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Indian Seafood Exports: Issues of Instability, Commodity
Concentration and Geographical Spread
C. Sarada, T. Ravisankar, M. K r i s h n a n a n d C. Anandanarayanan*
INTRODUCTION
Fluctuations in fish production from capture and culture, varlalions 111
lnternat~onalprices. adjustments in exchange rates and finally - the var~ablcvalues 01'
export earnings are grlm concerns for developing countries o f South Asia and Soul11
East Asia. Most of the developing countries (including India) obtain thu~rniaj~,r
share of export earnlngs from selected few items or commodities and the trade is
concentrated with a couple of nations. These developing coutitrics as commodity
producers and exporlers have hardly any instruments at their disposal to licdge
ag,ainst the adverse effects o f instabiltty of export earntngs. Managing commodity
prlcu risk and stabilising export earnings are still important policy issues for virtually
all low Income and commodity exporting countrlcs (Pal. 1992).
Imperative effects o f variability of export earnings at the macroeconomic level
are, disruptions in the investment planning process, misallocation ol'rcsou~ccsand
disturbances o f the internal balance of public finances, impacts on tlic ratc of
domestic savings, increastng internal and external indebtednessand problen~sill tl~c
balance of payments, which might result in unstable earnings and discourage fanners
liom producing for export and can lead to a "further" fall in export earnlngr and gross
National Product (GNP). The export oriented producers who suffer from eartilng
shortfall, will also cut back their consumption, which affects the puhlic finances I'hc
perslstencc o f abnormally depressed prices globally dur~ngthe 1980s has also
resulted in a sharp reduction in the living standards of developing coutitries 'l'hu> the
export-earning instability has to be considered as a development probleni becaurc it
dampens the growth rate, particularly as a result of its negative effect on product~vity
o f capital. Considering the strengths of Indian fisheries sector and steady
development in the past four decades, the causes of instabilities in production and
*Sctnl~,t(SS). Smwr Sc~mt~rt.
Prtnctpat Sclcntlrl nnd Ilcad, Teehn~eillOWmr, rrspccl~velyboccd 5 i m r e $
Ccnual Inn~luleol HracktvhwaVr Aquatullurc (CIHAI.It,d<au Ci>unc!l01 Agrzcullural Rcrcarcb.Chcnsl, -
Dlvl,lon.
bO0 028
The avthon .re gatcful to Ihr tchrca for ~aluahlcwggcrllons
Ponnlah. Dlnctor. CIRA, Chennsl md P Ravtchandran. Ibvrmcr
encoutlgcmcnt
lurlhcr. Ihc authors arc thankful lri A (I
i)crccliir f i r , CIIIA, Chonns~,lhr lhclr
INlllAlr 51 AFCIOD LXPORIS ISStII 5 0 1 INII*Lltl lTY.COMMOI>tlYCONCI NrK4IION
210
cxpon o f sealhod assume immense Importance in the ccanamic scenarto o f India and
hence the present stud> was conducted wlth the f o l l o u ~ n gohjectivcs: (1) 7'0 compute
concelllralion measures such as commodity concentrdtlon and geographic concentration coefficients (br lndian seafood exports. (2) l o estimate the ~nstabilityindices
for seafood exports from India and (3) 1
' o determine the s ~ g n i f i c a factors
~ ~ t that aflbct
the instability in xafood exports from lndla
Tlic paper is arranged as fbllows Sectlo11I1 d~scussesthe structure and pattern o f
firlieries dcvelop~nentIn lndia durlng the past four decndes The t l i ~ r drectlon deal,
\*1t11data and transformation prc~cedl~rcsSect~onI V descrthcs the theoretical and
computational framework. Sectlon V prcscnts the resullr and thc final sectloll gives a
summary o f findings and d~scussespollcy alternat~ves.
With an annual fish production of 6 ~ n ~ l l l ~cnctrlc
o n tonnes India occupies the
fourth position in fish production and second III aquaculture productio~iglobally The
annual domestlc per capita fish availability is 9 kilograms and seafoud cnpurt
earnlngs o f lndia is consistently over K\ 6,000 crorc* a ycar I:ish cuntrabuter 1.4 per
ccnt of gross domest~cprr,doct (Gl)P) i ~ n d4 5 per ccnt ofthe agr~cult~lral
601' 'I ill
the close of 1960 the ehpOIi ot Illdlan .;eak,od products iilalnly c<lnslstcd of drlcd
Itcms like dried fish, dr~edsl~r~mp.
shark lins, fish maws. etc I,rorn 1961, the expoti
o f dried seafood products was on thc decl~neand export.; of proces\cd Item\ were
making steady increases Some lmprovemellt in the product profile of seafood
exports was v~sibleafter 1966. 1 roletl and canned itcnis gained wider acceptance.
