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SECTION 2

Characterization and estimation of tuna fishing capacity


An analysis of the fishing capacity of the global tuna purse-seine fleet[14]

Chris Reid
Forum Fisheries Agency
PO Box 629, Honiara, Solomon Islands
E-mail: chris.reid@ffa.int
James E. Kirkley
College of William and Mary
Virginia Institute of Marine Science
School of Marine Science
Gloucester Point, VA 23062, USA
E-mail: jkirkley@vims.edu
Dale Squires
National Marine Fisheries Service
La Jolla, California, USA
E-mail: Dale.Squires@noaa.gov
Jun Ye
Department of Economics
University of California, San Diego, USA
E-mail: jye@weber.ucsd.edu

ABSTRACT

This paper presents the results of analyses conducted to measure tuna purse-seine fishing capacity in the eastern Pacific Ocean (EPO), western and central Pacific (WCPO), Indian and Atlantic oceans as part of FAO Project GCP/INT/851/JPN Management of tuna fishing capacity: conservation and socio-economics. The regional analyses were conducted using Data Envelopment Analysis (DEA), as recommended by the project's Technical Advisory Committee (TAC). The results of the regional analyses are then drawn together in an overview discussion of tuna purse-seine fishing capacity at a global level.

The level of aggregation and the period over which the DEA was conducted varied among the different regional purse-seine fisheries due to differing levels of data that were available for each of the fisheries. The DEA of the EPO and WCPO purse-seine fisheries were conducted at the vessel level during 1998-2002. The period over which the analysis was conducted was limited to 1998-2002, as limiting the number of years of analysis to the five most recent ones captures more recent fleet configurations, cost conditions and fishing patterns, and also helps to control for the potential shifts in capacity output due to technical change. Further, capacity for these fisheries was estimated under two measures: (1) under full variable input utilisation and maximum technical efficiency (TE) and (2) under full variable input utilisation, but with current levels of TE. The latter was done to try to account for variations in skipper skill levels in deriving estimated capacity output levels. In effect, it measures capacity utilisation purged for the effects of TE. The DEA of the Indian and Atlantic purse-seine fisheries were conducted at the fishery level during the 1981-2002 and 1991-2002 periods, respectively. This was done as the data available were extremely limited, and consequently the DEA could be conducted only at the fishery level as opposed to the vessel level. In order to ensure that sufficient observations were obtained it was therefore necessary to conduct the analysis over the whole period for which data were available.

The results of the DEA for the EPO purse-seine fishery indicated that there was considerable excess capacity in the fishery, and that the largest contributor, by far, to excess capacity was Class-6 vessels, although there was excess capacity for Classes 2-3 and 4-5 vessels as well. It was estimated that across the fishery excess capacity, defined as capacity output, purged for TE, minus observed catch, increased from about 120 000 tonnes in 1998 to close to 200 000 tonnes in 2002, an increase approaching 63 percent in five years. For yellowfin and bigeye it was also estimated that excess capacity, defined as capacity output, purged for TE, minus combined maximum sustainable yield, climbed from an excess of about 11 percent in 1998 to an excess of almost 70 percent by 2002. Technical change was estimated on a cumulative basis to have increased by about 60 percent during 1998-2002 for the fishery as a whole. Thus "fishing power" or the state of technology increased considerably, and was an important factor in the exhibited increase in fishing capacity and excess capacity over this period.

For the western and central Pacific purse-seine fishery it was estimated that, on average, during 1998-2002 the purse-seine skipjack fishing capacity was around 306 000 tonnes (35 percent) per annum greater than the actual catch levels. However, it noted that when purging for TE excess skipjack fishing capacity was only 137 000 tonnes (16 percent) per annum greater than the actual catch levels. In other words, only around 40 percent of the potential increase in catches could be realised through increases in variable input usage, given the biomass, environmental conditions and state of technology that prevailed over this period. Estimated excess fishing capacity, purged for TE, was at its highest level in 2000. It was hypothesised that this may have been caused by low skipjack prices in the second half of 1999 and throughout 2000, resulting in vessels reducing the number of days spent searching and fishing.

