Alan Greenspan: The challenge of measuring and modeling a dynamic
economy
Remarks by Alan Greenspan, Chairman of the Board of Governors of the US Federal Reserve
System, delivered at the Washington Economic Policy Conference of the National Association for
Business Economics, in Washington DC on 27 March 2001.
* * *
I am pleased to have this opportunity to address an issue of considerable importance to both business
economists and policymakers--that is, the challenge of measuring and modeling our dynamic
economy. Moreover, I would like to raise the issue of how much of our finite resources should be
directed to measuring and how much to modeling.
Business economics endeavors to understand the structure of an economy - how it works and, above
all, how to forecast it. It is a bedeviling job because the future, at root, cannot be foretold. The best we
can do is to construct probabilistic models that can inform the decisions of business executives and, of
course, economic policymakers who - of necessity - will be making their decisions armed with
incomplete information.
For a while in the 1960s, we were increasingly mesmerized by the possibilities of econometric models
as a crystal ball. However, history was not entirely kind to this endeavor. For one thing, especially
against the backdrop of the inflation of the following decade, it soon became apparent that our
theories of the macroeconomy were woefully inadequate. For another, even leaving aside the
shortcomings of our theory, we soon learned that the economic structure did not hold still long enough
to capture its key relationships. Its changing structure frustrated efforts to isolate a reasonably fixed
set of coefficients. In turn, the absence of fixed coefficients undermined the usefulness of the model as
a basis for projecting the future.
Econometricians recognized many of these difficulties, and so developed a vast and elegant literature
in support of this research program, covering a spectrum of topics ranging from
maximum-likelihoodestimation techniques to tests for coefficient stability to diagnostics for detecting undesirable properties
of the errors of these equations. The creativity that was applied to this effort is all the more impressive
because it took place in the context of a computing environment that was, by modern standards, truly
primitive.
But in time it became increasingly clear that, for all their theoretical advantages, these sophisticated
models did not reliably outperform a number of simple and far less costly reduced-form models, from
the money supply models that appeared to work well for a while during the 1970s, to astructural vector
autoregression models based on a handful of lagged variables that are still employed today.
To be sure, the large econometric models have been refined, incorporating the fruits of later
theoretical developments, including perhaps most importantly the insight that monetary policy could
not permanently influence the level of the unemployment rate. Moreover, a larger role was given to a
range of financial and expectational variables that earlier practitioners steeped in orthodox Keynesian
income determination tended to downgrade. Liquidity preference functions, to be sure, were included
even in the early versions of these large-scale models as necessary building blocks for determining
the equilibrium level of interest rates and income. But, overall, financial sector modeling was primitive.
Indeed, only modest progress has been made in this area since the Federal Reserve began to
produce our own flow of funds accounts in 1955.
A further, and perhaps more profound, challenge to the underlying validity of this style of modeling is
the possibility that, whereas standard models of the real economy determine a unique level of income,
the financial system appears to be capable of reaching myriad equilibria. In addition, the fundamental
forces that determine which of these equilibria will be selected may themselves be inherently
unpredictable.
We have built large-scale models of the United States and global economies at the Federal Reserve.
While recognizing their limitations, we do find them useful in research and analysis. But the experience
of the last 40 years underscores a fundamental dilemma of business economics. Should we endeavor
to continue to refine our techniques of deriving maximum information from an existing body of data?
Or should we find ways to augment our data library to gain better insight into how our economy is
functioning? Obviously, we should do both, but I suspect greater payoffs will come from more data
than from more technique.
Certainly, statistical systems in the United States, both public and private, are world class and, indeed,
in many respects set the world standard. But given the rapidly changing economic structure, one could
readily argue that more statistical resources need to be applied to understanding the complexities of
the newer technologies that confront analysts.
These newer technologies and the structure of output they have created have surfaced a set of
definitional problems that - although evident in a world of steel, fabrics, and grains - were never on the
cutting edge of analysis. I refer, of course, to the age-old problem of defining what we mean by a unit
of output and, by extension, what we mean by price. The dollar value of sales or GDP depends, of
course, on the specific accounting rules chosen. And while value in that context is uniquely defined,
the split between volume change and price change is always approximate.
