How Uber uses innovative management tactics to incentivize its drivers: A critical commentary on Noam Scheiber’s “How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons”

In a recent long-form article in the New York Times, reporter Noam Scheiber set out to detail some of the ways Uber (and similar companies, but mainly Uber) are engaged in “an extraordinary experiment in behavioral science to subtly entice an independent work force to maximize its growth.”

That characterization seems innocuous enough, but it is apparent early on that Scheiber’s aim is not only to inform but also, if not primarily, to deride these efforts. The title of the piece, in fact, sets the tone:

How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons

Uber and its relationship with its drivers are variously described by Scheiber in the piece as secretive, coercive, manipulative, dominating, and exploitative, among other things. As Schreiber describes his article, it sets out to reveal how

even as Uber talks up its determination to treat drivers more humanely, it is engaged in an extraordinary behind-the-scenes experiment in behavioral science to manipulate them in the service of its corporate growth — an effort whose dimensions became evident in interviews with several dozen current and former Uber officials, drivers and social scientists, as well as a review of behavioral research.

What’s so galling about the piece is that, if you strip away the biased and frequently misguided framing, it presents a truly engaging picture of some of the ways that Uber sets about solving a massively complex optimization problem, abetted by significant agency costs.

So I did. Strip away the detritus, add essential (but omitted) context, and edit the article to fix the anti-Uber bias, the one-sided presentation, the mischaracterizations, and the fundamentally non-economic presentation of what is, at its core, a fascinating illustration of some basic problems (and solutions) from industrial organization economics. (For what it’s worth, Scheiber should know better. After all, “He holds a master’s degree in economics from the University of Oxford, where he was a Rhodes Scholar, and undergraduate degrees in math and economics from Tulane University.”)

In my retelling, the title becomes:

How Uber Uses Innovative Management Tactics to Incentivize Its Drivers

My transformed version of the piece, with critical commentary in the form of tracked changes to the original, is here (pdf).

It’s a long (and, as I said, fundamentally interesting) piece, with cool interactive graphics, well worth the read (well, at least in my retelling, IMHO). Below is just a taste of the edits and commentary I added.

For example, where Scheiber writes:

Uber exists in a kind of legal and ethical purgatory, however. Because its drivers are independent contractors, they lack most of the protections associated with employment. By mastering their workers’ mental circuitry, Uber and the like may be taking the economy back toward a pre-New Deal era when businesses had enormous power over workers and few checks on their ability to exploit it.

With my commentary (here integrated into final form rather than tracked), that paragraph becomes:

Uber operates under a different set of legal constraints, however, also duly enacted and under which millions of workers have profitably worked for decades. Because its drivers are independent contractors, they receive their compensation largely in dollars rather than government-mandated “benefits” that remove some of the voluntariness from employer/worker relationships. And, in the case of overtime pay, for example, the Uber business model that is built in part on offering flexible incentives to match supply and demand using prices and compensation, would be next to impossible. It is precisely through appealing to drivers’ self-interest that Uber and the like may be moving the economy forward to a new era when businesses and workers have more flexibility, much to the benefit of all.

Elsewhere, Scheiber’s bias is a bit more subtle, but no less real. Thus, he writes:

As he tried to log off at 7:13 a.m. on New Year’s Day last year, Josh Streeter, then an Uber driver in the Tampa, Fla., area, received a message on the company’s driver app with the headline “Make it to $330.” The text then explained: “You’re $10 away from making $330 in net earnings. Are you sure you want to go offline?” Below were two prompts: “Go offline” and “Keep driving.” The latter was already highlighted.

With my edits and commentary, that paragraph becomes:

As he started the process of logging off at 7:13 a.m. on New Year’s Day last year, Josh Streeter, then an Uber driver in the Tampa, Fla., area, received a message on the company’s driver app with the headline “Make it to $330.” The text then explained: “You’re $10 away from making $330 in net earnings. Are you sure you want to go offline?” Below were two prompts: “Go offline” and “Keep driving.” The latter was already highlighted, but the former was listed first. It’s anyone’s guess whether either characteristic — placement or coloring — had any effect on drivers’ likelihood of clicking one button or the other.

