| 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 |
| Attanasio 2011 | 10 | 21 | 3,956 | Yes | Colombia | Technical and soft-skills in-classroom and on-the-job training |
| Ibarraran 2014 | 4 | 22 | 3,520 | Yes | Dominican Republic | Soft and technical skills training with an internship |
| Elias 2004 | 4 | 23 | 2,711 | No | Argentina | Technical and soft skills training with an internship |
| Santa Maria 2009 | 12 | 21 | 2,000 | No | Colombia | Soft-skills, basic skills, technical training and an internship |
| Honorati 2015 | 10 | 24 | 1,366 | Yes | Kenya | Training on technical and soft skills plus an internship |
| Acevedo 2017 i | 5 | 21 | 822 | Yes | Dominican Republic | Soft skills in-classroom training with an internship |
| Acevedo 2017 ii | 1 | 21 | 822 | Yes | Dominican Republic | Technical and soft skills in-classroom training with an internship |
| Theodos 2017 | -4 | 16-20 | 553 | Yes | United States | Weekly pre-work training, workshops, coaching and mentoring and paid internship |
| Alvares de Azevedo 2013 ii | 30 | 18-35 | 463 | Yes | Kenya | ICT training with internships and job placement support |
| Alvares de Azevedo 2013 i | 29 | 18-35 | 462 | Yes | Kenya | ICT and soft skills training with internships and job placement support |
| Auspos 1988 | 13 | 30 | 444 | Yes | United States | Pre-vocational and on-the-job training |
| Alzua 2016 | 14 | 24 | 407 | Yes | Argentina | Technical and soft skills in-classroom 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 | 5 | -6 | 15 | 7 |
| Acevedo 2017 ii | 1 | -9 | 11 | 7 |
| Alvares de Azevedo 2013 i | 29 | 13 | 46 | 3 |
| Alvares de Azevedo 2013 ii | 30 | 13 | 47 | 3 |
| Alzua 2016 | 14 | -3 | 31 | 3 |
| Attanasio 2011 | 10 | 5 | 16 | 14 |
| Auspos 1988 | 13 | -4 | 30 | 3 |
| Davis 2017 | 1 | -3 | 6 | 15 |
| Elias 2004 | 4 | -2 | 11 | 12 |
| Honorati 2015 | 10 | 1 | 19 | 8 |
| Ibarraran 2014 | 4 | -1 | 10 | 13 |
| Santa Maria 2009 | 12 | 4 | 19 | 10 |
| Theodos 2017 | -4 | -24 | 16 | 2 |
| Overall |