Will Rinehart Executive Summary June 17,
2019
The
Department of Justice’s current inquiry into advertising underscores its
importance to the digital ecosystem. But advertising needs to be understood
within its proper context. Not only is it one, albeit important, method of
supporting content online, but advertising also gains its value within a
platform setting. Four key lessons are worth remembering:
- The
value of platforms comes in bringing together two different groups of
users;
- The recent Ohio
v. American Express case charts an important path for consumer
welfare that should be at the core of antitrust analysis for platforms;
- Advertising
competition is occurring both in both online space and traditional
channels like TV and radio; and, finally,
- The value of
user data generated from platforms isn’t as straightforward as many think.
The Value
of Ad-Supported Networks
Platforms
such as Facebook, Google, Reddit, and Twitter are often derided, but both
empirical and anecdotal data indicate that they provide real value to their
users. Pew polling finds that most Americans say the Internet has brought
benefits in learning, sharing and diversifying the flow of information into
their lives. Even with all of the negative press, “74% of Americans say major
technology companies’ products and services have been more good than bad for
them personally.”[1] A variety of stories sharpen this
perceived reality, indicating that such platforms support meaningful human
interactions and have helped to expand access to content. On Facebook, for
example, chronic pain sufferers find solace.[2] Widows vent, rage, laugh, and cry
without judgement through the Hot Young Widows Club.[3] Reddit, while sometimes a place of
outrage, is also where a weight lifter with cerebral palsy became a hero, where
those with addiction can find healing, and where respectful disagreement can
live.[4][5][6]
These
indicators show that these platforms generate value, and indeed, one common way
of valuing free services such as Facebook and Google is to calculate the amount
of forgone wages. Every hour spent on the site is an hour not spent on other activities.
There is an opportunity cost to using online platforms such as Facebook and
Reddit. A conservative estimate from a couple years back suggests that users
spend about 20 hours a month on Facebook.[7] Since the current average wage is
$27.83 (as of May 2019), this calculation indicates that people roughly value
the site by about $6,700 over the entire year.[8] A study using data from 2016 using
similar methods found that American adults consumed 437 billion hours of
content on ad-supported media, worth at least $7.1 trillion in terms of
foregone wages.[9]
Multi-sided
platforms create value by bringing different economic agents together.[10] They
facilitate interaction among these agents and generate welfare for individual
agents by reducing transaction costs. Such platforms aren’t new. They’ve been
around for decades in industries like video games, credit cards, newspapers,
and radio stations. The Twin Cities’ Mall of America is as much a platform as
Google is. The Internet has only facilitated the creation of such platforms by
allowing agents to interact in real time. As will be discussed later, the core
concern with platforms is how the price for each side is optimally set.
Platforms
create value via two sources. Usage externalities stem from
the benefits that both sides get when they use the platform. These mostly come
from reduced transaction costs. For instance, consumers can save time by using
platforms such as OpenTable to reserve tables at restaurants. Restaurants also
save time and costs by using an online platform for reservations.
The
second source relates to the number of users on each side of the platform.
These membership externalities are generated by network
effects. The value created for users on one side of the platform increases
exponentially when more users join the other side of the platform. For example,
the value of a credit card for merchants increases as the number of cardholders
increase. Similarly, as the number of merchants that accept a certain credit
card increases, cardholders benefit from increased acceptability. When an
increase in one side of the market affects the value in the other, these
changes are known as indirect network effects. They serve as the
foundation of the platform economy.
Consider
a platform with two sides, users and advertisers. If users experience an
increase in price or a reduction in quality, then they are likely to exit or
use the platform less. Advertisers are on the other side because they can reach
users, so in response to the decline in user quality, advertiser demand will
drop even if the ad prices stay constant. The result echoes back. When
advertisers drop out, the total amount of content also recedes, and user demand
falls because the platform is less valuable to them. Demand is tightly
integrated between the two sides of the platform. Changes in user and
advertiser preferences have far outsized effects on the platforms because each
side responds to the other. In other words, small changes in price or quality
tends to be far more impactful in chasing off both groups from the platforms as
compared to one-sided goods. These are called demand interdependencies and
are a species of indirect network effect. Research on magazine price changes
confirms this theory.[11] The
demand on one side of the market is interdependent with demand on the other.
