Policy Challenge
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Program Type
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14
4
4
Profits
Management Planning (ePR)
27
1
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Jin 2020 3 N/A 1,253,284 Yes China Management + Planning components trained
Alibhai 2019 iii 12 33.22 1,065 Yes Ethiopia Management + Planning components trained
Martínez 2018 33 36 1,018 Yes Chile Management + Planning components trained
Glaub 2014 37 39.47 93 Yes Uganda Management + Planning 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 (%)
Alibhai 2019 iii 12 -3 28 27
Glaub 2014 37 2 71 11
Jin 2020 3 1 4 44
Martínez 2018 33 9 57 18
Overall