Policy Challenge
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Program Type
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22
5
6
Profits
Management (ePR)
29
14
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Giorcelli 2019 23 35 6,074 Yes Italy Management components trained
Bastian 2018 ii 18 36.7 2,657 Yes Tanzania Management components trained
Bastian 2018 i 8 36.7 1,771 Yes Tanzania Management components trained
Valdivia 2014 i 14 43 970 Yes Peru Management components trained
Anderson 2018 i 39 38.04 365 Yes South Africa Management components trained
Anderson 2021 68 30.93 348 Yes Uganda Management 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 2018 i 39 14 64 9
Anderson 2021 68 24 113 3
Bastian 2018 i 8 -25 40 6
Bastian 2018 ii 18 -13 48 6
Giorcelli 2019 23 15 31 50
Valdivia 2014 i 14 1 26 27
Overall