Asymmetric Pass-Through in U.S. Gasoline Prices.

AuthorChesnes, Matthew
PositionReport - Statistical data
  1. INTRODUCTION

    There is a large literature analyzing the cost to price pass-through in industries ranging from automobiles (Gron and Swenson (1982)), to cheese products (Kim and Cotterill (2008)), to the beef industry (Goodwin and Holt (1999)). The literature has not only focused on the ability of firms to successfully capture rents when input costs change, but also on how the rate of pass-through varies when the costs increase versus decrease. Peltzman (2000) analyzes over 200 consumer and producer products and find asymmetric adjustment in two-thirds of the products. Similarly, Meyer et al. (2004) surveys the literature and finds that symmetric adjustment is rejected in about one-half of the cases. Much of the extant literature comes to seemingly contradictory conclusions about the existence and causes of this phenomenon, though the differences may be due to different data sources, price series, aggregation over time and across geographic areas, as well as misspecified models. In this paper, I examine the dynamics of pass-through in gasoline prices using a detailed dataset available at a high frequency, across many cities, and at several price levels in the vertical distribution process for gasoline.

    Particularly in the gasoline industry, this asymmetric phenomenon is known as rockets and feathers, reflecting the fact that retail prices tend to increase quickly when costs (say, wholesale gasoline prices) rise, but drift down slowly when they fall. (1) Pass-through in the gasoline industry has been the focus of many studies for several reasons. Gasoline is a fairly homogeneous product and both retail and intermediate wholesale prices are relatively transparent compared with other industries. (2) Much of the variation in gasoline prices is driven by the price of crude oil, the key input into gasoline. (3) Crude oil is traded on a world market and the price is also transparent to market players and to consumers. In spite of the transparency of prices, there are dynamics present in the gasoline industry that are difficult to explain with competitive or oligopolistic economic models.

    The literature on rockets and feathers dates back to at least 1991 when Robert Bacon found evidence of an asymmetric response in gasoline prices in the UK. Since that time, others have found evidence of the phenomenon including Borenstein, Cameron, and Gilbert (1997, hereafter BCG), Ye et. al. (2005), Deltas (2008), Tappata (2009) and Lewis (2011). These studies all utilize some form of an error correction model (outlined below) and consider some combination of crude oil prices, wholesale prices (rack or spot), and retail gasoline prices. They also vary by the geography they consider and how the data are aggregated over time. There are also several papers that, due to either a different data source or model, find no evidence of an asymmetric price response including Godby (2000) and Gautier and Le Saout (2012). Bachmeier and Griffin (2003) test the results in BCG using daily data and a different methodology and find no evidence of asymmetric adjustment, however they only focus on the transmission of crude oil to spot gasoline prices.

    If it exists, there is little consensus on what causes an asymmetric response. BCG offer three potential explanations for asymmetric adjustment: focal-point pricing as a form of market power, inventory adjustment frictions in the face of positive and negative demand shocks, and differences in consumer search patterns when prices are rising and falling. Deltas (2008) and Verlinda (2008) look at how the asymmetric response varies with the level of retail market power and find more asymmetry in markets with relatively more retail market power. (4)

    Lewis (2011) and Tapatta (2009) posit that consumer search behavior could be causing the asymmetric response. If consumers are more likely to search for a low price when prices are rising or expected to rise, then competition will be fierce when costs are rising and margins tight. However, if prices are falling, consumers may search less and this provides retailers with short-term market power and allows them to slowly lower prices and increase their margins. Evidence of this explanation could be found in the difference in asymmetry between branded and unbranded gasoline prices. If consumers who purchase unbranded gasoline tend to search more intensively than branded customers who are loyal to a specific brand, cost shocks to unbranded rack prices would be passed on more quickly to retail prices.

