Domestic gains from offshoring? Evidence from TAA-linked US microdata

• 成果类型: Article
• 署名作者: Monarch, Ryan; Park, Jooyoun; Sivadasan, Jagadeesh
• 署名单位: Federal Reserve System - USA; Federal Reserve System Board of Governors; University System of Ohio; Kent State University; Kent State University Salem; Kent State University Kent; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF INTERNATIONAL ECONOMICS
• ISSN/ISSBN: 0022-1996
• DOI: 10.1016/j.jinteco.2016.12.008
• 发表日期: 2017
• 页码: 150-173
• 关键词: Outsourcing; Manufacturing; EMPLOYMENT; TRADE; PRODUCTIVITY; firm performance

摘要

We construct a new linked data set with over one thousand offshoring events by matching Trade Adjustment Assistance (TAA) program petition data to U.S. Census Bureau microdata. We exploit these data to study the short- and long-term effects of offshoring on domestic firm-level employment, output, wages, and productivity in this large sample of offshoring events. As implied by heterogeneous firm models with high fixed costs of offshoring, we find that the average offshoring firm in the TAA sample is larger, more productive, older, and more likely to be an exporter, than the average non-offshorer. After initiating offshoring, TAA-certified offshorers experience large declines in employment (0.38 log points), output (0.33log points) and capital (0.25log points), and a concomitant increase in capital and skill intensity, relative to their industry peers. We find no significant change in average wages or productivity measures. Even six years after the initial offshoring event, we find no recovery in employment, output, or capital, and a higher probability of exit. We find similar results (including decline in output, and unchanged wages and productivity) for the aggregate of non-TAA certified plants of multi-plant offshoring firms. We find that the substitution of domestic activity by offshoring is stronger for relatively lower wage, lower capital intensity, lower productivity offshorers. Our results are consistent across two separate difference-in-differences (DID) approaches, and a number of robustness checks. (C) 2016 Elsevier B.V. All rights reserved.