The sectoral trade losses from financial crises
Bibliographic record
Abstract
Abstract The “Great Trade Collapse” triggered by the 2008-2009 crisis calls for a careful assessment of the trade losses from financial crises. We adopt a more detailed perspective by looking at the response of different types of trade (i.e. consumption, intermediate, capital goods, and business services) following various types of financial crises (i.e. debt, banking, and currency crises) in 41 emerging markets. Estimations performed in the 1980-2019 period using a combination of impact assessment and local projections to capture a causal dynamic effect running from financial crises to the trade activity show that the collapse of total trade is long-lasting and mainly driven by the fall of intermediate goods and to some extent capital goods, while trade in consumption goods and business services is more resilient to crises. Therefore, financial crises could lead to considerable disruption of global value chains, as observed during the Global Financial Crisis (GFC), and easily spill over from one country to another through trade linkages. The examination of heterogeneity reveals that total and sectoral trade is more severely impacted in countries with a lower share of manufacturing exports, less diversified exported products, and trading partners, with lower demand from trading partners and when associated with a deterioration of the domestic and external financial conditions and sudden stops. By contributing to the understanding of the trade effects of financial crises, our analysis provides insightful support for the design and implementation of policies aimed at coping with these effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".