PRELIMINARY – DO NOT CIRCULATE
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Occupational Licensing of Uber Drivers*
Jonathan Hall1, Jason Hicks2, Morris M. Kleiner3, and Rob Solomon4
1 Uber Technologies, Inc.
2 University of Minnesota
3 University of Minnesota and NBER
4 Uber Technologies, Inc.
* Hicks and Kleiner do not have any financial relationship with Uber Technologies, Inc.
* We thank Jason Dowlatabadi, Libby Miskin, Yun Taek Oh , and Jonathan Wang for their excellent comments and research
assistance. We appreciate comments from participants at the American Economic Association annual meetings, Association for
Policy Analysis and Management annual meetings, National Bureau of Economic Research Labor Studies Meetings, University
of Minnesota, and the W.E. Upjohn Institute for Employment Research.
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Abstract
Two of the most rapidly growing trends in the labor market in the United States are the
online, on-demand economy and occupational licensing. Occupational licensing by state and
local governments is a potential barrier to entry for workers who want to drive for a
transportation network company, such as Uber. These occupational regulations are typically
justified by regulators as ensuring a minimum level of safety and quality. We examine the
influence of these regulations on quality and safety outcomes for consumers using star (quality)
ratings that riders give drivers following trips and telematics data from individual trips (fraction
of hard brakes and hard accelerations). More specifically, we compare safety and quality
outcomes on trips performed by drivers with and without an occupational license in overlapping
markets, exploiting the quasi-random assignment of trip requests to drivers. We find that
occupational licensing frequently does not improve safety and quality outcomes of rides. Even in
those specifications where there is a positive effect of occupational licensing the magnitude of
the effect is relatively small.
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1. Introduction
Two rapidly growing trends in the labor market are the expansion of the on-demand
workforce and occupational regulations imposed by the government on the labor market (Katz
and Krueger, 2016; Kleiner and Krueger, 2013). While less than 1% of the of the U.S. workforce
participated in the direct on-demand economy in 2015, the growth rate of the number of workers
earning income from on-demand platforms exceeded 100% each month during the fall of 2015
(Farrell and Greig, 2016a; Farrell and Greig, 2016b). One such platform is Uber, a transportation
network company (TNC) that matches individuals who need rides to individuals who are willing
to provide rides for a price. Based on data from Google Trends, Harris and Krueger (2015) infer
that Uber is the largest on-demand labor platform in the U.S., with up to two-thirds of all activity
in the app-based labor market and more than 900,000 active drivers in the United States. As of
2019, Uber operates in over 700 cities and 63 nations with more than three million drivers. The
growing scale of the on-demand labor market makes understanding the effects of occupational
regulation on the quality and safety outcomes for rides performed by Uber drivers increasingly
important and relevant.
The primary rationale for implementing occupational licensing, where a government
issued license is required to perform work for pay in a profession, is maintaining sufficient
quality levels of provided goods and services and protecting the health and safety of consumers
and the public (Kleiner, 2015). For example, requiring potential TNC drivers to complete a
driver education course or a fingerprint background check to receive a license may be intended
to protect consumers and the public from unsafe and unscrupulous drivers. However, licensing
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may also reduce quality by raising prices, and consequently, pushing consumers to opt for a less
expensive, lower quality substitute.
Transportation network company drivers face different occupational licensing
requirements in different but sometimes neighboring or overlapping markets, meaning that in
some places, a rider who requests a trip may experience randomness in the licensing status of the
driver assigned to their trip request. This randomness provides an environment for studying the
relationship between the occupational licensing status of a driver and the quality experienced by
a rider and the safety of a ride. Not surprisingly, we are unable to randomly assign potential
drivers to different licensing regimes, which would potentially allow for identification of the
causal effects of licensing on safety and quality outcomes for drivers. Instead, our empirical
strategy estimates the safety and quality effects that occupational licensing provides via a
combination of selection into driving and treatment on individual license holders. In this paper,
we examine three settings where a rider may be assigned either a professionally licensed or
unlicensed Uber driver: Houston (where licensing requirements changed in May 2017); New
Jersey (where licensed New York City drivers may perform pickups); and UberSELECT (an
unlicensed ride type to which a licensed UberBLACK driver may also be dispatched).
The deregulation of the Houston ridesharing market is a natural experiment that allows us
to compare ride outcomes on trips performed by previously licensed Uber drivers and never
licensed Uber drivers using a quasi-random assignment method. We find that star (quality)
ratings on rides performed by previously licensed drivers are either slightly higher or do not
differ from the star ratings on rides performed by never licensed drivers, depending on the model
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used. In contrast, trips performed by previously licensed drivers are associated with slightly
higher or no difference in levels of hard brakes and hard accelerations, depending on the model.
We also compare trips performed by New York City (licensed) and New Jersey (not
required to have an occupational license) Uber drivers and trips performed by UberBLACK
(required to have commercial insurance) and UberSELECT (not required to have commercial
insurance) drivers. Trips performed by licensed New York City drivers have slightly lower star
ratings than trips performed by unlicensed New Jersey drivers, while there is no difference in star
ratings between trips performed by UberBLACK and UberSELECT drivers. New York City and
UberBLACK drivers have lower levels of hard brakes and hard accelerations than New Jersey or
UberSELECT drivers, respectively.
Our paper proceeds as follows as follows: Section 2 presents a background on
occupational licensing, a discussion of the relationship between occupational licensing and
quality and safety outcomes for consumers, and a discussion of the regulation of the on-demand
economy as it relates to Uber and other TNCs. Section 3 describes our data and methodologies
for the Houston, New York City/New Jersey, and UberBLACK/UberSELECT analyses. Section
4 presents our detailed results, including tests of the robustness of our estimates. Section 5
briefly summarizes and discusses our results.
2. Background of Occupational Licensing
Occupational licensure is the process by which governments determine the qualifications
required to work in a trade or profession, after which only regulated practitioners can legally
receive pay for performing tasks and duties in the occupation. This form of regulation has
become one of the most significant factors affecting labor markets in the United States (Kleiner
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and Krueger, 2010; Kleiner and Krueger, 2013). Over the past several decades, the share of U.S.
workers holding an occupational license has grown dramatically (Figure 1). As of 2016, an
estimated 22% of the U.S. workforce had attained an occupational license, with the majority
doing so at the state level (U.S. Bureau of Labor Statistics, 2016). In contrast, only 5% of U.S.
workers were licensed at the state level in 1950 (Kleiner and Krueger, 2013). Estimates from a
recent White House report suggest that over 1,100 occupations are regulated in at least one state,
but fewer than 60 are universally licensed (i.e., licensed in all 50 states), which indicates
significant variation in the occupations state and local governments choose to regulate
(Department of the Treasury Office of Economic Policy et al., 2015).
To obtain an occupational license, workers must fulfill government requirements, which
include both human capital and non-human capital requirements. The human capital
requirements are often extensive and frequently include completing an education program or
receiving training and attaining experience. Additionally, practitioners often must periodically
complete continuing education requirements to maintain their license. Non-human capital
prerequisites include paying licensing and licensing renewal fees, passing exams, and fulfilling
minimum age requirements. Further, licensing authorities often have broad authority to prevent
individuals who do not exhibit “good moral character” from receiving a license and can be
mandated to restrict ex-offenders from being granted a license.
