Datafication

'Datafication and the Welfare State' by Lina Dencik and Anne Kaun in (2020) 1(1) Global Perspectives 12912 comments

 Both vehemently protected and attacked in equal measure, the welfare state as an idea and as a policy agenda remains as relevant as ever. It refers not only to a program of social welfare and the provision of social services, but also to a model of the state and the economy. According to Offe (1984), the welfare state in advanced capitalist economies is a formula that consists of the explicit obligation of the state apparatus to provide assistance and support to those citizens who suffer from specific needs and risks characteristic of the market society, and it is based on a recognition of the formal role of labor unions in both collective bargaining and the formation of public policy. Although actively dismantled in recent decades as globalization and neoliberalism have taken hold of much of the modern world-system, its future continues to be fought over. It serves as a model for society that is seen to privilege a commitment to decommodification, universal access, and social solidarity as a way to overcome the most prominent contradictions of capitalism. A product of the twinned global crises of the Great Depression and the Second World War, the modern welfare state therefore encapsulates a moment of political and economic settlement, a mechanism of stabilization that arguably could emerge only out of such crises. 

From the outset, technology, particularly information and communication technologies, has played a key role in the development of the welfare state (Hobsbawm 1994). It has been instrumental in the creation of bureaucracies and forms of population management that have long been central to the way the welfare state is administered. Gunnar and Alva Myrdal, for example, famously argued for social engineering based on statistics and the use of technology to solve the population crisis of Sweden in the 1930s and 1940s. Their suggestions are now considered central to the ideas and cornerstones of the Nordic welfare state model (Kananen 2014). The creation of databases and the monitoring of citizens was from early on a fundamental part of assessing population needs and determining allocation of resources, a type of surveillance that has been the subject of much critique for creating categories of “deserving” and “undeserving” citizens (Offe 1984). At the same time, the advent of digitization has also been seen as a challenge to the welfare state and its ability to deliver on its promises, disrupting labor relations, undermining social security, and changing the parameters of state governance. With growing trends such as mass data collection, automation, and artificial intelligence, these tensions have only intensified, putting the welfare state into further question (Petropoulos et al. 2019). 

At the time of writing this introduction, the question of not only the future of the welfare state but also how technology intersects with it has gained new pertinence as we find ourselves in the midst of another global crisis. The global pandemic brought about by the rapid spread of COVID-19 has put social welfare questions and the role of the state at the top of the agenda once more. The crisis is seen to have prompted a return of the Leviathan state, a social contract with an absolute sovereign in which the state provides the ultimate insurance against an intolerable human condition (Mishra 2020), and it has provided renewed impetus for demands for universal health care, stable employment, and a basic income (Standing 2020). Certainly, initial responses to the pandemic and ongoing lockdowns across the world have aligned around state interventions in the economy not seen in a generation, with governments designing various packages of increased public spending, which has (re)invited a rhetoric of the importance of economic planning and strong social security. 

Technology is proving to be at the heart of this crisis and how the welfare state might emerge from it. As “social distancing” speeds up the transition to social and economic life online, often presented as a seamless process, Big Tech has quickly (in partnership with governments) established itself as our (new) infrastructure for everything from health to education to work (Bharthur 2020). At the same time, Big Tech is also presented as a solution to the crisis through extensive data collection, contact tracing, and certification. At the time of writing, the big data analytics company Palantir is in talks with a number of governments, including those of the United Kingdom, Germany, and France, to provide data infrastructure for health services during the pandemic, and Google and Apple have announced a joint venture to develop infrastructure for contact-tracing apps that determine if an individual has been in close proximity to someone COVID-19 positive (Fouquet and Torsoli 2020; Kelion 2020). Furthermore, the EU Commission has requested metadata from large mobile phone carriers, including German Telekom and Orange, to calculate mobility patterns and track the spread of the coronavirus across Europe (Scott, Cerulus, and Kayali 2020). It is claimed that only anonymized and aggregated data will be collected and that data will not be used to control or sanction lockdown measures but to use this data to be able to predict where medical supplies will be needed most. 

These initiatives introduce new questions about the nature of surveillance in governance, the place of data protection frameworks such as the EU’s General Data Protection Regulation (GDPR), and the role of private companies in the delivery of public services that all form an important part of the contemporary debate on technology and the welfare state. As Baker (2020, p. para. 13) puts it, “for governments looking to monitor their citizens even more closely, and companies looking to get rich by doing the same, it would be hard to imagine a more perfect crisis than a global pandemic.” Moreover, the turn to data and the reliance on data-driven systems in governance introduces key epistemological and ontological assumptions about what constitutes relevant social knowledge for decision-making and how individuals and populations should be understood and managed. Data, on this premise, needs to be collected in as large a quantity as possible (total information capture), processed through automation, with the view to calculate all possible outcomes—a knowing of all risks—so as to preempt them before they occur (Andrejevic 2019). While a global crisis like the one we are currently in might present itself as a state of exception in these terms, the trend of datafication across social life is one that was already firmly in place. 

What does it mean to organize the welfare state around this trend of datafication? With this special issue, we take stock of this question and explore the multiple ways in which the practices, values, and logics that underpin the advancement of datafication intersect with the practices, values, and logics that form the basis of the public services that we commonly associate with the modern welfare state. The idea for this special issue emerged out of discussions in the Nordic research network Datafication, Data Inequalities and Data Justice, of which we are both members. It is perhaps no surprise that it is a Nordic context that spurred on the engagement with the welfare state as this has long been a central feature of Nordic societies, both as an idea and in practice. However, the question of how datafication impacts public services, particularly in relation to social welfare, is a global one and one that cannot be universalized, whether in terms of data-driven developments or their implications (Milan and Treré 2019). At the same time, the history of the modern welfare state is one that has most frequently been associated with Europe in what Judt (2007) has described as the “social-democratic moment” of the postwar period. This history is reflected in our contributions that predominantly engage with European and Western settings, while doing so in the context of globalization. Many of the issues discussed in our contributions are being raised elsewhere as technology infrastructures globalize and standardize practices (cf. Booth 2019).