Free health care for the poor: a good way to achieve universal health coverage? Evidence from Morocco
Bibliographic record
Abstract
Policies and programs aimed at giving access to health care free of charge for some segments of the population are increasingly being put in place by low- and middle income countries, going against the Washington-consensus era recommendation to impose user fees on public health care to insure a better quality of service. Yet, such policies may not be suited for middle-income countries, where user fees are not necessarily be the biggest barriers to health care. We study a nationwide example of such a policy with the generalization of the Medical Assistance Regime or RAMED in the Kingdom of Morocco, a policy aiming at giving free access to hospital care to the poorest quarter of the population. Using nationally representative panel data collected before, during and after the extension of the policy, we show that the removal of user fees did have a positive impact on access to health care, but that this impact comes mostly from poorer, rural households. We also study the impact on health expenditures, and find that it has not led to a decrease of the financial burden, except for the subset of urban households that have recurring health expenditures. Overall, our result show that in a middle income country , user fees, even modest, seem to significantly deter healthcare usage for the rural part of the population.
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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 | 0.005 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".