Abstract 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. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors affect taxi bookings to bound the effect of customer decisions while accounting for various confounding variables. We find that even in a unique market like Singapore, where incumbent taxi companies have app-based booking systems similar to those from ride-hailing companies like Uber, the estimated upper bound on the cross-platform substitution between ride-hailing services and taxi bookings is only 0.26. On the other hand, we show that incorporating surge price factor improves the precision of demand prediction by 12% to 15%. Our structural analyses based on a driver guidance system finds this improved accuracy in demand prediction reduces drivers’ vacant roaming times by 9.4% and increases the average number of trips per taxi by 2.6%, suggesting the price information is valuable across platforms, even if elasticities are low.
Highlights We estimate the cross-price elasticity of 0.26 between ride-hailing and taxi services. The results suggest up to 18% of taxi fares are due to cross-platform substitution. Using surge factors when guiding taxi drivers leads to 9.4% reduction in vacant time. We estimate that such saving could lead to 2.6% increase in the hourly taxi trips. Other markets with less comparable taxi service will have smaller effects.
The impact of ride-hail surge factors on taxi bookings
2021-12-02
Article (Journal)
Electronic Resource
English
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