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Insurance mechanisms to mediate economic risks
in marine fisheries
J. D. Mumford, A. W. Leach, P. Levontin, and L. T. Kell
Mumford, J. D., Leach, A. W., Levontin, P., and Kell, L. T. 2009. Insurance mechanisms to mediate economic risks in marine fisheries. – ICES
Journal of Marine Science, 66: 000–000.
Uncertainty affects the behaviour of fishers and fisheries regulators in a way that can adversely affect the sustainability of fish stocks,
fisheries income, and productivity. In agriculture, there has been a long history of using levy funds and public and private insurance
schemes to mediate economic risks to growers resulting from environmental variability and quarantine risks. In the United States, the
federal government continues to underwrite funds (collected by contracted private agents) that are used to protect contributors from
the effects of extreme weather and pest and disease losses. In Europe, there are examples of industry-based mutual funds to mediate
risks from exotic agricultural diseases. In agriculture, insurance mechanisms have been successful in reducing risk-inducing behaviour
by contractual compliance to risk-reducing codes of practice. For fisheries, insurance may provide a tool to address some elements of
uncertainty in a way that would help both the fishing industry and the regulators achieve objectives of sustainability, income security,
and productivity. This paper presents a brief review of insurance in agriculture and capture fisheries and uses a stochastic model to
illustrate how insurance funds could protect revenue and encourage increased sustainability of fisheries and improve compliance with
and enforcement of fisheries regulation. Although insurance may be a partial solution to unsatisfactory fisheries management and
fishing performance, some potential challenges to this novel approach are also discussed.
Keywords: capture fishery, feedback dynamics, fisher behaviour, harvest, indemnity, insurance, price, regulation, revenue, risk, sustainability,
uncertainty.
Received 13 October 2008; accepted 23 March 2009.
J. D. Mumford, A. W. Leach, and P. Levontin: Centre for Environmental Policy, Imperial College London, Silwood Park, Buckhurst Road, Ascot SL5
7PY, UK. L. T. Kell: Centre for Environment, Fisheries and Aquaculture Science (Cefas), Pakefield Road, Lowestoft, Suffolk NR33 OHT, UK.
Correspondence to J. D. Mumford: tel: þ44 207 5942206; fax: þ44 207 5942308; e-mail: j.mumford@imperial.ac.uk.
Introduction
Individual fishers can react to inherent risks in ways that increase
their own future risks and jeopardize the sustainability of fish
stocks. Faced with declining catches, fishers may react in several
ways that affect the uncertainties in fish stocks and revenues.
They may continue to fish at the regulated effort level, leave the
industry, or they might increase catchability and/or effort,
thereby overexploiting resources in the short term. Risk can be
reduced through structural design measures that move fishery
management to become more robust to the uncertainty that per-
vades fishery systems through, for example, adaptive management
that reduces uncertainty by learning about the fishery system over
time (Charles, 2007). Here, we ask if insurance can provide such a
tool by providing more resilience to uncertainty in a way that
would help fishers and regulators achieve shared objectives of sus-
tainability, income, and productivity. However, the nature of fish-
eries risk, ranging from incipient to acute unsustainability
(Cunningham and Maguire, 2002), may determine the forms of
insurance that are appropriate and the constraints on any potential
benefits from insurance.
Insurance mechanisms have been used to mitigate financial
risks presented by environmental and biosecurity uncertainties
in agriculture [USDA Risk Management Agency (RMA;
www.rma.usda.gov), Kartoffelafgiftsfonden in Denmark
(www.kartoffelafgiftsfonden.dk), Potatopol in Holland
(www.potatopol.nl)]. However, the sources of uncertainty that
can adversely affect fisheries are diverse and often more difficult
to define and predict than in agriculture (Hermann et al., 2004)
and, because fisheries are often a shared resource, individuals
lacking property rights have an incentive to exploit the resource
unsustainably. It is more difficult to define and enforce property
rights for fisheries than it is for agriculture; for example, fish
stocks are often exploited by several nations and fleets, whereas
agricultural property generally comes under a single national jur-
isdiction. In cases where property rights for fisheries have been
established through individual transferable quotas (ITQs;
Iceland, New Zealand, Australia, USA), the risk of stock collapse
has been reduced (Costello et al., 2008); insurance could comp-
lement such a property right system and help reduce various
risks further.
The factors that affect agricultural outputs are often evident
(weather, pest and disease outbreaks, etc.), whereas the question
of what caused a decline in fisheries is usually impossible to
answer with the same degree of confidence. Risks in agriculture
are often heterogeneous over large areas, whereas fisheries risks
may apply to the whole stock. Insurance that covers many
farmers planting a particular crop can expect to pay out to only
a few farmers in any year, because most events covered by
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ICES Journal of Marine Science Advance Access published April 20, 2009
agricultural insurance are location-specific and can be expected to
affect only a small proportion of the crop at the same time. The
difference from insuring a single fish stock is that it is much
more likely that many fishers would need to be compensated by
the insurance simultaneously, because the success of each
depends on the state of the entire stock and is not as area-specific
as for individual farms. Many fish stocks are cyclical owing to
broad climatic and ecological factors, whereas others exhibit varia-
bility attributable to fluctuations in recruitment success, mortality,
and migration. Continued overexploitation of fish stocks can itself
lead to greater variability in stock dynamics (Beddington and May,
1977). Risk is also introduced from socio-economic and political
spheres via interactions in prices, costs, labour availability, and
regulation (Hermann et al., 2004).
The fishing industry has repeatedly pointed to difficulties in
adjusting to large fluctuations in total allowable catch (TAC)
between years, whereas a lack of flexibility in TACs could threaten
the sustainability of fishery resources unless TACs were set conser-
vatively (Kell et al., 2005). Currently under the European Common
Fisheries Policy (CFP), management plans for many stocks specify
interannual bounds on TACs. An alternative, or complementary,
approach might be to reduce the variation in revenues by insur-
ance, rather than managing TACs directly. For instance, an insur-
ance contract can specify effort and catch restrictions for each
fisher, so the variability in catch will be reduced compared with
a management system that relies on TAC alone.
