Document Type : Original Article
Authors
1
Associate Professor, Department of Social Planning, Faculty of Social Sciences, University of Tehran, Tehran, Iran
2
Professor, Department of Social Communication, Faculty of Social Sciences, University of Tehran, Tehran, Iran.
3
PhD Candidate in Social Welfare, Department of Social Planning, Faculty of Social Sciences, University of Tehran, Tehran, Iran
10.22034/jss.2026.2089913.1949
Abstract
Background and Objective: This article assesses data governance in the Iranian Welfare Information Database (IWID) through critical policy analysis, drawing on data justice and historical institutionalism. Although Iran’s transition toward data-driven social policy seeks better targeting and more efficient resource allocation, the key question is whether this architecture is grounded in meaningful participation, transparency, accountability, and data justice.
Methods and Data: This qualitative study is based on more than 25 semi-structured interviews with senior managers, deputies, specialized advisors, and researchers in welfare and data; participation in more than 35 expert meetings; and analysis of official documents from 2024–2025. Data were interpreted through combined thematic analysis in dialogue with data justice and historical institutionalism.
Findings: The findings show that IWID reproduces a centralized model of data governance at three levels: citizenship, community, and institutional. Citizens become objects of visibility; local communities and civil society organizations are marginalized; and welfare institutions lack interpretive participation in data. This pattern is rooted in the post-revolutionary institutional trajectory and fragmented welfare institutions, which have constrained multilevel mechanisms of participation, transparency, and accountability.
Discussion and Conclusion: Without redesigning participation rules—including standardized self-reporting, the right to appeal and explanation, the right not to be datafied, meaningful roles for local communities, and differentiated analytical access for welfare institutions—data-driven social policy in Iran risks knowledge inequalities, concentration of interpretive power, and structural de-participation.
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