For yellowfin and bigeye combined in the WCPO purse-seine fishery it was estimated that during 1998-2002 excess purse-seine fishing capacity was around 72 000 tonnes (29 percent) per annum greater than the actual catch. However, it noted that when purging for TE excess yellowfin and bigeye fishing capacity was only 31 000 tonnes (12 percent) per annum greater than the actual catch levels. In other words, only around 40 percent of the potential increase in catches could be realised through increases in variable input usage, given the biomass, environmental conditions and state of technology that prevailed over this period. It was also estimated that during 1998-2002, on average, fishing capacity, purged for TE, for yellowfin and bigeye combined was in excess of the average catches between 2000 and 2002 by 47 666 tonnes, or 20 percent, but that no excess capacity existed in the fishery in 2002 when measured against the average 2000-2002 catch levels.

From the DEA for the Indian and Atlantic purse-seine fisheries it appears that there is excess capacity in both oceans. The more serious level of excess capacity exists for the Indian Ocean fishery. It was estimated that, on an annual basis, there was approximately 61 000 tonnes of excess capacity in the Indian Ocean fishery. In comparison, the Atlantic Ocean fishery had approximately 29 500 tonnes of excess harvesting capacity. Alternatively, if Indian and Atlantic Ocean vessels operated efficiently, fully utilized their variable inputs and harvested the average annual reported level of landings, fleet sizes could be reduced, respectively, from 40 to 31 (22.5 percent) in the Indian Ocean fishery and from 53 to 46 (13.2 percent) in the Atlantic Ocean fishery. These estimates are considered extreme lower-bound estimates of capacity due to the limited number of observations and inadequate information for considering different modes and nations' fishing activities.

At a global level for skipjack it appears that fishing capacity peaked in 1999, then declined in 2000 and 2001 and then returned to 2000 levels in 2002. Excess capacity followed a similar pattern, with a significant rise in 1999, followed by a decline of more than 50 percent in 2000 and 2001 and then by a small increase in 2002. Excess capacity as a percentage of catch also peaked in 1999; however, from then until 2002 it was in continuous decline. From the estimates it appears that global purse-seine fishing capacity for yellow-fin and bigeye was on a downward trend between 1998 and 2000, even though observed catch levels were rising, before increasing back to 1998 levels in 2001 as observed catch levels rose sharply and then declined again in 2002. Excess fishing capacity between 1998 and 2000 fell by over 40 percent, while excess capacity in 2001 was at levels similar to that seen in 1999. In 2002 excess capacity was less than those in 1998, 1999 and 2001, but greater than that in 2000.

Excess fishing capacity is a result of both technical inefficiency (or skipper skill) and under-utilisation of variable inputs. In other words, catches can be increased through either an increase in the efficiency of inefficient purse-seine vessels or through an increase in the utilisation of variable inputs such as increases in the numbers of days spent fishing and searching. In the analysis of the purse-seine fisheries of the EPO and the WCPO, fishing capacity, purged for TE, was also estimated. In both analyses under this measure, there was a significant reduction in the estimated level of fishing capacity. For the EPO, the estimated average excess capacity level, purged for TE, measured against the observed catches of skipjack and of yellowfin and bigeye combined during 1998-2002 were around half the level of the estimated excess capacity levels measured against observed catches. For the WCPO, average excess capacity level, purged for TE, measured against observed catches for skipjack and for yellowfin and bigeye combined during 1998-2002 were around 60 percent lower than the levels of the estimated excess capacity measured against observed catches. These results indicate that TE improvements (or increases in skipper skill levels) of inefficient vessels are required if capacity output levels are to be fully achieved.

1. Introduction

The Food and Agriculture Organization of the United Nations (FAO) is implementing a project on the management of tuna fishing capacity, FAO Project GCP/INT/851/JPN Management of tuna fishing capacity: conservation and socio-economics. The main objectives of the project are to identify, consider and resolve technical problems associated with the management of tuna-fishing capacity on a global scale, taking into account conservation and socio-economic issues.

The project's Technical Advisory Committee (TAC) met in April 2003. The TAC recommended that a data envelopment analysis (DEA) be undertaken to estimate the fishing capacity of industrial tuna fleets, including the purse-seine, pole-and-line and longline fleets. Subsequent to this it was decided that the analysis would be undertaken in a phased manner, with the analysis of purse-seine fishing capacity undertaken at the first stage and then, depending on the availability of appropriate data, the pole-and-line and longline fisheries at a later stage. This paper presents the results of the DEA of global purse-seine fishing capacity conducted at the regional level.

