Long-term unemployment in Japan in the global financial crisis and recession
Bibliographic record
Abstract
This paper examines the trends in long-term unemployment (unemployment for six months or more) in Japan across the period around the global financial crisis of the late 2000s and the subsequent Great Recession. Using data from the Labour Force Survey and Employment Status Survey, both conducted by the Statistics Bureau, Ministry of Internal Affairs and Communications, it uses decomposition analysis to illustrate some factors that change the long-term unemployment rates. While also shifting along with cyclical changes in the economy, the long-term unemployment rate and the share of long-term unemployed in the total unemployed have continued to rise over the last 30 years. From the mid-2000s, there was a large increase in the very long-term unemployed (people unemployed for over two years), accounting for more than a quarter of the total unemployed males in the mid-2010s. The decomposition analysis shows that the changes in the long-term unemployment rates are influenced to a large degree by the changes in the unemployment rate and the share of long-term unemployed in the total unemployed. The long-term unemployment rates are high for male workers, young workers (age 15‒24) and those whose highest level of education is high school or lower. The long-term unemployment rates are high in the three major metropolitan areas, while the share of long-term unemployed in the total number of unemployed is high in the rural areas.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".