Insurers' M&As in the United States during the 1990‒2022 period: Is the Fed monetary policy a causal factor?
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We investigate the causes of the gap in mergers and acquisitions (M&As) between life and nonlife insurers in the United States from 1990 to 2022. Our causality analysis indicates parallel trends between M&As in the life insurance and nonlife insurance sectors from 1990 to 2012, and a significant difference after 2012. There was a shock in the life insurance market that resulted in a reduction in M&As after 2012. Variable annuity sales and profitability in the life insurance sector declined after 2012. We find evidence that low interest rates observed after the implementation of the Fed's quantitative easing policy from 2008 to 2012 caused the difference in M&As between the life and nonlife sectors after 2012.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it