What Drives Successful Economic Diversification in Resource-Rich Countries?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The “resource curse” is often understood to imply poor growth in the non-resource sectors of the economy, but research into the diversification performance of resource-rich countries is limited. This paper surveys recent evidence and identifies empirical patterns in the economic diversification of resource-rich countries. Diversification is measured using the growth of per capita non-resource (manufacturing and services) sectors in domestic and export markets, which has a cleaner interpretation than competing measures. This measure is used to evaluate the long-term diversification of countries that started off as resource-dependent, and to rank countries according to their performance. We then identify policy-relevant correlates of diversification at the national level, including the acquisition of human capital, public and intellectual capital, and firm dynamism. More resource-dependent countries appear to perform worse on measures of human capital and intellectual capital, but more resource-abundant countries perform better on public capital and human capital accumulation. We examine the mechanisms behind diversification performance through in-depth case studies of Oman, Laos, and Indonesia, and conclude by identifying policy lessons and future research directions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.006 |
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 it