论文标题
“继续前进” - 使用监督学习调查发明家的族裔
'Moving On' -- Investigating Inventors' Ethnic Origins Using Supervised Learning
论文作者
论文摘要
专利数据提供了有关技术发明的丰富信息，但并未透露发明家的种族来源。在本文中，我使用监督的学习技术来推断此信息。为此，我构建了一个标有名称的95'202的数据集，并训练具有长短记忆（LSTM）的人工复发性神经网络（LSTM），以根据名称来预测民族起源。训练有素的网络在17个民族起源中的总体表现达到91％。我使用该模型对268万发明者的种族起源进行分类和调查，并提供有关其随着时间的流逝以及各个国家和技术领域的种族血统组成的新颖证据。在过去的几十年中，全球族裔起源组成变得越来越多样化，这主要是由于亚洲起源发明者的相对增加。此外，在美国，外国产物发明者的流行率尤其很高，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者流入美国新兴的高科技领域的驱动，而不是其他高收入国家的流入。
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.
