| Evaluation | Effect | Average Age | Observations | Experimental Evaluation | Country | Intervention |
|---|---|---|---|---|---|---|
| Attanasio 2011 | 4 | 21 | 3,956 | Yes | Colombia | Technical and soft-skills in-classroom and on-the-job training |
| Ibarraran 2014 | 2 | 22 | 3,520 | Yes | Dominican Republic | Soft and technical skills training with an internship |
| Honorati 2015 | 5 | 24 | 2,318 | Yes | Kenya | Training on technical and soft skills plus an internship |
| Santa Maria 2009 | 3 | 21 | 2,000 | No | Colombia | Soft-skills, basic skills, technical training and an internship |
| Alzua 2016 | 5 | 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 (%) |
|---|---|---|---|---|
| Alzua 2016 | 5 | -1 | 11 | 3 |
| Attanasio 2011 | 4 | 2 | 6 | 32 |
| Honorati 2015 | 5 | 2 | 8 | 12 |
| Ibarraran 2014 | 2 | 0 | 4 | 40 |
| Santa Maria 2009 | 3 | -1 | 6 | 13 |
| Overall |