THE UBER EFFECT: HOW TRANSPORTATION NETWORKING COMPANIES IMPACT AUTOMOTIVE FUEL CONSUMPTION

A Thesis submitted to the Faculty of the

Graduate School of Arts and Sciences of Georgetown University

in partial fulfillment of the requirements for the degree of

Master of Public Policy in Public Policy

By

Andrew S. Kitchel, B.S.

Washington, DC April 4, 2017

ii

Copyright 2017 by Andrew S. Kitchel

All Rights Reserved

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THE UBER EFFECT: HOW TRANSPORTATION NETWORKING COMPANIES IMPACT AUTOMOTIVE FUEL CONSUMPTION

Andrew S. Kitchel, B.S.

Thesis Advisor: Andrew S. Wise, Ph.D.

ABSTRACT

The growth of the sharing economy has shifted consumption habits of individuals

and how they approach transportation within the largest Metropolitan Statistical Areas

(MSAs) in the United States. The effect of Transportation Networking Companies

(TNCs) such as Uber and Lyft on public transportation use, car ownership, and traffic

congestion is well documented. In this analysis, I utilize fixed-effects multiple regression

to attempt to determine the relationship between the market entrance of such

transportation services into the 50 largest MSAs in the United States and the consumption

of automotive fuel by auto drivers. Using data from the Urban Mobility Report, the

National Transit Database, Uber, the Energy Information Administration, the Census

Bureau, and the Bureau of Economic Analysis, this study found a relationship between

TNC operation and reduced excess fuel consumption, however without a statistically

significant effect. This study suggests that TNCs and the sharing economy are continually

shifting individual consumption habits and, with further study and more data, has

important policy implications for city planning and urban transportation.

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This thesis and the work that went into research and writing are dedicated to Joe Biden.

Many thanks,

Andrew

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TABLE OF CONTENTS

SECTION 1: INTRODUCTION ...............................................................................................1

SECTION 2: BACKGROUND AND REVIEW OF RELEVEANT LITERATURE ...............4

Technology Adoption and Diffusion ...................................................................................4

Sharing Economy .................................................................................................................5

TNCs and Uber ....................................................................................................................8

Effects of Uber ...................................................................................................................10

Contribution to Existing Literature ....................................................................................11

SECTION 3: THEROETICAL FRAMEWORK .....................................................................13

SECTION 4: DATA AND DESCRIPTIVE STATISTICS .....................................................14 Descriptive Statistics ..........................................................................................................19

SECTION 5: EMPIRICAL MODELS.....................................................................................24

SECTION 6: EMPIRICAL RESULTS ...................................................................................28

SECTION 7: CONCLUSION AND POLICY RECOMMENDATIONS ..............................37

Policy Recommendations...................................................................................................38

Conclusion .........................................................................................................................39 APPENDIX: DATASETS .......................................................................................................41 BIBLIOGRAPHY ....................................................................................................................42

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LIST OF TABLES

TABLE 1. MERGED MASTER DATASET ..........................................................................19

TABLE 2. URBEN MOBILITY REPORT .............................................................................20

TABLE 3. EIA FUEL CONSUMPTION REPORT ................................................................21

TABLE 4. TRANSIT REPORT - 2014 ...................................................................................22

TABLE 5. TRANSIT REPORT - 2004 ...................................................................................23

TABLE 6. MODEL 1. EXCESS FUEL...................................................................................28

TABLE 7. MODEL 2. LOGGED FUEL .................................................................................29

TABLE 8. MODEL 3. MULTICOLLINEARITY ADJUSTMENT .......................................30

TABLE 9. COMPARISON OF MODELS 1, 2, AND 3 .........................................................36

LIST OF FIGURES

FIGURE 1. UBER EXPANSION ............................................................................................15

FIGURE 2. TTI AND CSI MEASURES IN 2014 ..................................................................17

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SECTION 1: INTRODUCTION

The purpose of this study is to gather data regarding the rise of sharing economy

transportation services and examine the effects that this has on the habits of individuals regarding

fuel consumption and transportation in urban areas. Research into the effects that the sharing

economy has on transportation habits has increased in recent years and has led to important

conclusions for future research and policy implications for Metropolitan Statistical Areas

(MSAs). I expected that this analysis would conclude that transportation networking companies

(TNCs) have led to a reduction in personal-vehicle driving, an increase in public transportation

usage, a reduction in traffic, and thus the hypothesis I tested is as follows: As a result of TNC

entry into MSAs, there would be a statistically significant reduction in the excess automobile

fuel consumption in these regions.

The rise of the sharing economy has made fundamental changes to the ways in which

both individuals and businesses interact with one another, choose accommodations, do their

shopping, and even how they seek transportation from point A to point B. Services such as Uber,

Airbnb, Instacart, and Lending Club have shifted the habits of the average consumer, especially

in urban areas where these services are more concentrated. Technological advances are a large

part of this evolution that has taken place since the dawn of the 21st century; individuals are now,

more than ever, able to request services from anywhere, on demand.

One of the earliest mentions of the term “sharing economy” (also known as the gig

economy, the shareconomy, collective consumption, and the peer economy, among others) was

from Harvard Law Professor, Lawrence Lessig, in 2008 referring to the concept of individuals to

enter into transactions of sharing and renting, rather than entering an agreement of ownership

(Kummer 2007). However, while Lessig may have popularized the term, the idea can be traced

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even earlier to another Harvard Law Professor, Yochai Benkler, who suggested “commons-based

peer production” (2002), and later in his paper Sharing Nicely: On Shareable Goods and the

Emergence of Sharing as a Modality of Economic Production, asserted that,

“The technological state of society, particularly the extent to which individual

agents can engage in efficacious production activities with material resources

under their individual control, affect opportunities for, and hence the comparative

prevalence and salience of, social, market (both price based and managerial), and

state production modalities… Technology does not determine the level of sharing.

But it does set threshold constraints on the effective domain of sharing as a

modality of economic production.” (Benkler 2004).

As the sharing economy continues to grow and change, we must be careful to determine

the effects that these changes are having on the regions and economies in which they are taking

place. For instance, potential effects include the use of public transportation, traffic congestion,

time spent in transit, and fuel consumption in the urban areas where TNCs such as Uber are most

prevalent.

In the 2016 study, Do Ride-Sharing Services Affect Traffic Congestion? An Empirical

Study of Uber Entry, the researchers examine the effects that the market entry of Uber has on the

traffic congestion of MSAs. The analysis concludes that Uber’s entry into a market serves to

decrease traffic congestion (Li 2016).

Further, in the research report, Shared Mobility and the Transformation of Public Transit,

the American Public Transportation Association (APTA) examines the relationship between

ride-sharing platforms and the use of public transportation in urban areas. The key findings of the

analysis include that transportation network users also use public transportation as a

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complementary service, spend less on transportation costs, and are less likely to own cars

(Shared Use Mobility Center 2016).

Using the groundwork that has been provided by these findings and analyses, I further

investigate the effect of sharing economy transportation services such as Uber. By drawing upon

the data sources on the expansion of these services, Metropolitan Statistical Area (MSA) excess

fuel consumption, traffic congestion, public transportation usage and consumption statistics,

personal driving and commuting, and economic indicators, I determine the changes that this 21st

century change has wrought upon the fuel use and transportation habits of individuals in the 50

largest metropolitan centers of the United States.

This paper will continue as follows. In section 2, I will review the relevant literature on

the subject and provide background information. Section 3 contains the theoretical framework,

and the methodology and limitations. In section 4, I will explain the data and methods of the

analysis. Section 5 consists of the empirical model. Section 6 contains the empirical results.

Section 7 includes the conclusions, policy implications of the findings, and recommendations for

future research.

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SECTION 2: BACKGROUND AND REVIEW OF RELEVANT LITERATURE

Academic studies increasingly have been addressing aspects of the sharing economy in

recent years as it continues to expand into new areas of consumption and daily life. The sharing

economy has been studied in a broad sense to provide insight into its effects on habits, markets,

and consumption; like the effects of previously emerging technologies and their diffusion. In

addition, the emergence of sharing economy transportation options has also been studied, and

this study continues, to better understand the effects and outcomes of these services on the status

quo. First, I will cover general literature on new technology entry and diffusion, and their impact

on consumer habits.

