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OCR for page 307
Microeconom~cs
of Technological Innovation
EDWIN MANSFIELD
We are still very far from having a satisfactory understanding of
the innovation process, the determinants of the rate of innovation,
the measurement of the rate and direction of technological change,
and the effects of changes in technology. In view of the enormous
difficulties that studies in these areas face, it is not surprising that
existing knowledge remains limited. Nonetheless, steadily progress
has been made in this area.
Until the 1950s, the economics profession generally was woefully deficient
in its treatment of technological change. With few exceptions, like Joseph
Schumpeter (1934), economists failed to recognize the central importance
of industrial innovation. In the past 30 years. however, a great deal of
research, much of it financed by the National Science Foundation, has been
carried out by economists to extend the* limited understanding of the nature,
determinants, and effects of technological change. Without question, si~,-
nificant progress has been made. New models have been constructed and
new kinds of data have been assembled. Yet we are far from a satisfactory
understanding of this very difficult topic.
This chapter describes briefly some of the principal work that has been
done to help answer the following questions: (1) What has been the effect
of research and development (R&D) on the rate of productivity growth?
(2) What has been the rate of return from investments in industrial innovation?
(3) What have been the size, determinants, and effects of imitation costs?
(4) How much effect have patents had on imitation costs and the rate of
innovation? (5) How great has been the rate of inflation in R&D? (6) What
factors deterTnine the rate of diffusion of an innovation? (7) To what extent
has the rate of international technology transfer increased?
307
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308
EDWIN MANSFIELD
While these are not the only questions that microeconomists have dealt
with, they clearly are among the most important. Since there is a considerable
literature on each of these questions, my treatment is selective, and it will
tend to focus on the kinds of work that my students and I have been doing
in recent years.
RELATIONSHIP BETWEEN R&D AND PRODUCTIVITY GROWTH
For both analytical and policy purposes, it is important to investigate the
relationship between the amount spent by an industry or firm on R&D and
its rate of productivity increase. Dunng the past 30 years a number of studies
of this kind have been carried out. They are by no means free of problems,
however, as I have indicated in detail elsewhere (Mansfield et al., 19821.
Perhaps the most important of their findings is that R&D has a very significant
effect on the rate of productivity increase in the industries and the time
periods that have been studied. In one of the earliest studies of this topic,
Minasian (1969) found that the rate of productivity increase in chemical firms
was directly related to their expenditures on R&D. His results indicated that,
during the period to which his data pertained, the marginal rate of return—
that is, the rate of return from an additional dollar spent was about 50
percent for R&D in chemicals. A study of my own indicated that the marginal
rate of return from R&D was about 40 percent or more in the petroleum
industry and about 30 percent in the chemical industry (Mansfield, 19651.
In agnculture, Gnliches (1964) found that output was related in a statistically
significant way to the amount spent on research and extension. Assuming a
6-year lag between research input and its returns, his results indicated a
marginal rate of return from agncultural R&D of 53 percent. Another study,
by Evenson (1968), used time-genes data to estimate the marginal rate of
return from agricultural R&D, which it found to be about 57 percent.
Peterson's study ( 1971 ) of R&D in poultry indicated a marginal rate of return
of about 50 percent.
Throughout the 1970s these studies were extended. Gnliches (1980) used
data for almost 900 manufacturing fibs to examine He relationship between
R&D and the rate of productivity growth. The results indicated that the
amount spent by a firm on R&D was directly related to its rate of productivity
growth. Also, he found that the private rate of return from R&D was about
17 percent. It seemed much higher than this in chemicals and petroleum and
lower in aircraft and electrical equipments
Terleckyj (1974) studied the effects of R&D expenditures on productivity
change in 33 manufacturing and nonmanufactunng industries during the years
1948 to 1966. In manufacturing, the results suggested about a 30 percent
rate of return from an indus~y's R&D on its own productivity. In addition,
his findings showed a very substantial effect of an indus~y's R&D on pro-
OCR for page 309
MICROECONOMICS OF TECHNOLOGICAL INNOVATION
309
ductivity growth in other industries, resulting in a social rate of return greatly
exceeding that of 30 percent. Nadin and Bitros (1980) constructed an econo-
metric model in which output was treated as an exogenous variable and
R&D, labor, and capital inputs were regarded as functions of input prices,
sales, the rate of capacity utilization, and the lagged dependent variables.
They found that labor productivity is significantly affected in both the short
run and the long run by the level of a firms R&D expenditures.
During the 1980s, studies by Griliches, Link, Nadiri, Scherer, Terleckyj,
and myself, among others, provided still further evidence concerning the
rates of return from R&D.2 In interpreting the rates of return obtained, both
in these studies and in those done before, it is important to distinguish between
private rates of return and social rates of return. The private rate of return
is the rate of return to the Finn carrying out the R&D; He social rate of return
is the rate of return to society as a whole. Since the Grin Hat carries out the
R&D frequently cannot appropriate many of the benefits of doing so, the
social rate of return may be considerably in excess of the private rate of
retum. Thus, many of the rates of return cited above, because they are social
rates of rehem, are likely to be higher than the private rate of return to the
firm carrying out the R&D.
