C. STRATEGIC CITIES
ultimate irony in the placeless world is that some places organize the
Castells 1998, p. 188
In 1967, at the crest of
the Old Economys development, John Kenneth Galbraith declared the
individual entrepreneur obsolete, saying only the group has the
information that decision requires (Galbraith 1967, 1985, p. 104). Today,
in the light of history, we see things differently. In retrospect, the
group in the traditional managerial corporation looks more like a
stultifying bureaucracy, where the safest tactic was the non-decision. (Larry
By the same token, the I.T.
case study in Part B was intended to show the vital role played by newcomers,
acting as entrepreneurs, to overthrow the established order and blast through
the tendencies toward stagnation that past success seems to breed. From that
standpoint, the difference between the U.S. economy, on the one hand, and those
of Japan or France or Germany, on the other, has seemed to lie in the superior
opportunities the U.S. has afforded newcomersgeeks, freaks, immigrants,
and other outsiders.
Yet the basis for Professor
Galbraiths verdict remains of interest. New technologies are not
necessarily easier to understand today than in 1967. Group (or, in todays
parlance, team) cooperation, consulting, and coordination are often
as crucial to product development and innovation today as they were
What has changed, it would
seem, is the legitimacy of hierarchy. A primary lesson of the last third of the
20th century was that hierarchy is antithetical to the free and open
flow of strategic information, the information that decision
requires. This, as many people have observed, is the impression one might
glean from the fates of the U.S.S.R. and of U.S. corporate dinosaurs (like
Sears or General Motors) alike.
More recently, of course,
the proliferation of computer networks both within and between organizations
has also made hierarchy less tenable. As a result, the 1990s saw powerful
tendencies toward flatter organizations; burgeoning alliances between large and
small firms; and deepening networks between firms and venture capitalists,
universities, and governments.
Information flows remain
vital, in other words. But now PC networks and spatial proximity provide
increasingly complementary channels for the horizontal transmission of
strategic information. One result, as manifested most vividly in the U.S. in
perhaps a dozen large and mid-sized cities, is a new system of innovation,
driven by partnerships between knowledge workers and venture
That is our current point
The purpose of this
section, then, is to consider which large American cities (more
specifically, metropolitan areas) are spearheading the New Economys next
round of developmentand to ask how they have emerged as centers of
Naturally, any such inquiry
needs to begin with a deep bow to Silicon Valley.
In "The Valley of Money's
Delight" (The Economist, March 29, 1997), John Micklethwait cites
economic cultures as the catalysts that
determine whether networks communicate. As he observes, "Research has
increasingly concentrated on clustersplaces (such as Hollywood or Silicon
Valley) or communities (such as the overseas Chinese) where there is 'something
in the air' that encourages risk-taking."
He lists 10 features of
Silicon Valley's economic culture that help explain the area's
- Tolerance of
- Tolerance of
- Reinvestment in the
- Enthusiasm for
- Promotion on
- Obsession with the
- Anybody can
This list points up the
fluidity of the Valley as an economic environment. One of the qualities it
conveys is a sense of loyalty to the place, rather than to the firm. By
extension, it suggests a milieu conducive to spin-offs and start-upsan
environment that can be termed Economy 2.
(Martin Kenney and Urs Von Burg 1999.)
Linked background sketches
on cluster theory offer further observations on the connection between
information flows and spatial access. A first module surveys neoclassical
approaches to cluster theory, those focusing mainly on spatial externalities. The second, on what I term
post-neoclassical models, considers network-based
industrial systems, path dependency, increasing returns, and dynamic
agglomeration economies. As a reminder that any such hard-and-fast dichotomy
between neoclassical and post-neoclassical views is fraught with peril, a
sidebar locates cluster theory within the spectrum of urban growth paradigms. A useful set of links on
cluster-based state economic development policies appears in the Hubert H.
at the University of Minnesota.
That said, we can turn
directly to a range of diverse views as to the role of large cities in the
American economy. The goal is to discern which specific cities are best
positioned at the Millennium to facilitate the information flows likely to
THE U.S. SYSTEM OF
CITIES: COMPETING VISIONS
A few framing points about
the U.S. system of cities can be offered now. The unifying theme is the role of
history as help or hindrance to a metropolitan areas economic
performance. From the standpoint of evolutionary economics, this is an issue of
path dependence. From the standpoint of cluster theory, it overlaps with the
question of specialization vs.
