ReseaRch PaPeR Journal of Agricultural and Marine Sciences 2020, 25(2): 46–57 DOI: 10.24200/jams.vol25iss2pp46-57 Received 16 Mar 2020 Accepted 14 May 2020 Dynamics of Physical Capital in Artisanal Fisheries and Policy Implications Shekar Bose1, Hussein Samh Al-Masroori2*, Salim Darwish Salim Al-Hasani3 Hussein Samh Al-Masroori2*( ) masroori@squ.edu.om, 2Depart- ment of Marine Science and Fisheries, College of Agricultural and Marine Sciences, Sultan Qaboos University. 3Ministry of Agriculture and Fisheries, Sultanate of Oman. 3Gibbon Street, Woolloongabba Queensland, 4102, Australia. Introduction The twin problems of over-investment and excess harvesting capacity are of major concerns for fish-eries in both developed (Zhang et al., 2018; Kirkley et al., 2002; Kirkley and Squires, 2003) and developing countries (Pomeroy, 2012; Purcell and Pomeroy, 2015). These problems are essentially linked to both physical and natural capital (Nøstbakken et al., 2011). The role of physical capital as a produced means of production is well established in economics in general (Hulten, 1991), and in natural resource industries like fisheries in partic- ular (Clark et al., 1979). Frequent occurrence of conflicts in fisheries due to excess capacity (Ahmed et al., 2006), threats to long term sustainability due to capital stuffing (Kirkley and Squires, 1998), and poor economic returns associated with over-investment are some of the legiti- mate justifications for the need to address the concerns of over-investment and excess capacity in small- and/or large-scale fisheries. This calls for effective management of overcapitalization in fisheries which is crucial for en- suring sustainability in fisheries and the flow of benefits to the society in the long run (Kirkley and Squires, 1998). Furthermore, recognizing the role of small-scale fisher- ies in development (de Melo Alves Damasio et al., 2016), the financing of small-scale fisheries whether by public or private funds has received attention of policy-makers in recent years both locally (Diffey et al., 2009; Setlur and Arbuckle, 2015) and globally (Heck et al., 2007; Holmes et al., 2014). However, a lack of knowledge about the sta- tus of physical capital engaged in fishing will not only undermine effectiveness of management but also intro- duce uncertainty to the future investment planning for the sector. Hulten (1991) emphasized that an estimate of capital, however imperfect, is crucial to understand the ديناميات رأس املال املادي يف مصايد األسامك الحرفية واآلثار املرتتبة عىل السياسات شيكار بوز1 وحسني سمح املرسوري2* وسامل درويش الحسني3 abstRact. The dynamics of physical capital stock and net investment in artisanal fisheries of Al-Seeb, a coastal fishing town of Muscat Governorate, were examined covering the period 2004-2013. Data was gathered from two sources namely the Ministry of Agriculture and Fisheries and field survey. A hedonic regression model was used to examine the influence of boat characteristics on the boat acquisition prices and then to derive the growth rate of physical capital stock. Boat characteristics comprising of ‘age’, ‘horsepower (hp)’ and ‘length (vl)’ were found to be statistically significant at the 5% level and carry sign consistent with the economic theory of depreciation and cost respectively. While inter-an- nual fluctuation of net investment was observed, a positive trend in cumulative investment with an average growth rate of 7.63% was experienced in the fishery during 2004-2013. A crude assessment of the operating costs and the gross revenue of surveyed boat-owners in 2015 showed that on average monthly economic profit of 571±169 SD OMR was received by individual boat-owner, which complements the positive trend in cumulative investment. These findings have important management and policy implications in relation to the effective management of harvesting capacity as well as attract investment in the fishery. Finally, some limitations of the study are discussed along with the indication of potential future research. KeywoRds: Sultanate of Oman, Al-Seeb, Capital Stock, Investment, Hedonic Model, Fishing Fleet. املســتخلص:تم يف هــذه الدراســة فحــص ديناميــات مخــزون رأس املــال املادي وصايف االســتثامر يف املصايد الحرفية يف والية الســيب مبحافظة مســقط يف الفــرتة 2004-2013 وذلــك بإســتخدام بيانــات جمعــت مــن وزارة الزراعــة والــروة الســمكية وعــن طريق املســح امليداين، وقد أســتخدم منــوذج إنحدار املنفعــة لفحــص تأثــر خصائــص القــارب عىل أســعار اقتنائه ثم إلشــتقاق معدل منــو رصيد رأس املال املــادي. وقد وجد أن خصائص القــارب التي تتكون مــن العمــر والقــوة والطــول كانــت ذات داللــة إحصائية عند مســتوى 5٪ وتحمل عالمــة تتوافق مع النظريــة االقتصادية لإلهالك والتكلفــة عىل التوايل، وبينــام وجــد تذبــذب ســنوي يف صــايف االســتثامر لوحــظ وجــود اتجــاه إيجــايب يف االســتثامر الرتاكمــي مبتوســط معــدل منــو 7.63٪ يف مصايــد األســامك خــالل الفــرتة 2004-2013، وقــد أظهــر تقييــم تكاليــف التشــغيل واإليــرادات اإلجامليــة ملالــي القــوارب الذيــن شــملهم االســتطالع يف عــام 2015 أن مالــك القــارب الفــردي حصــل يف املتوســط عــىل أربــاح اقتصاديــة شــهرية تبلــغ 571 ± 169 ريــال ُعــامين، وهــو مــا يؤكــد االتجــاه اإليجايب يف االســتثامر الرتاكمــي، ولهــذه النتائــج تداعيــات إداريــة وسياســية مهمة فيام يتعلــق باإلدارة الفعالة لقــدرة الحصاد