ED 251 295

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INSTITUTION

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SE 045 233

Technology, Innovation, and Regional Economic

Development.

Congress of the U.S., Washington, D.C. Office of

Technology Assessment.

OTA-ST1-238

Jul 84

174p.; For a related document, see ED 235 344.

Superintendent of Documert_ U.S. Government Printing

Office, Washington, DC 20402.

Reports - Research/Technical (143)

MF01/PC07 Plus Postage.

Community Programs; *Developmental Programs;

*Economic Development; *Government Role; Higher

Education; *Industry; Policy; Regional Programs;

Research and Development; *State Programs;

*Technological Advancement; Technology;

Universities

*Industrial Development

ABSTRACT

In recent years state and local governments,

universities, and private sector groups have become increasingly

active in promoting technological innovation and technology-based

business development in their local economies. These efforts have

resulted in productive new forms of partnership and cooperation at

all levels. While federal programs have sometimes supported these

efforts, and while'recent changes in federal policy have improved the

climate for high technology development initiatives, in most cases

both the initiative and the-ongoing leadership have come from

imaginative state and local leaders. This five-chapter report

provides: (1) an overview of high-technology development (HTD); (2) a

definition and analysis of high-technology industries; (3) a

discussion of entrepreneurship and venture capital in HTD; (4) a

discussion of state and local government, university, and private

sector initiatives for HTD; and (5) an examination of the federal

role in regional ,HTD. Three reports are appended: they concern (1)

the theoretical base for high-technology location and regional

development, (2) a regionei assessment of the formation and growth in

high-technology firms, and (3) a preliminary investigation of recent

evidence on high-technology industries' spatial tendencies. One

factor examined in the latter report is the nature and diversity

among high-technology industries in both growth performance and

locational tendencies. (JN)

***********************************************************************

Reproductions supplied by EDRS are the best that can be made

from the original document. ***********************************************************************
LIEPANDIASNT Of EDUCATION

NATIONAL INSTITUTE Of EDUCATION

EDUCATIONAL RESOURCES INFORMATION

CENTER {ERIC)

V/Ths document has been reproduced as

received from the person Of orgsmastarm

onginatava rt

Minor changes have been made to improve

reproduction quality.

Points a. view or opinions stated in Ints docu

mem do not lecessaniy represent offices' ME

position of poky.

Technology, Innovation,

and Regional Economic

Development

MMUS Of THE UNITED 1TAT6

040 of Technology Assessment wesnssesm. A. C Aosta
Office of Technology Assessment

Congressional Board of the 98th Congress

MORRIS K. UDALL, Arizona, Chairman

TED STEVENS, Alaska, Vice Chairman

Senate

ORRIN G. HATCH

Utah

CHARLES McC. MATHIAS, JR.

Maryland

EDWARD M. KENNEDY

Massachusetts

ERNEST F. HOLLINGS

South Carolina

CLAIBORNE PELL

Rhode Island

CHARLES N. KIMBALL, Chairman

Midwest Research Institute

EARL BEISTLNE

University of Alaska

CHARLES A. BOWSHER

General Accounting Office

CLAIRE T. DEDRICK

California Land Commission

House

GEORGE E. BROWN, JR.

California

JOHN r DINGELL

Mi,nigan

LARRY WNN, JR.

Kansas

CLARENCE E. MILLER

Ohio

JOHN H. GIBBONS

(Nonvoting)

Advisory Council

JAMES C. FLETCHER

University of Pittsburgh

S. DAVID FREEMAN

Tennessee Valley Authority

GILBERT GUDE

Congressional Research Service

CARL N. HODGES

University of Arizona

Director

JOHN H. GIBBONS

COOPER EVANS

Iowa

RACHEL McCULLOCH

University of Wisconsin

WILLIAM J. PERRY

Hambrecht & Quist

DAVID S. POI IER

General Motors Corp.

LEWIS THOMAS

Memorial Sloan-Kettering

Cancer Center

The Technology Assessment Board approves the release of this report. The views expressed in this report are not

necessarily those of the Board, OTA Advisory Council, or of individual members thereof.

3
Technology, Innovation,

and Regional Economic

Development

OTA Reports are the principal docuntentation of format assessment projects. These

projects are approved in advance by the Technology Assessment Board. At the conclusion of a project, the Board has the opportunity to review the report, but its release does

not necessarily imply endorsement of the results by the Board or its individual members.

/os 1

COON= OF Tin 1110110 Stan

01111Co loolloolow Aososempol

1+1n. Q F X610
Recommended Citation:

Technology, innovation, and Regional Economic Development ( Washington, D.C.: U.S.

Congress, Office of Technology Assessment, OTA-ST1.238, July 1984).

Library of Congress Catalog Card Number 84-601088

For sale by the Superintendent of Documents

U.S. Government Printing Office, Washington, D.C. 20402
Foreword

This report assesses the potential for local economic growth offered by high-technology

industries, the types of programs used by State and local groups to encourage development

of these industries, and the implications of these programs for Federal policy. As such, it

is a companion to the continuing series of OTA reports on the international competitiveness of U.S. industry and the meaning of industrial policy in the U.S. context. It was requested by the House Committee on Science and Technology, and letters of endorsement

were also received from the House Committee on Small Business; the House Task Force

on Industrial Innovation and Productivity; the Joint Economic Committee; and the House

Committee on Banking, Finance, and Urban Affairs.

In recent years State and local governments, universities, and private sector groups have

become increasingly active in promoting technological innovation and technology-based

business development in their local economies. These efforts have resulted in productive

new forms of partnership and cooperation at all levels. While Federal programs have sometimes supported these efforts, and while recent changes in Federal policy have improved

the climate for high-technology development initiatives, in most cases both the initiative

and the ongoing leadership have come from imaginative State and local leaders. OTA hopes

that, by documenting these efforts, it can help Congress to take State and local efforts and

perspectives into full consideration in the formulation of national policy.

OTA was assisted in this assessment by an advisory panel of individuals representing

a wide range of backgrounds, including industry, finance, and State and local government.

In addition, hundreds of State and local officials have provided information and dozens

of reviewers from universities, private companies, and government agencies have provided

helpful comments on draft reports. OTA expresses sincere appreciation to each of these

individuals. As with all OTA reports, however, the content is ihe responsibility of OTA

and does not necessarily constitute the consensus or endorsement of the advisory panel

or the Technology Assessment Board.

4. 1:11(42

ff:41444JOHN H. GIBBONS

Director
Technology, innovation, and Regional Economic Development Advisory Parel

William

Chairman and

William J. Abernathy*

Graduate School of Business

Harvard University

William F. Aikman

President

Massachusetts Technology Development Co

The Honorable Henry Cisneros

Mayor

San Antonio, Tex.

Ella Francis

President

Parkside Association of Philadelphia

Aaron Gellman

President

Gellman Research Associates, Inc.

Don Lee Gevirtz

Chairman

The Foothill Group, Inc.

George W. Haigh

Piesident and CEO

The Toledo Trust Co.

Quentin Lindsey

Science and Public Policy Advisor

Office of the Governor

State of North Carolina

C. Norris, Chairman

CEO, Control Data Corp.

Neal R. Peirce

Contributing Editor

National Journal

David V. Ragone

President

Case Western Reserve University

J. David Roessner

Technology St Science Policy Program

School of Social Sciences

Georgia Institute of Technology

John Stewart

Assistant General Manager

Tennessee Valley Authority

Ellen Sulzberger Straus

President, WMCA Radio

Member, New York City Partnership

Alexander B. Trowbridge

President

National Association of Manufacturers

Thomas L. Yount, Jr.

Commissioner of Employment Security

State of Tennessee

rp.

NOTE: The Advisory Panel has provided advice and suggestions, but it does not necessarily approve, disapprove, or endorse

the contents of this report, for which OTA assumes full responsibility.
Technology, innovation, and Regional Economic Development

Project Staff

John Andelin, Assistant Director,

OTA Science, information, and Natural Resources Division

William F. Mills,* and Nancy Naismith,** Program Manager

Science, Transportation, and Innovation Program

Paul B. Phelps, Project Director

Barry J. Holt, Analyst

Caroline S. Wagner, Research Assistant*

Jeffrey E. Seifert, Research Assistant**

Brian R. Land, Research Assistant***

Student Intern (fall 1983)

Nina Graybill, Editor

Walter Gunn, Student Intern (summer 1982)

Jay M. Berman, Student Intern (fall 1982)

Mary Ann Wall, Student Intern (spring 1983)

D. Douglas Caulkins, Faculty Intern (fall, 1983)

Marsha Fenn, Administrative Assistant

R. Bryan Harrison, Office Systems Coordinator

Betty Jo Tatum, Secretary

Consultants

Gwendolyn B. Moore

Ellis Mottur

Judith H. Obermeyer

'Through September 1983

After September 1983

Atter januatv 19g4
Contractors and Acknowledgments

Contractors

Catherine Armington, Candee Harris, and

Marjorie Odle, Business Microdata Project,

The Brookings Institution

Renee A. Berger

Cosmos Corp., Gwendolyn B. Moore and

Robert K. Yin, Principal Investigators

The Fantus Co.

Charles Ford Harding, Principal Investigator

Amy K. Glasmeier, Peter G. Hall, and Ann R.

Markusen, Institute of Urban and Regional

Development, University of California at

Berkeley

Christopher T. Hill and K. Nagaraja Rao,

Center for Pofiry Alternatives, Massachusetts

Institute r fe.chnology

John Ree Syracuse University, and Howard

Staff( rd, University of Cincinnati

Resea-,:h Triangle Institute,

Alvin Cruze, Principal Investigator

Rhett D. Speer

Carol'Steinbach

Venture Economics, Brian Haslett, Principal

Investigator

Acknowledgments

John Alic

Office of Technology Assessment

Jacques Bagur

Gulf South Research Institute

Michael Barker

Gallatin Institute

Dennis Barnes

University of Virginia

Donald Bielman

Microelectronics Center of North Carolina

Jerry Birchfield

Advanced Technology Development Center

Georgia Institute of Technology

Marjory Blumenthal

Office of Technology Assessment

Miles Boylan

Colby College

Charlotte Breckinridge

Congressional Research Service

Ray Brill

Small Business High Technology Institute

F. Paul Carlson

Oregon Graduate Center

Charilyn D. Cowan

National Governors' Association

Robert A. Cox

New England Innovation Group

Robert J. Crowley

Massachusetts Technology Development Corp.

Belden Hull Daniels

Counsel for Community Development, Inc.

vl

Nancy S. Dorfman

Center for Policy Alternatives

Massachusetts Institute of Technology

Albert A. Eisele

Control Data Corp.

J. D. Eveland

National Science Foundation

Jerry Feigen

Small Busineis Administration

Paul Feldman

Public Research Institute

Center for Naval Analyses

Irwin Feller

Institute for Policy Research and Evaluation

Pennsylvania State University

Miles Friedman

National Association of State Development

Agencies

David Goodman

Governor's Commission on Science and

Technology

State of New jersey

Julie Gone

Office of Technology Assessment

Aaron Gurwitz

Federal Reserve Bank of New York

Dan Hecker

Bureau of Labor Statistics

U.S. Department of Labor

David Hirschberg

U.S. Small Business Administration
Louis Jacobson

Public Research Institute

Center for Naval Analyses

Michael Kieschnick

Dimensional Credit Corp.

San Francisco, Calif.

Gretchen Kolsrud

Office of Technology Assessment

Brent Lane

Governor's Office

State of North Carolina

Karen Seashore Louis

Center for Survey Research

L'niversity of Massachusetts

V:,?.er 0. McGuire

McGuire, Barnes, Andrascik, Inc.

San Francisco, Calif.

Shelley Metzenbaum

Massachusetts Institute of Technology

Charles Minshall

Battelle Memorial Laboratories

James O'Connell

Control Data Corp.

Albert Paladino

Advanced Technology Ventures

Walter H. Plosila

Deputy Secretary of Commerce

State of Pennsylvania

David P. Richtmann

Participation Systems, Inc.

Winchester, Mass.

Corey Rosen

National Center for Employee Ownership

William K. Scheirer

U.S. Small Business Administration

10

Elliot Schwartz

Congressional Budget Office

Kenneth N. Sherman

Cambridge Research and Development Group

Brendan F. Somerville

National Association of Manufacutrers

Milton D. Stewart

INC. Magazine

James G. Strathman

Portland State University

Fred Tarp ley

Advanced Technology Development Center

Georgia Institute of Technology

Morgan Thomas.

University of Washington

Richard Thoreson

Office of Technology Assessment

Louis G. Tornatzky

National Science Foundation

Roger J. Vaughan

Gallatin Institute

Bertram Wake ley

Council of State Planning Agencies

Amy Walton

National Science Foundation

Philip Webre

Congressional Budget Office

Elizabeth Weems

Washington Office of the Governor

State of Illinois

Robert Weiss

Council of State Planning Agencies

VII
Contents

Chapter Page

1. Overview of High-Technology Development 3

2 Definition and Analysis of High-Technology Industries 17

3. The Roles of Entrepreneurship and Venture Capital in

High-Technology Development 33

4. State and Local Initiatives for High-Technology Development 53

5. The Federal Role in Regional High-Technology Development 75

Appendixes Page

A . High-Technology Location and Regional Development: The Theoretical Base 97

B. Formation and Growth in High-Technology Firms: A Regional Assessment 108

C. Recent Evidence on High-Technology Industries' Spatial Tendencies:

A Prel:minary Investigation 145

11 ix
CHAPTER 1

Overview of

High-Technology Development
Contents

Findings

Page

5

Implications for Federal Policy 10

Better Information Ii

Improved Coordination 12

1.3
CHAPTER 1

Overview of

High-Technology Development

OTA's investigation of regional high-technology

development (HTD) suggests that high-technology

industry is so difficult to define and isolate statistically, and so interdependent with other sectors of

the economy, that to define Federal policy for regional development based on distinctions between

"high technology" and "low technc logy" would be

artificial and possibly misleading. By any definition

used today, high-technology industries are expected

to grow somewhat faster than will overall employment over the next decade. Because of their relatively small employment base, however, these industries will directly account for only a small fraction

of total employment growth. It is likely that their

largest employment impacts will come through the

diffusion and widespread application of their products by other industries, "smokestack" and services

alike.

In short, while high-technology industries may

contribute significantly to the productivity and competitiveness of the overall economy, an emphasis on

those industries per se, rather than on the process

of technological innovation and diffusion, carries

a risk of distorting public policy and ignoring real

opportunities for promoting industrial competitiveness and sustainable economic development.

High-technology industries do represent an important component in some regional economies.

They are becoming more dispersed geographically,

7,

High-technology complex

14

Photo orodit: RCA

3
4 Technology, Innovation, and Regional Economic Development

but few communities are likely to develop large concentrations of microelectronics- and computer-based

firms like those in California's Silicon Valley c

Massachusetts' Route 128. New opportunities for

economic development are being created by other

emerging technologies, and even greater opportunities exist for the application of productivity-enhancing innovations in existing industries. Accordingly,

the greatest opportunities for most communities may

lie in encouraging business development and technological innovation throughout the local economy,

rather than simply attracting high-technology businesses from other. regions.

State and local groups have taken the initiative

in launching programs to promote technological innovation in their economies. While some of these

initiatives were no doubt inspired by the successes

of other regions, they are usually se ..-n as logical extensions of traditional economic development activities. Most HTD initiatives attempt to mobilize available resources or to create the institutional netwk rks

that encourage innovation and entrepreneurship.

The diversity and flexibility of these initiatives reflect the inventiveness of local leaders, as well as the

need to adapt to prevailing needs and conditions

in widely different areas. These initiatives have considerable potential to stimulate technological innovation, and in some regions they have already proven

to be a useful complement to existing economic development programs. To the extent that these State

and local initiatives can stimulate the national level

of research and development (R&D) and quicken

the pace of commercialization and diffusion of new

technologies, they may also contribute to the productivity of the entire U.S. economy.

Federal policies and programs have contributed

to regional HTD, usually as an indirect result of attempts to achieve broader national goals and purposes. For example, innovation-oriented policies

those intended to promote R&D and technological change at the national leveloften have significant impacts on regional development. Similarly,

community and economic development programs

e.g., block grants, business assistance, and education and traininghave sometimes stimulated technological innovation in local economies. Macroeconomic policies of the Federal Government have

had perhaps the largest and least intentional influence on regional HTD. Relatively little Federal effort has gone toward promoting technological innovation and regional development concomitantly; the

few Federal programs to do so have been largely experimental in nature, and designed to develop or

support State and local mechanisms.

However, OTA finds no evidence that an extensive new Federal effort, specifically and directly

targeted on this aspect of economic development,

would be necessary to promote regional HTD. Several recent changes in Federal policy promise to

create a better climate for State and local HTD initiatives. Many additional changes that have been

proposed for larger Federal policies and programs,

to achieve broader national objectives, might also

provide additional indirect benefits for regional

HTD.

Thus, it would seem most effective to continue

current Federal roles, and where possible improve

those roles by making Federal policies and programs

more sensitive to their regional impacts. Better informatioa would help to identify and refocus existing

policies and programs that can contribute to regional

HTD; such information would also be useful to State

and local clients. Much could also be achieved

through improved coordination of existing policies

and programs, perhaps as pal.: of a mechanism created to pursue objectives relating to industrial policy, and/or improved coordination with State and

local mechanisms, which may provide an effective

means of achieving national goals in the area of regional HTD. These conclusions are based on the

principal findings outlined below. Implications for

Federal policy are developed at the end of this

chapter.

15
Ch. 1Overview of HighTechnology Development 5

Findings

1. State and local groups have become increasingly active in encouraging technological innovation and high-technology development.

Encouraged by the success of such areas as Silicon

Valley and Route 128, public and private sector

groups in other regions are launching initiatives to

promote HTD in their own economies. The growing competition for HTD has generated literally

hundreds of these initiatives by State and local

governments, univers.ties, and private sector organizations. State and Local leaders are attracted to

HTD because they believe it promises new jobs,

clean industry, and rapid growth. Some also believe

that high-technology businesses can be a major force

in the revival of distressed regions and cities, especially in the Midwest.

These initiatives are seen as a logical and inevitable extension of more traditional economic development activities. They vary in their goals and

results, in part because they must be adapted to the

prevailing conditions and available resources in

widely different areas. The diversity of these efforts,

and the flexibility with which specific programs have

been structured, suggest that they are usually based

on a careful evaluation of a region's needs and its

existing industrial base. Many have considerable potential to stimulate technological innovation, and

in some regions they have already proven to be a

useful complement to existing economic development programs. In a number of specific program

areas, innovative State and local strategies appear

to be making a major contribution to regional HTD.

2. 1"...e efforts of State and local groups to promote high-technology development in their

regions may have implications for the national economy.

High-technology industries are a key source of the

innovative ideas, products, and processes that are

essential to revitalizing older industries and maintaining U.S. technological and economic competitiveness. State and local HTD initiatives naturally

are primarily concerned with regional economies,

but to the extent that these activities increase the

level of R&D or quicken the pace of commercializa16

don and diffusion of new technologies, they may

also ..ave potential for improving the productivity

and competitiveness of the en.-ire U.S. economy.

3. Depending on the definition of high technology used, jobs in high-technology industries

currently represent between 3 and 13 percent

of total U.S. employment.

According to three definitions used by the U.S.

Bureau of Labor Statistics (BLS), high-technology

industries represented 2.5 million, 5.7 million, or

12.3 million jobs in 1982, out of a total wage and

salary employment of 92 million.

4. Employment in high-technolf gy industries

grew at a faster rate over the past decade than

did overall employment, and it is likely to

continue doing so in the coming years. Nonetheless, because high-technology industries

are a smell sector of our economy, they will

directly provide only a small share of total

new jobs.

High-technology industries increased their employment by 25 to 40 percent (depending on definition) ever the decade between 1972 and 1982; during that time overall employment rose by 20 percent.

BLS projections indicate the likelihood of a similar

outcome for the period through 1995, although bo.h

growth rates are expected to be lower than in the

past. For example, the high-technology industries

included in the BLS' midrange definition increased

their employment from 4.47 million in 1972 to 5.69

million in 1982 (an average growth rate of 2.4 percent a year), and are projected to increase their

employment further by 1995 to between 7.72 .million and 7.89 million (2.0 to 2.2 percent a year average growth rate); overall employment brew at an

average rate of 1.9 percent a year from 1972 to 1982

and is expected to grow by 1.5 to 1.8 percent a year

in the near future, adding 25 million new jobs by

1995. By this projection, 8 to 9 percent of new jobs

will be in high-technology industries (slightly more

than 2 million out of the 25 million total).

It is important to note that the preceding projection of employment in high-technology industries
B Technology, innovation, and Regional Economic Development

IL

%Vim crodit Ford Alolor Co

High-technology innovations can help revitalize older U.S. Industries; here, robots weld automobile subframes

does not fully account for the impact of high technology on employment: other new jobs will be created elsewhere in the economy as a result of the use

of high-technology products. Unfortunately, this latter effect cannot be quantified.

BLS projections indicate that high-technology industries represent neither the fastest growing employment sector nor the one that will add the largest

absolute number of jobs. For example, employment

in construction is estimated to increase by 2.6 percent a year, and in service, by 2.1 percent. The nonhigh-technology portion of the service sector alone

is estimated to increase its employment by more than

9 million jobs.

5. Jobs in hightechnology industries play a ma.

jor role in the economies of many States and

communities.

Analysis of recent trends shows that some regions

and communities benefit more from HTD than

others. This is because high-ttchr4ogy employment

is concentrated in a few StaN'r and within those

States in a few large metropolitan areas. Using a

"moderate" definition of high technology, corresponding to about 6 million jobs in 1982, 60 percent of high-technology jobs are located in the 10

States with the highest levels of high-technology

employment, and 40 percent in the top five States.

Within these States, moreover, as much as 90 per-
Overview of High- Technology Development 7

cent of high-technology employment is located in

the five largest metropolitan areas, some of whose

labor forces are one-third in high-technology industries.

6. High-technology industry employment is

found predominantly in iarge multistate enterprises and became more dispersed geographically during the 1970's.

According to one study, 88 percent of high-technology employment was found in large multiestablishrnent firms, compared to 60 percent for all private employment. During the period 1976-80, new

and expanding branch facilities were the source of

most high - technology employment growth. The

South and the West, with the smallest shares of

high-technology employment in 1976, had the highest employment growth rates during this period.

Another study found that 80 percent of high-tech=

nology industries became more dispersed during

1972-77, with some of them opening plants in as

many as 100 new counties. However, between 37

and 48 percent of high-technology employment has

remained concentrated in the top five States since

1975.

7. While the conditions that led to concentrations of microelectronics firms in Silicon

Valley and Route 128 are unlikely to be replicated in other areas, new opportunities are

being created elsewhere by advances in microelectronics and other technologies and in

the application of new products and processes by existing industries.

The emergence of these archetypes was caused by

the confluence of technological opportunity, created

by fundamental advances in microelectronics that

opened up a wide range of potential applications,

with the preexisting socioeconomic conditions in

each region. The result was cumulative, leading to

agglomeration economies that tended to enhance

the region's resources and encourage high-technology entrepreneurship. In both regions, growth was

driven by "home grown" startups and local spinoffs

from firms already in the area.

Many of these conditions cannot be replicated

elsewhere, and few other communities are likely to

develop such large concentrations of microelectron36-737 0 - 84 - 2 : QT., 3 18

ics and computer firms. Some of the conditions can

be replicated, however, and these areas do provide

lessons for communities seeking to expand the technological base of their local economies. Furthermore,

new technological opportunities are being opened

by advances in other fields, such as robotics and

biotechnology. Finally, the applications of new products and manufacturing technologies also create new

opportunities for basic industries and the regions

in which they are concentrated.

8. The most important conditions for "home

grown" HTD are the technological infrastructure and entrepreneurial network that

encourage the creation of indigenous hightechnology firms and support their survival.

Many communities are trying to achieve longterm, sustainable high-technology growth by making the transition from branch plants to "home

grown" HTD, based on the creation of new firms

by local entrepreneurs. Indigenous HTD depends

in large part on the community's ability to mobilize

and integrate the various resourcesscientific, financial, human, and insti.utionalthat constitute the

region's technological infrastructure:

applied research and product development

activities at nearby universities, Federal laboratories, and existing firms;

informal communication networks that provide

access to information and technology transfer

from those R&D activities;

scientific and technical labor force, including

skilled craftsmen, newly trained engineers, and

experienced professionals (who also represent

a pool of potential entrepreneurs);

a network of experts and advisors (often augmented by university faculties) specializing in

hardware, software, business development, and

venture capital;

a network of job shoppers and otl.?r suppliers

of specialized components, subassemblies, and

accessories; and

proximity to complementary and competitive

enterprises, as well as distributors and customers.

Entrepreneurs, in general, tend to be moderate

risk-takers who function well in a supportive envi -.

ronment where they can obtain the information,
8 Technology, Innovation, and Regional Economic Development

resources, and assistance that will give their enterprises a reasonable chance of success. Regional and

local cultures can be especially supportive of entrepreneurship: the spinoff and startup of new firms

drove the development of Silicon Valley and Route

128. This finding has clear implications for other

regions, in that all communities can create support

networks, access to information, and other types of

resources that may encourage entrepreneurs.

9. Venture capital is becoming more widely

available for high-technology firms, although

by far the largest amounts are still flowing

to firms in California and Massachusetts.

Venture capital plays an important role in the

process of HTD, through both investment and business development assistance. Concerns have been

raised that gaps in venture capital availability may

hinder the creation and expansion of new high-technology firms. These gaps fall into three categories:

regional (location), sectoral (technology or industry), and stage of development (especially at the seed

or startup stage). There is some indication that market forces are working to correctnot exacerbate

trends concerning these potential gaps. Recent data

indicate that investments have risen in almost all

regions, but it appears that California investors still

generate the most venture capital and California

firms still attract much of the investment from other

regions. Some State and local programs are designed

to encourage local venture capital to "stay at home."

Seed capital, invested at the very early stages of a

new enterprise, is less well understood but may be

more of a problem for local HTD efforts than venture capital.

10. State and local initiatives often involve institutional innovations directed at the conditions thought to be supportive of indigenous HTD.

State and local HTD initiatives -re aimed at new

starts as well as brand. plants. They differ from

traditional economic development strategies primarily in their attention to the special needs of hightechnology firms and in their emphasis on creating

the cooperative institutional networks that constitute the technological infrastructure. These initiatives have resulted in new linkages between government, university, and industry that are aimed at

developing and integrating the technical anqntrepreneurial resources in their regions. They seek to

mobilize resources or remove barriers in six general

areas:

research, development, and technology transfer;

human capital, including education and

training;

entrepreneurship training and assistance;

financial capital;

physical capital; and

information gathering and dissemination.

11. Federcl rsitcies and programs have played

an important but usually indirect role in

State and local HTD initiatives.

The Federal role in regional HTD is usually indirect, even unintentional, and largely incidental to

the pursuit of other and more central national goals

and purposes. Moreover, little effort at the Federal

level has gone toward promoting technological innovation and regional economic development concomitantly. OTA's investigation has identified four

specific areas in which the Federal Government influences regional HTD:

R&D and innovation policies, including not

only Federal investments in R&D and tax incentives for private investments in innovation,

but also macroeconomic and trade policies and

regulatory policies in the areas of patents and

antitrust;

technology transfer programs that attempt

to increase innovation and growth of industrial

sectors by encouraging the diffusion and utilization of federally developed technologies by

private industry;

general regional development programs, including block grants and technical assistance,

which provide flexible funding tools that have

been put to innovative uses in many State and

local HTD programs; and

planning and demonstration projects that

facilitate new institutional linkages and encol :rage or support the creation of new HTD

mechanisms at the State and local levels, some

of which have been continued or copied elsewhere with little additional Federal support or

intervention.

19
Ch. 1Overview of High-Technology Development 9

Photo credit Tonoosmo Tichnoiogy Found Von

A region's "technological infrastructure"its scientific, institutional, human, and financial resourcesis Important

for the development of indigenous high-technology firms

12. The lack of a single, generally accepted definition of "high technology" constrains

analysis.

Some of the confusion surrounding the question

of HTD stems from the variety of definitions of

"high technology" and the vagueness with which

the term is sometimes used. For statistical purposes,

high-technology industries are usually defined as

those industry groups with higher than average

R&D spending and/or proportion of scientific and

20

technical workers. High-technology industry is far

from homogeneous, however; it is made up of disparate kinds of firms, using varying levels of technology, and producing different kinds of jobs at different times and places. Some of these industries

have grown faster than average, others more slowly,

and still others have contracted in recent years.

No definition, however, captures adequately the

full process of technological innovation. "High technology" can be restricted to a small group of re-
10 Technology, Innovation, and Regional Economic Development

search-intensive industries tbat manufacture new,

sophisticated, technology-intensive products. Alternatively, it can be defined broadly enough to include any industru that employs new methods or

machinery. This distinction is important because the

application of new products and processes usual;-

-has larger economic and employment impacts than

their development.

13. Statistical evidence relating specific characteristics of communities to regional patterns of high-technology development remains sketchy.

Efforts to correlate specific community characteristics with patterns of HTD indicate that recent

population growtha measure of the area's economic health and general attractivenessis related

to HTD. Other statistical research substantiates the

common wisdom that high-technology manufacturing plants are likely to locate in large metropolitan

areas with concentrations of scientific and technical manpower. Such analyses, however, are generally unable to reveal convincing relationships between HTD and many of the factors commonly

thought to be important for location decisions of

high-technology firms, such as proximity to a university or airport, cultural amenities, and business costs.

This inability may result from the high level of aggregation in the available data, for both industries

and metropolitan areas, as well as the complicated

nature of the HTD process.

14. It is difficult to measure the effectiveness

of State and local efforts to attract high-technology firms, due to inadequate data, the

newness of the efforts, and the complex

"ik- nature of HTD.

Many economists and analysts question whether

these efforts have anything more than a marginal

impact on HTD. Others see in the rush to H ID

not only the risk of failure but also the danger of

ignoring opportunities that are better suited to a region's needs and resources. The initiatives themselves are too varied and too recent to evaluate

systematically, and their most important effects may

be indirect and long term. However, the following

factors seem to increase the chances for success:

identifying and focusing on local needs and

resources;

adapting to external constraints;

local initiative, leadership, and partnership;

linkage with broader development efforts; and

sustained effort, often for a period of decades.

In other words, HTD may be best served when

States and cities emphasize: I) strengthening links

among financial, academic, and business communities; 2) improving the overall scientific and technological base of State and local economies; and 3) encouraging high-technology entrepreneurship and the

creation of new firms. These are in fact the centerpieces of many programs. In addition, HTD programs may be more likely to benefit individual communities and the Nation as a whole if State and local

efforts are supported or at least not hindered by policies and programs at the Federal level.

Implications for Federal Policy

The Federal Government already plays a significant role in regional HTD. Current Federal policies

exert important influences on regional economies,

and a wide range of existing Federal programs have

made useful contributions to State and local HTD

initiatives. However, OTA finds no compelling tea21
Ch. 1Overview of High-Technology Development 11

son to launch an extensive new Federal effort to expand the Government's role. First, small changes

in existing Federal programs could focus or enlarge

their contributions to regional HTD, and in most

cases the authority and mechanisms for doing so

already exist. Second, changes in Federal policy with

regard to taxes, R&D, technology transfer, and

other national goals have also helped to create a

more favorable climate for regional HTD over the

past decade. Third, several other changes in Federal policy that have been proposed to achieve

broader national objectives might also enhance the

roles now played by the Federal Government in regional HTD.

Similarly, it might be desirable to establish a new

mechanism to coordinate Federal efforts to promote

technological innovation and industrial competitiveness. It might also be appropriate to make these efforts more respotisive to, and supportive of, State

and local HTD initiatives, which have already demonstrated their potential for promoting both technological innovation and regional economic development. But to organize a new institution solely

around regional HTD, or to use regional HTD as

the organizing principle for sweeping policy changes,

would be to distort its relationship to larger national

goals and objectives.

In short, it would seem most effective to continue

the current Federal roles in regional HTD, and to

sharpen those roles by including a regional perspective in the making of policy and the implementing of programs for other purposesi.e., make

innovation programs more regional, development

programs more innovative, and macroeconomic policies more sensitive to both. This might best be pursued through incremental improvements in those

roles when it is possible to do so without sacrificing the original purpose of the policy or program

involved. This approach, however, imposes two important requirements:

better information for policymaking and program implementation, particularly with regard

to the impacts of current Federal programs and

the effectiveness of various State and local

mechanisms; and

improved coordination, both among complementary Federal programs and between the

Fe leral Government's efforts and those of

22

various State and local groups, which may present an alternative to new Federal programs in

this area.