This paper is structured as follows. Section 2 provides an overview of the definition of capacity and capacity utilisation (CU), as used in this report, and analytical methods used for measuring fishing capacity and excess (over-) capacity. In Sections 3 to 5 the methodology, data employed and results of the analyses conducted for the tuna purse-seine fisheries of eastern Pacific Ocean (EPO), western and central Pacific Ocean (WCPO), Indian Ocean and Atlantic Ocean are presented. Details of the analyses of the EPO conducted by Dale Squires and Jun Ye, the WCPO conducted by Chris Reid and the Atlantic and Indian Oceans conducted by James Kirkley are given in Sections 3, 4, and 5, respectively. Finally, in Section 6 the results of the regional analyses are combined in an overview discussion of tuna purse-seine fishing capacity at a global level.

2. Overview of the analytical approach

2.1 Capacity and capacity utilisation

Capacity is a short-run concept, where firms and industry face short-run constraints, such as the stock of capital or other fixed inputs, existing regulations, the state of technology and other technological constraints (Morrison, 1985). Capacity is defined in terms of potential output. This potential output can be further defined and measured, following either a technological-economic approach or an economic optimisation approach based directly on microeconomic theory (Morrison, 1985).[15] The two notions of capacity are distinguished by how the underlying economic aspects are included to determine the capacity output.

In either approach, CU is simply actual output divided by capacity output (Morrison, 1985). In the technological-economic approach, a CU value less than one implies that firms have the potential for greater production without having to incur major expenditures for new capital or equipment (Klein and Summers, 1966).

This paper, and those of FAO (1998), Kirkley and Squires (1999), FAO (2000) and Squires et al. (2003), focus upon the technological-economic measures of capacity, because the paucity of cost data in most fisheries mitigates against estimation of cost or profit functions to derive economic measures of capacity and CU. Similarly, the technological-economic approach is used by the United States (Corrado and Mattey, 1998) and most other countries to monitor CU throughout the economy.

The technological-economic capacity of a firm can be defined following Johansen's (1968, p. 52) definition of plant capacity as, "... the maximum amount that can be produced per unit of time with existing plant and equipment, provided the availability of variable factors of production is not restricted". Färe (1984) provides a formal proof and discussion of plant capacity.

Capacity output thus represents the maximum production that the fixed inputs are capable of supporting. This concept of capacity conforms to that of a full-input point on a production function, with the qualification that capacity represents a realistically-sustainable maximum level of output, rather than some higher unsustainable short-term maximum (Klein and Long, 1973). In practice, this approach gives maximum potential output, given full utilisation of the variable inputs under normal operating conditions, since the data used reflect normal operating conditions and existing market, resource stock and environmental conditions.[16] This approach gives an endogenous output, and incorporates the firm's ex ante short-run optimisation behaviour for the production technology, given full utilisation of the variable inputs under normal operating conditions.

The definition and measurement of capacity in fishing and other natural resource industries face a unique problem because of the stock-flow production technology, in which inputs are applied to the renewable natural resource stock to produce a flow of output. For renewable resources, capacity measures are contingent on the level of the resource stock. Capacity is, therefore, the maximum yield in a given period that can be produced, given the capital stock, regulations, current technology and state of the resource (FAO, 1998; Kirkley and Squires, 1999). Nonetheless, annual climate-driven ocean variability is clearly a key factor affecting fisheries. The monsoon and El Nino-Southern Oscillation events provide clear examples, since the distribution and catchability of fish varies. As a consequence, and due to annual changes in the size and species mix of the resource stocks, the target level and capacity output from the stock-flow production process can vary annually, and even seasonally.

An additional factor that is important to consider is the source of variations in the level of technical efficiency (TE) at which a vessel operates. Pascoe and Coglan (2002) found that differences in vessel characteristics explained around one third of the variation in TE of English Channel trawlers, and attributed the remainder to unmeasurable characteristics, such as skipper skill and differences in technology that could not be quantified. Other studies (e.g. Kirkley, Squires and Strand, 1998; Squires and Kirkley, 1999) have also suggested that much of the difference in efficiency among vessels may be due to differences in skipper skill. As such, in this study, where data permits, fishing capacity is estimated under two different measures. First, as discussed previously, it is estimated under full-variable input utilisation and maximum TE. Second, it is estimated under full-variable input utilisation, but with current levels of TE. The latter was done to try to account for variations in skipper skill levels in deriving estimated capacity output levels; in effect, it measures capacity utilization (CU) purged for the effects of TE.

