Analysing the profitability of the Spanish fleet after the anchovy moratorium using bootstrap techniques

Ecological Economics 70 (2011) 1154–1161

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Ecological Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e c o l e c o n

Analysis

Analysing the profitability of the Spanish fleet after the anchovy moratorium using bootstrap techniques M. Dolores Garza-Gil ⁎, Manuel M. Varela-Lafuente, Gonzalo Caballero-Miguez, Marcos Álvarez-Díaz University of Vigo, Spain

a r t i c l e

i n f o

Article history: Received 21 December 2009 Received in revised form 21 December 2010 Accepted 17 January 2011 Available online 23 February 2011 Keywords: Moratorium Profitability Small pelagic species Spanish fisheries

a b s t r a c t Given the low biomass level of the anchovy, the European Commission agreed to establish a moratorium on this species in the Bay of Biscay in 2005. Although the effects of managing the fishing activity with TACs, fees, taxes and ITQs on the stocks and future net profits are well documented in the specialized literature, scarce attention has been paid to analyze the effects of moratoriums on fisheries and the profitability of the fleet. This paper studies if the anchovy moratorium had a significant impact on the economic profitability of the fleet that fishes anchovy and other pelagic species in the North Atlantic waters. For this purpose, we compare the financial situations of the fleet before and after the moratorium from a statistical point of view using bootstrap, which is a flexible non-parametric and computationally intensive methodology. The results reveal the existence of a structural change in the financial performance of the fleet after the implementation of the moratorium. © 2011 Elsevier B.V. All rights reserved.

1. Introduction The Spanish purse seine fishery in the Atlantic is made up of 491 vessels, of which 346 fish in North-Western Cantabrian waters and the remaining 146 in Canary Island and Bay of Cadiz waters (MAPA, 2008). It is one of the fleets with the most vessels operating in these waters and it targets small pelagic species, such as sardine, horse mackerel, mackerel and the anchovy (MAPA, 2008; Ibermix, 2007). Regarding the management, the Total Allowable Catch (TAC) is the main regulatory mechanism included in the Common Fisheries Policy (CFP). The decision-making process of the TAC is as follows: the European Commission bases its determination of how much of each species can be caught (or, in such case, the establishment of a moratorium) on the biological assessments made by the Advisory Committee for Fisheries Management (ACFM) and the International Council for the Exploration of the Sea (ICES). On the basis of these scientific reports, the Commission listens to its advisory committee: the Scientific, Technical and Economic Committee for Fisheries (STECF). This committee is made up of guest scientists whose function is to examine the assessments done by the ACFM as well as to incorporate socio-economic aspects into the analysis. The Commission also has to consult with representatives from the fisheries and aquaculture sector and other social groups which constitute the Advisory Committee on Fisheries and Aquaculture (ACFA). After such consultations, the ⁎ Corresponding author at: Facultade de Ciencias Económicas e Empresariais, Campus de Lagoas-Marcosende s/n, 36310 Vigo, Galicia, Spain. Tel.: + 34 986 812 515; fax: + 34 986 812 401. E-mail address: [email protected] (M.D. Garza-Gil). 0921-8009/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2011.01.008

European Commission sets out the TACs for the following year, which are debated and approved by the Council of Fisheries Ministers. The agreed TACs by the Council usually exceed the maximum levels proposed by the Commission. There is no, in general, a specific regulation from European Union for the anchovy fishery. Only, and given the poor situation in which the anchovy has found itself over the last years, a precautionary TAC was implemented for several years and a moratorium for this species has been established in the Bay of Biscay since 2005. The Spanish government uses input control (area restrictions, entrance restrictions and gear regulation) and, in addition, some regional governments implement output control for some species (maximum catches by fishing day for the sardine case). Although the effects of managing the fishing activity with TACs, fees, taxes and ITQs on the stocks and future net profits are well documented (among others, Arnason, 1994; Neher et al., 1989; Townsend, 1990; Ward, 1994; Arnason and Gissurarson, 1999; Costa Duarte et al., 2000; Amstrong and Sumaila, 2001; Weitzman, 2002; Bjorndal and Lindroos, 2003; Russell and Potter, 2003; Hall and Mainprize, 2005), little attention has been paid to the effects of a moratorium on the profitability of the fleets involved in the affected fishery. Woodrow (1998) studies the reduction in the fishing capacity after cod moratorium in the Northeast coast of Newfoundland; and Nostbakken and Bjorndal (2003) and Nostbakken (2008) analyse the North herring fishery under different management regimes since the moratorium was lifted in this fishery. Given that in the middle of 2005 a moratorium on the anchovy was introduced, the aim of this paper is to verify if this restrictive measure had a significant impact on the economic profitability of the Spanish

