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Impact of COVID-19 on industries

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Impact of COVID-19 on industries

Abstract and Figures

The novel coronavirus disease 2019 (COVID-19) in the U.S. is part of the worldwide pandemic of this extremely infectious disease. As of November 2020, the U.S. has experienced 10,200,000 cases and 238,000 COVID-19 fatalities. The pandemic is still ongoing, with around 50,000 new cases and 800 deaths each day. A recent U.S. based report shows that the economic loss due to COVID-19 will be more than $22 trillion. Due to the lockdown and the urgency of a safe re-opening, the impact on different industries varies from state to state. A robust and granular level of analysis is needed to understand the impact of COVID-19 on various industries. This study collected data (before and after COVID-19) from the U.S. Bureau of Labor Statistics (USBLS) to perform a quantitative analysis. Using current employment statistics (CES) data from the USBLS, an observational before-after analysis was conducted by using the difference-in-difference method. The results show the highly-impacted industries, and the temporal patterns of the impact are also evaluated.
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COVID-19 in the Environment © 2022 Elsevier Inc.
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CHAPTER 9
Impact of COVID-19 on industries
Subasish Das
Texas A&M Transportation Institute, College Station, TX
9.1 Introduction
The United States (US) is still in the midst of one of the largest pandemics in the his-
tory of this country. As of November 5, 2020, the U.S. death rate had reached approxi-
mately 720 deaths per million people, which is the thirteenth-highest rate worldwide.
Throughout March and early April of 2020, several states, cities, and counties enforced
‘lockdown’ or ‘stay at home’ quarantine orders on their populations to reduce the spread
of the virus. The recent Coronavirus, the SARS-CoV-2 (severe acute respiratory syn-
drome coronavirus 2) or COVID-19, has made a long-term financial impact on U.S.
industries. According to the April 2020 Report of the U.S. Bureau of Statistics (USBLS,
2020a), “total nonfarm payroll employment fell by 20.5 million in April, and the unem-
ployment rate rose to 14.7 percent. The changes in these measures reflect the effects of
the coronavirus (COVID-19) pandemic and efforts to contain it. Employment fell sharply
in all major industry sectors, with particularly heavy job losses in leisure and hospitality.
The global pandemic from the spread of COVID-19 has generated an unprec-
edented amount of uncertainty due to the massive growth of hospitalizations, deaths,
and quarantine related crises (Saadat et al., 2020). COVID-19 is now considered to be
the greatest economic threat since the Great Depression; it has increased mortality and
morbidity rates, negatively affected mental health conditions, and had an enormous,
negative economic impact.A recently published journal estimated the overall cost of
the virus would be greater than $16 trillion (Cutler and Summers, 2020). This study
aims to perform a robust before-after analysis by collecting historical employment data
from the U.S. Bureau of Statistics. The findings of the study will be beneficial in under-
standing the temporal influence of this pandemic on several industrial sectors in the US.
9.2 Literature review
There has recently been a tremendous growth of COVID-19 related published articles.
However, there has been a limited number of studies conducted about the impact of
COVID-19 on different industries in the U.S. and other countries.
By examining the U.S. stock market data, Baek et al. (2020) determined a regime
change in market volatility due to the COVID-19. The results indicate a substantial
increase in total risk for the US stock market. Baker et al. (2020) also stated that
DOI: https://doi.org/10.1016/B978-0-32-390272-4.00004-X
Impact of COVID-19 on Socio-Economic Environment
192
COVID-19 had the greatest impact on stock market volatility in the history of pandemics.
Maneenop and Kotcharin (2020) explored the short-term impact of the COVID-19 on
52 listed airline companies by performing an event study methodology. The results show
that traders in Western countries react more responsively based on data than the rest of
the world. Kim etal. (2021) examined the impact of Asian American employment due to
the COVID-19 outbreak. The empirical results showed that Asian Americans are more
adversely affected by this pandemic than other racial groups. Using Household Pulse
Survey data (Ganson etal., 2020), estimated the associations between job insecurity and
depression thresholds among U.S. young adults during the COVID-19 pandemic. Dang
and Viet Nguyen (2020) explored the impact of COVID-19 on gender inequality by
using a six-country survey. The results showed that women are 24 percent more likely
to lose their job due to the COVID-19 outbreak. Huang etal. (2020) examined the
impact of the pandemic on the hospitality industry. The findings showed that the rise
of the pandemic is highly associated with the continued deterioration of this industry.
