Bank Bonus Pay as a Risk Sharing Contract

https://ssrn.com/abstract=3202916We show that banker bonuses cannot be understood exclusively as incentive contracts, but also incorporate a significant risk sharing dimension between bank shareholders and bank employees. This contrasts with the conventional view whereby diversified shareholders fully insure risk averse employees. However, financial frictions imply that shareholder value is concave in a bank's cash reserves---making shareholders effectively risk averse. The optimal contract between shareholders and employees then involves some degree of risk sharing. Using extensive payroll data on 1.26 million bank employee years in the Austrian, German, and Swiss banking sectors, we show that the structure of bonus pay within and across banks is compatible with an economically significant risk sharing motive, but difficult to rationalize based on incentive theories of bonus pay only

Using Social Network Activity Data to Identify and Target Job Seekers

An important challenge for many firms is to identify the life transitions of its customers, such as job searching, being pregnant, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firm opportunities to be more relevant to its customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms. Our proposed approach builds on the hidden Markov model (HMM) framework to recover the latent state of job search from noisy signals obtained from social network activity data. Specifically, our modeling approach combines cross-sectional survey responses to a job seeking status question with longitudinal user activity data. Thus, in some time periods, and for some users, we observe the “true” job seeking status. We fuse the observed state information into the HMM likelihood, resulting in a partially HMM. We demonstrate that the proposed model can not only predict which users are likely to be job seeking at any point in time, but also what activities on the platform are associated with job search, and how long the users have been job seeking. Furthermore, we find that targeting job seekers based on our proposed approach can lead to a 42% increase in profits of a targeting campaign relative to the approach that was used at the time of the data collection