occupational licensing of uber drivers* - stanford university · 2019, uber operates in over 700...

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PRELIMINARY – DO NOT CIRCULATE 0 Occupational Licensing of Uber Drivers* Jonathan Hall 1 , Jason Hicks 2 , Morris M. Kleiner 3 , and Rob Solomon 4 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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Page 1: Occupational Licensing of Uber Drivers* - Stanford University · 2019, Uber operates in over 700 cities and 63 nations with more than three million drivers. The growing scale of the

PRELIMINARY – DO NOT CIRCULATE

0

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.

Page 2: Occupational Licensing of Uber Drivers* - Stanford University · 2019, Uber operates in over 700 cities and 63 nations with more than three million drivers. The growing scale of the

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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,

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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

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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

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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.

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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

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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

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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

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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

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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.

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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,

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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

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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

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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

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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

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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

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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

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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.

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References

Angrist, J. D., & Guryan, J. (2008). Does teacher testing raise teacher quality? Evidence from

state certification requirements. Economics of Education Review, 27(5), 483-503.

Bardach, N. S., Asteria-Peñaloza, R., Boscardin, W. J., & Dudley, R. A. (2013). The relationship

between commercial website ratings and traditional hospital performance measures in the

USA. BMJ Qual Saf, 22(3), 194-202.

Barrios, J. M., Hochberg, Y. V., & Yi, L. H. (2018). The Cost of Convenience: Ridesharing and

Traffic Fatalities (No. 27). New Working Paper Series.

https://doi.org/10.1007/BF00748122

Beinstein, A., & Sumers, T. (2016, June 29). How Uber Engineering Increases Safe Driving with

Telematics. Uber Engineering. Retrieved from: https://eng.uber.com/telematics/

Carpenter, D. M., Knepper, Sweetland, K., & McDonald, K. (2017). License to Work: A

National Study of Burdens from Occupational Licensing: 2nd Edition. Institute for Justice.

Csere, C. (2014, May 9). The Spy Who Paid Me: Progressive Insurance Offers You a Discount-if

You Let Little Brother into Your Car. Car & Driver. Retrieved from:

https://www.caranddriver.com/news/a15363200/the-spy-who-paid-me-progressive-

insurance-offers-you-a-discount-if-you-let-little-brother-into-your-car/

Claims Journal. (2015, May 19). Hard Braking Most Likely Predictor of Future Crashes:

Progressive Data. Retrieved from:

https://www.claimsjournal.com/news/national/2015/05/19/263424.htm

Department of the Treasury Office of Economic Policy, Council of Economic Advisors, and

Department of Labor. (2015, July). Occupational Licensing: A Framework for

Policymakers. White House Report.

Deyo, D. (2016). Licensing and Service Quality: Evidence Using Yelp Consumer Reviews.

George Mason University Working Paper.

Dills, A. K., & Mulholland, S. E. (2018). Ride‐Sharing, Fatal Crashes, and Crime. Southern

Economic Journal, 84(4), 965-991.

Elvik, R. (2006). Economic deregulation and transport safety: A synthesis of evidence from

evaluation studies. Accident Analysis & Prevention, 38(4), 678-686.

Farrell, D., & Greig, F. (2016a). Paychecks, paydays, and the online platform economy: Big data

on income volatility. JP Morgan Chase Institute.

Farrell, D., & Greig, F. (2016b). The online platform economy: What is the growth trajectory?

JP Morgan Chase Institute.

Friedman, Milton. (1962). Capitalism and Freedom. Chicago: University of Chicago Press.

Page 37: Occupational Licensing of Uber Drivers* - Stanford University · 2019, Uber operates in over 700 cities and 63 nations with more than three million drivers. The growing scale of the

36

Greenwood, B. N., & Wattal, S. (2017). Show Me the Way to Go Home: An Empirical

Investigation of Ride-Sharing and Alcohol Related Motor Vehicle Fatalities. MIS

quarterly, 41(1), 163-187.

