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Chair der Sitzung: Martin Schneider, Universität Paderborn
Ort:Virtueller Raum 3
Bank Bonus Pay as a Risk Sharing Contract
Patrick Kampkötter1, Matthias Efing2, Harald Hau3, Jean-Charles Rochet3,4
1Eberhard Karls Universität Tübingen, Deutschland; 2HEC Paris; 3University of Geneva and Swiss Finance Institute; 4University of Zurich
We argue that risk sharing motivates the bank-wide structure of bonus pay. In the presence of financial frictions that make external financing costly, the optimal contract between shareholders and employees involves some degree of risk sharing whereby bonus pay partially absorbs earnings shocks. Using payroll data for 1.26 million employee-years in all functional divisions of Austrian, German, and Swiss banks, we uncover several empirical patterns in bonus pay that are difficult to rationalize with incentive theories of bonus pay - but support an important risk sharing motive. In particular, bonuses respond to performance shocks that are outside the control of employees because they originate in other bank divisions or even outside the bank.
IT Skills, Occupation Specificity and Job Separations
Christian Eggenberger, Uschi Backes-Gellner
Universität Zürich, Schweiz
This paper examines how workers’ earnings change after involuntary job separations depending on the workers’ acquired IT skills and the specificity of their occupational training. We expect that IT skills can reduce or amplify earnings losses of workers with specific occupational skill bundles. Our paper uses information on all skills that workers acquire in their occupational training. On one hand, we categorize IT skills found in training curricula into (a) generic IT skills useful in any context (e.g., configuring software) and expert IT skills (e.g., specific programming languages). On the other hand, we distinguish between more specific and less specific occupations by comparing that occupation’s skill bundle to the skill needs of the overall labor market. Our results show that generic IT skills are positively associated with higher earnings after involuntary separations; expert IT skills are not. The positive correlation between generic IT skills and earnings is strongest for workers in specific occupations, who otherwise have the largest earnings losses after involuntary separations.
Types of Commitment and Patterns of Participation and Loyalty on a Crowdworking Platform. A Case Study Applying Fuzzy-Set Qualitative Comparative Analysis
Paul Hemsen, Martin Schneider
Universität Paderborn, Deutschland
A new form of flexible employment in the digital economy is “crowdworking”: Companies source out online tasks to specialized crowdsourcing platforms. The platforms advertise the tasks to their registered free-lancers – the crowdworkers – who can perform a job in return for a fixed price. Crowdworking platforms that focus on tasks with considerable skill needs such as designing are dependent on committed expert workers – but they do not know much about a heterogeneous and potentially footloose crowd. In this paper, we therefore explore why workers are committed to a platform, how a platform’s incentive system matters for crowdworker commitment, and how commitment influences work hours (participation) and intention to stay (loyalty). The study is based on a survey among 204 workers registered with a text creation platform. It runs a sophisticated rating-based incentive system: Workers have access to more challenging tasks and better pay once they have achieved a higher performance rating (“five stars”). The system generates commitment because it appeals to differing worker motives for engaging on the platform. Based on their motives to do crowdwork and their levels of satisfaction with the incentive system, we identify six groups of committed workers applying fuzzy-set qualitative comparative analysis (fs/QCA). We also show that calculative commitment among workers is associated with more work hours, and affective commitment with a stronger intention to stay. Overall, this paper adapts the concept of organizational commitment to the platform context and shows how distinct groups of committed workers can be identified within a workforce crowd.