Impact of COVID-19 Response on Unemployment in Sri Lanka
Bibliographic record
Abstract
Goal number eight of the Sustainable Development Goals is set out as “Decent work and economic growth.” This goal recognizes that economic growth is not just an end, but also a means in itself of generating jobs – productive and meaningful work, an enduring measure of human development. Therefore, from a developmental perspective, it remains important to be vigilant not only about the impact on the economy but also about the creation of jobs and the extent of unemployment. In Sri Lanka, the government’s response to COVID-19 was akin to a “crush and contain” strategy, wherein the country was placed on the most stringent curfew-level lockdown for a period of 52 days after the first case of COVID-19 was detected on March 11th, 2020. Many Sri Lankan workers lost their jobs in the immediate aftermath of the lockdown. Statistics indicate that the total number of jobs in the economy contracted by 160,996 in the first quarter of 2020. Consequently, the COVID-19 response in Sri Lanka has already had a large impact on economic growth. Furthermore, measures taken by the government to preserve foreign reserves has resulted in further restrictions to importations and investments, which in turn have an impact on economic growth. This article translates estimates on economic impact to estimates of impact on unemployment, as it arises in this context of containing COVID-19 in Sri Lanka. The estimates indicate that following the curfew-level lockdown implemented in Sri Lanka between March and June 2020, overall unemployment increased to above 6% in the second quarter and will remain above 6% for the rest of the year. These estimates offer the government a means of evaluating some of the social and economic consequences that arise from curfew-level lockdowns, and point to the importance of developing more measured approaches of containing the spread of COVID-19, as the pandemic risks persist.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".