Ragnar Arnason
Fisheries Subsidies, Overcapitalization and Economic Losses
Paper presented at the workshop on
Overcapacity, Overcapitalization and Subsidies in European Fisheries
Portsmouth
October 28-30 1998
DRAFT
1.
Introduction
The economics of the world’s marine fisheries are heavily distorted not only by the
externalities stemming from the common property problem but also by direct and
indirect government subsidies to the fishing industry. Both kinds of distortions work
in the same direction. Both encourage excessive fishing effort, overinvestment in
fishing capital and overexploitation of the fish stocks. Thus, fisheries subsidies
generally exacerbate the common property problem.
The exact effect of fisheries subsidies depends on the fisheries management
system in place. Thus, adding government subsidies to a common property fishery
generally results in more economic waste in equilibrium coupled with a faster path
toward a situation of economic waste and a greater risk of permanent damage to the
biological resource. By contrast, government subsidies to a property-rights based
fishery, i.e. a fishery where the common property problem has been substantially
alleviated, may in some cases merely amount to a non-distortive financial transfer to
the holders of fishing property rights.
This paper explores some of the issues associated with subsidies in fisheries. It
begins by reviewing the available statistics on the global amount of and trends in
fisheries subsidies. It then goes on to examine the impact of subsidies on fishing
effort, fishing capital and the economic performance of the fisheries. The strategy of
investigation is to mix theoretical analysis with numerical calculations on the basis of
a simple fisheries model. In this way, it is hoped that the reader can be provided not
only with a qualitative sense of the impact of fisheries subsidies but a quantitative feel
as well.
2.
Subsidies and Capacity: Amount and Trends
The extent of subsidies, direct and indirect, in ocean fisheries is, perhaps
understandably, not a well-researched topic. However, by all available accounts, the
amount of fisheries subsidies worldwide can hardly be described as anything but very
substantial, at least relative to the total revenues of the industry. Thus, based on 1989
data, FAO in 1993 (FAO 1993, Wijkstrom 1998) estimated that global fisheries costs
exceeded revenues by 54.5 billion USD or 78%. According to FAO, this shorfall must
have been met by direct or indirect subsidies.
More recently, in a World Bank publication, Milazzo (1998) has produced a
much more detailed examination of the subsidy issue. According to his results
aggregate global fisheries subsidies are between 14 and 20 billion USD annually. This
amounts to some 17-25% of the industry’s revenues.
The difference between the Milazzo’s 1998 estimates and the FAO’s 1993
estimates of global fisheries subsidies is quite high. Wijkstrom (1998) attributes the
difference to a declining trend in fisheries subsidies. While it may be true that
fisheries subsidies are declining, it should also be kept in mind that neither of these
estimates can be regarded as very reliable. In particular, the FAO 1993 estimate was a
2
very rough one, little more than an informed guess. As a result the difference between
these two estimates can hardly justify an inference about trends in global fisheries
subsidies.
Nonetheless, the claim that global fisheries subsidies are declining may well
be true. Indeed, as mentioned by Wijkstrom, other evidence points in that direction.
Thus, it is known that major fishing nations such as the states forming the erstwhile
Soviet Union, Norway and Peru have drastically cut back on fisheries subsidies since
the late 1980s. Similarly, FAO surveys of several fishing nations indicate declines in
fisheries subsidies (Wijkstrom 1998).
Although Milazzo’s (1998) World Bank study carefully avoids pointing a
finger to the most extravagant fisheries subsidisers around the world, it is possible to
glean from his report the that the European Union, Japan and China feature close to
the top of that list. In fact, per volume of catch, the European Union may very well be
the world champion in this respect.
It is informative to compare fisheries subsidies with other food industry
subsidies. Many agricultural food products are notorious as recipients of high
subsidies. According to Milazzo’s study, fisheries are firmly placed in the same
premier class of subsidy receivers. Thus, including global trade protection (e.g. tariff
barriers) on top of financial subsidies, Milazzo’s study produced the following
comparison:
Table 1
Average Global Food Subsidies
(Including trade barriers)
Product
Wheat
Coarse grains
Rice
Oilseeds
Sugar
Beef and veal
Pork
Poultry
Lamb and mutton
Eggs
Fish
Subsidy
48%
36%
86%
24%
48%
35%
22%
14%
45%
14%
30-35%
Source: Milazzo, 1998
Given this high level of subsidies on top of the common property problem in
the world’s fisheries, it not surprising that the fisheries are heavily overcapitalized.
According to a recent FAO estimate (FAO 1995), the global fishing fleet in 1992
3
measured some 26 million GRT. Since then the fleet has still expanded by 0.7 million
GRT (Newton 1998). More importantly, however, due to technological advance and
refitting, the harvesting capacity of the fleet has increased much more, perhaps as
much as 22% (Newton 1998 and Fitzpatrick and Newton, 1998).
