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In Chapter 4, the book’s analytical focus moves to the actuarial profession and examines how changes in the structure of life insurance arrangements led actuaries to reconsider the nature of financial uncertainty. The chapter starts by identifying a key moment in debates about financial risk: the presentation by Sydney Benjamin of a new way of modelling the financial risk in unit-linked insurance contracts, using stochastic simulation techniques. While traditional actuarial modelling was mostly ‘deterministic’, representing the future merely according to point-based estimates, stochastic simulation modelling ascribed a likelihood to different scenarios and thereby allowed actuaries to account for the spread of possible outcomes. After describing Benjamin’s novel approach and identifying the sources of its contentiousness, the chapter reviews the ensuing actuarial debates about whether and how financial uncertainty should be modelled and then places this debate in the broader context of the actuarial field, showing how actuaries struggled to operationalize financial risk, but also grappled with the question of the appropriate place for mathematics and computing in actuarial thought and practice. The chapter concludes by considering how the introduction of this new mode of modelling financial risk – stochastic modelling – influenced life insurance and actuarial science more broadly.

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The emergence of COVID-19 in early 2020 led to a flood of numbers in public discourse, from the ever-updating count of daily cases of coronavirus to the fabled R-value. But this vast sea of statistics, data, league tables, indexes, metrics and indicators often led to more confusion than clarity. Therefore, we need to find better ways to think through these number-rich contexts - especially as we enter into the ‘post-pandemic’ era. To do so, this book puts forward the Life of a Number methodological approach. Seven particularly important numbers from the pandemic were analysed through a five-stage process that paid attention to demand, production, communication, public meaning and backgrounding. The narrative that emerges holds significance for COVID-19 but, more broadly, it forms the theoretical framework of data bounds. This concept helps scholars think through how the quantitative becomes a meaningful way to engage with, think about, discuss and change certain phenomena. Each chapter provides a distinct perspective on data bounds, with the conclusion putting forward a four-point toolkit to help scholars put data bounds into practice. It calls for academics to pay attention to media and communication, interrogate and appreciate quantitative realism, examine how data bounds can maintain or challenge power, and determine why some data bounds dominate. The methodological, empirical and theoretical contribution of this book is one that bridges the gap between Critical Data Studies and Media and Communication, providing a starting point for scholars looking to think through the ever-expanding net of data in the ‘post-pandemic’ world.

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Chapter 5 builds on the two preceding chapters and examines how many of the questions about financial risk and actuarial discretion continued to surface throughout the 1980s and ’90s. The chapter first describes how the continuous decline of interest rates put pressure on the capital base of insurance firms. One company in particular ended up with serious difficulties: the Equitable Life Assurance Society, a mutual company whose practices exemplified in extremis the problems in British life insurance more generally. I suggest in this chapter that the dramatic collapse of Equitable Life in 2000 gave rise to the perception that actuarial expertise had failed to keep up with the realities of financial capitalism. The chapter ends by describing how this crisis of actuarial expertise in turn provided a window of opportunity for proponents of modern finance theory to push for reforms in the calculative machinery of life insurance, drawing on the core models of modern finance theory and the derivatives departments of investment banks. The downfall of Equitable Life prepared the way for life insurers’ evaluation machinery to be overhauled more generally

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Queering science communication should give LGBTIQA+ people power and the chance to play. This conclusion summarizes the various contributions to this book and draws out some unresolved issues that deserve attention in future research and practical science communication endeavours.

Queering science communication must entail something more than recognizing LGBTIQA+ people are present in our discipline. The field must review underlying structures and values in our culture and together work out how to improve. Science communicators must expand the way we understand and discuss the diversity of humanity and then embrace these ways of being within our research and applied practices.

