Patent data is an established source of information for both scientific research and corporate intelligence. Yet, most patent-based technology indicators fail to consider firm-level dynamics regarding their technological quality and technological activity. Accordingly, these indicators are unlikely to deliver an unbiased view on the current state of firm-level innovation and are thus incomplete tools for researchers and corporate intelligence practitioners. In this paper, we develop DynaPTI, an indicator that tackles this particular shortcoming of existing patent-based measures. Our proposed framework extends the literature by incorporating a dynamic component and is built upon an index-based comparison of firms. Furthermore, we use machine-learning techniques to enrich our indicator with textual information from patent texts. Together, these features allow our proposed framework to provide precise and up-to-date assessments about firm-level innovation activities. To present an exemplary implementation of the framework, we provide an empirical application to companies from the wind energy sector and compare our results to alternatives. Our corresponding findings suggest that our approach can generate valuable insights that are complementary to existing approaches, particularly regarding the identification of recently emerging, innovation-overperformers in a particular technological field.

In this paper, we use a data-driven approach to predict the "green potential" of ISCO occupations based on their corresponding skills. With this information, we can investigate the relationship between environmental regulations and occupation-level employment in the manufacturing sector of 19 European countries for the period 1992-2010. Our empirical results highlight heterogeneous occupational employment changes in response to an increase in environmental policy stringency. More specifically, we find a decrease in labor demand for occupations with relatively low green potential and an increase for occupations with relatively high green potential. Thus, at least in the short term, greening the economy may create winners and losers across occupations and countries.

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.

Technologies evolve at different paces and their rate of improvement varies considerably. We demonstrate that the fastest technological progress currently occurs in the digital domain and empirically investigate the relationship between technologies’ improvement rates and breakthrough innovations as measured by forward citations of patents. Our empirical estimates suggest that patents from the digital sphere, as well as those related to fast-improving technologies, are associated with a higher probability to produce breakthrough innovations. We then investigate Swiss core industries’ specialization patterns toward these potential high-impact technologies and compare the state of cutting-edge innovation in Switzerland to other countries. Our findings imply that the Swiss innovation system is among the laggards regarding innovations in today’s fastest improving digital technologies.

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.