| Evaluación | Efecto | Edad promedio | Observaciones | Evaluación experimental | País | Intervención |
|---|---|---|---|---|---|---|
| Reis 2015 | 1 | 21-54 | 178,636 | No | Brasil | Aplicación práctica de capacitación profesional en aula |
| Diaz 2006 | 10 | 19 | 6,208 | No | Perú | Formación profesional y una pasantía en empresas privadas |
| Hirshleifer 2016 | 1 | 27 | 5,702 | Si | Turquía | 3 meses de programa de formación profesional |
| Miller 2003 | -65 | 19 | 1,221 | Si | Estados Unidos | Capacitación profesional según necesidades de los empleadore en ambiente laboral |
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).
| Evaluación | Efectos | Valor Mínimo | Valor Máximo | Peso en el efecto promedio (%) |
|---|---|---|---|---|
| Diaz 2006 | 10 | 6 | 13 | 28 |
| Hirshleifer 2016 | 1 | -1 | 3 | 34 |
| Miller 2003 | -65 | -484 | 354 | 0 |
| Reis 2015 | 1 | 0 | 1 | 38 |
| Overall |