JOURNAL ARTICLE

Substitution Between Corporate Social Responsibility Activities: Evidence from Hiring and Mistreating Unauthorized Workers and Pollution.

  • Published In: Management Science (INFORMS), 2026, v. 72, n. 4. P. 3411 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Huang, Ying; Li, Ningzhong; Zhou, Xiaolu 3 of 3

Abstract

This article investigates the substitution effect among corporate social responsibility (CSR) investments by examining how U.S. states’ staggered adoption of E-Verify mandates—regulations requiring employers to verify the work authorization of new hires to reduce hiring unauthorized workers and related workplace abuses—impacts plant-level pollution. The study finds that while E-Verify mandates improve labor-related CSR by reducing unauthorized employment and workplace violations, they lead to a significant increase in toxic chemical releases, particularly under universal mandates applying to all employers, with stronger effects in states with higher shares of unauthorized workers and in plants with inherently hazardous jobs. The increase in pollution is primarily attributed to reduced emission efficiency and, to a lesser extent, increased production, rather than changes in pollution abatement activities. These findings highlight a regulatory trade-off where enhancing one dimension of CSR (labor practices) may inadvertently diminish another (environmental performance), underscoring the importance of considering CSR as an integrated package in policy and research.

Additional Information

  • Source:Management Science (INFORMS). 2026/04, Vol. 72, Issue 4, p3411
  • Document Type:Article
  • Subject Area:Religion and Philosophy
  • Publication Date:2026
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2022.02310
  • Accession Number:192910467
  • 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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