Download Do We Have a “New” Macroeconomy?

Do We Have a “New” Macroeconomy?
J. Bradford DeLong1
[email protected]
http://www.j-bradford-delong.net/
March 2001
Abstract
Claims that we are in a “new economy” are more hushed today than they were
two years ago. Nevertheless, there is a good chance that the data processing and
data communications revolutions have altered the structure of the
macroeconomy and changed the pattern of the business cycle and productivity
growth to a degree rarely if ever seen before. The information-technology
revolution is the prime candidate for driving the acceleration in aggregate labor
productivity growth in the 1990s. Either America’s businesses are managed by
fools, or the boom in investment in information technology will pay dividends in
the form of accelerated aggregate labor productivity growth for at least a decade
to come. The acceleration in productivity growth is the prime candidate to
account for the reduction in the economy’s sustainable level of unemployment.
And improvements in information systems should diminish the aggregate
economy’s vulnerability to inventory fluctuations, which have for more than a
century been a principal driving force behind the business cycle.
Introduction
Claims today that we are in a "new economy" are more hushed than they were a year or
two ago. The crash of the technology-heavy NASDAQ market, the end of the tech-heavy
IPO boom, and the inability of investment analysts today to recall why they placed such
1
I would like to thank U.C. Berkeley’s Committee on Research for financial support, and Stephen Cohen,
Bob Cumby, Bob Gordon, Steve Oliner, Dan Sichel, Lawrence Summers, Hal Varian, Steve Weber, David
Wilcox, Janet Yellen, and John Zysman for helpful discussions.
2
high valuations on technology leaders only a few months ago2 have sent many who used
to claim that old economic rules were suspended into hiding.
Source: CBS Marketwatch and the Bureau of Labor Statistics.
If stocks fall significantly further, enthusiasm for the “new economy” will cool still more.
The wise bet is that stocks will fall significantly lower, for major market indices are still
very high relative to standard historical average fundamental patterns,3 and there is good
2
See Gretchen Morgenson (2001), “How Did They Value Stocks? Count the Absurd Ways,” New York
Times (March 18).
3
See Robert Shiller (2000), Irrational Exuberance (Princeton: Princeton University Press: 0691050627)
3
reason to suspect that the inflow of investors into the market during the 1990s has made it
more vulnerable and more volatile.4
However, just as the conventional wisdom over much of the past half decade became
increasingly overexuberant about the prospect of a “new economy,” so current opinion is
in danger of becoming overpessimistic. The U.S. economy is now in danger of falling
into recession: right now we are seeing just how skillful our central bankers are as they
try to reduce interest rates by just enough to offset the depressing effects on spending of
the NASDAQ crash in order to guide the U.S. economy to a “soft landing.” $4 trillion
worth of stock market wealth has vanished. But stock market crashes and recessions are
beside the point. As I wrote two years ago, if there is a “new economy” it is “not about…
smooth growth, permanently rising stock prices… or permanently low rates of
unemployment, interest and inflation” for “the business cycle… has existed for at least
200 years despite enormous changes in the economy. It is likely to exist for at least 200
more.”5
What, then, is the “new economy” —if it really exists—about? It is about structural
transformation: the technology revolutions in data processing and data communications
doing for information processing and organizational control something like what the
technology revolutions of the nineteenth-century industrial revolution did for materials
processing and transportation. I think that our current technological revolutions may well
be of the same order of magnitude as the industrial revolution’s transformation of the
primarily agriculture-and-craft economy powered by animal and human muscles of the
eighteenth century into the primarily industry-and-machine economy powered by steam,
4
See Brad Barber and Terrence O’Dean (2001), “The Internet and the Investor,” Journal of Economic
Perspectives 15:1 (Winter), pp. 41-54.
5
See Stephen S. Cohen, J. Bradford DeLong, and John Zysman (1999), “Tools for Thought” (Berkeley:
U.C. Berkeley xerox).
4
oil and electricity of the twentieth century:6 I have long been an optimist with respect to
the technology (if also a pessimist with respect to business models and stock market
valuations).
