ECONOMIC ANALYSIS OF FISHERY IN THE NORTHERN PERSIAN GULF

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1 ECONOMIC ANALYSIS OF FISHERY IN THE NORTHERN PERSIAN GULF ABDOULKARIM ESMAEILI Department of Agrcultural Economcs, College of Agrculture, Shraz Unversty, Shraz, Iran Tel: Fax: E-mal: There are many fshng landng areas n southern Iran, dstrbuted all along the northern Persan Gulf. Despte ncreasng effort, the total catch has fluctuated n recent years. Iran s facng wth over capacty of vessels and too many fshers, yet smultaneously poltcal, socal and economcal pressures exst for expanson of fshng effort. It s extremely dffcult to make management and resource allocaton among competng user groups. Ths study examnes the techncal effcency and proftablty of the fshng ndustry n order to select the best fshng vessels group. The results ndcate that wooden vessels of medum sze are more effcent than small fberglass ones. The result of proftablty analyss usng nternal rate of return, and beneft cost rato ndcate that bg vessels are n crtcal stage. Although small and medum vessels are proftable, but medum vessels are more economcally effcent than others. Key words: Persan Gulf, Proftablty, Effcency, Fshery Management Persan Gulf s a 6-mle-long body of water, whch separates Iran from Iraq, Saud Araba, Bahran, Kuwat, Qatar, and Unted Arab Emrates. Fsheres n the Persan Gulf play very mportant role for the local economy. There are three coastal provnces nvolvng fsheres n Iranan sde of the Gulf ncludes; Khozestan n the northwest, Hormozgan n the northeast and Boushehr n the mddle of the Gulf. Many commercal speces caught from Persan Gulf ncludes; demersal speces (39%), bg pelagc (38%), small pelagc (12%) and other speces (11%). Hormozgan s the most mportant provnce for Iranan fsheres n the Gulf (Esmael, 29). Around 3 percent of 38 tones fsh landngs n Iran come from the Hormozgan provnce. Ths amount of fsh s capture by 269 fshermen and 3825 fshng vessels. The major problem facng the fsheres n the regon s the uncertan avalablty of fsh and non proftablty of fshng vessels. Towards decreasng trend n total fsh landng and ncreasng trend n fsh vessel, per capta catch decreased n recent years (Fgure 1 & 2). 1

2 Fgure 1. Relatonshp between fsh catches and vessels ( ) catch fleet catch (Tons) Fleet (No.) Year Fgure2. Catch per vessel ( ) Tons/fleet Year Catch per vessel are fallng, despte the fact that effort for catch ncreased n recent years. The problem s that too many vessels are chasng too lmt fsh resource. Due to declne proftablty, neffcency, and poor management, some fsh stocks declned n recent years. The man purpose of ths study s to examne the proftablty of fshery ndustry and determne selected vessel group. Methods: The data for ths study came form the feld study of fshery n Hormozgan provnce, south of Iran. Totally 144 fsh vessels were selected usng stratfed random samplng method. The sample vessels were classfed nto four groups ncludes -3 ton, 3-2 ton, 21-5 ton, and above 5 ton capacty vessels. The nformaton was collected through face-to- face ntervews wth skppers. Effcency methodology s used for measurng factor nfluence fshery proftablty. Although ffty years ago, Farrell (1957) ntroduced a methodology for measurng effcency, hs 2

3 methodology undergong many refnements and mprovements. The model used n ths paper s based on Battese and Coell (1995) approach. They ntroduce followng stochastc fronter general model for estmaton effcency. βχ ln (y ) = + v u (1) Where y s output of the th vessel x s a vector of producton nputs, β s vector of parameters, v are ndependent dentcally dstrbuted random varables that measured errors v and exogenous shocks beyond the control of the manager, fnally s dstrbuted one sde and measured effcency. The neffcency determnants functon also as followng general form. u = δ + δz + w (2) Where z s vector of factors affectng effcency level, δ s vector of parameters and w s error term. Fronter effcency model (Eq.1) and neffcency model (Eq.2) are estmated together by maxmum lkelhood method. Secondary method used n ths study s fnancal analyss (engneerng economy). Fnancal analyss s a decson crteron that prescrbes how to select nvestment alternatves. Beneft cost rato and nternal rate of return crtera methods are used to analyss proftablty of fshng vessels. The role of economc analyss s to provde decson-makers (e.g. poltcans, fshery managers, fundng agences) wth nformaton that wll enable approprate choces and trade-offs to be evaluated concernng the allocaton of resources to capture fsheres. Proftablty for the fshermen s the am of government and t depends on the market prce of fsh and the unt cost of harvestng, prce beng a functon of the quantty of fsh landed and unt cost beng a functon of job opportunty and the prces of factor nputs to run fsheres. In economc engneerng study, the rate of return on nvestment s normally expressed as a percentage. The annual net proft dvded by total ntal nvestment represent the fracton whch, when multpled by hundred, s known as the percentage of return on nvestment. The usual procedure s to fnd the return on total orgnal nvestment, wth the value of the average net proft beng the nomnator. B = C EUAB EUAC EUAB: Equvalent Unform Annual Beneft EUAC: Equvalent Unform Annual Cost 3