The markets for lndian products \prcad fast to developed countries horn Ihc
traditional buycrs In developing countries 1111 1960, thc markets for lndian marine
products were largely confined to neighhour~ngcountries l ~ h cSri I-anka, My~inmar
and Singapore. This positlon cont~nuedas long as exports f i o n ~l n d ~ awere dolninated
by drled ~lems.When the froren and canllcd items increasingly figured in exports, thc
sophisticated and affluent markets like I1.S.A , France. Australia. Canada, Japan, ctc..
became important buyers. The U . S A, remalned the principal buyer o f our frozen
shrimp for a long time. But after 1977, Japan emcrged as the principal huycr o f
frozen shrimp followed by Western F.uropcan countries. For a number o f years. Japan
continued to be the single largest buyer o f our seafood products. Ilowever recently.
U.S.A. became the largest market for lndian products. Even though a declining trend
has been observed in 2003-04 (Table I ) compared to earlier years U.S A. has
continued to be the single largest buyer relegating Japan to the second pns~tionTllc
share of U S.A. for 2003-04 was 12.9 per cent in terms of quantity and 27.61 per
cent in terms of value. China is one o f the leading markets for fish items like ribbon
fish. crocker etc. China accounted for 3 1.75 per cent in volume and 10.03 per cent in
value of the total expon o f marine products from lndia 'The export o f seafood
products had grown to greater proportion as one o f the Important items o f India's
240
INDIAN JOUKNALOtAGRICULTURAL ECONOhllCS
export liom a few million US$ in 1961-62 to US$ 1330.76 million in 2003-04.
accounting for approximately 3.32 per cent o f the total exports from India. During the
eighties, the canned items slowly disappeared and frozen items gained prominence in
India's seafood trade. Amongst the frozen items also, there were changes in the
demand for differentiated products from various countries. While Japan ind~cated
their preference for headless sliell on shrimp, the l J S.A.demanded peeled shrimp
meat and the European countrtes preferred the IQF shrimp in frozen and cooked
Ibrm. The Europeali market also absorbed the major share o f cephalopods while
Japdn had taken a small share of' it. 'These frozen fish items had greater demand in the
buuth East Astan countries as well as In the M ~ d d l eEast In the 1970s. the export was
depend~nymainly o ~ shrimp
i
hut due to the expon promotional measures, it hecame
possible to d~vrrbifythe products In the cighties adding cephalopods (cultlefish, squid
and octopus) and frozen fish (such as pomfiet, rihhon fish. seer fish, mackcrcl. reef
cod. croakers, snapper, etc). While all these items hold good prospects, l ~ v efish,
chilled fresh water fish etc arc promising ~ t c ~ nfor
s the future (Ayyappan and
Krishnan, 2005). Duc l o the introduction o f scientific shrimp fanning, the export of
fiozen value added shrlmp continued as the major foreign exchange earner among
seafood products and the volume of fiozen shrimp exported during 2003-04 was
1.29.768 metric tonnes (Tahle 2)
111
DATA AN11 M t IHOPS
The value of total seafood cxpons from India from 1981-82 to 2003-04 were
obtained from various issues of Hevieus of Marrne 1'ruduct.s Erporb published by
Marltie Products txports Development Authority (MPEDA). Cochin. Indla. The data
on shrimp product~onwere obtained from MPEDA Data regarding GDP, F~slieries
Gross Doniestic Product (IYGDP) was obtained from Central Statistical
Organisation's web site \\\\I< n11,hpl IIC In. GDP excluding Fisher~es(NFCDP) was
coniputed by subtracting FGDP from GDP.
Mr.r~sure.\o j Co'o,,cw~rrrrlmn
Several concentration measures are ava~lahle In the literature, e.g., Tongan
(1994). Tepegne (2000). Erlat and Akyuz (2001). Campa and Fernandes (2004) used
different measures for different studies. l.he appropriatenessof a measure depends on
the data on which the estimates are based and with the purpose o f analysis. ~ h c
present study utilises the most widely employed Gini-Hirschman coefficient of
concentration, which defines the degree of concentration in a country's export as
1hl)lAN C L 4 f t XPORTS I S S l J t O F INFIAHILITY C O M M I ) U I I Y l ' O N ( ' I N ~ K A 1 I O N
241
Where G - Cornnlodity concentratton coeffic~ent (CC)/Geographic concentration
Export earnings of commodlt). group i in year titxport
coefficient (GC), X,,
earnings from country i in year t and X, = '1 otal export earnings in year 1.
-
A varlety of instability ind~cesare available in the literature from simple to
complex. The variance of expon grov'th 15 the simplest measure of export instability.