(1) City Roles in the
In a conference
announcement from the University of Newcastle (England) in 1998, the organizers
proposed a typology of cities based on 10 distinct city types. The conference
theme was Cities in the Global Information Society, so the taxonomy
can be understood in that light. Here is the list, along with examples
suggested by the organizers:
Newcastle, Pittsburgh, Essen)
York, Tokyo, Singapore)
- 2nd Tier regional and
(Amsterdam, Dublin, Milan, Taipei, Toronto,
(Pearl River Delta)
(Moscow, Warsaw, Budapest)
(Soweto, sub-Saharan Africa generally)
(Sunderland, Bangalore, Kingston
- Resorts and
Carolina [sic], Rotterdam)
(Malaysia's Multimedia Corridor and Japan's technopoles)
With a couple of obvious
modifications, a similar taxonomy could be applied to the U.S. system of
cities, using, say, categories 1-3 and 7-9.
In particular, asking which
of Americas largest cities are industrial in origin
(type 1) is a fruitful exercise.
Metropolitan Evolution (Revisited)
TABLE 10 links changes in manufacturing employment after
1970 to the mid-century industrial legacies of 30 large U.S. areas. It reveals a record of large losses by
industrially specialized areas.
The 30 metro areas
contained the 30 largest cities in 1970, when large-scale losses of
manufacturing jobs were about to begin. The areas are ranked by their
population sizes in 1910, at the end of the nations heavy
industrialization and before the automobile or electricity had had much impact.
This historical approach (introduced in Norton 1979) owes much to the
geographer John R. Borcherts proposed sequence of technology epochs in a
classic 1967 article, American Metropolitan Evolution.
The dozen areas that had
reached the largest size by 1910 can be termed industrial. The
dozen then smallest were deemed young. In between, such areas as
Los Angeles, Washington, D.C. and Seattle are anomalous, in that
much of their growth had occurred after 1910 but before 1950. Among the
variables that then align by age-class are (1) population density, (2)
industrial structure, and (3) unionization rates. (Norton 1979.)
Regionally, 11 of the
industrial areas were in the Manufacturing Belt, and 10 of the younger areas
outside it. (See MAP 2, which is adapted from Norton
1979, p 25.) At mid-century, the dozen industrial areas still had an average 35
percent of their 1950 workforces in manufacturing jobs. In contrast, the dozen
termed younger had an average of only 19 percent.
Their roles as exporters of
industrial goods to the rest of the U.S. and abroad left the mature metro areas
vulnerable to huge losses in manufacturing employment after 1970. The
combined manufacturing job losses from four of themNew York (down
658,000), Chicago (326,000), Philadelphia (277,000), and Detroit
(198,000)exceeded the entire U.S. loss (1,395,000). Most younger
areas added manufacturing jobs over the period, including a few (Atlanta,
Dallas-Fort Worth, Houston, San Diego, and Phoenix) with sizable absolute
As to changes in total
employment, the contrasts between industrial and younger areas are milder, but
still pervasive. The U.S. added 55 million payroll jobs from 1970 to 1998, for
a percentage gain of 78 percent. Relative to this national rate, three points
about the 30 areas might be made:
- A few industrial areas
(New York, Pittsburgh, Cleveland, Buffalo) had extremely low job
growthbelow 10 percent.
- The median figure for
the 11 older areas in the Manufacturing Belt, 35 percent (for Chicago), was
less than half the U.S. rate.
- The median for the 10
younger areas of the South and West was 157 percent, twice the national rate. Atlanta, Dallas, Houston, and Phoenix
added more than 1 million jobs (as did a now resurgent Chicago, Los Angeles,
Even in the late 1990s,
with brisk job growth nationwide, the older areas still lagged. As
FIGURES 14 and15 document,
aggregate job growth from 1995 to 1998 remained only about half the rate in
most older areas as in most younger ones.
In sum, the specialized
industrial roles of the mature areas led to large-scale losses of manufacturing
and sluggish growth in total employment. While not exactly news, this
remains point number one in any overview of the system of U.S.
From the standpoint of
cluster theory, we might put all this a different way:
be good for city growthor not! It all depends on the nature of the
activity, the pattern of demand from the rest of the world, and the chemistry
between the activity and learning on the part of the citys
workforce and knowledge base.
(3) The Ladder of
An opposite view comes from
David Warsh, who writes an economics column for the Boston Globe.
Prompted by the purchase of the Los Angeles Times by the Chicago
Tribune in early 2000, Warsh proposed an informal ranking of the leading
centers of U.S. influence. His admittedly impressionistic list refers to
education, finance, and media industries
and the capacity to absorb
the latest streams of immigration
. (Warsh 2000, p. E1.) By this
reckoning, the three largest cities, New York, Chicago, and Los Angeles, are
also the three most influential, the places where U.S. opinions and attitudes
Then there are the
other American cities of international importanceWashington, D.C.,
Boston, Miami, San Francisco and, possibly, Seattle
world centers in
certain fields. In this reckoning, Washington qualifies only because it
is the capital. Boston and San Francisco make the top 8 by virtue of their
financial and university strength. Miami qualifies as the gateway to Latin
America and the Caribbean, and Seattle as a high-tech nursery.
