Bibliographic record
Abstract
International trade fell dramatically between 2008 and 2009. We provide a micro-econometric investigation of the determinants of this fall, using data from a small open economy, Belgium. First, we find that changes in firm-country-product exports and imports occurred almost exclusively at the intensive margin. Whereas quantities and prices contracted sharply, the number of firms, the average number of destination and origin markets per firm, and the average number of products per market changed only very little. Second, we examine the contribution of various firm, product and country characteristics to the fall in the intensive margin, thereby testing some conjectures that have been put forward in the literature. Our econometric results point toward a broad-based and very homogenous fall in trade. Input-intensive and highly leveraged firms relying strongly on trade credit reduced their imports somewhat more, but the implied magnitudes are small, explaining very little of the variation in the firm-specific part of the trade fall. Last, we show that exports-to-turnover and imports-to-intermediates ratios at the firm level did neither systematically decrease nor reveal strong firm- or sector-specific patterns. All of our results therefore point to a demand-side explanation: the fall in trade was mostly driven by the fall in
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| 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".