Price Transmission in Thai Aquaculture Product Markets: An Analysis along
Value Chain and across Species
KEHAR SINGH
Former Research Associate, Aquaculture/Fisheries Centre,
University of Arkansas at Pine Bluff, AR 71601, USA
Presently Research Scientist (Agricultural/Resource Economics),
Canada Excellence Research Chair - Aquatic Epidemiology (CERC),
Atlantic Veterinary College, Charlottetown, PE C1A 4P3, Canada
Email: kesingh@upei.ca
MADAN M. DEY*
Professor
Aquaculture/Fisheries Centre,
University of Arkansas at Pine Bluff,
1200 North University Dr., Mail Slot 4912, Pine Bluff, AR-71601, USA.
Email: mdey@uaex.edu
Amporn Laowapong
Economist, Senior Professional
Department of Fisheries ,
Ministry of Agriculture and cooperative – Thailand
E-mail: amporn0108@gmail.com
Umesh Bastola
Former Graduate Assistant, Aquaculture/Fisheries Centre,
University of Arkansas at Pine Bluff, AR 71601, USA
Presently Ph D Student, School of Economic Sciences,
Washington State University, Pullman WA 99164, USA
Email: umesh.bastola@wsu.edu
*
Corresponding Author
1
Price Transmission in Thai Aquaculture Product Markets: An Analysis along
Value Chain and across Species
Abstract
We have examined the presence of price transmission asymmetry
along the value chain, and the price transmission across four main aquaculture
species in Thai fish market. This is an attempt to contribute to the horizontal and
vertical price transmission in the seafood markets literature including the price
transmission asymmetry in the developing countries. We did not find any
evidence of asymmetric price transmission in walking catfish (except in long-run),
vannamei shrimp and tilapia; however, it is evident in Thai seabass market;
wholesalers exercising some market power. In most of the cases, none of the
species considered affect significantly prices of other species at the same level of
value chain.
Key words Vertical price transmission, price transmission asymmetry, price
transmission across species, price transmission models, Thai fish market.
Running Title Price Transmission in Thai Fish Market
JEL Classification C22, D4, Q13
2
Introduction
Horizontal and vertical prices linkages are important areas of research in the food
markets. The extent to which a price shock at one market/level of value chain affects a
price in other market/value chain level provides an assessment of the functioning of
markets. The number of studies on horizontal price linkages in the seafood markets in
the developed world has increased recently; however, it is hard to find studies in the
developing countries. There are limited studies on vertical price transmission including
the asymmetric price transmission in seafood markets in the world. Lack of the price
transmission studies in seafood producing developing countries is primarily due to
unavailability of the time series price data across species, markets and along the value
chain.
The present study is an attempt to contribute to the horizontal and vertical price
transmission in the seafood markets literature including the price transmission
asymmetry in the developing countries. We have examined the presence of price
transmission asymmetry along the value chain, and the price transmission across four
main aquaculture species in Thai fish market. The fish species considered in the
analysis are vannamei shrimp (Penaeus vanamei), tilapia (Oreochromis niloticus),
walking catfish (Clarius sp.) and seabass (Lates calcarifer).
The fisheries sector including aquaculture plays a vital role in the food security
and economy of Thailand. In 2009, total fisheries production in the country was 3.78
million tons equivalent to 140,000 million baht (4,700 million US$) in value. The
contribution of individual management sub-sectors to the total production included:
marine capture (58%), inland capture (6%), coastal aquaculture (22%), and fresh water
culture (14%). Marine capture fishery is mainly for exports while the coastal and fresh
water aquaculture is for domestic consumption. Vannamei shrimp (Penaeus vanamei)
constitutes 60% of total coastal aquaculture culture production. Seabass (Lates
calcarifer) is the main marine finfish cultured in Thailand; about 63% of the total of
marine fin fish farms cultured seabass during 2007 (Department of Fisheries, 2007).
3
Tilapia (Oreochromis niloticus) and walking catfish (Clarius sp.) account for 32% and
19% of total fresh water production, respectively.
Recent studies on the spatial price linkages in seafood markets in the developed
world include Nielsen (2004); Asche et al. (2005); Nielsen (2005); Nielsen et al. (2007);
Vinuya (2007); Lopez and Asche (2008); Lopez (2009); Nielsen, Smit, and Guillen
(2009); Jimenez-Toribio, Guillotreau, and Mongruel (2010); Asche et al. (2012). Nielsen
(2004) found that the ‘Law of One Price’ is in force between the Norwegian and Danish
herring markets. Asche et al. (2005) examined market integration between wild and
farmed salmon on the Japanese market and found that the species were close
substitutes on the market, and that the expansion of farmed salmon had resulted in
price decreases for all salmon species. Nielsen (2005) identified strong integration of
European cod markets and partially integrated saithe markets. Nielsen et al. (2007)
found that markets for farmed trout are related toothed fish markets in Germany, and
that markets for these trout are more closely linked to markets for captured fish than to
farmed salmon. Using import price data from Japan, United States, and European Union,
Vinuya (2007) tested market integration and the ‘Law of One Price’ in the world shrimp
market. Norman-Lopez and Asche (2008) found that imports of fresh and frozen tilapia
fillets lie in different market segments, while fresh and frozen catfish fillets compete in
the same market. Norman-Lopez (2009) showed that fresh farmed tilapia fillets compete
with wild whole red snapper, wild fresh fillets of seabass, and back flounder in the U.S.
market. Nielsen, Smit, and Guillen (2009) identified a loose form of market integration
between 13 fresh and seven frozen fish species in Europe. They found that the Law of
One Price is in force on the fresh market within the segments of flatfish and pelagic fish
in Europe. Jimenez-Toribio, Guillotreau, and Mongruel (2010) examined the degree of
integration between the world market and the major European marketplaces of frozen
and canned tuna through both vertical and spatial price relationships. They found that
the European market for final goods segmented between the Northern countries
consuming low-priced canned skipjack tuna imported from Asia (mainly Thailand) and
the Southern countries (Italy, Spain) processing and importing yellowfin-based products
sold at higher prices. Asche et al. (2012) used detailed data on shrimp prices by size
class and import prices to conduct a co-integration analysis of market integration in the
4
U.S. shrimp market. They found a significant evidence of market integration, suggesting
that the ‘Law of One Price’ holds for this industry.
The literature analyzing vertical price linkages has concentrated on evaluations
of the links between farm, wholesale and retail prices (Vavra and Goodwin 2005). The
price relationships along the value chain provide insights into marketing efficiency, and
consumer and farmer welfare (Aguiar and Santana 2002). It is to mention here that the
relationships between two stages in the value chain are well developed by the theory of
derived demand; however, the high data requirements to estimate such relationships
often make it impossible to estimate. Therefore, analysis of just prices at different levels
of the market chain is more commonly employed.
Vertical price linkages in seafood markets are not studied much. A few recent
studies to site are: Jimenez-Toribio, Garcia-del-Hoyo, and Garcia-Ordaz (2003); Guillen
and Franquesa (2008); Jimenez-Toribio, Guillotreau, and Mongruel (2010). JimenezToribio, Garcia-del-Hoyo, and Garcia-Ordaz (2003) used prices concerning ex-vessel
markets, wholesale markets and foreign trade to study the impact of vertical integration
on price transmission in the fishing distribution channel of the Striped Venus (Chamellea
gallina).
Using weekly data, Guillen and Franquesa (2008) analyzed the price
transmission elasticity of the main twelve seafood products in the Spanish market chain
(Ex-vessel, Wholesale and Retail stages). Jimenez-Toribio, Guillotreau, and Mongruel
(2010) tested vertical price relationships between the price of frozen tuna paid by the
canneries and the price of canned fish in both Italy and France. The two species show
an opposite pattern in prices transmission along the value chain: price changes along
the chain are far better transmitted for the “global” skipjack tuna than for the more
“European” yellowfin tuna.
The asymmetric price transmission, i.e., increasing and decreasing prices at one
level of value chain transmit at different rates to another level, has received
considerable attention in agricultural economics. Meyer and von Cramon-Taubadel
(2004); Frey and Manera (2005) provide reviews of the literature on asymmetry price
transmission. However, the issue of asymmetric price transmission has been
overlooked in fish and fish product market studies (Jaffry 2005). A few studies to
5
mention are Jaffry (2005); Garcia (2006); Guillen and Franquesa (2008), Matsui et al.
(2011); and Nakajima et al. (2011). Gonzales et al. (2003) detected the asymmetric
price transmission in the distribution of wild cod and farmed salmon. Jaffry (2005) found
asymmetry in price transmission in the whole hake value chain in France. Garcia (2006)
studied the hake prices transmission along the Spanish market chain. Guillen and
Franquesa (2008) investigated the price transmission asymmetry in the main twelve
seafood products in the Spanish market chain (ex-vessel, wholesale and retail levels).
Matsui et al. (2011) analyzed Japanese blue fin tuna market and discussed that entities
having the market power shifted from upstream to downstream by tuna market structure
change. Using a threshold autoregressive rolling window regression model, Nakajima et
al. (2011) studied blue fin tuna market in Japan. The findings of this study supported
those of Matsui et al. (2011).
Common explanations of the existence of asymmetric farm-retail price
transmission in the food sector include: market power, search costs, consumer
response to changing prices, producer adjustment cost, and the behavior of markups
over the business cycle (Jaffry 2005). The presence of asymmetric price transmission is
often considered as an evidence of market failure (Meyer and Cramon-Taubadel 2004).
Peltzman (2000) found that asymmetric pricing is not just anecdotal, it’s closer to
universal, and asymmetric pricing to be as common in unconcentrated industries as it
was in concentrated industries.
Methodology
We have used following procedure to fulfill the objectives of the study:
i)
Testing for a presence of the unit-root, Granger causality, and cointegration;
ii)
Testing for the price transmission asymmetry along the value chain; and
iii)
Specifying and estimating the price transmission models.
Unit Root, Granger Causality and Cointegration Tests
6
Important issues in the price transmission analysis are: a) stationarity/non-stationarity of
the time series, b) the Granger causation, and c) co-integration of non-stationary time
series having same order of integration. Addressing these issues is important to decide
on the regression model to adopt for the price transmission analysis (stationarity/nonstationarity and cointegration) and the R.H.S. variables in the model (the Granger
causation). If the series under study are stationary at levels, one can use traditional
econometric tools like ‘ordinary least square’ estimation procedure to determine
relationships between those series. The non-stationary series having unit root may be
co-integrated if their order of integration is same; one can use the ‘error correction
models’ to determine the relationships. The ‘models in difference’ can be used for noncointegrated series having unit root.
There are two types of tests used to test whether a time series is stationary or
not: the unit root tests and the stationarity tests. The unit root tests test the null of a unit
root against an alternative of stationarity, or mean reversion. If the unit root null
hypothesis is rejected, then the series is said to be stationary. The presence of a unit
root in the time series representation of a variable has important implications for both
the econometric method used and the economic interpretation of the model in which
that variable appears. The Augmented Dickey Fuller (ADF) test of Dickey and Fuller
(1979), the generalized least squares ADF (DF-GLS), the Point Optimal tests (PT) of
Elliott, Rothenburg, and Stock (ERS) (1996), and the Phillips-Perron test (Phillips and
Perron 1988) are commonly used univariate unit root tests. The stationarity tests test
the null hypothesis of stationarity against a unit root alternative. If the test fails to reject
the null, the time series is said to be stationary. The tests most widely used are those of
7
Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) (1992); Saikkonen and Luukkonen
(1993); Leybourne and McCabe (1994).
As is well known in the applied economics literature, even a test with DF-GLS’s
favorable characteristics may still lack power to distinguish between the null hypothesis
of nonstationary behavior (I(1)) and the stationary alternative (I(0)). The Ng-Perron test
(Ng and Perron 2001) modifies the Phillips and Perron (1988) test in a number of ways
in order to increase the test’s size and power. This testing procedure ensures that nonrejections of the null hypothesis of the unit root are not due to a low probability of
rejecting a false null hypothesis, while rejections are not related to size distortions. The
Ng-Perron test constructs four test statistics that are based upon the GLS de-trended
data. These test statistics are modified forms of Phillips (1987) Zα statistics and Phillips
and Perron (1988) Zt statistics, the Bhargava (1986) R1 statistic which is built on the
work of Sargan and Bhargava (1983), and the ERS (1996) Point Optimal statistic.
Keeping in view the improved size and power of Ng-Perroni (2001) test over other
univariate unit root tests, we have used the same to test the null hypothesis of presence
of unit root in the series.
The next step is to determine whether the series having unit root are cointegrated
or not. Cointegration between two time series integrated of same order can be tested
with either by the Engle and Granger (1987) test or by the Johansen (1988) test; we
have used the latter one. The Johansen (1988) cointegration test is an unrestricted
cointegration test; Gonzalo (1994) discussed advantages/disadvantages of this test.
The issue of testing whether or not a variable precedes another variable, i.e., the
Granger causality (Granger 1969), is increasingly gaining attention in empirical research
8
(Hatemi-J 2012). We followed the Toda and Yamamoto (1995) procedure to test for the
Granger causality: i) determining maximum order of integration of two series, ii) setting
up a VAR model in levels, iii) selecting appropriate maximum lag length for variables in
the VAR model, iv) testing for serial autocorrelation in the model, v) re-estimating the
VAR model with appropriate lag length, and vi) testing the null hypothesis. As discussed
earlier seabass farm, wholesale and retail price series, and tilapia retail price series
price series are I(1), and all other series are (I(0). We have estimated appropriate
maximum lag order using: i) FPE (Final prediction error), ii) AIC (Akaike information
criterion), iii) SIC (Schwarz information criterion), and iv) HQIC (Hannan-Quinn
information criterion). Then we have estimated the VAR model with lag order equal to
maximum lag length selected using different information criteria plus maximum order of
integration of two series. Then we conducted (post-estimation test) to check for
autocorrelation in the model using the Lagrange-multiplier test (H0: no autocorrelation at
lag order). If autocorrelation is found in the selected lag length, we increased the lag
length until autocorrelation issue resolved and re-estimated the model. In the end we,
tested the null hypothesis using the Wald test, which has asymptotically chi-square
distributed with p degree of freedom under the null hypothesis. For this test, we included
only lag length selected on the basis of different information criteria; extra lags
(maximum order of integration and increased lags to resolve autocorrelation) used are
just to fix up the asymptotics.
Testing for the price transmission asymmetry along the value chain
9
Meyer and von Cramon-Taubadel (2004) provide a survey of the asymmetric price
transmission methods. The results of the Johansen (1988) cointegration test, which will
be discussed in the succeeding section, shows that none of the series having unit-root
are cointegrated. Therefore, we followed the Houck (1977) and Ward (1982) approach.
This approach basically splits the change in explanatory variable into positive and
negative changes.
We have considered three levels along the value chain: farm, wholesale and
retail. Based on the pair-wise Granger causality test, we determined the direction of
causation. The Granger causality test, which will be discussed in the results and
discussion section, shows unidirectional in some cases and bidirectional causation in
other cases; however, in some of the cases the price at one level of value chain (e.g.
wholesale) is caused by the prices at other levels of value chain (farm and wholesale).
Depending on these results, we have extended the Houck (1977) and Ward (1982)
model to consider two regressors. The empirical model used in this paper for testing its
asymmetry can be expressed as:
p