Governments worldwide are heavily involved in regulating fish-
eries, yet few of these fisheries can be described as being both eco-
logically and economically sustainable. The CFP of the European
Union has a reputation for management that has been criticized
for lack of sustainability (House of Lords, 2008). Insurance has
proved a useful tool in mitigating risks and promoting sustainabil-
ity in agriculture. Here, we provide a brief overview of agricultural
insurance mechanisms, review the literature on fisheries insurance,
and illustrate, with a simple herring-like stock example, the poten-
tial of simulation models to aid discussion of applicability of
insurance in capture fisheries.
Review of specific agricultural insurance
mechanisms
In agriculture, there are a number of examples of insurance being
used to mitigate financial risks for farmers and to promote sustain-
ability. The insurance mechanisms described are not intended as
instantly transferrable options in capture fisheries, but are used to
illustrate how insurance in certain situations altered the governance
and restructured responsibilities with respect to agricultural risk.
The RMA (www.rma.usda.gov) of the US Department of
Agriculture (USDA) provides insurance cover for more than $67
billion of agricultural risk annually in the United States (up 60%
since 2003), with more than a million voluntary subscribers
growing .100 insured commodities. The programme offers a
range of policy options to subscribers, who can insure against vari-
ation in price, yield or revenue, at a range of thresholds, about
either their own or local county average values over recent years.
The indemnity (compensatory payment) is underwritten by the
USDA and administered through a network of private insurance
agents contracted to the RMA. The programme has operated in
a steadily evolving form since 1938. It constitutes a significant
subsidy to American agriculture, but requires as a condition of
insurance that good agricultural practice be followed by farmers
and requires that the regulator obtains statistical information on
risks and effectively manages behaviour to achieve environmental
quality and social goals. These are important issues for fisheries,
for which it would be inadvisable to have a subsidy that simply
supported overcapacity that resulted in overexploitation of fish
stocks. Two particular difficulties arise: (i) what is a capture fish-
ery’s equivalent to “good agricultural practice”, and (ii) are actuar-
ial data obtainable?
In a more specific case in the Netherlands, an industry-based
agricultural insurance scheme has been used to mitigate financial
risk caused by occasional outbreaks of two exotic potato diseases,
potato brown rot and potato ring rot. For 2 years in the mid-1990s,
the Dutch government provided compensation for outbreaks of
these diseases, but would not continue to provide support after
the second year of high outbreaks. Potato growers initiated an
insurance scheme called Potatopol in 1997 (http://
www.potatopol.nl/; described in Waage et al., 2007). Potatopol is
an incorporated non-profit entity operated by potato growers
with a small professional management staff. Government assisted
the scheme with an initial grant of E250 000 to help establish the
programme. Growers pay an annual premium based on individual
criteria. Part of the contractual agreement is that if, in a bad year,
there is particularly heavy demand on the fund, then subscribers
may be obliged to make an additional payment into it, with a
fixed maximum. If there is a particularly bad year and the fund,
despite emergency premiums from subscribers, is still unable to
cover the necessary outlay, then Potatopol makes use of commer-
cial reinsurance to ensure that all insured risks are covered.
Commercial reinsurance premiums are paid out of the annual sub-
scriptions. The fund is capped at a predetermined level, commen-
surate with likely risk, and any excess funds remaining at the end of
the year are returned pro rata to the subscribers. Certain
risk-reducing activities are stipulated in the contract, and the
insurance scheme has mainly benefited the industry by providing
a significant incentive to risk-reducing behaviour by growers, a
benefit we address in relation to fisheries risk management later.
It has also encouraged the Dutch government to continue to
support research and regulation, all of which has contributed to
a dramatic fall in potato disease outbreaks and helped make the
capped insurance scheme viable.
By way of explanation, the concept of reinsurance is an exten-
sion of the concept of insurance, in that it passes on part of the
risk for which the original insurer is liable. Reinsurance contracts
are more specialist than general insurance contracts, but for
the most part they work in exactly the same way, except that
the insured is another insurer, known as the reinsured
(www.lloyds.com). Lloyd’s cite four reasons for the importance
of reinsurance:
(i) To protect against large claims. For example, for a fire in a
large oil refinery, a large city hit by an earthquake or the cat-
astrophic collapse of an important fish stock, insurers would
spread the risk by reinsuring part of what they have agreed to
insure with other reinsurers, so that the loss is not so severe
for any one insurer.
(ii) To avoid undue fluctuations in underwriting results. Insurers
want to ensure a balanced set of results each year without
peaks and troughs. They can therefore obtain reinsurance
that will cover them against any unusually large losses. This
maintains a cap on the claims to which the insurer is exposed.
Page 2 of 10 J. D. Mumford et al.
(iii) To obtain an international spread of risk. This is important
when a country is vulnerable to natural disasters and an
insurer is heavily committed in that country. Insurance
may be reinsured to spread the risk outside the country.
(iv) To increase the capacity of the direct insurer. Sometimes
insurers want to insure a risk but are not able to do so
alone because they do not have enough capital to cover the
whole risk. By using reinsurance, the insurer is able to
accept the risk by insuring the whole risk and then reinsuring
the part it cannot keep for itself with reinsurers.
In Denmark, potato growers are supported through a fund
(Kartoffelafgiftsfonden, www.kartoffelafgiftsfonden.dk; described
in Waage et al., 2007) set up by the Danish Potato Council
(Specialudvalget for Kartofler). The fund is administered jointly
by farmers, the Potato Council and Government. Growers pay a
compulsory levy based on their individual outputs. The fund
raises some E540 000 a year on approximately a million tonnes
of production. Compensation from the fund is set at 60% of
costs faced by a grower through an outbreak, but only covers
costs, revenues, and destruction costs, but not replacement seed,
borne in the initial year (as in the Netherlands Potatopol pro-
gramme). By 2004, a group of private insurance companies
offered additional insurance to potato growers to cover the pro-
portion of the loss from 60% up to 90% of the first-year costs,
and including the costs for buying new seed in the following
year. The insurance costs E20 per hectare of potatoes, and 10%
of potato farmers have taken out the insurance. The potato com-
pensation fund does not carry over unspent funds, but, unlike
Potatopol, surpluses are invested in potato research carried out
by public and private institutions competing for the funds. So
far, the fund has not been depleted by claims in any year.
