Productivity Growth: Recent Trends and Future Prospects

Productivity Growth: Recent Trends and Future Prospects

Order Code RL31428
CRS Report for Congress
Received through the CRS Web
Productivity Growth:
Recent Trends and Future Prospects
Updated July 9, 2003
Brian W. Cashell
Specialist in Quantitative Economics
Government and Finance Division
Congressional Research Service ˜ The Library of Congress

Productivity Growth:
Recent Trends and Future Prospects
Summary
While there are many economic variables over which policymakers have at least
some direct or indirect influence, productivity growth may be among those that
remain relatively removed from the influence of deliberate economic policy.
Although many policy proposals are advocated on the grounds that they will help
boost productivity, it may be that productivity growth rates have a greater influence
on policy than policy does on the growth of productivity. It seems that variations in
the productivity growth rate are so incompletely understood that there is no clear
consensus among economists about the best way for policymakers to promote it.
Nonetheless, an important question for policymakers is whether or not the surge
in productivity growth of the late 1990s will continue. Higher productivity growth
means higher real incomes, which in combination with progressive income tax rates
yields higher federal revenues. As long as Social Security operates on a pay-as-you-
go basis, productivity growth also extends the date of reckoning as far as the trust
fund balances are concerned because the incomes of those paying Social Security
taxes will grow more rapidly than the benefits. Whether or not productivity growth
continues at the rate it did in the late 1990s is a critical concern for those making and
using long-term economic forecasts.
Productivity is a highly cyclical variable, so that assessing its rate of growth at
any single point in time requires knowing the particular stage of the business cycle.
However, it is the long run trend rate of growth of productivity that is of particular
interest because that is the source of rising standards of living. Following the
business cycle peak in the fourth quarter of 1973, productivity growth slowed
substantially. Unfortunately, that slowdown remains poorly understood, which
makes it difficult to design policies that might counteract it.
In the second half of the 1990s, however, productivity growth appears to have
accelerated. This is unusual in a mature economic expansion, which suggested to
more than a few observers that it was not just a short-term phenomenon, but rather
a sign that there was an increase in the long-term economic growth rate.
The recent pickup in productivity growth is largely due to the rapid rate of
decline in the prices of computers and other information technology (IT) equipment.
The rapid drop in price of IT equipment stimulated substantial investment spending,
raising the amount of capital per worker. An important factor in those price declines
has been innovation in the manufacture of microprocessors. Whether or not that
rapid pace of innovation keeps up, and prices continue to fall will be important
factors in future rates of productivity growth. This report will be updated as
economic developments warrant.

Contents
The Cyclical Nature of Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Measuring Productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Problems in measuring real output . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Productivity Growth: Historical Patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Multi-factor productivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Accounting for Past Changes in Productivity Growth . . . . . . . . . . . . . . . . . . 9
Computers and Productivity Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
What Explains the Acceleration in Productivity Growth? . . . . . . . . . . . . . . 14
Will the Surge in Productivity Growth Persist? . . . . . . . . . . . . . . . . . . . . . . 17
List of Figures
Figure 1. Labor Productivity in 3 Long Expansions . . . . . . . . . . . . . . . . . . . . . . . 6
Figure 2. Multi-factor Productivity – Labor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Figure 3. Multi-factor Productivity – Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Figure 4. Chain-Weighted Price Index for
Computers and Peripheral Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Figure 5. Chain-Type Quantity Index – Investment in
Computers and Peripheral Equipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Figure 6. Real Private Non-Residential
Investment as a Percentage of GDP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
List of Tables
Table 1. Growth in Output per Labor Hour,
Private Business Sector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Table 2. Contributions to Productivity Growth . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Table 3. Sources of Productivity Growth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Productivity Growth:
Recent Trends and Future Prospects
Of all the economic variables policymakers keep an eye on, productivity growth
may be one of the most important, at least over the long run, because it measures the
rate of improvement in our national standard of living. Economy-wide increases in
productivity indicate increases in real production and incomes which have been
achieved without an increase in work. Consumers can buy more of those goods and
services (or leisure) that make their lives easier or more enjoyable. Even where
productivity growth is limited to certain industries, everyone benefits from the lower
prices (or the improved quality) for those goods and services.
While there are many economic variables over which policymakers have at least
some direct or indirect influence, productivity growth may be among those that
remain relatively removed from the influence of deliberate economic policy.
Although many policy proposals are advocated on the grounds that they will help
boost productivity, it may be that productivity growth rates have a greater influence
on policy than policy does on the growth of productivity. It seems that variations in
the productivity growth rate are so incompletely understood that there is no clear
consensus among economists about the best way for policymakers to promote it.
Nonetheless, productivity growth rates do have important consequences for
policymakers. The budget process, for example, typically looks at least five, if not
10, years ahead in setting spending and tax policies. With respect to Social Security,
the time frame is even longer. Over such an extended period of time, some insight
into the outlook for productivity growth is critical to projecting other economic
variables and establishing an economic baseline on which to base budget decisions.
Without at least some understanding of underlying factors, projections of
productivity may simply reduce to quantifying forecaster optimism.
In the second half of the 1990s, productivity growth accelerated. That it
happened well into an economic expansion was unusual, and it raised hopes that it
was more than a temporary phenomenon and represented an increase in the long-run
trend rate of growth. This report examines both the cyclical and long-run
characteristics of productivity growth, discusses the recent behavior of productivity,
and considers the factors that may determine its future performance.
The Cyclical Nature of Productivity
Distinguishing variations in productivity growth that are due to short-term
economic conditions from those that might be indicative of a change in long-term
trends can be difficult. Productivity is a cyclical variable, and tends to fluctuate
considerably over the short run. Changes in longer-term trends can only be identified
by comparing growth rates over successive business cycles.

