IDEAS home Printed from https://ideas.repec.org/r/tpr/restat/v99y2017i1p180-183.html
   My bibliography  Save this item

Why Do Tougher Caseworkers Increase Employment? The Role of Program Assignment as a Causal Mechanism

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Martin Huber & Mark Schelker & Anthony Strittmatter, 2022. "Direct and Indirect Effects based on Changes-in-Changes," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 432-443, January.
  2. 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.
  3. Wunsch, Conny & Strobl, Renate, 2018. "Identification of causal mechanisms based on between-subject double randomization designs," CEPR Discussion Papers 13028, C.E.P.R. Discussion Papers.
  4. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
  5. Viviana Celli, 2019. "Causal Mediation Analysis in Economics: objectives, assumptions, models," Working Papers 12/19, Sapienza University of Rome, DISS.
  6. Michael C Knaus & Michael Lechner & Anthony Strittmatter, 2021. "Machine learning estimation of heterogeneous causal effects: Empirical Monte Carlo evidence," The Econometrics Journal, Royal Economic Society, vol. 24(1), pages 134-161.
  7. 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.
  8. 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.
  9. Doerr Annabelle & Strittmatter Anthony, 2021. "Identifying Causal Channels of Policy Reforms with Multiple Treatments and Different Types of Selection," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 67-88, January.
  10. Michael C. Knaus & Michael Lechner & Anthony Strittmatter, 2022. "Heterogeneous Employment Effects of Job Search Programs: A Machine Learning Approach," Journal of Human Resources, University of Wisconsin Press, vol. 57(2), pages 597-636.
  11. Wifo, 2017. "WIFO-Monatsberichte, Heft 6/2017," WIFO Monatsberichte (monthly reports), WIFO, vol. 90(6), June.
  12. 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.
  13. Schütt, Christoph A., 2023. "The effect of perceived similarity and social proximity on the formation of prosocial preferences," Journal of Economic Psychology, Elsevier, vol. 99(C).
  14. Vikström, Johan & Söderström, Martin & Cederlöf, Jonas, 2021. "What makes a good caseworker?," Working Paper Series 2021:9, IFAU - Institute for Evaluation of Labour Market and Education Policy.
  15. David G. Lugo‐Palacios & Jonathan M. Clarke & Søren Rud Kristensen, 2023. "Back to basics: A mediation analysis approach to addressing the fundamental questions of integrated care evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 32(9), pages 2080-2097, September.
  16. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
  17. Viviana Celli, 2022. "Causal mediation analysis in economics: Objectives, assumptions, models," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 214-234, February.
  18. Martin Huber & Anna Solovyeva, 2020. "On the Sensitivity of Wage Gap Decompositions," Journal of Labor Research, Springer, vol. 41(1), pages 1-33, June.
  19. 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.
  20. 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 Kiel).
  21. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.
  22. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
  23. Martin Huber & Lukáš Lafférs, 2022. "Bounds on direct and indirect effects under treatment/mediator endogeneity and outcome attrition," Econometric Reviews, Taylor & Francis Journals, vol. 41(10), pages 1141-1163, November.
  24. Ulrike Huemer & Rainer Eppel & Marion Kogler & Helmut Mahringer & Lukas Schmoigl & David Pichler, 2021. "Effektivität von Instrumenten der aktiven Arbeitsmarktpolitik in unterschiedlichen Konjunkturphasen," WIFO Studies, WIFO, number 67250, March.
  25. 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.
  26. Joachim Wilde, 2022. "What drives trust of the long‐term unemployed in their caseworkers?," LABOUR, CEIS, vol. 36(2), pages 231-250, June.
  27. 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.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.