M.D. Garza-Gil et al. / Ecological Economics 70 (2011) 1154–1161

fleet in the North Atlantic waters. In particular, we study if the profitability ratios obtained over the period 2003–05 were statistically different from those values corresponding to the years after the implantation of the moratorium. For this purpose, a statistical contrast was carried out using the bootstrap method (Efron and Tibshirani, 1998). Bootstrapping is a statistical, non-parametric and computationally intensive methodology that allows calculating empirically confidence intervals without assuming a specific distribution of the variable under study. The construction of these intervals allows us to check if the moratorium meant a statistically significant decline in the profitability of the Spanish fleet. Dealing with problems concerning over-fishing implies taking appropriate measures with regard to the conservation of marine resources and the Common Fisheries Policy's structure. The search for a balance between the two issues probably requires the establishment of a programme to control and reduce fishing effort prior or simultaneous to the implementation of fish management plans and/ or, in our case, the establishment of a fishing moratorium. The possible negative effect of a moratorium, resulting in a reduction in return, could in turn generate an adjustment of fishing effort through the withdrawal of vessels, but also an increase in the pressure on other commercial species. Evidently, other factors may also influence fleet profitability, such as cost increases or the evolution of fish prices. This study explores these aspects and, in particular, how fleet profitability has been affected after the application of the moratorium. The study is structured in the following way. In section 2 the Spanish purse seine fleet in the North Atlantic is described. In Section 3 the financial ratios used in this study are showed, and the statistical hypothesis to be tested is explained. Section 4 presents a brief explanation of the bootstrap method. In Section 5, the results are showed. Finally, in Section 6 the main conclusions are commented. 2. Spanish purse seine fleet description The Spanish purse seine fleet in the North Atlantic is made up of relatively homogenous vessels insofar as their technical characteristics are concerned (Castro et al., 2007). As reflected in Table 1, the fleet has an average capacity of 34.2 gross tonnages (GT), a power of 151.8 kilowatt (KW) per vessel and a length of 21 m. The average life of the fleet is 20 years, with a crew of 8 per vessel. All of the vessels use nets made from synthetic materials, and are equipped with hydraulic haulers and electronic fish detectors. The fleet is located mainly in Galicia (49%) and the Basque Country (31%); the remaining vessels are distributed between Asturias (13%) and Cantabria (7%). This fleet targets pelagic species, mainly the sardine and the anchovy. Insofar as the sardine is concerned, since 1999 the Galician government (in Northwest of Spain) has implemented a maximum limit per fishing day in place (7000 kg per vessel in 2007). The anchovy has been subject to a precautionary TAC in area VIIIc for several years, but since the collapse of this species in 2005, the European Union established a moratorium which is still in place today

Table 1 Characteristics of the Spanish fleet involved. 2003–07. Source: Data estimated from a sample of 193 vessels and Ibermix (2007).

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Table 2 Volume and value of landings. 2003–07. Source: Data estimated from sample and ICES (2008). Volume (in tons)

2003

2004

2005

2006

2007

Sardine Horse mackerel Mackerel Anchovy Other species1 TOTAL landings

22,819 10,325 7645 3700 572 45,061

26,879 9066 11,377 7580 1066 55,968

31,564 13,724 12,910 104 4520 62,822

26,233 12,297 13,911 15,8 6452 58,909

25,782 12,158 26,630 4 12,155 76,729

Value (in 1000 €07)

2003

2004

2005

2006

2007

Sardine Horse mackerel Mackerel Anchovy Other species TOTAL

14,376 7744 3746 14,800 18,305 58,971

13,977 7343 5689 22,740 9867 59,616

16,413 15,645 11,361 426 15,429 59,274

13,641 15,248 12,903 66 18,917 60,776

16,501 16,170 18,108 18 10,094 60,891

1 Other species: Chub mackerel, Twaite shad, Bogue, Grapike and Sand smelt. The increase in other species is mainly due to the bogue (by 150% for the period), chub mackerel (75%) and twaite shad (9%) landings.

(ICES, 2008).1 As we can see in Table 2, for the period 2003–2007 the landings show a continued increase in horse mackerel and in mackerel (species not subjected to catch limits).2 It is also observed an average increase in the case of the sardine, but with a downward trend in recent years due to the substantial variability in the recruitment of this species since the mid-90s (ICES, 2008). Moreover, according to Central Market-Mercasa and the Spanish Ministry of Industry,3 the lack of the Cantabrian anchovy has generated an increase in the price of anchovy that comes from the Mediterranean and other places. It is even observed that the price of the anchovy went so far as to double the level previous to the implantation of the moratorium. With relation to the economic results, the costs and income data were estimated from personal interviews to the fishermen. The data collection was through direct survey of fishermen. The sample was selected by random simple method from the seines census for this fishery and the interviews were carried out over the telephone and mail. 193 were finally obtained, with a reply rate (questionnaires completed/those contacted) of 63%. The results in constant euros of 2007 (real or inflation-adjusted values) are showed in Table 3. The fleet operates with increasing costs over the period considered, due mainly to the increase in fuel and crew costs (Table 3). Furthermore, the joint income also shows an upward trend for the period 2003–07 (see Table 3). We can observe a steep reduction in the number of vessels throughout the period (13%). According to fleet census for this fishery, this reduction has occurred mainly in Cantabria (22.2%), Asturias (13.3%) and the Basque Country (11.4%), while in Galicia, the region in Northern Spain most dependent on fishing, there has been a 19.5% increase; therefore, we can affirm that a greater fleet concentration has occurred in this region. As we can see in Table 4, in average terms for the period 2003–07, the fleet generated an income of approximately one hundred and fifty-seven thousand Euros per vessel, and bears costs of one hundred and thirty-five thousand euros. The labour costs represent the highest percentage with a 58.5% of the total income per vessel. In turn, this

