Platform Design, Earnings Transparency and Minimum Wage Policies: Evidence from A Natural Experiment on Lyft
Rubing Li, Xiao Liu, Arun Sundararajan

TL;DR
This study examines how Lyft's policy of earnings guarantees and increased transparency affected driver behavior, platform performance, and stakeholder interests through a natural experiment, revealing significant increases in driver engagement and supply.
Contribution
It provides causal evidence on the effects of platform design and transparency policies on driver supply and platform outcomes using a natural experiment and advanced econometric methods.
Findings
Driver engagement and trips increased significantly.
Lower-earning drivers benefited most from the policy.
Supply spillovers positively affected rider demand.
Abstract
We study the effects of a significant design and policy change at a major ridesharing platform that altered both provider earnings and platform transparency, examining how it affected outcomes for drivers, riders, and the platform, and providing managerial insights on balancing competing stakeholder interests while avoiding unintended consequences. In February 2024, Lyft introduced a policy guaranteeing drivers a minimum fraction of rider payments while increasing per-ride earnings transparency. The staggered rollout, first in major markets, created a natural experiment to examine how earnings guarantees and transparency affect ride availability and driver engagement. Using trip-level data from over 47 million rides across a major market and adjacent markets over six months, we apply dynamic staggered difference-in-differences models combined with a geographic border strategy to…
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Taxonomy
TopicsTransportation and Mobility Innovations · Urban Transport and Accessibility · Transportation Planning and Optimization