Occupation licensing requirements also vary considerably across states. For example,
only seven states license dental assistants and 13 states license locksmiths. For states that do
license the same occupation, requirements to obtain a license can vary widely. For example,
Iowa requires 490 days of education and training to become a licensed cosmetologist and states
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such as New York and Massachusetts require only 233 days, while the national average is 372
days (Carpenter et. al., 2017). In addition, occupational licensing is often not clearly tied to
issues of health and safety. To illustrate, Michigan requires 1,460 days to become an athletic
trainer, but only 45 days to be licensed as an emergency medical technician (Carpenter et al.,
2017).
Transportation network company (TNC) drivers have experienced diverse occupational
licensing requirements across locales since beginning to operate in U.S. cities in 2012.
Regulation of TNC drivers often occurs at the city level with 69 cities having passed regulatory
legislation; however, 48 states, including Washington, D.C., have also passed legislation
regulating TNCs as of June 2017 (Moran, 2017; Moran et al., 2017). In some locales, TNC
drivers are required to obtain an occupational license to legally operate, while in other locales,
licensure is not required for drivers. Further, requirements associated with licensure vary from
requiring drivers to fulfill human capital requirements, such as driver education classes, to
government mandated fingerprint background checks, which are in addition to the commercial
background checks TNCs require for drivers.
In New York City, which is the most heavily regulated ride-hailing market in the U.S.,
Uber drivers must fulfill extensive requirements to obtain an occupational license, including
completing a Department of Motor Vehicle-approved defensive driving course/exam, a
Wheelchair Accessible Vehicle class, a 24-hour For Hire Vehicle (FHV) course/exam, and a
fingerprint background check. Further, the licensure process has upfront minimum costs of
approximately $2,000. In contrast, drivers in the state of New Jersey, who may not perform
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pickups in NYC, do not have to obtain an occupational license to legally operate; however, NYC
drivers can operate in New Jersey.
Prior to May 2017, Uber drivers who wanted to perform pickups on the Uber platform in
the city of Houston had to complete a fingerprint background check and pay $70, which was in
addition to the commercial background check already required by Uber. Houston, along with
New York City, were the only two cities in the U.S. that required Uber drivers to complete an
FBI fingerprint background check. Houston required an FBI fingerprint background check
because they viewed the commercial background check required by Uber as inadequate to assure
that potentially unsafe drivers did not operate in the city and claimed that several TNC license
applicants were revealed to have criminal records during the fingerprint background check (Paez,
2016). However, the fingerprint background check likely served as a significant barrier to entry
for potential Uber drivers in Houston. The number of appointments available to complete the
fingerprint background check was limited because the fingerprint background check was run by
one company statewide which created a backlog and increased the time and monetary cost to
receive a license. According to a survey performed by Uber, 67% of drivers who passed Uber’s
commercial background check chose not to complete Houston’s licensing process because the
process was too time consuming, complex, and costly (Wilson, 2016). In contrast, prior to May
2017, Uber drivers only operating in the Houston suburbs were not required to be licensed and
complete a fingerprint background check but could not legally perform pickups within Houston
city limits.
Lastly, drivers operating on the UberBLACK platform, a high-end, luxury ride service
offered by Uber in 35 markets in the U.S., must obtain an occupational license from their
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respective city or state licensing authority to perform pickups. The primary requirement
associated with licensure for UberBLACK drivers is mandated commercial insurance. In
contrast, drivers operating on the UberSELECT platform, a high-end, everyday ride offered by
Uber in 27 markets in the U.S. which frequently overlap with the UberBLACK product markets,
are not required to be licensed or have commercial insurance. In many of these overlapping
product markets, UberBLACK drivers are cross-dispatched on the UberSELECT platform,
which results in UberBLACK and UberSELECT drivers being eligible for the same pickup
requests.
Our identification strategy exploits the variation in licensure described above for drivers
in NYC and New Jersey, the city of Houston and the suburbs of Houston, and drivers on the
UberBLACK and UberSELECT platforms. We use variation in licensure in combination with
geographic overlap in the ability of these drivers to perform pickups to implement our quasi-
random assignment methodology. Uber drivers licensed in NYC can perform pickup requests
originating in New Jersey, and following deregulation of the TNC market in the city of Houston
in May 2017, unlicensed Uber drivers (previously unlicensed drivers and newly operating,
unlicensed drivers) were able to perform pickups in the city of Houston. To compare safety and
quality outcomes of trips performed by licensed and unlicensed Uber drivers, we include trips in
our sample when the driver who performed the pickup was closest to the pick-up request location
(based on estimated time of arrival) and the second closest driver to the pickup location for the
performed trip was of the opposite licensing status.
2.1. Occupational Licensing and Quality and Safety
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A key public policy justification for occupational regulation is protecting consumers and
the wider public from low-quality practitioners (Kleiner, 2006). Consumers may lack the
knowledge or information necessary to assess the quality of the product or service prior to its
provision. By setting minimum skills standards for entry into occupations, occupational licensing
is expected to increase the average skill levels in an occupation because low-quality practitioners
cannot meet the new, higher skill standard, and as a result, are pushed out of the occupation
(Koumenta et al., 2014). This is the justification for requiring licensure of drivers operating on
TNC platforms.
Licensure may cause consumers to receive a more standardized and higher-quality
product, while the resulting higher investments in training may enhance the skills base in the
economy (Shapiro, 1986). Quality is ensured through the regular monitoring of performance
standards, deviations from which can lead to penalties such as fines, additional required training,
or exclusion from practicing within the occupation (Kleiner and Todd, 2009; Thornton and
Timmons, 2013).
The effect of regulation on service quality also can be negative. Quality is not only linked
to skill, but also to quantity supplied. If an increase in quality through better trained practitioners
results in a subsequent decrease in their supply (due to aspiring practitioners not meeting the
entry requirements), and consequently an increase in prices, the overall service quality received
by customers may be reduced (Koumenta, et al. 2014). Consumers may perceive the government
regulated service to be of higher quality and demand more of the service, thus pushing up the
price of the service. Higher prices may cause some consumers to opt for lower quality services.
In the context of occupational licensing, such substitution is confined to ‘do-it-yourself’ services
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(Friedman, 1962; Kleiner, 2006). A more extreme unintended consequence of occupational
licensing could involve the decision not to consume the service at all, which may pose health and
safety risks.
Research has most frequently found little to no effect of occupational licensing
regulations on quality and safety outcomes for consumers. While the volume of research in this
area is limited, most research on quality and safety has focused on the education and healthcare
sectors (Angrist and Guryan, 2008; Kleiner et al., 2016; Kleiner and Kudrle, 2000; Larsen, 2015;
Sass, 2015; Timmons and Mills, 2015). In healthcare, malpractice insurance premiums (a
measure of safety) were not affected by more stringent occupational licensing requirements for
nurse practitioners, opticians, and dentists (Kleiner et al., 2016; Kleiner and Kudrle, 2000;
Timmons and Mills, 2015).
Researchers have also found that Yelp business ratings, which are similar to the star
ratings used by Uber, are an accurate measure of perceived service quality by consumers
(Bardach et al., 2013; Luca, 2016; Ranard et al., 2016). Recently, Deyo (2015) used a
differences-in-differences model (DID) to examine the relationship between occupational
licensing and Yelp ratings for four service-based occupations, massage therapists, manicurists,
cosmetologists, and barbers. She found that the licensing of an occupation and the stringency of
occupational licensing requirements were associated with lower Yelp ratings in a state, and
hence, lower service quality.