Cutler and Summers (2020) estimated a $16 trillion (nearly 90 percent of the annual
gross domestic product or GDP of the US) economic loss due to the outbreak. Since
the beginning of the outbreak in March 2020, 60 million people in the U.S. filed for
unemployment insurance. Although a human life is invaluable, economists employ a
measure known as ‘statistical lives’ to estimate the value of a human life. This study pro-
vides an estimate of cumulative financial costs due to the pandemic by evaluating the
lost output and the decline of health conditions.
9.3 Methodology
9.3.1 Data collection
The USBLS Current Employment Statistics (CES) survey is the most trustworthy
source of employment related data on the U.S. labor market (Ghanbari and McCall,
2016). The CES survey collects data, an indicator of Principal Federal Economic
Indicators, each month on employment frequencies, hours of employment, and earn-
ings. The USBLS publishes these preliminary estimates at the national level by 12
major industries (in addition of four broad categories), usually on the first Friday of
the subsequent month, with amendments to be published in the following two suc-
ceeding months (USBLS, 2020b). To achieve reliable data, preliminary data published
for August to October in 2020 were not used. This study collected data for April to
July of 2018 and 2019, and January to July of 2020. The industries included in the CES
data are as follows:
1. CES7000000001: leisure and hospitality (seasonally adjusted)
2. CES8000000001: other services (seasonally adjusted)
3. CES1000000001: mining and logging (seasonally adjusted)
4. CES5000000001: information (seasonally adjusted)
5. CES4200000001: retail trade (seasonally adjusted)
Impact of COVID-19 on industries 193
6. CES6000000001: professional and business services (seasonally adjusted)
7. CES6500000001: education and health services (seasonally adjusted)
8. CES2000000001: construction (seasonally adjusted)
9. CES3000000001: manufacturing (seasonally adjusted)
10. CES9000000001: government (seasonally adjusted)
11. CES5500000001: financial activities (seasonally adjusted)
12. CES1021100001: oil and gas extraction (seasonally adjusted)
13. CES0500000001: total private (seasonally adjusted)
14. CEU0500000001: total private (not seasonally adjusted)
15. CES0000000001: total nonfarm (seasonally adjusted)
16. CEU0000000001: total nonfarm (not seasonally adjusted)
Table 9.1 lists the measures of number of jobs by the sixteen sectors. The difference
between job frequencies of 2018–19 (the mean of 2018 and 2019 job data by sectors from
April to July) and 2020 (April to July) shows that majority of the sectors were impacted
during the COVID-19 pandemic (in exception of oil and gas extraction sector). The
Table 9.1 Measures of Number of Jobs by Dierent Sectors (April to July).
Code Industry Year Apr May Jun Jul
CES7000000001 Leisure and
hospitality
2018–19 (mean) 16,362 16,391 16,418 16,439
2020 8549 9954 11,933 12,566
Diff 7813 6437 4485 3873
Percent Change 47.75 39.27 27.32 23.56
CES8000000001 Other services 2018–19 (mean) 5852 5856 5870 5867
2020 4571 4816 5182 5340
Diff 1281 1040 688 527
Percent Change 21.89 17.76 11.72 8.98
CES4200000001 Retail trade 2018–19 (mean) 15,732 15,728 15,705 15,700
2020 13,287.6 13,673.5 14,531.5 14,785.4
Diff 2444.4 2054.5 1173.5 914.6
Percent Change 15.54 13.06 7.47 5.83
CES0500000001 Total private
(seasonally
adjusted)
2018–19 (mean) 126,958 127,132 127,318 127,464
2020 108,527 111,763 116,492 118,018
Diff 18,431 15,369 10,826 9446
Percent Change 14.52 12.09 8.5 7.41
CEU0500000001 Total private
(not seasonally
adjusted)
2018–19 (mean) 126,504 127,382 128,422 128,492
2020 108,159 111,866 117,311 118,808
Diff 18,345 15,516 11,111 9684
Percent Change 14.5 12.18 8.65 7.54
CES0000000001 Total nonfarm
(seasonally
adjusted)
