Statewide and Regionalist Parties’ Perspectives in the Long-Term Dynamics of Decentralization

The doctoral dissertation with the title “Statewide and Regionalist Parties' Perspectives in the Long-Term Dynamics of Decentralization” engages with patterns of decentralization over time in comparative perspective. How the multi-level state is organized is fundamental for territorial politics, and therefore why decentralization occurs is an important factor to understand the democratic system and how policy-making difficulties arise. This dissertation embraces a post-functionalist, rational choice assumption about political parties: governing statewide parties decentralize in their own interest. Otherwise, why would they distribute power? Additionally, an innovative neo-institutionalist perspective argues that, over time, arising multi-level institutions influence statewide parties’ calculations endogenously to subsequently decentralize. The establishment of regional democracy through the major reform of political decentralization should empower regional actors, and influence statewide parties’ strategies of decentralization. The dissertation also includes two new methodological procedures. In chapter 2, an analysis of decentralization dynamics over time (1950-2018) and in 19 democracies unveils that regional democracy affects statewide parties’ asymmetric decentralization decisions. Before political decentralization, ideological proximity between the center and regions with decentralization demands seems to predict decentralizing reforms. This pattern disappears after political decentralization, possibly due to statewide governments giving up on ideological considerations vis-à-vis regional executives. Furthermore, party-based explanations of asymmetric decentralization cannot be found in symmetric decentralization, highlighting the latter’s idiosyncrasy. In chapter 3, based on co-authored work with Leonce Röth and Lea Kaftan, we develop a procedure to generate optimized dictionaries to measure attention dynamics to territorial politics based on newspaper texts in Spain (1976-2019) and the UK (1900-2020), two prominent and complex cases of decentralization. We show how to efficiently develop this important text-as-data resource to compare attention patterns across political arenas (mass media and parliament). By measuring salience of the territorial issue and its sub-issue over time, we find that media emphasizes violence-related territorial sub-issue more, whereas parliament focuses on administrative and technical issues such as a fiscal authority decentralization. In chapter 4, also based on a co-authored investigation with Lea Kaftan and Leonce Röth, we argue that party positions conveyed by the media are key to understand the voter-party convergence link in democratic representation. Mediated party positions can help us fill the gap in territorial politics concerning party positions on territorial sub-issues. We develop a procedure to obtain mediated party positions from news text with sentiment analysis and topic models in an automatized manner. Accounting for news outlet differences and comparing our measures with established expert judgments, manifesto positions, and estimates based on parliamentary debates, we find valid mediated positions for statewide and regionalist parties on four territorial sub-issues in Spain

Optimized Dictionaries: A Semi-Automated Workflow of Concept Identification in Text-Data

Identifying social science concepts and measuring their prevalence and framing in text data has been a key task of scientists ever since. Whereas debates about text classifications typically contrast different approaches with each other, we propose a workflow that generates optimized dictionaries that are based on the complementary use of expert dictionaries, machine learning, and topic modeling. We demonstrate our case by identifying the concept of "territorial politics" in leading newspapers vis-à-vis parliamentary speeches in Spain (1976-2018) and the UK (1900-2018). We show that our optimized dictionaries outperform singular text-identification techniques with F1-scores around 0.9 for unseen data, even if the unseen data comes from a different political domain (media vs. parliaments). Optimized dictionaries have increasing returns and should be developed as a common good for researchers overcoming costly particularism