Productivity Growth in the
2000s
J. Bradford DeLong1
University of California at Berkeley and NBER
March 2002
First Draft
1
I would like to thank the University of California at Berkeley for financial support, and Chad Jones, Dan
Sichel, and Larry Summers for helpful discussions.
2
Abstract
The causes of the productivity growth slowdown of the 1970s
remain mysterious. By contrast, nearly all agree that the cause of
the productivity growth speed-up of the 1990s lie in the
information technology sector. The extraordinary pace of invention
and innovation in the information technology sector has generated
real price declines of between ten and twenty percent per year for
decades. Increased total factor productivity in the information
technology capital goods-producing sector coupled with
extraordinary real capital deepening as the quantity of real
investment in information technology capital bought by a dollar of
nominal savings grows have together driven the productivity
growth acceleration of the later 1990s.
Will this new, higher level of productivity growth persist? The
answer appears likely to be “yes.” The most standard of simple
applicable growth models—that of Oliner and Sichel—predicts
that the social return to information technology investment would
have to suddenly and discontinuously drop to zero for the upward
jump in productivity growth to reverse itself in the near future.
More complicated models that focus in more detail on the
determinants of investment spending or on the sources of increased
total factor productivity appear to strengthen, not weaken, forecasts
of productivity growth over the next decade.
3
I. Introduction
In the early 1970s, U.S. productivity growth fell off a cliff. Measured output per personhour worked in nonfarm business had averaged a growth rate of 2.88 percent per year
from 1947 to 1973. It averaged a growth rate of only 1.30 percent per year from 1973 to
1995. The deceleration in the growth rate of total real GDP was somewhat smaller: a
matter of –1.18 percentage points per year in output rather than the -1.58 percentage
points per year in labor productivity, as total real GDP growth slowed from 3.91 percent
per year averaged over 1947:1 to 1973:2 to 2.73 percent per year averaged real over
1973:2 to 1995:1. The social changes that brought more women into the paid labor force
in enormous numbers cushioned the effect of this productivity slowdown on the growth
rate of measured total real GDP, if not its effect on Americans’ material welfare. The
productivity slowdown meant that, according to official statistics, Americans in 1995
were only 70 percent as productive as their predecessors back in the early 1970s would
have expected them to be. The productivity slowdown gave rise to an age of diminished
expectations that had powerful although still debated effects on American politics and
society.2
In the second half of the 1990s American productivity picked itself up, and more-or-less
resumed its pre-1973 pace. Between the beginning of 1995 and the semi-official NBER
business cycle peak in March 2001, U.S. nonfarm-business output per person-hour
worked grew at an annual rate of 2.80 percent per year. (Extending the sample through
the 2001 recession to the likely trough point of 2001:4, the late-1990s growth rate is 2.69
percent per year.) Between the beginning of 1995 and the semi-official NBER business
cycle peak in March 2001, U.S. real GDP grew at a pace of 4.21 percent per year.
(Extending the sample through the 2001 recession to the likely trough point of 2001:4,
the late-1990s growth rate is 3.85 percent per year.) Non-economists tended to attribute a
large chunk of fast late-1990s growth to “cyclical” factors,3 but economists had a much
2
3
See Krugman (1994) for one interpretation of how the productivity slowdown made a big difference.
See, for example, Kosterlitz (2002).
4
harder time attributing more than a few tenths of a percentage point per year of late1990s growth to the business cycle.4 Moreover, as Susanto Basu, John Fernald, and
Matthew Shapiro have powerfully argued, there are stronger reasons for thinking that the
adjustment costs associated with moving to a more information technology capitalintensive growth path led actual growth to understate trend growth than for thinking that
cyclical factors led actual growth to overstate trend growth in the second half of the
1990s.5 And the extremely rapid run-up of stock prices indicated that at least the marginal
investor in equities anticipated that the acceleration of economic growth that started in
the mid-1990s would last for decades or longer.6
The causes of the productivity slowdown of the 1973-1995 or so period remain
disappointingly mysterious. Baily (2002) calls the growth-accounting literature on the
slowdown “large but inconclusive.” No single factor provides a convincing and coherent
explanation, and the residual position that a large number of growth-retarding factors
suddenly happened to hit at once is unlikely.7
By contrast, nearly all agree on the causes of the productivity speed-up of 1995-2001: it
is the result of the extraordinary wave of technological innovation in computer and
communications equipment—solid-state electronics and photonics. Robert Gordon
(2002) writes that cyclical factors account for “0.40” percentage points of the growth
acceleration, and that the rest is fully accounted for by information technology—an “0.30
[percentage] point acceleration [from] MFP growth in computer and computer-related
semiconductor manufacturing” and a “capital-deepening effect of faster growth in
computer capital… in the aggregate economy accounts [for] 0.60 percentage points of the
4
See Gordon (2002) and Gordon (2000).
See Basu, Fernald, and Shapiro (2001).
6
See Greenwood and Jovanovic (1999).
7
See Fischer (1988), Griliches (1988), Jorgenson (1988), and Gordon (2000b) and (2002). Jorgenson
(1988) convincingly demonstrates that the oil price shocks can account for slow growth in potential output
in the 1970s, but why does potential output growth remain slow after 1986 after real oil prices have fallen
again? Griliches (1988) finds that an explanation in terms of a slowdown in innovation is unattractive, but
Gordon (2000b) and (2002) finds such an explanation attractive.
5
5
acceleration.” Kevin Stiroh (2001) writes that “all of the direct contribution to the post1995 productivity acceleration can be traced to the industries that either produce
[information technology capital goods] or use [information technology capital goods]
most intensively, with no net contribution from other industries… relatively isolated from
the [information technology] revolution.” Oliner and Sichel (2000) write that “the rapid
capital deepening related to information technology capital accounted for nearly half of
this increase” in labor productivity growth, with a powerful “additional growth
contribution… com[ing] through efficiency improvement in the production of computing
equipment.” Jorgenson, Ho, and Stiroh (2001) reach the same conclusions about the
importance of information technology capital-deepening and increased efficiency in the
production of computing and communications equipment as major drivers of the
productivity growth acceleration, and they go on to forecast that labor productivity
growth will be as high in the next decade as it has been in the past half-decade.8
The failure of economists to reach consensus in their explanations of the productivity
slowdown has to leave one wary of the reliability of the consensus about the causes of the
productivity speed-up. This paper, however, will assume that this consensus is correct:
that the productivity growth speed-up in the second half of the 1990s was the result of the
technological revolutions in data processing and data communications. It will then go on
to ask what the boom of the past seven years means for productivity growth in the next
decade or so. Will the decade of the 2000s be more like the late 1990s, or more like the
1980s as far as growth in productivity and living standards is concerned?
The answer is that the smart way to bet is that the 2000s will be much more like the fastgrowing late 1990s than like the 1980s. The extraordinary pace of invention and
innovation in the information technology sector has generated real price declines of
between ten and twenty percent per year in information processing and communications
8
However, Jorgenson, Ho, and Stiroh expect total real GDP growth to slow because of slower growth in
hours worked—they forecast 1.1 percent per year growth in hours over the next decade, compared to 2.3
percent per year growth in hours from 1995 to 2000.
6
equipment for nearly forty years so far. There are no technological reasons for this pace
of productivity increase in these leading sectors to decline over the next decade or so. In
the consensus analysis, creased total factor productivity in the information technology
capital goods-producing sector coupled with extraordinary real capital deepening as the
quantity of real investment in information technology capital bought by a dollar of
nominal savings grows have together driven the productivity growth acceleration of the
later 1990s. It may indeed be the case that a unit of real investment in computer or
communications equipment “earned the same rate of return” as any other unit of real
investment, as Robert Gordon (2002) puts it. But the extraordinary cost declines had
made a unit of real investment in computer or communications equipment absurdly
cheap, hence the quantity of real investment and thus capital deepening in informationtechnology capital absurdly large.
Thus the most standard of simple applicable growth models—that of Oliner and Sichel
(2000)—predicts a very bright future for the American economy over the next decade or
so. Continued declines in the prices of information technology capital mean that a
constant nominal flow of savings channeled to such investments will bring more and
more real investment. As long as information technology capital earns the same rate of
return as other capital, then labor productivity growth should continue very high. The
social return to information technology investment would have to suddenly and
discontinuously drop to nearly zero, or the share of nominal investment spending devoted
to information technology capital would have to collapse, or both, for labor productivity
growth in the next decade to reverse itself and return to its late 1970s or 1980s levels.
Moreover, more complicated models that focus in more detail on the determinants of
investment spending or on the sources of increased total factor productivity appear to
strengthen, not weaken, forecasts of productivity growth over the next decade. It is very
difficult to argue that the speculative excesses of the 1990s boom produced substantial
upward distortions in the measured growth of potential output. The natural approach that
7
economists to model investment spending in detail—the approach used by Basu, Fernald,
and Shapiro (2001)—tells us that times of rapid increase in real investment are times
when “adjustment costs” are unusually high, and thus times when actual productivity
growth undershoots the long-run sustainable trend. Both a look back at past economic
revolutions driven by general-purpose technologies9 that were in their day analogous to
the computer in their effects10 and a more deeper look forward into the likely
determinants of productivity growth suggest a bright future.
This paper attempts to demonstrate these points in six sections, including this
introduction. Section II lays out the aggregate macroeconomic facts about the boom of
the later 1990s, and sketches a little bit of the history of the relevant data processing and
data communications technologies. Section III presents the simplest possible model that
can handle the phenomena of a technological revolution like that we are going through,
and shows that it predicts that it would be very difficult not to have rapid productivity
growth over the next decade. Section IV argues that the standard journalistic point often
heard these days that the collapse of the NASDAQ and the recession of 2001 invalidate
predictions of the “new economy” is simply wrong. Section V speculates about the
underlying determinants of the Solow productivity growth residual, and how they will be
affected by our ongoing technological revolution. And section VI provides a brief
summary of the conclusions.
