To address both excess capacity and overcapacity in a fishery, it must first be determined by fishery managers that a problem does in fact exist in that fishery.
In terms of measurement, the level of capacity utilization can be measured in a fishery both in indicative or qualitative terms and in analytical or quantitative terms. While quantitative metrics might be preferred, indicative measures are exceedingly practical in providing a first glimpse of the status of a fishery.
Secondarily, knowing the efficacy of a particular regulation in eliminating capacity requires an unbiased metric to determine the trend in capacity utilization over time.
It is critical to note that short run corrections in capacity levels might not persist over the long run if the underlying market incentives to over invest in capital and labor are not corrected by the regulation. For example, certain types of fishery management approaches, such as open access fisheries management, inevitably lead to overcapacity in a fishery, whilst other rights-based management approaches correct the underlying market incentives to over invest in capital and labor and prevent overcapitalization from occurring.
However, excess capacity can still develop in fisheries managed under these types of regulations. As a result, the management approach is a qualitative indicator of the existence of overcapacity, but not necessarily excess capacity. Quantitative metrics can be used to determine if excess capacity and overcapacity exist as well as providing a measure of their magnitude and direction of change over time in a fishery.
Qualitative assessments should use verifiable indicators that are based on scientific methods. The fundamental rationale of this approach is to apply common yardsticks to all fisheries, and minimize the role of subjective judgment. At the same time, it is recognized that the judgement, individual knowledge, and experience of the analysts will necessarily play an important role. The indicators approach has important advantages: it makes maximum use of existing information and it incorporates biological, management, and fleet-specific data.
Qualitative capacity indicators can be developed from bioeconomic theory based on existing conditions in or characteristics of a fishery. Clearly, no single indicator would be sufficient to make a determination of overcapacity in a fishery. A combination of indicators utilizing time trend information is needed to determine qualitative capacity levels in fisheries. Keeping in mind these practical difficulties and categories, it may be useful to consider the qualitative indicators of:
The annual report to the U.S. Congress entitled Status of Fisheries of the United States, prepared by the National Marine Fisheries Service, identifies fisheries that are:
If the species in a directed fishery are overfished, overcapacity almost certainly exists since overfishing and overcapacity are both symptoms of the same underlying management problem. Further, a fishery that is characterized as fully utilized or that may be approaching a condition of being overfished is also likely to exhibit overcapacity since fewer inputs in the production process could be used to provide the same level of harvest.
This indicator may apply somewhat differently to non-targeted and multispecies fisheries. The above general observations pertain to directed fisheries. However, many multiple species fisheries include a mix of overfished, fully utilized and developing fisheries. In these cases, the individual analyst in each region has to determine capacity levels on a case-by-case basis.
Put simply, incidental harvests in a fishery directed at another overfished and/or fully utilized species may or may not indicate overcapacity for the incidentally caught species.
Another qualitative indicator of overcapacity is the management environment of the fishery. The fundamental rationale for this indicator is that certain management categories are more likely, or tend, to be associated with overcapacity than others.
Under this indicator, there are three broad management categories:
These broad relationships, or associations, between management systems and capacity levels enjoy considerable support in the technical literature, and have been borne out in a major comparative study prepared by OECD in 1997.[20] Accordingly, while individual fisheries undoubtedly have their unique features, certain general relationships seem to emerge over time. It is assumed that, in most instances, open access fisheries tend to be associated with overcapacity; limited access fisheries usually have the same association, and harvest rights-based fisheries tend over time to eliminate overcapacity.
In open access fisheries, anyone can participate since there are no barriers to entry. More importantly, participants in open access fisheries have incentives to increase effort and investments as long as the fishery is profitable. Under these circumstances, overfishing and overcapacity almost always occur in the long run.
In looking at this issue, Hannesson (1987) found that free access led to over-exploitation, and that the optimal rate of exploitation is less than the maximum sustainable yield - in contradiction to the biological doctrine that fish stocks should be managed to give maximum sustainable yield (MSY).[21] Optimal catch capacity was also shown to depend on the cost of investment, but that the derivation of optimal harvesting and investment policies became very complicated in stochastic fishery models.
In limited access fisheries, new entrants are prohibited or restricted but existing permit holders can behave as though they are operating in an open access fishery. In this situation, a restrictive TAC in a limited access fishery could lead to some stock recovery, and existing participants will have incentives to invest in new capital equipment. Without further restrictions on investments, these types of fisheries tend to supply inputs at levels that result in overcapacity. In limited access systems in which permits are transferable, the over investment problem may be mitigated but not necessarily eliminated.
