Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium
Based on administrative data on unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity in effectiveness across programmes and unemployed. Simulations show that “black-box” reassignment rules that respect capacity constraints on average, increase, respectively decrease, the time spent in employment, respectively unemployment, by more than one month within 30 mont... Mehr ...
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Dokumenttyp: | journalarticle |
Erscheinungsdatum: | 2023 |
Schlagwörter: | Business and Economics / Social Sciences / Law and Political Science / Policy evaluation / Economics and Econometrics / active labour market policy / causal machine learning / modified causal forest / conditional average treatment effects |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-28550577 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://biblio.ugent.be/publication/01GK7MJBYVJDW45QCM01NK44YC |
Based on administrative data on unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity in effectiveness across programmes and unemployed. Simulations show that “black-box” reassignment rules that respect capacity constraints on average, increase, respectively decrease, the time spent in employment, respectively unemployment, by more than one month within 30 months of programme start. A shallow policy tree delivers a simple rule that realizes about 85% of this gain.