Vote choice under certainty, risk, and uncertainty

Voters are thought to be repelled by unclear party communication or all-out uncertainty about their ideological or programmatic positions. Our contribution builds on a series of survey experiments in the alternative states of certainty, risk, and uncertainty. Choice under risk occurs, for instance, when electoral platforms transmit blurred or unclear signals. However, the range of potential positions and their respective probabilities is considered common, exogenous knowledge: we find that these scenarios neither attract nor repel voters. In contrast, choice under uncertainty is given when potential outcomes or their respective probabilities are unknown to the voters and require endogenous cognitive abilities and endogenous signal processing: we demonstrate that choice under uncertainty tends to drive away voters. The experimental setup considers both spatial and non-spatial components of voter utility and their contextual conditions to bolster external validity and arrive at more internally and externally valid assessments of vote choice under various ‘‘states of nature’’

Do Tight Labor Markets Pay Off for the Unemployed? Evidence from Austria

This paper studies the impact of labor market tightness on employment outcomes for unemployed job seekers in Austria. Using administrative data from 2011 to 2022, we construct granular measures of labor market tightness based on regional and occupational vacancy-to-unemployment ratios. To address endogeneity concerns, we employ a shift-share instrumental variable strategy that leverages variation in tightness across occupations and regions. We find that a 1 percent increase in tightness raises the probability of finding a job within 3 months by 0.109 percentage points. Entry wages respond positively but modestly on average (elasticity of 0.015), with effects concentrated in high-tightness environments. In particularly scarce occupations, wage elasticities reach 0.075, indicating strong non-linear returns to tightness. Effects are larger for job seekers with lower pre-unemployment income or longer unemployment spells. Tighter markets also lead to higher job stability after reemployment, with increased likelihood of remaining employed beyond 12 months. These findings are consistent with standard search and matching models and underscore the role of labor market conditions in shaping job seekers’ bargaining power. This study provides new evidence on the benefits of tight labor markets for unemployed job seekers and informs policy efforts aimed at improving matching efficiency and mitigating labor shortages

Tax Audit Quality: The Role Of Experience And Technology Readiness In A Digital World

Society is digitising at a rapid pace and tax authorities must keep up with significant changes in how companies administer their tax liabilities. Tax auditors must be able to achieve an efficient and effective audit quality regardless of the degrees to which digitisation and digitalisation have been implemented by the audited companies. Previous research shows a positive correlation between audit experience and audit quality. However, it is unclear whether more experience—which was most likely acquired in traditional audits of accounting systems with low levels of digitalisation—is also beneficial in a changing environment with more highly digitalised companies. We argue that more experienced tax auditors are only superior to less experienced auditors in this changing environment if they are sufficiently willing and equipped to use new technologies and more digitised data. We expect—and find—that experienced tax auditors with adequate technology readiness achieve a higher audit quality in detecting information technology (IT) risks than tax auditors with less experience and/or less technology readiness. Our results, however, also show that more experienced tax auditors do not perform better when detecting traditional risks (i.e. non-IT-related risks) than less experienced auditors. Overall, our results suggest that experience without the propensity to embrace and use new technologies and more digitised data might not be enough to achieve the required audit quality levels in the future. We therefore emphasise the importance of appropriate training (in order to adopt new ways of working and acquire competencies to understand new technologies) and the strategic composition of the tax authorities’ audit teams

Ungewissheit als erstrebenswerte kollektive Praxis

Ungewissheit ist eine unausweichliche Bedingung des menschlichen und gesellschaftlichen Lebens. Sie auszuhalten kann jedoch unangenehm und anstrengend sein. Viele Menschen versuchen, Ungewissheit in ihrem Alltag zu reduzieren – mitunter auf Weisen, die nicht nur illusorisch, sondern auch sozial und demokratiepolitisch schädlich sein können. Der Vortrag beschäftigt sich damit, Ungewissheit nicht nur als individuelles Unbehagen oder Defizit zu begreifen, sondern vor allem als bewusste Haltung, die autoritären Versuchungen und schnellen Lösungen entgegenwirkt und Räume für Ambivalenzen und differenzierte Perspektiven öffnet

Legitime Überforderung: Psychiatrische Diagnosen im Kontext digitaler ‚Mental Health‘-Diskurse

Im Rahmen der 3. Suttner Vorlesung wird Dr.in Laura Wiesböck einen Einblick in die ambivalenten Auswirkungen der Verbreitung einer „psychotherapeutischen Kultur“ über digitale Kanäle geben. Sie stellt die Frage, warum hinderliche Gefühlslagen und Handlungsweisen gegenwärtig primär in pathologisierter Form anerkannt und ausgelebt werden und welche gesellschaftlichen Umstände und Akteur*innen dafür förderlich sind, dass Fragen zur emotionalen Ausgeglichenheit und Funktionalität immer mehr zu Fragen von Gesundheit oder Krankheit werden. Besonders auf die Problematik der Verbreitung amerikanischer Gesundheitsdiskurse zu „Mental Health“ und „Selfcare“ weist sie hin, da diese neoliberale Vorstellungen von radikaler Individualisierung, Wettbewerbsorientierung und Konsumzentrierung verstärken können

Exploring prosocial behaviors in times of a pandemic: Individuals’ lay perspective versus scientific measurements

