Station heterogeneity and the dynamics of retail gasoline prices

Transcription

1 Station heterogeneity and the dynamics of retail gasoline prices Julia González Carlos Hurtado April 2018 INCOMPLETE AND PRELIMINARY DRAFT. Abstract One of the main stylized facts in the dynamics of retail gasoline prices not driven by upstream pass through is the existence in some markets of asymmetric price cycles where a sharp price increase is followed by gradual price declines. Although some of the studies analyzing these cycles use station-level data, there is no empirical evidence on the heterogeneity of firms within a market in their cycling behavior. This paper aims to fill this gap in the literature. To do so, we propose a new cycling indicator which, unlike the one used in the literature so far, is well suited to identify cycles at the station level and overcomes some missclassification issues. By detecting asymmetric cycles at the station level we obtain variability in cycling behavior within markets, which can be exploited as an identification strategy to better estimate the effect of asymmetric cycles on the price level. This sheds light on the issue of competitive (i.e., Edgeworth cycles) vs collusive explanations behind these cycles that has been troublesome to tackle so far in the literature because of identification issues related to market-level confounders, which now we are able to control for. Keywords: retail gas price dynamics; Edgeworth cycles; firm heterogeneity; collusion. JEL classifications: L20, R32. University of Illinois at Urbana-Champaign, 214 David Kinley Hall, 1407 W Gregory St, Urbana, IL González: jgonzlz3@illinois.edu. Hurtado: hrtdmrt2@illinois.edu. 1

2 1 Introduction Much of the empirical literature on retail gasoline pricing is concerned with the analysis of how and why prices change over time. Although much of the work examines how shocks to crude oil prices and wholesale gasoline prices are passed through to retail prices, a rapidly growing area of the literature has also been focused on dynamics not driven by upstream prices. One of the main findings is the existence of a peculiar phenomenon observed in certain markets: asymmetric price cycles where a sharp price increase is followed by gradual price declines. These sawtooth-like cycles were first noticed in some southern and western markets in the United States (Allvine and Patterson, 1974), and have been shown to exist in most large and many medium-sized cities in Canada (Eckert, 2002; Noel, 2007; Atkinson, 2009) and Australia (Wang, 2009) since the 80s, and in some European cities (Foros and Steen, 2013; Siekmann, 2017). In the United States, the literature agrees these cycles reappeared in the 2000s only in several Midwest markets (Lewis, 2009; Doyle et al., 2010; Zimmerman et al., 2013). Although some of these studies use station level data, there is no empirical evidence on the heterogeneity of firms within a market in their cycling behavior. The determination of the existence of cycles at the market level is carried on based on mean prices, which may conceal different strategies by the gas stations on whether or not to engage in asymmetric price cycles. This paper aims to fill this gap in the literature. In terms of what drives these cycles, most of the empirical work has aligned with Maskin and Tirole (1988) s theory on Edgeworth cycles. This is one of the possible equilibrium outcomes of Maskin and Tirole s duopoly model, where firms alternate undercutting each other until eventually marginal cost is reached, at which point a war of attrition is initiated, with each firm randomizing between setting marginal cost and restoring price (relenting) to initiate a new cycle (Figure 1). Some predictions of this model and its extensions (Eckert, 2013; Noel, 2008) have been tested empirically, generally finding support for this explanation. However, a new strand of literature has suggested that these cycles may not be competitive but rather collusive in nature, or contain collusive elements (Foros and Steen, 2013; de Roos and Katayama, 2013; Byrne and de Ross, 2017). Asymmetric cycles have inspired investigations by federal competition authorities in a number of countries, leading in some cases to price regulations. Looking at the consequences of the cycles may shed light on whether they are the result of collusion or just a specific form of competition. In particular, one policy-relevant effect is whether they are associated with lower or higher prices; if we were to find lower (higher) prices in cycling markets, that would be evidence against 2

3 (in favor of) anti-competitive behavior. However, identifying a causal effect of these cycles is challenging; they typically persist for decades, and authors must often rely on cross sectional comparisons across cities (Noel, 2002; Doyle et al., 2010). Zimmerman et al. (2013) use a dif-in-dif approach with panel data, although they warn about a causal interpretation due to market structure changes confounding with cycle appearance. Noel (2015) claims a plausibly causal effect of cycles on price by exploiting a natural experiment. In this study, we detect asymmetric cycles at the station level; this gives us variability in cycling behavior within markets, which can be exploited as an identification strategy to better and more generally estimate the effect of asymmetric cycles on the price level, controlling for market-level confounders. Figure 1: Maskin & Tirole (1988) s Edgeworth cycles. Source: Maskin and Tirole (1988). 2 A new cycling indicator The cycling indicator used so far in the literature is the median first difference of the price. It relies on the assumption that, with asymmetric cycles, there would be more periods when the price decreases than periods when it increases, and then the median price change would be negative (a cutoff at -0.5 has been agreed). There are two drawbacks with this indicator. First, it is unsuited to deal with the identification of cycles at the station level: the large amount of days when individual gas stations do not change prices at all could render a median price change of zero for a perfectly cycling station. On the other hand, even at the market level this indicator may fail to correctly identify cycling markets under certain circumstances. For a negative median change to indicate asymmetric cycles, it is key that the undercut stages reach down to the same, steady marginal cost every time, which does not happen in the presence of price trends. The decreasing price phase will be cut short if the trend is increasing, 3

