Bibliographic record
Abstract
Cross-sectional data show Global North countries export higher quality products at a point in time. Product-level panel data can address if countries improve their export quality over time. The literature has addressed this practically relevant panel question only in small samples over the short term. We addressed it for a large sample, over the long run, focusing on the hitherto overlooked endogeneity between export quality and factor accumulation and the role of export composition. We utilized a two-tiered panel: the panel of countries and the panel of products each country trades. We found some evidence that middle-income countries often upgrade export quality within the same product, but that high- and low-income countries do this less often. Our results appear to support product cycle theory: some countries climb the value ladder, others are competed off from the ladder’s top, and new countries enter markets. Technology appears to be a potential basis for consolidating trade competitiveness over time, as skill accumulation becomes more widespread across countries and loses significance as an explanatory variable. Our results provide some explanation of why Global North countries might resist sharing technology. This research is timely with deadlocked multilateral trade negotiations and looming trade wars. It attempts to contribute to an evidence-based guide to trade policy.
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".