Does free complementary health insurance help the poor to access health care? Evidence from France
Bibliographic record
Abstract
The French government introduced a 'free complementary health insurance plan' in 2000, which covers most of the out-of-pocket payments faced by the poorest 10% of French residents. This plan was designed to help the non-elderly poor to access health care. To assess the impact of the introduction of the plan on its beneficiaries, we use a longitudinal data set to compare, for the same individual, the evolution of his/her expenditures before-and-after enrollment in the plan. This before-and-after analysis allows us to remove most of the spuriousness due to individual heterogeneity. We also use information on past coverage in a difference-in-difference analysis to evaluate the impact of specific benefits associated with the plan. We attempt at controlling for changes other than enrollment through a difference-in-difference analysis within the eligible (rather than enrolled) population. Our main result is the plan's lack of an overall effect on utilization. This result is likely attributable to the fact that those who were enrolled automatically in the free plan (the majority of enrollees), already benefited from a relatively generous plan. The significant effect among those who enrolled voluntarily in the free plan was likely driven by those with no previous complementary coverage.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".