4 Result The study focuses on the Hormozgan provnce n Northern Persan Gulf. The vessels of ths provnce consst of small fbreglass and medum wooden vessels. These vessels makng short trps close to the costal-land n the regon. Revenue and cost determne the proft of fshng operatons. Revenue s dependng on quanttes caught and prce obtaned for fsh. The man cost factors are operatonal cost, whch can be dvded n fuel cost, labor, food and ce cost, gear repar, vessel mantenance expenses and nsurance.totally 144 fsh vessels were selected and sample was classfed nto four groups ncludes -3 ton, 3-2 ton, 21-5 ton, and above 5 ton capacty vessels (Fgure 3). The nformaton was collected through face-to- face ntervews wth skppers. Fgure 3. Number of vessels n selected groups >Ton 3-2 Ton 21-5 Ton >5 Ton Descrptve statstcs of annual proft and vessel characterstcs are presented n Table 1. The table also presents soco-economcs characterstcs of the skppers. Table 1. Descrptve statstcs of vessel characterstcs and soco-economcs of skppers Parameter -3 ton 4-2 ton 21-5 ton 5< ton General Characterstcs Labor Engne Power (HP) Fuel Cost 15,973,85 23,594,74 38,238,578 63,143,167 Two-way Rado 6.9% 96.9% 94.1% 1% Total Cost (Rals) 7,25,727 29,393,676 54,495,18 68,28,333 Investment Vessel 3,57,458 37,656,25 575,738,235 1,182,222,222 (Rals) Investment nets (Rals) 16,43,277 56,732,823 82,822,39 144,572,67 Income (Rals) 77,325,424 26,749, ,874, ,777,778 Proft (Rals) 54,17,927 77,893,75 1,63,883 12,841,314 4

5 Soco-economcs characterstcs Skpper s Owner 86% 62.5% 66.7% 88.9% Prvate Ownershp 88.9% 58.1% 39.4% 11.1% Educaton 74.6% 81.3% 79.4% 66.7% Experence (Year) Parameter -3 ton 4-2 ton 21-5 ton 5< ton General Characterstcs Labor Engne Power (HP) Fuel Cost 15,973,85 23,594,74 38,238,578 63,143,167 Two-way Rado 6.9% 96.9% 94.1% 1% Total Cost (Rals) 7,25,727 29,393,676 54,495,18 68,28,333 Investment Vessel 3,57,458 37,656,25 575,738,235 1,182,222,222 (Rals) Investment nets (Rals) 16,43,277 56,732,823 82,822,39 144,572,67 Income (Rals) 77,325,424 26,749, ,874, ,777,778 Proft (Rals) 54,17,927 77,893,75 1,63,883 12,841,314 Soco-economcs characterstcs Skpper s Owner 86% 62.5% 66.7% 88.9% Prvate Ownershp 88.9% 58.1% 39.4% 11.1% Educaton 74.6% 81.3% 79.4% 66.7% Experence (Year) The fberglass vessels are small wth an average of 52.7 HP engne power, 3,57,458 and 16,43,277 Iranan Rals nvestment n vessel and equpment, respectvely. 4-2 ton vessels, relatvely small wth an average of HP engne power, 37,656,25 and 56,732,823 Iranan Rals nvestment n vessel and equpment, respectvely ton capacty vessels, relatvely bg wth an average of HP engne power, 575,738,235 and 82,822,39 Iranan Rals nvestment n vessel and equpment, respectvely. 51< ton vessels are bg wth an average of HP engne power, 1,182,222,222 and 144,572,67 Iranan Rals nvestment n vessel and equpment, respectvely. Annual ncome, cost and proft for four mentoned vessels groups are presents n table 1. Although bg vessels ncome and proft s hgher than small and medum vessels, the rate of return s relatvely low for bg vessels (fgure 4). 5

