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Econ ArticlesCreated 2/21/1996 |
J. Bradford DeLong
U.S. Department of the Treasury, and University of
California at Berkeley
Lawrence H. Summers
U.S. Department of the Treasury
Received March 1993, final version received September 1993
ABSTRACT
We extend and improve the database used in DeLong and Summers [1991]
and, focusing on developing economies, find that there is a very
strong growth-equipment investment association even when rich,
industrialized economies are not considered. Rapid growth is found
where equipment investment is high, and slow growth where equipment
investment is low. If there is a region where the post-WWII
growth-equipment nexus is weak, it is the well-integrated and very
rich region of western Europe--not the developing world.
Correspondence to: Lawrence H. Summers, Under Secretary of the
Treasury for International Affairs, 1500 Pennsylvania Avenue,
Washington, D.C. 20220
*The opinions expressed here are the opinions of the authors alone,
and are not the views or positions of any agency of the U.S.
government. We would like to thank Robert Barro, Robert Hall, Chad
Jones, Steven King, Jong-Wha Lee, Lant Pritchett, Paul Romer, Robert
Summers, and Robert Waldmann among others for helpful
discussions.
Running Title: Equipment Investment in Developing
Countries
Key Words: Growth, Aggregate Productivity, Development,
Investment
JEL Category Numbers: O11 O30 O40
I. Introduction
This paper continues the research project we began in "Equipment
Investment and Economic Growth" (DeLong and Summers [1991]). Our
1991 paper used data from Summers and Heston [1988, 1991], and from
detailed benchmark estimates of national economy price and quantity
structures from the U.N. International Comparison Project (see
Kravis, Heston, and Summers [1978] and [1982]) to show that in the
post-World War II era there is a strong cross-economy association
between output per worker growth and investment in machinery and
equipment.
Here we extend and improve our database, focusing on developing
economies, and find that the growth-equipment association remains
strong when the rich, industrialized economies are excluded from the
sample.
One might think that it would not: rich economies can make productive
use of the equipment that embodies modern machine technologies
because they have already developed the "human infrastructure" of
literacy, technology-handling skills, and organizational practices.
Perhaps poorer developing economies lack the human infrastructure to
support machine technology and are unable to benefit from high rates
of accumulation.
We believe this fear is overdrawn. Relatively poor economies appear
to benefit as much as do richer economies from an investment effort
concentrated on machinery and equipment. Rapid growth is found where
equipment investment is high, and slow growth where equipment
investment is low. If there is a region where the post-WWII
growth-equipment nexus is weak, it is the well-integrated and very
rich region of western Europe--not the developing world.
We believe that the strength of the growth-equipment nexus that we
find among the developing economies is especially impressive because
of the indifferent quality of much of national income and growth data
covering the world's poorer economies. Both growth rates and
investment rates are measured with considerable error, much more
error than is found in data covering the richer economies that have
well-developed government statistical departments. Substantial
measurement error tends to degrade the strength of estimated
statistical relationships. But even though the data are of relatively
poor quality, there remains strong statistical evidence of a powerful
equipment-growth association among developing economies.
Much of the effort that we have put into this project since we wrote
DeLong and Summers [1991] has been devoted to sharpening our
estimates of equipment investment rates. Our previous estimates of
the division of investment between equipment and structures rested
very heavily on benchmark year observations. In this paper we use a
much broader range of data to construct our estimates of investment
rates. This improvement in the database does provide us with
substantial payoffs: our results are sharper and more precise with
the new than they were with our old database.
Section II of this paper describes our data. It documents the
extraordinarily wide variation across economies in relative price and
quantity structures, and the associations between these variations
and economic policies. We stress the distinction between investment
effort--share of national product saved, plus capital inflows--and
investment: buildings constructed, and machines put into productive
use. Many of the policies that have been followed in the post-WWII
period, especially in the developing world, seem designed to maximize
"investment effort" while ensuring that each unit of "investment
effort" translates into as little actual investment as possible.
