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Why do tougher caseworkers increase employment? The role of programme assignment as a causal mechanism

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  • Huber, Martin

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  • Mellace, Giovanni

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  • Lechner, Michael

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Abstract

Previous research found that less accommodating caseworkers are more successful in placing unemployed workers into employment. This paper tries to shed more light on the causal mechanisms behind this result using semiparametric mediation analysis. Analysing very informative linked jobseeker-caseworker data for Switzerland, we find that the positive employment effects of less accommodating caseworkers are not driven by a particularly ef-fective mix of labour market programmes they use, but rather by other dimensions of the counselling process, possibly including threat effects of sanctions, pressure to accept jobs, and other factors related to the caseworker’s counselling style.

Suggested Citation

  • Huber, Martin & Mellace, Giovanni & Lechner, Michael, 2014. "Why do tougher caseworkers increase employment? The role of programme assignment as a causal mechanism," Economics Working Paper Series 1414, University of St. Gallen, School of Economics and Political Science.
  • Handle: RePEc:usg:econwp:2014:14
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    References listed on IDEAS

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    1. Gerfin, Michael & Lechner, Michael & Steiger, Heidi, 2005. "Does subsidised temporary employment get the unemployed back to work? Aneconometric analysis of two different schemes," Labour Economics, Elsevier, vol. 12(6), pages 807-835, December.
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    14. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
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    Citations

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    Cited by:

    1. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    2. Strobl, Renate & Wunsch, Conny, 2018. "Identification of causal mechanisms based on between-subject double randomization designs," CEPR Discussion Papers 13028, C.E.P.R. Discussion Papers.
    3. Martin Huber & Michael Lechner & Anthony Strittmatter, 2018. "Direct and indirect effects of training vouchers for the unemployed," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(2), pages 441-463, February.
    4. Martin Huber & Michael Lechner & Giovanni Mellace, 2016. "The Finite Sample Performance of Estimators for Mediation Analysis Under Sequential Conditional Independence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(1), pages 139-160, January.
    5. N. N., 2017. "WIFO-Monatsberichte, Heft 6/2017," WIFO Monatsberichte (monthly reports), WIFO, vol. 90(6), June.
    6. Amelie Schiprowski, 2020. "The Role of Caseworkers in Unemployment Insurance: Evidence from Unplanned Absences," Journal of Labor Economics, University of Chicago Press, vol. 38(4), pages 1189-1225.
    7. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2018. "Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence," Papers 1810.13237, arXiv.org, revised Dec 2018.
    8. Knaus, Michael C. & Lechner, Michael & Strittmatter, Anthony, 2017. "Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach," IZA Discussion Papers 10961, Institute of Labor Economics (IZA).
    9. Michael C. Knaus, 2020. "Double Machine Learning based Program Evaluation under Unconfoundedness," Papers 2003.03191, arXiv.org, revised Oct 2020.
    10. Prifti, Ervin & Daidone, Silvio & Davis, Benjamin, 2019. "Causal pathways of the productive impacts of cash transfers: Experimental evidence from Lesotho," World Development, Elsevier, vol. 115(C), pages 258-268.
    11. Ville Vehkasalo, 2020. "Effects of face-to-face counselling on unemployment rate and duration: evidence from a Public Employment Service reform," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 54(1), pages 1-14, December.
    12. Frölich, Markus & Huber, Martin & Wiesenfarth, Manuel, 2017. "The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 91-102.
    13. Viviana Celli, 2019. "Causal Mediation Analysis in Economics: objectives, assumptions, models," Working Papers 12/19, Sapienza University of Rome, DISS.
    14. Huber, Martin & Schelker, Mark & Strittmatter, Anthony, 2019. "Direct and Indirect Effects based on Changes-in- Changes," FSES Working Papers 508, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    15. Martin Huber & Anna Solovyeva, 2020. "On the Sensitivity of Wage Gap Decompositions," Journal of Labor Research, Springer, vol. 41(1), pages 1-33, June.
    16. Steinmayr, Andreas, 2014. "When a random sample is not random: Bounds on the effect of migration on household members left behind," Kiel Working Papers 1975, Kiel Institute for the World Economy (IfW).
    17. Rainer Eppel & Helmut Mahringer & Petra Sauer, 2017. "Österreich 2025 – Arbeitslosigkeit und die Rolle der aktiven Arbeitsmarktpolitik," WIFO Monatsberichte (monthly reports), WIFO, vol. 90(6), pages 493-505, June.

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    More about this item

    Keywords

    Unemployment; counselling style; active labour market policy; direct effects; indirect effects; causal mechanisms; causal channels; matching estimation;
    All these keywords.

    JEL classification:

    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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

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