The Effects of Chilean Coho Salmon and Rainbow TroutAquaculture on Markets for Alaskan Sockeye Salmon
ABBY WILLIAMS
Madison Gas and Electric, Post Office Box 1231, Madison, Wisconsin 53717, USA
MARK HERRMANN*School of Management, University of Alaska–Fairbanks, Post Office Box 757500,
Fairbanks, Alaska 99775, USA
KEITH R. CRIDDLE
School of Fisheries and Ocean Sciences, University of Alaska–Fairbanks,17101 Point Lena Loop Road, Juneau, Alaska 99801, USA
Abstract.—A simultaneous-equation equilibrium model of international salmonid markets is used to
examine the combined effect of variability in the landings of Alaska’s sockeye salmon Oncorhynchus nerka
and increases in the production of Chile’s Atlantic salmon Salmo salar, coho salmon O. kisutch, and rainbow
trout O. mykiss on Alaska’s sockeye salmon exvessel prices and revenues. While Atlantic salmon, coho
salmon, and rainbow trout from Chile and sockeye salmon from Alaska are not identical commodities, they
compete in many of the same domestic and international markets, and Alaska’s earnings in those markets
have declined as Chile’s production has increased. Although Alaska’s average annual harvests have remained
nearly constant, Alaska’s production is now a small percentage of the total salmonid production in a world
that eats significantly more salmonids than it did 25 years ago. Nevertheless, our model indicates that sockeye
salmon prices continue to be sensitive to interannual variations in the quantity of sockeye salmon harvested in
Alaska and to changes in the quantity of Atlantic salmon, coho salmon, and rainbow trout produced in Chile.
However, the model indicates that exvessel revenues are less sensitive than exvessel prices and that Alaska’s
recent levels of sockeye salmon landings have been near the maximum of the total exvessel revenue curve. In
addition, the model suggests that because Alaska’s share of the production of high-value salmon (Chinook,
coho, and sockeye salmon) has declined, exvessel prices for sockeye salmon are now less sensitive to changes
in Chilean production of Atlantic salmon, coho salmon, and marine-reared rainbow trout. The implication of
these findings is that, under current market conditions, exvessel revenues from Alaska’s sockeye salmon
fisheries can be maximized by maintaining catches at or near historic levels and that exvessel prices are
unlikely to continue to decline as rapidly as they did in the 1990s.
In the late 1980s, Alaska’s commercial wild salmon
fishery was regarded as prosperous and thriving.
Alaska accounted for over 40% of the world’s total
salmonid production and over 48% of the world’s
production of high-value salmonids—Chinook salmon
Oncorhynchus tshawytscha, coho salmon O. kisutch,
sockeye salmon O. nerka, Atlantic salmon Salmo salar,
and marine-reared rainbow trout O. mykiss. In addition,
salmon prices were at an all-time high. However, over
the last 20 years, even though Alaskan salmon harvests
have remained high and are touted as a biological
success, real (inflation-adjusted) prices and revenues
have declined to about one-fourth of their peak values
(see Figure 1) and have triggered widespread financial
hardship for fishermen, processors, and fishery-depen-
dent communities. The decline in real exvessel salmon
prices and revenues in Alaska has been variously
attributed to unfavorable exchange rates, poor eco-
nomic conditions in import markets, fluctuating run
sizes, and collusion among processors and in the
wholesale markets. However, statistical models of the
supply and demand for salmon have consistently
suggested that the overall decline in exvessel prices
can be best explained as a consequence of competition
in product markets engendered by the rapid expansion
of salmonid aquaculture (Anderson 1985a, 1985b;
Herrmann and Lin 1988; Lin et al. 1989; Herrmann et
al. 1993a; Herrmann 1994; Clayton and Gordon 1999;
Asche et al. 2001; Knapp et al. 2007).
In 1980, Atlantic salmon aquaculture was just
emerging from trial-scale operation and yielded about
27 million lb annually, or about 2% of world
production; by 1990, salmonid aquaculture (primarily
Atlantic salmon and coho salmon) annual yields had
* Corresponding author: [email protected]
Received April 22, 2008; accepted June 2, 2009Published online November 5, 2009
1777
North American Journal of Fisheries Management 29:1777–1796, 2009� Copyright by the American Fisheries Society 2009DOI: 10.1577/M08-102.1
[Article]
expanded to almost 637 million lb, a little over 27% of
world salmonid production; in 2005, salmonid aqua-
culture yields had expanded to over 3,373 million lb,
roughly one-and-three-quarters times the total salmon
supply from capture fisheries (see Figures 2, 3).
Production-scale salmonid aquaculture commenced
with Atlantic salmon in Norway, Scotland, Ireland,
and the Faroe Islands. Canada became an important
aquaculture producer in the mid-1980s with Atlantic
salmon and coho salmon. In the last decade, Chile has
emerged as a major producer of Atlantic salmon,
rainbow trout, and coho salmon (Bjørndal and Aarland
1999; Olson and Criddle 2008). Today, Norway, Chile,
the UK, and Canada are the leading aquaculture
producers of salmon and rainbow trout and, respec-
tively, account for 42, 40, 9, and 6% of current
production. Despite persistent popular myths to the
contrary, salmon and rainbow trout produced in
confined aquaculture are readily accepted by consum-
ers in the domestic and international fresh, frozen, and
smoked markets that were traditionally served by
commercial fisheries based in Alaska, British Colum-
bia, Russia, and Japan (Lin et al. 1989; Herrmann et al.
1993b; Wessells and Holland 1998; Asche et al. 2005).
The first markets for Alaskan wild salmon to be
adversely affected by increases in salmonid aquaculture
were European markets (such as in France) where
imports of cold smoked coho salmon, Chinook salmon,
FIGURE 2.—World production of salmon from capture fisheries, by producing region (FAO 2007).
FIGURE 1.—Real (1988 base year) exvessel prices and landings for Alaskan sockeye salmon, 1988–2007 (ADFG 2007).
1778 WILLIAMS ET AL.
and chum salmon O. keta from Alaska were displaced
by imports of Atlantic salmon from Norway, Ireland,
and Scotland. This was followed by losses in the
domestic (U.S.) markets for fresh and frozen salmon,
where the quantity of coho salmon and Chinook
salmon purchased from the commercial fisheries has
remained nearly constant at about 64 million lb, but
prices have tumbled in response to imports of Atlantic
salmon from Norway, British Columbia, and Chile,
which have risen from negligible levels in the early
1980s to over 390 million lb in 2005. The most recent
losses have occurred in the most valued market, Japan,
where imports of Chilean coho salmon and Chilean and
Norwegian rainbow trout have displaced imports of
Alaskan sockeye salmon and softened prices. In the
early 1990s, Japan annually imported an average of
187 million lb of Alaskan sockeye salmon; the recent
average is 64 million lb. Between 1989 and 2005,
Japanese annual imports of pen-reared salmon and
rainbow trout increased from 24 million to 370 million
lb (Figure 4). At the same time that salmonid
aquaculture production was increasing and putting
FIGURE 3.—World production of marine-pen-reared salmon and rainbow trout, by producing region (FAO 2007).
FIGURE 4.—Japanese imports of salmon, by species and origin (NMFS 2007).
ALASKAN SOCKEYE SALMON MARKETS 1779
pressure on the fresh, frozen, and smoked markets for
high-value salmon species, markets for canned sockeye
salmon and pink salmon O. gorbuscha salmon were
dwindling as a consequence of changes in consumer
preferences (Herrmann 1994).
This study examines the effects that Chilean Atlantic
and coho salmon and rainbow trout production is
having on Alaskan sockeye salmon prices and
revenues. Sockeye salmon was chosen as a focus both
because of its historical prominence and because it is
the highest-valued commercial salmon fishery in
Alaska. From 1980–2007, sockeye salmon accounted
for an average of 58% of the total value of Alaskan
salmon landings. In addition, Alaskan sockeye salmon
has historically been sold into the Japanese market
where it has faced ever increasing competition from
Chilean exports of coho salmon and Chilean and
Norwegian exports of rainbow trout.
The USA, Chile, and Norway are the main
competitors in the Japanese market for high-value
salmonids. While Alaska supplies fresh and frozen
sockeye salmon (mostly headed and gutted fish
[H&G]) to Japanese markets, Chile supplies fresh and
frozen coho salmon and rainbow trout (a mix of
boneless fillets and H&G), and Norway supplies fresh
and frozen Atlantic salmon and rainbow trout (a mix of
boneless fillets and H&G). The Japanese Ministry of
Finance (Ministry of Finance 2006) reports that
although Japan imported approximately 500 million
lb of salmon and trout in both 1994 and 2005, the
species mix and sources changed substantially (see
Table 1). In 1994, 48% of Japan’s salmon imports were
sockeye salmon, and 93% of those sockeye salmon
came from North America. In contrast, in 2005, only
25% of Japanese salmon and trout imports were
sockeye salmon, and of those, only 55% came from
North America (Ministry of Finance 2006). Over the
same time period, Japanese imports of coho salmon
increased from 16% to 32%, rainbow trout imports
increased from 12% to 23%, and Atlantic salmon
imports increased from 8% to 14% of total Japanese
salmonid imports. Nearly all of the imported Atlantic
salmon and rainbow trout came from Chile and
Norway. The change in market share for coho salmon
is particularly dramatic: in 1994, 59% of Japan’s
imports of coho salmon came from Chile and 41%from North America; in 2005, 97% of Japanese coho
salmon imports came from Chile. The growth and
development of salmonid aquaculture in Chile is
particularly important to the following analysis because
Chile’s salmon and rainbow trout products compete
directly in the principal markets for Alaskan sockeye
salmon, whereas much of Norway’s salmon and
rainbow trout products are exported to Europe where
they have displaced Alaskan exports of coho salmon
and Chinook salmon.
Model Specification
To better understand Alaska sockeye salmon
exvessel price and revenue determination, we con-
structed an international supply and demand equilibri-
um model for sockeye salmon, coho salmon, and
rainbow trout. Market models are simplifications of
complex interactions and reflect a balancing of prior
expectations based on theoretical relationships and
empirical estimates conditioned on limitations in
available data and informed by hypothesis testing.
The model (Figure 5) incorporates those elements of
international trade in salmonids that are most influen-
tial in determining prices and revenues for Alaskan
sockeye salmon. The model does not characterize trade
flows for low-value salmon (e.g., pink salmon and
chum salmon), European demand for wild or farmed
salmon and rainbow trout, or the allocation of salmon
and rainbow trout produced in the UK and nations
other than Norway, Chile, and Canada. While it is true
that there once was a thriving market for Alaskan
salmon in Europe, and while it appears that there may
be a resurgent European demand for Alaskan salmon,
sales of Alaskan salmon to Europe were inconsequen-
tial from 1990 through 2005. Although Norwegian
Atlantic salmon has exerted a driving influence on
historical salmon prices (Asche 1997; Asche et al.
