The Competitiveness of Nations
in a Global Knowledge-Based Economy
May 2003
Paul M. Romer
*
American Economic Review
Volume 86, Issue 2
May 1996, 202-206.
Index
III.
Neoclassical versus New Growth Theory
Whether new growth theory and economic history are a good match depends
on the kind of question one addresses and the kind of answer one expects. I find that they complement each other when I
try to answer questions about the world. Economists who believe that these lines of
inquiry can go their separate ways are addressing entirely different kinds of
questions or have a different notion of what it means to give a good answer.
Many recent attempts at testing models of growth proceed without making
any reference to evidence from economic history. They rely on data series for many countries,
typically for the last 30 or so years. They
focus on questions about models instead of questions about the world. A representative conclusion is that the right
model of economic growth is neoclassical in an extreme sense: it assumes that technology
is the same in all countries and concludes that exogenous differences in saving
and education cause all of the observed differences in levels of income and
rates of growth (N. Gregory Mankiw, 1995).
However, to take a specific case, differences in saving and education
do not explain why growth was so much faster in the United States than it was
in Britain around the turn of this century. In 1870, per capita income in the United
States was 75 percent of
per capita income in Britain. By 1929,
it had increased to 130 percent. In the
intervening decades, years of education per worker increased by a factor of 2.2
in Britain and by a nearly identical factor of 2.3 in the United States. In 1929, this variable remained slightly lower
in the United States. (Data
are taken from Angus Maddison [1995].) [1]
In addition, differences in rates of investment in the two countries
were not the result of exogenous differences in savings rates. The remarkable fact about the British economy
during this period is how much of domestic savings was devoted to investment
abroad. In the decade prior to 1913, net
domestic investment was roughly equal to net foreign investment (A. K. Cairncross, 1953 p. 121). By 1914, net foreign assets were equal to 1.5 times GDP. To understand what happened in Britain, one
must explain why investment abroad, especially in the United States, was so
attractive to British savers.
It is difficult to look at the data for these two countries without wondering
whether the well-documented technological developments in the United States are
not part of the story. Nevertheless, the
standard model-testing exercise does not even consider this possibility. Nor does it seek out any direct evidence that
would help one decide how important any differences in the technology might
have been. This would be a glaring flaw if the goal truly were to understand
events in the world, but it is as natural as a null hypothesis if all one wants
to do is test models.
A second approach recognizes the value of economic history but denies
the need for formal theory. It shows up
each time someone proposes a new piece of mathematical formalism. Only 30 years ago many economists still
objected to a mathematical statement of the relationship between output and
capital in terms of an aggregate production function and an aggregate stock of
capital, Y = F(K, L). Twenty
years ago, a different group of economists objected when labor economists used
mathematical equations and a new human-
* Department of Economics, University of California,
Berkeley, CA 94720. Comments by Gavin
Wright are gratefully acknowledged. This
work was supported by the Canadian Institute for Advanced Research.
1. Note added
in proof: Recent work by Claudia Goldin (pers. comm.) suggests that Maddison’s
data on education in the United States are flawed. Final judgment about the importance of
education should be withheld until better evidence becomes available.
202
capital variable H to capture the observation that a
person’s skills could be enhanced by investing in education or experience. Ten year ago, many economists readily
acknowledge that output of knowledge must somehow be related to the inputs
devoted to the production of knowledge, but they objected nevertheless when
growth theorists suggested that economists make another try at capturing these
relationships using mathematical expressions of the form dA/dt
= G(H, A).
Every time a familiar argument is translated for the first time from
natural language into mathematics, the same objections arise. “These equations are so simplistic, and the
world is so complicated.” This reflects
a misapprehension of the role of formal theory. Set aside models. The key is to understand what it means to
answer a question about the world In the
lead-up to his exposition of evolutionary theory, Richard Dawkins (1986 p. 11)
give a refreshingly straightforward description a what constitutes a good
answer to a such question:
If I ask an engineer how a steam engine works, I have
a pretty fair idea of the general kind of answer that would satisfy me. Like Julian Huxley, I should definitely not be
impressed if the engineer said that it was propelled by “force locomotif.” And
if he started boring on about the whole being greater than the sum of its
parts, I would interrupt him: “Never mind about that, tell me how it works.”
