IIFET 2006 Portsmouth Proceedings
COMPARING CAPACITY UTILISATION, ALLOCATIVE EFFICIENCY AND FINANCIAL
PERFORMANCE IN THE TRAWL FISHERIES OF BRITTANY (1994-2003)
Pascal LE FLOC'H, CEDEM, plefloch@univ-brest.fr
Simon MARDLE, CEMARE, simon.mardle@port.ac.uk
ABSTRACT
Overcapacity situations appear regularly in the activity of marine natural resource exploitation. The
measure of capacity utilisation and allocative efficiency for fishing vessels is an approach that can
determine the details of that overcapacity. On the one hand, DEA (Data Envelopment Analysis)
methodology can be used in the case of multi-production to produce individual measures of economic
performances, defined through the concept of efficiency. On the other hand, the assessment of financial
performance requires the availability of individual data on cost and earnings for the same set of fishing
firms. The former method is based on outputs (landings by species) and inputs (fixed and variable) and
provides a relative measure for firms (or Decision-making units) being compared, hence at least one firm
will lie on the frontier in a DEA analysis. In this context, capacity utilisation, technical efficiency and
economic (or revenue) efficiency are main results derived from a linear programming model. Allocative
efficiency can be derived from economic and technical efficiency as a final score. The latter approach
gives a measure of financial performance from individual bookkeeping data. Gross revenue and gross
surplus can then be used to compare financial performance of trawlers with their economic performance
computed from DEA model. Both methods are used and compared for the same set of vessels, namely the
commercial trawl fisheries of the French region of Brittany.
Keywords: Trawl fisheries, Capacity utilization, allocation efficiency, Data envelopment analysis
INTRODUCTION
Over-capacity is a phenomena that exists in many fisheries around the world. A great deal has been
written about how this occurs in open-access and more recently in ‘regulated’ open-access fisheries [1]. In
order to address this issue, capacity and capacity utilization must be measured. The preferred approach, as
suggested by the Food and Agriculture Organisation of the United Nations [2], is data envelopment
analysis (DEA). DEA has been used considerably in recent years in fisheries economics for the
measurement of vessel technical efficiency and capacity utilisation [3] and [4].
For the most part, in applications to fisheries, this has been implemented from a physical capacity point of
view. That is, capacity utilization is determined by the relationship between physical inputs (such as
engine power, vessel length and days at sea) and the production of a set of outputs (i.e. species caught).
However, in reality, even amongst vessels with similar physical attributes, the economic performance of
those vessels can differ dramatically.
In the fisheries literature, there are recent studies that have used DEA models for the measurement of
capacity utilization with an added economic dimension. For example, according to the revenue-based
approach defined by [5], [6] measure the capacity utilization of Danish North Sea trawlers. In another
example, [7] implement a ray economic capacity model [8] using profit as the key measure in order to
consider the capacity utilization of a Scottish trawl fleet. In the measurement of capacity utilization and
technical and economic efficiency of vessels, it follows [5] that a measure of allocative efficiency can be
derived from the measures of technical efficiency and economic efficiency. Other than [6] there are few
examples of such an application in marine fisheries. However, [9] also consider technical, economic and
allocative efficiencies for Chinese fish farms.
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IIFET 2006 Portsmouth Proceedings
The approach used in this paper to measure capacity incorporates an economic measure of inputs (i.e. fuel
costs) and a revenue based measure to compare performance of vessels. Here, in addition to capacity
utilization, we also consider technical, allocative and economic efficiency, and we use individual vessel
accounts in order to compare economic performance obtained from DEA model to actual financial
performance of the vessels. It is rarely possible in fisheries to have access to such data, and as such
provides for an innovative analysis. In this study, data for French trawlers operating in Southern Brittany
is used.
METHODS
Capacity utilisation
The origins for the current measurement of efficiency of production is typically regarded to be the work
of Farrell [10]. Since, several techniques for measurement have been developed. One such technique is
that of data envelopment analysis (DEA) which can be used to estimate technical, allocative and
economic efficiencies [11] and [12]. DEA is deterministic and as a result does not require prespecification of the frontier technology. It provides a relative measure for those firms i being compared,
hence at least one firm will lie on the frontier in a DEA analysis.
