2021 AWARENESS OF PANDEMICS AND THE IMPACT OF COVID-19 - DOCUMENTOS DE TRABAJO N.º 2123
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Awareness of pandemics
and the impact of COVID-19
2021
Documentos de Trabajo
N.º 2123
Alejandro Buesa, Javier J. Pérez
and Daniel SantabárbaraAwareness of pandemics and the impact of COVID-19
Awareness of pandemics and the impact of COVID-19 (*) Alejandro Buesa, Javier J. Pérez and Daniel Santabárbara Banco de EspaÑa (*) Corresponding author: Javier J. Pérez, javierperez@bde.es. DG Economics, Statistics and Research, Banco de España, calle de Alcalá, 48, Madrid, Spain. This is a preprint version of a refereed paper forthcoming in Economics Letters. Documentos de Trabajo. N.º 2123 May 2021
The Working Paper Series seeks to disseminate original research in economics and finance. All papers have been anonymously refereed. By publishing these papers, the Banco de España aims to contribute to economic analysis and, in particular, to knowledge of the Spanish economy and its international environment. The opinions and analyses in the Working Paper Series are the responsibility of the authors and, therefore, do not necessarily coincide with those of the Banco de España or the Eurosystem. The Banco de España disseminates its main reports and most of its publications via the Internet at the following website: http://www.bde.es. Reproduction for educational and non-commercial purposes is permitted provided that the source is acknowledged. © BANCO DE ESPAÑA, Madrid, 2021 ISSN: 1579-8666 (on line)
Abstract “Awareness” about the occurrence of viral infectious (or other) tail risks can influence their socioeconomic inter-temporal impacts. A branch of the literature finds that prior lifetime exposure to signicant shocks can affect people and societies, i.e. by changing their perceived probability about the occurrence of an extreme, negative shock in the future. In this paper we proxy “awareness” by historical exposure of a country to epidemics, and other catastrophic events. We show that in a large cross-section of more than 150 countries, more “aware” societies suffered a less intense impact of the COVID-19 disease, in terms of loss of lives and, to some extent, economic damage. Keywords: socioeconomic impact of pandemics, global health crises. JEL classification: E43, F41, N10, N30, N40.
Resumen La conciencia de los individuos y las sociedades sobre el alcance de las infecciones víricas y otros riesgos de cola puede influir en el impacto socioeconómico que estas dejan a lo largo del tiempo. La literatura muestra que la exposición a episodios negativos o extremos durante la trayectoria vital de las personas puede continuar afectándoles sustancialmente más adelante, ya que su percepción de la probabilidad de que estos eventos ocurran en el futuro se ve alterada. Este artículo utiliza la exposición histórica de un país a epidemias y otros eventos catastróficos como un instrumento de la conciencia de experiencias previas. Los resultados, utilizando una sección cruzada de más de 150 países, sugieren que en aquellas sociedades que se han mostrado «más conscientes», el COVID-19 ha tenido un menor impacto en términos de coste humano y, hasta cierto punto, también económico. Palabras clave: impacto socioeconómico de las pandemias, crisis sanitarias globales. Códigos JEL: E43, F41, N10, N30, N40.
1 Introduction
The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2, the virus that causes COVID-
19) came as a surprise for many individuals and nations, but not for others. Some governments and
individuals were more “aware” of the possibility of a pandemic outburst of this sort than others,
1 Introduction
for at least two reasons. First, a big part of the scientific community had been warning for at
The
leastsevere acutewith
a decade respiratory syndrome
increasing coronavirus-2
intensity (SARS-CoV-2,
about the likely theofvirus
appearance that causes
“disease X” (seeCOVID-
WHO,
19) came
2017; as a surprise
Daszah, 2020; defor Bolle,
many individuals
2021). On and
the nations, but not
other hand, for countries
some others. Some governments
or regions and
had been
individuals were
more affected more
over the“aware” of theby
past decades possibility
infectiousof diseases
a pandemic
(like,outburst
SARS in of 2002,
this sort
MERSthaninothers,
2012,
for at least
or Ebola in two 1 and/orFirst,
2014)reasons. other aextreme
big partnatural
of the events
scientific community
with had been of
very low frequency warning for ata
impacting
Figure 1: World-wide biological and other natural, extreme events, 1950-2020
least acommunity
given decade with(likeincreasing intensity
earthquakes, about
volcano the likely
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or tsunamis). of phenomena
Such “disease X”have
(see become
WHO,
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of events
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2021).
(see On the 1).
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Societies someprone
countries
to the
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x 1000 regions had been
of these
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or that past been
decades by infectious
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past, 2002,beMERS in 2012,
more prepared
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or Ebola
to new 1episode
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other extreme natural
a recurrent wave of events with very
an ongoing lowcase
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of biological events)-a
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literature have been subject
has highlighted to them through
some channels in a not-so-distant past, may
which the degree be more prepared
of “awareness” deter-
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to identify
mines a150
newand
the social episode -or ainter-temporal
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of an of
ongoing one (in
a pandemic. 2 case of biological
In Economics, events)-
Kozlowski,
600
in an earlyand
Veldkamp fashion, or might have
Venkateswaran developed
(2020) show thatmore resilient
the main and forward-looking
economic policycould
costs of a pandemic toolsarise
and
100
400
institutions
from changestoinmitigate their impact.
agents’ behaviour long after the immediate health crisis is settled. 3 Indeed, Jordà,
50 200
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Singh literature has highlighted
Taylor (2020) some channels
provide empirical through
evidence basedwhich the degree
on a wealth of “awareness”
of historical episodesdeter-
that
0 0
mines the social andlong-run
economic inter-temporal 2 In Economics, Kozlowski,
pandemics do have
1950 1960 economic 1980 impact
1970 consequences. Inofturn,
1990 a pandemic.
the epidemiological
2000 2010 literature shows
Veldkamp
that andHydrological
individual Venkateswaran
awareness (2020)
(human)Meteorological is show that
a relevant
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factor
Biological to economic
# of account
people forcosts
affected/dead the
by of aworldwide
pandemic
spreading
epidemics ancould
ofaxis)
(right arise
epidemic,
from changesthe
in agents’ behaviour 3 Indeed, Jordà,
by stressing interplay betweenlong after the
awareness immediate
and health crisis
disease outbreak (see,isamong
settled.others, Granell et
Source: EM-DAT database: https://www.emdat.be/.
