The Competitiveness of Nations in a Global Knowledge-Based Economy
Paul M.
Romer *
The Origins of Endogenous Growth
The Journal of Economic Perspectives, 8
(1
Winter 1994, 3-22.
|
Extracts
Version #1: The
Convergence Controversy
An Evaluation of the
Convergence Controversy |
The phrase “endogenous growth” embraces a diverse body
of theoretical and empirical work that emerged in the 1980s. This work distinguishes itself from
neoclassical growth by emphasizing that economic growth is an endogenous
outcome of an economic system, not the result of forces that impinge from
outside. For this reason, the
theoretical work does not invoke exogenous technological change to explain why
income per capita has increased by an order of magnitude since the industrial
revolution. The empirical work does not
settle for measuring a growth accounting residual that grows at different rates
in different countries. It tries instead
to uncover the private and public sector choices that cause the rate of growth
of the residual to vary across countries. As in neoclassical growth theory, the focus in
endogenous growth is on the behavior of the economy as a whole. As a result, this work is complementary to,
but different from, the study of research and development or productivity at
the level of the industry or firm.
This paper recounts two versions that are told of the
origins of work on endogenous growth. The
first concerns what has been called the convergence controversy.
The second concerns the struggle to
construct a viable alternative to perfect competition in aggregate-level
theory. These accounts are not surveys. They are descriptions of the scholarly
equivalent to creation myths, simple stories that economists tell themselves
and each other to give meaning and structure to their current research efforts.
Understanding the differences between
these two stories matters because they teach different lessons about the
relative importance of theoretical work and empirical work in economic analysis
and they suggest different directions for future work on growth.
* Paul M. Romer is Professor of Economics,
University of California, Berkeley, California.
3
Version #1: The
Convergence Controversy
The question that has attracted the most attention in
recent work on growth is whether per capita income in different countries is
converging. A crucial stimulus to work
on this question was the creation of new data sets with information on income
per capita for many countries and long periods of time (Maddison,
1982; Heston and Summers, 1991).
In his analysis of the Maddison
data, William Baumol (1986) found that poorer
countries like Japan and Italy substantially closed the per capita income gap
with richer countries like the United States and Canada in the years from 1870
to 1979. Two objections to his analysis
soon became apparent. First, in the Maddison data set, convergence takes place only in the
years since World War II. Between 1870
and 1950, income per capita tended to diverge (Abramovitz,
1986). Second, the Maddison
data set included only those economies that had successfully industrialized by
the end of the sample period. This
induces a sample selection bias that apparently accounts for most of the
evidence in favor of convergence (De Long, 1988).
As a result, attention
then shifted to the broad sample of countries in the Heston-Summers
data set. …
4
4-6-7-8-9
The implication from this work is that if you are
committed to the neoclassical mode, the kind of data exhibited in Figures 1 and
2 [HHC: figures not displayed] cannot be used to make you recant. They do not compel you to give up the
convenience of a model in which markets are perfect. They cannot force you to address the
complicated issues that arise in the economic analysis of the production and
diffusion of technology, knowledge, and information.
An Evaluation of
the Convergence Controversy
The version of the development of endogenous growth
theory outlined above skips lots of detail and smooths
over many complications that made this seem like a real controversy at the
time. In retrospect, what is striking is
how little disagreement there is about the basic facts. Everyone agrees that a conventional
neoclassical model with an exponent of about one-third on capital and about
two-thirds on labor cannot fit the cross-country or cross-state data. Everyone agrees that the marginal product of
investment cannot be orders of magnitudes smaller in rich countries than in
poor countries. The differences between
the different researchers concern the inferences about models that we should
draw from these facts. As is usually the
case in macroeconomics, many different inferences are consistent with the same
regression statistics.
10
This history has many elements in common with other
stories about the development of economics. The story starts with the emergence of new
data. These present anomalies that lead
to new theoretical models, some of which differ markedly from previous,
well-accepted models. Then a more
conservative interpretation emerges that accommodates the new evidence and
preserves much of the structure of the old body of theory. In the end, we have refined the set of
alternatives somewhat, but seem to be left in about the same position where we
started, with too many theories that are consistent with the same small number
of facts.
