[OC] 25 years of fashion models vs. the US population: almost no overlap in body fat, and even "plus-size" models sit below the average American woman Data Visualization
![[OC] 25 years of fashion models vs. the US population: almost no overlap in body fat, and even "plus-size" models sit below the average American woman Data Visualization](/api/images/reddit-maps/1tpz06d_1779962401512.jpg)
Data Analysis
What This Visualization Shows
This data visualization displays "[OC] 25 years of fashion models vs. the US population: almost no overlap in body fat, and even "plus-size" models sit below the average American woman" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on [https://www.pnas.org/doi/10.1073/pnas.2602380123](https://www.pnas.org/doi/10.1073/pnas.2602380123)
This is panels d–g from a new PNAS paper on the cultural evolution of beauty standards (Boucherie et al., 2026). It compares US-based female fashion models against US women aged 17–30 from the NHANES health survey, using Relative Fat Mass (RFM), an estimate of body-fat percentage derived from height, waist circumference, and sex.
What the panels show:
* **Panel d:** Models cluster tightly around an RFM of \~18% (underweight range), while the general population centers near \~38%. The two distributions barely overlap. Notably, even the high end of the model distribution, where the "plus-size" outliers live, stays well below the average American woman, whose mean (the blue dashed line) sits to the right of nearly the entire model curve. * **Panel e:** The annual gap (NHANES minus Models) sits at roughly 20 percentage points and is essentially flat over two decades. The fitted trend is \~0.01 points per year, i.e. no convergence. * **Panel f:** A PCA projection of body measurements separates the two groups along lines of constant RFM, suggesting selection happens primarily on body composition. * **Panel g:** PCA loadings, where bust/waist/hips drive PC1 (overall size) and height drives PC2 (slenderness).
The paper's broader finding: even though representational diversity (hair color, ethnicity, national origin, occasional plus-size casting) has increased, the *typical* model physique hasn't changed. The added variation comes from a few tail outliers, not a shift in the median, and the industry's "plus-size" category is still leaner than the typical woman it's nominally meant to represent.
**Source:** Boucherie, Kumar, Ledebur, Lohse & Śliwa (2026). "Cultural evolution of beauty standards." *PNAS*, Vol. 123, No. 21, e2602380123. [https://doi.org/10.1073/pnas.2602380123](https://doi.org/10.1073/pnas.2602380123) (open access)
**Tools/Methods:** Dataset of 793,199 model work records (2000–2024) scraped from models.com and fashionmodeldirectory.com, benchmarked against NHANES. RFM = 64 − 20 × (height/waist) + 12 × sex. Code and data public: GitHub (github.com/LCB0B/evolution-beautystd), Zenodo (zenodo.org/records/17638160). Figures are Python (matplotlib)., which allows us to understand complex relationships and insights within the data through visual storytelling.
Deep Dive into the Topic
Social and demographic data visualization provides insights into human behavior, population trends, and societal patterns that shape our communities. This type of analysis is essential for understanding social dynamics, planning public services, and addressing societal challenges through data-driven approaches.
Demographic visualizations often reveal important trends such as age distribution, migration patterns, education levels, and social mobility. These insights help urban planners design better cities, educators understand student populations, and healthcare providers allocate resources effectively. Social media analytics and survey data visualization can uncover public opinion trends, consumer preferences, and social movement patterns.
The power of social data visualization lies in its ability to make abstract social concepts tangible and actionable. By presenting complex social phenomena through charts, graphs, and interactive dashboards, researchers and policymakers can communicate findings more effectively and develop targeted interventions. This approach is particularly valuable in areas like public health, education policy, and social services planning.
Data Analysis and Insights
The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.
Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.
The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.
Significance and Applications
This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.
For analysts, researchers, and decision-makers, this type of data visualization provides essential insights for strategic planning and performance optimization. Whether addressing operational challenges, market analysis, or policy development, understanding data patterns helps create more effective strategies and solutions.
The broader significance lies in how this information contributes to our understanding of complex systems and relationships. This knowledge helps predict future trends, identify potential challenges, and develop more informed approaches to problem-solving and opportunity identification.
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About the Author

Alex Cartwright
Senior Data Visualization Expert
Alex Cartwright is a renowned data visualization specialist and infographic designer with over 15 years of experience in...