Assignment 1: Social Performance of Organizations

The company that I have selected is Uber. I have included a few articles, attached. 

Assignment 1: Social Performance of Organizations

According to the textbook, the current world economy is increasingly becoming integrated and interdependent; as a result, the relationship between business and society is becoming more complex. In this assignment, you will be researching a Fortune 500 company from an approved company list provided by your professor. Be sure to check the list before you begin.

Write a four to six (4–6) page paper in which you:

1. Specify the nature, structure, and types of products or services of your company, and identify two (2) key factors in the organization’s external environment that can affect its success. Provide an explanation to support the rationale.

2. Suggest five (5) ways in which the primary stakeholders can influence the organization’s financial performance. Provide support for the response.

3. Specify one (1) controversial corporate social responsibility concern associated with your company.

4. Submit a reference page with at least four (4) quality references that you have used for this paper. Note: Wikipedia and other Websites do not qualify as academic resources.

Your assignment must follow these formatting requirements:

· This course requires the use of new Strayer Writing Standards (SWS). The format is different from other Strayer University courses. Please take a moment to review the SWS documentation for details.

· Be typed, double spaced, using Times New Roman font (size 12), with one-inch margins on all sides; citations and references must follow SWS or school-specific format. Check with your professor for any additional instructions.

· Include a cover page containing the title of the assignment, the student’s name, the professor’s name, the course title, and the date. The cover page and the reference page are not included in the required assignment page length.

The specific course learning outcomes associated with this assignment are:

· Analyze the relationship between business and society, and the ways in which they are part of an interactive system.

· Recommend ways stakeholders can influence the destiny of both business and society.

· Analyze the various primary and secondary stakeholder groups, their roles, and relationships.

· Compare and contrast the concepts of corporate social responsibility and citizenship.

· Analyze ways ethical challenges affect the multiple functions of the business.

· Use technology and information resources to research issues in business and society.

· Write clearly and concisely about business and society using proper writing mechanics.

