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PRICE INTERACTION BETWEEN AQUACULTURE AND FISHERY AN ECONOMETRIC ANALYSIS OF SEABREAM AND SEABASS IN ITALIAN MARKETS by Roberta Brigante (IREPA) and Audun Lem (FAO) XIII EAFE CONFERENCE SALERNO 18-20 APRIL 2001 The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the Food and Agriculture Organization of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. The designations “developed” and “developing” economies are intended for statistical convenience and do not necessarily express a judgement about the stage reached by a particular country, country territory or area in the development process. The views expressed herein are those of the authors and do not necessarily represent those of the Food and Agriculture Organization of the United Nations nor of their affiliated organization(s). - iii - ABSTRACT This paper analyses the impact of aquaculture in the Mediterranean on the market for fish from capture fisheries, based on two species – seabream and seabass. The interaction between aquaculture and capture fish prices is investigated in order to verify a possible stabilization effect on the market as a result of increased supply. Econometrics models, unit roots and co-integration are applied to test the linkages between prices of farmed and captured products, using quarterly data from January 1991 to December 1998. The analysis indicates that captured and farmed species are not substitutes for each other, and there is no link between the prices. A long-run change in farmed species price has had no impact on the long-run price of captured species prices. - v - CONTENTS ABSTRACT iii INTRODUCTION 1 SECTION 1 – ANALYSIS OF THE SECTOR 3 World Fisheries and Aquaculture in 1998 3 SECTION 2 – METHODOLOGICAL ANALYSIS 5 SECTION 3 – THE DATA 7 SECTION 4 – THE ECONOMETRIC MODEL 9 THE SEABASS CASE ADL (4,4) Model estimate Analysis of the dynamic structure of the ADL (4,4) model: Redundant Variable test Reduced Model estimate Restrictions of the reduced model Distributed Lag Model Estimate Co-integration Analysis between LC and LA Error Correction Model estimate Granger Causality Test Comments and conclusions on the Seabass case 10 10 THE SEABREAM CASE ADL (4,4) Model estimate Analysis of the dynamic structure of the ADL (4,4) model: Redundant variable test Reduced Model estimate Restrictions on the reduced model Co-integration analysis First Difference Model estimate Granger Causality Test Paired Granger Causality Tests Comments and conclusions on the Seabream case 23 23 12 12 15 16 18 19 22 22 25 25 28 28 28 31 31 31 SECTION 5 – CONCLUSIONS 32 BIBLIOGRAPHY 34 FOREWORD The initiative to this paper was taken by Audun Lem, FAO who also supplied much of the data and supervised the actual work. The analysis was carried out by Roberta Brigante, IREPA under the scientific supervision of Professor Massimo Spagnolo, IREPA.. Price interaction between aquaculture and fishery 1 INTRODUCTION This report is the result of a study on the impact of aquaculture on capture fishing, using an empirical and econometric approach. The research focuses on two lines of enquiry: first, to understand whether, and, if so, how, the increase in supply of product from aquaculture in the last decade could be interpreted as a substitution process of the cultured product for the captured one; and, second, to verify whether the increased supply could have a stabilizing effect on the market. The authors try to answer these questions using a model to explain the dynamics of interaction between the time series of prices of captured and farmed species. In particular, the time series of prices are examined in order to establish if they are co-integrated. If a co-integration relationship exists, then a long-run relationship and a set of short-run adjustment parameters could also be found. The analysis is based on the Italian market during the period 1991 to 1998, examining the time series of prices of two species, seabass and seabream, where supply came both from capture and farm fisheries. The Italian market is used because of the ample consumption of the two species and because it absorbs about 70% of the production of seabass and seabream in the Mediterranean. Studying the Italian case seemed therefore appropriate. The report has five main sections. The first section illustrates briefly the world state of fishing and aquaculture. The purpose is to show the changing structure of fish production. Overexploitation of marine resources – the principal cause of impoverishment of fish stocks – is affecting the capture level, which in 1998 again showed a decrease. In contrast, aquaculture is in continuous growth and production represents nearly a quarter of total world fish production. In the second section, the methodology used for the empirical analyses is presented. In the third section, the data used for the analyses are presented. In the fourth section, the results of econometric analyses are presented. In the fifth section, conclusions are drawn and discussed. 2 Introduction Price interaction between aquaculture and fishery 3 SECTION 1 ANALYSIS OF THE SECTOR WORLD FISHERIES AND AQUACULTURE IN 1998 In 1998, world fish production was 116.9 million tonnes (t), showing a slight decrease from 1997 (see Table 1). Table 1. World fish production, 1990-1998 1990 1991 1992 Production 1993 1994 1995 1996 1997 1998 million tonnes 1998 % Var.(1) 98-97 (%) mvr(2) 90-98 Capture 85.5 84.4 85.3 86.5 91.4 91.6 93.2 93.3 87.9 75.2 -5.8 0.4 Aquaculture 13.1 13.7 15.5 17.9 20.8 24.5 26.8 28.8 29.0 24.8 +0.7 10.6 Total 98.5 98.1 100.7 104.4 122.2 116.0 119.9 122.1 116.9 100.0 -4.3 2.2 Notes: (1) Percentage change between 1997 and 1998. (2) mvr = marginal variation rate Source: FAO Around 75% of the 1998 production came from capture fisheries, with aquaculture in continuous growth. Capture and aquaculture production trends (t106) (Source: Based on FAO data. World production in 1998 was 4.3% less than 1997, but aquaculture production increased (Figure 1). Aquaculture is reliably forecast to continue to grow, to stabilize 2010 around the 35 million t/yr level. In contrast, capture fisheries production in 1998 was only about 87.9 million t, in comparison to the 93.3 million t in 1997. The decreasing trend in capture fisheries is due, broadly, to the excessive fishing effort that is one of the