39
IN WESTERN SUMBAWA AND EASTERN LOMBOK OF NUSA TENGGARA BARAT PROVINCE, INDONESIA
Joko Mariyono1, Karnan2, Didik Santoso3, Syarif Husni4
1Faculty of Economics, University of Pancasakti, Tegal, Central Java E-mail: mrjoko28@gmail.com
2,3Faculty of Teacher Training and Education, University of Mataram, Lombok, NTB E-mail: karnan.ikan@gmail.com, didik_santoso@yahoo.com
4Faculty of Agriculture, University of Mataram, Lombok, NTB E-mail: husnisyarif@ymail.com
ABSTRACT
Two-thirds of the area of Indonesia is covered by the sea. Marine fi shery is a natural resource that can be exploited to complement food production in Indonesia. The fi shing industry has potential as a primary production source of income generation. However, it has not been optimally managed. This study analyses the economics of traditional fi shery in Nusa Tenggara Barat where most coastal communities have economic reliance on this resource. For this study, the production function is fundamental and the basic criterion for production is economic effi ciency. Data were collected from panel surveys over the period of 2008 to 2010 within three zones comprising seven villages. The stochastic frontier method was used to measure performance. The results of the study show high total factor productivity, meaning that the current technology has been fully exploited. Fishery production shows increasing returns to scale because the fi shers make many return expeditions to fi shing grounds where they have found abundant fi sh. Increasing the number of return trips per unit time to a productive fi shing area increases the effi ciency of the capital equipment. On the other hand, continuing to use depreciated equipment and fi shing craft precludes investment in more effi cient technologies. The chances of achieving theoretical or full fi shing effi ciency are inhibited by cultural and socioeconomic factors as well as seasonal, temporal and spatial factors, which all act to vary the size of the catch. In conclusion, there is still room to improve fi shery performance by renewed investment in vessels, equipment and machinery.
Keywords: traditional fi shery, technical effi ciency, production function, economic analysis, bi- ased technological progress.
I. INTRODUCTION
At global level, marine fi sheries and aquaculture are several of the most important sectors, providing employ- ment for around 180 million people.
The sectors represent a signifi cant per- centage of the animal protein intake,
particularly in developing countries in the planet (FAO, 2010). Economically, fish and fishery-based products are the commodities that are most widely traded. The value of export in 2008 was more than US$85 billion (Dyck and Sumaila, 2010).
JEL Classifi cation: Q22, D24, O13.
In Indonesia, marine fi sheries are important; they provide employment and generate income. Because two- third of Indonesia’s area is ocean, ma- rine fi sheries have the highest potential to be exploited as renewable natural resources. Coastal communities mostly rely for their livelihoods on marine fi sheries: fi sh and fi sh-derived products provide employment through fi shery supply chains.
Marine fisheries have not been managed effi ciently, however. Coastal communities of Indonesia exploit these resources using traditional equipment only. Variations in terms of capacity, engine power and equipment of fi shing vessels between regions are very high. Such variations could lead to different standards of productive effi ciency. In short, there is lack of investment by Indonesian fi shers in modern and effective technology and in capital equipment for marine fi sheries. Many factors can be blamed for this conservatism, i.e. social, economic, cultural and natural. Social factors include too few years of formal education and training so that fishers are less productive and less efficient. Economic factors include low operating capital, and cultural factors reinforce strong beliefs that local traditions and methods should be followed. One important factor infl uencing productivity is the nature of the marine world, that is, seasonal phenomena. Marine fi shing grounds are dynamic. Many species of fish migrate from one place to another depending on conditions.
Many coastal communities in West Nusa Tenggara still use traditional fishing methods. Fishers take small boats equipped with small engines to catch various species of fi sh. The additional equipment used by fi shers is also simple and traditional and, consequently, productivity (size of catch) is low (so that it is the job of local government, in collaboration with private companies, to provide assistance to increase this productivity).
The objectives of this study are to analyse the dynamics of traditional fi sheries, to measure effi ciency, and to discover factors that affect effi ciency.
The results of this study are to provide important information for local gov- ernments, policy makers, researchers, academicians, and other private sector organisations to enable the welfare of fi shers in coastal communities to be improved.
