JOURNAL ARTICLE
From Local to Global: Offshoring and Asset Prices.
Published In: Management Science (INFORMS), 2023, v. 69, n. 3. P. 1420 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Bretscher, Lorenzo 3 of 3
Abstract
This article investigates how the potential to offshore production influences the cost of capital and risk premia across U.S. manufacturing industries. It introduces a novel industry-level measure of labor offshorability, constructed from occupation-level task data weighted by employment and wages, and finds that industries with low offshoring potential exhibit significantly higher excess stock returns compared to those with high offshoring potential. Empirical evidence links this offshorability premium to import competition from low-wage countries, showing that offshoring mitigates risks associated with foreign competition by enabling cost reductions and market share protection. A two-country dynamic trade model, distinguishing a high-wage (West) and low-wage (East) country, incorporates offshoring decisions and successfully replicates key empirical patterns, including the positive return spread between low and high offshorability industries and its amplification with import penetration. The model further predicts and the data confirm that offshoring lowers profit volatility, that the offshorability premium increases with consumer price sensitivity, and that the associated investment strategy has a positive consumption beta, underscoring offshoring’s role as a significant factor in industry risk and asset pricing.
Additional Information
- Source:Management Science (INFORMS). 2023/03, Vol. 69, Issue 3, p1420
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2023
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.4393
- Accession Number:162389394
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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