1999; Herrmann 1993, 1994; Herrmann et al. 1993b;
Kinnucan and Myrland 2002, 2005), and although
European markets are important outlets for wild and
farmed salmon and rainbow trout, including these
additional market flows would have added consider-
able complexity to the model without adding much to
our understanding of factors that currently drive price
formation for Alaskan sockeye salmon.
The model uses annual observations from 1989 to
2005. During the time period modeled, most of the
FIGURE 5.—Schematic representation of trade flows includ-
ed in the salmon model.
1780 WILLIAMS ET AL.
landings of Alaskan sockeye salmon were frozen and
sold into Japanese markets. Imports of coho salmon
from Chile and rainbow trout from Chile and Norway
are the principal substitute products that compete with
Alaskan sockeye salmon exports to Japan. During
1990–2005, Chile exported 95% of its coho salmon
production and 85% of its rainbow trout production to
Japan, meanwhile exporting 64% of its Atlantic salmon
production to the USA. Over the same period, Japan
has been the recipient of 70% of Alaska’s frozen
sockeye salmon exports and 58% of Norway’s rainbow
trout production. The USA is the leading export market
for Atlantic salmon from Chile and Canada; from 1990
through 2005, the USA absorbed 64% of Chile’s
output of Atlantic salmon and 96% of Canada’s output
of Atlantic salmon. Initially, most of the imports were
whole fresh salmon, while most current imports are
frozen fillets with substantial quantities of smoked
portions and prepared meals. Although United States
imports of fresh and frozen Atlantic salmon do not
directly influence the price of Alaskan sockeye salmon,
as important components of the world supply of
salmon, they indirectly influence sockeye salmon
prices and are included in the model.
The model includes 13 behavioral equations, 15
market clearing identities, and 28 endogenous vari-
ables. Among the behavioral equations are six demand
equations that represent the U.S. inverse demand for
Chilean and Canadian Atlantic salmon, and Japanese
inverse demand for Chilean coho salmon and rainbow
trout, Norwegian rainbow trout, and Alaskan sockeye
salmon. In addition, the behavioral equations include
six equations that describe the allocation of production
and landings of each species from their principal
sources into their principal markets: the allocation of
Atlantic salmon reared in Chile and Canada to the
USA, and the allocation of Chilean coho salmon,
Chilean and Norwegian rainbow trout, and Alaskan
sockeye salmon to Japan. The behavioral model also
includes an equation that describes exvessel price
formation for Alaskan sockeye salmon. The 13
behavioral equations are described below. The 15
market clearing identities are presented in Table 2.
Variable definitions are summarized in Table 3. Data
sources are documented in Table 4. Linear and
nonlinear model forms were examined for each of the
behavioral equations; specification of functional form
was based on maximizing goodness of fit (GOF) of
individual equations and the overall equation system,
and on minimizing the degree of serial correlation in
estimated residuals.
Demand equations.—The six demand equations
share several similarities. First, all six equations were
specified as inverse (price-dependent) demand func-
tions to reflect the fact that, in a given year, supply is
largely predetermined and world market prices adjust
to the quantity produced. Each demand equation
includes own per capita quantity imports, own price,
per capita income, and a range of substitute prices. All
prices were adjusted to eliminate the confounding
influence of inflation.1
In the equations that represent U.S. inverse demand
for Canadian and Chilean Atlantic salmon (equations 1
and 2), the demand for Atlantic salmon from each
country was modeled as a substitute for imports of
Atlantic salmon from the other country because they
have similar product attributes: they are produced using
similar production systems, systems that are, to a large
degree, owned by a handful of multinational corpora-
tions (e.g., Marine Harvest, Fjord, Cermac); they
depend on feeds manufactured by multinational
corporations that supply both markets (e.g., Skretting,
EWOS); and, during the time period modeled, virtually
all of the Atlantic salmon exported to the USA
originated from Chile and Canada. All variable
definitions are listed in Table 3.
Equation (1): U.S. inverse demand for ChileanAtlantic salmon.—The USA is the most important
export market for Chilean Atlantic salmon. The real
price of Chilean Atlantic salmon exported to the USA
was modeled as a linear function of the per capita
quantity of Chilean Atlantic salmon exported to the
USA, the real price of Canadian Atlantic salmon
TABLE 1.—Percent by weight of Japanese imports of salmon
and rainbow trout in 1994 and 2005.
Species 1994 2005
Sockeye salmon 48 25Coho salmon 16 32Rainbow trout 12 23Atlantic salmon 8 14Other salmon and trout 16 6
1 Our model is based on export data for the producingcountries rather than import data for consuming countries.This is primarily due to data limitations, particularly for Japan,where data on imports have only recently been reported at theindividual-species level. For the inverse demand curves, allprices and incomes used have been transformed into thecurrency of the importing country and deflated using anappropriate price index. Because the export prices are free onboard and the importers would pay cost, insurance, andfreight, the prices used in our model are not the actual pricespaid by the importing countries. However, lacking a completetime series of actual imported prices for the time periodmodeled, the exported prices will serve as a proxy to importedprices, and as long as changes in energy costs and the likeremain somewhat similar between exporting counties, thisshould not introduce bias into the modeled parameterestimates.
ALASKAN SOCKEYE SALMON MARKETS 1781
exported to the USA, U.S. real per capita income, and
the previous year’s per capita exports of Chilean
Atlantic salmon to the USA, that is,
AtlPCL!USt ¼ b10 þ b11ðAtlQCL!US
t Þ þ b12ðAtlPCA!USt Þ
þ b13ðIncUSt Þ þ b14ðAtlQCL!USt�1 Þ:
ð1ÞLagged per capita exports of Chilean Atlantic salmon
to theUSAwere included to reflect the trend of increasing
U.S. import demand for Chilean Atlantic salmon: as
importers and consumers have become more familiar
with Chilean Atlantic salmon, demand has grown.
Equation (2): U.S. inverse demand for CanadianAtlantic salmon.—The USA is also the main export
market for Canadian Atlantic salmon. The real price of
Canadian Atlantic salmon exported to the USA was
represented as a linear function of the per capita
quantity of Canadian Atlantic salmon exported to the
USA, the real price of Chilean Atlantic salmon
exported to the USA, U.S. real per capita income,
and lagged per capita exports of Chilean Atlantic
salmon to the USA, that is,
AtlPCA!USt ¼ b20 þ b21ðAtlQCA!US
t Þ þ b22ðAtlPCL!USt Þ
þ b23ðIncUSt Þ þ b24ðAtlQCL!USt�1 Þ:
ð2ÞLagged Chilean Atlantic salmon exports to the USA
are used to reflect the reduced amount that U.S.
importers were willing to pay for imports of Canadian
Atlantic salmon as U.S. importers established ever-
stronger market links with Chile.
Although the specification of equations (1) and (2) is
nearly symmetric, per capita Chilean Atlantic salmon
exports are included in both equations, while lagged
Canadian Atlantic salmon exports are omitted from
both equations. Our initial specification of equations (1)
TABLE 2.—Market clearing identities for the salmon market model (1–15) and the simulated variables (16–21).
1. Chilean Atlantic salmon export price to the USA Atl�PðCLPÞCL!USt ¼ AtlPCL!US
t ðPPIUSt Þ CLP
US$
��2. Chilean Atlantic salmon per capita exports to the USA AtlQCL!US
t ¼ Atl �QCL!USt =PopUSt
3. Canadian Atlantic salmon real export price to the USA AtlPðCA$ÞCA!USt ¼ AtlPCA!US
t
PPIUStPPICAt
�CA$
US$
���4. Canadian Atlantic salmon per capita exports to the USA AtlQCA!US
t ¼ Atl �QCA!USt =PopUSt
5. Chilean coho salmon real export price to Japan cohoPðCLPÞCL!JPt ¼ cohoPðJP¥ÞCL!JP
t
WPIJPtWPICLt
�CLP=US$
JP¥=US$
���6. Chilean coho salmon per capita exports to Japan cohoQCL!JP
t ¼ coho�QCL!JPt =PopJPt
7. Weighted real export price of Chilean and Norwegian rainbow trout to Japan
rt~PðJP¥ÞJPt ¼rtPðCLPÞCL!JP
t
�JP¥=US$
CLP=US$
�rtQCL!JP
t WPIJPt þ rtPðNOKÞNO!JPt
�JP¥=US$
NOK=US$
�rtQNO!JP
t CPINO
ðrtQCL!JPt þ rtQNO!JP
t ÞWPIJPt
8. Chilean rainbow trout real export price to Japan rtPðCLPÞCL!JPt ¼ rtPðJP¥ÞCL!JP
t
WPIJPtWPICLt
�CLP=US$
JP¥=US$
���9. Chilean rainbow trout per capita exports to Japan rtQCL!JP
t ¼ rt �QCL!JPt =PopJPt
10. Weighted real export price of Chilean coho and Alaskan sockeye salmon to Japan
salmon~PðJP¥ÞJPt ¼rtPðCLPÞCL!JP
t
�JP¥=US$
CLP=US$
�cohoQCL!JP
t WPICLt þ sockPAK!JPt
�JP¥
US$
�sockQAK!JP
t PPIUS
ðcohoQCL!JPt þsock QAK!JP
t ÞWPIJPt
11. Norwegian rainbow trout real export price to Japan rtPðNOKÞNO!JPt ¼ rtPðJP¥ÞNO!JP
t
WPIJPtCPINOt
�NOK=US$
JP¥=US$
���12. Norwegian rainbow trout per capita exports to Japan rtQNO!JP
t ¼ rt �QNO!JPt =PopJPt
13. Alaskan sockeye salmon real export price to Japan sockPAK!JPt ¼ sockPðJP¥ÞAK!JP
t
WPIJPtPPIUSt
�US$
JP¥
���14. Alaskan sockeye salmon per capita exports to Japan sockQAK!JP
t ¼ sock �QAK!JPt =PopJPt
15. Alaskan sockeye salmon landings not exported to Japan sock �QAK!Otht ¼ sock �QAKland
t � 1:35ðsock �QAK!JPt Þ
16. Canadian Atlantic salmon nominal export price to the USA Alt�PðCA$ÞCA!USt ¼ AtlPðCA$ÞCA!US
t ðPPICAt Þ17. Chilean coho salmon nominal export price to Japan coho�PðCLPÞCL!JP
t ¼ cohoPðCLPÞCL!JPt ðWPICLt Þ
18. Chilean rainbow trout nominal export price to Japan rt�PðCLPÞCL!JPt ¼ rtPðCLPÞCL!JP
t ðWPICLt Þ19. Norwegian rainbow trout nominal export price to Japan rt�PðNOKÞNO!JP
t ¼ rtPðNOKÞNO!JPt ðCPINOt Þ
20. Alaskan sockeye salmon nominal export price to Japan sock�PAK!JPt ¼ sockPAK!JP
t ðPPIUSt Þ21. Alaskan sockeye salmon nominal exvessel price sock�PAKexv
t ¼ sockPAKexvt ðPPIUSt Þ
1782 WILLIAMS ET AL.
TABLE 3.—Variables used in the estimation and simulation of the international supply and demand equilibrium model for
salmon (sources are given in Table 4).
