Estimating Elasticity of Demand for Fresh
Bluefin Tuna in the World’s Largest Fish Market
Kanae Tokunaga *
Abstract
This study estimates the demand elasticity for Bluefin tuna in the Japanese market
by using an instrumental variables approach. Variability in the catch by purse seine
vessels and fluctuations in the auctioned volume at Tsukiji Market are used as supply
shocks. In 2014, the International Union for the Conservation of Nature placed Pacific
Bluefin tuna the Red List following a sharp decline in its stock. This led to an increased
pressure to reduce catch. This study aims to show how a catch reduction would impact
prices. This study finds an elastic demand, which may be problematic because a catch
reduction would not be compensated by an increase in price.
1
Introduction
Japan is the largest consumer of tuna in the world. Over a quarter of global tuna catch is
consumed in Japan annually (OPRT, 2004). Among all tuna species, Bluefin tuna has the
highest price per kilogram. It is reported that the Japanese market accounts for 80% of the
global Bluefin tuna trade (McCurry, 2015).
In recent years, a growing concern for Bluefin tuna stock triggered a discussion about
tightening the regulations for Bluefin tuna fisheries. Pacific Bluefin tuna stock was placed
on the Red List by the International Union for Conservation of Nature (IUCN) following an
assessment that concluded that the stock is threatened and vulnerable to extinction (IUCN,
2014). In 2015, the Fishery Agency of Japan introduced a catch limit on juvenile tunas (i.e.
individuals that weighs less than 30 kilograms). While currently, there is no limit posted
on the catch of matured Bluefin tuna, there is an increasing pressure to decrease the catch
in order to conserve the stock.
* [email protected]
1
This study estimates the price elasticity of demand for fresh Bluefin tuna in the
Japanese market. There is a growing interest in understanding the implications of fishery
management on the seafood market. Yet, the limited availability of market data and
the complexity of fishery harvesting behaviors pose a challenge to conducting thorough
analysis. In this study, we adopt the instrumental variables approach and use auction
data from the Tsukiji Central Wholesale Market. Unlike agricultural commodities such as
grains and legumes, storability is limited for fresh seafood products. While tunas are often
frozen or canned, these products are treated differently from fresh products in the market.
Because Bluefins are usually consumed raw, the landed tunas are usually transported to
the wholesale market within a day. All of these reasons combined make fresh Bluefin tuna
the ideal candidate for the application of instrumental variables to estimate demand.
Among other existing studies that estimate the price elasticity of demand by applying
the instrumental variables approach, the most notable and relevant to this paper is Angrist
et al. (2000). The study uses weather as an instrument for supply shocks to measure the
elasticity of demand for Whitefish in the Fulton Fish Market in New York. The use of
weather and climate variables are perhaps more common in the study of agricultural
commodities, as described by Angrist and Krueger (2001).
Weather variables, such as precipitation and wind speed, serve as good instruments for
those fish species that are caught by methods sensitive to weather conditions. However,
they are not ideal for the estimation of Bluefin tuna demand for two reasons. First, purse
seine vessels, which account for a significant amount of Bluefin catch within Japanese
waters, are large enough to remain in the water for an average of two weeks. Because
of this, the operation’s sensitivity to weather conditions is difficult to measure. Second,
Bluefin tuna fisheries in Japan operate at various locations and times of year, using various
types of gears. No single domestic fishery operates year-round. This is because tuna is
highly migratory, and localized fisher folks can only target Bluefin tuna on a seasonal
basis. These two characteristics invalidate weather conditions as a potential instrument
for calculating demand elasticity.
Instead, this study uses two types of supply shocks for the estimation of the price
elasticity of demand for fresh Bluefin tuna. First, intra- and inter-annual variabilities in
purse seine landings are used as quasi-experiments that shift fresh Bluefin tuna supply.
Purse seine Bluefin tuna fishing takes place in the summer months. The landed volume by
the purse seine fishery is the largest among all Bluefin tuna fisheries in Japan. Judging by
the fact that 50% of the domestic catch quota for juvnile Bluefin is alloted for the purse
seine fishery, their impacts on the market are significant. Inter-annual variations in purse
seine landings are also considered. Although there is no conclusive study that explains the
2
causes of the variability, it is believed to be due to oceanographic conditions which cause
schools of Bluefin tuna to disperse. This makes it difficult for purse seine vessels to harvest,
which leads to lower landings in the years that the oceanographic conditions persist. We
also use daily and monthly fluctuations as supply shocks. We rely on the characteristics of
the purse seine Bluefin tuna fishery to form a quasi-experiment that shifts supply without
affecting demand.
Second, fluctuations in daily and monthly auctioned volume at Tsukiji Market are used
as a supply shock. To do this, the deviation from the time trend is calculated from daily
and monthly auction data. Because landed fresh Bluefin tuna is auctioned within a day,
fluctuations in landings are reflected in the daily auctioned volume at Tsukiji Market.
This characteristic makes the fluctuations in auctioned volume a suitable variable for
supply shocks. Our method for calculating supply shocks is partly motivated by Roberts
and Schlenker (2013), which estimated elasticities for agricultural commodities. They
calculate yield shocks by deriving deviations from the time trend that is approximated by
a restricted cubic spline. We adopt their approach to derive daily and monthly landing
shocks.
