FRBSF Economic Letter
2007-25; August 31, 2007
Changing Productivity Trends
As important as productivity growth is to the health of
the economy, much remains to be understood about how and
why its trend growth rate changes. This Economic Letter discusses some of the points of debate in the research
on these issues.
The data and some early explanations
 Figure 1 plots productivity growth over the last 50 years
based on calculations by the Bureau of Labor Statistics
of multifactor productivity (MFP) for the nonfarm business
sector, which "… measures the changes in output
per unit of combined inputs." Like most other such
constructs, this measure illustrates that productivity
grew rapidly in the 1960s, then slowed from the 1970s to
the mid-1990s, and then grew rapidly again.
Much ink has been spilt trying to explain the period
of slow productivity growth. A favorite early candidate
was
the dramatic rise in the price of oil during this period.
Nordhaus (2004) provides a recent restatement, arguing
that the slowdown was concentrated in industries that
are related to oil, such as pipelines and auto repair.
Yet,
the sharp decline in oil prices in 1986, for example,
did not lead to faster productivity growth. Others suggest
that the slowdown was due to diminishing returns to science
and technology in general (see the references in Griliches
(1993)).
An explanation related to information technology
Greenwood and Yorukoglu (1997) turn the argument about
diminishing returns on its head, arguing that the slowdown
resulted not from an exhaustion of technical possibilities
but from the opening up of new ones, specifically, the
introduction of information technologies (IT). The authors
argue that firms and workers will take a while to learn
how to use the new technology. For example, they point
to David's (1991) analysis of electrification in America.
Before electricity, factories used a single source of
energy—typically steam or water—to power all the machines
at once, using
a system of belts and drives. They continued to use this
single-power-source structure even after the advent of
electric power, using motors to drive groups of machines.
Over time, however, firms figured out that machines could
be powered individually, leading to more efficient production
processes; for instance, the production plans for one
machine no longer had to take account of when the other
machines
were running.
Critically, during this period, when both firms and workers
are learning what to do with the new technology, worker
productivity is likely to fall below what it was otherwise.
Thus, Greenwood and Yorukoglu (GY) argue that while new
technology ultimately leads to higher productivity, the
immediate response to the new technology is likely to
be a decrease in productivity.
The GY explanation elegantly links the 1970s and the
1990s. Productivity slowed down in the 1970s as workers
and firms
struggled to learn and implement the new information
technologies; as this process moved ahead, productivity
surged in the
1990s. And the idea that learning is costly and may be
accompanied by temporarily lower output growth is plausible
as well.
What is implausible is the speed of learning required
for this story. The productivity slowdown that began in
the
early 1970s lasted more than two decades. Could it really
take that long to learn about each of the new information
technologies that have emerged over this period? Most
importantly, would the productivity growth rate remain
depressed for
25 years? A recent counterexample is the creation of
the world wide web in the mid-1990s, which was followed
almost
immediately by waves of new uses that are still ongoing
as firms have continued to figure out ways to capitalize
on it. Note that productivity growth accelerated at about
the same time. This suggests that the IT revolution (and,
by implication, any other significant shift in technology)
should be viewed not as a single drawn-out process requiring
a long period of learning during which one must live
with below normal productivity growth, but rather as a
series
of related breakthroughs and inventions, each of which
is figured out and mastered (and the productivity gains
enjoyed) as it emerges.
 Also suspect in the GY analysis is their approach to
measuring technical progress. They recommend using the
price of capital
goods (defined here to include equipment as well as consumer
durables) relative to the price of consumer goods (defined
to equal nondurable consumer goods and services). This
recommendation not only can be justified by a formal
model, but its underlying intuition also is straightforward:
think
of how much the price of computers has fallen (relative
to the price of shirts, say) over the last two decades
and how much more productive computers have become over
the same time. Figure 2 shows that the decline in the
relative price of capital goods is not a recent phenomenon,
but
has been going on for some time. However, as Marquis
and Trehan (2007) point out, this price also depends upon
what
is happening to the price of consumer goods. When the
founder of Wal-Mart figured out how to create a more efficient
retail chain, for example, he caused the price of shirts
to fall relative to the price of numerically controlled
machines. But GY would see the resulting rise in the
relative
price of capital goods as a negative shock to capital
sector technology. More generally, the point is that the
change
in the behavior of the relative price of capital goods
in the 1970s could have been caused by changes in productivity
outside that sector.
An alternative that emphasizes the service sector
Griliches (1993) provides one set of arguments along
these lines, arguing that the productivity slowdown that
began
in the late 1960s tended to be concentrated in the
service sectors, where it was hard to measure (for example,
health
services) and not in sectors where measurement was
relatively easy (such as manufacturing).
How large a role measurement issues may have played has
been a matter of dispute, but others—such as Triplett
and Bosworth (2000)—have confirmed that productivity
did slow down by more in the service sector. In subsequent
research (2007), these authors argue that the service
sector
productivity deceleration that took place in the early
1970s has been reversed since the mid-1990s. Using a
data set that covers 34 service sector industries and spans
the 1987-2005 period, they calculate that annual MFP
growth
in the service sectors accelerated from 0.5% over the
1987-1995 period to 1.3% over 1995-2000 and to 1.5% over
the 2000-2005
period; the corresponding numbers for the goods sectors
are 1.8%, 2.3% and 1.9%. According to the authors: "The
services sector contributed three-quarters of the economy
wide acceleration in MFP after 1995, a contribution that
is without historical precedent" (p. 4).