Section III presents evidence on the strong association of equipment
investment and growth, on the robustness of this association to the
inclusion or exclusion of different ranges of the cross-country
distribution of per capita productivity levels, and on tests of
whether growth might be the cause and equipment investment the
effect. It concludes that the growth-equipment nexus among developing
economies is strong, and in all likelihood arises because the returns
on equipment investments are very high, but that it is not the sole
or overwhelming determinant of relative growth rates.
Section IV presents speculative conclusions about economic policy
that we draw from our studies. We believe that policies to boost
equipment investment above what might be thought of as
laissez-faire levels might produce large economic growth
benefits. We are much more confident that policies that reduce
equipment investment below what might be thought of as
laissez-faire levels destroy economic growth.
II. Assessing Economic Structures
Estimating Equipment Investment Rates
DeLong and Summers [1991] found a strong association between the
growth rate of GDP per worker over 1960-85 (in international dollars
as estimated by Summers and Heston [1991]) and our estimates of the
share of GDP devoted to machinery investment over 1960-85. The study
covered a sample of sixty-odd non oil-exporting economies that had
been at some point or other closely studied by the U.N. International
Comparison Project [ICP], which had constructed estimates of national
relative price and quantity structures for specific benchmark years
denominated in a common "international dollar" unit.
As we noted, the estimates of the share of equipment investment in
GDP used were not especially good. They depended heavily on the ratio
of equipment to total investment in benchmark years being good
proxies for the average ratio of equipment to total investment on
average over the sample. Our use of benchmark estimates confined our
cross-country sample to those economies that had served as benchmarks
in the ICP: Singapore and Taiwan, for example, were omitted from our
database.
In this paper we use a broader range of sources of information to
construct our estimates of equipment investment rates. This
improvement in the database does provide substantial payoffs: our
results are sharper and more precise with the new than they were with
our old database. First, we improve our estimates of real rates of
equipment investment by using sources of information on real
equipment investment besides national income accounts. The bulk of
machinery and equipment are imported. Trade statistics are a fruitful
source of data on equipment investment. In addition, the relative
price of equipment has a strong negative correlation with equipment
investment. Aitken [1991] has constructed estimates of the relative
price of equipment in the 1980s. Lee [1992] has compiled estimates of
real equipment imports over 1960-85.
We determined the relationship in our 1991 sample between our
estimates of equipment investment rates over 1960-85, the Lee
estimates of real equipment imports from the OECD over 1960-85, the
Aitken estimates of the relative price of capital, and two additional
variables--the total investment share and the average ratio of
national product per worker to the U.S., both from Summers and Heston
[1991]. We then used this relationship to project equipment
investment rates over 1960-85 for non oil-exporting economies omitted
from our 1991 sample. In estimating the relationship between
equipment investment and our proxies, we exclude the
equipment-exporting economies of the G-7 that produce domestically
the bulk of their equipment investment. We also exclude the three
largest outliers--the African economies Tanzania, Zambia, and
Zimbabwe, which we estimated in DeLong and Summers [1991] to have
high shares of equipment investment in GDP, but which have little
capacity to produce capital goods and low recorded equipment
imports.
By far the best predictor of equipment investment was the share of
equipment imports in GDP, which do move one-for-one with our 1991
estimates of national equipment investment rates.
For OECD nations, and for thirteen others for which unpublished data
was kindly provided by Robert Summers, we sharpen our estimates of
equipment investment by estimating the share of equipment investment
in total investment not from one benchmark year but for all years in
the 1960-85 sample. We find, however, that this improvement on our
earlier benchmark procedure has only small effects.
These improvements in our data generate substantial benefits. For our
full sample, the t-statistics on equipment investment that we report
below are more than two-thirds higher than the t-statistics in the
analogous regressions in our earlier work.
Price and Quantity Structures
Figure 1 documents that the
price of investment goods relative to the deflator for GDP as a whole
is much greater in poor than in rich economies. This shows one of the
major benefits of using ICP data: because of the divergence in
relative price structures, it is hazardous to attribute the same
meaning in terms of additions to the physical capital stock to
savings in poor as in rich economies. Poor economies require a
greater "investment effort," in terms of foregone consumption, to
produce the same physical investment.
Also noteworthy in figure 1 is the wide divergence in relative price
structures among the poorer economies. Richer economies have similar
price structures. But the relative price of investment goods can vary
by a factor of three in the bottom quartile of the distribution of
national average output per worker.
