Insurance for Economic Losses Caused by Pandemics — The Geneva Risk and Insurance Review (2020)
Summary
A 2020 article in The Geneva Risk and Insurance Review by Robert Hartwig, Greg Niehaus and Joseph Qiu, published online 14 September 2020. The abstract states that private insurance coverage for economic losses caused by pandemics is limited and attributes the low coverage to the high levels of capital that would be required to credibly insure such losses with cross-sectional pooling mechanisms. It states that pooling over time significantly reduces the required capital but as a practical matter likely requires a government able to borrow and tax. The introduction notes the International Monetary Fund estimate of global economic losses of $9 trillion from the COVID-19 pandemic.
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“Insurance for Economic Losses Caused by Pandemics — The Geneva Risk and Insurance Review (2020).”
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The Geneva Risk and Insurance Review (2020) 45:134–170
https://doi.org/10.1057/s10713-020-00055-y
ORIGINAL ARTICLE
Insurance for economic losses caused by pandemics
Robert Hartwig1 · Greg Niehaus1 · Joseph Qiu2
Received: 13 July 2020 / Accepted: 25 August 2020 / Published online: 14 September 2020
© International Association for the Study of Insurance Economics 2020
Abstract
Private insurance coverage for economic losses caused by pandemics is limited.
While many factors contribute to reduced demand and supply, we attribute the low
amount of coverage to the high levels of capital that would be required to credibly
insure pandemic economic losses with cross-sectional pooling mechanisms. Pooling
over time significantly reduces the required capital and therefore the cost of insur-
ance, but as a practical matter likely requires a government with the ability to bor-
row and tax. We also argue that insurance for economic losses due to pandemics
likely generates positive externalities for the macroeconomy. We therefore analyze
the general tradeoffs associated with different ways that a government can promote
such insurance.
Keywords Pandemic · Insurance · Public Policy · Capital
1 Introduction
Pandemics are a recurring natural phenomenon that throughout human history have
caused not only widespread sickness and death but also economic disruption on
a massive scale. The COVID-19 pandemic is no exception, with the International
Monetary Fund estimating global economic losses at $9 trillion (IMF 2020b).
But unlike other economically destructive natural phenomena such as hurricanes,
earthquakes and wildfires, the losses from which are substantively financed by pri-
vate insurers (at least in advanced economies), very little private insurance exists
to manage the financial consequences of pandemic risk. Indeed, very few prod-
ucts have ever been brought to market by private property–casualty insurers. This
All errors and omissions remain the authors’ responsibility. Opinions stated in this paper are the
authors’ personal views.
* Greg Niehaus
gregn@moore.sc.edu
1
Darla Moore School of Business, University of South Carolina, Columbia, USA
2
American Risk and Insurance Association (ARIA), Malvern, USA
Vol:.(1234567890)
The Geneva Risk and Insurance Review (2020) 45:134–170 135
paper examines the failure of private insurance markets and alternative risk-sharing
mechanisms to adequately address economic losses arising from pandemics. The
paper also identifies potential reasons for the lack of insurance, as well as potential
solutions.
The analysis and discussion are divided into three parts. In the next section, we
describe the current marketplace for pandemic insurance. While there are examples
of private insurance coverage for pandemic losses, the market for private pandemic
insurance is exceedingly thin in the sense that coverage options, especially those
for business interruption, are extremely limited or nonexistent for most individual
entities. Payments by private insurers to businesses suffering economic losses asso-
ciated with the COVID-19 pandemic are expected to account for only a small frac-
tion of total economic losses. In Sect. 3, we provide a conceptual framework that
helps explain why the private market for pandemic insurance coverage is naturally
thin. We conclude that a risk-sharing arrangement that operates over time could be
beneficial, in part to avoid the negative macroeconomic externalities associated with
a prolonged shutdown of many firms without pandemic business interruption cover-
age. Such an arrangement needs to involve a government with the ability to borrow
and tax. Alternative ways of incorporating government involvement in a risk-sharing
mechanism are presented in Sect. 4, along with an analysis of how the alternatives
differ on important dimensions. The paper concludes with a short summary.
2 Review of pandemic risk in property/casualty insurance
and reinsurance markets
Insuring against economic losses from pandemics poses a set of challenges to prop-
erty/casualty insurers that collectively have severely limited the insurability of the
risk in the private sector on a large scale. The result is that few products designed to
specifically address the risk of pandemics have ever been brought to market by prop-
erty–casualty insurers and reinsurers. Consequently, there exists a large gap between
the economic consequences associated with pandemics and the ability of insurers
and reinsurers to provide protection products to address those consequences.
This paper will explore many of the practical, institutional and theoretical obsta-
cles insurers confront in the underwriting and pricing of pandemic risk. The main
obstacle is the extreme aggregation/accumulation risk associated with pandemics,
which severely diminishes the ability to diversify and spread risk. In addition, there
is a balance sheet risk stemming from the negative correlation between the expected
magnitude of insured losses and the market value of financial assets held in the
insurer’s investment portfolio. The collective impact of these obstacles is to require
insurers to carry impractically high amounts of capital. Consider, for example, that
pandemic-related business continuity (i.e., business interruption) losses alone across
all businesses in the United States are estimated at $1 trillion per month, yet at year-
end 2019 the total capital resources (policyholder surplus) of the U.S. property–cas-
ualty insurance industry stood at approximately $800 billion (Hartwig and Gordon
2020).
136 The Geneva Risk and Insurance Review (2020) 45:134–170
To illustrate the importance of each of the obstacles mentioned above, it is useful
to consider how pandemic risk differs from other types of catastrophe risks, such as
hurricanes, earthquakes, and wildfires, which are routinely underwritten by insur-
ers and reinsurers. Each of these natural disasters impacts a limited number of poli-
cyholders for a limited period of time.1 The property and business continuity (i.e.,
business interruption) losses associated with hurricanes, for example, are largely
a coastal phenomenon with damaging winds typically dissipating over the span of
hours. In contrast, business continuity losses arising from pandemics, by defini-
tion, have the potential to impact virtually all policyholders, irrespective of loca-
tion and nearly simultaneously, with losses continuing over the span of months or
even years. The resulting accumulation of losses of the many (rather than the few)
prevents the pooling and redistribution of those losses, as essentially all policyhold-
ers are impacted, requiring the insurer to hold prohibitively large amounts of capi-
tal. Pandemic risk therefore cannot be readily spread, shared or diversified across
policyholders. Further, as the COVID-19 experience during the first quarter of 2020
demonstrated, pandemic-related losses are correlated with both financial market
losses and other insurance losses. During the first quarter of 2020, the policyholder
surplus of the U.S. property–casualty insurance industry fell by 9.3% to $744.9 bil-
lion, due in large part to a sharp decline in asset prices as well as the establishment
of COVID-19 loss reserves across numerous lines of insurance (Best 2020).
2.1 Supply and demand for pandemic insurance pre‑COVID‑19
Given the characteristics of pandemic losses and their financial impact on world
economies, insurers and reinsurers have generally asserted that with some excep-
tions, large-scale pandemic risk is not insurable in the private sector (see Sect. 3 for
more discussion on this topic). For this reason, insurers have no alternative but to
exclude coverage for virtually all future pandemic exposures from insurance policies
and reinsurance treaties. This does not mean that property–casualty insurers have
no exposure to pandemic-related losses. Indeed, Lloyd’s of London in May 2020
estimated that global insured losses arising from COVID-19 to the non-life insur-
ance industry could total $107 billion. Lloyd’s further estimated that its own losses
would range from $3 billion to $4.3 billion, comparable to the losses it sustained
from the September 11, 2001 terrorist attacks (Evans 2020a). Looking toward the
future: research indicates that pandemic risks are intensifying as globalization and
urbanization proceed apace, potentially costing as much as $23.5 trillion over the
next 30 years (Hilsenrath 2020).
The apparent paradox—that the paucity of pandemic insurance available in the
market does not preclude potentially large losses—results from several factors.
First, much of the industry’s exposure arises from contingency coverages—insur-
ance sold to businesses to offset losses triggered by one or more of a wide range of
1
Losses from floods and most terrorist acts are also limited to policyholders in a geographical area and
for a limited amount of time. While private insurance exists for these risks, there are also government
programs to insure or reinsure these losses (see Sect. 4).
The Geneva Risk and Insurance Review (2020) 45:134–170 137
covered events (i.e., risks), pandemic being only one of those events. Second, there
is an expectation on the part of insurers that litigation, regulation and legislation
will compel insurers, in some cases, to pay claims on policies where no coverage
was intended (and thus no premium collected) by the insurer. That latter exposure
represents a large and difficult-to-quantify liability for insurers, the ultimate cost for
which will likely not be known for years. Willis Towers Watson, a global advisory
and broking company, has estimated that insured COVID-19 business interruption
and event cancellation losses in the U.S. could range anywhere from $2.0 billion to
$22.7 billion, while workers’ compensation losses could be as little at $0.2 billion
but as high as $92 billion (Willis Towers Watson 2020). The enormous range in esti-
mates stems largely from uncertainty related to litigation, regulation and legislation
and from uncertainty about the severity of the pandemic.
In the subsections that follow, we examine the insurance market for “pure” pan-
demic risk. We then focus our discussion on COVID-19 and potential losses arising
from specific lines of insurance with contingent exposure to pandemics or where
exposure is incidental though still material in magnitude. We conclude with an
examination of shifts in market dynamics precipitated by the COVID-19 pandemic
in terms of changes in underwriting practices, pricing and the ability of the industry
attract capital.
2.2 Insurance markets for pandemic risk
As discussed above, property–casualty insurers have long maintained that insuring
against large-scale pandemic risk is economically infeasible. Note that this stands
in direct contrast to life insurers, where pandemic risk is inherent in all mortality-
based policies sold with no material adverse impact on the price or availability of
coverage. Life insurers routinely model the potential impact of pandemics on their
books of business and incorporate that risk into the price of their products, typically
ceding the associated extreme mortality risk to reinsurers and sometimes to capital
markets.2 This observation once again begs the question as to why pandemic risk is
deemed largely uninsurable in the context of property–casualty insurance.
2.3 Pure pandemic risk
The history of property–casualty insurance products specifically designed to manage
pandemic risk is a short one. We now examine two of the most recent approaches to
providing “pure” pandemic coverage in a market that historically has offered little-
to-no coverage to the majority of potential buyers.3 Both products illustrate some
2
See National Association of Insurance Commissioners (2020). Also, Solvency II includes capital
charges for life catastrophe risks, e.g., caused by pandemics. See e.g., AON (2011) and Kraut and Richter
(2015).
3
“Pure” pandemic coverage refers to insurance that covers pandemic-related losses and nothing else.
Later we consider other types of insurance where pandemic risk is just one of many types of risk that
could cause claims.
