Policy Challenge
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Program Type
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9
3
5
Firm Survival
All components (eFS)
14
3
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
McKenzie 2021 i 2 36 13,508 Yes Kenya All components components trained
McKenzie 2021 ii 1 36 5,822 Yes Kenya All components components trained
Michaelides 2012 10 N/A 2,979 Yes United States All components components trained
Chioda 2021 i 19 20 2,475 Yes Uganda All components components trained
Chioda 2021 ii 20 20 2,475 Yes Uganda 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 (%)
Chioda 2021 i 19 11 27 19
Chioda 2021 ii 20 12 28 19
McKenzie 2021 i 2 0 4 29
McKenzie 2021 ii 1 -2 5 27
Michaelides 2012 10 -11 32 6
Overall