primary causes of overexploitation and even extinction of some species. A lot of species are, in fact, completely exploited (some 44% of the stock), overexploited or in danger of extinction: this implies production crises, forced inactivity periods, and notable variations in capture quantities from one year to the next (causing serious fluctuations in prices). For this reason, FAO has elaborated the Code of Conduct for Responsible Fisheries, that points out behavioural principles for respecting and safeguarding aquatic life, focusing attention on ecosystem problems and biodiversity. In this scenario, aquaculture could provide the means to satisfy the growing demand for fish in the face of a decreasing supply from the wild. From 1990 to 1997, in fact, world per caput consumption grew from 13.3 kg to 15.9 kg, and this trend can be expected to continue in the next few years (Table 2 and Figure 2). Section 1 – Analysis of the sector 4 Table 2. World consumption of fish products per caput (kg/head) Year Per caput consumption (kg/head) 1990 13.3 1991 12.6 1992 13.0 1993 13.6 1994 14.1 1995 15.1 1996 15.4 1997 15.9 Source: FAO data Trend in per caput consumption of fish products from 1990 to 1997 (kg/head) (Source: Based on FAO data) . Price interaction between aquaculture and fishery 5 SECTION 2 METHODOLOGICAL ANALYSIS The method of analysis used in order to identify the appropriate model was the one proposed by the London School of Economics (LSE), generally referred to as “from general to particular.” This procedure, traditionally advocated by LSE, is developed in the opposite direction to classical econometrics. In the first phase, you assume a general model with a lot of lags in the endogenous variabiliy as well as in the exogenous variability. The general model can be formulated as follows: z m z yt ai yt i k ,i xk ,t i t i 1 [1] k 1 i 0 In the second phase, you test the correct specification of the general model, if the parameters are significant and stable. At this point you delete from the general model the notsignificant parameters and estimate the resultant model. On the parameters of the resultant model, the LSE method suggests imposing nine restrictions. In this way you have nine particular models, each of which subtends a specific economic behaviour. For example, if z=1 and m=1, the general model [1] becomes an Autoregressive Lags Model (ADL) (1,1) model: y ay t 1 1 xt 2 xt 1 t [2] Imposing the nine restrictions on the parameters of the model obtained [2], you have the following nine models: Type of model Restriction 1) static regression a = = 0 2) autoregressive model = = 0 3) tendency indicator model a = = 0 4) growth rates model a=1, = - 5) distributed lag model a= 0 6) partial adjustments model = 0 7) restriction to common factor model = - a 8) error correction model (ECM) K = (1 + 2) (1- a) 9) deferred model 1 = 0 6 Section 2 – Methodological analysis The most interesting model of these is the ECM model, coming from the ADL (1,1) model, namely: yt = 1 xt + (a-1)(y-cx)t-1 +et where: yt = yt - yt-1 xt=xt - xt-1; (y-cx)t-1 is the ECM term that represents the deviation from the long-run equilibrium; c is the co-integration coefficient. In this model, variations of yt depend from variations of the dependent variable yxt as well as from disequilibrium (y-cx)t-1 in the previous period. Thus far, this model is the most complete because it gives information about both short and long runs. The parameter 1 represents the short-run reaction; the parameter (a-1) represents the reaction of the dependent variable to the deviations from the static equilibrium. Engle and Granger (1987) showed that you can estimate an ECM model only if the variables are co-integrated. If a co-integration relationship does not exist, there is no long run equilibrium curve. In this case, the model that you can estimate is a first differences model. Price interaction between aquaculture and fishery 7 SECTION 3 THE DATA The models are estimated on the basis of time series of quarterly prices for captured and farmed seabass and seabream, collected in Italy from 1991 to 1998. Captured seabass and seabream prices time series are the result of monitoring of the operators of the sector, collected by IREPA. Farmed seabass and seabream prices time series are collected by FAO (GLOBEFISH) Captured seabass prices time series show a constant pattern over the years, with oscillations in the annual averages depending on the course of captures, in constant diminution since 1995. Of note is the jump each year in December because of Christmas festivities. Captured seabream prices time series show hard oscillations in the annual averages connected to the pattern of captures, in constant diminution since 1994. Note also a jump similar to that of seabass in December because of Christmas festivities. Farmed seabass and seabream prices time series show a negative trend. This is due to the expansion of Italian aquaculture supply, as well as to imports from Greece at extremely competitive prices. Some of these parameters are illustrated in the following figures, namely supply of captured and farmed seabass (Figure 3) and seabream (Figure 4) in Italy; and monthly prices for captured and farmed seabass (Figures 5 and 6) and seabream (Figures 7 and 8) in Italy. Figure 4. Seabream – trends in capture and aquaculture production in Italy, 1991-1998 (Source: FAO data) Seabass – trends in capture and aquaculture production in Italy, 1991-1998 (Source: FAO data) 6000 6000 5000 5000 4000 4000 3000 3000 2000 2000 1000 91 1000 92 93 94 QSC . 95 96 QSA 97 98 0 91 92 93 94 QOC 95 96 QOA 97 98 Section 3 – Data 8 Captured seabass: Trend of monthly prices in Italy, 1991-1998 (Source: IREPA) 52000 Farmed seabass: Trend of monthly prices in Italy, 1991-1998 (Source: IREPA) 24000 22000 48000 20000 44000 18000 40000 16000 36000 14000 12000 32000 91 92 93 94 95 96 97 91 98 92 93 94 95 96 97 98 PSA PSC Captured Seabream: Trend of monthly prices in Italy – 1991-98 (Source: IREPA) 44000 Farmed Seabream: Trend of monthly prices in Italy – 1991-98 (Source: FAO) 24000 22000 40000 20000 18000 36000 16000 32000 14000 12000 28000 10000 24000 8000 91 92 93 94 95 POC 96 97 98 91 92 93 94 95 POA 96 97 98 Price interaction between aquaculture and fishery 9 SECTION 4 THE ECONOMETRIC MODEL This analysis evaluates the impact of aquaculture on fisheries in terms of prices. The data are quarterly captured (C) and farmed (A) seabass and seabream prices time series collected in Italy from 1991 to 1998. The analysis uses the logarithmic specification of prices time series (LC and LA) because it accomodates the elasticity of the model. The variables of the general model are: The dependent variable is LC. The independent variables are: – the dependent variable LC, four times lagged; – the variable LA; – the variable LA, four times lagged; – three dummy variables – D1, D2, D3 – to accommodate the seasonal component; – one dummy variable – DU95 or DU94 – to consider exceptional values, such as a jump in capture quantity in 1995 for seabass and in 1994 for seabream; and – the deterministic trend T. In this model the author applied the London School of Economics (LSE) method: “from the general to the particular.” It implies the following steps: (i) Estimate the general model and perform a diagnostic test on the parameters. (ii) Delete from the general model the not-significant parameters and estimate the resultant model. (iii) On the parameters of the resultant model, impose the nine restrictions1 suggested with the LSE method in order to identify the appropriate model. 1 . For the nine restrictions, see Section 2. Section 4 – The econometric model 10 THE SEABASS CASE ADL (4,4) Model estimate The ADL (4,4) Model is the following: LC = 0 + 1LC(-1) + 2LC(-2) + 3LC(-3) + 4LC(-4) + 5LA + 6LA(-1) + 7LA(-2) + 8LA(-3) + 9LA(-4) + 10T + 11D2 + 12D3 + 13D4 + 14DU95 LS // Dependent variable is LC Sample: 1992:1 to 1998:4 No. of observations: 28 after adjusting endpoints Variable Coefficient SE T-statistic 10.81816 1.245899 8.683012 0.0000 LC(-1) -0.066298 0.136506 -0.485681 0.6353 LC(-2) 0.034547 0.153295 0.225365 0.8252 LC(-3) -0.161272 0.145304 -1.109891 0.2872 LC(-4) C Probability -0.184459 0.112594 -1.638267 0.1253 LA 0.058115 0.038667 1.502985 0.1567 LA(-1) 0.042316 0.042441 0.997041 0.3369 LA(-2) 0.107670 0.041425 2.599127 0.0220 LA(-3) 0.082267 0.041033 2.004876 0.0663 LA(-4) 0.073076 0.040565 1.801455 0.0949 T 0.009456 0.000915 10.32987 0.0000 D2 0.034853 0.009978 3.493140 0.0040 D3 0.043271 0.012884 3.358601 0.0051 D4 0.050324 0.011222 4.484355 0.0006 -0.040752 0.004981 -8.180987 0.0000 DU95 R-squared 0.979524 Mean dependent var 10.57359 Adjusted R-squared 0.957473 S.D. dependent var 0.034663 S.E. of regression 0.007148 Log likelihood 109.3562 Sum squared residual 0.000664 F-statistic 44.42031 Durbin-Watson stat 2.046943 Prob(F-statistic) 0.000000 Breusch-Godfrey Serial Correlation LM Test (4 lags) F-statistic 0.406406 Probability 0.799810 ARCH heteroscedasticity test (4 lags) F-statistic 0.839499 Probability 0.517113 The goodness of fit can be tested by the following graphs: 10.65 10.60 0.015 0.010 10.55 0.005 10.50 0.010 10.45 0.005 0.000 -0.005 0.000 -0.010 -0.005 -0.010 -0.015 92 93 94 Residual 95 96 Actual 97 Fitted 98 96:1 96:3 97:1 97:3 Recursive Residuals 98:1 98:3 ± 2 S.E. Price interaction between aquaculture and fishery 11 1.5 15 10 1.0 5 0.5 0 -5 0.0 -10 -0.5 -15 96:1 96:3 97:1 CUSUM 97:3 98:1 5% Significance 98:3 96:1 96:3 97:1 CUSUM of Squares 97:3 98:1 98:3 5% Significance Section 4 – The econometric model 12 The ADL (4,4) model gives a good interpolation of the data, in terms of R 2 (0.979524) and adjusted R2 (0.957473). This means the variation of LC is explained at 97.98% from the regression. Based on the T-statistic only the constant, the parameter LA(-2), as well as the trend and the dummy variables are significant at 5%. This means that the variation of the dependent variable LC could be determined from: the farmed prices at time t-2 (LA(-2)); from the deterministic trend; as well as from the seasonal component and from exceptional values, like a jump in the captured quantities. The stochastic components are not autocorrelated. That means the residual components for different periods (in this case, quarters) are not correlated. There is no heteroscedasticity in the model. Based on the recursive residuals test, the model results are stable, confirmed by Cusum and the Cusum of Squares test. Analysis of the dynamic structure of the ADL (4,4) model: Redundant Variable test This test can help you determine whether a subset of variables in an equation have zero coefficients and might thus be deleted from the equation. Redundant variables: LC(-1), LC(-2), LC(-3) and LC(-4) F-statistic = 3.506462; Probability = 0.037573 The variables are significant. Redundant variables: LA, LA(-1), LA(-2), LA(-3) and LA(-4). F-statistic = 19.76253; Probability = 0.000012 The variables are significant. Redundant variables D2, D3 and D4 F-statistic = 9.073950; Probability = 0.001671 The variables are significant. Redundant variables: LC(-4) and LA(-4) F-statistic = 2.460941; Probability = 0.1240062 The variables are not significant. Redundant variables: LC(-3) and LA(-3) F-statistic = 2.060766; Probability = 0.166957 The variables are not significant. Redundant variables: LC(-2) and LA(-2) F-statistic = 3.649222; Probability = 0.055224 The variables are significant. Redundant variables: LC(-1) and LA(-1) F-statistic = 0.507506; Probability = 0.613443 The variables are not significant. Based on these results we can delete from the ADL (4,4) model the not-significant variables and estimate the reduced model. Reduced Model estimate The reduced model is: LC = 0 + 1LC(-2) + 2LA + 3LA(-2) + 4T + 5D2 + 6D3 + 7D4 + 8DU95 LS // Dependent variable is LC Price interaction between aquaculture and fishery 13 Sample: 1991:3 to 1998:4 No. of observations: 30, after adjusting endpoints Variable Coefficient C SEr T-statistic Probability 7.803955 1.170543 6.666951 0.0000 -0.040780 0.136988 -0.297690 0.7689 LA 0.095512 0.046915 2.035856 0.0546 LA(-2) 0.215167 0.043197 4.981092 0.0001 T 0.007928 0.001078 7.357522 0.0000 D2 0.039113 0.007454 5.247308 0.0000 D3 0.044127 0.011199 3.940162 0.0007 0.049559 0.006666 7.434682 0.0000 -0.035383 0.006440 -5.494616 0.0000 LC(-2) D4 DU95 R-squared 0.924231 Mean dependent var 10.57559 Adjusted R-squared 0.895367 S.D. dependent var 0.034296 SE of regression 0.011094 Log likelihood 97.82278 Sum squared residual 0.002585 F-statistic 32.01975 Durbin-Watson stat. 1.600424 Probability (F-statistic) 0.000000 Breusch-Godfrey Serial Correlation LM Test (4 lags) F-statistic 2.376307 F-statistic 2.103784 Probability 0.092846 White