In fisheries and other renewable resource industries, excess capacity is often defined relative to some biological or bio-socio-economic reference point that accounts for sustainable resource use and a target resource stock size. Excess capacity, in a technological-economic approach, can be defined as the difference between capacity output and the target level of capacity output, such as maximum sustainable yield (MSY) or the catch rate corresponding to the fishing mortality of an alternative harvest (FAO, 1998). The target level of capacity output was defined by FAO (1998, p. 11), "Target fishing capacity is the maximum amount of fish over a period of time (year, season) that can be produced by a fishing fleet if fully utilized while satisfying fishery management objectives designed to ensure sustainable fisheries..."[17]. A similar conclusion was reached by FAO (2000). The target fishing capacity catch can be specified as, for example, MSY or maximum economic yield (MEY).

In this paper, however, we apply a different approach. Capacity and excess capacity are addressed primarily in terms of observed catch against capacity, or potential, catch, assuming that the potential catch is sustainable. In addition, however, in the analysis of the EPO an examination of excess capacity with regard to the AMSY[18] for yellowfin and bigeye is presented, and in the WCPO analysis excess capacity is examined in the context of recent scientific advice that there be no further increases in fishing mortality on yellowfin and bigeye stocks.

2.2 Measuring capacity using data envelopment analysis

DEA is a mathematical programming approach introduced by Charnes, Cooper and Rhodes (1978). The DEA approach seeks to derive the most technically-efficient production frontier, from either an input or an output orientation, by constructing a piece-wise linear technology. Although capacity may be estimated by numerous methods (e.g. a stochastic production frontier, peak-to-peak or surveys), we use DEA to estimate harvesting capacity for the EPO, WCPO, Indian Ocean and Atlantic Ocean. The estimation is restricted to a technological-economic approach in that the data are restricted to the physical quantity of inputs used in the production process and the physical quantity of output produced. The output-orientated approach of Färe (1984) is used in this study for estimating capacity. The output orientation seeks to determine the maximum expansion in outputs, given fixed input levels for some factors (fixed factors) and unrestricted levels for other factors (i.e. the variable factors). The fixed factors limit total production. Although the variable factors are unrestricted, DEA permits the determination of variable input usage consistent with the levels determined by the fixed factors.

The original approaches of Charnes, Cooper and Rhodes (1978) and Färe (1984) provided estimates of TE consistent with the notion of TE offered by Farrell (1957) (i.e. maximum expansion of output, given no change in inputs, or maximum reduction in inputs, given no change in outputs). The method of Färe (1984), later modified by Färe, Grosskopf and Kokkelenberg (1989), separates the factors of production into fixed and variable inputs, and subsequently solves a mathematical programming problem that permits the determination of a piece-wise production technology or frontier, which represents the efficient levels of output, given the fixed factors of production. The mathematical programming problem is the following:

[1]

where TECAP equals a measure of the potential expansion in outputs; 2 (2 31.0) is the inverse of an output distance function; ujm is the w* output of the jth producer or observation; xjn is the nth input for the jth producer; Fx and Vx, respectively, indicate vectors of fixed and variable factors; 8 is a measure of the optimum utilization of the variable inputs; and z is a vector of intensity variables that define the reference technology. If the value of 2 is 1.0, production is efficient and output cannot be expanded, and if [1]> 1.0, the potential output may be expanded by 2 - 1.0. Problem [1] imposes constant returns to scale; in our analysis we allow for variable returns to scale by imposing the constraint 3 zj = 1.0.

One limitation of problem [1] or the Färe, Grosskopf and Kokkelenberg (1989) model is that it imposes a radial expansion of outputs (i.e. all outputs expand by the same proportion, 2 - 1.0). This limitation, however, can be easily resolved through the use of directional distance vectors, the Russell (1985) measure, or the slack-based approach of Cooper, Seiford and Tone (2000), all of which permit non-radial expansions of outputs. We consider the Russell measure because of its ease of estimation. The use of either directional vectors or the approach of Cooper, Seiford and Tone require considerably complicated estimation algorithms. The Russell measure (RM) is as follows: m=l

[2]

where M is the number of outputs, and the variables are the same as those previously discussed. The division by M ensures an overall efficiency score of 1.0 or greater. We also impose variable returns to scale.

