A large literature examines the composition of cabinets in parliamentary systems, but very little attention has been paid to the size of those cabinets. Yet not only is the size of the cabinet related to the division of portfolios that may take place, cabinet size is also related to policy outcomes. In this article, a theory of party size is considered which examines how coalition bargaining considerations, intra-party politics and efficiency concerns affect the size of cabinets. Hypotheses derived from the theory are examined using an extensive cross-national dataset on coalition governments which allows us to track changes in cabinet size and membership both across and within cabinets.

CHIARAMONTE, A. C. A. (2014). Le elezioni politiche del 2013: uno tsunami senza vincitori. In C. Fusaro & A. Kreppel (Eds.), Politica in Italia. I fatti dell’anno e le interpretazioni - Edizione 2014 (pp. 51–70). BOLOGNA: Il Mulino.

Segnalazione bibliografica. Autori: James Adams, Catherine E. De Vries, Debra Leiter British Journal of Political Science January 2012 42 : pp 81-105 Abstract During the 1980s and the 1990s, the elites of the two largest Dutch parties converged dramatically in debates on income redistribution, nuclear power and the overall Left–Right dimension, paving the way for the Dutch party system's polarization on immigration and cultural issues. Did the Dutch mass public depolarize along with party elites, and, if so, was this mass-level depolarization confined to affluent, educated, politically engaged citizens? Analysis of Dutch Parliamentary Election Study respondents’ policy beliefs and partisan loyalties in 1986–98...

Multidimensional scaling (or MDS) is a methodology for producing geometric models of proximities data. Multidimensional scaling has a long history in political science research. However, most applications of MDS are purely descriptive, with no attempt to assess stability or sampling variability in the scaling solution. In this article, we develop a bootstrap resampling strategy for constructing confidence regions in multidimensional scaling solutions. The methodology is illustrated by performing an inferential multidimensional scaling analysis on data from the 2004 American National Election Study (ANES). The bootstrap procedure is very simple, and it is adaptable to a wide variety of MDS models. Our approach enhances the utility of multidimensional scaling as a tool for testing substantive theories while still retaining the flexibility in assumptions, model details, and estimation procedures that make MDS so useful for exploring structure in data.