JOURNAL ARTICLE

IN LIEU OF THE NLRA.

  • Published In: Wisconsin Law Review, 2026, v. 2026, n. 3. P. 493 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: RACABI, GALI 3 of 3

Abstract

The National Labor Relations Act (NLRA) deteriorates due to constitutional attacks and political sabotage. As labor law buckles, its preemption regime, a keystone of U.S. labor governance, has become unsustainable. This Article argues that, to survive, labor law must flip its federal default by empowering and expanding state-level labor institutions and by prying open the NLRA preemption doctrine. Eighteen states already maintain NLRA-like statutory frameworks, and fourteen more, as a state public policy, recognize workers' rights to unionize and act collectively. These under-examined laws hint at an alternative labor governance model in lieu of the NLRA. Building on emerging preemption challenges, weaknesses in federal enforcement, and employers' own challenges to the NLRA, this Article outlines legal strategies turning retreat into opportunity. After describing the slew of state private sector labor laws, this Article uncovers a hidden labor law principle against the use of preemption arguments that create regulatory "no-man's-lands," where workers have no recourse in state or federal labor law. It also introduces a "catch-22" argument, whereby employers who deny NLRA coverage or authority cannot shield themselves from state law with preemption arguments. Then, this Article explores how emerging state labor laws utilize novel trigger mechanisms and the ways regional cooperation can signal the outlines of a bottom-up labor governance model [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Wisconsin Law Review. 2026/05, Vol. 2026, Issue 3, p493
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2026
  • ISSN:0043-650X
  • DOI:10.59015/wlr.OLWH1889
  • Accession Number:193279098
  • Copyright Statement:Copyright of Wisconsin Law Review is the property of Wisconsin Law Review 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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