FAO has not published estimates of the needed reduction in the world’s
fishing fleets. However, high ranking FAO experts have proved willing to come
forward with such estimates. According to one recent estimate the reduction in global
fishing capacity required for reasonably efficient sustainable fisheries is of the order
of magnitude 50% (Garcia and Newton 1997).
This estimate may well be too conservative. Given an optimal sustainable
yield in ocean capture fisheries of 80 million metric tonnes this will lead to catch per
unit of fleet of approximately 6 metric tonnes per GRT. In Iceland however, in spite
of a significant excess fleet capacity, the actual yield per unit fleet capacity has in
recent years ranged between 12-15.mt/GRT. Moreover, similar performance has been
registered in several other property rights based fisheries around the world. If the
same level of capital efficiency can be replicated on average in the remainder of the
world’s fisheries, the fleet size require to take a sustainable yield of 80 million would
be about 6 million GRT, or just over 1/5 of its current size.
3.
Model
The understanding of the basic fisheries subsidies and overcapacity issue may be
helped by formulating the arguments in terms of a simple fisheries model.
Let the instantaneous profits of individual fishing firms be defined by the
function  as follows:
(1)
 = pY(e,x) – C(e) - Y(e,x),
where Y(e,x) represents the harvesting function with e denoting fishing effort or
fishing capital and x fish stock biomass. In what follows, we will refer to the variable
e as fishing effort. The variable can, however, just as easily be regarded as fishing
capital. The parameter p stands for the price of a unit of harvest so pY(e,x) represents
revenues. The function C(e) represents harvesting costs. So, the first two terms of
expression (1) represent harvesting profits in the usual sense. In what follows, I will
refer to these two terms as operating profits of the harvesting activity. In accordance
with accepted wisdom we assume that both Y(e,x) and C(e) are increasing in their
arguments with Y(e,x) at least semi-concave and C(e) at least semi-convex.
The last term of (1) also represents harvesting costs but of a different kind.
More precisely, Y(e,x) is intended to reflect the opportunity cost to the firm, in
addition to C(e), of harvesting the quantity Y(e,x). The unit price of harvesting, , is
central in this context. It may represent either an actual price or an imputed one. Thus,
in an ITQ fishery,  would be the market price of a unit of quota. Similarly, in a tax
managed fishery,  would represent the tax per unit of harvest. Under other fisheries
management systems,  would normally represent the imputed shadow value of
4
biomass to the firm. In a single owner fishery, this shadow value would be very close
to the socially optimal one. In the usual multi-firm fishery,  would generally be
much smaller, usually very close to zero.
The advantage of including the term Y(e,x) in the profit function is that the
economic efficiency of the fishery is reflected directly in the variable . Thus, 0
represents a typical common property fishery, while  that is close to the true social
shadow value of the biomass represents a well managed fishery.
Assuming sufficient concavity of , private profit maximization implies:
(2)
e = pYe(e,x) – Ce(e) - Ye(e,x) = 0,  active firms
which yields the optimal effort level of the firms as functions of p, x and .
The number of fishing firms (or vessels) will presumably evolve according to
the entry-exit function:
(3)
n = N(),
N(0)=0, N()>0,
where n>0 denotes the number of firms and n represents the change in the number of
firms.
Finally, the evolution of the biomass is given by the differential equation:
(3)
x = G(x) - Y(e,x),
where the function G(x) represents natural biomass growth.
For later reference, it is convenient to list two equations defining optimal
equilibrium1:
(4)
Gx(x) + Yx(e,x)Ce(e)/( pYe(e,x) – Ce(e)) = r,
(5)
G(x) - Y(e,x) = 0,
where r denotes the rate of time discount.
Now, employing a linear specification for the profit and entry functions
greatly simplifies the presentation without losing sight of the crucial attributes of the
subsidy issue. More precisely, let
(6)
Y(e,x) = aex, a>0,
(7)
C(e) = ce, c>0,
(8)
N() = b, b>0.
1
These equations follow easily from the necessary conditions for maximizing the present value of
rents from the fishery.
5
Moreover, let’s adopt the commonly used logistic form for the biomass growth
function. Namely:
(9)
G(x) = x - x2.
The linear specification in equation (6)–(8) makes aggregation over firms very
easy. For instance the aggregate profit function is now formally identical to the
individual one or:
(10)
  n = aEx -cE,
where the aggregate fishing effort is, Ene. Consequently we can proceed with the
aggregate industry just as for individual firms.
Note, moreover, that
 e + n e .
(11) E = n
Yet, another interesting implication of the linear specification following from (2) is:
(12)
 = (pY(e,x) – C(e)) /Y(e,x) = pa – c/x.
In other words: the private shadow value of harvesting equals average operating
profits of the harvesting activity. Note that (12) can be regarded as a demand function
for harvest by the fishing industry or, alternatively, the supply function of biomass.
The corresponding social demand function for biomass is given by the
dynamic optimality condition:
(12)
 -r = pYx(e,x) – ( Gx(x) -Yx(e,x)).
So, under the functional specifications of equations (6)-(9) we can derive the
following optimal equilibrium supply function of biomass:
(13)
 = p( - x)/(r + x).