AN ANALYSIS OF THE LABOR MARKET FOR UBER’S DRIVER-PARTNERS IN THE UNITED STATES JONATHAN V. HALL AND ALAN B. KRUEGER* Uber, the ride-sharing company launched in 2010, has grown at an exponential rate. Using both survey and administrative data, the authors provide the first comprehensive analysis of the labor market for Uber’s driver-partners. Drivers appear to be attracted to the Uber platform largely because of the flexibility it offers, the level of compensation, and the fact that earnings per hour do not vary much based on the number of hours worked. Uber’s driver-partners are more similar in terms of their age and education to the general workforce than to taxi drivers and chauffeurs. Most of Uber’s driver-partners had full- or part-time employment before joining Uber, and many continue in those positions after starting to drive with the Uber platform, which makes the flexibility to set their own hours especially valuable. Drivers often cite the desire to smooth fluctuations in their income as one of their reasons for partnering with Uber. Over the past few years, much speculation has arisen as to whether the so-called on-demand economy will positively or negatively affect the future of work, but little evidence exists to support either position. In this article, we study the characteristics, labor supply, and earnings of workers who provide car rides using the Uber platform. Drivers who partner with Uber (Uber refers to them as ‘‘driver-partners’’) provide transportation services to customers who request rides using Uber’s application (app) on their smartphones or other devices. Uber is a quintessential on-demand economy company, responsible for perhaps two-thirds of all activity in the platform-based labor market, according to Harris and Krueger (2015). This study provides the first detailed analysis of a representative, national sample of Uber driver-partners. We draw on anonymized administrative data from Uber on the driving histories, schedules, and earnings of drivers who used the Uber platform from 2012 to 2014, and on two surveys *JONATHAN V. HALL is affiliated with Uber Technologies, Inc. ALAN B. KRUEGER is a Professor in the Department of Economics and Woodrow Wilson School at Princeton University. Krueger acknowledges working as a consultant to Uber in December 2014 and January 2015 when the initial draft of this paper was written. Copies of the computer programs used to generate the results presented in the article are available from the authors at jvh@uber.com and akrueger@princeton.edu. KEYWORDs: technology, ride sharing, Uber, transportation industry, platform economy ILR Review, 71(3), May 2018, pp. 705–732 DOI: 10.1177/0019793917717222. The Author(s) 2017 Journal website: journals.sagepub.com/home/ilr Reprints and permissions: sagepub.com/journalsPermissions.nav conducted by the Benenson Strategy Group (BSG): a survey of 601 driverpartners conducted in December 2014 (BSG 2104) and a survey of 632 driver-partners conducted in November 2015 (BSG 2015). In addition, as a point of comparison, we report data on the characteristics of a representative sample of taxi drivers and chauffeurs, and of all workers, based on several government surveys. Uber has grown exponentially since it was first launched in the United States. After driver applicants qualify to partner with Uber, they are free to spend as much or as little time as they like offering their services to passengers in any given month.1 Whether drivers access the app on any given day, and when they decide to do so, is entirely up to the drivers’ discretion. This flexibility is appealing to driver-partners, but it creates a complication for counting the number of active driver-partners because, at any time, drivers can choose to pursue other work opportunities or can spend time taking care of non-work obligations, refraining from utilizing the Uber platform for a period of time and then returning to use the Uber platform in later months. To address this issue, we calculated the number of driver-partners who provided at least four trips to passengers in a given month (which we refer to as the number of ‘‘active partners’’). From a base of near zero in mid-2012, more than 460,000 driver-partners in the United States actively drove with Uber by the end of 2015. The number of active Uber driver-partners approximately doubled every six months from the middle of 2012 to the end of 2015. At this growth rate, every American would be an Uber driver within five years—which implies that the growth rate will inevitably slow down. One theme that emerges from the following analysis is that a tremendous amount of sorting takes place in the on-demand economy, and, by dint of their backgrounds, family circumstances, and other pursuits, Uber’s driver-partners are well matched to the type of work they are doing. Notably, Uber’s driverpartners are attracted to the flexible schedules that driving on the Uber platform affords. The hours that driver-partners spend using the Uber platform can, and do, vary considerably from day to day and week to week, depending on workers’ desires in light of market conditions. In addition, most driverpartners do not appear to turn to Uber out of desperation or because they face an absence of other opportunities in the job market—only 8% were unemployed just before they started working on the Uber platform—but rather because the nature of the work, the flexibility, and the compensation appeals to them compared with other available options. Even as the national unemployment rate fell to 5%, the number of active Uber drivers continued to rise. These findings likely relate to a broader, more generalized demand by many individuals for workplace policies that favor flexible work schedules, family-oriented leave policies, and telecommuting arrangements, over the 1 Although requirements vary by city, before they can utilize the Uber platform, a potential driver typically must 1) pass a background check and a review of his or her driving record; 2) submit documentation of insurance, registration, and a valid driver’s license; 3) successfully complete a city-knowledge test; and 4) drive a car that meets a quality inspection and is less than a certain number of years old. 706 ILR REVIEW standard nine-to-five work schedule, in order to support a more familyfriendly lifestyle. Historically, independent contractors have reported in surveys that they prefer their working arrangements to traditional employment relationships, and this tendency appears to be continuing in the on-demand economy. Demand for work opportunities that offer flexible schedules is partly driven by the aging of the workforce and the increase in secondary earners, and it will likely increase as a result of ongoing demographic trends. Flexible work opportunities such as Uber can also help workers smooth fluctuations in other sources of income (Farrell and Greig 2016a). In addition, if changes to the health care system help reduce job lock— by making health insurance more readily available and accessible to individuals—more people are likely to become entrepreneurs and take advantage of the flexibility and income-generating potential made possible by the on-demand economy. For these reasons as well, it is critical to understand how the on-demand economy is affecting work opportunities. In this article, we provide a step toward understanding the nature of work in the on-demand economy by reporting new evidence on hours of work, income, and the motivations and backgrounds of participants in one of its important segments: driver-partners using the Uber platform. We rely on survey data on drivers’ self-reported motivations and circumstances and on administrative data collected through the Uber app on driving histories, schedules, cross-city growth rates, and earnings. We also estimate Mincertype wage regressions using a combination of survey and administrative data. The analysis provides a complement to the extensive literature on contingent and alternative work arrangements in the United States. Literature Review The size, growth, and nature of the contingent workforce in the United States has long been debated, and such debate continues with the advent of the on-demand economy.2 One of the problems with this discussion, however, is that analysts have employed multiple definitions of contingent work, ranging from the self-employed to temporary workers to part-time workers to on-call workers. Contingent workers can be defined broadly or narrowly, and magnitudes and trends vary depending on the particular definition.3 In the Current Population Survey (CPS) administered in 1995, 2001, and 2005, the Bureau of Labor Statistics (BLS) included a supplemental module to collect information on various forms of contingent and alternative work arrangements. This survey provides the most informative data available, although it is now somewhat out of date.4 The BLS Contingent Worker 2 For example, in his critique of the ‘‘task rabbit’’ economy, Kuttner (2013) claimed, ‘‘The move to insecure, irregular jobs represents the most profound economic change of the past four decades.’’ 3 See Polivka (1996) for a thoughtful discussion of the definition of contingent and alternative work arrangements. 4 The BLS plans to administer the contingent worker supplement again in May 2017. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 707 Survey (CWS) found that the contingent workforce, defined as workers ‘‘who do not expect their jobs to last or who reported that their jobs are temporary,’’ was relatively small and did not grow between 1995 and 2005 (BLS 2005). In 1995, between 2.2% and 4.9% of the workforce was employed in a contingent position, depending on the definition, and in 2005 these figures ranged from 1.8% to 4.1%.5 These figures are clearly small, with no indication of an upward trend. Claims that contingent workers represent a much larger share of the workforce generally count part-time workers as contingent workers, even though part-time workers typically are employed in traditional employment relationships. As the BLS reported, ‘‘the vast majority of part-time workers (91%) were not employed in contingent arrangements.’’6 Nevertheless, data on part-time work do not point to an upward trend. As Bernhardt (2014: 5) noted, ‘‘After increasing during the 1970s, both the overall percent parttime and the percent involuntary part-time have been largely flat, with the exception of cyclical increases during recessions.’’ The share of workers in part-time positions (which BLS defines as usually working less than 35 hours a week) has shown little secular trend over the past three decades. In 1995, according to data from the CPS, 17.8% of all workers reported that they usually worked part-time hours. That figure fell to 16.8% in 2005 and to 16.5% in 2007, and then rose to 19.8% in 2009 during the Great Recession but has since declined. In 2014, approximately 18.3% of workers were in part-time positions, a level that hardly differs from 20 years earlier. Katz and Krueger (2016) extended the findings of BLS’s CWS by including a subset of the questions on alternative work arrangements on the Rand American Life Panel in fall 2015. They found that the share of workers in alternative work arrangements—defined to include freelancers, workers who were contracted out by one firm to work for another firm, temporary help agency workers, and on-call workers—increased from about 11% in 2005 to nearly 16% in 2015. Note that the CWS and the Rand data are limited to each individual’s main job. Many workers who participate in the ondemand economy may do so as a secondary job. Counting both main and secondary jobs, Katz and Krueger (2016) further found that only 0.5% of the workforce was involved in providing services directly to customers through an online intermediary, such as Uber or TaskRabbit. About twice as many workers said they provided services to customers through an offline intermediary, such as Avon. Other estimates also suggest that less than 1% of the US workforce participated in the on-demand economy in 2015, although the on-demand workforce was growing very rapidly. For example, Farrell and Greig (2016a) estimated that 0.6% of the working-age population (or approximately 0.4% of 5 See Cohany (1996) and http://www.bls.gov/news.release/pdf/conemp for the BLS statistics on contingent and alternative work arrangements cited in this section. 6 See http://bls.gov/news.release/pdf/conemp . 708 ILR REVIEW the workforce) worked in the on-demand economy based on the frequency of bank deposits from 30 online work platforms. Farrell and Greig (2016b) further found that, though decelerating, the annual growth rate exceeded 100% in the number of workers receiving income from these platforms each month during the fall of 2015. Based on data from Google Trends, Harris and Krueger (2015) inferred that Uber is by far the largest on-demand labor platform, which makes an understanding of the characteristics, labor supply behavior, and motivation of Uber’s driver-partners all the more important. BSG Survey of Uber’s Driver-Partners Uber contracted with the BSG to conduct a web survey of Uber’s driverpartners in December 2014 in 20 market areas that represented 85% of all of Uber’s US driver-partners (BSG 2014). The survey was conducted again in November 2015 in 25 market areas that currently represent 68% of Uber’s US driver-partners (BSG 2015). A total of 601 drivers completed the 2014 survey and 833 drivers completed the 2015 survey. Although the response rate to the surveys was only around 10%, based on a comparison of aggregated administrative data, the (weighted) respondents do not appear to be dissimilar from the full set of driver-partners in terms of their average work hours or hourly earnings.7 In this section we highlight findings from the surveys that are relevant for understanding the labor market for Uber’s driver-partners and their motivations for partnering with Uber. We contrast the demographic characteristics of Uber driver-partners with those of taxi drivers and chauffeurs (US Census occupation code 9140), based on data collected in the American Community Survey (ACS), as well as contrasting with all workers. We emphasize findings from the 2014 survey, and note any significant changes between the 2014 and 2015 surveys. Driver Demographics Table 1 summarizes the demographic characteristics of Uber’s driverpartners based on the 2014 BSG survey and reports the corresponding characteristics of taxi drivers and chauffeurs and the entire workforce in the same 20 markets surveyed by BSG, drawing from 2012 to 2013 ACS data.8 7 The BSG survey utilized a stratified design, and weights were derived to make the sample representative of all drivers in terms of the services they offered (uberX [low-cost], UberBLACK [premium], or both); other strata were drawn in proportion to the population and are self weighting. All statistics reported here from the BSG survey are weighted to reflect the survey design. Where cited, question numbers refer to the BSG survey. 8 The 20 markets in 2014 were Atlanta, Austin, Baltimore, Boston, Chicago, Dallas, Denver, Houston, Los Angeles, Miami, Minneapolis, New Jersey, New York City, Orange County, Philadelphia, Phoenix, San Diego, San Francisco, Seattle, and Washington, DC. The 24 markets in 2015 were Atlanta, Baton Rouge, Boston, Charlotte, Chicago, Columbus, Dallas, Denver, Detroit, Fresno, Houston, Indianapolis, Los Angeles, Miami, New York City, Oklahoma City, Philadelphia, Phoenix, Providence, Salt Lake City, San Antonio, San Francisco, Seattle, and Washington, DC. The 14 common markets were Atlanta, Boston, Chicago, Dallas, Denver, Houston, Los Angeles, Miami, New York City, Philadelphia, Phoenix, San Francisco, Seattle, and Washington, DC. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 709 Uber’s driver-partners are spread throughout the age distribution, mirroring the workforce as a whole rather than taxi drivers or chauffeurs. Of Uber’s driver-partners, 19% are under age 30, and 24.5% are age 50 or older. By contrast, taxi drivers and chauffeurs are substantially older, with 9% under age 30, and 44% age 50 or older. The greater representation of younger people among Uber’s driver-partners likely reflects that Uber provides a new opportunity and that older workers are less likely to change jobs, but it may also reflect entry barriers into the taxi driver and chauffeur professions that make it more difficult for younger people to obtain such jobs. Women make up 14% of Uber’s driver-partners, which exceeds the percentage of taxi drivers and chauffeurs who are women in the same markets (8%) but is less than the share of women in the workforce overall. Half of Uber’s driver-partners are married, which is slightly below the corresponding figure for taxi drivers and chauffeurs but close to the figure for all workers, probably, at least in part, a reflection of the varying age distributions. Note, however, that Uber’s driver-partners are slightly more likely to have children under the age of 18 living with them at home (Q17) than are taxi drivers and chauffeurs.9 Additionally, 71% of Uber’s driver-partners reported that they support financial dependents (Q19). Table 1. Characteristics of Uber’s Driver-Partners, Taxi Drivers and All Workers Variable Uber’s driver-partners (2014 BSG Survey) (%) Taxi drivers and chauffeurs (2012–13 ACS) (%) All workers (2012–13 ACS) (%) Age 18–29 19.1 8.5 21.8 30–39 30.1 19.9 22.5 40–49 26.3 27.2 23.4 50–64 21.8 36.6 26.9 65 + 2.7 7.7 4.6 Female 13.8 8.0 47.4 Less than high school 3.0 16.3 9.3 High school 9.2 36.2 21.3 Some college / Associate’s 40.0 28.8 28.4 College degree 36.9 14.9 25.1 Postgraduate degree 10.8 3.9 16.0 White non-Hispanic 40.3 26.2 55.8 Black non-Hispanic 19.5 31.6 15.2 Asian non-Hispanic 16.5 18.0 7.6 Other non-Hispanic 5.9 2.0 1.9 Hispanic 17.7 22.2 19.5 Married 50.4 59.4 52.6 Have children at home 46.4 44.5 42.2 Currently attending school 6.7 5.0 10.1 Veteran 7.0 5.3 5.2 Number of observations 601 2,080 648,494 Notes: ACS data pertain to the same 20 markets as the BSG survey and are for 2012 and 2013. ACS, American Community Survey; BSG, Benenson Strategy Group. 9 A caveat, however, is that the BSG question directed respondents to ‘‘include children living with you part time.’’ 710 ILR REVIEW Among those reporting an ethnic/racial background, Uber’s driverpartners are more likely to identify their ethnicity/race as white nonHispanic than are taxi drivers and chauffeurs in the same areas, although they are less likely to identify as white non-Hispanic than the workforce as a whole in those areas.10 Uber’s driver-partners are less likely to identify as black/African American non-Hispanic than are taxi drivers and chauffeurs, but the percentages who identified as Asian or Pacific Islander and Hispanic/Latino are similar for the two groups. Looking beyond the 20 market areas, the ethnic/racial composition of taxi drivers and chauffeurs in the United States as a whole closely matches that of Uber’s driverpartners who responded to the BSG survey.11 Nearly half of Uber’s driver-partners (48%) have a college degree or higher, considerably greater than the corresponding percentage for taxi drivers and chauffeurs (18%), and above that for the workforce as a whole as well (41%). Twelve percent of Uber’s driver-partners have no postsecondary education, whereas more than half (52%) of taxi drivers and chauffeurs have no postsecondary education. Seven percent of Uber’s driver-partners are currently enrolled in school, mostly taking classes toward a four-year college degree or higher. More highly educated individuals may be more likely to avail themselves of new technological options in the labor market when they become available, which may partly account for the higher level of education of Uber’s driver-partners. Seven percent of Uber’s driver-partners are veterans of the armed services, and 1% are members of the reserves. In addition, 6% of driverpartners have household members who are military veterans, 3% have household members who are active duty members of the armed services, and 2% have household members in the reserves. Based on the ACS data, 5% of taxi drivers and chauffeurs—and the same percentage of all workers—in the 20 areas BSG surveyed are veterans. Only two of the demographic characteristics that we examined registered statistically significant changes between the 2014 and 2015 surveys.12 First, the driver-partners were somewhat younger in 2015 than in 2014: 23% were in the 18–29 age bracket in 2015 compared with 19% in 2014. Second, the driver-partners were more likely to hold a postgraduate degree in 2015 (13.6% compared with 10.6% in 2014). Given the large number of demographic characteristics examined, and these relatively modest differences, 10The BSG and ACS race and Hispanic ethnicity questions differ because Hispanic ethnicity is listed with the other racial identities in the BSG race/ethnicity question (Q56), and then Hispanic ethnicity is also asked about specifically for all those who did not select Hispanic in Q56 in the following question (Q57). We have attempted to align the two surveys by reporting anyone who identified as Hispanic in either question as Hispanic, and then reporting the other groups exclusive of those indicating Hispanic origin, and excluding the 11% of respondents who did not provide an answer to Q56 or Q57. 11The nationwide figures for taxi drivers and chauffeurs are 42.3% non-Hispanic white, 24.5% nonHispanic black, 12.0% non-Hispanic Asian, 3.1% non-Hispanic other, and 18.0% Hispanic. 12For comparability, the samples were restricted to overlapping cities in 2014 and 2015 in these comparisons. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 711 we interpret this as evidence that the basic demographic distribution of Uber’s driver-partners was essentially unchanged from 2014 to 2015, despite the roughly fourfold increase in the number of driver-partners over this period. Driver Employment History The BSG survey provides retrospective information on driver-partners’ work experience that offers a picture of what they were doing prior to partnering with Uber. Approximately 80% of driver-partners reported that they were working full- or part-time hours just before they started driving on the Uber platform. Only 8% of driver-partners in 2014 (and 10% in 2015) said they were unemployed just prior to partnering with Uber. This low percentage is notable given that, for the economy overall, about 25% of new hires came from unemployment and 70% came from nonemployment in 2014 and 2015.13 The large share of drivers who partnered with Uber while they had another job suggests the role that Uber plays in supplementing individuals’ income from other sources. Prior to partnering with Uber, 6% of drivers were students, 4% were retired, and 3% were stay-at-home parents. Among those working prior to partnering with Uber, 81% reported that they had a permanent job that would be there until they left, were laid off, or were fired, and many appear to have continued in those jobs after partnering with Uber.14 Uber’s driver-partners worked in a wide range of jobs prior to partnering with Uber. Nearly 20% of drivers had worked in transportation services in their previous job, and 28% had worked as a driver at some point in their career, but no other industry accounted for more than 10% of drivers’ previous jobs. Just over one-third (36%) of driver-partners in 2014 were not actively looking for a new job prior to driving on the Uber platform. Only 25% were actively looking for a full-time job, another 25% were looking for a part-time job, and 10% were looking for either a part- or full-time job (Q8). Of those driver-partners actively looking for a job prior to partnering with Uber, 24% had been doing so for less than a month, 52% for one to six months, and 24% for more than six months (Q9). That more than one-third of driverpartners joined the Uber platform without actively searching for a job suggests that Uber provided a new alternative that enticed many people to engage in a work activity who might not have done so otherwise. 13These figures are based on transition rates reported by Fallick and Fleischman (2004) at http:// www.federalreserve.gov/econresdata/researchdata/feds200434.html. 14Among those who were working at a full-time job prior to partnering with Uber, 93% said their job was permanent. 712 ILR REVIEW Driving on the Uber Platform In 2014, drivers were split almost evenly among those who reported having no other job in addition to partnering with Uber (38%), those who had a part-time job in addition to partnering with Uber (30%), and those who had a full-time job in addition to partnering with Uber (31%). The 2015 survey found that a much larger share of those who had a job in addition to driving with the Uber platform had a full-time job as opposed to a part-time job. In 2015, 52% of driver-partners worked full-time on another job, 14% of driver-partners had a part-time job apart from partnering with Uber, and 33% of driver-partners had no other job. Not surprisingly, the administrative data indicate that, on average, those who do not have another job work the most hours per week with the Uber platform, and those who have a fulltime job apart from Uber worked the least hours per week with the Uber platform. For example, one-third of driver-partners who reported having no other job in 2014 worked more than 35 hours per week on the Uber app since starting to work with Uber, compared with 13% of those who reported having another part-time job, and 3% of those who reported having another full-time job. The 2014 survey asked driver-partners whether a variety of possible motivations were a major reason, minor reason, or not a relevant reason for why they partnered with Uber (Q22). The most common reasons (combining major and minor reasons) were ‘‘to earn more income to better support myself or my family’’ (91%); ‘‘to be my own boss and set my own schedule’’ (87%); ‘‘to have more flexibility in my schedule and balance my work with my life and family’’ (85%); and ‘‘to help maintain a steady income because other sources of income are unstable/unpredictable’’ (74%).15 Driving on the Uber platform provides an important source of income for driver-partners. For one-fifth of driver-partners (20%), Uber is their only source of personal income; and for another 12%, Uber is their largest but not only source of income. Nearly half of driver-partners view income earned on the Uber platform as a supplement to their income but not a significant source (48%) (Q61). Perhaps not surprisingly—given that most driver-partners had jobs they could, and often did, keep when they started partnering with Uber—71% of driver-partners in 2014 replied that partnering with Uber has increased their overall income, whereas only 11% replied that partnering with Uber has decreased their overall income (Q28R1). A variety of questions suggest that Uber’s driver-partners value the flexibility that the Uber platform permits, and many are drawn to Uber in large part because of this flexibility. Fifteen times as many drivers said Uber had made their lives better, rather than worse, by giving them more control over their schedule (74% compared with 5%). In addition, when asked directly 15The order was unchanged when considering only those reasons designated as a major reason, and the corresponding percentages were 76%, 64%, 63%, and 51%, respectively. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 713 (Q52), ‘‘Which of the following would you most prefer regarding your driving with Uber?’’ with responses describing an employment relationship and an independent contractor relationship, 79% chose the latter. Furthermore, when the driver-partners were asked what they would do if Uber were no longer available in their area, 35% (the largest group) said they would use another ride-sharing app platform, while only 21% said they would look for a full-time job in an unrelated industry (Q32).16 These findings suggest that considerable sorting occurs in the on-demand economy, and that those workers who value flexibility are the most likely to seek opportunities there. Female driver-partners were more likely than men to highlight the need for flexibility as a reason for becoming a partner with Uber, but both men and women appear to value the opportunity to set their own schedule. For example, 42% of women and 29% of men said that a major reason for driving with Uber was that they ‘‘can only work part-time or flexible schedules’’ because of ‘‘family, education, or health reason[s].’’ Further, female driverpartners were nearly 30 percentage points more likely than men to work an average of 15 or fewer hours per week (67% compared with 38%).17 Men, however, were slightly more likely than women to indicate that they would prefer a job where they choose their own schedule and can be their own boss over a nine-to-five job with some benefits (73% compared with 68%). Another aspect of Uber’s flexibility is that spending time on the platform can help smooth the transition to another job, as driver-partners can take off time to prepare for and search for another job at their discretion. Nearly one-third (32%) of driver-partners indicated that ‘‘to earn money while looking for a steady, full-time job’’ (2014: Q22R11) was a major reason for partnering with Uber, and this is particularly the case for students and for those who do not have another job or are working part-time on another job. Likewise, those who have no other job or another part-time job are about twice as likely as those with full-time jobs to say that they will continue with Uber until something better comes along (2014: Q50). These results suggest that Uber provides a helpful ‘‘bridge’’ for some driver-partners until they can find new jobs that are better matches for their skills and interests. The BLS contingent worker survey found that independent contractors were less likely to have health insurance coverage than were traditional employees. In 2015, 38% of Uber’s driver-partners received employerprovided health insurance—either from their own employer at another job or from a spouse or other family member’s employer—which is down from 49% in 2014. 16Other responses were drive a taxi (8%), look for a part-time job (19%), not look for a new job (12%), and other (5%). 17Hours are measured by hours with the Uber app on. Given that drivers could have another app on simultaneously, or could be conducting personal tasks with the Uber app on, our hours measure is an imperfect measure of hours working on the Uber platform. 714 ILR REVIEW Overall, 81% of driver-partners said they are very satisfied or somewhat satisfied with Uber in 2015. This value is essentially unchanged from 78% in 2014. Completing the Picture with Uber Administrative Data Uber collects extensive data on driver-partners’ trips, fares, and time through the Uber app. Below, we summarize findings based on Uber’s administrative data using aggregated, anonymized tabulations from Uber’s databases to round out the analysis of the labor market for Uber’s driverpartners. In particular, the data set contains information on payments to drivers, time with the app on, number of trips, and miles driven (with and without a passenger in the car). Uber does not store any demographic information collected from the driver application and background check processes in its databases. Figure 1 documents the exponential growth in the number of active Uber driver-partners in the United States from mid-2012, when uberX was launched, to the end of 2015. The spectacular growth of the number of active driver-partners over the past few years is evidence that Uber provides many workers a choice that they prefer to other available options or to not working at all. During the latest month for which we have data, December 2015, a total of 464,681 driver-partners completed four or more trips using the Uber platform. Figure 1. Number of Active Driver-Partners in United States Each Month Source: Uber administrative data. Notes: US UberBLACK and uberX driver-partners providing at least four rides in any month (1,085,765 individuals), which is how we define an active driver-partner. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 715 Geographically, Uber’s driver-partners are distributed across the country and are most common in the larger population centers. Figure 2 shows that Uber driver-partners are particularly prevalent in the large urban areas of the Northeast, Southeast, Midwest, and upper and lower West Coast. Figure 3 displays growth in the number of Uber’s driver-partners in each of the BSG metropolitan areas, indexed to the number of months since Uber started operating in the city through January 2016.18 The growth for the Austin market reflects the period prior to the suspension of operations in May 2016. The fastest growth in the number of driver-partners has been in Miami and Las Vegas, markets in which Uber only recently became fully operational. Future research can link these city-specific patterns to other city-level data to study the effect that Uber has had on the taxi industry and other outcomes.19 Predictors of the growth in the number of Uber drivers across cities provides some insights into the forces underlying Uber’s success. The outcome Figure 2. Active Driver-Partners, by Census MSA Notes: Number of Uber driver-partners who took at least four trips in October 2015, by Census Metropolitan Statistical Areas (MSA). 18The way in which Uber classified New Jersey trips in its database has changed over time, so, for the sake of consistency, New Jersey is omitted from this chart. Also, Orange County is reported as part of Los Angeles. 19One article in this vein is Greenwood and Wattal (2015), who examined the relationship between ride sharing and alcohol-related motor vehicle homicides across cities in California. 716 ILR REVIEW variable that we focus on is the log of the number of active driver-partners per month in 2015Q4 divided by the number of months that Uber has operated in the city. Because Uber started from a base of zero in 2009, the dependent variable reflects Uber’s growth rate per month that it launched in each city. We compiled a set of city characteristics for 97 cities in which Uber operates and regressed the growth measure on these characteristics. Results are summarized in Table 2. Column (1) provides estimates for the full sample of 97 cities, and columns (2) to (4) restrict the sample to 80 cities for which we have information on the number of taxi licenses. In all of the estimated models, the number of Uber drivers per month in operation rises with city population, and we cannot reject a unit elasticity. Cities with more taxi licenses per capita have added relatively more Uber drivers, suggesting an excess demand for ride-sharing services in cities with relatively more taxis, all else equal. Of note, the cost of a five-mile Uber ride has a statistically insignificant and small coefficient. The unemployment rate in a city is also unrelated to the growth in the number of Uber drivers, consistent with the observation made in light of Figure 1 that the exponential growth of Uber drivers held in periods of high and low unemployment. Real gross domestic product (GDP) and population density in a metropolitan area are both found to be unrelated to the number of Uber drivers. And, cities where Uber started earlier have added significantly more drivers per month than cities where Uber started later, suggesting that Uber was strategic in launching earlier in cities with greater latent demand for ridesharing services. Figure 3. Active US Driver-Partners over Time, by City Notes: Number of US UberBLACK and uberX driver-partners making at least one trip in the specified month, indexed to the number of months since Uber began in a given city or June 2012, whichever came later. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 717 Dynamics Because Uber offers a flexible work option with low barriers to entry, a large number of workers try the service, and some discontinue using it after a period of time while others continue for an extended period. As described in the previous section, driver-partners vary their length of time using the platform for many reasons. Those spending fewer hours may find that Uber is not a good match for their lifestyle or they may use Uber only when they are between jobs; others may find that it provides them with a flexible work schedule and source of income they have been looking for and so continue using the platform for much longer. Figure 4 reports the weekly continuation rate (i.e., the survivor curve) for all driver-partners who started on the platform in the first half of 2013. Within one month of becoming an active Uber driver-partner, 11% of driver-partners became inactive, which we defined as not using the service Table 2. Determinants of Growth of Uber Driver-Partners across Cities Dependent variable Log of average active monthly driver-partners in 2015 Q4 per number of months operating Variable (1) (2) (3) (4) Months operating 0.045*** 0.041*** 0.041*** 0.042*** (0.008) (0.008) (0.007) (0.007) Log of MSA population (2010) 1.097*** 1.098*** 0.876*** 0.895*** (0.325) (0.327) (0.313) (0.315) Log of MSA density (2010) 0.122 0.132 0.012 0.022 (0.090) (0.100) (0.100) (0.101) Log of MSA real GDP (2013) –0.267 –0.201 –0.028 –0.048 (0.293) (0.298) (0.282) (0.285) Log of MSA median taxi earnings (2015) 0.386 –0.056 –0.039 –0.093 (0.606) (0.628) (0.590) (0.598) Annual unemployment (2014) –0.061 –0.059 –0.035 –0.039 (0.050) (0.058) (0.055) (0.056) Log of number of taxi licenses per 1,000 people 0.301*** 0.289*** (0.084) (0.086) Cost of a five-mile Uber trip in 2014 0.015 0.012 (0.019) (0.020) Log of the number of cars per 1,000 (MSA) –0.316 (0.470) Constant –7.842*** –7.525*** –5.285** –3.357 (2.116) (2.208) (2.154) (3.593) Sample size 97 80 80 80 Adjusted R2 0.880 0.894 0.908 0.907 Residual standard error 0.546 (df = 90) 0.511 (df = 73) 0.476 (df = 71) 0.478 (df = 70) F statistic 118.780*** (df = 6; 90) 111.545*** (df = 6; 73) 98.252*** (df = 8; 71) 86.710*** (df = 9; 70) Sources: Data from Uber, Taxicab Fact Book (TLPA), US Census, and US Bureau of Economic Advisors. Notes: MSA, Metropolitan Statistical Areas; GDP, gross domestic product. *p \ 0.1;**p \ 0.05; ***p \ 0.01. 718 ILR REVIEW over the next six months. After half a year, 70% of those who started using Uber in the first half of 2013 were still actively using the system, and more than one-half of those who started in the first half of 2013 remained active a year after starting. One-third were still active two years after starting on the platform. These figures suggest that Uber provides a bridge for many people who are seeking another position in the labor market, and it provides a longer-term option for others. Uber’s driver-partners can select into providing various types of car service. The Uber platform offers several tiered service levels to potential riders. Roughly speaking, throughout the United States, UberBLACK is the premium option. Driver-partners on UberBLACK are commercially licensed drivers with ‘‘black cars’’ (i.e., limo-style cars) that adhere to specific vehicle standards. Many driver-partners on UberBLACK are employees or contract workers for limousine companies that use Uber’s technology. In most markets (New York City being a notable exception), driver-partners on uberX, the lower-cost product offered on the platform, may drive their personal automobiles, utilizing commercial insurance (with $1 million in liability and uninsured/underinsured motorist bodily injury coverage provided through the Uber platform) when conducting commercial activity. As previously stated, all Uber driver-partners must pass a background check prior to driving on the platform. Figure 5 indicates that Uber’s exponential growth is fueled by the spectacular growth of uberX driver-partners. The rapid growth of uberX is likely Figure 4. Continuation Rate for US Driver-Partners over Two Years Notes: US UberBLACK and uberX driver-partners who made their first trip between January and June of 2013 and had subsequently made at least four trips (11,267 individuals). We classified a driver-partner as becoming inactive at the start of any period in which he or she does not record a trip for the next six (or more) months. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 719 because of its availability in more US markets than UberBLACK, because of greater customer demand for a lower-cost service, because of lower entry barriers (e.g., absence of need for a commercial license and luxury car), and because of Uber’s promotional efforts. As previously noted, approximately two-thirds of Uber driver-partners work either full-time or part-time on another job. Therefore, it should not be surprising that most who drive with Uber do so part-time. The platform is conducive to a wide range of work schedules, as evidenced by there being little discernible relationship between hourly earnings and hours spent on the platform. Table 3 illustrates this pattern for uberX drivers during October 2015, which we selected as a month to represent more normal market conditions, as it falls after Uber’s summer fare cuts and before the holiday season. The table reports earnings per hour with the app on, broken down by amount of time spent driving per week in the six largest markets where Uber operates, and for the 2014 BSG markets combined.20 Reported earnings here are net of Uber’s fees but do not adjust for driver-partners’ expenses, which we try to estimate below.21 We also emphasize that the Figure 5. Active Uber Driver-Partners, by Service Notes: All US UberBLACK and uberX driver-partners making at least four trips in any month (1,149,024 individuals). 20Trimmed 1% means are presented rather than medians because the results were similar and because it is more appropriate to average trimmed means across cities than it is to use medians to derive an aggregate measure. 21Note also that the tables do not include earnings from promotional offers and incentives (most often hourly and monthly price guarantees conditional on driving a certain number or set of hours) that Uber occasionally offers to drivers, most often at the beginning of a driver-partner’s time on the network or around the launch of a new Uber market. This omission causes us to slightly understate drivers’ earnings. 720 ILR REVIEW hours measure is an imperfect and probably overstated measure of hours worked, as drivers can have an app turned on for another ride-sharing platform while their Uber app is on, and they can conduct personal tasks while the Uber app is turned on. In the combined set of 20 areas, more than half of uberX driver-partners chose to drive for 15 or fewer hours a week, and fully 83% chose to drive fewer than 35 hours a week.22 Yet the largest difference in hourly earnings across workers in the various hours categories was $ 0.66 (about 4%) between those driver-partners driving 16 to 34 hours a week and those driving 1 to 15 hours a week. Across all uberX drivers, earnings per hour each week are negatively correlated with hours logged with the app on that week, although this negative correlation may partly be a statistical artifact of the imprecision in measuring hours, as noise in hours will tend to induce a negative correlation attributable to division bias. A regression that instruments for hours worked in week t using hours worked during week t–1 found no evidence of an effect of hours worked on hourly earnings. In any event, there is little evidence that uberX drivers who work longer hours per week earn more per hour than those who work shorter hours, which may make Table 3. Distribution and Trimmed 1% Mean of Hourly Earnings of uberX Driver-Partners by Hours Worked, October 2015 Hours/week 1 to 15 16 to 34 35 to 49 More than 50 Market Percentage of driverpartners (%) Earnings per hour (US $) Percentage of driverpartners (%) Earnings per hour (US $) Percentage of driverpartners (%) Earnings per hour (US $) Percentage of driverpartners (%) Earnings per hour (US $) BOS 51 20.27 32 20.64 12 20.51 5 19.87 CHI 58 15.48 29 15.94 9 16.05 4 15.82 DC 52 17.71 31 18.27 12 18.21 5 17.57 LA 55 18.09 30 18.09 10 17.57 5 16.46 NY 24 23.13 32 24.46 27 24.48 17 23.86 SF 53 22.53 31 23.86 11 24.02 4 23.75 All BSG survey markets 53 18.75 30 19.41 12 19.33 5 18.81 Source: Uber. Notes: Data aggregated at the driver-partner-week level. Excludes incentive payments offered to new driver-partners in some markets. Earnings are net of Uber’s fees but do not adjust for driver-partners’ expenses. Final line reflects the 20 survey markets in the 2014 BSG surveys. Cities weighted by their trip distributions in October 2014. BOS, Boston; CHI, Chicago; DC, Washington; LA, Los Angeles; NY, New York City; SF, San Francisco; BSG, Benenson Strategy Group. 22Driver-partners who provided service on both uberX and UberBLACK during October 2015 are excluded from Table 3. Drivers who utilized the UberBLACK platform tended to log longer hours per week than did uberX drivers: 52% of UberBLACK drivers used the platform for 35 hours or more a week. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 721 the platform particularly attractive to drivers interested in working short hours. Figure 6 shows the distribution of weekly hours with the app turned on over time for all Uber drivers combined. As Uber has expanded, more driver-partners are utilizing the platform for 15 or fewer hours per week. At the same time, the percentage of those on the platform for more than 35 hours a week has declined. This result is due partly to uberX growing more rapidly than UberBLACK, and to uberX drivers driving fewer hours per week. Figure 7 likewise provides an analysis of driver earnings over time. Specifically, for each city we calculated the 1% trimmed mean and then computed a fix-weighted average across cities to hold constant the shifts across cities. Driver-partner earnings fluctuate from week to week, but in the 20 markets in the 2014 BSG survey, the average was $20.19 from June 2014 through October 2015. A regression of hourly earnings on time found no evidence of a time trend. That fares have trended downward while hourly earnings display no time trend suggests that hourly earnings are anchored to the drivers’ alternative wages, with the entry and exit of workers causing utilization rates to adjust to clear the market at a more or less fixed wage.23 23Hsieh and Moretti (2003) reached a related conclusion concerning the earnings of real estate agents, namely that entry and exit of real estate agents leads their real earnings to be invariant to fluctuations in housing prices. Figure 6. Distribution of Uber Driver-Partner Hours over Time Notes: All US uberX and UberBLACK driver-partners spending at least an hour online on the Uber app in a given week in 20 BSG US cities surveyed in 2014. BSG, Benenson Strategy Group. 722 ILR REVIEW Uber versus Taxi Table 4 illustrates the breakdown of Uber driver-partners (combining both UberBLACK and uberX driver-partners) by hours worked per week in October 2014, compared to taxi and limo drivers based on the ACS. Taxi drivers and chauffeurs work longer hours per week than do Uber’s driverpartners, with more than one-third of taxi drivers usually working 50 or more hours per week. Slightly more than half of Uber drivers use the platform for 15 hours or fewer per week, compared with just 4% of taxi and limo drivers. This drastically different allocation of work time likely reflects that the medallions required to operate a taxi are typically leased on a daily or weekly basis, which gives taxi drivers an incentive to work long hours over the duration of the lease. Uber driver-partners do not face this incentive, which enables them to flexibly select their hours and to better align their work schedules to customer demand. Figure 8 shows that driver-partners vary the number of hours in which they use the Uber platform by a considerable amount from week to week. In any given week, well more than half (64%) of driver-partners drive either 25% more or 25% less than the amount they drove during the previous week. Only 17% of driver-partners tend to drive within 10% of the amount of time they drove in the previous week. The within-driver, across-week coefficient of variation of hours with the Uber app turned on for drivers who Figure 7. UberX Driver Weighted-Average 1% Trimmed Mean Earnings over Time Notes: All US uberX and UberBLACK driver-partners spending at least an hour online using the Uber app in a given week in the 20 BSG US cities surveyed in 2014. A 1% trimmed mean of average hourly earnings across drivers was calculated for each city, and cities were weighted by the total number of trips provided in the city in October 2014 to hold constant changes in the distribution of drivers across cities. BSG, Benenson Strategy Group. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 723 were active throughout the same period is 0.35 for the 25th percentile driver, 0.54 at the median, and 0.81 for the 75th percentile driver. These figures indicate considerable variation in the amount of time drivers spend driving on the platform from week to week and are consistent with responses to the BSG survey, which indicated that drivers valued the flexibility that driving with the Uber app provides. Since the Uber platform applies a new model to an existing industry, it is instructive to compare driver-partner earnings to those in similar occupations. In particular, we compare Uber driver-partners to taxi drivers, limo Figure 8. Distribution of Changes in Work Hours by Percentage from Week to Week Notes: All pairs of weeks in which a US UberBLACK or uberX driver-partner spent at least one hour on the Uber app in the initial week. Sample period is August 31, 2014, through November 22, 2014 (170,505 individuals). Table 4. Distribution of uberX Driver-Partners and Taxi Drivers and Chauffeurs by Hours Worked Hours/week uberX driver-partners (October 2014) (%) uberX driver-partners (October 2015) (%) Taxi drivers and chauffeurs (ACS) (%) 1–15 51 55 4 16–34 30 29 15 35–49 12 10 46 50+ 7 6 35 Sources: Uber and 2012-13 American Community Survey (ACS). Notes: Data for Uber driver-partners pertain to each week when they worked at least one hour in October 2014. ACS hours based on ‘‘usual hours worked per week past 12 months.’’ All data are for BSG surveyed market areas. BSG, Benenson Strategy Group. 724 ILR REVIEW drivers, and chauffeurs based on the Occupational Employment Statistics (OES) survey (BLS 2014), which reports earnings for drivers who are employees (in contrast to Uber’s drivers, who are independent contractors). Taxi drivers, limo drivers, and chauffeurs who are on payroll likely do not bear expenses for gasoline, vehicle maintenance, depreciation, and so on. By contrast, these expenses are incurred by Uber driver-partners (although may be deductible from income taxes in many cases). As a consequence, we subsequently present estimates of drivers’ expenses to facilitate a comparison of net earnings. The data in Table 5 indicate that Uber’s driver-partners generally receive higher earnings per hour (before vehicle expenses) than do employed taxi drivers and chauffeurs. As long as drivers’ costs are less than $6.79 per hour, the net earnings of Uber driver-partners would exceed those of taxi drivers and chauffeurs, on average. Notice also that Uber’s driver-partners tend to earn more in the same markets that taxi drivers and chauffeurs tend to earn more. The Pearson correlation across the 19 areas with available data is 0.52. At least in the long run, the process of labor market equilibration in the presence of varying local labor market conditions should generate a positive correlation in the wages of individuals doing similar work in the same market. Expenses Uber’s driver-partners are not reimbursed for their driving expenses, such as gasoline, maintenance, depreciation, or insurance, but employed Table 5. Comparison of Net Hourly Earnings (before Vehicle Expenses) of Uber Driver-Partners and Hourly Wages of Taxi Drivers and Chauffeurs, October 2015 Earnings per hour or hourly wages Market Uber drivers-partners (net earnings per hour) (US $) OES taxi drivers and chauffeurs (hourly wages) (US $) BOS 20.86 12.96 CHI 16.23 12.54 DC 18.45 14.26 LA 18.43 14.53 NY 23.69 15.74 SF 23.87 13.92 Average BSG survey Uber markets 19.35 12.56 Source: Uber. Notes: Data aggregated at the driver-partner-week level. Drivers utilizing all Uber platforms are included in sample. Excludes incentive payments offered to new driver-partners in some markets. Earnings are net of Uber’s fees but do not adjust for expenses. Final row reflects the 20 survey markets in the 2014 BSG surveys. Cities weighted by their trip distributions in October 2014. OES data from the May 2015 Occupational Employment Statistics survey (BLS 2014). OES, Occupational Employment Statistics; BOS, Boston; CHI, Chicago; DC, Washington; LA, Los Angeles; NY, New York City; SF, San Francisco; BSG, Benenson Strategy Group. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 725 driver-partners covered by the OES data may not have to cover these costs. Costs vary for each driver-partner depending on their model of car, driving style, traffic, and other factors. Note also that drivers may partially offset their costs by deducting work-related expenses from their income for tax purposes, including depreciation or leasing fees, gasoline, maintenance, insurance, mobile device and data fees, and license and registration fees. We disregard possible tax deductions in our calculation below, however, leading to a somewhat overstated estimate of driver costs. To derive estimates of Uber driver-partner costs tailored to each category of vehicle that drivers operate, we utilize cost data from the American Automobile Association’s (AAA) ‘‘Your Driving Costs’’ reports.24 Each year, AAA reports estimates of the five-year cost of ownership for the top five selling vehicles in each of five categories: small, medium, and large sedan, truck, and minivan. We combine these data with estimates of average miles driven per hour while the Uber app is on, which is derived from drivers’ Global Positioning System (GPS) data, for a random sample of 2,000 driver days per city in each of the 20 BSG cities surveyed. Costs in the AAA report are broken down on a per mile basis for variable costs (e.g., gas) and a per year basis for fixed costs (e.g., insurance and taxes). Only one variable cost of interest is not explicitly provided, marginal depreciation. Instead, AAA provides annual depreciation estimates for vehicles driven 10,000, 15,000, and 20,000 miles per year. We derive per mile depreciation estimates from these data as follows: we assume that average per mile depreciation over the first 10,000 miles is the same as it is between mile 10,000 and mile 15,000. Additionally, we assume that the average per mile depreciation between mile 15,000 and mile 20,000 applies to miles driven in excess of 20,000. We apply the AAA cost figures to two scenarios: driving full-time and driving part-time on the Uber platform. In the case of full-time driver-partners we include the fixed costs of the vehicle under the assumption that the individual purchased a new car specifically to earn money as a professional driver and otherwise would have had recourse to another car for personal use. For full-time drivers, we further assume that the car is used mostly for providing ride-sharing services, but partly for personal use. Specifically, fixed costs are spread across 35,000 business miles (approximately the distance one would travel in 2,000 hours of professional driving) and 15,000 personal miles. We compute costs for full-time drivers under two assumptions: 1) excluding insurance and registration fees, as these costs would be required if the car were to be used for personal driving absent Uber; and 2) including insurance and registration fees, as these costs would be additional if the car were used exclusively for professional driving or if the driver would not have used the car absent Uber. In the part-time case, we disregard fixed costs, assuming that drivers are using a car they already owned, 24See http://exchange.aaa.com/automobiles-travel/automobiles/driving-costs/#.Vx7mc5MrJdA. 726 ILR REVIEW which would have depreciated regardless of driving on the platform and that they would have been responsible for insurance and registration fees regardless of occasionally driving on the Uber platform. Under these assumptions, Table 6 reports estimates of hourly costs for five vehicle types for part-time (column (1)) and full-time drivers (columns (2) and (3)). Drivers’ hourly expenses vary depending on their model of car and fullor part-time status. For part-time drivers, costs range from $2.94 to $4.38 per hour, and for full-time drivers they vary from $3.76 to $6.46 per hour. Thus, the AAA expense data suggest that, taking expenses into account, the average Uber driver-partner is likely to earn at least as much per hour, and probably more, than the average taxi driver and chauffeur. Earnings Regressions We next consider how earnings vary across Uber driver-partners. Table 7 presents earnings regressions using the BSG 2014 survey data, in which the dependent variable is the log of the earnings per hour net of Uber fees.25 The column labeled (1) presents results from a model with explanatory variables that relate to driving with the Uber platform, such as whether the driver provides rides under the UberBLACK service and the driver’s average weekly hours since partnering with Uber, as well as city dummy variables. The second column adds variables reflecting the drivers’ personal characteristics, such as race, experience, and education. Tenure at Uber is defined as the number of months the driver has used the platform. Table 6. Estimated Hourly Expenses by Vehicle Type and Part-Time and Full-Time Driver-Partners Vehicle type Part-time (US $) (1) Full-time (US $) (2) Full-time with insurance and registration (US $) (3) Small sedan 2.94 3.76 4.29 Medium sedan 3.60 4.79 5.33 Large sedan 4.25 5.83 6.38 4WD SUV 4.38 5.94 6.46 Minivan 4.02 5.34 5.84 Source: Authors’ calculations based on AAA data and Uber. Notes: Uber collects GPS data on average driving speed per hour with app on. See text for further details. AAA, American Automobile Association; GPS, Global Positioning System. 25It is not possible for us to link the BSG survey data back to Uber administrative data. Consequently, we are limited to the survey data collected by BSG and the administrative information that Uber provided to BSG. Thus, we cannot estimate driver expenses because we do not know the type of car or mileage that each driver drove. Moreover, the earnings per hour data provided to BSG indicated the decile interval of the drivers’ hourly earnings in 2014, not the exact hourly earnings. We use the log of the midpoint of the interval as the dependent variable. UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 727 The city dummies are jointly highly significant, as might be expected in light of the results shown in Tables 3 and 5. Drivers who provide the UberBLACK service earn more per hour than those who exclusively provide uberX rides, which also might be expected given that UberBLACK requires Table 7. Earnings Regressions for Uber Driver Partners, 2014 Dependent variable Log of midpoint of decile hourly earnings Variable Means (1) (2) Log of midpoint of weekly hours 2.987 –0.034 –0.013 (0.066) (0.061) UberBLACK driver-partner (= 1 if yes) 0.354 0.562*** 0.480*** (0.165) (0.158) Finance car 0.472 0.099 0.064 (0.099) (0.085) Lease car 0.103 0.271* 0.225 (0.150) (0.149) Short-term rental/lease car 0.053 0.234 0.195 (0.314) (0.290) Other car procurement 0.036 –0.832 –0.595 (0.513) (0.520) New service fee 0.295 –0.132 (0.257) Education 2.396 0.013 (0.009) Tenure (time using Uber app in months) 6.712 0.054** (0.027) Tenure squared 96.900 –0.002* (0.001) Experience 7.074 0.010* (0.005) Experience squared 361.562 –0.00000 (0.0002) Female 0.121 0.031 (0.123) Black 0.186 –0.181 (0.228) Hispanic 0.151 –0.399 (0.254) Asian 0.158 –0.273 (0.177) Other 0.140 –0.010 (0.192) City dummies Yes Yes Constant 1.121*** 0.916*** (0.221) (0.225) Adjusted R2 0.336 0.353 Residual standard error 1.246 (df = 575) 1.229 (df = 564) Source: BSG 2014 survey data. Notes: N = 601. Mean(Dep. var. = 2.40, SD(Dep. var.) = 1.53. BSG, Benenson Strategy Group. *p \ 0.1; **p \ 0.05; ***p \ 0.01. 728 ILR REVIEW a luxury car and drivers who are typically commercially licensed. A quadratic relationship exists between earnings and accumulated seniority using the Uber platform, with earnings peaking after about 14 months. Drivers with more potential experience (defined as age minus education minus 6) also have slightly higher hourly earnings. Drivers’ education, race, and sex are not statistically significant predictors of earnings. Conclusion In this article, we have attempted to provide the first comprehensive description of Uber’s driver-partners, based on both survey data and administrative data. Several findings are worthy of emphasis and exploration in further research. First, the Uber platform provides a great deal of flexibility for driverpartners, and this characteristic of work in the on-demand economy may attract workers who supply labor to the sector more generally. Responses to the BSG survey indicated that many driver-partners valued the flexibility to choose their hours and days of work. Furthermore, the administrative data indicate that a large share of driver-partners avail themselves of this flexibility and vary their hours from week to week. Compared with traditional taxi drivers, Uber driver-partners tend to work substantially fewer hours per week. For example, taxi drivers and chauffeurs were five times more likely to work 50 or more hours per week than were Uber driver-partners. The high fixed costs of obtaining a medallion to drive a taxi in many areas could explain the longer hours worked by taxi drivers. The finding that hourly earnings for Uber’s driver-partners are essentially invariant to number of hours worked during the week also makes Uber an attractive option to those who want to work part-time or intermittently, as other part-time or intermittent jobs in the labor market may entail a wage penalty. Second, Uber’s driver-partners are more similar in terms of age and education to the general workforce than to taxi drivers and chauffeurs. Many factors may have contributed to this result. 1) The US economy was operating at less than full employment during the period studied, and so more highly educated and younger workers may have had fewer alternatives available than is normally the case. Uber may have represented a particularly attractive bridge option for these workers. 2) Entry barriers in traditional taxi and limo services may prevent a broader segment of the workforce from gaining such jobs. 3) A segment of the general public may be drawn to Uber over traditional taxi and chauffeur jobs because Uber permits greater flexibility in terms of scheduling. That new drivers continued to partner with Uber at an accelerating rate in late 2014 and 2015, when the economy strengthened and the unemployment rate fell below 6%, suggests that weakness in the economy was not the major reason driverpartners partnered with Uber. 4) Most driver-partners were employed prior to joining Uber. These considerations suggest that Uber has attracted UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 729 driver-partners with a wide range of backgrounds because they value the type of opportunity for flexible work that Uber provides. Third, although it is difficult to compare the after-tax net hourly earnings of Uber’s driver-partners with that of taxi drivers, it appears that Uber driver-partners earn at least as much as taxi drivers and chauffeurs, and in many cases they earn more. The prospect of higher compensation likely explains, in part, why the number of Uber driver-partners has grown at an exponential rate (along with lower entry barriers and flexibility). Another aspect of Uber that can influence the pay of driver-partners vis-a`-vis taxi drivers is that customers rate their driver-partner when they take a trip with Uber, and driver-partners’ ratings are made available to potential customers. This leads Uber’s driver-partners to develop reputations, which may serve as an incentive to perform well to develop and maintain a good reputation. By contrast, taxi drivers typically are anonymous and customers are not aware of their reputations. Reputations matter in markets.26 Driverpartners are rewarded for having a good reputation, which could lead Uber’s driver-partners to earn more than taxi drivers. Furthermore, driverpartners who expect to do a good job and want to develop a strong reputation are more likely to be attracted to Uber than to traditional taxi service.27 Estimating the impact of driver-partners’ reputations on their earnings is an important topic for further research. The wage regressions that we present find little evidence of earnings differences by driver education, gender, or race, but we do find a return to early experience using the Uber platform. Finally, Uber’s growth rate has varied considerably across cities. Understanding why Uber grew more rapidly in certain cities could provide insights into the likely future path of the on-demand sector. For example, if inefficient taxi regulations and restricted supply of taxi licenses contributed importantly to Uber’s rapid expansion, then demand for on-demand services may be slower in fields other than for-hire transportation services. Uber likely represents a substantial fraction of the work facilitated by digital matching platforms, yet it seems to be part of a larger trend in work in the United States. Several recent studies documented the exponential growth in work facilitated by digital platforms,28 much of which is not ride-sharing work.29 Estimates of the size of the labor force that uses digital platforms for finding work vary widely and reach as high as 8% of all American adults over the course of a year (Smith 2016). Smith also documented that, among workers who report that digital platform income is ‘‘essential or important,’’ the 26See, for example, Cabral and Hortacxsu (2010) for research on the relationship between sellers’ ratings and sales on eBay, which, like Uber, is an online marketplace that uses a ratings system to build reputations for both sellers and buyers. 27This sorting effect could partly explain why Uber’s driver-partners are more highly educated than traditional taxi drivers and chauffeurs. 28See, for example, Farrell and Greig (2016a, 2016b), Manyika et al. (2016), and Smith (2016). 29Smith’s (2016) survey suggested that one-quarter of respondents who have worked using an online job platform in the past year worked in ride-sharing. 730 ILR REVIEW modal reason stated for using those platforms is a need to control their schedule. Manyika et al. (2016: 59) found that 87% of digitally enabled independent workers are independent ‘‘by choice,’’ compared with 69% of independent workers as a whole. Topics for future research in this field include estimating the welfare gains associated with digitally enabled worker independence over scheduling, understanding what work is and is not amenable to platform-based allocation, and studying the impact of the emergence of digital platforms in certain sectors on traditional jobs in the same sector. References Bernhardt, Annette. 2014. Labor standards and the reorganization of work: Gaps in data and research. UC Berkeley: Institute for Research on Labor and Employment, January. Accessed at http://www.irle.berkeley.edu/workingpapers/100-14 (January 12, 2015). [BLS] Bureau of Labor Statistics. 2005. Contingent and alternative employment arrangements, February 2005. July 27. Washington, DC: U.S. Department of Labor. ———. 2014. Occupational employment and wages, May 2013, 53-3041 Taxi drivers and chauffeurs. April 1. Washington, DC: U.S. Department of Labor. Accessed at https: //www.bls.gov/oes/current/oes533041.htm (January 10, 2015). [BSG] Benenson Survey Group. 2014. The driver roadmap. Internal survey of Uber driverpartners. December. Accessed at http://www.bsgco.com/uber (January 2016). ———. 2015. The driver roadmap 2.0. Internal survey of Uber driver-partners. November. Accessed at https://2q72xc49mze8bkcog2f01nlh-wpengine.netdna-ssl.com/wp-content/ uploads/2015/12/BSG_Uber-Driver-Roadmap-2.0_12.7.15_FIN2 (May 2017). Cabral, Luı´s, and Ali Hortacxsu. 2010. The dynamics of seller reputation: Theory and evidence from eBay. Journal of Industrial Economics 58(1): 54–78. Cohany, Sharon R. 1996. Workers in alternative employment arrangements. Monthly Labor Review 119(10): 31–45. Fallick, Bruce, and Charles A. Fleischman. 2004. Employer-to-employer flows in the U.S. labor market: The complete picture of gross worker flows. Finance and Economics Discussion Series (FEDS) Working Paper 2004-34. Washington, DC: Federal Reserve Board. Farrell, Diana and Fiona Greig. 2016a. Paychecks, paydays and the online platform economy: Big data on income volatility. JPMorgan Chase & Company Institute. Accessed at https://www.jpmorganchase.com/corporate/institute/document/jpmc-institute-volati lity-2-report (January 2017). ———. 2016b. The online platform economy: What is the growth trajectory? JPMorgan Chase & Company Institute. Accessed at https://www.jpmorganchase.com/corporate/ institute/institute-insights.htm (January 2017). Greenwood, Brad N., and Sunil Wattal. 2015. Show me the way to go home: An empirical investigation of ride sharing and alcohol related motor vehicle homicide. Fox School of Business Research Paper No. 15-054. Accessed at http://ssrn.com/abstract=2557612 or http://dx.doi.org/10.2139/ssrn.2557612 (January 2017). Harris, Seth, and Alan Krueger. 2015. A proposal for modernizing labor laws for twenty-firstcentury work: The ‘‘independent worker.’’ The Hamilton Project, Discussion Paper 2015- 10. Washington, DC: Brookings Institution. Hsieh, Chang-Tai, and Enrico Moretti. 2003. Can free entry be inefficient? Fixed commissions and social waste in the real estate industry. Journal of Political Economy 11(5): 1076–122. Katz, Lawrence F., and Alan Krueger. 2016. The rise and nature of alternative work arrangements in the United States, 1995–2015. Accessed at https://krueger.princeton.edu/sites/ default/files/akrueger/files/katz_krueger_cws_-_march_29_20165 (April 2016). Kuttner, Robert. 2013. The Task Rabbit economy. American Prospect 24(5), October 10. Accessed at http://prospect.org/article/task-rabbit-economy (January 2015). UBER’S DRIVER-PARTNERS IN THE US LABOR MARKET 731 Manyika, James, Susan Lund, Jacques Bughin, Kelsey Robinson, Jane Mischke, and Deepa Mahajan. 2016. Independent work: Choice, necessity, and the gig economy. McKinsey Global Institute, October. Accessed at http://www.mckinsey.com/global-themes/employ ment-and-growth/independent-work-choice-necessity-and-the-gig-economy (January 2017). Polivka, Anne E. 1996. Contingent and alternative work arrangements, defined. Monthly Labor Review 119(10): 3–9. Smith, Aaron. 2016. Gig work, online selling, and home sharing. Pew Research Center, November 21. Accessed at http://www.pewinternet.org/2016/11/17/gig-work-online-sell ing-and-home-sharing/ (January 2017). Taxicab Fact Book. 2014. New York City Taxi and Limousine Commission. Accessed at http://www.nyc.gov/html/tlc/downloads/pdf/2014_taxicab_fact_book (January 2015). 732 ILR REVIEW Copyright of ILR Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.