In fisheries where quite specific harvest rights exist, fishermen have incentives to use only the capacity required to take their allotted quotas or shares. If there is overcapacity, the fishery will tend over time to reduce that excess to an optimum level. Some overcapacity may remain for some time in the fishery after harvest rights-based arrangements were first introduced. However, such systems give fishermen incentives to reduce inputs, thus eliminating overcapacity in the long run.
Subject to the qualifications noted above, the sheer presence of open access and (to a lesser degree) limited access management systems may be considered as indicators of overcapacity in fisheries, whereas harvest rights-based management systems may be considered an indicator of no overcapacity in a fishery.[22]
The ratio between harvest levels and quotas is another management-related indicator of overcapacity, especially because most managed fisheries operate under harvest guidelines; usually a TAC.
Overcapacity may be thought to exist if harvest level exceeds the TAC on a regular basis. Under this indicator, it is assumed that the target, or optimal, level of capacity is that level that is necessary to harvest the TAC in a single species fishery during a fishing season.
It should be noted that this is not a perfect measure of overcapacity. For one thing, effective enforcement and monitoring of the harvest levels could close the fishery before the TAC is exceeded. For another, this indicator does not work well in multi-species fisheries. Nevertheless, under most circumstances, a harvest-to-TAC ratio that exceeds “one” on a regular basis indicates at least the potential for overcapacity to exist.
Another indicator of overcapacity is the “race for fish” in which fishermen harvest the TAC before the end of fishing season.
The total catch level divided by the days fished may be used as a qualitative indicator of overcapacity. If the number of days fished declines progressively for a number of years, that may be an indicator of overcapacity.
This indicator is not a perfect test of overcapacity for the same reasons as the harvest-to-TAC relationship. However, an increase over time of this ratio could indicate the potential for overcapacity in a fishery.
Controversies surrounding the setting of the TAC and the extent to which setting its sub-allocation or distribution among different user groups may also be an indicator of overcapacity in a fishery.
Typically, disputes occur between commercial fishermen using different gear types or residing in different areas, and/or between commercial and recreational fishermen. Evidence that the determination and sub-allocation of TACs are accompanied by a meaningful level of political controversy suggests that there may be a potential for the existence of overcapacity in that fishery. Obviously, this is an extremely rough indicator of overcapacity for the simple reason that it is difficult to evaluate objectively the seriousness and intensity of these differences.
Another qualitative indicator of overcapacity is the trend in unused permits, or latent permits. By defining latent permits to be permits issued to fishermen that have never been used to harvest fish, it follows that the ratio of active permits to total permits (active plus latent) may be used as an indicator of overcapacity.
A relatively large number of latent permits, or a low ratio of active to total permits, would indicate the potential for overcapacity in a fishery. Further, as this ratio declines, the likelihood that overcapacity exists in the fishery probably increases.
This is not a perfect measure of overcapacity since speculators who never intend to harvest fish may hold a permit in the hope of benefiting by selling or leasing the permit if they are made transferable. In addition, fishery managers may decide to purchase or cancel inactive permits. Nevertheless, a relatively low and declining ratio of active to total permits may under certain conditions indicate overcapacity in a fishery.
A decline over time in catch per unit of effort (CPUE) implies overfishing and overcapacity. However, the CPUE indicator of overcapacity must be used with care.
Fluctuating TACs under a constant-fishing-mortality management strategy could mask this effect. The CPUE could remain constant or improve even with overcapacity in the fishery as the TAC increases with the recovery of the stock. In addition, CPUE trends could remain constant or increase for schooling species even though overall stock abundance is declining.
In general, in fisheries where TACs and harvest levels are fairly constant, a declining trend in CPUE over time probably indicates overcapacity.
A number of quantitative methods have been developed in the economics literature that may be used to estimate various types of fishing capacity. Three general approaches to estimating technical capacity are:
The “peak-to-peak” method of Klein (1960) and the DEA model developed by Fare et al. (1989) based on Johansen (1968), are two approaches that have been used to estimate capacity utilization in fisheries.
SPF is an alternative method that has been used to estimate efficient (frontier) production in fisheries (Kirkley, Squires, and Strand, 1995) and may also be a useful method for developing a measure of capacity under certain circumstances.
