Advances in Agriculture

Volume 2017, Article ID 5878725, 7 pages

https://doi.org/10.1155/2017/5878725

## Estimating Profit Efficiency of Artisanal Fishing in the Pru District of the Brong-Ahafo Region, Ghana

^{1}Department of Agricultural and Resource Economics, University for Development Studies, Tamale, Ghana^{2}Department of Agricultural Economics, Agribusiness and Extension, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana^{3}Discipline of Agricultural Economics, University of KwaZulu-Natal, Pietermaritzburg, South Africa

Correspondence should be addressed to Gideon Danso-Abbeam; hg.ude.sdu@maebbaosnad

Received 26 February 2017; Accepted 26 November 2017; Published 21 December 2017

Academic Editor: Claus A. Soerensen

Copyright © 2017 Edinam Dope Setsoafia et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

This study evaluated the profit efficiency of artisanal fishing in the Pru District of Ghana by explicitly computing profit efficiency level, identifying the sources of profit inefficiency, and examining the constraints of artisanal fisheries. Cross-sectional data was obtained from 120 small-scale fishing households using semistructured questionnaire. The stochastic profit frontier model was used to compute profit efficiency level and identify the determinants of profit inefficiency while Garrett ranking technique was used to rank the constraints. The average profit efficiency level was 81.66% which implies that about 82% of the prospective maximum profit was gained due to production efficiency. That is, only 18% of the potential profit was lost due to the fishers’ inefficiency. Also, the age of the household head and household size increase the inefficiency level while experience in artisanal fishing tends to decrease the inefficiency level. From the Garrett ranking, access to credit facility to fully operate the small-scale fishing business was ranked as the most pressing issue followed by unstable prices while perishability was ranked last among the constraints. The study, therefore, recommends that group formation should be encouraged to enable easy access to loans and contract sales to boost profitability.

#### 1. Introduction

Fish is a source of high quality and cheap animal protein essential to balancing diet. According to the World Bank [1], the primary source of animal protein for more than 50% of the world’s population is fish. Globally, most of the total fish supply is obtained from marine and inland capture fisheries, and the remainder is derived from aquaculture. Both marine fish and aquaculture play a vital role in the global supply of fish. In 2006, there was a total of 110 million tonnes world production of fish out of which 47% came from aquaculture [2]. In Africa, 5% of the total population obtain their source of livelihood from fisheries industry.

Ghana has a lot of lakes, lagoons, and rivers for fish production [3, 4]. The Volta river basin (including Volta lake, the largest man-made lake in Africa and the second largest in the world) dominates the country’s riverine systems [5]. The Volta lake has attracted several migrant artisanal fisheries and settlers. The country is also blessed with a marine coastline of about 550 kilometers which stretches from Aflao in the eastern part of the country to Half-Assini in the western part. These two water bodies (i.e., Volta lake and the coast) serve as a main source of fish, which is used in the production of meals and medicines for both human and animal consumption [6]. Artisanal fisheries form a significant contribution towards food security, income, and employment in many Ghanaian households. Artisanal fishing is a traditional or subsistence fishing comprising small-scale, low-technology, and low-capital fishing practices undertaken by individual fishing households (based on the definition of artisanal fishing, the study, therefore, used artisans and small-scale fishers interchangeably). Most of these artisans are of coastal, island, or inland ethnic groups. The artisan fishing uses traditional fishing technology such as rod and tackle, cast nets, and small traditional fishing boats. Artisanal fishing is less intensive and stressful than the modern industrial fishing techniques. According to Tetteh [7], the small-scale subsector of Ghana’s fisheries provides about 30,000 people with employment. In 2012, the fisheries subsector contributed approximately 1.5% to the country’s agricultural Gross Domestic Product (GDP) [8, 9].