Better Information

The most straightforward options for congressional action would be to continue those Federal

programs that have provided indirect support for

regional HID, or have been used for these purposes

by innovative local officials; to assess the potential

contributions of other Federal programs; and perhaps to redirect agency efforts or reallocate funding to programs that can do so most effectively while

still achieving their primary purposes. To pursue any

of these options, however, will require better information than is currently available on the role of

high-technology industry in regional economies and

the impacts of Federal policy on regional economic

change. Such information might take three general

forms:

An information clearinghouse, containing a

comprehensive and up-to-date list of State and

local initiatives, as well as Federal programs that

have supported or influenced re,:onal HTD

directly or indirectlyin ways similar to those

identified by OTA. Periodic directories or online computerized data bases could be used, for

example, but perhaps the most useful form

would be similar to the "project bank" established by the White House Task Force on Private Sector Initiatives. This would provide potential users with information on what types

of assistance are available, how they have been

used by other communities, and what results

can be expected from different combinations of

programs in different regions.

Monitoring and evaluation of Federal programs, both past and current, in order to identify those that have made (or might make) the

most significant contributions to regional HTD.

This would include, at the minimum, retrospective evaluations of the regional impacts of specific Federal programs, similar to those normally

carried out by the General Accounting Office

(GAO). In particular, the National Science

Foundation (NSF) could be encouraged to evaluate and disseminate the results of its three "experiments" in regional HTD, described in chap-
12 Technology. Innovation, and Regional Economic Development

ter 5. It might also include ongoing evaluations

of the implementation and impacts of recent

policy changes that may influence regional

HTD. Both GAO and the Department of Commerce, for example, have recently released

reports on the implementation of the Stevenson-Wydler Technology Innovation Act of

1980, although neither report directly addresses

the impacts on regional HTD.

In addition, State and local governments

would benefit significantly from comparative

analyses of two specific types: the diffe7;_ntial

impacts of the Federal programs in different regions or situations; and the effectiveness and

transferability of different types or combinations

of HTD initiatives. Analyses of both types have

been supported in the past by the Economic

Development Administration's (EDA) Economic Research Division Development Administration in the Department of Commerce, although that office currently lacks the budget

to publish a bibliography of its most recent research. NSF has recently solicited proposals for

research on the effectiveness of State and local

HTD initiatives, and should be encouraged to

disseminate the results widely to State and local

officials.

Statistical data on the structure and dynamics

of regional economies, which would be invaluable for monitoring economic changes and formulating economic policy at all levels of government, Such data are currently collected by the

Bureau of the Census and Bureau of Labor Statistics, often on the basis of information supplied by the States. However, compilations and

analyses of these data are not always available

to State and local development officials on a

timely basis or in a usable form. The results of

the 1982 census of Manufactures, for example,

will not be available until 1986; the statistical

analyses conducted for this assessment were

based on data for 1972 and 1977. Similarly,

modeling and other econometric studies of regional economies, concentrating on structural

changes and the impacts of both technological

change and public policies, would have immeasurable value for designing State and local economic development strategies. Several Federal

Reserve Banks now maintain models of their

region's economy and have made them available to local groups for planning purposes.

Providing these types of information would not

require a new Federal institution. It would probably

impose additional program costs in the form of new

reporting requirements for executive agencies, and

possibly on the recipients of Federal assistance. It

would increase costs very little in most cases. It also

has the advantage of familiarityit would work

through existing mechanisms, where lines of communication have already been established, participants are familiar with the procedures, and the costs

and benefits of the programs are (or could be) fairly

well determined. In addition, it would maintain the

variety and resulting flexibility of the existing Federal roles in regional HTD, which appear to contribute to their usefulness. Finally, this option could

be implemented quickly, in a number of different

ways, and at a number of levels, some of which

would require no change in legislation or appro.,

priation,

Improved Coordination

Federal programs have had unsystematic and indirect but important impacts on regional HTD when

existing Federal programs are utilized in conjunction with a Stare or local HTD initiative. Thus, it

might be desirable to coordinate existing Federal

programs that benefit regional HTD (in order to give

them greater coherence) and to integrate Federal efforts with HTD initiatives at the local, State, or regional levels. This would increase efficiency by

avoiding duplication and increasing leverage. It

might also prove possible to implement Federal programs through mechanisms created by individual

cities and States, or by several States with similar

needs and opportunities.

Since most of these functions already exist, it

would not be necessary to create a new Federal

agency to carry them out. Several executive branch

agencies, notably the Department of Commerce and

the Small Business Administration (SBA), are already taking steps to coordinate their efforts with

those of State and local governments. In other cases

this could be accomplished, without new authority, through increased coordination between Federal agencies at the regional level. For example, there

23
Ch. 1Overview of HighTechnology Development 13

is a natural complementarity between the research

activities of the EDA and those of the various Federal Reserve branches; similarly, much could be

gained by coordinating the complementary activities of SBA (business development), Department of

Housing and Urban Development (community and

economic development), and the Departments of Labor and Education (jobs Training Partnership Act,

vocational education, and science and mathematics

initiatives for human resource development). Alternatively, lead-agency responsibility could be assigned

to an appropriate existing agency (e.g., Commerce,

NSF).

Nor would the creation of a coordinating mechanism at the Federal level be required for regional

HTD alone. If a mechanism is created for broader,

related purposes, however, then it might be appropriate to give it this additional responsibility. For

example, these functions could be assigned to an

agency similar to or subordinate to any of the numerous mechanisms that have been proposed for

purposes of formulating and implementing a national industrial policy e.g., regional subcouncils, as opposed to (or in addition to) the sectoral subcouncils, foreseen for the proposed Council on Industrial

Competitiveness (H.R. 4360 and 4362). This mechanism might also provide a central location for

gathering and disseminating the types of information outlined above.

24

Another alternative would be the "federalization"

of regional HTD through block grants or matching funds to States and communities to support their

HTD initiatives. This option does not require the

creation of new agencies or programs at the Federal level, and the assistance need not displace existing community development programs. It could

be limited to planning grants and startup costs, or

could include ongoing support or State technical

extension services, seed capital mechanisms, and

other initiatives. This alternative would maintain

an explicitly experimental approach: it would recognize the many State and local HTD initiatives as

a testing ground, and mandate an explicit Federal

role in supporting, monitoring, evaluating, and

disseminating information on successes and failures.

By supporting the creation of many initiatives at

the State and local level, this alternative might lead

to greater innovation, variety, and specificity than

would be possible or appropriate at the Federal level.

It would also protect against the consequences of

a single, centrally determined policy or program design that turns out to be wrong-headed. The grants

could be made on a competitive basis, allowing individual areas to design what they feel to be most

needed or most effective for their economies. These

grants would probably include a strong matching

requirement to ensure both seriousness and prior

networking on the part of recipients.
CHAPTER 2

Definition and Analysis of

High-Technology Industries

25
Contents

Page Definitions of High-Technology Industry 17

Technological Innovation and Industrial Evolution 17

Definitions Used in This Assessment 18

Shortcomings of the Definitions 20

Size, Structure, and Growth of High-Technology Industry 20

Firm Size 22

Employment Growth Rates 23

High-Technology Employment Projections 23

Geographical Patterns of High-Technology Industry Location 23

Factors Influencing Patterns of High-Technology Industry Location 28

List of Tables

Table No.

Page I. Five Definitions of High-Technology Industry 19

2. Employment in the High-Technology Industries, 1972, 1980, and 1982 21

3. Employment injhree Groups of High-Technology Industries,

1972, 1980, 1982 22

4. Employment in Three Groups of High-Technology Industries,

1972, 1980. 1982 and Projected 1995 24

5. Employment Change by Type and Region for High- and Low-Technology

Manufacturing and Business Services as a Percent of 1976 Employment Within Region and Sector, 1976-80 26

6. Employment in Three Groups of High-Technology Industries in

10 States With Highest Le.,els of High-Technology Employment, 1982 Annual Averages 27

7. High-Technology Employment as a Percent of Total Nonagricultural

Employment in Top 10 States Under Three Definitions,

1982 Annual Average 27

8. SMSAs With the Highest and Lowest Percentage of Employment in High-Technology Industries, 1977 27

9. Location Factors Influencing New Manufacturing Plant 29

Llat of Figures

Figure No. Page 1. Employment in High-Technology Industries, 1972-83 23

2. Regional Shares of High-Technology (H/T) Employment and

All Industry Employment _1976, 1980 25

26
CHAPTER 2

Definition and Analysis of

High-Technology Industry

Definitions of High-Technology Industry

Technological Innovation and

industrial Evolution

The lack of a generally accepted definition of

"high-technology industries" complicates the investigation of the role these industries play in the economy and in national, State, and local development

policies. To some, the term "high-technology" refers

to a vague notion of industries involved with computers, telecommunications, electronics, biotechnology, and other emerging and rapidly evolving

technologies.

Researchers have attempted to define such industries with greater precision by classifying them

according to quantifiable criteria related to the industrial use of science and technology. Their job is

complicated by the fact that high technology itself

is constantly changing as innovations render earlier

advances obsolete. Certainly, many technologies

considered advanced 20 years agoplastics or aerospace, for instancehave matured and are now considered standard. Moreover, the structure and needs

of yesterday's high-technology industries are quite

different from what they once were, and may also

be different from those of the "new" high-technology

industries.

Thus, the dynamic nature of the U.S. economy

results from the birth, growth, maturation, and decline of various industrial sectorsand much of that

change is the result of changes in the current state

of high technology. Even if a uniform rn,-2n!ng were

somehow assigned to the term, the factors that define high technology (and any policies designed to

influence high-technology industries) must reflect the

fact that industries and technologies evolve.

This industrial evolution, described by changes

in what is considered high technology and what is

considered standard, is driven by technological innovation, the process by which society generates and

uses new products and manufacturing processes.

27

This process consists of activities surrounding the

generation, research, development, introduction,

and diffusion of new or improved products, processes, and services for both public and private uses.

What do we know about technological innovation?1

Innovation is a complex process.

Its essence is uncertainty about the outcome.

Innovation can be costly and time-consuming.

The economic and social impacts of innovations

occur through their diffusion and widespread

use.

Basic scientific research seems to underlie technological change in complex and indirect, but

important, ways.

The innovation process differs from industry to

industry.

Financial and manpower resources are necessary, but not sufficient, for innovation.

Both large and small firms play important roles

in innovation, and those roles differ from industry to industry.

Investment in industrial innovation activities

responds, like other investments, to economic

forces.

Since concepts of technological advance and innovation implicitly drive discussions of high-technology industry, quantifiable measures of innovative

capacity or behavior are needed to facilitate analysis. Since the innovative behavior of firms and industries is very difficult to measure, two traditional

measures of scientific activity are generally used as

proxies to classify industries by innovative capacity

or activity: 1) relative research and development

(R&D) spending levels, usually as a percentage of

sales; and 2) relative levels of scientific, engineerISubstance for most of these items can be found in Mary Ellen Mope,

"The Process of Technological Innovation in Industry: A State of

Knowledge Review for the Congress," in joint Economic Committee,

U.S. Congress, Study on Economic Change, Research and Development, vol. 3, 1980. See also Federal Support fbr R&D and Innovaon, Congressional Budget Office study, April 19&1.

17
18 Technology, Innovation, and Regional Economic Development

ing, and technical (SE&T) personnel in the industry's total work force. In addition, some researchers hay_ separated out fast-growing industries or

used measures of indirect R&D inputs.' These criteria are then used to rank industries, as defined by

the Standard Industrial Classification (SIC) codes,'

and lines are drawn to distinguish between those

that, on average, spend a higher proportion of their

resources on R&D ancl/or employ a higher proportion of technical workers.

Definitions Used in This Assessment

Bureau of Labor Statistics (BLS).A recent

Monthly Labor Review article presented data on

three groups of"high-technology" industries developed by BLS. These three definitions illustrate the

differences that arise from applying different criteria,

and they provide some bounds for the analysis that

follows.4

Group 1. Industries in this group employ a

proportion of technology-oriented workers

greater than 1.5 times the average for all industries, or 5.1 percent of total employment. Industries with fewer than 25,000 employees were

excluded, resulting in a list of 48 three-digit industries. Three out of four industries in this list

are manufacturing industries.

Group 2.Industries in this group display ratios

of R&D expenditures to sales greater than twice

the average for all industries, or a minimum of

6.2 percent. Nonmanufacturing industries were

excluded from this analytical cut due to data

limitations. The resultinglist included only six

three-digit industries,

Group 3.Industries in this group had to satisfy

criteria concerning both the relative R&D ex-

-

'See, for example, Lester Davis. "New Definition of HighTech Re

veals That U.S. Competitiveness in This Area Has Been Declining,"

111,ifirs, Arnerka, Oct. 18, 1982, pp. 18.23; and F. M. Scherer, "In,

terindustry Technology Flows in the United States, Research Policy

11, l982. pp. 227,245.

'SIC codes are used to classify business establishment by their types

of business activities. Industries are divided into major groups, numbered 01 through 99. Each of these groups can be divided further, with

each !eve! .f detail adding another digit to the code. For example, in

the Manufacturing Division, Transportation Equipment is represented

by SIC 37, Motor Vehicles and Motor Vehicle Equipment by SIC 371,

and Truck Trailers by SIC 3715. These levels of disaggregation are re- ferred to as the two-, three, and fourdigit SIC codes.

'Richard Riche, Daniel E. Hecker, and John U. Burgan, "High Technology Today and Tomorrow: A Small Slice of the Employment Pie,"

Monthly Labor Review, November 1983, pp. 50-58.

penditures and the proportion of technologyoriented workers. In this case, the proportion

of technology-oriented workers had to be greater than the average for all manufacturing industries (6.3 percent), and the R&D-to-sales

ratio had to be close to or above the average

for all industries (3.1 percent). In addition, some

industries were excluded based on subjective

evaluations of their major products, while two

industries which provide technical services to

manufacturing industries were added. This

group includes 28 three-digit industries.

These three definitions (referred to hereafter as

Groups 1, 2, and 3) incorporate the features most

commonly used in establishing classifications of

high-technology industries. Moreover, they provide

a range of industry groupings within which most

other definitions found in the literature will fit. The

scope of Group 1, for example, is quite broad and

represents over 12 million jobs, while Group 2 includes a very narrow range of industries and represents fewer than 3 million jobs. Group 3, the "moderate definition," represents about 6 million jobs.

By comparison, total nonagricultural employment

was almost 93 million in 1982, and employment in

the manufacturing sector was about 22 million.

Group 3 corresponds closely to two other definitions used to investigate the structure and regional

distribution of high-technology industry for this

assessment. One, used by the Brookings Institution

in conjunction with a Dun & Bradstreet data base,

includes 96 four-digit SICs and represents slightly

more jobs than Group 3.5 The other, used by researchers at the University of California at Berkeley

in conjunction with data from the Bureau of the

Census, includes 99 four-digit SICs but represents

slightly fewer jobs than Group 3.6 These definitions

are presented in table I.

'Catherine Armingron, Candee Harris, and Marjorie Odle, Forma.

non and Growth in High Technology Firms: A Regional Assessment

(Washington, DC: The Brookings Institution, under contract with

OTA), The analysis and data were prepared for the National Science

Foundation under grant No. IS1 8212970, with additional analysis pre

pared for OTA under an interagency agreement with the Small Busi

!less Administration. Original data development work was funded by

SBA contract No. 2641-0A79.

6Amy K. Glasmeier, Peter 0. Hall, and Ann R. Markusen, Recent

Evidence on HighTechnology industries' Spatial Tendencies: A Preliminary Investigation, University of California Institute for Urban and

Regional Studies, under contract with ()TA. The development of the

data base and the majority of the descriptive analysis were done under

contract with the National Science Foundation, contract No. SES

82-08104.
Ch. 2- Definition and Analysis of High-Technology industries 19

Table 1.Five Definitions of High-Technology industry

Bureau of Labor Statistics

SIC Industry 1 2 3 arookings Berkeley

131 Crude petroleum and natural gas X - - X -

1321 Natural gas liquids - - X - -

162 Heavy construction, except highway and street X - - - -

281 Industrial inorganic chemicals - - X X X

282 Plastic materials and synthetics ... X X X X

283 Drugs X X X X X

284 Soaps, cleaners, and toilet preparations X - X - X

285 Paints and allied products X - X - X

286 Industrial organic chemicals X - X X X

287 Agricultural chemicals X - X - X

289 Miscellaneous chemical products X - X X X

291 Petroleum refining X - X X X

301 Tires and inner tubes X - - - -

3031 Reclaimed rubber - - - - X

324 Cement, hydraulic X - - - -- 348 Ordnance and accessories X - X X X

351 Engines and turbines X - X X X

352 Fami and garden machinery X - - - -

353 Construction, mining, and material handling machinery X - - X X

354 Metalworking machinery X - - - X

355 Special industry machinery, except metalworking X - X - -

356 General industrial machinery X - - X X

357 Office, computing and accounting machines X X X X X

358 Refrigeration and sirvice Industry machinery X - - - -

361 Electric transmission and distribution equipment X - X - X

362 Flectrical industrial apparatus X - X X X

363 Household appliances X - - - -

364 Electric lighting and wiring equipment X - -

365 Radio and TV receiving-equipment X - X X X

366 Communication equipment X X X X X

367 Electronic components and accessories X X X X X

369 Miscellaneous electrical machinery X - X - -

371 Motor vehicles and equipment . X - - -

372 Aircraft and parts X X X X X

3743 Railroad equipment - - - - X

376 Guided missiles and space vehicles X X X X

381 Engineering, laboratory, scientific, and research instruments X - X X X

382 Measuring and controlling instruments X - X X X

383 Optical Instruments and lenses X - X X X

384 Surgical, medical, and dental instruments X -r X X X

3851 Ophthalmic goods - - - - X

388 Photographic equipment and supplies X - X X X

3872 Watches, clocks - - - X -

483 Radio and TV broadcasting X - - - -

489 Communication services, n.e.c X - - - -

491 Electric services X - - - -

493 Combination electric, gas, and other utility services X - - - -

506 Wholesale trade, electrical goods X - - -

508 Wholesale trade, machinery, equipment, and supplies X - - -

737 Computer and data processing services X - X X -

7397 Commercial testing laboratories - - - X -

7391 Research and development laboratories ., X - X X -

891 Engineering, architectural, and surveying services X - - - -

892 Noncommercial educational, scientific, and research

organizations X - .... X -

1980 employment total (millions) 12.6 2.5 6.2 6.7 4.8a

81977 data.

NOTE: The Berkeley and Brookings deflnitlons do not include employment in all four -digit SIC codes included In the three -diglt SIC codas listed,

SOURCE. Wort, ly Labor Review, November 1953; apps. S and C of this sport.

29
20 Technology, Innovation, and Regional Economic Development

Shortcomings of the Definitions

As discussed above, these definitions attempt to

capture aspects of the technological innovation process. Although the measures employedrelative

R&D spending or SE&T employment of industriesallow analysis, they are imperfect proxies, so

the definitions are less than ideal. The definitions

share several characteristics that affect their usefulness. Some of these are discussed here.

Standard Industrial Classifications.The lists

consist only of SIC codes, not individual firms or

establishments. While the industries on the list share

the relatively high reliance on R&D and SE&T

workers, they are far from homogeneous. Moreover,

the firms included in any 4particular SIC code can

vary in size, structure, and other characteristics that

influence their role in and their use of the technological innovation process. Furthermore, the criteria

are applied to industry averages, not firms. Therefore, not every firm in each industry class on the

list satisfies the criteria, although they are more likely

to than firms in industries not on the list. The lists

simply reflect groups of firms that, while sharing a

common product and together satiisfying certain criteria, can be quite different.

Product vs. Process Distinctions.Second,

since the SIC codes are product-oriented, the lists

are too. Within this framework, therefore, the narrower lists exclude some industries whose products

are not considered high-technology, and do not

spend a lot on R&D relative to their sales, but which

may nevertheless rely heavily on high-technology

processes or inputs. The agricultural and forestry

industries, for example. rely heavily on new chemicals and innovative production techniques, yet are

excluded from the lists. Similarly, the shoe and textile industries are modernizing their production processes with computer-aided design and manufacturing (CAD/CAM) equipment. To a large extent,

however, these process improvements represent the

outputs of other industries; developing a consistent

list that includes both the producers and users of

advanced-technology products and processes might

require different criteria from those used for the producers and would pose some very difficult problems.

Moreover, such lists would still suffer from aggregation problems similar to those discussed above

the firms within each SIC code are not homogeneous, and differences between firms' use of technology would be obscured.

Service Sector.Another important definitional

issue concerns recognition of a number of innovative

or "high-technology" firms in the service sector. The

production of computer software, for instance, is an

innovative, high-growth, and technology-driven

sector, yet it remains camouflaged in SIC 737, computer programming services. Perhaps parts of the

software industrylike segments of the printing

industrywould be more appropriately classified in

the high-technology manufacturing sector. Further,

the relationships between the service industries and

other industries tend to be lost when c!,.ssifying industries as above. To the extent that many service

companies can be considered extensions of firms

they support, their employment might be appropriately credited to the supported industries. What may

appear as a small definitional issue thus may have

important implications for comparing growth rates

between the manufacturing and service sectors, and

the producers and users of technology.

Size, Structure, and Growth of High-Technology Industry

The employment level in the so-called "high-technology" industries varies substantially depending on

the definition chosen. Table 2 shows the degree of

variability in size and growth for individual industries, while table 3 illustrates the effects of definitions by comparing the BLS groups to national employment levels. Table 3 shows that in 1982, of the

30

92 million wage and salary workers in the United

States, 2.5 million were employed in the industries

in Group 2, 5.7 million in Group 3, and 12.3 million in Group 1. Even the broadest definition

(Group 1), which includes numerous nonmanufacturing industries, accounts for less than 14 percent

of all wage and salary workers. While that percent-
Ch. 2-Definition and Analysis of High-Technology Industries 21

Table 2.-Employment in High-Technology Industries, 1972, 1980, and 1982 (In thousands)

High-technology

groups Employment Percent change

SIC industry 1 2 3 1972 1980 1982 1972-80 1972-82

131 Crude petroleum and natural gas X 139.3 219.6 281.7 57.7 102.2

162 Heavy construction, except highway and street X 495.1 858.5 633.9 33.0 28.1

281 industrial inorganic chemicals X X 141.2 161.1 153.5 14.1 8.7

282 Plastic materials and synthetics X X 228.7 204.8 182.7 -10.0 -20.1

283 Drugs X X X 159.2 196.1 199.8 23.2 25.5

284 Soaps, cleaners, and toilet preparations X X 122.4 140.9 245.3 15.1 18.7

285 Paints and allied products X X 68.6 65.1 59.7 -5.1 -13.0

286 industrial organic chemicals X X 142.8 174.1 174.3 21.9 22.1

287 Agricultural chemicals X X 56.4 72.0 67.1 27.7 19.0

289 Miscellaneoui chemical products X X 90.0 93.3 91.5 3.7 1.7

291 Petroleum refining X x 151.4 154.8 169.0 2.3 11.6

301 Tires and inner tubes X 122.1 114.8 101.9 6.0 -16.5

324 Cement, hydraulic X 31.9 30.9 28.5 -3.1 -10.6

348 Ordnance and accessories X X 81.9 63.4 71.4 -25.6 -12.8

351 Engines and turbines X X 114.8 135.2 114.8 18.0 0.2

352 Farm and garden machinery , X 135.0 169.1 130.8 25.3 -3.1

353 Construction, mining, and material handling

machinery X 293.7 389.3 340.9 32.6 18.1

354 Metalworking machinery X 286.0 373.1 320.3 30.5 12.0

355 Special industry machinery, except metalworking X X 176.9 207.3 179.4 172 1.4

356 General industrial machinery X 267.5 323.7 283.2 21.0 5.9

357 Office, computiniand accounting machines X X X 259.6 432.2 489.7 66.5 88.8

358 Refrigeration and service industry machinery X 164.4 174.2 161.3 6.0 -1.9

361 Electric transmission and distribution equipment X X 128.4 122.5 110.1 -4.6 -142

362 Electrical industrial apparatus X X 209.3 239.9 211.8 14.6 1.2

363 Household appliances X 186.9 r163.2 142.0 -12.7 -25.0

364 Electric lighting and wiring equipment X 204.4 209.2 186.9 2.4 -8.8

365 Radio and TV receiving equipment X X 139.5 108.8 94.6 -22.0 -32.2

366 Communication equipment X X X 458.4 541.4 555.7' 18.1 212

387 Electronic components and accessories X X X 354.8 553.8 568.7 56.0 60.3

369 Miscellaneous electrical machinery X X 131.7 152.7 141.3 15.5 7.3

371 Motor vehicles and equipment X 874.8 788.8 690.0 -9.8 -21.1

372 Aircraft and parts X X X 494.9 652.3 611,8 31.8 23.6

376 Guided Missiles and space vehicles X X X 92,5 111.3 1127.3 20.3 37.5

381 Engineering, laboratory, scientific, and research

instruments X X 64.5 76.8 75.7 19.1 17.4

382 Measuring and controlling instruments *X X 159.8 245.3 244.3 53.7 53.1

383 Optical instruments and lenses X X 17.8 33.0 32.5 87.5 84.7

384 Surgical, medical, and dental instruments X X 90.5 155.5 160.4 71.8 77.2

386 Photographic equipment and supplies X X 117.1 134.8 138.3 15.0 18.1

483 Radio and TV broadcasting , X 142.7 199.6 216.4 39.9 51.6

489 Communication services, n.e.c.b X 29.7 .1 91.0 122.6 206.4

491 Electric services X 312.0 391.0 415.1 25.3 33.0

493 Combination electric, gas, and other utility services X 183.4 196.7 1 .4 7.3 8.2

506 Wholesale trade, electrical goods X 3312 421.4 434.9 272 31.3

508 Wholesale trade, machinery, equipment, and

supplies X 888.6 1,307.7 1,344.9 50.6 54.8

737 Computer and data processing services X X 106.7 304.3 357.5 185.2 235.1

7391 Research and development (aboratories X X 110.7 163.1 162.7 47,3 47.0

891 Engineering, architectural, and surveying services . X 339.3 544.9 568.7 60.1 67.8

892 Noncommercial educational, scientific, and

research organizations X 111.8 113.5 117.8 1.5 5.4

&Group 1. inctuties industries with a proportion of technology-orientad workers (engineers, life and physical scientists, mathematical special!

technicians, and computer specialists) at least 1 -5 times the average for all industries.

Group 2. includes industries with a ratio of R&D expenditures to net sales it West twice the average for all Industries.

Group 3. includes manufacturing Industries with a ProPortion 'of technokstotionted workers equal to or greater than the average for all

and a ratio of R&D expenditures to sales close to or above the average for all industries. Two nortminuficturing Industries which provide

technology manufacturing industries also are included.

bNot elsewhere classified.

SOURCE: Monthly Labor Ravi**, November 1463.

sit, engineering and science

31

manufacturing Industries,

technical support to high.
22 Technology, Innovation, and Regional Economic Development

Table 3.Employment in Three Groups of High-Technology Industries, 1972, 1980, 1982 (In thousands)

Employment Percent change

Employment grouping 1972 1980 1982 1972-80 1972-82

All wage and salary workers 76,547.0 02,811.2 91,950.1 21.0 20.1

Group 9,989.7 12,550.1 12,349.8 25.6 23.6

Percent of total employment 13.1 13.6 13.4

Group 2 1,819.4 2,486.9 2,543.0 38.7 39.8

Percent of total employment 2.4 2.7 2.8

Group 3 4,468.9 5,894.8 5,891.1 27.4 27.3

Percent of total employment 5.8 6.2 62 .1

SOURCE: BMW of Libor Statistics.

age is not insignificant, Group I includes many industry groups generally not considered producers

of high-technology goodsheavy construction,

motor vehicles, electric services, farm machinery,

and wholesale trade in machinery and supplies, for

example.

At the other extreme, Group 2 accounts for only

2.8 percent of the work force, but it excludes R&D

laboratolics, optical instruments, and many of the

chemical-related industries. Finally, Group 3, falling between Groups 1 and 2, accounts for 6.2 percent of the work force. This definition is very similar to definitions commonly used by researchers and

those on which much of die statistical analysis for

this assessment was based.

These groups exclude some industries that might

otherwise be considered high-technology, include

some unexpected industries, and in general can be

changed with minor modifications to the criteria.

The distinction between high- and low-technology

industries may also ignore the interdependence of

these sectors.

In 1982, for example, U.S. business devoted onehalf of its capital spendingsome $56 billion in 1972

dollars to computers, instruments, electronics and

communications equipment, up from one-third in

1977 and one-quarter in 1972.7 Basic industry is an

important customer for the products and processes

developed and marketed by the so-called high-technology industries. Indeed, a steel plant that invests

in advanced process technologies, modern instrumentation and control systems, and state-of-the-art

materials-handling equipment is a high-technology

undertaking. Some of the differences between hightechnology industries and others may be worth

1" Shackles on Growth in the Eighties," Fortune, (Xt. 4, 1982.

noting, howeverthe size of firms and the employment growth rates, in particular.8

Firm Size

High-technology industries are composed of firms

that are on average larger, and rely more on branch

facilities, than those in low-technology manufacturing and service industries.9 Analysis by the Brookings Institution shows that such establishment's

employ an average of 69 employees, while those in

low-technology manufacturing and service industries

employ an average of 32, and those in other industries, an average of 13. Eighty-eight percent of employment in the high-technology industries is found

in firms with more than 100 employees. By contrast,

72 percent of employment in low-technology manufacturing and service industries and 58 percent in

other industries is found in these larger firms. Further, compared with 60 percent of all private jobs,

percent of employment in the high-technology

industries is found in multiestablishment firms.

Finally, while about one-third of jobs in low-technology manufacturing and service industries are

found in firms' out-of-State affiliates, such affiliates

provide half of the jobs in the high-technology industries.°

'Unless otherwise noted, data in this chapter concerning the structure and location patterns of high-technology industries come from

the work of Armington, Odle, and Harris, at the Brookings institution and Glastneler, Markusen, and Hall, at University of CaliforniaBerkeley.

"Low-technology manufacturing and service industries" are those

manufacturing and service industries that do not satisfy the criteria

for inclusion in the "high-technology" list. "Other industries" refers

to those not considered manufacturing or service industries, such as

agriculture, construction, wholesale and retail trade, and some mining, for example.

10Arrnington, et al., op. cit.
2-0.finition d Analysis of 1419101pchnology witistOes 23

Employment Growth Rates

Another difference between these industries concerns employment growth rater,. Regardless of which

of the three BLS definitions is considered, the aggregate employment growth rates of these groups

were higher than that of total wage and salary employment during .he periods 1972.80 and 1972-82

(see table 3). Like that in most other industries, the

growth in the listed industries was neither continuous nor steady. Some of the industries included

in the listings had cyclical employment losses, especially apparent during the 1974-75 recession. Because

the industries are a relatively small percentage of

,total employment, the magnitude of changes is also

relatively small. For example, during that period,

employment in Group 2 and Group 3 industries fell

by only about 200,000 and 500,000 respectively. Figure 1 shows the employment trends of each group

of industries from 1972 to 1982.

Figure 1.Employment In High-Technology

Industries, 197243

10,600.0

9,600.0

8,600.0

43 7,600.0

6,600.0

5,600.0

4,600.0

3.600.0

2,600.0

1,600.0

1972 1974 1976 1978 1980 1982

Years

SOURCE Bureau of Labor Statistics

High-Technology

Employment Projections

Although high-technology industries grew, in the

aggregate, faster than other groups of industries, they

will prollably provide approximately the same proportior of jobs in the future. Projecting future

employment trends is beyond the scope of OTA's

assessment. An overriding difficulty comes from a

basic inability to see job types and industries that

simply do not exist yet. Complicating this is uncertainty about the impact of automation on employment in basic industries and the ability of hightechnology industries to provide jobs for displaced

blue-collar workers. Also unknown is how many

jobs will go to lower cost labor markets in foreign

countries as high-technology production gears up.