Technology Adoption and Diffusion

When technological innovation leads to breakthroughs, the diffusion of these new

technologies can lead to substantial changes in the habits and behaviors of consumers.

Technologies such as the personal computer and the cellular phone have had considerable effects

on how individuals communicate, make purchases, consume education, and conduct business. As

new innovations grow and overtake aging products, a diffusion process occurs by which these

new technologies spread and become the norm. This process can make the market broader and

allow a wider set of individuals to participate; it can allow individuals to substitute new

technology for older ones; and it can stimulate continuing innovation (Norton and Bass 1987).

Diffusion theory states that new technologies must be widely adopted to succeed and

individuals follow a normally distributed adoption pattern, a five-stage decision-making process:

knowledge, persuasion, decision, implementation, and confirmation (Rogers 2003). In addition,

the consequences of adoption of a new technological innovation include three different

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subgroups: direct or indirect, desirable or undesirable, and anticipated or unanticipated (Rogers

2003). However, while diffusion theory relies on the assumption that individuals will make

decisions based on economic principles, Redmond suggests that an institutional perspective of

individualism is also needed to understand consumer trends including technology (2003).

Further, communication has often been thought of as a limiting factor in the spread of new

innovations and their diffusion into wide use from innovators to adopters (Redmond 2003).

There are three major variables in the diffusion of innovation, per Barbara Wejnert. The

first is the “characteristics of innovations,” which include whether the consequences of diffusion

are public versus private, and the costs versus benefits of the innovation (Wejnert 2002). The

second variable is the “characteristics of innovators” that encompasses factors such as

familiarity, status, socioeconomic position, and personal characteristics (Wejnert 2002). The

third variable is the “environmental context” of the innovation, including geography, societal

culture, and politics (Wejnert 2002). These factors are important to consider when examining the

effects that Uber has had on consumer transportation habits and the context in which the service

became widely adopted in urban areas. Next I will cover the emergence of the sharing economy.

Sharing Economy

The sharing economy is a recent phenomenon that has created a new type of consumption

method by which individuals can be directly connected to and easily and efficiently share goods

and services. Technological advances have played a key role in the emergence of the sharing

economy as the clear majority of individuals in American urban areas now have networked

access to one another at all times by way of smart phones. The sharing economy has penetrated

the markets in areas such as real estate and living accommodations, delivery services,

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crowdfunding platforms, loans and financing, and of course the urban transportation industry. In

2014, the worldwide sharing economy market was estimated to be worth $26 billion and was

predicted that it would grow to over $100 billion in the coming years (Cannon 2016), matching

the market share of traditional rental sectors by 2025 (PwC 2014).

While the emergence of the sharing economy is a recent phenomenon and much of the

literature on the subject is relatively fresh (within the last five years), the idea of its emergence

was implied as early as 2002 by Yochai Benkler. First mentioned in the form of “commons-

based peer production,” as a new form of production that would be facilitated by advances in

networked technology and large groups of individuals to work on projects together (Benkler

2002). Later, it was posited that technology was a central (but not limiting) factor of this type of

production that, “… sets threshold constraints on the effective domain of sharing as a modality of

economic production” (Benkler 2004). Soon after, the term “sharing economy” was applied by

Lawrence Lessig when discussing the role of the Internet in the emergence of this type of

production (Kummer 2007).

The sharing economy, also often referred to as collaborative consumption and peer-to-

peer markets, has grown rapidly over the last decade and has extended to many aspects of

consumer life. One of the pioneers of the sharing economy is Airbnb, which connects property

owners with potential renters and facilitate transactions between them for short-term rental

agreements. Recent research has uncovered that Airbnb is having impacts on the price-setting

behavior of hotels, and in deeply penetrated markets they are “… successfully competing with,

differentiating from, and acquiring market share from incumbent firms” (Zervas 2016). A key to

Airbnb’s success (and that of the sharing economy) is the ability for property owners to

maximize the consumption of their commodity by utilizing extra bedrooms and second

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properties while not in use, or allowing for use of property while they are on vacation. Like other

aspects of the sharing economy, the key is that individuals are “… monetizing under-utilized

infrastructure” (Kalathil 2016). Another important aspect of the success of the sharing economy

is the reduction in transaction costs that are facilitated by the technology that individuals have in

their pockets (Sundararajan 2013).

The sharing economy has not necessarily come into being without scrutiny and without

ruffling the feathers of more entrenched industries and regulations. While the sharing economy

has many positive aspects and the goals of those participating are generally in alignment with

municipal and city governments, they are often at odds with these regulators (Cannon 2014).

City regulations are generally based on the status quo of entrenched sectors and interests in each

area due to default bias, and the emergence of peer-to-peer services can be a foreign and

threatening prospect. In recent years, companies like Airbnb and Uber have been at odds with

city regulators in many areas, which have gone as far as the firms leaving certain markets due to

regulatory issues (Vock 2016, Cannon 2014).1 Many of these firms have resolved regulatory

disputes in the areas where they operate, and moving forward, both governments and sharing

economy companies should seek to be more responsive and work together to best serve the

individuals and regional economies in which they operate (Cannon 2014).

In addition, there has been criticism of the moniker ‘sharing economy’ because it may not

give an accurate signal of the intent behind this type of consumption. The reason for this is

because it implies that individuals are sharing purely out of the goodness of their hearts, and

there are certainly some selfless reasons for collaborative consumption such as sustainability and

reduced impact on the environment (Hamari 2016). However, on balance, the primary incentive

1 Uber and Lyft opted to withdraw from the Austin, TX market rather than subject their drivers to intrusive background check regulations that were implemented as a response to Transportation Networking Company (TNC) operation in the city.

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for participating in the sharing economy is financial. While the name we use to describe this new

consumption pattern may not be of the utmost importance to actually understanding it, a more

accurate term may be peer-to-peer business (Tuttle 2014). I now turn to TNCs as a specific

aspect of the sharing economy.

TNCs and Uber

Another of the pioneers of the sharing economy is Uber, a transportation networking

company (TNC) which uses a mobile app to facilitate the connection of vehicle and rider for

transportation in cities. TNCs have become more and more common over the last decade, with

small local or regional services giving way to national and global companies. Examples include

Car2go, Zipcar, ReachNow, Via, Lyft, and Uber. In large MSAs of the United States, these

services have become almost ubiquitous, as they are present in at least 57 of the largest 60

metropolitan areas. Uber has taken the largest market share of any of these companies as one of

the earliest and most aggressive firms.

Uber was founded as a startup in 2009, but the mobile app and car service went into

active service in 2011 in the San Francisco area. The idea is simple: an individual seeking a ride

can download the app, specify his or her departure location and destination, and connect to an

independently contracted driver who will arrive shortly. All payment information and

transactions take place digitally with tip included in the fare, so no physical money or credit card

ever changes hands. Riders have options available to specify the type of car a rider would like or

to share the car with other riders in a sort of carpool. The service quickly spread to cities like

New York City, Seattle, Chicago, and Boston in 2011 and moved to international cities by the

end of that year (Uber Statistics 2016).

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As of 2016, Uber operates in 60 countries and more than 300 cities (Uber Statistics

2016), and in late 2015 the company was given a valuation of $62.5 billion (Newcomer 2015). In

addition, there are more than 160,000 active independently contracted drivers and while car

rentals have stayed relatively consistent in recent years, Uber use has pulled almost level with

traditional taxi services (Uber Statistics 2016). The success of Uber has allowed the company to

branch out into areas such as food service, delivery, and is even working toward using a fleet of

driverless cars to transport customers (Newcomer 2015). Indeed, other efforts have followed the

lead of Uber, such as a similar car-hailing company called Lyft. While very similar in practice,

Lyft generates a fraction of the revenue that Uber is able to bring in due to Uber’s first-mover

advantage. However, it does appear that Lyft benefits from Uber operating in a market because it

provides exposure and competition and it has generated higher rider engagement with its friendly

business model (Huet 2015).

