These authors contributed equally to this work.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

The fishing industry in Abu-Dhabi, United Arab Emirates (UAE), plays an important role in diversifying food sources in order to enhance national food security. The fishing industry is facing an increasing risk that may impact the sustainability (

Fishing is one of the oldest traditional industries in the United Arab Emirates (UAE) and is typically inherited from former generations. Pearl extraction and fishing were dominant sources of income before oil and natural gas discoveries in the country. The majority of the fish catch originates from the Emirate of Abu Dhabi, which includes more than 65% of the UAE sea area in its borders. The Food and Agriculture Organization (FAO) [

In order to control fishing efforts, the Emirate of Abu Dhabi introduced new regulations in 2003 [

In order to estimate UAE fishing self-sufficiency ratios, time series for fish production, export, import and consumption have been analyzed. The self-sufficiency ratio (SSR) is a percentage of the locally-produced quantity of product over the total supply available for the consumption of such a product. The total quantity of supply available for consumption is calculated by subtracting product exports from the total of locally-produced product and adding product imports from abroad. The fish catch in UAE increased steadily from 70,000 metric tons in 1982 to 115,000 metric tons in 1999. Production then started declining until it reached 77,700 metric tons in 2010. Meanwhile, fish consumption in the UAE steadily increased from 76,400 metric tons in 1982 to 192,640 metric tons in 2010. The differences between consumption and production are filled with imports from other countries to balance the food production in addition to imports (supply) and consumption (demand) in the UAE. Fish imports in the UAE increased from 8780 metric tons in 1982 to 132,720 metric tons in 2010. The UAE fish self-sufficiency ratio (SSR) historically increased from 92% in 1982 to 129% in 1991, when the country was exporting fish to neighboring countries. However, the SSR has declined in recent years (40% in 2010) to the lowest level since 1982 (

UAE fish self-sufficiency ratio (1982–2010).

Source: Arab Organization for Agricultural Development, Statistical Yearbook (1982–2012) [

Total fish catch originating from Abu-Dhabi Emirate, UAE (2001–2010).

Source: Abu-Dhabi Environment Agency [

Since 2004, the total fish catch has stayed below the level of 7000 metric tons annually in the Emirate of Abu-Dhabi after the annual increase up to 2003 (

Efficiency differences in fisheries are often attributed to differences in technology and the skill of the skipper and crew, as shown in Kirkley

Aisyah

Singh

Singh

Tingley

The above studies show the importance of decision-making in relationship to different management alternatives at the fishermen decision-maker (DM) level under various risk and uncertainty conditions. Successful fishing practices were found to be those that considered the impact of boat capacity, fishing effort, size of capital investment, boat length, labor training and management, as well as the number and sizes of traps. Previous studies also highlighted the importance of diversification, economies of scale and the catch of high-value fish management options on fishing profitability and both technical and economic efficiency.

The overall goal of this research is to address concerns related to risk and uncertainty in the fishing industry in the Emirate of Abu-Dhabi, UAE. The specific objective of this paper is to quantify and rank UAE fishermen preferences concerning various fishing management alternatives at different levels of attitudes towards risk. To achieve the specific research objective, several statistical and stochastic analysis techniques were employed, including descriptive statistic and stochastic dominance techniques (first/second degree stochastic dominance, stochastic dominance with respect to a function and stochastic efficiency with respect to a function) to rank the fishing management alternatives (methods of fishing, sizes of traps and number of traps) based on a well-accepted economic indicator (profitability represented by gross margin). The stochastic dominance techniques were applied across a spectrum of risk attitudes and preferences ranging from risk neutral to risk averse. The significance of this research stems from the DM need to identify less risky management alternatives, the adoption of which will hopefully retain more UAE fishermen in the business, attract a new generation of fishermen and augment the sustainability of this important food sector to ensure a sufficient and long-term supply of fish in the UAE.

