Policy Challenge
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Program Type
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7
4
6
Firm Productivity
All components (ePY)
12
2
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
McKenzie 2021 i 3 36 5,598 Yes Kenya All components components trained
Michaelides 2012 1 N/A 2,979 Yes United States All components components trained
McKenzie 2021 ii 6 36 2,655 Yes Kenya All components components trained
Chioda 2021 i 14 20 2,478 Yes Uganda All components components trained
Chioda 2021 ii 17 20 2,478 Yes Uganda All components components trained
Anderson 2022 i 2 37.7 273 Yes Nigeria All components 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 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