Local and state regulatory issues have created problems for Uber and its expansion, as

previously mentioned, as well as opposition from taxi interests, and controversy with how Uber

classifies its drivers as independent contractors without the benefits of employee status. Many

areas have temporarily or permanently implemented regulations that have caused Uber to leave

or to not enter in the first place. These include cities in upstate New York, Austin, and Seattle,

among numerous others; however, for the most part, Uber has been allowed to return

(McAndrew 2016, Vock 2016). Uber continues to not operate Austin, Texas and upstate New

York cities such as Rochester and Buffalo.

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Effects of Uber

TNCs like Uber and Lyft have created a paradigm shift in how individuals move around

their cities for commuting, school, and other transportation needs. Studies have shown that Uber

and similar services are changing how individuals use public transportation such as buses and

trains, commute times and traffic congestion, rates of emissions, and even car ownership in

certain areas.

One of the primary concerns with the rise of Uber has been the effect that it may or may

not have on traffic congestion, especially considering that the areas in which it operates the most

are large cities with busy traffic cycles. Researchers from Arizona State University’s Carey

Business School examined the effect that the entry of Uber has on traffic congestion of urban

areas and found that there is a significant effect. They concluded that the entry of Uber into a

market significantly decreases traffic congestion and may be an important aspect of any plan to

reduce it (Li 2016). The study suggests that this may be due to increased total vehicle capacity,

reduction in car ownership, and fewer cars on the road looking for fares (Li 2016).

Another effect of Uber that has merited study is the relationship between it and public

transportation usage in cities. In 2016 the American Public Transportation Association (APTA)

examined the relationship between ride-sharing platforms and the use of public transportation in

urban areas. The key findings of the analysis are that individuals who increasingly use shared

transportation methods also are more likely to take advantage of public transportation and are

spending less in transportation costs (Shared Use Mobility Center 2016). It also finds that ride-

sharing services tend to complement public transportation, and are more akin to a substitute for

driving a car than a substitute for public transit (Shared Use Mobility Center 2016). Further, the

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analysis finds that as individuals increasingly use TNCs, they are less likely to own cars (Shared

Use Mobility Center 2016).

The effect of car-sharing services on vehicle ownership has been examined in multiple

studies in addition to the Shared Mobility report. In one such study, Martin, Shaheen, and

Lidicker (2011) found that joining a car-sharing service has a statistically significant negative

effect on car ownership; that is, individuals who become members of these services are less

likely to own vehicles over time. This finding is echoed by a study on San Francisco’s

pioneering service, CarShare, which found that members of this service tended to sell their

current cars and postpone or forego purchasing a new one (Cervero 2007). In addition, this study

suggests that participation in a ride-sharing service is a ‘self-reinforcing’ behavior in that it is

associated with not owning a car, and not owning a car is associated with using the service

(Cervero 2007). Lastly, for the single metropolitan area that was addressed in the study,

increased use of car-sharing, the tendency for these vehicles to be small and fuel efficient,

mindfulness of impact by the members, and fewer cars on the road, have all led to a decreasing

trend of gasoline consumption and greenhouse gas (GHG) emissions (Cervero 2007).

Contribution to Existing Literature

The effects of TNCs have been substantial to the habits and effects of transportation by

individuals in large metropolitan areas. And as these services become more institutionalized and

part of the status quo, we must be vigilant to explore all the effects that are taking place and to

best measure and understand them.

Previous studies have suggested that Uber and other TNCs are having significant impacts

on transportation within cities, including habits, public transportation, social effects, and

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consumption. Many of these effects have been studied, but one area that has received less

attention is the effect that these services have had on fuel consumption from automobile use in

urban areas. Cervero (2007) did find a relationship between car-sharing service use and a

downward trend in fuel consumption and emissions in the San Francisco area, however I seek to

determine if this effect is consistent across disparate cities. In addition, today these services are

much more common and accessible, with Uber and Lyft dominating the industry. This study

seeks to determine if the entry and expansion of TNCs, and specifically Uber, has had a

significant effect on the consumption of auto fuel in major metropolitan areas of the United

States.

I next turn to the theoretical framework that guides my analysis.

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SECTION 3: THEORETICAL FRAMEWORK

I use the theoretical framework that follows to explore the effect that Uber and TNC

entry exerts on automotive fuel consumption in urban areas. This theoretical framework serves as

the basis for the empirical model that I use to test my hypothesis and determine the effects of

TNC entry on consumption.

FC = f(U, PT, T, D, e) (1)

FC represents fuel consumption; U represents the presence or absence of Uber operation (and

other TNCs) in an urban area; PT represents indicators related to public transportation such as

ridership and fuel; T represents traffic congestion and driving indicators; D represents

demographic and economic indicators; and e represents the error and omitted variables.

The theoretical framework serves to illustrate the relationship between the entrance of

services such as Uber and personal use of automobiles for transportation in large urban areas. By

observing each area in a time before the entrance (2004) and a time after use of TNCs becomes

commonplace (2014), I am able explore the shifts in habit that occur due to this change. The idea

is that once this new option is available, an average individual will adjust their habits with regard

to personal driving, public transportation, and even car ownership, which will change their

overall transportation portfolio. Previous studies have analyzed the significant effects that not

only the sharing economy, but specifically that TNCs like Uber have had on public

transportation, car ownership, regulatory action, and traffic congestion. This study seeks to

utilize those results and to build upon the base of knowledge in order to better understand the

consumption habits of individuals before and after the adoption of Uber.

I next describe the data I use for my study.

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SECTION 4: DATA AND DECRIPTIVE STATISTICS

The data that I used to explore my hypothesis come from multiple sources to best

construct a complete picture of the effect that Uber entry into Metropolitan Statistical Areas has

had in the United States. These include fuel consumption and sales data from the Urban Mobility

Report (UMR) from the Texas A&M University Transportation Institute (TAMUTI) and United

States Energy Information Administration (EIA), dates of the entry of Uber into markets from

the Uber newsroom and from media outlets, traffic and consumption information in the Urban

Mobility Report, ridership and fuel consumption by public transportation services from the

Federal Transit Administration’s (FTA) National Transit Database (NTD), as well as

demographic and control data from the United States Census Bureau, the Bureau of Economic

Analysis (BEA), and the Bureau of Labor Statistics (BLS).

I used multiple econometric models to best analyze the relationship between Uber entry

into urban areas and fuel consumption from transportation. The models consider data from two

specific years before and after the expansion of Uber into most major metropolitan areas in the

United States. These years are 2004 for before, and 2014 for after, because much of the data that

are being used are only available up to 2014, and because these two years offer a good snap shot

of the pre- and post-Uber operation states of these regions in the United States. Each

Metropolitan Statistical Area has two observation rows to account for the values of each variable

in the years 2004 and 2014.

The total master dataset is a combination of these data sources merged to include the

most important indicators and control variables for this analysis. This dataset is comprised on

100 observations; two for each of the 50 largest MSAs in the United States in each of the years

of the analysis. Each observation has MSA- and year-specific data points for five public

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transportation variables, eight variables for automobile and road use, four demographic and

economic variables, an Uber service variable, and the fuel consumption variable.

The primary dependent variable that I utilize will be a measure of excess automotive fuel

consumption in urban areas of interest from the UMR data. This variable is a measure of the

excess fuel that is consumed by private individuals in automobiles beyond the amount that is

required for transit from their departure location to their destination. This excess use can be due

to congestion, traffic, seeking parking, waiting, and other reasons, and it is measured in

thousands of gallons of fuel. Gasoline and diesel fuel sales statistics from the EIA were utilized

to augment the dataset, however the limitations of this data due to missing values and level of

analysis disallowed use as a dependent variable. In addition, a second dependent variable that

will be used is a natural logarithm transformation measure of excess fuel consumption from the

UMR. The regression models will consider these values in each of the two years of interest to

estimate the relationship between Uber and fuel consumption.