What I would want to hear is something
about how the parts of an engine interact with each other to produce the
behavior of the whole engine. I would
initially be prepared to accept an explanation in terms of quite large
subcomponents, whose own internal structure and behavior might be quite
complicated and, as yet, unexplained. The
units of an initially satisfying explanation could have names like fire-box,
boiler, cylinder, piston, steam governor. Given that the units each do their particular
thing, I can then understand how they interact to make the whole engine move.
Of course I am
then at liberty to ask how each part works. Having previously accepted the fact that
the steam governor regulates the flow of steam, and having used this fact in my
understanding of the behavior of the whole engine, I now turn my curiosity on
the steam governor itself.
The central element in this account of what Dawkins calls hierarchical
reductionism is a recognition that explanation operates on many levels that
must be consistent with each other. What
theories do is take all the available complicated information about the world
and organize it into this kind of hierarchical structure.
In building this structure, good theory indicates how to carve a system
at the joints. At each level, theory
breaks a system down into a simple collection of subsystems that interact in a
meaningful way. Dawkins could have used
a simple theory that makes a bad split of the engine into its front and back
halves. Instead, he uses a simple theory
that makes a good split into the fire-box, the boiler, and so on. What growth
theory must do is provide a good, simple split of the opportunities available
in the physical world.
III. Neoclassical versus New Growth Theory
Neoclassical growth theory explains growth in terms of interactions
between two basic types of factors: technology and conventional inputs. At the next level, conventional inputs are
subdivided into physical capital, labor, and human capital. The initial split into technology and
conventional inputs is promising, because technology does differ from all other
inputs. However, for technical reasons,
neoclassical theory mapped this split onto the theoretical dichotomy between
public and private goods. This means
that the theory leads to a dead end when one tries to understand the details
about technology in a second-stage analysis analogous to Dawkins’s
investigation of the steam governor. Technology
in the model does not correspond to anything in the world. It is possible to understand capital in terms
of things like machine tools that can be observed, but for a description of
technology, neoclassical theory only relates to things that live in models - shifting
production possibility frontiers and the like.
The obvious real-world candidates for technology simply are not public
goods. For example, a promising line of
work in the 1960’s
203
studied embodied technological change. Implicitly, it modeled technology as designs
for machines. This line of work lost its
momentum, perhaps because of the difficulty people had in reconciling what is
known about machine design with an initial cut that makes technology a public
good. In their evolutionary alternative
to neoclassical growth theory, Richard Nelson and Sidney Winter (1982) rejected
the public-good assumption and represented technology as routines followed
within firms. Recent generations of
neoclassical growth theorists have not followed up on either approach and have
contented themselves with a force locomotjf explanation:
“Technological change causes economic growth.”
New growth theory started on the technology-as-public-good path and
worried about where technology came from, but it soon backed up and reconsidered
the initial split that economists make in the physical world. New growth theorists now start by dividing the
world into two fundamentally different types of productive inputs that can be
called “ideas” and “things.” Ideas are nonrival goods that could be stored in a bit string. Things are rival goods with mass (or energy). With ideas and things, one can explain how
economic growth works. Nonrival ideas can be used to rearrange things, for
example, when one follows a recipe and transforms noxious olives into tasty and
healthful olive oil. Economic growth
arises from the discovery of new recipes and the transformation of things from
low to high value configurations.
This slightly different initial cut leads to insights that do not
follow from the neoclassical model. It
emphasizes that ideas are goods that are produced and distributed just as other
goods are. It removes the dead end in
neoclassical theory and links microeconomic observations on routines, machine
designs, and the like with macroeconomic discussions of technology.
In an analysis of American and British growth, the insight that is most
relevant concerns scale. By definition,
a nonrival idea can be copied and communicated, so
its value increases in proportion to the size of the market in which it can be
used. For example, if barriers to trade
meant that a computer operating system written in the state of Washington could
only be used within that state, it would be worth far less than if it could be
used all over the world. If there were
only a few olive trees, no one would have bothered to figure out how to use the
olives. If people can sometimes establish
property rights over a nonrival good like an operating
system or a recipe (a possibility precluded by the public-good approach)
differences in scale will change the rewards for producing new ideas.
