Pareto-efficient solutions and regulations of congested ride-sourcing markets with heterogeneous demand and supply
Introduction
Recent years have witnessed the rapid growth of ride-sourcing services offered by Uber, Lyft, Didi and their rivals. As an innovative sharing economy and gig-economy that connects travelers who want an on-demand ride and private car users who work as full-time or part-time ride service providers, the ride-sourcing service has attracted much attention from researchers. While the emergence of ride-sourcing services brings convenience to travelers, it also arouses many debates and challenges. One major question is whether and how the government should regulate the ride-sourcing market. In fact, some regulations or policies already took effect, especially in metropolitan cities. For example, New York City sets a minimum per-trip payment standard for all drivers working for High-Volume For-Hire Services (referring to ride-sourcing services, such as Uber). As per the minimum rates on Feb 01, 2020, the wheelchair-accessible vehicles (WAV) should be paid at a rate higher than $1.429 per mile and $0.502 per minute, while the non-WAV should be paid at a rate higher than $1.103 per mile and $0.502 per minute, by Uber, Lyft, among other ride-sourcing companies (see NYC, 2020). Although it does not directly control drivers’ mean earnings per hour, the minimum per-trip Payment Formula would result in estimated typical gross hourly earnings before expenses of at least $25.76 per hour for drivers (see NYC rules, 2020a). In addition, New York City has regulations on the utilization rate of vehicles. According to the NYC rules for the utilization rate, namely, the percentage of a driver’s on-duty time spent with a passenger in their car, the ride-sourcing companies with lower utilization rates than a certain standard would be required to pay higher driver compensation per trip to offset their drivers’ waiting time for dispatching (see NYC rules, 2020b). As reported, under the new regulation announced by Mayor Bill de Blasio in June 2019, Uber, Lyft and their competitors must mandate their drivers to carry a passenger at least 69% of their time while operating in Manhattan below 96th Street (see NY daily news, 2019).
On Sep 10, 2019, California legislators approved a landmark bill, i.e., Assembly Bill 5 (AB5)1, that requires companies like Uber and Lyft to treat their drivers as employees (Conger and Scheiber, 2019). Under this new regulation, ride-sourcing drivers are no longer contractors but employees who will be guaranteed by ride-sourcing platforms with basic protections like a minimum wage and unemployment insurance. This regulation is essentially a minimum income regulation that mandates the platform to ensure the drivers’ income per hour is larger than the minimum wage. However, Voters in California recently approved a ballot measure that exempts drivers for ridesharing services from AB5. More specifically, drivers for app-based ride-sourcing companies are classified as independent contractors instead of employees unless the companies set drivers’ hours, compel acceptance of specific ride requests, or prevent drivers from working for other companies (O'Brien, 2020). In December 2018, the Court of Appeal in the UK supported the rules of the Employment Tribunal, under which Uber drivers are classified as employees instead of self-employed contractors, and should be entitled to employee benefits, such as national minimum wage and holiday-pay.
Since 2016, Didi Chuxing and its rivals are required by the authorities of Beijing and Shanghai to only employ local permanent residents as their drivers (Li, 2016). This regulation is similar to the fleet size control in taxi markets, which controls the number of drivers by issuing a limited number of licenses. There are two major motivations for this policy: first, the government tries to restrain the vehicle fleet size for the traffic congestion is severe in big cities; second, the emergence of ride-sourcing services substantially compromises the benefits of taxi drivers, who protest against ride-sourcing drivers. However, a reduction in vehicle fleet size may also undermine the rights of non-local residents to work as a ride-sourcing driver, and compromise the benefits of passengers by increasing their waiting time (as a result of the supply shortage).
Clearly, the interests and benefits of different stakeholders, including drivers, passengers, ride-sourcing platforms and others, are typically in partial conflicts with each other. Due to the complexity of ride-sourcing markets, it remains a challenge to understand the aggregate implications of these regulatory schemes on different stakeholders’ interests. As a result, it is still unclear whether these regulations can effectively strike a good balance between these partially conflicting concerns. In addition, it is also interesting and important to explore some other effective regulatory schemes that have never been implemented but can potentially lead to a socially desirable outcome.
To address these issues, in this study, we attempt to provide a systematic analysis of a few important regulatory schemes2 by discussing whether each of them can achieve a targeted Pareto-efficient outcome, and what are their impacts on the platform’s decisions and the resulting realized demand and supply. We will investigate these issues under several market scenarios, such as the markets with homogeneous/heterogeneous drivers and mild/heavy traffic congestion. By conducting this, we are able to spell out the impacts of drivers’ heterogeneity and traffic congestion externality on the performance of various regulatory schemes. Our study contributes to the literature by offering a few interesting and new managerial insights, which include but are not limited to:
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In the market scenarios with homogeneous drivers and no/mild traffic congestion, only by regulating the commission and service level can the government induces the platform to choose the targeted Pareto-efficient strategy. If drivers have heterogeneous reservation rates, the commission regulation is still effective, but the service level regulation does not work. In the presence of traffic congestion, both commission and service level regulations are not Pareto-efficient.
