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The Inclusive Cost of Pandemic Influenza Risk



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Victoria Y. Fan
Dean T. Jamison
Lawrence H. Summers
Working Paper 22137
1050 Massachusetts Avenue
Cambridge, MA 02138
March 2016
The authors thank Branden Nakamura and Pamela Krueger (University of Hawai‘i) and Jennifer Nguyen
(University of Washington) for valuable research assistance. Kristie Ebi (University of Washington)
provided guidance to the literature on carbon emission levels and their costs. Julian Jamison and Olga
Jonas of the World Bank provided helpful suggestions. The Bill & Melinda Gates Foundation provided
partial financial support for this research through grants to the University of California, San Francisco,
for the ‘Commission on Investing in Health (CIH), Phase 3’ and to the University of Washington for
the ‘Disease Control Priorities Network’. The views expressed herein are those of the authors and
do not necessarily reflect the views of the National Bureau of Economic Research.
At least one co-author has disclosed a financial relationship of potential relevance for this research.
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The Inclusive Cost of Pandemic Influenza Risk
Victoria Y. Fan, Dean T. Jamison, and Lawrence H. Summers
NBER Working Paper No. 22137
March 2016
JEL No. H51,I15,I18
Estimates of the long-term annual cost of global warming lie in the range of 0.2-2% of global
income. This high cost has generated widespread political concern and commitment as
manifested in the Paris agreements of December, 2015. Analyses in this paper suggest that the
expected annual cost of pandemic influenza falls in the same range as does that of climate change
although toward the low end. In any given year a small likelihood exists that the world will again
suffer a very severe flu pandemic akin to the one of 1918. Even a moderately severe pandemic, of
which at least 6 have occurred since 1700, could lead to 2 million or more excess deaths. World
Bank and other work has assessed the probable income loss from a severe pandemic at 4-5% of
global GNI. The economics literature points to a very high intrinsic value of mortality risk, a
value that GNI fails to capture. In this paper we use findings from that literature to generate an
estimate of pandemic cost that is inclusive of both income loss and the cost of elevated mortality.
We present results on an expected annual basis using reasonable (although highly uncertain)
estimates of the annual probabilities of pandemics in two bands of severity. We find:
Expected pandemic deaths exceed 700,000 per year worldwide with an associated annual
mortality cost of estimated at $490 billion. We use published figures to estimate expected income
loss at $80 billion per year and hence the inclusive cost to be $570 billion per year or 0.7% of
global income (range: 0.4-1.0%).
For moderately severe pandemics about 40% of inclusive cost results from income loss. For
severe pandemics this fraction declines to 12%: the intrinsic cost of elevated mortality becomes
completely dominant.
The estimates of mortality cost as a % of GNI range from around 1.6% in lower-middle income
countries down to 0.3% in high-income countries, mostly as a result of much higher pandemic
death rates in lower-income environments.
The distribution of pandemic severity has an exceptionally fat tail: about 95% of the expected
cost results from pandemics that would be expected to kill over 7 million people worldwide.
Victoria Y. Fan
Office of Public Health Studies
1960 East-West Road, Biomed D-204
Honolulu, HI 96822
Dean T. Jamison
University of California, San Francisco
50 Beale St., Suite 1200
San Francisco, CA 94105
Lawrence H. Summers
Harvard Kennedy School of Government
79 JFK Street
Cambridge, MA 02138
and NBER
March 28, 2016
The Inclusive Cost of Pandemic Influenza Risk
The recent Ebola outbreak in Guinea, Liberia and Sierra Leone reminded the world that
enormous economic and human costs result from the uncontrolled spread of deadly infection
Less noticed was that a pandemic with characteristics similar to that of influenza in 1918 would
have killed about 10 times as many people in those three countries as did Ebola. Worldwide the
death total from such a pandemic would be on the order of 2500 times higher than WHO’s
estimate of a little over 11,300 Ebola deaths through the end of the epidemic on March 17, 2016
(World Health Organization, 2016).
One important dimension of the cost of a pandemic lies in its impact on income.
Premature deaths reduce the labor force; illness leads to absenteeism and reduced productivity;
resources flow to treatment and control measures; and individual and social measures to reduce
disease spread can seriously disrupt economic activity. The World Bank has generated estimates
of these costs (Burns, Mensbrugghe, and Timmer 2008; Jonas 2013). The World Bank studies
found, among other things, that a 1918-severity pandemic might reduce global GDP by about
5% and that the disruptive effects of avoiding infection would account for about 60% of that
total. McKibben and Sidorenko (2006) examined consequences of a range of pandemic
severities including an ‘ultra’ scenario (toward the upper end of the range of estimates for
severity in 1918). They concluded this extreme scenario would lead to income losses of over
12% of GNI worldwide and over 50% in some developing countries.
The second major dimension of pandemic cost lies in the intrinsic value of lives
prematurely lost and of illness suffered. Efforts to measure the costs of premature mortality and
illness remain imperfect but, that said, extensive empirical findings do appear in the economics
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literature, particularly for the costs of premature mortality (Viscusi, 2014; Hammitt and
Robinson, 2011; Lindhjem, Navrud and Braathen, 2010). While valuation of mortality change
appears most frequently in the environmental economics literature (see, for example, OECD
2014), the report of The Lancet Commission on Investing in Health – Global Health 2035
systematically applied these methods to understanding of global health (Jamison, Summers et
al. 2013). Our purpose in this paper is to estimate the magnitude of this second dimension of
pandemic cost using standard methods. As large as the direct impact of a pandemic on income
appears to be, we conclude that this second, intrinsic, dimension of cost far exceeds the cost of
lost income. The inclusive cost of a pandemic is the sum of its adverse impact on income and
of the intrinsic cost of premature mortality and illness.
Our paper assesses the expected annual cost of a pandemic with risk r (expressed as the
annual probability of a pandemic in %) and severity s (expressed as the fraction of the world
population that dies from the pandemic). It uses the historical and modelling literatures to
generate expected values of r and s, and uses those values to generate estimates of mortality
and its associated costs.