Humanitarian crises like the Covid‐19 pandemic pose significant challenges to society, prompting scientific debate on whether such situations elicit more prosocial or more selfish behavior. Despite the restrictions imposed by the pandemic, current evidence indicates a continued display of various prosocial behaviors. This research aims to enhance the understanding of what constitutes prosocial behavior from both individuals’ lay and scientific perspectives. For this purpose, we analyzed lay perspectives via an open question in a representative survey ( N = 446) and qualitatively categorized the reported prosocial behaviors inductively with content analysis. The qualitative content analysis revealed three clusters of prosocial behaviors: promoting the welfare of others, health‐protective measures, and supporting society. Additionally, we conducted a systematic literature review to identify the scientific perspective view (i.e., focusing on the empirical measurements) on prosocial behaviors studied during the pandemic. Although behaviors promoting the welfare of others (e.g., donations) were the most commonly studied in the literature review, participants reported more health‐protective behavior, such as hand‐washing, which was not traditionally considered to be prosocial before the pandemic. The comparison between individuals’ lay and scientific perspectives highlighted some prosocial behaviors that warrant future investigation (e.g., supporting the economy, home office)

The impact of prior education on student success in higher education: how do different school types influence success in different fields of study?

The course of an individual’s education is shaped by a series of pivotaldecisions. Each of these decisions has the potential to influenceeducational pathways and success. This study examines the impact ofupper secondary school type on success in higher education inAustria. Austria offers a high degree of diversification in its educationalsystem, with a range of academic and vocational pathways availablefrom the age of 14. I introduce the concept of disciplinarycounterparts which describes types of high schools and fields of studythat share the same or a similar field of education. Based onadministrative data of students, separate Cox proportional hazardsmodels for 16 fields of study are fitted. The findings indicate that noschool type has a consistently higher graduation probability across allfields of study. Students from business vocational high schools (VHS)and technical VHS have a higher graduation probability in fields ofstudy that are considered disciplinary counterparts than students fromother types of VHS and academic high schools. This study emphasisesthe significance of prior education and the necessity of providingsupport structures for students who change fields between the uppersecondary and tertiary level to enhance success for all students

AI Agents as Infrastructures: Mediating Power Dynamics in Global AI Platform Ecosystems between the US and China

Previous research has shown that the political economy of Generative AI (GenAI) is shaped by infrastructural dominance of US-based Big Tech companies—Google, Microsoft, Amazon—which control cloud computing, data access, proprietary software, and pools of AI talent. However, the emergence of task-oriented AI agents and the growing influence of Chinese tech firms potentially reshuffle this political economy. AI agent systems, such as GPT Builder (OpenAI), Copilot Studio (Microsoft), and Coze (ByteDance), integrate foundation models with complementary components to perform specific tasks in programmable, modular, and increasingly customized ways. As such, AI agents are not only reconfiguring the technical architectures of GenAI development pipelines, but are also operating as infrastructural middleware towards the platformization and customizable industrialization of GenAI. These shifts call for a renewed examination of infrastructural mediation and control of GenAI platform ecosystems across both U.S. and Chinese contexts. Hence, this study aims to investigate how AI agents are mediating technical interdependencies and power asymmetries within the global political economy of GenAI by asking: What are the technical architectures of AI agent systems in the U.S. and China? How do these systems operate differently as infrastructural components within broader AI ecosystems? And how do they reflect or reshape the shifting political-economic relations? To address these questions, this study will conduct a comparative case study of two leading AI agent builders—GPT Builder from the U.S. and Coze from China. Employing a “technographic” approach, it will critically scrutinize publicly available materials including products pages, blog posts, press releases and relevant media and industry reports. The analysis will first map distinct orchestrations of core components such as foundation models, tools, memory systems, knowledge base and prompting on each builder. Based on this computational overview, this study will then situate these building systems within their respective platform architectures and AI technology stacks in each country to unfold the underlying infrastructural relations and power structures. By examining the institutional distinctions between two cases, this study will offer empirical insight into how OpenAI and ByteDance are institutionalizing distinct modes of infrastructural dominance and interdependence. It argues that the political economy of GenAI is being reconfigured through an increasingly contested geopolitical terrain, where AI agents function as a new infrastructural layer enabling ecosystem-based digital dependency and rentier economy. These new dynamics extend infrastructural control beyond the lock-ins in cloud infrastructures of US-based tech companies, signalling a shift toward customized industrialization and governance over downstream ecosystems. In doing so, this research will lay a foundation for further critical inquiry, policy regulation and public engagement on AI governance and platform power

Retrieval from mixed sampling frequency: generic identifiability in the unit root VAR

The “retrieval from mixed frequency sampling” approach based on blocking—described e.g., in Anderson et al. (Econom Theory 32:793–826, 2016a)—is concerned with retrieving an underlying high frequency model from mixed frequency observations. In this paper, we investigate parameter-identifiability in the Johansen (Likelihood-based inference in cointegrated vector autoregressive models. Oxford University Press, Oxford, 1995) vector error correction model for mixed frequency data. We prove that from the second moments of the blocked process after taking differences at lag N (N is the slow sampling rate), the parameters of the high frequency system are generically identified. We treat the stock and the flow case