Uber seems to offer better service in areas with more white people. That raises some tough questions
An Uber car is seen parked with the driver’s lunch left on the dashboard in Los Angeles in July. (Lucy Nicholson/Reuters)
Jennifer Stark is a computational journalist at the College of Journalism at the University of Maryland, College Park. Nicholas Diakopoulos is an assistant professor in the College of Journalism at University of Maryland, College Park. This is a guest contribution to Wonkblog.
The goal of Uber’s surge-pricing algorithm is to influence car availability by dynamically adjusting prices. When surge is in effect, and prices are higher, the idea is that the supply of drivers is increased while at the same time demand is decreased. We previously reported that it appears that rather than increase the absolute supply of drivers by getting more cars on the road, existing driver supply is instead redistributed geographically to places with more demand. If drivers are relocating to areas with surge-pricing, those areas will experience reduced wait times for their car, or better service, but the areas the drivers are moving away from will experience longer wait times, or poorer service. So who gains, and who loses? Which neighborhoods get consistently better or worse service?
Our analysis of a month’s worth of Uber data throughout D.C. suggests an answer: The neighborhoods with better service — defined as those places with consistently lower wait times, the pickup ETA as projected by Uber — are more white.
We collected data on wait times — Uber’s estimate for how long you will wait between requesting your car and it arriving — and surge pricing via the Uber API for 276 locations in D.C. every three minutes for four weeks from Feb. 3 to March 2. We didn’t want to miss any surges, so we chose three minutes, knowing that surges in D.C. are no shorter than three minutes. The surge-pricing data was then used to calculate the percentage of time surging. Data were analyzed by census tracts, which are geographic areas used for census tabulations, so that we could test for relationships with demographic information. Only uberX cars were included in our analysis since they are the most common type of car on Uber. (In the interest of making the data analysis transparent, all our code can be viewed online.)
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Source: Washington Post