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Productivity is most often measured by output per man-hour. It is typically
procyclical. In a recession, productivity tends to decline, or grow less rapidly. As
the recession ends, and the economy begins to expand, productivity growth usually
picks up.
At the beginning of an economic contraction, demand for goods and services
declines but firms may be slow to lay off workers both because they may have
invested a considerable amount of time and money in their recruitment and training,
and because there are costs associated with laying those workers off and then re-
hiring them when business recovers. The other input to production, physical capital,
is relatively fixed in the short run. So, at the beginning of an economic downturn,
output tends to fall more rapidly than either labor or capital, and so measured
productivity declines.
If the contraction continues and production falls enough, firms will begin to lay
off workers. But, at first, they will tend to be those most recently hired with the least
amount of training and who were relatively less productive than those hired before.
Reducing the quantity of labor employed tends to moderate any initial deceleration
in measured productivity growth.
As the contraction comes to an end and the economy begins to expand again,
firms can increase their output initially by putting idle capital back to work and
taking advantage of any under-utilized labor already on hand. This increase in output
with little or no increase in either labor or capital is reflected in relatively rapid
productivity growth. Once increasing demand can no longer be satisfied with
existing capacity, additional labor will be added. Those hired first will tend to be
those relatively more experienced. As more and more labor is hired, the contribution
to output of each additional hire tends to drop. Thus, as the expansion ages,
productivity growth slows from the rates earlier on in the expansion.
Measuring Productivity
Productivity is a ratio of the quantity of output produced to the quantity of
inputs used in its production. An increase in the quantity of output with no increase
in either labor or capital would be an increase in productivity. As long as output is
rising faster than the contribution of labor and capital, measured productivity will
rise.
There are two different measures of productivity published by the federal
government. Both are published by the Bureau of Labor Statistics of the Department
of Labor (BLS). The measure of output used by BLS in the calculation is based on
data from the national income and product accounts published by the Bureau of
Economic Analysis of the Department of Commerce (BEA). The first, and probably
the one most often cited in press reports, is labor productivity. Labor productivity
is measured in terms of average output per hour. It is a ratio of the quantity of output
to the hours of work done. If the quantity of output rises by the same proportion as
the amount of work, then the economy is only producing more either because there
are more workers, or workers are putting in longer hours, and there is no productivity
growth. If output rises faster than hours worked, labor productivity is also increasing.

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The second measure, known as multi-factor productivity, also referred to as total
factor productivity, accounts for increases in both hours worked and growth in the
capital stock. Multi-factor productivity typically rises more slowly than labor
productivity because there is a more complete accounting of inputs. The difference
between the two measures is mainly attributable to increases in the ratio of capital to
labor.
Problems in measuring real output. Production of goods and services is
necessarily measured in terms of dollar values because that is the only unit of
measure common to all of the factors involved. The dollar value of output, however,
reflects both prices and quantities. Distinguishing between changes in output that are
“real” (i.e., indicative of changes in quantity), and changes that are only due to
variations in the general price level is a difficult problem.
Productivity measures are based on inflation-adjusted measures of output. The
way in which price change is measured can thus affect measures of productivity
growth. If existing price indexes understate the rate of inflation, that will cause
estimates of productivity growth to be overstated
Few goods or services stay the same from year to year. Over time, most
products acquire new characteristics that make it difficult to compare them with
earlier models. An increase in the price of a car, for example, may reflect rising
prices throughout the economy, but it may also reflect new features such as catalytic
converters or airbags. Ideally, those price increases due to the addition of these new
characteristics would not affect the price index for cars. Even though the same
number of cars might be sold in successive periods, the newer model car might
provide a better (e.g. safer or less polluting) service over its useful life. A more
difficult problem is the introduction of an entirely new product because there is no
price from an earlier period with which to compare the introductory price.

