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‘Moving On’—investigating inventors’ ethnic origins using supervised learning

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  • Matthias Niggli
Abstract
Patent data provides rich information about technical inventions, but does not disclose the ethnic origin of inventors. In this article, I use supervised learning techniques to infer this information. To do so, I construct a dataset of 96′777 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 91.4% across 18 ethnic origins. I use this model to predict 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.

Suggested Citation

  • Matthias Niggli, 2023. "‘Moving On’—investigating inventors’ ethnic origins using supervised learning," Journal of Economic Geography, Oxford University Press, vol. 23(4), pages 921-947.
    • Handle: RePEc:oup:jecgeo:v:23:y:2023:i:4:p:921-947.
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    More about this item

    Keywords

    supervised learning; innovation; patents; migration;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • F22 - International Economics - - International Factor Movements and International Business - - - International Migration
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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