Economics Of Science Research Paper

There is a productive tension between viewing science as a social instrument and as a social purpose. In the first view, science is a social investment in the production and dissemination of knowledge that is expected to generate economic returns as this knowledge is commercially developed and exploited. This approach has the apparent advantage that the standard tools of economic analysis might be directly employed in choosing how to allocate resources to science and manage their use. In the second approach, science is assumed to be a social institution whose norms and practices are distinct from, and only partially reconcilable with, the institutions of markets. While this second approach greatly complicates the analysis of resource allocation and management, it may better represent the actual social organization of science and the behavior of scientists, and it may therefore ultimately produce more effective rules for resource allocation and better principles for management. Both approaches are examined in this research paper, although it is the first that accounts for the majority of the economics of science literature (Stephan 1996).

1. The Economic Analysis Of Science As A Social Instrument

In arguing for a continuing high rate of public funding of science following World War II, US Presidential Science Advisor Vannevar Bush (1945) crafted the view that science is linked intrinsically to technological and economic progress as well as being essential to national defense. The aim of ‘directing’ science to social purposes was already well recognized, and had been most clearly articulated before the war by John D. Bernal (1939). What distinguished Bush’s argument was the claim that science had to be curiosity-driven and that, in companies, such research would be displaced by the commercial priorities of more applied research. The view that science is the wellspring of economic growth became well established within the following generation, giving rise to statements like ‘Basic research provides most of the original discoveries from which all other progress flows’ (United Kingdom Council for Scientific Policy 1967).

The concept of science as a source of knowledge that would be progressively developed and eventually commercialized became known as the ‘linear model.’ In the linear model, technology is science reduced to practical application. The ‘linear model’ is an oversimplified representation that ignores the evidence that technological change is often built upon experience and ingenuity divorced from scientific theory or method, the role of technological developments in motivating scientific explanation, and the technological sources of instruments for scientific investigation (Rosenberg 1982). Nonetheless, it provides a pragmatic scheme for distinguishing the role of science in commercial society.

If science is instrumental in technological progress and ultimately economic growth and prosperity, it follows that the economic theory of resource allocation should be applicable to science. Nelson (1959) and Arrow (1962) demonstrated why market forces could not be expected to generate the appropriate amount of such investment from a social perspective. Both Arrow and Nelson noted that in making investments in scientific knowledge, private investors would be unable to capture all of the returns to their investment because they could not charge others for the use of new scientific discoveries, particularly when those discoveries involved fundamental understanding of the natural world. Investment in scientific knowledge therefore had the characteristics of a ‘public good’, like publicly accessible roads. This approach established a basis for justifying science as a public investment. It did not, however, provide a means for determining what the level of that investment should be.

Investments in public goods are undertaken, in principle, subject to the criterion that benefits exceed costs by an amount that is attractive relative to other investments of public funds. To employ this criterion, a method for determining the prospective returns or benefits from scientific knowledge is required. The uncertainty of scientific outcomes is not, in principle, a fundamental barrier to employing this method. In practice, it is often true that the returns from investments in public good projects are uncertain, and prospective returns often involve attributing to new projects the returns from historical projects. Griliches (1958) pioneered a methodology for retrospectively assessing the economic returns on research investment, estimating that social returns of 700 percent had been realized in the period 1933–55 from the $2 million of public and private investments on the development of hybrid corn from 1910–55. Other studies of agricultural innovation as well as a limited number of studies of industrial innovation replicated Griliches’ findings of a high social rate of return (see Steinmueller 1994 for references). Mansfield (1991) provides a fruitful approach for continuing to advance this approach. Mansfield asked R&D executives to estimate the proportion of their company’s products and processes commercialized in 1975–85 that could not have been developed, or would have been substantially delayed, without academic research carried out in the preceding 15 years. He also asked them to estimate the 1985 sales of the new products and cost savings from the new processes. Extrapolating the results from this survey to the total investment in academic research and the total returns from new products and processes, Mansfield concluded that this investment had produced the (substantial) social rate of return of 28 percent.

