Working Paper Series,
IFAU - Institute for Evaluation of Labour Market and Education Policy

Abstract: Sickness insurance guarantees employees the right to take leave from work when they are sick, but is vulnerable to excessive use because monitoring of recipients’ health is difficult and costly. In terms of costs, it would be preferable to focus monitoring on individuals whose sickness absence it strongly affects. This paper studies targeted monitoring in the setting of a large-scale randomised experiment where medical certificate requirements were relaxed for some workers. I employ a machine learning method, the generalised random forest, to identify heterogeneous effects on the duration of workers’ sickness absence spells. This allows me to compute treatment effect estimates based on an extensive set of worker characteristics and their potentially complex relationships with each other and with sickness absence duration. The individuals who are most sensitive to monitoring are characterised by a history of extensive sick leave uptake, low socioeconomic status, and male gender. The results suggest that a targeted policy can achieve the same reduction in monitoring costs as took place during the experiment at a 51 percent smaller loss in terms of increased sickness absence. Monitoring all insured individuals is estimated to be inefficient, but the benefits of targeted monitoring are estimated to exceed the costs.

Keywords: Sickness Absence; Monitoring; Heterogeneous Effects; GRF

JEL-codes: C21; I18; J22

Language: English

58 pages, November 8, 2024

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