Emerging Trends in The Social and Behavioral Sciences

Economics of Privacy and User‐Generated Content

Item

Title

Economics of Privacy and User‐Generated Content

Author

Tucker, Catherine

Research Area

Social Institutions

Topic

Work and the Economy

Abstract

The Internet has allowed an unprecedented expansion of the data firms can collect and the amount of content users can upload. Understanding the forces that shape the demand and supply of this content is critical for understanding the likely evolution of the Internet. We review foundational and cutting‐edge research on the economics of privacy and user‐generated content (UGC), and sketch out issues for future research.

Related Essays

Intellectual Property (Economics), Michele Boldrin and David K. Levine

Misinformation and How to Correct It (Psychology), John Cook et al.

Political Advertising (Political Science), Erika Franklin Fowler

Innovation (Economics), Adam B. Jaffe

Technology Diffusion (Economics), Adam B. Jaffe

Search and Learning in Markets (Economics), Philipp Kircher

The Psychological Impacts of Cyberlife Engagement (Psychology), Virginia S. Y. Kwan and Jessica E. Bodford

Network Research Experiments (Methods), Allen L. Linton and Betsy Sinclair

Data Mining (Methods), Gregg R. Murray and Anthony Scime

Retrieval‐Based Learning: Research at the Interface between Cognitive Science and Education (Psychology), Ludmila D. Nunes and Jeffrey D. Karpicke

The Impact of Learning Technologies on Higher Education (Psychology), Chrisopher S. Pentoney et al.

Text Analysis (Methods), Carl W. Roberts

Digital Methods for Web Research (Methods), Richard Rogers

Education in an Open Informational World (Educ), Marlene Scardamalia and Carl Bereiter

Emerging Trends: Shaping Age By Technology and Social Bonds (Sociology), Annette Spellerberg and Lynn Schelisch

Content Analysis (Methods), Steven E. Stemler

Information Politics in Dictatorships (Political Science), Jeremy L. Wallace

Identifier

etrds0093

extracted text

Economics of Privacy and
User-Generated Content
CATHERINE TUCKER

Abstract
The Internet has allowed an unprecedented expansion of the data firms can collect
and the amount of content users can upload. Understanding the forces that shape the
demand and supply of this content is critical for understanding the likely evolution of
the Internet. We review foundational and cutting-edge research on the economics of
privacy and user-generated content (UGC), and sketch out issues for future research.

INTRODUCTION
Let us step back in time to the early 1990s, when the Internet had not yet
begun to revolutionize the provision of content (news, books, TV, movies,
and music), and most content was not yet held in digital form. For many people now, that world is a dim memory. Newspapers financed the provision of
news content through advertising, which advertisers bought in the hopes of
reaching the newspaper’s readership, but there was no way to track whether
the advertising had worked or not, or to target particular demographics. Publishers financed the production of physical books via publishing houses that
were highly selective about which books to take on, and which distributed
books through a tight network of bookstores and in a pre-specified order
(hardback and then paperback). Music production was similarly controlled
by music labels that produced physical vinyl records, and later CDs, and
distributed them through a tight network of physical music stores, and individual writers and musicians got a small cut. Movies and TV, again financed
through advertising, were released and distributed through a small number of channels and in a predetermined order, in order to maximize revenue. Although remnants of this system still exist 20 years on, the economics
of privacy and “User Generated Content” indeed, of knowledge itself have
changed radically and permanently. Understanding this change is the key to
understanding future developments in privacy and content production.
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

2

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

This change, for many working in the content industries, has been terrifying. Newspaper and TV advertising have become much less attractive to
advertisers than newer, cheaper, more targeted and more trackable forms of
online advertising. The result has been massive layoffs at newspapers, and
a simultaneous explosion in news content created by amateurs for free on
the Internet. As newspapers and TV news, both supported by untargeted
advertising, have declined in quality and in their ability to finance expensive
investigative bureaus, organizations such as Wikileaks have taken advantage
of data digitization to provide scoops larger and more significant than any
by newspapers in decades, and have done it based on a nonprofit donation
model.
As older channels have weakened, user-generated content has become
exponentially more important. Users now generate news, music and literary
materials and find fewer barriers to reaching a market for them (Keen,
2008). The distinction between self-presentation (the materials you might
“publish” to your Facebook page) and publication of creative productions is
progressively breaking down. Hundreds of millions of people will happily
upload information about their preferences, tastes and ideas as a mechanism
of self-expression, in a way that makes that information accessible to advertisers. The level of privacy necessary to facilitate the commercial exploitation
of the Internet therefore turns out to be somewhat lower than theorists
had supposed. Generational shifts are also producing an Internet-using
population that, relative to the Internet-using population of 1995, is much
less sensitive to privacy concerns, especially where a sense (or illusion)
of control over one’s personally identifiable information is maintained.
Increasingly, it is not the content itself—what might have originally been
termed an “artistic work” that creates profit, but the ways in which that
content can be monetized, either by the platform that advertises next to it,
or via live performances and syndication.
FOUNDATIONAL WORK
There are two streams of literature that are crucial for understanding the
intersection between privacy and user-generated content from an economics
perspective.
The first is the classic literature on the economics of privacy. Posner (1981);
Stigler (1980) set out the classic view of privacy-seeking as distortion of information. Here, privacy stands for secrecy. In such models, privacy generally
operates as a cost that prevents the flow of information necessary to facilitate
functioning markets. (Hirshleifer, 1971) suggests that keeping information
private does not “[lead] to any improvement in productive arrangements”
and provides “an incentive for individuals to expend resources in a socially

Economics of Privacy and User-Generated Content

3

wasteful way.” This view of privacy has been questioned in recent theoretical
research that integrates notions of behavioral economics into theoretical
models (Acquisti, John, & Loewenstein, 2008) to help explain consumers’
preferences for privacy, and also to set out why there may be a role for
regulation in some cases to protect consumer privacy. Other work that
has documented an economic need for privacy has used straightforward
questions of price discrimination to understand consumer motivations for
privacy which amount to more than simple secrecy. Hui and Png (2006)
provide a nice review of this literature.
Other work has attempted to understand the tradeoffs involved in providing strong privacy protections, in that doing so can reduce otherwise desirable market outcomes such as the adoption of electronic medical records
(Miller & Tucker, 2009) and lifesaving medical technologies (Miller & Tucker,
2011), the ability of small firms to enter digital markets (Campbell, Goldfarb,
& Tucker, 2011; Goldfarb & Tucker, 2012a), and even the ability of municipalities and states to limit certain kinds of advertising (Goldfarb & Tucker,
2011).
The second pertinent stream of literature for understanding the economic
role of user-generated content is the economics literature on platforms.
This literature describes the role of network operators in facilitating the
interaction between two (or more) groups of users, when each group of
users values the presence of the other group (Armstrong, 2006; Rochet &
Tirole, 2006). This is important for understanding user-generated content,
because it seems natural to suppose that users who generate content will be
more likely to value a platform to disseminate their creative work if there are
other people to consume the content (Ahn, Duan, & Mela, 2011). One paper
that ably makes the link between this platform literature and user-generated
content is Hoffstetter, Shriver, and Miller (2009), who examine how an
exogenous shift in wind speeds promotes blog postings about windsurfing,
which in turn promotes participation in the network. Similarly, Albuquerque
et al. (2010) describes this audience-feedback progress for an online magazine
platform.
MORE RECENT WORK
User-generated content online can take many forms. The largest stream
of research in marketing and economics has evaluated the effects of
user-generated content in the forms of reviews and quality information,
starting with early work such as Mayzlin (2004) and Chevalier and Mayzlin
(2006). This is a very large literature, and it is hard to do it justice within
the confines of a short article. Recent papers include Zhao, Yang, Narayan,
and Zhao (2012), who model how consumers learn about products through

