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136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175tors of production is not restricted”. 2 This concept ofcapacity conforms to that of full-input utilization (i.e.,maximum utilization of the variable inputs given thefixed factors of production) on a production function,with the qualification that capacity represents a sustainablemaximum level of output (Klein and Long,1973). The Johansen concept of capacity, however,was unbounded. The technological-economic measurewas initially offered by Färe (1984) and is a weakerversion of the Johansen measure because output orproduction is bounded by the fixed factors of production.In the context of fisheries, this weaker concept ofthe Johansen measure of capacity corresponds to themaximum catch a vessel can produce if inputs arefully utilized given the biomass, the fixed inputs, theage structure of the fish stock, and the present stateof technology. This concept of capacity output cannotequal the output level that can be realized only atprohibitively high cost of input usage, and hence, iseconomically unrealistic. The capacity output is measuredrelative to the observed best-practice frontier(i.e., based on observed points) and based on observedinput and output levels. It is, therefore, not an absolutetechnically derived number based on an engineering 3notion of maximum possible catch; instead, the observedinput and output levels reflect changes inducedby economic behavior of firms. That is, the observedbest-practice frontier is established by the existing fleetand implicitly reflects economic decisions made byvessel operators.The decision to maintain a given level of capacityor vessel size is a long-run decision based on expectationsabout future production possibilities (e.g.resource stock, environmental conditions, and regulation),and input and output prices. Capacity output isdetermined relative to a given point in time fixed, andhence, is a short-run concept. This is consistent withthe definition offered by Johansen (Prochaska, 1978).CU is also a short-run concept, since current output or2 Klein and Long (1973, p. 744) state that, “Full capacityshould be defined as an attainable level of output that can bereached under normal input conditions—without lengthening acceptedworking weeks, and allowing for usual vacations and fornormal maintenance.” In addition, Färe (1984) developed a formalproof of the existence for Johansen’s definition of capacity.3 Which means what is possible to produce if the vessel isworking at maximum physical load.N. Vestergaard et al. / Fisheries Research 1445 (2002) 1–12 3production can be adjusted given changes in input and 176output prices, but only subject to fixed inputs and the 177available technology. Over the long run, there is not a 178capacity problem in most industries, because the firm 179adjusts its capital stock and production level to the ap- 180propriate levels and all available inputs are utilized in 181terms of their most effective long-run equilibrium lev- 182els. However, in open-access fisheries, because of the 183“Tragedy of the Commons,” excess capacity is gen- 184erally expected in which the firms or vessels collec- 185tively, as the fleet, tend to harvest a level of catch that 186exceeds the sustainable long-term target level and the 187fish stocks tend to become overfished. 188In the case of fisheries, the concept of capacity 189needs to address several specific issues. 4 An addi- 190tional issue for fisheries, as compared to many other 191industries, is that the fishermen harvest from a fixed 192pool of resources where nature limits production 193and the individual fisher’s ability to control catches 194(Prochaska, 1978). Measuring capacity in a renewable 195resource industry is, therefore, more complicated than 196measuring capacity output in a more “conventional” 197industry because the measure must be conditional 198upon the resource stock. The production technology 199for a fishery is stock-flow, in which inputs are ap- 200plied to the resource stock to yield a flow of catch 201(output). Hence, if the capacity output is measured 202over a time period, the measure must reflect changes 203in the resource stock as well as changes in the capital 204stock. 205Resource abundance and availability, however, may 206vary because of unexpected or seasonal changes in en- 207vironmental conditions. If output or production levels 208vary seasonally or change because of extremely un- 209usual environmental changes, capacity measures need 210to incorporate and recognize these changes. 211Many fisheries throughout the world involve pro- 212duction of more than one output. Hence, multi-product 213or multi-species production is likely to be the norm 214rather than the exception (Clark, 1985), and any em- 215pirical method for estimating and assessing capacity 216must be able to account explicitly for multiple outputs. 217Another issue important for determining a method for 218assessing capacity in fisheries is the mobile nature of 219the vessel. Vessel operators may often switch from 2204 For an overview of capacity in fisheries see Kirkley and Squires(1999).UNCORRECTED PROOF

