taylor

The Politics of Technological Change:

International Relations versus Domestic Institutions (8500 words)

By Mark Zachary Taylor

Department of Political Science Massachusetts Institute of Technology

contact: [email protected]

Paper prepared for the Massachusetts Institute of Technology

Department of Political Science Work in Progress Colloquia

April 1, 2005 (Boston, Massachusetts)

ABSTRACT Technological innovation is under-theorized and little studied by international relations scholars. Despite the fact that technology appears as a variable in every major debate within international relations scholarship, few IR theorists attempt to explain where relative technological power comes from or how international politics fits into explanations of national innovation rates. Instead, technological innovation is widely assumed to be either random, scientifically determined, or structured solely by domestic institutions, and hence exogenous to international politics. This paper disputes these claims, bringing together recent findings about the political economy of technological innovation in order to show that much of this “accepted wisdom” about technology is incorrect. Instead, it will be argued that it is international relations, not domestic institutions, that are key to explaining variance in national innovation rates.

“...the economy that breaks through the apparent technological stagnation of the present will undoubtedly become the technological innovator and global power of the future.”

–Robert Gilpin in War & Change in Wor d Politics l 1

I. Introduction Technological innovation is of central importance to the study of international relations (IR), affecting

almost every aspect of the sub-field.2 First and foremost, a nation’s technological capability has a significant

effect on its economic growth, industrial might, and military prowess; therefore relative national technological

capabilities necessarily influence the balance of power between states, and hence have a role in calculations of

war and alliance formation. Second, technology and innovative capacity also determine a nation’s trade profile,

affecting which products it will import and export, as well as where multinational corporations will base their

production facilities.3 Third, insofar as innovation-driven economic growth both attracts investment and

produces surplus capital, a nation’s technological ability will also affect international financial flows and who

has power over them.4 Thus, in broad theoretical terms, technological change is important to the study of IR

because of its overall implications for both the relative and absolute power of states. And if theory alone does

not convince, then history also tells us that nations on the technological ascent generally experience a

corresponding and dramatic change in their global stature and influence, such as Britain during the first

industrial revolution, the United States and Germany during the second industrial revolution, and Japan during

the twentieth century.5 Conversely, great powers which fail to maintain their place at the technological frontier

generally drift and fade from influence on international scene.6 This is not to suggest that technological

innovation alone determines international politics, but rather that shifts in both relative and absolute

technological capability have a major impact on international relations, and therefore need to be better

understood by IR scholars.

Indeed, the importance of technological innovation to international relations is seldom disputed by IR

theorists. Technology is rarely the sole or overriding causal variable in any given IR theory, but a broad

overview of the major theoretical debates reveals the ubiquity of technological causality. For example, from

Waltz to Posen, almost all Realists have a place for technology in their explanations of international politics.7 At

the very least, they describe it as an essential part of the distribution of material capabilities across nations, or an 1 Gilpin, Robert. War and Change in world Politics (Cambridge Univ. Press, 1981), p. 182 2 Technology is defined here as a physical product, or a process of handling physical materials, which is used as an aid in problem solving. More precisely, technology is a product or process which allows social agents to perform entirely new activities or to perform established activities with increased efficiency. Innovation is defined as the discovery, introduction, and/or development of new technology, or the adaptation of established technology to a new use or to a new physical or social environment. 3 Saxenian, Anna-Lee. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. (Harvard University Press, 1994); Krugman, Paul. Geography and Trade. (MIT Press, 1991); Krugman, Paul ed. Strategic Trade Policy and the New International Economics. (MIT Press, 1986); Helpman, Elhanan. “Increasing Returns, Imperfect Markets, and Trade Theory” in Handbook of International Economics ed. R. W. Jones & P. B. Kenen. (North Holland, 1984) pp. 325-366. 4 Strange, Susan Casino Capitalism (Basil Blackwell, 1986) 5 And perhaps China and India during the twenty-first century 6 The Netherlands, Sweden, France, and Russia, were each at one time or another simultaneously global leaders in science and technology, as well as influential great powers, but eventually lost both titles simultaneously 7 Waltz, Kenneth Theory of International Politics (1979); Posen, Barry R. Sources of Military Doctrine: France, Britain and Germany Between the World Wars (Cornell Univ. Press, 1986)

Mark Zachary Taylor 3/25/2005

indirect source of military doctrine. And for some, like Gilpin quoted above, technology is the very cornerstone

of great power domination, and its transfer the main vehicle by which war and change occur in world politics.8

Jervis tells us that the balance of offensive and defensive military technology affects the incentives for war.9

Walt agrees, arguing that technological change can alter a state’s aggregate power, and thereby affect both

alliance formation and the international balance of threats.10 Liberals are less directly concerned with

technological change, but they must admit that by raising or lowering the costs of using force, technological

progress affects the rational attractiveness of international cooperation and regimes.11 Technology also lowers

information & transactions costs and thus increases the applicability of international institutions, a cornerstone

of Liberal IR theory.12 And in fostering flows of trade, finance, and information, technological change can lead

to Keohane’s interdependence13 or Thomas Friedman et al’s globalization.14 Meanwhile, over at the “third

debate”, Constructivists cover the causal spectrum on the issue, from Katzenstein’s “cultural norms” which

shape security concerns and thereby affect technological innovation;15 to Wendt’s “stripped down technological

determinism” in which technology inevitably drives nations to form a world state.16 However most

Constructivists seem to favor Wendt, arguing that new technology changes people’s identities within society,

and sometimes even creates new cross-national constituencies, thereby affecting international politics.17 Of

course, Marxists tend to see technology as determining all social relations and the entire course of history,

though they describe mankind’s major fault lines as running between economic classes rather than nation-

states.18 Finally, Buzan & Little remind us that without advances in the technologies of transportation,

communication, production, and war, international systems would not exist in the first place.19

Yet, despite the fact that technology appears as a variable in every major debate within international

relations scholarship, few IR theorists attempt to explain where relative technological power comes from or how

international politics fits into explanations of national innovation rates. For example, for all their concern about

technological capabilities, structural realists tend to cast innovation as a within-system variable, and therefore

offer no explanation for it in their theories. Robert Gilpin, the realist most concerned with technological change,

8 (Gilpin 1981) 9 Jervis, Robert xxxx 10 Waltz, Kenneth N. Theory of International Politics (McGraw-Hill, 1979) 11 Krasner, Stephen D.(ed.) International Regimes (Cornell Univ. Press, 1983) 12 Keohane, Robert O. After Hegemony: Cooperation and Discord in the World Political Economy (Princeton Univ. Press, 1984). 13 Katzenstein, Peter J. Between Power and Plenty: Foreign Economic Policies of Advanced Industrial States (Univ of Wisconsin Press, 1978); Keohane, Robert O. Power and Interdependence: World Politics in Transition (Little, Brown, 1977). 14 Greider, William. One World, Ready or Not: The Manic Logic of Global Capitalism (Simon & Schuster, 1997); Ohmae, Kenichi The Borderless World: Power and Strategy in the Interlinked Economy (HarperBusiness, 1999); Thurow, Lester C. Head to Head: The Coming Economic Battle Among Japan, Europe, and America (Morrow, 1992); Friedman, Thomas L. The Lexus and the Olive Tree (Farrar, Straus, Giroux, 1999) 15 Katzenstein, P Cultural Norms and National Security ; Wendt p.110-111, Wendt 16 Wendt, Alex "Why a World State is Inevitable: Teleology and the Logic of Anarchy" in European Journal of International Relations 9(4) (2003). 17 For a typical example see Simon, Craig “Internet Governance Goes Global” in International Relations in a Constructed World (Oxford Univ. Press, 1988). 18 For an alternate view of Marxism see Bimber, Bruce “Three Faces of Technological Determinism” in Does Technology Drive History: The Dilemma of Technological Determinism, edited by Merrit Roe Smith and Leo Marx, (MIT Press, 1994) pp. 79-100. 19 Buzan, Barry and Richard Little "The Idea of International System: Theory Meets History" International Political Science Review 15(3), (1994) pp. 231-56.


does observe that all technologically innovative states eventually decline due to diminishing returns, increasing

consumption, rising costs of protection and production, and the diffusion of technical skills to peripheral states.

However, beyond being “on the periphery of the system”, Gilpin is unclear as to why some peripheral states

become innovative, and hence powerful, and some do not. Liberal IR theorists tend to treat technological

progress as purely exogenous, and thus have little to offer on the question of national innovation rates beyond

the generalized cure-all’s of free trade, foreign investment, and participation in international institutions. Those

few Constructivists who write about innovation see it as broadly driven by ideas, identities, norms and culture,

but give us little insight as to how these systematically translate into differences in national technological power.

