Healthcare Facility Choice and User Fee Abolition: Regression Discontinuity in a Multinomial Choice Setting
Bibliographic record
Abstract
Summary We apply parametric and non-parametric regression discontinuity methodology within a multinomial choice setting to examine the effect of public healthcare user fee abolition on health facility choice by using data from South Africa. The non-parametric model is found to outperform the parametric model both in and out of sample, while also delivering more plausible estimates of the effect of user fee abolition (i.e. the ‘treatment effect’). In the parametric framework, treatment effects were relatively constant—around 10%—and that increase was drawn equally from home care and private care. In contrast, in the non-parametric framework treatment effects were largest for large (and poor) families located further from health facilities—approximately 5%. More plausibly, the positive treatment effect was drawn primarily from home care, suggesting that the policy favoured children living in poorer conditions, as those children received at least some minimum level of professional healthcare after the policy was implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".