Singh andWu
al., 2013; Taylor (2020)
et al., 2012;provide
Samantalempirical evidence based2014;
and Chattopadhyay, on aorwealth
Wang of
et historical
al., 2020).episodes that
pandemics
1
do have
Just to quote long-run
the most economic
prominent consequences.
examples of the past 20Inyears,
turn,as the epidemiological
noted in WHO (2017):literature
the Severeshows
Acute
Respiratory
Against Syndrome
this (SARS) appeared
background, in forpaper
this the first
wetime in to
test 2002, and extent
what spread across
more hemispheres
“aware” in just sixsuffered
societies months;
that individual (human) awareness is a relevant factor to account for the spreading of an epidemic,
the Middle East Respiratory Syndrome (MERS), identified in 2012, spread to 26 countries in three years and is still
active;
aby less the Ebolaimpact
intense outbreak(both
that erupted
humanin the spring
and of 2014ofspread
economic) through the whole
the outbreak
COVID-19 diseaseregion of West
spread. Africa
Our aiminet
isa
matterstressing theto interplay
of weeks; date, and in between awareness
particular since 2015 and disease
a total of 86 countries (see,
and among
territories others,
have Granell
reported evidence
to
al.,2shed
2013;some light
of mosquito-transmitted
Wu et al.,in2012;
understanding
Zika-virus infection.the striking heterogeneity among countries in the incidence
Samantal and Chattopadhyay, 2014; or Wang et al., 2020).
Infectious diseases, in particular those that turn into pandemics, lead to significant human and socioeconomic
of 1theFor
costs. pandemic and its economic costs. To test the(2018),
hypothesis at ethand we take the COVID-19
following
Just tohistorical
quote theevidence see, among
most prominent others, of
examples Bloom et al.
the past 20 years, as or noted
Smith al. (2019).
in WHO (2017):For thethe
Severe Acute
crisis, see
Respiratory IMF (2020)
Syndrome or Sapir (2020).
(SARS) appeared for of
theawareness,
first time in using
2002, and spread across hemispheres in just six
steps.
3
On First,
related we construct indicators measures of historical exposure tomonths;
virual
the Middle Eastgrounds,
Respiratory Lin Syndrome
and Meissner (2020),
(MERS), when studying
identified in 2012, the
spreadlinktobetween public
26 countries in health performance
three years in
and is still
the early days
active; the Ebola of the COVID-19
outbreak pandemic and
that eruptedevents. those
in the spring during the Spanish Influenza pandemic of 1918-20, find that
outbreaks,
experience andSARS
with otheris catastrophic
associated with lower Next,of we
mortality
2014 spread
build
today,
through the
measures whole
of countries
the region ofof
incidence West
theAfrica
COVID- in a
matter of weeks; to date, and in particular since 2015 a total of in
86acountries
sample ofand 33 territoriesworldwide.
have reported evidence
19 pandemic, both from
of mosquito-transmitted the human
Zika-virus infection.and economic points of view. Finally, we estimate spatial
2
Infectious diseases, in particular those that turn into pandemics, lead to significant human and socioeconomic
econometric models
costs. For historical linking
evidence see,both
amongsets of indicators
others, Bloom et al.using a or
(2018), cross-section
Smith et al. of about
(2019). For150
the countries
COVID-19
crisis, see IMF (2020) or Sapir (2020).
across
3
On the world.
related The Lin
grounds, spatial econometric
and Meissner framework
(2020), allows
when studying theus to between
link control public
for the proximity
health among
performance in
the early days of the COVID-19 pandemic and those during the Spanish Influenza pandemic of 1918-20, find that
countries, a direct
experience with SARSamplifier of spillovers
is associated with lower from countries
mortality more
today, in exposed
a sample of 33to the pandemic
countries to the others.
worldwide.
We also include other geographical and socioeconomic controls, including lockdown and curfew-type
BANCO DE ESPAÑA 7 DOCUMENTO DE TRABAJO N.º 2123
measures adopted by governments, a key element identified in the literature (see e.g. Ferraresi et
al., 2020).
The rest of the paper is organized as follows. In Section 2, we outline the econometric method-400 1.800
Figure 1.600
350 1: World-wide biological and other natural, extreme events, 1950-2020
Figure 1: World-wide biological and other natural, extreme events, 1950-2020 1.400
300
Figure #1:of events
World-wide biological and other natural, extremeInhabitants
events,x 1000
1950-2020
400 1.200
1.800
250 # of events Inhabitants x 1000
400 1.800
1.000
1.600
350 # of events Inhabitants x 1000
200
400 1.800
350 1.600
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1.400
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150
350 1.600
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0
200 800
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150 1950 1960 1970 1980 1990 2000 2010 800
100 600
150 400
Hydrological Meteorological Geophysical Biological # of people affected/dead by epidemics worldwide (right
600axis)
100
50 400
200
100
400
50 200
0 database: https://www.emdat.be/.
Source: EM-DAT 0
50 1950 1960 1970 1980 1990 2000 2010 200
0 0
1950 1960 1970
Geophysical 1980
Biological 1990 2000 2010 worldwide (right axis)
0
Hydrological Meteorological # of people affected/dead by epidemics 0
Against this1950 1960 in this
background,
Hydrological
1970paper we
Meteorological
1980test to1990
Geophysical
what extent
Biological
2000 more2010 “aware” societies
# of people affected/dead by epidemics worldwide (right axis)
suffered
Hydrological Meteorological
a less intense
Source: impact
EM-DAT (both
database: humanGeophysical Biological
and economic)
https://www.emdat.be/.
