What are the determinants of permanent and temporary non take-up of the French minimum social benefit?
Bibliographic record
Abstract
We investigate the determinants of non-take-up of the French minimum social benefit (Revenu de Solidarité Active [RSA], Active Solidarity Income) using the Tax and Social Income Survey and distinguishing between people in situations of either permanent or temporary eligibility. We use the monthly dimension of the dataset in order to study the dynamics of non-take-up. We find that poverty levels in the department of residence and having two or more children decrease the probability of RSA non-take-up, in cases where individuals are permanently or temporarily eligible while professional inactivity, having rental resources and being in a couple increase the likelihood of submitting a claim in both cases. In the case of permanent eligibility, we find that RSA non-take-up in a given quarter increases the probability of non-take-up in the following quarter. Moreover, the amount of the RSA, the level of sanctions, residing in a priority district and the "back and forth" between claimants and social agencies are important factors affecting non-take-up in the case of permanent eligibility while take-up for the Activity Bonus and social agencies' credibility in delivering accurate and reliable information, are salient in the temporary eligibility case.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".