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Spillovers of Program Benefits with Mismeasured Networks

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  • Lina Zhang

Abstract

In studies of program evaluation under network interference, correctly measuring spillovers of the intervention is crucial for making appropriate policy recommendations. However, increasing empirical evidence has shown that network links are often measured with errors. This paper explores the identification and estimation of treatment and spillover effects when the network is mismeasured. I propose a novel method to nonparametrically point-identify the treatment and spillover effects, when two network observations are available. The method can deal with a large network with missing or misreported links and possesses several attractive features: (i) it allows heterogeneous treatment and spillover effects; (ii) it does not rely on modelling network formation or its misclassification probabilities; and (iii) it accommodates samples that are correlated in overlapping ways. A semiparametric estimation approach is proposed, and the analysis is applied to study the spillover effects of an insurance information program on the insurance adoption decisions.

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  • Lina Zhang, 2020. "Spillovers of Program Benefits with Mismeasured Networks," Papers 2009.09614, arXiv.org.
    • Handle: RePEc:arx:papers:2009.09614
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    References listed on IDEAS

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