Bibliographic record
Abstract
The paper applies modified Oaxaca-type analyses on the eighteen available waves of the British Household Panel Survey to decompose the wage gap among full time employees from either side of the North-South divide and identify its components that can be attributed to measurable worker- and labour market characteristics, and the part due to differences in the returns to these endowments. Further, by applying Juhn, Murphy and Pierce’s (1991) methodology, it is analysed, how changes in these underlying factors could explain the one quarter decline in the wage gap over the 1991 – 2009 period. The paper confirms the existence of a differential treatment effect by showing that only one fifth of the wage gap can be explained by observable differences. The magnitude of the unexplainable coefficient effect is so large, that the remarkable improvements in Northern occupational structure and human capital levels over the period could only translate into an actual decline in the wage gap, because it coincided with a period of increasing inequality among Northern occupational wage premia, which – as a by-product – increased the average Northern wage and this way counterbalanced the effects of the increasing Southern returns to experience, that alone could have increased the initial pay gap by half.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".