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The social group appeal of the Greens and the PRRP in a realigned party competitive space

The social group appeal of the Greens and the PRRP in a realigned party competitive space

Speaker: Hauke Licht (University of Innsbruck) with Leonce Röth (Ludwig-Maximilian University Munich)

Wednesday, April 9, 14:00–14:45 (Irish time)

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Abstract: While it is increasingly recognised that Greens and PRRPs are shaping the cultural and economic transformation of societies, it remains an open question whether this is mainly a manifestation of issue voting or attitudinal value change and/or more deeply rooted in the structural transformation of group identities. We address this question by theorising and measuring the group appeals of Green parties and PRRPs from the supply side of party competition. Conceptually, we develop a two-dimensional space of group identities (vertically and horizontally advantaged versus disadvantaged groups) and argue that the expected appeals within these dimensions should be derived from the parties' ideologies. Empirically, we use a variety of semi-supervised text-as-data methods to classify social group appeals in a full sample of all Green and PRRP manifestos from 1980-2024 in 33 countries, adding mainstream parties in selected countries as a benchmark (Germany, Sweden, UK and Switzerland). Based on this classification, we show the prevalence and composition of groups across and within the vertical and horizontal dimensions, and discuss what a supply-side perspective on group appeals can add to the debate on the socio-structural underpinnings of party competition in times of realignment.

About the speaker: Hauke is an Assistant Professor at the University of Innsbruck, specializing in computational methods for analyzing political rhetoric. His research explores rhetorical strategies in democratic representation, multilingual text analysis, and party competition across countries and languages. Hauke’s work has been published in Journal of Politics, Political Analysis, and Computational Communication Research, and has received awards for innovative data science.