| Evaluation | Effect | Average Age | Observations | Experimental Evaluation | Country | Intervention |
|---|---|---|---|---|---|---|
| Iacovone 2022 i | 5 | 43 | 3,146 | Yes | Colombia | Management + Soft-skills + Accounting components trained |
| Iacovone 2022 ii | -2 | 43 | 3,146 | Yes | Colombia | Management + Soft-skills + Accounting components trained |
| Drexler 2014 i | 7 | 40.2 | 525 | Yes | Dominican Republic | Management + Soft-skills + Accounting components trained |
| Bruhn 2018 | 15 | 42.19 | 314 | Yes | Mexico | Management + Soft-skills + Accounting components trained |
| Anderson 2022 ii | 8 | 37.7 | 271 | Yes | Nigeria | Management + Soft-skills + Accounting components trained |
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 (%) |
|---|---|---|---|---|
| Anderson 2022 ii | 8 | -10 | 25 | 6 |
| Bruhn 2018 | 15 | -2 | 32 | 6 |
| Drexler 2014 i | 7 | -8 | 21 | 8 |
| Iacovone 2022 i | 5 | 0 | 11 | 40 |
| Iacovone 2022 ii | -2 | -7 | 3 | 40 |
| Overall |