Global cities in specialized realms, these five fall just below the top three,
New York, Chicago, and Los Angeles.
This is not to rob a dozen other U.S. cities of their significance.
But the hierarchy is well-established, and here, as in Europe, the oldest
cities tend to remain at the top. (Warsh, p. E1, emphasis
This curious generalization
may have some relevance to media and entertainment. But it completely misses
the dynamic of renewal by which younger centers have restored the American
economy to global leadership. A sense of that dynamic can be seen in the
recent upheavals in the U.S. system of citiesindexed not only by job
growth, but also migration choices and I.T. roles.
(4) Tech-Poles: The
Milken Institute List
Consider, for example, the
Milken Institutes 1999 ranking of Tech-Poles. These are the
U.S. metropolitan areas that stand out by virtue of their size and
specialization in a broad range of high-tech activities. (DeVol 1999, p. 67.)
When an areas percentage share of U.S. high-tech output is multiplied by
its high-tech output location quotient, the result finds San Jose (Silicon
Valley) the runaway leader, followed by Dallas, Los Angeles, Boston, and
Seattle. The next five are Washington, D.C., Albuquerque, Chicago,
New York, and Atlanta.
In other words, four of the
top five areas are from the South and West, as are three of the next five (once
we recognize that the Washington, D.C., areas high-tech center of gravity
is northern Virginia). That adds up to six of the top seven metropolitan
areas from the South and West, as measured across the gamut of high-tech
A still sharper regional
watershed can be seen for domestic migration.
(5) Magnet Metros: The
Niles Hansen contends that
domestic migration flows in the 1990s give a clean read on the economic
opportunities offered by major metropolitan areas. (Hansen 2000.) In part this
view is based on the observation by Glaeser that domestic migration flows offer
a better indicator of an areas success than per capita income growth,
because the latter may include a bribe component in wages to offset
The areas with the largest
1990-1997 in-flows can be found below what Hansen terms the Seattle-Atlanta
line. (See MAP 3, which is based on data presented in
Hansen 2000, Table 2.) The numbers range from over 300,000 in Atlanta, Phoenix,
and Las Vegas to gains between 120,000 and 162,000 in Seattle, Portland,
Dallas, Denver, Austin, Raleigh (which is just north of the line), and
In terms of size, all the
magnet metros had fewer than five million residents in 1995. The
largest was Dallas, with 4.7 million resident. The next largest were Atlanta,
3.6 million, Seattle, 3.4, and Denver, 2.3. That meant that no magnet metro was
as large as any of the 8 largest areas: New York, Los Angeles, Chicago,
Washington, San Francisco, Philadelphia, Boston, and Detroit. Each of these 8
largest had over five million people, and each had net domestic outflows.
The map serves as a visual
reminder that size is but one of several linked variables. It portrays a
regional realignment from high-density, high-cost, older areas in the
Manufacturing Belt and California to younger, low-density centers. In turn,
this shows up in the data as a move from larger to smaller cities.
The net effect, Hansen
concludes, has been a definite shift downward in the urban hierarchy in
terms of where Americans want to live and work. (Hansen 2000, p. 12.) And
as he demonstrates, the shift is not only from the largest to mid-sized metros,
but also from the Manufacturing Belt and California to younger areas in the
diagonal band between them.
(6) The Perils of
Specialization, Continued: I.T. Hardware
Just as specialized roles
proved a heavy load for industrial metros after 1970, so too did high profiles
in computer production and electronics between 1986 and 1996. (TABLE 11.) The precipitants were declining U.S. employment
in computer production (SIC 357), slow job gains in electronics (SIC 367), and
decentralization of both to rural states.
Thus the three areas
with the greatest initial specialization in computer and electronics production
accounted for over half of all hardware jobs lost nationwide from 1986 to 1996.
(See Equation 1.) Phoenix, Boston, and Los
Angeles combined for hardware losses of 73,000 jobs.
How different is the lesson
here from that of the de-industrializing industrial cities after
1970? The two cases seem closely related, and not only because the I.T.
hardware losses are one component of the larger losses in manufacturing
employment in older areas. In each story, initial production centers
specialized in sectors that would add little or no employment nationally, a
scenario that tends to be accompanied by rapid dispersal to competing domestic
sites, including non-metro locations.
