Your Time Has a Price—Lyft Tested It

Why It Matters

A higher, data‑driven value of time could reshape federal transportation policy and enable ride‑hailing firms to price services more precisely, affecting investment decisions and consumer costs.

Key Takeaways

• Lyft’s experiment measured riders’ willingness to pay for reduced wait times.
• Average value of time estimated at $20 per hour, double federal estimate.
• Experiment varied both price and wait time across the United States market.
• Findings could reshape DOT and EPA calculations for transportation investment decisions.
• Real‑time pricing may manage demand, improve efficiency, and boost revenue.

Summary

Lyft’s chief economist explained a nationwide field experiment designed to quantify how much riders value their time when waiting for a ride.

The test randomly offered riders either the nearest car with a three‑minute wait or a farther vehicle with a longer wait, while simultaneously adjusting the fare. By observing the trade‑off between price and wait time, Lyft calculated an implicit value of time of roughly $20 per hour, more than twice the $10‑$11 per hour figure traditionally used by the U.S. Department of Transportation.

“I called the DOT and the EPA,” the economist said, noting that the results suggest a need to rethink how time‑saving benefits are priced in transportation projects. The experiment covered riders in every major market across the United States, providing a robust data set.

If regulators adopt this higher valuation, infrastructure projects, congestion‑pricing schemes, and ride‑hailing pricing models could shift dramatically, giving companies new levers to manage demand and potentially increasing revenue while improving overall system efficiency.

Original Description

Have you taken a Lyft, shopped at Walmart, or used Facebook in the last decade? If so, you’ve likely been part of an experiment.

In this clip, John List explains how real-world field experiments—like Lyft testing what your time is worth—help firms and economists learn what actually drives behavior. It’s the same approach behind his new textbook Experimental Economics: Theory in Practice.