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When drivers are homogeneous, the optimal trip fares decrease while the optimal wages increase as the target solution moves from monopoly optimum to social optimum, along the Pareto-efficient frontier. In contrast, the optimal wages exhibit an opposite trend along the frontier when drivers are heterogeneous. This property affects the designs of regulations in different market scenarios, e.g., only a minimum wage regulation influences the platform’s decision in the former case, while only by utilizing a maximum wage regulation can the government affects the platform’s choices.
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Heavy traffic congestion may flip over the trends of key variables, such as trip fare, wage, fleet size, along the Pareto-efficient frontier moving from monopoly optimum to social optimum. This substantially affects the government’s policies: with light traffic congestion, the government tends to use price cap or minimum fleet size provision to force the platform to increase consumer and driver surplus; in contrast, with heavy traffic congestion, the government will try to mitigate the negative impacts of traffic congestion externality by capping the vehicle fleet size or setting a maximum wage.
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Under some regulations, such as minimum per-hour income regulation, the platform may select a part of drivers who are willing to join the platform by driver rationing. If no regulation is imposed, the platform will choose to recruit all drivers who intend to participate.
This paper is organized as follows. Section 2 presents a review of the related literature. Section 3 introduces a model for depicting the equilibrium state of a ride-sourcing market, and presents a bi-objective maximization problem to obtain the set of Pareto-efficient solutions. Section 4 explores some important properties of the Pareto-efficient frontier, while Section 5 investigates the regulatory effects of a variety of regulatory regimes for the government to achieve a predetermined or targeted Pareto-efficient solution. Section 6 carries out extensive numerical studies to elucidate the theoretical results found in Section 5 and provide additional managerial insights. Finally, Section 7 concludes the paper and discusses future research directions.
Section snippets
Literature review
A wide variety of issues have been examined so far, including the stationary equilibrium analyses of the ride-sourcing market (Zha et al., 2016, Ke et al., 2020), geometrical matching and order dispatching (Xu et al., 2017, Zha et al., 2018b, Yang et al., 2020a), coordination between supply and demand (Bai et al., 2018), spatial, temporal, surge, static pricing strategies (Cachon et al., 2017, Castillo et al., 2017, Bimpikis et al., 2019), drivers’ working schedules through a whole day (Zha et
Modelling settings
This section first presents a model to delineate the intriguing relationship between the endogenous variables and decision variables of a standard ride-sourcing market, which serves as a foundation for the analyses of government regulations. Consider a market where passengers can opt for ride-sourcing service offered by a monopoly ride-sourcing platform or other transportation modes (such as public transit service). Denote by the average trip fare per order charged to passengers, by the
Optimal solutions and their properties
Generally speaking, the platform aims to maximize its own profit while the government is concerned with the social welfare. As shown by many previous studies (Yang and Yang, 2011, Zha et al., 2016), the first-best solution (social optimum) is unattainable since the platform earns a negative profit and will not participate in the market at social optimum, unless a certain amount of government subsidy is implemented. Therefore, the government may set a targeted social welfare that may ensure a
Analytical results of government regulations
As aforementioned, the objective of government regulation is to induce the platform to voluntarily choose the predetermined (or targeted) Pareto-optimal solution set by the government. This section will investigate multiple regulatory schemes and attempt to answer two major questions: (1) can the government induce the platform to choose the targeted Pareto-optimal solution by using these regulations voluntarily? (2) what are the impacts of these regulations on the realized demand and supply? To
Numerical studies on regulatory outcomes
In this section, we conduct numerical studies to examine the regulatory outcomes of the regulations discussed so far, under market scenarios where the theoretical results cannot be obtained: (1) with heterogeneous drivers and no traffic congestion; (2) with heterogeneous drivers and traffic congestion. For ride-sourcing market with homogeneous drivers and no traffic congestion, its theoretical results can be obtained, and the corresponding numerical studies are provided in Appendix A.2.
Conclusion
This paper first discusses the properties of the Pareto-efficient frontier that connects the social optimum and monopoly optimum, and then investigates the regulatory effects of various regulation approaches, including price-cap regulation, fleet size regulation, wage regulation, minimum utilization regulation, commission regulation, and demand regulation. It is interesting to find that many endogenous variables exhibit monotonic properties along the Pareto-efficient frontier. For example, as
CRediT authorship contribution statement
Jintao Ke: Conceptualization, Methodology, Writing – original draft. Xinwei Li: Conceptualization, Methodology, Writing – original draft. Hai Yang: Conceptualization, Methodology, Writing – review & editing. Yafeng Yin: Conceptualization, Methodology, Writing – review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank the editor and the three reviewers, whose useful comments have helped us improve the exposition of this study. The work described in this paper was substantially supported by the National Key Research and Development of China (No. 2018YFB1600900), the National Natural Science Foundation of China (No. 72001014), Hong Kong Research Grants Council under project HKU15209121, HKUST162086 and a grant from NSFC/RGC Joint Research Scheme under project N_HKUST627/18 (NSFC-RGC 71861167001).
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