Additionally, Kleiner and Todd (2009) used the stringency of state bonding requirements
for mortgage brokers to examine the effects of occupational regulation on housing loans and
foreclosure rates. The authors found that higher required bonding levels (in dollars) resulted in
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reductions in subprime loans, higher foreclosure rates, and higher interest rates on brokered loans
(Kleiner and Todd, 2009).
In the transportation industry, little research has focused on the direct effects of
occupational licensing on quality and safety outcomes, particularly for rides performed by for-
hire vehicle drivers. In an unpublished working paper, Saito (2013) found that in Japan the
reduction of entry regulations in prefectures (equivalent to U.S. states) for taxicab drivers did not
increase the number of accidents per kilometer. In a meta-analysis, Elvik (2006) found that there
were no changes in road or airline safety due to economic deregulation, but rail safety increased
because of deregulation of the train industry.
While to our knowledge no previous research has examined the effect of occupational
licensing on the safety and quality of rides performed by TNC drivers, several studies have
broadly examined the relationship between the initial entrance of Uber into markets and overall
accident and crime rates in these markets (Barrios et al., 2018; Dills and Mulholland, 2018;
Greenwood and Wattal, 2017; Peck, 2017; Martin-Buck, 2016). Generally, the researchers found
that the introduction of Uber reduced alcohol-related collisions, fatal accidents, and other crimes
often associated with alcohol consumption, such as physical and sexual assaults. However,
Barrios et al. (2018) purport to find that the entrance of ridesharing services in U.S. cities was
associated with an increase in overall motor vehicle fatalities and fatal accidents, such as
pedestrian deaths.
To summarize, the main justification for implementing occupational licensing regulations
is increasing the quality and safety of services provided for consumers. However, determining
the impact of regulations on service quality is difficult because of the corresponding effects of
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regulation on labor supply. Similarly, any net effect on price will depend on the characteristics of
the service and consumer demand. Service quality and safety can be difficult to measure,
resulting in limited data availability, which is evidenced by the paucity of research examining the
effects of occupational licensing on quality and safety outcomes. Our ability to obtain firm level
data on the provision of ride-sharing services by licensed and unlicensed drivers allows us to
conduct a unique analysis examining the effect of occupational licensing on key quality and
safety outcome measures on rides performed by Uber drivers.
3. Data and Methods
All data used in our analyses of ride-sharing are provided by Uber. The data are
organized at the trip level, which allows us to identify a driver’s licensing status and
demographic characteristics, various quality and safety outcomes on an individual trip, and other
important driver, rider, vehicle, and trip characteristics that may affect quality and safety
outcomes.
3.1. Measuring Quality
One approach in assessing the quality of Uber rides for consumers is through trip ratings
of drivers. After completing a trip, riders rate the quality of the trip on a scale of one to five stars,
with one star associated with the lowest quality and a five with the highest quality. Riders are not
required to rate their trip; however, the interface of the Uber app is designed to encourage riders
to provide a quality rating by immediately displaying the rating option on a customer’s
smartphone screen after a trip is completed. These ratings reflect the perceived overall quality
and safety of trip, potentially including factors such as the friendliness of the driver, efficiency
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and knowledge of the route, driving safety, and cleanliness and quality of the vehicle. Across all
datasets, riders provide a trip rating for approximately 45% of trips. Star ratings are right skewed,
with nearly 86% of trips receiving a five-star rating. In contrast, less than 2% of trips receive one
star. See Figure 2, which includes data for all rated personal transportation trips across all Uber
products/platforms, excluding UberTaxi, completed in the U.S. between June 2012 and January
2017. The distribution of driver trip ratings is also relatively consistent across locales. See Figure
3, which includes the five U.S. cities with the highest UberX trip volume that were operational
with the UberX product as of January 2014. UberX is the most common and among the lowest
cost ride-sharing option provided by Uber.
To proxy for safety, we also use hard brakes and hard accelerations telematics data in our
analyses. Analysis from Progressive Insurance found that hard braking is one of the most likely
predictors of future crashes (Claims Journal, 2015). Braking and acceleration events on
individual trips are constructed using sensor data collected through the Uber app on a driver’s
smartphone. Telematics data for rides performed on the Uber platform first became available in
March 2016.
The telematics variables we use are the fraction of hard brakes on a trip,
(# 𝑜𝑓 ℎ𝑎𝑟𝑑 𝑏𝑟𝑎𝑘𝑖𝑛𝑔 𝑒𝑣𝑒𝑛𝑡𝑠 𝑜𝑛 𝑎 𝑡𝑟𝑖𝑝
𝑇𝑜𝑡𝑎𝑙 # 𝑜𝑓 𝑏𝑟𝑎𝑘𝑖𝑛𝑔 𝑒𝑣𝑒𝑛𝑡𝑠 𝑜𝑛 𝑎 𝑡𝑟𝑖𝑝 ), the fraction of hard accelerations on a
trip,(# 𝑜𝑓 ℎ𝑎𝑟𝑑 𝑎𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑒𝑣𝑒𝑛𝑡𝑠 𝑜𝑛 𝑎 𝑡𝑟𝑖𝑝
𝑇𝑜𝑡𝑎𝑙 # 𝑜𝑓 𝑎𝑐𝑐𝑒𝑙𝑒𝑟𝑎𝑡𝑖𝑜𝑛 𝑒𝑣𝑒𝑛𝑡𝑠 𝑜𝑛 𝑎 𝑡𝑟𝑖𝑝 ), and indicators for whether a trip had greater than 20%
hard brakes or greater than 20% hard accelerations. Any brake or acceleration on a trip with a
force greater than 3.06 m/s2 is considered a hard brake or hard acceleration, which is consistent
with transportation industry standards (Csere, 2014; Beinstein and Sumers, 2016). Whether a
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driver has greater than 20% hard brakes or hard accelerations on a trip is also a standard metric
for identifying if a trip has a disproportionately high percentage of hard brakes or hard
accelerations. Both sets of telematics metrics are used because the distribution of the percentage
of hard brakes or hard accelerations on trips could influence the proportion of trips that are
identified as “safe” trips using the 20% hard brakes or hard accelerations threshold. For example,
the proportion of trips with greater than 20% hard brakes may differ significantly if hard braking
events tend to occur in relatively high or relatively low proportions on individual trips versus if
hard braking events tend to be relatively evenly distributed across trips.