And one last example. Scheiber writes:

Consider an algorithm called forward dispatch — Lyft has a similar one — that dispatches a new ride to a driver before the current one ends. Forward dispatch shortens waiting times for passengers, who may no longer have to wait for a driver 10 minutes away when a second driver is dropping off a passenger two minutes away. Perhaps no less important, forward dispatch causes drivers to stay on the road substantially longer during busy periods — a key goal for both companies. Uber and Lyft explain this in essentially the same way. “Drivers keep telling us the worst thing is when they’re idle for a long time,” said Kevin Fan, the director of product at Lyft. “If it’s slow, they’re going to go sign off. We want to make sure they’re constantly busy.” While this is unquestionably true, there is another way to think of the logic of forward dispatch: It overrides self-control. * * * Uber officials say the feature initially produced so many rides at times that drivers began to experience a chronic Netflix ailment — the inability to stop for a bathroom break. Amid the uproar, Uber introduced a pause button. “Drivers were saying: ‘I can never go offline. I’m on just continuous trips. This is a problem.’ So we redesigned it,” said Maya Choksi, a senior Uber official in charge of building products that help drivers. “In the middle of the trip, you can say, ‘Stop giving me requests.’ So you can have more control over when you want to stop driving.” It is true that drivers can pause the services’ automatic queuing feature if they need to refill their tanks, or empty them, as the case may be. Yet once they log back in and accept their next ride, the feature kicks in again. To disable it, they would have to pause it every time they picked up a new passenger. By contrast, even Netflix allows users to permanently turn off its automatic queuing feature, known as Post-Play. This pre-emptive hard-wiring can have a huge influence on behavior, said David Laibson, the chairman of the economics department at Harvard and a leading behavioral economist. Perhaps most notably, as Ms. Rosenblat and Luke Stark observed in an influential paper on these practices, Uber’s app does not let drivers see where a passenger is going before accepting the ride, making it hard to judge how profitable a trip will be.

Here’s how I would recast that, and add some much-needed economics:

Consider an algorithm called forward dispatch — Lyft has a similar one — that dispatches a new ride to a driver before the current one ends. Forward dispatch shortens waiting times for passengers, who may no longer have to wait for a driver 10 minutes away when a second driver is dropping off a passenger two minutes away. Perhaps no less important, forward dispatch causes drivers to stay on the road substantially longer during busy periods — a key goal for both companies — by giving them more income-earning opportunities. Uber and Lyft explain this in essentially the same way. “Drivers keep telling us the worst thing is when they’re idle for a long time,” said Kevin Fan, the director of product at Lyft. “If it’s slow, they’re going to go sign off. We want to make sure they’re constantly busy.” While this is unquestionably true, and seems like another win-win, some critics have tried to paint even this means of satisfying both driver and consumer preferences in a negative light by claiming that the forward dispatch algorithm overrides self-control. * * * Uber officials say the feature initially produced so many rides at times that drivers began to experience a chronic Netflix ailment — the inability to stop for a bathroom break. Amid the uproar, Uber introduced a pause button. “Drivers were saying: ‘I can never go offline. I’m on just continuous trips. This is a problem.’ So we redesigned it,” said Maya Choksi, a senior Uber official in charge of building products that help drivers. “In the middle of the trip, you can say, ‘Stop giving me requests.’ So you can have more control over when you want to stop driving.” Tweaks like these put paid to the arguments that Uber is simply trying to abuse its drivers. And yet, critics continue to make such claims: It is true that drivers can pause the services’ automatic queuing feature if they need to refill their tanks, or empty them, as the case may be. Yet once they log back in and accept their next ride, the feature kicks in again. To disable it, they would have to pause it every time they picked up a new passenger. By contrast, even Netflix allows users to permanently turn off its automatic queuing feature, known as Post-Play. It’s difficult to take seriously claims that Uber “abuses” drivers by setting a default that drivers almost certainly prefer; surely drivers seek out another fare following the last fare more often than they seek out another bathroom break. In any case, the difference between one default and the other is a small change in the number of times drivers might have to push a single button; hardly a huge impediment. But such claims persist, nevertheless. Setting a trivially different default can have a huge influence on behavior, claims David Laibson, the chairman of the economics department at Harvard and a leading behavioral economist. Perhaps most notably — and to change the subject — as Ms. Rosenblat and Luke Stark observed in an influential paper on these practices, Uber’s app does not let drivers see where a passenger is going before accepting the ride, making it hard to judge how profitable a trip will be. But there are any number of defenses of this practice, from both a driver- and consumer-welfare standpoint. Not least, such disclosure could well create isolated scarcity for a huge range of individual ride requests (as opposed to the general scarcity during a “surge”), leading to longer wait times, the need to adjust prices for consumers on the basis of individual rides, and more intense competition among drivers for the most profitable rides. Given these and other explanations, it is extremely unlikely that the practice is actually aimed at “abusing” drivers.

As they say, read the whole thing!

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