Demand Estimation Using Managerial Responses to Automated Price Recommendations

Daniel Garcia, Juha Tolvanen () and Alexander Wagner
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Juha Tolvanen: Department of Economics, University of Vienna, 1090 Vienna, Austria

Management Science, 2022, vol. 68, issue 11, 7918-7939

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 algorithmic 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 adjustments 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 empirical framework can be applied directly to other decision-making situations in which recommendation systems are used.

Keywords: big data; causal inference; machine learning; revenue management; price recommendations (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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http://dx.doi.org/10.1287/mnsc.2021.4261 (application/pdf)

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Working Paper: Demand Estimation Using Managerial Responses to Automated Price Recommendations (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:11:p:7918-7939

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