High-End Variety Exporters Defying Distance: Micro Facts and Macroeconomic Implications
Bibliographic record
Abstract
We develop a new methodology to identify high-end variety exporters in French firm-level data. We show that they do not export to many more countries, but they export to more distant ones. This comes with a greater geographic diversification of their aggregate exports. These facts are explained by a lower sensitivity to distance of high-end variety export(er)s. We also show that high-end export(er)s are more sensitive to the average income of the destination country. Because of this different sensitivity to gravity variables, the within-product specialization of a country in the production of high-end varieties is likely to affect its export growth and volatility. We show that a higher sensitivity to per capita income tends to increase the volatility of high-end variety exports. However, a lower sensitivity to distance reduces volatility through a greater geographic diversification. Furthermore, we point out that a lower sensitivity to distance allows high-end varieties to benefit more from growth in more distant markets.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".