4

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

this user-generated content. There has also been work that suggests how
profitable these new sources of data can be for firms. Archak, Ghose, and
Ipeirotis (2011); Netzer, Feldman, Goldenberg, and Fresko (2012); Decker
and Trusov (2010); Lee and Bradlow (2011) discuss how the mining of
product reviews helps market research and the development of product
features. Ghose, Ipeirotis, and Li (2012) discusses how such data can be
used for rankings, and Tirunillai and Tellis (2012) show the linkage between
this form of user-generated content and stock market returns. This type of
research deals with consumer opinions, which are typically less personally
sensitive than other forms of user-generated content, so there has not
been much consideration given to how privacy concerns might temper
the production of such user-generated content. One potentially acceptable
mechanism would be the “behavior-based price discrimination” described
by Fudenburg and Villas-Boas (2006), where consumers’ own reviewing
behavior is used to generate personalized prices.
There has been less academic work relating to the active production of
creative content such as videos and blog postings. Gal-Or, Geylani, and
Yildirim (2010) studies the production of user-generated news content.
Ghose and Han (2011) study the production of user-generated content on
mobile networks. There is little economics work on visible user-generated
content platforms such as Youtube, but the topic has received attention from
computer science academics. (Cha, Kwak, Rodriguez, Ahn, 2007) provide
an excellent overview of Youtube user-generated content, examining the
lifecycle of uploaded videos, how much illegal content is in the system,
and how the system might be made more efficient. There have also been a
few papers that look directly at the intersection of user-generated content
and privacy, focusing mainly on social media and business networking
sites. Early examples of this are Gross and Acquisti (2005); Acquisti and
Gross (2006), who investigate privacy concerns on Facebook. There is also
an information systems literature that uses survey research to try and
understand consumer behaviors (Xu, 2007; Xu, Teo, Tan, & Agarwal, 2012)
regarding the release of information in social media settings. However,
for much of the economically focused empirical work, the focus has not
been predominantly on privacy concerns surrounding the content, but
instead on privacy concerns surrounding the advertising that supports
the content Tucker (2011, 2012). This research suggests that striking the
right balance on privacy protection is essential for establishing successful
advertising-supported social network platforms.
This highlights a general tension between the notions of privacy and
user-generated content. In particular, the very nature of user-generated
content is that it is there to be broadcast and consumed. On the other hand,
the very nature of privacy concerns is that users are, for various reasons,

Economics of Privacy and User-Generated Content

5

reluctant to have information pertaining to them consumed by others.
One might initially observe that the users who are least comfortable with
the content they generate being broadcast, consumed or used to target
advertising, will also be the least likely users to contribute publicly to
creating content. However, it is also possible that users generate content
as a means of self-expression, without being fully aware of the ways that
their contribution can be reproduced, disseminated or even used to harm
their offline reputation. This tension is often highlighted in discussions
about how age affects the consumption and broadcast of user-generated
content. A survey-based literature has evolved in legal studies that asks
directly about perceptions of privacy (Sheehan and Hoy (2000), Marwick,
Diaz, and Palfrey (2010), etc.). For example, Hoofnagle, King, Li, and Turow
(2010) show that there is a perception of in creased attention to privacy over
time. In general, this work, alongside more empirically based work such as
Goldfarb and Tucker (2012b), suggests that people say they are more focused
on privacy than they were 5 years ago. This could have benefited from the
wider publicity given to shifts in commercial privacy policies for major
websites in the press, and with increased discussion of mass government
surveillance. Consistently with recent discussions in law and philosophy,
this shift may reflect a change in consumers’ perceptions over what contexts
warrant privacy.
(Zittrain, 2009) (p. 212), among others, links how the kind of technology
that enables user-generated content changes perceptions of what is public
and what is private, arguing that changes in technology “threaten to push
everyone toward treating each public encounter as if it were a press conference.” It is still very early to determine how such new psychological shifts
may affect consumers’ economic decision-making, and we are not aware of
systematic or empirical research that would give insight into it. Reflecting
the legal perspectives in this stream of research, Taylor (2004) develops an
economic model of information-sharing across firms that provides one explanation of why the context in which data is used may matter: if data is used
to price-discriminate, and changes in technology mean that firms can sell the
data to other firms even outside their sector, then many rational consumers
will choose to keep information private in order to prevent future price discrimination. Prospectively, such models can potentially be used in the future
to understand how the motivation to broadcast user-generated content intersects with privacy concerns.
ISSUES GOING FORWARD
This discussion illustrates that this is a very new area. I conclude with three
major avenues that would be particularly useful areas for future research.

6

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Motivation for production of user-generated content: One open question
is exactly what are the intrinsic and extrinsic motivations are for the production of user-generated content. Typically research has modeled this in
terms of size of audience. This makes sense give the earlier literature on network effects, but there appear to be instances where adding more content at
the margin brings no clear benefit to the user. For example, there are 4815
reviews of the movie “Mean Girls” starring Lindsay Lohan on Amazon.com,
of which 4650 were glowing five-star reviews. What motivated that Amazon
user to post the 4650th positive review for the movie? Without understanding the motivations for users of posting user-generated content even in the
absence of a likely audience, it is difficult to understand the likely distortions
in the quality of the content produced. Works of Daugherty et al. (2008); Moe
and Schweidel (2012); Godes and Silva (2011) have examined how the need
to correct previous user-generated content can affect behavior, but there are
too many instances where people post user-generated content that reinforces
earlier user-generated content for this to be a full explanation.
Optimal privacy protections for user-generated content. There is a tension
between the desire by many producers of user-generated content to expose
their thoughts and creative works to the largest possible audience, and a
desire to protect these original works from the eyes of undesirable readers
such as bosses or an insurance agency. One answer to this dilemma is to
encode into social media sites very granular privacy controls. However,
understanding how best to resolve the tension of allowing both privacy
controls but in a way that is not cumbersome and easy to understand
represents a huge technical challenge, especially in the context of a user
base that is generally not well informed about the effects of different privacy
architectures on their user experience. Another, more cumbersome answer
is legislation that would limit and shape the uses to which user-generated
content can be put. This represents an exceptional challenge for policymakers and economists, who need to think about what incentives would lead
to a welfare-improving outcome, while dealing with a rapidly changing
and novel technological environment. Early efforts appear to be underway
in some US states and in the European Union to forbid employers or
potential employers from requiring as a condition of employment access to
employees’ or candidates’ online social media profiles.
Optimal privacy protections over time for user-generated content. One
crucial issue when it comes to privacy and user-generated content is the
question of the appropriateness of privacy protection over time. The classic
concern over privacy online is that I may have time-inconsistent preferences.
For example, I may, as a college student think that user-generated content
involving naked guitar playing is amusing and rewarding and that I do
not need to impose strict privacy controls when posting it. However,

Economics of Privacy and User-Generated Content

7

after I graduate and am pursuing a career in accountancy, such videos
may come back to haunt me, and I may retrospectively wish that I could
have employed stricter privacy protections to prevent the release of such
materials. This illustrates a tension between the digital and persistent nature
of user-generated content that makes it hard to retrospectively apply new
privacy protections to it. More broadly, the predigital era enabled individuals to shift their self-presentation over time without it being possible to hold
their previous incarnations or opinions against them; the digital era may
increase the pressure on individuals to present a more consistent image over
the course of early adulthood through to old age. Further research is needed
to understand whether users do indeed have time-inconsistent preferences
when it comes to privacy, or whether users initially are uninformed about
the consequences of their privacy choices. Given these inconsistencies,
further research is needed to understand whether there is a role for privacy
protection and laws to help address such scenarios.
REFERENCES
Acquisti, A., & Gross, R. (2006). Imagined communities: Awareness, information
sharing, and privacy on the Facebook. In 6th Workshop on Privacy Enhancing Technologies, pp. 36–58.
Acquisti, A., John, L. K., & Loewenstein, G. (2008). What is privacy worth? Mimeo,
CMU.
Ahn, D.-Y., Duan, J. A., & Mela, C. F. (2011). An equilibrium model of user generated
content. Working Papers 11–13, NET Institute.
Albuquerque, P., Pavlidis, P., Chatow,U., Chen, K.-Y., Jamal, Z., & Koh, K.-W. (2010).
Evaluating promotional activities in an online two-sided market of user-generated
content. Simon School Working Paper No. FR 10-08.
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product
features by mining consumer reviews. Management Science, 57(8), 1485–1509.
Armstrong, M. (2006, Autumn). Competition in two-sided markets. RAND Journal of
Economics, 37(3), 668–691.
Campbell, J. D., Goldfarb, A., & Tucker, C. (2011). Privacy regulation and market
structure. Mimeo, University of Toronto.
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., & Moon, S. (2007). I tube, you tube,
everybody tubes: analyzing the world’s largest user generated content video system. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement,
pp. 1–14. ACM.
Chevalier, J., & Mayzlin, D. (2006). The effect of word of mouth online: Online book
reviews. Journal of Marketing Research, 43(345–354).
Daugherty, T., Eastin, M., & Bright, L. (2008). Exploring consumer motivations for
creating user-generated content. Journal of Interactive Advertising, 8(2), 1–24.
Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from
online product reviews. International Journal of Research in Marketing, 27(4),
293–307.

