Unpacking Pay Standards: a response to the UC Berkeley Labor Center

By Alison Stein, Economist at Uber

Michael Reich and Ken Jacobs’ blog post combines a series of unreasonable assumptions and approaches to paint a predictably negative view of the recently submitted ballot initiative. We would like to take the time to highlight our disagreement with two key components of their analysis. In particular, we will dispute their characterization of on-app time, as well as their usage of the IRS reimbursement rate as a proxy for the cost of driving.

On-App Time

The authors are correct in pointing out that drivers and couriers spend a significant portion of their on-app time available but unengaged. It is an oversimplification, however, to characterize this time as the period “between dropping off passengers and getting their next ride”. This scenario is only one component of what is captured by this on-app time. During this time, drivers are free to reject or ignore any dispatch a company might send them, behaviors that create more on-app time. They might be located in the central business district during rush-hour, or they might be checking on market demand in a far-flung suburb late at night. They could have their app on while commuting or running errands. They may be simultaneously on-app or even engaged with another platform.

Given the diversity of behaviors that on-app time encompasses, it is not reasonable to analogize it to the time between customers, when an on-shift cashier must stand at the counter. Nor, do we believe, is it reasonable to argue that a company should be compensating for all of this time. A cashier cannot, for instance, show up to work whenever or wherever they want. They cannot choose to ignore certain customers, or to take an unscheduled break. And they certainly cannot alternate serving customers of competing retailers. For the analogy to the cashier to hold, and for this time to be reasonably compensable, on-app time would need to become subject to monitoring and control by Uber, an outcome that we believe to be highly undesirable from the point of view of a driver or courier.

Freedom from control during on-app time is the essential difference that distinguishes the experience of working with network companies from traditional employment and is what provides the flexibility that drivers so value [1, 2]. Not enforcing limits on drivers’ on-app time means that they are free to elect to use the app whenever and wherever they want. They are further free to reject or cancel offers that do not align with their preferences. In the context of network companies and app-based drivers, flexibility is synonymous with agency during on-app time.

Any pay standard that compensates for on-app time will necessarily impinge upon drivers’ access to truly flexible work. Tying prices to on-app time forces companies to intervene to manage the amount of on-app time in the market, exactly as every employer in the traditional economy does — with shifts, schedules, fixed wages, and mandatory work minimums. As noted above, this comes at the cost of drivers’ flexibility. One need look no further than the regulation Reich recommended in New York City to see this dynamic at play. Rideshare companies there are now being forced to ration on-app time between drivers, who can no longer freely access the platform. When there is not sufficient demand, drivers are prevented from using Uber or Lyft. Drivers’ dislike of these policies has been made clear through large-scale protests. California drivers would similarly lose flexibility under such a standard.

Ultimately, we believe that these network companies represent a unique opportunity in the labor market for workers that highly value flexibility. Work on these platforms may not make sense for everyone, and the majority of workers will likely continue to opt for more traditional employment relationships. But attempting to regulate these companies by analogy to traditional employment will always fundamentally constrain their ability to deliver on this flexibility, displacing the workers whose personal circumstances make it a non-negotiable.


  1. Chen et al. estimate that drivers’ receive more than twice the surplus they would have in less flexible working arrangements.

Cost Estimates

As Reich notes in his study of ridesharing in New York City, “a comprehensive accounting of all vehicle-related expenses is essential” to understanding driver earnings. This remains true in California, and is precisely why we also do not rely on the IRS rate, but instead conduct our own study on the cost of driving. Our internal analysis, summarized below, estimated the average cost of a marginal mile to be $0.258.

It is important to note that our results are not without context. Similar studies of cost have found values similar to ours. The Rideshare Guy found his marginal expenses in a Prius to be $0.195 per mile. Zoepf et al. calculated a cost of $0.30, using more conservative assumptions about MPG, and allocating fixed insurance costs to miles driven with network companies.

We can also use the AAA’s costs pamphlet to get quick contextualizing numbers. We adjust their fuel estimate to account for California’s higher cost, add in maintenance, and split out per-mile depreciation to arrive at $0.215 and $0.259 for Hybrids and Medium Sedans, respectively.

Cost Study

The population of drivers and couriers active across these platforms in California is disproportionately composed of part-time workers; only 17.5% of all engaged time in a quarter is attributable to Uber drivers who averaged at least 30 hours a week [1]. For couriers, we would expect this figure to be even lower. As such, we infer that most drivers did not purchase their vehicles for the purpose of driving with a network company. With this backdrop, we consider the marginal cost of a vehicle mile to be most relevant. This figure is composed of fuel costs, the cost of variable depreciation, and the cost of maintenance and repairs.

We use internal data to understand our mix of vehicle types, which skews much more towards hybrids and high-MPG vehicles than either national or state averages. Data on the cost of ownership for each vehicle type is taken from publicly available external sources, as well as vendors that partner with Uber.

Fuel Costs

We combine internal VIN data with DataONE’s VIN decoder to get each vehicle’s combined fuel economy. We then multiply by regional gas price data from the Energy Information Administration (EIA) to account for the relatively high cost of gas in California. The average cost of fuel per mile logged on our platform was $0.112 for the period studied.


To understand the amount a vehicle on the platform depreciates with each mile, we used publicly available data on depreciation amounts for specific make/model/years for various mileage levels. We queried this data at broad intervals to get the information needed to allow us to sketch out a cost curve for each vehicle (see example plot). At each point, we can use the slope given by this data to understand the depreciation associated with an incremental mile. The final component of this analysis is an assumption about total mileage to-date for these vehicles. As we do not observe this, we make a conservative assumption, and use average usage data from the National Highway Traffic Safety Administration. This is conservative, as driver-partners likely drive more than the average person. Using a smaller figure for miles to-date puts the vehicle on a steeper part of the curve, inflating likely depreciation costs. We find an average depreciation per-mile of $0.049.

Maintenance and Repairs

We used public estimates of the cost of maintenance and repairs per mile by make/model/year. We join that to our internal data to estimate the average cost of maintenance and repairs, which we find to be $0.097 per mile.

Ultimately, we chose to move forward with $0.30, a figure higher than our own internal estimate, to be conservative and have committed to pegging our estimate to inflation to ensure that it remains an accurate measure over time.


  1. Reich cites an external study that finds that “10 percent of transportation platform drivers account for about 57 percent of driver earnings” to imply that a large share of work on the platform is done by drivers that work close to full time. We do not observe the same pattern in supply for this cohort using our internal data.



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