Figure 1. Uber Expansion. (Source: Uber Statistics Report)

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The independent variables that I include are a binary variable for Uber operation in the

MSA that takes on one of two possible values (0 or 1) depending on if Uber is operating in the

area. The value of this variable depends on the corresponding year of observation. In the fall of

2015, Uber was operating in over 300 cities worldwide and over 200 in the United States (Uber

Statistics 2016). Due to regulatory barriers, two of the MSAs in this analysis do not currently

allow Uber to operate within the area; the service does not operate in Buffalo, New York, and

while Uber did operate in Austin, Texas in 2014, it has since pulled out of the market due to city

policy.2

Another independent variable that is included in the models is fuel consumption of public

transportation services in each of the areas of interest from the NTD. Also from the NTD, I use

two variables for passenger ridership on public transit that include passenger miles traveled and

actual number of passenger rides. These data were amassed by compiling statistics from the

National Transportation Database for each of the public transit service and company in each of

the MSAs for street and rail transportation methods. After compilation into datasets for each

MSA, the data for fuel use and passenger ridership were merged into the master dataset. These

variables are measured in thousands of gallons of gasoline or diesel consumed, total number of

passenger rides, and total number of passenger miles on public transportation.

From the UMR, I use two variables for the number of miles traveled which are measures

for average daily miles of travel on highways and arterial streets. These data were collected by

the Texas A&M University Transportation Institute and is measured in surfaces miles traveled.

Further control variables from the UMR include a categorical variable for urban area size based

on the categories provided by TAMUTI and corresponds to “very large,” “large,” and “medium” 2 Uber and Lyft opted to withdraw from the Austin, TX market rather than subject their drivers to intrusive background check regulations that were implemented as a response to Transportation Networking Company (TNC) operation in the city.

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Metropolitan Statistical Area categories. In addition, average fuel prices for both gasoline and

diesel are included and measured in average real dollars per gallon over each of the two time

periods. The Travel Time Index (TTI) and the Commuter Stress Index (CSI) are indices that are

created from data collected for the UMR to provide numerical values for these aspects. The TTI

is a ratio of the time it takes to drive a distance in peak traffic periods to the time it takes to drive

the same distance at normal speed limits.3 The CSI is a similar measure is based only on the

busier direction of travel during peak hours. Both the TTI and CSI indices take on values

between 1 and 2 with high values corresponding to more congestion. Figure 2 is a map that

shows relative TTI (size of circle) and CSI (shade of circle) values in the MSAs included in this

analysis.

Figure 2. TTI and CSI measures in 2014.

3 For instance, if peak travel is 15 mins and normal is 10 minutes, the TTI would be equal to 1.5.

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Annual congestion cost measures the cost generated by lost time and excess fuel use due to

congestion and is measured in dollars. Last from the UMR is an indicator of the number of

commuting drivers on highways and arterial roads in each of the MSAs during each year.

In addition, demographic and economic variables including population, employment, and

income are included for each year in each MSA. The population data were collected from the

United States Bureau of the Census estimates for the years 2004 and 2014. The employment data

for each of the years were also collected from the Census Bureau and are measured as the

average percent of the population that was classified as unemployed in each of the two years of

analysis. Income statistics for each of the MSAs were collected from the Bureau of Economic

Analysis’ Regional Data tool on their website and is measured as Gross Domestic Product per

Capita in real dollars.

In the pages that follow, Table 1 shows descriptive statistics for these data in the merged

dataset. Table 2 shows the descriptive statistics for the Urban Mobility Report dataset, Table 3

shows the descriptive statistics for the EIA Fuel Consumption Report dataset, and Tables 4 and 5

show the descriptive statistics for the National Transit Database Public Transportation datasets.

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DESCRIPTIVE STATISTICS Table 1. Master Merged Dataset Variable Observations Mean Std. Dev. Min. Max. MSA 0 STATE 0 YEAR 100 2009 5.025 2004 2014 POP 100 2864350 3082517 820000 1.9e7 POPCAT 100 2.2 0.603 1 3 UBERYEAR 22 2012.045 0.899 2010 2013 UBER 100 0.49 0.502 0 1 EXCESSFUEL 100 44004.4 48549.44 7445 296701 PUBGAS 97 477660.3 847701.1 0 3676734 PUBDIES 97 7377935 12900000 0 8.96e7 PASSMILES 98 8.86e8 2.83e9 2.27e7 2.19e10 PASSRIDES 98 1.69e8 5.36e8 6457653 4.26e9 DRIVEHW 100 25886.15 24562.84 5422 139275 DRIVEART 100 24645.12 23279.02 5649 126010 PRICEGAS 100 2.663 0.708 1.78 4.21 PRICEDIESEL 100 2.846 0.848 1.8 4.86 TTI 100 1.247 0.073 1.11 1.43 CSI 100 1.301 0.106 1.11 1.62 CONGCOST 100 2552.9 2991.573 504 16218 COMMUTERS 100 1268.23 1081.213 406 5881 EMPLOYMENT 98 0.058 0.0108 0.037 0.083 GDP 99 55209.32 12049.18 27226 104862 LOGFUEL 100 10.339 0.776 8.915 12.601 PUBFUEL 97 7855595 1.31e7 0 8.96e7 PUBFUELCAT 100 2.175 0.837 1 3

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Table 2. Urban Mobility Report Variable Observations Mean Std. Dev. Min. Max. Area 0 PopGroup 0 Year 0 Population 202 1667.475 2471.003 105 19040 PopRank 202 50.891 29.157 1 101 AutoCommuters 202 752.609 917.825 51 5881 FreewayMiles 202 14909.53 20457.15 480 139275 ArterialMiles 202 14692.83 19162.25 1025 126010 ValueofTime 202 15.885 1.789 14.1 17.67 CommercialVal 202 84.105 9.959 74.17 94.04 AverageGas 202 2.665 0.712 1.77 4.21 AverageDies 202 2.844 0.848 1.77 4.86 ExcessTotal 202 24891.72 39043.97 660 296701 ExcessRank 202 51 29.227 1 101 ExcessPerCom 202 17.718 5.853 2 35 PerConRank 202 48.262 29.555 1 101 TotalDelay 202 55989.93 94064.45 1685 628241 DelayRank 202 51 29.227 1 101 DelayPerCom 202 39.975 13.085 6 82 DPerConRank 202 49.569 29.188 1 101 TTIVal 202 1.199 0.077 1.05 1.43 TTIRank 202 48.708 28.539 1 101 CSIVal 202 1.247 0.106 1.07 1.62 CSIRank 202 49.119 28.528 1 101 CongestionCost 202 1434.441 2378.763 40 16218 CongestionRank 202 50.960 29.20503 1 101 CongestionPer 202 978.178 317.179 149 2069 CongestPerRank 202 50.926 29.217 1 101

21

Table 3. EIA Fuel Consumption Report Variable Observations Mean Std. Dev. Min. Max. Date 402 14517.14 3536.577 8415 20620 USTotalGas 402 53481.14 12416.28 17945.5 67183.3 EastCoast 271 15177.44 4549.565 2115.7 21808.8 NewEngland 197 1390.876 517.1744 615 2275 Connecticut 32 125.0313 41.241 6.5 157.8 Massachusetts 203 870.399 358.822 246.8 1445.8 RhodeIsland 161 194.2435 78.659 64.7 387 Vermont 6 2.367 0.489 1.5 2.8 CentralAtlantic 269 5851.248 1360.101 1592/7 8343.8 Maryland 101 53.642 18.152 10.5 120.9 NewJersey 228 1527.003 239.948 969.9 2317.8 NewYork 271 2486.933 564.679 962 3631.4 Pennsylvania 232 2090.706 429.191 732.1 2972.6 LowerAtlantic 213 9497.844 1427.207 554.1 11784.5 Florida 238 4544.23 1328.724 1.7 6243.5 Georgia 220 1472.261 571.0113 121 2312.9 NorthCarolina 217 720.483 246.473 2.3 1001.5 SouthCarolina 228 819.501 155.442 285 1018.8 Virginia 247 823.986 363.861 46.6 1270.2 Midwest 273 15687.33 3906.696 8658.7 21668 Illinois 215 2609.896 661.813 865.5 3415.1 Iowa 187 183.3519 51.401 96.5 322 Michigan 211 2501.498 436.835 1896.6 3404.5 Missouri 200 830.358 194.4353 5.7 1243.8 NorthDakota 94 8.29 5.327 2 24 Ohio 251 4324.868 930.489 2892.3 5881.5 SouthDakota 189 19.618 5.003 7.8 35 Tennessee 242 1197.086 369.273 24.6 2064.2 Wisconsin 104 695.1731 144.4708 407.3 875 GulfCoast 269 7862.019 3005.963 496.5 10562.2 Alabama 190 442.067 56.790 332.1 623.1 Louisiana 215 993.247 166.735 685.5 1313.2 Texas 239 6114.429 1835.634 121.8 7923.1 RockyMountain 273 1848.914 797.3308 362.6 3199.4 Colorado 273 1342.014 595.468 216.2 2156.8 Utah 198 445.239 152.099 91.2 798.6 Wyoming 254 47.834 21.737 10 104.7 WestCoast 269 10504.8 3024.428 5259.5 15004.9 Alaska 152 250.1612 61.842 108.6 342.8 California 254 7193.322 1401.005 4177.3 9115 Nevada 158 148.641 55.589 4.1 251.5 Oregon 271 401.166 200.062 99.6 677.5 Washington 272 1002.9 471.877 245.7 1692.5

