Disrupting the Business and Legal Landscape

258 Pages·2016·8.39 MB·English

THE SHARING ECONOMY: DISRUPTING THE BUSINESS AND LEGAL LANDSCAPE

Panel 402
NAPABA Annual Conference
Saturday, November 5, 2016
9:15 a.m.

1. Program Description
Tech companies are revolutionizing the economy by creating marketplaces that connect individuals who “share”
their services with consumers who want those services. This “sharing economy” is changing the way Americans
rent housing (Airbnb), commute (Lyft, Uber), and contract for personal services (Thumbtack, Taskrabbit). For every
billion-dollar unicorn, there are hundreds more startups hoping to become the “next big thing,” and APAs play a
prominent role in this tech boom.

As sharing economy companies disrupt traditional businesses, however, they face increasing regulatory and
litigation challenges. Should on-demand workers be classified as independent contractors or employees? Should
older regulations (e.g., rental laws, taxi ordinances) be applied to new technologies? What consumer and privacy
protections can users expect with individuals offering their own services? Join us for a lively panel discussion with
in-house counsel and law firm attorneys from the tech sector.

2. Panelists
Albert Giang
Shareholder, Caldwell Leslie & Proctor, PC
Albert Giang is a Shareholder at the litigation boutique Caldwell Leslie & Proctor. His practice focuses on
technology companies and startups, from advising clients on cutting-edge regulatory issues to defending them in
class actions and complex commercial disputes. He is the rare litigator with in-house counsel experience: he has
served two secondments with the in-house legal department at Lyft, the groundbreaking peer-to-peer ridesharing
company, where he advised on a broad range of regulatory, compliance, and litigation issues. Albert also
specializes in appellate litigation, having represented clients in numerous cases in the United States Supreme
Court, the United States Court of Appeals for the Ninth Circuit, and California appellate courts. He has been
recognized as one of the “Most Influential Minority Lawyers” by the Los Angeles Business Journal, a “Rising Star” in
the appellate field by Los Angeles Magazine, and one of the “Best Under 40” by the National Asian Pacific
American Bar Association. Prior to joining Caldwell Leslie & Proctor, Albert served as a law clerk for the Honorable
Richard A. Paez, United States Court of Appeals for the Ninth Circuit, graduated with distinction from Stanford Law
School, and received his Bachelor of Arts degree magna cum laude from Amherst College.

Loni Mahanta
Managing Counsel, Employment/Litigation at Lyft, Inc.
Loni Mahanta is Senior Counsel, Employment/Litigation at Lyft, Inc., the groundbreaking peer-to-peer ridesharing
company. In addition to overseeing employment issues at Lyft, she plays a leading role in the widely-followed
disputes over whether Lyft and Uber’s drivers should be classified as independent contractors or employees. Prior
to joining Lyft, Loni was an attorney at Folger Levin LLP and Crowell & Moring LLP, where she specialized in
employment litigation. She obtained her B.A. in International Relations from Stanford University and graduated
magna cum laude and Order of the Coif from Hastings College of the Law.

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Jennifer Lam
Senior Product Counsel, Airbnb
Jennifer Lam is the Senior Product Counsel at Airbnb, where she plays a leading role in the company’s product and
privacy work. She previously spent four years as in-house counsel at Zynga, where she was lead counsel on issues
of privacy, product, promotions, and content. Prior to working at these innovative startups, Jennifer was an
Associate at Kasdan, Simonds, Riley & Vaughan and graduated from Golden Gate University School of Law.

Steven Siger
Managing Counsel, Thumbtack
Steve Siger is Managing Counsel at Thumbtack, a groundbreaking company that connects businesses and
customers with local service professionals. At Thumbtack, Steve is the primary legal advisor to the Product,
Engineering, and Marketing teams, while also working on government affairs, trust and safety, and other legal
issues. Previously, Steve served as Senior Counsel at Uber, where he led the team responsible for all regulatory
and legal policy affairs for Uber in the western United States; as Chief of Staff and Acting Deputy Assistant Attorney
General in the U.S. Justice Department’s Office of Legal Policy; as a Law Clerk to the Honorable Judith W. Rogers on
the United States Court of Appeals for the District of Columbia Circuit; as an Associate at Latham & Watkins LLP;
and on the advance staff of Obama for America. Steve graduated with distinction and pro bono distinction from
Stanford Law School, and received his B.A. in Political Science magna cum laude and with distinction from Yale
University.