Of all the goods and services produced, computers may be changing the most
rapidly from year to year. The prices of computers have been falling, and their
performance has been improving dramatically. Rather than simply track change in
the price of a “computer” from one year to the next, the BEA attempts to track
changes in the price of “computing power.”1 That means that it tries to take into
account changes in memory, processing speed, and other features when estimating
price change in successive models of computers.
Some sectors of the economy may be easier to measure than others. In the case
of manufactured goods there is at least a tangible product that can be counted even
though there may be difficulties in assessing changes in its quality or other
characteristics. In the case of services, it can be difficult even to define what is being
produced. Take medical care, for example. In the case of physician services, what
should be measured as production, the number of office visits per hour? Should
success at treating various ailments be taken into account?
1 J. Steven Landefeld and Bruce T. Grimm, “A Note on the Impact of Hedonics and
Computers on Real GDP,” Survey of Current Business, December 2000, pp. 17-22.

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Some have argued that because the service sector accounts for a growing share
of total national output, and because it is more difficult to measure productivity in the
service sector, that overall measures of productivity have become more prone to
error. At one time it was suggested that at least part of the slowdown in productivity
growth that began, by most accounts, in 1973 may have been due to measurement
problems associated with the increased size of the service sector.2 More recent
evidence suggests that is unlikely to have been the case. Those sectors considered
to be harder to measure account for some of the larger productivity gains since 1995.3
Productivity Growth: Historical Patterns
How does productivity growth in the economic expansion of the 1990s compare
with those of the past? Table 1 presents data for annual rates of growth in labor
productivity between successive business cycle reference dates for the post-War era.
Two observations are apparent from the data in the table. First, there is clearly
a tendency for productivity growth to be procyclical. That is to say that productivity
growth is higher during expansions (trough to peak) than during contractions (peak
to trough). Second, it is clear from the data that, since the business cycle peak in the
fourth quarter of 1973, productivity growth has been quite a bit slower than was the
case before.
In order to separate changes in long-term trends from those changes that reflect
more variable short run economic conditions, analysts often compare data from
similar points in successive business cycles. A look at the peak-to-peak rates of
growth in productivity for past business cycles reveals that productivity growth fell
by about half after 1973.
2 Zvi Griliches, “Productivity, R&D and the Data Constraint,” American Economic Review,
Volume 84, Issue 1, March 1994, pp. 1-23.
3 Robert J. Gordon, Recent Productivity Puzzles in the Context of Zvi Griliches’ Research,
paper presented to meetings of the American Economic Association, Jan 5, 2002, 17 pp.

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Table 1. Growth in Output per Labor Hour,
Private Business Sector
business cycle reference dates
average annual rate of growth from:
(year and quarter)
(percent)
peak to
trough to
peak to
peak
trough
peak
trough
peak
peak
1948:4
1949:4
1953:3
3.6
4.4
4.2
1953:3
1954:2
1957:3
1.6
2.6
2.4
1957:3
1958:2
1960:2
2.1
3.1
2.8
1960:2
1961:1
1969:4
1.1
3.4
3.2
1969:4
1970:4
1973:4
2.9
3.2
3.1
1973:4
1975:1
1980:1
0.3
1.7
1.4
1980:1
1980:3
1981:3
-1.7
3.2
1.6
1981:3
1982:4
1990:3
-0.3
1.9
1.6
1990:3
1991:1
2001:1
-1.0
2.1
2.0
Sources: National Bureau of Economic Research; Department of Labor,
Bureau of Labor Statistics.
For much of the post-World War II era, the U.S. experienced relatively rapid
rates of productivity growth. Between the fourth quarter of 1948 and the fourth
quarter of 1973, output per labor hour in the private business sector grew at an annual
rate of 3.2%. Between the fourth quarter of 1973 and the first quarter of 2001, that
rate fell to 1.7% per year. Most economists point to 1973 as the beginning of an
extended period of slower productivity growth. This drop in the rate of productivity
growth has been the focus of much economic research. Thus far, the slowdown
remains poorly understood.
The economic expansion which began in March 1991 and ended in March 2001
(120 months) was the longest U.S. expansion on record.4 Only two other expansions
have been long enough to allow meaningful comparisons. The expansion of the
1960s began in February 1961 and ended in December 1969 (106 months), and the
expansion of the 1980s began in November 1982 and ended in July 1990 (92
months). Figure 1 compares productivity growth in the latest economic expansion
with those two earlier expansions. For each expansion represented, productivity is
set equal to 100 at the initial cycle trough (i.e., the starting point of the expansion).
4 The Business Cycle Dating Committee of the National Bureau of Economic Research is
widely acknowledged as the arbiter of U.S. business cycle reference dates. See its website
at: <http://www.nber.org/cycles.html>.