This pattern is present even more strongly in the pattern across
nations of the real relative price of equipment. Figure
2 plots the price of machinery and equipment relative to GDP for the
year 1980 against 1980 output per worker. The downward slope of
relative equipment prices as output per capita levels rises is not
surprising. Equipment is highly tradable: the relative price of
equipment is close to the inverse of the national product deflator.
Again we see divergence in price structures among poorer economies.
We believe that a large part of these divergences must be traced to
differences in economic policies: differences in exchange rate policy
that affect the gap between the current exchange rate and the PPP
exchange rate, or differences in trade policy that drive a wedge
between the internal price and the world price of equipment.
The wedge between investment and investment effort is especially
important because many have pointed to the lack of correlation
between a developing economy's investment effort and its growth rate.
Krueger [1990] argues that physical investment cannot have a very
high social marginal product because India--which Krueger estimates
has raised its (nominal) gross investment share from 14 to 22 percent
over the post-independence period--has exhibited poor growth
performance. But examine figure
2. In India, like in Argentina, the savings rate is relatively
high but equipment is expensive--more than twice as expensive in
relative terms as in Korea in 1980. Thus equipment investment as a
share of GDP in India over 1960-85 is about half of the sample
average even though the savings share is near the mean. India
demonstrates not that boosting investment is unproductive, but that
policies that boost saving while simultaneously raising the relative
price of investment in equipment and structures are unproductive. We
suspect that restrictions on imports of capital goods have ensured
that the Indian government's attempts to support investment have had
effects not on quantities but on prices: India's policies have
managed to enrich industrialists instead of encouraging
industry.1
Figure 3 shows how high
relative prices of equipment go with a low real share of equipment
investment in GDP. To some degree the association shown in figure 3
is spurious, especially among the poorest economies for which data
are of lower quality. For a given level of nominal spending on
equipment investment in the nominal national product accounts, a
lower (measured) relative price of equipment leads to a higher
(estimated) share of equipment investment in GDP. Discounting
for measurement error, figure 3 provides powerful evidence that a
good way to reduce one's investment in equipment is to pursue
policies that elevate the relative price of equipment.
III. The Growth-Equipment Nexus
Table 1 presents basic regressions of GDP per worker growth rates
over 1960-85 on average rates of estimated equipment investment,
other investment, on the log of output pr worker in 1960, and on the
labor force growth rate. The sample consists of eighty-eight non
oil-exporting nations, up from the sixty-one in our 1991 sample.
While our estimates of output per worker growth, initial output per
worker levels, and labor force growth are the same as in DeLong and
Summers [1991], our estimates of equipment and other investment rates
are significantly improved.
The first line of table 1 reports the regression using our standard
specification on the entire eighty-eight economy sample. The four
right hand-side variables account for nearly half of the variation in
growth rates over 1960-85 in the database. And there is a very strong
partial association between equipment investment and growth: each
extra one percentage point devoted to equipment investment is
associated with an 0.302 percentage point increase in the annual GDP
per worker growth rate.
Figure 4 presents the leverage
plot--the partial scatter diagram--of equipment investment and growth
corresponding to the regression in the first line of table 1. The fit
is significantly better than in the analogous regression of our
earlier work. There the four independent variables accounted for
twenty-nine percent of the variation in output per worker growth
rates. Here the four independent variables account for almost
half.
Stratified Samples
Abramovitz [1986] discussed the problems of development in a
framework in which "convergence"--the ability of poorer countries to
catch-up to the richer by adopting modern technologies and investing
in capital-intensive production methods--was limited by "social
capability"--whether an economy had the human infrastructure of
skills, formal educational attainments, and organizational practices
necessary to take advantage of the machines and techniques of the
industrial revolution. Landes [1990] writes of how the task of
catching-up to the industrial leaders in productivity levels requires
"the creation and acceptance of a new ethic of personal behavior,"
and how development has "been most readily effected in those
societies, like the Japanese, which had already developed appropriate
time and work values before the coming of modern industry." This line
of argument raises the possibility that structural relationships that
hold for the relatively rich already industrialized nations of the
world economy's core may not hold for those economies at the
periphery. Perhaps the strong growth-equipment nexus that we have
found in earlier work does not hold if the richer, industrialized
economies are excluded from our cross-country sample.