Variable Definition Source
sock �QAKlandt Alaskan sockeye salmon landings (lb) 4
sock �QAK!JPt Alaskan sockeye salmon exports to Japan (lb) 5
sockQAK!JPt Alaskan sockeye salmon per capita exports to Japan (lb/person) 1
sock �QAK!Otht Alaskan sockeye salmon landings not exported to Japan (lb) 1
sock�PAKexvt Alaskan sockeye salmon nominal exvessel price (US$/lb) 4
sockPAKexvt Alaskan sockeye salmon real exvessel price (US$/lb) 2, 4
sock�PAK!JPt Alaskan sockeye salmon nominal export price to Japan (US$/lb) 5
sockPAK!JPt Alaskan sockeye salmon real export price to Japan (US$/lb) 1
sockPðJP¥ÞAK!JPt Alaskan sockeye salmon real export price to Japan (JP¥/lb) 2, 4
sock�TRAKexvt Alaskan sockeye salmon nominal exvessel revenue (US$) 4
Alt �QCAt Canadian Atlantic salmon total exports (lb) 3
Alt �QCA!USt Canadian Atlantic salmon exports to the USA (lb) 3
AltQCA!USt Canadian Atlantic salmon per capita exports to the USA (lb/person) 1
Alt�PðCA$ÞCA!USt Canadian Atlantic salmon nominal export price to the USA (CA$/lb) 3
AltPðCA$ÞCA!USt Canadian Atlantic salmon real export price to the USA (CA$/lb) 1
AltPCA!USt Canadian Atlantic salmon real export price to the USA (US$/lb) 2, 3
Alt �QCLt Chilean Atlantic salmon total exports (lb) 6
Alt �QCL!USt Chilean Atlantic salmon exports to the USA (lb) 6
AltQCL!USt Chilean Atlantic salmon per capita exports to the USA (lb/person) 1
Alt�PðCLPÞCL!USt Chilean Atlantic salmon nominal export price to the USA (CLP/lb) 6
AltPCL!USt Chilean Atlantic salmon real export price to the USA (US$/lb) 2, 6
Alt�PCL!USt Chilean Atlantic salmon nominal export price to the USA (US$/lb) 1
Alt�PðCLPÞCL!Otht Chilean Atlantic salmon nominal export price to other countries (CLP/lb) 6
coho �QCLt Chilean coho salmon total exports (lb) 6
coho �QCL!JPt Chilean coho salmon exports to Japan (lb) 6
cohoQCL!JPt Chilean coho salmon per capita exports to Japan (lb/person) 1
coho�PðCLPÞCL!JPt Chilean coho salmon nominal export price to Japan (CLP/lb) 6
cohoPðCLPÞCL!JPt Chilean coho salmon real export price to Japan (CLP/lb) 1
cohoPðJP¥ÞCL!JPt Chilean coho salmon real export price to Japan (JP¥/lb) 6, 7
salmon~PðJP¥ÞJPt Weighted average real price of Chilean coho and Alaskan sockeye salmon exported to Japan (JP¥/lb) 1rt~PðJP¥ÞJPt Weighted average real price of Chilean and Norwegian rainbow trout exported to Japan (JP¥/lb) 1rt �QCL
t Chilean rainbow trout total exports (lb) 6rt �QCL!JP
t Chilean rainbow trout exports to Japan (lb) 6rtQCL!JP
t Chilean rainbow trout per capita exports to Japan (lb/person) 1rt�PðCLPÞCL!JP
t Chilean rainbow trout nominal export price to Japan (CLP/lb) 6rtPðCLPÞCL!JP
t Chilean rainbow trout real export price to Japan (CLP/lb) 1rtPðJP¥ÞCL!JP
t Chilean rainbow trout real export price to Japan (JP¥/lb) 6, 7rt �QNO
t Norwegian rainbow trout total exports (lb) 9rt �QNO!JP
t Norwegian rainbow trout exports to Japan (lb) 9rtQNO!JP
t Norwegian rainbow trout per capita exports to Japan (lb/person) 1rt�PðNOKÞNO!JP
t Norwegian rainbow trout nominal export price to Japan (NOK/lb) 9rtPðNOKÞNO!JP
t Norwegian rainbow trout real export price to Japan (NOK/lb) 1rtPðJP¥ÞNO!JP
t Norwegian rainbow trout real export price to Japan (JP¥/lb) 6,7
CA$/US$ Canadian–U.S. exchange rate 2
CLP/US$ Chilean–U.S. exchange rate 2
NOK/US$ Norwegian–U.S. exchange rate 2
JP¥/US$ Japan–U.S. exchange rate 2
PPIUSt U.S. producer price index for foods and feeds (base ¼ 1982) 2
PPICAt Canadian producers price index for foods and feeds (base 1997) 3
WPICLt Chilean wholesale price index (base ¼ 1992) 7
CPINOt Norwegian consumer price index for food (base ¼ 2000) 9
WPIJPt Japanese producer price index for agricultural products (base ¼ 2000) 7fuelCPINOt Norwegian fuel price index 9
PopUSt U.S. population (thousands) 2
PopJPt Japan population (millions) 8
IncUSt U.S. real personal per capita income 2
IncJPt Real per capita Japan private final consumption expenditures 7
I91
Indicator variable for 1991
I96
Indicator variable for 1996
I03
Indicator variable for 2003
ALASKAN SOCKEYE SALMON MARKETS 1783
and (2) included both lagged variables; lagged Cana-
dian Atlantic salmon exports were dropped from these
equations because the associated coefficient estimates
were not significantly different from zero. Imports of
Chilean Atlantic salmon are the fastest-growing source
of salmon consumed in the USA, and in 2000, Chile
overtook Canada as the leading exporter of Atlantic
salmon to the USA While the USA had imported
salmon from Canada for many years (initially from the
Pacific coast capture fisheries, and currently from a
combination of capture fisheries landings and aquacul-
ture), the Chilean export market was still relatively new,
and it is hypothesized that it took time to establish the
marketing relationships where importers were as
experienced with importing salmon from Chile as they
were with importing salmon from Canada. Moreover,
since 2000, Chilean exports of Atlantic salmon to the
USA have averaged about 1.6 times the quantity of
Canadian exports of Atlantic salmon to the USA.
The next four inverse demand equations (equations 3
through 6) were developed to describe the Japanese
demand for Chilean coho salmon, Chilean rainbow trout,
Norwegian Atlantic salmon, and Alaskan sockeye
salmon. Like the USA, which mostly imports Atlantic
salmon from Chile, Japan relies on Chile as the primary
source of coho salmon and rainbow trout. The most
difficult challenge in modeling the Japanese inverse-
demand equations was that prices of Chilean coho
salmon, Chilean and Norwegian rainbow trout, and
Alaskan sockeye salmon are highly collinear, and thus it
was not possible to include prices for all of these
substitutes in each demand equation. To alleviate multi-
collinearity, we used linear combinations of the prices of
substitutes. While these instrumental variables saved
degrees of freedom and alleviated collinearity, the inter-
pretation of the associated coefficients is less specific.
Equation (3): Japanese inverse demand for Chileancoho salmon.—Japan is the principal export market for
Chilean coho salmon. The real price of Chilean coho
salmon exported to Japan was characterized as a log-
linear function of the per capita quantity of Chilean
coho salmon exports to Japan, the quantity-weighted
average real price of Chilean and Norwegian rainbow
trout exported to Japan, the real price of Alaskan
sockeye salmon exported to Japan, and Japanese real
per capita income, that is,
cohoPðJP¥ÞCL!JPt ¼ b30 þ b31logeðcohoQCL!JP
t Þþ b32loge
rt~PðJP¥ÞJPt� �
þ b33logesockPðJP¥ÞAK!JP
t
� �þ b34ðIncJPt Þ: ð3Þ
Equation (4): Japanese inverse demand for Chilean
rainbow trout.—Japan is also the principal export
market for Chilean rainbow trout. The Japanese real
price of Chilean rainbow trout exports was described as
a linear function of the per capita quantity of Chilean
rainbow trout exported to Japan, the quantity-weighted
average real price of Chilean coho and Alaskan
sockeye salmon exported to Japan, the real price of
Norwegian rainbow trout exported to Japan, Japanese
real per capita income, lagged per capita Chilean
rainbow trout exports to Japan, and an indicator
variable to address unusual conditions in this market
in 1996, that is,
rtPðJP¥ÞCL!JPt ¼ b40 þ b41ðrtQCL!JP
t Þþ bsalmon
42~PðJP¥ÞJPt
þ b43½rtPðJP¥ÞNO!JPt � þ b44ðIncJPt Þ
þ b45ðrtQCL!JPt�1 Þ þ b46ðI96Þ:
ð4ÞLagged per capita imports of Chilean rainbow trout
were included because Chilean rainbow trout is a
relatively new product to Japan, and therefore importer
and consumer preferences for Chilean rainbow trout
were still being formed during the time period used for
estimation of model coefficients. Inclusion of the
indicator variable reduces the influence that the
unusual price observed in 1996 would have had on
the fit of this equation, but allows the 1996 prices to
influence the fit of other equations and preserves the
time sequence of observations needed to represent
dynamic linkages.
Equation (5): Japanese inverse demand for Norwe-gian rainbow trout.—Japan is an important export
market for Norwegian rainbow trout. The real price of
Norwegian rainbow trout exports to Japan was depicted
as an exponential function of the per capita quantity of
Norwegian rainbow trout exports to Japan, the
quantity-weighted average real price of Chilean coho
and Alaskan sockeye salmon exported to Japan, the
real price of Chilean rainbow trout exports to Japan,
TABLE 4.—Sources for the data in Table 3.
Number Source
1 Derived by identity; see Table 22 Economagic (2007)3 Statistics Canada (2007)4 ADFG (2007)5 NMFS (2007)6 Ramirez, A.G., Chilean Ministry of Fisheries, aff. personal
communication7 DSI (2007)8 Web Japan (2007)9 Statistics Norway (2007)
10 Equation 14
1784 WILLIAMS ET AL.
Japanese real per capita income, and a categorical
variable introduced to account for unusual conditions
in this market in 1991, that is,
logertPðJP¥ÞNO!JP
t
� � ¼ b50 þ b51ðrtQNO!JPt Þ
þ bsalmon52
~PðJP¥ÞJPtþ b53
rtPðJP¥ÞCL!JPt
� �þ b54ðIncJPt Þ þ b55ðI91Þ: ð5Þ
Inclusion of the categorical variable was again
motivated by a desire to reduce the influence that the
unusual price observed in 1991 would have had on the
fit of this equation while preserving the time sequence
of observations.