II. LITERATURE REVIEW AND FRAMEWORK Previous studies
Many studies of marine fi shery eco- nomics have been done, most of them in developed countries where advanced technology is used extensively in fi sh- ing industries. For example, Holland et al., (1999) analyse the special economic policies of fi sheries in Canada, the US, the European Union, Japan, Taiwan, Norway and Australia. Fox et al., (2006) conduct ex-post analyses of the effects of policies in Australia on the produc- tivity of marine fi sheries in that coun-
try. Pascoe and Coglan (2002) analyse the effi ciency of marine fi sheries in England to assess the contribution of inputs. Kompas, Che and Grafton (2004) measure technical effi ciencies of input controls in Australian fi sheries.
Based on fish exports data, Indonesia, along with other devel- oping countries, such as Thailand, Chile and China, showed strong fi sh export performance and increased competitiveness (Klasra and Fidan, 2005). However, economic studies on Indonesian marine fi sheries nationally are still limited, so are the studies of marine fi sheries at sub-district level or at village level. There is some recent study by Hulaifi (2011) on a sub-district in Malang, East Java. Meanwhile, Tajerin et al., (2003), and Manadiyanto, Pranowo and Sudarmanto (2002) have made financial analyses of coastal fi sheries in Tuban and on the northern coast of Java, respectively. The main findings is that these fisheries need modernising due to them generating low income. Others studies are mostly
on sociocultural aspects, which explore the socioeconomic conditions of fi sher households. Thus, studies of the eco- nomics of marine fi sheries still require more data. Compared to previous works, this study’s signifi cant contribu- tion is in terms of a more advanced analysis based on multi-year data and econometric modelling.
Theoretical framework
) , , : ,
(L K A f
Y (1)
This study used production func- tion framework, which describes the technical relations between inputs and outputs, and is generally formulated as:
Figure 1. Measure of technical effi ciency
where Y is output; L, labour; K, capital; A, total factor productivity; and a and b are coeffi cients of technology.
Economic performance of a catch is measured in terms of technical effi ciency, which is a measure of the difference between actual and potential production. Note that potential pro- duction is the highest that is achievable with the same mix of inputs.
Technical effi ciency is diagrammed in Figure 1. Let Y be a single output produced using a single input N. At point B, YB is actual output that is produced with input of N*. At point A, using the same amount of N*, output can reach the potential yield of YA. It means that the technical efficiency is the ratio of YB to YA. Many factors affect technical effi ciency and socioeconomic factors have signifi cant roles in this. Note that the change in production factors does not directly affect the level of production.
Technical efficiency can be measured using deterministic and stochastic methods (Greene, 1993).
Deterministic methods are suitable for measuring the effi ciency of high- technology industries, where variations in productivity between firms are mostly because of efficiencies.
Stochastic methods are suitable for measuring the effi ciency of agricultural industries, where variations in production between fi rms are because of differences in efficiency and in natural resources. Respectively, Sharma, Leung and Zaleski (1999) and Sahoo, Mohapatra and Trivedi (1999) show the superiority of stochastic models in analysing the effi ciency of agriculture, and of deterministic models in steel industries.
Aigner et al (1977) and Meeusen and van den Broeck (1977) defi ne a stochastic frontier production function model in which the disturbance term has two parts: a systematic component and a one-sided component. The systematic component captures
random variations in output because of factors outside the control of the producer. The one-sided component specifies the technical inefficiency relative to the stochastic frontier. This is to capture variations in production at the same level of inputs. If a producer has lower production than other producers do with the same mix of inputs, that producer is said to be less effi cient than others are.
III. MODEL ANALYSIS
(2) where A was total factor produc- tivity; Y, total production; L, number of trips; K, capital; e, exponential op- eration; α β δ φ θ were coeffi cients of technology (production elasticity); v, one side component of effi ciency; and u was an error term or random varia- tion in output. Time trend t affected A and coeffi cients of technology at the same time. Taking logarithmic op- eration on left and on right-hand sides, equation (2) resulted in:
(3)
(4) This study used an extended Cobb–
Douglas production function, which was formulated as:
where (^) represented a logarithmic operation.