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Figure 1. Labor Productivity in 3 Long Expansions
Source: Department of Labor, Bureau of Labor Statistics.
The chart shows that the expansions of the 1960s and the 1980s followed, more
or less, the typical pattern described earlier. Rates of growth tended to be more rapid
earlier in the expansions than was the case towards the end of the expansion. It is
also clear that productivity grew much more rapidly in the expansion of the 1960s
than in the 1980s.
The expansion of the 1990s did not follow the typical pattern. For about the
first half of the expansion it did seem to be typical with productivity growth slowing
from its pace at the start. But after about four years, productivity growth
unexpectedly accelerated. That productivity growth accelerated at the midpoint of
the expansion has been interpreted as evidence that there has been a shift in the long-
run trend.
Multi-factor productivity. BLS also publishes an alternative set of
productivity measures which account not only for labor used to produce goods and
services, but also for the role that capital plays in the production process – multi-
factor productivity, as described above.
Capital comes in two forms, physical capital and human capital. Physical
capital refers to the land, plant, equipment, and inventories that are used in the
production of goods and services. Human capital results from investments in
education and training which yield a more capable workforce.

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In calculating the multi-factor measure of labor productivity, the input of labor
takes changes in labor quality into account. In other words, an adjustment is made
which reflects investments in education and training, otherwise known as human
capital. Figure 2 compares growth in this measure of labor productivity in the current
expansion with previous business cycles. (These data are only available annually).
Figure 2. Multi-factor Productivity – Labor
Source: Department of Labor, Bureau of Labor Statistics.
Figure 2 shows again that the expansions of the 1960s and 1980s exhibited,
more or less, typical patterns of productivity growth. That is to say, growth rates
were relatively more rapid early in the expansions and relatively less rapid as the
expansions drew to a close. As was the case with the previous measure of labor
productivity, the expansion of the 1990s was atypical. Although productivity growth
was slower in the second, third and fourth year than in the first year of the expansion,
productivity growth accelerated in the fifth year and continued at a relatively rapid
pace.

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Figure 3 compares growth in multi-factor, also known as total factor,
productivity over each of the three long postwar expansions. This measure accounts
for contributions of both labor and capital in the production of goods and services.
Because capital’s contribution to the production of goods and services is also taken
into account, it rises by less than do the other measures which take only labor hours
into account.5
Figure 3. Multi-factor Productivity – Total
Source: Department of Labor, Bureau of Labor Statistics.
The chart, like the two earlier ones, shows that the expansions of the 1960s and
1980s, were fairly typical in terms of the cyclical behavior of productivity, and that
productivity growth in the 1960s was much faster than it was in the 1980s. It also
shows that the cyclical behavior of productivity growth in the 1990s expansion was
typical through the first 4 years, but was much weaker than in the other two long
expansions. After the fourth year, however, productivity growth accelerated.
The unexpected acceleration in productivity in the second half of the 1990s,
exhibited by all of these measures, prompted many observers to speculate that there
had been an important change that some characterized as the “new economy.” It was
claimed that, with the expansion of the internet and the widespread use of computers,
we were on the verge of an era of much more rapid economic growth.
5 Remember that productivity is a ratio of output produced to the inputs used in its
production. By adding capital to the inputs, the ratio falls.

