BLS WORKING PAPERS

U.S. Department of Labor

U.S. Bureau of Labor Statistics

Office of Employment Research and Program Development

Labor Market Effects of Local Spread of COVID-19

Michael Dalton, U.S. Bureau of Labor Statistics

Working Paper 524

June 2020

JEL codes: J01, J21, J63

Keywords: COVID-19, coronavirus, recession, unemployment
Labor Market Effects of Local Spread of COVID-19∗

Michael Dalton†

June 5, 2020

Abstract

This paper explores the short-term local labor market impact of the spread of COVID-19 in the

United States, using the Current Employment Statistics survey and the Current Population Survey

microdata. It uses the longitudinal aspect of both these surveys to measure changes in employment

for business establishments and household members. I match the survey respondent to the measured

local incidence of confirmed COVID-19 cases, using confidential information on county of location,

to estimate the impact of the local incidence of the virus, after controlling for multiple measures of

government intervention. I find the greatest declines in employment in counties with higher incidence

of COVID-19. These effects vary by industry: leisure / hospitality and other services have large

declines in employment relative to the effect of the incidence of the virus, while the employment

decline in construction and transportation and warehousing depends more on the local incidence of

the virus. Finance / insurance, a very telework-friendly industry, is unaffected by the incidence of

the virus. These short-term employment effects have implications for future employment patterns

as government restrictions are relaxed and business owners begin to decide whether to open their

businesses while the virus is still active in the United States.

JEL codes: J01, J21, J63

Keywords: COVID-19, Coronavirus, Recession, Unemployment

† Bureau of Labor Statistics, 2 Massachusetts Ave NE, Washington, DC, 20002, 202-691-7403, dalton.michael@bls.gov.

Opinions expressed here do not reflect those of the federal government or the Bureau of Labor Statistics.

∗Gratitude to Jeffrey Groen, Elizabeth Handwerker, Mark Loewenstein, and Anne Polivka for very helpful

comments in improving the paper.

1
1 Introduction

The spread of COVID-19 has had a sudden and historic impact on the United States labor

market. Through depressed demand from consumers choosing to stay home, government

mandates for businesses to close, supply chain and shipping disruptions, and income losses

and greater uncertainty leading to further declines demand, the negative effects on the economy are extremely broad. While the significance of the initial employment collapse is clear,

the path of the recovery is less so. We do not know how the labor market will respond as

government restrictions are lifted and businesses are urged to open and employees to return

to work while the virus continues to spread. Concerns about the safety of employees and

customers and the hesitancy of consumers to return to their prior spending patterns is creating a great deal of uncertainty about which jobs are likely to return quickly, and which

jobs are most sensitive to local ebbs and flows of virus incidence.

There is already some evidence that government shutdown orders alone do not explain

the pattern of the recession. Chetty et al (2020) find that individuals began changing behavior—with both consumer spending and employment falling—prior to state governments

issuing shutdown orders. This shows that individuals are responsive to information about

the virus, even if the government is not immediately acting. Baek et al (2020) find only 25%

of unemployment claims can be explained by the implementation of stay-at-home orders. To

the extent that living in a location where the virus is spreading makes the virus more salient

in affecting behavior, local spread may have localized impacts on employment and economic

activity. Furthermore, the incidence of the virus also affects both the perceived and real

probability of getting the virus oneself. This suggests other channels affecting employment

need to be thoroughly examined.

While population density, climate, humidity, and local government action are potential

contributing factors to the spread of the virus, the exact path of the virus through individual

locations cannot be clearly predicted. Thus, the relationship between employment change

and the timing and scale of the spread of the virus thus far can be used to study what people

and what jobs may have difficulty returning as the economy re-opens – and which jobs may

fluctuate in response to the contemporaneous local incidence of the virus.

2
To understand the relationship between employment change and the local spread of the

virus, this paper attempts to separate out the employment impact of the Covid-19 pandemic

into a local component from a more aggregate impact combining a nationwide component and

effects from local government mandates. Past and recent research has tried to estimate the

extent that labor markets are localized, as opposed to national. Manning and Petrongolo

(2017) find that policies and individual behaviors are at least partially bounded by the

borders of the local labor market, suggesting that an unexpected event, such as the broad

economic impact of the spread of the virus, will have localized effects on the labor market

separate from the effect on the entire country. Most research thus far on the labor market

impact of the virus has focused on broader impacts, which have been large: Kahn et al

(2020) estimate a 30% decline in online job postings across the United States; Loewenstein

and Dey (2020) estimate that the workers in the bottom quintile of wages make up over

half of the employment in industries particularly exposed to the virus; Barrero et al. (2020)

estimate that 42 percent of recent layoffs will result in permanent job loss. By separating

out the larger, broad effect and the effect of local government intervention from the local

effect on employment, this paper gives insight about which jobs are most at-risk of sustained

employment loss and which jobs are at-risk for more uncertainty in the coming months.

