Price Transmission Relationships in Tuna Value Chain in Thailand

Umesh Bastola1,3, Madan Mohan Dey1, and Amporn Laowapong2

1Aquaculture / Fisheries Center, University of Arkansas at Pine Bluff, 1200 North

University Drive, Mail Slot 4912, Pine Bluff, AR 71601, USA.

2Economic Analysis of Fishery Commodities Section, Department of Fishery, Kasetsart

Campus, Chatuchak Bangkok 10900 Thailand.

3 Presently Ph D Student, School of Economic Sciences, Washington State University,

Pullman WA 99164, USA

Correspondence:

Madan Mohan Dey

E-mail: mdey@uaex.edu

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ABSTRACT

Thailand is the largest importer of skipjack tuna as well as the largest exporter of canned

tuna in the world. This particular study focuses on studying the trend of tuna prices in the

import (frozen skipjack) and export (canned) level, exploring causal relationships, and

examining asymmetry in price transmission in the tuna value chain in Thailand. The two

price series were found to be cointegrated with canned tuna price Granger causing the

skipjack tuna price. A vector error correction model (VECM) showed that 41% of shock

in the long-term equilibrium adjusted in the current month and fully adjusted in 1.4

months. Test of price transmission asymmetry using von Cramon-Taubadel (1999)

approach suggested symmetric relationship indicating identical response in the skipjack

tuna price due to any policy intervention that either increases or decreases the canned

tuna price in Thailand.

Key words: Price Transmission Asymmetry, Cointegration, Tuna, Thailand, Value Chain

Running Head: Price transmission in tuna fishery in Thailand

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Introduction

Tuna is one of the most valuable seafood commodities in Thailand. It was the

second highest seafood product to earn export value (24% of total seafood export value)

after shrimp (38%) in the country in 2009, when 224,542 Million Baht1 (35 % of total

food export) of seafood were exported (Laowapong, 2010). Similarly, tuna is the most

dominant seafood imported in Thailand which accounted for about 52% of total fish

import value (35.8 Billion Baht) in 2009. Thailand is the world’s leading importer of

frozen tuna and exporter of canned tuna (GLOBEFISH, 2010).

The major export of Thai tuna is in canned form (98%) in air-tight containers with

a negligible proportion (2%) in frozen vacuum pouch (Figure 1). Almost 95% of the raw

material used in canning factories comes from import in the form of frozen whole tuna

while 50% of the domestic catch is the supply to these factories. Of the total processed

tuna, about 90% is exported and the remaining is consumed in the domestic market. The

major importing countries of Thai canned tuna in the order of decreasing volume

imported in 2009 are the U.S., Egypt, Libya, Australia, Canada, and Japan

(GLOBEFISH, 2010). The main market segments of the tuna value chain in Thailand are

i) first-hand market from domestic supply, ii) import market of frozen tuna, iii) final

domestic market, and iv) final export market of canned tuna (Figure 1). Among these,

import market of frozen tuna and final export market of canned tuna are the two most

important market segments both in terms of volume and value of fish marketed. This

study investigates trends and relationships between tuna prices in these two levels of tuna

supply chain in Thailand.

1 1 US$=34.3351 Baht in 2009, 31.7270 in 2010.

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Exploring relationships among prices at different levels of supply chain render

valuable information for policy implication. In one hand, it allows for investigating

existence of potential market power in the supply chain while on the other hand, it also

identifies future effects of policy intervention in one level of the chain on the other.

Besides, there may have existed seasonal trend in prices and marketing margins at

different levels of supply chain, the knowledge of which may help to formulate policy

options that benefits for all those who rely on seafood industry for their livelihood.

Price transmission relationships have been widely studied in agricultural sector

while a few of such studies are also conducted in seafood marketing sector. Meyer and

Taubadel (2002) have presented a survey of the literature on asymmetry in price

transmission. They have also elucidated the possible causes of asymmetry in price

transmission as: existence of market power, adjustment and menu costs, and other causes

such as government intervention, asymmetric information, and inventory management.

Some of the studies that examined price transmission relationships in seafood marketing

include: Jaffry (2005) in French hake; Floros (2007) in different fish species in Greece

and UK; Asche, Jaffry, and Hartmann (2007) in salmon in Norway; and Shinoj et al.

(2008) in major fish markets in India. However, no study has yet analyzed price

transmission relationships in seafood markets in Thailand.

