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Is Vienna still a just city?
(2022)
Research on multi-level implementation of EU legislation has almost exclusively focused on the national level, while little is known about the role of subnational authorities. Nevertheless, it is a prerequisite for the functioning of the European Union that all member states and their subnational authorities apply and enforce EU legislation in due time. I address this research gap and take a closer look at the legal transposition process in the German regional states. Using a novel data set comprising detailed information on about 700 subnational measures, I show that state-level variables, such as political preferences and ministerial resources, account for variation in the timing of legal transposition and repeatedly lead to subnational delay. To conclude, the paper addresses the role of subnational authorities in the EU multi-level system and points to their interest in shaping legal transposition in order to counterbalance their loss of competences to the national level.
In countries with long-standing agency traditions, the creation of new agencies rarely comes as a large-scale reform but rather as one structural choice of many possible, most notably a ministerial division. In order to make sense of these choices, the article discusses the role of political design-focusing on the role of political motivations, such as ideological turnover, replacement risks and ideological stands toward administrative efficiency-and organizational dynamics-focusing on the role of administrative legacies and existing organizational palettes. The article utilizes data on organizational creations in the Norwegian central state between 1947 and 2019, in order to explore how political design and organizational dynamics help us understand the creation of agencies relative to ministry divisions over time. We find that political motives matter a great deal for the structural choices made by consecutive Norwegian governments, but that structural path dependencies may also be at play.
Can we rely on computational methods to accurately analyze complex texts? To answer this question, we compared different dictionary and scaling methods used in predicting the sentiment of German literature reviews to the "gold standard " of human-coded sentiments. Literature reviews constitute a challenging text corpus for computational analysis as they not only contain different text levels-for example, a summary of the work and the reviewer's appraisal-but are also characterized by subtle and ambiguous language elements. To take the nuanced sentiments of literature reviews into account, we worked with a metric rather than a dichotomous scale for sentiment analysis. The results of our analyses show that the predicted sentiments of prefabricated dictionaries, which are computationally efficient and require minimal adaption, have a low to medium correlation with the human-coded sentiments (r between 0.32 and 0.39). The accuracy of self-created dictionaries using word embeddings (both pre-trained and self-trained) was considerably lower (r between 0.10 and 0.28). Given the high coding intensity and contingency on seed selection as well as the degree of data pre-processing of word embeddings that we found with our data, we would not recommend them for complex texts without further adaptation. While fully automated approaches appear not to work in accurately predicting text sentiments with complex texts such as ours, we found relatively high correlations with a semiautomated approach (r of around 0.6)-which, however, requires intensive human coding efforts for the training dataset. In addition to illustrating the benefits and limits of computational approaches in analyzing complex text corpora and the potential of metric rather than binary scales of text sentiment, we also provide a practical guide for researchers to select an appropriate method and degree of pre-processing when working with complex texts.
To meet the Paris Agreement targets, carbon emissions from the energy system must be eliminated by mid-century, implying vast investment and systemic change challenges ahead. In an article in WIREs Climate Change, we reviewed the empirical evidence for effects of carbon pricing systems on technological change towards full decarbonisation, finding weak or no effects. In response, van den Bergh and Savin (2021) criticised our review in an article in this journal, claiming that it is "unfair", incomplete and flawed in various ways. Here, we respond to this critique by elaborating on the conceptual roots of our argumentation based on the importance of short-term emission reductions and longer-term technological change, and by expanding the review. This verifies our original findings: existing carbon pricing schemes have sometimes reduced emissions, mainly through switching to lower-carbon fossil fuels and efficiency increases, and have triggered weak innovation increases. There is no evidence that carbon pricing systems have triggered zero-carbon investments, and scarce but consistent evidence that they have not. Our findings highlight the importance of adapting and improving climate policy assessment metrics beyond short-term emissions by also assessing the quality of emission reductions and the progress of underlying technological change.
Typen von Forschungsdesigns
(2022)
Sozialwissenschaftliche Forschungsdesigns umfassen alle wesentlichen Entscheidungen, die im Forschungsprozess getroffen werden müssen. Der Beitrag unterscheidet drei rundlegende Typen von Forschungsdesigns: x-zentriert, y-zentriert und kontrastiv. Das x-zentrierte Design versucht einen theoretisch spezifizierten kausalen Effekt zu identifizieren und dessen Größe möglichst genau und ohne Verzerrungen zu schätzen. Das y-zentrierte Design versucht mehrere komplementäre Theorien über kausale Effekte so zu kombinieren, dass bestimmte Phänomene möglichst gut erklärt werden. Das kontrastive Design vergleicht die Erklärungskraft von zwei oder mehr konkurrierenden Theorien. Die Unterscheidung der drei Typen ist für qualitative Fallstudien ebenso relevant wie für Experimente oder statistische Studien mit Beobachtungsdaten. Der Beitrag grenzt die drei Typen voneinander ab, erklärt ihre jeweiligen Annahmen und diskutiert ihre Vor- und Nachteile sowie die Möglichkeiten und Grenzen ihrer Kombination. Daneben diskutiert er den Unterschied zwischen Modellen und Theorien sowie die Bedeutung des Sparsamkeitsprinzips bei der Entwicklung und Bewertung wissenschaftlicher Theorien und Erklärungen.
Voting for Votes
(2022)
Scholars frequently expect parties to act strategically in parliament, hoping to affect their electoral fortunes. Voters assumingly assess parties by their activity and vote accordingly. However, the retrospective voting literature looks mostly at the government's outcomes, leaving the opposition understudied. We argue that, for opposition parties, legislative voting constitutes an effective vote-seeking activity as a signaling tool of their attitude toward the government. We suggest that conflictual voting behavior affects voters through two mechanisms: as a signal of opposition valence and as means of ideological differentiation from the government. We present both aggregate- and individual-level analyses, leveraging a dataset of 169 party observations from 10 democracies and linking it to the CSES survey data of 27,371 respondents. The findings provide support for the existence of both mechanisms. Parliamentary conflict on legislative votes has a general positive effect on opposition parties' electoral performance, conditional on systemic and party-specific factors.
Real options are widely applied in strategic and operational decision-making, allowing for managerial flexibility in uncertain contexts. Increased scholarly interest has led to an extensive but fragmented research landscape. We aim to measure and systematize the research field quantitatively. To achieve this goal, we conduct bibliometric performance analyses and bibliographic coupling analyses with an in-depth content review. The results of the performance analyses show an increasing interest in real options since the beginning of the 2000s and identity the most influential journals and authors. The science mappings reveal six and seven research clusters over the last two decades. Based on an in-depth analysis of their themes, we develop a research framework comprising antecedents, application areas, internal and external contingencies, and uncertainty resolution through real option valuation or reasoning. We identify several gaps in that framework, which we propose to tackle in future research.