Ride Sharing and Overcoming Taxi Discrimination

An interesting story from NPR caught my eye: how ride sharing apps make it easier for riders to overcome racial discrimination. The story tells the tale of Skinny Pants Guy, “a dude who was in his mid-20s — slim, with neat, shoulder-length locks, skinny chinos, loafers and a leather briefcase slung across his torso — standing on the corner, his arm raised skyward. He was trying without luck to hail a cab. …”If I was carrying a gun, where could I even hide it?” he said to us in exasperation.”

Although technically illegal and opening taxi drivers to regulatory punishment and fines if they refuse to accept passengers, racial discrimination among taxi drivers does occur.

Many people of color are embracing these services as a way to avoid discrimination from traditional taxicab drivers. There’s more than anecdotal evidence that that discrimination is widespread. A yearlong investigation by a local reporter in Washington, Russ Ptacek, found last year that taxicab drivers were significantly less likely to stop for black fares than for white fares who were dressed the same.

Phenomena like taxi driver discrimination are an economic conundrum — even in the case of Skinny Pants Guy, who is professionally dressed and unlikely to pose a threat to the driver, why choose no fare rather than pick up the passenger?

Gary Becker’s 1957 book The Economics of Discrimination analyzed this phenomenon, and Becker’s model (inserting a “race taste” parameter into a standard utility function) showed that such discrimination was harmful to both the person discriminated against and the person practicing the discrimination. This model has its limits, of course; if you’re working in a standard framework with stable preferences, this taste parameter represents inherent racism and uses that framework to estimate the cost of such preferences. It doesn’t allow for changing preferences as time and circumstances change. An alternative model that can explain the same phenomenon without such a taste parameter would be a screening model — based on location (good/bad neighborhood) and/or race and/or clothing, a taxi driver could draw inferences based on history or preconceptions and choose whether or not to accept the passenger.

Ride sharing, and technology features such as the ability of riders to rate drivers and vice versa, give riders competing alternatives. What drives this beneficial outcome? Are drivers more willing to pick up riders because they know that they will get to submit a rider rating ex post? Are drivers more willing to pick up riders because the fact that they actively chose Uber or Lyft signals to the driver that such a rider in less likely to be a threat? If that’s the case, why is that the case?

Whatever the motivation, I’m intrigued by this aspect of ride sharing because it’s an unintended benefit, and a broader social benefit of ride sharing. One reason why I’m so interested in ride sharing (see how many posts Mike and I have done!) is that in the process of enabling asset owners to monetize their “dead capital”, it taps into what Adam Smith called our “fellow-feeling” — it inclines us more to sympathy (in the Smithian sense) with the other person, and to act with tolerance. I also think that the reciprocal/mutual ratings system is an institutional design that harnesses fellow-feeling, by giving both drivers and riders an incentive to imagine being in the situation of the other party, and to consider the effects of their behavior on the other party.