II. The Pattern of Growth in the Later 1990s
Nominal spending on information technology capital rose from about one percent of GDP
in 1960 to about two percent of GDP by 1980 to about three percent of GDP by 1990 to
between five and six percent of GDP by 2000.
9
See Bresnahan and Trajtenberg (1995).
See Crafts (2002).
10
8
9
10
Period
1947:1-1973:2
1973:2-1995:1
1995:1-2001:2
1995:1-2001:4
Productivity
Growth (Real
Output per
Hour)
0.02875224
0.0130173
0.0280261
0.02690108
(1) Even before the mid-1990s, ICT had a much bigger impact on growth than steam and
at least a similar impact to that of electricity in a similar early phase.
11
(2) The Solow productivity paradox stems largely from unrealistic expectations. In the
early phases of general purpose technologies their impact on growth is modest because
the new varieties of capital have only a small weight relative to the economy as a whole.
(3) If there has been an ICT productivity paradox, it comprises an apparent absence of
TFP spillovers; in this respect, the contribution made by electricity in the 1920s through
its impact on the reorganization of factory work has probably not yet been matched by
ICT.
examining the impact of the two previous technological breakthroughs with similar
claims to be regarded as general purpose technologies (Bresnahan and Trajtenberg,
1995), namely, steam and electricity, in a growth accounting framework. The results of
this exercise are striking: they suggest that the growth contribution of ICT in the past 25
years has exceeeded that of steam and at least matched that of electricity over comparable
periods and that the true paradox is why more should have been expected of ICT.
A straightforward generalization of this is used by Oliner and Sichel (2000) which
features different varieties of capital (including computer hardware, software and
communication equipment as types of ICT capital) whose growth contributions are
weighted by their shares in income, and in which TFP growth is decomposed into TFP
growth in making ICT capital and in other activities weighted by output shares. The
contribution of innovations in ICT is captured through two components: extra TFP
growth and through the three additional capital inputs. This is similar to the endogenous
innovation based growth accounting for the expanding varieties growth models of Romer
(1990) and Grossman and Helpman (1991), as set out in Barro (1999).
A different strand of endogenous growth economics is adopted by Schreyer (2000) in
which ICT capital goods are 'special' in that they provide knowledge spillovers or other
positive externalities to the economy similar to the formulation in Romer (1986).
Basu, Fernald, and Shapiro (2001) estimate that because of adjustment costs productivity
growth in the second half of the 1990s undershot the long-run technology trend by half a
percentage point per year because of adjustment costs.
Of the actual 2.86 percent annual growth of output per hour, 0.40 is attributed
to a cyclical effect and the remaining 2.46 percent to trend growth, and the latter is 1.04
points faster than the 1972-95 trend. How can this acceleration be explained? A small
part on lines 6 and 7 is attributed to changes in price measurement methods and to a
slight acceleration in the growth of labor quality. All of the remaining 0.89 points can be
directly attributed to computers. The capital-deepening effect of faster growth in
computer capital relative to labor in the aggregate economy accounts of 0.60 percentage
12
points of the acceleration (line 9a) and a 0.30-point acceleration of MFP growth in
computer and computer-related semiconductor manufacturing account (line 10) sum to an
explanation of 0.90 points, compared to the 0.89 acceleration in trend that needs to be
explained.
There is no sign of a fundamental transformation of the U. S. economy. There
has been no acceleration of MFP growth outside of computer production and the rest of
durable manufacturing. Responding to the accelerated rate of price decline of computers
that
occurred between 1995 and 1998, business firms throughout the economy boosted
purchases
of computers, creating an investment boom and "capital deepening" in the form of faster
growth of capital relative to labor. But computer capital did not have any kind of magical
or
extraordinary effect ó it earned the same rate of return as any other type of capital.
If, as Gordon says, computer capital “earned the same rate of return” then why did
growth accelerate? The answer is that the rate of return that remains the same is the rate
of return on a marginal addition to the real capital stock
Stiglitz channels the ghost of Friedrich Hayek: "Most
downturns have been
inventory recessions. They tend to be short-lived; as companies
deplete their inventories, things improve. This is different. It's not
just an inventory downturn, but also a case of overcapacity in areas
where there was lots of investing -- IT, telecom.… These represent
a significant share of investment in the late 1990s," said Stiglitz.
Things won't improve until industry gets rid of excess equipment
and employees, he says. "The real restructuring takes time."
The Bubble-Boom of the 1990s?
If one wishes to minimize the possibility and the magnitude of technology-driven secular
productivity acceleration in the second half of the 1990s, there are two analytical
strategies to pursue. The first is to stress the cyclical component of 1990s economic
growth. The second is to argue that the internet bubble led standard statistical methods to
overstate the productivity of the American economy in the second half of the 1990s.
13
__________ and Franco Malerba (1999). "Industrial Dynamics and the Evolution of Firms'
and Nations' Competitive Capabilities in the World Computer Industry," in Mowery
and Nelson, eds. (1999b), pp. 79-132.
Brynjolfsson, Erik (1996). "The Contribution of Information Technology to Consumer
Welfare," Information Systems Research, 7:3, September, pp. 281-300.
Eller, Jonathan (2000). "U. S. Inflation and Unemployment in the late 1990s: A Reexamination of the Time-Varying NAIRU," Northwestern University senior honors
thesis, May.
Goldin, Claudia (1998). "America's Graduation from High School: The Evolution
and
Spread of Secondary Schooling in the Twentieth Century." Journal of Economic History, vol.
58 (June), pp. 345-74.
Gordon, Robert J. (1977). "Can the Inflation of the 1970s Be Explained?" BPEA 1:1977,
253-77.
_________ (1982). "Inflation, Flexible Exchange Rates, and the Natural Rate of
Unemployment." In Workers, Jobs, and Inflation, edited by Martin N. Baily. Washington:
Brookings, 88-152.
_________ (1990). The Measurement of Durable Goods Prices. University of Chicago Press for
NBER.
_________ (1997). "The Time-Varying NAIRU and its Implications for Economic Policy."
Journal of Economic Perspectives 11(1): 11-32.
_________ (1998). "Foundations of the Goldilocks Economy: Supply Shocks and the
Time-Varying NAIRU," Brookings Papers on Economic Activity, vol. 29 (no. 2), pp. 297-333.
__________ (2000a). "Interpreting the `One Big Wave' in U. S. Long-term Productivity
Growth," in Bart van Ark, Simon Kuipers, and Gerard Kuper, eds., Productivity,
Technology, and Economic Growth, Kluwer Publishers, 2000, pp. 19-65.
__________ (2000b). "Does the New Economy Measure Up to the Great Inventions of the
Technology and Economic Performance, Page 45
Past?" Journal of Economic Perspectives, vol. 14 (Fall), pp. 49-74.
Ip, Greg (2000). "Market on a High Wire," Wall Street Journal, January 18, p. C1.
Jorgenson, Dale W. and Kevin J. Stiroh (2000). "Raising the Speed Limit: U. S. Economic
Growth in the Information Age," Brookings Papers on Economic Activity, 31:1, pp. 125-211.
Katz, Lawrence F., and Alan B. Krueger (1999). "The High-Pressure U. S. Labor Market of
the 1990s," Brookings Papers on Economic Activity, 30:1, pp. 1-65.
Lum, Sherlene K. S., and Brian C. Moyer (2000). "Gross Domestic Product by Industry for
1997-99," Survey of Current Business, vol. 80, no. 12, December, pp. 24-35.
Mishel, Lawrence, Jared Bernstein, and John Schmitt (1999). The State of Working America
199899. Ithaca NY: Cornell University Press.
Mowery, David C. (1999). "The Computer Software Industry," in Mowery and Nelson, eds.
(1999b), pp. 133-68.
__________ and Richard R. Nelson (1999a). "Explaining Industrial Leadership," in
Mowery
and Nelson, eds. (1999b), pp. 359-82.
14
__________ and Richard R. Nelson, eds. (1999b). Sources of Industrial Leadership: Studies of
Seven
Industries. Cambridge UK: Cambridge University Press.
Oliner, Stephen D., and Daniel E. Sichel (2000). "The Resurgence of Growth in the Late
1990s: Is Information Technology the Story?" Journal of Economic Perspectives, vol. 14, Fall,
pp. 3-22.
__________ (2001). "The Resurgence of Growth in the Late 1990s: Is
Information
Technology the Story?" Updated presentation presented at the meetings of the
American Economic Association, January 7.
Parker, Robert, and Bruce Grimm (2000). "Software Prices and Real Output: Recent
Developments at the Bureau of Economic Analysis," paper presented at NBER
productivity workshop, March 17.
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in Ralph Landau and Nathan Rosenberg, eds., The Positive Sum Strategy: Harnessing Technology
for Economic Growth. Washington DC: National Academy Press, pp. 17-32.
Sichel, Daniel E. (1997). The Computer Revolution: An Economic Perspective. Washington:
Brookings.
Triplett, Jack E. (1999). "The Solow Computer Paradox: What do Computers do to
Productivity?" Candian Journal of Economics, 32:2, April, pp. 309-34.
Uchitelle, Louis (2000). "Economic View: Productivity Finally Shows the Impact of
Computers," New York Times, March 12, section 3, p. 4.
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Economic Review, vol. 80 (September), pp. 651-68.
15
The average workweek did not rise above its mid-1994 level, ever
16
THE SOLOW PRODUCTIVITY PARADOX IN HISTORICAL PERSPECTIVE
By
Nicholas Crafts
(London School of Economics)
November 2001.
