Did the COVID-19 pandemic impact income distribution?
Bibliographic record
Abstract
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".