Intersectional Data and the Social Cost of Digital Extraction: A Pigouvian Surcharge

2026-01-13

AI Summary

This study addresses how digital capitalism, through large-scale data extraction, exposes intersecting identities—such as race, gender, and disability—thereby generating structural privacy externalities that disproportionately impose social costs like discrimination and risk on marginalized groups. To redress this imbalance, the paper develops a political economy framework that, for the first time, integrates intersectional social identities into data pricing mechanisms. Drawing on information-theoretic measures—specifically mutual information and entropy reduction—it proposes a model-agnostic Pigouvian surcharge scheme. This mechanism internalizes the social costs of data extraction, embeds normative social value judgments, and is applicable across parametric, nonparametric, and machine learning models. Furthermore, it offers regulators a calibratable institutional tool to enact redistributive protections in the face of asymmetric digital power.

Technology Category

• Machine Learning: Privacy
• Game Theory and Economic Paradigms: Mechanism Design
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Security and Privacy: Data transparency and provenance
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

Contemporary digital capitalism relies on the large-scale extraction and commodification of personal data. Far from revealing isolated attributes, such data increasingly exposes intersectional social identities formed by combinations of race, gender, disability and others. This process generates a structural privacy externality: while firms appropriate economic value through profiling, prediction, and personalization, individuals and social groups bear diffuse costs in the form of heightened social risk, discrimination, and vulnerability. This paper develops a formal political economic framework to internalize these externalities by linking data valuation to information-theoretic measures. We propose a pricing rule based on mutual information that assigns monetary value to the entropy reduction induced by individual data points over joint intersectional identity distributions. Interpreted as a Pigouvian-style surcharge on data extraction, this mechanism functions as an institutional constraint on the asymmetric accumulation of informational power. A key advantage of the approach is its model-agnostic character: the valuation rule operates independently of the statistical structure used to estimate intersectional attributes, whether parametric, nonparametric, or machine-learned, and can be approximated through discretization of joint distributions. We argue that regulators can calibrate this surcharge to reflect contested social values, thereby embedding normative judgments directly into market design. By formalizing the social cost of intersectional data extraction, the proposed mechanism offers both a corrective to market failure and a redistributive institutional shield for vulnerable groups under conditions of digital asymmetry.

Problem

• intersectional data
• privacy externality
• digital extraction
• social cost
• informational asymmetry

Innovation

• mutual information
• Pigouvian surcharge
• intersectional data
• privacy externality
• model-agnostic valuation