Pandemics: history, risk, severity
Papers in the literature define pandemic severity in different ways hence it is important to
specify the definitions we use in this paper. For simplicity we define severity in terms only of
mortality although in practice differing case fatality rates lead to different numbers of severe
cases for any given level of mortality. Global Health 2035 introduced the term ‘standardized
mortality unit’ (SMU) to convey mortality rates that are small. The SMU is 10-4 and hence, for
example, the pandemic of 1957-58 would be characterized as having a global death rate of 3
SMU rather than 0.03%. In the world’s 2015 population of 7.35 billion, 1 SMU corresponds to
March 28, 2016
735,000 deaths. Seasonal influenza causes about 250-500,000 deaths per year (WHO, 2014). We
define severe pandemics as having mortality rates of 10 SMUs or greater, and moderately severe
pandemics as having a severity less than 10.
The historical record suggests that the 1918 influenza was an outlier among outliers, with
unusual circumstances including the co-occurrence of World War I. No other influenza pandemic
on record had such devastatingly high mortality rates, with estimates ranging from 20 million to
50 million (or more) excess deaths over the period 1918-20, but concentrated in 1918. (20
million deaths would comprise 1.1% of the world’s 1918 population.) In addition to the severe
pandemic of 1918 the sparse record suggests that there have been about 12-17 other pandemics
since 1700. Of these we identify six as having substantial excess mortality, with mortality rates
in the range of 3-8 SMU (Table 1).While the world may be expected to experience moderately
severe to severe pandemics several times each century, there is consensus among influenza
experts that an event on the very severe scale of the 1918 pandemic may be plausible but
remains historically and biologically unpredictable (Taubenberger, Morens, and Fauci, 2007). A
modelling exercise for the insurance industry concluded that the ‘return period’ would be 100-
200 years for a 1918-type pandemic, but acknowledged major uncertainty (Madhav, 2013).
While a biological replica of the 1918 flu would no doubt result in lower mortality rates than
occurred in 1918 (Madhav, 2013), both that study and other analysts point to the possibility that
exceptionally transmissible and virulent viruses could lead to global death rates substantially
higher than in 1918 (see McKibben and Sidorenko, 2006, or Osterholm, 2005).
India suffered a disproportionate share of global pandemic mortality in 1918 (Davis,
1951). In general, lower income parts of the world suffered more in 1918. Morens and Fauci
(2007) and Madhav (2013) argue, very plausibly, that a modern epidemic would likewise
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Table 1. Worldwide mortality from selected influenza pandemics, 1700-2000a
Year Estimated worldwide
deaths (millions)
Estimated world
population (millions)
Severity, s
(fraction of world
population killed,
measured in SMUs)b
1729c 0.4 720 6
1781-82c 0.7 920 8
1830-33c 0.8 1150 7
1898-1900c 1.2 1630 7
1918-20c,d 20-50 1830 110-270
1957-58c 1 2860 3
1968-69c,e 1-2 3540 3-6
a For pandemics to include in this table we chose those in the period from 1700 to the present whose severity we
could ascertain from the literature. Morens and Fauci (2007, figure 4) and Morens and Taubenberger (2011) identify
12-17 pandemics in the period from 1700 but many of those resulted in substantially lower mortality than for those
in this table (or had mortality levels we could not ascertain).
b The standardized mortality unit (SMU) represents a 10-4 mortality risk and is used to represent small numbers as
integers. For example the 1729 pandemic led to an elevation in mortality of 0.06% of the world’s population which
is more conveniently expressed as 6 SMUs. In the world’s 2015 population 1 SMU corresponds to 735,000 deaths.
c Potter (2001).
d Beveridge (1991); Ghendon (1994); Johnson and Mueller (2002).
e Hampson and Mackenzie (2006).
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disproportionally affect poor countries. That said, China’s mortality rate in 1918 was low,
probably because of lower case fatality rates rather than lower incidence (Cheng and Leung
2007). This points to the possibility of great within income group heterogeneity in a modern pandemic.
Our intention in this paper is not to provide a new review of the literature on mortality in
previous pandemics but rather to select plausible values from that literature to define our
reference cases while emphasizing, with Taubenberger and colleagues, the uncertainty inherent
both in the history and in projections to be drawn from it. In light of this literature (and its
attendant uncertainty) we develop and report results for two representative levels of severity.
Table 2 defines the severity levels we use and indicates the levels of annual risk assigned to
them. Box 1 provides the background to the calculation of expected severity that Table 2
The effort proceeds in two steps. First, information on pandemic severity is used to
generate increases in age-specific death rates for the world and for each of the World Bank’s
four income groups of countries. Second, the literature on valuation of changes in mortality
rates is used to generate estimates of the age-specific costs of mortality increase, and hence of
the total cost.
We begin by estimating the change in a population’s age-specific mortality rate for the
two severity reference cases. Estimates of the age-specific excess mortality rates of different
populations from the 1918 pandemic are consistent in their shape by having a unique inverted-
U shaped distribution, whereby adults aged 15 to 60 experienced elevated rates compared to the
elderly (Murray et al., 2006; Luk, Gross, and Thompson, 2001). We thus used the specific U.S.
data for age distribution of excess deaths to generate age distributions for the world, adjusting
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Table 2: Worldwide pandemic risk – two representative scenarios, 2015
Moderately severe
(< 10 SMU)b
Severe pandemic
( 10 SMU)a Any
1. Annual probability, rb 2% 1.6% 3.6%
2. Return time, 1/r 50 years 63 years 28 years
3. Average severity (SMU)c 2.5 58 27
3. Expected severity, sd 0.05 SMU 0.93 SMU 0.98 SMU
a See footnote b, Table 1.
b These severity states are mutually exclusive. Hence the annual probability of any pandemic is
[ 1 – (1-0.2) (1-0.016) ] = 3.6%.
c The ‘average severity’ of a pandemic in a given severity range is the expected value of severity given that a pandemic did
in fact occur in that range – e.g. 2.5 SMUs is the expected severity given that a pandemic of severity s < 10 SMUs has
d ‘Expected severity’ is average severity times probability of occurrence [ = row (3) x row (1) ].