6 Fgure 4. Rate of return for selected vessels groups 2.% 15.% 1.% 5.%.% <3 ton 3-2 ton 21-5 ton >5ton The annual rate of return for <3ton, 3-2 ton, 21-5 ton, and >5ton vessels groups are calculated 165, 3, 23, and 7 percent respectvely. The rate of return relatvely low for bg vessels compare to nterest rate n agrculture sector (14%), ndustral sector (17%) and annual nflaton rate (17.5%). It means >5 ton vessels group s non proftable. Another alternatve method for select the most proftable vessels group s beneft cost rato. For any vessels group to reman under consderaton, ts beneft-cost rato must exceed one. The beneft cost rato for <3ton, 3-2 ton, 21-5 ton, and >5ton vessels groups are calculated 3.23, 1.73, 1.4 and.54, respectvely (Fgure 5). Fgure 5. Beneft cost rato for selected vessels groups <3 Ton 3-2 Ton 21-5 Ton > 5 Ton It could be conclude that three smaller vessel groups reman under consderaton. The correct alternatve (the most proftable vessels group) can be selected by applyng the prncple of ncremental analyss. In ncremental analyss for comparng one alternatve to another, frst determne the cash flow representng the dfference between the two cash flows. Then the 6

7 decson whether to select a partcular alternatve rests on the determnaton of the economc desrablty of the addtonal ncrement of nvestment requred by one alternatve over the other (Thuesen and Fabrycky 1993). The ncremental nvestment s consdered to be desrable f t yeld a return that exceeds the mnmum attractve rate of return or the beneft-cost rato exceed one. The calculaton of ncremental nvestment for both methods (rate of return and beneft cost rato) are ndcated that 21-5 ton vessels are the most proftable vessels. In other worlds, although rate of return and beneft-cost rato are hgher for <3ton vessels, but the ncremental rate of return and ncremental beneft-cost rato are exceed mnmum attractve rate of return (MARR) and one, respectvely. Comparng 3-2 ton and 21-5 ton ndcated that ncremental nvestment s less than one n beneft-cost rato method. Productvty of labor and captal are dfferent among vessels groups. Although labor productvty s hgher for large vessels, but captal productvty s larger for small vessels (table 2). On the whole, medum vessels are more effcent than small and large vessels n the regon. Table 2. Labor and captal productvty n vessels groups <3ton 3-2 ton 21-5 ton >5ton Vessel productvty Equpment productvty Total productvty captal captal captal Labor productvty Effcency technque s used to analyze proftablty of fshery vessels. For the proftablty nvestgaton, assumng Coob-Daglass functonal form, the proft functon mentoned n equaton 1 can be wrtten as, LnΠ 5 = β + β LnX j= 1 j j + v u (3) Π X Where represents the th vessel for =1, s the proft of th vessel and j represents the amount of nput j used by the th vessel. The dependent varables are defned as follows. X 1 represents the average mantenance cost used n th vessel, measured n US$. 7

8 X 2 denotes per capta catch for th vessel, measured n kg. X 3 represents the average gear cost used n th vessel, measured n US$. X 4 represents the average fuel cost used n th vessel, measured n US$. X 5 represents the average food & ce cost used n th vessel, measured n US$. The neffcency model n ths study as followng form. u = δ + 6 k = r δ Z k k + w (4) u Where s the neffcency of th vessel and dependent varables as follows. Z 1 represents port exstence, Z 1 =1 f vessel fshng regon have port, otherwse Z 1 =. Z 2 denote tow-way rado exstence, Z 2 =. Z 2 =1 f the vessel have two-way rado, otherwse Z 3 denotes educaton, Z 3 =1 f skppers educated, otherwse Z 3 =. Z 4 denotes property rght regme, property. Z 4 =1 f the owner s one person and Z 4 = f common Z 5 denotes skppers age, measured n year. Z 6 represents cooperatve actvty, Z 6 =1 vessel fshng regon cooperatve evaluated do not Z have good actvty, otherwse 6 =. The stochastc fronter model and neffcency model are estmated n one-stage by the econometrc package FRONTIER 4.1 (Coell 1996). The parameter estmated wth correspondng standard errors s presented n Table 2. The fronter model of the proft functon coeffcents s used to nvestgate economc effcency and proftablty. Estmates of the parameters of the fronter and neffcency models are presented n Table 3. Parameters estmated have expected mpacts on proftablty and effcency n both models. In the fronter model, the coeffcents pertanng mantenance cost, gear cost, fuel cost are sgnfcant and have the expected negatve sgns, mplyng that an ncrease n the mentoned costs causes a decrease n proftablty. The sgn of food & ce coeffcent s accordng to the expectaton but not sgnfcant. The postve sgn of per capta catch varable suggests that ncrease n catch leads to hgher proftablty. 8