But in this paper I am supposed to perform a narrower task than to survey the “new
economy” as a whole.7 Here my task is to assess whether the data processing and data
communications technological revolutions have had a significant impact on the
macroeconomy—on the patterns of unemployment, inflation, growth, boom, and
recession for the economy as a whole. My answer is a firm “probably”: the time span is
too short for any conclusion to be much stronger than an informed guess, but it is
probably the case that the data processing and data communications revolutions have had
a significant impact on macroeconomic variables and patterns. Comparing the U.S.
macroeconomy today to the U.S. macroeconomy of the 1980s, I am struck by six
significant differences:
Over the past decade it has become increasingly clear that innovation in computer and
communications technologies is proceeding at an extraordinary pace. The pace of
technological advance is well-described by what has come to be called "Moore's Law"-the rule of thumb that Intel cofounder Gordon Moore's set out a generation ago that the
density of circuits we can place on a chip of silicon doubles every eighteen months with
little or no significant increase in cost. Moore's Law has held for thirty years; it looks like
it will hold for another ten at least. Moore's Law means that means that today’s
computers have 66,000 times the processing power of the computers of 1975. In ten years
computers will be more than 10 million times more powerful than those of 1975--at
roughly constant cost.
6
See David Landes (1969), The Unbound Prometheus: Technological Change and Industrial Development
in Western Europe from 1750 to the Present (Cambridge: Cambridge University Press: 0521094186).
7
However, performing this broader task is on my agenda. See J. Bradford DeLong and Lawrence H.
Summers (forthcoming), “An Overview of the ‘New Economy’” (Jackson Hole: Federal Reserve Bank of
Kansas City).
5
In a normal case one would say that rapid improvements in a particular branch of
industry run into diminishing returns. The first candlepower of light one can produce
after dark--with a candle or an oil lamp steady enough to read by--is a really big deal. The
ten-thousandth is not. But each upward leap in computer processing power seems to bring
a new dimension of capabilities and uses: the first computers produced tables useful for
calculating artillery trajectories. The next generation were used not to make sophisticated
but to make the extremely simple calculations needed by the Census Department and by
the human resource departments of large corporations. The next generation of computers
were used to stuff data into and pull data out of databases in real time--airline
reservations processing systems and insurance systems.
Computers came to American business as wordprocessors and what-if machines, devices
to answer questions like "what if this paragraph looked like that?" or "what if the growth
rate were twice as fast?" And now computers have become embedded into objects as
sensors and controllers, and extended from machines to access the worldwide library that
we are now building. For paralleling the revolution in data processing capacity has been a
similar revolution in data communications capacity.
I won't say whether this particular leap forward in technology is larger than in the past-bigger than the steam engine or the automobile. How could I? What metric would we
use? In their day television, or the internal combustion engine, or the railroad, or the
steam engine were technological leaps that transformed the economy as well. And you
only have to begin thinking about the problems of measuring changes in economic
structure and rates of economic growth across structural transformations before you
conclude that the problems of measurement are as unsolvable as the problem of trying to
draw an accurate two-dimensional map of the surface of the earth.
Today I am going to focus on a subset of these issues: what this "new economy" means
for macroeconomic policy--even though it may well be the case that the microeconomic
effects (on antitrust doctrines and policy, say) are more important.
6
So I have six points to make here today:
*
First, that computer technology finally is a big deal for the macroeconomy as a
whole
*
Second, that computer technology is almost surely a bigger deal than our standard
measurements suggest.
*
Third: one macroeconomic consequence is likely to be a decline in the inventory
cycle.
*
Fourth, a second macroeconomic consequence is likely to be an improved labor
market--the one we are seeing now.
*
Fifth, that macro policy becomes more difficult because rules of thumb break
down.
*
Sixth, that someday macro policy will become even more difficult because the
tools of monetary policy may--not now, but at some date in the distant future--lose a lot
of their power.
Computer Technology Finally Has a Large, Direct, Measured Effect on Growth...
