Third-Party Performance Pay to Improve Local Government Accountability: A Field Experiment in Burkina Faso
Bibliographic record
Abstract
Can local government accountability be improved by giving community-based organizations a financial stake in their local government's performance?In a field experiment in Burkina Faso, we test a "third-party performance pay" scheme for community-based organizations (CBOs).Selected CBOs are promised a variable cash grant that is proportional to changes in their local government's performance scores over a two-year period.We test if third-party performance pay (1) motivates CBOs to actively lobby for better municipal performance, (2) increases accountability and problem-awareness of municipal decision makers and (3) ultimately leads to improvements in municipal government performance.We also investigate if the incentive scheme had any unintended consequences for the internal functioning of the beneficiary CBOs.*Note to readers: This document is the unblinded replication of a results-blind analysis report that was previously uploaded to the AEA RCT registry.The blind analysis was conducted using datasets where all variables indicating treatment assignment or treatment status had been removed, masked, or replaced with simulated (randomly permuted) treatment identifiers.The original datasets were encrypted and safeguarded by research team members who are not authors of this study.During the blind analysis, we developed and refined our analytical framework, data cleaning and estimation strategies with knowledge of the data, but without knowledge of the actual results.Furthermore, we incorporated results-blind expert feedback from seminar presentations and recorded participants' expectations about the eventual, unblinded results via a prediction survey.
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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 | 0.030 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".