For nearly half a century (cf. Hendrik S.
Bibliographic record
Abstract
omists have known that trade flows were two to three times more volatile than GDP despite the fact that standard theories predicted an elasticity of one. A major puzzle developed in the fourth quarter of 2008 as standard econometric mod-els, which already incorporated these very high elasticities, could only predict 70 to 80 percent of the decline in world trade (see the estimates in Organization of Economic Cooperation and Development (OECD) 2009). Over the next two years, economists have tried, with little success, to improve on this “70 per-cent solution. ” Much progress has been made in building theoretical models to explain why trade elasticities might differ from one, and calibration exercises soon began to match the econometric evidence. For example, Rudolfs Bems, Robert C. Johnson, and Kei-Mu Yi (2010) argue that once one takes into account input-output linkages, one can replicate the elasticity of imports with respect to GDP of three and explain 70 percent of the decline in world trade. Similarly, Jonathan Eaton et al. (2011) obtained an 80-percent solu-tion using a more elaborate general equilibrium model. As a result of this, many economists have been arguing that one needs to consider trade finance-based explanations for why calibration exercises to date have underestimated the decline in world trade. This paper reviews some of the evidence that financial factors may have resulted in a greater decline in exports than were predicted in models without financial frictions. We provide two new
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".