On the effects of active labour market policies among individuals reporting to have severe mental health problems

Author:

Tübbicke Stefan1ORCID,Schiele Maximilian1

Affiliation:

1. Research Department for Basic Income Support and Activation Institute for Employment Research (IAB) Nuremberg Germany

Abstract

AbstractOn the one hand, unemployment is known to have detrimental effects on individuals' mental health. On the other hand, poor mental health reduces re‐employment chances quite drastically, creating a vicious cycle. Active labour market policies (ALMPs) such as training programs or wage subsidies have been shown to ameliorate negative effects on mental health and improve labour market integration on average for the general unemployed population. In the context of individuals with severe mental health issues, however, it is unclear whether these interventions can be expected to deliver similar positive effects. In fact, one may argue that they have the potential to worsen employment prospects of individuals by adding additional stress to their pre‐existing mental health problems. Hence, this paper estimates the long‐term causal effects of ALMPs on the labour market integration of individuals with self‐reported severe mental health issues and compares estimates to individuals without such issues using unique combined survey and administrative data. Effects are estimated using the innovative double machine learning method and show that ALMPs do not only improve labour market integration of unemployed individuals with severe mental health issues, but they do so more effectively than for other unemployed individuals.

Publisher

Wiley

Subject

Public Administration,Sociology and Political Science,Development

Reference73 articles.

1. PASS‐Befragungsdaten verknüpft mit administrativen Daten des IAB (PASS‐ADIAB) 1975–2015;Antoni M.;FDZ‐ Datenreport,2017

2. How effective are unemployment benefit sanctions? Looking beyond unemployment exit;Arni P.;Journal of Applied Econometrics,2013

3. Moving towards best practice when using inverse probability of treatment weighting (iptw) using the propensity score to estimate causal treatment effects in observational studies;Austin P. C.;Statistics in Medicine,2015

4. Health‐related effects of welfare‐to‐work policies;Ayala L.;Social Science & Medicine,2013

5. Bach P. Chernozhukov V. Kurz M. S. &Spindler M.(2021).DoubleML – An object‐oriented implementation of double machine learning in R. ArXiv Paper No. 2103.09603 [stat.ML].

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