Elasticity of output with respect to input was shown as:
Returns to scale were shown by summing production elasticity with re- spect to K and L, which was formulated as:
RS = α+(+δt+t (5) If RS<1, RS=1 or RS>1, the pro- duction technology exhibited decreas- ing, constant, or increasing returns to scale, respectively.
Meanwhile, technological change can be tracked using
Data and location
This study used unbalanced panel data for the period of 2008, 2009 and 2010, in seven locations on western Sum- bawa and the southern part of eastern Lombok. Total sample size for this study was 108 fi sher households. Their locations were grouped in three zones:
zone-0 included three villages (Senutuk, Benete and Maluk), zone-1 comprised two villages (Lb Lalar and Poto Tano) and zone-2 comprised two villages (Tj Luar and Ujung). Division of zones was based on the fi sh market and num- ber of fi shers. Zone-0 was the closest to the capital city of a sub-district and many fi sh buyers were within this zone.
Because it was such a strategic location, fi shers were concentrated within this zone. This was reasonable since it was easier for them to sell their catches if they stayed here. Many fi shers from other zones, which were four to six hours by boat from zone-0, sold their catches at zone-0 fi sh markets as well.
In terms of technology, fi shers in all zones had very similar boats and equip- ment.
The socioeconomic condition of fi sher households was also similar: the primary occupation was fi shing, and the secondary occupation might be as a small trader or running a very small farm with help from family members.
Data were collected monthly from each fi sher household, using structured questionnaires. In this analysis, data were aggregated to avoid the effects of shortcomings of individual responses, that is, incomplete information. The t
K t L
Yˆ ˆˆ2
(6)
In this case, there were two types of technological change: Hicks-neutral, which was shown by 2 and ‘bi- ased’ technological change, which was shown by LˆKˆ. If δ ≠ 0 and ≠ 0, the production technology indicated L- and K-biased technological change (Mariyono, Kompas and Grafton, 2010). Calculations of elasticity and rate of technological change were based on average values of L, K and t.
Source of variation in technical effi ciency is analysed using model as follows:
t IZ OZ
S 5 6 7
4
ED EX FM kons
v 1 2 3 (7)
where FM was family member;
EX was experience as a fi sher; ED was level of education; S was season; OZ was main zone, IZ was zone I; and t was year (2008=1)
The dynamics of catch per unit effort (CPUE), which represented aver- age production per trip, was analysed using a model as follows:
OZ t t S OZ Const
CPUE 1 2 3 4 * (8)
defi nitions of variables used in this study were as follows:
Y: total production, total biomass caught during one month (kg)
CPUE: average production or Y per trip (kg) Trip: number of fi shing
K: total value of boat and fi shing gear (IDR) Season: dummy variable for a fi sh season:
February March, April, June, July and Novem- ber (1 for season, 0 otherwise)
FM: number of family members in a household ED: standard of education (in years)
EXP: experience as fi sher (in years)
All data were analysed using statistical software in order to get the estimates of production functions, the factors affecting effi ciency and the dynamics of CPUE
IV. ESTIMATION RESULTS AND DISCUSSION
The estimates of production func- tions are in Table 1. Production had signifi cant coeffi cients, including the joint test of squared time trend. Total factor productivity was high, indicating that the technology had been used to almost its full capacity: boats and fi sh- ing gear had been fully utilised. It was hard for fi shers to increased produc- tivity unless they increase the available resources.
Production elasticity with respect to fi shing trips was high (0.97), mean- ing that increasing the number of trips by 10 per cent could as well increase total catch by 9.7 per cent. One of the interesting features was that the production function exhibits increas- ing returns to scale (IRS). This means that doubling the number of trips at the same time would do more than double the catch. In marine fi sheries, this could occur because during the peak fi shing season, the fi shers would increase the number of times they go fi shing once they observed an abun- dance of fi sh in the same area. As a result, the following trip will catch more fi sh than before.
The production function showed trip-intensive technological change, which means that production leads to more trips over time. This was consis- tent with IRS phenomena, where fi sh- ers tended to increase the number of their fi shing trips, which was an indica- tion that the fi sh stocks (or supplies) tended to increase over time. If the fi sh population increased, the same amount of effort would result in a bigger catch.
