Emerging economies, productivity growth and trade with resource‐rich economies by 2030
Bibliographic record
Abstract
Rapid economic growth in some emerging economies in recent decades has significantly increased their global economic importance. If this rapid growth continues and is strongest in resource‐poor Asian economies, the growth in global demand for imports of primary products also will continue, to the ongoing benefit of natural resource‐rich countries. This paper explores how global production, consumption and trade patterns might change over the next two decades in the course of economic development and structural changes under various scenarios. We employ the GTAP model and version 8.1 of the GTAP database with a base year of 2007, along with supplementary data from a range of sources, to support projections of the global economy to 2030. We first project a baseline assuming that trade‐related policies do not change in each region but that factor endowments and real GDP grow at exogenously estimated rates. That baseline is compared with two alternative scenarios: one in which the growth rates of China and India are lower by one‐quarter and the other in which this slowdown in emerging economies leads to slower productivity growth in the primary sectors of all countries. Throughout the results, implications are drawn out for natural resource‐abundant economies, including Australia and New Zealand.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".