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Table 4. Transit Report – 2014 Variable Observations Mean Std. Dev. Min. Max. ReporterName 0 ReporterType 0 TimeservingT 4617 -1.89e12 1.38e7 -1.89e12 -1.89e12 TimeservingP 4586 -1.89e12 2.75e7 -1.89e12 -1.89e12 VehiclesOp 1805 63.151 214.548 1 5238 VehiclesAv 1805 73.4133 244.4662 0 5323 VehiclesIn 3491 46.036 138.261 0 3138 TotalPMiles 4386 1025116 7234251 0 3.56e8 TotalMiles 4957 841423 6339414 0 3.45e8 Deadhead 4380 113390.7 709160.3 0 2.13e7 Scheduled 2541 1197581 8785288 0 3.56e8 TotalHHours 4375 66956.52 478633.8 0 2.00e7 TotalEHours 4951 55619.84 409621.5 0 1.89e7 DeadHeadHours 4374 6653.381 50288.89 0 1730677 CharterServ 676 456.108 4684.572 0 91757 SchoolBus 642 2.567 65.041 0 1648 TrainsinOp 570 146.068 490.942 0 5234 U 340 634485.9 2659142 10 3.91e7 V 340 610033.4 2562696 10 3.79e7 W 340 24452.46 118566 0 1202208 X 340 33972.71 142094.8 4 2192630 Y 340 32060.48 134263.3 4 2081480 Z 340 1912.226 8856.611 0 111150 Unlinked 4949 2142859 4.2e7 0 2.74e9 ADAUPT 476 152881.4 455576 0 6448134 Sponsored 522 12772.01 43218.57 0 465538 PassengerM 4439 1.3e7 1.9e8 0 1.12e10 DaysofServ 3264 128.857 105.996 0 365 DaysnoTop 1624 0.092 1.863 0 52 StrikeComm 0 DaysnoStrik 0 0.159 0.672 0 7 EmergencyC 0 BRTNonStat 1805 0.007 0.212 0 7.9 MixedTraff 1805 129.389 355.254 0 5624

23

Table 5. Transit Report – 2004 Variable Observations Mean Std. Dev. Min. Max. Trs_Id 92 4791.394 2660.138 1 9193 Mode_Cd 0 Service_Cd 0 TimePeriod 0 Time_Serv 4490 -1.83e12 3.24e10 -1.89e12 -1.69e12 Time_Service 4490 1527.305 725.361 0 2400 VehiclesIn 7172 40.578 150.619 0 3767 Passengers 7172 10.186 138.368 0 5171 VehicleMil 7172 442333 3167246 0 1.22e8 VehicleOr 7172 32169.28 281990.7 0 1.53e7 PassMiles 7172 141260 4575245 0 3.5e8 RevMiles 7172 135040 4412601 0 3.4e8 ScheduledMi 7172 136376.4 4524948 0 3.51e8 PassengerT 7172 6570.048 244082 0 1.96e7 PassengerU 7172 6151.398 230855.4 0 1.86e7 VehicleSc 7172 268189.2 2542119 0 1.02e8 CharterBus 7172 61.635 2478.811 0 201473 SchoolBus 7172 1.939 117.979 0 8215 UnlinkedPass 7172 1254146 2.5e7 0 1.76e9 PassengerY 7172 6530794 1.17e8 0 8.34e9 OperatedNum 0 DaysOperate 7172 105.809 131.9284 0 367 Strikes 7172 0.065 1.285 0 46 Declared 7172 0.113 1.160 0 61 ADAUPT 0

I now turn to the empirical models I will estimate.

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SECTION 5: EMPIRICAL MODELS

Model 1 LOGFUEL = β0 + β1UBER + β2POP + β3PUBGAS + β4PUBDIES + β5PASSMILES + β6PASSRIDES + β7DRIVEHW + β8DRIVEARTERIAL + β9PRICEGAS + β10PRICEDEISEL + β11TTI + β12CSI + β13CONGCOST + β14COMMUTERS + β16EMPLOYMENT + β17GDP + e

(2) Model 2 EXCESSFUEL = β0 + β1UBER + β2POP + β3PUBGAS + β4PUBDIES + β5PASSMILES + β6PASSRIDES + β7DRIVEHW + β8DRIVEARTERIAL + β9PRICEGAS + β10PRICEDEISEL + β11TTI + β12CONGCOST + β13COMMUTERS + β14EMPLOYMENT + β15GDP + e

(3) Model 3 EXCESSFUEL = β0 + β1UBER + β2POP + β3PUBFUEL + β4PASSRIDES + β5DRIVEHW + β6DRIVEARTERIAL + β7PRICEGAS + β8TTI + β9CONGCOST + β10EMPLOYMENT + β11GDP + e

(4) EXCESSFUEL is the measure of excess liquid fuel consumed in MSAs; LOGFUEL is a natural log transformation of the EXCESSFUEL variable; MSA indicates the Metropolitan Statistical Area of interest; YEAR is the year of analysis, either 2004 or 2014; POP is the population of the are based on the UMR from the year’s Census data; POPCAT is the categorical variable for population; 1, 2, or 3; UBERYEAR is the year in which Uber entered an area; UBERMONTH is the month in which Uber entered an area; UBER is a binary variable to indicate the operation of Uber in an urban area; PUBFUEL is fuel consumption from public transportation services; PUBGAS is gasoline consumption from public transportation services; PUBDIES is diesel consumption from public transportation services; PASSMILES is the measure of passenger miles traveled on public transit services; PASSRIDES is the number of passenger rides on public transportation services; DRIVEHW is number of miles driven on highways in each area; DRIVEARTERIAL is number of miles driven on arterial roads in each area; PRICEGAS is the average price of gas in each area; PRICEDIESEL is the average price of diesel in each area; TTI is the Transportation Time Index from the UMR; CSI is the Commuter Stress Index from the UMR; CONGCOST is the measure of the annual cost of congestion in each area; COMMUTERS is the indicator for the number of drivers in each area; EMPLOYMENT is a measure of the average employment rate in each area; GDP is an economic measure of per capita income in each area; LYFT is a binary indicator for the operation or Lyft in each area; STATE indicates the state in which the urban area is located; e is the random error.

25

EXCESSFUEL serves as a dependent variable in my models and is a measure of excess

liquid fuel consumed in Metropolitan Statistical Areas from the EIA and UMR, and is measured

in thousands of gallons for the years 2014 and 2004. In addition, LOGFUEL is a natural

logarithm transformation of this variable for use in a log-linear regression model. My

expectations for the independent variables are as follow.

UBER is a binary indicator variable for the presence or absence of Uber operation in the

given urban area and year, and will serve as the primary independent variable. Theses data are

collected from press releases, the Uber website, and the Uber Statistics Report from 2016. My

expectation was that the coefficient for UBER will be negative in all models effecting a decrease

in fuel consumption due in part to the operation of Uber in each of the Metropolitan Statistical

Areas.

