Unemployment, Gender and Labor Force Participation in Spain: Future Trends in Labor Market
Bibliographic record
Abstract
quarter of 2012, almost three percentage points higher than one year earlier and almost 12 percentage points higher than at the end of 2008. Although the exponential growth of Spanish unemployment is mainly caused by a lower demand for labor, there is also a second cause, viz. an increased supply of labor, reflected by higher participation rates. In this paper we investigate how participation rates are affected by business cycle fluctuations, while accounting for different labor market behaviour of men and women. Based on an analysis using Spanish quarterly data over the period 1976-2012, we find evidence for a linear discouraged worker effect for men (i.e., decreasing participation rates during recessions), implying that male participation rates will continue to show a weak but sustained decrease as long as unemployment keeps rising. On the contrary, we find a significant ‘added’ worker effect (i.e., increasing participation rates during recessions) for women, but only when unemployment rates are below a certain threshold. Since the Spanish unemployment rate just recently (in 2012) passed this threshold, our results suggest that the added worker effect for women no longer applies and that, accordingly, the recent increase in female participation rates now comes to an end.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".