2018–19 (mean) 149,442 149,623 149,824 149,989
2020 130,303 133,028 137,809 139,570
Diff 19,139 16,595 12,015 10,419
Percent Change 12.81 11.09 8.02 6.95
Impact of COVID-19 on Socio-Economic Environment
194
Code Industry Year Apr May Jun Jul
CEU0000000001 Total nonfarm
(not seasonally
adjusted)
2018–19 (mean) 149,380 150,187 150,835 149,733
2020 130,317 133,432 138,502 139,076
Diff 19,063 16,755 12,333 10,657
Percent Change 12.76 11.16 8.18 7.12
CES1000000001 Mining and
logging
2018–19 (mean) 732 734 735 733
2020 653 633 626 620
Diff 79 101 109 113
Percent Change 10.79 13.76 14.83 15.42
CES2000000001 Construction 2018–19 (mean) 7346 7372 7390 7404
2020 6556 7012 7171 7202
Diff 790 360 219 202
Percent Change 10.75 4.88 2.96 2.73
CES3000000001 Manufacturing 2018–19 (mean) 12,731 12,744 12,763 12,776
2020 11,489 11,729 12,062 12,103
Diff 1242 1015 701 673
Percent Change 9.76 7.96 5.49 5.27
CES6000000001 Professional
and business
services
2018–19 (mean) 21,047 21,084 21,121 21,153
2020 19,254 19,414 19,725 19,887
Diff 1793 1670 1396 1266
Percent Change 8.52 7.92 6.61 5.98
CES6500000001 Education and
health services
2018–19 (mean) 23,792 23,826 23,874 23,924
2020 21,805 22,193 22,760 22,979
Diff 1987 1633 1114 945
Percent Change 8.35 6.85 4.67 3.95
CES5000000001 Information 2018–19 (mean) 2842 2845 2851 2852
2020 2609 2569 2576 2565
Diff 233 276 275 287
Percent Change 8.2 9.7 9.65 10.06
CES9000000001 Government 2018–19 (mean) 22,484 22,491 22,506 22,525
2020 21,776 21,265 21,317 21,552
Diff 708 1226 1189 973
Percent Change 3.15 5.45 5.28 4.32
CES5500000001 Financial
activities
2018–19 (mean) 8633 8647 8659 8674
2020 8566 8585 8605 8620
Diff 67 62 54 54
Percent Change 0.78 0.72 0.62 0.62
CES1021100001 Oil and gas
extraction
2018–19 (mean) 144 145 146 146
2020 155.4 153.5 153.6 155.2
Diff −11.4 −8.5 −7.6 −9.2
Percent Change −7.92 −5.86 −5.21 −6.3
Table 9.1 (Cont’d)
Impact of COVID-19 on industries 195
leisure and hospitality sector was hardly impacted, with half of job reduction in April
compared to the average number of jobs in this sector in April of 2018 and 2019. The job
loss measures improved in May and July due to people’s intention to return to work as
many industries have started to operate partial or full starting from May or June in 2020.
The percentages of job losses during COVID-19 months are shown in Table 9.2. The
overall trend shows that the reduction is higher in the early months (April or May) com-
pared to the late months (June or July) during this pandemic. Two sectors (information and
manufacturing) show a slightly opposite trend with greater job losses in the later months
compared to the job losses in the earlier months. The overall month by month trends and
the upticks of these two sectors are also visible in Fig. 9.1. Fig. 9.2 shows the number of
COVID-19 cases and related deaths from January to July of 2020. Although the number
of cases and deaths are higher during the summer months, the stay home orders and lock-
downs were in place during the earlier months (March and April), which were associated
with the high number of job losses during April and May (see Table 9.2).
Table 9.3 lists the number of average jobs during the first three months and the last
four months of January to July job market data in 2020. There are job reductions in
all sectors in the later months. However, the impact varies widely by sectors. The most
impacted sector was ‘leisure and hospitality, with a 35.23 percent reduction of jobs. The
least impacted sector was ‘oil and gas extraction’ with a job reduction of only 1.28 percent.
9.4 Dierence in dierences
Difference in Differences (DID) is a robust observational before-after study. In DID, or
double difference approach, a treatment qualification variable occurs in a way that the
Table 9.2 Percentage Change in Number of Jobs by Dierent Sectors (April to July).
