It's an assumed fact that the nation's economy today is driven by high-tech.
Wherever our economic attention turns, high-tech is often the focus, whether
it concerns job creation, the stock market, university research or workforce
Numerous policy initiatives have been passed on the sole argument
that local or state prosperity will be shackled in the new economy
without a high-tech development strategy. Woe is the region or state
that performs poorly in one of many quasi-scientific indexes published
every year that plot and rank high-tech growth. Earlier this year,
a headline in the Columbus, Ohio, business press reported the unenviable:
"Study finds Columbus lagging in high-tech economy." Oftentimes,
it's a simple matter of economic envy. "Dreams of high-tech glory
passing [Orange County] by," lamented a headline in last year's Los Angeles Times.
In an oft-cited speech last year, David Kidwell, then-dean of the
business school at the University of Minnesota, charged that "Minnesota
is not a major player in the new high-tech economy emerging in the
21st century," and argued that "[t]he key to our future economic
success is to foster a high-tech renaissance in Minnesota. If successful,
this high-tech activity can drive new economic growthcreating
new wealth and high-paying jobsand help maintain our region's
high standard of living."
Such rhetoric is hard to disagree with, because everyone knows
high-tech is the future, right? Probably, but we're so obsessed
with high-tech that a couple of important details are routinely
overlooked: First, and most importantly, we don't know (or can't
agree on) how to define and draw clear lines around high-tech. A
look at almost any two studies of high-tech industries will likely
uncover two fundamentally different definitions of high-tech, neither
of which might hold water in just a few years because the industry
changes so quickly.
This annoying little bugaboo means research to date on the high-tech
industry has built only a thin, widely dispersed foundation, which
provides little solid footing for further research that might provide
better information on and analysis about the industry. As a result,
despite our hyperventilation about all things high-tech, there are
very little public data on the high-tech industry and its true impact
in the economy. In other words, we know high-tech is important in
today's economy, but don't ask us how we know. Please.
To most, the semantic hair-splitting involved in defining high-tech
is something for economists and policy wonks to settle. As Supreme
Court Justice Potter Stewart so aptly said about pornography, a
person might not be able define it, but they know it when they see
it. But there are real consequences for failing to go beyond such
a quaint measuring stick. For example, it's hard to imagine effective
policy to encourage "a high-tech renaissance" given that the target
is broad, out of focus and fast-moving.
Efforts to define and assess high-tech and other "emerging" industries
have engaged academics, business and government agenciesincluding
the Federal Reservefor years. But the complexities of delimiting
and measuring such an amorphous beast have foiled all attempts at capture.
The prey remains, largely, at-large.
No one argues anymore about the importance of high-tech in today's economy.
As a 1997 study by the Bureau of Labor Statistics (BLS) said, "High-technology
industries are the most important source of strategically transformative
products and processes in the U.S. economy. Changes in employment patterns
in these industries thus command the interest of researchers, policymakers
and the general public."
Business and policy emphasis on high-tech has only accelerated
since then, and for good reason. High-tech businesses have a strong
reputation for innovation and global competitiveness, which brings
with it high average wages and strong employment growth, according
to studies by the BLS and the American Electronics Association (AeA).
So what's the problem? In fact, there isn't one if you don't want
to quibble about methodologies and other details. But then you also
have to be willing to gloss over a little thing called accuracy
and be content with old information on a fast-changing industry.
A recent internal report on tracking high-tech by the Dallas Fed
outlines a few of the problems. Data on high-tech employment is
available, but such figures "lag the current state of high-tech
by at least eight months ... [and] give us only a partial picture
of high-tech manufacturing." Data on high-tech output would
be particularly useful but "are even more out of date,"
the report stated, often running two or more years behind. Numerous
contacts also noted that up-to-date regional data is particularly
difficult to come by.
That's not to say that past and current research efforts on high-tech
are inaccurate, or not useful. Far from it. They have provided the
best information to date on the high-tech industry. But such efforts
also have what one economist in the Federal Reserve System called
"serious data and methodological problems" that ultimately
need to be ironed out. That will likely take a lot of heavy lifting.
