DISCUSSION PAPER SERIESIZA DP No. 13411

A Literature Review of the Economics of
COVID-19

Abel BrodeurDavid Gray
Anik Islam
Suraiya Jabeen Bhuiyan

JUNE 2020

DISCUSSION PAPER SERIESIZA DP No. 13411

A Literature Review of the Economics of
COVID-19

Abel Brodeur

Anik Islam

David Gray

Suraiya Jabeen Bhuiyan

University of Ottawa and IZA
University of Ottawa

University of Ottawa
University of Ottawa

JUNE 2020

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IZA DP No. 13411

JUNE 2020

ABSTRACT
A Literature Review of the Economics of
COVID-19
The goal of this piece is to survey the emerging and rapidly growing literature on theeconomic consequences of COVID-19 and government response, and to synthetize theinsights emerging from a very large number of studies. This survey (i) provides an overview
of the data sets used to measure social distancing and COVID-19 cases and deaths; (ii)reviews the literature on the determinants of compliance and effectiveness of social
distancing; (iii) summarizes the literature on the socio-economic consequences of COVID19 and government interventions, focusing on labor, health, gender, discrimination andenvironmental aspects; and (iv) discusses policy proposals.
JEL Classification:

E00, I15, I18, J20

Keywords:

COVID-19, coronavirus, employment, lockdowns

Corresponding author:
Abel Brodeur
Department of Economics
Faculty of Social Sciences
University of Ottawa
120 University
Ottawa, ON K1N 6N5
CanadaE-mail: abrodeur@uottawa.ca

1Introduction

The World has been gripped by a pandemic over the first half of 2020. It was identified as a
new coronavirus (severe acute respiratory syndrome coronavirus 2, or SARS-CoV-2), and later
named as Coronavirus Disease-19 or COVID-19 (Qiu et al., 2020). While COVID-19 originatedin the city of Wuhan in the Hubei province of China, it has spread rapidly across the world,
resulting in a human tragedy and tremendous economic damage. By mid-June, there had
been over 8 million cases of COVID-19 globally, with over 436,000 deaths.
Given the rapid spread of COVID-19, countries across the World have adopted several
public health measures intended to prevent its spread, including social distancing (Fong et al.
(2020)).1 As part of social distancing, businesses, schools, community centres, and nongovernmental organization (NGOs) have been required to close down, mass gatherings havebeen prohibited, and lockdown measures have been imposed in many countries, allowing
travel only for essential needs.2 The goal is that through social distancing, countries will beable to “flatten the curve”, i.e., reduce the number of new cases related to COVID-19 fromone day to the next in order to halt exponential growth and hence reduce pressure on
medical services (John Hopkins University, 2020a).
The spread of COVID-19 is expected to result in a considerable slowdown of economic
activities. According to an early forecast of the International Monetary Fund (2020a), theglobal economy would contract by about 3 percent in 2020. The contraction is expected to
be of far greater magnitude than that of the 2008-2009 Global Financial Crisis. However,in its latest update (June 2020), the International Monetary Fund (2020b) revised theforecast to 4.9 percent contraction in 2020. The report cites the following reasons forthe updated forecast: i) greater persistence in social distancing activities; ii) lower
activity during lockdowns; iii) steeper decline in productivity amongst firms which haveopened up for business; and iv) greater uncertainty.3 The economic implications will be
wide-ranging and
1 According to Mandavilli (2020), this strategy saved thousands of lives both during other pandemics such as the

Spanish flu of 1918 and more recently in Mexico City during their 2009 flu.
2 According to CDC (2020), social distancing (or physical distancing) means keeping space between yourself andother people outside home. To practice social/physical distancing: i) stay at least 6 feet (about 2 arms’ length)from other people: ii) do not gather in groups; and iii) avoid crowded places and mass gatherings.
3 World Bank (2020) forecasts a 5.2 percent contraction in global GDP. Similarly, OECD (2020) forecasts a fall inglobal GDP by 6 percent to 7.6 percent, depending on the emergence of a second wave of COVID-19.

2

uncertain, with different effects on the labor markets, production supply chains, financial
markets, and the World economy. The negative economic effects may vary by the stringencyof the social distancing measures (e.g., lockdowns and related policies), its length of
implementation, and the degree of compliance.

In addition, the pandemic and thegovernment intervention may lead to mental health distress, increased economic inequality,
and affect some socio-demographic groups particularly adversely.
The goal of this piece is to survey the emerging literature on the economic
consequences of COVID-19 and government response, and to synthesize the insights
emerging from a growing number of studies. Figure 1 illustrates the number of National
Bureau of Economic Research (NBER) working papers that have been released related to thepandemic between March 2020 and May 2020.4 By the end of May, there had been 106
papers related to COVID-19. Similarly, over 60 discussion papers on the pandemic werereleased by the IZA Institute of Labor Economics5 during March-May 2020.

Figure 1: COVID-19 Publications in NBER
20

120

18

106

No. of publications

87

14
78

80

12
61

10
8

40

38

6
28

4

20

15

2
0

60

52

Cumulative publications

100

16

3

7
0

Week 4 Week 5 Week 1 Week 2 Week 3 Week 4 Week 1 Week 2 Week 3 Week 4
March

April

May

Source: Author’s Research drawn from the NBER website

4 The list of NBER working papers is available here: https://www.nber.org/wp_covid19.html.
5 The list of IZA discussion papers is available here: https://covid-19.iza.org/publications.

3

We hope our literature review will help better inform academic and policy debate. We list
throughout our paper government responses and how they vary across local, provincial (orstate), and federal governments. We investigate policies implemented not only aimed at
mitigating the deaths and the morbidities related to COVID-19, i.e. the direct health andpublic health-related issues, but also those measures providing a cushion in the form of shortrun income maintenance and subsidies to business. We also cover interventions aimed ataddressing the more persistent scarring economic effects that are expected to manifest
themselves in the longer run.
This paper will focus on five broad areas: i) measurement of spread of COVID-19 andsocial distancing, ii) the degrees of disease transmission, effectiveness, and compliance withsocial distancing, iii) economic and financial impacts of COVID-19, iv) socioeconomic
consequences of lockdowns, and v) governmental response to the pandemic.
The rest of the paper is structured as follows. Section 2 focuses on the background of
COVID-19, including a historical context of pandemics and evolution of COVID-19. Section 3
provides an outline on the measurement of COVID-19 and social distancing by documenting
and describing the most popular data sources. Section 4 discusses the socioeconomic
determinants and effectiveness of social distancing. Section 5 focuses on the economic andfinancial impacts, including the plausible mechanisms and economic modelling of infections.
Section 6 reviews the literature on socioeconomic consequences of social distancing
measures, focusing on the labor, health, gender, discrimination, and environmental aspects.
Section 7 touches upon the policy measures. Section 8 provides the conclusion.

2

Background

2.1

History of Pandemics and Economic Impact

Pandemics are not new and have occurred at different stages in human history (Ferguson etal., 2020). Table 1 below provides a historical timeline of major pandemics across the World.
While there have been many outbreaks and human catastrophes, there has been a notablerise in the frequency of pandemics from the year 2000 and thereafter. This is particularly due
4to increased emergence of viral disease amongst animals (Madhav et al., 2017). Given the risein the frequency of pandemics, many researchers including Garrett (2007), Keogh-Brown etal. (2008) and most recently Madhav et al. (2017) and Fan et al. (2018) argue that a largescale global pandemic was inevitable. Ferguson et al. (2020) from the Imperial College London
COVID-19 Response Team claim that COVID-19 is the most serious episode since the 1918
Spanish Influenza pandemic. Despite the comparisons, Barro (2020) concludes that the nonpharmaceutical interventions implemented during 1918 Spanish Influenza pandemic were
not successful in reducing overall deaths. This was because the interventions were notmaintained for a sufficiently long period of enough time. He estimates that the mean durationof school closings and prohibitions of public gatherings was only 36 days, whereas the mean
duration of quarantine/isolation was 18 days (0.05 years). These numbers were quite small
compared to the number of days that the 1918 Spanish influenza pandemic was active.

Table 1: Major Pandemics: Historical Timeline
NameTime Period

Antonine Plague165-180

Japanese smallpox
epidemic

735-737

Plague of Justinian541-542

Black Death

1347-1351

New World Smallpox
Outbreak1520-onwards

Great Plague of London

1665

Italian Plague

1629-1631

Type/Pre-human

Estimated

host

Death Toll

Believed to be either
smallpox or measles
Variola major virus
Yersinia pestis
bacteria/rats, fleas
Yersinia pestis
bacteria/rats, fleas
Variola major virus
Yersinia pestis
bacteria/rats, fleas
Yersinia pestis
bacteria/rats, fleas

5 million

1 million

30 to 50 million

200 million

56 million

100,000

1 million

5Cholera Pandemics 1-6

1817-1923

V. cholerae bacteria

1 million+

Third Plague

1885

Yersinia pestis

12 million

bacteria/rats, fleas(China & India)

Yellow Fever

Late 1800s

Virus/Mosquitoes

Russian Flu

1889-1890

H2N2 (avian origin)1 million

Spanish Flu

1918-1919

H1N1 virus/pigs

40 to 50 million

Asian Flu

1957-1958

H2N2 virus

1.1 million

Hong Kong Flu

1968-1970

H3N2 virus

1 million

HIV/AIDS

1981-present

Virus/chimpanzees

25 to 35 million

Swine Flu

2009-2010

H1N1 virus/pigs

200,000

SARS

2002-2003

Ebola

2014-2016

MERS

2015-present

Coronavirus/bats,
civetsEbolavirus/ wild
animals
Coronavirus/bats,camels100,000-150,000
(US)

770

11,000850

Source: World Economic Forum (2020)

Pandemics are expected to have a severe negative impact on economic activities, atleast in the short run. According to Jonas (2013), the impact ranges from: i) avoidance reaction
due to social distancing measures (e.g., individuals might forgo consumption and purchasesof certain goods and services), ii) small direct costs (e.g., hospitalization and medical costs),iii) larger indirect costs (loss of labor, production), and iv) offsetting and cascading effects(disruption of services, travel and others). A number of studies tried to anticipate theeconomic loss from a pandemic.6 For example, Jonung and Roeger (2006) forecasted that ahypothetical global pandemic would lead to 1.6 percent drop in GDP for the European Union

6 Please see Boissay & Rungcharoenkitkul (2020, p. 2) for a list of studies estimating the economic loss from pastor hypothetical pandemics.

