Demand Estimation Using Managerial Responses to Automated Price Recommendations
提出一个利用管理者对算法价格建议的延迟调整来识别需求弹性的新框架，并用欧洲酒店数据验证，发现控制函数法比双重差分法更稳健。
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. This paper was accepted by Omar Besbes, revenue management and market analytics. 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 .