1、1Asymmetric transmission between terminal and shipping point prices for selected fruits Byeong-Il Ahn* and Hyunok Lee*June 2012*Department of Food and Resource EconomicsKorea UniversityAnam-dong Seongbuk-gu, Seoul, 136-701South Koreaand*Department of Agricultural and Resource Economics University of
2、 California, Davis Davis, CA 95616This research was funded in part by California Department of Food Azzam, 1999; Meyer and von Cramon-Taubadel, 2004; Kaufmann, 2005; Ahn and Kim, 2008). Following this line of literature, commonly referred to as asymmetric price transmission, the present study adds t
3、o the price transmission literature on specialty crops by investigating the structure of price transmission in the context of the vertical market chain for fruit markets in the United States. Focusing on the initial shipping point and terminal (wholesale) links in the marketing chain, we examine sho
4、rt-term as well as cumulative price responses of terminal prices to changes in shipping point prices. In addition to providing empirical evidence related to asymmetry, previous studies on asymmetric price transmission explored its interpretations. If the markets were efficient (and some additional c
5、onditions are met), a price shock in one market affects the price of the related market in a symmetric fashion. This suggests that the test of asymmetry could be used to investigate market efficiency, and that the evidence of asymmetry would be consistent with a market with asymmetric transaction co
6、sts, market power or some other deviation from perfect competition (Meyer and von Cramon-Taubadel, 2004; Carmen and Sexton, 2005; Koutroumanidis et al., 2009).3A number of studies of agricultural commodities suggested that the main driver of asymmetric price transmission in a vertical marketing chai
7、n is market power (McCorristonet al., 1998; Azzam and Schroeter, 1995; Chen and Lent, 1992; Bunte and Peerlings, 2003; Carmen and Sexton, 2005). A party with market power can influence the price to increase profits. Under such a situation, market participants with market power exploit the situation
8、differentially depending on the direction of the initial price shock. We explore the implications of our test results for the underlying market structure of marketing chains considered in this study. Of course, evidence of asymmetry does not necessarily imply market power. Carlton (1989) suggests th
9、at unless the change in marginal cost (the procuring price from the upstream marketing chain) is sufficiently large, retailers do not implement a price change due to a menu or re-pricing cost. Bettendorf and Verboven (2000) supported this claim empirically in the context of vertical markets for coff
10、ee. Other explanations include Ball and Mankiw (1994) who focused on asymmetric adjustment of nominal prices during the time of inflation, and Reagan and Weitzman (1982) who showed how competitive industries result in asymmetric price transmission through their inventory holding behavior. Finally, B
11、ailey and Brorsen (1989) pointed out that asymmetric (or imperfect) information about costs may cause asymmetric price transmission.1 We formally test the asymmetry of price transmission between the shipping point and terminal prices using weekly price data spanning from 1998 to 2011. The model sepa
12、rates the two regimes of the initial price shock and incorporates time lags of both explanatory and dependent variables, which enables us to investigate the price adjustment process between prices in different stages of the marketing chain. The empirical models are discussed in the next section. Dat
13、a description and preliminary tests on data and model specification, including the tests on lag order, unit-root, causality and cointegration follow. We then report the model estimation results, and conclude the paper with a summary and implications. 1 For more discussion, see Peltzman (2000) who in
14、vestigated various plausible causes for price asymmetries, such as market concentration, inventories, inflation-related asymmetric “menu costs“ of price changes, or the fragmentation of the marketing chain. His study supports none of these causes, except for the level of fragmentation of the marketi
15、ng chain.4Empirical ModelThe salient features of the models used in testing asymmetric price transmission involve the segmentation of the regimes, differentiated by the sign of the initial price shock. Early studies adopting regime segmentation include Wolffram (1971) and Houck (1977).2 Allowing the
16、 possibility of non-instantaneous price adjustments, Ward (1982) and Boyd and Brorsen (1988) extended Houcks model by incorporating lagged explanatory variables in the vertical marketing chains of fresh vegetables and pork in the United States.3 The length of lags in this framework corresponds to th
