Uber and the Economics of Discrimination
For years, minority communities have faced discrimination when trying to hail a cab. In the District of Columbia where I live, the problem has been documented and continues to persist. While there are punishments for failing to serve someone due to their skin color, these “refusal to haul” laws are difficult to enforce. Ridesharing apps like Uber, while not a panacea, have clearly benefited a variety of underserved communities by allowing individual exchange to push back against racial profiling.
Broadly speaking, three features of ridesharing apps help to upend markets inefficiencies that often occur due to personal prejudice: personal reputation is imbued via the review system; local market demand can be discovered; and the introduction of competitive pressure helps to solve a matching problem.
For one, personal reputation gets outsourced via the rating and payment system. As a profession, taxi drivers are subject to some of the highest workplace violence, up to 33 times higher than average. Nonfatal assaults, homicides, robberies, and verbal abuse are among the highest for taxicab drivers, while the perpetrators of the less serious actions typically face few legal consequences. In all, one can imagine that the job breeds risk aversion. Sadly, many people use skin color as a proxy for trustworthiness, leading to the consistent problem that minorities face when trying to hail a cab. With Uber, personal reputation is quantified. To be sure, Uber does not alleviate the problems of discrimination after the transaction when a rating is assigned. But, again, this is a much better situation than was typically the case before. Moreover, because there is no cash exchange, the possibility for robbery is severely diminished, and if there are problems, identifying information on the rider is kept which allows for ex post enforcement. All combined, the safety of the driver is better assured with Uber, putting downward pressure on discriminatory acts.
Enabled by the rating system and granular information on consumer demand, Uber has been able to move into underserved neighborhoods of all kinds. As evidenced by their data, Uber serves these neighborhoods 4 times out of 10, whereas taxicabs are required to do this once a day via law. In the past, minority neighborhoods were often overlooked for taxi service because they were assumed to be unsafe. This knowledge asymmetry disallowed for countless transactions to occur, which can now happen via the trust that the app enables. Even those areas that are not minority communities but have low per capita demand now can receive service as well. All of this is in part due to knowledge problems of where consumers demand service, buttressed by a supply problem that medallions and the taxicab commissions have enabled.
Lastly, and interestingly enough, the expansion of the taxi market has an effect on matching, as Becker first laid out in his seminal book on the economics of discrimination. Of course, if more drivers are competing for the same number of consumers, then on the margins, discriminating against certain riders because of their skin color or how they present themself becomes more costly. Similarly, if we assume that the number of consumers is outpaced by the number of potential drivers, then absolute number of drivers who don’t have discriminatory preferences would be a larger number relative to consumers, thus allowing for more matches to occur.
It should be noted that apps will not be the end all to our social ills, but far too often people underestimate the effects that the expansion of the market can have in alleviating problems.