Given the similarity of privately owned aquaculture to agricul-
ture, we should expect that insurance schemes would be applied to
aquaculture before they are extended to wild capture fisheries and,
indeed, insurance in aquaculture is already widespread (Hotta,
1999).
Review of the literature on applying insurance
to wild fisheries
Many capture-fishing risks have been, and continue to be, covered
by insurance, including vessel, gear, and crew safety policies.
However, the application of insurance to catch, price, or revenue
variation is more problematic, because there has been less actuarial
information on which to base risk assessments related to pro-
duction variables in wild fisheries, which explains the scarcity of
examples in the literature. Such problems arise from the cryptic
nature of fish stocks and the difficulty in attributing causes to
losses on an actuarial basis. The harvests of several specific
marine fisheries are already covered in Japan by a government-
backed Mutual Insurance Scheme, in which the aim is to maintain
a viable industry to secure production capacity (Fisheries Agency,
2005). The scheme enables fishers to share risks, shielding individ-
ual fishers from ruin caused by natural disasters and other uncer-
tainties. However, the distinguishing feature of these fisheries is
that the species are, as in aquaculture, geographically well-defined
and contained, such as kelp, sedentary shellfish, and algae. The fol-
lowing section summarizes two important published studies con-
cerning the application of insurance to genuinely wild capture,
common resource, mobile fisheries; the first is a theoretical appli-
cation of insurance theory by Ludwig (2002), and the second is a
more applied approach in which the USDA considered extending
RMA crop-insurance principles to wild sockeye salmon in Bristol
Bay, Alaska (Greenberg et al., 2001; Hermann et al., 2004).
Ludwig (2002) begins with the premise that fisheries manage-
ment needs to be precautionary and builds on the idea that
taxes and charges can be better instruments in achieving
risk-averse management of fisheries than direct regulations (such
as TAC or effort control), then demonstrates the utility of insur-
ance with some simple models. The insurance regime is manda-
tory because one of the objectives of an insurance regime, as
conceived by Ludwig (2002), is to place an extra burden (rather
than bestow a subsidy) on fishers. In this context, the fishers are
creating the risks of stock collapse which are borne by the
general public, and the compulsory purchase of insurance by
fishers would partly shift the burden back onto the generators of
risk. Ludwig does not consider designing an insurance scheme
according to the needs of fishers, but rather as a tool to internalize
the hazard that excessive fishing effort can have on an ecosystem.
He claims that a bond or insurance regime can achieve several
objectives that are summarized in Table 1.
Ludwig (2002) uses a stochastic surplus production model with
three different harvest control rules: constant harvestrate, constant
catch, and adjustable harvest rates, based on abundance level to
obtain a target catch. He claims that the main difficulties in
setting up an insurance regime are political, institutional, and phi-
losophical, and that sound actuarial calculations can be made for
fisheries. He does not substantiate this last claim. However, the
management strategy evaluation (MSE) approach would allow
the equivalent of an actuarial basis, because management is
based on modelled populations rather than real population attri-
butes (Kell et al., 2007). Therefore, if the response behaviour of
fish stocks and fishing effort can be modelled plausibly in the
MSE approach, it would form a suitable foundation on which to
add insurance as a management component, because the necessary
actuarial data could be generated as a component of model output.
Sockeye salmon case study
In 2001, the RMA of the USDA contracted the University of Alaska
Fairbanks Agricultural and Forestry Experiment Station to scope a
pilot crop-insurance programme for the Bristol Bay commercial
salmon fishery (Greenberg et al., 2001; Hermann et al., 2004). It
was the first attempt to extend USDA crop insurance to wild fish-
eries. The report concluded that until the fishery reached stability,
it would be difficult to design and administer an insurance policy
that would benefit the industry. As a result, an insurance pro-
gramme was not set up. However, the initial design phase ident-
ified many practical issues regarding guarantees, insurance
triggers, and indemnity payouts relevant to the design of potential
insurance schemes in other wild fisheries.
The salmon study drew some important differences between
risk factors and insurance schemes in agriculture and wild fish-
eries. The RMA identifies three important components for crop
insurance, peril, moral hazard, and adverse selection. In agricul-
ture, peril is defined as unanticipated/unavoidable events that
affect some outcome, such as low yields caused by bad weather,
fire, and uncontrollable pest-induced losses. In fisheries, the defi-
nition of peril needs to be modified because it is difficult, perhaps
impossible, to develop a sound actuarial basis to determine the
contributory effect of natural events to catches in a given year.
For this reason, the authors suggested that peril in wild fisheries
should be redefined as an outcome: low catches or low fishery
Insurance mechanisms to mediate economic risks in marine fisheries Page 3 of 10
ex-vessel revenues rather than identifiable causes. Moral hazard is
defined by the RMA as producers taking an action to maximize
their return from the insurance product by wilfully undermining
their production of the insured crop. Controlling moral hazard
requires good risk insurance design to avoid incentives for harvest-
ers to “fish” the insurance. Good design would likewise ensure
that insurers were able to differentiate between legitimate and ille-
gitimate claims and, conversely, prevent insurers from rejecting
legitimate claims. A marine fishery equivalent to “best agricultural
practice” was not easy to define, which would have made it
difficult for loss adjusters to identify causes and weights of contri-
buting factors. For these reasons, individual performance-based
guarantees were rejected, and various group-based catch-
per-unit-effort triggers were simulated in the salmon-study
calculations of modelled insurance payouts. The third RMA com-
ponent, adverse selection against the insurance provider, occurs
when the insured person has better knowledge of the relative
risk of a particular situation than does the insurance provider.
