More New Evidence on Asymmetric Gasoline Price Responses.

AuthorFaber, Riemer P.
  1. INTRODUCTION

    Asymmetric price responses occur when prices rise more rapidly after an increase in costs than they decline after a decrease in costs (see Peltzman (2000)). Many consumers and policy makers are suspicious that this pricing behavior is common in gasoline markets. Borenstein, Cameron, and Gilbert (1997) (henceforth BCG) perform an early study to this topic and confirm the suspicion. They take the production and distribution chain of gasoline and study at each part of the chain whether the price responds asymmetrically with respect to its upstream price. The four prices in the chain are the crude oil price, spot market price, wholesale price, and retail price.

    After the study of BCG, many similar papers appeared. Geweke (2004) surveys this literature. He mentions two possible aggregation issues that may arise in this literature. The first possible issue is aggregation over time. In this case, the analysis does not include all (possible) price adjustments. This problem can occur if data have a lower frequency than the frequency of price decisions or input cost changes (Geweke (1978)). For example, BCG use a weekly spot market price and a semi-monthly retail price. The second possible issue is aggregation over space. In this case, the analysis focuses on a geographic area like a national or local market instead of individual firms. This problem can occur if data are aggregated over individuals or if the estimation method does not take into account possible differences between firms. For example, BCG use the average of the retail price in 33 cities and others, Balmaceda and Soruco (2008), Hosken, McMillan, and Taylor (2008), Verlinda (2008), Noel (2009), and Lewis (2011), use data on individual firms, but pool these data and report the asymmetry in the market as a whole. However, individual firms set prices, not the market as a whole. It might be that not all gasoline stations have the same pricing strategy because, for example, they do not operate under the same conditions (competition, ownership structure, location, etc.). Moreover, even if all stations adjust prices asymmetrically, the degree to which they do may differ between stations. Finally, Robertson and Symons (1992), Pesaran and Smith (1995), Hsiao (2003), and Baltagi, Bresson, and Pirotte (2008) argue that pooling across individuals may possibly give biased results in (dynamic) estimations if there exists parameter heterogeneity.

    Bachmeier and Griffin (2003) study whether Geweke's first aggregation issue is relevant. More specifically, they study the first part of the chain and explain the daily spot market price by the daily price of crude oil. They do not find asymmetric price responses when they use daily data, but they do find asymmetry when they repeat the analysis with weekly data. As a consequence, the first issue that Geweke mentions is important. (1)

    This paper studies Geweke's second aggregation issue. More specifically, I study the second part of the chain and explain daily retail prices of individual gasoline stations by the daily spot market price. My main interest is whether there exist differences between stations. To my knowledge, this question has not been studied before, possibly due to the large data requirements. I find that the data are not poolable across stations. A separate analysis for each individual station shows that 38% of the stations in my sample respond asymmetrically. Therefore, asymmetric pricing is a feature of individual firms. Geweke's second aggregation issue is important. Together with the results of Bachmeier and Griffin (2003), this finding shows that each decision of each decision maker is informative for understanding asymmetric price responses and the underlying motives. Subsequently, I study whether there exist differences in the characteristics of stations that do and do not adjust prices asymmetrically. I look at 35 (sometimes overlapping) characteristics. For example, I study whether stations that adjust prices asymmetrically have higher price levels, are geographically clustered, or have a certain ownership structure. I find that asymmetric pricing seems to be a phenomenon that is randomly distributed across stations.

    Section 2 describes the market and data set. Section 3 specifies the model. Section 4 presents the estimation results and Section 5 studies characteristics of stations that respond asymmetrically. Section 6 contains further discussion. Section 7 concludes.

  2. MARKET AND DATA

    This paper studies the retail gasoline market in the Netherlands. (2) There are around 4,300 gasoline stations (BOVAG (2006)). Most stations use the brand of an oil company. There are five large oil companies. Broadly speaking, there are three ownership models: some stations are company-owned and company-operated, other stations are company-owned and dealer-operated, and the remaining stations are dealer-owned and dealer-operated. A station's operator sets the price (although oil companies publish nonbinding national suggested prices). Rough estimates indicate that around 80% of the stations are dealer-operated. Nearly all stations set prices on a daily basis. Almost all gasoline sold is bought at the Amsterdam-Rotterdam-Antwerp (ARA) spot market, which supplies large parts of western Europe. A price for this spot market is published once a day. This price is the standard spot market price in the market (see, e.g., Shell (2001)).