9 Table3. The parameter estmated for fronter and neffcency model Parameter Estmaton Standard Error Fronter Model β 25.76***.83 β 1 β ***.32.17*.87 β -3.26***.41 3 β 4-2.5***.36 β Ineffcency Model δ.79***.233 δ 1 δ *** δ δ δ δ.39***.89 6 γ.34*.25 2 σ.82***.11 *, **, *** statstcally sgnfcant at the 1%, 5%, 1% levels, respectvely. 9

10 The coeffcents of neffcency model have expected sgns. Postve sgn of parameters n the neffcency model mply negatve effects on the economc effcency and the contrary. Although the coeffcent of port exstence and cooperatve actvty are sgnfcant, the other parameters (two-way rado, educaton, property regme and age) are non-sgnfcant. The value of γ suggests that the varance of proft neffcency effects accounts 34 percent of total proft varance. Although ths amount relatvely low, but hgher than that found by Fouseks et al. (23). The overall techncal effcency s calculated from the model, for the entre sample, s 56 percent. It s mportant to note that captal opportunty cost was not ncluded nto the model. Wth respect to hgher nvestment n bg vessel compare to the smaller fberglass one, the effcency would be change f captal cost ncluded nto to the model. Dscusson: In ths study a fronter proft model and engneerng economy were used to assess the economc effcency and proftablty among fshery vessels n the south of Iran. The soco-economc, nfrastructure and nsttuton factors had sgnfcant mpact on proftablty. Overcomng these constrants would contrbute to ncrease n proftablty of fshery and decrease n overfshng. The result also shows that, on the whole, medum vessels are more effcent than small and large vessels n the regon. In addton, the large vessels are non-proftable. The mean economc effcency for the sample vessels s low. Ths means that there s potental for ncreasng proftablty of fshng vessels. Although food & ce coeffcent was not sgnfcant, all other coeffcents n the fronter model were sgnfcant and had expected sgns. Cooperatve actvty and port exstence are two mportant varables n neffcency model. Vessels proftablty also depends on others soceconomc factors, local development and provson of nfrastructure, whch affect the skpper's access to nputs and technologes. It would be nterestng to dentfy the mentoned factors and use n the tme seres-cross secton (pooled data) Forter model. Based on the fact that cross secton data was used n the study, t was mpossble to use fsh prce n the fronter model. Ths s one of the lmtatons of the research; therefore, t could be recommended that researcher use tme seres data n the future studes. References Battese, G., T. Coell, 1992, Fronter producton functons, techncal neffcency and panel data: wth applcaton to paddy farmers n Inda, J. Prod. Analyss, 3, Battese, G., T. Coell, 1993, A stochastc fronter producton functon ncorporatng a model for techncal neffcency affects workng Papers n Econometrcs and Appled Statstcs, No. 69. Department of Econometrcs, Unversty of New England, Armdale. Battese, G., T. Coell, 1995, A model for techncal neffcency effects n a stochastc fronter producton functon for panel data, Emprcal Economcs, 2, Coell, T., 1996, A gude to fronter, verson 4.1. A computer program for fronter producton functon. CEPA workng paper 96/7, Department of Econometrcs, Unversty of New England, Armdale. 1

11 Esmael, A., 29, Envronmental change and fshery management n the northern Persan Gulf, Journal of Envronmental Plannng and Management, 52, Farrell, M. J., 1957, The Measurement of producton effcency,.journal of Royal Statstcal Socety, 12, Forsund, F. R., C. A. K. Lovell, and P. Schmdt, 198, A survey of fronter producton functon and of ther relatonshp to effcency measurement, Journal of Econometrc, 13, Fouseks, P., S. Kolonars, 23, Techncal effcency determnaton for fsheres: a study of trammel netters n Greece, Fsheres Research, 63, Krkley, J., D. Squres, and I. Strand, 1995, Assessng techncal effcency n commercal fsheres: the md-atlantc sea scallop fshery, Amercan Journal of Agrcultural Economcn, 77, Pascoe, S., J. Andersen, and J. De Wlde, 21, The mpact of management regulaton on techncal effcency of vellels n Dutch beam trawler fshery, European Revew of Agrcultural Economcs, 28, Pomeroy R. S., 1992, Economc studes of small-scale fsheres: A comparson of methodologes. Asan Fsheres Scence, 5, Sharma, K., P. Leung, 1999, Techncal effcency of the long-lne fshery n Hawa: an applcaton of a stochastc producton fronter, Marne Resource Economcs, 13, Squres, D., 1987, Fshng effort: t's testng specfcaton and nternal structure n fsheres economcs and management, Journal of Envronmental Economcs Management, 14, Thuesen G J., W. J. Fabrycky, 1993, Engneerng Economy, Prentce-Hall, Inc. New Jersey. 11