# Assessing the Efficiency of Small-Scale and Bottom Trawler Vessels in Greece

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

**Y**, is an m × n matrix of outputs and

**X**is an h × n matrix of inputs. Both matrices contain data for all n DMUs. The technical efficiency (TE) measure can be formulated as follows:

subject to:

−

**y**

_{i}+

**Y**λ ≥ 0

θ

**x**−

_{i}**X**λ ≥ 0

λ ≥ 0

**y**, and

_{i}**x**, represent the output and input of DMU i respectively and

_{i}**Y**λ and

**X**λ are the efficient projections on the frontier. A measure of θ

_{i}= 1 indicates that the DMU is technically efficient. Thus, 1 − θ, measures how much the DMU i’s inputs can be proportionally reduced without any loss in output.

Subject to:

−

**y**+

_{i}**Y**λ ≥ 0

θ

**x**

_{i}−

**X**λ ≥ 0

**NI**′λ = 1

λ ≥ 0

**NI**′λ = 1 where

**NI**is a n × 1 vector of ones. This constraint allows only the comparison of firms of similar size, by forming a convex hull of intersecting planes, so that the data is enveloped more tightly. Scale efficiency can be calculated by conducting both a CRS and a VRS DEA upon the same data. If there is a difference in the two TE scores for a particular DMU, then this indicates scale inefficiency, and the SE score is equal to the ratio of the CRS TE score to the VRS TE score.

