Why are Some Regions More Innovative than Others? The Role of Firm Size Diversity

Large labs may spawn spin-outs caused by innovations deemed unrelated to the firm's overall business. Small labs generate demand for specialized services that lower entry costs for others. We develop a theoretical framework to study the interplay of these two localized externalities and their impact on regional innovation. We examine MSA-level patent data during the period 1975-2000 and find that innovation output is higher where large and small labs coexist. The finding is robust to across-region as well as within-region analysis, IV analysis, and the effect is stronger in certain subsamples consistent with our explanation but not the plausible alternatives.

Superhuman science: How artificial intelligence may impact innovation

New product innovation in fields like drug discovery and material science can be characterized as combinatorial search over a vast range of possibilities. Modeling innovation as a costly multi-stage search process, we explore how improvements in Artificial Intelligence (AI) could affect the productivity of the discovery pipeline in allowing improved prioritization of innovations that flow through that pipeline. We show how AI aided prediction can increase the expected value of innovation and can increase or decrease the demand for downstream testing, depending on the type of innovation, and examine how AI can reduce costs associated with well-defined bottlenecks in the discovery pipeline. Finally, we discuss the critical role that policy can play to mitigate potential market failures associated with access to and provision of data as well as the provision of training necessary to more closely approach the socially optimal level of productivity enhancing innovations enabled by this technology

The Knowledge Filter and Economic Growth: The Role of Scientist Entrepreneurship

Assesses the prevalence of and trends in the commercialization of research by university scientists funded by the National Cancer Institute. Analyzes levels of entrepreneurship in patenting choices and the role of university technology transfer offices

The Knowledge Filter and Economic Growth: The Role of Scientist Entrepreneurship

Assesses the prevalence of and trends in the commercialization of research by university scientists funded by the National Cancer Institute. Analyzes levels of entrepreneurship in patenting choices and the role of university technology transfer offices

A mix of small and large firms can be key to regional innovation

Areas such as Silicon Valley and Boston are often held up as examples of innovative regions to be emulated, but what makes them this way? By analysing patent data on computers and communication technology, Ajay K. Agrawal, Iain Cockburn, Alberto Galasso, and Alexander Oettl, argue that the mix of large and small firms in a region is very important to regional innovation. He writes that regions where a number of small and large lists coexist are more productive in terms of innovation, when compared to those that have only a small number of large firms or a large number of small ones

ABO blood group-incompatible living donor kidney transplantation: a prospective, single-centre analysis including serial protocol biopsies

Background. ABO incompatible kidney transplantation using antigen-specific immunoadsorption is increasingly performed but data on outcome, complications and protocol biopsies are still scarce. The present prospective single-centre study was aimed at these issues. Methods. This was a prospective single-centre cohort study of 10 successive ABO incompatible living donor kidney transplantations at the University Hospital Basel from September 2005 to October 2007. The following parameters were closely monitored during the whole follow-up: graft function, albuminuria, blood group antibody titres, CD19+ cell count, total IgG and IgG subclasses, CMV antigenaemia, decoy cells in the urine, EBV and polyoma BK virus PCR in the blood. Protocol biopsies were performed on Days 0 and 7 after 3, 6, 12 and 18 months. Results. Patient and graft survival is 100% after a median follow-up of 489 days (range 183-916 days). Median serum creatinine is 137 μmol/l (range 70-215 μmol/l), and median urine albumin-creatinine ratio (UACR) is 3.1 mg/ mmol (range 0.6-7.8 mg/mmol) at the time of the last follow-up. All patients had sustained diminished CD19+ cell count and/or total IgG concentrations. Neither CMV antigenaemia nor EBV replication in the blood was observed. Seven patients had positive polyoma BK virus replication in the blood but none developed polyoma virus-associated nephropathy (PVAN). Protocol biopsies revealed rejection Banff IIa in three patients on Day 7, and in one patient after 3 and 6 months. Banff Ia rejection was found in five patients. All rejection episodes resolved. Mild signs of chronic antibody-mediated rejection were observed in five patients. Conclusions. ABO-incompatible kidney transplantation seems to be successful and safe. Modifications of the current protocol may be possible and may further reduce potential side effects and cost

Systemic inflammation in decompensated cirrhosis: Characterization and role in acute-on-chronic liver failure.