Put the other way around,
one of the ways the U.S. as a geographical entity retains employment relative
to offshore locations is by offering both competing centers of innovation and
lower-cost (including non-metropolitan) environments.
In any case hardware was
only half the story of metro I.T. growth during the break-up of the old
computer industry (Grove 1993, p. 57).
(7) In Sum: Diversity
and Adaptive Capacity
We are exploring the
geographical origins of the New Economy in the U.S. Regionally, the PC
revolution had largely western coordinates, as Part B showed. As the New
Economy moved into high gear in the mid-1990s around the Internet, a different
geographical logic took over. The underlying forces shaping place competition
increasingly came to include media and finance, not just I.T. Accordingly, the
creation of technology-based start-ups would now depend on resources available
to a few of the most diversified of the faded industrial centers, the
industrial cities identified in Map 2.
From either perspective,
that of the U.S. resurgence during the PC revolution or the Internet explosion
of the late 1990s, the diversity of the system of cities may well have added to
the U.S. economys adaptive capacity. As Clem Tisdell observes,
Industrial diversity (more generally diversity of driving attributes
in dynamic systems) can have value in increasing the likelihood that an economy
(or system) can jump to a superior state. (Tisdell 1999, p.
>By extension, we might
surmise that continentality and regional diversity aided the U.S. immeasurably
in its shift from mature industries and cumbersome managerial corporations to
new forms and sources of growth.
Now we come to the third
basic tendency transforming the U.S. system of cities. The first point has been
the influence of historyknown in the cluster literature as
path-dependencein the economic performance of the 30 large areas
we are monitoring. The second is domestic migration flows, as shaped by
the influences that make mid-sized younger centers from Seattle to Atlanta
magnet metros. The third is the agglomeration of knowledge
workers in a dozen or so major areas blessed by a favorable mix of venture
capital firms, universities and research institutes, and a crackling
atmospheretypically a high-amenity location where ideas and possibilities
are, in Alfred Marshalls term, in the air.
Where, then, did
concentrations of I.T. workers grow most rapidly between the mid-1980s and the
mid-1990s? In light of the stagnant job growth in computer and electronics
hardware employment, the answer turns largely on software and other computer
We will find that the
geography of job growth in software had a logic opposite that of hardware. That
is, the places that specialized most in software and other computer services in
the mid-1980s would then go on to record the largest software job gains over
the next decade. Since software and other computer services added jobs at a
rapid clip during this interval, for most of the 30 areas the employment gains
easily outweighed computer hardware losses. (Exceptions were two hardware
centers, Los Angeles and Phoenix.)
In turn, some of the
initially specialized areas saw software expansion interact with the local
venture-capital base to spur new technology-based business creation, as
measured by initial public offerings (IPOs). The result for Boston and New
York, industrial cities in terms of the timing of their
industrialization, has been a dramatic comeback in the innovation race, fueled
in good part by specializations in higher education, finance, and
Hence the spatial chemistry
for innovation to be documented now. The indicator to be used is the IPO, the
issuance by a privately held company of common stock to the general public.
While 600,000-800,000 new businesses are formed each year in the US, only about
400 companies reach the moment of an IPO. To that extent, IPOs can be viewed as
survivors of a selection process to single out elite start-up companies
promising investors high profits because they can do something
newSchumpeters touchstone for innovation.
Part Real, Part Surreal:
The Internet Gold Rush
At the same time, this may
be the dimension to the New Economy best described my Mark Zandis term,
part real, part surreal (Zandi 1998). Realistically, an IPO can be
viewed as an attempt on the part of promoters to sell a new idea to
the investment community. During the Internet Gold Rush of 1998 and 1999 some
IPOs have had more hype than content, as the shakeout of dot-coms in
April 2000 demonstrated. To that extent, IPOs are an imperfect measure of
innovationan indicator of market fads as well as of genuine new
For now, suppose we view
IPOs as a rite of passage for an idea-based start-up firm, a moment of truth
when the firms defining premise is put to the test of the market. The question is, where are such new
ideas most likely to occur, to be put into practice, and to reach the stage of
As a working hypothesis, we
might surmise that IPOs in the late 1990s were most frequent where knowledge
workers could hook up with venture capitaliststhe suppliers not only of
money, but of management expertise of the kind most technology-based start-ups
Accordingly, the topics to
be explored now are (1) the new lineup of software centers, where I.T. workers
are concentrated; (2) the prominence of venture capital (VC) firms in the 30
areas; and (3) the rate of IPOs in an area per million residents.