Much of the problem stems from the fact that "high-tech"
is a ubiquitous term, malleable enough to fit almost any economic
blank. This makes measuring and tracking the high-tech industry,
particularly at the microeconomic level, much more difficult compared
to a well-defined, output-based industry like mining. (This means
we also have a poor grasp of high-tech's secondary impact as new
technologies seep into other industries, but that's an entire discussion
Studies attempting to define high-tech typically use either an
input or output strategy. Input studies identify high-tech companies
based on the intellectual investments in a company's core product
or process. Most often, such studies define high-tech by the level
of research and development and by occupation classifications (for
example, average number of scientists and engineers, which acts
as a proxy for research and other intellectual inputs). In such
studies, the sophistication of the product itself is unimportantthink
of the many tape and other adhesive products from research and engineering
Output studies, on the other hand, classify industries as high-tech
based on the perceived sophistication and value-added of products
and serviceslike computers or software programming. This study
method is less common, mostly because of the subjective analysis
required in drawing a line between high- and low-tech.
Both types of studies often use the government's Standard Industrial
Classification (SIC) system. A huge public database on private industry,
the SIC system classifies different types of companies in increasing
detail using two-, three- and four-digit codes (see sidebar for example). Because there are no SIC categories for high-tech,
researchers must pick SIC codes that they believe correspond with
agreed-upon inputs (high R&D) or outputs (software) that are considered
high-tech. Once finalized, employment and various other data can
be gathered for each SIC code and then aggregatedvoilà,
you've just defined and tracked high-tech.
Not so fast, others say. While useful as a starting point, such
methodology is nonetheless rife with problems. Probably the most
recognized indexes of the high-tech economy come from the AeA, the
nation's largest high-tech trade organization and publisher of a
series of high-tech progress reports. Cyberstates, for example,
narrowed high-tech into three categories: high-tech manufacturing,
communication services, and software and computer-related services.
It then chose 45 SIC codes at the four-digit level that it believed
corresponded to these three broad groupings. [These 45 were listed
under 13 different industry groupings, or three-digit SIC codes.]
On its Web site, the AeA acknowledged that in producing its reports,
"we found that there is no consensus on the definition of the
high-tech industry. ... This means the definition of the high-tech
industry varies greatly depending on what combination of products
and services are selected from the SIC codes to define the industry."
To illustrate: The BLS has published a handful of studies on the
high-tech industry in the last two decades. A 1999 report by the
BLS used occupation and R&D intensity to determine which SIC codes
represented high-tech. The final list consisted of 29 different
three-digit SIC codesmore than two times those of the AeA
definitionand included such industries as petroleum, cigarettes,
soaps and cleaners, paint, plastics, agricultural chemicals, drugs,
aerospace, and motor vehicles and equipment.
There is a small handful of industrieslike electronic components,
computers and communications equipmentincluded in most studies
of the high-tech industry. But even here, a 1997 BLS study pointed
out that "problems are also present even in apparently uncontroversial"
classifications. For example, computer and office equipment (SIC
357) includes producers of such low-tech items as staple removers
and hole punches, not to mention that most computer makers today
"use mass-produced components assembled in highly routine settings
with minimal engineering and scientific input," the report
said. "[B]ecause the effects of technological change can be
seen in almost every industry, these uncertainties are inevitable
in any high-tech study, no matter how precise the classifying metric."
The fact that there is little agreement (indeed, even discussion)
on the matter has meant that a variety of high-tech indexes and
rankings are showing up, each arguably as valid as the next. A high-tech
index from Economy.com, a provider of economic, financial and industry
research, had 11 three-digit SIC codes, four of which are not included
in either the AeA or BLS definitions. A Brookings Institution review
of 14 studies found varying use of 45 different three-digit SIC
codes to define high-tech, accompanied by almost 100 different four-digit
Another recent Brookings report summarized the inherent difficulties.
"Any classification system is, at best, an imperfect means
of describing the activities of diverse and quickly changing business
enterprises. ... Any definition that relies on groups of [SIC] codes
is likely to only roughly capture the product and market specializations
of the firms and areas being analyzed."
SIC codes, the report said, imply "that there is a great deal
of homogeneity among these high-tech firms. ... [Rankings] that
group inherently disparate firms such as medical devices, semiconductors,
telecommunications and software together into a single category
of 'high-technology' and attempt to explain their behavior as if
they were homogenous units driven by a common set of factors, are
likely to be substantially misleading and incomplete."
These little methodological nuances might seem trivial, but they
heavily influence where regions and states rank in high-tech studies.
For example, the Midwest ranked next to last of nine regions in
the concentration of high-tech employment in 1998, according to
a 2000 report by the AeA. However, the study did not include either
the aerospace or automotive industries, both of which are mature
but research-intensive industries. Including both industries in
the tabulations boosted the Midwest's rank from eighth to third,
and the state of Michigan shot up from 17th to fourth, according
to a follow-up study by the Michigan Economic Development Corp.