The use of DEA to calculate fishing capacity output and CU is illustrated in Figure 2.1. DEA, using the observed landings for different-sized vessels and a measure of the capital stock or fixed inputs, such as gross registered tons (GRT), determines the output or landings that are the greatest for any given vessel size, assuming that the variable inputs are fully utilized (variable inputs are thereby unconstrained) under normal operating conditions, where normal operating conditions are reflected in the data. DEA calculates a frontier or maximum landings curve, as determined by the best-practice vessels, which represents fishing capacity output. Landings directly on the best-practice production frontier represent full capacity CU or CU = 1. When a vessel produces at less than full capacity, as represented by an output lying below the frontier in Figure 2.1, the CU is less than one, i.e. CU < 1. Thus, in Figure 2.1 B represents the size of the landings, A denotes the excess capacity (vis-à-vis observed production), A + B denotes capacity output, and the ratio A/(A + B) represents CU, so that CU < 1 in this case.

The production frontier, established by the best-practice vessels (the ones on the frontier) and estimated by DEA, gives capacity output, given the fixed inputs or capacity base, the states of technology, the environment and the resource stocks, provided that the variable inputs (fishing effort) are fully utilized under normal operating conditions. The production frontier (also called the reference technology), established by the best-practice vessels, and also estimated by DEA, gives technically-efficient output, given the fixed inputs, states of technology and the environment and resource stocks when the variable inputs are utilized at the observed levels. Hence, the difference between capacity output and technically-efficient output is that variable inputs are fully utilized in the former and are utilized at the observed levels (which could be fully utilized) in the latter.

FIGURE 2.1
Data envelopment analysis

Alternative methods for measuring capacity and CU have been proposed in the literature, most notably duality-based measures using cost, profit or revenue functions (Morrison, 1985; Squires, 1987; Segerson and Squires, 1995a and 1995b; Färe et al., 2002). Unlike duality-based econometric estimates of cost, profit or revenue functions, DEA does not impose an underlying functional form, so that estimation is not conditional upon the functional specification. Unlike the cost, profit or revenue function approach, DEA can estimate primal measures of capacity in a multiple-product environment without imposing separability assumptions on the outputs (Segerson and Squires, 1990). DEA can be used when prices are difficult to define, or behavioural assumptions, such as cost minimisation, are difficult to justify or cost data are unavailable.

The DEA approach has limitations. First, it is a non-statistical approach, which makes statistical tests of hypotheses about structure and significance of estimates difficult to perform, although there are several non-parametric tests that can be performed to test the results of DEA. Second, because DEA is non-statistical, all deviations from the frontier are assumed to be due to inefficiency. Third, estimates of capacity and CU may be sensitive to the particular data sample (a feature shared by the dual cost, profit or revenue function approach).


[14] The views expressed in this paper are those of the authors, and do not necessarily reflect the views of the U.S. National Marine Fisheries Service or the Forum Fisheries Agency or its member countries.
[15] In the economics approach, capacity can be defined as that output pertaining to one of two economic optimums: (1) the tangency of the short- and long-run average cost curves (Chenery, 1952; Klein, 1960; Friedman, 1963), so that the firm is in long-run equilibrium with respect to its use of capital, or (2), the tangency of the long-run average cost curve with minimum short-run average total cost curve (Cassels, 1937; Hickman, 1964).
[16] Klein and Long (1973, p. 744) state that, "Full capacity should be defined as an attainable level of output that can be reached under normal input conditions - without lengthening accepted working weeks, and allowing for usual vacations and for normal maintenance". The U.S. Bureau of the Census survey uses the concept of practical capacity, defined as "the maximum level of production that this establishment could reasonably expect to obtain using a realistic employee work schedule with the machinery and equipment in place" and assuming a normal product mix and down-time for maintenance, repair, and cleanup.
[17] Fishing capacity is generally defined by FAO (1998, 2000) as follows:

Fishing capacity is the maximum amount of fish over a period of time (year, season) that can be produced by a fishing fleet if fully utilized, given the biomass and age structure of the fish stock and the present state of the technology. Fishing capacity is the ability of a vessel or fleet of vessels to catch fish, i.e. YC = Y(EC, S).

In this general definition, YC denotes current yield/catch, EC denotes current effort, and S denotes stock size (biomass). Fishing capacity thus represents the maximum amount of fish caught by a fleet fully utilizing its variable economic inputs under normal operating conditions, given the fleet's capital stock (vessels, gear, and equipment, including FADs), biomass, and harvesting technology. Normal operating conditions refers to those operating conditions faced by fishing vessels in the normal conditions of the time period in which they operate.
[18] AMSY is the Average Maximum Sustainable Yield and is an average over a number of years to account for fluctuations that may occur over time.


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