IDEAS home Printed from https://ideas.repec.org/p/usg/econwp/202108.html
   My bibliography  Save this paper

Active labour market policies for the long-term unemployed: New evidence from causal machine learning

Author

Listed:
  • Goller, Daniel
  • Harrer, Tamara
  • Lechner, Michael
  • Wolff, Joachim

Abstract

We investigate the effectiveness of three different job-search and training programmes for German long-term unemployed persons. On the basis of an extensive administrative data set, we evaluated the effects of those programmes on various levels of aggregation using Causal Machine Learning. We found participants to benefit from the investigated programmes with placement services to be most effective. Effects are realised quickly and are long-lasting for any programme. While the effects are rather homogenous for men, we found differential effects for women in various characteristics. Women benefit in particular when local labour market conditions improve. Regarding the allocation mechanism of the unemployed to the different programmes, we found the observed allocation to be as effective as a random allocation. Therefore, we propose data-driven rules for the allocation of the unemployed to the respective labour market programmes that would improve the status-quo.

Suggested Citation

  • Goller, Daniel & Harrer, Tamara & Lechner, Michael & Wolff, Joachim, 2021. "Active labour market policies for the long-term unemployed: New evidence from causal machine learning," Economics Working Paper Series 2108, University of St. Gallen, School of Economics and Political Science.
    • Handle: RePEc:usg:econwp:2021:08
    as