Although projections are fraught with uncertainty, the Department of Labor's Bureau of Labor Statistics prepares employment projections of roughly

12 years. The latest BLS projections of moderate,

high, and low growth extend through 1995. For each

of the three groups defined previously and using

either 1980 or 1982 as a base, high-technology

employment is projected to grow somewhat faster

than total wage and salary under all three growth

alternatives. As table 4 shows, the size of the total

pool of wage and salary workers is projected to grow

by between 26 and 31 percent between 1982 and

1995, depending on economic conditions projected.

Although employment in the high-technology industries is projected to grow by between 32 and 39

percent, depending on scenario and definition, the

proportion of the work force in the industries remains small."

"For Group 2, the low growth alternatives shows higher 1995 employ

ment than the moderate alternative. This is because higher defense

spending is assumed in the low alternative than in the moderate alter

native, and Group 2 has a high proportion of its employment in three

defense- related industries; communications equipment, aircraft and

parts, and guided ,rnifsilcs and space vehicles.

Geographical Patterns of High-Technology

Industry Location

Although a few regions maintain a reputation for

large concentrations of high-technology industries,

these industries have been dispersing throughout the

Nation, along with the population and other manufacturing activity. This spreading of high-technology industries holds promise for areas that have not
Table 4.-Employment In Throe Groups of tfigb-Tactatoloity Industries, 1972, 1988, 1982 and Projeotod 1995 On thousands)

'Employment grouping

Employment

Projected 1995 employment

alternatives 1980.95

Percent change

1962-95

1972 1980 1982 Low Moderate High Low Moderate High Low Moderate High

Al! wage and Ulan/

76,547.0 92,611.2 91,950.1 115,382.9 117,744.9 120,531.1 24.6 27.1 30.1 25.5 28.1 31.1

__workers

7-Group 1 9,989.7 12,550.1 12,349.6 16,260.7 16.612.9 18,931.6 29.6 32.4 34.9 31.7 34.5 37.1

Percent of total s employment t3.1 13.8 13.4 14.1 14.1 14.0 - mMi *MIN Group 2 1,819.4 2,466.9 2,543.0 3,517.5 3,409.6 3,452.9 41.4 37.1 38.8 38.3 34.1 35.8

Percent of total

employment 2.4 2.7 2.8 3 0 2.9 2.9 - - TM* Group 3 4,468.9 5,694.8 5,691.1 7,746.6 7,719.8 7,890.0 38.0 35.6 38.5 3P.1 35.6 38.6

.Percent of total

employment 5.8 6.2 6.2 6.7 6.6 6.5 - - - _.

SOURCE: Bureau of Labor Statistics.

34

,

I
Ch. 2Definition and Analysis of High- Technology Industries 25

previously benefited from HTD. Researchers at Univeriity of California-Berkeley have found that while

some high-technology industries contracted during

the 1970's, most often became more dispersed. On

average, they appeared in 18 percent more counties in 1977 than in 1972.

Regions.Analysis by the Brookings Institution

reveals that the regional distribution of high-technology industry employment roughly parallels that

of total nonagricultural employment. The Northeast and perhaps the West have higher proportions

of high-technology employment, and tLe South a

lower one, ;dative to the distribution of total

employment (fig. 2). ikirther, more than two-thirds

of the high-technology employment in the South

is found in branches of out-of-State firms, while the

other regions average less than half. Of the fourregions, however, the South also had the highest net

rate of employment growth in these industries; the

West had the second highest rate of employment

growth, followed by the North Central and the

Northeast.

In general, the trend indkntes a convergence of

the regional shares of high-technology employment.

Further, in all regions, employment growth from the

creation of new establishments and the expansion

of existing businesses was higher for high-technology

industries than for other industries (table 5). These

regions are quite broad, however, and within them,

the distribution and growth patterns of hightechnology industries can vary greatly.

States.Once again, definitional issues complicate analysis ofthe distribution of these industries.

First, the definition affects the rankings because individual industries are not evenly distributed: a State

with a large concentration of a particular industry

would be affected by whether that industry is inFigure 2.Regional Shares of High-Technology (11/T) Employment and Ali industry Employment 19781 1910

34

32

30

28

26

24

22

20

18

18

14

12

10

8

8

4

2

0 fr

I. Northeast North Central

SOURCE. Office of Technofogy Assessment with Brookings InstitutiOn date.

35

South West
26 Technology, innovation, and Regional Economic Development

Table 5.- Employment Change by Type and Riskin for High- and LowTechnology Manufacturing and Business

Services as a Percent of 1978 Employment Within Region and Sector, 1978-80

Net Formations Expansions Contractions Closures

U.S. Total:

High 19.4% 23.8% 24.6% -11.3% -17.8%

Low 11.7 19.0 21.0 -10.5 -17.7

Northeast:

High 7.5 16.3 21.3 -13.7 -16.4

Low 4.3 14.2 18.9 -11.6 -17.2

North Central:

High 12.5 19.6 19.2 -10.4 -15.9

Low 8.5 16.3 18.6 -10.5 -15.8

South:

High 34.1 34.2 27.9 -9.6 -18.5

Low 15.8 22.5 22.3 -10.0 -19.0

West:

High 29.3 28.4 33.5 -10.9 -21.7

Low 25.0 26.8 27.8 -9.6 -20.0

SOURCE: Th Brookings instituUon.

cluded.'2 Rankings also vary according to the choice

of absolute or relative levels of high-technology

employment. Using the BLS Group 3 definition, for

example, the 10 States with the highest number of

workers in high-technology industry in 1982 accounted for 60 percent of all high-technology employment; the top five States accounted for 41 percent, a percentage that has remained fairly level since

1975 (table 6)."

The list of States with the highest percentage of

workers employed in high-technology industries,

however, is dramatically different. Table 7 shows

that high-technology industries represent a high percentage of the Delaware work force under Groups

I and 3, which include the chemical industry, but

not under Group 2. California, on the other hand,

ranks not higher than fifth under even the narrowest

list. In general, the top 10 States are dominated by

the New England region, where high-technology industries hold a large share of a proportionately large

manufacturing sector.

Metropolitan Areas. -Just as the national and

regional data hide some patterns apparent at the

State level, high-technology concentrations can vary

.':For example, Ohio and Illinois fall within the top five States in

hightechnolcsgy employment, if the BLS Group 1 definition (the

broadest definition, accounting for 14 million jobs) were used. This

can probably be attributed to the inclusion of motot vehicle and in

dustrial machinery industries, among others. If those industries are ex

chided under the more restrictive Group 3 definition (accounting for

about 6 million jobs), Illinois drops to seventh place and Ohio falls

out of the top 10. Set' Riche, et al., op. cit.

"Riche, et al., op cit., p. 57.

36

within States and may have a major impact on local

economies, even in a State not dominated by hightechnology industries. Analysis by University of

California-Berkeley researchers shows that the percentage of an area's labor force employed in hightechnology industries in 1977 varied from almost

20 percent to less than 0.05 percent for the 277

Standard Metropolitan Statistical Areas (SMSAs),

with a median of 4.4 percent. Table 8 lists the

SMSAs with the highest and lowest ratios for 1977.

Only one of the top 10 SMSAs-San Jose, in the

heart of California's Silicon Valley, is in a top 10

State under the comparable BLS Group 3 definition. A high ratio of high-technology employment

is not, however, a guarantee of prosperity: of the

50 SMSAs with the highest ratios in 19,77, 12 had

high-technology employment losses during the

period 1972-77.14

Although the evidence points to growth of hightechnology industries throughout the country, recent data from the BLS indicates that most hightechnology employment remains in the largest metropolitan areas. In California, Texas, and Michigin,

for example, 91 percent, 75 percent, and 79 percent,

respectively, of the high-technology jobs are found

in the each State's five largest SMSAs. In general,

this trend is similario that found for all manufacturing, 64 to 74 percent of which is located in

metropolitan areas."

14(.11asmeier, et al., op. cit.

'sRiche, et al., op. ca., p 56.
Ch. 2-Definition and Analysis of HighTechnology industries 27

Table 6.- Employment in Three Groups of High-Technology Industries in 10 States With Highest Levels of

High-Technology Employment, 1982 Annual Averages (in thousands)

Group 1 Group 2 Group 3

Total United States 13,038.3 Total United States 2,633.7 Total United States 5,943.4

Top 10 States 7,489.5 Top 10 States 1,737.4 Top 10 States 3,566.6

California 1,527.5 California 610.6 California 933.1

Texas 1,068.4 New York 205.3 New York 493.4

New York 924.0 Massachusetts 160.7 Texas 372.0

Ohio 683.0 Texas 157.6 New Jersey 316.8

Illinois 672.0 New Jersey 116.9 Massachusetts 305.5

Michigan 651.0 Florida ' 108.1 Pennsylvania 277.0

Pennsylvania 615.4 Connecticut 98.5 Illinois 261.5

New Jersey 521.7 Illinois 96.2 Ohio 247.8

Massachusetts 450.0 Pennsylvania 93.3 Connecticut 185.8

Florida 378.5 Washington 90.2 Florida 173.7

Share Share Share

Top 10 57.4% Top 10 66.0% Top 10 60.0%

Top 5 37.4 Top 5 47.5 Top 5 40.7

'Because fourth-quarter 1982 data wire not avaltsbia at the time of publication, a 9-mon

SOURCE: Bureau of Labor Statistics.

th veraCe wail used.

Table 7. -High- Technology Employment as a Percent of Total Nortagricultoral Employment in Top 10 States

Under Three Definftions,1982 Annual Average

Group 1 Group 2 Group 3

Total United States 13.4 Total United States 2 8 Total United States 6.2

Delaware 24.0 New Hampshire 7 2 Delaware 16.2

New Hampshire 21.0 Vermont 7 0 Connecticut . 13.0

Michigan 20.4 Connecticut 6 9 New Hampshire 12.5

Connecticut 20.3 Arizona 6 8 Vermont 11.7

Vermont 18.9 California .6.2 Massachusetts 11.7

Indiana 17.8 Massachusetts 61 New Jersey 10.3

Massachussetts 17.2 Washington 5 7 California 9.5

Texas 17.0 Kansas 4 7 Arizona 9.0

New Jersey 16.9 Utah 4 2 Washington 8.2

Kansas 16.5 Colorado 3 9 Kansas 7.8

Ohio 16.5

a9 -month warns

SOURCE: Bureau of Labor Stalisttcs

Table 8.-SMSAs With the Highost and Lowest Percentage of Employment in High-Technology industries, 1977

Highest percentage Lowest percentage

Rockford, IL 19.6 Killeen, TX a MelboumeTitusville, FL 18.0 Columbia, MO

Wichita, KS 17.7 Grand Forks, ND-MN a San Jose, CA 17.4 Pueblo, CO 0.1

Binghamton, NY 15.2 Anchorage, AK 0.1

Lake Charles, LA 14.6 Clarksville, TNKY 0.2

Cedar Rapids, IA 14.6 Honolulu, Hi 0.2

Bloomington, IN 12.9 Great Fails, MT 0.3

Johnson City, TNWV 12.8 McAllen Pharr, TX 0.4

Longview, TX 12.7 Laredo, TX 0.4

Median-4.4

iess Mn t 0.1 percent.

SOURCE: Glasmaiar, et ai

37
28 Technology, innovation, and Regional Economic Development

Factors Influencing Patterns of

High-Technology Industry Location

Some aspects of traditional business location

theories can be applied to the location decisions of

high-technology industries, but statistical evidence

is sketchy. Firms and industries choose to locate in

certain areas for a variety of complex reasons. Regional economic lifecycle theories suggest that each

community has different needs and can offer different resources to firms. Similarly, each industry and

every firm requires its own mix of resources from

its location, and offers its unique mix of benefits and

impacts. Moreover, these needs and impacts change

as firms, industries, and communities evolve.'6

In general, factors affecting location of busintsses

have been separated into two types: 1) those relating

to the costs of moving materials, products, people,

and ideas; and 2) those relating to the attributes of

areas. Traditionally, with industries relying on large

quantities of bulky raw materials, or the transportation of heavy products, the transportation costs

weighed heavily. To the extent that the high-technology industries are different from basic industriesin that they rely less on heavy raw materials

and produce goods that are costly relative to their

weighttheir bulk transportation needs may be considered less important than the need to attract and

transport people. Similarly, there may be some distinctions in the need for skilled or unskilled labor,

energy use, ability to pay taxes, or in any of the other

traditional location factors.

The results of two surveys that point to these differences and.the complex nature of location decisions are shown in table 9. The first part of the table

shows that the factors considered important by hightechnology and non-high-technology plants are similar, although perhaps weighted differently. The second part of the table indicates that the search for

a high-technology plant location really consists of

two searchesfirst for a broad region, then for a

site within the regionand that the factors may vary

between stages. Moreover, not only can the factors

influencing location decisions vary by the kind of

ijohn Revs and Howard Stafford, High-Technology Location and

Regional Development: The Theorrnad Base, OTA contractor paper,

May 1483.

firm and the stage of the site selection process, but

they also depend on the type of facility, and whether

the facility is an expansion or relocation of an existing firm or a new business formation.'7 Accurate

generalizations about the factors that influence the

location of high-technology firms and facilities are

clearly difficult to make.

Statistical evidence to document the relationships

between characteristics of communities and hightechnology development from sources other than

surveys remains quite sketchy. The Brookings and

Berkeley studies show that, although data limitations and aggregation problems plague such analysis, statistical analysis can reveal some relationships.") For the most part, however, the correlations

were weak. Brookings tested 13 independent variables against business formations and employment

growth for both high- and low-technology industries in a sample of 35 SMSAs for the period 1976-

80. Berkeley tested 19 independent variables in 219

SMSAs against three measures of HTD for the period 1972-77.: ratio of high-technology to total employment, change in high-technology employment, and

change in the number of high-technology plants.

In general, these analyses "explained" differences

in high-technology formations (new establishments)

better than differences in high-technology dependence (share oc local employment) or high-technology

employment gmwth. None of these patterns was explained very well, however, and the resulting correlations cannot be considered statistically strong.

Data aggregation problems hindered the analysis,

but the lack of clear results also points to the complicated nature of industrial location decisions. Correlations from available data indicate that the following community characteristics are significantly

associated with HTD:

Population growth during the previous 5 years,

an indicator of general attractiveness and growth

potential, had a strong relationship to formations and employment growth in both the highand low-technology manufacturing sectors.

"Ibid.

l'Artnington, et al., op, cit.; and Glassmeier, et al., op. cit.

38
Ch. 2Definition and Analysis of High-Technology Indust:1es 29

Table 9.Location Factors Influencing New Manufacturing Plants

sign-technology and non-high-lechnoiogy plants

Rank High-technology plants Non-high-technology plants

1 Labor

2 Transportation availability

3 Quality of life

4 Markets access

5 Utilities

6 Site charactiristics

7 Community characteristics

B Business climate

9 Taxes

10 Development organizations

Labor

Market access

Transportation availability

Materials access

Utilities

Regulatory practice

Quality of life

Business climate

Site characteristics

Taxes

SOURCE: H. A. SUMO* Slimy of 106 Plante, 193.

High-technology plants

Rank Selection of region Selection within region

1 Leior siciilslairaiiabillty

2 Lab ,r coats

3 Tax kilimale within region

4 Acad,mlo institutions

5 Cost of living

6 Transportation

7 Markets access

8 Regional regulatory practices

9 Energy costatavallability

10 Cultural amenities

Labor availability

Statellocal tax structure

Business climate

Cost of propertylconstruction

Transport availability for people

Ample area for expansion

Proximity to good schools

Proximity to amenities

Transport facilities for goods

Proximity to customers

SOURCE: Joint Economic Committee., U.S. COnaliill. location of High To:boob& Finns and Ragionai Eoprtomia Dovalopinent,

June 1. 1202. tables 01.5 and f1. PP- 72 sad 25.

Proportion of the labor force in technical occupations was strongly related to high-technology

formations and even more strongly to formations of tiny high-technology establishments (a

statistical proxy for new spinoff and startup

firms), but not to low-technology manufacturing or other formations. Larger city sizes and

larger labor forces appear to be correlated highand low-technology formations alike.

High rates of population growth probably represent an amalgam of characteristics that seem to make

an area attractive for migration of people and businesses in general. The higher percentage of technical workers in an area could represent a variety of

situations, from an existing concentration of facilities employing such workers to influences of colleges or universities. Other evidence suggests that

higher per-capita levels of Federal defense spending

go hand in hand with high-technology industries.

This may simply reflect the fact that the definitions

of high-technology industry tend to be weighted by

defense-oriented industries.

Only more disaggregated regional and industrial

data would allow more convincing statistical evidence of the relationship between specific community characteristics and high-technology development. Whether such analysis would ever really be

convincing, however, remains unclear, because of

the complex nature of both economic development

and multifaceted characteristics 'of technology-based

industry. In addition, even when correlations between certain factors and high-technology development are revealed, establishing causal relationships

is very difficult. Finally, since the available data are

dominated by large, multiestablishment firms, analysis of high-technology location patterns focus implicitly on factors that influence the location of

branch facilities. The patterns and factors for the

creation of new firms may be slightly different, but

they are overwhelmed by data on the branching of

existing firms.
CHAPTER 3

The Roles of Entrepreneurship

and Venture Capital in HighTechnology Development
Contents

P;q.ze

Introduction . 3 3

I iigh--Technology Complexes 3 i

Overview 13

Technological Infrastructure and Agglomeration 34

Small Firms and Innovation 35

Outlook for Other Regions

Entrepreneurship and High-Technology Development 37

Factors in the Promotion of Entrepreneurship 37

Entrepreneurial Networks 39

Venture Capital and Regional High-Technology Development 41

Role and Structure of the Venture Capital Industry 41

Recent History of the Venture Capital Industry 42

Fa, tors Influencing Regional Distribution of Venture Capital 43

Re,rent Patterns of Venture Capital Investment Activity 45

Venture Capital Gaps and Efforts to Fill Them 45

Venture Capital Sources 46

Venture Capital Recipients 47

Interregional Flows 48

Fiiture Trends in Venture Capital Distribution 50

List of Tables

rdbit, l'age

10. Estimated Organized Venture Capital Pool, 1982 42

I I. Commitments to Independent Private Venture Capital Firms 43

12. Sources of Venture Capital Investment, by Region, 1980-82 46

1 3. Leading Sources of Venture Capital, by Stlte, 1980-82 47

14. Distribution of Venture Capital Investments and Companies Financed,

by Region, 1980.82 48

15. Regional Flow of 1982 Venture Capital Investment 49

ft. Leading Venture Capital Recipient States, 1980.82 49

List of Figures

hgctrt.. Page

3. Fairchild Begat Tree (1957-1970) 38

4. Profile of a Company Startup by Venture Capital 42

41
CHAPTER 3

The Roles of Entrepreneurship and Venture

Capital in High-Technology Development

introduction

What role is played by new high-technology firms?

What are the conditions that lead to their creation

and survival? The large, expanding firms whose

branch plants appear to drive so much of high-technology development (HTD) were once small and

young, after all, and the success of California's

Silicon Valley and Massachusetts' Route 128 are

based in part on their role as "seedbeds" for these

new startups. In addition, research indicates that

small, new firms play an important role in the processes of technological innovation, new product development, and job creation)

This chapter begins with an examination of existing high-technology centers and the theoretical ex-

'See for example Louis C,. Tornatzky, et al., The Process of Technological Innovation; Reviewing the Literature (Washington, DC; National Science Foundation, May 1983).

planations for these geographic concentrations of

innovative activity. The combination of conditions

that make them so productive of new technologies

and new firms constitutes what has variously been

called the "technological infrastructure," "a omeradon effect," or "entrepreneurial network." There follows a general consideration of entrepreneurship,

the role it plays in the creation of new "high-technology firms, and the reasons it flourishes in existing

centers of HTD. One of the most important reasons may be a relative abundance of venture capital in those regions. Thus, the chapter concludes

with an examination of the special role of venture

capital in high-technology entrepreneurship, the

growth and structure of the venture capital industry, and recent trends in the availability of venture

capital in different regions.

High-Technology Complexes

Overview

Certain regions appear to offer a more promising

environment. for new technology-based businesses

than others. The patterns identified by recent research suggest, for example, that while high-technology growth in the South is dominated by the

formation of new branches by multistate firms,

growth in the Northeast and West is driven by the

formation of independent firms and local branches.'

The economic theories reviewed in chapter 2 sug-

'Catherine Armington, Carrier Harris, and Marjorie Odle, Formation and Growth in High Technology Farm.: A Regional Assessment

(The Brookings Institution, Washington, DC, under contract with

OTA). The analysts and data were prepared for the National Science

Foundation under grant No. 1St 8212470 with additional analysis prepared for (TA under an interagency age menu with the Small Business Administration, Original data development work was funded by

SBA contract No. 2h41.0A-79,

42

gest that this results from a "regional economic

development lifecycle." That is, the initial concentration of high-technology firms reaches a self-sustaining "critical mass," at which time sufficient external economies develop to ensure the further

growth of HTD.

R.;:search on tiie origins and growth of existing

high-technology complexesthe "archetypes" of

Silicon Valley and Route 128suggests that these

HTD success stories are essentially spontaneous and

idiosyncratic.' That is, their emergence was caused

_ _ . ....._. I(istories and tomparatire analyses of Silicon Valley and Route 128

an be found in the following sources: Peter 3. Brennan. "Advanced

Technology Center, Santa Clara Valley, California," Scientific American, vol. 244, No. 3, March 1981, pp. SC-1-10; Nancy S. Dorfman,

"Route 128; The Development of a Regional High Technology Center," Research Policy. vol, 12, 1983, pp. 299.31f); see also her Nfiksa

chusetts' High Technology Boom in Perspective: An investigation of

Irs Dimensions, Causes and the Role of New Firms, CPA 82-2 (Cam33
34 Technology, Innovation, and Regional Economic Development

by the fortuitous confluence of technological opportunity, created by fundamental advances in

microelectronics that opened up a wide range of innovations and potential applications, with the preexisting socioeconomic conditions in each region. In

both cases, furthermore, a critical catalytic role was

played by a particular person, firm, or institution.

The result was cumulative, leading to lomeration economies that tended to enhance the region's

resources and encour3e high-technology entrepreneurship , In both of these regions, growth was

driven by local startups and spinoffs from firms

already in the area.

Many of these conditions cannot be replicated

elsewhere, at least in electronics, because the regions

that have already developed such complexes provide a comparative advantage in the creation of advanced products and new high-technology firms.

Some of the conditions can be replicated, however,

particularly those involving components of the technological infrastructure such as informal communication networks and institutional cooperation. In

addition, the negative impacts of increasing concentration, combined with the maturation of the technologies themselves, have led to the dispersion of

these boom industries out of their original seedbeds.

This creates opportunities for additional communities to attract production facilities, on which a

local scientific and technical base can be built. Furthermore, simild opportunities are being opened by

advances in other technologies, such as robotics and

biotechnology. Finally, the applications of new products and manufacturing technologies also create new

oppe .unities for declining industries and the regions

in which they are concentrated.

Technological Infrastructure

and Agglomeration

The various resourcesscientific, financial, human,

and institutionalon which HTD depends constibridge, MA: Center for Policy Alternatives, Massachusetts Institute

of TeL 1982), Robert Premus, Location of High Technology

Firms and Regional Economic Development. staff study prepared for

the Subcommittee on Monetary and Fiscal Policy, joint Economic Com

mittee, serial 94.670 0 (Washington, DC: U.S. Government Printing

Office, June 1,1982), app. A: and AnnaLee Saxenian, Silicon Valley:

Regional Prototype or Histoncal Exception? paper p ,rated at the Coo

fetence on Microelectronics in Transition: Industrial Transformation

and Social Change, University of California at Santa Cruz, May 1983,

43

Lute the region's technological infrastructure. Of interest to the present investigation are the following:

applied research and product development activities at nearby universities, Federal laboratories, and existing firms;

informal communication networks that provide

access to information and technology transfer

from those research and development (R&D)

activities;

scientific and technical labor force, including

skilled craftsmen, newly trained engineers, and

experienced professionals (who also represent

a pool of potential entrepreneurs);

a network of consultants (often augmented by

university faculties) specializing in hardware,

software, business development, and venture

capital;

a network of job shoppers and other suppliers

of specialized components, subassemblies, and

accessories; and

proximity to complementary and competitive

enterprises, as well as distributors and customers.

A Sampling of Route 128 High-Technology Firms

* Location of soma of Ma high-tecnnotogy firma in th.
Ch. 3The Roles of Entrepreneurship and Venture Capital in High-Technology Development 35

R&D activities appear to be particularly important, and industrial R&D tends to be concentrated

in a few major urban areas specializing in particular technological applications. This gives an advantage to firms in those regions developing related innovations, because of both technology transfer and

the demand for their products from existing firms.

Research at universities and Federal laboratories exerts a decentralizing influence on the geographic

location of R&D.4 This influence varies among industries, and appears to be greatest in biomedical,

computer, and energy R&D. In the past, university and Federal R&D has not resulted in significant

agglomeration if industrial R&D was not also present, but there is evidence that industrial R&D is

also becoming less concentrated. The most common

sites for decentralized R&D are production installations, and chapter 2 has shown that these branch

plants are also becoming more dispersed.

The resources provided by the technological infrastructure are particularly advantageous in hightechnology complexes, which are largely made up

of small, specialized companies that often depend

on other firms for supplies, services, and markets.

Concentration of these resources in one area enhances their productivity by creating external economies of scale, in both production and marketing,

similar to the internal economies created by the size

and vertical integration of much larger corporations.

This agglomeration of new firms attracts and retains

skilled professionals in the region, promotes informal communication among them, strengthens and

diversifies the technological infrastructure, and

draws venture capital to the region by creating opportunities for profitable investment.'

Agglomeration also places entrepreneurs at the

(-enter of competitive turmoil, where they are able

to identify new market niches and have the incentive to fill them quickly. In addition, the agglomeration appears to encourage entrepreneurial activity,

both by providing local role models and by providing a supportive environment that reduces risk and

uncertainty, either actually or subjectively. As a re-

'Edward J. Malec k "Science, 'Technology, and Regional Economic

Development: Review and Prospects," Research Policy, vol. 10, 1981,

pp. 31. 334; see also Irwin Feller, "Invention, Diffusion and Industri,

al Los anon," in lot namics of Manufacturing Activity, L,

Collins and 1). F. Walker (eds.) (New York; Wiley, 1975), pp. 83.107.

`Dorfman, op. s p. 508.

suit of this "network of mutual dependency," an industry undergoing rapid technological development

can be expected to show "a tendency . . . to grow

faster in a region the greater the degree of agglomeration there, other things being equal."6

Other evidence underscores the importance of the

"threshold" phenomenon of the agglomeration model. A recent study of the formation of new and small

technology-based firms since 1975 reveals that the

principal determinant of the geographic distribution

of these firms is the presence of similar firms and

the presence of major research universities and independent research institutes. The strong positive

correlation between new and older firms indicates

that, if the economic conditions in a region are

favorable to existing high-technology firms, they are

likely to be advantageous for new firms as well.7

Small Firms and Innovation

In addition, a large body of research indicates that

small, new firms play an important role in the processes of technological innovation, new product development, and job creation. In general, the rate

of innovation within organizations is positively correlated with its size; larger firms may have greater

innovative potential since they can operate larger

R&D departments and bring greater resources to

bear upon problems.8 This generalization may not

hold for high-technology firms, however. Some researchers claim a superior innovative potential of

small firms because they are more vigorous and respond more quickly than larger firms to new ideas

and market conditions.9 This is most likely to be

p, 107,

'Stephen G. Graham, The alcrmirarnts of the Geographical Distribution of the Formation New and Small Technology.Based

doctoral dissertation, Department of Finance and Insurance, Michigan State University. 1981.

'Everett M. Rogers, Diffusion of Innovations, /d ed. (New York: Free

Press, 1983); Joseph Schumpeter, Capitalism, Socialism, ,ind Democracy,

2d ed. (New York; Harper & Row, 1942); John Kenneth Galbraith,

The New Industrial State, 3d ed. (Boston: Houghton Mifflin, 1967).

9Kenneth J. Arrow. "Innovation in Large and Small Firms," in Entrepreneurship, Joshua Ronen (ed.) (Lexington, MA: Lexington Books,

1983), pp. 15.28; Small Businesses Are More Active as Inventors Than

as Innovators in the Innovation Process, PAD-82.19 (Washington, DC:

U.S. General Accounting Office, 1981); Lynn Bollinger, Katherine

Hope, and James M. Utterback. "A Review of Literature and Hypotheses on New Technology based Firms," Research Policy, vol. 12, pp.

1 14; PierteAndre Julien and Christian Lafrance, "Towards the Formalization of 'Small is Beautiful': Societal Effectiveness Versus Economic Efficiency." Futures, June 1983, pp. 211.221; James Brian Quinn.

44
36 Technology, innovation, and Regional Economic Development

the case in agglomerations, for the reasons outlined

above; it is because of their responsiveness to market opportunities that existing high-technology complexes have been such prolific "incubators" of new

firms and products.

Other research suggests that firm size may not be

a decisive factor in innovation, and indeed that a

variety of sizesfrom very small to very large, like

that found in the agglomerations described above

may be a key to continued innovation in an industry and growth in a region.° Because innovative

activity is "inherently untidy," this variety within

an industry or region hedges against the possibility

that opportunities for innovation will be ignored

by a set of overly homogeneous firms." In addition,

many high-technology firms do not survive, and

even successful firms have shown a tendency to locate their production operations in areas outside the

original seedbed. As a result, the continued creation of spinoffs and other new starts will be necessary to sustain the process of innovation and regional HTD.

Outlook for Other Regions

The above discussion, and the experience of existing high-technology complexes, make clear the

importance of entrepreneurship and startups in technological innovation and regional HTD. Many of

the conditions that led to these agglomerations cannot be replicated elsewhere, at least in the particular areas of microelectronicsmerchant semiconductors and microcomputersin which Silicon Valley

and Route 128 specialize. Because of their self-sustaining concentration of complementary businesses,

these regions provide a comparative advantage in

"Technological Innovation, Entrepreneurship, and Strategy," Sloan

Management Review, spring, 1979, pp. 19-30; Karl H. Vesper, Entrepreneurship and National Policy (Chicago: Hello Institute for Small

Business Policy, 1983); Stephen Feinman and William Fuentevilla, Indicators of International Trends in Technological Innovation (Jenkintown, PA: Oellmann Research Associates, 1976).

I°George Gilder, "Should We Sacrifice Our Future To Preserve the

Past?" INC., vol. 2, No. 11, November 1980, pp. 93.98; Devendra Saha!,

"Technology, Productivity, and Industry Structure," Technologkal Forecasting and Social Change, vol. 24, 1983, pp, 1-13.

"Devendra Saha!, "Invention, Innovation, and Economic Evolution,"

Technological F(vecasting and Social Change, vol, 23, 1983, pp.

213.235.

Photo credit. MI Corp.

Advances in robotics and other technologies provide

economic opportunities for a variety of regions

the creation of advanced products and new hightechnology firms in these high-technology sectors.'2

However, the technological opportunities in

microelectronics remain too numerous for any one

firmor regionto exploit. Similar opportunities

are also being opened by advances in other technologies, such as robotics i:nd biotechnology. In addition, the applications of new products and manufacturing technologies also create new opportunities

for declining industries and the regions in which

they are concentrated.I3 Finally, the negative impacts of increasing concentration, combined with

the maturation of the technologies themselves, have

"Dorfman, op. cit., p 310-, Donald L. Koch, William N. Cox, Delores

W, Steinhauer, and Pamela V. Whigharn, "High Technclogy: The

Southeast Reaches Out for Growth Industry," Economic Review, Federal Reserve Bank of Atlanta, vol. 68, No, 9, September 1983, pp, 4.19,

"Lynn E. Brown, "Can High Tech Save the Great Lakes States?"

New England Econornk Review, November-December 1983, pp. 19-33.

45
Ch. 3The Roles of Entrepreneurship and Venture Capital in High-Technology Development 37

led to dispersion of these boom industries out of

their original seedbeds.

This creates opportunities for additional communities to attract production facilities, on which a

local scientific and technical base can be built. In

addition, some of the conditions that led to agglomeration in Silicon Valley and Route 128 can be replicated elsewhere, particularly those involving components of the technological infrastructure such as

informal communication networks and institutional cooperation. The efforts of other States and localities to create these conditions are discussed in

chapter 4. Their success in achieving long-term, sustainable HTD may depend on their ability to mobilize the resources created by branch plants as the

ingredients for "home grown" HTD, and to encourage creation and expansion of indigenous firms by

local entrepreneurs. The following section examines

the process of technological entrepreneurship and

the conditions that encourage and support it.

