Sharing diseconomy: impact of the subsidy war of ride-sharing companies on urban congestion

ABSTRACT

As a typical example of the sharing economy, ride-sharing, in theory, can improve market efficiency and enhance the welfare of consumers and service suppliers. However, negative externalities resulted from ride-sharing, such as quasi-public goods, are often overlooked. In this paper, we used AutoNavi’s big data to measure traffic congestion in cities, and built DID, PSM-DID and DDD models to analyse the impact of the subsidy war among ride-sharing companies on the traffic congestion in Shanghai. The subsidy war led to an increase in the congestion index of Shanghai by 0.068–0.077, which was equivalent to an increase in transit time by 3.51%–5.53%. Excessive subsidies offered by ride-sharing companies to vie for market share will eventually distort the market. As a result, increased social costs will ultimately harm the well-being of the entire society, leading to diseconomy.     

New mobility service users' perceptions on electric vehicle adoption

The recent growth of new mobility services such as car-sharing (ZipCar, Car2Go) and ride-hailing (Uber, Lyft) has interesting implications for new vehicle technologies. We explore the users of the services and their relation to electric vehicles preferences by analyzing two large-scale mobility service surveys. A number of categories (car-share usage, ride-hail usage, commute mode, demographics, current vehicle attributes, environmental attitudes, technology attitudes, and life-stage information) are examined in order to determine the likelihood a respondent considers purchasing an electric vehicle in the future. Survey respondents explicitly expressed that exposure to ride-hailing did not increase their propensity for wanting to purchase an electric vehicle in the future. However, we run a full suite of cross-validation models and find that in addition to the typical factors used in modeling preferences, the use of new mobility services statistically increases the predictive power of our model to identify preferences for electric vehicles.