heteroscedasticity test Probability 0.077845 ARCH heteroscedasticity test (4 lags): F-statistic 2.155201 Probability 10.65 0.03 10.60 0.02 10.55 0.109553 0.01 0.03 10.50 0.02 0.01 0.00 10.45 -0.01 0.00 -0.01 -0.02 -0.02 -0.03 -0.03 92 93 94 Residual 95 96 Actual 97 98 95:3 Fitted 96:1 96:3 97:1 97:3 Recursive Residuals 98:1 98:3 ± 2 S.E. 1.6 15 10 1.2 5 0.8 0 0.4 -5 0.0 -10 -15 -0.4 95:3 96:1 96:3 CUSUM 97:1 97:3 98:1 5% Significance 98:3 95:3 96:1 96:3 97:1 CUSUM of Squares 97:3 98:1 98:3 5% Significance 14 Section 4 – The econometric model Price interaction between aquaculture and fishery 15 The reduced model also gives a good interpolation of data, in terms of R2 (0.924231) and adjusted R2 (0.895367). This means that the variation of LC derives 92.42% from the regression. These results are inferior in comparison to those from the model ADL (4,4), indicating that the ADL (4,4) reduced model involves a worsening of the regression. Based on T-statistics, only the constant, the parameter LA(-2), and the trend and the dummy variables are significant at 5%. This means that the variation of the dependent variable LC could be determined from: the farmed prices at time t-2 (LA(-2)); from the deterministic trend; as well as from the seasonal component and from exceptional events like an increase in the capture quantities. The stochastic components are not autocorrelated. That means the residual components relative to different periods (in this case, quarters) are not correlated. There is no heteroscedasticity in the model. Based on the recursive residuals test, the model is unstable; based on Cusum and the Cusum of Squares test, it is stable. Restrictions of the reduced model The nine restrictions are tested by the Wald test. The Wald test deals with hypotheses involving restrictions on the coefficients of the explanatory variables. The restrictions may be linear or non-linear, and two or more restrictions may be tested jointly. Output from the Wald test depends on the linearity of the restriction. For linear restrictions, the output is an F-statistic and a chi-squared statistic with associated p-values. Accepting the Null hypothesis of the test implies the possibility of estimating the resultant model. (i) Wald test for the static model F-statistic = 13.71840; Probability = 0.000155 Chi-square = 27.43680; Probability = 0.000001 H 0 : 1 3 0 H A : 1 , 3 , 0 (ii) Wald test for the AR 1 model F-statistic = 26.38602; Probability = 0.000002 Chi-square = 52.77205; Probability = 0.000000 H 0 : 2 3 0 H A : 2 , 3 , 0 (iii) Wald test for the tendency indicator model F-statistic = 2.162065; Probability = 0.140020 Chi-square = 4.324129; Probability = 0.115087 H 0 : 1 2 0 H A : 1 , 2 0 (iv) Wald test for the growth rates model F-statistic = 31.27552; Probability = 0.000001 Chi-square = 62.55105; Probability = 0.000000 H 0 : 1 1, 3 2 H A : 1 1, 3 2 (v) Wald test for the distributed lags model F-statistic = 0.088620; Probability = 0.768865 Chi-square = 0.088620; Probability = 0.765939 H 0 : 1 0 H A : 1 0 (vi) Wald test for the partial adjustments model F-statistic = 24.81128; Probability = 0.000063 Chi-square = 24.81128; Probability = 0.000001 H 0 : 3 0 H A : 3 0 (vii) Wald test for the restriction to common factor model F-statistic = 25.77946; Probability = 0.000050 Chi-square = 25.77946; Probability = 0.000000 H 0 : 3 1 * 2 H A : 3 1 * 2 (viii) Wald test for the deferred model F-statistic = 4.144712; Probability = 0.054575 Chi-square = 4.144712; Probability = 0.041765 H 0 : 2 0 H A : 2 0 Section 4 – The econometric model 16 Based on these results, we accept the Null hypothesis of the test only for case (v), the distributed lag model. That implies the possibility of estimating the resultant model. Distributed Lag Model Estimate The model is the following: LC = 0 + 1LA(-2) + 2T + 3D2 + 4D3 + 5D4 + 6DU95 LS // Dependent variable is LC Sample: 1991:3 to 1998:4 No. of observations: 30, after adjusting endpoints Variable Coefficient SE T-statistic C 7.771893 0.294993 26.34606 0.0000 LA(-2) 0.270152 0.029308 9.217677 0.0000 T 0.007155 0.000664 10.78206 0.0000 D2 0.047476 0.006389 7.431041 0.0000 D3 0.059942 0.006380 9.395743 0.0000 0.055470 0.006125 9.056476 0.0000 -0.030688 0.004364 -7.032540 0.0000 D4 DU95 Probability R-squared 0.908629 Mean dependent var 10.57559 Adjusted R-squared 0.884793 S.D. dependent var 0.034296 SE of regression 0.011641 F-statistic 38.12029 Sum squared residual 0.003117 Probability (F-statistic) 0.000000 Log likelihood 95.01426 Durbin-Watson stat 1.645804 Breusch-Godfrey Serial Correlation LM Test (4 lags) F-statistic 2.580668 Probability 0.070413 White heteroscedasticity test F-statistic 1.720894 Probability 0.151939 ARCH heteroscedasticity test (4 lags) F-statistic 4.296370 Probability 0.010745 The goodness of fit can be tested by the following graphs: 10.65 0.04 10.60 0.03 10.55 0.02 10.50 0.01 0.03 0.02 0.01 10.45 0.00 0.00 -0.01 -0.01 -0.02 -0.02 -0.03 -0.03 92 93 94 Residual 95 96 Actual 97 Fitted 98 95:3 96:1 96:3 97:1 97:3 Recursive Residuals 98:1 98:3 ± 2 S.E. Price interaction between aquaculture and fishery 17 1.6 15 10 1.2 5 0.8 0 0.4 -5 0.0 -10 -0.4 -15 95:3 96:1 96:3 CUSUM 97:1 97:3 98:1 5% Significance 98:3 95:3 96:1 96:3 97:1 CUSUM of Squares 97:3 98:1 98:3 5% Significance Section 4 – The econometric model 18 The model also gives a good interpolation of data, in terms of R2 (0.908629) and adjusted R (0.884793). This means that the variation of LC derives 90.86% from the regression. These results are inferior to those from the reduced model, so the reduction of the reduced model in the distributed lag model involves a worsening of the regression. Based on T-statistics, only the constant, the parameter LA(-2), as well as the trend and the dummy variables are significant at 5%. This means that the variation of the dependent variable LC could be determined from: the farmed prices at time t-2 (LA(-2)); from the deterministic trend; as well as from the seasonal component and from exceptional events like a increase in the captured quantities. 