Average data by vessel Number of vessels: 2003 Number of vessels: 2007 Age of vessel (years) Capacity (GRT/vessel) Capacity (GT/vessel) Power (HP/vessel) Power (kW/vessel) Length (m/vessel) Crew on board (No./vessel)

397 346 20 34.2 27.5 187.0 151.8 21 8

1 The Spanish government provided funding (basically from FIFG and EFF) to shipowners and crew since 2005: €45 per day and for not more 40 days per year. (APA/2150/2005, APA/3132/2006, and APA/2260/2007 regulations). 2 The effects from increasing pressure on these species are not evaluated by ICES. The spawning stock biomass in relation to precautionary limits is unknown and, in absence of a reliable assessment and precautionary point, the state of the stocks cannot be evaluated by the biologists (ICES, 2008). 3 The data were collected from http://www.mercasa.es and http://www.mityc.es.

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Table 3 Economic results of the fleet⁎. 2003–07. Source: Data estimated from sample. (in 1000 €07) Total costs

1

- Energy Consumption - Other Current Costs - Vessel costs - Crew share Total income Gross Value Added (GVA) Gross Cash Flow (GCF) Depreciation Net Profit (NP)

2

Interests Net Profit Number of vessels

2003

2004

2005

2006

2007

45592.9 (122.6; 1.07) 4293.7 (9.9; 0.92) 3665.5 (10.8; 1.2) 6035.3 (14.9; 0.98) 31747.3 (95.2; 1.2) 58797.0 (159.5; 1.08) 44802.4 (133.3; 1.18) 13204.0 (52.3; 1.6) 4522.7 (14.0; 1.23) 8681.2 (45.7; 2.1) 539.0 (2.96; 2.2) 8142.1 (45.2; 2.2) 397

46948.9 (130.4; 0.99) 4744.8 (9.3; 0.70) 3901.4 (16.50; 1.51) 6054.9 (14.7; 0.87) 32247.7 (100.2; 1.11) 59447.1 (119.0; 0.71) 44746.0 (94.45; 0.75) 12498.2 (75.08; 2.14) 4944.7 (16.3; 1.18) 7553.5 (73.4; 3.47) 758.5 (5.66; 2.66) 6795.0 (74.18; 3.90) 357

47672.2 (108.4; 0.80) 4846.0 (10.3; 0.75) 3821.0 (13.4; 1.24) 6058.0 (14.8; 0.86) 32947.5 (181.5; 0.87) 53459.5 (102.4; 0.67) 38734.8 (80.7; 0.73) 5787.3 (54.4; 3.30) 4843.9 (17.24; 1.25) 943.4 (55.5; 20.66) 763.2 (5.8; 2.67) 180.2 (57.6; 112.2) 351

50139.9 (113.8; 0.79) 5053.1 (10.7; 0.73) 4902.8 (18.7; 1.33) 6308.9 (15.5; 0.86) 33875.0 (83.8; 0.86) 54893.0 (105.3; 0.67) 38628.1 (83.3; 0.75) 4753.1 (59.2; 4.32) 4489.8 (15.74; 1.22) 263.3 (60.54; 79.80) 696.7 (5.2; 2.59) −433.4 (62.1; −49.7) 347

51460.7 (115.4; 0.78) 5584.7 (11.6; 0.72) 5475.2 (20.9; 1.32) 6434.6 (16.0; 0.86) 33966.1 (82.7; 0.84) 54942.0 (107.7; 0.68) 37447.5 (84.3; 0.78) 3481.4 (61.51; 6.11) 3767.0 (12.52; 1.15) −285.6 (62.23; −75.37) 712.3 (5.0; 2.4) −998.0 (63.2; −21.9) 346

⁎Standard deviation and coefficient of variation between brackets. 1 These costs do not include the financial burdens of the companies or the taxes paid. 2 Net Profit before taking away the interest.

data enables us to obtain a gross value added of around one hundred and thirty-three thousand Euros per vessel per year (Table 4). This leads to the generation of a net economic return for the period, in terms of gross cash flow, of over twenty-one thousand Euros and net profits of nine thousand Euros (after depreciation) and seven thousand Euros per vessel (after interest payments). The evolution of these variables throughout the period 2003–07 is showed in Table 5. It can be seen that the total costs per vessel increased by around 5%. The entries “Other Current Costs”, “Energy Costs” and ”Crew Share” experienced the most significant increase (11%, 8% and 4%, respectively), especially in the last two years of the period considered. On the other hand, the average annual rate of the total income per vessel also increased notably, although at a lesser rate than the costs (3.5%). This allows the gross value added to increase at around 2% on average. However, the increase in income is not sufficient to compensate for the increase in costs over the period

1

3. Financial ratios and statistical testing hypothesis Through the survey process we have also obtained information relating to the value of the investment in a fishing vessel with the average characteristics outlined above. This value stands at I = 326,940 euros (constant euros of the year 2007). This value includes, on the one hand, the initial value of acquiring the fishing vessel and all of the equipment necessary to carry out fishing activity and, on the other, the cost of modernising and renewing the engines. Moreover, the survey process allows us to get the necessary data to construct the Average Annual Rate of Return (ARR). This financial ratio

Table 5 Economic results of the fleet per vessel. 2003–07. Source: Data estimated from sample and Banco de España (2008).