# of people affected/dead by epidemics worldwide (right axis)
of the COVID-19 disease spread. Our aim is
to Source:
shed some light
EM-DAT in understanding
database: the striking heterogeneity among countries in the incidence
https://www.emdat.be/.
Source: EM-DAT database: https://www.emdat.be/.
of the pandemic
Against and its economic
this background, in thiscosts.
paper To
we test to
thewhat
hypothesis at hand
extent more we take
“aware” the following
societies suffered
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steps.
a less First,this
intense we background,
construct
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(bothindicators
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of awareness,
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using extent more
themeasures
COVID-19 “aware”
of historical
disease societies
exposure
spread. tosuffered
Our virual
aim is
Against this background, in this paper we test to what extent more “aware” societies suffered
a
toless
shedintense
outbreaks,some impact
andlight (both human
otherincatastrophic
understanding and
the economic)
events. of themeasures
COVID-19
Next, weheterogeneity
striking build of the
among disease spread.
incidence
countries inoftheOur
the aim is
COVID-
incidence
a less intense impact (both human and economic) of the COVID-19 disease spread. Our aim is
to
19 shed
of the some light
pandemic,
pandemic bothandin
fromunderstanding
its the humancosts.
economic the
and striking
To testheterogeneity
economic points
the among
of view.
hypothesis countries
at Finally,
hand wewe in the
the incidence
estimate
take spatial
following
to shed some light in understanding the striking heterogeneity among countries in the incidence
of the First,
pandemic
econometric
steps. modelsandlinking
we construct its economic
both sets
indicators costs. To test using
of ofawareness,
indicators the hypothesis
using at
a cross-section
measures handofweabout
of historical take the countries
150
exposure following
to virual
of the pandemic and its economic costs. To test the hypothesis at hand we take the following
steps.
across First,
the
outbreaks, weother
world.
and construct indicators
Thecatastrophic
spatial of awareness,
econometric
events. we using
framework
Next, measures
allows
build of the
us to control
measures of historical
for theexposure
incidence of the to
proximity virual
among
COVID-
steps. First, we construct indicators of awareness, using measures of historical exposure to virual
outbreaks,
countries,
19 pandemic,aand other
direct
both catastrophic
amplifier
from the humanevents.
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and Next, we build
countries
economic measures
more
points view.oftothe
exposed
of theincidence
pandemic
Finally, ofto
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we estimatetheCOVID-
others.
spatial
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19 pandemic,
We also include
econometric both
otherfrom
models linkingtheboth
geographicalhuman andand
sets economic points
socioeconomic
of indicators aofcross-section
controls,
using view. Finally,
including lockdown we estimate
of about and150 spatial
curfew-type
countries
19 pandemic, both from the human and economic points of view. Finally, we estimate spatial
econometric
measures
across models
theadopted
world. bylinking
The spatialboth
governments, sets
a key
econometric of element
indicators
framework using
allowsa us
identified incross-section
the literature
to control of the
for about
(see e.g.150 countries
Ferraresi
proximity amonget
econometric models linking both sets of indicators using a cross-section of about 150 countries
across
al., the aworld.
2020).
countries, direct The spatial
amplifier of econometric
spillovers from framework
countriesallows us to control
more exposed to thefor the proximity
pandemic among
to the others.
across the world. The spatial econometric framework allows us to control for the proximity among
countries,
We The a direct
rest of the
also include amplifier
paper
other is of spillovers
organized
geographical from countries
andassocioeconomic
follows. more 2,
In Section exposed
controls, we to the
outline
including pandemic
the andtocurfew-type
econometric
lockdown themethod-
others.
countries, a direct amplifier of spillovers from countries more exposed to the pandemic to the others.
We
ologyalso
measures include
and adopted other
describe bythegeographical
data used. and
governments, In socioeconomic
Section
a key element controls,
3 weidentified
discuss in including
the main
the lockdown
results
literature of the
(see and curfew-type
paper,
e.g. and et
Ferraresi in
We also include other geographical and socioeconomic controls, including lockdown and curfew-type
measures
Section 4 adopted
al., 2020). we draw by some governments, a key element identified in the literature (see e.g. Ferraresi et
policy implications.
measures adopted by governments, a key element identified in the literature (see e.g. Ferraresi et
al., The
2020).
rest of the paper is organized as follows. In Section 2, we outline the econometric method-
al., 2020).
2 The
ology restdescribe
of the paper
Methodology
and the datais
andorganized
data
used. In as follows.
Section In Section
3 we discuss 2,
thewemain
outline the econometric
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and in
The rest of the paper is organized as follows. In Section 2, we outline the econometric method-
ology
Sectionand describe
4 we the data
draw some policyused. In Section 3 we discuss the main results of the paper, and in
implications.
ology and describe
Methodology the data used. a In Section 3 we discuss the 150
main results ofanthe paper, and in
Section 4 we drawWe someregress,
policyfor large
implications. cross-section of over countries, indicator of the
Section
incidence 4 we draw
of the some policy
pandemic (S) onimplications.
