JOURNAL ARTICLE

The Legal Classification of Human Capital in 'Markets, Hybrids and Hierarchies'.

  • Published In: Chinese Journal of Comparative Law, 2023, v. 11, n. 3. P. 1 1 of 3

  • Database: Legal Source 2 of 3

  • Authored By: Ang, Lance 3 of 3

Abstract

This article examines the legal classification of hybrid workers—those engaged in non-standard employment relationships that blend characteristics of employment and self-employment—within the context of Singapore’s labour law, comparing it to the United Kingdom’s trinary approach under the Employment Rights Act 1996 (ERA 1996). It argues that Singapore’s current binary framework inadequately addresses the complexities of platform and gig work, which challenges traditional employee/independent contractor distinctions and calls for statutory recognition of an intermediate "hybrid worker" category. Drawing on the UK’s limb (b) worker category, the article proposes legislative reforms for Singapore to recalibrate the coordination-risk bargain between capital and labour, ensuring firms internalize enterprise risks commensurate with their control over workers while preserving labour market flexibility. It further discusses appropriate individual and collective rights for hybrid workers, emphasizing the need for a calibrated baseline of protections that reflect varying degrees of worker dependence and subordination. The article concludes that such reforms would promote a more sustainable capitalist framework by balancing economic efficiency with social protections in evolving labour markets.

Additional Information

  • Source:Chinese Journal of Comparative Law. 2023/12, Vol. 11, Issue 3, p1
  • Document Type:Article
  • Subject Area:Law
  • Publication Date:2023
  • ISSN:20504802
  • DOI:10.1093/cjcl/cxae003
  • Accession Number:176131833
  • Copyright Statement:Copyright of Chinese Journal of Comparative Law is the property of Oxford University Press / USA 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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