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Proceedings of the National Academy of Sciences of the United States of America
related to the increasing opportunities for extracting the returns to discoveries by selling or licensing off the rights, as opposed to having to exploit them directly. They also found that intermediaries and markets, supportive of such trade in technological information by reducing transaction costs, appear to have evolved first in geographic areas with a record of high rates of patenting, and that the existence of these and like institutions may in turn have contributed to the persistence over time of geographic pockets of high rates of inventive activity through self-reinforcing processes.
The paper by Keith Pavitt (6) was perhaps more explicitly focused on the design of technology policy than any other presented at the colloquium. Making reference both to the weak association across nations between investment in R&D and economic performance, and to the paucity of evidence for a direct technological benefit to the information provided by basic research, he argued that the major value of such activity is not in the provision of codified information, but in the enhancement of capacity to solve technological problems. This capacity involves tacit research skills, techniques and instrumentation, and membership in national and international research networks. In his view, the exaggerated emphasis on the significance of codified information has encouraged misunderstanding about the importance of the international “free-rider” problem and a lack of appreciation for institutional and labor policies that would promote the demand for skills and institutional arrangements to solve complex technological problems.
One afternoon of the colloquium was devoted to papers on economic issues in medical technology. Many economists have long been concerned that the structures of incentives in the systems of health care coverage used in the United States have encouraged the development of medical technologies whose value on the margin is small, especially relative to their cost. The paper by Mark McClellan (7) presented new evidence on the marginal effects of intensive medical practices on outcomes and expenditures over time, using data on the treatment of acute myocardial infarction in the elderly from 1984 through 1991 from a number of hospitals. In general, McClellan found little evidence that the marginal returns to technological change in heart attack treatment (catheterization is the focus here) have declined substantially; indeed, on the surface, the data suggest better outcomes and zero net expenditure effects. Because a substantial fraction of the long-term improvement in mortality at catheterization hospitals is evident within 1 day of acute myocardial infarction, however, McClellan suggests that procedures other than catheterization, but whose adoption at hospitals was related to that of catheterization, may have accounted for some of the better outcomes.
Lynn Zucker and Michael Darby (8) followed with a discussion of their studies of the processes by which scientific knowledge comes to be commercially exploited, and of the importance of academic researchers to the development of the biotechnology industry. Employing a massive new data set matching detailed information about the performance of firms with the research productivity of scientists (as measured by publications and citations), they found a very strong association between the success of firms and the extent of direct collaboration between firm scientists and highly productive academic scientists. The evidence is consistent with the view that “star” bioscientists were highly protective of their techniques, ideas, and discoveries in the early years of the revolution in genetic sequencing, and of the significance of bench-level working ties for the transmission on technological information in this field. Zucker and Darby also suggest that the research productivity of the academic scientists may have been raised by their relationships with the firms because of both the opportunities for commercialization and the additional resources made available for research.
The paper by Alan Garber and Paul Romer (9) begins by reviewing the arguments that lead economists and policy makers to worry that market allocation mechanisms, if left alone, may not allocate an optimal amount of funds to research activity. They then consider the likely costs and benefits of various ways of changing the institutional structures that determine the returns to research, including strengthening property rights for innovative output and tax subsidy schemes. The discussion, which is weighted to medical research, points out alternative ways of implementing these schemes and considers how their relative efficacies are likely to differ with the research environment.
Iain Cockburn and Rebecca Henderson (10) followed with an empirical investigation of the interaction between publicly and privately funded research in pharmaceuticals. Using a confidential data set that they gathered, they begin by showing that for their sample of 15 important new drugs there was a long and variable lag between the date of the key enabling scientific discovery and the market introduction of the resultant new chemical entity (between 11 and 67 years). In at least 11 of the 14 cases the basic discoveries were done by public institutions, but in 12 of those same cases the major compound was synthesized at a private firm, suggesting a “downstream” relationship between the two types of research institutions. They stress, however, that private sector research scientists often publish their results and frequently co-author with scientists from public sector institutions, suggesting that there are important two-way flows of information. There is also some tentative evidence that the research departments of firms that have stronger ties to the public research institutes are more productive.
Steve Berry, Sam Kortum, and Ariel Pakes (11) analyze the impact of the lowering of emission standards and the increase in gas prices on the characteristics and the costs of producing automobiles in the 1970s. Using their construct of a “hedonic” cost function, a function that relates the costs of producing automobiles to its characteristics, they find that the catalytic converter technology that was introduced after the lowering of emissions standards in 1975, did not increase the costs of producing an auto (though it may have hurt unmeasured performance characteristics). However, the more sophisticated three-way and closed-loop catalysts and the fuel injection technologies, introduced following the further lowering of emissions standards in 1980, increased costs significantly. They also show that the miles per gallon rating of the new car fleet increased significantly over this period, with the increases occurring primarily as a result of the introduction of new car models. Though the new models tended to be smaller than the old, there was also an increase in the miles per gallon in given horsepower weight classes. This, together with striking increases in patenting in patent classes that deal with combustion engines following the 1973 and 1979 gas price hikes, suggests a significant technological response, which allowed us to produce more fuel efficient cars at little extra cost.
Since the founding of Sematech in 1987, there has been much interest in whether this consortium of United States semiconductor producers has been effective in achieving the goal of promoting the advances of United States semiconductor manufacturing technology. The original argument for the consortium, which has received substantial support from the federal government, was based on the ideas that it would raise the return to, and thus boost, spending on investment in process R&D by increasing the extent to which new knowledge would be internalized by the firms making the investments, and increase the social efficiency of the R&D conducted by enabling firms to pool their R&D resources, share results, and reduce duplication. Douglas Irwin and Peter Klenow (12) have been studying whether these expectations were fulfilled, and here review their findings that: there are steep learning curves in production of both memory chips and microprocessors;