Responsible researcher: Eduarda Miller Figueiredo
Original title: The Value of Flexible Work: Evidence from Uber Drivers
Authors: M. Keith Chen, Judith A. Chevalier, Peter E. Rossi and Emily Oehlsen
Intervention Location: United States
Sample Size: 197,517 drivers
Sector: Job Market
Variable of Main Interest: Salary
Type of Intervention: Uber (flexible work)
Methodology: Multivariate Probit
Summary
Companies have launched business models to meet demand for services using independent providers at unconventional hours. Although these companies do not offer many of the traditional employment benefits, they do provide an opportunity for workers to receive pay on a flexible schedule. This article seeks to analyze behavioral patterns of Uber drivers and provide preliminary evidence on the types of flexibility sought by these drivers. Using a Multivariate Probit model, they found that the drivers in the sample experience large changes in salary that are not consistent from one week to the next and therefore attribute great value to flexible working arrangements.
In recent years, several companies have launched business models that seek to match demand for services with independent contractors who work intermittent or nonstandard schedules. Although these companies do not offer many of the traditional employment benefits, they do provide an opportunity for contractors to receive compensation on a flexible schedule.
Literature has examined flexible practices in the workplace. The Board of American Advisors [1] (2010) reports that 81% of employers would allow some employees to periodically change arrival and departure times within an hourly range, 27% of employers would do the same for most or all employees . However, only 41% would allow some employees to change their arrival and departure times on a daily basis. Thus, employers typically appear to have preferences for certain hours of the day worked by employees. Furthermore, the literature also suggests that lower-paid workers have less flexibility than higher-paid workers (Bond and Galinsky, 2010).
Therefore, this article seeks to analyze behavior patterns of Uber drivers and provide preliminary evidence on the types of flexibility sought by these labor suppliers.
Uber is a platform on which drivers, once approved, can use their own – or rented – cars to offer rides whenever they want. There are no minimum hour requirements and few restrictions on maximum hours. The fares paid by passengers are defined at city level and dynamically adjust (increase) when demand is high in relation to the supply of drivers in a given area.
Since drivers can work whenever they want, a driver's earnings in a given hour are effectively determined by the driver's marginal willingness to work. However, salaries are uncertain and pay can be quite low. In other words, the main advantage is also an important disadvantage: drivers can work whenever they want.
Hall and Krueger (2016) examine research evidence and administrative data from Uber. They document that drivers cite flexibility as a reason to work for Uber and that for many it is a part-time activity, secondary to more traditional employment (Hall and Krueger, 2016; Campbell, 2018).
Given that Uber drivers work largely part-time, it is not surprising that their pattern of hours worked does not resemble the pattern of hours worked by those in conventional jobs. In Figure 1 there is a comparison of the work habits of drivers with the work habits of employed men over 20 years old in 2014 (ATUS[2]).
Figure 1: Comparison of Uber driver activity with ATUS workers
The graph in Figure 1 shows the share of these drivers working each of the 168 hours of the week. It is clear that work for ATUS mainly occurs between 9am and 5pm, whereas Uber drivers are more likely to work at 6pm or 7pm than 2pm or 3pm. While ATUS men are about half as likely to work Saturday afternoon, Uber drivers are more likely to work Saturday afternoon and evening.
The authors used data provided by Uber, which has all the hours of the platform's drivers in the United States from September 2015 to April 2016, totaling 197,517 drivers, with 881,826,744 hourly observations [3] . The focus of the study was the UberX service, as it is the service with the majority of Uber trips. Specifically, the data consists of an anonymous driver identifier and a record of active time spent in the system, driving time, city and payments.
The authors divided time into discrete hours as a unit of observation, 168 hours per week. A worker considered “active” is one who is active (transporting a passenger or picking up a passenger) for at least 10 minutes within that hour. The salary is calculated as the driver's total salary for that hour, divided by the minutes worked, times 60. On the Uber platform, drivers are expected to pay both for the capital costs of their vehicle and for all operating costs of the vehicle. Thus, such costs were incorporated into the driver's reserve salary.
The authors used a Multivariate Probit model, with a latent regression for each time period. Furthermore, the labor supply model has two important points: (i) the censoring point varies according to the observation, as observed wages vary between observations; and (ii) the labor supply reserve wage model imposes an exact restriction on the coefficient that achieves identification – that is, that the wage restriction on the latent variable of the model is equal to -1.
Figure 2 shows a graph of variations in the expected salary and the logarithm of the expected salary. It is possible to see that even within the city, week and day of the week, there is a large variation, with a standard deviation of more than 3 dollars per hour, which corresponds to a variation in wages of at least 10%.
Figure 2: Variation in Expected Salaries.
A: Expected Salary. B: Expected Salary Log.
The authors' results suggest that Uber drivers do not have homogeneous preferences regarding time of day and day of the week. Preferences for rush hour from Monday to Friday are very heterogeneous.
It was also found that drivers in the sample experience large changes in pay that are not consistent from one week to the next, and therefore they may place a high value on the flexible work arrangement. For example, by randomly selecting 100 drivers in Philadelphia who worked Monday through Thursday nights, the authors found that although most of the city's average wages hover around $20, there is a week when the actual wage is very high.
The authors find that the average driver earns about $21.67 per hour, with a surplus of about $10 per hour, suggesting a reservation wage of $11.67 per hour. The reserve salary includes the cost of the driver's time as well as driving costs.
The ability to work split shifts and unconventional hours at Uber is valuable to drivers. Where adaptation to positive and negative reservation wage shocks is possible in the Uber-style labor supply arrangement. A conventional work arrangement rarely allows workers to choose to work more if they unexpectedly run out of money.
That is, the authors documented an important value analysis of flexible work arrangements: the ability to adapt work schedules to time-varying reservation wages.
The expectation is that technology will allow the growth of more types of work in the style of the Uber platform. Although such arrangements can have important disadvantages compared to traditional careers with their employment rights, flexibility is an important source of value in such arrangements.
References
Bond, James T., and Ellen Galinsky. 2011. “Workplace Flexibility and Low-Wage Employees.” https://familiesandwork.org/downloads/WorkFlexandLowWageEmployees.pdf .
Campbell, Harry. 2018. “The Rideshare Guy 2018 Reader Survey.” https://docs.google.com/document/d/1g8pz00OnCb2mFj_97548nJAj4HfluExUEgVb45HwDrE/edit .
Council of Economic Advisors. 2010. “Work-Life Balance and the Economics of Workplace Flexibility.” https://digitalcommons.ilr.cornell.edu/key_workplace/714 .
Hall, Jonathan V., and Alan B. Krueger. 2016. “An Analysis of the Labor Market for Uber Driver-Partners in the United States.” Working Paper no. 22843 (November), NBER, Cambridge, MA.
[1] Council of Economic Advisors (2010).
[2] American Time Use Survey.
[3] Such numbers were found from a series of cleansing of the original database.
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