An example of the supply (1) and optimal demand function (1) for biomass
is drawn in Figure 1.
6
Figure 1
The -function: The supply and demand of biomass
Shadow value of biomass
2
2
1( x)
2( x)
0
2
2
0
0.01
0.5
1
x
Biomass
1.5
2
2
Now, as mentioned, the 1-schedule represents the optimal social demand for
biomass. Thus, the optimal equilibrium occurs at the intersection of the two curves at
a relatively high level of biomass in Figure 1. Alternatively, for a social biomass
demand curve of =0, corresponding to the classical common property fishery,
equilibrium occurs at a fairly low level of biomass in the diagram. For >0,
representing more effective fisheries management, equilibrium biomass occurs at a
higher biomass level.
4. The Impact of Subsidies
Fisheries subsidies, as other subsidies, may take many forms. Often, however,
fisheries subsidies depend directly or indirectly on harvest rates and/or fishing effort.
Fisheries subsidies may also appear as lump sum subsidies payable to fishing firms
independently of fishing effort and harvesting rates.2 To capture this let us define a
subsidy function as follows:
(14)
S(e,Y(e,x)) = s0 + s1e + s2Y(e,x),
where s0 denotes the lump sum subsidy and s1 and s2 are unit effort and harvest
subsidies, respectively. Note that these subsidies may well be negative reflecting taxes
rater than subsidies. For a true subsidy scheme, however, S(e,Y(e,x))>0.
With the subsidies, the firms’ profit function becomes:
2
A common type of fisheries subsidy, investment subsidies, may be regarded as a mixture of a
fishing effort subsidy and a lump sum subsidy.
7
(15)
° =  + S(e,Y(e,x)) = pY(e,x) – C(e) - Y(e,x) + S(e,Y(e,x)).
Profit maximization then implies:
(16)
°e = e + Se + SyYe = 0.
Comparison of equation (15) with equation (2) shows that subsidies will
generally affect fishing effort and consequently biomass. A little analysis reveals only
two noteworthy exceptions to this result.
(i)
(ii)
Se + SyYe = 0
Y(e,x) = -(es1 + Y(e,x)s2),
The first exception says that the marginal effect of fishing effort on the subsidy
received by the firm is zero. Consequently, the subsidies have no impact on profit
maximizing fishing effort. This, however, is of little practical relevance. Considering
for instance for the specification in (14), the first exception can only happen when
s1= -s2 Ye(e,x).
This means one of two things. First, one of the subsidy components is actually a tax
exactly cancelling the effect of the other in which case the arrangement is not really a
subsidy. Second, both subsidy components are identically zero meaning that the
subsidy only consists of the lump sum part, s0.
It is important to realize, however, not only the lump sum subsidy will leave
the fishery unaffected. Through the entry-exit condition, equation (3), it will affect the
number of firms in the industry. As conveyed in equation (3), equilibrium in the
industry requires that company profits be zero. Under a lump sum subsidy scheme this
is now
° =  + s0.
Thus clearly the number of firms and hence aggregate effort will be affected although,
perhaps, the fishing effort of existing fishing firms remains constant.3
The other exception is somewhat more interesting. It basically means that the
subsidies will be matched exactly by a change in the opportunity cost of harvesting, .
While this appears extremely unlikely in general, there is one fisheries management
regime, namely a well managed ITQ system, under which this might apply. In fact, it
is easily shown that if, under the ITQ system, an imposition of subsidies is not
accompanied by a change in the TAC, fishing effort (individual and aggregate) will
not change and the subsidy will be matched exactly by a corresponding increase in .4
Now, under the ITQ fisheries management system, , represents the market price of
3
4
Actually, since biomass will have to adjust, so will the fishing effort of the existing fishing firms.
Note that this applies equally to the lump sum subsidy, s0, as s1 and s2.
8
quota. Thus, the imposition of subsidies will be reflected in the corresponding capital
gain to the quota holders.
This, exception, while perhaps slightly more relevant than the first, is
nonetheless of little consequence. First, notice that under the subsidy scheme, the
quota holders would like to alter the TAC (increase it) and hence would exert some
pressure on the quota authority to do so. Second, a fisheries subsidy scheme is
unlikely to be deemed politically appropriate for a well managed ITQ fishery.
We have thus established that, perhaps apart from the well-managed ITQ
fishery, any true subsidy scheme will affect aggregate fishing effort and biomass.
What is the direction of this impact? Simple comparative statics exercise shows, not
unexpectedly, that subsidies increase equilibrium fishing effort and, consequently,
reduce equilibrium biomass. Most likely fishing subsidies also reduce the sustainable
yield. Examples of the effects of (a) fishing effort subsidy and (b) harvest subsidies
are illustrated is Figure 2
Figure 2
Equilibrium Fisheries Model: The Effect of Subsidies
1
1
1.5
Revenue ( effort )
Costs ( effort )
2
Revenue ( effort )
Revenue2 ( effort )
0.5
Costs2 ( effort )
1
Costs ( effort )
0
0
0
0
0
1
effort
2
2
(a) Fishing effort subsidy
0
0
0
1
effort
2
2
(b) Harvest subsidy
What is the cost of these subsidies? To a certain extent the cost can be inferred
from the sustainable yield diagram. In case (a), the fishing effort subsidy, it is vertical
distance between the two cost functions at the new level of fishing effort. In case (b),
the cost amounts to the vertical distance between the new and the old revenue
function at the new level of fishing effort.