6

(EU) due to both demand and supply side factors. Other studies analyze the impact with ahistorical comparison. For example, ‘how would the casualty numbers during the 1918
Spanish Influenza pandemic transpire today?’ Barro et al. (2020) estimate that, holding
everything else constant, the 2.1 percent death rate during the Spanish Influenza pandemic
in 1918-1920 would translate to roughly 150 million deaths worldwide (compared to theWorld’s population of 7.5 billion in 2020) during COVID-19 pandemic. The authors also findthat, on average, the 2.1 percent death rate corresponds to 6 percent decline in GDP and 8
percent fall in private consumption.
2.2

Evolution of COVID-19

According to Zhu et al. (2020), the first pneumonia case was discovered on December 8, 2019in a wet market in Wuhan, the capital city of Hubei Province of China. Afterwards, several
clusters of patients with such pneumonia were reported throughout late December 2019.
Table 2 below provides a timeline of key events, starting from January 2020.

Table 2: COVID-19 - Timeline
Date

Events

4 January 2020

WHO reports cluster of pneumonia cases in Wuhan, Hubei, China

7 January 2020

WHO identifies COVID-19

11 January 2020

China announces 1st death from COVID-19

13 January 2020

1st official case of COVID-19 reported outside China in Thailand

17 January 2020

Authorities in the Nepal, France, Australia, Malaysia, Singapore, South
Korea, Vietnam and Taiwan confirm cases

21 January 2020

1st case of COVID-19 reported in the United States of America (US)22 January 2020

WHO finds evidence of human-to-human transmission from China

23 January 2020

30 January 2020
31 January 2020

China imposes lockdown in the cities of Wuhan, Xiantao and Chibi of
the Hubei province
WHO declares COVID-19 to be a Public Health Emergency ofInternational Concern
US declares COVID-19 a domestic public health emergency

7

2 February 2020
9 February 2020

14 February 2020
15 February 2020
23 February 2020

1st death due to COVID-19 outside of China in Philippines
The death toll in China surpasses that of 2002-03 Severe AcuteRespiratory Syndrome (SARS)
Egypt reports 1st case of COVID-19, the 1st case in the African
continent
France reports 1st death from COVID-19 outside of Asia
COVID-19 cases rise in Italy in what becomes the largest outbreakoutside of Asia

26 February 2020

Brazil confirms 1st case of COVID-19, the 1st case in South America

27 February 2020

1st case of community transmission reported in the US

29 February 2020

1st death due to COVID-19 in the US

8 March 2020

Over 100 countries report COVID-19 cases
Italy imposes quarantine in Lombardy region

11 March 2020

WHO declares COVID-19 a pandemic

13 March 2020

Donald Trump declares national emergency in the US

17 March 2020
19 March 2020
21 March 2020

All 50 states in US have at least one confirmed case of COVID-19
California first state to implement ‘stay-at-home’ order in US
Italy’s death toll surpasses that of China
EU suspends public deficit rules to inject fiscal stimulus acrosscountries
The White House and Senate leaders of both the Democratic and

25 March 2020

Republican parties in the US come to an agreement on a US$2 trillion
stimulus to aid workers, businesses, and the health-care system in
response to the pandemic

26 March 2020

US leads the world in COVID-19 cases

2 April 2020

Global cases of COVID-19 reach 1 million

8 April 2020

11 April 2020

China lifts lockdown in Wuhan, 76 days after it was sealed off tocontain COVID-19
US records 2,000 deaths in one day, the highest single-day death tollrecorded by any country8

15 April 2020

Global cases of COVID-19 reach 2 million

24 April 2020

US’s death toll surpasses 50,000

27 April 2020

Global cases of COVID-19 reach 3 million

28 April 2020

COVID-19 cases in US surpass 1 million

21 May 2020

Global cases of COVID-19 surpass 5 million
Brazil surpasses Russia as the country with the 2nd highest number of

22 May 2020

cases, after the US

27 May 2020

US’s death toll surpasses 100,000

Figure 2 shows the cumulative cases and deaths from the COVID-19 pandemic. Notethat both cases and deaths started rising from March 2020. As of 23 June, total, cumulative
cases were over 9.2 million, while there were over 470,000 deaths across the World. Table 3
lists the top 10 countries in terms of COVID-19 cases and deaths. The table shows that US, UK,
Brazil, and some countries in the European Union (Italy, France, Spain, Germany) are in the
top 10 in terms of COVID-19 cases and deaths.

Figure 2: COVID-19 Cases and Deaths – Global Pandemic (as on 23 June)
10,000,000

600,000

9,262,754

9,000,000
472,171

500,000

7,000,000

400,000

6,000,000
5,000,000

300,000

4,000,000
200,000

3,000,000
2,000,000

COVID-19 Deaths

COVID-19 Cases

8,000,000

100,000

1,000,000
22-Jan-20

22-Feb-20

22-Mar-20

22-Apr-20

COVID-19 CASES

22-May-20

22-Jun-20

COVID-19 DEATHS

Source: Data from the Coronavirus Resource Center, John Hopkins University

9

Table 3: Cumulative Cases: Top 10 Countries (as of 23 June)
Rank Country

Confirmed Cases(Cumulative)

Country

Confirmed Deaths(Cumulative)

1US

2,364,874

US

121,662

2

Brazil

1,145,906

Brazil

52,645

3

Russia606,043

UK

43,165

4

India

456,183

Italy

34,644

5

UK

308,334

France29,7236

Peru

260,810Spain

28,327

7

Chile

254,416

Mexico23,377

8

Spain

247,086India

14,476

9

Italy

239,410

Iran

9,996

10

Iran

212,501

Belgium

9,722

Source: Data from the Coronavirus Resource Center, John Hopkins University.

Compared to previous pandemics, COVID-19 has a disproportionate impact on the elderly
from a health perspective. The lockdown measures, however, are more global in scope andscale than their predecessors, and they have disrupted international supply chains as well as
aggregate demand and consumption patterns. This in turn has led to heightened financial
market turbulence and amplified the economic shock. Moreover, greater borrowing andhigher debt levels among firms and households during this time make the short-term shocks
more potent compared to previous pandemics (Boissay and Rungcharoenkitkul, 2020).

3

Measurement of COVID-19 and Social Distancing

3.1

Measurement of COVID-19

Before reviewing the potential economic impact, socioeconomic consequences, andgovernmental response, it is important to contextualize the data related to COVID-19.
Without such data, it will not be possible to comprehend the scope of the pandemic. Timely
and reliable data inform the World of how the disease is spreading, what impact the pandemic
10

has on the lives of people around the world, and whether the counter measures taken are
successful or not (Roser et al., 2020).
The three key indicators are: i) the total number of tests, ii) the number of confirmed
COVID-19 cases, and iii) the number of COVID-19 deaths. These numbers are provided bydifferent local, regional and national health agencies/ministries across countries over theWorld. However, for research and educational purposes, the data are accumulated by theCenter for Systems Science and Engineering at John Hopkins University.7 The databaseprovides the numbers as well as visual maps of COVID-19 cases across the World. The cases
are reported at the provincial level for China, at the city level for US, Australia and Canada,
and at the country level for all other countries (Dong et al., 2020). The data are corroborated
with the WHO,8 the Center for Disease Control (CDC) in US, and the European Center for
Disease Control (ECDC).
Based on these figures, the Case Fatality Rate (CFR) is calculated as the number ofconfirmed deaths divided by the number of confirmed cases and is used to assess themortality rate of COVID-19.9 However, Roser et al. (2020) caution against taking the CFR
numbers at face value to understand mortality risks,10 because the CFR is based on thenumber of confirmed cases. Due to limited testing capacities, not all COVID-19 cases can be
confirmed. Moreover, the CFR reflects the severity of the disease in a particular context at aparticular point in time. Therefore, CFR changes over time and is sensitive to the location andpopulation characteristics.
Recent studies show that there are large measurement errors associated with COVID19 case numbers. Using data on influence-like illnesses (ILI) from the CDC, Silverman et al.
(2020) show that ILIs can be a useful predictor of COVID-19 cases. The authors find that there
was an escalation of ILI patients during March 2020. These cases could not be properly
identified as COVID-19 cases due to the lack of testing capabilities during the early stages. The
authors suggest that the surge in ILIs may have corresponded to 8.7 million new COVID-19

7 Please see the link for the numbers and visual representation. https://coronavirus.jhu.edu.
8 Please see the WHO COVID-19 Dashboard. https://covid19.who.int.
9 Please refer to John Hopkins University (2020b) for CFR across countries.
10 Please see the link for further details. https://ourworldindata.org/mortality-risk-covid.

11cases between March 8 and March 28. Intuitively, the number suggests that almost 80
percent cases of COVID-19 in US were never diagnosed.
While the above dataset focuses on counts and tests, the COVID Tracking Project11 in
the US provides additional data on patients who have been hospitalized, are in intensive careunits (ICU), and are on ventilator support for each of the 50 states. It also grades each state
on data quality. Recently, it has included COVID Racial Data Tracker,12 which shows the raceand ethnicity of individuals affected by COVID-19. All of these combined measures andstatistics provide a more comprehensive perspective of the spread of the pandemic in the US.

3.2

Measurement of Social Distancing

In comparison to measuring the spread of the virus, social distancing is not easy to quantify.
We determined from the literature that there are three main techniques of quantification: i)measures of the mobility of the population, ii) modelling proxies, and iii) the formation ofindices. Proxies and indices are based on data related to the spread of infection and to theimplementation of policies, respectively. On the other hand, the movement of people is basedon their observed travelling patterns. Mobility measures have been used extensively in thelast two months to understand mobility patterns during the COVID-19 pandemic (Nguyen etal., 2020). However, mobility data providers have slight differences in their methodologies.
Table 4 provides a summary of how different mobility data providers compile their data.

Table 4: Social Distancing – Mobility Measures and How They Work
Mobility Measures

How Do They Work?
Google Mobility13 aggregates anonymous data from

Google LLC Community

users’ mobile-device-location history. It shows how visits

Mobility Reports

to, or length of stay at, different types of location changeover time compared to a baseline period. The reports

11 Please see the link for further details. https://covidtracking.com/data.
12 Please see the link for further details. https://covidtracking.com/race.
13 Please see the link for further details. https://www.google.com/covid19/mobility.

