Abstract: We study discriminatory policies of the tsarist government against Jewish entrepreneurs. No general incorporation law existed in Imperial Russia. Every single corporate charter had to be approved by the Ministry of Finance and signed by the tsar. Since 1890, the tsarist government started to include discriminatory clauses in some corporate charters that banned Jewish entrepreneurs from share ownership and/or purchasing property. What are the causes and consequences of discrimination in the short and long run? Using newly digitized data on the universe of Russian manufacturing factories in 1890, we find higher incidence of discriminatory clauses in corporate charters issued between 1891 and 1913 in more capital-intensive industries with fewer Jewish incumbents. To evaluate economic implications of discriminatory policies, we assemble a novel dataset from corporations’ balance sheets in 1885–1894. We find that corporations with discriminatory clauses in their charters were more likely to issue debt than equity. What was the effect of discriminatory barriers on potential market entrants? To address this question, we define counterfactual entrepreneurs as the universe of factory owners in 1890. Combining ethnicity information from one million individual WWI casualty records and the merchant guilds’ records to predict entrepreneurs’ ethnicity, we find evidence that barriers for Jewish entrepreneurs led to the incorporation of relatively less productive non-Jewish entrepreneurs. Finally, we show that discriminatory policies had broader economic implications including for the corporations without restrictions in their charters: corporations with Jewish founders that publicly traded on the St. Petersburg Stock Exchange in 1865–1913 underperformed post-1890.

Abstract: Conflict over power distribution within a leadership group is a central feature of authoritarian politics. In contemporary autocracies, power is increasingly concentrated in the hands of the leader. Studies have explored how personalization shapes the use of repression. We know less about how the personalization of power shapes strategies of information control that generate voluntary compliance within society. To gain leverage on this question, we combine data on gradations of personalism with novel data on state control over education and the media across 212 authoritarian regimes from 1950 to 2010. We show that in the process of concentrating power leaders not only increase state control over education and the media but also crucially shape their content to indoctrinate. Findings answer several calls to move beyond the study of repression for understanding the politics of non-democracies and have implications for research on personalism and authoritarian politics.

Abstract: How do autocrats maintain power? Understanding what strategies authoritarian leaders use to generate mass compliance and elite loyalty is a central question in comparative politics. However, existing scholarship typically examines different political control strategies in isolation, and few works consider the broad range of strategies that autocrats can employ or assess how these strategies jointly explain regime survival. Drawing on rich data from the Varieties of Democracy and Varieties of Indoctrination datasets, we present one of the first attempts to comprehensively map the use of six strategies of repression, co-optation, and indoctrination across 229 regimes from 1946 to 2010. Furthermore, we use model-based clustering and Bayesian model stacking to explore patterns in how autocrats combine different strategies and identify which set of strategies best predict autocratic regime breakdown. The paper’s rich data, empirical approach, and findings offer novel evidence and insight into debates about authoritarian ruling strategies and longevity.

Abstract: On what basis can we claim a scholarly community understands a phenomenon? Social scientists generally propagate many rival explanations for what they study. How best to discriminate between or aggregate them introduces myriad questions because we lack standard tools that synthesize discrete explanations. In this paper, we assemble and test a set of approaches to the selection and aggregation of predictive statistical models representing different social scientific explanations for a single outcome: original crowd-sourced predictive models of COVID-19 mortality. We evaluate social scientists' ability to select or discriminate between these models using an expert forecast elicitation exercise. We provide a framework for aggregating discrete explanations, including using an ensemble algorithm (model stacking). Although the best models outperform benchmark machine learning models, experts are generally unable to identify models' predictive accuracy. Findings support the use of algorithmic approaches for the aggregation of social scientific explanations over human judgement or ad-hoc processes.

[“COVID-19 Model Challenges” Shiny app] [Pre-registration]