It is worth noting that for effort subsidies, the total cost of the subsidies is
greater than the one corresponding to the initial fishing effort level. This is due to the
expansion in fishing effort resulting from the subsidy. In contrast, for harvest
subsidies, the eventual cost of the subsidy is less that appears at the initial effort (and
harvest) level. The reason is the reduction in harvest levels as a result of increased
fishing effort.5 So, in a certain perverse sense, it may be preferable to governments
contemplating fisheries subsidy to the industry to make it depend on harvest rather
5
Actually, if the initial bionomic equilibrium occurs to the left of the MSY effort level the effect may
be reversed.
9
than per unit of fishing effort, for in the former case the total amount of subsidy is
likely to contract over time.
Given that there is no improvement in the profitability of the fishery, the social
cost of the subsidy may be taken to equal subsidy itself. This, however, is too
optimistic. There are at least two additional cost items of significance. First there is
the cost of operating the subsidy system itself. Obviously, this cost can be quite high.
Second, in the case where the subsidy leads to a reduction in the sustainable yield,
there may be a corresponding loss in the consumers’ surplus. Even for a relatively low
elasticity of demand, this cost may easily turn out to be quite substantial.
To throw some further light on the possible magnitudes in question, let’s
consider a particular numerical example. More precisely we employ the following
specification of the key equations defined in section 3:
(17)
 = p aex - ce,
(18)
x = x -x2 - aex,
(19)
e = N() = b
The exact values of the economic and biological parameters in this model are,
of course, of no great significance provided they are empirically plausible. However,
if only for added concreteness, the parameter values have been chosen so as to reflect
roughly the known general features of the global marine fisheries as described in
section 2 . More precisely:
Parameter
p
a
c


b
Value
1.0
1.0
0.85
1.6
0.6
1.0
So, for biomass measured in units of 100 million metric tonnes, these
parameters values suggest a virgin stock biomass of the currently exploited species of
some 266 million metric tonnes and a maximum sustainable yield of some 107
million metric tonnes. This is close to the middle of the range suggested by FAO
(1997). Moreover assuming a competitive fishery, the steady state global harvest and
fisheries profitability results are similar to those experienced in recent years. The
steady state solution to this model in yield-effort space is illustrated in Figure 3.
10
Figure 3
Numerical Example: Sustainable Yield and Costs
(Units on vertical axis 100 million tonnes)
1.5
Revenue ( effort )
1
Costs ( effort )
0
0
0
0
0.5
1
effort
Effort
1.5
1.6
Let us now consider an effort subsidy, i.e. a payment per unit of effort. The
total amount of this subsidy obviously depends on the actual effort level. However,
given the parameter values discussed above, this subsidy would in equilibrium
amount to 20% of revenues under a competitive fisheries management regime. This of
course is within the range estimated by FAO (1993) and Milazzo (1998)
The static equilibrium solution (i.e. a discount rate of zero) to this model yield
the results listed in Table 2:
Table 2
The numerical model: Static equilibrium outcomes
Competitive
Variable
Optimal
fishery
No subsidy
Fishing effort
0.34
1.00
Harvest
76.6
92.6
Industry profits
44.7
0
Subsidy
0
0
Social profits
44.7
0
Competitive
fishery
Subsidy
1.08
82.6
0
17.7
-17.7
Fishing effort measured as an index. All other variables measures in million metric
tonnes of catch.
While the results in Table 1 should not be regarded as an attempt at describing
the global fisheries situation, they certainly provide an idea of the possible
quantitative impacts of fisheries subsidies . Comparing columns 3 and 4, it is obvious
that these costs can easily be substantial. Thus, accpeting the model as a stylized
11
description of the global fishery, effort subsidies amounting to almost 18 million
metric tonnes of fish (about 21% of revenues) lead to a significant increase in global
fishing effort, compared to the unsubsidized effort level, and a reduction in global
harvest of some 10 million metric tonnes. Most importantly, perhaps, the entire
subsidy is a waste of money.
So, as demonstrated in Table 1, effort subsidies simply make a bad situation
even worse. Instead of technically attainable social profits amounting to almost 60%
of revenues, fisheries mismanagement in the form of competition and subsidies lead
to social losses of over 20% of revenues.
