Heterogeneous causal effects of labour market programs: A machine learning approach
Articles and reports: 11-522-X202200100017Description: In this paper, we look for presence of heterogeneity in conducting impact evaluations of the Skills Development intervention delivered under the Labour Market Development Agreements. We use linked longitudinal administrative data covering a sample of Skills Development participants from 2010 to 2017. We apply a causal machine-learning estimator as in Lechner (2019) to estimate the individualized program impacts at the finest aggregation level. These granular impacts reveal the distribution of net impacts facilitating further investigation as to what works for whom. The findings suggest statistically significant improvements in labour market outcomes for participants overall and for subgroups of policy interest. Issue Number: 2022001Author(s): Handouyahia, Andy; Awad, Georges; Rikhi, Tristan; Aouli, EssolabaMain Product:Statistics Canada International Symposium Series: Proceedings
