FRBSF Economic Letter
2003-08; March 21, 2003
Technological Change
This Economic
Letter summarizes the papers presented at the conference "Technological
Change," held at the Federal Reserve Bank of San Francisco
on November 14-15, 2002, under the joint sponsorship of the Bank and
the Stanford Institute for Economic Policy Research. The papers are
listed at the end and are available online.
In the latter part of the twentieth century, information technology (IT)
came to be used everywhere—in offices, factories, and homes—and
transformed the way things are done in activities as diverse as jet aircraft
design, document production, and home entertainment. This technology also
has improved tremendously, as evidenced, for instance, by the quick succession
of more powerful computers with faster processors, greater storage capacity,
and so forth.
Two of the conference papers noted that the use of computers in diverse
applications was similar to the use of earlier technologies, such as steam
and electricity, and looked at the evolution of those older technologies
to understand both how computers diffused through the economy and the
effects they were likely to have on it. Another theme at the conference
was "technological embodiment," which refers to technological
change that is embedded in the machine and is the reason one must buy
a new computer every few years in order to use the latest technology.
Among other things, embodiment can explain why it took a long time for
the effects of technological change in the computer industry to show up
in higher productivity in the economy. Other papers at the conference
were concerned with the spread of technology across countries, asking,
for instance, whether the process of technological diffusion ensured that
all countries grew at the same rate.
Industrial revolutions and the diffusion of
technology
Nick Crafts (2002) uses two recent developments in economic analysis
to study his well-known finding that the pace of productivity growth and
technological innovation during the industrial revolution was not as rapid
as had been believed. The first is work on general purpose technologies
(GPTs), which Lipsey, et al. (1998) define as "...a technology that
initially has much scope for improvement and eventually comes to be widely
used, to have many uses, and to have many...technological complementarities."
The most cited examples of GPTs include electricity, steam, and IT. Second,
because conventional methods of growth accounting do not account for the
improvement in the quality of capital over time (and so tend to understate
the contribution of technological change to growth), Crafts uses some
recent techniques that explicitly account for embodiment.
An analysis of data incorporating these new developments leads him to
confirm his earlier conclusion, which is that it takes a long time for
GPTs to have a significant impact on productivity. In fact, he finds not
only that steam power had a relatively small impact on productivity growth
initially, but also that this impact was smaller than that of comparable
GPTs, like electricity and IT, at a similar point in their development.
An important reason was that the real price of steam power stayed high
for many decades. And while he does find that using the new method of
growth accounting rather than the traditional method raises the estimated
effect of technological change on British output growth between 1780 and
1860, the difference is not very large.
Atkeson and Kehoe (2001) study technology diffusion during the "second
industrial revolution" (1860-1900), when a host of new technologies
were invented, including those based on the use of electricity. Economic
historians have argued that the full effects of these technologies were
not felt until many decades after their introduction. It is not hard to
see why; for instance, in order to reap the benefits of electrification,
manufacturing firms had to replace old machinery (which relied principally
on steam power) and reorganize their production processes.
The authors' model reproducing this slow diffusion contains two key assumptions.
First, new technologies are embodied in capital goods. Second, a plant's
productivity rises with its age, reflecting a process of learning-by-doing.
The authors then consider what happens when there is a sustained acceleration
in the productivity of capital goods. While the standard model for studying
this phenomenon predicts a rapid transition to a higher long-run growth
rate that is at odds with the historical experience, the authors' model
yields a pattern of slow diffusion of new technologies through the economy,
which is similar to the pace of electrification of the manufacturing sector
in the first part of the last century.
Interestingly, their model does not imply slow diffusion during the "Information
Technology Revolution. " Recent high rates of embodied technological
progress imply that, compared to the past, capital goods now get obsolete
much more quickly and firms have less time to accumulate experience with
their capital (the learning-by-doing effect). As a result, firms scrap
their old capital much more rapidly than before. This prediction of rapid
diffusion counters the arguments of some economic historians, who use
the slow diffusion of technology in the early twentieth century to explain
why the introduction of IT late in the twentieth century did not have
a more immediate impact on productivity growth.
International diffusion of technology
In a world with embodied technology, trade in capital goods provides
a means for the international diffusion of technology. Caselli and Wilson
(2002) look at what determines the kinds of capital goods countries import
and the effects of these decisions on a country's level of income. They
begin by specifying a production function where output depends upon labor
and different kinds of capital and show that this can be rewritten as
the product of two terms: a conventional production function where output
depends upon the quantity of labor and of capital plus a term that contains
information about the different kinds of capital in use. They hypothesize
that the amount a country invests in a particular kind of capital depends
upon the relative efficiency of that capital and upon its complementarity
with various characteristics of the country in question (such as the skill
level of its labor force). The relative efficiency of capital depends
upon the amount of research and development embedded in it.
Since most countries acquire embodied technologies by importing capital
from a relatively small number of technological "leaders," they
argue that capital imports provide a measure of technology adoption by
"follower" countries. Data on capital imports then can be used
to draw inferences about the kinds of capital investments different countries
make. They find a wide variation in the kinds of capital imported by different
countries, with the mix depending upon country-specific factors such as
human capital (or the skill level of the countries workers), institutions
(such as property rights), and the level of financial development. They
also show that taking the quality of capital into account provides a significantly
better explanation of income differences across countries than a specification
where only the quantity of capital is accounted for.