12

have six locational categories: i) retail and recreation, ii)
grocery and pharmacy, iii) parks (parks, beaches, etc.), iv)
transit stations (subways, bus, train stations), v)
workplaces, and vi) residential areas.
‘Unacast’14 has a Social Distancing Scoreboard. It uses
location data from cellphones to compare the number of
average visitations for each day to its ‘normal’ levels‘Unacast’prior to the pandemic. The ‘Scorecard’ assigns a letter
grade of A through F based on peoples’ social distancing
behavior. The assigned score is based on three different
metrics: i) percent change in the average distancetravelled; ii) percent change in “non-essential visitation";and iii) change in “human encounters".

‘Safegraph’‘Safegraph’15 data track the GPS locations from millions of
US cellphones to construct a daily panel of census-blocklevel aggregate movements measures.
‘Baidu’ Maps16 track the population flow of more than
300 cities in China every day. This includes the flow of‘Baidu’ Maps

passengers and urban travel intensity as well as city
migration trends. This platform was used to track theearly spread of COVID-19 from Wuhan.

Mobility data17 are more dynamic, available at a daily rate, and they can be used to measure
the effect of social distancing on other aspects, such as adherence to shelter-in place policies
or labor employment (Gupta et al., 2020). They also offer key insights into human behavior.
For example, ‘Safegraph’ data suggest that social activity in the US started declining

14 Please see the link for further details. https://www.unacast.com/covid19
15 Please see the link for further details. https://www.safegraph.com/dashboard/covid19-commerce-patterns
16 Please see the link for further details. http://research.baidu.com/Blog/index-view?id=133
17 Please see Oliver et al. (2020) for a detailed analysis as to why mobility data is scarcely used in case of

epidemics, even though it might be useful.

13

substantially, rapidly, and well before lockdown measures were imposed (Farboodi et al.,2020).
Nevertheless, mobility data have their own restrictions. Mobility data are a proxy for timespent in different locations. They do not allow one to determine the context of the contacts(needed to understand the spread of COVID-19), i.e. whether they place in the workplace orin the general community (Martín-Calvo et al., 2020). Those situations involve different levelsof risk of transmission. In regards to the productive activities of the individuals that are
tracked, information on the context is also indeterminate. For those who are working virtuallyfrom their homes, for instance, these measures do not capture the value added from the timethat they allocate to their jobs. It is also likely that the quality of these measures candeteriorate when overall unemployment rates and job disruptions are high (Gupta et al.,2020).18 Telecom operator data are deemed to be more representative compared to location
data, as telecom data are not limited to people with smartphones, GPS locator, and history oftravel using GPS location (Lomas, 2020).

4Social Distancing: Determinants, Effectiveness and Compliance

A large range of social distancing policies have been implemented,19 ranging from full-scale
lockdowns to voluntary self-compliance measures.20 For example, Sweden imposed relatively
light restrictions (Juranek and Zoutman, 2020). Large-scale events were prohibited, andrestaurants and bars were restricted to table service only; however, private businesses weregenerally allowed to operate freely. The populations were encouraged to stay home if they
were feeling unwell and to limit social interaction if possible (M. Andersen et al., 2020).
Stringent social distancing measures are implemented in countries with a greater
proportion of the elderly population, a higher population density, a greater proportion of

18 Mobility measures track work-location based on movement to workplace from a reference point i.e. home.

However, if a person works from home or becomes unemployed, there will not be a distinct workplace reference
point. Hence, the quality of mobility measures is expected to fall.
19 TheWHO Health System Response Monitor provides a cross country analysis and other details:
https://analysis.covid19healthsystem.org/
20 People tend to adopt social distancing when there is a specific incentive to do so in terms of risk of health andfinancial health cost (Makris, 2020). Maloney and Taskin (2020) also attribute voluntary actions to either fear ora sense of social responsibility.

14

employees in vulnerable occupations, greater degrees of democratic freedom, moreinternational travelling, and further distance from the equator (e.g., Jinjarak et al., 2020).
Appealing to a game theory approach, Cui et al. (2020) argue that states linked by economic
activities will be “tipped”21 to reach a Nash equilibrium, where all other states comply withshelter-in-place policies. Social distancing policy determinants have been linked to party
leader characteristics, political beliefs and partisan differences (Baccini and Brodeur, 2020;
Barrios and Hochberg, 2020; Murray and Murray, 2020).22 Barrios and Hochberg (2020)
correlate the risk perception for contracting COVID-19 with partisan differences. They findthat, in absence of social distancing imposition, counties in the US which have higher DonaldTrump-vote shares are less likely to engage in social distancing. This persists even when
mandatory stay-at-home measures are implemented across states. Allcott et al. (2020) find asimilar pattern. In addition, the authors show through surveys that Democrats and Republican
supporters have different risk perceptions about contracting COVID-19 and hence theimportance of social distancing measures.
Researchers are trying to establish the effectiveness of social distancing policies in
reducing social interaction and ultimately COVID-19 infections and deaths. Abouk and Heydari(2020) show that reductions in out-of-home social interactions in the US are driven by a
combination of policy and voluntary measures, with strong causal impact of state-wide stayat-home orders, and more moderate impacts of non-essential business closures andlimitations placed on bars/restaurants. Ferguson et al. (2020) argue that multiple
interventions are required to have a substantial impact on transmission. The optimal
mitigation strategy, which is a combination of case isolation, home quarantine, and social
distancing of high-risk groups (aged over 70), would reduce the number of deaths by half, and

21 “A tipping set is a set of players with the following property: if all members of this set choose to implement

shelter-in -place policies, then the best response of every other agent will be to follow suit. So the member of
the tipping set can drive all others to the adoption of shelter-in-place policies, even in the absence of a federal
mandate for such policies.” (Cui et al., 2020, p. 4).
22 Baccini and Brodeur (2020) find that US states with Democratic governors are 50 percent more likely to
implement lockdown/stay-at-home orders. Moreover, governors without term limits were 40 percent more
likely to implement stay-at-home orders.

15the demand of beds in intensive care units (ICUs) by two-thirds in the UK and US.23 Note that
this set of interventions falls well short of an economic shutdown.
Similarly, Dave et al. (2020b) find that counties in Texas that adopted shelter-in-placeorders earlier than the statewide shelter-in-place order experienced a 19 to 26 percent fall in
COVID-19 case growths two weeks after implementation of such orders. Andersen et al.
(2020) find that temporary paid sick leave, a federal mandate enacted in the US, whichallowed private and public employees two weeks of paid leave, led to increased compliancewith stay-at-home orders. On a more global scale, Hsiang et al. (2020) show that social
distancing interventions prevented or delayed around 62 million confirmed cases,
corresponding to averting roughly 530 million total infections in China, South Korea, Italy,
Iran, France, and USA.
Testing is another facet of COVID-19 which has been investigated (Baunez et al., 2020;
Gollier and Gossner, 2020). This process is crucial in informing about the number of people
who have fallen ill with COVID-19 and to understand in real-time whether the dynamics ofthe pandemic are accelerating or decelerating (Baunez et al., 2020).24 Moreover, it allows
tested non-infected individuals to rejoin the workforce without being a risk to others.
However, according to Gollier and Gossner (2020), there is insufficient production level oftests in order to conduct mass-testing across all affected countries. These authors call for“group-testing” as a way to get around the problem, but there might be practical problems
related to such measures e.g., the maximum number of people present in a group and theacceptable error band for tests in groups. Baunez et al. (2020) suggest “test allocation” acrossregions in a specific country based on the marginal benefit of testing. Using data for Italy, the23 Fang et al. (2020) argue that if lockdown policies were not imposed in Wuhan, then the infection rates would

have been 65% higher in cities outside Wuhan. Hartl et al. (2020) show that growth rate of COVID-19 cases inGermany dropped from 26.7 percent to 13.8 percent after implementation of lockdown in the country.
Greenstone and Nigam (2020) project that 3 to 4 months of social distancing would save 1.7 million people in
USA by October 2021, 630,000 of which would be due to avoided overcrowding of ICUs across hospitals. Friedson
et al. (2020) argue that early intervention in California helped reduced significantly COVID-19 cases and deaths
during the first three weeks following its enactment.
24 Using data for testing numbers and socioeconomic characteristics at the zip-code level in New York City, Borjas(2020) finds that there is a significant correlation between tests conducted, positive test results, and income.
This suggests that people residing in poorer neighborhoods are less likely to be tested. However, if they aretested, they have a higher chance of obtaining a positive test result.

16

authors find that the allocation of tests was not efficient in relation to the criteria provided
by the authors.
Another important related issue is the determinants of compliance behavior [e.g. (Y.
Fan et al., 2020)]. The documented socioeconomic determinants of compliance to lockdowns(or safer-at-home orders) include, among others, income, trust and social capital, beliefs,
public discourse, and to some extent, news channel viewership. Chiou and Tucker (2020)
show that Americans living in higher-income regions with access to high-speed internet are
more likely to comply with social distancing directives. Coven and Gupta (2020) find that lowincome neighborhoods in New York City comply less with shelter-in-place activities during
non-work hours. According to the authors, this pattern is consistent with the fact that lowincome populations are more likely to be front-line workers and are also are more likely tomake frequent retail visits for essentials, making for two compounded effects.
Individual beliefs need to be taken into consideration, as they affect behavior andcompliance. Based on an experimental setup with participants in US and UK, Akesson et al.
(2020) conclude that individuals over-estimated the infectiousness of COVID-19 relative toexpert suggestions (i.e., compared to the true R0 number).25 If they saw/heard expert opinion,
individuals were prone to correct their beliefs. However, the more infectious COVID-19 wasdeemed to be, the less likely they were to undertake social-distancing measures. This wasperhaps due to beliefs that the individual will contract COVID-19 regardless of social
distancing practices.
Briscese et al. (2020) model the impact of “lockdown extension” on compliance using
a representative sample of residents from Italy. The authors find that, if a given hypotheticalextension is shorter than expected (i.e. a positive surprise), the residents are more willing toincrease self-isolation. Therefore, to ensure compliance, these authors suggest that it isimperative for the government/local authorities to work on communication and to manage
peoples’ expectations.

25 Provided that all individuals in the population are susceptible to an infection, R 0 number is the expected

number of cases that can be generated from one case in the population (Fraser et al., 2009).

