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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]

This paper sheds new light on the drivers of civil service reform in US states. We first demonstrate theoretically that divided government is a key trigger of civil service reform, providing nuanced predictions for specific configurations of divided government. We then show empirical evidence for these predictions using data from the second half of the 20th century: states tended to introduce these reforms under divided government, and in particular when legislative chambers (rather than legislature and governor) were divided. [ABSTRACT FROM AUTHOR]

Bureaucratic discretion and executive delegation are central topics in political economy and political science. The previous empirical literature has measured discretion and delegation by manually coding large bodies of legislation. Drawing from computational linguistics, we provide an automated procedure for measuring discretion and delegation in legal texts to facilitate large-scale empirical analysis. The method uses information in syntactic parse trees to identify legally relevant provisions, as well as agents and delegated actions. We undertake two applications. First, we produce a measure of bureaucratic discretion by looking at the level of legislative detail for US states and find that this measure increases after reforms giving agencies more independence. This effect is consistent with an agency cost model, where a more independent bureaucracy requires more specific instructions (less discretion) to avoid bureaucratic drift. Second, we construct measures of delegation to governors in state legislation. Consistent with previous estimates using non-text metrics, we find that executive delegation increases under unified government. [ABSTRACT FROM AUTHOR]

We examine the causal effect of legislative activity on private benefits, which have been largely neglected by previous research in legislative studies. By relying on a natural experiment in New Zealand, where randomly selected Members of Parliament (MPs) are given the opportunity to propose legislation, we find evidence for a causal relation between proposing a (successful) bill and the private benefits MPs receive, in terms of gifts and payments for services. We conclude that the allocation of private benefits depends on legislative performance. [ABSTRACT FROM AUTHOR]

I propose a new majoritarian bargaining model in which more than one implicit proposal can be on the table at the same time. Institutional differences from system to system affect [...]

A response by Massimo Morelli to the letter to the editor regarding his article "Demand Commitment and Legislative Bargaining" published in the previous issue is presented.