Did the COVID-19 pandemic impact income distribution?
Notice bibliographique
Résumé
This analysis aims to explore how employee income distribution performed during the first year of the COVID-19 pandemic; it further aims to compare it with a pre-pandemic scenario (2019) and with the financial and the sovereign debt crisis. By referring to the EU Labour Force Survey (LFS) database for six EU Member States (Denmark, Estonia, Greece, Ireland, Italy, and Portugal), and by using transition matrices and a selection of mobility indices as empirical tools, the direction and the magnitude of the movement across quantiles experienced by employees are explored. For each of the years under scrutiny, the transition across quintiles is computed between two very close periods (e.g. from one quarter to another). Sudden changes in the structure of the transition matrices and the value of the respective mobility indicators, when observed in comparison with a ‘benchmark’ year, may be interpreted either as a shock to the economic system, or the (counter) effect of automatic stabilisers and discretionary public policy measures (and as a combination of the two). The direction and the magnitude of the change may depend on different factors, including the kind of crisis, labour market and market income response, along with the design and timing of public policy discretionary cushioning measures. This conclusion emerges from the comparison of results collected for the COVID-19 crisis with those of the Great Recession: Two different kinds of crisis, two different sets of transmission mechanisms from the origin of the crisis to the real economy, two different responses of the labour market and of the public policy intervention. During the COVID-19 crisis, the overall level of income mobility increased, while during the financial crisis and sovereign debt crisis it decreased. The reason lies both in the different magnitude of flows from employment to unemployment and in the type and timing of the measures taken. As for the COVID-19 pandemic vs a pre-pandemic scenario, in-depth observation of the transition matrices and of the relative mobility indices suggests an increase of the overall mobility that is explained by specific movements of the ‘upward’ and ‘downward’ movers, as well as from the patterns followed by the proportion of individuals belonging to the single quantiles. When the figures for different indicators are broken down, it seems that there is a general worsening condition of females compared to males, of the youngest (16-29-year-olds) and of employees without tertiary education (ISCED 6-8). This research has received funding from the Horizon 2020 research and innovation programme of the EU under grant agreement No.101016233, H2020-SC1-PHE CORONAVIRUS-2020-2-RTD, PERISCOPE (Pan European Response to the Impacts of Covid-19 and future Pandemics and Epidemics).
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 | 0,001 | 0,006 |
| 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 | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».