138 The Geneva Risk and Insurance Review (2020) 45:134–170
of the key challenges insurers and capital markets encounter in the market for pan-
demic insurance, driven by actual experience immediately prior to and during the
COVID-19 pandemic. A more general framework for illustrating the challenges of
supplying pandemic risk insurance is developed in Sect. 3 of this paper.
2.3.1 Traditional insurance approach: PathogenRX
PathogenRX is arguably the best-known recent example of a property-casualty
insurance product designed to address pure pandemic risk. Marketed by the global
commercial insurance broker Marsh LLC, PathogenRX was jointly developed by
Munich Re, one of the world’s largest reinsurers, and Metabiotica, a start-up focused
on helping governments and industry estimate, mitigate and manage epidemic risk.
PathogenRX was introduced in 2018, approximately 18 months before the first con-
firmed cases of COVID-19 were reported in Wuhan, China, in late 2019. Despite
the propitious timing of PathogenRX’s launch and its backing by some of the largest
and best-known insurance names in the world, only one policy was ever sold in the
pre-COVID era (Ratliff 2020).
The failure of PathogenRX in the market occurred despite very flexible terms and
conditions and a general recognition by corporations and governments that pandem-
ics posed a material risk to their operations even before COVID-19, based in part on
their experience with the 2003 Severe Acute Respiratory Syndrome (SARS) pan-
demic. According to Marsh’s marketing materials, PathogenRX also offers buyers:
• The ability to model and estimate infectious disease risks,
• Customizable insurance coverage for losses stemming from infectious diseases,
including for loss of gross profit, loss of revenue, and extra expense, and
• Parametric (i.e., pre-defined) coverage triggers, such as a mortality count or pub-
lic health emergency declaration.
Marsh’s target clients included companies in industries particularly vulnerable
to outbreaks, epidemics and pandemics, including hospitality and travel, sports and
entertainment, higher education, retail, manufacturing and mining (Marsh LLC
2020).
It should be noted that PathogenRX as of this writing (August 2020) continues
to be offered by Marsh and the product’s marketing materials have been revised to
mention COVID-19. It is unclear whether the pandemic has led to actual sales. How-
ever, property–casualty insurers typically experience a surge in sales after major
catastrophic events (e.g., sales of flood and earthquake insurance have been docu-
mented to increase after major events), perhaps as market participants gain a better
understanding of the risk, the risk becomes more salient, or risk aversion increases.4
At the same time, it is likely that underwriting and pricing have been adjusted to
4
See Born and Klein (2019) for an overview of the literature on flood insurance in the U.S., including
factors affecting demand.
The Geneva Risk and Insurance Review (2020) 45:134–170 139
reflect actual COVID-19 experience, resulting in more restrictive terms and condi-
tions and higher premiums.
2.3.2 Capital market approach: the World Bank’s Pandemic Emergency Financing
Facility
Another notable but structurally very different approach to using insurance to man-
age pandemic risk involved the World Bank’s Pandemic Emergency Financing
Facility (PEF). Beginning in July 2017 with a term of three years, the PEF provided
up to $425 million in insurance in the form of bonds and swaps (World Bank 2020).
Interestingly, the PEF has been widely derided and deemed a failure even though
the facility performed exactly as intended, paying out $195.8 million to 64 of the
world’s lowest income countries affected by the COVID-19 pandemic. Criticism of
the PEF has focused on its perceived slowness to pay and its cost. The severity of
criticism led the World Bank in July 2020 to formally abandon plans for a “PEF
2.0,” despite a concurrent intensification of the pandemic throughout much of the
developing world (Hodgson 2020).
The perceived failure of the PEF despite its functional success derives from the
parametric triggers used to determine if and when payouts would be made. The
facility was designed to provide payouts to cover “surge costs” incurred by poor
countries in the event of pandemics arising from six categories of virus, coronavirus
being one of them. Multiple triggers had to be met before payouts could be made.
These triggers were related to the number of infections and deaths, rates of spread,
degree of geographic spread and duration of the pandemic. It was not until April 16,
2020 that the last of the trigger conditions was certified as having been met. By that
time, the pandemic was well underway. In the view of many, the lag deprived poor
nations of the funds desperately needed to battle the spread of the virus precisely
when those funds were needed most. The PEF has also been criticized as overly
generous to investors who collectively received interest payments totaling nearly
$100 million through February 2020, the result of annual coupons on the bonds that
ranged as high as 11.5%.
It is worth noting that the “failure” of World Bank’s Pandemic Emergency
Financing Facility is a somewhat subjective characterization (Lane and Beckwith
2020). First, as previously noted, the facility did indeed pay nearly $200 million to
64 low income nations. Further, the assertion that the coupons on the bonds were
excessive is debatable. The reality is that bonds bearing the 11.5% coupon—associ-
ated with the B tranche of the issuance—carried a material 9.44% (i.e., nearly 1-in-
10) chance of sustaining at least some loss each year over the facility’s three-year
term. The relatively high probability of loss reflects the broad spectrum of infectious
diseases covered—coronavirus (e.g., SARS, MERS, COVID-19), Ebola, Rift Valley
Fever and Lassa Fever, among others. Moreover, as the bonds began to trade on the
secondary market shortly after issuance, market quotes remained close to par, sug-
gesting fair pricing relative to the opportunity cost of capital at that time (mid-2017).
One additional criticism of the PEF centers on the assertion that insurance,
including ILS, are relatively expensive methods for delivering disaster relief and
that funds for recovery from large-scale disasters (including pandemics) can be
140 The Geneva Risk and Insurance Review (2020) 45:134–170
more cheaply accessed via after-the-fact borrowing. In the case of the World Bank,
which is rated AAA by Standard and Poor’s, its cost of debt will be incrementally
above that of advanced economy sovereign debt. The World Bank could therefore
theoretically borrow on behalf of its less creditworthy client states, lending to them
at a much lower interest rate than they could obtain on their own (assuming they
can access credit at all). Lane and Beckwith (2020) point out that such an approach
(i) subjects the World Bank to default risk, thereby jeopardizing its own credit rat-
ing, and (ii) presumes credit will be cheap and plentiful for the indefinite future.
The issue of post-event funding alternatives to finance pandemic losses is addressed
more thoroughly in Sect. 4 of this paper.
As we discuss in Sect. 3 below, the fact that very few pure pandemic risk prod-
ucts have ever been brought to market by insurers likely reflects the impracticality of
insuring against pandemics in general, at least in the private sector, given the enor-
mous capital requirements to underwrite the risk. The problems faced by Marsh’s
PathogenRX and the World Bank’s PEF suggest that even very sophisticated efforts
to provide pandemic risk solutions can fail in the marketplace for a multitude of
reasons.
2.4 Contingent coverage for pandemic risk
In contrast to the market for pure pandemic risk in which coverage options are a rar-
ity, pandemic risk is sometimes explicitly covered in the form of contingency cover-
ages. The best known and largest market for contingency coverage is event cancel-
lation insurance. Typically, payouts are contingent on the occurrence of a specific
event. Event cancellation insurance is commonly purchased by organizers of sport-
ing and entertainment events, conferences and associated venues. Such policies are
highly customized but are typically underwritten with communicable disease exclu-
sions which preclude coverage for epidemics and pandemics. However, communica-
ble disease coverage can sometimes be purchased as an extension to the policy for
an additional premium. While relatively few buyers purchase the extension, there
are some high-profile exceptions. The organizers of the Wimbledon Tennis Tourna-
ment in London, for example, had for 17 years purchased pandemic insurance as
part of its event cancellation coverage, paying $2 million per year in premium. Can-
cellation of the 2020 tournament due to COVID-19 resulted in a reported payout
of $141 million by the event’s insurers (Forbes 2020).5 The International Olympic
Committee (IOC), organizer of the 2020 Tokyo Olympics—which were postponed
until 2021—likewise purchased coverage that included cancellation due to pandem-
ics. The IOC received a negotiated sum to cover some of the losses associated with
the postponement.
5
Wimbledon’s organizers announced in June 2020 that pandemic coverage for the 2021 tournament is
unavailable, citing insurance market conditions. Other major sporting events have reported a similar pull-
back in the market by insurers amid the COVID-19 pandemic.
The Geneva Risk and Insurance Review (2020) 45:134–170 141
2.5 Other coverages with potentially material pandemic risk exposure
Beyond event cancellation insurance, a wide range of other coverages exist that
can give rise to direct and indirect pandemic-related losses. A detailed discussion
of all potentially exposed lines of insurance is beyond the scope of this paper,
but a few key lines are worth mentioning both to illustrate the breadth of existing
industry exposure and gauge the magnitude of the coverage gap. Among the lines
with the most incidental exposure to pandemic loss are workers’ compensation,
commercial general liability, trade credit and directors and officer’s insurance.
The nature of each line’s exposure to pandemic and potential COVID-19 losses is
discussed below:
• Workers’ compensation insurance provides wage replacement, medical and
death benefits for all injuries and illnesses that occur in the course of employ-
ment, including those arising from occupational exposure to communicable
diseases. Healthcare workers are the most exposed and as of mid-2020 approxi-
mately 100,000 workers in the U.S. had contracted COVID-19 and more than
500 had died (CDC 2020). As discussed previously, workers’ compensation
insurers are expected to incur billions in COVID-19 claims. Millions of other
workers in other industries who are designated as “essential workers” (a defi-
nition that varies by state) are also potentially exposed. Their ability to claim
workers’ compensation benefits depends on a demonstration that COVID-19 was
contracted out of the course of their employment. As mentioned previously, esti-
mated COVID-19 losses in the workers’ compensation line are highly uncertain,
ranging from $0.2 billion to $92 billion (Willis Towers Watson 2020).
• Commercial general liability insurance protects businesses from a variety of
claims that can arise during business operations. The insurers that write these
policies are therefore concerned that U.S. businesses could be the subject of
extensive litigation alleging negligence in allowing the coronavirus to spread
(e.g., to customers, students, etc.). Willis Towers Watson has estimated that
insured losses in the U.S. could reach $27 billion in a worst-case scenario.
Losses of this magnitude would be highly disruptive to insurance markets and
severely impair the ability of businesses to recover from shutdowns. To address
this concern, insurers and businesses support federal efforts to implement a tem-
porary liability shield that would protect businesses, healthcare facilities and
educational institutions against most COVID-related litigation (Werner et al.
2020).
• Trade credit insurance is purchased by businesses wishing to insure their
accounts receivable against the possibility of default or insolvency of the coun-
terparty in the transaction. Trade credit insurance therefore reduces credit risk.
