While a recent paper, Data Valuation and Law, is not itself an “empirical paper,” it is certainly “ELS-adjacent” and, as such, I thought it might interest some. This is particularly so as both AI and “big data” become increasingly important.
Motivating Jordan Berry (USC) and D. Daniel Sokol’s (USC) paper is the observation that “while data has become increasingly valuable and important, the law’s attempts to value data have lagged, remaining confused and underdeveloped.” To improve matters in this regard, the paper advances a “real options” approach for framing data valuation not because the approach is perfect but, rather, because “it is the least bad alternative available.” The paper’s abstract follows.
“Data has become an increasingly valuable asset. Numerous areas of law—including contracts, corporate law, IP, antitrust, tax, privacy, and bankruptcy—require parties and courts to determine the value of assets, including data. Unfortunately, data valuation has been hindered by a lack of clarity over what data is and why it is valuable. This lack of clarity also increases the chances of legal decisionmakers valuing data in inconsistent ways, which would create further confusion, inefficiencies, and opportunities for regulatory arbitrage.
This article proposes a unified framework for valuing data that will promote consistent valuations across fields of law. It begins by conceptualizing data as building blocks: It is of little value on its own. But when placed in skillful and creative hands, it can unlock choices for its holders—choices they would not otherwise have—that can generate tremendous profits. Thus, data constitutes what is known as a ‘real option.’ This article shows how using real options to value data can significantly improve upon existing data valuation practices.”