an indicator of awareness (E), and a number of control variables
(X), including a “spacial lag”. For country i and time unit t the model takes the form:
K
Si,t = θW Si,t + β0 Ei,t + φk Xk,i,t i,t (1)
k=1
where θW Si,t captures the autocorrelation of the effects of the pandemic between close countries
BANCO DE ESPAÑA 8 DOCUMENTO DE TRABAJO N.º 2123
through the spatial weighting matrix W . For N countries, this object contains N 2 elements where
the element wi1 ,i2 captures the distance from country i1 to country i2 . The main diagonal is filledK
Si,t = θW Si,t + β0 Ei,t + φk Xk,i,t i,t (1)
k=1
2 Methodology and data
where θW Si,t captures the autocorrelation of the effects of the pandemic between close countries
through the spatial
Methodology Weweighting matrix
regress, for W . For
a large N countries,
cross-section this object
of over 150 countries, N 2indicator
contains an elements of
where
the
the element
incidence of w
the 2 captures(S)
i1 ,ipandemic theon
distance from country
an indicator i1 to country
of awareness (E), andi2a. number
The main diagonalvariables
of control is filled
with including
(X), zeros. Accounting
a “spacialfor the For
lag”. proximity
countryamong
i and countries
time unit ist the
key,model
given takes
that the
the health
form: situations
of closer geographies are likely to be more connected. While the concept of distance can refer to
K
a variety of economic, social or
Si,tgeographical
= θW Si,t + βattributes,
E
0 i,t + we adopt
φk X the latter in our analysis. We
k,i,t i,t (1)
respiratory diseases (such as MERS and SARS, among k=1others), and, more specifically,
use two alternative approaches: (i) a more traditional contiguity approach, whereby only adjacent on SARS-
where
CoV-1;
countries θWnumber
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all countries in .that
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2000-2019.
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Indicators
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with of
of incidence
awareness
zeros. Accounting for of We
the the pandemic
proxy
proximity amongFirst,
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with
countries is key, the
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given the human
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of
focus casualties,
on or the
eventslosses:
that (i)disaster
Annual
occurred inhas
theprompted
growth ofthe declaration
rate2000-2019.
period GDP of Revisions
in52020; (ii) a state of
emergency
to 2020 GDP in growth
a country. Epidemic
forecasts diseases
by the are grouped
International withinFund
Monetary natural disasters
(IMF) (biological).
with respect to the pre-
Indicators
pandemic of incidence
We combine information
outlook, of in
proxied by the
the pandemic
EM-DAT
forecasts with First, as the
population
published by regards
IMF the
statistics direct human
from the
in November incidence,
World
2019. WeBank we
take and
the
focus on the
construct the
projections fatality
IMF’srates
following
from measures
flagship of disaster6 awareness
of COVID-19.
publication We compute
World theOutlook.
accumulated
by country:
Economic (i) number number of April
of epidemic
Specifically, the deaths at
episodes
2020
aaffecting
given reference
vintage, morecan
that date
than
be 100inpeople;
seen a an
as given country
(ii)
initial withinasthe
estimate aoffraction
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theofnumber
the incidence focus
the ofoninhabitants,
pandemic, outbreaks
based on tolimited
allow
linked to
4 7
for cross-country
For comparability.
our benchmark
within-the-year specifications
information, Weresults,
andand
the show we
Novemberresults
use thefor
2020 three
contiguity
one. reference dates:
approach, but 1-month
all results usingafter the
the other
measure are available upon request.
pandemic th death was reported), 3-months after the
5
Results outbreak (proxiedconstructed
for related measures by the date at which
different the 10
thresholds for the affected population are available upon request
and provide very similar results. In addition, if awareness is linked to preparedness, there are indices that proxy the
same
latter. date,
One is and the cumulative
the Global number
Health Security Index of cases
(GHS as see
Index: of 31 December 2020. Looking at developed
https://www.ghsindex.org/about/) the results
by
the Nuclear Threat Initiative, the Johns Hopkins Center for Health Security and The Economist Intelligence Unit.
using different
The GHS Index isreference dates
a quantitative allowsonushealth
indicator to account
security for
and the factcapabilities
related that, as across
the pandemic developed
195 countries. Results
using this index are available upon request, and show no robust link between GHS and pandemic incidence.
worldwide,
6 governments and individual citizens took social distancing measures and actions. Thus,
Source: Johns Hopkins Coronavirus Resource Center: https://coronavirus.jhu.edu/.
7
as In all case
regards we hypothesis
our trim the upper
of and lower 5% of“awareness”,
pre-existing the forecasts’ distribution
an assumed to advantage
prevent distortion from outliers.
may have weakened
BANCO DE ESPAÑA 9 DOCUMENTO DE TRABAJO N.º 2123
over time.
Second, regarding economic incidence, we look at indicators based on economic losses for the
whole of 2020. This is motivated by the fact that the use of higher frequency data (either monthly orfocus on the fatality 6 We compute the accumulated number of deaths at
Figurerates of COVID-19.
2: COVID-19 incidence (Y-axis) and “awareness” (X-axis).
a given reference date in a given country as a fraction of the number of inhabitants, to allow
Deaths 1-month vs. # epidemics
Deaths 1-month vs. # epidemics
Deaths 3-month vs. # epidemics
Deaths 3-month vs. # epidemics
Deaths end-2020 vs. # epidemics
Deaths vs. # epidemics
15
6
for cross-country comparability. We show results for three reference dates: 1-month after the
6
4
pandemic outbreak (proxied by the date at which the 10th death was reported), 3-months after the
4
10
2
2
same date, and the cumulative number of cases as of 31 December 2020. Looking at the results
5
0
0
using different reference dates allows us to account for the fact that, as the pandemic developed
-2
-2
0
0 1 2 3 4 0 1 2 3 4 0 1 2 3 4
worldwide, governments and individual citizens took social distancing measures and actions. Thus,
Deaths 1-month vs. # disasters
Deaths 1-month vs. # disasters
Deaths 3-months vs. # disasters
Deaths 3-month vs. # disasters
Deaths end-2020 vs. # disasters
Deaths vs. # disasters
as regards our
respiratory hypothesis
diseases (such of
as pre-existing
MERS and “awareness”,
SARS, amonganothers),
assumed advantage
and, may have on
more specifically, weakened
SARS-
15
6
6
4
over time.
