Latin America and COVID-19: Shutting Down in a World of Informal and Tiny Firms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Employment losses in 2020 were larger in Latin America than in any other region. We show that the prevalence of informality, micro-entrepreneurship, and jobs-not-fit for remote work in nonessential sectors accounts for this outcome in both simulations and ex post data. When considering lockdowns and demand shocks, amplified by input–output linkages and a Keynesian multiplier, these distinctive characteristics imply that the risk of employment losses in a typical Latin American economy is at least five times higher than that of a counterfactual United States. Our framework explains over 70 percent of the observed cross-sector variation in work hours in the second quarter of 2020. Early blanket lockdowns, such as those implemented in part of Latin America, affect informality differently and outweigh other factors. JEL Classification Codes: F; O47; O20; O17
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it