p


q


q


ln Pi*   0t   l cum(ln Pj ) t  l   l cum(ln Pj ) t  l    m cum(ln Pm ) t  l   l cum(ln Pm )   , (1)
l 0
l 0
m0
m0
where, cum and ln stand for cumulative and natural logarithmic value,
respectively. Subscripts ‘i’, ‘j’ and ‘k’ stands for value chain level; ‘l’ and ‘m’ denote lag
number; t is the time; ln Pi *  ln Pt  ln Pt 0 ; (ln Pt  )  ln Pt  ln Pt 1 , if ln Pt  ln Pt 1 and 0
otherwise; and (ln Pt  )  ln Pt  ln Pt 1 , if ln Pt  ln Pt 1 and 0 otherwise.  t is the error
component. If the price series on the LHS of the equation are stationary at levels without
trend, we did not use the time as a variable on the RHS of the equation.
10
The null hypotheses of no difference tested against the alternate hypotheses of
inequality are as follows:








(2.2)




(2.3)




Null H 0S 1 :  l   l against alternate H1S 1 :  l   l for l  1,2,3,...
(2.1)
Null H 0S 2 :  m   m against alternate H1S 2 :  m   m for m  1,2,3,...
Null H 0S 3 :  l   m against alternate H1S 3 :  l   m for l  m
Null H 0S 4 :  l   m against alternate H1S 4 :  l   m for l  m
(2.4)
)
(2.5)
Null H 0L1  :   l    l against alternate H1L1  :   l    l


o
o
o
o
l 1
l 1
l 1
l 1
q
q
q
q
m 1
o
m 1
q
m 1
o
m 1
q
l 1
m1
l 1
m1
o
q
o
q
l 1
m 1
l 1
m 1