Code Industry Apr May Jun Jul
CES7000000001 Leisure and hospitality 47.75 39.27 27.32 23.56
CES8000000001 Other services 21.89 17.76 11.72 8.98
CES4200000001 Retail trade 15.54 13.06 7.47 5.83
CES0500000001 Total private (seasonally adjusted) 14.52 12.09 8.5 7.41
CEU0500000001 Total private (not seasonally adjusted) 14.5 12.18 8.65 7.54
CES0000000001 Total nonfarm (seasonally adjusted) 12.81 11.09 8.02 6.95
CEU0000000001 Total nonfarm (not seasonally adjusted) 12.76 11.16 8.18 7.12
CES1000000001 Mining and logging 10.79 13.76 14.83 15.42
CES2000000001 Construction 10.75 4.88 2.96 2.73
CES3000000001 Manufacturing 9.76 7.96 5.49 5.27
CES6000000001 Professional and business services 8.52 7.92 6.61 5.98
CES6500000001 Education and health services 8.35 6.85 4.67 3.95
CES5000000001 Information 8.2 9.7 9.65 10.06
CES9000000001 Government 3.15 5.45 5.28 4.32
CES5500000001 Financial activities 0.78 0.72 0.62 0.62
CES1021100001 Oil and gas extraction −7.92 −5.86 −5.21 −6.3
Impact of COVID-19 on Socio-Economic Environment
196
treatment group is considered at some time point, but the control group is never con-
sidered. While before-after studies use the difference of the treatment group before and
after the treatment, DD uses the difference between the two before and after differences
across the treatment and control groups (Lee, 2016).
To understand what is essentially identified by DD, suppose that the treatment is a
law implemented at t = t0 to affect possibly everybody, not just those with Q = 1, and
YX tt Qt
tU
it it it
qi
t
=+
[]
+≤
[]
+
1
ββ β
x11
00 0 (9.1)
where Qit is a time-varying qualification dummy. Here β0 is the treatment effect for
everybody, but if there are other changes at t0 affecting everybody (e.g., a weather 131
Fig. 9.2 COVID-19 cases and deaths in the U.S. from January to July 2020.
0
5,00,000
10,00,000
15,00,000
20,00,000
25,00,000
January February MarchApril MayJuneJuly
Cases Deaths
Fig. 9.1 Monthly trends of the percentage of job loss during COVID-19.
-10
0
10
20
30
40
50
Leisure and hospitality
Other services
Retail trade
Total private (seasonally
adjusted)
Total private (not
seasonally adjusted)
Total nonfarm
(seasonally adjusted)
Total nonfarm (not
seasonally adjusted)
Mining and logging
Construction
Manufacturing
Professional and business
services
Education and health
services
Information
Government
Financial activities
Oil and gas extraction
Percentahe of Job Loss
Sector
Apr May Jun Jul
Impact of COVID-19 on industries 197
132 Matching, RD, DD, and Beyond change), then β0 will pick up the effects of the
other changes as well. In contrast, βq is the extra effect only for those with Qit = 1.
Hence, tt0,
EY QXEY QX
it it it it it it q
(,)( ,)=−
==10
β
Under
EU QXEU
QX
it it it it it it
(,)(
,)
=− =
10
(9.2)
What is identified in DD is not β0 + βq, but the extra effect βq on the subpopulation
Qit = 1, which is the effect of the interaction between Qit and the time dummy 1[t0t]
for the post-treatment or “treatment-on” periods. The condition involving Uit is the
aforementioned removal of the unobserved confounder effect (Lee, 2016).
9.5 Results and discussions
To examine the impact of COVID-19 on job frequencies, this study used DID, a
function used in open source software R package ‘fixest’ (Berge, 2020). This packed
offers a special tool to add interactions in estimations. In this method, the variable ‘job
frequency by month (January to June 2020)’ was interacted with the values taken by
‘job frequency in March 2020’ by specifying it as a reference value. Table 9.4 shows the
Table 9.3 Measures of Number of Jobs by Dierent Sectors (January to July 2020).
