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IREPA Onlus – Istituto Ricerche Economiche Pesca e Acquacoltura
The Relationship between Fleet Capacity, Landings, and the Components Parts of Fishing Effort
XIII EAFE CONFERENCE Salerno 18-20 April 2001 The Main Objectives
The aim of this research is to study the relationship between Landings, Effort and Fleet Capacity. It is well known that this kind of relationship, has been classified as catch effort model by the fisheries literature.
In this research we use
neoclassic production function theory
as methodological framework. Following this approach, the relationship between Landings, Effort and Fleet Capacity will become a production function where landings is the dependent variable and Effort and Fleet Capacity are the independent variables.
In the production function
input.
the stock size is also included as
Analytic specification of the short run production function have been developed. In the long run analysis the dynamic of each factor, stock included, has been considered. This kind of analysis has also been studied using also the neoclassic Growth theory approach (Coppola,1998).
The Data Bank
For the Italian fisheries
the time series cover a period of 28 years starting from 1972 to 1999 and concerning each of the 47 species observed by the National Statistical Institute (ISTAT).
The data are geographically subdivided into different levels: i) 10 coastal areas; ii) 15 administrative regions; iii) 44 maritime districts; iv) 54 Italian provinces and 302 boat enrolment offices.
Catches
(tons) Data available: monthly production by species and by boat enrolment offices.
Prices
Data available: monthly prices by species and by boat enrolment offices.
Fishing Fleet
Data available: number of vessels, GRT, HP, length, year of construction, crew, fishing system (MAGPIII), fishing days and by boat enrolment offices.
Scientific Background and Methodology
In the fisheries economics, the relationship between landing and fishing effort has been modelled as a Catch-Effort model. In the well known Catch Effort model (Shaefer [1954], Fox [1970], Pella and Tomlinson [1969], it is assumed that in short run the amount of the catch is proportional to the size of the stock and to the level of effort. In formulas we can write:
Y=qEP
where
Y
are the catch,
q
the catchability coefficient,
E
the Effort and
P
the size of the stock
.
The models differentiate owing to the long run law of growth. In the Shaefer model it is a logistic curve while in the Fox model is a Gompertz curve. When the neo-classic production function theory has been applied to the fisheries, we deal with several specific problems. Shortly they are: - the role of the stock in the production function. The stock may be considered as input factor, as externality, or as both (input factor and externality); - the means and the measure of the effort; - the functional form; - the Long run analysis modelling. In the Long run analysis we have to take into account the dynamic of the factor (Biomass and Effort). The stock growth according biological rules and fishing mortality. The effort in the catch effort model is a control variable and it has not an own dynamics; The short-run Catch Effort model may be considered a production function, where
Y
is the output, and
E
is the input
.
function with respect to
E
If
P
is not considered an input, this model is a linear production . It means that in the short run, when the stock size is constant, increasing the effort a higher level of catch will be obtained with the same constant average productivity. In the other case (
P
is considered as an input), the same function became a production function with increasing return to scale (a Cobb Douglas with return to scale equals to 2). Obviously this is a not realistic. Many authors have considered a Cobb Douglas where and are respectively the elasticity of the effort and the stock size. An other functional form proposed (Coppola 1996, Coppola Pascoe 1998) is the Spillman [1923] production function applied to the fisheries
Y
P
( 1
R
e E
)( 1
R
s S
)
where
Y = catch P = size of the entire biomass E = fishing effort S = part of the entire biomass R
e
,
R
s
= parameters ; 0
R
e
1 e 0 <
R
s
< 1
The factor
S,
size of the stock, is the natural factor of choice in production function.
In the open access the use cost of to the stock is near to zero. It implies that also that the marginal productivity of the stock tends to zero, or alternatively, the size of the stock use tends to be infinite. The production function becomes:
Y
P
( 1
R
e E
)
where
Y = catch P = size of the entire biomass E = fishing effort
Once defined the production function in the short run we want to individuate the production function in the long run. Differently from the short run, in the long run the size of biomass
P
is not constant and can change both for the natural growth and for the fishing activity. For sake of semplicity, we may assume that ( whereare respectively the intrinsic growth rate and the Carrying Capacity.
E
is, as usual, a control variable
.
The long-run equation is
Y
K
1 1
a
( 1
R E
) ( 1
R E
)
Empirical Framework
The Estimation of the model depends on the functional form of the production function to estimate and on the on the available data. The Shaefer model and the Fox model are clearly linear model and can be estimated with OLS method. Coppola (1993, 1994) has estimated the Shaefer and the Fox models with data concerning the Italian case. Both papers include dynamic models estimated with using LSE methodology developed by Hendry (1979). This permits to distinguishes and to estimate the short-run and the long run elasticity of the effort. Besides the estimates of the model, several test have been also implemented. Particular attention has been given to the cointegration analysis. In the catch effort model, cointegration analysis tests the existence of a long-run relationship between catch-effort relationship. In other term, with cointegration we test if the long run equilibrium between catch and effort really exists. The Spillman production function is a non-linear model and it is not possible to use OLS in order to estimate it. Also to test the cointegration between the variable is quite difficult too. Tsoa, Shrank and Roy (1985) Pascoe (1998) estimated a non linear model.
Coppola and
Expected Results
-To improve the theoretical models for the fishery production function; -To estimate the relationship between landings, stocks, capacity and other inputs for: -the Italian trawl fishery, -the Scottish North Sea demersal fishery and for -the Danish North Sea demersal fishery; - To identify the components of fishing effort in the three chosen fisheries; - To quantify the influence of technical change; •
Teams involved
Centre for Fishery Economics Research (CFER)
contact person: Philip Rodgers, e-mail: [email protected]
•
Danish Institute of Fisheries Economics Research (DIFER)
contact person: Henning P. Jorgensen, e-mail: [email protected].
•
Istituto Ricerche Economiche Pesca ed Acquacoltura (IREPA)
contact person: Vincenzo Placenti, e-mail: [email protected]
•