Containing volatility : windfall revenues for resource-rich low-income countries
Bibliographic record
Abstract
An abundance of natural resources is \n both an opportunity and a challenge for developing \n countries. Several resource-rich, low-income countries \n receive amounts of foreign aid that are similar to or larger \n than their actual or potential revenues from natural \n resources. In such countries, the donors may have an \n opportunity to help a government to use its resource \n revenues productively and minimize the magnitude of risks \n created by resource rents. Development of aid instruments \n tailored for such purposes might be helped by model-based \n analysis of the effects of foreign aid on resource-rich, \n low-income economies and its interactions with the flows of \n natural resource revenues. This paper develops a growth \n model a la Barro in which the government receives windfalls \n (from natural resources and foreign aid) and rent-seeking \n agents contest for public funds. The key conclusion is that \n making aid countercyclical helps to achieve higher economic \n growth, and so does conditioning disbursements on \n enhancement of public capital. Introducing elements of \n insurance in the design of both aid products financing \n investments in infrastructure and social services and \n supporting policy and institutional reforms may help to \n achieve both of these objectives.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".