Identification and Estimation of Spillover Effects in Randomized Experiments
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I study identification, estimation and inference for spillover effects in experiments where units' outcomes may depend on the treatment assignments of other units within a group. I show that the commonly-used linear-in-means (LIM) regression identifies a weighted sum of spillover effects with some negative weights, and that the difference in means between treated and controls identifies a combination of direct and spillover effects entering with different signs. I propose nonparametric estimators for average direct and spillover effects that overcome these issues and are consistent and asymptotically normal under a precise relationship between the number of parameters of interest, the total sample size and the treatment assignment mechanism. These findings are illustrated using data from a conditional cash transfer program and with simulations. The empirical results reveal the potential pitfalls of failing to flexibly account for spillover effects in policy evaluation: the estimated difference in means and the LIM coefficients are all close to zero and statistically insignificant, whereas the nonparametric estimators I propose reveal large, nonlinear and significant spillover effects.
Date: 2017-11, Revised 2020-11
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