To do this, I measure the number of cases of COVID-19 per capita in a county as of the

reference period in the surveys, and combine this with longitudinal employer and employee

histories in the microdata of the Current Employment Statistics (CES) survey and the Current Population Survey (CPS). These allow me to observe changes in employment at the

establishment (for the CES) and individual (for the CPS) levels. Additionally, I construct

measures at the county level to control for government-mandated restrictions in order to

address the potentially confounding impact on employment and spread of virus. Relying on

the randomness of the timing and extent of the local spread of the virus, focusing on changes

at the microdata level, and using data from previous months, I estimate which parts of the

changes in the local labor markets can be attributed to the local spread versus the broad

economic impact and government interventions. I then use these estimates to assess how the

impact of the virus on local employment varies by industry.

3
1.1 Data

Incidence of COVID-19 at the county level is acquired from the New York Times, which

provides daily updates1. The number of cases reflects positive test results as reported from

individual states and the Center for Disease Control and Prevention (CDC). The per-capita

measure is calculated using county-level population data from the 2010 Decennial Census.

The areas with the highest incidence as of April were in the northeast, the south, dotting

around the Great Lakes, and the southwest. While the hardest hit areas tended to be densely

populated areas, there is still variation as some rural areas (e.g., Dakota county, Nebraska)

were especially hard hit, and some dense urban areas (e.g., the Bay Area) did not have a

high incidence of the virus. This geographic variation will be useful in analyzing the effects

of the virus on the labor markets.

To observe employment changes at the establishment-level, I use the Current Employment Statistics (CES)2

. This is a monthly survey of establishments that asks about number

of employees, hours worked, and aggregate earnings of workers. The CES has a panel design

that surveys most establishments for typically more than 24 consecutive months. As a result,

changes in employment from one month to the next can be estimated at the establishment

level in the CES microdata. Additionally, using these microdata allows me to match establishments with county-level COVID-19 information. The reference period for the CES survey

is the pay period that includes the 12th of the month. For this reason, I use the measure of

confirmed COVID-19 cases per capita in the county as of the last day of the week for the

most recent reference period.

The map in Figure 1 shows the change in employment across establishments within a

MSA from March to April 20203. Some patterns are readily apparent: darker shading in

the northeast, around the Great Lakes, the southern tip of Florida, the southwest, and a

few patches along the west coast. Comparing to a map of COVID-19 incidence (Figure B1),

there is a clear correlation between higher incidence of COVID-19 and greater decline in

1 Available at https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html (The New York

Times, 2020). Last accessed May 31, 2020.

2For information about the CES, visit https://www.bls.gov/web/empsit/cesprog.htm.

3MSA definitions based on Bureau of Labor Statistics definitions: https://www.bls.gov/oes/current/msa_def.htm

4
Figure 1: Change in employment in MSAs in April 2020, Current Employment Statistics

employment at the establishment level.

To examine employment effects for individuals, I use the Current Population Survey

(CPS). This is a nationally representative monthly survey of 60,000 households, asking a

variety of questions that cover demographic and labor force concepts. The labor force questions are also primarily focused on the week that including the 12th day of the reference

month. Like the CES, the CPS has a panel aspect. In any given month, approximately 75%

of the households sampled were also sampled the previous month. This longitudinal aspect

allows for observing transitions in labor market status from one month to the next4. I match

households to their county of residence to estimate how the local incidence of COVID-19 is

related to labor force transitions for these surveyed individuals.

A confounding aspect in studying the relationship between COVID-19 incidence and

changes in employment is the extent of simultaneous government intervention. While it is

difficult to get a consistent quantitative measure of the extent of any intervention across

counties and states, the New York Times provides some data about "stay-at-home" orders

for states and counties5, based on primary sources from governments. Using this information,

4For more information about the CPS, visit https://www.bls.gov/cps/documentation.htm

5 An archive of stay-at-home orders are reported here: https://web.archive.org/web/*/https://www.nytimes.com/interactiv

stay-at-home-order.html

5
the date of first stay-at-home order is determined for each county6

. For 32 states, the first

stay-at-home order is the same for all counties, as it was mandated at the state level. For the

remaining states, some counties acted before the state as a whole. For instance, Oklahoma

never made an official stay-at-home mandate, though some counties within Oklahoma chose

to make these orders separately. The number of weeks between the first government mandate

for the county and the reference period for the surveys is the key control for local shutdowns7.