3.2. Quasi-random Assignment Identification Strategy
Our quasi-random assignment approach uses Uber’s dispatch algorithm to identify
comparison trips for licensed and unlicensed Uber partner-drivers that perform trips in
overlapping geographic areas. The quasi-random assignment of rides occurs because Uber’s
dispatch algorithm considers nearby professionally licensed and unlicensed drivers as eligible for
dispatch for certain trips but does not use a driver’s licensing status as criteria for trip
assignment. While the dispatch algorithm has evolved over time, the algorithm is primarily based
on a driver’s proximity to a rider’s location (estimated time of arrival). Each ride request in our
data has a queue associated with the request, which is a rank order list of drivers to receive the
pick-up request. Each driver listed in a queue also has an associated predicted estimated time of
arrival (ETA) to the pick-up location. We include trips in our analyses performed by licensed
drivers when a licensed driver has the shortest ETA and an unlicensed driver has the second
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shortest ETA, and we include trips performed by unlicensed drivers when an unlicensed driver
has the shortest ETA and a licensed driver has the second shortest ETA.1
Our identification strategy (quasi-random assignment) hinges on both licensed and
unlicensed drivers being nearly equally close to the pick-up location, but the closest driver
performs the pickup and second closest driver does not. Our datasets are only comprised of trips
in which the driver with the shortest ETA accepted the pickup request. Thus, our datasets do not
include trips when the driver with the shortest ETA rejected the pickup request, and the request
was fulfilled by another driver in the queue. Uber partner-drivers cannot see the destination of a
rider before they accept or reject a ride request, which limits concerns about selection occurring
at the trip level due to drivers of different licensing types preferentially accepting trips based on a
rider’s destination.
3.2.1. Houston Metropolitan Area: Previously Licensed and Never Licensed Uber Drivers
(Previously Unlicensed and New, Unlicensed Drivers)
On May 29, 2017, statewide regulations for TNCs in Texas went into effect that
superseded previous city level regulations in Houston. Texas House Bill 100 eliminated the
mandatory occupational licensing of Uber drivers who performed pickups in the city of Houston.
In the city of Houston, drivers were previously required to complete a fingerprint background
check to receive an occupational license. In contrast, Uber drivers in the Houston Metropolitan
1 We included all trips in our datasets in which the drivers with the shortest and second shortest ETAs were of
differing licensing types, regardless of the licensing status of other drivers who were in relatively close proximity to
the pickup location (e.g., the third, fourth, five shortest ETA, etc.). There may be concerns that ride assignment is
not agnostic to licensing status if drivers of one licensing status are disproportionately represented among drivers
with the shortest ETAs. As a result, we intend to conduct robustness checks using datasets in which a trip is only
included if there are a minimum of two drivers from each licensing type among the five drivers with the shortest
ETAs.
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Area (HMA) who did not go through the licensure process with the city of Houston could not
legally perform pickups within Houston city limits; however, these unlicensed drivers could
legally perform pickups in the suburbs surrounding Houston. After deregulation of the Houston
TNC market, previous restrictions on unlicensed drivers performing pickups in Houston were
lifted.
We first use the quasi-random assignment of ride requests in the post-deregulation period
in the HMA to compare quality and safety outcomes for rides performed by previously licensed
and previously unlicensed Uber drivers. We include rides in the sample performed by drivers
who first became active on the Uber platform before May 17, 2017, which is the date the Texas
Senate passed the legislation effectively deregulating the TNC industry in the state. The dataset
for the previously licensed/previously unlicensed Uber driver analysis comprises all UberX trips
performed in the HMA from September 1, 2017 - December 12, 2017 that met our quasi-random
ride assignment criteria.
We also use the quasi-random ride assignment approach to compare quality and safety
outcomes for rides performed by previously licensed and new, unlicensed Uber drivers who
entered the TNC market in the HMA after deregulation. We include rides in the sample
performed by new, unlicensed drivers if the signup date of the driver (i.e., the date on which a
driver first created an account on the Uber platform) was after May 29, 2017, which is the date
the statewide regulations were signed into law by the Governor of Texas and went into effect.
The dataset for the previously licensed/new, unlicensed Uber driver analysis comprises all
UberX trips performed in the HMA from September 1, 2017 - December 6, 2017 that met our
quasi-random ride assignment criteria.
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We are unable to randomly select individuals to be licensed or unlicensed drivers when
they initially sign up to operate as an Uber driver. As a result, individuals who choose to be
licensed or unlicensed may differ on characteristics that influence quality and safety outcomes of
rides. Since the Houston licensing regime did not have a human capital requirement, any
difference between licensed and unlicensed drivers should largely reflect selection, as opposed to
driver training directly improving a driver’s safety or quality. To better understand these
mechanisms, we control for driver level characteristics/observables that may affect quality and
safety outcomes, but definitionally cannot be randomized between the driver types at the trip
level using our quasi-random ride assignment approach. Additionally, we control for the star
rating a driver gave a rider on a trip because this may influence the star rating a rider gives a
driver on a trip, even though the rating given by the driver is not revealed to the rider in the Uber
app. We also include trip level variables as controls to improve the statistical efficiency of our
model. Inclusion of these variables should not alter the coefficient estimates of our parameter of
interest (ride was performed by a previously licensed driver) because the values of these trip
level variables should be randomly distributed across trips performed by previously unlicensed
and new unlicensed drivers. We use the program TripMatchR, which was designed by John
Horton at New York University, to control for pickup and destination locations on individual
rides. TripMatchR algorithmically implements a geography-based clustering approach that
partitions trip locations into “regions” or “clusters” by partitioning the trips into iso-count
regions (equal number of pickups per region).
We use ordinary least squares (OLS) to estimate our models with star ratings (ranging
from one to five), fraction of hard brakes, and fraction of hard accelerations as dependent
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variables. We use linear probability models (LPM) for our analyses with greater than 20% hard
brakes and greater than 20% hard accelerations as our dependent variables. All models use
robust standard errors clustered at the driver level. Model 1 is the specification used with star
ratings as the dependent variable.
(1) 𝑄𝑅𝑟𝑙 = 𝛽0 + 𝛽1𝑋𝑟𝑙′ + 𝛽2𝑋𝑟𝑙
′′ + 𝛿1𝐿𝑖𝑐𝑒𝑛𝑠𝑒𝑑𝑙 + 𝛼𝑟𝑙 + 𝜂𝑟𝑙 + 𝜖𝑟𝑙
where 𝑄𝑅𝑟𝑙 is the star (quality) rating for ride r by a driver of previous licensing status l.
𝐿𝑖𝑐𝑒𝑛𝑠𝑒𝑑𝑙 is the effect of a ride being performed by a driver who previously completed the TNC
licensing requirements in the city of Houston. X’ is a vector of driver level covariates, including
age, gender, driver experience (previous number of trips performed), driver experience2, and
vehicle model and year, as well as the rider rating. X” is a vector of trip and rider level
covariates, including client fare (USD), predicted estimated time of arrival (ETA), trip distance
(miles), trip duration (seconds), rider experience (previous number of trips), driver surge
multiplier, and a rider’s most frequent pickup city. 𝛼 are geography controls (pickup and drop-
off location). 𝜂 are hour of the day (HOD) and day of the week (DOW) controls.
Model 2 is the specification used with fraction of hard brakes, fraction of hard
accelerations, greater than 20% hard brakes, and greater than 20% hard accelerations as
dependent variables.
