My paper “The Drivers of Social Preferences: Evidence from a Nationwide Tipping Field Experiment” with Uri Gneezy, John List, and Ian Muir is now out as an NBER working paper. We analyze over 40 million tipping outcomes on the Uber app in the summer of 2017 to study tipping behavior and social preferences in the field. The immensity of the data set, both in terms of the number of observations and the number of features about each individual outcome we can observe, helps us determine facts about tipping behaviors and norms that were difficult to assess in previous studies.
As a first fact, roughly 16% of trips were tipped on Uber at the time.1 In the four weeks of data we collected, 60% of riders who took at least 10 trips never tipped. Conditional on tipping, people tip a little more than $3 on average (about 26% of the fare), so the average tip, including cases where the rider did not tip, is about $0.50. Tipping frequency on Uber is lower than tipping frequency in other contexts like restaurants. This could be a consequence of different norms and cultural expectations in the two settings–Uber did not encourage riders to tip in the earlier days of the app. It could also be because the tipping decision is made privately on Uber after the ride is over, whereas in many other contexts the tipping decision is more public, adding social pressure to the decision. Across cities, tips are lowest in large cities, especially in California and in the Northeast, and highest in less densely populated areas.
A key result is that rider characteristics are about 3 times as informative of the amount tipped as driver characteristics. Knowing who the rider is on a trip is much more predictive of the tip amount than knowing who the driver is. We show this to be true in various cities across the United States.
A rider’s rating is especially predictive of how much they will tip. Higher rated riders tip more, after controlling for a large number of other factors like the time and location of the trip. Riders also tip more after they just joined Uber, and then start tipping less as they get more experience taking rides. One possible explanation is that initially they overestimate tipping norms on Uber, and as they get more experienced they adjust their tipping downwards. Using a proxy for the gender of a rider constructed from their first name, we find that women tend to tip less than men. We also find that riders who use a default app language other than English tip less. We do not speculate on reasons in the paper, but it is possible that perceived tipping norms or perceived pressure to tip vary by demographics.
On the driver side, we similarly find high ratings and less experience are associated with higher tips. Declines in tips with experience could suggest that drivers try especially hard on their first few trips before getting comfortable, or that they decide tips are not worth the effort. Drivers whose default app language is not English get tipped less as well.
Women get tipped more than men on average. In particular, younger women get tipped more than younger men, with the gap shrinking with age.
Both male and female riders tip women more, but male riders are much more responsive to the age of female drivers than they are to the age of male drivers. Men tip younger women about seven cents more on average than older women (roughly a 14% increase, give or take), while they tip younger men about 3 cents more than older men. Female riders tend to not vary tips as much based on the age of the driver.
Tips also vary based on characteristics of where riders and drivers are from. Riders from ZIP codes that are high income and have a lower share of African American or Hispanic residents tend to tip more. Drivers from low income areas and areas with a higher share of Hispanic residents get tipped less on average.
Finally, tips seem to be positively correlated with several dimensions of trip quality. Picking up a rider on time is associated with getting higher tips, as is having fewer hard brakes and accelerations on the ride.
In addition to uncovering these facts, we explore some aspects of social preferences in the field. First, we look at repeated interactions between a rider and a driver. It is not common for a rider to match with the same driver twice, but our dataset is large enough that we still observe a substantial number of tip outcomes on repeat interactions. Riders tip on average 27% more the second time they see the same driver than they do the first time.
There are two reasonable explanations: strategic reciprocity, or some kind of behavioral or norms response. Under the strategic reciprocity explanation, when a rider matches with the same driver twice, they realize that repeat interactions happen more frequently on Uber than they originally thought. As a result, they should tip all of their drivers more to avoid bad or awkward encounters in the future. A testable implication of this model is that riders should tip other drivers more too. This is not borne out in the data–only the single trip with a repeat interaction gets tipped more. This gives evidence for some kind of behavioral response instead, like social connection built between the rider or driver, or different norms governing repeat interactions and single encounters.
We might reasonably think that riders and drivers build social connection by spending more time with each other and having a conversation. If the rider or driver is less comfortable using English, then we might expect conversation to be less likely, which could hinder social connection and mitigate the repeat interaction effect. However, we find that even when the rider or driver uses a default app language other than English, repeat interactions result in an increase in tips on par with the effect in the general pool of Uber users. If higher tips happen on repeat interactions because of social connection, this is at least suggestive evidence that conversation is not the mechanism through which social connection is built.
Finally, we look at the effect of default tip options on tipping levels. Riders can always enter a custom tip, but most people tend to choose one of the default options when they do tip. Uber ran an experiment early on varying the default options shown to riders. While we show that higher defaults did lead to slightly higher tips, we show the effect of defaults are much smaller on Uber than what the literature suggests for taxis.2 On Uber, tips happen privately, while on taxis tips happen in front of the taxi driver. Our results suggest that defaults can be more influential in setting tipping norms when the tipper is monitored. That is, norm setting through defaults and monitoring of the tipper complement each other in boosting tip levels.
This paper is long and packed with results, some of which I did not have space to discuss here. I hope you take a look at the full paper if you are interested in learning more.
If you like econometrics, also check out my other newly released paper, “Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings,” with Ali Hortacsu, John List, Ian Muir, and Jeffrey Wooldridge. We use simulation to study best practices for analyzing cluster-randomized field experiments with panel data.
- We only focus on UberX trips in the study.
- Haggag and Paci (2014)