Timing of stay-at-home orders may not be the only dimension that governments have restricted economic behavior. I add an additional measure of intervention based on anonymized

data published by Google at the county level that measures mobility 8. The data made publicly available is a measure of the number of visits to specific types of establishments by day,

relative to a benchmark set in January 2020. This paper uses the retail space (e.g., visits to

movie theaters, restaurants, museums, and shopping centers) mobility measure. The sample

of individuals used to create the data are people who choose to have their location history

on while using Google services. The representativeness of the sample is not defended by

Google, and due to the anonymized nature of the data, it cannot be validated as representative of the population. However, the overall patterns observed in the data are intuitive,

suggesting this may be a good approximation for movement relative to businesses. There is

an across-the-country steep decline in visits to retail spaces, with an even larger decline in

many areas that had a higher incidence of the virus. By combining measures of government

limiting movement from home (length of stay-at-home order) and limiting businesses (decline in visits to retail), both sides of the potential impact from government interventions on

employment are being captured9.

6 Stay-at-home orders are treated the same as “shelter-in-place”, “lockdown”, and “shutdown” orders.

7Figure B2 shows the number of days prior to April 18th that the county had a stay-at-home order.

8Google LLC "Google COVID-19 Community Mobility Reports". https://www.google.com/covid19/mobility/

Accessed: May 31st, 2020.

9Figure B3 shows the mobility measure as of March 14th, 2020 (Panel A) and April 18th, 2020 (Panel B).

6
Figure 2: Decline in Employment in April 2020 as COVID-19 Increases, by Industry

2 Labor Market Consequences of Spread of Virus

2.1 Analysis of Current Employment Statistics Microdata

2.1.1 Graphical Analysis

In each panel of Figure 2, there are plots of four separate months of data from the CES,

with the horizontal axis showing the number of confirmed cases of COVID-19 per 100,000

residents at the county level, and the vertical axis showing the average percentage change

in employment across establishments in that bin, weighted by establishment employment.

Including April 2019 helps identify any monthly seasonal effects that may confound the

analysis, and April 2019 works well as a control month as it definitively is unaffected by

COVID-19.

7
For most industries, there is an overall shift downwards of the April 2020 line relative

to the other three months of data. This shift can be thought of as the broad economic

impact of COVID-19 faced by all geographies, and the gradient of the line as the additional

impact due to local incidence of the virus. However, there are confounding factors, such

as government intervention, that muddle the interpretation of the gradient. This will be

addressed more directly in the following section and these graphs are meant for illustrative

purposes of overall patterns.

Figure 2 makes clear that the effects of the pandemic on employment thus far are very

heterogeneous. Leisure/Hospitality has the biggest shift downward, bottoming out at 49%

decline in at the highest incidence rate in April 2020. The other services industry (NAICS

code 81) also has a big shift downwards, and, within this sector, the industries with the largest

declines in employment are nail salons, personal care services, beauty salons, and barber

shops. Manufacturing and construction both have notable downward shifts and a negative

trend as the incidence rate increases. Retail trade and transportation and warehousing do

not appear to have much of an overall shift but do have a trend downward as the incidence

rate increases. The finance and insurance sector appears unaffected in both shift and trend.

2.1.2 Econometric Model

The next step in the analysis is examining these patterns in a more rigorous regression

framework to address potentially confounding factors. The primary confounding factor is

that government interventions are likely to impact both the economy and the spread of

COVID-19. For instance, if an area were to shut down completely and with full compliance

from the residents, we may expect the incidence to be low in that area while employment

would be drastically reduced, too. In this example, the concepts are statistically related,

but the incidence of the virus is not the immediate causal relationship. I include 2 measures

to control for government interventions: the county-level measure of number of days since

a stay-at-home order went into effect is included in the model and the mobility measure on

change in visits to retail spaces. All official stay-at-home orders occurred after the March

2020 reference period, so will only have positive values for the April 2020 respondents. For

the Google mobility measure, April 2019 is assumed to have no change relative to Google’s

8
chosen benchmark of January 2020. These measures work as good proxies for government

intervention because they cover both the impact on individuals’ movement from home (stayat-home orders) and the consumers relationship to businesses (visits to retail). One thing to

note is that the mobility measure may actually be controlling for some of the effect of the

incidence of the virus on economic activity, which will attenuate the estimated effect of the

incidence downward. Holding government intervention constant, one channel through which

living in an area with a higher incidence of the virus can affect employment would be a drop

in demand resulting from consumers choosing not to visit the stores. This illustrates the

difficulty in estimating this impact: the virus has hit so quickly that it is difficult to untangle

these different causal pathways. For this reason, the analysis errs on the side of caution of

including the mobility measure that may attenuate the results on local incidence.

