Has the significant increase in fixed-rate, non-refinanceable student loan debt in the United States over the last 20 years affected the transmission of monetary policy to household consumption? Several studies have emphasized the importance of refinancing as a key mechanism through which households fund consumption in response to expansionary monetary policy. In this paper, I estimate consumption responses to expansionary monetary policy shocks for households with different types of debt to study the implications of the increased share of student loan debt in the household balance sheet. I find that households with fixed-rate non-refinanceable student loan debt have a muted consumption response compared to mortgagors and households with variable-rate student loan debt. Mortgagors and variable-rate student loan holders have significantly larger consumption responses, approximately $4000, or 40% more over four years. I then calculate the implied effect on average household consumption responses given the composition of the household balance sheet in 2007 versus 2019. I find that the reallocation of household debt has reduced average monetary policy responses by around 10% after two years.

This paper describes the construction of the Distributional Financial Accounts (DFAs), a new dataset containing quarterly estimates of the distribution of U.S. household wealth since 1989, and provides the first look at the resulting data. The DFAs build on two existing Federal Reserve Board statistical products — quarterly aggregate measures of household wealth from the Financial Accounts of the United States and triennial wealth distribution measures from the Survey of Consumer Finances — to incorporate distributional information into a national accounting framework. The DFAs complement other existing sources of data on the wealth distribution by using a more comprehensive measure of household wealth and by providing quarterly data on a timely basis. We encourage policymakers, researchers, and other interested parties to use the DFAs to help understand issues related to the distribution of U.S. household wealth.

In this note, we describe an algorithm, developed in Carlson, Shan, and Warusawitharana (2013), to match banks that are geographically close and are similar in size and business model. Concurrently, we also release a data set of matched banks obtained from applying this algorithm from 1998 to 2014, as well as some of the associated computer programs.