Abstract
We provide a new framework to identify demand elasticities in markets where managers rely on algorithmic recommendations for price setting and apply it to a data set containing bookings for a sample of midsized hotels in Europe. Using nonbinding algorith- mic price recommendations and observed delay in price adjustments by decision makers, we demonstrate that a control-function approach, combined with state-of-the-art model- selection techniques, can be used to isolate exogenous price variation and identify demand elasticities across hotel room types and over time. We confirm these elasticity estimates with a difference-in-differences approach that leverages the same delays in price adjust- ments by decision makers. However, the difference-in-differences estimates are more noisy and only yield consistent estimates if data are pooled across hotels. We then apply our control-function approach to two classic questions in the dynamic pricing literature: the evolution of price elasticity of demand over and the effects of a transitory price change on future demand due to the presence of strategic buyers. Finally, we discuss how our empiri- cal framework can be applied directly to other decision-making situations in which recom- mendation systems are used.
Original language | English |
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Pages (from-to) | 7918-7939 |
Number of pages | 22 |
Journal | Management Science |
Volume | 68 |
Issue number | 11 |
Early online date | 2022 |
DOIs | |
Publication status | Published - Nov 2022 |
Bibliographical note
Funding Information:History: Accepted by Omar Besbes, revenue management and market analytics. Open Access Statement: This work is licensed under a Creative Commons Attribution 4.0 International Li-cense. You are free to copy, distribute, transmit and adapt this work, but you must attribute this work as “Management Science. Copyright © 2022 The Author(s). https://doi.org/10.1287/mnsc.2021.4261, used under a Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/.” Funding: D. Garcia acknowledges financial support from the Austrian Science Fund [Single Project “Understanding Consumer Search” FWF-P 30922]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2021.4261.
Publisher Copyright:
© 2022 The Author(s).
Keywords
- Causal inference
- Machine learning
- Demand estimation
- Revenue management
- Price recommendations
- Big data
- Industrial organization
- price recommendations
- big data
- causal inference
- machine learning
- revenue management
Fields of Science and Technology Classification 2012
- 502 Economics