There are alternative methods to estimating demand elasticities for fish. A review
by Asche et al. (2007) indicates the systems of equations approach, such as the Almost
Ideal Demand System (AIDS) by Deaton and Muellbauer (1980) or the Rotterdam System
by Theil (1965) and Barten (1968), to be a common approach. In an analysis of the
Japanese seafood market, Eales et al. (1997) finds an inelastic price response to a change in
consumption by using the AIDS model. In addition, retail demand for tunas in Japan is
found to be inelastic by Wessells and Wilen (1994).
While previous studies that calculate elasticities of demand do not distinguish between
different species of tuna, this study distinctively analyzes the Bluefin tuna market. While
there is evidence for substitutability among tropical tuna species, such as that of Yellowfin
and Bigeye tuna, as presented by the market cointegration analysis of Bose and McIlgorm
(1996), to the author’s knowledge, the substitutability between Bluefin tuna and other
tunas is not yet fully understood. It is beyond the scope of this paper to test for market
cointegration of different species of tunas. Rather, this paper focuses on understanding
fresh Bluefin tuna. At Tsukiji Market, different species of tunas are auctioned separately
and treated as different products. Fresh and frozen products are also auctioned separately
and treated as different products. This indicates there exists a distinct market for fresh
Bluefin tuna. In addition, management measures are usually implemented at a species
level. Hence, a distinction between different species of tuna during market analysis adds
value.
3
2
Background: Bluefin Tuna Fisheries in Japan
In Japan, coastal fishermen target Bluefin tuna by using various types of gears at various
locations. The main gears used are longline, trolling, setnet, and purse seine. Coastal
fishing in Japan is managed by either region-based fishery cooperative associations or by
gear-based associations (Makino and Matsuda, 2005). All of the purse seine vessels that
target Bluefin tuna belong to the purse seine gear association and land in Sakaiminato.
Other gears are managed by local fishery cooperative associations. Because Bluefin tuna is
a migratory species, no fishery targets Bluefin tuna year-round.
While almost all the Bluefin tuna that are caught in the coastal and off-shore waters of
Japan are sold fresh, the catch from distant waters is sold either fresh or frozen. A report
by World Wildlife Fund Japan indicates that the majority of fresh Bluefin are sold via
wholesale markets, while frozen ones are sold directly to supermarket chains and trading
companies without going through wholesale markets1 .
Since 2004, the Bluefin tuna catch by purse seine vessels in the Sea of Japan has
increased dramatically. Purse seiners that target Bluefins operate in the Sea of Japan by
targeting those that congregate for spawning. The identified spawning area is depicted in
Figure 1 in dark blue. Although Pacific Bluefin tuna migrate throughout the Pacific Ocean,
they spawn in just two areas, the larger of which is located near Taiwan and the Okinawa
Islands, and the smaller of which is located in the Sea of Japan.
The spawning of Bluefin tuna in the Sea of Japan takes place in July and August. The
fishing season for purse seine vessels starts at the end of May and lasts until July. From
2011 to 2015, 11 purse seine vessels landed their catch in Sakaiminato Port. In short,
approximately half of the supply of fresh Bluefin caught in Japanese domestic waters is
landed by a small number of vessels at Sakaiminato Port in about a 70-day period each
year.
Bluefin tuna fishing by purse seine vessels started in 1982 (Tottori Prefecture Fisheries
Research Institute, 1995). Because purse seine vessels can only target fish that swim in
a large group, Bluefin was not considered a good target by purse seine fishermen until a
purse seine vessel targetting sardines and mackerel found a congregation of Bluefin tunas
in the Sea of Japan, and later, scientific research found a spawning area nearby. Prior to
this discovery, the majority of Bluefin tuna were caught by longline, pole-and-line and
set-net. As a result of the discovery, Bluefin tuna became a primary target in the summer
months when they congregate to spawn in the Sea of Japan.
1 WWF
Japan (in Japanese) http://www.wwf.or.jp/activities/2009/09/625310.html (Last accessed November 18, 2016)
4
Figure 1: Bluefin Tuna Spawning Ground in the Sea of Japan
Note: Dark blue area shows the Bluefin tuna spawning ground. Spawning ground information was obtained from
the Fisheries Agency of Japan (FAJ, Fishery Agency of Japan, 2014)
In recent years, media reports have surfaced of a conflict between the purse seine
fishery and pole-and-line fishermen. Relatively smaller scale pole-and-line fishermen
argue that an increased harvest of Bluefin tuna by purse seine vessels have caused a stock
decline, which has led to the worsening of their catch (?). While there is no scientific
argument to support this claim, it has put the problem of Bluefin tuna stock decline on
the public radar, resulting in an increase in pressure to tighten the regulation of Bluefin
tuna fisheries.
3
Data
This study uses auction data from the Tsukiji Central Wholesale Market in conjunction
with purse seine landings data. A complete set of variables is listed in Table A1 in the
appendix. Tsukiji data is an under-explored data set, and is only used by a few studies (Ex.
Wakamatsu and Miyata (2016)). Existing papers on the analysis of the Japanese seafood
5
market, in general, used either import-export data compiled by the national government
(Ex. Asche et al. (2005)) or the landings information of Yaizu Port, which is one of the
ports that host distant water vessels (Ex. Bose and McIlgorm (1996)).