2 The stochastic components are not autocorrelated. That means the residual components relative to different periods (in this case, quarters) are not correlated. There is no heteroscedasticity in the model. Based on a recursive residuals test, the model results are unstable, and similarly based on a Cusum test. Based on the Cusum of Squares test, the results are stable. Co-integration Analysis between LC and LA A group of non-stationary time series data is co-integrated if there is a linear combination of them that is stationary, i.e. the combination does not have a stochastic trend. The linear combination is called the Co-integrating Equation. Its normal interpretation is as a long-run equilibrium relationship. A non-stationary time series can often be made stationary if it is differenced once or several times. If a time series is non-stationary but its first difference is stationary, it is said to have a unit root and to be integrated of order one (I(1)). A time series that has to be differenced d times before it becomes stationary is integrated of order d (I(1)). The order of a time series can be tested by an Augmented Dickey Fuller (ADF) test. The ADF test involves running a regression of the first difference of the series against the series lagged once, lagged difference terms, and, optionally, a constant and a time trend. The test for a unit root is a test on the coefficient of the lagged dependent variable in the regression. If the coefficient is significantly different from zero, the hypothesis that y contains a unit root is rejected and the hypothesis is accepted that y is stationary rather than integrated. The output of the ADF test consists of the T-statistic of the Coefficient of the lagged test variable and critical values for the test of a zero coefficient. If the Dickey-Fuller T-statistic is smaller (in absolute value) than the reported critical values, you cannot reject the hypothesis of non-stationarity and the existence of a unit root. You would conclude that your series may not be stationary. You may then wish to test whether the series is I(1) (integrated of order one) or integrated of a higher order. A series is I(1) if its first difference does not contain a unit root. You can repeat the ADF test on the first difference of your series to test the hypothesis of integration of order 1 against higher orders. You can repeat the test on second differences if you find that the first difference may be non-stationary. ADF test on LC ADF Test Statistic = -3.322718; 1% Critical Value* = -3.6576 Since ADF Test Statistic > % Critical Value*, then LC is integrated. ADF test on LC ADF Test Statistic = -8.820744; 1% Critical Value* = -2.6423 Since ADF Test Statistic < 1% Critical Value*, then LC is not integrated, LC is I(1). * MacKinnon critical values for rejection of hypothesis of a unit root. Price interaction between aquaculture and fishery 19 ADF test on LA ADF Test Statistic = -1.050449; 1% Critical Value* = -2.6395 Since ADF Test Statistic > 1% Critical Value*, then LA is integrated. ADF test on the LA ADF Test Statistic = -6.421043; 1% Critical Value* = -2.6423 Since ADF Test Statistic < 1% Critical Value*, then LA is not integrated, LA is I(1). ADF test on the RESID series (the RESID series represents the residuals of the static regression of LC on LA) ADF Test Statistic = -3.326980; 1% Critical Value* = -2.6395 Since ADF Test Statistic < 1% Critical Value*, then RESID is not integrated. the RESID is a linear combination between LC and LA. That means that RESID is I(0) and implies that LC and LA are co-integrated. Error Correction Model estimate Since LC and LA are co-integrated, we can estimate the ECM, using the following model: LC = 0 + 1LA + 2ECM(-1) + 3T + 4D2 + 5D3 + 6D4 + 7DU95 LS // dependent variable is LC Sample: 1991:2 to 1998:4 No. of observations: 31, after adjusting endpoints Variable C Coefficient SE T-Statistic Probability -0.058882 0.014016 -4.201170 0.0003 0.074322 0.065475 1.135125 0.2680 -0.097751 0.252552 -0.387053 0.7023 T 0.000534 0.000747 0.714531 0.4821 D2 0.084059 0.011174 7.522743 0.0000 D3 0.054003 0.010335 5.225375 0.0000 LA ECM(-1) D4 0.069282 0.009596 7.219618 0.0000 DU95 -0.002286 0.006805 -0.335955 0.7400 R-squared 0.817426 Mean dependent var 0.002693 Adjusted R-squared 0.761859 S.D. dependent var 0.037023 S.E. of regression 0.018067 F-statistic 14.71086 Sum squared resid 0.007508 Prob(F-statistic) Log likelihood 85.06289 Durbin-Watson stat 2.168804 0.000000 Breusch-Godfrey Serial Correlation LM Test (4 lags) F-statistic 0.645325 Probability 0.636912 White heteroscedasticity test F-statistic 2.069333 F-statistic 0.151918 Probability 0.079810 ARCH heteroscedasticity test (4 lags) Probability 0.960128 Ramsey RESET Test (4 lags) F-statistic 1.001378 Probability 0.431187 Ramsey RESET Test (3 lags) F-statistic 0.019303 F-statistic 0.027934 Probability 0.996232 Ramsey RESET Test (2 lags) Probability 0.972489 Section 4 – The econometric model 20 The goodness of fit can be tested by the following graphs: 0.10 0.06 0.05 0.04 0.00 -0.05 0.04 -0.10 0.02 0.02 0.00 -0.15 0.00 -0.02 -0.02 -0.04 -0.04 -0.06 -0.08 -0.06 92 93 94 95 Residual 96 Actual 97 98 95:3 Fitted 96:1 96:3 97:1 97:3 Recursive Residuals 98:1 98:3 ± 2 S.E. 1.6 15 10 1.2 5 0.8 0 0.4 -5 0.0 -10 -0.4 -15 95:3 96:1 96:3 CUSUM 97:1 97:3 98:1 5% Significance 98:3 95:3 96:1 96:3 97:1 CUSUM of Squares 97:3 98:1 98:3 5% Significance Price interaction between aquaculture and fishery 21 Section 4 – The econometric model 22 The model gives a good interpolation of data, in terms of R2 (0.817426) and adjusted R2 (0.761859). This means that the variation of LC derives 81.74% from the regression. These results are inferior to those from the ADL (4,4) model. Thus the reduction of the reduced model in the distributed lag model involves a worsening of the regression. Base on the statistic T alone, the constant and the dummies variables are significant. This means that the variation of the dependent variable LC could be determined only from the seasonality. The variable LA is not significant and that means that the short-run adjustment parameter has no influence on the variation of the dependent variable. The variable ECM(-1) is also not significant, so the long-run adjustment parameter has no influence on the variation of the dependent variable. The stochastic components are not autocorrelated. That means the residual components relative to different periods (in this case, quarters) are not correlated. There is no heteroscedasticity in the model. Based on the Ramsey Reset test, the model is correctly specified. On the base of the recursive residuals test, the model results are unstable; similarly from Cusum and Cusum