(2) 𝐻𝐵𝑟𝑙, 𝐻𝐴𝑟𝑙, 𝐻𝐵20𝑟𝑙, 𝐻𝐴20𝑟𝑙 = 𝛽0 + 𝛽1𝑋𝑟𝑙′ + 𝛽2𝑋𝑟𝑙
′′ + 𝛿1𝐿𝑖𝑐𝑒𝑛𝑠𝑒𝑑𝑙 + 𝛼𝑟𝑙 + 𝜂𝑟𝑙 + 𝜖𝑟𝑙
where 𝐻𝐵𝑟𝑙 is the fraction of hard brakes on ride r by a driver of previous licensing status l, 𝐻𝐴𝑟𝑙
is the fraction of hard accelerations 𝐻𝐵20𝑟𝑙 is whether a ride has greater than 20% hard brakes,
19
and 𝐻𝐴20𝑟𝑙 is whether a ride has greater than 20% hard accelerations. 𝐿𝑖𝑐𝑒𝑛𝑠𝑒𝑑𝑙 is the effect of
a ride being performed by a driver who previously completed the TNC licensing requirements
mandated by the city of Houston. X’ is a vector of driver level covariates, including age, age2,
gender, experience (previous number of trips performed), and vehicle model and year. X” is a
vector of trip level covariates, including ln(trip distance), ln(trip duration), and driver surge
multiplier. 𝛼 are geography controls (pickup and drop-off location). 𝜂
are hour of day (HOD)
and day of week (DOW) controls. Additionally, we include device operating system as a control
variable in our telematics models, which is an indicator for whether a driver uses an Android or
iPhone (iOS operating system) smartphone, because internal Uber findings indicate that the
operating system affects the measurement of hard accelerations on trips.
3.2.2. New York City and New Jersey Uber Drivers
We also use the quasi-random ride assignment approach to compare quality and safety
outcomes of UberX rides performed in New Jersey by licensed New York City Uber drivers and
unlicensed New Jersey Uber drivers. Uber drivers who perform pick-ups in New York City must
obtain an occupational license from the New York City Taxi & Limousine (NYC TLC). In
contrast, New Jersey UberX drivers are not required to obtain an occupational license or fulfill
any of the NYC TLC licensing requirements. Both New York City and New Jersey drivers can
fulfill Uber pickup requests originating in New Jersey. We exploit this geographic overlap in
pick-up capability and the border discontinuity in licensing requirements for New York City and
New Jersey drivers to compare quality and safety outcomes of rides performed by licensed and
unlicensed drivers. The dataset for this analysis comprises UberX rides performed in New Jersey
by licensed New York City drivers and unlicensed New Jersey drivers between July 18, 2016
20
and July 15, 2017 that fulfill our quasi-random ride assignment criteria. We use models (1) and
(2) described above to compare star (quality) ratings and telematics outcomes, respectively, on
UberX trips performed by New York City and New Jersey drivers. Since the licensing
requirements in New York City include human capital requirements, such as defensive driving
and driver education training, licensure may result in better safety outcomes on trips performed
by licensed drivers relative to trips performed unlicensed drivers after controlling for immutable
driver characteristics.
3.2.3. UberBLACK and UberSELECT Drivers
Lastly, we use the quasi-random ride assignment approach to compare quality and safety
outcomes for rides performed by UberBLACK drivers, who are professional drivers mandated to
have commercial insurance by state and local governments, with rides performed by
UberSELECT drivers, who have no commercial insurance or licensing requirements and only
need standard automobile insurance. Commercial insurance is a specific insurance type that is
required for transporting passengers, and the cost of the insurance ranges from $4,000-$6,000 per
year (Thune, 2018). UberBLACK drivers are responsible for the costs of commercial insurance.
In contrast, UberSELECT drivers are only required to pay for standard automobile insurance,
which costs an average of $936 per year (National Association of Insurance Commissioners,
2018). Uber purchases the extra liability insurance necessary for UberSELECT drivers to
transport passengers in their vehicles, but the increased cost is not borne by the UberSELECT
drivers. The added commercial insurance costs for UberBLACK drivers potentially creates an
incentive for these drivers to drive more carefully because an accident or other safety related
incident could significantly increase their insurance costs. Thus, we expect both selection and
21
treatment effects resulting from the commercial insurance requirement to be embedded in the
relationship between licensing and safety.
All vehicles operated by drivers on the UberBLACK platform must be black in color, and
typical vehicles makes eligible for use on the UberBLACK platform include Audi, BMW,
Cadillac, Lincoln, and Mercedes, among others. In contrast, there are no color restrictions for
vehicles operating on the UberSELECT platform and typical vehicle makes include Audi, BMW,
Cadillac, Lincoln, Volvo, and Chevrolet, among others. Overall, there is significant overlap in
vehicle quality and eligible makes and models across UberSELECT and UberBLACK. We
include 10 cities in our analysis where UberBLACK drivers are cross dispatched on the
UberSELECT platform (Figure 4), meaning customers who request an UberSELECT product
may receive an UberBLACK driver if that driver has a shorter ETA to the UberSELECT request
location.
Our dataset for this analysis comprises all trips performed by UberSELECT and
UberBLACK drivers between August 8, 2016 and July 16, 2017 that met our quasi-random ride
assignment criteria. We use Models 1 and 2 described above to compare star (quality) ratings and
telematics outcomes, respectively, on trips performed by commercially insured UberBLACK
drivers and outcomes for trips performed by UberSELECT drivers (unlicensed product).
However, in both models, we did not include the vehicle make and year as a control because of
the large overlap in vehicle types across the UberBLACK and UberSELECT platforms.
Additionally, in Model 2, we include an indicator for whether a driver was using a leased vehicle
as a control variable because as professional drivers, UberBLACK drivers disproportionately use
leased vehicles when operating on the Uber platform.
22
3.2.5. Robustness Tests
The right skewedness of the distribution of star ratings suggests that the distance between
each rating level may not be equal, particularly the distance between a five- and four-star rating
potentially being greater than the distances between the other star ratings. Figure 5 shows
quantile transformations for the previously licensed/new, unlicensed Uber drivers data. The
quantile transformations indicate that the distances between the star ratings may be unequal,
following an ordinal scale. Given the possibility of unequal distances between the star ratings
levels, we use an ordered logit model as a sensitivity test in our analysis with star ratings as a
dependent variable. We evaluate the marginal effects for the ordered logit model at the
likelihood of the occupational regulation variable resulting in a five-star rating compared to all
other ratings.
As a robustness test for our OLS models with fraction of hard brakes and fraction of hard
accelerations as dependent variables (continuous in [0,1]), we use fractional response regression
models. This approach will potentially allow us to avoid model misspecification and eliminate
the possibility of predicted values for fraction of hard brakes and hard accelerations falling
outside the [0,1] interval. Further, fractional response models may capture non-linear
relationships, particularly when values for the fraction of hard brakes or hard accelerations are
near 0 or 1. Fractional response regression uses a logit model and quasi-likelihood estimation. As
a sensitivity test for our LPMs with greater than 20% hard accelerations and 20% hard brakes as
dependent variables we estimated logistic regressions. These robustness tests were only
conducted for our previously licensed/new, unlicensed Uber driver analysis in Houston.
4. Results
23
4.1. Houston Metropolitan Area: Previously Licensed and New, Unlicensed Uber Drivers
Table 1 provides descriptive statistics (mean, standard deviation, minimum, and
maximum) for driver level variables for trips performed by Uber drivers who were previously
licensed in the City of Houston and new, unlicensed drivers who only began operating in the
HMA after deregulation. Previously licensed drivers tend to rate their passengers lower than new
drivers by 0.11 ratings points and have performed roughly 2,293 more previous trips than new
drivers, on average. The lower passenger ratings observed on trips performed by previously
licensed drivers is likely due, in part, to previously licensed drivers having significantly more
experience (r = -0.12). Females perform approximately 13% of the trips performed by previously
licensed drivers in our sample, while, in contrast, female performed approximately 20% of the
trips performed by new drivers. Overall, females represent roughly 19% and 31% of previously
licensed drivers and new drivers, respectively, in our sample. These substantial gender
differences between previously licensed drivers and new drivers are likely due, in part, to
deregulation lowering the barriers to entry to be an Uber driver, and thus, incentivizing drivers
who want to work part time to enter the ridesharing labor market. The average age of a
previously licensed driver in our sample is 44, while the average age of a new driver is 38, which
indicates that younger drivers may have begun driving with Uber in Houston following
deregulation.