The preceding discussion could lead one to conclude that the development of a comprehensive methodology for assessing the rate of return based on scientific research was only a matter of greater expenditure on economic research. This conclusion would be unwarranted. Efforts to trace the returns from specific government research efforts (other than in medicine and agriculture) have been less successful. The effort by the US Department of Defense Project Hindsight to compute the returns from defense research expenditures not only failed to reveal a positive rate of return, but also rejected the view that ‘any simple or linear relationship exists between cost of research and value received’ (Office of the Director of Defense Research and Engineering 1969). Similar problems were experienced when the US National Science Foundation sought to trace the basic research contributions underlying several major industrial innovations (National Science Foundation 1969). In sum, retrospective studies based on the very specific circumstances of ‘science enabled’ innovation or upon much broader claims that science as a whole contributes a resource for commercial innovation seem to be sustainable. When these conditions do not apply, as in the cases of specific research programs with uncertain application or efforts to direct basic research to industrial needs, the applicability of retrospective assessment, and therefore its value for resource allocation policy, is less clear.

More fundamentally, imputing a return to investments in scientific research requires assumptions about the ‘counter-factual’ course of developments that would have transpired in the absence of specific and identified contributions of science. In examples like hybrid corn or the poliomyelitis vaccine, a reasonable assumption about the ‘counter-factual’ state of the world is a continuation of historical experience. Such assumptions are less reasonable in cases where scientific contributions enable a particular line of development but compete with alternative possibilities or where scientific research results are ‘enabling’ but are accompanied by substantial development expenditures (David et al. 1992, Mowery and Rosenberg 1989, Pavitt 1993).

For science to be analyzed as a social instrument, scientific activities must be interpreted as the production of information and knowledge. As the results of this production are taken up and used, they are combined with other types of knowledge in complex ways for which the ‘linear model’ is only a crude approximation. The result is arguably, and in some cases measurably, an improvement in economic output and productivity. The robustness and reliability of efforts to assess the returns to science fall short of standards that are employed in allocating public investment resources. Nonetheless, virtually every systematic study of the contribution of science to economy has found appreciable returns to this social investment. The goals of improving standards for resource allocation and management may be better served, however, by analyzing science as a social institution.

2. Science As A Social Institution

The economic analysis of science as a social system begins by identifying the incentives and constraints that govern the individual choices of scientists and this may reflect persistent historical features of science or contemporaneous policies. Incentives may include tangible rewards such as monetary awards, intangible, but observable, rewards such as status, and less observable rewards such as personal satisfaction. Similarly, constraints should be interpreted broadly, including not only financial limitations but also constraints stemming from institutional rules, norms, and standards of practice. The following simplified account suggests one of several ways of assembling these elements into a useful analytical framework.

Becoming a scientist requires substantial discipline and persistence in educational preparation as well as skills and talents that are very difficult to assess. Scientific training may be seen as a filter for selecting from prospective scientists those who have the ability and drive to engage in a scientific career. In addition, the original work produced during research training demonstrates the capacity of the researcher and provides a means for employers to assess the talents of the researcher (David 1994). Analyzing science education as an employment filter is a complement to more traditional studies of the scientific labor market such as those reviewed by Stephan (1996). The employment filter approach may also waste human resources by making schooling success the only indicator of potential for scientific contribution. If, for example, the social environment of the school discourages the participation or devalues the achievement of women or individuals from particular ethnic groups, the filter system will not perform as a meritocracy.

The distinctive features of science as a social system emerge when considering the incentives and constraints facing employed scientists. Although there is a real prospect of monetary reward for outstanding scientific work (Zuckerman 1992), many of the incentives governing scientific careers are related to the accumulation of professional reputation (Merton 1973). While Merton represented science as ‘universalist’ (open to claims from any quarter), the ability to make meaningful claims requires participation in scientific research networks, participation that is constrained by all of the social processes that exclude individuals from such social networks or fail to recognize their contribution. The incentive structure of seeking the rewards from professional recognition, and the social organization arising from it, is central to the ‘new economics of science’ (Dasgupta and David 1994).