As noted, software centers
had employment gains that swamped their losses in hardware. (TABLE 12.) For example, of the 30 large areas, Washington,
D.C., was most specialized in SIC 737 (computer services) in 1986. Thanks in
large part to the explosion of software and telecommunications in northern
Virginia, the D.C. area also had the largest gain in computer services
employment, over 50,000. At the other end of the spectrum, the least
specialized area in 1986, New Orleans, had the smallest increase in computer
Here we have a stylized
dichotomy, which in this case may be accurate. The contrast is between two
southern centers, the National Capital Region (with its abundance of government
agencies, including the Pentagon, the outsourced private contractors, the media
covering the federal government, the spectrum of universities, and the
tradition of intellectual conflict and ferment) and New Orleansa city
whose chief claim to fame is the Marti Gras. The first led the list in terms of
job growth in computer services. The second came in last.
How general was this
tendency? To find out, we can test changes in employment from 1986 to 1996
against initial location quotients. (A location quotient expresses
the proportion of a sector like I.T. in a places total employment,
relative to the same proportion for the U.S. Hence location quotients above
unity would indicate that the area is more specialized in the activity than the
nation as a whole.)
Equation 2 indicates that a
difference of one point in 1986 location quotients between areas was associated
with an increment of 10,000 computer-services jobs over the decade after 1986.
Whatever the bundle of variables represented by the initial location quotients,
together they account for nearly 60 percent of the variation in job
In contrast to hardware
jobs, then, this was an example of virtuous specialization. In a rapidly growing employment sector
nationwide, initial centers tended to grow as rapidly in percentage terms as
others, hence scoring larger absolute gains.
Does Venture Capital
Now we come to the
financing mechanism. The starting point is that lead VC firms tend to
stay local. The reason is their need for routine face-to-face
contact with supported early-stage firms. As a Silicon Valley journalist notes,
If you need to meet with a company every week or other week to get it off
the ground, you dont want to have to jump on a plane and cross three time
zones to do itespecially if you generate high returns off companies based
in your own proverbial backyard. (Shawn Niedorf, New Yorkers Not
Talk of Town, San Jose Mercury-News, on-line, March 7,
At the same time,
Niedorfs qualifier (especially
) points up the key
premise in her argument. What if you cannot find promising companies right in
your backyard? Which comes first, the VC chicken or the start-up egg? At this
point a word about the origins of VCand its migration westmay come
Venture capital was
invented in the form of Bostons American Research and Development (ARD)
at the end of World War II as a deliberate attempt to incubate new activities
to offset the decline of New Englands ancient industries. By the 1960s,
venture capital also took hold in Silicon Valley, where Shockley Semiconductor
had enhanced the presence of Hewlett-Packard and the Stanford Research Park.
Both Boston and Silicon Valley would go on to become the nations primary
VC centers and hotbeds of technology-base start-ups.
On the other hand, New York
or Chicago venture capitalists may take part in syndications, through
co-investments with lead VC firms elsewhereSilicon Valley,
Massachusetts, or more recently Texas, for example. This was the tendency
documented in a 1992 study of VCs role in 8 major centers. The authors
classified 8 VC centers as technology-oriented (Silicon Valley and Denver),
financial-oriented (New York and Chicago), or hybrids of the two (Boston,
Minneapolis-St. Paul, Texas, and Connecticut). (Florida and Smith 1992, p.
At that time they found
that just 7 percent of the investments made by New York venture
capitalists were made in-state, vs. 70 percent in-state in California. In
between was Massachusetts, whose VC firms made 40 percent of their placements
in-state, and 30 percent to California start-ups. (Florida and Smith, p. 193.)
(A different angle on the feasibility of long-distance relationships, as
facilitated by airline connections between emerging and established centers,
appears in a recent study of innovation in Texas
Florida and Smiths
study of the 8 VC centers appeared in 1992. In the meantime some things have
changed, such as the rise of New York Citys Silicon Alley,
which specializes in media-based Internet start-ups. One might therefore expect
to find deepening ties between Wall Street venture capitalists and Silicon
VC "Funds" as an
indicator of Local Supply
The hypothesis, then, is
that the frequency of IPOs in an area will increase, the greater the supply of
venture capital in the area. How, then, should be measure supply?
Lacking more precise data, a good indicator of the size of an areas
venture capital base is the number of separate funds being maintained by
the areas VC firms. Each fund in a VC firms portfolio
represents a separate sector (e.g., biotech, network software, or e-commerce).
And each has a separate and finite duration (perhaps five or 10 years), to be
liquidated at maturity. (TABLE 13.)
