Spillover Effects in Empirical Corporate Finance: Choosing the Proxy for Treatment Coverage

Abstract

The existing literature indicates that spillovers can lead to a complicated bias in the estimation of causal effects in empirical corporate finance. We show that, under the assumption of simple random treatment assignment and when the proxy chosen for the group-level treatment coverage is the leave-one-out average treatment, such a spillover bias exists if and only if the average indirect effects on the treated and untreated groups are different. We quantify the gains in spillover bias reduction using Monte Carlo exercises. We propose a Wald test to statistically infer the presence of bias. We illustrate the application of this test to bear out spillovers in firms’ employment decisions.

Keywords

• spillover bias
• average direct effect
• average indirect effect
• causal graph