The technical efficiency (TE) measure can by obtained by solving the following DEA modelii:
Max  i
subject to
 i y im    j y jm
m
j

j
x jn  xin
n
(Eq. 1)
j
j  0
where  i is a scalar outcome denoting how much the production or outputs, ym, of each firm, i, can
increase by using inputs, xn, (both fixed and variable) in a technically efficient configuration. In this case,
both variable and fixed inputs are constrained to their current level. In this case,  i represents the extent
to which output can increase through using all inputs efficiently, and is therefore output-based. The
*
technically efficient level of output ( yTE
) is defined as  i multiplied by observed output (y). As defined,
this model represents a constant returns to scale (CRS) assumption. In order to model variable returns to
  1 or
scale (VRS) or non-increasing returns to scale (NIRS), the constraints
 1
j j
j j

respectively are required. The level of technical efficiency is estimated as:
TEi  1
i .
(Eq. 2)
Following [13], a measure of capacity output can be found using:
Max  i'
subject to
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
IIFET 2006 Portsmouth Proceedings
 i' y im    j y jm
m
j

j
x jn  xin
n  
(Eq. 3)
j
j  0
where  i' is a scalar denoting the multiplier that describes by how much the output of firm i can be
expanded. In the estimation of capacity, only fixed factors are considered where inputs are separated into
fixed factors (i.e. set  ) and variable factors (i.e. set ̂ ). Capacity utilisation (CU) for firm i is defined
as:
CU i  1
 i'
.
(Eq. 4)
An unbiased estimate of capacity utilisation (CU*) for firm i is estimated by removing the effects of
technical efficiency from the capacity utilisation measure (i.e. equation 7 divided by equation 2), and is
achieved by the following equation:
1
CU
i
i
CU i* 

1
TEi
 i'

 i'
i
(Eq. 5)
Allocative efficiency
An economic efficiency (EE) measure can be obtained by solving the following revenue-maximising
DEA model [5]:
m
Max  Pik yik*
k 1
subject to
*
y im
   j y jm  0
m
j
xin    j x jn  0
n
(Eq. 6)
j
j  0
where y ik* is the revenue maximising or economically efficient production of the kth output of the ith
firm and Pik is the observed price of that output. The economic efficiency index is then calculated as the
ratio between actual revenue and potential revenue using:
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IIFET 2006 Portsmouth Proceedings
m
EE i 
P
y ki
P
*
ki
k 1
m
k 1
ki
ki
(Eq. 7)
y
The output-based measure of allocative efficiency for the ith firm can be obtained using [10] and [13]:
AE i 
EE i
(Eq. 8)
TEi
Main efficiencies measures are represented in figure 1. Technical efficiency (TE) is given by the distance
0A/0B. Only pure technical efficiency is calculated as the restriction,
  1 , is included in the
j j

analysis. Economic efficiency (EE) is depicted by 0A/0D and refers to a revenue maximisation principle.
Finally, allocative efficiency (AE) is illustrated through 0B/0D, which is an optimal combination of fish
landings taking into account theirs ex-vessel prices. This description corresponds to an output orientation
model (a given level of inputs).
Y2
B
Y*2
0
D
A
C
Y*1
Y1
Figure 1. Output orientation
Financial performance
In this paper, we retain the distinction between economic performance and financial performance [14].
Here, economic performance is related to the concept of efficiency, which can be measured in term of
technical efficiency and economic efficiency, as defined above to measure allocative efficiency either in
the input direction (cost minimisation) or in the output direction (profit maximisation). Financial
performance is based on the concept of revenue or income and is described through indicators as gross
revenue and gross surplus.
The analysis of financial performance can be undertaken from economic information of fishing boats,
defined as individual firms, collected either through skipper-owner’s surveys or from bookkeeping. The
commercial fishing fleet of the French region of Brittany is well informed by the Regional Economic
Observatory of Fisheries which is a NGO created by a professional fishermen organisation. This
Observatory collects bookkeeping and landings data by individual units. Accounting database gives
information on costs and earnings. Variable costs are collected for fuel, crew share and gears. Fixed costs
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IIFET 2006 Portsmouth Proceedings
are represented through maintenance and repairs. The item “other costs” associates other variables and
fixed expenses, that are usually less sensitive with respect to variations in landings (ex-vessel prices,
volume, abundance) and costs.
Table I: Costs and Earnings taken into account in the different parameters
Parameter
Costs/Income included
Income
Value of landings = fish
Other = subsidies, compensations, hire of vessel, rescue, etc.