## 3. Results

#### 3.1. Small-Scale Vessels

#### 3.2. Bottom Trawlers

## 4. Discussion

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

- Farrell, M.J. The measurement of productive efficiency. J. R. Stat. Soc. Ser. A
**1957**, 120, 253–290. [Google Scholar] [CrossRef] - Grafton, R.Q.; Kirkley, J.; Kompas, T.; Squires, D. Economics for Fisheries Management; Ashgate Publishing, Ltd.: Aldershot, UK, 2006. [Google Scholar]
- Pascoe, S.; Kirkley, J.E.; Greboval, D.F.; Morrison, C.J. Measuring and Assessing Capacity in Fisheries: Issues and Methods; Food & Agriculture Org.: Rome, Italy, 2003. [Google Scholar]
- Charnes, A.; Cooper, W.W.; Rhodes, E. Measuring the efficiency of decision making units. Eur. J. Oper. Res.
**1978**, 2, 429–444. [Google Scholar] [CrossRef] - Coelli, T.J. A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program; CEPA Working Paper; University of New England: Armidale, Australia, 1996. [Google Scholar]
- Lindebo, E.; Hoff, A.; Vestergaard, N. Revenue-based capacity utilisation measures and decomposition: The case of Danish North Sea trawlers. Eur. J. Oper. Res.
**2007**, 180, 215–227. [Google Scholar] [CrossRef] - Pascoe, S.; Coglan, L. The contribution of unmeasurable inputs to fisheries production: An analysis of technical efficiency of fishing vessels in the English Channel. Am. J. Agric. Econ.
**2002**, 84, 585–597. [Google Scholar] [CrossRef] - Tsitsika, E.V.; Maravelias, C.D.; Wattage, P.; Haralabous, J. Fishing capacity and capacity utilization of purse seiners using data envelopment analysis. Fish. Sci.
**2008**, 74, 730–735. [Google Scholar] [CrossRef] - Fousekis, P.; Klonaris, S. Technical efficiency determinants for fisheries: A study of trammel netters in Greece. Fish. Res.
**2003**, 63, 85–95. [Google Scholar] [CrossRef] - Idda, L.; Madau, F.A.; Pulina, P. Capacity and economic efficiency in small-scale fisheries: Evidence from the Mediterranean Sea. Mar. Policy
**2009**, 33, 860–867. [Google Scholar] [CrossRef] - Reid, C.; Squires, D.; Jeon, Y.; Rodwell, L.; Clarke, R. An analysis of fishing capacity in the western and central Pacific Ocean tuna fishery and management implications. Mar. Policy
**2003**, 27, 449–469. [Google Scholar] [CrossRef] - Kumbhakar, S.C.; Lovell, C.A.K. Stochastic Frontier Analysis; Cambridge University Press: Cambridge, UK, 2000. [Google Scholar]
- Kirkley, J.; Paul, C.J.M.; Squires, D. Capacity and capacity utilization in common-pool resource industries. Environ. Resour. Econ.
**2002**, 22, 71–97. [Google Scholar] [CrossRef] - Sharma, K.R.; Leung, P. Technical efficiency of the longline fishery in Hawaii: An application of a stochastic production frontier. Mar. Resour. Econ.
**1998**, 13, 259–274. [Google Scholar] [CrossRef] - Vestergaard, N.; Squires, D.; Kirkley, J. Measuring capacity and capacity utilization in fisheries: The case of the Danish Gill-net fleet. Fish. Res.
**2003**, 60, 357–368. [Google Scholar] [CrossRef] - Pascoe, S.; Mardle, S.; Tingley, D. Capacity appraisal in the English Channel fisheries. Available online: http://www.faoadriamed.org/pdf/publications/td13/PMT-td-13.pdf (accessed on 14 July 2016).
- Lee, S.-G.; Rahimi Midani, A. Productivity change under the vessel buyback program in Korean fisheries. Fish. Sci.
**2015**, 81, 21–28. [Google Scholar] [CrossRef] - Coelli, T.; Grifell-Tatje, E.; Perelman, S. Capacity utilisation and profitability: A decomposition of short-run profit efficiency. Int. J. Prod. Econ.
**2002**, 79, 261–278. [Google Scholar] [CrossRef] - Simar, L.; Wilson, P.W. Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models. Manag. Sci.
**1998**, 44, 49–61. [Google Scholar] [CrossRef] - Bogetoft, P.; Otto, L. Benchmarking with DEA, SFA, and R; International Series in Operations Research & Management Science; Springer: New York, NY, USA, 2011; Volume 157. [Google Scholar]
- Gocht, A.; Balcombe, K. Ranking efficiency units in DEA using bootstrapping an applied analysis for Slovenian farm data. Agric. Econ.
**2006**, 35, 223–229. [Google Scholar] [CrossRef] - Simar, L.; Wilson, P.W. Statistical Inference in Nonparametric Frontier Models: The State of the Art. J. Product. Anal.
**2000**, 13, 49–78. [Google Scholar] [CrossRef] - Tingley, D.; Pascoe, S.; Coglan, L. Factors affecting technical efficiency in fisheries: Stochastic production frontier versus data envelopment analysis approaches. Fish. Res.
**2005**, 73, 363–376. [Google Scholar] [CrossRef] - Squires, D. Public regulation and the structure of production in multiproduct industries: an application to the New England otter trawl industry. Rand J. Econ.
**1987**, 18, 232–247. [Google Scholar] [CrossRef] - Holland, D.S.; Sutinen, J.G. Location choice in New England trawl fisheries: Old habits die hard. Land Econ.
**2000**, 76, 133–149. [Google Scholar] [CrossRef] - Färe, R.; Grosskopf, S.; Lee, H. A nonparametric approach to expenditure-constrained profit maximization. Am. J. Agric. Econ.
**1990**, 72, 574–581. [Google Scholar] [CrossRef] - Banker, R.D.; Chang, H.; Natarajan, R. Estimating DEA technical and allocative inefficiency using aggregate cost or revenue data. J. Product. Anal.
**2007**, 27, 115–121. [Google Scholar] [CrossRef] - Portela, M.C.A.S. Value and quantity data in economic and technical efficiency measurement. Econ. Lett.
**2014**, 124, 108–112. [Google Scholar] [CrossRef] - Del Hoyo, J.J.G.; Espino, D.C.; Toribio, R.J. Determination of technical efficiency of fisheries by stochastic frontier models: A case on the Gulf of Cadiz (Spain). ICES J. Mar. Sci. J. Cons.
**2004**, 61, 416–421. [Google Scholar] - Esmaeili, A. Technical efficiency analysis for the Iranian fishery in the Persian Gulf. ICES J. Mar. Sci. J. Cons.
**2006**, 63, 1759–1764. [Google Scholar] [CrossRef] - Ali, F.; Parikh, A.; Shah, M.K. Measurement of economic efficiency using the behavioral and stochastic cost frontier approach. J. Policy Model.
**1996**, 18, 271–287. [Google Scholar] [CrossRef]

**Figure 1.**Histograms of (

**a**) Technical Efficiency (TE) and (

**b**) Scale Efficiency (SE) scores of the small-scale vessels

**Figure 2.**Histograms of (

**a**) Technical Efficiency (TE) and (

**b**) Scale Efficiency (SE) scores of bottom trawlers.

Variable | Small-Scale Vessels | Bottom Trawlers | ||
---|---|---|---|---|

Mean Value (€) | St. Deviation | Mean Value (€) | St. Deviation | |

Input variables | ||||

Personnel cost | 9400 | 6594 | 81,243 | 44,842 |

Fuel cost | 4861 | 5905 | 118,825 | 57,005 |

Running cost | 3377 | 5354 | 70,983 | 46,019 |

Repair and maintenance cost | 2178 | 2265 | 20,229 | 12,854 |

Output variable | ||||

Revenues | 19,655 | 16,602 | 359,080 | 277,570 |

**Table 2.**Descriptive statistics of Technical Efficiency (TE), Scale Efficiency (SE) and scale of operation for small-scale vessels.