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Box 1: Estimating Pandemic Severity and Risk
Following usage in the insurance industry we define risk, r(s), in terms of ‘exceedance
probability’, the annual probability of a pandemic having a severity exceeding s. Again following
insurance industry usage, the ‘return time’ for s is the expected number of years before there will
be a pandemic of at least severity s. If t(s) is the return time then t(s) = r(s)-1. For example, if the
annual probability of a pandemic of severity at least s is 1% then its return time will be 100
If we had access to a function r(s) showing exceedance probability as a function of
severity our analysis could proceed using the expected value of severity of all pandemics. As r(s)
is the complementary cumulative of the density for s, we would have:
Box equation 1.1: expected value of s = rds
Modelled estimates of the function r(s) are not (publicly) available so we approximate in two
steps. We label pandemics with global s 10 SMUs as ‘severe’. (As defined in the text, 1 SMU
corresponds to a 10-4 mortality risk.) We label pandemics with global s < 10 as moderately
severe and, for the first step in our assessment of expected severity, we use recent history as a
straightforward guide to frequency and severity of moderately severe pandemics. In particular
we assume two such pandemics per century in this severity range and that the average severity is
2.5 SMUs globally. The expected annual severity of moderately severe pandemics is then, 0.02 x
2.5 = 0.05 SMU, corresponding to expected annual deaths of a little over 35,000 worldwide.
We turn next to Box equation 1.1 to estimate the contributions to expected severity from
pandemic severity greater than 10 SMUs worldwide (or 4 SMUs in the U.S.). Let s*(x) be the
contribution of pandemic severity greater than x to expected pandemic severity. Information
available from AIR (2016) allows calibration of r(s) for the U.S. with s 4:
s*(4) = r
[Available data allow us to calibrate only an exceedance probability function, r(s), for the U.S.
Hence we start with that and translate to world values from severity ratios available in Madhav
(2013).] The calibration points to a very ‘fat-tailed’ distribution. The hyperbolic family of
complementary cumulative distributions provides natural candidates for r(s) and we parameterize
the hyperbolic in terms of its expectation and the fatness of its tail (see Jamison and Jamison
2011, Table 2, in the formally identical context of discounting). Thus
Box equation 1.2: r(s) = [ 1 + m( 1 – f )s ] - [ 1 + 1 / ( 1 – f ) ] ,
where 1/m is the expected value of s, and f indicates the fatness of the tail (smaller values imply
a fatter tail). Our calibration yields a value of m of 1.8 and of f = -2. Hence s*(0) = 1 / 1.8 = 0.56.
s*(4) is thus given by:
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s*(4) = 0.56 - 1  3
and the integral can be approximated to be 0.38. (For small values of s, Box equation 1.2
substantially overestimates r when the equation for r(s) has been calibrated to fit larger values of
s. Thus the need for this two-step procedure.) Hence s*(4) = 0.56 - 0.38 = 0.18, which is the
contribution to expected severity in the U.S of severity levels 4. We infer global severity from
U.S. severity using the approach described in the main text.
Madhav (2013), using the AIR model, estimates that a 1918-type pandemic would kill 21
to 33 million people in today’s world. She reports a mid-range severity for the U.S. of such a
pandemic of 8.8 SMU with a return time of 100-200 years. Box equation 1.2 predicts that the
return time for a pandemic of at least that severity is about 175 years.
It is worth commenting that our calibrated value of -2 for f, the tail fatness parameter in
box equation 1.2, implies that the distribution of exceedance probabilities is very fat tailed
indeed. An exponential distribution for r(s) could be considered to be neither fat nor thin tailed.
Calibrating an exponential as we did for the hyperbolic – so that the contribution to expected
severity of severity 4 is equal to 0.18 – gives r(s) = e -0.57s , and a return time for a 1918-type
pandemic of 150 years – quite close to the 175 years of box equation 1.2. But for s = 4 in the
U.S. (over 7 million deaths worldwide) the exponential gives an unrealistic return time of only
10 years whereas box equation 1.2 gives 63 years. AIR (2016) estimates that an extreme
pandemic with s = 30 in the U.S. (and perhaps 100 million deaths worldwide) has a return time
of 1000 years and box equation 1.2 gives 875 years. The exponential gives 27 million years.
We hardly need reiterate the uncertainty surrounding the numbers we use to reflect the
likelihood of pandemics of varying levels of severity. That said, our numbers represent
conservative choices that are broadly consistent with historical experience and modelling
parameters. [Substantially greater severities and likelihoods have been discussed – both by
Madhav (2013) and elsewhere in the literature (McKibben and Sidorenko, 2006; Osterholm,
2005; DeBruin, et al, 2006).] As Morens and Taubenberger have put it (2011, p. 277): “With
human influenza the only certain thing seems to be uncertainty.” We would slightly modify that
to assert the virtual certainty that, sooner or later, the world will again suffer a severe pandemic.
March 28, 2016
for greater absolute increases elsewhere. The fatality rate among young adults, although high in
the 1918 pandemic influenza, was low relatively in the 1957 and 1968 epidemics – (Simonsen
et al, 1998). We also use an alternative and more typical distribution of excess mortality where young
children and the elderly are disproportionally affected as well as a combination of the two resulting from
assuming the same proportional increase in mortality for all age groups. Our final calculations are based on
the assumption that moderately severe pandemics will have age distributions like those of 1957 and 1968
whereas severe pandemics will have age distributions of death like that of 1918.
