Policy Challenge
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Program Type
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22
5
6
Profits
Gestión (sPR)
29
14
Evaluación Efecto Edad promedio Observaciones Evaluación experimental País Intervención
Giorcelli 2019 23 35 6,074 Si Italia Componentes entrenados: Gestión
Bastian 2018 ii 18 36.7 2,657 Si Tanzania Componentes entrenados: Gestión
Bastian 2018 i 8 36.7 1,771 Si Tanzania Componentes entrenados: Gestión
Valdivia 2014 i 14 43 970 Si Perú Componentes entrenados: Gestión
Anderson 2018 i 39 38.04 365 Si Sudáfrica Componentes entrenados: Gestión
Anderson 2021 68 30.93 348 Si Uganda Componentes entrenados: Gestión

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 (%)
Anderson 2018 i 39 14 64 9
Anderson 2021 68 24 113 3
Bastian 2018 i 8 -25 40 6
Bastian 2018 ii 18 -13 48 6
Giorcelli 2019 23 15 31 50
Valdivia 2014 i 14 1 26 27
Overall