Null H 0L 2 :   m    m against alternate H1L 2 :   m   im
(2.6)
Null H 0L 3  :   l    m against alternate H1L 3  :   l    im




(2.7)
Null H 0L 4 :  l    m against alternate H1L3 :  l   im
H
(2.8)
The equality of the coefficients of the positive change and negative change
S1
0

and H 0S 2 provides the test on short run asymmetry. The equality of the coefficients


for the sum of positive change and sum of negative change H 0L1 and H 0L 2 gives the
information on long run price transmission asymmetry. Testing the null hypotheses
H 0S 3 and H 0L 3 provides the evidence whether degree of positive changes in two
regressors on the changes in the dependent variable are significantly different from
each other or not in short run and long run, respectively. Similarly rejection of null
hypotheses H 0S 4 and H 0L 4 provides evidence of significant difference in the influence of
negative changes in two independent variables on the dependent variable.
Specifying and Estimating the Price Transmission Models
11
We have identified the regressors based on the Granger causality test results. If a price
series on the left-hand side (LHS) and the right-hand side (RHS) price series (price
series which are the Granger cause of the series on the RHS) do not have unit root, we
have used a price transmission in levels (eq. 3.1). However, if any of the price series on
the LHS and RHS have a unit root and two or more price series are not cointegrated,
we have used a model in difference (eq. 3.2).
ln Pik   0t   ilk (ln Pik )t  l   ilv (ln Piv )t  l   jlk (ln Pjk )t  l   t ,
l 0

vk


l
j





 ln Pik   0t   ilk (ln Pik ) t  l   ilv (ln Piv ) t  l   jlk (ln Pjk ) t  l   t ,
l 0
vk
l
(3.1)
l
j
l
(3.2)
where subscript ‘i' and ‘j’ denote the species, ‘v’ and ‘k’ denote value chain level,
‘l’ denote lag order and ‘t’ denote time. ‘P’ stands for price series, ‘ln’ is the natural
logarithmic value,  denote parameter and  t is the error component. If the price series
on the LHS of the equation are stationary at levels without trend, we did not use the
time as a variable on the RHS of the equation. Since we have used logarithmic form,
therefore, the estimated parameters (  ) are price short run price transmission
LR
elasticities. The long run elasticities along the value chain (VC
) and across species
( SLR ) are computed as follows:




