Since the 1990s, research and development (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/information technology (IT) domain. The increase in foreign R&D on the firm side has been largely concentrated within software and IT-intensive multinational corporations (MNCs). 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 United Kingdom, Canada, and Japan. In this paper we will document three important phenomena: (1) the globalization of R&D by US 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. We argue that the shortage in software/IT-related human capital resulting from the large IT- and software-biased shift in innovation drove US MNCs abroad, and particularly drove them abroad to “new hubs” with large quantities of science, technology, engineering, and mathematics (STEM) workers who possessed IT and software skills. Our findings support the view that the globalization of US multinational R&D has reinforced the technological leadership of US-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.
This chapter reviews the empirical economics literature on the impact of trade liberalization on firms’ innovation-related outcomes. We define and examine four types of shocks to trade flows: import competition, export opportunities, access to imported intermediates, and foreign input competition. Our review reveals interesting heterogeneities at the country and firm levels. In emerging countries, trade liberalization appears to spur productivity and innovation. In developed countries, export opportunities and access to imported intermediates tend to encourage innovation, but the evidence on import competition is mixed, especially for firms in the United States. At the firm level, the positive effects of trade on innovation are more pronounced at the initially more productive firms, while the negative effects are more pronounced at the initially less productive firms.
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 US federal government with the goal of developing radically new technologies. In this paper, we 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. We identify the ARPA model’s domain as mission-oriented research on nascent S-curves within an inefficient innovation system. Finally, we describe some of the challenges to implementing the ARPA model, and we comment on the role of ARPA in the landscape of research-funding approaches.
On the thirty-fifth anniversary of the adoption of the Orphan Drug Act (ODA), we describe the enormous changes in the markets for therapies for rare diseases that have emerged over recent decades. The most prominent example is the fact that the profit-maximizing price of new orphan drugs appears to be greater today than it was in 1983. All else equal, this should reduce the threshold for research and development (R&D) investment in an economically viable product. Further, the small size of patient populations for orphan drugs, together with the increasing prevalence of biologics among orphan drugs, have created a set of natural monopoly-like markets in which firms face little competition, even after the end of formal periods of patent protection and market exclusivity. Additionally, the evolving technologies of drug development—in particular, the increasingly common use of auxiliary endpoints in clinical trials and the use of biomarkers for patient selection for treatment—now allow manufacturers to target smaller populations. Taken together, these changes raise doubts about whether the ODA encourages the development of products that otherwise would not have been brought to market—or whether, instead, it simply rewards the producers of inframarginal products. After presenting empirical support for our claims of an evolving marketplace, we discuss the tradeoffs associated with reshaping the ODA for the twenty-first century.
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. 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 intellectual property (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.
We review the evidence that artificial intelligence (AI) is having a large effect on the economy. Across a variety of statistics—including robotics shipments, AI start-ups, and patent counts—there is evidence of a large increase in AI-related activity. We also review recent research in this area that 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. We 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, including universal basic income and guaranteed employment.
