Capital Flows to Latin America and the Caribbean: First Quarter 2020 in Times of COVID-19
Bibliographic record
Abstract
Highlights • In the first quarter of 2020, the spread of the coronavirus, together with a precipitous decline in commodity prices, radically changed the financial landscape for Latin American and Caribbean (LAC) issuers. After a record breaking issuance of US$ 38 billion worth of bonds in January, bond issuance dried-up in February and March, bringing total quarterly issuance to US$ 45 billion. • On March 26, however, Panama successfully placed a sovereign bond in cross-border markets to secure additional resources to combat the COVID-19 pandemic. It was followed in April by other four sovereigns from the region – Peru, Guatemala, Mexico and Paraguay – all tapping international debt markets with sizeable issuances for coronavirus funding. There was strong demand for the bonds. Together, these five sovereign issuances amounted to US$ 13 billion in new cross-border bonds. • There were five international green bond issuances in the first quarter (all in January), two new and three re-openings, and a social bond placed by Ecuador, which became the first sovereign issuer to sell social bonds in the international market. In April, Guatemala issued US$ 500 million in social bonds to finance eligible social investments directly and indirectly related to COVID-19 prevention, containment and mitigation efforts. • The shocks of the COVID-19 pandemic and plunge in commodity prices were amplified by greater risk aversion, with volatility reaching a historic peak in mid-March. LAC spreads widened 357 basis points in the first quarter as a result, and were at 703 basis points at the end of March, close to the peak reached in November 2008 during the global financial crisis (765 basis points). Equities and currencies in the region also weakened significantly amid the coronavirus outbreak. The MSCI Latin American index was down 46% by the end of March. • There were nine more negative credit rating actions than positive in the region in the first quarter. In April alone, there were fourteen negative actions, eleven of them downgrades. All of the negative actions in April mentioned the impact of the coronavirus outbreak and of plummeting commodity prices. The commodity-exporters of the region, particularly oil-exporters, were the hardest hit by the downgrades.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".