The AeA and others note that sound data collection is complicated
because many new industries widely considered to be high-tech are
not fully captured by current government statistics. That's because
the SIC system has not been updated since 1987. Many believe the
transition to the new North American Industry Classification System
(NAICS) will address some of the data problems. But even if it does
improve the classification system, it does not (at least not yet)
draw the subjective line between what is high-tech and what is not.
Nor is it dynamic enough to identify and include emerging, growing
technology industries. A salient example of the new system's limitations:
NAICS does not recognize or classify biotechnology.
A new breed
To feed the growing interest in the high-tech sector and the new economy,
other, less scientifically based indexes are gaining popularity. Many
take a more holistic approach, including both traditional measures of
high-tech (like employment) as well as indicators of underlying conditions
believed to nurture high-tech industry.
The Massachusetts Technology Collaborative, for example, puts
together an annual index on the state's "innovation economy." It
features 30 indicators on such things as business friendliness (including
average pay and CEO ratings), entrepreneurship and idea generation
(IPOs and patents), human resources (workforce education and computer
access in the classroom) and technology and investment (federal
R&D and venture capital).
Similar indexes are produced by the Milken Institute and the Progressive
Policy Institute, and tabulated state by state. Milken's "New Economy
Index" uses 12 (non-SIC) data sets, ranging from higher education
rates to exports to R&D and business startups. The Progressive Policy
Institute has a 16-point surveythe third in a series of such
indicesit calls the "Metropolitan New Economy Index." Measures
(fairly) unique to this effort are jobs in "gazelle" (or fast-growing)
companies, broadband capacity and Internet domain names.
These efforts get away from a concrete definition of high-tech
and instead attempt to measure those things assumed to correlate
with or contribute to a healthy high-tech sector. There is general
agreement regarding some of the basic factors that encourage high-tech
development, like high levels of college-educated workers. But the
sheer variety of indicators used by such studies suggests they more
likely reflect the leanings of authors and sponsoring organizations.
The Fed and high-tech
This issue does have important, if indirect, ramifications for the Federal
Reserve. As the economy's central pulse-taker, the Fed depends heavily
on data to determine the health of the economy. Without any reliable measurement
models for high-tech, the Fed arguably cannot provide as clear a diagnosis
on the economy's health as it otherwise might.
Some would arguewith some validitythat there's a "so-what"
factor to consider. For starters, the Fed is mainly a secondary,
rather than primary, gatherer of data on the current economy. It
gobbles up reams of research, but the vast majority of it (for example,
employment figures) is tracked, tabulated and aggregated by somebody
else (like the BLS). To the extent that tracking high-tech is important,
the Fed isn't set up to solve that problem directly. What's more,
existing data does manage to capture high-tech activity, but it
gets categorized and analyzed under more traditional economic frameworks
like manufacturing or business services.
But the Cleveland Fed's 2000 annual
report argued that good information is important to the Fed's
monetary stabilization policy, and data models have not kept up
with the shift in the economy. "If the central bank is to conduct
monetary policy appropriately, then reasonable management information
systems are imperative," the report said. "But the past decadethe
New Economyshould have taught us this: The apparatus we currently
employ for making sense of the economy-that is, the measurements
we employ to distill information about the American economic enterprise
into a comprehensible form-are simply inadequate to the task."
The Dallas Fed's report said, "The high-tech sector has been one
of the major drivers of growth of the Texas economy in the past
decade. In order to track the performance of the high-tech sector
in the region we need a dependable and up-to-date measure of high-tech
To gauge opinions and activities on this issue within the Fed,
the Minneapolis Fed sent an informal e-mail poll to economists at
the 11 other Fed banks, all of whom are involved in compiling the
Beige Book, an anecdotal summary of economic conditions prepared
for each Federal Open Market Committee meeting. Nine of 11 districts
replied, although responses from two districts provided little information
on the high-tech tracking issue.
The seven remaining responses indicate there is uneven attention
and concern about this issue among district banks. Very few [including
the Minneapolis Fed] have formal efforts to systematically track
high-tech sectorsagain, largely because Fed data gatherers
depend on other organizations for much of the raw data that goes
into setting monetary policy.
"St. Louis does not do high-tech tracking [via an index or other
measure] ... nor have we considered it or discussed it. This is
probably because the high-tech sector as narrowly defined is relatively
small in our district," said Howard Wall, research officer and regional
economics coordinator for the St. Louis Fed. According to economist
Tim Schiller of the Philadelphia Fed, "We are currently researching
high-tech in our area, so our monitoring efforts for this industry
are still evolving."