    Download full text from publisher

    File URL: http://ux-tauri.unisg.ch/RePEc/usg/econwp/EWP-2108.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Sarah Bernhard & Eva Kopf, 2014. "Courses or individual counselling: does job search assistance work?," Applied Economics, Taylor & Francis Journals, vol. 46(27), pages 3261-3273, September.
    3. Lechner, Michael, 2018. "Modified Causal Forests for Estimating Heterogeneous Causal Effects," IZA Discussion Papers 12040, Institute of Labor Economics (IZA).
    4. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    5. Michael Gerfin & Michael Lechner, 2002. "A Microeconometric Evaluation of the Active Labour Market Policy in Switzerland," Economic Journal, Royal Economic Society, vol. 112(482), pages 854-893, October.
    6. Michael Lechner & Conny Wunsch, 2009. "Are Training Programs More Effective When Unemployment Is High?," Journal of Labor Economics, University of Chicago Press, vol. 27(4), pages 653-692, October.
    7. Kluve, Jochen, 2010. "The effectiveness of European active labor market programs," Labour Economics, Elsevier, vol. 17(6), pages 904-918, December.
    8. repec:adr:anecst:y:2008:i:91-92:p:17 is not listed on IDEAS
    9. Zhengyuan Zhou & Susan Athey & Stefan Wager, 2023. "Offline Multi-Action Policy Learning: Generalization and Optimization," Operations Research, INFORMS, vol. 71(1), pages 148-183, January.
    10. David Card & Jochen Kluve & Andrea Weber, 2010. "Active Labour Market Policy Evaluations: A Meta-Analysis," Economic Journal, Royal Economic Society, vol. 120(548), pages 452-477, November.
    11. Achatz, Juliane & Trappmann, Mark, 2011. "Arbeitsmarktvermittelte Abgänge aus der Grundsicherung : der Einfluss von personen- und haushaltsgebundenen Barrieren," IAB-Discussion Paper 201102, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    12. 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.
    13. Annette Bergemann & Gerard J. Van Den Berg, 2008. "Active Labor Market Policy Effects for Women in Europe - A Survey," Annals of Economics and Statistics, GENES, issue 91-92, pages 385-408.
    14. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    15. K. Hohmeyer, 2012. "Effectiveness of One-Euro-Jobs: do programme characteristics matter?," Applied Economics, Taylor & Francis Journals, vol. 44(34), pages 4469-4484, December.
    16. Jonathan M.V. Davis & Sara B. Heller, 2020. "Rethinking the Benefits of Youth Employment Programs: The Heterogeneous Effects of Summer Jobs," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 664-677, October.
    17. Barbara Sianesi, 2004. "An Evaluation of the Swedish System of Active Labor Market Programs in the 1990s," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 133-155, February.
    18. David Card & Jochen Kluve & Andrea Weber, 2018. "What Works? A Meta Analysis of Recent Active Labor Market Program Evaluations," Journal of the European Economic Association, European Economic Association, vol. 16(3), pages 894-931.
    19. Lechner, Michael & Smith, Jeffrey, 2007. "What is the value added by caseworkers?," Labour Economics, Elsevier, vol. 14(2), pages 135-151, April.
    20. Barbara Boelmann & Anna Raute & Uta Schönberg, 2020. "Wind of Change? Cultural Determinants of Maternal Labor Supply," RF Berlin - CReAM Discussion Paper Series 2020, Rockwool Foundation Berlin (RF Berlin) - Centre for Research and Analysis of Migration (CReAM).
    21. Lechner, Michael & Wunsch, Conny, 2013. "Sensitivity of matching-based program evaluations to the availability of control variables," Labour Economics, Elsevier, vol. 21(C), pages 111-121.
    22. 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.
    23. 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.
    24. Tamara Harrer & Andreas Moczall & Joachim Wolff, 2020. "Free, free, set them free? Are programmes effective that allow job centres considerable freedom to choose the exact design?," International Journal of Social Welfare, John Wiley & Sons, vol. 29(2), pages 154-167, April.
    25. Caliendo, Marco & Mahlstedt, Robert & Mitnik, Oscar A., 2017. "Unobservable, but unimportant? The relevance of usually unobserved variables for the evaluation of labor market policies," Labour Economics, Elsevier, vol. 46(C), pages 14-25.
    26. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    27. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    28. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    29. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.
    30. Marco Caliendo & Ricarda Schmidl, 2016. "Youth unemployment and active labor market policies in Europe," IZA Journal of Labor Policy, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-30, December.
    31. Bernhard, Sarah & Wolff, Joachim, 2008. "Contracting out placement services in Germany : is assignment to private providers effective for needy job-seekers?," IAB-Discussion Paper 200805, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    32. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    33. repec:eme:ijmpps:v:34:y:2013:i:1:p:486-516 is not listed on IDEAS
    34. Sarah Carpentier & Karel Neels & Karel Van den Bosch, 2014. "How Do Exit Rates from Social Assistance Benefit in Belgium Vary with Individual and Local Agency Characteristics?," Research in Labor Economics, in: Safety Nets and Benefit Dependence, volume 39, pages 151-187, Emerald Group Publishing Limited.
    35. Blázquez, Maite & Herrarte, Ainhoa & Sáez, Felipe, 2019. "Training and job search assistance programmes in Spain: The case of long-term unemployed," Journal of Policy Modeling, Elsevier, vol. 41(2), pages 316-335.
