Evolution and Current Practices of Ride Services

Abstract

In the past decade we have seen a rapid transformation in the ride service market with the advent of decentralized Ride Hailing (RH) services (e.g., Uber, Lyft), who gained significant market share at the expense of traditional Vertically Integrated (VI) taxi companies.However, it is not clear what the endgame of this competition between these two models will be. What is more, the current development of driverless car technology, which leads to another form of Vertically Integrated service model, is posing further questions on how the industry will shape in the future. In the first chapter, we analyze this question by game-theoretically modeling entry and competition between decentralized Ride Hailing and Vertically Integrated ride services. First, by comparing monopoly models, we find that due to the advantage of centralization, the VI model is more efficient in increasing firm profits, reducing delays and increasing social welfare compared to the RH model. However, in competition, the RH firm predominantly reverses this disadvantage, and gains the upper hand in the market with lower delays, higher market share, and higher profits due to its flexibility in setting its supply compared to the VI firm, which projects the success of the ride-hailing firms compared to the traditional taxi model. Furthermore, the entry of the RH firm into the market always improves social welfare, while the entry of an inefficient VI firm may reduce industry service levels and surprisingly decrease social despite introducing competition. Our results suggest that entry of a costly self-driving car technology may in fact hurt the industry as a whole and social welfare in a market that is served predominantly by a ride-hailing company, and this technology should be approached carefully by the industry and the regulators. In the second chapter, we examine the effect of offering ride distance information to the drivers in the ride-hailing two-sided market.This is of our interest because currently, the drivers cannot observe the riders' destination. However the leading companies in the ride-hailing business such as Uber and Lyft are experimenting the effect of offering this information to the drivers, but the effect is yet uncertain. It may introduce efficiency, or it may aggravate the cherry picking behavior which will be detrimental to the firm. We develop a game theoretical model of (i) a ride-hailing firm where the drivers do not observe the riders' ride distance (the unobservable case), and (ii) where they can observe (the observable case). We compare the two cases to determine when it would benefit the ride-hailing firm to offer this information to the drivers. We also compare consumer surplus and social welfare to see if the firm's decision may be in conflict with the social planner's. Our main finding is that when consumers are patient, the drivers' cherry picking behavior introduces efficiency to the operations of the observable case, making it optimal for the firm to offer observability to the drivers. However as consumers become impatient, this cherry picking behavior becomes burdensome for the observable case, and thus the firm's optimal decision is to not give observability. Furthermore, under certain parameter regions, the firm's decisions are in conflict with the social planner's objective, thus the government may need to devise a policy to maximize social welfare.