Anthropometric within-country Inequality and the Estimation of Skill Premia with Anthropometric Indicators
Bibliographic record
Abstract
Recently, a method to measure inequality has been proposed that is based on an- thropometric indicators. Baten (1999, 2000) argued that the coefficient of variation of human stature (henceforth ‘CV’) is correlated with overall inequality in a society, and that it can be used as indicator, especially where income inequality measures are lack- ing. This correlation has been confirmed in further analyses, for example by Pradhan et al. (2003), Moradi and Baten (2005), Sunder (2003), Guntupalli and Baten (2006), Blum (2010a), van Zanden et al. (2010), see also Figure 1 and Table 1. The idea is that average height reflects nutritional conditions during early childhood and youth. Since wealthier people have better access to food, shelter and medical resources, they tend to be taller than the poorer part of the population. Hence, the variation of height of a cer- tain cohort may be indicative of income distribution during the decade of their birth. The aim of this study is firstly to provide an overview of different forms of within- country height inequality. Previous studies on the aspects of height inequality are re- viewed. Inequalities between ethnic groups, gender, inhabitants of different regions and income groups are discussed. In the two final sections, we compare height CVs of anthropological inequality with another indicator of inequality, namely skill premia. We also present estimates of skill premia for a set of countries and decades for which “height CVs”, as they will be called in the following, are available.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".