Costs and Benefits of Dollarization: Evidence from North, Central, and South America
Bibliographic record
Abstract
This paper examines the macroeconomic costs and benefits of dollarization. Economic theory suggests that the main benefit is enhanced price stability, while the main cost is higher business-cycle volatility if the dollarizing country's output is not sufficiently correlated with that of the U.S. Data from 1950Data from -1997 are used to estimate various cost and benefit measures for nineteen North, Central, and South American countries. The paper finds that these cost and benefit factors exhibit substantial variability across the countries considered. Furthermore, they are strongly positively correlated: countries (such as Peru) that have a lot to gain from dollarization, also have a lot to lose from it; while countries (such as Canada) that have little to lose by dollarizing, have also little to gain by it. The empirical results can be also used to compare net benefits for individual countries, showing, for example, that Chile is a better dollarization candidate than Mexico.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".