Tsukiji Market is the largest wholesale seafood market in the world. The market, as
measured by annual trade volume, is five times larger than the second largest fish market
in the world, the Fulton Fish Market in New York City. The Fulton Fish Market is studied
extensively in Graddy (1995), Angrist et al. (2000), and Graddy (2006). Unlike Fulton Fish
Market, which uses one-on-one price negotiation between a seller and buyer, prices of fish
are determined through auction at the Tsukiji Market. Only fish that did not get sold via
an auction are directed to one-on-one negotiation.
Tsukiji Central Wholesale Market’s daily auction data is compiled and made available to
the public by the Tokyo Metropolitan Government’s Central Wholesale Market Division2 .
The data covers daily auction results from 2004 to the present. There are a few weaknesses
to this data set. First, the sales volume is aggregated for each species, and it does not
allow us to distinguish between the domestic catch and imports. Second, about 20% of the
observations have missing price information. It is worthwhile noting that the daily mode
price, instead of a mean price, is reported.
To overcome the shortcomings of the daily data, monthly summary information from
Tsukiji Market is used3 . This data set includes the total sales volume and the average price
of domestic and imported fish auctioned at Tsukiji Market from 2002 to 2015.
The auctioned volume from the daily data, which includes both the domestic catch and
imports, is adjusted by multiplying the daily total auctioned volume by the ratio of the
domestic catch in the total auctioned volume as calculated from the monthly summary
data. The analysis uses this adjusted daily auctioned volume data instead of the aggregated
data to approximate domestic catch.
In conjunction with the Tsukiji Market data, purse seine landings information is
obtained from the Sakaiminato City Government. For the years from 2011 to 2015, the
date of landing and the landed volume is available for every single landing of Bluefin tuna
by purse seine vessels. To compensate for the short time series of the purse seine landings
information, yearly summary data is also obtained from the Sakaiminato City Government.
This time series covers the years from 1982 to 2015. This yearly summary data includes
the average ex-vessel price, the annual total landed volume, and the number of landings.
Table 1 shows the descriptive statistics of the key variables. The data from the first day
2 Daily auction data (in Japanese) is available from http:www.shijou-nippo.metro.tokyo.jp/SN/SN_
Sui_Nengetu.html
3 Monthly auction data (in Japanese) is available from http://www.shijou-tokei.metro.tokyo.jp/
6
Table 1: Tsukiji Whole Sale Market Data Descriptive Statistics (2004 - 2014 during Sakaiminato Season)
Mean
Daily Max Price in JPY (USD)
Log (Daily Max Price in JPY)
Daily Mode Price in JPY (USD)
Log (Daily Mode Price in JPY)
Daily Sales Vol. (kg)
Log (Daily Sales Vol.)
Std. Dev.
7,683 (75.34)
8.875
2,469 (23.66)
7.703
16,368
9.522
3,040
0.378
1,373
0.446
9,997
0.618
Max
Min
25,000 (239.54)
10.127
13,333 (127.75)
9.498
76,678
11.247
2,700 (25.87)
7.901
608 (5.83)
6.410
1,958
7.580
Figure 2: Purse Seine Bluefin Tuna Landings at Sakaiminato (in metric ton)
of the sales at the Tsukiji Market is dropped, as the prices on that day are significantly
higher than the other days. The first auction of the year is called a celebratory market, and
many auctioneers bid much higher than usual. In the year 2013, the celebratory market
price went up to 700,000 JPY (approximately 7,000 USD) per kilogram.
Figure 2 shows the yearly landings by purse seine vessels. The landed volume of
Bluefin tuna by purse seine vessels increased dramatically in 2004 and 2005, and though
it has since decreased, it remains elevated above previous levels . While the cause of this is
not clear, this increase in landings coincides with an increase in fishing effort. In a typical
year, purse seine fishing of Bluefin tuna starts in late May and lasts until the end of July.
In each year, 8 to 12 purse seine vessels participate in the fishery. Each vessel spends
approximately two weeks at sea. A vessel comes back to the port once it has harvested
7
Figure 3: Purse-Seine Bluefin Tuna Landings at Sakaiminato (Number of Times)
Figure 4: Purse Seine Bluefin Tuna Catch per Fishing Trip (Metric Ton)
8
a reasonable amount of Bluefin tuna, though this amount varies depending on various
factors. As Figure 3 shows, the number of times that the vessels return to port to land
their catch increased by 2.5 times in 2004.
Given this shift in the purse seine vessels’ effort level, we only include the data after
the year 2004 in the analysis that use variations in the purse seine catch as instruments.
Also, because the purse seine Bluefin fishery adopted new voluntary measures to limit the
catch in 2015, we exclude data from the year 2015 in the analysis.
4
Estimation Framework
In the estimation of the price elasticity of demand, the naı̈ve approach would be to regress
quantity against price using the ordinary least square method. However, there are two
problems associated with this approach because the observed quantities and prices are at
the intersection of supply and demand curves. Firstly, because there are more coefficients
than the number of equations, the coefficients will not be identified4 . Second, the ordinary
least square estimator is biased and inconsistent due to the endogeneity problem. To
overcome these problems, an instrumental variables approach can be applied.