Fuel cost
Fuel
Lubricant
Crew share
Crew share
Crew premium, owner premium
Social insurance
Gears
Fishing gear expenses
Maintenance
Maintenance and repair for the vessel
Others costs
Landings costs, Port taxes, Ice, food, Leasing, Insurance, Miscelleaneous costs
Source: Thébaud et al., (2005) [15]
The collection of data to measure economic and financial performance of fishing fleets in the European
Union is now ruled by the Council Regulation No 1543/2000 and the Commission Regulation No
1639/2001. According to Member States, economic information is provided by bookkeeping or field
survey methods. A comparison of economic and financial indicators estimated from these two collection
methods was made in the French case [16] and [17].
DATA
A comparative approach between financial and economic performances makes sense if individual
information is delivered for a constant sample of units. Through the Regional Economic Observatory,
landings and bookkeeping data are available from 1994 to 2003 by individual units. Hence, a constant
sample of fishing vessels was constituted. These fishing boats belong to three segments of trawlers
located in South Brittany. As constant samples are required to study economic and financial performance
on several years, size of samples is consequently low for each segment (13 units for 12-16 meters, 5 units
for 16-20 meters, 11 units for 20-25 meters).
Table II: Technical parameters (mean value) for constant samples
Age
Length
Tonnage
Engine power
in 2003
(meter)
(GRT)
(kw)
12-16 m
23.3
14.8
31.2
214.8
16-20 m
23.4
18.0
38.6
305.4
20-25 m
16.7
21.4
78.8
410.9
From Observatoire Economique Régional des Pêches de Bretagne
In a short-term analysis between 1999-2003, [18] considered the definition of fleets and fisheries for
South Brittany trawlers. The above fleet categories were accepted. As a further part of the analysis, it was
shown that different vessel strategies are used by different classes of vesslels. For example, large South
Brittany otter trawlers were indicated to have six different strategies based on landing profiles.
The smallest group is composed of vessels of 12-16 meters exploiting resources mainly in inshore
fisheries (VIIIa area). The second segment characterises trawlers of 16 to 20 meters. Most of the time,
vessels belonging to this segment produce fish in offshore fisheries (WIIIa, VIIh areas), but a few of them
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IIFET 2006 Portsmouth Proceedings
spend fishing time in the same grounds as the first segment units. The biggest trawlers compose the third
segment, with a length of 20-25 meters. These largest fishing units produce fish essentially in offshore
fisheries (VIIh-g areas).
Figure 2. Fisheries
In terms of average landings, different trends can be observed for the three fleets. Average annual
landings per vessel vary in quantities between 46 tons and 65 tons for the [12-16m[ sample, between 101
and 139 tons for the [16-20m[ sample, and between 192 and 250 tons for the largest trawlers. Annual
gross revenues are comprised between 242 and 279 k€ (in constant euro 2003) for the smallest units,
between 389 and 436 k€ for the intermediate segment, and between 627 and 725 k€ for the [20-25m[
sample. Average landings in value have known strong overall increases from 1994 to 2003 for the [2025m[, excepted in 2000. Conversely, trend was declining from 1997 to 2002 for the [12-16m[ and [1620m[ segments, in value and volume for the former and mainly in volume for the latter.
The set of target species varies considerably according to the segment fleet observed. The smallest units,
so called 12-16 meters, exploit simultaneously and mainly five stocks (nephrops, anglerfish, megrim,
hake and sole). Cod and whiting stocks have to be added to the above list in the case of the intermediate
segment, 16-20 meters. The biggest trawlers produce a larger panel of fish in offshore fisheries, including
those cited previously. Eventually, three species have been selected as outputs in DEA analysis
(nephrops, anglerfish, megrim) because they are considered as the main valuable products for all three
segments. Others species, targeted fishes and by-products, are gathered in a fourth category. Landing
values have been inflated to 2003 values using a Fisher price index.