Harris, S. D., & Krueger, A. B. (2015). A Proposal for Modernizing Labor Laws for Twenty-

First-Century Work: The “Independent Worker.” The Hamilton Project, Discussion Paper

2015-10. Washington, D.C.: Brookings Institution.

Katz, L. F., & Krueger, A. B. (2016). The rise and nature of alternative work arrangements

in the United States, 1995-2015(No. w22667). National Bureau of Economic Research.

Kleiner, M. M. (2006). Licensing occupations: Ensuring quality or restricting competition?. WE

Upjohn Institute.

Kleiner, M. M. (2015, March). Reforming occupational licensing policies. Washington, D.C.:

The Hamilton Project, Brookings Institution.

Kleiner, M. M., & Krueger, A. B. (2013). Analyzing the extent and influence of occupational

licensing on the labor market. Journal of Labor Economics, 31(2), S173-202.

https://doi.org/10.1086/669060

Kleiner, M. M., & Kudrle, R. T. (2000). Does regulation affect economic outcomes? The case of

dentistry. The Journal of Law and Economics, 43(2), 547-582.

Kleiner, M. M., Marier, A., Park, K. W., & Wing, C. (2016). Relaxing occupational licensing

requirements: Analyzing wages and prices for a medical service. The Journal of Law and

Economics, 59(2), 261-291.

Kleiner, Morris M., and Richard M. Todd. "Mortgage broker regulations that matter:Analyzing

earnings, employment, and outcomes for consumers." Editor, David Autor, Studies of labor

market intermediation. University of Chicago Press, 2009.183-231.

Koumenta, M., Humphris, A., Kleiner, M., & Pagliero, M. (2014). Occupational regulation in the

EU and UK: Prevalence and labour market impacts. Final Report, Department for Business,

Innovation and Skills, School of Business and Management, Queen Mary University of

London, London.

Larsen, B. (2015). Occupational licensing and quality: Distributional and heterogeneous effects

in the teaching profession. Working Paper. Retrieved from:

https://web.stanford.edu/~bjlarsen/Larsen%20(2015)%20Occupational%20licensing%20and

%20quality.pdf

Luca, Michael. (2016). Reviews, Reputation, and Revenue: The Case of Yelp.com. 12–016.

Cambridge, Mass. Retrieved from: http://www.hbs.edu/faculty/Publication

Files/12016_a7e4a5a2-03f9- 490d-b093-8f951238dba2.pdf

Martin-Buck, F. (2016). Driving Safety: An Empirical Analysis of Ridesharing’s Impact on

Page 38: Occupational Licensing of Uber Drivers* - Stanford University · 2019, Uber operates in over 700 cities and 63 nations with more than three million drivers. The growing scale of the

37

Drunk Driving and Alcohol-Related Crime. https://doi.org/10.1111/j.1756-

8765.2012.01221.x

Moran, M. (2017). Transportation Network Companies. Transportation Policy Research Center.

Testimony to the Texas Senate Committee on Business and Commerce.

Moran, M., Ettelman, B., Stoeltje, G., Hansen, T., and Pant, A. (2017, October). Policy

Implications of Transportation Network Companies: Final Report. Texas A&M

Transportation Institute, PRC 17-70 F.

Paez, T. (2016). Safety, Effectiveness & Best Practices for Vehicle-for-Hire Criminal

Background Checks. City of Houston Administration & Regulatory Affairs Department.

Peck, J. L. (2017). New York City Drunk Driving After Uber (No. 13). Economic Working

Papers. https://doi.org/10.1109/MC.2014.290

Ranard, B. L., Werner, R. M., Antanavicius, T., Schwartz, H. A., Smith, R. J., Meisel, Z. F., &

Merchant, R. M. (2016). Yelp reviews of hospital care can supplement and inform

traditional surveys of the patient experience of care. Health Affairs, 35(4), 697-705.

Saito, K. (2013). Deregulation and Safety: Evidence from the Taxicab Industry. Working Paper.

