurement error in minimum wage evaluations using survey data

Author

Listed:
  • Bossler, Mario

    (Institute for Employment Research (IAB), Nuremberg, Germany ; LASER)

  • Westermeier, Christian

    (Institute for Employment Research (IAB), Nuremberg, Germany)

Abstract

"We assess the role of measurement error in minimum wage evaluations when the treatment variable - the bite - is inferred from a survey wage distribution. We conduct Monte Carlo experiments on both simulated and empirical distributions of measurement error derived from a record linkage of survey wages and administrative data. On the individual level treatment effects are downward biased by more than 30 percent. Aggregation of the treatment information at the household, firm or region level does not fully alleviate the bias. In fact, the magnitude and direction of the bias depend on the size of the aggregation units and the allocation of treated individuals to such units. In cases of a strongly segregated allocation, measurement error can cause upward biased treatment effects. Besides aggregation, we discuss two possible remedies: the use of a continuous treatment variable and dropping observations close to the minimum wage threshold." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Bossler, Mario & Westermeier, Christian, 2020. "Measurement error in minimum wage evaluations using survey data," IAB-Discussion Paper 202011, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:202011
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    References listed on IDEAS

    as
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    Cited by:

    1. Devereux, Kevin & Studnicka, Zuzanna, 2024. "Non-monotonic employment effects by market structure and minimum wage level," CLEF Working Paper Series 66, Canadian Labour Economics Forum (CLEF), University of Waterloo.
    2. Lucas Hafner & Benjamin Lochner, 2022. "Do minimum wages improve self-rated health? Evidence from a natural experiment," Empirical Economics, Springer, vol. 62(6), pages 2989-3014, June.
    3. Börschlein, Benjamin & Bossler, Mario, 2021. "A new machine learning-based treatment bite for long run minimum wage evaluations," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242441, Verein für Socialpolitik / German Economic Association.

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    More about this item

    Keywords

    Bundesrepublik Deutschland ; Befragung ; Datengewinnung ; Messfehler ; Integrierte Erwerbsbiografien ; Lohnhöhe ; Mindestlohn ; Antwortverhalten ; Validität ; Wirkungsforschung ; IAB-Haushaltspanel ; 2011-2015;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • J38 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Public Policy

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