# Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship

## TL;DR

This study uses 3D body scans and machine learning to analyze how physical appearance relates to family income, revealing significant gender differences and supporting the attractiveness premium hypothesis.

## Contribution

It introduces a novel dataset with 3D body scans and applies machine learning to address measurement errors in studying appearance-income links.

## Key findings

- Significant association between physical appearance and income.
- Gender differences in the appearance-income relationship.
- Evidence supporting the attractiveness premium hypothesis.

## Abstract

We study the association between physical appearance and family income using a novel data which has 3-dimensional body scans to mitigate the issue of reporting errors and measurement errors observed in most previous studies. We apply machine learning to obtain intrinsic features consisting of human body and take into account a possible issue of endogenous body shapes. The estimation results show that there is a significant relationship between physical appearance and family income and the associations are different across the gender. This supports the hypothesis on the physical attractiveness premium and its heterogeneity across the gender.