A decade or more ago it became a commonplace to wonder where the aggregate
macroeconomic benefits of computers were. Back then Dan Sichel had an answer: the
economy was large, and in that context computer investment was small.
But now computer investment is large. The end of the government's budget deficit and
the rising inflow of capital from abroad have boosted total investment--and at the margin
one of every two new investment dollars goes to information technology. And the price
of computers has fallen, so each dollar of nominal investment in information technology
buys much more computer power today than it did a decade ago.
7
Dan Sichel and Steve Oliner conclude that eighty percent of the acceleration in measured
productivity growth in the second half of the 1990s is due to information technology
investment.
But Other, Unmeasured Effects May Be as Large...
Moreover, there are large chunks of the economy in which productivity growth is not
well-measured at all. Many of these chunks are ones in which we would expect computer
investment to yield substantial gains. Are we measuring them all? The conclusion has to
be "no." As Alan Blinder has written, "retailing over the Internet may offer many benefits
to consumers (examples: cheaper comparison shopping, 24-hour availability, no travel
costs, etc.), but such gains will never be counted in GDP, and hence will never appear in
the productivity statistics."
However, there are no firm, reliable numbers on the magnitude of official statistics'
understatement of productivity growth. There are only guesses. The Boskin CPI
Commission had a guess of about 1% per year, but even they did not speculate on
whether the magnitude of the understatement was growing.
Consequences: The Decline of the Inventory Cycle?
Managers say that one of the principal benefits of new computer-and-communications
technologies is better inventory control. Businesses today can control their inventories
much more effectively. Interest and storage costs are lower. Stock-outs are less frequent,
and less costly.
8
What are the macroeconomic implications of better microeconomic inventory
management? Over the past hundred years, as much as two-fifths of the quarter-toquarter variability of production growth rates about trend has been due to fluctuations in
inventory investment. Already we have seen economy-wide reductions in inventory-tosales ratios of about one-fifth, and greater reductions in the length of time goods spend in
the inventory pipeline.
If the reduction in inventories made possible by modern information and communications
technologies is close to reaching its limit, then we can expect that one consequence of the
new economy is to moderate the inventory-driven portion of the business cycle. If the
reduction in inventories has just begun--if improved information flow will truly make the
new economy a just-in-time economy--then we can expect that the inventory-driven
portion of the business cycle will be severely reduced, or effectively eliminated.
Consequences: A Lower "Natural Rate" of Unemployment?
The use of unemployment as a measure of cyclical conditions has gone awry at least
twice: in the late 1970s, as the natural rate rose, and in the late 1990s, as the natural rate
has fallen. In the late 1970s belief that there was still room for substantial expansion
without accelerating inflation proved false: the actual natural rate of unemployment
turned out to be higher than believed by two percentage points. In the late 1990s belief
that there was no room for substantial expansion without accelerating inflation proved
false: the actual natural rate of unemployment turned out to be lower than believed by
two percentage points.
One way to interpret these facts is to say: "too bad for natural rate theories." Stable
natural rate theories worked well only in the U.S.--they never worked well in Europe, for
example. So why should we be surprised that their success in the U.S. is only temporary
and transitory?
9
A second thing to say is that workers' real wage aspirations are a function of the
unemployment rate: the higher is unemployment, the smaller is the amount by which
workers aspire to raise their real wages. In equilibrium, the rate of unemployment
consistent with non-accelerating inflation--the natural rate of unemployment--will be that
rate of unemployment at which workers' real wage aspirations are equal to economy-wide
productivity growth.
Thus the higher the rate of productivity growth, the lower the natural rate of
unemployment.
This wage-aspiration theory is certainly a candidate explanation for both the surprisingly
unfavorable inflation-unemployment tradeoff the U.S. experienced in the 1970s, and the
surprisingly favorable inflation-unemployment tradeoff the U.S. has experienced in the
1990s. If it is correct, the low current natural rate is another benefit that the U.S. is
reaping from the new economy.
Consequences: A Broken Speedometer?
