1
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 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 1Average Global Food Subsidies
(Including trade barriers)
Product / Subsidy
Wheat / 48%
Coarse grains / 36%
Rice / 86%
Oilseeds / 24%
Sugar / 48%
Beef and veal / 35%
Pork / 22%
Poultry / 14%
Lamb and mutton / 45%
Eggs / 14%
Fish / 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 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) = pY(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 pY(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 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 = pYe(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(0)=0, N()>0,
where n>0 denotes the number of firms and represents the change in the number of firms.
Finally, the evolution of the biomass is given by the differential equation:
(3) = 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 equilibrium[1]:
(4)Gx(x) + Yx(e,x)Ce(e)/( pYe(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) = aex, a>0,
(7)C(e) = ce, c>0,
(8)N() = b, b>0.
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 = aEx -cE,
where the aggregate fishing effort is, Ene. Consequently we can proceed with the aggregate industry just as for individual firms.
Note, moreover, that
(11)= e + n.
Yet, another interesting implication of the linear specification following from (2) is:
(12) = (pY(e,x) – C(e)) /Y(e,x) = pa – 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 = pYx(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.
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 + s1e + s2Y(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:
(15)° = + S(e,Y(e,x)) = pY(e,x) – C(e) - Y(e,x) + S(e,Y(e,x)).
Profit maximization then implies:
(16)°e = e + Se+SyYe = 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)Se+SyYe = 0
(ii)Y(e,x) = -(es1 + 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 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
(a) Fishing effort subsidy (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 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) = paex - ce,
(18) = x -x2 - aex,
(19)= 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 / Valuep / 1.0
a / 1.0
c / 0.85
/ 1.6
/ 0.6
b / 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.