As mentioned above, the overall costs of fisheries subsidies does not end with
the subsidy itself. It also includes the cost of running the subsidy system and the
reduction in consumer surplus due to a reduction in fish supply. Given the availability
of close substitutes in consumption, the long term elasticity of fish price with respect
to quantity is presumably quite low. Consequently, the reduction in consumers’
surplus stemming from a contraction in supply is also low. Thus, assuming an
constant elasticity of demand function with the elasticity of price equal to –0.1, the
loss in consumer surplus associated with the subsidy scheme is equivalent to about 0.7
million metric tonnes of catch. Given a management cost of the subsidy scheme of
between 1 and 5% of the subsidies then brings these two items up to 1-1.5 million
metric tonnes of fish. Thus a the overall global costs of fisheries subsidies amounting
to almost 18 million metric tonnes of fish may be between 19 and 20 million metric
tonnes of fish.
The above results only apply to the long run steady state. Let us now turn our
attention to what happens along the dynamic adjustment paths toward such a state.
The dynamics of the system defined by (13) – (15) are well known Wilen (1977),
Amundsen et al. (1993). The following figure illustrates these dynamics in biomasseffort space.
Figure 4
Competitive Fishery: Biomass-Effort Dynamics
2
Effort
Z
2
n 2
Y
n 2
1
f( nn )
g ( nn )
0
0
0
0
1
2
Z
Y
 nn
n 1 n 1
Biomass
3
2.7
12
The solid curves in Figure 4 represent adjustment paths. The dotted lines in the
diagram represent biological (downward sloping line) and economic (vertical line)
equilibrium schedules, respectively. The intersection of these two curves represents an
overall fishery equilibrium. Given the model specification, this equilibrium is
cyclically stable. Two adjustment paths to the equilibrium starting from two different
initial positions are drawn in the diagram. Along the adjustment paths, biomass
fishing effort and consequently harvest and profits evolve in a cyclical manner. It is of
some importance to realize, that the area to the right of the economic equilibrium line
(the vertical curve in the diagram) represents positive industry profits and,
consequently, increasing fishing effort, while the area to the left of the economic
equilibrium line represents economic losses and, consequently, reduction in fishing
effort.
Now consider the impact of subsidies. For this purpose we compare two
adjustment paths, one with no subsidy as illustrated in Figure 4 and one with subsidy.
The subsidy considered is a fixed effort subsidy as specified above. the results are
illustrated in Figure 5
Figure 5
Competitive Fishery: Adjustment paths with and without subsidies
(With a subsidy: Solid line. Without subsidy: Dotted line)
1.971
2
1.5
X
n 2
Z
1
n 2
0.5
0.01
0
0
0.372
0.5
1
1.5
X
Z
n 1 n 1
2
2.5
3
2.66
As shown in Figure 5, the subsidized fishery not only results in a lower biomass and
higher fishing effort equilibrium, it also exhibits a higher fishing effort than the
unsubsidized fishery at all biomass levels along the adjustment path.
As shown in Figure 5, the adjustment paths are generally cyclical. The time
paths of profits and subsidies and profits (before subsidies) are illustrated in Figure 6.
13
Figure 6
Competitive fishery: The time path of profits and subsidies
(profits1 = industry profits, no subsidization scheme.
profits2 = industry profits, subsidization scheme less the subsidy)
1.0
1
0.5
profits1 ( n )
profits2 ( n )
subsidy ( n )
0
0.5
0.8
0
0
50
n
Periods
100
100
Now, industry profits less subsidies are a measure of fisheries rents. Figure 6
demonstrates that along the dynamic adjustment path, fisheries rents in the subsidized
fishery are always less than those in the unsubsidized fishery. The difference is
covered by the subsidies which in this example are quite substantial. The present
value of the dynamic paths illustrated in Figure 6 at 5% rate of discount are presented
in Table 3 below.
Table 3
Present values of dynamic paths
(Rate of discount 5%)
Paths
Unsubsidized competitive profits
Subsidized competitive profits
Sudsidies
Subsidized competitive profits less subsidies
Optimal path profits
Present value
2.66
2.87
2.94
-0.07
8.78
Fraction of an
optimal-like
path
0.29
0.33
0.33
0.001
Table 3 contains a couple of noteworthy results. First, the present value of the
competitive fishery is strictly positive. The reason is that the fishery starts from a
14
virgin stock biomass level and consequently yields some rents along the initial phases
of the adjustment path toward a bio-economic equilibrium. Nevertheless, the results
amounts to a qualification of the naive assertion that common property fisheries yield
no net returns to their participants. In fact, according to the results expressed in the
last column of Table 3, the value of the competitive fishery constitutes a substantial
fraction of the optimal one. However, this quantitative results certainly does not
generalize. The value of the competitive path depends upon the parameters of the
problem not the least the speed of adjustment parameter. Thus doubling the
adjustment speed parameter reduces the value of the competitive path by over 40%.
Second, the present value of the subsidized path to the companies is just
slightly superior to the unsubsidized path. So at the cost of subsidies of 2.94 metric
tonnes of fish the outcome of the fishing companies is only improved by 0.21 metric
tonnes or just 7% of the subsidy. Nevertheless, the static result that subsidies are a
complete waste has to be modified in the current context.