Lines two through five of table 1 successively exclude from the
sample those economies in the sample of the row above with the
highest initial levels of output per worker, focusing on poorer and
poorer slices of the distribution, until line five examines only
those economies with 1960 GDP per worker levels less than ten percent
of the U.S. level. But the equipment investment coefficient does not
fall--if anything, it rises (although not by statistically
significant amounts).
It is not the case that the strong equipment investment coefficient
is being driven by one or two outliers. Some observations--chiefly
Botswana and Singapore--do have enormous identifying variance, and
their inclusion or exclustion can shift the magnitude of the
equipment investment coefficient by a fifth. But these extremely
influential data points corresponding to very poor economies
neutralize each other. For every Botswana, a poor outlier economy
that our regression fits extremely well, there is a Zambia: a poor
economy that our regression fits relatively badly. Table 2 excludes
the range of the distribution in which falls Botswana--the range of
economies with 1960 GDP per worker levels less than five percent of
the U.S.[2] But the
exclusion of these very poorest economies does not appreciably shift
the equipment investment coefficient. Botswana's exclusion lowers the
coefficient: removing it alone reduces the coefficient from 0.305 to
0.263 in the full sample. But the exclusion of Zaire, Mozambique, and
Tanzania from the sample raises the coefficient. The net effect is
nearly zero: it is not the case that the very poorest economies in
the sample are in any real sense atypical.
Pritchett [1990] pointed out that the existence of a strong
relationship between growth and equipment investment among poorer
developing countries is difficult to find in the database underlying
our 1991 paper. Using our earlier database, coefficients are
sensitive to the exact specification: no one can say with confidence
that the strong growth-equipment association found in the sample as a
whole applies to the developing economy subsample. Here. with better
data, we can make stronger statements: the same growth-equipment
nexus does hold in the developing economy subsample.
Continent Effects
The set of independent variables we use in our basic specification is
parsimonious. We have experimented with adding to the right hand side
other variables that are plausible determinants of growth, and failed
to find an alternative specification with a greater number of
independent variables that produces a markedly smaller coefficient on
equipment investment. Our failure does not prove that the
equipment-growth nexus is a structural one: it could be the case that
equipment investment is a good proxy for some other factor, perhaps
related to education or to what Abramovitz calls "social capability,"
for which we possess no good measures.
An alternative way of exploring these questions would start from the
proposition that location--the continent on which on economy is
located--has a strong connection with at least the
culturally-determined factors that are placed under the category of
"social capability." It could well be that because of cultural or
other factors some continents have had much more favorable
opportunities for growth than have others, or that economies in some
continents receive more growth from investments in equipment than
others. And perhaps the strong growth-equipment associations that we
find in our cross-economy regressions are to some degree the result
of the omission of factors correlated with location.
Table 3 reports key
coefficients from a regression that includes continent-specific
effects, and from a regression that includes both continent-specific
effects and allows for an interaction of continent with equipment
investment. There are statistically significant continent-specific
effects: the null that the regression intercepts are the same across
continents is rejected at the .023 level. But allowing for these
continent-specific differences in growth has little effect on the
equipment investment coefficient: the coefficient estimated is 0.278,
as opposed to 0.305 using our full sample and the basic
specification. Omission of growth-causing factors correlated with
location is not a source of the strong equipment investment
coefficient we estimate.
Table 3 also examines whether the strength of the growth-equipment
association varies from one continent to another. The table shows no
statistically significant sign that the strength of the
growth-equipment nexus varies across continents. The null hypothesis
that all of the continent-equipment interaction terms are zero fails
to be rejected, with a marginal significance level of 0.161. However,
the differences in the point estimates of the strength of the
growth-equipment association across continents are large--varying
from a high of 0.450 for Africa to a low of 0.006 for Europe. Table 3
does suggest that the growth-equipment nexus is, of anything,
potentially stronger in regions containing developing economies: the
three continents--Africa, Asia, and Latin America--in which
developing economies are most heavily concentrated have the three
highest point estimates of the continent-specific strength of the
growth-equipment association.
