An analysis of the Norwegian economic policy during COVID-19.
Notice bibliographique
Résumé
This paper discusses the impact of COVID-19 on the Norwegian economy in the short, medium, \nand long term. Moreover, it will discuss and analyze the economic policy response and its \neffects. In addition, we will look at Norway in comparison with the rest of the world, primarily \nother OECD countries. \n\nThe motivation for this is gaining perspective for the unique situation we are in and how it may \naffect the future. COVID-19 affects day-to-day lives unlike any previous recessions in modern \ntimes, and while most countries have taken on debt, Norway can fall back on transfers from the \nGovernment Pension Fund Global. Therefore, it is highly relevant to look closer at how the \npandemic impacted the Norwegian economy and discuss how economic policy handled the \nturbulence. For this purpose, we will discuss the impact and economic policy response from the \nfirst quarter of 2020 until the end of the first quarter of 2021. \n\nWe will primarily look at how the real economic variables were hit when analyzing the impact. \nThe real economic variables involve the variables that affect Norway's GDP in aggregate form. \nWe will also discuss potential long-term consequences as a result of the pandemic. To do so, we \nwill look at consequences for long-term trend growth on output and regulations of the economic \nframework. By this, we mean covering the most important factors of the Norwegian economy. \nThis will lay a good foundation to clarify and discuss the economic-political response. In that \nway, we will cover monetary policy, fiscal policy, and the interaction between them. \n\nThe basis for the economic policy framework is established in chapter 2. We provide the theory \nthat is used for discussing and analyzing the economic response here. For monetary policy, \nNorges Bank's flexible inflation targeting is used as a basis. This involves Norges Bank \nstabilizing inflation near the target in the medium term. Flexible inflation targeting implies that \nthe central bank weighs stable inflation against the developments in output and demand. A model \nestablished by Røisland and Sveen (2018) will be used for the monetary policy analysis.\n \nWhen it comes to fiscal policy, Norway is in a unique position due to the oil fund. The usage of \noil revenues and fiscal policy's influence on aggregate output is crucial for this part of the paper. 3 \nMoreover, the potential effects of increased public spending are covered. Lastly, the chapter \ncovers the interaction between fiscal and monetary policy. \n\nChapter 3 starts with a presentation of how the impact and responses of COVID-19 around the \nworld were. Further, we cover how the world's economies are connected and how it affects \nNorway. The chapter continues and ends with data and economic responses from the real \neconomy, inflation, the labor market, and the foreign exchange market. \n\nIn chapter 4, we discuss and analyze the economic policy response. We cover Norges Bank's \nassessments and its rate decisions from the first quarter of 2020 until the first quarter of 2021. \nFurther, we assess this against the model we established in chapter 2 and compare this to other \nOECD countries. Additionally, we discuss how the credit market was impacted. \n\nIn the next part of the chapter, we discuss the role of fiscal policy during the pandemic and \nwhich long-term consequences it faces. We then discuss how the balance between monetary- and \nfiscal policy is crucial and how the roles have changed since the financial crisis. We end the \nchapter with a discussion of the long-term development and structural changes. \n\nFinally, chapter 5 concludes the paper.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».