Retrieved from: http://www.law.ntu.edu.tw/aslea2014/file/saito.pdf

Sass, T. R. (2015). Licensure and worker quality: A comparison of alternative routes to

teaching. The Journal of Law and Economics, 58(1), 1-35.

Shapiro, C. (1986). Investment, moral hazard, and occupational licensing. The Review of

Economic Studies, 53(5), 843-862.

Thornton, R. J., & Timmons, E. J. (2013). Licensing one of the world’s oldest professions:

massage. The Journal of Law and Economics, 56(2), 371-388.

Timmons, E. J., & Mills, A. (2018). Bringing the effects of occupational licensing into focus:

optician licensing in the United States. Eastern Economic Journal, 44(1), 69-83.

U.S. Bureau of Labor Statistics. (2016). Data on Certificates and Licensing. Retrieved from:

http://www.bls.gov/cps/certifications-and-licenses.htm#highlights

Wilson, L. (2016). Uber, city of Houston reach agreement over background checks.

Click2Houston. Retrieved from: https://www.click2houston.com/news/uber-city-of-houston-

reach-agreement-over-driver-background-checks

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Figure 1: Share of Workers in the U.S. with an Occupational License

Figure 2: Driver Ratings Distribution on US Trips

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Figure 3: Driver Ratings Distribution on U.S. UberX Trips by City

Figure 4: Cities in UberBLACK/SELECT Analysis

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Figure 5: Quantile Transformations of Star Ratings

Figure 6: Percent Change Regression Results (Licensed Driver Variable) – Previously

Licensed/New, Unlicensed Drivers in Houston

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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

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Figure 9: Fraction of Hard Brakes by Locale - UberBLACK/UberSELECT Drivers

Figure 10: Fraction of Hard Accelerations by Locale - UberBLACK/UberSELECT Drivers

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Figure 11: Percent Change Regression Results (Commercial Driver Variable) –

UberBLACK/UberSELECT Drivers

Table 1: Driver Level Descriptive Statistics – Previously Licensed/New, Unlicensed Drivers

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Table 2: Trip Level Descriptive Statistics – Previously Licensed/New, Unlicensed Drivers

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Table 3: Dependent Variable Descriptive Statistics – Previously Licensed/New, Unlicensed

Drivers

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Table 4: Driver Level Descriptive Statistics – Previously Licensed/Previously Unlicensed

Drivers

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Table 5: Trip Level Descriptive Statistics – Previously Licensed/Previously Unlicensed

Drivers

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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

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Table 8: Trip Level Descriptive Statistics - New York City/New Jersey Drivers

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Table 9: Dependent Variable Descriptive Statistics - New York City/New Jersey Drivers

Table 10: Driver and Rider Level Descriptive Statistics - UberBLACK/UberSELECT

Drivers

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Table 11: Trip Level Descriptive Statistics - UberBLACK/UberSELECT Drivers

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Table 12: Dependent Variable Descriptive Statistics - UberBLACK/UberSELECT Drivers

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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

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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

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Table A5: Trips with > 20% Hard Accelerations Results– Previously Licensed/New,

Unlicensed Drivers

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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

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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

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Table B5: Trips with > 20% Hard Accelerations Results – Previously Licensed/Previously

Unlicensed Drivers

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Appendix C

Table C1: Star (Quality) Ratings Results - New York City/New Jersey Drivers

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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

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Table C5: Trips with > 20% Hard Accelerations Results - New York City/New Jersey

Drivers

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Appendix D

Table D1: Star (Quality) Ratings Regressions - UberBLACK/UberSELECT Drivers

Table D2: Fraction of Hard Brakes Regressions - UberBLACK/UberSELECT Drivers

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Table D3: Fraction of Hard Accelerations Regressions - UberBLACK/UberSELECT

Drivers

Table D4: Trips with > 20% Hard Brakes Regressions - UberBLACK/UberSELECT

Drivers

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Table D5: Trips with > 20% Hard Accelerations Regressions - UberBLACK/UberSELECT

Drivers