But the "new economy" has bad as well as good consequences for the making of
monetary policy. For one thing, when historical patterns no longer apply standard rules of
thumb no longer apply either. In the absence of standard rules of thumb, it becomes more
difficult to try to guide the economy into the narrow zone in which price stability and full
employment are both attainable.
How to make Federal Reserve policy with a broken speedometer is a very hard problem.
More weight has to be placed on the immediate past and less weight on the distant past in
deciding what to do. But it is not clear to me how to think constructively about such
issues.
10
Consequences: Do Traditional Tools of Monetary Policy Break?
The last of the consequences I wish to note is that perhaps the new economy will--not
now, but someday--break the standard tools of monetary policy. The Federal Reserve
uses its open market operations--sales or purchases of U.S. Treasury securities for reserve
deposits at the Fed--to manipulate interest rates and so affect aggregate deamnd The
unique role of commercial banking reserves in the payments system means that small
open market operations have large consequences for the intertemporal price structure.
The Federal Reserve controls interest rates remarkably easily.
But this will hold true only as long as deposits at U.S. commercial banks are the means of
payment of choice in the economy. And as financial innovation proceeds and as the new
economy grows, there is a good chance that deposits at U.S. commercial banks will cease
to be the means of payment of choice.
And if this happens, what becomes of the Federal Reserve's ability to control interest
rates and affect aggregbate demand?
So far there is no sign that the unique role of bank reserves in the U.S. payments system
is fading away. There are no signs that Federal Reserve power to shape interest rates is
diminishing. But someday there may be.
Conclusion: Other Issues
And there are a host of macroeconomic issues I haven't considered: Day traders.
Contagion. The magnitude of the peso and East Asian financial crises.
11

First, the sheer magnitude of the apparent changes in macroeconomic patterns. With
the two exceptions of the Great Depression and the post-1970 productivity slowdown,
for the past century large structural changes in the economy have been accompanied
by little in the way of changes in the business cycle. (Moreover, for both the Great
Depression and the productivity slowdown it has been remarkably difficult to trace
the macroeconomic patterns to underlying structural changes in the economy. 8)

Second, the sharp decline in business cycle volatility. As Christina Romer has pointed
out, the largest break in the cyclical variability of the unemployment rate and output
growth comes in the mid-1980s, with cyclical variability since the end of the Volcker
disinflation being an order of magnitude smaller than in any previous era.9

Third, the sharp acceleration in productivity growth in the mid-1990s. No one in the
mid-1970s expected the productivity growth slowdown that then struck the economy
to last for long, but by the mid-1990s no one expected the era of diminished
expectations and slow productivity growth to come to an end so quickly.10

Fourth, the remarkably rapid fall in the unemployment rate consistent with stable
inflation. At the start of the 1990s many economists believed that the natural rate of
unemployment in the U.S. economy was somewhere between 6.5 and 7.0 percent. By
8
See Barry Eichengreen (1996), Golden Fetters: The Gold Standard and the Great Depression (Oxford:
Oxford University Press: 0195101138); Paul Romer (1987), “Crazy Explanations for the Productivity
Slowdown,” NBER Macroeconomics Annual 2.
9
See Christina Romer (1999), “Changes in Business Cycles: Evidence and Explanations” (Cambridge:
NBER Working Paper 6948).
10
For example, consider the subtitle of Paul Krugman (1995), Peddling Prosperity: Economic Sense and
Nonsense in an Age of Diminished Expectations (Cambridge: MIT Press: 0393312925).
12
the end of the decade it seemed as though the natural rate might be as low as 4.0
percent.

Fifth, the decline in measures of the aggregate inventory-to-sales ratio. After
showings remarkable stability at about 24 months for nearly half a century, the
inventory-to-shipments ratio in American durable manufacturing has fallen smoothly
and steadily by about a third in the past decade.

Sixth, increased financial volatility—both in the form of increasingly virulent
international financial crises, and in the form of increased domestic stock market
volatility. Few a decade ago anticipated that the 1990s would see so many major
financial crises. And few anticipated that the 1990s would see stock prices in the U.S.
drift as far away from standard measures of long-run fundamentals as they did.
