How Platform Transparency Shapes Provider Choices: Evidence from A Natural Experiment on Lyft

Rubing Li (), Xiao Liu () and Arun Sundararajan ()
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Rubing Li: Stern School of Business, New York University, New York, NY, USA
Xiao Liu: Stern School of Business, New York University, New York, NY, USA
Arun Sundararajan: Stern School of Business, New York University, New York, NY, USA

No 25-09, Working Papers from NET Institute

Abstract: In February 2024, Lyft introduced a policy guaranteeing drivers at least 70\% of rider payments while also increasing per-ride earnings transparency. The rollout of this policy was staggered, first introduced in “major markets” that were more urban, and provided a natural experiment to assess how platform transparency and earnings guarantees affect ridesharing availability, driver engagement and rider satisfaction. Using trip-level data from over 47 million rides in a major urban market and its neighboring suburban markets across six months, we applied dynamic staggered difference-in-differences models to measure the causal effects of these platform design changes on supply- and demand-side outcomes, and ensuing ridesharing performance. We show that the design change led to substantial changes in driver engagement, with separate effects from the guarantee and the transparency. Drivers increased their working hours and utilization, leading to more completed trips and higher per-hour and per-trip earnings. These effects were strongest for drivers with lower pre-policy earnings and greater income uncertainty. We unpack the economic mechanism by which increased supply also had a positive spillover on demand, boosting rider app engagement and booking conversions and lowering wait times in high demand areas. We also provide some evidence that points to platform transparency potentially leading to unintended strategic driver behavior. Finally, we develop a counterfactual simulation framework that models ride production as a function of driver supply hours and rider intents. Using this model, we identify optimized supply reallocation strategies that could increase overall ride production by up to 39\%.

Keywords: Ride-hailing; Platform Regulation; Gig Workers; Transparency; Earning Floor; Pricing (search for similar items in EconPapers)
JEL-codes: L1 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2025-09
New Economics Papers: this item is included in nep-tre
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