How do the results of the studies of the 1980s compare with those of the
studies of the 1970s and 1960s? In general, they are quite consistent, in the
sense that they continue to indicate that the level of R&O seems to be closely
related to the rate of productivity grown, and that the marginal rate of return
from investment in R&D is high, although perhaps not as high as in earlier
years. The high marginal social rate of return from R&D is important because
it suggests that there may be an undennvestment in R&D, a phenomenon
that many economists attribute partly to the differences between private and
social rates of return from innovative activities.
SOCIAL AND PRIVATE RETURNS FROM SPECIFIC INNOVATIONS
The types of econometric studies just described have been employed by
economists to estimate the social rates of return from investments in new
technology. But they are by no means He only type of study carried out by
economists for this purpose. A number of microeconomic studies of the
returns from specific innovations have been carried out as well. To estimate
He social benefits from an innovation, economists have often used a model
of the following sort. If the innovation results in a shift downward in the
supply curve for a product, they have used the Yea under the product's
demand curve between the preinnovation and postinnovation supply curves
as a measure of the social benefit from the innovation during the relevant
time period. If all other prices remain constant, this area equals the social
value of He additional quantity of the product plus the social value of the
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310
EDWIN MANSFIELD
resources saved as a consequence of the innovation. Consequently, if one
compares the stream of R&D (and other) inputs relating to the innovation
with the stream of social benefits measured in this way, it is possible to
estimate the social rate of return from the investment in the new technology.
The first such studies concerned only agricultural R&D. Although only a
few studies of this sort were conducted, notably by Gnliches (1958), Peterson
(1971), and Schmitz and Seckler (1970), the results were quite consistent in
the sense that Hey all indicated that the rate of return from agricultural R&D
in the United States has tended to be high. Until the 1970s, no such estimates
were made for ~ndustnes other than agriculture. In an attempt to help fill
this gap, my co-workers and I estimated the rate of return from the investment
in 17 industrial innovations, which occurred in a variety of industries and
which stemmed from fins of quite different sizes (Mansfield et al., 1977b).
Most of these innovations were of average or routine importance, not major
breakthroughs. Although this sample cannot be regarded as randomly chosen,
there is no obvious indication that it was biased toward very profitable
innovations socially or privately—or relatively unprofitable ones.
To estimate the social rate of return from the investment in each of these
innovations, we extended the model described above to include the pricing,
behavior of the innovator, the effects on displaced products, and the costs
of uncommercialized R&D and of R&D done outside the innovating orga-
nization. The results indicate that the median social rate of return from the
investment in the innovations studied was 56 percent, a very high figure.
On the other hand, the median private rate of return was 25 percent. (In
interpreting the latter figure, it is important to note that innovation is a risky
activity; see Mansfield et al., 1971.)
Information also was obtained concerning the returns from the innovative
activities of one of America's largest fins, from 1960 to 1972. For each
year this fin had made an inventory of the technological innovations re-
sulting from its R&D and estimated its effects on its profit stream in detail.
When the average rate of return from this f~n's total investment in innovative
activities was computed, the result was 19 percent, which is not too different
from the median private rate of return (25 percent) noted above. Also, lower
bounds were computed for the social rate of return from this firm's invest-
ment, which was about double that for its private rate of return (see Mansfield
et al., 1977a).
To extend our sample and replicate our analysis, the National Science
Foundation commissioned two studies, one by Robert R. Nathan Associates
and one by Foster Associates. Their results, like ours, indicated that the
median social rate of return tends to be very high and much higher than the
median private rate of return. Based on its sample of 20 innovations, Nathan
Associates (1978) found the median social rate of return to be 70 percent
and the median private rate of return to be 36 percent. Foster Associates
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MICROECONOMICS OF TECHNOLOGICAL INNOVATION
311
(1978), based on its sample of 20 innovations, found the median social rate
of return to be 99 percent and the median private rate of return to be
24 percent.
These findings pertain to the average rate of retum. As pointed out in the
previous section, econometric investigations indicate that the marginal rate
of return has also tended to be high. In sum, practically all of the studies
carried out to date indicate that the average social rate of return from in-
vestments in new technology in both agriculture and industry has tended to
be very high. Moreover, the marginal social rate of return also seems high,
generally at least 30 percent. As I have stressed repeatedly elsewhere, there
are very important problems and limitations inherent in each of these studies.
Certainly, they are frail needs on which to base policy conclusions. But
recognizing this fact, it nonetheless is remarkable that so many independent
studies based on so many types of data result in so consistent- a set of
conclusions. As noted above, many economists view these conclusions as
evidence of an underinvestment in civilian technology.