وجذب االســتثامر يف املصايــد، ويف نهاية الورقة متت مناقشــة بعض قيود الدراســة مع اإلشــارة إىل البحوث املستقبلية املحتملة. الكلامت املفتاحية: سلطنة عامن، السيب، رصيد رأس املال، اإلستثامر، منوذج املنفعة، أسطول الصيد 47ReseaRch PaPeR Bose, Al-Masroori, Al-Hasani process of economic growth. In economics, the description of the term capital has been conceived as deferred consumption, as a stock of durable goods, or as a flow of factor services (Hulten, 1991). In case of fisheries as argued by Squires (1988) that the quantity of capital measured as the flow of services from the number of boats (i.e. physical capital stock) operating in a fishery. The economic theory of capital in fisheries, differentiates three types of capital namely physical (e.g. boat, equipment), natural (fish stock), and human (crews, skills and expertise). The interdependen- cies of natural and physical capital (Boyce, 1995; Burt and Cummings, 1970) under various conditions such as functional form of investment costs (Boyce, 1995) and degree of malleability (Clark et al., 1979) were studied in fisheries. It was argued that favorable condition of growth in natural capital and other form of economic incentives (e.g. subsidy) leads to investment decisions by fishers and entry and exit behavior of fishers, other things being equal (Le Floc’h et al., 2011). However, it is important to note that exit from fisheries may be diffi- cult due to lack of alternative employment opportunities (Bose et al., 2013; Ikiara and Odink, 1999). Eisenack et al. (2006) argued that for a developing fishery, a time gap existed between the growth of physical and natural capi- tal, and due to partial substitutability between these two forms of capital the growth in physical capital continues even after decline of the natural capital. In general, while the conservation and management of natural capital are the primary objectives of management authorities, the regulatory environment is likely to exert influence on fishers’ investment decisions relating to physical capital (Nøstbakken et al., 2011). The capital market imperfec- tions in relation to the borrowing and lending constraint which has particular relevance to small-scale fisheries in Oman where collateral is needed by the financial in- stitutions (Diffey et al., 2009). Other factors affecting investment decision include boat age and size, future revenues, operating costs (e.g. fuel, and labour), stock status of the main target species, and the impact of man- agement measures (e.g. mesh size restrictions) and fleet size (Tidd et al., 2011). The main objective of this case study were three-fold considering the above-mentioned strategic importance of small-scale fisheries sector in Oman. First, the extent of influence of basic boat characteristics on the acquisi- tion price of boat. Second, an empirical assessment of the dynamics of physical capital stock and net invest- ment in artisanal fisheries during 2004-2013. Third, ascribing importance to the view of ‘fishing costs’ as a key driver of fishers’ investment decisions, among oth- ers, and the scarcity of such cost information (Lam et al., Table 1. Descriptive statistics of sample used for hedonic regression Year Boat Boat Length (ft) Boat Price (OMR) Engine (hp) Engine Price (OMR) No. Mean SD CV (%) Mean SD CV (%) Mean SD CV (%) Mean SD CV (%) 2004 9 22.89 5.49 23.98 1614.53 971.31 60.16 102.78 92.4 89.90 1938.98 1383.32 71.34 2005 8 23.37 5.66 24.22 1537.87 839.75 54.60 109.37 96.49 88.22 2058.91 1382.51 67.15 2006 8 21.62 4.24 19.61 1361.36 704.28 51.73 88.12 86.89 98.60 2164.27 1842.41 85.13 2007 8 21.62 4.24 19.61 1474.83 803.87 54.51 88.12 86.89 98.60 1697.81 1056.14 62.21 2008 6 21.17 4.21 19.89 1429.34 772.88 54.07 70.83 76.77 108.39 1517.64 1101.61 72.59 2009 6 21.17 4.21 19.89 1676.81 784.26 46.77 70.83 76.77 108.39 1506.67 934.74 62.04 2010 4 22.75 4.57 20.09 1825.53 517.31 28.34 93.75 90.12 96.13 2194.17 1308.22 59.62 2011 4 24.50 4.51 18.41 2142.88 954.55 44.55 143.75 98.1 68.24 2882.25 1189.67 41.28 2012 2 27.00 2.83 10.48 NA NA NA 140 84.85 60.61 4053.75 1773.07 43.74 Figure 1. Map of Oman and study location 48 sQU JoURnal of agRicUltURal and MaRine sciences, 2020, VolUMe 25, issUe 2 Dynamics of Physical Capital in Artisanal Fisheries and Policy Implications and the harvesting function is well established in fish- eries literature, and the dynamic behaviour of physical capital stock is used for explaining the rate of invest- ment associated with fisheries resource use. Therefore, research on capital-stuffing and investment behavior of fishers is valuable as it may provide necessary signal for investment incentives (or dis-incentives) to the private sector which is one of strategic objectives of the sector. The existence of capital market imperfections in relation to the borrowing and lending constraints (Diffey et al., 2009) further rationalizes the importance of the present research to identify market constraints and thereby, en- hance the knowledge of the relevant actors to formulate appropriate marketing strategies to accommodate the market dynamics of foreign origin. Case Study Profile Fisheries are an integral part of Oman’s economy and have occupied an important place in the national pol- icy agenda as the ‘Oman Vision 2020’ - a long-term de- velopment