It is instrumental in facilitating international trade where complex supply chains
amplify the risk. The deep, global economic recession that ensued as coun-
tries attempted to contain the spread of the coronavirus precipitated a massive
increase in credit risk. The deteriorating finances of businesses around the world
exposed insurers in the U.S. and London markets (where the majority of this
142 The Geneva Risk and Insurance Review (2020) 45:134–170
coverage is written) to losses estimated to be as high as $6 billion (Willis Towers
Watson 2020).6
• Directors and officer’s insurance (D&O) protects members of an organization’s
board of directors and its management against personal loss if they are sued as a
result of serving on the organization’s board or management team. The COVID-
19 pandemic is expected to lead to extensive litigation against corporations, their
boards and managers arising from allegations that the company’s response to the
pandemic was inadequate and/or that statements the company made regarding
its exposure to loss and finances were misleading and caused financial injury
to stakeholders (e.g., shareholders). The sharp increase in business bankrupt-
cies during the pandemic had by mid-2020 already contributed to an increase in
D&O claims. Willis Towers Watson estimates that losses in the D&O line could
reach $4 billion.
2.6 Pandemic coverage post‑COVID
The immediate impact of COVID-19 on property–casualty insurance markets has
been to intensify a pre-existing “hard market” for most major lines of commercial
insurance. Hard markets are typically characterized by a tightening of underwriting
criteria and a sustained upward trend in pricing—reflecting both a reassessment of
risk and reduction in available capacity. Market surveys for the first quarter of 2020
revealed the largest and broadest rate increases since 2003, a period when markets
were still absorbing the shock of the September 11, 2001 terrorist attacks (Council
of Insurance Agents and Brokers 2020).
At the same time, insurers are planning for the future. Amid the pandemic, numer-
ous insurers and reinsurers in the U.S., Europe and in the Bermuda market have
collectively raised billions of dollars in the form of both equity and debt offerings.
Issuance of insurance-linked securities has also continued apace, running ahead of
2019 volumes. These companies and investors have cited the favorable rate environ-
ment and numerous market dislocations to which capital can be opportunistically
deployed at attractive rates of return. These capital raises are consistent with a pat-
tern observed for decades within the industry whereby hard market periods, which
generally occur following major market shocks (e.g., September 11 attacks, Hur-
ricane Katrina), serve to attract additional capital even amid heightened uncertainty.
At least some of this capital will likely be allocated to new product offerings that
involve pandemic risk, often through excess and surplus lines markets which offer
insurers more flexibility in terms of price and policy language. Insurers are also har-
nessing the creativity and ingenuity within the InsurTech sector. Lloyd’s, for exam-
ple, has established an innovation accelerator—Lloyd’s Lab—which is working with
a variety of start-up firms to expedite COVID-19 related products and innovations
and to improve understanding of how to model and create products that better pro-
tect customers against future pandemics and other systemic risks (Lloyd’s 2020).
6
Estimate includes potential losses for political risk insurance and surety lines.
The Geneva Risk and Insurance Review (2020) 45:134–170 143
2.7 Section summary
Insurers, reinsurers and capital markets have historically struggled to bring com-
mercially viable pandemic insurance products to market. Potential losses can easily
exceed the industry’s capital, surplus and premium resources. Pandemic losses are
also correlated with financial market losses. Collectively, these factors pose a sys-
temic risk to the industry and potentially the overall economy. Insurers also struggle
with a legal, regulatory and legislative environment that makes contracting for pan-
demic risk a costly and uncertain process. These environmental obstacles compound
the inherent challenges in modeling pandemic risk. Under such conditions, fre-
quency and severity of losses cannot be precisely modeled, creating an obstacle to
accurate pricing. Consequently, it is unlikely that private insurance markets will be
able to offer affordable, widely available commercial insurance products that insure
against some of the costliest economic consequences of pandemics, especially those
arising from business continuity risks. In the next section we more formally address
the supply-side challenges in the market for pandemic insurance, and in Sect. 4 we
discuss the potential role of a government in overcoming those challenges.
3 A framework for analyzing pandemic insurance
The previous section documented that prior to COVID-19 the private insurance
market for economic losses from pandemics related to a pause of economic activ-
ity was thin in the sense that relatively few entities had coverage and the amount of
coverage that was purchased was low relative to the potential size of losses.7 In this
section, we examine a number of non-mutually exclusive, potential explanations for
a thin pandemic insurance market.
Some potential explanations can be viewed as originating on the demand side
and arise from an underestimation of either the likelihood and/or the severity of
pandemic losses. The underestimation of pandemic risk could occur for many rea-
sons, including decision makers underestimating the probability of a pandemic, or
underestimating the speed and/or extent to which a virus spreads, or underestimat-
ing the probability and/or duration of a government imposed shutdown of economic
activity, or being overly optimistic about the ability of scientists to develop treat-
ments and vaccines. Regardless of why, underestimation of the risk could lead to
low demand and therefore a thin market. Demand can be further reduced by people’s
perception that if a truly disastrous pandemic event occurs, governments likely will
intervene and provide disaster assistance.
It is unlikely that widespread underestimation of pandemic risk will occur in the
near future given the recent experience with COVID-19.8 On the other hand, the
7
Other types of economic losses were indemnified by private market insurance products during COVID,
such as event cancellation, trade credit, workers’ compensation.
8
See Ratliff (2020) for an interesting story of the development of the pandemic insurance product
PathogenRX, its lack of success in the marketplace, and how risk perceptions changed in January 2020.
144 The Geneva Risk and Insurance Review (2020) 45:134–170
response of governments in developed countries to COVID-19 in providing aid (see
Richter and Wilson 2020 and Sect. 4) has likely increased the perception that gov-
ernments will react similarly in future pandemics, which will decrease the incentive
of entities to mitigate risk and to purchase insurance for losses caused by pandemics.
While we acknowledge that a lack of demand likely contributes to a thin market,
we focus in this section on why it is costly to supply insurance for economic losses
caused by a pandemic. We argue that pandemic risk has characteristics that would
likely lead to a thin market even in the absence of distortions that reduce demand.
To focus attention on the supply side of insurance, we assume that those exposed to
pandemic risk have a good understanding of the risk and would be willing to pay a
“reasonable” loading over and above the expected loss for insurance if it was avail-
able. Risk aversion on the part of individuals can explain their willingness to pay
more than the expected loss, and financial market imperfections (see e.g., Froot and
Stein 1997) can explain businesses’ willingness to pay more than the expected loss.
Of course, as the loading (price) on the insurance increases, the quantity of insur-
ance demanded is expected to decline, i.e., the market is expected to become thinner.
We discuss both a private market supplying the insurance and a government sup-
plying the insurance for the purpose of identifying situations in which one would
have advantages/disadvantages relative to the other. While certainly motivated
by the COVID-19 pandemic, this section does not consider the current situation;
instead, it identifies and discusses general issues that must be addressed to provide
a successful pandemic risk-transfer scheme for the future. It is therefore conceptual,
not descriptive nor empirical.
3.1 Conceptual framework/setting
We consider the risk associated with insuring N entities for the economic losses
associated with business closures due to an infectious disease. We think of these N
entities as being in a given country, but a broader perspective is also possible. The
entities could be individual businesses or represent a group of businesses, say in a
given geographical region. Without specifying the size or economic conditions of
an entity, assume that entity i has a probability of pi of economic losses Li from a
breakout of an infectious disease in a given year. Presumably Li is related to the
amount of economic activity in i. For now, we will not specify a probability distribu-
tion for p or L, as below we consider various hypothetical examples.
We consider the possibility that the probability of a loss and/or the severity of a
loss could be correlated across entities, with the idea that epidemics (disease is con-
tained to a few entities) would be characterized by a low correlation, and that pan-
demics (disease spreads across many entities) would be characterized by a relatively
high correlation. We specify the correlation structure in examples 3 and 4.
Note that the underlying risk structure (the probability distributions for the prob-
ability of a loss, p, the severity of losses, L, and the correlation structure is assumed
to be constant over time periods. This type of stationarity is almost certainly vio-
lated in practice as, all else equal, the likelihood that multiple entities would expe-
rience losses (the correlations) increases with economic integration (globalization)
The Geneva Risk and Insurance Review (2020) 45:134–170 145
and decreases with advances in medicine. Similarly, the probability that an entity i
experiences a loss can change over time and the magnitude of economic losses can
change over time, e.g., with economic activity and medical breakthroughs. These
aspects of the problem are ignored.
3.2 The capital required for pooling risk cross‑sectionally
Initially, we focus on the amount of capital needed to provide a credible mecha-
nism to pay claims that occur in a given time period. In the subsequent section, we
analyze pooling risk over time. We refer to the time period as a year. We ignore all
administrative costs (e.g., costs incurred to enroll entities and pay claims) associated
with implementing a pooling mechanism. We define a credible mechanism as one
that would provide a very high probability of being able to pay all losses that occur
within a year. To achieve this high probability, the pooling mechanism must have
access to sufficient financial resources to pay most losses that could occur, which we
somewhat arbitrarily set as the 99th percentile value of total potential losses.9
We consider the pooling mechanism as occurring at the aggregate level that
includes all N entities. This could be through a private insurance market or possi-
bly a government program. Regardless of whether a private market or a government
solution is used, we assume that the pandemic risk is managed as a standalone pool
of risk, independent from the rest of the risks that exist. In other words, we ignore
potential risk-sharing benefits across types of risk.10
We further assume that a private insurance market works well in the sense that
insurers/reinsurers can transfer risk among themselves costlessly, so that each
insurer holds a proportional share of the aggregate pandemic risk portfolio. In other
words, the risk is shared efficiently among insurers via a frictionless reinsurance
market (see Borch 1962). Under these assumptions, each insurer/reinsurer, would
pay a proportion of all pandemic losses, although the proportion could vary across
insurers/reinsurers based on their risk tolerance, which we do not specify. Also, with
this assumption, the total amount of financial resources needed to provide a credible
(99% probability) pooling mechanism of all pandemic losses would be the same for
a private market as for a standalone government program.
Below we calculate the amount of financial resources needed in several hypothet-
ical examples to illustrate some of the factors that determine the cost of providing a
credible insurance mechanism. In each of the examples, we define the aggregate lia-
bility for pandemic risk as the expected value of pandemic losses, the assets needed
to credibly insure the risk as the 99th percentile value of the distribution of total
pandemic losses, and the required capital as the difference between the assets and
the liability. Figure 1 illustrates these definitions for an arbitrary probability density
function for pandemic losses. For simplicity, we initially assume that the assets are
invested in risk-free, zero-return securities.
9
As a benchmark, Solvency II seeks a probability of insolvency equal to 99.5%.
10
See Ibragimov et al. (2009) and Mildenhall (2020) for explanations of why pooling some types of risk
exposures with others may not be desirable.
146 The Geneva Risk and Insurance Review (2020) 45:134–170
We assume that a private market has a cost of holding capital. To identify these
costs, consider an equity investor in an insurer. Conceptually, the investor could have
alternatively invested in a portfolio of mutual funds that has the same asset portfolio
as the insurer. The difference is that there is likely to be more information frictions
(agency costs and information asymmetry costs) with an investment in the insurer
because the insurer’s managers are not only managing the asset portfolio but also the
liability side of the balance sheet (Kielholz 2000). In addition, there are potential tax
costs associated with the insurer investment (see e.g., Niehaus 2020). The purpose
of this paper is to focus on the amount of capital needed, not the capital costs per
dollar of capital.