Abstract
A growth accounting methodology is used to compare the contributions to growth in
terms of capital-deepening and total factor productivity growth of three general-purpose
technologies, namely, steam in Britain during 1780-1860, electricity and information and
communications technology in the United States during 1899-1929 and 1974-2000,
respectively. The format permits explicit comparison of earlier episodes with the results
for ICT obtained by Oliner and Sichel. The results suggest that the contribution of ICT
was already relatively large before 1995 and it is suggested that the true productivity
paradox is why economists expected more sooner from ICT.
This paper attempts to fill this gap by examining the impact of the two previous
technological breakthroughs with similar claims to be regarded as general purpose
technologies (Bresnahan and Trajtenberg, 1995), namely, steam and electricity, in a
growth accounting framework. The results of this exercise are striking: they suggest that
the growth contribution of ICT in the past 25 years has exceeeded that of steam and at
least matched that of electricity over comparable periods and that the true paradox is why
more should have been expected of ICT.
A straightforward generalization of this is used by Oliner and Sichel (2000) which
features different varieties of capital (including computer hardware, software and
communication equipment as types of ICT capital) whose growth contributions are
weighted by their shares in income, and in which TFP growth is decomposed into TFP
growth in making ICT capital and in other activities weighted by output shares. The
contribution of innovations in ICT is captured through two components: extra TFP
growth and through the three additional capital inputs. This is similar to the endogenous
innovation based growth accounting for the expanding varieties growth models of Romer
(1990) and Grossman and Helpman (1991), as set out in Barro (1999).
A different strand of endogenous growth economics is adopted by Schreyer (2000) in
which ICT capital goods are 'special' in that they provide knowledge spillovers or other
positive externalities to the economy similar to the formulation in Romer (1986). To
capture this idea in the one sector model, equation (1) is rewritten as
Y = AK£\ + £] L1 - £\ (3)
17
where £] > 0 represents the impact of the knowledge spillover on output. If the
contribution of capital is still weighted by sK = £\, then the standard Solow residual
becomes
£GY/Y - £GK/K - £GL/L = £GA/A + £]£GK/K (4)
so it comprises both exogenous technological change and the growth effect from
spillovers.
sK
sL
Nevertheless, the sectoral pattern of labor productivity growth, which was heavily
skewed towards contributions from ICT-intensive industries (Stiroh, 2001), and the
evidence of micro studies that find important lagged productivity gains from
reorganizations of work facilitated by ICT (Brynjolffson and Hitt, 2000) suggest that a
significant part of the economy wide TFP acceleration in these years may have been due
to TFP spillover effects from ICT investment.
It should be noted that parts of the service sector are among the main users of ICT and
that the response of output to ICT may be masked by measurement problems (McGuckin
and Stiroh, 2000).
The main results of this benchmarking exercise are:
(1) Even before the mid-1990s, ICT had a much bigger impact on growth than steam and
at least a similar impact to that of electricity in a similar early phase.
(2) The Solow productivity paradox stems largely from unrealistic expectations. In the
early phases of general purpose technologies their impact on growth is modest because
the new varieties of capital have only a small weight relative to the economy as a whole.
(3) If there has been an ICT productivity paradox, it comprises an apparent absence of
TFP spillovers; in this respect, the contribution made by electricity in the 1920s through
its impact on the reorganization of factory work has probably not yet been matched by
ICT.
18
19
20
21
22
TECHNOLOGY AND ECONOMIC PERFORMANCE IN THE AMERICAN
ECONOMY
Robert J. Gordon
Working Paper 8771
http://www.nber.org/papers/w8771
NATIONAL BUREAU OF ECONOMIC RESEARCH
1050 Massachusetts Avenue
Cambridge, MA 02138
February 2002
This paper examines the sources of the U. S. macroeconomic miracle of 1995-2000 and attempts
to
distinguish among permanent sources of American leadership in high-technology industries, as
contrasted with
the particular post-1995 episode of technological acceleration, and with other independent
sources of the economic
miracle unrelated to technology. The core of the American achievement was the maintenance of
low inflation in
the presence of a decline in the unemployment rate to the lowest level reached in three decades.
The post-1995
technological acceleration, particularly in information technology (IT) and accompanying revival
of productivity
growth, directly contributed both to faster output growth and to holding down the inflation rate,
but inflation was
also held down by a substantial decline in real non-oil import prices, by low energy prices
through early 1999, and
by a temporary cessation in 1996-98 of inflation in real medical care prices. In turn low inflation
allowed the Fed
to maintain an easy monetary policy that fueled rapid growth in real demand, profits, and stock
prices, which fed
back into growth of consumption in excess of growth in income.
The technological acceleration was made possible in part by permanent sources of American
advantage
over Europe and Japan, most notably the mixed system of government- and privately-funded
research universities,
the large role of U. S. government agencies providing research funding based on peer review, the
strong tradition
of patent and securities regulation, the leading worldwide position of U.S. business schools and
U. S.-owned
investment banking, accounting, and management-consulting firms, and the particular importance
of the capital
market for high-tech financing led by a uniquely dynamic venture capital industry. While these
advantages help
to explain why the IT boom happened in the United States, they did not prevent the U. S. from
experiencing a
23
dismal period of slow productivity growth between 1972 and 1995 nor from falling behind in
numerous industries
outside the IT sector.
The 1995-2000 productivity growth revival was fragile, both because a portion rested on
unsustainably
rapid output growth in 1999-2000, and because much of the rest was the result of a doubling in
the growth rate
of computer investment after 1995 that could not continue forever. The web could only be
invented once, Y2K
artificially compressed the computer replacement cycle, and some IT purchases were made by
dot-coms that by
early 2001 were bankrupt. As an invention, the web provided abundant consumer surplus but no
recipe for most
dot-coms to make a profit from providing free services. High-tech also included a boom in
biotech and medical
technology, which also provided consumer surplus without necessarily creating higher
productivity, at least within
the feasible scope of output measurement.
Part of this change in perception was an illusion based on a change in the measuring rod.
The annual
growth rate of output per hour for 1972-95 was 1.1 percent per year based on data
available prior to 1999 but
jumped to 1.5 percent per year as a result of data revisions announced in late 1999.
The essence of the miracle was the conjunction of low unemployment and low
inflation. Fed policy avoided any sharp upward spike in short-term interest rates such as
had
happened during the previous expansion in 1988-89 because of the perception that
accelerating inflation was not a problem, despite a much lower unemployment rate than
the
minimum achieved in the earlier expansion. Policy reactions were less aggressive in the
late
1990s than in the late 1980s, because the economy appeared to have experienced a sharp
change in behavior along at least two dimensions. Unemployment could be allowed
to
decline because inflation remained low. The second change of behavior was in the
growth
of productivity. After resigned acceptance of the so-called "productivity slowdown,"
more
than two decades following 1973 when output per hour grew at barely one percent per
annum
24
(well under half of the rate experienced before 1973), analysts were astonished to observe
productivity growth at a rate of nearly three percent as the average annual rate for 19962000
and an unbelievable 5.3 percent in the four quarters ending in mid-2000.3
Falling unemployment, low inflation, and accelerating productivity growth brought
many other good things in their wake. In February, 2000, the American economy set a
record
for the longest business expansion since records began in 1850. Profits surged and, at
least until early in the year 2000, stock market prices grew even faster than profits,
showering
households with unheard-of increases in wealth that in turned fueled a boom in
consumption
and an evaporation of household saving (at least as conventionally measured, excluding
capital
gains).
For the first time since the early 1970s, gains in real income were enjoyed by
households in the bottom half of the income distribution, and in April, 2000, the
unemployment rates for blacks and Hispanics reached the lowest levels ever recorded.
One way of describing the changing relationship between
technology and economic performance is through Robert M. Solow's famous 1987 saying
that
"we can see the computer age everywhere but in the productivity statistics." For a decade
economists took "Solow's paradox" as a truism, noting the continuing co-existence
of
explosive growth in computer use and dismal growth in labor productivity, and they
differed
only in their explanations.6 But by 1999-2000 a consensus emerged that the
technological
revolution represented by the New Economy was responsible directly or indirectly not
just
for the productivity growth acceleration, but also the other manifestations of the miracle,
including the stock market and wealth boom and spreading of benefits to the lower half
of
the income distribution. In short, Solow's paradox is now obsolete and its inventor has
admitted as much.
The unemployment rate in 1999-2000 fell to four percent,
the lowest rate since the 1966-70 period during which inflation accelerated steadily. Yet
in
25
1998 and early 1999, prior to the 1999-2000 upsurge in oil prices, inflation not only
failed to
accelerate but rather decelerated.
I have attempted to use a
common framework to explain why the performance of the 1970s was so bad and of the
1990s was so good, pointing to the role of adverse supply shocks in the earlier episode
and
beneficial supply shocks more recently. In my interpretation (1998) inflation in 1997-98
was
held down by two "old" supply shocks, falling real prices of imports and energy, and by
two
"new" supply shocks, the accelerating decline in computer prices (see Figure 9 below)
and a
sharp decline in the prices of medical care services made possible by the managed care
revolution. In retrospect my analysis, while still valid regarding the role of the supply
shocks,
did not place sufficient emphasis on the productivity growth revival as an independent
source
of low inflation. Between 1995 and late-2000 wage growth accelerated substantially
from 2.1
to above 6 percent at an annual rate, thus appearing to validate the Phillips curve
hypothesis
of a negative tradeoff between unemployment and wage growth. However, soaring
productivity growth during the same period prevented faster wage growth from
translating
into growth in unit labor costs (defined as wage growth minus productivity growth). If
productivity growth were to decelerate, then it added one more element to the list of
transitory elements that had held down inflation in the late 1990s. Any of the items on
the
list ó falling relative import and energy prices, a faster rate of decline in computer prices,
moderate medical care inflation, and the productivity growth revival itself ó could turn
around and put upward rather than downward pressure on the inflation rate.
