Policy Challenge
Path 2 Copy Created with Sketch.
Program Type
Path 2 Copy Created with Sketch.
3
5
12
Adult Employment
Technical and soft skills in-classroom and OJT training (eAEM)
4
0
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Davis 2017 1 18 5,076 Yes United States Part-time job with job mentor and social-emotional training
Ibarraran 2014 -1 22 3,520 Yes Dominican Republic Soft and technical skills training with an internship
Attanasio 2011 5 21 2,863 Yes Colombia Technical and soft-skills in-classroom and on-the-job training
Elias 2004 -2 23 2,711 No Argentina Technical and soft skills training with an internship
Honorati 2015 4 24 2,318 Yes Kenya Training on technical and soft skills plus an internship
Santa Maria 2009 13 21 2,000 No Colombia Soft-skills, basic skills, technical training and an internship
Acevedo 2017 i 1 21 1,819 Yes Dominican Republic Soft skills in-classroom training with an internship
Acevedo 2017 ii 1 21 1,819 Yes Dominican Republic Technical and soft skills in-classroom training with an internship
Theodos 2017 -4 16-20 555 Yes United States Weekly pre-work training, workshops, coaching and mentoring and paid internship
Alvares de Azevedo 2013 ii 9 18-35 463 Yes Kenya ICT training with internships and job placement support
Alvares de Azevedo 2013 i 14 18-35 462 Yes Kenya ICT and soft skills training with internships and job placement support
Auspos 1988 5 30 444 Yes United States Pre-vocational and on-the-job training

Graph of Effects

This graph shows the effects of [Number of Evaluations] evaluations of [Program Type] programs. The first evaluation, [Evaluation name 1], estimated an effect of [Estimated effect Eval. 1] points (graphed with the top grey circle). Because this is an estimate, there is uncertainty about what the true effect is. But we know that the true effect will probably lie between a low value of [True effect lie between – Low value, Eval. 1] and a high value of [True effect lie between – High value, Eval. 1] (graphed with the top horizontal line). We can combine the estimates from all the evaluations and compute an average effect of [Estimated effect, Average] (graphed with the black circle). There is also uncertainty about where the true average effect is. But we know that the true average effect probably lies between [True effect lie between – Low value, Average] and [True effect lie between – High value, Average] points (graphed with the bottom horizontal line).
Evaluation Effects Low Value High Value Weight in Average Effect (%)
Acevedo 2017 i 1 -3 5 10
Acevedo 2017 ii 1 -2 5 10
Alvares de Azevedo 2013 i 14 6 22 4
Alvares de Azevedo 2013 ii 9 1 17 4
Attanasio 2011 5 1 9 9
Auspos 1988 5 -3 14 4
Davis 2017 1 -1 3 13
Elias 2004 -2 -4 0 13
Honorati 2015 4 1 7 11
Ibarraran 2014 -1 -4 2 11
Santa Maria 2009 13 7 19 6
Theodos 2017 -4 -11 3 5
Overall