How much do means-tested benefits reduce the demand for annuities?
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Means-tested retirement benefits create incentives to cash out pension wealth. Individuals trade off the advantages from annuitization, receiving longevity risk insurance, to the disadvantages, giving up ‘free’ wealth in the form of means-tested supplemental income. We quantify the impact of means-tested benefits with a calibrated life-cycle model, demonstrating that they substantially reduce the desire to annuitize especially for low and intermediate levels of pension wealth. Using an administrative dataset on pension choices, we show that the model's predicted fraction of retirees choosing the annuity is able to match the annuitization pattern of occupational pension wealth observed in Switzerland. On the base of our model, we also assess alternative policies such as mandatory annutization and tougher asset tests.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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