Despite the above-mentioned natural potentials and significance of fish industry in the country, the sector faces some challenges that militate against its growth. These include perishability of fresh fish and lack of information about the management of the industry by these artisans [10]. For instance, small-scale fishers may not have the financial management skill to adequately manage their resources to optimize their revenue. According to Bank of Ghana Report [11] on the Fishery industry in 2008, fish production declined severely from 6% of the GDP in 1993 to 3.9% in 2006. Ghana imports fish to supplement its consumption demand, particularly in the lean season, albeit, Ghana is a fishing country. An integral aspect of fish production is fish marketing which ensures that the right commodity is available at the right time, price, and place [12]. Over the years, governments have implemented intervention programmes such as subsidizing the cost of outboard motors and fuel cost towards improving productivity growth and an efficiency of the artisanal fisheries as well as increasing per capita income of the indigenous fishermen. However, the outcomes have been below expectation, due to poor implementation, monitoring, and evaluation of the intervention programmes. Further, the marketing of fish is characterized mainly by the problems of seasonality, perishability, and poor storage. The seasonality of fish makes it abundant at certain periods of the year (July to August) during which there is glut leading to high supply over demand forcing fishermen to sell at a “give-away” price. Fish has been highly perishable after harvesting as it requires proper preservation and storage to increase its shelf life [13]. This negatively affects profit efficiency of the artisanal fishers and also serves as a disincentive for people to go into fishing and its related businesses. At other periods of the year (January to February), there is a great scarcity of fish leading to high prices. Addressing these obstacles through empirical evidence is a necessary step towards attaining self-sufficiency in the Ghanaian fishing industry. Estimating profit efficiency provides a mean to capture farm level production specialization that allows higher incomes reserved by farms through the production of differentiated output to compensate for higher cost incurred [14].

Moreover, most studies in efficiency have not looked at the artisanal fishing subsector, especially in the study area. In the Pru District of Ghana where most households are into artisanal fishing and food security of great concern, the sustainability of the fishing business cannot be achieved without economic viability. The study, therefore, investigates the profit efficiency among artisanal fishers in the Pru District of Ghana by estimating their profit efficiency levels, identifying the sources of profit inefficiency and assessing some of the constraints of artisanal fishing.

#### 2. Methodology

##### 2.1. Study Area, Data, and Sampling Technique

The study was conducted in the Pru District which is geographically located in the Brong-Ahafo region of Ghana, and its capital is Yeji. The population is mainly rural with their primarily economic activity being agriculture. Due to the presence of the Volta lake in the district, fishing is a significant activity that employs the majority of the people (fishers, fishmongers, cold store operators, among others). Primary data collected from small-scale fishing households was used for the study. The data consisted of fishermen’s sociodemographic characteristics, inputs and outputs of fishing, the cost of fishing, and prices of fish, among others. A semistructured questionnaire was used to collect data through personal interviews. The communities well known for fishing activities were taken from the Fisheries Department under the Ministry of Food and Agriculture [MoFA], Ghana. Six villages were randomly selected, and twenty respondents were chosen from each community giving a total sample size of 120.

##### 2.2. Analytical Framework

Sociodemographic characteristics were reported using descriptive statistics such as frequencies, percentages, and averages. The data was examined using stochastic profit frontier model and Garrett ranking technique.

###### 2.2.1. Theoretical Framework of Stochastic Frontier Model

Over the past three decades, the two main components of production efficiency, technical and allocative, have been analysed in literature. However, both measures can be merged into one system, where more efficient estimates can be obtained by the simultaneous estimation of the system [15]. The technical efficiency component is mostly measured using the famous frontier production function [16]. However, [17] argues that frontier production approach to measuring efficiency may not be appropriate when production units face different prices and have different resource endowment. Hence, stochastic profit efficiency function is used to estimate efficiency directly [18]. The stochastic profit frontier combines the concepts of both technical and allocative efficiency, and, therefore, any errors in the production are assumed to be translated into lower profits [19]. Profit efficiency refers to the ability of an artisanal fisher to achieve highest possible benefit or gain, given the prices of inputs and levels of fixed factors used in fishing. In this case, the small-scale fishers are assumed to choose a combination of variable inputs and outputs that maximize profit subject to technology constraint. Artisans who fall below the frontier or do not operate on the frontier are not profit-efficient. This model assumes that factors outside the artisans control can also affect profit levels estimated against a random term which is a component of the composite error. The stochastic profit frontier model was used to determine the profit efficiency of artisanal fishing in the Pru District. The study applies the Battese and Coelli [20] procedure by postulating a profit function assumed to behave in a manner consistent with the concept of the stochastic frontier model [21, 22].