Entrepreneurship and High-Technology Development

Factors in the Promotion

of Entrepreneurship

The individual inventor/entrepreneur has been

respected, praised, and, during some periods in

American history, elevated to the status of culture

hero." Despite fluctuations, there is a clear trend

toward greater public appreciation of the entrepreneur following a partial eclipse during the 1960's and

early 1970's.'5

Favorable local and regional cultures can enlarge

the pool of potential entrepreneurs. In the classic

high-technology complexes of California and New

England, for example, entrepreneurial activity is

especially strong and highly respected: role models

abound, starting one's own firm before age 35 is a

common ambition, and it is not unusual for employees to leave a firm and starftheir own after seeing the success of a former colleague. When the

entrepreneur becomes a local culture hero, the "cascading" of entrepreneurial activity may become

almost explosive. An example is the 35 new firms

spawned by former employees of Fairchild Camera

between 1957 and 1970 (see fig. 3).

Much of the literature on eriffrepreneurship has

been devoted to describing the psychological traits

associated with successful individuals. Different re-

"Material in this set tion is based on the staff paper, "The Role of

Entrepreneurship in High-Technology Innovation," December 1983,

prepared for OTA by Professor Douglas Caulkins of Grinnell College

while serving as a faculty intern.

"David C. McClelland, The Achieving Society (New York: Van

Nostrand, 1%1); Alex Inkeles, "The American Character," The Center Magazine, November/December 1983; Samuel Roman, Blaming

Tedino/ogy. The lrrational Search for Scapegoats (New York: St.

Martin's Press, 1981).

searchers have emphasized different characteristics:

innovation (the creation of new combinations of

products and processes);Is the search for novelty or

opportunity in a climate of uncertainty;" or the

need for achievement, is Entrepreneurs dislike repetitive work and tend to be nonconformists, but they

are only moderate risk-takers.'9 Studies focusing specifically on high-technology entrepreneurs suggest

that dissatisfaction with previous employment is

often a factor in their decision to establish a new

firm." Extensive familiarity with the industry is the

norm, and high-technology entrepreneurs tend to

maintain widespread networks of informal contacts

in the industry, often including former empIoyers.21

This su ests that efforts to encourage HTD

should include assistance from a network of specialists engaged in different aspects and stages of product creation, production, and marketing. This

should not be seen as a denial of the importance

of inch 'dual creativity and commitment, but instead

as a recognition of the need for a supportive social

environment for the entrepreneur.

'6Schumpeter, cap. cit.

"Ronen, op, cit., pp. 148-149; Yvon Gasse, "Elaborations on the

Psychology of the Entrepreneur," in Encyclopedia of Entrepreneurship,

Calvin A. Kent, Donald L. Sexton, and Karl Vesper (eds.) (Englewood

Cliffs, NJ: Prentice-Hall, 1982), p. 59.

IsMcClelland, opcir,

19M, F. R. Kets De Vries, "The Entrepreneurial Personality: A Person at the Croseavads," Journal of Management Studies, vol. 14, 1977,

p, 38; Robert H. Brockhaus, Sr., "Elaborations on the Psychology of

the Entrepreneur," op. cit., Kent, Sexton and Vesper (eds.), pp. 41.43.

T*Brockhaus, op, crt,, pp. 51.52; C. Cornegys, "Cognitive Dissonance

and Entrepreneurial Behavior, " Journal of Small Business Management,

January 1976.

"Brockhaus, op. cit., p. 51.

46
Figure 3.Fairchild Begat Tree (1957-1970)

Fairchild

Rhein

Semiconductor

1959

1.

Blenetics

1961

TeledyneAmtico

1981

Union Carbide

Electronics

1964

General-Micro

Electronics

1963

Miracle Hill

Electronics

1968

Applied Materials

Technology

1967

American

Micro-Systems

1966

Electronic

Arrays

1967

Nortec

Electronics

1968

Sernprex

1966

Qualidyne

1968

International

Computer

Modules

1970

National

Semiconductor

1967

Hayfield

Electronics

1956

Advanced

Memory Systems

1968

Analog

intergrated

Microsystems

1968

Intel

1968

Communications Control Data

Transistor Systems

1969

7.

1989

Advanc xf

Micro-Devices

1969

Cartesian

1968

Russell,

McC tanning,

Roach & O'Connor

1969

Precision

Monollthics

1969

Litronix

1970

NOTES

1. Acquired by Raytheon In 1461,

2 Two founders were from Semtconductrr Corp.

3. Assets of Atciectro acquired In rilortaititeitiari of National which moved from Connecticut to California.

4. Acquired by Ford-Philo* in 1966.

5. Other founders wens from Circuit Engineering i Design, Fairchltd, GE, and Union Carbide.

6. Three founders from AMI, and three from Plawlett4Pagiuni.

1. Two founders from Fairchild.

& One founder from Philoo-Ford Miorosiectronics.

9. FOur founders from Fairchild , and one from ITT Samicanductor.

10, Two founders from Fairchild, and one from SIIITHMIltaill, Inc., and one from Peripheral Systems Carp.

WW1

Industries

1970

10.

SOURCE: From Kirk P. Grahaim, "Factors Influencing the Formation of Technics) Companies" in Cooper and Komives, 1972; with permission.
Ch. 3The noise of Entrepreneurship and Venture Capital in High-Technology Development 39

auto oat* Utah Linavition Clow

Inventor -entrepreneur, right, from the Utah Innovation

Center, and his High Frequency Jet Ventilator

for infants and newborns

Entrepreneurial Networks

The essential role of the entrepreneurial network

is to bring technological innovations successfully

into the marketplace. The entrepreneurial process

begins with the recognition of an opportunity for

a new product, but it ends (if successful) with the

routinized production and marketing of a commercial product. Technological entrepreneurship may

begin with one individual's ideas, but it will necessarily involve many people and institutions before

the process comes to an end. It is useful, therefore,

to think not in terms of individual entrepreneurs

but of entrepreneurial networks, the components

of which are involved in different phases of the process.22 Successful entrepreneurship within existing

firms depends on assembling an appropriate team

and creating communication networks across departmental boundaries.23 For individual entrepreneurs, the region's technological infrastructure and

numerous contacts in the industry can supply a similar support network.

Many entrepreneurial networks for new starts

have been assembled fortuitously. The first step in

"Lionel A. Cox, "Transfer of Science and Technology in Successful

Innovation," Forest Products finanal, vol. 24, No. 9, September 1974,

pp. 44.45.

"Rosabeth Moss Kanter, The change Masters: Innovation for ProJuctiotv in the American Con- oration (New York: Simon & Schuster,

198;1, p. 28.

36-737 0 - 84 - 4 : QL 3

the formation of new firms may occur when a potential entrepreneur is not encouraged by his present employer to pursue an innovation. In this case

the frustrated employee may "spin off' a new company to develop the product himself. This may explain the greater instance of startups in regions that

have' a large number of existing firms in similar

sectors.

The process of creating the firmsolving design

problems, developing a business plan, obtaining financing, and assembling the needed management

and technical teamis often a perilous task. Many

high-technology firms do not survive beyond the

first or entrepreneurial stage, often because the

founding entrepreneur lacks the necessary managerial experience or skills. Existing high-technology

complexes, because of their preexisting support networks and other agglomeration economies, serve to

reduce risk and uncertainty. This has led to their

reputations as successful "incubators" of new starts.

The experience = fined during the past 30 years

in Silicon Valley and Route 128 has demonstrated

the importance of technological entrepreneurship

to regional HTD. It has also created a sufficient

knowledge base to support the deliberate cultiva,

Lion of entrepreneurship and new starts.24 Many of

the functions that were previously performed informally, and even haphazardly, by components of the

entrepreneurial network have now been institutionalized in the venture capital industry. In addition,

a number of universities, often in collaboration with

other public and private sector groups at the State

and local level, have launched programs to train and

assist potential entrepreneurs.

In an increasing number of cases, furthermore, existing firms encourage an employee to start a "spinout" firm by assisting with capital, laboratory space,

and technical support. In these cases the parent firms

themselves become (or provide) members of the entrepreneurial network. Tektronix and Control Data

are among the firms which have assisted internal

entrepreneurs with spinout firms.

Finally, a number of large corporationsin some

cases, early leaders in the high-technology field that

are concerned about losing their innovativeness

have experimented with techniques for promoting

z4"The New Entrepreneurs," The Economist, Dec. 24, 1983, pp. 61.73,
40 Technology, innovation, and Regional Economic Development

Moto civdir Trummom Taohnotogy Foundation

Technology for Energy Corp. In Knoxville, a spinoff from the University.of Tennessee and Oak Ridge National Laboratory

corporate entrepreneurship. Among the more successful organizational experiments have been the

"venture management" or "incubator groups" set

up within major corporations such as Xerox, Exxon, 3M, and IBM. These product-development

units can have a core membership of persons with

common skills, such as electrical engineering, with

other specialists joining and leaving as needed during the development of an innovation. A second

approach is to bring together individuals with dif4 9

ferent skills to carry the innovation through the entire cycle of development. The latter approach has

generally been more productive of successful innovations, in part because it tends to minimize the potential discontinuity of the entrepreneurial network.25

"Michael J. Brand and John Van Maanen, "Individuals, Groups,

and ICA: hnological Innovation,"' Chemrech, September 1983, pp.

528-533.
Ch. 3The Roles of Entrepreneurship and Venture Capital in High-Technology Development 41

Venture Capital and Regional High-Technology

Development

Role and Structure of the

Venture Capital Industry

Vehture capital investment is primarily regarded

as the early stage financing of relatively small, rapidly

growing companies (see fig. 4).26 27 Since the recession of 1974, however, venture capitalists have taken

on an expanded role in business development providing expansion financing for young companies

that do not yet have access to public equity markets, and financing for leveraged buyouts in which

managers purchase divisions of major corporations

with the objective of revitalizing existing businesses.

Three key attributes characterize venture capital

investment:

It involves some potential equity participation

for the venture capitalist, either through direct

purchase of stock or through warrants, options,

or convertible securities.

It is a long-term investment discipline that often

requires a period of 5 to 10 years for investments

to provide a significant return.

Venture capitalists are active, ongoing participants whose experience and specialized skills

add value to their investment in a developing

business; they are not passive investors offering only capital.

26The following sections are based on the contractor report, "Ven.

cure Capital Investment: Regional V -inations," prepared for OTA by

Venture Economics, Brian Has lett, principal investigator, December

1983, The statistics in the text and tables are derived from the Ven,

cure Economics data base, which contains information on the investments of venture capital firms and SfilCs accounting for more than

80 percent of the industry's total investment activity and on more than

4,000 portfolio companies dating to the 1960's.

'Tar ly stage financing can be defined in finer stages:

Semia relatively small amount of capital provided to prove a business or technology conceiCt. It may invc.:.e product development

but rarely any marketing.

Startupfinancing provided for use in product development and

initial marketing. Companies may have been in business a short

time (I year or less), but have not sold their product commercially.

First stage -financing provided to companies that have expended

initial capital (often in developing a prototype) and require funds

to initiate commercial manufacturing and sales.

In a sense, therefore, the venture capital industry institutionalizes many of the functions, and benefits, of the technological infrastructure or entrepreneurial network. The assistance that venture

capitalists provide to entrepreneurs and young growing businesses is usually in such areas as:

long-range planning;

defining financial needs and arrangements;

market planning and marketing assistance;

recruiting and evaluating management;

finding external technical expertise and vendor/

supplier relations; and

contributing business experience through ongoing counsel and support.

These skills are learned through an apprenticeship

processyounger associates spend years working for

more experienced venture capitalists until they gain

the requisite experience and credibility to form new

firms. These spinoffs from existing firms have been

a major source of growth in recent years, measured

in terms of number of venture firms.

The formal venture capital industry consists of

three major types of professional organizations:

independent private venture capital firms; venture

capital subsidiaries of financial and nonfinancial corporations; and Small Business Investment Companies (SBICs) (see table 10). Independent privie firms

include family groups and, to a greater extent, professional partnerships funded by both individual and

institutional investors (see table 11). A number of

financial corporations, most notably bank holding

companies, have established venture capital sub

sidiaries to invest in businesses that do not meet the

parent company's. usual investment or loan criteria.

In addition, many large industrial corporations (including General Electric, Lubrizol, Texaco, Textron,

and Xerox) have venture capital investment groups.

Among the approximately 360 regular SBICs licensed by the Federal Government, some 200 are

involved primarily in venture capital investment

rather than loans to small businesses; this group of

50
a Technology, Innovation, and Regional Economic Development

Figure 4.Profile of a Company Startup by Ventura Capital

Profit

Start

Production

Market

introduction

Expansion of

production

Exploitation of

markets

Search for Competition

capital

Establishing Organizational

image problems of growth

Search for management

and other staff cs Further

financing

Further

financing

Divestment

Stock

exchange

launch

1038

SOURCE: Nature Migazinte, vol. 307, Feb. 3, 1984, p. 403.

Table 10.Estimated Organized Venture Capital Pool,

1882

Amount of capital

under management

(billions)

Private venture capital firms $4.4

Corporate subsidiaries

(financial and nonfinancial) 1.9

Small business Investment companies' 1.3

Total $7.6

*ibis inciudas only Si lics engaged primarity in equity invosting.

SOURCE: Venture Economics, Inc.

SBICs includes many that are affiliated with bank

holding companies. Other sources of risk capital

available to entrepreneurs include the public new

issues market, wealthy individuals, and local investors with a few dollars to back an acquaintance or

relative. The term "informal investors" refers to the

latterfinancially sophisticated individuals who often have previous experience with new ventures.28

1Informal investors are an appropriate source of external risk capital for many ventures, but few data are available on informal investment activity. The data, analysis, and observations presented in thu

chapter deal only with the formal venture capital industry.

51

Recent History of the

Venture Capital Industry

As is the case with any industry, the structure and

behavior of the venture capital industry is greatly

influenced by external factors.29 Among the most

significant of these are Federal tax policy with respect to capital gains; laws and regulations affecting pension funds and other major sources of investment capital; the market for new public stock

offerings by small companies; and the track record

of experienced professional venture capitalists themselves. Only a limited number of private venture capital firms existed in the 1950'sli several of which were

family funded firms. Passage of the Small Business

Investment Act of 1958, which created the SBIC

program, was a major influence on the development

of the venture capital industry in the United States.

By providing tax advantages, potential Government

leverage, and a vehicle designed for small business

'Tor a detailed examination of the venture capital industry, see U.S.

General Accounting Office, "Government,Indiztry Cooperation Can

Enhance the Venture Capital Process," GAO/AFMD-82.35, August

1982.
Ch. 3The Roles of Entrepreneurship and Venture Capital in High-Technology Development 43

Table 1 1. Commitments to independent Private Venture Capital Firms

1981 1982 (first six months)

Source

Capital

committed

(millions)

Percent

of total

Capital

committed

(millions)

Percent

of total

Pension funds $ 200 23 $484 34

Individuals and families 201 23 283 20

Insurance companies 132 15 199 14

Foreign 90 10 197 14

Corporations 142 17 170 12

Endowments and foundations 102 12 90

Total $1,423 100 $867 100

SOURCE: Ventura Economics,

financing, the Act encouraged the formation of hundreds of SBICs.

In the early 1960's, however, there were relatively

few experienced venture capital managers, and many

of these newly created SBICs failed. These failures

discouraged many potential investors, and a declining public market for new issues in the early and

mid-1970's forced venture capitalists to operate

across a far broader spectrum of investment opportunities, particularly later -stage expansion financings, which provided adequate returns and a lower

risk exposure than early stage investments. Underwritings fell from $1.4 billion in 1969 to only $16

million per year in 1974 and 1975, and high capital

gains taxes combined with epressed stock market to create an unfavorable e virohment for the

industry. The total pool of organ d venture capital was essentially static from 1969 rough 1977.

Sharp reductions in capital gains tax 1978

made risk-taking by entrepreneurs far more re

ing and attracted a substantial influx of funds to

venture capital firms. The total venture capital pool

managed by professional firms grew threefold, from

some $3 billion in 1977 to $9 billion at mid-year

1983. Moreover, recent changes in Federal regulations have made it easier for pension funds to invest in venture capital partnerships; as a result, approximately 30 percent of the new capital for

professional firmssome $924 million between 1978

and 1982has been provided by pension funds (see

table 10). The successful track records of experienced

venture capitalists (whose returns in many cases

have only been realized and reported since the mid1970's) have also attracted the attention of investors.

Established venture capitalists are now bringing in

record amounts of money.

The substantial increase in the venture capital

pool has had an impact on the size of venture capital firms and on the scale and dispersion of venture

capital investment activity. Several new venture capital "megafunds" run by small, well-respected management groups have attracted over $100 million.

Most general-purpose funds, by contrast, range between $20 million and $30 million, These larger

funds can make larger investments than have been

traditional and still achieve diversity in their portfolios.

Factors Influencing Regional

Distribution of Venture Capital

Focusing by Stage of Business Develop.

went. The role of venture capital and the skills

required of the venture capitalist vary considerably

with each stage in the development of a portfolio

company. A venture firm that concentrates on laterstage financing is generally dealing with more mature

companies which require less attention, but the

number of individual investments handled by each

partner is still small. When a firm concentrates on

early stage and startup investments, there are even

fewer portfolio assignments per professional, since

such investments usually involve more substantial

involvement and time.

In recent years, however, a new group of venture

capital "seed" funds have been formed to address

the financing needs of very early stage companies.

The financing of seed and startup ventures is an

essentially local activity. Some venture capitalists

cite, as an informal rule, that they do not wish to

look at seed or startup situations more than 250 or

300 miles from their office. Thus, a focus on seed

52
44 Technology, innovation, and Regional Economic Dew lorment

and startup investment situations will necessarily

confine investments to the region where the venture firm is located. It follows, therefore, that entrepreneurs in an area with few or no venture capital firms focusing on seed or startup situations will

have a more difficult time accessing venture capital

than those in an area with numerous venture firms

focusing on early stage financing. Later-stage financing requires less daily involvement by the venture

capitalist, and thus investments can be'sought from

a broader geographic area.

However, the growth of these seed funds has been

paralleled by the growth of informal "feeder" relationships between local seed funds, which often lack

the capital to provide successive rounds of financing rapidly growing companies, and larger national

funds that can provide portfolio firms with laterstage financing. For the larger firms, which may lack

the manpower to identify and develop a large number of startups, this affiliate relationship is one way

to provide themselves with a continuing flow of

quality investments.

Focusing by Industry.Venture capitalists often

focus on particular industries, primarily because it

is difficult to develop the recil ite expertise in more

than a har,dful of sophisticated technologies. This

is darticularly true for investment in electronics, biotechnology, and other high-technology industries.

A review of investments during the early 1980's

indicates the following distribution of portfolio companies:

computer-related fieldsbetween 30 and 40

percent;

communications-10 percent;

other electronics-related areas-12 to 14 percent;

medical-related areas-7 to 8 percent;

genetic engineering-4 percent; and

industrial automation-3 to 4 percent.

The remaining 20 to 34 percent of investments were

in energy, industrial products, consumer-related

areas, and other manufacturing and services.

The industry focus of a particular venture firm

is a function of both the expertise of its professional

staff and its geographical locationi.e., ir,clustries

in the venture firm's "home market" which offer investment opportunities. Some venture capital firms,

however, have hired partners with technical exper53

Moto credit McDonnell bouyeee *Wong ion co.

tise to broaden their investment opportunities. Coinvestment syndicates have also developed, allowing venture firms to expand their industry exposure

by sharing technical expertise as well as risk. The

expectations of the investors supplying the capital

can also be a major determinant of investment strategy. Specifically, some corporate investors are looking for "windows" on new technology and are thus

inclined to direct their venture capitalists towards

particular industries and particular technologies.

Technological Infrastructure.Another factor

influencing regional variations in venture capital

activity is the presence or absence of the professional, technical, and commercial services needed to support new high-technology firms. Those needed

services include, but are not limited to, lawyers, accountants, commercial loan officers in banks and

other traditional business lenders, R&D persc,nnel,

educators, large corporations, and responsive State

and local government. How these professionals and

institutions view the new business development and

venture investment process clearly affects entrepreneurial activity and the concomitant investment of

venture capital.
Ch. 3The Roles of Entrepreneurship and Venture Capital in High-Technology Development 45

How er, the outlook of lawyers, accountants,

and loan officers tends to be conservative in many

regions. Moreover, research staffs at large laboratories, major corporations, and leading educational

institutions can also constrain entrepreneurial activity by focusing on proven technologies and resisting

potential- new applications. Business schools have

ten to emphasize the administration of the large,

esta fished corporations rather than the .processes

by ich new businesses develop. Large corporations also frequently have the conservative biases

associated with any large bureaucratic institution,

and some seem to have lost the entrepreneurial spirit

of their early days. Governments often use tax, land

use, and other regulatory policies to preserve existing

jobs and businesses rather than to create new entities.

The result is that a whole series of professionals

and institutions with (often legitimate) conservative

biases lack both the inclination to take risksthe

essence of entrepreneurship and venture investing

and an understanding of this risk-taking process.

Entrepreneurs seeking assistance from such professionals and institutions may be discouraged in their -------

'°Robert F t. Hayes and William J. Abernathy, "Managing Our Way

to Economic Decline," Harvard Business Review, vol. 58, No. 4,

July/August 1980, pp. 67-77.

efforts. Investors expressing an interest in venture

capital may be likewise discouraged.

A healthy venture investment climate in a region

requires the existence of a cadre of professionals and

managers in key institutions who understand the

venture investment process and the requirements,

particularly the risk-taking element, of the entrepreneurial and business-development prods. Regions

with a limited history of venture capital activity

often need to develop this professional and institutional infrastructure if they wish to expand the volume of entrepreneurial activity and venture capital

investment.

Traditional centers of venture capital activity and

HTD have this kind of entrepreneuriai network, and

efforts are under way to replicate it elsewhere. For

example, analysis'of Route 128 and Silicon Valley

suggests that the Massachusetts Institute of Technology and Stanford University made a significant

contribution to the development of Those two regions. Awareness of their potential contribution has

led universities in other regions to become increasingly active in stimulating local entrepreneurial activity and business de velopment. Similar initiatives

have also been launched by State and local governments and by, private sector groups throughout the

United States. These activities are described in

chapter 4.

Recent Patterns of Venture Capital Investment Activity

Venture Capital Gaps and Efforts

To Fill Them

Concerns have been raised that gaps in the availability and accessibility of venture capital may hinder the creation and expansion of new high-technology firms." Two principal types of gaps are of

interest to the present investigation: stage of development (especially at seed or startup stage), and re-

"Venture capital availability" refers to the amount of capital committed to venture capital firms and thus available for investment in

husines.ses. "Venture capital accessibility," on the other hand, refers

to how readily this available capital can be obtained by entrepreneurs

seeking funding. "Venture capital investment activity" refers to the

disbursement of that capital by venture capital firms as they make invest.

meats in kcImpames.

54

gional. Recent evidence suggests that market forces

are working to correctnot exacerbatethese potential gaps.

Seed Capital Gaps.There is a significant obstacle to any rapid increase in professionally managed seed capital investment: the limited human resources of the venture capital community relative

to the demands of new ventures. The new venture

capital funds specializing in seed and startup capital are narrowing this gap, but most of them are

based in the San Francisco and Boston areas. However, even large venture capital funds invest in one

or two seed financings each year; these investments

are the most time-intensive, but they often bring

the greatest financial returns.
46 Technology, Innovation, and Regional Economic Development

In 1982, about 5 percent of venture capital financings were in seed stage companies, compared with

about 19 percent in startups (product development

and initial marketing), 44 percent in expansions

(firms already shipping a product), and 11 percent

in leveraged buyouts and other investments.32 The

amounts invested were more heavily skewed toward

the later stages of development, but about half of

the companies that received expansion financing

had previously received seed, startup, and/or firststage financing from professional venture capitalists.

The percenrages are likely to be somewhat biased

toward expansion financings because venture capitalists continue to support companies already in

their portfolios.

Regional Gaps. -As competition within the industry intensifies, venture capitalists are filling some

of the geographical gaps that have existed since the

mid-1970's. More and more new areas are being explored for investment opportunities, either by new

funds or by branches and affiliates of venture capital firms located in major centers. Although New

York, California, Massachusetts, and Illinois have

the greatest concentration of venture capital firms,

new partnerships have been formed since 19 Si in

10 other States Colorado, Connecticut, Georgia,

Maryland, Ohio, Oregon, Mode Island, Texas, Virginia, and Washington.

In addition, an unprecedented 110 new venture

capital funds are currently seeking $3.5 billion of

t.ftir thr t'l 1r .1),,t' ',Irnu,nr invtcred, 1,*(11

fur(' ( i urrr,rl, i rn" 1482, p.

new venture capital. Of these, two-thirds are located

in the four leading centers-the San Francisco Bay

Area, New York City, Boston, and Chicago. Fairfield County in Connecticut and Minneapolis-St.

Paul account for another 14 percent. But 12 States

have potential new or enlarged funds: Colorado,

Florida, Georgia, Iowa, Maryland, Michigan, Missouri, New Mexico, North Carolina, Pennsylvania

Texas, and Washington.

A recent analysis suggests, however, that increasing the amounts of venture capital being managed

in a region will not, in itself, increase the amount

of venture investment activity there. Instead, the

impact of increased dollars on the number of companies financed varied markedly from State to State

during the period 1977-82." This supports the contention that local capital is only one of many important ingredients in the technological infrastructure necessary for new business development.

Venture Capital Sources

The geographical sources of formal venture capital are relatively concentrated and have not changed

significantly in the last 3 years, despite a 60-percent

increase in the size of the pool. Nevertheless, certain regions and States show an increased presence

as a source of venture capital. Table 12, which presents the regional breakdown for 1980-82, shows

that roughly 75 percent of formal venture capital

comes from three regions-California/Southwest,

"I. 'cimirt. (7apitai jourtuil, Septrrober 1482, p. tit- tablcs 14

:Ind 15 bloo..

Table 12.-Sources of Venture Capital Investment, by Region, 1980-82

Region

Amount

invested

(millions)

1980 1981

Amount

Share invested

(percent) (millions)

Share

(percent)

1982

Amount

invested

(millions)

Share

(percent)

California/Southwest $217 25.9 $ 272 25.1 $ 371 27.6

New York/New Jersey 234 28.0 301 27.8 355 26.4

New England .... 171 20.4 247 22.8 282 21.0

Midwest 116 13.9 134 12.4 177 13.1

Mid-Aitantic 63 7.5 72 6.6 62 4.6

Gulf Coast/Southwest 21 2.5 25 2.3 54 4.0

Pacific Northwest 4 0.5 10 0.9 19 1.4

Rockies 3 04 2 0.2 13 1.0

-Plains 3 0.4 9 0.8 7 0.5

South 5 0.6 12 1.1 6 0.4

Total from known sources $837 100.0 $1,084 100.0 $1,346 100.0

SOURCE Venture Economics. Inc

55
Ch. 3-The Roles of Entrepreneurship rand Venture Capital in High-Technology Development 47

New York/New Jersey, and New England. The Gulf

Coast/Southwest, Pacific Northwest, and Rockies

showed significant increases in 1982, due primarily

to investments originating in Texas, Washington,

and Colorado respectively. Investments from the

Midwest have increased steadily, while the Mid-Atlantic, South, and Plains States declined in both

amount and share in 1982 after increases in 1981.

This pattern of concentration is confirmed in table

13, which compares the leading sources of venture

capital by State for 1980-82. The top eight States

represent over 90 percent of the pool, the top four

States over 75 percent, and the top two States-California surpassed New York in 1982-account for

half of the new venture capital sources in the United

States. These States clearly dominate their regions,

as well: Illinois and Minnesota, for example, provided 88 percent of the new venture capital from

the Midwest.

Venture Capital Recipients

The regional pattern of venture capital distribution is even more concentrated than that of sources,

as shown in table 14. For most of the past decade,

venture capital investment activity has been concentrated in the California/Southwest region, specifically northern California. New England (specifically Massachusetts) has been a distant second,

followed at an even greater distance by the Gult

Coast /Southwest (specifically Texas) and New

York/New Jersey (specifically New York). These geographic investment patterns did nor change significantly during the period 1980.82, although the total

amount of new venture capital investments increased by 80 percent and the number of companies

financed by 50 percent. The top two regions increased their dominance in 1982, accounting for 61

percent of the investments and 56 percent of the

companies financed by the venture capital industry in 1982.

But as venture capital disbursements increased

fiorn $1.1 billion in 1980, to $1.4 billion in 1981,

and $1.8 billion in 1982, even a constant percentage of the total investment means an increase in the

amount of venture capital received. Investments increased significantly in the Midwest and Rockies,

and the Pacific Northwest increased its share of both

dollars and deals. The South and Plains have seen

relatively flat levels of investment activity, however,

and the Mid-Atlantic has experienct O. a steady decline in venture capital invesvii,7-,t activity.

Table 15 shows a similar pattern at the State level:

over 80 percent of all venture capital investment are

concentrated in just 10 States. The top five venture

capital recipients (California, Massachusetts, Texas,

New York, and Colorado) did not change during

the period, and their share of venture capital activity increased from 68 percent to almost 77 percent

of total investments. California steadily increased

its dominant position, accounting for almost 40 percent of the companies financed and over 45 percent

of the amount invested in 1982.

The second tier of States are more volatile: New

Jersey has risen slowly from tenth to seventh place,

while Michigan suddenly appears in eighth place in

1982 on the basis of some rather large investments,

Table 13.-Leading Sources of Venture Caps.- 5y State, 198082

State of investor

..._...... ........ Amount

(millions)

1980 1981

. _ Share Amount

(percent) (minions)

. Share

(percent)

1982

Amount

(millions)

Share

(percent)

California $216 25.8 $ 270 24.91 $ 370 27.5 New York 223 26.6 2913 2613 340 25 -3 Massachusetts ........ , . . 144 17.2 171 15.8 167 12.4 Illinois 67 8.0 90 8.3 135 10.0 Connecticut 19 2.3 55 5.1 107 7.9 Texas ......... 19 2.3 25 2.3 52 3.9

Pennsylvania 44 5.3 58 54 35 2.6 Minnesota 18 2.2 26 2.4 21 1.6

Eight-State total ... 750 89.6 985 90.9 1,227 91.2

Total from known sources $837 100.0 $1,084 100.0 $1,346 100.0

SOURCE Venture Economics, Inc

56
48 Technology, Innovation, and Regional Economic Development

Table U.-Distribution of Venture Capital Investments and Companies Financed, by Region, 1980-82

Region

CalifomiatSouthwest

Year

1980

1981

1982

New England 11981

1982

Amount

Invested

(millions)

$ 350

$ 599

$ 833

142

239

278

New York/New Jersey 1980 91

1. :1 80

1982 173

Gulf Coast/Southwest 1980 132

1981 162

1982 158

Midwest

Rockies

South

1980

1981

1982

82

109

124

1980 68

55

1982 82

Mid-Atlantic

1980 54

59

1982 62

Pacific Northwest

Plains

1980 76

1981 65

1982 52

1980 20

1981 33

1982 50

1980 , 10

1981 8

1982 10

Share

(percent)

34.1

42.5

45.7

13.8

17.0

15.3

8.9

5.7

9.5

12.9

11.5

8.7

8.0

7.7

6.8

6.6

3.9

4.5

5.3

4.2

3.4

7.4

4.6

2.9

2.0

2.3

2.7

Companies

financed

208

247

353

104

138

148

55

65

80

62

57

76

60

73

72

33

36,

47

39

35

38

26

35

44

Average amount

invested per

Share company

(percent) (millions)

34.2 $1.7

34.4 $2.4

39.3 $2.4

17.1 1.4.

19.2 1.7

16.5 1.9

9.0 1.7

9.1 1.2

8.9 2.2

10.2 2.1

7.9 2.8

8.5 2.1

9.9 1.4

10.2 1.5

8.0 1.7

5.4 2.1

5.0 1.5

5.2 1.7

4.3 1.4

4.9 1.7

4.2 1.6

6.4 2.9

4.9 1.9

4.9 1.2

18

25

32

3.0

3.5

3.6

1.1

1.3

1.6

1.0

0.6

0.5

3

7

8

0.5

1.0

0.9

3.3

1.1

1.2

United States 1980 $1,025

1 $1,409

1982 $1,822

100.0

100.0

100.0

808

718

898

100.0

100.0

100.0

$1.7

$2.0

$2.0

SOURCE Venture Economics inc

and Minnesota appears in ninth on the basis of a

larger number of smaller investments. Illinois, although a growing source of venture capital (see table

13), experienced a decline in the share invested in

the State; Connecticut, on the other hand, is becoming more prominent as both a source and a recipient of venture capital.

