Health expenditure data almost always include extreme values. Such heavy tails can be a threat to the commonly adopted least squares methods. To accommodate extreme values, we propose the use of an estimation method that recovers the often ignored right tail of health expenditure distributions....

We examine the impact of local racial demonstrations, such as Black Lives Matter (BLM) protests, and the subsequent racial justice movement following the death of George Floyd on racial disparities in Paycheck Protection Program loan disbursements to small businesses. Using...

Our research examines the impact of dwindling community bank numbers on community investment and economic development. Initially, we confirm the vital role of community banks' small business lending in local development. Contrary to popular belief, we find that a decrease in community banks...

We show how to measure the welfare effects arising from increased data availability. When lenders have more data on prospective borrower costs, they can charge prices that are more aligned with these costs. This increases total social welfare, and transfers surplus from borrowers to lenders. We...

Bank branch density, defined as the number of bank branches to total deposits, has significantly declined over the past decade, fueled by a confluence of branch closings and the almost doubling of deposits between 2016 and 2022. During this period, banks with low branch density benefited from...

The traditional model of bank-led financial intermediation, where banks issue demandable deposits to savers and make informationally sensitive loans to borrowers, has seen a dramatic decline since 1970s. Instead, private credit is increasingly intermediated through arms-length transactions, such...

In recent years, assets of non-bank financial intermediaries (NBFIs) have grown significantly relative to those of banks. These two sectors are commonly viewed either as operating in parallel, performing different activities, or as substitutes, performing substantially similar activities, with...

Causal inference is of central interests in many empirical applications yet often challenging because of the presence of endogenous regressors. The classical approach to the problem requires using instrumental variables that must satisfy the stringent condition of exclusion restriction. At the...

We develop a novel method for the identification of monetary policy shocks. By applying natural language processing techniques to documents that Federal Reserve staff prepare in advance of policy decisions, we capture the Fed's information set. Using machine learning techniques, we then predict...

We study how monetary policy affects subcomponents of the Personal Consumption Expenditures Price Index (PCEPI) using local projections. Following a monetary policy contraction, the response of aggregate PCEPI turns significantly negative after over three years. There are stark differences in...