III
Research Applications of Human Resource Data

Paula Stephan in her background paper used data from the Survey of Doctorate Recipients to document three trends in the deployment of scientists and engineers in the United States—the increased importance of industry as an employer of PhDs relative to universities and government; the increased importance of service industries as employers of scientific and engineering talent relative to the manufacturing sector; and the movement of PhDs from the laboratory into non-R&D positions. In addition, the workshop incorporated two sessions discussing examples of productive uses of human resources data in illuminating aspects of innovation—first, the growth of biotechnology and second the growth of alliances among firms and collaborations between firms and university researchers.

RESEARCH ON BIOTECHNOLOGY

The prominence of human resource data in studies of the emergence and growth of firms applying the scientific and technical advances of microbiology and biochemistry is no surprise. First, there is no standard industry classification for biotechnology and thus no ready-made universe of firms whose characteristics can be studied independent of their creators and managers. Second, those founders and managers tend to be scientists and engineers on whom there is considerable public information. Leading practitioners of this analysis include Lynne Zucker and Michael Darby, who have studied the emergence of biotechnology in the United States and abroad (inter alia, Darby et al., 1999; Zucker et al., 1994).



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Using Human Resource Data to Track Innovation: Summary of a Workshop III Research Applications of Human Resource Data Paula Stephan in her background paper used data from the Survey of Doctorate Recipients to document three trends in the deployment of scientists and engineers in the United States—the increased importance of industry as an employer of PhDs relative to universities and government; the increased importance of service industries as employers of scientific and engineering talent relative to the manufacturing sector; and the movement of PhDs from the laboratory into non-R&D positions. In addition, the workshop incorporated two sessions discussing examples of productive uses of human resources data in illuminating aspects of innovation—first, the growth of biotechnology and second the growth of alliances among firms and collaborations between firms and university researchers. RESEARCH ON BIOTECHNOLOGY The prominence of human resource data in studies of the emergence and growth of firms applying the scientific and technical advances of microbiology and biochemistry is no surprise. First, there is no standard industry classification for biotechnology and thus no ready-made universe of firms whose characteristics can be studied independent of their creators and managers. Second, those founders and managers tend to be scientists and engineers on whom there is considerable public information. Leading practitioners of this analysis include Lynne Zucker and Michael Darby, who have studied the emergence of biotechnology in the United States and abroad (inter alia, Darby et al., 1999; Zucker et al., 1994).

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Using Human Resource Data to Track Innovation: Summary of a Workshop At the workshop other researchers described a series of studies that have examined, among other aspects of the development of industrial biotechnology, the following phenomena the contribution of basic research conducted in universities, government laboratories, and some large companies to the formation of new start-ups; tendencies not only to regional concentration of firms but also to regional specialization in certain types of biotechnology products or services; how scientists and engineers are employed within firms, not only in management and research but also in a variety of other functions such as quality assurance, regulatory compliance, and manufacturing design; how people with scientific and technical training populate service functions external to the biotechnology companies—venture capital, law, investment banking, and accounting firms; how biotechnology developments depend on informal networks of professionals that cut across public and private research and the nonprofit sector and that in some cases arise among graduate students and postdoctoral students before they become involved in entrepreneurial activity; the high degree of mobility and cross-fertilization among firms; and the feedback to university research via industry funding that tends to be associated with higher faculty productivity, albeit at some cost in openness of research. Biotechnology in Maryland Maryann Feldman of Johns Hopkins University presented her study of the genesis and evolution of the biotechnology industry in Maryland since the earliest firms were established in the early 1970s. Maryland is home to the third or fourth highest concentration of biotech firms in the United States. Feldman conducted her research by pulling together midrodata from a number of sources. She found 240 firms, 40 of them publicly traded. The median size is 14 employees; the mean size is just over 50 employees. By finding out where the founders were employed before starting a firm, she discovered that most were spun off from large supplier firms, such as Litton Bionetics, Life Technologies, Inc., and Bethesda Research Labs, Inc., rather than universities. Those founded by academics were more likely to be from leading universities outside Maryland. The Walter Reed Army Institute of Research and the National Institutes of Health