    The Edgeworth price cycle model of Maskin and Tirole (1988) may also explain the dynamics. They show that competition may lead to relatively slow price undercutting down to cost and a rapid rise or resetting of the cycle initiated by a single firm and quickly followed by all its competitors. Several studies have found evidencing of price cycles in gasoline markets (seeEckert (2002), Noel (2007, 2009), Lewis and Noel (2011), and Zimmerman et al. (2013)). While price cycle models do not deal directly with the response of prices to changes in costs, cycles and asymmetric pass-through may be related. Lewis and Noel (2011) show that cost shocks are passed through faster in markets that feature price cycles. However, the speed of pass-through may be unrelated to the asymmetric response to positive and negative cost shocks. Pass-through rates may be fast, though asymmetric, so cycling cities may show evidence of asymmetric pass-through even if the speed of pass-through is faster than in non-cycling cities.

    This paper is similar to BCG in that I analyze several different prices (crude oil, spot gasoline, rack, and retail prices) over a long period of time. However, unlike BCG who use weekly and bi-weekly data from the Lundberg Survey, I have access to daily data on all prices. I also avoid a modeling assumption by BCG (discussed below) which was questioned by Bachmeier and Griffin (2003) and instead use a more standard approach. I also consider how asymmetry varies across different U.S. cities, between branded and unbranded prices, and how asymmetric pass-through is correlated with market concentration and the existence of price cycles.

    I find evidence of asymmetry in the crude oil to gasoline spot price, the spot to rack, and the rack to retail price relationships. (5) The rack to retail relationship shows the strongest asymmetry at both the city and national level. On average, retail prices rise three to four times as fast as they fall. Asymmetry varies significantly across cities with the strongest rack to retail asymmetry in Salt Lake City, Louisville (Indiana) and Cleveland. New York shows the least asymmetric pass-through. Estimates based on weekly data show more asymmetric pass-through as retail prices are predicted to increase faster following a cost (rack price) increase and fall slower following a cost decrease. Branded gasoline features significantly more asymmetry compared with unbranded gasoline in response to changes in the rack price, consistent with the consumer search explanation for asymmetric pass-through. Computing the impact of asymmetric pass-through over time shows significant differences by year, though on average, retail prices would be about 2.45 cents per gallon (cpg) lower if they fell as quickly as they rose.

    Finally, I find that cities that show more asymmetric pass-through tend to feature more price cycling and have a faster overall speed of pass-through. I also find evidence that asymmetry is positively related to market concentration. The overall brand-level Herfindahl-Hirschman Index is about 14% higher for cities in the top quartile of asymmetry relative to the bottom quartile.

    The paper proceeds as follows. In section 2,I outline the model that I employ and specification tests are run to justify its use. I discuss the data and provide basic descriptive statistics in section 3 and present the results of my model in section 4, including asymmetric pass-through results for different geographic areas, at different levels of time aggregation, and for different products. I also present city-specific factors that may be related to the magnitude of asymmetric pass-through. Section 5 concludes.

  2. MODEL

    I estimate an error correction model (ECM) frequently used in the literature, though in various forms (e.g., Bachmeier and Griffin (2003)). I estimate the model individually for each city (metro area) (6) and for a national specification that allows for different price levels and markups in each city. The latter regression measures the average pass-through rate across all cities, while allowing the long-term relationship to vary by city. I allow for a difference in the pass-through of positive and negative upstream price changes. While I estimate the model for several pairs of upstream and downstream prices, for simplicity, the following is the rack to retail pass-through model for a given city:

    [mathematical expression not reproducible] (1)

    Note [DELTA][Retail.sub.t-i] = [Retail.sub.t-i]-[Retail.sub.t-(i-1)]. Lag lengths are determined by minimizing the Bayesian Information Criterion (BIC):

    BIC = K* log(N)+ N* [Log(RSS/N)], (2)

    where K is the number of parameters to be estimated, N is the number of observations, and RSS= [epsilon]'[epsilon] from equation 1.I could allow the lag lengths to vary separately for positive and negative changes as well as for rack and retail prices. However, since determining the optimal lag lengths for each price series, versus using a fixed (and equal) lag length for all, does not affect the qualitative results, in the analysis below I fix the lag length at 21 days in all regressions. (7) This also allows me to compare regressions across cities and over time since I will utilize the same specification in each. The expression [z.sub.t-1] = [Retail.sub.t-1] - [[gamma].sub.0]-[[gamma].sub.1] [Rack.sub.t-1] is the error correction term, and it captures the long-run relationship between the upstream and downstream...

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