3. Moderator
Albert Lin
Partner, Ice Miller
Albert Lin is a partner in the Columbus, Ohio office of Ice Miller, LLP. Lin’s practice focuses on government
regulatory enforcement, complex litigation, and appeals. Prior to joining Ice Miller, Lin served as General Counsel
to Ohio Attorney General Richard Cordray. As a member of the AG’s senior staff, Lin helped develop the office’s
strategic plan and had primary responsibility for major financial litigation, criminal justice, gaming, and other
matters. Prior to joining the AG’s office, Lin was an associate at Vorys, Sater, Seymour & Pease, LLP in Columbus,
Ohio, where his practice focused on government regulatory matters and complex litigation. Lin also served as a
law clerk to two federal judges: The Honorable R. Guy Cole, Jr. (6th Cir.) and The Honorable Ann Aldrich (N.D. Ohio).
4. Outline
a. Definition(s) of “Sharing Economy”
i. “Sharing economy is an umbrella term with a range of meanings, often used to describe
economic and social activity involving online transactions. Originally growing out of the
open-source community to refer to peer-to-peer based sharing of access to goods and
services, the term is now sometimes used in a broader sense to describe any sales
transactions that are done via online market places, even ones that are business to
consumer (B2C), rather than peer-to-peer…. [A] means of describing a generally more
democratized marketplace, even when it’s applied to a broader spectrum of services.
Also known as shareconomy, collaborative consumption or peer economy, a common
academic definition of the term refers to a hybrid market model (in between owning
and gift giving) of peer-to-peer exchange. Such transactions are often facilitated via
community-based online services.” [Wikipedia]