But &ng tn lluctuat~otlsin export volulnes and values, deviations from trend in
exports could be more ideal measure of export ~nstability Varlous corrections for
trend are available in the Ilterature, "11,!moving avcragcs, llnear and exponential
trends (Pinsuwana, 1991, Rhat and Nlrmala. 2001. Drvkota, 20041. I:.ach o f these
methods has its own advantages and d~sadvantages.The present study anenlpts to
compute the Instability o f ehparts hy using the measme, based on the avcrage percent
devlatlon of the ohscrved values pmcecd from an exponcntial path (Paudyal, 1988).
'The instability index (11) can he expressed by the formula.
Whcre 11 = Instability indcx, X t = Est~mdtcdtrend \aloe, X ,
=
Actual value.
X
=
Mean o f the actual valuc.
'The literature on the determinants of export instability is well established. The
deternittlants are commodity concentratton (CC), geographical concentration (GC),
the ratio of food and raw lnatcrials l o lotal exports, per capita income of exporting
country, openness of the economy and export shares in world trade, etc. The
empirical evidence on Ihc relationsh~pbetween these determ~nant~
and export
instability appears inconclusive. Studies like Massell (1970) and O'Brien (1972)
show no significant link between expon instability and its alleged determinants.
However. studies like Paudyal (1988), and Tegegne (2000) showed that these
The cross-country analysis by earlier studies
determinants do affect export ~nstah~lity.
implicitly assume a unique relationship between a given ehplanatoV variable and the
degree of export instability across the countries. Thus, estimates using
cross section
data to find the average relationshtps does not provlde much lnfomation on the
boliaviour o f producers o f specific commod~tlcs111 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~lahlctime series
studies do not address the Issues o f non-statlonary nature o f data. Hmce it could not
he ruled out that these estilnates are estimated from spurious rcgressioti. MullorSebast~an(1988) argued that studlcs, which lump together the exports o f all goods.
are misleading because export instahllity I I a~gtven product is influenced hy the
characteristics of the indiv~dualproduct and degree o f development of the exporting
country. Accordingly. the present study colifi~ieitself to instability in llidia~lseafood
exporl earnings and uses thc tltne serles data after considering the non-statlonary
nature oftlic data.
I:arlier statistical cv~dencebconclude that illstability index o f exports are largcly
associated with the dcgrcc o f commodity cu~ice~~tration
o f cxports, per capita i t ~ c o n ~ c
and with the concentration o f exportr by eeoeraphical area o f 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 o f commodity concentratloti (CC).
Geographic Conce~itratiot~
((iC)and Instability ill countp's GDl' ~ h reflects
~ h~ C I .
capita lncomc ofthe chporting country.
IISFFX
f(C'C. GC, Il(il)P)
.. . (7)
To separate out the effects o f the fishcries GDP on instability o f scafood expons
equation 3 is redefined as follows
IISFEX = f (CC. GC, IIFGDP. IINFGDP)
.... (4)
Wliere IIFGDP represents lnstabillty index o f 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 o f major commodity, global demand
conditions affecting the major commodity, internal supply condit~otisshould 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~dercdas one of the determinants Thus
equation 4 can he written as
IISFEX =f (CC, GC, IIFGDP, IINFGDP, IISHPR)
....( 5 )
N D l A N JOlJKNALOTAGRICULTURAL kCONOMICS
244
(.'~~mpu/u/ronol
Framework
To establish cause and effect relationship between major determinants and
seafood export instabil~tya double log-linear function is preferred because o f ease of
tliterpretation and best l i t Thus the long-run seafood export instab~lityfunction is
specified as follows
liisfex =Pa+ PIIcc+ p,Igc+ p,liifgdpi
P, liinfgdp+ p,
.... ( 6 )
liishpr+ E ,
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~nglong-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~ngfirst differences of
the va~iahlcs,before any estimations are done, to mahe the series stationary. This
procedure of differencing has a ~najordrawback: 11eliminates all ~nformationabout
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 o f 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 o f a 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 p r i m assumptions
about the nature of interrelationship. Secondly, it clearly distinguishes between longrun and short-run effects since both levels and first differences o f 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 o f 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.
INDIAN SEAFOOD EXPORTS
ISSllES OF INS1AHII.IIY. ('OMMODITY CONCFNTHATION
Aliisfex =~,,+fi,fblcc,., t ~ ,Alngc,,
f + f i , f ~ l l i i f ~ d ~ +p,fAli!nfgdp,,
,,,
213
.
., (7)
to, i ~ l t i s h ~ r+BECM(-I)+
,,
c,
Whcre A is the difference operator. ECM(-I) is error-correction term lagged by one
period In the cotntegrating regression, for integrating short term dynamics tn the
long-run seafood export functton, This functlon allows to estimate short-run
relationships betwccn Instability index o f Indian seafood exports and its
determinants, e,, the error term follows normal ltidependent and identically
distributed (i.i.d) properties. The coefficient 6 measures the response o f instability
Index of seafood expons In each prriod from the long-ru11 equiltbrium with the
co~ntegration equation normaliscd on ~yfex. The coefficient 6 represents the
propartton of the d~sequllihriumin iisfex in one period corrected in the next period 6.
is ehpected to have a negative sign and be statistically significant.