The new economics of science builds upon socio- logical analyses (Cole and Cole 1973, Merton 1973, Price 1963) of the mechanisms of cumulative reinforcement and social reward within science. From an economic perspective, the incentive structure governing science is the result of the interactions between the requirement of public disclosure and the quest for recognition of scientific ‘priority’, the first discovery of a scientific result. Priority assures the alignment of individual incentives with the social goal of maximizing the scientific knowledge base (Dasgupta and David 1987). Without the link between public dis- closure and the reward of priority, it seems likely that scientists would have an incentive to withhold key information necessary for the further application of their discoveries (David et al. 1999).

As Stephan (1996) observes, the specific contribution of the new economics of science is in linking this incentive and reward system to resource allocation issues. Priority not only brings a specific reward of scientific prestige and status but also increases the likelihood of greater research support. Cumulative advantage therefore not only carries the consequence of attracting attention, it also enables the recruitmentof able associates and students and provides the means to support their research. These effects are described by both sociologists of science and economists as the Matthew effect after Matthew 25:29, ‘For to every one who has will more be given, and he will have abundance; but from him who has not, even what he has will be taken away.’ As in the original parable, it may be argued that this allocation is appropriate since it concentrates resources in hands of those who have demonstrated the capacity to produce results.

The race to achieve priority and hence to collect the rewards offered by priority may, however, lead to inappropriate social outcomes because priority is a ‘winner take all’ contest. Too many resources may be applied to specific races to achieve priority and too few resources may be devoted to disseminating and adapting scientific research results (Dasgupta and David 1987, David and Foray 1995), a result that mirrors earlier literature on patent and technology discovery races (Kamien and Schwartz 1975). Moreover, the mechanisms of cumulative advantage resulting from achieving priority may reduce diversity in the conduct of scientific research. This system has the peculiarity that the researchers who have the greatest resources and freedom to depart from existing research approaches are the same ones who are responsible for creating the status quo.

The principal challenges to the view that science is a distinct social system are the growing number of scientific publications by scientists employed in private industry (Katz and Hicks 1996) and the argument that scientific knowledge is tightly bound to social networks (Callon 1994). Private investments in scientific research would appear to question the continuing validity of the ‘public good’ argument. For example, Callon (1994) contends that scientific results are, and have always been, strongly ‘embedded’ within networks of researchers and that ‘public disclosure’ is therefore relatively useless as a means of transfer for scientific knowledge. Gibbons et al. (1994) argue that research techniques of modern science have become so well distributed that public scientific institutions are no longer central to scientific activity.

While the arguments of both Callon and Gibbons et al. suggest that private scientific research is a direct substitute for publicly funded research, other motives for funding and publication such as gaining access to scientific networks suggest that public and private research are complementary (David et al. 1999). The growing reliance of industry on science provides a justification for investing in science to improve the ‘absorption’ of scientific results (Cohen and Levinthal 1989, Rosenberg 1990). Employed scientists need to be connected with other scientific research colleagues who identify ‘membership’ in the scientific community with publication, and labor force mobility for employed scientists requires scientific publication. Thus, it is premature to conclude that the growing performance of scientific research in industry or publication of scientific results by industrial authors heralds the end of the need for public support of science.

The growing significance of private funding of scientific research does, however, indicate the need to improve the socioeconomic analysis of the incentive and governance structures of science. Empirical work on the strategic and tactical behavior of individual scientists, research groups, and organizations is urgently needed to trace the implications of the changing environment in which the social institutions of science are evolving. Ultimately, these studies should be able to meet the goal of developing better rules for allocating and managing the resources devoted to science.

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