Deductions from the Export Basket: Capabilities, Wealth and Trade
Bibliographic record
Abstract
This paper re-explores the relation between a country's level of wealth and the mix of products it exports.We argue that both are simultaneously determined by countries' capabilities i.e. by countries' productivity and quality levels for each good.Our theoretical setup has two features.(1) Some goods have fewer high-quality producers/countries than others i.e. there is Ricardian comparative advantage.(2) Imperfect competition allows high-and low-quality producers to coexist, which we refer to as 'product ranges'.These two features generate a very particular non-monotonic, general equilibrium relationship between a country's export mix and its wage (GDP per capita).We show that this non-monotonicity permeates the 1980-2005 international data on trade and GDP per capita.Our setup also explains two other facets of the data: (1) Product ranges are huge and (2) for the poorest third of countries, changes in export mix substantially over-predict growth in GDP per capita.This suggests that the main challenge for low-income countries is to raise quality and productivity in their existing product lines.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".