4 and will be artificially long with negative trends, generating excess Type-I and Type-II errors, respectively (Figure 2). Also, in markets with lengthy cycle phases, a relatively short sample period can have a stage underrepresented, leading to false rejection of cycling behavior. Figure 2: Median price change indicator in the presence of price trends. In this paper we propose a new cycling index that explodes a different implication of the asymmetry of the price cycles. Sharp increases followed by gradual decreases means that the rate at which the prices go upwards is faster than the rate at which they go down. We construct the Cycling Ratio as the average change in increasing stages divided by the average change in decreasing stages: CR = +p p = [ΣT t=1 t p 1(t τ + )]/[Σ T t=11(t τ + )] [Σ T t=1 t p 1(t τ )]/[Σ T t=11(t τ )] Stations with an indicator of 1 or lower are unambiguously non-cyclers. Whenever there is asymmetry in the cycles, the Cycling Ratio will be greater than 1, but the average increasing slope should be significantly greater than the average decreasing slope to conservatively categorize that station as cycler. We use as a benchmark threshold a Cycling Ratio of 1.5. That is, whenever prices increase at least 50% faster than they decrease, we classify that gas station as cycler. This indicator is suited to identify asymmetry in the cycles at the station level, and, since it does not rely on how long the stages last, it is robust to the existence of price trends. 4

5 3 Data We use a novel database of daily regular gasoline prices at the station level in the continental United States. We gathered these data from GasBuddy.com, a website that provides information on gas prices reported by consumers through a smartphone app. Consumers use this app to check prices at stations nearby to compare them and find good deals, but they also have cash incentives to report or update prices as they spot them. The use of this type of consumer-reported data is not new in the academic research on retail gas prices 1, although most of them analyze Canadian markets. We retrieved this data every day at 10:00 pm during the 72 days from February 25 to May 7, For each day and gas station in our sample, we observe the price (if reported in the last 24 hours), how long ago it was reported, station location, and other features (brand, convenience store, etc.). We have a total of 4,569,581 observations from 90,052 urban gas stations. For the Cycling Ratio calculation, we keep stations that have prices reported for at least half of the days in the analyzed period, which reduces our sample to 70,201 stations (4,212,658 observations). 4 Indicators performance In Figure 3 each gas station in the sample is represented by a point colored depending on its cycling behavior. Blue points represent non-cycling stations, whereas red points are cyclers: darker red indicates a stronger cycling activity. Although, consistent with previous findings, there is a high concentration of cycling stations in the Midwest, there are other areas in the country with prominent presence of cyclers as well. In fact, the median price change indicator is prone to Type-I error: it misclassifies 25% of the metropolitan areas as non-cycling when they actually present cycling patterns (Figure 4). In most cases, it is because the prices in our data period present positive trends; in others, because the market develops cycles with lengthy phases (see examples in Figure 5). 1 See Atkinson (2008) for a description of the advantages and potential issues. 5

6 Figure 3: Station cycling behavior heterogeneity in the US. Note: Blue points represent non-cycling stations. Grades of red/orange indicate stations with different levels of cycling activity. 6

7 Figure 4: Cycling Ratio vs median price change classification of markets. Cycling Ratio Median price change Note: Each point represents a market, defined as a Core-Based Statistical Area. Figure 5: Old indicator in the presence of price trends and lengthy phases. price Mar-01 Apr-01 May-01 cycling ratio = 3.17; med var price = Tampa, FL price Mar-01 Apr-01 May-01 cycling ratio = 3.23; med var price = Salt Lake City, UT 7

