(NAS Colloquium) Science, Technology and the Economy

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Proceedings of the National Academy of Sciences of the United States of America

Table 5. Multinomial logit coefficients

	Explanatory variables
Category: Type of coauthor institution	Time trend	Degree to which firm is pro-publication	Degree to which R&D decisions are made by a single individual	Size of firm’s drug discovery effort in $m	Constant
Hospital	0.046*	0.071	0.029	–0.008*	–2.734
	(0.021)	(0.045)	(0.038)	(0.002)	(1.783)
Nonprofit	0.367	0.207*	0.007	–0.008*	–3.640
	(0.027)	(0.059)	(0.048)	(0.002)	(2.252)
Public, including	–0.058*	0.363*	–0.077**	–0.005*	4.494*
NIH	(0.023)	(0.055)	(0.043)	(0.002)	(1.922)
SELF	–0.041*	–0.018	0.061**	–0.001	6.761
	(0.018)	(0.039)	(0.034)	(0.001)	(1.557)
University	0.021	0.104*	0.039	–0.007*	0.489
	(0.019)	(0.041)	(0.034)	(0.001)	(1.598)
Dependent variable: Type of coauthor institution 1980–1988 data: 26,501 observations. Reference category: Private. Standard errors are in parentheses. , Significant at 5% level. *, Significant at 10% level.

We hesitate to over-interpret these results: confounding with aggregate time trends, the small sample imposed by incomplete data, difficulties with lags, causality, and a variety of other measurement problems discussed in previous papers mean that they are not as statistically robust as we would prefer. Furthermore, they are offered as descriptive results rather than tests of an underlying behavioral model. Nonetheless, they offer support for the hypothesis that the ability to access and interact with public sector basic research activity is an important determinant of the productivity of downstream private sector research.

Table 6. Determinants of patent output at the firm level

	Model
	1	2	3	4
Intercept	5.159* (1.032)	5.292* (1.042)	4.252* (0.859)	4.037* (0.839)
Percent of coauthorships with universities	7.340* (1.611)	6.897* (1.680)	5.137* (1.789)	4.493* (1.759)
Papers per research dollar		0.005 (0.006)		0.061* (0.026)
Firm dummies			Yes	Yes
Time trend	–0.227* (0.045)	–0.231* (0.045)	–0.203** (0.038)	–0.211* (0.037)
RMSE	0.987	0.987	0.777	0.754
R-squared	0.293	0.301	0.611	0.638
Intercept	4.043* (1.358)	2.380* (1.198)	2.551* (1.146)	2.515* (1.118)
Percent of publications by top 10 authors	2.052 (1.489)	3.897* (1.717)	3.358* (1.646)	3.236* (1.613)
Percent of coauthorships with universities			4.870* (1.749)	4.305* (1.726)
Papers per research dollar				0.056* (0.002)
Firm dummies		Yes	Yes	Yes
Time trend	–0.142* (0.048)	–0.129* (0.036)	–0.179* (0.039)	–0.187* (0.038)
RMSE	1.093	0.792	0.757	0.738
R-squared	0.132	0.595	0.635	0.658
Ordinary least-squares regression. Dependent variable: Important patents per research dollar. 1980–1988 data, 84 observations. Standard errors are in parentheses. RMSE, root mean squared error. , Significant at 5% level. *, Significant at 10% level.

Conclusions and Implications for Further Research

The simple linear model of the relationship between public and private research may be misleading. Information exchange between the two sectors appears to be very much bidirectional, with extensive coauthoring between researchers in pharmaceutical firms and researchers in the public sector across a wide range of both institutions and nationalities. Our preliminary results suggest that participating in this exchange may be an important determinant of private sector research productivity: The relationship between public and private sectors appears to involve much more than the simple, costless, transfer of basic knowledge from publicly funded institutions to profit-oriented firms.

Without further work exploring the social rate of return to research it is, of course, difficult to draw conclusions for public policy from these results. However they do suggest that any estimate of the rate of return to public research, at least in this industry, must take account of this complex structure. They are also consistent with the hypothesis that public policy proposals that curtail the flow of knowledge between public and private firms in the name of preserving the appropriability of public research may be counterproductive.

We would like to express our appreciation to those firms and individuals who generously contributed data and time to this study, and to Gary Brackenridge and Nori Nadzri, who provided exceptional research assistance. Lynn Zucker and Michael Darby provided many helpful comments and suggestions. This research was funded by the Sloan Foundation, the University of British Columbia Entrepreneurship Research Alliance (Social Sciences and Humanities Research Council of Canada grant 412–93–0005), and four pharmaceutical companies. Their support is gratefully acknowledged.

1. Arrow, K. (1962) in The Rate and Direction of Inventive Activity, ed. Nelson, R. (Princeton Univ. Press, Princeton), pp. 609–619.

2. Zucker, L. (1991) in Research in Sociology of Organizations, ed. Barley, S. & Tolbert, P. (JAI, Greenwich, CT), Vol. 8, pp. 157–189.

3. Merton, D. (1973) in The Sociology of Science: Theoretical and Empirical Investigation, ed. Starer, N.W. (Univ. Chicago Press, Chicago), pp. 439–460.

4. Dasgupta, P. & David, P.A. (1987) in Arrow and the Ascent of Modern Economic Theory, ed. Feiwel, G.R. (N.Y. Univ. Press, New York), pp. 519–542.

5. Dasgupta, P. & David, P.A. (1994) Res. Policy 23, 487–521.

6. Henderson, R., Jaffe, A. & Trajtenberg, M. (1994) Universities as a Source of Commercial Technology: A Detailed Analysis of

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