One of
the most common arguments against platform power is the worry that they will
start excluding producers, retailers, advertisers, and app developers.[12] Platforms,
however, have a strong incentive to include all users because the effects
reverberate through both sides of the platform. That isn’t to say that the
pricing structure will be the same, and indeed the optimal pricing strategies
for each part of the network is important to understand network behaviors.
Pricing
Structure in Platforms
In work
fundamental to his 2003 Nobel Prize, economist Jean Tirole found that prices
charged by platforms are fundamentally different from those of traditional
businesses.[13] Three
basic assumptions, which hold in the real world, help to set the stage for this
conclusion. First, there are two distinct customer groups connected by the
platform. Second, positive externalities exist between members of those groups.
And finally, a two-sided platform provides a good or service that facilitates
exchange of value in the face of these externalities. Altogether, Tirole helped
to prove that an increase in marginal cost on one side of the platform doesn’t
necessarily increase prices on that side. Thus, the profit-maximizing price for
one side may be below the marginal cost or even negative. Here, negative prices
mean that the consumer is getting a benefit without paying for it.
For
example, OpenTable gives consumers bonuses for signing up while charging
restaurants fees for having them on their network. In this structure, the
network effects, or the membership externalities, of having
consumers use OpenTable increases value for restaurants as they have access to
a larger user base. OpenTable then charges fees to the restaurants in order to
recover costs. This price structure is common: One entity is given discounts or
charged no fee and the other side is charged fees greater than the marginal
cost. Indeed, the cost structure of Twitter, Facebook, and Google only make
sense through this lens.
In the
last two decades, economics has been adapting to the insights and the
challenges of platforms. In the case of a one-sided business, such as a
laundromat or a mining company, there is one downstream or upstream consumer,
so demand is fairly straightforward. But platforms are more complex since value
must be balanced across the different participants in a platform, which leads
to demand interdependencies, as explained earlier.
In an
article cited in the Supreme Court’s Ohio v. American Express (or Amex)
decision—which focused on the ability of credit-card companies, as platforms,
to prevent merchants from “steering” purchasers toward a particular kind of
credit card—economists David Evans and Richard Schmalensee explained the
importance of the integration of platform economics into competition analysis:
“The key point is that it is wrong as a matter of economics to ignore
significant demand interdependencies among the multiple platform sides” when
defining markets.[14] If they
are ignored, then the typical analytical tools will yield incorrect
assessments.
While it
didn’t employ the language of demand interdependencies in its Amex decision,
the Supreme Court did agree with that general assessment:
To be
sure, it is not always necessary to consider both sides of a two-sided
platform. A market should be treated as one sided when the impacts of indirect
network effects and relative pricing in that market are minor. Newspapers that
sell advertisements, for example, arguably operate a two-sided platform because
the value of an advertisement increases as more people read the newspaper. But
in the newspaper-advertisement market, the indirect networks effects operate in
only one direction; newspaper readers are largely indifferent to the amount of
advertising that a newspaper contains. Because of these weak indirect network
effects, the market for newspaper advertising behaves much like a one-sided
market and should be analyzed as such.
How the
Court reached that conclusion is worth exploring. In contrast to newspapers,
credit card payment platforms “cannot make a sale unless both sides of the
platform simultaneously agree to use their services,” so, “two-sided
transaction platforms exhibit more pronounced indirect network effects and interconnected
pricing and demand.” The Court seems to connect two-sidedness with the
simultaneity requirement. But it isn’t the simultaneous nature of credit cards
that makes them two-sided markets, but their demand interdependencies.
Newspapers also have strong demand interdependencies even though they may not
feature the simultaneity of credit cards, in contradistinction to the Amex decision.