Equation (6): Japanese inverse demand for Alaskansockeye salmon.—Japan has been the single largest
market for Alaskan sockeye salmon. The real price for
Alaskan sockeye salmon exports to Japan was
represented as a log-linear function of: the quantity of
Alaskan sockeye salmon exported to Japan, the real
price of Chilean exports of coho salmon to Japan,
Japanese real per capita income, the lagged sum of
exports to Japan of Chilean coho salmon and Chilean
and Norwegian rainbow trout, and a categorical
variable introduced to account for unusual conditions
in this market in 1991, that is,
sockPðJP¥ÞAK!JPt
¼ b60 þ b61 logeðsockQAK!JPt Þ
þ b62 logecohoPðJP¥ÞCL!JP
t
� �þ b63logeðIncJPt Þþ b64 logeðcohoQCL!JP
t�1 þrt QCL!JPt�1 þrt QNO!JP
t�1 Þþ b65ðI91Þ:
ð6ÞOur initial specification of this equation included the
quantity-weighted price of Japanese imports of Chilean
and Norwegian rainbow trout; this variable was
dropped from the final model specification because
the associated coefficient was not statistically signifi-
cant, possibly due to the correlation between this price
and the price of Chilean coho salmon. To overcome
this problem and to incorporate the dynamic effects of
a maturing relationship between Japanese importers
and Norwegian and Chilean exporters, the model
included the lagged sum of Japanese imports of
Chilean and Norwegian salmon and rainbow trout.
During the time period covered, there were incredible
and rapid changes in the salmon and rainbow trout
markets. In 1990, Alaskan sockeye salmon was the
dominant high-value salmon–rainbow trout species
imported into Japan. Today, Alaskan sockeye salmon
has been relegated to niche markets. Because Japanese
imports of pen-reared coho salmon, Atlantic salmon,
and rainbow trout were comparatively novel, it could
be expected that it might take Japanese importers some
time to establish market channels for these products.
Thus, over time, it could be expected that imports of
pen-reared salmonids from Chile and Norway would
have an increasingly negative effect on the export price
of Alaskan sockeye salmon to Japan. The lagged sum
of Japanese imports of pen-reared salmonids from
Chile and Norway was included to capture dynamic
changes in the willingness to pay for Alaskan sockeye
salmon that go beyond the effect of changes in current
import prices alone.
Allocation equations.—Supply equations are not
specifically modeled in this study. The production of
free-ranging salmon can be characterized as a stochas-
tic process that can be affected by natural variations in
survival and growth, changes in habitat, and catches in
capture fisheries. Alaska’s salmon fisheries are man-
aged to achieve escapement objectives and guideline
harvest levels (GHLs). The number of fishing vessels is
limited by area and gear, and vessel size and gear
characteristics are also regulated. In season, managers
use estimates of escapement and run strength to
determine the frequency and duration of open fishing
periods. The intent of management is to ensure that
fishing does not occur unless the lower-bound
escapement objective is exceeded, and that harvesting
prevents escapements from exceeding the upper-bound
escapement objective. Operating in the context of these
derby fisheries, license holders have the choice to
participate or not participate in the open fishing
periods. Exceptionally low preseason price offers have
occasionally resulted in strikes or low participation
rates. For example, nearly 40% of Bristol Bay, Alaska,
sockeye salmon drift-gill-net permit holders chose to
forego fishing in 2002; the remaining 60% of Bristol
Bay, Alaska, sockeye salmon drift-gill-net permit
holders succeeded in harvesting the GHL (Schelle et
al. 2004). Similarly, when runs are unexpectedly strong
or unusually compressed, processors have limited their
daily purchases to avoid exceeding the capacity of their
processing lines, and escapements have exceeded the
upper-bound escapement objective. While harvests of
native salmon stocks could be reduced through
mismanagement or sustained through judicious man-
agement, there is little evidence to suggest that harvests
can be augmented beyond their historic baseline.
Similarly, there is little evidence that interannual
variability can be eliminated, and there is little reason
to expect that the productivity of Alaska’s salmon
fisheries is insulated against long-term cyclic or forced
variation in climate. That is, harvests of Alaska’s native
salmon stocks can be characterized as draws from a
stationary stochastic process.
ALASKAN SOCKEYE SALMON MARKETS 1785
In contrast, the quantity of salmon and rainbow trout
produced in confined aquaculture systems is only
constrained by the costs and availability of production
inputs, survival rates, the availability of suitable
production sites, and differences between production
costs and product prices. The history of steadily
increasing quantities of pen-reared salmon and rainbow
trout (Figure 2) and steadily increasing efficiency in
production (Asche 1997; Asche et al. 2001; Olson and
Criddle 2008) suggests that the long-run supply of pen-
reared salmon and rainbow trout has not yet reached a
binding capacity constraint. Together, increased pro-
ductivity and increased extent of production have
fueled the long-run expansion of salmon aquaculture
and depressed the price of salmon taken in the capture
fisheries. Although modeling the supply of pen-reared
salmon and rainbow trout is outside the scope of this
work, the model developed in this paper is designed to
allow for simulations that explore the likely conse-
quences of continued increases in aquaculture produc-
tion.
In place of supply equations, we have developed
equations that describe the allocation of supply to final
markets (equations 7 through 12). The structure of
these equations takes into account a variety of factors,
including the extent to which each country’s supply is
dominated by a single buyer, the distance between
supply sources and end markets, local prices for the
product relative to prices available in alternative
markets, and the export momentum to a given market.
Equation (7): allocation of Chilean Atlantic salmonto the United States.—The USA is the primary export
market for Chilean Atlantic salmon. The quantity of
Chilean Atlantic salmon exported to the USA was
represented as a linear function of the price that
Chilean producers receive for exports of Atlantic
salmon to the USA, the price that Chilean producers
receive for exports of Atlantic salmon to other
countries, the total tonnage of Chilean exports of
Atlantic salmon to the USA and other countries, and an
indicator variable to account for unusual conditions in
this market in 2003, that is,
Atl �QCL!USt ¼ b70 þ b71
Atl�PðCLPÞCL!USt
� �þ b72
Atl�PðCLPÞCL!Otht
� �þ b73ðAtl �QCLt Þ
þ b74ðI03Þ:ð7Þ
Although the USA has been the principal export
market for Chilean Atlantic salmon, approximately
one-third of the Chilean Atlantic salmon exports during
1989–2005 went to other countries, and thus there was
some opportunity for price arbitrage. To account for
this possibility, both export market prices were
included in the equation. Because these Atlantic
salmon exports went to many other countries and were
small relative to Chilean Atlantic salmon exports to the
USA, they were modeled as an exogenous variable.
Equation (8): allocation of Canadian Atlanticsalmon to the United States.—Virtually the entire
supply of Canadian Atlantic salmon is exported to the
USA. The quantity of Canadian Atlantic salmon
allocated to the U.S. market was modeled as a linear
function of the real price of Canadian Atlantic salmon
exported to the USA and the total quantity of Canadian
Atlantic salmon available for export, that is,
Atl �QCA!USt ¼ b80 þ b81
AtlPðCA$ÞCA!USt
� �þ b82ðAtl �QCA
t Þ: ð8ÞEquation (9): allocation of Chilean coho salmon to
Japan.—Likewise, because virtually all Chilean coho
salmon is exported to Japan, the quantity of Chilean
coho salmon exported to Japan was modeled as a linear
function of the real price of Chilean coho salmon
exported to Japan and the total quantity of Chilean
coho salmon available for export, that is,
coho �QCL!JPt ¼ b90 þ b91
cohoPðCLPÞCL!JPt
� �þ b92ðcoho �QCL
t Þ: ð9ÞEquation (10): allocation of Chilean rainbow trout
to Japan.—Although approximately 85% of Chilean
rainbow trout exports for 1989–2006 went to Japan, the
allocation function modeling this behavior is a bit more
complex than the previous two allocation equations.
The quantity of Chilean rainbow trout allocated to
Japan was modeled as an exponential function of the
real price of Chilean rainbow trout exports to Japan, the
total quantity of Chilean rainbow trout available for
export, and the fraction of the total Chilean production
of rainbow trout exported to Japan during the
preceding year, that is,
logeðrt �QCL!JPt Þ ¼ b100 þ b101 loge
rtPðCLPÞCL!JPt
� �þ b102 logeðrt �QCL
t Þ
þ b103 logert �QCL!JP
t�1
rt �QCLt�1
!:
ð10ÞEquation (11), which determines the allocation of
Norwegian rainbow trout to Japan, also includes a
variable that represents a lagged export share. In both
cases, the product is relatively new, and it takes time to
build up the relationships between the exporters and
importers in the marketing chain. It is hypothesized that
1786 WILLIAMS ET AL.
as the market shares of Chilean and Norwegian
rainbow trout has increased in Japan, it has become
easier to export to Japan. As the share of Chilean
rainbow trout exported to Japan increases, it is
expected that the quantity exported to Japan in the
current year will also increase.
Equation (11): allocation of Norwegian rainbowtrout to Japan.—Three of the four modeled factors that
describe the determination of the quantity of Chilean
rainbow trout exported to Japan were also used to
model the quantity of Norwegian rainbow trout
exported to Japan: the real prices paid for Norwegian
rainbow trout exports to Japan, the total amount of
Norwegian rainbow trout available for export, and the
percent of total Norwegian rainbow trout production
that was exported to Japan during the previous year. In
addition, this allocation equation includes the deflated
consumer price index of energy in Norway. The form
of the Norwegian rainbow trout allocation equation is
logeðrt �QNO!JPt Þ ¼ b110 þ b111 loge
rtPðNOKÞNO!JPt
� �þ b112 logeðrt �QNO
t Þ
þ b113 logert �QNO!JP
t�1
rt �QNOt�1
!
þ b114 logefuelCPINOtCPINOt
� �:
ð11ÞAlthough it might be expected that the price of
energy would also be important in the Chilean
allocation equations, Chile allocated 95% of its coho
salmon and 85% of its rainbow trout exports to Japan,
while Norway exported just 58% of its rainbow trout to
Japan during this period. Energy costs are highly
correlated with overall shipping costs, and the
difference in the transportation costs to deliver rainbow
trout from Norway to Japan rather than the European
Union or Russia is substantial, so energy prices are
more influential in the choice of export market for
Norway than for Chile.2
Equation (12): allocation of Alaskan sockeye salmonto Japan.—The per capita quantity of Alaskan sockeye
salmon exported to Japan was modeled as an
exponential function of the real price for Alaskan
sockeye salmon exported to Japan, total Alaska
landings of sockeye salmon, and the per capita quantity
of Alaskan sockeye salmon exported to Japan during
the previous year, that is,
logeðsock �QAK!JPt Þ ¼ b120 þ b121 loge
sockPðJP¥ÞAK!JPt
� �þ b122 logeðsock �QAKland
t Þþ b123 logeðsock �QAK!JP
t�1 Þ:ð12Þ
The lagged quantity of Alaskan sockeye salmon
exported to Japan was used to represent the effect that
rapidly changing patterns in Alaskan sockeye salmon
exports to Japan have had over time.
