ArticleProceedings of the National Academy of Sciences of the United States of America2024
Augmenting the availability of historical GDP per capita estimates through machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Cultural proximity reveals a local premium in collective memory.PNAS nexus · 2026Article
- GDP prediction of The Gambia using generative adversarial networks.Frontiers in artificial intelligence · 2025Article
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3 authors.
Funding
Abstract
Can we use data on the biographies of historical figures to estimate the GDP per capita of countries and regions? Here, we introduce a machine learning method to estimate the GDP per capita of dozens of countries and hundreds of regions in Europe and North America for the past seven centuries starting from data on the places of birth, death, and occupations of hundreds of thousands of historical figures. We build an elastic net regression model to perform feature selection and generate out-of-sample estimates that explain 90% of the variance in known historical income levels. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which these data are not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 y, body height in the 18
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