Corporate Finance Program Meeting Fall 2020

This paper provides a comprehensive assessment of financial intermediation and the economic effects of the Paycheck Protection Program (PPP), a large and novel small business support program that was part of the initial policy response to the COVID-19 pandemic in the US. Granja, Makridis, Yannelis, and Zwick use loan-level microdata for all PPP loans and high-frequency administrative employment data to present three main findings. First, banks played an important role in mediating program targeting, which helps explain why some funds initially flowed to regions that were less adversely affected by the pandemic. The top-4 banks alone account for 36% of total pre-policy small business loans, but disbursed less than 3% of all PPP loans in the first round of funding. Second, the researchers exploit regional heterogeneity in lending relationships and individual firm-loan matched data to show that the short- and medium-term employment effects of the program were small compared to the program's size. Third, many firms used the loans to make non-payroll fixed payments and build up savings buffers, which can account for small employment effects and likely reflects precautionary motives in the face of heightened uncertainty. Limited targeting in terms of who was eligible likely also led to many inframarginal firms receiving funds and to a low correlation between regional PPP funding and shock severity. Their findings illustrate how business liquidity support programs affect firm behavior and local economic activity and how policy transmission depends on the agents delegated to deploy it.

In emerging markets, a significant share of corporate loans are denominated in dollars. Using novel data that enables us to see currency and the cost of credit, in addition to several other transaction-level characteristics, Gutierrez, Ivashina, and Salomao re-examine the reasons behind dollar credit popularity. They find that a dollar- denominated loan has an interest rate that is 2% lower per year than a loan in Peruvian Soles. Expectations of exchange rate movements do not explain this difference. The researchers show that this interest rate differential for lending rates is closely matched by the differential in the deposit market. Their results suggest that the preference for dollar loans is rooted in the local household preference for dollar savings and a banking sector that is closely matching its foreign assets and liabilities. Gutierrez, Ivashina, and Salomao find that borrower competitive pressure increases the pass-through of this differential.

Aggregate bank lending to firms expands following a number of adverse macroeconomic shocks, such as the outbreak of COVID-19 or a monetary policy tightening. Using loan-level supervisory data, Greenwald, Krainer, and Paul show that these dynamics are driven by draws on credit lines by large firms. Banks that experience larger drawdowns restrict term lending more -- crowding out credit of smaller firms. Using a structural model, the researchers show that credit lines are necessary to reproduce the flow of credit toward less constrained firms after adverse shocks. While credit lines increase total credit growth, their redistributive effects exacerbate the fall in investment.

Which firms invest in artificial intelligence (AI) technologies, and how do these investments affect individual firms and industries? Babina, Fedyk, He, and Hodson provide a comprehensive picture of the use of AI technologies and their impact among US firms over the last decade, using a unique combination of job postings and individual-level employment profiles. The researchers introduce a novel measure of investments in AI technologies based on human capital and document that larger firms with higher sales, markups, and cash holdings tend to invest more in AI. Firms that invest in AI experience faster growth in both sales and employment, which translates into analogous growth at the industry level. The positive effects are concentrated among the ex ante largest firms, leading to a positive correlation between AI investments and an increase in industry concentration. However, the increase in concentration is not accompanied by either increased markups or increased productivity. Instead, firms tend to expand into new product and geographic markets. Their results are robust to instrumenting firm-level AI investments with foreign industry-level AI investments and with local variation in industry-level AI investments, and to controlling for investments in general information technology and robotics. Babina, Fedyk, He, and Hodson also document consistent patterns across measures of AI using firms' demand for AI talent (job postings) and actual AI talent (resumes). Overall, their findings support the view that new technologies, such as AI, increase the scale of the most productive firms and contribute to the rise of superstar firms.

Donaldson, Morrison, Piacentino, and Yu develop a model of a firm in financial distress. Distress can be mitigated by filing for bankruptcy, which is costly, or preempted by restructuring, which is impeded by a collective action problem. They find that bankruptcy and restructuring are complements, not substitutes: reducing bankruptcy costs facilitates restructuring, rather than crowding it out. And so does making bankruptcy more debtor-friendly, under a condition that seems likely to hold now in the U.S. The model gives new perspectives on current relief policies (e.g., DIP finance subsidies) and on long-standing legal debates (e.g., about APR violations).

A new form of collateralized lending has emerged, most prominently in developing countries, that is facilitated by a "lockout" technology, which allows the lender to temporarily disable the flow value of the collateral to the borrower without physically repossessing it. Gertler, Green, and Wolfram explore the effect of this new technology both in a model and in a randomized controlled trial using school-fee loans collateralized with a solar home system. They find that securing a loan with lockout drastically reduces default rates (by 15 pp) and increases the lender's rate of return (by 5 pp per month). Employing a variant of the Karlan and Zinman (2009) methodology, the researchers decompose the total effect and find that roughly one-third of the total effect is attributable to (ex-ante) adverse selection and two-thirds of the effect is attributable to (ex-post) moral hazard. Access to a school-fee loan significantly increases school enrollment and school-related expenditures without detrimental effects to household's balance sheet.

Broccardo, Hart, and Zingales study the relative effectiveness of exit (divestment and boycott) and voice (engagement) strategies in promoting socially desirable outcomes in companies. They show that in a competitive world exit is less effective than voice in pushing firms to act in a socially responsible manner. Furthermore, the researchers demonstrate that individual incentives to join an exit strategy are not necessarily aligned with social incentives, whereas they are when well-diversified investors are allowed to express their voice. Broccardo, Hart, and Zingales discuss what social and legal considerations might sometimes make exit preferable to voice.