Neo-Marxists such as Immanual Wallerstein and Andre Gunder Frank, as well as physiologist Jared Diamond,

argue more thoroughly that, at root, the sources of technological difference are accidental: historical, ecological,

and geographic.20 But they offer little explanation as to why states like Japan can buck these trends and penetrate

the “core” of technologically advanced states. Conversely, traditional Marxists describe an ever increasing spiral

of economic concentration amongst social elites in the great powers, which will ultimately lead to technological

stagnation.21 Yet they are none too specific as to why different industrialized countries fall into different levels

of stagnation, and some not at all.

This gap is stunning: if technology and innovation are so causally important to international politics,

then why are these phenomena not better explained in IR theory as dependent variables? In fact, despite the

clear influence of politics on technological innovation, this phenomenon is only sparsely studied by political

scientists at all.22 Rather, this area has largely become the purview of small number of economists and

sociologists who often ignore or misconjecture important political variables in their analysis. And as if in

retaliation, most of the political scientists who discuss technological variables often neglect the enormous body

of innovation research that has developed over the past fifteen years in the other social sciences.

Recent research by comparative political economists provides us with a basis for crossing this divide

between the different social science approaches to innovation, as well as the empirical evidence and theoretical

motivation for investigating international relations variables as causal factors.23 This article will bring together

recent findings about the political economy of technological innovation in order to show that much of the

current conventional wisdom held in political science about technology is incorrect, and that there exists a deep

and fundamental causal relationship between international politics and technological change. It will be argued

that the decisions to innovate, to allocate resources towards innovation, and to permit the social changes caused

by new technology are all deeply political decisions, not random or scientifically determined ones. Furthermore,

20 Wallerstein, Immanuel M. The Modern World-System (Academic Press, 1974); Diamond, Jared Guns, Germs, and Steel: The Fates of Human Societies. Frank, Andre Gunder The World System: Five Hundred Years or Five Thousand? (Routledge, 1993). 21 Marx, Karl Capital: A Critique of Political Economy (International Publishers, 1967); Lenin, Vladimir I, Imperialism (Vanguard Press, 1926) 22 Gene Skolnikoff, Dick Samuels, Harvey Sapolsky, and David Hart are rare and valuable exceptions. 23 Hall, Peter A. and David Soskice. “Introduction” in Varieties of Capitalism : The Institutional Foundations of Comparative Advantage, Edited by Hall, Peter A. and David Soskice, (Oxford University Press, 2001); Mokyr, Joel. 2000. The Gifts of Athena: Historical Origins of the Knowledge Economy. Princeton, N.J. : Princeton University Press; Taylor, Mark Zachary “Empirical Evidence Against Varieties of Capitalism’s Theory of Technological Innovation” International Organization 58(3) (Summer 2004).


it will be theorized that each nation’s relationships with lead innovating countries determines its technological

trajectory. In other words, this article will argue that international relations are as important as domestic

institutions, and perhaps more so, in determining national innovation rates.

II. Empirical Puzzles and The Failure of Existing Theories

Why are some countries more technologically innovative than others? This question remains under-

theorized by IR scholars because most theorists simply “black-box” the innovation process, and offer no model

of it. More specifically, IR theorists, and indeed most political scientists, tend to assume that the rate and

direction of technological innovation are either 1) random, 2) scientifically & technically determined, or 3)

structured solely by domestic politics & institutions. Regardless of which of these three assumptions is adopted,

the end result is that innovation is always exogenous to IR theory and vice-versa. That is, randomness and

scientific & technical determinism are explicitly outside of political causality; meanwhile, most domestic

politics explanations posit an ungeneralizeable combination of institutions, policies, and historical contingencies

acting to produce a new technology, which then springs forth Athena-like onto the international arena. Hence,

even a domestic politics approach to technological change produces a result that is exogenous from an IR

perspective.

Yet none of these three widely-held assumptions about the causes of technological innovation have ever

been empirically tested or theoretically justified. Rather, they are entirely based on incomplete theory, anecdotal

observation, and stylized facts. Therefore, our first step is to ask what the empirical data tell us about national

rates of technological innovation? More specifically, is there is any factual basis for the widespread assumption

that aggregate technological change is either a random or scientifically determined phenomenon, and therefore

apolitical? Or that innovation is purely a function of domestic institutions; for example, a natural product of

democracy, free markets, or government decentralization?

In order to answer these questions, we need to look at cross-national data on relative technological

performance. By far, the most frequently used quantitative measure of national innovation rates is patents.

Empirical research has shown that if we weight patents by the number of citations they receive, then we can use

them as a good measure of national levels of innovation across long periods of time (see Appendix I for a fuller

discussion of the use of patents as an innovation measure). Therefore we focus our attention on the cross-

national patent data.

Figures 1a-1c (below) graph the citations-weighted patent rates (per capita) of twenty-one industrialized

democracies over the 1975-1995 period. Since the United States is by far the most innovative country in the

world during this time period, the data has been normalized to show each country’s innovation rate relative to

that of the United States. The top graph presents data on those countries that are consistently the world’s most

innovative nations, the middle graph shows the mid-level innovators, and the bottom graph highlights those


Figure 1a-c: Total Citations-Weighted Patents per Capita (US = 1.00) Most Innovative Countries

0

0.18

0.36

0.54

0.72

0.9

1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

SwitzJapanSwedenGermnyCanada

Mid-Level Innovative Countries

0

0.18

0.36

0.54

0.72

0.9

1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

NethldsUKFrance

DenmarkAustriaBelgiumAustraliaNorway

ItalyIrelandNewZlndSpain

Rapidly & Increasingly Innovative Countries

0

0.18

0.36

0.54

0.72

0.9

1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995

TaiwanIsraelFinlandS Korea

Source: National Bureau of Economic Research (2001)


countries which have had the most significant increases in innovation rates during the twenty year period.24 Note

that each of the graphs uses the same vertical scale, and hence can be compared against one another. With this

aggregate data in hand, we can begin to make some initial judgments about the plausibility of various common

assumptions about national innovation patterns.

Does Randomness Explain National Innovation Rates? If innovation was a purely random phenomenon, then no nation should remain amongst the world’s

technological leaders for very long, and newly innovative nations should regularly rise up. However, the first

thing we notice in Figures 1a-1c is that we do not see randomness in national innovation rates. Instead, we see

some enduring regularities, with the United States, Japan, Switzerland, and Sweden consistently placing

amongst the world’s leading innovators, while Spain, New Zealand, Norway, and Italy are repeatedly the least

innovative of the industrialized world. This is especially puzzling when we consider that almost every

industrialized nation expends a considerable share of its resources on the pursuit of technological progress. Yet,

despite the seemingly clear policy and fiscal requirements for promoting innovative behavior, some countries

are consistently more successful than others at technological innovation, even amongst the industrialized

democracies. Note also that amid the regular patterns, there is some occasional but significant jockeying for

relative position. These regularities need explaining, as do the countries which violate them. For example, why

did post-war Japan, a recently devastated and occupied late-industrializer with little or no depth in Western

science, rapidly “out-innovate” France with its advantages of generous Marshall-Plan benefits and hundreds of

years of experience performing cutting-edge scientific research and technological innovation?25 Or take the

Nordic countries, with their relatively similar sized economies, histories, government institutions, and ethnic

constituencies. Given these similarities why do we see such drastic variance in Nordic innovation rates? Why

Finland’s recent and sustained rise in innovation since the mid-1980s, or Sweden’s relative persistence as a lead

innovator, rather than Norway which consistently hugs the bottom of the pack?

How About Other Popular Theories About National Innovation Rates?

