Conclusion

Overview The United States spends more money on research and has more Nobel Laureates than any other country. It is the unquestioned global leader in science. But even while other countries are spending more on research and development (R&D), purse strings are tightening in the United States, and taxpayers want to know that their money is well spent. But by and large, science investments are based on subjective decisions and, often, flawed data (1). A major reason is that there is no systematic answer to the very specific question of the link between federal R&D and economic growth. As Ben Bernanke has pointed out, scholars do not know much about how federal support for R&D affects economic activity (2). The US government must and can do better: You cannot manage what you cannot measure. As a House Science committee chair noted, While many of us would agree that science has had a positive impact on our lives, I think we actually know very little about how the process of innovation works. What kinds of research programs or institutional structures are most effective? How do investments in R&D translate to more jobs, improved health, and overall societal wellbeing? How should we balance investments in basic and applied research? With millions of Americans out of work, it becomes more critical than ever that we find answers to these questions. (3) Hitherto, the examination of the results of federal expenditures on scientific research has tried to directly link research grants to bibliometric measures, like publications. This book argues that such an approach is the wrong framework to use: Documents do not do science, people do science. Science is not a slot machine wherein funding generates results in nice tidy slices in three- to five-year time intervals. In fact, research ideas – the black box between research funding and results – are transmitted through networks in long, circuitous, and often nonlinear fashion, over quite long periods. So, the right framework begins with identifying the right unit of analysis – people – and examining how research funding builds public and private networks. The evidence is clear that people and networks are the drivers of innovation: The vibrant growth of Silicon Valley, Boston, San Diego, and the Research Triangle was driven by each region’s research institutions and the people within them.