As already mentioned Table 3 also contains comparison with the results of a
so-called optimal-like path. A few words concerning the nature of this path are in
order. Since the numerical model employed is linear in control variables, the optimal
path must be a bang-bang one (Clark and Munro, 1975). Therefore, starting from a
virgin stock equilbrium, the initial segment of the optimal path would normally
follow the upper bound on fishing effort. However, there is little knowledge about this
upper bound, there is a certain indeterminacy in specifying the optimal path. The
course taken here is to specify a dynamic upper limit on fishing effort, namely that
defined by the initial effort development of the competitive path. So, the optimal path
first follows this path until the biomass has been brought down to the optimal long
term level at which point there is a jump (a bang) to the corresponding equilibrium
fishing effort level. This optimal-like path along with the subsidized and unsubsidized
competitive paths is illustrated in Figure 7.
15
Figure 7
Subsidized and unsubsidized competitive paths and the optimal-like path
1.971
2
1.5
Effort
X
n 2
Z
n 2
AA
1
n 1
0.5
0.01
0
0
0.372
0.5
1
1.5
X
Z
 AA
n 1 n 1
n 0
Biomass
2
2.5
3
2.66
Subs idy
No subsidy
Optimal
5.
Why governments may choose to subsidize fisheries
In view of the meager benefits of fisheries subsidies to the fishing firms reported in
Table 3, one may wonder why fishing industry organizations campaign for such
supports and why government choose to grant them. The reason may lie partly in the
dynamic aspect of the situation and partly in the institutional aspects of the subsidy
bargaining process.
First, it is obvious from looking a Figures 4 and 5, that the value of a given
subsidy scheme to the industry depends very much on its position at the time when
the subsidies are introduced. Thus, if the subsidies are introduced at a point where the
industry is already suffering losses and contracting or just about to, the short term
effort enhancing impact of the subsidies will be minimized and the gain to the
industry, compared to a situation of no subsidies, may be quite substantial. This is
demonstrated in Table 4 which lists the present value of profits for competitive paths
with different initial points.
16
Table 4
Impact of a subsidy scheme at various initial points of fisheries development
Present value
Initial Point
I. Virgin stock equilibrium
Unsubsidized fishery
Subsidized fishery
Subsidies
II. Bionomic equilibrium
Unubsidized fishery
Subsidized fishery
Subsidies
II. Maximum effort
(biomass=0.85, effort=1.71)
Unubsidized fishery
Subsidized fishery
Subsidies
Present value
(Seen from the beginning of the fishery)
2.66
2.87
2.94
2.66
2.87
2.94
0
0.405
3.737
0
0.225
2.08
-1.684
-1.225
6.396
-0.147
-0.107
0.56
So, as demonstrated in Table 4, depending on the initial point, the net benefits to the
industry of subsidies may actually be quite substantial. Therefore, at certain stages of
the fishery, industry representatives may quite rationally lobby for subsidies. Also,
interestingly, the efficiency of the subsidies in terms of the fraction of the subsidies
being retained by the industry also depends on the initial point. Thus, starting at the
bionomic equilibrium point almost 10% of the subsidies are actually translated into
fishing industry benefits compared to 7% if the initial point is the virgin stock
equilibrium or the point of maximum effort.
Now, the introduction of new subsidies inevitably results in an initial
improvement in industry profitability. While, as we have seen, this improvement will
to a substantial extent by eaten away by expansion in the industry, the temporary
benefit may be quite substantial. This is illustrated in Figure 8 which traces out the
profitability of the industry from a position of bionomic equilibrium with and without
subsidies.
17
Figure 8
The temporary impact of subsidies
0.163
0.2
0.1
unsubsidized ( n )
subsidized ( n )
0
0.077 0.1
0
1
5
10
15
n
20
25
30
30
Figure 8 shows clearly the initial positive impact of subsidies on industry
profitability. Thus, given a relatively short time horizon (or equivalently high discount
rates) by the representatives of industry and government, an agreement on increased
subsidies may not seem so irrational. This holds, in particular, if there are other sociopolitical constraints preventing lump sum financial transfers to the members of the
fishing industry.
6.
Subsidies and Efficient Fisheries Management Systems
Above we have seen that subsidies generally have a detrimental impact on fisheries
leading to even greater fishing effort and overcapitalization, lower fish stocks and less
economic rents from the fisheries than would be the case in an unsubsidized common
property fishery. This raises the question of the impact of subsidies in an efficiently
managed fishery.6 Will subsidies in that situation only amount to a straight financial
transfer or will they also result in economic losses to society? The answer is that it
depends on the nature of the management regime.
The operating program of a well-managed fishery are described by equations
(1) to (3) in section 3, where the imputed shadow value of biomass, , equals the
social one. Given this it is easy to demonstrate that subsidies will generally affect the
optimal behaviour of the industry both in equilibrium and long adjustment paths.
Consider for instance the equilibrium equation:
6
Of course, one may wonder about the need for subsidies in an efficiently managed fishery. One
situation, in which subsidies in such a fishery may be empirically relevant is when a poorly
managed subsidized has been brought under an efficient fisheries management regime.