8

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Fudenburg, D., & Villas-Boas, J. M. (2006). Behavior based price discrimination
and customer recognition. Handbooks in Information Systems (Vol. 1, pp. 377–435,
Chapter 7). Amsterdam, the Netherlands: Elsevier.
Gal-Or, E., Geylani, T., & Yildirim, T. (2010). User-generated content in news media.
Technical report, working paper.
Ghose, A., & Han, S. P. (2011). An empirical analysis of user content generation and
usage behavior on the mobile internet. Management Science, 57(9), 1671–1691.
Ghose, A., Ipeirotis, P. G., & Li, B. (May/June 2012). Designing ranking systems for
hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520.
Godes, D., & Silva, J. C. (2011). Sequential and temporal dynamics of online opinion.
Marketing Science, 31, 448–473.
Goldfarb, A., & Tucker, C. (2012a). Privacy and innovation. In Innovation Policy and
the Economy (Vol. 12, NBER Chapters.). Cambridge, MA: National Bureau of Economic Research, Inc.
Goldfarb, A., & Tucker, C. (2012b). Privacy and innovation. Innovation Policy and the
Economy, 12(1), 65–90.
Goldfarb, A., & Tucker, C. E. (2011). Privacy regulation and online advertising. Management Science, 57(1), 57–71.
Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social
net- works. In Proceedings of the 2005 ACM Workshop on Privacy in the Electronic
Society, WPES ’05, New York, NY, USA, pp. 71–80. ACM.
Hirshleifer, J. (1971). The private and social value of information and the reward to
inventive activity. American Economic Review, 61(4), 561–74.
Hoffstetter, R., Nair, H. S., Shriver, S., & Miller, K. (2009). Social ties and user generated content: Evidence from an online social network. Working Papers 09-28, NET
Institute.
Hoofnagle, C. J., King, J., Li, S., & Turow, J. (2010). How different are young adults
from older adults when it comes to information privacy attitudes and policies?
Mimeo, Berkeley.
Hui, K., & Png, I. (2006). Economics and information systems. In Handbooks in Information Systems (Vol. 1, Chapter 9: The Economics of Privacy). Amsterdam, the
Netherlands: Elsevier.
Keen, A. (2008). The cult of the Amateur: How blogs, MySpace, YouTube, and the rest of
today’s user-generated media are destroying our economy, our culture, and our values.
Crown Publishing Group.
Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881–894.
Marwick, A. E., Diaz, D. M., & Palfrey, J. (2010). Youth, privacy, and reputation.
Mimeo, Harward Law School.
Mayzlin, D. (2004). Promotional chat on the internet. Marketing Science, 25, 155–163.
Miller, A., & Tucker, C. (2011). Can healthcare information technology save babies?
Journal of Political Economy, 2, 289–324.
Miller, A. R., & Tucker, C. (2009, July). Privacy protection and technology adoption:
The case of electronic medical records. Management Science, 55(7), 1077–1093.

Economics of Privacy and User-Generated Content

9

Moe, W. W., & Schweidel, D. A. (2012). Online product opinions: Incidence, evaluation, and evolution. Marketing Science, 31(3), 372–386.
Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (May/June 2012). Mine your
own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521–543.
Posner, R. A. (1981). The economics of privacy. American Economic Review, 71(2).
Rochet, J.-C., & Tirole, J. (2006, Autumn). Two-sided markets: A progress report.
RAND Journal of Economics, 37(3), 645–667.
Sheehan, K. B., & Hoy, M. G. (2000). Dimensions of privacy concern among online
consumers. Journal of Public Policy and Marketing, 19, 62–73.
Stigler, G. J. (1980). An introduction to privacy in economics and politics. The Journal
of Legal Studies, 9(4), 623–644.
Taylor, C. R. (2004, Winter). Consumer privacy and the market for customer information. RAND Journal of Economics, 35(4), 631–650.
Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? dynamics of usergenerated content and stock performance. Marketing Science, 31, 198–215.
Tucker, C. (2011). Social networks, personalized advertising, and privacy controls.
Mimeo, MIT.
Tucker, C. (2012). Social advertising. Journal of Marketing Research. Mimeo, MIT.
Xu, H. (2007). The effects of self-construal and perceived control on privacy concerns.
In 28th Annual International Conference on Information Systems (ICIS 2007), Montreal,
Canada.
Xu, H., Teo, H.-H., Tan, B. C. Y., & Agarwal, R. (2012). Effects of individual self- protection, industry self-regulation, and government regulation on privacy concerns:
A study of location-based services. Information Systems Research, 23, 1342.
Zhao, Y., Yang, S., Narayan, V., & Zhao, Y. (2012). Modeling consumer learning from
online product reviews. Marketing Science, 32, 153–169.
Zittrain, J. (2009). The future of the internet—and how to stop it. New Haven, CT: Yale
University Press.

CATHERINE TUCKER SHORT BIOGRAPHY
Catherine Tucker is the Mark Hyman Jr. Career Development Professor and
Associate Professor of Marketing at MIT Sloan. Her research interests lie in
how technology allows firms to use digital data to improve their operations
and marketing, and in the challenges, this poses for regulations designed
to promote innovation. She has particular expertise in online advertising,
digital health, social media, and electronic privacy. Generally, most of her
research lies in the interface among Marketing, Economics, and Law. She
has received an NSF CAREER award for her work on digital privacy and
a Garfield Award for her work on electronic medical records.
Dr. Tucker is Associate Editor at Management Science and a Research
Associate at the National Bureau of Economic Research. She teaches MIT
Sloan’s course on Pricing and the EMBA course Marketing Management

10

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

for the Senior Executive. She has received the Jamieson Prize for Excellence
in Teaching as well as being voted “Teacher of the Year” at MIT Sloan. She
holds a PhD in economics from Stanford University and a BA from Oxford
University.
RELATED ESSAYS
Intellectual Property (Economics), Michele Boldrin and David K. Levine
Misinformation and How to Correct It (Psychology), John Cook et al.
Political Advertising (Political Science), Erika Franklin Fowler
Innovation (Economics), Adam B. Jaffe
Technology Diffusion (Economics), Adam B. Jaffe
Search and Learning in Markets (Economics), Philipp Kircher
The Psychological Impacts of Cyberlife Engagement (Psychology), Virginia S.
Y. Kwan and Jessica E. Bodford
Network Research Experiments (Methods), Allen L. Linton and Betsy Sinclair
Data Mining (Methods), Gregg R. Murray and Anthony Scime
Retrieval-Based Learning: Research at the Interface between Cognitive Science and Education (Psychology), Ludmila D. Nunes and Jeffrey D. Karpicke
The Impact of Learning Technologies on Higher Education (Psychology),
Chrisopher S. Pentoney et al.
Text Analysis (Methods), Carl W. Roberts
Digital Methods for Web Research (Methods), Richard Rogers
Education in an Open Informational World (Educ), Marlene Scardamalia
and Carl Bereiter
Emerging Trends: Shaping Age By Technology and Social Bonds (Sociology),
Annette Spellerberg and Lynn Schelisch
Content Analysis (Methods), Steven E. Stemler
Information Politics in Dictatorships (Political Science), Jeremy L. Wallace



Economics of Privacy and
User-Generated Content
CATHERINE TUCKER

Abstract
The Internet has allowed an unprecedented expansion of the data firms can collect
and the amount of content users can upload. Understanding the forces that shape the
demand and supply of this content is critical for understanding the likely evolution of
the Internet. We review foundational and cutting-edge research on the economics of
privacy and user-generated content (UGC), and sketch out issues for future research.

INTRODUCTION
Let us step back in time to the early 1990s, when the Internet had not yet
begun to revolutionize the provision of content (news, books, TV, movies,
and music), and most content was not yet held in digital form. For many people now, that world is a dim memory. Newspapers financed the provision of
news content through advertising, which advertisers bought in the hopes of
reaching the newspaper’s readership, but there was no way to track whether
the advertising had worked or not, or to target particular demographics. Publishers financed the production of physical books via publishing houses that
were highly selective about which books to take on, and which distributed
books through a tight network of bookstores and in a pre-specified order
(hardback and then paperback). Music production was similarly controlled
by music labels that produced physical vinyl records, and later CDs, and
distributed them through a tight network of physical music stores, and individual writers and musicians got a small cut. Movies and TV, again financed
through advertising, were released and distributed through a small number of channels and in a predetermined order, in order to maximize revenue. Although remnants of this system still exist 20 years on, the economics
of privacy and “User Generated Content” indeed, of knowledge itself have
changed radically and permanently. Understanding this change is the key to
understanding future developments in privacy and content production.
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

