Abstract This paper studies the effects of peers on the adoption of a Youth Employment Subsidy in Chile since its inception. We examine the effects that former classmates' and coworkers' adoption have on one's adoption. Identification comes from discontinuities in the assignment rule that allow us to construct valid instrumental variables for peers' adoption. Using a comprehensive set of administrative records, we find that classmates and especially coworkers play significant roles in the adoption of the subsidy. Peer effects are determined during the early stages of the program's implementation and vary by network characteristics and the strength of network ties.

This paper proposes a novel matching estimator where neighbors used and weights are endogenously determined by optimizing a covariate balancing criterion. The estimator is based on finding, for each unit that needs to be matched, sets of observations such that a convex combination of them has the same covariate values as the unit needing matching or with minimized distance between them. We implement the proposed estimator with data from the National Supported Work Demonstration, finding outstanding performance in terms of covariate balance. Monte Carlo evidence shows that our estimator performs well in designs previously used in the literature.