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

Abstract

Conditional cash transfer programs are now used extensively to encourage poor parents to increase investments in their children’s human capital. These programs can be large and expensive, motivating a quest for greater efficiency through increased impact of the programs’ imposed conditions on human capital formation. This requires designing the programs’ targeting and calibration rules specifically to achieve this result. Using data from the Progresa randomized experiment in Mexico, this article shows that large efficiency gains can be achieved by taking into account how much the probability of a child’s enrollment is affected by a conditional transfer. Rules for targeting and calibration can be made easy to implement by selecting indicators that are simple, observable, and verifiable and that cannot be manipulated by beneficiaries. The Mexico case shows that these efficiency gains can be achieved without increasing inequality among poor households.