BASIC RESEARCH AND PRODUCTIVITY
While the foregoing studies provide valuable information concerning the
relationship between total R&D input and productivity change, they tell us
nothing about the effect of the composition of an industry's or firm's R&D
on its rate of productivity change. In particular, they tell us nothing about
the role of basic research in promoting productivity increase. Basic research
is defined by the National Science Foundation as "original investigation for
the advancement of scienuf~c knowledge . . . which [does] not have im-
mediate commercial objectives." Does basic research, as contrasted win
applied research and development, make a significant contribution to an
industry's or firm's rate of technological innovation and productivity change?
Although the studies cited above indicated that an indusmy's or f~rrn's R&D
expenditures have been directly related to its rate of productivity change,
they were unable to shed light on this question because no attempt was made
to separate basic research from applied research and development.
About 5 years ago, an econometric study was carried out to determine
whether an industry's or f~rrn's rate of productivity change in recent years
had been related to the amount of basic research it perfo~ed, when other
relevant variables (such as its rate of expenditure on applied R&D) were
held constant. This study (Mansfield, 1980, 1981) has various limitations,
but its results are of interest, particularly since so little research has been
done on this subject. The findings indicate that there was a statistically
significant and direct relationship between the amount of basic research
carried out by an industry or firm and its rate of increase of total factor
productivity, when its expenditures on applied R&D were held constant. To
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EDWIN MANSFIELD
some extent, this may reflect a tendency for basic research findings to be
exploited more fully by the industries and Grins that were responsible for
them. Or it may reflect a tendency for applied R&D to be more effective
when carried out in conjunction with some basic research.
Whether the relevant distinction is between basic and applied research is
by no means clear: there is some evidence that basic research may be acting
to some extent as a proxy for long-term R&D. Holding constant the amount
spent on both applied R&D and basic research, an industry's rate of pro-
ductivity increase seems to be directly and significantly related to the extent
to which its R&D is long term. This constituted the first systematic evidence
that the composition, as well as the size, of an industry's or fien's R&D
expenditures affect its rate of productivity increase. However, the study was
really just a beginning. Much more work is required in this area, since the
composition of R&D is for many purposes as important as its total amount.
CENTRAL ROLE OF IMITATION COSTS AND TIMES
Economists have long recognized that the costs of imitating new products
have an important effect on the incentives for innovation in a market econ-
omy. As Arrow (1962) and others have pointed out, if fins can imitate an
innovation at a cost that is substantially below the cost to the innovator of
developing the innovation, there may be little or no incentive for the innovator
to carry out the innovation. In Heir discussions of the innovation process,
economists frequently have called attention to the major role played by the
costs of imitation, but there has been little or no attempt to measure those
costs, to test various hypotheses concerning the factors influencing them, or
to estimate their effects.
In the first empirical study of this topic (Mansfield et al., 1981) data were
obtained from fimns in the chemical, drug, electronics, and machinery in-
dustries concerning the cost and time of imitating (legally) 48 product in-
novations.3 By imitation cost is meant all costs of developing, and introducing
the imitative product, including applied research, product specification, pilot
plant or prototype construction, investment in plant and equipment, and
manufacturing and marketing start-up. (If there was a patent on the inno-
vation, the cost of inventing around it is included.) By imitation time is
meant the length of time that elapses from the beginning of the imitator's
applied research (if there was any) on the imitative product to the date of its
commercial introduction.
The sample of firms for this study was chosen more or less at random
from among the major firms in these four industries in the Northeast, and
He new products were chosen more or less at random from among, Hose
introduced recently by these firs. For 34 of the products, the data are based
on acn}a1 experience, because the new product had already been imitated.
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MICROECONOMICS OF TECHNOLOGICAL INNOVATION
313
For the remaining 14 products, no imitator had appeared as yet, but the
innovating Finn provided detailed estimates that we regarded as reliable.
Also, for all 48 products, data were obtained from the innovating fib con-
ce~ning the costs of the innovation, as well as the time it took to bring the
innovation to market (from the beginning of applied research to the date of
its commercial introduction).
The ratio of the imitation cost to the innovation cost averaged about 0.65,
and the ratio of the imitation time to the innovation time averaged about
0.70. There was considerable variation about these averages, however. For
about half of the products, the ratio of imitation cost to innovation cost was
either less than 0.40 or more than 0.90. For about half of the products, the
ratio of imitation time to innovation time was either less third 0.40 or more
than 1.00. Products with a relatively high (low) ratio of imitation cost to
innovation cost tended to have a relatively high (low) ratio of imitation time
to innovation time.