DRIVEHW, DRIVEARTERIAL, DRIVE, PRICEGAS, PRICEDIESEL, TTI, CSI,

CONGCOST, COMMUTERS are all independent variables from the Urban Mobility Report that

act as control variables for the analysis. They represent miles driven on highways, miles driven

on arterial roads, miles driven on both highways and arterial roads, average price of gasoline,

average price of diesel, the Transportation Time Index, the Consumer Stress Index, the cost of

congestion, and number of commuters. These variables provide information about fuel use,

driving volume, gas prices, travel time, and congestion from the years 2004 and 2014. I expected

that DRIVEHW, DRIVEARTERIAL, and DRIVE to all have positive coefficients because

increases in miles driven should result in increased consumption of fuel. I also expected that TTI,

CSI, CONGCOST, and COMMUTERS to have positive coefficients as an increase in each of the

indices, costs of congestion, or number of commuters should also result in an increased volume

of fuel consumed in a given year. My expectations for PRICEGAS and PRICEDIESEL were

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more tentative due to the necessity of driving for many individuals and small fluctuations in

prices having minimal effect due to the inelastic price elasticity of demand of auto fuel. But, I

assumed that increased prices of gas or diesel would result in less fuel being consumed, and

therefore I expected a negative coefficient for both.

PUBGAS, PUBDIES, PUBFUEL, PASSMILES, PASSRIDES are independent variables

from the National Transportation Database that act as control variables for the analysis. They

represent amount of gasoline consumed by public transportation services in Metropolitan

Statistical Areas, diesel consumed by public transportation services in MSAs, gasoline and diesel

consumed by public transportation in MSAs, number of passenger miles traveled on public

transit services, and number of passenger rides on public transportation services. These variables

describe public transportation trends regarding fuel use and passenger use in the years 2004 and

2014. My expectation for PUBGAS, PUBDIES, and PUBFUEL was that each would have a

negative coefficient due to the relationship observed in previous literature that increased public

transportation use decreases individual fuel consumption and that public transit and TNCs are

complementary transportation services. I also expected that PASSMILES and PASSRIDES

would have negative coefficients because as more passengers ride public transportation services

for longer distances, the amount of fuel consumed by individuals should decrease.

POPULATION, POPCAT, EMPLOYMENT, and GDP are demographic and economic

measurement variables from to control for differences between the Metropolitan Statistical

Areas. This information comes from the United States Bureau of the Census, Bureau of

Economic Analysis (BEA), and Bureau of Labor Statistics (BLS) statistics. I expected that the

POP variable for MSA population in each year would have a positive coefficient because as

population increases, the amount of fuel consumed increases as well, holding all other variables

27

constant. The POPCAT variable only was used for sensitivity testing, but I expect that it would

result in a positive coefficient for the same reasons. The expectation for the coefficient for

EMPLOYMENT was less clear-cut than many of the others because increased unemployment

may lead to less car ownership and less transportation, but it is also unclear whether the

contractors that drive Uber vehicles are considered to have full employment or not. With this in

mind, I expected that the coefficient for EMPLOYMENT to have a positive value suggesting

that increased employment leads to increased consumption of fuel. I also expected that the

coefficient for GDP would be positive because as income increases, more people would consume

fuel.

I now discuss the results of my estimated equations.

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SECTION 6: EMPIRICAL RESULTS

To estimate the effect of Uber entry on the consumption of automobile fuel in American

metropolitan statistical areas, I estimated three fixed effects regression models, absorbing MSA

using difference-in-differences, to determine the presence and magnitude of this effect. In

addition, I estimated multiple variants of my models and alternative models to test the sensitivity

of my analysis and determine the strength and predictive power of my results.

Each of the three main models that I estimated resulted in significant F-statistics and high

R-squared outcomes, suggesting that my regression models have a good fit and are statistically

significant. The primary effect of interest is that of the indicator variable for Uber on the

dependent variables measuring excess fuel consumption. Based on previous literature and my

exploration, I expected that the presence of Uber in an MSA would have a significant effect on

fuel consumed by individuals, and my models suggested that my hypothesis was correct.

Table 6. Model 1. Excess Fuel. Variable Coefficient Robust SE t-score p-value UBER -1231.814 767.409 -1.61 0.118 POP 0.0123 0.0034 3.66 0.001*** PUBGAS 0.0007 0.00042 1.61 0.118 PUBDIES -0.0002 0.00008 -2.79 0.009*** PASSMILES 1.61e-6 2.76e-6 0.58 0.563 PASSRIDES 3.81e-6 0.00001 0.33 0.744 DRIVEHW -0.341 0.129 -2.65 0.012** DRIVEART 0.278 0.151 1.84 0.075* PRICEGAS 1475.058 2995.68 0.49 0.626 PRICEDIES -779.69 2547.517 -0.31 0.762 TTI 109062.7 31658.55 3.44 0.002*** CONGCOST 3.109 1.212 2.56 0.015** COMMUTERS 11.45 5.8 1.97 0.057* EMPLOYMENT 9.299 18.42 0.5 0.617 GDP 0.088 0.067 1.31 0.2 Constant -154551.8 39206.46 -3.94 0.000***

Pr > F(15, 32) = 0.0001 * p<0.1; ** p<0.05; ** p<0.01 R-squared: 0.9998 Adjusted R-squared: 0.9933

29

Model 1 shows the results for the first model I estimated in which the fuel variable

(EXCESSFUEL) was not transformed and measured in thousands of gallons to determine the

volume effect of Uber presence on excess fuel consumption (Table 6). The result for this model

is close to being statistically significant at the 90 percent level (p=0.118) and the coefficient is -

1,231.814. This model suggests that Uber entry into a MSA has an important effect and that the

magnitude of this effect is equal to a reduction in 1,231,814 gallons of excess fuel between the

years 2004 and 2014. While not strictly statistically significant at or above the 90% level, Model

1 is in line with the hypothesized results of the analysis. Also, Model 1 appears to have

considerable statistical significance in the control variables and fit, suggesting that there is a

linear relationship between the presence of Uber services and excess fuel consumption.

Table 7. Model 2. Logged Fuel. Variable Coefficient Robust SE t-score p-value UBER -0.046 0.026 1.77 0.087* POP 6.89e-8 7.6e-8 0.91 0.371 PUBGAS -1.89e-8 1.06e-8 -1.79 0.084* PUBDIES -2.7e-10 2.46e-9 -0.11 0.913 PASSMILES -9.97e-12 1.24e-10 -0.08 0.936 PASSRIDES -9.99e-12 4.91e-10 -0.02 0.984 DRIVEHW -3e-6 3.9e-6 -0.77 0.447 DRIVEART 1.6e-6 5.23e-6 0.30 0.763 PRICEGAS -0.125 0.122 -1.03 0.313 PRICEDIES 0.144 0.099 1.44 0.160 TTI 0.589 1.592 0.37 0.714 CSI 1.004 1.436 0.70 0.490 CONGCOST 0.00008 0.00003 2.73 0.010 COMMUTERS 0.0003 0.00018 1.99 0.056 EMPLOYMENT -0.0007 0.00048 -1.46 0.155 GDP 3.37e-6 2.23e-6 1.51 0.141 Constant 7.221 0.669 10.78 0.000***

Pr > F(13, 31) = 0.0001 * p<0.1; ** p<0.05; ** p<0.01 R-squared: 0.9992 Adjusted R-squared: 0.9977

Model 2 shows the results for the model I estimated in which the fuel variable was

transformed using a natural logarithm (LOGFUEL) in order to determine the percentage effect of

30

Uber presence on excess fuel consumption (Table 7). The result for this model was statistically

significant at the 90 percent level (p=0.087) with a coefficient of 0.046, suggesting that the move

from no Uber operation to Uber operation in a MSA explains a 4.495 percent change in excess

fuel consumption between the years 2004 and 2014. The resulting coefficient for Uber operation

in Model 2 did have a slightly stronger statistical significance than that in Model 1. This result is

in line with Model 1, my hypothesis, and the literature exploring the effects on Uber and TNCs

on transportation habits of individuals in cities.

Table 8. Model 3. Multicollinearity Adjustment.