17、e duration of the price adjustment, and the coefficients on the lagged explanatory variables indicate the magnitude of their impacts on the dependent variable. The main drawback of this approach relates to the time series properties of the data. Recognizing that this simple approach is not consisten
18、t with common time series properties of the data, Borenstein et al. (1997) and von Cramon-Taubadel (1998) applied a cointegration method to the tests of asymmetric transmission between the crude oil and retail gasoline prices and between producer and wholesale prices in the German pork market, respe
19、ctively. As a more comprehensive time-series approach, Krivonos (2004) adopted an error correction model and characterized the long-run equilibrium price transmission in the world versus producing-county coffee markets. Developing the empirical model starts with defining the relationship between the
20、 current and lagged prices in a marketing chain simply composed of the downstream and upstream markets. Let , denoting the downstream price at time t, depend on its own lagged values dtPand the contemporaneous and lagged upstream prices, . Then, a typical autoregressive uPdistributed lag (ADL) model
21、 with the lag length of n can be specified as: . This equation assumes a symmetric relationship ni tniuitdtidt PbaP100 2 Even though the typical asymmetric specification originates from Wolffram and Houck, the basic conceptualization of asymmetric price transmission goes back to Farrell (1952), who
22、first investigated empirically the irreversibility behavior of the demand function of habitual consumption goods. 3 Houcks (1977) model was developed to test the asymmetry in supply response in the U.S. milk and pinto beans markets by extending the basic model concept by Wolffram (1971), who segment
23、ed the initial price shock into increasing and decreasing phases. 5between the changes in explanatory variables and the dependent variable. This symmetric relationship is more immediate when the equation is expressed in differences, where signifies a change from the value of the previous 101niuitnid
24、tdt PbaPperiod. To incorporate the possibility of asymmetric transmission, we need to separate the explanatory variables depending on the direction of the change, which can be specified using binary variables, and : iAiBi(Model 1) 111100nnnnddduutitiitiitiititPaPaAbBPbe , , 100tiiifAoherwstiifAoherw
25、stiifoherws0tiifPohrwsEquation (1) allows two types of asymmetric price transmission tests. First, we can test for short-term asymmetric price transmission between and . If were uitPdtuitPsymmetrically transmitted to , estimated coefficients and would be the same. dtPibiThus, asymmetric price transm
26、ission exists with respect to the ith lagged upstream price if two coefficients are significantly different from one another. The second test is for cumulative asymmetric price transmission. If the influences of and on the 10nuitiiBP10nuitiidependent variable are symmetric, the cumulated coefficient
27、s would be the same as 10nib. Thus, the hypothesis of symmetric cumulative price transmission would be rejected 10niibif these sums were significantly different from one another. The short-term symmetry implies cumulative symmetry, but not vice versa. Further, the short-term asymmetry does not imply
28、 cumulative asymmetry. Similar tests between the contemporaneous and lagged dependent variables apply. That is, the short-term price adjustment is said to be asymmetric if the data reject and the cumulative price adjustment is asymmetric if the data 0:iiHa6reject . 110:nniiHaNote that tests on asymm
29、etric price transmission provide information about market efficiency (or inefficiency) and market inefficiency is indicated by the asymmetry on the b coefficients. Further, asymmetry confirmed with further statistical evidence of being significantly 1nigreater than is termed positive asymmetry, whic
30、h is consistent with the fact that higher 1nibprofits are earned by downstream participants than what they could have earned under the efficient market (Carman and Sexton, 2005). Another alternative model specification relates to the time series nature of the price variables. Although equation (1) c
31、an capture the cumulative effects in price transmission, these models essentially do not consider the effects of price transmission when price variables deviate from their long run path. In general, differenced variables (such as , and ) tend dtPituitPto be stationary, however, the original price va
32、riables may meander without showing the constant mean or variance over time. Although the prices are not stationary, if a linear relationship between these price variables is stable and the residual from this linear relationship is white-noisy, we say that cointegration exists between these variable
33、s (Anders, 1995). If the existence of cointegration is identified, the asymmetric price transmission model can be extended to specify the long-run adjustments by introducing the error-correction terms, as in von Cramon-Taubadel and Loy (1996). Given the error correction model can be extended based o