With the exception of the era of the Great Depression, it is not possible to find another
period in the twentieth century with so much apparent change in the macroeconomy as
we have seen in the past decade. Moreover, in at least four of the elements listed above,
the prime candidate for responsibility is some aspect of the technological revolutions in
data processing and data communications. Only in the case of diminished
macroeconomic volatility is it hard to draw a link between new technologies and changed
economic performance.
Structural Change and Macroeconomic Volatility
The past hundred and fifty years have seen the world’s advanced industrial economies
shift from primarily agricultural to primarily industrial and now primarily service
13
economies. They have seen the rise of sophisticated systems of consumer credit that
allow households to smooth their spending over time. They have seen the rise of the
modern social insurance state to serve as a sea-anchor for the economy by virtue of the
large relative size of its spending programs. They have seen the rise of systems of deposit
insurance to reduce the probability of a massive chain of bankruptcies and thus a fullfledged financial panic. They have seen the government take on responsibility for
managing the macroeconomy.
Yet in spite of all these structural changes, the American business cycle in the last quarter
of the twentieth century has looked remarkably like the American business cycle in the
last quarter of the nineteenth century.
[to be written]
The Sharp Acceleration in Productivity Growth
In the nonfarm business sector—the part of the economy on which productivity studies
typically focus—output per labor hour rose between 1995 and 2000 at 2.5 percent per
year, more than double the pace of the preceding quarter century since 1970. This
acceleration of productivity growth raises for the first time in a generation the likelihood
of reasonably rapid and broad-based real income growth, if the jump in productivity
growth can be sustained. In the long run, productivity growth and average income growth
must correspond. An era like that of 1970-1995 in which productivity growth is slow
must be, in Paul Krugman’s phrase, an “age of diminished expectations.”
14
Back before 1995 critics of visionaries who saw the computer as transforming the world
pointed to slow and anemic growth in aggregate labor productivity. As Nobel Prizewinning MIT economist Robert Solow posed the question, if the computer is so important
"how come we see the computer revolution everywhere but in the [aggregate]
productivity statistics?"11 After Solow wrote productivity performance worsened still
further. In the decade and a half before Solow asked his question in 1987 output per hour
grew at 1.1 percent per year. In the eight years after 1987 output per worker grew at only
0.8 percent per year.
This "productivity paradox" was sharpened because at the microeconomic level
economists and business analysts had no problem finding that investments in high
technology had enormous productivity benefits. MIT economist Erik Brynjolffson and
his coauthors found typical rates of return on investments in computers and networks of
more than fifty percent per year. Firms that invested heavily in information technology
and transformed their internal structures so that they could use their new technological
capabilities flourished in the 1980s and 1990s--and their lagging competitors did not.12
But then, beginning in 1992, the American economy began a stunning investment boom.
From 1992 to 2000, business fixed investment grew at an average annual rate of 11% per
year. the share of business fixed investment in GDP jumped from 9% to 13%, with more
than half of the additional investment going into computers and related equipment. And
as the information technology investment boom took hold, productivity growth and
growth in real GDP accelerated as well. Real GDP rose by an average of 3.9% per year
between 1995 and 2000. Nonfarm business output per hour worked grew at 2.7% per
year.
11
Robert Solow (1987), New York Review of Books
See Erik Brynjolfsson and L. Hitt (1996), "Paradox Lost? Firm-level Evidence on the Returns to
Information Systems Spending," Management Science (April); Erik Brynjolfsson (1993), "The Productivity
Paradox of Information Technology," Communications of the ACM, Vol. 36, No. 12, Dec. 1993; Erik
Brynjolfsson and L. Hitt (1998), "Beyond the Productivity Paradox," Communications of the ACM
(August).
12
15
One of the chief factors driving the boom in real investment was the extraordinary gains
in productivity in the manufacture of semiconductors and computer equipment. The
market price of computing power has fallen more than ten thousand-fold in a single
generation. As a result, the installed base of information processing power has increased
at least million-fold since the end of the era of electro-mechanical calculators in the
1950s. The share of nominal GDP devoted to information technology investment has not
grown much since the 1980s. But because of falling prices, the real share has grown very
swiftly.