Equation (13): Alaskan exvessel price of sockeyesalmon.—The model was designed to highlight trade
flows and product demands that are most influential in
the determination of exvessel price and revenue for
Alaskan sockeye salmon. An exvessel price equation
was estimated as part of the system of behavioral
equations. While not explicitly modeled as a compo-
nent of the behavioral model, exvessel revenues can be
derived from estimates of exvessel price.
The real exvessel price of Alaskan sockeye salmon
was modeled as a linear function of the real price of
Alaskan sockeye salmon exported to Japan, the
quantity of Alaskan sockeye salmon landings not
exported to Japan, and the lagged ratio of Alaskan real
exvessel price of sockeye salmon to the real export
price of Alaskan sockeye salmon exported to Japan,
that is,
sockPAKexv
t ¼ b130 þ b131ðsockPAK!JPt Þ
þ b132ðsock �QAK!Otht Þ
þ b133sockPAKexv
t�1
sockPAK!JPt�1
� �: ð13Þ
As previously discussed, during the period modeled,
70% of all Alaskan sockeye salmon harvests were
exported to Japan as minimally processed fresh or
frozen products. However, in recent years more Alaska
sockeye salmon is being sold elsewhere; since 2000,
approximately half of sockeye salmon was exported
fresh and frozen to Japan and half sold elsewhere.
Alaskan sockeye salmon catches not exported to Japan
were included in this equation to represent the effects
that increases in landings used for other markets have
on exvessel price. Finally, lagged exvessel price shares
were used to represent market friction; current exvessel
prices are a reflection of past prices.
Although not modeled explicitly, real exvessel
revenue for Alaskan sockeye salmon can be derived
from the product of estimates of Alaskan real exvessel
price of sockeye salmon for any level of landings as
follows:
2 It is noteworthy that Russia is now the largest importer ofNorwegian rainbow trout, surpassing Japan in 2004. However,Russia was not an important export market for Norwegianrainbow trout until 1999. Because of the country’s late entryinto the market, the Russian segment could not be modeledexplicitly.
ALASKAN SOCKEYE SALMON MARKETS 1787
sock�TRAKexv
t ¼ sockPAKexv
t ðsockQAKland
t ÞðPPIUSt Þ: ð14ÞMarket clearing identities.—Market clearing identi-
ties are equations that transform input data series so
that they are conformable for inclusion in the
behavioral equations. The transformations include
adjustments for inflation, changes in exchange rates,
and changes in population. The 15 market clearing
identities included in our model (Table 2) are used to
derive time series for the endogenous variables.
Coefficient estimates and model performance.—The
market model for sockeye salmon, coho salmon,
Atlantic salmon, and rainbow trout features 13
behavioral equations, 15 identities, and 28 endogenous
variables to be estimated in an equation system that
includes several nonlinear relationships and embeds
dependent variables as explanatory variables in other
equations. Because of the limited number of observa-
tions available for parameter estimation, and because of
the complexity of the 13 behavioral equations, it was
not feasible to use a three-stage least-squares estima-
tion approach, so the equation system was estimated
using seemingly unrelated regression (SUR). In
comparison with single-equation estimation approach-
es, SUR reduces error covariances associated with
parameter estimates. Coefficient estimation and sensi-
tivity analyses were completed using Statistical
Analysis Software (SAS) 9.1 (SAS 2007). Coefficient
estimates, SEs, and summary statistics are reported in
Table 5. The P-values are one-sided P-values where thecoefficients on the variables could be signed a priori
according to economic theory; in the cases that they
could not, such as for the indicator variables, the P-values are two-sided. All elasticities and flexibilities
mentioned in the text to follow were estimated at mean
levels.
The estimated values of R2 on the transformed
dependent variables (0.712–0.999) reported in Table 5
indicate that the equation system provided an accept-
able fit to historical values of the dependent variables.
However, these historical fits for systems of equations
are better explored by the use of GOF statistics on the
untransformed variables. To check for serial correla-
tion, a common indicator of model misspecification,
the Durbin–Watson (DW) statistic was computed for
each equation. None of the DW statistics are so
extreme that the null hypothesis of no serial correlation
can be rejected; however, a few of the DW statistics are
in the indeterminate range, which may be a function of
the fact that the time series is rather short or indicative
of adjustment lags that exceed the 1-year lags modeled
in some equations.
Results
Salmon Demand Equations
The U.S. inverse demand equations for Atlantic
salmon, equations (1) and (2), performed well; there
was a high degree of correlation between the dependent
price and explanatory variables (R2 ¼ 0.846 for
equation 1; R2¼ 0.930 for equation 2), and statistically
significant coefficient estimates for all the explanatory
variables (Table 5). The equations show a clear
substitution relationship between the Atlantic salmon
from Chile and Canada. Additionally, the statistical
relationship between lagged Chilean Atlantic salmon
and the price of Canadian Atlantic salmon suggests that
USA importers have reduced their willingness to pay
for Canadian Atlantic salmon as they have established
relationships with Chilean exporters. The price flexi-
bility for the time-lagged quantity of Chilean Atlantic
salmon with respect to the price of Canadian Atlantic
salmon indicates that for each 1% increase in Chilean
Atlantic salmon exports to the USA, willingness to pay
for Canadian Atlantic salmon has decreased by 0.10%.
The Japanese inverse demand equations (equations
3–6) describe the price formation for exports of
Alaskan sockeye salmon, Chilean coho salmon, and
Chilean and Norwegian rainbow trout to Japan. The
inverse demand for each of these commodities depends
TABLE 5.—Coefficient estimates, P-values (parentheses), and summary statistics for model equations.
Equation bn0
bn1
bn2
bn3
1 �0.013 �2.0 3 10�5 (0.00) 0.711 (0.00) 79.9 (0.00)2 0.017 �2.0 3 10�5 (0.00) 0.253 (0.00) 39.1 (0.00)3 �2,119 �71.2 (0.00) 200.3 (0.00) 17.1 (0.15)4 50.06 �6.0 3 10�5 (0.03) 0.131 (0.19) 0.549 (0.01)5 4.26 �4.1 3 10�7 (0.00) 0.003 (0.00) 0.001 (0.06)6 53.1 �109.8 (0.00) 74.0 (0.00) 325.8 (0.00)7 2.6 3 108 19,760 (0.04) �32,218 (0.00) 0.54 (0.00)8 �1.6 3 10�7 3.9 3 10�8 (0.00) 0.98 (0.00)9 �7.4 3 106 756,701 (0.01) 0.98 (0.00)10 �0.712 0.13 (0.00) 1.02 (0.00) 0.49 (0.00)11 �1.60 0.41 (0.09) 1.01 (0.00) 0.60 (0.00)12 �8.42 0.10 (0.12) 0.91 (0.00) 0.52 (0.00)13 �0.0015 0.46 (0.00) �1.5 3 10�11 (0.01) 0.0056 (0.03)
1788 WILLIAMS ET AL.
on contemporaneous or lagged prices of one or more of
the others. For all of the Japanese demand equations,
the linear functional form was the default so that the
elasticities could vary with both the mean-level value
of the dependent and independent variable. However,
using the degree of serial correlation in the error terms
as a possible indication of an incorrect functional form,
alternative functional forms were used when substantial
serial correlations were found in the residuals to the
linear model.
The estimated demand equations indicate that,
within Japanese markets, there is extensive substitution
between Chilean and Norwegian rainbow trout,
Chilean coho salmon, and Alaskan sockeye salmon.
Each of the equations provided a good fit to the historic
data (R2 ¼ 0.889 for equation 3; R2 ¼ 0.712 for
equation 4; R2¼ 0.922 for equation 5) and statistically
significant coefficient estimates for almost all of the
explanatory variables (Table 5).
In the equation that describes Japanese demand for
Chilean coho salmon (equation 3), the cross-price
flexibility between the price of Chilean coho salmon
and the price of Chilean and Norwegian rainbow trout
is 0.96, indicating that a 1% increase (decrease) in the
price of imported rainbow trout leads to a 0.96%increase (decrease) in the price of Chilean coho salmon
exported to Japan. This variable is statistically
significant (P-value , 0.00). The cross-price flexibility
associated with the price of Alaskan sockeye salmon
exports to Japan had a P-value of 0.146, indicating a
larger probability of a type I error; however, there are
strong a priori reasons to believe that these two species
are also substitutes and the elevated SE on the
estimated parameter may be an artifact of the high
degree of collinearity between the prices of Chilean
and Norwegian rainbow trout and Alaskan sockeye
salmon export prices. Nevertheless, the higher proba-
bility of a type I error—that there is not a significant
effect from price variations in imported Alaska sockeye
salmon on variations in the price of imported Chilean
coho salmon—is probably also due to the diminishing
importance of Alaskan sockeye in the Japanese market.
Cross-price elasticities indicate that Chilean coho
salmon prices exports are more strongly affected by
variations in Japanese rainbow trout import prices than
they are by variations in the import price for Alaskan
sockeye salmon. Although this may seem counterintu-
itive, in 2005 exports of Chilean and Norwegian
rainbow trout to Japan were nearly four times as large
as exports of Alaskan sockeye salmon to Japan and
now are of more importance in the Japanese consump-
tion of salmon and trout. Indeed, the residual share of
the Japanese market that continues to be filled by
Alaskan sockeye salmon may represent a niche market
(i.e., a market that is characterized by a more narrowly
defined group of specialized consumers who are
willing to pay a premium for specific sockeye salmon
products).
In the equation that characterizes Japanese inverse
demand for Chilean rainbow trout (equation 4), the
weighted real price of salmon (Alaska sockeye salmon
and Chilean coho salmon combined) and the real price
of Norwegian rainbow trout were included as substitute
prices. Norwegian exports of rainbow trout to Japan
had a statistically significant P-value. The associated
cross-price flexibility indicates that, at the mean, every
1% increase (decrease) in the price of Norwegian
rainbow trout is expected to result in a 0.56% increase
(decrease) in the price of Chilean rainbow trout in the
Japanese market. The P-value associated with the
coefficient on the real weighted price of Alaskan
sockeye salmon and Chilean coho salmon (0.193)
indicates a moderate possibility of type I error, yet it is
likely that this is a result of the high degree of
collinearity among the prices of the modeled substi-
tutes. The coefficient associated with Japanese con-
sumption expenditures returned a stronger type I error
probability of 0.393. However, economic theory and
TABLE 5.—Extended.
