http://hdl.handle.net/10900/42132 Tue, 19 May 2026 04:54:09 GMT 2026-05-19T04:54:09Z http://hdl.handle.net/10900/172528 Following the Traces of Curious Minds: Intellectual Curiosity and its Behavioral Manifestations Across Learning Contexts Schumacher, Aki Intellectual curiosity is a desire for information and knowledge that drives individuals to seek intellectually stimulating activities and environments (Litman & Spielberger, 2003; Mussel, 2013a; von Stumm & Ackerman, 2013). It plays a central role in how humans learn and develop, both in everyday life and formal education (Grossnickle, 2016; von Stumm et al., 2011). Curiosity-driven learning has been widely studied, from basic cognitive mechanisms to its function in classrooms. However, research across disciplines often focuses on different aspects, making it difficult to form a comprehensive understanding of the curiosity-driven learning process (Hassin & Shohamy, 2020). Moreover, although information-seeking and learning behaviors are seen as central expressions of curiosity, the behavioral mechanisms underlying knowledge acquisition have received limited attention so far. To address these gaps, this dissertation investigated curiosity-driven learning as a multi-level process shaped by state-level dynamics, person characteristics, and the learning context. Each of these levels play an important role but they also interact to influence how we seek and acquire knowledge. Two key objectives guided this dissertation: (1) to integrate disciplinary perspectives by examining intellectual curiosity across states, traits, and learning contexts; and (2) to enrich our understanding of intellectual curiosity by focusing on its behavioral expressions. To this end, the dissertation introduced behavioral trace data as a promising method for capturing curiosity-driven learning behaviors across varied settings. Three empirical studies were conducted to address these objectives, each exploring different facets of curiosity. These studies investigated intellectual curiosity at different degrees of granularity, moving from cognitive mechanisms captured by basic metrics of information seeking to linking students’ trait curiosity to their real-world learning behaviors. Collectively, this dissertation contributes to a richer and behaviorally grounded account of intellectual curiosity, deepening our understanding of how intellectual curiosity supports human learning.; Die Dissertation ist gesperrt bis zum 19. September 2027 ! Sun, 19 Sep 2027 00:00:00 GMT http://hdl.handle.net/10900/172528 2027-09-19T00:00:00Z http://hdl.handle.net/10900/172302 Effects of CSR Reporting and Governance Regulation Haase, Annika This thesis examines first- and second-order effects of EU regulations of Corporate Social Responsibility (CSR) disclosure and corporate governance, namely the Non-Financial Reporting Directive and the Sec-ond Shareholder Rights Directive. In three empirical studies, the thesis provides evidence regarding two questions: (1) How is CSR reporting enforced, and does enforcement work? And (2) does shareholder voting on remuneration policies (Say on Pay (SoP)) affect executives’ compensation contracts, especially also with respect to CSR-based components? Based on a survey conducted with European enforcement bodies between 2021 and 2022, the first study documents in a systematic fashion country-level heterogeneity in the scope of enforcement, the related enforcement activities, in the institution(s) assigned with the task to carry out enforcement, in the audit of CSR reporting, and in the endowment with human resources. In contrast, (potential) enforcement actions are quite comparable between EU member states. The second study builds upon the findings of the first study by exploiting a regulatory “shock” to the enforcement in Sweden identified in the survey. Specifical-ly, this study investigates whether enforcement shapes CSR reporting (quality) and has economic effects. Results show that Swedish firms increased causal reasoning, improved report readability, and decreased boilerplate disclosures following the initiation of enforcement procedures. These findings suggest that (the threat of) enforcement induced affected Swedish firms to improve their CSR reporting. Cross-sectional tests further reveal that increases in CSR reporting quality are consistently more pronounced for firms with presumably low CSR reporting incentives. Moving on to economic effects, evidence presented in the study suggests that the initiation of CSR reporting enforcement procedures reduced information asym-metries among investors and induced firms to increase CSR activities. The third and last study of the thesis shifts the focus from CSR reporting to corporate governance. Speaking to the second question, the study explores whether (the threat) of shareholder voting on execu-tives’ remuneration affects remuneration packages. My results show that treated firms significantly re-duced total and abnormal compensation, largely driven by a reduction in performance-based compensa-tion components. These effects are more pronounced for firms that for the first time introduced (manda-tory) SoP votes. In contrast, the design of remuneration packages is only partially affected by SoP indi-cating that SoP mainly affects particularly visible elements of remuneration policies (i.e., level of compen-sation). Mon, 17 Nov 2025 00:00:00 GMT http://hdl.handle.net/10900/172302 2025-11-17T00:00:00Z http://hdl.handle.net/10900/171950 Social Media, Social Networks, Stereotypes, and Sustainability – Essays in Behavioral Economics Moritz, Raphael Digital platforms and social media have fundamentally transformed how individuals signal information, while