Synergies or trade-offs in university life sciences research.

Major legislative, legal, and technological changes paved the way for a period of remarkable growth in the patenting of life science research by U.S. universities in the 1980s and 1990s. (1) During this time, the relative importance of life science patents granted to U.S. universities grew from 10% of all university patents in 1980 to almost 25% in 1999. (2) This dramatic expansion in the role of life sciences research occurred in a period when the annual number of patents granted to U.S. universities grew almost tenfold from 340 patents granted in 1980 to 3,274 in 1999. Similarly, funding to support research and education activities in the life sciences at major research universities nearly doubled in constant dollar terms, with an especially rapid expansion occurring in the 1990s (National Science Foundation 2000). As a leading edge technology for U.S. universities, life science patenting has clearly become a major research priority over the past two decades, but many would ask, at what cost? This article analyzes a particular aspect of this question by identifying the impact of life science patents on other university life science research outputs, namely, published articles and doctorates.

Researchers concerned about university-level trade-offs associated with the expansion of patenting tend to focus on three potential negative outcomes: (a) universities moving away from basic research to pursue commercial patents (Kennedy 2000; Dasgupta and Ray 1994; Blumenthal et al. 1996); (b) universities placing priority on the establishment of intellectual property rights instead of on knowledge generation and idea sharing and those intellectual property rights making public research more difficult (Rai and Eisenberg 2003; Campbell et al. 2002; Blumenthal et al. 1996); and (c) university research quality declining as patent activity increases (Henderson, Jaffe, and Trajtenberg 1998; Sampat, Mowery, and Ziedonis 2003). All three of these trade-offs can be translated into university research production outcomes, the first two into fewer and lower-quality journal articles and potentially fewer doctorates, and the third into lower-quality patents or articles (i.e., ones with fewer citations). Concerns about these tradeoffs are especially heightened in discussions of patenting trends in public, land grant universities, which historically have been viewed as institutions dedicated to creating public goods in their research and teaching enterprises (Atkinson et al. 2003).

Most quantitative research on the impacts of academic patenting has focused on effects outside the university. Some important examples are the Jensen and Thursby's (2001) examination of the private investment incentives associated with universities having the right to offer exclusive licensing of their patents, and the Zucker, Darby, and Brewer (1998) exploration of the synergy between top scientists and biotech firms where universities and companies are proximately located. (3) However, only with respect to the evolution of patent quality has there been any systematic empirical analysis done on the effects of increased patenting on university research performance, with the most recent evidence on patent citations suggesting no significant changes (Sampat, Mowery, and Ziedonis 2003). One case study at MIT has shown complementarities between patents and other research outputs for university scientists (Agrawal and Henderson 2002), but its results are limited to two departments at the top patenting university in the country. In the sociological literature, Owen-Smith and Powell (2003) suggest that high-quality research generates both highly cited articles and a rich pool of potential patent opportunities for enterprising technology transfer offices to exploit. This positive outcome is the main "synergy" of interest in this article; that is, the degree to which there are scope economies associated with high-quality research generating both patents and traditional research outputs (articles and trained students) in a more cost effective manner than if those research outputs were produced separately.

Using panel data for U.S. universities, this article explores the evidence for two types of increasing returns (scale and scope economies) in the production of three major life science research outputs: patents, articles, and doctorates. These measures are important indicators of potential synergies associated with the portfolios of university research outputs. While they are not, in and of themselves, direct welfare measures, they can help to identify whether more production of these university research outputs, separately or jointly, is more cost efficient. The methods used below allow the construction of both "overall" scope and scale estimates as well as a distribution across university sizes and type to investigate whether scale and scope outcomes are more prevalent in life sciences research.

The methodological approach builds on Baumol, Panzar, and Willig's (1988) framework by constructing a university multiple-output cost function. We present panel data estimates of the multiple-output cost function from fixed-effects and random-effects models for both quantity and quality-adjusted outputs.

The panel data econometrics advance previous cost-function estimations aimed at identifying the underlying properties of university production processes, as do the quality adjustments made on output quantities. These empirical innovations are made possible by a data set that combines annual data from 1981 to 1998 for ninety-six U.S. universities on life science research expenditures, patents, journal articles, and doctorates, including citation data for the patents and journal articles that can be used to construct quality-adjusted output measures. The analysis starts with nonparametrically smoothed costs surfaces that provide visual evidence of scope and scale economies in the production of patents and articles, especially among universities with medium to large production levels. Econometric estimates from a series of panel data models provide the basis for testing systematically for the presence of economies of scale and scope. The estimates reveal economies of scale in both the quantity and quality-adjusted data, and economies of scope in only the quality-adjusted data. Comparisons across university types reveal the strongest scale and scope economies in land grant universities.

The organization of the article is as follows. In the next section, an explanation is given for how scale and scope are measured in a multiproduct cost function. The estimation strategy is presented in the third section followed by a section introducing the panel data set on U.S. university life science research, and explaining how the quality-adjusted research outputs are constructed. In the fifth section, we provide the results of the empirical analysis, which is followed by a conclusion.

Measuring Scale and Scope in a Multiproduct Cost Function

Standard analyses of patent production both in industry and at universities, (Hausman, Hall, and Griliches 1984) have used a production function approach to estimate the determinants of patent production. Building on Arora (1995), a recent piece by Graff, Rausser, and Small (2003) tests for complementarities in reduced form production function models among private firms. These techniques rest heavily on key assumptions regarding the nature of complementarities and the validity of some exclusion restrictions, which are unlikely to be satisfied in the typical university setting where output prices are difficult to measure. (4)

A more promising line of inquiry for identifying synergies or trade-offs among multiple outputs involves using the dual, i.e., a cost-minimization framework as set forth by Baumol, Panzar, and Willig (1988). Since their work on scale and scope economies first appeared, this cost function approach has been applied extensively to many sectors including universities (de Groot, McMahon, and Volkvein 1991; Cohn, Rhine, and Santos 1989). Previous university applications of the Baumol, Panzar, and Willig (1988) framework involve either cross-sectional analyses, or pooled versions of panel data.

Typical multiproduct cost function estimations are based on a version of the following equation,

(1)

C(Y, w) = [a.sub.o] + [summation over (j)] [b.sub.j][Y.sub.j] + 1/2 [summation over (j)] [summation over (k)] [C.sub.jk][Y.sub.j][Y.sub.k]

+ [summation over (l)] [d.sub.l][w.sub.l] + 1/2 [summation over (l)] [summation over (m)] [d.sub.lm][w.sub.l][w.sub.m],

where C(Y, w) is the total cost of producing a vector of outputs Y with a vector of input prices w, and [a.sub.o], [b.sub.j], [c.sub.jk], [d.sub.l], [d.sub.lm] are scalars. (5) The coefficient estimates, [b.sub.j] and [C.sub.jk], are then used as evidence for synergies and trade-offs and as arguments in the construction of estimates for ray economies of scale and economies of scope using formulas presented below. In order for the cost function to be valid it must satisfy homogeneity of degree 1 in input prices. The procedure for ensuring this is described below in the empirical implementation section.

Ray Economies of Scale and Scope

Following the standard formulas in Baumol, Panzar, and Willig (1988), we calculate ray economies of scale and scope from the cost function parameters. They are calculated as follows:

1. Ray economies of scale: The ray economies of scale for the joint production process are defined by:

(2) [S.sub.n](Y) = C(Y)/[[summation].sub.j] [Y.sub.j][partial derivative]C(Y)'/[partial derivative][Y.sub.j]

where ray economies of scale exist if [S.sub.n] (Y) is greater than 1.