Why Growth Performance Differed across Countries in the Recent Crisis: the Impact of Pre-crisis Conditions
Bibliographic record
Abstract
The growth performance of countries proved to be very different during the recent financial crisis. The objective of the paper is to investigate why, despite the fact that the crisis hit countries simultaneously, the length and depth of the crisis turned out to be very different across countries. We apply principal component analysis to derive a single indicator for growth performance which includes different aspects of GDP dynamics before and after the crisis. Then we apply multivariate regressions analysis to analyze whether pre-crisis economic conditions and/or structural characteristics can explain the differences in growth performance in a sample of 37 countries. We focus primarily on industrialized countries but also include dynamic emerging economies. The pre-crisis conditions we investigate include the fiscal situation, trade competitiveness, output and credit growth; the structural characteristics we selected were country size, openness, the share of specific sectors and per capita income. The three indicators which proved to explain most robustly the cross country differences in the recent crisis and thus could also be used as predictors for future crises are the current account position, credit growth and GDP growth in the run-up period. Trade competitiveness improved the performance in the crisis. Past credit and GDP growth impaired country performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".