In fisheries, harvests are dependent on biological phenomena. In
the salmon case study, run strength over time may be correlated
with previous events and therefore, to some extent, could be
predictable. Fishers may be able to predict insurable events in
years when poor runs were expected, which would severely com-
promise an insurance programme. A multiyear obligation to
subscribe to insurance was suggested as a solution to prevent
fishers taking out insurance only in those years when they were
anticipating payouts.
Unlike in crop insurance, the insurable units in fisheries are
rarely homogeneous: fishing opportunities do not determine indi-
vidual performance. For this reason, the report suggested that
indemnity payouts should be paid based on average performance
histories (APH) of individual fishers within the fleet so that, in
poor years, they would be compensated commensurate with
their fishing performance in previous years, assuming demonstra-
bly similar effort.
The sockeye salmon fishery in Alaska was suffering from poor
prices at the time of the study as a result of other salmon species
gaining favour in the Japanese market. As a result, Bristol Bay
fishers desired revenue-based triggers so they would be covered
for poor catches and/or lower prices. Finally, the report raised a
concern that insurance could interfere with the economically
and ecologically based need to reduce capacity in the fishery by
essentially subsidizing fishers that would otherwise leave either
permanently or temporarily.
An illustrative model of fisheries insurance
The success of insurance in agriculture, aquaculture, and certain
wild fisheries in Japan makes the issue of wider use of insurance
in fisheries worthy of further investigation. Setting up experiments
to discover features of a viable insurance scheme, as was attempted
in the salmon study, is costly. For a particular fishery, it is cost-
efficient to use a simulation approach to analyse the implications
of different sources of uncertainty for insurance costs and the
effectiveness of a particular insurance scheme to mitigate risks.
Here, we construct a simple model to illustrate the potential for
model-based investigations of fisheries insurance issues and to
provide some graphical representations of how an insurance
regime can function in fisheries. Simulation models have been
widely used to evaluate alternative management decisions (Kell
et al., 2007). Adding an insurance component to such models
can provide a measure of risk via the calculation of insurance pre-
miums. The cost of insurance is generally familiar to stakeholders
and can be used as a way of measuring the costs of risk mitigation.
Therefore, expanding MSE models with insurance features would
be an easy-to-communicate method for quantifying the benefits of
reducing uncertainty (through either different management
actions or improvements to stock assessments).
General insurance modelling methods
A stochastic population dynamics model of a herring-like stock
was developed to illustrate how the economic and biological stab-
ility of a fishery can be affected by an insurance regime. The model
is purely illustrative and makes use of various components
from agricultural insurance examples mentioned above to build
Table 1. Summary and comments on the insurance paper of Ludwig (2002).
Ludwig’s points Observations
Prevent risky exploitation by increasing the costs through premiums
which are proportional to risks, so rendering the most risky activities
unprofitable
Insurance may keep fishers in business that otherwise would have been
forced to leave
Shift the risk burden from the public to the fishers—the “polluter pays”
principle
Punitive insurance premiums, as envisioned by Ludwig (2202), will not
necessarily lead to greater social fairness. It is not always possible to
determine who within an industry is causing the loss, or how much,
so everyone in the industry pays, even if they do not cause loss
A clear link between a harvesting strategy and a premium would alter
the behaviour of fishers and make them less likely to cause harm to
the stocks and the ecosystem
In theory, modelling approaches such as risk assessments or MSE can
be useful in linking the size of premiums to harvesting strategies, but
the potential effectiveness of insurance to influence the behaviour of
fishers is unknown. The difficulties of monitoring activities at sea
could make fisheries insurance less effective than agricultural
insurance at lowering risks, through its influence on individual
behaviour
The assessment of risk depends on the state of knowledge, so charging
for risk would provide financial incentives for industry-sponsored
research
Knowledge, particularly the ability to predict future harvest, creates a
problem for insurance by enabling fishers to “fish” the insurance by
purchasing the cover in anticipation of payouts. On the other hand,
there is no guarantee that the knowledge obtained would be useful
enough to reduce insurance premiums. A final case, in theory, could
be that information would obviate the need for insurance by
effectively eliminating risk
Page 4 of 10 J. D. Mumford et al.
a notional hybrid insurance mechanism for a capture fishery. It
also includes features identified by Greenberg et al. (2001) as
being necessary for a capture fishery scheme: “peril” is defined
as an outcome (low revenue because of low catch and/or price)
rather than a specific unavoidable event, the insurance guarantee
is based on overall fleet catch or gross revenue rather than individ-
ual catch, and indemnity payments are based on individual APH.
The model allows us to examine the effects that insurance has on
collective fisher revenue risks as well as possible benefits through a
reduced stock-collapse risk that would result from insurance-
modified fisher behaviour. It also provides a transparent mechan-
ism for calculating premiums to cover risks (to the levels that
could be chosen by subscribers) under different scenarios.
The model is not intended to be a meticulous econometric or
MSE tool and is deliberately simple in many respects, because its
purpose here is to serve as an illustration for the discussion and
as a proof of concept. This is why it contains assumptions, such
as having perfect knowledge of stock sizes to define maximum sus-
tainable yield (MSY), and lacks harvest control rules that react to
the perceived developments in stock dynamics, although such fea-
tures can be added easily if necessitated by a new set of questions.
We assume that regulators aim to manage the fishery according to
a MSY objective by controlling effort rather than catch. However,
the model allows for implementation uncertainty and for the stra-
tegic behaviour of fishers, who may respond to recent declines in
revenue with a short-term increase in effort. Harvest rate is a
random variable with the mean based on MSY equilibrium.
Short-term response is modelled by allowing this random value
to increase within set limits for 1 year following a decline in
revenue; this seasonal increase is permitted until the decline in
revenue is halted. With insurance, the fisher receives a payment
when revenue falls below a pre-set trigger. The model assumes
that the fisher complies with the contractual obligation not to
increase fishing effort unless warranted by a decline in revenue.
This is an example of a contractual agreement under an insurance
regime, and not an instance of a particular interpretation of socio-
economic theory of effort – revenue interaction.
The insurance policies modelled here are based on systems
employed in agricultural risk management, such as the USDA
RMA. Revenue shortfalls are covered at preselected levels.