2

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

This change, for many working in the content industries, has been terrifying. Newspaper and TV advertising have become much less attractive to
advertisers than newer, cheaper, more targeted and more trackable forms of
online advertising. The result has been massive layoffs at newspapers, and
a simultaneous explosion in news content created by amateurs for free on
the Internet. As newspapers and TV news, both supported by untargeted
advertising, have declined in quality and in their ability to finance expensive
investigative bureaus, organizations such as Wikileaks have taken advantage
of data digitization to provide scoops larger and more significant than any
by newspapers in decades, and have done it based on a nonprofit donation
model.
As older channels have weakened, user-generated content has become
exponentially more important. Users now generate news, music and literary
materials and find fewer barriers to reaching a market for them (Keen,
2008). The distinction between self-presentation (the materials you might
“publish” to your Facebook page) and publication of creative productions is
progressively breaking down. Hundreds of millions of people will happily
upload information about their preferences, tastes and ideas as a mechanism
of self-expression, in a way that makes that information accessible to advertisers. The level of privacy necessary to facilitate the commercial exploitation
of the Internet therefore turns out to be somewhat lower than theorists
had supposed. Generational shifts are also producing an Internet-using
population that, relative to the Internet-using population of 1995, is much
less sensitive to privacy concerns, especially where a sense (or illusion)
of control over one’s personally identifiable information is maintained.
Increasingly, it is not the content itself—what might have originally been
termed an “artistic work” that creates profit, but the ways in which that
content can be monetized, either by the platform that advertises next to it,
or via live performances and syndication.
FOUNDATIONAL WORK
There are two streams of literature that are crucial for understanding the
intersection between privacy and user-generated content from an economics
perspective.
The first is the classic literature on the economics of privacy. Posner (1981);
Stigler (1980) set out the classic view of privacy-seeking as distortion of information. Here, privacy stands for secrecy. In such models, privacy generally
operates as a cost that prevents the flow of information necessary to facilitate
functioning markets. (Hirshleifer, 1971) suggests that keeping information
private does not “[lead] to any improvement in productive arrangements”
and provides “an incentive for individuals to expend resources in a socially

Economics of Privacy and User-Generated Content

3

wasteful way.” This view of privacy has been questioned in recent theoretical
research that integrates notions of behavioral economics into theoretical
models (Acquisti, John, & Loewenstein, 2008) to help explain consumers’
preferences for privacy, and also to set out why there may be a role for
regulation in some cases to protect consumer privacy. Other work that
has documented an economic need for privacy has used straightforward
questions of price discrimination to understand consumer motivations for
privacy which amount to more than simple secrecy. Hui and Png (2006)
provide a nice review of this literature.
Other work has attempted to understand the tradeoffs involved in providing strong privacy protections, in that doing so can reduce otherwise desirable market outcomes such as the adoption of electronic medical records
(Miller & Tucker, 2009) and lifesaving medical technologies (Miller & Tucker,
2011), the ability of small firms to enter digital markets (Campbell, Goldfarb,
& Tucker, 2011; Goldfarb & Tucker, 2012a), and even the ability of municipalities and states to limit certain kinds of advertising (Goldfarb & Tucker,
2011).
The second pertinent stream of literature for understanding the economic
role of user-generated content is the economics literature on platforms.
This literature describes the role of network operators in facilitating the
interaction between two (or more) groups of users, when each group of
users values the presence of the other group (Armstrong, 2006; Rochet &
Tirole, 2006). This is important for understanding user-generated content,
because it seems natural to suppose that users who generate content will be
more likely to value a platform to disseminate their creative work if there are
other people to consume the content (Ahn, Duan, & Mela, 2011). One paper
that ably makes the link between this platform literature and user-generated
content is Hoffstetter, Shriver, and Miller (2009), who examine how an
exogenous shift in wind speeds promotes blog postings about windsurfing,
which in turn promotes participation in the network. Similarly, Albuquerque
et al. (2010) describes this audience-feedback progress for an online magazine
platform.
MORE RECENT WORK
User-generated content online can take many forms. The largest stream
of research in marketing and economics has evaluated the effects of
user-generated content in the forms of reviews and quality information,
starting with early work such as Mayzlin (2004) and Chevalier and Mayzlin
(2006). This is a very large literature, and it is hard to do it justice within
the confines of a short article. Recent papers include Zhao, Yang, Narayan,
and Zhao (2012), who model how consumers learn about products through

4

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

this user-generated content. There has also been work that suggests how
profitable these new sources of data can be for firms. Archak, Ghose, and
Ipeirotis (2011); Netzer, Feldman, Goldenberg, and Fresko (2012); Decker
and Trusov (2010); Lee and Bradlow (2011) discuss how the mining of
product reviews helps market research and the development of product
features. Ghose, Ipeirotis, and Li (2012) discusses how such data can be
used for rankings, and Tirunillai and Tellis (2012) show the linkage between
this form of user-generated content and stock market returns. This type of
research deals with consumer opinions, which are typically less personally
sensitive than other forms of user-generated content, so there has not
been much consideration given to how privacy concerns might temper
the production of such user-generated content. One potentially acceptable
mechanism would be the “behavior-based price discrimination” described
by Fudenburg and Villas-Boas (2006), where consumers’ own reviewing
behavior is used to generate personalized prices.
There has been less academic work relating to the active production of
creative content such as videos and blog postings. Gal-Or, Geylani, and
Yildirim (2010) studies the production of user-generated news content.
Ghose and Han (2011) study the production of user-generated content on
mobile networks. There is little economics work on visible user-generated
content platforms such as Youtube, but the topic has received attention from
computer science academics. (Cha, Kwak, Rodriguez, Ahn, 2007) provide
an excellent overview of Youtube user-generated content, examining the
lifecycle of uploaded videos, how much illegal content is in the system,
and how the system might be made more efficient. There have also been a
few papers that look directly at the intersection of user-generated content
and privacy, focusing mainly on social media and business networking
sites. Early examples of this are Gross and Acquisti (2005); Acquisti and
Gross (2006), who investigate privacy concerns on Facebook. There is also
an information systems literature that uses survey research to try and
understand consumer behaviors (Xu, 2007; Xu, Teo, Tan, & Agarwal, 2012)
regarding the release of information in social media settings. However,
for much of the economically focused empirical work, the focus has not
been predominantly on privacy concerns surrounding the content, but
instead on privacy concerns surrounding the advertising that supports
the content Tucker (2011, 2012). This research suggests that striking the
right balance on privacy protection is essential for establishing successful
advertising-supported social network platforms.
This highlights a general tension between the notions of privacy and
user-generated content. In particular, the very nature of user-generated
content is that it is there to be broadcast and consumed. On the other hand,
the very nature of privacy concerns is that users are, for various reasons,

Economics of Privacy and User-Generated Content

5

reluctant to have information pertaining to them consumed by others.
One might initially observe that the users who are least comfortable with
the content they generate being broadcast, consumed or used to target
advertising, will also be the least likely users to contribute publicly to
creating content. However, it is also possible that users generate content
as a means of self-expression, without being fully aware of the ways that
their contribution can be reproduced, disseminated or even used to harm
their offline reputation. This tension is often highlighted in discussions
about how age affects the consumption and broadcast of user-generated
content. A survey-based literature has evolved in legal studies that asks
directly about perceptions of privacy (Sheehan and Hoy (2000), Marwick,
Diaz, and Palfrey (2010), etc.). For example, Hoofnagle, King, Li, and Turow
(2010) show that there is a perception of in creased attention to privacy over
time. In general, this work, alongside more empirically based work such as
Goldfarb and Tucker (2012b), suggests that people say they are more focused
on privacy than they were 5 years ago. This could have benefited from the
wider publicity given to shifts in commercial privacy policies for major
websites in the press, and with increased discussion of mass government
surveillance. Consistently with recent discussions in law and philosophy,
this shift may reflect a change in consumers’ perceptions over what contexts
warrant privacy.
(Zittrain, 2009) (p. 212), among others, links how the kind of technology
that enables user-generated content changes perceptions of what is public
and what is private, arguing that changes in technology “threaten to push
everyone toward treating each public encounter as if it were a press conference.” It is still very early to determine how such new psychological shifts
may affect consumers’ economic decision-making, and we are not aware of
systematic or empirical research that would give insight into it. Reflecting
the legal perspectives in this stream of research, Taylor (2004) develops an
economic model of information-sharing across firms that provides one explanation of why the context in which data is used may matter: if data is used
to price-discriminate, and changes in technology mean that firms can sell the
data to other firms even outside their sector, then many rational consumers
will choose to keep information private in order to prevent future price discrimination. Prospectively, such models can potentially be used in the future
to understand how the motivation to broadcast user-generated content intersects with privacy concerns.
ISSUES GOING FORWARD
This discussion illustrates that this is a very new area. I conclude with three
major avenues that would be particularly useful areas for future research.

