Job Sick Leave: Detecting Opportunistic Behavior

We utilize a large administrative dataset of sickness leave in Italy, i) to investigate whether private firms are more effective than the public insurer in choosing who to monitor, and ii) to study the correlation between potentially opportunistic behavior and the observable characteristics of the employee. We find that private employers are more likely to select into monitoring employees who are fit for work despite being on sick leave, if the public insurer is not supported by any data-driven tool. However, the use of a scoring mechanism, based on past records, allows the public insurer to be as effective as the employer. This result suggests that the application of machine learning to appropriate databases may improve the targeting of public monitoring to detect opportunistic behavior. Concerning the association between observable characteristics and potentially opportunistic behavior, we find that males, employees younger than 50, those on short leaves or without a history of illness are more likely to be found fit for work.