2212222232242252262272282292302312322332342352362372382392402412422432442452462472482492502512522532542552562572582592602612622632642654 N. Vestergaard et al. / Fisheries Research 1445 (2002) 1–12one fishery to another during a given period of time,or from one period to another.The ability to change fisheries raises complex issuesabout aggregating measures of capacity for differentfisheries. That is, what level of aggregation should beconsidered when assessing capacity output? The levelof aggregation determines the outcome of the analysis.A high level of aggregation including all fisherieswithin the year of the whole fleet shows the overalllevel of capacity and CU. However, the problem is thatthere may be fisheries with very high CU and fisherieswith low CU that can counterbalance so the combinedCU result is not alarming. The fisheries with high-lowCU are typically high value fisheries, and hence, themost important economically. If the fisheries are technologicallydistinct, they may be treated separately.In open-access fisheries, in which the access to eachsingle fishery is not excluded, a problem called latentcapacity might arise. This problem has its origin in thefact that the fishing effort can change allocation betweenthe fisheries during the season. A fishery witha high CU might in the next period have a low CUbecause of incoming vessels resulting in other fisherieshaving a high CU, all things equal. An assessmentof the excess capacity in this kind of fishery hasto take the regulation into account. A decommissioningscheme oriented towards reducing capacity in afishery with both high- and low-valued species, therefore,may subsequently only reduce the capacity of thelow-valued components, while not effectively reducingcapacity relative to the high-valued species.3. Empirical methodologyThe methodology used in this paper to empiricallyestimate and assess capacity is DEA. The DEA approachis a mathematical programming technique forwhich an optimal solution is determined given a setof constraints. The approach has been widely used tofind the technical efficiency of firms (Charnes et al.,1994). This approach can also be used to measure capacityand CU following Färe et al. (1989, 1994). Theapproach readily incorporates multiple output and inputtechnologies.Färe et al. (1989) demonstrated that an outputorientedmeasure of technical efficiency could beused to estimate the capacity output and optimumvariable factor usage. An output-oriented measure of 266technical efficiency determines the maximum possi- 267ble expansion of outputs (i.e., the frontier production) 268with no change in the fixed factors of production. 269The frontier or best-practice technology is a refer- 270ence technology or production frontier that depicts 271the most technically efficient combination of inputs 272and outputs. The production frontier is formed as 273a non-parametric, piece-wise, linear combination of 274observed best-practice activities. 275Under the Färe et al. (1989) framework, only the 276fixed inputs are bounded at their observed level, al- 277lowing the variable inputs to vary and be fully utilized. 278This is slightly different from the concept offered by 279Johansen, because it explicitly allows the fixed factors 280to restrict output. The approach provides a scalar mea- 281sure or efficiency score, θ1 ∗ , that indicates the percent- 282age by which the production of each output of each 283firm may be increased (i.e., the score measures the dis- 284tance between the observed output and the frontier). If 285the solution is 1.25, the capacity output is 1.25 times 286the observed output. The CU is then simply 1/1.25 = 2870.8. The DEA approach also provides the optimal uti- 288lization rate of variable inputs, λ ∗ jn, or the utilization of 289the variable inputs required to produce at full capacity 290output. 291Estimation of capacity output may be obtained by 292solving a mathematical or linear programming prob- 293lem. Initially, designate the vector of outputs as u and 294the vector of inputs by x. There are m outputs, n in- 295puts, and j firms or observations. Capacity output and 296the optimum or full input utilization values require 297solving the following equations: 298Maxθ,z,λ θ 1subject to 300θ 1 u jm ≤J∑z j u jm ,j=1J∑z j x jn ≤ x jn , n ∈ α,j=1J∑z j x jn = λ jn x jn ,j=1m = 1, 2,... ,M,n ∈ˆα,UNCORRECTED PROOF299301302303