17

Trust in government and social capital also play key roles. Bargain and Aminjonov(2020) show that residents in European regions with high levels of trust decrease mobility
related to non-necessary activities compared to regions with lower levels of trust. Similarly,
Brodeur et al. (2020c) find that counties in the US with relatively more trust in others decrease
their mobility significantly more once a lockdown policy is implemented. They also provide
evidence that the estimated effect on compliance post-lockdown is especially large for trustin the press, and relatively smaller for trust in science, medicine or government. Barrios et al.
(2020) and Durante et al. (2020) also provide evidence that regions with stronger higher civic
culture engaged in more voluntary social distancing.
Simonov et al. (2020) analyze the causal effect of cable news on social distancing
compliance. The authors examine the average partial effect of Fox News viewership, a news
channel that mostly defied expert recommendations from leaders of the US and global healthcommunities on COVID-19 and on compliance and find that 1 percentage point increase in
Fox News viewership reduced the propensity to stay at home by 8.9 percentage points.

5

COVID-19: Potential Economic and Financial Impacts

5.1

Plausible Mechanisms

To understand the potential negative economic impact of COVID-19, it is important tounderstand the economic transmission channels through which the shocks will adversely
affect the economy. According to Carlsson-Szlezak et al. (2020a) and Carlsson-Szlezak et al.
(2020b), there are three main transmission channels. The first is the direct impact, which isrelated to the reduced consumption of goods and services. Prolonged lengths of thepandemic and the social distancing measures might reduce consumer confidence by keeping
consumers at home, wary of discretionary spending and pessimistic about the long-term
economic prospects. The second one is the indirect impact working through financial market
shocks and their effects on the real economy. Household wealth will likely fall, savings will
increase, and consumption spending will decrease further. The third consists of supply-side
disruptions; as COVID-19 keeps production halted, it will negatively impact supply chains,labor demand, and employment, leading to prolonged periods of lay-offs and rising
unemployment. In particular, Baldwin (2020) discusses the expectation shock by which there18

is a “wait-and-see” attitude adopted by economic agents. The author argues that this iscommon during economic climates characterized by uncertainties, as there is less confidence
in markets and economic transactions. Ultimately, the intensity of the shock is determinedby the underlying epidemiological properties of COVID-19, consumer and firm behavior in theface of adversity, and public policy responses.
Gourinchas (2020, p. 33) summarizes the effect on the economy by stating: “A modern
economy is a complex web of interconnected parties: employees, firms, suppliers, consumers,
and financial intermediaries. Everyone is someone else’s employee, customer, lender, etc.”
Due to the very high degrees of inter-connectiveness and specialization of productive
activities, a breakdown in the supply chains and the circular flows will have a cascading effect.
Baldwin (2020) describes the impact of COVID-19 on the flows of income in the economy.
First, households do not get paid and hence reduce their consumption and savings levels. The
decrease in savings reduce investment and hence ultimately diminish the capital stock.26
Second, households reduce their demand for imports, which in turn reduces income for the
rest of the World, and hence the country’s exports decrease. Third, the demand/supply
shocks cause disruption in domestic and international supply chains. Fourth, all of theprevious shocks and disruptions lead to a fall in output – causing reductions in the usage ofthe factors of production. In this case, labor is more affected than capital through reduced
working hours or layoffs and hence lower earnings
It is also important to understand the processes that generate recoveries from
economic crises. Carlsson-Szlezak et al. (2020a) explain different types of recovery after
shocks through the concept of “shock geometry”. There are three broad scenarios of
economic recoveries, which we mention in ascending order of their severity. First, there is
the most optimistic one labelled ‘V-shaped’, whereby aggregate output is displaced andquickly recovers to its pre-crisis path. Second, there is the ‘U-shaped’ path, whereby output
drops swiftly but it does not return to its pre-crisis path. The gap between the old and new
output path remains large. Third, in the case of the very grim ‘L-shaped’ path, output drops,
and growth rates continue to decline. The gap between the old and new output path

26 In a dynamic model, the reduction in capital stock will occur in the next period, as is the case in standard

dynamic general equilibrium models.

19

continues to widen. Notably, Carlsson-Szlezak et al. (2020b) state that after previous
pandemics, such as the 1918 Spanish Influenza, the 1958 Asian Influenza, the 1968 Hong Kong
influenza, and the 2002 SARS outbreak, economies have experienced ‘V-shaped’ recoveries.
However, the COVID-19 economic recovery is not expected to be straightforward. This isbecause the effects on employment due to social distancing measures/lockdowns are
expected to be much larger. According to Gourinchas (2020), during a short period, as muchas 50 percent of the working population might not be able to find work. Moreover, even if
no containment measures were implemented, a recession would occur anyway, fueled by the
precautionary and/or panic behavior of households and firms faced with the uncertainty ofdealing with a pandemic as well as with an inadequate public health response (Gourinchas,2020).
5.2

Susceptible-Infected-Recovered (SIR) Epidemiological Models

A key tool used by epidemiologists is the seminal SIR model developed by Kermack et al.
(1927). A large number of studies estimate and forecast disease scenarios for COVID-19 using
a SIR model of the pandemic (e.g., Atkeson et al. (2020)).27 In these models there are threestates of health: i) susceptible (S) (at risk of getting infected), ii) infected (I) (and contagious),and iii) recovered/resistant (R) (previously infected). Those who have died from the disease
are no longer contagious. These models assume that susceptible people interact with infected
people at a given rate. The infected people recover over time at a given rate and acquire
immunity.28 Eventually, the susceptible population declines over time as people gain ‘herd
immunity’ from COVID-19.29 These different rates of infection, recovery and their associatedprobabilities form the main parameters of the SIR models.
The SIR models help simulate the effect of social distancing measures on the spreadof the infection. If only infected cases are isolated, the infection reaches a peak within 4months and reduces quickly. With social distancing measures, the infection reaches a peakaround the same point in time, however, the number of reported cases is significantly lower.

27 See Ellison (2020) for a brief survey of results.
28 The simplest versions of these models abstract from the fact that there is a chance of re-infection. In such acase, the infected people would rejoin the pool of agents in the S group.
29 Please see D’souza and Dowdy (2020) for details on gaining herding immunity for COVID-19.

20

If containment measures are lifted early, there is a chance of re-emergence of the infection.
These qualitative outcomes give a sense of the role of social distancing on COVID-19
transmission (Anderson et al., 2020).
An underlying assumption in these epidemiology models is that the transitions
between the states of health are exogenous with respect to economic outcomes. This meansthat the expected decrease in consumption activities or hours worked due to COVID-19 are
not accounted for in SIR models. This condition cannot be ignored because of the “lives vs.
livelihood” tradeoff that weighs heavily in any general analysis of pandemics thatincorporates the public health outcomes and the economic outcomes. A central focus of thisstrand of the literature is the efficiency of that tradeoff, i.e. how to reduce the rate of
infections at the lowest possible costs to economic welfare.
Eichenbaum et al. (2020a) address that question by integrating a macroeconomic
general equilibrium model with the standard SIR model. In their SIR-Macro model, theprevalence of infection depends on the degree of interaction between agents when
consuming and working, as well as the random chance of contracting the virus. Therefore,
the susceptible population can lower the chances of infection by reducing their consumption
activities and their labor supply (outside of their residences). Based on their assumptions andcalibration techniques, Eichenbaum et al. (2020a) find that aggregate consumption fell by 9.3
percent over a 32-week period. On the other hand, labor supply or hours worked followed a
U-shaped pattern, with a peak decline of 8.25 percent in the 32 nd week from the start of thepandemic. However, long-run declines in hours worked are lower because a higher proportionof the population survive and return to work compared to the counterfactual.
While the SIR-Macro model abstracts from real-world problems such as bankruptcy
costs, mass hysteria, loss of effective labour supply, and also do not consider dynamics
present in other models, such as consumption uncertainty, price rigidities (which would makeconsumption and hours worked fall further), there are certain caveats that have beenaccounted for in the literature, namely incomplete information, infection externalities andrisks across sub-populations. These are explained below.
Infected populations might be asymptomatic and might unknowingly increase
infection. Berger et al. (2020) outline a Susceptible-Exposed-Infectious-Recovered (SEIR)
21

