The Impact of Ride-Hail Surge Factors on Taxi Bookings

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced form estimation with structural analyses using machine-learning-based demand predictions. We estimate an upper-bound of the cross-price elasticity of taxi bookings to surge factors of only 0.26, but incorporating surge factors into a demand-prediction model improves the out-of-sample accuracy by 12-15%. Our structural analyses based on a driver guidance system finds the improved accuracy reduces drivers' vacant roaming times by 9.4% and increases average trips per taxi by 2.3%, suggesting the price information is valuable across platforms, even if elasticities are low.

Agarwal, Sumit and Charoenwong, Ben and Cheng, Shih-Fen and Keppo, Jussi, Fickle Fingers: Ride-Hail Surge Factors and Taxi Bookings (March 26, 2019). Agarwal, Sumit, Ben Charoenwong, Shih-Fen Cheng, and Jussi Keppo. "The impact of ride-hail surge factors on taxi bookings." Transportation Research Part C: Emerging Technologies 136 (2022): 103508., Available at SSRN: https://ssrn.com/abstract=3157378 or http://dx.doi.org/10.2139/ssrn.3157378