Bibliographic record
Abstract
Abstract Empirical evidence has suggested a “resource curse” exists, in which countries with abundant resources may have higher initial consumption but then grow more slowly. The effect appears to be dependent on a country's political structure. Theoretical models not typically accounted for historical exceptions, or have not shown the effect exists in a dynamic growth setting. We derive the resource curse effect in an optimal growth model augmented with a political process. The economy has a finite nonrenewable resource, and the government planner can choose to over‐extract natural resources relative to the efficient path by distorting the discount rate, but in so doing incurs political costs that depend on the presence of democracy. Government planners in non‐democratic countries usually have more autonomy in policymaking than those in democratic countries; therefore, the political cost is lower for non‐democratic countries. We show that the incentive for the planner to distort the extraction path is larger, the higher is the initial resource endowment. Consistent with empirical evidence, the distortion raises short‐term consumption but lowers the long‐term growth rate, and institutional differences create corner solutions that explain why some resource‐abundant countries avoid the curse. These results are robust to the inclusion of autonomous technological change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".