It is worth noting that the imitation cost was no smaller than the innovation
cost for about one-seventh of the products. This was not due to any superiority
of the imitative product over the innovation. Instead, in a substantial per-
centage of the cases, it was due to the innovator's having, a technological
edge over its rivals in the relevant field. Often this edge was due to superior
"know-how" that is, better and more extensive technical information based
on highly specialized experience with the development and production of
related products and processes. Such know-how is not divulged in patents
and is relatively inaccessible (at least for a period of time) to potential
Imitators.
Thus, these data indicate that innovators routinely introduce new products
despite the fact Mat other firms can imitate those products for about two-
thirds (often less) of the cost and time expended by the innovator. In some
cases, this is because, although other firms could imitate these products in
this way, there are other barriers to entry (for example, lack of a well-known
brand name) that discourage potential imitators. But to a greater extent (at
least in this sample), it seems to be due to a feeling on the part of the
innovators that, even if imitators do begin to appear in a relatively few years,
the innovation will still be profitable.
Patents and Imitation Costs
In recent years, economists have also begun to study more systematically
We effects of patents. Of particular interest is the question: To what extent
is the ratio of imitation cost to innovation cost affected by whether the
innovator has patents on the new product? Contrary to popular opinion, patent
protection does not make entry impossible, or even unlikely. Within 4 years
of their introduction, 60 percent of the patented successful innovations in
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EDWIN MANSFIELD
the above sample were imitated. Nonetheless, patent protection generally
increased imitation costs. To obtain information concerning the size of this
increase, my co-workers and I asked the finns in the sample described above
to estimate how much the imitation cost for each patented product increased
because it was patented. The median estimated increase was 11 percent.4
We also asked the firms to estimate how much the imitation cost for each
unpatented product would have increased if it had been patented. The median
estimated increase was only about 6 percent. (Indeed, for two of these prod-
ucts, patent protection would have reduced the money and time required for
imitation because without patent protection the innovator was able to keep
secret the essential information underlying the product, whereas if the product
had been patented, some of the inflation would have had to be disclosed. )
The fact that a patent resulted in a larger increase in the imitation costs of
the patented products than of the unpatented products was, of course, a major
reason why some products were patented and others were not.
Patents had a larger impact on imitation costs in ethical drugs than in the
other industries sampled, which helps to account for survey results indicating
that patents are regarded as more important in ethical drugs than elsewhere.
The median estimated increase in imitation cost due to patent protection was
about 30 percent in ethical drugs, in contrast to about 10 percent in chemicals
and about 7 percent in electronics and machinery Without patent protection,
it frequently would have been relatively inexpensive (and quick) for an
imitator to determine the composition of a new Mug and to begin producing
it. These results are in accord with the conclusion of Taylor and Silberston
(1973) that the lack of patent protection would reduce the rate of expenditure
on innovative activity to a greater extent in drugs than in other industnes.
Imitation Costs, Entry, and Concentration
Let us turn now from the determinants of imitation costs to their effects
on market entry and industry concentration. Holding constant the discounted
profit (gross of the imitation cost) that the imitator expects to earn by imitating
a new product, the new product is more likely to be imitated if the imitation
cost is small. To discourage market entry, We innovator may adopt pricing
(and other) policies to reduce the imitator's expected discounted gross profit
if the imitation cost is low. Taking this into account, is it still true that the
probability of entry is inversely related to the size of the imitation cost? To
find out, my co-workers and I determined whether each product innovation
in the sample described above was imitated within 4 years of its introduction.
(Innovations that had been on the market fewer than 4 years and unsuccessful
innovations clearly had to be omitted.) We then carried out a logit analysis5
to detennine whether We ratio of imitation cost to innovation cost influenced
the probability that entry of this sort occurred within ~ years. Based on the
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MICROECONOMICS OF TECHNOLOG1C~ INNOVATION
315
results, imitation cost seemed to be related in the expected way to whether
entry occurs.
Imitation cost may also affect an industry's level of concentration. One
would expect an industry's concentration level to be relatively low if its
members' products and processes can be imitated easily and cheaply. Existing
econometric findings, which are based on limited data, have been entirely
consistent with this hypothesis. Given the large number of factors influencing
an industry's concentration level, it is interesting that this relationship is
relatively close. Differences among industries in the technology transfer
process (including transfers that are both voluntary and involuntary from the
point of view of the innovator) may be able to explain much more of the
nterindustry variation in concentration levels than is generally recognized.
PATENTS AND THE RATE OF INNOVATION
One of the most important and controversial questions concerning the
patent system is: What proportion of innovations would be delayed or not
introduced at all if they could not be patented? To shed light on this question,
economists have camed out carefully designed surveys to determine the
proportion of their patented innovations that Grins report they would have
introduced (with no appreciable delay) if patent protection had not been
available. Although answers to such questions have obvious limitations and
must be treated with caution, they should shed some light on this topic, about
which so little is known. According to the Grins in one such study (Mansfield
et al., 1981), about one-half of the patented innovations would not have been
introduced without patent protection. The bunk of these innovations occurred
In the drug industry. Excluding drug innovations, the lack of patent protection
would have affected less than one-fourth of Me patented innovations in the
sample.
