Quadrupling historical GDP per capita estimates through machine learning

Résumé

Can we use data on the biographies of hundreds of thousands 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 700 years 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 85% of the variance in known historical GDPs per capita. We use this model to generate GDP per capita estimates for countries, regions, and time periods for which this data is not available and validate them by comparing them with three proxies of economic output: body height in the 18th century, wellbeing in 1850, and church building activity in the 14th and 15th century. Additionally, we show our estimates reproduce the well-known reversal of fortune between southwestern and northwestern Europe between 1300 and 1800. These findings validate the use of fine-grained biographical data as a method to produce historical GDP per capita estimates.