Moving On – Investigating Inventors‘ Ethnic Origins Using Supervised Learning

Niggli, Matthias (6 May 2022). Moving On – Investigating Inventors‘ Ethnic Origins Using Supervised Learning (Unpublished). In: Bern Data Science Day 2022. University of Bern. 06 May 2022.

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Patent data provides rich information about technical inventions, but does not disclose
the ethnic origin of inventors. In this paper, I use supervised learning techniques to infer this
information. To do so, I construct a dataset of 95′ 202 labeled names and train an artificial
recurrent neural network with long-short-term memory (LSTM) to predict ethnic origins based
on names. The trained network achieves an overall performance of 91% across 17 ethnic origins.
I use this model to classify and investigate the ethnic origins of 2.68 million inventors and
provide novel descriptive evidence regarding their ethnic origin composition over time and
across countries and technological fields. The global ethnic origin composition has become
more diverse over the last decades, which was mostly due to a relative increase of Asian origin
inventors. Furthermore, the prevalence of foreign-origin inventors is especially high in the
USA, but has also increased in other high-income economies. This increase was mainly driven
by an inflow of non-western inventors into emerging high-technology fields for the USA, but
not for other high-income countries.

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