Next, using the UN’s current “World Population Prospects" (United Nations, 2015) age
distributions of populations and life tables, we calculated excess deaths and the estimated
reduction in life expectancy using these age-specific mortality rates (Preston, Heuveline, and
Guillot, 2000). Table 3 shows the results for our severity categories. Our expected annual
pandemic death total, across both severities is 720,000 (or about 1.2% of the number of
actually occurring deaths). A consequence of this expected increase would be that life
expectancy at birth would decrease – by about 0.3-0.4 years in low and lower-middle income
Next we place dollar values on the changes in mortality rates. Our specific calculations
followed the methods used in Global Health 2035 (Jamison, Summers et al., 2013; Appendix
3), but with a slight change in some numbers. In particular, we used values of v of 0.7, 1.0, 1.3 and
1.6% of income per capita per SMU of mortality increase, i.e. per 1/10,000 increase in
mortality risk for one year for countries in each of the World Bank’s four income groups of
countries. (0.7% was used for low-income; 1.0% for lower-middle; 1.3% for upper-middle;
and 1.6% for high-income.) In calculating the value of change in mortality at age a we used as
a reference the literature’s value as a fraction of gross national income per capita for 35 year
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Table 3: Expected deaths from pandemic risk, by country income group, 2015a
Income levelb
Low Lower-
middle Upper-
middle High World
1. Population (millions) 640 2900 2400 1400 7350
2. Moderately severe pandemics
2.1 Relative pandemic severityc 4 3 2 1 -
2.2 Expected annual pandemic-related
mortality rate, in SMU 0.08 0.06 0.04 0.02 0.05
2.3 Expected excess deaths per year
[ = (1) x (2.2) ] 5100 18,000 9600 2800 37,000
3. Severe pandemics (all severities
3.1 Relative pandemic severityc 10 7 4 1 -
3.2 Expected annual pandemic-related
mortality rate, in SMU 1.8 1.26 0.72 0.18 0.93
3.3 Expected excess deaths per year
[ = (1) x (3.2) ] 120,000 370,000 170,000 25,000 680,000
4. Expected totals
4.1 Expected mortality rate in SMUb 1.88 1.32 0.76 0.2 0.98
4.2 Expected excess deaths per year
[ = (2.3) + (3.3) ] 120,000 390,000 180,000 28,000 720,000
Footnotes on next page.
March 28, 2016
Footnotes, table 3
a Very substantial uncertainty adheres to all numbers in rows 2-4 of this table. We judge that ± 40% reasonably reflects this
uncertainty. AIR’s (Madhav, 2013) mortality estimates for a 1918-type pandemic occurring today are given ± 22% and we
have amplified that somewhat. Rather than tediously report a ± 40% range, this table reports only our point estimates
except for our estimate of total annual expected deaths where we state the range.
b We use the World Bank’s income level classification of countries (World Bank, 2015).
c ‘Relative severity’ indicates severity in each income group relative to the high-income group. This ratio is assumed to be
different for each level of severity. Our estimates for severe pandemics come from AIR (Madhav, 2013, Figure 3). AIR
estimates a narrow range of mortality rates across high income countries (6-11 SMUs) for their model of a 1918 type
pandemic, and the relative severities we indicate are consistent with the HIC rates and AIR’s estimate of 21 to 33 million
deaths globally in such a pandemic. Evidence for the moderate pandemics of 1957-58 and 1968-69 suggest a more
compressed range for these less severe pandemics, and our relative severity numbers in row 2.1 reflect this.
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olds. This amount was adjusted up or down for ages other than 35 in proportion to the ratio of
life expectancies at those ages to life expectancy at age 35. Hence for a given value of overall
mortality the mortality cost will depend on which of the age distributions of excess pandemic
mortality described above is assumed. (Important strands of the benefit-cost literature choose
not to adjust the value of mortality risk for age. We have repeated our calculations to test the
sensitivity of our results to this alternative assumption and found a change of only about 5% in
our headline number.)
Table 4 shows the results of our calculation of the intrinsic cost of pandemic risk using
values of v of 0.7-1.6% of GNI per SMU, depending on income category. We stress again that
these are expected annual values of loss associated with the indicated risks of pandemics in the
severity ranges we have chosen. Expected costs of an actual severe pandemic would be about 60
times as large. The World Bank likewise expresses income loss figures as expected annual
values but uses different values for annual pandemic risk. Table 4 shows our estimate of the
expected annual loss for the world as a whole from the intrinsic cost of pandemic risk to be
-0.6% of global income or about $490 billion per year. Loss varies by income group, from a little
over 0.3% in high-income countries to 1.6% in lower-middle income countries.
To obtain an estimate of inclusive annual pandemic costs, we add an estimate of
expected income losses. We have previously referred to estimates in the literature of the
income loss from pandemics of differing levels of severity (McKibben and Sidorenko, 2006;
Burns, Mensbrugghe and Timmer, 2008; and Jonas, 2013). Our severity categories differ from
theirs so it is difficult to use directly their estimates of income loss. That said we feel values of
1% of global income as the income loss from a moderately severe pandemic, as we define it,
and 4% of global income for a severe pandemic would be consistent with the estimates
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Table 4: Mortality costs of pandemic risk, by country income group, 2015
(age-dependent VSMU)
Income levela
Low Lower-
middle Upper-
middle High World
1. Economic parameters
1.1 Income, Y (trillions of 2013 $) 0.7 6 20 54 80
1.2 Per person income, y (2013 $) 780 2300 8200 41,000 11,000
1.3 vb 0.7% 1.0% 1.3% 1.6% -
2. Pandemic costsc
2.1 Expected annual mortality cost, C
(billions of 2013 $)d -7 -100 -200 -180 -490
(-290 to -690)
2.2 Annual mortality cost, c
[as a % of income = (2.1) ÷ (1.1)] -1.1% -1.6% -1% -0.34% -0.62%
(-0.37 to -0.87%)
a We use the World Bank’s income data and income level classification of countries (World Bank, 2015).
b We use ‘v’ to denote the value of a 1 in 10,000 risk of death, expressed as a % of per capita GNI. The dominant position
in the literature is that lower income countries should have lower values for v (Hammitt and Robinson, 2011). The
literature provides only weak quantitative guidance on how v should vary with y, if at all, and the numbers we have chosen
should be viewed as reasonable assumptions within the spirit of the literature.
c Very substantial uncertainty adheres to these cost estimates (footnote a, Table 3). We judge ± 40% to reasonably reflect
this uncertainty but report that range only for our estimates of worldwide costs.
d For any given value of s, our calculation of the intrinsic cost of a pandemic depends on the age distribution of deaths from
the pandemic, and the calculations reported here use different age distributions for pandemics of different severities. In
particular for moderately severe pandemics we assume an older age distribution of deaths, typical of such pandemics. For
severe pandemics we assume the younger age distribution of deaths that characterized the 1918 pandemic.