Interregional Flows

Many of the differences between regions or States

as sources versus recipients of investment can be explained by the mobility of venture capital. Table 16

presents in matrix form the flow of venture capital

between regions in 1982. The California/Southwest

region owes its dominance to two factors: 75 percent of the venture capital originating in the region

was also invested there, and it also attracted almost

one-half of the investments made by venture capitalists in other regions. On average, in fact, only

25 percent of the venture capital raised in other regions was invested in the same region; New England

invested only 33 percent of its venture capital in local

companies. Only in the South and Pacific Northwest

was the majority of venture capital invested at home:

in the South, a number of small SBICs have concentrated their limited resources on investments

close to home; in the Pacific Northwest, one major

firm has made investment in emerging Northwest

companies a major focus of its strategy.

57
Table 15.-Reglonal Flow of 1982 Venture Capital investment (millions of dollars)

Region of investor

Region of venture capital recipient Total by

region of

investor

New York/ Gulf Coast! California! Pacific New England New Jersey Mid-Atlantic South Midwest Southwest Plains Rockies Southwest Northwest New England

New Vatic/New Jersey

Mid-Atlantic

South

Midwest

.. Gulf Coast/Southwest

Plains

Rockies

CalifomistSouthwest

Pacific Northwest

Foreign Investors

Region unknown°

1 Total by region of recipient

$ 93

61

5-

16

9

33-

10

51

$ 19

66

2

5

17

.-

11

61

$ 6

15

4-

12

12

13

$52

$ 9

11

2

3

5

4

2-

4

n

$ 7

15

1 38

12

5

10

44

$ 20

31

3

2

12

24

2

1

22

3

38

$

3

2

1

1

2

$ 7

14-

1 17

5

13

13

12

19

$118

136

42-

64

7

2

7

280

8

36

134

$ 3

3

1 -

6

1

8

11

1 14

$ 282

355

62

6

177

54

7

13

371

19

78

398

$278 $173 $62 $124 $158 $10 $82 $833 $50 $1,822 - Investment of lees than $1 million.

INA to rounding. In some oases table entries do not equal the row or column to its bThle category includes teViletere for which no location is identified as well as financing from unidentified investors. For exempla, in a $3 million financing, three of the venture capital Accounting for $4 million but the source of the additional $1 million Investors may be unknown may be unidentified, In some cases the unidentified Investor may be a venture which only makes occasional investments. capital fund which prefers to keep a low profile or a firm

SOURCE: Venture Economics, Inc

Table 16.-Leading Venture Capital Recipient States, 198082

1980 1 1 1982

Share of

Share of

companies Share of

Share of

companies Amount Average

Invested investment financed investment financed invested Share Companies Share per company State (percent) (percent) State (percent) (percent) State (millions) (percent) financed (percent) (millions) _Rank

1 CA 33.7 33.1 CA 41.7 32.7 CA $ 829 45.5 349 38.9 $2.0 2 MA 12.0 13.8 MA 12.8 14.5 MA 224 12.3 114 12.7 2.3 3 TX 10.5 7.6 TX 9.9 5.6 TX 143 7.6 68 7.6 2.1 4 NY 6.5 4.9 NY 3.2 6.8 NY 134 7.4 59 6.6 2.3 5 CO 5.3 3.9 CO 3.1 3.9 CO 67 3.7 35 3.9 1.9 6 PA 4.1 2.3 CT 2.7 2.6 CT 39 2.1 23 2.6 1.7 7 IL 2.8 3.1 OH 2.6 2.2 NJ 38 2.1 21 2.3 1.8 8 GA 2.5 1.6 NJ 2.5 2.2 MI 34 1.9 13 1.4 2.6 9 MD 2.4 0.7 IL 2.3 2.8 MN 32 1.8 26 2.9 1.2 10 NJ 2.3 3.9 PA 2.1 2A PA 30 1.6 17 1.9 1.8

10-State total 82.1 75.0 82.8 75.8 $1,570 86.2 725 80.7 $2.2 U.S. total . 100.0 100.0 100,0 100.0 $1,822 100.0 898 100.0 $2.0

SOURCE: Venture Economics, Inc

58
so Technology, Wootton, and Reglomit Emtnonic Development

Future Trends In- Venture-Capital--

Distribution

Only a relatively small number of pension funds

have invested in venture capital firms to date, but

as this investment alternative becomes more widely

accepted more pension funds are expected to participate. Major corporations are also recognizing

venture capital as a "window" on emerging technologies, and this recognition may also stimulate

capital inflow. Venture capital investment is also

becoming more international in scope: foreign investors have provided an increasing portion of the

funds raised in the last 3 years; in the first 6 months

of 1983, foreign sources supplied 21 percent of the

$1.15 billion raised, second only to pension funds

(see table 11). In addition, European investors are

increasingly investing directly in U.S. portfolio companies along with U.S. venture capitalists, thereby

leveraging the domestic investment pool.

Underlying technological changes favorable to entrepreneurial activity also seem likely to accelerate,

thereby creating more investment opportunities.

Demographic factors will also affect entrepreneurial

activity, as a generation of American managers and

engineers reach the "entrepreneurial age bracket,"

between 30 and 45, when individuals have accumulated the experience and capital needed to launch

a new enterprise. A continuing constraint on the

expansion of venture capital activity will be the industry's human resource base, but as more individuals gain experience through the apprenticeship process it should he possible to sustain the growth of

the industry.

many-State-art& iorat-govermn

have begun to focus their attention on en

ship and the venture capital process as part of their

broader economic development strategies. These initiatives, which are discussed in the following chapter,

often include efforts to increase the amount of risk

capital available to local entrepreneurs. Venture

capitalists have played an active role in designing

these programs, and very few of them involve government-run investment funds. This reflects an

emerging consensus that State-run funds would have

difficulty in securing private coinvestors, retaining

experienced venture capital managers, or resisting

short-term pressures on investment decisions. Instead, most efforts involve public/private cooperation to enhance the venture capital process itself.

Public sector roles in this effort have included:

encouraging private investment by reducing

State taxes on capital committed to new companies;

enabling' State employee pension funds to invest in venture capital partnerships;

helping venture capital firms to secure capital

from sources within the State;

facilitating the seed capital process by providing local entrepreneurs with training, technical assistance, and help in preparing presentations to venture capitalists; and

developing communications networks with the

national venture capital community, in order

to bring local opportunities to their attention.
CHAPTER 4

State and Local Initiatives for

High-Technology Development
Contents

Page

Introduction 53

The Actors 53

State Government 54

Local Government 55

Universities 56

The Private Sector 57

Federal Government Involvement 58

The Initiatives 59

Research, Develoment, and Technology Transfer 59

Human Capital Development 62

Entrepreneurship Training and Assistance 63

Financial Assistance 64

Physical Capital 65

Information Gathering and Dissemination 68

Additional Factors 69

Identifying Local Needs and Resources 69

Adapting to External Constraints 70

Local Initiative and Partnership 70

Linkage With Broader Development Efforts 71

Sustained Effort 71
CHAPTER 4

State and Local Initiatives for

High-Technology Development

Introduction

In the past 10 to 20 years, several regions of the

United States have developed strong local economies based on fast-growing "high-technology"-Mdustries. Encouraged by these successes, State and

local governments, universities, and private sector

groups are launching initiatives to promote similar

high-technology development (HTD) in other regions.' They are optimistic because high-technology

industries are spreading beyond their original

strongholds, and because recent policy changes have

lifted more of the responsibility for economic development to the State and local levels. While hightechnology industries represent no more than 15 percent of total U.S. employment, they play a major

role in many local economies and could be a force

in the revival of distressed regions and cities, especially in the Midwest.

The intense competition for HTD has generated

literally hundreds of State and local initiatives, but

it is too soon to tell whether they have anything

more than a marginal impact on HTD. Some critics

view such competition as a zero-sum gamei.e., resources spent to entice a firm to locate in one city

'These initiatives have been described in detail in two OTA background papers: Ce,)stis of State Government Initiatives for High-Technology Industri:11 Development, OTiVBP.ST1-21, May 1983; and En.

couraging High 't..chnologv Development, OTABP-ST1.25, February

1984.

or State rather than in another are wasteful from

a national viewpoint since no new jobs result. But

States and cities are also beginning to take a number of actions to encourage technological innovation and economic activity that would not happen

without government intervention.

These institutional innovations seek to strengthen

the linkages among the financial, academic, and business communities; promote entrepreneurship; and

improve the overall scientific and technological base

of State and local economies. In short, the initiatives

attempt to develop the cooperative support networks

that constitute the technological infrastructure for

HTD, either by mobilizing the necessary local resources or removing barriers to innovation and entrepreneurship.

The initiatives are as varied as the locales in which

they were launched, but they seem to share three

common goals; job creation, business development,

and economic diversification. This chapter examines the roles played by the principal actors in State

and local HTD and the interdependence of their

efforts. However, the main emphasis of the chapter

is on describing the goals and impacts of specific

types of initiatives. Although most of these initiatives are very recent, some successes can already be

identified. The final section of the chapter examines the factors that may increase the chances of

success.

The Actors

State and local governments, universities, and private sector groups have all become more active in

encouraging HTD, and their roles are examined

below. This organization is somewhat misleading,

howeveronly rarely are the initiatives completely

independent. For example, State government programs usually involve the participation of university, local government, and/or private sector groups,

just as university and local initiatives often seek to

create closer and more productive relationships with

private industry. Federal Government programs

have also played at least an indirect role: many of

62

53
54 Technology, Innovation, and Regional Eco nomic.pe _

the initiatives were encouraged by Federal pilot studies, and others have made innovative use of Federal funds, planning grants, and other development

tools.

State Government

High-growth, technology-based firms and the industries they compose have become the targets of

increasingly numerous State economic development

strategies. In some cases, the strategies involve

organizational innovations designed to identify, integrate, and mobilize existing State resources for

technological innovation. The strategies also often

include the redirection of existing activities or the

creation of new programs specifically designed to

promote HTD. An OTA census conducted in January 1983 identified 153 State government programs

with at least some features directed toward HTD;

of these, 38 were specifically dedicated to the creation, expansion, attraction, or retention of hightechnology firms.'

In order to gather more detailed information on

State government initiatives, OTA conducted a survey and comparative analysis of high-technology initiatives in 16 States-8 that had implemented dedicated programs before 1981, and 8 that initiated

dedicated programs in 1981-82.3 The sample States

selected for the survey were:

Pre-N81 Stares

California

Connecticut

Georgia

Massachusetts

New York

North Carolina

Tennessee

Pennsylvania

1981-82 States

Indiana

Illinois

Michigan

Minnesota

Missouri

New Mexico

Ohio

Rhode Island

OTA found that these States approach HT!' in

many ways. States with dedicated initiatives, cor example, often had a sophisticated research base and

considerable high-technology industry before those

programs were established; their objective in part

is to strengthen and retain what is already there.

States whose economic base consists primarily of

'C'ensus of ,Crat (;,,vernment Initiatives for High-Technologr In

dustnat I)t.vvit Innen( c\Xi':11)Ington, 1X:; Congrt.-ss, ()ffitse of Tet.h,

nologv Assessmehr, (If A firSTI-21, Mau 1983).

'Stare IniriarRes 'lrvey, prepared fot ()TA by the Rescark:h Triangle

Institute, Alvin M 'run'. investigator, May I9S1.

older, basic industries tend to emphasize economic

diversification and the application of new production technologies in traditional manufacturing sectors. Still other States, notably the less highly industrialiied ones, aggressively pursue the production

facilities of expanding high-technology firms in order to bolster their industrial base and build a founda,.ion for future development.

These approaches suggest that attention to HTD

is not distinct from economic development activities in general. In most cases, State officials consider

their high-technology initiatives to be a natural and

even inevitable extension of their different economic

development strategies. In many cases, the office responsible for HTD emerged through the evolution

of a more traditional economic development agency.

OTA's investigation found that many initiatives

involved bOth short- and long-term goals, and that

Most were well integrated with other State efforts.

Often a high-technology task force or commission

appointed by the Governor is the primary mechanism for identifying needs and formulating policy

recommendations; the Governor's office was the initiator of more than half the programs investigated.

Legislatures also play a role, since programs may require enabling legislation and legislative appropriations. However, that role varies widely, from little

or no involvement to being the primary impetus in

HTD efforts. Similarly, the State economic development office takes a lead policy role in some States,

but plays a less direct role in others.

Most of the State initiatives studied involved the

participation and support of university officials and

business leaders. However, local governments were

directly involved in fewer than half of these State

programs; they generally participated indirectly,

through their legislative representatives. The private

sector most often provided advice and consultation,

although that sector has been the primary initiator

of certain State programs. University officials also

advised, but they too have been the driving force

behind certain programs.

About one-third of the programs in the survey

States were classified as "labor and technical assistance" (primarily training programs). States with

older initiatives had a slightly higher percentage of

"high-technology education" programs, which may

reflect their greater university resources. States with

63
Ch. 4State and Local initiatives for WO-Technology Development 55

more recent initiatives had a slightly higher percentage of "general industrial development" programs

with special provisions for high-technology firms,

as well as programs involving capital assistance.

However, while many financial assistance programs

help firms to locate seed or venture capital, very few

provide risk capital themselves.

Most of the 153 initiatives were launched between

1980 and 1983, and the vast majority (85 percent)

have undergone no formal evaluation. Nevertheless,

most survey respondentsState officials and hightechnology executives alikewould favor additional

initiatives by both State and Federal Governments.

The Task Force on Technological Innovation of

the National Governors' Association (NGA) has

reported that, while most States are actively pursuing short-term efforts to compete for technologybased research and manufacturing firms, they are

also developing medium- and long-term strategies

to encourage modernization in traditional industries

and to create a favorable environment for entrepreneurship and technological innovation. As a

result of these activities, according to the NGA report, both the impetus for technological innovation

and "the real and effective initiative for economic

development and for the provision of jobs are shifting from the Federal Government to the Stites."

While the report asserts that "the preliminary results

to date have been impressive," it acknowledges

that most of these State initiatives are too new to

evaluate.4

Local Government

Substate and local HTD efforts are driven by the

prospect of increased jobs and a stronger tax base.

The localities are often influenced by the tremendous contributions that high-technology companies

have made to the local economies of Valley

and the Boston area; indeed, some communities

have sought to promote themselves as "Silicon

Mountain," "Silicon Coast," or "Silicon Plain."

OTA has identified five types of communities, based

on the degrees of difference between the successful

ask Fort t Tee hnological Innovation, Technology and Growth:

State hutratives in 'Technological innovation (Washington, DC: Na.

clonal Governors' .Assodanon. October 1983). See also State Initiatives

to Technohwit al Innovation: Preliminary Report of Survey Findings

(Washington. IX:: National Governors' Asmxiation, February 1983).

36-737 0 - 84 - S : QL 3

high-technology models and the localities that seek

to emulate them:

high-technology centers, which already have a

strong base of high-technology firms, research

universities, and venture capital;

diluted high-technology centers, whose large

high-technology base is spread throu, a larger

and more mature local economy;

spillover communities, located near hightechnology centers, whose proximity allows

them to exploit the centers' resources, amenities,

and high-technology base;

technology installation centers, where the presence of a major research facility attracts specialized suppliers and creates a local base of

researchers and skilled workers that can be exploited for economic development; and

bootstrap communities, which lack most of the

characteristics of high-technology centers but

offer low operating costs and high quality of life

that make them attractive for branch plants of

expanding high-technology companies.

In order to determine what types of local HTD

programs have been attempted, how well they have

worked, and their transferability to other communities, OTA analyzed 54 separate high-technology

initiatives in 22 communities:5

Huntsville, AL

Phoenix, AZ

San Diego, CA

Colorado Springs, CO

Brevard County, FL

Orlando, FL

Chicago, IL

Lowell, MA

Montgomery County, MD

Minneapolis, MN

Albuquerque, NM

Binghamton, NY

Cincinnati, OH

Portland, OR

Philadelphia, PA

Oak Ridge, TN

Austin, TX

San Antonio, TX

Salt Lake City, UT

Burlington, VT

Seattle, WA

Milwaukee, WI

The most common types of substate and local initiatives identified in this investigation include the

following:

land use, planning, and zoning;

university improvements;

vocational-technical training;

incubator buildings;

marketing programs;

sl,ocal Htgh-Technology Initiatives Study, prepared for ()TA by The

Fantus Co., Charles Ford Harding, principal investigator, April 1983,

64
58 Technology, innovation, and Regional EC01101711C Development

high-technology task forces; and

venture capital.

While some it tiatives sought to stimulate the creation of new local companies built around innovative products, the localities directed their greatest efforts toward attracting branch operations of

large high-technology firms, which pay more immediate dividends in terms of job creation.

The programs of the 22 coiamunities studies have

not been subjected to rigorous comparative analysis or evaluation, and thus their value in helping

to create high-technology centers cannot be

determined.

Universities

Colleges and universities play two major roles in

technological innovation and its diffusion through

the economy. First, they train and educate scientists and other technical personnel and expand the

base of scientific and technical information. Second,

they transfer this talent and information to the private sector, thereby fostering the commercialization

and diffusion of innovation. This cooperation between the educational and industrial sectors is not

a new phenomenonStanford Industrial Park,

which dates from the 1940's, and\Research Triangle

Park, developed in the 1950's, are primary examples

of such cooperation

The need for this interaction is even greater today,

Universities, industry, and State governments have

developed programs to address not only the preparation of students but also the needs of new and

expanding high-technology businesses, particularly

the need for increased research and development

(R&D' and technology transfer and the need to provide technical/vocational skills to the local work

force.' As the growth of Federal R&D funding has

'Thc tnatri,11 relating to univerNities IN drawn from the following dcx

t S t icnet,d Ae,ount;ng t)ttiet, The Federal Koh- in FostcrIng

L er.,/tv Industry Col1x-rarion, (iA() ,TAD 83-22, May 198 ;;

t National ienc Foundation, i'mverstry/Industry Resear-h

Rclanon,hips: ,k1vrhs, Realities and Porermab, NSB 81.1. (ktober

"t Emerge as art important Catalyst in the New

Hess I)evelopment Venture Capaal Journal, vol. 23, No.

1`)8;, pp, 7,12; and

National Otivcrnors. Asso, anon, Task I ,,rte on Imhnological

Innovation. l ca ',noir wy an ;rowth: Start' lnInanves in Tech/Jo1, /0,a! Innowanon, Cat t, I983.

65

e'

Photo omen: Tennessee Technology Foundation

Oak Ridge Associated Universities, a nonprofit association of more than 50 colleges and universities acting as

prime contractor for high-technology research, training,

education, and information activities

slowed in the face of inflation and budgetary pressures, the education sector has become more active

in soliciting research funds from industry. In tuan,

industry supports university R&D because it requires the long-term, basic research on which universities concentrate,

A study by the U.S. General Accounting Office

(GAO) concludes that these developing linkages between university and industry not only can enhance

technological innovation but also can stimulate

regional economic development.? Resea ch and

science parks affiliated with universities attract new

high-technology firms to their areas, and they may

also provide seedbeds for spinoff companies. Technical centers and industrial extension services benefit existing local businesses by increasing the rate

of innovation diffusion and increasing access :o facilities, equipment, and expertise. Cooperative research activities provide industry with early access

to the results of university research and improved

training for scientific and engineering personnel,

according to the GAO study.

Institutional differences between the university

sector and industry can make such cooperation and

collaboration difficult. However, several of the newest contract agreements between industry and universities reflect possible resolutions, including the

right of individual university researchers to publish

their findings. Another issue is the ownership of in7GAO, op. cu., pp. 15, 24, 40, 47-48.
Ch. 4State and Local Initiatives for High-Technology Development 57

tellectual property, particularly in light of the increased patentability of university-conducted applied

research.

In addition, several States are working with their

public universities to set up programs to stimulate

innovation and business development. A number

of community and junior colleges, in collaboration

with their State, local government, or local industry, are providing training in technical skills needed

by high-technology industry. In some cases, these

programs take the form of a general training course;

in others, these institutions work directly with a local

firm to train the labor needed for expansion. The

private sector also is cooperating with State and local

governments to improve the quality of math and

science education, and to provide training, retraining, and employment development.

The Private Sector

Private sector participation is an important feature

in the design, operation, and success of HTD initiatives at the State and local levels.8 These HTD efforts, after all, are aimed at influencing the decisions

of individual entrepreneurs and firms about where

to start, expand, or relocate their business activities. Increasingly, however, corporations and individual executives are participating as a stimulus or

collaborator in HTD efforts of State governments,

local communities, and universities. Business is

directly affected not only by business conditions but

also by conditions in the external environment. During the 1970's, State and local governments and the

private sector began to assume a larger role in community development, and the early 1980's have seen

further reductions in Federal funding for economic

development and a further transfer of responsibility

to local jurisdictions. The effort to secure additional

public and private resources has led to the creation

of local partnerships involving Government, community groups, and the private sector. This trend,

1.01401-Led in many cases by similar changes in State

policies, is expected to continue.

Studies by SRI International indicate that this

changing environment represents both a challenge ----

"l he fnIhro.ing n1;41(.1-131 Is based on the c ontrat tor report, Private

tie rf v 11)1( hi! t 0: I figh Tt\ h flt>logy and the l.is al Economy, prepared

for krir.-\ bt genet: A. Btlger wah rt-SCar, h assi,,tatux by Robert

iuskind. Apr.! lus

Photo credit llsesschesethr institute of Technology

MIT's fusion research laboratory is named after Nabisco

Brands, Inc., which donated the laboratory building

and an opportunity for the private sector: local

governments sometimes lack the manpower and experience to deal with the complex problems of HTD,

while corporate action or public/private partnership

has proven especially successful in such areas as economic development, job creation, and education

and training. SRI also found that there are several

different approaches that any company can undertake, regardless of its size.9

'Tom Chmura, rt al., Redefining Partnership- Developing Public/Pri

vate Approaches to Community Problem Solving; A Guide for Local

Officials (Menlo Park, CA: SRI International, January 1982), p. 6. See

also SRI International, "Developing Public/Private Amoak hes to Community Problem Solving," Management Information Service Report

{International City Management Association), vol. 14, No, 7, July 1982,

whole issue, Both reports are based on research conducted by the Public

Policy Center of SRI International, with funding from the Office of

Community Planning and Development of the U.S. Department of

Housing and t lrban Development.

66
Sr" Technology, Innovation, and Regional Economic Development

Business involvement in regional HTD often results from company policies that reflect the personal

beliefs and commitment of their executives. In other

cases, business involvement addresses community

problems that affect the general business climate or

a particular firm's operating costs and profits: adequate public services and facilities in order to operate and grow; a well-trained labor force; and a reasonable local tax burden. An important byproduct

of public/private ventures is the improved communication and understanding that results tetween

participants from local government and the business community.

Private sector firms and executives have a wide

range of resources that can be applied to problemsolving and economic development in their communities. Different types of firms possess different

kinds of resources, and these resources often determine the roles firms play, the problems they address,

and the specific initiatives they launch. In general,

however, these strategies can be classified as'follows:

business investment and operations, notably site

location decisions, but also including targeted

bank deposits and real estate development, preferential hiring or procurement practices, and

expanded employee services;

education development, including philanthropic contributions, loaned personnel, donated

equipment, technology transfer mechanisms,

and cooperative research arrangements;

business development and risk capital, including

entrepreneurship training and assistance, small

business incubators, and geographic investment

pools for venture and seed capital; and

business/civic advocacy, usually through trade

or business executive associations, to express

support for public leaders or policies, encourage

participation by other firms, and promote community involvement by individual employees.

These four strategies are generic to all businesses,

but the resulting initiatives show distinctive patterns

associated with particular industries. Financial institutions, for example, find investment and business development a logical extension of their normal activities; their decisions are motivated by profit,

but they also take into consideration the special

needs of the community, such as housing or neighborhood revitalization. Nonfinancial corporations,

67

on the other hand, are more likely to use philanthropic contributions as the mechanism for community involvement. In addition, patterns of involvement often reflect the particular self-interest

of the firm: pharmaceutical companies make donations to medical schools, accounting firms give to

business schools, and high-technology arms focus

their donations on engineering or computer science

programs.

High-technology firms have made use of all of

these strategies. As nonfinancial institutions, they

seldom use special investment strategies, but hightechnology businesses have made substantial contributions to educational institutions, often commingled with investments in cooperative R&D programs. Company size affects the firm's ability to

draw upon internal resources: large, well-established

firms such as IBM, Honeywell, Sperry, or Xerox are

able to draw upon vast amounts of capital, personnel, business experience, and a longstanding network of contacts. Also, as with other corporations,

high-technology firms tend to focus their involvement near the headquarters, although there are numerous examples of company involvement at branch

sites.

Federal Government Involvement

Representatives of the Federal Government have

participated directly in the initiatives of several cities. For example, the High Technology Task Force

in Chicago was chaired by the director of the Argonne National Laboratories. Major Federal R&D

installations frequently have provided the base

around which high-technology programs are built.

In fact, the reduction of Federal support for such

installations has provided the impetus for some local

economic development programs directed at hightechnology companies. Also, military bases have

proved to be good sources of skilled labor for hightechnology companies located ai an area. In such

cases, the Federal Government has in effect subsidized technical, training for workers who subsequently feed into the local private economy.

In addition, many of these State and local HTD

initiatives made extensive use of funding and development tools provided by the Federal Government. In its survey of 54 local H I D initiatives, for
Ch. 4State and Local Initiatives for HighTechnology Development 59

example, OTA found that the 22 communities surveyed had made use of several Federal programs:

Urban Development Action Grants

Industrial Development Bonds 5

Economic Development Administration grants 4

Community Development Block Grants 3

Comprehensive Education and

Training Act programs 2

Free Trade Zone 2

Appalachian Regional Commission programs 2

Small Business Administration loan programs 1

These Federal programs were not designed specifically to help with regional HTD. Block grants,

training programs, planning grants, and loan funds

are highly flexible instruments and have been used

for a variety of projects, depending on the needs or

desires of different communities. When used imag-,

inatively and in conjunction with other efforts by

entrepreneurial local leaders, these programs have

proven to be an effective adjunct to regional HTD

efforts. (A more detailed discussion of the role of

Federal policies and programs is presented in ch. 5.)

The Initiatives

The preceding section suggests the diversity, as

well as the interdependence, of the roles played by

State and local groups in promoting regional HTD.

In most cases, however, their efforts are aimed either

at mobilizing the necessary local resources or at removing barriers to HTD. The emphasis of the resulting initiatives falls into six general categories:

research, development, and technology transfer;

human capital;

entrepreneurship training and assistance;

financial assistance;

physical capital; and

information gathering and dissemination.

Research, Development, and

Technology Transfer

Perhaps the most fundamental initiatives are those

that aim to quicken the flow of innovation itself.

Since most basic research is still performed by universities, many of these initiatives focus on improving linkages between universities and industry.

Some, such as joint research ventures and research

consortia, involve formal, long-term collaboration

between a university and one or more companies.

Others, such as research centers and technical extension services, provide technical assistance or perform short-term research for local firms in exchange

for fees or other support. In other cases, alumni

groups ;have become active in patenting and commercializing the results of university research.

In all of these cases, the object of the initiative

is to make university resources more widely available, to raise the level of formal and informal communication between academic and industrial researchers, and to increase the speed with which

research results become available to industry. Recent cent studies suggest that, given strong leaderslip and

a stable source of funding, such initiatives can contribute to regional economic development by reorienting university research to Yaard the needs of

industry, by attracting outside firms to the region,

by improving the productivity of existing firms, or

by encouraging the creation of new firms.°

The OTA census found a number of State hightechnology initiatives directed toward research, developulent, and technology transfer. For example,

the Pennsylvania Technical Assistance Program offers technical information and assistance to all State

businesses, particularly in the area of technology

transfer. Other initiatives offer services to a specific

set of industries or businesses involved in technological innovation. The Biomedical Research Park,

a joint effort between Illinois and Chicago, is set

up to assist biomedical firms with innovation and

development.

University initiatives in industrial R&d) are driven

in part by the need to diversify funding sources, retain faculty, and attract students!. However, changing technological and economic conditions have also

"'NSF. op. cit.. p. 11.

68
60 Technology, innovation; and Regional Economic Development .

led to a greater emphasis or .ommercializing. research results, particularly when the university has

royalty rights. These university initi-tives include:

research and science parks, clustefs of researchntensive firms and facilities on a site near a

university;

research and technical centers that disseminate

information, provide technical assistance, and

perform short- or long-term research for local

businesses in exchange for fees and other support; and

university/industry collaboration, including

'cooperative research ventures and research consortia.

University-based research centers perform applied

research in exchange for fees and other support,

allowing firms to pool their resources and thereby

avoid duplicated expenses for facilities and equipment. The university benefits from the fees and increased research activities and from improved student training. The concentration of technical

know-how can make these research centers fertile

ground for creation of new high-technology businesses. Because these centers also have been cited

as a major factor in the development of high-technology complexes, a number of States have begeto encourage their development.

For example these research centers include the

North Carolina Center for Microelectronics, the

Center for Applied Microelectronics at the University of Wisconsin, and the California Microelectronics Innovation and Computer Opportunities

(MICRO) research center. The University of Minnesota has joined the ranks of microelectronics research centerieby raising $6 million from businesses

in the State. The Surface Science Center at the University of Pittsburgh will provide basic and applied

research results applicable to indust--.4 al technologies,

and the University of Wyoming set up an Industrial Fund to provide applied n , earch results to

area businesses.

Several universities have also set up special offices

or technical centers to provide short-term technic al assistance t', local businesses, including patent

:,earches, technical staff, and other research services. Rather than establishing long-term research

agenda, these centers tend to emphasize technologi

transfer and consulting services. Such centers car

be particularly helpful in communities with fragmented industrial bases where firms are unable to

pool their resources effect,iyely.

The Center for Industrial Cooperation at the State

University of New York at Stony Brook, for example, provides research and technical assistance on

specific industry projects for 15 dues-paying industrial affiliates. Another technical center is the George

Mason Institute at George Mason University in Virginia, which provides technical assistance to hightechnology business and education groups in the

State. The Delaware Technical and Community

College (with fundinerom the U.S. Economic Development Administration) is setting up a similar

center to work with technology-based businesses in

Delaware; the school also is working with General

Motors to develop a joint training and retraining

---ram for auto workers. The University of Mis-

-.xi also is working closely with the auto industry

to train and retrain workers for the new technical

demands of automated manufacturing. The University of Wisconsin has an Industry Research Program

that provides business with information on the results of its research.

Several university/industry research partnerships

have been formed to match the special technical

needs of high-technology industry and the unique

resources of the educational sector. The two most

common forms of cooperative ventures are joint ventures between a university and a single firm, and

research consortia involving several companies and/

or universities. Such arrangements can take many

legal forms, including long-term research contracts

69

Photo credit: Co orgi Moon Unirersity

George Mason Institute In Virginia
Ch. 4State and Local Initiatives for HighTechnology Development 61

and limited partnerships. Recent Federal legislation

enables industrial partners to obtain tax credits for

investments in universiti; research, in addition to

capital gains treatment for profits on the products

of the research.

Cooperative research ventures represent a very

small portion of university research, although several substantial ventures have been launched in the

last few years. The most visible and substantial agreements have been signed between Harvard University and Monsanto; Washington University and

Mallinckrodt; Harvard Medicel School and Seagrams; Massachusetts Institute of Technology and

Exxon; Carnegie-Mellon and Westinghouse; and

Washington University and Monsanto. The relationships vary from simple corporate grants to complex contracts giving the industrial partner control

over intellectual products.

The direct grants approach is exemplified by the

$6-million, 5-year immunogenetics program sponsored by DuPont at'Harvard; the $7-million, 10-year

combustion science grant from Exxon to MIT; and

the $5-million, 5-year tobot development project

sponsored by Westinghouse at the Carnegie-Mellon

Robotics Institute. These grants are targeted for specific research and have a turn-back arrangement so

that the corporation can benefit from inventions.

A few universities, seeing the potential for income

from cooperative research, have become entrepreneurial. Stanford 1.iniversity in 1981 created

Engenics, a for-profit company to develop large-scale

chemical processin techniques, arid the Center for

Biotechnology, a nonprofit research organization

provided with $2 million by the six corporate supporters of Engenics. Stanford holds 30 percent of

the equity in Engenics.