Variable | Mean | Standard Deviation | CV | Min | Max |

TE | 0.42 | 0.18 | 43.9% | 0.15 | 0.79 |

SE | 0.81 | 0.20 | 24.9% | 0.17 | 1 (51 vessels) |

Scale of operation | DMUs | ||||

IRS | 43 vessels (63%) | ||||

CRS | 51 vessels (22%) | ||||

DRS | 35 vessels (15%) |

**Table 3.**Variables that define groups with different Technical Efficiency (TE) scores in the small-scale fishing segment.

Variable that Define Groups | Average TE | Z Score | Result | |
---|---|---|---|---|

Length class | 5–6 m | 0.50 | 2.65 ** | Small vessels have higher TE |

6–12 m | 0.40 | |||

Level of education | basic | 0.44 | 2.89 ** | Skippers with basic education perform better |

advanced | 0.38 | |||

Vessels registered in “Thessaly” region | Yes | 0.42 | 1.79 * | Vessels in this region have higher TE |

No | 0.38 | |||

Vessels registered in “South Aegean” and “Crete” | Yes | 0.50 | −1.67 * | Vessels in these regions have higher TE |

No | 0.41 | |||

Young skipper (less than 40) | Yes | 0.43 | 2.11 ** | Vessels whose skipper is very young have less TE |

No | 0.37 |

**Table 4.**Variables that define groups with different Scale Efficiency (SE) scores in the small-scale fishing segment.

Variable that Define Groups | Average SE | Z Score | Result | |
---|---|---|---|---|

Fishing activity is the main source of income | Yes | 0.82 | −2.81 ** | Vessels whose skippers’ main income is fishing present higher SE |

No | 0.71 | |||

Old skipper (more than 65) | Yes | 0.71 | 1.67 ** | Vessels whose skipper is old, have less SE |

No | 0.82 |

**Table 5.**Spearman correlations of Technical Efficiency (TE) and Scale Efficiency (SE) scores of the small-scale vessels with technical and socioeconomic variables.

TE | SE | |
---|---|---|

Length | −0.23 ** | 0.19 ** |

Gt | −0.20 ** | 0.21 ** |

Revenues | 0.02 | 0.24 ** |

Days at sea | −0.13 ** | 0.15 ** |

Unpaid labour to total labour | −0.22 ** | −0.05 |

**Table 6.**Descriptive statistics of Technical Efficiency (TE), Scale Efficiency (SE) and scale of operation of the bottom-trawlers.

Variable | Mean | Standard Deviation | CV | Min | Max |

TE | 0.68 | 0.14 | 20.0% | 0.42 | 0.86 |

SE | 0.73 | 0.21 | 28.7% | 0.30 | 1 (3 vessels) |

Scale of operation | DMUs | ||||

IRS | 31 vessels (91%) | ||||

CRS | 3 vessels (9%) | ||||

DRS | 0 vessels |

**Table 7.**Spearman correlations of Technical Efficiency (TE) and Scale Efficiency (SE) scores of the bottom-trawlers with technical and socioeconomic variables.

TE | SE | |
---|---|---|

Length | 0.06 | 0.34 ** |

Gt | 0.07 | 0.30 * |

Revenues | 0.11 | 0.58 ** |

Days at sea | −0.25 | 0.12 |

Gross cash flow | 0.59 ** | 0.65 ** |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Pinello, D.; Liontakis, A.; Sintori, A.; Tzouramani, I.; Polymeros, K.
Assessing the Efficiency of Small-Scale and Bottom Trawler Vessels in Greece. *Sustainability* **2016**, *8*, 681.
https://doi.org/10.3390/su8070681

**AMA Style**

Pinello D, Liontakis A, Sintori A, Tzouramani I, Polymeros K.
Assessing the Efficiency of Small-Scale and Bottom Trawler Vessels in Greece. *Sustainability*. 2016; 8(7):681.
https://doi.org/10.3390/su8070681

**Chicago/Turabian Style**

Pinello, Dario, Angelos Liontakis, Alexandra Sintori, Irene Tzouramani, and Konstantinos Polymeros.
2016. "Assessing the Efficiency of Small-Scale and Bottom Trawler Vessels in Greece" *Sustainability* 8, no. 7: 681.
https://doi.org/10.3390/su8070681