ࡱ> 5@ 0"bjbj22 8&XXu::::Z5Z5Z5856\877:(8(8@8S9S9S9[[[[[[[$^RZ`R[/CO9S9/C/C[::(8@8\LLL/C:*(8@8[L/C[LLMVT@d:YU(87 0G"Z5CvT U\0\Tx`WJ>`YU::::gU\`US9<L >?S9S9S9[[D.4dL^4VERTICAL PRICE LINKAGES IN THE EGYPTIAN TILAPIA MARKET HUSSIEN A. HEBICHA Central laboratory for Aquaculture Research, Agricultural Research Center, Abassa, Abou Hammad, Sharkia, Egypt. Abstract This paper investigates price transmission asymmetry of Tilapia in the Egyptian market. Monthly wholesale, retail, and quarterly farm gate prices covering the period from 1998 to 2006 were used in the analysis. Retail to wholesale price spread was larger than that between wholesale and farm level. Retail prices were relatively less volatile than wholesale and farm prices. Upstream to downstream price linkages revealed symmetric price transmission between farm and wholesale levels. Short run asymmetric price transmissions were apparent between wholesale and retail market chains; however, price transmission was symmetric in the long run. Wholesale price changes took three months to be fully reflected in retail prices. On the other hand, analyzing price linkages from downstream to upstream showed the existence of short run price asymmetry and long run price symmetry between retail and wholesale marketing levels. There was timing asymmetry for price changes at the retail level to be completely transmitted to the wholesale level. The wholesale to farm level had symmetric price transmission, however price increases at the wholesale was fully transmitted while price decreases was less than fully transmitted to the farm level. INTRODUCTION In Egypt, tilapia production from all sources amounted to 349 thousand ton representing about 36% of total fish production, while aquaculture production of tilapia species was estimated at 259 thousand tons (General Authority for Fish Resources Development, 2006). Governmental fish farms sell their production to wholesalers through biddings, while private farms sell their fish to wholesalers through tentative agreement. Some wholesalers act as retailers while others sell through auction in central markets where registered auctioneers charge a percentage of the fish value from wholesalers. Fish sold as it is to consumers. No marketing services are added to fish from farm to retail level except grading, icing, and transportation. The issue of price transmissions along the marketing channels for agricultural products has attracted research interest among economists. There is a common concern that prices are sticky or slowly responsive to price changes in other levels of the marketing channels. Furthermore, there may be a difference in the way and the speed that prices at a marketing level adjust to an increase or a decrease in the prices at another level. The concept of price transmission is particularly important in the analysis of welfare effects of changes in agricultural policy and in the analysis of introducing new technologies that changes the costs and consequently the prices at a specified marketing level. Empirical studies of price transmission across market levels have tried to clarify whether or not asymmetries do occur. The studies are based on estimating models of vertical price linkages and using it to evaluate how downstream prices move in relation to upstream price changes or vice versa. Most of these studies have been based on a model adopted by Houck (1977) that was latter modified by ward (1982). Ward (1982) examined price transmission for a number of fresh vegetables in U.S. and found that wholesale prices lead both retail and shipping point prices. He found that wholesale price decreases were reflected at the retail more than were price increases and that wholesale price decreases were more fully passed to the shipping point relative to wholesale price increases. Kinnucan and Forker (1987) studied the margins in the U.S. milk sector and concluded that there was asymmetry, but in the opposite direction to that found for fresh produce by Ward. Weekly f.o.b. and retail prices were used to examine price transmission process for lemon and orange marketing channels in four retail markets in U.S. (Pick et al., 1990). Their results showed that in the short run, retail prices and margins were more responsive to f.o.b. price increases than they were to price decreases. However, price movements were symmetric in the long run. Bernard and Willett (1996) have found that wholesale price causes both farm and retail prices and farm prices causes retail prices in the U.S. broiler industry. In addition, they indicated the existence of price asymmetry between wholesale and both farm and retail levels and between farm to retail level. Assuming that causation running from upstream to downstream, Brooker et al. (1997) utilized weekly data over the period from 1988 to 1993 to study price transmission for fresh vegetables. Asymmetric price transmissions were evident for bell peppers at shipping pointwholesale and wholesaleretail levels. Worth (1999) examined the vertical linkages between f.o.b. and retail prices of six vegetables assuming that f.o.b. price lead retail price. There was evidence that retail prices for carrots and tomatoes showed greater response to f.o.b. price increases. Aguiar