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In this book, Natalia Forrat describes two models of authoritarianism: the first in which people see the state as their team leader and the other where they trust informal (non-state) leaders and see the state as a source of perks or punishment. Forrat compares the structures of political machines in four Russian regions, finding that the two maintaining unity-based authoritarianism demonstrated a stable performance across multiple elections, while the other two delivered less stable results. Carefully crafted and sophisticated, Forrat's theory of authoritarian power sheds new light on state-society relations in Russia and helps explain the divergent patterns of regime maintenance strategies in authoritarian countries throughout the world.
List of contents
- Acknowledgments
- Introduction
- Chapter 1: Authoritarian Power in Statist and Anti-Statist Societies
- Chapter 2: A Tale of Four Regions
- Chapter 3: Team State vs Outsider State: Public Organizations in Statist and Anti-Statist Societies
- Chapter 4: The Political and the Civic: Political Machines in Anti-Statist and Statist Societies
- Chapter 5: The Riddle of Russian Authoritarianism
- Conclusion
- References
- Appendix 1: Quantitative Analysis
- Appendix 2: Fieldwork Data Collection and Analysis
- Appendix 3: Data on Social Policy in Kemerovo Region
- Appendix 4: Newspaper Publications Related to Residential Committees and Community Centers in Kemerovo and to Their Role in Politics
- Appendix 5: Newspaper Publications Related to Veterans' (Pensioners') Organizations and Their Role in Politics
- Appendix 6: List of Interviews
- Index
About the author
Natalia Forrat is a Lecturer in the Center for Russian, East European, and Eurasian studies at the University of Michigan's International Institute. She is a scholar of authoritarianism, state-society relations, mobilization, and civil society. She obtained her PhD from Northwestern University and held academic fellowships and appointments at Stanford, the University of Notre Dame, and the University of Michigan.