The stochastic profit frontier model is specified aswhere is the normalized profit (gross margin) of the fisherman, is the price of the normalized variable input, is the level of fixed factor in fishing, and is the error term. is the symmetric error term and assumed to be an independently and identically distributed two-sided error term representing the random effects, measurement errors, omitted explanatory variables, and statistical noise; is the one-sided error term. The profit efficiency of the th artisanal fisher can be expressed as the ratio of the observed profit to the predicted maximum profit and specified aswhere is the profit efficiency, is observed profit, and is the maximum (potential) profit. The profit efficiency ranges between zero and one . That is, . The parameters of the stochastic profit frontier function were estimated by the maximum likelihood function using STATA 13. The maximum likelihood estimates of the stochastic profit frontier model provide the estimates of and gamma , where gamma explains the variation of the total profit from the frontier profit. The gamma estimate is specified as .

Here lies between zero and one and represents the share of the inefficiency in the overall residual variance. The gamma values ranging between zero (0) and one indicate the presence of profit inefficiency. A value of 1 indicates a deterministic frontier while that of zero suggests the absence of inefficiency. Thus, such absence of inefficiency favours the use of the average response model estimation due to the absence of the inefficiency effect term (). is the variance of the error term associated with the profit inefficiency effects, and that associated with random noise factor is . represents the overall variance of the model and the three are related as [23].

The main hypothesis tested in this study is whether or not there exists profit inefficiency in the operations of the sampled artisanal fishers in the study area. The null and the alternate hypotheses are stated as : absence of profit inefficiency, : presence of profit inefficiency.

The hypotheses above were tested with the generalized likelihood ratio test (* λ*) which can be expressed asThe generalized likelihood ratio (LR) has a mixed chi-square distribution set at a level of % significance and degrees of freedom, where is the number of variables included in the inefficiency model [24]. is the model under the null hypothesis that there is no inefficiency () while is an alternate hypothesis that profit inefficiency exists ().

###### 2.2.2. Empirical Model

The study employed the Cobb-Douglas production function due to its flexibility and its popularity. It also meets the requirements of being self-dual; thus, it allows an examination of economic efficiency [22]. The study used the model of [25] to specify the stochastic profit frontier with the inefficiency components where all the parameters were estimated together in a single step maximum likelihood estimation. The frontier model for estimating profit efficiency of artisanal fish catch is specified aswhere is normalized profit. (For the normalized profit, the gross margin is divided by the market price of the fish output. The gross margin is the difference between the total revenue and the total variable cost considering the inclusion of some fixed cost as explanatory variables in the profit equation [24].) is the cost of maintenance, is cost of storage, is the price of labour, is the price of needle, is size of rope, is the price of paddle, and is the cost of the boat. is characteristic of artisanal fishers related to fishing and is error term. The profit efficiency of the th farmer is given by [5, 6], wherewhere denotes the age of fisherman, denotes fishing experience, denotes educational level, and denotes household size.

###### 2.2.3. Garrett Ranking Technique

The Garrett ranking technique was used to rank constraints that militate against the activities of fishermen. The fishermen were allowed to rank constraints which were converted into score value using the formula for Garrett ranking technique as specified below:where is rank of constraint = the th constraint by the th artisan. is number of constraints ranked by th fishers. The percentage position calculated using the above formula was converted into scores using Garrett’s table. The scores of each constraint were added to each constraint from which total values and mean values of the scores were computed.

#### 3. Empirical Results and Discussions

##### 3.1. Demographic Characteristics of Respondents

The entire 120 respondents interviewed are males (Table 1). This may be due to the nature of fishing. Fishing involves physical strength and thus is undertaken by men while females are mainly engaged in the processing and marketing. Fishing in the Pru District is primarily dominated by the age group 31–40 years representing about 39% of the total sample size. This is closely followed by the age group 41–50 years representing about 26.7%. There is no fisher below 20 years while 7.5% are above 60 years; thus, the aged are somehow engaged in fishing. The majority (75.8%) of the artisanal fishers are married. In the Ghanaian fishing communities, the entire family mostly engage in the business with the males going to the sea while the females participate in the marketing and processing. Table 1 also reveals that out of the 120 respondents sampled, those without formal education are 43.3% and those with basic/primary education are also 43.3%. Also, 16 respondents representing 13.3% had up to senior high school education while none of them had a tertiary education. Tijani et al. [26] and Hyuha [27] indicated that the acquisition of higher education improves the quality of labour and also the probability of adopting new techniques. The majority (68.3%) of the fishermen are Christians while Muslims and Traditionalists recorded 11.7% and 17.5%, respectively.