The patent trends shown in Figures 1a-1c also belie many of the causal variables often invoked to

explain such puzzles. Figure 2 below lists several of these independent variables, those most frequently used to

explain national differences in innovation rates & high tech production patterns. Juxtaposed alongside these

causal explanations is a list of the world’s top 5 innovators, by almost any measure (patents, scientific

publications, productivity rates, high technology exports), during the past sixty years. It does not take long to

notice that each of the causal variables on the left varies enormously across the countries on the right, and yet

24 The term “mid-level” is used here to remind us that there are approximately one hundred countries that produced little or no patented innovation during this period (defined as 10 or fewer patents). The only other countries not included in Figure 1 that innovated at a comparable level to those graphed are the USSR/Russia, South Africa, Hungary, and Hong Kong, each of which would be in the mid-level group. These were omitted since they are generally not considered to be amongst the industrialized democratic nations. 25 Although the citations-weighted patent data only goes back to the mid-1970s, we know that from the early 1960s onwards Japan rose become a lead innovator in automobiles, semiconductors, and consumer electronics, rather than France which attempted to innovate in these same industries. Furthermore raw patent counts from the NBER patent database show that Japan annually produced more international patents than France as far back as the series goes (1963), and on a per capita basis back to 1969-1970.


each of these countries is a persistent lead innovator. Let’s briefly review some of the hypotheses behind these

causal factors. It is important to note that the purpose here is exploratory, not to definitively prove or thoroughly

explain any particular hypothesis; rather, we want to get a sense of just how muddled and indeterminate current

theories are, and how conflicting and contradictory the different lines of causality:

Figure 2: Common Explanations of National Innovation Rates & Top 5 Innovators

Military ▀ USA Size ▀ Japan Scarcity of Labor/Natural Resources ▀ Switzerland Barriers to Entry/Increasing Returns ▀ Germany Free-riding ▀ Canada Culture

1) Military force has long been associated with technological change, and several prominent historical

sociologists have argued that the development of military technical ability, either weapons systems or their

production processes, largely determines national innovation rates.26 However, a cursory look at the countries in

Figure 2 tends to contradict this thesis. It might apply quite well to the United States, where defense-related

innovation consistently spills over into the civilian economy, but it fails to explain the high rates of innovation

in the other countries, especially during the Cold War. Indeed Samuels (1994) has shown that the reverse

causality appears to be true in Japan.27 2) Since innovation requires innovators, it is only common sense to

hypothesize that either the relative size of states’ economies or populations should significantly determine

variance in national innovation rates, but it is hard to fit highly innovative small states like Switzerland or

Canada into this particular explanation. 3) Reversing the “size” argument, economists using Hicks theory of

relative factor prices have suggested scarcity of labor, or perhaps natural resources, creates incentives for

technological change in order to compensate for the scarcity, however Japan has historically been labor rich and

resource poor, while the United States has suffered the opposite condition.28 4) Since technology is typified by

high barriers to entry and increasing returns, it follows that once a country achieves high rates of innovation,

then it will tend to maintain them, therefore early developers should have an innovative advantage. But not only

is this violated by late-industrializers such as Japan and Sweden, but note the relative decline of the UK in

Figure 1, which led the first industrial revolution, and was the source of much primary research upon which the

second industrial revolution and computer and internet revolutions were based. Perhaps more striking is the

often ignored case of China, which nearly industrialized during the 14th century, but instead slipped into an

innovative atrophy so grave that the Chinese eventually had to re-learn from Europeans many of its own 26 Smith, Merritt Roe ed. Military Enterprise and Technological Change: Perspectives on the American Experience (MIT Press, 1985); Stuart Leslie, The Cold War and American Science (Columbia Univ.Press, 1994); Edwards, Paul The Closed World: Computers and the Politics of Discourse in Cold War America (MIT Press, 1996); Parker, George The Military Revolution: Military Innovation and the Rise of the West, 1500-1800 (Cambridge Univ. Press, 1996). 27 Samuels, Richard J. "Rich Nation, Strong Army": National Security and the Technological Transformation of Japan (Cornell Univ. Press, 1994) 28 Hicks, J Theory of Wages (MacMillan, 1932); Habakkuk, H.J. American and British Technology in the 19th Century (Cambridge Univ. Press, 1962). See also Leontief, Wassily “Domestic Production and Foreign Trade: The American Capital Position Re-examined” Economia Internazionale 7 (1954).


indigenous inventions.29 5) Students of Gershenkron reverse the previous argument, hypothesizing that late-

industrializers not only have the strongest incentives to leap ahead to the technological frontier, but also have the

advantage of free-riding on the scientific achievements of those advanced countries which came before.30

However, the persistence of the United States above Japan, or the other Europeans above Alice Amsden’s rising

East Asia giants would appear to contradict Gershenkron’s thesis. 6) Finally, Dore (1987), Cipolla (1994), and

countless others have highlighted the role of culture in fostering innovation, and yet history has yet to produce a

major cultural group that does not innovate.31

The point here is not to argue that none of the causal variables described above have any effect on

national innovation rates, but rather to defuse some widely accepted, but unsubstantiated, generalizations about

the sources of relative technological power. The variables listed in Figure 2 are frequently paraded out as

“accepted wisdom” during discussions of national innovation rates. And certainly some of them might make

sense when used to explain a particular country’s innovation rate at a specific point in time. Yet on closer

consideration, we find that not one of them can be consistently applied across time and space to explain the

world’s most innovative countries. Therefore it is reasonable to suspect that none of these variables alone,

especially those that contradict each other, has any overall systematic or deterministic explanatory power, and

that better theory is needed.

Do Scientific & Technical Determinism Explain Innovation Rates?

Another reason why political scientists tend to ignore technological innovation is that innovation was

long assumed to be apolitical. Even those social scientists who attempted to deal systematically with

technological change (including Marx, Schumpeter, and Solow) generally regarded it, and the underlying body

of scientific knowledge upon which it drew, as a “black box” proceeding according to its own internal processes

largely independent of political or economic forces. The argument here has been that technological change

proceeds according to its own internal scientific dynamics in a drive towards greater and greater technical

efficiency, and in a manner primarily determined by the objective physical laws of nature.32

However, as scholars have delved deeper into the history and sociology of technological change,

scientific determinism has come to be seen as neither theoretically coherent nor supported by the factual

evidence. What researchers have found is that, while the laws of physics and chemistry often set the boundaries

on mankind’s ability to manipulate nature,33 these physical laws do not determine man’s technological choices

29 Needham, Joseph, Science and Civilisation in China (Cambridge Univ. Press, 1954); Mokyr, Joel The Lever of Riches: Technological Creativity and Economic Progress (Oxford Univ. Press, 1990). 30 Gerschenkron, Alexander Economic Backwardness in Historical Perspective (Harvard Univ Press, 1962). 31 Dore, Ronald Taking Japan Seriously: A Confucian Perspective on Leading Economic Issues (Stanford Univ. Press, 1987); Cipolla, Carlo M. Before the Industrial Revolution: European Society and the Economy, 1000-1700 (Norton, 1994); Diamond, Jared M. Guns, Germs, and Steel: The Fates of Human Societies (W.W. Norton & Co., 1997). 32 Winner, Langdon Autonomous Technology: Technics-Out-Of-Control as a Theme in Political Thought (MIT Press, 1977); Barnes, B "The Science-Technology Relationship: A Model and a Query" Social Studies of Science 12 (1982); Illinois Institute of Technology, Technology in Retrospect and Critical Events in Sciences (TRACES) (IIT Research Institute, 1968). 33 Though there are those who argue that the laws of science themselves are susceptible to politics, see Feyerabend, Paul Against Method: Outline of an Anarchistic Theory of Knowledge (Verso, 1975); for a less extreme interpretation, Kuhn, Thomas S. The Structure of Scientific Revolutions (Univ. of Chicago Press, 1962).


within those bounds. Nor do the laws of science necessarily privilege some technical choices over others in a

deterministic manner. In fact, social choices about new technology arise even in the most objective and

scientific instances of technological change. These choices generally involve physical trade-offs which must be

decided upon politically, and often with unknown physical and social ramifications. For example, decisions

about whether to pursue digital or analog computing,34 AC or DC electrical power,35 different types of rail

systems,36 and countless other technical decisions were made on the basis of politics, not pre-determined by any

sort of physical law.

Conversely, some technical choices may have little scientific value but immense cultural, ethical, or

ideological meaning. These latter include choices about risks, access, power, control, and the distributive effects

of a new technology. Such choices may be purely political in nature, but they can drastically affect the physical

form and technical operation of new technology, what capabilities a technology will have, and how the

technology is to be physically used and controlled.37 This type of causality is not confined to innovation in

developing countries38 or solely in cases of infamous political progeny such as weapons systems,39 birth

control,40 or nuclear power,41 but has been shown to be at work affecting innovation in general, such as in

automobiles,42 the internet,43 medical technologies,44 cruise missiles,45 numerically-controlled machines,46 and

even seemingly insignificant technologies like fluorescent lights or bicycles.47 Of course, the debate over

technology’s effect on social outcomes is still ongoing;48 my point here is to acknowledge the hundreds of case

studies which reveal that technology itself is anything but technically determined. Rather, politics consistently

appear to play a major role in shaping new technology and determining innovation rates.

