Knowledge production function: lessons from biotechnology

International Journal of Technology Management

Volumn 19, Issue 1, 2000, Pages 165-178

  Stephan, Paula ^a,d,e,f   Audretsch, David ^b,d,g   Hawkins, Richard ^c  

^a GEORGIA STATE UNIVERSITY   (United States)

^b INDIANA UNIVERSITY   (United States)

^c UNIVERSITY OF WEST FLORIDA   (United States)

^d Department of Economics ^*

^e North Atlantic Treaty Organization   (Italy)

^f NATIONAL RESEARCH COUNCIL   (United States)

^g WZB BERLIN SOCIAL SCIENCE CENTER   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FINANCE; INDUSTRIAL ECONOMICS; INDUSTRY; KNOWLEDGE BASED SYSTEMS; MARKETING; PRODUCTION ENGINEERING; RESEARCH AND DEVELOPMENT MANAGEMENT; SOCIETIES AND INSTITUTIONS;

COMMERCIALIZATION; INNOVATION; KNOWLEDGE PRODUCTION FUNCTION; SPILLOVER PROCESS;

BIOTECHNOLOGY;

EID: 0033871625     PISSN: 02675730     EISSN: None     Source Type: None    
DOI: 10.1504/ijtm.2000.002810     Document Type: Article

References (31)

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10. Feldman, M.P. (1994) 'Knowledge complementarity and innovation'. Small Business Economics, Vol.. 6, No. 5, pp.363-372.
11. Stephan, P. (1996) 'The economics of science', Journal of Economic Literature, Vol. 34, pp.1199-1235.
12. Mansfield, E. (1995) 'Academic research underlying industrial innovations: sources, characteristics, and financing', Review of Economics and Statistics, Vol. 77, No. 1, pp.55-65.
13. Citation counts for articles for which the scientist is not a first author are much more difficult to obtain. The number of citations received by an article reflects a variety of other factors besides intellectual productivity. For example, the number of citations received by an article may be correlated with the prestige of the author's graduate school and/or the prestige of the author's present institutional affiliation. In addition, the citation count may reflect the size, nature, and growth rate of the author's field and the extent of cronyism. Nonetheless, the vast amount of work done by Eugene Garfield at the Institute for Scientific Information, as well as the work of others in this field, documents that citation counts, particularly for very heavily cited works, are extremely highly correlated with other measures of accomplishment, such as the receipt of awards and membership in academies. For a summary of studies validating the relationship between citation counts and the quality of work done by a scientist, see Garfield [14].
14. Garfield, E. (1979) Citation Indexing - Its Theory and Application in Sciences, Technology, and Humanities, New York, Wiley.
15. The data have been linked to two additional databases for other projects not discussed here. The Investment/CDA database was used by Stephan and Everhart (forthcoming) [16] to investigate the security transactions of university scientists in the database who have a large enough financial position in the company to require disclosure. Stephan has also linked the data to the file prepared by the Center for Research and Security Prices (CRSP) in order to follow the stock-trading history of each company for a period of up to 420 days.
16. Stephan, P. and Everhart, S. (forthcoming) 'The changing rewards to science: the case of biotechnology', Small Business Economics.
17. Link, A.N. and Rees, J. (1990) 'Firm size, university based research, and the returns to R&D', Small Business Economics, Vol. 2, No.1, pp.25-32.
18. Werth, B. (1994) The Billion-Dollar Molecule: One Company's Quest for the Perfect Drug, New York, Simon and Schuster.
19. Audretsch, D.B. and Stephan. P. 'Knowledge spillovers in biotechnology: sources and incentives', The Journal of Evolutionary Economics, Vol. 9, No. 1, pp.97-107.
20. The academic trajectory includes those whose careers have been at hospitals, research foundations and the government.
21. Scientists who followed another type of path or whose career trajectory was indeterminate are excluded.
22. Although this may be a result of age (they are considerably older), it is likely that with age the results would not be altered. Science is noteworthy for persistent inequality which age merely amplifies [11].
23. Burrill, G.S. (1987) Biotech 88: Into the Market-place, San Francisco, Arthur Young High Technology Group.
24. Audretsch, D.B. and Stephan, P. (1996) 'Company-scientist locational links: the case of biotechnology', American Economic Review, Vol. 86, No. 3, pp.641-652.
25. Zucker, L.G., Darby, M.R. and Brewer, M.B. 'Intellectual capital and the birth of US biotechnology enterprises', National Bureau of Economic Research, Cambridge, working paper No. 4653.
26. Pisano, G.P., Teece, W. and Teece, D. (1988) 'Joint ventures and collaboration in the biotechnology industry', in David Mowery, (Ed.), International Collabourative Ventures in US Manufacturing, pp. 183-222.
27. Twenty-two of the 54 firms designate the chair of the scientific advisory board in the prospectus. In all but two instances, the chair is employed at a university. In the case of one firm, the scientific advisory board is chaired by co-chairs, both of whom work at a university.
28. Stephan, P. (1997) 'Capitalizing the human capital of university professors: the case in biotechnology', presented at the higher education meetings of the National Bureau of Economic Research.
29. The biotechnology firms that we are studying come from Standard Industrial Classification code 2834 (pharmaceutical preparations), SIC code 2835 (diagnostic substances), SIC code 2836 (biological products), SIC code 2860 (organic chemicals), and SIC code 8731 (physical research). SIC codes for new NASDAQ (National Association of Securities Dealers Automated Quotations) firms being placed on the Center for Research and Security Prices tape are generally coded at the three-digit level, although occasionally at the four digit level. This means that the portfolio used to construct the MKT variable is a bit more inclusive than the particular SIC codes from which the prospectuses were taken. Specifically, SIC codes 2830-2839, 2860-2869, and 8730-8739 are included in constructing the MKT variable. The maximum number of firms that entered the index was 202; the minimum was 181.
30. Because several firms that were scheduled to make a public offering pulled or delayed the offering, only 45 firms are used in the Stephan study. Furthermore, the three firms in the sample that offered their stock in bundles (shares of stock plus warrants), were not included in the price equation since it is impossible to estimate the value of warrants on day one. These three firms were included in the proceeds equation, however, but another firm (Ligand) is excluded in the estimates of the proceeds equations because a large portion of its public offering was sold to Allergan Pharmaceuticals.
31. Many specifications of the offer-price equation were estimated. Some of these entered the reputation variable in a non-linear form. The best fits are presented in Table 3. In addition, in unrcported regressions a variable was included that measures the citation count of the scientist having the most citations in the ith firm. It is only marginally significant when included, suggesting that, svith the exception of the Nobel laureates, the reputation of the 'team' is more important than the reputation of the most-cited scientist.