Each method has strengths and weaknesses, and the choice of the appropriate model will vary depending on the nature of the fishery, the data available, and the intended use of the capacity measure
The peak-to-peak method is best suited when capacity related data are especially limited, for example when data are limited to catch and number of participants. The approach is called peak-to-peak because the periods of full utilization, called peaks, are used as the primary reference points for the capacity index.
In practice, a peak year is often identified on the basis of having a level of output per producing unit that is significantly higher than both the preceding and following years. Capacity output is compared to actual output in different time periods to give measures of capacity utilization after adjusting catch levels for technological change.
The peak-to-peak method requires data on landings and participants, such as vessel numbers, and some identification of a technological time trend. Minimum fleet sizes (number of vessels) that correspond to different levels of capacity can be calculated.
The peak-to-peak method is quite simple to apply even when sparse data are available. The method has been applied to fisheries and examples can be found in the literature; e.g., Kirkley and Squires (1999), Ballard and Roberts (1977) and Garcia and Newton (1995). However, peak-to-peak has a number of shortcomings that should be considered when evaluating the meaning of the capacity measure it provides.
In most cases, peak-to-peak can be expected to provide only a rough measure of capacity since the number of vessels or other measures of physical capital are only a loose proxy for the actual catching power of the fleet. The analysis ignores economic factors that impact what the fleet will actually catch. If only the total number of participants and catch are used in the model, differences in capacity across gear types or other sectoral disaggregations cannot be identified; thus the index may not account for changes in the composition of the fleet that may have significantly changed its overall capacity.
Determining the impacts of removing different groups of participants from a fishery will not be possible since the capacity of individual producing units is not identified.
Also, if significant changes in fishery regulations or other factors that impact capacity have occurred, this measure of capacity may not be a reliable predictor of current capacity.
Finally, the measure is based on observations over time where both the resource stock and the intensity of capital input utilization have varied.
DEA uses linear programming methods[24] to determine either
DEA models were originally designed to measure technical efficiency. Fare et al. (1989) proposed a variation on the standard output oriented model that is designed to measure capacity output and capacity utilization assuming unconstrained use of variable inputs. Thus, to be at the frontier of maximum production, firms must be efficiently producing the most output for a given level of fixed inputs. This primal approach was extended by Fare, Grosskopf, and Kirkley (2000). They developed a multioutput DEA measure based on a revenue or cost function framework that could be applied to a multi-species fisheries. Firms that are not on the frontier can be below it either because they are using inputs inefficiently or because they are using lower levels of the variable inputs relative to firms on the frontier.
DEA has several attributes that make it a useful tool for measuring capacity in fisheries. Capacity estimates can be calculated for multispecies fisheries if certain, fairly strong, assumptions are made about the nature of production.[25] DEA readily accommodates multiple outputs (e.g., species and market categories) and multiple types of inputs such as capital and labor. The analysis accepts virtually all data possibilities, ranging from the most limited (catch levels, number of trips, and vessel numbers) to the most complete (a full suite of cost and revenue data), where the more complete data improve the analysis.
The DEA model may also include constraints on outputs of particular species (e.g., bycatch or trip limits). Since DEA identifies the efficiency and capacity of individual firms, it can be used to identify operating units (individual vessels or vessel size classes) that can be decommissioned to meet various objectives.
Capacity estimates can also be made for different groups of firms (e.g., by region and vessel size class) and the number of operating units could be determined by adding the capacities of each operating unit until the total reaches a target. If data on input costs or output prices are available, DEA can be used to measure both technical and allocative efficiency of firms, i.e., the model will calculate how much costs could be reduced or revenues increased by efficiently producing the optimal product mix.[26]
As with the other capacity measurement methods, DEA has a number of potential shortcomings.
First, a quite significant problem with DEA is that it is typically a deterministic model. Random variations in measured output (which may have been caused by measurement error or simply by normal variation in catch rates) are interpreted as inefficiency and influence the position of the frontier. In effect, the model assumes that vessels should be able to duplicate the highest catch rates observed. Recent research in the economics literature has focused on methods to overcome this problem.
Second, capacity output is based on observed practice and the economic and environmental conditions at the time observations were made. If fishermen were not operating at capacity in the past it may not be possible to identify the true technical capacity, and changing conditions may have altered what the fishermen can produce currently.