plan, reflects the country’s desire to achieve food security, enhance fishers’ income, and maximize so- cio-economic benefits from the sector (Bose et al., 2010; MNE, 2007). The superior economic performance of the sector is a prerequisite for the achievement of these stra- tegic objectives. Small-scale artisanal fisheries in Oman have been the dominant both in terms of landings and value. During the past decade or so, the sector’s contribution to Gross Domestic Product (GDP) has remained stable around 0.6% (at 2000 constant prices) (Bose et al., 2010; Al-Sub- hi et al., 2013). With the advent of economic diversifi- cation policy guided by ‘the Vision for Oman’s Econ- omy-2020’ the fisheries sector has attracted renewed 2011). This study aimed to provide a crude estimate of monthly average operating costs and revenue per boat experienced in 2015. Due to time and resource limita- tions, the cost estimate was produced using the logbook information obtained from ten owner-operators partic- ipated in the study. From an academic perspective, this study can provide a wider relevance to artisanal fisheries sector in devel- oping countries as this type of quantitative assessment is relatively limited in fisheries literature. In addition, the authors are unaware of any empirical work available or devoted explicitly to address the matter at hand in Oman. More importantly, the lack of knowledge about the existing fleet capacity and the extent of their utiliza- tion has been highlighted as major issues, among others, confronting the management of fisheries sector in Oman (Setlur and Arbuckle, 2015; Al-Siyabi and Bose, 2018). In the absence of such information fisheries managers may have the tendency to use the number of operating boats as a measure of capital stock and investment in the tradi- tional fisheries sector. However, such measure would not be adequate as it ignores changes in the quality of such capital over time (Kirkley and Squires, 1998; Kendrick, 1961). So far, there is negligible attempt to collect and compile statistics on either the capital stock or the level of investment in the sector. This study also contributes to the practical realm, as it provides policy makers and investors with knowledge that has the potential to assist in designing effective strategic actions with regard to in- vestment in fisheries. Therefore, this case study not only fills the existing knowledge gap but also complements the existing global literature by adding country-specific information. The link between aggregate physical capital stock 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 0 50000 100000 150000 200000 250000 300000 V al ue (t ho us an d O M R ) L an di ng s (m t) Year Landings (mt) Value (OMR 1000) Figure 2. Landings and Value of Artisanal Fisheries, Oman 49ReseaRch PaPeR Bose, Al-Masroori, Al-Hasani interest and accordingly the authority has been promot- ing public-private partnerships to attract private invest- ment in the sector. Recently, efforts have been directed to, amongst others, (i) modernize fishing operations and improve fleet performance, (ii) initiate effective fisheries governance mechanisms; (iii) ensure sustainable use of fisheries resources; and (iv) optimize socio-economic benefits from the sector. The present study conducted in Al-Seeb area which is a coastal fishing town located in the northwest part of the Muscat Governorate. The selection of the study site is influenced by several factors, such as, time and costs, information availability and geographical prox- imity. It is envisaged that the replication of this study would be useful for generating insights into the pattern of fishery investment and subsequently the design of effective management policies in relation to the future investment in the fishery. Muscat Governorate is one of the seven coastal governorates of Oman (See Figure 1). Muscat Governorate has six Wilayah (Province) namely Muscat (capital city), Qurayyat and Al-Seeb, Bawshar, Al-Amrat and Mutrah. Figures 2, 3, and 4 exhibit the performance of total fish landings and gross value at the national (Oman), governorate (Muscat) and Wilayah (Al-Seeb) level re- spectively covering the period 2004-2015. From these figures, it can be noted that at the national level showed a distinct positive trend during the study period, while the same pattern was not evident at the governorate and Wilayah level. However, a resemblance was observed in the behavioral pattern of landings and gross value at the governorate and Wilayah level. Further comparative analysis comprising landings, gross value, fishing boats, and number of fishers are provided in the results section. Methodology Data Ideally, the economic value of capital services should be reflected in rental price. In the absence of such rental market the imputed price of capital services consists of two cost elements, namely the opportunity cost and the depreciation cost associated with the investment and the capital equipment respectively (Jorgenson, 1974; cited by Squires, 1988). There is negligible published database on actual transaction prices of new boats and, therefore, the factory quotation prices for new boats available for the period 2004-2013 were used in this analysis. In the absence of price information for other capital items, such as