6

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Motivation for production of user-generated content: One open question
is exactly what are the intrinsic and extrinsic motivations are for the production of user-generated content. Typically research has modeled this in
terms of size of audience. This makes sense give the earlier literature on network effects, but there appear to be instances where adding more content at
the margin brings no clear benefit to the user. For example, there are 4815
reviews of the movie “Mean Girls” starring Lindsay Lohan on Amazon.com,
of which 4650 were glowing five-star reviews. What motivated that Amazon
user to post the 4650th positive review for the movie? Without understanding the motivations for users of posting user-generated content even in the
absence of a likely audience, it is difficult to understand the likely distortions
in the quality of the content produced. Works of Daugherty et al. (2008); Moe
and Schweidel (2012); Godes and Silva (2011) have examined how the need
to correct previous user-generated content can affect behavior, but there are
too many instances where people post user-generated content that reinforces
earlier user-generated content for this to be a full explanation.
Optimal privacy protections for user-generated content. There is a tension
between the desire by many producers of user-generated content to expose
their thoughts and creative works to the largest possible audience, and a
desire to protect these original works from the eyes of undesirable readers
such as bosses or an insurance agency. One answer to this dilemma is to
encode into social media sites very granular privacy controls. However,
understanding how best to resolve the tension of allowing both privacy
controls but in a way that is not cumbersome and easy to understand
represents a huge technical challenge, especially in the context of a user
base that is generally not well informed about the effects of different privacy
architectures on their user experience. Another, more cumbersome answer
is legislation that would limit and shape the uses to which user-generated
content can be put. This represents an exceptional challenge for policymakers and economists, who need to think about what incentives would lead
to a welfare-improving outcome, while dealing with a rapidly changing
and novel technological environment. Early efforts appear to be underway
in some US states and in the European Union to forbid employers or
potential employers from requiring as a condition of employment access to
employees’ or candidates’ online social media profiles.
Optimal privacy protections over time for user-generated content. One
crucial issue when it comes to privacy and user-generated content is the
question of the appropriateness of privacy protection over time. The classic
concern over privacy online is that I may have time-inconsistent preferences.
For example, I may, as a college student think that user-generated content
involving naked guitar playing is amusing and rewarding and that I do
not need to impose strict privacy controls when posting it. However,

Economics of Privacy and User-Generated Content

7

after I graduate and am pursuing a career in accountancy, such videos
may come back to haunt me, and I may retrospectively wish that I could
have employed stricter privacy protections to prevent the release of such
materials. This illustrates a tension between the digital and persistent nature
of user-generated content that makes it hard to retrospectively apply new
privacy protections to it. More broadly, the predigital era enabled individuals to shift their self-presentation over time without it being possible to hold
their previous incarnations or opinions against them; the digital era may
increase the pressure on individuals to present a more consistent image over
the course of early adulthood through to old age. Further research is needed
to understand whether users do indeed have time-inconsistent preferences
when it comes to privacy, or whether users initially are uninformed about
the consequences of their privacy choices. Given these inconsistencies,
further research is needed to understand whether there is a role for privacy
protection and laws to help address such scenarios.
REFERENCES
Acquisti, A., & Gross, R. (2006). Imagined communities: Awareness, information
sharing, and privacy on the Facebook. In 6th Workshop on Privacy Enhancing Technologies, pp. 36–58.
Acquisti, A., John, L. K., & Loewenstein, G. (2008). What is privacy worth? Mimeo,
CMU.
Ahn, D.-Y., Duan, J. A., & Mela, C. F. (2011). An equilibrium model of user generated
content. Working Papers 11–13, NET Institute.
Albuquerque, P., Pavlidis, P., Chatow,U., Chen, K.-Y., Jamal, Z., & Koh, K.-W. (2010).
Evaluating promotional activities in an online two-sided market of user-generated
content. Simon School Working Paper No. FR 10-08.
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product
features by mining consumer reviews. Management Science, 57(8), 1485–1509.
Armstrong, M. (2006, Autumn). Competition in two-sided markets. RAND Journal of
Economics, 37(3), 668–691.
Campbell, J. D., Goldfarb, A., & Tucker, C. (2011). Privacy regulation and market
structure. Mimeo, University of Toronto.
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., & Moon, S. (2007). I tube, you tube,
everybody tubes: analyzing the world’s largest user generated content video system. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement,
pp. 1–14. ACM.
Chevalier, J., & Mayzlin, D. (2006). The effect of word of mouth online: Online book
reviews. Journal of Marketing Research, 43(345–354).
Daugherty, T., Eastin, M., & Bright, L. (2008). Exploring consumer motivations for
creating user-generated content. Journal of Interactive Advertising, 8(2), 1–24.
Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from
online product reviews. International Journal of Research in Marketing, 27(4),
293–307.

8

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Fudenburg, D., & Villas-Boas, J. M. (2006). Behavior based price discrimination
and customer recognition. Handbooks in Information Systems (Vol. 1, pp. 377–435,
Chapter 7). Amsterdam, the Netherlands: Elsevier.
Gal-Or, E., Geylani, T., & Yildirim, T. (2010). User-generated content in news media.
Technical report, working paper.
Ghose, A., & Han, S. P. (2011). An empirical analysis of user content generation and
usage behavior on the mobile internet. Management Science, 57(9), 1671–1691.
Ghose, A., Ipeirotis, P. G., & Li, B. (May/June 2012). Designing ranking systems for
hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520.
Godes, D., & Silva, J. C. (2011). Sequential and temporal dynamics of online opinion.
Marketing Science, 31, 448–473.
Goldfarb, A., & Tucker, C. (2012a). Privacy and innovation. In Innovation Policy and
the Economy (Vol. 12, NBER Chapters.). Cambridge, MA: National Bureau of Economic Research, Inc.
Goldfarb, A., & Tucker, C. (2012b). Privacy and innovation. Innovation Policy and the
Economy, 12(1), 65–90.
Goldfarb, A., & Tucker, C. E. (2011). Privacy regulation and online advertising. Management Science, 57(1), 57–71.
Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social
net- works. In Proceedings of the 2005 ACM Workshop on Privacy in the Electronic
Society, WPES ’05, New York, NY, USA, pp. 71–80. ACM.
Hirshleifer, J. (1971). The private and social value of information and the reward to
inventive activity. American Economic Review, 61(4), 561–74.
Hoffstetter, R., Nair, H. S., Shriver, S., & Miller, K. (2009). Social ties and user generated content: Evidence from an online social network. Working Papers 09-28, NET
Institute.
Hoofnagle, C. J., King, J., Li, S., & Turow, J. (2010). How different are young adults
from older adults when it comes to information privacy attitudes and policies?
Mimeo, Berkeley.
Hui, K., & Png, I. (2006). Economics and information systems. In Handbooks in Information Systems (Vol. 1, Chapter 9: The Economics of Privacy). Amsterdam, the
Netherlands: Elsevier.
Keen, A. (2008). The cult of the Amateur: How blogs, MySpace, YouTube, and the rest of
today’s user-generated media are destroying our economy, our culture, and our values.
Crown Publishing Group.
Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881–894.
Marwick, A. E., Diaz, D. M., & Palfrey, J. (2010). Youth, privacy, and reputation.
Mimeo, Harward Law School.
Mayzlin, D. (2004). Promotional chat on the internet. Marketing Science, 25, 155–163.
Miller, A., & Tucker, C. (2011). Can healthcare information technology save babies?
Journal of Political Economy, 2, 289–324.
Miller, A. R., & Tucker, C. (2009, July). Privacy protection and technology adoption:
The case of electronic medical records. Management Science, 55(7), 1077–1093.

Economics of Privacy and User-Generated Content

9

Moe, W. W., & Schweidel, D. A. (2012). Online product opinions: Incidence, evaluation, and evolution. Marketing Science, 31(3), 372–386.
Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (May/June 2012). Mine your
own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521–543.
Posner, R. A. (1981). The economics of privacy. American Economic Review, 71(2).
Rochet, J.-C., & Tirole, J. (2006, Autumn). Two-sided markets: A progress report.
RAND Journal of Economics, 37(3), 645–667.
Sheehan, K. B., & Hoy, M. G. (2000). Dimensions of privacy concern among online
consumers. Journal of Public Policy and Marketing, 19, 62–73.
Stigler, G. J. (1980). An introduction to privacy in economics and politics. The Journal
of Legal Studies, 9(4), 623–644.
Taylor, C. R. (2004, Winter). Consumer privacy and the market for customer information. RAND Journal of Economics, 35(4), 631–650.
Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? dynamics of usergenerated content and stock performance. Marketing Science, 31, 198–215.
Tucker, C. (2011). Social networks, personalized advertising, and privacy controls.
Mimeo, MIT.
Tucker, C. (2012). Social advertising. Journal of Marketing Research. Mimeo, MIT.
Xu, H. (2007). The effects of self-construal and perceived control on privacy concerns.
In 28th Annual International Conference on Information Systems (ICIS 2007), Montreal,
Canada.
Xu, H., Teo, H.-H., Tan, B. C. Y., & Agarwal, R. (2012). Effects of individual self- protection, industry self-regulation, and government regulation on privacy concerns:
A study of location-based services. Information Systems Research, 23, 1342.
Zhao, Y., Yang, S., Narayan, V., & Zhao, Y. (2012). Modeling consumer learning from
online product reviews. Marketing Science, 32, 153–169.
Zittrain, J. (2009). The future of the internet—and how to stop it. New Haven, CT: Yale
University Press.