and Santana (2002) used monthly-deflated prices to investigate price transmission behavior in Brazil for three groups of agricultural products differ with respect to market concentrations and product storability. Their results showed that price transmission in most of the markets was asymmetric, and that market concentration and product storability may not be necessary for asymmetric price transmission. They explained price asymmetry by the high and growing inflation rates in the economy. Romain et al. (2002) identified short and long run asymmetries between farm and retail fluid milk markets in New York City and Upstate New York. However, these asymmetries were no longer significant after the enforcement of retail price ceiling law in 1991. Girapunthong et al. (2003) investigated price transmission for U.S. tomatoes assuming that farm prices led changes in wholesale and retail prices. They found evidence of symmetric price transmission between farm and retail levels and asymmetric price transmission between wholesale and both producers and retailers. Retail prices respond more to rising wholesale prices than to falling prices while wholesale prices respond more to declining than to rising producer prices. Among the reported causes of price transmission asymmetry in commodity markets are theories of local market power, search cost, perishability of the product, government intervention in the pricing system, imperfect information among economic agents, and the expectations about inflation rates and price increases. The purpose of this paper is to study the price linkages among farm, wholesale, and retail stages of the market chain of tilapia in Egypt over time. The focus is on the resulting price transmission, the possibility of differences in the adjustment process to price increases versus price decreases, and on the amount of time required for the adjustment to take place. MATERIALS AND METHODS Data The data used in this study were obtained from Central Agency for Public Mobilization and Statistics (CAPMAS). The data include monthly retail and wholesale prices, consumer price index for food items, and quarterly farm gate prices. The reported prices are averages for all grades or sizes of tilapia. The data cover the period from 1998 to 2006. All price were expressed in real terms using CPI for food (January 2007=100) as a deflator. Model Estimate The estimation technique used here is based on a variable splitting technique to estimate irreversible function similar to that proposed by Houck (1977) and endorsed in many previous studies. Assuming that causality goes from (X) to (Y) and that (X) have different impact on (Y) according to whether its sign is positive or negative. The model was estimated by ordinary least squares method in the following form Yt = c0T + "ri=0 ai XIt + "sj=0 bj XDt + Et Where Yt is the change in Y at period (t) from its initial value at period (0), T is time trend, XIt is "ni=1 (XIt - XIt-1) if XIt > XIt-1 and zero otherwise, XDt is "ni=1 (XDt - XDt-1) if XDt < XDt-1 and zero otherwise, c0, ai, and bj are parameters to estimate, and Et is an error term. This specification allows testing for short run and long run asymmetries. Testing the null hypotheses of whether the speed of adjustment are equivalent for rising versus falling prices (i.e. Ho: ai = bJ, i=0, 1,..., r; j=0,1,..., s) are tests of short run price transmission asymmetries. If the null hypothesis is rejected for at least one of the equality, it implies the existence of asymmetry. These tests can be classified into test of contemporaneous short run asymmetry (Ho: a0 = b0) and tests of distributed lag short run asymmetries (Ho: ai = bJ, i=1,..., r; j=1,..., s). In addition, if r `" s, it implies distributed lag short run asymmetry, i. e. differences in the timing of price pass-through. The test of long run price asymmetry or the cumulated effect is made by testing the null hypothesis, Ho: "ri=k ai = "sj=k bj with k element of [0, min (r, s)], rejection of the null hypothesis indicates differences in the amount of pass-through and therefore indicates the existing of long run price asymmetry. If changes in X are fully reflected in Y, one would expect that "ri=k ai = 1 and "sj=k bj =1. It should be noted that short run asymmetry could occur without long run asymmetry and vice versa. Normal statistical inference for linear regression on non-stationary time series is not valid and casts doubts on the reliability of the results (Kennedy, 1992; Charemza and Deadman, 1992). In addition, non-stationarity of the series implies a different econometric method. Therefore, before estimating the equation, all price series have to be tested to determine whether they were stationary. Causality In competitive markets, price movements or causality flowed from farm to wholesale to retail. However, concentration at any of the market levels may cause price at this level to lead other prices. The direction of price flows has to be assumed or tested. Causality tests can be used to determine the direction of the price flows between market levels. Granger causality tests were used to establish these directions (Gujarati, 1988; Kennedy, 1992). The test was performed by regressing retail price on lagged