Policy Challenge
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Program Type
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7
4
6
Firm Productivity
Todos los componentes (sPY)
12
2
Evaluación Efecto Edad promedio Observaciones Evaluación experimental País Intervención
McKenzie 2021 i 3 36 5,598 Si Kenia Componentes entrenados: Todos los componentes
Michaelides 2012 1 - 2,979 Si Estados Unidos Componentes entrenados: Todos los componentes
McKenzie 2021 ii 6 36 2,655 Si Kenia Componentes entrenados: Todos los componentes
Chioda 2021 ii 17 20 2,478 Si Uganda Componentes entrenados: Todos los componentes
Chioda 2021 i 14 20 2,478 Si Uganda Componentes entrenados: Todos los componentes
Anderson 2022 i 2 37.7 273 Si Nigeria Componentes entrenados: Todos los componentes

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 2022 i 2 -15 20 6
Chioda 2021 i 14 8 21 17
Chioda 2021 ii 17 10 24 17
McKenzie 2021 i 3 -1 7 22
McKenzie 2021 ii 6 -1 12 18
Michaelides 2012 1 -4 6 20
Overall