Policy Challenge
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Program Type
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30
5
6
Management Skills
Management Soft-skills Accounting (eENS)
43
15
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Dammert 2019 ii 29 40.43 529 Yes Liberia Management + Soft-skills + Accounting components trained
Drexler 2014 i 10 40.2 525 Yes Dominican Republic Management + Soft-skills + Accounting components trained
Bruhn 2018 3 42.19 378 Yes Mexico Management + Soft-skills + Accounting components trained
Anderson 2022 ii 27 37.7 267 Yes Nigeria Management + Soft-skills + Accounting components trained
Iacovone 2022 i 62 43 134 Yes Colombia Management + Soft-skills + Accounting components trained
Iacovone 2022 ii 62 43 134 Yes Colombia Management + 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 (%)
Anderson 2022 ii 27 9 45 17
Bruhn 2018 3 -15 21 17
Dammert 2019 ii 29 17 41 20
Drexler 2014 i 10 -4 25 19
Iacovone 2022 i 62 38 86 14
Iacovone 2022 ii 62 37 86 14
Overall