Note that this indicator
measures where placements originatenot where they land. Since the
purpose of a VC placement is to bring the early-stage firm to a successful IPO,
linking IPOs to where placements land would be tautological, explaining
nothing. (That is, when a Chicago venture capitalist has a placement in Silicon
Valley, the IPO is all but certain to occur in the Valley.) In contrast, we are
testing Shawn Niedorfs maxim: lead or solo VC firms prefer, in effect, to
stay home because of the need for frequent face-to-face contact with supported
In short, the premise is
that start-ups in a given metropolis are more likely to find VC
financing and assistance if more VC funds are being run there.
An IPO a Day:
A word about the IPO data.
From May 1996 to November 7, 1999, 1,532 IPOs were launched in the U.S. That
averages over 400 per year, or more than one a day. The three and a half years
surveyed is the interval covered by the data-base in Hoovers on-line IPO
directory (http://www.hoovers.com/ipo/). The
data-base permits counts by industry, by state, and by metropolitan
Over that interval from
mid-1996. about three-eighths of the total count have been in some sense
digital, linked to computing, semiconductors, software, networks,
or e-commerce. (The proportion rose sharply in 1999, as the Gold Rush gathered
speed, to about 60 percent.)
In absolute terms a handful
of areas dominated the metro landscape for IPOs over the period from July 1997
to late October 1999. New York and Silicon Valley each were home to about 200
IPOs. Adding Los Angeless 94 and Route 128s 90 gives a figure for
the four top metros of over half of the 30-area totaland about 40 percent
of the U.S. total (TABLE 14). Like its progenitor
venture capital, IPO activity thus tends to be concentrated in a few major
In addition, the large
number of IPOs for the New York area suggests a sharp increase in start-up
activity, triggered in part by media-linked Internet firms. No longer does
money raised by venture capitalists in New York all go to other
At the same time, some
unexpected places also have high IPO rates, once we discount the effect of
absolute population size.
IPO Rates by Area,
Relative to Population
population, how do individual areas compare to the U.S. averages, i.e., about
six IPOs of all kinds, and about two digital IPOs, per million
residents? (To repeat: digital offerings include not just Internet issues, but
any that relate to computers, electronics, or software.)
For IPOs generally, the
highest rate was Silicon Valley (approximated by combining the San Francisco
and San Jose metropolitan areas). It had nearly 30 IPOs per million residents,
about twice the rate of any other area. As in the Milken Institute ranking of
high-tech output noted above, the San Jose/San Francisco region is in a class
A dozen other areas on the
list came in above the U.S. average. Other entries include second-place Denver
(above Boston or New York), Seattle, Atlanta, San Diego, Baltimore, and
In contrast, both the
smaller areas in the South and West and the more heavy-metal areas
of the Midwest lagged the national averages.
For digital IPOs per se,
the top five entries in TABLE 15, are Silicon Valley,
Denver, Route 128, Seattle, and Washington, D.C. (i.e., including northern
Virginia). By contrast, Philadelphia, Houston, Kansas City, and Chicago are
less prominent digitally than for IPOs in general.
Digital IPO Rates as a
Function of the Two Variables
Denver aside, we seem to
have arrived back to a list of the usual suspects for clusters of
innovation. To what extent does this outcome reflect the proposed explanatory
variablesthe supply of venture capital and the relative size of an
areas software sector? Two measures of the latter influence have been
found relevant. One refers to the share of an areas total employment in
software jobs in 1996. Another refers to the rate of growth of employment in
computer services between 1986 and 1996.
It turns out that
three-fourths of the differences among areas in digital IPO rates per million
residents can be statistically explained in this framework. (In
Equation 3, in other words, the adjusted R2
he implication is that the
five areas just mentioned have unusually high rates of IPO activity because
large numbers of technically talented people are concentrated in places
offering relatively easy access to venture capitalincluding not only the
funding, but the management expertise that comes with it.
Still, given the element of
hucksterism that permeated IPOs during the Internet Gold Rush of the late
1990s, it seems advisable to compare the IPO results to more traditional
The obvious question, in
other words, is whether we might find a better indicator with which to monitor
changes in innovative performance in the U.S. system of cities.
An approach that is
sometimes advanced is to compare patent rates in competing metropolitan areas.
For example, OhUallachain (1999, p. 613) observes, Innovation is
not the product of lone individuals nudging technology forward, but encompasses
many interdependent people, firms, and institutions working within networks of
social and economic relations. It turns out, however, that the article is
not about innovation at all, but about patents, tabulated relative to
population in U.S. metropolitan areas in 1996.
