Evaluation of the Covid-19 pandemic impact on the global economy
Bibliographic record
Abstract
Since people became aware last February of the spread of the coronavirus epidemic, the world economy has suffered an unprecedented shock that has rattled the economic paradigm. As suggested by trends in the sub-quarterly indicators, the GDP growth figures already reflected, in their provisional version, the economic effects of lockdowns on the last two weeks of the first quarter. However, given the severity of the containment measures, significant downward and upward revisions to GDP could be expected. We then assess the impact of the shock on the global economy using input-output tables from the World Input-Output Database (WIOD). The various measures enacted for the month of April had an impact of -19% on added value at the global level. Not all sectors and countries were affected in the same way. At the sectoral level, the hotel and catering branch recorded a 47% fall in added value at the global level. Geographically, Europe was the area hit hardest, in particular Spain, Italy and France, with drops in added value of more than 30 points. Although Germany suffered a smaller fall in activity, in connection with less restrictive containment measures overall, the country is nevertheless suffering from its high exposure to foreign demand. Modelling then makes it possible to describe the impact of the activity shock on labour demand for the month of April. However, while the adjustment of labour demand to the production shock is very marked, the final impact on salaried employment ultimately appears, at least in Europe, to be weak compared to the potential job losses, due to the implementation of measures for short-time working. The United States, lacking such a mechanism,has experienceda greater destruction of salaried jobs, reaching 14.6% of total salaried employment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".