A disaggregated empirical analysis of the determinants of IMF arrangements: Does one model fit all?
Bibliographic record
Abstract
Abstract Does one model fit all when it comes to the determinants of IMF programs? Certainly claims have been made by the IMF that capital account crisis (CAC) countries are discernibly different in terms of the characteristics that lead them to borrow from it, while other research has claimed that it is only Asian economies that are different from the rest. This paper sets out to examine these issues. It tests various forms of a fairly conventional model to see whether some forms better fit certain groups of countries than others. It then uses the favoured models to estimate the probability of countries having an IMF arrangement. In particular it examines countries that have been identified by the Fund as CAC countries, but it also looks at a number of comparator countries. The findings suggest that there are some differences between low income and middle income countries. Pressures in the foreign exchange market are significant for the latter but not for the former. The paper also discusses differences between regions and within regions. Broadly speaking the findings confirm that Asian economies around the time of the 1997/1998 crisis tended to turn to the IMF for financial support more quickly than would have been anticipated on the basis of the existing best‐fitting models. The paper also discusses the implications of the findings for policy and for the reform of the IMF. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".