Financial Development, Credit, and Business Cycles
Bibliographic record
Abstract
Abstract How does financial development affect the magnitude of the business cycles fluctuations? We examine this question in a general equilibrium model with heterogeneous agents and endogenous credit constraints based on Kiyotaki (1998). We show that there is a hump‐shaped relationship between the degree of financial frictions and the amplification of unexpected productivity shocks. This nonmonotonic relation is due to the fall in financial frictions having two opposite effects on the response of output. One effect is the reallocation of productive inputs between agent types, which, while active, increases with the fall in financial frictions. The other effect is the change in the demand of inputs, which decreases with the fall in financial frictions. At low levels of financial development, the reallocation effect dominates and a fall in financial frictions increases the amplification of productivity shocks. In contrast, at higher levels of financial development, a fall in financial frictions decreases the shock amplification because the reallocation effect disappears while the effect on the demand of inputs is still present.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".