Table 4 Average economic results of the fleet for the period 2003–07. Source: Data estimated from sample.

Total costs 1a - Energy Consumption - Other Current Costs - Fixed vessel costs - Crew share Total income Gross Value Added (GVA) Gross Cash Flow (GCF) Depreciation Net Profit (NP)2 Interests Net Profit

and, therefore, the gross and net surpluses before and after interest payments drop slightly on average over the period considered under analysis.

Annual average Period 03–07 (1000 €07)

%

Annual average per vessel (1000 €07)

48362.9 4904.4 4353.2 6178.4 32926.9 56307.7 40871.8 7944.8 4513.7 3431.1 694.0 2737.2.3

85.9 8.71 7.73 10.97 58.47 100.00 72.6 14.1 8.0 6.1 1.2 4.9

135.1 13.7 12.2 17.2 91.9 156.7 133.6 21.7 12.6 9.1 1.9 7.2

These costs do not include the financial burdens of the companies or the taxes paid. 2 Net profits without taking away the interest.

(in 1000 €07)

2003

2004

2005

2006

2007

Total costs1 - Energy Consumption - Other Current Costs - Vessel costs - Crew share Total income Gross Value Added (GVA) Gross Cash Flow (GCF) Depreciation Net Profit (NP)2 Interests Net Profit Inflation rates Nominal interest3

115.3 10.9 9.2 15.2 80.0 148.5 113.2 33.2 11.4 21.8 1.4 20.4 3.00 4.11

132.0 13.4 10.9 17.0 90.3 166.5 125.3 35.0 13.8 21.1 2.1 19.0 4.00 4.02

136.0 13.8 10.9 17.3 94.0 152.3 110.3 16.5 13.8 2.7 2.2 0.5 4.60 3.42

144.5 14.6 14.1 18.2 97.6 158.2 111.3 13.7 12.9 0.76 2.0 −1.24 3.10 3.78

148.7 16.1 15.8 18.6 98.2 158.8 108.2 10.1 10.9 −0.82 2.1 −2.9 4.70 4.24

1 2 3

These costs do not include the financial burdens of the companies or the taxes paid. Net Profit before taking away the interest. Government bond yields (10 years).

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gives information about the amount of revenue that an investment generates over a given period of time as a percentage of the amount of capital invested. Analytically, the ratio is defined as P

∑ Rt

ð1Þ

t =1

P I

ARRp =

t = 1; :::; P

where ARRp is the Average Annual Rate of Return in the period p, Rt is the average net return of the fleet at year t, I is the value of the investment of a representative fishing vessel and P the total number of years of the period being considered (P = 2 for the period 2003–04, and P = 3 for the period 2005–07). In addition, and due to the fishery could involve both physical and financial risks, a Risk-adjusted Rate of Return (RARR) is also calculated. In particular, we consider in our study the most simple and known methods to measure risk-adjusted performance: the Sharpe Ratio. This financial ratio describes how the volatility of a stock might affect expected return, and the potential rewards for investors prepared to take the risk. A high value of the ratio indicates potentially high returns in relation to the risk taken. The ratio is formally estimated by means of the following mathematical expression RARRp =

ARRp −rfp σ ARR p

ð2Þ

where RARRp is the Sharpe ratio in the period p, ARRp is the Average Annual Rate of Return during the period p, rf is the risk-free return4 and σp is the average standard deviation of ARRp calculated as P

∑ σ tARR

σp =

t=1

P

ð3Þ

and where σtARR is the standard deviation of ARRp. Our study also includes the calculation and analysis of the Investment Payback Period (PP). This ratio provides information about the number of years that are required to recover the initial investment. Mathematically the ratio is calculated as follows PPp =

I

. : ∑ Rt P P

t =1

Finally, it is worth noting that all these variables are estimated in average terms for a standard vessel and in constant monetary units relating to the year 2007. The goal of this study is to verify if the values of these financial ratios calculated for the period before the moratorium (2003–2004) are statistically different from those values estimated for the period after the moratorium (2005–2007). Formally, we contrast the null hypothesis H0 : F2003−04 = F2005−07 against the alternative H1 : F2003−04 ≠ F2005−07 where Fp is the financial ratio under study for the Spanish purse seine fleet in the period p. The decision rule that allows us to accept or reject the null hypothesis can be carried out by constructing a confidence interval and then checking whether the null value is in the interval corresponding to the value of the alternative hypothesis. If the null value is inside the interval, then the null hypothesis can be accepted

4

The risk-free return is that one obtained by the 90-day Government Treasury bills.