304305z j ≥ 0,λ jn ≥ 0,j = 1, 2,...J,n ∈ˆα,306 where z j is the intensity variable for the jth observa-307 tion; θ 1 the technical efficiency score or the proportion308 by which output may be expanded when production309 is at full capacity; and λ ∗ jnthe ratio of optimum use of310 input x jn to observed input use of x jn .311 Capacity output is then determined by multiplying312 θ1 ∗ by actual production. CU, based on observed out-313 put, may be calculated as follows:314CU(observed) =uθ1 ∗u = 1315 This measure provides a ray measure of capacity out-316 put and CU in which the multiple outputs are expanded317 in fixed proportions relative to their observed values318 (Segerson and Squires, 1990). The ray measure con-319 verts the multiple-output problem to a single-product320 problem by keeping all outputs in fixed proportions.321 This ray measure corresponds to a Farrell (1957) mea-322 sure of output-oriented technical efficiency due to the323 radial expansion of outputs. 5324 Färe et al. (1994) noted that this ray CU measure325 may be biased downward, because the numerator in326 the CU measure, the observed outputs, may not be327 produced in a technically efficient manner. A techni-328 cally efficient measure of outputs may be obtained by329 solving a problem where both the variable and fixed330 inputs are constrained to their current levels. The331 outcome (which can be called θ2 ∗ ) shows the amount332 by which production can be increased if production333 is technically efficient. The technically efficient com-334 bination of outputs, conditional on observed input335 levels, may be determined by solving another linear336 programming problem, which is similar to the capacity337 problem:338339340Maxθ,z θ 2subject toθ 2 u jm ≤J∑z j u jm ,j=1θ ∗ 1m = 1, 2,... ,M,5 A non-radial expansion of outputs would correspond toKoopmans (1951) notion of technical efficiency.N. Vestergaard et al. / Fisheries Research 1445 (2002) 1–12 5J∑z j x jn ≤ x jn ,j=1n = 1, 2,... ,N,z j ≥ 0, j = 1, 2,... ,J. 342The technically efficient output vector is calculated by 343multiplying θ2 ∗ by observed production for each out- 344put. The technically efficient or “unbiased” ray mea- 345sure of CU is then the following: 346CU(efficient) = θ 2 ∗uθ1 ∗u = θ 2∗UNCORRECTED PROOFθ ∗ 1The output-oriented measure can be used in several 348ways. For each vessel, the capacity output is deter- 349mined. Summing over vessels by given criteria (e.g. 350regional or gear-type) the necessary number of ves- 351sels can be found where the total reach some spec- 352ified target (e.g. total allowable catch, TAC). In the 353multi-species case, this can be done for each species. 354We stress, however, that summing over each vessel 355presents a lower bound for the industry or fleet level 356of capacity (i.e., the industry or fleet level of capacity 357is greater than or equal to the sum of the vessel levels 358of capacity). 359The variable input utilization outcome, λ ∗ jn, mea- 360sures the ratio of optimal use of variable input to 361observed use; the optimal variable input usage is the 362variable input level which gives full technical effi- 363ciency at the full capacity output level. If the ratio of 364the optimal variable input level to the observed vari- 365able input level exceeds (falls short of) 1.0 in value, 366there is a shortage (surplus) of the ith variable input 367currently employed and the firm should expand (con- 368tract) use of that input. 3693.1. Second-stage analysis 370Several external factors not included in the analy- 371sis might influence the CU scores. Coelli et al. (1998) 372suggest a second-stage analysis, where the scores ob- 373tained by the DEA analysis in the first stage are re- 374gressed on variables that are expected to influence the 375scores. Using the results from the second-stage re- 376gression, the efficiency scores can be adjusted. In the 377case of a fishery, the variables could be season, size 378of vessel, home port, etc. In our case of the Danish 379Gill-net fleet, where only 1 year is included and also 380341347

3813823833843853863873883893903913923933943953963973983994004014024034044054064074084094104114124134144154164174184194204214224236 N. Vestergaard et al. / Fisheries Research 1445 (2002) 1–12the vessels are relatively equal in size, only the portis included in the second-stage analysis. Capacity andCU might, however, vary widely by port, which inturn reflects differences in institutional practices, resourceavailability and abundance, and market conditions(Squires and Kirkley, 1996). This variation incapacity and CU can be evaluated using a Tobit analysis.Tobit analysis accounts for censoring of the capacitymeasure at zero and of CU measure at both zeroand 1. The capacity and CU measures regressed uponport dummy variables, without an intercept, gives aone-way analysis of variance with 5−1 = 4 degrees offreedom when the null hypothesis of equal