Based on this evidence, they conclude that "Baumol's
disease" has been cured. This refers to Baumol (1967),
who pointed out that in an economy where productivity was
growing in some but not all sectors (with teaching and
hospitals among many examples of the latter), economic
growth would slow down over time, unless consumers were
willing to reduce the share of income they spent on goods
with little or no productivity growth. His account provided
an explanation for what happened in the U.S. during the
1970s, when productivity growth slowed down and employment
in the service sector grew much faster than in manufacturing.
While Bosworth and Triplett's careful measurement and
analysis highlight the role of MFP changes in the service
sector
both in the productivity slowdown of the 1970s and the
acceleration of the 1990s, we do not know if these changes
are statistically significant. For example, it has been
argued that the only clear evidence of productivity change
in services lies in the wholesale and retail trade sectors.
Nor do the authors provide any explanation of what caused
the changes in service sector productivity growth. It
is difficult to know, for instance, what to make of the "measurement
explanation" of the 1970s productivity slowdown in
light of the recent acceleration in service sector productivity.
Another aspect of the Bosworth and Triplett argument
is that the acceleration in MFP growth in the service sector
is not related to either previous or contemporaneous
IT
investment. Note that this is not the same thing as saying
that increasing amounts of IT investment do not affect
output per worker or labor productivity.
But others have emphasized the role of IT (and the capital
goods sector more generally) in the recent productivity
acceleration. Basu and Fernald (2006) argue that the
recent productivity surge represents the effects of earlier
IT
investment. In particular, they argue that cheaper IT
allows firms to reorganize production in radically different
and
more productive ways and also fosters complementary innovations.
Further, it takes some time for these productivity enhancing
effects of IT investment to manifest themselves. In their
empirical analysis, they find that, during the 2000s,
productivity growth accelerated by more in industries that
had high
IT investment growth rates over 1987-2000. Using a different
data set and techniques, Marquis and Trehan (2007) argue
that the productivity deceleration of the 1970s resulted
from a common shock that had dissimilar effects on different
sectors of the economy (which is consistent with a deceleration
in service sector productivity), but that the acceleration
of the 1990s is located in the capital goods sector alone.
Conclusions
It is tempting to look for a common explanation for the
productivity deceleration of the 1970s and the acceleration
of the 1990s. But this turns out to be a difficult task.
An alternative view is that periods of rapid productivity
growth represent changes in the level of productivity
due to unrelated innovations that come along every so often.
For example, one might see rapid productivity growth
as
the economy moved from a pre-IT environment to an IT-based
environment, but the faster growth would dissipate once
the new technology was fully incorporated into the economy.
This view still leaves us without an explanation of the
1970s most people can agree with. While analyses suggest
that some sectors slowed down by more than others, the
reasons for that are far from obvious and do not appear
to be related to the IT sector. There is greater agreement
that the productivity acceleration of the 1990s was related
to IT, though here again not everyone agrees about what—if
any—other factors may have been involved. Bharat Trehan
Research Advisor
References
Basu, Susanto, and John Fernald. 2006. "Information
and Communications Technology as a General Purpose Technology:
Evidence from U.S. Industry Data." Federal
Reserve Bank of San Francisco Working Paper 2006-29.
Baumol, William J. 1967. "Macroeconomics of Unbalanced
Growth: The Anatomy of Urban Crisis." American
Economic Review 57, pp. 415-426.
Bosworth, Barry, and Jack Triplett. 2007. "The Early
21st Century Productivity Expansion Is Still in Services." International
Productivity Monitor 14 (Spring) pp. 3-19.
Bureau
of Labor Statistics. (BLS measure of MFP and discussion).
David, Paul. 1991. "Computer and Dynamo: The Modern
Productivity Paradox in a Not-too-Distant Mirror." Technology
and Productivity: The Challenge for Economic Policy.
Paris: Organization for Economic Cooperation and Development,
pp. 315-348.
Greenwood, Jeremy, and Mehmet Yorukoglu. 1997. "1974." Carnegie-Rochester
Conference Series on Public Policy 46, pp. 49-95.
Griliches, Zvi. 1993. "Productivity, R&D, and
the Data Constraint." American Economic Review 83, pp. 1-23.
Marquis, Milton, and Bharat Trehan. 2007. "On Using
Relative Prices to Measure Capital-Specific Technological
Progress." Mimeo, Federal Reserve Bank of San
Francisco.
Nordhaus, William. 2004. "Retrospective on the 1970s
Productivity Slowdown." NBER Working Paper 10950.
Triplett, Jack, and Barry Bosworth. 2000. "Productivity
in the Services Sector." Brookings Economic Papers,
January.
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do not necessarily reflect the views of the management
of the Federal Reserve Bank of San Francisco or of the
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