Robust Market Interventions

TL;DR

This paper develops spectral methods to identify conditions under which market interventions can reliably increase social surplus despite uncertainties and imprecise information about strategic firms and market dynamics.

Contribution

It introduces the recoverable structure condition, linking large-scale complementarities to the feasibility of robust interventions in strategic markets.

Findings

Recoverable structure enables robust interventions under uncertainty.

Spectral analysis of the Slutsky matrix reveals key principal components.

Large-scale complementarities are crucial for intervention success.

Abstract

When can interventions in markets be designed to increase surplus robustly -- i.e., with high probability -- accounting for uncertainty due to imprecise information about economic primitives? In a setting with many strategic firms, each possessing some market power, we present conditions for such interventions to exist. The key condition, recoverable structure, requires large-scale complementarities among families of products. The analysis works by decomposing the incidence of interventions in terms of principal components of a Slutsky matrix. Under recoverable structure, a noisy signal of this matrix reveals enough about these principal components to design robust interventions. Our results demonstrate the usefulness of spectral methods for analyzing imperfectly observed strategic interactions with many agents.