18
Gx + YxCe/e = r
G(x) – Y(e,x) = 0.
Subsidies will affect the marginal profits of fishing effort and perhaps also the
marginal costs. Thus clearly, the marginal stock effort term, YxCe/e, in the first
equation will be affected and, consequently equilibrium fishing effort and biomass.
More to the point, it is easy to show that that harvesting and effort subsidies
generally reduce the marginal stock effect term and consequently lead to a lower
equilibrium biomass and higher equilibrium fishing effort. Interestingly, interest rate
subsidies, to the extent that they affect the rate of discount, r, work in the opposite
(but, presumably, just as harmful) way.
So, provided the management system in place allows the fishing industry to
adjust to the subsidies, it will choose to do so and thus deviate from the socially
optimal path.
To give an idea of the likely quantitative impacts of subsides, let’s return to
the numerical model of the previous sections. Assuming the same type and level of
effort subsidies as above, namely 0.15fishing effort, we find the following:
Table 5
Optimal equilibrium with and without subsidies
Without subsidy
Fishing effort
Biomass
Harvest
Present value of profits
Present value of rents
Present value of subsidies
0.56
1.74
0.97
10.32
10.32
0
With subsidy
(approx. 7% of revenues)
0.60
1.66
1.00
12.09
10.20
1.89
As shown in Table 5, effort subsidies amounting to roughly 7% of revenues lead to a
modest increase in equilibrium fishing effort and reduction in equilibrium biomass.
Fisheries rents, however, are reduced only slightly (1%). Thus, most of the subsidies
(94%) represent a financial transfer to the fishing industry.
The above result applies to a fishery that is managed so as to maximize the
present value of profits to the fishing industry itself. Most ocean fisheries, however,
are not managed by the fishing industry but a government agency that may not be
susceptible to industry influence. Thus, for instance in an ITQ managed fishery,
where the TAC is independent of subsidies, the industry is not in the position to adapt
its operations to the existence of subsidies. Therefore, neither fishing effort nor
biomass will change and it is easy to show that the amount of the subsidies will be
19
exactly reflected in the market value of quotas. To see this consider the profit function
under subsidies:
 = pY(e,x) – C(e) - Y(e,x) + s0 + s1e + s2 Y(e,x) = 0.
Taking total differentials and imposing the condition that de = dx = 0 yields:
d = Y(e,x) d = ds0 + ds1e + ds2 Y(e,x)  dS,
where dS represents the total change in the subsidy. Recognizing that under the
conditions specified Y(e,x) is a constant proves the assertion.
In the special case where only the harvesting subsidy is changed we get
exactly.
d = ds2.
7.
Good and Bad Subsidies
Milazzo (1998, e.g. p. 14) raises the question of “undesirable” vs. “desirable”
subsidies. Apparently undesirable subsidies are those that reduce economic efficiency
by encouraging overcapacity and overfishing. Desirable subsidies, by contrast, are
those that are not only economically non-distortive but corrective in the sense that
they further economic efficiency. Examples of these “good” subsidies are those
designed to (a) counter the consequences of the common property problem by e.g.
reducing the fishing fleet or (b) alleviate the consequences of other externalities such
as pollution from fishing operations. Let us now briefly examine these ideas.
Consider first subsidies to remove unnecessary fishing vessels from the fishing
fleet. The basic observation to make is that unless there is a good fisheries
management system in place, measures of this type are not going to have any long
term effect on fisheries profitability. The reason is very simple. Subsidies of this kind,
do not constitute a modification of the fisheries management system and consequently
do not alter the fundamental common property problem. Therefore the same forces
that generated the initial inefficiency are still at work. Moreover, given the fisheries
management system and other operating conditions of the fishery, the initial capital
level is presumably an equilibrium one for the industry. If it were not, it would be
adjusted in time anyway. Therefore, any reduction in the number of fishing vessels
that may be accomplished by capacity reduction subsidies will be counteracted by an
expansion of fisheries inputs that can act as substitute for fishing vessels. Most
importantly, as there is no change in the fisheries management system, the long term
profitability of the industry will not change.
This argument can be expressed more formally along the lines of the basic
model of section 3 as follows: Write the fisheries profit function as:
(k,e,x,,p) = 0,
20
Where k and e denote vectors of capital and effort inputs respectively and p is the
corresponding vector of prices. As before, x denotes fish stock biomass and  the
private evaluation of the shadow value of the stock given the fisheries management
system. Now, imagine that one element of k, k, say, is somehow reduced and
constrained at a new lower level. The above equation will nonetheless still apply.
However, since the fisheries management system is unchanged, , is unaltered and we
obtain the following adjustments.
(20)
 Ykiki/k +  Yeiei/k + x/k = 0.
Hence, other inputs and the biomass change so as to keep industry profits and,
consequently, fisheries rents unchanged. This means that the subsidy is a complete
waste of funds.
