The Incentive Game under Target Effects in Ridesharing: A Structural Econometric Analysis

Posted: 13 Nov 2019

See all articles by Xirong Chen

Xirong Chen

University of International Business and Economics (UIBE)

Zheng Li

North Carolina State University - Department of Agricultural & Resource Economics

Liu Ming

The Chinese University of Hong Kong, Shenzhen

Weiming Zhu

University of Navarra, IESE Business School

Date Written: November 2, 2019

Abstract

To wield a flexible self-scheduled supply to match the ever-changing demand and maintain market shares, ride-sharing platforms such as Uber and Didi strive to keep more registered drivers active on the road, especially during peak hours in which the demand tends to be the highest. Platforms have been providing monetary rewards to incentivize self-scheduled drivers to work longer. However, drivers' responses to bonuses can be affected by the target effect, which if present may undermine the effectiveness of such bonus schemes.

In this paper, we provide a theoretical and empirical analysis of the effectiveness of the monetary bonus scheme in the context of the ridesharing industry, where the drivers are highly heterogeneous and set income targets. We first model driver's decision-making processes and the platform's optimization problem as a Stackelberg game, and show that driver's working hours do not increase monotonically with the bonus rate under the target effect and that the platform may not use all its budget on bonuses to maximize capacity or profit. Then, we develop a novel empirical strategy and perform a reduced-form analysis to provide evidence on the existence of the drivers’ income-targeting behavior utilizing comprehensive datasets that were obtained from a leading ridesharing platform. Using the equilibrium outcome from the driver's problem, we also structurally probe the existence and the magnitude of the target effect for each driver. Finally, we perform a counterfactual analysis to calculate the optimal bonus rates for different scenarios utilizing the characteristics of heterogeneous drivers derived from the estimation outcomes. We find that, compared to the platform's previous bonus-setting, the optimal bonus strategy improves the capacity level during peak hours by as much as 24%, boosting the total profit by $5.1 million per month.

Keywords: empirical and behavioral operations, structural estimation, sharing economy

Suggested Citation

Chen, Xirong and Li, Zheng and Ming, Liu and Zhu, Weiming, The Incentive Game under Target Effects in Ridesharing: A Structural Econometric Analysis (November 2, 2019). Available at SSRN: https://ssrn.com/abstract=3479675

Xirong Chen

University of International Business and Economics (UIBE) ( email )

10, Huixin Dongjie
Changyang District
Beijing, Beijing 100029
China

Zheng Li

North Carolina State University - Department of Agricultural & Resource Economics ( email )

Box 8109
3332 Nelson Hall
Raleigh, NC 27695-8109
United States

Liu Ming

The Chinese University of Hong Kong, Shenzhen ( email )

Shenzhen, Guangdong 518172
China

Weiming Zhu (Contact Author)

University of Navarra, IESE Business School ( email )

Avenida Pearson 21
Barcelona, 08034
Spain

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