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and we have statistical arguments that allow us to affirm that the moratorium did not have a significant impact on the profitability of the Spanish purse seine fleet in the North Atlantic. On the contrary, if the null value lies outside the interval, the null hypothesis can be rejected and we can affirm that the moratorium had a significant effect on the profitability of the fleet. The next section describes a statistical methodology known bootstrapping to calculate empirically confidence intervals. The bootstrap is a data-based simulation method for statistical inference. The main advantage of bootstrap techniques is that they require no assumptions about the statistical distribution of the financial ratios. That is, it is possible to construct accurate intervals without having to make normal theory assumptions. This fact implies a great flexibility and a substantial advantage in comparison with the standard intervals based on the premises of the classical statistics. 4. The bootstrap method The Spanish purse seine fishery in the Atlantic comprises 491 vessels. From the total of the vessels, we obtain financial information of a random sample of n = 128 vessels {x1, x2,..., x128}, were xi is a vector that contains the information of the vessel i (∀ i = 1,...,128).  From this sample we calculate the financial ratio for the period p Fˆ p , and use this value to estimate the true but unknown value of the whole fleet(Fp). Besides the point estimate, it is also useful to provide intervals that allow us to know between what values the true value of the ratio (Fp) is likely to lie. Moreover, these intervals are useful in order to determine the acceptation or rejection of the null hypothesis in the proposed statistical contrast. The advances of modern computing power have boosted the use of resampling techniques such as bootstrapping to construct empirical confidence intervals. The bootstrap intervals can be constructed without making restrictive assumptions concerning the statistical distribution of Fp. An important characteristic of the bootstrap is that it allows us to get more accurate intervals (Brownstone and Valletta, 2001), and these intervals can be asymmetric (intervals that are longer on the left or right). The bootstrap method employed in this study was programmed based on the following computational algorithm: 1st step: A bootstrap sample {x1*, x2*,..., x128 * } is obtained by randomly sampling 128 times with replacement from the original sample{x1, x2,..., x128}. For example, a possible bootstrap sample would be {x1*, x2*, x3*, x4*, x5*..., x128 * } = {x93, x5, x111, x5, x40..., x2}. 2nd step: Using the bootstrap sample, we calculate the financial ratio under consideration over the period p (Fp*). 3rd step: Repeat B times the steps 1 and 2 to get B values of the financial ratio({Fbp}Bb = 1). These B values define the empirical sampling distribution of the ratio. An important issue is how to determine B, the number of bootstrap replications. As B approaches infinity, the empirical sampling distribution of the ratio obtained by means of the bootstrap method approaches the true statistical distribution of the financial ratio (Mooney and Duval, 1993). In general, it is assumed B=1,000 as a rough minimum number of replications (Efron and Tibshirani, 1998). In our specific study, we have set a number of bootstrap replications equals to B = 10,000. 4th step: Given the empirical distribution of the financial ratio F, the next step is to construct a confidence interval. A range of procedures has been proposed for the construction of bootstrap confidence intervals such as the normal approximation method, the percentile method, the percentile tmethod, the bias-corrected percentile and the accelerated bias-corrected method. The optimal choice of one or another method depends on the specific problem object

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of analysis. Nevertheless, the accelerated bias-corrected method has been shown to perform better under a wider variety of assumptions (Briggs et al., 1997) and, at the present level of development, it is recommended for general use (Efron and Tibshirani, 1998). For all these reasons, this is the method that has been applied in this study.5

The accelerated bias-corrected interval (BCa) with a level of confidence (1 − α) is given by     ðα Þ ðα Þ BCa : Flo ; Fup = F 1 ; F 2 where Flo and Fup are the lower and upper bound of the interval associated to the ratio Fp, respectively. F (αi) indicates the 100·αi th percentile of the B bootstrap replications ({Fpb}Bb = 1) already calculated in the third step. α1 and α2 are estimated using the expressions z0 + zα   α 1 = Φ z0 + 1−a· z0 + zðαÞ

α 2 = Φ z0 +

!

! ð1−α Þ z0 + z   : 1−a· z0 + zð1−α Þ

In this case Φ(·) is the standard normal cumulative distribution function and z(α) is the 100·α th percentile point of the standard normal distribution. The parameter z0 is the bias-correction term and can be estimated from the proportion of bootstrap replications ({Fbp}Bb = 1) less than the original value of the financial ratio (Fˆp ) 0

n o1 B b ˆ ; ∀b = 1; :::; B ∑ I F b F p p B C −1 B C zˆ0 = Φ Bb = 1 C @ A B

where Φ− 1(·) is the inverse function of a standard cumulative distribution function and I(·) is an indicating function ( Ið·Þ =

1

if

Fpb b Fˆp

0 otherwise

) ∀b = 1; ::::; B:

The parameter a is known as the acceleration parameter because it refers to the rate of change of the standard error of Fˆp regarding the true value Fp. Efron and Tibshirani (1998) suggest the use of a jackknife method to estimate a  n  ð·Þ ði Þ 3 ∑ Fˆp −Fˆp i=1 aˆ =  n   3 = 2 ð·Þ ðiÞ 2 6· ∑ Fˆp −Fˆp i=1

ði Þ

where Fˆp is the replicate of the financial ration over the period p with the ith observation removed, and n

ð·Þ Fˆp

ðiÞ ∑ Fˆp

=

i=1

n

:

5 In our study, the confidence intervals were also computed using the other methods. The resulting intervals were quite similar among them. These results are not reported in this study, but they can be sent by request.

