Autocracies with Adjectives: The Need for Better Classifications of Autocratic Regimes

This paper will investigate how regime datasets conceptualize and measure authoritarian regimes. The surge of authoritarianism renewed scholarly interest in regime typologies, as many new and mixed forms of authoritarianism push the limits of existing nomenclatures. Recent academic debates also problematize that core institutions including parliaments, party systems, and judiciaries were overlooked in existing regime typologies. Datasets on authoritarian governance already exist, but the classification of cases is wildly inconsistent across and within datasets. What explains these inconsistencies, despite the use of similar or even the same regime categories? And how can these classifications be augmented to better capture contemporary hybrid forms of authoritarian rule? It will be argued that while some differences come down to the coding of specific cases, the underlying conceptualization of autocratic regimes and the logical organization of regime categories systemically undermine the measurement validity of regime datasets. By analyzing how sub-types of autocratic regimes were operationalized in the codebooks of datasets like Geddes, Wright, and Frantz’s 2014 ‘Autocratic Breakdown and Regime Transitions’ dataset, Magaloni, Chu, and Chin’s ‘Autocracies in the World’ data from 2013, and Bell’s 2016 ‘Rulers, Elections, and Irregular Governance’ dataset, this paper can dissect the conceptual origin of coding inconsistencies. By troubleshooting the design of regime indicators for autocracies, this analysis can pave the way for a more accurate classification of contemporary autocracies and more complex regime types.