Working Paper: NBER ID: w26380
Authors: Bharat Chandar; Uri Gneezy; John A. List; Ian Muir
Abstract: Even though social preferences affect nearly every facet of life, there exist many open questions on the economics of social preferences in markets. We leverage a unique opportunity to generate a large data set to inform the who’s, what’s, where’s, and when’s of social preferences through the lens of a nationwide tipping field experiment on the Uber platform. Our field experiment generates data from more than 40 million trips, allowing an exploration of social preferences in the ride sharing market using big data. Combining experimental and natural variation in the data, we are able to establish tipping facts as well as provide insights into the underlying motives for tipping. Interestingly, even though tips are made privately, and without external social benefits or pressure, more than 15% of trips are tipped. Yet, nearly 60% of people never tip, and only 1% of people always tip. Overall, the demand-side explains much more of the observed tipping variation than the supply-side.
Keywords: tipping; social preferences; ride-sharing; Uber; field experiment
JEL Codes: C93; D63; D64
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| rider gender (J16) | tipping (J33) |
| rider ratings (R48) | tipping (J33) |
| number of trips (R41) | tipping propensity (D64) |
| driver gender (J16) | tipping (J33) |
| driver age (R48) | tipping (J33) |
| fare levels (R48) | tipping (J33) |
| service quality (L15) | tipping (J33) |