Demand Estimation Using Managerial Responses to Automated Price Recommendations - Daniel Garcia; Juha Tolvanen; Alexander K. Wagner (Center for Economic Studies and Ifo Institute (CESifo), 2021)
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: L13; L83; D12; 330; big data; causal inference; machine learning; revenue management; price recommendations; demand estimation