Is a country lucky to have natural resources? : a cross-country study of how mineral and fuel industries affect economic growth using panel data.
Bibliographic record
Abstract
There is a body of empirical Literature finding that an abundance of natural resources actually hinders a country's economic growth. This is a counterintuitive result given the historical experience of countries such as Australia, Canada and the United States. Theories have also been developed to explain this empirical finding. One family of explanations incorporates the Dutch Disease into endogenous growth by assuming that an expansion of the resource sector changes the composition of output, which in turn determines productivity growth. The concern is that new theories are being developed without solid empirical evidence. Other prominent explanations for resource abundance hindering development include the Singer-Prebisch thesis, the prevalence external shocks and rent seeking due to the high and usually unequal distribution of the profits from these industries. In this study a data set using 54 countries with annual observations from 1983 to 2000 is used. A key advantage over previous research is the use of a more precise measure of resource abundance. The total rent earned from a country's mineral and fuel industries is used. Another important feature of this study is the investigation of how an expansion of the resources sector may affect growth several years after the expansion has occurred, as it provides a test of the new theories that make use of the Dutch Disease. Minimal support for resource abundance detracting from economic growth is found. The volatility of exports is unimportant. Countries rich in natural resources are not more prone to Government corruption. Finally, the more recent explanations involving the Dynamic Dutch Disease are rejected.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".