In a similar spirit, Varga
observes, This chapter, using a large data set of US patents, presents
the first industrially and spatially detailed analysis of recent trends of
innovative activity in the United States (Varga 1999, p. 230).
Invention, Not Innovation
Unfortunately, and apart
from any other limitations of
data, patents do not measure innovation. Formally, of course, patents are
granted by the U.S. Patent Office when it accepts applications to register new
ideas, whether for business procedures (as in the recent Amazon single-click
case) or for new hardware or industrial processesor (notoriously, of
late) for a chemical formula to be used by pharmaceuticals companies. The
patent then confers monopoly rights to the holder, normally for a period of 20
Innovation is a separate
step: the commercialization of invention. In a Schumpeterian framework, for
example, the four key processes are invention, innovation, emulation, and
diffusion. The invention, which may or may not get patented, is the initial
idea. The innovation is the process of putting the idea into practice for a
profit. Emulation is what happens when competitors swarm to provide
the same product at a lower price, subject to patent restrictions (as when
Compaq reverse-engineered BIOS chip for the IBM PC in the mid-1980s, opening
the doors to clones). Diffusion refers to the time interval
required for an innovation to become widely adopted.
In case the difference is
not clear, consider the potentially unnerving case whereby British Telecom is
considering pressing a claim that it had applied for (in 1980) and received (in
1989) a patent for a process closely resembling if not identical to the
hyperlink. This realization occurred by accident only in the year 2000, when
someone stumbled upon an old patent record. (Bray 2000, p. D1.) If such a
patent exists and proves valid, the question ariseswhat happened? A
certain rough justice might be served, in that hyperlinks were first joined to
the Internet by an Englishman, Tim Berners-Lee, in a project at CERN, the
European particle physics consortium in Switzerland, in 1990.
The point here, however, is
that the idea was never put into practice by British Telecom, who seem not to
have known what to do with it. If so, they had plenty of company in the
numerous U.S. managerial corporations who came up with ideas and then had no
clue how to proceed. The classic example, among many, is Xerox, whose Palo Alto
Research Center (PARC) came up with a dazzling series of revolutionary PC
ideas, not one of which Xerox ever commercializedbecause they did not
improve Xeroxs position in the copier market.
In organizational terms,
patents can perhaps best be understood as a running tabulation of what happens
in corporate R&D labs, as a look at the U.S. Patent Offices top 10
patenting organizations will tend to confirm. In 1996, for example, the 10 U.S.
organizations with the largest number of patents were IBM, Motorola, the U.S.
government, General Electric, Eastman Kodak, Xerox, Texas Instruments, 3M,
AT&T, and Hewlett-Packard (O hUallachain, p. 624). Only two of these,
T.I. and H-P, are from the South or West, and they are both anomalies in their
own regions by virtue of their relatively advanced age.
How accurate are patents as
indicators of corporate and other invention? A recent
study by Cohen, Nelson, and
Walsh (2000) points up the indicators limitations. Surveying 1478 R&D
labs in U.S. manufacturers in 1994, they found that of the several ways firms
protect the profits due to invention
patents tend to be the least
emphasized by firms in the majority of manufacturing industries and secrecy and
lead time tend to be emphasized most heavily. By the same token, when
patents were employed, it was not necessarily to protect a new discovery but
alternatively to attain negotiating leverage, to block other firms
patents of related discoveries, or to prevent suits.
Even as measures of
inventive activity, in short, patents leave something to be desired.
What Do Patents Show
about the Geography of R&D or Inventive Activity?
Taken on their own terms,
what do such studies of the geography of patent activity reveal? As might be
expected, adding location as a dimension creates new measurement
One concerns the location
of the discovery itself vs. the location of the patents ownership.
Tabulations that locate patent activity according to where the patent is owned,
not where the inventor (or R&D lab) is located, tend to distort the
picture, as when the 23 percent of Arizonas patent activity attributable
to a Motorola facility there in 1996 might have been credited to the parent
companys state, Illinois. Another is that patents have traditionally not
covered software code, which instead comes under copyright laws. To that
extent, patent counts will tend to slight metros specializing in I.T. (Both
these cautionary points are made by O hUallachain, p. 628).
Such quibbles aside, what
do the two recent patent studies reveal about the U.S. system of cities? Both
tend to bring out the inventiveness of traditional metros in the
Manufacturing Belt. O hUallachain, for example, finds that the 87 metros
of the Manufacturing Belt accounted for half of all metropolitan patents in
1996, when they had only 44 percent of the metropolitan population.
