Policy Challenge
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Program Type
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7
4
7
Firm Productivity
Management Accounting (ePY)
14
0
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Calderon 2020 4 46.04 1,112 Yes Mexico Management + Accounting components trained
Drexler 2014 ii -3 40.2 526 Yes Dominican Republic Management + Accounting components trained
Anderson 2022 iii 1 37.7 273 Yes Nigeria Management + Accounting components trained
Anderson 2022 iv 1 37.7 271 Yes Nigeria Management + Accounting components trained
Higuchi 2019 ii 14 45.9 176 Yes Tanzania Management + Accounting components trained
Higuchi 2019 iii 34 44.5 173 Yes Tanzania Management + Accounting components trained
Higuchi 2019 i 23 44.9 161 Yes Tanzania Management + 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 iii 1 -17 18 13
Anderson 2022 iv 1 -16 19 13
Calderon 2020 4 -6 13 29
Drexler 2014 ii -3 -17 12 17
Higuchi 2019 i 23 1 46 9
Higuchi 2019 ii 14 -7 35 10
Higuchi 2019 iii 34 12 56 9
Overall