Acute‐on‐chronic liver failure (ACLF) in cirrhosis is characterized by acute decompensation (AD), organ failure(s), and high short‐term mortality. Recently, we have proposed (systemic inflammation [SI] hypothesis) that ACLF is the expression of an acute exacerbation of the SI already present in decompensated cirrhosis. This study was aimed at testing this hypothesis and included 522 patients with decompensated cirrhosis (237 with ACLF) and 40 healthy subjects. SI was assessed by measuring 29 cytokines and the redox state of circulating albumin (HNA2), a marker of systemic oxidative stress. Systemic circulatory dysfunction (SCD) was estimated by plasma renin (PRC) and copeptin (PCC) concentrations. Measurements were performed at enrollment (baseline) in all patients and sequentially during hospitalization in 255. The main findings of this study were: (1) Patients with AD without ACLF showed very high baseline levels of inflammatory cytokines, HNA2, PRC, and PCC. Patients with ACLF showed significantly higher levels of these markers than those without ACLF; (2) different cytokine profiles were identified according to the type of ACLF precipitating event (active alcoholism/acute alcoholic hepatitis, bacterial infection, and others); (3) severity of SI and frequency and severity of ACLF at enrollment were strongly associated. The course of SI and the course of ACLF (improvement, no change, or worsening) during hospitalization and short‐term mortality were also strongly associated; and (4) the strength of association of ACLF with SI was higher than with SCD. Conclusion: These data support SI as the primary driver of ACLF in cirrhosis

Іншомовні аспекти фахової між культурної комунікації в сучасній вітчизняній і зарубіжній науковій літературі

У статті розглядаються основні напрямки сучасних вітчизняних і зарубіжних наукових досліджень з іншомовної фахової міжкультурної комунікації. Aspects of the professional foreign language of intercultural communication in modern domestic and foreign scientific literature. The paper discusses the main directions of current domestic and foreign scientific research in the field of professional foreign language intercultural communication

Helpfulness and Productivity: Implications of a New Taxonomy for Star Scientists

Atlanta Conference on Science and Innovation Policy 2009This presentation was part of the session : Science and Innovation WorkforceThe need to hire the best and the brightest - "the war for talent" - has long been one of the most pressing strategic concerns facing managers (Kapur and McHale, 2005; Guthridge, Komm, and Lawson, 2008). This concern is largely driven by the observation that high performers, or stars, account for the generation of a disproportionately large level of output. The vice-president of engineering of Google, Alan Eustace, noted to the Wall Street Journal in 2005 that "one top-notch engineer is worth 300 times or more than the average", and that he "would rather lose an entire incoming class of engineering graduates than one exceptional technologist" (Tam and Delaney, 2005). Why is this? How do stars so greatly influence the performance of organizations? The existing performance taxonomy for scientists focuses exclusively on individual output, classifying a scientist as either a Star or a Non-Star. The seminal work by Zucker, Darby, and Brewer (1998), for example, defines stars as the top 0.75% of contributors to the genetic sequence database GenBank, a group that accounts for almost 17% of contributions. Recent work by Groysberg, Lee, and Nanda (2008) examines the skill portability of the top 3% of security analysts when they move firms, using a ranking of the perceived effectiveness of security analysts and Azoulay, Graff, Zivin, and Wang (2008) look at the impact of eminent scientists using a variety of measures; such as research funding, citations, and patenting. In all of these articles, the definition of a star is based solely on productivity, in other words, we define stars by what they physically produce. This uni-dimensional classification of star scientists is surprising as innovation is most often characterized as a communal process. Communal interactions matter for two reasons. First, innovation is more often a result of the recombination of existing knowledge and ideas, rather than the discovery of something fundamentally novel (Gilfillan, 1935; Nelson and Winter, 1982). As knowledge frontiers continue to expand, combinations of increasingly specialized levels of human capital are required to reach the forefront of knowledge (Wuchty, Jones, and Uzzi, 2007; Jones, 2008). It is this recombination of specialized ideas, either through formal collaborations (coauthorships, joint ventures, etc.) or informal means (discussions and comments from helpful individuals), that leads to innovation. Second, the exchange of knowledge is to a large extent governed through social channels. Individuals possess only finite levels of knowledge and knowledge search is costly; social forces can reduce barriers to knowledge flow through geographic proximity (Jaffe, Trajtenberg, and Henderson 1993), labor mobility (Almeida and Kogut, 1999; Oettl and Agrawal, 2008), social networks (Singh 2005), and membership in ethnic communities (Agrawal, Kapur and McHale 2008). While innovation is a communal process, the inability to perfectly contract between parties on knowledge exchange leads to failures in the market for knowledge and a decrease in knowledge transfer (Arrow 1962). As such, conditions that facilitate knowledge sharing or spillovers in the absence of formal contractual environs are of great value to firms. Ultimately, if our concern is to understand the mechanisms by which an individual maximizes his performance, simply understanding the productivity inputs of an individual would suffice. However, the strategy and economics literatures focus on performance measures at the organization and regional levels, and as such, mechanisms in which individuals influence the productivity of others become important as these mechanisms directly influence the performance of organizations and regions. Hence, mechanisms by which individuals generate spillovers are of paramount concern to scholars of strategy and economics. The importance of social factors on innovation illuminates the deficiency of our current productivity-focused conceptualization of star scientists (Stars versus Non-Stars). To expand our current conceptualization of star scientists, I develop a new taxonomy of star scientists by incorporating a social dimension: helpfulness to others. This new taxonomy allows an individual to not only vary along a productivity dimension but also along a helpfulness dimension. The objective of this paper is threefold. First, I expand upon the current dichotomous conceptualization of stars by developing a taxonomy that not only incorporates a star's individual productivity but also his helpfulness. In doing so, I move beyond the current uni-dimensional classification and redefine what it means to be a star. Second, I propose a measure to classify individuals into this new taxonomy. And third, I use this taxonomy to assess the extent to which different star types influence the productivity of others. Following prior studies (Allison and Long, 1990; Azoulay, Graff, Zivin, and Wang, 2008) I measure individual productivity using Impact Factor-weighted publication counts. Helpfulness, on the other hand, is measured by academic journal acknowledgements as acknowledgements are generally made to those who have helped in the development of the work. Using these measures of productivity and helpfulness, I classify a sample of 415 immunologists and examine their influence on the productivity of their coauthors. Coauthorship is used to pinpoint the timing of the formation of an interpersonal tie between the immunologist and a potential recipient of spillovers. It is this collocation in social space that allows the coauthor the potential to benefit from any spillovers the star may provide. By placing a star in both productivity and helpfulness space, while keeping the classifications discrete, I am able to classify an individual as one of four types: an All-Star, a Lone Wolf, a Maven, or a Non-Star. I define an All-Star as an individual with both high productivity and high helpfulness. A Maven is an individual with average productivity but high helpfulness. A Lone Wolf is someone who has high productivity but average helpfulness, and a Non-Star has both average productivity and average helpfulness. Restrictively, the current dichotomous conceptualization of stars groups both All-Stars and Lone Wolves together, and overlooks Mavens. By expanding on the current classification, I am able to examine the influences of individuals who vary both in their productivity and their helpfulness. Examining the changes in productivity from coauthoring with various star types would be an appropriate empirical exercise if coauthoring relationships were chosen at random, but clearly they are not. The problem with endogenous coauthor selection is that the coauthors selected by an immunologist may be chosen due to their productivity, thus producing spurious correlations between an individual's productivity and their coauthorship network. For this paper, I examine the decrease in productivity of coauthors when an immunologist dies. Across a number of specifications, the productivity of the coauthors of All-Stars that die decreases on average by 35% relative to the decrease in productivity when a Non-Star dies. More interestingly, coauthors of Mavens that have died experience a 30% decrease in productivity, while the coauthors of Lone Wolves experience decreases in productivity of only 19% on average. By expanding the current conceptualization of star scientists and focusing on both the productivity and helpfulness dimensions of scientists, I find that spillovers are most likely to be generated from individuals with high helpfulness. As a result, the literature has largely overemphasized the importance of Lone Wolves, yet overlooked and consequently underemphasized Mavens