    Retail prices are published daily on the website of Athlon Car Lease. This company leases cars to other firms. When the driver fills up his car, the station electronically sends the bill to the lease company. As a result, Athlon Car Lease obtains gasoline price quotations from 120,000 drivers, who fill up on average twice a week, from all over the country. It publishes these data on its website and I downloaded the data daily over the period 30 May 2006-20 July 2008 (783 days). The data set includes about 3,600 stations. Stations that the data set does not include are randomly distributed over the country and seem to be mostly smaller or nonactive. I only use observations for the standard type of gasoline (95 RON, so-called Euro95). The data set contains approximately 2,300 unique price quotations per day for this type of gasoline. (3) During the sample period, the daily average retail price varies between 39 and 68 cents per liter (in euros, excluding taxes). (4) The spot market price that I use is the daily Platt's Barges FOB Rotterdam High (Premium Gasoline 10 PPM with 95 RON). This spot market price increases 269 times and decreases 281 times during the sample period. I convert all prices to prices per liter (excluding taxes) in euros. (5) I match individual stations in the data set to lists of station-specific characteristics and to characteristics of the area in which the station is located. (6)

  3. MODEL SPECIFICATION

    To study whether retail prices of individual stations respond asymmetrically to changes in the spot market price, I model for each station the relation between the retail price and spot market price. Augmented Dickey-Fuller unit root tests indicate that the spot market price and retail price of 98% of the stations are integrated of order 1. To take into account possible cointegration between these series, I estimate for each station an asymmetric error correction model. BCG and Bachmeier and Griffin (2003) use the same model. Therefore, an advantage of this model is that it is easy to compare results. (7) The long-run relationship between the retail price and spot market price is:

    [P.sub.i,t] = [[alpha]*.sub.i]] [Spot.sub.t-2] + [c*.sub.i] + [[tau]*.sub.i]] [Time.sub.i] + [[lambda]*.sub.i] [Mix.sub.t] + [[epsilon]*.sub.i,t] (1)

    where [P.sub.it] is the retail price of station i on day t, [Spot.sub.t-2] is the spot market price on day t-2, and [c*.sub.i] is a station-specific constant (it contains, for example, the average markup). Timet is a time trend that captures a possible inflationary increase in the margin, [Mix.sub.t] is a dummy variable that is 1 after 1 January 2007 (law requires the addition of biofuels from that date onward), and [[epsilon]*.sub.i,t] is an error term. All prices are in euros per liter excluding excise duty and VAT. (8)I use the two-day lagged spot market price, since the spot market price has the highest correlation with the retail price when I use a two-day lag. (9) The one-day lagged spot market price is the most recent quotation available at the moment that a station sets its price. If I use the one-day lagged spot market price in Equation (1), then the estimated long-run impact of the spot market price is similar.

    If the residuals of Equation (1) are stationary, a cointegrating relation exists. In that case, I can superconsistently estimate the coefficients in Equation (1) and define a short-run relation between the retail price and spot market price:

    [mathematical expression not reproducible] (2)

    where [l.sub.it] is an error term. I also include the one-day lagged change in the spot market price. Thus, the model always includes at least the one-day and two-day lagged change in the spot market price. (10) The term[[epsilon]*.sub.i,t-1] reflects the difference between the retail price and its equilibrium value on day t-1. Therefore, the coefficient [[gamma].sub.i] measures the speed of adjustment toward the equilibrium retail price. To study asymmetric price responses, I transform Equation (2) into an asymmetric short-run relation:

    [mathematical expression not reproducible] (3)

    where for each variable z: [DELTA][z.sup.+] = max{[DELTA]z,0} and [DELTA][z.sup.-] = min{[DELTA]z,0}. And where [mathematical expression not reproducible] and [mathematical expression not reproducible]. A plus (minus) as superscript to a coefficient indicates that the coefficient belongs to a variable with increases (decreases). Asymmetric price responses can exist due to a larger impact of increases than of decreases in current and lagged...

To continue reading

Request your trial

VLEX uses login cookies to provide you with a better browsing experience. If you click on 'Accept' or continue browsing this site we consider that you accept our cookie policy. ACCEPT