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Accounting for Past Changes in Productivity Growth
The long-term trend rate of growth in productivity apparently slowed in the early
1970s. The search for causes attracted the attention of many analysts. If there is to
be any hope of designing policies likely to stimulate growth in living standards, or
of making projections of productivity growth, it would seem that some understanding
of past variations would be critical. It would be particularly gratifying if some single
dominant factor could be identified so that policies to counteract it could be designed.
Unfortunately, results to date might be described as unsatisfying.
The prime suspect, at least initially, was the OPEC oil price hikes which, in
1973, roughly doubled the price of crude oil. The fact that this price “shock” was
more or less coincident with the slowdown in productivity growth made it a likely
candidate. The theory was that, because of the suddenly higher cost of energy, much
of the existing capital stock became obsolete.6
Since 1973, however, there have been additional oil price shocks with no
appreciable effect on the rate of productivity growth. Between 1979 and 1981, oil
prices more than doubled again. In 1986, the price of oil fell by nearly half. In real
terms, the average price of oil in 1998 was less than it was in 1973.7 That only one
of the large oil price changes was associated with a change in productivity growth
suggests that, if there is a single factor to blame for the 1973 slowdown, it is likely
to be found elsewhere.
Another potential cause which has been examined is spending on research and
development. Griliches examined the role of R&D spending in the productivity
slowdown.8 He found a decline in R&D spending relative to GDP beginning in the
mid-1960s. That timing would seem to make it a good suspect in the 1973
slowdown. But, other countries that also experienced productivity decelerations had
no corresponding drop in R&D spending. That doesn’t mean R&D played no role,
but it would be easier to implicate R&D spending if there had been other instances.
Griliches also points out that, in the 1970s, the number of patents granted in the
U.S. declined. That, paired with an increase in the dollar level of R&D spending,
resulted in a decline in the number of patents per R&D dollar. Griliches suggests that
this decline in the number of patents relative to R&D spending leaves open the
possibility of diminishing returns to R&D spending and wonders if it is possible that
there exists some sort of technological frontier near which the opportunities for
invention become scarce.
6 Martin Neil Baily, “Productivity and the Services of Capital and Labor,” Brookings
Papers on Economic Activity 1, 1981, pp. 1-50.
7 D e p a r t m e n t o f E n e r g y d a t a a v a i l a b l e o n t h e w e b a t :
<http://www.eia.doe.gov/emeu/aer/txt/tab0519.htm>.
8 Zvi Griliches, “Productivity, R&D, and the Data Constraint,” American Economic
Review, March 1994, volume 84, number 1, pp. 1-23.

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In an attempt to identify causes of the post-1973 slowdown in real output
growth, Angus Maddison analyzed a range of likely factors and tried to estimate the
contribution of each one.9 Maddison was only able to “explain” 41% of the 1.4% per
year deceleration in real output. Of the 14 separate factors examined, the largest was
found to have accounted for less than one-seventh of the slowdown.
Thus, while it might be satisfying if a single cause for the productivity
slowdown could be found, it seems more likely that there are multiple factors at
work. If the slowdown in productivity had affected all industries more or less
equally, that might have favored the case for a single cause for the slowdown,
although one factor would not necessarily be expected to affect industries to the same
degree. However, when productivity trends are examined for individual industries,
some are found to have fared quite well in comparison with others.10
Economist Robert Gordon has offered a slightly different perspective on the
1973 productivity slowdown. He maintains that, rather than explaining why
productivity growth slowed in the 1970s and 1980s, the focus instead should be on
why it was so rapid earlier in the century.11 Gordon argues that, in the 1970s,
productivity growth simply fell back to its long run trend rate, and that the growth
experienced earlier in the century was unusually rapid due to a number of
technological advances. Among the advances he cites are the spread of electric
motors, the internal combustion engine, and the telephone and its derivatives.
Gordon argues that, together, these innovations had a much greater economic effect
than the electronic computer. Gordon goes on to suggest that the economy may be
on a long-run curve of diminishing returns to technological advancement, and that
the various modifications and improvements to devices such as computers are less
important than their initial introduction.
It may be that there will always be some portion of productivity growth or
variation in productivity growth that will remain unexplained. Early theoretical
models of economic growth treated that part of productivity not due to capital
accumulation as “exogenous,” which is economic jargon for a variable which does
not react to the internal influences of an economic system, but rather is external and
independent.12 If that is true, there will always be limits to the extent of influence
public policy can have on productivity growth.
9 Angus Maddison, “Growth and Slowdown in Advanced Capitalist Economies,” Journal
of Economic Literature, volume 25, number 2, June 1987, pp. 649-681.
10 Robert J. Gordon, Problems in the Measurement and Performance of Service-Sector
Productivity in the United States, National Bureau of Economic Research, Inc. Working
Paper #5519, March 1996.
11 Robert Gordon, “Comments,” in Ben S. Bernanke and Julio J Rotemberg, eds., NBER
Macroeconomics Annual 1996, MIT Press, pp. 259-267.
12 Robert Solow, “Technical Change and the Aggregate Production Function,” Review of
Economics and Statistics, volume 39, August 1957, pp. 312-320.