values of itself and lagged values of wholesale prices. An F-test was used to test the null hypothesis that lagged values of the wholesale prices were not significant. If the null hypothesis is rejected, it implies that wholesale price cause or lead retail price. On the other hand, it is necessary to test whether retail price leads wholesale price by using the same procedure. Causality test requires generous lags that are not practically possible for quarterly data in this study. Therefore, the tests were applied to monthly data, wholesale and retail prices, while bidirectional causal relationships were assumed for farm price linkages. RESULTS AND DISCUSSIONS Unit Root Tests Results Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests including intercept and trend were used to test for the stationarity of the price series (Kennedy, 1992; Charemza and Deadman, 1992). The null hypothesis of a unit root (non-stationarity) was rejected for each of price series in the level and thus it is considered that all the series are stationary (Table 1). Causality Results The null hypothesis that retail price does not cause wholesale price can be rejected at 0.06 level of significance with F-value equals 2.89. Meantime, the null hypothesis that wholesale price does not cause retail price can be rejected at 0.10 level of significance with F-value equals 2.35. The results were sensitive to the length of the lags used; therefore, they do not present strong evidence of causality. Accordingly, the causality was assumed to be bidirectional. Farm price variations are not transmitted directly to the retail level. On the contrary, farm price changes are usually filtered by wholesalers. Therefore, four equations for price linkages were estimated, two for price flow from farm to wholesale and from wholesale to retail and the other two for price flow from retail to wholesale and from wholesale to farm. Prices Descriptive Statistics and Trend The behavior of tilapia retail, wholesale, and farm real prices over the period of analysis is illustrated in Figure 1. Prices seemed to move together although they appeared to have negative trend over the time period. Actually, the estimated price growth rates indicated that retail, wholesale, and farmer real prices were declining at the rate of 0.95%, 1.2%, and 1.3% per quarter, respectively. Meantime, retail, wholesale, and farm nominal prices were growing at the rate of 1.2%, 1.3%, and 1.4% per quarter, respectively. This may be explained by increasing inflation rates during this time period. In addition, it can be seen from the graph that the spread between retail and wholesale prices started to increase from the second quarter of 2002 reached the maximum in the last quarter of 2004, then started to decrease. If price changes are passed through immediately and completely from one level of the marketing chain to another, then one would expect prices at the different levels to have similar variances (Pick et al., 1990; Worth, 1999). Tilapia price coefficient of variability as well as standard deviation decrease as the product goes down the market chain (Table 2). This may be explained by the availability of more substitutes and Table 1. Unit Root Tests for Price Series of Tilapia in Egypt. SeriesADF TestP.P. TestReal Retail Price (Quarterly)-3.33 (0.07)-3.34 (0.07)Real Wholesale Price (Quarterly)-3.68 (0.03)-3.94 (0.02)Real Farm Price (Quarterly)-4.10 (0.01)-3.77 (0.03)Real Retail Price (Monthly)-4.03 (0.01)-4.08 (0.01)Real Wholesale Price (Monthly)-3.75 (0.02)-5.35 (0.001) Notes: (1) ADF is the augmented Dickey-Fuller test and P.P. is Phillips- Perron test. (2) Figures in parentheses are the significance levels.  sources at the retail level. These differences in price variability indicate that prices at the retail level do not respond or adjust fully to price changes at the farm or wholesale level. The retail to wholesale spread is about twice that between wholesale and farm. This implies that relatively more services are added to tilapia between retail and wholesale levels, as compared to wholesale and farm levels. Since marketed tilapia is neither stored nor processed, most of the spread between retail and wholesale can be explained by the cost and risk of holding a perishable commodity at the retail level, or retailers may have more barraging power over wholesalers so that they can extract economic profits. Price Asymmetry Results The equations for monthly data were estimated with an initial lags of three months. The number of month lagged was then reduced to include only the lagged Table 2. Summary Statistics of Tilapia Real Prices ItemMean (L.E./Kg)Std. Dev. (L.E./Kg)Max. (L.E./Kg)Min. (L.E./Kg)C.V. (%)Farm Price (FP)10.881.6313.937.7915.00Wholesale Price (WP)11.391.6014.078.2014.06Retail Price (RP)12.381.4615.089.2211.80 coefficients that were significantly different from zero. Durbin Watson and BreuschGodfrey L.M. tests were used to detect for the presence of serial correlation in the initial OLS estimates. Estimation of the autocorrelation coefficient was obtained using Chocrane-Orcutt iterative procedure. The PraisWinston procedure was used to correct for the autocorrelation. The estimated equations are shown in Table 3. The estimated