Equation bn4
bn5
bn6
df R2 DW
1 �1.9 3 10�5 (0.00) 11 0.846 1.912 �6.3 3 10�4 (0.00) 11 0.930 1.703 276.7 (0.01) 11 0.889 2.414 0.012 (0.39) 5.9 3 10�5 (0.03) �38.6 (0.00) 9 0.712 1.645 0.0002 (0.03) 0.197 (0.00) 10 0.922 2.116 �86.4 (0.00) �87.1 (0.00) 10 0.807 2.317 4.6 3 107 (0.00) 11 0.984 1.698 13 0.998 1.489 13 0.999 1.58
10 12 0.999 2.0311 �0.99 (0.01) 11 0.973 2.0012 12 0.973 1.6813 12 0.778 1.66
ALASKAN SOCKEYE SALMON MARKETS 1789
knowledge about the market suggest that both of these
explanatory variables are important determinants of the
real price of exports of Chilean rainbow trout to Japan
and, as was discussed earlier, it was considered better
to leave these variables in the equation than to risk the
biases associated with the omission of relevant
explanatory variables. While inclusion of collinear
variables does not bias model forecasts, caution should
be exercised in interpreting forecasts that involve
variations in the weighted price of Alaskan sockeye
salmon and Chilean coho salmon, and variations in
Japanese consumption expenditures.
The indicator variable (I96) was included in equation
(4) to prevent the unusual price observed in 1996 from
unduly influencing coefficient estimates. Fisheries
marketing and trade journals provide some indications
as to why 1996 might have been an unusual year with
prices substantially below those otherwise predicted by
the model. For example, industry observers indicate
that 1996 was characterized by an unexpectedly weak
demand for sashimi, which redirected a large share of
rainbow trout imports into the lower-valued tei-en
(lightly) salted fillet market, thereby depressing the
average price of Chilean rainbow trout (Atkinson
1996).
The Japanese inverse demand for Norwegian
rainbow trout (equation 5) included the weighted real
prices of Chilean coho and Alaskan sockeye salmon
and the real price of Chilean rainbow trout as substitute
variables. The weighted real price of Chilean coho and
Alaskan sockeye salmon in the Japanese market had a
mean-level, cross-price flexibility of 0.57. The real
price of Chilean rainbow trout returned a cross-price
flexibility of 0.19. The indicator variable I91
was
included to prevent the unusually higher observed than
predicted price in 1991 from unduly influencing
coefficient estimates. Industry observers suggest that
early year reports of a chaotic Japanese demand
resulted in many of the European producers shifting
away from feed additives that promoted the rich, red
flesh color that the Japanese market demands. This,
coupled with increased EU demand (resulting from
‘‘aggressive PR campaigns’’), forced Japan to bid
higher than expected for Norwegian trout, all else equal
(Atkinson 1991).
The last of the demand equations, Japanese inverse
demand for Alaska sockeye salmon (equation 6), also
performed well (R2¼ 0.807, and statistical significance
for all coefficient estimates at 5% or 10% significance
levels). The cross-price flexibility between Alaskan
sockeye salmon and Chilean coho salmon, 0.32,
indicates that a 1% increase (decrease) in the price
for Chilean coho salmon leads to a 0.32% increase
(decrease) in the price for Alaskan sockeye salmon
exported to Japan. This equation included a variable
which represented Japanese total per capita imports of
Chilean coho salmon and Chilean and Norwegian
rainbow trout. These aquaculture products started out
relatively new to Japan (during the modeled period),
especially in comparison with sockeye salmon which
has historically been Japan’s most important salmon
import. As the lagged quantity of the sum of farmed
salmon and rainbow trout exports to Japan increases by
1% at the mean, the price of Alaskan sockeye salmon
decreases by 0.37%, indicating a strong movement
between the market buildup of Chilean coho salmon
and Chilean and Norwegian rainbow trout and the
decreased Japanese willingness to pay for Alaskan
sockeye salmon. This is in addition to the effect that
increased aquaculture production has had on price and
captures the dynamically changing market shares by
importers who became evermore accustomed to
Chilean coho salmon and Chilean and Norwegian
rainbow trout over this time period. An indicator
variable was included to prevent the model from
grossly overestimating Japanese demand for Alaskan
sockeye salmon in 1991. The reason for the model’s
tendency to overestimate demand in 1991 is not
entirely clear. However, this was the year of the big
salmon price drop and has been the focus of previous
research (e.g., Herrmann 1992). Price decreases during
this time period led fishermen to charge that Japanese
purchasers and Bristol Bay processors colluded to fix
exvessel prices, a charge that the plaintiffs were unable
to substantiate in court.
Salmon Allocation Equations
The Chilean allocation of Atlantic salmon to the
USA (equation 7) explained virtually all the variation
in the allocated salmon (R2 ¼ 0.984). Most of the
attributed allocation is due to increases in total exports,
where a 1% increase in total Chilean Atlantic salmon
exports leads to a 0.84% increase in allocation to the
USA. Although the U.S. market has been the leading
outlet for Chilean Atlantic salmon exports, even during
this period, nearly one-third of Chile’s Atlantic salmon
exports have gone into other markets. This suggests
that some price arbitration may take place. It was
estimated that a 1% increase in the price of Chilean
Atlantic salmon exported to the USA would lead to a
0.17% increase in the quantity exported to the USA,
while an increase in the price of Chilean Atlantic
salmon to markets outside the USA would decrease
allocation to the USA by 0.26%. An indicator variable,
I03, was included to correct initial underestimates of
Chilean exports of Atlantic salmon to countries other
than the USA in 2003. The reasons for this temporary
dip in the Chilean allocation of Atlantic salmon to the
1790 WILLIAMS ET AL.
USA are unclear and probably reflect activities in
European markets that were not represented in our
model.
Variations in Canadian allocations of Atlantic
salmon to the USA (equation 8) are almost entirely
explained (R2 ¼ 0.998) by just two variables. This is
not surprising since the USA is nearly the sole market
for Canadian exports of Atlantic salmon, having
received nearly 95% of Canada’s Atlantic salmon
production during the time period considered in this
analysis.
The Chilean allocation of coho salmon to Japan
(equation 9) is similar, but with Japan as the sole
purchaser. Again, virtually all variation in allocation is
captured by information about variations in total
Chilean coho salmon exports and the price received
from Japanese importers (R2 ¼ 0.999).
Although all of the allocation equations were
initially specified as linear, the last three allocation
equations were estimated in double-log form to allow
for nonlinear responses and to eliminate serial
correlation in the residuals. All three equations
provided a good fit to the historic data (R2 ¼ 0.999
for equation 10; R2¼0.973 for equation 11; R2¼ 0.973
for equation 12) and statistically significant coefficient
estimates for almost all of the explanatory variables.
In addition to the logarithms of its own export price
and its own total exports, the logarithm of the
allocation of Chilean rainbow trout to Japan (equation
10) includes the logarithm of the previous year’s
percent of total Chilean rainbow trout exported to
Japan. This variable captures the momentum of this
novel commodity as it made inroads into the Japanese
market channels.
The allocation of the logarithm of Norwegian
rainbow trout exports to Japan (equation 11) is similar
to the Chilean rainbow trout allocation equation.
Recall, that of all the products modeled, the allocation
to Japan of Norwegian rainbow trout was the lowest in
percentage terms, just 58% (much of this being due to
recent increases in the allocation of Norwegian
rainbow trout to Russia).
The logarithm of the allocation of Alaskan sockeye
salmon to Japan (equation 12) was modeled using the
logarithms of own price and total landings as
explanatory variables. Additionally, the equation
included the logarithm of the previous year’s export
quantity, which helped to explain some of the current
Japanese imports. This was expected as the relationship
between Alaskan exporters and Japanese importers,
although well established, has undergone rapid changes
during this time period.
In conclusion, for the allocation equations there are
some features that hold across all equations. By far the
most influential variable in explaining the allocation of
a particular species to its principal export market is the
volume of production as indicated by total salmon or
trout exports or landings. In the six allocation
equations, the elasticity of total exports (or landings)
with respect to quantity ranged from 0.84 to 1.03,
associated P-values showing statistical significance at a1% level. The allocated goods export price was
significant in most cases at a 10% level, but less
influential and with price elasticities ranging between
0.05 and 0.41. Finally, in all equations, the variables
explained a large portion of the variation in the
allocated good (or the transformed good) as exhibited
by R2-values ranging between 0.973 and 0.999.
Sockeye Salmon Exvessel Price Equation
Equation (13) was constructed to explore factors that
directly affect the exvessel price of Alaskan sockeye
salmon. The exvessel price equation performed
acceptably as R2 was equal to 0.778 and coefficient
estimates were statistically significant at 1% and 3%significance levels. Despite recent declines in the
fraction of Alaskan sockeye salmon exported to Japan,
the price elasticity associated with variations on the
Japanese real import price is near unity indicating that,
in percentage terms, there is a nearly one-to-one
relationship between the price of Alaskan sockeye
salmon exports to Japan and exvessel prices in Alaska.
Alaskan landings of sockeye salmon going into other
markets and product forms is also a statistically
important determinant of exvessel prices, and the
elasticity of landings (going to products and places
other than the Japanese market for fresh or frozen
sockeye salmon) is �0.17, indicating that, all else
equal, as landings increase (decrease) by 1%, the
Alaska exvessel price for sockeye salmon will decrease
(increase) by 0.17%. Finally, there is a positive
relationship between the previous year’s share of the
exvessel price to the Japanese export price and the
current exvessel prices.
Goodness of Fit
Historical simulation GOF statistics are the preferred
means of assessing the predictive accuracy of a system
of equations over the estimation period. Evaluating each
equation separately does not provide information on
how they interact in the modeled system. Individual
equation GOF statistics are used to incorporate
intertemporal and intratemporal linkages, which exist
within the market response model. These interdepen-
dencies are explicitly incorporated into the dynamic
model simulation, where each of the equations in the
market response model is solved in its reduced form.
Thus, model simulation provides a more robust measure
ALASKAN SOCKEYE SALMON MARKETS 1791
of actual model performance. Model simulations were
conducted using the Newton algorithm in SAS (SAS
2007). The historic dynamic simulation was performed
on the system of equations for the 1990 to 2005 period.
The GOF statistics are reported in Table 6.
The GOF statistics include the correlation between
the actual and predicted values of the endogenous
variables (r), the mean percent error, the root mean
square percent error, and the Theil inequality coeffi-
cient (U1). The Theil inequality coefficient is a
measure of forecast accuracy, where 0 is a perfect
forecast, 1 is a forecast that performs no better than a
naı̈ve forecast (a forecast of repetition of the previous
time period’s value), and a value greater than 1 is
worse than the naı̈ve forecast.