Indemnity payments are triggered when the revenue falls below
the covered proportion of a historical average (e.g. the previous
5 years). The size of an insurance payment depends on an
agreed coverage level (CL), and a premium calculated for that
level of coverage.
Several fund-creation and management options are available
for industry, including: fixed premium, variable fund; variable
premium, fixed fund; invest or return surplus in fund, at various
intervals; frequency of premium or fund review; liabilities of the
fund can be limited to a fixed level or left uncapped; reinsure
upper tail of liability or leave unmet. Our example model has
been developed for a fixed on/off premium variable fund with
capped liabilities (using reinsurance), but any system could be
simulated and their impacts evaluated.
The insurance liability is split between an industry-generated
mutual fund and private reinsurance. The industry fund covers
the higher frequency, lower cost end of the annual payout distri-
bution, whereas commercial reinsurance is used to protect the
industry fund from high cost, lower probability events at the
upper end. We calculate the size of a premium needed to guarantee
that the insurance fund is sufficient to cover losses after the first 10
years of operations in 75% of the simulations. In the illustration,
payouts from the mutual fund are capped at the 75th percentile.
The excess above the maximum mutual fund payment cap is
covered by commercial reinsurance; the premium charged to the
fund for reinsurance is calculated separately. During the first 10
years of operation, the mutual fund is allowed to borrow money
at 8% interest to cover any payouts beyond the value of the
fund. The insurance fund is assumed to earn 5% annual interest
when not used to make payments. The model-fund building and
liability-capping method is similar to that used in Potatopol.
The herring-like population model is stochastic and
age-structured, with lognormal process errors in a Beverton–
Holt type stock–recruitment relationship. Prices are considered
to be elastic with respect to the supply of fish. The MSY equili-
brium harvest rate is computed from mean values of parameters
(stock–recruitment, maturity, mortality, and weights-at-age)
based on the ICES assessment of herring stocks, and this rate is
the base harvest level applied throughout the runs of the model.
For a full description of the model, see the Appendix.
To illustrate the role of insurance, five scenarios were modelled:
(1) No insurance, no effort increase on falling revenue. Expected
fishery performance: noisy but stable mean net revenue;
(2) No insurance, effort increase on falling revenue. Expected
fishery performance: noisy and declining mean net revenue;
(3) Insurance, no effort increase on falling revenue. Expected
fishery performance: smoothed revenue, lower mean net
revenue than Scenario 2 at the beginning, but higher than
Scenario 2 at the end of 30 years;
(4) Insurance, effort would increase on falling revenue, but is less
likely to occur because revenue is maintained by insurance
payouts. Expected fishery performance: smoothed revenue,
lower mean net revenue than Scenario 2 at the beginning,
but higher than Scenario 2 at the end of 30 years;
(5) Insurance, 1% annual increase in fishing mortality attribu-
table to an increase in catchability, no increase in effort on
falling revenue. Expected fishery performance: long-term
downturn in stock stability and revenue.
Outputs of the model are used to illustrate various points in the
Discussion below.
Discussion
Cunningham and Maguire (2002) observed that: “ ...uncertainty
is a major factor of unsustainability, and that its effects increase
as the fishery management system becomes more elaborate. In
the absence of management, fishers are confronted with uncertain-
ties related to the natural variability of the environment and of that
of the markets. Under active management, uncertainties about
management decisions, their effects and their implementation
are added. Current fishery management approaches have evolved
from control theory which may not be appropriate to control
unpredictable and complex systems such as fisheries”.Charles
(2007) argued that structural uncertainty (model, implemen-
tation, and institutional) is best dealt with by robust management,
e.g. by creating an adaptive portfolio of mutually reinforcing man-
agement tools. Insurance could be one tool in that portfolio.
The purpose of a potential capture-fishery insurance scheme
varies between industry and regulators. For industry, we have
assumed that the focus is principally on revenue (a product of
Insurance mechanisms to mediate economic risks in marine fisheries Page 5 of 10
catch and price) set against individual or fleet average records and
effort deployed. The primary role of insurance from this perspec-
tive would be to use the fund to reduce short-term revenue risks,
i.e. to bolster income in years of low catches and/or price
(Greenberg et al., 2001, Herman et al., 2004). The variability in
costs, which together with revenues determine the profitability
of a fishery, would need to be covered by other mechanisms,
such as forward contracts on fuel and long-term labour price
agreements. Figure 1a illustrates the smoothing of revenue (after
allowing for the cost of insurance premiums) that occurs at 80
and 100% CLs; the 100% CL offers greater smoothing of net
revenue for a commensurately higher premium.
A necessary condition for the start of the insurance scheme
would be to reduce overcapacity in the fishery at the start of the
scheme, or else there would be no incentive for fishers to harvest
at a sustainable rate or to pay the start-up costs. Additionally, in
the first years of an insurance scheme, start-up costs, required to
build an industry-based mutual fund, could be prohibitive. For
Potatopol in the Netherland, the government assisted by providing
a one-off grant towards the fund. To illustrate the effect of initial
fund-building requirements, Figure 2 shows median revenue tra-
jectories from 1000 iterations of the model for each of the five
scenarios over 30 years. Scenarios 1 (without insurance) and 3
(with insurance) show revenues from fishing to “perfect knowl-
edge”, strictly enforced MSY. Note how the insurance start-up
costs (to create the mutual fund) in Scenario 3 generate lower
net revenues for fishers in the first 10 years than with Scenario
1. This effect is also seen in the with/without insurance compari-
son of Scenarios 2 and 4.
The industry might be reluctant to bear the full cost of mitigat-
ing the risk of revenue fluctuation especially because the costs vary
also, so smoothing the variability in revenue does not necessarily
translate into smoothing the variability in profits. However, the
same problems have been addressed in agricultural insurance,
where it is common to insure revenue (outputs) rather than
costs (inputs in agriculture include fertilizer, seed, water, and
labour). The solution partly lies in the level of government
support of insurance. The government interest in supporting
insurance schemes is to ensure greater stability of food supply,
protecting the producers, and securing a more sustainable use of
natural resources by forcing the industry to accept “good practice”
contracts in return for lower risks. Enforcing best-practice
standards also prevents issues of moral hazard in which growers
Figure 2. Median revenues for five modelled scenarios: (1) no
insurance, no effort increase on falling revenue; (2) no insurance,
effort increase on falling revenue; (3) insurance, no effort increase on
falling revenue; (4) insurance, effort increase on falling revenue; and
(5) insurance, 1% annual increase in fishing mortality (100% CL for
Scenarios 3–5; maximum effort increase¼1.7 for Scenarios 2 and 4).
