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Demand Estimation Using Managerial Responses to Automated Price Recommendations

Daniel Garcia (), Juha Tolvanen and Alexander Wagner

No 9127, CESifo Working Paper Series from CESifo

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 dataset containing bookings for a sample of mid-sized hotels in Europe. Using non-binding 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 is 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 time as well as 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; demand estimation (search for similar items in EconPapers)
JEL-codes: D12 L13 L83 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-ind and nep-pay
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