| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP12224 |
| DP12224 Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach | |
| Michael Lechner; Anthony Strittmatter | |
| 发表日期 | 2017-08-18 |
| 出版年 | 2017 |
| 语种 | 英语 |
| 摘要 | We systematically investigate the effect heterogeneity of job search programmes for unemployed workers. To investigate possibly heterogeneous employment effects, we combine non-experimental causal empirical models with Lasso-type estimators. The empirical analyses are based on rich administrative data from Swiss social security records. We find considerable heterogeneities only during the first six months after the start of training. Consistent with previous results of the literature, unemployed persons with fewer employment opportunities profit more from participating in these programmes. Furthermore, we also document heterogeneous employment effects by residence status. Finally, we show the potential of easy-to-implement programme participation rules for improving average employment effects of these active labour market programmes. |
| 主题 | Labour Economics |
| 关键词 | Machine learning Individualized treatment effects Conditional average treatment effects Active labour market policy |
| URL | https://cepr.org/publications/dp12224 |
| 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/541035 |
| 推荐引用方式 GB/T 7714 | Michael Lechner,Anthony Strittmatter. DP12224 Heterogeneous Employment Effects of Job Search Programmes: A Machine Learning Approach. 2017. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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