The instrumental variables approach works as follows. First, by denoting the log
transformed prices and quantities as q and p, with superscript S and D to denote supply
and demand respectively, along with the error term u and v, the estimation equations for
supply and demand can be expressed as
Supply equation:
qtS = β0 + β1 ptS + γzt + ut
Demand equation:
qtD = α0 + α1 ptD + vt .
In the supply equation, zt represents the exogenous supply shock.
The prices and quantities that we observe in the data are at the intersections of supply
and demand curves. Therefore, we have q∗ = qS = qD and p∗ = pS = pD , where ∗ denotes
equilibrium. By solving the two equations, we have
pt∗ =
β 0 − α0
γ
1
+
zt +
(u − vt ) ≡ δ0 + δ1 zt + t
α1 − β1 α1 − β1
α1 − β 1 t
The right hand side of the equation is the reduced form estimation model. However,
4 The discussion on the identification of the constant price elasticity of demand is detailed in the Appendix
A.
9
this model lacks an economic intuition. A better estimation approach would be to use the
two-stage least square method. The reduced form estimation model serves as a first-stage
estimation model for the two-stage least square estimation. The first stage estimation
model can be expressed as:
p̂t = δ0 + δ1 zt + t .
The second stage estimation model uses the estimated price pˆt so that the second stage
estimation model can be expressed as
qt = α0 + α1 p̂t + σ X + errort .
The basic idea used in this study is similar to the one used in Angrist et al. (2000),
which used weather conditions as exogenous supply shocks. A key qualification for any
instrument used to estimate demand elasticity is that it must be an exogenous supply
shock that shifts the supply curve without affecting the demand curve.
In this study, we use the intra- and inter-annual variations in landings in the purse
seine Bluefin tuna fishery as instruments. There are four important characteristics that
make the instruments valid. First, purse seine landings account for a large portion of
the domestic Bluefin tuna catch. Second, the purse seine fishing season only lasts for
approximately 70 days. Third, a landing by a purse seine vessel is far larger than a landing
by any other gear. Fourth, landings by purse seine vessels do not take place every day of
the season. In the years from 2011 to 2014, there were only 22 landings, on average, per
season. These characteristics indicate that each landing by a purse seine vessel shift the
supply of fresh Bluefin tuna in the Japanese market. We estimate with two variables as
instruments: 1) the landed volume by a purse seine vessel on the previous day, and 2) a
dummy variable indicating whether or not there was a landing by a purse seine vessel on
the previous day.
In addition, inter-annual variations in catch by purse seine vessels serve as a quasiexperiment for supply shocks. From Figure 2, even after the increase in the trips made
by purse seine vessels after the year 2004, the landed volume is low in some years. In
particular, the landings in years 2009, 2010, and 2012 fall below the 1st quintile of the
landings after 2004. In these years, the catch per fishing trip is also low (Figure 4). Because
such drops are most likely due to unknown oceanographic conditions, as previously
discussed, we could clearly rule out the possibility of price influencing this variable. From
this, we estimate price elasticity of demand by using 1) a dummy variable indicating low
catch years, and 2) average annual catch per fishing trip as instruments.
We can also use fluctuations in daily auctioned volume at the Tsukiji Market as in10
Figure 5: Tsukiji Daily Auctioned Volume (in Kilogram)
Note: The colored lines show approximated time trends with spline knots equal to 4 (blue),
9 (red), and 132 (green). The gray shaded area is the 95% confidence interval.
struments. Because of the limited storability for Bluefin tuna, and the fluctuations in the
auctioned volume at Tsukiji Market are most likely due to the fluctuations in landings.
From this, we can calculate deviations from the time trend as a proxy for supply shocks.
To do this, we first calculate the time trend by using the cubic spline method5 .
Figure 5 shows the daily auctioned volume of fresh Bluefin tuna at Tsukiji Market. The
graph shows great variations in daily auctioned volume. The colored lines are time series
trends approximated by using natural cubic splines with spline knots equal to 4 (blue), 9
(red), and 132 (green). The gray shaded area is the 95% confidence interval of the time
trend approximation. The fluctuations are calculated by taking the difference between the
observed data and the time trend.
One of the unique aspects of Bluefin tuna fishing in Japan is that all coastal and offshore Bluefin tuna fisheries are seasonal. This causes a monthly fluctuation in Bluefin
catch. This can be observed clearly in the monthly summary data (Figure 6). Again, the
colored lines show approximated time trends with spline knots equal to 4 (blue), 5 (red),
and 13 (green). The gray shaded area is the 95% confidence interval of the time trend
approximation. By following the same steps used to calculate the daily fluctuations, we
can also calculate the monthly fluctuations to be used as instruments.
In total, six instruments are used to estimate the own-price elasticity of demand. Two
stage least square methods are used to estimate the value by using the six instruments.
5 Pollock (2007) discusses that the spline method is an appropriate strategy to model a local characteristics
of time trends without sacrificing global trend.
11
Figure 6: Tsukiji Monthly Auctioned Volume (in Metric Ton)
Note: The colored lines show approximated time trends with spline knots equal to 4 (blue),
5 (red), and 13 (green). The gray shaded area is the 95% confidence interval.