The nature of the costs associated with a government holding the necessary level
of capital is likely to differ from a private market. For example, there would not be
tax costs associated with the government holding the capital. However, there would
likely be other costs. For example, politicians might be tempted to use the funds for
other purposes or invest the funds to achieve other political goals besides providing
a pooling mechanism for infectious disease costs. We can think of these political
costs as a form of agency costs associated with a government structure as opposed
to a private corporate structure. There may also be efficiency losses in having a gov-
ernment entity manage the pooling arrangement as opposed to private insurers. We
defer additional discussion of these costs to Sect. 4.
We now present a series of hypothetical examples. The purpose of these rela-
tively elementary examples is to illustrate how the required amount of capital var-
ies with important characteristics of pandemic risk, specifically, with uncertain loss
severity, cross-sectional correlation in the frequency of losses, and cross-sectional
correlation in the severity of losses. The focus is not on the absolute amount of capi-
tal needed in a given example, but on the relative amount of capital across the exam-
ples. Researchers, practitioners, and instructors in the risk and insurance field can
skip the examples and go right to the subsection titled “Summary of Examples”,
which is before Sect. 3.3. The examples might be useful for students and those new
to the underlying principles of insurance.
3.2.1 Example 1: ideal conditions
Assume that there are 1000 entities, the probability of an event (p) is 0.02 for each
entity, and the severity of an event (L) is the same for each entity and equals $100.
Importantly, assume that the outcome for one entity is independent of another entity.
In this simple case, the distribution for total losses for all 1000 entities has a bino-
mial distribution with an expected value $2000 and a 99th percentile value equal
to $3100. Thus, prior to the realization of losses, the total liability is $2000 and
the assets needed to have a credible pooling arrangement is $3100, which implies
that the amount of capital required to provide credible insurance is $1100, or a little
more than one-half the expected total loss. On a per entity basis, the expected cost is
$2, and the amount of capital required is $1.1. Stated differently, the required capital
per entity is 55% of the expected loss.
This example illustrates the great benefits of pooling risk. If each entity had to
hold enough assets to ensure that it would be able to pay all its own losses, then
The Geneva Risk and Insurance Review (2020) 45:134–170 147
Probability
Density
Losses
Total Losses
Expected Value 99th %le
Liability Assets
|------------------ Capital ------------------|
Required for
Credible pooling
Fig. 1 Illustration of assets and capital needed for a credible pooling mechanism for a given probability
density function for total losses
each entity would need to set aside $100. If this risk is pooled using a mutual agree-
ment among the 1000 entities, each entity would need to contribute $3.1 and there
would be sufficient funds to pay every entity’s loss with probability 0.99. The con-
tribution is just 55% more than each entity’s expected loss. Alternatively, an insurer
could offer each entity credible insurance by raising equity capital equal to about
$1100 and charging a premium to each entity that covers the expected claim costs
per entity, $2, plus the cost of compensating the capital providers.11
This example assumes ideal conditions for pooling risk and therefore provides
a benchmark for the subsequent examples. The probability of a loss and the size of
the loss if it occurs are known and are the same for all entities, the outcomes across
entities are independent of each other, the outcomes do not depend on the decisions
of any of the participants, and there are a relatively large number of entities. The
remaining examples relax some of these assumptions by incorporating characteris-
tics of pandemic risk. As we will see, the pandemic characteristics generally make
pooling risk more costly.12
3.2.2 Example 2: uncertainty in severity of losses
The magnitude of the losses that an entity incurs from a pandemic is highly uncer-
tain, because economic shutdowns can vary in terms of scope (how many busi-
nesses stop operating), degree (is there a complete shutdown or reduced activity),
11
Finding the exact amount of capital to raise is more complicated than this paragraph suggests as the
premium depends on the amount of capital and the amount of capital depends on the premium. See Har-
rington and Niehaus (2003) for a simple model incorporating the simultaneity. Of course, finding the
optimal amount of capital in practice is much more complicated.
12
If entities differ in their underlying probability distributions, specifically with respect to the “fatness”
of the tail of the distribution, then pooling exposures may not be beneficial. See Ibragimov et al. (2009)
and Mildenhall (2020).
148 The Geneva Risk and Insurance Review (2020) 45:134–170
and duration (weeks, months, or years). We therefore introduce uncertainty in the
magnitude of the loss if a loss occurs (hereafter referred to as severity), but we con-
tinue to keep the probability of a loss equal to 0.02. We assume that each entity has
the same probability distribution for severity. The severity experienced by each indi-
vidual entity is an independent draw from a lognormal distribution with an actual
mean of $100 and an actual standard deviation equal $200.13 With this assump-
tion, the expected severity is the same as in the previous example, $100, but there
is considerable uncertainty in the magnitude of the losses that each entity with a
loss experiences. Figure 2 illustrates the density function of the assumed lognormal
distribution.
With these assumptions, the expected loss per entity is still $2, but the 99th per-
centile value is $5.4 per entity, implying that in a mutual arrangement, each entity
would need to contribute 170% [($5.4 − $2)/$2] more than their expected loss, or a
little over three times the amount that was needed in Example 1 when losses were
fixed at $100. The more uncertainty there is in losses, all else equal, the greater the
cost of providing a credible pooling arrangement.
3.2.3 Example 2b: incorporating limits
Insurers can reduce the uncertainty in their loss payments by incorporating limits on
their coverage. Suppose, for example, that the pooling mechanism provides cover-
age only for losses up to $500 per entity in the pooling arrangement described in
Example 2. Given the assumed lognormal distribution for losses, only about 3% of
the participants would have losses exceeding this amount. Incorporating this limit
lowers both the expected value of the payment that a participant will receive (from
$2 to $1.75), as well as the 99th percentile value of payments per entity (from $5.4
to about $3.5). Consequently, the required contribution per entity would drop from
170% to about 100% of the expected claim payment.
The lower the coverage limit, the less capital required to provide a credible
insurance mechanism for the coverage provided, all else equal. For example, if the
coverage limit in our example was further reduced to say $200 per entity, then the
required contribution per entity would be about 80% of the expected claim pay-
ment. However, the lower the limit, the less coverage that is being provided, i.e., less
risk sharing is taking place. Nevertheless, coverage limits can be an effective tool
to promote a private market for relatively low pandemic losses. The desirability of
coupling a private market with relatively low limits with a government offering (or
possibly requiring) excess coverage above the private market limits, either directly
13
We are assuming that severity has a lognormal distribution with a mean of $100 and a standard devia-
tion of $200, not that the log of severity is normally distributed with a mean of $100 and a standard
deviation of $200. To elaborate, sometimes (but not here) the lognormal distribution is characterized by
parameters, say m and s, which are the mean and standard deviation of the log of the random variable.
In that case, the actual mean and standard deviation of the lognormally distributed random variable are
exp(m + s2/2) and exp(m + s2/2))(exp(s2 − 1)1/2.
The Geneva Risk and Insurance Review (2020) 45:134–170 149
6 360
5.0% 90.0% 5.0%
0.016
0.014
0.012
0.010 Lognorm(100,200)
0.008
@RISK Course Version Minimum 0.00
Maximum +∞
University of South Carolina Mean 100.00
Std Dev 200.00
0.006
0.004
0.002
0.000
-100 100 200 300 400 500 600 700 800 900
0
Fig. 2 Lognormal distribution with mean of $100 and standard deviation = $200 used to model uncer-
tainty in the severity of losses
to consumers or through a reinsurance arrangement with private insurers will be dis-
cussed in Sect. 4.14
3.2.4 Example 3: correlation in loss severity
In the previous examples, all the entities were assumed to have the same probability
distribution for the frequency and severity of losses and the outcomes for one entity
were independent of the other entities. We now introduce correlation by modeling
the average severity experienced by all of the entities. Specifically, we assume the
average severity has a lognormal distribution with an expected value of $100 and a
standard deviation of $200. In this setting, if the average severity is larger (or lower)
than expected, then that higher (lower) than expected severity applies to all of the
entities that have a loss, which intuitively induces a positive correlation in the indi-
vidual entities’ losses conditional on a loss occurring. We continue to assume that
the probability of a loss for all entities is 0.02 and independent of each other.
The result is that the amount of capital required to have a credible pooling
arrangement increases dramatically. For a mutual pooling arrangement, each entity
would have to contribute capital equal to $15 in addition to the expected loss of $2.
Stated differently, the capital required is 750% of the expected loss. Thus, correla-
tion in losses increases the required capital dramatically.
14
We also investigate allowing the probability that an entity has a loss, p, to be uncertain such that the
expected probability for each entity still equals 0.02. Specifically, for each individual entity, we assume
that the probability of a loss is independently drawn from a Beta distribution with parameters equal to 1
and 49. We keep the severity of the loss if it occurs as a lognormal distribution with a mean of $100 and
standard deviation of $200 (as in Example 2). Even though we have added uncertainty in the probability
that an entity has a loss, the amount of capital needed is very similar to Example 2.
150 The Geneva Risk and Insurance Review (2020) 45:134-170
3.2.5 Example 4: additional correlation through the frequency of a loss
In the next example, we assume that the loss frequency that applies to all entities is
uncertain. That is, a probability of a loss is selected randomly and that probability
applies to all the entities. This is a way of incorporating correlation in the occur-
rence of a loss across entities (see Hisakado et al. 2006). Intuitively, when one entity
has a higher (lower) than expected probability of a loss, then all the entities have a
higher (lower) than expected probability of a loss.
Specifically, we assume that the probability of a loss that applies to all entities
is drawn from a Beta distribution with parameters | and 49, which is illustrated in
Fig. 3. This distribution implies that, on average, the probability of an entity having
a loss is 0.02, as has been the case in all of the examples. We continue to assume
that the average severity of losses has a lognormal distribution as was the case in
Example 3. With these assumptions, the 99th percentile value of losses is about $21
per entity, implying that capital equal to about $19 per entity is needed to provide
credible insurance. In a mutual arrangement, each entity would need to contribute
950% more than their expected loss to have a credible pooling arrangement. Thus,
correlation in the occurrence of losses also has a significant effect on the amount of
required capital.
3.2.6 Example 5: pandemic versus natural catastrophe risk
We now provide an example to compare insurance for pandemic risk to insurance
for a natural catastrophe risk. The idea underlying the example is that natural catas-
trophe risk is usually limited in that it impacts only entities within a given geo-
graphical region. Therefore, catastrophe risk is likely to have correlation in losses
for entities in a given geographical area, but not for all entities. In comparison to
the setting described above for pandemic risk, natural catastrophe risk would likely
cause correlation within groups of entities, not across all entities. We therefore esti-
mate the capital that would be needed to provide a credible pooling arrangement if
instead of applying the probabilistic assumptions of Example 4 to all 1000 entities,
they were applied to separate groups of 20 entities each. This imposes correlation in
frequency and severity for the 20 entities within each group, but keeps the frequency
and severity across groups uncorrelated. With 20 entities per group, there are 50
groups. The amount of capital required per entity in this case is about $4.4, or 220%
of the expected loss. This is high, but not close to the 950% that we found for the
pandemic risk in Example 4.