Uber’s Secret Gold Mine.

Authors:

CARSON, BIZ

 (AUTHOR)

Source:

Forbes

. 2/28/2019, Vol. 202 Issue 1, p32-34. 3p. 2 Color Photographs, 1 Graph.

Document Type:

Article

Subject Terms:

*

Profit

*

Capitalists & financiers

*

Local delivery services

Company/Entity:

Uber
 Eats (Company)

NAICS/Industry Codes:

492210

 Local Messengers and Local Delivery

People:

Khosrowshahi, Dara

Abstract:

The article discusses the success of the food-delivery company Uber Eats, led by executive Jason Droege, and the possibility that it could generate $1 billion in revenue in 2019, in addition concern by Uber chief executive officer (CEO) Dara Khosrowshahi that it may not make a profit. The article also discusses the success of Uber Eats competitors such as Grubhub and whether investors will continue to support Uber Eats.

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0015-6914

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134397060

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Uber’s Secret Gold Mine 

Contents

1.

Gobbling Market Share

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STRATEGIES

Strategies

Uber eats could make up a tenth of the ride-hailing giant’s revenue this year, impressive news for investors in its ipo. but well-capitalized rivals are already trying to tap the same vein.

When early investors were pitched on Uber’s original plan for a car-service app in 2008, it wasn’t until the second-to-last slide that they heard delivery could be another moneymaker for the business. Ten years later, delivery is no longer an afterthought. According to projections from its CEO, Dara Khosrowshahi, Uber Eats is on track to deliver some $10 billion worth of food worldwide this year, up from an estimated $6 billion-plus last year. Uber takes a 30% cut and a delivery fee, then pays drivers, suggesting that Uber Eats could generate at least $1 billion in revenue this year, or an estimated 7% to 10% of the total. That means Uber Eats is already among the planet’s largest food-delivery services and ranks second in the U.S. behind rival Grubhub (likely $1 billion in 2018 revenue) and ahead of competition like Caviar, Postmates and DoorDash.

Uber could certainly use the extra calories. The money-losing San Francisco-based company was valued at some $76 billion when it last raised money, in August 2018, and bankers hope its IPO, slated for later this year, could boost that to $120 billion. The problem is, there is no way Uber’s core ride-hailing business is worth that much. Its explosive growth is showing signs of slowing, and internationally the taxi service has struggled, selling its China operations to local rival Didi Chuxing in August 2016, as well as its stakes in Southeast Asia. Uber’s self-driving-car business, once considered the answer to rising driver costs, suspended testing and fired workers after an autonomous Uber killed a pedestrian in March 2018. Now, as Uber prepares to tell investors why they should buy its stock instead of rival Lyft’s, Uber Eats looks like a distinguishing factor.

“When I first joined Uber, I think Uber was much more associated with ride-hailing and Eats was this interesting part-time endeavor,” says Khosrowshahi, who took over as CEO in August 2017. “It has since exploded, in a good way, into a truly significant business.”

But despite the growth, Uber Eats is losing lots of money, and even Khosrowshahi doesn’t know when it will be profitable. Potential Uber investors will have to decide: Is food delivery a smart bet on future growth or a fool’s errand in a crowded market?

