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

TL;DR

This paper introduces a formal framework for quantifying and internalizing the social costs of intersectional data extraction in digital capitalism, proposing a Pigouvian surcharge based on information theory to regulate data markets.

Contribution

It develops a model-agnostic pricing rule using mutual information to assign value to intersectional data, integrating normative social considerations into market regulation.

Findings

Proposes a mutual information-based surcharge for data extraction.

Operates independently of statistical models used for intersectional attribute estimation.

Provides a normative, calibratable mechanism to address social externalities in digital markets.

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.