IZA DP No. 11267: Better Together? Social Networks in Truancy and the Targeting of Treatment
Truancy correlates with many risky behaviors and adverse outcomes. We use detailed administrative data on by-class absences to construct social networks based on students who miss class together. We simulate these networks and use permutation tests to show that certain students systematically coordinate their absences. Leveraging a parent-information intervention on student absences, we find spillover effects from treated students onto peers in their network. We show that an optimal-targeting algorithm that incorporates machine-learning techniques to identify heterogeneous effects, as well as the direct effects and spillover effects, could further improve the efficacy and cost-effectiveness of the intervention subject to a budget constraint.