5. Results and discussion In the estimation of the net return we will use the gross cash flow obtained by the owners of the fishing vessels after deducting the labour costs (wages and salaries and employers' social security costs), and the normal operating costs (mainly fuel consumption, vessel maintenance, costs relating to fishing gear and other costs involved in fishing activity). We believe that this is information to which fishermen have relatively easy access, either on account of their own experiences or knowledge of the sector. Therefore, we understand that this indicator is more illustrative. An investor in a fishing company really bears in mind this indicator when accepting or rejecting an investment in a fishing vessel. Furthermore, and given that in economic fisheries literature other variables are usually employed (Davidse et al., 1999; Whitmarsh et al., 2000; European Commission, 2001; Surís-Regueiro et al., 2002; Flaaten and Heen, 2004; Garza-Gil and Amigo-Dobaño, 2008), we will also use the net surplus calculated under two different scenarios. In the first one, we will consider the difference between the gross cash flow and the imputed depreciations (both of the vessel as well as the engine and other vessel equipment), given that this is a variable usually applied in financial literature. In the second scenario, we will use the difference between the gross cash flow and the imputed depreciations and interest, as proposed by the European Commission to evaluate the profitability of the European fleet's different segments (European Commission, 2001). In Table 6 we show the results obtained for the different financial ratios (ARR, RARR and PP) in the three scenarios proposed for the economic return: the Gross Cash Flow and the Net Profit before and after interest payments. Using the gross cash flow, the standard purse seine fleet vessel over the period 2003–07 obtained an average annual rate of return (ARR) of 6.64%. This rate is relatively high in comparison with other fisheries. This financial outcome is mainly explained by the decreasing number of vessel during this period, which is estimated close to the 13% of the total number of vessels. The risk-adjusted rate (RARR) presented a percentage of 0.21%, and the investment payback period (PP) was of around fifteen years, a shorter period of time than the average life of the vessel (twenty years). Considering the scenarios put forward for the net profits, the ARR drops significantly and stands at 2.79% and 2.20%, respectively; and the RARR reflected a very low or even negative value (0.01% and −0.02%, respectively). These last scenarios also imply an investment payback period of approximately thirty-six and forty-five years, which would imply that the investor cannot recover the investment given the average life of the vessel. On the other hand, we do not know what fishermen's expectations are with regards to return when they make their investment. Therefore, it would be necessary to look to other indicators which would make it possible to evaluate the return more precisely. One of the indicators which could serve as a reference, bearing in mind that we have information for a five-year period, is the long-term return on the public debt (in particular, the return on government bonds). This return reached an average nominal rate of 3.91% (Banco de España, 2008) in the period 2003–07 (see Table 5). Using this reference, the ARR reached for the fleet was higher than the return on government bonds in the three scenarios contemplated. Therefore, the expectations of obtaining economic returns by making an investment in this fleet are higher than those that would be generated through other capital investments. Given that the goal of this study is to analyse if the moratorium had a statistically significant impact on the profitability of the fleet, the whole period is divided into two parts: the period before the moratorium (2003–04) and the period after the moratorium (2005– 07). Table 6 shows the ARR, RARR and PP for these two periods. In all cases, the ARR and RARR for the period 2003–04 are higher than for

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Table 6 Annual Average Rate of Return (ARR), Risk-Adjusted Rate of Return (RARR) and Investment Payback Period (PP)⁎. 2003–2007

Gross Profit Net Profit = Gross Profit–Depreciation Net Profit = Gross Profit–Depreciation– Interests

2003–2004

2005–2007

ARR (%)

RARR (%)

PP (years)

ARR (%)

RARR (%)

PP (years)

ARR (%)

RARR (%)

PP (years)

6.64 (4.12, 9.23) 2.79 (0.20, 5.18) 2.20 (−0.48, 4.66)

0.21 (0.08, 0.35) 0.01 (−0.14, 0.14) −0.02 (−0.17, 0.11)

15.06 (10.89, 24.18) 35.81 (17.82, 212.04) 45.50 (–, 610.10)

10.44 (7.31, 13.08) 6.58 (3.57, 8.77) 6.05 (2.87, 8.20)

0.42 (0.26, 0.56) 0.24 (0.08, 0.36) 0.21 (0.06, 0.33)

9.58 (7.71, 13.64) 15.20 (11.36, 27.81) 16.54 (12.15, 34.33)

4.10 (1.08, 7.22) 0.27 (−2.93, 3.25) −0.37 (−3.64, 2.61)

0.06 (−0.11, 0.25) −0.15 (−0.33, −0.01) −0.18 (−0.35, −0.01)