Other university programs sponsored by individual firms focus on particular academic problems. For

example, IBM has a launched a $50 million program

of grants and equipment donations to improve manufacturing engineering, and Exxon sponsors a $16.8-

million engineering faculty assistance program to

supplement junior faculty salaries.

Recently, several companies have organized into

consortia to pool resources for several universities

and special programs. For example, the 10 major

makers of semiconductors (including Honeywell,

Hewlett-Packard, and IBM) have established the

Semiconductor Research Cooperative, which will

idehtify generic research needs and work with university research departments.

Research consortia may i:Iclude either one company and several universities, several companies and

one university, or several companies and several universities. An example is the research center at Purdue University in Indiana, jointly sponsored by five

corporations, to develop computer prototypes. Similarly, Pennsylvania State University has 20 industrial sponsors for a cooperative program in recombinant-DNA technology. Other examples include

the Caltech Silicon Systems Project, Stanford's Center for Integrated Systems, the Polymer Affiliates

Program at Drexel University, and (perhaps the most

complicated example) the Center for Biotechnology

Research.

The Center for Biotechnology Research is sponsored by Engenics Corp. (itself a Stanford spinoff)

along with six other companies: Bendix, Elf Technologies, General Foods, Kopvenco, Mead, and

MacLaren. Three universities are involved: Stanford, the University of California, and MIT. Resulting patents will be held by the universities, with the

center receiving royalties, and the contributing corporations having exclusive rights to the patent licenses. The object of the center is to provide multiyear funding for university research and to enhance

the effectiveness and efficiency of basic and applied

research. It will also allow universities to benefit from

a product's financial success, as well as providing

industry with incentives to justify long-term research

investments.

Technology transfer, through patenting and licensing, has traditionally been handled by university administrators, but recently this important commercialization function has also been assumed by

private nonprofit alumni foundations. Some of these

foundations are independent of the university, but

all of them rely on university research capability for

inventions that can be commercialized.

The Wisconsin Alumni Research Foundation is

the largest and oldest university technology-transfer

operation. It is a multimillion dollar operation. Its

1929 patent of vitamin D has provided $14 million

in license income.
62 Technology, innovation, and Regional Economic Development

More typical is the University of Virginia Alumni

Patent Foundation. Funds for the program were provided initially by the alumni foundation, but subsequent funds were raised from the private sector and

from 4X)yalty and licensing agreements. Patent income averages between $50,000 and $100,000 per

year. The foundation has processed approximately

200 faculty and alumni inventions; working with

patent attorneys, arranging for licensing, and iden

tifying market opportunities.

The Washington Research Foundation (WRF), a

nonprofit organization established in 1982, seeks to

increase Washington State's share of the market in

high-technology products and processes. WRF plans

to work closely with the State's universities as well

as other research centers. A bank loan of up to $1

million has been guaranteed by pledges from individuals, law and accounting firms, and manufacturing establishments.

Human Capital Development

Other initiatives focus on developing the human

capital that is needed to exploit these technological innovations. Two important secondary :fleets

of the university/industry collaboration discussed

above are: 1) improving science and engineering

training, and providing continuing education for

those already env wed by induar. y. For many initiatives, however, these are the principal goals. Some

universities, for instance, provide student internships

in high-technology companies or, in cooperation

with State governments and local employers, offer

special training or retraining programs for-technical workers. Local governments frequently lobby for

engineering programs at nearby State colleges or develop special "magnet" high schools or technologybased curricula in their vocational education

programs.

Several high-technology companies also contribute funds, equipment, or personnel to upgrade

science and mathematics instruction in the local

public schools. In other cases, local initiatives focus

on creating employment opportunities for engineers

or technical workers who might otherwise leave the

area because of cutbacks at a nearby research in

stallation.

71

An OTA survey revealed that about half of all

State HTD initiatives involve high-technology training or education programs." Training programs,

which made up one-third of those surveyed, often

operate through grants to other organizations, and

many of these programs have obtained funding from

Federal sources. Some States are analyzing the use

of customized job training (i.e., specifically tailored

to the needs of potential employers) in connection

with new Federal efforts under the Job Training Partnership Act of 1982 discussed in chapter 5.

High-technology education programs represented

almost one-fourth of the initiatives in the survey

States. An example is California's MICRO program,

which provides funding for graduate fellowships and

faculty research projects, and is supported by matching grants from private industry. Several universities

have also established industry internstap programs

designed to provide students with practical experience in technology-based businesses. Lehigh University in Pennsylvania, for example, has a cooperative master's and Ph.D. program that combines

professional work (directed by industry advisors)

with study and research conducted at the university. Similar programs exist at Carnegie-Mellon University and the University of Detroit, among others.

A number of local communities have also developed engineering programs at local universities.

These initiatives, which 4an be used to develop a

technical work force in technology installation and

bootstrap communities, include efforts to create an

engineering department at a university that has not

had one, add graduate programs, upgrade the overall

quality of a program, or attract faculty members with

specializations in areas of importance to local industry. Local governments in diluted centers, teshnology installation centers, and bootstrap communities have also used vocational-technical training

initiatives to attract high-technology firms. These

initiatives have taken the form of adding specific

training programs required by local industry or the

development of high-technology "magnet" gh

schools.

I

".+r,Irc Intriarivo Survey, prepared for VTIA by the RCM:ilft h 'Triangle

Institute, Alvin M. lruze, print ipal investigator, 'Say
Ch. 4State and Local Initiatives for High-Technology Development 63

Business executivesworking as individuals, participating on advisory councils, or as members of

a business organizationshave also focused their attention on strengthening educational institutions.

Corporate practices regarding education can he

viewed as initiatives to create the innovations and

intellectual infrastructurethe raw materialsthey

need to survive. Many high-technology firms contribute funds to universities, and several trade associations have issued policy statements encouraging

their membership to give at the "2-percent level."

For example, the American Electronics Association

has set a goal of 2 percent of each member firm's

annual research budget to be contributed to universities for supplementing faculty salaries and developing research facilities. The Massachusetts High

Technology Council asked its members in January

1982 to raise their level of support for higher education to 2 percent of their annual R&D expenditures. In December 1982, they announced they had

met their $15 million goal.

Another method of providing resources is lending

personnel whose technical skills can assist educational institutions. High-technology companies such

as IBM and Xerox have been leaders in this area,

particularly for training programs.

The Harris Corp. operates an extensive program

with local junior and senior high schools in Florida.

Company personnel give lectures and work with

schc pens' 'net to promote interest in science and

atics. Harris' activities are motivated by a

desire to retain their present employees (whose children attend these schools) and engender positive attiatudes toward technology among high school students (who are potential future employees).

Honeywell is involved in the creation of a new

magnet program in a local high school in Minnesota.

This program will focus on science and math skills

but also will promote a broad skills base. Honeywell

has work, with the school system to develop a strategic plant for technical skills development, and the

company has contributed funds as well as lending

personnel.

Donating equipment represents a comparatively

small but growing component of private sector initiatives by high-technology firms. According to Independent Sector, an association representing nonprofit organizations, the value of corporate noncash

72

giving (equipment and materials) was approximately

$6 billion in 1983. Deductions created by the Economic Recovery Tax Act of 1982 are expected to

increase corporate equipment donations. Corporations also view donating equipment as cultivating

a market for their high-technology products.

Entrepreneurship Training

and Assistance

A special subset of human capital is entrepreneurship, and many HTD initiatives are designed to provide training, technical and management assistance,

and other support needed by those who create new

technology-based companies. The OTA census identified 15 State initiatives, undertaken in conjunction with State universities, that were designed in

part to equip inventors or entrepreneurs with the

skills needed to commercialize emerging technologies. These 15 programs are only a fraction of the

training efforts of U.S. colleges and universities: the

number of entrepreneurship courses grew from fewer

than 10 in 1960 to more than 200 in 1980, and in

1984 as many as 400 colleges and universities offered

courses in the creation a management of small

businesses." In some cases hese programs are supported by the private secto which sees in them an

opportunity to promote the values of capitalism as

well as the university's role in technological innovation and business development.

The University of Texas, for example, has not

only a Chair of Free Enterprise (established in 1976)

but also an Institute for Constructive Capitalism,

funded by Mobil, Shell, Tenneco, and other corporations. Similarly, two leading venture capitalists

have recenly endowed a chair at the Harvard Business Schoc I devoted to the creation and management of new business ventures.

Wichita State University established a Center for

Entrepreneurship and Small Business Management

in 1977. The initiative is suppo4ed by over 50 area

businesses, and the force behind its creation and

development is a professor who is also a successful

entrepreneur. In addition to seminars and publications, the Center is about to start a small business

incubator.

"umversitto Emerge ,s, an Important t :atiflyst in the New Nisi.

Developmenr Prose~.," Verrrire (7,ifitta/ 2 3. Ni. 5,

Aug.isr 1981, p.
64 Technology, Innovation, and Regional Economic Development

In addition to courses for full-time students, many

universities also provide seminars and conferences

on business development topics, notably on how

to raise venture capital, or provide technical and

management assistance to local entrepreneurs and

inventors. Baylor and Case Western Reserve, among

other universities, provide innovation evaluation

programs in addition to courses and seminars. Carnegie-Mellon and the University of Pittsburgh jointly sponsor the Pittsburgh Enterprise Corp. to foster new business development. The MIT Enterprise

,Forum, sponsored by the alumni association, conducts "incubator forums" in several cities. These efforts have been successful in involving local professionalslawyers, accountants, bankers, consultants,

and government officials, as well as university officialsin the local entrepreneurial network.

One of the most highly developed set of initiatives

for promoting high-technology entrepreneurship

and small business development has been created

by the private sector in Minnesota, in coopers ion

with the University of Minnesota and State a, 'd

local governments. Star Co (Start-a-Company

sponsored by the Minnesota Business Partnership

is a program through which established firms assist

in the creation of new small businesses through technology spinoff, management consulting, and/or

equity investments. Some 35 large corporations have

already committed to assist in the startup of two

new companies apiece, and smaller firms will assist

in the startup of one new company, A related initiative is the Minnesota Project Innovation (MPI),

launched in November 1983, which in addition to

technology spinoff and entrepreneurship assistance

will help the State's small high-technology firms

compete for grants under the Federal Government's

new Small Business Innovation Research (SBIR) program. MPI, created at the recommendation of the

Governor's Commission on SBIR grants and initially funded by a State grant, will be coordinated

through and use the resources of the Control Data

Business and Technology Center in Minneapolis.

Private sector participation in such initiatives is encouraged by State legislation passed in 1983 that provides tax credits for technology transfers or investments in qualified small businesses, as well as for

contributions to private sector organizations like

StarCo, MPI, the Minnesota Cooperation Office,

and the Minnesota Seed Capital Fund.

Financial Assistance

Thirty- four'of the 68 State government initiatives

surveyed by OTA provide some form of financial

assistance to high-technology firms. Most of this

assistance is indirect, taking the form oftax credits,

industrial revenue bonds, or loan guarantees. While

many State programs help firms to locate seed or

venture capital, very few provide risk capital themselves.

Most of the local representatives interviewed in

the course of the OTA assessment recognized the

importance of venture capital to HTD, but few expressed satisfaction with their initiatives to fill this

need. Planned and existing efforts included seminars

or conferences for venture capital firms and local

entrepreneurs, the identification of local venture

capital resources, and consulting assistance in procuring venture capital. Several cities, including

Cleveland and Cincinnati, were also developing

local venture capital funds. However, effective local

venture capital programs presuppose a substantial

number of high-technology innovations in a community each year, something few communities experience.

Many colleges and universities have begun to take

a mere active role in financing new technology-based

companies." These investments are usually made

from the university's endowment or alumni fund,

with capital gains rather than new business development as the object. In some cases, universities invest directly in companies that have spun off from

research and technical centers. Examples include

Boston, Brown, Harvard, Lawrence, and Stanford

Universities; the Universities of Chicago, Notre

Dame, and Rochester; Rensselaer Polytechnic Institute; and Grinnell College in Iowa. In other cases

universities work to make capital available to new

starts by investing in venture capital partnerships.

About $350 million has been invested in such partnerships, most of it since 1980, by such universities

as Harvard, MIT, Stanford, and Yale. Other universities (including Carnegie-Mellon, Georgia Tech,

Case Western Reserve, and the University of Pennsylvania's Wharton School) are supporting the formation of local seed capital funds for startups and

' op, k it, pp. 1 1-12.
Ch. 4State and Local initiatives for High-Technology Development 65

spinoffs, often in connection with their incubator

facilities and entrepreneurship assistance programs.

However, formal venture capital is still dominated

by independent firms and corporate subsidiaries,

which tend to resist geographic requirements. These

investments tend to go where the returns are expected to be greatest, which in recent years has

meant California and Massachusetts. Several universities and local governments have tried to attract

these investments to their areas by holding venture

capital conferences. In addition, several State and

local governments, in cooperation with local business groups and foundations, have recently established venture capital funds with explicit geographic

requirements.

Seed capital, invested at the earlier and riskier

stages of a new venture, does tend to stay local, but

only about 5 percent of venture activity is targeted

for seed efforts, and there are only a few firmsperhaps 5 or 10 nationwidethat specialize in seed investments. One example is the Bay Venture Group,

which was established and completed its first deal

in 1976. The limited partners are individuals with

a net worth in excess of $40 million. They assume

that from concept (seed) to public offering will take

from 8 to 12 years. Their deals are made on the market promise of "several hundred million dollars" in

sales per year. Ideas are found "word of mouth,"

and the firm provides significant technical assistance.

Another, Npha Fund, is based in Palo Alto and

raised $13 million from individuals, corporations,

al. -3 endowment funds to support seed investments,

primarily in the San Francisco Bay Area.

The local economic impact may be even more pronounced for informal seed capital investments.

Where there is little local risk capital activity, the

private sector has sometimes sought to establish a

presence by creating investment vehicles to pool

local risk capital and encourage local entrepreneurs:

The Minnesota Seed Capital Fund was an outgrowth of the Minnesota Business Partnership,

a statewide business executives group. The fund

has attracted capitalization of $10 million from

pension funds and individual investors. It targets its investments instate to firms in the startup and early development stages. It works closelv with the Minnesota Cooperation Office, an

74

independent nonprofit organization that provides technical assistance to new businesses.

The Michigan Investment Fund (MIF) is a limited partnership that was initiated by the

Charles S. Mott Foundation. The Foundation,

working with a nonprofit small business expert,

developed a blueprint for a limited partnership

to serve primarily the economic needs of the

State. MIF plans to direct 60 percent of its investment instate, but not all the funds will be

invested in high-technology firms. The remaining 40 percent will be used to establish relationships with out-of-State venture firms in hopes

that those investments will lead later to capital returning to Michigan. (The Mott Foundation has a blueprint for a similar endeavor that

will involve three Michigan counties. Presently

in the planning stage, the Flint River Capital

Fund will work closely with the General Motors Institute on new technologies.)

The Cincinnati Chamber of Commerce, with

the aid of the Gannett Foundation, is in the

planning stage of creating a venture capital firm.

The firm will not be required to invest in Cincinnati. The Chamber feels that a local presence will enhance the likelihood of promoting

entrepreneurship but will not be directly responsible for generating this capability.

In Cleveland, the Gund Foundation sponsored

a study of the city's economic profile that recommended the creation of three entitiesone

for research coordination, one for technical

assistance, and one to provide local venture capital. The first two are in the planning stage; the

Primus Fund has capitalization of $30 million

and will start making investments in 1984.

These investments will be limited to Ohio, with

an emphasis on the greater Cleveland area, and

will be targeted for "high-growth" opportunities.

Physical Capital

Local governments often seek to encourage I-ITD

through changes in land use and zoning, as well as

the provision of public services and facilities. Research and science parksparcels of land set aside

for research-inter firms and facilities, with varying tax incentives and eligibility requirementsare

the most common form of this type of initiative.
66 Technology, Innovation, and Regional Economic Development

These parks are usually accompanied by improvements in local utilities, transportation systems, and

other infrastructure. More than 150 research and

science parks have been developed in the United

States since the 1950's, and all five types of communities identified earlier have engaged in this type of

initiative.

Research and science parks have also been built

by universities on sites adjacent-to the campus, often

in conjunction with entrepreneurship programs or

technical centers. Four basic benefits can result from

locating a research or science park near a university. First, increased interaction and easier communication between university and industry researchers helps to broaden the mutual understanding of

problems and needs. Second, business gains quicker

access to new developments through increased information and knowledge transfer. Third, business

also gains access to student workers and faculty consultants, as well as to laboratory, computer, library,

and other resources. Finally, the increased interaction opens opportunities for creating new businesses

and new university/industry programs.

The Stanford Research Park in California is often

cited as the model for university/industry science

parks, as is the Research Triangle Park in North

Carolina, although it was originally a State government initiative. A more recent example of this initiative is at the Washington State University, which

recently established a research and industrial park

to provide consulting opportunities for faculty, employment opportunities for students, and enhanced

rese -ch funding for the university. The University

of Utah has a science park specializing in biomedical

research and development. Rensselaer Polytechnic

Institute, a leader in university/industry cooperation, has a new high-technology industrial park 15

minutes from campus. The University of Illinois is

working with the State to set up a high-technology

research park on land donated by the city of Chicago. Similar efforts are underway in Florida, Michigan, New jersey, Connecticut, and several other

States.

Science and research parks are not without risk,

however: they take a long period of time to become

successful (often 15 to 30 years); carrying costs can

be high if suitable users are not attracted quickly;

and the parks can monopolize large tracts of valuable land that might be put to other productive uses.

Research Triangle, NC

Durham

Duke University

1.85

Chapel Hill

University of

North Carolina

Research

Triangle

0 Raleigh

North

Carolina

State

Some communities ultimately have had to relax

their usage criteria to attract nontechnological users,

but once undermined in this manner the research

parks lose much of their appeal to technology-based

companies. Recent studies suggest that as many as

50 percent of all high-technology parks have failed

to achieve their intended goals because of poor planning, insufficient promotion, or lack of ties with

nearby universities." Even among parks that were

started by or closely affiliated with universities, only

6 out of 27 were clearly successful; 16 to 18 were

failures because they had attracted no industry or

less than planned, and the remainder were still in

some stage of development."

Incubator facilities, which provide low-cost office

and laboratory space for entrepreneurs and struggling firms, are another physical capital initiative

of local governments. These facilities are most often

built in areas where the quantity of high-quality

speculative space for small users is limited. Such

areas include inner-city portions of diluted centers

and-smaller communities without a large high-technology base. Incubator facilities require experienced

real estate management, and (as with research parks)

carrying costs can be high if they are not utilized.

In addition, technology-based tenants often require

technical and management assistance.

"Charles W. Minshall, An Overview of Trends in Science and High

Technology Parks, Economics and Policy Analysis Occasional Paper

No. 37 (Columbus, OH: Battelle Columbus Laboratories, October

1983), pp. 18-21.

15Southern Regional Education Board, Sites for High Technology

tiviries, draft based on report by Battelle Institute, June 1983.

75
Ch. 4State and Local Initiatives for HighTechnology Development 67

Several universities also have established incubator facilities to make their resources available to new

businesses or entrepreneurs developing a new product or process. Such a center recognizes and formalizes the university's role as a seedbed for new

technologies and new technology-based companies.

This approach incorporates and exploits several

resources of the 'university, including low-cost office and laboratory space, as well as access to capital, business planning, and management advice from

faculty members and local professionals. While some

of these centers extend eligibility to qualifying small

business, their emphasis is on the enterprising student or faculty member who needs a start in commercializing a promising innovation.

The oldest such facility is the University City Science Center in Philadelphia, founded by 23 colleges

and universities in 1967, but similar centers exist

at Rensselaer Polytechnic Institute, Georgia Tech,

Carnegie-Mellon, MIT, Wichita State, and the University of Missouri. The Utah Innovation Center,

set up by the University with an NSF grant in 1978,

has continued as a private concern following the

loss of its Federal funding. (See ch. 5 for a discus

sion of the NSF Innovation Center Experiment.)

Moreover, recent data on the role that small business plays in innovation and job creation have

sparked interest within the private sector regarding

incubator facilities and technical assistance centers.

These facilities often nrovide technical and financial assistance as well as low-cost office and laboratory space for high-technology firms in the seed and

startup stages.

For example, the Advanced Technology Development Center at the Georgia Institute of Technology is a new effort to promote indigenous hightechnology industry in the Atlanta area. The effort

is State-initiated, but the private sector will contribute $1.7 million of the projected $5.1-million budget.

Facilities now under construction will provide lowcost space for entrepreneurs. As of 1982, the CenUtah Innovation Center, Phase

Photo °Wit' Utah Innovation Canter
68 Technology, Innovation, and Regional Economic Development

ter had worked with 30 companies. One Of its most

successful programs is an annual venture capital conference that brings together startup hopefuls with

potential investors.

Control Data Corp.'s Business and Technology

Centers (BTCs) approach the need for incubators

as a profit-making opportunity. BTCs provide entrepreneurial firms with technical assistance and

basic shared services at reasonable rates. The original

Control Data BTC, established in St. Paul in 1979,

has been highly effective in promoting the survival

and growth of small entrepreneurial firms; out of

126 new companies representing over 1,000 new

jobs, the survival rate is 88 percent over the first

5 years. BTCs have already been established in 12

other U.S. cities, and Control Data plans to open

as many as 13 additional BTCs during 1984.

Information Gathering

and Dissemination

The first step in almost any State or local hightechnology strategy is the creation of a task forte

or commission, usually with university and private

sector participation. Task forces serve to focus local

attention and often have a pronounced networking effect. They also perform a valuable service in

gathering information about the needs and problems that can be addressed through HTD; the institutional and economic resources that can be

brought to bear; the kinds of actions that might be

undertaken; and the experience of other States and

communities with similar initiatives.

OTA identified several instances in which task

force recommendations were the basis for subsequent State initiatives, and in some cases the task

force itself became a permanent council or foundation charged with implementing and overseeing

these activities. Examples include the Connecticut

Product Development Corp., created in 1972 by legislation growing out of the State's Full Employment

Task Force; Georgia's Advanced Technology Development Center, created as a result of a study commissioned by the Governor in 1979; the Bay State

Skills Carp., which evolved from a gubernatorial

plan to meet Massachusetts' need for more skilled

and trained workers; and the Tennessee Technology Foundation, created as a result of recommendations of the Governor's Technology Corridor

Task Force.

Local task forces usually are appointed by mayors,

although they are sometimes an adjunct of the

chamber of commerce. They generally include representatives from industry, education, and government. They are distinct from other initiatives in that

they are not designed to overcome some limitation

in a community's ability to attract or retain hightechnology companies. Instead, they have a designing function and, in some cases, participate in implementation. They also have a pronounced networking effect and serve to focus local attention and

resources on high-technology economic development.

High-technology information dissemination usually takes the form of government marketing programs aimed at target firms and industries. The

OTA census found that the services most frequently

offered by dedicated State programs involve information dissemination-17 programs link industry

and university resources, and 8 others involve promotional activities aimed at high-technology prospects, in part to inform them of the existing HTD

resources and activities in the State. These initiatives

often involve extensive cooperation with individual

communities.

At the local level, virtually all communities conduct marketing programs to attract new industry.

However, those localities with the most sophisticated

programs directed at high-technology companies

tend to be those that already have experienced the

greatest success in attracting them. These include

communities in all categories, with the exception

of the high-technology centers themselves, but the

programs differ in their focus depending on the type

of community involved. For example, the spillover

communities are most likely to direct their efforts

toward companies located in the city to which they

are adjacent, while bootstrap communities primarily seek to attract branch plants of expanding technology-based companies. The key ingredients of

these initiatives are the identification of specific firms

for which the community would have the greatest

appeal, the improvements to the required infrastructure or amenities, and a concerted marketing effort

through mail, telephone, and personal visits.

Business groups also undertake promotional campaigns, usually advocating desired changes in public policy but occasionally aimed at increasing the

development efforts of member firms. These advo77
Ch. 4State and Local Initiatives for High-Technology Development 89

cacy programs build consensus and bring private

prestige to bear on public problems, just as public

advocacy programs give recognition to the contribinions of business groups and individuals. National

trade associations tend to focus on Federal policy,

although as the locus of governmental responsibility for economic development shifts to State and

local governments, State business groups can be expected to gain in influence.

The Massachusetts High Technology Council

(MHTC) is one of most successful business/civic advocacy organizations in the Nation. In 1979, the

Council established a "social contract" with the

Massachusetts government to create 60,000 jobs if

the State brought total taxes to a level competitive

with the 17 other States against which local hightechnology firms competed for technical talent.

Taxes have dropped, and MHTC has fulfilled its

part of the contract.

Additional Factors

Whether the initiatives investigated by OTA hold

promise for promoting both technological innovation and regional economic development is unclear.

Most have been launched in the past 3 to 5 years,

and the majority have undergone no formal evaluation or comparative analysis. Moreover, given the

differences in their goals and mechanisms, absolute

criteria for success will be difficult to determine

some are designed to attract new industry in the

short run, while others are building the technological infrastructure for growth in the future. Many

involve institutional changes that might take decades to bear fruit.

In fact, since their most important effects may be

indirect, the effectiveness of these initiatives will

always be difficult to measure. In some cases, relatively mature initiatives have been very slow to produce any significant results, while more recent programs elsewhere are already considered successful.

Furthermore, many of the States and communities

investigated by OTA had already experienced a considerable amount of HIT) before launching their initiatives, and other regions have experienced a great

deal of HTD even without a dedicated initiative.

No single factor explains why some communities

and regions have been more successful than others

in nurturing and benefiting from HTD. For every

!manorial determinant identified in economic theory

or implicit in government practice, examples can be

provided of cities that have several or all of the ingredients but have not yet achieved success. The

traditional factors .rong research university,

skilled labor pool, available financing, the presence

of corporate headquarters, transportation, good climate, and cultural amenitiesmay not always he

enough. OTA's investigation suggests that the following additional factors may also be important for

State and local HTD initiatives:

identifying local needs and resources;

adapting to external constraints;

local initiative and partnership;

linkage with broader development efforts; and

sustained effort, often over a period of decades.

In short, it appears that cooperation and commitment by public and private individuals and organizations may provide the necessary catalyst to bring

the ingredients together.

Identifying Local Needs and Resources

Successful H I L) initiatives generally reflect a sense

of the region's distinctive attributes and potential.

Different regions have different needs and different

resources with which to address them. As a result,

no single approach or program design will work in

all settings. Although individual States and communities can learn from the successes of others, implementation must be region-specific, This requires

a detailed knowledge of local conditions and a clear

recognition of the local attributes, both strengths

and weaknesses, that will influence a region's ability

to attract or spawn high-technology industry.

Analysis of this type is typically conducted by

high-technology task forces representing govern78
70 Technology, Innovation, and Regional economic Development

ment, university, and industry. In some cases, this

function has been performed by economic development officials or by outside consultants. In any case,

successful initiatives have required careful planning,

and some of them have been based on comprehensive, long-term master plans. At the minimum, the

analysis should identify explicitly, in advance, not

only what the community can realistically expect

to achieve through HTD, but also the resources that

will be required to achiive these goals.

In many cases, successful initiatives simply redirect

or build on preexisting resources and activities, often

by creating new institutional linkages. The increased

emphasis on the roles of universities and entrepreneurs, for example, reflects a recognition that they

constitute an important and possibly underutilized

resource for regional economic developmenVtimilarly, most State initiatives reflect an awareness of

the existing industrial base and the role it plays as

both the source of technological opportunities and

a potential market for new products and processes.

Adapting to External Constraints

Three a many factors over which a community

has little control. These include physical factors such

as climate, terrain, and proximity to existing hightechnology centers, as well as a variety of policies

and regulations imposed by higher levels of government. Successful States and communities recognize

these external constraints and adjust their objectives

and strategies accordingly. By doing so they also

avoid the conflicting goals and inflated expectations

that might otherwise lead to constraints within the

community.

Those without an existing high-technology base,

for example, typically focus their initial marketing

efforts on branch plants rather than on rerearchor technology-intensive establishments. Over time,

as these branch plants create a skilled labor force

and technical infrastructure, the communities may

be able to attract more sophisticated operations and

encourage local spinoffs. The time required for this

process is another constraint. Even when initiatives

are successful, H I1) does not represent a quick fix

for regional economic problems. In addition, no successful State or community focuses its efforts exclusively on these industries; most HTD initiatives are

components of a broader economic development

st rategy.

79

Local Initiative and Partnership

High-technology development efforts have generally been most successful when they are initiated

and implemented locally. Some communities receive

substantial help from State governments in developing university resources and complementing the

local marketing program. Others have found innovative uses for funding and development tools made

available by the Federal Government. But in most

cases, the objectives and strategies are developed

locally, and local representatives play a major role

in the design and implementation of the initiatives.

While the organization responsible for the program or the degree of collaboration may vary between initiatives or communities, institutional collaboration is almost always present in successful

initiatives. In fact, the form the initiative takes may

be less important than the reordering of institutional

relations it engenders. This cooperation or "partnership" among government, universities, and local

business groups emerges most easily at the local level,

where the public and private sectors are less distinct.

Stable political climate and local government with

an efficient, pro-business image have a positive influence on the level of institutional involvement and

cooperation. So too does the existence of organizational mechanisms or a network of intermediaries

and brokers to bring together public and private

leaders. Also important is the past history of public/

private initiatives in the community: a strong history of collaborative efforts provides a foundation

of positive experience, as well as building trust and

understanding between business, government, and

community groups. States and communities that

have benefited most from these factors have three

characteristics in common:

A social and organizational culture that promotes an underlying consensusa common

civic perspective and a positive attitude about

the region's attributes and prospects.

An environment that nurtures entrepreneurial

leaders, both public and private, who combine

an established track record for innovation, a

broad view of their community's promise, and

the ability to recognize and exploit changing

opportunities.

A network of business/civic advocacy organizations that attracts the membership of top

officers of major companies and receives from
Ch. 4State and Local Initiatives fo1 ligh- Technology Development 71

them the commitment of time and effort to

work on issues of mutual concern, including cooperation with the public sector.

Linkage With Broader

Development Efforts

Successful high-technology initiatives are seldom

undertaken in isolation, and the most substantial

results often come from multiple initiatives and those

that are components of a broader de lopment strategy. This requires complementarity twein FITQ

programs and more traditio..al econ in" ic Svelopment activities, as well as coordination between the

HTD initiatives of different sectors. This restructuring of institutional relationships, aimed at mobilizing the technological infrastructure for HTD, is in

fact the principal difference between HTD and general economic development initiatives.

Most State officials consider their high-technology

initiatives to be a logical and perhaps inevitable extension of more traditional economic development

efforts. This attitude apparently is correctthe majority of high-technology executives who stated that

their location decisions had been influenced by a

State program identified a general economic development or training program, rather than a hightechnology initiative. Efforts to attract high-technology branch plants, for example, are generally part

of a broader effort to strengthen or diversify the industrial base. Similvrly, most local strategies involve

not only incubator facilities and technical centers

but also more traditional initiatives to make the

community more attractive to technology-based

firms, such as training progra s, educational improvements, or the constructio of a cultural center. Likewise, efforts to i .1crease oval venture capital activity are usually tied to university

improvements, entrepreneurship assistance, and

technical extension services. The failure to create

such linkages between university and industry was

a leading cause of failures in science and research

parks. 16 k,

°1v9insha(I, up, sir.

36-737 0 84 QL 3

The same may be true of the role of Federal programs in State and local HTD initiatives. Block

grants and tax-free bonds 4e highly flexible instruments and have been used for a wide variety

of projects, depending on the needs (or desires) of

different communities. Their influence is usually indirect and diffuse: they provide funds and development tools that can be very effective when used imaginatively, in conjunction with other efforts, by

entrepreneurial local leaders.

Sustained Effort

HTD does not represent a quick fix for regional

economic problems, and few States or communities

have developed large concentrations of high-technology establishments in a short period of time.

Route 128 and Research Triangle Park, for instance,

might have been considered failures if their results

had been measured too soon. Based on the few initiatives that have been in place for a significant

period, a minimum of 10 or even 20 years may be

required to translate these institutional innovations

into a significant number of local firms and jobs that

can be attributed to products created by indigenous

entrepreneurs or research establishments. As a

result, success will depend in part on sustained effort and commitment, including stable long-term

funding.