The modelling strategy adopted to estimate VECM involvcs three steps
Step I Tesrjor Srarronorrh
Before conducting cointegration tests, it is necessary to establi5h the univariate
time series properties of the variables l o confirm all the variables are non-stationary
and integrated o f the same order. This is performed by unit-root test, viz., Augmented
Dickey-Fuller (ADF) test. This test fitids out the order of integration, which is the
minimal number oftimes a series has to be differenced until 11becomes stationary.
Step 2: Derermrnal~onof Oprimum Lag Lengrh
The cointcgration test is based on vector auto regression and is sensitivc to the
number of lags included in the model, therefore first we should determine the optimal
number of lags used in the cointegration test One way to determine the number o f
lags is to select the model with minimum information criterion which are based 011
log-likelihood and penalise the inclusion of additional regressors (Greene, 1993). The
study utilises Akaike ~nformationcriterion (AIC) to choose the optimum lag length
Step:3 Coinregrarion Tesr and VECM
The purpose o f cointegration test is to determine whether a group o f nonstationary series is cointegrated or not. The presence o f cointegration enables to
form a vector error correction mechanism to analyse both the short and long-run
nlationship among cointegrated series.
INDIAN JOIIKNAI.OF AORICIII.TURN. ECONOMIC?
246
'There are a number of alternative cointepratlon tests (Engle and Granger 1987;
Stock and Watson. 1993 and Johansen, 1988 and 1995). The common objective o f
these tests is to determine ifthere exists a long-run relationship among all variables.
Johansen cointegration procedure has properties which are superior to alternative
methods o f cointegration testing. This method treats all the variables as endogenous
and take care of the endogenetty ~ r o h l e mby prov~dingan estimation procedure that
does not require arbitrary cho~ceof a variable for normalisation. I t also allows tests
for tnultiple cointegrations (Chosh. 2003). Even though this methodology is quite
complex. the underlying inference is straightforward. I n order to find the possible
cointegrating vectors the data is div~dedinto two groups. the variables in their levels
and thelr first differences, using the technique o f canonical correlation, the linear
combinations are stationary atid thus so are the cointegrating vectors (Jaffry el al..
1998) The present study empir~callyevaluates cointegration bchveen variables by
utilising MI. method o f cointegration developed by Sohansen (1988, 1995) and
exlcnded hy Johansen and Juselius (1994).
I h e ind~vidualvariahles uerc processed according to thc neth hods described.
hefore attempting the estimation of ~tiodelas detailed in the foregoing sections. The
sumniary statistics are presented in Table 3.
TAIIt.1
-.
Vr8tahlc
11)
CC
1iC
IlhrFY
1 1 1
IINI'ODP
IISHPH
3
IIIMMAKY S I A I I F I I C S (It MOI1EL VARIABLES
1981.1991
Standwd
Mean
Ikvlrinn
PL_.-
3hi
hhXV3
4 113
24905
0810
XI
I 872
a
-1981-2004
1991-2MM
Mean
Standard
Dcv8auon
A-151
Mran
(6)
75748
Standard
Ilrvm!nn
.. .
(71
35 843
60109
22491
6701
1510
3 1 022
13 381
20013
1 I560
22358
12427
0614
I320
11914
IU7IJ
6624
13448
1 1 884
7912
5308
70600
1162
$ 5 041
3 450
30
100
1931
3 011
9486
1 0 3 1 3
Commodity concentration reflects the compositton o f exports. I t also ~ndicates
the direction of growth in lerms of variety, product differentiation and sources o f
value realisation. It is therefore an important parameter to be examined for export
data analysis. For computing commodity concentration (CC), careful selection o f
specific commodities is crucial. As composition o f Indian sea food exports have
changed with the passage of time as detailed under Section 11, the items contributing
maximum value were selected. I n the early 1970s Indian seafood exports basket
INDIAN S!-AFWDLIPORT!, ISSIIFSOF MST4BII.IrY.COMMOOITY CONCENFRATION
247
contained merely 37 Items (~nclud~ng
processed) By 2000. it rose to 141 items
These product forms can be ma~nlycategorised into the following: frozen shrimp (6
product forms), frozen fin fish (22). live items (5) chilled items (5), canned items (5)
dried items (30). shell Items (3). cunle fish (13) squid (14). frozen lobster (6) others
(32). t:ven though different product forms are exported from India, the major
commod~tiesare frozen shrimp, f r o ~ e nfish. frozen cuttle fish, frozen squid which
constitutes nearly 90 per cent of the earnings from seafood expons. Hence, these
items arc conridered in the computation of commodity concentration and rest were
included as "others". Mean value o f commodity concentration has decreased
ind~cati~ig
the fact that lndia divers~fiedher exports geographrcally during the 1990s
as compared to the 1980s since the index value decreased from 81.37 per cent to
70.60 percent (Table 3) The results (not presented here) also ~ndicatea gradual
dccrease In commod~tyconcentration from 87.13 per cent in 1981-82 to 68.16 per
cent In 2003-04 which reflects the country's diversifying profile o f exports T h ~ sis
confirmed by the facl that though the value added portion in seafood exports IS onl:
20 per cent. this segment is increasing at 55 per cent annually, tlowever, major
commodities still ~nakea substantial contribution to the total value realised from
cxports in value terms
Geographic concentration (GC) was computed co~is~dering
Japan, U S A , and
Western European countries and rest as "others" In splte o f decrease in GC index
value from 66.89 per cent (1981-91) to 55.04 per cent (1991-2004) ind~catingin
general that thc geographic concct~tratio~~
of exports was h~gh.I t also meant that the
markets for Indian seafood exports remained stable during t h ~ period.
