Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
Economics and Statistics Administration
Page 1
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
“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.
In addition to defining these “digital matching services” the report offers
an initial assessment of its size and scope based on publicly available data
on its largest firms, as well as an examination of its potential effect on
consumers and service providers. The report closes with an overview of
the benefits and challenges emerging from the growth of these firms.
U.S. Department of Commerce
Economics and Statistics Administration
Office of the Chief Economist
Digital Matching Firms:
A New Definition in the
“Sharing Economy” Space
By
Rudy Telles Jr.
ESA Issue Brief
#01-16
Special thanks to
William Hawk and
Jasmine Joung for
conducting research on
digital matching firms,
as well as many
substantive suggestions
(See back cover for
a full list of
acknowledgements.)
June 3, 2016
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
Economics and Statistics Administration
Page 2
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.
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
Economics and Statistics Administration
Page 3
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.
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
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4. To the extent that tools and assets are necessary to provide a service, digital matching firms rely
on the workers using their own.
Unlike employees of traditional firms that use assets owned by the firm to perform services, these
workers either own or have personal access to the assets that are used to provider services. Often,
the assets must meet a set of criteria that the firm dictates. For example, Lyft requires that vehicles
used by their service providers be from 2004 or later.
3
Box 1. A Plethora of Descriptors and Misnomers: Why We’re Not Describing
“Sharing” or “Collaborative” Firms
Digital matching firms have been referred to by many names and descriptors. Among the most
ubiquitous labels for the collection of these firms in both media and academic reporting are the
“sharing” and “collaborative” economies. However, terms such as “sharing” and “collaborative” do
not adequately characterize what makes firms like Uber, Taskrabbit, and Airbnb innovative. As
discussed at length in this report, these firms provide a platform for consumers and service
providers to connect and complete a transaction safely and efficiently, using the capital assets of
the service providers themselves, when such assets are required to provide the service.
Service providers using their own underutilized assets to provide a service are often characterized
as “sharing” or “collaborating” with consumers, but this implies services being provided for free;
the reality is that the so-called “sharing” or “collaboration” in these cases is not free. Service
providers are simply using their assets to earn money. In digital matching firms, service providers
bear the cost and risk of providing a service and, in many cases, use an asset they already own for
another purpose, but they are not “sharing” their assets any more than a traditional taxi company
is sharing its cars or a hotel is sharing its rooms. There are some true “sharing” economy platforms
that help individuals provide their assets to others free of charge, such as Freecycle, which provides
a place for people to give away their possessions. However, Freecycle is essentially free retail, and
retail is not in the scope of our definition of digital matching services.
Similarly, a true “collaborative” economy consisting of individuals utilizing online platforms to
provide services and/or produce products also exists, as in the environments that produced the
UNIX operating system or R statistical software, for example. However, the types of activities
conducted through the “collaborative” economy do not accurately describe the kinds of
transactions conducted via digital matching platforms.
The use of a narrow set of conditions that define a digital matching firm is an intentional effort to
separate firms using an innovative online business model from firms using more standard business
3
According to Lyft’s published requirements for Lyft vehicles, some states and cities require that service providers
use a newer model vehicle, such as Seattle, where service providers must have 2006 or more recent vehicle
models.
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
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models that have a strong online component and from online activities that cannot be characterized as
being provided by “firms.” (See text box: “Sharing” Firms that Are Not Digital Matching Firms).
Many digital matching firms share certain other attributes, but we did not consider them decisive or
critical for defining digital matching firms because not all digital matching firms or the services provided
by these firms have these attributes. For one, service providers utilizing digital matching platforms use
capital assets that they already own to provide services, if such assets are required, but there are also
providers that purchase or rent assets specifically to provide services via digital matching platforms.
For example, an individual may purchase a car via Uber’s partnership with a network of car dealers and
lenders for the express purpose of providing transportation services through the Uber app, or lease a
vehicle on a short-term basis for use with the Lyft or Uber platform; rental companies such as Breeze
exist to provide vehicles for digital matching transportation firms. A provider may also purchase a
condominium to rent out on Airbnb, or purchase a bicycle or tools to use for paid tasks such as
delivering packages or assembling furniture via the Taskrabbit app.
While digital matching firms may set quality standards for their service providers, those standards may
differ from those of their non-digital matching competitors. For instance, digital matching firm service
providers may lack occupational licenses in the industries in which they provide services, and may be
considered “amateurs.”
4
However, digital matching firm service providers are not always amateurs, and
some digital matching apps connect consumers with professionals.
Along with the wide variety of services offered are a range of pricing structures, as lodging digital
matching platforms, such as Airbnb, and task platforms, such as Taskrabbit, allow service providers to
set their own rates, relying on consumers and service providers to adjust prices themselves. Other firms,
such as Uber and Lyft, set prices internally, and both consumers and service providers are reliant on the
digital platform to determine cost of service.
4
For example, many localities, such as New York City, require that individuals complete a certification course
and/or obtain a chauffeurs license before they can legally drive a taxi.
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
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Box 2. “Sharing” Firms that Are Not Digital Matching Firms
5
Many companies that are commonly classified as members of the “sharing” and/or “collaborative”
economy fall outside of the scope of our definition of digital matching firms. These include:
1. Firms that provide online classifieds such as Craigslist do, in fact, match consumers with goods
and service providers, but lack rating systems and also do not process transactions via their
own digital platform.
2. Companies that provide assets that are shared by multiple consumers on an ad-hoc basis, such
as “bikesharing” and “carsharing” firms or movie rental kiosks. We exclude these firms because
the assets provided are owned by the firm itself on a self-service basis. These firms operate
more like rental services but without the need for a staff of retail salespeople.
3. Online retailers such as Amazon since a large portion of their sales consist of items
warehoused by Amazon itself or provided via authorized, traditional third-party retailers. (In
some cases, however, these large retailers have subsidiaries that provide services that fit our
definition. Amazon Mechanical Turk, for example, connects consumers with freelancers who
provide a service for a fee.)
4. Firms that facilitate the matching of a service without facilitating a monetary transaction.
Examples of these platforms include couchsurfing, freecycle, Maine Tool Library,
Neighborgoods. These firms are appropriately classified as part of the “sharing economy,” as
the peer-to-peer transactions that take place via these apps involve the sharing or giving away
of goods and services, often for altruistic purposes.
