| Evaluation | Effect | Average Age | Observations | Experimental Evaluation | Country | Intervention |
|---|---|---|---|---|---|---|
| Giorcelli 2019 | 7 | 35 | 6,074 | Yes | Italy | Management components trained |
| Bastian 2018 ii | -1 | 36.7 | 2,718 | Yes | Tanzania | Management components trained |
| Bastian 2018 i | 0 | 36.7 | 1,812 | Yes | Tanzania | Management components trained |
| Valdivia 2014 i | 6 | 43 | 1,037 | Yes | Peru | Management components trained |
| Anderson 2018 i | -2 | 38.04 | 440 | Yes | South Africa | Management 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 2018 i | -2 | -8 | 4 | 8 |
| Bastian 2018 i | 0 | -1 | 1 | 36 |
| Bastian 2018 ii | -1 | -2 | 0 | 36 |
| Giorcelli 2019 | 7 | 4 | 10 | 19 |
| Valdivia 2014 i | 6 | -16 | 28 | 1 |
| Overall |