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provided by these authors. Using our estimates of the annual probabilities of such pandemics
(Table 2) we get expected annual income losses globally of $16 billion for moderately severe
pandemics and of $64 billion for severe pandemics for a cost of about $80 billion per year.
Table 4 shows an expected annual mortality cost for pandemics of $490 billion of which 95%
is from severe pandemics. This suggests that income losses are only a small fraction of
inclusive costs (about 12%) for severe pandemics but a much larger 40% of inclusive costs for
moderately severe pandemics. The expected annual inclusive cost of pandemics is the sum of
the income loss and the mortality cost or about $570 billion per year. This represents slightly
over 0.7% of global income with a range of perhaps 0.4-1.0% (Table 4).
Expected annual pandemic costs appear substantial. This discussion section provides
some comparative perspective, assesses sensitivity to key assumptions and discusses
limitations to this study.
Comparators: It is worth comparing the inclusive cost of pandemic risk with the
estimated costs of global warming. As with pandemic risk, much uncertainty is attached both
to the magnitude of future global warming and to what its costs will be (or even whether costs
should be modelled as affecting the level or the growth rate of income – Moore and Diaz,
2015). In contrast to the very modest number of studies of potential pandemic cost, Tol (2013)
points to literally hundreds of studies on the cost of climate change, although Pizer et al (2014)
point to the weakness of the key part of the literature that deals with the ‘social cost of carbon’
or SCC. Global CO2 emissions were on the order of 36,000 million tons in 2013, containing
6,200 million tons of carbon (“Emissions | Global Carbon Atlas” 2015). Estimates of the social
cost of carbon vary widely. But if the SCC were around $120 per ton then the cost of CO2
emissions in 2013 would be about 1% of world income. $120 per ton, although high, is well
March 28, 2016
within the range of available estimates (Nordhaus, 2010; Tol, 2013). To the cost of carbon in
CO2 must be added its cost in methane, which Smith et al (2013) estimate to be substantial.
The synthesis of the 2014 report of the Intergovernmental Panel on Climate Change provides
the following assessment of a now-extensive literature: “…the existing incomplete estimates of
global economic losses for warming of 2.5°C above pre-industrial levels are 0.2 to 2.0% of
income…” (IPCC, 2015). Our expected annual inclusive cost of pandemic risk (at 0.7% of
global income) lies about 25% of the way up from the low end of the range of the IPCC’s
estimated range for global warming.
While most studies of the cost of global warming fail to include the intrinsic cost of
increased mortality risk, the effect of doing so may be modest. The IPCC report (2014)
anticipates that there will be increased risks, with very high confidence, of ill health due to
heat waves and fires, of undernutrition from diminished food production in poor regions, and
of increased food- and water-borne diseases and some vector-borne and infectious diseases.
Modest reductions in cold-related mortality and morbidity will be offset by the magnitude and
severity of the aforementioned increased risks. Although the IPCC presents scenarios of health
risks, the aggregate impact of climate change to mortality was not summarized. But the
gradual nature of warming allows time for (costly) adaptations that could be expected to
reduce the mortality consequences. A recent paper points to potentially important mortality
gains in the U.S. from keeping U.S. emissions consistent with global warming of 2°C (Shindell
et al, 2016). These benefits appear to flow almost entirely from reduced pollution rather than
slower atmospheric warming. Most health costs of climate change are, then, likely to be
included in the income cost of adaption rather than being additional to it.
Another useful comparator for pandemic risk lies in deaths from selected alternative
causes. The expected annual number of pandemic flu deaths for 2015 in our reference cases is
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720,000 (Table 3). To this might reasonably be added 300,000 deaths per year from seasonal
flu (WHO, 2014) for a total of over one million. By comparison, GH2035 (Appendix Table
A1.9a), using WHO data, reported the following for 2011:
TB 0.98 million deaths
HIV/AIDS 1.6 million deaths
Maternal 0.28 million deaths
Cancers 7.9 million deaths
Ischaemic heart disease 7.0 million deaths
Stroke 6.2 million deaths
It is clear that the expected annual number of pandemic flu deaths is large on the scale of
killers with high salience in low-income countries although much smaller than the major
global killers of cancer and cardiovascular disease.
We are aware of one other study that estimates disease cost using valuation of
estimated mortality. Watkins and Daskalakis (2015) found very high burdens from rheumatic
heart disease using methods closely related to ours. Far more studies assess the burden of
specific environmental risk factors (OECD, 2014).
Sensitivity to assumptions: The methods used to value mortality risk have limitations. The
valuation of health risks – including fatalities, illness, and injuries – is inherently difficult because
money is often an ineffective substitute for dimensions of human well-being. In practice,
however, these estimates are obtained from ex post observations of the labor market and reflect
how people in fact differentially value and trade-off very small fatality risks for income.
Substantial variation exists both in the estimated value of a small mortality risk at a given age in
the U.S. and in how the valuation (v) should vary across ages and countries (see Lindhjem,
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Navrud and Braathen, 2010, and Hammitt and Robinson, 2011). And, as we have made clear
throughout, the estimates we use for pandemic risk, r, and severity, s, remain subject to inherent
Hammitt and Robinson (2011) have assembled the evidence that the value of mortality risk as a
percent of income in low-income countries may be less than for high-income countries. GH2035 did not
include this potential effect in its calculations. In this paper we do include adjustment for this, which
leaves estimates of cost in high income countries unchanged but reduces our estimated cost for the world
as a whole. As previously noted we assessed the sensitivity of our results to alternative assumptions
on this point (and others) and concluded our main findings to be robust to the specific
assumptions made.
