"Immigration, Innovation, and Knowledge Diffusion"

This dissertation studies the relationship between immigration and innovation. It adopts an empirical perspective and focuses on Switzerland, combining the use of patent data and immigration administrative records. The first essay is based on a novel database on immigrant inventors and explores their role in Swiss R&D activities, their productivity, and inventive career before and after immigration. It shows that highly productive inventors are over-represented among immigrants and that foreign inventors tend to enter Switzerland early in their careers and with little prior inventive experience. The second essay studies the innovation effects of the Agreement on the Free Movement of Persons (AFMP), which lifted immigration restrictions for EU citizens to Switzerland after its introduction in 2002. It exploits a natural experiment generated by regional differences in the exposure to the AFMP and influx of foreign workers. It finds that the AFMP introduction increased regional patenting in Switzerland and did not depress the inventive output of its neighbouring countries. In addition, Swiss inventors did not reduce their inventive activity due to newly arrived foreigners. Further results show that the AFMP had a positive effect on incumbent inventors' productivity, increasing the number of foreign collaborators and the variety of technical knowledge available.The third essay also focuses on the AFMP and studies the effects of the Swiss and the German labor markets' integration on technical knowledge diffusion. Using patent citation data and textual similarity measures, it demonstrates that the abolition of immigration restrictions for commuters residing in the German state of Baden-Wuerttemberg and working in Switzerland increased the diffusion of German inventions patented in commuters' residential locations into Switzerland.

Institutions and Incentives in Knowledge Production and Diffusion: From Science to Innovation

Charles Chadi Ayoubi

This thesis presents four essays providing novel empirical and theoretical insights on the incentives and institutional structures that favor knowledge production and diffusion. The first two studies analyze these processes in the realm of scientific research, while the last two essays evaluate broader applications with vast social welfare implications. The first essay (chapter 2) of this dissertation, in collaboration with Michele Pezzoni and Fabiana Visentin, exploits a dataset on all applicants to a prestigious Swiss grant to explore a central process in academic life: the application for funds. The results suggest that scientists applying to a grant significantly increase their publicationsâ quality and quantity, learn more, and extend their collaboration network. Beyond the effect of applying, receiving the research funds increases the probability of co-authoring with co-applicants, but it does not have any additional effect on other scientific outcomes. These results justified the title of the chapter since, as it is the case in the Olympics, in research grants, âthe important thing is not to win, it is to take part.â The second essay (chapter 3), also in collaboration with Michele Pezzoni and Fabiana Visentin, uses the same empirical context to explore the determinants of knowledge flows among collaborating scientists. The chapter proposes a novel methodology based on journal references to track knowledge flows among researchers working together. The results suggest that geographical distance does not significantly affect the knowledge flows between team members, but the cognitive distance separating two members does. More specifically, there is an inverted U-curve effect of cognitive distance on the learning among team members: the higher the distance between two scientists in terms of subjects studied, the more they exchange knowledge, up to the point when the distance becomes detrimental because they have too little common ground to communicate. The third essay (chapter 4), in collaboration with Boris Thurm, goes beyond the exploration of the determinants for scientistsâ knowledge production and diffusion to delve into the incentives of all individuals to exchange knowledge. The chapter has two major contributions. First, it acts as a literature review of the empirical evidence on non-financial incentives for knowledge diffusion, such as social recognition, career prospects, and moral considerations. Second, the chapter proposes a simple economic model with heterogeneous agents holding both selfish and moral motives to derive some novel policy implications. The last essay (chapter 5), in collaboration with Dominique Foray, delves into a specific case of knowledge diffusion, the integration of machine learning technologies in healthcare. The analysis suggests that machine learning has the potential for spurring innovation in healthcare but faces several institutional levers. Collecting quantitative data on patents and publications, and qualitative data on hospitals, the results show that machine learning affects healthcare in different ways than older information and communication technologies. The apparition of new business models encourages tech giants to enter the healthcare sector. These patterns have the potential to increase social welfare by reducing externalities in terms of innovation complementarities, but they pose new challenges such as competition policy and human capital formation.

EPFL2020

Institutions and Incentives in Knowledge Production and Diffusion: From Science to Innovation

Charles Chadi Ayoubi

EPFL2020