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Is the German Retail Gasoline Market Competitive? A Spatial-Temporal Analysis Using Quantile Regression

Author

Listed:
  • Alex Kihm
  • Nolan Ritter
  • Colin Vance

Abstract

We explore whether noncompetitive pricing prevails in Germany’s retail gasoline market by examining the influence of the crude oil price on the retail gasoline price, focusing specifically on how this influence varies according to the brand and to the degree of competition in the vicinity of the station. Our analysis identifies several factors other than cost—including the absence of nearby competitors and regional market concentration—that play a statistically significant role in mediating the influence of the oil price on the retail gas price, suggesting moderate price-setting power among stations.

Suggested Citation

  • Alex Kihm & Nolan Ritter & Colin Vance, 2016. "Is the German Retail Gasoline Market Competitive? A Spatial-Temporal Analysis Using Quantile Regression," Land Economics, University of Wisconsin Press, vol. 92(4), pages 718-736.
    • Handle: RePEc:uwp:landec:v:92:y:2016:i:4:p:718-736
    Note: DOI:
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    Citations

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    Cited by:

    1. Frondel, Manuel & Horvath, Marco & Vance, Colin & Kihm, Alexander, 2019. "Increased market transparency in Germany's gasoline market: What about rockets and feathers?," Ruhr Economic Papers 810, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. LeSage, James P. & Chih, Yao-Yu & Vance, Colin, 2019. "Markov Chain Monte Carlo estimation of spatial dynamic panel models for large samples," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 107-125.
    3. Koh, Kanghyok & Jeon, Sungho & Lee, Jinhyuk, 2022. "The effects of price competition on firms' operations and market price: Evidence from a retail gasoline market," Energy Economics, Elsevier, vol. 108(C).
    4. Palencia-González, Francisco J. & Navío-Marco, Julio & Juberías-Cáceres, Gema, 2020. "Analysis of brand influence in the rockets and feathers effect using disaggregated data," Research in International Business and Finance, Elsevier, vol. 52(C).
    5. Julio César Arteaga & Daniel Flores, 2022. "Price Regulation and Fraud—with Special Emphasis on Gasoline Retailing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 60(2), pages 175-192, March.
    6. Haucap, Justus & Heimeshoff, Ulrich & Siekmann, Manuel, 2016. "Selling gasoline as a by-product: The impact of market structure on local prices," DICE Discussion Papers 240, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    7. Horvath, Marco, 2019. "Germany's market transparency unit for fuels: Fostering collusion or competition?," Ruhr Economic Papers 836, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    8. Leonardo C. B. Cardoso & Carlos Frederico A. Uchôa & Williams Huamani & David R. Just & Raúl V. Gomez, 2022. "Price effects of spatial competition in retail fuel markets: the impact of a new rival nearby," Papers in Regional Science, Wiley Blackwell, vol. 101(1), pages 81-105, February.
    9. LeSage, James P. & Vance, Colin & Chih, Yao-Yu, 2017. "A Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel duopoly pricing," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 46-55.
    10. Michal Kvasnička, 2022. "Can we ignore spatial dependence when evaluating mergers?," Empirical Economics, Springer, vol. 62(3), pages 1323-1344, March.
    11. Bergantino, Angela S. & Capozza, Claudia & Intini, Mario, 2020. "Empirical investigation of retail fuel pricing: The impact of spatial interaction, competition and territorial factors," Energy Economics, Elsevier, vol. 90(C).
    12. Michal Kvasnička & Rostislav Staněk & Ondřej Krčál, 2018. "Is the Retail Gasoline Market Local or National?," Journal of Industry, Competition and Trade, Springer, vol. 18(1), pages 47-58, March.

    More about this item

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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