Limitations: A key limitation of this study is that it used historical mortality estimates and
modelled estimates from various sources to estimate excess pandemic mortality in 2015. While
the AIR modelling efforts (Madhav, 2013) explicitly account for potentially increased risks
associated with increased air travel and mobility of persons and goods, as well as increased
urbanization, we lacked access to the full results of that study. Likewise while AIR attempted to
account for decreased risks associated with increased incomes, schooling, and access to health-
care services (including vaccination, antiviral medications, improved infection control, increased
surveillance, and real-time communications), we could only indirectly use that information.
Increased global temperature may reduce the case fatality rates of flu, but may also increase the
transmissibility of the virus. Population level immunity against a particular influenza strain likely
varies by region and by age distribution, although the extent of that variation is not known. In
1918 a few countries (e.g. China and Mexico) did not experience the typical inverted-U
distribution of excess age-specific mortality from flu. In Mexico, for example, the elderly were
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not spared from excess mortality as seen in the U.S., although its working-age population were
hit as hard as other regions (Chowell et al. 2010). In China mortality rates were low at all ages.
The characteristics of new pandemic viral strains depends on poorly understood patterns of
immunity and the complex and poorly understood process of viral evolution and genetic
reassortment in dynamic ecosystems (Morens, Folkers, and Fauci 2004).
An additional limitation of this study is that it does not include an estimated value of the intrinsic
undesirability of nonfatal illness or of pandemic fear – significant characteristics of population response
to SARS in Taiwan (Liu et al. 2005). The high media salience and associated fear may also lead
populations to overreact to mild pandemics – increasing their cost beyond what might be considered
optimal (Brahmbhatt and Dutta, 2008). The economics literature currently provides value estimates
almost entirely for mortality risk, but when appropriate valuations of illness and fear become available
our results may be shown to be underestimates for this reason.
World Bank studies estimate that around 5% of global income as the probable income loss from
a 1918-severity pandemic. In this paper we estimate the intrinsic cost of the excess deaths from potential
pandemics and add that to income loss to provide an estimate of the expected annual inclusive cost of a
very severe pandemic. Our estimate of the expected number of pandemic deaths per year is 720,000
(subject to major uncertainty). The expected annual inclusive cost that results for the world is $570
billion or 0.7% of global income. In comparison, the Intergovernmental Panel on Climate Change
estimates the likely cost of global warming to fall in the range 0.2 to 2% of global income annually.
Posner (2004) has argued that economics and the social sciences more generally pay far too little
attention to potentially catastrophic events, although a literature is now beginning to emerge (Barro and
Jin, 2011 and Pindyck and Wang, 2013). We find it natural to conclude that the academic and policy
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attention provided to pandemic risk falls well short of a reasonably estimated comparison of that risk
with its consequences. That said recent trends encourage. Japanese Prime Minister S. Abe, as he
prepares to host the G-7 in 2016, has placed high priority on dealing with health crises (Abe, 2015). And
a recent Commission, hosted for WHO and the World Bank by the U.S. National Academy of Medicine,
points to practical and significant financial and organizational steps to improve pandemic preparedness
and response (Sands, 2016).
It remains for other efforts to assess the costs and probable impact of investments to reduce the
likelihood or probable severity of a pandemic. These investments potentially range from R&D toward a
universal flu vaccine through pre-investment in manufacturing capacity for (or stockpiling of) drugs and
vaccines to implementing global programs to immunize humans, swine and birds against seasonal flu.
Important investments along these lines are indeed being made. It is our sense, however, that given this
paper’s cost estimates for pandemic risk the economic benefits of further investments are likely to
substantially exceed their costs.
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... There have been several studies that address the costs of epidemics and pandemics, both in terms of the costs of past pandemics or in modeling potential costs for possible pandemics in the future (see Burns et al., 2006;McKibbin and Sidorenko, 2006;Jonung and Roeger, 2006;Fan et al., 2016;Prager et al., 2016;Jordá et al 2020). These kinds of projections generally model the macroeconomic effects on income or the share of GDP. ...
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The Coronavirus is one of the most influential infectious diseases of the 21 st century. This study investigates the relationship between stock market returns and gold market returns for five of the most affected countries between January 02, 2020, and December 31, 2020, by using the Hatemi-J Asymmetric Causality Test. The results show that because of the demand for liquidity, the atmosphere of panic, and the perception of gold as a safe-haven, the causal relationship is not strong for each country.
... World Bank (2017) published a report with estimates of GDP loss during a pandemic. They used a methodology based on the study of Fan et al. (2015). As far as Greece is concerned, the World Bank estimated that the loss would amount to 0.44 percent of its GDP. ...
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This book includes thirty-one selected studies that have been published in various ATINER academic journals since the beginning of the COVID-19 pandemic. All of these studies have undergone a double-blind peer review process and have been accepted for publication. These studies cover research related to COVID-19 from a variety of research fields that include Health; Mass Media and Communication; Sociοlogy; Business and Economics; Tourism; Education; and Law.
... Few examples include smallpox, measles, polio, Ebola, Zika etc. Even before Covid-19 era, the world economy suffered an estimated loss of $570 billion every year due to various epidemic outbreaks [1]. However, there are few epidemics that have shaped human history as much as Covid-19-estimated losses in United States (US) alone is approximately $16 trillion [2]. ...
We formulate an optimal control problem to determine the lockdown policy to curb an epidemic where other control measures are not available yet. We present a unified framework to model the epidemic and economy that allows us to study the effect of lockdown on both of them together. The objective function considers cost of deaths and infections during the epidemic, as well as economic losses due to reduced interactions due to lockdown. We tune the parameters of our model for Covid-19 epidemic and the economies of Burundi, India, and the United States (the low, medium and high income countries). We study the optimal lockdown policies and effect of system parameters for all of these countries. Our framework and results are useful for policymakers to design optimal lockdown strategies that account for both epidemic related infections and deaths, and economic losses due to lockdown.
