This paper investigates the influence of creditor control rights on the pricing of corporate loans. We construct a novel dataset, which combines hand-collected covenant violations data with individual borrower, creditor, and loan contract information. Our data allows us to distinguish between creditors that receive direct control rights after a covenant violation and creditors that do not receive control rights after a violation. By comparing the loan terms of these two creditor types, we can isolate the impact of creditor control rights on loan pricing from the impact of other factors related to a covenant violation. We find that creditors exploit control rights to overprice new loans, which is a key determinant of the loan premium puzzle. In addition, we uncover novel cross-sectional and time-series loan pricing patterns that can be explained by creditor control rights.

We examine whether banks manage climate transition risk by securitizing corporate loans. We present two novel results. First, banks are more likely to securitize loans when borrowers increase their carbon emission intensity. Second, securitization serves as a vehicle to shift transition risk, especially when banks do not have market power to price transition risk into loan contracts. Exploiting the Trump election as an exogenous shock to transition risk, we establish a causal link between transition risk and loan securitization. Our estimates show stronger effects for banks that do not exhibit preference for sustainable lending and domestic lenders. We discuss how our paper speaks to the debate on the design of bank climate policies.

This paper studies how the California Consumer Privacy Act (CCPA), a comprehensive privacy law that grants users control over their data, affects fintech lending. To develop hypotheses we build a parsimonious screening model. Consumers apply for loans with banks and a fintech that has a superior but data-intensive screening technology. However, consumers dislike sharing their data, and in particular with the fintech. We empirically show that the introduction of the CCPA, by assuaging concerns about data sharing, increases mortgage applications with fintechs relative to banks. Consistent with applicants' greater willingness to share data, fintechs make greater use of non-traditional data to improve screening. In turn, they deny more applications and can offer lower interest rates.

This paper predicts recessions using the dispersion of deposit rates offered by banks on insured deposits. An increase in the dispersion of deposit rates can accurately predict recessions over long time horizons at the county, state, and national levels. We find that the growth of deposits, particularly uninsured deposits of riskier banks, slows down at the onset of a downturn, regardless of whether the downturn was preceded by a credit boom. In turn, riskier banks increase their deposit rates to attract more funding to support their balance sheet. The resulting increase in the dispersion of deposit rates predicts an impending economic downturn.

I show that the long-term decrease in the nominal short rate since the 1980s contributed to a decline in banks' supply of business loans, firm investment and new firm creation, and an increase in banks' real estate lending. The driving force behind these relationships was the shift in banks’ funding mix from time deposits (CDs) to savings deposits, which was caused by the decrease in the nominal rate. I show that banks finance business lending with time deposits because of their matching interest-rate sensitivity and liquidity. A lower nominal rate reduces the spread on liquid deposits (e.g., savings deposits), leading households to substitute towards them and away from illiquid time deposits. In response to an outflow of time deposits, banks cut the supply of business loans and increase their price. The decrease in business lending leads to reduced investment at bank-dependent firms and a lower entry rate of firms in industries that are highly reliant on external funding. I document these relationships both in the aggregate, and in the cross-section of banks, firms and geographic areas. For identification, I exploit cross-sectional variation in banks' market power and business credit data. I develop a general equilibrium model which captures these relationships and shows that the transmission mechanism I document is quantitatively important.

We examine the value of skilled foreign labor in finance by leveraging on two natural experiments. We find that hedge funds that secure more H-1B visas in random lotteries deliver higher alphas, Sharpe ratios, and information ratios. Moreover, an unexpected reduction in the H-1B quota undermined the performance of hedge funds that were dependent on H-1B workers. The superior performance of funds with high H-1B allocations can be attributed to highly-educated STEM majors operating systematic strategies. Notwithstanding the valuable skills that foreign workers possess, racial and ethnic homophily induces some fund managers to eschew foreign labor.

We propose a precise definition of greenwashing in asset management that combines ESG self-labels, sustainability scores of portfolio holdings, and funds’ voting behavior. Armed with this definition, we are able to quantify the prevalence of greenwashing in the US mutual fund industry. Although self-labeled ESG funds dominate non-ESG funds in terms of ESG ratings and voting support for ESG proposals, 24% of them are greenwashers according to our definition. Greenwashers are more likely to belong to larger and older fund families and less likely to be offered by signatories of the United Nations Principles for Responsible Investment. Importantly, while retail investors do not distinguish between greenwashers and true ESG funds, institutional investors are not deceived by the former. Our results suggest that accusations of ubiquitous greenwashing in asset management exaggerate the true extent of the problem. However, there is room for regulation aimed at enhanced ESG disclosure, at least for those funds that target retail investors.

I study the causal effect of bond investor demand on the financing and investment decisions of nonfinancial firms using granular data on the bond transactions of U.S. insurance companies. Liquidity inflows from insurance premiums combined with insurers' persistent investment preferences identify bond demand shifts, which raise bond prices and reduce firms' financing costs. In response, firms issue more bonds, especially when they have well-connected bond underwriters. The proceeds are used for investment rather than shareholder payouts, particularly by financially constrained firms. The results emphasize that bond investors significantly affect corporate financing and investment decisions through their price impact.

Using a dataset of more than 38 million consumer-bank relationships in 518 districts of Southern Peru, we find that first-time borrowers receive credit and better loan terms from the bank where their families are more central. Our results are explained by informal-oriented banks using family ties as a strategy toward the unbanked. They give first credits to retain the relatives of the recipients as clients, and they also screen first-time borrowers using the credit behavior of their relatives.

This paper studies how interconnected plants distribute additional liquidity from banks through the supply chain. Using a spatially segmented bank branch expansion rule in India, we find that direct exposure to additional bank credit allows plants to hold less precautionary cash and increase bank debt. Directly exposed plants pass through liquidity to customer plants as short-term trade credit. This liquidity spillover improves sales, employment, and productivity at customer plants. Structural estimation yields an average credit multiplier of 1.48. Our results underscore the credit multiplier effects of production networks and the importance of financial integration among firms with limited banking services.