📖 ABSTRACT/OVERVIEW
This study provides an original theoretical and empirical investigation of subnational authoritarianism within Nigeria's formally democratic federal system, examining how competitive elections coexist with authoritarian governance practices at the state level in a phenomenon constitutive of democratic backsliding. Existing theories of democratic backsliding focus predominantly on national-level executive aggrandisement, insufficiently theorising the subnational dynamics that sustain authoritarian enclaves within multiparty federal systems. Drawing on subnational authoritarianism theory, democratic backsliding literature, and recent comparative politics scholarship from 2019 to 2024, the study employs a mixed-methods comparative research design. A subnational authoritarianism index is constructed for all 36 Nigerian states using indicators of executive discretion, legislative capture, judicial independence, civil society space, and media freedom, drawing on data from 2011 to 2023. Quantitative cluster analysis identifies distinct state-level democratic regime typologies. Comparative qualitative case studies of four strategically selected states representing authoritarian and democratic poles are conducted through 55 elite interviews and process tracing. The study's original theoretical contribution is the Competitive Authoritarian Subnational State Model, which theorises the conditions under which electoral competition at state level fails to produce democratic governance accountability, centring the mechanisms of incumbent resource monopolisation, opposition fragmentation, and security apparatus deployment. This model advances existing competitive authoritarianism theory into federal subnational contexts, with global applicability. Findings carry implications for democratic reform theory and Nigeria's constitutional governance. Keywords: subnational authoritarianism, democratic backsliding, competitive elections, Nigeria, state governance
Need Complete Chapters of the Above Topic?
Get high-quality, Zero-AI research materials with current citations.
Request via WhatsApp 💬