Daniel Goller, Ph.D.

Tournaments are frequently used incentive mechanisms to enhance performance. In this paper, we use field data and show that skill disparities among contestants asymmetrically affect the performance of contestants. Skill disparities have detrimental effects on the performance of the lower-ability contestant but positive effects on the performance of the higher-ability contestant. We discuss the potential of different behavioral approaches to explain our findings and discuss the implications of our results for the optimal design of contests. Beyond that, our study reveals two important empirical results: (a) affirmative action-type policies may help to mitigate the adverse effects on lower-ability contestants, and (b) the skill level of potential future contestants in subsequent tournament stages can detrimentally influence the performance of higher-ability contestants but does not affect the lower-ability contestant.

We analyze the causal influence of a positive reputation shock for a particular occupation on career choice. In the unpredictable event that a person from one's own country wins a (gold) medal in a particular occupation at the World Championship of Vocational Skills (WorldSkills), searches for apprenticeship vacancies increased significantly by around 7%. Most importantly, the increase in searches for apprenticeship vacancies in the current year has also led to a 2.5% increase in the number of contracts signed in the winning occupation, and there are indications that these apprenticeships have a better match between employers and employees.

Career and job fairs are frequently used instruments to improve the matching between employees and employers as well as between employees and professions. Despite their importance, however, their impact has been under-researched. Using an innovative dataset that measures searches on the largest platform for apprenticeship vacancies in real-time and the necessity that some fairs had to switch from in-person to virtual during the COVID-19 phase unexpectedly, we can show that virtual fairs not only had a stronger immediate positive impact on the number of searches for apprenticeship vacancies but also expanded the search radius for different professions.

We investigate the effectiveness of three different job-search and training programmes for German long-term unemployed persons. On the basis of an extensive administrative data set, we evaluated the effects of those programmes on various levels of aggregation using Causal Machine Learning. We found participants to benefit from the investigated programmes with placement services to be most effective. Effects are realised quickly and are long-lasting for any programme. While the effects are rather homogenous for men, we found differential effects for women in various characteristics. Women benefit in particular when local labour market conditions improve. Regarding the allocation mechanism of the unemployed to the different programmes, we found the observed allocation to be as effective as a random allocation. Therefore, we propose data-driven rules for the allocation of the unemployed to the respective labour market programmes that would improve the status-quo..