Employee Flight Risk and Capital Structure Decisions

Abstract

We examine how employee flight risk —the risk that a firm will suffer productivity losses and incur significant search and training costs when some of its mobile workers leave—affects capital structure decisions. We proxy for this risk with the ex-ante cross-industry labor mobility of a firm’s workers using a novel dynamic textual measure for this mobility derived from network centrality. To help establish causality, we rely on the Double/Debiased Machine Learning (DML) estimator, which offers a rigorous framework to estimate causal effects by leveraging the strong predictive power of machine learning methods. Higher employee flight risk compels firms to adopt more conservative capital structures. This effect is stronger for firms with limited access to external capital, that are in labor-intensive industries, or with a larger number of workers who are skilled, in managerial occupations, or who have lower costs of switching employers. Conversely, the effect is weaker for firms in strongly performing industries and after an exogenous increase in the supply of workers. Our evidence implies that employee flight risk, which can be especially acute during financial strain, leads to more cautious capital structure choices because it increases a firm’s expected costs of financial distress.