CAREER: Understanding Economic Growth: Firms, Inventors, and Ideas

本研究的主要重点是了解创新、技术进步和经济增长之间的联系，并研究改善美国经济增长和福利的最佳产业政策。具体而言，本研究侧重于创新和经济增长的三个主要组成部分：(i)发明者，（ii）公司，（iii）想法（专利）。该项目的第一部分通过将美国专利记录与1880年至1940年的联邦人口普查数据联系起来，作为对未来的指导，研究了美国创新的黄金时代。然后研究了专利发明与长期经济增长的关系，并提出了分析创新与增长的宏观和微观关键决定因素的框架。在过去的几十年里，整个经济的创业率下降了超过12%，而企业增长率中的要素再分配和分散指标下降了超过20%。该项目的第二部分研究美国经济的这些变化。项目的这一部分侧重于理解美国经济的增长和创新模式，并利用美国经济的微观数据将各种经验规律联系在一起。我们的目标是了解这些变化的原因，并提出能够重建企业家精神和创造就业机会的政策。第三部分对专利市场进行了研究。非实践实体（npe）如何影响创新和技术进步？该项目为该主题提供了新的理论和实证证据。在这样做的过程中，该项目为一场辩论提供了信息，这场辩论将npe描述为善意的中间人，帮助将知识产权重新分配给最有生产力的地方，或者是利用专利制度榨取租金、从而损害创新的骗子。作为该项目的一部分，PI还计划编写一套全面的在线经济增长教育材料，供所有人免费使用。其目标是向更广泛的学生传播高质量的免费材料，包括那些没有机会在顶级学府学习的学生。这一研究结果将有助于美国经济的增长、就业机会的创造和竞争力的提高。通过结合微观和宏观视角，我们对宏观经济增长和技术进步的理解可以显著提高。宏观经济是由许多微型公司组成的，这些公司雇佣发明家来产生新的创新想法。因此，这一建议——通过关注思想、发明家和企业行为的微观经济学——旨在通过利用关于创新和企业行为的丰富信息，捕捉企业层面创新行为的丰富异质性，从而揭示宏观经济增长。此外，形式化模型允许我们使口头概念具有可操作性，并与数据对抗。因此，本提案还侧重于将一般均衡增长理论与数百万美国专利、发明家和公司的丰富微观数据相结合。通过使用结构估计技术，该建议将允许PI不仅了解潜在机制，而且还可以进行反事实政策练习，PI可以在其中研究最优产业政策设计。

The main focus of this research is to understand the links between innovation, technical progress, and economic growth and to study the best industrial policies to improve economic growth and welfare in the U.S. Specifically, this research focuses on three major components of innovation and economic growth: (i) inventors, (ii) firms, and (iii) ideas (patents). The first part of this project examines the golden age of American innovation by undertaking a major data matching exercise linking U.S. patent records with data from Federal Censuses between 1880 and 1940 as a guide to the future. Then it studies the relationship between patented inventions and long-run economic growth, and outlines a framework for analyzing key macro- and micro-level determinants of innovativeness and growth. Over the last several decades economy-wide startup rates have declined in excess of twelve percent, while measures of factor reallocation and dispersion in firm growth rates have declined in excess of twenty percent. The second part of the project studies these changes in the U.S. economy. This part of the project focuses on understanding patterns of growth and innovation in the U.S. economy and ties together various empirical regularities using micro data from the U.S. economy. The goal is to understand the reasons for these changes and propose policies that can reconstruct entrepreneurship and job creation. The third part of this project studies the market for patents. How do non-practicing entities (NPEs) impact innovation and technological progress? This project provides new theoretical and empirical evidence on the subject. In doing so the project informs the debate that has portrayed NPEs alternatively as benign middlemen that help to reallocate intellectual property to where it is most productive or stick-up artists that exploit the patent system to extract rents, thereby hurting innovation. As part of the project, the PI also plans to prepare a comprehensive set of online education materials on economic growth that will be freely accessible to everyone. The goal is to disseminate high-quality, but free materials to a broad range of students, including those who did not get the chance to study at a top institution. The results of this research will contribute to economic growth, job creation and competitiveness of the U.S. economy.Our understanding of macroeconomic growth and technological progress can be significantly improved by combining micro and macro perspectives. A macroeconomy is made of lots of micro firms that hire inventors to produce new innovative ideas. Therefore, this proposal--by focusing on the microeconomics of ideas, inventors, and firm behavior--aims to shed light on macroeconomic growth by capturing the rich heterogeneity in innovation behavior at the firm level by using the wealth of information on innovation and firm behavior. In addition, formal models allow us to make verbal notions operational and confront them with data. Therefore, this proposal also focuses on combining general equilibrium growth theories with rich micro data on millions of U.S. patents, inventors, and firms. By also using structural estimation techniques, this proposal will allow the PI not only to understand the underlying mechanisms, but also to do counterfactual policy exercises where the PI can study optimal industrial policy design.