A great deal of historical analysis has addressed the performance of
the British and American economies around the turn of the century. For general discussions, see Nathan Rosenberg
(1981), Nelson and Gavin Wright (1992), and Moses Abramovitz
and Paul David (1996). From the
beginning, observers have pointed to the abundance of natural resources in the
United States as an early advantage, especially in agriculture. The surprising conclusion that emerges from
recent historical scholarship is that resource abundance also interacted with
scale to create a technological lead in manufacturing that persisted well into
the 20th century.
The United States started as little more than an
importer of European technology, but by the first decades of the 19th century,
distinctively American technologies began to emerge. Entrepreneurs and inventors developed specialized
machines that economized on human effort and made prolific use of the natural
resources and energy that were available (Rosenberg, 1981). Other nations in the new world also faced low
prices for natural resources relative to labor. For example, Maddison’s
(1995) data suggest that Australia had the highest level of GDP per capita from
1870 to 1900 because its stock of resources was so large relative to its population.
What made the United States unique was
the combination of resource abundance and large markets (Abramovitz
and David, 1996). In 1820, the
population was 534,000 in
Argentina, 33,000 in Australia, and 9.6 million in the United States. Moreover, even at this early date, the United
States had a transportation system and a commercial infrastructure that
effectively linked most of its citizens into a truly national market. By 1870, the population had grown to 1.8 mil-
204
lion in Argentina, 1.6 million in Australia, and 40 million
in the United States, a third more than lived in the United Kingdom at that
time.
As Rosenberg (1963, 1981) has observed, large markets - which were also
populated here by relatively homogeneous consumers - mattered, because they
encouraged firms to incur the design and setup costs necessary for long
production runs of standardized goods assembled from interchangeable parts. As he emphasizes, they also mattered because
they induced large markets for specialized machines. The differences in incentives created by
market size were presumably of great consequence when populations differed by a
factor of 10 or 20 and flows of goods between nations were still relatively
limited. More direct evidence that
market size and incentives did matter for invention can be inferred from
Kenneth Sokoloff ‘s (1988) evidence on the geographic distribution of patent
awards in the United States. His data
show that inventive activity was concentrated around locations that had access
to cheap transportation, and that it expanded into new areas when the transportation
system improved.
Resource abundance and scale effects were therefore key elements in the
development of production using specialized machinery, standardized goods, and
interchangeable parts. By the middle of the
19th century, when the British first started to take notice, this system was
used in only a few industries, gun-making most famously. Other important industries in the United
States, such as iron-making, still lagged behind their British counterparts. It took another half century or more for per
capita output in the United States to move ahead of Britain’s. Scale effects continued to be crucial in this
later period as well.
In the beginning, machinery was made in machine shops that were part of
large manufacturing enterprises like textile mills. When markets grew, these shops eventually separated
from their parent firms and began to operate as suppliers to many firms. However, the growth in potential markets came
not just through growth in the industry of the parent firm. Most of it came from growth in other
industries because of what Rosenberg (1963) has identified as a process of
technological convergence which created an additional scale effect distinct
from the one associated with population size. Firms engaged in the production of many different
kinds of goods (including machine tools themselves) all used the same kinds of
machinery to shape first wood, then metal. Thus, the former machine shop of the textile
mill sold not just to other textile firms, but to all manner of manufacturing
enterprises. As a result, the
proliferation of specialized machine tools was limited only by the extent of
what came to be a very large market.
Thus, scale acted through larger markets for both final goods and
capital goods. Scale in this sense was
determined by a large population, an integrated market, and technological
convergence. A large quantity of natural
resources was important initially because it changed the price of materials
relative to labor, thus encouraging the use of machinery. Over time, abundant quantities of potential
natural resources created an additional scale effect relating to the supply
of things that could be transformed by any particular new idea. This effect was most obvious in the
development of uses for by-products (Rosenberg, 1985). For example, the quantity of animal waste grew
with the expansion of the meat-packing industry. Its geographic concentration also increased as
refrigeration and the railroad made it possible for meat-packing to be
separated from the site of final consumption. This increase in the volume of animal byproducts
and its concentration created incentives for firms to come up with new nonrival goods - literally, in this case, new recipes - for
making use of raw materials that had previously been discarded as waste. This process ultimately led to the development
of a by-products industry that was one of the early users of industrial
chemistry.
