Employment and economic growth in the conditions of COVID-19 pandemics: Cross-country comparisons
Bibliographic record
Abstract
Introduction. The factor of a sharp slowdown in economic growth in almost all countries since the beginning of 2020 has been quite atypical. Whereas previously we had seen shocks mainly related to economic processes, this time the “black swan” was a public health emergency – the new coronavirus pandemic COVID-19. A feature of this crisis was the unprecedented measures of states to restrict the movement of citizens, as well as the suspension of the activities of both industrial enterprises and enterprises in the sphere of trade and services. The aim of this work is to make cross-country comparisons of the impact of increased unemployment caused by the COVID-19 pandemic on different countries’ economic growth. Theoretical analysis. The relationship between the actual output gap and potential and cyclical unemployment rates has traditionally been studied according to the well-known law of A. Okun. Okun’s Law can be viewed as a linear algebraic equation for the function of real gross domestic product (GDP). The essence of the law is that with an increase in cyclical unemployment, total output should decrease, since the number of people employed in GDP production falls. Empirical analysis. Cross-country comparisons of economic growth and characteristics of the labor market in Russia, the USA, China, Canada and Germany from 2000 to 2020, including the period of the new coronavirus infection pandemic, were carried out. Results. Based on the analysis of time series of GDP and the unemployment rate, it is shown that, depending on the depth and effectiveness of state support measures for business in terms of maintaining employment, deviations of the actual values of GDP from those calculated in accordance with Okun’s empirical law are observed. The largest and smallest deviations in real GDP changes from the predictions for the first half of 2020 have been recorded in Germany and Canada, respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".