coefficientsbetween all port dummy variables is applied. Themodel we consider for the Tobit regression for CU is asfollows:CU =5∑α i D ii=1where i indexes individual ports and 5 denotes the totalnumber of ports. The null hypothesis of equal CU forall ports is H 0 : α I =···=α i =···=α 5 . With a Tobitregression, the appropriate test of the null hypothesisis the Wald test with a χ 2 distribution with 4 degreesof freedom. If the null hypothesis is rejected, then asequence of other tests is needed where the ports arecompared in pairs. With five different ports the numberof pairs will be 10.3.2. Partial capacity measuresSeveral measures permitting non-radial or non-proportionalchanges in outputs have been developed,e.g. Russell (1985) and Zieschang (1984), but theyhave not, so far, been used to estimate capacity. PartialCU measures developed by Segerson and Squires(1990) in the parametric case are here applied in thenon-parametric framework. A partial CU approachvaries only a single output. All other outputs are heldfixed at their actual levels. The partial measure canbe seen as the first stage in the asymmetric Färe efficiencymeasure (Färe et al., 1983) adjusted to the caseof capacity. A partial CU measure is defined as theobserved output level divided by the capacity level ofthe output of concern given the actual output levelsof all other products and fixed factor. The numericalvalue of this CU measure will vary across productsso it is not unique for a given firm. The partial CU 424measures might indicate that the degree of over cap- 425italization in the fishery can vary considerably across 426products (Segerson and Squires, 1990). There may be 427more excess capacity or higher rates of CU in the 428fishery of one species than another. The stocks in the 429North Sea are managed on a species-by-species ba- 430sis. For species that are closer to full partial CU (i.e., 431close to 1) or have lower levels of excess capacity, the 432future demand for that species is likely to be of more 433importance in determining the future expansionary or 434contractionary forces in the fishery than is the demand 435for the species with lower CU or higher excess capac- 436ity. The partial CU measure is estimated for cod and 437plaice, since these are the most important species in 438the fishery. The partial CU for a given firm and species 439is as follows: 440CU(partial) = 1UNCORRECTED PROOFθ ∗ 3where θ3 ∗ is the score obtained by solving the following 442DEA-problem: 443Maxθ,z θ 3subject to 445θ 3 u j1 ≤u jm =J∑z j u j1 ,j=1J∑z j u jm ,j=1J∑z j x jn ≤ x jn , n ∈ α,j=1m = 2,... ,M,z j ≥ 0, j = 1, 2,... ,J, 449where species 1 is the species for which the partial 450measure is found. 4514. The Gill-net fleet and fishery—background 452and data 453The Danish fisheries are normally divided into hu- 454man consumption and industrial fisheries. The human 455441444446447448

456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501consumption fisheries, which comprise many fisheries,are defined as those where no species are landed forindustrial purpose. The industrial fisheries are fisherieswhere some of the species are landed for industrialpurpose (processing of meal and oil), meaning thatspecies caught in these fisheries can also be landed forhuman consumption. The human consumption fisheriesare, in general, multi-species fisheries (i.e., morethan one species is caught in one setting of the gearor in one trip). In several of the fisheries, differentgear types (e.g. trawl, gill-netters, Danish Seiners) areused.A large part of the Danish human consumption fleetis multipurpose, which can participate in several fisheriesduring the year, including industrial fisheries.Relative prices between species and inputs, regulatoryconstraints, production costs, biological conditions,and change in seasons are factors that determinethe choice of fishery. The gill-netters participatein the mixed human consumption fishery harvestingroundfish and flatfish in the North Sea and Skagerrak.The catch composition varies over the year and betweenfishing grounds. The gears involved are trawl,gill-net, and Danish Seine. The target species alsovaries over the year and according to the gear typeused; however, cod, haddock, saithe, plaice, and soleare the main species, with cod being the most important.Nearly all the gill-netters participate in the fisheryin the North Sea and about half of them also participatein the Skagerrak fishery. Only a few gill-netterstake part in the fishery in Kattegat and the BalticSea.4.1. The regulation and the regulatory processThe EU council annually determines the TAC forquota species in the exclusive economic zones (EEZs)of the EU member states. A fixed scale (called theprinciple of relative stability) divides the TACs amongthe member states into national quotas. The memberstates then