Indian Seafood Exports: Issues of Instability, Commodity ...

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oliaviour of producers of specific commod~tlcs 111 thc choset~ cuuntrlcs. Only few studics such as Love (1992). Wilson (1994). Slnha (1999). Tegegne (2000). used time series analysis on an indwidual country hasis Hut, most ofthe ava~lahlc time series studies do not address the Issues of non-statlonary nature of data. Hmce it could not he ruled out that these estilnates are estimated from spurious rcgressioti. Mullor- Sebast~an (1988) argued that studlcs, which lump together the exports of all goods. are misleading because export instahllity II~ a gtven product is influenced hy the characteristics of the indiv~dual product and degree of development of the exporting country. Accordingly. the present study colifi~ie itself to instability in llidia~l seafood exporl earnings and uses thc tltne serles data after considering the non-statlonary nature oftlic data. I:arlier statistical cv~denceb conclude that illstability index of exports are largcly associated with the dcgrcc of commodity cu~ice~~tration of cxports, per capita it~con~c and with the concentration of exportr by eeoeraphical area of dest~nation (Paudyal. 1988. Sinha, 1999. 'I egegtle. 2000). I'hus, instahil~ty index of seafood ehports earnings could he expressed as a funct~oti of commodity concentratloti (CC). Geographic Conce~itratiot~ ((iC) and Instability ill countp's GDl' ~ h reflects ~ h ~CI. capita lncomc ofthe chporting country. IISFFX f(C'C. GC, Il(il)P) .. . (7) To separate out the effects of the fishcries GDP on instability of scafood expons equation 3 is redefined as follows IISFEX = f (CC. GC, IIFGDP. IINFGDP) .... (4) Wliere IIFGDP represents lnstabillty index of fisheries GDI' and IINFGDP Instability index ofnon-fisheries GVP. 'I'egegne (2000) argued that apan from the other key determinants of exports income fluctuations, the relative Importance of major commodity, global demand conditions affecting the major commodity, internal supply condit~oti should also be considered. Among seafood exports, frozen shrimp is the single largest expon item. The proportion of exports from cultured shrimp production has kept rising, implying that ally instability caused ill seafood cxports could be mainly due to fluctuations in the cultured shrimp production Thus to capture the flucluations In seafood exports, cultured shrimp production is also cons~dercd as one of the determinants Thus equation 4 can he written as IISFEX = f (CC, GC, IIFGDP, IINFGDP, IISHPR) ....(5)
244 NDlAN JOlJKNALOT AGRICULTURAL kCONOMICS (.'~~mpu/u/ronol Framework To establish cause and effect relationship between major determinants and seafood export instabil~ty a double log-linear function is preferred because of ease of tliterpretation and best lit Thus the long-run seafood export instab~lity function is specified as follows liisfex =Pa+ PIIcc+ p,Igc+ p,liifgdpi P, liinfgdp+ p, liishpr+ E , ... .(6) Where, l = Natural logarithm, ii = Instability Index, sfex = Indian .seafood exports, cc .- Commodity Concentration, gc = Geographic Concentration, fgdp = Fisheries GlIP, nfgdp = non-fisheries GDP, shpr = Cultured shrimp production, E, = error term assumed to be identical and independent and E - N(o,o'). Po, PI. I) ,P, IDd. PI are the coefficients to be estimated. Estimat~ng long-run relatiollsliip such as in equation (6) is likely to pose some prohlcms because thc valiables in the analysis typically exhibit multicollinearity and tion-stationarity. 'Thcsc problenis arc oRcn dealt with by tak~ng first differences of the va~iahlcs, before any estimations are done, to mahe the series stationary. This procedure of differencing has a ~najor drawback: 11 eliminates all ~nformation about the Ikmg run relatiomhip and thcreby only short-run effects was explained (Maddala, 1092). Thus the modelling ol'srafood exports ~nstab~lity should be based on lnethods which cxplicltl) take the non-stationarity features of the data into account. The theory of cointegration techniques and vector error correction lnodels (VECM) addresses these issues in an efficient and s~gll~ficant manner (Engle and Granger. 1991) which i~npl~es that cointegration tests are superior when investigating the relationships that are believed to be ofa long run nature. There are several advantages In using VEC'M. I'irstly, the VECM approach treats the variables as determined wlthin the salne system, without a prim assumptions about the nature of interrelationship. Secondly, it clearly distinguishes between longrun and short-run effects since both levels and first differences of the variables enter the VECM. Thirdly, the speed of adjustment towards the long-run relationship can be directly estimated. Finally, the VECM has a sound statistical foundation based on the theory of cointegration developed by Engle and Granger (1987). Despite the above advantages these models can become easily over-parameterised, as each variable is allowed to affect the other variable at a number of lags. The results can also be sensitive to the chosen lag length, although there are significance tests that can be used to detennine the appropriate number of lags to be chosen. Thus transformation of equation 6 by incorporating error correction (EC) term can be represented as follows.
Page 1 and 2: -- - - - -.- - -- -- Indian Seafood
Page 3 and 4: 240 INDIAN JOUKNALOt AGRICULTURAL E
Page 5: Where G - Cornnlodity concentratton
Page 9 and 10: 246 INDIAN JOIIKNAI.OF AORICIII.TUR
Page 11 and 12: 24R INDIAN IOIIRNAI. Uk AOKlCULTtJR
Page 13 and 14: 250 INIIIAN JOIJRNAL Or AGKICUl.nlR
Page 15: 211 INDIAN JOURNAL 01 AGRICUL.TURA1

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