It’s a question familiar to Jason Droege, the 40-year-old protégé of former CEO Travis Kalanick. Droege has run Uber Eats since its 2014 inception, and some of the most critical voices he had to overcome were from Uber’s pre-IPO investors, who thought the company was on a path to re-create the terrible economics of Web 1.0 failures Web van, which blew through over $700 million trying to reengineer grocery delivery in the late 1990s, and Kozmo.com, which spent nearly $300 million trying to deliver video games and convenience-store fare. Droege shrugs off the comparisons—and the competition. “The world was telling us this was a crowded space. But our hypothesis was it wasn’t,” he says.

Making money on delivery isn’t easy. Sure, Uber Eats gets a hefty chunk of a restaurant’s bill and charges a delivery fee, generally between $2 to $8. But Uber has to pay the driver to pick up and drop off the food, plus market the service. Uber’s share of the bill is lower, on average, than in the ride-hailing business. Restaurants are, at best, semi-willing partners that can ill afford a 30% blow to their bottom lines. And since Uber isn’t (yet) willing to have your meal share a ride with a paying customer, there are fewer network efficiencies to capitalize on.

Its largest competitor, publicly traded Grubhub, has proved you can make a profit in this business. That success has made it a formidable rival, and it’s not the only one: Just in the U.S., Uber competes against Square subsidiary Caviar, well-capitalized startups DoorDash and Postmates, and the potential giant in the wings, Amazon.

Kalanick recruited Droege, with whom he had cofounded a file-sharing startup as undergraduates at UCLA, in March 2014 to head what was loosely called Uber Everything. His mandate: Find a service that could become as big as ride-hailing. Droege tried delivering everything from diapers and deodorant to daisies and dry cleaning. Nothing worked—except food.

After a few stunts like delivering ice cream and BBQ on the Fourth of July, Uber made its first serious attempt with Uber Fresh. Fresh had drivers circling city blocks with coolers full of soups and sandwiches ready for delivery within minutes. On launch day in Los Angeles in August 2014, the Uber team sold hundreds of meals in an hour and a half, a giant leap from the eight orders a day for deodorant. “The signal spike was big,” Droege says.

It was the right market but the wrong product. Magical as it was to have a driver show up with a burrito in 5 minutes at the tap of an app, Droege realized customers would wait 30 minutes if they could order any meal they wanted. Internally the team quietly started work on Project Agora (Greek for marketplace) to launch Uber Eats. They started in Toronto in 2015, chosen because competition was lighter than in a city like New York, and then expanded to Miami, Houston and secondary cities like Tacoma, Washington. A couple of markets (Miami and Atlanta) became profitable in 2017, proving that the business was possible, at least in certain places.

But just as Uber Eats was getting traction, Uber’s executive team fell apart in the wake of reports of sexual harassment, gender discrimination and questionable business ethics. Ultimately, Kalanick was ousted, and other groups, like self-driving cars, lost their department heads. But Droege and his team of nearly 2,000 remained mostly unscathed. He admits it was a “tough year,” but he told his team to keep their heads down and execute.

What’s most exciting to Uber executives is that many Eats customers don’t even use the ride-hailing service: Last year, four of every ten people who used Eats were new to Uber, giving the company access to fresh customers who might later be convinced to give the car service a try.

“Of all the side bets that Uber has made over the years, whether it’s autonomous or delivering other things or different modalities of transportation, this has come out as the clear number one in scale and executive attention,” says Mike Ghaffary, the former CEO of delivery rival Eat24.

Eats is closing in on Grubhub, still the U.S. market leader. In 2016, Grubhub controlled over half the market, says Wedbush analyst Ygal Arounian. Its market share dropped to 34% in 2018, while Eats’ grew from 3% to 24%. ” The pace of their expansion has caught everyone off guard,” Arounian says.

But the tailwinds helping Eats, such as a generation turning to their phones first when hungry, also propelled its opponents. In 2018, DoorDash raised about $1 billion in venture funding and nearly tripled its valuation to $4 billion. Postmates also raised $400 million in the last six months of 2018 and now has a valuation of $1.9 billion. Both competitors also benefit from their single-minded focus on food delivery.

To trim costs, Uber Eats batches orders so a driver can pick up multiple meals at once. It’s also enticing customers with free delivery from restaurants that already have a courier en route. But Khosrowshahi draws the line when it comes to pairing passengers with pad thai: “We don’t want your experience to suffer because it may be good for our business.”

To grow further, Uber Eats needs to win over more customers and restaurants. Droege is betting partnerships with McDonald’s and Starbucks will entice customers to open the Uber Eats app instead of a competitor’s. Uber is also copying Grubhub’s core business model and letting some restaurants do their own deliveries in exchange for a bigger take of the bill.

Success depends on convincing restaurant owners like Simon Mikhail, of Si-Pie Pizzeria in Chicago, that Eats trumps its rivals. Mikhail works with more than a dozen delivery services, but only Uber Eats approached him with an idea for a virtual restaurant, after it noticed how many folks in the neighborhood were searching for fried chicken. Now he sells 160 pounds of chicken a week, exclusively through Uber Eats app. “They do cut into profit a little bit, but it’s worth it,” he says.

Will investors decide that Uber Eats is also worth it? That ‘s now up to Droege to deliver.

FINAL THOUGHT

* “A man seldom thinks with more earnestness of any thing than he does of his dinner.” —SAMUEL JOHNSON

Gobbling Market Share

SINCE 2016, UBER EATS HAS GROWN FROM LESS THAN 5% OF THE U.S. FOOD DELIVERY MARKET TO NEARLY 25%—AND IT’S EXPECTED TO JUST KEEP GETTING FATTER.

1ESTIMATE. SOURCE: WEDBUSH SECURITIES ESTIMATES

GRAPH: Gobbling Market Share

PHOTO (COLOR): Chow time: Since the launch of Uber Eats, its leader, Jason Droege, has had 18% of all of his meals from Eats, including this bowl of noodles.

PHOTO (COLOR): Dara Khosrowshahi, Uber’s CEO, has left much of Eats to Droege: “Honestly, I’m there to do the corporate grunt work,” he says.

Choose Language

Too Good to Be True? A Comment on Hall and Krueger’s Analysis of the Labor Market for

 

Uber’s Driver-Partners.

Images

  

Authors:

Berg, Janine

 

8

hesj@queensu.ca

Johnston, Hannah

1

Source:

ILR Review

. Jan

2

01

9

, Vol.

7

2 Issue 1, p

3

9-

6

8.

30

p. 1 Color Photograph, 1 Diagram, 1 Chart.

Document Type:

Article

Subject Terms:

*

Industrial policy

*

Wages

*

Labor market

Automobile drivers

Author-Supplied Keywords:

for-hire vehicle industry

gig economy

labor market analysis

taxi industry

Uber

Company/Entity:

Uber Technologies Inc.

NAICS/Industry Codes:

926110

 Administration of General Economic Programs

Abstract:

In their comment on the article on Uber driver-partners by Jonathan Hall and Alan Krueger, the authors analyze the article’s methodological problems, including sample bias, leading questions, selective reporting of findings, and an overestimation of driver earnings, which do not account for the full range of job-related expenses and is based on outdated data. The authors also argue that Hall and Krueger make unsubstantiated claims that extend beyond the scope of their research and ignore a rapidly growing literature that is critical of the Uber model as well as the broader for-hire vehicle industry in which Uber operates. As policymakers grapple with how to respond to transport network companies, the authors argue that a fuller understanding of the costs and benefits of services such as Uber is critical for making informed policies.

[ABSTRACT FROM AUTHOR]

 

Copyright of ILR Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s express written permission. However, users may print, download, or email articles for individual use.

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Author Affiliations:

1Janine Berg is a senior economist at the International Labour Office (ILO) in Geneva, Switzerland. The views expressed in this article are her own and do not necessarily reflect the views of the ILO. Hannah Johnston is a doctoral candidate in the Department of Geography and Planning at Queen’s University, Kingston, Ontario

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Too Good to Be True? A Comment on Hall and Krueger’s Analysis of the Labor Market for Uber’s Driver-Partners 

Contents

1.

Uber’s Survey of Its Drivers

2.

Non-response Bias

3.

Missing Questions

4.

Framing of Questions

5.

Insufficient Analysis of Findings: Job Satisfaction

6.

Earnings of Uber Drivers Compared to Taxi Drivers

7.

Taxi Reference Category

8.

Calculating Uber Drivers’ Expenses

9.

Tax Rates and Social Programs

10.

Municipal Licensing Costs

11.

Urban Geographies

12

.

The AAA Comparison

13.

Fare Decreases and Outdated Data

14.

Flexibility

15.

Working-Hour Variability

16.

Driving for Hire: A Demand-Driven Service

17.

Financial Dependence on Income from Uber

18.

Uber’s Practices to Ensure a Ready Supply of Drivers

19.

Ratings

20.

A Labor Market Analysis That Is Too Narrow

21.

Leasing and the Transformation of America’s Taxicab Labor Market

22.

Effect of TNCs on FHV Drivers

23.

TNC Impact on Medallion Owners

24.

Market Externalities and Predatory Pricing

25.

Summary: Context Is Crucial

26.

Conclusion

27.

Footnotes

28.

References

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In their comment on the article on Uber driver-partners by Jonathan Hall and Alan Krueger, the authors analyze the article’s methodological problems, including sample bias, leading questions, selective reporting of findings, and an overestimation of driver earnings, which do not account for the full range of job-related expenses and is based on outdated data. The authors also argue that Hall and Krueger make unsubstantiated claims that extend beyond the scope of their research and ignore a rapidly growing literature that is critical of the Uber model as well as the broader for-hire vehicle industry in which Uber operates. As policymakers grapple with how to respond to transport network companies, the authors argue that a fuller understanding of the costs and benefits of services such as Uber is critical for making informed policies.

Keywords: labor market analysis; for-hire vehicle industry; gig economy; Uber; taxi industry

A survey of Uber drivers showed that the vast majority are happy working for the company. They greatly value the flexibility in terms of when and how much to work. . . . They also seem happy with the pay.

—Testimony given by Joe Kennedy, Senior Fellow, Information Technology and Innovation Foundation, to the House Small Business Committee Hearing, United States House of Representatives, May 24, 2016

Research on the economic and social impact of the on-demand economy are key reference points for governments as they grapple with how best to regulate changing labor markets. In testimony before the House Small Business Committee and in other official proceedings, the article by Hall and Krueger has been cited for documenting drivers’ contentment with the flexibility of the Uber model and its pay. Yet the findings presented in their article are fraught with methodological problems and unsubstantiated claims. Given the impact that research can have on the lives of working people, we must accurately convey the social and economic costs and benefits of restructuring labor markets—a responsibility grossly overlooked by Hall and Krueger.

Our comment presents four criticisms of the Hall and Krueger study. First, there are methodological concerns, including a poorly constructed survey and a flawed analysis of job-related costs. Second, the authors present an incomplete portrayal of the situation of Uber drivers. Third, the authors make unsubstantiated claims that parrot Uber’s corporate narrative about the virtues of the company’s business model. These claims are not grounded in the authors’ research and are at odds with a growing body of literature, not cited by the authors, that presents a more critical analysis of the working conditions of Uber drivers. And fourth, the authors provide an incomplete labor market analysis that fails to account for the impact of transport network companies (TNCs), such as Uber and Lyft, on taxi and other for-hire vehicle (FHV) drivers, despite the paper’s recurring comparisons of Uber vs. Taxi.

In addressing the four aforementioned critiques, we present a structured response. We address the first two criticisms with a critique of the survey, the authors’ use of the survey, and their earnings calculations. We discuss Hall and Krueger’s unsubstantiated claims that reinforce Uber’s corporate narrative in two sections on driver flexibility and driver ratings, where we introduce research that examines how Uber’s business practices are used as disciplinary and management tools. Finally, we address some of the missing elements in the authors’ “labor market analysis” of Uber drivers by explaining the evolution of the taxi labor market in the United States over the past several decades, which is key to understanding the current situation of FHV drivers. We also briefly explain Uber’s competitive practices and the consequences of these practices for FHV drivers, cities, and the public alike.

Uber’s Survey of Its Drivers

Hall and Krueger’s description of Uber drivers draws from two surveys conducted by the Benenson Strategy Group (BSG) under contract for Uber. The first survey was conducted in December 2014 and included 601 Uber drivers in 20 US cities. The second survey was conducted in November 2015 and included 833 respondents in 24 US cities.1 [ 1] Hall and Krueger have shared the survey questionnaire as well as the aggregated responses.2 [ 2]

Both surveys collect information on standard demographic characteristics, education, previous work experience, household income, and experiences driving for Uber. Some overlap occurs between the two surveys, though the 2015 survey is considerably longer (with 135 questions and sub-questions as opposed to 95 in 2014). The surveys provide some useful information for analyzing the financial condition and dependence of workers on the platform, as well as their motivation and experience driving for Uber.

Our criticism of the survey concerns methodological shortcomings, including possible non-response bias that skewed the survey sample, and missing questions. We are also concerned about bias in the survey with respect to the framing of some of the questions as well as the authors’ incomplete presentation of the survey results. As we point out, the authors emphasize some data over others, or do not provide sufficient context to analyze the survey results.

Non-response Bias

As acknowledged by Hall and Krueger, the survey response rate, at “around” 10%, is low. Although the authors state that “the (weighted) respondents do not appear to be dissimilar from the full set of driver-partners in terms of their average work hours or hourly earnings” (p. 709), they do not provide an accounting of this weighting. The article states that the sample was stratified, and that the weighting adjusts for this, but this is not the only weighting issue involved. It is possible there were differential response rates across groups of respondents, in which case a post hoc weight would be needed.

The risk of non-response bias is particularly high given this was a company-sponsored survey. [55] explained how employee surveys can be helpful management tools, if they are perceived by workers as having the right intentions, such as workplace improvement. Had Uber notified drivers that they had contracted a survey company to hear about drivers’ concerns, the response rate might have been higher than 10%. The low response rate likely suggests alternative worker perceptions of the survey, such as those suggested by [63] who explained how many workers will not respond “when they feel the [company] survey will not result in meaningful action and change” (p. 243).

A greater concern would be if non-response bias resulted from failure to respond, or failure to respond honestly, out of fear of reprisal from the company. Indeed, shortly after the 2014 survey results were released, Newsweek published an article questioning the validity of the survey results, in part, based on interviews with Uber drivers who said they would not have responded to the survey had they been selected, with one driver indicating fear of retribution from the company ([40]). If drivers did not respond (or did not respond honestly) for fear that their accounts would be deactivated3 [ 3]—perhaps a more salient concern among those with greater financial dependence on the platform—a non-response bias is likely in the sample.

Given the low response rate, an investigation of non-response bias should have been included in Hall and Krueger’s article. Analyzing and accounting for non-response bias can be done through follow-up surveys to non-respondents as well as by examining the characteristics of the respondents, as certain socioeconomic characteristics may contribute to a lower response rate. Comparing the sample to the “full set of driver-partners in terms of their average work hours or hourly earnings” (p. 709) is not sufficient for addressing this concern; it merely asserts that the variables chosen by the authors for sample comparison are the only possible cause of response propensity ([26]). The failure to address non-response bias calls into question Hall and Krueger’s claim that the study is based on a “representative, national sample” of Uber drivers (p. 705, our emphasis).

Missing Questions

Another concern about the survey is missing questions or questions that solicit incomplete information. One critical question missing from both the 2014 and 2015 surveys is the average number of hours the person drives for Uber in a typical week. As a result, analysis of other survey questions, such as how much the person spends per month on fuel costs related to driving (2015: Q53R2), is rendered impossible. Moreover, this information is necessary for analyzing the survey responses, given key differences between part-time and full-time drivers. Full-time drivers comprise a smaller percentage of the total number of registered drivers, but they drive a much higher proportion of hours and are fundamental to Uber’s ability to offer a reliable service.4 [ 4] Using the parlance of Uber, they are the company’s “top partners.” Assuming that the intent of the survey was to solicit feedback from its drivers, it is thus surprising this question was not included. Moreover, Hall and Krueger use the administrative data on hours worked in their earnings regression, but they do not use it to delve into possible differences between the part-time and full-time workforce. Rather, they limit the analysis to a presentation of aggregated data.

