Risk equalization in The Netherlands: an empirical evaluation
Bibliographic record
Abstract
The Netherlands relies on risk equalization to compensate competing health insurers for predictable variation in individual medical expenses. Without accurate risk equalization insurers are confronted with incentives for risk selection. The goal of this study is to evaluate the improvement in predictive accuracy of the Dutch risk equalization model since its introduction in 1993. Based on individual-level claims data (n = 15.6 million), we estimate the risk equalization models that have been successively applied in The Netherlands since 1993. Using individual-level survey data (n = 8735), we examine the average under-/overcompensation by these models for several relevant subgroups in the population. We find that in the course of years, the risk equalization model has been substantially improved. Even the current model (2012), however, does not eliminate incentives for risk selection completely. To achieve the public objectives, further improvement of the Dutch risk equalization model is crucial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".