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Do spontaneous Self-Regulatory modes of thought vary as a function of personality, demographic background, and goal category?
No full textHidden Commitments and Future Opportunities: Implicit Moral Philosophies in Consumer Psychology
No full textDas Sondervermögen Infrastruktur – der Königsweg zur Ertüchtigung der Verkehrsinfrastruktur?
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Where Do Women Win Primaries? Asymmetric Opportunity Theory in Congressional Nominations
No full textWomen remain descriptively underrepresented in Congress, with primary elections shown to contribute to this underrepresentation. Because the value of winning a primary depends on the district partisanship and incumbent status, we analyze the kinds of districts where women won congressional primaries between 2006 and 2020. Republican women were less likely than their Democratic counterparts to win primaries across all types of districts. Democratic women were less successful in competitive general election districts, suggesting concerns about ‘electability’ among primary voters when nominating women. Republican women ran less often in conservative districts—likely due to gendered perceptions about women’s suitability for office—contributing to the partisan asymmetry of women in Congress. In both parties, women were strategic in selecting where to run, rarely winning incumbent primaries and disproportionately targeting open seats. Our findings help clarify how the nomination process exacerbates the gender gap in descriptive representation in Congress
Transformation des Politikzyklus durch Open Data und künstliche Intelligenz
No full textDie Digitalisierung und die damit verbundene digitale Transformation führen zu tiefgreifenden Veränderungen in Politik und Verwaltung. Neue Technologien wie Open Data (OD) und Künstliche Intelligenz (KI) beeinflussen zunehmend, wie politische Entscheidungen getroffen und Verwaltungsprozesse gestaltet werden. Im vorliegenden konzeptionellen Artikel wird die Frage beantwortet, wie OD und KI den Politikzyklus in seinen Phasen verändert. Zur Beantwortung wurde zunächst ein Überblick über den aktuellen Forschungsstand erstellt. Anschließend wurden im Rahmen der einzelnen Phasen die möglichen Einflüsse von OD und KI am Beispiel des Politikfelds Mobilität bzw. des Projekts zur Mobilitätsdateninfrastruktur (MODI) aufgearbeitet. Im Ergebnis wird deutlich, dass OD und KI nicht nur datenbasierte Entscheidungen unterstützen können, sondern auch die Anzahl und Rolle der beteiligten Akteure sowie deren Werkzeuge verändern
Illiberal Norms, Media Reporting, and Bureaucratic Discrimination: Evidence from State-Citizen Interactions in Germany
No full textRecent research on the rise of radical right-wing parties highlights the activation of deeply rooted illiberal norms in society, including hostility toward marginalized groups. Negative media reporting on immigration may reinforce this trend. Although previous studies have examined the effect of far-right normalization on voting, little is known about its broader societal impacts. Therefore, we ask how regional variation in anti-immigrant sentiments interacts with negative reporting to shape the behavior of street-level bureaucrats. Our theory posits that street-level bureaucrats are more likely to engage in discrimination if they work in areas with widespread anti-immigrant sentiments; a pattern that can be amplified by negative news on immigrants. To test our expectations, we conducted a preregistered survey experiment with 1400 German welfare caseworkers. Our findings reveal that regional norms significantly affect the likelihood of discrimination, especially after exposure to negative media frames. These results raise concerns about the impartiality of state institutions amidst rising illiberal norms
Co-Movement, Factors, and Forecasting: Data-Driven and Neural Network Advances in Financial Equity Market Analysis
No full textIn recent decades, data-driven and neural network methods have gained significant attention among academics and financial practitioners in equity market research. This thesis comprises four quantitative studies introducing innovative, data-driven approaches to measuring and predicting key financial variables in equity markets. First, we analyze global equity market co-movement over 25 years using a dynamic spatial model, focusing on major financial crises. Second, we employ neural networks to forecast downside deviation for investment factor timing and evaluate the results through an investment strategy. Third, we develop a neural network-based approach to predict daily realized volatility by transforming intraday data into images and using them as predictors. Finally, building on these insights, we propose a mixed-input, mixed-frequency neural network for next-day volatility forecasting, integrating intraday images, heteroscedastic autoregressive regressors, and market data. Given equity markets' increasing complexity and rapid evolution, this dissertation contributes to equity market research by advancing measurement and forecasting modeling techniques. This contribution is further reinforced by the publication of two of the four studies in international peer-reviewed journals: Dirkx and Heil (2022) in Expert Systems with Applications and Heil et al. (2022) in the Journal of International Money and Finance