LR
VC
   ilv  1    il  and  SLR    jl  1    il 
l

l 0

l

l 0

(4)
The analyses have been done on the STATA12 software (STATACORP LP,
Texas, U.S.). Equations 1, 3.1 and 3.2 were estimated using the Cochrane-Orcutt
regression, which corrects for the auto-correlation, if any, in the time series. We have
used EView6 software (IHS Inc.) for the Granger causality test. Using F-test in
STATA12, we tested the null hypotheses given in equations 2.1 to 2.8.
12
We have used monthly price data on different fish species at different levels of
supply chain, collected by different agencies. The time period of data used ranges from
January 2001 to October 2010 (Appendix 1). Data on farm-gate price and wholesale
level price of seabass, catfish, and tilapia were obtained respectively from Office of
Agriculture and Cooperative and Fish Market Organization under Ministry of Agriculture
and Cooperatives, Thailand, while the retail prices were obtained from Ministry of
Commerce. Prices on black tiger shrimp were obtained from central shrimp wholesale
market, Sakot Sarom, Thailand.
Results and Discussion
Unit Root, Granger Causality and Cointegration
Table 1 presents the unit root test results for different time series under study. The price
series namely, shrimp farm, shrimp wholesale, shrimp retail, walking catfish wholesale,
tilapia farm and tilapia wholesale are stationary at levels, whereas the price series
namely walking catfish farm and walking catfish retail are trend stationary (table 1).
Seabass farm, wholesale and retail price series, and tilapia retail price series price
series have unit root. These series are stationary in first difference without a linear
trend; we have taken the liberty not to present these results in table 1.
For the pair wise Granger causality test, tables 2A and 2B present the selected
number of lags. Tables 3A (along the value chain) and 3B (across the species) show
results of the pairwise Granger causality test. The test rejected following null
hypotheses (table 3A):
i).
Shrimp wholesale price does not Granger cause shrimp farm price,
13
ii).
shrimp retail price does not Granger cause shrimp wholesale price,
iii).
shrimp wholesale price does not Granger cause shrimp retail price,
iv).
shrimp retail price does not Granger Cause shrimp farm price,
v).
walking catfish farm price does not Granger cause walking catfish
wholesale price,
vi).
walking catfish retail price does not Granger cause walking catfish
wholesale price,
vii).
seabass farm price does not Granger cause seabass wholesale price,
viii).
tilapia wholesale price does not Granger cause tilapia farm price,
ix).
tilapia retail price does not Granger cause tilapia farm price, and
x).
tilapia farm price does not Granger cause tilapia retail price.
Across the value chain, the Granger causality tests rejected following null
hypotheses up to 0.10 levels of significance (tables 3B):
i).
Walking catfish retail price does not Granger cause seabass retail price,
ii).
walking catfish retail price does not Granger cause tilapia retail price,
iii).
walking catfish retail price does not Granger cause shrimp retail price,
iv).
walking catfish farm price does not Granger cause seabass farm price,
v).
walking catfish farm price does not Granger cause tilapia farm price,
vi).
shrimp wholesale price does not Granger cause walking catfish wholesale
price,
vii).
seabass wholesale price does not Granger Cause walking catfish
wholesale price, and
14
viii).
tilapia wholesale price does not Granger cause walking catfish wholesale
price.
Therefore, we conclude that shrimp retail price, shrimp wholesale price and
walking catfish farm price are the Granger cause of shrimp farm prices. Shrimp
wholesale and walking catfish retail prices are the Granger cause of shrimp retail prices.
Shrimp retail price is a Granger cause of shrimp wholesale price. Walking catfish farm
price, walking catfish retail price and seabass farm price are the Granger cause of
shrimp farm price, seabass retail price, and seabass wholesale price, respectively.
Walking catfish farm price, walking catfish retail price, and wholesale prices of shrimp,
seabass and tilapia are the Granger cause of walking catfish wholesale price. Retail
and wholesale prices of tilapia and walking catfish farm price are the Granger cause of
tilapia farm price, whereas farm and wholesale prices of tilapia and walking catfish retail
price are the Granger cause of tilapia retail price. None of the price series considered is
a Granger cause of walking catfish farm and retail prices, and tilapia wholesale price.
Our results suggest that prices in the Thai fish sector are not determined at one
end and then passed down or up along the supply channel. That is, pricing patterns in
the Thai fish sector are not just cost or demand driven. We found the direction of
causality from retail to farm prices in vannamei shrimp; however, the direction of
causality also found from wholesale to retail prices. In case of walking catfish, the
pricing patterns are both supply and demand driven. The retail market shocks in case of
tilapia are directly transmitted to farmers, and vice-versa. The wholesale prices of
seabass adjust to shocks in farm prices; however, shocks in retail market remains
confined to retail market. Tiffin and Dawson (2000) while studying the United Kingdom
15
lamb market found that lamb prices were determined in the retail market, and then
passed upward along the supply chain. Goodwin and Holt (1999) and Goodwin and
Harper (2000) found that retail market shocks were confined in retail markets for the
most part, but farm markets adjusted to shocks in wholesale markets. However, BenKaabia, et al. (2002) found both supply and demand shocks were fully passed through
the marketing channel; i.e., they found complete price transmission. Saghaian (2007)
found that beef price causality in the U.S. markets at different levels of the supply
channel are bi-directional, influencing and being influenced by each other at each stage.
We have tested the cointegration along value chain for seabass; and at the retail
level of value chain among seabass and tilapia. Other price series are either stationary
at levels or trend stationary or there is only one price series having unit root at
farm/wholesale level of value chain. Table 4 presents the results of the Johansen
Cointegration test. The Trace and Eigen value statistics failed to reject the null
hypothesis of maximum rank equal to ‘0’ in all other cases, which shows absence of
cointegration between those price series.
Price Transmission Analysis
Equation 3.1 and 3.2 are a general model used to study the price transmission relations
in Thai fish market. These models have AR-terms; therefore, it is necessary to decide
the number of lags of AR terms. We have selected the lags using FPE, AIC, HQIC and
SBIC criteria (table 5).
Walking Catfish
16
Table 6A presents the estimates of equation 1 for walking catfish wholesale price and
asymmetry price tests results. F-tests failed to reject all null hypotheses of no difference
up to 0.10 levels of significance except for long run asymmetry test hypothesis for
walking catfish retail prices, where the difference of the sum of positive change and
negative change coefficients is statistically significant at 0.07 levels. The long run
elasticity (sum of coefficients) of wholesale price with increasing retail prices (0.62) is
significantly lower than decreasing retail prices (1.14). This means positive demand
shocks in the walking catfish retail market are transmitted at a lower rate than negative
shocks to the walking catfish wholesale market in the long run.
Table 6B provides the estimates of the price transmission models for the walking
catfish farm, wholesale and retail prices. As stated earlier, we did not find any of the
price series along the value chain and across the species at the same level of value
chain as a Granger cause for farm and retail prices (tables 3A and 3B). Also these price
series are trend stationary (table 1), and lag length selection criteria showed optimum
lag length three for farm prices and lag length two for retail price (table 5). The
estimated models show very low but positive trends in walking catfish farm and retail
prices (table 6B). Both farm and retail current prices of walking catfish are positively
influenced by its previous month prices and negatively with two month lagged price
(table 6B).
