Institutionalization and Effectiveness of Macroprudential Policy: Evidence from Latin American Countries
Bibliographic record
Abstract
This work analyzes the relationship between macroprudential policy and different credit balances in Latin America distinguishing between loosening and tightening regulatory direction and focusing on the individual impact of each macroprudential instrument. Using quarterly data for ten countries covering the 2000-2019 period and employing panel OLS with fixed effects and covariance matrix, corrected by Arellano’s method, we analyze the full sample and compare the pre-and-post 2009 subperiods to assess the role of intensifying macroprudential policy institutionalization on its effectiveness. Positive results were predominant for the total, corporate, and real estate credit balance in the loosening measures. The tightening occurred in the balance of consumer credit and real estate. These effects, mainly from 2009 onwards, with the beginning of the institutionalization of macroprudential policy. The findings contribute to a more granular understanding of macroprudential policy impact, highlighting the importance of policy direction, specific instruments, credit type and the institutional context in Latin America.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".