Ricerca

Ricerca

Ricerca

Segnalazione bibliografica. Autori: Thomas Brauninger, Martin Brunner, Thomas Daubler European Journal of Political Research, December 2011 Abstract It is well known that different types of electoral systems create different incentives to cultivate a personal vote and that there may be variation in intra-party competition within an electoral system. This article demonstrates that flexible list systems – where voters can choose to cast a vote for the list as ordered by the party or express preference votes for candidates – create another type of variation in personal vote-seeking incentives within the system. This variation arises because the flexibility of party-in-a-district lists results from voters'...

Do parties with different ideological origins adjust their policies in response to the binding commitments that derive from the European integration process? This paper examines whether party platforms have adapted to the ideological content of EU treaty provisions – based on ‘neoliberalism’ and ‘regulated capitalism’ – across a range of policy areas The analysis builds on existing research which has examined how party families respond to the challenges and opportunities of the integration process. This is the first study that focuses on long-term party policy adjustment across different policy areas by examining whether there has been a shift away from core ideological goals towards the direction of EU policy. The main finding is that there has generally been a shift towards the direction of EU policy across all party families in both member and non-member states. The findings have implications for the quality of representation and functioning of democracy in the member states since the deepening of the European integration process reduces ideologically distinct policy alternatives across party families and can hinder policy innovation

To cite the article: Trastulli F (2022) More Left or Left No More? An In-depth Analysis of Western European Social Democratic Parties' Emphasis on Traditional Economic Left Goals (1944–2021). Front. Polit. Sci. 4:873948. doi: 10.3389/fpos.2022.873948. The article is open access and can be accessed here. Trastulli FPS 06/2022Download Abstract The ideological evolution of Western European social democratic parties has received considerable scholarly attention over the decades. The most widespread view concerns the alleged programmatic moderation and convergence with the mainstream right of this...

Utilizing data that allows for the placement of both of the candidates running and voters on the same ideological scale, I model proximity voting in the 2010 House elections. I demonstrate that though the literature predominantly emphasizes partisanship and incumbency, relative distance from the candidates also plays a significant role in the voting decision. Additionally, I show that these proximity effects are conditional upon the type of candidate running and the individual's partisan attachment. In total, these results show that while the rates of partisan voting and incumbent victory are high in House elections, voters do consider ideological proximity and can punish candidates who take positions that are too far out of line.

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.