Walking catfish wholesale price series is influenced by its farm and retail prices,
and also vannamei shrimp, seabass and tilapia wholesale prices. Seabass wholesale
price has unit root, and walking catfish wholesale price is stationary at levels without
trend (table 1). Therefore, we have used model in difference without trend. The
17
estimates of the model (table 6B) show that walking catfish farm price do not have any
significant influence on its wholesale price, whereas its retail price affected its wholesale
price significantly. Walking catfish current month retail price does not affect its current
wholesale price, whereas one and two month lagged retail price has positive (short run
elasticity = 1.25) and negative (short run elasticity = -0.97), respectively, on walking
catfish wholesale prices. Two month lagged vannamei shrimp wholesale price affects
walking catfish current month wholesale price significantly (short run elasticity = 0.40).
Current seabass wholesale price has negative and previous month has positive
influence on walking catfish wholesale price. Only current month tilapia wholesale prices
influence walking catfish wholesale prices significantly. In nutshell a positive and a
negative changes in current month tilapia and seabass whole prices, respectively, lead
to a positive change in current month walking catfish wholesale price. The reverse is
true for effects of previous month wholesale prices of tilapia and seabass on current
month wholesale price of walking catfish.
Vannamei Shrimp
We have presented the estimated price transmission asymmetry models (eq. 1) for
vannamei shrimp farm, wholesale and retail prices in table 7A, and the asymmetric price
transmission hypotheses tests results in table 7B.hypothesis. None of the estimated
coefficients in vannamei shrimp farm price model are statistically significant up to 0.10
levels of significance. However, in case of wholesale/retail price models, current price
coefficients of positive as well as negative cumulative changes in retail/wholesale prices
are significant, and magnitudes of coefficients are almost equal. This means absence of
asymmetric price transmission in Thai vannamei shrimp markets at farm, wholesale and
18
retail levels of value chain. This is confirmed by the hypotheses test results given in
table 7B.
Vannamei shrimp farm, wholesale and retail prices are stationary in levels
without trend. Walking catfish retail price, which is trend stationary at levels, is the
Granger cause of vannamei shrimp retail price. At the same level of value chain, price
of none of the species understudy is the Granger cause of vannamei shrimp farm and
wholesale prices. The test results showed the absence of asymmetric price
transmission in Thai vannamei shrimp market along the value chain. Therefore, we
have used model given in equation 3.1 (table 7C) to work out price transmission
relationships.
One month lagged prices of vannamei shrimp have significant influences on its
current prices at respective levels of value chain; however, degree of influence is
considerably higher at wholesale and retail levels than at farm level. Vannamei shrimp
current wholesale price also affects vannamei shrimp farm price significantly; the short
run price transmission elasticity of vannamei shrimp farm price with respect to its
wholesale price is very low (0.30). Current and one month lagged vannamei shrimp
wholesale/retail prices affect current vannamei shrimp retail/wholesale prices
significantly. The log run price transmission elasticity of vannamei shrimp
wholesale/retail price with respect to its retail/wholesale price is 0.84/0.77.
Seabass
All seabass price series have the unit roots (table 1); however, they are not cointegrated
(table 4). Seabass farm price is the Granger cause of its wholesale price; the hypothesis
of the Granger causality is rejected in other price pairs of seabass along value chain.
19
Keeping in view these results, we have estimated equation 1 for seabass wholesale
price (table 8A). The coefficient of current cumulative positive change in seabass retail
price is significant; however, the coefficient of current cumulative negative change in
retail seabass price is non-significant up to 0.10 levels of significance. The coefficients
of lagged (one and three month lags) cumulative negative change in retail seabass
price are significant too. This means that if the seabass wholesalers pay higher prices
(say 1%) to the farmers, they immediately receive higher prices (0.58%) from the
seabass retailers. However, if the wholesalers pay lower prices to the farmers, they do
not pass the decrease to the retailers immediately. They pass around 20% of decreased
price to the retailers in next month and about 26% in third month. Less than 50% of
decrease and 70% of increase in wholesalers’ purchase price is passed to the retailers
in the long run. This indicates, and is confirmed by asymmetry hypotheses tests results
(table 8A), presence of short run as well as long run asymmetric price transmission
between seabass price in Thailand. It is to mention here that seabass production is
mainly based on cage culture, which requires very high investments. Seabass farmers
are well organized too. Retailers have very low, if any, control over prices.
We have estimated models in difference given in equation 3.2 for seabass farm,
wholesale and retail prices (table 8B). One month lagged seabass retail and farm prices
influence respective prices. One month lagged farm price of walking catfish affects
seabass farm price. Seabass current farm price is only factor which affects seabass
wholesale price significantly. Three month lagged walking catfish retail price has
significant influence on seabass retail price.
Tilapia
20
Tilapia wholesale and retail prices are the Granger cause of tilapia farm price, and
wholesale and farm prices of its retail price. Tilapia retail price have unit root, whereas
wholesale and farm price series are stationary. The results of the asymmetric price
transmission model shows that six month lagged cumulative positive change in
wholesale price and five month lagged cumulative change in retail price affect tilapia
farm price significantly (table 9A); however, there is no evidence of asymmetric price
transmission in tilapia markets along the value chain in Thailand (table 9B).
The estimates of the price transmission model (table 9C) for tilapia retail price
show that walking catfish retail price influence tilapia retail price significantly (price
transmission elasticity in current month = 0.32, and long run price transmission elasticity
= -0.06). Recent historical prices affect tilapia prices at all levels of value chain.
Conclusions and Policy Implications
We have examined the presence of price transmission asymmetry along the value
chain, and the price transmission across species in Thai fish market. This is an attempt
to contribute to the horizontal and vertical price transmission in the seafood markets
literature including the price transmission asymmetry in the developing countries.
We found unidirectional Granger causation in some cases and bidirectional
Granger causation in other cases; however, in some of the cases the price at one level
of value chain is Granger caused by the prices at other levels of value chain. Therefore,
we have extended the Houck (1977) and Ward (1982) asymmetric price transmission
model to consider two regressors, which allow the researchers to test the hypotheses
“whether degree of positive/negative changes in two regressors on the changes in the
dependent variable are significantly different from each other or not in short run and
21
long run”. We estimated the price transmission relationships using regressors along the
value chain and across the species at the same level of value chain.
There is no evidence of short run asymmetric price transmission from either retail
or farm level to wholesale level; however, there is weak evidence of long run
asymmetric price transmission from retail to wholesale price. We did not find any
evidence of asymmetric price transmission in Thai fish market for vannamei shrimp and
tilapia in short- and long run. Short run and long run price transmission asymmetry is
evident in Thai seabass market; wholesalers exercising some market power.
In most of the cases, none of the species considered affect significantly prices of
other species at the same level of value chain. The exceptions to this are: i) walking