34 Edwards, Paul N. The Closed World: Computers and the Politics of Discourse in Cold War America (MIT Press, 1996); Redmond, Kent C. From Whirlwind to MITRE: The R&D Story of the SAGE Air Defense Computer (MIT Press, 2000) 35 Hughes, Thomas P. Networks of Power: Electrification in Western Society, 1880-1930 (Johns Hopkins University Press, 1983) 36 Dunlavy,Colleen Politics and Industrialization (Princeton Univ. Press, 1994). 37 Bijker, Wiebe E., Thomas P. Hughes, and Trevor Pinch The Social Construction of Technological Systems (MIT Press, 1989); Bijker, Wiebe E. Of Bicycles, Bakelites, and Bulbs: Toward a Theory of Sociotechnical Change (MIT Press, 1995). 38 Steinfeld Edward, "Chinese Enterprise Development and the Challenge of Global Integration," in Shahid Yusuf (ed.) East Asian Networked Production (World Bank, forthcoming); Keniston, Kenneth and Deepak Kumar (eds.) IT Experience in India: Bridging The Digital Divide (SAGE Publications, 2004); Amsden, Alice H. The Rise of "The Rest": Challenges to the West from Late-Industrializing Economies (Oxford University Press, 2001). 39 MacKenzie, Donald A. Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance (MIT Press, 1990); Flank, Steven M. Reconstructing rockets--the politics of developing military technology in Brazil, India, and Israel / by Steven M. Flank. (PhD Thesis, MIT 1993). 40 Asbell, Bernard The Pill: A Biography of the Drug that Changed the World (Random House, 1995); Reed, James From Private Vice to Public Virtue: The Birth Control Movement and American Society Since 1830 (New York, 1978) 41 Bauer, Martin ed. Resistance to New Technology (Cambridge Univ. Press, 1995) 42 Beasley, David R. The Suppression of the Automobile: Skulduggery at the Crossroads (Greenwood Press, 1988) 43 Richard J. Barber Associates Inc. The Advanced Research Projects Agency, 1958-1974 (Washington D.C., National Technical Information Service, 1975) 44 Douglas Starr, Blood: An Epic History of Medicine and Commerce (Alfred A. Knopf, 1998); Drake, Alvin W., Stan N. Finkelstein, Harvey M. Sapolsky The American Blood Supply (MIT Press, 1982); Epstein, Steven, Impure Science: AIDS, Activism, and the Politics of Knowledge (Univ. of California Press, 1996). 45 Jodie M. Sweezy and Austin G. Long. From Concept to Combat: Tomahawk Cruise Missile Program History and Reference Guide, 1972-2004. (forthcoming) US Doc PMA-280/Tomahawk All-Up Round Program Office, Naval Air Station Patuxent River, Maryland. 46 Noble, David F. America By Design: Science, Technology, and the Rise of Corporate Capitalism (Knopf, 1977) 47 Bijker, Wiebe E. Of Bicycles, Bakelites, and Bulbs: Toward a Theory of Sociotechnical Change (MIT Press, c1995. 48 Merritt Roe Smith and Leo Marx (eds.) Does Technology Drive History?: The Dilemma of Technological Determinism (MIT Press, 1994) .


This evidence suggests a possibility: perhaps the fundamental problem with several of the explanations

bulleted above in Figure 2 is that they generally ignore politics. In doing so, the explanations in Figure 2 ignore

the fact that technological innovation is a fundamentally political phenomena. When studied at the micro-level,

technological progress appears not to be a scientifically determined drive towards greater and greater technical

efficiency, nor is it a random draw. Rather the decision to innovate, to allocate resources towards innovation,

and to permit the social changes caused by new technology are all political decisions. We must therefore ask

what social scientists have had to say about the politics of technological change.

III. Political Institutions & Endogenous Technological Change By far, the most developed set of theories that use politics to explain variance in national innovation

rates are those based on domestic institutions. As observed above, the exogeneity of technological change was

taken for granted by most economists throughout the 19th and mid-20th centuries. This attitude changed

gradually during the Cold War, as vast expenditures by the US government and industry on R&D made it

increasingly clear that technological innovation could be made responsive to economic and political needs, a

fact punctuated by the Soviet launch of Sputnik and later by the Japanese and German economic “miracles”. In

response, innovation scholars during the 1960’s began to investigate whether certain supply-side or demand-side

variables could explain why even developed nations followed different technological trajectories.49 This

somewhat inconclusive debate was followed in the late-1970s and 1980s by a plethora of case and country

studies which tended to emphasize the importance of this or that policy, these or those historical conditions, but

failed to produce any generalizable theory about the rate or direction of national innovation.

A recurring problem encountered in these debates was the contradiction between empirical observation

and certain fundamental tenets of the economics of science. Specifically, Kenneth Arrow had argued that much

productive knowledge takes the form of unpatentable laws of nature and advances in basic science, and is

therefore a non-excludable public good available to everyone without charge.50 And while patents and trade

secrets act as temporary solutions to this appropriability problem in the area of applied knowledge, history has

shown that the original inventors of technology often do not capture most of the benefits of their innovations

when these inventions are transferred across borders, and that these transfers take place even in spite of

considerable efforts to stop them. Theoretically speaking then, in the long-run, developed nations should not

display significant variation in either per capita innovation rates or in the type of innovative activities which

they pursue. Yet, as Figs. 1a-1c show, differences appear to abound.

One possible solution to this paradox is institutions. Institutions are perhaps the only variables which

both influence the incentives for innovative behavior and which differ across nations. Indeed, political scientists

and economists have long recognized the capacity of government, labor, regulatory, and legal institutions to

49 Mowery, David C. and Nathan Rosenberg. “The Influence of Market Demand Upon Innovation: A Critical Review of Some Recent Empirical Studies” Research Policy 8(2) (1979) pp. 103-153. 50 Arrow, Kenneth “Economics of Welfare and the Allocation of Resources for Invention” in The Rate and Direction of Inventive Activity Nelson, Richard R. ed. (Princeton University Press, 1962)


inhibit free market exchange and thereby hamper innovation. But it was not until Paul Romer (1990)

endogenized technological change that social scientists began to take seriously the ability of institutions to

actively enhance aggregate economic performance through their effects on the rate and direction of

technological progress.51 Political economic research on this question then began to probe the roles of specific

national institutions and policies on innovation rates, for example, examining the effects of different science

policies, trade regimes, legal frameworks, or financial institutions. Often using a case study approach, these

types of inquiries evolved into the study of “national innovation systems” (NIS). Led by scholars such as

Richard Nelson, Bengt-Ake Lundvall, and Charles Edquist, the NIS approach brought to light the complexity of

the innovation process and the diversity of factors involved in it.52 Michael Porter, Scott Stern, and Jeffrey

Furman (2002) have since advanced this line of research by synthesizing NIS theory with Porter’s cluster-based

thesis of national industrial competitive advantage, and used statistical analysis of patent data to argue their

hypotheses.53

More recently, comparative political economists have suggested more parsimonious institutional

theories. In their research on “varieties of capitalism”, Hall & Soskice argue that the more a nation allows

markets to structure its domestic economic relationships, the more radically innovative it will be. Conversely,

the more a polity chooses to coordinate economic relationships via non-market mechanisms, the more slowly

and incrementally innovative it will be.54 Finally, Daniel Drezner (2001), Joel Mokyr (2000), and others have

argued that decentralized government structure may be the key to explaining high innovation rates.55

Decentralized states are seen as agile, competitive, and well structured to adapt to innovation’s gale of creative

destruction. Meanwhile, centralized states, have come to be viewed as rigid and thus hostile to the risks, costs,

and change associated with new technology, or prone to cling too long to fool-hearty or outdated technological

projects.