Thus far we have examined several manifestations of the American economic miracle
of the late 1990s without focussing explicitly on the single most important factor which
made all of this possible, namely the sharp acceleration in productivity growth that
started at the end of 1995 and that was presumably caused entirely or in large part by the
technological acceleration that we have labelled the "New Economy." Figure 7 divides
the postwar into three periods using the standard quarterly data published by the Bureau
of Labor Statistics (BLS), the "golden age" of rapid productivity growth between 1950:2
and 1972:2, the dismal slowdown period extending from 1972:2 to 1995:4, and the
revival period since 1995:4.
26
The average annual growth rate of nonfarm private output per hour during the period
1972:2-1995:4 is 1.42 percent in the newly revised data.
Every major industrialized country experienced a sharp slowdown in productivity
growth after 1973, and the extent of the slowdown in most countries was greater than in
the United States. During 1960-73 growth in productivity (ALP) in the 15 countries of
the European Union was double and in Japan quadruple that in the U. S. In the 1970s
and 1980s productivity growth slowed down everywhere, but later than in the U. S., and
by the first half of the 1990s productivity growth in Europe and Japan had converged to
that of the U. S. Thus the productivity slowdown was universal in the developed world
rather than being unique to the U. S.
The post-1972 slowdown in the U. S., Japan, and Europe can be traced back to the
sources of the "golden age" which began around the time of World War I in the United
States (Gordon, 2000a). A set of "great inventions" of unprecedented scope and
importance, including electricity and the internal combustion engine, had been developed
during the Second Industrial Revolution of 1860-1900 and began the process of diffusion
through the structure of the economy and society soon after the turn of the century
(Gordon, 2000c). The productivity acceleration of the "golden age" occurred as the
electric motor revolutionized manufacturing, as the internal combustion engine
revolutionized ground transport and allowed the invention of air transport, and as other
innovations in chemicals, petroleum, entertainment, communication, and public health
transformed the standard of living in the United States between 1900 and the 1950s. In
addition to the original advantages of the United States, particularly economies of scale
and a wealth of natural resources (Wright, 1990), the dislocation of the two world wars
and the turbulent interwar period delayed the diffusion of many of these innovations in
Europe and Japan until after 1945, but then the rich plate of unexploited technology led to
a period of rapid catch-up, if not convergence, to the U. S. frontier. This interpretation
explains the post-1972 productivity slowdown as resulting from the the inevitable
depletion of the fruits of the previous great inventions. The faster productivity growth in
Europe and Japan during 1950-72, and the greater magnitude of their slowdowns, and the
delayed timing of the slowdown into the 1980s and 1990s, is explained by the late start of
Europe and Japan in exploiting the late 19th century "great inventions
How important has the New Economy and IT revolution been in creating the U. S.
productivity revival which appears directly or indirectly to be responsible for most other
dimensions of the late-1990s U. S. economic miracle? Fortunately we do not need to
explore this question from scratch, since recent academic research has produced a
relatively clear answer which is summarized and interpreted in this section. The basic
answer is that the acceleration in technical change in computers, peripherals, and
semiconductors explains most of the acceleration in overall productivity growth since
27
1995, but virtually all the progress has been concentrated in the durable manufacturing
sector, with surprisingly little spillover to the rest of the economy.
To provide a more precise analysis we must begin by distinguishing between the
growth in output per hour, sometimes called average labor productivity (ALP), from the
growth of multi-factor productivity (MFP). The former compares output growth with
that
of a single input, labor hours, while the latter compares output with a weighted average of
several inputs, including labor, capital, and sometimes others, including materials,
energy,
and/or imports. ALP always grows faster than MFP, and the difference between them is
the contribution of "capital deepening," the fact that any growing economy achieves a
growth rate of its capital input that is faster than its labor input, thus equipping each unit
of labor with an ever-growing quantity of capital.
How have computers and the New Economy influenced the recent productivity
growth revival? Imagine a spontaneous acceleration in the rate of technological change
in the computer sector, which induces a more rapid rate of decline in computer prices and
an
investment boom as firms respond to cheaper computer prices by buying more
computers. In response, since computers are part of output, this acceleration of technical
change in
computer production raises the growth rate of MFP in the total economy, boosting
the growth rate of ALP one-for-one. Second, the ensuing investment boom raises the
"capital deepening" effect by increasing the growth rate of capital input relative to labor
input and thus increasing ALP growth relative to MFP growth.
In discussing the New Economy, it is important to separate the computer-producing
sector from the computer-using sector. No one denies that there has been a marked
acceleration of output and productivity growth in the production of computer hardware.
The real issue has been the response of productivity to massive computer investment
by the 96 percent of the economy engaged in using computers rather than producing
them. If the only effect of the technological breakthrough in computer production on the
non-computer economy is an investment boom that accelerates the growth rate of capital
input, then non-computer ALP growth would rise by the capital-deepening effect, but
there would be no increase in non-computer MFP growth. Let us call this the "direct"
effect of the New Economy on the non-computer sector. Sometimes advocates of the
revolutionary nature of the New Economy imply that computer investment has a higher
rate of return than other types of investment and creates "spillover" effects on business
practices and productivity in the non-computer economy; evidence of this "spillover"
effect would be an acceleration in MFP growth in the non-computer economy occurring
at the same time as the technological acceleration in computer production.
28
In 1999 nominal final sales of computers and peripherals plus fixed investment in
software
represented 3.5 percent of nominal GDP in the nonfarm nonhousing private business
economy. Thus the "non-computer part of the economy" represents 96.5 percent of
nonfarm nonhousing private business output. Final
sales of computer hardware is an unpublished series obtained from Christian Ehemann of
the BEA; the other
series in this calculation appear in the Economic Report of the President, February 2000,
Tables B-10 and B-16.
What is the counterpart of the New Economy in the official output data? The
remarkable event which occurred at the end of 1995 was an acceleration of the rate of
price
change in computer hardware (including peripherals) from an average rate of -12 percent
during 1987-95 to an average rate of -29 percent during 1996-98. Computers
did not become more important as a share of dollar spending in the economy,
which stagnated at around 1.3 percent of the nonfarm private business economy. The
counterpart of the post-1995 acceleration in the rate of price decline was an acceleration
in the rate of technological progress; apparently the time cycle of Moore's Law shortened
from 18 months to 12 months at about the same time
We use the recent results of Oliner and Sichel (2000, 2001) to compute the contribution
of
computers and semiconductors both to capital deepening and to the MFP acceleration in
the
overall economy. Second, we summarize my recent study (Gordon, 2000b) that adds two
elements to the work of Oliner and Sichel.
Of the actual 2.86 percent annual growth of output per hour, 0.40 is attributed
to a cyclical effect and the remaining 2.46 percent to trend growth, and the latter is 1.04
points faster than the 1972-95 trend. How can this acceleration be explained? A small
part on lines 6 and 7 is attributed to changes in price measurement methods and to a
slight acceleration in the growth of labor quality. All of the remaining 0.89 points can be
directly attributed to computers. The capital-deepening effect of faster growth in
computer capital relative to labor in the aggregate economy accounts of 0.60 percentage
points of the acceleration (line 9a) and a 0.30-point acceleration of MFP growth in
computer and computer-related semiconductor manufacturing account (line 10) sum to an
explanation of 0.90 points, compared to the 0.89 acceleration in trend that needs to be
explained.
There is no sign of a fundamental transformation of the U. S. economy. There
has been no acceleration of MFP growth outside of computer production and the rest of
29
durable manufacturing. Responding to the accelerated rate of price decline of computers
that
occurred between 1995 and 1998, business firms throughout the economy boosted
purchases
of computers, creating an investment boom and "capital deepening" in the form of faster
growth of capital relative to labor. But computer capital did not have any kind of magical
or
extraordinary effect ó it earned the same rate of return as any other type of capital.
The dependence of the U. S. productivity revival on the production and use of
computers waves a danger flag for the future. Consider the possibility that the
accelerated
1995-98 29 percent rate of price decline for computers does not continue. Already in the
year
ending in 2000:Q4 the rate of price decline slowed from 29 to 12 percent, the same as
between 1987 and 1995. If in response the growth rate of computer investment were to
slow
down to a rate similar to that before 1995, then the main source of the productivity
revival
identified by Oliner and Sichel (2000, 2001) would disappear, and with it much of the U.
S.
economic miracle.
In fact, the slope of the
price-quantity relationship was appreciably flatter during 1972-87 than during 1987-95 or
1995-99. If the demand curve has not shifted, the inverse of these slopes is the price
elasticity
of demand, namely -1.96, -1.19, and -1.11 in these three intervals, which can be
compared
with Brynjolfsson's (1996, p. 292) estimated price elasticity of
-1.33 over the period 1970-89. The apparent decline in the price elasticity is consistent
with
the view that the most important uses of computers were developed more than a decade
into
the past, not currently.
A longstanding puzzle in the retardation of British
economic growth after the 1870s is the fact that many inventions initially made by British
inventors were brought to commercial success in the U. S., Japan, and elsewhere. This
issue
30
of who captures the fruits of innovation suggests that the British were not alone in losing
out.
The U. S. invention of videotape was followed by exploitation of the consumer VCR
market
that was almost entirely achieved by Japanese companies. The Finnish company Nokia
took
over leadership in mobile phones from Motorola. Within any economy there are winners
and
losers as upstart companies (Intel, Microsoft) seize the advantage in developing
technology
while leaving older competitors (IBM, Wang, Digital Equipment, Xerox) behind.
While predicting technological developments in advance is exceedingly difficult, there
is an ample literature which points to particular national characteristics that help to
explain,
at least in retrospect, why particular inventions and industries came to be dominated
by
particular countries.
