Using a new dataset of UK-syndicated loans, we document a significant loan cost disadvantage incurred by privately held firms. For identification, we use the distance of a firm's headquarters to London's capital markets as a plausibly exogenous variation in corporate structure (i.e., public/private) choice. We analyze the channels of the loan cost disadvantage of being private by documenting the importance of: the higher costs of information production, the lower bargaining power, the differences in ownership structure, and the differences in secondary market trading. Interestingly, we find no evidence that lenders price expected future performance into the loan spread differential.

[Using a new dataset of UK-syndicated loans, we document a significant loan cost disadvantage incurred by privately held firms. For identification, we use the distance of a firm's headquarters to London's capital markets as a plausibly exogenous variation in corporate structure (i.e., public/private) choice. We analyze the channels of the loan cost disadvantage of being private by documenting the importance of: the higher costs of information production, the lower bargaining power, the differences in ownership structure, and the differences in secondary market trading. Interestingly, we find no evidence that lenders price expected future performance into the loan spread differential.]

We investigate the predictive power of loan spreads for forecasting business cycles, specifically focusing on more constrained, intermediary-reliant firms. We introduce a novel loan-market-based credit spread constructed using secondary corporate loan-market prices over the 1999 to 2023 period. Loan spreads significantly enhance the prediction of macroeconomic outcomes, outperforming other credit-spread indicators. We also explore the underlying mechanisms and differentiate between borrower fundamentals and financial frictions. Evidence suggests that supply-side frictions are a decisive factor in the forecasting ability of loan spreads.

We analyze government interventions in the eurozone banking sector during the 2008–2009 financial crisis. Using a novel data set, we document that fiscally constrained governments “kicked the can down the road” by providing banks with guarantees instead of fully-fledged recapitalizations. We econometrically address the endogeneity associated with bailout decisions in identifying their consequences. We find that forbearance prompted undercapitalized banks to shift their assets from loans to risky sovereign debt and engage in zombie lending, resulting in weaker credit supply, elevated risk in the banking sector, and, eventually, a greater reliance on liquidity support from the European Central Bank.

We analyze pricing differences between U.S. and European syndicated loans over the 1992–2014 period. We explicitly distinguish credit lines from term loans. For credit lines, U.S. borrowers pay significantly higher spreads, but lower fees, resulting in similar total costs of borrowing in both markets. Credit line usage is more cyclical in the United States, which provides a rationale for the pricing structure difference. For term loans, we analyze the channels of the cross-country loan price differential and document the importance of: the composition of term loan borrowers and the loan supply by institutional investors and foreign banks. Received September 18, 2015; editorial decision July 13, 2016 by Editor Philip Strahan.

A two-sided “credit-line channel”—relating to drawdowns and repayments—explains the severe drop and partial subsequent recovery in bank stock prices during the COVID-19 pandemic. Banks with greater exposure to undrawn credit lines saw larger stock price declines but performed better outside of crises periods. Despite deposit inflows, high drawdowns led to reduced bank lending, suggestive of capital encumbrance upon drawdowns. Repayments of credit lines unencumbered capital which explains the stock price recovery starting Q2 2020. Bank provision of credit lines resembles writing put options on aggregate risk, and we propose how to incorporate this feature into bank stress tests.