For the instrumental variables to yield consistent estimates and behave better than the
ordinary least square estimators, instruments need to be correlated with the endogenous
regressor (Murray, 2006). To verify our choice of instruments, two tests are performed.
The weak instrument test performs an F-test on the first-stage regression and checks the
correlation between the instrument and the endogenous regressor. The Wu-Hausman test
checks whether the instrumental variables yield
more consistent results than the ordinary least square estimator.
5
5.1
Results
Main Results
Table 2 shows the reduced form, or first-stage, estimate and the estimated price elasticity
of demand with four instruments. For all the specifications, we limit our analysis to the
purse seine fishing season. The season is determined by the first and the last day of the
landings.
Two of the instruments are created from the characteristic that purse seine landings do
not take place everyday. Column (4) uses the landed volume by purse seine vessels on a
previous day as an instrument. Column (5) uses a dummy variable that indicates whether
there was a landing by a purse seine vessel on a previous day.
The other two instruments are based on the inter-annual variabilities in purse seine
12
landings. We define the years that fell below 1,219 metric tons, those that make up the
first quintile of the annual harvest for the years 2004 to 2014, as the low catch years.
By this definition, the years 2009, 2010, and 2012 are defined as the low-catch years.
Column (7) uses a dummy variable that indicates the low purse seine catch years as
instrument. Column (8) uses annual average landed volume per trip as an instrument.
Because purse seine vessels may come back without filling to the vessel’s storage capacity
when stock conditions are not ideal, this variable would be an appropriate measure for
stock conditions.
The estimated elasticity of demand ranges between -1.247 to -2.163. The adjusted
R square values are low for all models. The adjusted R square value is negative for the
estimation (7). However, this is not of a concern because the adjusted R square values do
not have any statistical meaning in the estimations using the 2-stage least square method
(Wooldridge, 2010).
From the reduced form estimation, we find that the coefficient for the variable of the
landed volume by a purse seine vessel, or vessels, is very small. There are two possibilities
for why this is the case. First, the correlation between this instrument and the endogenous
variable is weak. If this is the case, it poses a serious problem, as the weak correlation
causes the instrumental variable estimator to behave worse than the ordinary least square
estimator (Yogo, 2004). Second, the instrument may not be properly controlled for by
other variables. This is less problematic if we can identify and add sufficient control
variables. Despite the small coefficient value, the variable passes the F-test that tests
for weak instruments. All other instruments pass both the weak instrument and the
Wu-Hausman test, suggesting that the instrumental variable estimator is more consistent
than the ordinary least square estimator.
Table 3 summarizes the results of the same four instrumental variables with varying
specifications to control for confounding factors. The estimated elasticity of demand
ranges from -1.238 to -2.149. The logged daily maximum auction price is included for the
specifications (2), (4), (6) and (8). This variable is included as a proxy for the quality of fish.
A day of the year variable is included to check whether the auctioned volume changes as
the purse seine season progresses. A day of the week variable is included because previous
studies indicate demand differs throughout the week (Ex. Angrist et al. (2000)).
All specifications pass the weak instrument test and the Wu-Hausman test.
13
14
171
0.307
0.303
Observations
R2
Adjusted R2
Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01
NA
NA
7.849∗∗∗
(0.036)
171
0.234
0.230
NA
NA
7.902∗∗∗
(0.044)
−0.465∗∗∗
(0.065)
(2)
411
0.022
0.019
NA
NA
7.660∗∗∗
(0.026)
0.142∗∗∗
(0.047)
(3)
411
0.035
0.032
NA
NA
−0.013∗∗∗
(0.004)
8.110∗∗∗
(0.108)
(4)
171
0.142
0.137
***
***
19.991∗∗∗
(1.269)
171
0.216
0.211
***
***
19.121∗∗∗
(1.388)
X
−1.247∗∗∗
(0.180)
−1.360∗∗∗
(0.165)
X
(6)
(5)
411
−0.679
−0.684
***
***
26.156∗∗∗
(4.736)
X
−2.163∗∗∗
(0.615)
(7)
Instrumental Variable
2SLS
Reduced Form (1st Stage)
OLS
−0.00001∗∗∗
(0.00000)
Weak Instrument Test
Wu-Hausman Test
Constant
PS ave. land. vol. per trip (IV)
PS low catch year (IV)
PS prev. day. land. dum.(IV)
PS prev. day land. vol. (IV)
Price elas. of demand
(1)
Log(Auctioned Volume)
Log(Price)
Dependent Variable:
Table 2: Reduced Form and IV Estimates (No Covariates)
411
−0.033
−0.035
***
***
21.671∗∗∗
(2.926)
X
−1.581∗∗∗
(0.380)
(8)
15
Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01
Observations
R2
Adjusted R2
IV PS land. vol. prev. day
IV PS land. prev. day dum.
IV Low PS catch yr.