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Using Human Resource Data to Track Innovation: Summary of a Workshop laboratories were also the source of founders of many firms, accounting for the fact that two of the core technologies in the Maryland biotech industry are vaccines and genomics/gene therapy. Feldman had plans to repeat the study periodically. Because many of the companies tracked a year ago have gone out of business or merged, while other new firms have emerged, tracking the movement of scientific expertise will be one way to see what has been happening. Biotechnology and the University of California As director of the University of California’s (UC’s) industry/university cooperative research program, Suzanne Huttner oversees the university’s biotechnology and other high-technology initiatives. She participated in a study of the role that basic research and research training at the university had played in the development of the California biotechnology economy. California has been the location of about one-third of U.S. biotechnology companies since the emergence of the industry; the UC system accounts for approximately 10 percent of National Institutes of Health extramural funding. Led by Cherisa Yarkin, an economist at UC Berkeley, the study followed the people in the industry to find out where they were from, how they were deployed, and how they moved around. One-quarter of the firms in California were founded by a member of the UC faculty and 85 percent of the firms employed graduates of UC with advanced degrees in science and engineering. One hundred percent of those firms with 20 or more employees employed UC graduates. UC graduates with advanced degrees in molecular biology and related life sciences fields were working in most parts of a company including regulatory affairs, quality assurance, manufacturing, scale-up operations involving bioprocess engineering, and business development. Entirely new occupations are emerging that require expertise based on training in two or more disciplines. One such emerging field, bioinformatics, is experiencing severe labor shortages, because it requires advanced training in both molecular biology and computer sciences. The study found that advanced training in the life sciences is also a characteristic of those staffing investment banking, venture capital, law firms, and other services supporting the biotechnology industry. Huttner concluded that failure to trace the education and employment backgrounds of workers in all sectors of the biotechnology industry, including the service and supply infrastructure of investment capital and legal services, as well as the production and business development workforce within firms, will result in badly underestimating the contri-

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Using Human Resource Data to Track Innovation: Summary of a Workshop butions of public investment, especially in research training, to the economy. Scientific Networking and Entrepreneurial Success Walter Powell of the Stanford University School of Education described his research on the role of scientific networks in the biotechnology industry (Powell et al., 1998). The project focused on how firms survive and grow by both doing research and absorbing ideas generated elsewhere. This line of inquiry, which involved assembling a detailed database on staffing, economic performance measures, and patterns of collaboration among firms, found high rates of mobility of scientists and complex patterns of interaction among them. Critical advances, such as the discovery of the BRACA-1 gene and development of the mouse model of Alzheimer’s disease, involve collaborators from a variety of institutions—universities, government laboratories, nonprofit research institutes, research hospitals, biotechnology companies, and pharmaceutical houses. In the case of the BRACA-1 gene, the paper describing the discovery in Nature had 33 authors at 13 institutions; 11 of them changed employers in the 3-month interval between the submission of the paper and its publication. Powell characterized the innovation process as competition among networks of scientists rather than competition between firms. Firms depend on recruiting researchers as employees or collaborators for access to these scientific networks, which speed the time in which they can bring products to market and generate revenues. Little is known about the formation and operation of these networks except that some of the most important relationships are formed in graduate school or postdoctoral appointments and many of them are too informal to be recorded as contractual ties. Industry/University Relationships Eric Campbell of the Massachusetts General Hospital and Harvard Medical School described research on the impact of industry funding on the research and other activities of a sample of basic biomedical and clinical faculty in the 50 universities that received the most funding from NIH in 1993. Approximately 28 percent of the life sciences faculty surveyed in these 50 most-research-intensive universities had industry support for 1   This effect reversed, however, when faculty received more than two-thirds their research funding from industry. They published significantly fewer articles in the past 3 years.

