Policy Challenge
Path 2 Copy Created with Sketch.
Program Type
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3
10
13
Enrollment In Secondary
School inputs (eES)
5
1
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Cullen 2006 -2 Grade 8 N/A Yes United States Students obtained access to preferred schools, such as magnet schools
Adukia 2014 5 Grade 7 53,338 No India The Total Sanitation Campaign provided construction of school latrines
Rosati 2007 2 Grade 9 48,957 No Mexico Computers, equipment, training, materials, incentives
Cristia 2014 4 Grade 9 33,583 No Peru Schools received hardware, software, training and internet access
Zhang 2009 6 Grade 10 9,424 Yes China Students obtained access to magnet schools
Ome 2012 1 Grade 11 9,283 No Colombia Introduction of examination for teachers to get hired or promoted
Estrada 2015 -1 Grade 8 8,365 No Mexico Federal teacher hiring program based on national standardized test
Pires 2015 3 15 2,330 No Chile The reform increasing the number of schooling hours by 30%
McEwan 2015 -10 Grade 7 1,386 No Honduras Flexible teacher contracts and alternative recruitment practices
Khandker 2009 11 14 1,237 No Bangladesh Improvement of roads, construction of culverts and small bridges
Fairlie 2013 1 Grade 8 1,123 Yes United States Computers to schoolchildren for home use
Glewwe 2009 9 Grade 8 887 Yes Kenya Schools received official government textbooks and teachers guides
Kemple 2000 11 Grade 10 345 Yes United States Academic and vocational training and career development

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 (%)
Adukia 2014 5 3 7 10
Cristia 2014 4 -28 36 0
Cullen 2006 -2 -4 0 10
Estrada 2015 -1 -6 4 6
Fairlie 2013 1 -2 4 9
Glewwe 2009 9 3 15 5
Kemple 2000 11 4 18 4
Khandker 2009 11 6 16 7
McEwan 2015 -10 -18 -2 3
Ome 2012 1 0 2 12
Pires 2015 3 1 5 10
Rosati 2007 2 1 3 12
Zhang 2009 6 4 8 11
Overall