coefficients of price movements and asymmetry hypotheses testing are shown in Table 4. Upstream to Downstream Price Linkages The estimated equation for farm-wholesale price linkages explained about 98% of the variations in wholesale price. The estimated coefficients were all statistically significant. The coefficients for price increases and decreases had positive signs indicating that farm and wholesale prices move together. The estimated coefficient for price increases was less than that for price decreases by 0.04, however, this difference was not statistically significant (Calculated F=0.81). This implied the existence of price transmission symmetry between farm-wholesale levels. Price increases at the farm level was fully transmitted to the wholesale level while price decreases was more than completely transmitted to the wholesale level. The estimated coefficients for wholesale-retail equation showed that both price increases and decreases for the three months were positive and significant indicating that wholesale and retail prices move up and down together. In other words, there were two-month lags before retail price fully adjusted to wholesale price changes. This indicates that the time of price pass through was the same for price increases or decreases. The estimated coefficients for wholesale price decreases were larger than that for wholesale price increases for the three months. Previous research had found that retail prices tended to reflect more of a wholesale price decrease than a wholesale price increase and explained that rising retail prices might reduce sales and increased the incidence of spoilage (ward, 1982). Hypothesis testing showed that there was asymmetry in price transmission only for the initial month (calculated F=128.34) leading to the conclusion that the response of tilapia retail prices to whole- Table 3. Estimated Coefficients of Price Asymmetry in the Egyptian Tilapia Market Between 1998 and 2006 ItemUpstream to Downstream Price LinkagesDownstream to Upstream Price LinkagesFarm ! WholesaleWholesale ! RetailRetail ! WholesaleWholesale! FarmTrend0.025 (0.014) (0.08)-0.05 (0.004) (0.00)-0.05 (0.002) (0.00)-0.03 (0.014) (0.031)Price Increase1.01 (0.028) (0.00)0.681 (0.12) (0.00)0.610 (0.09) (0.00)0.993 (0.026) (0.00)Price Decrease1.05 (0.029) (0.00)0.773 (0.12) (0.00)0.519 (0.09) (0.00)0.931 (0.026) (0.00)Price Increase(-1) -------0.404 (0.13) (0.00)0.446 (0.10) (0.00) -------Price Decrease (-1) -------0.510 (0.13) (0.00)0.417 (0.09) (0.00) -------Price Increase (-2) -------0.212 (0.11) (0.06)0.101 (0.09) (0.26) -------Price Decrease (-2) -------0.241 (0.12) (0.04)0.346 (0.09) (0.00) -------Adjusted R20.980.910.950.98D.W.1.442.051.851.45LM Test Statistic2.28 (0.13)a0.06 (0.79)a0.43 (0.50)a2.17 (0.14)aNotes: Figures in the first set of parentheses are the estimated standard errors of the coefficients and the figures in the second set of parentheses are the significance levels. a: is the probability level to reject the null hypothesis of no serial correlation in the residuals. sale price changes was significantly different for increases or decreases of wholesale prices. A direct interpretation of the estimated coefficients is as follows: a one pound increase in the wholesale price of tilapia leads to a L.E. 0.681 increase in the retail price during the initial month, while a one pound decrease in the wholesale price leads to L.E. 0.773 decrease in retail price. There is about a L.E. 0.092 per Kg difference in the amount that the retail price responds to a one pound wholesale price changes Table 4. Symmetry Hypothesis Tests for The Estimated Tilapia Price Coefficients between Different Market Levels Between 1998 and 2006. Null HypothesesUpstream to Downstream Price LinkagesDownstream to Upstream Price LinkagesFarm ! WholesaleWholesale ! RetailRetail ! WholesaleWholesale! Farma0 = b0F= 0.81 do not reject Ho aF= 128.34 reject HoF= 108.82 Reject HoF= 2.30 do not reject Hoa1 = b1 -------F= 0.23 do not reject HoF= 0.03 do not reject Ho -------a2 = b2 -------F= 0.02 do not reject Ho ------- -------a0 = 1t = 0.357 do not reject Hot = - 0.269 do not reject Hob0 = 1t = 1.72 reject Hot = - 2.65 reject Ho" ai = " bjF= 0.25 do not reject HoF= 0.23 do not reject Ho -------" ai = 1F= 1.068 do not reject HoF= 0.113 do not reject Ho" bj = 1F= 3.53 do not reject HoF= 1.92 do not reject HoNotes: All hypothesis tests are made at the 95% confidence level; ai and bj represent coefficients of price increases and decreases, respectively. during the initial month. The amount of total retail adjustment occurring in the initial, second, and third month were 52.5%, 31.1%, and 16.3% for price increases and 50.7%, 33.5%, and 15.8% for price decreases, respectively. The difference between the cumulative effect of wholesale price decreases (1.282) and wholesale price increases (1.067) was statistically significant only at 0.62 level of significance (Calculated F=0.25). Therefore, the null hypothesis of long run symmetry was not rejected. Wholesale price changes were fully transmitted to retail prices in the long run. Downstream to Upstream Price Linkages The variables included in the retail-wholesale equation explained 95% of the variations in tilapia wholesale