catfish price affects tilapia price at retail level in short as well as long run, ii) three month
lagged walking catfish retail price affects seabass current retail price, iii) one month
lagged walking catfish farm price influences seabass farm price, and iv) vannamei
shrimp two month lagged price, current tilapia price and current and one month lagged
seabass price affect significantly walking catfish prices at wholesale level. In all these
cases, the price transmission elasticities are positive except for long run elasticity in
case i (where it is negative but close to zero) and current month seabass wholesale
price in case iv where it is -1.11. These results indicate lack of competition among
different species in Thai seafood market. However, walking catfish faces some
competition from tilapia in short run at wholesale level.
Price transmission relationships along the value chain shows that walking catfish
retail prices (one month and two month lagged) influence significantly its wholesale
price in short run. Vannamei shrimp retail and wholesale prices affects each other in
22
short run as well as long run. Vannamei shrimp’s current wholesale price also
influences its current farm price. Seabass current farm price affects its wholesale price.
None of the prices along value chain in tilapia affect each other significantly.
The results of the study have important policy implications. Various studies (Dey
et al. 2008a; Dey et al. 2008b) indicate that, given elastic income elasticity of demand
for fish, there will be tremendous increase in demand for various types of fish in
Thailand over time due to population growth and increases in per capita income. Dey et
al. (2008a) also indicates that fish exports from Thailand are expected to rise
particularly of tilapia, cultured shrimp and high-value marine fish like seabass. It is
projected that consumer prices of the various species studied are expected to rise faster
than the posited inflation rate of 3.5%during 2005-2020, except for tilapia (with a yearly
rise of 2.6%) (Dey et al. 2008a). The findings of no asymmetric price transmission of
retail prices of aquaculture products , indicating that increases in the retail price of the
aquaculture products are likely to pass fully to the primary markets , are beneficial to
aquaculture farmers in the country. In recent years, almost all increases in fish
production have come from aquaculture sector. However, increasing fish supply from
aquaculture will exert a downward pressure on prices of aquaculture products. But if
market prices fall due to the expansion of products, retailers might also be able to easily
pass through falling prices to farmers, and thereby farmers’ revenue might fall. Thus,
there is a need to monitor the likely effect of aquaculture expansion on farm prices. The
aquaculture products should have a favorable market outlook to ensure economic
viability of the concerned farm enterprises.
23
Aquaculture harvests are seasonal in nature. Like in other developing countries,
many fish farmers in Thailand are often forced to sell their produces during the
harvesting season. If retail and/or wholesale prices drop due to some market
phenomenon, farmers will have to sell their produces at that low price. This signifies the
importance of better storage facilities and transport infrastructure in rural markets.
Policies that encourage small-scale farmers to form collective arrangement for
marketing will be helpful.
24
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28
Species
Shrimp
Seabass
Walking
Catfish
Tilapia
Asymptotic
critical
values*
Value
Chain
Level
F
W
R
F
W
R
F
W
R
F
W
R
1%
5%
10%
Table 1
Ng-Perron Unit Root Tests
Constant
MZa
MZt
MSB
MPT
LL
-16.20
-12.21
-18.11
-3.01
-0.10
-5.17
-2.83
-2.47
-3.00
-1.19
-0.07
-1.53
0.17 1.58
0.20 2.02
0.17 1.37
0.40 8.08
0.70 30.42
0.30 4.95
-5.17
-13.87
-0.53
-12.96
-8.69
1.16
-13.80
-8.10
-5.70
-1.42
-2.52
-0.30
-2.28
-2.00
0.55
-2.58
-1.98
-1.62
0.28 5.21
0.18 2.20
0.55 19.68
0.18 2.88
0.23 3.13
0.47 21.37
0.17 1.78
0.23 3.17
0.28 4.45
Constant + Linear Trend
MZa
MZt
MSB
MPT
LL
1
2
1
0
0
1
-24.91
-20.42
-31.12
-6.26
-7.53
-15.03
-3.53
-3.14
-3.92
-1.70
-1.83
-2.72
0.14 3.68
0.15 4.80
0.13 3.05
0.27 14.53
0.24 12.33
0.18 6.18
1
2
1
0
0
1
0
0
1
0
1
0
-22.21
-27.56
-18.31
-23.11
-29.74
-2.68
-23.80
-17.30
-14.20
-3.33
-3.71
-2.98
-3.34
-3.86
-0.97
-3.42
-2.91
-2.62
0.15 4.10
0.13 3.31
0.16 5.23
0.14 4.29
0.13 3.07
0.36 28.26
0.14 4.03
0.17 5.48
0.19 6.67
0
0
1
0
0
0
MZa and MZt = Modified forms of Phillips (1987) Za statistics, Phillips and Perron (1988) Zt statistics, MSB = the
Bhargava (1986) R1 statistics, MPT= the Elliott, Rothenberg, and Stock Point Optimal (ERS, 1996) Point Optimal
statistic, LL= Lag length using Spectral GLS-detrended AR based on SIC.
The unit root hypothesis is rejected in favor of stationarity when MBS and ERS are smaller than their respective
critical values, and MZa and MZt are greater than their respective critical values.
Figures in bold indicate rejection of null hypothesis up to the 0.05 level of significance.
*Ng-Perron (2001, Table 1)
29
Table 2A
Lag Length Selection for the Pairwise Granger Causality Test
Price Series
FPE AIC HQIC SBIC Selected Lag*
Vannamei Shrimp Farm and Wholesale Price
3
3
3
1
3+0+0=3
Vannamei Shrimp Wholesale and Retail Price
3
3
3
1
3+0+0=3
Vannamei Shrimp Farm and Retail Price
2
2
1
1
2+0+0=2
Walking Catfish Farm and Wholesale Price
1
1
1
1
1+0+0=1
Walking Catfish Wholesale and Retail Price
2
2
2
2
2+0+0=2
Walking Catfish Farm and Retail Price
2
2
2
1
2+0+0=2
Seabass Farm and Wholesale Price
2
2
1
1
2+1+0=3
Seabass Wholesale and Retail Price
2
2
1
1
2+1+0=3
Seabass Farm and Retail Price
2
2
2
1
2+1+0=3
Tilapia Farm and Wholesale Price
5
5
1
1
6+0+0=6
Tilapia Wholesale and Retail Price
1
1
1
1
1+1+0=2
Tilapia Farm and Retail Price
1
1
1
1
1+1+0=2
Vannamei Shrimp and Seabass Retail Price
2
2
2
1
2+1+0=3
Vannamei Shrimp and Walking Catfish Retail Price
2
5
2
2
2+1+0=3
Vannamei Shrimp and Tilapia Retail Price
2
2
1
1
2+1+0=3
Seabass and Walking Catfish Retail Price
5
5
4
2
6+0+1=6
Seabass and Tilapia Retail Price
2
2
2
1
2+1+0=3
Walking Catfish and Tilapia Retail Price
2
2
2
1
3+0+0=3
Shrimp and Seabass Farm Price
1
1
1
1
1+1+0=2
Shrimp and Walking Catfish Farm Price
2
2
2
1
2+1+0=3
Shrimp and Tilapia Farm Price
2
2
1
1
2+0+0=2
Seabass and Walking Catfish Farm Price
2
2
2
1
2+1+0=3
Seabass and Tilapia Farm Price
1
1
1
1
1+1+0=2
Walking Catfish and Tilapia Farm Price
1
1
1
1
1+1+0=2
Shrimp and Seabass Wholesale Price
3
3
3
1
3+1+0=4
Shrimp and Walking Catfish Wholesale Price **
5
5
2
2
5+0+1=6
Shrimp and Tilapia Wholesale Price
3
3
2
2
3+0+0=3
Seabass and Walking Catfish Wholesale Price
1
6
1
1
1+1+0=2
Seabass and Tilapia Wholesale Price
6
6
1
1
6+1+0=7
Walking Catfish and Tilapia Wholesale Price
2
2
1
1
2+0+0=2
* Selected lag is equal to lag length based on different criteria plus maximum order of integration plus additional
lag(s) to adjust for autocorrelation. All shrimp and walking catfish price series, and tilapia farm and WPs series are
I(0) levels. All seabass and tilapia RP series are I(1). ** Autocorrelation at lag 5
30
Table 3A
Pairwise Granger Causality Tests on Prices of Fish Species in Thailand along Value Chain
FNull Hypothesis
Obs
Prob.
Statistic
Vannamei Shrimp WP does not Granger Cause Vannamei Shrimp FP
78
5.78 0.00
Vannamei Shrimp FP does not Granger Cause Vannamei Shrimp WP
0.89 0.45
Vannamei Shrimp RP does not Granger Cause Vannamei Shrimp WP
66
2.42 0.08
Vannamei Shrimp WP does not Granger Cause Vannamei Shrimp RP
2.82 0.05
Vannamei Shrimp RP does not Granger Cause Vannamei Shrimp FP
67
5.29 0.01
Vannamei Shrimp FP does not Granger Cause Vannamei Shrimp RP
0.62 0.54
Walking Catfish WP does not Granger Cause Walking Catfish FP
91
0.59 0.45
Walking Catfish FP does not Granger Cause Walking Catfish WP
14.13 0.00
Walking Catfish WP does not Granger Cause Walking Catfish RP
90
0.36 0.70
Walking Catfish RP does not Granger Cause Walking Catfish WP
14.74 0.00
Walking Catfish RP does not Granger Cause Walking Catfish FP
91
2.15 0.12
Walking Catfish FP does not Granger Cause Walking Catfish RP
0.35 0.71
Seabass WP does not Granger Cause Seabass FP
63
0.44 0.64
Seabass FP does not Granger Cause Seabass WP
3.28 0.04
Seabass RP does not Granger Cause Seabass WP
62
0.76 0.52
Seabass WP does not Granger Cause Seabass RP
1.09 0.36
Seabass RP does not Granger Cause Seabass FP
64
0.24 0.87
Seabass FP does not Granger Cause Seabass RP