31
32
33
34
35
36
37
Projecting Productivity Growth:
Lessons from the U.S. Growth Resurgence
Dale W. Jorgenson
Harvard University
Mun S. Ho
Resources for the Future
Kevin J. Stiroh °Ø
Federal Reserve Bank of New York
December 31, 2001
This paper analyzes the sources of U.S. labor productivity growth in the post-1995 period
and
presents projections for both output and labor productivity growth for the next decade.
Despite the recent downward revisions to U.S. GDP and software investment, we show
that
information technology (IT) played a substantial role in the U.S. productivity revival. We
then outline a methodology for projecting trend output and productivity growth. Our
basecase
projection puts the rate of trend productivity growth at 2.24 percent per year over the
next decade with a range of 1.33 to 2.98 percent, reflecting fundamental uncertainties
about
the rate of technological progress in IT-production and investment patterns. Our central
projection is only slightly below the average growth rate of 2.36 percent during the 19952000
period.
This uncertainty is only magnified by the observation that recent productivity
estimates remain surprisingly strong for an economy in recession. The Bureau of Labor
Statistics (BLS, 2001b) estimates that business sector productivity grew 1.5 percent per
year
from the third quarter of 2000 to the third quarter of 2001, while business sector output
grew
only 0.1 percent per year.
We then turn to the future of U.S. productivity growth. Our overall conclusion is that
the projections of Jorgenson and Stiroh (2000), prepared more than eighteen months ago,
are
largely on target. Our new base-case projection of trend labor productivity growth for the
38
next decade is 2.24 percent per year, only slightly below the average of the period 19952000
of 2.36 percent per year. Our projection of output growth for the next decade, however, is
only 3.34 percent per year, compared with the 1995-2000 average of 4.60 percent, due to
slower projected growth in hours worked.
The starting point for projecting U.S. output growth is the projection of future growth
of the labor force. The growth of hours worked of 2.24 percent per year from 1995-2000
is
not likely to be sustainable because labor force growth for the next decade will average
only
1.10 percent. An abrupt slowdown in growth of hours worked would have reduced output
growth by 1.14 percent, even if labor productivity growth had continued unabated. We
estimate that labor productivity growth from 1995-2000 also exceeded its sustainable
rate,
however, leading to an additional decline of 0.12 percent in the trend rate of output
growth, so
that our base-case scenario projects output growth of 3.34 percent for the next decade.
Basu, Fernald, Shapiro (2001) present an alternative approach to estimating trend
growth in total factor productivity by separately accounting for factor utilization and
factor
accumulation. They extend the growth accounting framework to incorporate adjustment
costs,
scale economies, imperfect competition, and changes in utilization. Industry-level data
for the
1990s suggests that the post-1995 rise in productivity appears to be largely a change in
trend
rather than a cyclical phenomenon, since there was little change in utilization in the late
1990s. While Basu et al. are clear that they do not make predictions about the
sustainability of
11Both Roberts (2000) and French (2001) employ the Stock and Watson (1998) method of
dealing with the zero
bias.
these changes, their results suggest that any slowdown in investment growth is likely to
be
associated with a temporary increase in output growth as resources are reallocated away
from
adjustment and toward production.
39
40
The Economic Impact of Information Technology
Kevin J. Stiroh*
Federal Reserve Bank of New York
January 18, 2001
Summary
One of the defining features of the U.S. economy in the 1990s is the massive expenditure on
information technology equipment and software. With the U.S. economy surging over the same
period, this raises an obvious and important question: what is the economic impact of information
technology? Not surprisingly, there is a large and growing literature on the question and this
essay
reviews the empirical research done by economists and business analysts. The essay begins with a
discussion of how economists think about information technology and measure it using the
hedonic
theory of prices. The next section reviews the empirical estimates of the impact of information
technology, both at the macroeconomic level of the entire U.S. economy and then at the more
microeconomic level of individual firms or industries. In both cases, the evidence is building that
information technology is having a substantial economic impact. The final section of the essay
discusses some of the conclusions about information technology and the potential impact on the
U.S.
economy in the future.
41
Information Technology and the U.S. Productivity Revival:
A Review of the Evidence
Kevin J. Stiroh
January 10, 2002
Abstract
Aggregate, industry, and firm level studies all point to a strong connection between information
technology (IT) and the U.S. productivity revival in the late 1990s. At the aggregate level, growth
accounting studies show a large and growing contribution to productivity growth from both the
production and the use of IT. At the industry level, industries that produce or use IT most
intensively have shown the largest increases in productivity growth after 1995. At the firm level,
ITintensive
firms show better performance than their peers, and several specific case studies show how
IT improves real business practices. This accumulation of evidence from a variety of studies
suggests a real productivity impact from IT.
The revival in U.S. productivity growth in the late 1990s is a critical development for the
U.S. economy because faster productivity growth contributes to rising living standards, helps
keep
inflationary pressures under control, and supports business profitability. Understanding the
sources
of productivity growth and why it fluctuates, however, is a difficult task and many factors
contribute
to its rise and fall.
This paper focuses on one specific factor – information technology – and reviews the
evidence on whether IT has contributed to the recent period of strong productivity growth. While
some discussion remains, there is a growing consensus that IT has played a critical role. At its
heart,
this is a story of profound technological progress in the production of IT assets like computer
hardware, software, and telecommunications equipment. This generates large productivity gains
in
the industries that produce IT assets, and provides powerful incentives for other firms to invest in
the
latest IT assets. The evidence from aggregate, industry, and firm level studies is building that
these
effects are economically large and have contributed substantially to the U.S. productivity revival.
42
43
44
45
Information Technology and the U.S. Productivity Revival:
What Do the Industry Data Say?
Kevin J. Stiroh°Ø
Federal Reserve Bank of New York
This Draft: December 4, 2001
First Draft: January 24, 2001
Abstract
This paper examines the link between information technology (IT) and the post-1995 U.S.
productivity revival. Industry-level data show a broad productivity growth resurgence that reflects
both the production and the use of IT. The most IT-intensive industries experienced significantly
larger productivity gains than other industries and there is a strong link between IT capital shares
and
the relative acceleration of labor productivity. A novel decomposition shows that all of the direct
contribution to the post-1995 productivity acceleration can be traced to the industries that either
produce IT or use IT most intensively, with no net contribution from other industries that are
relatively
isolated from the IT revolution.
46
47
48
49
50
51
52
53
The New Economy: Post Mortem or Second Wind?
By
Martin Neil Baily
Senior Fellow,
How Well Does Growth Accounting Explain Shifting Productivity Trends?
Table 1 shows the growth accounting estimates made by the Bureau of Labor
Statistics
(BLS) covering the periods 1948-73 and 1973-95. Increases in capital services
per hour worked accounted for a modest 28 percent of the growth in output per
hour over the period 1948-73 in non farm business. This figure increased to 50
percent over the period 1973-95 but capital services per hour worked provides
almost no explanation of the slowdown in labor productivity. The slowdown in
labor productivity growth that took place after 1973 is matched by an equal
decline in the unexplained residual item of MFP growth.
A large but inconclusive literature developed in the 1980s attempting to
understand why productivity growth slowed after 1973. Growth accounting failed
to explain the post-73 acceleration; how does it do in explaining the pick-up in
growth after 1995? Table 2 shows three estimates of the decomposition of the
increase of productivity growth after 1995, focusing on the differences in growth
rates pre and post-1995. The first updates Steven Oliner and Daniel Sichel
(2000)1, the second updates the estimates reported in Baily and Lawrence
(2001),2 and the third is from Dale Jorgenson, Mun Ho and Kevin Stiroh (2001).
The results differ from those previously available largely because there
was a major downward revision of the GDP and hence productivity data made
last summer. Much of the revision came when the estimates of software
investment were revised down. The first two columns differ for two main reasons.
Oliner and Sichel base their non farm business productivity growth only the
product side of the National Income accounts, while my preferred estimate
averages the income and product figures from the accounts, which are both valid
estimates of non farm output. This makes the acceleration of labor productivity
larger, because estimated income grew more rapidly than estimated production.
Second, the two columns estimate the contribution of the IT sector to MFP
differently.3 It is important to note that neither column uses an adjustment for
business cycle effects. Jorgenson et al. apply their growth accounting to a
broader definition of the economy, so the figures are not directly comparable.
According to both Oliner and Sichel and Jorgenson et al, the speed-up in labor
productivity growth after 1995 is largely ‘explained’ by the growth accounting
framework, and the explanation is mostly an IT story. The rapid accumulation of
IT capital provided a large boost to labor productivity (adding 0.59 percentage
point in Oliner-Sichel), more than offsetting the slowing of the contribution of
other capital. There was a small effect of labor quality (education and experience
54
effects). And then a boost from faster MFP within the IT sector. Of course that is
an MFP residual effect, but it is much less mysterious than the traditional MFP
residual. It is well-known that the computer and semiconductor industries
increased their pace of productivity advance after 1995 as a result of a faster rate
of introduction of new generations of chips and intense competitive pressure.
There is a small (0.23 percentage point in Oliner-Sichel) step-up in
‘other MFP’, but if some fraction of the productivity acceleration were cyclical,
that mystery component could easily vanish, in which case the whole
acceleration is explained or at least can be easily described.
Following the data revisions of last summer, therefore, the latest Oliner and
Sichel and
Jorgenson et al. results are consistent with the view that the post-95 acceleration
in the economy as a whole was driven by the IT sector. Faster MFP in the IT
sector added directly to productivity growth and the resulting decline in the price
of IT capital induced rapid capital accumulation that added to labor productivity in
the rest of the economy. Robert Gordon also reaches very much the same
conclusion.
Second, microeconomic analyses of plants and firms find substantial adjustment
costs to
investment and lags between investment and productivity. The growth
accounting approach
assumes increases in capital intensity have an impact on productivity in the same
year they occur. That is an important issue because the coincident timing of the
productivity surge and the IT investment surge is a key reason for thinking the
latter caused the former.
