Research choice and finance in university bioscience.

AuthorBuccola, Steven
  1. Introduction

    Much of the economics of science is concerned with factors underlying the direction and productivity of laboratory work. The factors are highly varied, including alternative elaborations of the scientist's incentive structure, human capital and training, specialty field and scientific opportunities, laboratory infrastructure and assistance, professional network and culture, and institutional reputation and support. Policy implications include research institution design (Holmstrom 1989), administrative structure (Landry and Amara 1998), reporting protocols (Levitt and Snyder 1997), strength and structure of intellectual property rights (Thursby and Thursby 2003; Dillon 2005), and size and allocation of public funding (Cockburn and Henderson 1998; David, Hall, and Toole 1999; Diamond 1999).

    The importance of such work lies in the broad social controversy over whether, and if so how, publics ought to intervene in the traditionally autonomous character of scientific communities, for example, by encouraging greater exposure to market forces. Dasgupta and David (1994) argue that economic forces normally conducive to dynamic efficiency are unavailable in the relations between university-based open science and commercial research and development (R&D). Perhaps as a result, popular opinion about scientific work reflects widely divergent opinions, ranging from an awe of science's obvious technological power to a suspicion that it has betrayed the social good by selling itself to commercial interests (Sheldon 2003).

    A major obstacle in evaluating these concerns, and in guiding public policy, is the partly ineffable nature of scientific knowledge. Scientific inputs and outputs are difficult for third parties to monitor or measure, so scientists have substantial control over how and whether their results are disseminated. And science often is pursued--at least in academia--for nonmonetary rewards that are difficult to elucidate, quantify, or observe (Dasgupta and Maskin 1987; Rosenberg and Nelson 1994; Stephan 1996). Price-mediated supply and demand models are, in particular, largely inappropriate to upstream scientific inquiry. Following Merton (1973), analysis instead has focused on how scientists' choices are influenced by their norms, reward structures, and institutional environments. Much empirical work is confined to subsets of factors for which data are available and which illuminate selected topics (Breschi, Lissoni, and Montobbio 2005; Walsh, Cho, and Cohen 2005; Azoulay, Ding, and Stuart 2007). Cohen, Nelson, and Walsh (2002) trace impacts of public research on industry R&D success. Agrawal and Henderson (2002) and Geuna et al. (2004) examine faculty-industry program and funding relationships in a single university. Others concentrate on generic differences in the way upstream and downstream research is best managed (Aghion, Dewatripont, and Stein 2005).

    We contribute to this literature by developing and estimating a bench-level model of how a subset of university bioscientists design, finance, and communicate their research. The model enables us to address in a new way some of the fundamental questions in public science policy: Does private support steer university research toward more applied or privately appropriable inventions and thus away from publicly accessible knowledge? In this Bayh-Dole era, is basic research still substantially less excludable than applied research? Does private funding facilitate public funding or vice-versa? How do investigators' professional norms affect what they study? These topics cannot adequately be addressed without considering other major influences on academic science, including human capital, institutional environment, and in-kind contract terms (Xie and Shauman 1998).

    We draw on literature in both the time-series and cross-sectional dimensions. The time-series tradition has focused on aggregate scientific effort and outcome, embodied in research input-output relations and factor demands (Jaffe 1989; Griliches 1990; Jaffe, Trajtenberg, and Henderson 1993) or scientific labor supplies (Levin and Stephan 1991; Ehrenberg 1992; Leslie and Oaxaca 1993). The cross-sectional tradition instead has concentrated on research programs themselves, allowing a detailed look at scientists' objectives, funding sources, and institutional environments. Studies of university-industry relationships by Blumenthal et al. (1986, 1996), Curry and Kenney (1990), Campbell and Bendavid (2003), Breschi, Lissoni, and Montobbio (2005), and Walsh, Cho, and Cohen (2005) fall in this genre. So do Mansfield's (1995, 1998) surveys of research firms' university relationships; Hall, Link, and Scott's (2003) analysis of commercial research projects; and Zucker, Darby, and Brewer's (1998) and Toole and Czarnitzki's (2005) focus on the influence of leading academic scientists. Huffman and Evenson (1993) have characterized the culture and institutional environment of university agricultural research in particular.

