This article studies how the rise of financial technology (Fintech) lending affects credit access, interest rates, and social welfare. We consider a lending competition model with two incumbent banks and a Fintech lender, which use different information and technologies to assess borrower creditworthiness. We show that Fintech lending could negatively affect high-quality borrowers’ access to credit when the Fintech lender’s screening accuracy is superior to that of the banks. Furthermore, Fintech lending may worsen the allocative efficiency of credit and reduce social welfare under some conditions. Analytical and numerical results suggest that Fintech lending mostly reduces the expected interest rates.

Lenders’ simultaneous equity holdings introduce conflicts of interest among members of syndicated loans. We argue that lenders address such within-syndicate conflicts with financial covenant design to improve contracting efficiency. We show that loans with higher conflicts rely less on performance-based covenants, which serve as tripwires to facilitate ex-post control transfer and require coordination among syndicate members. Instead, high-conflict loans rely more on capital-based covenants to align shareholder-creditor interest ex-ante and incentivize shareholder monitoring. Overall, these results suggest that such conflicts can reduce capital flexibility and renegotiation efficiency for the borrowers.

We estimate the effects of student loan access on educational attainment and labor market returns in New Zealand. We exploit the introduction of a national policy mandating a 50% pass rate for student loan renewals using a regression discontinuity design. Retaining loan access increases reenrollment for students around the threshold, and a majority eventually graduate with a bachelor's degree within seven years. We find that retaining student loan access leads to large labor market returns for struggling students. The additional debt from further borrowing is small relative to the earnings returns and declines quickly due to faster repayment.

We build a publicly available database that tracks economic activity in the United States at a granular level in real time using anonymized data from private companies. We report weekly statistics on consumer spending, business revenues, job postings, and employment rates disaggregated by county, sector, and income group. Using the publicly available data, we show how the COVID-19 pandemic affected the economy by analyzing heterogeneity in its effects across subgroups. High-income individuals reduced spending sharply in March 2020, particularly in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent, dense areas. Those businesses laid off many of their employees, leading to widespread job losses, especially among low-wage workers in such areas. High-wage workers experienced a V-shaped recession that lasted a few weeks, whereas low-wage workers experienced much larger, more persistent job losses. Even though consumer spending and job postings had recovered fully by December 2021, employment rates in low-wage jobs remained depressed in areas that were initially hard hit, indicating that the temporary fall in labor demand led to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the effects of fiscal stimulus policies designed to stem the downward spiral in economic activity. Cash stimulus payments led to sharp increases in spending early in the pandemic, but much smaller responses later in the pandemic, especially for high-income households. Real-time estimates of marginal propensities to consume provided better forecasts of the impacts of subsequent rounds of stimulus payments than historical estimates. Overall, our findings suggest that fiscal policies can stem secondary declines in consumer spending and job losses, but cannot restore full employment when the initial shock to consumer spending arises from health concerns. More broadly, our analysis demonstrates how public statistics constructed from private sector data can support many research and real-time policy analyses, providing a new tool for empirical macroeconomics.