Policy Challenge
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Program Type
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18
3
3
Firm Productivity
Gestión Planificación (sPY)
40
-3
Evaluación Efecto Edad promedio Observaciones Evaluación experimental País Intervención
Alibhai 2019 iii -5 33.22 1,184 Si Etiopía Componentes entrenados: Gestión + Planificación
Martínez 2018 22 36 1,035 Si Chile Componentes entrenados: Gestión + Planificación
Glaub 2014 55 39.47 95 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 -5 -13 4 40
Glaub 2014 55 21 89 21
Martínez 2018 22 13 31 39
Overall