Does Self-Regulation Generate Desired Social Outcomes? Evidence from Payment Transparency in Africa
Bibliographic record
Abstract
We investigate the effectiveness of multi-stakeholder initiatives (MSIs) as a governance mechanism in tackling global sustainability challenges, focusing on the Extractive Industries Transparency Initiative (EITI). We evaluate whether MSIs, seen as an alternative to industry self-regulation, lead firms to commit to and achieve higher social or environmental standards. We explore the symbolic versus substantive nature of these voluntary commitments and their actual impact on social outcomes. By examining the EITI, which aims to reduce corruption in extractive industries through payment transparency, we assess the impact of EITI membership on transparency performance and local perceptions of corruption. Utilizing data on the staggered adoption of mandatory disclosure by firms in Europe and Canada, we find that EITI-supporting firms show better transparency. Additionally, geo-coded survey data from Africa reveals that local perceptions of corruption decrease near areas of new investments by EITI-supporting firms. These findings indicate that self-regulation initiatives can yield positive social outcomes. The study contributes to the debate on the social impact of private-sector CSR initiatives and the effectiveness of MSIs in addressing sustainability challenges.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".