The main purpose of this study is to develop an improved knowledge of the

dynamics of tuna value chain in Thailand. The specific objectives include: i) to examine

trends and seasonality in prices of tuna at two important levels of value chain (import

market of frozen tuna and export market of canned tuna); ii) to explore the causal

relationships among the import price of frozen tuna and the export price of canned tuna;

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and iii) to examine the asymmetry of price transmission relationships between the two

price series.

Theoretical framework

There may exist a causal relationship, either short-term or long term, between

these two price series. Identifying these relationships may have valuable implications for

any policy interventions. Hence, this study has tried to explore if any such relationships

exists among the two price variables of tuna value chain.

Time series data are generally non-stationary. Basically, various economic forces

play role to affect variables like price over time. Hence, employing ordinary least squares

regression techniques to investigate causal relationships among variables in time series

data may result into spurious results. Therefore, testing whether data is stationary or

contains unit root is the first step in any time series analysis.

Unit root test of price series

In this study, we have used Augmented Dicky-Fuller (ADF) (1979, 1981) test to

check the presence of unit root in the price series. If there is no unit root, then the price

series are said to be stationary in levels or integrated of order zero (denoted I(0)). If there

is a unit root but differencing the series once makes it stationary, then it is said to be

integrated of order one (I(1)).

The null hypothesis of the ADF test is that the series contains unit root (I(1))

versus alternative hypothesis of no unit root (I(0)). The ADF tests for each series in log

values were performed in levels and first differences with a constant as well as with a

constant and a trend. The appropriate number of lags chosen was based on Schwarz

Information Criteria (1978) for each test.

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Granger Causality Test

Once the data are found to have unit root, we can test for Granger causality to see

if there exists any causal relationships between variables. A time series X is said to

Granger-cause Y if X values provide statistically significant information about future

values of Y. Its mathematical formulation is based on linear regression modeling of

stochastic processes (Granger 1969). Hence, one way of application of Granger causality

test in marketing research may be to identify whether a price variable causes another

price variable to change or not. The null hypothesis is formulated such that its rejection

allows us to conclude that the particular variable Granger causes the other variable. Here,

we have used pairwise Granger causality test to detect the causal relationships between

the two price series.

Test of Cointegration and Vector Error Correction Model

Two or more unit root series are said to be cointegrated if their linear

combinations are stationary. Cointegration analysis involves establishing statistically

sound long-run relationships between time series data. Series that are non-stationary but

have a common stochastic trend may be co-integrated. There are two approaches for

testing cointegration, namely, Engle and Granger test (1987) and Johansen test (Johansen

1988). In this study, we followed Johansen’s cointegration test procedure. This test is

based on vector autoregressive (VAR) system. In this framework, two asymptotically

equivalent tests for cointegration, a trace test and a maximum eigen value test have been

used. A vector error correction model for each cointegrating equations have been

developed to see how various forces react to establish a long-run equilibrium between the

given cointegrated pairs of price series.

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Test of price asymmetry

Method followed by von Cramon-Taubadel (1999) outlined as below have been used in this

study to study the price transmission asymmetry in the supply chain:

Where, ECT is the error correction term obtained from the cointegration equation. The test of

asymmetry in price transmission is the test of null and alternative hypotheses given as:

The rejection of null hypothesis indicates that the price transmission is

asymmetric while the failure to reject the null hypothesis confirms symmetric price

transmission relationship.

Source data

Monthly time-series data obtained from the FAO GLOBEFISH (GLOBEFISH

AN 11101, 11102) with time period from Jan 2005-May 2010 were used in this study.

Two price series namely, frozen skipjack tuna price and canned tuna price in the Thailand

tuna supply chain, were analyzed. The frozen skipjack price averaged 2.1 USD per kg

with a standard deviation of 0.34 USD while the canned tuna price averaged 12.2 USD

per carton2 with a standard deviation of 4.88 USD. Both the series were transformed to

natural logarithms before conducting econometric analysis.

Trends and seasonality in prices

Figure 2 depicts the price movement patterns of each price series over time. There

is no large fluctuation in prices over time. Both the price variables are found to move in a

similar fashion. Monthly seasonal price index was calculated for each series. For that,

2 One carton contained 48 six-ounce-chunks of tuna.

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prices at each individual month were averaged, which were then divided by the sum of

such averages of all 12 months, and then multiplied the ratio by 1200. As shown in Fig.