r
N o structural
shift was observed durinp, the period under study. The figures remaining h ~ g h l y
etahle, as can be seen from decrease in standard deviation from 5.46 per cent to 3 5
percent, the country's seafood exports rema~nedstable. Again, looking at the data on
the geographical spread o f exports, with the ascendancy o f the Euro, lndia has been
making deeper inroads into the EU markets; 27.42 per cent o f Indian seafood was
exponed to the EU in 2004-05 with 23 37 per cent to the US and 18 per cent to Japan
Us~ngequation 2, the Indian seafood export instability was worked out, following
absolute average percentage deviations method o f the observed values uslng an
exponentla1 path o f trend values. The mean values o f ~nstabilityindex o f seafood
exports were 4.37 per cent during the 1980s and 39.10 per cent during the 1990s,
indicating that the instability in sea food exports has increased over the period o f
time. The potential o f seafood exports as a source o f enhanced revenue for propping
up the foreign exchange reserves and the national CDP was discovered only in the
early 1990s. The instabil~tyin the 1990s may be due to the gaps in institutionalising
the growth hhich led to short term fluctuations in the sector (Shanger al., 2001).
The decrease i n instability indices o f fisheries GDP from 24.91 per cent in the
1980s to 20.02 per cent in the 1990s with reduced standard deviation values o f 13.38
per cent in the 1980s and 11.56 per cent in the 1990s indicate robust growth o f the
fisheries sector. But the estimates also show that there is a need for stabilising the
INDIANIOIIRNAI. Uk AOKlCULTtJRAL ECONOMICS
24R
sectors w ~ t h i nthe economy. The value o f instability indices of
growth of the
non-fisheries CiDP increased from 0.81 per cent to 11.95 per cent with its
corresponding standard deviation increas~ngfrom 0.67 per cent to 10.71 per cent.
Instability in growth of lead sectors has been attributed to slow growth o f the
agricultural sector (1.5 per cent) in the current plan period leading to an overall
decline in the perfornlance o f the economy.
Hefore estimating the relationship hetwecll seafood exports instabil~tyand its
determinants, the integration properties or stationar~fy properties has to be
determined, The unlt root test? werc perlnr~nedfor all the variahles by employing
ADF test on hotli levels and First d~fferencesof the variahles. The results presented in
l'able 4 indicate that the AIIF test in levels docs not all,>% for rejecting the null
lhypothcs~s(Ho) hut it can he rejccted at first differences. 'This means that all these
variables are integrated o f the order onc, i.e. I( 1).
IAHI 1.4 AII(IMCN'T1I)IIICKFY IliLi.1 K UNrl ROOTTt'SI KLSULT'1
--
1'81111111~1
-.
.
- _ J P L
Vrr~shle$
lririr~
It /
ipc
12!/gdp
.
...
.
.-
lcrcli
-I lui
-il 8.16
-I 283
-2 922
-1421
--
At llnl d~flcren~cs
14)
-4 101
-4 905
.5 13R
.5 380
-5216
Ilcc~s~on
(51
!(I)
I(!)
l(ll
1(1)
1111
As mentioned in the methodology, the Johnnsen ~ ~ i n t e g r a t i otest
n is sensitive to
lag length. I t is essential to identify the appropriate lag length before proceeding for
the co~ntegrat~on
test. One of the most commonly used procedures to identify the lag
length is to estimate VAR using un-differenced data and compare their AIC (Chin
and Fang, 2003). Bascd on AIC results, optimum lag length indicated is I for the
equation. Since all the variables are I( 1 ) Johansen multivariate cointegration test was
and estimate the cointegration
applied to determine rank of impact matrix
equations. A n unrestricted intercept and a linear trend in the variables, but not i n the
co-integrating vectors enter the system. The results o f A,,.,, and hmax in Table 5
Indicate that the tank of Il can be set to 2 based on A.,
statistic at both 5 per cent
indicated that there exists one coand I per cent level o f significance and
integrating equation at both 5 per cent and I per cent level o f significance.
