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This thesis is structured in two main parts that contain our analyses using individualand aggregate-level data.

Our first part introduces a new dataset on the individual level, which we use to investigate economic questions in the fields of labor economics and economics of education. More specifically, it provides detailed information about graduates from a mid-size German university (Saarland University) and their respective careers.

The part consists of four chapters: after a detailed introduction, we provide a comprehensive description of the new dataset on employment biographies of university graduates, explaining the origin of the data, present several descriptive statistics and select our sample for later analysis. In the next chapter, we conduct our main empirical analysis, i.e., we investigate the first full-time wage of our sample of graduates in a total of three sections: we estimate a simple Mincerian wage equation, examine returns to specific types of work experiences acquired during higher education, and provide an analysis of the gender wage differential within the first full-time employment among our sample of graduates from Saarland University. We conclude this part with a short summary of our main results and a discussion.

Our part on aggregate-level data is entirely concerned with Synthetic Control Methods (SCM) for comparative case studies on the aggregate level. Similar to the first part, we begin with a comprehensive introduction. Our next chapter contains a description of SCM, an explanation on how to conduct inference and a summary of recent articles on the topic. In the third chapter, we discuss certain pitfalls and problems regarding the implementation of SCM. Finally, we apply SCM to investigate the impact of graphic cigarette warning labels on smoking rates in Canada.

We conclude the thesis with some remarks on the scientific contribution of our work and an outlook on potential future…

Schlagworte

Volkswirtschaftslehre, Economics of Education, Economics, Statistics, Causal Interference, Individual-Level Data, Aggregate-Level Data, Program Evaluation