Two-sided markets and ride-hailing Can academia hold the keys to industry insights?

Abstract I conduct a literature review of academic articles surrounding ride-hailing to attempt to glean in-sights about these platforms. I present multiple papers around topics such as two-sided markets, mul-ti-homing and dynamic platform competition. I show that the literature, both theoretical and empiri-cal, surrounding these topics indeed gives us tools to analyze not only single ride-hailing platforms, but also the industry itself. I use these methods to produce insights from a local and temporal market case study

Regulating TNCs: Should Uber and Lyft Set Their Own Rules?

We evaluate the impact of three proposed regulations of transportation network companies (TNCs) like Uber, Lyft and Didi: (1) a minimum wage for drivers, (2) a cap on the number of drivers or vehicles, and (3) a per-trip congestion tax. The impact is assessed using a queuing theoretic equilibrium model which incorporates the stochastic dynamics of the app-based ride-hailing matching platform, the ride prices and driver wages established by the platform, and the incentives of passengers and drivers. We show that a floor placed under driver earnings pushes the ride-hailing platform to hire more drivers and offer more rides, at the same time that passengers enjoy faster rides and lower total cost, while platform rents are reduced. Contrary to standard economic theory, enforcing a minimum wage for drivers benefits both drivers and passengers, and promotes the efficiency of the entire system. This surprising outcome holds for almost all model parameters, and it occurs because the wage floors curbs TNC labor market power. In contrast to a wage floor, imposing a cap on the number of vehicles hurts drivers, because the platform reaps all the benefits of limiting supply. The congestion tax has the expected impact: fares increase, wages and platform revenue decrease. We also construct variants of the model to briefly discuss platform subsidy, platform competition, and autonomous vehicles

Full Potential of Future Robotaxis Achievable with Trip-Based Subsidies and Fees Applied to the For-Hire Vehicles of Today

As described by Grush and Niles in their textbook, The End of Driving: Transportation Systems and Public Policy Planning for Autonomous Vehicles, there are two distinct market states for the future of automobility as vehicles become increasingly automated. The first, Market-1, is comprised of all vehicles that are manufactured and sold to private owners and used as household vehicles. This private consumer fleet will—through automated driver assistance systems (ADAS)—be increasingly capable of hands-off operation, even self-driving in certain environments such as limited-access expressways. The second category, Market-2, represents all the vehicles made expressly for the service market, i.e., roboshuttles and robotaxis, meant to be eventually driverless in prepared, defined areas and streets. Ford, GM, Lyft, Uber, Waymo, and dozens of other companies assert that they are preparing vehicles for Market-2. The main thesis in this perspective is that a productive, efficient system of on-demand Market-2 mobility can evolve from incentive-based governance—here termed “harmonization management.” This approach strikes a contrast with rigid regulation of a style seen with big city taxicabs and based on using constrained service classifications or per-vehicle medallion approaches. This essay recommends that transportation authorities set up systems of robust pricing signals—incentives and fees—delivered through a universal, mandatory system providing efficient, equitable distribution of these signals

Market Structure and Congestion Externalities: Theory and application to the ride-hailing industry

The encompassing theme of this dissertation is the analysis of markets that feature market power and negative externalities. I focus mainly on congestion externalities, as externalities that affect only market participants. The first chapter evaluates the efficiency of private pricing of congestible resources. I develop a model of congestible resource use that explicitly considers a bivariate distribution of reservation values and sensitivities to congestion across potential users. This model highlights the importance of the correlation between reservation values and sensitivities to congestion to judge the efficiency of private pricing. Numerical results based on a road pricing example show that monopolistic pricing can range from very inefficient (price too high) when the correlation is negative to almost complete efficiency when it is strong and positive. The second chapter studies ride-hailing markets mediated by digital platforms like Uber. I extend the model of the first chapter to include a supply side of drivers. A monopolistic platform chooses prices on both sides of the market to maximize profit. I calibrate the model to the morning peak period of Bogota, Colombia. The results show that the price gap imposed by a monopolistic platform corresponds to about two thirds of the net marginal external cost caused by an additional ride hailer. A congestion charge on ride hailing is then justified. However, the optimal congestioncharge, as a tax on the price charged to riders, covers only 50% of the marginal external cost. The last chapter explores the effects of modifying several assumptions of the ride-hailing model developed in the second chapter. The main modification is to move from a monopolistic market structure to a duopoly. I show that absent any differentiation between platforms, competition leads to zero profits. This result supports the idea that ride-hailing markets gravitate towards a single platform. Assuming a small amount of differentiation, the duopoly equilibrium reduces the price charged to riders and increases the size of the market. This expansion reduces overall welfare due to the external effect on traffic congestion and calls for a higher congestion charge

Synergies between app-based car-related shared mobility services for the development of more profitable business models

Purpose: Emerging shared mobility services are an opportunity for cities to reduce the number of car single trips to both improve traffic congestion and the environment. Users of shared mobility services, such as carsharing, ridesharing and singular and shared ride-hailing services, often need to be customers of more than one service to cover all their transport needs, since few mobility providers offer more than one of these services from a single platform. On the other hand, providers offering these services separately do not optimize costly resources and activities, such as the vehicles or the technology. Hence, the aim of this paper is to find synergies between the different app-based car-related shared mobility services that foster the development of new business models, to increase the profitability of these services. Design/methodology/approach: The research approach is built on the literature of car-related shared mobility services business models, supported by the review of certain outstanding services websites, and face-to-face interviews with users and drivers of these transport services. The analysis is presented by means of the Business Model Canvas methodology. Findings: Based on the synergies found, this paper suggests a few different approaches for services to share some resources and activities. Originality/value: This study identifies the common features of carsharing, ridesharing and singular and shared ride-hailing services to develop more profitable business models, based on providing the services in aggregated form, or outsourcing activities and resources. In addition, the implications of these proposals are discussed as advantages and drawbacks from a business perspectivePeer ReviewedPostprint (published version

Horizontal Collusions Organized by Uber: Time for a Change in Canada

This paper argues that Uber’s ordinary operation should be characterized as organizing horizontal cartels among drivers that not only fix the fares of ride- hailing services using its platform but also allocate customers. Uber-led cartels, therefore, violate section 45(1) of the Competition Act5 of Canada. In doing so, this paper analyzes the relationships between Uber and drivers and argues that (i) Uber is the organizer of price-fixing and market allocation collusions among drivers, (ii) the collusions are horizontal, and (iii) they are per se illegal. The first section discusses the general structure of peer-to-peer markets. The second section examines factors indicating the illegality of cartels organized by Uber under the Competition Act of Canada. The last section will provide some legal and technical solutions that may help Uber to remedy the anti- competitiveness of its platform

Ecosystem Competition and the Antitrust Laws

I. Introduction II. Three Examples of Ecosystem Competition ... A. Middleware and Operating Systems ... B. E-Books and Tablets … C. Connected and Automated Vehicles III. How Ecosystem Competition Enhances Consumer Welfare IV. Implications for Antitrust Law and Polic