A Snapshot of the Crisis of COVID-19: Estimate of the Employment Impact Due to Lockdowns in Cagayan de Oro City, Philippines
Bibliographic record
Abstract
When coronavirus disease 2019 (COVID-19) became a national health crisis, the local government of the Cagayan de Oro City (CDOC) did not implement total lockdown. The COVID-19 Adjustment Measure Program adopted by the local government probably affected the April 2020 Labor Force Survey that showed that Region 10 posted an employment rate of 88.9%, which is higher than the national average of 82.3% (Department of Labor and Employment, Region Office No. X (DOLE-X). NorMin secures highest employment rate amid COVID 19. 2020. Available from: https://pia.gov.ph/news/articles/1044898 [Accessed 9th May 2021]). Despite the regional figure being 6.6 percentage points higher than the national one, there is a decrease in employed persons by around 400,000 from 2.302 million persons employed in April 2019 to 1.883 million in April 2020 (Department of Labor and Employment, Region Office No. X (DOLE-X). NorMin secures highest employment rate amid COVID 19. 2020. Available from: https://pia.gov.ph/news/articles/1044898 [Accessed 9th May 2021]). Hence, the study determines the effect of COVID-19 protective measures implemented by the government on the economy of CDOC. Using the barangay-level and selected sectoral-level data on business registration, and employment data between 2010 and 2019, the study estimates that one-week lockdown means a ₱1,825 loss of income for a minimum-wage employee. One-month lockdown costs ₱7,300 foregone income, while one-quarter lockdown (or a half of six months) is equivalent to ₱21,900 income loss. We recommend 10 policy interventions, but the government should also think big and invest its resources into programs that create a multiplier effect on the economy. Multipliers are interventions that create ripples or positive impacts on other sectors and/or economic participants.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".