Pr > F(11, 36) = 0.0001 * p<0.1; ** p<0.05; ** p<0.01 R-squared: 0.9997 Adjusted R-squared: 0.9922

Model 3 shows the fixed effects regression results for a third model that omits some of

the control variables included in previous models in order to account for multicollinearity

between control variables (Table 8). In this model, public transit passenger miles (PASSMILES),

diesel price (PRICEDIES), and commuters on the road (COMMUTERS) were omitted; and

public transit gasoline and diesel (PUBGAS and PUBDIES) were combined into a single

variable for total public transit fossil fuel use (PUBFUEL). The effect of Uber operation in this

model was not statistically significant at the 90 percent level (p=0.189) with a coefficient of -

Variable Coefficient Robust SE t-score p-value UBER -932.666 696.675 -1.34 0.189 POP 0.0176 0.0024 7.41 0.001*** PUBFUEL -0.00007 0.00007 -1.05 0.301 PASSRIDES 8.61e-6 2.27e-6 3.8 0.001*** DRIVEHW -0.459 0.134 -3.42 0.002*** DRIVEART 0.540 0.145 3.72 0.001*** PRICEGAS 626.434 485.278 1.29 0.205 TTI 112215.5 35483.45 3.16 0.003*** CONGCOST 2.480 1.163 2.13 0.040** EMPLOYMENT 19.203 18.569 1.03 0.308 GDP 0.141 0.074 1.90 0.065* Constant -164171.7 43906.94 -3.74 0.001***

31

932.666. This suggests that the entry of Uber into a MSA does decrease the consumption of

automobile fuel by 932,666 gallons between the years 2004 and 2014, though not in a

statistically significant manner. This result is still in line with the hypothesized effect of Uber

entry into metropolitan areas, however, the omission of important control variables such as

driving indicators is likely the cause for the change in coefficient magnitude and significance.

Multicollinearity of the control variables was present, however some of the variables are

inherently correlated because of the similar collection methods and measures (such as arterial

and highway driving, or public transit passenger miles and passenger rides).

Other variables of interest in the regression analysis are the control variables that were

included in each of the models. Population (POP), public transit diesel use (PUBDEIS), driven

miles on highways and arterial roads (DRIVEHW and DRIVEART), the transportation time

index (TTI), the cost of congestion (CONGCOST), and the number of commuters on the road

(COMMUTERS) each had a statistically significant effect on the amount of excess fuel in Model

1. Public transit gasoline use (PUBGAS), public transportation passenger miles and rides

(PASSMILES and PASSRIDES), the average price of gasoline and diesel (PRICEGAS and

PRICEDIES), rates of unemployment (EMPLOYMENT), and GDP per capita in each MSA

(GDP) each did not show a statistically significant effect (Table 6).

The coefficient for population translated into a positive 12.3 gallons of additional excess

fuel consumed for each additional resident in an MSA; this is equivalent to the prediction of a

positive coefficient. Driven miles on arterial roads had a coefficient 0.278 gallons of fuel

consumed for every additional mile traveled on arterial roads. Interestingly, the coefficient for

miles driven on highways suggested that for each additional mile on the highway, excess fuel

consumption decreases by 0.341 gallons, in opposition to my prediction, perhaps due to the

32

increase in fuel efficiency per highway mile driven compared to those in the city. The coefficient

for congestion cost translated into 3.109 gallons of excess fuel for every $1,000 of costs due to

congestion of roads. The coefficient for commuters on the road suggests that for each additional

commuter, there will be 11.45 gallons of additional excess fuel consumption. In addition, the

coefficient for the Transportation Time Index showed that excess fuel consumption increases by

10,906,270 gallons for each tenth of a point that the index increases.4 This result (other than for

highway driving) was expected as each of these variables correlate with the amount of fuel used

by automobiles; increased population leads to more cars on the road, more miles driven on roads

leads to increased consumption of fuel, increased congestion results in more excess fuel

consumed, and more commuters suggests increased consumption.

The amount of diesel consumed by public transit showed a statistically significant effect

at the 99 percent level with a slightly negative coefficient, showing a decrease of 0.2 gallons of

excess fuel per gallon of transit diesel. Interestingly, the coefficient for public transit gasoline use

was positive, showing an increase of 0.7 gallons of excess fuel for every gallon, although it is not

statistically significant. Approximately 94 percent of fossil fuel use by public transportation

services in the MSAs in this analysis is in the form of diesel for busses, which at least partially

explains the significance of the diesel variable. This result suggests that increased use of public

transportation decreased the amount of fuel consumed by automobile commuters. This is due to

the relationship that is created between TNCs and public transportation; the combination allows

for adequate transportation without having to own a car. Often public transit on its own is

inadequate as a single entity of transportation and the entry of TNC service into MSAs provides

that bridge. This is in line with previously discussed literature which suggests that TNCs and

4 In this analysis, the TTI has a minimum of 1.11 and a maximum of 1.43.

33

public transit are complementary services which are often used as substitutes for owning or

driving a personal car.

In addition to the above control variables were the results for the variables that did not

show statistically significant effects on the analysis. Neither the coefficient for passenger miles

on public transit nor that for number of passenger rides on public transit were statistically

significant. However, each of the coefficients were positive (0.0016 and 0.0048 additional

gallons of excess fuel per mile or ride, respectively), albeit with a very small magnitude, which

went against the expectation of a negative sign that would suggest more public transportation use

leads to less fuel consumption. The coefficient for gasoline price was positive (1,475.1),

suggesting that more excess fuel is consumed as the price of gasoline increases. This was also

against my expectations, but may be because the average price of gasoline was higher in 2014

and populations had increased. The coefficient for diesel price showed the expected result, a

decrease in 779,690 gallons of excess fuel for every dollar that diesel prices increase. Although

the results of these two variables are interesting, neither of these coefficients had statistical

significance. Both the coefficients for employment and for GDP per capita showed positive

coefficients (9.299 and 0.088, respectively), following my prediction that as employment and

personal income increase, the personal consumption of automotive fuel will also increase. Once

again though, neither of these coefficients for these variables showed a statistically significant

effect. As noted before, multicollinearity may have a significant impact on all of these results.

The control variables in Model 2 generally followed the trends of those in Model 1, but

less statistical significance was found, suggesting that there is a linear relationship between

excess fuel consumption and these variables. The only control variable that showed statistical

significance in Model 2 was public transit gasoline consumption (PUBGAS), with a negative and

34

diminuative coefficient of -0.0000000189, suggesting that for each additional gallon of public

transportation gasoline, excess fuel was reduced by 0.000002 percent. Other variables that had

differing results from Model 1 included PASSMILES, PASSRIDES, PRICEGAS, PRICEDIES,

and EMPLOYMENT. In Model 2, the coefficients for public transit passenger miles and

passenger rides were again statistically insignificant and of small magnitudes, however each of

the signs switched to negative which is in line with my initial expectations. In addition, the

coefficients for average price of gasoline and diesel were not statistically significant, but the

signs were also the opposite of Model 1. I expected each coefficient to have a negative sign, but

in Model 2, PRICEGAS had a negative sign and PRICEDIES has a positive sign, the opposite of

Model 1. The coefficient for employment also switched signs in Model 2, suggesting the

opposite effect than what I expected, however, again the magnitude was small and the result was

not statistically significant. Overall, the poor performance of this version of the model leads me

to favor Model 1 over Model 2.

Model 3 was a larger adjustment from Model 1 due to the omission of multiple control

variables, however many of the variables that were included had similar results to that of Model

1. The coefficients for population, arterial and highway miles driven, the Transportation Time

Index, and the cost of congestion remained statistically significant, had similar magnitudes, and

retained the same signs as Model 1. However, interesting differences occurred with the

coefficients for public transit passenger rides and for GDP per capita. The coefficient for public

transit passenger rides was statistically significant at the 99 percent level and suggested that for

every additional passenger ride, excess fuel consumption increased by 0.00861 gallons. This may

be due to the fact that passenger miles on public transit was not included in this model. In

addition, the coefficient for GDP per capita remained small and positive, but was statistically

35

significant at the 90 percent level in this analysis. Model 3 is helpful because it continues to

show the effect that Uber has on excess fuel consumption in MSAs, while attempting to

accounting for the multicollinearity. However, Model 3 is less comprehensive than Model 1 due

to the omission of certain control variables.