The Size and Scope of Digital Matching Firms
The apparent dramatic emergence of digital matching firms begs a number of questions about their size
and scope, including what are the total revenues of all digital matching companies and how many
people are engaged in providing services within this paradigm?
As the discussion below indicates, the evidence for definitive answers to these questions is limited.
Though there are many high-profile privately held startups for which there are estimated large market
values, much of the market analysis that has been done to date is broad in scope and speculative —
there is little systematic information on the number and characteristics of individuals acting as providers
on the digital matching platforms. Information about the customer experience and consumer surplus
from using digital matching platforms versus more traditional business models is often anecdotal,
5
For a list of firms that do seem to qualify as digital matching firms, refer to the “Appendix: Examples of Digital
Matching Firms” following the conclusion of this report.
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
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although some solid economic research on specific markets—such as the price and market effects with
respect to specific firms in specific cities—is beginning to emerge.
Estimates of the Size and Growth of Digital Matching Firms
Relatively little economic research on digital matching firms exists, and teasing out data about the size
and value of these firms is difficult. Most digital matching companies are private and not subject to the
disclosure requirements of publicly-held companies, limiting the availability of reliable data on factors
such as yearly revenues. Therefore, estimates of the size and growth of digital matching firms tend to
come from private-sector surveys of consumers and service providers.
PricewaterhouseCooper (PwC), consulting firm MBO Partners, investment research group PiperJaffrey,
and the JPMorgan Chase Institute have each released reports that attempt to estimate the size and
growth of the “sharing” and “collaborative” economies. These studies inevitably include many firms and
industries that fall outside of the scope of our analysis. However, it’s worth examining the few studies
that do exist, as many of the companies we classify as digital matching firms are represented in their
analyses. Even using a broader definition than the one we propose, all of these studies suggest that the
“sharing” economy comprises a relatively small portion of the overall economy.
In 2014, a study by PwC
6
presented an estimate that five key sharing sectors—travel, car sharing,
finance, staffing, and music and video streaming—had global revenues of about $15 billion in 2014 with
the potential to increase to around $335 billion by 2025. In addition, PwC surveyed 1000 consumers in
order “to comprehend consumer attitudes toward the sharing economy.” According to the PwC data, 8
percent of all adults have participated in some form of automotive sharing, and 1 percent have served
as providers under this new model, “chauffeuring passengers around or loaning out their car by the
hour, day or week”. The PwC study also suggests that service providers in the “sharing economy,” which
they estimate to comprise 7 percent of the U.S. population, are made up of a wide variety of age and
income groups though their estimates include firms that we would not consider digital matching firms.
The consulting firm MBO Partners produces an annual report titled the “State of Independence in
America”
7
that examines the U.S.’s “independent workforce, or those who work 15+ hours a week as an
independent contractor.” In a proprietary supplement to this report titled “Independent Workers and
the On-Demand Economy,” MBO estimates that 2.7 million Americans, or 9 percent of independent
workers provide services through “on-demand economy platforms,” and that roughly 500,000 of the
estimated 2.7 million U.S. on-demand independent workers are estimated to provide services for Uber,
Lyft, and Airbnb, suggesting that service providers in the digital matching economy are concentrated in a
small number of firms. Further the report found that, of those surveyed, independent workers in the on-
demand economy reported lower earnings than independents not using these platforms and
marketplaces. The data shows that 36 percent of independent workers using on-demand platforms
reported earnings of $25,000 or less compared to 22 percent of independent workers not using such
platforms. At the other end of the spectrum, only 17 percent of those providing services through on-
demand platforms reported earning $75,000 or more; that compares to 28 percent of independent
6
PricewaterhouseCooper. “The Sharing Economy.” Consumer Intelligence Series. April 2015.
7
MBO Partners. “MBO Partners State of Independence in America 2015.”
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workers that do not use on-demand platforms. However, like the PwC survey, MBO Partners included
many companies in their report, such as Amazon and Ebay, which do not fit our criteria for digital
matching firms.
The JPMorgan Chase & Co. Institute released a report titled “Paychecks, Paydays, and the Online
Platform Economy: Big Data on Income Volatility,”
8
that attempted to estimate the effect of what they
call the “online platform economy” on income volatility. The authors used an anonymized sample of 1
million people who were customers of JPMorgan between October 2012 and September 2015, and a
dataset of more than 260,000 individuals who have offered goods or services on an online platform.
They estimate that more than 4 percent of adults, or approximately 10.3 million people, participated in
the “online platform economy” over the three-year period of their study, and that 1 percent of adults
earned income from an online platform in a given month. Moreover, the Institute estimated a 47-fold
increase in the number of adults that earned income from online platforms over the course of the three-
year period.
Investment research group PiperJaffray produced a report titled “Sharing Economy: An In-Depth Look At
Its Evolution & Trajectory Across Industries”
9
that estimated total “sharing” revenues from short-term
person-to-person (P2P) home rentals, such as Airbnb, at 2 percent of the U.S. accommodations market,
which includes hotels, hostels, bed and breakfasts, cruises and other short-term and P2P rentals.
However, this report predicts that by 2025, P2P home rentals could represent as much as 10 percent of
accommodation bookings, with revenue of $107 billion. In addition, Uber and other “ridesharing”
companies are estimated to make up more than 5 percent of the $90 billion global taxi marketplace.
As noted earlier, the information available about the collective size of digital matching firms is sparse,
the reports consist of small survey samples, and every study includes firms that that are outside the
scope of our definition. For that reason, these estimates are not entirely applicable for our purposes.
However, despite the inclusion of firms and industries that do not reflect the digital matching economy,
these studies suggest that digital matching firms are quickly growing in size, yet remain a relatively small
part of the greater U.S. economy.
The Largest Digital Matching Firms
Current estimates of the size and growth of the ”sharing” economy may not be appropriate for our
purposes given the inclusion of firms that fall outside of our defined digital matching firm parameters.
However, publicly released estimates of the size and growth of the largest individual digital matching
firms are available and provide an insightful, if imperfect, glimpse into the rapid growth of the most
successful firms.
8
JPMorgan Chase & Co. Institute. “Paychecks, Paydays, and the Online Platform Economy: Big Data on Income
Volatility.” February 2016.
9
Olson, Michael J., Samuel J. Kemp. “Sharing Economy: An In-Depth Look At Its Evolution and Trajectory Across
Industries.” PiperJaffray Investment Research. March 2015.