This paper is divided into four sections. The first section summarized the overall UAE fisheries’ socio-economic indicators, a literature review of previous studies and the research objective, which focuses on quantifying and ranking UAE fishermen preferences concerning various fishing management alternatives at different levels of attitudes towards risk. The second section of this paper describes the methods used, including stochastic first and second degree stochastic dominances, stochastic dominance with respect to a function and stochastic efficiency with respect to a function, to assess the risk facing UAE fishermen. The third section includes the results and discussion, starting with the analysis of the Abu-Dhabi fishermen survey results and continues with the ranking of the fishermen management alternatives using various ranking methods. The fourth section of this paper includes a summary and conclusion, which summarizes the importance of considering both decision-maker’s attitude towards risk and economic inefficiencies in managing UAE fisheries that can be used for designing successful fisheries policies.

In economic systems, there is generally risk associated with production quantities, input prices, output prices, interest rates, quantities demanded by consumers, market share, government regulations and weather conditions, among others. For a business, a manager does not have control over many of these variables,

As previously discussed, several well-known decision criteria for stochastic dominance exist, including first degree stochastic dominance (FSD), second degree stochastic dominance (SSD) and stochastic dominance with respect to a function (SDRF). Hardaker _{a} (a measure of how much a person would pay to avoid risk), for the SSD criterion is from 0 to +∞. SSD assumes a utility function of a positive, but decreasing slope, ^{1}(X) > 0 and U^{2}(X) < 0 with SSD, and A is preferred to B if _{a} absolute risk aversion coefficients. These risk-preference intervals are bounded by a lower risk aversion coefficient, r_{aL}, and an upper risk aversion coefficient, r_{aU}, which characterize the general degree of risk aversion for a manger. A risk-efficient set of strategies will include the choices preferred by each manager having risk preferences consistent with the restrictions imposed by the lower to upper interval. Furthermore, Hardaker

A more discriminatory procedure than SDRF that considers the full range of decision-maker preferences is stochastic efficiency with respect to a function (SERF). SERF is more efficient than other stochastic dominance techniques, because it can be applied for any utility function form based on the full range (_{a}). A positive r_{a} indicates that the DM is risk averse, while a negative r_{a} indicates that the DM is a risk taker (a risk-neutral DM is more concerned about the expected return on their investment, not on the risks they may be assuming). Furthermore, SERF is a preferable method, due to the fact that it is one of few risk analysis techniques that can be used to easily visualize the stochastic frontier across the entire r range, where preferences for a particular alternative may be illustrated.

SERF identifies and orders utility efficient alternatives in terms of certainty equivalents (CEs) for a specified risk preference. Hardaker ^{−1} (w, r(w))
_{a}(w)w)
_{a}(w) is the absolute r_{a} with respect to wealth. Given a random sample of size n from alternative w (

A negative exponential utility function conforms to the hypothesis that managers prefer less risk to more given the same expected return and assumes managers have constant absolute risk aversion, as suggested by Lien _{a} that a DM should use with SERF [

In this study, UAE fishing management alternatives (methods, size of traps and number of traps) were considered for the descriptive statistic and stochastic dominance analyses. Fishermen catch determines revenues, because the fishermen are a price taker in the market. Subtracting the cost of operations from revenues determines the gross margin or revenue over variable cost. The fishing management alternatives are analyzed under various levels of fishermen attitudes towards risk. SERF is a practical method for analyzing the fishing management alternative problem, because SERF considers both the variability of gross margins and DM attitude towards risk. As stated above, SERF calculates a CE, which is the sure amount of money with the same utility as the expected utility of a risky alternative [

SERF is a procedure for ranking risky alternatives based on the certainty equivalent (CE) for risk alternatives and the absolute risk aversion coefficients (ARAC). Hardaker

The Simetar © 2008 risk analysis software [_{aL}) equal to 0.0 (representing a neutral decision-maker) and an upper absolute risk aversion coefficient (r_{aU}) equal to 0.004 (representing a risk-averse decision-maker) were used to perform the analysis. For the SERF analysis, gross margin CE curves by fishing management alternatives (fishing methods, size of traps and number of traps) were produced by calculating 25 CE values for each curve over the entire range (0.0 to 0.004) of absolute risk aversion (r_{a}). Stochastic efficiency with respect to a function (SERF) is considered a sufficient ranking method. Hardaker [

The data for this study were extracted from face-to-face interviews with Emirate of Abu-Dhabi, UAE, fishermen. The United Arab Emirates Ministry of Water and Environment (MOEW) [