Yet, the Court was correct in defining the market as a transactional one, where
cardholders and merchants are intimately connected.
Justice
Breyer’s dissent in Amex offers one path to understand optimal
pricing. As he wrote,
But while
the market includes substitutes, it does not include what economists call
complements: goods or services that are used together with the restrained
product, but that cannot be substituted for that product. See id., ¶565a, at
429; Eastman Kodak Co. v. Image Technical Services, Inc., 504 U. S. 451, 463
(1992). An example of complements is gasoline and tires. A driver needs both
gasoline and tires to drive, but they are not substitutes for each other, and
so the sale price of tires does not check the ability of a gasoline firm (say a
gasoline monopolist) to raise the price of gasoline above competitive levels.
As a treatise on the subject states: “Grouping complementary goods into the
same market” is “economic nonsense,” and would “undermin[e] the rationale for
the policy against monopolization or collusion in the first place.” 2B Areeda
& Hovenkamp ¶565a, at 431.
Here, the
relationship between merchant-related card services and shopper-related card
services is primarily that of complements, not substitutes. Like gasoline and
tires, both must be purchased for either to have value. Merchants upset about a
price increase for merchant related services cannot avoid that price increase
by becoming cardholders, in the way that, say, a buyer of newspaper advertising
can switch to television advertising or direct mail in response to a
newspaper’s advertising price increase.
Still, it
isn’t the case that “both must be purchased for either to have value.” That is
perfect complementarity, which is rare. When the price of gasoline increases,
then the demand for tires is likely to decrease as well. This relation doesn’t
need to run the other way, however. When the price of tires decreases, the
demand for gasoline doesn’t typically inch up. This kind of asymmetric demand
relationship is counter to the kind of relationship on platforms where demand
is linked on both sides.
Justice
Breyer buries the lede. Attributing a price increase to firms in the tire
market might be wrong if demand fluctuations in the adjacent gasoline market
partially caused those prices changes. In other words, the reason why
complementary demand matters in the first place is to ensure that the court’s
analysis is correct. Going back to Evans and Schmalensee, “The key point is
that it is wrong as a matter of economics to ignore significant demand
interdependencies among the multiple platform sides” when defining markets. If
you do, you get the assessments wrong.
To his
credit, Breyer does rightly point out the thin definition offered by the
majority,
I take
from that definition that there are four relevant features of such businesses
on the majority’s account: they (1) offer different products or services, (2)
to different groups of customers, (3) whom the “platform” connects, (4) in
simultaneous transactions.
Having
simultaneous transactions isn’t the defining feature of two-sidedness, and if
the lower courts come to rely on this feature to define platforms, then some
assessments of competitive effects are likely to be wrong. Instead, the courts
should be focused, as they have been for some time, on consumer welfare.
Consumer
Welfare and Antitrust
The
courts have long interpreted Section 2 of the Sherman Act to focus on
enforcement of conduct rather than mere outcomes. Starting with Alcoa,
Justice Hand’s opinion made it clear that “The Act does not mean to condemn the
resultant of those very forces which it is its prime object to foster: finis
opus coronat.”[15] This idea was reaffirmed by Justice
Scalia in Trinko who noted that “the possession of monopoly
power will not be found unlawful unless it is accompanied by an element of
anticompetitive conduct.”[16] While
some boosters of market intervention might want to change this standard, this
focus on consumer welfare shouldn’t change. It’s important to note that a focus
on conduct doesn’t preclude antitrust enforcement of platforms.
