Body size and wages in Europe: A semi-parametric analysis
Bibliographic record
Abstract
Evidence of the association between wages and body size –typically measured by the body mass index– appears to be sensitive to estimation methods and samples, and varies across gender and ethnic groups. One factor that may contribute to this sensitivity is the non-linearity of the relationship. This paper analyzes data from the European Community Household Panel survey and uses semi-parametric techniques to avoid functional form assumptions and assess the relevance of standard models. If a linear model for women and a quadratic model for men fit the data relatively well, they are not entirely satisfactory and are statistically rejected in favour of semiparametric models which identify patterns that none of the parametric specifications capture. Furthermore, when we use height and weight in the models directly, rather than equating body size with the body mass index, the semi-parametric models reveal a more complex picture with height having additional effects on wages. We interpret our results as consistent with the existence of a wage premium for physical attractiveness rather than a penalty for unhealthy weight.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".