34、n the results of the cointegration test, the presentation of the error correction model will be deferred until we perform the cointegration test. DataOne distinct characteristic of fresh fruits is perishability, which surely contributes to short-term fluctuation of market prices. This implies that p
35、rice transmission can be relatively in short term and the data used to examine price transmission necessarily have to reflect such 7short terms. In light of this, we searched for time series price data with short intervals. The Marketing Service at the U.S. Department of Agriculture provides weekly
36、prices of major agricultural commodities at various marketing channels. We have chosen fresh strawberries, apples, table grapes and fresh peaches as representative fruits, and for each of these fruits, we collected weekly prices at the shipping point and terminal market. In terms of selecting the lo
37、cation of the shipping point and terminal market, we picked the shipping point in the region that is associated with the largest production and the terminal market that likely handles the largest volume. Obviously, the terminal prices and shipping point prices correspond to the upstream price and do
38、wnstream price in our model, respectively. While our data period spans from 1998 to 2011, depending on its season, the data series for each fruit begins and ends in different months. Data details are provided in the appendix. These two price series tend to move together, and fluctuate considerably o
39、ver the time period investigated. Price fluctuations are larger in the second half of the period for both series. The time pattern of the price fluctuation suggests the possibility of non-stationarity of price series, which violates the time series property of constant mean and variance. The co-move
40、ment of these series further suggests the possibility of cointegration, as is often manifested by the parallel pattern of non-stationary variables. The time series properties of the data will be formally investigated next. Preliminary testsIn this section we first consider the choice of lag orders a
41、nd causality and then investigate time series properties of the data. We select appropriate lag orders based on statistical criteria, and perform the causality tests to identify the relationship between shipping point and terminal prices. We conduct unit root tests on the variables used in empirical
42、 equations to avoid spurious regression and erroneous interpretation of estimated results. We also test for cointegration between the wholesale and factory prices for the possibility of including long-run relations of these time series vectors in the empirical estimation. 8Lag order choice and causa
43、lity tests The following vector autoregressive (VAR) model is used to determine the lag orders and conduct the causality test:1212.T TTTTTTT Tt t t ktktS SSSSSSSSt t t ttPPPabPabab eWithin the VAR formulation expressed as above, the optimum lag order is selected using the Akaikes information criteri
44、on (AIC) and Schwartz Bayesian Information Criterion (SBIC). We considered up to the fifth lag, and the lag order that produces the minimum information criteria is selected (Anders, 1995). Table 1 presents the values obtained under the two information criteria for each order of lags considered. Usin
45、g these criteria, we selected the lag order one for apples, lag order four for table grapes, lag order three for strawberries, and lag order four for peaches.4In specifying the empirical equations, our underlying assumption is that the current terminal price is influenced by the shipping point price
46、s, consistent with the usual assumption that downstream prices are affected by upstream prices.5 However, this is not always the case, and an easy example is the case of market power. Under the influence of market power, the party with market power influences the price to his/her advantage and thus
47、acts as an initiating party in price causality. Thus, with no prior knowledge about the industry, we need to empirically evaluate causality. To check the causality between prices, we used the estimation results from the VAR model. From the first equation of the VAR model ( ), we can say that 11kkTdd
48、TdSt ititPabP4 Due to the space limitation, we do not present the full estimation results of the VAR model. 5 The opposite direction of causality is, of course, plausible as in Koutroumanidis et al., which deals with a market where imports represent a large share of domestic consumption, and the imp
49、ort price leads the consumer price. They find that under such market conditions, causality is from downstream to upstream markets, i.e., the consumer price affects the producer price.9causes if we reject the null hypothesis H0: . Likewise, using SPT 0.21dkdbbthe results of the second equation of the VAR model ( ), is 11kSuuTuTt ititPaPsaid to cause , if we reject the null hypothesis H0: . Table 2 shows S 0.21kathe causality