The GOF statistics show that the model fits the
historical data fairly well. The lowest correlation
coefficient was 0.62 (Alaskan sockeye salmon export
price to Japan), and three equations had correlation
coefficients close to 1.00 (U.S. demand for Canadian
Atlantic salmon, Japanese demand for Chilean exports
of coho salmon, and Japanese demand for Chilean
exports of rainbow trout). The variable of most
importance for our study, Alaska sockeye salmon
revenue, had a correlation coefficient of 0.91. The
mean absolute percentage errors are within a reason-
able range, from 1.5% for Canadian exports of Atlantic
salmon to the USA to 20.6% for Alaskan sockeye
salmon exvessel prices and Alaska sockeye salmon
revenue. The CVs for the same equations are 2.3% and
29.6%. The Theil U1 statistics indicate a reliable
historical forecast, the lowest value being 0.02 (for
Canadian exports of Atlantic salmon to the USA and
Chilean exports of coho salmon to Japan) and the
highest, 0.21 (for Alaskan sockeye salmon exvessel
prices).
Sensitivity Analyses
The estimated model can be used to explore
probable changes in the dependent variables in
response to changes in exogenous variables. We used
the model to explore the market consequences of
variations in the quantity of Atlantic salmon, coho
salmon, and rainbow trout produced in Chile and to
explore the shape of the exvessel demand and revenue
curves for Alaskan sockeye salmon. In the first
scenario, we simulated potential changes in Chilean
production of Atlantic salmon, coho salmon, and
rainbow trout and examined the likely effects on
exvessel prices and revenues in Alaska’s sockeye
salmon fisheries. These simulations should not be
construed as predictions of impending changes in the
U.S. and Japanese markets; rather, the simulations
characterize what could have occurred to salmon
markets if Chile had suddenly increased its output of
salmon and rainbow trout in 2005 and disposed of the
additional production in markets as they existed in
2005. In the second scenario, we varied Alaska
sockeye salmon production to trace out exvessel
demand and revenue curves. The results are character-
izations of price changes that would have occurred in
2005 without the markets having time to adjust or for
exporting countries to develop new markets. So, these
price changes, particularly in regard to changing
farmed salmon and rainbow trout supplies, represent
an upper bound on likely long-term price changes.
Scenario 1: Increased Chilean Production of CohoSalmon and Rainbow Trout
Alaska’s sockeye salmon, once the unquestioned
champion in the Japanese salmon market, is now
dwarfed by Japanese imports of Chilean coho salmon
and rainbow trout, and Norwegian Atlantic salmon and
rainbow trout (see Figure 4). For example, in 2005,
Chile’s combined exports of coho salmon and rainbow
trout to Japan were 300 million lb, while Alaska’s 2005
sockeye salmon exports were a mere 79 million lb.
The combined total exports of Chilean coho salmon
and rainbow trout to all countries for 2005 was 338
million lb, and therefore each 1% increment represents
an increase in total coho salmon and rainbow trout
exports (mainly being exported to Japan) of 3.4 million
lb. The simulated effect on these increased exports of
Chilean coho salmon and rainbow trout to Japan are
shown in Tables 7–9. For each 1% increase in exports
of Chilean coho salmon, it is estimated that the
exvessel price of Alaskan sockeye salmon would
decrease by approximately US$0.0017/lb, average
TABLE 6.—Goodness-of-fit statistics; MA%E ¼ mean
percent error, RMS%E ¼ root mean percentage error or
coefficient of variation (CV¼1003SD/mean), r¼ correlation
between the observed and predicted values, U1 ¼ Theil
inequality coefficient (see Table 3 for variable definitions).
Equation Variable MPE RMSPE r U1
1 Atl�PðCLPÞCL!USt 6.0 8.4 0.85 0.09
2 Atl�PðCA$ÞCA!USt
4.3 5.7 0.87 0.05
3 coho�PðCLPÞCL!JPt 11.9 14.6 0.76 0.135
4 rt�PðCLPÞCL!JPt 9.7 12.2 0.84 0.11
5 rt�PðNOKÞNO!JPt 8.3 9.6 0.80 0.10
6 sock�PAK!JPt 9.9 11.7 0.62 0.123
7 Atl �QCL!USt 11.1 21.6 0.99 0.06
8 Atl �QCA!USt 1.5 2.3 1.00 0.02
9 coho �QCL!JPt 3.4 6.3 1.00 0.02
10 rt �QCL!JPt 2.9 3.3 1.00 0.03
11 rt �QNO!JPt 15.2 18.0 0.97 0.17
12 sock �QAK!JPt 8.2 10.3 0.98 0.11
13 sock�PAKexvt 20.6 29.6 0.65 0.21
14 sock�TRAKexvt 20.6 29.6 0.92 0.18
1792 WILLIAMS ET AL.
exvessel revenues decreasing by $0.46 million. For
each 1% increase in exports of Chilean rainbow trout, it
is estimated that the exvessel price of Alaskan sockeye
salmon would decrease by approximately $0.0012/lb
and that average exvessel revenues would decrease by
approximately $0.34 million. In combination, each 1%
increase in Chilean rainbow trout and coho salmon
exports would have reduced Alaskan sockeye salmon
exvessel prices by approximately $0.0030/lb and
Alaskan sockeye salmon exvessel revenues by approx-
imately $0.80 million.
Although these estimated losses to the Alaskan
sockeye salmon fishery are not trivial, they are small
in comparison with the historical effects that salmon
aquaculture has had on markets for Alaskan salmon,
especially as it is noted that an additional 25% increase
in Chilean exports to Japan would be roughly
equivalent to the current exports of Alaskan sockeye
salmon to Japan. Moreover, these estimates are upper
bounds. Actual losses would be somewhat smaller if the
increases were anticipated and sellers had time to
develop new markets. With 2005 Chilean coho salmon
and rainbow trout exports to Japan equivalent to about
four times the level of Alaskan sockeye salmon exports
to Japan, our findings suggest that the residual market
share held by Alaskan sockeye salmon in Japan may not
be as vulnerable to increased exports of Chilean coho
salmon and rainbow trout as it has been in the past.
Indeed, Alaskan exports of sockeye salmon to Japan
have stabilized since 1998 at around an average of 62.3
million lb, while the previous 8-year period (1990 to
1997) average for exports was 172.5 million lb.
Scenario 2: Alaska Sockeye Salmon Exvessel Demand
and Revenue Curves
Sockeye salmon is the most valuable and most
abundant of the high-valued salmon species harvested
in Alaska. In 2005, Alaskan sockeye salmon fisheries
yielded 267 million lb and generated $194 million in
exvessel revenue at an exvessel price of $0.73/lb. The
estimated demand and revenue curves are presented in
Figure 6. The exvessel demand curve shows prices
varying from $0.51/lb for sockeye salmon landings of
346.9 million lb to $0.98/lb for sockeye salmon
landings of 186 million lb. The model indicates that
2005 Alaskan exvessel revenues from sockeye salmon
would have been maximized at 258.4 million lb of
landings with an associated exvessel price of $0.75/lb
and exvessel revenue of $193.8 million. This is nearly
identical to the actual 2005 landings of 266.9 million
lb, exvessel price of $0.73/lb, and exvessel revenue of
$193.7 million. It is also very similar to the 2007
landings of 276.9 million lb, nominal exvessel price of
$0.75, and nominal exvessel revenue of $206.4 million,
particularly if the 2007 price and revenue are
discounted to 2005 price levels. This indicates, all else
equal, that recent Alaskan landing levels of sockeye
salmon are near the peak of the total revenue curve and
TABLE 8.—Simulated effects of increased Chilean exports
of rainbow trout on exvessel prices and revenues for Alaskan
sockeye salmon, 2005.
%Increase
Rainbow troutexports
(million lb)
Sockeye salmonprice
(US$/lb)
Sockeye salmonrevenue
(million US$)
0 164.9 0.7257 193.681 166.5 0.7245 193.352 168.2 0.7232 193.023 169.8 0.7220 192.684 171.5 0.7207 192.345 173.1 0.7194 191.9910 181.4 0.7127 190.2115 189.6 0.7057 188.3320 197.9 0.6982 186.3425 206.1 0.6903 184.2230 214.4 0.6819 181.98
TABLE 9.—Simulated effects of combined increases in
Chilean exports of coho salmon and rainbow trout on exvessel
prices and revenues for Alaskan sockeye salmon, 2005.
%Increase
Total exports(million lb)
Sockeye salmonprice
(US$/lb)
Sockeye salmonrevenue
(million US$)
0 337.6 0.7257 193.681 341.0 0.7228 192.892 344.4 0.7198 192.103 347.7 0.7168 191.294 351.1 0.7137 190.485 354.5 0.7106 189.6510 371.4 0.6946 185.3715 388.2 0.6775 180.8020 405.1 0.6590 175.8825 422.0 0.6390 170.5330 438.9 0.6169 164.64
TABLE 7.—Simulated effects of increased Chilean exports
of coho salmon on exvessel prices and revenues for Alaskan
sockeye salmon, 2005.
%Increase
Coho salmonexports
(million lb)
Sockeye salmonprice
(US$/lb)
Sockeye salmonrevenue
(million US$)
0 172.7 0.7257 193.681 174.4 0.7240 193.222 176.2 0.7223 192.773 177.9 0.7206 192.324 179.6 0.7189 191.865 181.3 0.7172 191.41
10 190.0 0.7088 189.1615 198.6 0.7004 186.9120 207.2 0.6920 184.6825 215.9 0.6837 182.4630 224.5 0.6753 180.23
ALASKAN SOCKEYE SALMON MARKETS 1793
that incremental variations from these catch levels are
unlikely to substantially impact exvessel prices or
revenues, while substantial increases of decreases from
these levels of landings can be expected to be
accompanied by substantial decreases in total exvessel
revenue. The relationship between prices, revenues,
and landings are subject to change over time as factors
outside of the model, such as potential new markets,
influence salmon markets. Likewise, the current
worldwide recession could negatively impact salmon
prices in ways the model cannot predict.
Discussion
Over the past 25 years, the wild Alaskan salmon
fishing industry has faced intense competition in key
domestic and international markets and has experi-
enced dramatic decreases in real exvessel prices. Most
of this reduction in exvessel prices can be attributed to
the increased volume of Atlantic salmon, coho salmon,
and rainbow trout produced in aquaculture. Alaska has
gone from supplying as much as 64% of the world’s
supply of high-value salmon (Chinook salmon, sock-
eye salmon, and coho salmon) in 1983 to supplying as
little as 5% in 2002. These changes have been
particularly difficult to weather because Alaska’s
fisheries have operated as limited-entry derbies that
have induced fishermen to invest in costly modifica-
tions to vessels and gear, and to bid up the price of
limited access permits to levels that could not be
afforded when salmon prices declined. Many studies
have documented the effects of ever-increasing quan-
tities of salmon and rainbow trout from aquaculture on
exvessel prices offered for salmon harvested in capture
fisheries (Anderson 1985b; Asche 1997; Asche et al.
