Employment fluctuations and dynamics of the aggregate average wage in Poland, 1996–2003<sup>1</sup>
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Bibliographic record
Abstract
Abstract The aggregate average wage is often used as an indicator of economic performance and welfare, and as such often serves as a benchmark for changes in the generosity of public transfers and for wage negotiations. Yet if economies experience a high degree of (non‐random) fluctuation in employment, the composition of the employed population will have a considerable effect on the computed average. In this paper we demonstrate the extent of this problem using data for Poland for the period 1996–2003. During these years the employment rate in Poland fell from 51.2 percent to 44.2 percent and most of this fall occurred between the end of 1998 and the end of 2002. We show that about a quarter of the growth in the average wage during this period could be attributed purely to changes in employment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it