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Structural Empirical Evaluation of Job Search Monitoring

Author

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  • van den Berg, Gerard J.

    (University of Groningen)

  • van der Klaauw, Bas

    (Vrije Universiteit Amsterdam)

Abstract

We structurally estimate a novel job search model with endogenous job search effort, job quality dispersion, and effort monitoring, taking into account that monitoring effects may be mitigated by on-the-job search and search channel substitution. The data are from a randomized experiment conducted in the Netherlands. They include registers of post-unemployment outcomes like wages and job mobility, and survey data on measures of search behavior. As such we are the first to study monitoring effects on post-unemployment outcomes. We find that the option to climb the job ladder reduces substitution between search channels during unemployment and compensates for adverse long-run effects of monitoring on wages. We use the structural estimates to compare monitoring to counterfactual policies against moral hazard, like re-employment bonuses and changes in the unemployment benefits path. Replacing monitoring by an overall benefits reduction in a way that is neutral to the worker results in slightly smaller effects with lower administrative costs.

Suggested Citation

  • van den Berg, Gerard J. & van der Klaauw, Bas, 2013. "Structural Empirical Evaluation of Job Search Monitoring," IZA Discussion Papers 7740, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp7740
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gürtzgen, Nicole & (né Nolte), André Diegmann & Pohlan, Laura & van den Berg, Gerard J., 2021. "Do digital information technologies help unemployed job seekers find a job? Evidence from the broadband internet expansion in Germany," European Economic Review, Elsevier, vol. 132(C).
    2. Lochner, Benjamin & Merkl, Christian & Stüber, Heiko & Gürtzgen, Nicole, 2021. "Recruiting intensity and hiring practices: Cross-sectional and time-series evidence," Labour Economics, Elsevier, vol. 68(C).
    3. Paul Muller & Bas van der Klaauw & Arjan Heyma, 2020. "Comparing econometric methods to empirically evaluate activation programs for job seekers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 526-547, August.
    4. Lachowska, Marta & Meral, Merve & Woodbury, Stephen A., 2016. "Effects of the unemployment insurance work test on long-term employment outcomes," Labour Economics, Elsevier, vol. 41(C), pages 246-265.
    5. Malory Rennoir & Ilan Tojerow, 2019. "Évaluation de l’ensemble du dispositif de contrôle de la disponibilité des chômeurs, tel que mis en œuvre au sein du Forem," ULB Institutional Repository 2013/292150, ULB -- Universite Libre de Bruxelles.
    6. De Groote, Olivier, 2019. "A dynamic model of effort choice in high school," TSE Working Papers 19-1002, Toulouse School of Economics (TSE), revised Jun 2020.

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    More about this item

    Keywords

    search channels; treatment; job mobility; job duration; wage; active labor market policy; search effort; unemployment duration; multi-tasking; randomized social experiment;
    All these keywords.

    JEL classification:

    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • J65 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment Insurance; Severance Pay; Plant Closings
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy
    • J62 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Job, Occupational and Intergenerational Mobility; Promotion
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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