fishing gears, electrical equipment, and consis- tent price index series for heavy machinery, the nom- inal prices of tangible assets such as boats and engine along with the corresponding quality attributes age, boat length, and engine horsepower are considered in this study. Data were collected from two sources. First, the price quoted by Omani companies submitted to the Ministry of Agriculture and Fisheries during 2004-2013 for 53 boats of various sizes (ranging from 16ft to 33ft) and engine types were collected. The acquisition pric- es were not available. Second, data on registered fishing boats operating in Al-Seeb coastal water along with their characteristics, such as size, engine power, and boat age were collected from the local Ministry office. It was noted that the quoted prices for the same boat size and engine power from different companies differ in a given period due to different technical specification of boats and add-ons (such as warranty condition, and insurance period) coming with the deals. The average value was used to represent such cases. As mentioned earlier, a 0 5000 10000 15000 20000 25000 30000 0 5000 10000 15000 20000 25000 30000 35000 V al ue (t ho us an d O M R ) L an di ng s (m t) Year Landings (mt) Value (OMR 1000) Figure 3. Landings and Value of Artisanal Fisheries, Muscat Governorate 50 sQU JoURnal of agRicUltURal and MaRine sciences, 2020, VolUMe 25, issUe 2 Dynamics of Physical Capital in Artisanal Fisheries and Policy Implications simple logbook was prepared (in Arabic) to collect in- formation on basic operation costs. Ten owner-opera- tors nominated by the local office of the Ministry were engaged during February-April 2015 to record costs per trip incurred by individual fisher for two weeks (14 trips per fisher). In fisheries, boats are treated as a durable but depre- ciable asset. In estimating physical capital stock and net investment in the New England commercial fisheries (Kirkley and Squires, 1988) followed hedonic regression method and used data on boat characteristics, such as gross registered tonnage (GRT), length, and engine pow- er. The basic idea behind the hedonic approach is to con- sider a fishing boat as a bundle of characteristics, such as length, age, engine horsepower (hp) and to generate estimate of the missing prices when quality changes. Empirical Model The basic model adopted in this study is mainly drawn from the study by Kirkley and Squires (1988). It involves two steps. First, a hedonic model was estimated, and second step growth rate of capital stock was derived by making use of the estimates of implicit price from the first step. Broadly speaking, the hedonic approach hy- pothesizes that the price of a commodity is influenced by its characteristics (Rosen, 1974). Kirkley and Squires (1988) have argued that the hedonic approach offers an attractive characteristic for estimating capital stock in fishing industries such that it incorporate changes in the quality of capital over time which should be reflected in boat acquisition prices. To represent this, the following Cobb-Douglas functional form was employed. C=constant.(age)α.(vl)β.(hp)γ.(exp)u Eq.(1) where, C is the list price or acquisition price of boat, age is age of the boat, vl is boat length, hp is engine horse- power, and u is white noise error term. From a theoret- ical standpoint, the relationship expected between the price and the characteristics are as follows: α<0,β>0,and γ>0. The hedonic approach is adopted because there is no market information available for used or rent- ed boats. The application of hedonic regression on the available data provides implicit prices of the chosen quality dimensions which would also provide signal to the cost of fishing effort. The age of a boat, was counted from the year the boat was first registered in the boat registry of the Ministry. Size (measured as boat length) and capacity (measured as horsepower) are two import- ant characteristics of fishing boats as they are routinely used for fishing effort measures. The stability of the esti- mated coefficients obtained from the hedonic regression model would not be tested due to lack of enough obser- vation for each expected period. In empirical analysis, a linear, semi-logarithmic, and log linear form of the model is frequently used. As there is no a priori reason to assume that price and quality be- have in a particular fashion, a Box-Cox transformation was applied to decide whether linear or log-linear func- tional form of the model fit the data best as follows: Eq.