Bibliographic record
Abstract
In this paper, I examine the role of household income in determining who bribes and how much they bribe in health care in Peru and Uganda.I find that rich patients are more likely than other patients to bribe in public health care: doubling household consumption increases the bribery probability by 0.2-0.4percentage points in Peru, compared to a bribery rate of 0.8%; doubling household expenditure in Uganda increases the bribery probability by 1.2 percentage points compared to a bribery rate of 17%.The income elasticity of the bribe amount cannot be precisely estimated in Peru, but is about 0.37 in Uganda.Bribes in the Ugandan public sector appear to be fees-for-service extorted from the richer patients amongst those exempted by government policy from paying the official fees.Bribes in the private sector appear to be flat-rate fees paid by patients who do not pay official fees.I do not find evidence that the public health care sector in either Peru or Uganda is able to price-discriminate less effectively than public institutions with less competition from the private sector.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 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".