A second confounding factor is that certain geographic factors may affect both the spread

of the virus and the change in employment (e.g., population density, climate, industry mix

within sectors, and other unobserved characteristics). Figure B4 already illustrates that

the differences between months of data cannot be explained fully by common patterns in

geographic areas that had a higher incidence rate as of April 2020 since April 2019 does

not reflect the same trend as April 2020. MSA fixed effects in the regresson control for

both observed and unobserved factors that are common across areas, and will make sure the

relationship between change in employment and incidence of virus is not solely explained

by local characteristics of MSAs. Furthermore, dummies for each month in the analytical

sample and additional controls for firm size, industry, population of county and whether the

establishment is part of a multiunit firm are included.

∆employmentict =

X

t

αt + βCOV IDct+

γ0DaysSinceM andatect + γ1DaysSinceM andatec

∗t

δMobilityct+

X

m

θmI(c ∈ m) + Xictσ + ict

(1)

Equation 1 shows the regression model to be estimated, where

9
• ∆employmentict is the change in employment from the previous month for establishment

i in county c at month t,

• αtis the month-specific national effect for month t

10

,

• COV IDct is the county-level COVID-19 number of cases for month t per 100 residents,

• DaysSinceM andatect is the number of days since a stay-at-home mandate in county c

as of month t,

• DaysSinceM andatec

∗t

is the average number of days since a stay-at-home mandate in

the counties within 30km of county c as of month t,

• Mobilityct is the Google mobility measure as of the reference date for month t, which

can take a value between 100 and -100 and is a benchmark relative to visits to retail

spaces in January 2020,

• I(c ∈ m) represents an indicator for county c being in MSA m,

• Xict represents the vector of control variables specific to establishment i in county c in

month t, and

• ict is the idiosyncratic error term.

The key estimate of interest is β. The local government intervention effects are captured

by γ0 and δ, and the broad national impact is captured by αApril2020. For the regression,

COV IDct is the number of cases in county c in the 4 weeks leading up to (and including)

the end of the reference period in month t. The interpretation of β is relative to recent cases,

which would allow for a county to have an improving (or worsening) incidence rate for future

months when applying the estimates to new data.

For Mobilityct, there are some gaps in the data for some counties due to lack of available

data to construct the anonymized measures by Google. The rolling average for a county

across the nearest date to the reference period is used. For counties with no data near the

reference period, the state average on that date is used to fill the missing value for the county.

10αApril2019 is left out of the estimation so that the coefficients αt estimates are relative to April 2019.

10
2.1.3 Regression Results

Table 1 shows the results for the full sample and by industry of the full model depicted

in equation 111. The sample includes establishments responding to the CES in consecutive

months for April 2019, February 2020, March 2020, and April 2020. Each row in the table

shows results from a separate regression, where the goal is to estimate the slope on per capita

incidence of COVID-19 at the county level (β) and compare it to the effects from the size of

the national effect (αApril2020) and the proxies for local government intervention effects (γ0

and δ). The aggregate effect shown in the second column is for an establishment in April

2020 and assumes the establishment is in a county that has had 3 weeks of a shutdown and

30% fewer visits to retail spaces relative to the baseline. The p-value underneath the effect

is for the F-test of the linear combination of the coefficients being different from zero:

αApril2020 + 3 × γ0 + (−30) × δ = 0 (2)

For the full sample, an establishment in a county going from 1% of the population with

a confirmed case of COVID-19 in the previous month to 0% will have a 4.3% increase in

employment. The estimated aggregate non-incidence effect gives a decline in employment

of 7.4%. A 4.3% improvement in employment relative to the 7.4% aggregate, non-incidence

effect is very large, economically significant, and statistically significant. This shows the important role of controlling local virus transmission in protecting against further employment

declines.

11For each month, the Saturday of the week of the reference period (12th of the month) is used to determine

the number of COVID-19 cases. For April 2019, 0 cases are reported. As of February 15th 2020, 9%

of establishments were in a county with at least one confirmed case. As of March 14th 2020, 57% of

establishments were in a county with at least one confirmed case. As of April 18th 2020, 99% of establishments

were in a county with at least one confirmed case.

11
Table 1: Heterogenous Impact of Local of Spread of Virus, Current Employment Statistics

survey

Dep. Var: Percent Change in Employment from Previous Month

Sample...

