The Governance of Nonprofits and Their Social Impact: Evidence from a Randomized Program in Healthcare in the Democratic Republic of Congo
Bibliographic record
Abstract
Substantial funding is provided to the healthcare systems of low-income countries. However, an important challenge is to ensure that this funding is used efficiently. This challenge is complicated by the fact that a large share of healthcare services in low-income countries is provided by nonprofit health centers that often lack (i) effective governance structures and (ii) organizational know-how and adequate training. In this paper, we argue that the bundling of performance-based incentives with auditing and feedback (A&F) is a potential way to overcome these obstacles. First, the combination of feedback and performance-based incentives—that is, feedback joined with incentives to act on this feedback and achieve specific health outcomes—helps address the knowledge gap that may otherwise undermine performance-based incentives. Second, coupling feedback with auditing helps ensure that the information underlying the feedback is reliable—a prerequisite for effective feedback. To examine the effectiveness of this bundle, we use data from a randomized governance program conducted in the Democratic Republic of Congo. Within the program, a set of health centers was randomly assigned to a “governance treatment” that consisted of performance-based incentives combined with A&F, whereas others were not. Consistent with our prediction, we find that the governance treatment led to (i) higher operating efficiency and (ii) improvements in health outcomes. Furthermore, we find that funding is not a substitute for the governance treatment; health centers that only receive funding increase their scale but do not show improvements in operating efficiency or health outcomes. This paper was accepted by Lamar Pierce, organizations. Funding: This research was supported by the Agence Nationale de la Recherche [Grant Investissements d’Avenir (LabEx Ecodec)]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4846 .
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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 | 0.033 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".