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Chapter 7 is the conclusion. We provide a short and selective synopsis of our argument and briefly review, and elaborate on, the empirical illustrations from previous chapters. Theoretically, we suggest that cross-class solidarity, which has sometimes been linked to dense networks of civic associations, is likely to originate in low information and encompassing social insurance programs. The chapter also discusses promising avenues for future research.
Chapter 1 introduces the topic and motivates our study. It explains the general logic of our argument and introduces the methods and evidence we rely on. The chapter gives an overview of the book’s organization and main insights and hence serves as a preview.
Chapter 5 extends our framework to credit markets, which are not usually analyzed by scholars of the welfare state, yet fulfill many of the same income-smoothing functions. Much like in private insurance markets, more and better information allows for better risk classification, which enables lenders to tie interest rates more directly to default risk. This results in inequality because individuals with a higher risk of default are almost always lower income, and more information either raise their interest rates or cut them off from credit markets altogether. The welfare state matters, too, because generous social protection lowers default risk – something lenders take into account. Based on a data set containing the 39 million single-family loans that Freddie Mac purchased or guaranteed in the past two decades, we show that the interest rate spread markedly increased over time and we test, using a regression discontinuity design, whether information could have plausibly caused this increase. We also test whether social protection influences access to credit for different income groups and find that it does.
Chapter 4 chronicles the status quo and innovations in underwriting practices in the life insurance domain and shows how private markets deal with information problems and how they eagerly capitalize on novel ways – such as tracking devices – to mitigate asymmetric information. Using quantitative analysis, the chapter also shows that private life insurance markets are more developed in country-years with better information, but that partisanship mediates this relationship. Life insurance is an interesting domain to study because it has many parallels to health insurance, yet the former is mostly private, while the latter is mostly public. The chapter discusses the emergence of a supplementary private health insurance market, but it also documents the continued popularity of public solutions in areas where the time-inconsistency problem cannot be overcome by private actors.
Chapter 6 focuses on labor market developments and preferences for unemployment policies. Using data from Germany, we show that increasingly, labor market risks can be predicted with a small set of observables (education, occupation, and location), while the relevance of private information has declined over time. Polarization over unemployment policies has risen at the same time. We also explore – theoretically and empirically – how people translate their labor market situation into political preferences and show the importance of social networks in the process. Lastly, the chapter describes a case study of a fascinating reform in the Swedish unemployment insurance system, which shows what happens when unemployment insurance contributions and benefits are tied to unemployment risk, as would happen in a private market. Thus, the Swedish case provides a window into the (possible) future of segmented social policy programs that we predict will become more commonplace.
Chapter 3 offers a historical account of the emergence of the welfare state. In the absence of private or public insurance and faced with new, poorly understood, and existential risks, workers set up mutual aid societies (MASs) to cope with industrialization and urbanization. At their peak, MASs covered up to half of the (male) population but only protected against a small fraction of the risks of unemployment, disability, disease, old age, and death. While MASs tried to mitigate adverse selection and moral hazard through monitoring, they faced a double bind: they attracted bad risks while losing good risks to commercial insurance. Nor could they cope with correlated risks or support PAYG arrangements (the time-inconsistency problem). Ultimately, they were replaced by compulsory public social policy programs that mandated all citizens to join a common risk pool. The state had the power of compulsion, the ability to overcome the time-inconsistency problem, and majority support for social insurance in the absence of effective private alternatives. The result was a massively redistributive welfare state.
A core principle of the welfare state is that everyone pays taxes or contributions in exchange for universal insurance against social risks such as sickness, old age, unemployment, and plain bad luck. This solidarity principle assumes that everyone is a member of a single national insurance pool, and it is commonly explained by poor and asymmetric information, which undermines markets and creates the perception that we are all in the same boat. Living in the midst of an information revolution, this is no longer a satisfactory approach. This book explores, theoretically and empirically, the consequences of 'big data' for the politics of social protection. Torben Iversen and Philipp Rehm argue that more and better data polarize preferences over public insurance and often segment social insurance into smaller, more homogenous, and less redistributive pools, using cases studies of health and unemployment insurance and statistical analyses of life insurance, credit markets, and public opinion.