Policy Challenge
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Program Type
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14
3
5
Firm Productivity
Management Soft-skills (ePY)
25
2
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Karlan 2011 0 N/A 2,966 Yes Peru Management + Soft-skills components trained
Bloom 2013 9 N/A 1,831 Yes India Management + Soft-skills components trained
Valdivia 2014 ii 2 43 1,120 Yes Peru Management + Soft-skills components trained
Georgiadis 2016 i 61 N/A 390 Yes United Kingdom Management + Soft-skills components trained
Georgiadis 2016 iii 14 N/A 291 Yes United Kingdom Management + Soft-skills 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 (%)
Bloom 2013 9 1 18 23
Georgiadis 2016 i 61 43 79 16
Georgiadis 2016 iii 14 -6 35 14
Karlan 2011 0 -6 6 24
Valdivia 2014 ii 2 -5 9 24
Overall