Third, the growth accounting framework formalizes what is essentially a oneobservation
correlation. Labor productivity accelerated at about the same time that IT
investment surged. The assumption is made that the IT capital surge caused the
productivity surge, but reverse causality is also possible.
To determine the importance of all these issues we have to go down a level to
look at
industry data and case studies. If the growth accounting is correct, then the
productivity
acceleration should be showing up in the industries that invested in all the IT
capital. The availability in 2000 of output by industry data prepared by the Bureau
of Economic Analysis using new price deflators spawned a series of analyses of
the productivity acceleration that changed the perspective on the slowdown. I will
discuss the findings of these studies shortly, but before that it is worth looking at
the latest industry numbers since updated and revised figures were released in
November 2001. Table 3 shows labor productivity growth estimates from 1989-
55
95 and 1995-2000. Each industry’s output reflects the value added in that
industry (gross product
originating) and labor input is measured by the number of full-time equivalent
employees.
These new figures confirm the important conclusion that emerged in the literature
cited above, namely that the increase in labor productivity growth took place in
service industries as well as in durable goods manufacturing. In particular there
was a surge in productivity in wholesale and retail trade and in the finance sector.
What about the link to IT capital? Baily and Lawrence carried out a simple
exercise that
is repeated in the last two rows of Table 3. The industries were divided into those
that were more IT-intensive and those that were less IT-intensive, measured by
IT capital in 1995 relative to value added. The IT-intensive group had much faster
productivity growth throughout and a
larger productivity acceleration.
Stiroh has gone well beyond this simple calculation and estimated the impact of
IT
intensity. His strongest results come from defining IT intensity based on the
share of IT capital
services in total capital services in 1995. If that is high, he argues, it “identifies
industries
expending tangible investment on IT and reallocating assets toward high-tech
assets.” (page 10).
He also argues that it is important to use a measure that is exogenous, not one
that is defined over
the same period as the productivity revival. Hence he uses the IT intensity in
1995. He finds that
industries that are above the median in their IT intensity, by his measure, have
much larger
increases in labor productivity after 1995. His findings are pretty robust,
remaining strong even
after excluding outliers and making other tests. One place that gives weaker
results is when IT
intensity in 1995 is calculated as IT services per FTE. He notes that industries
that had invested
heavily in IT to reduce labor in the early 90s would have had difficulty making
further labor
productivity gains in the late 90s.
The McKinsey Global Institute (2001) recently completed an analysis of the
acceleration
56
in US productivity growth. They main part of this study consisted of case studies,
which I will
discuss later, but they also offer an alternative regression analysis with mixed
results. Starting
with value added data, they find that if each industry is considered as one
observation, there is
almost no correlation between the acceleration in labor productivity growth by
industry after
1995 and the surge in IT capital per employee by industry. On the other hand, if
each industry is
8
weighted by employment, there is a positive and significant correlation.10 The
study argued
further that the concentration of the acceleration in a few industries is revealing
because these
industries account for much of the total acceleration, but a much smaller fraction
of the total
surge in IT capital accumulation. This again, they argue, undermines the
IT/productivity link.
No statistical evidence can be definitive, but there is a pretty strong case for
saying there
is some connection between IT and the productivity acceleration. But how close
that connection
is and exactly how it works is not certain. Case study evidence can provide more
information.
Case Study Evidence The main contribution of the McKinsey study was a series
of eight
industry case studies, looking in detail at what had happened to productivity in
the 90s. Six
industries that had contributed disproportionately to the productivity acceleration
were included,
wholesale and retail trade, computers and semiconductors, telecom and
securities. And retail
banking and hotels were included as two industries that had invested heavily in
IT but had
experienced no surge in productivity.11
Based on these case studies, the study concluded that competitive pressure was
the main
driver of the productivity acceleration, by forcing improvements in business
operations. In the
retail trade case, they found that Wal-Mart played a pivotal role, both because of
its own large
size and high productivity, but also because it put competitive pressure on other
retailers. In the
57
semiconductor industry, Intel came under competitive pressure from AMD and
responded by
shortening its product cycle. There was an accelerated decline in the price of
microprocessors
that was translated into an acceleration of measured productivity in this industry.
Going in the
opposite direction, the study found that hotels and retail banks were facing
weaker competitive
pressure and were able to earn large profits without pushing as hard to improve
efficiency.
In general, the case studies make clear that productivity improvements come
from a
variety of sources. Improvements in retail productivity came about through
organizational
improvements, the advantages of large-scale “big box” stores and by a shift to
higher value goods
associated with the growth in the number of high-income consumers.
There are examples where IT investments yielded little payoff. Banks invested
very
heavily in powerful computers that were not really needed for the tasks that most
of the bank
employees were performing. However, there are also examples where IT did
contribute
substantially to productivity. Two of the industries were high-tech producers. The
telecom
industry is a direct application of IT. The Internet allowed much higher
productivity in the
10 The case for weighting the observations is that the variance in productivity
measures may be larger for
small industries.
11 Note, however, that the latest BEA data now find increased growth in banking
productivity after 1995.
9
securities industry, and IT is used heavily in the giant wholesale and retail
sectors. Wal-Mart, for
example, has relied heavily on IT as an enabler of its efficient supply chain. It
operates the
largest commercial database in the world.
The relation between IT and productivity revealed in the cases is a complex one,
and
often IT systems developed prior to 1995 were vital facilitators of productivity
improvements
after 1995. This suggests some of the impact of the surge in spending after 1995
may show up as
58
faster growth in future years.
Conclusions from the Industry and Case Study Data. There are pitfalls in any
industrylevel
analysis of productivity because the data are not collected in a way that really
allows the
researcher to allocate outputs and inputs by industry, particularly inputs. Deciding
which
industries are using IT capital intensely is problematic, for example. One example
of the kind of
problem posed is business services. This is a large industry that has grown a lot
and is a large
investor in IT. There is no serious measure of real output or productivity for this
industry, so it is
very hard to know the extent to which the industry or its IT may be contributing to
the
acceleration of productivity in other industries.
Another difficulty in drawing strong conclusions about the IT--productivity link
from the
industry data results from the fact that a main driver of the surge in IT capital was
the accelerated
price decline that encouraged everyone to invest more. Investing in IT became
the thing to do.
Ex post, some companies or industries used their IT capital well and others not
so well. Some
industries were subject to unrelated productivity shocks and some to swings in
measured
productivity resulting from vagaries in the measurement process. Without
controls for these
‘other factors’ it is hard to separate out the impact of greater or lesser amounts of
IT capital
accumulation.
Despite these difficulties the industry data and case studies are valuable. It does
look as
if the productivity acceleration, even though it is concentrated, spread outside
just the IT
producing sector. There are signs that IT using industries have done relatively
well in
productivity growth, but clearly other factors are important. It is hard to look at the
industry data
and the detailed case studies and conclude the productivity acceleration was just
IT-driven.
Other Indicators of Structural Change: What do they Show?
10
59
There are three aspects of US macroeconomic performance in the 1990s that
give an alternative
reading on the economy in the late 90s.12 They are related to productivity, but
rely on different
data.
Rising Real Wages and Low Unemployment Average hourly earnings, adjusted
for
consumer prices, declined by 0.5 percent a year from 1978-95 and then
increased at 2.0 percent a
year from 1995-2000, a difference of 2.5 percent a year. The corresponding
acceleration of real
consumption wages was 1.9 percent for compensation per hour and 1.3 percent
for the
employment cost index.13 It appears that the strong economic performance and
increased
productivity growth of the late 90s translated into much faster real wage growth.
This result is
important because it shows that the economy in the late 90s did not just generate
faster computers
and communications equipment, it affected the ability of the economy to deliver
consumer goods
in return for hours of work. It changed the path of workers’ real consumption
wages, including
hourly employees. Some part of this was likely the result of the very strong labor
demand in the
period, but not that much, because average real wages are not strongly procyclical. Moreover,
real wages grew even faster from the third quarter of 2000 to the third quarter of
2001 than 19952000, using any of the three measures of wages. So the pattern of solid real
consumption wage
performance has continued despite demand weakness in the economy.
Based on the experience of the 70s and 80s, estimates of the non-accelerating
rate of
unemployment (NAIRU) for the US were 6 to 6_ percent a year. Certainly the
experience of the
80s expansion seemed to confirm this, where wage and price inflation started to
worsen as the
unemployment rate fell below 6 percent. By contrast, unemployment fell below 6
percent in
September of 1994 and moved to around the 4 percent mark. There was no sign
of accelerating
inflation until 2000, in fact core inflation fell over much of the 90s. Several writers
have linked
60
the improvement in the inflation/unemployment tradeoff to the change in the path
of real wages.14
Nominal wage changes do not increase immediately when productivity growth
improves, and so
unit costs fall relative to trend and inflation is held down.
In summary: the experience of the labor market provides confirmation of a
structural
change in the US economy in the 90s that is somewhat independent of specific
productivity
numbers. The hedonic price indexes for computers have changed the measured
rate of
12 For a more extended discussion of these issues see Baily (2001).
13 In all three cases consumer prices are measured by the chain price index for
personal consumption
expenditure, excluding food and energy. Food and energy do of course affect
workers living standards, but
the fluctuations in the prices of these items are largely unrelated to the
performance of the nonfarm
business sector. Using the core CPI shows smaller, although still strong,
increases in real wage growth.
See Baily (2001) Table 5. The employment cost index starts only in 1980.
14 Blinder (2001), Ball and Moffitt (2001) and Council of Economic Advisers
(2001).
11
productivity growth, but they have relatively little impact on consumer prices and
cannot be the
main reason why unemployment stayed so low for so long.