Effect of

COVID-19

Cases per 100

in County

Aggregate

Impact Separate

from Incidence

Sample Size

...Full Sample

-0.043

(0.0022)

[<.001]

-0.074

[<.001] 735725

...Leisure/Hospitality

-0.056

(0.0091)

[<.001]

-0.38

[<.001] 63375

...Retail Trade

-0.041

(0.0034)

[<.001]

-0.01

[<.001] 254757

...Transportation and

Warehousing

-0.039

(0.0089)

[<.001]

-0.036

[<.001] 30311

...Health Care

-0.031

(0.0072)

[<.001]

-0.1

[<.001] 52363

...Manufacturing

-0.082

(0.016)

[<.001]

-0.096

[<.001] 28824

...Professional Business Services

-0.026

(0.0093)

[0.0056]

-0.053

[<.001] 37132

...Other Services

(except Public Admin)

-0.056

(0.017)

[0.0011]

-0.23

[<.001] 18242

...Construction

-0.13

(0.009)

[<.001]

-0.1

[<.001] 54187

...Finance and Insurance

0.0011

(0.0019)

[0.58]

-0.011

[<.001] 98980

Each row is a separate OLS regressions, with robust standard errors are in parentheses and p-values are

in square brackets. Sample includes all establishments with valid responses for the variable in both the

response month and previous month from April 2019, February 2020, March 2020, and April 2020. Included

in each regression are the controls for government interventions. The second results column represents the

estimated employment effect for a county with 3 weeks of stay-at-home orders and a 30% decline in visits

to retail establishments in April 2020. Additional controls are fixed effects for MSA, firm size, industry,

whether the establishment is part of a multi-unit firm and population of county.

12
The industries with the largest magnitude drop in employment separate from incidence

are leisure / hospitality and other services with 38 and 23% declines in employment in April

2020, respectively. The largest slope with respect to the incidence of the virus is construction,

with a coefficient translating to a 13% decline in employment for every 1% of the county

population that is confirmed with the virus in the preceding month. The only industry with

a null effect on the incidence of the virus is finance / insurance. Additionally, finance is

the only industry where the effect of number of days since a mandate also has a null effect

on employment. One potential explanation for these results is that telework is common in

finance, resulting in less of an impact from the virus itself, and any employment loss in April

2020 is based around broad economic trends for this industry. Construction, on the other

hand, is not telework-friendly, so this may partly explain why it has the largest response to

local virus incidence. Also, some construction jobs may involve being in close proximity to

customers’ homes (for instance, home remodeling), and customers choose to forgo these jobs

to avoid contact.

Comparing the ratio of the local incidence effect relative to the separate, aggregate employment impact provides insight into the industries that may return quicker as the virus

subsides locally, but also means there is more uncertainty looking into the future as the employment will fluctuate in sync with the return of the virus. Creating the ratio by dividing

the first column estimates with the second column estimates, construction, transportation

and warehousing, and retail trade have a ratio greater than 1. This means 1% of the county’s

population having confirmed cases of COVID-19 will have a bigger impact on the industry’s

employment than the separate aggregate effect. It is likely that employees in these industries

may benefit the most from getting the spread of the virus under control locally.

2.2 Employment Changes Within Households: Analysis of Current Population

Survey microdata

Similar patterns between the household survey and establishment survey are observed. Figure 3 shows the percentage of respondents who were employed in the previous month and

were then on layoff in the current month12. On layoff is the most common labor force tran12On layoff also includes respondents who were listed as “employed but absent” and gave a reason for their

absence as some “other” reason. In April 2020, a number of respondents gave a response that COVID-19

13
Figure 3: More Workers Leaving Employment as COVID-19 Spreads, by Industry

sition state for those leaving employment in the previous month in the CPS. A large shift

upwards can be seen for April 2020 for many industries. Similar patterns emerge as in the

CES: construction has a steep gradient transitioning from employment to on layoff as the

county has more COVID-19 cases, and transportation and warehousing also shows increasing

loss of employment as the incidence increases. The biggest upward shifts in lines for April

2020 are in leisure / hospitality and other services.

An analogous regression model to that of equation 1 is used for the CPS, instead modeling

the likelihood that respondent i employed in the previous month will be on layoff in the

current month. For these regressions, I use data from the same months as the CES– April

2019, February, March, and April 2020, restricting the data to respondents 16 and older

was the reason for their absence, which should include the respondent in the “on layoff” category. In order

to not miss this group in the analysis, they are included for all months with the “on layoff” unemployed

respondents.

