Making Conditional Cash Transfer Programs More Efficient: Designing for Maximum Effect of the Conditionality

Conditional cash transfer programs are now used extensivelyto encourage poor parents to increase investments in their children’shuman capital. These programs can be large and expensive, motivatinga quest for greater efficiency through increased impact of theprograms’ imposed conditions on human capital formation.This requires designing the programs’ targeting and calibrationrules specifically to achieve this result. Using data from theProgresa randomized experiment in Mexico, this article showsthat large efficiency gains can be achieved by taking into accounthow much the probability of a child’s enrollment is affectedby a conditional transfer. Rules for targeting and calibrationcan be made easy to implement by selecting indicators that aresimple, observable, and verifiable and that cannot be manipulatedby beneficiaries. The Mexico case shows that these efficiencygains can be achieved without increasing inequality among poorhouseholds.