Efficient Subsidies via Supply Re-usability

39 Pages Posted: 11 Sep 2019

See all articles by Shumin Ma

Shumin Ma

City University of Hong Kong (CityU) - School of Data Science

Qi Wu

City University of Hong Kong, School of Data Science

Date Written: February 1, 2019

Abstract

Surge pricing finds equilibrium prices in periods of excessive demand or scarce supply. Effective or not, it is a business risk when riders perceive spiking prices as exploitation of people's emergency. This paper studies subsidy policies that avoid the downside of surge pricing when accommodating the fluctuation of demand. We show that the re-usability of driver supply presents a hidden capacity. Tapping it wisely through supply-side subsidies prescribes a non-pricing alternative to the current pricing policies without the need for either hiking price or recruiting new drivers. We use a queueing model together with the Stackelberg game to analyze how to optimally subsidize a reusable pool of driver supply, both myopically and in the long run. Knowing drivers are self-interested and given a specific structure of base trip fare, our analysis shows that injecting a healthy dose of myopic subsidy into the matching process reverts unfavorable decisions of individual drivers. In aggregate, the induced supply multiplier effect significantly boosts vehicle circulation and effective demand. We also show that, on the other hand, the impact of myopic subsidies on long-run throughput is not monotonic. There is a physical limit in terms of how much the platform can tap this hidden capacity. However large the budget of incentive, its size is intrinsically constrained by the spatial structure of base trip fare and the distribution of customer travel distances.

Keywords: Ride matching, Subsidy policy, Queueing model, Stackelberg game

Suggested Citation

Ma, Shumin and Wu, Qi, Efficient Subsidies via Supply Re-usability (February 1, 2019). Available at SSRN: https://ssrn.com/abstract=3447306 or http://dx.doi.org/10.2139/ssrn.3447306

Shumin Ma

City University of Hong Kong (CityU) - School of Data Science ( email )

Kowloon
Hong Kong

Qi Wu (Contact Author)

City University of Hong Kong, School of Data Science ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

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