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Private firms better target sick leave monitoring without data tools. Machine learning enables public insurers to match private sector effectiveness in detecting opportunistic behavior.

Keywords:
absenteeismfitness for workmoral hazardsick leave insurance

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Area of Science:

  • Health Economics
  • Public Policy
  • Data Science

Background:

  • Sickness absence represents a significant cost to economies.
  • Effective monitoring is crucial to mitigate costs associated with fraudulent or opportunistic sick leave.
  • The efficiency of monitoring by private firms versus public insurers is a key policy question.

Purpose of the Study:

  • To compare the effectiveness of private firms and public insurers in monitoring sickness leave.
  • To identify employee characteristics correlated with potentially opportunistic sick leave behavior.
  • To explore the role of data-driven tools, including machine learning, in enhancing monitoring efficiency.

Main Methods:

  • Analysis of a large administrative dataset of sickness leave in Italy.
  • Comparative analysis of monitoring strategies employed by private employers and the public insurer.
  • Statistical analysis to identify correlations between employee characteristics and opportunistic behavior.

Main Results:

  • Private employers more effectively monitor employees fit for work on sick leave when the public insurer lacks data-driven tools.
  • The public insurer achieves comparable effectiveness to private employers when utilizing a scoring mechanism based on past records.
  • Machine learning applications show potential for improving the targeting of public monitoring for opportunistic behavior.
  • Observable characteristics such as being male, younger than 50, on short leaves, or lacking a prior illness history are associated with a higher likelihood of being found fit for work while on sick leave.

Conclusions:

  • Data-driven tools, particularly machine learning, can significantly enhance the public insurer's ability to detect opportunistic sick leave.
  • Targeted monitoring, informed by data analytics, can improve the efficiency and fairness of sickness absence management.
  • Understanding employee characteristics associated with opportunistic behavior can inform policy and intervention strategies.