Facebook Consumers Face Challenges in Valuing Personal Data
Hurdles More Acute for Women, Black, and Low-Income Users
Personal data generate significant value for digital platforms, as a source of revenue or as input that makes it easier to determine how to target algorithms. While platforms can quantify the value of users’ data to the platform, users face hurdles in pinpointing the value of data to themselves, which jeopardizes the equitable allocation of the benefits from consumer data. A new study examined how Facebook consumers’ valuations of their personal data change once they have received information about the value of that data and how valuations vary across demographic groups. The study identified substantial differences in users’ valuations of their social media data by gender, race, and income.
The study, by researchers at Carnegie Mellon University (CMU), the University of Texas at Austin (UT Austin), and the Massachusetts Institute of Technology, was published as a working paper.
“As policymakers explore introducing data dividends and companies experiment with new business models around data markets, it is essential to understand the economic valuations of consumers’ personal data,” says Ananya Sen, assistant professor of information technology and management at CMU’s Heinz College, who coauthored the study. “Policymakers need to account for how users perceive their digital footprint and how these perceptions vary across different demographic groups, especially ones that are underrepresented.”
Individuals’ valuations of their personal information are very uncertain and marred by problems associated with asymmetric information—when one party in an economic transaction has more knowledge than the other. Not only do consumers rarely know how their data are used, they also lack information on the value of their data or the costs when their data are misused. Unlike established markets for goods (e.g., cars, company shares), where consumers have access to information on the market valuation of an item, markets for personal data have transactions that are unique to each individual, exacerbating consumers’ costs to learning their preferences.
If consumers cannot correctly determine the market value of their own data, data markets fail to achieve the purpose they ought to fulfill: enabling an equitable allocation of the benefits extracted from consumer data. Such failure is problematic if the inability to determine fair valuations is disparately distributed across different socioeconomic groups, with some groups experiencing a disadvantage in obtaining compensation for their data.
In this study, researchers used a mechanism, which allowed every participant to achieve the best possible outcomes by acting on their true premises, to determine consumers’ willingness to share their social media data for monetary compensation. They estimated distributions of valuations of social media data before and after an information treatment (e.g., making consumers more aware of the value by others or the costs when their data are compromised). In particular, they studied what amount of compensation Facebook users required to share the entirety of their profile data—including pictures and private messages—with researchers, and how consumers’ distribution of data valuations differed by demographic group.
Before the information treatment, the study found significant differences in users’ valuations of their social media data by gender, race, and income in both groups. Historically, marginalized groups reported significantly lower valuations. Specifically, White users valued their data more highly than Black users, males valued their data more highly than females, and low-income users valued their data more highly than higher-income users. These findings held even after controlling for income and education.
After the participants received information about their data, almost a third of the users in both groups revised their data valuations, with most increasing them. In both groups, providing information led to a reduction in differences in data valuations, but only by increasing the valuations of low-valuation individuals, a group in which female, low-income, and Black participants were overrepresented. Dispersion and heterogeneity in valuations across these demographic groups decreased but did not disappear after the information intervention.
“Strategies such as information campaigns by policymakers that reduce information asymmetries may be helpful to consumers, especially those from marginalized groups,” explains Alessandro Acquisti, professor of information technology and public policy at CMU’s Heinz College, who coauthored the study. “But we now know that consumers’ valuations of their personal data are only partly influenced by market information, which may impair the ability of markets to build a more equitable data economy.”
Among the study’s limitations are that it explored data from just one platform.
The research was funded by the McCombs School of Business at UT Austin; the Center for Technology, Innovation and Competition at the University of Pennsylvania; and the NET Institute.
Summarized from a Working Paper, Information Frictions and Heterogeneity in Valuations of Personal Data by Collis, A (University of Texas at Austin), Moehring, A (Massachusetts Institute of Technology), Sen, A (Carnegie Mellon University), and Acquisti, A (Carnegie Mellon University).Copyright 2021. All rights reserved.
About Heinz College of Information Systems and Public Policy
The Heinz College of Information Systems and Public Policy is home to two internationally recognized graduate-level institutions at Carnegie Mellon University: the School of Information Systems and Management and the School of Public Policy and Management. This unique colocation combined with its expertise in analytics set Heinz College apart in the areas of cybersecurity, health care, the future of work, smart cities, and arts & entertainment. In 2016, INFORMS named Heinz College the #1 academic program for Analytics Education. For more information, please visit www.heinz.cmu.edu.