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Outsourcing Regulation: Analyzing Nongovernmental Systems of Labor Standards and Monitoring
Open accessAbstract
A range of new nongovernmental systems for advancing labor standards and enforcement have emerged over the last 5 years. This article comparatively assesses these multistakeholder systems of codes of conduct and monitoring, discusses their underlying models of regulation, and proposes a set of criteria for evaluating their effectiveness, including their legitimacy, rigor, accountability, and complementarity. Critical issues are raised about the transparency of existing initiatives, independence of monitors, convergence of standards, and dynamics among nongovernmental regulation, unions, and state enforcement. The article concludes by arguing that with increased transparency, improved technical capacities, and new mechanisms of accountability to workers and consumers, nongovernmental monitoring could complement existing state regulatory systems.
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McCubbins, M. D., & Schwartz, T. (1984) · American Journal of Political Science · doi:10.2307/2110792 · Cited by 2,936
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Can non‐state global governance be legitimate? An analytical framework
Bernstein, S., & Cashore, B. (2007) · Regulation & Governance · doi:10.1111/j.1748-5991.2007.00021.x · Cited by 890
Perspectives on multinational enterprises in emerging economies
Meyer, K. E. (2004) · Journal of International Business Studies · doi:10.1057/palgrave.jibs.8400084 · Cited by 846
Algorithmic regulation: A critical interrogation
Yeung, K. (2018) · Regulation & Governance · doi:10.1111/rego.12158 · Cited by 842
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Metadata
- Authors
- O'Rourke, D.
- Venue
- Policy Studies Journal
- Publisher
- Wiley
- Published
- 2003-03
- Volume
- 31
- Issue
- 1
- Pages
- 1-29
- ISSN
- 0190-292X (ISSN-L)
- 1541-0072
- OpenAlex
- W2097627183
- Cited by
- 581
- References
- 1
- FWCI
- 53.91
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
