The COVID-19 shock and consumer creditAugust 7, 2020
The ongoing COVID-19 pandemic has severely impacted the US economy, with rising unemployment (Coibon et al. 2020a), income losses for many households (Cajner et al. 2020), reduced consumer spending (Coibon et al. 2020b), and overall increases in economic uncertainty (Baker et al. 2020a). Baldwin and Weder di Mauro (2020) provide a comprehensive overview of the economic consequences of the pandemic. This large economic shock also had profound implications for the consumer credit market.
Using credit card data from the Federal Reserve’s monthly Y-14M reports, in a new paper (Horvath et al. 2020), we examine the early impact of the COVID-19 shock on both the use and availability of credit in the US consumer credit card market through March 2020. We estimate the local effect of both pandemic severity and policy responses in the form of non-pharmaceutical interventions (NPIs) on use and availability of credit. Moreover, we examine differences in credit market outcomes for borrowers of different creditworthiness.
While most counties in the US still had not registered any confirmed COVID-19 cases by mid-March, some regions were already affected severely at this point. Figure 1 visualizes data on COVID-19 cases from the Johns Hopkins COVID-19 Data Repository (Dong et al. 2020). As can be seen, there were clusters of affected counties in Washington State, the Bay Area, New York State, and Southern Florida by mid-March.
Figure 1 Confirmed COVID-19 cases per 100,000 as of 15 March 2020
Notes: This figure illustrates the number of confirmed COVID-19 cases per 100,000 population across US counties as of 15 March 2020.
Source: Johns Hopkins COVID-19 Data Repository (Dong et al. 2020) and authors’ own calculations.
At the same time, many counties had already enacted some NPIs, such as large gathering bans and the closure of public venues, schools, and universities by mid-March. We use data from the Coronavirus Intervention Dataset provided by Keystone Strategy (Keystone 2020) to construct a simple county-level measure of NPI stringency, by adding up the number of NPIs. As shown in Figure 2, by mid-March most counties with NPIs had inherited their NPI measures from state legislation and were therefore subject to a high degree of NPI stringency relative to their number of confirmed cases. This allows us to disentangle the effect of the pandemic itself from the effect of NPIs. It is important to note that by mid-March, the most restrictive NPIs, such as shelter-in-place orders and lockdowns, had not yet been implemented in any county. Therefore, our analysis does not inform the discussion on these more restrictive public health interventions.
Figure 2 Non-Pharmaceutical Intervention stringency as of 15 March 2020
Notes: This figure illustrates the simple NPI stringency indicator across US counties, calculated as the number of NPIs as of 15 March 2020.
Source: Keystone Strategy Coronavirus Intervention Dataset (Keystone 2020) and authors’ own…