Economic Impact of Investments in R&D and Science and Engineering Workers

美国经济在创新方面处于全球领先地位，其劳动力高度移动的。本课题研究创新与劳动力流动之间的关系，考察劳动者（特别是科学家和工程师）的流动在促进和扩散经济中的创新方面的作用。该研究探讨了公司和机构的科学和工程劳动力如何影响生产力：在一家公司，将具有不同就业历史的工人混合在一起有助于产生新的想法并促进创新吗？工人在企业之间的流动是否有助于在经济中的企业之间传播新思想和转移技术？探讨这些问题有助于更好地理解硅谷等充满活力的区域集聚经济以及经济增长背后的机制。这项研究对劳动力流动、创新、研发投资和科学与工程工作者的相关政策产生了影响。如果通过工人流动传播思想和技术对经济增长很重要，那么制定影响劳动力市场内部或地区、州和国家之间工人流动的政策--例如，税收和医疗保险政策、住房和城市政策、移民政策、知识产权规则以及就业中的竞业禁止协议的执行，都需要对它们对经济增长和生产率的影响保持敏感。该项目使用有关工人就业历史和雇主特征的详细的经济范围内雇主-雇员关联数据，结合有关企业研发的数据，分析技术在经济中的扩散。该项目产生了新的估计私人回报的研发，在公司和企业建立的水平。有大量证据表明，企业之间的研发溢出效应是重要的，其影响至少与研发投资的直接私人回报具有相同的数量级。该项目开发的R D溢出的来源和溢出的幅度在公司和建立一级的新证据。本研究探讨了两个相互关联的问题：（一）工人流动是企业间知识溢出的重要来源吗？(ii)如何劳动力流动介导的知识溢出关系到企业之间的技术溢出在以前的研究记录？特别是，劳动力流动介导的知识溢出的技术溢出的来源，或者他们是一个额外的和单独的效果？该项目的研究有助于理解科学和工程工作者以及从事或参与研发活动的其他职业的工人的工资构成;并扩大了对科学和技术进步如何刺激经济活动以及对研发和工人的投资如何通过经济传播的理解。该研究方法特别有前途，因为它使用了广泛经济规模的丰富和全面的微观数据。该研究通过开发机器学习方法来识别研发业绩较好的小型企业，并将企业的研发投资分配给企业的相关机构，为NSF/Census研发调查数据的收集和利用做出了贡献。该奖项反映了NSF的法定使命，通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

The U.S. economy is a global leader in innovation, and its workforce is highly mobile. This project investigates the link between innovation and labor mobility, examining the role of the movement of workers (especially scientists and engineers) in fostering and diffusing innovation in the economy. The research addresses how the science and engineering workforce of firms and establishments affect productivity: In a firm, does mixing together workers with diverse employment histories help generate new ideas and foster innovation? Does movement of workers between firms help spread new ideas and transfer technology across firms in the economy? Answering these questions provides a better understanding of dynamic, regional agglomeration economies, such as Silicon Valley, and the mechanisms behind economic growth. This research has ramifications for policies relating to labor mobility, innovation, and investment in R&D and science and engineering workers. If the spread of ideas and technology through worker mobility is important to economic growth, then the formulation of policies that influence mobility of workers within labor markets or among regions, states, and countries -- e.g., tax and health insurance policies, housing and urban policy, immigration policy, intellectual property rules and enforcement of noncompete covenants in employment -- need to be sensitive to their effect on economic growth and productivity.The project uses detailed, economy-wide employer-employee linked data on workers' employment histories and employer characteristics, combined with data on firms' R&D, to analyze the diffusion of technology through the economy. The project produces new estimates of the private returns to R&D, at the firm and business establishment level. There is substantial evidence that R&D spillovers between firms are important and have an impact that is at least of the same order of magnitude as the direct, private returns to R&D investment. The project develops new evidence on the sources of R&D spillovers and the magnitudes of spillovers at the firm and establishment levels. The research addresses two interrelated questions about R&D spillovers: (i) Are worker flows an important source of knowledge spillovers between firms? (ii) How do labor mobility-mediated knowledge spillovers relate to the technology spillovers between firms documented in previous research? In particular, are labor mobility-mediated knowledge spillovers the source of technology spillovers, or are they an additional and separate effect? The project research contributes to the understanding of wage formation for science and engineering workers, and workers in other occupations that are exposed to or participate in R&D activities; and expands understanding of how scientific and technological advances stimulate economic activity, and how investments in R&D and in workers propagate through the economy. The research approach is especially promising because of its use of rich and comprehensive microdata at economy-wide scale. The research contributes to NSF/Census R&D survey data collection and utility, by developing machine learning methods for identifying smaller R&D-performing firms, and allocating firms' R&D investments to the relevant establishments of the firm.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.