prices. Each of the estimated coefficients for price increases and for price decreases was positive and significantly greater than zero, indicating that tilapia wholesale and retail prices move up and down together. The retailwholesale price relationship had different significant lag coefficients for price increases and decreases. There were three and two-month lags before wholesale price was fully adjusted to retail price decreases and increases, respectively. This indicated that the time of price passthrough from retail to wholesale level was not the same for price increases and decreases and the presence of price transmission asymmetry in third month. The estimated coefficients for retail price decreases were smaller than that for retail price increases. Hypothesis testing showed that there was asymmetry in the initial month (Calculated F=108.8) leading to the conclusion that the response of tilapia wholesale prices to retail price changes was significantly different for increases or decreases of retail prices. The results indicated that a one pound increase in the retail price of tilapia resulted in a L.E. 0.61 increases in the retail price during the initial month, while a one pound decrease in the retail price resulted in L.E. 0.519 decreases in wholesale price. There was about a L.E. 0.091 per Kg difference in the amount that the wholesale prices respond to a one pound retail price changes during the initial month. The results indicated that 57.8% of the total impact of a one pound increase in retail price on wholesale price is felt immediately and 100% after one month, while 40.5% of the total impact of a one pound decrease in retail price on wholesale price is felt immediately, 73% after one month, and 100% at the end of the third month. However, the null hypothesis of long run symmetry was not rejected. Retail price changes were fully reflected in the wholesale prices in the long run. The independent variables included in the wholesale-farm equation explained 98% of the variations in farm prices. The estimated coefficients of wholesale price increases and decreases were positive and significantly different from zero. The equation showed that the effect of wholesale price increase on farm price was greater than that of wholesale price decrease by about 0.06. However, this difference was not statistically significant (F=2.31) implying that price transmission between wholesale-farm levels was symmetric. Hypotheses testing indicated that wholesale price increases was fully transmitted to farm price while wholesale price decreases was only partially passed through. SUMMARY AND CONCLUSION This paper analyzed the price transmission between market levels for tilapia in Egypt. Monthly retail, wholesale, and quarterly farm gate prices over period 1998 through 2006 were used in the analysis. Prices were expressed in real terms using the CPI for food as deflator (January 2007=100). The price volatility differed between market levels. Prices were less volatile at the retail level. Price causality analysis between retail and wholesale levels indicated that retail had greater effect on wholesale prices than otherwise. Wholesale price showed a greater response to retail price increases than to decreases. In addition, the results indicated the existence of short run price asymmetry and that wholesale prices take less time to adjust for retail price increases than for price decreases. However, long run price transmission symmetry was revealed and price changes at the retail level were fully transmitted to the wholesale level. On the other hand, retail prices were more responsive to wholesale price decreases than to price increases. This result may be explained by the perishable nature of the product. There was no difference in the time required for wholesale price changes to be fully transmitted to the retail level. Short run price transmission asymmetry existed in the initial month, however price changes at the wholesale level were fully transmitted to the retail chain and long run price symmetry was evident. The wholesale stage appeared to have more market power than retailers because of the timing and short run price transmission asymmetries. Detailed data on wholesale expenses and lower frequency data are needed for further exploration of the causes of timing and price asymmetries. Short run price transmission symmetry existed between farm and wholesale marketing levels whether causation ran from upstream or downstream. Price increases at either farm or wholesale level were fully transmitted to the other level. However, price decreases at the farm level were more than fully transmitted to the wholesale and price decreases at the wholesale level were less than completely transmitted to the farm level. It should be noted that only quarterly data were available at the farm level. Perishable products tend to be marketed rapidly; therefore, further research utilizing lower frequency data should be implemented to explore the farm-wholesale price linkages. REFFERENCES Aguiar, D. R. D. and J. A. Santana. 2002. Asymmetry in Farm to Retail Price Transmission: Evidence from Brazil. Agribusiness 18(1): 37-48. Bernard, J. C. and L. S. 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