Partial Identification of Spatial Production Networks

2026-06-10

AI Summary

This study addresses the challenge of identifying exposure risk within regional production networks in the absence of cross-state buyer-seller linkage data. By modeling the missing intermediate input structure as an unknown coupling constrained by aggregate regional activity levels, supply-side limitations, and bilateral freight flows, the authors integrate transportation linear programming with partial identification theory to quantify, for the first time, the tightest possible bounds on exposure risk supported by publicly available data—without relying on strong regionalization assumptions. The analysis reveals that common proportional regionalization assumptions for key commodity sectors are inconsistent with observed freight data. Incorporating bilateral freight constraints substantially sharpens the identified sets; however, considerable uncertainty persists for service and mixed sectors, highlighting the sensitivity of conclusions to prevailing modeling assumptions.

Abstract

Which regional exposure conclusions are identified when public data do not observe buyer-seller links across states? We study this question by treating the missing intermediate-input spatial kernel as an unknown coupling constrained by regional activity margins, support restrictions, and auxiliary shipment moments. For linear exposure statistics, the sharp identified set is computed by transportation linear programs. Applying the method to U.S. state-sector data, we find that shipment data are inconsistent with the spatial diffuseness implied by proportional regionalization in key goods sectors. However, they do not identify a unique regional production network or a precise ranking of state exposure to local shocks. Bilateral shipment restrictions tighten the bounds, but much of the remaining uncertainty comes from large service and mixed sectors that are weakly covered by goods-movement data. The results show which exposure conclusions are supported by public data and which are imposed by maintained regionalization assumptions.

Problem

• Partial Identification
• Spatial Production Networks
• Regional Exposure
• Intermediate Inputs
• Shipment Data

Innovation

• partial identification
• spatial production networks
• transportation linear programming
• intermediate-input linkages
• regional exposure