The Data Welfare State is a new volume in the Data Justice book series published by Sage, a project that seeks to understand the implications of datafication for social justice and resistance. Co-authored by Anne Kaun and Anu Masso, researchers based at Södertörn University in Sweden and Tallinn University of Technology in Estonia, respectively, the book situates itself within conversations on new media and communication technology, public policy, and public administration. The volume promises a global perspective on what the authors term the “data welfare state” – a formation where welfare governance and automation technologies converge. Situating its analysis firmly within the specific socio-political and cultural context of European countries, the text interrogates the practical and theoretical implications of leveraging data practices for the public good.
In the introduction and reiterated in the conclusion, Kaun and Masso explicitly state that one of the central aims of The Data Welfare State is to explore how data-based automation can be developed or governed in ways that remain consistent with the ethical and social principles of the welfare state. The authors describe their project as an attempt to understand how automation might be aligned with core values like equality, justice, and care, while still responding to the pressures of digitalisation and efficiency.
The book distinguishes itself through a novel writing style. Each chapter opens with a speculative mise-en-scène, presumably drawn from empirical encounters. These short stories, perhaps the book’s most inventive stylistic feature as it jumps back in history and forward into a future yet to come, interpellate and orient the reader towards the affective conditions where citizens and caseworkers meet with algorithmic systems in the data welfare state. They lend concreteness to a topic often treated abstractly, and they serve as methodological framing devices rather than mere literary ornament. On the methodological side, the ambition of this book is evident in its hybrid approach, combining a large-scale representative survey (N = 4,501) across Estonia, Germany, and Sweden, with qualitative interpretation and historical reconstruction. Kaun and Masso weave together methods of sociology, communication, and cultural analysis into a single transdisciplinary form.
This work dissects the emerging data welfare state through a structure that moves from historical synthesis to empirical critique. Chapter 1, “Data Welfare State”, establishes the conceptual foundation by tracing the historical co-evolution of welfare and computing across three stages of change, relying on the SAC (structure, agency, culture) principle to model social transformation and identifying the “paradox of openness and closure” as a fundamental tension. This is followed by Chapter 2, “The Mundanisation of Algorithmic Public Services”, which employs institutional analysis to document how automation becomes naturalised in routine practices (e.g., the Swedish Trelleborg model). This mundanisation is actively enabled by staff undertaking crucial mediating roles such as digital care work and phatic labour. Chapter 3, “Experiences of Data Welfare”, grounds the study in quantitative data, reporting results from a comparative, representative survey (N = 4,501) across Estonia, Germany, and Sweden. This chapter analyses citizen perceptions of automated decision-making (ADM) across key variables – awareness, trust, risk perception, suitability, and values – to develop a detailed typology of adaptation, classifying groups like the “informed sceptics” and “advocates of efficiency”. Chapter 4, “(Re-) Configuring Data Welfare”, addresses resistance, casting data welfare as a political concern. It documents forms of civic engagement and scenario-based questioning across sensitive domains. Finally, the Conclusion, “Crisis in the Welfare Question: Rethinking the Welfare State in the Age of Automation”, defines the data welfare state as a fundamental social change aimed at enhancing well-being while considering the potential vulnerability of all parties involved. It offers a programmatic manifest outlining three prescriptive conditions for future governance: 1) without real welfare no data welfare, 2) without a focus on people no data welfare, and 3) without public data infrastructures no data welfare.
A mark of a great text is inviting a conversation, which this book’s analysis of the contemporary datafication of welfare state does. However, the text appears contaminated by a series of metonymic moves, performing an intellectual sleight of hand that repeatedly substitutes the object of its critique.
The first substitution occurs at the conceptual level. “Automation”, “datafication”, “digitalisation”, and “computation” are frequently conflated into a bundle of interconnected processes. This ontological flattening is a metonymic move, allowing the quantity of data to stand in for the qualitative differences of the medium. The book’s claim that the data welfare regime’s distinctiveness is closely tied to the process of datafication reveals this bias. This sleight of hand is most evident when the “paradox of openness and closure” is presented as a core friction. The paradox represents the tension between the governmental commitment to enhancing knowledge creation through open data sharing and the crucial imperative to safeguard that data against risks related to privacy, security, and detrimental economic interests. This move substitutes a deep critique of automation’s operational function with a more familiar ownership dilemma, bracketing deeper questions about whether this new medium is truly novel or merely a linear extension of an inherently mechanistic state bureaucracy found in archives, recordkeeping, and dossier technologies, as it seeks to commensurate welfare values.
A second metonymic substitution occurs in the book’s very framing. The text claims to analyse a “data welfare state”, but the evidence it mobilises may point to a different subject entirely. The experimental nature of pilot studies and automation attempts, often not fully implemented, suggests that the true object of study is a labified welfare state, where the experiment functions as a metonym for a regime that does not yet exist in full operation. Even when it does, the data welfare state remains under construction. This substitution is sustained by a form of welfare idealism that appears to chase a “real” welfare state, as seen in the programmatic conclusion and the use of Esping-Andersen’s typology – arguably logocentric – and risks romanticising a pre-data welfare state. This framing permits “frictions” to be treated as novel consequences of datafication rather than as pre-existing systemic failures of bureaucratic and idealised welfare models, failures that the new medium of datafication has revealed rather than engendered.
The final substitution is rhetorical. The book’s claim to global relevance is another metonymic leap. Asking the specific cultural contexts of Estonia, Germany, and Sweden to stand for the globe does not sit well with a critical perspective. This self-academic zeal culminates in a programmatic conclusion that substitutes hyperbolic language of “bold visions” for a grounded theory of change. The call to “retool” the system is overstated, feeling hollow against the book’s own evidence of constrained agency. By perpetually demonstrating that automation brings “automation success stories as well as disruptions” and has both “advantages and the dark side”, the text offers a “status report” on implementation, but in its final move, presents this report as the manifesto for resistance it promised to be in the introduction.
Torn between normativity and critical theory, the ambitious book delivers a detailed description of the datafication of the welfare conjuncture, drawing on the traditions of European Idealism.