Philipp Koch, Viktor Stojkoski, and César A. Hidalgo

PNAS 121 (39) e2402060121

The scarcity of historical GDP per capita data limits our ability to explore questions of long-term economic development. Here, we introduce a machine learning method using detailed data on famous biographies to estimate the historical GDP per capita of hundreds of regions in Europe and North America. Our model generates accurate out-of-sample estimates (R2 = 90%) that quadruple the availability of historical GDP per capita data and correlate positively with proxies of economic output such as urbanization, body height, well-being, and church building activity. We use these estimates to reproduce the reversal of fortunes experienced by southern and northern Europe and the historical role played by Atlantic ports. These findings show that machine learning can effectively augment the historical availability of economic data.

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