'Economic Impact of COVID-19: Households' Financial Distress Part 1' (2020-03-23)
Summary
An Economic Impact of COVID-19 article from the Federal Reserve Bank of Richmond dated March 23, 2020, titled COVID-19 and Households' Financial Distress Part 1: Employment Vulnerability and (Financial) Pre-Existing Conditions, by Kartik Athreya, Ryan Mather, Jose Mustre-del-Río and Juan M. Sánchez. It measures financial distress as the share of people 30 days or more delinquent on a credit card payment and divides U.S. zip codes into five quintiles. With Census LEHD LODES data, it finds that roughly 11 percent of the most financially distressed group works in Accommodation and Food, compared with about 7 percent of the least distressed. It reports only a slight relationship for other sectors, with 32 percent and roughly 27 percent of workers in the highest and lowest quintiles employed in the sectors examined.
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“'Economic Impact of COVID-19: Households' Financial Distress Part 1' (2020-03-23).”
Original at www.richmondfed.org. Archived at Pandemic Darlings, https://pandemicdarlings.com/source-documents/unequal-employment-impacts-of-covid-19-9d25455a9a/
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March 23, 2020
Economic Impact of COVID-19
COVID-19 and Households’ Financial Distress
Part 1: Employment Vulnerability and (Financial)
Pre-Existing Conditions
By Kartik Athreya, Ryan Mather, Jose Mustre-del-Río, and Juan M. Sánchez
COVID-19, and the public’s reaction to pursue people are distressed. Moreover, the reductions
social distancing, will inevitably affect differ- in consumption in some geographies will likely
ent industries in different ways. Some jobs, like create further distress at the community level,
computer programming and farming, require as research following the Great Recession sug-
little face-to-face interaction with clients and can gests. On the scale of an entire economy, then,
often be completed from home. Others, how- we would expect that losses to income or wealth
ever—particularly those that specialize in “social” that fall most heavily on people who are vulner-
services like accommodation and food services able in this way will have a greater overall effect
(think restaurants)—will face significant chal- than losses that fall on people who are more
lenges in these conditions. And while sectors will financially stable.
be hit unevenly, so too will people: A general fea-
ture of our economy is that relatively secure and To predict how COVID-19 may affect household
high-paying jobs are the ones most often easily consumption and balance sheets, we will focus in
transferred to remote locations, while already- this post on the Accommodation and Food sector,
insecure and relatively low-paying jobs are over- which, as we note above, is particularly
whelmingly less suited to remote delivery. vulnerable to a shock that keeps people from
gathering. It is also a large sector, representing
These features imply that the burden of this close to 10 percent of total employment in the
illness will not only be borne unevenly across United States and an even larger proportion
occupations, but that the largest burdens will fall of the sectors we view as likely to be the most
on populations that are already the most vulner- affected by social distancing guidelines. We will
able to economic shocks. In a recent working examine whether this sector tends to employ
paper, we showed that communities in which people from zip codes with high financial distress.
a higher percentage of people are financially By “financial distress,” we mean the percentage
distressed will cut their consumption much of people who go 30 days or more delinquent on
more in reaction to a given decline in wealth a credit card payment over the course of a year.
than communities where a lower percentage of Difficulty making timely payments is likely a good
March 2020 – Richmond Fed Page 1
indicator of an overall lack of financial capacity and, Beginning with Arts, Entertainment, and Recre-
hence, of vulnerability to temporary shocks, especially ation, we see that the more financially distressed
severe ones like the one we are experiencing. are not more likely to be part of this sector.
To do this, we divide all U.S. zip codes into five evenly
sized groups, or “quintiles,” defined by their incidence
of financial distress. In what follows, we denote the
zip codes with the lowest financial distress as group
“one” and the zip codes with the highest financial
distress as group “five.”
To obtain data on employment by industry and zip
code, we utilize the Census LEHD LODES dataset.
Similarly, we see that this holds also for
Construction, Retail Trade, and Other services, in
the following three figures.
It is clear from this that, indeed, Accommodation and
Food is an area of the U.S. economy where the most
financially distressed are most often seen. Comparing
the bottom quintile with the top, we see that roughly
11 percent of the most financially distressed group is
working in this sector, while among the least finan-
cially distressed, the number is up to 40 percent lower
(at 7 percent, roughly). The relationship is also sys-
tematic: The geographic regions of the country that
are more financially distressed have a greater share
of their employment located in Accommodation and
Food.
If there is better news, it is that Accommodation and
Food—while critical and large in size—is perhaps
understandably unique in the “double whammy” it
features for workers. We show next that for a vari-
ety of other sectors there seems to be only a slight
positive relationship between zip-code incidence of
financial distress and the share of workers employed
in potentially vulnerable industries.
Page 2
One way of seeing that Accommodation and Food is
especially susceptible is that, overall, 32 percent of
workers in the highest quintile of financial distress
work in the sectors examined here, while a similar
proportion (roughly 27 percent) of workers in the
low-est quintile also work in these sectors. Thus, the
share of employment in vulnerable sectors taken as
whole is not very different across zip codes with
different distress levels. But our analysis makes clear
that the picture of overall “calm” hides the much
clearer pat-tern within the sector that covers
businesses whose services are clearly important for
our lives and liveli-hoods.
In the next installment of this series, we will use the
facts described here as inputs to a model we have de-
veloped to deliver an initial projection of what could
be the possible response of household consumption
and balance sheets to drops in income.
Katrik Athreya is executive vice president and direc-
tor of research at the Richmond Fed. Ryan Mather is
a research associate and Juan M. Sanchez is an assis-
tant vice president at the St. Louis Fed. Jose Mustre-
del-Rio is a senior economist at the Kansas City Fed.
Endnotes
1
The data used to construct this measure were used in our
aforementioned working paper and came originally from the
New York Federal Reserve/Equifax CCP dataset for 2018. It is
available for public download here.
2
Importantly, we use data from the LEHD LODES database
that are summed to the zip-code level in the area where the
workers live, as opposed to the area where they work. Thus,
even though a worker from a highly financially distressed zip
code may work in a zip code with low financial distress, we will
count their job as being filled by someone in the highly finan-
cially distressed community. Data are available at the two-digit
level in the NAICS framework.
This article may be photocopied or reprinted in its
entirety. Please credit the authors, source, and the
Federal Reserve Bank of Richmond and include the
italicized statement below.
Views expressed in this article are those of the authors
and not necessarily those of the Federal Reserve Bank
of Richmond or the Federal Reserve System.
Page 3
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