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Unraveling electoral volatility : the influence of conflicting attitudes in multi-party systems
Get PDFOver the past two decades, vote switching has become increasingly common. One underexplored factor driving this behavior is the impact of internal sources of conflict, particularly political ambivalence. While the relationship between ambivalence and vote switching has been examined within the American two-party system, its role in multi-party systems (MPS) remains largely unstudied.
This dissertation makes three key contributions. First, it develops a theoretical framework outlining how party ambivalence, leader ambivalence, party-leader disagreement, and coalition disagreement influence electoral behavior in multi-party contexts. Second, it introduces and validates an alternative measure of ambivalence tailored to MPS. Third, it identifies and empirically assesses which sources of internal conflict most significantly affect voting behavior, with a specific focus on vote switching.
Findings indicate that all three forms of conflict exert a meaningful influence on vote switching within MPS. Among them, political ambivalence emerges as one of the most consistent and significant predictors, substantially increasing the likelihood of a voter switching parties
Einseitige Einführung von Kurzarbeit mit individualarbeitsrechtlichen Mitteln : Änderungskündigung, Vertragsanpassungsanspruch, Zustimmungsobliegenheit
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Essays in international economics and industrial organization
Get PDFThis dissertation sheds light on the effects of trade policy. Chapter 1 provides theoretical analysis of firm behavior in a model in which firms make geographical importing decisions under oligopolistic competition. In Chapter 2, I calibrate the model from Chapter 1 to the Ukrainian production dataset and use the calibrated model to evaluate the welfare effects of input trade liberalization that happened during the accession of Ukraine to the World Trade Organization (WTO) in 2008. In Chapter 3, I study the aggregate and distributional effects of unilateral trade liberalization for a small open economy in which firms face credit constraints and set variable markups
Essays on the econometric analysis of structural instabilities and systemic risk
Get PDFThis dissertation consists of three chapters in a study of structural instabilities and systemic risk analysis. A system of interest is assumed to be interconnected, but the underlying structure is unknown or unobservable.
Chapter 1 develops a method to detect and localize the points of structural instabilities in the cross-correlation structure of a panel. Cross-correlation structures contain valuable information about the underlying linkages among variables and the channels of spillovers across cross-sectional units. Instabilities in these structures often signal structural changes within the system. We propose a novel method for detecting instabilities in cross-correlation structures using a latent factor model framework. We introduce a suitable object — the column space of the loading matrix (the factor space) — to capture structural correlation changes while being free from the inherent identification issue of the latent model. The resulting detection criterion is based on an intuitive distance measure between two factor spaces, integrating both the detection and localization of breakpoints. In applications, our methods effectively detect instability points consistent with the development of the subprime mortgage crisis, as well as major policy changes such as the repeal of the Glass–Steagall Act and the U.S.–China trade war.
Chapter 2 proposes a novel framework to identify the most influential units behind structural breaks. In a system represented by panel data, a break in the cross-correlation structure can empirically indicate volatility propagation from individual (idiosyncratic) dimensions to the entire system. Individual units contributing the most to this break can act as systemic risk components, potentially driving further instability across the system. We propose a novel method to detect these main contributors — referred to as 'granular units'— as an early detection tool for potential systemic risk components. Assuming a standard approximate latent factor structure to model system covariance dynamics agnostically, we introduce a straightforward influence measure to evaluate the contributions of individual (idiosyncratic) second moments to the structural break. Applied to S&P 100 daily return data across major economic crisis periods, the proposed detection scheme effectively identifies likely sources of systemic risk from early crisis stages.
