Quantifying and Improving Market Efficiency in App Based Ridesharing

Principal Investigator(s):

Ravi Bapna, Associate Dean, Carlson School of Management

Project summary:

The advent of multiple ridesharing apps in various cities provides consumers choice and flexibility in meeting their transport needs. Although these ridesharing services afford consumers convenience and access to transportation, cross-platform search costs exist (e.g., in New York City, at least seven ridesharing services are currently in operation), as do significant information asymmetries. These factors, in tandem with consumers' use of heuristic decision rules in the face of choice and information overload, can lead to market inefficiencies. The prior literature has lent relatively little attention to quantifying the overall welfare generated by the existence of multiple competing ridesharing services. While economic theory predicts the law of one price, given that consumers bear arguably little or no switching costs, prior evidence from Internet markets for books, electronics, and consumer goods provides empirical evidence that price dispersion continues to manifest in digital markets. This study is measuring the economic efficiency of the ride-sharing market by quantifying price dispersion across geographies, between ridesharing options. Subsequently, the study aims to design an agent-based intervention in the form an app (called Lyber), that operates as middleware, aggregating search results over multiple ridesharing platforms in a user's location. Users who leverage this tool will be able to specify their travel needs, and the app will provide consumers with the utility of maximizing choice across multiple ride sharing platforms. Again, economic theory predicts that as awareness and adoption of the coordinating agent increases, the market should move closer to equilibrium and maximal efficiency. The study's theoretical predictions are being tested via a field experiment conducted in two stages.

Project details: