International shark fin markets and shark management: an integrated market preference-cohort analysis of the blacktip shark (Carcharhinus limbatus)
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Ecological Economics 40 (2002) 117– 130
www.elsevier.com/locate/ecolecon
ANALYSIS
International shark fin markets and shark management: an
integrated market preference –cohort analysis of the blacktip
shark (Carcharhinus limbatus)
Quentin S.W. Fong a,*, James L. Anderson b,1
a
Marine Ad6isory Program/Fishery Industrial Technology Center, School of Fisheries and Ocean Sciences,
Uni6ersity of Alaska Fairbanks, 118 Trident Way, Kodiak, AK 99615, USA
b
Department of En6ironmental and Natural Resource Economics, Coastal Institute, Uni6ersity of Rhode Island,
1 Greenhouse Road, Kingston, RI 02881, USA
Received 15 November 2000; received in revised form 10 October 2001; accepted 31 October 2001
Abstract
The increasing demand for shark fins in Asia, and the publicity resulting from finning and discarding live sharks,
has generated concern regarding the sustainability of the world’s shark populations. These concerns can be attributed
to the shark’s life history, which is characterized by a pattern of slow growth, late maturity, few offspring, and long
life, making populations vulnerable to overexploitation. Once overexploited, shark stocks will be slow to recover due
to these constraints. Despite an increase in consumption and trade of shark fins and other shark products, and the
vulnerability of shark populations once overexploited, little effort has been expended to understand the biology and
economics of sharks and shark fisheries until recently. This study adds to the understanding of linkages between shark
product markets, specifically shark fins, and the biology of shark populations by explicitly incorporating multi-at-
tribute market information into bioeconomic modeling. Results from conjoint analysis of the Hong Kong dried,
processed end-user markets is incorporated into a blacktip shark (Carcharhinus limbatus) cohort model to estimate the
optimal harvest size and age that maximize economic value. Results show that optimal harvest sizes and ages for all
mortality and discount factor scenarios are greater than the maturation sizes and ages for both male and female
blacktip. Policy implications for this study are also discussed. © 2002 Elsevier Science B.V. All rights reserved.
Keywords: Shark fin market; Shark management; Bioeconomic model
1. Introduction
Bioeconomic models utilize an integrated eco-
nomic and biological systems approach to evalu-
* Corresponding author. Tel./fax: + 1-907-486-1516. ate the performance of fishery resources using
E-mail addresses: qfong@sfos.uaf.edu (Q.S.W. Fong), different management strategies. Traditionally,
jla@uri.edu (J.L. Anderson). most bioeconomic analysis in the fisheries man-
1
Tel.: + 1-401-874-4568.
0921-8009/02/$ - see front matter © 2002 Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 1 - 8 0 0 9 ( 0 1 ) 0 0 2 7 3 - 7118 Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130
agement literature examines issues such as extrac- Until recently, none of the aforementioned
tion rates and fleet size and/or capacity with seafood marketing studies has explicitly incorpo-
simplistic assumptions about market behavior. rated multi-attribute market values into captured-
Demand functions are estimated using highly ag- based fishery bioeconomic analysis. Larkin and
gregated data to generate price estimates. Little Sylvia (1999) explicitly incorporate intrinsic fish
attention has been given to using realistic market quality into a standard bioeconomic fisheries
information, especially when considering the model for Pacific whiting that includes the har-
multi-attribute nature of fishery products (e.g. vesting and processing sectors. Intraseasonal price
Carroll et al., 2001). This is important, because in for Pacific whiting is estimated by a multi-at-
the economics of the international food marketing tribute, seemingly unrelated regression model that
system, which includes seafood, it is consumers incorporates flesh composition, product form, and
who are the driving force for product selection hedonic price function for surimi products.
and consumption (Schaffner et al., 1998). While This research also explicitly incorporates multi-
lack of understanding of consumer tastes and attribute market information to bioeconomic
preferences may prevent successful marketing of modeling. Here, a framework that incorporates
fishery products by producers, the misunderstand- market information of a shark product, shark fin,
ing of consumer product markets by policymakers is merged with the biological growth function of a
may promote ineffective fishery management shark is presented. Specifically, the fishery man-
schemes. This can result in welfare losses to all agement objective of harvesting shark fins that are
of the most preferred quality to maximize eco-
resource users (e.g. Homans and Wilen, 1997).
nomic return to society is investigated. This is
The incorporation of product characteristics in
achieved by incorporating the results of a conjoint
bioeconomic analysis for fishery management was
analysis of dried processed shark fin in Hong
investigated by Gates (1974), who illustrated the
Kong into a bioeconomic model of the blacktip
importance of product size as a function of mar-
shark. The optimal harvest size and age that
ket price for ex-vessel demand analysis. Subse-
maximizes economic value of the shark fin set
quently, several bioeconomic studies have
(caudal, dorsal, and two pectoral fins) for a single
incorporated size-dependent market prices in ex-
cohort of blacktip shark under different biological
amining optimal resource management strategies and economic scenarios is estimated.
