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Add To Calendar 21/02/2017 14:00:0021/02/2017 14:20:00America/ChicagoAquaculture America 2017TECHNICAL EFFICIENCY OF THE EUROPEAN FISH AND SEAFOOD PRODUCTION INDUSTRY Room 12The World Aquaculture Societyjohnc@was.orgfalseanrl65yqlzh3g1q0dme13067DD/MM/YYYY

TECHNICAL EFFICIENCY OF THE EUROPEAN FISH AND SEAFOOD PRODUCTION INDUSTRY

José L. Fernández Sánchez*, José M. Fernández Polanco, Ignacio Llorente,
Elisa Baraibar Diez, María D. Odriozola Zamanillo, and Ladislao Luna Sotorrío
 
IDES Research Group
Faculty of Economics and Business Administration
University of Cantabria
Avda. de los Castros s/n 39005 Santander (Spain)
fernandezjl@unican.es

The purpose of this research is to evaluate the technical efficiency of the European (EU28) fish and seafood production industry over the period 2008-2013. To estimate technical efficiency, we adopt time-invariant SF models of Translog and Cobb-Douglas production functions for panel data in which the inefficiency effect (ui) has a normal distribution truncated at zero. Maximum likelihood (ML) estimates of the parameters of the Translog and Cobb-Douglas functions as well as the technical efficiency estimate for each country were obtained simultaneously using the xtfrontier command of the statistical package STATA. Technical efficiency scores for each country and production system (fishing and farming) were estimated using industry-level data from EUMOFA to get yearly values of fish and seafood production by each type of production system and from STECF to get yearly values of total employment (number of employees), vessel power (Kw), and total production assets (constant €) for each country.

Maximum Likelihood (ML) parameter estimates of the European fish and seafood production (fishing and farming) industry inefficiency frontier models are presented in Table 1. Regarding the fishing production system, the likelihood ratio test value for the Translog production function was significant at the 1% level (chi-square = 223.31). On the other hand, the validity of the Translog specification over the Cobb-Douglas one in the case of the aquaculture production system was strongly rejected (chi-square = 5.69). Moreover, the sum of the estimated parameters of the factors in the Cobb-Douglas fishing production function indicates a value above the unity (0.0976 + 1.2792 > 1) what enables us, therefore, to say that the situation of increasing returns to scale is established in the European fish and seafood fishing production system. Conversely, the sum of the estimated parameters of the factors in the farming production function indicate a value below the unity (0.3478 - 0.0185 < 1) whereby it can be inferred that the farming production system presents decreasing returns to scale.

We report estimation results of TE using the Translog SF production function for the fishing production system and the Cobb-Douglas SF production function for the farming production system in Table 2. These results show that the mean values of TE in the European production (fishing and farming) industry are relatively low being the fishing production system mean value (44.5%) much larger than the farming production system mean value (24.6%). In addition, according to these figures, countries with the most efficient fishing industry are Denmark, Estonia, and Spain with technical efficiency scores over 90% of the potential level of output. By the contrary, countries with the less efficient fishing industry are Malta, Lithuania, Finland, and Belgium with values lower than 30%. On the other hand, British and German farms are, on average, among the most technically efficient with scores over 80%, whereas Estonia, Slovenia, Portugal, Cyprus, and Romania are the countries with the less efficient farms in Europe with scores below 10%.

Therefore, these results show significant differences of inefficiency among the countries under study so that there is a room for improvement in the European fish and seafood production industry to be more efficient technically.







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