based on Kermack et al. (1927) to account for this incomplete information. They bring forward
the idea of increasing testing of susceptible populations to identify infected-asymptomaticpatients and quarantining this segment of the population. The authors find that the targeted
quarantine policy would have a lower negative impact on the economy compared to thestandard uniform quarantine policy. Similarly, Eichenbaum et al. (2020b) conclude that ‘smart
containment’ policies, which are a combination of testing and quarantining of infected
people, would render the economic activity vs. public health tradeoff more favorable.
Eichenbaum et al. (2020a) focus on the infection externality problem. They mention
that the competitive equilibrium is not Pareto optimal, as agents do not consider that theiractions impact the infection and death rates of other economic agents. To properly internalize
the externality, the authors suggest that the containment measures are optimal if they aretightened over time in proportion to the spread of infection. If a strict containment policy is
enforced from the beginning, it will have a much more severe impact on the economy.
Bethune and Korinek (2020) focus on the infection externality in a more formal manner. The
authors develop Susceptible-Infected-Susceptible (SIS) and SIR models to quantify theinfection externalities using a decentralized and then the social planners’ approach. The
authors find that in a decentralized approach, infected individuals continue to engage ineconomic activities to maximize their utility. On the other hand, susceptible agents do reduce
their activities to reduce the risk of infection. The resulting behavioral outcome is that
infected individuals do not engage in adequate social distancing, as they do not internalize
the effects of their activities on the overall infection risk. Based on the model assumptions
and calibration for the US economy, the results suggest that the infection persists for morethan two years. In contrast, with the social planner approach, the planner forcibly reduces
the activity of infected agents to mitigate the risks to susceptible agents and eventually to
reduce infections to zero. In addition, the authors calculate the marginal cost of additional
infection to be $80,000 in the decentralized approach and $286,000 with the social planner’s
approach (nominal 2020 dollars). This shows that private agents underestimate the cost of
the externality, and the social planner’s approach of containment of the infected population
is Pareto efficient compared to a uniform containment policy.
Acemoglu et al. (2020) introduce heterogeneity of risks across sub-populations. The
different sub-populations (young, middle-aged, and old) have different infection, morbidity,
22and fatality rates, as well as different levels of interaction with others. These conditions giverise to targeted quarantine measures. This is because a differential lockdown between
different risk groups (aggressive lockdown of older groups compared to younger ones) can
reduce the number of lives lost and negative economic outcomes to a greater extent
compared to uniform lockdown measures for all age groups. The authors find that with auniform lockdown lasting 434 days, the total number of fatalities reaches 1.8 percent of the
population, with economic costs of about 24.3 percent of annual GDP. On the other hand, atargeted lockdown policy lasting 230 days reduces fatalities to 1 percent of the populationand the economic cost to 10 percent of annual GDP. Similarly, Aum et al. (2020b) shows thatthe progression of the virus in South Korea and UK can be effectively managed with aggressive
testing and contact tracing, which can in turn reduce both the economic and health costs.
An interesting question is whether differentiating containment/social distancing
measures across sectors and occupations can help to reduce the extent of lives lost and lower
the severity of the economic downturn. Bodenstein et al. (2020) and Krueger et al. (2020)
focus on this aspect through their variants of the SIR-Macro model.
Bodenstein et al. (2020) rely on a supply-side perspective that is centered on theeffects of the pandemic on the parts of the economy that provide essential inputs. The
authors develop an integrated framework by combining a standard SIR model containing two
groups of a heterogeneous population with a macroeconomic model. The transmission
mechanism between the epidemiological variables and the economic variables is through thechange in labor supply, i.e. infected people cannot participate in the workforce, which is adirect cost of the disease. The economic activities are divided between two groups: “core”
and “non-core” sectors with a low degree of substitutability in production between them.
The former produce raw and intermediate inputs, while the latter produces final-stage
outputs. The indirect cost stems from the fact that the slowdown/closure of core industries
will affect non-core industries through input-output linkages – what is typically called the‘supply chains’ in the media. The social distancing measures help to attenuate deaths andmorbidities, hence to curb the decrease in labor supply. The model shows that the absenceof social distancing leads to a negative 40 percent deviation from steady state in output inthis two-sector economic model. This contraction shrinks to a negative 20 percent deviationfrom the steady state with the enforcement of social distancing. Intuitively, “All else equal, a23lower infection peak shields better the core sector, resulting in economic gains (while
reducing the strain on the national health care systems). However, these gains now imply
some economic losses from reducing the labor supply and some economic gains fromsmoothing out the infection peak.” (Bodenstein et al., 2020, p. 23).
Krueger et al. (2020) also focus on the heterogeneity across sectors by introducing a
multi-sector economy with varying degrees of elasticity of substitution of consumption acrossgoods. In this case sectors differ according to the riskiness of consuming their respective
services. Based on their model, susceptible households substitute consumption from thehigh-infection sector with those from the low-infection sector in the event of an outbreak.
This re-allocation of spending patterns helps maintain a relatively stable consumption pathand lowers the risk of being infected from participating – as either a provider or a consumer- in high-infection activities. According to the authors, with all other things equal, this“reallocation” of economic activity may helps reduce the number of infections i.e. flatten the
curve.
Other researchers try to model the endogenous response of economic agents and timevarying nature of infection risks. Quaas (2020) and Dasaratha (2020) provide theoretical
propositions of behavioral responses to various changes in policies or infection levels. Alfaro
et al. (2020) modify the existing SIR models to account for optimizing decisions on social
interaction based on the infection risks. Typically, infection rates are taken as exogenous in
SIR models. However, after accounting for heterogeneity in preferences, they find that
preference traits, such as patience, altruism, and reciprocity, play important roles in reducing
the infection externalities. An approach that balances strict social distancing restrictions withsocial preferences is expected to help mitigate the economic and public health costs. To
provide an example, Argente et al. (2020) find that public disclosure of COVID-19 cases in
Seoul, South Korea led to a decrease in foot-traffic to neighborhoods/areas with more cases.
These data were calibrated into an SIR model with a heterogenous population to account for
infection transmission and economic outcomes. The authors find that, compared to a scenario
with no disclosure, public disclosure led to a decrease in infection by 400,000 cases anddeaths by 13,000 cases over a period of 2 years. The same policy is also expected to lower
economic costs by 50%. Fernández-Villaverde and Jones (2020) extend the endogenous
behavioral response by accounting for time-variation of infection rate or Ro parameter in SIR
24models. Using a Susceptible-Infectious-Recovered-Died (SIRD) model and different values ofR0 across countries, they find that forecasts prior to the peak death rates are ‘noisy’. Afterthe peak has occurred, however, these forecasts converged well with the actual data. Liu etal. (2020) find that COVID-19 growth rates can be forecasted by autoregressive fluctuations
and also suggested that the forecasts contain a lot of uncertainties due to parameter
uncertainties and realization of future shocks.30 Pindyck (2020) estimates how different
values of the R0 parameter affect death rates, durations of pandemics, and the possibility ofa ‘second wave’ of infection. The author also analyzes the benefits of social distancing
measures in terms of value of statistical life (VSL) and its implications in terms of realism.

5.3

Macroeconomic Impacts

As COVID-19 unfolds, many researchers have been trying to think about the economic impact
from a historical perspective. Ludvigson et al. (2020) find that, in a fairly conservative scenario
without non-linearities, pandemics such as COVID-19 are tantamount to large, multipleperiod exogenous shocks. Using a ‘costly disaster’ index, the authors find that multi-period
shocks in US (assumed to be a magnitude of 60 standard deviations from the mean of thecostly disaster index for a period of 3 months) can lead to a 12.75 percent drop in industrial
production, a 17 percent loss in service employment, sustained reductions in air travel, andmacroeconomic uncertainties which linger for up to five months. Jordà et al. (2020) analyze
the rate of return on the real natural interest rate (the level of real returns on safe assets
resulting from the demand and supply of investment capital in a non-inflationary
environment) from the 14th century to 2018. Theoretically, a pandemic is supposed to induce
a downward negative shock on the real natural interest rate. This is because investment
demand decreases due to excess capital per labor unit (i.e. a scarcity of labor being utilized),
while savings flows increase due to either precautionary reasons or to replace lost wealth.
The authors find that real natural rate remains depressed for a period of 40 years, decreasing
to -1.5 percent within 20 years.

30 Please see Hong et al. (2020) for an intuitive explanation behind the uncertainties through the caveats related

to the R0 number in managing COVID-19 risks.

25

However, analysis using historical data might not be sufficient. According to Baker etal. (2020b), COVID-19 has led to massive spike in uncertainty, and there are no close historicalparallels. Because of the speed of evolution and timely requirements of data, the authors
suggest that there is a need to utilize forward-looking uncertainty measures to ascertain its
impact on the economy. Using a real business cycle (RBC) model, the authors find that a
COVID-19 shock31 leads to year-on-year contraction of GDP by 11 percent in 4th quarter of2020. According to the authors, more than half the contraction is caused by COVID-19induced uncertainty. Coibion et al. (2020b) use surveys to assess the macroeconomic
expectations of households in US. They find that it is primarily lockdowns, rather than COVID19 infections, that lead to drops in consumption, employment, lower inflationary
expectations, increased uncertainty, and lower mortgage payments.32
Other researchers have examined the role of global supply chains. Bonadio et al.
(2020) use a quantitative framework to simulate a global lockdown as a contraction in labor
supply for 64 countries. The authors find that the average decline in real GDP constitutes amajor contraction in economic activity, with a large share attributed to disruptions in global
supply chains. Elenev et al. (2020) model the impact of COVID-19 as a fall in worker
productivity and a decline in labor supply which ultimately adversely affect firm revenue. The
fall in revenue and the subsequent non-repayment of debt service obligations create a wave
of corporate defaults, which might bring down financial intermediaries. Céspedes et al. (2020)
formulate a minimalist economic model in which COVID-19 also leads to loss of productivity.
The authors predict a vicious cycle triggered by the loss of productivity causing lower
collateral values, in turn limiting the amount of borrowing activity, leading to decreased
employment, and then lower productivity. The COVID-19 shock is thus magnified through an‘unemployment and asset price deflation doom loop’.33
Mulligan (2020) assesses the opportunity cost of “shutdowns” in order to document
the macroeconomic impact of COVID-19. Within the National Accounting Framework for the

31 This shock is formulated from the Standard &Poor’s (S&P) 500 Volatility Index (VIX) and the news-based

economic policy uncertainty (EPU) developed by Baker et al. (2016). Refer to Baker et al. (2020b) for details.
32 According to Coibion et al. (2020b), these findings have implications on monetary and fiscal policies and theirrespective transmission mechanisms.
33 Please see Fornaro and Wolf (2020) for a broader explanation of the mechanism.

26

US, the author extrapolates the welfare loss stemming from “non-working days”, the fall inlabor-capital ratio resulting from the absence/layoff of workers, and the resulting idle
capacity of workplace. After accounting for dead-weight losses stemming from fiscal stimulus,
the replacement of normal import and export flows with black market activities, and theeffect on non-market activities (lost productivity, missed schooling for children and young
adults), the author finds the welfare loss to be approximately $7 trillion per year of shutdown.
Medical innovations such as vaccine development, contact tracing, and workplace risk
mitigation can help offset the welfare loss by around $2 trillion per year of shutdown.
Consumption patterns and debt responses from pandemic shocks have not been
analyzed prior to COVID-19 (Baker et al., 2020a). Using transaction-level household data,
Baker et al. (2020a) find that households sharply increased their spending during the initialperiod in specific sectors such as retail and food spending. These increases, however, were
followed by a decrease in overall spending. Binder (2020) conducted an online survey of 500
US consumers to understand their concerns and responses related to COVID-19, whichindicated items of consumption on which they were spending either more or less. They findthat 28 percent of the respondents in that survey delayed/postponed future travel plans, andthat 40 percent forewent food purchases. Interestingly, Binder (2020) finds from the surveys
that consumers tend to associate higher concerns about COVID-19 with higher inflationary
expectations, a sentiment which is found to be a proxy for “pessimism” or “bad times”.
The economic impact of shocks such as pandemics is usually measured with aggregate
time series data, such as industrial production, GDP growth, unemployment rate, and others.
However, these datasets are available only after a certain lag - usually months or until the endof the quarter. On the other hand, economic shocks resulting from COVID-19 are occurring
at real time. In order to analyze the economic impact at a higher frequency, Lewis et al. (2020)
developed a weekly economic index (WEI) using ten different economic variables to track the
economic impact of COVID-19 in the US. According to the study, between March 21 andMarch 28, the WEI declined by 6.19 percent. This was driven by a decline in consumer
confidence, a fall in fuel sales, a rise in unemployment insurance (UI) claims, and othervariables. Similarly, Demirguc-Kunt et al. (2020) estimate the economic impact of social
distancing measures via three high-frequency proxies (electricity consumption, nitrogendioxide emissions, and mobility records). The authors find that social distancing measures led
27to a 10 percent decline in economic activity (as measured by electricity usage and emissions)
across European and Central Asian countries between January and April.

