Policy Challenge
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Program Type
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14
4
4
Profits
Gestión Planificación (sPR)
27
1
Evaluación Efecto Edad promedio Observaciones Evaluación experimental País Intervención
Jin 2020 3 N/A 1,253,284 Si China Componentes entrenados: Gestión + Planificación
Alibhai 2019 iii 12 33.22 1,065 Si Etiopía Componentes entrenados: Gestión + Planificación
Martínez 2018 33 36 1,018 Si Chile Componentes entrenados: Gestión + Planificación
Glaub 2014 37 39.47 93 Si Uganda Componentes entrenados: Gestión + Planificación

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 (%)
Alibhai 2019 iii 12 -3 28 27
Glaub 2014 37 2 71 11
Jin 2020 3 1 4 44
Martínez 2018 33 9 57 18
Overall