Impact of the Coronavirus Pandemic on Labor Market
Bibliographic record
Abstract
The coronavirus pandemic has affected all spheres of society, including a significant impact on labor markets. The article presents a statistical analysis of trends in the Russian labor market in the context of the spread of coronavirus infection in 2020 and in the first half of 2021. On average, in 2020, the number of people employed in the economy field decreased by almost 2%, respectively, the number of unemployed increased by 25%. Within 2020, the peak of unemployment growth occurred in the third quarter of 2020, when the decline in the number of employed reached 1,9 million people, and the increase in the number of unemployed – 1,5 million people. At the same time, the unemployment rate increased to 6,4%, i.e. by almost a half compared to the corresponding period of 2019. Starting from the fourth quarter of 2020, the recovery of the Russian labor market began. By July 2021, the unemployment rate had dropped to 4,8%. Such serious transformations of the Russian labor market required an analysis of the dynamics and structure of employment in various sections: by type of economic activity, by subjects of the Russian Federation, by gender, by age groups, by place of residence. Along with this, the authors compared the indicators of the unemployment rate, which was calculated using two methods used in Russian statistical practice. An international comparison of the unemployment rate for 2020–2021 was carried out. The indicators characterizing the underutilization of labor, including the number and level of potential labor, are considered. The dynamics of wages in 2019–2021 is considered. Special attention is paid to the analysis of the situation with workers’ wages at the most acute moment of the crisis – in April 2020.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".