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Biopower, governmentality and the making of health inequities of migrant domestic workers in Hong Kong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

This research examines the health-seeking experiences of migrant domestic workers (MDWs) in Hong Kong, expanding the discussion on migrant health inequities related to governance. Drawing on Foucauldian concepts of biopower and governmentality, we investigate how power/knowledge and regimes of truth influence MDWs’ decision-making processes and their experiences accessing healthcare resources and asserting legal rights. Data were collected through ethnographic methods, including participant observation and oral history interviews. Our findings reveal that the Hong Kong government employs two levels of strategies to minimize healthcare costs and responsibilities for MDWs. The first strategy shifts healthcare responsibilities onto employers through privatization and outsourcing, instilling fear in employers and subsequently eliciting shame in MDWs. This results in victim-blaming, discouraging MDWs from seeking assistance and rendering them disposable. The second strategy arises when MDWs fall ill after contract termination; their visa status changes to visitor, excluding them from public healthcare entitlements under an algorithmic eligibility system even if they have pending legal claims. This system algorithmically reclassifies sick MDWs with pending legal cases as “non-eligible persons,” effectively denying them access to public health resources. We propose a concept of “algorithmic truth as governance” to capture this mechanism. It refers to data-driven algorithmic technologies that generate real-time, automated classifications presented as neutral and objective truths, which authorities use to legitimize exclusive enforcement. Collectively, these strategies not only legitimize exclusionary policies but also align with neoliberal ideologies aimed at expelling unproductive labor, thereby perpetuating health inequities.

Original languageEnglish
Article number118449
JournalSocial Science and Medicine
Volume383
DOIs
Publication statusPublished - 25 Jul 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Algorithm
  • Governance
  • Health inequities
  • Migrant domestic workers

ASJC Scopus subject areas

  • Health(social science)
  • History and Philosophy of Science

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