Electoral Research Abstracts - Segnalazioni bibliografiche

Electoral Research Abstracts - Segnalazioni bibliografiche

Electoral Research Abstracts - Segnalazioni bibliografiche

This article provides a detailed set of coding rules for disaggregating electoral volatility into two components: volatility caused by new party entry and old party exit, and volatility caused by vote switching across existing parties. After providing an overview of both types of volatility in post-communist countries, the causes of volatility are analysed using a larger dataset than those used in previous studies. The results are startling: most findings based on elections in post-communist countries included in previous studies disappear. Instead, entry and exit volatility is found to be largely a function of long-term economic recovery, and it becomes clear that very little is known about what causes ‘party switching’ volatility. As a robustness test of this latter result, the authors demonstrate that systematic explanations for party-switching volatility in Western Europe can indeed be found.

A wide range of studies find that democracies experience more terrorism than non-democracies. However, surprisingly little terrorism research takes into account the variation among democracies in terms of their electoral institutions. Furthermore, despite much discussion of the differences in terrorist groups’ goals in the literature, little quantitative work distinguishes among groups with different goals, and none explores whether and how the influence of electoral institutions varies among groups with different goals. The argument in this article posits that electoral institutions influence the emergence of within-system groups, which seek policy changes, but do not influence the emergence of anti-system groups, which seek a complete overthrow of the existing regime and government. The study finds that within-system groups are significantly less likely to emerge in democracies that have a proportional representation system and higher levels of district magnitude, while neither of these factors affects the emergence of anti-system groups.

Abstract Under evaluative voting, the voter freely grades each candidate on a numerical scale, with the winning candidate being determined by the sum of the grades they receive. This paper compares evaluative voting with the two-round system, reporting on an experiment, conducted during the 2012 French presidential election, which attracted 2,340 participants. Here we show that the two-round system favors “exclusive” candidates, that is candidates who elicit strong feelings, while evaluative rules favor “inclusive” candidates, that is candidates who attract the support of a large span of the electorate. These differences are explained by two complementary reasons: the opportunity for the voter to support several candidates under evaluative voting rules, and the specific pattern of strategic voting under the two-round voting rule.

Abstract This article examines the electoral impact of spillover effects in local campaigns in Britain. For the first time, this is applied to the long as well as the short campaign. Using spatial econometric modelling on constituency data from the 2010 general election, there is clear empirical evidence that, in both campaign periods, the more a party spends on campaigning in constituencies adjacent to constituency i, the more votes it gets in constituency i. Of the three major political parties, the Liberal Democrats obtained the greatest electoral payoff. Future empirical analyses of voting at the constituency scale must, therefore, explicitly take account of spatial heterogeneity in order to correctly gauge the magnitude and significance of factors that affect parties' parliamentary performance.

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.