3.2.7 Summary of the examples
These hypothetical examples, which are summarized in Table 1, illustrate that the
amount of capital required to provide a credible cross-sectional pooling arrangement
increases substantially if there is correlation in the frequency and severity of losses,
both of which characterize pandemic losses. Example | provides a baseline case of
when pooling works well—i.e., a large number of exposure units, loss distributions
are known, losses are not correlated across exposures, and the interested parties
HE
The Geneva Risk and Insurance Review (2020) 45:134–170 151
0.0010 0.0593
90.0% 5.0%
50
45
40
35
30 Beta(1,49)
25
@RISK Course Version Minimum 0.0000
Maximum 1.0000
University of South Carolina Mean 0.0200
20 Std Dev 0.0196
15
10
5
0
0.00 0.04 0.08 0.12 0.16 0.20
Fig. 3 Beta distribution with parameters 1 and 49 used to model uncertainty in the frequency of losses
cannot influence the loss distribution. In this case, the capital required is 55% of the
expected loss costs. The introduction of uncertainty in losses in Example 2 increases
the capital required by a factor of three (from 55 to 170%). Correlation in the aver-
age severity of losses across entities (Example 3) increases the required capital sub-
stantially to about 750% of the expected loss costs. If there is also correlation in
the probability of the probability of a loss (Example 4), then the required capital is
about 950% of the expected loss costs. Of course, these numbers are contingent on
the specific assumptions made about the probability distributions, but they illustrate
the important general point that even when pooling arrangements can be organized
and managed at zero costs (which is what we assumed), the capital required to pro-
vide a credible pooling arrangement for pandemic risk would be substantial.15
3.3 Pooling over time
The previous section indicates that a credible cross-sectional pooling arrangement
for the economic losses associated with pandemics can require a large amount of
capital. We now analyze the possibility of pooling the risk over time, under the
assumption that the losses in one year are independent of those in another year. To
illustrate conceptually how this might work, we consider a 50-year time period and
treat all the entities in a given year as a separate cohort. We think of pooling over
time as each of the 50 annual cohorts agreeing to share losses equally with the other
49 annual cohorts. For simplicity, we ignore the time value of money.
To illustrate the potential benefits of pooling over time, we compare the capi-
tal required for all the entities under the cross-sectional pooling arrangement
15
It is also worth noting that the correlation in the probability of a loss in Example 5 is relatively mod-
est, as there is less than a 2.5% probability of having more than 75 entities experience a loss at the same
time.
152 The Geneva Risk and Insurance Review (2020) 45:134–170
Table 1 Summary of the hypothetical examples
Example #
1 2 3 4
Assumptions
Frequency Brnlli-prob fixed Brnlli-prob Brnlli-prob Binom-prob
(0.02) independ- Fixed fixed uncrtn
ent (0.02) (0.02) (Beta)
independ- independ- correlated
ent ent
Severity Fixed ($100) Uncertain Uncertain Uncertain
(LogN) (LogN) (LogN)
independ- correlated correlated
ent
Number of entities experiencing a loss
Mean 20 29 20 20
10th percentile 14 14 14 2
25th percentile 17 17 17 6
75th percentile 23 23 23 28
90th percentile 26 26 26 47
Required capital per entity to have
credible insurance
Amount of capital per entity ($) 1.1 3.4 15 19
Capital/(ExpLoss) (%) 55% 170% 750% 950%
in Example 4, which was 950% of the expected loss, to the capital that would be
needed for a credible pooling arrangement over 50 years. To do this, we estimate the
probability distribution of aggregate losses from Example 4 and assume that a sepa-
rate independent draw from this estimated distribution determines the aggregate loss
in each of the 50 years. We then simulate the mean and 99th percentile value of the
average loss over the 50 years, which are used to find the amount of capital needed
as the difference between the 99th percentile value and the mean value. The details
are in the “Appendix”.
Note that the sequential nature of time is ignored in this analysis. Instead, one can
think of pooling over time as requiring each annual cohort to contribute the annual
expected loss plus the required annual capital for each of the 50 years and then the
total losses over all 50 years are paid. Alternatively, one can think of the pooling
arrangement as receiving funds each year and paying out losses each year as they
occur, where any excess funds are carried over to the following year and any deficit
is borrowed from the participants at a zero interest rate.
The exact results depend on the distributional assumptions for aggregate losses
each year, but the message is clear: the amount of overall capital required would be
substantially lower than in Example 4. Under one distributional assumption (Inverse
Gauss distribution), the capital required per entity per year is 125% of the expected
loss, which is substantially lower than what would be required in a cross-sectional
pooling arrangement, which was 950% of the expected loss. Under an alternative
The Geneva Risk and Insurance Review (2020) 45:134–170 153
distribution assumption (Lognormal distribution), each entity would need to con-
tribute capital equal to 325% of the expected loss each year with pooling over time.
A problem with pooling risk over time is finding a way to actually implement the
arrangement so that sufficient funds are available each year that a large loss could
occur. To illustrate, using the example above, each year the arrangement would
receive 125% more than the expected loss, which equals $4500 ($2000 × (1 + 1.25))
in contributions. This amount exceeds the expected annual payout of $2000, but a
large loss could occur in one of the early years before a sufficient buffer has been
accumulated. Indeed, in the example, there is over a 10% probability that a loss
larger than $4500 could occur in any given year, including the first year.
A potential response is that the pool can borrow funds to make up any shortfall
that might occur. However, the feasibility of obtaining credible commitments from
all the relevant entities is questionable, and if it were possible, the transaction costs
would be high. In a dynamic economy with exit and entry, the arrangement would
need to identify the terms under which entities could leave and newly created enti-
ties could join the arrangement over time.
There are additional reasons that a viable pooling arrangement over time would
be difficult, if not impossible, to arrange and implement in a private market setting.
For example, if there are changes over time in the underlying probability distribu-
tions describing the frequency and/or the severity of losses that are different across
entities, then those entities that experience lower frequency and/or severity might
defect from the large pool, and possibly form smaller pools with other entities that
have similar changes.16 Therefore, differential changes across entities in economic
growth or in the quality of public health services might make a pooling arrange-
ment unstable over time. In addition, given that frequency and severity of losses
can change because of an entity’s actions and policies regarding public health and
economic growth, there is also a moral hazard problem associated with pooling over
time.
Given the problems associated with arranging a private market pool over time,
it is reasonable to ask whether a government could arrange such a pooling arrange-
ment. One big advantage of a government arranging the pooling arrangement over
time is that it has the ability to borrow funds and then tax future cohorts, which
helps reduce the capital problem (see e.g., Gordon and Varian 1988). In addition, a
government has fiat power to force participation, which could help reduce adverse
selection. On the other hand, pooling over time requires a long-term commitment,
and governments will be tempted to react to short-term political developments. In
Sect. 4, we further discuss the potential advantages and disadvantages with imple-
menting a government solution to pooling risk over time.
16
There is a formal literature on the stability of sharing arrangements. See e.g., Ligon et al. (2002) and
Genicot and Ray (2003).
154 The Geneva Risk and Insurance Review (2020) 45:134-170
3.4 Other considerations affecting the cost and benefits of a pooling
arrangement
The previous sections focused on the amount of capital associated with providing
a credible pooling mechanism for the economic losses from pandemics. There are
other characteristics of pandemics that could make a private market “thin” in the
sense that coverage is available for only a relatively small proportion of potential
or expected losses. We now discuss some of these factors.
3.4.1 Correlation of asset values to losses
As discussed above, pooling arrangements need to hold assets in excess of their
expected loss payments. These assets typically are invested in financial securities,
which ideally generate positive returns from interest, dividends, and capital gains.
In general, the returns on the financial securities held by insurers are uncertain,
which when combined with the uncertainty associated with loss payments (dis-
cussed above) can affect the credibility of the pooling arrangement to pay all
losses. The overall effect of uncertain asset returns depends on three factors.
First, higher expected returns on the assets, all else equal, increases the cred-
ibility of the pooling arrangement. Intuitively, the investment returns on the
assets provide additional financial resources to pay losses. Second, greater the
uncertainty associated with investment returns (sometimes measured by standard
deviation of returns), all else equal, reduces credibility. Intuitively, more uncer-
tainty in asset returns increases the likelihood of obtaining very low asset values
and therefore increases the likelihood of not having sufficient resources to pay
all losses. Third, the problem with the pandemic losses is that they arise from an
economic slowdown or shutdown, which also tends to lower asset returns. Thus,
when pandemic losses are high, asset returns tend to be low. The greater is the
negative correlation between asset returns and loss payments, all else equal, the
lower is the credibility of a pooling mechanism. To maintain the same credibil-
ity, the pooling mechanism will need to hold additional capital, which makes the
pooling arrangement even more costly to operate.
3.4.2 Expected frequency and severity differs across entities
In the examples above, we assumed that prior to the revelation of any random
variables, all of the entities, as well as the insurer, were homogeneous in their
expectation of the probability of an entity having a loss (0.02) and in their expec-
tation of the magnitude of the loss if a loss occurred ($100). In practice, both
the severity of losses and the probability of a loss are likely to vary across enti-
ties in observable ways. Loss severity will be higher, for example, for entities
with greater economic activity (revenue), those engaged in activities that are not
viewed as being essential, those with a more cautious clientele, those located
in more densely populated areas, etc. Similarly, the probability of a loss will be
HE
The Geneva Risk and Insurance Review (2020) 45:134–170 155
higher for entities located in areas with a more mobile population and that are
more densely populated.
In theory, this heterogeneity, provided it is symmetrically known, does not pre-
sent insurmountable problems for a pooling arrangement. To avoid an adverse selec-
tion problem (the high expected loss entities participate but the low expected loss
entities do not), the contributions to a mutual pooling arrangement or the premi-
ums paid to a private insurer would need to vary based on the expected losses of
each entity.17 A potential alternative is to not vary the contributions/premiums, and
instead make participation mandatory. This approach, however, would likely give
rise to political costs as those entities with low expected losses would want to reduce
their subsidization of those entities with high expected costs.
A more difficult problem arises if those who are operating the pooling arrange-
ment cannot distinguish entities with high expected losses from those with low
expected losses, but that the entities know their own expected losses. In this case,
the adverse selection problems cannot be solved by varying contributions/premiums
because the information needed to determine the appropriate premiums is private.
For example, a retail business’s susceptibility could depend on its clientele, about
which the owner is likely to have better information. On the other hand, understand-
ing the scope, degree, and duration of a pandemic often requires specialized scien-
tific knowledge, and thus the “experts” (scientists) are the ones who will likely have
superior information, not the exposed entities.
