We investigate the gender gap in issue attention among members of parliament (MPs) by applying automated text analytic techniques to a novel data set on Italian parliamentary speeches over a remarkably long period (1948–2020). We detect a gendered specialization across issues that tends to disappear as women’s shares in parliamentary groups increase. We then investigate whether women’s access to previously male-owned issues brings with it a different agenda, operationalized as a different vocabulary. We detect a U-shaped pattern: language gender specificity is high when female MPs are tokens in parliamentary groups with a large preponderance of men; it decreases when their shares start increasing and grows again when they constitute a considerable minority. We argue that this pattern is consistent with the theory of tokenism, and it is produced by the interlinkage of commitment to shared norms and the distribution of “activation thresholds” among female MPs.

A primary challenge for researchers that make use of observational data is selection bias (i.e. the units of analysis exhibit systematic differences and dis-homogeneities due to non-random selection into treatment). This article encourages researchers in acknowledging this problem and discusses how and – more importantly – under which assumptions they may resort to statistical matching techniques to reduce the imbalance in the empirical distribution of pre-treatment observable variables between the treatment and control groups. With the aim of providing a practical guidance, the article engages with the evaluation of the effectiveness of peacekeeping missions in the case of the Bosnian civil war, a research topic in which selection bias is a structural feature of the observational data researchers have to use, and shows how to apply the Coarsened Exact Matching (CEM), the most widely used matching algorithm in the fields of Political Science and International Relations.

The election of President Mattarella is a turning point in Matteo Renzi’s attempt to reform the constitution. This choice determined the loss of Forza Italia’s support to the constitutional reform, thus leaving the Renzi cabinet and the Democratic Party alone on the Yes-side. Our goal is to assess the degree of Renzi’s misjudgment by comparing two theoretical perspectives on voting behavior in direct-democratic settings through a nested design. Our results highlight that vote choices can be explained by both the systematic and the heuristic modes of information processing. Respondents’ agreement with the content of the reform (systematic mode) and a positive evaluation of the Renzi cabinet (heuristic mode) are key predictors of Yes vote. Instead, the negative assessment of the economic situation is a crucial driver of No vote. Lastly, we build a bridge between these two perspectives by showing that partisan attachments conditioned the effect of respondents’ content evaluation on voting behavior.

A lively debate among students of parliamentary democracy concerns how coalition governments build their policy proposals. Some scholars maintain that government declarations mirror the position of the median party in parliament; others argue that these proposals better agree with the weighted mean of the coalition parties’ electoral promises. This article sheds light on this puzzle by investigating the role played by several political actors in shaping government declarations on two dimensions: the ideological left–right scale and a genuinely policy-based welfare scale. The results reveal that the agenda setters on the two dimensions do not coincide. On the left–right scale, the prime minister’s party plays a leading role. On the welfare scale, government declarations are affected by the party of the median legislator in parliament and by the parties of the labour and social affairs ministers. Furthermore, government declarations on the welfare dimension tend to drift rightwards with adverse economic conditions.