In addition, in Models 1, 2, and 3, the error terms are statistically significant at the 99

percent level and have large magnitudes. This suggests that there is a significant effect of omitted

variables that I was unable to include in my models. As previously discussed, the greatest

limitation to my analysis is my inability to find certain data collected at the MSA-level. Should

such data become available in the future, it would certainly add detail and explanatory power to

these models.

Below is Table 9, which shows the results for Models 1, 2, and 3 in a comparison format.

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Table 9. Comparison of Models 1, 2, and 3. Model 1 Model 2 Model 3 UBER -0.046 -1,231.814 -932.667 (0.087)* (0.118) (0.189) POP 0.00000007 0.012 0.018 (0.371) (0.001)*** (0.001)*** PUBGAS -0.00000002 0.001 (0.084)* (0.118) PUBDIES -0.00000001 -0.0002 (0.913) (0.009)*** PASSMILES -0.00000001 0.000001 (0.936) (0.563) PASSRIDES -0.00000001 0.000004 0.000008 (0.984) (0.744) (0.001)*** DRIVEHW -0.000003 -0.341 -0.459 (0.447) (0.012)** (0.002)*** DRIVEARTERIAL

0.000002 0.279 0.540

(0.763) (0.075)* (0.001)*** PRICEGAS -0.125 1,475.058 626.434 (0.313) (0.626) (0.205) PRICEDEISEL 0.144 -779.690 (0.160) (0.762) TTI 0.590 109,062.704 112,215.500 (0.714) (0.002)*** (0.003)*** CONGCOST 0.00008 3.109 2.480 (0.01)*** (0.015)** (0.040)** COMMUTERS 0.0003 11.453 (0.56)* (0.057)* EMPLOYMENT -0.0007 9.299 19.203 (0.155) (0.617) (0.308) GDP 0.000003 0.088 0.141 (0.141) (0.200) (0.065)** PUBFUEL -0.0001 (0.301) Constant 7.221 -154,551.790 -164,171.700 (0.000)*** (0.000)*** (0.001)***

R2 0.9992 0.9998 0.9997 Adj-R2 0.9977 0.9933 0.9922 Pr > F 0.0001 0.0001 0.0001

* p<0.1; ** p<0.05; *** p<0.01

In the next, and final, section, I discuss the policy implications of these findings.

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SECTION 7: CONCLUSION AND POLICY RECOMMENDATIONS

The purpose of this study is to build upon the literature that has explored the effects of

Uber and other TNCs on transportation in MSAs by determining if these services have led to a

decrease in consumption of automotive fuel. Through the process of collecting a wide array of

data, building models, and performing background research, I used empirical model to determine

the significance of these effects. The previous literature on the subject suggests that TNCs have

had significant effects on individual transportation habits in urban areas, leading me to believe

that my model would show an effect on fuel consumption. With my model, I anticipated that I

would draw important conclusions about the presence of Uber and its operation in urban areas.

The results of this analysis have important policy implications relevant to urban areas and

how policy-makers will pursue transportation policy in the years to come. Urban planning

regarding transportation should take the innovation and changing habits of the sharing economy

into account in order most efficiently serve the population. Transportation from point A to point

B is one of the most important aspects of municipal policy in large metropolitan areas and

understanding the changes in how individuals are choosing transportation options is vital.

Changing transportation consumption practices are relevant to planning for future transportation

infrastructure and public transit, to regulations on TNCs and ride-sharing services, and on fuel

taxation policy.

The policy recommendations that grow out of this analysis are based on the results of my

empirical models and the extent to which the operation of Uber and other TNCs reduces

consumption of excess fuel. Based on previous literature, these services have a significant effect

on transportation by increasing public transportation use, reducing traffic congestion, and

reducing car ownership, among others. These are generally regarded as positive changes, and any

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evidence that fuel consumption is decreasing or growing at a reduced rate should alert

policymakers to incentivize and grow these services to maximize positive effects.

Policy Recommendations

The policy recommendations that grow out of these analyses are based on the evidence

that Uber, along with other factors, are creating a reduction in the amount of excess automotive

fuel is consumed by individuals. They are as follow: implement regulatory policies for TNCs

that allow their operation alongside other transportation services; model TNC growth and plan

public transportation expansion alongside it; and consider reduced reliance on fossil fuels when

planning municipal taxation policies.

While all but three of the MSAs in this analysis have allowed Uber and other TNCs to

operate, one of the largest factors impeding the growth of operations has been municipal and

state regulatory barriers in areas such as Austin, Texas, Upstate New York, and many mid-sized

cities. Based on this analysis and previous literature, TNCs appear to be increasing ease of

transportation and reducing excess fuel consumption. Due to this evidence, city administrators

should work to shape transportation regulatory policies that allow entry and maintenance of TNC

services alongside other forms of transportation.

City and state governments should also work to build upon this analysis and previous

research to better understand the growth of Uber and other TNCs in metropolitan areas.

Modeling this growth will serve as an integral part in determining future plans for public

transportation services, as such services appear to be complementary with the use of TNCs and

could result in increased ridership. In addition to the planning and growth aspects of this

relationship, city and state governments should also seek to identify best practices for

39

transportation taxation policy based on these findings. Evaluation and analysis of changing

behavior and trends in consumer transportation could have important implications for how

municipalities and states collect revenue through the transportation sector. Should this evaluation

conclude that changes in habits are maintained, taxation on transportation can be diversified

across transportation mediums.

Conclusion

This study is an attempt to find statistically significant evidence that TNC operation in

the largest Metropolitan Statistical Areas of the United States is affecting the amount of

consumer automobile fuel consumed. The results of the fixed effects regression analysis between

the years 2004 (before Uber entry) and 2014 (after Uber entry) showed that Uber is influencing

the amount of excess fuel that is consumed in metropolitan areas, however the results were

slightly less than statistically significant. In all three models, the sign and magnitude of the

“Uber effect” suggested that the hypothesized outcome was indeed the case, however statistical

significance was not always achieved.

My analysis also confirmed much of the previous literature about the effects of TNCs on

consumer habits in metropolitan areas. This includes that the utilization of TNCs by residents of

cities is complementary with use of public transportation services, and individuals tend to

substitute this combination for driving a personal automobile. In addition, the operation of Uber

has an important relationship with the number of commuters on the road and congestion of

highways and arterial roads. In addition, it is clear from my analysis that populations and number

commuters on the road has a strong relationship with the amount of automotive fuel that is

consumed.

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Furthermore, the collection of data and the analyses performed for this project made very

clear that more data on consumer habits and fuel consumption at the Metropolitan Statistical

Area-level should be sought out in order to best understand the effects of TNCs on consumer

habits. This raises an important point for future research: better data and collection. Currently, it

is very difficult to find data related to this topic that is collected at the MSA-level. The simple

reason that I deduced after speaking to the Energy Information Administration, Census Bureau,

Department of Energy, Department of Transportation, Department of Commerce, and Federal

Highway Administration is that they are simply not collected by the government. The

government should prioritize the collection of these data in order to best predict and plan for the

future of metropolitan transportation policy. In addition to the federal and state governments,

MSAs should invest in collecting this information in order to better understand how the sharing

economy is impacting transportation. Should more data related to this topic collected at the

granular level become available in the future, further research can be conducted into these

effects.

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APPENDIX: DATASETS

Employment Statistics. (2016). United States Bureau of the Census. https://www.census.gov/topics/employment.html

Motor Gasoline Sales to End Users. (2016). Energy Information Administration.

https://www.eia.gov/dnav/pet/pet_cons_refmg_d_nus_VTR_mgalpd_m.htm National Transit Database. (2004). Federal Transit Administration. Energy Consumption.

https://www.transit.dot.gov/ntd National Transit Database. (2014). Federal Transit Administration. Energy Consumption.

https://www.transit.dot.gov/ntd National Transit Database. (2004). Federal Transit Administration. Service.

https://www.transit.dot.gov/ntd National Transit Database. (2014). Federal Transit Administration. Service.

https://www.transit.dot.gov/ntd Regional Economic Database. (2017). Bureau of Economic Analysis.

https://www.bea.gov/itable/iTable.cfm?ReqID=70&step=1#reqid=70&step=1&isuri=1 Urban Mobility Report. (2015). Texas A&M University Tranportation Institute.

https://mobility.tamu.edu/ums

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