Randomized Regulation: The Impact of Minimum Quality Standards on Health Markets
Bibliographic record
Abstract
This paper presents results from the first randomization of a regulatory reform in the health sector. The reform established minimum quality standards for patient safety, an issue that has become increasingly salient following the Ebola and COVID-19 epidemics. In the experiment, all 1,348 health facilities in three Kenyan counties were classified into 273 markets, and the markets were then randomly allocated to treatment and control groups. Government inspectors visited health facilities and, depending on the results of their inspection, recommended closure or a timeline for improvements. The intervention increased compliance with patient safety measures in both public and private facilities (more so in the latter) and reallocated patients from private to public facilities without increasing out-of-pocket payments or decreasing facility use. In treated markets, improvements were equally marked throughout the quality distribution, consistent with a simple model of vertical differentiation in oligopolies. This paper thus establishes the use of experimental techniques to study regulatory reforms and, in doing so, shows that minimum standards can improve quality across the board without adversely affecting utilization.
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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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".