But the high-tech industry is itself evolving, and at a much faster
pace, which complicates matters further. "In our view, the main
constraint to tracking the high-tech sector is that it is not clearly
defined and changes over time," according to Jason Bram, an economist
with the New York Fed.
For example, printing and TV broadcasting used to be considered
high-tech, but no longer. If e-commerce is considered high-tech,
then Wal-Mart would qualify because of its online merchandising
and state-of-the-art inventory and information systems.
"If some aspects of a firm's business seem high-tech and others
seem low-tech, as in these examples, how do you treat it?" Bram
said. "This is why we feel that defining the sector is the biggest
hurdle. If what you are really looking for is industries with strong
future growth potential, then your best bet would probably be to
come up with a set of rapidly growing industries using BLS growth
The motivation for districts to track any information on a region's economy
"is to provide data to the [district bank] president for FOMC meetings,"
said Yolanda Kodrzycki, assistant vice president and economist with the
Boston Fed. But with little hard data available, districts are trying
new strategies to fill the information gaps.
The Dallas Fed created its own index for high-tech output in three
areas: computers, telecommunications and electronic components.
Though the index is still in the experimental stage, "we have
a measure where we know whether [high-tech] is adding to [district]
growth," said Mine Yucel, Dallas Fed assistant vice president.
To improve its district Beige Book summary, Kodrzycki said the
Boston Fed has added additional contacts in high-tech fields with
a significant presence in the district, like computer makers, semiconductors,
software and IT. "It's sort of a natural to beef up coverage
of those areas."
Rather than invest heavily on internal research on high-tech, Kodrzycki
and others at the Boston Fed are involved in outside efforts with
similar research goals. She is on the advisory boards of Massachusetts
Technology Collaborative and Mass High Tech, the latter of
which is a regional journal that tracks and publishes various high-tech
indicators. Such external efforts are a "really low-cost way
to develop measures that are useful to us" at the Boston Fed,
At the Atlanta Fed, "We're beginning to devote more efforts
to tracking the high-tech sector in the district, particularly in
Atlanta, in part because state officials in the Southeast are devoting
considerable resources to attracting and retaining jobs in that
sector," said Madeline Zavodny, an Atlanta Fed economist. In
the past, she said, "we have primarily relied on newspaper
accounts and reports from our branch directors for information on
the high-tech sector. We hope to develop other methods but aren't
yet sure how to best track this sector." An analyst with direct
experience in the high-tech sector was recently hired and "is
now helping us figure out how to best track it and to develop contacts
At times, high-tech's volatility can make it difficult to find
and maintain reliable industry contacts, said Yucel of the Dallas
Fed. The industry is "very secretive," she said, adding
there can be "great difficulty establishing contact for even
a few years" with the same individuals.
But by the same token, "being the Fed puts us in a special
position" to attract leaders in the high-tech industry to a
district bank's board, according to Harvey Rosenblum, senior vice
president and research director for the Dallas Fed. "They give
us an entree we might not otherwise get" regarding conditions
in the high-tech sector.
Rosenblum also pointed out that high-tech is not the first industry
to pose measurement problems. When he was at the Chicago Fed years
ago, Rosenblum said, researchers typically looked only at mature
industries with easy-to-measure output. While useful, he said such
efforts "were not measuring anything that was new to the economy."
So he helped create what he called "the shadow Beige Book"
that looked specifically at new businesses and industries.
In a similar vein, tracking high-tech was not as important as
the underlying strategy to seek out and understand new and changing
segments of the economy. "This is something the Fed is always
working on," Rosenblum said. The Dallas Fed's high-tech index
"is part of trying to keep a handle on it."
And efforts to better track high-tech will likely continue to engage
Fed economists, academics and business peoplejust a step behind
the industry they are trying to capture.
Code Name: High-Tech
Many studies on high-tech use the government's Standard Industrial
Classification (SIC), which breaks down industries and industry
groupings into increasing detail using corresponding two-, three-
and four-digit codes. Various data on private industry-for example,
employment and salesare collected by the government at each
SIC level, which many researchers use to analyze economic and business
The industries selected as high-tech so qualify only because a researcher
or study has determined them to have some correlation to what is
commonly believed to represent high-tech. All the industries listed
at right have been considered high-tech in at least one previous
28 Chemicals and pharmaceuticals
281 Industrial chemicals
Alkalines and chlorine
2813 Industrial gases
282 Plastics material and synthetics
2822 Synthetic rubber
2823 Cellulose manmade fibers
35 Industrial machinery
351 Engines and turbines
353 Metalworking machinery
357 Computer and office equipment
3578 Calculating and
367 Electronic components and accessories