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Computers and Productivity Growth
Robert Solow, a major contributor to the theory of economic growth, is often
quoted for his remark that the effect of computers can be seen everywhere but in the
productivity statistics.13 Through the 1980s and early 1990s, there seemed to be no
big payoff from the growing stock of computers. That presented a puzzle to those
who expected significant returns.
But it is now believed that computers have had much to do with the acceleration
in productivity growth since the mid-1990s. Whether or not that is the case and how
computers have affected productivity growth are important in trying to assess how
durable the acceleration will prove to be.
It is getting to the point where consumers expect the rapid pace of innovation
in the manufacture of computers to continue. It is also widely assumed that the speed
and memory capacity of those computers will continue to improve at a steady pace.
This rapid rate of technological advance in the development and manufacture of
computers was predicted in 1965 by Gordon E. Moore, one of the co-founders of
Intel Corporation.14 Specifically “Moore’s Law” predicted that the number of
transistors that could be put on a computer chip would double every 18 months.
Whether or not that prediction was a self-fulfilling prophecy may be open to
question, but the fact is that the pace of technological advance in the manufacture of
computers has vindicated Moore’s Law over time.
Because of the rapid innovation in the production of computer chips, the prices
of computers, as well as other goods related to information processing and
communications, sometimes referred to collectively as information technology (IT),
have been falling steadily for some time. Figure 4 shows the chain-weighted price
index, published by BEA, for computers and related equipment from 1959 through
early 2003. Because the changes are so large the chart is plotted on a logarithmic
scale. Prices for computers have clearly been on a steady downward trend. Between
1959 and 1995, computer prices fell at an average annual rate of nearly 17%, and
between 1995 and 2003 prices fell at an annual rate of 19.9%.
13 Robert Solow, “We’d Better Watch Out,” New York Times Book Review, July 12, 1987,
p. 36.
14 Gordon E. Moore, Cramming more components onto integrated circuits, Electronics,
Volume 38, Number 8, April 19, 1965. See also the Intel web site:
[http://www.intel.com/research/silicon/mooreslaw.htm].

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Figure 4. Chain-Weighted Price Index for
Computers and Peripheral Equipment
Source: Department of Commerce, Bureau of Economic Analysis.
These price declines reflect substantial improvements in the quality of
computers. BLS has developed a procedure for estimating price indexes for goods
whose characteristics are changing rapidly. These are referred to as “hedonic” price
indexes. Hedonic price indexes attempt to estimate a statistical relationship between
prices and a set of characteristics, such as memory and processor speed.
These price indexes are important to the measurement of productivity, because
estimating price change is necessary to estimating change in real output and thus
productivity. If the rate of price decline in computers is overestimated, then
measures of productivity will be overstated. Most studies estimate that, in the late
1990s, prices for personal computers alone fell at an annual rate of somewhere
between 30% and 40%.15
Rapid declines in computer prices have, not surprisingly, stimulated a surge in
investment. Figure 5 shows the chain-linked quantity index for investment in
computers and related equipment from the national income and product accounts
(NIPA). Although there are data back to 1959, production of computers was
negligible until the 1980s. Thus, even though real output of IT equipment was
increasing rapidly, it did not account for a very large share of total output until
recently.
15 J. Steven Landefeld and Bruce T. Grimm, “A Note on the Impact of Hedonics and
Computers on Real GDP,” Survey of Current Business, Dec. 2000, pp. 17-22.

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Figure 5. Chain-Type Quantity Index – Investment in
Computers and Peripheral Equipment
Source: Department of Commerce, Bureau of Economic Analysis.
Increased investment in computers made a direct contribution to the increase in
economic growth of the late 1990s. Between 1973 and 1995, real GDP increased at
an average annual rate of 2.9%. Of that 2.9% growth, production of information
processing equipment and software contributed 0.3 percentage points. Between 1995
and 2000, real GDP grew by an average of 3.9%, and information processing
equipment and software contributed 0.7 percentage points of that growth.
Computers have affected growth in productivity in at least two ways. First,
there has been rapid productivity growth in the production of computers which, as
computers accounted for an increasing share of total production, tended to raise the
overall measure of productivity growth. Second, the sharp drop in computer prices
has stimulated increased investment in computers, which has contributed to an
increase in the overall amount of capital available to the workforce. This is often
referred to as “capital deepening.” Increases in the capital stock generally tend to
raise worker productivity.
In part because of increased spending on IT equipment, the overall rate of
investment spending rose significantly in the 1990s. Figure 6 presents real fixed
investment as a share of total GDP since 1987. Since the end of the 1990s
expansion, the ratio has fallen, but it remains above its 1995 level.

CRS-14
Figure 6. Real Private Non-Residential
Investment as a Percentage of GDP
Source: Department of Commerce, Bureau of Economic Analysis.
There remains the question, however, whether or not the acceleration in
productivity growth is limited to developments in the world of information
technology.
What Explains the Acceleration in Productivity Growth?