Table 2 provides descriptive statistics for key trip level variables for previously licensed
and new Uber drivers, including trip distance, trip duration, driver surge multiplier, rider
experience, ETA to the rider pickup location, and ETA differences between the first and second
closest drivers to the pickup location. Importantly, the mean values for each of the variables are
24
very similar for trips performed by previously licensed and new Uber drivers, which is critical
for assuring that randomization occurred at the ride level. In particular, ETA for new drivers is
only 0.02 minutes longer than that of previously licensed drivers. Further, the average difference
in ETA between the closest driver (who performed the pickup) and the second closest driver for
each partner-driver combination (Closest: previously licensed driver – Second closest: new
driver and Closest: new driver – Second closest: previously licensed driver) is nearly identical.
Table 3 contains the descriptive statistics for the dependent variables in our models (star
ratings, fraction of hard brakes, fraction of hard accelerations, greater than 20% hard brakes, and
greater than 20% hard accelerations) for previously licensed and new, unlicensed Uber drivers.
Previously licensed drivers have 0.008 lower star ratings than new drivers, on average, and 0.3
and 0.4 percentage point higher fractions of trips with greater than 20% hard brakes and 20%
hard accelerations, respectively. The mean values of the fraction of hard brakes and hard
accelerations are very similar across both driver types.
Appendix A contains the complete OLS and LPM regression results with star (quality)
ratings and the telematics variables as dependent variables (Tables A1-A5). In the results tables,
the first specification (1) contains only our variable of interest (previously licensed). In the
second specification (2), we include the previously licensed variable and driver level controls. In
the third specification (3), our estimates also include trip level controls. Due to our quasi-random
ride assignment methodology, the values of these variables should be randomly distributed
across partner-driver types, and thus, including these variables should not alter the coefficient
estimates on our previously licensed variable. In the fourth specification (4), we include controls
for pickup and drop-off locations of a trip. In the fifth and most complete specification (5), we
25
include controls for the hour of the day and the day of the week when a trip began. The values of
the geography and time variables are included in models (4) and (5). The results of the
robustness tests are briefly discussed in the text below and generally conform to the findings of
our OLS and LPM models.
Figure 6 displays our key results and contains percent effect estimates
(𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡
𝐷𝑒𝑝𝑒𝑛𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝑚𝑒𝑎𝑛 ∗ 100) for the previously licensed variable in the full specifications for
each of our dependent variables. The coefficients on the previously licensed variables (and
corresponding percent effects) are all positive, but small (ranging from 0.10% to 3.29% effects)
and not significant (p > 0.10) in the full specification for each dependent variable model. The
coefficient on the previously licensed variable in the fully specified ordered logit model, which
includes star ratings as the dependent variable, is positive (0.0279), but not significant (p =
0.352). Additionally, in the fractional response regressions, the coefficient on the previously
licensed variable in the fraction of hard brakes model (0.005) and hard accelerations model
(0.006) are positive, but not significant with p-values of 0.825 and 0.818, respectively. Lastly,
the coefficients on the previously licensed variable in the models for greater than 20% hard
brakes (p = 0.378) and greater than 20% hard accelerations (p = 0.316) are not significant at any
level. Together the results do not indicate that previously licensed drivers have better star
(quality) ratings, lower levels of hard brakes, or lower levels of hard accelerations than new,
unlicensed drivers.
4.2. Houston Metropolitan Area: Previously Licensed and Previously Unlicensed Uber Drivers
Table 4 provides descriptive statistics (mean, standard deviation, minimum, and
maximum) for driver level variables for trips performed by Uber drivers who were previously
26
licensed in the City of Houston and previously unlicensed Uber drivers who first became active
on the Uber platform prior to deregulation. On average, previously licensed drivers give their
passengers lower ratings than previously unlicensed drivers, the age of a driver on a trip
performed by a previously licensed driver is less than that on a trip performed by a previously
unlicensed driver, and females perform a lower percentage of trips conducted by previously
licensed drivers relative to trips conducted by previously unlicensed drivers. The numerical
differences between previously licensed and previously unlicensed drivers for these driver
characteristics are nearly identical to the differences between previously licensed and new,
unlicensed drivers described previously. However, in contrast, the difference in driver experience
between previously licensed drivers and new, unlicensed drivers is greater than that between
previously licensed drivers and previously unlicensed drivers, which is unsurprising given
previously unlicensed drivers were active on the Uber platform before deregulation, unlike new,
unlicensed drivers. Previously licensed drivers had conducted, on average, 1,818 more previous
trips than previously unlicensed drivers.
Table 5 provides descriptive statistics for trip distance, trip duration, driver surge
multiplier, rider experience, ETA, and ETA difference for previously licensed and previously
unlicensed Uber drivers. Like the sample of trips performed by previously licensed and new,
unlicensed drivers, the mean values for each of these variables are very similar for trips
performed by previously licensed and new Uber drivers, which again is important because our
identification strategy relies on the quasi-random assignment of trips to previously licensed and
previously unlicensed drivers.
27
Table 6 provides descriptive statistics for star (quality) ratings, fraction of hard brakes,
fraction of hard accelerations, greater than 20% hard brakes, and greater than 20% hard
accelerations for previously licensed and previously unlicensed Uber drivers. The average star
ratings for both driver types are identical (4.823). Previously licensed drivers have a 0.20
percentage point greater fraction of hard brakes and hard accelerations, on average, then
previously unlicensed drivers. Further, previously licensed drivers have 0.9 and 0.8 percentage
point higher fractions of trips with greater than 20% hard brakes and 20% hard accelerations,
respectively.
Appendix B contains the full OLS and LPM regression results for our previously
licensed/previously unlicensed Uber driver analysis (Tables B1-B5). For each dependent
variable, the coefficient on the previously licensed variable remains very similar across
specifications after driver controls are added to the model. Figure 7 shows percent effect
estimates for the previously licensed variable in the full specifications for the star rating and
telematics dependent variables. The percent effects for the previously licensed variable are
positive and significant at a minimum of the 5% level in all models except in fraction of hard
accelerations model (p < 0.10). The percent effects of a trip being performed by a previously
licensed driver relative to a previously unlicensed driver range from 0.29% in the star ratings
model to 9.21% and 12.69% in the greater than 20% hard brakes and 20% hard acceleration
models, respectively.
Previously licensed drivers may not have better telematics outcomes than previously
unlicensed drivers, in part, because the licensure process in the City of Houston did not require
potential drivers to complete human capital requirements to improve ride safety. Additionally,
28
given the significant challenges previously faced by drivers to complete the fingerprint
requirement (i.e., one provider offering fingerprinting services for the city of Houston) driver
selection effects resulting from the licensure process may partially explain the results. For
example, previously unlicensed drivers who operate on the Uber platform part-time for
supplemental income and were unwilling to go through the Houston licensure process may be
less aggressive drivers than previously licensed drivers who are more likely to choose to work
full-time on the platform.