(e.g. Thunberg et al., 1998). The consideration of
size as a product characteristic that affects market
price has certainly added a realistic dimension to 2. Background
fishery management analysis. However, in certain
fisheries, particularly those with foreign ethnic Shark fin, a product that is traditionally con-
markets such as bluefin tuna and shark fins, the sumed in Hong Kong, Singapore, Macao, China,
use of product size alone may not be sufficient for and other countries with large ethnic Chinese
demand and bioeconomic analysis. Indeed, recent populations, is one of the most valuable food
marketing studies of seafood products using vari- items in the world. For instance, in 1998, the
ous multi-attribute utility approaches have average price for dried processed caudal fins 25.4
demonstrated that size is only one of several cm (10 in.) in length was US$ 415.00 retail in
product attributes that end-users evaluate (e.g. Hong Kong (Fong and Anderson, 2000). As a
Zucker and Anderson, 1998). For example, results consequence of liberalization and increasing
from hedonic price analysis of fresh North At- spending power of the Asian middle class, the
lantic bluefin tuna (Thunnus thynnus), show that demand for shark fins has increased significantly.
fat content, color, shape, freshness, and size are For instance, Hong Kong, a trader, processor,
significantly correlated to ex-vessel price (Carroll and consumer of shark fins, and the most impor-
et al., 2001). McConnell and Strand (2000) found tant market for shark fins in the world, increased
similar results from tuna in Hawaii. shark fin imports more than 214% from 2648 mtQ.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130 119 in 1985 to 8323 mt in 1998 (Vannuccini, 1999; the Melbourne market to get an indication of how Hong Kong Census and Statistics Department, prices change with changing quantity. However, 2001). Similarly, shark fin imports by Thailand they could not make any conclusive statements increased 42% from 97 to 138 mt (Food and from this portion of the study since imports and Agriculture Organization, 2001). domestically caught sharks, which do not enter The increasing demand for shark fins in Asia, the Melbourne market, were not included in the and the publicity resulting from finning and dis- analysis. Thus, the authors treat the price of shark carding live sharks, have generated concern re- meat as exogenous and assume that price remains garding the sustainability of the world’s shark constant over the simulated 30-year period. This populations. These concerns are due to the nature is a restrictive and unrealistic assumption. This of the shark’s life cycle, which makes them vulner- study adds to the understanding of linkages be- able to overexploitation (Holden, 1977). Once tween shark product markets, specifically shark overexploited, shark stocks are slow to recover. fins and shark biology. Other biological factors, such as schooling by age, sex, and reproductive state, also make some shark species (e.g. blue shark, Prionacae glauca) highly 3. Bioeconomic model vulnerable to overfishing. High fishing mortality may deplete certain segments of the age class, The overall structure of the model is presented which may significantly affect the reproductive in Fig. 1. First, the biological growth of an indi- dynamics of shark populations (Anonymous, vidual blacktip shark is modeled with respect to 1996). the length and weight of three fin types— caudal, Despite an increase in consumption and trade dorsal, and pectoral. Results of a shark fin prefer- of shark fins and other shark products, and the ence analysis, conjoint analysis, is applied to cal- vulnerability of shark populations once overex- culate the utility index of the dried, processed fin ploited, relatively little effort has been spent to set as a function of blacktip shark growth. Fi- understand the biology and economics of sharks nally, the harvest size (age) for a blacktip shark and shark fisheries until recently (e.g. Pascoe et al., 1992). This lack of research in the biology and economics of sharks may stem from the tradition- ally small scale of shark fisheries relative to other fisheries, a lack of understanding of ethnic mar- kets for shark products, and the incidental by- catch nature of many shark fisheries. Further, little attention has been paid by domestic and international fishery management institutions to the management of shark stocks. Bonfil (1994) found that only three (Australia, New Zealand, and the US) out of 26 countries with reported annual shark landings of over 10 000 mt have domestic shark research programs and manage- ment plans. Few existing economic studies concerning sharks address the linkage between shark prod- ucts and shark harvest management. Pascoe et al. (1992) developed a bioeconomic model to esti- mate the effects of different management options for the southern shark fishery in Australia. They measured the price flexibilities of shark meat in Fig. 1. Blacktip shark market – cohort model.
120 Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130
cohort that maximizes the Hong Kong shark fin ship between dried, processed shark fins and
buyer’s utility for dried, processed fins under dif- blacktip shark growth. First, fin length measure-
ferent mortality rates and discount rates is ments are made from the tip of the fin to the
estimated. middle base of the fin and along the anterior edge
of the shark fin. These measurements are done
3.1. Biological component because Hong Kong shark fin buyers use the
anterior edge measurement as an indicator of fin
Age and growth estimates for blacktip shark of size while Al-Quasmi (1994) uses Food and Agri-
both sexes are represented by a von Bertalanffy culture Organization/World Health Organiza-
growth function: tion’s standard (FAO/WHO, 1987).
