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We study the consequences of populism for economic performance and the quality of bureaucracy. When voters lose trust in representative democracy, populists strategically supply unconditional policy commitments that are easier to monitor for voters. When in power, populists try to implement their policy commitments regardless of financial constraints and expert assessment of the feasibility of their policies, worsening government economic performance and dismantling resistance from expert bureaucrats. With novel data on more than 8,000 Italian municipalities covering more than 20 years, we estimate the effect of electing a populist mayor with a close‐election regression discontinuity design. We find that the election of a populist mayor leads to smaller repayments of debts, a larger share of procurement contracts with cost overruns, higher turnover among top bureaucrats—driven by forced rather than voluntary departures—and a sharp decrease in the percentage of postgraduate bureaucrats. [ABSTRACT FROM AUTHOR]

This paper empirically shows that the imbalance between an ethnic group's political and military power is crucial to understanding the likelihood that such a group engages in a conflict. We develop a novel measure of a group's military power by combining machine-learning techniques with rich data on ethnic group characteristics and outcomes of civil conflicts in Africa and the Middle East. We couple this measure with available indicators of an ethnic group's political power as well as with a novel proxy based on information about the ethnicity of cabinet members. We find that groups characterized by a higher mismatch between military and political power are between 30% and 50% more likely to engage in a conflict against their government depending on the specification used. We also find that the effects of power mismatch are non-linear, which is in agreement with the predictions of a simple model that accounts for the cost of conflict. Moreover, our results suggest that high-mismatched groups are typically involved in larger and centrist conflicts. The policy implication is that power-sharing recommendations and institutional design policies for peace should consider primarily the reduction of power mismatches between relevant groups, rather than focusing exclusively on equalizing political power in isolation. [ABSTRACT FROM AUTHOR]

We introduce and assess the use of supervised learning in cross-domain topic classification. In this approach, an algorithm learns to classify topics in a labeled source corpus and then extrapolates topics in an unlabeled target corpus from another domain. The ability to use existing training data makes this method significantly more efficient than within-domain supervised learning. It also has three advantages over unsupervised topic models: the method can be more specifically targeted to a research question and the resulting topics are easier to validate and interpret. We demonstrate the method using the case of labeled party platforms (source corpus) and unlabeled parliamentary speeches (target corpus). In addition to the standard within-domain error metrics, we further validate the cross-domain performance by labeling a subset of target-corpus documents. We find that the classifier accurately assigns topics in the parliamentary speeches, although accuracy varies substantially by topic. We also propose tools diagnosing cross-domain classification. To illustrate the usefulness of the method, we present two case studies on how electoral rules and the gender of parliamentarians influence the choice of speech topics. [ABSTRACT FROM AUTHOR]