8 5 Cycling heterogeneity The overall market price cycling behavior is determined ultimately by the aggregation of individual station prices. In Figure 6 we can see how cycling markets have on average a larger proportion of cycling stations. However, interestingly, not all stations behave the same way within a market, regardless it is overall cycler or not. Figure 6: Market cycling behavior and proportion of cycling stations. Cycling Ratio Share of cycling stations Note: Each point represents a market, defined as a Core-Based Statistical Area. An example of the distribution of cycling behavior across stations within a market is given in Figure 7. Cyclers and non-cyclers coexist, and may be as close as in the same block (see Figure 8). 6 Price gap A cross-section comparison over time of cycling vs non-cycling markets in the United States is shown in Figure 9. Markets with asymmetric cycles seem to charge lower prices, which is consistent with previous results. However, as the literature warns, cross-section comparisons and even panel-data analysis cannot claim causality of the effect of cycles on prices, because of the presence of market-level confounders that are either unobservable, or observable characteristics of the market structure that are very closely correlated with the presence of cycles, like the existence of certain brands 8

9 Figure 7: Heterogeneity in cycling behavior: the case of Chicago. Note: Blue points represent non-cycling stations. Grades of red indicate levels of cycling activity. 9

10 10 Figure 8: Cycling and non-cycling stations coexist yards away from each other in Chicago.

11 (Zimmerman et al., 2013). Figure 9: Cycling and non-cycling market prices dynamics. price Mar-01 Apr-01 May-01 Non-cyclers Cyclers To tackle the issue of identifying the effect of cycles on price levels, we exploit the heterogeneity of cycling behavior within local markets. In the Chicago example of Figure 10, the average price dynamics of cyclers and non-cyclers are depicted; the price gap suggests that cycling stations tend to charge lower prices. We formally estimate this price gap at the United States level by regressing the station-day prices on the cycling indicator, controlling for station characteristics (including brand), demographic controls, and market fixed effects (which take care of the market-level confounders) interacted with time fixed effects. We find an average cycling effect of -4 cents. In Figure 11 the (negative of the) coefficients of the cycling indicator interacted with time fixed effects are shown; it can be seen how non-cycling stations have almost always charged higher prices on average. This result holds in almost all markets across the United States (Figure 12). 7 Remarks Our results suggest that the presence of asymmetric price cycles in some retail gasoline markets is unlikely to be driven by collusive behavior among gas stations, since we find cycling stations charge on average lower prices. However, our findings are not exactly consistent with the presence of Edgeworth cycles equilibrium either. In Maskin and 11

12 Figure 10: Cycling vs non-cycling station price gap in Chicago. Figure 11: Cycling effect on price level over time. (-) date-cycler FE (cents) Mar-01 Apr-01 May-01 12

13 Figure 12: Cycling price gap distribution across markets. Tirole s model and its extensions, the underlying assumption is that firms within a market either all engage in asymmetric cycles or neither does, whereas we show that cycling and non-cycling strategies take place simultaneously by different firms within a market. In order to shed light on what drives this particular kind of equilibrium, we are left to study what predicts cycling behavior, in particular by exploring station location and how firms react to neighboring stations cycling strategies. References Allvine, F., Patterson, J Highway robbery: an analysis of the gasoline crisis. Indiana University Press. Atkinson, B Retail gasoline price cycles: evidence from guelph, ontario using bi-hourly, station specific retail price data. Energy Journal, 30, Atkinson, B On retail gasoline pricing websites: potential sample selection biases and their implications for empirical research. Review of Industrial Organization, 33, Byrne, D. P., de Ross, N Learning to co-ordinate: a study in retail gasoline. Working Paper. 13

14 Doyle, J., Muehlegger, E., Samphantharak, K Edgeworth cycles revisited. Energy Economics, 32, Eckert, A Retail price cycles and response asymmetry. Canadian Journal of Economics, 35, Eckert, A Empirical studies of gasoline retailing: A guide to the literature. Journal of Economic Surveys, 27, Foros, O., Steen, F Vertical control and price cycles in gasoline retailing. The Scandinavian Journal of Economics, 115, Lewis, M Temporary wholesale gasoline price spikes have long-lasting retail effects: the aftermath of hurricane rita. Journal of Law and Economics, 52, Noel, M. D Edgeworth price cycles in retail gasoline markets. MIT dissertation. Noel, M. D Edgeworth price cycles and focal prices: computational dynamic markov equilibria. Journal of Economics and Management Strategy, 17, Noel, M. D Do edgeworth price cycles lead to higher or lower prices? International Journal of Industrial Organization, 42, Noel, M Edgeworth price cycles: evidence from the toronto retail gasoline market. Journal of Industrial Economics, 55, de Roos, N., Katayama, H Gasoline price cycles under discrete time pricing. Economic Record, 89, Siekmann, M Characteristics, causes, and price effects: Empirical evidence of intraday edgeworth cycles. DICE Discussion Paper, No Wang, Z (mixed) strategy in oligopoly pricing: evidence from gasoline price cycles before and under a timing regulation. Journal of Political Economy, 117, Zimmerman, P., Yun, J., Taylor, C Edgeworth price cycles in gasoline: evidence from the united states. Review of Industrial Organization, 42,