TLt = L [1− e − K(t − t0)] (1) The weight, in grams, of the dried, unprocessed
shark fin samples is also recorded. These measure-
where TL is the total length of the blacktip in ments are used to estimate the relationship be-
centimeters; L the attainable maximum size, is tween the length and weight of dried, unprocessed
176 cm total length; K the rate that approaches shark fins. Dried, unprocessed shark fins of each
L is 0.27; and t0 is the age at which the fish type are processed into the end-user product
would have been zero size is − 1.20 year form— dried, processed fins. Before final process-
(Branstetter, 1987). The subscript t represents age ing, dried, unprocessed fins are rehydrated until
in quarters; t=1.0, 1.25, 1.50, …, 30, assuming fin lengths became constant. Length measure-
the blacktip has an average life expectancy of 30 ments of the rehydrated fins are used as a proxy
years. The total length equation is then converted for fresh shark fins to estimate the relationship
to pre-caudal length by using the following equa- between fresh and dried unprocessed fin lengths.
tion estimated by Castro (1996) in millimeters: After processing the fin sets into final dried, pro-
PCLt = − 23.1+0.74TLt (2) cessed form, length measurements were taken
from the processed fins and used to calculate the
where PCL is the pre-caudal length of a blacktip conversion ratios of dry, unprocessed fins to
(mm) and TL is the total length. dried, processed fins. Weight conversion ratios
To estimate the functional relationships be- between dried, unprocessed and dried, processed
tween shark and fin growth in terms of fin size shark fins were calculated following Al-Quasmi
and weight, data from Al-Quasmi (1994) and (1994) (Tables 18, 19). The following sections
measurements from a commercial sample of describe the estimated linkages by fin type.
blacktip shark fins are utilized. These relation-
ships are estimated by a series of regressions that 3.1.1. Caudal fin
linked dried, processed fin size to the von Berta-
lanfy growth function of blacktip shark. The first 3.1.1.1. Fin size estimate. The relationship be-
step of the relationship is estimated by the rela- tween fresh caudal fin and fresh dorsal fin is
tionship between fresh dorsal fin and pre-caudal represented by:
length of the blacktip shark as reported by Al-
Quasmi (1994): ln(MidCaut )
FDFt =0.5524 (0.33)+ 0.1608 (14.85)PCLt = 0.2670 (1.21)+0.8777 (12.81)ln(FDFt )
R 2 =0.83 (3) R 2 = 0.79 (4)
where FDFt is the fresh dorsal fin size measured where ln denotes natural logarithmic transforma-
from the shark fin tip to the middle base of the tion; MidCau is the middle length measurement
fin. Numbers in parenthesis represents the t-ratio. of the fresh caudal fin; FDF is the middle length
A sample of 36 sets of dried, unprocessed measurement of the fresh dorsal fin.
blacktip shark fins was obtained from Guyana to The relationship between the fresh and dried
complete the estimation of the functional relation- unprocessed fin is represented by:Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130 121
DryMidCaut =0.7999 (0.81) sented by:
+ 0.8768 (17.27)MidCaut DryMidDort = 0.7486 (1.03)
R 2 =0.97 (5) +0.8877 (20.94) FDFt
where DryMidCau is the middle length measure- R 2 = 0.98 (10)
ment of the dried caudal fin. where DryMidDor is the middle length measure-
The relationship between the two length mea- ment of the dried caudal fin; FDF is the middle
surements of the dried caudal fin is represented length measurement of the fresh dorsal fin.
by: The relationship between the two length mea-
surements of the dried dorsal fin is represented by:
DryOutCaut =3.17596 (1.64)
DryOutDort = − 0.8986 (−0.28)
+ 1.009 (9.33)DryMidCaut
+1.4841 (7.51)DryMidDort
R 2 =0.91 (6)
R 2 = 0.90 (11)
where DryOutCau is the anterior length measure-
ment of the dried caudal fin. where DryOutDor is the anterior length measure-
The relationship between dried caudal fin and ment of the dried dorsal fin.
dried processed caudal fin based on five samples is The relationship between dried caudal fin and
represented by: dried processed caudal fins based on five samples
is represented by:
DryProcOutCaut = 0.96DryOutCaut (7)
DryProcOutDort = 0.96DryOutDort (12)
where DryProcOutCau represents the anterior where DryProcOutDor represents the anterior
length measurement of the dried processed caudal length measurement of the dried processed dorsal
fin. fin.
3.1.1.2. Fin weight estimate. The relationship be- 3.1.2.2. Fin weight estimate. The relationship be-
tween the weight and length of the dried caudal tween the weight and length of the dried dorsal fin
fin is represented by: is represented by:
ln(DryCaugmt ) ln(DryDorgmt )
= − 4.5691 (− 3.87) = − 5.2522 (−5.96)
2
+ 2.8660 (7.445)ln(DryOutCaut ) R =0.87 +3.0628 (10.82)ln(DryOutDort ) R 2 = 0.95
(8) (13)
where DryCaugm is the dried caudal fin weight in where DryDorgm is the dried dorsal fin weight in
grams. grams.
The weight relationship between dried caudal The weight relationship between dried caudal
fins and processed dried caudal fin based on five fins and processed dried caudal fin based on five
samples is represented by: samples is represented by:
DryCauKgt =0.74DryCauKgt (9) DryProcDorKgt = 0.530DryDorKgt (14)
where DryCauKg is the dried processed caudal fin where DryDorKg is the dried processed dorsal fin
in kilograms. in kilograms.
