Energy Governance, Transnational Rules, and the Resource Curse: Exploring the Effectiveness of the Extractive Industries Transparency Initiative (EITI)
Bibliographic record
Abstract
Transnational standards for disclosure have become a defining feature of global governance and sound economic development, yet little is known about their effectiveness. This study statistically explores the efficacy of such standards for the important case of the Extractive Industries Transparency Initiative (EITI), an international non-governmental organization which maintains a voluntary standard for revenue transparency in the extractive industries. As of November 2015, 31 countries were “EITI Compliant” and another 49 were “EITI Candidates.” In total, 49 countries had disclosed payments and revenues worth some $1.67 trillion in more than 200 “EITI Reports”, and over 90 major companies involved in oil, gas, and mining are committed to supporting the EITI. The EITI has also received support from 84 global investment institutions that collectively manage about $16 trillion in energy infrastructural assets. Moreover, the European Union, African Union, G8 and G20, and the United Nations have all endorsed the EITI. This article provides the first broad empirical examination of the EITI’s effectiveness in improving governance and economic development outcomes in its member countries using non-parametric tests, regression analysis, and data from the World Bank. We analyze the performance of the first 16 countries to attain EITI Compliance Status over the period of 1996–2014. We find, interestingly, that in most metrics EITI countries do not perform better during EITI compliance than before it, and that they do not outperform other countries. We postulate four possible explanations behind the relative weakness of the EITI: a limited mandate, its voluntary nature, stakeholder resistance, and dependence on strong civil society.
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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 | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".