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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.
List of contents
1. Introduction; 2. A theoretical framework; 3. A brief analytical history of social protection; 4. Private markets for life and health insurance; 5. Credit markets; 6. Labor market risks; 7. Conclusion.
About the author
Torben Iversen is Harold Hitchings Burbank Professor of Political Economy at Harvard University. His most recent book (co-authored with David Soskice) is Democracy and Prosperity: Reinventing Capitalism through a Turbulent Century (2019).Philipp Rehm is Associate Professor of Political Science at the Ohio State University. His research interests are located at the intersection of Political Economy and Political Behavior.
Summary
This book explores, theoretically and empirically, the consequences of 'Big Data' for the politics of social protection. It argues that the information revolution enables the formation of evermore fine-grained insurance pools, which in turn threatens the solidarity of the welfare state. The book also considers policies to limit marketization.
Foreword
This book explores, theoretically and empirically, the consequences of 'Big Data' for the politics of social protection.