Overall, the star (quality) ratings and telematics results from our previously
licensed/previously unlicensed Uber driver models from Houston indicate that previously
licensed drivers have significantly higher star (quality) ratings than previously unlicensed
drivers, but also greater fractions of hard brakes on trips and a higher likelihood of having a trip
with either greater than 20% hard brakes or greater than 20% hard accelerations. While the
percent effect in the greater than 20% hard brakes and hard accelerations models for previously
licensed drivers relative to previously unlicensed drivers appear of moderate size, the mean
number of trips in the dataset with greater than 20% hard brakes and greater than 20% hard
accelerations are 8.95% and 8.2%, respectively. As a result, the observed effect sizes only
increase the percentage of trips with greater than 20% hard brakes and greater than 20% hard
accelerations to 9.77% and 9.24% of trips, respectively.
4.3. New York City and New Jersey Uber Drivers
Table 7 provides descriptive statistics for key driver and rider variables for our New York
City/New Jersey Uber drivers analysis. The average age of drivers on trips performed by
licensed NYC drivers (38 years old) and unlicensed New Jersey drivers (39 years old) are nearly
29
identical. However, similar to the composition of drivers in our HMA datasets, females
performed a lower percentage of trips conducted by NYC drivers (3%) relative to trips conducted
by New Jersey drivers (11%). Additionally, NYC drivers performed, on average, 355 more
previous trips than New Jersey drivers.
Table 8 provides descriptive statistics for various trip level variables. As with our
previous HMA analyses, the average trip distance, trip duration, ETA, rider experience (in Table
7), ETA to the rider pickup location, and ETA differences between the first and second closest
drivers to the pickup location are all very similar on trips performed in New Jersey by licensed
NYC Uber drivers and unlicensed New Jersey Uber drivers.
Table 9 provides descriptive statistics for the star (quality) ratings and telematics
dependent variables. New York City Uber drivers have 0.043 lower star ratings, on average, than
New Jersey Uber drivers. However, NYC drivers have slightly lower levels of hard brakes (0.3
percentage points) and accelerations (0.32 percentage points) than New Jersey drivers, as well as
lower percentages of trips with greater than 20% hard brakes (0.7 percentage points) and hard
accelerations (0.5 percentage points).
Appendix C contains the complete OLS and LPM regression results for our NYC/NJ
Uber driver analysis (Tables C1-C5). For each dependent variable, the coefficient on the
licensing coverage variable remains similar across specifications. Figure 8 displays percent effect
estimates for the licensing coverage variable in the full specifications for all dependent variables.
The percent effects for the licensing coverage variable are negative and significant at a minimum
of the 5% level in all models. The percent effects of a trip being performed by a licensed NYC
driver relative to an unlicensed NJ driver range from -0.96% in the star ratings model to -8.3% in
30
the fraction of hard accelerations model, which indicates a decrease in the mean fraction of hard
accelerations from 5.6% to 5.14% of accelerations. Licensed New York City drivers may have
lower star ratings than NJ drivers because the heavily structured licensing process in NYC
focuses on fulfilling human capital requirements related to ride safety, not necessarily quality. As
a result, NYC drivers may disproportionately emphasize safety over perceived quality when
providing rides.
Overall, the star (quality) ratings and telematics results from our previously NYC/NJ
Uber driver models indicate that licensed NYC drivers have slightly lower star (quality) ratings
on trips than unlicensed NJ drivers, but also modestly lower fractions of hard brakes and hard
accelerations and a lower likelihood of a trip having either greater than 20% hard brakes or
greater than 20% hard accelerations.
4.4. UberBLACK and UberSELECT Drivers
Table 10 contains descriptive statistics of important driver level variables for trips
included in the UberBLACK and UberSELECT driver analysis. While the age of drivers is very
similar on trips performed by UberBLACK and UberSELECT drivers, females performed eight
percent of UberSELECT trips, but only two percent of UberBLACK trips. Additionally,
UberBLACK drivers performed, on average, 1,562 more previous trips than UberSELECT
drivers.
Table 11 contains descriptive statistics for key trip level variables. Importantly, the
difference in ETA between the closest and second closest driver to the pickup location is very
similar regardless of whether an UberBLACK driver is closest and an UberSELECT driver is
second closest (1.26 minutes) or an UberSELECT driver is closest and an UberBLACK driver is
31
second closest (1.29 minutes). Trip distance, trip duration, and driver surge multiplier are also
very similar for trips performed by UberBLACK and UberSELECT drivers.
Table 12 contains descriptive statistics for the ratings and telematics dependent variables
included in the analysis. The average star ratings are similar for rides performed by UberBLACK
drivers (4.845) and UberSELECT drivers (4.829). However, rides performed by UberBLACK
drivers have lower fractions of hard brakes (0.71 percentage points lower) and hard accelerations
(0.8 percentage points lower) than rides performed by UberSELECT drivers, as well as lower
percentages of trips with > 20% hard brakes (1.5 percentage points lower) and hard accelerations
(1.74 percentage points lower). These patterns of lower levels of hard brakes and accelerations
for UberBLACK drivers relative to UberSELECT driver partners are consistent across all ten
locales included in the analysis. Figures 9 and 10 display the fraction of hard brakes and fraction
of hard accelerations by locale for trips performed by UberBLACK and UberSELECT drivers.
Appendix D contains the full OLS and LPM regression results for our
UberBLACK/UberSELECT driver analysis (Tables D1-D5). For each dependent variable, the
coefficient on the commercial driver variable remains similar across specifications after driver
controls are added to the model. Figure 11 displays percent effect estimates for the commercial
driver variable from the full specifications for each dependent variable. The coefficient on the
commercial driver variable (and corresponding percent effect) in the star (quality) ratings model
is positive, but small (0.32% effect) and not significant (p > 0.10). In contrast, the percent effects
for the commercial driver variable are negative and significant at a minimum of the 5% level in
each of the telematics dependent variable models, ranging in effect from -6.02% in the fraction
of hard brakes model to -9.1% in the greater than 20% hard accelerations model. The percent
32
effect for the greater than 20% hard accelerations model indicates that if a trip is performed by a
commercial driver, the likelihood of a trip having greater than 20% hard accelerations is reduced
by 9.1%. Overall, our results do not show that trips performed by a commercially licensed
UberBLACK drivers have lower ratings than trips performed by UberSELECT drivers.
However, trips performed by UberBLACK drivers are associated with somewhat lower fractions
of hard brakes and hard accelerations and lower likelihoods of having either greater than 20%
hard brakes or greater than 20% hard accelerations.
5. Conclusion and Discussion
Two of the most rapidly growing institutions in the labor market in the U.S. are the
online, on-demand economy and occupational licensing. Occupational licensing by state and
local governments is a potential barrier to entry for drivers who want to drive on a transportation
network company, such as Uber. We use a quasi-random ride assignment identification strategy
to compare quality and safety outcomes on UberX rides in two areas (New Jersey and the
Houston Metropolitan Area) where Uber drivers with and without occupational licenses overlap
when operating on the UberX platform. In the HMA, we compare ride outcomes of previously
licensed Uber drivers to both previously unlicensed Uber drivers and new, unlicensed Uber
drivers after deregulation of the TNC market. Additionally, we use the quasi-random ride
assignment approach to compare ride outcomes in ten locales for drivers who operate on
different Uber platforms (UberBLACK and UberSELECT) but can fulfill the same pickup
requests.