But the institutional explanations for differences in national innovation rates have suffered serious

theoretical criticisms and empirical setbacks in the recent years. For example, critics point out that NIS-type

approaches fail to produce (or to test!) any general hypotheses or theory. Instead, NIS generates some fairly

complex models of national innovation, with some 20-30 major independent variables (policies and institutions),

each of which may play a role in technological innovation depending on its configuration vis-à-vis the other

variables. And since the successful operation of each NIS variable often depends upon its context, we find

ourselves with a rapid proliferation of viable national innovation systems. In the end, the NIS approach results in

a large typology of institutions, not testable theories of causality. Meanwhile, although the causal stories put

51 Romer, Paul M. “Endogenous Technological Change” Journal of Political Economy 98(5)(1990) pp. S71-S102. 52 Nelson, Richard R. National Innovation Systems: A Comparative Analysis (Oxford Univ. Press, 1993); Lundvall, Bengt-Ake National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning (St. Martin's Press, 1992); Edquist, Charles Systems of Innovation : Technologies, Institutions, and Organizations (Pinter, 1997). 53 Furman Jeffrey, Michael Porter, and Scott Stern "The Determinants of National Innovative Capacity" Research Policy 31(6): 899-933 (Aug 2002) 54 Hall & Soskice (2001) 55 Drezner, Daniel. “State Structure, Technological Leadership and the Maintenance of Hegemony.” Review of International Studies 27(1) (2001) pp. 3-25; Mokyr, Joel. The Gifts of Athena: Historical Origins of the Knowledge Economy (Princeton Univ. Press, 2000).


forward by proponents of “varieties of capitalism” theory and government decentralization are both theoretically

appealing and dovetail with some widely held stereotypes about national differences in innovation, both theories

are contradicted by the empirical evidence. Taylor (2004, 2005) and others have analyzed data on international

patents and scientific publications to show that neither theory properly describes national innovative behavior

over time or across countries.56

So where does this leave us? We have seen that technological innovation touches almost every aspect of

international relations, and is mentioned by all major theoretical approaches to the sub-field. Yet, few IR

theorists attempt to explain where relative technological power comes from or how international politics fits into

explanations of national innovation rates. The reason for this theoretical gap is that IR scholars, like most

political scientists, assume that innovation is exogenous to IR and vice-versa. More specifically, we generally

assume that the rate and direction of technological innovation are either 1) random, 2) scientifically &

technically determined, or 3) structured solely by domestic politics & institutions. However, the empirical

evidence fails to support any of these assumptions. This is especially disturbing in the case of institutional

explanations, which have become the orthodoxy over the past several decades. In fact, the study of national

innovation rates has in large part been taken over by the study of comparative institutions, with no consideration

given to international relations.57 We therefore have to ask how and why we find ourselves in this strange

predicament.

IV. Why Pure Institutional Explanations Fail Why do institutional explanations for national innovation rates satisfy us so well theoretically, but fail

us empirically? And if institutional explanations are indeed incorrect, then how can we explain their continued

prevalence in explaining the politics of technological innovation? One possible answer to former question is that

we have simply not yet identified the right institutions, or combination of institutions, that affect innovation

rates. However, a preponderance of recent empirical evidence points to a more fundamental problem, one which

also helps us to answer the latter question. Institutional theories appear to fail because they are based on faulty,

or at least incomplete, assumptions about the public goods nature of science. According to Arrow (1962) the

basis for technological change is the accumulation and development of ideas and scientific knowledge. This

assumption was carried forward by Romer (1990) as the basis of his endogenous growth theory. In the Arrow-

Romer approach, ideas and scientific knowledge are assumed to be public goods, with free-riders benefiting

enormously from the investment, hard-work, and sacrifices of the scientists and inventors who produce them.

Scholars also note that innovation is plagued with high levels of risk and uncertainty, imperfect information, and

high transactions costs. So when nations seek to promote technological innovation, they have to overcome a

combination of all of these problems.

56 Taylor, Mark Z. "Empirical Evidence Against Varieties of Capitalism's Theory of Technological Innovation" International Organization 58(3) (Summer 2004); Taylor, Mark Z. “Government Structure and the Politics of Technological Innovation” Paper presented at APSA Annual Meeting in Chicago (2004). 57 Rosenberg, Acemoglu,


In political science, the solution to each of these problems is: institutions. Institutions solve the free-

rider problem by providing selective incentives. They also act to lower information & transaction costs, and to

lower and spread risk & uncertainty. Hence as political scientists, when we see the problems associated with the

production of ideas and scientific knowledge, we are naturally drawn to institutional explanations. Moreover,

the empirical data appears at first to back this up: as we saw in Figure 1, the variance in innovation rates is

across countries; and since government institutions are what determine borders, we can similarly infer that

institutions are the primary causal variables.58 So theoretically and empirically, it is natural to look to institutions

for the sources of variance in national innovation rates.

However, in the last two decades, economists and business scholars who study innovation (including

Pavitt (1987), Nelson (1990), Rosenberg (1990), and Amsden (2001)) have moved away from a pure public

goods characterization of innovation based on Arrow’s & Romer’s “ideas”, and towards a more sophisticated

conceptualization of innovation as an activity based on “basic science” and “tacit knowledge.” This distinction

between “ideas & knowledge” and “basic science & tacit knowledge” may seem trivial, but it significantly alters

the solution to the collective action problems associated with innovation, and therefore has important

implications for the ability of national institutions to affect technological change. This becomes evident when

we understand how basic science and tacit knowledge interact to produce new technology.

Basic science can be defined as either 1) facts or data observed in reproducible experiments, or 2)

theories or relationships between facts.59 Basic science is non-rival and minimally excludeable.60 It is also easily

codified in equations, diagrams, books, and blueprints. Basic science can therefore largely be treated as a public

good, and poses a significant collective action problem.61 In modern times, this problem has been solved through

research subsidies, universities, and selective incentives of security, social prestige, tenure, life-style.62 However

forty years of case study and statistical evidence suggest two important amendments to the traditional view of

basic science held in political science. First, the belief that technology is simply “applied science” is not always

empirically born out; more often than not, the causal arrow points the other way. In astrophysics, metallurgy,

engines, communications, munitions, and medicine, a piece of fully functioning technology often precedes, and

in fact provokes, the science which explains how it works.63 Second, and more importantly, basic science does

not provide technical ability. There are numerous examples of societies with advanced scientific knowledge that

58 Olson, Mancur “Big Bills Left on the Sidewalk” Journal of Economic Perspectives 10, 2 (1996) pp. 3-24 59 Nelson, Richard R. “The Simple Economics of Basic Scientific Research” Journal of Political Economy 67(3) (June 1959) pp. 297-306. This type of knowledge is termed “explicit knowledge” within the sociology literature. 60 Arrow, Kenneth, 1962; Romer, Paul. 1990. 61 Nelson, Richard R. (1959); Rosenberg, Nathan “Why Do firms Do Basic Research (with their own money)?” Research Policy 19 (1990) pp. 165-174. 62 Others would add that personality and psychology traits unique to innovators, as well as circumstance and serendipity also play significant roles, see Stern, Scott “Do scientists pay to be scientists?” Management Science 50 (6): 835-853 (June 2004) 63 Rosenberg, Nathan, Inside the Black Box: Technology and Economics (Cambridge Univ. Press, 1982); Barnes, Barry and David Edge eds. Science in Context: Readings in the Sociology of Science (Open University Press, 1982), especially p147-185.


do not innovate at the technological frontier.64 As an illustration, note that the basic science behind atomic

weaponry can be easily found in physics, chemistry, and engineering texts. But, even a team of Nobel Prize

winning economists could not likely build a functioning atomic bomb if merely given a stack of science texts

and some fissile material. They would more probably destroy themselves in the process, or starve because they

had quit their teaching jobs to pursue the project. Why? Not because Nobel laureates in economics are

unintelligent, but because social scientists tend to lack the training and experience in (i.e. the tacit knowledge of)

bomb-making, circuitry, chemistry, machining, or the proper handling of explosives or radioactive materials.65

So while knowledge of basic science can certainly aid technological innovation, it is neither intrinsically

sufficient nor determinate.

Tacit knowledge is knowledge that cannot be codified, but can only be transmitted via training or gained

through personal experimentation.66 Tacit knowledge is non-rival and largely excludeable, therefore it cannot be

treated like a public good.67 The simplest example of the nature and value of tacit knowledge is that one does not

learn how to ride a bike, or build an atomic bomb, from a textbook.68 Perhaps more relevant to our discussion is

that tacit knowledge has been shown to be central to the creation of new products and processes, and their

adaptation and use in mass-production.69 And if tacit knowledge is a private good, then acquiring it becomes a

new problem for would-be innovators to solve. Furthermore, one’s ability to acquire some forms of tacit

knowledge depends on one’s already existing stock of knowledge: it is easier to learn how build that atomic

bomb if you have already mastered the basics of building conventional explosives. Therefore, tacit knowledge is

not only a private good, but its cost can vary enormously due to learning curves and other similar barriers to

entry. Finally, much empirical analysis of economically relevant tacit knowledge during the 1980s strongly

suggests that tacit knowledge gained through experimentation is far more costly and risky than that transmitted

through training or embodied in existing technology.