One important reason why patents frequently are not regarded as crucial
is that Hey often have only a limited effect on the rate at which imitators
enter the market. For about half of the innovations, the firms in this study
believed Hat patents had delayed the entry of imitators by less than a few
months. Although patents generally increased the imitation costs, they did
not increase the costs enough in these cases to have an appreciable effect on
the rate of entry. But although patent protection seems to have only a limited
effect on entry in about half of the cases, it seems to have a very important
effect in a minority of ~em. For about 15 percent of the innovations, patent
protection was estimated to have delayed the time when the first imitator
entered the market by 4 years or more.
In another study (Mansfield, 1985) based on a random sample of 100 firms
from 12 industries (excluding very small firms) in the United States, the
results indicate that patent protection was judged by He fimns to have been
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316
EDW N MANSFIELD
essential for the development or introduction of 30 percent or more of the
inventions commercialized in 2 industries pharmaceuticals and chemicals.
In another 3 industries (petroleum, machinery, and fabricated metal prod-
ucts), patent protection was estimated to be essential for the development
and introduction of about 10 to 20 percent of their inventions. In the remaining
7 industries (electrical equipment, office equipment, motor vehicles, instru-
ments, primary metals, rubber, and textiles), patent protection was estimated
to be of much more limited importance in this re Hard. Indeed, in office
equipment, motor vehicles, rubber, and textiles, the firms were unanimous
in reporting that patent protection was not essential for the development or
introduction of any of their inventions during the years 1981 to 1983.
This does not mean, however, that firms make little use of the patent
system. On Me contrary, even in those industries in which practically all
inventions would be introduced without patent protection, the bulk of the
patentable inventions seem to be patented. And in such industries as phar-
maceuticals and chemicals, in which patents are important, over 80 percent
of the patentable inventions are reported to have been patented. Clearly,
firms generally prefer not to rely on trade secret protection when patent
protection is possible. Even in industnes, like motor vehicles, in which
patents are frequently said to be relatively unimportant, about 60 percent of
the patentable inventions seem to be patented.
Despite We frequent assertions Nat films are making less use of the patent
system than in Me past, the evidence does not seem to bear this out. Even
in electronics, where "potting" (that is, black boxing, such as the encap-
sulation of products in epoxy resin to deter imitation) is said to have cone
into prominence, and patents are claimed to be less important, the firms in
our sample reported no such trend. This is important because it is the first
systematic evidence concerning Me extent to which the reduction in Me patent
rate during the 1970s was due to a shift away from patents and toward trade
secrets and over forms of protection. If, as some responsible observers have
claimed, "the so-called patent decline may be merely a patent bypass"
(Shapley, 1978:848-~49), it is important that policymakers be aware tI}at
this is the case. Based on these results, there is no indication Mat this is
- true.
PRICE INDEXES FOR R&D INPUTS
Economists, policymakers, and analysts are interested in the changes over
time in real R&D expenditure- that is, the changes in the amount of real
resources devoted to R&D. To estimate such changes, it is necessary to have
price indexes for R&D inputs. Unfortunately, Imt~l very recently no such
price indexes existed. Official government statistics in the United States use
the GNP deflator to deflate R&D expenditures. The relevant government
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MICROECONOMICS OF TECHNOLOGICAL INNOVATION
317
agencies are well aware that the GNP deflator is only a rough approximation,
but little has been known about the extent to which the results would change
if price indexes for R&D inputs were constructed and used instead of the
GNP deflator.
In the late 1970s, Goldberg (1978) and Schankerman (1979) constructed
price indexes for R&D inputs. These price indexes were based on the use
of proxies (that is, series that were thought to be highly correlated with the
relevant input prices). For example, Goldberg used data from the National
Survey of Professional, Technical, and Clerical Pay to represent the changes
over time in the level of wages for R&D engineers and scientists, and Schank-
e~an used the Index of Cost of Matenals of the Bureau of Economic Analysis
as a proxy for R&D materials prices.
Although indexes based on proxies are interesting and useful, there are
obvious advantages in constructing price indexes for R&D inputs based on
data obtained from firms regarding actual prices and expenditures. Many
observers have urged that such indexes be constructed. Recently, a study
financed by the National Science Foundation was camed out along these
lines, based on detailed data obtained from about 100 fins in 12 industries
(Mansfield et al., 1983; Mansfield, 1985~. The study results indicate that,
if one is interested in making short-term comparisons of total real R&D
expenditure in the nation as a whole, the GNP deflator Is reasonably adequate.
For example, in comparisons of successive years, the percentage change in
real R&D expenditure based on the GNP deflator is generally within a per-
centage point of that based on the R&D price index produced by the study.