In a
piece in the New York Times in April, legal scholar Lina Khan
worried that this case would “effectively [shield] big tech platforms from
serious antitrust scrutiny.”[17] Law
professor Tim Wu followed up with an op-ed in the New York Times expressing
similar concern about the ability of agencies and courts to go after bad
platform actors:
To reach
this strained conclusion, the court deployed some advanced economics that it
seemed not to fully understand, nor did it apply the economics in a manner
consistent with the goals of the antitrust laws. Justice Stephen Breyer’s
dissent mocks the majority’s economic reasoning, as will most economists,
including the creators of the “two-sided markets” theory on which the court
relied. The court used academic citations in the worst way possible — to take a
pass on reality.[18]
As Amex stands,
Google, Facebook, and other platforms more evidently fall into the newspaper
category than the payments category under the majority’s opinion, meaning its
holding doesn’t apply directly to them. Yet the opinion didn’t define what
“weak indirect network effects” actually mean in practice, so this case doesn’t
leave Google and Facebook untouched by any means. Here, the Court made an
error. If these two companies do face scrutiny, then they should be subjected
to the platform standard that Amex has begun to outline.
Economists
and antitrust scholars have been actively working to extend traditional
antitrust analysis to apply it to platform businesses.[19] The
Court agreed; the totality of the platform needs to be understood. As such, the
Department of Justice should work to extend the analysis first begun by the
Supreme Court and pioneered by organizations such as the OECD.[20] To
understand the competitive nature within the digital advertising space
properly, the Department should consider how each side responds to incentives,
prices, and demand from the other side, as well as, how the participants in the
platform compete with other, more traditional offerings.
The
Changing Nature of Advertising Competition
Many are
quick to mark online advertising as a submarket within advertising, but a
closer look tells of a dynamic ecosystem where traditionally distinct markets
have come into direct competition with one another. The proliferation of
outlets, both online and offline, and the convergence of content mean that
advertisers have increasingly shifted their resources to target audiences
across multiple media channels like radio, TV, and social media.[21] The Association of National
Advertisers has charted the rise of this multi-channel advertising scheme
throughout the past decade and finds that most marketers now use an integrated
marketing method.[22] The spread of messages across
numerous channels means that some lower value advertisers continue to advertise
on a limited set of publishers, which has the effect of reducing demand for
advertising and thus ad prices.[23] In other words, more outlets generally
translates into lower ad prices as knowledgeable advertisers pick and choose
specific channels. Indeed, as research has found, concentration in the online
ad market leads to less revenue for those platforms because it allows for a
more efficient targeting of keywords through superior information.[24]
A number
of studies into the effect of TV ads on online behavior fundamentally challenge
the belief that the two worlds are separate. Studies of financial services
advertising through television find that Google searches are affected.[25] Similarly, TV ads for trucks have
been shown to increase both the company’s search numbers and their competitors
as well.[26] Research has also shown that
“television advertising does influence online shopping and that advertising
content plays a key role.”[27]
While
Google and Facebook are the biggest players in the online advertising space,
the rise of new players indicate it hasn’t stultified. Amazon has grown from
just 2 percent of the market in 2016 to around 9 percent this year. Projections
put Amazon on a track for serious growth, suggesting the retailer could make up
as much as 14 percent of the segment in just five years.[28] Already, the other players have
felt the squeeze. The cost per click on Google, an important metric of ad cost,
dropped 29 percent from last year.[29]
Amazon
isn’t the only threat on the horizon. While it hasn’t deployed its ad tech,
GIFY, the company that places gifs on web sites and phones, could become a
major powerhouse.[30] Fortnite is also a looming
competitor. In its 2018 annual report, Netflix explained that its biggest
threat wasn’t other traditional content outlets like FX or Disney, but a
completely new platform, saying, “We compete with (and lose to) Fortnite more
than HBO.”[31] Fortnite has hit a wall in terms of
revenue, however, but it could continue to grow if it worked to integrate more
promotions within its platform, a plan they seem to be undertaking.[32] Spotify is another unusual
competitor in this space, but has found a niche with its ability to analyze
songs as people listen and target based on perceived mood.[33]
There are
limits to the ad-supported ecosystem, however, because there is only so much
attention in the attention economy. The research firm Midia put a fine point on
this when they said earlier this year that “engagement has declined throughout
the sector, suggesting that the attention economy has peaked. Consumers simply
do not have any more free time to allocate to new attention seeking digital
entertainment propositions, which means they have to start prioritising between
them.”[34] Disappointing quarterly results
from a few of the major games publishers could portend a change for the rest of
the players in the attention economy. As Midia analyst Karol Severin observed,
“competition within the attention economy is now more intense than ever
before.”
