Demand Estimation Using Managerial Responses to Automated Price Recommendations

Daniel Garcia, Juha Tolvanen, Alexander K. Wagner*

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in FachzeitschriftArtikelPeer-reviewed

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.
OriginalspracheEnglisch
Seiten (von - bis)1-22
Seitenumfang22
FachzeitschriftManagement Science
Frühes Online-Datum2022
DOIs
PublikationsstatusVeröffentlicht - 2022

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