US AND UK LABOUR MARKETS BEFORE AND DURING THE COVID-19 CRASH
Bibliographic record
Abstract
We examine labour market performance in the US and the UK prior to the onset of the Covid-19 crash. We then track the changes that have occurred in the months and days from the beginning of March 2020 using what we call the Economics of Walking About (EWA) that shows a collapse twenty times faster and much deeper than the Great Recession. We examine unemployment insurance claims by state by day in the US as well as weekly national data. We track the distributional impact of the shock and show that already it is hitting the most vulnerable groups who are least able to work from home the hardest – the young, the least educated and minorities. We have no official labour market data for the UK past January but see evidence that job placements have fallen sharply. We report findings from an online poll fielded from 11–16 April 2020 showing that a third of workers in Canada and the US report that they have lost at least half of their income due to the Covid-19 crisis, compared with a quarter in the UK and 45 per cent in China. We estimate that the unemployment rate in the US is around 20 per cent in April. It is hard to know what it is in the UK given the paucity of data, but it has gone up a lot.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| 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) | 0.005 | 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".