The process errors are lognormally distributed so that the median
MSY values are slightly higher than the MSY calculated
under-deterministic conditions.
Figure 1. An example of how insurance works in Scenario 3
(insurance, no effort increase on falling revenue) at two revenue CLs,
80 and 100% CL for two trajectories, (a) where insurance mitigates
losses during unexpectedly bad year(s) (see year 9), and (b) where
insurance provides a soft landing after a period of extraordinary luck
(see years 6–11).
Page 6 of 10 J. D. Mumford et al.
could reduce inputs (and hence costs) and demand compensatory
payments for lowered yield falsely attributed to insured losses.
The advantages of insurance are inversely proportional to
controllability of various uncertainties: the less controllable the
system, the greater the need for insurance. To illustrate this, we
modelled two scenarios (Scenario 1, without insurance, and
Scenario 3, with insurance) where we assume perfect knowledge
of expected MSY values and total compliance with management.
Differences between Scenarios 1 and 3 are small: insurance in
Scenario 3 bolsters revenues in poorer years and smoothes the
lows in poor ones, while softening fall in revenue for medium-
term downturns (Figure 1). In the two scenarios, there is no
linkage between revenue and effort, so the simulated fishers
harvest to the regulated MSY level, giving them relatively constant
revenues over the 30-year simulation.
In fisheries where TACs are less strictly enforced and there is a
tendency for fishers to increase effort in the event of downturns in
revenue, the long-term benefit of insurance might be to reduce
stock risk by removing the incentive to increase effort after a
year in which revenue decreased (illustrated in Figure 2,
Scenarios 2 and 4). This anticipates that insurance imposes a con-
tractual obligation not to increase fishing effort in the pay-out
year. Note that the initial years for Scenarios 2 and 4 are even
more divergent than Scenarios 1 and 3, increasing effort in
Scenario 2 resulting in short-term benefits (and serious long-term
stock risks), whereas the fund start-up costs of Scenario 4 cause net
revenue to drop before stabilizing at a higher level than the non-
insurance equivalent (from year 13 on).
Insurance payouts may provide a “soft landing” when there are
short, sharp declines in harvest (such as in Figure 1b, years 6– 11),
giving a few years reprieve from lost revenue and allowing for
longer term structural or regulatory adjustment. Where there is
a long-term decline in catch, insurance is not likely to be able
to help.
As shown by the salmon insurance study (Greenberg et al.,
2001; Hermann et al., 2004), the level of insurance is important
in determining the extent of the smoothing/cushioning effect on
subscriber revenues. However, the salmon insurance studies did
not include premium estimates and payments. Consequently,
smoothing of revenue (net of premiums) may not be as straight-
forward as expected; Figures 1a and 1b illustrate the fact that
short-term revenue downturns may be cushioned by higher CLs,
but at times, choosing higher rate coverage could lead to more
variable revenues attributable to irregular premium payments
and/or mutual fund dividend returns.
The focus of regulators tends to be towards increasing the sus-
tainability of exploitation and hence production. It is likely that
the primary requirement of an insurance instrument would
focus on increasing sustainability of production rather than
solely protecting revenues. This could be built into a MSE model-
ling approach to test fisheries management actions. Where fishers
were simulated to increase short-term fishing effort to chase falling
revenues compared with previous years (Scenarios 2 and 4), the
risk of stock collapse increases (Figure 3), and the expected reven-
ues over time decrease (Figure 2). However, insurance triggers
compensatory payments in years with falling revenue (set against
the insurance cover level), and this reduces the pressure to increase
fishing effort, so lessening the ecological risk from overfishing
(compare Scenarios 4 and 2 in Figure 3). Scenario 5 simulates
“technological creep”, a steady improvement in catching efficiency
through investment in better technology. In that scenario, the
actions of fishers not only maintain income but also increase
income through increased short-term catch, and it could be that
insurance stimulates investment in fishing technology by reducing
risk. If so, then insurance only smoothes revenue in the short term,
whereas steady increases in fishing effort cause long-term declines
in the stock and hence long-term reductions in revenue. This
possibility was also cited as a concern by Greenberg et al. (2001)
and Hermann et al. (2004).
Previous work (Mumford et al., 2008) used a purely reactive
approach to insurance payment that acted like the salmon insur-
ance example (Greenberg et al., 2001), in which the sole purpose
was to limit the low points of revenue for the fishers. Scenarios
2 and 4, with the implicit concept of contractual compliance not
to increase fishing pressure, introduce dynamic feedback
between the behaviour of fishers and the stock. There is a potential
role for insurance to reduce the insured’s risk behaviour, as was
seen in the reduction of potato disease outbreaks in the
Netherlands after the implementation of Potatopol. For example,
in Scenario 4, insurance payments reduced the destabilizing
effect on stocks caused by short-term increases in effort after
revenue declines. This system resembles Ludwig’s (2002) proposal
and is more prospective than that described in Mumford et al.
(2008), because it reduces financial risk to fishers each year
while satisfying regulators by dampening ecological risks from
overfishing in the future.
