Advances in the understanding of the molecular basis of diseases have expanded the number of plausible therapeutic targets for the development of innovative agents in recent decades. However, although investment in pharmaceutical research and development (R&D) has increased substantially in this time, the lack of a corresponding increase in the output in terms of new drugs being approved indicates that therapeutic innovation has become more challenging. Here, using a large database that contains information on R&D projects for more than 28,000 compounds investigated since 1990, we examine the decline of R&D productivity in pharmaceuticals in the past two decades and its determinants. We show that this decline is associated with an increasing concentration of R&D investments in areas in which the risk of failure is high, which correspond to unmet therapeutic needs and unexploited biological mechanisms. We also investigate the potential variations in productivity with regard to the regional location of companies and find that although companies based in the United States and Europe differ in the composition of their R&D portfolios, there is no evidence of any productivity gap.

We draw on diverse data sets to compare the institutional organization of upstream life science research across the United States and Europe. Understanding cross-national differences in the organization of innovative labor in the life sciences requires attention to the structure and evolution of biomedical networks involving public research organizations (universities, government laboratories, nonprofit research institutes, and research hospitals), science-based biotechnology firms, and multinational pharmaceutical corporations. We use network visualization methods and correspondence analyses to demonstrate that innovative research in biomedicine has its origins in regional clusters in the United States and in European nations. But the scientific and organizational composition of these regions varies in consequential ways. In the United States, public research organizations and small firms conduct R&D across multiple therapeutic areas and stages of the development process. Ties within and across these regions link small firms and diverse public institutions, contributing to the development of a robust national network. In contrast, the European story is one of regional specialization with a less diverse group of public research organizations working in a smaller number of therapeutic areas. European institutes develop local connections to small firms working on similar scientific problems, while cross-national linkages of European regional clusters typically involve large pharmaceutical corporations. We show that the roles of large and small firms differ in the United States and Europe, arguing that the greater heterogeneity of the U.S. system is based on much closer integration of basic science and clinical development.

Reputation is an important social construct in science, which enables informed quality assessments of both publications and careers of scientists in the absence of complete systemic information. However, the relation between reputation and career growth of an individual remains poorly understood, despite recent proliferation of quantitative research evaluation methods. Here, we develop an original framework for measuring how a publication's citation rate Δc depends on the reputation of its central author i, in addition to its net citation count c. To estimate the strength of the reputation effect, we perform a longitudinal analysis on the careers of 450 highly cited scientists, using the total citations C i of each scientist as his/her reputation measure. We find a citation crossover c × , which distinguishes the strength of the reputation effect. For publications with c < c × , the author's reputation is found to dominate the annual citation rate. Hence, a new publication may gain a significant early advantage corresponding to roughly a 66% increase in the citation rate for each tenfold increase in C i . However, the reputation effect becomes negligible for highly cited publications meaning that, for c ≥ c × , the citation rate measures scientific impact more transparently. In addition, we have developed a stochastic reputation model, which is found to reproduce numerous statistical observations for real careers, thus providing insight into the microscopic mechanisms underlying cumulative advantage in science.computational sociology | science of science | networks of networks | Matthew effect | sociophysics C itation counts are widely used to judge the impact of both scientists and their publications (1-4). Although it is recognized that many factors outside the pure merit of the research or the authors influence such counts, little effort has been devoted to identifying and quantifying the role of the author-specific factors. Recent investigations have begun to study the impact the individual scientists have through collaboration and reputation spillovers (5, 6), two integrative features of scientific careers that contribute to cumulative advantage (7-9). However, the majority of citation models avoid author-specific effects, mainly due to the difficulty in acquiring comprehensive disambiguated career data (10-13). As the measures are becoming increasingly common in evaluation scenarios throughout science, it is crucial to better understand what the citation measures actually represent in the context of scientists' careers. Moreover, how does reputation affect a scientist's access to key resources, the incentives to publish quality over quantity, and other key decisions along the career path (14-18)? In addition, what role does reputation play in the mentor-matching process within academic institutions, in the effectiveness of single/double blinding in peer review, and in the reward system of science (14,15,19)?It is against this background that we have developed a quantitative framework with the goal of isola...

No abstract

Production systems, traditionally analyzed as almost independent national systems, are increasingly connected on a global scale. Only recently becoming available, the World Input-Output Database (WIOD) is one of the first efforts to construct the global multi-regional input-output (GMRIO) tables. By viewing the world input-output system as an interdependent network where the nodes are the individual industries in different economies and the edges are the monetary goods flows between industries, we analyze respectively the global, regional, and local network properties of the so-called world input-output network (WION) and document its evolution over time. At global level, we find that the industries are highly but asymmetrically connected, which implies that micro shocks can lead to macro fluctuations. At regional level, we find that the world production is still operated nationally or at most regionally as the communities detected are either individual economies or geographically well defined regions. Finally, at local level, for each industry we compare the network-based measures with the traditional methods of backward linkages. We find that the network-based measures such as PageRank centrality and community coreness measure can give valuable insights into identifying the key industries.

