Health Insurer Bargaining Power and Firms’ Incentives to Manage Earnings: Evidence From an Economic Shock
Bibliographic record
Abstract
Health insurance premiums account for a significant portion of the cost base of U.S. corporations. A recent study finds that health insurance premiums increase for firms that experience positive profit shocks, suggesting that the U.S. health insurance market is not perfectly competitive. Motivated by this finding and the economic importance of health insurance premiums, this is the first study to examine firms’ earnings management incentives in the face of insurance carriers with strong bargaining power. We use an innovative data set for a large sample of U.S. firms with detailed information on insurance premiums and insurance plan characteristics. Using an economic shock to insurance firms’ bargaining power and difference-in-differences tests, we find that firms manage their reported earnings downward when insurance providers have strong bargaining power. We further show that this effect is more pronounced in settings in which there are ex ante reasons to expect stronger incentives to manage earnings downward. We also provide preliminary evidence suggesting that downward earnings management has the intended effect of mitigating future increases in health insurance premiums. Our analyses highlight an inefficient health insurance market as an important determinant of firms’ financial reporting choices.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".