MOD: Estimating the Effect of Exposure to Superstar Scientists: Evidence from Academia and the Biopharmaceutical Sector

通过衡量研究资助的“培训”效果和研究工作的协同效应，该项目提供了关于公共研究的好处和适当的资助水平的长期辩论背后的重要和缺失的因素。地理影响力的衡量标准也为当前的政策问题提供了信息，即美国相对于高质量研究对企业在国际背景下的区位选择所施加的吸引力的竞争力。此外，由于这项研究的一部分探索了知识溢出的运作机制，该项目应该产生关于跨组织的人才配置的有价值的见解，以及影响代理人之间信息流动的技术和政策如何对经济中的技术创新水平和速度产生重要影响。因此，本项目的目的是实证地估计“超级明星科学家”的溢出对生物医学领域科学进步的重要性。这项研究依赖于调查人员在过去几年中收集的独特数据集。该数据集匹配了1977至2003年间医学院教师和NIH受赠者的完整名册的出版产出、NIH资金和专利。这项研究的中心模型估计了接触“超级巨星”科学家对学术生命科学领域其他研究人员的科学生产力产生的影响。曝光被认为是一个多维的结构，有三个不同的影响渠道：(A)合著性(“社会距离”)；(B)同一地点(“地理距离”)；(C)研究重点的重叠/互补(“科学距离”)。因为科学家不是随机定位或合作的，所以特别关注的是准实验，它可以帮助梳理因果关系和单纯的相关性。作为这项研究的一部分，开发了开源软件工具来构建单个科学家之间的社会和科学距离的测量。除了对政策辩论的智力贡献外，这个项目还将产生几个额外的影响。首先，为测量研究人员之间的社会和科学距离而开发的软件将对对科学和技术政策感兴趣的广泛学者有用。开放源码格式的目的是鼓励未来的用户修改和改进软件，以便这一领域的研究工具继续进步。该软件连同源代码和用户手册将向公众免费提供，供科学政策学者和其他感兴趣的各方使用。其次，这项研究产生的软件预计将通过公开提供一些交叉文件，极大地降低匹配生物科学中个人层面的专利、出版和研究资金数据的成本。最后，调查结果将通过各种媒体传播给广泛的受众，从而促进与学院的同事以及生物医药行业的政策制定者和公司的公开对话。

By measuring the "training" effects of research grants and synergies across research efforts, this project provides significant and missing elements in the calculus behind the perennial debates about the benefits from public research and appropriate levels of funding. Measures of geographic influence also inform current policy questions about U.S. competitiveness vis-a-vis the magnetic pull that high quality research exerts on the locational choice of firms in an international context. Moreover, since part of this research explores the mechanisms through which knowledge spillovers operate, the project should generate valuable insights about the allocation of talent across organizations and how the technologies and policies that influence the flow of information between agents has important implications for the level and rate of technological innovation within the economy.The purpose of this project, therefore, is to empirically estimate the importance of spillovers from "superstar scientists" for scientific progress in the biomedical area. The study relies on a unique dataset that the investigators have assembled over the past several years. The dataset has matched publication output, NIH funding and patents for the complete roster of medical school faculty and NIH grantees between the years 1977 and 2003. The central model of this study estimates the effect that exposure to "superstar" scientists exerts has on the scientific productivity of other researchers within the academic life sciences. Exposure is assumed to be a multidimensional construct, with three distinct channels of influence: (a) co-authorship ("social distance"); (b) co-location ("geographic distance"); and (c) overlap/complementarity of research foci ("scientific distance"). Because scientists do not locate or collaborate at random, particular attention is given to the quasi-experiments that can help tease apart causal relationships from mere correlations. As part of this study, open-source software tools are developed to construct measures of social and scientific distance between individual scientists. Beyond intellectual contributions to policy debates, this project will have several additional impacts. First, the software developed to measure social and scientific distance between researchers will be useful to a wide range of scholars interested in science and technology policy. The open-source format is designed to encourage future users to modify and improve the software so that research tools in this area continue to advance. This software along with source code and user manuals will be made publicly available at no charge for use by science policy scholars and other interested parties. Second, the software resulting from this study is expected dramatically to reduce the cost of matching individual-level patent, publication and research funding data in the biosciences by making a number of cross-walk files publicly available. Finally, the findings will be disseminated through various media to a wide range of audiences, thereby facilitating an open dialogue with colleagues in the academy, as well as policy makers and firms in the biomedical industry.