Policy Challenge
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Program Type
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-1
7
10
Firm Productivity
Management Accounting Planning (ePY)
5
-6
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Fairlie 2015 -3 42.08 2,965 Yes United States Management + Accounting + Planning components trained
Buvinic 2020 3 38.29 1,878 Yes Indonesia Management + Accounting + Planning components trained
Alibhai 2019 ii -5 35.84 1,113 Yes Ethiopia Management + Accounting + Planning components trained
Lafortune 2018 ii 9 45 514 Yes Chile Management + Accounting + Planning components trained
Lafortune 2018 i 3 45 512 Yes Chile Management + Accounting + Planning components trained
Brooks 2016 -4 28.9 238 Yes Kenya Management + Accounting + Planning components trained
Bruhn 2013 -1 28.14 170 Yes Bosnia & Herzegovina Management + Accounting + Planning components trained
Higuchi 2015 iv -44 44.2 105 Yes Vietnam Management + Accounting + Planning components trained
Higuchi 2015 vi 50 44.2 82 Yes Vietnam Management + Accounting + Planning components trained
Higuchi 2015 v -66 44.2 66 Yes Vietnam Management + Accounting + 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 ii -5 -15 5 15
Brooks 2016 -4 -23 14 7
Bruhn 2013 -1 -29 26 4
Buvinic 2020 3 -4 11 19
Fairlie 2015 -3 -8 2 24
Higuchi 2015 iv -44 -78 -10 3
Higuchi 2015 v -66 -119 -14 1
Higuchi 2015 vi 50 10 91 2
Lafortune 2018 i 3 -10 15 12
Lafortune 2018 ii 9 -4 21 12
Overall