Variation across entities in their expected losses also raises the issue of whether
a pooling arrangement should provide equal compensation to all participants even
though they may differ in their actual losses. All else equal, compensating entities
for their actual loss (indemnity contracts) provides better risk sharing. The downside
with providing compensation based on the size of the loss is that it requires measur-
ing the loss, which is costly. In addition to reducing administrative costs, a flat ben-
efit can also reduce moral hazard, a topic to which we now turn.
3.4.3 Moral hazard
Potential moral hazard problems arise whenever the actions of participants in a
pooling arrangement can influence either the probability of a loss or the severity of a
loss. One way to mitigate moral hazard is to monitor participant’s behavior and write
contracts that are contingent on this behavior (although this may be costly). Another
way to mitigate moral hazard is to limit the extent to which risk is shared by includ-
ing deductibles, coinsurance, limits, exclusions, etc. Regarding the latter approach,
making participants pay part of the losses that occur (i.e., placing some of the risk
on participants) forces the participants to internalize the impact of their behavior
on the expected losses. A third method of mitigating moral hazard is to have non-
indemnity insurance contracts in which the payment by the insurer is determined by
either a set of event parameters (e.g., the number of countries affected or number of
17
The contribution needed to cover the cost of capital would also depend on each entity’s marginal con-
tribution to the uncertainty in total losses. See e.g., Bauer and Zanjani (2016).
156 The Geneva Risk and Insurance Review (2020) 45:134–170
deaths) and/or a loss index (e.g., the losses suffered in a geographical region), which
are correlated with an individual entity’s losses but not directly influenced by the
entity. These parametric and index triggers are sometimes used in insurance-linked
securities, such as catastrophe bonds.18 Indeed, the pandemic bond issued by the
World Bank in 2017 and discussed in Sect. 2 used a parametric trigger.
Moral hazard is likely to be an issue with pandemic risk for several reasons. The
economic costs associated with an interruption of normal business activity depend
on managers’ actions to control costs and generate revenue during the interruption
period. If a business receives compensation for all or most of its additional costs
and its lost revenue, then it will have little incentive to find and implement innova-
tive ways to prepare for and respond to an interruption to lower the costs incurred.
Also, decisions regarding the timing, extent, and duration of business interruptions
are often made by a regional or national governing body, and political officials will
naturally tend to initiate, broaden, and extend a forced interruption if the cost is born
mostly by future generations or taxpayers who are not the politician’s constituents.
3.4.4 Samaritan’s dilemma
If participation in either a private or public pooling mechanism is voluntary, then
some entities may not participate, because they have the expectation that if a major
loss occurs, public assistance will provide ex post coverage at no cost. This is some-
times called the “Samaritan’s dilemma” (Buchanan 1977). A solution is mandatory
participation.
3.4.5 Positive externalities associated with a pooling mechanism
Most of the discussion to this point has focused on the private costs and benefits
associated with pooling risk, i.e., the benefits and costs to the entities in the pooling
arrangement. However, an important consideration is the potential benefits that oth-
ers in society receive as a result of having a pooling arrangement that compensates
entities for their loss. One potential external benefit is that it reduces the likelihood
that business relationships among contracting parties or between the business and
its employees will be damaged or disrupted. Also, by helping to maintain economic
activity, even at a lower than normal level, compensation to entities that experience
a loss can reduce the magnitude and duration of a recessionary economic period.
A disadvantage of purely private insurance mechanism is that these positive exter-
nalities would not be taken into consideration in the pricing and distribution of the
insurance. Potential solutions include government subsidies for private insurance or
even government provision of the insurance at reduced rates.
18
The use of non-indemnity triggers to reduce moral hazard is discussed in Niehaus and Mann (1992),
Doherty and Richter (2002), Doherty and Smetters (2005), Froot and O’Connell (2008), and Finken and
Laux (2009). The tradeoff is greater basis risk. Teh and Woolnough (2019) provide an overview of the
literature and an analysis of basis risk associated with index-based contracts.
The Geneva Risk and Insurance Review (2020) 45:134–170 157
Also, the lack of insurance for the economic losses associated with a business
interruption implies that additional risk is placed on lending institutions. Given the
connectedness of the financial industry, the failure of some financial institutions
could jeopardize the solvency of other institutions. Contagion can also occur if
information about the solvency problems of one or a few institutions spread to other
institutions. In short, there is potential failure of, or significant damage to, the entire
financial system (systemic risk) due to uninsured pandemic losses. To the extent that
a functional risk-transfer mechanism for the economic losses from a pandemic can
reduce the likelihood of a bad recession, there is a positive externality associated
with having such a risk-transfer mechanism.
3.5 Section summary
The most important problem facing the private supply of pandemic insurance is the
large amount of capital required to make the insurance credible. Other problems
(correlation between pandemic losses and insurer assets, moral hazard, adverse
selection, low take-up rates due to underestimation of risk) are also important con-
siderations. While a private market could provide some coverage by limiting the
amount of coverage and the number of entities covered, widespread, high-limit cov-
erage is not likely to be forthcoming from the private market. There are, however,
positive externalities associated with avoiding long periods of depressed economic
activity, which broad coverage could help prevent. Comprehensive government pro-
grams designed to directly address large-scale business continuity losses from pan-
demics are necessary to address this risk prospectively and could, over time, poten-
tially encourage the innovation of limited specialized pandemic coverages by private
insurers and reinsurers. It is useful to think about broad coverage provided by a gov-
ernment as a pooling arrangement over time, which utilizes the government’s ability
to transfer resources over long time periods by borrowing and taxing. The tradeoffs
associated with different ways of organizing a government mechanism are analyzed
in the next section.
4 Government’s role
Since society has a vested interest in mitigating and compensating pandemic losses
and a private market alone cannot provide sufficient insurance coverage, we con-
sider how a government can promote greater sharing of future pandemic risks. Dur-
ing COVID-19, governments around the world not only imposed social-distancing
measures to control the spread of the virus, but also actively provided significant
financial resources to businesses and individuals, with the intention to protect
employment and avoid bankruptcies. Current government actions, however, are not
necessarily the optimal mechanism for the future. In this section, we outline four
general approaches available to governments which aim to facilitate and support the
sharing of pandemic risk. We evaluate these four approaches based on the degree to
which they are likely to achieve specific public policy goals.
158 The Geneva Risk and Insurance Review (2020) 45:134-170
4.1 Four approaches to government involvement
4.1.1 Approach 1: provide post-event aid
One approach is for the government to let the private market deal with pandemic
risk without government involvement. As explained above, the private market will
fail to provide substantial coverage and many entities will suffer large uncompen-
sated losses if a pandemic occurs. The government, on an ad hoc basis, can then
“come to the rescue” and provide aid to those impacted. This approach, which is
depicted in Fig. 4, involves no pre-event financing nor pre-event commitment for
how the aid will be allocated if an event occurs. The government reacts to a pan-
demic event as it is occurring and allocates resources to the entities affected by the
pandemic. The necessary funds are borrowed, placing the cost burden on current
and future taxpayers.
The COVID-19 pandemic was handled by most governments using the post-event
aid approach. Alpert (2020) provides a description of the aid provided by 14 differ-
ent countries, including the dates the aid was announced, the amount of aid, and ref-
erences to news articles about the aid. The Coronavirus Aid, Relief, and Economic
Security (CARES) Act in the U.S. is an example of post-event aid; it deployed
over $2 trillion in economic relief to workers, families and small businesses. The
UK’s government subsidized up to 80% of the salaries of eligible workers when the
COVID-19 outbreak occurred in the country. It also provided $379 billion to sup-
port business loans. Germany initiated an Economic Stabilization Fund with $650
billion to support loan payments and take equity stakes in struggling companies.
It subsequently provided $54 billion to small businesses and the self-employed to
cover operating costs. It is worth noting that in addition to explicit government relief
funds, post-event aid can also be provided in implicit forms, such as legislation that
limits a company’ liability from pandemic-related lawsuits or reduces a firm’s pay-
roll taxes.
4.1.2 Approach 2: provide reinsurance
Prior to a pandemic event, a government could provide reinsurance to insurers that
sell primary pandemic coverage to businesses. The reinsurance coverage would be
for losses above a designated threshold and would have a designated limit and might
contain a co-share provision. The government reinsurer would reimburse insurers
based on the terms of the reinsurance contract. Although the government reinsurer
could have accumulated some resources during periods without an event, a major
pandemic would likely require government borrowing with tax payments to support
the debt. This approach is depicted in Fig. 5.
Several countries use government reinsurance schemes to cover terrorism risk.
See Michel-Kerjan and Pedell (2005) for an overview and comparison of the French,
German, and U.S. systems that were set up in the wake of the 9/11 attacks.’
'9 As an alternative to government reinsurance, a government could provide contingent financing to
insurers. Pool Re in the UK provides an example. Pool Re was created as a mutual reinsurer for property
insurers and reinsurers in the UK after the IRA bombing of the Baltic Exchange in 1992. Pool Re col-
HE
The Geneva Risk and Insurance Review (2020) 45:134–170 159
Fig. 4 Provide post-event aid
4.1.3 Approach 3: provide insurance
The government could provide voluntary insurance coverage directly to entities
that are exposed to pandemic risk. With this scheme, the government insurer col-
lects premiums and is authorized to borrow funds in case a pandemic occurs prior
to accumulation of sufficient premiums. This approach, which is depicted in Fig. 6,
would require the government to either create the organizational structure to market
the insurance directly to insureds or pay commissions to existing marketing entities
in the private insurance industry. Since pandemics are relatively rare events, it would
likely not be efficient for the government to develop a new organizational structure
to pay claims; instead, it could pay claims through existing government entities, such
as the Federal Emergency Management Agency (FEMA) in the U.S., or third parties
such as banks, insurers, or claims processing companies in the private market.
The National Flood Insurance Program (NFIP) in the U.S.is an example of a gov-
ernment program that directly indemnifies against future losses. The NFIP markets
its policies through participating insurance agents and insurers, who also participate
in the claims process.20 Outside the U.S., governments of many countries also face
insurance coverage issues related to catastrophe risk and often provide insurance
directly to the policyholders. These schemes are sometimes referred as “residual
markets”.21 For example, Taiwan has material earthquake risk and therefore estab-
lished Taiwan Residential Earthquake Insurance Fund (TREIF) to provide residen-
tial earthquake insurance. In New Zealand, the Earthquake Commission (EQC) also
supplies residential earthquake insurance directly to homeowners.