Based on the 131 fishermen interviewed, the survey sample results indicated that the average fishing boat age was about 22 years with a standard deviation (SD) of nine years. The average fishing boat market price was 142,000 Dirhams (AED) with an SD of 93,000 Dirhams. The United Arab Emirate Dirham (AED) is the official currency of the UAE (one USD = 3.675 AED). A boat license costs UAE fishermen about 220 AED on average, and survey results showed that there was on average four workers per boat earning a salary of 712 AED per worker monthly. The average boat capacity was around 208 horsepower with an SD of 67.5 horsepower; the average boat length was 32 feet (9.8 m) with an SD deviation of 14.4 feet (4.4 m). The number of fishing trips per month ranged from two to 22 trips. On average, each fisherman went on six fishing trips per month, with each trip taking four days on average. Investigation of the socio-economic characteristics of Abu-Dhabi fishermen showed that 97% of the fishermen were boat owners with only 3% renting boats for fishing. The survey results showed that 53% of the fishermen were UAE nationals, while 47% were citizens of other countries (e.g., India, Iran and Oman). When survey respondents were asked if they purchased boat insurance, only nine fishermen (or 7%) responded yes. Furthermore, only 37 survey respondents (or 28%) indicated that their sons were interested in pursuing fishing as a profession and career. These findings confirm the fact that the next generation does not consider fishing in Abu-Dhabi as a secure source of income (and would not want to enter the business and sustain it for the future). Five different species of fish were identified as common in the Abu-Dhabi fishermen daily catch, including Hamour, Kan’ad-Chabbat, Sultan Ibrahim, and Safi Arabi

The survey results also focused on the estimation of the gross margin per boat on an annual basis. The gross margin was calculated by subtracting the variable costs, which are costs that vary with the quantity of the fish catch and include the costs of labor, fuel, maintenance and boarding (such as food, nets, water and ice). Labor and fuel costs combined represent 89% of the total variable costs for an average Abu-Dhabi fisherman. Labor and fuel represent 62% and 27% of the variable costs, respectively. Laborers are commonly foreigners; it costs boat owners about 4370 AED on average to hire a worker from abroad. The economic results of the Emirate of Abu-Dhabi, UAE, fishermen survey are presented in

Descriptive statistics of selected economic variables based on the Emirate of Abu-Dhabi, UAE, fisheries survey.

Variable | Annual total revenue (TR) (AED) | Annual variable costs (VC) (AED) | Annual gross margin (GM) = TR − VC (AED) |
---|---|---|---|

Mean | 298,173 | 159,112 | 139,061 |

Median | 276,000 | 157,680 | 125,320 |

Standard deviation (SD) | 185,899 | 52,415 | 174,042 |

Coefficient of variation (SD/mean) | 0.62 | 0.33 | 1.25 |

Number of observations | 131 | 131 | 131 |

Descriptive statistics of annual gross margins (AED) for various methods of fishing based on the Emirate of Abu-Dhabi, UAE, fisheries survey.

Statistical measure | Traps | Thread | Nets |
---|---|---|---|

Mean | 167,551 | 65,078 | 67,274 |

Median | 159,360 | 66,430 | 45,280 |

Standard deviation (SD) | 175,891 | 169,834 | 142,885 |

Coefficient of variation (SD/mean) | 1.05 | 2.61 | 2.12 |

Number of observations | 94 | 10 | 27 |

First degree stochastic dominance (FSD) can be determined by visually considering the Cumulative Distribution Functions (CDFs) between various management alternatives. As previously stated, Alternative A completely dominates Alternative B if the CDF of Alternative A is always below and to the right of the CDF of B (_{A} (_{B} (

To further investigate the statistical differences between the three sets of management alternative distributions (fishing methods, traps sizes and number of traps), Kolmogorov-Smirnov tests were performed with results reported in

Sample annual gross margin (AED) cumulative distribution functions (CDFs) for different methods of fishing.

Sample annual gross margin (AED) cumulative distribution functions (CDFs) for trap sizes.

Sample annual gross margin (AED) cumulative distribution functions (CDFs) for the number of traps.

Kolmogorov–Smirnov (K-S) statistical significance tests of cumulative distribution functions (CDFs) for pairs of fishing management alternatives (methods, size of traps and number of traps).

