This research carries out a technical efficiency assessment for a small-scale fishery in Mexico. Technical efficiency (TE) is measured as the deviation of the firm (i.e. fisher) production from the best practice production frontier (Kumbhakar and Lovell 2000). Under TE, fishing production is assumed to be stochastic because the fishing is sensitive to different random factors including weather and other environmental influences (Squires et al. 2003). The analysis of TE of small-scale fisheries is relevant for several reasons. There is a little information on the small-scale fisheries real contribution to livelihoods and economies in developing countries (FAO 2009). The information on small-scale artisanal and recreational fisheries is scarcer and harder to track with 12 million fishers compared with 0.5 million in industrialized countries (Worm et al. 2009). Indeed, while this type of fishery is common in countries like Mexico, the number of analyses assessing their efficiency is very limited. These small-scale fisheries have the potential to generate significant profits and be more resilient to shocks and crises; two important elements to poverty alleviation and food security. But at the same time, these small-scale fisheries may overexploit stocks, harming the environment and generating only a marginal profit level (Gordon 1954; Anderson 1986). Aiming to identify and assess the variables that constrain TE, this research seeks to provide information that would help in the development of guidance as how to remedy production inefficiencies and provide information to define the strategies to use on the assistance of the fishing communities. Improving TE enhances harvest and may contribute to better use and distribution of scarce resources. It may even help to deal “partially” with the open access problem, but clearly, the decision maker need to be careful because increasing the catch may lead to a probable overexploitation of the resource, making the open access problem a more severe problem. Thus, TE output is a partial solution subject to the existence of a fishery management institution that procures the sustainable use of the fishery. As the fishers become more efficient they provide more food and increase their competitiveness representing a way to improve their profit and alleviate the poverty. Overall, while improving TE is only a part of the puzzle, a comprehensive analysis is required to improve the management of the fisheries. This analysis use data from the Lázaro Cárdenas reservoir (LCR), a small-scale fishery where commercial fishers face weak governmental regulation; as a result, the lake has been overharvested and used inefficiently (Tovar et al. 2009), introducing a classic common property situation. From a total of 148 fishers registered in the three cooperatives, only about 100 were active at the time of the survey. 111 fishers were interviewed, and then our survey is basically a census. From 111, only 89 observations were used after dropping one outlier and 21 others because of missing values. It is desirable to have proficient fishers but, what factors constrain efficiency? Aiming to contribute in the knowledge of small-scale fisheries, this study seeks to estimate the drivers of TE in LCR. The research hypothesis is that fisher skills and education level are the main contributors to technical efficiency. The empirical model to estimate TE follows Squires et al. (2003) and Grafton et al. (2000), using the production frontier approach proposed by Battese and Coelli (1995). The model includes labor, effort capacity (eci) to measure the fishing effort, and factor capacity (fci) to measure the inputs used in the fishing activity. Both eci and fci, result from a linear combinations of other variables to account for very different technologies. Effort capacity captures the energy used to move the vessel based on the fisher’s expenditure on gasoline and a dummy variable if the fisher rows the boat. Factor capacity is a proxy of capital stock, and it is a function of the number of nets used by the fisher and a dummy identifying if the fisher angles. Therefore, the model approach allows for the possibility that any fisher can use motor and row and use the net and angling. The technical efficiency equation to estimate the contribution of various factors to a fisher’s inefficiency includes the education level, the number of years of fishing experience, the time (years) that the fisher has using the boat, the number of persons in home to assess the family size of the fisher, the size of the boat, and three dummies to indicate if the fisher has taken a class to improve his fishing techniques, if the fisher shares the boat, and if fishing is the primary source of income. The model equations were jointly regressed using a maximum likelihood procedure, using the program Frontier 4.1 (Coelli and Henningsen 2011) in the statistical package “R.” The model tests favors a Cobb Douglas over a Translog, and it supports the truncated over the half normal distribution. In the production function variables, labor, eci and fci are significant and positive, and variables such as education, fishing experience, and training have a negative sign, thus they have a positive effect on efficiency. In other variables, time using the motor has a negative effect on efficiency; and family size and fisher income have a significant a positive effect to increase efficiency. The results are policy relevant. Knowing the factors that constrain TE brings a guide for policy makers to consider how efficiency can be improved. If the stock does not decline, it may increase the fishers’ revenue and hopefully reduce the poverty. However, it should be carefully assessed. As the fishery is a resource of open access, improvements in TE without restrictions on entry may lead to a faster collapse of fishery. Improving the net income of the fishers and avoiding the overexploitation of resource stock both need to be tackled. It is not an easy task, restrictions in the open access to control the overexploitation without a correct economic assessment could leave the fishers in more poverty. Moreover, measures implemented on the technology side partially solve the problem (Grafton 2006). Improving the fishing capital (nets, motors, boats) is one policy option, but it may be more effective improving the education level of the fishers. This research shows one element of the puzzle by emphasizing key variables to improve the efficiency, but a comprehensive analysis is required to improve the management. Previous research has emphasized the relevance of technology and fisher’s skills to improve the management of the fishery (Salas 2007; Hilborn 2007a; Grafton et al. 2006; Anderson 1986). This research not only corroborates previous research done for large fisheries, but also contributes in the knowledge of TE in small-scale fisheries. As Squires et al. (2003), Esmaeili (2006), and Akanni (2008, 2010), this assessment has a relatively high TE. This finding is interesting to corroborate in future research to see if most small-scale fisheries have a similarly high TE.


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