CRS-15
Prior to the recent acceleration in productivity growth, most analyses found that
computers were not yielding much benefit. One reason for that is that, until recently,
computers accounted for a relatively small share of the total capital stock.16
But, that view has now changed. Although perhaps not yet embracing all of the
claims of proponents of a “new economy,” economists are encouraged that the
acceleration in productivity growth of the late 1990s may mean that the economy is
on a higher growth path and that computers have had a lot to do with it.
Two recent studies, discussed below, found considerable evidence that the
computer, or more generally IT equipment, is behind most of the recent acceleration
in productivity growth. There is also evidence of a modest “spillover” into other
sectors of the economy. In other words, investment in computers can raise the
productivity of the workers who use them, but it may also lead firms to change the
way they operate leading to further productivity gains.
The first study, by Oliner and Sichel at the Federal Reserve Board, found that
of a 0.9 percentage point increase in the growth rate of total factor productivity from
the first half of the 1990s to the second half, all of it could be accounted for by
advances in the production of computers themselves and the also by the use of those
computers.17 Table 2 presents a breakdown of Oliner and Sichel’s accounting for
productivity growth for selected periods since 1974.
Table 2. Contributions to Productivity Growth
1974-1990
1991-1995
1996-2001
change
(1)
(2)
(3)
(3) - (2)
Growth rate of labor productivity
1.36
1.54
2.43
0.89
Contributions from:
Capital Deepening
0.77
0.52
1.19
0.67
Information technology capital
0.41
0.46
1.02
0.56
Other capital
0.37
0.06
0.17
0.11
Labor quality
0.22
0.45
0.25
-0.20
Multi-factor productivity
0.37
0.58
0.99
0.41
Semiconductors
0.08
0.13
0.42
0.29
Computer hardware
0.11
0.13
0.19
0.06
Software
0.04
0.09
0.11
0.02
Communication equipment
0.04
0.06
0.05
-0.01
16 Stephen D. Oliner and Daniel E. Sichel, “Computers and Output Growth Revisited: How
Big is the Puzzle?” Brookings Papers on Economic Activity, 2:1994, pp. 273-334.
17 Stephen D. Oliner and Daniel E. Sichel, Information Technology and Productivity:
Where Are We Now and Where Are We Going? Board of Governors of the Federal Reserve
System, May 2002, 78 pp.

CRS-16
Other nonfarm business
0.11
0.17
0.23
0.06
Source: Oliner and Sichel.
Oliner and Sichel found that of a 0.89 percentage point increase in average labor
productivity between the early and late 1990s, 0.56 was due to increased investment
in IT related capital (an increase from 0.46 to 1.02), and 0.35 was due to increased
productivity in the production of IT equipment (an increase from 0.26 to 0.61 in the
combined computer and semiconductor sectors). Thus, the contribution of IT
equipment to the increase in productivity was greater than the overall increase.
Oliner and Sichel also found that labor quality’s contribution to productivity growth
declined during the 1990s. That is likely related to cyclical factors as the
unemployment rate fell and the available pool of skilled workers shrank.
Oliner and Sichel, using an economic model, attempted to assess the
implications of recent developments in the technology sector for prospects for
continued rapid productivity growth. They conclude that productivity growth is
likely to fall somewhere in the range of 2 - 2¾% over the next ten years.
A second study, by Jorgenson, Ho, and Stiroh, came to similar conclusions.18
Table 3 presents the results of their analysis.
Table 3. Sources of Productivity Growth
1959-1973
1973-1995
1995-2001
change
(1)
(2)
(3)
(3) - (2)
Growth rate of labor productivity
2.63
1.33
2.02
0.69
Contributions from:
Capital Deepening
1.13
0.80
1.39
0.59
IT Capital Deepening
0.19
0.37
0.85
0.48
Other Capital Deepening
0.95
0.43
0.54
0.11
Labor Quality
0.33
0.27
0.22
-0.05
Total Factor Productivity
1.16
0.26
0.40
0.14
Information Technology
0.09
0.21
0.41
0.20
Non-information Technology
1.07
0.05
-0.01
-0.06
Source: Jorgenson, Ho, and Stiroh.
18 Dale W. Jorgenson, Mun S. Ho, and Kevin J. Stiroh, Lessons From the U.S. Growth
Resurgence, paper prepared for the First International Conference on the Economic and
Social Implications of Information Technology, held at the U.S. Department of Commerce,
Washington, D.C., on January 27-28, 2003, 28 pp.

