Influence of height on likelihood of employment, occupational sorting, and earnings in 27 post‐communist countries
Bibliographic record
Abstract
OBJECTIVES: We study the influence of height on labour market outcomes using micro-data from a recent survey that cover 27 post-communist countries. Specifically, we focus on the influence of height on three dimensions of labour market outcome: (1) likelihood of employment, (2) occupational sorting, and (3) earnings. METHODS: We use micro-data from 2016 Life-In-Transition survey (LITS) which was jointly conducted by the European Bank for Reconstruction and Development and the World Bank. We run several types of regression to show how height influences (1) likelihood of employment, (2) occupational sorting, and (3) earnings. RESULTS: When controlling for a comprehensive set of covariates, for each 10 cm increase in height, the probability of getting a job increases by 1% points for males and by 3 for females. Equally, for each 10 cm increase in height, the probability of getting a job increases by 2% points in urban areas and rural areas. Our findings demonstrate that taller women and men are more likely: (a) being an employer rather than an employee; (b) to be employed in higher-paid and more prestigious sectors of finance, insurance, and real estate; (c) to be employed in private enterprises. Finally, when occupational sorting and socio-demographics are controlled for, a 10 cm increase in height results in a 5% increase in earning for men, and a 12% increase in earnings for women. CONCLUSIONS: Using a diverse sample of 27 post-communist countries, we found that taller individuals have better labour market outcomes in terms of employment, occupational sorting, and earnings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".