Making the Poor Pay for the Rich: Capital Account Liberalization and Reserve Accumulation in the Developing World
Bibliographic record
Abstract
Since the 1990s emerging market and developing countries (EMDCs) have been accumulating massive amounts of international reserves. The fundamental factor behind this reserve hoarding is financial in nature rather than trade-related, stemming from the widespread adoption of capital account liberalization in EMDCs, the resulting exposure to heightened financial volatility, and the consequent need to accumulate reserves as a self-insurance against potential disruptions in capital flows. Precautionary reserve hoarding, however, follows a circular logic that not only imposes heavy opportunity costs on EMDCs but also defeats the very purpose of capital account liberalization. When EMDCs accumulate reserves to hedge against capital account shocks, they are essentially recycling privately incurred short-term capital inflows into publicly incurred capital outflows, engaging in a reverse carry-trade that neither makes any economic sense nor results in any net transfer of financial resources from abroad. The net effect of this circular logic behind financial openness and precautionary reserve accumulation is a regressive and inequitable shifting of the costs of financial volatility from richer to poorer countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".