2012
DOI: 10.1111/j.1467-6451.2012.00488.x
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Price Dispersion Over the Business Cycle: Evidence from the Airline Industry

Abstract: This study provides empirical evidence documenting how price dispersion moves with the business cycle in the airline industry. Performing a fixed-effects panel analysis on seventeen years of data covering two business cycles, we find that price dispersion is highly pro-cyclical. This effect is especially pronounced for legacy carriers relative to low-cost carriers. We show that our empirical result is consistent with firms' implementing second-degree price-discrimination tactics.

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Cited by 21 publications
(13 citation statements)
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References 38 publications
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“…Proposition is consistent with Gerardi and Shapiro (), who provide industry‐level evidence for a negative relationship between competition and price dispersion, and the industry‐level evidence in Cornia, Gerardi, and Shapiro (), which implies price dispersion is procyclical.…”
supporting
confidence: 86%
“…Proposition is consistent with Gerardi and Shapiro (), who provide industry‐level evidence for a negative relationship between competition and price dispersion, and the industry‐level evidence in Cornia, Gerardi, and Shapiro (), which implies price dispersion is procyclical.…”
supporting
confidence: 86%
“…7 Favara and Imbs (2015) exploit state-level exposure to bank 6 In Section I, we also discuss related literature on the strategic use of debt in bargaining games. 7 A recent empirical macro literature also studies the causes and consequences of credit shocks for house prices (Jordà, Schularick, and Taylor (2015)), price-discrimination markups (Cornia, Gerardi, and Shapiro (2012)), business cycles (Borio and Lowe (2002), Mian, Sufi, and Verner (2017), and Krishnamurthy and Muir (2017)), and stock markets (Hansman et al (2018)). See Mian and Sufi (2017) for a survey of recent work on credit-driven business cycles.…”
Section: A Related Literaturementioning
confidence: 99%
“…Escobari (2012) collects data on ticket prices for 81 flights (route-date combinations). Gerardi and Shapiro (2009) and Cornia et al (2012) use large publicly available data on airline tickets (the DB1B data set), but, unlike Escobari's data, a flight is a route-quarter combination. Here the data set is large and the definition of the good is as narrow as one can hope for.…”
Section: Introductionmentioning
confidence: 99%