In the complex landscape of the digital economy, the interplay between data sellers and buyers shapes the contours of modern commerce. Xueying Zhao’s job market paper (JMP) sheds light on a crucial aspect of this ecosystem: how data sellers can maximize profits by tailoring supplemental signals to buyers with private information. Here, I try to translate this into easier language with an example give an insight into the mechanism, discuss the implications for consumer welfare, and the broader policy ramifications or discussion in a broader geopolitical context.
The Mechanism: Tailoring Supplemental Data
Consider a local bakery aiming to optimize its advertising spend by targeting customers most likely to purchase its products. The bakery possesses private data about its existing customers but faces the challenge of selection bias and incompleteness. Enter a data seller, such as Google or Facebook, which holds vast troves of information—browsing history, social interactions, and location data.
These platforms create tailored ad-targeting solutions. For instance, Google or Facebook might offer the bakery data on “users nearby who recently searched for ‘pastries.’” This is designed to supplement the bakery’s private insights, enhancing the effectiveness of its targeting.
The pricing of these solutions is based on how much the additional signal increases the bakery’s targeting efficiency, effectively reflecting its willingness to pay. Despite the bakery’s private information, the platform can achieve a “first-best outcome” by perfectly complementing the bakery’s limited data for a broader audience. Such mechanisms, including features like Facebook’s “lookalike audiences,” have been instrumental in shaping advertising strategies and were infamously utilized during events like the Brexit referendum.
Policy Implications and Consumer Welfare
Zhao’s framework highlights critical dynamics in information markets:
- Consumer Surplus Extraction: The data seller often extracts significant consumer surplus by perfectly tailoring its offerings. This raises concerns about fairness and the long-term implications for consumers.
- Consumption vs. Wealth: While consumers may feel “better off” in terms of consumption, this may not translate into improved long-term financial security, such as asset accumulation. Policymakers, focused on GDP maximization, may overlook these nuances, ignoring externalities like environmental degradation and health impacts.
- Global Taxation Challenges: Data marketers operate on a global scale, extracting vast profits that are often transfer-priced to avoid taxation. Efforts like the UK and France’s push for Digital Services Taxes (DST) since 2016 have faced resistance, particularly from the US. The OECD’s global tax accord represents a step forward, but geopolitical challenges threaten its implementation.
Advocating for Robust Policy Responses
To counteract tax avoidance and ensure fairness, I advocate for a crude DST with revenues as the tax base rather than net profits. A collective push for such measures can pave the way for better instruments like the OECD accord. Without coordinated action, countries risk falling into a “divide and rule” trap.
Leveraging Data for Societal Gains
Beyond taxation, data has untapped potential for addressing critical challenges like climate change. With better governance, data can enable more targeted fiscal interventions, reducing inefficiencies and supporting global efforts for climate action. I’ve explored these ideas in my work on the Informational Boundaries of the State paper, which examines how data can empower more effective state interventions.
Broader Connections and Structural Challenges
The challenges extend beyond governance to systemic issues like skill shortages, which I’ve discussed in my Performative State Capacity and Climate (In)action paper. These shortages, often a legacy of austerity and demographic changes in Western Europe, hinder the ability of states to capitalize on available data.