The Value
of Data
Like any
other asset, the value of data lies in its ability to earn revenue, but the relationship
between revenue and user data isn’t straightforward.[35] Most valuations of big data simply
divide the total market capitalization or revenue of a firm by the total number
of users.[36] In its 2018 annual report, Facebook
calculated that the average revenue per user was around $112 in the United
States and Canada.[37] Antonio Garcia-Martinez recently
used this data point in Wired magazine to place an upper limit
to the value of data.[38] And Douglas Melamed argued in a
recent Senate hearing that the upper-bound value should at least be cognizant
of the acquisition cost for advertisements—putting the total user value at
around $16 (although he cautiously noted that this estimate was likely
inaccurate).[39] Similarly, when Microsoft bought
LinkedIn, for example, reports suggested that they were buying monthly active
users at a rate of $260.[40]
Yet it is
misstep to equate the advertising dollars going to tech platforms with the
value of user data. Understanding multi-sided platforms requires understanding
the goods traded on the user side and the advertiser side. Advertisers spend
money on platforms because people are there, just as advertisers spend money on
TV, print, and radio because people watch television, read newspapers, and
listen to the radio. On Google, Facebook, Instagram, Twitter, and Reddit, user
demand comes as a result of the shared content, which is an experience good.[41] Advertiser demand in turn relies
upon total user demand, since they are trying to get their messages to users.
For advertisers, the inference data explain which groups of
people—sorted by age, gender, or location—clicked on a web site, liked a page,
shared it, or left the platform.
The
demand for users is tightly coupled with demand for advertisers, leading to
demand interdependencies, which were explored by the American Action Forum last
year.[42] As noted then,
Demand is
tightly integrated between the two sides of the platform. Changes in user and
advertiser preferences have far outsized effects on the platforms because each
side responds to the other. In other words, small changes in price or quality
tends to be far more impactful in chasing off both groups from the platforms as
compared to one-sided goods.
While
data is important to the overall maintenance of the platform, much of this data
is valuable only within the platform’s relationships.
The
bankruptcy proceedings for Caesars Entertainment, a subsidiary of the larger
casino company, offer a unique example of this problem. As the assets were
being priced in the selloff, the Total Rewards customer loyalty program got
valued at nearly $1 billion, making it “the most valuable asset in the bitter
bankruptcy feud at Caesars Entertainment Corp.”[43] But the ombudsman’s report
acknowledged that it would be a tough sell because of the difficulties in
incorporating it into another company’s loyalty program. Although it was Caesars’
most valuable asset and helpful in it generating cash flow for that company,
its value to an outside party in generating cashflow was an open question. The
data itself, apart from the company’s systems, was not obviously valuable at
all.
Some
businesses have tried to separate out data from the broader information
ecosystem, but they have met with little success. The pay-to-surf business
model was popular in the late 1990s until the dot-com crash swept the companies
under.[44] Owen Thomas recalled what happened
in the San Francisco Chronicle: “AllAdvantage, a Hayward company that
exemplified the approach, had to yank its initial public offering and auction
off its assets after blowing through millions of dollars.”[45] Later, both Handshake and Datacoup
began offering payments for data.[46] But Handshake went under while
Datacoup isn’t taking new users. Wired editor Gregory Barber
went another route and became his own data entrepreneur. He sold his location
data, Apple Health data, and Facebook data, and all he got was a paltry 0.3
cents.[47]
Data
Innovation
Why
couldn’t Barber sell his data for a large sum? Data is often valued within a
relationship, but practically valueless outside of it. There is a term for this
phenomenon, as economist Benjamin Klein explained: “Specific assets are assets
that have a significantly higher value within a particular transacting
relationship than outside the relationship.”[48] Since data is a highly specific
asset, granting platforms control should be a more efficient outcome.