6

Socio-economic Consequences

We now review studies documenting the socioeconomic consequences of COVID-19 andlockdowns. Social distancing and lockdown measures have been shown to affect labormarkets, mental health and well-being, racial inequality and gender roles. The environmental
implications, while likely to be positive, also need careful analysis.34
6.1

Labor Market OutcomesA large number of studies document the effect of COVID-19 on hours of work and job losses(e.g., Adams-Prassl et al., 2020; Béland et al. 2020c; Coibion et al., 2020a; Kahn et al., 2020).
The unemployment increases observed in the US are partly driven by lockdown/social
distancing policies (Rojas et al., 2020). Accounting for cross-state variation in the timing ofbusiness closures and stay-at-home mandates in US, Gupta et al. (2020) find that theemployment rate in the US falls by about 1.7 percentage points for every extra 10 days that astate experienced a stay-at-home mandate during the period March 12 - April 12.
Coibion et al. (2020a) find that the unemployment/job loss in the US is more severe
than one might judge based on the rise in unemployment insurance (UI) claims, which is to
be expected given the low coverage rate for UI regimes in the US. They also calculate a severe
fall in the labor participation rate in the long run accompanied by an increase in “discouraged
workers” (unemployed workers who have actively stopped searching for work effectively
withdrawing from the labour force). This might be due to the disproportionate impact of
COVID-19 on the older population. Aum et al. (2020a) find that an increase in infections leads
to a drop in local employment in the absence of lockdowns in South Korea, where there were

34 A number of studies also investigate the effect of income and occupations on COVID-19 transmission [e.g.

Baum and Henry (2020), Lewandowski (2020)]. Using an instrumental variable approach, Qiu et al. (2020) findthat the spread of COVID-19 cases “between cities” in China is much smaller compared to “within city” spread.
Cities with higher income levels (measured by GDP per capita ay the city level) are more likely to have higher
transmission rates owing to more social interactions and higher levels of economic activity.

28

no government mandated lockdowns. This number increased for countries such as the US andthe UK where mandatory lockdown measures were imposed.
Adams-Prassl et al. (2020) analyze the inequality in job/income losses based on thetype of job and individual characteristics for the US and the UK. The authors find that workers
who can perform none of their tasks from home are more likely to lose their job. The study
also finds that younger individuals and people without a university education weresignificantly more likely to experience drops in their income. Yasenov (2020) finds thatworkers with lower levels of education, younger adults, and immigrants are concentrated in
occupations that are less likely to be performed from home. Similarly, Alstadsæter et al.
(2020) find that the pandemic shock in Norway has a strong socio-economic gradient, as it
has disproportionately affected the financially vulnerable population, including parents withyounger children.
Béland et al. (2020c) discuss heterogeneous effects across occupations and workers
in the US. They show that occupations that have a higher share of workers working remotely
were less affected by COVID-19. On the other hand, occupations with relatively more workers
working in proximity to others were more affected. They also find that occupations classified
as ‘more exposed to disease’ are less affected. This finding is possibly due to the number of
essential workers in these occupations. Based on these results, it can be reasonably expectedthat workers might change (or students might select different) occupations in the short or
medium-term.
Kahn et al. (2020) show that firms in the US have dramatically reduced job vacancies
from the 2nd week of March 2020. The authors find that the job vacancy declines occurred at
the same time when UI claims increased. Notably, the labor market declines (proxied throughthe reduction in job vacancy and the increase in UI claims) were uniform across states, withno notable differences across states which experienced the spread of the pandemic earlier
than others or implemented stay-at-home orders earlier than others. The study also findsthat the reduction in job vacancies was uniform across industries and occupations, except for
those in front line jobs, e.g. nursing, essential retail and others.
With the enforcement of social distancing measures, work from home has become
increasingly prevalent. The degree to which economic activity is impaired by such social
29

distancing measures depends largely on the capacity of firms to maintain business processes
from the homes of workers [Alipour et al (2020), Papanikolaou and Schmidt (2020)].
Brynjolfsson et al. (2020) find that the increase in COVID-19 cases per 100k individuals is
associated with a significant rise in the fraction of workers switching to remote work and thefall in the fraction of workers commuting to work in the US. Interestingly, the authors findthat people working from home are more likely to claim UI compared to people who are stillcommuting to work and are likely working in industries providing essential services.
Dingel and Neiman (2020) analyze the feasibility of jobs that can be done from home.
They find that 37 percent of jobs can be feasibly performed from home, which is related tothe extent that the job involves face-to-face interaction. According to Avdiu and Nayyar(2020), the job-characteristic variables of home-based work (HBW) and face-to-face (F2F)
interaction differ along three main dimensions, namely: i) temporal (short run vs. mediumrun); ii) the primary channel of effects (supply and demand of labour); and iii) the relevant
margins of adjustment (intensive vs. extensive). They argue that the supply of labor inindustries with HBW capabilities and low F2F interactions (e.g. professional, scientific andtechnical services) might be the least affected.

Nevertheless, those industries andoccupations with HBW capabilities and high F2F interactions are likely to experience negative
productivity shocks. For example, teachers in high schools and universities might provide
lectures online through web-based applications during lockdown restrictions. This mode ofteaching, however, is not as interactive as standard classroom sessions. As lockdown
restrictions are lifted, industries with low HBW capabilities and low F2F interactions (e.g.
manufacturing, transportation and warehousing) might be able to recover relatively quickly.
The risk of infection through physical proximity can be mitigated by wearing personal
protective equipment (PPE) and by taking other relevant precautionary measures. However,those industries with low HBW capabilities and high F2F interactions (e.g. accommodationand food services, arts entertainment and recreation) are likely to experience slower
recoveries, as consumers might be apprehensive about patronizing them, e.g. going tocinemas and restaurants.
From the firm’s perspective, there are large short-term effects of temporary closures,the (perhaps permanent) loss of productive workers, and declines in job postings
characterized by strong heterogeneity across industries. Bartik et al. (2020) survey a small
30

number of firms in the US and document that several of them have temporarily closed shopand reduced their number of employees compared to January 2020. The surveyed firms were
not optimistic about the efficacy of the fiscal stimulus (CARES Act loan program) implemented
by the federal government. Campello et al. (2020) find that job losses have been more severe
for industries with highly concentrated labor markets (i.e., where hiring is concentrated
within few employers), non-tradable sectors (e.g., construction, health services), and creditconstrained firms. Hassan et al. (2020) discern a pattern of heterogeneity of firm resilience
across industries in the US and around the World. Based on earnings call reports, they provide
evidence that some firms are expecting increased business opportunities in the midst of the
global disruption (e.g., firms which make medical supplies or others whose competitors arefacing negative impression after the outbreak of COVID-19). Barrero et al. (2020) measure thereallocation of labor in response to the pandemic-induced demand response (e.g., increased
hiring in delivery companies, delivery-oriented restaurant/fast food chains, technology
companies).

6.2

Health Outcomes

The impact of the pandemic on physical health and mortality has been documented in manystudies (e.g., Goldstein and Lee, 2020; Lin and Meissner, 2020).35 A growing number of studies
also document worsening mental health status and well-being e.g. Brodeur et al. (2020c);
Davillas and Jones (2020); de Pedraza et al. (2020); Tubadji et al. (2020). Chatterji and Li (2020)
document the effect of the pandemic on the US health care sector. The authors find that
COVID-19 is associated with a 67 percent decline in the total number of outpatient visits perprovider by the week of April 12th - 18th 2020 relative to the same week in prior years. Thismight have negative health consequences, especially amongst individuals with chronic healthconditions. Others such as Alé-Chilet et al. (2020) explore the drop in emergency cases in
hospitals around the world.

35 Lin and Meissner (2020) show that places that performed poorly in terms of mortality in 1918 were more likely

to have higher mortality during COVID-19. They also find that countries more strongly affected by SARS are likely
to have lower mortality rates from COVID-19.

31

Nevertheless, during a crisis such as the COVID-19 pandemic, it is common for everyone toexperience increased levels of distress and anxiety, particularly the sentiment of social
isolation (American Medical Association, 2020). According to Lu et al. (2020), social distancing
or lockdown measures are likely to affect psychological well-being through lack of access toessential supplies, discrimination or exclusion by neighbors or other groups, financial loss,
boredom, and frustration due to lack of information. They determine that maintaining a
positive attitude (in terms of severity perception, the credibility of real-time updates, andconfidence in social distancing measures) can help reduce depression.36 Public mental health isalso affected by the cognitive bias related to the diffusion of public death toll statistics(Tubadji et al., 2020).
Using the Canadian Perspective Survey Series, Béland et al. (2020b) find that older

individuals and employed individuals who have less than a high school education reported
lower mental health status. Their assessment also reveals that those who missed work not
due to COVID-19, and those who were unemployed, showed declines in mental health. Usingpanel data in the UK, Etheridge and Spantig (2020) report a large deterioration in the state ofmental health, with much larger effects for women.37
The implementation of lockdown policy also adversely affected public mental health.
Armbruster and Klotzbücher (2020) demonstrate that there were increases in the demand forpsychological assistance (through helpline calls) due to lockdown measures imposed inGermany. The authors find that these calls were mainly driven by mental health issues suchas loneliness and depression. Brodeur et al. (2020a) show that there has been a substantial
increase in the search intensity on ‘boredom’ and ‘loneliness’ during the post-lockdown
period in nine Western European countries and the US during the first few weeks oflockdowns.
Fetzer et al. (2020) find that there has been broad public support for COVID-19
containment measures. However, some of the respondents believe that the general public

36 Using pre-COVID-19 data, Hamermesh (2020) provide evidence that, adjusted for numerous demographic andeconomic variables, happiness is affected by how people spend time and with whom.
37 Galasso et al. (2020) rely on survey data from eight OECD countries and provide evidence that women are
more likely to agree with restraining public policy measures and to comply with them.