Total Factor Productivity Growth
How much of the growth-equipment nexus is associated with total
factor productivity growth, and how much with capital deepening
holding total factor productivity constant? The lack of accurate
estimates of investment rates in the 1950s, and thus of
capital-output ratios as of 1960, provides a substantial obstacle to
calculating good estimates of total factor productivity growth for
our cross-section samples.
We prefer to take another approach that calculates a lower bound to
the proportion of the growth-equipment nexus that is attributable to
a correlation between equipment investment and total factor
productivity growth. Countries that had high investment shares after
1960 had, in all likelihood, high investment shares before 1960 and
high capital-output ratios in 1960 as well. If we assume that
capital-output ratios in 1960 were uncorrelated with post-1960
investment rates--and with other right-hand side variables in our
regressions--then we overstate the average change in the
capital-output ratio and the proportion of relative differences in
growth rates that can be attributed to differences in capital
deepening.
Table 4 reports estimates of total factor productivity growth
regressed on our standard four variables, including equipment
investment, under the assumption that 1960 and 1985 capital-output
ratios are uncorrelated. The first two lines report regressions using
our developing economies sample, including only economies with GDP
per worker levels less than thirty percent of the U.S. The second two
lines include all our data. Lines one and three assume a fifteen
percent per year net rate of return on capital at the sample mean in
1985.[3] Lines two and
four assume a thirty percent per year net rate of return at the
sample mean.[4]
All four lines of table 4 show a strong correlation between equipment
investment and total factor productivity
growth.[5] The positive
correlation of equipment investment and TFP growth is lower for the
high rate of return case, but the high rate of return case also
contains a strong negative correlation between structures
investment and TFP growth. Figure
5 shows the partial scatter of equipment investment and estimates
of total factor productivity growth, calculated assuming that 1960
and 1985 capital-output ratios are uncorrelated, corresponding to the
final row of table 4. Figure 5 is very similar to earlier figures in
this paper: countries that have experienced rapid output growth have
done so because of rapid TFP growth even if we attribute a high share
of the product to capital.
Should anyone be surprised at our finding that effectively all of the
growth-machinery nexus is due to the correlation between equipment
investment and TFP growth? No. This conclusion could have been
reached through theoretical reasoning alone: standard models that
equate the rate of return on investment to the marginal product of
capital contain a profound "investment pessimism." Even large shifts
in investment rates have next to no effects on long-run growth
rates.
In the case of equipment investment, the investment pessimism of
standard models is amplified by the high rate at which equipment
depreciates. Differences in equipment investment rates on the order
of five percent of GDP boost the steady-state equipment capital stock
by only a quarter of annual GDP or so. At standard rates of return,
this increment can support only a boost to GDP of less than ten
percent, and boosts GDP growth rates over the quarter century of our
sample by only a third of a percentage point per year.
Instrumental Variables Estimates Using Developing Economy
Samples
Either equipment investment leads to processes that boost TFP, or
there are some other variables omitted from our analysis that boost
TFP and equipment investment together. We suspect that the principal
causal chain runs from equipment investment to growth. Here we
examine the instrumental variables relationship between growth and
equipment in developing countries for three sets of instruments:
tariff and non-tariff barriers to trade, savings rates, or relative
equipment prices.
Table 5 presents results, using a sample, of developing economies
that corresponds to the fourth line of table 1. For two of the three
sets of instruments--savings shares of national product, and relative
equipment prices--the instrumental variables regressions show an
association between growth and equipment investment as strong as did
the ordinary least squares regression. For the third set of
instruments--tariff and non-tariff barriers to trade--the point
estimate of the equipment investment coefficient is near zero.
However, the standard error is extremely large.
A strong growth-equipment investment connection in ordinary least
squares regressions might be due to reverse causation--fast growth
might be the cause and equipment investment the effect. It is more
difficult to make this argument for the instrumental variables
regressions in lines one and three of table 5. If rapid growth were
the cause of high equipment investment, then equipment investment
would be high in economies where demand for equipment was high--and
so equipment prices would be high. But in the first stage regression
underlying line one of table 5, there is a negative relationship
between equipment prices and equipment quantities, suggesting that
equipment investment is high when the supply is favorable, and thus
that equipment investment is the cause, not the effect of high
growth. A similar argument could be made for line three. Standard
theory predicts that where growth is strong for exogenous reasons
savings should be low--not high.