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Market intelligence group VB Profiles
10
estimated that, worldwide, there are now 17 companies in the
sharing or collaborative economies each worth more than $1 billion, with 60,000 employees and $15
billion in funding. As with the other studies discussed above, we would not consider many of these
companies part of our more narrow definition of digital matching firms, but the list does include digital
matching companies such as Uber, Lyft, and Airbnb, as well as other digital matching companies such as
Chegg, which specializes in online textbook rentals.
Uber—a privately held company—is the largest digital matching firm based on market valuation.
The Wall Street Journal reported that its market value was estimated to be $62.5 billion in
December 2015, up from $60 million in 2011.
11
If this is accurate, its current valuation is higher
than 80 percent of all S&P 500 companies. Reuters forecasts that Uber, from the 20 percent cut
it takes from every ride, will generate approximately $2 billion in revenue worldwide in 2015.
12
Airbnb—also a privately held firm—is the second largest digital matching firm based on market
valuation and the largest lodging accommodations provider among digital platform firms. The
Wall Street Journal reported Airbnb’s estimated value at more than $25 billion, which is more
than that of the Marriott hotel chain.
13
The valuation increased from approximately $10 billion
in April, 2014.
10
Koetsier, John. “The sharing economy has created 17 billion-dollar companies (and 10 unicorns).” VentureBeat.
June 4 2015.
11
Isaac, Mike, Leslie Picker. “Uber Valuation Put at $62.5 Billion After a New Investment Round.” The New York
Times. December 3, 2015.
12
Zhang, Shu, & Gerry Shih. “Uber seen reaching $10.8 billion in bookings in 2015: fundraising presentation.”
Reuters. August 21, 2015.
13
Alba, Davey. “Airbnb Confirms $1.5 Billion Funding Round, Now Valued at $25.5 Billion.” Wired. December 7.
2015.
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
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Box 4. Disruption and Convergence
As with the introduction of e-commerce in the 1990s, Internet-based technologies in the form of
digital matching apps have the potential to disrupt existing markets. The growth of the digital
matching firms appears to have begun to cause some disruption in traditional industries such as
transportation services and lodging, with the potential to do so in a variety of other industries.
Airbnb and other lodging-centric digital matching firms may have already had an effect on hotel
revenues in some areas. According to a Boston University study titled “The Rise of the Sharing
Economy: Estimating the Impact of Airbnb on the Hotel Industry,” each additional 10 percent
increase in the size of Airbnb listings in Texas resulted in a 0.37 percent decrease in monthly hotel
revenues. There is also some evidence that digital matching firms may have had an effect on the
supply of long-term rentals in some areas, as some landlords in major cities, such as New York City,
have chosen to operate homes as short-term rentals via Airbnb rather than lease them in a more
traditional manner, decreasing the supply and potentially raising the price of rental properties within
that market. To combat what they consider ad-hoc hotels, regulators in New York City have since
proposed heavy penalties for property owners who violate the city’s ban on short-term rentals.
14
In the transportation industry, the rise of firms such as Lyft and Uber likely have negatively affected
the value of taxi medallions in New York City, as the price fell to roughly $805,000 in early 2015,
down 23 percent from 2013’s peak of $1.05 million; corporate medallions, which may be owned in
fleets, were down 28 percent from their peak.
15
Taxi industry revenue has fallen considerably in a
number of cities as well; in Seattle, taxi revenues dipped 28 percent in two years.
16
At the same time, in an effort to compete with digital matching firms, traditional industries are
beginning to incorporate digital matching technology into their services, in a process known as
convergence, lowering their own costs and improving the consumer experience. For example, the
Curb app for taxi services works much like Uber, connecting consumers with taxi drivers representing
90 taxi companies in 60 cities and allowing consumers to pay for rides via the app.
Along with incorporating technologies from digital matching firms into their business models, some
regulatory hurdles are being modified to help make traditional firms more competitive with digital
matching firms. For example, the New York City Taxi Commission has removed geography questions
from the taxi license test in response to a decline in the number of driver applicants, while also
acknowledging that reliable GPS technology has made rote knowledge of the New York City area less
important for driver success and customer satisfaction.
17
14
Gonzalez, Juan. “NYC Council to propose tough penalties for landlords who use sites like Airbnb, in effort to keep
affordable housing.” New York Daily News, June 10, 2015.
15
Barro, Josh. “New York City Taxi Medallion Prices Keep Falling, Now Down About 25 Percent.” The New York
Times, Jan. 7, 2015.
16
Samuelson, Rob. “Seattle taxi revenue dropping precipitously due to Uber and Lyft.” Seattle Sun Times. June 13,
2015.
17
Worland, Justin. “Cab Drivers No Longer Required to Learn N.Y.C.’s Streets.” Time. March 9, 2015
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Benefits and Challenges Introduced by Digital Matching Firms
Benefits of Digital Matching Platforms
With the potentially rapid growth of digital matching firms, an important question is whether or not
they benefit consumers, workers, and the overall economy. Given the developing nature of the sector,
there is insufficient data to make any definitive judgments. However, given its inherent characteristics,
digital matching firm technology has the potential to provide a number of benefits. This section explores
the benefits often associated with the digital matching platforms.
1. Provides Lower Prices for Consumers Due to Reduced Transaction and Overhead Costs for the
Service Provider: Transaction costs are the time, money, skill, and effort needed to facilitate a
market transaction. Every day, consumers demand goods and services that could be provided by
professionals and non-professionals in their communities. These market exchanges are often
facilitated through firms, brokers, and sometimes government agencies. Digital matching platforms
potentially reduce the costs of coordinating these transactions by connecting consumers with
service providers directly and often in real-time, ostensibly cutting out the traditional firm and
middlemen that would otherwise be needed to link them.
There is some evidence that these lower costs for the service provider has resulted in lower prices
for consumers. For example, a Business Insider article reported that in 2014, an Uber ride was less
expensive than a taxi in all but two of the 21 large cities studied, so long as surge pricing wasn’t
activated.
18
In addition, the previously discussed PwC survey found that, of those polled, 56 percent
cited “better pricing” as the reason for their preference for “automotive sharing economy models.”
The PiperJaffray report, which was also previously discussed, found that private accommodations
available through digital platforms, such as Airbnb, are generally less expensive than hotels in cities
throughout the world (see table).