But economists who accept this interpretation come to
the conclusion that we do not have enough data only because they restrict
attention to the kind of statistical evidence illustrated in Figures 1 and 2. They fail to take account of all the other
kinds of evidence that are available. My
original work on growth (Romer, 1983; 1986) was
motivated primarily by the observation that in the broad sweep of history,
classical economists like Malthus and Ricardo came to
conclusions that were completely wrong about prospects for growth. Over time, growth rates have been increasing,
not decreasing. [4] Lucas (1988) emphasized the fact that international
patterns of migration and wage differentials are very difficult to reconcile
with a neoclassical model. If the same
technology were available in all countries, human capital would not move from
places where it is scarce to places where it is abundant and the same worker
would not earn a higher wage after moving from the Philippines to the United
States.
The main message of this paper is that the convergence
controversy captures only part of what endogenous growth has been all about. It may encompass a large fraction of the
recently published papers, but it nevertheless represents a digression from the
main story behind endogenous growth theory. The story told about the convergence
controversy also tends to reinforce a message that I think is seriously
misleading - that data are the only scarce resource in economic analysis.
Version #2: The
Passing of Perfect Competition
The second version of the origins of endogenous growth
starts from the observation that we had enough evidence to reject all the
available growth models throughout the 1950s, 1960s, and 1970s. What we lacked were good aggregate-level
models. This version of the origins of
endogenous growth is therefore concerned with the painfully slow progress we
have made in constructing formal economic models at the aggregate level. It suggests that progress in economics does
not come merely from the mechanical application of hypothesis tests to data
sets. There is a creative act associated
with the construction of new models that is also crucial to the process.
4. See Kremer (1993)
for a stimulating look at this question from a very long-run point of view.
11
The evidence about growth that economists have long
taken for granted and that poses a challenge for growth theorists can be
distilled to five basic facts.
Fact #1: There are many firms in a market economy. The
fact is so obvious that we often do not bother to state it, but it clearly will
not do to have a model in which there are overwhelming forces that tend to
concentrate all output in the hands of a single, economy-wide monopolist.
Fact #2: Discoveries differ from other inputs in the
sense that many people can use them at the same time. The
idea behind the transistor, the principles behind internal combustion, the
organizational structure of a modern corporation, the concepts of double entry
bookkeeping - all these pieces of information and many more like them have the
property that it is technologically possible for everybody and every firm to
make use of them at the same time. In
the language of public finance, ordinary goods are rival goods, but information
is nonrival.
Fact #3: It is possible to replicate physical
activities. Replication implies that the aggregate production
function representing a competitive market should be characterized by
homogeneity of degree one in all of its conventional (that is, rivals inputs.
If we represent output in the form Y = AF(K,
H, L), then doubling all three of K, H, and L should allow a
doubling of output. [HHC: A is the level of technology; H
is human capital] There is no need to
double the nonrival inputs represented by A because
the existing pieces of information can be used in both instances of the
productive activity at the same time. (The assumption that the market is competitive
means that the existing activity already operates at the minimum efficient
scale, so there are no economies of scale from building a single plant that is
twice as large as the existing one.)
If farming were the relevant activity instead of
manufacturing, we would clearly need to include land as an input in production,
and in the economy as a whole, it is not possible to double the stock of land. This does not change the fundamental
implication of the replication argument. If aggregate output is homogeneous of degree 1
in the rival inputs and firms are price takers, Euler’s theorem implies that
the compensation paid to the rival inputs must exactly equal the value of
output produced. This fact is part of
what makes the neoclassical model so simple and makes growth accounting work. The only problem is that this leaves nothing
to compensate any inputs that were used to produce the discoveries that lead to
increases in A.
Fact #4: Technological advance comes from things that
people do. No economist, so far as I know, has ever been willing
to make a serious defense of the proposition that technological change is
literally a function of elapsed calendar time. Being explicit about the issues here is
important nevertheless, because it can help untangle a link that is sometimes
made between exogeneity and randomness. If I am prospecting for gold or looking for a
change in the DNA of a bacterium that will let it eat the oil from an oil
spill, success for me will be dominated by chance. Discovery will seem to be an exogenous event
in the
12
sense
that forces outside of my control seem to determine whether I succeed. But the aggregate rate of discovery is
endogenous. When more people start
prospecting for gold or experimenting with bacteria, more valuable discoveries
will be found. This will be true even if
discoveries are accidental side effects of some other activity (finding gold as
a side effect of ditch-digging) or if market incentives play no role in
encouraging the activity (as when discoveries about basic molecular biology
were induced by government research grants). The aggregate rate of discovery is still
determined by things that people do.