Market Value of Corporations According to Robert Hall (2001), the ratio of the
market
value of corporations to the replacement cost of their capital (Tobin’s Q) nearly
tripled in the
1990s. In part, this increase in market value came about because stocks were
valued by different
criteria at the beginning and end of the decade. In particular, the risk premium
may have
declined. Moreover, stock prices reached unsustainable levels in 1999 (the end
point of Hall’s
calculation), and have fallen since then. Technology stocks certainly went
through a bubble, and
the overall stock market may also have experienced an increase that was not
driven by the
fundamentals.
The surprising thing about the increase in market value, however, is not that it
has
61
weakened from its extreme highs, but how robust it has been. Despite the
economic downturn
and the uncertainty induced by the September 11 attacks, stock price indexes
remain very high.
Even if Tobin’s Q were to fall sharply from its level at the end of 1999, it would
still be well
above unity, its value at the beginning of the 90s. This means that markets have
a persistent
belief that corporations now hold valuable intangible capital assets.
Wall Street pays some attention to productivity data, but not a lot. Market
valuations are
based on expectations about profits. Back in the mid-90s before clear signs of
improved
productivity had emerged, Wall Street analysts started to increased their
expected profit growth
figures and market valuations, which had been rising strongly since the mid-80s,
started to rise
even faster. History may look back at the current period as one where the market
turned out to be
dramatically over-valued. But for the present, Wall Street seems convinced that
there is still
strong profit growth potential in the corporate sector. It retains the belief that the
economy has
experienced a structural change.
Capital Inflow and the Dollar Figure 1 shows the rapid increase in the net inflow
of
foreign capital into the United States, heavily concentrated in direct foreign
investment and
securities.15
As a consequence of the foreign appetite for these US investment opportunities,
the
portfolio of assets held by foreigners has shifted into equities and direct
ownership of US
companies. These two classes of investment constituted roughly 50 percent of all
assets held by
foreigners in 1999, up from around 30 percent in 1990.
The desire of foreign companies to buy into the United States does not mean
they are just
buying high-tech or IT companies. The new economy in the United States is
apparently making
12
traditional industries attractive too. The breakdown of direct foreign investment by
industry
62
shows increased investment in high-tech sectors such as electronic components,
computer
services and telecom. But there was also a large increase in investment spread
throughout a range
of different industries, including traditional manufacturing industries and heavy IT
users such as
insurance and financial services.16 Equity investment may have been more
heavily skewed to the
tech sector, but I do not have a breakdown of that capital inflow.
When the US economy started to slow dramatically and then move into
recession,
triggered in part by the terrorist attacks, one might have expected the value of the
US dollar to fall
as the appetite for US assets declined. Instead, the US dollar has remained very
high against both
the Euro and the Yen. The weakness in Japan is a separate issue, but the
continued strength of
the dollar against the Euro and other currencies remains something of a surprise.
It appears the
rest of the world believed in the increased profitability of capital in the US and
continues to
believe that the prospects for asset returns are stronger in the US than in the
other main industrial
countries. The inflow of capital and the strength of the dollar can be seen as
another piece of
evidence supporting the view of a structural change in the US economy.17
Causes and Future Prospects
Based on the data given above and the studies described, the weight of the
evidence points clearly
to the conclusion that there was a structural break in the performance of the US
economy in the
1990s. Despite revisions, the aggregate productivity data still show a break in the
productivity
trend. The case study evidence supports the view that the pace of innovation
picked up in a range
of different industries. The changes in the labor market, the rise in the market
valuation of
corporate capital, and the inflow of capital to the US, all support the conclusion
that structural
change took place. This combination of economic outcomes suggests
productivity growth and
the return on capital both increased in the 90s.
A caveat is in order. Should the improvement in economic performance be
described as
63
having created a new economy? There are reasons to be skeptical about the
term ‘new economy.’
After all, the economy is like a large oil tanker, it does not change direction
quickly and much
economic activity today is similar to what it was in the early 1990s. Most
employment and
15 The “other private assets” category includes bank loans.
16 See the table at http://www.bea.doc.gov/bea/di/fdi-ind.htm
17 Jaume Ventura (2001) disagrees with this interpretation. He argues that US
residents experienced a very
large increase in wealth in the 90s and responded by increasing their
consumption and borrowing from the
rest of the world. However the fact that US residents could attract funds as easily
as they did suggests a
perception by foreigners that returns had increased in the US.
13
output remain in traditional industries and we learned in 2001 that the business
cycle is still alive.
In fact the current recession, which involves large swings in inventory and
equipment investment,
looks rather like the old-style recessions of the period after World War II. While I
use the term
new economy, because it captures the idea of a shift in the economy in the 90s, I
recognize that it
probably overstates the extent of change.
With that terminology disclaimer out of the way, the question is whether there is
some
compromise or consensus explanation of the productivity improvement that
emerges. Certainly
advances in IT are changing the way business is done. And as we have seen,
the aggregate
growth accounting evidence and some analyses of the industry data make a
case that a major part
of the productivity acceleration was the result of these advances. But for the
following reasons, I
judge that IT was not the sole reason for the productivity shift after 1995.
First, I prefer the growth accounting framework used in Baily-Lawrence. This
framework indicates there was a significant increase in MFP growth after 1995
outside the IT
hardware sector. Second, the growth accounting framework itself may overstate
the causal link
from IT capital to growth if there is reverse causality. Third, the large increase in
real wage
64
growth, together with the extraordinary growth in the market valuation of
corporations does not
seem consistent with a model where there is faster MFP growth only in the IT
hardware sector
that affects other sectors only through IT capital accumulation. Fourth, the case
studies provided
evidence that innovative business practices, that in some cases were unrelated
to IT, contributed
heavily to faster productivity. Fifth, the regressions from the industry data and the
case studies
indicate that IT capital in place prior to 1995 facilitated productivity growth after
1995. This puts
into question the immediate and direct linkage from rapid IT capital accumulation
to rapid
productivity growth that is built into the growth accounting exercise. And finally,
the failure of
the growth accounting framework to explain the slowdown in the 70s leaves
some residual
skepticism about its ability to fully capture the speed up.
Whether the improved economic performance of the 90s was wholly IT driven or
only
partly IT driven, there remains the question of why did the pace of innovation
speed up in the 90s.
What was it about the economic conditions in the 90s that fostered stronger
economic
performance? One simple way to describe the shifts in the productivity trend is
that there was a
pool of innovations and investments to be exploited after World War II, and these
generated the
fast trend of productivity over the period. There was then a lull as the easy ways
to raise
performance had already been found. During the 1970s and 1980s there was a
lot of change
taking place and old capital was being destroyed. There was an ongoing push of
economic change
and innovation, and the IT revolution was in the making, but the benefits were not
yet showing
14
up, at least in measured productivity. Some time in the 1990s a new flow of
productivityenhancing
innovations came on stream, the economic environment was favorable, and
there has
been a return to something closer to the postwar trend of faster growth.
65
While there is no proof here, one can see some of the reasons why the
environment in the
US economy in the 1990s was favorable both for rapid innovation and rapid
diffusion of
innovation, both IT related and otherwise. There has been heightened
competition in an
increasingly deregulated economy facing strong international competition. IT
innovation is driven
by the demand for improved technologies in the using industries. The United
States has
competitive service industries, often on a global scale, and this encourages them
to seek out new
technologies to improve their own productivity. If the new economy were entirely
the result of a
random surge in the flow of innovation, then all countries should have had similar
changes
together. The new technologies are available globally. In practice the United
States has been well
ahead of most of the industrial countries and a reason for this is that the United
States has
competitive markets in the industries that are using IT.
When innovations occur in one area, they can bring benefits. But when
complementary
innovations occur together the effects can be greatly increased. The 90s were a
particularly
favorable time because a combination of rapid advances in computing power,
software and
communications capabilities formed such a set of complementary innovations.
Large amounts of
data can be processed and presented in a way non-technical personnel can use
and then
transmitted to remote locations within the same firm or to other firms.
The policy environment contributed to the creation of the right environment for
growth
and innovation. Policies to deregulate industries and to maintain domestic
competition and
increase international competition have been stressed. Funds have been
provided to support basic
research and education. And the mix of monetary and fiscal policy has lowered
interest rates and
encouraged investment.
Future Prospects If you believe the productivity revival was structural, but it came
from
66
the surge in productivity growth within the computer sector, plus some cyclical
component, there
is a case for a rather pessimistic view of future productivity growth. First, the
huge rate of decline
in computer prices of the late 90s may not continue indefinitely and, second,
there may be
diminishing returns to investment in IT capital. The collapse of investment in the
past year seems
consistent with this view, and has caused a large adjustment in some commercial
forecasters’
predictions of productivity growth. Macroeconomic Advisers, for example,
predicts labor
productivity growth to be fairly strong in the second half of 2002, with a cyclical
bounce, but to
average only 1.6 percent for the four quarters of 2003, because of the collapse of
IT investment.
15
Using ‘conservative’ assumptions, Oliner and Sichel (2002) argue that the
productivity growth
trend could fall slightly below 2 percent.
The basis for pessimism in the IT-only scenario can be questioned, however.
First, IT
prices could well continue to fall rapidly since the technological drivers of
improved performance
and the intense competition are still at work.18 And even if the price declines do
moderate, there
is an offset, a “share effect.”19 The share of IT capital in total capital has been
rising rapidly, with
the effect of pushing up the rate of real capital accumulation. The growth rate of
the total capital
stock is a weighted average of the growth rates of the components. The fastergrowing
components (IT equipment) are a growing share of the total. Jorgenson et al.
(2001) are actually
rather optimistic about the future prospects for labor productivity, even though
they view the
revival as being IT driven. Jorgenson et al. anticipate a growth rate of 2.24
percent a year, not
much slower than in the late 90s, based on the assumption of continued rapid
decline of processor
and computer prices. There is a wide error band around their central figure,
ranging from a
pessimistic estimate of 1.33 percent and an optimistic estimate of 2.98.