(2) Parameter ‘λ’ which was determined by the data defines the functional form as: if λ=1, it represents linear speci- 0 1000 2000 3000 4000 5000 6000 7000 0 2000 4000 6000 8000 10000 12000 V al ue (t ho us an d O M R ) L an di ng s (m t) Year Landings (mt) Value (OMR 1000) Figure 4. Landings and Value of Artisanal Fisheries, Al-Seeb 51ReseaRch PaPeR Bose, Al-Masroori, Al-Hasani fication, and if λ=0, it reduces to log-linear specification. Each observation of the dependent variable (i.e. boat price) was first divided by the geometric mean (GM = 3118.32) of the dependent variable and two versions (with and without taking log of the dependent variable) of the regression model (Equation 1) was run. The values of residual sum squares (RSS) were used to conduct a Chi-square test to decide between the linear and log-lin- ear functional forms. The test result (see the result sec- tion) supported a log-linear version of the model. As the model is in log-linear form, the resulting regression coefficients can be interpreted as partial price elastici- ty with respect to the variable concerned, other things being equal. Following Grillches (1961), a semi-logarith- mic form of the model (Equation 1) with respect to the age variable was also experimented to accommodate the possibility of geometric patterns of depreciation of boat value with age. The first data set was used to estimate the param- eters from the two versions of Equation 1. At the sec- ond-stage, the parameter estimates from Equation 1 were used to generate estimate of capital stock per boat per year based on the second data set that include physi- cal characteristics of the registered boats that have been operating in the Al-Seeb fisheries. The estimated capital stock per boat was then summed for all boats in a year to obtain the estimate of total capital stock. It was assumed that the value of all capital services was the sum of the values of individual capital services (Squires, 1988). Net investment was then calculated as the change in total capital stock per unit of time (i.e. year). Results Some relevant comparative statistics are also calculated from the data presented in the Figures 1-3. During 2004- 2015, the average growth rate of total landings in Oman was about 5.65% per annum whereas Muscat governor- ate and Al-Seeb were witnessed negative growth rate of -4.08% and -0.09% respectively. However, during the same period, the positive trends in total value were ob- served for Oman, Muscat governorate and Al-Seeb, was observed the average growth rate of 9.71%, 4.90% and 7.31%, respectively. Furthermore, for Muscat and Al- Seeb, a downward trend was observed for total landings around mid 2000-2012 and then a positive trend after 2012. The sharpest decline in landings appeared to take place in 2008. With reference to number of fishers and boats positive trends were observed for Oman, Mus- cat and Al-Seeb but differed in extent, with the average growth rates of 3.40%, 1.60% and 2.41% for number of fishers and 2.03%, 1.61%, and 2.11%, respectively. The variation in total landings as measured by the co- efficient of variation (CV) for Al-Seeb was the highest (45.47%) followed by Muscat governorate (32.94%) and Oman (24.92%). On the other hand, the variation ob- served in total value was the highest for Oman (37.18%) followed by Al-Seeb (33.49%) and the governorate (22.91%). The descriptive statistics of the sample used for he- donic regression model are presented in Table 1. It was mentioned earlier that a Box-Cox transformation was followed to decide between the two conventional versions namely the linear and the log-linear models. The estimated χ2 test value (38.25, 8 df ) supported the log-linear specification of the model. Table 2 provides the empirical results of the hedonic regression mod- -60000 -40000 -20000 0 20000 40000 60000 N et In ve st m en t ( O M R) Year Figure 5. Net Investment in the Fishery during 2004-2013 52 sQU JoURnal of agRicUltURal and MaRine sciences, 2020, VolUMe 25, issUe 2 Dynamics of Physical Capital in Artisanal Fisheries and Policy Implications el specified in Equation 1 in two forms (log linear and semi-log with respect to age) along with the summary statistics and diagnostics. The potential ‘non-linearity’ in variable was also experimented by including square root term for each variable in to the model but failed to generate any theoretically convincing results. Some important results can be noted from Table 1. The average boat length was more or less similar across the years, which indicated the degree of homogeneity in small-scale fisheries with respect to boat size. Howev- er, the results of boat power exhibited relatively great- er degree of heterogeneity in the sample as reflected in the coefficient of variation (CV) estimate. This was because of the fact that some boats were using twin-en- gine. This was also reflected by the fact that the aver- age price of engine is higher than that of boat except for 2009. Furthermore, the variability (as indicated by CV estimates) of average boat price was lower than engine power and engine price which to some extent consistent with the homogeneity in boat size as mentioned earlier. Apart from higher capital cost (i.e. fixed cost), the use of twin-engine also exerted influence on variable costs due to recent rise in fuel price. It is noted that a limited number of quality dimen- sions explain a very large fraction of the total variability of the list price as measured by R2 value. The coefficient of the variable ‘age’ carries an expected sign as expect- ed from the economic theory of depreciation and the associated t-value suggests that the variable is statisti- cally significant at the 5% level. The coefficient of age in Model 1 is the elasticity, which is the predicted average proportionate change in boat list price due to propor- tionate change in boat age, holding other qualities con- stant. On the other hand, semi-log form the coefficient of age can be interpreted as the percentage change in the boat list price due to a unit change in