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ii. “Increasingly, consumers and independent service providers are engaging in
transactions facilitated by an Internet-based platform. The digital firms that provide the
platforms are often collectively referred to as belonging to the ‘sharing’ or
‘collaborative’ economies, among other descriptors. [W]e narrow the focus and
propose a definition of ‘digital matching firms’ that exhibit the following characteristics:
(1) They use information technology (IT systems), typically available via web-based
platforms, such as mobile ‘apps’ on Internet- enabled devices, to facilitate
peer-to-peer transactions.
(2) They rely on user-based rating systems for quality control, ensuring a level of
trust between consumers and service providers who have not previously met.
(3) They offer the workers who provide services via digital matching platforms
flexibility in deciding their typical working hours.
(4) To the extent that tools and assets are necessary to provide a service, digital
matching firms rely on the workers using their own.” [U.S. Department of
Commerce, Digital Matching Firms: A New Definition in the “Sharing Economy”
Space (June 2016), available at http://www.esa.gov/sites/default/files/digital-
matching-firms-new-definition-sharing-economy-space.pdf.]
iii. Span different industries: Lyft, Uber, Airbnb, Thumbtack
iv. Sometimes used interchangeably with: peer-to-peer economy, collaborative
consumption, on-demand economy, gig economy
b. Economic impact
i. PARTICIPATION: According to a 2015 PWC report:
(1) 44% of US adults are familiar with the sharing economy.
(2) 18% of US adults have participated in the sharing economy as a consumer.
(3) 7% of the US population are providers in the sharing economy.
(4) The five key sharing sectors—travel, car sharing, finance, staffing, and music
and video streaming—have global revenues of roughly $15 billion today, which
are projected to increase to around $335 billion by 2025.
(5) “Airbnb averages 425,000 guests per night, totaling more than 155 million
guest stays annually—nearly 22% more than Hilton Worldwide, which served
127 million guests in 2014. Five-year-old Uber operates in more than 250 cities
worldwide and as of February 2015 was valued at $41.2 billion—a figure that
exceeds the market capitalization of companies such as Delta Air Lines,
American Airlines and United Continental.”
ii. PUBLIC OPINION: According to a 2015 PWC report:
(1) Among US adults familiar with the sharing economy, 86% agree it makes life
more affordable, 83% agree it makes life more convenient and efficient, 72%
agree they feel that the sharing economy experience is not consistent, and 69%
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agree they will not trust sharing economy companies until they are
recommended by someone they trust.
(2) Among consumer who have tried the sharing economy, 57% agree that “I am
intrigued by companies in the sharing economy but have some concerns about
them.”
(3) Among consumer who have tried the sharing economy, 64% say that peer
regulation is more important than government regulation.
iii. FUTURE: According to a 2015 PWC report:
(1) Among US adults familiar with the sharing economy, 51% could see themselves
being providers in the next two years, and 72% could see themselves being
consumers in the next two years.
c. Legal challenges
i. Regulatory
(1) Airbnb case study
(2) “We initiate this proceeding to protect public safety and encourage innovators
to use technology to improve the lives of Californians. The purpose of this
Rulemaking is not to stifle innovation and the provision of new services that
consumers want, but rather to assess public safety risks, and to ensure that the
safety of the public is not compromised in the operation of these business
models. The Commission invites all interested parties to participate in this
proceeding to ensure that regulation is not a hindrance, but continues to be
the safety net that the public can rely on for its protection.” [California Public
Utilities Commission, Order Instituting Rulemaking (2012), Rulemaking 12-12-
011 (discussing transportation network companies)]
ii. Employment / Employee Classification
(1) Lyft case study
(2) Borello factors:
(a) “[T]he principal test of an employment relationship is whether the
person to whom service is rendered has the right to control the
manner and means of accomplishing the result desired.... [S]trong
evidence in support of an employment relationship is the right to
discharge at will, without cause.”
(b) Additional factors include: “(a) whether the one performing services is
engaged in a distinct occupation or business; (b) the kind of
occupation, with reference to whether, in the locality, the work is
usually done under the direction of the principal or by a specialist
without supervision; (c) the skill required in the particular occupation;
(d) whether the principal or the worker supplies the instrumentalities,
tools, and the place of work for the person doing the work; (e) the
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length of time for which the services are to be performed; (f) the
method of payment, whether by the time or by the job; (g) whether or
not the work is a part of the regular business of the principal; and (h)
whether or not the parties believe they are creating the relationship of
employer-employee.” [S. G. Borello & Sons, Inc. v. Dep’t of Indus.
Relations, 48 Cal.3d 341, 350–51 (1989)]
(3) “As should now be clear, the jury in this case will be handed a square peg and
asked to choose between two round holes. The test the California courts have
developed over the 20th Century for classifying workers isn’t very helpful in
addressing this 21st Century problem. Some factors point in one direction,
some point in the other, and some are ambiguous. Perhaps Lyft drivers who
work more than a certain number of hours should be employees while the
others should be independent contractors. Or perhaps Lyft drivers should be
considered a new category of worker altogether, requiring a different set of
protections. But absent legislative intervention, California’s outmoded test for
classifying workers will apply in cases like this. And because the test provides
nothing remotely close to a clear answer, it will often be for juries to decide.
That is certainly true here.” [Cotter v. Lyft, Inc., 60 F. Supp. 3d 1067, 1081–82
(N.D. Cal. 2015)]
iii. Privacy/Consumer Protection
(1) Case study
(2) FTC Guidelines:
(a) Start with security.
(b) Control access to data sensibly.
(c) Require secure passwords and authentication.
(d) Store sensitive personal information securely and protect it during
transmission.
(e) Segment your network and monitor who’s trying to get in and out.
(f) Secure remote access to your network.
(g) Apply sound security practices when developing new products.
(h) Make sure your service providers implement reasonable security
measures.
(i) Put procedures in place to keep your security current and address
vulnerabilities that may arise.
(j) Secure paper, physical media, and devices.

