Does It Pay to Do Novel Science? The Selectivity Patterns in Science Funding

Public funding agencies aim to fund novel breakthrough research to promote the radical scientific discoveries of tomorrow. Identifying the profiles of scientists being financed to pursue their research is therefore crucial. This paper shows that the funding process is not always awarding the most novel scientists. Exploiting rich data on all applications to a leading Swiss research funding program, we find that novel scientists have a higher probability of applying for funds than non-novel scientists, but they get on average lower ratings by grant evaluators and have fewer chances of being funded. We discuss the implications for the allocation of scientific research spending.

The organisation of science: topics, incentives and funding.

Omar Ballester Gonzalez

Ever since the links between the development of new technologies and economic growth became evident, researchers have attempted to study how the creation of knowledge fosters progress. If pushing the frontier of knowledge has an impact on progress and well-being, it is essential to pursue some form of science policy. Policymakers rely on the scholarly work of researchers in order to understand the likely impact of new policies and investments, and evaluate the state of the art in science and innovation policy. Therefore, the work of social scientists, economists of science and information scientists, among others, is vital to the characterisation, understanding and management of science. In recent years, the availability and quality of science data (including bibliographic data and metadata, funding, relational databases, ontologies and classifications) has boosted the empirical work in the depiction of the organisational structure of science. In turn, policy analysis has been able to accurately identify many unsuspected effects of past investments and policy decisions both at the macro and micro level. Using topic models, we develop a novel method for evaluating the robustness of different text-to-text similarity models. Employing that procedure, we find that the neural-network-based paragraph embeddings approach seems capable of providing statistically robust estimates of document--document similarities. Finding methods to estimate the similarity between individual publications is an area of long-standing interest in the information science and scientometrics communities. These techniques enable researchers to build indicators and classification methods based on the analysis of large text corpora. We show that the most widely used techniques suffer from inconsistencies upon retraining, and provide a procedure to evaluate and compare the quality of different methods, regardless of the data. Next, we present a game-theoretic model of rewards to scientific contributions. Our model of science may help explain the resulting social organisation of science from a simple social dilemma model. We model a researcher's payoff as a common-pool resource game, intrinsically connecting the appropriability of scientific output to a scientist's optimal strategy. This simple model of reward allocation sheds new light on a variety of behaviours that have been observed amongst researchers. Finally, we propose an empirical analysis of the relationship between basic knowledge generation and spillovers to innovation. Using the United States' 2001 ban on federal funding of human embryonic stem cells (hESC), we disentangle the effect that policy had on downstream innovation. We employ recently developed data on patent-to-scientific-article citations to measure the spillovers, and we characterise the causal impact of the policy on subsequent innovation with a difference-in-differences estimator. Our estimates suggest that in the years following the policy, scholarly publications subject to the ban received 65 to 80 per-cent fewer patent citations than the control group. We then apply topic modelling techniques to examine changes in the direction of science. In particular we build a topic-variety metric. Our findings show that variety decreased in the aftermath of the policy. Our results suggest that even the most modest policy changes have a profound impact on downstream innovation and the advancement at the frontier.

EPFL2020

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

The organisation of science: topics, incentives and funding.

Omar Ballester Gonzalez

EPFL2020

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

Charles Chadi Ayoubi

EPFL2020