CoV-1; (iii) number of natural disasters affecting more than 0.1% of the country’s population. We
4
10
2
Second, regarding
andeconomic
focus onincidence, weoccurred
look at in
indicators based 5
on economic losses for the
2
restrict our sample events that the period 2000-2019.
5
0
0
whole of 2020. This is motivated by the fact that the use of higher frequency data (either monthly or
-2
-2
0
Indicatorswould
respiratory
quarterly) of incidence
diseases
0 1
severely asofMERS
(suchreducethe
ourpandemic
and SARS,
2
sample First,
amongasto
3
of countries, regards
others),
between the
and, direct
40 more
4
human
5
countriesincidence,
and 70specifically,
0
we
on SARS-1
(depending
2 3 4 5 0 1 2 3 4 5
IMF first revision vs. # epidemics IMF rev.6 1-year vs. # epidemics GDP 2020-2019 vs. # epidemics
focuson
CoV-1;
also on thenumber
(iii)
availablefatalityofrates
control of COVID-19.
natural disasters
variables, presented We compute
affecting
later), more
IMF revisions (ST) vs. # epidemics
with than the accumulated
0.1%
a marked of the
bias number
country’s
towards ofeconomies.
deathsWe
population.
advanced at
IMF revisions (MT) vs. # epidemics GDP variation vs. # epidemics
10
0
0
arestrict
given our
reference date in aallows
given country asoccurred
a in
fraction of period
the number 5
tosample and focus on eventsto that ourin analysis
the 150of countries,
2000-2019. inhabitants, to aallow
-5
Resorting annual data us include some with fair
0
-5
-10
for cross-country comparability. We show results for three(see reference dates:More
1-month after the
-10
representation of advanced and emerging market economies Table A1). specifically, we
-15
-10
Indicators
pandemic of
outbreak incidence
(proxied of the pandemic First, asth regards the direct human incidence, we
of by the date at which the 10 growth death rate was ofreported), 3-months after the
-20
-20
use the following measures economic losses: (i) Annual GDP in 2020; (ii) Revisions
focus date,
on theand fatality rates of COVID-19. 6 We compute the accumulated number of deaths at
-25
-15
-30
same
to 2020 GDP growth
0 the cumulative
1
forecasts bynumber
2 of cases
the International 3 as Monetary
of 31 December
4 0
Fund (IMF)2020. with
1 Looking
respect2at the
to theresults
pre- 3 4 0 1 2 3 4
a given
using reference
different date
reference in a
dates given
allows country
us as a fraction
to published
account of the number of inhabitants, to allow
Notes:
pandemic Human incidence
outlook, indicators
proxied by the(in logs): “Deaths
forecasts 1-month”for bythe
refers theto fact
the
IMF that,
number asCOVID-19
of the pandemic
in November Wedeveloped
casualties
2019. per
takemillion
the
inhabitants in the 1st month after the 10th casualty was registered; “Deaths 3-months”, three months after the 10th casualty;
for cross-country
worldwide,
“Deaths end-2020”,
comparability.
governmentsas of 31 and
December
We
individual show results
citizens
2020. Economic
for
tookEconomic
social
incidence
three reference
distancing
indicators:
dates:
“IMF 1measures
1-month
st revision”and
refers
after
actions.
to the
the
Thus,
difference
projections from IMF’s flagship publication World Outlook. Specifically, the April 2020
in GDP growth forecasts for 2020 between the April-2020 and October-2019 th IMF World Economic Outlook reports; “IMF
pandemic
as regards
rev. outbreak
our
1-year” (proxied
hypothesis
refers by
of an
to the forecast the date
pre-existing at
differences between which the
“awareness”, 10 death
an assumed
the October-2020 was reported),
advantage
and October-2019 IMF WEO 3-months
maybased
have on
reports. after
weakened the
As regards
vintage, that can be seen as initial estimate of the incidence of the pandemic,
indicators of “awareness”: “# epidemics” refers to the number of epidemic episodes suffered by a country between 2000
limited
same
over date,
andtime. and
2019 that the cumulative
affected more than 100number people; “#ofdisasters”
cases as of 31
refers to theDecember 2020. Looking at the results
7 number of biological and other natural disasters
within-the-year information,
suffered by a country between 2000 and
andthe 2019November
that affected 2020 one.0.1%
more that of its population.
using different
Second, reference
regarding dates
economic allows us
incidence, toweaccount
look atfor the
indicators fact that,
based asonthe pandemic
economic developed
losses
5
Results for related measures constructed different thresholds for the affected population are available uponfor the
request
worldwide,
whole governments
and provide very
Control variables
of 2020. This Toand
similar results. Inindividual
control
is motivated addition, ifcitizens
byfor awareness
thefactors
fact took social
is linked
potentially
that the use ofto distancing
preparedness,measures
affecting
higher there are and
the evolution
frequency of actions.
dataindices the
(either that proxyThus,
pandemic
monthly the
or
latter. One is the Global Health Security Index (GHS Index: see https://www.ghsindex.org/about/) developed by
as
the regards
other Nuclear our
thanwould
quarterly) hypothesis
Threat
“awareness”,
severely of include
Initiative,we
the pre-existing
Johns
reduce ourHopkins“awareness”,
Center
the following
sample of countries,antoassumed
forvariables
Health Security advantage
in theand
between 40 The may have
andEconomist
analysis: urban weakened
Intelligence
population
70 countries Unit.
(depending as
The GHS Index is a quantitative indicator on health security and related capabilities across 195 countries. Results
aover
using
also time.
percentage
on index of
thisavailable total
arecontrol population
available in 2019;
upon request,
variables, the
and show
presented noaverage
robust
later), atemperature
withlink GHSbetween
betweenbias
marked 1991 incidence.
and pandemic
towards advancedandeconomies.
2016; the
6
Source: Johns Hopkins Coronavirus Resource Center: https://coronavirus.jhu.edu/.
