Electoral Research Abstracts - Segnalazioni bibliografiche

Electoral Research Abstracts - Segnalazioni bibliografiche

Electoral Research Abstracts - Segnalazioni bibliografiche

Two new studies challenge the prevailing consensus that proportional representation (PR) systems produce greater ideological congruence between governments and their citizens than majoritarian ones. This has led to what has become known as the ‘ideological congruence controversy’. G. Bingham Powell claims to resolve this controversy in favour of PR systems. Specifically, he argues that the results from the two new studies are based on an anomalous decade and that PR systems generally do produce greater government congruence. In addition, he also asserts that PR systems exhibit less variability in government congruence. In this article, the empirical evidence for these two claims is re-evaluated using exactly the same data as employed by Powell. The analysis indicates that although PR systems produce better and more consistent representation in the legislature, they do not hold an advantage when it comes to representation at the governmental level.

Although many studies of clientelism focus exclusively on vote buying, political machines often employ diverse portfolios of strategies. We provide a theoretical framework and formal model to explain how and why machines mix four clientelist strategies during elections: vote buying, turnout buying, abstention buying, and double persuasion. Machines tailor their portfolios to the political preferences and voting costs of the electorate. They also adapt their mix to at least five contextual factors: compulsory voting, ballot secrecy, political salience, machine support, and political polarization. Our analysis yields numerous insights, such as why the introduction of compulsory voting may increase vote buying, and why enhanced ballot secrecy may increase turnout buying and abstention buying. Evidence from various countries is consistent with our predictions and suggests the need for empirical studies to pay closer attention to the ways in which machines combine clientelist strategies.

This article analyses the impact of party systems on human well-being and argues that multiparty systems are associated with better welfare outcomes for two primary reasons: first, multiparty systems provide representation to multiple issue-dimensions in society, thereby indicating a more inclusive system, which ensures that diverse societal interests are taken into account during formulation of welfare policies. Second, multiparty systems also indicate a competitive party system, which provides incentives for parties to perform effectively while in office and propels parties to appeal to multiple segments of society by providing broader welfare services. The impact of party systems on human well-being is tested on a global sample of 68 democratic countries from 1975–2000. The findings show support for the hypothesized relationship between party systems and human well-being.

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.

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.