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Zeitschrift für Energiewirtschaft

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21.05.2019

Asymmetric Diesel Retail Pricing Strategies: Depending On Brands and Population Densities?

verfasst von: Sebastian Kreuz, Prof. Dr. Felix Müsgens

Erschienen in: Zeitschrift für Energiewirtschaft | Ausgabe 2/2019

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Abstract

Asymmetric cost pass-through between crude oil and retail fuel prices (“Rockets and Feathers”) has been analysed for different countries and time periods. However, few studies have been conducted for the German market. Furthermore, this study differentiates between company types and regions in this context. We compare price setting behaviour of independent and major brand stations following oil price changes. Furthermore, we differentiate between regions with higher and lower population densities. We establish an error correction model with a novel data set from the years 2011 to 2012. Our results confirm significant differences between price setting behaviour after oil price changes of major brand stations and other stations.

Vorheriger Artikel Citizens’ Initiatives in the Context of Germany’s Power Grid Expansion: a Micro-econometric Evaluation of Their Effects on Members’ Protest Behavior

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We also include research results for the German gasoline market because literature on German diesel price characteristics is currently rare.

We also analysed an asymmetric model approach (see Table 9 and Eq. 7), which is an alternative approach in the literature. We found comparable results to the reference case presented in Sect. 5.

Many studies however analyse that retail prices in general are influenced by such characteristics (see e.g. Barron et al. 2004; Firgo et al. 2015 and the literature cited therein).

Orange County is a county in California, USA, comprising 2460 km², i.e. less than 1% of the German landmass.

Santiago is the capital of Chile with a landmass of 641 km² (the Santiago Metropolitan Region comprises 15,403 km²).

See ibid, p. 630.

More reported prices on Monday may be explained by higher demand on Mondays (FCO 2011).

When data points did not include specific brand names, we included them in Non IPS and Non Top Five subsets. This is a cautious approach as we may underestimate differences between subsets.

We also considered a more disaggregated model (directly based on the daily data)—but dividing our dataset into two complementary sub-groups (e.g. PopDens high and PopDens low) results in days without observations.

It can be debated whether Jet, which is found to have lower prices than the other brand stations (FCO 2011), should be included in this group. Changing our categorization by putting all Jet retail stations out of the Top Five category and into the category of all other retail stations (i.e. Top Four [Aral, Shell, Total, Esso] vs. Non Top Four), we find more significant results regarding price differences between Top Four and Non Top Four. Using this approach, Top Four stations are significantly more expensive. A two-sample t‑test finds Top Four vs. Non Top Four: 2.1691** (0.0312).

Using daily opening prices (instead of closing prices) does not change our results in a meaningful way.

The Durbin-Watson statistic (DW-test, row 7) is applied to analyse autocorrelation of the error terms. The DW-test finds no evidence of autocorrelation.

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Titel: Asymmetric Diesel Retail Pricing Strategies: Depending On Brands and Population Densities?
verfasst von: Sebastian Kreuz
Prof. Dr. Felix Müsgens
Publikationsdatum: 21.05.2019
Verlag: Springer Fachmedien Wiesbaden
Erschienen in: Zeitschrift für Energiewirtschaft / Ausgabe 2/2019
Print ISSN: 0343-5377
Elektronische ISSN: 1866-2765
DOI: https://doi.org/10.1007/s12398-019-00252-x

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