Body shape matters: Evidence from machine learning on body shape-income relationship
Bibliographic record
Abstract
The association between physical appearance and income has been of central interest in social science. However, most previous studies often measured physical appearance using classical proxies from subjective opinions based on surveys. In this study, we use novel data, called CAESAR, which contains three-dimensional (3D) whole-body scans to mitigate possible reporting and measurement errors. We demonstrate the existence of significant nonclassical reporting errors in the reported heights and weights by comparing them with measured counterparts, and show that these discrete measurements are too sparse to provide a complete description of the body shape. Instead, we use a graphical autoencoder to obtain intrinsic features, consisting of human body shapes directly from 3D scans and estimate the relationship between body shapes and family income. We also take into account a possible issue of endogenous body shapes using proxy variables and control functions. The estimation results reveal a statistically significant relationship between physical appearance and family income and that these associations differ across genders. This supports the hypothesis on the physical attractiveness premium in labor market outcomes and its heterogeneity across genders.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".