3.1.2. Dorsal fin 3.1.3. Pectoral fin
3.1.2.1. Fin size estimate. The relationship be- 3.1.3.1. Fin size estimate. The relationship be-
tween the fresh and dried unprocessed fin is repre- tween fresh pectoral fin and fresh dorsal fin is122 Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130
represented by: samples is represented by:
ln(MidPect ) DryProcPecKgt = 0.42DryPecKgt (20)
=1.1582 (6.35)+ 0.7438 (13.09)ln(MidDort ) where DryPecKg is the dried processed pectoral fin
2 in kilograms.
R =0.79 (15)
where MidPec is the middle length measurement of 3.2. Utility index for an indi6idual shark
the fresh pectoral fin.
The relationship between the fresh and dried 3.2.1. Conjoint analysis and consumer choice
unprocessed fin is represented by: A market preference model, conjoint analysis, is
used to determine the utility of the shark fin set to
DryMidPect = − 1.1327 (−1.37)
Hong Kong shark fin importers/processors as a
+ 1.0006 (25.80)MidPect function of blacktip shark growth. Conjoint analysis
is a form of multi-attribute utility model, which all,
R 2 =0.99 (16)
or in part, link to the notion that utility is derived
where DryMidPec is the middle length measurement from the attributes that the good possesses (e.g.
of the dried pectoral fin. Lancaster, 1966). It is assumed that the utility Hong
The relationship between the two length measure- Kong shark fin importers/processors obtained from
ments of the dried pectoral fin is represented by: a specific shark fin product is a function of the utility
derived directly from the product’s attributes and
DryOutPect = 3.2687 (2.18)
levels of those attributes and indirectly from the
+1.1607 (15.61)DryMidPect profits associated with the product’s attributes
(Lancaster, 1966). For example, a Hong Kong shark
R 2 =0.97 (17)
fin buyer may prefer medium-sized dried, processed
where DryOutPec is the anterior length measure- dorsal shark fin to large-sized dried, processed
ment of the dried pectoral fin. pectoral shark fin. The utility derived from a given
The relationship between dried pectoral fin and product may then be expressed in general form as
dried processed pectoral fins based on five samples a quasi-concave, twice continuously differentiable
is represented by: utility function:
DryProcOutPect = 0.96DryOutPect (18) U(sh )= U{Xh ; y(Xh )} (21)
where DryProcOutPec represents the anterior where U(sh ) is the utility the buyer derives from the
length measurement of the dried processed pectoral hth composite dried, processed shark fin product sh ;
fin. Xh is a vector of levels making up the composite
product sh ; y(Xh ) is the profit function associated
3.1.3.2. Fin weight estimate. The relationship be- with the product’s attributes. Since a decision maker
tween the weight and length of the dried pectoral obtains some degree of satisfaction from each
fin is represented by: product, the alternative selected for consumption
would be the one that provides the highest satisfac-
ln(DryPecgmt )
tion. For example, a shark fin buyer would choose
= − 4.7353 (− 7.16) product s4 over product s2, only if U(s4) is greater
than U(s2). However, the utility of the shark fin
+ 2.7462 (13.56)ln(DryOutPect ) R 2 =0.87
importer/processor is not directly observable and is
(19)
unknown. The utilities, therefore, are treated as
where DryPecgm is the dried pectoral fin weight in random variables, and the probability of choosing
grams. alternative dried, processed shark fin product s4 over
The weight relationship between dried pectoral s2 is equal to the probability that U(s4) is greater
fins and processed dried pectoral fin based on five than U(s2) (Manski, 1977).Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130 123
3.2.2. Conjoint analysis model specification and Table 1
Results of conjoint model estimation (ordered logit)
estimation
Conjoint analysis of dried, processed shark fin Variable Coefficient S.E. T-ratio
was conducted with Hong Kong shark fin im-
porter/processors. This method uses field experi- Constant 2.78 0.66 4.20**
ments by asking respondents to rank or rate Size 2.32 0.23 10.19**
Dorsal −8.36 0.89 −9.41**
products with predetermined attributes and levels
Pectoral −13.11 1.38 −9.47**
of attributes to measure the buyer’s preference or v(1) 3.22 1.72 1.87*
utility as the dependent variable (Green and Sri- v(2) 6.16 0.75 8.26**
navasan, 1990). Here, the conjoint analysis evalu- v(3) 7.68 0.88 9.34**
ates the utility function of Hong Kong shark fin v(4) 10.18 1.85 5.50**
v(5) 14.26 1.76 8.12**
importers/processors directly by asking respon-
v(6) 17.31 1.82 9.49**
dents to rate a set of stimuli from 0 to 10, with 0 v(7) 18.91 1.90 9.93**
being the least preferred, and 10 being the most v(8) 20.43 1.99 10.26**
preferred. In this case, a reduced design of 11 dried, v(9) 23.76 2.84 8.38**
processed shark fins was obtained by using an
Log likelihood function =−192.58; N =187; Restricted log
asymmetrical factorial orthogonal experimental
likelihood=−448.41; 2 =511.66**; **, significant at the
plan (Addelman, 1962). The attributes included 0.01% level; *, significant at the 10% level.
were fin size and type. The conjoint model em-
ployed in this research uses the traditional non-in- individuals in the experiment, and m is the number
teraction-effect model, which is assumed to be of stimulus in the conjoint experiment. Maximizing
additive in levels of the attributes (e.g. Green and L(h, v) provides estimates of the parameters h and
Srinavasan, 1990): v (McKelvey and Zvoina, 1975).
