We investigate heterogenous employment effects of Flemish training programmes. Based on administrative individual data , we analyse programme effects at various aggregation levels using Modified Causal Forests (MCF) , a causal machine learning estimator for multiple programmes . While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and types of unemployed. Simulations show that assigning unemployed to programmes that maximise individual gains as identified in our estimation can considerably improve effectiveness . Simplified rules, such as one giving priority to unemployed with low employability, mostly recent migrants, lead to about half of the gains obtained by more sophisticated rules .
 Based on administrative data of 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 across programmes and unemployed. Simulations show that 'black-box' rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness : up to 20 percent more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70 percent of this gain .
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We investigate heterogenous employment effects of Flemish training programmes.
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individual data , we analyse programme effects
data of unemployed in Belgium, we estimate the labour market effects of three training programmes
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(MCF)
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for multiple programmes
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types of
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assigning
'black-box' rules that reassign
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individual gains as identified in our estimation
estimated individual gains
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. Simplified rules, such as one giving priority to unemployed with low employability, mostly recent migrants, lead to about half of the gains obtained by more sophisticated rules
: up to 20 percent more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70 percent of this gain