Code Industry Jan-Mar Apr-Jul Diff Perc
CES7000000001 Leisure and hospitality 16,600 10,751 5849 35.23
CES8000000001 Other services 5909 4977 932 15.77
CES0500000001 Total private (seasonally adjusted) 129,193 113,700 15,493 11.99
CES0000000001 Total nonfarm (seasonally
adjusted)
151,922 135,178 16,744 11.02
CES1000000001 Mining and logging 711 633 78 10.97
CES5000000001 Information 2892 2580 312 10.79
CEU0500000001 Total private (not seasonally
adjusted)
127,535 114,036 13,499 10.58
CES4200000001 Retail trade 15,643 14,070 1573 10.06
CEU0000000001 Total nonfarm (not seasonally
adjusted)
150,434 135,332 15,102 10.04
CES6000000001 Professional and business services 21,510 19,570 1940 9.02
CES6500000001 Education and health services 24,509 22,434 2075 8.47
CES2000000001 Construction 7602 6985 617 8.12
CES3000000001 Manufacturing 12,834 11,846 988 7.7
CES9000000001 Government 22,729 21,478 1251 5.5
CES5500000001 Financial activities 8832 8594 238 2.69
CES1021100001 Oil and gas extraction 156 154 2 1.28
Impact of COVID-19 on Socio-Economic Environment
198
results from the difference in difference method. The adjusted R2 value of the developed
model is 0.80. The estimates for each month are statistically significant. The estimates
indicate that there is a growth of total jobs from January to February. By keeping the
‘job frequencies in March 2020’ as the reference, the study showed a drastic reduction
in jobs in April and May of 2020. The estimates for June 2020 show lower job loss
compared to May 2020. The descriptive statistics also provide similar evidence. Fig. 9.3
illustrates the estimates of the month-based job frequencies.
9.6 Conclusion
The impact of COVID-19 on the U.S. economy is enormous. Many top economists
consider this outbreak as the most disastrous economic recession since the Great
Depression. The laid off workforce needs to spend less due to their job loss, which
will impact other businesses in the long run. With the loss of subsequent business
opportunities, economic growth will decline. As the virus is still not contained in the
US, this loss will stay longer, and full recovery is not expected in the near future. This
study collected historical CES data from the USBLS and showed the highly impacted
Table 9.4 Estimates of Dierence in Dierence Models.
Estimate Std. Error t-value Pr(>|t|)
Month: Jan −632.31 51.493 −12.28 < 2.20E-16
Month: Feb −161.47 35.413 −4.5597 < 5.29E-06
Month: Apr −1068.7 64.226 −16.639 < 2.20E-16
Month: May −1589 88.765 −17.901 < 2.20E-16
Month: Jun −1113.8 78.743 −14.144 < 2.20E-16
Adj. R20.79882
R2-Within 0.09424
Log-likelihood −35,994.75
Fig. 9.3 Interaction plot of job measures from January to June 2020.
123456
Month
–1500 –1000 –50
00
Estimate and 95% Conf. int.
Impact of COVID-19 on industries 199
sectors (leisure and hospitality, other services, and total private) by performing both
year-based and month-based analysis. Additionally, this study applied DID, a sophis-
ticated before-after observational study, to show the statistical significance of job loss
during the COVID-19 pandemic. By using data from all sectors, the DID results show
a large decline of jobs during April and May and a slight recovery in June.
The current study has some limitations. It has used only data until July 2020 as the rest
CES data are preliminary estimates. Additionally, the DID analysis was limited to all job
losses. There is a need for sector based DID analysis, which was not performed in this study.
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This study examines the short-term impact of the 2019 novel coronavirus (COVID-19) outbreak on 52 listed airline companies around the world by using event study methodology. The results demonstrate that airline stock returns declined more significantly than the market returns after three major COVID-19 announcements were made. Overall, traders reacted differently during the three selected events. The strongest overreaction was noted in the post-event periods of the World Health Organization's and President Trump's official announcements. Moreover, the findings confirm that traders in western countries are more responsive to recent information than those in Asian countries. The findings call for immediate policy designs in order to protect the airline industry around the globe.
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The outbreak of COVID-19 has caused concerns globally. On 30 January WHO has declared it as a global health emergency. The easy spread of this virus made people to wear a mask as precautionary route, use gloves and hand sanitizer on a daily basis that resulted in generation of a massive amount of medical wastes in the environment. Millions of people have been put on lockdown in order to reduce the transmission of the virus. This epidemic has also changed the people's life style; caused extensive job losses and threatened the sustenance of millions of people, as businesses have shut down to control the spread of virus. All over the world, flights have been canceled and transport systems have been closed. Overall, the economic activities have been stopped and stock markets dropped along with the falling carbon emission. However, the lock down of the COVID-19 pandemic caused the air quality in many cities across the globe to improve and drop in water pollutions in some parts of the world.