However, the production function showed capital-saving technological change, meaning that the use of capital tended to decrease. This was an indica- tion that catching fi sh needed simpler fi shing gear. Fishers only needed to take and use the particular fi shing gear that was suitable for the season and the species of fi sh.
Table 1. Production frontier of fi sheries
Variable Coefficient St. error z-test p>|z|
TFP 2.544 0.651 3.91 0.000
Trip 0.731 0.150 4.87 0.000
Capital 0.658 0.247 2.67 0.008
t*Trip 0.240 0.055 4.33 0.000
t*Capital -0.182 0.076 -2.39 0.017
t -0.540 0.344 -1.57 0.117
t2 -0.018 0.063 -0.29 0.770
2 total= 232.48, P >2= 0.000; 2 (t=t2=0 ) = 8.10, Log likelihood = -154.33482 P >2= t=t2=0 ) = 0.0174
2 ((α+β+2δ+2φ)-1=0) = 8.40, P>2 =0.0037
Note that the production func- tion showed negative technological progress. This was largely because of depreciation of capital, that is, boat, engine and fi shing equipment. Most boats and engines used by fi shers in the area were old. This was consistent with the fact that the total factor productiv- ity of the production function was high or almost at full capacity. In this case, capital and other fi shing gear needed regeneration or renewal.
Figure 2 shows the standards of technical effi ciency across the regions.
On average, technical effi ciency was 0.66, which was still low. This means that using current technology, fish catches can be increased if the effi cien- cy increases. Compared to traditional fi sheries in Nigeria, the effi ciency was nearly the same, with a range of 55 to 84 per cent effi ciency. This is probably because of Nigerian fi shers’ experience and education (Kareem, Aromolaran and Dipeolu, 2009).
advance their technology to improve production because their technical ef- fi ciency is already high (Shapiro, 1983;
Belbase and Grabowski, 1985).
Table 2 shows an analysis of inefficiency. The number of family members had a positive and signifi cant effect on technical efficiency. This means that the larger a household, the more technically effi cient it is. Involv- ing more family members had two effects: the fi rst related to economies of scale, that is, an increase in labour input leads to higher production. The second was the effi ciency effect, that is, more labour involvement leads to more technical effi ciency, which is probably because of a better division of labour.
This was consistent with the common understanding that traditional agricul- tural and fi shery practices need more labour to be more effi cient.
Figure 2. Technical effi ciency across regions
0 0.2 0.4 0.6 0.8 1
Senutuk Benete Maluk Lb Lalar P Tano Tj Luar Ujung Efficiency
Note that effi ciency in two regions of zone-0 was very high but in other regions it is quite low. Thus, these other regions still had enough leeway to in- crease production using their current technology. The two more efficient regions (Senutuk and Maluk) need to
Table 2. Factors affecting technical effi - ciency
Variable Coefficient St. error z-test p>|z|
Constanta 4.8262 0.0103 470.7 0.00
Family member 0.0750 0.0010 77.0 0.00
Experience -1.5056 0.0031 -489.3 0.00
Education -1.5822 0.0029 -536.5 0.00
Season 0.0000 0.0002 0.1 0.95
Zone-0 0.4079 0.0007 606.6 0.00
Zone-1 0.7491 0.0015 504.4 0.00
Year 1.5056 0.0031 488.8 0.00
R2 = 0.9999
F( 7, 244) = , Prob > F = 0.0000
Experience could have negative effects on technical effi ciency, though.
Experience accumulates with age, meaning that the more experienced fi shers are the older but are less able to and less willing to change their ways.
Were the fi shers still young and more amenable to technical innovation, their
increasing experience would lead to greater effi ciency.
An inadequately educated work force could have negative effects on the effi ciency of an industry, particu- larly industries that relied on advanced technology. Traditional fi sheries did not need highly educated fi shers. Note that the years spent in formal education by fi shery workers in all these areas under study were much the same. Most fi shers had only an elementary school education; perhaps around 15 per cent fi nished high school.
Seasons did not affect effi ciency, meaning that there is no difference in technical effi ciency between high and low seasons. Zone-0 and zone-1 showed greater technical efficiency than zone-2. One interesting feature is that effi ciency of fi sh catching in- creased over time because the produc- tion function under went technological regress. If the consistency of catch was relatively stable but the production frontier decreases, technical effi ciency would increase.