7 Second, regarding economic incidence, we look at indicators based on economic losses for the
average
In all household
Resorting case trimsize
toweannual in 2019;
thedata
upperallows grosstonational
us 5%
and lower include
of income per capita,
in ourdistribution
the forecasts’ analysis PPP150
some
to (current
prevent US
countries,
distortion dollars).
fromwith In
a fair
outliers.
whole of 2020.
addition,
representation This
via dummy is motivated
variables,
of advanced and weby the factmarket
control
emerging thatthe
for the use of higher
geographical
economies frequency
(seelocation ofdata
Table A1). each (either
More countrymonthly
withinwe
specifically, or
a
quarterly)
continental would
use the following severely
(Africa,reduce
groupmeasures our sample
Oceania,
of economicNorth of (i)
countries,
America,
losses: to between
rate40
South-Central
Annual growth and 70in
America,
of GDP countries
Asia, (ii)(depending
2020; Europe), and
Revisions
also on available
distinguish
to 2020 GDP control
between
growth variables,
emerging
forecasts by presented
markets versuslater), with
advanced
the International a marked
economies,
Monetary bias
Fundand towards
small
(IMF) advanced
versus
with largeeconomies.
respect tocountries
the pre-
Resorting
(a
pandemic to annual
dummyoutlook,
that takes data
value allows
proxied theus
1 ifthe
by to include
population
forecasts is in our
above
published analysis
bythe IMFsome
themedian all150
inofNovembercountries,
countries with
in the
2019. We a fair
sample).
take the
representation
projections ofwe
In addition,
from advanced
control
IMF’s and
flagship emerging
for the market
incidence
publication economies
of policy
World (see
decisions,
Economic Table A1). More
as measured
Outlook. specifically,
by the
Specifically, thewidely-used
April 2020we
use
Non the following
that canmeasures
Pharmaceutical
vintage, of an
economic
Intervention
be seen as losses:(NPIs),
indicator
initial estimate (i)ofAnnual
thethe growth of
Oxford
incidence rate of pandemic,
COVID-19
the GDPGovernment
in 2020; (ii)on
based Revisions
Response
limited
to 2020 GDP
Tracker growth
of Hale
within-the-year et al. forecasts
(2020).and
information, by the
The the International
indicator
November Monetary
is available
2020 Fundset
for7 a large
one. (IMF) with respect
of countries. Moretostringent
the pre-
pandemic
containment
5 outlook,
Results for related proxied
policies
measures by thestringent
more forecasts
(e.g. constructed published
lockdowns
different by theaffected
or the
thresholds for IMF in
curfews) November
entail 2019. inWe
an increase
population are available take
the
upon the
index.
request
and provide very similar results. In addition, if awareness is linked to preparedness, there are indices that proxy the
projections
Ex ante,
latter. One one
from
is themay
IMF’s
think
Global
flagship
that
Health
publication
more
Security
World
“awareness”
Index
Economic
might
(GHS Index:
Outlook. with
be associated Specifically, the developed
April 2020
the implementation
see https://www.ghsindex.org/about/) of
by
the Nuclear
vintage, Threat
that can Initiative,
be seen thean
as Johns Hopkins
initial Centeroffor
estimate Health
the Security
incidence of and
theThe Economist
pandemic, Intelligence
based on Unit.
limited
moreGHS
The effective health
Index is policies.
a quantitative Nonetheless,
indicator it is
on health unclear
security andwhether “more aware”
related capabilities acrosscountries were
195 countries. more
Results
using this index areinformation,
within-the-year available upon request,
the and show no 2020 link7between GHS and pandemic incidence.
robustone.
prone
6 to the implementation and November
of policies in the spirit of those captured by the index, or they rather
Source: Johns Hopkins Coronavirus Resource Center: https://coronavirus.jhu.edu/.
7
5
In
resorted all case
Results we
toforothertrim
related the upperconstructed
measures
alternatives and lower
-such as5% of thethresholds
different
intensive forecasts’ distribution
testingfor the contact
and topopulation
affected prevent
tracing-distortion from upon
are available
that allowed outliers.
themrequest
not
and provide very similar results. In addition, if awareness is linked to preparedness, there are indices that proxy the
latter.
to follow Onetheis the Global Health
stringent lockdown Security Index (GHS
approach. WithIndex: see https://www.ghsindex.org/about/)
the available developed by
data we cannot test either hypothesis.
the
BANCO DE ESPAÑA 10 DOCUMENTO
Nuclear ThreatDEInitiative,
TRABAJO N.º 2123 the Johns Hopkins Center for Health Security and The Economist Intelligence Unit.
Nevertheless,
The GHS Index to is aaccount
quantitative for potential endogeneity
indicator on concerns
health security withcapabilities
and related our empirical
acrossapproach we explore
195 countries. Results
using this index are available upon request, and show no robust link between GHS and pandemic incidence.
the6 Source:
link between indicators
Johns Hopkins of awareness
Coronavirus Resource and the NPI
Center: indicator in a very simple way, by regressing
https://coronavirus.jhu.edu/.
7
In all case we trim the upper and lower 5% of the forecasts’ distribution to prevent distortion from outliers.Figure 2: COVID-19 incidence (Y-axis) and “awareness” (X-axis).
Figure 2: COVID-19 incidence (Y-axis) and “awareness” (X-axis).
Figure
Deaths 1-month vs. #2:epidemics
COVID-19Deaths
incidence (Y-axis)
3-month and “awareness”
vs. # epidemics
Deaths 1-month vs. # epidemics
(X-axis).
Deaths end-2020 vs. # epidemics Deaths 3-month vs. # epidemics Deaths vs. # epidemics
Figure
Deaths 1-month vs. #2:epidemics
COVID-19Deaths
incidence (Y-axis)
3-month and “awareness”
vs. # epidemics (X-axis).
