The gains from agglomeration economies are believed to be highly localized. Using confidential Census plant‐level data, we show that large industrial plant openings raise the productivity not only of local plants but also of distant plants hundreds of miles away, which belong to large multi‐plant, multi‐region firms that are exposed to the local productivity spillover through one of their plants. This “global” productivity spillover does not decay with distance and is stronger if plants are in industries that share knowledge with each other. To quantify the significance of firms' plant‐level networks for the propagation and amplification of local productivity shocks, we estimate a quantitative spatial model in which plants of multi‐region firms are linked through shared knowledge. Counterfactual exercises show that while large industrial plant openings have a greater local impact in less developed regions, the aggregate gains are greatest when the plants locate in well‐developed regions, which are connected to other regions through firms' plant‐level (knowledge‐sharing) networks.

We develop a bottom-up approach to estimate the slope of the primitive form of the New Keynesian Phillips curve, which features marginal cost as the real activity variable. Using quarterly micro data on prices, costs, and output, we estimate dynamic pass-through regressions that identify the slope as a function of primitive parameters. We find a high slope for the cost-based Phillips curve, which contrasts with the low estimates of the conventional output gap–based formulation found in the literature. We reconcile by showing that the output elasticity of marginal cost is low, at least during moderate inflation periods (e.g., pre-pandemic). (JEL C51, E12, E23, E31, E32, L60)