Accordingly, Metropolitan residents in the manufacturing belt remain the
most industrious inventors (p. 613).
differ because he monitors changes in patent activity over time, from 1983 to
1992. He finds a general shift in patent activity from the metros of the
Manufacturing Belt to areas in the South and West, led by patents registered
for I.T. On the other hand, some centers in the Belt retained strong presences
in chemicals and pharmaceuticals, and in high-technology machinery.
Philadelphia, for example, remained strong in the former, and Chicago in the
latterindeed, Chicago ranked second in 1992 among all areas in terms of
high-technology patents. (Varga, p. 225).
The Stellar Patent
Performance of the Three Super-States, 1978-1998
Relying purely on patent
data, then, the two studies together suggest that the strong performance of
Manufacturing Belt metro areas in 1996 may have been a legacy effect. This
impression holds up when we perform a new comparison of state patent data over
time. We can begin with the 10 states that had most patents in the late 1970s.
Seven were from the Manufacturing Belt, and the other three were California,
Florida, and Texas, the proverbial super-states when it comes to
population and employment growth. (The data set has been compiled and provided
by Brian Ceh, who also alerted me to the increasing prominence of the latter
Among the 10 major states
in terms of late-1970s patent activity, we can compute the increase in patents
generated over the next 20 years. The national count doubled (from 44,762 to
90,676, up 103 percent). But counts roughly tripled in Florida, California, and
Texas. As FIGURE 18 shows, Massachusetts came in at the
national average, while the remaining six states had increases of less than
two-thirds the U.S. pace.
A line is also included in
Figure 18 to show state population changes over the same interval. With the
possible exceptions of Massachusetts (where patents outperformed
population, as it were) and New Jersey (where the opposite can be seen), the
two indicators show a remarkable correspondence.
The conclusion? For
inventive activity no less than for population, the U.S. experienced a
pronounced shift away from the Manufacturing Belt in the 1980s and 1990s. On
average, in other words, the three super-states had increases in
patent activity at least triple that of such traditional industrial states as
New York, Michigan, Ohio, Illinois, Pennsylvania, and New Jersey.
This brief look at patents,
though hardly definitive, suggests four plausible conclusions:
- The results reveal that
per capita inventiveness as a measure is likely to underestimate
the speed of the regional transformation, because both patent activity and
population have shifted at a rapid pace.
- The widely noted U.S.
comeback in patent activity during the 1990s (which defied predictions by
analysts such as Michael Porter) has depended directly on the supercharged
patent performance of the growing states in the South and West, as symbolized
here by Florida, Texas, and California.
- In the end there is not
much difference in the geographical implications as between IPOs and patents as
indicators of the geographical dispersal of creativity over the past few
decades. Both indicators, the one
of innovation, the other of inventiveness, point up the growing prominence of
younger metros and regions within the economy as sources of technological
- The greatest exceptions
to point (3) are the two resurgent industrial cities, New York (a major IPO
seedbed) and Chicago, buoyed by high-tech (but not I.T.) patent
activity. Here the indicators give different results, and each must be
Large urban places
are not anachronisms in the information age, they are the dominant places in
the information age (Drennan 1999, p. 314). But which ones, specifically, emerge
from among our working list of 30 of the largest U.S. metropolitan areas? In
Galbraiths telling term, which have emerged as strategic cities, places
offering the information that decision requires? In light of the indicators we have considered, the first 10
or so areas come quickly to mind, although the exact order remains
- San Francisco/Silicon
Valley, of course.
- These days, (2) Boston
and Route 128 (again).
- Washington, D.C., which
thanks to northern Virginia is almost as prominent in telecommunications as in
- Dallas, especially when
understood as the center of the emerging complex of Texas cities.
- Seattle, as symbolized
by the worlds largest philanthropy, the Bill and Melinda Gates
- Los Angeles, like New
York a media-rich location in an age of convergence between content and the
- Denver, a relatively
unknown powerhouse and a regional capital.
- New York, by virtue of
finance and media.
- Chicago, another
industrial city like New York and Boston, and a standout in terms
of Old-Economy high-tech patents.
- Either Atlanta, by the
indicators and as a regional capital, or
- San Diego, an amenity
center now liberated from but still technically enriched by its long-time
But I see that this top-10
list, loosely based on the empirical indicators we have surveyed, omits such
obvious smaller candidates as Albuquerque, Austin, Boise, Miami, Minneapolis,
Orlando, Portland (Oregon), or the North Carolina complexon the basis of
size alone. Needless to say, such smaller centers are increasingly prominent in
national and global networks of research, production, and innovation.
Come to think of it,
recognizing that some other highly innovative cities have been omitted from our
list is as good a way as any to do justice to the energizing geography of
the New Economy.