CATHERINE TUCKER SHORT BIOGRAPHY
Catherine Tucker is the Mark Hyman Jr. Career Development Professor and
Associate Professor of Marketing at MIT Sloan. Her research interests lie in
how technology allows firms to use digital data to improve their operations
and marketing, and in the challenges, this poses for regulations designed
to promote innovation. She has particular expertise in online advertising,
digital health, social media, and electronic privacy. Generally, most of her
research lies in the interface among Marketing, Economics, and Law. She
has received an NSF CAREER award for her work on digital privacy and
a Garfield Award for her work on electronic medical records.
Dr. Tucker is Associate Editor at Management Science and a Research
Associate at the National Bureau of Economic Research. She teaches MIT
Sloan’s course on Pricing and the EMBA course Marketing Management

10

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

for the Senior Executive. She has received the Jamieson Prize for Excellence
in Teaching as well as being voted “Teacher of the Year” at MIT Sloan. She
holds a PhD in economics from Stanford University and a BA from Oxford
University.
RELATED ESSAYS
Intellectual Property (Economics), Michele Boldrin and David K. Levine
Misinformation and How to Correct It (Psychology), John Cook et al.
Political Advertising (Political Science), Erika Franklin Fowler
Innovation (Economics), Adam B. Jaffe
Technology Diffusion (Economics), Adam B. Jaffe
Search and Learning in Markets (Economics), Philipp Kircher
The Psychological Impacts of Cyberlife Engagement (Psychology), Virginia S.
Y. Kwan and Jessica E. Bodford
Network Research Experiments (Methods), Allen L. Linton and Betsy Sinclair
Data Mining (Methods), Gregg R. Murray and Anthony Scime
Retrieval-Based Learning: Research at the Interface between Cognitive Science and Education (Psychology), Ludmila D. Nunes and Jeffrey D. Karpicke
The Impact of Learning Technologies on Higher Education (Psychology),
Chrisopher S. Pentoney et al.
Text Analysis (Methods), Carl W. Roberts
Digital Methods for Web Research (Methods), Richard Rogers
Education in an Open Informational World (Educ), Marlene Scardamalia
and Carl Bereiter
Emerging Trends: Shaping Age By Technology and Social Bonds (Sociology),
Annette Spellerberg and Lynn Schelisch
Content Analysis (Methods), Steven E. Stemler
Information Politics in Dictatorships (Political Science), Jeremy L. Wallace


Economics of Privacy and
User-Generated Content
CATHERINE TUCKER

Abstract
The Internet has allowed an unprecedented expansion of the data firms can collect
and the amount of content users can upload. Understanding the forces that shape the
demand and supply of this content is critical for understanding the likely evolution of
the Internet. We review foundational and cutting-edge research on the economics of
privacy and user-generated content (UGC), and sketch out issues for future research.

INTRODUCTION
Let us step back in time to the early 1990s, when the Internet had not yet
begun to revolutionize the provision of content (news, books, TV, movies,
and music), and most content was not yet held in digital form. For many people now, that world is a dim memory. Newspapers financed the provision of
news content through advertising, which advertisers bought in the hopes of
reaching the newspaper’s readership, but there was no way to track whether
the advertising had worked or not, or to target particular demographics. Publishers financed the production of physical books via publishing houses that
were highly selective about which books to take on, and which distributed
books through a tight network of bookstores and in a pre-specified order
(hardback and then paperback). Music production was similarly controlled
by music labels that produced physical vinyl records, and later CDs, and
distributed them through a tight network of physical music stores, and individual writers and musicians got a small cut. Movies and TV, again financed
through advertising, were released and distributed through a small number of channels and in a predetermined order, in order to maximize revenue. Although remnants of this system still exist 20 years on, the economics
of privacy and “User Generated Content” indeed, of knowledge itself have
changed radically and permanently. Understanding this change is the key to
understanding future developments in privacy and content production.
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

2

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

This change, for many working in the content industries, has been terrifying. Newspaper and TV advertising have become much less attractive to
advertisers than newer, cheaper, more targeted and more trackable forms of
online advertising. The result has been massive layoffs at newspapers, and
a simultaneous explosion in news content created by amateurs for free on
the Internet. As newspapers and TV news, both supported by untargeted
advertising, have declined in quality and in their ability to finance expensive
investigative bureaus, organizations such as Wikileaks have taken advantage
of data digitization to provide scoops larger and more significant than any
by newspapers in decades, and have done it based on a nonprofit donation
model.
As older channels have weakened, user-generated content has become
exponentially more important. Users now generate news, music and literary
materials and find fewer barriers to reaching a market for them (Keen,
2008). The distinction between self-presentation (the materials you might
“publish” to your Facebook page) and publication of creative productions is
progressively breaking down. Hundreds of millions of people will happily
upload information about their preferences, tastes and ideas as a mechanism
of self-expression, in a way that makes that information accessible to advertisers. The level of privacy necessary to facilitate the commercial exploitation
of the Internet therefore turns out to be somewhat lower than theorists
had supposed. Generational shifts are also producing an Internet-using
population that, relative to the Internet-using population of 1995, is much
less sensitive to privacy concerns, especially where a sense (or illusion)
of control over one’s personally identifiable information is maintained.
Increasingly, it is not the content itself—what might have originally been
termed an “artistic work” that creates profit, but the ways in which that
content can be monetized, either by the platform that advertises next to it,
or via live performances and syndication.
FOUNDATIONAL WORK
There are two streams of literature that are crucial for understanding the
intersection between privacy and user-generated content from an economics
perspective.
The first is the classic literature on the economics of privacy. Posner (1981);
Stigler (1980) set out the classic view of privacy-seeking as distortion of information. Here, privacy stands for secrecy. In such models, privacy generally
operates as a cost that prevents the flow of information necessary to facilitate
functioning markets. (Hirshleifer, 1971) suggests that keeping information
private does not “[lead] to any improvement in productive arrangements”
and provides “an incentive for individuals to expend resources in a socially

Economics of Privacy and User-Generated Content

3

wasteful way.” This view of privacy has been questioned in recent theoretical
research that integrates notions of behavioral economics into theoretical
models (Acquisti, John, & Loewenstein, 2008) to help explain consumers’
preferences for privacy, and also to set out why there may be a role for
regulation in some cases to protect consumer privacy. Other work that
has documented an economic need for privacy has used straightforward
questions of price discrimination to understand consumer motivations for
privacy which amount to more than simple secrecy. Hui and Png (2006)
provide a nice review of this literature.
Other work has attempted to understand the tradeoffs involved in providing strong privacy protections, in that doing so can reduce otherwise desirable market outcomes such as the adoption of electronic medical records
(Miller & Tucker, 2009) and lifesaving medical technologies (Miller & Tucker,
2011), the ability of small firms to enter digital markets (Campbell, Goldfarb,
& Tucker, 2011; Goldfarb & Tucker, 2012a), and even the ability of municipalities and states to limit certain kinds of advertising (Goldfarb & Tucker,
2011).
The second pertinent stream of literature for understanding the economic
role of user-generated content is the economics literature on platforms.
This literature describes the role of network operators in facilitating the
interaction between two (or more) groups of users, when each group of
users values the presence of the other group (Armstrong, 2006; Rochet &
Tirole, 2006). This is important for understanding user-generated content,
because it seems natural to suppose that users who generate content will be
more likely to value a platform to disseminate their creative work if there are
other people to consume the content (Ahn, Duan, & Mela, 2011). One paper
that ably makes the link between this platform literature and user-generated
content is Hoffstetter, Shriver, and Miller (2009), who examine how an
exogenous shift in wind speeds promotes blog postings about windsurfing,
which in turn promotes participation in the network. Similarly, Albuquerque
et al. (2010) describes this audience-feedback progress for an online magazine
platform.
MORE RECENT WORK
User-generated content online can take many forms. The largest stream
of research in marketing and economics has evaluated the effects of
user-generated content in the forms of reviews and quality information,
starting with early work such as Mayzlin (2004) and Chevalier and Mayzlin
(2006). This is a very large literature, and it is hard to do it justice within
the confines of a short article. Recent papers include Zhao, Yang, Narayan,
and Zhao (2012), who model how consumers learn about products through