U(sh )= i%ij x (h)
ij + mij mij N(0, 1) (22)
3.2.3. Utility index formulation
where U(sh ) is the random utility that an individual Maximizing the log likelihood function in Eq.
derives from hth product, iij is the parameter (23) provides estimates for h, the coefficients of the
matrix that represents the relative importance of independent variables and v, the threshold levels
the levels, x (h)
ij represents the deterministic indepen- between ratings. The estimated equation is:
dent variable matrix associated with attribute j and
level i for product h, and mij is the random error Usc = 2.8+ 2.4Sz− 8.3Dor− 13.1Pec (24)
term. where Usc is the utility score for the three fin types,
An ordered logit model was used to analyze the caudal, dorsal, and pectoral, at various fin sizes; Sz
rating data generated by the conjoint experiment. is fin size; Dor is dorsal fin, and Pec is pectoral fin.
For an independent sample of n individuals, the log Both Dor and Pec are coded in dummy variables.
likelihood function, L(h, v), is: All estimated coefficients are significant at the
0.01% level (Table 1). The estimated utility score
L(h, v) Eq. (24) and the estimated threshold level (v) are
n m then used to calculate the probability of a dried,
= % % Rq,y log(Lq,y (vj − 1 −i%X) processed shark fin being rated in a certain category
q = 1y = 1
(e.g. rating= 10) for a given fin size and fin type.
−Lq,y − 1(vj − i%X)) (23) The ordered logit model specification captures
where L(·) is the logistic distribution function the preference structure for dried, processed shark
e(·)/1 +e(·); v’s are the unknown threshold variables fin by a representative shark fin processor/importer
to be estimated with h where j= 0, …, 9; i is the in Hong Kong. It is assumed that the shark fin
matrix for the coefficients h; and X is the matrix processor/importer assigned ratings to product
for the independent variables constant, fin size, profiles in the conjoint experiment relative to his/
dorsal fin, and pectoral fin; n is the number of her most desired product profile. Thus the specific124 Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130
utility score for an index is calculated from the proposed by Peterson and Wroblewski (1984) are
logistic probability function for the most preferred used for sensitivity analysis. The Peterson and
rating (rating=10), the estimated utility score Wroblewski (1984) natural mortality function is:
(Usc) from Eq. (24), and the estimated v9 (23.759;
1.92 − 0.25
significant at 0.01%) that represents the lower Mt = Wt (29)
4
bound threshold level for the most preferred rat-
ing. The formula for the utility index, which where Mt is the quarterly natural mortality, and Wt
represents the probability of being the most pre- is the dry weight of individual shark in grams,
ferred dried, processed shark fin product is: assuming dry weight is 0.2 of wet weight. This
UWi,Sz =1−
e[23.7 − (2.8 + 2.4Sz − 8.3Dor − 13.1Pec)] mortality function simulates the decrease in natu-
ral mortality as the size of a shark increases with
age in a cohort.
1+e[23.7 − (2.7 + 2.4Sz − 8.3Dor − 13.1Pec)]
(25) The weight of an individual blacktip shark is
determined by:
where UWi is the utility per unit weight for the
three fin types; i is fin type, and 23.7 is the WKGt = (2.51×10 − 9)TLM3.12
t (30)
estimated lower bound threshold level for the most
where WKGt is the wet weight of an individual
preferred rating from the estimated ordered logit
blacktip shark (kg), and TLMt is total shark length
model.
(mm) (Castro, 1996).
The utility index for fin type, i, is calculated as
The total utility of a cohort using the utility
the product of the utility per unit weight, UWi, and
index approach by conjoint analysis is represented
the dried, processed weight, DPWi, for fin type i:
by:
UIi =UWi ×DPWi (26) TUIt × Nt
TUCt = (31)
where UIi is the utility index for fin type i. (1+r)t
The total utility index for an individual blacktip where TUCt is the total utility index for the cohort
is the sum of the utility indexes of the three fin at age t; TUIt the total utility index for an individ-
types, taking into account that sharks have one ual shark; Nt the number of sharks in the cohort;
caudal, one dorsal, and two pectoral fins: and r is the discount rate, which is set at 0, 0.02,
0.03, 0.05, 0.07, 0.1, and 0.2, respectively.
TUI =% UIi (27)
where TUI is the total utility index for an individ- 4. Optimal harvest
ual blacktip shark.
