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• W2767810069 abstract "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 reduced-form linear-in-means 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 reduced-form linear-in-means coefficients are all close to zero and statistically insignificant, whereas the nonparametric estimators I propose reveal large, nonlinear and significant spillover effects." @default.
• W2767810069 created "2017-11-17" @default.
• W2767810069 creator A5026785831 @default.
• W2767810069 date "2022-01-01" @default.
• W2767810069 modified "2023-09-25" @default.
• W2767810069 title "Identification and estimation of spillover effects in randomized experiments" @default.
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• W2767810069 doi "https://doi.org/10.1016/j.jeconom.2021.10.014" @default.
• W2767810069 hasPublicationYear "2022" @default.
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