16
Source: Jack Triplett (1999), "Computers and the Digital Economy" (Washington,
DC: Brookings Institution).
As Federal Reserve Board economist Dan Sichel pointed out in the early 1990s, the thenfailure to see the computer revolution in the aggregate productivity statistics should not
have come as a surprise. In the 1970s and 1980s computers were simply too small a share
of total investment and total GDP to expect to see a strong imprint on aggregate
economic growth. However, as Oliner and Sichel pointed out, what was true in the 1980s
was no longer true by the mid-1990s. Increasing rates of decline in computer prices have
boosted real investment by enough that according to their calculations the information
technology revolution accounts for two-thirds of the acceleration in labor productivity
growth in the 1990s. The productivity slowdown that began in the earl 1970s appears to
be over--and computers and communications are the prime candidate to be awarded
responsibility for this recent acceleration in American economic growth.
17
Nevertheless, it is hard to draw strong conclusions about measured productivity growth
and the information technology revolution. The time period during which productivity
growth has accelerated is very short. It is possible—even if unlikely—that it is a cyclical
phenomenon. Northwestern economist Robert Gordon maintains that the productivity
acceleration is confined to the relatively small durable manufacturing sector of the
economy. Since the principal benefit of the information technology revolution is
supposed to show itself in increased productivity on the part of those who use computers,
the concentration of productivity growth in a relatively narrow sector is somewhat
disturbing news.
18
However, we can be more optimistic about (i) future productivity growth triggered by the
information-processing revolution, and (ii) real as opposed to measured productivity
growth.
First, Stanford historian of technology Paul David has observed that it takes considerable
time for an economy to restructure itself to take full advantage of the potential opened up
by a revolutionary technology. David claims that it took forty years for the American
economy to realize the productivity potential of the dynamo. Electric power became a
reality in the 1880s. But it was not until the 1920s that there had been enough
experimentation and use of electricity-based technologies for businesses to learn how to
use electric power effectively, and for the inventions of Edison and Westinghouse to pay
off in giant leaps in industrial-sector productivity. There is no reason to think that the
modern information technology revolution will be any different.
Thus a part of the disconnect between observers of technology and national income
accountants in the 1970s and 1980s arose because the two groups focused on different
things. Observers of technology look at the leading edge of innovation and
implementation. National income accountants see changes reflected in their aggregate
data only when what was the leading edge becomes standard practice. Economic
historian David Landes points out that something very similar happens when historians of
technology and historians of national income try to date the original industrial revolution.
Historians of technology look at inventions and innovations and date it to the 1760s.
Historians of national income do not see a marked acceleration of economic growth until
the 1840s and 1850s when industrial technology diffused widely.
Second, there are systematic flaws in the process of measurement that do lead us to
overstate inflation and understated true economic growth in recent decades. Popular
awareness of these flaws has been largely the result of the Boskin Commission, which
guessed that true economic growth had been understated in recent decades by somewhere
around one percent per year--enough to double material productivity in 72 years.
19
In industries comprising one-eighth of the entire American economy, measures of real
output growth assume no change in productivity growth: estimates of production are
created from estimates of employment growth alone. Anyone who goes to the bank today
and went a generation ago can gauge an enormous--and mostly pleasant--shift largely
driven by banks' use of information and network technology to provide extra flexibility,
convenience, and service. Yet prior to 1999 important elements of this improvement in
service were completely missed by measurements of GDP.
Moreover, all this leaves completely to one side the problems in assessing increases in
productivity that do not generate an immediate revenue stream from final consumers to
producers. Does anyone doubt that Americans' material standard of living--as consumers,
at least--was raised in the 1950s and 1960s by the coming of network television? But
where do network television programs appear in measures of real GDP growth?
Nowhere. Because network television's revenue model is based on advertising, the
creation of network television programs shows up in the national income and product
accounts as an intermediate cost but not as a final service--it shows up as a reduction in
measured economic productivity.