0.71 0.55
Tilapia WP does not Granger Cause Tilapia FP
83
1.93 0.09
Tilapia FP does not Granger Cause Tilapia WP
1.70 0.13
Tilapia RP does not Granger Cause Tilapia WP
87
0.60 0.55
Tilapia WP does not Granger Cause Tilapia RP
3.06 0.05
Tilapia RP does not Granger Cause Tilapia FP
91
2.62 0.08
Tilapia FP does not Granger Cause Tilapia RP
3.42 0.04
RP= Retail Price, WP= Wholesale Price, RP = Retail Price, F-statistics in bold shows rejection of hypothesis up
to the 0.10 level of significance.
31
Table 3B
Granger Causality Tests on Prices of Fish Species in Thailand across Value Chain
Null Hypothesis
Obs F-Statistic
Prob.
Seabass RP does not Granger Cause Vannamei Shrimp RP
64
1.32 0.28
Vannamei Shrimp RP does not Granger Cause Seabass RP
0.21 0.89
Walking Catfish RP does not Granger Cause Seabass RP
61
3.77 0.00
Seabass RP does not Granger Cause Walking Catfish RP
0.62 0.72
Tilapia RP does not Granger Cause Walking Catfish RP
91
0.75 0.53
Walking Catfish RP does not Granger Cause Tilapia RP
2.58 0.06
Walking Catfish RP does not Granger Cause Shrimp RP
66
2.38 0.08
Vannamei Shrimp RP does not Granger Cause Walking Catfish RP
0.76 0.52
Tilapia RP does not Granger Cause Vannamei Shrimp RP
66
1.60 0.20
Vannamei Shrimp RP does not Granger Cause Tilapia RP
1.31 0.28
Tilapia RP does not Granger Cause Seabass RP
64
1.43 0.24
Seabass RP does not Granger Cause Tilapia RP
0.11 0.96
Seabass FP does not Granger Cause Vannamei Shrimp FP
65
0.43 0.65
Vannamei Shrimp FP does not Granger Cause Seabass FP
0.78 0.46
Walking catfish FP does not Granger Cause Vannamei Shrimp FP
78
0.65 0.58
Vannamei Shrimp FP does not Granger Cause Walking Catfish FP
0.89 0.45
Tilapia FP does not Granger Cause Vannamei Shrimp FP
79
1.89 0.16
Vannamei Shrimp FP does not Granger Cause Tilapia FP
0.70 0.50
Walking Catfish FP does not Granger Cause Seabass FP
64
2.47 0.07
Seabass FP does not Granger Cause Walking Catfish FP
0.43 0.73
Tilapia FP does not Granger Cause Seabass FP
65
1.16 0.32
Seabass FP does not Granger Cause Tilapia FP
1.13 0.33
Tilapia FP does not Granger Cause Walking Catfish FP
91
0.77 0.47
Walking Catfish FP does not Granger Cause Tilapia FP
3.92 0.02
Seabass WP does not Granger Cause Vannamei Shrimp WP
61
1.54 0.20
Vannamei Shrimp WP does not Granger Cause Seabass WP
0.95 0.44
Walking Catfish WP does not Granger Cause Vannamei Shrimp WP
74
1.52 0.19
Vannamei Shrimp WP does not Granger Cause Walking Catfish WP
3.22 0.01
Tilapia WP does not Granger Cause Vannamei Shrimp WP
74
1.01 0.39
Vannamei Shrimp WP does not Granger Cause Tilapia WP
1.81 0.15
Walking Catfish WP does not Granger Cause Seabass WP
63
0.26 0.77
Seabass WP does not Granger Cause Walking Catfish WP
2.75 0.07
Tilapia WP does not Granger Cause Seabass WP
58
0.66 0.70
Seabass WP does not Granger Cause Tilapia WP
1.71 0.13
Tilapia WP does not Granger Cause Walking Catfish WP
87
3.35 0.04
Walking Catfish WP does not Granger Cause Tilapia WP
0.29 0.75
RP= Retail Price, WP= Wholesale Price, RP = Retail Price, F-statistics in bold shows rejection of hypothesis up to
the 0.10 level of significance.
32
Table 4
Unrestricted Cointegration Rank Test: Trace and Maximum Eigen value
Constant
Constant + Linear Trend
Maximum
rank
trace statistic
Max Eigen Value
trace statistic
Max Eigen Value
Seabass: Farm and Wholesale Prices
0
13.50
8.59
21.80
15.17
1
4.91
4.91
6.63
6.63
Seabass: Retail and Wholesale Prices
0
18.27
13.38
19.63
13.56
1
4.89
4.89
6.07
6.07
Seabass: Retail and Farm Prices
0
17.21
11.11
19.19
13.17
1
6.10
6.10
6.02
6.02
Retail Prices: Seabass and Tilapia
0
15.71
14.09
20.62
15.24
1
1.61
1.61
5.38
5.38
Critical Value 5%
0
19.96
15.67
25.32
18.96
1
9.42
9.24
12.25
12.52
Figures in bold indicates rejection of null hypothesis at the 0.05 level of significance.
33
Table 5
Lag Length Selection for Auto-regression in Price Transmission Model
Price Series
FPE AIC HQIC SBIC Selected Lag
Vannamei Shrimp Farm Price
1
1
1
1
1
Vannamei Shrimp Wholesale Price
3
3
3
3
3
Vannamei Shrimp Retail Price
2
2
2
2
2
Walking Catfish Farm Price
3
3
3
1
3
Walking Catfish Wholesale Price
1
1
1
1
1
Walking Catfish Retail Price
2
2
2
2
2
Seabass Farm Price
2
2
1
1
2
Seabass Wholesale Price
1
1
1
1
1
Seabass Retail Price
3
3
3
3
3
Tilapia Farm Price
1
1
1
1
1
Tilapia Wholesale Price
2
2
2
2
2
Tilapia Retail Price
1
1
1
1
1
34
Table 6A
Estimates and Tests for Asymmetric Price Transmission in Walking Catfish Prices in
Thailand along the Value Chain
Coefficient
Variable
Lag
Sig. Level
Symbol Estimates
Dependent Variable: Cumulative Change in (∆) Walking Catfish Wholesale Price
Cumulative + ∆ Walking Catfish Farm Price
Cumulative - ∆ Walking Catfish Farm Price
Cumulative + ∆ Walking Catfish Retail Price
Cumulative - ∆ Walking Catfish Retail Price
0
0.5874
0.0250
1
-0.0327
0.8760
0
0.2120
0.2300
1
0.3379
0.1360
0
0.1652
0.6090
1
1.0967
0.0140
2
-0.6397
0.0600
0
0.5761
0.3120
1
1.6022
0.0090
2
-1.0324
0.0630
0.0214
0.5530
Constant
Adjusted R-squared = 0.4344, Durbin-Watson Statistics = 1.8394
Testing for Asymmetry
F-stat
(df= 1, 77)
Null Hypotheses (H0)
35
Sig. Level
1.38
0.2438
1.15
0.2862
0.00
0.9575
0.37
0.5436
0.41
0.5232
0.33
0.5682
3.30
0.0731
1.02
0.3158
0.31
0.5766
0.07
0.7950
1.27
0.2625
+ ∆ = positive change, - ∆ = negative change
Table 6B
Estimated Price Transmission Equations for Walking Catfish in Thailand
Variable
Lag
Coefficient
Sig. Level
Dependent Variable: Walking Catfish Farm Price
Walking Catfish Farm Price
1
0.9165
0.0000
2
-0.2910
0.0450
3
0.2003
0.0660
Trend
0.0006
0.0230
Constant
0.5455
0.0130
Adjusted R-squared = 0.8744, Durbin-Watson Statistics = 1.9843
Dependent Variable: ∆ Walking Catfish Wholesale Price
∆ Walking Catfish Wholesale Price
1
-0.1580
0.2060
∆ Walking Catfish Farm Price
0
0.1971
0.1850
1
0.1239
0.3970
∆ Walking Catfish Retail Price
0
0.0774
0.8440
1
1.2524
0.0020
2
-0.9776
0.0060
∆ Vannamei Shrimp Wholesale Price
0
-0.0523
0.6890
1
0.1178
0.4320
2
0.4051
0.0220
3
-0.3158
0.1010
4
0.1654
0.3380
5
0.0430
0.7720
∆ Seabass Wholesale Price
0
-1.1133
0.0120
1
1.1883
0.0100
∆ Tilapia Wholesale Price
0
0.2052
0.0080
1
-0.0794
0.3710
2
0.0698
0.3730
Constant
0.0014
0.8520
Adjusted R-squared = 0.4274, Durbin-Watson Statistics = 1.9822
Dependent Variable: Walking Catfish Retail Price
Walking Catfish Retail Price
1
1.3004
0.0000
2
-0.4159
0.0000
36
Trend
0.0005
Constant
0.4204
Adjusted R-squared = 0.9719, Durbin-Watson Statistics = 2.0082
∆ = first difference
Note: All price series are in Natural Logarithmic form
0.0220
0.0050
Table 7A
Estimates for Asymmetric Price Transmission in Vannamei Shrimp Prices in Thailand
Coefficient
Variable
Lag
Sig. Level
Symbol Estimates
Dependent Variable: Cumulative Change in (∆) Vannamei Shrimp Farm Price
Cumulative + ∆ Vannamei Shrimp Wholesale Price
Cumulative - ∆ Vannamei Shrimp Wholesale Price
Cumulative + ∆ Vannamei Shrimp Retail Price
Cumulative - ∆ Vannamei Shrimp Retail Price
0
0.2824
0.1590
1
-0.1714
0.3960
2
-0.1417
0.4870
3
0.1190
0.2700
0
0.2542
0.1920
1
0.1710
0.4040
2
-0.2196
0.3020
3
-0.0813
0.5000
0
0.0166
0.9300
1
0.3601
0.0600
2
0.0610
0.7410
0
0.1926
0.2930
1
-0.0964
0.6180
2
0.3200
0.1000
Constant
0.0361
0.7030
Adjusted R-squared = 0.5124, Durbin-Watson Statistics = 1.6498
Dependent Variable: Cumulative Change in (∆) Vannamei Shrimp Wholesale Price
37
Cumulative + ∆ Vannamei Shrimp Retail Price
Cumulative - ∆ Vannamei Shrimp Retail Price
0
0.9203
0.0000
1
0.1137
0.3190
2
-0.1296
0.2540
3
0.0929
0.4040
0
0.7814
0.0000
1
0.1819
0.1520
2
0.0755
0.5390
3
-0.0664
0.6040
Constant
-0.0243
Adjusted R-squared = 0.7538, Durbin-Watson Statistics = 1.8153
Dependent Variable: Cumulative Change in (∆) Vannamei Shrimp Retail Price
0.5980
Cumulative + ∆ Vannamei Shrimp Wholesale Price
Cumulative - ∆ Vannamei Shrimp Wholesale Price
0
0.8564
0.0000
1
-0.0533
0.6780
2
0.2062
0.1070
3
-0.2159
0.0520
0
0.8845
0.0000
1
-0.0423
0.7480
2
-0.1276
0.3410
3
0.1120
0.3760
Constant
0.0203
Adjusted R-squared = 0.7659, Durbin-Watson Statistics = 1.8440
+ ∆ = positive change , - ∆ = negative change
Table 7B
Tests for Asymmetric Price Transmission in Vannamei Shrimp Prices in Thailand
Null Hypotheses (H0)
F-stat
Sig. Level
Dependent Variable: Cumulative ∆ Vannamei Shrimp Farm Price (df = 1, 49)
38
0.01
0.9219
1.23
0.2736
0.06
0.7999
1.27
0.2660
0.02
0.8866
0.6440
0.42
0.5192
2.48
0.1216
0.89
0.3489
0.01
0.9384
0.53
0.4682
0.00
0.9731
0.63
0.4310
0.30
0.5851
Dependent Variable: Cumulative ∆ Vannamei Shrimp Wholesale Price (df = 1, 54)
0.54
0.4643
0.14
0.7133
1.28