4

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

this user-generated content. There has also been work that suggests how
profitable these new sources of data can be for firms. Archak, Ghose, and
Ipeirotis (2011); Netzer, Feldman, Goldenberg, and Fresko (2012); Decker
and Trusov (2010); Lee and Bradlow (2011) discuss how the mining of
product reviews helps market research and the development of product
features. Ghose, Ipeirotis, and Li (2012) discusses how such data can be
used for rankings, and Tirunillai and Tellis (2012) show the linkage between
this form of user-generated content and stock market returns. This type of
research deals with consumer opinions, which are typically less personally
sensitive than other forms of user-generated content, so there has not
been much consideration given to how privacy concerns might temper
the production of such user-generated content. One potentially acceptable
mechanism would be the “behavior-based price discrimination” described
by Fudenburg and Villas-Boas (2006), where consumers’ own reviewing
behavior is used to generate personalized prices.
There has been less academic work relating to the active production of
creative content such as videos and blog postings. Gal-Or, Geylani, and
Yildirim (2010) studies the production of user-generated news content.
Ghose and Han (2011) study the production of user-generated content on
mobile networks. There is little economics work on visible user-generated
content platforms such as Youtube, but the topic has received attention from
computer science academics. (Cha, Kwak, Rodriguez, Ahn, 2007) provide
an excellent overview of Youtube user-generated content, examining the
lifecycle of uploaded videos, how much illegal content is in the system,
and how the system might be made more efficient. There have also been a
few papers that look directly at the intersection of user-generated content
and privacy, focusing mainly on social media and business networking
sites. Early examples of this are Gross and Acquisti (2005); Acquisti and
Gross (2006), who investigate privacy concerns on Facebook. There is also
an information systems literature that uses survey research to try and
understand consumer behaviors (Xu, 2007; Xu, Teo, Tan, & Agarwal, 2012)
regarding the release of information in social media settings. However,
for much of the economically focused empirical work, the focus has not
been predominantly on privacy concerns surrounding the content, but
instead on privacy concerns surrounding the advertising that supports
the content Tucker (2011, 2012). This research suggests that striking the
right balance on privacy protection is essential for establishing successful
advertising-supported social network platforms.
This highlights a general tension between the notions of privacy and
user-generated content. In particular, the very nature of user-generated
content is that it is there to be broadcast and consumed. On the other hand,
the very nature of privacy concerns is that users are, for various reasons,

Economics of Privacy and User-Generated Content

5

reluctant to have information pertaining to them consumed by others.
One might initially observe that the users who are least comfortable with
the content they generate being broadcast, consumed or used to target
advertising, will also be the least likely users to contribute publicly to
creating content. However, it is also possible that users generate content
as a means of self-expression, without being fully aware of the ways that
their contribution can be reproduced, disseminated or even used to harm
their offline reputation. This tension is often highlighted in discussions
about how age affects the consumption and broadcast of user-generated
content. A survey-based literature has evolved in legal studies that asks
directly about perceptions of privacy (Sheehan and Hoy (2000), Marwick,
Diaz, and Palfrey (2010), etc.). For example, Hoofnagle, King, Li, and Turow
(2010) show that there is a perception of in creased attention to privacy over
time. In general, this work, alongside more empirically based work such as
Goldfarb and Tucker (2012b), suggests that people say they are more focused
on privacy than they were 5 years ago. This could have benefited from the
wider publicity given to shifts in commercial privacy policies for major
websites in the press, and with increased discussion of mass government
surveillance. Consistently with recent discussions in law and philosophy,
this shift may reflect a change in consumers’ perceptions over what contexts
warrant privacy.
(Zittrain, 2009) (p. 212), among others, links how the kind of technology
that enables user-generated content changes perceptions of what is public
and what is private, arguing that changes in technology “threaten to push
everyone toward treating each public encounter as if it were a press conference.” It is still very early to determine how such new psychological shifts
may affect consumers’ economic decision-making, and we are not aware of
systematic or empirical research that would give insight into it. Reflecting
the legal perspectives in this stream of research, Taylor (2004) develops an
economic model of information-sharing across firms that provides one explanation of why the context in which data is used may matter: if data is used
to price-discriminate, and changes in technology mean that firms can sell the
data to other firms even outside their sector, then many rational consumers
will choose to keep information private in order to prevent future price discrimination. Prospectively, such models can potentially be used in the future
to understand how the motivation to broadcast user-generated content intersects with privacy concerns.
ISSUES GOING FORWARD
This discussion illustrates that this is a very new area. I conclude with three
major avenues that would be particularly useful areas for future research.

6

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Motivation for production of user-generated content: One open question
is exactly what are the intrinsic and extrinsic motivations are for the production of user-generated content. Typically research has modeled this in
terms of size of audience. This makes sense give the earlier literature on network effects, but there appear to be instances where adding more content at
the margin brings no clear benefit to the user. For example, there are 4815
reviews of the movie “Mean Girls” starring Lindsay Lohan on Amazon.com,
of which 4650 were glowing five-star reviews. What motivated that Amazon
user to post the 4650th positive review for the movie? Without understanding the motivations for users of posting user-generated content even in the
absence of a likely audience, it is difficult to understand the likely distortions
in the quality of the content produced. Works of Daugherty et al. (2008); Moe
and Schweidel (2012); Godes and Silva (2011) have examined how the need
to correct previous user-generated content can affect behavior, but there are
too many instances where people post user-generated content that reinforces
earlier user-generated content for this to be a full explanation.
Optimal privacy protections for user-generated content. There is a tension
between the desire by many producers of user-generated content to expose
their thoughts and creative works to the largest possible audience, and a
desire to protect these original works from the eyes of undesirable readers
such as bosses or an insurance agency. One answer to this dilemma is to
encode into social media sites very granular privacy controls. However,
understanding how best to resolve the tension of allowing both privacy
controls but in a way that is not cumbersome and easy to understand
represents a huge technical challenge, especially in the context of a user
base that is generally not well informed about the effects of different privacy
architectures on their user experience. Another, more cumbersome answer
is legislation that would limit and shape the uses to which user-generated
content can be put. This represents an exceptional challenge for policymakers and economists, who need to think about what incentives would lead
to a welfare-improving outcome, while dealing with a rapidly changing
and novel technological environment. Early efforts appear to be underway
in some US states and in the European Union to forbid employers or
potential employers from requiring as a condition of employment access to
employees’ or candidates’ online social media profiles.
Optimal privacy protections over time for user-generated content. One
crucial issue when it comes to privacy and user-generated content is the
question of the appropriateness of privacy protection over time. The classic
concern over privacy online is that I may have time-inconsistent preferences.
For example, I may, as a college student think that user-generated content
involving naked guitar playing is amusing and rewarding and that I do
not need to impose strict privacy controls when posting it. However,

Economics of Privacy and User-Generated Content

7

after I graduate and am pursuing a career in accountancy, such videos
may come back to haunt me, and I may retrospectively wish that I could
have employed stricter privacy protections to prevent the release of such
materials. This illustrates a tension between the digital and persistent nature
of user-generated content that makes it hard to retrospectively apply new
privacy protections to it. More broadly, the predigital era enabled individuals to shift their self-presentation over time without it being possible to hold
their previous incarnations or opinions against them; the digital era may
increase the pressure on individuals to present a more consistent image over
the course of early adulthood through to old age. Further research is needed
to understand whether users do indeed have time-inconsistent preferences
when it comes to privacy, or whether users initially are uninformed about
the consequences of their privacy choices. Given these inconsistencies,
further research is needed to understand whether there is a role for privacy
protection and laws to help address such scenarios.
REFERENCES
Acquisti, A., & Gross, R. (2006). Imagined communities: Awareness, information
sharing, and privacy on the Facebook. In 6th Workshop on Privacy Enhancing Technologies, pp. 36–58.
Acquisti, A., John, L. K., & Loewenstein, G. (2008). What is privacy worth? Mimeo,
CMU.
Ahn, D.-Y., Duan, J. A., & Mela, C. F. (2011). An equilibrium model of user generated
content. Working Papers 11–13, NET Institute.
Albuquerque, P., Pavlidis, P., Chatow,U., Chen, K.-Y., Jamal, Z., & Koh, K.-W. (2010).
Evaluating promotional activities in an online two-sided market of user-generated
content. Simon School Working Paper No. FR 10-08.
Archak, N., Ghose, A., & Ipeirotis, P. G. (2011). Deriving the pricing power of product
features by mining consumer reviews. Management Science, 57(8), 1485–1509.
Armstrong, M. (2006, Autumn). Competition in two-sided markets. RAND Journal of
Economics, 37(3), 668–691.
Campbell, J. D., Goldfarb, A., & Tucker, C. (2011). Privacy regulation and market
structure. Mimeo, University of Toronto.
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y., & Moon, S. (2007). I tube, you tube,
everybody tubes: analyzing the world’s largest user generated content video system. In Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement,
pp. 1–14. ACM.
Chevalier, J., & Mayzlin, D. (2006). The effect of word of mouth online: Online book
reviews. Journal of Marketing Research, 43(345–354).
Daugherty, T., Eastin, M., & Bright, L. (2008). Exploring consumer motivations for
creating user-generated content. Journal of Interactive Advertising, 8(2), 1–24.
Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from
online product reviews. International Journal of Research in Marketing, 27(4),
293–307.