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Using Human Resource Data to Track Innovation: Summary of a Workshop their research. Faculty with industry support published significantly more articles in the most recent 3-year period,1 compared with the average during their own careers and as well as compared with researchers without industry funding. Their teaching loads were about the same, and they devoted significantly more time to service activities. They were also more likely to have applied for, received, and licensed a patent and to have research become the basis of a start-up company. Campbell estimated that studies that involve assembling a sample from scratch as quickly as possible take about 6 months of the time of the principal investigator and three or four graduate students and cost a minimum of $500,000. RESEARCH ON COLLABORATIONS AND PARTNERSHIPS Another productive area of innovation research using human resources data is also concerned with the transfer of technological information across institutional boundaries and ultimately into commercial activities, without being limited to a particular technology or industry. University Technology Transfer In the second part of his presentation, Donald Siegel summarized research on factors associated with the productivity of technology transfer offices of universities (Siegel et al., 1999). Commercialization of technologies by universities has increased greatly, from 300 patents in 1980 to 2,412 in 1997, from 276 licensing agreements in 1980 to 3,328 in 1997, and from 35 corporate start-ups in 1980 to 333 in 1997. Some universities do more than others. Siegel and his colleagues analyzed a survey of 113 university technology transfer offices affiliated with the Association of University Technology Managers, which has detailed information on sources and deployment of staff and their compensation together with information on university policies regarding the disposition of intellectual property and faculty and institutional involvement in start-up firms. They linked these data with data from NSF and the Bureau of the Census to create environmental controls. They found that adding environmental controls did not explain much of the variation in relative productivity among the offices, suggesting that individual characteristics and organizational and personnel policies matter more. Siegel noted that systematic assessment of the human resources characteristics and practices of successful university technology transfer offices could have practical policy consequences in identifying best practices, which in turn might facilitate more efficient spillovers of scientific knowledge.

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Using Human Resource Data to Track Innovation: Summary of a Workshop Research Joint Ventures Albert Link, an economist at the University of North Carolina at Greensboro, discussed recent research on research partnerships, both between companies and between companies and universities. He suggested that information on individual scientist and engineer participants is often revealing of the motivation and significance of the institutional linkages and the degree of collaboration. For example, firms infrequently assign top-level personnel to joint ventures, preferring to keep their principal human talent assets and core technology competencies proprietary. The characteristics of the researchers in interfirm collaborations can be a proxy for the intensity of firm involvement and a predictor of their economic consequences. Corporate collaborators with university researchers, on the other hand, tend to seek out star scientists with competencies that the firms are lacking. Indeed, it is somewhat misleading to speak of industry-university collaborations; the firms are generally much less interested in institutional than in individual capabilities. Networks with Formal and Informal Elements Diana Hicks of CHI Research spoke of possibilities of tying together databases on technical publications and patents, which are produced by people, with other human resources data to help measure the intangible elements of firm assets. For example, it appears that papers published by industrial researchers are more highly cited than those by academic researchers in certain fields of the biological and other sciences. If industry is the best place to do important research, who is doing it? What are the trends in the backgrounds of industrial researchers in those fields? An analysis of research publications in Great Britain shows that the growth is taking place at companies that publish one to 10 papers a year, indicating that employment of scientists with advanced degrees is increasing most rapidly in small companies, where their expertise can be more closely related to innovation (Hicks and Katz, 1997). Research activity in the service sector, as measured by publications, is even more concentrated in small firms. The percentage of papers coauthored with university researchers increased in nearly every industry apart from agriculture from the early 1980s to the early 1990s. Turning to U.S. data, Hicks examined the science and technology linkages of DuPont along eight dimensions: co-authoring, co-patenting, patent-to-patent citation (each way), paper-to-paper citation (each way), and patent-to-paper citation (each way).2 Such an analysis shows, for 2   Paper-to-patent citations were not analyzed.

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Using Human Resource Data to Track Innovation: Summary of a Workshop example, with whom DuPont scientists are publishing and upon whose research DuPont scientists are capitalizing. A similar analysis can be done of patents. The result is a visual map of the breadth and depth of the intellectual network in which DuPont operates—the institutions with which they collaborate in research and patenting and how often and the institutions whose work they have used or absorbed and vice versa, again with what frequency. The analysis shows how interlinked the structure of scientific research is. Hicks cautioned, however, that the effort to pull the data together was very intensive and expensive, and a considerable investment would be needed to extend the analysis to more companies and include human resources data. Hicks estimated that it might take $3 million to $4 million to clean and integrate up to 10 years of data from the various citation and human resources databases and another $25,000 a year to maintain it, excluding the cost of getting data on sources of research funding. Others said that estimate might be low, perhaps very low, especially for annual operating costs. On the other hand, Stephan observed that there is a substantial unmeasured cost in failing to improve the data already collected and their linkages, because they are revealing less and less about trends in industrial innovation.