18
The price of Uber services will rise during “peak” periods, when consumer demand for rides is highest. According
to Uber’s FAQ, “At times of high demand, the number of drivers [Uber] can connect you with becomes limited. As a
result, prices increase to encourage more drivers to become available.” These often include periods of inclement
weather, holidays, and near areas in which special events are taking place. Given that Uber surge pricing is variable
and that rates can climb by many multipliers of the base fare, it’s possible that consumers, on the whole, pay more
for Uber.
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2. Provides Flexible Employment Schedules and Additional Income for Workers: People who need
extra income and/or can’t work traditional hours are often able to provide services via digital
matching firms. Low barriers of entry and the utilization of ubiquitous, common capital assets, such
as cars, bicycles, and extra bedrooms allow individuals to work during their “off” hours or while
they’re otherwise unemployed
19
. For example, in a survey commissioned by Uber, 80 percent of
their “driver-partners” were working full or part-time jobs just before they started driving on the
Uber platform, and two-thirds of that group reported having a full-time job. In addition, of those
19
Evidence indicates that firms have encouraged individuals to purchase capital assets such as cars for use with the
digital matching app. For example, Uber has a vehicle financing service that connects borrowers, including those
with poor credit, with auto dealers. Individuals who purchase an asset to use specifically with a digital matching
app in-fact may be losing the flexibility benefits that are ostensibly one of the draws of being a service provider for
a digital matching firm, as they are now responsible for the payments and maintenance of that asset, and thus
must work.
Table 1:
City
Private Rental Index ($)
Hotel Index ($) Difference (%)
Singapore 68 202 67
Seoul 48 142 66
Rio de Janerio 102 257 60
Hong Kong 73 174 59
Barcelona 65 149 56
Zunich 87 199 56
New York 114 255 55
London 75 165 55
Istanbul 69 151 55
Budapest 45 99 54
Cannes 108 231 53
Palma de Mallorca 73 150 52
Sydney 94 195 52
Frankfurt 76 159 52
Melbourne 75 155 52
Los Angeles 94 192 51
Florence 76 152 50
Nice 89 174 49
Paris 98 183 47
Munich 87 159 46
Miami 115 214 46
Rome 87 155 44
Lisbon 63 113 44
Madrid 65 115 44
Milan 84 152 44
Seville 64 113 43
Prague 64 112 42
Berlin 65 112 41
Vienna 77 130 40
Venice 119 183 35
Brussels 93 136 32
Source: Study commissioned by W imdu and converted from EUR to USD by Piper Jaffray
Hotel vs. Private Rental Costs Throughout the World
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surveyed, more than half had never previously worked as a driver, whether it be for a taxi, limo, or
other for-hire transportation company, suggesting that the Uber platform provided an introduction
into a new line of full or part-time work for the majority of its service providers.
20
Further, the previously discussed JPMorgan Chase Institute study found that earnings from labor
platforms helped to offset low or zero-income periods for workers with high levels of income
volatility, notably when they were between jobs and when their income dipped. Although the
number of people participating in what they call the “online platform economy” increased
tremendously during the three-year period of their study, individuals mostly utilized online
platforms as a secondary source of income, and their reliance on platforms for income remained
stable over time in both the fraction of months that participants were active and the fraction of total
income earned on platforms in active months.
Aside from providing employment opportunities for the unemployed and workers who require
supplemental incomes, digital matching platforms also offer opportunities for non-traditional
working populations, such as retired people and individuals with disabilities or health issues. Some
companies are actively recruiting senior citizens; Uber, for example, announced a partnership with
AARP's Life Reimagined, which would give members who sign up to be new drivers a bonus after
they provide 10 rides through the service.
3. Leverages Excess Capacity: Digital matching firms provide a platform for service providers to take
advantage of underutilized assets. Turo, for example, capitalizes on the existence of idle private
vehicles by allowing users to rent out their cars to others when they’re not using them.
Transportation services provided by Uber and Lyft capitalize on both underutilized cars that are
theoretically sitting unused and drivers with both the time and desire to work. Rooms listed for rent
on Airbnb or HomeAway are often guest rooms or in houses that are currently vacant due to
vacation, travel, or other life events.
4. Potentially Stimulates New Consumption: By providing consumers access to services that were
previously either unavailable or less convenient, digital matching firms may be able to access
untapped markets and increase overall consumption. However, it’s possible that total consumption
in the economy could actually decrease as consumers shift away from the more traditional economy.
For example, if urban consumers begin using digital matching apps for their transportation needs to
a large enough degree, they may hold off on purchasing a car, which could potentially decrease
overall consumption in the economy. Reliable data examining the stimulative effect of digital
matching firms is currently sparse.
5. Improves the Consumer Experience: The innovations introduced by digital matching firms could
considerably lessen the inconvenient aspects of service transactions, increasing consumer welfare.
For example, both Lyft and Uber allow consumers to pay for their services via their respective apps,
removing the post-ride in-person transaction that is often required when they use traditional taxi
20
Hall, Jonathan V, & Alan Krueger. “An Analysis of the Labor Market for Uber’s Driver-Partners in the United
States.”
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services. The apps also utilize GPS technology to allow consumers to track their driver so that they
know in real-time when he or she will arrive.
6. Provides a Mechanism for Trust between Consumers and Individual Service Providers: Digital
matching firms, via rating systems within their platforms, have provided the consumer an efficient
mechanism through which they are willing to trust complete strangers to provide goods and
services. Relying on crowdsourced information to establish trust between a consumer and a
company is not new, as people have long used relatives, friends, co-workers, and neighbors to
choose a company or specific service provider. However, robust public ratings systems may be a
more efficient guidepost when deciding whether or not, for example, they will stay in a spare
bedroom or have a stranger clean their house, mount their TV, or cook their food. Further, many
digital matching firms are able and willing to ban service providers who fall below ratings thresholds,
acting as an incentive for better service.
Challenges Introduced by the Digital Matching Platforms
Digital matching firms and their technologies have the potential to provide a number of benefits, but
there are possible downsides to the emergence of these firms, most notably to service providers
themselves. Partly because service providers are typically not classified as employees of the firm, risks
are often shifted from the digital matching firm (that provides the platform) to the service provider
(often an individual). There are also potential concerns about customer privacy and access to these
services that need to be considered when evaluating the overall costs and benefits of these new
services.
1. Potential Income Instability: Service providers in the digital matching economy are fully reliant on
the digital matching platform’s ability to connect them with consumers, and they are not
guaranteed to be matched. Thus, service providers in the digital matching economy are often
unsure at any given time whether or not their services will be in demand. Also, in the case of digital
matching firms that set rates themselves, service providers are unsure of the price until they begin
providing those services, and the prices may change at any time.
