The Economic Impact of Corruption Scandals in Latin America: Evidence from the Sovereign Bond Market
Bibliographic record
Abstract
We explore seven Latin American (LATAM) countries from 2011 to 2018 to assess whether corruption scandal events induce sovereign spreads reactions and consequently affect economic soundness. We focus on the direct and short-term reactions of sovereign spreads and investigate the medium-term impact of scandal events on economic soundness. We find that corruption scandal announcements instantaneously inflate sovereign spreads; the next-day impact is even stronger. Corruption scandals in one country are found to positively impact neighbouring countries (through sovereign yield deflation mechanisms) but induce lower FDI inflows and inverse contagion effects in the wider region. These results highlight the critical role played by scandals in the dynamics of borrowing costs faced by LATAM economies. The results may be employed by policymakers to forecast the consequences for their country's cost of debt and modify fiscal strategies accordingly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".