32fail to adhere to health measures, and that the governmental response has been insufficient.
These respondents have significant correlation with lower mental health. If governments areseen to take decisive actions, then the respondents altered their perception about
governments and other citizens, which in turn improved mental health.
6.3

Gender and Racial Inequality

A growing literature points out that COVID-19 has had an unequal impact across genders andacross genders across OECD countries; specifically, women and racial minorities such as
African-Americans and Latinos have been unduly and adversely affected.
While recessions typically affect men more than women, many studies provide
suggestive evidence that COVID-19 has a disproportionate impact on women’s socioeconomic
outcomes (Adams-Prassl et al. (2020), Forsythe (2020), Yasenov (2020)). Alon et al. (2020)
argue that women’s employment is concentrated in sectors such as health care andeducation. Moreover, the closure of schools and daycare centers led to increased childcare
needs, which would have a negative impact on working mothers/single mothers.
Béland et al. (2020a) analyze the domestic violence aspect of COVID-19 in Canada. The
authors find that work arrangements such as remote work is not increasing women’s
perceived impacts of COVID-19 on the levels of family stress and domestic violence. Instead,
women’s concerns regarding their inability to meet financial obligations due to COVID-19
contributed to a significant increase in reported family stress and domestic violence. They
also suggest that women’s concerns about maintaining social ties is positively associated withconcerns regarding domestic violence and family stress from confinement.
Fairlie et al. (2020) examine the variation in unemployment shocks amongst minority
groups in the US. The authors find that Latino groups were disproportionately affected by the
pandemic. The authors attribute the difference to an unfavorable occupational distribution(e.g., more Latino workers work in non-essential services) and to lower skills amongst Latino
workers. Borjas and Cassidy (2020) determine that the COVID-19 shock led to a fall in
employment rates of immigrant men compared to native men in US, which was in contrast to
the historical pattern observed during previous recessions. The immigrants’ relatively highrate of job loss was attributed to the fact that immigrants were less likely to hold jobs that33

could be performed remotely from home. The likelihood of being unemployed during Marchwas significantly higher for racial and ethnic minorities (Montenovo et al., 2020).
Schild et al. (2020) find that COVID-19 occasioned a rise of Sinophobia across theinternet, particularly when western countries started showing signs of infection. Bartos et al.
(2020) document the causal effect of economic hardships on hostility against certain ethnic
groups in the context of COVID-19 using an experimental setup. The authors find that the
COVID-19 pandemic magnifies hostility and discrimination against foreigners, especially from
Asia.
6.4

Environmental Outcomes

The global lockdown and the considerable slowdown of economic activities is expected tohave a positive effect on the environment (He et al., 2020; Almond et al., 2020; Cicala et al.,2020). He et al. (2020) show that lockdown measures in China led to a remarkable
improvement in air quality. The Air Quality Index and the fine particulate matter (PM2.5)
concentrations were brought down by 25 percent within weeks of the lockdown, with larger
effects in colder, richer, and more industrialized cities. Similarly, Almond et al. (2020) focusedon air pollution and the release of greenhouse gases in China during the post COVID-19. The
authors determined that, while nitrogen dioxide (NO2) emissions fell precipitously, sulphur
dioxide emissions (SO2) did not fall. For China as whole, PM2.5 emissions fell by 22 percent;however, ozone concentrations increased by 40 percent. These variations show that there is
not an unambiguous decrease in air pollution due to slowdown of economic activities. The
reduction can be attributed to less personal vehicle travel causing lower nitrous oxide (NO2)emissions. Brodeur et al. (2020b) examine the causal effect of ‘safer-at-home’ policies on air
pollution across US counties. They find that ‘safer-at-home’ policies decreased air pollution(measured as PM2.5 emissions) by almost 25%, with a larger effect for populous counties.38
Cicala et al. (2020) focus on the health and mortality benefits of reduced vehicle travel andelectricity consumption in the US due to the stay-at-home policies. The authors suggest that

38 Andree (2020) focuses on the effect of pollution on COVID-19 cases, finding that PM2.5 is a highly significant

predictor of COVID-19 incidence using data from 355 municipalities in Netherlands.

34

reductions in emissions from less travel and from lower electricity usage reduced deaths byover 360 per month.
Based on the research discussed above, Table 5 provides a summary of the literature
focusingonthesocioeconomic

outcomesofsocialdistancing/stay-at-homeorders/lockdowns, including measures and methodologies.

Table 5: Socioeconomic Outcome of COVID-19 Lockdowns: Summary of Studies
Country

Socioeconomic

Socioeconomic

domainOutcome Measure

Methodology

Author(s)Home Policies on

Difference-in-

Béland etLabor Market

Differencesal. (2020c)

Distancing Policies on

Difference-in-

Gupta et al.

Labor Market

Differences(2020)

Impact of Safer-atUS

Labor Market

Outcomes
Impact of Social
US

Labor Market

Outcomes
Impact of Safer-atUS

Labor Market

Home Policies on
Different

Data Analysis

Yasenov(2020)

Occupations
Impact on Job Market
Conditions in
USLabor Market

Response to COVID-

OLS

19 infections andRojas et al.
(2020)

Mitigation Policies
Impact of Quarantine
China

Mental Health &and Attitudes

Quantile

Lu et al.

Wellbeing

Towards COVID-19

Regression(2020)

on Depressive
35

Symptoms andHappiness
Impacts of SpendingUS

Mental Health &Wellbeing

Time “With Whom”
and “How” during

OLS

Lockdowns on

Hamermesh(2020)

Happiness
Effects of COVID-19

CanadaMental Health &Wellbeing

and lockdown on
Individuals’ Mental
Health and Financial

ProbitBéland etRegressional. (2020b)

Difference-in-

Tubadji etDifferencesal. (2020)and Work Concerns
UK, Italy

Mental Health &and Sweden Wellbeing

GlobalCausal Effect of
Lockdown on Mental
HealthMental Health &Effect of Lockdown

Wellbeingon Mental Health

OLS

Fetzer et al.
(2020)

Disproportionate
Effect of COVID-19 on
Gender Equality (inUS

Gender Inequality

terms of labor force

Surveyparticipation,

Alon et al.
(2020)

childcare needs,
workplace flexibility)
Effect of COVID-19
GenderCanadaand subsequent

Inequality/Domestic confinement on
Violencefamily stress andOLS

Béland etal. (2020a)

domestic violence36Effect of COVID-19
China

Environment

Lockdown on Air
Pollution
Effect of COVID-19

China

Environment

Lockdown on Air
Quality

Difference-in-

Almond etDifferencesal. (2020)

Difference-in-

He et al.

Differences(2020)

Impact of Social
Distancing on
Emissions andExpected Health
US

Environment

Effects Through

OLS

Reduced Personal

Cicala et al.
(2020)

Vehicle Travel andElectricity
Consumption.

US

Environment

Causal Effect ofDifference-in-

Safer-At-HomeDifferences/

Orders on Pollution

Synthetic

and Collision

Control

Externalities

MethodBrodeur etal. (2020b)

Incidence of COVID19 Lockdown andNetherlands Environment

Connections with Air
Pollution Exposure:

OLS

Andree(2020)

Evidence from theNetherlands

7

Policy Measures

The economic literature deals with many different policy measures. In order to bringcoherence to our discussion of them, we organize our discussion according to five broad37

topics: i) the types of policy measures, ii) the determinants of government policy, iii) the
lockdown measures and their associated factors, iv) the lifting of lockdowns, and v) the
economic stimulus measures.
To mitigate the negative effects of public health controls on the economy and tosustain and promote public welfare, governments all around the World have implemented avariety of policies in a very short time frame. These include fiscal, monetary, and financial
policy measures (Gourinchas, 2020). The economic measures vary across counties in breadthand scope, and they target households, firms, health systems and/or banks (Weder di Mauro,2020).
Using a database of economic policies implemented by 166 countries, Elgin et al.
(2020) employ Principal Component Analysis (PCA) to develop their COVID-19 Economic
Stimulus Index. The authors correlate the standardized index with predictors of government
response, such as population characteristics (e.g. median age), public-health-related
measures (e.g. number of hospital beds per capita), and economic variables (e.g. GDP per
capita). They find that the economic stimulus is larger for countries with higher COVID-19
infections, median age and GPD-per-capita. In addition, the authors develop a ‘Stringency
Index’, which includes the measures such as school closures and travel restrictions. The
authors find that the ‘Stringency Index’ is a not a significant predictor of economic stimulus,
which suggests that public health measures do not drive economic stimulus measures (Wederdi Mauro, 2020).
On a similar note, Porcher (2020) has created an index of public health measures using
the PCA technique. The index is based on 10 common public health policies implemented
across 180 countries to mitigate the spread of COVID-19. The index is designed to measure
the stringency of the public health response across countries. The author finds that,
abstracting from the COVID-19 case numbers and deaths, countries which have better publichealth systems and effective governance tend to have less stringent public health measures.
Cheng et al. (2020) develop the CoronaNet – COVID-19 Government Response
Database, which accounts for policy announcements made by countries globally since 31
December 2019. The information that is contained in the data base is categorized according
to: i) type of policy, ii) national vs. sub-national enforcement, iii) people and geographic region38

targeted by the policy, and iv) the time frame within which the policy is implemented. Table
6 provides a description of the government response database for 125 countries.
The dataset shows variations across policy measures. The policy most governments have
implemented in response to COVID-19 is “external border restriction”, i.e., the one whichseeks to restrict access to entry through ports.39 The authors find that external border
restrictions have been imposed by 186 countries. Similarly, the second most common policy
measure, implemented by 153 countries, is “school closures”. However, in terms of policies
which have implemented the greatest number of times, “obtaining or securing healthresources” come first. This includes materials (e.g. face masks), personnel (e.g. doctors,
nurses) and infrastructure (e.g. hospital). The second most implemented policy is “restriction
on non-essential businesses”. In terms of stringency of policy enforcement, “emergency
declaration” and formation of “new task force” or “administrative reconfiguration to tackle
pandemic” are implemented with 100 percent stringency.
Due to these idiosyncratic differences between policy responses across countries over time,
the authors use a Bayesian dynamic item-response approach to measure the implied
economic, social and political cost of implementing a particular policy over time. They further
develop a ‘Policy Activity Index’. Intuitively, the index gives a higher rank for policy activity tocountries that are more willing to implement a ‘costly’ policy i.e. meet the implied cost. Basedon the ‘Policy Activity Index’, the authors determine that school closure is the costliest to
implement across the 125 countries. Mandatory business closure and social distancing
policies come second. Moreover, internal border restrictions are seen to be more costly
compared to external border restriction.

