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Einleitung
(2022)
Ausblick
(2022)
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.
“Broadcast your gender.”
(2022)
Social media platforms provide a large array of behavioral data relevant to social scientific research. However, key information such as sociodemographic characteristics of agents are often missing. This paper aims to compare four methods of classifying social attributes from text. Specifically, we are interested in estimating the gender of German social media creators. By using the example of a random sample of 200 YouTube channels, we compare several classification methods, namely (1) a survey among university staff, (2) a name dictionary method with the World Gender Name Dictionary as a reference list, (3) an algorithmic approach using the website gender-api.com, and (4) a Multinomial Naïve Bayes (MNB) machine learning technique. These different methods identify gender attributes based on YouTube channel names and descriptions in German but are adaptable to other languages. Our contribution will evaluate the share of identifiable channels, accuracy and meaningfulness of classification, as well as limits and benefits of each approach. We aim to address methodological challenges connected to classifying gender attributes for YouTube channels as well as related to reinforcing stereotypes and ethical implications.
Is There a Rural Penalty in Language Acquisition? Evidence From Germany's Refugee Allocation Policy
(2022)
Emerging evidence has highlighted the important role of local contexts for integration trajectories of asylum seekers and refugees. Germany's policy of randomly allocating asylum seekers across Germany may advantage some and disadvantage others in terms of opportunities for equal participation in society. This study explores the question whether asylum seekers that have been allocated to rural areas experience disadvantages in terms of language acquisition compared to those allocated to urban areas. We derive testable assumptions using a Directed Acyclic Graph (DAG) which are then tested using large-N survey data (IAB-BAMF-SOEP refugee survey). We find that living in a rural area has no negative total effect on language skills. Further the findings suggest that the “null effect” is the result of two processes which offset each other: while asylum seekers in rural areas have slightly lower access for formal, federally organized language courses, they have more regular exposure to German speakers.
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.