Based on the largest Eigen value we considered the first cointegrating equation
and normalised long-run colntegration equation on liisfex variable. The cointegrating
vector corresponding to maximal Eigen value ( i e., dominant long run relationship) is
presented as follows.
(n)
L.,
IAOLC 5 JOllANSrN COINTt(iKAIl0N TESl RtSIJLTS
II~PULCEIC~Llgon
no o l ('I
($1
(11
Nunc
Atinnst 1
Atnli,rl2
Ir a ~ c
5 per rmt
I pcr c c a
rlaltrlrc
crtt~irl
valuc
crbllcal
valur
vnlac
h.,
I21
(3)
0937
0171
IIhh3
A1moql3
0 ?66
Almon4
0242
M -.
nmo,l
5 ..
..- -0--018
13q6X
77 56
46 61
23 76
621
I
I4l
4721
?QOX
1541
1~7h
~
-
( I 728)
(0 102)
5 per ccnl
sr~llc~l
l l v r csnt
.,A
valvr
crlllcal valllc
(bl
58 I 2
17)
39 17
4 5 111
309(
7607
$4 46
21 65
2004
I l h / e x = -1 5.462' Icc I 4.902' /yct0.?03'l,!/grlp
(2.214)
\lul~slic
1%
103 18
V4 15
b8q2
-
Mux-I I E C ~
IBl
22 84
3146
2707
3871
32 24
17 56
21197
qR1
1407
3 76
2( 52
1863
I1 3811
6 65
-
.
0.774' l,infgd/~ I 0.610' I,r,\lrpr
(0 093)
(0 077) .. (10)
t
lo thc abobe long-rut1 model all coefficients liave the ant~cipatedsigns indicattng
yc. !i/pf/i,. ~rnfydparrd mhla. have positwe effect on the tnstahility in cxpons and cc
concentration lndicates
have negative effect Thus one untt increase in the geographic
4.9 ltntts incrcasc 111the tnstahil~t! index o f lnd~anseafood exports. 1he lnipact of
instahilttv doe to non-fiqheries GIN' is more comoared to ~nstah!l~tv
In fisher~esGDP
as reflected by the magnitude o f coeficients. Similarly. instahllity in shrimp
production also has a postttvc Impact on instahtlity tndex
Ilowever, commod~tyconcentration appears to have a negative effect on the
instabilih. in seafood exports indtcarina that tf commoditv concentration decreases bv
one unit the instability o f exports wtll increase by 15.46 tirnea. In this conlext this
may be interpreted as a fall in the proportion o f high value frozen shrimp in favour o f
lower value items in the composition o f Indian seafood exports ovcr ttme Table 6
provides the shon run coefficiietits o f the instability equation including errorcorrection tern.
TAHL.1 h I STtMATl'S 01 SHOKI-RlININDIAN ~kAF001~
t X I ' O H I S INSIAHIII1Y MOIICI
The international seafood market is governed by both price and non-price factors.
In the post-WTO scheme o f globalised scenario, product improvement and
diflerentiation is more of a norm than an exception. Therefore, the short-term
coefiicients are smaller than their long run counter parts. This suggests that the
impact o f these variables causing instability in Indian seafood exports requires tlme
250
INIIIANJOIJRNALOr AGKICUl.nlRAL ECONOMICS
for adjustment. The est~mated coeffic~ents show that all the var~ables except
instability in cultured shrimp production did not show sign~ficantshort-run impact on
~nstab~lity
111seafood exports. The significance of this short run effect is minimised
hy thc error-correction term, whlch is s~pn~ficant
with the expected sign and of a
fairly larger magniludc. 'l'h~sfindlng not only supports the validity of long-run
in
equilibrium relationsh~pamong the variables but also indicates that instab~l~ty
scafood exports is sensit~veand tends to depart from the equilibrium value in the
previous period. Its magnitude indicates that deviation from the long-run 8s adjusted
fairly quickly when 54.6 per cent o f disequilibrium is recovered in each period. The
results also substantiate the fact that the seafood expon sector I 5 responsive to the fast
changing profile of the market. Conforming to I-lazard Analysis and Critical Control
Points (HACCP) standards, improvcmcnts in processing and packaging standards and
develop~neiitand marketing o f niche products to select the markets are enabled
quickly in order to take advantage o f the gains from trade even in the short run (Dey
el u l . 2005).
