JOURNAL ARTICLE

Economic Substance Behind Texas Political Anti-ESG Sanctions.

  • Published In: Management Science (INFORMS), 2026, v. 72, n. 4. P. 2976 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Rajgopal, Shiva; Srivastava, Anup; Zhao, Rong 3 of 3

Abstract

This article investigates whether the polarized political stances on environmental, social, and governance (ESG) investing between so-called blue (Democratic) and red (Republican) states in the U.S. are reflected in their pension funds’ investment strategies, focusing on a 2021 Texas law that bans state agencies from investing in funds that "boycott" energy companies. The study finds that the Texas-banned ESG funds, despite their pro-ESG branding, are largely index or closet index funds with only a modestly lower allocation to energy stocks (3.5%) compared to similar non-ESG funds (5.7%), challenging the notion of a true boycott. Furthermore, Texas and other red states’ pension funds do not significantly overweight energy sector investments relative to blue states’ pension funds, and banned funds generally outperformed control funds before the ban. The findings suggest that the strong pro- and anti-ESG rhetoric by politicians is not mirrored in the actual investment policies of their states’ pension funds, indicating that ESG has become a politicized term with limited economic substance in this context.

Additional Information

  • Source:Management Science (INFORMS). 2026/04, Vol. 72, Issue 4, p2976
  • Document Type:Article
  • Subject Area:Geography and Cartography
  • Publication Date:2026
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2024.05180
  • Accession Number:192910497
  • 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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