3, though canned tuna prices are relative stable across different months, the skipjack tuna

prices reveal some seasonal variation. The skipjack prices tend to be generally lower

from January to July and comparatively higher in other months.

Results and Discussions

The unit root tests have been performed at level data and the first difference

including both: 1) a constant and 2) a constant and a linear trend. The tests are applied to

the variables in logarithms. The results of the ADF test indicate that the null hypothesis

of having unit root could not be rejected for both series at level but rejected at first

difference, and hence, both are integrated of order one (I(1)) (Table 1).

Table 2 shows the result from pairwise Granger causality tests. We found that the

canned tuna price Granger caused skipjack tuna price, but not vice versa. Hence the

Granger causality was unidirectional.

Testing for the presence of cointegrating relationships between the two variables,

Johansen’s cointegration test yielded the following results (Table 3). From Table 3, it can

be seen that there exists one cointegration equation between skipjack and canned tuna

prices as given by trace test and maximum eigen value test. The resulting cointegrating

equation is as follows:

ln Skipjack Tuna = -2.9189+ 1.0006 ln Canned Tuna

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Thus, from the above cointegrating equations, 1% increase in canned tuna

price causes about the same percent increase in skipjack price. This indicates that there is

a unitary elastic relationship between these two prices.

The error correction model for skipjack tuna resulted in the following

equation:

Similarly, the error-correction model in case of canned tuna is expressed by the

following relationship:

The models illustrate how deviations from long run equilibrium in series are

restored due to adjustment processes. The first ECM is significant with a R-square value

of 0.2393. The error correction coefficient (or the long-run adjustment parameter) in the

model is also significant at the 1% level. The error correction coefficient shows that 41%

of any shock in the equilibrium is corrected in the current period. The value (λ = (1-

0.413)/0.413 = 1.42} implies that for the shock in the system to be fully adjusted, it

requires 1.42 months of time. None of the other coefficients are statistically significant.

In the second ECM with canned tuna as the dependent variable, we found that none of the

coefficients as well as the model R-square was statistically significant. This result is in

consistent with the results shown by the Granger causality test where found unidirectional

causality from canned tuna to skipjack tuna price.

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The price transmission relationship given by the von Cramon-Taubadel (1999)

approach is expressed as:

Here, the estimated coefficients of the positive and negative residual terms were

not statistically significant. The null hypothesis of equality of these two coefficients

could not be rejected at the 5% level (F1, 58 = 0.150; p-value = 0.697). Hence, it indicated

that the price transmission relationship in tuna supply chain was symmetric.

SUMMARY AND CONCLUSION

The trends and relationships among prices of tuna in Thailand, have been

explored in this study. Imported frozen skipjack prices are found to be relatively less

fluctuating over time as compared to canned tuna price. Nevertheless, prices in these two

series are found to move in a similar fashion. The skipjack tuna price showed seasonality

in prices, being lower from February to July.

A unidirectional Granger causality was observed where canned tuna price was

found to influence the skipjack tuna price. Moreover, the two price series were

cointegrated depicting a long-run equilibrium relationship. The statistically significant

terms in VECM showed that 41 % of deviations in the long run equilibrium were

adjusted in the current period and gap above the equilibrium in one month was found to

produce downward pressure on skipjack tuna price in the subsequent month.

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Furthermore, the price transmission in the tuna value chain in Thailand was found to be

symmetric. Thus, the response of skipjack price to increase in the price of canned tuna

was identical to that to the price decrease.

To conclude, any policy that affect the one level of tuna supply chain in Thailand

would affect the other level. However, any shock in the system would be adjusted fast

enough and maintain the long-run equilibrium. Any policy intervention that triggers price

increase in one level of the supply chain will not have differential response in the other

level of the chain to the price decrease, thus, providing information on potential effect of

any policy intervention on tuna industry in Thailand.

An on-going fish supply-demand projection analysis, currently being

implemented by the International Food Policy Institute/World Bank/University of

Arkansas at Pine Bluff/FAO, indicates that the retail price of tuna is expected to increase

over the next 20 years or so (up to 2030). Our finding of symmetric price transmission

between frozen skipjack tuna and canned tuna in Thailand has a positive implication for

tuna fishermen in the region. The results reveal that tuna canneries in Thailand do not

have market power in the buyer market; increases in export price of canned tuna are

expected to pass fully to the import market of frozen skipjack tuna. Given that the

volume of frozen import of tuna is about 8 times higher than that of domestic catch in

Thailand, the welfare of tuna fishermen in Thailand is expected to be improved with

increase in price of tuna in the global market.