24.37 (13.56, 85.55) 374.22 (160, 1318) –

⁎ Bootstrap confidence intervals between brackets.

the period 2005–07. The payback period for the initial investment in the vessel increases as the rate of return drops. Moreover, we can also observe that the average return of the fleet over the period 2003–04 (10.44%) was much higher than the average return of the government bonds (4.08%). Conversely, for the period 2005–07 we observe that the investment in government bonds gave a slightly higher profitability (4.13% against 4.10%). The financial comparison between profitabilities is even worse if we consider the two scenarios defined for the net profits (4.13% versus 0.27% and −0.37%, respectively). Given these results, we can affirm that the Spanish purse seine fishery in the Atlantic underwent a worsening in its levels of profitability after the moratorium. The investment in the fleet became less attractive than the investment in a free-risk and slightly profitable asset. One important question that still remains without answering is if the values of the financial ratios corresponding to the period 2003–04 are statistically different from those values obtained in the period 2005–07. In other words, we must check if there was a structural change from the year in which the moratorium was imposed (the year 2005). For this purpose, we test the statistical hypothesis 

H0 : F2003−04 = F2005−07 H1 : F2003−04 ≠ F2005−07

by means of the construction of bootstrap confidence intervals. Table 6 also reports the bootstrap confidence intervals, and Fig. 1 displays graphically these intervals and the values of the financial ratios considered in this study. As we can observe in Fig. 1, for all cases the ARR and RARR percentages corresponding to the period 2005–07 are below the lower bound of the interval associated to the ratio for the period 2005–07. As a consequence, we have statistical arguments that allow us to state that the null hypothesis can be rejected and, therefore, we confirm from a statistical point of view that the moratorium had a significant and negative effect on the profitability of the fleet.6 On the other hand, we can also observe that the intervals for the period 2005– 07 are wider. This characteristic reveals that the volatility in the profitability of the fleet after the moratorium is much higher, indicating that the risk assumed by the investor is also higher during this period. An interesting comment can be made regarding the investment payback period (PP) based on the gross profits. Given the profits during the period before the moratorium, the number of years necessary to recover the investment was 9 years and almost 6 months. After the moratorium, the number of years increased up to the 24 years and 4 months, which is out of the upper bound of the bootstrap interval constructed for the period before the moratorium. Consequently, we can reject the null hypothesis and, therefore, the

6 The increase of the total cost observed during the period has reinforced the decreasing trend of the financial ratios. In any case, the test based on bootstrapping confirms a structural change in the financial ratios.

number of years required to recoup the investment is statistically different for both periods. The analysis according to the different definitions of net profit is not very suitable and accurate in this case since we get a PP values and intervals extremely high. The explanation to this limitation is that this ratio is very sensitive to a low or negative profitability. 6. Conclusions The main target species of the Spanish purse seine fleet in the North Atlantic have traditionally been the anchovy and the sardine, as well as other small and medium-sized pelagic species. The fleet was characterized by a relatively high profitability and a short investment payback period in comparison with the average life of a standard vessel belonging to this fleet. From the period 2003–2004, the Spanish fleet's standard vessel, using the gross operating surplus, obtained an ARR of around 10.44%, a risk-adjusted rate of return of 0.42% and a payback period of approximately nine years and half, a considerably shorter period of time than the average life of the vessel (twenty years). On the other hand, if we consider the scenarios proposed for the net profits, the ARR and RARR would stand at around 6% and 0.20%, respectively, and the payback period is around sixteen years. These levels of profitability are still higher than those that would be generatedthrough other capital investments (i.e. long-term return on the public debt). Given the low biomass level of the anchovy, a moratorium was imposed in 2005. After this year, the profitability of the fleet declined. In gross terms, the ARR and RARR went down to 4.10% and 0.06%, respectively; the payback period goes up to twenty-four years. After deducting depreciation and interest, we see that the financial situation of the fleet is critical since there are huge losses. Specifically, the ARR and RARR reflected negative values of −0.37% and −0.18%, respectively. The construction of confidence intervals using the bootstrap technique allows us to verify the existence of a structural change in the year 2005. We verify from a statistical point of view that the profitability of the fleet before the implantation of the moratorium was statistically higher. Nevertheless, the losses observed after the moratorium were mitigated thanks to the reduction in the number of boats and the important increase in landings (albeit of less valuable species) which alleviates for the drop in catches of a species with a higher market value (the anchovy and sardine). The progressive increase in pressure on other non-regulated species, which at this moment in time show no sign of being over-fished, could generate sustainability problems in the future. Therefore, it is necessary to supervise and control the fishing effort on these species. If the situation of these stocks was to worsen, and improvements in the regulated species (sardine and anchovy) were not occurring either, then it might be necessary to reduce fishing effort through, for example, the application of ship buyback programmes. It is also worth mentioning that other factors have contributed to deteriorate the profitability of the fleet during the

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Fig. 1. Decision rule based on the bootstrap confidence intervals.

