Evaluation of the impact of COVID-19 factors on income inequality in the European Union.
Bibliographic record
Abstract
The Coronavirus, also known as COVID-19, orginated in China and within a few months has rapidly widespread around the world. With the start of the second quarter of 2020, the anxiety and uncertainity about the unknown virus has put pressure on countries in the world. Questions about the COVID-19 were arising: how many cases and deaths of COVID-19 the world can expect, how long it will last and what impact COVID-19 pandemic will have on the world‘s economy. The growing number of COVID-19 cases encouraged countries around the world to take action to prevent the spread of the virus. Preventing actions like wearing facemasks, restricting movements between countries, banning entertainment (such as concerts, various performances, sports) and many more were taken. Such restrictions led to a slowdown in economic activity. The impact of the COVID-19 pandemic can be assessed from a variety of economic measurements and indicators, but income inequality has long been one of the most threatening trends in the global economy and one of the most pressing issues in today‘s world. Thus, the aim of this study is to analyze how COVID-19 affected income inequality. In the theoretical part, the analysis of economic indicators‘ changes shows that COVID-19 has quite a big impact on economic indicators. That is why it is very importatnt to analyze the relationship between COVID-19 and income inequality. The theory analyzes the definition of income inequality by both foreign and Lithuanian authors, as well as the impact of income inequality on economic growth. Theoretical part also analyzes measurements of income inequality as well as presents major COVID-19 factors influencing income inequality. Various concepts and definitions are used in order to define income inequality in different contexts, and there are also many reasons for income inequality. For example, tax systems, unemployment, limited access to education, unequal distribution of wealth and many others. Rising income inequality can lead to financial crises, increase personal and institutional debts, change people‘s communication with other members of society and slowdown the economic growth. Correlation and regression analyzes are used in order to analyze the impact of COVID-19 on income inequality in European Union countries. The study examines how COVID-19 factors such as working from home, cases and deaths from COVID-19 and household savings influenced income inequality in EU.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".