The tentative conclusion is that (I) measured productivity growth accelerated in the
1990s, and a substantial part of that acceleration is likely to be due to the information
technology revolution, and (ii) we would expect a substantial part of the real productivity
gains to escape measurement entirely. It is probable that, as far as productivity growth is
concerned, we do have a new mcroeconomy.
The Fall in the Natural Rate of Unemployment
Back at the start of the 1990s most macroeconomists estimated that the economy’s
natural rate of unemployment was between 6.5 and 7.0 percent. If unemployment fell
below that level, it was argued, inflation would begin to accelerate. Thus a Federal
20
Reserve that wished to avoid major recessions by maintaining the public’s confidence in
its lack of tolerance for inflation could not afford to let the unemployment rate fall below
6.5 percent.
These estimates were based on long historical experience. Since the 1960s, whenever
unemployment had fallen below 6.5%, inflation had accelerated. This had happened in
the early 1970s, in the late 1970s, and in the late 1980s. But since1993 things have been
very different. The decline in unemployment in the 1990s was accompanied not by rising
but by falling inflation. Only in the last year or two have there been any signs of
accelerating inflation in the United States.
21
By now the deviation between what inflation is and what one would have predicted
inflation would be from the pre-1993 pattern is substantial. The simplest model for
inflation takes the change in the inflation rate to be a linear function of the lagged
unemployment rate. If the pre-1993 pattern had held for the entire decade of the 1990s
and if the unemployment rate had followed its historical path, we would expect the
inflation rate this year to be nearly 6 percent, instead of the 2.5 percent that the consensus
forecast anticipates.
22
What is the possible link between the information technology revolution and a lower
natural rate of unemployment? It is not yet plausible to argue that online job searches
have made the labor market’s frictions less important. It is, however, possible that the
natural rate of unemployment is linked to the rate of economy-wide productivity growth.
The era of slow productivity growth from the mid-1970s to the mid-1990s saw a
relatively high natural rate. By contrast, rapid productivity growth before 1973 and after
1995 has been associated with a natural rate.
A higher rate of productivity growth allows firms to pay higher real wage increases and
still remain possible. If workers' aspirations for real wage growth themselves depend on
23
the rate of unemployment and do not depend directly on productivity growth, then a
speedup in productivity growth will reduce the natural rate.
[to be finished]
Manufacturing Inventories in the Economy
To the extent that modern computer and communications technologies can be put to work
as true information technologies, they will improve businesses' abilities to know about
and manage their goods in the pipeline from initial raw materials to final sales.
24
Tighter inventory control should mitigate business cycle fluctuations…
Financial Crises and Stock Market Volatility
The story of the "new economy" and the boom of the 1990s seems more like another
revival of a very old story: a speculative boom set in motion by new technological
developments that is then carried to ridiculous excess and collapses, leaving some glad
that they found people to sell their businesses and positions to before the inevitable
rendezvous with reality and others holding the bag as they found themselves unable to
find a still greater fool before asset prices peaked. This was the story of the "new
dimensions of political economy" of the 1960s, the new era prosperity of the 1920s, and
so forth back to the South Sea Bubble all came to a crshing end. New technologies were
unable to sustain the hopes built on them,
[to be written]
Conclusion
Federal Reserve Chair Alan Greenspan is certainly a believer that there is a new
macroeconomy. He sees “a deep-seated [and] still developing shift in our economic
landscape” caused by “an unexpected leap in technology.
There are eras when advancing technology and changing organizations transform not just
one production sector but the whole economy and the society on which it rests. Such
25
moments are rare. But today we may well living in the middle of one. We are living
through the rise of what will soon be the dominant source of economic development:
information technology. Information technology builds tools to manipulate, organize,
transmit, and store information in digital form. It amplifies brainpower in a way
analogous to that in which the nineteenth century Industrial Revolution’s technology of
steam engines, metallurgy and giant power tools multiplied muscle power.
[to be written]
26
Notes