CRS-17
According to Jorgenson, Ho, and Stiroh’s estimates, of a 0.69 percentage point
rise in average labor productivity growth during the 1990s, increased investment
(capital deepening) accounted for 0.59 percentage point, and improved productivity
in the IT sector itself contributed another 0.20 percentage point of the acceleration.
The evidence suggests that increased productivity in the sector producing IT
equipment has had a modest direct effect on total factor productivity. By far the
more important factor has been the declining price of IT equipment stimulating a
surge in investment and increasing the size of the capital stock.
Remember that total factor productivity measures changes in output that are not
accounted for by changes in economic inputs such as labor and capital. There is no
doubt that computers are raising productivity of many firms, but, as long as economic
statistics measure them correctly, the increased share of work that computers do will
not show up in increased multi-factor productivity because that measure of
productivity tracks the increase in output not associated with the increase in
investment in computers.19 It is unclear whether or not computers have had any
“spillover” effects on multi-factor productivity beyond their direct contribution to
growth in output.
There is some evidence to suggest that those spillover effects – of computers on
total factor productivity – are fairly small. Jorgenson and Stiroh found that those
sectors of the economy that invest most heavily in computers and IT equipment, such
as financial services, had among the lowest rates of productivity growth measured.20
Jorgenson, Ho, and Stiroh also estimated projections of growth in average labor
productivity. They projected that productivity growth would range between 1.14%
and 2.38% over the next decade, with a base case of 1.78%, just below the 1995 -
2001 rate of growth.
In another study, Robert Gordon found that most of the acceleration in labor
productivity was attributable to capital deepening and faster productivity growth in
the production of computers and IT equipment.21 Of the roughly 0.2 percentage point
increase in total factor productivity, most was accounted for by faster productivity
growth in the manufacture of durable goods. That suggests that any spillover effects
of computers on the overall economy were limited.
Will the Surge in Productivity Growth Persist?
Perhaps the most important question for policymakers is whether or not the
surge in productivity growth of the late 1990s will continue. Higher productivity
19 Barry P. Bosworth and Jack E. Triplett, What’s New About the New Economy? IT,
Economic Growth and Productivity, Brookings Institution, December, 2000, 35 pp.
20 Dale W. Jorgenson and Kevin J. Stiroh, Raising the Speed Limit: U.S. Economic Growth
in the Information Age, Brookings Papers on Economic Activity, 2000(1), volume 2, pp.
125-212.
21 Robert J. Gordon, Technology and Economic Performance in the American Economy,
Working Paper 8771, National Bureau of Economic Research, February 2002,58 pp.

CRS-18
growth means higher real incomes, which in combination with progressive income
tax rates yields higher federal revenues. As long as Social Security operates on a
pay-as-you-go basis, it also extends the date of reckoning as far as the trust fund
balances are concerned because the incomes of those paying Social Security taxes
will grow more rapidly than the benefits. Whether or not productivity growth
continues at the rate it did in the late 1990s is a critical concern for those making and
using long-term economic forecasts.22
It seems likely that the increase in productivity growth of the late 1990s was a
shift to a higher long-run trend rate of growth rather than an event limited to the most
recent cyclical expansion. But there is still no guarantee that faster growth will
endure through the next expansion.23
The major difficulty in projecting productivity growth remains an imperfect
understanding of past variations. Some of the sources of productivity growth are
clearly understood. Increased investment and a growing capital stock raise labor
productivity. Increased education and training also contribute. But aside from the
contributions of human and physical capital, much less is certain. To a great extent,
projections of productivity still reflect the optimism or pessimism of the forecaster.
It is clear however that the recent pickup in productivity is largely attributable
to the rapid rate of decline in the prices of computers and other IT equipment. An
important factor in those price declines has been innovation in the manufacture of
microprocessors. Whether or not that rapid pace of innovation keeps up, and prices
continue to fall will be important factors in future rates of productivity growth.
However, ultimately there may be limits to the number of transistors that can be put
on a single computer chip.
As computer prices have fallen, their use has become much more widespread.
Because of falling prices it has become profitable to put computers to uses with
smaller and smaller returns. But, as long as recent rates of innovation in the
production of computers and IT equipment continue, productivity might at least be
expected to continue growing more rapidly than it did between 1973 and 1995.

22 Paul W. Blauer, Jeffrey L. Jensen, and Mark E. Schweitzer, Productivity Gains, How
Permanent? Federal Reserve Bank of Cleveland Economic Commentary, September 1,
2001.
23 See also: CRS Report RS21527, The Performance of Productivity During the Recent
Slowdown: What Does It Mean for Future Living Standards? , by Marc Labonte.