decide the distribution of their respectivenational quota among fishermen. Since there isno banking of national quotas, the member states designthe regulation to ensure full utilization of theirquotas.The Danish regulation of the fishery for cod, haddock,saithe, and sole is based on the Danish share ofthe TAC’s divided into quarterly total quotas for theN. Vestergaard et al. / Fisheries Research 1445 (2002) 1–12 7whole fishery, which in turn are divided into rations 502for a given period, 6 in some cases depending on the 503size of vessels. The number of participating vessels, 504however, is not regulated for these fisheries. During 505a given time period, therefore, rations can decline or 506the ration period can be shortened. If the Danish quota 507for a species is caught before the end of the year, the 508fishery is simply closed. 7 509In the beginning of the year, the Danish Ministry 510of Fisheries sets both the size of the quarterly quotas 511and rations based on the experience from former years 512and based on the size of the total Danish quota. Over 513the year, the Ministry closely monitors the fishery by 514recording all catches, and if necessary the regulation 515is changed, so that the quota is not exceeded. The 516purpose of the regulation is, in general, to achieve a 517better distribution of the fisheries over the year and a 518better utilization of the Danish quotas compared to a 519free fishery of the quotas. The regulatory instruments, 520quarterly quotas and rations, are used to stretch out 521the fishery over the whole year. In summary, it can be 522concluded that the cod and saithe fishery in all four 523areas has been constrained by the limited TAC. Sole 524has been constrained in the North Sea. The TAC for 525plaice in the Skagerrak was exploited over 90%, but 526there was no regulation. 527Access to the Danish fisheries is restricted. To 528achieve an entry right, two authorizations are needed, 529one, recognition as a commercial fisherman and two, 530a vessel license, where recognition is a necessary 531condition for the vessel license. A vessel license can 532be obtained only if corresponding capacity leaves the 533fishery. 534Only with permission from the Ministry is an ex- 535tension of the existing number of vessels allowed. For 536the purpose of management, capacity output has been 537assumed to be related to a number of physical inputs 538used by the vessel. The inputs are GRT, length, width, 539depth, hold capacity, and engine power. However, the 540relationship between these physical measures of ca- 541pacity and capacity output of vessels is not straightfor- 542ward, and capacity can be increased through changing 543the combination of these inputs, or through changes 5446 It is possible in a number of cases for the fishermen to transferration from one period to the next.7 Sometimes a fishery is closed if the quarterly quota is caught.The fishery opens again at the start of the next quarter.UNCORRECTED PROOF

5455465475485495505515525535545555565575585595605615625635645655665675685695705715725735745755765775785795805818 N. Vestergaard et al. / Fisheries Research 1445 (2002) 1–12in other inputs not included in the management definitionof physical capacity. 8The primary purpose of the regulations is to harmonizethe total capacity of the fleet to the resourcestock conditions. Regulation of the total existing capacityis based on control of the physical inputs relatingto the capacity of the individual vessels. Thissystem can regulate the individual vessels, but cannotcontrol the total fishing effort, because the access toeach fishery, in general, is non-regulated. The mosteconomically attractive fisheries will attract effort andeach fisherman will try to use his ration first, becauseonce the quarterly quota is exhausted, the fishery isclosed. The conclusion is that the overall limited accessto the Danish fishery and limited possibilities toextend the existing capacity will not reduce the overcapacity in the most profitable fisheries, although theeffort in the least attractive fisheries may be reduced.From the point of efficiency, the result is that too mucheffort is attracted into certain fisheries. Therefore, thesituation emerges where the overall capacity problemis solved on the sector level but not in all fisheries.4.2. DataData necessary for analyzing capacity output andCU were available only for gill-net vessels larger than20 GRT. As a consequence, the analysis was limitedto 69 vessels (i.e., only those vessels larger than 20GRT). Available data pertained to trip-level data andvessel operations during 1993, and consisted of thefollowing information: (1) the volume and value ofthe landed catch of each of the following species: cod,haddock, saithe, plaice, sole and other species (addedtogether); (2) the month of landing; and (3) the fishingarea.The trip information allows for a division of theannual fishery activity based on month and area. Thegill-netters participate only in the mixed human con-8 Equating the capital stock (physical inputs such as vesselsize and engine power) to capacity implicitly assumes a linearrelationship between the capital stock and capacity. In general,these coincide only if there is but one fixed input or stock ofcapital, all variable inputs are in fixed proportions to the fixedinput, production is characterized by constant returns to scale (a1% increase in all inputs, both variable and fixed, increases catchby one percent) (Berndt and Fuss, 1989). In fisheries, there is anadded condition, that of a constant fish stock(s).sumption fishery in the North Sea and Skagerrak, 582which can probably be divided into several different 583fisheries; given the available data, however, it is not 584possible to precisely further divide the fishery into 585other fisheries. 586There is no information available about the length 587of the trips, 9 and hence, no information on the variable 588inputs per trip was available. It was decided to aggre- 589gate the trip-level information to annual activity per 590vessel. For each vessel, therefore, the total landings 591(output) and the number of trips (representing variable 592input), together with information on the KW and GRT 593(representing fixed factors or the capital stock), were 594used in the analysis. 10 5955. Results and discussions 596Of the 69 Danish gill-net vessels, 36 (38) vessels 597had a (ray) CU based on technically efficient produc- 598tion (CU based on observed production) less than 1 599(Fig. 1). Nearly 2/3 (43 vessels out of 69) of the fleet 600had a CU higher than 0.9, while 10 vessels had a CU 601less than 0.8. Using the CU measure based on ob- 602served output shows that 40 vessels had a CU higher 603than 0.9 and 20 vessels had a CU less than 0.8. The 604average CU was 0.92 (0.88) with a standard deviation 605of 0.11 (0.16) (Table 1). These CU values illustrate 606that a minor, but significant part, of the gill-net fleet 607had relatively low levels of CU. These results are in 608accordance with those obtained in Vestergaard (1998), 609where the gill-net was shown to be more efficient than 610other types of gear in the Danish human consumption 611fishery. 612The second-stage analysis sought to determine 613whether or not the homeport might explain some of 614the variance in CU-scores. Of the 69 vessels, 48 were 615from the port of Hvide Sande. Of these 48 vessels, 61630 had a CU less than 1. The observation that 30 out 617of 48 vessels from the port of Hvide Sande had a CU 618score less than 1 suggests that this fleet may have 619more excess capacity than the rest of the fleet. The 6209 Since the fisheries in question are human consumption fisheries,where the trip length varies between 1 and 5 days, it is not assumedthat the use of trips instead of number of days will give biasedresults when looking at similar vessels.10 Because of the lack of better data on the variable inputs, therelatively homogenous vessel group of gill-netters was selected.UNCORRECTED PROOF

621622623624625626627628629630631632633634635636637638639N. Vestergaard et al. / Fisheries Research 1445 (2002) 1–12 9Fig. 1. Distribution of capacity utilization scores.Table 1Average CU, variable input utilization, number of vessels with CU equal or different to 1 aCU(observed) CU(efficient) VIU CU cod CU PlaiceAverage (standard deviation) 0.88 (0.16) 0.92 (0.11) 1.27 (0.39) 0.93 (0.15) 0.85 (0.28)NR with CU = 1 31 33 57 50NR with CU < 1 38 36 12 19NR with VIU = 1 31NR with VIU < 1 2NR with VIU > 1 36a CU: ray CU; VIU: variable input utilization; Cu cod and Cu plaice : partial CU of cod and plaice, respectively.result of the test of the null hypothesis that CU wasequal for all ports could not be rejected (a χ 2 value of8.19 with a probability of 0.085), at the significancelevel of 5%.The distribution of the variable input utilizationrates had the same pattern as the CU rates (Fig. 2).About half of the vessels could increase the use ofvariable inputs, although doing so would increase outputby only one-half the difference between observedoutput and capacity output (Table 2). On average, thevariable input utilization rate is 1.27 (standard deviationis 0.16), indicating that vessels should increasethe number of trips compared to the optimal numberof trips by 27% (Table 1) if vessel operators desire tooperate at full capacity output.Capacity output and technically efficient outputwere calculated using the estimated scores obtainedfrom the DEA problems. The capacity and technicallyefficient output levels were calculated for eachspecies and aggregated to obtain an estimate of ex- 640cess capacity for each species (Table 2). For example, 641the total fleet production of cod could at full capacity 642have been 651,730 kg higher, which corresponds to an 643Fig. 2. Distribution of variable input utilization scores (VIU).UNCORRECTED PROOF