2. Fewer Benefits and Protections for Service Providers: Since many digital matching firm service
providers are classified as independent contractors, they are not eligible to receive many benefits,
such as a minimum wage, overtime pay, health and life insurance benefits, collective bargaining
rights, retirement and savings plans, protections from discrimination, and sick leave. Workers who
sign up for their own benefits must additionally devote their own unpaid time to what is normally
provided by human resource departments. In addition, service providers are often not compensated
if, for instance, a client is running late or reneges on a service request.
3. Service Providers are Responsible for their Own Training: A digital matching company lacks the
incentive to train its service providers lest they be classified as employees. For that reason, service
providers either must already possess the knowledge and experience to provide a service or are
forced to train themselves. Thus, for example, laborers providing handyman services via platforms
such as Taskrabbit, must either already know how reliably to mount a TV, or teach themselves to do
so.
4. Capital Investment and Maintenance Costs are the Responsibility of the Service Provider: Digital
matching firms rely on service providers to use and maintain their own capital assets. If, for
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example, a service provider’s car breaks down or their tool malfunctions, he or she must cover
replacement and repair costs. Further, when providing services through car transportation platforms
such as Uber, service providers are responsible for fuel costs, depreciation, and insurance
coverage.
21
5. Consumer Privacy and Security: Digital matching firms by their very nature collect and have access
to a substantial amount of consumer and service provider information, whether it be a consumer’s
credit card information, home address, location, or travel history. As with all firms that conduct
business via the digital economy, the safe handling and legal usage of such data by digital matching
firms must be considered.
6. Access: In order to utilize digital matching platforms as either a consumer or a service provider, one
must at least have access to the Internet, and also, in many cases, a smartphone. According to the
Pew Research Center
22
, about two-thirds of American adults now own a smartphone, up from 35
percent in 2011. Although smartphone access has grown considerably, one-third of U.S. adults are
effectively unable to utilize many digital matching applications without assistance, and many of
those without smartphones are those with lower levels of educational attainment and those on the
lower end of the socioeconomic spectrum. For instance, only 50 percent of Americans making less
than $30,000/year own a smartphone, compared with 84 percent smartphone ownership among
those making $75,000/year or more. Only 52 percent of Americans with a high school degree or less
own a smartphone compared with 78 percent ownership among those with college degrees.
Integrating Digital Matching Firms into the Regulatory
Framework
When startups with innovative business models emerge, it may take time to figure out how they fit into
the regulatory framework. For example, the rise of what are now considered traditional online retailers
such as Amazon and Ebay brought with them a multitude of complicated policy issues that are still being
debated, such as how, where, and when to tax purchases.
23
If regulations are inequitable, this may lead
to market distortions. The purpose of this section is to provide an overview of several of the issues that
have emerged to-date as some of the prominent digital matching firms have increased their market
share over the past few years. Although not a comprehensive list, these issues include:
1. Worker Classification: Currently, many digital matching firm service providers are classified as
independent contractors, and not employees. As discussed earlier, in the United States this
distinction carries with it differences in rules and regulations related to areas such as unemployment
insurance, workers’ compensation, training, and health insurance coverage. The IRS has a list of 20
factors that “may be examined in determining whether an employer-employee relationship exists.”
These factors include worker training and set hours of work. Government regulators are examining
21
https://www.uber.com/driver-jobs. Viewed on 3/24/2016.
22
Pew Research Center, “U.S. Smartphone Use in 2015.” April 1, 2015.
23
For example, Amazon currently only collects sales taxes for transactions that take place in just over half of U.S.
States.
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whether or not these service providers should be classified as employees, and some are ruling that
they should be.
California’s Labor Commission, for example recently ruled that Uber drivers should
be classified as employees. The U.S. Department of Labor also recently reemphasized concern over
companies claiming their workers as independent contractors when they should be employees,
although the guidance did not specifically reference the “sharing” economy or any of the other
commonly used designations.
24
However, in anticipation of similar rulings, some companies, such as
Shyp, have begun converting their independent contractors into employees.
Further, companies are required to withhold income taxes, pay unemployment taxes, and pay and
withhold Social Security and Medicare taxes for workers classified as employees. As mentioned
above, California regulators recently ruled
25
that Uber drivers should be classified as employees, not
independent contractors. If that ruling stands, Uber will be required to incur the administrative costs
necessary to collect these employment-related taxes, as well as pay the Federal Unemployment tax
(FUTA) and cover half of their employees’ social security and Medicare taxes
26
.
The sharp disparity between the way contractors and employees are regulated has led some
27
to
question whether a third worker classification should be enacted that covers workers who fall
somewhere between independent contractors treated as self-employed businesspeople and
traditional employees that are generally entitled to certain benefits and worker protections.
Our understanding of the extent of the worker classification issue is challenged because of the
limited availability of data on this segment of the workforce, although the federal government is
currently conducting several efforts to collect better data on the subject. (Box 5. Expanding the
Collection and Availability of ‘Sharing’ Firm Data).
2. Taxation and Compliance: The applicability of hotel taxes to room and residence rentals via Airbnb
and other lodging-specific digital matching firms have been raised in a number of localities. Initially,
digital matching firms did not require that service providers pay lodging taxes that are typically
required of hotels and other lodging establishments, potentially reducing government revenue and
creating a competitive advantage for lodging-specific digital matching firms. Many localities, such as
Santa Monica, California have banned the use of Airbnb-like services for short-term lodging unless
the service provider obtains a business license and pays a hotel tax.
28
In response, Airbnb has agreed
to collect taxes in several cities, including the District of Columbia and Portland, Oregon in order to
meet local tax collection responsibilities while not burdening potential service providers with the
need to apply for licenses or collect taxes themselves.
24
https://www.dol.gov/whd/workers/misclassification/ai-2015_1.htm
25
http://www.reuters.com/article/us-uber-tech-drivers-lawsuit-idUSKCN0Y02E8
26
https://www.irs.gov/businesses/small-businesses-self-employed/understanding-employment-taxes
27
For example, The Hamilton Project at the Brookings Institute released a report titled “A Proposal for
Modernizing Labor Laws for Twenty-First-Century Work: The “Independent Worker” that examined alternative
employee classifications for gig economy workers.