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REFERENCES

Asche, F., S. Jaffry, and J. Hartmann. 2007. Price transmission and market integration:

vertical and horizontal price linkages for salmon. Applied Economis 39:2535-2545.

Dickey, D.A., and Fuller, W.A. (1979). Distribution for the Estimates for Auto

Regressive Time Series with a Unit Root. Journal of the American

Statistical Association 74:427-31.

Dickey, D.A., and Fuller, W.A. (1981). Likelyhood Ratio Statistics for Autoregressive

Time series with a Unit Root. Econometrica 49:1057-72.

Engle, R.F., and Granger, C.W.J. (1987). Cointegration and Error Correction

Representation, Estimation and Testing. Econometrica 55: 251-76.

Floros, C. 2007. Causality and price transmission between fish prices: new evidence from

Greece and UK. European Journal of Social Sciences 4(2):147-159.

GLOBEFISH. 2010. Globefish Market Report: Tuna-May 201, Thailand. FAO

Granger, C.W.J. (1969). Investigating causal relations by econometric models and cross-

spectral methods. Econometrica 37:424-438.

Jaffry, S. 2005. Asymmetric price transmission: A case study of French hake value chain.

Marine Resource Economics 19:511-523.

Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of Economic

Dynamics and Control 12:231-254.

Johansen, S., and Juselius, K. (1990). Maximum Likelihood Estimation and Inference on

Cointegration with Applications to the Demand for Money. Oxford Bulletin of

Economics and Statistics 52:169-210.

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Laowapong, A. 2010. Fisheries Value Chain: Kingdom of Thailand. A report submitted

to Food and Agriculture Organization for the project entitled A Value-chain

Analysis of International Fish Trade and Food Security with an Impact Assessment

of the Small-scale Sector.

Schwarz, G. (1978). Estimating the Dimension of a Model. Annals of Statistics 6:461-

464.

Shinoj, P., B. Ganesh Kumar, R. Sathiadhasb, K.K. Dattaa, M. Menona, and S.K. Singha.

2008. Spatial price integration and price transmission among major fish markets in

India. Agricultural Economics research Review 21:327-335.

von Cramon-Taubadel, S., and J.P. Loy. 1999. The identification of asymmetric price

transmission processes with integrated time series. Jahrbucher for

Nationalokonomieund Statistik 218:85-106.

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Table-1: Summary of Augmented Dickey Fuller test of unit root for the two price

variables at their level and first difference.

Variable name

ADF test at level ADF test at first differences

Constant Constant and trend Constant Constant and trend

Skipjack Tuna -2.110 -2.029 -9.237** -9.245**

Canned Tuna -1.401 -1.147 -7.361** -7.391**

** denotes rejection of null hypothesis of having unit root at the 1% significance level.

* denotes rejection of null hypothesis of having unit root at the 5% significance level.

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Table 2. Results of the pairwise Granger causality test.

 Null Hypothesis F-Statistic Decision

 Canned tuna does not Granger cause skipjack tuna 6.325** Reject H0

 Skipjack tuna does not Granger cause canned tuna 1.880 Accept H0

*denotes null hypothesis rejected at 5%.

** denotes null hypothesis rejected at 1%.

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Table 3. Results of cointegration rank test between skipjack and canned tuna prices

No. of

cointegration

equation

Trace Maximum Eigen Values

Statistics p-value* Statistics p-value*

1  13.748  0.028  13.483  0.019

*MacKinnon-Haug-Michelis (1999) p-values.

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FIGURE CAPTIONS

Figure 1. Marketing channel of tuna in Thailand in 2009.

Figure 2. Prices of frozen skipjack tuna (c & f Thailand) and canned tuna (c & f USA).

Figure 3. Seasonal variation in prices of frozen skipjack tuna (c & f Thailand) and canned

tuna (c & f USA).

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Figure 1: Market channel of tuna in Thailand in 2009.

90%10%

Tuna from Thai vessels >100,000 tons

Domestic consumption

Factories

Export

Tuna from import >800,000 tons

50%50%

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Figure 2. Prices of frozen skipjack tuna (c & f Thailand) and canned tuna (c & f USA). (1

carton = 48*6 oz chunks v/f). Source (GLOBEFISH AN 11101, 11102).

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Figure 3. Seasonal variation in prices of frozen skipjack tuna (c & f Thailand) and canned

tuna (c & f USA).

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