Augmenting the availability of historical GDP per capita estimates through machine learning

TL;DR

This paper introduces a machine learning approach using biographical data to estimate historical GDP per capita across Europe and North America over 700 years, validated by multiple historical proxies and revealing economic shifts like the reversal of fortunes.

Contribution

The study presents a novel method combining biographical data and elastic net regression to produce detailed historical GDP estimates, validated against diverse proxies.

Findings

Achieved 90% variance explanation in known income levels.

Reproduced the reversal of economic fortunes between southwestern and northwestern Europe.

Generated comprehensive historical GDP datasets with confidence intervals.

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 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 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 this data is not available and externally validate our estimates by comparing them with four proxies of economic output: urbanization rates in the past 500 years, 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 and find this is largely driven by countries and regions engaged in Atlantic trade. These findings validate the use of fine-grained biographical data as a method to produce historical GDP per capita estimates. We publish our estimates with confidence intervals together with all collected source data in a comprehensive dataset.