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Airline price responses in the face of demand shocks: European lessons from the COVID-19 pandemic

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  • Borsati, Mattia
  • Fageda, Xavier

Abstract

We exploit the exogenous demand shock in the aviation sector that resulted from the spread of the COVID-19 pandemic to investigate how airlines responded in terms of pricing strategies. By using monthly fare data for flights operated within the EU-27 member states over the period 2017–2022 and by quantifying the intensity of pandemic cycles through a stringency index, we estimate price regressions at the route–airline level. Our findings reveal that the demand drop has prompted lower fares and increased intertemporal price dispersion. Furthermore, we show that fare cuts are not explained by airlines operating fewer flights. By analyzing heterogeneous price responses to the demand shock as determined by various route and airline characteristics, we find a stronger reaction on international markets and greater resilience of low-cost airlines. We also show that the regulations in the form of public service obligations and state aid programs for airlines helped the beneficiaries but not travelers.

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  • Borsati, Mattia & Fageda, Xavier, 2024. "Airline price responses in the face of demand shocks: European lessons from the COVID-19 pandemic," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:transe:v:186:y:2024:i:c:s1366554524001285
    DOI: 10.1016/j.tre.2024.103537
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    More about this item

    Keywords

    Airline pricing; COVID-19; Network airlines; Low-cost airlines; State aid;
    All these keywords.

    JEL classification:

    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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