VI
<'ON< L.USIONS
Stahilisatiol~lncasures can be rcalised internall) by the government, using
don~csticstabilisation schemes or externally by the international community, using
different policy !nsfrulilents with or w~thoutmarket intervention. Routine measures
to rc~ncdy thc adverse effects o f unstable expon earnings are stabilisation of
remunerative prices o f expon products (though price stabilisation alone cannot
stahilise export earnings), rationalising balance o f payments, restoring balance
between foreign exchange outflow and inflow: management o f government revenue
(since cxport production provides foreign exchange earnings and tax revenues)
ensuring the cash llow of the sector in general and producer incomes in particular.
Cons~der~ng
the instab~lity of export volumes, a strategy aimed at export
diversification in general, measures to stabil~sesupply o f seafood products by using
research and technology development would be the most appropriate. With regard to
price tluctuations of com~iiodities, in addition to price stabilisation policies,
appriipriate improve~nent in thc product profile with emphasis on value added
products which circumvent the price factors including anti dumping duties and bonds
could provide the way out The primary fishery producers, viz., small farm operators
who account for the bulk of fish production. can stahilise thelr own revenues by using
instruments of risk management like forward contracts with input dealers for sale of
the produce. At the global level, the Minneapol~sGrain Exchange (MGE) is the only
commodity market which entertains forward trading o f seafood. Therefore adoption
o f advanced strategies in marketing helps to minimise the price risk and ensure
assured returns to seafood exports
Received Februav 2006.
Revision accepled May 2006,
INDIAN SEAF1NJll k X M K T S ISSULS OF INSTABIL.ITY, COMMODITY CONCCNTRArlON
211
AYYRPPB~.
A S and M Krlshnon (2005). "Ofthe111 Attempts Nrcded. Thr Ihndu Sume,,d Ayrirrirurr.
pp 124-136
Hhal K Sham and V Ntrmala 12001). "lnnah~l~r)of Agncuruial Exponr and Econom~ig r o d h I"
Ind~a"lndronFionomic Joc,rnol. Vol 8. No 3 . pp 87-91
Campa. Jort Mnnucl and ( i bcmande, Nuna (20041. Sourcrr 01 Gamr /?om lnrernorionai Po,,/i~lto
I)~vrrrrjicorrun.CEPR D~rcurs~oli
Papcr Nu 4390 h I t ~ , i : s ~cum'g~ctrj~ct
r~~
556712
Chln. I. and ti I-imp 120111)."Culnlegre~onAnnlysls ol Singapore ller~dcnt~al
Market". 10"' I.uropesl
Real Crolc Soctcty Conlerencc. 2001
1)evkota. S C (20041. (bust.? o f h p o r t lnrlvbliin rn hepol. lntcrnationul lrndc 0410002. Economlcr
Workkng Papcr Archtvc k.conWPA
IDcy Madan Mohan. Ruehlano M flrluncs and Maflur~udlnAhnlcd l2OOS). "D~saggregntedAnalys~so f
Flih Supply. Ilemand and 1r d c ~n Arm l l a s c l ~ nModcl
~
and I rllmation Ssalegy'. Aqzmcuirurr
Economir~sundMonopamenr. Vol 9. No 1-2. pp 111-1411
t.nglc. R F and C W J (irangcr (1987). "('cl-lntcgratlon and I.rror ('orrection Rcprescntst~on.
Csumiluon and Tcrt~ng". Lcononresrco. Vul 55, pp 251-276
Englc. R I a i d C W J Granger (1991). Long-Kun Lconomic Krlation,~h~psKeadmpr ~n Co~nrc$m,ron
Oxford IIn~vers~t)
Press, OxRlrd
Eriat. (i, and Akyur (2001). "Countv Crrnccntrvt8on 01 l u r l t ~ hthponr and lmpons over I~me".
E c o n u m ~Wch Innltutc. www economic wehln<tltulc org
Ghorh. M (20031. "Spala1 Inleuallon of Whcat Markets I" lndiu tv~dcnccsfrom ro~ntegrnl~on
Icsls".
Oxford U~~vriapmeml
Stud>es.Vol31. No 2. pp 159-71
Grecnc. W (1993). Lconomuln< Anuij,~ir,Mac Millan. Nc\r Yoik
laIl?v. S G Taylor. S I'ascoc and U Zahala 11998). hlarkrl Drlinrorcon offilth Sp91'1<.6 in Sputn.
