2021•Journal of Fisheries and Marine Sciences EducationRequires access

An Analysis on the Spatial Efficiency Analysis by Located Type of Middle Level Fishing Village in Gyeongsangnam-do

Jong-Cheon Kim, Jin-Gon Son

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Abstract

The purpose of this study is to analyze the production efficiency of fishing village in Gyeongsangnam-do using Bootstrap-DEA. This study analyzed 40 capture fishery type fishing village in Gyeongsangnam-do. First, the study estimates technical, pure technical, and scale efficiency of each fishing village based on traditional DEA under the assumption of CRS and VRS. The average value of technical efficiency for fishing village was estimated at 0.45 in the CCR model and the average pure technical efficiency value in the BCC model was estimated at 0.69. As a result, we show that both CCR and BCC models can reduce inputs to improve efficiency. Second, Estimation of efficiency by location type in the Bootstrap-CCR model showed that most of the top groups were suburban, and coastal villages were mostly inefficient. On the other hand, the Bootstrap-BCC model showed that the coastal village DMU A36 had the highest pure technology efficiency. Third, as a result of analyzing the impact of returns to scale in 40 fishing village, the portion of coastal village types was the highest among the fishing village operating on the constant returns to scale. Next, out of 29 fishing village with the increasing returns to scale, suburban type accounted for 18, and coastal village type accounted for 5 out of 7 fishing villages with the decreasing returns to scale.

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What this paper is about

The purpose of this study is to analyze the production efficiency of fishing village in Gyeongsangnam-do using Bootstrap-DEA. This study analyzed 40 capture fishery type fishing village in Gyeongsangnam-do. First, the study estimates technical, pure technical, and scale efficiency of each fishing village based on traditional DEA under the assumption of CRS and VRS. The average value of technical efficiency for fishing village was estimated at 0.45 in the CCR model and the average pure technical efficiency value in the BCC model was estimated at 0.69. As a result, we show that both CCR and BCC models can reduce inputs to improve efficiency. Second, Estimation of efficiency by location type in the Bootstrap-CCR model showed that most of the top groups were suburban, and coastal villages were mostly inefficient. On the other hand, the Bootstrap-BCC model showed that the coastal village DMU A36 had the highest pure technology efficiency. Third, as a result of analyzing the impact of returns to scale in 40 fishing village, the portion of coastal village types was the highest among the fishing village operating on the constant returns to scale. Next, out of 29 fishing village with the increasing returns to scale, suburban type accounted for 18, and coastal village type accounted for 5 out of 7 fishing villages with the decreasing returns to scale.

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Available abstract

The purpose of this study is to analyze the production efficiency of fishing village in Gyeongsangnam-do using Bootstrap-DEA. This study analyzed 40 capture fishery type fishing village in Gyeongsangnam-do. First, the study estimates technical, pure technical, and scale efficiency of each fishing village based on traditional DEA under the assumption of CRS and VRS. The average value of technical efficiency for fishing village was estimated at 0.45 in the CCR model and the average pure technical efficiency value in the BCC model was estimated at 0.69. As a result, we show that both CCR and BCC models can reduce inputs to improve efficiency. Second, Estimation of efficiency by location type in the Bootstrap-CCR model showed that most of the top groups were suburban, and coastal villages were mostly inefficient. On the other hand, the Bootstrap-BCC model showed that the coastal village DMU A36 had the highest pure technology efficiency. Third, as a result of analyzing the impact of returns to scale in 40 fishing village, the portion of coastal village types was the highest among the fishing village operating on the constant returns to scale. Next, out of 29 fishing village with the increasing returns to scale, suburban type accounted for 18, and coastal village type accounted for 5 out of 7 fishing villages with the decreasing returns to scale.

Key concepts: Fishing, Fishing village, Scale (ratio), Returns to scale, Estimation, Geography, Fishery, Production (economics)

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