A Comparison of Labour Market Responses to the Global Downturn
Bibliographic record
Abstract
The global economic downturn has led to a crisis in labour markets, with an estimated 15.2 million job losses across the OECD economies, equivalent to a rise in the OECD unemployment rate from 5.5 to 8.9 per cent. Initially, the rise in unemployment appeared lower than expected. In Holland, Kirby and Whitworth (2009) we demonstrated a simple rule of thumb between output growth and the unemployment rate in the OECD as a whole, based on Okun's approach. Using a dataset that spans the period 1988–2008, regression analysis suggests that on average a 1 per cent decline in output is associated with a rise of 0.6 percentage points in the unemployment rate across the OECD economies. Between the first quarter of 2008 and the first quarter of 2009, output in the OECD economies declined by 4.8 per cent. The unemployment rate rose by 1.9 points over this period, as compared to 2.9 per cent given by the rule of thumb. However, the labour market tends to lag production. While most of the major economies started to grow again in the second or third quarters of 2009, OECD unemployment continued to rise into the final quarter of the year, with a cumulative increase in the OECD unemployment rate of 3.4 percentage points — even higher than that suggested by our rule of thumb.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".