A Micro Approach to the Issue of Hysteresis in Unemployment: Evidence from the 19881990 Labour Market Activity Survey
Bibliographic record
Abstract
This paper uses a rich set of microeconomic labour market data—the 198890 Labour Market Activity Survey published by Statistics Canada—to test whether there is negative duration dependence in unemployment spells. It updates and extends similar work carried out by Jones (1995) who used the 198687 Labour Market Activity Survey. Applying hazard model estimation, the analysis finds some evidence of negative duration dependence at the microeconomic level, which is consistent with the de-skilling hypothesis of hysteresis. These microeconomic estimates of negative duration dependence are used to compute macroeconomic estimates of hysteresis in unemployment. The results suggest that hysteresis effects from de-skilling are very small at the macro level, contributing less than 0.1 percentage points to the aggregate unemployment rate. The small estimated size of this hysteresis effect may explain why evidence of hysteresis has been so difficult to find at the macroeconomic level. The paper also shows that Unemployment Insurance (UI) benefits reduce the probability of exiting from unemployment and that unemployment duration does not seem to be prolonged by reservation-wage effects.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".