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

Xirong Chen (), Zheng Li (), Liu Ming () and Weiming Zhu ()
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Xirong Chen: University of International Business and Economics, Beijing 100029, China
Zheng Li: North Carolina State University, Raleigh, North Carolina 27695
Liu Ming: The Chinese University of Hong Kong, Shenzhen 518172, China
Weiming Zhu: IESE Business School, University of Navarra, Pamplona 31009, Spain

Manufacturing & Service Operations Management, 2022, vol. 24, issue 2, 972-992

Abstract: Problem definition : We study a ridesharing platform’s optimal bonus-setting decisions for capacity and profit maximization problems in which drivers set daily income targets. Academic and Practical Relevance : Sharing-economy companies have been providing monetary rewards to incentivize self-scheduled drivers to work longer. We study the effectiveness of the monetary bonus scheme in the context of the ridesharing industry, where the drivers are highly heterogeneous and set income targets. Methodology : We model a driver’s decision-making processes and the platform’s optimization problem as a Stackelberg game. Then, utilizing comprehensive datasets obtained from a leading ridesharing platform, we develop a novel empirical strategy to provide evidence on the existence of drivers’ income-targeting behavior through a reduced-form and structural analysis. Furthermore, we perform a counterfactual analysis to calculate the optimal bonus rates for different scenarios by using the characteristics of heterogeneous drivers derived from the estimation outcomes. Results : Our theoretical model suggests that the drivers’ 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. We empirically demonstrate that the drivers engage in income-targeting behavior, and furthermore, we estimate the income targets for heterogeneous drivers. Through counterfactual analysis, we illustrate how the optimal bonus scheme varies when the platform faces different driver compositions and market conditions. We also find that, compared with the platform’s previous bonus setting, the optimal bonus strategy improves the capacity level during peak hours by as much as 26%, boosting the total profit by $4.3 million per month. Managerial implications : It is challenging to develop a flexible self-scheduled supply of drivers that can match the ever-changing demand and maintain the market share of the ridesharing platform. When offering monetary bonuses to incentivize drivers to work longer, the drivers’ income-targeting behavior can undermine the effectiveness of such bonus schemes. The platform needs to understand the heterogeneity of drivers’ behavioral preferences regarding monetary rewards to design an effective bonus strategy.

Keywords: sharing economy; empirical analysis; structural estimation (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:24:y:2022:i:2:p:972-992

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