Fact #5: Many individuals and firms have market power
and earn monopoly rents on discoveries.
Even though the information from discoveries is nonrival (as noted in fact 2), economically important
discoveries usually do not meet the other criterion for a public good; they
typically are partially excludable, or excludable for at least some period of
time. Because people and firms have some
control over the information produced by most discoveries, it cannot be treated
as a pure public good. This information
is not like a short-wave radio broadcast that everyone can access without the
permission of the sender. But if a firm
can control access to a discovery, it can charge a price that is higher than
zero. It therefore earns monopoly
profits because information has no opportunity cost.
The neoclassical model that was developed and applied
by Robert Solow (1956, 1967) and others constituted a
giant first step forward in the process of constructing a formal model of
growth. The discussion of the
convergence controversy, framed as it was almost entirely in terms of the
neoclassical model, illustrates the model’s power and durability. Like any model, the neoclassical model is a
compromise between what we would like from a model and what is feasible given
the state of our modeling skills. The
neoclassical model captured facts 1, 2, and 3, but postponed consideration of
facts 4 and 5. From a theoretical point
of view, a key advantage of the model is its treatment of technology as a pure
public good. This makes it possible to
accommodate fact 2 - that knowledge is a nonrival
good - in a model that retains the simplicity of perfect competition. The public good assumption also implies that
knowledge is nonexcludable, and this is clearly
inconsistent with the evidence summarized in fact 5 - that individuals and
firms earn profits from their discoveries. This assumption was useful, nevertheless, as
part of an interim modeling strategy that was adopted until models with nonrivalry and excludability could be formulated.
Endogenous growth models try to take the next step and
accommodate fact 4. Work in this
direction started in the 1960s. For
example, Karl Shell (1966) made the point about replication noted above,
showing that it left no resources to pay for increases in A. He proposed a model in which A is
financed from tax revenue collected by the government. Recent endogenous growth models have tended to
follow Arrow (1962) and emphasize the private sector activities that contribute
to technological advance rather than public sector funding for research. A subset of these models has tried to
incorporate
13
both fact
4 (that technological advance comes from things people do) and fact 5 (the
existence of monopoly rents). These are
sometimes referred to as neo-Schumpeterian models because of Schumpeter’s
emphasis of the importance of temporary monopoly power as a motivating force in
the innovative process. [5] In addition, there are two other distinct kinds of endogenous growth
models. Spillover models have already
been mentioned. Linear models will be
described below. [6]
With the benefit of hindsight, it is obvious that
growth theorists would eventually have to do what economists working at the
industry and firm level have done: abandon the assumption of price-taking
competition. Otherwise, there is no hope
of capturing fact 5. Even at the time,
the point received at least some attention. In his 1956 paper, Solow
remarked in a footnote on the desirability of extending the model to allow for
monopolistic competition. One of his
students, William Nordhaus (1969), subsequently
outlined a growth model that did have patents, monopoly power, and many firms. For technical reasons, this model still
invoked exogenous technological change, so it is not strictly speaking a model
of endogenous growth - but it could have been extended to become one. Because a general formal treatment of
monopolistic competition was not available at the time, little progress in this
direction took place for the next 20 years.
Even though it is obvious in retrospect that
endogenous growth theory would have to introduce imperfect competition, this
was not the direction that the first models of the 1980s pursued. Both my model (1986) and Robert Lucas’s model
(1988) included fact 4 without taking the final step and including step 5. In both of these models, the technology is
endogenously provided as a side effect of private investment decisions. From the point of view of the users of
technology, it is still treated as a pure public good, just as it is in the
neoclassical model. As a result, firms
can be treated as price takers and an equilibrium with
many firms can exist.
This technique for introducing a form of aggregate
increasing returns into a model with many firms was first proposed by Alfred
Marshall (1890). To overturn the
pessimistic predictions of Malthus and Ricardo, he
wanted to introduce some form of aggregate increasing returns. To derive his downward sloping supply curve
from an industry with many firms, Marshall introduced the new notion of
increasing returns that were external to any individual firm. External effects therefore entered into
economics to preserve the analytical
5. Of course, Stigler’s
law applies in this case: The person that any result is named after was not the
first person to derive or state the result. It just helps to have a label so that you can
keep track of the players without a scorecard.
