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，其劳动力具有高度的流动性。这个项目调查了创新和劳动力流动之间的联系，考察了工人(特别是科学家和工程师)的流动在促进和传播经济中的创新方面所起的作用。这项研究解决了公司和机构的科学和工程劳动力如何影响生产率的问题：在一家公司中，将不同工作经历的员工混合在一起是否有助于产生新想法和促进创新？工人在公司之间的流动是否有助于在经济中的公司之间传播新想法和转移技术？回答这些问题有助于更好地了解硅谷等充满活力的区域集聚经济体，以及经济增长背后的机制。这项研究对与劳动力流动、创新和对研发以及科学和工程工作者的投资相关的政策产生了影响。如果通过劳动力流动传播思想和技术对经济增长很重要，那么影响劳动力市场内或地区、州和国家之间劳动力流动的政策的制定--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竞业禁止公约的执行--需要敏感于它们对经济增长和生产力的影响。该项目使用详细的、全经济范围内与雇主和雇员相关的数据，包括工人的就业历史和雇主特征，并结合公司研发的数据，分析技术在经济中的传播。该项目对公司和商业机构层面的私人研发回报做出了新的估计。有大量证据表明，企业之间的研发溢出是重要的，其影响至少与研发投资的直接私人回报相当。该项目就研发溢出的来源以及企业和机构层面的溢出程度提出了新的证据。这项研究解决了关于研发溢出的两个相互关联的问题：(I)劳动力流动是企业间知识溢出的重要来源吗？(Ii)劳动力流动中介的知识溢出与先前研究中记录的企业之间的技术溢出有何关系？特别是，劳动力流动中介的知识溢出是技术溢出的来源，还是另一种单独的影响？该项目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；并扩大对科学和技术进步如何刺激经济活动的理解，以及对研发和工人的投资如何在经济中传播。这种研究方法特别有前景，因为它在整个经济范围内使用了丰富和全面的微观数据。这项研究通过开发机器学习方法来识别规模较小的研发表现良好的公司，并将公司的研发投资分配给公司的相关机构，为NSF/人口普查R&D调查数据的收集和利用做出了贡献。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.