Individual Private Pension Insurances : the effects of sociodemographics characteristics on poverty
Bibliographic record
Abstract
Funded private pillars are well developed in many countries such as the United States, the United Kingdom, Canada, Switzerland and the Netherlands. In France, the development of occupational pension plans has been limited. However, in this country, households tend to \nincrease their personal saving by contracting life insurances in order to finance their retirement. \nBy encouraging the individualization of retirement planning, reforms of public pension systems \ncould expose a vulnerable part of the population to the risk of poverty. Among households, lowincome \nand women are particularly vulnerable during their working life and then during their retirement period. In France and in other OECD countries, the trend to have more individualized pension raises the question of the poverty during the retirement period. \nIn this context, could life insurances and private pension contracts avoid the risk of poverty ? \nIn this paper, we analyze the behaviour of life insurance holding in France, by using an \neconometric model and studying statistically the risk of poverty among Retired.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".