COVID-19 and the Informality-driven Recovery: The Case of Colombia's Labor Market
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
This paper documents the impact of the COVID-19 pandemic and associated lockdowns on the Colombian labor market using household micro-data. About a quarter of employment was temporarily disrupted at the height of the first pandemic-induced lockdown in 2020. Women, the young, and the less educated were the most affected groups. Since then, a remarkable recovery, led by a rebound in informal employment, has taken place. By adjusting both employment levels and hours faster, the informal sector acted as an important margin of adjustment, particularly in those industries most affected by the first lockdown. The informal sector also appears to have played a role in decreasing the sensitivity of aggregate employment to more recent lockdowns in 2021, as the economy has learned to cope with pandemic restrictions, although the possibility of higher informality rates becoming embedded remains an substantial downside risk for long-term productivity.
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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 | 0.002 | 0.003 |
| 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) | 0.001 | 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