CEMARE Rrrearch Papcr 140. Cencr lor the Lcunumlcr and Mnnagemenl i d Aquallc
Ksrourccs(CAMARE). Depanment of Cconom~cr.lln~verrrtyof Ponsnloulh. 1.ock.iua) Rond,
Ponsmoalh. iln8lrd Klsgdom
Jahanren. S I IYX8). ' Slar~sr$cal
Anal)rt\ of ('m~nlcgtat~ng
V~ctnr\.".lr>urnuidbcorioni,i.
rhoomicr ond
Control. \'a1 12. pp 231-254
Johansen. S.(1995). Ltbbhood-Hosed Inlrrencr ,n (biaeprared t'scror Autorqqrurrirr d4odelr. Oxlard
linsvcrs~l)Press, Oxford
Johansen. S, and K . Juscl<ur(1994). "ldent~fical~on
of thc I.ang-Run and the Shon. Run Saucturc An
Appikcallon lo the 1SI.M Mudcl.".l?urnoi ofEconametncs, Vol. 63. pp 7-3h
1 . 0 ~ ~J. (1992). "Exnon lnrtabtltly and the Domcrtlc Economy Oucl~onsof Cauralif)." Journal of
Development ~ ~ u d Vol
c r 28,pp 735-742
Maddala. G.S (1992), lnrroduct~onto Ec'eanomerrics(Second Ed~laon).Englcwond Cltfl\. NJ Prcntkcc
Hall. Ine
Massell. B (1970). "Expon lnstah~llty and Cconomlc Struelurc". Amencan Economic R e v ! ~ w .
Scplembcr, pp 618-630
Mullor-Scbasttan, A (19RR). "A New Approach lo the Relalconsh~phelwecn Expon lnslahnl~ryand
Ecunom~cDcvclopmcnr Economic Devrlnpmer ond Culrurvl (aorrge, Vol 36. pp. 2 17-36
VRncn. P (1972). "On Commod~tyConcenVatnnn of Expnns I" Devcloplng Countrtcs". fi;conomrc
Inrema-lonole, Vol 25, pp 697-717
Pal. Suresh (1992). "Agricultural Expon o f lndla Issues of Grouzh and Instab~liry" Indron Journal 01
Agncvlrurai Donomrcs, Vol47. No 2, Apnldune. pp IRS-194
Paudyal. S R (1988). Forelpn Trade. Agd ond Developmetu rn Nepal. Commonwealth Publi,hcrs. New
l k l h ~ l.n d ~ a
Ptnruuana 1. l19911.
. . "An AAnas~s of Markcl Stah~l~lv
. for Exnon
. of That Frozen Shrtmnr bv
lnnar~thmic
Vartance
Method Master Thesis". Kuasan Un1vcr51lv.~o
-~
-.. 117
Shang. Y.C. PingSun Lcung and Yung C Shang (2001). "Comparauvc Economics of Shrlmp Farmxng
~n Asla", on P~ngSunLcung and Khem R S h m s (Edr) (2001). Donomtcs and Mmagemcnt g(
.
"
.
211
INDIAN JOURNAL 01 AGRICUL.TURA1. EC ONOMICS
Shrimp and Gorp F o r m ~ n gin Asca. A Coliecl,on o/Rerrorch Popers hared on the ADlJ/,+'ACA
I h r m Per/wmoncr Suwey. Network of Aquaculture Centcr~ ~n Ana-Pa~ific (NACAl (20011
I%sngkok.1halland. pp 33.46
Slnha. I' Ilipcndra (1999). Exporl Inrlobilib: lnve,~ln~enl
ond Eronnmrr Growth tn A ~ i a nColinlr!es A
Time Scrtel A ~ O / Y E I SCenler
.
Dlscusslon I'npcr No 799. Economic Growth Ccnler, Yalc Untrcnlty.
Connrcr!cur
Stock. J I1 and M W Watson (1993). "A Simple hstirnator nl'Coir,agiat~ng Vcclon ~n Higher-Ordci
lntcgralcd Sysrems". Cronomrlrico. Vol 61. pp 7X3-820
lcgugnc. A (2000). "('ommotllt) Conc~.ntr"laln and Lkpon l'nrnlnps In\rahlll@ Cv~dunccfrom Airlcso
rounlrte\." C e n l ~ r hlr Ivannric R<,,rr,nh on dfricil School of I3u\>nr\s. Moniclair Svslr
Llslvcrslty
llppcr
Moorclair
Accc\scd
Scprcmher.
2005
Ihup, ~1ph.1tt>~tnl~I,~br
\,<ICI . l c l ~ ~ ~ lIpK' lA! 11hlf125I < ~ L ~ I I l'ltll
G , pcll
logan. S (1994). Porcrgn lradr R r ~ t m sand linde 1,~hrrab:arton m Rrkev Dunng !he iPXf1.~Ashgvtc
I'uhlirhtng Lld .Aldershot. Avchuty. tnplund
Wllsun. P (1994). "lirpan Earnings In'irahtlily or S~ngdpote.1957-1988 A l ~ m cFcries Anal)sli.'
Journal ~ ( A s r o ~ D o n o m r r Vul
r . 5 , pp 399.412