An often-overlooked aspect of flood-plain mapping is the fact that these maps designate stark boundaries, with households falling either inside or outside of areas designated as “flood zones.” Households inside flood zones must insure themselves against the possibility of disasters. However, costly insurance may have pushed lower-income households out of areas officially designated a flood risk and into physically adjacent areas. While not designated an official flood risk, Federal Emergency Management Agency (FEMA) and disaster data shows that these areas are still at considerable risk of flooding. In this post, we examine whether flood maps may have inadvertently clustered those households financially less able to bear the consequences of a disaster into areas that may still pose a significant flood risk.
The National Flood Insurance Program (NFIP) was designed to reduce household and lender flood-risk exposure and “encourage lending.” In this post, which is based on our related study, we show that in certain situations the program actually limits access to credit, particularly for low-income borrowers—an unintended consequence of this well-intentioned program.
Climate change could affect banks and the financial systems they anchor through various channels: increasingly extreme weather is one (Financial Stability Board, Basel Committee on Bank Supervision). In our recent staff report, we size up this channel by studying how U.S. banks, large and small, fared against disasters past. We find even the most destructive disasters had insignificant or small effects on bank stability and small and positive effects on bank income. We conjecture that recovery lending after disasters helps stabilize larger banks while smaller, local banks’ knowledge of “unmarked” (flood) hazards may help them navigate disaster risk. Federal disaster aid seems not to act as a bank stabilizer.
How informed or uninformed are bank depositors in a banking crisis? Can depositors anticipate which banks will fail? Understanding the behavior of depositors in financial crises is key to evaluating the policy measures, such as deposit insurance, designed to prevent them. But this is difficult in modern settings. The fact that bank runs are rare and deposit insurance universal implies that it is rare to be able to observe how depositors would behave in absence of the policy. Hence, as empiricists, we are lacking the counterfactual of depositor behavior during a run that is undistorted by the policy. In this blog post and the staff report on which it is based, we go back in history and study a bank run that took place in Germany in 1931 in the absence of deposit insurance for insight.
About one in twenty American households are unbanked (meaning they do not have a demand deposit or checking account) and many more are underbanked (meaning they do not have the range of bank-provided financial services they need). Unbanked and underbanked households are more likely to be lower-income households and households of color. Inadequate access to financial services pushes the unbanked to use high-cost alternatives for their transactional needs and can also hinder access to credit when households need it. That, in turn, can have adverse effects on the financial health, educational opportunities, and welfare of unbanked households, thereby aggravating economic inequality. Why is access to financial services so uneven? The roots to part of this problem are historical, and in this post we will look back four decades to changes in regulation, shifts in the ownership structure of retail financial services, and the decline of free/low-cost checking accounts in the United States to search out a few of the contributory factors.
Blickle, Kovner, and Viswanathan share a synopsis of a recent conference featuring new research in financial intermediation and expert perspectives on corporate credit markets.
A key part of understanding the stability of the U.S. financial system is to monitor leverage and funding risks in the financial sector and the way in which these vulnerabilities interact to amplify negative shocks. In this post, we provide an update of four analytical models, introduced in a Liberty Street Economics post last year, that aim to capture different aspects of banking system vulnerability.