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The final chapter brings together the lessons of the six empirical chapters to put forward a four-part toolkit to help scholars put data bounds into practice. First, scholars must pay attention to media and communication by examining media ecosystems. Second, they must both interrogate and appreciate the power of quantitative realism. Third, academics need to examine how data bounds can maintain or challenge power. Finally, and the hardest task of all, those interested in taking this approach must attempt to determine why some data bounds dominate over others. The chapter concludes by asking ‘Is there any hope?’, with the answer pointing to the role of social and political movements rather than politicians, journalists and experts.

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The shape and scope of data bounds are not just determined by quantitative realism, policy, identity construction or the data itself. Data bounds emerge into – and so are shaped by – historical norms. This chapter examines how this occurred through the projection of ‘90,000 cases per day’ by 19 July 2021 by Christina Pagel. This projection failed to ‘take off’ like other figures during the pandemic. While some would point to its simplicity (and the way it ultimately proved to be wide of the mark), this chapter argues that it did not circulate because of the way inequality had been normalized in the UK. This historical context meant that Trade-Off did not factor inequality into the equation of risk – the effect on the most deprived in society was considered acceptable collateral for the freedoms of those less deprived. This was the reason that Pagel’s projection was ignored: 90,000 cases offered very little threat to certain groups in society and a considerable threat to others. Such a case study emphasizes the need to understand and interrogate the contingencies into which data bounds emerge.

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When a book focuses on the quantitative, it can often emphasize the way people use numbers to understand, rationalize or compute. But the quantitative is emotive too. This chapter focuses on the now iconic data visualization from the pandemic: the peak and troughed graph of daily cases, hospitalizations and deaths. Specifically, it examines the way the graph was presented by a Sky News journalist – and how this performance flouted the convention of presenting these types of graphs about death. Using genre studies analysis, it argues that the data visualization was imbued with the emotional experience of those living through Trade-Off. One that can be defined by both uncertainty – the rising and falling of cases – but also of large-scale tragedy – the thousands of people who died per day. The journalists’ animated performance lacked the sobriety that the presentation of such a culturally important graph needed. In doing so, it pointed to the emotion with which we often imbue data visualizations.

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The opening empirical chapter outlines Trade-Off: the dominant data bound of the pandemic that positioned economic indicators and health metrics in direct opposition to each other. As GDP growth, unemployment and trade improved, cases, hospitalizations and deaths declined – and vice versa. The chapter argues that Trade-Off dominated because of the government’s response to coronavirus. In adopting a mitigation strategy, they engaged in a constant trade-off: they would introduce national lockdowns when health metrics worsened – even if this affected the economy badly – and then open up society when health metrics were suitably low – allowing the economy to grow again. This stood in direct opposition to other countries, mainly in East Asia and Australasia, that took an elimination or containment approach. It was here that an alternative data bound emerged: Protect Both. In these countries, the prevailing logic was that over the long term you either protected both the economy and health or saw them both decline. The message from this chapter is clear: policy structured the data bound that emerged.

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Chapter 9 examines the emergence of the pension de-risking market, which in many ways is exemplary of the changes in the insurance industry more generally. The chapter provides a brief historical overview of what became known as the ‘pensions crisis’ that led to the closure of many defined benefit pension schemes, leaving the legacy defined benefit pension liabilities to be picked up by insurance companies. The chapter then analyses the structure of this newly invigorated market field before analysing the ties with this field and the adjacent market field of asset management. The chapter concludes by arguing that this development illustrates the changing logics underpinning private pensions: from a relational model based on trust to one that revolves primarily around the financial logic of risk-based capital

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Insurance in the Age of Finance

Insurance is an important – if still poorly understood – mechanism for dealing with a broad variety of risks associated with modern life.

This book conducts an in-depth examination of one of the largest and longest-established private insurance industries in Europe: British life insurance. In doing so, it draws on over 40 oral history interviews to trace how the sector is changed since the 1970s, a period characterised by rampant financialisation and neoliberalisation.

Combining insights from science and technology studies and economic sociology, this is an unprecedented study of the evolution of insurance practices and an invaluable contribution to our understanding of financial capitalism.

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