Our analysis indicates that occupational licensing has either no effect or a small negative
effect on safety outcomes for rides in the HMA, indicating that the previous licensure process in
33
the city of Houston did not “weed out” unsafe drivers. The licensure process in Houston consists
primarily of drivers completing a fingerprint background check but does not require drivers to
acquire human capital, which could improve safety outcomes. Previously licensed Uber drivers
in Houston did have higher star (quality) ratings than previously unlicensed Uber drivers;
however, the gain in star ratings potentially due to licensure is relatively small and is not
observed when we compare previously licensed drivers to new, unlicensed drivers.
In our NYC/NJ analysis, we found that licensed NYC Uber drivers have slightly lower
star ratings than unlicensed NJ Uber drivers, but lower levels of hard braking and hard
accelerations across telematics models relative to NJ drivers. These results may be due to a
licensure process in NYC that provides additional specific human capital, such as driver training,
to improve safety outcomes (i.e., completion of Driver Education and Defensive Driving
Courses), but also de-emphasizes quality outcomes.
Commercially licensed UberBLACK drivers also have lower levels of hard braking and
hard accelerations across all telematics dependent variable models relative to UberSELECT
drivers; however, we observe no difference in star ratings on rides performed by UberBLACK
and UberSELECT drivers. These findings are consistent with the requirement that UberBLACK
drivers carry high cost commercial insurance, which could be subject to greater price increases
following an accident relative to the standard automobile premiums required for UberSELECT
drivers. The higher cost of commercial insurance might incentivize UberBLACK drivers to
operate more safely, but not to offer higher quality rides (as reflected by star ratings). However,
lower rates of hard brakes and hard accelerations for UberBLACK and NYC Uber drivers could
also be due to selection effects where unobserved characteristics of previously licensed drivers
34
lead to better telematics outcomes. For example, previously licensed drivers may have frequently
served as professional drivers (i.e., taxi drivers or chauffeurs) before becoming Uber drivers, and
the relationship between licensing status and estimated quality may reflect the benefits of this
additional driving experience. Regardless, the magnitude of the reductions in hard brakes and
hard accelerations is generally small, potentially indicating minimal practical effects of licensure
on safety outcomes.
Overall, our quasi-random ride assignment approach indicates that the influence of
occupational licensing on consumer quality and safety outcomes is often either negative,
insignificant, or small. These results suggest that occupational licensing has a limited effect on
enhancing the quality experience for Uber riders and improving safety outcomes of Uber rides in
the cities and time periods included in our analyses.
35
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38
Figure 1: Share of Workers in the U.S. with an Occupational License
Figure 2: Driver Ratings Distribution on US Trips
39
Figure 3: Driver Ratings Distribution on U.S. UberX Trips by City
Figure 4: Cities in UberBLACK/SELECT Analysis
40
Figure 5: Quantile Transformations of Star Ratings
Figure 6: Percent Change Regression Results (Licensed Driver Variable) – Previously
Licensed/New, Unlicensed Drivers in Houston
41
Figure 7: Percent Change Regression Results (Licensed Driver Variable) – Houston:
Previously Licensed/Previously Licensed Drivers in Houston
Figure 8: Percent Change Regression Results (Licensed Driver Variable) – New York
City/New Jersey Drivers
42
Figure 9: Fraction of Hard Brakes by Locale - UberBLACK/UberSELECT Drivers
Figure 10: Fraction of Hard Accelerations by Locale - UberBLACK/UberSELECT Drivers
43
Figure 11: Percent Change Regression Results (Commercial Driver Variable) –
UberBLACK/UberSELECT Drivers
Table 1: Driver Level Descriptive Statistics – Previously Licensed/New, Unlicensed Drivers
44
Table 2: Trip Level Descriptive Statistics – Previously Licensed/New, Unlicensed Drivers
45
Table 3: Dependent Variable Descriptive Statistics – Previously Licensed/New, Unlicensed
Drivers
46
Table 4: Driver Level Descriptive Statistics – Previously Licensed/Previously Unlicensed
Drivers
47
Table 5: Trip Level Descriptive Statistics – Previously Licensed/Previously Unlicensed
Drivers
48
Table 6: Dependent Variable Descriptive Statistics – Previously Licensed/Previously
Unlicensed Drivers
Table 7: Driver and Rider Level Descriptive Statistics - New York City/New Jersey Drivers
49
Table 8: Trip Level Descriptive Statistics - New York City/New Jersey Drivers
50
Table 9: Dependent Variable Descriptive Statistics - New York City/New Jersey Drivers
Table 10: Driver and Rider Level Descriptive Statistics - UberBLACK/UberSELECT
Drivers
51
Table 11: Trip Level Descriptive Statistics - UberBLACK/UberSELECT Drivers
52
Table 12: Dependent Variable Descriptive Statistics - UberBLACK/UberSELECT Drivers
53
54
Appendix A
Table A1: Star (Quality) Rating Regression Results – Previously Licensed/New, Unlicensed
Drivers
Table A2: Fraction of Hard Brakes Results – Previously Licensed/New, Unlicensed Drivers
55
Table A3: Fraction of Hard Acceleration Results – Previously Licensed/New, Unlicensed
Drivers
Table A4: Trips with > 20% Hard Brakes Results – Previously Licensed/New, Unlicensed
Drivers
56
Table A5: Trips with > 20% Hard Accelerations Results– Previously Licensed/New,
Unlicensed Drivers
57
Appendix B
Table B1: Star (Quality) Rating Regression Results – Previously Licensed/Previously
Unlicensed Drivers
Table B2: Fraction of Hard Brakes Regression Results – Previously Licensed/Previously
Unlicensed Drivers
58
Table B3: Fraction of Hard Accelerations Regression Results – Previously
Licensed/Previously Unlicensed Drivers
Table B4: Trips with > 20% Hard Brakes Results – Previously Licensed/Previously
Unlicensed Drivers
59
Table B5: Trips with > 20% Hard Accelerations Results – Previously Licensed/Previously
Unlicensed Drivers
60
Appendix C
Table C1: Star (Quality) Ratings Results - New York City/New Jersey Drivers
61
Table C2: Fraction of Hard Brakes - New York City/New Jersey Drivers
Table C3: Fraction of Hard Accelerations - New York City/New Jersey Drivers
Table C4: Trips with > 20% Hard Brakes Results - New York City/New Jersey Drivers
62
Table C5: Trips with > 20% Hard Accelerations Results - New York City/New Jersey
Drivers
63
Appendix D
Table D1: Star (Quality) Ratings Regressions - UberBLACK/UberSELECT Drivers
Table D2: Fraction of Hard Brakes Regressions - UberBLACK/UberSELECT Drivers
64
Table D3: Fraction of Hard Accelerations Regressions - UberBLACK/UberSELECT
Drivers
Table D4: Trips with > 20% Hard Brakes Regressions - UberBLACK/UberSELECT
Drivers
65
Table D5: Trips with > 20% Hard Accelerations Regressions - UberBLACK/UberSELECT
Drivers
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