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iv. Economic & Tort Relationship
(1) Doe v. Uber case study
(2) “In sum, the Court cannot determine – as Uber effectively argues –that as a
matter of law sexual assault by Uber drivers is always outside the scope of
employment, if the drivers are in fact ultimately found to be employees. The
California Supreme Court has left this question open…. Plaintiffs may, or may
not, ultimately prevail on their vicarious liability claims…. To the extent that
these are close questions, the Court finds that they are more appropriately
resolved at a later stage of the litigation.” [Doe 1 et al. v. Uber Technologies
Inc., No. 3:15-cv-04670 (N.D. Cal.)]
5. Q&A
6. Resources
a. Sharing economy cases to watch
i. O’Connor v. Uber Techs., Inc., No. C-13-3826 EMC (N.D. Cal.): Misclassification,
employment issues
ii. Airbnb Inc. v. City and County of San Francisco, No. 3:16-cv-03615 (N.D. Cal.):
Regulatory issues, First Amendment, Communications Decency Act preemption
iii. Doe 1 et al. v. Uber Technologies Inc., No. 3:15-cv-04670 (N.D. Cal.): Tort law, vicarious
liability issues, common carrier liability
b. Attachments
i. U.S. Department of Commerce, Digital Matching Firms: A New Definition in the “Sharing
Economy” Space (June 2016), available at http://www.esa.gov/sites/default/files/digital-
matching-firms-new-definition-sharing-economy-space.pdf.
ii. Molly Cohen & Arun Sundararajan, Self-Regulation and Innovation in the Peer-to-Peer
Sharing Economy, 82 U. Chi. L. Rev. Dialogue 116 (2015), available at
https://lawreview.uchicago.edu/page/self-regulation-and-innovation-peer-peer-
sharing-economy.
iii. Vanessa Katz, Regulating the Sharing Economy, 30 Berkeley Tech. L.J. 1067 (2015),
available at
http://scholarship.law.berkeley.edu/cgi/viewcontent.cgi?article=2083&context=btlj.
iv. California Public Utilities Commission, Decision Adopting Rules and Regulations to
Protect Public Safety While Allowing New Entrants to the Transportation Industry,
Rulemaking 12-12-011 (September 19, 2013), available at
http://docs.cpuc.ca.gov/PublishedDocs/Published/G000/M077/K112/77112285.PDF.
v. Federal Trade Commission, Start with Security – A Guide for Business (June 2015),
available at https://www.ftc.gov/system/files/documents/plain-language/pdf0205-
startwithsecurity.pdf.
vi. Cotter v. Lyft (N.D. Cal.) – Order on Motions for Summary Judgment
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vii. Doe v. Uber (N.D. Cal.) – Order on Motion to Dismiss
viii. Doe v. Internet Brands (9th Cir.) – Order on Motion to Dismiss

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U.S. Department of Commerce
Digital Matching FirmsE: Ac oNnewom Deicfisn itaionnd i nS tthaet “isSthiacrsin Ag Edcmonionmisyt”r Saptaiocen
Office of the Chief Economist

Digital Matching Firms:

A New Definition in the
“Sharing Economy” Space

Executive Summary
Increasingly, consumers and independent service providers are engaging
in transactions facilitated by an Internet-based platform. The digital firms
that provide the platforms are often collectively referred to as belonging
to the “sharing” or “collaborative” economies, among other descriptors.
However, in this paper, we narrow the focus and propose a definition of
By
“digital matching firms” that exhibit the following characteristics:
Rudy Telles Jr.
1. They use information technology (IT systems), typically available
via web-based platforms, such as mobile “apps” on Internet-
ESA Issue Brief enabled devices, to facilitate peer-to-peer transactions.
#01-16
2. They rely on user-based rating systems for quality control,

ensuring a level of trust between consumers and service providers
who have not previously met.

3. They offer the workers who provide services via digital matching
platforms flexibility in deciding their typical working hours.
Special thanks to
4. To the extent that tools and assets are necessary to provide a
William Hawk and
service, digital matching firms rely on the workers using their
Jasmine Joung for
own.
conducting research on
digital matching firms, In addition to defining these “digital matching services” the report offers
as well as many an initial assessment of its size and scope based on publicly available data
substantive suggestions on its largest firms, as well as an examination of its potential effect on
(See back cover for consumers and service providers. The report closes with an overview of
the benefits and challenges emerging from the growth of these firms.
a full list of
acknowledgements.)

