Policy Challenge
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Program Type
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28
2
2
Firm Productivity
Soft-skills Accounting (ePY)
70
-14
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Karlan 2015 4 35.29 212 Yes Ghana Soft-skills + Accounting components trained
Gonzalez-Uribe 2018 55 30.33 145 No Chile Soft-skills + Accounting 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 (%)
Gonzalez-Uribe 2018 55 24 87 47
Karlan 2015 4 -19 27 53
Overall