Spillovers of Program Benefits with Missing Network Links

TL;DR

This paper develops a flexible method to estimate spillover effects of programs in networks with missing links, accounting for non-random missingness and providing bias reduction in unbounded degree networks.

Contribution

It introduces a novel approach for identifying and estimating spillover effects in partially observed networks with non-i.i.d. missing links.

Findings

Method effectively reduces bias in unbounded degree networks.

Accurately estimates treatment and spillover effects in simulated and real data.

Re-examines spillover effects of home computer use on children's learning.

Abstract

The issue of missing network links in partially observed networks is frequently neglected in empirical studies. This paper addresses this issue when investigating the spillovers of program benefits in the presence of network interactions. Our method is flexible enough to account for non-i.i.d. missing links. It relies on two network measures that can be easily constructed based on the incoming and outgoing links of the same observed network. The treatment and spillover effects can be point identified and consistently estimated if network degrees are bounded for all units. We also demonstrate the bias reduction property of our method if network degrees of some units are unbounded. Monte Carlo experiments and a naturalistic simulation on real-world network data are implemented to verify the finite-sample performance of our method. We also re-examine the spillover effects of home computer use on children's self-empowered learning.