6
Quantity (tons)
Value (k€2003)
Quantity (tons)
12-16 m
Average landings per vessel (kE2003)
360
2003
370
0
2002
380
20
1994
390
40
2003
2002
2001
2000
220
1999
0
1998
230
1997
10
1996
240
1995
20
400
60
2001
250
2000
30
410
80
1999
260
420
100
1998
40
430
120
1997
270
440
140
1996
50
160
1995
280
Average landings per vessel (tons)
290
60
Average landings per vessel (kE2003)
70
1994
Average landings per vessel (tons)
IIFET 2006 Portsmouth Proceedings
Value (k€2003)
16-20 m
720
250
700
200
680
660
150
640
100
620
600
50
580
2003
2002
2001
2000
1999
1998
1997
1996
560
1995
0
Average landings per vessel (kE2003)
740
1994
Average landings per vessel (tons)
300
Value (k€2003)
Quantity (tons)
20-25 m
Figure 3. Average annual landings per vessel
From Observatoire Economique Régional des Pêches de Bretagne
Fixed inputs are represented by length, engine power and catch per unit of fuel consumption. As
abundance index is not available on a monthly basis, a proxy has been built as a catch per unit of fuel
consumption from landings by selected species (kg) divided by fuel consumption (litres). In empirical
applications of DEA, abundance index is considered as fixed inputiii or more precisely “…DEA model
also included biomass levels…as additional fixed environmental parameters…”[19]. Variable input is
included through fuel consumption (litres).
In this paper, all units were considered in the same linear programming analysis. Consequently, the
constant sample include all 29 fishing boats for which information on inputs and outputs is available on a
monthly basis during ten years (from 1994 to 2003). Fifty-one months during which boats had no fishing
activity have been removed from the analysis to avoid a bias in capacity utilisation and efficiency scores.
13 observations were moved for 12-16 meters boats, 12 observations for 16-20 meters boats, and 26
observations for 20-25 meters boats. If the model is specified on a monthly basis, scores are averaged
from individual data based on monthly production by boat and by year. Hence, number of data is 3429
(1547 for the 12-16 meters, 588 for the 16-20 meters sample, and 1294 for the 20-25 meters). Boats 1 to
13 belong to the smallest group (12-16 meters class), boats 14 to 18 are the 16-20 meters units, and boats
19 to 29 are the 20-25 meters vesselsiv.
Table III: Description of number of observations used in the DEA analysis
12-25 meters
Boats (constant sample)
29
Available observations
3429
(boat x month x year)
Moved observations from the analysis
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IIFET 2006 Portsmouth Proceedings
RESULTS
In the literature on DEA models, allocative efficiency can be often presented as a final score to identify
the best practise in term of economic performance of the individual fishing companies [9] and [20]. The
availability of individual bookkeeping data for the constant sample gives the opportunity to check
financial performance, computed through gross revenue per meter and gross surplus per meterv, against
allocative efficiency. Moreover, it appears interesting to rely allocative efficiency scores to unbiased
capacity utilisation (CU*). This comparative approach is an innovative analysis, providing several
indicators on individual fishing firm’s performance during the decade 1994-2003.
Big trawlers used in average 76% of their capacity utilisation under a CRS assumption and 79% under a
VRS assumption. Mixed trawlers (16-20 meters) used respectively 63% (CRS) and 82% (VRS) of their
CU. The inshore fleet reached a mean value by 53% with CRS and 87% with VRS. Allocative efficiency
reaches respectively under CRS (constant return to scale) and VRS (variable return to scale) a mean value
by 0.89 and 0.9 for the 20-25 meters class (table IV). The intermediate segment, 16-20 meters, obtain a
mean value by 0.88 under CRS and by 0.93 under VRS. The smallest segment produces a lower optimal
allocation of fish products compared to others classes under CRS, with a mean value by 0.82. On the
other hand, small trawlers record a better result than big trawlers under VRS, with a mean score by 0.92.
Table IV: Mean comparison of CU (unbiased capacity utilisation) and AE (allocative efficiency) scores
12-16 meters 16-20 meters 20-25 meters All units
CUCRS
0.53
0.63
0.76
0.63
CUVRS
0.87
0.82
0.79
0.83
AECRS
0.82
0.88
0.89
0.86
AEVRS
0.92
0.93
0.90
0.91
Subscripts CRS and VRS for constant return to scale and variable return to scale, respectively.
The best allocation of outputs (nephrops, anglerfish, megrim and others species), given their landings
prices, is obtained by the biggest units if the CRS assumption is retained (figure 4). Boat 8 is the only one,
belonging to the smallest segment, amongst the best units with a score by 0.93 in term of allocative
efficiency. If the VRS assumption is used, 9 boats out of the first ten are small trawlers.
As a result of the adoption of VRS, fishing vessel size cannot be considered as an argument to explain
allocative (in)efficiency. Best combinations of prices and quantities to maximise gross revenue have been
attributed to smallest trawlers (specifically boats 2, 3 and 11). These three vessels perform both in term of
CU (with no excess capacity situation) and AE. The coefficient of correlation is higher with VRS
(R²=49%) than with CRS (R²=34%).