39 Harris (2020a) maps the incidence of COVID-19 in New York City with subway usage. The author finds that the

shutoff of subway ridership in Manhattan correlates strongly with the substantial increase (doubling of newcases) in this borough. This is arguably due to alternative modes of transport (e.g. local train lines, bus lines)
leading to “closer interaction” amongst riders.

39

Table 6: Summary Statistics of COVID-19 Government Response Dataset
Cumulative Total
Number ofType of Policies

Implemented
Policies

Obtaining or Securing Health

Number ofCountries
which have
Implemented
Policies

Stringency of
PolicyEnforcement
(%)

2342

148

54

1855

135

92

1583

169

90

1102

161

87

External Border Restrictions

1064

186

83

Public Awareness Campaigns

609

137

23

Restrictions on Mass Gathering

575

159

87

Social Distancing (Voluntary)

518

127

71

373

99

80

345

104

100

Emergency Declaration

330

114

100

Health Monitoring

318

110

71

Internal Border Restrictions

313

111

89

Health Testing

283

98

67

Curfew

172

91

95

Resources
Restriction of Non-Essential
Businesses
School Closures
Quarantine/Lockdown/Stay-atHome Measures

Restrictions on Non-Essential
Government Services
New Task Force/Configuration of
Administration to Tackle
Pandemic

Source: Cheng et al. (2020)

40

Optimal lockdown policies have been studied mostly using epidemiologymacroeconomic models, some of which are oriented around the dichotomy between the casein which the choices (and responses) are made by private agents and the case in which the
choices are made by a social planner (Acemoglu et al. (2020), Alvarez et al. (2020), Berger etal. (2020), Bethune and Korinek (2020), Eichenbaum et al. (2020a)). Jones et al. (2020) argue
that in contrast to private agents, the social planner will seek to front-load mitigation
strategies, i.e. impose strict lockdowns from the beginning to reduce spread of infection andlet the economy fall into a recession or a depression. Others add that the outcomes are
dependent on the parameters of these models. The optimal policy reflects the rate of time
preference, epidemiological factors, the hazard rate for a vaccine discovery, the learning
effects in the health care sector, and the severity of output losses due to a lockdown(Gonzalez-Eiras and Niepelt, 2020). The intensity of the lockdown depends on the gradientof the fatality rate as a function of the infected and on the assumed value of a statistical life
(VSL). The absence of testing increases the economic costs of the lockdown and shortens theduration of the optimal lockdown (Alvarez et al., 2020). Chang and Velasco (2020) argue thatthe optimality of policies depends on peoples’ expectations. For instance, fiscal transfers must
to large enough to induce people to stay at home to reduce the degree of contagion;otherwise they might not change their behavior, which will increase the risk of infection.
Their analysis also provides a critique of the use of SIR models, as the parameters used inthese models (which remain fixed in value) would shift as individuals change their behavior in
response to policy.40
With the decrease in daily death rates, policies regarding re-opening of economy areof primary importance.41 Gregory et al. (2020) describe the lockdown measure as a “loss of
productivity”, whereby relationships between employers and labors are suspended,
terminated, or continued. They further explain that post COVID-19, the speed and the type
(V-shaped or L-shaped) of recovery depend on at least three factors: i) the fraction of workers
who, at the beginning of the lockdown, enter unemployment while maintaining a relationship

40 Chang & Velasco (2020) compare their critique of SIR models with that of the Lucas (1976) critique of macro-econometric models for policy estimation.
41 See, for example, T. M. Andersen et al. (2020), Glover et al. (2020) and Zhao et al. (2020) for how to relax
restrictions and whether the lockdowns should remain in place.

41

with their employer, ii) the rate at which inactive relationships between employers andemployees dissolve during the lockdown, and iii) the rate at which workers who, at the endof the lockdown, are not recalled by their previous employer can find a new, stable jobs
elsewhere (Gregory et al., 2020).
Harris (2020b) points out the importance of utilizing several status indicators (e.g.,
results of partial voluntary testing, guidelines for eligibility of testing, daily hospitalization
rates) in order to decide the course of action on re-opening the economy. Kopecky and Zha(2020) state that decreases in deaths are either due to implementation of social distancing
measures or to herd immunity, which is hard to identify using standard SIR models. They
argue that with the ‘identification problem’, there will be considerable uncertainty about
restarting the economy. Only comprehensive testing can help resolve this ambiguity by
quickly and accurately identifying new cases so that future outbreaks could be contained byisolation and contact-tracing measures (Kopecky and Zha, 2020).
Agarwal et al. (2020) develop a synthetic control group from mobility restriction
interventions applied in different countries to understand the trade-off between different
levels of interventions in US. They find that a small decrease in mobility reduces the numberof deaths; however, after registering a 40 percent drop in mobility, the benefits from mobility
restrictions (in terms of the number of deaths) diminish. Using a counterfactual scenario, theauthors find that lifting severe mobility restrictions and retaining moderate mobility
restrictions (e.g. by imposing limitations in retail and transport locations) might effectively
reduce the number of deaths in US. Others such as Rampini (2020) make the case forsequential lifting of lockdown measures for the younger population at the initial stages
followed by the older population at later stages. The authors state that the lower effect on
the younger population group is a fortunate coincidence, and thus the economic
consequences of interventions can be greatly reduced by adopting a sequential approach.
Oswald and Powdthavee (2020) make a similar case for releasing the younger populationfrom mobility restrictions first on the grounds of higher economic effectiveness and theirgreater resilience against infections.

42

As some US states reopened, researchers now focus on the immediate
consequences.42 Nguyen et al. (2020) find that four days after reopening, mobility has
increased by 6 to 8 percent, with greater increases across states which were late adopters oflockdown measures. These findings have important implications for the resurgence of cases,
hospital capacity, and further deaths. Dave et al. (2020a) analyze the effect of lifting the
shelter-in-place order in Wisconsin, after the Wisconsin Supreme Court abolished it, on social
distancing and COVID-19 cases and find no statistically significant impact.
It is important to recognize that the economy also faces a “flatten the curve” problem.
According to Gourinchas (2020), without substantial and timely macroeconomic intervention,
the output lost from the economic downturn will be greatly amplified, especially as economic
agents try to protect themselves from COVID-19 by reducing consumption spending,investment spending, and engaging in lower credit transactions.43 Gourinchas (2020) suggeststhat there should be cross-regional variation in government responses based on country
characteristics. Therefore, the type and level of fiscal and monetary stimulus designed tobuffer the economic downturn will vary significantly across countries.
With high amounts of government debt and historically low interest levels existing inmost developed countries, Bianchi et al. (2020) suggest a coordinated monetary and fiscal
policy to tackle the COVID-19 economic fallout. They recommend that fiscal policy should be
used to enact an emergency budget with a ceiling on the debt-to-GDP ratio. This measure
would increase aggregate spending, raise the inflation rate, and reduce real interest rates.
The monetary authorities would coordinate with fiscal policy authorities by adopting an
above-normal inflation target. In the long run, governments would try to balance the budget,and future monetary policy would aim to bring inflation back to normal levels.
Bigio et al. (2020) focus on the cases for government transfers vs. credit subsidy
policies. They determine that the optimal mix between them depends on the level of financial

42 See Balla-Elliott et al. (2020) for a survey of small American businesses’ expectations about re-opening.
43 Collard et al. (2020) explain that efficient allocation is determined by the marginal rate of substitution between

consumer utility and infection risks. For dynamic models, the resource allocation is determined by the interplay
between immunization and infection externalities. Hall et al. (2020) analyze the maximum amount ofconsumption people would be willing to forgo to avoid death from COVID-19. The authors find that the decreasein consumption ranges from 41 percent to 28 percent, depending on the average mortality rate for a group.

43

development in the economy. According to these authors, economies with a developed
financial system should utilize credit policies. On the other hand, developing economies
should engage in more transfer spending. Guerrieri et al. (2020) explore whether a supply
shock such as COVID-19 leads to a fall in excess demand in a multi-sector economy withincomplete markets. They find that a negative supply shock can lead to overreaction in termsof falling demand, especially in cases where there is low substitutability across goods,
incomplete markets, and liquidity constraints amongst consumers. They argue that the
optimal policy response is to combine loosening of monetary policy with enhanced social
insurance. In contrast, unconventional policies such as wage subsidies, helicopter drops ofliquid assets, equity injections, and loan guarantees can keep the economy in a fullemployment, high-productivity equilibrium (Céspedes et al., 2020). These policies can stop
the cycle of negative feedback loops between corporate default and financial intermediary
weakness, which can create a macro-economic disaster (Elenev et al., 2020). Didier et al.
(2020) discuss the policy menu, priorities and trade-offs of providing direct financing to firms.

8

Conclusion

This study delved into the research related to the economics of COVID-19 that has beenreleased in a relatively short time. The aim is to bring coherence to the academic and policy
debate and to aid further research.
Before delving into the impacts of COVID-19 and government response on theeconomy, we documented the most popular data sources to measure COVID-19 known
cases/deaths and social distancing. We first pointed out that COVID-19 cases and deaths
suffer from measurement error due to many factors including testing capacity. Mobility
measures using GPS coordinates from cell phones have been used extensively to measuresocial distancing. However, there are certain caveats that apply, particularly in terms ofprivacy concerns and the representativeness of data. The paper also reviewed different
research related to social distancing itself, particularly in regards to its determinants, its
effectiveness in mitigating the spread of COVID-19, and its compliance. Going forward, social
distancing and its measurements will continue to play a key role in academic research andpolicy development.
44

Going forward, the policy measures related to COVID-19 will continue to be an
important area of research. These measures, which have varied both in terms of scope andimplementation, are expected to yield a profound economic and social impact. This study
tried to bring coherence to these issues by covering different public health and economic
stimulus measures as well as providing a review of the literature on policy determinants,
optimal lockdown measures, factors affecting lifting of lockdown, and combinations of fiscal
and monetary policy measures aimed at ‘flattening the recession curve’.

45

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