We show that labor market frictions are first-order for understanding credit markets. Wage growth and labor share forecast aggregate credit spreads and debt growth as well as or better than alternative predictors. They also predict credit risk and debt growth in a cross section of international firms. Finally, high labor share firms choose lower financial leverage. A model with labor market frictions and risky long-term debt can explain these findings, and produce large credit spreads despite realistically low default probabilities. This is because precommitted payments to labor make other committed payments (i.e., interest) riskier.

We document that the aggregate hiring rate of publicly traded firms in the U.S. economy negatively predicts stock market returns and long-term cash flows, and positively predicts short-term cash flows. In addition, through a variance decomposition, we show that the time-series variation in the aggregate hiring rate is mainly driven by changes in discount rates and short-term expected cash flows, with no contribution from variation in long-term expected cash flows. We estimate a neoclassical dynamic model with labor market frictions and show that labor adjustment costs and time-varying risk are essential for the model to replicate the empirical patterns.

Previous studies have identified a negative relation between firms’ hiring rates and future stock returns in the cross-section. We document that this relation is significantly steeper in industries that rely relatively more on high-skill workers than low-skill workers. A long-short portfolio sorted on firm-level hiring rate earns an average annual return of 8.6% in high-skill industries, and only 0.9% in low-skill industries. Moreover, this pattern is not explained by the standard CAPM. These findings are consistent with a neoclassical model with labor force heterogeneity and labor market frictions if it is more costly to replace high-skill than low-skill workers. Received August 14, 2015; editorial decision December 31, 2016 by Editor Leonid Kogan.