Distant Reading of the German Coalition Deal: Recognizing Policy Positions with BERT-based Text Classification

In postwar Germany, the federal government is usually formed by several political parties (Schmidt, 2007, p. 97). Over the past 16 years, these government coalitions were led by the Christian Democratic parliamentary group (CDU/CSU), most recently in cooperation with the Social Democratic Party (SPD), which, following the federal election in 2021, was unwilling to negotiate with their former partner, calling for new alliances to achieve a majority in parliament. Finally, the leaders of the Free Democratic Party (FDP), the Greens and SPD, despite mixed support from the party bases, signed a coalition agreement. Some journalists even regarded the FDP, which gained access to two key ministries, the secret winner of the negotiations (Fürstenau, 2021), also because the Greens did not see some of their desired climate change policies implemented (Lauter, 2021). In this research, we are interested in how the coalition agreement was assembled regarding the individual party contributions. To that end, we utilize methods from Natural Language Processing, which have seen widespread adoption in political science (Wilkerson and Casas, 2017; Merz et al., 2016; Rauh, 2015; Slapin and Proksch, 2008).