Deaths end-2020 vs. # epidemics
15
6
Deaths 1-month vs. # epidemics Deaths 3-month vs. # epidemics Deaths vs. # epidemics
6
15
Deaths 1-month vs. # epidemics Deaths 3-month vs. # epidemics Deaths end-2020 vs. # epidemics
6
Deaths 1-month vs. # epidemics Deaths 3-month vs. # epidemics Deaths vs. # epidemics
6
64
15 10 15
Deaths 1-month vs. # epidemics Deaths 3-month vs. # epidemics Deaths end-2020 vs. # epidemics
6 4
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vs. #
# epidemics
epidemics Deaths 3-month
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vs. #
# epidemics
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Deaths vs. #
# epidemics
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4 2 66 4
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10
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055
00 -2
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0
0 1 Deaths 1-month
2 vs. # disasters 3 4 0 1 Deaths 3-month
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Deaths 1-month vs. # disasters Deaths 3-months vs. # disasters4 Deaths end-2020 vs. # disasters
015
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6
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0
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1
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2 3
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4
15
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6
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6
64
15 10 15
Deaths 1-month vs. # disasters Deaths 3-months vs. # disasters Deaths end-2020 vs. # disasters
6 4
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Deaths 1-month vs.
vs. #
# disasters
disasters Deaths 3-month
Deaths 3-month vs.
vs. #
# disasters
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# disasters
disasters
4 2 66 4
15
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10
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10
510
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00 -2
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-2
IMF first1 revision vs.3 # epidemics IMF rev.1 1-year vs. # epidemics GDP 2020-2019 vs. 3# epidemics
0
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2 (ST) vs. # epidemics 4 5 0 IMF revisions
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IMF first revision vs. # epidemics IMF rev. 1-year vs. # epidemics GDP 2020-2019 vs. # epidemics
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100
IMF first IMF rev. GDP
-5 0
-5-15-10-500-10 -5 0
revision
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IMF revisions (ST) vs.
(ST) vs.
vs. #
# epidemics
# epidemics
epidemics 1-year
IMF
IMF (MT)vs.
revisions (MT)
revisions vs. #
vs.
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# epidemics
epidemics 2020-2019
GDP variation
GDP
vs.
variation vs.
vs. # epidemics
# epidemics
# epidemics
10 0
-500
0-10 10
-5 -10 -5
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-15
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0 1 2 3 4 0 1 2 3 4 0 1 2 3 4
-10
-15
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-30 -20
0 1 2 3 4 0 1 2 3 4 0 1 2 3 4
-20
-25
-15
Notes:
0
Human 1
incidence
2
indicators
3 4
(in logs):
0
“Deaths
1
1-month”
2
refers
3
to 4the number 0
of COVID-19
1 2
casualties
3
per
4
million
-25
-25
-15
-30
-15
-30
inhabitants
Notes:
0
0 Human in
1 the
1 1st22 month
incidence 3 after the
indicators
3 4 10th
4 (in casualty
logs):
0
0 was
“Deaths
1
1 registered;
1-month”
2
2 “Deaths
refers
3
3 3-months”,
to 44the number
0
0 three months
of COVID-19
1
1 2
2 after the 10th
casualties
3
3 4 casualty;
per
4 million
st revision” refers to the
“Deaths
Notes: end-2020”,
inhabitants
Human in the 1stas
incidence of indicators
month 31after
December 10th
the(in 2020. Economic
casualty
logs): incidence
was registered;
“Deaths 1-month” indicators:
“Deaths
refers to the “IMF 1three
3-months”,
number months after
of COVID-19 the 10th
casualties difference
per casualty;
million
in GDP growth forecasts of for 2020 between the Economic
April-2020 and October-2019 IMF World st Economic
“Deaths end-2020”,
inhabitants
Notes: Human in the 1stas
incidencemonth 31after
December
indicators the 10th
(in 2020.
casualty
logs): incidence
was registered;
“Deaths 1-month” indicators:
“Deaths
refers to the “IMF
3-months”,
number 1three months Outlook
revision”
of COVID-19 refers
after to reports;
the theth
10
casualties per
“IMF
difference
casualty;
million
rev.GDP
in 1-year”
growth refers toasthe
forecasts forecast
of for 2020 differences
between thebetween theincidence
April-2020 October-2020 forand
and October-2019 October-2019
IMF World IMF
st EconomicWEO reports. As regards
Tracker
“Deaths of Hale
end-2020”, 1et al. (2020).
31 December The indicator
2020. was is
Economic available a3-months”,
indicators: large
“IMF set months Outlook
ofrevision”
1three countries.
refers to reports;
More theth “IMF
stringent
difference
st
st th
inhabitants in the month after the 10th casualty registered; “Deaths after the 10 th casualty;
indicators
rev.
in 1-year”
GDP
“Deaths of refers
growth “awareness”:
end-2020”, toasthe
forecasts of for“#
forecast
31 epidemics”
2020 differences
between
December refers
2020. to the
thebetween
April-2020
Economic number
the of epidemic
October-2020
and October-2019
incidence and IMF
indicators: episodes 1suffered
October-2019
World
“IMF IMFbyWEO
st Economic
st revision” aOutlook
country
reports.
refers between
theAs
to reports; 2000
regards
“IMF
difference
and 2019
indicators
rev. 1-year”that affected
of refers
“awareness”:
to the more “# than
forecast 100 people;
epidemics”
differences “#todisasters”
refers the numberrefers
of to theand
epidemicnumber of biological
episodes suffered and
by other
aOutlook
countrynatural Asdisasters
between 2000
containment
in GDP growth policies
forecasts (e.g.
for 2020 more
between thebetween
stringent the
April-2020 October-2020
lockdowns or
and October-2019 curfews)October-2019
IMF entail
World IMF
an
EconomicWEO
increase reports.
in the
reports; regards
index.
