We show that, for their flights, easyJet, Ryanair and Southwest set a sequence, based on the seats’ order of sale, of non-strictly monotonically increasing fares. A model where prices cannot be continuously adjusted due to organizational frictions and technological bottlenecks provides a theoretical framework supporting the use of price sequences. Consistent with the model’s main assumption, the evidence from easyJet’s flights indicates that sequences remain “static”, i.e., unchanged, for about 35 hours on average or even longer depending on a flight’s load factor, its selling rate and the time to departure. Dynamic pricing, which corresponds to a modification of the sequence, may occur in two forms, with seats being either marked-down or marked-up. The latter tends to occur predominantly during the day, suggesting that human decision- making often complements the algorithm’s operativity. Thus, we study the conditions for classifying dynamic pricing as inter-temporally discriminatory and provide some managerial implication arising from the use of price sequences

Capacity, Static and Dynamic pricing: theory and evidence / C.A. Piga, M. Alderighi. - (2024 Dec 13). [10.2139/ssrn.5029318]

Capacity, Static and Dynamic pricing: theory and evidence

M. Alderighi
2024

Abstract

We show that, for their flights, easyJet, Ryanair and Southwest set a sequence, based on the seats’ order of sale, of non-strictly monotonically increasing fares. A model where prices cannot be continuously adjusted due to organizational frictions and technological bottlenecks provides a theoretical framework supporting the use of price sequences. Consistent with the model’s main assumption, the evidence from easyJet’s flights indicates that sequences remain “static”, i.e., unchanged, for about 35 hours on average or even longer depending on a flight’s load factor, its selling rate and the time to departure. Dynamic pricing, which corresponds to a modification of the sequence, may occur in two forms, with seats being either marked-down or marked-up. The latter tends to occur predominantly during the day, suggesting that human decision- making often complements the algorithm’s operativity. Thus, we study the conditions for classifying dynamic pricing as inter-temporally discriminatory and provide some managerial implication arising from the use of price sequences
lgorithm; dynamic pricing; competition policy; machine learning
Settore ECON-04/A - Economia applicata
13-dic-2024
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5029318
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1155215
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