IV Annual PS land. per trip
Weak Instrument Test
Wu-Hausman Test
Constant
Saturday
Friday
Thursday
Wednesday
Tuessday
Day of the year
Log(Daily max price)
Elas. of demand
***
***
−0.007∗∗∗
(0.002)
−0.311∗∗
(0.128)
−0.424∗∗
(0.190)
0.173
(0.128)
0.040
(0.134)
0.003
(0.130)
20.325∗∗∗
(1.143)
X
***
***
171
0.364
0.332
−1.255∗∗∗
(0.163)
0.132
(0.106)
−0.007∗∗∗
(0.002)
−0.305∗∗
(0.129)
−0.410∗∗
(0.192)
0.156
(0.130)
0.053
(0.135)
0.016
(0.132)
19.380∗∗∗
(1.360)
X
−1.238∗∗∗
(0.161)
171
0.369
0.342
(2)
(1)
171
0.321
0.292
***
***
X
−0.006∗∗∗
(0.002)
−0.310∗∗
(0.132)
−0.395∗∗
(0.198)
0.175
(0.133)
0.053
(0.139)
0.009
(0.135)
20.764∗∗∗
(1.217)
−1.304∗∗∗
(0.172)
(3)
171
0.308
0.273
***
***
X
−1.330∗∗∗
(0.175)
0.138
(0.111)
−0.007∗∗∗
(0.002)
−0.303∗∗
(0.134)
−0.377∗
(0.201)
0.158
(0.135)
0.067
(0.141)
0.024
(0.138)
19.840∗∗∗
(1.442)
(4)
411
−0.614
−0.642
***
***
X
0.0003
(0.003)
−0.274∗∗
(0.136)
−0.446∗∗
(0.210)
−0.023
(0.137)
−0.077
(0.140)
−0.127
(0.136)
26.128∗∗∗
(4.395)
−2.149∗∗∗
(0.635)
(5)
Log(Auctioned volume)
Dependent Variable:
Table 3: IV Estimates (with Covariates)
411
−0.391
−0.418
***
***
X
−1.995∗∗∗
(0.544)
0.228∗
(0.120)
−0.001
(0.003)
−0.264∗∗
(0.126)
−0.433∗∗
(0.197)
−0.021
(0.127)
−0.054
(0.132)
−0.098
(0.128)
23.188∗∗∗
(3.351)
(6)
411
0.081
0.065
X
***
***
−0.002
(0.002)
−0.270∗∗∗
(0.102)
−0.569∗∗∗
(0.146)
−0.028
(0.103)
−0.115
(0.104)
−0.146
(0.102)
21.863∗∗∗
(2.595)
−1.530∗∗∗
(0.374)
(7)
411
0.142
0.125
X
***
***
−1.464∗∗∗
(0.345)
0.166∗
(0.090)
−0.003
(0.002)
−0.263∗∗∗
(0.099)
−0.550∗∗∗
(0.145)
−0.026
(0.100)
−0.095
(0.102)
−0.123
(0.100)
20.042∗∗∗
(2.167)
(8)
5.2
Fluctuations as Supply Shock
Table 4 summarizes reduced form and instrumental variable estimations with daily fluctuations in auctioned volume as instruments. These specifications include observations from
the years 2004 to 2014 for all days that the data is available6 .
The coefficients for supply shocks (i.e. daily fluctuations) in the reduced form estimation show significant but small values. However, these instruments pass the weak
instrument test. Using fluctuations as supply shocks yields an estimated price elasticity
of demand ranging from -2.603 to -4.793. These estimates are slightly higher in absolute
value than the specifications using the purse seine landings as instruments.
Estimation results from the daily fluctuations in auctioned volume are presented in
Table 5. The results with different covariates and time trend estimates are presented. The
estimated elasticity of demand values are more elastic using these fluctuations than when
using purse seine landings as fluctuations. The values range from -2.343 to -3.591.
The estimates from the specifications that only consider the purse seine season have
lower absolute values. The estimates from the specifications that consider all days, with a
dummy variable that indicates the purse seine seasons are negative and significant. Given
these observations, the results hint that the elasticity of demand may be slightly less elastic
during the purse seine season.
A typical purse seine season normally lasts from the late May to the end of July. The
more inelastic demand estimate during purse seine seasons may be explained by the fact
that the overall seafood supply is low in the summer months. Low overall seafood supply
limits availability of alternatives, which makes the market more dependent on Bluefin
tuna. Indeed, an interview with the local fishery research institute suggests that purse
seine Bluefin tuna fishery markets to fill seasonal void in the seafood market7 .
6 In
the data set, the price information is missing for about 20% of the days.