1,00
1,00
0,98
B27
y = 0,1927x + 0,7351
R2 = 0,3363
B8
B16
B14
0,90
B22
B20
B18
B1
B15
0,85
B2
0,80
B25 B24
B3
0,96
B29
B19
B23
B26
Allocative efficiency
Allocative efficiency
0,95
B21
B17
B28
B11
B4
B6
B12
B10
B5
B9
B3
y = 0,287x + 0,6759
R2 = 0,4944
B2
B27
B11
B14
B16
B8
B18
0,94
0,88
B26
B4
B25
B5
B23
B17
B19
B10
B12
B6
B28
B22
B13
B20
0,86
B9
B21
B15
0,90
B1
B29
B24
0,92
0,84
0,75
B13
0,82
B7
B7
0,70
0,30
0,40
0,50
0,60
0,70
0,80
Unbiased capacity utilisation
0,90
0,80
0,60
1,00
0,65
0,70
0,75
0,80
0,85
Unbiased capacity utilisation
CRS
VRS
Figure 4. Allocative efficiency and Unbiased Capacity Utilisation
8
0,90
0,95
1,00
IIFET 2006 Portsmouth Proceedings
Comparing economic performance (scores providing by DEA model) with financial results proves to be an
innovative analysis. Figures 5 and 6 depict individual location of trawlers both through allocative
efficiency, gross revenue per meter of length and gross surplus per meter of length.
It can be noted a relationship between allocative efficiency and gross revenue per meter (R² is equal to
25% under CRS and 20% under VRS).
40000
40000
B29
B25
B10
B9
B21
B22
30000
25000
B23
B19
B14
B24
B17
B18
B13
20000
B16
B15
B8
B4
B7
15000
B1
B5
B6
B2
10000
B27
B26
B02
B28
B11
B12
Gross Revenue per meter (€2003)
Gross Revenue per meter (€2003)
B29
y = 73627x - 38804
R2 = 0,2463
35000
B3
5000
0
0,70
0,75
0,80
0,85
0,90
0,95
B22
30000
B26
B24
B12
B11
B9
B18
B17
B13
20000
B14
B10
B28
25000
B27
B21
B19
B20
B16
B15
B8
B7
15000
B4
B6
B5
B2
B1
10000
B3
y = -1E+06x 2 + 3E+06x - 1E+06
R2 = 0,1971
5000
0
0,80
1,00
B23
B25
35000
0,85
0,90
Allocative Efficiency
Allocative Efficiency
0,95
1,00
VRS
CRS
Figure 5. Allocative efficiency and average Gross Revenue per meter (€2003)
As allocative efficiency is based on economic efficiency weighted by technical efficiency, figure 5 shows
for each vessel its maximised revenue in relation to average gross revenue. Gross revenue is very close for
biggest trawlers, average landings value by boat and per meter for the 20-25 meters class during the period
1994-2003 was 32063 €, with a t-value by 10.7. Situation is more contrasted for the 12-16 meters class.
Average gross revenue is estimated to 18118 € with a t-value by 2.8. In the case of VRS model, a
polynomial curve is needed to generate a trend between AE and gross revenue per meter. Most of small
units are located below or near the curve, excepted boat 11.
In figure 6, allocative efficiency is relied on short term profit, gross surplus per meter (figure 6). Gross
surplus is computed as the difference between total revenue (gross revenue and compensation, see table I)
and operational total costs (wages, fuel, gear, maintenance, others operational costs). Allocative efficiency
indice explain only 7% of gross surplus with VRS model (5% with CRS model).
