Siona Listokin

doi:10.19080/ASM.2022.07.555713

Opinion

Priceless or Worthless? Formal Privacy Valuation in U.S. Policy Proposals

Siona Listokin*

Associate Professor, George Mason University, United States

Submission: June 22, 2022; Published: June 30, 2022

*Corresponding author: Siona Listokin, Associate Professor, George Mason University, 3351 Fairfax Drive, Arlington VA 22201, United States

How to cite this article:Sushil. Strategic Knowledge Management for Competitive Business. Ann Soc Sci Manage Stud. 2022; 7(3): 555713. DOI: 10.19080/ASM.2022.07.555713

Opinion

Assigning formal economic value to consumer data privacy is difficult in public policy and law, in part because of the lack of a traditional marketplace for privacy and the context-specific nature of consumer preferences. Consumers often express high valuation for data privacy while accepting low transaction prices in practice, a contradiction known as the privacy paradox [1]. Privacy value is also highly context dependent [2]. Perhaps because of the lack of a clear market price, privacy legislation and proposals in the U.S. typically do not use traditional cost-benefit analysis in the policy process and most formal valuations focus on industry-side loss estimations.

Policy assessment tools that quantify, in dollar values, the combined effects of potential projects are generally concerned with efficiency of collective decision making [3]. An obvious case of such quantification of “privacy” value could occur in crafting or implementing policy specifically regarding industry that depends on collected consumer data for their revenue models. For example, as part of the January 2013 decision from the Federal Trade Commission not to sue Google for anticompetitive behavior in online search advertisement, regulators decided that the risk of consumer privacy violations in online advertisement markets was small.

At the same time, the FTC decided that the potential benefits to consumers and industry from not regulating were very high. Understanding the “economics of privacy” wherein privacy tradeoffs are given similar treatment as traditional welfare considerations is an important tool for the main U.S. privacy regulator (for example, then-acting FTC Chairman Maureen Ohlhausen noted the need “to deepen the FTC’s understanding of the economics of privacy” in 2017.)

The early development of COVID-19 offered another context in which to consider the practical value of consumer data privacy. A number of COVID-19 policy alternatives, such as smartphone enabled contact tracing, involve potential privacy costs and thus offer opportunities to establish and study norms for privacy valuation in public policy. The advantage of looking at data collection in the context of public health is that the tradeoff is no longer between individual risk and preferences versus industry profit; rather, the tradeoff involves collective public health.

In a review of COVID-19 policy proposals that are listed in PubMed.gov, a repository of 30 million citations of biomedical research hosted by the United States National Library of Medicine, there are a few dozen articles are not primarily centered around medical treatment or vaccine development and involve cost benefit analyses that measure net economic welfare of various lockdown and tracing policies. These analyses could potentially include consumer privacy as a tradeoff in an efficiency-maximizing model, wherein massive data collection in pursuit of public health is a quantifiable cost expressed in real dollar terms to balance quantifiable health, lifestyle and commerce benefits. Yet it is hard to find a single study that attempts to quantify the privacy tradeoff; where privacy is a cost consideration at all, it is most often an “intangible” or nonquantifiable one. Practically speaking, policy evaluations in the COVID-19 context treat privacy as an add-on outside of the core analysis by excluding them from calculations.

Privacy policy, regarding COVID-19 and beyond, is harmed by this add-on treatment. There are methods to quantify and monetize privacy costs and benefits for policy analysis and these should be utilized to more fully involve privacy in the regulatory and legislative proposal process. Public finance identifies two main methods to quantify intangible impacts, hedonic pricing and contingent valuation [4]. At the most basic, policy analysts could include privacy in monetized calculations with value ranges that incorporate consumer surveys (high end valuation) and empirical studies of consumer behavior (low end valuation). The published COVID-19 policy studies, in contrast, treat privacy as an established good without value

The implication for future privacy legislation proposals is that the key output of the anticipated course of action will not be assigned a value, leaving it a subjective proposition in the face of quantifiable counterpoints.