6. Richard Nelson and
Sidney Winter (1982) developed an alternative evolutionary model of growth. Their verbal, descriptive style of theory,
which they label appreciative theory, was flexible enough to accommodate facts
1-5. This style of work can be thought
of as a complement to formal theory, not a substitute for it. It leaves open the problem of constructing a
formal theory that could accommodate these facts.
14
machinery
of supply and demand curves and price taking in the presence of increasing
returns. The analysis of other kinds of
external effects - smoke, bees, and so on - came later. [7]
As noted in the previous discussion of spillover
models, Arrow (1962) constructed a model along these lines… For technical
reasons, Arrow, like Nordhaus, did not emphasize the
fact that his model could lead to sustained, endogenous growth. For the parameter values that he studies, if
the size of the population is held constant, growth eventually comes to a halt.
Lucas’s model has a very similar underlying structure. There, it is investments in human capital
rather than physical capital that have spillover effects that increase the
level of the technology… Both of these models accommodated facts 1-4 but
not fact 5. [8]
In my first paper on growth (Romer,
1986), I assumed… that it is spillovers from private research efforts that lead
to improvements in the public stock of knowledge A. This seemed appealing because it
recognized that firms did research and development on purpose and that the
relevant spillovers or incomplete property rights were associated with the
results from research and development. (In the microeconomic analysis of research and development at the
industry level, Zvi Griliches
(1979) used this same kind of formulation.) But to make this model fit within the
framework of price-taking with no monopoly power, I assumed that… research is a
nonrival good and fact 3, that only rival goods need
to be replicated to double output…
My sleight of hand… may seem like a trifling matter in
an area of theory that depends on so many other short cuts. After all, if one is
7. For an explicit
treatment showing that Marshallian external
increasing returns is ultimately an untenable way to model any process
involving learning or knowledge, see Dasgupta and Stiglitz (1988).
8. Lucas actually makes
A depend on per capita H rather than total H. ‘The
difference between these two formulations is not relevant for the discussion
here, but is important for some of the other implications of the model.
9. For consistency with
the rest of the discussion, I distinguish here between R and K. In
the paper, I actually dropped physical capital from consideration so that I
have only one state variable to deal with. This leads to a potential confusion
because I also used the symbol K for knowledge instead of
R.
15
going to do violence to the complexity of economic activity by assuming
that there is an aggregate production function, how much more harm can it do to
be sloppy about the difference between rival and nonrival
goods? Unfortunately,
quite a bit. The distinctions
between rival and nonrival inputs, and the
distinction between excludable and nonexcludable
goods, are of absolutely fundamental importance in modeling and in policy
formulation.
For years, the economic analysis of science and
technology policy consisted of little more than a syllogism. The major premise was that the government
should provide public goods and the private sector should provide private
goods. The minor premise was that basic
research is a public good and applied research is a private good. Once you think carefully about nonrivalry and excludability, it is clear that the major
premise is misleading because it understates the possible role for collective
action. Governments can usefully provide
goods that are nonrival but are not true public
goods, because they are potentially excludable.
The minor premise is simply wrong. Applied research is not an ordinary private
good. Discussion in policy circles is
now taking place using new terms - critical technologies, generic research, and
pre-competitive research - that are only vaguely defined but that take the
discussion outside of the simple dichotomy between public goods and private
goods. This is probably useful, but it
would lend needed structure to this discussion if participants paid more
attention to the distinction between the two different aspects of publicness (nonrivalry and nonexcludability) and looked more formally at the different
kinds of policy challenges that nonrivalry and nonexcludability present.
The linear model branch of endogenous growth theory
pursued even more aggressively the strategy I used. [10]
If I could treat the part of knowledge that firms
control as an ordinary input in production - that is, as an input that is rival
and hence is not associated with increasing returns - why bother to allow for
any nonrival inputs at all? In effect… These
models assumed that research R, physical capital K, and human
capital H were like ordinary inputs. If there are no nonrival
goods, there are no increasing returns. It is then a relatively simple matter
to build a perfectly competitive model of growth. To simplify still further,
these models often aggregate R, K, and H into a single broad
measure of capital. Suppose we call it X… Relative to the neoclassical model,
these models capture fact 4 - that technological change comes from investments
that people make - at the cost of abandoning fact 2, that technology or
knowledge is a nonrival good.
