In the midst of a post-job interview / project downtime stretch this past March, I decided to scratch an itch I’ve had for a while and test the overall experience of driving for Uber. I’ve always wondered about the product experience from a driver’s perspective, and hey, why not make a few bucks while finding out?
I was steeled for a lengthy process, but signing up was rather simplistic; I only had to upload images of my active insurance, registration and drivers license. As I waited to clear the Uber background check I detailed my Ford Escape, upped my mobile data plan, and within just a few days, I was on the road.
Since Uber launched a number of years back, I’ve had a rather conflicted perspective on the entire ride-sharing industry, unsure as to whether I found their disruption innovation to be of actual value to workers or an example of a tech firm taking advantage of an entire ecosystem of labor. The reality I experienced over three straight weeks of 30+ hours of driving was something a bit more nuanced.
Context Is King
Heading into this experiment, I realized there was going to be a drastic difference between driving within a saturated area such as New York City and my current home town of Greensboro, NC—from fare prices to Uber’s percentage cut to the degree of fare consistency, location makes a difference. So before I jump into my findings, let me provide some context regarding the environment I found myself navigating:
- As a sprawling home to 270,000 people, Greensboro has a small downtown footprint—three blocks wide by six blocks long—surrounded by traditional suburbia and urban sprawl that extends out to a good number of office parks, gated communities and strip malls. It can take upwards of 30 minutes to drive from the northernmost to southernmost tip of its borders.
- There’s one sporadically busy bus and train station downtown, and one small international airport about 20 minutes away.
- Five sprawling college campuses call Greensboro home, along with a host of technical and trade schools. The student population alone is close to 35,000.
- Nearby major cities include High Point (20 mins), Winston-Salem (30 mins), Raleigh-Durham (1 hr) and Charlotte (1.5 hrs)
- Sundays and Mondays are practically dead, with not many options for food or drink available after 9pm, if establishments are open at all.
Fares and Surge
As one might imagine, catching fares in such a vast physical space with limited activity can be a challenge. Aside from very particular hours of the day—from 8 to 9:30am and 5 to 6:30pm on weekdays, and from 10pm to 2am from Thursday through Saturday—consistency is severely lacking. The job becomes a crap shoot with so many neighborhoods and businesses to cover in guessing where fares will pop up, especially while competing with an unknown number of fellow Uber drivers chasing the same rabbit.
Whereas large cities have relatively consistent Surge™ zones over populated areas of both work and living activity, Greensboro drivers don’t enjoy such a luxury. The most recognizable Surge zone is found downtown, often later in the evening as people make their way home from clubs and restaurants. Every now and then a random Surge zone appears on the map, but it’s often during down times, and it seems to be literally based on a couple of fares vying for one or even zero drivers in the area. Uber markets Surge to the public as a feature designed to get more drivers on the road to service passengers with high wait times, but in reality, by the time an active driver (let alone someone at home or in a coffee shop) makes it across town to attempt to tap into an announced Surge it has already passed.
From a driver perspective in larger cities, Surge pricing is a nice-to-have in order to augment consistent fare activity in larger cities. In locations such as Greensboro, Surge is a core necessity in bringing hourly rates to even a semi-respectable level, much closer to how a waiter making $2.50 per hr relies on tips to pay the bills.
When expanding into cities with low population density, high degrees of urban sprawl and a limited adoption of technology, it’s abundantly clear to me that Uber can’t just roll out the same program.
(Not) Sharing The Load
In January, Uber reduced the passenger rate in the Piedmont-Triad area by 20%. This price point reduction was rolled out to help grow the Uber passenger base, which is an understandable approach and one that would help drivers as well. The problem is that while Uber passed such a significant savings to passengers, they didn’t supplement the loss of income for drivers, as they kept their 25% overall take of driver earnings in effect.
Essentially, drivers in non-dense, growth markets are paying for Uber to expand their market, while the company keeps raking in the profits. If I were making solid money over those three weeks, I wouldn’t care much. So how was my pay? Here’s the breakdown:
Week One: 32.18 hours driving
25% Uber fee: ($142.80)
Gas & carwash: ($100)
Total Income: $328.39 [$10.19 per hour]
Week Two: 30.92 hours driving
25% Uber fee: ($99.96)
Gas & carwash: ($100)
Total Income: $199.95 [$6.47 per hour]
Week Three: 36.12 hours driving
25% Uber fee: ($140.92)
Gas & carwash: ($100)
Total Income: $322.87 [$8.94 per hour]
Over the course of three weeks, working an average of 33.07 hours, I only made $8.58 per hour—not to mention the cost of taxes (10%) or the depreciation of my vehicle by putting an average of 1,000 miles per week. Unless I had zero other options to earn, why would I continue driving for Uber when in order to meet a living wage threshold in my area, I’d have to make at least $10.53 per hour?
The Longtail Of Drivers
Aside from drivers who have limited options to earn income, you have to wonder why anyone would commit to a full-time schedule picking up fares. Maybe my three weeks doesn’t accurately represent the opportunities of all drivers. Or maybe Uber didn’t design their service with full-time drivers in mind.
Uber portrays their archetypical driver in the following commercial:
Uber is framed as beneficial to each person’s life based on the flexibility Uber provides; not based on the amount they can make while driving. Uber has tapped into a workforce version of what Chris Anderson coined The Long Tail well over a decade ago.
In an online retail environment, The Long Tail represents product that might never see the light of day on a brick & mortar shelf. Think Amazon, and the millions of books available to you, regardless of the size of the print run. The web makes such product availability possible, as the concept of physical shelves is replaced by a singular coded product template that represents each and every book in stock.
Most importantly, when you stack the long tail on end, overall sales will equal or surpass the big ticket items; the “popular” head of the tail. While such a static results proposition doesn’t match the actual results of part-time vs. full-time Uber driver productivity, it doesn’t need to, as new long-tail drivers are constantly replacing the attrition of fellow moonlighters.
Uber’s business model is steeped in the surface notion that the tail is what matters; that people moonlight with Uber to make ends meet, so the traditional definition of an “employee”—and every labor expectation that comes with it—shouldn’t apply. And with the constant turnover that occurs in the < 10 hours per week driver class, Uber benefits by having a turnstile of new drivers willing to forgo basic labor rights, such as a minimum wage or benefits. This becomes the overarching approach to classifying Uber drivers, as they redirect attention to the moonlighters while full-time drivers aren’t treated any differently.
Noah Zatz makes a similar point in his post at OnLabor:
[…] the much-touted majority of Uber drivers working very few hours per week are performing far less than the majority of the work. And the seemingly marginal group of full-time drivers actually are doing about half the work, far more than those driving the fewest hours. The precise numbers are sensitive to how one subdivides the drivers (using 1-10, 11-29, 30-44, and 45+ hours per week might look worse for Uber) and to the distribution within each bracket, but the basic point is robust.
Uber has created a worker ecosystem that benefits only those who need to occasionally earn, and doesn’t reward those who invest the most amount of time catching fares.
While the experience didn’t leave a great taste in my mouth, as a design thinker, I’m not interested in litigating Uber into traditional labor-law compliance. I’m much more interested in exploring the possibilities in the interface & business model that can help drivers while continuing to earn revenue for Uber. And yes, during my time delivering drunks to their homes in the middle of the night, these scenarios were exactly what I was considering.
In Part II, I’ll present interface and business model suggestions to improve both the life and behavioral expectations of the Uber driver.