STATISTICAL ANALYSIS OF INCOME OF THE POPULATION OF UKRAINE: IMPACT OF COVID-19
Bibliographic record
Abstract
One of the important components of the economy of any state - income policy. The article analyzes the level of income of the population of Ukraine during 2010-2020. The labor market, employment are the most dynamic elements of a market economy. They not only intertwine the interests of workers and employers, but also reflect economic, political, demographic, social and other processes that significantly affect the demand and skills of workers, employment and unemployment, social protection and living standards. In addition to the nominal dynamics of income, the change in the real level of income was studied, during which it was found that the real available income level decreased significantly during 2014-2015. The impact of the coronary crisis on the well-being of the population is significant, as during the quarantine the incomes of Ukrainians significantly decreased, especially in the second quarter of 2020. In addition to the analysis of income, its components were studied. The lion's share is wages, but the share of social transfers is not much less than the share of wages. This ratio of components is a disproportion, in highly developed countries the situation is opposite. The article also considers the dynamics of the average and minimum levels of wages during 2010-2020, found that the average wage is much higher than the minimum, so there is a disparity in wages in Ukraine. Also, inequality in wages is observed on the following grounds: regional, sectoral and gender. A comparison of the average salary of Ukraine and a number of European countries was made. In Ukraine, labor is paid the lowest, which is one of the main reasons for labor migration. The article provides recommendations for improving the conditions of workers and employers in the labor market, which will lead to the development of Ukraine's economy as a whole.
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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 | 0.004 |
| 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 | 0.000 |
| 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".