Are Labor Force Participation Rates Really Non-Stationary? Evidence from Three OECD Countries
Bibliographic record
Abstract
In this paper we first examine the labor force participation rates (LFPR henceforth) for Australia, Canada and the USA and endogenously determine several structural break points in the series and discuss their possible causes. We employ a class of generalized univariate processes, called fractionally integrated processes (Granger and Joyeux, 1980; Hosking, 1981) with structural breaks. They are flexible enough to capture the mean-reverting dynamics in the series. Therefore, they allow modeling the Labor force participation rate movements over time better than the standard time series models. In order to examine the possibility of mean reversion, we use a test developed by Robinson (1994) which permits testing I(d) hypothesis allowing for breaks at known times. The results of our analysis indicate that the LFPRs that we consider are stationary. As a result we can conclude that the informational value of the unemployment rates about the behavior of labor markets and the causes of joblessness are useful in Australia, Canada and the USA. In these countries we can talk about one-to-one correspondence between the long-term changes in unemployment rates and the long-term changes in employment rates and that unemployment rate is a useful indicator of joblessness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".