Identifying Socially Disruptive Policies

TL;DR

This paper develops a method to bound social disruption effects of policies using network data, revealing larger impacts than traditional regression analysis in experimental settings.

Contribution

It introduces a novel approach to bounding social disruption effects from experimental network data, addressing the challenge of partial identification.

Findings

Large disruptive effects identified using bounds

Traditional regression underestimates social disruption

Eigenvalue rearrangement provides informative bounds

Abstract

Social disruption occurs when a policy creates or destroys many network connections between agents. It is a costly side effect of many interventions and so a growing empirical literature recommends measuring and accounting for social disruption when evaluating the welfare impact of a policy. However, there is currently little work characterizing what can actually be learned about social disruption from data in practice. In this paper, we consider the problem of identifying social disruption in an experimental setting. We show that social disruption is not generally point identified, but informative bounds can be constructed by rearranging the eigenvalues of the marginal distribution of network connections between pairs of agents identified from the experiment. We apply our bounds to the setting of Banerjee et al. (2021) and find large disruptive effects that the authors miss by only considering regression estimates.