Benhabib and Spiegel (2002) examine the role of human capital in the
process of technology diffusion across countries and show that the way
this diffusion takes place matters for the long-run distribution of per
capita income across countries. They point out that several previous studies
(including one of their own) adopted a specification for the technology
diffusion process that ensures that (a measure of) productivity in all
follower countries will grow at a pace determined by technological innovation
in the leader country. However, it is possible to specify the diffusion
process in other ways, including those in which diffusion gets weaker
as the geographical distance between the follower and the leader increases.
Indeed, if the human capital stock of a follower is sufficiently low,
this kind of process implies that productivity growth in the follower
country may never catch up with the leader.
Using data on productivity growth rates for a sample of 84 countries
over the 1960-1985 period, they find that human capital (schooling) facilitates
catch-up in productivity across countries. However, their results also
favor the specification of the diffusion process which implies that productivity
growth in some follower countries may never catch up with the leader.
They estimate that an average of 1.78 years of schooling was required
in 1960 to ensure that productivity growth in a given country caught up
(eventually) with productivity growth in the U.S. Under this criterion,
they identify 27 countries that were predicted to exhibit slower productivity
growth than the U.S. Over the next 35 years, 22 of these countries did
fall farther behind the U.S. in productivity growth, while the bulk of
the nations in their sample tended to catch up with the U.S. in productivity
growth
Some other implications of technological change
Hornstein, Krusell, and Violante (2002) present a model in which the
interaction of embodied technological change with labor market institutions
helps to determine key labor market characteristics, such as the unemployment
rate and the distribution of wages across different kinds of workers.
The model also provides an explanation for the differences in the behavior
of U.S. and European labor markets in recent years. For example, in 1965
the unemployment rate was lower in virtually every European country than
in the U.S. However, while the U.S. unemployment rate rose by only 1.7%
over the next 30 years, the average increase for European countries was
8.4%.
To understand how their model works, note first that capital must be
used for a minimal period in order to recover investment costs. Labor
costs matter as well. The U.S. economy has relatively low unemployment
benefits, which implies low labor costs, so that capital can remain in
use for a relatively long time. In contrast, Europe has high benefits
and high labor costs, which forces firms to scrap capital earlier. Now
consider what happens when there is an increase in the pace of technological
change. The assumption of embodied technology means that the benefits
of faster technological change can be obtained only by faster replacement
of machines. This is relatively easy to do for U.S. firms, but it is hard
for European firms because the life of capital in Europe is already very
short. European firms must be compensated along some other margin; in
their model this occurs through an increase in the probability that a
firm's search for a worker will be successful. An increase in this probability,
in turn, requires a larger pool of available workers, which is accomplished
through longer spells of unemployment. In a quantitative exercise with
their model, they show that a 2 percentage point increase in the rate
of embodied technological progress raises the unemployment rate by less
than 1 percentage point in a U.S.-type economy but by more than 8 percentage
points in a European-type economy.
Greenwood, Seshadri, and Vandenbroucke (2002) use technological change
to explain variations in fertility rates over time. According to the authors,
two features stand out in the data on the fertility of U.S. women over
the last 200 years. The first is a drastic decline: the average white
woman had seven children in 1800 but only two in 1990. The second is a
surprising recovery in fertility between the mid-1940s and the mid-1960s—the
"baby boom."
Their model explains both features of the data by technological progress,
although of different kinds. The long-run decline in fertility is explained
by technological progress in the market sector. Ongoing technological
progress over this period has raised individuals' wage rates. The implicit
cost of having children has risen as a consequence, because individuals
now must give up a greater amount of consumption goods for every hour
spent on raising children; this tends to lower fertility. By contrast,
technological progress in the household sector tends to raise fertility,
because it frees up the time women used to spend on household tasks. Of
course, having more children is not the only possible response to more
free time; women could decide to spend some of this time in market activities
as well, i.e., their labor force participation rates could increase as
well. The authors argue that a burst of technological progress occurred
in the household sector around the 1940s, arising from the second industrial
revolution. For instance, refrigerators entered household service in the
1920s and the first fully automatic washing machine appeared in the 1930s.
Consistent with their model, these innovations were followed by a period
of rising fertility and rising female labor force participation rates.
In fact, the biggest percentage increase in fertility during the baby
boom was among working women.
Bharat Trehan
Research Advisor
Conference
papers
Atkeson, Andrew, and Patrick J. Kehoe. 2001. "The
Transition to a New Economy after the Second Industrial Revolution."
Benhabib, Jess, and Mark M. Spiegel. 2002. "Human
Capital and Technology Diffusion."
Caselli, Francesco, and Daniel Wilson. 2002. "Importing
Technology."
Crafts, Nick. 2002. "Productivity
Growth in the Industrial Revolution: A New Growth Accounting Exercise."
Greenwood, Jeremy, Ananth Seshadri, and Guillaume Vandenbroucke. 2002.
"The
Baby Boom and Baby Bust: Some Macroeconomics for Population Economics."
Hornstein, Andreas, Per Krusell, and Giovanni L. Violante. 2002. "Vintage
Capital as an Origin of Inequalities."
Reference
Lipsey, R.G., C. Bekar, and K. Carlaw. 1998. "What Requires Explanation?"
In General Purpose Technologies and Economic Growth, ed. E. Helpman.
Cambridge, MA: MIT Press.
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