Distributional Effects of Social Security Reforms: the Case of France
Bibliographic record
Abstract
This paper uses a calibrated dynamic life-cycle model to quantify the long-run distributional impact of two opposite Social Security reforms: modifying the parameters of a defined benefit (DB) plan (such as in France with Ayrault’s reform) or switching to a notional defined contribution (NDC) plan (such as in Italy). Both reforms yield an inequal distribution of welfare losses. Low-skilled workers are the main losers of the reforms. This is so for different reasons in each reform. In the case of Ayrault’s reform, low-skilled individuals delay retirement by 2 years, up to age 62. In switching to a NDC scheme, low-skilled workers’pensions fall substantially. In NDC schemes, inequalities along the working-life are directly translated into inequalities in pension levels. The switch from a DB plan to the Italian reform yields substantial welfare losses, pensions drastically fall, and individuals save more. Since low-skilled workers do not save as much as middle or high-skilled workers, the switch to NDC schemes leads to a more unequal society in terms of asset distribution.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".