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.
MLA
Giroud, Xavier, et al. “Propagation and Amplification of Local Productivity Spillovers.” Econometrica, vol. 92, .no 5, Econometric Society, 2024, pp. 1589-1619, https://doi.org/10.3982/ECTA20029
Chicago
Giroud, Xavier, Simone Lenzu, Quinn Maingi, and Holger Mueller. “Propagation and Amplification of Local Productivity Spillovers.” Econometrica, 92, .no 5, (Econometric Society: 2024), 1589-1619. https://doi.org/10.3982/ECTA20029
APA
Giroud, X., Lenzu, S., Maingi, Q., & Mueller, H. (2024). Propagation and Amplification of Local Productivity Spillovers. Econometrica, 92(5), 1589-1619. https://doi.org/10.3982/ECTA20029
Supplement to "Propagation and Amplification of Local Productivity Spillovers"
Xavier Giroud, Simone Lenzu, Quinn Maingi, and Holger Mueller
The replication package for this paper is available at https://doi.org/10.5281/zenodo.12698079. The authors were granted an exemption to publish parts of their data because either access to the data is restricted or the authors do not have the right to republish them. They were also granted an exemption from publishing parts of their code, because the terms of use of their the data does not allow them to share information on some of the variables. The journal checked the public parts of the replication package for their ability to reproduce the results in the paper and approved online appendices. The replication package contains information on how authors can obtain access to the original data and code, archived by the data provider for a period of at least 10 years. During that time, authors commit to assist users who, having obtained access to the confidential part of the package, may have trouble reproducing the results generated from the confidential data and codes. Given the highly demanding nature of the algorithms, the reproducibility checks were run on a simplified version of the code, which is also available in the replication package.
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