“IMF
suffered
and 2019 bythat
ofa refers
country
affected between
more 2000
than andpeople;
100 2019 that affected
“#todisasters” more that
refers
of to 0.1%
theand of episodes
number its population.
of biological and a other natural
indicators
rev. 1-year” “awareness”:
to the “#
forecastepidemics”
differencesrefers
between the number
the October-2020epidemic October-2019suffered
IMFbyWEO country
reports. Asdisasters
between 2000
regards
suffered
and 2019bythat
ofa “awareness”:
country
affectedbetween
more“# 2000
than andpeople;
100 2019 that affected
“#todisasters” more that
refers
of to 0.1% of episodes
the number its population.
of biological anda other natural disasters
Ex ante,
indicators
suffered
one may think that more
epidemics” “awareness”
refers the number might be
epidemic associated with
suffered the
by implementation
country between 2000of
and 2019bythat
a country
affectedbetween
more than 2000100
andpeople;
2019 that affected more
“# disasters” that
refers to 0.1% of its population.
the number of biological and other natural disasters
Control
suffered by
more variables
effective health To
a country between control
2000 and for
policies. 2019 factors
itpotentially
that affected
Nonetheless, is more that 0.1%
unclear affecting
whether the evolution
of its population.
“more of the pandemic
aware” countries were more
Control variables To control for factors potentially affecting the evolution of the pandemic
Control
other
pronethanto variables
“awareness”,
the To we
implementation control
includefor the
factors
of policies potentially
following
in the spirit affecting
variables
of those the the
incaptured evolution
analysis:
by theurban of the
index, pandemic
population
or they as
rather
other thanvariables
Control “awareness”, To we include
control for the following
factors variables
potentially in the the
affecting analysis: urban
evolution population
of the pandemic as
other
a resortedthanto“awareness”,
percentage of total
other we include
population
alternatives -such theintensive
in as
2019;following
the averagevariables
testing and in the analysis:
temperature
contact tracing- urban
between that population
1991allowed
and 2016;
themtheas
not
a percentage
other of total population
than “awareness”, we include in 2019; the average
the following temperature
variables between urban
in the analysis: 1991 and 2016; the
population as
aaverage
percentage
to follow the of
household total
sizepopulation
stringent in 2019; gross
lockdown in 2019;
approach. the
national
Withaverage
income temperature
per
the availablecapita,
data we between
PPP cannot 1991either
(current
test USand 2016; the
dollars).
hypothesis.In
average
a percentagehousehold
of totalsizepopulation
in 2019; gross national
in 2019; income per
the average capita, PPP
temperature (current
between 1991US anddollars).
2016; the In
average
addition, household
via dummy size in 2019;
variables, gross
we national
control for income
the per capita,
geographical PPP
location (current
of
Nevertheless, to account for potential endogeneity concerns with our empirical approach we explore each US
countrydollars).
within Ina
addition, via dummy
average household sizevariables,
in 2019; we control
gross for the
national geographical
income location
per capita, PPP of each country
(current withinIna
US dollars).
addition,
continental viagroup
the link between dummy variables,
(Africa,
indicators Oceania,we control
Northand
of awareness for the NPI
America,
the geographical
indicator location
South-Central veryofsimple
in aAmerica, each country
Asia,
way, within
Europe), anda
by regressing
continental
addition, viagroup dummy (Africa, Oceania,
variables, North for
we control America, South-Central
the geographical America,
location Asia,
of each Europe),
country withinanda
continental
distinguish group
between (Africa,
emerging Oceania,
markets North
versusAmerica,
advanced South-Central
economies, America,
and small
one on the other, i.e. we compute a simple correlation coefficient. For that purpose, we calculate Asia,
versus Europe),
large and
countriesthe
distinguish
continental between emerging
group (Africa, marketsNorth
Oceania, versusAmerica,
advancedSouth-Central
economies, and small versus
America, Asia,large countries
Europe), and
distinguish
(aaverage
dummyvalue between
that of theemerging
takes value 1 ifmarkets
stringency the
index versus
population advanced
one month is above economies,
and three monthsand
the median of all
aftersmall
the versus
countries large
in
10th death the countries
wassample).
notified
(a dummy that
distinguish takesemerging
between value 1 ifmarkets
the population is above economies,
versus advanced the medianand of all countries
small versus in the countries
large sample).
(aindummy
In
each that takes
addition,
country, value
weascontrol
well as 1forif the
the population
incidence
average ofisfull
for the above
policy the median
yeardecisions,
2020. As asofmeasured
shownall in
countries
by A2
Table in the
the in sample).
widely-used
the Annex,
In addition,
(a dummy we control
that takes value 1forif the incidence ofispolicy
the population above decisions,
the median asofmeasured by the
all countries widely-used
in the sample).
theInPharmaceutical
Non addition, we
correlation control
between for theindicator
Intervention
fatalities incidence
and of policy
(NPIs),
stringency thedecisions,
Oxford
indicators as measured
COVID-19
is statistically not by the widely-used
Government
significantlyResponse
different
NonInPharmaceutical
addition, we control Intervention
for theindicator
incidence(NPIs),
of policy thedecisions,
Oxford COVID-19
as measured Government Response
by the widely-used
Non
fromPharmaceutical
zero for most ofIntervention
the indicators indicator
used. For (NPIs), the Oxford
the regression COVID-19
analysis, Government
we extract Response
the residuals of the
Non Pharmaceutical Intervention indicator (NPIs), the Oxford COVID-19 Government Response
previous regressions and include them as an additional control in the human incidence variables’
specifications. These residuals capture the part of the stringency policies that are not associated
to awareness.
BANCO DE ESPAÑA 11 DOCUMENTO DE TRABAJO N.º 2123
3 Results
We provide some initial descriptive evidence in Figure 2, were we display scatterplots relating ourYou can also read