In decades past, we struggled about what we meant by output - and hence price - nonetheless an
average price of hot rolled steel sheet and a corresponding total tonnage was precise enough for most
analytic needs. By the same token, tons of steel per work hour in a rolling mill yielded rough
approximations of underlying productivity for most purposes.
Output per hour in an economy dominated by such goods, or even services, for which the definition of
a typical unit of output was reasonably unambiguous, was a meaningful and relatively robust statistic.
Our data systems in the early post-World War II years were by and large adequate to the task.
But over time, and particularly during the last decade or two, an ever-increasing share of GDP has
reflected the value of ideas more than material substance or manual labor input. This ongoing
development is imposing significant stress on our statistical systems. We know, presumably uniquely,
the dollar value of a software application. But when comparing software-application values over time,
how much of the change is volume and how much is price? The answer, in principle, requires
judgments about very fundamental issues in measurement: What are the underlying determinants of
consumer value preference, and how does this good or service contribute to that preference, taking
account of all the other goods and services being consumed? Problems that were always latent in
defining steel prices and quantities but rarely rose to this level of significance are threatening to
seriously challenge our measurement systems in an age of the microprocessor, fiber optics, and the
laser.
These latent problems have emerged in full view in the pricing of medical services. Perhaps the
inherent complexity of this undertaking is most clearly revealed by posing the question, what do we
mean by a standardized unit of medical output? Is it the procedure, the treatment, or the outcome?
What does the fee charged for the bundle of services associated with cataract or arthroscopic surgery
represent? How does one value the benefits to the patient of shorter hospital stays, more comfortable
recoveries, and better physical outcomes? Clearly, the unadjusted fee for a single medical procedure
does not adequately represent its "price."
The price indexes for medical services used to be constructed by pricing a variety of inputs - for
example, a night in the hospital, or an hour of a physician's time. A few years ago, the Bureau of Labor
Statistics began moving toward pricing the treatment paths of particular diagnoses, the better to
capture changes in the mix of inputs used to treat a given disease. For example, many surgical
procedures that used to require an overnight stay in a hospital now can be performed on an outpatient
basis, and the producer price index and consumer price index are now better able to measure the
price decline associated with that change. Interestingly, when such techniques are applied to
individual medical procedures they appear almost without exception to indicate falling prices at least
since the mid-1980s. This has raised significant questions as to whether our current measures of
overall medical service price inflation are capturing the appropriate degree of productivity advance
evident in medicine.
Indeed, the level of real gross product per hour for medical services embodied in our overall
productivity measures declined between 1990 and 1999 (the last year for which data are available).
This is implausible and raises obvious questions of the validity of the price deflators currently
employed. Thus, while progress is visible, enormous measurement challenges remain in measuring
prices of medical services.
But there are deeper issues as well, associated with the valuation of a consumer's time. Clearly the
shorter stay of a cataract patient is of value to that patient. In other words, today's techniques allow the
surgeon to deliver more consumer value per hour of operating time, other things being equal. A full
measure of the output of the medical sector would take account of this reduction in recovery time and
attribute it to the medical sector.
While many issues of measurement arise in the context of services, even the measurement of some
goods prices presents considerable challenges. High-technology goods are a case in point. Academic
research in this area dates to the mid-1960s, but its application in the measurement of real output
gained prominence with the introduction of hedonic price indexes for computers and peripherals by the
Bureau of Economic Analysis in 1985. More recently, the efforts undertaken by statistical agencies
have intensified, spurred by the accelerated pace of technological innovation, which has yielded an
ever-expanding range of new products and product variants, as well as by the rising share of these
goods in our economic value added.
Thus, much progress has been made by the BEA, Census, and the BLS, with which I am sure you are
familiar. This morning, I should like to alert you to some of the research in these areas coming from
the Federal Reserve. Much of our work in this regard has been focused on improving our published
statistics on industrial production.