How then
should ownership of those assets be allocated? A broader legal and economic
discussion—with its origin in the merger between Fisher Body, an automobile
parts provider in Detroit, and General Motors in 1926—has sprung up around this
question. Before the deal, GM bought car bodies directly from Fisher and then
mounted them on frames and sold the completed cars to consumers. In this sense,
the car bodies were intermediate goods, in much the same way that data is an
important intermediate good. But what if Fisher Body, after signing a long-term
contract with GM, decided to ask for more money for their parts? Final
production would cease, leading to what is known as the holdup problem.[49]
Much
research into contracts, mergers, and the control of assets developed as a
result of this scenario, and in 2016, Oliver Hart received the Nobel for
Economics as a direct result of this work. As one review of his work explained,
[T]he
optimal allocation of property rights—or governance structure—is one that
minimizes efficiency losses. Thus, in a situation where party A’s investment is
more important than party B’s investment, it is optimal to allocate property
rights over the assets to party A, even if this discourages investment by party
B.[50]
Even if
data is jointly created, joint control isn’t the most efficient outcome. When
one party’s investment in the data does not boost the total value that much,
then it is better for the other person to own both assets. In the parlance of
economics, the party with higher marginal returns from investment should be
given control, which is why platforms, and not users, spend so much time and
effort to understand what is happening on the platform. Some politicians might
want to change this ownership division, but it makes sense from an efficiency
standpoint. Changing it would result in less efficiency.
Conclusion
Consumers
already benefit tremendously from ad-supported platforms. And policies meant to
rebalance an already unequal relationship where consumers win is likely to harm
the ecosystem to their detriment. Competitive analysis fails when it doesn’t
correctly capture the importance of the various players in multi-sided
platforms.
[1] Pew, Americans
Feel Better Informed Thanks to the Internet, https://www.pewinternet.org/2014/12/08/better-informed/.
[2] Toni
Bernhard, In Defense of Facebook, https://www.psychologytoday.com/us/blog/turning-straw-gold/201201/in-defense-facebook.
[3] Lucy
Rock, The Hot Young Widows Club is out to change the way we grieve, https://www.theguardian.com/lifeandstyle/2018/feb/24/the-hot-young-widows-club-is-out-to-change-the-way-we-grieve.
[4] Reddit, Adaptive
athlete with cerebral palsy performs a 70kg power clean, https://www.reddit.com/r/GetMotivated/comments/7jc48d/image_adaptive_athlete_with_cerebral_palsy/
[5] Reddit, Ex-addicts
of reddit, what was your rock bottom?, https://www.reddit.com/r/AskReddit/comments/5pb2u4/exaddicts_of_reddit_what_was_your_rock_bottom/.
[6] Bryan
Clark, Reddit’s r/changemyview is a template for how all online
discussion should be, https://thenextweb.com/socialmedia/2019/01/23/reddits-model-community-offers-a-prototype-for-controversial-discussions/.
[7] Zeynep
Tufecki, Mark Zuckerberg, Let Me Pay for Facebook, https://www.nytimes.com/2015/06/04/opinion/zeynep-tufekci-mark-zuckerberg-let-me-pay-for-facebook.html.
[8] Economic
News Release: Table B-3. Average hourly and weekly earnings of all employees on
private nonfarm payrolls by industry sector, seasonally adjusted https://www.bls.gov/news.release/empsit.t19.htm.
[9] David
S. Evans , The Economics of Attention Markets, https://www.competitionpolicyinternational.com/the-economics-of-attention-markets/.
[10] David
S. Evans, The Antitrust Economics of Multi-Sided Platform Markets, http://digitalcommons.law.yale.edu/cgi/viewcontent.cgi?article=1144&context=yjreg.
[11] Minjae
Song, Estimating Platform Market Power in Two-Sided Markets with an
Application to Magazine Advertising, http://www.simon.rochester.edu/fac/MSONG/papers/Song-twosided.pdf.
