Fisheries management, capacity measurement, Resource /Energy Economics and Policy, Q22,

Asche, Frank

English

Research Papers in Economics

105
Marine Resource Economics, Volume 22, pp. 105–108 0738-1360/00 $3.00 + .00
Printed in the U.S.A. All rights reserved Copyright © 2007 MRE Foundation, Inc.
Capacity Measurement in Fisheries:
What Can we Learn?
FRANK ASCHE
University of Stavanger
Key words   Fisheries management, capacity measurement.
JEL Classification Code   Q22.
Introduction
Since the Food and Agriculture Organization of the United Nations (FAO) report on
fishing capacity, there has virtually been an explosion of researchers’ interest in ca-
pacity measurement in fisheries (FAO 1998). This interest follows directly from the
seminal work of Gordon (1954), who predicted that in a poorly managed fishery one
will observe rent dissipation and a level of effort which is too high. This excessive
effort presents itself as overcapacity in the form of too many vessels and excessive
input factor use. This is also why the main focus in traditional command-and-control
fisheries management has been the control of input factor use, such as restricted ac-
cess and limitations on gear type, vessel size, and other inputs. In the recent general
economics literature, there have been substantial developments in economic theory
and methods to measure capacity utilization in a variety of industries. Given the fo-
cus on overcapacity in fisheries management, it is only natural that this has fueled
the interest of fisheries economists to employ these tools.
There is little doubt that measurement of capacity utilization in a fishery can
provide useful information with respect to similar issues as in other industries.
Moreover, in many ways a measure of capacity utilization can be regarded as an in-
dex of how well a fisheries’ management system works. In addition, the fact that
poorly managed fisheries generate substantial redundant capacity due to rent seek-
ing from the fishermen makes capacity measurement potentially an even more
interesting management issue as indicated by Gordon (1954), particularly at the fleet
level. In a number of countries, employment in the fishing industry is also an impor-
tant objective in fisheries management. An important question when attempting to
improve management then becomes—how much must capacity (i.e., employment)
be reduced to achieve these goals?
However, the measurement of capacity also becomes more challenging in re-
newable resource industries like fisheries because of the effects different
management regimes have on incentives and thereby the way fishermen operate.
There are also substantial differences with respect to different approaches to capac-
Frank Asche is a professor at the University of Stavanger, N-4036 Stavanger, Norway, and affiliated
with the Centre for Fisheries Economics, Institute for Research in Economics and Business Administra-
tion, Bergen, Norway, email: Frank.Asche@uis.no.
The author acknowledges the financial support of the European Commission (FAIR contract no. CT
2001-01535). The views expressed herein are those of the author and are not to be attributed to the Eu-
ropean Commission.Asche 106
ity utilization measurement and the information they provide in different settings.
When using measures of capacity utilization in fisheries management, one should be
very careful in assessing what information these measurements actually convey.
What is Optimal Capacity?
As far as I can see, the most difficult question in measuring fishery capacity utiliza-
tion is to find a measure of the optimal capacity. All measures of capacity utilization
involve a ratio between actual and full, or optimal, capacity utilization, and this is
accordingly a key measure. However, the special nature of the fishing industry
makes optimal capacity very difficult to measure. In particular, the importance of
the state and variability of the stocks and the changes in economic incentives due to
different management regimes makes capacity measurement in fisheries very differ-
ent from traditional industries. This also makes the measure of full (or optimal)
capacity very important but very difficult to obtain in practice. In theory, full capac-
ity utilization is obtained when a fishery is optimally managed in an economic as
well as a biological sense. While this is a clear reference point, it is extremely diffi-
cult to measure empirically. If one deviates from this point, is it along the optimal
cost schedule given the stock or is it along the rent dissipating revenue schedule? As
shown by Homans and Wilen (1997), the difference can be substantial.
In applied work, one has to measure full capacity in one way or another. In the
technical inefficiency literature—the parametric using Stochastic Frontiers (SF), as
well as the nonparametric using Data Envelopment Analysis (DEA)—this is gener-
ally taken to be the most efficient observation in the data set. For instance, Dupont
et al. (2002) states that, “the analysis is restricted to observed output-input relation-
ships.” However, as discussed by Wilen (2007), while the best firms are likely to be
close to, if not at, full capacity in a sample of agricultural firms, the issues with re-
spect to stock and management systems are likely to make a substantial difference
with respect to what is the best observed capacity. Thus, with this approach, one has
no way to assess how far the best firm in the sample is from the best firm in an opti-
mally managed fishery.
A different approach follows Morrison (1985) and is based on estimates of cost,
revenue, or profit functions as in Squires (1988), Dupont (1990), Bjørndal and Gor-
don (1993), and Weninger (1998) where either the output or some input factors can
be treated as fixed. Using this approach, one can solve for the optimal level of a
fixed output or input, and accordingly, one is not restricted to the best practice in the
sample. With such parametric models, model stability is of course an issue, and one