Different initiatives have different time horizons,

however. Some regions have been able to strengthen

their economies quickly by attracting branch plants

of technology-based companies. More immediate

benefits can be achieved through short- and medium-term strategies, such as administrative reforms

and infrastructure improvements that lay the foundation for subsequent private investment. Educational and venture capital initiatives, on the other

hand, take longer to show results. This suggests the

possibility of a staged, incremental approach to HTD

in which different organizations launch complementary initiatives with different time horizons that

build on one another. The exact timing and mix

of mechanisms in such a strategy are subjects for

further research.

80
CHAPTER 5

The Federal Role in Regional

High-Technology Development
.4

Contents

r.ige

Introduction 75

R&D and Innovation Policies

Technology Transfer Programs 78

Technical Information 79

Technology Brokering 79

Extension Services 80

Recent Policy Developments 81

Pending Legislation 82

Regional Development Programs and Tools 8.3.

Community Development 84

Business Development 87

Education and Training

Planning, Demonstration, and Models

State Technical Services Program 91

National Science Foundation 91

Table

Page

17. Growth in Federal Outlays for Grants, by Function 83

Figures

Pdge

5. Federal, Private.% and Tot &D, 1953-84

0. Federal R&D by Budget 'unc in, 1953j5

#. 7r. 82

41;'.
CHAPTER 5

The Federal Fit le in Regional

1-f!cih-Technology Development

introduction

Fiscal, monetary, and other broad Federal policies

influence the general economic climate for State and

locai development efforts, just as they affect the environment for all economic activities In addition, Federal policies have had important impacts on regional

high-technology development (HTD) when existing

Federal programs or resources were utilized

junction with ;3 State or local HTD irlit tri

some cases the Government's role was rel.

rect: Federal officials served as members of task

and commissions commissions in several communities, and other

initiatives were supported by Federal planning

grants. More frequently, however, the Federal role

has been an indirect or even unintentional result

of the pursuit of an agency's primary mission.

Major Federal research and development (R&D)

installations, for example, frequently have provided

the base or nucleus around which high-technology

initiatives hay, been built. In addition, many State

and local initiatives have made innovative use of

block grants, training programs, loan funds, and

other development tools provided by the Federal

Government. "these Federal programs provide highly flexible instruments that have been used for a wide

variety of projects, depending on ,:he needs of different communities. They were not sa cifically designed to assist in regional HTD, but they have

nevertheless proven to be an effective adjunct to reHTD efforts when used imaginatively, and

in conjunction with other efforts, by entrepreneurial

leaders.

A's investigation has identified four different

. or areas in which the Government influences

regional HTD. The first of these roles is very broad

in ma ire:

policies to encourage R&D and technological

innovation, including not only Federal it-Ives,

ments in R&D-and tax incentives for private

investments in innovation, but also macroeconomic and trade policies and regulatory policies

in the areas of 1.atents and antitrust.

These basic national policies exert a profound influence on the economic environment for all business activity, especially technological innovation.

However, the broad impacts of the policies are beyond the scope of this assessment. They are discussed briefly below, but more atteation will he

given to three other roles in which the regional impacts of Federal policies and programs are more direct and more easily identified:

technology transfer programs that attempt to

increase innovation and growth of industrial

sectors by encouraging the diffuskI and utilization of federally developed technologies by

private industry;

general regional development programs, including block grants and technical assistance, which

provide flexible funding tools that have been

put to innovative uses in many State and local

HTD programs; and

& planning and demonstration projects that facilitate new institutional linkages and encourage

or support the creation of new HTD mechanisms at the State and local levels, some of

which have been continued or copied 'elsewhcre

with little additional Federal support or intervention,

The following sections discuss the nature 01 these

Federal roles and provide exar ,pies of programs

whose 'activities have influenced regional HTD or

supported State and local HTD initiatives. Recent

changes in these policies, and proposed changes embodied in pending legislation, are also discussed.

83 75
76 Technology, innovatron, and Regional Economic Development

R&D and Innovation Policies

The Federal Government encourages R&D and

teelmologic al innovation through two activities:

funding R&D directly, and creating an economic

climate conducive to private investment in R&D

and innovation.' Direct funding of R&D is usually

justified in terms of public goods, knt which the

Government is the principal customer, or social returns from basic research in which the private sector is likely to underinvest. However, private firms

are the major actors in the commercialization and

diffusion of new technologies, and the Government

also encourages technological innovation through

policies that make private R&D investments more

attractive. These policies are directed at the U.S.

economy as a whole, rather than the needs of particular industrial sectors or geographical regions, and

identifying their impacts on regional HTD is beyond

the scope of this assessment. Nevertheless, their general influence on regional HTD may be greater than

that of any specific program discussed in this

chapter.

Direct funding repsesents the most visible Government role in encouraging R&D and innovation. In

recent years the Federal Government has funded

about half of the R&I..) conducted in the United

States, most of it for public goods related to the missions of particular agencies. National defense is the

archetypal public good, and defense-related spendina claims an increasingly large share of the Federal R&D budgetfrom 48 percent in 1980 to 70 percent in the fiscal year 1984 budget request, the

highest defense share since 1962 (see figs. 5 and 6).

Defense-related R&D is heavily oriented toward development activities (over 80 percent) and is concentrated primarily in the aerospace and electronics

industries. C..sommereial spinoffs from defense R&D

have benefited the semiconductor and computer industries in SiliLaal Valley and Route 128, but these

civilian applications ale long-term, and unpredictable, and tend to he minor relative to the Federal

R&D ITIVe:-tItIt'Ilt Iii ptiblic: 4400(1S,2

i'd c,.(;r I ,Y,41.

/;,1( (W,I,};:tlgt,,t1, IX ". l Con

Apr,i

.? ;. A 1,,t nn 110(14 ofhi

84

Direct funding of R&D also provides social returns in areas where private industry is likely to

underinvest, particularly long-term basic research

in which the risks are high and the private returns

smaller than the social returns. Universities carry

out over half of the basic research conducted in the

United States, and funding university research is

also an aspect of the Government's educational responsibilities. A major trend in recent civilian R&D

budgets has been a sharp shift away from development and toward basic research, reflecting the Administration's belief that applied R&D should be

left to the private sector. Applied research funding

has changed little in recent R&D budgets, despite

its key role in transforming laboratory research into

commercial products and processes.' This trend may

be partially offset by increases in private sector applied research, often in collaboration with universities.

In addition to direct funding, the Federal Government also promotes innovation throiigh policies that

encourage increased private sector investment in

R&D and new technologies. Fiscal, monetary, regulatory, procurement, and trade policiesall have

indirect effects that, collectively, may have more influence on technological innovation and regional

H 1 D than programs specifically targeted on R&D.4

Changes in tax policy with regard to capital gains

and in regulations affecting pension fund investments, for example, contributed to a tripling of the

formal venture capital pool between 1978 and 1983.

The regional impacts of this increase have been

mixed: while investments have increased in most

regions, venture capital activity remains concentrated in a few States, and it is unclear whether there

has been a corresponding increase in local seed capital activity.

Recent changes in tax policy, notably the Economic Recovery Tax Act of 1981 (ERTA), have also

increased the attractiveness of investment in new

plants and equipment, thereby speeding the diffuflor,,, , it , gcncf,11 vrc of thi, scc 1111ot tic Ilwilttz, 'Illy In

Ochirt. 1)( C(71/gTCY.It)nal Blitigrt

lift, 1:, utni.cr lctriiT, and tXruhre, Et\ft.1,11 tilipport

/31,,,mrs, Vt :iNhulgtt IX Congtc-sion;11 B4,1114-t ()ffitc, jornbsry 1'454)
Ch. 5The Federal Role in. Regional HighTechnology Development 77

Figure 5.Federal, Private, and Total R&D, 1953-84

22

20

18

16

14

12

10

8

6

4

2

1975 1980

Calendar years

1981 1982 1983 1984

Figure 6.Federal R&D by Budget Function, 1953-85

1955 1960 1965

SOUKE Notional Science Foundation

1970 1975 1980

Calendar years

,ion of lit- process technologies. ERTA also inreiised the incentives for equipment donations to

universities, which figured prominently in many of

the uiversity/industry initiatives investigated by

OTA, and instituted a 25-percent incremental tax

relit for increased R&D expenditures. Private sector investment in R&D has in fact accelerated since

the late but this trend predates the recent

( flanges in macroeconomic policy, Its causes probably lie in industry's belief in the importance of

R&D for international competitiveness.'

A recent analysis by the Congressional Budget C.ffice :BO suggests that the overall impact of R&I)

incentive~ ha, been relatively small but that, with

,c)trie specifi( changes, the incremental tax credit

inwht usefulk be extended beyond its scheduled

termination at the end of 1985.' Similarly, some

1981 1982 1983 1984 1985

reconsideration of antitrust .t.iforcement might be

desirable, particularly with regard to joint R&D ventures, but such changes should be carefully evaluated

to determine their effect on competition, as well as

their technological impacts.? These new institutional

linkages are key components in several of the regional IITD initiatives described in chapter 4.

Debate on national R&D and innovation policies

is seldom concerned with regional HTD efforts, but

Federal R&D does have differential effects on regional e! < onomies through patterns of high-technology procurement, the distribution of university

R&D expenditures, and the location of Federal

R&D facilities. Government procurement ( an provide a large stable demand, establish pilot production facilities, or SO design and performance standards that require the use of new technologies.

stt
78 Technology, Innovation, and Regional Economic Development

For example, the Department of Defense (DOD)

contributed to the growth of high-technology complexes in Silicon Valley and Route 128 by concentrating research and procurement contracts there

during the 1950's. These practices have also been

used by DOD to promote the machine tool industry during the 1970's and the robotics and super

computer industries during the 1980's. Similar practices also promoted the development of new energy

technologies in the 1970's. However, CBO has

found little evidence that procurement can play the

same role in civilian technologies where the Government does not dominate the market.8

State and local groups have little control over procurement patterns and are more likely to lay the

foundations for regional HTD through university

improvements that attract additional Federal research funding. Indeed, many of the university research institutes investigated by OTA derive a large

share of their operating budgets from Federal research contracts. As civilian R&D shifts from applied to basic research, however, it has become increasingly important that universities also strengthen

. st,

/

their ties with industry, in order to generate technologies with local business and employment potential.

The location of Federal R&D laboratories and engineering facilities can have a major effect on local

economies, though not necessarily on HTD, by

bringing jobs and technically trained personnel to

a community. MIT's Lincoln Lab, for example,

played a large role in the initial development of hightechnology industry in the Boston area, just as Oak

Ridge has contributed to HTD in eastern Tennessee

and NASA's Marshall Space Flight Center to that

of Huntsville, AL.qhe Environmental Protection

Agency became the largest employer in North Carolina's Research Triangle Park, and helped to ensure

the Park's success, when it decided to locate its 1,500 -

employee Research Center there in 1965. Other Federal R&D facilities, however, have had only a slight

impact on local economies. Many were located in

remote areas for security reasons, and others never

developed active linkages with local universities and

businesses. While these Federal installations may

provide a valuable resource on which State and local

initiatives can build, their contribution to regional

development really depends on how active they are

in transferring new technologies to private firms.

Technology Transfer Programs

Exe:Tt for the few instances where industrial development is regarded as a national security con -

cern, the Federal Government most directly stimulates ter hnologiLal innovation in private industry

through technology transfer programs, which make

available the result:. of federally funded R&D or support their application. Funds spent on disseminating

knowledge gained through Federal R&D are considerably smaller than the amounts expended to gain

that information, as one would expect. The fruits

of technology transfer are beneficial to the national

economy, but the precise benefits have been difficult to quantifrand it is difficult to say how important technology transfer is to regional HTD. These

programs are nationwide, so their regional impacts

are determined by the exicnc to which they are used

by firms in one area.

56

However, Federal technology transfer programs

are more likely to contribute to regional HTD than

are Federal R&D programs, for two reasons. First,

the more than 700 Federal laboratories and technology development centers are located throughout the

Nation, in communities both large and small, urban and rural. To the extent that technology transfer takes place on a decentralized basis, therefore,

Federal R&D facilities can become a vital component in the technological infrastructure of the areas

in which they are located. Second, many of the

1ITD initiatives launched by State and local governments and universities focus on technology transfer and improved linkages between academic research and industrial applicati". Asa result, these

State and local mechanisms are in a better position

to act as a clearinghouse or broker for information
Ch. 5The Federal Role in Regional High-Technology Development 79

about Federal technologies and access to Federal personnel.

OTA has identified three general modes of Federal technology transfer, as well as numerous examples of successful interaction between Federal, State,

and local efforts. With regard to direct technology

transfer, the three typical approaches have been;

technical information, usually in the form of

newsletters, directories, or computer data bases

that list Federal technologies available for licensing and use by private firms;

technology brokering by agency staffs, who

either seek out potential users of existing federally owned technologies, or provide refer, al

services for inventors seeking technical or financial resources; and

technical extension services, which provide consultants to assist firms in solving technical problems in a certain field of specialization.

A fourth form of technology is engineering demonstratior., in which an agency takes the responsibility for demonstrating the feasibility and commer

dal potential of a new technology. Demonstration

projects have been most common in defense- and

energy-related industries; they have less general potential for promoting regional HTD based on civilian or consumer technologies.

Technical information

The main effort of technical information services

is the dissemination of research results, usually in

printed form. For example, the Department of Interior's Bureau of Mines issues a newsletter; NASA

publishes selected abstracts of technologies and a

selection of useful computer programs; and DOD

has a single facility (the Defense Technical Information Service) that holds all the technical abstracts

its contractors or potential contractors might need.

The largest data base of this type is maintained by

the Department of Commerce's National Technical Information Service (NTIS), which catalogs most

of the federally funded technology available for

licensing by the private sector.'' Each agency serves

qA.11),311v agen(Ic,, ,,uppt.311 trc, htlolog,j tranSfer prOgflinl

that k ritcr n tht Ci1.4stillnotnon of !nforrnatIon, int.lud:r.; the fc,11krAlng.

National Oceanic and Atmospheric Administration;

Department of Energy c.,(. linicai Inform:loon Centers;

t)eparttnent of Agriculture Cooperanyt: Extension Seance, Tt\

different clients and provides different services, usually as a result of the agency's mission. In most cases,

however, technology has been transferred primarily to industry, rather than to State and local governments. In addition, the transfer function has been

essentially passivethe information is available to

all corners, but it is up to the user to identify those

with commercial potential.

A major change is being brought about by the

creation of a Center for Utilization of Federal Technologies (CUFT) in NTIS and Offices of Research

and Technological Application (ORTAs) in Federal

laboratories, both of which were mandated by the

Stevenson-Wydler Technology Innovation Act of

1980 (Public Law 96-480). While still founded on the

idea that the simple communication of technical information will lead to commercialization, CUFF and

the various ORTAs also involve an explicit commitment to technology evaluationactive attempts

to identify and assess R&D projects with potential

application to the needs of State and local government or the private sector. CUFT serves as a central clearinghouse for this information and for subsequent licenaig activities. CUFF also helps State and

local governments to identify both a product or

process they need and a manufacturer that will develop and/or package it for the governments to buy.

CUFT also notifies small business associations and

State innovation groups of the availability of Federal laboratory technology."'

Technology Brokering

Technology brokering programs differ from technical information programs primarily in providing

direct contact between Government researchers and

industrial end-users of federally developed technologies. When agencies broker technologies, they actively seek out potential users of federally developed

technologies or provide such technologies in response to problems posed by industry. Technology

brokering is thus more effective at transferring techht a! Inforrnati,m Sycterw., Patent I.R.enws Prtytarn, ,tnd Sient

and Edtkatton Adnurnstration; and

Dtpartment-of -Trantperrtstioft.4:triti-n-clap r;44g: .'''v

(ahith concentrates on dissenunatIng tt ansportat ion tOlnologics

to State and local governments).

h-rhc StrItns,t,nk.Viiicr 7ctchnology /nth tvatton Al ( q. I A Re,

port n' the Pre:sic/ern anal the Congreto (A'ashtngton, IN: 1:.S. Depart:tient Fcbruary 1954, pr. 20. 2r5,

be
80 Technology, Innovation, and Regional Economic Development

nologies than are passive technical information services, but the additional cost of providing one-to-one

assistance to firms also makes it much more expensive.

NASA's Technology Utilization (TU) program is

one of the best known of this type. Initiated in 1962

to bring new aerospace technologies to the attention of American industry, TU's first efforts were

in the publishing and distribution of NASA R&D

results. By the mid-1960's, TU staffers had found

that the availability of technical information alone

would not transfer technologies effectively, so they

began a process called "people interaction"matching potential users to information and skills at

NASA installations." In 1971, NASA became even

more active in technology brokering through technology adaptation, the re-engineering of NASA

technology for other uses, such as a firefighter

breathing system based on spacesuit technology.

Another part of the TU prc;;ram with particular

relevance to regional HTD initiatives is NASA's

nine Industrial Applications Centers. All are located

at universities, where they offer information services, workshops, and technical assistance to industrial clients, most of which are small manufacturing firms. Fees cover about half the cost of these

centers; NASA's subsidy averages about 28 percent,

with the balance coming from university funds. In

Florida and Kentucky, the State governments match

the NASA subsidy for their centers in exchange for

exclusive assistance to businesses located in those

States*?

Extension Services

Extension services are distinguished from technology brokering by their decentralized structure and

interactive operation. They provide much the same

H;nne, I. Haggerty, .';11;(,)Ff; 106 i (Washingwn, IX:, National

Aerntiuti, s and Spat c Administration, Tet hnclogy Utilization and

lndutr ,Affairs ), tyon. !slay 1Q8 it, pp. I 4-135, Two programs not

mentioned in r hc I rc entarn,n 0.1*(1 to fit into this category. The Cott,

nnr3t Assoc at Program is a joint effort of NASA and the Amen.

an 111,tirwt, o,f Acron aunts ;in:.1 Astronautics, whereby the ALAA in

tr, du, cs ii,io.itro,p,,Le firms to NASA, its technologists, and its

n<tivrtrc The ni 'RtSi.1)Esi ha tige Agreements program exposes

timis to NASA tis Ines. and NA SA.'.1.1.1 offices to firms, through

per,nnci tv.1):ingc,

WhIttyn, and Leonard Ault, 74.csentation to

the Feder 11 inretigetu E ,ircimating Group of the Federal Labora

tor% ( Fir, I,)S3

services as technology broLrs, actively seeking out

potential users of publicly owned technologies and

providing technical solutions to questions posed by

client firms. But most technical extension services

are based at State universities and colleges, rather

than at a few Federal facilities, resulting in closer

and more widespread linkages with regional economies. In addition, they have an interactive relation with their clients: extension services not only

respond to client requests, but a'so try to convince

firms to adopt technologies that will increase productivity.

The most extensive, best known, and probably

most successful of these is the Cooperative Extension Service of the U.S. Department of Agriculture

(USDA), founded in 1911, which is part of an integrated national system of agricultural research, development, and technology transfer that traces its

origins to the Morrill Act of 1862. Recent attention

to the needs of technologically advanced manufacturing has led to proposals for a Federal extension

service modeled after USDA's. While technical extension to manufacturers may work, however, the

model of agricultural extension may be faulty.

Both NASA's TU program and the State Technical Services (STS) program have been compared

to USDA's extension efforts, usually unfavorably.

To some extent the relative lack of success in nonagricultural technical extension has been due to

underfimdingNASA's entire technology transfer

budget was $9 million in fiscal year 1983, and STS

expended less than $11 million during its 1965-69

lifetime, USDA's extension budget was $334

million it fiscal year 1984 alone.

However, differences in scale may be less important than differences in the nature of the technologies, user groups, and markets that are involved.

Technical assistance to manufacturers requires an

added effort to adapt existing technologies to specific: applications because of the diversity of manucacturing firms.' 3

"Everett M. Rogers, J. D. Eveland, and Alden S. Kean, Extending

the Agricult,iral Extension Model, Institute for Communication Re.

search, Stanford C,!niyersity, September 1976, NSF contract No, 75,

SP.0265, p. 117; set ills() William A. Hetzner, Louts Tornatzky, and

Katherine Klein, "Manufacturing Technology in the 1980's: A Survey

of Federal Programs and Practices," Ntinagernent Science, vol, 29, No.

8, August 1 84, pp, 951-461.
Ch. 5The Federal Role in Regional High-Technology Development 81

In addition, technical problems are not the principal cause of failure for manufacturing innovations.

Research has shown that new products from small,

high.technology firms are more likely to fail because

of inadequate management, unfavorable markets,

or both. J4 This suggests that entrepreneurship assistance, of the sort already provided by many State

and local HTD initiatives, may he more effective

than technical assistance from the Federal Government.

Recent Policy Developments

When Congress required NASA to disseminate

the as-yet-unknown benefits of space exploration

(National Aeronautics and Space Act of 1958, Public

Law 85.568, sec. 203), it became the first and, until

1980, the only Federal agency explicitly required to

engage in technology transfer. In the past decade,

however, there has been increasing congressional

attention to technology transfer. Congress first addressed the issue of Government patent policy after

research showed that only 5 percent of federally

owned patents were ever commercialized, compared

with 33 percent of university patents. 15 Congress

passed the Patent and Trademark Amendments of

1980 (Public Law 96-517), which allowed small businesses, universities, and nonprofit research institutes to retain title to discoveries made under Government contract. Legislation was introduced but

not passed by the 97th Congress that would apply

these rights to all Federal contractors. Patent rights

were extended to all Federal contractors by Presidential Memorandum on February 18, 1983, and bills

to make this extension law have again been introduced in the 98th Congress: the Uniform Patent

Procedures Acts (S. 2171 and H.R. 4964).

Patent rights for extramural inventions made with

Federal assistance represent a relatively minor facet

of technology transfer, however. Before Public Law

96.517 was signed into law, the Federal Government

received le: ; than 8 percent of the more than 70,000

domestic patents granted each year.i6 Of broader

.'Sumner !Over :Ind Eldon E Swev:v, "Viv Innovations Fad" re, h.

1CV1V14 , NO. 6, lvfarch i: 41

,n;:un Ii.rsettatn, Ch`VerT/T7W/Ir Parent y: Th 04'11(1'41W Of

!fit R.1,Thng From Federally Funded RScI) (WashIngton, TY7:

s I.11)rary ongre,s, :ongressli /nal Research Service, April Ic+.44),

Il ,'N)17. See ako 1.;.`;', Department ,f Commerce, op cat

ted.), "Implii..ttions of the In novatIon Prot es,.

4:,%' Stu7)kilate Tee hnolop al Innovaion," n The (Irrgon

irl011 'ewer Evpyruncnr: 17 (Eugene. OR: n Ver

Oroz,,n, I ac, crnl,er 1,W), p

public policy importance is the transfer of the results

of Federal intramural R&D to industry. A formal

Federal laboratory role in technology transfer coalesced in 1974 with the formation of the Federal

Laboratory Consortium, a voluntary association of

technology transfer officers. Among the over 700

Federal research installations, however, technology

transfer efforts varied widely. Especially within the

numerous DOD labs, prominence of technology

transfer depended on its position on an individual

administrator's or organization's agenda.

Congress acted to standardize this role with the

passage of the Stevenson-Wydler Technology Innovation Act of 1980 (Public Law 96-480). It was designed to establish a national policy to enhance technological and industrial innovation by stimulating

technology transfer a .,d encouraging the development and diffusion of industrial technologies. It incorporated sevcral suggestions from President Carter's Industrial Innovation Message of October 1979,

including the creation of CUFT to coordinate the

dissemination of all intramural R&D results. Among

its other provisions, Public Law 96-480 required federally funded laboratories to establish ORTAs; to

set aside 0.5 percent of their R&D budgetsfor technology transfer activities; and to assign a full-time

staff person in laboratories with annual budgets over

$20 million (sec. 11). It also called for the creation

by the Department of Commerce (DOC) and the

National Science Foundation (NSF) of Centers of

Industrial Technology (CITs), to pursue generic technologies and promote linkages between university

and industry (secs. 6, 7, and 8); and for the establishment of a program for exchange of scientific personnel between universities, industries, and Federal

laboratories (sec. 13).

Recent evaluations indicate that Federal agencies

are implementing some but not all provisions of Public Law 96-480. A recent survey by GAO found that

most Federal laboratories are covered by an ORTA,

and that the agencies surveyed have snent the required 0.5-percent set-aside for technology transferan estimated $177 million in fiscal year 1982.'7

(;At did not evaluate the effectiveness of section

11 in enhancing technology transfer, but its survey

reveals several features of -agency implementation

that might constrain the Act's contribution to reAgo), tes' At tl< r4) Implement Set non 11 of the Stevenson

trvdter re..t //rug( 1,,c' Innovation Act of I (4`11) (NX'ashtrIgton, IX:: U.s.

;over nment Al c.ountIng ()ffice, forthcoming), CiAt)/RI)E1.),S4 -hO.
82 Technology, Innovation, and Regional Economic Development

gional HTD by restricting both the scope and the

degree of interaction with State and local initiatives:

Four agencies collectively representing 85 percent of the Federal R&D budgetDOD,

NASA, DOE, and NSFhave requested waivers of the staffing requirement, primarily because ORTAs would duplicate existing technology transfer activities.

Although laboratories with an ORTA show a

higher level of technology transfer than laboratories without them, only 45 percent of the 236

laboratories surveyed were covered by ORTAs

at the local or facility level, while 36 percent

were covered by an ORTA at the centralized

or headquarters level, and 19 percent of laboratories were not covered by any ORTA, often

because of personnel limitations or uncertainty

about agency policies.

Over half of the fiscal year 1982 funds 'available to ORTAs were spent on technical information disseMination, while a lack of resources

for adaptive engineering was cited as an obstacle

to providing technical assistance to State and

loci governments.

A broader review of the implemttation of Public Law 96-480, conducted by DOC ncludes that

the Administration has "made substantial progress

in carrying out the intent of [the) Stevenson-Wydler

Act."is It has done so, however, through a "comprehensive strategy' based on "noninterventionist"

initiatives, including tax incentives, changes in patent and antitrust procedures, and reallocation of

Federal R&D funding.

IOC has not implemented the CITs and accompanying grants wailed for by sections 6 and 7, citing

budget constraints and the greater effectiveness of

antitrust legislation (speca,,cally the National Productivity and Innovation Act of 1983, H.R. 3878/

S. 1841) in achieving this purpose. 19 Indeed, many

,:ooperative or joint R&D initiatives similar to the

CITs have been undertaken by universities and the

private sector. DOC concludes that "the Govern1)(1' "(,(11` 't t ,T111711.'1, ( It . T 4, crilrihisls

, j

0

meat has a significant opportunity to support the

intent of section 6 by providing technical assistance

for . universities and other nonprofit organizations that request it."" NSF's University/Industry

Coc:erative R&D Experiment, begun in 1973, is

counted by both DOC and NSF as creating CITs

under section 8 of the 1980 Act.

Pending Legislation

Several other bills now pending and likely to be

reintroduced in the 99th Congress also address the

Federal role in technology transfer and the development and diffusion of commercial technologies

in ways that might increase the Federal contribution to regional HTD. The Manufacturing Sciences

and Technology Research and Development Act of

198 3 (S. 1286/H.R. 4415), for instance, would permit DOC to establish and support Centers for Manufacturing Research and Technology Utilization similar to the CITs authorized by Public Law 96-480.

The Advanced Technology Foundation Act (H.R.

4361) would establish a new Federal agency to initiate and support cooperative applied research

through loans and grants, and would also create a

Federal Industrial Extension Service.

The National Professions and Technology Foundation Act (H.R. 4245) would also create a new Federal agency, combining some of the functions of

DOC and NSF, that woulce promote not only technology transfer but also development of scientific

and technical manpower and of a national information and statistics policy. The National Technical Clearinghouse Fund Act (H.R. 2514/S. 808)

would establish a central clearinghouse to enhance

technology transfer to private industry and the general public. Another bill (H.R. 2525) would establish a National Commission on Technological Innovation and Industrial Modernization to develop

a national industrial policy, including analysis of

public policies that hinder industrial competitiveness and strategies for the development and extension of new technologies.

1
Ch. 5-The Federal Role in Regional HighTechnology Development 83

Regional Development Programs and Tools

While R&D and technology transfer programs

have provided an 2ffective stimulus to cooperative

HTD initiatives involving universities and private

industry, OTA's investigation suggests that the Federal Government has more frequently contributed

to H I D initiatives by State and local government

through grants and other assistance to community

and economic development. In a survey of 54 local

HTD initiatives in 22 communities, OTA found that

local governments had made use of the following

Federal programs:

t.:rhan Development Action Grants

Industrial Development Bonds . Et.onomic Development Administration grants

Commutut v Development Block Grants . Comprehensive Education and Training Act

programs

Free Trade lone

Appalachian Regional Commission programs .. Small Business Administration loan programs .

4

There are currently almost '1, programs providing nearly $100 billion in grants to State and local

governments for a wide variety of public services and

activities (see table 1(). One justification given for

this Federal involvement is that States might underinvst in activities whose benefits spill over into

other States. Other rationales tor Federal assistance

are benefits from centralized coordination, whether

in cffcctivene or efficiency, and equitable distribution (it. re,(,urces for providing public goods and

services, whether for particular recipients or for

States and localities generally.n Because Federal assistance programs have grown considerably over the

past 20 years, the current Administration has proposed a massive realignment of responsibility between the Federal Government and the States.

These Federal programs offer State and local governments flexible tools for regional economic development, including funds, technical assistance and

information, planning assistance, and job training

or retraining programs. Block grants, training programs, planning grants, and loan funds have been

used for a wide variety of projects, often in ways

unanticipated or unintended in the original legislation, depending on the needs or desires of different commuqities. These programs were not designed

specifically to promote regional H 11), and in general their influence on regional H I I) is indirect and

even serendipitous. Nevertheless, they have proven

to be an effective adjunct to regional HTD initiatives

when utilized imaginatively, and in conjunction with

other I-ITD efforts, by entrepreneurial local leaders.

an tAtt:ndo.1 tilt u..ton t)t this topti., se't' Stimita t i,rl.ten.rn,

Roivrta Drs,v,, P.11'1'11.1:1 RUggIt...., and Suzanne Feder

rl (;,1 ernment in a kral .`+ter77: CIrrt.nt Intrgovrnmnt0( Prograrn ()prron for (.1);Ingc. (WaOimpon, IX': (.1.S. C.ongrussional

t)tlac, August It)S I. Table 17.-Growth in Federal Outlays for Grants, by Function (in millions of current dollars)

Program areas

trItraStruc fury and di, ewment

1960

Outlays...

1970 1980

Percentage

growth in

real terms

1960.80

1982

outlays

Percentage

change In

real terms

1980.821

Energy $ 6 $ 25 $ 499 2,420 $ 509 7

Nailonai resources and environment 108 429 5,362 1,404 4,871 -17

Transportation 2,999 4,538 13,087 32 12,171 15 Community and regional development 109 1,780 6,486 1,703 5,379 24 Education, training, and social services 526 6,390 21,862 1,101 16,589 -36 Income security and health

Income security 2,635 5,819 18,495 186 21,930 2 Health . 214 agza 45,758 f,819 18,839 3 General purpose ltf,cal assistance 159 430 8,478 1,441 6,347 37

Other 264 753 1,445 58 1,559 9

Total outlays for grants $7,020 $24,014 $91,472 334 $88,194 17

.#1,. 1,,ops defense agr,cuii,, ,,onmeice veterzils programs, administration of just e. and general government

,), fjudypt for 0,,!Qy9 4.1pial075 for eacrr spending CafErcj oy were used

J1
84 Technology, Innovation, and Regional Fconornic Development

OTA's investigation of the impacts of these Federal programs on regional HTD concentrates on two

of the categories listed in table 17:

community and regional development, specifically development grants and business development programs that have contributed to

high-technology industrial development initiatives: and

education, training, and sodal services, specifically science and mathematics education, vocational education, and training programs that

have contributed to high-technology manpower

initiatives.

The resulting list and the specific examples provided

are not comprehensive; in fact, although several directories of such programs exist, there is at present

no central, user-oriented source of information for

public officials who might wish to undertake similar projets." OTA's intention is instead to provide

"See, for e! ample, Office of Management and Budget, Catalog

of Federal 17)ortiesti, Assistanc. 17th ed. (Washington, DC.: U.S. Government Printing Offic., June 1983). The Catalog, first published in

Igt1S, contains qt., assistance programs of all types (including economic

development; offered by SI different Federal agencies; it describes briefly

the types of projects funded, but provides neither descriptions of in

novatis c program 11SC! nor combinations employed by other cornrmi.

nines.