Table 3 explains the spatial and temporal analysis of catch per unit effort (CPUE). On average, CPUE in zone-0 was higher than that of the other zones. This is understandable be- cause technical effi ciency in zone-0 was the highest. CPUE was signifi cantly de- pendent on the season; there was a sea- sonal pattern to CPUE. Overall, CPUE increased over time because technical effi ciency increased over time as well, despite any technological regress. This indicates that the rate of increase in
technical effi ciency is greater than that of technological regress. Note that the value of R2 was very low, which indicates that many factors aside from the model infl uence the dynamics of CPUE.
The CPUE in zone-0 slightly decreased over time. In such a zone, the number of fi shers was continu- ally increasing because the zone was strategic for marketing. Fishers from other zones prefered to catch fish in this zone during the high season.
Another factor is the combination of technological regress and high techni- cal effi ciency. In this zone, actual and potential production decreased simul- taneously, but the rate of technical regress was higher than that of the fall in technical effi ciency.
V. CONCLUSION AND POLICY RECOMMENDATION
Based on estimates of production function, source of variation in ef- ficiency, and spatial and temporal performance of CPUE, this study concludes that technology adopted by fi shers in the community is almost at its full capacity. The traditional fi shers tend to increase the number of trips (trip-augmented technological change) Table 3. Spatial and temporal performance of CPUE
Variable Coefficient St. error z-test p>|z|
Constanta 32.20 9.39 3.43 0.001
Zone-0 28.90 15.14 1.91 0.056
Season 9.43 4.04 2.33 0.020
Trend (t) 13.32 3.11 4.28 0.000
Trend*Zone-0 -22.19 5.16 -4.3 0.000
2= 30.87, Prob > 2= 0.0000 R2= 0.1424
and to reduce capital use (capital-saving technological change). As capital and other equipment are already aged, the potential of fi sheries production decreases over time.
Level of technical effi ciency is still low, particularly in zone-1 and zone-2.
As expected in traditional agricultural practices, the higher number of fam- ily members is a positive factor that infl uences effi ciency. For fi shers who are already old, each additional year of experience leads to a decrease in efficiency. Efficiency in zone-1 and zone-2 is lower than that in zone-0, but the CPUE in those zone increases over time. In contrast, CPUE in zone-0 is the highest, but it tends to decreases overtime. Generally, level of technical effi ciency increases over time. Com- bination of movement in technical effi ciency and in technological regress explains the different productivity of catches among zones. Seasonal factors infl uence CPUE. During the peak of the fi shing season the number of fi sh- ers in zone-0 increases signifi cantly.
This study recommends that the technology of traditional marine fi sheries in the community needs to be improved because the capital and other equipment have already been obsolete and out-of-date. In particular, this will help to increase production in zone-0. Additional labour is required to increase efficiency and, eventu- ally, to improve production. Regarding zone-0, it is recommended that fi shers from other sites should not operate in this zone to avoid over fi shing. If the
fi shing gear can be upgraded, it would be better that those fi shers operate in other zones, especially in zone-1 and zone-2, since there is room to increase production by improving effi ciency us- ing the same fi shing gear as at present.
However, unlike in zone-0, regenerat- ing technology in both these zones might be less cost-effective because the level of effi ciency is still low.
During the low season, or when the weather is not conducive to fi sh, it would be difficult for those who rely solely on marine fi shery to get an income. Local government, private sectors, and other local institutions are expected to support these coastal communities running other productive activities that generate income, for example growing high-value vegetables and other food crops. It is a possibility because adequate land and agricultural infrastructure are available to the com- munity.
ACKNOWLEDGMENTS
The study results from collaboration between the Center for Coastal and Marine Studies (P3L), University of Mataram and PT Newmont Nusa Tenggara. Financial support for this study was provided by PT Newmont Nusa Tenggara. The authors thank Mr Wagimin Hadi Sastra, Mr Faozan Maulad and Mr Basar of Commu- nity Development, PT Newmont Nusa Tenggara, for their valuable feedback but the authors are responsible for interpretation of results and any errors.
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