Price Transmission in Thai Aquaculture Product Markets: An Analysis along Value Chain and across Species

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 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 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) 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 Jim enez-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 co4 integration analysis of market integration in the 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 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) ii) iii) Testing for a presence of the unit-root, Granger causality, and cointegration; Testing for the price transmission asymmetry along the value chain; and Specifying and estimating the price transmission models Unit Root, Granger Causality and Cointegration Tests 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 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-Perron i (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 (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 (H 0: 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 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 * = α 0t + ∑ α l+ cum∆ (ln Pj+ ) t − l + ∑ α l− cum∆ (ln Pj− ) t − l + ∑ β m+ cum∆ (ln Pm+ ) t − l + ∑ β l− cum∆ (ln Pm− ) + ε , l =0 l =0 m=0 m=0 (1) 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 − otherwise; and ∆(ln Pt ) = ln Pt − ln Pt −1 , if ln Pt < ln Pt −1 and otherwise ε t is the error 10 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 0.5874 0.0250 -0.0327 0.8760 0.2120 0.2300 0.3379 0.1360 0.1652 0.6090 1.0967 0.0140 -0.6397 0.0600 0.5761 0.3120 1.6022 0.0090 -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 0.9165 0.0000 -0.2910 0.0450 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 -0.1580 0.2060 ∆ Walking Catfish Farm Price 0.1971 0.1850 0.1239 0.3970 ∆ Walking Catfish Retail Price 0.0774 0.8440 1.2524 0.0020 -0.9776 0.0060 ∆ Vannamei Shrimp Wholesale Price -0.0523 0.6890 0.1178 0.4320 0.4051 0.0220 -0.3158 0.1010 0.1654 0.3380 0.0430 0.7720 ∆ Seabass Wholesale Price -1.1133 0.0120 1.1883 0.0100 ∆ Tilapia Wholesale Price 0.2052 0.0080 -0.0794 0.3710 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 1.3004 0.0000 -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 0.2824 0.1590 -0.1714 0.3960 -0.1417 0.4870 0.1190 0.2700 0.2542 0.1920 0.1710 0.4040 -0.2196 0.3020 -0.0813 0.5000 0.0166 0.9300 0.3601 0.0600 0.0610 0.7410 0.1926 0.2930 -0.0964 0.6180 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 0.9203 0.0000 0.1137 0.3190 -0.1296 0.2540 0.0929 0.4040 0.7814 0.0000 0.1819 0.1520 0.0755 0.5390 -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 Retail Price Cumulative - ∆ Vannamei Shrimp Wholesale Price 0.8564 0.0000 -0.0533 0.6780 0.2062 0.1070 -0.2159 0.0520 0.8845 0.0000 -0.0423 0.7480 -0.1276 0.3410 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 0.6440 0.02 0.8866 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 0.5696 Dependent Variable: Cumulative ∆ Vannamei Shrimp Retail Price (df = 1, 57) df = degree of freedom for F-test 39 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 0.4947 0.0000 Vannamei Shrimp Retail Price 0.0649 0.5620 0.1416 0.2540 0.0704 0.5460 Vannamei Shrimp Wholesale Price 0.3054 0.0090 -0.1717 0.2340 -0.1408 0.2920 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 0.8658 0.0000 -0.3884 0.0250 0.3469 0.0110 Vannamei Shrimp Retail Price 0.7778 0.0000 -0.4878 0.0010 0.1414 0.3650 -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 0.9086 0.0000 -0.1737 0.2010 Vannamei Shrimp Wholesale Price 0.8321 0.0000 -0.7700 0.0000 0.1482 0.3690 -0.0064 0.9290 Walking Catfish Retail Price -0.2388 0.2370 0.4223 0.1910 -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 0.5874 0.0000 -0.0474 0.7140 0.2063 0.1140 -0.0886 0.4560 0.1183 0.1770 0.2005 0.0430 -0.1372 0.1650 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 0.7074 0.0000 -0.1671 0.3160 -0.1626 0.2160 ∆ Walking Catfish Retail Price 0.5428 0.1450 0.0928 0.8580 -0.5254 0.3280 0.9517 0.0470 -0.0213 0.9610 -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 0.2381 0.0590 ∆ Walking Catfish Farm Price 0.0376 0.6640 0.1914 0.0200 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 0.0842 0.4960 ∆ Seabass Farm Price 0.2639 0.0000 0.1074 0.1300 Constant 0.0033 0.0830 Adjusted R-squared = 0.3840, Durbin-Watson Statistics = 1.9870 ∆ = change/first difference 42 Note: All price series are in Natural Logarithmic form Table 9A Estimates for Asymmetric Price Transmission in Tilapia Prices in Thailand Coefficient Variable Lag Symbol Estimates Sig Level Dependent Variable: Cumulative Change in (∆) Tilapia Farm Price Cumulative + ∆ Tilapia Wholesale Price Cumulative - ∆ Tilapia Wholesale Price Cumulative + ∆ Tilapia Retail Price 43 0.2639 0.2150 0.1071 0.6800 -0.1528 0.5910 0.0206 0.9370 -0.0701 0.7790 -0.2190 0.3930 0.4350 0.0620 0.2903 0.1590 -0.1105 0.6210 -0.0259 0.9040 -0.2455 0.2480 -0.0949 0.6800 0.4362 0.0620 -0.0570 0.7970 0.4995 0.5570 0.4465 0.6660 -1.0667 0.2000 Cumulative - ∆ Tilapia Retail Price 0.4902 0.5940 -0.7139 0.5050 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 FNull Hypotheses (H0) stat Sig Level Dependent Variable: Cumulative Change in (∆) Tilapia Farm Price (df = 1, 48) 44 0.01 0.9354 0.29 0.5934 0.09 0.7631 0.48 0.4913 0.00 0.9478 3.07 0.0862 2.23 0.1418 0.53 0.4694 0.00 0.9947 0.44 0.5080 1.64 0.2071 0.29 0.5936 0.07 0.7866 0.04 0.8356 0.21 0.6503 0.12 0.7310 Dependent Variable: Cumulative Change in (∆) Tilapia Retail Price (df = 1, 60) 45 1.24 0.2695 0.13 0.7159 0.56 0.4554 0.00 0.9897 0.02 0.8967 0.50 0.4843 0.89 0.3494 0.88 0.3510 1.91 0.1721 0.11 0.7396 0.29 0.5916 0.08 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 -0.2825 0.0090 ∆ Walking Catfish Retail Price 0.3259 0.0040 -0.1252 0.2790 -0.2847 0.0110 ∆ Tilapia Wholesale Price -0.0109 0.6840 -0.0175 0.5250 ∆ Tilapia Farm Price 0.0193 0.4860 -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 0.5449 0.0000 Walking Catfish Farm Price 0.1080 0.5550 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 0.8043 0.0000 -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 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 48 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 PillipsPerron 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 Kwiatkowski-Phillips-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 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 [...]... 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... 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... 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,... 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. .. 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... Economics 81(3): 630-7 Goodwin, Barry K and Daniel C Harper (2000) Price Transmission, Threshold Behavior, and Asymmetric Adjustment in the U.S pork sector Journal of Agricultural and Applied Economics 32(3): 543-53 Guillen , Jordi and Ramon Franquesa 2008 Price Transmission and Market Power Analysis in the Spanish Seafood Market Chain Espai de Recerca en Economia Working Papers in Economics No 190 Accessed... 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. .. 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... 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... 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