The Political Economy of Government Revenues in Post-Conflict Resource-Rich Africa: Liberia and Sierra Leone
Bibliographic record
Abstract
This paper examines the post-war strategies of Liberia and Sierra Leone to generate revenues from their natural resources. We document the challenges faced by the government of the two countries, contrasting measures taken to address these challenges as well as the outcomes. We complement the analysis with an analytical model which explores the implications of exploiting natural resources in the aftermath of a civil conflict before public management institutions are developed, as observed in Liberia and Sierra Leone. The key lesson is that resource-rich countries emerging from conflict face a difficult trade-off between relatively large longer-term gains which accrue when institutional capacity is developed prior to exploiting the resources, and smaller short-term revenues that come with immediate exploitation of the resources. The findings call attention to the potential role of the international community in developing post-conflict countries' natural resource and revenue institutional capacity, as well as transparent corporate and government institutions for resource management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".