How workers respond to social rewards: evidence from community health workers in Uganda
Bibliographic record
Abstract
This paper investigates the effect of a non-financial incentive-a competitive annual award-on community health workers' (CHWs) performance, an issue in the public health literature that has not been explored to its potential. Combining data on a competitive social 'Best CHW' award with the monthly performance of 4050 CHWs across Uganda, we examined if introducing social recognition awards improved the performance of CHWs. In contrast to predominant explanations about the effect of awards on motivation, our first multilevel mixed-effect models found that an award within a branch (consisting of ∼30 CHWs) was negatively associated with the performance of the local peers of the winning CHW. Models focused on non-winning branch offices revealed two additional findings. First, a branch showed underperformance if a CHW from any of the three neighbouring branches won an award in the previous year, with average monthly performance scores dropping by 27 percentage points. Second, this negative association was seen only in the top 50th percentile of CHWs. The bottom 50th percentile of CHWs exhibited increased performance by 13 percentage points. These counter-intuitive results suggest that the negative response from high performers might be explained by their frustration of not winning the award or by emotions such as envy and jealousy generated by negative social comparisons. Our results suggest that more fine-grained examination of data pertaining to motivators for CHWs in low-income countries is needed. Motivational incentives like awards may need to be customized for higher- and lower-performing CHWs.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".