Good Policies or Good Fortune: What Drives the Compression in Emerging Market Spreads?
Bibliographic record
Abstract
Since 2002, spreads on emerging market sovereign debt have fallen to historical lows. Given the close links between sovereign spreads, capital flows to emerging markets, and economic growth, understanding the factors driving these spreads is very important. We address this issue in two stages. First, we use factor analysis to study the extent to which emerging market bond spreads are driven by global factors, as opposed to country-specific macroeconomic fundamentals. Using data on different U.S. asset classes, we identify a common factor, linked to global financial conditions. Second, we use this common factor in a panel estimation framework to analyze the degree to which the fall in spreads is driven by better macroeconomic policies. Our results show that the common factor is not responsible for the reduction in spreads. Instead, emerging markets have benefited considerably from better macroeconomic policies, including lower inflation and lower debt. Therefore, a reversal of the benign global conditions need not necessarily have a substantial negative impact on financing conditions for emerging markets.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".