of the Tottori Prefecture Fishery Division Sakaiminato Office by the author (November 2015)
7 Interview
16
17
Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01
3
2,983
0.164
0.164
No. of spline knots
Observations
R2
Adjusted R2
9
2,983
0.163
0.163
NA
NA
8.158∗∗∗
(0.006)
8.159∗∗∗
(0.006)
NA
NA
−0.00003∗∗∗
(0.00000)
−0.00003∗∗∗
(0.00000)
(2)
132
2,983
0.027
0.027
NA
NA
8.163∗∗∗
(0.009)
−0.00002∗∗∗
(0.00000)
(3)
3
2,344
−3.191
−3.193
***
***
9
2,344
−3.145
−3.147
***
***
30.248∗∗∗
(1.045)
−2.603∗∗∗
(0.128)
−2.619∗∗∗
(0.128)
30.376∗∗∗
(1.048)
(5)
(4)
132
2,344
−12.739
−12.745
***
***
48.123∗∗∗
(4.657)
−4.793∗∗∗
(0.570)
(6)
Instrumental Variable
2SLS
Reduced Form (1st Stage)
OLS
Weak Instrument Test
Wu-Hausman Test
Constant
Supply shock
Elas. of demand
(1)
Log(Auctioned Volume)
Log(Price)
Dependent Variable:
Table 4: Daily Fluctuations as Instruments (No Covariates)
18
4
No
2,344
−2.127
−2.150
No. of spline knots
Only incl. PS season
Observations
R2
Adjusted R2
Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01
X
X
***
***
22.753∗∗∗
(0.788)
4
Yes
411
−1.031
−1.119
X
X
***
***
25.925∗∗∗
(1.777)
−2.484∗∗∗
(0.223)
0.300∗∗
(0.136)
−2.498∗∗∗
(0.123)
0.718∗∗∗
(0.076)
Day of the week FE
Year FE
Weak Instrument Test
Hausman-Wu
Constant
PS season dummy
Log(Daily max P)
Elas. of demand
(2)
(1)
4
No
2,344
−4.892
−4.938
X
X
***
***
−3.591∗∗∗
(0.271)
0.836∗∗∗
(0.111)
−1.222∗∗∗
(0.157)
30.747∗∗∗
(1.752)
(3)
9
No
2,344
−2.001
−2.022
X
X
***
***
22.507∗∗∗
(0.766)
−2.444∗∗∗
(0.119)
0.697∗∗∗
(0.074)
(4)
9
Yes
411
−1.020
−1.108
X
X
***
***
25.887∗∗∗
(1.770)
−2.478∗∗∗
(0.222)
0.298∗∗
(0.136)
(5)
9
No
2,344
−4.531
−4.574
X
X
***
***
−3.477∗∗∗
(0.256)
0.806∗∗∗
(0.106)
−1.166∗∗∗
(0.150)
30.085∗∗∗
(1.666)
(6)
Log(Auctioned Volume)
Dependent Variable:
Table 5: Daily Fluctuations as Instruments (with Covariates)
132
No
2,344
−4.658
−4.700
X
X
***
***
26.777∗∗∗
(1.647)
−3.371∗∗∗
(0.320)
1.062∗∗∗
(0.149)
(7)
132
Yes
411
−0.808
−0.886
X
X
***
***
25.090∗∗∗
(1.726)
−2.343∗∗∗
(0.221)
0.272∗∗
(0.129)
(8)
132
No
2,344
−4.690
−4.735
X
X
***
***
−3.527∗∗∗
(0.343)
0.819∗∗∗
(0.123)
−1.191∗∗∗
(0.188)
30.379∗∗∗
(2.129)
(9)
Estimation results from monthly fluctuations in auctioned volume are presented in
Table 6. The estimated elasticity of demand values form a narrow range from -1.887 to
-1.938. The results of the weak instrument test show that the instruments pass for all
specifications. The results of the Wu-Hausman test indicate that the instrumental variables
are consistent, while the ordinary least square estimator would be inconsistent, thereby
supporting the use of monthly fluctuations as instruments.
The estimated values of elasticity of demand are close to the estimated values from the
specifications using purse seine landings as instruments.
19
20
Note: ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01
NA
156
0.515
0.512
No. of spline knots
Observations
R2
Adjusted R2
NA
156
0.517
0.514
NA
NA
3,537.282∗∗∗
(47.431)
3,536.606∗∗∗
(47.508)
NA
NA
−0.006∗∗∗
(0.0005)
−0.006∗∗∗
(0.0005)
(2)
NA
156
0.517
0.514
NA
NA
3,535.190∗∗∗
(47.415)
−0.006∗∗∗
(0.001)
(3)
4
156
0.231
0.226
***
***
5
156
0.231
0.226
***
***
27.867∗∗∗
(1.454)
−1.938∗∗∗
(0.179)
−1.936∗∗∗
(0.179)
27.854∗∗∗
(1.455)
(5)
(4)
13
156
0.251
0.246
***
***
27.430∗∗∗
(1.430)
−1.884∗∗∗
(0.176)
(6)
4
156
0.604
0.567
X
***
***
26.865∗∗∗
(1.081)
−1.887∗∗∗
(0.132)
(7)
Instrumental Variable
2SLS
Reduced Form (1st Stage)
OLS
Year FE
Weak Instrument Test
Wu-Hausman Test
Constant
Supply shock
Log(Price)
(1)
Log(Auctioned Volume)
Log(Price)
Dependent Variable:
Table 6: Monthly Fluctuations as Instruments
5
156
0.604
0.567
X
***
***
26.866∗∗∗
(1.081)
−1.888∗∗∗
(0.132)
(8)
13
156
0.604
0.567
X
***
***
26.865∗∗∗
(1.081)
−1.887∗∗∗
(0.132)
(9)
6
Conclusion and Discussion
This study estimated the elasticity of demand for fresh Bluefin tuna in Japan by using the
instrumental variables approach. Because Japan is the largest consumer of Pacific Bluefin
tuna, understanding the characteristics of Japanese demand for Bluefin helps to predict
the overall market consequences of tighter conservation measures.