What does all this mean for innovation? First, the characteristics of basic science are such that we still

have the traditional public goods problem, high risks, uncertainty, transaction costs, and imperfect and costly

information. Therefore institutions are still important as solutions to these problems. Second, the private goods

characteristics of tacit knowledge demand a new focus on international relations rather than domestic

institutions. Why? Because the high costs and risks of generating new tacit knowledge suggest that innovators 64 Needham, Joseph (1954); Botelho, Antonio J. “The Politics of Resistance to New Technology: Semiconductor Diffusion in France & Japan until 1965” in Bauer (1995); Diamond, Jared (1997); Chase, Kenneth Firearms: A Global History to 1700 (Cambridge Univ. Press, 2003). 65 For opposing arguments on this very point see the Harvard Nuclear Study Group (Albert Carnesdale et al.) Living With Nuclear Weapons (Bantam, 1983); MacKenzie D, Spinardi G “Tacit Knowledge, Weapons Design, and the Uninvention of Nuclear-Weapons” American Journal of Sociology 101 (1): 44-99 (July 1995). 66 Polanyi, Michael, Personal Knowledge: Towards a Post-Critical Philosophy (Harper, 1962); Polanyi, Michael The Tacit Dimension (Anchor Day, 1966) 67 Von Hippel, E "Sticky Information and the Locus of Problem-Solving: Implications for Innovation" Management Science (40) (1994). 68 For opposing arguments on this very point see the Harvard Nuclear Study Group [Albert Carnesdale et al.] Living With Nuclear Weapons (Bantam, 1983); MacKenzie D, Spinardi G “Tacit Knowledge, Weapons Design, and the Uninvention of Nuclear-Weapons” American Journal of Sociology 101 (1): 44-99 (July 1995). 69 Langlois, Richard N. “Knowledge, Consumption, and Endogenous Growth” Journal of Evolutionary Economics 11 pp. 77-93 (2001); Ernst D, Kim L “Global Production Networks, Knowledge Diffusion, and Local Capability Formation” Research Policy 31(8-9) pp. 1417-1429 (Dec 2002)


should prefer to use existing tacit knowledge wherever possible. Thus the goal is to maximize domestic

scientists’ and engineer’s access to tacit knowledge that has been developed elsewhere, and not have to

constantly re-invent costly wheels. And since this particular good exists in the minds and actions of scientists

and engineers in other countries, we need to focus on international relationships. Here I do not mean general

globalization as prescribed by free market theorists (such as openness to trade, labor migration, and financial

capital), or participation in international organizations as advised by liberal institutionalists. Rather, I mean

those specific forms of international relations which result in increased exposure to tacit knowledge, such as

those listed below in Figure 3.

Figure 3: Tacit Knowledge Transfer Mechanisms -technology licensing -purchases of patents & intellectual property -imports of capital goods & high technology products -overseas training & education -foreign consultants & technical assistance -attendance to international expositions, conferences, & lectures -overseas plant visits -consults with foreign capital goods & high technology suppliers/consumers -mergers & acquisitions, joint R&D projects -immigration of scientists, engineers, and highly skilled labor -establishing R&D facilities in high-tech countries -inward FDI in production and R&D facilities from more advanced countries

It is also worth highlighting here the priority of education. Scientific and technical training are necessary

to increase the number of people who are capable of enjoying any tacit knowledge they come into contact with.

This sometimes referred to as “absorptive capacity”. So referring back to our atomic bomb example, while we

may not fear the atomic capabilities of a team of Noble Prize winning economists, we might indeed become

apprehensive if the same science texts and uranium were found in the hands of a team of merely average

laboratory chemists, physicists, electricians, and engineers.70

V. Empirical Evidence: A First Look If international relationships are so important in explaining differences in national innovation rates, then

their effects should be evident in the empirical data. That is, countries with more of the international

relationships such as those listed in Figure 3, and higher levels of them, should be observed to innovate

relatively more than countries that are less well connected, regardless of the quality of their domestic

institutions. Ideally one would want to conduct both statistical analysis and case studies in order to test the

theory outlined above. Since the independent variables vary across both time and country, statistical analysis

should involve panel data, preferably with alternate and competing measures of the major variables in order to

triangulate observations of phenomena that are admittedly difficult to measure. Case studies could then

corroborate the statistical findings, and allow us to confirm whether any relationship suggested by the aggregate

70 Carnesdale et al. (1983); MacKenzie and Spinardi (1995).


data can indeed be observed at the micro-level. Case studies would also allow us to go beyond patent data, and

judge with greater scrutiny the pace and degree of innovation being performed. Of course, such case studies

should be careful to randomize across technologies, and to maximize variation across domestic institutions,

international relationships, and time periods, in order to compensate for the constraints of small-N analysis.

Obviously, these in-depth analyses are beyond the scope of this brief working paper. However, we can examine

some basic statistical data for prima facie evidence.

Data In order to conduct a probative test of international relations versus domestic institutions explanations of

technological change, we need at least one quantitative measure of each variable. For these purposes, I retain the

use of citations-weighted patents as my measure of technological innovation.71 As my measure of international

relationships, I use science and engineering PhD’s awarded by United States’ graduate schools to foreign

students. Clearly, this measure does not represent the diverse number of tacit knowledge transfer mechanisms

listed in Figure 3. But since formal education and training of scientists and engineers by top researchers is

perhaps the most effective vehicle of tacit knowledge transfer, then this measure should be the most likely to

provide evidence in support of an international relations mechanism for technological change.72 Furthermore, we

would expect that the most useful international relationships would be those geared towards relatively more

innovative countries, preferably the lead innovator. In other words, Finland should gain far more tacit

knowledge by establishing multiple strong ties with the United States as opposed to creating these same ties

with Mexico. Therefore, according to an international relations hypothesis of technological change, those

nations which send more people to the US to receive advanced training in science and engineering should be

more innovative than others.

For domestic institutions, I focus on those most invoked by the conventional wisdom: democracy and

free markets. As my measure of democratic institutions, I employ Polity2 from the University of Maryland’s

Polity IV Database, which ranks nations on a -10 to +10 scale of institutionalized democracy. As my measure of

markets, I use the "Economic Freedom of the World Index" produced by the Fraser Institute which ranks the

strength of nations’ market institutions on a 1-10 scale. This index is a composite measure which attempts to

quantify and combine: size of government sector in the national economy, legal structure & security of property

rights, access to sound money, free trade, and degree of government regulation of finance, labor, and private

71 To better get at innovation rates, a more thorough analysis might also employ citations-weighted scientific publications and high-technology exports as a percentage of GDP, and perhaps a factor analysis of all three variables. 72 Granted, there is no best way to acquire existing tacit knowledge. Empirical studies on knowledge appropriability and spillovers have found no single superior method of acquiring foreign know-how, or even tacit knowledge held by domestic competitors. Hence, different countries might choose different combinations depending on their availability, costs, & benefits. Therefore results found using any single measure should be interpreted with care. Coe, David T. and Helpman, Elhanan “International R&D Spillovers” European Economic Review 39, p. 859-887 (1995); Levin, R, Klevorick, A, Nelson RR, and Winter SG "Appropriating the Returns from Industrial R&D" Brookings Papers on Economic Activity (3) pp. 783-820 (1987); Mansfield, Edward “How Rapidly Does New Industrial-Technology Leak Out” Journal of Industrial Economics 34(2) (Dec 1985): 217-223; Griliches Z. “The Search For Research-and-Development Spillovers” Scandinavian Journal of Economics 94: S29-S47 Suppl. S (1992); Henderson R, Jaffe AB, Trajtenberg M “Universities as a Source of Commercial Technology: A Detailed Analysis of University Patenting, 1965-1988” Review of Economics and Statistics 80(1): 119-127 FEB 1998


business. Both of these institutional measures are frequently used by social scientists who study comparative

institutions, and therefore allow to conduct our quick probe with relative confidence. A sample of the summary

statistics is provided below. If the domestic institutions hypothesis is correct, then those nations with higher

levels of democracy and free markets should be more innovative than others, regardless of the number of

students sent to train in the US. If international relationships matter more, than the data should show the

opposite pattern.

