Policy Challenge
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Program Type
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1
7
14
Completing Secondary
School inputs (eCS)
3
-1
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Gorlitz 2015 -3 Grade 12 6,507,474 Yes Germany Curriculum reform that rose instruction time and curriculum standards
Jackson 2012 6 Grade 8 179,710 No Trinidad & Tobago Attending same-sex schools
Cullen 2006 -4 Grade 8 N/A Yes United States Students obtained access to preferred schools, such as magnet schools
Lavy 2002 3 Grade 10 N/A No Israel Schools received bonus based on students performance
Rosati 2007 0 Grade 8 48,957 No Mexico Computers, equipment, materials, training, incentives for teachers
Fryer 2011 -6 Grade 11 27,995 Yes United States Schools received payments for meeting annual performance targets
Dobbie 2011 4 Grade 11 23,713 No United States Admission to a school with higher achieving peers and more resources
Fairlie 2013 0 Grade 8 11,514 Yes United States Students received computers for home use
Deming 2011 5 Grade 11 1,865 Yes United States Students obtained access to preferred schools
Kemple 2008 1 Grade 11 1,428 Yes United States Academic and vocational training and career development
Glewwe 2009 5 Grade 8 887 Yes Kenya Schools received official government textbooks and teachers guides
Kemple 2000 14 Grade 11 345 Yes United States Academic and vocational training and career development
Leme 2012 3 Grade 7 263 No Brazil Teachers received lesson plans, training; students received textbooks
Bando 2010 0 Grade 8 250 Yes Mexico Parents associations received $500-$700 grant and training

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 (%)
Bando 2010 0 -4 4 5
Cullen 2006 -4 -7 -1 7
Deming 2011 5 0 10 4
Dobbie 2011 4 2 6 10
Fairlie 2013 0 -2 2 10
Fryer 2011 -6 -10 -2 6
Glewwe 2009 5 1 9 5
Gorlitz 2015 -3 -4 -2 12
Jackson 2012 6 2 10 6
Kemple 2000 14 5 23 2
Kemple 2008 1 -3 5 6
Lavy 2002 3 -1 7 6
Leme 2012 3 0 6 8
Rosati 2007 0 0 0 12
Overall