In the estimates that used variations in purse seine landings as supply shock instruments, the estimated constant own-price elasticity of demand ranged from -1.247 to
-2.163, depending on the specifications. In the estimates that used the daily fluctuations in
the auctioned volume at the Tsukiji Market as supply shock instruments, the estimated
constant own-price elasticity of demand ranged from -2.444 to -4.739, when we did not
control for the purse seine season. The results from the specifications that controlled
for the purse seine season indicated slightly less elastic demand during the purse seine
season. In the estimates that used the monthly fluctuations in the auctioned volume at the
Tsukiji Market as supply shock instruments, the estimated constant own-price elasticity of
demand ranged from -1.887 to -1.938.
To the author’s knowledge, there is currently no estimate of the elasticity of global
demand for Bluefin tuna. This study could reasonably serve as an approximation of the
elasticity of global demand as the Japanese market accounts for 80% of the fresh Bluefin
tuna traded in the world.
One previous study found the retail price elasticity of demand for tuna to be inelastic
(Wessells and Wilen, 1994). Yet another study by Sun et al. (2015) found unit elastic
demand for Skipjack and Yellowfin tuna in the canned tuna market. Because Bluefin tuna
has the highest price of any tuna, and can be perceived as a luxury food item, our findings
of an elastic demand fit with intuition.
An elastic demand may be a problem for Bluefin tuna fisheries, as they may not be
able to attain enough price increases if Bluefin supply is reduced to cover the loss from
the reduced catch. This may pose a challenge to the industry if catch limits are imposed.
The results also suggest the possibility that demand for Bluefin tuna in the summer may
be less elastic than in other seasons. If there is indeed a variability in demand elasticity
across seasons, with a more inelastic summer demand, a catch limit imposed on purse
seine fisheries may be less damaging overall.
Crona et al. (2015) warns that seafood prices are not working as a signal for ecosystem
health. The conclusion that Bluefin tuna demand is elastic may be interpreted as evidence
that Bluefin tuna prices are not working as a signal for the species’ population health.
To fully address this, future studies should look at the substitutability among different
21
species of fish.
As we see in the Bluefin tuna fisheries in Japan, many coastal fisheries often target
different species at different times throughout the year. Those seasonal target choices may
be studied further to understand the supply elasticities for different seafood products.
Furthermore, in the presence of seasonal fisheries, a species-based regulation may impacts
different fisheries differently, or uniformly, if demand elasticity differ across seasons. To
address this, future studies should further investigate seasonality in the seafood market.
22
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25
Appendix
A: Identification of the Elasticity of Demand
First, by denoting the log transformed prices and quantities as q and p with superscript
S and D to denote supply and demand respectively, the estimation equations for the the
supply and demand can be expressed along with the error term u and v as
Supply equation:
qtS = β0 + β1 ptS + γzt + ut
Demand equation:
qtD = α0 + α1 ptD + vt
In the supply equation, zt represents exogenous supply shocks.
The prices and quantities that we observe in the data are the intersections of supply
and demand curve. Therefore, we have q∗ = qS = qD and p∗ = pS = pD . By solving the two
equations, have
pt∗ =
and
qt∗ =
β0 − α0
γ
1
+
zt +
(u − vt ) ≡ δ0 + δ1 zt + t ,
α1 − β1 α1 − β1
α1 − β1 t
α1 γ
(β0 − α0 )α0
α1
+
zt +
(u − vt ) ≡ θ0 + θ1 zt + ηt .
α1 − β 1
α1 − β1
α1 − β1 t
From the exogenous supply shock zt , we are able to identify α0 from θ0 /δ0 and α1 from
θ1 /δ1 . Hence, by using an exogenous supply shock, we are able to identify the elasticity of
demand.
26
Appendix B: Supplementary Figures and The List of Variables
Figure A1: Purse Seine Landings vs Tsukiji Adjusted Auctioned Volume
27
Figure A2: Purse Seine Landing on Previous Day vs Tsukiji Adjusted Auctioned Volume
28
Figure A3: Tsukiji Market Auctioned Price and Volume
29
30
JPY/kilogram
JPY/kilogram
Mid price**
Low price***
*Tokyo Metropolitan Government defines high price as the highest price of the day
**Tokyo Metropolitan Government defines mid price as mode price of the day
***Tokyo Metropolitan Government defines low price as mode of the prices below the mid price of the day
Tsukiji Market
Times
Metric ton
Number of fish
1000 JPY
Date
Date
Metric ton
Kilogram
JPY/kilogram
Number of landings
Landed volume
Number of landed fish
Landed value
First day of landing
Last day of landing
Landed volume
Sales volume
High price*
Year Sakaiminato total
Year Sakaiminato total
Year Sakaiminato total
Year Sakaiminato total
Year Sakaiminato total
Year Sakaiminato total
Day Vessel
Day Aggregate
Day Major domestic port, other domestic port total, overseas total
Day Major domestic port, other domestic port total, overseas total
Day Major domestic port, other domestic port total, overseas total
Sakaiminato
Units
Variable Name
Dataset
Table A1: List of Variables
1982 - 2015
1982 - 2015
1982 - 2015
1982 - 2015
1982 - 2015
2000, 2002 - 2015
2011 - 2015
2004 - 2015
2005 - 2015, include missing data
2006 - 2015, include missing data
2007 - 2015, include missing data
Time Period