The same motivation led to the investments that were needed to take
advantage of other natural resources. Because
of the quantities of resources that were available and the large markets for
goods, large investments in basic technologies for extracting and processing
these resources could be sustained. This
enabled the United States to become the world’s leading supplier of virtually
every industrial raw material, a fact that is reflected in high and increasing
intensity of resources in U.S. exports from 1880 to 1930 (Wright, 1990). With the exceptions of wood and land, the
United States achieved leadership in most raw mate-
205
rials because of its intensive use
of its endowment, not because of the endowment itself (Wright, 1990). Because of the “congruence” (in the
terminology adopted by Abramovitz and David [1996]) between
the U.S. strength in intensive resource use and its early strength in
manufacturing technologies, it developed a technological lead over the rest of
the world that expanded throughout the first half of this century (Nelson and
Wright, 1992).
Scale effects are clearly not the only interesting factor in this
story. For example, new institutions
like the United States Geological Survey, the private university, the large multidivisional
firm, and the specialized research laboratory were important as well. Concerning the scale effects themselves, the
arguments presented here will not tell historians anything they did not already
know. The relatively modest contribution
that new growth theory can make is to move the issue of scale up in the
conceptual hierarchy. Scale effects
should no longer be treated in the manner of a growth accountant like Edward
Dennison, (i.e., as a kind of afterthought that had something to do with plant
size). They should be treated in the
manner of Adam Smith: as a fundamental aspect of our economic world that
follows from the nonrival character of ideas.
If new growth theorists have their way, the first distinction
economists will draw when looking at the physical world will be the one that
separates rival things from nonrival ideas. Right from the start, this should be the way
the physical world is carved up into a small number of interacting elements
analogous to pistons and boilers. When
the resulting theoretical framework is combined with the evidence and
inferences from economic history, economists will be able to give a more
convincing answer to the question of how industrial growth works and why it
emerged first in America.
Abramovitz, Moses and David,
Paul A. “Convergence and Deferred Catch-up: Productivity Leadership and the
Waning of American Exceptionalism,” in Ralph Landau,
Timothy Taylor, and Gavin Wright, eds., The
mosaic of economic growth. Stanford, CA: Stanford University Press, 1996,
pp. 21-62.
Cairncross, A. K. Home and foreign
investment 1970-1913. Cambridge: Cambridge University Press, 1953.
Dawkins,
Richard. The blind watchmaker. New York:
Norton, 1986.
Maddison,
Angus. Monitoring the world economy, 1820-1992.
Paris: Organization for Economic Cooperation and Development, 1995.
Mankiw, N. Gregory. “The Growth of Nations.” Brookings Papers on Economic
Activity, 1995, (1), pp. 275-326.
Nelson,
Richard R. and Winter, Sidney. An
evolutionary theory of economic change. Cambridge, MA:
Belknap, 1982.
Nelson, Richard R. and Wright, Gavin. “The Rise and Fall of
American Technological Leadership: The Postwar Era in Historical Perspective.” Journal
of Economic Literature, December 1992, 30(4), pp. 1931-64.
Rosenberg,
Nathan. “Technological Change in the Machine Tool Industry, 1840-1910.” Journal of Economic History, December 1963, 23(4), pp.
414-43; reprinted in Perspectives on technology. Armonk,
NY: Sharpe, 1976, pp. 9-31.
______________ . “Why in America?” in Otto Mayr and Robert C. Post, eds., Yankee enterprise, the
rise of the American system of manufactures. Washington, DC: Smithsonian
Institution Press, 1981; reprinted in Exploring
the black box. Cambridge: Cambridge University Press, 1995.
______________ . “The
Commercial Exploitation of Science by American Industry,” in Kim B. Clark,
Robert H. Hayes, and Christopher Lorenz, eds., The
uneasy alliance. Boston: Harvard Business School Press, 1985, pp. 18-51.
Sokoloff, Kenneth L. “Inventive
Activity in Early Industrial America: Evidence from Patent Records.” Journal
of Economic History, December 1988, 48(4), pp. 8 13-50.
Wright, Gavin.
“The Origins of American Industrial Success, 1879-1940.” American Economic
Review, September 1990, 80(4), pp. 651-68.
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The Competitiveness of Nations
in a Global Knowledge-Based Economy
May 2003