28
Sam Sanders, ”Santa Monica Cracks Down On Airbnb, Bans 'Vacation Rentals' Under A Month,” NPR, May 13,
2015, http://www.npr.org/sections/thetwo-way/2015/05/13/406587575/santa-monica-cracks-down-on-airbnb-
bans-vacation-rentals-under-a-month
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Box 5. Expanding the Collection and Availability of ‘Sharing’ Firm Data
Given the relative infancy of digital matching firms and the broader “sharing economy,” limited
federal data is available about the size, scope, and growth of these firms or about the makeup of
their employees and contractors. The lack of data makes it difficult for researchers and
policymakers to study trends in this area. However, efforts are underway to expand the
availability of data on these firms and the workers that provide services through them.
For example, the Department of Labor and the U.S. Census Bureau will reintroduce the
Contingent Worker Supplement (CWS) as part of the Current Population Survey in 2017
29
. The
CWS was conducted 5 times from 1995 to 2005 in an attempt to measure more accurately the
size of the contingent workforce
30
. With adjustments to reflect questions relevant to digital
matching firms or to the ‘sharing economy,’ the CWS could be an important source of data on
workers, both within the government and in the private sector, who participate in these parts of
the economy but are not traditional employees.
Further, expanding the ability of federal statistical agencies to use limited Federal tax information
holds promise for improving data on digital matching firms or the broader ”sharing economy.”
Such data access potentially would enable the statistical agencies to measure income from
sources such as payments made to a person who is not an employee, sources which are
particularly relevant to these parts of the economy. Allowing statistical agencies access to this
type of data would require a change in the tax code to expand the use of tax information for
statistical purposes; for example, under current law, the Bureau of Economic Analysis only has
access to Federal tax information of corporations (FTI) and the Bureau of Labor Statistics has no
access to FTI for use in the statistics it produces. This barrier to accessing business tax
information is a roadblock preventing measurement of the sharing economy’s financial size and
employment scope.
3. Equal Access to Services for Individuals with Disabilities: A large number of businesses in the
United States are included in the 12 categories that are considered “public accommodations” and
are therefore covered by the American with Disabilities Act (ADA), including restaurants, hotels,
movie theaters, schools, day care facilities, recreation facilities, taxi services and doctors’ offices. For
instance, taxis services are required to ensure that a certain percentage of their fleets are equipped
to transport passengers with disabilities; in the District of Columbia, each taxi and sedan company
29
Secretary Tom Perez, “Innovation and the Contingent Workforce,” U.S. Department of Labor blog, January 25,
2016.
30
According to the Department of Labor, contingent workers are “persons who do not expect their jobs to last or
who reported that their jobs are temporary. They do not have an implicit or explicit contract for ongoing
employment. Alternative employment arrangements include persons employed as independent contractors, on-
call workers, temporary help agency workers, and workers provided by contract firms.”
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with 20 or more vehicles must dedicate a portion of its fleet to wheelchair accessible vehicles.
31
Hotels and even bed and breakfast facilities must comply with ADA regulations regarding
architectural barriers. Digital matching firms that provide transportation services may not be
equipped to provide service to the disabled, and it is unclear whether or not they are required to do
so under the ADA or related statutes and regulations. Also not clear is whether authorities require
most rooms and houses listed on websites of lodging-specific digital matching firms to be ADA
compliant.
4. Consumer Safety and Service Provider Certification: Traditional firms must often pass rigorous
regulatory checks, such as health and safety inspections in hotels and in restaurants, to ensure that
their services are safe for consumers. Service providers in certain traditional industries are also
often subject to additional screening and certification requirements, such as ensuring the
contractors they employ are licensed to conduct handyman services or have taxi licenses for their
cabs. Digital matching firms may not meet these same consumer safety requirements. Airbnb and
other lodging-specific digital matching firm service providers, for instance, are not subject to the
health and sanitation inspections common among hotels and bed and breakfast facilities. Providers
of handyman services through digital matching platforms may not have the required contractors’
licenses to do specific requested tasks. Firms such as Taskrabbit hedge against such issues by
providing insurance coverage in the event of an accident, but their service providers potentially
remain unlicensed.
Traditional service providers must also comply with federal, state and local environmental
regulations to which digital matching service providers may not be subject. Hotels, for example,
must adhere to a number of basic federal requirements under the Clean Air Act, Clean Water Act,
and the Resource Conservation & Recovery Act and Toxic Substances Control Act, among others.
Conclusion
In this paper, we proposed a definition for “digital matching firms” as firms that use Internet and
smartphone-enabled apps to match service providers with consumers, help ensure trust and quality
assurance via peer-rating services, and rely on flexible service providers who, when necessary, use their
own assets. Notwithstanding the challenges of defining and measuring digital matching firms in the
context of the greater economy, their rapid growth suggests that these firms are providing a unique and
valuable platform to connect consumers and service providers. We found that many digital matching
firms have grown considerably during the past five years, and although reliable public data about the
size and scope of the digital matching economy as a whole is scarce, there are a number of digital
matching firms that are reportedly valued in the billions of dollars, with Uber and Airbnb leading the
pack at $62.5 billion and more than $25 billion, respectively. However, these firms remain a small part of
the greater economy.
31
According to the DC Taxicab Commission Disability Advisory Committee, the portion of its fleet dedicated to
wheelchair accessible vehicles must be at least 6 percent by December 31, 2014; at least 12 percent by December
31, 2016; and at least 20 percent by December 31, 2018.
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We discussed a number of potential benefits and challenges introduced by digital matching firms, with
benefits including potentially lower transaction costs for services, flexible employment opportunities for
service providers, the leveraging of excess capacity, improved customer experience, and the potential
for stimulating new consumption in the economy. However, the introduction of digital matching firms is
not without potential downsides. These detriments include potential income instability for service
providers, the need for service providers to take care of their own asset maintenance costs,
responsibility for obtaining the assets (such as a car or room) that they use to provide services, fewer
worker benefits, and access issues for individuals who don’t have a readily available Internet source
and/or smartphone.
Finally, we discussed some of the challenges that are emerging with the growth of this particular
innovative business model. Like ecommerce firms in the 1990s, digital matching firms are promoting
debate about how to capture the benefits of technology driven change without abandoning important
aspects of the current industrial organization, such as workers’ rights, consumer safety, equal access,
environmental protection, and privacy. .