... During global health emergencies, the severity of outbreaks and the economic burden usually depend on the preparedness of a nation and her economic resilience [44]. Most underdeveloped nations are faced with a huge and distorted economic impact; for instance, during the severe influenza pandemic where about 4%-5% loss gross national income was recorded [45]. It was also reported that some countries such as Guinea, Sierra Leone, and Liberia lost about $2.2 billion in their gross domestic products to Ebola in 2015 [46]. ...
Contexto: Las pymes representan aproximadamente el 50 % del PIB mundial, y debido a la pandemia por la covid-19, se han visto gravemente afectadas. Este documento proporciona un panorama general de factores que incidieron en la economía de las pymes y presenta algunas soluciones para mantener su operación en tiempos de dificultades. Metodología: La elaboración del manuscrito esta soportada en literatura relevante publicada entre 2019 y 2021. La estrategia de búsqueda se aplicó a través de las bases de datos Embase, Web of Sciences, Scopus, bajo términos de búsqueda clave covid-19, pandemic, small enterprises, medium-sized enterprises, global economic, economic growth, developing countries, emerging economies, economic impact, financial fragility, government action*, credit risk, travel restriction*, e-commerce, digital technologie*, digital transformation, emerging market*, supply chain*, small business. Posteriormente, se efectuó la clasificación y análisis documental a través del método Raceer (recopilación, almacenamiento, elaboración esquemática conceptual, enlace de unidades informativas, redacción). Resultados: El análisis de la información relevante reveló que la economía de las pymes cumplió un papel importante en la fuerte caída del PIB y en el aumento de los niveles de pobreza y desempleo a nivel mundial, y que aquellas que querían mantenerse en operación debieron reinventarse e incursionar con nuevas estrategias de negocio. Conclusiones: La pandemia generada por la covid-19 produjo cambios significativos en hábitos de compra, métodos de fabricación y formas de trabajar que impactaron gravemente el desempeño de las pymes. Para lidiar con este nuevo escenario económico, se espera que la tecnología y la innovación direccionen a nuevos modelos de negocio de rápida adaptación.
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The year 2020 has witnessed the highly infectious disease Corona Virus outbreak impacted across the globe. This unpredictable and unprecedented calamity has pushed economies to struggle and strive. Most of the sectors in the economy were severely hit, which led to financial suffering. There is a paradigm shift in the circular flow of income, which has affected the lifestyle and changed people's spending and investment habits. This study aims to understand how the Covid-19 pandemic has influenced the financial decision-making and investment preferences of retail investors. This research paper also studies the changes in the spending pattern of people during the lockdown due to Covid-19. A sample survey was conducted through a structured questionnaire to determine the impact of the pandemic on individual investment decisions in the city of Lucknow. A random sampling technique has been used to collect the data for the study. The study's findings show that people's lifestyles and spending habits have changed significantly due to the pandemic fear and lockdown. The study also indicates that there has been a shift in the spending preference of people towards healthy products and essentials.
Background: Although our daily life and economics were severely affected by COVID-19, cost analysis of the disease has not been conducted in Iran. Hence, we aimed to perform a cost analysis study and then estimate direct medical costs of COVID-19. Methods: A cross-sectional study was performed in Tehran and recorded medical files from March 1, 2020, to September 1, 2020, were examined. A predefined electronic form was developed and all required variables were included. All people whose both first and final diagnoses were COVID-19 positive and were admitted in governmental hospitals were considered for inclusion. Using stratified random sampling method, 400 medical records were evaluated to gather all data. STATA 14 was used for data analysis. Results: We evaluated 400 medical records and the age of patients ranged from 22 to 71 years. The mean cost of COVID-19 was 1434 USD. Of 400 patients, 129 of them had underlying disease and statistical significance was observed in people who had underlying diseases than people who did not have underlying disease. Conclusion: Beds and medications were the most important factors that added to the costs. COVID-19 has undoubtedly imposed a high financial burden on the health system. It is highly recommended that patients with positive test result be strictly encouraged to stay at home and adhere to safety protocols.
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The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors that hamper or support efforts to curb global spread of the disease. In this paper, we analyzed COVID-19-related comments collected from six social media platforms using natural language processing (NLP) techniques. We identified relevant opinionated keyphrases and their respective sentiment polarity (negative or positive) from over 1 million randomly selected comments, and then categorized them into broader themes using thematic analysis. Our results uncover 34 negative themes out of which 17 are economic, socio-political, educational, and political issues. Twenty (20) positive themes were also identified. We discuss the negative issues and suggest interventions to tackle them based on the positive themes and research evidence.
The Covid-19 disease has had major consequences around the world for both public health and the real economy. This economic crisis generated by COVID-19 turns out to be different from previous crises in aspects such as the urgency, scope and magnitude of the negative shock on demand and supply. Countries such as El Salvador, Honduras, Guatemala and Nicaragua, located in Central America, which are among the poorest in Latin America implemented anti-Covid-19 measures since March 2020. Such as restricting mobility and temporarily shutting down non-essential economic activities. As a result, households and businesses are facing an economic downturn due to the pandemic, with effects across the supply chain and from the demand side, because customers can't leave. For this analysis, the average impact on the sales of exporting companies will be estimated. The results indicate that all companies experienced a sudden drop-in economic activity. Permanently closed exporting firms accounted for 6% of employment, compared with 1% for all other firms in the domestic market. This is a first review of the effects of Covid-19 mitigation measures on the performance of exporting companies in four Latin American countries. The study uses a longitudinal database to perform a descriptive analysis of company conditions and company survival. A difference model is used to estimate the average impact on the sales of exporting companies. The control variables were the characteristics of the company, as well as whether it was temporarily closed due to Covid-19. The results indicate that all companies experienced a sudden drop-in economic activity. Permanently closed exporting firms accounted for 6% of employment, compared with 1% for all other firms in the domestic market. The estimation model indicates that for businesses that temporarily closed during this period, there was a further 8% reduction in sales. This article contributes to the literature in several aspects. First, the results complement articles investigating the economic impact of COVID-19 by providing quantitative evidence on the pandemic situation in four Central American countries. Second, longitudinal data provides a unique perspective on how companies have been reacting to the pandemic, as they allow us to control a few variables that can alter analysis in other types of structures. Third, I further examine how the impact of the pandemic on businesses varies by country based on ownership structure and other characteristics. With the observed consequences, our results provide information that can help us consider the broader economic implications of the impact of COVID 19, as well as the design of strategies for recovery.