[12] Lina
M. Khan, What Makes Tech Platforms So Powerful?, https://promarket.org/wp-content/uploads/2018/04/Digital-Platforms-and-Concentration.pdf#page=15.
[13] Jean-Charles
Rochet and Jean Tirole, Platform Competition in Two-sided Markets, http://www.rchss.sinica.edu.tw/cibs/pdf/RochetTirole3.pdf.
[14] David
S. Evans and Richard Schmalensee, The Antitrust Analysis of Multi-Sided
Platform Businesses, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2214252.
[15] Justice
Learned Hand, United States v Aluminium Company of America, et. al., http://www.tcd.ie/Economics/staff/masseyp/term1lecture7.htm.
[16] Justice
Antonin Scalia, Verizon Communications Inc. v. Law Offices of Curtis V.
Trinko, LLP, https://www.justice.gov/atr/competition-and-monopoly-single-firm-conduct-under-section-2-sherman-act-chapter-1#N_7_.
[17] Lina
M. Kahn, The Supreme Court Case That Could Give Tech Giants More Power, https://www.nytimes.com/2018/03/02/opinion/the-supreme-court-case-that-could-give-tech-giants-more-power.html.
[18] Tim
Wu, The Supreme Court Devastates Antitrust Law, https://www.nytimes.com/2018/06/26/opinion/supreme-court-american-express.html.
[19] David
S. Evans, The Antitrust Economics of Multi-Sided Platform Markets, http://digitalcommons.law.yale.edu/cgi/viewcontent.cgi?article=1144&context=yjreg.
[20] Organisation
for Economic Co-operation and Development, Rethinking Antitrust Tools
for Multi-Sided Platforms, http://www.oecd.org/daf/competition/Rethinking-antitrust-tools-for-multi-sided-platforms-2018.pdf.
[21] Ad
Age, ANA survey says marketers make progress with integrated programs, https://adage.com/article/btob/ana-survey-marketers-make-progress-integrated-programs/285466.
[22] Nielsen, ANA
and Nielsen study reveals multi-screen advertising to rise dramatically, https://www.nielsen.com/us/en/press-room/2013/ana-and-nielsen-study-reveals-multi-screen-advertising-to-rise.html.
[23] Susan
Athey, Emilio Calvano, and Joshua S. Gans, The Impact of Consumer
Multi-homing on Advertising Markets and Media Competition, https://pubsonline.informs.org/doi/10.1287/mnsc.2016.2675.
[24] Francesco
Decarolis and Gabriele Rovigatti, Concentration and Internet
Advertising: The Rise of Buyer Power, http://papers.nber.org/conf_papers/f112403/f112403.pdf.
[25] Mingyu
Joo, Kenneth C. Wilbur, Bo Cowgill, and Yi Zhu , Television Advertising
and Online Search, https://pubsonline.informs.org/doi/10.1287/mnsc.2013.1741.
[26] Rex
Yuxing Du, Linli Xu, and Kenneth C. Wilbur, Immediate Responses of
Online Brand Search and Price Search to TV Ads, https://journals.sagepub.com/doi/abs/10.1177/0022242919847192.
[27] Jura
Liaukonyte, Thales Teixeira, Kenneth C. Wilbur, Television Advertising
and Online Shopping, https://pubsonline.informs.org/doi/abs/10.1287/mksc.2014.0899.
[28] Audrey
Schomer, THE RISE OF AMAZON ADVERTISING: This is exactly what Amazon is
doing to siphon billions of ad dollars from Google and Facebook and why brands
love it, https://www.businessinsider.com/the-rise-of-amazon-advertising-2019-5.
[29] Sara
Salinas, The most important numbers from Alphabet’s Q4 earnings report
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[30] Nicole
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https://www.americanactionforum.org/comments-for-record/comments-for-the-department-of-justices-competition-in-television-and-digital-advertising-workshop/#ixzz5rJ7FK4dh
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