However, for long-term comparisons of national R&D expenditure, the use
of the GNP deflator can result in substantial errors. Thus, whereas real R&D
expenditure went up by 26 percent in the years 1969 to 1981, based on the
GNP deflator, it really went up by only 15 percent, according to He R&D
price Index.
The reason why the GNP deflator performs worse in He long run than in
the short run is that, for He vast majority of years for which we have data,
it has tended to underestimate the rate of inflation in R&D. This problem is
especially severe in particular industries. For example, based on the GNP
deflator, real R&D expenditure in the chemical industry grew by about
42 percent during 1969-1981, but based on the R&D price index, it grew
by only about 22 percent during this period. Similar errors occur in the oil,
primary metals, fabricated metal products, rubber, automobile, instruments,
and "other" industries. In all of these industries, the GNP deflator results
in an overestimation of the 1969-1981 growth of real R&D expenditure of
15 percentage points or more.
Given the obvious importance of the R&D figures to economic analysis
in this area, the availability of these improved R&D price indexes should be
a significant step forward. This is the sort of data improvement that tends
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318
EDWIN MANSFIELD
to be invisible to people who are not involved closely with empirical work.
But such improvements can be important.
THE DIFFUSION OF INNOVATIONS
Whereas R&D puce indexes have attracted the attention of relatively few
economists, the diffusion of innovations has been the focus of a considerable
amount of work. Although we are far from having completely satisfactory
models of the diffusion process, substantial progress has been made in this
area.
In general, the diffusion of a major new technique tends to be a slow
process. For example, measuring from the date of first commercial appli-
cation, it generally took more than 10 years for all of the major American
firms in the bituminous coal, steel, railroad, and brewing industries to begin
using a sample of important new techniques. (Among the innovations in-
cluded in this sample were the shuttle car, trackless mobile loader, and
continuous mining machine in the bituminous coal industry, and the by-
product coke oven, continuous annealing, and continuous wide strip mill in
the steel industry.) More recently, similar findings have been reported for
the chemical and other industries. Also, Me rate of diffusion vanes widely.
Sometimes it took decades for Fisons to install a new technique, but in other
cases they imitate the innovator very quickly. To some extent, these differ-
ences may reflect a tendency for the diffusion process to go on more rapidly
in more recent times than in the past (Mansfield, 1961, 19681.
Based on the available evidence, the rate of diffusion of an innovation
depends on the average profitability of the innovation; the variation among
firms in the profitability of He innovation; the size of He investment required
to introduce the innovation; and the number of firms in the industry, their
average size, the inequality in their sizes, and the amount that they spend
on research and development. Using these variables, one can explain a large
proportion of the variation among innovations in the rate of diffusion. More-
over, this seems to be the case in a wide vaneW of industries and in other
countries as well as in the United States. Econometric models using these
variables seem to be useful devices for technological forecasting (Mansfield,
19776~.
According to studies of a number of industries, fibs in which the expected
returns from the innovation are highest tend to be quickest to introduce an
innovation. Also, holding constant the profitability of the innovation, large
firms tend to introduce an innovation more quickly than do small firms. In
some industries, this may be due to the fact Hat larger firms although not
necessarily the largest ones are more progressive than small fins. But
even if the larger fimns were not more progressive, one would expect them
to be quicker, on the average, to begin using a new technique, for reasons
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MICROECONOMICS OF TECHNOLOGICAL INNOVATION
319
discussed elsewhere (Mansfield, 196Sb). Also, holding other factors con-
stant, firms with younger and better-educated managers tend to be quicker
to introduce new techniques—or at least, this seems to be the case in in-
dustnes in which the relevant data have been collected.
Companies also differ greatly with regard to the intrafirm rate of diffu-
sion the rate at which, once it has begun to use the new technique, a firm
substitutes it for older methods. A considerable amount of this variation can
be explained by differences among firms in the profitability of the innovation,
the size of the fin, and the fimn's liquidity. Also, there is a tendency for
late starters to catch up. That is, firms that are slow to begin using an
innovation tend to substitute it for older techniques more rapidly than those
that are quick to begin using it. It is also relevant to note that the same sort
of process occurs on the international scene: countries that are slow to begin
using, an innovation tend to substitute it for older techniques more rapidly
than countries that are quick to begin using it. The reasons for this tendency,
both at the company and national levels, seem clear enough (Mansfield,
1968a; Nasbeth and Ray, 19741.
Sociologists have studied the nature and sources of information obtained
by managers concerning new techniques. The sources of information some-
times vary depending on how close the manager is to adopting the inno-
vation. For example, in agriculture, mass media are most important sources
at the very early stages of a manager's awareness of the innovation, but
friends and neighbors are most important sources when a manager is ready
to try the innovation. Also, there is evidence of a ''two-step flow of
communication." The early users of an innovation tend to rely on sources
of information beyond their peer group's experience; after they have begun
using the innovation, they become a model for their less expert peers,
who can imitate their performance (Rogers, 1962, and subsequent pub-
lications).
