Welfare Effects of Banning Genetic Information in the Life Insurance Market: The Case of BRCA1/2 Genes
Bibliographic record
Abstract
This paper is a contribution to the debate about whether regulations that ban insurance companies from access to individuals’ genetic tests may lead in the near to medium term future to substantial adverse selection costs. We choose the specific possibility of widespread knowledge based on genetic testing for the so-called breast cancer (BRCA1/2) genes. We use a data set including economic, demographic, and relevant family background information to simulate the market for 10-year term life insurance targeted at women aged 35 to 39. Using standard welfare economic analysis for various information and regulatory scenarios concerning genetic test results, we find generally only modest adverse selection costs associated with such a regulatory ban. However, for family background groups which are at high risk for carrying one of the BRCA1/2 genes, the efficiency cost of adverse selection may be significant especially if a large fraction of women within such groups were to obtain genetic test results. These results, therefore, suggest some caution in developing regulations which protect individuals’ genetic privacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".