The failure of line two of table 5 to produce a strong equipment
investment coefficient is, however, disappointing. This failure could
be rationalized: our estimates of barriers to trade are for the most
part from the 1980s, from near the end of our sample period, and are
perhaps not ideal measures of average trade policy stance over the
sample period.
What Is the Social Rate of Return to Equipment Investment?
Our TFP growth regressions suggest that a one percentage point
increase in the equipment investment share of GDP is associated with
an increase of approximately 0.2 percentage points per year in the
TFP growth rate. Suppose that equipment investment yields a net
private rate of return of fifteen percent that is a high estimate of
the worldwide average return on private business investments, and an
associated gross rate of return on the order of twenty-five percent
per year. What then is the social rate of return to equipment
investment?
The calculation of the social rate of return hinges on whether it is
correct to view the association of equipment investment and TFP
growth as reflecting a causal relationship, and on the timing of
whatever external rise in TFP might be induced by equipment
investment. If the relationship is causal and if the extra rise in
TFP happens immediately--at the moment of installation, as new
equipment is brought on line and workers and organizations learn the
skills necessary to use it efficiently--then the net social rate of
return to equipment investment could be as high as thirty-five
percent per year: fifteen percent in extra privately-appropriable
value created through capital deepening, and approximately twenty
percent through the external effects induced. Models like that of
Aghion and Howitt [1992] in which private investments in new types of
equipment raise productivity at the moment of such investments
suggest such a front-loading of the TFP boost.
If the cross-section regressions will bear a causal interpretation,
net social rates of return from equipment investment in the
range of twenty-five percent per year or more are defensible under
the maintained hypothesis that the large coefficient on equipment
investment arises because equipment investment is a trigger of
learning-by-doing and thus of substantial total factor productivity
growth. To the extent that causality flows in the other direction as
well, the social rate of return will be somewhat lower. To be more
precise would require a much sharper vision of the process of
productivity growth and on-the-job-training than we possess, and more
confidence that the growth-equipment nexus is in fact a causal
one.
IV. Implications and Conclusions
We have documented that a strong connection between equipment
investment and productivity growth holds for developing countries. We
have also reported instrumental variables regressions that produce
the same association between growth and machinery. Our
instruments--savings rates, trade barriers, and the relative price of
equipment--are variables that are in large part determined by
economic policy, and only very indirectly affected by output per
worker growth. Thus our evidence suggests that a large portion of the
growth-machinery investment relationship arises from a causal nexus
between equipment and growth.
If this argument is correct, our conclusions have clear implications
for development policy. Policies that make it difficult for
developing economies to import and install machinery and equipment
are likely to be disastrous. Moreover, there is a case that the
social returns from investments in equipment are greater than the
private profits obtained: if so, then "getting relative prices right"
in the sense of attaining a relative price structure most conducive
to rapid growth and long-run welfare would involve pushing the real
relative price of equipment below what might be thought of as its
laissez faire value.
However, it is important to note that the possibility that some
activist policies to shape relative price structures could promote
growth does not imply that a typical government would be well-advised
to seek to put such policies into effect. Westphal (1990), for
example, a strong believer that Korea's government has significantly
accelerated growth, believes that governments that attempt to promote
growth by means of industrial policies usually fail--for reasons
familiar to theorists of the rent-seeking society. Reflection on the
broad range of attempts to supplement the invisible hand by the
visible hand of industrial policy in countries like India, Argentina,
and Ghana as well as Korea and Japan adds force to his argument.
If we have correctly identified the growth-equipment nexus as a
powerful source of economic growth, than those who wish to argue that
East Asian "developmental states" have generated extraordinary
benefits because the microeconomic management exercised by
their activist governments do a better job of allocating resources
for growth than market forces face a roadblock at the beginning of
their argument. In our regressions, growth in East Asia has not been
extraordinarily high, in the sense of diverging from the general
pattern given the macroeconomic fundamentals. Growth has been rapid,
yes: but we would have expected growth to be rapid because
fundamentals--especially rates of equipment accumulation--have been
very favorable. Hong Kong is the sole outstanding positive outlier in
our regressions among East Asian economies.