of Squares test. Granger Causality Test The Granger approach to the question of whether X causes Y is to see how much of the current Y can be explained by past values of Y, and then to see whether adding lagged values of X can improve the explanation. Y is said to be Granger-caused by X if X helps in the prediction of Y, or equivalently if the coefficients of the lagged Xs are statistically significant. Pairwise Granger Causality Tests Sample: 1991:1 to 1998:4 Lags: 4 Null Hypothesis Observations [1] LA does not Granger Cause LC 28 [2] LC does not Granger Cause LA F-Statistic Probability 0.61456 0.65737 1.54295 0.23005 [1] Since F-statistic = 0.61456 < F0.058,19 = 2.48, then LA does not Granger Cause LC [2] Since F-statistic = 1.54295 < F0.058,19 = 2.48, then LC does not Granger Cause LA Comments and conclusions on the Seabass case Any of the estimated models gives satisfactory results. Not all the parameters of the estimated models are significant and in some case the models are not stable and correctly specified. Although there is a co-integration relationship between the LC and LA series, the ECM model does not furnish satisfactory results: the short-run adjustment parameter, LA, has no influence on the variation of the dependent variable. The ECM(-1) variable is also not significant, so the lung-run adjustment parameter has no influence on the variation of the dependent variable. These results mean that the two different product forms – captured seabass and farmed seabass – are not substitutes for each other and there is no link between their prices. Therefore a long-run change in price of one product has no impact on the long-run price of the other product. The Granger causality test confirms this results. In both cases, in fact, we accept the null hypothesis: LA does not Granger Cause LC in case [1] and LC does not Granger Cause LA in case [2]. Price interaction between aquaculture and fishery 23 THE SEABREAM CASE ADL (4,4) Model estimate The ADL (4,4) Model is the following: LC = 0 + 1LC(-1) + 2LC(-2) + 3LC(-3) + 4LC(-4) + 5LA + 6LA(-1) + 7LA(-2) + 8LA(-3) + 9LA(-4) + 10T + 11D2T + 12D3T + 13D4T + 14DU94 LS // dependent variable is LC Sample: 1992:1 to 1998:4 No. of observations: 28, after adjusting endpoints Variable Coefficient SE T-Statistic Probability C 2.267910 3.768348 0.601831 0.5576 LC(-1) 0.098210 0.139733 0.702842 0.4945 LC(-2) 0.193248 0.144248 1.339694 0.2033 LC(-3) -0.241864 0.145356 -1.663942 0.1200 LC(-4) 0.267353 0.164226 1.627961 0.1275 LA 0.278185 0.110766 2.511457 0.0260 LA(-1) -0.008705 0.124759 -0.069776 0.9454 LA(-2) -0.100967 0.128454 -0.786015 0.4459 LA(-3) -0.046655 0.123526 -0.377692 0.7118 LA(-4) 0.353730 0.111651 3.168173 0.0074 T 0.013865 0.003310 4.189143 0.0011 D2T -0.004188 0.002089 -2.005172 0.0662 D3T -0.002600 0.001791 -1.451676 0.1703 D4T 0.001823 0.001747 1.043843 0.3156 -0.084154 0.033863 -2.485116 0.0273 DU94 R-squared 0.922137 Mean dependent var. 10.28433 Adjusted R-squared 0.838284 S.D. dependent var. 0.065112 SE of regression 0.026184 Sum squared resid. 0.008913 Log likelihood 73.00417 F-statistic 10.99708 Durbin-Watson stat. 1.743384 Probability (F-statistic) 0.000052 Breusch-Godfrey Serial Correlation LM Test (4 lags) F-statistic 0.828567 Probability 0.539346 ARCH Test ( 4 lags) F-statistic 2.379440 Probability 0.088057 The goodness of fit can be tested by the following graphs: 10.5 0.10 10.4 10.3 0.04 10.2 0.02 10.1 0.05 0.00 0.00 -0.05 -0.02 -0.04 -0.06 -0.10 92 93 94 Residual 95 96 Actual 97 98 Fitted 96:1 96:3 97:1 97:3 Recursive Residuals 98:1 98:3 ± 2 S.E. Section 4 – The econometric model 24 1.5 15 10 1.0 5 0.5 0 -5 0.0 -10 -0.5 -15 96:1 96:3 97:1 CUSUM 97:3 98:1 5% Significance 98:3 96:1 96:3 97:1 CUSUM of Squares 97:3 98:1 98:3 5% Significance Price interaction between aquaculture and fishery 25 The ADL (4,4) model gives a good interpolation of data, is in terms of R2 (0.922137) and adjusted R2 (0.838284). This means the variation of LP0C is explained at 92.21% from the regression. Base on the statistic T, the parameters LA and LC(-4), as well as the trend and the dummy variable DU94, are significant at 5%. This means that the variation of the dependent variable LC could be determined from: the farmed prices at time t and at time t-4 (LA and LA(4)); from the deterministic trend; as well as from the seasonal component and from exceptional events like an increase in the captured quantities. The stochastic components are not autocorrelated. That means the residual components relative to different periods (in this case, quarters) are not correlated. There is no heteroscedasticity in the model. Based on the recursive residuals test, the model is stable, confirmed by the Cusum test. Based on Cusum of Squares test results, it is unstable. Analysis of the dynamic structure of the ADL (4,4) model: Redundant variable test This test can help you determine whether a subset of variables in an equation have zero coefficients and could thus be deleted from the equation. Redundant variables: LC(-1), LC(-2), LC(-3) and LC(-4) F-statistic = 1.834613; Probability = 0.182390 The variables are significant. Redundant variables: LA, LA(-1), LA(-2), LA(-3) and LA(-4) F-statistic = 3.591426; Probability = 0.029284 The variables are significant. Redundant variables: D2T, D3T and D4T F-statistic = 3.878624; Probability = 0.035059 The variables are significant. Redundant variables: LC(-4) and LA(-4) F-statistic = 6.480220; Probability = 0.011158 The variables are not significant. Redundant variables: LC(-3) and LA(-3) F-statistic = 1.419719; Probability = 0.276900 The variables are not significant. Redundant variables: LC(-2) and LA(-2) F-statistic = 1.425754; Probability = 0.275533 The variables are not significant. Redundant variables: LC(-1) and LA(-1) F-statistic = 0.251064; Probability = 0.781659 The variables are not significant. On the basis of these results we can delete from the ADL (4,4) model the not-significant variables and estimate the reduced model. Reduced Model estimate The reduced model is the following: LC = 0 + 1LC(-4) + 2LA + 3LA(-4) + 4T + 5D2T + 6D3T + 7D4T + 8DU94 LS // dependent variable is LC Section 4 – The econometric model 26 Sample: 1992:1 to 1998:4 No. of observations: 28, after adjusting endpoints Variable Coefficient SE T-Statistic Probability C 1.838552 2.130271 0.863060 0.3989 LC(-4) 0.343826 0.142974 