Framing of Questions

A basic tenet in questionnaire design is to avoid combining two opinions into a single question. Doing so forces respondents to answer two questions at once when their opinions about the two may diverge. As [62] explained:

Even the rankest beginner would wince at a question like “In the coming election do you support Senator Pace and peace, or do you support Governor Guerra and war?” Less blatant examples may slip by the inexperienced question formulator. For example, consider: “Are you in favor of building more nuclear power plants so that we can have enough electricity to meet the country’s needs, or are you opposed to more nuclear power plants even though this would mean less electricity?” Conjoined here are questions . . . with the implied relationship that nuclear power plants are the only way to increase the supply of electricity. Such a tactic could easily be used to load a question in favor of one particular kind of response. (p. 133)

Double-barreled questions can also be formulated as one-and-a-half-barrel questions when additional considerations are included in the answer. This tactic occurred in the BSG questionnaire in 2014 with the following forced choice question (Q38): “If both were available to you, at this point in your life, would you rather have: a steady 9-to-5 job with some benefits and a set salary or a job where you choose your own schedule and be your own boss?” Few workers would state that they do not want schedule flexibility and autonomy. Had a question been included to solicit their views on “a job with a regular and guaranteed income that is sufficient to support one’s family,” similar high marks would likely have been recorded.

The 2015 survey improved on the formulation of Q38 by making the opposition clearer, but it also misleadingly characterized the opposing contractual terms, likely skewing the response. It asks (Q52):

Which of the following would you most prefer regarding your driving with Uber: being classified as an employee of Uber so you could be eligible for a minimum wage, health care and other benefits, but you would not have the flexibility to set your own schedule; or being classified as an independent contractor for Uber so you would have the flexibility to set your own schedule, but you are not eligible for a minimum wage, health care or other benefits.

Hall and Krueger report the results of this question on page 714, stating that 79% of respondents indicated their preference for an independent contractor relationship, but they provide no details of the question. This presentation is problematic as readers may incorrectly assume that the question was appropriately framed. Furthermore, if Uber really wanted to capture what the drivers valued, they should have teased out the label (employee or independent contractor), as many people have preconceptions of what these two things mean. Then separately, they should have asked drivers how much they valued each of the possible features: schedule flexibility, income guarantees, and other job-related benefits.

More troubling, the question mischaracterizes what is included by law in an employment relationship and thus forces workers to choose between two alternatives based on misinformation. A worker who is classified as an employee according to US employment law is not obligated to work from 9 to 5 or to have an inflexible schedule. Many American workers have little control of their schedule, but this is because of employer practices, not employment law as detailed in the Fair Labor Standards Act (FLSA). Put differently, employee status is not incompatible with schedule flexibility. Indeed, working-time laws in the United States only designate payment of overtime after 40 hours a week for non-exempt occupations. The FLSA does not guarantee minimum weekly hours, nor does it impose requirements on notification of schedules. Of importance to drivers, working time would include time spent driving to pick up a passenger, thus for the total time that the driver is at the disposal of their employer, the employee would be guaranteed the minimum wage. In addition, under the FLSA, drivers would be eligible for reimbursement of their driving expenses, which is likely to be of significant value to Uber drivers but was not mentioned in the survey question.

The phrasing of Q38 (2014) and Q52 (2015) is particularly disconcerting given the high-profile legal proceedings involving Uber drivers who claim they have been misclassified as independent contractors. The framing of these questions does not meet the academic rigor expected in peer-reviewed research and seems better suited to the purpose of public relations. And indeed, the company has disseminated widely the finding that drivers prefer their current contractual status as independent contractors, likely with a view to influencing policymakers as they grapple with regulation of TNCs.

Insufficient Analysis of Findings: Job Satisfaction

One of the headline findings of the Hall and Krueger study as well as the Uber press release based on the BSG survey is that 81% of the 2015 drivers responded that they were very satisfied or somewhat satisfied with driving with Uber. Hall and Krueger merely report this finding at the end of the section on the BSG survey results (p. 715); they do not provide information on how this figure compares with other workplace studies of job satisfaction, nor do they offer any analytical explanation of the concept of job satisfaction and the shortfalls in its measurement.

One of the reasons this question produced a favorable response is the rating scale. The survey allows workers to choose from only one of four options: very satisfied, somewhat satisfied, not very satisfied, not at all satisfied. A middle option—”neither satisfied nor dissatisfied”—is not offered as a choice. This omission can bias responses upward, as drivers who feel neutral are forced to choose between “somewhat satisfied” or “not very satisfied” ([22]). By contrast, the Conference Board, a business membership and research organization that has published an annual job satisfaction survey in the United States since 1987, uses a 5-point scale ranging from most satisfied to least satisfied and offering a neutral middle category. Using this scale, only 48.3% of American workers reported in 2014 they were satisfied with their jobs ([16]).

Hall and Krueger could have compared their findings with the 2015 American Working Conditions Survey carried out by researchers at the RAND Corporation, which uses the same 4-point scale of the Uber survey. It found job satisfaction rates (percentage satisfied or very satisfied) for its nationally representative sample of American workers that ranged from 80% to 92%, depending on the age, sex, and education of the respondent ([38]). Non-college graduate men scored the lowest (81% for the men between the ages of 35 and 49 and over 50, and 83% for men under the age of 35), no different from the level reported for Uber drivers. These comparative data are necessary for interpreting the results of the Uber survey.

The use of a single-measure job satisfaction indicator is problematic given the extensive literature that shows it is not reliable. [49] discussed this issue in his study of job satisfaction among UK occupations based on data from the British household panel survey. He found that “poorly paid childcare workers with low negotiable skill have higher overall job satisfaction levels than sales managers enjoying fat bonuses; cleaners with low negotiable skill qualifications are likely to have far higher levels of job satisfaction than the school teachers whose classrooms they tidy up” (p. 526). The author explained this anomaly by the tendency for respondents to answer single-measure job satisfaction questions based on the intrinsic characteristics of the job (the work that the person actually performs, autonomy, stress at work) rather than extrinsic characteristics such as pay, contractual status, or prospects for promotion ([49]). For this reason, the academic literature is clear that single-item measures of job satisfaction are misleading and should be replaced, or at least complemented, with multiple-item measures of job satisfaction ([43]; [ 8]). [43] contrasted the findings from two questionnaires administered to university teachers that used single- versus multiple-item questions of job satisfaction and reported an overestimation of 31 percentage points using the single-item measure.

Using single-measure job satisfaction would be understandable if this were the only information available, as is commonly the case in publicly available data, but both the 2014 and the [11] BSG surveys asked drivers to rate their satisfaction on specific aspects of driving for Uber (nine aspects were surveyed in 2014 (Q

51

): schedule flexibility, personal safety while driving, passengers’ treatment of drivers, Uber smartphone app, communication between Uber and partner drivers, how surge pricing works, the amount of business you receive from Uber, income, and the rating system). In 2015, the survey included 15 questions covering the topics of 2014 as well as asking about communication with the company and driver control over where they drive (Q29). The multiple-item questions provide a more nuanced picture of drivers’ job satisfaction that should have been used in an academic assessment. For example, even though overall job satisfaction remained high in 2015, the percentage of drivers that reported they were satisfied or very satisfied with their income from driving with Uber fell from 65% in 2014 to 56% in 2015.

Earnings of Uber Drivers Compared to Taxi Drivers

Hall and Krueger state that drivers are attracted to Uber because of the “level of compensation” (p. 705) and that “taking expenses into account, the average Uber driver-partner is likely to earn at least as much per hour, and probably more, than the average taxi driver and chauffeur” (p. 727). We dispute the calculations provided by the authors of Uber driver earnings vis-à-vis taxi drivers. One problem concerns the reference group that Uber drivers are compared with: “employees.” The other problem concerns their calculations, which understate Uber drivers’ expenses. Their flawed calculations overestimate Uber drivers’ earnings and position Uber as the higher-earning option for drivers, perpetuating the company’s long-standing practice of using inflated wage statistics to lure drivers ([30]).

Taxi Reference Category

A major methodological problem with the authors’ earnings comparison is that the earnings data they use for taxi drivers and chauffeurs does not adequately capture a typical taxi drivers’ earnings. The authors use occupational employment statistics (OES) data as if they were representative of the “average taxi driver and chauffeur” (p. 727), when in fact most taxi drivers are self-employed and thus not included in the OES data, which covers only employees. Furthermore, the occupational earnings data include other types of drivers who should not be included in the comparison, such as hearse drivers.5 [ 5]

According to [ 6] data, 188,860 drivers were employees in the “taxi drivers and chauffeurs” occupational category in May 2016. Labor force statistics from the 2016 Current Population Survey reveal 500,000 workers were employed in this same occupational category, which includes both self-employed and employees who report this occupation as their main job. While not directly comparable with the OES data, it does suggest that nearly two-thirds of drivers in this occupational category are self-employed. Studies of the taxi industry confirm this observation, noting that “employees [are] most common among limo drivers” ([56]).

Our review of BSG-surveyed taxi markets further confirms the prevalence of self-employment in the industry. In comparing the number of BLS taxi drivers and chauffeurs in a given metropolitan area to the number of reported taxicab medallions (the city-issued license to operate a cab), which we use as a conservative proxy for the number of drivers, we find that the number of medallions often exceeds the number of BLS taxi and chauffeur employees. For example, Philadelphia has 1,800 medallions but only 1,700 employees; Dallas has 2,000 medallions and 1,850 employees; Washington, DC, has 6,191 registered taxicabs and only 540 BLS taxi and chauffeur employees. Acquiring information on livery drivers is more challenging, as even in major cities such as Boston, the industry subsector remains largely unregulated. Livery cars, however, add to the total number of FHV on the road, and thus, workers in the sector. When accounting for livery drivers, many of whom are also independent contractors, the percentage of taxi driver employees represents an even smaller portion of the total occupational category. For example, in New York City, the BLS data tally 12,580 employed taxi drivers and chauffeurs. This compares to 13,587 yellow cab medallions and more than 143,647 licensed drivers ([19]). In this market, employees account for fewer than one in ten drivers.

Calculating Uber Drivers’ Expenses

Hall and Krueger provide two estimates to calculate Uber drivers’ net earnings. The first is a gross earnings-per-hour figure for drivers, by city, that is net Uber fees but does not account for driving-related expenses. Then the authors generate a cost estimate of hourly expenses for part- and full-time Uber drivers, with a breakdown by vehicle size to determine take-home earnings. Uber’s proprietary data set leaves us poorly positioned to either critique or affirm Hall and Krueger’s computations regarding net hourly earnings; however, in unpacking driving expenses, we find that the proposed figures underestimate the true cost of driving.

Our contention that Hall and Krueger have underestimated driver costs is based on five findings. First, the authors make no mention of the self-employment taxes paid by independent contractors. Second, they fail to account for additional licensing costs in highly regulated markets. Third, variation in cities’ transportation infrastructure suggests that the national mileage estimate used in their analysis is not appropriate. Fourth, the use of national American Automobile Association (AAA) data as a proxy for vehicle ownership costs underestimates costs in the BSG cities; variation based on driver demographics is also insufficiently addressed. And fifth, Uber has implemented multiple fare decreases since 2014 that most certainly affect the associated costs and earnings of Uber drivers; existing research suggests these changes have resulted in higher hourly expenses.

Tax Rates and Social Programs

Failure to account for additional tax obligations of Uber drivers leads to inflated hourly earnings. As independent contractors, Uber drivers are solely responsible for FICA contributions (payroll taxes), effectively doubling their payroll tax rate to 15.3% (compared with 7.65% paid by employees) on income below $127,000. Independent contractors may also be subject to state tax contribution rates. As Krueger has noted elsewhere, “A positive hourly wage premium for independent contractors could partially reflect a compensating differential for lower benefits and the need to pay self-employment taxes” ([35])—an important consideration when directly comparing earnings of taxi and limousine employees with Uber drivers.

In addition to no employer-provided benefits, independent contractors are not covered by labor laws that govern wages and hours, family and medical leave, workers’ compensation,6 [ 6] and unemployment. As self-identified low-income workers (according to the BSG survey, 54% of respondents described themselves as poor, working class, or lower-middle class) in an industry with exceptionally high rates of violence and injury,7 [ 7] lack of workers’ compensation and disability coverage can be risky.

Hall and Krueger suggest that the independent contractor status of drivers allows them to “partially offset their costs by deducting work-related expenses from their income for tax purposes” (p. 726); however, existing research suggests that many gig workers are unfamiliar with how to navigate tax codes. A recent survey, conducted by the Kogod Tax Policy Center at American University to assess on-demand workers’ understanding of their tax-filing obligations, found that almost half of survey respondents were unaware of available deductions, credits, or liabilities they could claim for tax purposes ([ 9]). Similarly, [42], in their qualitative analysis of Internet discussion forums of TNC drivers, found that drivers were confused about which driving-related expenses could be considered tax deductible. They concluded that drivers chose to participate in rideshare platforms without full knowledge of driving-related expenses. As they explained, “the rideshare industry is one in which many of the operating costs, and the burdens of estimating such costs, are borne by drivers. . . . some of these costs (including tax costs) may not be particularly salient to drivers at the time of their labor supply decisions” ([42]: 67).

Municipal Licensing Costs

FHV industries are commonly regulated at city or municipal levels; the process of developing a cost model on a statewide or national basis, as done by Hall and Krueger, flattens the market and glosses over important differences between cities. Locally determined regulatory provisions governing taxi permits and licensing eligibility affect the cost of driving and should have been considered in their analysis.

In various cities, Uber has fiercely opposed regulation; however, in other markets, including BSG-surveyed cities, Uber has acquiesced to regulatory mandates. Such regulation results in additional driver expenses. Although Uber left San Antonio, Texas, for approximately six months in 2015,8 [ 8] and Austin, Texas, in 2016 after the cities voted to require TNC drivers to be fingerprinted, Uber has complied with regulations in valuable markets. In New York, for example, initial licensing fees paid for by Uber drivers now total as much as $726.50,9 [ 9] plus any costs associated with visiting a physician, all of which are borne by the driver. Additionally, drivers must pay a new vehicle registration of $550 for a two-year license plus a $75 inspection fee if the vehicle has more than 500 miles on it. Within the BSG-surveyed cities, New York City is an outlier because of these additional costs; nonetheless, due diligence requires that additional licensing and registration fees be accounted for.

Urban Geographies

Urban infrastructure affects mobility within cities, a fact that is insufficiently addressed in Hall and Krueger’s estimate of driver expenses. The built environment of each city surveyed as part of the BSG survey is unique and differentially affects the rate of travel and thus the earnings of Uber drivers. Hall and Krueger estimate, on average, across 20 BSG cities that drivers would cover 35,000 business miles in 2,000 hours, an average speed of 17.5 miles per hour.

Congestion and traffic, however, make a huge impact on the abilities of drivers to perform under the terms suggested by Hall and Krueger. This detail leads us to ask whether the assumption of 17.5 miles per hour produces a reasonable expense estimate for drivers. Research on taxi speeds in New York City, for example, reveals an average taxi speed of less than 13 miles per hour in 2015 ([67]), which suggests that within the BSG sample significant variation occurs between cities. While 17.5 miles per hour may be a predictable rate of travel in Phoenix, where infrastructure was built to accommodate the automobile, it may not be a reasonable estimate for old-city Philadelphia, whose narrow streets were designed for the horse and carriage. To better account for these variations, Hall and Krueger should have estimated driver mileage and hours by city.

The AAA Comparison

Hall and Krueger use national AAA estimates on the cost of car ownership to estimate the hourly cost of driving for Uber. The authors offer two cost calculation scenarios: driving full-time and driving part-time. The part-time scenario excludes insurance registration and fees, assuming that these costs would be incurred regardless, whereas the full-time scenario incorporates a national estimated cost of insurance and registration fees, assuming that drivers have purchased the car explicitly for the purpose of driving for Uber. Once insurance and registration fees are accounted for (or, in the case of part-time drivers, disregarded), the authors add a per-mile driving fee and then convert the amount to an hourly estimate.

Vehicle ownership costs have substantial geographic variability. Insurance rates differ drastically between urban and rural areas, by driver age, and by miles driven. While [ 1] AAA estimates for insurance range between $981 and $1,08110 [10] depending on vehicle type ([ 1]), cities surveyed by BSG have a wide range of variability relative to average insurance rates. For example, a 2014 comparison of automobile insurance rates reveals that the Phoenix metropolitan area is generally 10% below the national average, Philadelphia and San Francisco are 10% higher, and Miami and New York are 34% and 36% higher, respectively. Detroit, surveyed by BSG in [11], boasts the highest insurance rates in the nation, at 165% above the national average ([20]; [33]).