Macroeconomic Advisers
67
is also more optimistic beyond 2003, when they predict a resumption of
investment spending
picks and a productivity trend of well over 2 percent. And Oliner and Sichel
(2002) project as
much 2_ percent as the future productivity trend under more optimistic
assumptions.
The McKinsey Global Institute, which argues that IT is only a secondary factor in
the
productivity surge made a forecast of future labor productivity growth based upon
the case
studies. They looked at each industry under study and asked to what extent
would the drivers of
productivity in the 90s continue into the future and were there innovations in
process that would
increase productivity. They estimated growth at 2.0 percent a year going forward
with a range of
1.6 to 2.5 percent.
As I have noted, my view of the determinants of recent productivity puts less
weight on
IT than Jorgenson et al. or Oliner and Sichel, but somewhat more weight than
that ascribed by the
cases studies. On this basis, how much of the post-95 growth surge will
continue? The basic
drivers of the acceleration, rapid improvements in IT, strong competition and the
impact of
globalization are in place for a continuation of growth. One cannot necessarily tell
how new
technologies will be used, but going forward the Internet is likely to add to
productivity, whereas
it was not a major factor in the 90s growth. Robert Litan and Alice Rivlin (2001)
estimate that
the Internet will add about a quarter of a percentage point to future growth. And
although they do
18 For example, John Markoff reports in the June 30th New York Times (2001)
on an advance made at Intel
“demonstrating that the semiconductor industry will be able to continue shrinking
its basic building blocks
at a torrid pace at least until the end of this decade.”
19 Daniel Sichel has pointed this out.
16
not allow one to put a specific number on productivity, the “other” indicators of a
new economy-the robust stock market and the strong dollar--are still signaling that markets
expect continued
68
high returns to capital, which will contribute to growth.
Despite a weak economy, labor productivity over the six quarters since mid 2000
grew at
about 1.5 percent at an annual rate. Non farm output declined in the second,
third and fourth
quarters of 2001 and yet there was positive labor productivity growth in all three
quarters. This
has never occurred before over the postwar period. With the caveat that recent
data are often
revised, this suggests that the trend of labor productivity is well above 1.5
percent.
I take as a starting point the figure that labor productivity grew at about 2.7
percent a year
1995-2000, based on an averaging of income and product estimates. At one
time, it was thought
that the productivity trend was continuously accelerating, but once the output
data were revised,
that view became hard to sustain. To the contrary, it seems much more likely that
some part of
the growth over this period was either temporary or cyclical. There was a boom in
financial
markets that drove revenues through the roof in the financial services sector and
pushed measured
productivity growth unsustainably high. There was a spending boom that pushed
productivity in
the trade sector way up. Some part of that productivity increase does not seem
sustainable.
Therefore, 2.5 percent is a reasonable upper bound on the trend. The fact that
technology and
competition remain strong, plus the evidence of recent productivity data, suggest
2.0 percent
growth is a reasonable lower bound to the current labor productivity trend.20
A Second Wind but at a Lesser Strength Despite different perceptions of the
drivers of
productivity, it seems that the literature I have reviewed above all concludes that
the productivity
trend is likely to be in the range of 2 to 2_ percent a year. That is a strong
performance that will
increase wages and living standards and allow potential GDP growth in the range
2.8 to 3.3
percent a year. It is certainly enough to say the new economy will get a second
wind. That does
not mean we will go back to the 90s. This rate of GDP growth is far slower than
the rate of over
69
4 percent achieved 1997-2000. Moreover, other elements of the new economy
may look weaker.
20 One negative for productivity growth over the next year or so is the impact of
September 11. As others
have noted, this could reduce underlying growth for a period, but should not
affect the longer run trend.
The attacks of September 11 will have some lasting effects on the level of
productivity in the US and hence
a short-term effect on productivity growth. One quick way to estimate the impact
is as follows: If private
business were to hire 200,000 additional security personnel, about a 50 percent
increase over the current
amount, and the all-in cost per person were $100,000, then this would add to $20
billion or about 0.2
percent of GDP. If there were also extra travel time, higher inventory etc, the
result could be a reduction of
productivity growth by about 0.2 to 0.3 percentage point for a couple of years.
The forecasting firm,
Macroeconomic Advisers, has suggested the productivity effect of the attacks
would be smaller. They
expect around 100,000 extra employees at a cost of $54,000 per person, giving a
total cost of only $5.4
billion. Running this through their macro model, they estimate the productivity
effect as 0.01 to 0.02
percentage point per year, although the effect was more persistent in their view.
17
Even though the new economy is likely to continue with faster productivity, this
does not
imply that stocks or the dollar will remain as strong. Future corporate earnings
could justify the
current level of the stock market or stimulate further growth, but they may very
well not do so.
Even a substantial fall in the market would still leave Tobin’s Q well above unity,
consistent with
a sizeable valuation of intangible capital, and that is the main expectation based
on the new
economy. For the dollar: At some time in the future the dollar is very likely to
decline even if the
returns to capital in the US remain high. The dynamics of servicing a rising
foreign debt suggest
this, plus new economy opportunities may develop more strongly in other
countries, changing the
relative attractiveness of investing in the US.
The continuation of solid productivity growth, occurring in industries that produce
70
consumer goods and services as well as those making investment goods, implies
real wages
should continue to grow more rapidly going forward than they did during the post1973 period.
However, there is no guarantee that the NAIRU will stay as low as it seemed to
be in the 90s. In
many specifications of the Phillips curve, an increase in the trend rate of growth
of real wages
induces only a temporary shift in the inflation/unemployment tradeoff.21 I would
expect some
persistent benefit in lower unemployment coming from faster productivity growth,
largely
because the NAIRU was much lower in the 50s and 60s when real wage growth
was rapid than in
the 70s and 80s when real wages were weak. But given the wide disagreements
in the profession
of the nature or even existence of a Phillips curve, there is no definitive evidence
to prove this
view.
In summary: Even though I conclude that the ‘new economy’ will get a second
wind,
this does not mean a return to the economic euphoria of the late 1990s.
Policy Implications
The high level of competitive intensity in the US economy has been important in
stimulating
innovation in both the high-tech sector and in the old economy, or the traditional
industries.
Policy must act to sustain this by maintaining open international markets and
avoiding
protectionist measures. Maintaining competitive intensity also means pursuing an
anti-trust
policy that allows industry change to take place, including consolidations, but that
resists anticompetitive
actions by dominant players that could discourage innovation and encourage
inefficiency. Anti-trust policy is not just about improving static efficiency, it is also
about
maintaining the competitive environment for innovation and productivity growth.
21 See Baily (2001) for a more extended discussion of this issue and additional
references.
18
Regulatory policy is just as important for productivity growth. The deregulation
movement of the 70s and 80s surely contributed to the flowering of innovation in
the 90s. Good
71
regulatory policy is not just a matter of getting rid of regulations, however, it
should set the rules
of the game for competitors in a way that enhances efficiency and innovation.
One example is
regulatory policy towards the use of wireless spectrum, an area of potential future
technology
growth. The current allocation of wireless spectrum is very inefficient, and a more
economically
rational allocation of wireless spectrum would enhance the development of this
important area of
the new economy.22
There is not the space here to deal with the issue of skills, but I want to
acknowledge
their importance. The returns to education and skill have risen dramatically in the
past twenty
years, contributing to a widening of the wage and income distribution. The
increased deployment
of IT has also changed the skill requirements of jobs. So it seems evident that
education and
training have become more important. In practice it is hard to create effective
programs to
enhance skills. Much of the skill acquisition necessary comes in training on the
job. But workers
who are better prepared before they enter or re-enter the workforce are more
likely to be trained
on the job and are more able to take advantage of training. Since innovation and
productivity
growth often involve the loss of jobs in some firms and industries as others
expand, it is essential
that workers have the opportunity to acquire new skills and become re-employed.
Good macroeconomic policy contributed to the favorable economic environment
of the
90s. Monetary policy encouraged growth in the mid 90s, tightened up at the end
of the decade as
growth started running too fast. And it acted strongly to mitigate the impact of the
downturn and
recession of 2001. It may face new challenges going forward, either if the
recovery fails to
materialize or if the turnaround is too strong. As I noted earlier, the combination
of low
unemployment and low inflation achieved in the 90s may not be as easily
reached over the next
five years. In the 90s there was an exaggerated view of what monetary policy
can accomplish
72
that may engender disappointment in the future.
Fiscal policy in the 90s contributed to growth by keeping interest rates low and
stimulating investment. Going forward, it will be necessary to rely more heavily
on domestic
saving as a source of investment funds than was the case in the 90s. The US
economy needs to
be rebalanced with a higher saving rate in the US, lower growth of imports, and a
smaller current
account deficit. Maintaining budget surpluses can contribute positively to this
rebalancing and
sustain the environment for growth. I noted earlier that budget planners
consistently
underestimated the improvement in the fiscal situation in the 90s, but that lesson
now seems to
22 See Martin Baily, Robert Willig, Peter Orszag and Jonathan Orszag, (2001).
19
have been learned too well. The danger today, even if the new economy gets a
second wind, is
that budget planners will overestimate tax revenues and underestimate spending,
thereby eroding
or eliminating the budget surpluses.
Conclusion
When running or playing a racquet sport it is easy to start too strong and then
become winded and
exhausted. After setting a better pace, the body fortunately seems to get a
second wind. The
exhaustion lessens and breathing becomes easier, although one does not have
quite the same
energy as was available in the beginning. That seems an appropriate analogy for
the economy in
recent years. The enthusiasm for IT and for the shares of high-tech companies
went too far.
Growth became unsustainably rapid and a slowdown inevitable. But the
excessive optimism of
the 90s should not give way to excessive pessimism now. Many of the drivers of
growth in the
90s, those linked to IT and those unrelated to it, remain in place.
73
74
75
76
77
78
79
80
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