Measures to Manage Capital Flows in Emerging Economies: Recent Experiences*
Bibliographic record
Abstract
After a brief reversal during the recent financial crisis, private capital inflows to EMEs are once again surging. The IMF has revised its long-standing opposition to capital controls to come out with indicators of the need to impose capital controls. In this context, this paper assesses the extent to which emerging economies had a macroeconomic „need to impose CFMs‟ using the IMF criteria, both for the period immediately preceding the Great Recession and the period since. We find that in the majority of country-year pairs in which there was an inflow surge in our sample, the IMF criteria for the need to impose capital controls were not satisfied. Yet, the 22 countries in our sample took around 500 capital flow measures (both easings, tightenings and institutional changes) during this period (2004-2010). Most of these measures were pure capital controls rather than prudential-type measures. Most measures were also undertaken by countryyear pairs that did not satisfy IMF criteria for their need. Moreover, the crisis marked a large drop in the measures liberalizing both inflows and outflows –– a trend that has gone largely unnoticed hitherto.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".