Bibliographic record
Abstract
Financial crises in emerging economies in the 1980s and 1990s often entailed abrupt declines in foreign capital inflows, improvements in trade balance, and large declines in output and total factor productivity (TFP). This paper develops a two-sector small open economy model wherein heterogeneous firms face collateralized credit constraints for investment loans. The model is calibrated using Mexican data, and explains the economic downturn and subsequent recoveries following financial crises. In response to a sudden tightening of credit availability, the model generates a large decline in external debt, an improvement in trade balance, and declines in output and TFP, consistent with the stylized facts of sudden stop episodes. Tighter borrowing constraints lead firms to reduce investment and production, which in turn results in some firms holding capital stock disproportionate to their productivity levels. This disrupts the optimal allocation of capital across firms, and generates an endogenous fall in measured TFP. Furthermore, the subsequent recovery is driven by the traded sector, since the credit crunch is more persistent among domestic financing sources relative to foreign financing sources. This is consistent with the experience of Mexico, where the relatively fast recovery from the 1994-95 crisis was driven mainly by the traded sector, which had access to international financial 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".