In addition to this, it is important to realize that there are normally significant
costs associated with a capacity reduction subsidy scheme. First, one of its effects, as
we have seen, is to distort the capital and input configuration of the fishing operations.
Second, a subsidy scheme of this type is inevitably quite costly to operate. This cost,
must of course, be subtracted from the gross benefits generated. Third, the existence
of a capacity reduction subsidy scheme inevitably encourages the expectation of
something similar taking place in the future. Hence, the expected costs of undertaking
capacity investments is correspondingly reduced. Therefore, this kind of a capacity
reducing scheme may actually end up by encouraging a capacity increase over and
above what would otherwise apply in bioeconomic equilibrium. Therefore, it appears,
at least in the case of a poorly managed fishery, that this allegedly “good” subsidy is
not so good after all.
In the case of a well-managed fishery, the negative effects of a capacity
reducing subsidies may not be as great as in the poorly managed case. Under certain
circumstance, they may even be completely nondistortive and merely amount to a
financial transfer as discussed in section 6 above. In other cases, they will distort the
economic signals to the industry and generate certain inefficiencies, such as too rapid
reduction in the fishing fleet. The other disadvantages discussed above also apply.
Therefore, also in the case of a well-managed fishery capacity-reduction subsidies
don’t appear to be so good. Moreover, in the case of a well-managed fishery, it is a
question why there should be a need for capacity reduction subsidies at all.
So the conclusion is that capacity reduction subsidies are generally far from
being “good” in the sense of Milazzo (1998). At best under a well-designed and
operated fisheries management system, they are totally ineffective with a social net
cost amounting only to the cost of operating the system. In most fisheries, however,
they represent a waste equivalent to the amount of the subsidies plus generally quite
significant costs due to further distortions in the fishery and the expenses of operating
the system.
Externality reduction subsidies (such as pollution abatement subsidies) fare
somewhat better. To the extent that they actually reduce externalities they may serve a
useful purpose. However, as demonstrated in equation (20) above, they will almost
certainly lead to countervailing distortions in the use of fisheries inputs. In addition,
21
the costs of operating the system must not be forgotten. These cost will, most likely,
waste some of the benefits.
So, the overall conclusion must be that these allegedly good subsidies are not
particularly good after all. First of all, they are usually totally ineffective in enhancing
the efficiency of the fisheries. Moreover, by conveying inappropriate economic
signals to the fishing firms, they are generally economically distortive. In fact, only in
exceptional cases are they nondistortive. Finally, these schemes are costly to operate.
Thus, broadly speaking, they appear little or no better than the other category of
subsidies referred to as “bad” subsidies.
8.
Capacity reduction and the spill-over effect
There is another implication of capacity reduction schemes that should be kept in
mind. Usually capacity reduction schemes are implemented over a limited range of
fisheries, often on national basis. Consequently, the displaced fishing capital will
exhibit a tendency to move to other fisheries. Now, according to FAO assessments
(FAO, 1995), most fisheries of the world are already fully or overcapitalized.
Consequently, the shift of fishing capacity from one fishery to another represents a
cost, at least from the global perspective. Obviously, this cost must be added to the
local or national cost of capacity reduction schemes discussed in the previous section.
More dramatically, it appears that the cost associated with shifting the fishing capital
from a fishery to another will normally exceed the maximum possible benefits of
reducing the fishing capital in the first fishery. The reason is that the costs of
increased fishing capital in one fishery generally exceeds the benefits of an equivalent
reduction in fishing capital in another fishery, provided the initial allocation of the
available fishing capital was efficient at the outset.
To see this more clearly, consider two fisheries; fishery 1 and fishery 2. Let
the profit functions in these two fisheries be given by (k1) and ( k2), where k1 and k2
refer to the level of fishing capital in the two fisheries, respectively. Let the overall
available fishing capital be K. Thus,
K = k1 + k2.
Now, assume that the fishing firms are initially able to allocate fishing capital freely
between the fisheries. It follows that
(21)
k1(k1) = k2(K-k1).
Global profits are given by
(k1) = (k1) +  (K-k1).
Now, let k1° represent the initial level of fishing capital in fishery 1. By
Taylor’s theorem, the change in global profits from moving fishing capital between
the fisheries is:
22
  (k1)-( k1°) = (k1 - k2)dk1 + (k1,k1 + k2,k2)(k1)2.
By (21) k1 - k2=0 and as required by profit maximization (concavity) k1,k1+k2,k2<0.
Hence:
(22)
 < 0.
The result expressed in (22) states that any artificial reallocation of fishing
capital across previously unmanaged fisheries can only reduce global fisheries rents.
It is important to realize that this occurs in spite of the fishing capital reduction being
fully implemented and effective in the first fishery without any of the countervailing
distortions and costs discussed in section 7. The policy inference seems to be twofold:


Capacity reduction schemes with the help of subsidies or other means are
much more likely to cost more than they bring in and, therefore, should be
avoided.
If capacity reduction schemes are employed anyway, the displaced fishing
capital (or an equivalent capacity) should be destroyed.
23
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