age, other things being equal. The variables ‘horsepower (hp)’and ‘length (vl)’ are statistically significant at the 5% level with expected sign that is consistent with the theory of cost which predicted that the boat cost should increase with the increase in the characteristics of power and size. These results are in line with Kirkley and Squires (1998). Furthermore, it is important to note that boats engine power and length have the highest influence on the acquisition price of boats, which is consistent with the descriptive statistics presented in Table 1. To demonstrate the empirical validity of the model a series of diagnostics has been applied and the results are presented in Table 2. It can be seen that in all cases, the results from the econometric diagnostics do not exhibit any deviations from the classical linear regression prop- erties. The J-B LM test was used to examine the normali- ty of the residuals at the 5% level (Jarque and Bera, 1980). The test statistic follows a χ2 (with 2 df ) distribution and the value of the test statistic indicates that the null hy- pothesis of normality cannot be rejected. The B-P-G test was used to test the null hypothesis of homoscedasticity of the residuals at the 5% level (Breusch and Pagan, 1979; Godfrey, 1978). The test statistic follows a χ2 (with 2 df ) distribution and the test value was insignificant at the 5% level thereby the null hypothesis of homoscedastic error variances cannot be rejected. The Lagrange Multiplier (LM) test statistic with two lags was used to test the null hypothesis of no autocorrelation at the 5% level. The test result supported the null hypothesis of no autocorrela- tion. Comparing the output of summary statistics, fore- cast performance, and model selection criteria, it can be 0 200 400 600 800 1000 1200 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 Cu m ul at iv e In ve st m en t ( 10 00 O M R) Year Figure 6. Cumulative Investment in the Fishery: 2004-2013 53ReseaRch PaPeR Bose, Al-Masroori, Al-Hasani seen that Model 1 performs only marginally better than Model 2. Therefore, it was decided to use the parameter estimates of Model 1 in the second stage. At the second-stage the parameter estimates from Equation 1 were used to generate estimate of capital stock per boat per year based on the second data set that include physical characteristics of the registered boats that have been operating in Al-Seeb water. The net in- vestment was calculated as the change in total capital stock per unit of time (i.e. year) for the period 2004- 2013 and it is presented in Figure 5, which exhibits in- ter-annual fluctuation in net investment in the fishery. Despite this year-to-year fluctuation, the cumulative of net investment in the fishery experienced a positive trend during 2004-2013 as depicted in Figure 6. Table 3 presents the descriptive statistics of boat characteristics, engine and gear price, maintenance costs, and operating costs per trip per boat gathered from the logbook survey data. It is noted that the boats involved in cost analysis ranging in length from 22 feet to 25 feet with an average horsepower of about 104 hp. These figures are within the range presented in Table 1. Furthermore, the largest variability with regard to engine price is consistent with the result presented in Table 1. On average, the fishing trip lasted for about 8 hours. The operating costs include fuel, oil, food bait and ice, and the average cost per trip for individual fisher was estimated to be about 15 OMR. On average, fuel cost has the highest share (about 53%) followed by the bait cost (28%) in the total trip cost. This information is of important use for future research in this area. The highest share of fuel cost has important cost implication for boats that are using twine-engine as stated earlier. To get an idea about the extent of economic benefits generated from fishing activities, crude estimates of the average monthly net value earned per boat are present- ed in Table 4 based on the survey data and the data from the statistical yearbook. The gross value is evaluated at farm gate prices of fish species. In the calculation of monthly operational costs per boat, it was assumed that individual boat makes 20 trips per month. Therefore, the estimate was conservative in nature. Fishers’ physical access to market can improve their Table 2. Empirical results of the hedonic regression model Model 1 (log-log) Model 2 (Semi-log w.r.t. age) Variable Coeffi- cient t-value Variable Coefficient t-value ln(age) -0.23 -4.61 age -0.03 -4.50 ln(hp) 0.49 9.97 ln(hp) 0.49 9.85 ln(l) 0.47 2.26 ln(l) 0.49 2.37 Constant 4.98 10.49 Constant 4.71 10.12 Summary Statistics: R2 0.95 0.94 Residual Sum Square (RSS) 0.78 0.81 Log likelihood 37.20 36.82 F statistics (Joint test) 287.50 283.16 (p = 0.00) (p = 0.00) Regression Diagnostics: Normality: LM test(χ22 df ) 2.23 (p = 0.33) 2.27 (p = 0.32) Serial correlation: Durbin Watson (D-W) LM Test (χ22 df ) 2.06 2.05 1.52 (p = 0.47) 0.98 (p = 0.38) Heteroscedasticity: B-P-G test (χ23 df ) 0.10 (p = 0.96) 0.11(p = 0.95) Forecast Performance: RMSE 0.121 0.122 MAE 0.102 0.103 MAPE 1.274 1.288 Model Selection Criteria AIC -1.23 -1.22 SC -1.08 -1.07 H-Q -1.17 -1.16 Table 3. Descriptive statistics of boat operation costs, Al-Seeb Item Min Max Mean SD