Proponents of the linear model and the neoclassical
model have sometimes been drawn into pointless arguments about which model is
worse. Proponents
10. One of the early
linear models was Uzawa (1965). Important recent papers in this line of
work include Becker, Murphy, and Tamura (1990), Jones and Manuelli
(1990), and Rebelo (1991).
16
of the
linear growth models point out that the neoclassical model fails to capture
fact 4. Proponents of the neoclassical
model observe that the linear model cannot capture fact 2…
This is not a very useful debate. There are circumstances in which each model
can be a useful expositional device for highlighting different aspects of the
growth process, but presumably the agenda for the profession ought to be to
capture both facts 2 and 4 and pick up fact 5 to boot.
Two steps were required for the neo-Schumpeterian
models of growth to emerge. The first
was that after struggling for years to preserve perfect competition, or at
least price-taking in the presence of external effects, growth theorists had to
decide to let go. It helped that
economists working on industrial organization had given them something else to
hang onto. By the late 1970s, there were
aggregate models with many firms (fact 1), each of which could have market
power (fact 5). The most convenient such
model was developed by Avinash Dixit
and Joseph Stiglitz (1977). William Ethier
(1982) subsequently showed how their model of preferences over many goods could
be interpreted as a production function that depended on a large number of
inputs in production.
Once people who were interested in growth recognized
that this approach offered the alternative to a competitive market structure,
there was only one technical detail that remained to be resolved, the detail
that had kept both Nordhaus and Arrow from producing
models of endogenous growth. All models
of growth need at least one equation which describes the evolution of something
like A(t). [11]…
Mathematically, this kind of formulation is not robust...
When we use this same kind of model to study population growth, this lack of
robustness does not raise any particular
11. Sometimes other
variables like H or K are used in place of A, but the
basic issues are the same.
17
difficulties.
We understand that functional forms are
always approximations, and that a linear differential equation leading to
exponential growth is a particularly convenient approximation. But Nordhaus and
Arrow both worked at a time when there was real concern about the knife-edge
character of the assumptions about φ [HHC: where φ
is the exponential growth rate of A, or the level of technology]. [12] If it was less than one, growth eventually stopped. If it was even slightly greater than one,
everything blows up. As a result,
economists stayed well away from the edge and assumed… [it]… to be strictly less than 1. In a model like Nordhaus’s,
growth can be kept going only by adding a second kind of knowledge A2 that
grows exogenously…
By the late 1980s, economists like Kenneth Judd (1985)
and Gene Grossman and Elhanan Helpman
(1989) were working out models of growth with monopolistic competition. Like Nordhaus and
Arrow, they stayed well away from the case where… [it]… was equal to 1. Judd invoked exogenous technological change to
keep his economy growing. Grossman and Helpman were investigating the connection between trade and
growth, and settled for an analysis of transitional dynamics of the model as it
converged to a steady state level of income where growth stopped. In each model, monopoly profits motivate
discovery…
12, See
Stiglitz (1990) for a discussion of how people
working on growth at the time perceived this problem.
18
…
Research on endogenous growth models in which
monopoly profits motivate innovation has progressed rapidly since then and has
uncovered a number of unexpected connections between market size, international
trade, and growth, as the article by Grossman and Helpman
in this symposium explains.
The economics profession is undergoing a substantial
change in how we think about international trade, development, economic growth
and economic geography. [13] In each of these areas, we have gone through a progression that starts
with models based on perfect competition, moves to price-taking with external
increasing returns, and finishes with explicit models of imperfect competition.
It is likely that this pattern will
repeat itself in other areas like the theory of macroeconomic fluctuations.
