[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123563-en":3,"doc-seo-123563-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123563,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Active labour market policies for the long-term unemployed - New evidence from causal machine learning","The study examines the effectiveness of three job-search and training programmes for German long-term unemployed individuals. Using a large administrative dataset, the analysis estimates effects at multiple aggregation levels with Causal Machine Learning, including heterogeneity by participant characteristics and local labour market conditions. Results indicate participants benefit from the programmes, with placement services performing best, effects emerging quickly and persisting. While effects are largely homogeneous for men, women show differential benefits when local conditions improve. The observed programme assignment is as effective as random allocation, motivating data-driven allocation rules.","Active labour market policies for the long-term unemployed: New evidence from causal machine learning  \nDaniel Goller 1,3, Tamara Harrer 2, Michael Lechner 1, Joachim Wolff2 *  \n1Swiss Institute of Empirical Economic Research, University of St. Gallen  \n2 Institute for Employment Research, Nuremberg  \n3Centre for Research in Economics of Education, University of Bern  \nThis version: June 2021  \nAbstract: 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.  \nKeywords: Policy evaluation, Modified Causal Forest (MCF), active labour market programmes, conditional  \naverage treatment effect (CATE) JEL classification: J08, J68 .  \nAddresses for correspondence:  \nDaniel Goller, Centre for Research in Economics of Education, University of Bern, Schanzeneckstrasse 1, CH- 3001 Bern, Switzerland, [Daniel.Goller@vwi.unibe.ch](Daniel.Goller@vwi.unibe.ch)  \nMichael Lechner, Swiss Institute for Empirical Economic Research (SEW), University of St. Gallen, Varnbüelstrasse 14, CH-9000 St. Gallen, Switzerland, Michael.Lechner@unisg.ch, [www.michael-lechner.eu](www.michael-lechner.eu)[ ](www.michael-lechner.eu)[Tamara Harrer](Tamara Harrer), Institute for Employment Research, Regensburger Str. 104, D-90478 Nuremberg, Germany, [Tamara.Harrer@iab.de](Tamara.Harrer@iab.de)  \nJoachim Wolff, Institute for Employment Research, Regensburger Str. 104, D-90478 Nuremberg, Germany, [Joachim.Wolff@iab.de](Joachim.Wolff@iab.de)  \n* Michael Lechner is also affiliated with CEPR, London, CESIfo, Munich, IAB, Nuremberg, IZA, Bonn, and RWI, Essen. Joachim Wolff is also affiliated with the Ludwig-Maximilian University, Munich, LASER, Nuremberg, and GLO, Essen. Support of the Swiss Science Foundation (grant SNST 407540_ 166999) and of the IAB under grant for the project “ Estimating heterogeneous effects of the schemes for activation and integration on welfare recipients’ outcomes: Enhanced analyses by the application of machine learning algorithms” is gratefully acknowledged. A previous version of the paper was presented at the Causal Machine Learning Workshop, 2020, St. Gallen. We thank the participants for their helpful comments and suggestions. The usual disclaimer applies.  \n1 Introduction  \nBringing means-tested benefit recipients back to work is among the hardest tasks for employment agencies. Still, it is of high interest for all, the society, the state, and most importantly for the unemployed, to increase their chances to find decent jobs and leave longterm unemployment. The classical approach of employment agencies in industrialised countries is to provide active labour market programmes (ALMP), such as job-search and training programmes, to selected unemployed individuals. It is therefore of great interest to labour market authorities to understand whether those ALMP are beneficial for the long-term unemployed and to understand which programme works best and for which types of unemployed persons. Furthermore, a key issue is to learn how to allocate the programmes efficiently.  \nThis study shares the interest of policy makers in gaining a better under","cbCaisjlQklS9vQe","https://ap.wps.com/l/cbCaisjlQklS9vQe","pdf",3870180,1,84,"English","en",105,"# Introduction\n## Policy challenge and goals\n## Prior evidence and research gap\n## Causal machine learning and treatment-effect heterogeneity\n## Modified Causal Forest (MCF) approach\n# Evidence on active labour market policy evaluation","[{\"question\":\"Which programmes are evaluated for the German long-term unemployed?\",\"answer\":\"The paper evaluates three job-search and training programmes, assessing their effectiveness for long-term unemployed participants.\"},{\"question\":\"What does the analysis find about overall programme effectiveness and timing?\",\"answer\":\"Participants benefit from the programmes, with placement services most effective. Effects materialize quickly and are long-lasting across programmes.\"},{\"question\":\"How do the effects differ across men and women, and what role do local conditions play?\",\"answer\":\"Effects are relatively homogenous for men, while women experience differential effects. Women benefit especially when local labour market conditions improve.\"}]","Active labour market policies for the long-term unemployed - New evidence from causal machine learning | PDF",1785817367,212,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"active-labour-market-policies-for-the-long-term-unemployed-new-evidence-from-causal-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/active-labour-market-policies-for-the-long-term-unemployed-new-evidence-from-causal-machine-learning/123563/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which programmes are evaluated for the German long-term unemployed?","Question",{"text":75,"@type":76},"The paper evaluates three job-search and training programmes, assessing their effectiveness for long-term unemployed participants.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the analysis find about overall programme effectiveness and timing?",{"text":80,"@type":76},"Participants benefit from the programmes, with placement services most effective. Effects materialize quickly and are long-lasting across programmes.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the effects differ across men and women, and what role do local conditions play?",{"text":84,"@type":76},"Effects are relatively homogenous for men, while women experience differential effects. 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