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September 4, 2020

Economic Impact of COVID-19
The Effect of Lockdown Measures on Unemployment
By Marios Karabarbounis, Reiko Laski, James Lee, and Nicholas Trachter
Unemployment increased sharply in all regions
of the United States as a result of the public
health response to the COVID-19 pandemic. But
in some states the unemployment rate increased
by fewer than 5 percentage points between February and June, while in others the rate increased

more than 10 percentage points. (See Figure 1.)
Can we explain state-level differences in unemployment rates based on differences in the
extent and duration of lockdowns and activity
restrictions across states?

Figure 1: Change in Unemployment Rate Between February and June, Across States

10

5

0

Kentucky
Utah
Idaho
Mississippi
Maine
Oklahoma
District of Columbia
New Mexico
Montana
Nebraska
North Dakota
Wyoming
South Dakota
North Carolina
Kansas
Missouri
Louisiana
Arkansas
Georgia
Maryland
Alabama
Wisconsin
Texas
Iowa
Arizona
Minnesota
West Virginia
Virginia
Connec�cut
Washington
South Carolina
Tennessee
Alaska
Ohio
Vermont
Florida
Oregon
Colorado
Indiana
Pennsylvania
Delaware
Rhode Island
New Hampshire
California
Hawaii
Illinois
Michigan
Nevada
New York
New Jersey
Massachuse�s

Percentage Points

15

State

Source: Bureau of Labor Sta�s�cs

September 2020 – Richmond Fed

Page 1

Connecting Unemployment and Lockdowns
To study the relationship between unemployment
and lockdowns, we rely on the measure “time spent
at home” to reflect the extent of restrictions on daily
activity. Figure 2 shows a clear positive association
between the increase in the unemployment rate
and the growth in time spent at home between
March 2 and June 30. To measure time spent at
home we rely on data from SafeGraph, which
provides changes in time spent at home at the
census block group level. We compute state
averages by weighting each census block group by
its population. Each cross in the figure corresponds
to a state. The dashed line represents an
unweighted trend, while the solid line represents
the population-weighted trend. The rela-tionship is
positive and stronger when weighting by
population, which suggests a stronger link between
time spent at home and unemployment in heavily
populated states.

Of course, an official lockdown is not the only
reason why time spent at home may increase. For
example, more time spent at home could reflect
voluntary choices due to a preference to avoid
risk. Indeed, households in many states restricted
their daily activities prior to official lockdowns
and remained indoors well after lockdown
orders were lifted. Disentangling the effects of
mandatory stay-at-home orders from voluntary
restriction of daily activities requires careful
consideration that goes beyond the scope of our
analysis in this article. In fact, we interpret time
spent at home as reflecting a broader measure of
lockdown which encompasses both official and
voluntary restrictions.
Another complicating factor is reverse causality.
More time spent at home in a state may itself be
the result of higher unemployment in the state.
The most natural explanation for variation across
states is differences in industry composition.

Figure 2: Change in Unemployment Rate and Time Spent at Home, Across States

20

Weighted by Popula�on

Difference in Unemployment Rate
Between February and June

Unweighted
15

10

5

0

0

100

200

300

400

Average Growth in Time Spent at Home
Between March 2 and June 30
Sources: Bureau of Labor Sta�s�cs, SafeGraph

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Industry Composition as a Driver of the Spatial
Variation of Time Spent at Home
To measure a state’s exposure to sensitive industries, we use a metric constructed by WalletHub:
the share of employment from highly affected
industries. This measure is a weighted average of
the shares of employment in highly affected industries by state, with exact weights determined
by WalletHub. Figure 3 presents a scatterplot of
the share of employment from highly affected
industries (x-axis) and the average growth in the
time spent at home variable (y-axis). Surprisingly,
there is no connection between the exposure of
a state to the pandemic and the time spent at
home.

It has been widely documented that leisure and
hospitality, and service sectors involving in-person
interactions, have been impacted substantially more
than other industries during the pandemic. As a
result, it is possible that rising unemployment rates
in some states could be due to greater exposure to
these sensitive sectors, which subsequently creates
a larger mass of people staying at home. In other
words, variation in time spent at home across states
could be a result of variation in industry exposure to
the common aggregate shock, rather than the result
of lockdown measures. In the following section, we
analyze if industry composition is behind the positive relationship we observe in Figure 2.

Figure 3: Industry Exposure to the Pandemic and Time Spent at Home, Across States

Average Growth in Time Spent at Home
Between March 2 and June 30

400

Weighted by Popula�on
Unweighted

300

200

100

0

30

40

50

60

70

Share of Employment From Highly Affected Industries
Score Out of 100
Sources: SafeGraph, WalletHub

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In Figure 4, we again analyze the relationship between time spent at home and unemployment with
one difference: We control for industry composition.
We perform this in two steps. First, we regress time
spent at home on the industry exposure variable
(which gives a small and insignificant coefficient as
we see in Figure 3). Then, we use the residuals of the
regression — which represent the growth in time
spent at home controlling for industry composition — as the main variable on the x-axis. As before,
we find a strong correlation between the (residual)
time spent at home and the change in unemployment. Thus, time spent at home — which is partially
affected by official lockdowns — affects the unemployment rate even after controlling for industry
composition.

between the amount of time spent at home and
the unemployment rate. Our simple analysis also
shows that industry composition is not driving
the relationship between time spent at home
and unemployment. This reinforces the idea
that lockdown measures have direct effects on
unemployment.
There are several other forces that could affect
the interpretation of the correlation between
time spent at home and unemployment that we
do not consider here. For example, as we studied
in a previous report, population density could
explain spatial variation in time spent at home.
Another option is that there is spatial variation in
attitudes toward risk. For example, if the popula-

Figure 4: Change in Unemployment Rate and “Controlled” Time Spent at Home, Across States

Difference in Unemployment Rate
Between February and June

20

15

10

5

0

Weighted by Popula�on
Unweighted
-100

0

100

200

300

Average Growth in Time at Home between March 2 and June 30,
Controlling for Share of Employment from Highly Affected Industries

Sources: Bureau of Labor Sta�s�cs, SafeGraph, WalletHub

Concluding Remarks
In this article, we studied the effect of lockdown
measures on unemployment. Using time spent at
home — which is a broad measure of mobility —
and spatial variation, we find a positive relationship

tion in states with a higher increase in time spent at
home is also more risk-averse, then the increase in
unemployment may not be fully attributed to the
extent of the lockdown.
Page 4

Marios Karabarbounis is an economist, Reiko Laski
and James Lee are research associates, and Nicholas
Trachter is a senior economist in the Research Department at the Federal Reserve Bank of Richmond.
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 5


Federal Reserve Bank of St. Louis, One Federal Reserve Bank Plaza, St. Louis, MO 63102