Evaluation of the impact of COVID-19 factors on income inequality in the European Union.
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».