1999; Herrmann 1993, 1994; Herrmann et al. 1993).
This study has focused on Alaskan sockeye salmon and
indicates that while the downward pressure on
Japanese import prices and on exvessel prices for
Alaskan sockeye salmon has slackened but not abated,
it appears that Alaskan sockeye salmon may now
occupy a niche position in the Japanese market and
may be somewhat insolated from the extreme down-
ward pressures of the last 25 years.
The sensitivity analyses indicate that, without
concomitant increases in consumer demand, substantial
increases in Chilean coho salmon and rainbow trout are
likely to further decrease exvessel prices and revenues
for sockeye salmon fishermen in Alaska. Nevertheless,
the relative magnitude of these impacts is likely to be
substantially less than the relative magnitude of
impacts experienced in the 1990s. It is estimated that
for each 5% increase in Japanese imports of Chilean
coho salmon, the exvessel price of Alaska sockeye
salmon will decrease by approximately $0.0084/lb and
exvessel revenues will decrease by $2.25 million.
Similarly, for increases in the Japanese imports of
Chilean rainbow trout, it is estimated that Alaskan
sockeye salmon exvessel price will decrease by
approximately $0.0063/lb for the first 5% increase,
with an associated decrease in exvessel revenue
decrease of $1.69 million. In combination, the first
5% increase in Chilean exports of coho salmon and
rainbow trout can be expected to decrease Alaskan
sockeye salmon exvessel prices by $0.0151/lb, with an
associated decrease in the exvessel revenues in the
Alaskan sockeye salmon fishery of approximately
$4.03 million.
The estimated exvessel demand curve for Alaskan
sockeye salmon indicates moderate price sensitivity,
but over a rather wide range of landings. It is estimated
that prices will vary from $0.51/lb for sockeye salmon
landings of 346.9 million lb to $0.98/lb for sockeye
salmon landings of 186 million lb. The associated total
exvessel revenue curve indicates that exvessel revenues
vary relatively little over a wide range of landings. The
maximum total revenue is estimated at $193.8 million
at landings of 258.4 million lb. For landings of 186
million lb, the estimated exvessel revenue is $182.5
million, and for landings of 346.9 million lb, total
exvessel venue is $176.8 million. This suggests that
FIGURE 6.—Simulated 2005 Alaskan sockeye salmon exvessel demand and revenue curves.
1794 WILLIAMS ET AL.
sockeye salmon exvessel revenue is not very sensitive
to changes in landings that are within the range of
recent guideline harvest levels.
With all of the changes that the Alaskan wild salmon
industry has endured over the past two and a half
decades, the model suggests that future impacts of
increased aquaculture production are unlikely to be as
substantial as were the impacts felt in the 1990s.
Although revenues are highly unlikely to approach the
highs (in real terms) of the late 1980s, they may have
finally leveled off a bit. It may be that Alaska wild
salmon marketing has been successful in securing
loyal, niche markets that provide some insulation from
the rapid price declines experienced during the 1990s.
The model presented in this paper suggests that
under current market conditions, to the extent that
Alaska’s escapement-based management regime max-
imizes expected sustainable yields, it also maximizes
expected sustainable revenues. However, in years with
particularly strong runs of sockeye salmon, allowing
escapements to exceed the upper-bound escapement
objective could help avert undesirable decreases in
exvessel price. A more likely scenario is for the
development of new niche markets for Alaskan
sockeye salmon that would insulate exvessel prices
from unusually strong runs without the need to reduce
harvest. Nevertheless, measures taken to bolster
exvessel prices will not address the root of economic
failure in the Alaskan salmon fisheries: hundreds of
vessels racing one another for a finite number of fish.
While it is convenient for fishermen and managers to
ascribe the indigence of the salmon fishery to the
onslaught of farmed salmon, the fundamental problem
is the primeval allocation structure that encourages
spendthrift investment in vessels and gear (Pearse and
Wilen 1979; Karpoff 1987). The problem is with an
allocation structure that drives up the cost of catching
fish until all profit is dissipated (Schelle et al. 2004;
Knapp et al. 2007). Regardless of the downward
pressure on Alaska salmon prices because of increased
farmed salmon production, the long-run profitability of
Alaska’s salmon fisheries will not improve until this
archaic allocation regime is replaced with an individual
or cooperative catch–share system (Knapp 2008).
Acknowledgments
This paper is a result of work funded by the Alaska
Sea Grant College Program project ASG05-00. We
also thank the anonymous reviewers as well as the
associate editor and editor for the North American
Journal of Fisheries Management for their very
thorough comments. All opinions are the authors’
and do not necessarily represent the Alaska Sea Grant
College Program.
References
ADFG (Alaska Department of Fish and Game). 2007. Salmon
fisheries in Alaska: catch, effort, and value information.
Available: www.cf.adfg.state.ak.us/. (March 2007.)
Anderson, J. L. 1985a. Private aquaculture and commercial
fisheries: bioeconomics of salmon ranching. Journal of
Environmental Economics and Management 12:353–
370.
Anderson, J. L. 1985b. Market interactions between aquacul-
ture and the common property commercial fishery.
Marine Resource Economics 2:1–24.
Asche, F. 1997. Trade disputes and productivity gains: the
curse of farmed salmon production. Marine Resource
Economics 12:67–73.
Asche, F., T. Bjørndal, and J. A. Young. 2001. Market
interactions for aquaculture products. Aquaculture Eco-
nomics and Management 5:303–318.
Asche, F., H. Bremnes, and C. R Wessells. 1999. Product
aggregation, market integration, and relationships between
prices: application to world salmon markets. American
Journal of Agricultural Economics 81:588–581.
Asche, F., A. G. Guttormsen, T. Sebulonsen, and E. H.
Sissener. 2005. Competition between farmed and wild
salmon: the Japanese salmon market. Agricultural
Economics 33:333–340.
Atkison, B. 1991. North European trout. BANR: Bill
Atkinson News Report: 1.
Atkison, B. 1996. Chilean coho. BANR: Bill Atkinson News
Report: 1–3.
Bjørndal, T., and K. Aarland. 1999. Salmon aquaculture in
Chile. Aquaculture Economics and Management 3:238–
253.
Clayton, P. L., and D. V. Gordon. 1999. From Atlantic to
Pacific: price links in the US wild and farmed salmon
market. Aquaculture Economics and Management 3:93–
104.
DSI (Data Services and Information). 2007. OECD statistics.
Available: www.statistischedaten.de/_shop/campus2.
php. (March 2007).
Economagic. 2007. Economic times series. Available: www.
economagic.com. (March 2007).
FAO (Food and Agriculture Organization of the United
Nations). 2007. Fishery statistical collections. Available:
www.fao.org/fishery/statistics. (March 2007.)
Herrmann, M. 1992. The Alaska salmon price crash of 1991.
Arctic Research of the United States 6(2):34–36.
Herrmann, M. 1993. Using an international econometric
model to forecast Alaska salmon revenues. Marine
Resource Economics 8:249–271.
Herrmann, M. 1994. The Alaska salmon fishery: an industry
in economic turmoil. Journal of Aquatic Food Product
Technology 3(3):5–22.
Herrmann, M., and B.-H. Lin. 1988. The demand and supply
of Norwegian Atlantic salmon in the United States and
the European Community. Canadian Journal of Agricul-
tural Economics 36:459–472.
Herrmann, M., R. C. Mittelhammer, and B.-H. Lin. 1992.
Applying Almon-type polynomials in modeling season-
ality of the Japanese demand for salmon. Marine
Resource Economics 7:3–14.
Herrmann, M., R. C. Mittelhammer, and B.-H. Lin. 1993a. An
international econometric model for wild and pen-reared
ALASKAN SOCKEYE SALMON MARKETS 1795
salmon. Pages 187–216 in U. Hatch and H. Kinnucan,
editors. Aquaculture: models and economics. Westview
Press, Boulder, Colorado.
Herrmann, M., R. C. Mittelhammer, and B.-H. Lin. 1993b.
Import demands for Norwegian farmed Atlantic salmon
and wild Pacific salmon in North America, Japan, and the
EEC. Canadian Journal of Agricultural Economics
41:111–124.
Karpoff, J. M. 1987. Suboptimal controls in common resource
management: the case of the fishery. Journal of Political
Economy 95:179–194.
Kinnucan, H. W., and Ø. Myrland. 2002. The relative impact
of the Norway–EU salmon agreement: a midterm
assessment. Journal of Agricultural Economics 53:195–
220.
Kinnucan, H. W., and Ø. Myrland. 2005. Effects of income
growth and tariffs on the world salmon market. Applied
Economics 37:1967–1978.
Knapp, G., C. A. Roheim, and J. L. Anderson. 2007. The great
salmon run: competition between wild and farmed
salmon. World Wildlife Fund, Washington, D.C.
Knapp, G. 2008. The Chignik salmon cooperative. Pages 335–
348 in R. Townsend, R. Shotton, and H. Uchida, editors.
Case studies in fisheries self-governance. FAO (Food and
Agriculture Organization of the United Nations) Fisher-
ies Technical Paper 504.
Lin, B.-H., M. Herrmann, Y. Y. Lin, and R. C. Mittelhammer.
1989. Forecasting the price of farmed Atlantic salmon: an
integrated econometric and time series approach. Agri-
business 5:477–488.
Ministry of Finance. 2006. Trade statistics of Japan. Available:
www.customs.go.jp/. (September 2006.)
NMFS (National Marine Fisheries Service). 2007. Foreign
trade information online database. Available: www.st.
nmfs.gov/st1/trade. (March 2007).
Olson, T. K., and K. R. Criddle. 2008. Industrial evolution: a
case study of Chilean salmon aquaculture. Aquaculture
Economics and Management 12:1–18.
Pearse, P. H., and J. E. Wilen. 1979. Impact of Canada’s
Pacific salmon fleet control program. Journal of the
Fisheries Research Board of Canada 36:764–769.
Schelle, K., K. Iverson, N. Free-Sloan, and S. Carlson. 2004.
Bristol Bay salmon drift gill-net fishery optimum number
report. Commercial Fisheries Entry Commission, Report
04-03N, Juneau, Alaska.
SAS (Statistical Analysis System). 2007. SAS, version 5.1 for
Windows. SAS Institute, Cary, North Carolina.
Statistics Canada. 2007. Imports and exports (international
trade statistics). Available: cansim2.statcan.ca/. (March
2007.)
Statistics Norway. 2007. Fishing and fish farming. Available:
www.ssb.no/fiskeri_havbruk_en/. (January 2007.)
Web Japan. 2007. Japan commodities. Available: web-japan.
org/stat/index.html. (January 2007.)
Wessells, C. R., and D. Holland. 1998. Predicting consumer
choices for farmed and wild salmon. Aquaculture
Economics and Management 2:49–59.
1796 WILLIAMS ET AL.
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