10000
10000
y = -179169x 2 + 323411x - 141789
R2 = 0,0719
9000
y = 7398x - 2501,5
R2 = 0,0528
8000
Gross Surplus per meter (€2003)
Gross Surplus per meter (€2003)
9000
B29
7000
B25
B27
B23
6000
B12
5000
B22
B9
B10
B13
4000
B11
B28
B24
B21
B20
3000
B7
B4
B5
2000
1000
0
0,70
B19
B14
B18
B15
B3
B2
B26
B16
B1
B17
B8
B6
0,75
0,80
0,85
0,90
0,95
8000
1,00
B25
6000
B23
B27
B28
B12
B22
5000
B26
B19
B20
3000
B5
B7
B4
B9
B24
B11
B10
B13
4000
B21
B15
B14
B18
B16
B3
2000
B1
B6
1000
0
0,80
Allocative Efficiency
B29
7000
0,85
B17
0,90
B2
B8
0,95
1,00
Allocative Efficiency
CRS
VRS
Figure 6. Allocative efficiency and Gross Surplus per meter (€2003)
Optimal allocation of fish outputs, given ex-vessel prices, is poorly correlated with short term profit. In the
case of trawlers under 16 meters, linear regression is characterised by a decreasing slope (with both
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IIFET 2006 Portsmouth Proceedings
models CRS and VRS) whereas boats above 16 meters display increasing gross surplus per meter with a
better allocative efficiency score.
DISCUSSION
In our study, only the biggest trawlers, with an overall length up to 16 meters, can be defined as units in
large scale fisheries. The smallest units, less of 16 meters, spend their fishing time exclusively in coastal
areas. Consequently, their labour input is more significant than capital input compared to the over 16
meters trawlers. In certain circumstances, financial results can be considered as irrelevant to measure
performance of fishing boats. This is particularly the case for small scale fisheries, where “non-wage
labour is a major input” [21]. Conversely, financial performance, based on bookkeeping data, becomes a
more significant indicator for large scale fisheries, where capital component is high compared to labour
input. In the case of trawl fisheries of Brittany, the smallest units display heterogeneous economic and
financial performance compared to 16-20 and 20-25 meters segments. Non-wage labour is potentially an
explanation to this difference. Boats 9, 10, 11, 12 and 13, belonging to the 12-16 meters class, display
gross surplus per meter as high as than big vessels (between 4000 and 5000 € per meter) but with a
greater scale of allocative efficiency. Potentially, skipper-owners can be rewarded exclusively from his
entrepreneur profit, explaining a decreasing relationships between economic and financial performance on
small boats.
In this paper, we have shown that there is some correlation between economic performance measured by
DEA and via accounting (gross revenue and gross surplus per meter). However, there are significant
differences that could impede the successful implementation of a policy designed to deal with the issue of
over-capacity. Particularly, capital value should be assessed for fishing boats to be included in this type of
analysis. Here, financial performance was estimated only from gross revenue and gross surplus.
Consequently, these results were affected exclusively by the cost of capital in the short run (gears, repairs
and maintenance). A comparison between economic performance, from DEA model, and net profit
(taking into account the full component of capital, depreciation and opportunity cost) could be a next
outcome of this analysis.
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i
In the original DEA literature, the generalised term decision making unit (DMU) is used. However, in economic
application, the firm is the common concept.
ii
This model is in fact a linear programming model, and in this case for ease of solution is denoted in the dual-form
(see for example Färe et al (1994), Charnes et al (2001) and Coelli (2000) for a complete derivation).
iii
Separate proxies for biomass abundance, estimated from landings and fuel consumption, have been computed
according to length class of trawlers. If target outputs (anglerfish, nephrops, megrim) are exploited in offshore
fisheries by big trawlers (20-25 meters), these species are caught in inshore marine area by small vessels (12-16
meters). Mixed trawlers (16-20 meters) share their fishing time between offshore and inshore fisheries.
iv
According to a rule of thumb suggested by Cooper et al. (Cooper et al., 2000), number of observations should be
equal to or greater than max [(outputs x inputs) ; 3x(ouputs + inputs)]. In this empirical work, number of outputs is
equal to 4 (anglerfish, nephrops, megrim, others species) and inputs are 7 (length, engine power, fuel consumption
and four different proxies for biomass abundance). Consequently, number of observations should be equal to or
greater than 33. Here, the size of constant sample is 29 boats but individual data are given by month and by year.
Finally, 3459 available observations have been used in DEA model.
v
Length (meter) and engine power (Kw) are fixed inputs in DEA model to represent technical characteristics of
trawlers. Gross revenue and gross surplus are weighted by length for two reasons. Firstly, as individual units belong
to three segments of trawlers in term of length (12-16 meters, 16-20 meters, and 20-25 meters), the comparative
approach between DEA scores and financial performance requires to use weighting factors. In DEA model,
parameters

j
j
 1 represent weighting factors. Secondly, fishing vessel’s length appears more significant than
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engine power (Kw) in regression analysis between financial scores (gross revenue or gross surplus) and allocative
efficiency.
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