Policy Challenge
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Program Type
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1
10
17
Profits
Management Accounting Planning (ePR)
7
-4
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Fairlie 2015 -2 42.08 2,721 Yes United States Management + Accounting + Planning components trained
Bulte 2017 236 43.77 2,420 Yes Vietnam Management + Accounting + Planning components trained
Buvinic 2020 17 38.29 1,856 Yes Indonesia Management + Accounting + Planning components trained
Gine 2011 -50 38.6 1,181 Yes Pakistan Management + Accounting + Planning components trained
Alibhai 2019 ii 2 35.84 1,010 Yes Ethiopia Management + Accounting + Planning components trained
Lafortune 2018 ii 43 45 362 Yes Chile Management + Accounting + Planning components trained
Lafortune 2018 i 10 45 361 Yes Chile Management + Accounting + Planning components trained
del Mel 2014 -19 37.71 356 Yes Sri Lanka Management + Accounting + Planning components trained
Higuchi 2015 iv 229 44.2 336 Yes Vietnam Management + Accounting + Planning components trained
Higuchi 2015 i 23 43.7 314 Yes Vietnam Management + Accounting + Planning components trained
Higuchi 2015 vi -81 44.2 260 Yes Vietnam Management + Accounting + Planning components trained
Brooks 2016 1 28.9 237 Yes Kenya Management + Accounting + Planning components trained
Higuchi 2015 v 785 44.2 212 Yes Vietnam Management + Accounting + Planning components trained
Higuchi 2015 iii -51 43.7 190 Yes Vietnam Management + Accounting + Planning components trained
Mano 2012 7 44.2 139 Yes Ghana Management + Accounting + Planning components trained
Higuchi 2015 ii 120 43.7 127 Yes Vietnam Management + Accounting + Planning components trained
Bruhn 2013 -15 28.14 99 Yes Bosnia & Herzegovina 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 2 -9 14 13
Brooks 2016 1 -2 4 23
Bruhn 2013 -15 -55 25 2
Bulte 2017 236 -85 557 0
Buvinic 2020 17 8 27 15
del Mel 2014 -19 -77 40 1
Fairlie 2015 -2 -6 1 23
Gine 2011 -50 -180 80 0
Higuchi 2015 i 23 -46 93 1
Higuchi 2015 ii 120 -418 658 0
Higuchi 2015 iii -51 -173 72 0
Higuchi 2015 iv 229 -520 977 0
Higuchi 2015 v 785 -1711 3281 0
Higuchi 2015 vi -81 -112 -50 3
Lafortune 2018 i 10 -25 45 3
Lafortune 2018 ii 43 1 85 2
Mano 2012 7 -5 19 13
Overall