2.404822 0.0265 LA 0.232601 0.078135 2.976925 0.0077 LA(-4) 0.258044 0.080913 3.189142 0.0048 T 0.013535 0.002919 4.637063 0.0002 D2T -0.002869 0.001437 -1.997081 0.0603 D3T -0.002471 0.001511 -1.635629 0.1184 D4T 0.002142 0.001186 1.806741 0.0867 -0.075700 0.022680 -3.337685 0.0035 DU94 R-squared 0.889521 Mean dependent var 10.28433 Adjusted R-squared 0.843003 S.D. dependent var 0.065112 SE of regression 0.025799 F-statistic Sum squared resid 0.012646 Probability (F-statistic) Log likelihood 68.10594 Durbin-Watson stat. 1.592515 9.12221 0.000000 Breusch-Godfrey Serial Correlation LM Test (4 lags) F-statistic 1.552114 Probability 0.237907 White heteroscedasticity test F-statistic 2.383313 Probability 0.063325 ARCH heteroscedasticity test (4 lags) F-statistic 2.001892 Probability 0.135124 The goodness of fit can be tested by the following graphs: 10.5 0.10 10.4 0.05 0.10 10.3 0.05 10.2 0.00 10.1 0.00 -0.05 -0.05 -0.10 -0.10 92 93 94 Residual 95 96 Actual 97 98 94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3 Fitted Recursive Residuals ± 2 S.E. 1.6 15 10 1.2 5 0.8 0 0.4 -5 0.0 -10 -0.4 -15 94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3 CUSUM 5% Significance 94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3 CUSUM of Squares 5% Significance Price interaction between aquaculture and fishery 27 The reduced model gives also a good interpolation of data, in terms of R 2 (0.889521) and adjusted R2 (0.843003). This means that the variation of LC derives 88.95% from the regression. These results are inferior to those from the model ADL (4,4), so the reduction of the model ADL (4,4) in the reduced model involves a worsening of the regression. Based on the statistic T, only the constant, the parameter LC(-4), LA and LA(-4), as well as the dummy variable, are significant at 5%. This means that the variation of the dependent Variable LC could be determined from: the captured price at time t-4 (LA(-4)); from the farmed price at time t and at time t-4 (LA, LA(-4)); and from exceptional events like an increase in the captured quantities. The stochastic components are not autocorrelated. That means the residual components relative to different periods (in this case, quarters) are not correlated. There is no heteroscedasticity in the model. Based on the recursive residuals test, the model is stable, as well as based on the Cusum test. Based on the Cusum of Squares test, it is unstable. Section 4 – The econometric model 28 Restrictions on the reduced model The nine restrictions are tested by the Wald test. (i) Wald test for the static model F-statistic = 7.737048; Probability = 0.003483 Chi-squared = 15.47410; Probability = 0.000436 H 0 : 1 3 0 H A : 1 , 3 , 0 (ii) Wald test for the AR 1 model F-statistic = 6.924188; Probability = 0.005512 Chi-square = 13.84838; Probability = 0.000984 H 0 : 2 3 0 H A : 2 , 3 , 0 (iii) Wald test for the tendency indicator model F-statistic = 5.884440; Probability = 0.010259 Chi-square = 11.76888; Probability = 0.002782 H 0 : 1 2 0 H A : 1 , 2 0 (iv) Wald test for the rates of growth model F-statistic = 20.95566; Probability = 0.000016 Chi-square = 41.91132; Probability = 0.000000 H 0 : 1 1, 3 2 H A : 1 1, 3 2 (v) Wald test for distributed lags model F-statistic = 5.783171; Probability = 0.026537 Chi-square = 5.783171; Probability = 0.016180 H 0 : 1 0 H A : 1 0 (vi) Wald test for the partial adjustments model F-statistic = 10.17063; Probability = 0.004830 Chi-square = 10.17063; Probability = 0.001427 H 0 : 3 0 H A : 3 0 (vii) Wald test for the restriction to common factor model F-statistic = 10.74012; Probability = 0.003964 Chi-square = 10.74012; Probability = 0.001048 H 0 : 3 1 * 2 H A : 3 1 * 2 (viii) Wald test for the deferred model F-statistic = 8.862081; Probability = 0.007747 Chi-square = 8.862081; Probability = 0.002912 Based on these results, we reject the null hypothesis of each impossibility of estimating the resultant model. H 0 : 2 0 H A : 2 0 test. That implies the Co-integration analysis For discussion of the concepts of unit root and co-integration, see relevant section. * indicates MacKinnon critical values for rejection of hypothesis of a unit root. ADF test on LC ADF Test Statistic = -4.465239; 1% Critical Value = -3.6576 Since ADF Test Statistic < 1% Critical Value*, then LC is not integrated. ADF test on LA ADF Test Statistic = -6.237515; 1% CriticalValue* = -4.2949 Since ADF Test Statistic < 1% CriticalValue*, then LA is not integrated. Since the LA and LC series are both stationary, a co-integration relationship does not exist. In this case we can estimate the model at first difference. First Difference Model estimate The model is: LC = 0 + 1LA + 2T + 3D2T + 4D3T + 5D4T + 6DU94 LS // dependent variable is LC Price interaction between aquaculture and fishery 29 Sample: 1991:2 to 1998:4 No. of observations: 31, after adjusting endpoints Variable Coefficient SE T-Statistic Probability C -0.005883 0.023717 -0.248037 0.8062 LA 0.079551 0.154230 0.515793 0.6107 T -0.005184 0.002737 -1.894129 0.0703 D2T 0.007550 0.002576 2.931189 0.0073 D3T 0.007033 0.001735 4.052618 0.0005 D4T 0.009438 0.001707 5.529507 0.0000 DU94 -0.010827 0.041434 -0.261317 0.7961 R-squared 0.625758 Mean dependent var. Adjusted R-squared 0.532197 S.D. dependent var. 0.003370 0.087854 SE of regression 0.060089 Sum squared resid. 0.086657 Log likelihood 47.14965 F-statistic 6.688269 Durbin-Watson stat. 2.977549 Probability (F-statistic) 0.000295 Breusch-Godfrey Serial Correlation LM Test (4 lags) F-statistic 3.913448 Probability 0.016629 Breusch-Godfrey Serial Correlation LM Test (1 lag) F-statistic 7.526868 Probability 0.011574 White heteroscedasticity test F-statistic 0.882398 F-statistic 8.053418 Probability 0.571470 ARCH heteroscedasticity test (4 lags) Probability 0.000370 ARCH heteroscedasticity test (1 lag) F-statistic 8.092822 Probability 0.008216 Ramsey RESET Test (4 lags) F-statistic 5.393020 Probability 0.004111 Ramsey RESET Test (3 lags) F-statistic 7.120685 F-statistic 6.179006 Probability 0.001766 Ramsey RESET Test (2 lags) Probability 0.007419 The goodness of fit can be tested by the following graphs: 0.2 0.2 0.1 0.0 0.1 -0.1 0.2 -0.2 0.1 0.0 -0.3 0.0 -0.1 -0.1 -0.2 -0.2 92 93 94 Residual 95 96 Actual 97 Fitted 98 94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3 Recursive Residuals ± 2 S.E. Section 4 – The econometric model 30 1.6 