must assume that the fishermen use a similar technology in an “optimal” manage-
ment situation as in the management situation under which the data was collected.
This can be reasonable when one moves from open access to an optimally managed
fishery as well as from individual nontransferable quotas to transferable quotas
(ITQs) if the harvesting technology does not change substantially from one manage-
ment system to the other. However, it can be very problematic when going from
regulated open access, restricted access, or any other command and control regime
with some race to fish incentives to management systems with substantially changed
economic incentives and harvesting technologies.
Using a parametric approach, Dupont (1990) and Weninger (1998) also compute
optimal fleet size (conditioned on stock and technology). Dupont (1990) finds that
in the Canadian Pacific salmon fishery, 22% of the vessels and 37% of the tonnage
are redundant. In the U.S. surf clam and quahog fisheries, Weninger (1998) finds
that a fleet of 128 vessels can be reduced to between 21 and 25; i.e., a reduction ofCapacity Measurement in Fisheries 107
about four-fifths of the number of vessels, when individual vessel quotas (IVQs) are
introduced. These results are also comparable to what has been observed in Iceland
and other countries where ITQs have been introduced, as number of vessels has
been reduced by as much as 50% and more. These estimates are also higher than
those commonly found in studies where one measures capacity utilization relative to
best practice in the sample, as in the technical inefficiency literature.
To find optimal fishing capacity, one also needs to take the biological aspect of
fisheries management into account and find the optimal stock size and harvest. This
requires bioeconomic modeling. However, as shown by Homans and Wilen (1997),
the differences can be dramatic, as they found optimal capacity to be only one tenth
of regulated open-access capacity. If one also takes into account the impact of
changes in harvesting practices and revenues due to changed incentives in different
management regimes as in Homans and Wilen (2005), the difference between the al-
ternative management situations can be even more substantial. These results indicate
that bioeconometrics, as advocated by Smith (2006), is a more appropriate approach.
However, such models are far from trivial to estimate. Furthermore, they may con-
tinue to encounter issues such as over-simplified specifications of effort, as
addressed in bioeconomic modeling by Squires (1987). In addition, one still will
need to find the optimal stock size. Given changing environmental conditions, such
as changing water temperatures and ecological system interactions, that is not an
easy undertaking.
Concluding Remarks
There is little doubt that measurement of capacity utilization in fisheries is interest-
ing from a fisheries management perspective, as this is a good indicator of how well
a management system works or does not work. However, given the difficulties in-
volved in measuring optimal capacity, one may wonder if it is able to provide
anything more than an indication of the magnitude of the management problem. As
discussed above, different approaches to measurement of capacity utilization will, in
many cases, give highly varying outcomes as the approach to measure optimal of
full capacity varies.
More importantly, as discussed by Wilen (2007), overcapacity is a symptom of
poor management, not the cause. Hence, improving capacity utilization should not
be a management goal in itself since it requires management of the command-and-
control type. The fisheries economics literature clearly shows that such management
systems do not work with respect to the economic management of a fishery, as dis-
cussed, for example, in Wilen (2000). Hence, different restrictions on capacity or
input factor use will, in general, only contribute to the inefficiency in a fishery.
Moreover, from a fisheries management perspective, buyback programs do not
work, although the people who receive the money transfer certainly benefit.
The main message that comes from the empirical literature is that the overca-
pacity in many fisheries is substantial, as indicated for example by Dupont et al.
(2002), Weninger (1998), and Homans and Wilen (1997), each using different em-
pirical or theoretical approaches. Their results indicate that the excess capacity is at
least half the number of vessels in a fleet, making more than half the fleet redun-
dant. This also implies that any capacity reducing scheme, such as buyback
programs that target a small percentage of the capacity, will most likely not have
any impact from a management perspective.Asche 108
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Assessing Efficiency Gains from Individual Transferable Quotas: An Application to the Mid-Atlantic Surf Clam and Ocean Quahog Fishery.

Bioeconometrics: Empirical Modeling of Bioeconomic Systems.

Capacity Utilization Measures and Excess Capacity in Multi-Product Privatized Fisheries. Resource and Energy Economics 24(2):193–210.

Fishing Effort: Its Testing, Specification, and Internal Structure in Fisheries Economics and Management.

Markets and Rent Dissipation in Regulated Open Access Fisheries.

Primal and Dual Capacity Utilization: An Application to Productivity Measure in the U.S. Automobile Industry.

Production Technology, Costs, and Multiproduct Industry Structure: An Application of the Long-Run Profit Function to the New England Fishing Industry.

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Report of the FAO Technical Working Group on the Management of Fishing.

The Economic Theory of a Common Property Resource.

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Thoughts on Capacity Analysis: Is Capacity Analysis Giving Policy Makers Information they

Capacity Measurement in Fisheries: What Can we Learn?

Capacity Measurement in Fisheries: What Can we Learn?

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