The size of the payouts, and therefore the premiums, is influ-
enced by all the factors that contribute to the variability in predic-
tions. We can use insurance models to explore how changing the
assumptions regarding the variability of model parameters could
affect insurance. This is useful because certain sources of uncer-
tainty are ultimately controllable: knowledge can be improved,
reducing uncertainty in the estimates of model parameters, and
fishing can be controlled to reduce both the level of exploitation
and the variability of harvest rates. In the Danish
Kartoffelafgiftfonden, for example, any unspent insurance fund at
the end of each year is invested in research to reduce potato
disease risks in the future. This also conforms to Ludwig’s
(2002) concept of an insurance fund contributing to research. It
would be possible to develop a model, similar in essence to our
Figure 3. Probability of stock-collapse risk that SSB falls below B
pa
(precautionary level to maintain a stock) in a particular year under
the five scenarios: (1) no insurance, no effort increase on falling
revenue; (2) no insurance, effort increase on falling revenue; (3)
insurance, no effort increase on falling revenue; (4) insurance, effort
increase on falling revenue; and (5) insurance, 1% annual increase in
fishing mortality (100% CL in Scenarios 3–5; maximum effort
increase¼1.7 for Scenarios 2 and 4).
Insurance mechanisms to mediate economic risks in marine fisheries Page 7 of 10
illustrative model (described in the Appendix), to investigate the
benefits of reducing the controllable sources of uncertainty
measured by the lowered cost of insurance.
We also suggest that insurance, through the creation of an
industry-based fund, may help to transform the governance fra-
mework. In the agriculture examples, insurance has led to the
establishment of more convergent objectives and behaviour
among stakeholders (industry, regulators, and consumers) with
particular reference to economic and agricultural sustainability.
This occurred through:
(i) changes in responsibility (shifting the burden of risk), and
increased trust between regulators, industry, and scientists;
(ii) incentives for industry to increase knowledge to reduce
uncertainty and hence premiums (interest from an industry
insurance fund could pay for research and such industry-
funded research might be more trusted by industry, improv-
ing compliance with the resulting scientific advice);
(iii) immediate feedback into the system through fisher antici-
pation of insurance stabilizing revenue, rather than lagged
responses by regulators that may be too late to influence
dynamic ecological processes.
Currently under the European CFP, the interannual variability
in TACs for many stocks is constrained to allow the industry to
plan their activities better. This, as Cunningham and Maguire
(2002) point out, requires a level of control that may not be appro-
priate for unpredictable and complex systems such as fisheries;
instead, insurance might provide a robust way of achieving a
similar result without the need for such strict control.
The CFP is also a potential setting for a Europe-wide insurance
regime. If all stocks were insured in Europe, then at least in the
future where structural reforms have taken place and fleet overca-
pacity is eliminated, a universal insurance scheme could be justi-
fied. It might make sense to set up a single-crop insurance
scheme in agriculture, with discrete homogenous units, but insur-
ing a single fish species may not work where fishers catch more
than one species. Moreover, it could be expensive, because in a
single-species fishery, risks cannot be spread except over time.
The conditions needed for the introduction of insurance should
be determined, such as the impact of an insurance system on
levels of stock health, using the MSE framework.
The ability to switch between species (Nagasaki and Chikuni,
1989; Matsuda and Katsukawa, 2002) in multispecies fisheries
may itself act as a form of insurance, in the sense of a risk mitiga-
tion measure, against uncertainty in stock abundance. However,
under the CFP, for example, regulations such as relative stability
(where catch quotas are set on historical rights) limit the ability
of fishers to switch between stocks and would consequently
prevent fishers switching from depleted to abundant stocks.
The use of an insurance model to calculate annual premiums
also provides a transparent logical method of converting risk
into a convenient (monetized) metric. Using the principles of an
insurance approach to uncertainty (and not necessarily an actual
insurance scheme), it is possible to place a value on the various
components of uncertainty that arise from lack of accuracy or
other causes of non-credibility. This could be done for the
various sources of uncertainty item by item and in combination,
to estimate the added value of reducing each component of uncer-
tainty. The values from various case studies could be tested with
stakeholders to see if they accept the relative values of any com-
ponent (and overall) improvement. It may also be a way of intro-
ducing a value for mutual trust among stakeholders (showing
them what it costs to disagree). For actual insurance, the agree-
ment to subscribe and adopt accepted practices can add to trust,
because subscribers are paying premiums and signing contracts
and can suffer a penalty for not behaving as prescribed.
The Economist newspaper recently (20 September 2008) head-
lined that “Scientists find proof that privatising fishing stocks
can avert disaster”, based on the paper of Costello et al. (2008)
on ITQs. Such “privatized” stocks are more likely to have appro-
priate levels of control and stakeholder commitment that would
favour insurance than other fisheries. Models that incorporate
uncertainty explicitly will add further opportunities to explore
the potential for insurance.
Conclusions
There is a role for economic mechanisms that help to address the
many issues of uncertainty that affect adverse behavioural
responses by fishers and short-term regulatory changes.
Agriculture has provided some examples of how crop insurance
against certain perils has reduced risk through changing the
adverse behaviour and reducing controllable uncertainty through
research, development, and dissemination of good practice.
Insurance can provide some mitigation against these uncertainties,
and along with other measures that give a long-term stake in the
fishery, might result in significant improvements in insured fish-
eries compared with uninsured fisheries.
There are, of course, limits to the role of insurance:
(i) concern that insurance could interfere with efforts to reduce
capacity in the fishery by essentially subsidizing fishers who
would otherwise leave either permanently or temporarily
(Greenberg et al., 2001);
(ii) financial incentives may mean that insurance payouts do not
reduce fishing effort (as in our Scenario 5, with increasing
technological capacity in harvesting), so enforcement by gov-
ernment and/or industry bodies would still be required.
A major obstacle to any insurance programme is overcapacity
in most capture fisheries (FAO, 2005). Once this is resolved and
the industry faces more normal risks, then insurance may offer
real benefits to transforming the governance framework. Further
research and more detailed modelling approaches may offer
greater insights into how insurance mechanisms could be
implemented, and identify fishing levels in which insurance pre-
sents a tool for increasing stability in stocks and sustainability of
exploitation.
Acknowledgements
This study has been carried out with financial support from the
Commission of the European Communities, specific RTD pro-
gramme “Specific Support to Policies”, SSP-2005- 022589
“Precautionary Risk Methodology in Fisheries”. It does not necess-
arily reflect its views and in no way anticipates the Commission’s
future policy in this area.