Pairs of fishing management alternatives | K-S test values | D values | Conclusion |
---|---|---|---|

D value is greater than the absolute value of the greatest difference between each pair; cannot conclude the presence of significant difference between the three distributions. | |||

Traps/Threads | −0.034 | 0.140 | |

Traps/Nets | 0.041 | ||

Threads/Nets | 0.074 | ||

D value is less than the absolute value of the greatest difference between each pair; can conclude the presence of significant difference between the three distributions. | |||

Large/Medium | −0.256 | 0.166 | |

Large/Small | 0.322 | ||

Medium/Small | 0.389 | ||

D value is greater than the absolute value of the greatest difference between each pair; cannot conclude the presence of significant difference between the three distributions. | |||

Less than 100/100–120 | 0.173 | 0.205 | |

Less than 100/More than 120 | 0.19 | ||

100–120/More than 120 | −0.144 |

Second degree stochastic dominance (SSD): gross margins based on indicators for the methods of fishing, the size of traps and the number of traps.

Fishing methods | Traps | Threads | Nets | SSD dominance ranking |
---|---|---|---|---|

Traps | -- | Dominant | Dominant | 1 |

Threads | Not Dominant | -- | Not Dominant | 3 |

Nets | Not Dominant | Dominant | -- | 2 |

Large | -- | Dominant | Dominant | 1 |

Medium | Not Dominant | -- | Dominant | 2 |

Small | Not Dominant | Not Dominant | -- | 3 |

100 traps or less | -- | Not Dominant | Not Dominant | 3 |

More than 100 to 120 traps | Dominant | -- | Not Dominant | 2 |

More than 120 Traps | Dominant | Dominant | -- | 1 |

Stochastic dominance analyses include specific assumptions related to DM attitude towards risk.

Stochastic dominance with respect to a function (SDRF) rankings for the fishing management alternatives based on the Emirate of Abu-Dhabi, UAE, fisheries survey.

Management alternatives | Efficient set ranking with an absolute risk aversion coefficient = 0.0 | Efficient set ranking with an absolute risk aversion coefficient = 0.004 |
---|---|---|

Thread | Most Preferred | Most Preferred |

Traps | 2nd Most Preferred | 2nd Most Preferred |

Nets | 3rd Most Preferred | 3rd Most Preferred |

Large | Most Preferred | 3rd Most Preferred |

Medium | 2nd Most Preferred | 2nd Most Preferred |

Small | 3rd Most Preferred | Most Preferred |

More than 120 traps | Most Preferred | Most Preferred |

More than 100 to 120 traps | 2nd Most Preferred | 2nd Most Preferred |

100 traps or less | 3rd Most Preferred | 3rd Most Preferred |

Stochastic efficiency with respect to a function (SERF) for fishing methods (negative exponential utility function).

Stochastic efficiency with respect to a function (SERF) for the size of traps (negative exponential utility function).

Stochastic efficiency with respect to a function (SERF) for the number of traps (negative exponential utility function).

The Emirate of Abu-Dhabi, UAE, fisheries survey of risk and uncertainty analysis showed both agreement and disagreement based on the analysis of the fishing management alternatives. All stochastic dominance risk analysis methods showed that the trap fishing method alternative was preferred over the net and thread alternatives at all levels of attitude towards risk. Furthermore, all stochastic dominance risk analysis methods showed that the large number of traps (100 traps or more) alternative was preferred over the smaller and medium number of traps alternatives. However, the SERF analysis results showed that a DM who is risk neutral would prefer large-sized traps over medium- or small-sized traps. This is not the case for a risk-averse DM, who would prefer the small-sized trap alternative over the medium- and large-sized alternatives.

The Emirate of Abu-Dhabi, UAE, fishermen operate under extreme risk and uncertainty conditions. The UAE fishery sector has experienced multiple challenges in the last three decades (1980–2010). To analyze the sources of risk that face both fishermen and fisheries in the Emirate of Abu-Dhabi, UAE, two major risk analysis methods were considered: standard stochastic dominance techniques (first/second degree stochastic dominance and stochastic dominance with respect to a function) and stochastic efficiency with respect to a function (SERF). Stochastic dominance techniques and SERF allow the DM to rank risky fishing management alternatives (

All the authors contributed equally to this work.

The authors declare no conflict of interest.