Figure 4: 1995 Summary Statistics Mean Standard Dev Max Min Polity II 3.39 6.92 +10 -10 Economic Freedom 6.13 1.24 + 9.082 + 3.442 US PhD’s Earned 64 264 +2671 0 Basic Stats: Individual Time Series I used time-series data to investigate nine countries, the results are reproduced below in Figures 5-8. The

years indicated on the X-axis (1960-1996) are the same for each graph. The values on the Y-axis accurately

track measures of the Polity II score and the Economic Freedom Index. As for PhD’s, in some graphs, the Y-

axis values track the number of US science-engineering PhD’s earned, in others they track either 10’s or 100’s

of PhD’s (as indicated in each graph’s legend). Note however that the Y-axis values do not systematical or

absolutely represent the innovation measure. This is due to the fact that, since there are truncation issues with

patent citations over time, I benchmarked each nation’s patent activity against a baseline: the patent activity of

the lead innovator (the US). This time-series of benchmarks was then scaled up or down in order to fit it with its

particular graph. Therefore the Y-axis values have no specific meaning for the innovation measure, except

perhaps comparatively from year to year within a given country. Rather, what we are looking for in the

innovation rate is how it behaves over time: is the trend up, down, or stable? Do changes in the trend precede or

follow changes in institutions or student flows?

The simple time-series data generally appear to confirm the international relations hypothesis, while

contradicting the domestic institutions hypothesis. For example, three of the countries studied (Fig 5: Taiwan,

South Korea, & Israel) are amongst the world’s most rapidly and increasingly innovative countries listed at the

beginning of this paper (recall Figure 1c above). In each of these cases, note how the rise in relative innovation

rates, tends to track and follow the trend of US PhD’s earned, but not the changes in institutional quality. Recall

that Taiwan remained under martial law for four decades until 1987, and one-party rule until 1991 when

President Chiang Ching-kuo gradually liberalized and democratized the system. Meanwhile South Korea was

ruled by various autocrats and military dictators until its first democratic elections in 1987. Hence both Taiwan

and South Korea democratized after their surge in innovation rates had begun, not before. What about markets?

Israel is instructive here. Israel has always had a high level of democracy, but from 1970-1980 its economic

market institutions suffered from an increase in non-market government coordination, subsidies, and transfers.

Yet Israel’s innovation rate increased despite this move towards what Hall & Soskice might call a coordinated


market economy.73 Hence neither a strong democracy nor strong markets seem to be a prerequisite for high

levels of innovation in these countries.

Three more countries (Fig 6: Portugal, Greece, and Venezuela) provide us with cases where domestic

institutions improved dramatically, but with no apparent effect on their national innovation rate. In both Portugal

and Greece, we see significant shifts in both their level of democratic institutions and strength of their economic

market institutions, but with no significant correlating change in relative innovation rates. In fact, the trend of

their relative innovation rates appears stable over time. In Venezuela, we perhaps do begin to see a small but

systematic improvement in relative innovation rates after 1984, but that nation had been highly democratic for

some fifteen years by then, hence it is difficult draw a strong correlation there. Perhaps more interesting, is the

fact that Venezuela’s economic market institutions moved significantly from “liberal” to “corporatist” at the

same time that its innovation rate seems to improve. Hence a decline in market institutions was accompanied by

rising innovation rates. Furthermore, note that in both Venezuela and Portugal, the innovation rates appear to

have some relationship with their level of earned US PhD’s in science and engineering. Despite a constant

increase, Portugal never earns more than 20 US science PhD’s per annum until 1995 (and suffers from relatively

stagnant innovation), whereas Venezuela hits that level of PhD’s by 1974 and continues to climb into the low

60’s by 1988 (and sees a slowly climbing relative innovation rate). Thus, Portugal and Venezuela suggest that

highly trained international students may matter more than domestic institutions. Of course, Greece bucks this

logic, with far more growth in US science PhD’s but little systematic improving in relative innovation rate.

In Figure 7 we find Sweden and Switzerland, two of the world’s top innovators, but both are on the

relative decline. Notice in the graph that this relative decline in innovation rates occurs despite the fact that both

countries have maximum levels of democracy, and increasingly free economic markets. Perhaps this is

explained by the fact that each country is on par with Portugal for their very low level of earned US PhD’s in

science and engineering (generally less than 20 per annum). So again, we see relative innovation rates reflecting

the international relationships, not the domestic institutions. Finally, observe the chart of New Zealand which

was specifically selected for its outlier rankings on the independent variables. Of all countries measures, New

Zealand has the most promising domestic institutions, with a combination of the highest level of democracy

possible and the greatest improvement in market institutions over time. Yet New Zealand’s innovation rate is

stagnant, just like its rate of earned US PhD’s in science and engineering.

Basic Stats: Cross-National Aggregates Of course, the time-series data above are not as clear and compelling as one might like them to be, and

the countries chosen could also be outliers. I therefore add some simple statistical graphs of the aggregate cross-

national data. Note that in these cross-sectional analyses the measure of innovation has changed. Since I am now

examining the 1975-1995 period as a whole, truncation effects are less worrisome and I do not need to

benchmark. Instead, I use as my innovation measure the natural log of citations-weighted patents. Logging the

73 Hall, Peter and David Soskice Varieties of Capitalism (2001).


patents makes them less sensitive to outliers and allows us to interpret the results in terms of elasticities; log

models are also consistent with much of the prior work in this type of research.74

The results of the simple cross-sectional comparisons are reproduced in Figures 9-11. Observe the

graphs dealing with economic institutions. There does seem to be a loose but observable relationship between

economic institutions and national innovation rates, both as a function of base-level of market quality and

change in market quality over time. However, the relationship between democratic institutions and innovation is

far more tenuous. There appears to be a very loose relationship between base-level of democracy and

innovation, but either no relationship or a negative relationship between innovation and change in democracy

over time. Now look at the three graphs of international relationships and national innovation rates. Here the

positive relationship is much tighter, whether we look at imports of capital goods, inward FDI, or graduates of

advanced US science and engineering programs. All three of these different measures of international

relationships show a much clearer correlation with national innovation rates than do any of the domestic

institutional measures.

Of course, since these graphs are cross-sectional we cannot be sure of the time dynamics, and therefore

of the causality. As far as these graphs are concerned, the innovation rates could be the result of the international

relationships, or the cause of them. Also, different causal relations might hold for nations at different levels of

development or size. So again, we are left waiting for a combination of time-series cross-section statistical

analysis (preferably with alternating measures of institutions and international relations) and case studies. These

analyses are currently underway, and should be completed in the coming months.

Results? But these prima facie probes do tell us some useful things. First, they strongly suggest that “good”

domestic institutions are not a necessary condition for, or producer of, technological performance. Most of the

countries in the time-series contradicted the hypothesis that democracy and markets determine innovation rates,

while the cross-national graphs showed only a loose relationship at best. Second, my probe weakly suggests that

international relationships may indeed matter for national innovation rates. None of this proves anything, but it

does suggest that this is fruitful ground for additional research. And that international relations may indeed be as

important, and perhaps more important, than domestic institutions in explaining innovation. Most importantly, it

provides ample basis for questioning the current orthodoxy within the social sciences which insists that domestic

institutions are the best answer to the important questions of international political economy. It also suggests

that IR scholars should be getting more involved in questions of technology, since they may hold a comparative

advantage in providing the theories and analysis to answer them.

74 Furman Jeffrey, Porter Michael, Stern Scott, "The Determinants of National Innovative Capacity" Research Policy 31(6): 899-933 August 2002; Jones, C. Introduction to Economic Growth (New York: WW Norton & Company, 1998).


VI. Conclusions & Implications This paper highlights the importance of technological change for explaining outcomes in international

politics, and notes the absence of any cohesive explanation in political science for why some countries are more

technologically innovative than others. It brings together recent findings about the political economy of

technological innovation in order to show that technological change is not a random or scientifically determined

process. It also questions the domestic institutions approach to explaining differences in national innovation

rates. Specifically, it argues that international relations are just as important as domestic institutions, and perhaps

more so, in determining national technological capabilities. International relations are important because

innovation depends on both basic science and tacit knowledge. And while domestic institutions may be

important for the provision of basic science, they are a second-best strategy for generating costly and risky tacit

knowledge, which can be acquired more cheaply and easily abroad. Some initial evidence is produced showing

that countries with more and stronger international relations that conduct tacit knowledge also tend to be more

innovative than nations with fewer and weaker international relations that conduct tacit knowledge. Moreover,

several countries with strong domestic institutions, but weak international relations, are less innovative than

countries with weaker institutions but multiple and strong international relationships.