Turning to other factors that also influence the rate of diffusion of an
innovation,- the diffusion process may be slowed by bottlenecks in the pro-
duction of Me innovation as in the case of the Boeing 707. Also, the extent
of advertising and other promotional activities used by producers of the new
product or equipment will have an effect. So, too, will the innovation's
requirements with respect to knowledge and coordination. The diffusion
process will be impeded if the innovation requires new kinds of knowledge
on the part of the user, new types of behavior, and the coordinated efforts
of a number of organizations. If an innovation requires few changes in
sociocultural values and behavior patterns, it is likely to spread more rapidly.
Also, the policies adopted by relevant labor unions influence the rate of
diffusion. For example, some locals of the painters' union have refused to
use the spray gun In addition, the users' willingness to take risks can have
an important influence on an innovation's rate of diffusion. Nonetheless,
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320
EDWIN MANSFIELD
while the diffusion process is probably better understood than many other
aspects of technological change, much more research is needed.7
INTERNATIONAL TEClINOLOGY TRANSFER
One aspect of the diffusion process that has received considerable attention
in recent years is international technology transfer. Economists have long
recognized Nat the transfer of technology is at the heart of the process of
economic growth, and that the progress of both developed and developing
countries depends on the extent and efficiency of such transfer. In recent
years, economists also have come to realize (or rediscover) the impact of
international technology transfer on the size and patterns of world trade.
The work of Hufbauer (1966), Tilton (1971), Schwartz, myself, and others
indicates that technology is being transferred across national boundaries more
rapidly than in the past. Based on a sample of chemical, semiconductor, and
pharmaceutical innovations, this was found to be true even when a variety
of other relevant factors were held constant. In considerable part, this is due
to the growing influence of multinational Finns, many of which are heavily
involved in transfemng technology. U.S.-based multinational fimns are trans-
femng their technology to their foreign subsidiaries much more quickly than
in the past. One study of technology diffusion found that in 1969 to 1978
about 75 percent of the technologies that were transferred to subsidiaries in
developed countries were less than 5 years old, in 1960 to 196S, the proportion
was about 27 percent (Mansfield and Romeo, 19801.
Nations that spend relatively large amounts on R&D (in the relevant in-
dustry) tend to be relatively quick to begin producing a new product, even
if they are not the innovator. This finding is analogous to the finding (cited
above) that firms that spend relatively large amounts on R&D tend to be
quick adopters of new technology developed by others. Both for entire nations
and individual firms, R&D provides a window on various parts of the en-
vironment, and it enables the nation or firm to evaluate external developments
and react more quickly to them. In some economic models, R&O is viewed
as an invention-producing or innovation-producin~, activity. While correct
as far as it goes, this view misses much of the point of R&D, which is that
it is also aimed at a quick response to rivals and at clever modification,
adaptation, and improvement of their results.
In many important industries, like pharmaceuticals, international technol-
ogy transfer is being promoted by the fact that companies have been carrying
on increasing shares of their R&D overseas. About 10 percent of the R&D
camed out by U.S. firms is performed outside die United States. In some
industries, again like pharmaceuticals, this percentage is much larger. When
compared with me total R&D expenditures in various host countnes, the size
of overseas R&D is perhaps even more striking. In the early 1970s, about
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MICROECONOMICS OF TECHNOLOGICAL INNOVATION
321
one-half of the industrial R&D performed in Canada and about one-seventh
of the industrial R&D performed in the United Kingdom and West Ge~any
was done by U.S.-based films (Mansfield, 19841.
International technology transfer has also been promoted by the fact that,
in many areas, the process of innovation has been internationalized. For
example, in the pharmaceutical industry, a new ding is no longer discovered,
tested, and commercialized, all within a single country. Instead, the discovery
phase often involves collaboration among laboratories and researchers located
in several different countries, even when they are within the same firm. And
clinical testing generally becomes a multicoun~y project. Even in the later
phases of drug development, such as dosage formulation, work often is done
in more Man one country. In contrast, older economic models of He process
of international technology transfer often tended to assume that innovations
were camed out in a single country, generally the United States, and Hat
the technology resided exclusively within that country for a considerable
period after the innovation's initial commercial introduction. Economists are
in the process of replacing these models with others that conform more closely
to current conditions.
Etht;CTS ON OTHER COUNTRIES OF THE
OIJTELOW OF U.S. TECHNOLOGY
As pointed out earlier, technology transfer lies at He heart of the process
of economic development. Innovations are primarily responsible for many
increases in output per capita. How rapidly innovations spread and thus
raise per capita output in countries other than the innovating nation depends
on the process of technology transfer. The multinational firm is, of course,
a major agent in the process of international technology transfer, but its role
is highly controversial. Many host countries, although eager for modern
technology, are suspicious of the activities of multinational firms.