The effects of this general trend may be far-reaching. Ultimately, it may force economists to
reconsider some of the most basic propositions in economics. For example, I am convinced that both markets
and free trade are good, but the traditional answer that we give to students to
explain why they are good, the one based on perfect competition and Pareto
optimality, is becoming untenable. Something more interesting and more
complicated is going on here. [14]
In each of the areas where our understanding has
changed, evidence that challenged the models of perfect competition and
supported the models with imperfect competition had been apparent all along. Everyone knew that there was lots of
intra-industry trade between developed nations and little trade between the
North and the South. Everyone knew that
some developing countries grew spectacularly while others languished. Everyone knew that people do the things that
lead to technological change. Everyone
knew that the number of locally available goods was limited by the extent of
the market in the city where someone lives and works.
In evaluating different models of growth, I have found
that Lucas’s (1988) observation, that people with human capital migrate from
places where it is scarce to place where it is abundant, is as powerful a piece
of evidence as all the cross-country growth regressions combined. But this kind of fact, like the fact about
intra-industry trade or the fact that people make discoveries, does not come
with an attached t-statistic. As a
result, these kinds of facts tend to be
13. Paul Krugman has made influential contributions in all of these
areas. See Krugman
(1990, 1991, 1993) for a discussion of the changes in
these fields.
14. Romer
(forthcoming) offers a demonstration that, for example, the costs of trade
restrictions in a developing country can be far greater in the context of a
model with imperfect competition than they are in a model with perfect
competition.
19
neglected
in discussions that focus too narrowly on testing and rejecting models.
Economists often complain that we do not have enough
data to differentiate between the available theories, but what constitutes
relevant data is itself endogenous. If
we set our standards for what constitutes relevant evidence too high and pose
our tests too narrowly, we will indeed end up with too little data. We can thereby enshrine the economic orthodoxy
and make it invulnerable to challenge. [15] If we do not have any models that can
fit the data, the temptation will be to set very high standards for admissible
evidence, because we would prefer not to reject the only models that we have.
When I look back on my work on growth, my greatest
satisfaction comes from having rejected the first round of external effects
models that I tried. I am glad that I
was able to learn something about robustness and nonrivalry
from struggling with these models, but was still able to let go when a better
alternative became apparent. My greatest
regret is the shift I made while working on these external effects models, a
shift that took me away from the emphasis on research and knowledge that
characterized my 1986 paper and toward the emphasis on physical capital that
characterized the empirical work in the paper cited in the discussion of
convergence (1987a). This paper
contributed to the convergence controversy and to an emphasis on the exponents
on capital and labor in aggregate production. I am now critical of this work, and I accept
part of the blame. Looking back, I
suspect that I made this shift toward capital and away from knowledge partly in
an attempt to conform to the norms of what constituted convincing empirical
work in macroeconomics. No international
agency publishes data series on the local production of knowledge and inward
flows of knowledge. If you want to run
regressions, investment in physical capital is a variable that you can use, so
use it I did. I wish I had stuck to my
guns about the importance of evidence like that contained in facts 1 through 5.
If macroeconomists look only at the cross-country
regressions deployed in the convergence controversy, it will be easy to be
satisfied with neoclassical models in which market incentives and government
policies have no effect on discovery, diffusion, and technological advance . But if we make use of all of the available evidence,
economists can move beyond these models and begin once again to make progress
toward a complete understanding of the determinants of long-run economic
success. Ultimately, this will put us in
position to offer policy-makers something more insightful than the standard
neoclassical prescription - more saving and more schooling. We will be able to rejoin the ongoing policy
debates about tax subsidies for private research, antitrust exemptions for
research joint ventures, the activities of multinational firms, the
15. In their discussion
of real business cycle theories and the kind of evidence used to test them,
Greg Mankiw (1989) and Robert Solow
(1988) have both made a similar point about explicit statistical versus broader
kinds of evidence.
20
effects of government procurement, the feedback between trade policy
and innovation, the scope of protection for intellectual property rights, the
links between private firms and universities, the mechanisms for selecting the
research areas that receive public support, and the costs and benefits of an
explicit government-led technology policy. We will be able to address the most important
policy questions about growth: In a developing country like the Philippines,
what are the best institutional arrangements for gaining access to the
knowledge that already exists in the rest of the world? In a country like the United States, what are
the best institutional arrangements for encouraging the production and use of
new knowledge?
I have benefitted
from comments by Jeffrey Frankel, Alan Krueger, David Romer,
Carl Shapiro, and Timothy Taylor on early drafts of this paper. This work was supported by NSF Grant #SES
9023469 and by the Canadian Institute for Advanced Research.
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