Other key differences are in play regarding AAA estimates. The AAA estimate is based on a 47-year-old male driver who commutes three to ten miles a day on his way to work, not someone who works as a professional driver and logs 35,000 business miles per year. Although most drivers are male, BSG findings report that 19.1% of Uber drivers are between the ages of 18 and 29; another 30.1% are between 30 and 39. Uber drivers skew young, which is an important consideration given the negative correlation between age and insurance costs in the United States. Actual insurance cost differentials can be quite extreme. For example, industry research on the 10 most expensive cities for 2014 extended automobile insurance (all of which are covered in the BSG survey), priced for a 26-year-old driver, reported premiums of $2,225 in Atlanta, $2,859 in Chicago, and $3,169 in Miami. Calculated for a Toyota Camry, these figures are much higher than the [ 1] AAA estimate of $1,007 used by Hall and Krueger. The additional costs for young drivers are particularly important considerations given Uber’s targeted marketing to college students ([64].). Individual driver expenses may also vary based on driving style, vehicle type, fuel requirements, driving record, and other factors11 [11] that are more difficult to account for on a generalized basis.

Although driving expenses may vary individually, estimating driver costs could have been done in a more nuanced way. Small changes to the BSG survey would have provided more fruitful information about driver-related expenses. The 2015 survey, which includes a question about driver expenses (car payments, fuel costs, insurance, repairs, regular maintenance, registration, car wash or interior cleaning, and parking costs) might have asked respondents to specify what percentage of these costs are work-related; instead, by not asking this information, Hall and Krueger position their findings as a “best estimate” despite its many flaws.

The survey could have also asked respondents to provide exact figures for known expenses, such as car payments, or Hall and Krueger could have used internal driver expense estimates, such as those leaked to the press in 2016. The leaked data, analyzed by BuzzFeed and subsequently confirmed by Uber, found that once expenses were accounted for, hourly driver earnings in Denver, Houston, and Detroit (all surveyed in [11] by BSG) were $13.17, $10.75, and $8.77, respectively—rates much lower than the authors’ estimate of $19.35 per hour ([41]). Alternatively, Uber could have also used data from its partner, SherpaShare, an app that provides a means for Uber drivers to monitor their expenses.12 [12] Given the company’s relationship with this app, it raises the question as to why the authors did not use this data to calculate Uber drivers’ expenses as this would arguably have produced a more accurate estimate.

Fare Decreases and Outdated Data

Hall and Krueger’s analysis is outdated. They state that “as long as drivers’ costs are less than $6.79 per hour, the net earnings of Uber drivers-partners would exceed those of taxi drivers and chauffeurs, on average” (p. 725). Because the data are from 2014, this conclusion should be written in past tense, as Uber dropped its rates in 2015 and 2016 in major cities across the United States.13 [13] Although the 2014 trip data correspond with the BSG survey year, the authors could have used more up-to-date data to calculate their earnings estimates. Other research by the first author ([28]; [18]) confirms this author’s access to up-to-date data. Given the importance of the debate around TNCs, we point to the political repercussions of presenting outdated driver earnings as if they were contemporarily relevant.14 [14]

Uber justified its 2015–16 rate cuts by telling drivers that price cuts stimulate demand and increase the volume of work. In the [11] BSG survey, Uber drivers reported they were working more; only a minority of respondents associated increases in work volume with higher earnings. When the BSG survey asked (Q50): “Compared to your first few months driving for Uber, which of the following comes closest to how you feel about driving with Uber now?” Twenty-eight percent of respondents reported they were driving more but making less money overall; 21% indicated they were driving more just to make the same amount of money. Only 21% of respondents indicated they were driving more and making more money. (Thirty percent of drivers indicated they had not increased their amount of driving.)

Drivers’ reports of increased hours worked without financial benefit is supported by other Uber-sponsored research including by Hall himself. One recent study determined that driver’s short-term earnings shifted in the same direction as the fare change. In the long run, the study found “little change in the hourly earnings rate despite large changes in the base fare index [in 2015 and 2016 and that] only driver utilization (the time drivers spend working with a paying passenger in their car) seems to show a persistent change in levels” ([27]). Similar to Hall and Krueger, [27] failed to account for driving-related costs, a problematic omission given the likely case that higher utilization results in higher mileage, and thus, increased expenditures and lower net earnings. Furthermore, if Hall and Krueger’s unsubstantiated claim that drivers “can conduct personal tasks while the Uber app is turned on” (p. 721) is true, then increased driver utilization results in a loss of these earning opportunities following a fare decrease (similar claims made in [14]; [27]).

Flexibility

Throughout their article, Hall and Krueger reiterate that people are attracted to driving for Uber because of the flexibility it provides. One of the pieces of evidence they give to support this argument is the high degree of variability in working hours. As they explain, “driver-partners vary the number of hours in which they use the Uber platform by a considerable amount from week to week” (p. 723).15 [15] Hall and Krueger’s presentation of this evidence paints a distorted picture of working-hour variability. We critique the authors’ claim that Uber provides flexible work opportunities and instead argue that workers’ schedules are highly dictated by the availability of work and their financial dependence on income from Uber. Furthermore, Uber’s practices to ensure a ready supply of drivers limit driver choice and control, undermining the flexibility that is extolled by the authors and the company alike.

Working-Hour Variability

Uber drivers have distinct profiles: those who drive short-hour “gigs” to complement other income sources and those who drive long hours, week after week, and rely on Uber as an important source of income. Although short-hour drivers constitute half of Uber’s workforce, as a percentage of hours driven the smaller share of drivers who work more than 35 hours per week (19% according to Table 4 of Hall and Krueger’s article) are critical for ensuring that Uber can provide a reliable service. Indeed, the 7% of drivers who work more than 50 hours per week account for approximately 19% of total Uber hours driven (Table 1). The proportion of this small but dedicated cohort is only one percentage point lower than the proportion of hours driven by short-hour gig workers who make up 51% of the total driving fleet.16 [16]

Graph

Distribution of Uber Drivers by Hours per Week and by Approximate Share of Hours Driven

Hours per week

% UberX drivers, 2014

Approximate share of hours driven (%)

1–15

51

19.9

16–34

30

36.6

35–49

12

24.6

50+

7

18.8

1 Source: Derived from Table 4, Hall and Krueger (2018).

2 Notes: We estimated the share of hours driven by multiplying the midpoint of the hours band by the percentage of Uber drivers and then dividing the share by total hours. For 50+, we calculated the hours at 55. We use 2014 data as their discussion in the article and their Figure 8 are based on 2014 data.

The information on share of hours driven is important for analyzing the data presented in Figure 8 of Hall and Krueger on variability of working hours. As variation is measured by the percentage change in total working hours between two given weeks, greater variation is likely among the short-hour segment of Uber drivers, which would skew the information presented in the figure. A driver who works 5 hours one week and 8 hours the next week would have a 60% variation, but one who drives 45 hours one week and 48 hours the next would have only a 7% variation. By merging full- and part-time drivers, Hall and Krueger present working-hour variation easily attributed to demand fluctuations caused by sports games, festivals, or adverse weather conditions as overly significant, which contributes to inflated estimates of flexibility.

Data from another Uber-affiliated study ([14]) shed additional light on the predictability of total driver hours worked. The authors present a transition matrix of hours worked in five-hour intervals ranging from zero hours to 50+ hours for a series of contiguous weeks. Their matrix confirms the distribution of working hours reported in Hall and Krueger, but it also reveals that while some variability occurs between weeks, short-hour drivers and long-hour drivers remain in their respective categories. Of those drivers who worked 50+ hours per week in week t, 47% of them worked 50+ hours in week t+1 (23% worked 46–50 hours; 14% worked 41–45 hours, and 9% worked 37–40 hours). Consistent, predictable patterns are also observed among drivers working more than 40 hours in week t, as well as among short-hour drivers, who typically remain in the short-hour bands. Thus, while total working hour predictability is greatest among the highest volume drivers, their research also reveals a clear pattern of drivers’ tendency to work within a predictable band from one week to the next ([14]).

Driving for Hire: A Demand-Driven Service

Like other for-hire drivers, Uber drivers must work when there is a demand for their services. [14] found high rates of work of Uber drivers during evenings and weekends, and that “an hour of labor supplied in the late evening/early morning hour, especially on the weekends is more remunerative than an hour of labor supplied during the day” (p. 15). The study also found that payouts are highest in periods when the reservation wage is high, indicating that late nights and early mornings are less desirable times for drivers to work ([14]). Research by [58], based on interviews with Uber drivers, revealed that despite drivers’ preference to work during the day, little work is available. They wrote, “The workday of a driver has a substantial vacuum of activity in the middle of the day, and the lost income will have to be recouped by driving when the app tells them there’s demand. In many cases that means that they feel compelled to work outside of conventional office hours, e.g., weekends and late evenings. Rather than freeing up time for family and social leisure activities, drivers have little work when everyone else is at work, and more when everyone else is free” (p. 33).

Financial Dependence on Income from Uber

Drivers who are less financially dependent on Uber may be better able to balance work and non-work commitments; those who are most dependent on their income from Uber (ostensibly full-time drivers and those who have financed cars in order to drive for Uber) must drive when earning opportunities are high despite the occupational risks associated with night work. Financial woes of dependent drivers have increased because of Uber’s fare decreases (or in some cases, increases of Uber’s commission), lowering per-trip earnings and increasing pressure to drive more. As mentioned, the [11] BSG survey found that of the drivers who report driving more, 70% were driving more for the same or less income (Q50). The BSG survey also revealed that 78% of respondents spend the money they earn from driving for Uber on essential items, such as rent, utilities, and debt. Likely included in this category are the 65% of Uber drivers who finance, lease, or rent their vehicles (2014: Q20). Many drivers acquire vehicles explicitly to drive for Uber, with one-quarter of Uber drivers stating they would have a less expensive vehicle or would not have a vehicle at all if they were not driving for Uber (2014: Q21). When Uber represents a significant or irreplaceable source of household income, driver scheduling decisions will be steered by those times when work is available, often at asocial hours.

Uber’s Practices to Ensure a Ready Supply of Drivers

To meet demand for its services, Uber has designed its application to ensure a steady supply of drivers. In general, drivers are able to log on and off to the application, but once logged on they must adhere to Uber’s regulations on the acceptance and cancellation of rides—constraints that effectively impede drivers from using more than one TNC application at a time. Although Uber’s regulations vary by locality, drivers are constrained by how often they can decline or cancel rides (Figure 1; [50]). This process is all the more problematic given that drivers must accept rides prior to knowing the final destination of the passenger, making it hard to judge how much time it will take or how profitable the trip will be ([37]; [52]).

Acceptance and Cancellation Criteria for Uber Drivers, San Francisco, 2015 Source: Rosenblat (2015).

Graph

Another tool that Uber employs to ensure a supply of drivers is guaranteed earnings incentives to work during specific times. Typically, these guarantees require drivers to work within specified hours, provide a pre-specified number of rides, and have a guaranteed acceptance rate—all aspects of company control that undermine driver flexibility (Figure 2; [51]).

Example of Guaranteed Earnings Scheme Source: Rosenblat (2016).

Graph

The tension between driver preference and economic need is what makes surge pricing, nudging,17 [17] and other incentives effective methods to lure drivers to their cars, and to keep them from logging off once they are driving ([57]). According to [52], “When Uber sets low rates for routine work, incentive-based pay steers drivers into working under much stricter and less flexible conditions in the hopes of higher earnings, such as hourly wage guarantees which vary according to the terms of each guarantee” (p. 3763). The authors explain that hourly guarantees are a way of scheduling shift work in order to ensure that drivers are available to meet consumer need at times of high demand. These practices may boost participation from part-time drivers; however, they serve as disciplinary and management tools for the full-time workforce who provide a disproportionate number of rides. Moreover, they undermine the flexibility that the authors and the company extoll.

Ratings

In their conclusion, Hall and Krueger introduce the issue of ratings, stating that “driver-partners are rewarded for having a good reputation, which could lead Uber’s driver-partners to earn more than taxi drivers” (p. 730).18 [18] Yet the authors’ claim is unfounded; they do not provide evidence of how ratings improve workers’ earnings. Moreover, their statement suggests that ratings are beneficial to Uber drivers despite qualitative studies (as discussed in more detail below) and the BSG survey, which reveal high levels of driver dissatisfaction with the ratings system. By asserting that ratings are beneficial to Uber drivers, Hall and Krueger ignore how worker rating systems are used as a tool of managerial discipline.

Most online platforms include rating systems, though the purposes of the ratings vary widely depending on the architecture of the platform. As [17] explained, when online platforms allow workers to set their own prices, reputation systems can help workers get more business and higher earnings. For example, on Airbnb, reputation systems reward highly rated hosts with higher rankings on search results and greater market exposure, thus allowing them to increase their pricing. But on platforms such as Uber, where prices are fixed by the platform and services are standardized, reputation systems are instead used to remove poor performers from the ecosystem ([17]).

Uber’s rating system performs a managerial assessment with passengers empowered to act as a middle manager ([52]). Alert functions notify drivers who are underperforming, and the rating system provides context for communicating desired behaviors to its drivers. For example, messages such as “You received a ‘Talks too much’ complaint” will direct drivers to a website that gives them advice on how to interact with riders ([53]: 3). Additionally, Uber offers training videos giving advice to drivers who “aspire to 5-star ratings.” Such advice encourages drivers to provide bottled water or phone chargers to passengers, to ask about music preferences or temperature, and to read social cues on whether the passenger wants to engage in conversation or not ([53]), with drivers “perform[ing] emotional labor in exchange for ratings instead of tips” ([52]: 3775).

An important source of discontent among drivers is the difficulty they encounter in disputing low ratings they feel are undeserved. One problem with the rating system is that passengers may express their frustrations with the application, the fare (especially during surge pricing), or the route chosen by the application by rating the driver poorly—even though drivers have no control over these features of Uber ([45]; [52]). As is well known, drivers can be deactivated if their average ratings fall below a certain threshold, usually 4.6 or 4.7 on a 5-point scale. For drivers who have invested in a vehicle to be able to drive for the company, deactivation is a particularly daunting prospect.

Not surprisingly, Uber’s rating system is a source of discontent among drivers. This finding has been documented in qualitative studies of Uber drivers ([45]; [52]; [53]) and was also evident in the BSG survey, with 63% of drivers agreeing in 2014 with the statement “the rating system is unfair” ([10]: Q52R8).19 [19]

In November 2017—three years after the first BSG survey was conducted—Uber updated its rating system by requiring passengers who rate drivers lower than 5 stars to select reasons why (e.g., GPS problems, traffic).20 [20] If the reason is for something that is out of the drivers’ control, it will not be reflected in the driver’s rating.

A Labor Market Analysis That Is Too Narrow

Hall and Krueger present their study as a labor market analysis of Uber drivers. We contend this perspective is too narrow; to fully understand the labor market for Uber drivers it is necessary to understand the interlinkages between TNCs and the overall market of FHVs. Indeed, the 2014 BSG survey revealed that of Uber drivers with additional employment, one in five is a black car or limo driver, one in six is a taxi driver, and one in ten worked for another TNC (2014 BSG: Q27). In addition, when asked what drivers would do if Uber were no longer available, 43% indicated that they would seek work as a taxi driver or for another TNC (Q32). Disruption in the FHV market affects the industry broadly as TNCs, taxis, and limos are competing services; segmenting the labor market as Hall and Krueger have done makes it impossible to fully understand the economic situation of FHV drivers, including those who drive for Uber.

In response, we offer a brief discussion of some aspects of the FHV market that should have been considered as part of Hall and Krueger’s article, but were not. We examine how the reclassification of taxi drivers from employees to self-employed beginning in the 1970s created labor market conditions that facilitated Uber’s market entry in the 2010s. This history is key to understanding the current situation of FHV drivers in the United States, including their working conditions and earnings. We then turn to the impact that Uber has had on FHV drivers in general, on medallion owners, and on the cities the FHV industry serves by examining the competitive strategies the company has employed to gain market share.

Leasing and the Transformation of America’s Taxicab Labor Market

As noted earlier in this commentary, most taxicab drivers work as independent contractors. Despite Hall and Kruger’s contention that drivers prefer being self-employed, a historical perspective reveals that this employment classification was not sought by drivers. Up until the late 1970s, taxicab garages hired drivers as employees, shared meter earnings, covered work-related expenses such as vehicle maintenance and gasoline, and frequently contributed to pension schemes and benefit plans negotiated by the drivers’ unions.

