Policy Challenge
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Program Type
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3
5
10
Completing Secondary
Conditional cash transfers (eCS)
5
1
Evaluation Effect Average Age Observations Experimental Evaluation Country Intervention
Baez 2011 5 16 816,038 No Colombia Monthly transfers of $18 conditional on school attendance
Glewwe 2012 0 Grade 7 182,006 Yes Brazil Monthly payments conditional on school enrollment and attendance
Heinrich 2005 5 Grade 8 N/A No Argentina Annual payment of $192 conditional on attendance and grade progression
Dubois 2012 3 Grade 8 27,788 Yes Mexico Monthly $34-$41 transfers conditional on school attendance
Barrera-Osorio 2015 1 Grade 9 10,947 Yes Colombia Students received bimonthly transfers conditional on attendance
de Brauw 2015 4 Grade 10 6,056 No Brazil Transfers to households conditional on school attendance
Dearden 2009 7 Grade 12 3,803 No United Kingdom Students received weekly payments, retention and achievement bonuses
Zavakou 2011 0 Grade 10 2,347 No Colombia Monthly $18 transfer conditional on school attendance
Barrera-Osorio 2011 4 Grade 9 529 Yes Colombia Students received bimonthly transfers conditional on attendance
Behrman 2005 -3 Grade 8 208 Yes Mexico Monthly $77 transfers conditional on regular school attendance

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 (%)
Baez 2011 5 4 6 16
Barrera-Osorio 2011 4 -1 9 7
Barrera-Osorio 2015 1 -1 3 14
Behrman 2005 -3 -8 2 7
de Brauw 2015 4 -7 15 2
Dearden 2009 7 5 9 13
Dubois 2012 3 2 4 16
Glewwe 2012 0 0 0 17
Heinrich 2005 5 -1 11 6
Zavakou 2011 0 -12 12 2
Overall