14
employed in the previous month13. The only other difference are the basic controls included

in the Xict of equation 1: person-level controls for gender, race, age, education, occupation,

industry, in addition to MSA fixed effects and county population.

13 8%, 57%, 99% of the respondents were in a county with at least one confirmed COVID-19 case in

February, March, April 2020, respectively.

15
Table 2: Heterogenous Impact of Local of Spread of Virus, Current Population Survey

Dep. Var: Y/N Respondent is on Layoff

Sample...

Effect of

COVID-19

Cases per 100

in County

Aggregate

Impact Separate

from Incidence

Sample Size

...Full Sample

0.018

(0.0061)

[0.0036]

0.14

[<.001] 149291

...Leisure/Hospitality

0.0004

(0.029)

[0.99]

0.39

[<.001] 11951

...Retail Trade

0.025

(0.029)

[0.39]

0.16

[<.001] 14596

...Transportation and

Warehousing

0.15

(0.042)

[<.001]

0.11

[<.001] 6641

...Health Care

0.014

(0.013)

[0.29]

0.1

[<.001] 20373

...Manufacturing

-0.0021

(0.022)

[0.92]

0.14

[<.001] 14344

...Professional Business Services

0.036

(0.02)

[0.07]

0.064

[<.001] 11848

...Other Services

(except Public Admin)

-0.033

(0.043)

[0.45]

0.28

[<.001] 6169

...Construction

0.13

(0.036)

[<.001]

0.12

[<.001] 9888

...Finance and Insurance

-0.00041

(0.0092)

[0.96]

0.043

[<.001] 6990

Each row is a separate OLS regressions, with robust standard errors are in parentheses and p-values

are in square brackets. Sample includes all establishments with valid responses for the variable in

both the response month and previous month from April 2019, February 2020, March 2020, and

April 2020. Included in each regression are the controls for government interventions. The second

results column represents the estimated employment effect for a county with 3 weeks of stay-at-home

orders and a 30% decline in visits to retail establishments in April 2020. Additional controls are fixed

effects for MSA, gender, age, education, occupation, industry, and population of county. Results are

weighted by CPS person weights.

16
Table 2 shows the results from the separate regressions by industry of employment in

the previous period, analogous to the CES results in Table 1. Similar to the CES results,

the biggest aggregate effects separate from the incidence are in the leisure/hospitality and

other services industries. Examining the gradient on the incidence of the virus by industry,

construction again has one of the larger effects in the CPS data: an increase of 1% of the

county population confirmed getting the virus in the month leads to an 13% increase in the

probability of going from employed to on layoff for workers in construction; this matches

the 13% decline in employment for establishments in this industry observed in Table 1. In

addition to construction, transportation and warehousing also has a large negative impact

on employment of local incidence relative to the aggregate effect in both the CES and CPS,

with ratios larger than 1. Lastly, finance / insurance is the only displayed industry with a

null impact of the spread of the virus in both the CES (Table 1) and CPS (Table 2).

3 Discussion

The common results between the CES and CPS tell an important story: not only was there

a broad negative impact on employment due to COVID-19 in April 2020, but this impact

was worse in local labor markets with a higher incidence of the virus. Variation in these

patterns between industries provides suggestive evidence about which jobs may return in

the short- or medium-term as the spread of the virus continues to be uneven throughout the

country. Leisure / hospitality and other services are the sectors with the highest employment

decline in employment relative to the effect of the incidence of the virus, and are likely

to have a slower recovery of employment even as the incidence is reduced and lockdowns

begin to be lifted. Some industries may have more uncertainty attached to them as their

employment is dependent upon the local spread of the virus – construction and transportation

and warehousing have the largest negative effect of the local incidence of the virus relative

to the aggregate, separate employment impact in both the CPS and CES. The industry with

the smallest employment declines –in terms of local impacts – is finance / insurance.

Dingel and Neiman (2020) estimate that finance / insurance and professional services

have some of the highest proportion of telework-eligible employees . Telework allows the

17
employees to continue doing their jobs without compromising the health of employees or

customers, and are less likely to be impacted by government stay-at-home orders. As seen in

both data sources, these industries have been minimally impacted. As long as the demand

holds steady, the impact of COVID-19 will continue to have the smallest effect for these

industries, though uncertainty remains surrounding demand the future of demand.

Leisure / hospitality and other services are industries that are cyclical, as demand tends

to decline for these industries during recessions. A decline in employment for these industries

can be partially explained by a drop in demand resulting from more income uncertainty of

consumers as confidence in the economy has taken a sharp decline14. Also, both of these

industries rely heavily on in-person interactions with the customers. With the growing

health concerns and struggle to find ways to maintain the customer base in a safe way, these

industries may be slower to recover even as the virus is more under control. The amount of

demand that returns to industries may be a good indication about the duration of impact

that COVID-19 will have on the economy.