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period 2005–07. The fall of the economic returns is likely to be also due to a higher increase in costs. Acknowledgements This study was made possible thanks to the financial support from the Xunta de Galicia (PGIDIT06CSC30001PR) and Ministry of Science and Innovation (ECO2009-10324). The authors also appreciate the reviews and suggestions provided by three anonymous referees. References Amstrong, C., Sumaila, U.R., 2001. Optimal allocation of TAC and the implications of implementing an ITQ management system for the North-East Arctic cod. Land Economics 77, 350–359. Arnason, R., 1994. Theoretical and Practical Fishery Management. In: Loayza, E.A. (Ed.), Managing Fishery Resources, World Bank Discusion Papers: Fishery Series. Washington D.C. , pp. 3–10. Arnason, R., Gissurarson, H., 1999. Individual Transferable Quotas in Theory and Practice. The University of Iceland Press, Reykjavik. Banco de España, 2008. Boletín Estadístico. Madrid, Junio 2008 http://www.bde.es. Bjorndal, T., Lindroos, M., 2003. International Management of North Sea Herring: Paper presented at the 12th Annual Conference of European Association of Environmental and Resources Economists. Briggs, A.H., Wonderling, D.E., Mooney, C.Z., 1997. Pulling cost-effectiveness analysis up by its bootstraps: A non-parametric approach to confidence interval estimation. Health Economics 6 (4), 327–340. Brownstone, D., Valletta, R., 2001. The Bootstrap and Multiple Imputations: Harnessing Increased Computing Power for Improved Statistical Tests. Journal of Economic Perspectives 15 (4), 129–141. Castro, J., Cardador, F., Santurtún, M., et al., 2007. Proposal of fleet segmentation for the Spanish and Portuguese fleets. WD06 presented at ICES WGHMM07. Costa Duarte, C., Brasao, A., Pintasilgo, P., 2000. Management of the Northern Atlantic Bluefin Tuna: An application of C-Games. Marine Resource Economics 14, 21–36. Davidse, W.P., McEwan, L.V., Vestergaard, N., 1999. Property Rights in fishing: from state property towards private property? A case study of three EU countries. Marine Policy 23, 537–547.

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Efron, B., Tibshirani, R.J., 1998. An introduction to the bootstrap. Chapman & Hall, Boca Raton, Florida. European Commission, 2001. Green Paper on the Future of the Common Fisheries Policy. Brussels, COM (2001); 135, 2001. Flaaten, O., Heen, K., 2004. Fishing vessel profitability and local economic link obligations: the case of Norwegian trawlers. Marine Policy 28, 451–457. Garza-Gil, M.D., Amigo-Dobaño, L., 2008. The profitability of the artisanal Galician fleet. Marine Policy 32, 74–78. Hall, S.J., Mainprize, B.M., 2005. Managing by-catch and discards: how much progress are we making and how can we do better? Fish and Fisheries 6, 134–155. Ibermix, 2007. Identification and segmentation of mixed-species fisheries operating in the Atlantic Iberian Peninsula waters. EC Project, FISH/2004/03-33. ICES, 2008. Report of the Working Group on the Assessment of Mackerel, Horse Mackerel, Sardine and Anchovy. Headquarters, ICES CM 2007/ACFM; 31. MAPA, 2008. La Agricultura, la Pesca y la Alimentación en España, Informe Anual. Ministerio de Agricultura, Pesca y Alimentación, Madrid. Mooney, C.Z., Duval, R.D., 1993. Bootstraping: a nonparametric approach to statistical inference. Sage University Paper Series on Quantitative Applications to Social Sciences, Series No. 07-095. Sage, Newbury Park, CA. Neher, P., Arnason, R., Mollet, N. (Eds.), 1989. Right Based Fishing. Kluwer Academic Publishers, Netherlands. Nostbakken, L., 2008. Stochastic modelling of the North Sea Herring Fishery under alternative management regimes. Marine Resource Economics 23, 65–86. Nostbakken, L., Bjorndal, T., 2003. Supply functions for North Sea Herring. Marine Resource Economics 18, 345–361. Russell, I.C., Potter, E.C.E., 2003. Implications of the precautionary approach for the management of the European eel. Fisheries Management and Ecology 10, 395–401. Surís-Regueiro, J.C., Varela-Lafuente, M.M., Garza-Gil, M.D., 2002. Profitability of the fishing fleet and structural aid in the European Union. Marine Policy 26, 107–119. Townsend, R.E., 1990. Entry Restrictions in the Fishery: Survey of the Evidence. Land Economics 66 (4), 359–378. Ward, J.M., 1994. The Bioeconomic Implications of a Bycatch Reduction Device a Stock Conservation Management Measure. Marine Resource Economics 9, 227–240. Weitzman, M., 2002. Landing Fee vs. Harvest Quotas with Uncertain Fish Stocks. Journal of Environmental Economics and Management 43, 325–338. Whitmarsh, D., James, C., Pickering, H., Neiland, A., 2000. The profitability of marine commercial fisheries: a review of economic information needs with particular reference to the UK. Marine Policy 24 (3), 257–263. Woodrow, M., 1998. A case study of fisheries reduction programs during the Northern Cod Moratorium. Ocean 6. Coastal Management 39, 105–118.