Our staff's multiyear work in this area began in 1998 with the development of new measures of the
domestic output of semiconductors. Next, we revised our procedures for estimating the production of
computers, and more recently we have introduced new series for an important component of the
output of the communications equipment industry - local area networking (LAN) equipment, which
provides the infrastructure critical to expanding the productive uses of information technology.
In total, these high-tech goods--semiconductors, computers, and LAN equipment - currently represent
less than 8 percent of total manufacturing output. However, their production, as we measure it, rose at
an average annual rate of around 50 percent in the second half of the 1990s, and, taken together, they
contributed two-thirds of the increase in manufacturing output between 1995 and 2000. Indeed, U.S.
production of semiconductors in 1996 eclipsed motor vehicle assemblies as the largest four-digit
manufacturing industry in nominal value-added terms.
The characteristics of these goods present the range of complexities that one faces in measuring
quality-adjusted prices. First, many are wholly new products: For example, switches--the largest single
segment of LAN hardware--did not enter the market until 1993. And even "older" high-tech products,
such as computers, are now bundled together in ways that offer an enormous variety of combinations
of characteristics related to speed, memory, networking capability, and graphics capability, to mention
just a few. For all of these goods, product cycles are truncated by rapid innovation. For instance, in
1995, 10 megabit-per-second Ethernet switches dominated that market; last year, the two most
popular switches operated at rates of 100 and 1,000 megabits per second. Product lives for
semiconductors and computers can be even shorter; some computer models have remained on the
market for only a couple of months.
In such an environment, the availability of detailed micro-level data describing the attributes of these
goods is crucial. One means of defining the unit of output is to unbundle the characteristics of a
high-tech product and to price each of them separately. This so-called "hedonic" technique - now
applied by the BEA to items that account for 18 percent of GDP--is one approach.1 In our work at the
Federal Reserve, we have developed hedonic price indexes for network routers and switches using, in
the case of the former, data from product catalogs and, in the case of the latter, privately produced
reports evaluating the performance of the products.
However, hedonics are by no means a panacea. Most important, the measured characteristics may be
acting only as proxies for the qualities of the services that buyers ultimately value. This, again, raises
the difficult issue of the appropriate scope for value measurement and poses the question of whether
the correct approach may be to move toward directly pricing the services we obtain from our
information processing systems rather than pricing separately the individual hardware components
and the software.
The Federal Reserve staff has found that, when detailed data are available on prices and on
quantities, we can produce results that are comparable to those based on hedonics, using the
conceptually simpler "matched model" approach.2 Indeed, we have taken this approach in constructing
1
J. Steven Landefeld and Bruce T. Grimm, "A Note on the Impact of Hedonics and Computers on Real GDP," Survey of
Current Business (December 2000).
quantity and price indexes for several high-tech items. In the case of semiconductors, we relied on
data from three private vendors for information on nearly 100 unique microprocessors, more than 200
types of memory chips, and more than 80 other chips. We also acquired nominal sales and unit value
data for about 1,100 distinct computer models.
Neither hedonic nor matched-model techniques are sufficient to deal with the introduction of wholly
new products that differ fundamentally in their characteristics from their predecessors. This will
continue to be one of our major ongoing challenges.
I am encouraged by the progress that economists and economic statisticians have been making to
date in tackling the daunting task of measuring real output and prices in a rapidly changing economy.
The challenge that lies ahead is, indeed, large, and to meet it will require the support of the business
and academic communities to supply the information and to help develop the tools that our statistical
agencies require.
The information revolution, itself, will also surely play an important role. For example, high-tech
information systems might some day allow statistical agencies to tap into a great many economic
transactions on a basis close to real time. More generally, I am certain that the possibilities for
creatively harnessing technology for the improvement of economic measurement are much broader in
scope - although, as in many other areas of endeavor, the precise directions those advances will take
are difficult to predict. If we had the appropriate database, of course, who knows?