Results from a multi-attribute marketing analy-
3.3. Utility index for cohort sis were incorporated into a market preference–co-
hort model of the blacktip shark. The optimal
To calculate the utility index for the blacktip harvest size of the blacktip shark was investigated
shark, the initial population of the cohort is as- for the conjoint preference–cohort model under
sumed to be 10 000, with both sexes combined. The four natural mortality scenarios. Within each nat-
quarterly numbers-at-age for the blacktip shark ural mortality scenario, the effects of seven dis-
cohort is: count factors were also simulated. These results are
presented in Table 2 and Figs. 2–5.
Nt + 1 = Nt · e − M/4 (28)
Three quarterly natural mortality parameters,
where Nt is the number of sharks at age t, expressed 0.025, 0.05, and 0.075, are used to determine the
in quarters; and M is the natural mortality rate. optimal harvest size/age of the blacktip shark.
Three quarterly natural mortality rates, 0.025, Results show that as quarterly natural mortality
0.050, 0.075, and a natural mortality function increases from 0.025 to 0.075 at any given discountQ.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130 125
rate, the optimal harvest size/age for the blacktip ity scenarios, since a shark cohort of a small-size
shark decreases. For example, at a discount rate class (i.e. younger age) would be more vulnerable
of 0.03, the optimal harvest size estimated with to predation than a cohort of a large-size class.
the conjoint market– cohort model decreases from Results show that the size-dependent mortality
171.88 (12.50 years of age) to 169.34 cm (10.75 conjoint market–cohort model provides the least
years), then to 166.62 cm (9.50 years) as the conservative optimal harvest sizes/ages of all mor-
quarterly mortality rate increases from 0.025 to tality scenarios (Table 2 and Fig. 5). For example,
0.075 (Table 2 and Figs. 2– 4). at zero discount rate, the optimal harvest size/age
The performance of the conjoint market– co- for the size-dependent mortality scenario is 169.34
hort model using a size-dependent natural mortal- cm (10.75 years), as opposed to 172.86 cm (13.50
ity function is also investigated (Peterson and years), 170.19 cm (11.25 years), 167.24 cm (9.75
Wroblewski, 1984). This function assumes that as years) for 0.025, 0.050, and 0.075 constant quar-
the size of an individual shark increases with age terly mortality rates, respectively.
(expressed in weight), the natural mortality rate Seven discount rates, ranging from 0 to 20%,
for the cohort decreases. This assumption is an are used to examine optimal harvest size and age
improvement in realism over the constant mortal- of the blacktip shark. These rates are used to
Table 2
Optimal harvest for conjoint market–cohort model
Natural mortalitya Discount rate (%) Total utility index Optimal harvest sizeb (cm)/age (years)
0.025 0 86.01 172.86/13.50
2 66.29 172.15/12.75
3 58.56 171.88/12.50
5 46.18 171.27/12.00
7 36.89 170.94/11.75
10 26.87 170.19/11.25
20 10.71 167.82/10.00
0.050 0 25.49 170.19/11.25
2 20.51 169.34/10.75
3 18.47 169.34/10.75
5 15.08 168.87/10.25
7 12.42 168.36/10.25
10 9.41 167.24/9.75
20 4.15 165.24/9.00
0.075 0 8.99 167.24/9.75
2 7.42 166.62/9.50
3 6.76 166.62/9.50
5 5.64 165.95/9.25
7 4.74 165.24/9.00
10 3.69 164.48/8.75
20 1.76 162.79/8.25
P & Wc 0 10.77 169.34/10.75
2 8.72 168.87/10.50
3 7.84 167.82/10.00
5 6.47 167.82/10.00
7 5.36 167.24/9.75
10 4.09 166.62/9.50
20 1.84 164.48/8.75
a
In quarters.
b
Total length.