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Rheumatic heart disease (RHD) is a neglected disease of poverty that is the most common paediatric cardiovascular condition in developing countries. Most RHD deaths occur in children and working-age adults, in whom the economic impact of premature death is high. Despite RHD being a preventable disease, global research and development funding for RHD was recently estimated at US$1·7 million, or about 0·1% of all global health funding. Decisions to scale up costly medical and surgical interventions for RHD are hindered, in part, by lack of evidence for the so-called return on investment that could be achieved through prevention of RHD-related mortality. We conducted a modelling study using data from 107 countries to estimate the economic impact of excess mortality from RHD.
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Integrated assessment models compare the costs of greenhouse gas mitigation with damages from climate change to evaluate the social welfare implications of climate policy proposals and inform optimal emissions reduction trajectories. However, these models have been criticized for lacking a strong empirical basis for their damage functions, which do little to alter assumptions of sustained gross domestic product (GDP) growth, even under extreme temperature scenarios. We implement empirical estimates of temperature effects on GDP growth rates in the DICE model through two pathways, total factor productivity growth and capital depreciation. This damage specification, even under optimistic adaptation assumptions, substantially slows GDP growth in poor regions but has more modest effects in rich countries. Optimal climate policy in this model stabilizes global temperature change below 2 °C by eliminating emissions in the near future and implies a social cost of carbon several times larger than previous estimates. A sensitivity analysis shows that the magnitude of climate change impacts on economic growth, the rate of adaptation, and the dynamic interaction between damages and GDP are three critical uncertainties requiring further research. In particular, optimal mitigation rates are much lower if countries become less sensitive to climate change impacts as they develop, making this a major source of uncertainty and an important subject for future research.
The value of risks to life as measured by the risk-money trade-off plays a fundamental role in economic analyses of health and safety risks and serves as the principal benefit measure for government risk regulation policies. The hedonic models that have been employed to generate empirical estimates of the value of statistical life (VSL) have produced a substantial literature on VSL based on market behavior. Segmentation of labor market opportunities requires that the hedonic approach be altered for disadvantaged labor market groups. Stated preference models often serve a beneficial function, particularly with respect to valuing risks other than acute accident risks and developing estimates of how utility functions depend on health status. The VSL varies with age, income, the cause of death (e.g., cancer), and other factors. This chapter also examines the risk-risk analysis approach and provides a comprehensive survey of the use of VSL by government agencies.
An emissions trajectory for the US consistent with 2 °C warming would require marked societal changes, making it crucial to understand the associated benefits. Previous studies have examined technological potentials and implementation costs and public health benefits have been quantified for less-aggressive potential emissions-reduction policies (for example, refs,), but researchers have not yet fully explored the multiple benefits of reductions consistent with 2 °C. We examine the impacts of such highly ambitious scenarios for clean energy and vehicles. US transportation emissions reductions avoid ∼0.03 °C global warming in 2030 (0.15 °C in 2100), whereas energy emissions reductions avoid ∼0.05-0.07 °C 2030 warming (∼0.25 °C in 2100). Nationally, however, clean energy policies produce climate disbenefits including warmer summers (although these would be eliminated by the remote effects of similar policies if they were undertaken elsewhere). The policies also greatly reduce damaging ambient particulate matter and ozone. By 2030, clean energy policies could prevent ∼175,000 premature deaths, with ∼22,000 (11,000-96,000; 95% confidence) fewer annually thereafter, whereas clean transportation could prevent ∼120,000 premature deaths and ∼14,000 (9,000-52,000) annually thereafter. Near-term national benefits are valued at ∼US$250 billion (140 billion to 1,050 billion) per year, which is likely to exceed implementation costs. Including longer-term, worldwide climate impacts, benefits roughly quintuple, becoming ∼5-10 times larger than estimated implementation costs. Achieving the benefits, however, would require both larger and broader emissions reductions than those in current legislation or regulations.
Pandemics and epidemics have ravaged human societies throughout history. The plague, cholera, and smallpox killed tens of millions of people and destroyed civilizations. In the past 100 years, the "Spanish Flu" of 1918-1919 and HIV-AIDS caused the deaths of nearly 100 million people. Advances in medicine have transformed our defenses against the threat of infectious disease. Better hygiene, antibiotics, diagnostics, and vaccines have given us far more effective tools for preventing and responding to outbreaks. Yet the severe acute respiratory syndrome (SARS), the Middle East respiratory syndrome (MERS), and the recent West African Ebola outbreak show that we cannot be . . .
How likely is a catastrophic event that would substantially reduce the capital stock, GDP, and wealth? How much should society be willing to pay to reduce the probability or impact of a catastrophe? We answer these questions and provide a framework for policy analysis using a general equilibrium model of production, capital accumulation, and household preferences. Calibrating the model to economic and financial data, we estimate the mean arrival rate of shocks and their size distribution, the tax on consumption society would accept to limit the maximum size of a catastrophic shock, and the cost to insure against its impact.
If an influenza pandemic struck today, borders would close, the global economy would shut down, international vaccine supplies and health-care systems would be overwhelmed, and panic would reign. To Emit the fallout, the industrialized world must create a detailed response strategy involving the public and private sectors.