    We assume markets are indeed present in academic bioresearch in an implicit sense. Funding agents provide support to university bioscientists in exchange for research with certain goals. Scientists pursue research plans in exchange for monetary and in-kind support and for the journal publications that enhance their professional careers. (1) Scientists' laboratory plans depend on the financial support they attract and on the scientists' human capital, professional norms, research discipline, and university environment.

    Our 2003-2004 survey of university bioscientists conducting agriculturally related work gives insight into relationships as yet unexamined in the literature. For example, less patentable or excludable research tends to be more basic, and more basic research less excludable, suggesting policies that strengthen intellectual property rights promote applied research at the expense of basic research. However, the relationship between basicness and nonexcludability is, controlling for other factors, rather weak. Furthermore, public funding encourages a research that is more basic but, likely on account of Bayh-Dole influences, more excludable as well. Private funding promotes work that is more applied and more excludable. The volume of public and private support to an individual scientist militate against one another.

  2. Research Program Choice

    University scientists are motivated by a variety of interests, among them prestige, scientific curiosity, money for themselves and their laboratories, and professional or ethical norms (Merton 1973). Achieving one depends partly on the others. Curiosity is indulged directly through the type of research conducted. Prestige depends on the type of research, on publication success, and on grant performance, the last depending in turn on research type, publishing record and grantsmanship effort, and university infrastructure.

    Decision Elements

    To express these relationships more schematically, consider a scientist with utility

    U = U(C, G; X, N), (1)

    where C is the vector of research program characteristics; G is its research budgets distinguished by funding source; N is the scientist's professional norms; and X is other variables such as the scientist's human capital and her university's culture and infrastructure. Equation 1 allows, through N, an explicit representation of the scientist's utility preferences about the substance and conduct of academic research. We assume she chooses research program characteristics C that maximize Equation 1, with first-order conditions

    [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (2)

    where i, j index the elements of C, and [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] are the scientist's professional norms relevant to the ith research characteristic.

    In a long-run setting, the scientist does not take financing opportunities G in Equation 1 as given. Rather, they depend on research program choices C, on human capital and other exogenous factors X, and on unobservable efforts the scientist and her university devote to winning grants from particular sources. Denoting such efforts [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], we may specify the grant-effort success functions as [G.sub.m] = [G.sub.m]([G.sub.n[not equal to]m], C ; X, [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]), where m, n index funding sources. Grant-writing effort is related to the utility importance the scientist and university attach to grants. Letting [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] be the subset of professional norms associated with preferences for grant support from the mth agency, we can rewrite the scientist's grant successes in estimable form

    [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)

    where [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] is an observable proxy for [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]. Equation 3 expresses potential jointness among research funding successes, often called crowd-in or crowd-out effects. The scientist's long-run optimization problem consists of solving the first-order conditions in Equation 2 simultaneously with the funding-success relations in Equation 3.

    Measures of professional or business norms, sometimes called propensities, have been employed extensively in models of scientific behavior (e.g., Merton 1973; Jaffe 1986; Harter 1994; Hall, Jaffe, and Trajtenberg 2001; Thursby and Thursby 2002; Campbell and Bendavid 2003; Stern 2004; Walsh, Cho, and Cohen 2005). Only broad proxies to such preferences are normally possible with aggregate time series, while more direct observations can be obtained with individual scientist data. In any event, allowing directly for professional norms seems particularly important in scientist-level studies because variations in utility parameters and thus unobservable effort would not otherwise be taken into account (Green 2003). Equations 2 and 3 make that explicit.

    Two...

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