The rise of marketplaces for goods and services has led to changes in the mechanisms used to ensure high quality. We analyze this phenomenon in the Uber market, where the system of pre-screening that prevailed in the taxi industry has been diminished in favor of (automated) quality measurement, reviews, and incentives. This shift allows greater flexibility in the workforce but its net effect on quality is unclear. Using telematics data as an objective quality outcome, we show that UberX drivers provide better quality than UberTaxi drivers, controlling for all observables of the ride. We then explore whether this difference is driven by incentives, nudges, and information. We show that riders’preferences shape driving behavior. We also find that drivers respond to both user preferences and nudges, such as notifications when ratings fall below a threshold. Finally, we show that informing drivers about their past behavior increases quality, especially for low-performing drivers.
Susan Athey, Juan Camilo Castillo, and Bharat Chandar, “Service Quality in the Gig Economy: Empirical Evidence about Driving Quality at Uber”, TNIT working paper, September 2019.
TNIT working paper, September 2019