This work deals with the current support landscape for Social Entrepreneurship (SE) in the DACH region. It provides answers to the questions of which actors support SE, how and why they do so, and which social ventures are supported. In addition, there is a focus on the motives for supporting SE as well as the decision-making process while selecting social ventures. In both cases, it is examined whether certain characteristics of the decision-maker and the organization influence the weighting of motives and decision-making criteria. More precise, the gender of the decision-maker as well as the kind of support by the organization is analyzed. The concrete examples of foundations and venture philanthropy organizations (VPOs) will give a deeper look at the SE support motives and decision-making behavior. In a quantitative empirical data collection, by means of an online survey, decision-makers from SE supporting organizations in the DACH region were asked to participate in a conjoint experiment and to fill in a questionnaire. The results illustrate a positive development of the SE support landscape in the German-speaking area as well as the heterogeneity of the organizational types, the financial and non-financial support instruments and the supported social ventures. Regarding the motives for SE-support, a general endeavor to change and to create an impact has proven to be particularly important at the organizational and the individual level. At the individual level female and male decision-makers have subtle differences in their motives to promote SE. Robustness checks by analyzing certain subsamples provide information about that. Individuals from foundations and VPOs, on the other hand, hardly differ from each other, even though here individuals with a rather social background face individuals with a business background. At the organizational level crucial differences can be identified for the motives, depending on the nature of the organization's support, and again comparing foundations with VPOs. Especially for the motives 'financial interests', 'reputation' and 'employee development' there are big differences between the considered groups. Eventually, by means of cluster analysis and still with respect to the support motives, two types of decision-makers could be determined on both the individual and the organizational level. In terms of the decision-making behavior, and the weighting of certain decision-making criteria respectively, it has emerged that it is worthwhile having a closer look: The 'importance of the social problem' and the 'authenticity of the start-up team' are consistently the two most important criteria when it comes to selecting social ventures for supporting them. However, comparing male and female decision-makers, foundations and VPOs, as well as the two groups of financially and non-financially supporting organizations, there are certain specifics which are highly relevant for SE practice. Here as well a cluster analysis uncovered patterns of criteria weighting by identifying three different types of decision-makers.

Many developed countries, including Germany, face a steady rise in the share of individuals obtaining higher education. While rising education itself bears a series of advantages as extensively studied in previous literature, it is also conceptually linked to a higher likelihood of working in an occupation that does not match one’s normal qualifications. Previous studies have predominantly evaluated how demographic or job‐related aspects correlate with the likelihood of being educationally ﴾mis﴿matched. However, they have largely ignored institutional facets of the educational system or industrial organization. Moreover, little is known about how private wealth affects educational mismatch or whether job satisfaction is homogenously affected among individuals once such a mismatch occurs. The five projects collected in this thesis aim to answer these open questions in the literature for Germany, using data from the Socio‐Economic Panel and employing different time intervals between 1984 and 2022. Beginning with the educational system in early childhood, Chapter 2 evaluates the impact of school‐starting age on the likelihood of over‐ and undereducation. It exploits the exogenous variation in school‐entry rules across federal states and years in Germany with regression discontinuity designs. The results report a negative impact of school‐starting age on the likelihood of undereducation, but no systematic relationship with overeducation. Subsequently, Chapter 3 explores the variation in education costs by leveraging the quasi‐experimental setting induced by the time‐limited introduction of tuition fees in several German federal states between 2006 and 2014. The increase in education costs among treated graduates results in a significantly higher likelihood of overeducation, which endures even several years post‐graduation. Chapter 4 focuses on the industrial relations system and examines the correlation between trade union membership and the likelihood and extent of educational ﴾mis﴿match. The results reveal that trade union members report significantly less overeducation at both the intensive and extensive margin and also a higher likelihood of being matched compared to non‐members. Furthermore, the heterogeneity analysis provides evidence that this correlation is driven by improved bargaining power instead of informational advantages. Chapter 5 focuses on private wealth as a determinant of educational mismatch by investigating the impact of a wealth shock through inheritances, lottery winnings or gifts on the likelihood of over‐ and undereducation. Due to the diminishing marginal returns of wages with increasing windfall gains the likelihood of undereducation is expected to decrease, while that of overeducation is expected to increase. Empirically, these suppositions are supported for overeducation, as its likelihood increases significantly after the windfall gain. Further analyses reveal that this effect is driven by individuals switching occupations while increasing their leisure time, and it materializes only for medium to large windfall gains. Contrary to the previous chapters, Chapter 6 focuses on educational mismatch, more precisely on overeducation, as the independent variable. In particular, it investigates the correlation between overeducation and job satisfaction. The results align with the previously established negative correlation for private sector employees exclusively. In contrast, interaction and subsample analyses reveal a positive correlation for public sector employees. This link is driven by individuals with a high degree of altruistic motivation and family orientation.