How competitive are income annuity providers over time?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The 2019 SECURE Act provides safe harbor protections to employers who evaluate the costs of providing guaranteed income including gathering information on competing providers. Annuities can be more difficult to evaluate than mutual funds because annuity expenses can be opaque, financial strength matters, and insurer competitiveness can change over time. We find significant variation in the payout rates across providers over time. While the payout rankings of annuity companies (e.g., best to worst) are fairly sticky over the short‐term, over the full period of the analysis the correlation declines effectively to zero (vs. the initial rankings). This suggests individuals or institutions who choose a single annuity provider based on income payout should revisit the decision regularly to ensure the quotes are still competitive. Companies for which immediate annuities are a higher fraction of total sales tend to rank higher and remain so more persistently over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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