We are tempted to argue that to the extent that interventionist
governments have aided development, they have done so because a
byproduct of their concentration on export promotion established a
relative price structure that made machinery and equipment
disproportionately cheap. This does not mean that good economic
policies have not been very important in economic growth. In the
Korean case, for example, government policies are the source of
Korea's effective educational system, of its relatively unbloated
government, of its relatively low equipment prices, and of its
relatively high rates of equipment investment. The relative
stagnation of the Korean economy in the 1950s under the Rhee
régime, and subsequent rapid growth beginning soon after the
initial reforms of the Park régime, point strongly to a key
role played by government policies in creating the fundamental
foundations for growth.
But a focus on government "guidance" and on the directive
bureaucracy-firm relationship draws attention away from what are
perhaps the true sources of rapid growth. Argentina, India, and many
others have all used similar rhetoric to justify "interventionist"
policies: all the programs are rationalized by similar appeals to
"Schumpeterian" rather than "Ricardian" advantage, and to the crucial
role of industry in economic development (see Johnson [1982],
Srinivasan [1989]). But in only a few East Asian cases does anyone
even try to claim that such policies have succeeded. Note also that
we are far from being the first the first to stress the association
between equipment investment and economic growth. Before World War II
was even a quarter of a century Díaz Alejandro [1970] argued
that Argentina's extraordinarily poor economic performance in the
post-World War II period was due to a very low rate of investment in
machinery and equipment generated by counterproductive policies.
Jones [1991] has found that a distorted relative price structure that
makes investment in equipment difficult and expensive can cripple
economic growth.
In addition, economic historians like Rostow [1960] and Gerschenkron
[1962] have also interpreted successful and unsuccessful growth
largely as the results of rapid or slow acquisition and installation
of machinery and equipment. A low price of machines--the ability to
make or acquire industrial capital goods cheaply--and a high quantity
of savings devoted to the purchase of machinery and equipment have
always featured prominently in economic historians' discussions of
the sources of modern economic growth. The reference to an industrial
revolution, rather than to a productivity-growth revolution or to a
standard-of-living revolution, is shorthand for the key role that
economic historians have attributed to mechanization and machinery in
driving the tremendous explosion of wealth over the past two
centuries.
Moreover, an old development economics tradition dating from the
early post-World War II years (for example Hirschman [1958]) and the
modern "new growth theory" tradition dating from the late 1980's (for
example Romer [1986]) point in the same direction. Both traditions of
analysis stress the importance of external economies or "linkages" as
fundamental sources of growth. And on the microeconomic side authors
like Mowery and Rosenberg [1989] have argued that learning-by-doing
plays a key role not only in using existing capital goods efficiently
and productively but also in generating technological change and
total factor productivity growth. They suggest that a necessary
prerequisite to productivity growth is a wide range of experience
using existing technologies. In such a case, equipment investment
might well be a necessary prerequisite for rapid growth.
Thus our central argument is far from a novelty.
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Figure 1
Relative Prices of Investment Goods, and Relative Levels of Output
per Worker, 1960-85
Figure 2
Relative Prices of Machinery and Equipment, and Levels of Output per
Worker, 1980
Figure 3
Estimated Equipment Investment and Equipment Prices
Figure 4
Partial Scatter of Equipment Investment and Growth: Full
Sample
Figure 5
Partial Scatter of Equipment Investment and Total Factor Productivity
Growth
Table 1
Equipment Investment and Growth: Regressions Using Different
Stratified Subsamples
Table 2
Equipment Investment and Growth: Regressions Using Different
Stratified Subsamples and Omitting the Very Poorest Economies
Table 3
Continent-Specific Effects
Table 4
Estimates of Total Factor Productivity Growth Regressed on Equipment
Investment
Table 5
Instrumental-Variables Estimates of the Growth-Equipment Nexus in the
Developing Economy Sample
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Econ ArticlesCreated 2/21/1996 |
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Professor of Economics J. Bradford DeLong, 601
Evans |