    36. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    37. Lasse Bork & Stig V. Møller & Thomas Q. Pedersen, 2020. "A New Index of Housing Sentiment," Management Science, INFORMS, vol. 66(4), pages 1563-1583, April.
    38. Heckman, James J. & Lalonde, Robert J. & Smith, Jeffrey A., 1999. "The economics and econometrics of active labor market programs," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 3, chapter 31, pages 1865-2097, Elsevier.
    39. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    40. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    41. Jonathan M.V. Davis & Sara B. Heller, 2017. "Using Causal Forests to Predict Treatment Heterogeneity: An Application to Summer Jobs," American Economic Review, American Economic Association, vol. 107(5), pages 546-550, May.
    42. Barbara Boelmann & Anna Raute & Uta Schönberg, 2020. "Wind of Change? Cultural Determinants of Maternal Labor Supply," Working Papers 914, Queen Mary University of London, School of Economics and Finance.
    43. repec:taf:applec:44:y:2012:i:34:p:4469-4484 is not listed on IDEAS
    44. Martin, John P. & Grubb, David, 2001. "What works and for whom: a review of OECD countries' experiences with active labour market policies," Working Paper Series 2001:14, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    45. Eva Kopf, 2013. "Short training for welfare recipients in Germany: which types work?," International Journal of Manpower, Emerald Group Publishing Limited, vol. 34(5), pages 486-516, August.
    46. Blank, Rebecca M., 1989. "Analyzing the length of welfare spells," Journal of Public Economics, Elsevier, vol. 39(3), pages 245-273, August.
    47. Boelmann, Barbara & Raute, Anna & Schönberg, Uta, 2020. "Wind of Change? Cultural Determinants of Maternal Labor Supply," IAB-Discussion Paper 202030, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    2. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies," Papers 2410.23322, arXiv.org, revised May 2025.
    3. Jonas Wood & Julie Maes & Karel Neels, 2025. "The Effectiveness of Active Labour Market Policies for Long‐Term Unemployed Jobseekers in Flanders," Social Inclusion, Cogitatio Press, vol. 13.
    4. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Cockx, Bart & Lechner, Michael & Bollens, Joost, 2023. "Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium," Labour Economics, Elsevier, vol. 80(C).
    3. Goller, Daniel & Lechner, Michael & Moczall, Andreas & Wolff, Joachim, 2020. "Does the estimation of the propensity score by machine learning improve matching estimation? The case of Germany's programmes for long term unemployed," Labour Economics, Elsevier, vol. 65(C).
    4. Gabriel Okasa, 2022. "Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance," Papers 2201.12692, arXiv.org.
    5. 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.
    6. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    7. Michael Lechner & Jana Mareckova, 2022. "Modified Causal Forest," Papers 2209.03744, arXiv.org.
    8. Federica Mascolo & Nora Bearth & Fabian Muny & Michael Lechner & Jana Mareckova, 2024. "From Average Effects to Targeted Assignment: A Causal Machine Learning Analysis of Swiss Active Labor Market Policies," Papers 2410.23322, arXiv.org, revised May 2025.
    9. 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.
    10. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).
    11. Burlat, Héloïse, 2024. "Everybody’s got to learn sometime? A causal machine learning evaluation of training programmes for jobseekers in France," Labour Economics, Elsevier, vol. 89(C).
    12. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    13. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    14. Caliendo, Marco & Mahlstedt, Robert & Mitnik, Oscar A., 2017. "Unobservable, but unimportant? The relevance of usually unobserved variables for the evaluation of labor market policies," Labour Economics, Elsevier, vol. 46(C), pages 14-25.
    15. Katharina Dengler, 2019. "Effectiveness of sequences of classroom training for welfare recipients: what works best in West Germany?," Applied Economics, Taylor & Francis Journals, vol. 51(1), pages 1-46, January.
    16. Daniel Boller & Michael Lechner & Gabriel Okasa, 2025. "The effect of sport in online dating: evidence from causal machine learning," Palgrave Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
    17. Arranz, José María & García-Serrano, Carlos, 2024. "You'll never seek alone: The impact of active labour market policies on finding a job," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 58, pages 1-20.
    18. Muller, Paul & van der Klaauw, Bas & Heyma, Arjan, 2017. "Comparing Econometric Methods to Empirically Evaluate Job-Search Assistance," IZA Discussion Papers 10531, Institute of Labor Economics (IZA).
    19. 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, July.
    20. Bernhard Boockmann & Tobias Brändle, 2019. "Coaching, Counseling, Case‐Working: Do They Help the Older Unemployed Out of Benefit Receipt and Back Into the Labor Market?," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 436-468, November.

    More about this item

    Keywords

    Policy evaluation; Modified Causal Forest (MCF); active labour market programmes; conditional average treatment effect (CATE);
    All these keywords.

    JEL classification:

    • J08 - Labor and Demographic Economics - - General - - - Labor Economics Policies
    • J68 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Public Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:usg:econwp:2021:08. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/vwasgch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.