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.

中文翻译：

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“继续前进”——使用监督学习调查发明家的种族起源

专利数据提供了有关技术发明的丰富信息，但并未披露发明人的种族来源。在这篇文章中，我使用监督学习技术来推断这些信息。为此，我构建了一个包含 96'777 个标记姓名的数据集，并训练了一个具有长短期记忆 (LSTM) 的人工循环神经网络，以根据姓名预测种族起源。经过训练的网络在 18 个种族中的整体表现达到了 91.4%。我使用这个模型预测和调查了 268 万发明家的种族起源，并提供了关于他们的种族起源构成随着时间的推移以及跨国家和技术领域的新的描述性证据。在过去几十年中，全球种族起源构成变得更加多样化，这主要是由于亚洲起源发明人的相对增加。此外，外国发明人在美国的流行率特别高，但在其他高收入经济体中也有所增加。这一增长主要是由于非西方发明家流入美国的新兴高科技领域，而不是其他高收入国家。