c
Peterson and Wroblewski (1984).126 Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130 Fig. 2. Total utility index from dried processed shark fins for the blacktip shark cohort (initial population = 10 000; natural mortality=0.025 per quarter). simulate the divergence between the social and 5. Summary and conclusions private opportunity cost of capital, time refer- ence, and risk premium. Real discount rates be- The increasing demand for shark products, in- tween 0 and 5% have been suggested as an cluding shark fins, and the life-history pattern of appropriate social discount rate for the 30-year long living, late maturity, and low-reproductive horizon (Clark, 1990). The differences can be potential of sharks have generated concerns re- attributed to the differences in risk premium garding the health of the world’s shark stocks. perceptions. These concerns are generated not only by regu- Results show that in all scenarios, size (age) latory agencies and non-governmental organiza- of optimal shark harvest decreases as discount tions but also from resource users. A survey of rate increases (Table 2). For example, given a shark fin importers/processors in Hong Kong, discount rate of 3%, the optimal harvest size the center for shark fin trade and consumption and age for blacktip under a size-dependent nat- in the world, has shown that more than 41% of ural mortality conjoint market–cohort simula- the respondents expressed concerns of overhar- tion is 167.82 cm (10.00 years of age). vesting of sharks (Fong and Anderson, 2000). Alternately, the optimal harvest size and age for Responding to these concerns, an International size-dependent natural mortality given a 20% Plan of Action for Conservation and Manage- discount rate is 164.48 cm (8.75 years of age), 2 ment of Sharks (IPOA-SHARKS) was devel- years younger than the case with a lower dis- oped by the FAO Technical Working on the count rate (Fig. 5). Conservation and Management of Sharks (Food
Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130 127 and Agriculture Organization, 2000). The IPOA- elicited by conducting a conjoint analysis using real SHARKS calls and provides guidelines to coun- dried processed shark fins, a common product tries with directed and non-directed shark catches form at the retail/wholesale level. The results of the to adopt a national plan of action for conservation conjoint analysis in the form of a utility index is and management of shark stocks. Countries con- then integrated with the biological growth function sidering or adopting the guidelines are encouraged of the blacktip shark. The objective of determining to ‘implement harvesting strategies consistent with the optimal harvest size/age of a cohort of blacktip the principles of biological sustainability and ratio- shark under different discount factors and mortal- nal long-term economic use’ (Food and Agricul- ity scenarios are investigated. Results from the ture Organization, 2000). preference–cohort analysis show that given the The objective of our work is to develop an reproductive maturation size of 145.00 cm (5.25 analytical framework for shark management in the years) for males and 158.00 cm (7.25 years) for context of using market information to develop females, optimal harvest sizes and ages for all and incorporate economic incentives to help ensure scenarios are greater than the maturation sizes/ biological sustainability and rational economic use ages for both sexes (Castro, 1996). For example, of shark populations. This is achieved by assem- the optimal harvest size based on Hong Kong bling a bioeconomic model by examining the link- buyer’s preference and age for the size-dependent ages between the shark fin market and biological mortality function is 167.82 cm (10.00 years)— parameters of sharks based on best scientifically 2.75 years beyond the maturation age for the male available information in conjunction with original and female blacktip, respectively. data (shark fin market and fin length/weight con- Shark stocks are currently being managed by version). Specifically, the preference structure of management measures such as reduce harvest lev- Hong Kong shark fin importers/wholesalers is els or effort, use of alternate gears, reduce adverse Fig. 3. Total utility index from dried processed shark fins for the blacktip shark cohort (initial population = 10 000; natural mortality=0.05 per quarter).
128 Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130 Fig. 4. Total utility index from dried processed shark fins for the blacktip shark cohort (initial population = 10 000; natural mortality=0.075 per quarter). effects on essential fish habitats, implement mini- Many of the targeted and non-targeted shark mum sizes, and time-area closures (Shotton, 1999). fisheries employ gear types that produce high Results from our work show that Hong Kong mortality. The use of gear types (e.g. trawl, gill-nets shark fin end-users prefer larger sized fins. More- etc.) that harvest and kill sharks non-selectively over, our results also show that the optimal harvest would render size limit management measures age/size of the shark is beyond the maturation of ineffective. Alternate gear-types such as hook and the representative shark. These results can be line, which under-sized sharks or sharks that have utilized by policy makers to internalize economic low quality fins can be released live, may also be incentives and strengthen existing management used as a management measure. policies to manage shark stocks in a biologically Third, rights-based fishing should be introduced and economically sustainable manner. For in- as a management measure. Rights-based systems, stance, size limits can be imposed such that har- whether in the form of individual transferable vested sharks have had the opportunity to quota, individual fishing quota, cooperatives or reproduce to help ensure biological sustainability. community development quota, allocate property While this measure helps biological sustainability, rights of specific fish stocks to resource users it also ensures harvesting of high quality (preferred usually on a percentage of the total allowable catch larger sized) shark fins. This harvest strategy would (TAC). This management system gives resource then also be consistent with the ‘rational long-term users the flexibility and motivation to maximize economic use’ guideline set forth by IPOA— economic gain in a sustainable fashion. For exam- sharks (Food and Agriculture Organization, 2000). ple, shark fishers for fins would be able to decide Second, gear types that are size selective with when and how to fish in the most economically low mortality should be used to harvest sharks. efficient manner given the TAC and other manage-
Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130 129
Fig. 5. Total utility index from dried processed shark fins for the blacktip shark cohort (initial population = 10 000; natural
mortality= Peterson and Wroblewski, 1984).
ment measures. This flexibility will generate eco- References
nomic incentives for shark fishers to selectively
harvest sharks that correspond to market sig- Addelman, S., 1962. Orthogonal main-effects plans for asym-
nals—in this case, larger sized sharks for their metrical factorial experiments. Technometrics 4, 21 – 46.
