The Elusive Costs of Sovereign Defaults
Bibliographic record
Abstract
Few would dispute that sovereign defaults entail significant economic costs, including, most notably, important output losses. However, most of the evidence supporting this conventional wisdom, based on annual observations, suffers from serious measurement and identification problems. To address these drawbacks, we examine the impact of default on growth by looking at quarterly data for emerging economies. We find that, contrary to what is typically assumed, output contractions precede defaults. Moreover, we find that the trough of the contraction coincides with the quarter of default, and that output starts to grow thereafter, indicating that default episode, rather than a further decline, seems to mark the beginning of the economic recovery. This suggests that whatever negative effects a default may have on output, they are driven by its anticipation, independently of whether or not the country ultimately decides to validate it. * We would like to thank Eduardo Cavallo and other participants in the December 2005 IPES pre-conference for usefull comments, and Mariano Alvarez for excellent research assistance. The views expressed in this paper and the authors ’ do not necessarily reflect those of the Inter-American Development Bank. 2 Hell, the last thing I should be doing is tell a country we should give up our claims. But there comes a time when you have to face reality. The problem historically has not been that countries have been to eager to renege on their financial obligations, but often too reluctant.1
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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 | 0.007 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".