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Appendix: Examples of Digital Matching Firms
This list was compiled for the purpose of testing whether it was possible to develop a firm-based
definition that captures the uses of a “new technology,” where the new technology is the on-demand or
digital matching platform business model. The majority of this research was done in the latter half of
2015 using the methods described below the table. An effort has been made to ensure the firms in the
list are still in operation and continue to meet the definition. However, given the dynamism of
entrepreneurially activity in this area, the list should not be viewed as authoritative or as a
comprehensive list of companies using the digital matching platform model.
Category Company Name Company Website
Art Rental Art.sy artsy.net
Art Rental TurningArt turningart.com
Art Rental Artsicle artsicle.com
Bike Sharing Spinlister, Inc. (formerly Liquid) https://www.spinlister.com/
Car Sharing Turo, Inc. http://www.Turo.com/
Car Sharing Getaround, Inc. https://www.getaround.com/
Car Sharing SnappCar http://www.snappcar.com/
Car Sharing BMW Car Sharing, LLC (DriveNow) https://us.drive-now.com/
Care Dog Vacay, Inc. http://dogvacay.com/
Care A Place for Rover, Inc. http://www.Rover.com/
Care UrbanSitter, Inc. https://www.urbansitter.com/
Care Care.com, Inc. https://www.care.com/
Care Swifto https://swifto.com/
Care The Good Bear, Inc. (Doggybnb) http://doggybnb.com/
Care Zingy http://www.zingypet.com/
Clothing Swaps Dig N'Swap http://www.dignswap.com/
Delivery Postmates, Inc. https://postmates.com/
Delivery Food Lovers United Co. https://www.fluc.com/
Delivery Square, Inc. (Caviar) https://www.trycaviar.com/
Delivery DoorDash, Inc. https://www.doordash.com/
Dining Feastly, Inc. https://eatfeastly.com/
Dining EatWith Media Ltd. http://www.eatwith.com/
Dining Greased Watermelon LLC (LeftoverSwap) http://leftoverswap.com/
Dining SpoonRocket, Inc. https://www.spoonrocket.com/
Dining Munchery, Inc. https://munchery.com/
Dining Sprig, Inc. http://sprig.com/
Dining Gobble
Errands TaskRabbit, Inc. (formerly RunMyErrand, Inc.) https://www.taskrabbit.com/
Errands JobRunners, LLC https://www.job-runners.com/
Errands Zaarly, Inc. https://www.zaarly.com/
Errands Dolly, Inc. https://getdolly.com/
Errands RedBeacon http://www.redbeacon.com/
Errands Expert Bids https://www.expertbids.com/
Errands Fancy Hands, Inc. https://www.fancyhands.com/
Errands Gorilly, Inc. http://www.gorilly.com/
Errands Alfred Club, Inc. https://www.helloalfred.com/
Errands NeighborFavor, Inc. https://favordelivery.com/
Errands Campus Bellhops, LLC https://getbellhops.com/
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
Economics and Statistics Administration
Page 24
Errands Shyp, Inc. http://www.shyp.com/
Errands Crowdflower http://www.crowdflower.com/
Fashion Tradesy, Inc. https://www.tradesy.com/
Fashion Le Tote, Inc. https://www.letote.com
Fashion RocksBox, Inc. https://www.rocksbox.com/
Fashion Rent the Runway, Inc. https://www.renttherunway.com/
Fashion Bag Borrow or Steal bagborroworsteal.com
Fashion Shopittome shopittome.com
Fashion The Outnet theoutnet.com
Funding Kickstarter, Inc. http://www.kickstarter.com
Funding RocketHub, Inc. http://www.rockethub.com/
Funding IndieGoGo Inc. https://www.indiegogo.com/
Funding Prosper prosper.com
Funding LendingTree, LLC (formerly Tree.com, Inc.) https://www.lendingtree.com/
Funding LendingClub Corporation https://www.lendingclub.com/
Funding Enterprise Den enterpriseden.com
Funding Startsomegood startsomegood.com
Funding Pozible pozible.com
Gardens Servicevines
General Online Rental AnyHire anyhire.com
Goods Sharing Sugar Packet, Inc. (doing business as NeighborGoods) http://neighborgoods.net/
Goods Sharing HeyNeighbor, LLC http://www.heyneighborapp.com/
Goods Sharing 1000 Tools, Inc. https://www.1000tools.com/
Goods Sharing Boatbound, Inc. https://boatbound.co/
Goods Sharing Streetbank streetbank.com
Goods Sharing Zi Group SA (Zilok) http://us.zilok.com/
Goods Sharing
Sparkplug Marketplace, Inc.
http://www.sparkplug.it/
Goods Sharing Friends With Things friendswiththings
Goods Sharing Toolzdo toolzdo.com
Goods Sharing RentStuff rentstuff.com
Homesharing Couchsurfing International, Inc. https://www.couchsurfing.com/
Homesharing Airbnb, Inc. https://www.airbnb.com/
Homesharing FlipKey, Inc. https://www.flipkey.com/
Homesharing HomeAway, Inc. (formerly CEH Holdings) http://www.homeaway.com/
Homesharing Roomorama https://www.roomorama.com/
Homesharing Lifealike Limited (doing business as onefinestay) http://www.onefinestay.com/
Media and Entertainment Fon Wireless, Ltd. https://corp.fon.com/
Media and Entertainment SoundCloud Ltd. http://soundcloud.com/
Media and Entertainment Earbits, Inc. http://www.earbits.com/
Misc Services Wello wello.com
Misc Services Nanny in the Clouds nannyintheclouds.com
Parking Parkcirca www.parkcirca.com
Parking Parking Panda Corp. (Parking Panda) https://www.parkingpanda.com/
Personal Services Rent a Friend rentafriend.com
Personal Services Hire a Boston Wingwoman hireawingwoman.com
Professional and Freelance Amazon.com, Inc. (Amazon Mechanical Turk) https://www.mturk.com
Professional and Freelance Upwork Global, Inc. (formerly Elance-oDesk, Inc.) https://www.upwork.com/
Professional and Freelance Fiverr International Ltd. https://www.fiverr.com/
Professional and Freelance Thumbtack, Inc. https://www.thumbtack.com/
Professional and Freelance SpareHire, Inc. https://www.sparehire.com/
Professional and Freelance Websoft, Inc. (doing business as Guru.com) http://www.guru.com/
Professional and Freelance Wonolo, Inc. http://wonolo.com/
Professional and Freelance Gig Bureau, LLC (doing business as GigSalad) https://www.gigsalad.com/
Professional and Freelance Peers Benefit Corporation http://www.peers.org/
Professional and Freelance Turuly, Inc. (doing business as BlogMutt) https://www.blogmutt.com/
Professional and Freelance Gigwalk, Inc. http://www.gigwalk.com/
Professional and Freelance Creative Circle, LLC https://www.creativecircle.com/
Ridesharing Uber Technologies, Inc. http://www.uber.com
Ridesharing Lyft, Inc. https://www.lyft.com/
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
Economics and Statistics Administration
Page 25
As digital matching firms are a relatively new phenomenon and the companies have been commonly
referred to as part of the “sharing” or “collaborative” economy, research into existing digital matching
firms was conducted via basic online searches. The majority of these firms were found using Google
search of the words “sharing economy,” or “collaborative economy,” which directed us to several news
and journal articles written on the topic. We then researched the companies and compiled a list of those
that have the characteristics of digital matching firms.
