Evaluation of education and training impacts for the unemployed: Challenges of new data

被引:0
作者
Cerqua, Augusto [1 ]
Urwin, Peter [2 ]
Thomson, Dave [3 ]
Bibby, David [3 ]
机构
[1] Sapienza Univ Rome, Dept Social Sci & Econ, Ple Aldo Moro 5, I-00185 Rome, Italy
[2] Univ Westminster, Sch Org Econ & Soc, 35 Marylebone Rd, London NW1 5LS, England
[3] FFT Educ Datalab, 11 Tufton St, London SW1P 3QB, England
关键词
Active labour market programme; Full and partial treatment; Endogenous selection; LABOR-MARKET PROGRAMS; CAUSAL INFERENCE; MATCHING METHODS; RETURNS; EXPOSURE; EARNINGS; DROPOUTS; COLLEGE; BIAS;
D O I
10.1016/j.labeco.2020.101907
中图分类号
F [经济];
学科分类号
02 ;
摘要
This study utilises an exceptionally rich English administrative dataset, to estimate employment impacts from training voluntarily initiated by unemployed individuals. A Coarsened Exact Matching approach is adopted, in a dynamic evaluation framework, to estimate impacts up to 5 years from training start. We identify economically and statistically significant impacts, estimated separately for (i) all training starters, (ii) the partially, and (iii) fully treated. Investigation of possible endogenous selection into partial/full treatment, using distance to training provider as an instrumental variable, suggests inclusion of extensive employment and learning histories in a matching framework, justifies invocation of the conditional independence assumption for comparisons of full/partial treatment. The partially treated secure a return that is, on average, 2 percentage points lower than full treatment. Thus, an 'intention to treat' approach would not alter conclusions on the efficacy of training; but using the partially treated to estimate counterfactual outcomes risks understating returns.
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页数:20
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