One of the most important unanswered questions concerning He transfer
of technology via the multinational firm is: How big have the economic
benefits to countries outside the United States been from technology transfer
of this sort? Put somewhat more precisely, bow much lower would total
output outside the United States have been if technology transfer of this sort
had not occurred, but if He relevant technology and goods were perhaps
available from He United States or elsewhere? Economists have begun to
assemble some quantitative evidence bearing on this question. Based on the
limited data available, it has been suggested Hat total annual output of
countries outside He United States would have been at least $35 billion, or
at least 1 percent, less if technology transfer of this sort had not occurred
(Mansfield and Romeo, 19801. (Note Hat this estimate is a lower bound.)
However, much more work is required in this area. International technology
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322
EDWIN MA~SFIEI~:)
transfer, like so many other aspects of technological change, is very imper-
fectly understood.
CONCLUSIONS
Microeconomists have been making steady pro press in the 30 years that
have elapsed since the economics profession began to direct a substantial
proportion of its energies and resources to the study of technological change.
About 15 years ago, I was asked by the National Science Foundation to write
a report (which subsequently appeared in Science) describing what we thought
we knew in this area and what types of research needed to be done (Mansfield,
19721. It is gratifying to say that significant progress has been made on
practically all of the topics identified there as being in great need of fumier
work.
In a chapter of this length, it is impossible to do more than sample some
of the many kinds of research that microeconomists have carried out in the
past 30 years. A wide variety of econometric models, empirical investiga-
tions, computer simulations, and other kinds of output has resulted from the
extensive work (largely financed by the National Science Foundation) that
has been done. I have made no attempt to cover all, or even most, of the
relevant results. Among the interesting contributions in recent years have
been Paul David's (1975) diffusion studies, George Eads's (e.g., 1980) policy
research, Zvi Gnliches's (1958, 1964, 1980, 1984) and Dale Jorgenson's
econometric investigations (e.g., Gollop and Jorgenson, 1980), the theoret-
ical work of Kamien and Schwartz (1982), the dynamic models of Burton
Klein (1977), the computer simulation models of Nelson and Winter (1982),
the empirical studies of Pavitt and his associates at Sussex University's
Science Policy Research Unit (e.g., 1974), Nathan Rosenberg's historical
investigations (1982), Richard Levin's (1984) and F. M. Scherer's statistical
studies (e.g., 1982), and Vernon Ruttan's innovation models (e.g., Evenson
et al., 19791. Many of these investigators are contributors to this volume,
so I have not attempted to summarize their results, since they obviously have
both an absolute and comparative advantage in this regard.8
Finally, although we clearly know a great deal more about Me economics
of technological change than we did 30 years ago, it is evident that we are
still very far from a satisfactory understanding of the innovation process, the
determinants of Me rate of innovation, the measurement of Me rate and
direction of technological change, and Me effects of changes in technology.
In view of the enormous difficulties Mat studies in these areas face, it is not
surprising that existing knowledge remains limited.
The extent to which economists are able and willing to work with, and
learn from, technologists and scientists may play a significant role in deter-
mining how successful we are in chipping away at the many perplexing
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MICROECONOMICS OF TECHNOLOGICAL INNOVATION
323
problems that remain To my mind, economists frequently have been far too
insular and divorced from technological realities. This volume is a very
welcome step in the right direction. I hope that it will set Me stage for more
extensive and effective collaboration between economists and technologists
in attempts to deal win We many fundamental problems in this area that
concern us all.
NOTES
1. Also, he found that the returns from R&D seemed to be lower in industries in which much
R&D is federally funded.
2. Many of these studies are con~ned in or referred to in Griliches (1984).
3. Much of this section and the next two sections draw heavily on Mansfield et al. (1981).
4. The empirical results presented in this section are largely from Mansfield et al. (1981).
5. A logit analysis is a statistical technique used to estimate the effects of independent variables
on a dependent variable that assumes values of zero or one.
6. Blac}cmar~ (1971) and others have found this model useful in their forecasting studies, and Hsia
(1973) and others have found it useful in studies of countries other than the United States.
;'. Models devised by economists to represent the diffusion process have been used (with some
success) by a variety of funs and government agencies. Although the applicability and power
of such models should not be exaggerated, they have proved to be reasonably helpful, if crude,
devices, when used with proper caution.
8. This list does not include many of the major figures in this field, let alone the promising younger
economists on which the future development of the field will largely depend. Several dozen
economists are currently doing interesting work in this field; I have named primarily those who
participated in the Symposium on Economics and Technology held at Stanford University,
March 17-19, 1985.
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Representative terms from entire chapter:
imitation cost