In the mid-to-late 1970s, taxicab garages around the country began introducing the practice of leasing. While there were geographic variations in how the imposition of leasing occurred, the process was overwhelmingly involuntary and described as a move to disempower workers and destroy collective bargaining. For example, in 1976 in Minneapolis, Minnesota, taxicab drivers protested early measures to institute leasing, warning that lease drivers would “receive no pension, no insurance, nor vacation benefits, cannot receive unemployment compensation or workman’s compensation, and still make less money on the street than commission drivers” and that the practice would destroy the strength of the drivers’ unions.21 [21]

In 1975, drivers in Arlington, Virginia, reported similar objections to leasing in a newsletter article describing that of all the drawbacks of leasing, the “worst blow” was the dissolution of the employee–employer relationship. “First, the lease has the driver agreeing ‘that there does not exist between them (AYC22 [22] and drivers) the relationship of employer–employee . . . either express or implied, but that the relationship of the parties hereto is strictly that of lessor–lessee.’ The company has obviously hired a smart labor lawyer because the National Labor Relations Act specifically excludes independent contractors from coverage by the protections of the NLRA” (emphasis in original).23 [23] Similar trajectories and timelines occur in New York City and San Francisco markets, both of which shifted to a leasing model by the late 1970s. Once drivers were no longer considered employees but instead self-employed independent contractors, taxi unions around the country systematically lost their right to collective bargaining ([21]).

Like the FHV industry as a whole, Uber’s business model hinges on classifying drivers as independent contractors. Hall and Krueger state that “historically, independent contractors have reported in surveys that they prefer their working arrangements to traditional employment relationships” (p. 707), yet the historic reality of the reclassification of taxi drivers as independent contractors does not support this assertion.

Effect of TNCs on FHV Drivers

Though taxicab drivers suffered with the introduction of leasing, by being forced to take on many of the expenses associated with operating taxicabs (gasoline and tolls, for example) and to bear all financial risks, the entrance of TNCs into the FHV market has made FHV drivers’ situation worse.

With some exceptions, most local taxi markets have been regulated through the issuing of medallions or taxi licenses. These licenses served two related purposes: to deter congestion by limiting the number of FHVs on the road and to restrict supply so that a decent income could be earned from driving a cab or limo. The entrance of TNCs such as Uber has created more competition among taxi and other FHV drivers, and despite increases in overall ridership, the oversupply of drivers points to a decrease in FHV driver earnings. In San Francisco, for example, the average number of trips for the city’s 1,812 registered taxicabs decreased 65% between 2012 and 2014 ([23]); although earnings data are difficult to obtain for a myriad of reasons discussed earlier, for individuals who drive on a full-time basis, a two-thirds decrease in the number of trips undoubtedly decreases earnings. During roughly the same period, 11,000 Uber drivers were added to San Francisco’s streets ([48]), and a more recent study estimated the total number of TNC drivers at 45,000 with most rides occurring at and contributing to peak congestion ([59]). These indications support the findings of the [11] BSG survey, mentioned earlier, whereby 49% of Uber drivers report they are driving more for less or the same amount of money. In addition, in response to an open-ended question asked of Uber drivers on why they expected their income from Uber to decrease, 142 drivers (34% of 417 respondents, Q47) stated it was because there were “too many drivers on the road.”

Notwithstanding increases in congestion and the negative effect that too many drivers has on the earnings of taxi and full-time TNC drivers, Uber has resisted implementing a cap on the number of vehicles ([ 7]). Uber’s impact on congestion and competition undermines the ability of FHV transport jobs to provide decent income to drivers—factors that led to taxi regulations and limits on the number of vehicles on the streets of cities such as New York in the first place. Though these observations are disregarded by the authors, they were not lost on New York City driver Doug Schifter, who ended his life in early 2018 on the steps of City Hall after publicly posting the following: “There was always meant to be numbers of cars below the demand. That was the guarantee of a steady income. Now the politicians have flooded the streets with unlimited cars and some 3000 new ones every month still coming. There is not enough work for everybody that pays a living. They are destroying many thousands of families financially.”24 [24]

The company’s disinterest in instituting a vehicle cap likely reflects its business model whereby profits are derived from the number of rides and associated fares. As Bhairavi Desai, executive director of the New York Taxi Workers Alliance, explained, unfettered access is not good for drivers: “The more [Uber] cars there are covering the streets, especially during prime times, the better the chance Uber has to deduct a commission off of the fare for themselves. So each individual driver will be earning less money, but Uber’s profit goes up” ([15]). Uber has not uniformly disrupted monopolies; Uber is trying to create a new one.

TNC Impact on Medallion Owners

Lease drivers predominate in most urban taxi markets, but a small number of workers own their taxicabs and the permits to operate them. The losses experienced by these workers since the arrival of TNCs ([32]) should be considered in analyses of the benefits of Uber.25 [25] In April 2014, New York City unrestricted medallions were selling for as much as $1.024 million; by November 2017, unrestricted medallion prices had fallen to the $130,000 to $500,000 range. Moreover, unrestricted medallion foreclosures nearly tripled in 2017, as compared with 2015. Chicago has experienced similar trends, with the median price of medallions falling from a high of $340,000 in early 2014 to $42,000 in late 2017.

Uber’s narrative suggests that the negative effects of disrupting the medallion system are experienced exclusively by privileged monopolists who control large shares of the market. But across the country, medallion ownership regulations differ, with many jurisdictions promoting driver ownership and creating barriers to the financialization of the taxi industry fueled by private medallion sales. For example, in Trenton, New Jersey, taxicab permits are non-transferable ([39]), and for decades San Francisco explicitly sought to ensure that active drivers held taxi medallions. San Francisco described Proposition K (1978–2007) as follows: “Medallions are not transferable, and the medallion holder is required to drive a minimum number of hours every year in order to retain the right to keep the permit (. . .). If the medallion holder does not drive sufficient hours, the permit can be taken away by the City and given to the next person on the waiting list for a medallion” ([60]). In these cases, regulatory oversight can ensure driver control as well as income for the city when new medallions are issued or transferred.

Market Externalities and Predatory Pricing

Medallion owners, including driver-owners, are not the only losers when taxi market regulation is dismantled. In some cities, stakeholders include the public writ large. When FHVs are treated like a private industry operating for public good, regulators can institute additional fees and charges that can be used for broader public interest. In New York, for example, a tax of 50 cents per taxi ride is remitted to the city in support of public transit, which corresponded to a 2014 MTA payment of $87.5 million; in 2015, as a result of competition with TNCs, yellow cab trips decreased by more than 52,000 per day and resulted in an annual loss of MTA fares of nearly $9.5 million.26 [26] Similarly, a 30-cent charge per taxi ride is included in all fares to help offset the cost of making New York’s taxi fleet more accessible to people with disabilities. Although some cities have instituted similar fees for TNCs, Uber has at times lobbied against these measures ([44]; [ 2]). Unfettered TNC growth can also lead some riders to migrate from public transportation to TNCs, undermining the use and sustainability of public transportation ([46]).

Hall and Krueger document the exponential growth of Uber, noting that the number of drivers doubled every six months from the middle of 2012 to the end of 2015, which they argue reflects Americans’ desire for flexible working arrangements. But another factor explains its tremendous growth: predatory pricing. The Uber model has rested on setting fares at below cost to attract customers and to undercut competition from other FHVs. Transportation industry expert [31] estimated that in 2015 Uber passengers were paying only 41% of the actual cost of their trips. Investor money has subsidized these losses, while bonuses and other incentives have been used to attract drivers ([61]). As [31] explained,

The growth of Uber is entirely explained by massive predatory subsidies that have totally undermined the normal workings of both capital and labor markets. . . . the price signals that allow drivers and customers to make welfare maximizing decisions have been deliberately distorted, and the laws and regulations that protect the public’s interest in competition and efficient urban transport have been seriously undermined. Absolutely nothing in the “narrative” Uber has used to explain its growth is supported by objective, verifiable evidence of its actual competitive economics. (pp. 34–35)

Uber’s strategy has also rested on extensive lobbying efforts at the state level that have led to state laws that strip municipalities of the ability to regulate TNCs, while retaining regulations on the taxi industry.27 [27] According to [ 7], 41 states have passed laws removing some or all of local governments’ ability to set industry standards for TNC services. This move has not only undermined the ability of local regulators to develop transportation frameworks suited to the particular needs of local markets but has also hurt drivers. As [34] explained, local taxi regulatory bodies have served as an avenue for traditional taxi drivers to negotiate some of the terms and conditions of their work despite their independent contractor status. The viability of this approach, however, is predicated on the existence of industry oversight.

Summary: Context Is Crucial

There is ample room to improve working conditions and remuneration in the FHV sector, including taxis. Hall and Krueger’s conclusions—that Uber drivers are happy and earn well—are devoid of historical context, disregard TNC impact on the larger FHV sector, and ignore market externalities. Labor market analyses used for policy purposes should fully explain the labor market, and a proper analysis requires institutional context. This element is notably absent from their research. We do not offer a full history of FHV services in the United States, nor would we expect that from Hall and Krueger, but evaluating what on-demand labor markets mean for the future of work, as the authors portend to do, requires that Uber be situated squarely within the ecosystem of FHV transportation services. It requires understanding the history that bore the current conditions, as well as analysis of the (anti)competitive practices Uber has used to gain market share and the consequences of these practices. Segmenting the labor market, as the authors have done, disregards the impact that one subsector has upon another—a significant oversight given that many Uber drivers work for multiple FHV services simultaneously. An analysis that is too narrow also deflects discussion of market externalities and social dumping. It entrenches the taxis vs. Uber debate and stifles inquiry into the larger questions that researchers and policymakers should be asking: What sort of regulation of FHVs, including TNCs, is best for local urban transport markets? How can we improve working conditions of FHV drivers, including TNCs? Is there anything new about on-demand work?

Conclusion

Hall and Krueger’s article has been cited in committee hearings of the U.S. Congress,28 [28] at a Federal Trade Commission workshop on the sharing economy,29 [29] on the California State Treasurer’s website (as part of “peer-reviewed” work),30 [30] and likely in other policy venues. The regulatory questions are not settled, and articles published in scientific journals can skew policymakers’ opinions.

Yet the article by Hall and Krueger, and the survey it is based on, are fraught with methodological problems—sample bias, leading questions, incomplete reporting of findings, flawed earnings calculations, unsubstantiated claims, and outdated data. These limitations do not restrain the authors from asserting their findings confidently, nor has it restrained the company from using these findings in support of its position in political and regulatory debates. The authors advance corporate claims of flexibility, extoll the benefits of driver ratings, and champion the “be your own boss” narrative without offering evidence to support their claims or to refute the growing body of literature that is critical of the on-demand labor practices of Uber and other similar companies.

What is most troubling about this article is that the authors have access to the information that would have allowed them to present not only a more timely analysis but a more rigorous one. Unfortunately, they do not. In a prescient article published in Science on computational social science, [36] warned “there might emerge a privileged set of academic researchers presiding over private data from which they produce papers that cannot be critiqued or replicated. Neither scenario will serve the long-term public interest of accumulating, verifying, and disseminating knowledge” (p. 721). It seems this concern has come to pass.

This article is written in response to Jonathan V. Hall and Alan B. Krueger’s 2018 article titled “An Analysis of the Labor Market for Uber’s Driver-Partners in the United States,” ILR Review 71( 3): 705–732.

For information regarding the data used for this article, please address correspondence to the authors at or .

Footnotes

1

 According to [10], [11]) the two surveys were conducted among 
Uber
 driver-partners in top US markets who provided at least four rides in the month prior to fielding. The survey was conducted over the Internet and respondents were given a financial incentive and guaranteed anonymity. The [10] survey reported that “weights were derived to make the sample representative of all drivers in terms of the services they offered.” For 2015, “quotas and weights were used to ensure the samples were representative of the actual 
Uber
 driver-partner population at the time of fielding.”

2

 Hall extended an invitation to inspect the data at 
Uber
 headquarters for the purpose of replicating the article’s results; unfortunately, this option was not feasible for us.

3

 During the same time frame, there had been some press articles about 
Uber
 canceling the account of a driver who was critical of the company. See https://
www.forbes.com/sites/ellenhuet/2014/10/16/

uber

-driver-deactivated-over-tweet/#758d74356a4c
.

4

 We return to this issue in the section on flexibility.

5

 According to the BLS, occupational category 53-3041, Taxi Drivers and Chauffeurs, includes workers who “Drive automobiles, vans, or limousines to transport passengers. May occasionally carry cargo. Includes hearse drivers.” See https://

www.bls.gov/oes/current/oes533041.htm

.

6

 Workers’ compensation is provided in select cities for independent taxi drivers, but not universally.

7
 Taxi drivers have significant occupational safety and health risks, with 14.9 fatalities in taxi drivers and chauffeurs compared to 3.3 per 100,000 workers in other occupations, as a result of homicide and motor vehicle accidents ([12]).

8

 The inclusion of San Antonio in the [11] BSG survey raises additional questions about the representativeness of the sample given that 
Uber
 did not operate in San Antonio for a six-month period during 2015. In fact, 
Uber
 signed an agreement with the city of San Antonio to re-admit services on October 13, just 18 days before the BSG was conducted in the city. See [54]; [24].

9

 Cost estimate includes license fee $252, drug test $26, defensive driving course $60, wheelchair accessible vehicle $75, 24-hour taxi school $150, license exam $75, and fingerprints and photos $88.50. For more details, see 

http://www.nyc.gov/html/tlc/html/industry/drivers.shtml

.

AAA insurance estimates are $981 for a small sedan, $1,007 for a medium sedan, and $1,081 for a large sedan. We have not considered the additional cost of commercial insurance, but given higher costs associated with the regular use of a personal vehicle for business purposes, we believe this should have been considered by the authors.

Additional variations would also include car financing rates. While rates are dependent on individual circumstances, rates of 

Uber’s

 wholly owned subsidiary, Xchange, were widely reported to target low-income drivers, charging as much as 22.75% interest ([29]; [25]; [47]).

See https://

www.sherpashare.com/

.

According to [31], 
Uber
 drivers retained 77% of each passenger dollar in 2016, down from 82% in 2014–15.

In January 2017, 
Uber
 agreed to pay a $20 million fine to the Federal Trade Commission for misleading drivers with inflated promises about potential earnings ([30]).

Unlike the survey, which was restricted to active drivers, this analysis is based on data that include any driver who spent at least one hour on the 
Uber
 app in the initial week.

[70] also made this point, arguing that 
Uber’s
 attention to its secondary workers is part of its strategy to prevent reclassification of its drivers as employees.

Nudging is the dispatching of a subsequent ride before the completion of the current ride to encourage drivers to continue to work.

The BSG survey did not ask drivers what their rating was, just like it did not ask about hours worked and earnings. However, the authors avail themselves of the administrative information on earnings per hour, but not of the administrative information on ratings. Had they, ratings could have easily been modeled in the earnings regression as an explanatory variable to assess the validity of their claim. In addition, a separate study by Hall and colleagues ([18]) that analyzed the gender pay gap among drivers did not include ratings in the regression analysis despite access to this information.

Q52R8 had 601 responses. The 2015 survey asked a different question on the ratings system, this time with a positive phrasing. “Now you will read some things people could say about 
Uber
 in particular. How well does this describe 
Uber
?” Q23R24: “Has a fair rating system.” Only one-third (269) of interviewees responded to this question. Of these, 52% responded that it describes 
Uber
 well and 31% stated it does not (15% were neutral and 2% responded don’t know).

See https://

www.
uber
.com/newsroom/180-days-ratings/

.

Larry Wieland, Guild of Taxi Drivers and Associated Workers, [68].

Arlington Yellow Cab.

[3].

From Doug Schifter’s Facebook page; see also [4], https://

www.nytimes.com/2018/02/06/nyregion/livery-driver-taxi-
uber
.html

. Between January and July of 2018, six New York City drivers committed suicide; financial distress has been cited repeatedly as a contributing factor.

According to [31], only New York, Boston, and Chicago have ever seen medallions with any significant value.

Calculated from total taxi trips, as reported in the 2016 NYC Taxicab Fact Book ([19]).

In 2017, the firm spent $1.8 million in lobbying for a statewide TNC bill in New York ([5]) and $392,000 in Florida where a statewide bill legalizing TNC services passed ([13]). In 2016, the firm spent $1.36 million lobbying at the federal level ([69]).

[65]; [66].

See https://

www.ftc.gov/news-events/events-calendar/2015/06/sharing-economy-issues-facing-platforms-participants-regulators

.

See 

http://www.treasurer.ca.gov/newsletter/2017/201701/column.asp

.

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