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Appendix
Population model description
The model is implemented in R, using three-dimensional arrays
storing numbers of herring in billions by age (a), year (y), and
simulation (s). Before starting the insurance regime, we assume
that the stock has been exploited at a sustainable MSY level for
100 years to establish a historical record of population trends.
The harvest is assumed to take place at the beginning of the
year, and spawning in the middle of the year; age at recruitment
is taken to be 1:
Ca;y;s¼Na;y;sHa;y;s;ðA1Þ
where Cdenotes the catch (in billions of fish), Nthe number of
herring (in billions of fish), and Hthe harvest rate.
Yield (Y) is given in millions of tonnes:
Yy;s¼X
a
Ca;y;sWa;y;s;ðA2Þ
where Wis the weight (kg) of individual fish at age.
NaþDt;yþDt;s¼ðNa;y;sCa;y;sÞexpðMa;y;sDtÞ;ðA3Þ
where Dtis equal to half a year and Mthe instantaneous rate of
natural mortality.
The spawning-stock biomass (in millions of tonnes) is denoted
by SSB:
SSBy;s¼X
a
NaþDt;yþDt;sMata;y;sWa;y;s
;ðA4Þ
where Mat denotes the proportion of sexually mature herring by
age, and
N1;yþ1;s¼
a
sSSBy;s
b
sþSSBy;s
1y;s;ðA5Þ
is a stochastic stock– recruitment relationship that gives the
number of age-1 herring for the following year, where
a
and
b
are Beverton–Holt recruitment-function parameters, and
e
is a
lognormally distributed process error with specified precision
(Figure A1). The population dynamics of herring (and fish in
general) are driven largely by the variability in recruitment
success from year to year. The parameters of the stock –recruit-
ment function are based on the estimates of the recruitment
relationship for Norwegian spring-spawning herring (Clupea har-
engus); the recruitment time-series for Norwegian spring-
spawning herring for the past 57 years is shown in Figure A1,
along with a random modelled trajectory for a herring-like stock
over a sample period of the same length.
For the older-than-recruitment-age groups, the transition from
year to year is modelled by
Naþ1;yþ1;s¼NaþDt;yþDt;sexpðMa;y;sDtÞ:ðA6Þ
For insurance calculations, we use the average price per kilo-
gramme of catch (P
y,s
), rather than an age-specific price, P
a,y,s
:
Insurance mechanisms to mediate economic risks in marine fisheries Page 9 of 10
Py;s¼P
a
Pa;y;sCa;y;sWa;y;s
Yy;s
:ðA7Þ
To illustrate the functioning of the insurance regime, we simu-
late 1000 iterations over 30 years for each scenario. For that period,
we calculate the size of the insurance payouts for a policy of 80%
revenue CL, i.e. compensation would be triggered if revenue falls
below 80% of the average. First, for each year and iteration, we cal-
culate the average revenue (Py;s) for the preceding 5 years:
Py;s¼meanðYy6;sPy6;s;...;Yy1;sPy1;sÞ:ðA8Þ
The trigger, T
y,s
, for insurance payment is based on the average
revenue, Py;s, and on the CL selected, which we assume for now
is 80%:
Ty;s¼80%Py;s:ðA9Þ
If the simulated revenue (yieldprice) Y
y,s
P
y,s
is ,T
y,s
, then an
insurance payment IP
y,s
is the difference between the current
revenue and the trigger:
IPy;s¼ðTy;sYy;sPy;sÞ:ðA10Þ
To calculate the premium, we use a search algorithm that finds
the minimum premium required such that the insurance fund
raised is sufficient to cover up to the 75th percentile value of the
annual payouts over all simulated scenarios (we implement a
“while” loop that increments the premium until it is sufficient
for the 75% of the least costly simulations). The insurance fund
has a pre-set limit at which it is capped, so that annual
premium payments are suspended while the fund is at or above
its pre-set limit, and once the fund is at or above the pre-set
limit, the interest earned is returned to policy-holders yearly.
This is referred to as the mutual fund. During the first 10 years,
the fund is allowed to borrow money if needed at 8% interest; con-
versely, the money not used for payouts is invested at a 5% annual
rate of interest (Figure A2). The operating costs are assumed to
add 10% to the total premium collected for the mutual fund.
The interest rates and operational cost values are arbitrary esti-
mates based on reasonable rates during 2007.
The upper 25% of liability is covered by private reinsurance
bought in the market. The premium for reinsurance, C, is calcu-
lated by adding a 25% profit margin to the expected annual rein-
surance payouts (re-IP
y,s
, the total payout less the reinsurance
threshold level at the 75th percentile of annual payouts) in the
extreme 25% of the simulations:
C¼mean
smeanðre IPy;sÞ
y
!
1:25:ðA11Þ
Fisher behaviour is modelled as follows: whenever the revenue
falls from one year to the next, fishers increase the following year’s
effort proportionally to the change in revenue, aiming to compen-
sate for falling revenue. However, it is assumed that this increase
relative to the level compliant with the MSY management strategy
is never greater than 70% of the effort at MSY. This is because we
assume that there is a practical, as well as a contractual, limit to the
additional effort fishers could make:
Eyþ1¼min 1:7EMSY;Py1;s
Py;s
EMSY
:ðA12Þ
Additionally, we assume that unless the revenue is falling, the effort
stays at the MSY level, i.e. it does not decline when revenues rise.
Revenue, denoted by P
y,s
, that affects fisher behaviour is inclusive
of insurance compensation and dividend surplus fund payouts,
and less premium payments.
doi:10.1093/icesjms/fsp100
Figure A1. Random recruitment trajectory (line) alongside the
estimated recruitment time-series for the Norwegian
spring-spawning herring stock for 1951– 2007 (points).
Figure A2. Building an insurance fund over 30 years in four simulated
scenarios, assuming 100% revenue CL. Note that the fund value
(y-axis) sometimes exceeds the pre-set upper limit of the fund because
of the fixed premium level, but it drops back to the pre-set limit
soon after because payments stop until the fund falls below the limit
again as a result of payouts.
Page 10 of 10 J. D. Mumford et al.