Construction and transportation and warehousing may be more prone to having individual worksites shut down due to illness, instead of being shut down across the entire industry.

Though these industries are not very telework friendly, for many of the occupations, employees can primarily do the same jobs without direct contact with customers. The risk is

primarily contained to only employees interacting with one another, which may explain why

these jobs have more resiliency when the incidence of the virus is low.

This paper estimates the relationship between local employment changes and the number

of local confirmed cases of Covid-19. The interpretation of these coefficients may change

as testing becomes more widespread and the proportion of confirmed cases to actual cases

approaches 1. As a result, this methodology may over-estimate the relationship between

employment impacts and cases of the virus. As of April 2020, many cases are unconfirmed

because a number of people who are infected are not getting tested, or are asymptomatic and

never seek out testing. Thus, the coefficients presented here should be considered as an upper

bound for the impact of additional confirmed cases on employment, as testing becomes more

14 https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/survey-us-consumersentiment-during-the-coronavirus-crisis

18
widespread and a higher proportion of people who have the virus are tested. The extent

of this estimation issue is not clear15. Conversely, it is possible that the behavioral effects

are strictly based on salience of COVID-19 – regardless of the number of unconfirmed cases,

more confirmed cases is what drives behavioral changes, in which case, the coefficients are

not overestimated.

Going forward, examining other dimensions of business decision making in terms of how

the payroll responds to changes in COVID-19 will be valuable to understand the dynamics

that are occurring in the labor market. Additionally, analyzing how these dynamics relate

to firm size and local geographic concentration of industries will help elucidate the future of

the labor market through the remainder of the pandemic and after.

References

[1] Baek, ChaeWon, Peter B. McCrory, Todd Messer, and Preston Mui (2020), “Unemployment effects of stay-at-home orders: Evidence from high frequency claims data”,

IRLE Working Paper No. 101-20.

[2] Barrero, Jose Maria, Nick Bloom and Steven J. Davis, “COVID-19 Also a Reallocation

Shock”, 2020, Working paper.

[3] Blanchard, Olivier Jean, and Lawrence F. Katz. 1992. “Regional Evolutions.” Brookings Papers on Economic Activity (1): 1–61,

[4] Chetty, Raj, et al. "Real-Time Economics: A New Platform to Track the Impacts

of COVID-19 on People, Businesses, and Communities Using Private Sector Data."

(2020).

[5] Dey, Matthew, Harley Frazis, Mark Loewsenstein, Hugette Sun, “Ability to Work from

Home: Evidence from Two Surveys and Implications for the Era of COVID-19”, 2020.

[6] Matthew Dey and Mark A. Loewenstein, "How many workers are employed in sectors

directly affected by COVID-19 shutdowns, where do they work, and how much do

they earn?," Monthly Labor Review, U.S. Bureau of Labor Statistics, April 2020,

https://doi.org/10.21916/mlr.2020.6.

[7] Dingel, Jonathan and Brent Neiman, “How Many Jobs Can be Done at Home?”, No.

wp26948. National Bureau of Economic Research, 2020.

[8] Kahn, Lisa B., Fabian Lange, and David G. Wiczer. Labor Demand in the time of

COVID-19: Evidence from vacancy postings and UI claims. No. wp27061. National

Bureau of Economic Research, 2020.

15Estimates of the gap is between confirmed positive cases and actual infections vary widely. For example,

one estimate from a company that tested raw sewage for COVID-19 says that there was about a 4:1 ratio

of infections to confirmed positive tests https://www.sun-sentinel.com/coronavirus/fl-ne-coronavirus-miamidade-tracking-spread-via-sewage-20200501-2fwgszzckjfc3get3frfrhw33a-story.html

19
[9] Manning, Alan, and Barbara Petrongolo. "How local are labor markets? Evidence from

a spatial job search model." American Economic Review 107.10 (2017): 2877-2907.

20
Appendix to Dalton (2020) “Labor Market Effects...”

A Additional Tables

These tables show the building up of the full model depicted in 1. The first row restricts all

coefficients to zero except for β. The second row restricts the coefficients on γ0, γ1, and δ to

be zero. The third row includes γ0 and γ1 in the estimates, and the following row estimates

the full model. As a robustness check, the last row removes the New York City-specific

counties.

