This project resulted in the production of three research papers, each focusing on distinct aspects of data economics: the value of data, data privacy, and data markets. These papers were presented at numerous venues, including invited seminars at leading universities, conferences, and workshops. They enhance our understanding of data economics, a booming field. The grant also funded the professional development of young PhD scholars. Finally, this work inspired further research on the topic, which is now in progress.

In "The Value of Data Records" (2024, The Review of Economic Studies), we addressed a key question: How much value does each individual's data contribute to multi-billion-dollar industries? Personal data fuels industries like search engines, social media, e-commerce, and job-matching platforms. It is often compared to the "new oil" of modern economies. However, the specific value of each individual's data remains unclear, especially when data is pooled to maximize revenues from ad auctions, obscuring individual contributions.

To address this, our paper proposes a classical approach rooted in information design literature. We characterize the value of each data record for intermediaries that use data to influence the behavior of strategic agents. Our analysis reveals that the value of a data record comprises two components. The first is the direct payoff the intermediary obtains (e.g., when a user trades with an advertiser). The second is a novel externality that arises when the intermediary pools records to withhold information from agents. This externality is a hallmark of intermediation problems due to inherent conflicts of interest.

Our analysis has two main implications. First, we show that the values of data records are a useful benchmark for compensating individuals for their data, contributing to policy debates about data dividends and data unions. Second, we draw an analogy between how an intermediary values data records and consumer theory, enabling the application of analytical tools to study data markets and optimal data acquisition strategies.

In "Privacy and the Value of Data" (2023, American Economic Association, Papers & Proceedings), we examined how data-privacy laws affect the value of personal data for firms and impact consumer welfare. We extend the model in "The Value of Data Records" to incorporate consumer privacy protection by introducing elicitation constraints. In our model, an e-commerce platform intermediates between a monopolistic seller and heterogeneous consumers, aiming to maximize consumer surplus by influencing the seller's pricing based on consumer information.

Our analysis yields three main insights. First, protecting consumer privacy can increase or decrease the value of some consumers' data while leaving others unaffected. Second, privacy protection impacts data usage by platforms and consumer payoffs, benefiting some consumers but harming others, especially those with no reason to withhold data. Third, privacy protection raises average transaction prices but limits trade, negatively impacting the platform.

In "Competitive Markets for Personal Data" (2024, working paper, with extended abstract in the 2024 Proceedings of the ACM EC Conference), we investigate whether competitive markets for personal data promote efficient allocations and maximize consumer welfare. This question is motivated by policy debates on data market design. Currently, consumers have limited control over their data use and are often imperfectly compensated. This can lead to inefficiencies and market failure.

We model an economy where consumers own their data and can sell it to a platform at a given price, assuming a competitive market. The platform provides a service, intermediating consumers with a third-party merchant from whom they can buy products. The platform uses consumers' data to inform the merchant about their willingness to pay, affecting merchant profits, consumer surplus, and data prices.

Our main result shows that the efficiency of this competitive economy relies on the platform's objective. When the platform's and merchant's objectives align, the equilibrium allocation is efficient, maximizing consumer welfare. Conversely, when the platform's objective aligns more with consumers, the equilibrium can be inefficient due to the externality discussed in "The Value of Data Records."

We propose three alternative market designs to correct this inefficiency. The first involves a "data union," an intermediary managing consumers' data on their behalf and returning proceeds as data dividends. The second involves a "data tax" on consumers when they trade their data. The third involves making markets "more complete," by letting the price of a data record also depend on its intended use.