Abstract
Adverse selection harms market efficiency and access to essential services, particularly for disadvantaged groups. Risk equalization policies attempt to mitigate this by compensating agents for risk disparities, but often fall short of addressing interactions between risk factors. Using health insurance data from the Netherlands, we present a machine learning approach to capture unanticipated interactions that impact medical expenditure risk. We compare our novel approach to a state-of-the-art statistical model. We find that our approach explains an additional 1.5% of medical expenditure, equivalent to 571 million euros over all individuals in the Dutch market. In particular, this translates into better compensation for low- and high-cost groups that are especially vulnerable to adverse selection. These findings confirm the significance of risk factor interactions in explaining medical expenditure risk, and support the adoption of machine learning alongside statistical models to further mitigate selection incentives in risk equalization policies.
| Original language | English |
|---|---|
| Article number | 106564 |
| Journal | Economic Modelling |
| Volume | 130 |
| DOIs | |
| Publication status | Published - Jan 2024 |
Bibliographical note
Funding Information:We would like to gratefully thank the editor and the two anonymous reviewers for their considered comments that helped improve this article. We would like to also extend our gratitude to Johan Visser and Professor Pierre Koning for their valuable feedback during this study. Finally, we would like to thank the Ministry of Health, Welfare and Sport and the Dutch Association of Health Insurers for providing access to the administrative dataset used in this study. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Publisher Copyright:
© 2023 The Authors
Keywords
- Health insurance
- Machine learning
- Model fitting
- Risk equalization
- Risk selection