As a student of science and technology policy—and therefore unencumbered by any externally imposed need to relate my analyses to the assumptions and methods of mainstream neoclassical theory—I find what Krugman calls “more exciting, more dynamic” theorizing and what Fagerberg calls “appreciative” theorizing, far more useful in doing my job. More to the point of this paper, while the above differences have been largely irrelevant to past analyses of technology’s economic importance, they are turning out to be critical in two important areas of policy for the future: the justification of public support for basic research and the determinants of the level of private support of R&D. They will therefore need to be addressed more explicitly in future. So, too, will the largely uncharted and unmeasured world of software technology.

The Usefulness of Basic Research

The Production of Useful Information? In the past, the case for public policy for basic research has been strongly supported by economic analysis. Governments provide by far the largest proportion of the funding for such research in the Organization for Economic Cooperation and Development (OECD) countries. The well-known justification for such subsidy was provided by Nelson (3) and Arrow (4): the economically useful output of basic research is codified information, which has the property of a “public good” in being costly to produce and virtually costless to transfer, use, and reuse. It is therefore economically efficient to make the results of basic research freely available to all potential users. But this reduces the incentive of private agents to fund it, since they cannot appropriate the economic benefits of its results; hence the need for public subsidy for basic research, the results of which are made public.

This formulation was very influential in the 1960s and 1970s, but began to fray at the edges in the 1980s. The analyses of Nelson and Arrow implicitly assumed a closed economy. In an increasingly open and interdependent world, the very public good characteristics that justify public subsidy to basic research also make its results available for use in any country, thereby creating a “free rider” problem. In this context, Japanese firms in particular have been accused of dipping into the world’s stock of freely available scientific knowledge, without adding much to it themselves.

But the main problem has been in the difficulty of measuring the national economics benefits (or “spillovers”) of national investments in basic research. Countries with the best record in basic research (United States and United Kingdom) have performed less well technologically and economically than Germany and Japan. This should be perplexing—even discouraging—to the new growth theorists who give central importance to policies to stimulate technological spillovers, where public support to basic research should therefore be one of the main policy instruments to promote technical change. Yet the experiences of Germany and Japan, especially when compared with the opposite experience of the United Kingdom, suggest that the causal linkages run the other way—not from basic research to technical change, but from technical change to basic research. In all three countries, trends in relative performance in basic research since World War II have lagged relative performance in technical change. This is not an original observation. More than one hundred years ago, de Tocqueville (5) and then Marx (6) saw that the technological dynamism of early capitalism would stimulate demand for basic research knowledge, as well as resources, techniques, and data for its execution.

At a more detailed level, it has also proved difficult to find convincing and comprehensive evidence of the direct technological benefit of the information provided by basic research. This is reflected in Table 1, which shows the frequency with which U.S. patents granted in 1994 cite (i.e., are related to) other patents, and the frequency with which they cite science-refereed journals and other sources. In total, information from refereed journals provide only 7.2% [= 0.9/(10.9+0.9+0.7), from last row of Table 1] of the information inputs into patented inventions, whereas academic research accounts for ≈17% of all R&D in the United States and in the OECD as a whole. Since universities in the USA provide ≈70% of refereed journal papers, academic research probably supplies less than a third of the information inputs into patented inventions than its share of total R&D would lead us to expect.

Furthermore, the direct economic benefits of the information provided by basic research are very unevenly spread amongst sectors, including among relatively R&D-intensive sectors. Table 1 shows that the intensity of use of published knowledge is particularly high in drugs, followed by other chemicals, while being virtually nonexistent in aircraft, motor vehicles, and nonelectrical machinery. Nearly half the citations journals are from chemicals, ≈37.5% from electronic-related products and only just over 5% from nonelectrical machinery and transportation. And in spite of this apparent lack of direct

Table 1. Citing patterns in U.S. patents, 1994

 

No. of citations per patent to

Share of all citations to journals

Manufacturing sector

No. of patents

Other patents

Science journals

Other

Chemicals (less drugs)

10,592

9.8

2.5

1.2

29.1

Drugs

2,568

7.8

7.3

1.8

20.6

Instruments

14,950

11.8

1.0

0.7

16.3

Electronic equipment

16,108

8.8

0.7

0.6

12.2

Electrical equipment

6,631

10.0

0.6

0.6

4.4

Office and computing

5,501

10.0

0.7

1.0

4.3

Nonelectrical machinery

15,001

12.2

0.2

0.5

3.3

Rubber and miscellaneous plastic

4,344

12.4

0.4

0.6

1.9

Other

8,477

12.2

0.2

0.4

1.9

Metal products

6,645

11.6

0.2

0.4

1.5

Primary metals

918

10.5

0.8

0.7

1.0

Building materials

1,856

12.6

0.5

0.7

1.0

Food

596

15.1

1.3

1.6

0.9

Oil and gas

998

15.0

0.6

0.9

0.7

Motor vehicles and transportation

3,223

11.3

0.1

0.3

0.4

Textiles

567

12.4

0.3

0.8

0.2

Aircraft

905

11.6

0.1

0.3

0.1

Total

99,898

10.9

0.9

0.7

100.0

Data taken from D.Olivastro (CHI Research, Haddon Heights, NJ; personal communication).



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