Chapter 3 designs a new sequential early warning framework for structural changes that accommodates a broad range of instabilities in the underlying latent network. Network and factor models are two important techniques for analyzing interconnected systems, and we demonstrate that an interconnected system can naturally have a dual representation through our network-factor model. This modeling enables the analysis of instabilities in the latent network using various tools from factor analysis. This online warning framework can be of practical importance for application to network-supported data in which the underlying structure is unknown or unobservable
The WebAI paradigm of innovation research : extracting insight from organizational web data through AI
Get PDFThis paper introduces the WebAI paradigm as a promising approach for innovation studies, business analytics, and informed policymaking. By leveraging artificial intelligence to systematically analyze organizational web data, WebAI techniques can extract insights into organizational behavior, innovation activities, and inter-organizational networks. We identify five key properties of organizational web data (vastness, comprehensiveness, timeliness, liveliness, and relationality) that distinguish it from traditional innovation metrics, yet necessitate careful AI-based processing to extract scientific value. We propose methodological best practices for data collection, AI-driven text analysis, and hyperlink network modeling. Outlining several use cases, we demonstrate how WebAI can be applied in research on innovation at the micro-level, technology diffusion, sustainability transitions, regional development, institutions and innovation systems. By discussing current methodological and conceptual challenges, we offer several propositions to guide future research to better understand i) websites as representations of organizations, ii) the systemic nature of digital relations, and iii) how to integrate WebAI techniques with complementary data sources to capture interactions between technological, economic, societal, and ecological systems
The welfare effects of explicit and implicit subsidies on fossil fuels
Get PDFWe examine the welfare effects of removing explicit and implicit fossil fuel subsidies, the latter entailing Pigouvian pricing of local externalities from fossil energy consumption. We map a multi-region, multi-sector general equilibrium model to granular data on subsidies, local marginal external costs, and national income and product accounts. On average, unilateral Pigouvian pricing improves a country’s welfare by 3.7%, generates fiscal revenues equal to 2.5% of consumption, and reduces the carbon price needed to meet the Paris climate target by 76%. Non-market welfare gains exceed market-related losses, benefiting most countries. Local air pollution pricing accounts for 90% of net benefits. About one third of countries would already meet their climate targets, making additional policies like carbon pricing redundant. For all countries combining Pigouvian energy pricing with carbon pricing increases welfare compared to relying on carbon pricing alone. Removing explicit subsidies has a minor impact on welfare and emissions. Global Pigouvian energy pricing would reduce global emissions by 32%, while increasing global welfare by 2.4%. Our findings underscore the potential of Pigouvian energy pricing to align economic, fiscal, and climate goals
Do China's special economic zones increase incentives to invest in R&D?
Get PDFChina's special economic zones (SEZs) have been established to foster business growth and innovation by improving the institutional context of specific sub-regional areas. We examine the effect of SEZs on the contribution of research and development (R&D) to the market value of firms located in these areas. The market value reflects investors' expectations of future returns to R&D, providing crucial information for strategic investment decisions. Larger R&D contributions to the market value create stronger incentives for firms to invest in innovation. Empirical results suggest that the contribution of R&D to the market value increases through the SEZs program, particularly for R&D intensive firms. This suggests that regional policies, while increasing incentives to innovate, may widen the gap between less and more R&D intensive firms, potentially impacting competition and long-term growt
The participation of young firms in public procurement
Get PDFPublic procurement offers sizable market opportunities for young firms. We investigate the firm- and founder-level characteristics determining young firms' decision to apply for public tenders, as well as the procurers' selection of an awardee. We distinguish between observable and unobservable characteristics as well as price-based tenders (tenders awarded solely on the price criterion) and criteria-based tenders (tenders awarded based on additional criteria next to the price). Using representative survey data for 4,314 young firms in Germany, we estimate a multinomial two-stage selection model. In the first stage, firms decide to "not apply," to "apply for price-based tenders," or to "apply for criteria-based tenders." In the second stage, procurers choose the awardee among the applicants of each tender type. We find the firm and founder determinants largely differ with regard to the first and second stage, as well as price- and criteria-based tenders
Long-term discrimination effects on adolescent health behaviors and well-being in four countries
Get PDFObjective: Adolescence is a pivotal foundation for lifelong health and a phase vulnerable to the adverse effects of discrimination. We assessed the impact of perceived discrimination on adolescent well-being over 2 years and the mediating effects of protective (physical activity, nutrition, sleep) and risky (substance use) health behaviors.
Methods: Adolescents (N = 9,957; Mage = 14.90 years) from the CILS4EU multinational panel (a longitudinal survey in four European countries) were examined across three waves. Direct and indirect relationships were analyzed using path models, adjusting for health behaviors, well-being, and control variables (age, gender, socioeconomic status, migration, religion) assessed in Wave 1.
Results: Adolescents reported the most discrimination instances within the school environment. Perceived discrimination at Wave 1 was significantly associated with decreased well-being at Wave 3 (β = -.04, p < .001) and decreased protective (physical activity: β = -.02, nutrition: β = -.04, sleep: β = -.04) and increased risky (substance use: β = .03) health behaviors at Wave 2. Nutrition and sleep mediated the relationship between perceived discrimination and well-being; no mediation was found for physical activity and substance use.
Conclusions: Even in observational data with 1-year assessment intervals, detrimental long-term effects of perceived discrimination on adolescent well-being are apparent, mediated through changes in nutrition and sleep behaviors. These results extend previous research—predominantly focusing on substance use—showing that perceived discrimination also predicted fewer protective health behaviors. Adolescence represents a strategic window for addressing discrimination and promoting healthy behaviors and well-being to mitigate long-term health disparities