We introduce a model of proportional growth to explain the distribution Pg(g) of business-firm growth rates. The model predicts that Pg(g) is exponential in the central part and depicts an asymptotic power-law behavior in the tails with an exponent ‫؍‬ 3. Because of data limitations, previous studies in this field have been focusing exclusively on the Laplace shape of the body of the distribution. In this article, we test the model at different levels of aggregation in the economy, from products to firms to countries, and we find that the predictions of the model agree with empirical growth distributions and size-variance relationships.proportional growth ͉ preferential attachment ͉ Laplace distribution G ibrat (1, 2), building on the work of the astronomers Kapteyn and Uven (3), assumed the expected value of the growth rate of a business firm's size to be proportional to the current size of the firm, which is called the law of proportionate effect (4, 5). Several models of proportional growth have been subsequently introduced in economics to explain the growth of business firms (6-8). Simon and co-workers (9-12) extended Gibrat's model by introducing an entry process according to which the number of firms rise over time. In the framework of Simon and co-workers, the market consists of a sequence of many independent ''opportunities'' that arise over time, each of size unity. Models in this tradition have been challenged by many researchers (13-17) who found that the firmgrowth distribution is not Gaussian but displays a tent shape.Here we introduce a general framework that provides a unifying explanation for the growth of business firms based on the number and size distribution of their elementary constituent components (18)(19)(20)(21)(22)(23)(24)(25). Specifically, we present a model of proportional growth in both the number of units and their size, and we draw some general implications on the mechanisms that sustain business-firm growth (7,11,21,(26)(27)(28). According to the model, the probability density function (PDF) of growth rates is Laplace in the center (13) with power-law tails (29, 30) decaying as P g (g) ϳ g Ϫ , where ϭ 3. Also, because of data limitations, previous studies in this field focus on the Laplace shape of the body of the distribution, which, however, is an unconditional object (31). Using a database on the size and growth of firms and products, we characterize the shape of the whole growth-rate distribution. We test our model by analyzing different levels of aggregation of economic systems, from the ''micro'' level of products to the ''macro'' level of industrial sectors and national economies. We find that the model accurately predicts the shape of the PDF of growth rate at all levels of aggregation studied. The Theoretical FrameworkWe model business firms as classes consisting of a random number of units. According to this view, a firm is represented as the aggregation of its constituent units such as divisions (22), businesses (20), or products (21). Accordingly, on a different level...

Understanding how institutional changes within academia may affect the overall potential of science requires a better quantitative representation of how careers evolve over time. Because knowledge spillovers, cumulative advantage, competition, and collaboration are distinctive features of the academic profession, both the employment relationship and the procedures for assigning recognition and allocating funding should be designed to account for these factors. We study the annual production n i ðtÞ of a given scientist i by analyzing longitudinal career data for 200 leading scientists and 100 assistant professors from the physics community. Our empirical analysis of individual productivity dynamics shows that (i) there are increasing returns for the top individuals within the competitive cohort, and that (ii) the distribution of production growth is a leptokurtic "tent-shaped" distribution that is remarkably symmetric. Our methodology is general, and we speculate that similar features appear in other disciplines where academic publication is essential and collaboration is a key feature. We introduce a model of proportional growth which reproduces these two observations, and additionally accounts for the significantly rightskewed distributions of career longevity and achievement in science. Using this theoretical model, we show that short-term contracts can amplify the effects of competition and uncertainty making careers more vulnerable to early termination, not necessarily due to lack of individual talent and persistence, but because of random negative production shocks. We show that fluctuations in scientific production are quantitatively related to a scientist's collaboration radius and team efficiency.career trajectory | labor market | science of science | tenure | computational sociology I nstitutional change could alter the relationship between science and scientists as well as the longstanding patronage system in academia (1, 2). Some recent shifts in academia include the changing business structure of research universities (3), shifts in the labor supply demand balance (4), a bottleneck in the number of tenure track positions (5), and a related policy shift away from long-term contracts (3, 6). Along these lines, significant factors for consideration are the increasing range in research team size (7), the economic organization required to fund and review collaborative research projects, and the evolving definition of the role of the academic research professor (3).The role of individual performance metrics in career appraisal, in domains as diverse as sports (8, 9), finance (10, 11), and academia, is increasing in this data rich age. In the case of academia, as the typical size of scientific collaborations increases (7), the allocation of funding and the association of recognition at the varying scales of science [individual ⇆ group ⇆ institution (12)] has become more complex. Indeed, scientific achievement is becoming increasingly linked to online visibility in a considerable reputation tournament (13)....

No abstract