A New Taxonomy for Star Scientists: Three Essays

It is surprising that the prevailing performance taxonomy for scientists (Star versus Non-Star) focuses only on individual output and ignores social behavior since scholars often characterize innovation as a communal process. To address this deficiency, I expand the traditional taxonomy that focuses solely on productivity and add a second, social dimension to the taxonomy of scientists: helpfulness to others. Using a combination of academic paper citations and Impact Factor-weighted publications to measure scientist productivity as well as the receipt of academic paper acknowledgements to measure helpfulness, I classify scientists into four distinct categories of human capital quality: All-Stars, who have both high productivity and helpfulness; Lone Wolves, who have high productivity but average helpfulness; Mavens, who have average productivity but high helpfulness; and Non-Stars, who have both average productivity and helpfulness. The first study examines the impact of 415 immunologists on the performance of their coauthors. Looking at the change in quality-adjusted publishing output of an immunologist's coauthors after the immunologist's death, I find that the productivity of an All-Star's coauthors decreases on average by 35%, a Maven's coauthors by 30% on average, and a Lone Wolf's coauthors by 19%, all relative to the decrease in productivity of a Non-Star's coauthors. These findings suggest that our current conceptualization of star scientists, which solely focuses on individual productivity, is both incomplete and potentially misleading as Lone Wolves may be systematically overvalued and Mavens undervalued. The second study builds upon the first study's finding that Mavens have a large impact on the performance of their coauthors. Using salary disclosures from 2008 at the University of California, I examine the extent to which each star type is compensated differently. While Mavens have a larger impact on the performance of their coauthors than Lone Wolves, Mavens are compensated less, providing preliminary evidence that these performance effects are spillovers. The third study examines the likelihood of an immunologist's mobility as a function of his observable and unobservable human capital. The greater a scientist's productivity (observable to the market), the greater his inter-institution mobility, while the greater a scientist's helpfulness (unobservable to the market), the lower his inter-institution mobility.Ph