Boat size (feet) 22 25 23.9 1.2 Engine Power (hp) 60 200 103.5 43.5 Engine Price (OMR) 1500 5000 2604.1 1240.6 Fishing Gear (units) 10 83 38.7 25.4 Fishing Gear Price (OMR) 525 5350 2215.9 1471.7 Maintenance Cost (OMR) 150 1250 517.8 332.6 Fishing Trip (hours) 2 19 8.1 3.8 Cost/trip/boat (OMR) Fuel 1 67 8.1 9.6 Oil 0.7 23.5 2.4 2.5 Food 0.2 33.5 2.7 3.4 Bait 0.5 15 4.3 3.2 Ice 0.5 8 1.5 1.1 Total 4 75.9 15.4 14.3 Source: Field survey 54 sQU JoURnal of agRicUltURal and MaRine sciences, 2020, VolUMe 25, issUe 2 Dynamics of Physical Capital in Artisanal Fisheries and Policy Implications profitability, as the prevailing market price was higher than that of farm gate. One of the reasons for the es- tablishment of the central wholesale market in Oman in April 2014 was to give fishers access to market and ad- dress pricing inefficiency (Al-Busaidi et al., 2016; Al-Jabri et al., 2015). Furthermore, the extent of profitability was also influenced by the economic value of species caught, which differed from species to species. The monthly net revenue figures ranged from 297 in July (summer month) to 842 in February (winter month). On average, the monthly net revenue per boat was about 571 ± 169 OMR. The findings indicates the fact that fishing has been a profitable profession and this is reflected in the estimates of cumulative net investment. Personal discus- sion with fishers also revealed the same. However, up- to-date, comprehensive, and quality economic data are required to convey precise assessment of the economic situation of the sector. Expenditure figures presented in Table 4 are only a part of the costs involved in fishing operations. The cost of the boat in the form of depreci- ation allowance needs to be accounted. In addition, the opportunity costs of capital and the owner’s labor and management have to be accounted for in order to arrive at the results of this economic activity. Discussion The degree of homogeneity in relation to boat length and the degree of heterogeneity to engine power are consistent with the findings by Al-Siyabi and Bose (2018). At least two important implications of these find- ings are as follows: (i) the homogeneity aspect may help reduce the conflicts, which arise from unequal catch share between large and small boats, and (ii)) the appar- ent heterogeneity to engine power along with the high- est share of fuel cost (on average 53%) has important economic implications as the use of twin-engine exerts influence on operating costs. In addition, the recent rise in fuel price as noted by Al-Siyabi and Bose (2018) exert- ed upward pressure on the operation costs. Higher fuel costs may discourage fishers’ future in- vestment decisions in the sector as it could reduce net gain unless it is compensated by the revenue earned through either increase in fish price or increase in total landings, or a combination of both (Tidd et al., 2011). Tidd et al. (2011) found a statistically significant influ- ence of fuel prices on vessel entry and suggested that the vessels would enter at lower costs of subsidized fuel. The highest degree of variation in total landings mea- sured by the coefficient of variation (CV) and its sharp- est decline experienced in 2008 for the study area signal the level of uncertainty in generating revenue from fish- ing. This is reflected by the variability in fishers’ income as shown in Table 3. The authority may promote diver- sification rather than specialization of fishing portfolio through fishers’ participation in additional or more di- verse fisheries to mitigate seasonal variation in their in- come. This approach is common in fisheries. Anderson et al. (2017) proposed such strategy to buffer against in- come variability due to seasonality in fisheries. In a local context, alternative strategic initiatives such as aquacul- ture development (Al-Siyabi and Bose, 2018) and fishing tourism (Al-Busaidi et al., 2016) are also suggested to en- able fishers to choose related but alternative profession. This type of approach would also help reducing fishing pressure and if economically viable would encourage relatively inefficient fishers to exit from harvesting op- erations. The results of the hedonic model in relation to boat age, length and horsepower are consistent with the the- oretical predictions and have important management implications. The statistical significance of the variable boat ‘age’ can be related to operational efficiency of the fleet as older vessels are expected to be less efficient than the newer one. Tidd et al. (2011) argued that old- er vessels are more likely to exit the fishery. The statis- tical significance of the variables ‘horsepower (hp)’ and ‘length (vl)’ predicted that the boat cost would increase with the increase in the characteristics of power and size. The results are also consistent with other empirical stud- ies (Tidd et al., 2011). The implication of these results is that boat size and horsepower together with the high Table 4. Crude estimates of the average monthly net value earned per boat in Al-Seeb Number of boats 521 Number of fishers 1201 Cost (OMR)/trip/boat 15.366 Cost (OMR)/boat/month * 307.320 Month Value (OMR) Value (OMR)/boat Income (OMR)/boat January 593000 1138.2 830.9 February 599000 1149.7 842.4 March 448000 859.9 552.6 April 321000 