The Impact of the COVID-19 Pandemic on Regional Labor Markets in Ukraine
Why this work is in the frame
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Bibliographic record
Abstract
In March 2019, the World Health Organization (WHO) announced the global outbreak of COVID-19 to be a pandemic. The probable source of the virus was Wuhan (China), from where it quickly spread all over the world. Experts say that from the start of the pandemic in Ukraine and to the second quarter of 2022, 5.04 million cases of infection were recorded. The fraction of people who have received at least one dose of the COVID-19 vaccine is 36.4%, making Ukraine the least vaccinated country in Europe. High morbidity rate made it necessary to introduce quarantine measures that have negatively affected the socio-economic development of Ukraine as a whole, and regional labor markets, in particular. The article is aimed at assessing the impact of the coronavirus pandemic on regional labor markets in Ukraine. To achieve this aim, the following tasks were solved: a) to analyze the COVID-19 dynamics as for Ukraine’s regions, taking into account the aggravation of the humanitarian crisis; b) to identify the situation in regional labor markets by economic activity indicators; c) to analyze state regulation as for its compliance with the state social guarantees in labor relations. It has been determined that the most negative situation with the COVID-19 epidemic is present in the regions with the largest cities of the country (which are home to a population of 1,029,049 to 2,611,327 people). It proves that a particularly negative impact of the pandemic was made on the regional labor markets with high economic activity and strong economic potential. It is established that the pandemic has led to employment reduction in all the regions, which went hand in hand with the increase in informal employment of economically active population. Rising unemployment and wage arrears are also signs of the negative impact of the COVID-19 pandemic on Ukraine’s regional labor markets. It can be concluded that the key problems requiring managerial decisions are those connected with inclusive economic recovery after COVID-19 and the fulfillment of state social guarantees in the labor market in almost every region in Ukraine, given the impact of the ongoing military conflict and the need for economic recovery after it.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it