Hysteresis Effect on Unemployment for Men and Women: A Panel Unit Root Test for OECD Countries
Bibliographic record
Abstract
There are two major hypotheses about the dynamics of unemployment in the literature: (i) natural rate of employment hypothesis, (ii) unemployment hysteresis hypothesis. The natural rate of employment hypothesis (NAIRU) implies a stable relationship between unemployment and business cycles in the long run. This means economic shocks arising in the business cycle creates a temporary imbalance in the unemployment rate. In other words, after an economic shock, the unemployment rate will return to long-term equilibrium level. In contrary to this argument, according to unemployment hysteresis hypothesis, unemployment move away from equilibrium state due to economic shocks and this state continues in the long run. The persistent imbalance means that unemployment have mean-deviation in the long run. As econometric approach, the unemployment series have an unstable trend. If unemployment rate is stable in the long run, the hysteresis effect becomes not valid. That is why the relationship between unemployment rate and business cycle will be the main issue of this study. According to that the hysteresis effect on unemployment rate in OECD countries has been analyzed. In this research the hysteresis effect on unemployment for women and men has also been separately examined. Thus, the research would allow us to distinguish and compare the gender differentiation within the OECD countries. The models have been estimated using yearly unemployment rate data from 1991 to 2014 for OECD countries and obtained from ILO statistics. Within the mentioned context above, Hysteresis effect has been analyzed with Panel Unit Root Tests, which both allowing and not allowing structural breaks. It is expected that the hysteresis effect on unemployment differs in terms of both gender and country level.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".