4.1.4 Approach 4: provide social insurance
Under a social insurance scheme, many or all entities are required to participate, and
all participating entities are entitled to receive event-contingent benefits. Typically,
Footnote 19 (continued)
lects funding from its member insurance companies and pays a “premium” to the UK Government for
its commitment to lend funds to cover terrorism losses in case Pool Re does not have sufficient financial
resources. In addition, Pool Re also purchases reinsurance from the private market. By the end of 2019,
Pool Re accumulated a fund of £6.7B to pay potential claims (Ralph 2020) and it purchased £2.4B of pri-
vate reinsurance coverage to enhance its claim-paying capacity in 2020 (Evans 2020b).
20
The Pension Benefit Guaranty Corporation (PBGC), which insures private defined benefit obligations
is the U.S., is another example of a government insurer.
21
Individual states in the U.S. also provide residual market mechanisms for catastrophe risk. For exam-
ple, states on the gulf coast and in the southeast have residual markets for coastal property.
160 The Geneva Risk and Insurance Review (2020) 45:134–170
Fig. 5 Provide reinsurance
Fig. 6 Provide insurance
participants are required to make pre-event payments, often in the form of a tax. An
administrative system is needed to process and pay the contingent benefits. For most
schemes, both the pre-event payments and the benefits paid from a social insurance
system increase with potential losses, and are capped at a relatively modest level
of potential losses. The idea is to provide modest coverage for a broad spectrum of
population. Figure 7 provides a visual description of a social insurance mechanism.
Unemployment insurance in the U.S. is an example of a social insurance mecha-
nism. Employers and employees are required to pay taxes so that individuals can
receive monetary payments if they become unemployed. Unemployment benefits are
determined by the states and paid for through a combination of federal and state
taxes. The social security system is another U.S. example. Payroll taxes are used to
pay the retirement and disability benefits of workers.
Germany has a social insurance program called Kurzarbeit, which pays work-
ers who have been furloughed a percentage of their lost wages. For example, if
an employee without children is moved from full-time employment to half-time
employment, then the program will pay 60% of one-half of the employee’s salary
(IMF 2020a; Chazan and Milne 2020). Italy’s Cassa Integrazione Ordinaria, which
is a program similar to Germany’s Kurzarbeit, has been expanded as result of the
COVID-19 pandemic. Under this system, employers who have suspended or reduced
work activity due to the pandemic can apply for emergency support that would enti-
tle most employees to 80% of their salary. Employees in industries particularly hard
hit by the pandemic (such as tourism and entertainment) are eligible for benefits of
longer duration than other workers (KPMG 2020).
More broadly, the COVID-19 pandemic has led the European Union (EU) to
deepen its commitment to collective action for the benefit of displaced workers
within its member states. Specifically, the European Commission has proposed
a new policy instrument known as temporary Support to mitigate Unemployment
Risks in an Emergency, or SURE (European Commission 2020). Under the SURE
Initiative, members are eligible for up to 100 billion Euros in loans granted on
favorable terms. The purpose of the loans is to help EU Member States defray the
The Geneva Risk and Insurance Review (2020) 45:134–170 161
Fig. 7 Provide social insurance
increased costs of their national short-term unemployment programs. In this sense,
the SURE Program is akin to a social reinsurance scheme in that it is the social
insurance mechanism in the member state that is the recipient of assistance.
4.2 Evaluation of the four approaches
Sections 2 and 3 identified the need for government intervention, and Sect. 4.1 iden-
tified four general ways that government could (and does in some cases) intervene in
insurance markets. We now consider the relative strengths and weaknesses of these
schemes in achieving various public policy goals. Specifically, we consider the fol-
lowing four goals: (1) operational efficiency, (2) matching compensation with losses,
(3) incentives for risk mitigation, and (4) macroeconomic impacts.
4.2.1 Goal 1: operational efficiency
Operational efficiency can be defined as how much money is used to cover oper-
ating expenses as opposed to indemnify losses. In the insurance market, this is an
important consideration. For example, the U.S. P&C insurance industry has an
expense ratio of 27%, according to National Association of Insurance Commission-
ers (2019), meaning 27% of the P&C industry premium collected is spent on agency
fees, broker commissions, employee salaries, and other operational costs. In 2018,
these costs were $168 billion. Operational efficiency is influenced by both system
design and business execution. All else equal, a system will be more efficient if (a)
it has fewer profit-seeking “middlemen” and (b) less bureaucracy/better day-to-
day business execution. A common view is that private business is more effective
than government regarding (b) but that well-designed government involvement can
reduce middlemen.
Among the four government schemes, an advantage of a social insurance scheme
is that it cuts out the “middlemen” because it can use existing government entities,
such as the tax authority, to collect payments from participants. On the other hand,
a social insurance system is unlikely to have an efficient method of verifying that
losses occurred (and their magnitudes). For example, the tax authority would likely
need to employ additional auditors who can verify losses while the pandemic is
ongoing so that timely payments can be made to keep entities viable. Alternatively,
benefits could follow a schedule based on payroll or revenue, which would improve
operating efficiency, but worsen the matching of compensation to losses (see below).
Yet another option would involve a parametric trigger (e.g., official pandemic dec-
laration) with benefits being paid to all eligible entities without any regard to actual
162 The Geneva Risk and Insurance Review (2020) 45:134–170
loss. While this approach avoids entirely the need for auditors to determine loss,
thereby accelerating the payment of losses and materially lowering administration
costs, it is the worst option in terms of matching compensation to losses.
Post-event aid may appear to be relatively efficient as well, especially consider-
ing that the administrative costs are only incurred when a pandemic occurs, which
(hopefully) is relatively rare. On the other hand, the ad hoc nature and the lack of
planning associated with post-event aid is likely to lead to inefficiencies in the dis-
tribution of relief funds and poor matching of compensation with losses. To reach
as many entities as possible, as quickly as possible, and without prior planning, the
government will likely incur high costs; also, the potential for graft is high.
If the government serves as the reinsurer or insurer, costs will be incurred in
developing the administrative structure for distribution, financial management,
claim services, etc., as well as the ongoing operating costs of providing these admin-
istrative functions, once they are established. These costs are likely to be greater if
the government directly provides insurance rather than reinsurance (since the lat-
ter involves interaction just with private insurers, while the former requires deal-
ing with the primary insured entities and potentially their intermediaries (i.e., agents
and brokers). Moreover, the lack of competitive forces could reasonably be expected
to make a government insurer less efficient than a private insurer.
In Table 2, we summarize this discussion by ranking the operational efficiency of
providing post-event aid as medium, reinsurance as medium, insurance as low, and
social insurance as high.
4.2.2 Goal 2: matching compensation with losses
An effective insurance mechanism would accurately measure losses and provide
compensation based on those losses. This is important from the perspective of the
welfare of the individual insureds and from the societal perspective of using an
insurance mechanism to reduce the impact of a pandemic on the macroeconomy. We
discuss the macroeconomic issues below; here we focus on the individual insureds.
Ideally, a pandemic insurance mechanism would replace a substantial share of an
entity’s losses, but to mitigate moral hazard (see below), it would not fully compen-
sate for all losses. Finding the optimal amount of coverage depends on many factors,
but all else equal it is better to have a higher correlation between compensation and
losses.22 Also, a system that provides compensation in excess of losses is wasteful of
resources (inefficient—see above), creates incentives to increase the risk, and likely
generates negative opinions about the insurance mechanism, which reduces political
support.
A problem with a post-event aid system is that it will likely be hastily designed
and pushed through the legislative process so that compensation can be provided
quickly. As a consequence, it is likely to sacrifice the goal of matching compensa-
tion with losses in order to get aid distributed quickly. In response to COVID-19,
22
Note that this policy goal could be also be classified as part of the efficiency goal, given that a scheme
that provides resources to those without losses is using resources inefficiently.
The Geneva Risk and Insurance Review (2020) 45:134–170 163
Table 2 Comparison of different ways that government can be involved in pandemic risk sharing with
respect to four public policy goals
Goal Government provides
Post-event aid Reinsurance Primary insurance Social insurance
Operational efficiency Medium Medium Low High
Risk mitigation incentives Low High Medium Low
Match funds with needs Low High Medium Low
Impact on overall economy Medium Medium Medium High
the U.S. government’s Economic Impact Payments were sent to individuals through
the tax system.23 Often payments did not correlate with losses and some entities
did not receive compensation because the entity did not file taxes.24 Likewise, the
Federal Pandemic Compensation Program, which provided unemployed workers
with an additional $600 above their state unemployment insurance benefit, resulted
in a median wage replacement rate of 134%, with 68% of workers receiving more in
unemployment benefits than they earned from their jobs (Ganong et al. 2020). Also,
the U.S. Paycheck Protection Program (PPP) for COVID-19 business relief was dis-
tributed through the banking system, so business owners needed to apply for PPP at
the banks. Different banks have different policies, systems and practices, which cre-
ated discrepancies in fund distributions.
A social insurance mechanism that provides a relatively uniform amount of com-
pensation compared to the losses suffered by entities is likely to have a relatively
low correlation between compensation and losses. In other words, simple social
insurance systems will do poorly relative to this goal. Moreover, attempts to improve
a social insurance scheme along this margin are likely to undermine some or all of
the operational efficiency advantages of social insurance discussed above.
Provided entities understand the risk, a private insurance market with voluntary
primary coverage (and government provided reinsurance) is likely to be signifi-
cantly better at achieving this second goal, since it allows entities with greater cover-
age needs to voluntarily choose to purchase more insurance coverage from private
insurers.25
23
There are multiple channels through which the government can distribute compensation payments,
including the tax authority (IRS), the disaster management system (FEMA), and entities with lending
authorities (SBA and Federal Reserve). The tax authority likely has the broadest reach, and therefore
would likely be an appropriate entity to disburse funds in a pandemic.
24
Some businesses did not file taxes because taxes “pass through” to the individual owners.
25
This advantage of utilizing a private insurance market (with government provided reinsurance) can
be undermined if entities systematically underestimate the risk. In this case, entities would purchase less
insurance than if they were fully informed. Evidence of low take-up rates exists in several existing insur-
ance programs. For example, the current terrorism take-up rate for the coverage provided by the Terror-
ism Risk and Insurance Act (TRIA) embedded in property policies is about 60% (Marsh 2019). The take-
up rate of residential earthquake insurance in California is only about 10% (Marshall 2018).
164 The Geneva Risk and Insurance Review (2020) 45:134–170
As summarized in Table 2, our ranking of how the various schemes match com-
pensation with losses is as follows: when the government provides post-event aid—
low, reinsurance—high, insurance—medium, and social insurance—low.
4.2.3 Goal 3: incentives for risk mitigation
Reducing risk by compensating entities for their pandemic losses (which is a good
thing) can also adversely affect their incentives to reduce the probability of a loss
occurring and/or reduce the magnitude of the loss once it occurs (which is a bad
thing). Therefore, the impact of a risk-sharing scheme on incentives for risk mitiga-
tion is an important consideration when evaluating alternative ways that a govern-
ment can be involved in providing pandemic risk insurance. (Note that mitigation
incentives should include incentives for innovation to help businesses continue to
operate, while keeping employees and customers safe.)