64464564664764864965065165265365465565665765865910 N. Vestergaard et al. / Fisheries Research 1445 (2002) 1–12Table 2Fleet capacity and CU (gill-netters, Kilo)Cod Haddock Saithe Plaice Sole OtherCatch 4,369,095 123,121 412,577 1,566,142 268,426 1,227,071Technical efficient output 4,617,355 125,389 426,275 1,644,746 285,472 1,279,219Capacity output 5,020,825 132,631 451,611 1,764,363 314,116 1,375,729Excess capacity 651,730 9,510 39,033 198,221 45,690 148,658Excess capacity (%) 14.9 7.7 9.5 12.7 17.0 12.1Ray CU(observed) 0.87 0.93 0.91 0.89 0.85 0.89Ray CU(efficient) 0.92 0.95 0.94 0.93 0.91 0.93Partial capacity output 4,777,448 1,883,159CU(cod), CU(plaice) 0.91 0.83excess capacity for cod of 15.9%. In total, the excesscapacity for each species shows fundamentally thesame results and varies within the same range as thoseon the vessel basis with CUs around 0.85–0.95. Codand sole have the highest excess capacity, which isin accordance with how the regulation proceeded thisyear. Surprisingly, saithe has a lower excess capacitythan plaice, which could indicate that plaice is a moreimportant species for the gill-net fleet than saithe.Haddock and saithe have the lowest excess capacity.The distribution of the partial CU measures for codand plaice shows that a very high share of the vesselsdid not have the ability to increase output (Fig. 3).However, the average partial CU for cod and plaicewas significantly lower than 1. The partial CU andthe CU (observed) (or CU(efficient)) measure for codFig. 3. Distribution of partial capacity utilization scores for codand plaice.were not very different on an aggregate basis (Table 2), 660which indicated that cod was one of the species that 661determined the capacity. For plaice, the situation was 662slightly different. The partial CU for plaice was less 663than both the CU (observed) and CU(efficient), which 664showed that the potential output of plaice was higher 665than both the actual and capacity output. This result 666suggests that the vessels had excess capacity in its 667production of plaice. 11 668Analysis and results were applied to only a single 669year—1993. The measures of capacity, therefore, were 670conditional on the regulatory and resource conditions 671that prevailed in 1993. Changes in these conditions 672might alter the results, which our analysis would not 673depict. As a consequence, the results presented in this 674paper might not be very indicative of capacity output 675levels and CU under different regulatory and resource 676conditions. An additional shortcoming of the approach 677used in this paper to estimate capacity output is the 678deterministic nature of DEA. 12 That is, DEA assumes 679all deviations from the frontier are caused by ineffi- 680cient operations, which in fact, some deviations may 681be induced by events beyond the control of the ves- 682sel operator. The use of annual data, however, likely 68311 One reviewer notes that cod and plaice might be harvestedusing different fishing techniques. Therefore, if plaice is bycatchin cod nets this can explain the lower partial CU. However, thedata do not allow for a division into cod and plaice netters.12 A stochastic approach, the stochastic production frontier, canbe used. However, there are also shortcomings with this approach.It cannot handle the case of multiple output, unless very complexstochastic multiple output distance functions are used, and so farit has not been used to estimate capacity. The stochastic productionfrontier, with out without a multiple output distance functionspecification, also cannot handle the case of zero-valued outputs.UNCORRECTED PROOF

684685686687688689690691692693694695696697698699700701702703704705706707708709710711712713714715716717718719720721722723reduces the possibility of attributing deviations fromthe frontier to inefficiency.6. ConclusionsThe Danish gill-net fleet exhibits moderate symptomsof over capacity. For the most important species,cod, excess capacity is 14.9% and varies between 7.7and 17.0% for the other species. There is some potentialfor reducing fleet capacity. On average, the overallCU is 0.88, and when measured with technically efficientproduction, overall CU is 0.92, with nearly halfof the vessels displaying a CU less than 1. CU doesnot systematically vary by port.The non-radial measure, the partial CU measure,indicates how much the production of one output canbe increased keeping the other outputs (along with thefixed factors and resource stock) fixed. For cod, thepartial CU measure is relatively high, showing comparativelylittle excess production of cod. 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