Third, capacity output is based on observed practice and the economic and environmental conditions at the time observations were made. If fishermen were not operating at capacity in the past it may not be possible to identify the true technical capacity, and changing conditions may have altered what the fishermen can produce currently.
SPF analysis is an econometric approach that can be used to estimate the maximum potential output (i.e., catch) for the observed factors of production (Kirkley and Squires, 1998). The estimated frontier production function can be used to estimate the capacity of a vessel, firm, or individual by predicting output with their actual level of fixed inputs and a maximum level of variable inputs.
SPF can be used to calculate both technical and allocative efficiency if data on input and output prices are available.[28] Additional advantages of SPF relative to the other approaches are that it is designed to handle noisy data and it allows for the estimation of standard errors and confidence intervals.
While SPF has the same shortcomings as DEA to varying degrees, the usual problems and assumptions associated with parametric analysis are also present. The selection of a distribution for the inefficiency effects may affect the capacity measure. The SPF approach is only well developed for single-output technologies unless a cost-minimizing objective is assumed.
To accommodate multiple outputs in a multiple species fishery, SPF requires representing the production technology in terms of one output as a function of normalized outputs. The representation of jointness in production is limited if species are heterogeneous in price, catchability and costs of production. The data requirements include firm or vessel output and input quantities, but richer models can be estimated if prices are available.
While qualitative indicators have limitations, they can suggest the existence of overcapacity in a fishery. While no single qualitative indicator would be sufficient, a combination of indicators could be used to make a determination if overcapacity existed in a fishery. Qualitative indicators show if overcapacity exists at a point in time, but do not indicate the magnitude of the problem or the direction of change. In addition, the expertise of the analyst can influence the application of these indicators.
Even with limited data, quantitative capacity measurement techniques may be able to provide information on capacity output and the number of operating units. Where data permits the use of either the SPF or DEA methods, a much richer set of management guidance may be offered. Since both of these methods are based on vessel level information, managers may be able to identify measures for particular fleet components or may facilitate the design of capacity reduction programs.
Regardless, it is prudent to use bioeconomic analyses to determine the actual details of which management system should be used to achieve capacity reductions, how many vessels of which type need to be eliminated, or which regulations will work best for different fishery management approaches in fisheries characterized by large, medium, and small scales of operation or in artisanal fisheries.
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[19] This section is
reproduced from Ward et al. (2000). “Assessing Capacity and Excess capacity
in Federally Managed Fisheries, A Preliminary and Qualitative Report.”
National Marine Fisheries Service, Offices of Science and Technology and
Sustainable Fisheries, Silver Spring, Maryland, September, 131 pp. [20] Towards Sustainable Fisheries: Economic Aspects of the Management of Living Marine Resources (Paris: OECD, 1997). [21] The result that this optimal rate of exploitation may be greater than the MSY rate when a higher discount rate exists becomes ambiguous when the higher discount rate implies a higher required rate of return on capital. [22] At a point in time, excess capacity could exist in a harvest rights-based fishery. Excess capacity could exist to respond to random market or recruitment fluctuations. This level of excess capacity should not be of concern to fishery managers because it would be short run in duration and not like the persistent overcapacity in the long run. [23] This section is taken from Ward, John (1999). “Report of the National Task Force for Defining and Measuring Fishing Capacity.” Draft report, National Marine Fisheries Service, Office of Science and Technology, Silver Spring, Maryland, June. [24] Mathematical programming, which includes linear programming, is the optimization of an objective function given a series of constraints. [25] Since outputs and inputs are expanded in fixed proportions, the model assumes and imposes Leontief separability, but does not test for it. [26] Technical efficiency occurs when the maximum level of output is produced with the inputs (e.g., capital and labor) available to the firm. Allocative efficiency in input selection involves selecting that mix of inputs that produce a given quantity of output at minimum cost given the input prices that prevail. [27] This is taken from Kirkley and Squires (1998); and, Coelli, Tim, D.S. Prasada Rao, and George E. Battese (1998) An Introduction to Efficiency and Productivity Analysis. Kluwer Academic Publishers, Boston. [28] Technical efficiency occurs when the maximum level of output is produced with the inputs (e.g., capital and labor) available to the firm. Allocative efficiency in input selection involves selecting that mix of inputs that produce a given quantity of output at minimum cost given the input prices that prevail. |
