| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP14270 |
| DP14270 Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium | |
| Michael Lechner; Bart Cockx; Joost Bollens | |
| 发表日期 | 2020-01-04 |
| 出版年 | 2020 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Labour Economics |
| 关键词 | Policy evaluation Active labour market policy Causal machine learning Modified causal forest Conditional average treatment effects |
| URL | https://cepr.org/publications/dp14270 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/543161 |
| 推荐引用方式 GB/T 7714 | Michael Lechner,Bart Cockx,Joost Bollens. DP14270 Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium. 2020. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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