Who is paid in pay-for-performance? Inequalities in the distribution of financial bonuses amongst health centres in Zimbabwe
Bibliographic record
Abstract
Although pay-for-performance (P4P) schemes have been implemented across low- and middle-income countries (LMICs), little is known about their distributional consequences. A key concern is that financial bonuses are primarily captured by providers who are already better able to perform (for example, those in wealthier areas), P4P could exacerbate existing inequalities within the health system. We examine inequalities in the distribution of pay-outs in Zimbabwe's national P4P scheme (2014-2016) using quantitative data on bonus payments and facility characteristics and findings from a thematic policy review and 28 semi-structured interviews with stakeholders at all system levels. We found that in Zimbabwe, facilities with better baseline access to guidelines, more staff, higher consultation volumes and wealthier and less remote target populations earned significantly higher P4P bonuses throughout the programme. For instance, facilities that were 1 SD above the mean in terms of access to guidelines, earned 90 USD more per quarter than those that were 1 SD below the mean. Differences in bonus pay-outs for facilities that were 1 SD above and below the mean in terms of the number of staff and consultation volumes are even more pronounced at 348 USD and 445 USD per quarter. Similarly, facilities with villages in the poorest wealth quintile in their vicinity earned less than all others-and 752 USD less per quarter than those serving villages in the richest quintile. Qualitative data confirm these findings. Respondents identified facility baseline structural quality, leadership, catchment population size and remoteness as affecting performance in the scheme. Unequal distribution of P4P pay-outs was identified as having negative consequences on staff retention, absenteeism and motivation. Based on our findings and previous work, we provide some guidance to policymakers on how to design more equitable P4P schemes.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".