Private insurance typically provides incentives for risk mitigation through the
underwriting process and by having insureds bear some risk through various policy
provisions, such as deductibles, coinsurance, limits, and exclusions. Insurers’ under-
writing processes differentiate the risk profiles of potential insureds and charge pre-
miums accordingly. Insurers usually are effective at identifying those entities that
have mitigated the risk or are committed to do so. Insurers offer a lower premium to
such insureds because they expect lower future claims. For example, when residents
living in Florida make their houses more resistant to hurricane damage, they usually
can receive a reduced premium.26
Given private insurers’ experience and expertise in managing moral hazard,
there are advantages of a government program utilizing private insurers to market
and price pandemic insurance and to settle pandemic claims. Thus, an important
argument for including the private insurance sector in the pandemic risk solution
is to incentivize risk mitigation. Research has shown that risk mitigation, even not
implemented at full scale, can significantly lower potential losses (McMorrow et al.
2013).
Theoretically, a government insurance program could operate like a private
insurer and mimic the underwriting processes and contract design features used by
private insurers. However, a monopoly government insurer would not face com-
petitive pressures to develop and use innovative approaches to provide incentives
for insured entities to mitigate risk. Furthermore, a government insurance program
would likely be subject to political pressure to suppress rates for insureds with the
greatest pandemic risk,27 thus blunting the effectiveness of underwriting as method
of providing incentives for risk mitigation.
26
Despite insurers’ incentive and capability to mitigate moral hazard, not all moral hazard will be elimi-
nated for at least two reasons. First, mitigation efforts are costly and therefore it is not optimal to elimi-
nate moral hazard. Second, some types of moral hazard will not be impacted by commercial insurance
contracts. For example, a population’s vulnerability to a pandemic depends on a number of co-morbidi-
ties of the individuals in the population, which depend largely on individual behavior, not business prac-
tices.
27
For examples of rate suppression in the National Flood Insurance Program, see Kousky and Shabman
(2014).
The Geneva Risk and Insurance Review (2020) 45:134–170 165
Social insurance programs tend to treat participants similarly in terms of pric-
ing and benefits. This occurs in part because social insurance programs typically
place greater emphasis on covering as many people as possible and on maintaining
relatively equal treatment of the insureds. Consequently, underwriting is likely to be
limited compared to the private insurance sector, and the tax or premium formula
used by a social insurance program is not likely to reflect risk mitigation measures
or have as much differentiation of across observable risk classes. Thus, the risk miti-
gation incentives from a social insurance scheme are not as strong as the schemes
that utilize the private insurance sector.28
The mitigation incentives provided by a post-event aid scheme depends on the
public’s perception of the likelihood that aid will be provided and the comprehen-
siveness of that aid. If entities view that the government will compensate most
losses, then the entities will have little incentive to mitigate pandemic risk. This is
the Samaritan’s dilemma problem (Raschky and Schwindt 2016). On the other hand,
if entities do not perceive that aid will be forthcoming but they understand the mag-
nitude of the risk that they face, then they will have strong incentives to mitigate.
To summarize, among the four government schemes discussed, private insurance
companies underwriting pandemic risk upfront and the government serving as the
insurance industry’s backstop reinsurer is likely the most effective approach from
the risk mitigation perspective; it therefore receives a ranking of high in our Sum-
mary Table 2. The government serving as an insurer receives a grade of medium,
and both the post-event aid and social insurance programs receive a low grade.
4.2.4 Impact on the overall economy
A pandemic insurance mechanism is important for the overall health of the econ-
omy. This point is illustrated by the impact of terrorism insurance coverage post
9/11. The lack of terrorism insurance, primarily driven by insurers not willing to
cover the risk, created broader economic issues. Examples include business loans
not being approved and construction projects halted because there was no proof of
insurance coverage.29 The same situation could emerge for pandemic insurance post
COVID-19. Insurance, whether provided directly by the government or through the
private sector (and then reinsured), helps to avoid this type of market failure. More
generally, a pandemic insurance mechanism can keep businesses operating, reduce
unemployment, and help maintain consumer spending, all of which help prevent an
extended recessionary period.
Do the four mechanisms differ in their impact on the overall economy? Post-event
aid differs from the others in that it is not in place when a pandemic occurs. It must
first work its way through the political process, which creates at least temporary
28
See Feldstein (1974) for classic evidence that social security reduces private savings. For a general
analysis of social insurance programs, see Feldstein’s presidential address to the American Economic
Association (Feldstein 2005).
29
These issues led to the Terrorism Risk and Insurance Act (TRIA) and its multiple renewals since its
initial passage.
166 The Geneva Risk and Insurance Review (2020) 45:134–170
macroeconomic damage as well as uncertainty about the extent to which business
will be able to recover from a shutdown. On the other hand, it is a more flexible
tool than the other programs in that it can be tailored to the needs at the time. It can
be scaled based on the damage observed and adjusted over time. The latter advan-
tages suggest that post-event aid can also be used along-side any of the other three
schemes to augment benefits if needed. As we have mentioned several times already,
the possibility of augmenting benefits, however, makes the Samaritan’s dilemma
worse.
Mandatory social insurance is likely to provide greater macroeconomic benefits
to society because of its broad reach and because a large proportion of benefits paid
will go to those with a high marginal propensity to consume (e.g., low-to-middle
income families, small businesses). Mandatory social insurance programs, therefore,
essentially provide a safety net for the economy. The government provision of insur-
ance or reinsurance, assuming that they are voluntary, would help bolster the mac-
roeconomy during a pandemic, but the degree to which they help depends on the
extent to which the pandemic insurance is purchased.30 Therefore, as summarized in
Table 2, we give post-event aid, reinsurance, and insurance a grade of medium, and
social insurance a grade of high.
4.3 Section summary
Our evaluation of the four pandemic risk-financing schemes is summarized in
Table 2 using the four goals discussed above. There is not a clear conclusion that
can be drawn based on these analyses, as each approach has its own advantages and
disadvantages. However, our sense is that the worst option is to wait until another
pandemic occurs, and then provide post-event aid. This option performs poorly on
three of the four goals outlined above. The one goal on which it ranks relatively high
is operational efficiency, in large part because it does not incur costs until another
pandemic event occurs. The other three approaches have advantages and disadvan-
tages and therefore the choice among them will depend on one’s relative weighting
of the various policy goals. In addition, we have simply provided a foundation for
organizing one’s thoughts about the issues. There are many details that would need
to be fleshed out and these details could impact the relative rankings of the various
approaches.
30
Another consideration is adverse selection, which is likely to be important in the context of pandemic
risk because of the high level of heterogeneity and information asymmetry. For example, if an insurer
collects a fixed percentage of a company’s income as a “pandemic insurance premium”, the industries
with a low-risk profile (e.g., office workers who can mostly work from home) will likely opt out because
these low-risk industries know they are subsidizing the high-risk industries with their participation.
Eventually, the risk pool will consist of mostly high-risk insureds, effectively leading to underfunding
and insufficient coverage. A solution for adverse selection is to make the insurance mandatory. Of the
four government insurance schemes discussed in this paper, only social insurance is mandatory.
The Geneva Risk and Insurance Review (2020) 45:134–170 167
5 Conclusion
Pandemics represent a recurrent scourge on humanity that throughout recorded
history are responsible for not only incalculable human suffering but also extreme
and lasting economic damage. The COVID-19 pandemic, which swept through
the world in 2020 and is still developing in many countries, was no exception.
Unquestionably, economic interdependencies in our modern, globalized society
accelerated the spread of the virus and intensified its economic consequences,
throwing the global economy into its sharpest contraction since the Great
Depression.
Certainly, many executives, risk managers, and insurance professionals knew
that the occurrence of a pandemic and its associated economic losses was a pos-
sibility, if not a certainty at some point in the future. However, as we document
in Sect. 2 of this paper, the private insurance market provided limited insurance
coverage for economic losses caused by a pandemic. Indeed, very few insurance
products protecting against “pure” pandemic risk have ever been brought to mar-
ket. While insurers are exposed to pandemic loss through some products with
contingent or incidental exposures, our analysis suggests that the overwhelming
majority of pandemic risk is uninsured.
What explains the limited coverage provided by the private market? While
acknowledging there might be reasons on the demand side, such as underesti-
mation of the frequency and severity of pandemics, we argue in Sect. 3 that a
pandemic insurance market fails largely because the amount of capital needed to
supply credible insurance coverage is prohibitively high. More specifically, we
use hypothetical examples to explain how correlation in the frequency and sever-
ity of losses, both key characteristics of pandemics, leads to an extraordinarily
high capital requirement. The examples also illustrate that the required capital
drops considerably if the risk is pooled over time, as opposed to across entities in
a given time period.
We conclude that government involvement is needed to solve the private insur-
ance market failure, due to government’s unique ability to tax, borrow and pool risk
across time. Moreover, we argue that an effective pandemic risk-sharing mechanism
would generate positive externalities for the macroeconomy by reducing the disrup-
tion in economic activity and preventing a prolonged economic recession.
The paper then suggests that a government can support the sharing of pandemic
risk by providing either (1) post-event aid, (2) reinsurance, (3) primary insurance,
or (4) social insurance. We evaluate the strengths and weaknesses of these schemes
based on four policy goals: operational efficiency, matching compensation with
losses, incentives for risk mitigation, and macroeconomic impacts. We conclude that
post-event aid meets the fewest policy goals, while the other three approaches have
advantages and disadvantages on different dimensions. Despite there not being a
clear conclusion for what is the optimal design for the future, we identify important
tradeoffs associated with different forms of government involvement.
Acknowledgements The authors appreciate the comments from an anonymous referee and the comments
and guidance from the editors.
168 The Geneva Risk and Insurance Review (2020) 45:134–170
Appendix
Additional Explanation of the Assumptions Used to Analyze Pooling over Time in
Sect. 2.2.
In Example 4, we assume that the frequency of claims that applies to all enti-
ties has a binomial distribution with an uncertain probability of a loss. The uncer-
tainty about the probability is described by a Beta distribution with parameters 1
and 49, which implies that on average the probability is 0.02, as is the case in all the
other examples. The average severity of losses has a lognormal distribution with an
expected value equal to $100 and standard deviation equal to $200.
Using these assumptions, we simulate 5000 outcomes for the aggregate loss for
all 1000 entities. We then find the probability distribution that fits these aggregate
loss outcomes, using the AIC statistic to rank the fitted distributions. The distribu-
tion with the lowest AIC statistic is a lognormal distribution with a mean of $2650
and a standard deviation of $14,342. The distribution with the second lowest AIC
statistic is the Inverse Gauss distribution with a mean of $1946 and standard devia-
tion of $5050.
Using each of these distributions, we simulate the mean and 99th percentile value
of the average loss over the 50 years, which are then used to find the amount of capi-
tal needed as the difference between the 99th percentile value and the mean value.
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