For IR theorists, the implications of this are striking. Technological innovation, by potentially redefining

every nation’s capabilities, ideas, and preferences, thereby affects the international political structure.

Technology and technological change are thus the link between unit-level and systems-level phenomena. A

domestic institutions explanation of national innovation rates would essentially endogenize great power

relations. Any nation that would change its place in the international political order would need only change its

institutions in order to affect its relative material power. Those nations threatened by such a shift would need to

undermine the challenger’s domestic institutions in order to disrupt technological change and thereby ward off

the potential threat. However, an international relations explanation of national innovation rates changes this

whole dynamic. If access to the tacit knowledge held by the lead innovators is the key to improving challenger

nation’s innovation rates, then control over this access becomes control over the international system.

Furthermore, ideologies and institutions of openness and competition become weapons that can be used

shrewdly by lead innovators to lock challengers into subordinate positions; while practices such as outsourcing,

open science education, or foreign direct investment might be taken advantage of by challengers to acquire the

strategic tacit knowledge necessary to improve their relative position at the expense of the lead innovator.

It bears repeating that the theory and evidence reported here are only a first cut, and more research

needs to done. The results shown here need to be confirmed by additional quantitative (non-patent) measures of

innovation and institutions, as well as qualitative measures at the case-study level. Additional international

avenues of tacit knowledge flows need to be considered, and contrasted with the influence of domestic

institutions. Finally, cross-national technological case studies need to be performed in order to confirm that the

causal mechanisms suggested by the aggregate level statistical analysis are indeed what is going on at the micro-

level.


Appendix I: Using Patents as a Measure of National Innovation Rates The most frequently used measure of innovation is patents. The debate over the proper use of patent data has proceeded vigorously and with increasing sophistication over the past several decades. The current consensus holds that patent data are acceptable measures of innovation when used in the aggregate (e.g. as a rough measure of national levels of innovation across long periods of time), but are less appropriate when used as a measure of micro-level innovation (to compare the innovativeness of individual firms or specific industries from year to year). And while this debate is ongoing and is better recounted elsewhere, this section will address some of the more pressing issues surrounding patent measures and their use in testing theory.75

Strictly speaking, a patent is a temporary legal monopoly granted by the government to an inventor for the commercial use of her invention, where the invention can take the form of a process, machine, article of manufacture, or compositions of matters, or any new useful improvement thereof. (USPTO)76 A patent is a specific property right which is granted only after formal examination of the invention has revealed it to be nontrivial (i.e. it would not appear obvious to a skilled user of the relevant technology), useful (i.e. it has potential commercial value), and novel (i.e. it is significantly different than existing technology). As such, patents have characteristics which make them a potentially useful tool for the quantification of inventive activity. First, patents are by definition related to innovation, each representing a “quantum of invention” that has passed the scrutiny of a trained specialist and gained the support of investors and researchers who must dedicate time, effort, and often significant resources for its physical development and subsequent legal protection. Second, patent data are widely available, and are perhaps the only observable result of inventive activity which covers almost every field of invention in most developed countries over long periods of time. Third, the granting of patents is based on relatively objective and slowly changing standards. Finally, the United States Patent and Trademark Office and the European Patent Office provide researchers with centralized patenting institutions for the two largest markets for new technology. In practical terms, this allows researchers to get around the issue of national differences in patenting laws as well as providing two separate and fairly independent data pools.

Given these qualities, patents have been used as a basis for the economic analysis of innovative activity for over thirty-five years. Current use began with the pioneering work of Frederic Scherer and Jacob Schmookler who used patent statistics to investigate the demand-side determinants of innovation.77 However, the labor intensive nature of patent analysis, which used to involve the manual location and coding of thousands of patent documents, severely limited the extent (or at least the appeal) of their use in political and economic research. These limitations were eased somewhat during the 1970s when the advent of machine-readable patent data sparked a wave of econometric analysis.78 In the late 1980s, the use of patent data was further facilitated by computerization, which increased the practical size of patent datasets into millions of observations. Most recently, Hall, Jaffe, & Trajtenberg at the NBER have compiled a statistical database of several million patents complete with geographic, industry, and citation information, which I will use later to test the VOC claims.79

However, patents do have significant drawbacks which somewhat restrict, but by no means eliminate, their usage as an index of innovation. First, there is the classification problem, in that it is difficult to assign a particular industry to a patent, especially since the industry of invention may not be the industry of eventual production or the industry of use or benefit. One can address this issue, where possible, by using different patent datasets with assorted systems and levels of patent classification. Second, it is not yet clear what fraction of the universe of innovation is represented by patents, since not all inventions are patentable and not all patentable inventions are patented. This problem is exacerbated when attempting comparative research since different industries and different countries may exhibit significant variance in their propensity to patent. One can address these concerns by using publications data in addition to patents. And although patents and publications both may be imprecise measures of innovation, as long as this measurement error is random and uncorrelated with the

75 For a review of the debate see Griliches 1990; Trajtenberg 1990; Archibugi and Pianta 1996; Harhoff, Narin, Scherer, and Vopel 1999; Eaton and Kortum 1999; Jaffe, Trajtenberg, and Fogarty 2000; Hall, Jaffe and Trajtenberg 2000, 2001. 76 Designs and plant life can also be patented, however most econometric analysis of patent data is confined to utility patents granted for inventions such as those listed above. For a fuller description of patents and patent laws, classifications, and the application process see http://www.uspto.gov/main/patents.htm. 77 Scherer 1965; Schmookler 1966. 78 Summaries of which can be found in Griliches 1984; Pakes 1986; and Griliches, Hall, and Pakes 1987. 79 Hall, Jaffe, and Trajtenberg 2001.


explanatory variables, then regressions using this data should produce unbiased estimates of the coefficients (and generally with inflated standard errors).

Finally, some critics point out that patents vary widely in their technical and economic significance: most are for minor inventions, while a few represent extremely valuable and far-reaching innovations. Moreover, it has been found that simple patent counts do not provide a good measure of the radical-ness, importance, or “size” of an innovation. Simple patents counts correlate well with innovation inputs such as R&D outlays, but they are too noisy to serve as anything but a very rough measure of innovation output.80 Therefore I use patent counts which have been weighted by forward citations. Forward citations on patents have been found to be a good indicator of the importance or value of an innovation, just as scholarly journal articles are often valuated by the number of times they are cited. The idea here is that minor or incremental innovations receive few if any citations, and revolutionary innovations receive tens or hundreds. Empirical support for this interpretation has arisen in various quarters: citation weighted patents have been found to correlate well with market value of the corporate patent holder, the likelihood of patent renewal and litigation, inventor perception of value, and other measures of innovation outputs.81

One potential weakness here is that it is often unclear what fraction of a nation’s innovation is actually patented, or to what degree selection bias exists in any given set of patent data. This problem is exacerbated when we consider that different countries may exhibit significant variance in their propensity to patent. However, at the national level, patents have also been found to correlate highly with other measures which we generally associate with aggregate innovation rates, including GDP growth, manufacturing growth, exports of capital goods, R&D spending, capital formation, Nobel Prize winners, scientific publications, etc.82 Therefore, although citations-weighted patents are by no means a perfect measure of innovation, and should always be corroborated by other measures wherever possible, they can be used with some confidence to judge the relative innovative performance of different countries.

80 Griliches 1984. 81 Trajtenberg 1990; Hall, Jaffe, and Trajtenberg 2000; Lanjouw and Shankerman 1997, 1999; Jaffe, Trajtenberg, and Fogarty 2000. 82 Amsden, Alice H. and Mona Mourshed “Scientific Publications, Patents and Technological Capabilities in Late-Industrializing Countries" Technology Analysis and Strategic Management 9(3) (1997).


Figure 5: Cases of Rising Innovation Rates Which Do Not Match Institutional Change Taiwan

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Figure 6: Cases of Positive Institutional Change But Stagnant Innovation Portugal

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Figure 7: Cases of Positive Institutions but Relative Decline in Innovation

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Figure 8: Largest Change in Market Institutions + Maximum Democracy

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Figure 9: Economic Institutions vs. Innovation

Base Level of Market Institutions v. Innovations

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Fraser Institute's Economic Freedom Index, 1975

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Figure 10: Demcratic Institutions vs. Innovation

Base Level of Democracy v. Innovation

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Figure 11: International Relationships vs. Innovation

Imports of Capital Goods vs. Innovation

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taylor

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