8

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Fudenburg, D., & Villas-Boas, J. M. (2006). Behavior based price discrimination
and customer recognition. Handbooks in Information Systems (Vol. 1, pp. 377–435,
Chapter 7). Amsterdam, the Netherlands: Elsevier.
Gal-Or, E., Geylani, T., & Yildirim, T. (2010). User-generated content in news media.
Technical report, working paper.
Ghose, A., & Han, S. P. (2011). An empirical analysis of user content generation and
usage behavior on the mobile internet. Management Science, 57(9), 1671–1691.
Ghose, A., Ipeirotis, P. G., & Li, B. (May/June 2012). Designing ranking systems for
hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science, 31(3), 493–520.
Godes, D., & Silva, J. C. (2011). Sequential and temporal dynamics of online opinion.
Marketing Science, 31, 448–473.
Goldfarb, A., & Tucker, C. (2012a). Privacy and innovation. In Innovation Policy and
the Economy (Vol. 12, NBER Chapters.). Cambridge, MA: National Bureau of Economic Research, Inc.
Goldfarb, A., & Tucker, C. (2012b). Privacy and innovation. Innovation Policy and the
Economy, 12(1), 65–90.
Goldfarb, A., & Tucker, C. E. (2011). Privacy regulation and online advertising. Management Science, 57(1), 57–71.
Gross, R., & Acquisti, A. (2005). Information revelation and privacy in online social
net- works. In Proceedings of the 2005 ACM Workshop on Privacy in the Electronic
Society, WPES ’05, New York, NY, USA, pp. 71–80. ACM.
Hirshleifer, J. (1971). The private and social value of information and the reward to
inventive activity. American Economic Review, 61(4), 561–74.
Hoffstetter, R., Nair, H. S., Shriver, S., & Miller, K. (2009). Social ties and user generated content: Evidence from an online social network. Working Papers 09-28, NET
Institute.
Hoofnagle, C. J., King, J., Li, S., & Turow, J. (2010). How different are young adults
from older adults when it comes to information privacy attitudes and policies?
Mimeo, Berkeley.
Hui, K., & Png, I. (2006). Economics and information systems. In Handbooks in Information Systems (Vol. 1, Chapter 9: The Economics of Privacy). Amsterdam, the
Netherlands: Elsevier.
Keen, A. (2008). The cult of the Amateur: How blogs, MySpace, YouTube, and the rest of
today’s user-generated media are destroying our economy, our culture, and our values.
Crown Publishing Group.
Lee, T. Y., & Bradlow, E. T. (2011). Automated marketing research using online customer reviews. Journal of Marketing Research, 48(5), 881–894.
Marwick, A. E., Diaz, D. M., & Palfrey, J. (2010). Youth, privacy, and reputation.
Mimeo, Harward Law School.
Mayzlin, D. (2004). Promotional chat on the internet. Marketing Science, 25, 155–163.
Miller, A., & Tucker, C. (2011). Can healthcare information technology save babies?
Journal of Political Economy, 2, 289–324.
Miller, A. R., & Tucker, C. (2009, July). Privacy protection and technology adoption:
The case of electronic medical records. Management Science, 55(7), 1077–1093.

Economics of Privacy and User-Generated Content

9

Moe, W. W., & Schweidel, D. A. (2012). Online product opinions: Incidence, evaluation, and evolution. Marketing Science, 31(3), 372–386.
Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (May/June 2012). Mine your
own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521–543.
Posner, R. A. (1981). The economics of privacy. American Economic Review, 71(2).
Rochet, J.-C., & Tirole, J. (2006, Autumn). Two-sided markets: A progress report.
RAND Journal of Economics, 37(3), 645–667.
Sheehan, K. B., & Hoy, M. G. (2000). Dimensions of privacy concern among online
consumers. Journal of Public Policy and Marketing, 19, 62–73.
Stigler, G. J. (1980). An introduction to privacy in economics and politics. The Journal
of Legal Studies, 9(4), 623–644.
Taylor, C. R. (2004, Winter). Consumer privacy and the market for customer information. RAND Journal of Economics, 35(4), 631–650.
Tirunillai, S., & Tellis, G. J. (2012). Does chatter really matter? dynamics of usergenerated content and stock performance. Marketing Science, 31, 198–215.
Tucker, C. (2011). Social networks, personalized advertising, and privacy controls.
Mimeo, MIT.
Tucker, C. (2012). Social advertising. Journal of Marketing Research. Mimeo, MIT.
Xu, H. (2007). The effects of self-construal and perceived control on privacy concerns.
In 28th Annual International Conference on Information Systems (ICIS 2007), Montreal,
Canada.
Xu, H., Teo, H.-H., Tan, B. C. Y., & Agarwal, R. (2012). Effects of individual self- protection, industry self-regulation, and government regulation on privacy concerns:
A study of location-based services. Information Systems Research, 23, 1342.
Zhao, Y., Yang, S., Narayan, V., & Zhao, Y. (2012). Modeling consumer learning from
online product reviews. Marketing Science, 32, 153–169.
Zittrain, J. (2009). The future of the internet—and how to stop it. New Haven, CT: Yale
University Press.

CATHERINE TUCKER SHORT BIOGRAPHY
Catherine Tucker is the Mark Hyman Jr. Career Development Professor and
Associate Professor of Marketing at MIT Sloan. Her research interests lie in
how technology allows firms to use digital data to improve their operations
and marketing, and in the challenges, this poses for regulations designed
to promote innovation. She has particular expertise in online advertising,
digital health, social media, and electronic privacy. Generally, most of her
research lies in the interface among Marketing, Economics, and Law. She
has received an NSF CAREER award for her work on digital privacy and
a Garfield Award for her work on electronic medical records.
Dr. Tucker is Associate Editor at Management Science and a Research
Associate at the National Bureau of Economic Research. She teaches MIT
Sloan’s course on Pricing and the EMBA course Marketing Management

10

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

for the Senior Executive. She has received the Jamieson Prize for Excellence
in Teaching as well as being voted “Teacher of the Year” at MIT Sloan. She
holds a PhD in economics from Stanford University and a BA from Oxford
University.
RELATED ESSAYS
Intellectual Property (Economics), Michele Boldrin and David K. Levine
Misinformation and How to Correct It (Psychology), John Cook et al.
Political Advertising (Political Science), Erika Franklin Fowler
Innovation (Economics), Adam B. Jaffe
Technology Diffusion (Economics), Adam B. Jaffe
Search and Learning in Markets (Economics), Philipp Kircher
The Psychological Impacts of Cyberlife Engagement (Psychology), Virginia S.
Y. Kwan and Jessica E. Bodford
Network Research Experiments (Methods), Allen L. Linton and Betsy Sinclair
Data Mining (Methods), Gregg R. Murray and Anthony Scime
Retrieval-Based Learning: Research at the Interface between Cognitive Science and Education (Psychology), Ludmila D. Nunes and Jeffrey D. Karpicke
The Impact of Learning Technologies on Higher Education (Psychology),
Chrisopher S. Pentoney et al.
Text Analysis (Methods), Carl W. Roberts
Digital Methods for Web Research (Methods), Richard Rogers
Education in an Open Informational World (Educ), Marlene Scardamalia
and Carl Bereiter
Emerging Trends: Shaping Age By Technology and Social Bonds (Sociology),
Annette Spellerberg and Lynn Schelisch
Content Analysis (Methods), Steven E. Stemler
Information Politics in Dictatorships (Political Science), Jeremy L. Wallace



Media

Economics of Privacy and User‐Generated Content