Innovation Policy and the Economy

A conference on Innovation Policy and the Economy 2018 took place on April 17 in Washington DC. Research Associates Josh Lerner of Harvard University and Scott Stern of MIT organized the meeting, which was sponsored by the Ewing Marion Kauffman Foundation. These researchers' papers were presented and discussed:

Nicholas Bagley, University of Michigan; Amitabh Chandra, Harvard University and NBER; Craig Garthwaite, Northwestern University and NBER; and Ariel Dora Stern, Harvard University

Precision Medicine and the Orphan Drug Act

Pian Shu, Georgia Institute of Technology, and Claudia Steinwender, MIT

Innovating in a Global Economy

The past several decades saw remarkable growth in international trade. Operating in an increasingly globalized competitive environment, firms today face unique opportunities and challenges in deciding whether, when, and how to innovate. Shu and Steinwender review the recent economics and management literature studying the link between trade liberalization and firms' innovation-related outcomes. They first classify trade shocks by direction and target market. They then summarize the theoretical predictions and empirical evidence on the impact of trade shocks on innovation. Finally, the researchers discuss policy implications, empirical limitations, and opportunities for future research.

Joshua Gans, Ajay K. Agrawal, and Avi Goldfarb, University of Toronto and NBER

The Economics of Artificial Intelligence

Recent progress in artificial intelligence (AI) - a general purpose technology affecting many industries has been focused on advances in machine learning, which we recast as a quality-adjusted drop in the price of prediction. Gans, Agrawal, and Goldfarb study how will this sharp drop in price impact society. Policy will influence the impact on two key dimensions: diffusion and consequences. First, in addition to subsidies and IP policy that will influence the diffusion of AI in ways similar to their effect on other technologies, three policy categories - privacy, trade, and liability - may be uniquely salient in their influence on the diffusion patterns of AI. Second, labor and antitrust policies will influence the consequences of AI in terms of employment, inequality, and competition.

Pierre Azoulay, MIT and NBER; Erica Fuchs, Carnegie Mellon University; Michael Kearney, MIT; and Anna Goldstein, Stanford University

Funding Breakthrough Research: Promises and Challenges of the "ARPA Model" (NBER Working Paper No. 24674)

From its 1958 origin in defense, the Advanced Research Projects Agency (ARPA) model for research funding has, in the last two decades, spread to other parts of the U.S. federal government with the goal of developing radically new technologies. Azoulay, Fuchs, Kearney, and Goldstein propose that the key elements of the ARPA model for research funding are: organizational flexibility on an administrative level, and significant authority given to program directors to design programs, select projects and actively manage projects. They identify the ARPA model's domain as mission motivated research on nascent S-curves within an inefficient innovation system. Finally, the researchers describe some of the challenges to implementing the ARPA model, and comment on the role of ARPA in the landscape of research funding approaches.

Lee G. Branstetter, Carnegie Mellon University and NBER; Britta Glennon, Carnegie Mellon University; and J. Bradford Jenson, Georgetown University and NBER

The IT Revolution and the Globalization of R&D (NBER Working Paper No. 24707)

Since the 1990s, R&D has not only become less geographically concentrated, but there has been especially fast growth in less developed emerging markets like China and India. One of the distinguishing features of the R&D globalization phenomenon is its concentration within the software/IT domain. The increase in foreign R&D on the firm side has been largely concentrated within software and IT-intensive multinationals. This concentration is mirrored on the country side. New R&D destinations such as India, China, and Israel look very different in the types of innovative activity being done there than older R&D destinations such as Germany, France, the U.K., Canada, and Japan. Branstetter, Glennon, and Jensen document three important phenomena: (1) the globalization of R&D by U.S. MNCs, (2) the growing importance of software and IT to firm innovation, and (3) the rise of new R&D hubs, and the differences in the type of activity done there. The researchers argue that the shortage in software/IT-related human capital resulting from the large IT- and software-biased shift in innovation drove U.S. MNCs abroad, and particularly drove them abroad to "new hubs" with large quantities of STEM workers who possessed IT and software skills. The researchers findings support the view that the globalization of U.S. multinational R&D has reinforced the technological leadership of U.S.-based firms in the information technology domain and that multinationals' ability to access an increasingly global talent base could support a high rate of innovation even in the presence of the rising (human) resource cost of frontier R&D.

Jason Furman, Harvard Kennedy School, and Robert Seamans, New York University

Artificial Intelligence and the Economy

Furman and Seamans review the evidence that artificial intelligence (AI) is having a large effect on the economy. Across a variety of statistics -- including robotics shipments, AI startups, and patent counts -- there is evidence of a large increase in AI-related activity. The researchers also review recent research in this area which suggests that AI and robotics have the potential to increase productivity growth but may have mixed effects on labor, particularly in the short run. In particular, some occupations and industries may do well while others experience labor market upheaval. The researchers then consider current and potential policies around AI that may help to boost productivity growth while also mitigating any labor market downsides including evaluating the pros and cons of an AI specific regulator, expanded antitrust enforcement, and alternative strategies for dealing with the labor market impacts of AI.

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