15 10 1.2 5 0.8 0 0.4 -5 0.0 -10 -0.4 -15 94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3 CUSUM 5% Significance 94:3 95:1 95:3 96:1 96:3 97:1 97:3 98:1 98:3 CUSUM of Squares 5% Significance Price interaction between aquaculture and fishery 31 The first differences model doesn’t give a good interpolation of data in terms of R 2 (0.625758) and adjusted R2 (0.532197). This means that the variation of LC derives 62.57% from the regression. Based on the T-statistic, only the dummy variables are significant. This means that the variation of the growth rate of LC depends only on the seasonally component. The stocastic components are not autocorrelated. That means that the residual components relative to different periods (in this case, quarters) are not correlated. There is heteroscedasticity in the model. Based on the Ramsey Reset test, the model is correctly specified. Based on the recursive residuals test, the model is stable, confirmed by the Cusum and Cusum of Squares test. The disadvantage of this model is that by differentiating the variables the long-run information is lost. Granger Causality Test For discussion of the concepts of unit root and co-integration, see relevant section. Paired Granger Causality Tests Sample: 1991:1 to 1998:4 Lags: 4 Null Hypothesis: [1] LA does not Granger Cause LC [2] LC does not Granger Cause LA Observations F-Statistic Probability 28 2.44236 0.08208 1.2686 0.31684 [1] Since F-statistic = 2.44236 < F0.058,19 = 2.48, then LA does not Granger Cause LC [2] Since F-statistic = 1.26863 < F0.058,19 = 2.48, then LC does not Granger Cause LA Comments and conclusions on the Seabream case Any of the estimated models gives satisfactory results. Not all the parameters of the estimated models are significant and in some case the models are not stable and incorrectly specified. The series LC and LA are both stationary; this excludes linkage between the two series, and the two different product forms – captured seabream and farmed seabream – are not substitutes for each other. Therefore there is no link between their prices, so that a long-run change in one price has no impact on the long-run price of the other product. The short-run adjustment parameter – LA – also has no influence on the variation of dependent variable LC, as shown from the first difference model. The Granger causality test confirms this results. In both cases, in fact, we accept the null hypothesis, namely that: LA does not Granger Cause LC in case [1], and LC does not Granger Cause LA in case [2]. Section 5 – Conclusions 32 SECTION 5 CONCLUSIONS In this study, the impact of aquaculture on fishing has been analysed using an empirical and econometric approach. Based on the estimated models and on the Augmented Dickey-Fuller tests on time series, we conclude that captured and farmed species are not substitutes for each other with no link between their prices, and a long-run change in farmed species prices has no impact on the longrun price of captured species. In terms of econometrics, the time series for seabass prices are cointegrated, and a relationship could be found. However, the short- and long-run adjustment parameters of the ECM model are not significant. The time series for seabream prices is stationary, and the short-run adjustment parameter of the first difference model is not significant. The Granger causality test confirms these results. In both cases – seabass and seabream – neither farmed species price affects the captured species price, and neither captured species price affects the farmed species price. These results indicate that there are two separate markets: a market for captured products and a market for farmed products. The first is characterized by a long-term stable prices; the second is characterized by a decreasing prices, linked to increasing farmed production and lowcost imports. The expansion and the success of the farmed product is due to the stability of the supply and to its low market price. The low price is due to the lower cost of the farmed product imported into Italy from Greece. Italy is in fact the target-market of producers of seabass and seabream from the entire Mediterranean area, and in particular of Greek producers. This situation can be attributed to the increasing seafood demand in Italy, with growth in imports and increased dependence on foreign supplies. The increasing demand has made the Italian market interesting to international suppliers. These, at the beginning of the 1990s, were attracted by the high prices at that time. As supplies increased, competition caused a sharp and continuous decrease in farmed seabass and seabream prices. In such a context, it is questionable if the farmers can continue to sell their production simply by reducing prices. Farm-gate prices have for many fishfarmers, especially in Italy where production costs are higher than in Greece, now reached a break-even point. In the future, factors such as quality and distribution will increase their importance as competitive parameters. Also for this reason, capture fisheries is not an activity that should suffer the consequences of the low price politics of aquaculture, but can in fact benefit from it. Fisheries, in fact, should defend its market quotas: by valorizing the quality of the captured product; and by identifying target buyers willing to pay higher prices for superior quality products. In fact, members of sensory test panels repeatedly express their preference for the captured seabass and seabream, commenting that farmed fish are more fatty; softer; and with less taste than the captured type. Price interaction between aquaculture and fishery 33 From the estimates presented here, we can conclude that the markets for farmed and captured fish should be considered separate. In fact, the variables linked differ either in terms of product characteristics or in terms of market demand. Finally, the impact of aquaculture on capture fisheries should not be considered as a substitution process of the farmed product for the captured one. Undoubtedly, the fishing sector is in a state of crisis, but the causes have to be sought in marine environment degradation and overexploitation of marine resources. Bibliography 34 BIBLIOGRAPHY Asche, F. 1995. A system approach to the demand for salmon in the European Union. 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