companies increasingly signal corporate sustainability by linking executive pay to ESG performance rather than solely financial metrics. Yet promises of reduced bias, enhanced accountability, and increased sustainability remain largely unfulfilled. This dissertation examines whether non-traditional information from social media can overcome entrenched discrimination and whether ESG metrics in executive compensation genuinely drive corporate sustainability. Through large-scale field experiments and novel hand-collected data, this dissertation makes several key contributions: First, it provides causal evidence of how social media information affects discrimination in informal markets—contexts overlooked by previous research despite their critical socioeconomic role. Using randomized experiments with fictitious social media profiles, the research shows that carefully designed social media signals can eliminate ethnic discrimination. Second, it introduces visual stereotypes, causally identifying how minority stereotypes shape economic outcomes (shared housing) and social outcomes (network formation). In contrast to non-stereotypical profiles, the results indicate that stereotypical content significantly reinforces bias. Third, it reveals that personality signals of agreeableness and emotional stability significantly improve acceptance rates across contexts, while conscientiousness shows no impact. Fourth, it demonstrates that enhanced salience of information fails to mitigate discrimination. Finally, it contributes the first comprehensive hand-collected dataset on ESG metrics, their weights, and fulfillment in executive compensation. Results from approximately 6,900 housing applications reveal persistent ethnic discrimination, with minority applicants facing 52% lower callback rates. However, social media profiles challenging stereotypes eliminate this gap, while stereotype-reinforcing profiles increase discrimination to 65%. Similar patterns emerge in the formation of personal social networks. In corporate settings, while ESG metrics are increasingly common in executive pay, most impose negligible financial consequences, revealing widespread "window dressing" where sustainability measures carry minimal pay risk. These findings demonstrate that introducing non-traditional information—social media signals or ESG metrics—does not automatically promote equity, accountability, or sustainability. Instead, the design, weighting, and processing of such information prove critical. The dissertation provides evidence that well-crafted interventions can reduce bias, but warns against superficial adoption of new information channels without genuine commitment to their underlying objectives. Fri, 07 Nov 2025 00:00:00 GMT http://hdl.handle.net/10900/171950 2025-11-07T00:00:00Z http://hdl.handle.net/10900/171898 Essays on the Theory and Application of Post-Regularization Inference and Selection Correction in Censored and Distribution Regression Models Erhardt, Pascal This dissertation explores the integration of modern machine learning techniques into classical econometric frameworks, with a focus on enhancing inference in models subject to outcome censoring or distributional heterogeneity. By bridging these domains, the thesis contributes both theoretical advances and empirical applications that demonstrate the added value of high-dimensional methods in addressing long-standing challenges in applied econometrics. The first essay develops a framework for post-selection inference in high-dimensional Tobit mo-dels. By combining maximum likelihood estimation with double machine learning and Neyman orthogonalization, it establishes asymptotically valid inference in settings with censored outco-mes. Simulations confirm its robustness, and an empirical application to HIV drug resistance data illustrates its practical relevance. This chapter demonstrates how regularization techniques can be successfully adapted to classical econometric models. The second essay investigates the effects of Germany’s statutory minimum wage using a post-double selection logistic distribution regression model. This high-dimensional approach allows for consistent inference across entire outcome distributions, eliminating reliance on ad hoc variable selection. Results show that the reform raised hourly wages at the bottom of the distribution wit-hout significant adverse effects on working hours, while effects on monthly earnings were more nuanced. The findings reconcile earlier mixed evidence and underscore the value of machine learning methods for labor market applications. The third essay applies a distribution regression model with sample selection correction to analy-ze the evolution of the gender wage inequality in Germany. The approach uncovers heterogene-ous patterns of unobserved selectivity across both full-time and part-time employment. Results indicate that the narrowing of the full-time gender wage gap over time is largely explained by changes in selectivity patterns and improvements in women’s characteristics, while part-time employment continues to exhibit pronounced gender differences, albeit with signs of conver-gence. Together, these essays demonstrate that incorporating machine learning into econometric analy-sis improves inference, reduces reliance on restrictive assumptions, and broadens the scope of research questions that can be rigorously addressed. The dissertation discusses and applies new tools for handling non-random sample selection and heterogeneous effects, and points toward a fruitful future agenda at the intersection of econometrics and statistical learning. Wed, 05 Nov 2025 00:00:00 GMT http://hdl.handle.net/10900/171898 2025-11-05T00:00:00Z