June 3, 2016
Office of the Chief Economist  Economics and Statistics Administration  Page 1
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Introduction
The Internet—particularly when accessed on smartphones and other mobile devices—is enabling sellers
and buyers to conduct market transactions in ways that had not been possible in the past. What began
as small and informal online exchanges of goods and services via message boards and rudimentary
websites has, with the widespread adoption of fast, reliable mobile smartphones and access to GPS,
evolved into a collection of firms that connect millions of consumers with other private citizens who can
provide goods and services quickly and efficiently.
One can now open an app and quickly arrange and pay for a car ride; book lodging for the night in a
private residence; or arrange for a local provider to clean a house, cook food, or even assemble one’s
furniture or mount a television. Conversely, a person with free time and the right combination of skills
and/or underutilized personal assets can use these same digital platforms to provide on-demand goods
and services for profit, all on his or her own schedule, with low barriers of entry.
In the decade since the emergence of firms such as Uber, a transportation services company, and
Airbnb, a platform for travel arrangements and reservation services, the number of people engaged in
both obtaining and providing goods and services through digital matching platforms has grown
considerably. A small number of digital matching firms are estimated to have valuations that rival many
of the world’s largest firms.1
In this paper, we define “digital matching firms” as entities that provide online platforms (or
marketplaces) that enable the matching of service providers with customers. By identifying a set of
common characteristics that define these “digital matching firms,” we can explore what is new and
unique about the phenomenon that is being called (among other names) “the sharing economy.” We
then examine the size, scope, and growth of the digital matching firms, with the caveat that there is a
relative dearth of public data available on these companies. Finally, we discuss the potential benefits
and detriments of the growth in digital matching firms to both the buyers and providers of the
services—that is, to consumers and workers. In this final context we also discuss some of the policy
challenges that have emerged as some firms using this business model have rapidly expanded and
begun to compete with existing firms in established markets.
Defining Characteristics of Digital Matching Firms
The companies that have pioneered this relatively new phenomenon have been classified by a number
of names including the “sharing economy,” “e-lancing,” the “ICT-enabled economy,” among others. (See
text box: A Plethora of Descriptors and Misnomers: Why We’re Not Describing “Sharing” or
“Collaborative” Firms). In our examination of this subset of the broader digital economy, we wish to
narrow our focus and define the “digital matching firms” as consisting of firms with business models
that exhibit the following characteristics:

1 Uber is currently valued at approximately $62.5 billion (New York Times), while Airbnb has an estimated valuation
of more than $25 billion. For comparison, Ford and Honda are worth approximately $60 billion, while GM has a
market value of around $55 billion. The Hilton Hotel chain has a valuation of nearly $28 billion.
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Digital Matching Firms: A New Definition in the “Sharing Economy” Space

1. Digital matching firms use information technology (IT systems), typically available via web-based
platforms such as mobile “apps” on Internet-enabled devices, to facilitate peer-to-peer transactions.

Digital matching firms have introduced a variety of apps and other Internet platforms that provide a
marketplace for secure, reliable, and efficient transactions between individuals. Digital matching
platforms often allow individuals to access peer-to-peer services in real-time and also allow the
digital matching firms to handle the financial transaction between the consumer and provider. For
example, an Uber passenger pays for her ride using a credit card via the Uber app itself, with the
firm paying the driver. This contrasts with a traditional taxi ride, during which the passenger pays
the driver directly. In short, the worker providing the service has no role in collecting payment from
the consumer.

2. Digital matching firms rely on user-based rating systems for quality control, ensuring a level of trust
between consumers and service providers who have not previously met.2

In order to facilitate peer-to-peer transactions, digital matching firms all utilize some form of rating
system to ensure a level of trust between individuals that are most often strangers. In addition to
requiring public disclosure of aggregate ratings, service providers are often required to maintain a
consumer feedback rating above a certain threshold in order to continue providing services via their
platforms. Our definition only requires that a rating system is in place to evaluate service providers,
but many rating systems are bilateral, giving service providers a sense of security about the integrity
of the person to whom they are, for example, renting out an asset.

3. Individuals who provide services via digital matching platforms have flexibility in deciding their typical
working hours.

Service providers for digital matching firms have the work flexibility of traditional freelance workers,
which is why they are often referred to as “e-lancers.” Individuals only offer services when they
choose, assuming they meet conditions that the digital service firm may set, such as: maintaining
adequate user feedback ratings; having government-required licensing, training, and insurance; and
having quality assets. As such, digital matching firm service providers, who are often not legally
classified as “employees” of the digital matching firm, are often not required to be on call or work a
specific amount by the digital matching firm in order to be eligible to provide services in the future.

2 It is worth noting that peer-to-peer financial services firms such as Lending Club or Funding Circle may or may not
be considered digital matching firms, depending on how one interprets both the underlying peer-to-peer nature of
the loans themselves and the necessity of a peer-to-peer rating system. Financial firms have robust rating systems,
such as credit scores, that are independent from the peer-to-peer rating systems typical of a transportation,
lodging, or peer-asset rental platform, but provide a mechanism for the trust necessary for consumers to use
digital matching firms. In this paper, we will consider peer-to-peer lending companies as digital matching firms.
Office of the Chief Economist  Economics and Statistics Administration  Page 3