Al-Quasmi, A.M., 1994. Physical – chemical characterization of
fins. Further, ‘ownership’ by resource users also
shark fins. M.S. thesis, Food Science and Nutrition De-
generates the incentive to biologically conserve partment, University of Rhode Island, 164 pp.
shark stocks to ensure economic sustainability. Anonymous, 1996. The implications of biology for the conser-
This work has developed a framework to merge vation and management of sharks. Paper prepared by the
economics and ecosystems, in this case a market Shark Specialist Group of the Species Survival Commis-
system and an apex predator in the ecosystem, to sion of the IUCN — The World Conservation Union for
optimally manage scarce resources using the best the 13th Meeting of the CITES Animals Committee, 23 – 27
September 1996, Pruhonice, Czech Republic.
available scientific information. The paucity of
Bonfil, R., 1994. Overview of world elasmobranch fisheries.
biological and economic data prevented us from FAO Fisheries Technical Paper No. 341, FAO, Rome,
conducting a full model incorporating intertempo- Italy, 119 pp.
ral species and fleet dynamics. Future work in- Branstetter, S., 1987. Age and growth estimates for blacktip,
cludes continuing work on determining the Carcharhinus limbatus, and Spinner, C. bre6ipinna, sharks
population dynamics of specific shark popula- from the Northwestern Gulf of Mexico. Copeia 1987,
tions, obtaining biological data that is pertinent to 964 – 974.
Carroll, M., Anderson, J.L., Martinez-Garmendia, J., 2001.
‘rational economic use’, such as shark fin length
Pricing of US North Atlantic bluefin tuna and implications
and weight, incorporate other uses of sharks (e.g. for management. Agribus 17, 243 – 254.
meat and non-use values) in analysis, and obtain- Castro, J.I., 1996. Biology of the blacktip shark, Carcharhinus
ing costs structures for processing and fleet opera- limbatus, off the Southeastern United States. Bull. Mar.
tions. This work is only the first step. Sci. 59, 508 – 522.130 Q.S.W. Fong, J.L. Anderson / Ecological Economics 40 (2002) 117–130
Clark, C.W., 1990. Mathematical Bioeconomics: The Optimal conomic analysis of a commercial fishery. Am. J. Agric.
Management of Renewable Resources. Wiley, New York, p. Econ. 81, 29 – 43.
384. Manski, C., 1977. The structure of random utility models.
FAO/WHO, 1987. First draft of codex worldwide standard for Theor. Dec. 8, 229 – 254.
shark fins. Codex Alimentarius Commission, Joint FAO/ McConnell, K., Strand, I., 2000. Hedonic price for fish: tuna
WHO Food Standards Program, Document CX/LA 87/9, prices in Hawaii. Am. J. Agric. Econ. 82, 133 – 144.
Rome, 8 pp. McKelvey, R.D., Zvoina, W., 1975. A statistical model for the
Food and Agriculture Organization, 2000. The International analysis of ordinal level dependent variables. J. Math. Soc.
Plan of Action for the Conservation and Management of 4, 103 – 120.
Sharks. FAO, Rome. Pascoe, S., Battaglene, T., Campbell, D., 1992. A bioeconomic
Food and Agriculture Organization, 2001. FAO Yearbook, model of the southern shark fishery. ABARE Research
Fishery Statistics, Commodities. FAO, Rome. Report 92.1, Project 9343.103, Australia, 60 pp.
Fong, Q.S.W., Anderson, J.L., 2000. Assessment of the Hong Peterson, I., Wroblewski, J.S., 1984. Mortality rate of fishes in
Kong shark fin trade. INFOFISH Int. 1/2000, 28 – 32.
the pelagic ecosystem. Can. J. Fish. Aquat. Sci. 41, 1117 –
Gates, J.M., 1974. Demand price, fish size and the price of fish.
1120.
Can. J. Agric. Econ. 22, 1 –12.
Schaffner, D.J., Schroder, W.R., Earle, M.D., 1998. Food
Green, P.E., Srinavasan, V., 1990. Conjoint analysis in market-
Marketing: An International Perspective. WCB/McGraw-
ing: new development with implications for research and
Hill, Boston, MA, p. 487.
practice. J. Market. 54, 3 –19.
Shotton, R. (Ed.), 1999. Case Studies of the Management of
Holden, M.J., 1977. Elasmobranchs. In: Gulland, J.A.
(Ed.), Fish Population Dynamics. Wiley, London, pp. 187 – Elasmobranch Fisheries. FAO Fisheries Technical Paper
215. No. 378, FAO, Rome, 920 pp.
Homans, F.R., Wilen, J.E., 1997. A model of regulated open Thunberg, E.M., Helser, T.E., Mayo, R.K., 1998. Bioeconomic
access resource use. J. Environ. Econ. Manage. 32, 1 –21. analysis of alternative selection patterns in the United States
Hong Kong Census and Statistics Department, 2001. Hong Atlantic silver hake fishery. Mar. Resour. Econ. 13, 51 – 74.
Kong Trade Statistics: Imports. Hong Kong Special Admin- Vannuccini, S., 1999. Shark utilization, marketing and trade.
istrative Region, People’s Republic of China. FAO Fisheries Technical Paper No. 389, FAO, United
Lancaster, K., 1966. A new approach to consumer theory. J. Pol. Nations, Rome, 470 pp.
Econ. 74, 132 – 157. Zucker, D.A., Anderson, J.L., 1998. Implications of choice
Larkin, S.L., Sylvia, G., 1999. Intrinsic fish characteristics and behavior and preferences in niche markets. Aquacult. Econ.
intraseason production efficiency: a management-level bioe- Manage. 2, 61 – 70.You can also read