The articles used to identify these firms are cited below the table along with the names of companies
mentioned..
1. Bloomberg Brief. “The Sharing Economy.” (2015). Available at:
http://newsletters.briefs.bloomberg.com/document/4vz1acbgfrxz8uwan9/front
2. Botsman, Rachel. “The Sharing Economy Lacks a Shared Definition”. Fast Company and Inc.
(2013). Available at: http://www.fastcoexist.com/3022028/the-sharing-economy-lacks-a-
shared-definition
3. Fast Company. Available at: http://www.fastcompany.com/3042248/the-gig-economy-wont-
last-because-its-being-sued-to-death
4. Federal Reserve Bank of Richmond. Available at:
https://www.richmondfed.org/publications/research/econ_focus/2014/q4/cover_story
5. “Find Work.” Peers.org. (2015). Available at: http://www.peers.org/find-work/
Ridesharing Sidecar Technologies, Inc. https://www.side.cr/
Ridesharing Tripda, Inc. https://www.tripda.com/
Ridesharing GoCarShare http://gocarshare.com/
Ridesharing Shuddle, Inc. https://shuddle.us/
Ridesharing AtoB LLC (Coride) https://www.coride.com/
Ridesharing Zimride https://zimride.com/
Ridesharing carma https://carmacarpool.com/
Ridesharing wingz https://wingz.me/
Ridesharing Nuride http://www.nuride.com/
Ridesharing Jayride http://us.jayride.com/
Taxi Sharing Taxi2
Taxi Sharing Weeels http://www.bandwagon.io/about#main
Teaching CoachUp, Inc. https://www.coachup.com/
Teaching Chegg, Inc. (Chegg Tutors) https://www.chegg.com/tutors/
Teaching Skillshare, Inc. http://www.skillshare.com/
Teaching Udemy, Inc. https://www.udemy.com/
Teaching Service Scout, Inc. (doing business as TakeLessons) https://takelessons.com/
Teaching Myngle http://www.myngle.com/
Teaching Glovico http://www.glovico.org/
Teaching
RiffRaff Community, Inc.
http://www.riffraff.me/
Teaching Livemocha
Textbook Rental Chegg
Textbook Rental CampusBookRentals
Textbook Rental BookRenter
Toy Rental BabyPlays babyplays.com
Unique Experiences Vayable, Inc. http://www.vayable.com/
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
Economics and Statistics Administration
Page 26
6. Forbes. Available at: http://www.forbes.com/sites/groupthink/2014/07/07/how-the-hotel-
industry-got-blindsided-and-why-yours-could-be-next/
7. Geron, Tomio. “Airbnb and the Unstoppable Rise of the Share Economy”. Forbes. (2015).
Available at: http://www.forbes.com/sites/tomiogeron/2013/01/23/airbnb-and-the-
unstoppable-rise-of-the-share-economy/
8. Jolly, Jennifer. “Dog Needs a Walk? There’s an App for that”. The New York Times. (2015).
Available at: http://well.blogs.nytimes.com/2015/07/07/dog-needs-a-walk-theres-an-app-for-
that/?action=click&contentCollection=Your%20Money&module=MostEmailed&version=Full&re
gion=Marginalia&src=me&pgtype=article&_r=0
9. LaBrecque, Sarah. “Eight of the Best Sharing Economy Companies”. The Guardian. (2014).
Available at: http://www.theguardian.com/sustainable-business/eight-best-sharing-economy-
companies
10. Scholz, Trebor. “Platform Cooperativism vs. the Sharing Economy”. Public Seminar. (2015).
Available at: http://www.publicseminar.org/2015/04/platform-cooperativism-vs-the-sharing-
economy/#.VaUvKvlVhBc
11. Sundararajan, Arun. “Peer-to-Peer Businesses and the Sharing (Collaborative Economy):
Overview, Economic Effects, and Regulatory Issues.” (2014). Available at:
http://smallbusiness.house.gov/uploadedfiles/1-15-2014_revised_sundararajan_testimony.pdf
12. Tanz, Jason. “How Airbnb and Lyft Finally Got Americans to Trust Each Other”. Wired: Business.
(2014). Available at: http://www.wired.com/2014/04/trust-in-the-share-economy/
13. The Economist. “All Eyes on the Sharing Economy”. The Economist: Technology. (2013).
Available at: http://www.economist.com/news/technology-quarterly/21572914-collaborative-
consumption-technology-makes-it-easier-people-rent-items
Acknowledgments
In addition to those whose work is cited, the author would like to thank the following
persons who provided comments, suggestions, and other contributions to this report:
Department of Commerce Economics and Statistics Administration: Ellen Hughes-
Cromwick, Chief Economist; Rob Rubinovitz, Deputy Chief Economist; Sabrina Montes,
Team Lead and Economist; David Langdon, Team Lead and Economist; David Beede,
Economist; Regina Powers, Economist; Sue Helper, former Chief Economist;
White House Council of Economic Advisers: Nirupama Rao; Robert Seamans; Martha
Gimbel; Harris Eppsteiner; Sam Himel
Digital Matching Firms: A New Definition in the “Sharing Economy” Space
Office of the Chief Economist
Economics and Statistics Administration
Page 27
Department of Labor: Heidi Shierholz; Tanya Goldman; Jeff Vockrodt
Department of Treasury: Karen Dynan; Tara Watson; Ryan Nunn
White House National Economic Council: JJ Raynor
Any errors in the report are solely the authors’ responsibility. The author also wishes to
express that the inclusion and/or discussion of any company is not to be characterized
as an endorsement of neither the firm itself nor the services it provides.