0.2630
0.72
0.4009
0.33
Dependent Variable: Cumulative ∆ Vannamei Shrimp Retail Price (df = 1, 57)
df = degree of freedom for F-test
39
0.5696
0.02
0.8801
0.00
0.9555
0.09
0.0921
3.20
0.0791
1.22
0.2746
Table 7C
Estimated Price Transmission Equations for Vannamei Shrimp in Thailand
Variable
Lag
Coefficient
Sig. Level
Dependent Variable: Vannamei Shrimp Farm Price
Vannamei Shrimp Farm Price
1
0.4947
0.0000
Vannamei Shrimp Retail Price
0
0.0649
0.5620
1
0.1416
0.2540
2
0.0704
0.5460
Vannamei Shrimp Wholesale Price
0
0.3054
0.0090
1
-0.1717
0.2340
2
-0.1408
0.2920
3
0.0523
0.4090
Constant
1.1629
0.0020
Adjusted R-squared = 0.7733, Durbin-Watson Statistics = 1.9003
Dependent Variable: Vannamei Shrimp Wholesale Price
Vannamei Shrimp Wholesale Price
1
0.8658
0.0000
2
-0.3884
0.0250
3
0.3469
0.0110
Vannamei Shrimp Retail Price
0
0.7778
0.0000
1
-0.4878
0.0010
2
0.1414
0.3650
3
-0.2840
0.0350
Constant
0.1552
0.4380
Adjusted R-squared = 0.9270, Durbin-Watson Statistics = 1.9603
Dependent Variable: Vannamei Shrimp Retail Price
Vannamei Shrimp Retail Price
1
0.9086
0.0000
2
-0.1737
0.2010
Vannamei Shrimp Wholesale Price
0
0.8321
0.0000
1
-0.7700
0.0000
2
0.1482
0.3690
3
-0.0064
0.9290
Walking Catfish Retail Price
0
-0.2388
0.2370
1
0.4223
0.1910
2
-0.2511
0.1730
Constant
0.5322
0.0900
Adjusted R-squared = 0.9400, Durbin-Watson Statistics = 2.0047
Note: All price series are in Natural Logarithmic form
40
Table 8A
Tests for Asymmetric Price Transmission in Seabass Prices in Thailand
Coefficient
Variable
Lag Symbol Estimates Sig. Level
Dependent Variable: Cumulative Change in (∆) Seabass Wholesale Price
Cumulative + ∆ Seabass Farm Price
Cumulative - ∆ Seabass Farm Price
0
0.5874
0.0000
1
-0.0474
0.7140
2
0.2063
0.1140
3
-0.0886
0.4560
0
0.1183
0.1770
1
0.2005
0.0430
2
-0.1372
0.1650
3
0.2645
4.5826
0.0030
0.0000
F-stat
(df = 1, 51
Sig. Level
8.18
0.0061
1.79
0.1871
3.32
0.0743
4.55
0.0378
91.29
0.0000
Constant
Adjusted R-squared = 0.0897, Durbin-Watson Statistics = 1.6685
Testing for Asymmetry
Null Hypotheses (H0)
+ ∆ = positive change , - ∆ = negative change
41
Table 8B
Estimated Price Transmission Equations for Seabass in Thailand
Variable
Lag Coefficient
Sig. Level
Dependent Variable: ∆ Seabass Retail Price
∆ Seabass Retail Price
1
0.7074
0.0000
2
-0.1671
0.3160
3
-0.1626
0.2160
∆ Walking Catfish Retail Price
0
0.5428
0.1450
1
0.0928
0.8580
2
-0.5254
0.3280
3
0.9517
0.0470
4
-0.0213
0.9610
5
-0.3462
0.2980
Constant
0.0009
0.8650
Adjusted R-squared = 0.4802, Durbin-Watson Statistics = 1.9852
Dependent Variable: ∆ Seabass Farm Price
∆ Seabass Farm Price
1
0.2381
0.0590
∆ Walking Catfish Farm Price
0
0.0376
0.6640
1
0.1914
0.0200
2
0.0492
0.5480
Constant
0.0002
0.9520
Adjusted R-squared = 0.0766, Durbin-Watson Statistics = 2.0226
Dependent Variable: ∆ Seabass Wholesale Price
∆ Seabass Wholesale Price
1
0.0842
0.4960
∆ Seabass Farm Price
0
0.2639
0.0000
1
0.1074
0.1300
Constant
0.0033
0.0830
Adjusted R-squared = 0.3840, Durbin-Watson Statistics = 1.9870
∆ = change/first difference
Note: All price series are in Natural Logarithmic form
42
Table 9A
Estimates for Asymmetric Price Transmission in Tilapia Prices in Thailand
Coefficient
Estimate
Sig.
Variable
Lag
Symbol
s
Level
Dependent Variable: Cumulative Change in (∆) Tilapia Farm Price
Cumulative + ∆ Tilapia Wholesale Price
Cumulative - ∆ Tilapia Wholesale Price
Cumulative + ∆ Tilapia Retail Price
Cumulative - ∆ Tilapia Retail Price
43
0
0.2639
0.2150
1
0.1071
0.6800
2
-0.1528
0.5910
3
0.0206
0.9370
4
-0.0701
0.7790
5
-0.2190
0.3930
6
0.4350
0.0620
0
0.2903
0.1590
1
-0.1105
0.6210
2
-0.0259
0.9040
3
-0.2455
0.2480
4
-0.0949
0.6800
5
0.4362
0.0620
6
-0.0570
0.7970
0
0.4995
0.5570
1
0.4465
0.6660
2
-1.0667
0.2000
0
0.4902
0.5940
1
-0.7139
0.5050
2
0.7437
0.4040
Constant
2.0702
Adjusted R-squared = 0.0897, Durbin-Watson Statistics = 1.6685
Dependent Variable: Cumulative Change in (∆) Tilapia Retail Price
0.2080
Cumulative + ∆ Tilapia Wholesale Price
-0.0867
0.1190
0.0559
0.3500
0.0771
0.1790
-0.0439
0.3600
0.0133
0.8180
0.0219
0.7130
0.0327
0.5970
0.0203
0.7520
0.0877
0.1840
0.0450
0.4450
-0.0489
0.4020
-0.0084
0.8910
Cumulative - ∆ Tilapia Wholesale Price
Cumulative + ∆ Tilapia Farm Price
Cumulative - ∆ Tilapia Farm Price
Constant
2.0702 0.2080
Adjusted R-squared = 0.0617, Durbin-Watson Statistics = 1.7371
+ ∆ = positive change , - ∆ = negative change
Table 9B
Tests for Asymmetric Price Transmission in Tilapia Prices in Thailand
Fsta Sig.
Null Hypotheses (H0)
t
Level
Dependent Variable: Cumulative Change in (∆) Tilapia Farm Price (df = 1, 48)
0.0
1
0.9354
0.2
9
0.5934
0.0
9
0.7631
0.4
8
0.4913
0.0
0
0.9478
44
3.0
7
2.2
3
0.0862
0.1418
0.5
3
0.4694
0.0
0
0.9947
0.4
4
0.5080
1.6
4
0.2071
0.2
9
0.5936
0.0
7
0.7866
0.0
4
0.8356
0.2
1
0.6503
0.1
2
0.7310
Dependent Variable: Cumulative Change in (∆) Tilapia Retail Price (df = 1, 60)
1.2
4
0.2695
0.1
3
0.7159
0.5
6
0.4554
0.0
0
0.9897
0.0
2
0.8967
0.5
0
0.4843
0.8
9
0.3494
0.8
8
0.3510
1.9
1
0.1721
0.1
1
0.7396
0.2
9
0.5916
45
0.0
8
0.7765
df = degree of freedom for F-test
Table 9C
Estimated Price Transmission Equations for Tilapia in Thailand
Variable
Lag Coefficient Sig. Level
Dependent Variable: ∆ Tilapia Retail Price
∆ Tilapia Retail Price
1
-0.2825
0.0090
∆ Walking Catfish Retail Price
0
0.3259
0.0040
1
-0.1252
0.2790
2
-0.2847
0.0110
∆ Tilapia Wholesale Price
0
-0.0109
0.6840
1
-0.0175
0.5250
∆ Tilapia Farm Price
0
0.0193
0.4860
1
-0.0314
0.2630
Constant
0.0045
0.3080
Adjusted R-squared = 0.2006, Durbin-Watson Statistics = 1.9823
Dependent Variable: Tilapia Farm Priceii
Tilapia Farm Price
1
0.5449
0.0000
Walking Catfish Farm Price
0
0.1080
0.5550
1
0.2709
0.1390
Constant
0.1026
0.7960
Adjusted R-squared = 0.4635, Durbin-Watson Statistics = 1.9731
Dependent Variable: Tilapia Wholesale Price
Tilapia Wholesale Price
1
0.8043
0.0000
2
-0.0458
0.6850
Constant
0.7182
0.0030
Adjusted R-squared = 0.5635, Durbin-Watson Statistics = 1.9922
∆ = first difference
Note: All price series are in Natural Logarithmic form
46
Appendix 1
Price Data and Summary Statistics
Level
Seabass
Farm
Jan 05 – July 10
108.75
9.59
8.82
Wholesale
Jan 05 – May 10
116.48
10.65
9.14
Retail
Jan 05 – July 10
140.84
23.92 16.98
Farm
Jan 03 – Sep 10
26.98
3.08
11.41
Wholesale
Jan 03 – Aug 10
30.12
3.15
10.46
Retail
Jan 03 – Oct 10
46.45
6.33
13.62
Farm
Jan 03 – Sep 10
19.47
3.11
15.98
Wholesale
Jan 03 – May 10
29.25
4.57
15.61
Retail
Jan 03 – Oct 10
37.91
4.64
12.25
Farm
Jan 05 – Sep 10
119.28
14.68 12.31
Wholesale
Jan 05 – Sep 10
135.58
18.31 13.50
Retail
Jan 05 - Sep10
215.48
19.24
Hybrid Walking
Catfish
Tilapia
Vannamei Shrimp
(50pcs/kg)
Time period
Average
S.D.
(Baht/kg)
Species
47
C.V.
8.93
i
We have also used the ADF and Pillips-Perron unit root tests, and the Kwiatkowski-
Phillips-Schmidt-Shin stationarity test. The ADF test concluded that walking catfish
wholesale and tilapia farm price series are stationary at levels at the 0.05 level of
significance, and shrimp retail, walking catfish farm, and tilapia wholesale price series
are trend stationary. The Pillips-Perron test showed shrimp farm, walking catfish
wholesale, and tilapia farm and wholesale series as stationary at levels, and walking
catfish farm as trend stationary at the 0.05 level of significance. The KwiatkowskiPhillips-Schmidt-Shin test concluded that shrimp farm, wholesale and retail price series,
seabass retail price series, and walking catfish and tilapia wholesale prices series are
trend stationary at the 0.05 level of significance. All tests conducted including Ng-Perron
test showed that tilapia retail price series, seabass farm, wholesale and retail price
series have unit root.
ii
Walking Catfish Farm Price, Tilapia Wholesale Price and Tilapia Retail Price are the
Granger cause of Tilapia Farm Price. However, we did not find significant fit for Tilapia
Farm Price = f(Walking Catfish Farm Price, Tilapia Wholesale Price, Tilapia Retail Price,
Autoregressive terms of Tilapia Farm Price). The null hypotheses that Tilapia Wholesale
Price is a Granger cause of Tilapia Farm Price, and Tilapia Retail Price is a Granger
cause of Tilapia Farm Price were rejected at 0.1 levels. Therefore, we dropped Tilapia
Wholesale Price and Tilapia Retail Price from the model, and re-run the model Tilapia
48
Farm Price = f(Walking Catfish Farm Price, Autoregressive terms of Tilapia Farm Price).
Since Tilapia Farm Price and Walking Catfish Farm Price at stationary at levels without
a trend, therefore, we used a model in levels.
49