In Table A1, the coefficient for the national effect in April 2020 is cut in half when

controlling for number of days since the county shut down, and then cut in half again once

controlling for state-COVID-period fixed effects. This implies a large portion of the impact

of the national trends are due to government orders. It then declines by about 70% when

the mobility measure is estimated in the model. The coefficient on the local incidence of the

virus declines significantly with the inclusion of the baseline controls, but has a more modest

decline when the government intervention controls are added. In the full model, there is

still an economic and statistically significant (p-value of less than .1%) impact of the local

incidence of the virus on employment.

Table A2 is analogous to Table A1 but using the CPS data. The national coefficient

decreases significantly when adding in government intervention measures, though not as

steep of a decline as in Table A1. The coefficient on incidence of COVID-19 has a much

steeper decline when adding in demographic controls than observed in the CES data in Table

A1. The coefficient maintains statistical significance at the 1% level. The magnitude of the

coefficient on incidence increases when the New York City observations are removed, though

the standard error also increases, pushing the statistically p-value just outside the range of

5% statistical significance.

1
Table A1: Impact of Local of Spread of Virus, Current Employment Statistics survey

Dep. Var: Percent Change in Employment from Previous Month

Model...

Effect of

COVID-19

Cases per 100

in County

Dummy

effect of

April 2020

(relative to

April 2019)

# of Weeks

County

Shutdown

Prior to 4/18

Google

Mobility

Measure

of Retail

Sample Size

...Univariate

OLS

-0.13

(0.0021)

[<.001]

735725

...OLS w/

Basic

Controls

-0.058

(0.0021)

[<.001]

-0.083

(0.00084)

[<.001]

735725

...OLS w/

Shutdown

Measure

-0.051

(0.0021)

[<.001]

-0.038

(0.0021)

[<.001]

-0.015

(0.00067)

[<.001]

735725

...Full

OLS Model

-0.043

(0.0022)

[<.001]

-0.016

(0.0023)

[<.001]

-0.011

(0.00069)

[<.001]

0.00082

(4e-05)

[<.001]

735725

...Full Model

without NYC

-0.048

(0.0026)

[<.001]

-0.016

(0.0023)

[<.001]

-0.011

(0.0007)

[<.001]

0.00082

(4.1e-05)

[<.001]

723637

Each row is a separate OLS regressions, with robust standard errors are in parentheses and p-values are

in square brackets. Sample includes all establishments with valid responses for the variable in both the

response month and previous month from April 2019, February 2020, March 2020, and April 2020. Basic

controls are fixed effects for MSA, firm size, industry, whether the establishment is part of a multi-unit

firm and population of county. Results are weighted by establishment employment.

2
Table A2: Impact of Local of Spread of Virus, Current Population Survey

Dep. Var: Y/N Respondent is on Layoff

Model...

Effect of

COVID-19

Cases per 100

in County

Dummy

effect of

April 2020

(relative to

April 2019)

# of Weeks

County

Shutdown

Prior to 4/18

Google

Mobility

Measure

of Retail

Sample Size

...Univariate

OLS

0.15

(0.0069)

[<.001]

149291

...OLS w/

Basic

Controls

0.024

(0.0058)

[<.001]

0.13

(0.0022)

[<.001]

149291

...OLS w/

Shutdown

Measure

0.021

(0.006)

[<.001]

0.093

(0.0043)

[<.001]

0.015

(0.0015)

[<.001]

149291

...Full

OLS Model

0.018

(0.0061)

[0.0036]

0.084

(0.0049)

[<.001]

0.014

(0.0015)

[<.001]

-0.00034

(8.1e-05)

[<.001]

149291

...Full Model

without NYC

0.023

(0.012)

[0.052]

0.083

(0.005)

[<.001]

0.014

(0.0015)

[<.001]

-0.00034

(8.2e-05)

[<.001]

143251

Each row is a separate OLS regressions, with robust standard errors are in parentheses and p-values are

in square brackets. Sample includes all civilians employed in the previous month from the CPS months

April 2019, February 2020, March 2020, and April 2020. Basic controls in the regressions are fixed effects

for MSA, gender, age, education, occupation, industry, and population of county. Results are weighted by

CPS person weights.

B Additional Figures

3
Figure B1: COVID-19 incidence as of April 18th, 2020, total confirmed cases per 100,000

population in county

Figure B2: Most variation in stay-at-home orders are at the state level

4
Figure B3: Google mobility data show a steep decline in visits to retail spaces from March

to April 2020, relative to the baseline in January 2020

5
Figure B4: Decline in Employment in April 2020 as COVID-19 Increases, Current Employment Statistics

Figure B5: More Workers Leaving Employment as COVID-19 Spreads, Current Population

Survey

6