616.1 308.8 May 447000 858.0 550.6 June 370000 710.2 402.9 July 315000 604.6 297.3 August 439000 842.6 535.3 September 467000 896.4 589.0 October 535000 1026.9 719.6 November 500000 959.7 652.4 December 457000 877.2 569.8 Total 5491000 10539.3 6851.5 * assuming that a boat will make 1 trip per day and work for 20 working days a month. Source: Field survey and MAF data. 55ReseaRch PaPeR Bose, Al-Masroori, Al-Hasani fuel costs may affect stock status and the economic vi- ability of fishers, which in turn discourage future invest- ment in fisheries. With regard to the parameter estimates of the model, one may argue that the use of list prices of boats and engines introduces bias in estimates depending on the extent to which they deviate from the actual acquisition price. Furthermore, prices of used boats or rental market were not available. Due to a lack of sales data (new or used), validity of parameter estimates with specific refer- ence to boat age could not be checked. It should also be noted that the boat acquisition price series were not in- flation adjusted due to a lack of suitable (e.g., price index for equipment) and consistent price index for the study period. However, this may not undermine the results. It is argued that available machinery and durable equip- ment price index failed to incorporate quality changes adequately (Griliches, 1961), and therefore the process of deflation can introduce errors in measurement of price variable (Hulten, 1991). Usher (1980) mentioned the choice of price index reflects the equivalent perfor- mance of the old and new capital goods which is con- trary to the real situation. The estimated parameters may be upward bias due to the use of quoted price rath- er than the acquisition price. However, this upward bias may be partly compensated as other components of cap- ital costs, which are not included in the present analysis. While inter-annual fluctuation in net investment in the fishery is observed, the cumulative investment trend was positive. The negative net investment could be in- fluenced by various factors. For example, disinvestment in 2011 may be due to negative growth in landings as depicted in Figure 4 and social upheaval experienced in the country (Valeri, 2015). On the other hand, the posi- tive net investment was influenced by the influx of new entrant. For instance, 25 new boats added to the registry in 2005. The positive trend in cumulative net investment for the period 2004-2013 reflects the overall dynamics of physical capital. Therefore, a convincing case can be made to support government investments and develop infrastructure. The positive trends in relation to number of fishers and boats were observed for Oman, Muscat and Al-Seeb signal economic potential of fisheries. In a recent case study, Al-Siyabi and Bose (2018) noted the existence of under-utilized fishing capacity and the pres- ence of technical inefficiency in the utilization variable inputs (i.e. crews and fishing time) in small-scale fisher- ies. Based on these findings, Al-Siyabi and Bose (2018) argued that there was potential for improving produc- tion without having to incur any additional physical capital costs. Therefore, this signal of economic poten- tial can be used as a tool to attract private investment in the sector, which is one of the strategic goals of the sector as mentioned earlier. However, a precautionary approach needs to be adopted and any effort to increase investment must strike a balance with the stock status. Otherwise, the call for public-private partnership can inevitably be limited. While the findings in this study have important stra- tegic implications with regard to future course of actions in the context of investment in small-scale fisheries, there are a number of limitations of this paper that need to be mentioned. First, a variety of potential variables such as fishing gear and equipment, and shore-based tangible assets like cars and buildings, are not consid- ered as the data for such variable are not readily avail- able. Second, the estimate of capital stock is based on the registered fishing boats in the local office, which is different from the number of boats as published in the yearbook. This discrepancy is due to the time lag in li- cense renewal requirements. Third, estimates generated in this study used registered boats of Al-Seeb fisheries only, therefore, it should not be treated as an indication of net investment in the overall fisheries sector of Oman. Conclusion This case study uses a hedonic approach for examin- ing the dynamics of physical capital stock and net in- vestment in Al-Seeb artisanal fisheries. In spite of the above-mentioned limitations, the study has identified important information gaps in this area of research, set the groundwork for a more complete research study in the future, and form the basis for generating insights into the pattern of fishery investment and subsequently the design of effective management policies in relation to the future investment in the fishery. While the present case study has provided useful information, the robust- ness of empirical data and conclusions needs to be veri- fied with further studies to a point where generalization of results can be accomplished. 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