A more fix used anti usable sitirce fOr local officials may be The 14'S

(;Lade to Government Resources for Esonotnic Development (Wash.

ington, NtufheastMidwest In:it:tine, December 1482). which pro,

%ides des& riptions of trVer Mt Fctiet al assistance pwarris, UTOS-ncltt'd

by type of nerd, and detailed examples of the uses, including 1-171)

proie:ts, to which they have been put by State and local governments,

The (iinoic; also contains descriptions of other Federal resources. including pros urement, tax credits, revenue bonds. and :he location of

Feder, a] fat dines, Wh:lh may also contribute to regional HTD. In ad

chnon, it contains a directory of nonYdend resources, SI1Ch as State

c,on,itnic development programs and private sector involvement, that

an ix used for al programs.

A dire( tc 'Tv that f,s uses spettfitally on lugh-technolt %v. business :ts..sist

aine is Plenty ;rtNT1Wald td,), Sour, Ciukte of GOV(.11)111eM

t10100' and Fal th t<il .4 ssistant o (Wellesly Hills, MA: Capital Publishing I. , l)ttohcr This source lists Federal programs for

information assistant e, let hnologv transfer, R&D support, and gen

era] business titian, ;rig, as %..sell as Stan: government programs for

re; )li):)g, with descriptions of tisers' experiences ys

them.

f h inclucies a detailed of State programs

the Dire, tory of lmenrives tor Bcilincs Investment anti Develop

mc:,r rn rile I 'Tlre,1 ;r:rtt. A State-hs -Stare ( ;tilde tWasfungton,

'7},,,u i.t ; otnpileti by the f 'rhan Institute in wri.

rioti 101 Thy Nlit;.,flid As4,t ration of St: t Development ,Agee

tics .mndthl ,Nat;on,;1 t i,unt:1 fit l Thai") iiOntnnit. DeVhlpriltlIi. 1 his

lirctti ,iv st,i t,m;ius IV !deftfat. to Federal otogt

X 's 1th. l'todu,rRity, Technology iind limos anon is :ur

fftifly ;1 int Ir.' tiNed oiiteet,rry of Federal, State, and pri.

att asslst pr, for small, vet linologi, ally 4ophistft attNi firms.

ruhlislied sm;dat ,itektorics in the past, oriented tosard the

prR.ITI th;IT) 1.,u1)/1. user',

92

illustrative examples of the interaction between Federal policies and State and local HTD efforts, in order to reveal areas in which their interaction might

be strengthened or improved.

Community Development

Numerous Federal programs, administered by several different agencies, provide financial support for

community development or business assistance activities through grants, direct loans, loan guarantees,

and technical assistance. In fiscal year 1982, the Government spent about $8 billion for community and

regional development and several billions more in

loans and loan guarantees to small business. The

Federal programs that most frequently contributed

to HTD initiatives investigated by OTA include:

Community Development Block Grants

(CDBGs), administered by the Department of

Housing and Urban Development (HUD),

which provide about $3.5 billion annually to

some 1,900 communities in support of housing,

infrastructure, and other locally chosen projects;

Urban Development Action Grants (UDAGs),

also administered )y HUD, which provide $0.5

billion annually t,) stimulate commercial and

industrial development in distressed coramunines;

a variety of grant, loan, and assistance programs

administered by the Economic Development

Administration (FDA), which promote the

long-term recovery and growth of communities,

adversely affected by economic change; and

business development programs, including both

financial and management assistance, administered by FDA and the Small Business Administration, plus tax-free industrial Development

Bonds (1DBs) that must be approved by the Department of the Treasury.

Community Development Block Grants.The

CDBO Program was created by the Housing and

Community Development Act of 1974 (Public Law

93-383) to consolidate a number of categorical assistance programs established by the Housing Act of

1949, 1954, 1955, 1956, 1965, and 1966. As such

they represent part of a trend toward consolidation

of Federal assistance programs in the 1970's that led

to greater flexibility in resource allocation and greater reliance on State and local administrative machinery. CDR', is the largest development assist-
Ch. 5The Federal Role in Regional High-Technology Development 85

ance program, with funds provided on a formula

basis for improvements such as housing rehabilitation, streets and roads, waterworks, and other public

facilities. It was originally designed to provide housing and expanded economic opportunities for lowand moderate-income neighborhoods rather than

industrial or commercial development.

Changes made in the program by the Housing and

Community Development Act of 1977 (Public Law

95-128) expanded eligibility to include grants to nonprofit neighborhood organizations, local development corporations, and Small Business Investment

Companies. Only a portion of CDBG funds$400

million, or about 10 percent in fiscal year 1981

were used for economic development activities.

However, amendments contained in the Omnibus

Budget Reconciliation Act of 1981 (Public Law 97-

35) further expanded eligibility to include private,

for-profit firms engaged in economic development,

and gave local jurisdictions greater discretion in how

they spend program funds.

As a result of these changes, CPBOs have become

a more useful tool for HTD initiatives that emphasize financial as well as physical capital. OTA has

identified the following examples of CDBGs used

for HTI) initiatives:

Philadelphia Industrial Development Corp.--

C:1)13(3 money was used to set up a revolving

loan fund.

Phoenix SBA Loan Program. The city finances its loan program through CDBG funds

and by selling debentures.

Cincinnati, Ohio, Venture Capital Fund.

CDR() funds were used to provide a venture

capital pool.

Recent emphasis by the Administration and Congress has been on further consolidating and reducing the CDBG program and decreasing Federal involvement in its operation. The 1981 amendments,

for example, transferred responsibility for the Small

Cities program from HUD to the States, on the condition that the States consult local officials on how

the funds will he used, provide planning and technical assistance, and provide 10 percent matching

funds from State resources.

This could serve to increase linkages between State

and local government efforts to promote HTD, but

concern has been expressed that the needs of urban areas may be ignored in State governments

dominated by rural and suburban interests. By reducing the reporting requirements, the amendments

may also have reduced both accountability and the

availability of valuable information on project design and effectiveness. The most recent amendments, contained in the Housing and Urban-Rural

Recovery Act of 1983 (Public Law 98-181), reauthorized CDBG for 3 years at reduced and flat authorization levels.

Urban Development Action Grants.The

1977 amendments also created the UDAG Program

to assist distressed communities that are undertaking economic revitalization programS designed to

generate jobs and increase tax revenues. UDAGs

are designed to improve the feasibility of otherwise

marginal projects by leveraging private sector financial participation, typically at a ratio of about 6 to

1. This requirement can contribute to the success

of HTD initiatives like those investigated ,by OTA

by stimulating local networking and partnership and

by creating stronger linkages between local, university, and private sector HTD efforts. This has proven most useful in developing physical capital for

HTD, such as incubator facilities and technologybased industrial parks.

OTA identified the following applications of

UDAG funds to HTD

New Haven Science Park.UDAG funds were

used for site preparation at Science Park, a large

tract of land being developed near Yale University for companies engaged in R&D on new

products and processes, and related manufacturing.

San Antonio, TX, Vista Verde South Development Area.Control Data has constructed

a 60,00X0 -square foot building in a l45 -acre

urban redevelopment area located in San Antonio's inner city and employs 300 people making electronic components. Th, project was

funded by an $18.8 million UDAG and a $4

million CDBG.

' Chicago, Biomedical Research Park.Both the

City and State have worked closely with Applied Molecular Genetics to secure a $2 million

UDAG and an $8 million IRB. The University of Illinois, the city, and the State are cooperating in a joint ventu.-e to acquire 46 acres

of land adjacent to the University's west side

93
86 Technology, innovation, and Regional Economic Development

campus and its genetics research center. The

site is planned as a high-technology park.

Lowell, MA.The city obtained a $5 million

UDAG, which was loaned to Wang at 4-percent interest as an incentive to locate in Lowell.

Philadelphia, University City Science Center.Federal urban renewal funds were used to

prepare the site for this 16-acre urban research

center, and a $5 million UDAG has been received for construction of a residential and conference center.

Philadelphia, Business and Technology Center.Control Data purchased a 300,000-squarefoot building, with the help of a $1.3 million

UDAG. The building will be renovated and

leased to other companies, and serve as an anchor for HTD in the surrounding 60-acre industrial park.

Milwaukee, Hilltop Parish Research Park.This

30-acre site on the city's northwest side was

rezoned for research activities only, and the city

acquired a $200,000 UDAG for site preparation.

Economic Development Administration

(EDA).EDA was created by the Public Works and

Development Act of 1965 (Public Law 89-136) to

support the long-range economic development of

areas with severe unemployment, low family income,

and/or significant population loss. FDA was primarily concerned with rural development until 1969,

when it began to offer assistance r'ar urban economic

development as well. Amendm_nts passed in 1976

(Public Law 94.487) greatly expanded FDA's mandate by reducing the population requirements for

eligibility and adding several new assistance programs. EDA activity reached its peak in 1979, when

the agency administered over $1.0 billion in aid,

much of it aimed at private firms. By 1983 its budget

had been cut by more than half, and the Administration has recommended abolishing EDA.

Although the HUD economic development programsCDBG and UDAGhave a much larger

budr,,et than the EDA programs, all of EDA's programs are focused on economic development, whit:

only small portions of total HUD funds are spent

cm activities whose specific purpose is to promote

economic development. OTA has identified four

FDA programs that have encouraged or supported

State and local HTD initiatives.

94

EDA's Public Works and Development Facilities

Program provides grants and loans for physical capital improvements, public services, and the acquisition and rehabilitation of property for development.

Eligible public works projects include industrial parks

and sites, vocational or skill training facilities, recreation projects, and hospitals. As a result, these grants

can contribute to local HTD initiatives involving

both physical and human capital. Following are

examples of EDA Public Works projects promoting

HTD:

$2,075,000 to the city of Boston in March 1978

for development of the Crosstown Industrial

Park. The Digital Equipment Corp. has located

a plant in the park which employs several hundred local workers.

$1,125,000 for a skill training center at Lowell,

MA, to offer instruction in computers and electronics.

$820,000 to the Occupational Industrialization

Center of Roxbury, MA, for renovation of a

building for a high-technology skill center.

$700,000 to the Occupational Industrialization

Center of Port Chester, NY, for another skill

center for high-technology instruction.

$500,000 for the construction of a computer skill

training center at Springfield, MA.

EDA's Special Economic and Adjustment Assistance Program supports a wide range of economic

activities, with eligibility based on need resulting

from "sudden and severe economic dislocation" or

"long -term economic deterioration." Adjustment

Assistance funds have been used not only for industrial land banking infrastructure and for neighborhood revitalization, but also for economic planning and to establish revolving loan funds. OTA

has identified three major revolving loan funds for

HTD that were capitalized in part by EDA grants:

$3 million to the Massachusetts Technology Development Corp.;

$1 million to the Connecticut Product Development Corp.; and

$1 million to the New York Science and Technology Foundation.

EDA's Technical Assistance Program (TAP) provided $8..3 million for 154 grants in fiscal year 1982,

among which were 36 university business assistance

centers that provide feasibility and marketing assist-
Ch. 5The Federal Role in Regional High-Technology Development 87

ance to existing businesses and help establish new

business. In fiscal year 1983 there were 38 centers

receiving $3.5 million in grants out of a TAP budget

of $8.0 million. A few of these celters are particularly active in technology transfer and local HTD

initiatives, notably those at Florida, Iowa, Oregon,

and Pennsylvania State Universities; the Universities

of Michigan and Southern California; and the Georgia Institux of Technology. A fiscal year 1983 TAP

grant also supported the efforts of the National

Council for Urban Economic Development to establish a comprehensive information and technical assistance program aimed at helping cities prepare new

strategies to meet the needs of high-growth firms.

TAP grants have also supported the U.S. Conference of Mayors' Research and Education Foundation, which hopes to strengthen local economic and

technnlogy development strategies through better

cooperation between cities, universities, and Federal Laboratories.

Business Development

In addition to assistance to State and local governments, the Federal Government also provides support for local economic development through business assistance programs administered by EDA and

the Small Business Administration (SBA). These include not only loan and loan guarantee programs,

but also equity and bond programs that provide a

pool of investment funds for local financial-capital

initiatives. Tax-free industrial revenue bond (IRB)

authority, granted by the Department of the Treasury, also falls in this category; IRBs were in fact the

second most frequent form of Federal participation

in local HTD initiative investigated by OTA.

EDA Business Development Loan Guarantees. This program provides loan guarantees for

firms in depressed areas, and also guarantees leases

held by a business enterprise. The program has recently been reduced in scale and changed from a

loan and guarantee program to one providing only

guarantees. In fiscal year 1982, 14 loans were approved for guarantee, but in fiscal year 1983 only

2 were approved and none has been approved thus

far in fiscal year 1984, with funding at $33 million.

Examples of high-technology companies using this

program include:

36-737 0 84 - 7 QL 3

Univox Corp., a Los Angeles company that

makes sea water purification devices, primarily for defense purposes, received a $2 million

direct loan in fiscal year 1982, when the direct

loan program still existed.

Systems Management of America arp. (Norfolk, VA), a defense contractor that makes computer equipment for submarines, received a $5

million loan guarantee in -fiscal year 1983.

Small Business Investment Companies

(SBICs).This program, established in 1958, has

evolved into a system in which locally organized private investment companies licensed by SBA are eligible to obtain funds from either the Federal Financing Bank or SBA. Such companies must have a

minimum capitalization of $500,000, and they receive Federal funds at rates of interest close to or

below that which the Government pays for the

money. SBICs are obligated to use the funds obtained for equity investments and long-term capital loans to small or disadvantaged businesses.

State governments have not been allowed to put

capital in these firms, although some States have

either established public corporations that invest in

SBICs or given tax breaks to SBICs go that they

may assist high-technology small businesses. Some

of the more successful SBIC-filianced companies inAdvanced Micro Devices; A

c. icy °systems; Computer and C

Tecnnology; and Teledyne. The Adm

imposed a moratorium on the lice

dahl; Amerimmunication

nistration has

sing of new

SBICs, although legislation has been introduced to

increase the funding available to this program (H.R.

3020, S. 1323). (See ch. 3 for a further discussion

of SBICs in local seed and venture capital markets.)

Section 503 Certified Development Companies. SBA 503 loans are designed to assist small

businesses by providing long-term financing (not to

exceed 25 years) through the sale of debentures by

the Federal Financing Bank. Loans are to assist small

businesses in the acquisition of land and buildings,

construction, expansion, renovation and modernization, machinery, and equipment. Several local initiatives investigated by OTA have used the SBA

503 loan program to assist fledgling high-technology

firms.

95
88 Technology, Innovation, and Regional Economic Development

Small Business Revitalization (SBR) Program.

HUD and SBA are cosponsors of this economic

development program, through which States can leverage private sector investment. Each participating Governor establishes a coordinated small business job creation system, based on private sector

financing and local leadership. The system is designed to deliver financial assistance to all parts of

the Stateespecially to small and rural communities

that may lack the staff or technical expertise to

assemble loan packages. Thirty States have now chosen to provide part of the funds and participate.

Phytofarms of America, Inc., a high-technology agribusiness in De Kalb, , secured a $2 million loan

through the SBR program to save 30 jobs, create

60 new ones, and purchase an existing plant from

a Fortune 500 company.

Small Business Development Centers

(SBDCs).Under the SBDC program, States can

match their shares of a $65 million Federal-authorization to run university-based management assistance centers for small business. Unlike many other

SBA programs, SBDCs have a legislative mandate

(Public Law 96.302) to assist in technology transfer, make use of Federal laboratory facilities and

equipment, and coordinate and conduct research

they deem worthwhile. Since the States decide how

SBDC money is spent, the program also offers an

opportunity to concentrate business development

efforts on high technology firms. Texas has proposed

doing this through an SBDC at the University of

"Texas at Arlington.

It is not known how often SBDCs assist high-technology firms, but evidence suggests they are a minor beneficiary. For instance, one evaluation of the

program found that only 10 percent of SBDC clients

were in manufacturing, and that only 4 of the 181

SI3LX.: offices established by October 198.3 were

based in university engineering or technical departments." An informal OTA survey of six large Federal Laboratories found that only one had been contacted by the SBDC, although in that instance the

SBDC was apparently an effective liaison between

'dentists and businessmen.

....... In, At) Et diti,gri.,) thy E,,,th,mi. r m.4,7 I)cI'ci,V+ITIc11( (..cr)tur Pr. tgrw'n tW,1,11Ington, [)(

--1;A

96

Industrial Revenue Bonds (IRBs).The interest

from these State and local government bonds is exempted from Federal taxes by the U.S. Treasury.

IRBs have been used extensively by municipal governments to attract investment for industrial sites

that include high-technology firms, such as Science

Park in New Haven, CT. Totaling almost $20 billion in fiscal year 1981, IR dwarf all other forms

of State and local developmen resources, and they

represent a crucial element in any HTD initiatives.

Education an TYaining

Education and training are ndamental for developing the human capital n ded to exploit innovations and providing a technically skilled work

force for regional HTD. About half of the State government HTD initiatives identified in OTA's census

involved education and training programs. Many

private sector initiatives also involve education improvements, often through loaned personnel or

donated equipment. These State and local efforts

are supported by Federal policies and programs in

three areas: science and mathematics education;

vocational education; and training or retraining

programs.

Science and Mathematics Education.Public

education is a constitutional responsibility of the

States, but its quality affects the Nation's economic

productivity. The launch of Sputnik in 1957

prompted concerns similar to those voiced today

about the quality of U.S. math and science education. Congress responded at that time with a massive

Federal education program, the National Defense

Education Act of 1958. After the Sputnik crisis,

however, Federal education policy shifted its focus

from the quality of education to equality of access.

Now, faced with issues such as the level of innovation, economic productivity, international competitiveness, and the impact of new technologies on the

work force, attention has again been directed toward

math and science education. A number of commission reports have raised concern about the quality

of U.S. education, specifically such problems as a

short4 I qualified math and science teachers, insufficient high school math and science course requirements, and a lack of priority for excellence in

education.
Ch. 5The Federal Role in Regional HighTechnology Development

Legislation now pending and likely to be reintroduced in the 99th Congress includes proposals for

initiatives such as tax incentives for companies

donating computers and technical equipment to

schools; loans and incentives dividuals to

become science and math teacht.. rograms for

joint teacher/industry employment; and funding for

staff development programs. Two bills ye emerged

as the major packages of education legislation in the

98th Congress: the Emergency Mathematics and

Science Education Act (H.R. 1310) passed the House

on March 2, 1983; the similar Education for Economic Security Act (S. 1285) was reported to the

Senate in June 1983 but no further action has been

taken, Both bills authorize $425 millfon for a variety of new programs designed to improve teaching

and increase the number of both engineering personnel and science and math teachers at all educational levels. The two bills, however, have structural

differences. While both .aquire the sharing of administrative responsibilitie oy the Department of Education and National Science Foundation, different

programs and roles are assigned to each organization. Also, a second major difference is the manner in which the formula grants are distributed to

State and local entities, with population the major

factor in the Senate bill, while poverty level and

school-age population are given equal weight in the

House bill.

Vocational Education Programs.The Department of Education, which administers the Vocational Education Program (VocEd), has no dedicated

high-technology training programs. VocEd has been

criticized in the past both for training for obsolete

skills and for using outmoded equipment. However,

a number of local VocEd programs have been directed toward skills needed for high-technology industries, such as:

Triton College, Rivergrove, IL (CAD/CAM

training);

Piedmont College, Greenwood, SC (robotics);

Tri-County Technical College, Pendelton, SC

(microelectronics);

Rivard Community College, Rivard flounty,

FL (robotics); and

Lively Area Vocational Technical Center, Tallahassee, FL (laser optics and electronics).

VocEd is awaiting reauthorization this year, and

several of the reauthorization bills specifically address high-technology training. The House has

passed the Vocational Technical Education Act of

1983 (H.R. 4164), which extends vocational education for 5 years and attempts to correct these problems by authotizing appropriations for activities such

as industry/education partnerships for training in

high-technology occupations; adult training, retraining, and employment development; and entrepreneurship programs. Other reauthorization bills

would consolidate vocational and adult education

programs through block grants to the States (S.

1039/H.R. 2940), amend the program to emphasize

high-technology training .(H.R. 4793, S. 1094), or

provide additional incentives for equipment donations (H.R. 3280 and H.R. 4244). Funding levels

were $729 million in fiscal year 1983 and $738 mil.

lion in fiscal year 1984.

Training.Federal training programs are generally justified on the grounds of providing equity to

poor and disadvantaged individuals; easing and

quickening the transition of displaced workers; and

training the work force to use new technologies efficiently. Major policy questions surrounding training are the criteria that should be used to determine

eligibility for training programs (e.g., poor, disadvantaged, handicapped, displaced, or minority) and the

appropriate level of involvement for the public and

private sectors. Currently, Federal training programs

account for only .7 percent of total training expend-_

itures in the United States; State and local programs

account for most of the government-sponsored

training.

Private industry currently spends about $30 billion on job training and retraining, according to the

American Society for Training and Development,

an amount equal to three times the level spent by

Federal, State, and local governments combined.

The 200 or 300 largest companies account for the

majority of training programs. Still, many believe

that U.S. industry underinvests in human capital

and should spend more on worker training. Hence,

a number of proposals now before Congress call for

additional incentives to private industry to increase

their investmentF in human capital.

The first Federal civilian job training initiative

since the New Deal was the Area Redevelopment

Act of 1%1 (ARA). As part of its provisions to help

economically depressed localities attract new industries, ARA authorized a limited amount of train97
90 Technology, Innovation, and Regional Economic Development

ing to ensure the availability of a skilled work force

for newly created jobs. Persistently high unemployment rates led to the enactment, in 1962, of the

Manpower Development and Training Act

(MDTA), which authorized a broader array of training services and allowances and replaced the training programs of the ARA. In 1964 Congress passed

the Johnson Administration's Economic Opportunity Act, which instituted a wide range of programs

designed to eradicate poverty, including training

programs targeted for the poor, minorities, and

youth. Training and retraining are also available to

displaced workers under EDA's Trade Adjustment

Assistance, a result of legislation dating to 1962, but

this training provision has seldom been used.24

The proliferation of training programs during the

late 1960's led to the enactment in 1973 of the Comprehensive Employment and Training Act (CETA),

which consolidated many of the existing work and

training programs. CETA was amended in 1974,

when a public service jobs program was added, and

again in 1976 when the public sector employment

program was expanded. The last rewrite of CETA,

in 1978, targeted training toward low-income and

disadvantaged individuals, but added a Private Sector Initiative Program to involve industry in training activities. CETA was frequently criticized for

creating too many public sector jobs and having too

little private industry involvement. Examples of

CETA projects that were part of local HTD initiatives include:

Colorado Springs, CO, Institute for Business

and Industrial Technology.IBIT is a skill center that has been in operation for nearly 2 years.

It trains students to fill entry-level technical positions and offers an associates degree in electron-

"Steve Carnovitz, "Trade Adjustment Assistance: What Went

Wrong " Journal of the Institute of Socioeconomic Studies, vol. 9, No.

1, spring 1984, pp. 26-39.

ics. The Institute was funded by CETA until

October 1983, when the Job Partnership Training Act took over.

Montgomery County, MD, Upgrading Skills

Training Program.This program is run by the

County Department of Economic Development

with CETA /JPTA funds. It involves all types

of firms, including high-technology. It identifies

firms that need to upgrade their employees'

skills and then works with the company to design a curriculum and select employees for the

program.

The job Training Partnership Act of 1982 (jTPA,

Public Law 97-300) replaced CETA in October 1983.

Like CETA, JTPA is meant to train and employ

the chronically jobless, especially disadvantaged

youths and the structurally l .iemployed. The main

differences between the two are that private industry councils (PICs), rather than local government,

will decide what training will be provided (based on

what jobs are available); and that no public sector

jobs will be created. Like recent changes in general

development programs, described above, JTPA thus

represents a consolidation and reduction in Federal

assistance to State and local governments, combined

with increased State and local responsibility frc pr8=

gram administration. However, by also increasing

private sector participation through local PICs,

JTPA may also contribute to local networking and

institutional cooperation, which OTA has found

to contribute to successful HTD initiatives.25

2Turther discussion of education- and training-related issues in high -

technology industry cat) be found in the following OTA publications:

Informational Technology and Its Impact on American Education

(OTA-C1T-187, November 1982); Automation and the Workplace: Sr

lected Labor, Education, and Training Issues A Technical Memorandum (OTA-TM-C1T-25, March 1983); Computerized Manufacturing

Technology: Employment, Education, and the Workplace (OTA-C1T235, April 1984); and Technology and St r 'aural Unemployment: Retraining Adult Displaced Workers (ongoing).

Planning, Demonstration, and Models

In addition to the more general programs described above, a small number of Federal programs

have contributed more directly to regional HTD initiatives by encouraging State and local groups to

98

establish new institutional linkages or administrative mechanisms. These programs have been very

small in comparison with CDI3G or CETA, and

only a few were longstanding. Nevertheless, they
Ch. 5The Federal Role in Regional High-Technology Development 91

have allowed local groups to experiment with different approaches to regional HTD, and in some

cases they have developed or demonstrated models

for State and local HTD initiatives that have been

successfully replicated elsewhere.

It is difficult to determine precisely how effective

these programs have been in promoting HID, since

they have yet to be evaluated in a rigorous or comparative fashion. However, the fact that they have

been considered or adopted by other States and localities suggests that the Federal Government has

played a useful role in encouraging, these experiments. The principal examples of this role iden...ied

in OTA's investigation were the State Technical

Services Program (STS) and several cooperative programs supported by NSF.

State Technical Services Program

STS was one of the few Federal programs specifically designed to assist States governments in improving their capabilities for dealing with problems

involving science and technology. STS was designed

to accelerate the utilization of new technologies

tirough locally planned programs to put them directly in the hands of business and industry. Established by the State Technical Service Act of 1965

(Pak Law 89-182), STS had given grants to all 50

States, totaling $10.9 million, by fiscal year 1969.

There were few restrictions on use, and in fact one

criticism of STS was that it provided little guidance

to States on how to assist high-technology Indus- _ try. Some States, like Pennsylvania and North Carolina, started industrial extension services modeled

after agricultural extension; others, like New York,

formed science and technology organizatios modeled after NSF. Many States, however, failed to establish permanent programs.

An evaluation of STS by A. D. Little, Inc., found

that benefits to firms from the three or four most

successful cases in each State were at least three times

the total Federal expenditure for the extension service in a year.26 The eventual benefits and return in

taxes were estimated to be well above the cost of

the program. The evaluation recommended that

Federal support be continued and even increased,

"Arthur I). Little, Inc., ogr a rn Evaluation of the Office of Stan:

al Servic'es, prepared for 1X)C's Office of Program Planning,

contra No. 9..35335, October 1969, pp. 17, 24, and 29.

and that greater attention be given to increar...i.4

communication between States in order to facil: tate

exploitation of useful results and techniques. Nevertheless, few of the efforts begun under STS were successful enough to receive further State funding after

the end of Federal support. The exceptions were

notable, howeverseveral of the programs identified

by OTA's census of State government HTD initiatives were founded under STS, and several others

were based on these successful models.

The Pennsylvania Technical Assistance Program (PENNTAP) is one existing vestige of STS.

PENNTAP offers assistance to industry through

field offices at 24 campuses of Pennsylvania

State University. PENNTAP generally reacts to

requests rather than initiating technology trans,

fer, a mode that both PENNTAP administrators and NSF observers say works best, in part

because extension officials may not know the

needs and constraints of potential clients.

PENNTAP was in turn the precurscr of Pennsylvania's ambitious Ben Franklin Partnership

initiative.

National Science Foundation

OTA also identified three NSF programs designed

to develop and demonstrate HTD mechanisms. Two

were directed to university settings, while the other

(like STS) was aimed at State and local governments.

All three have been successfully replicated.

Innovation Centers Experiment.NSFs Innovation Centers Experiment was initiated in 1973 to

increase university coursework and clinical experience in the commercialization of new ideas, and thus

heighten the entrepreneurialism of participating

students. The program also experimented with indirect aid to high-technology entrepreneurs through

three different types of university-based assistance

programs: product testing and evaluation services;

technical extension; and incubation facilities. A total

of 10 Centers were funded by the end of the program in fiscal year 1981. Total NSF grants amounted

to about $5.2 million; a small amount of fiscal year

1981 funds is still available, since the Innovation

Centers received funding for 5 years to ensure stability. NSF has only recently begun a final evaluation of the experiment, which was terminated in
92 Technology, Innovation, and Regional Economic Development

1981 in favor of the University/Industry Cooperative R&D Experiment.

Of the three original Centers, one concentrated

on student entrepreneurship (MIT), one provided

an incubation facility for small local firms (CarnegieMellon), anc1 one sold consulting services to private

innovators (University of Oregon). Six of the later

Innovation Centers concentrated on new product

and new business development, rather than product evaluation or student entrepreneurialism; the

seventh was an extension service (University of

Arkansas). Later facilities concentrated more on

assisting local entrepreneurs by providing technical

assistance and incubator facilities, space on campuses

for business offices, and laboratories near students

and faculty. As such, they represent models of several types of HTD initiatives described in chapter

4: R&D and technology transfer; entrepreneurship

training and assistance; and physical capital.

An interim NSF evaluation hailed the experiment

a success.z7 In the first 4 years the Innovation

Centers participated in the creation of over 30 new

ventures, of which 23 have reached the market. Sales

total of these firms Qeceeded $30 million, approximately 1,000 jobs were created, and more than $6

million in tax revenues were generated. More than

2,000 students gained instruction or experience in

the entrepreneurship, invention, or innovation processes. The study found the major factors determining program success included the caliber of directors, the quality of work, the attitudes of established

university departments, stability and flexibility

through 5-year block funding, and NSF's yielding

of patent rights to participant firms.

The incubator facilities at Universities of Utah and

New Mexico are widely considered to be the most

successful centers; MIT's center has also been called

a success, although it no longer concentrates on student entrepreneurship. Only one innovation center

has become financially successful, and several factors have contributed to the problems experienced

by other centers: the Federal Government's expenditure gained it no rights to innovations at the

centers, and the Federal subsidy was often competing or generating competitors with existing pri27Analvses of Five National Science Foundation Experiments To Stimulate Increased Technological Innovation in the Private Sector, NSF

82,32, 1982,

100

vate sector business consulting firms. University acquisition of equity through the Innovation Centers

might also create an additional problem: conflict of

university interests if it both educates entrepreneurs

and takes an interest in their businesses.

Facilities similar to the NSF Innovation Centers

have been established by other State governments,

universities, and private sector groups in recent

years. None have been modeled directly on these

10 centers, although numerous delegations have

visited and studied them to learn whatever lessons

might be applied to their own regions and situations.

Innovation centers elsewhere that have consulted

NSF include:

Georgia Tech's Advanced Technology Development Center, which has an incubator facility akin to the Carnegie-Mellon Innovation

Center model.

University of Wisconsin at White Water, which

has established an invention evaluation facility that learned from the unsuccessful University of Oregon Innovation Center.

University of Michigan at Ann Arbor, Institute for Research and Technology, Industrial

Development Division, which operates a management and technical assistance center akin

to the extension service NSF supported at the

University of Arkansas.

University of Missouri at Rolla, which is developing a multiple service center including incubator, produce evaluation, and technical and

management services, which combines elements

of several NSF centers.

University/Industry Cooperative Research

Centers.This experiment, which was begun at the

same time as the Innovation Centers in 1973, was

designed to promote innovation through R&D and

technology transfer rather than entrepreneurship

assistance. Cooperative R&D attempted to move

technology to the marketplace by matching university research capabilities with the needs of industry, which shares the cost of the research with the

Federal Government. NSF initially funded three

centers from among 14 university and corporate applicants who received planning grants in 1973. Criteria for selection were the institutions' ability to

plan the cooperative effort, obtain industry commitments (like UDAGs, the grants require proof of
Ch. 5The Federal Role in Regional High-Technology Development 93

private participation), and agree on allocation of patent rights. NSF guaranteed + years of funding and

Federal declination of patent rights resulting from

the research conducted at the centers.

MIT, North Carolina State University (NCSU),

and the Mitre Corp. received awards. MIT performed cooperative polymer processing research,

while NCSU operated an extension service to assist

the furniture industry, and Mitre acted as a broker

to bring together university and industry expertise

for energy research. Since 1973, Mitre's center has

closed, NCSU's has switched its focus from furniture

to signal processing, and 10 additional centers have

been funded, all of them located at universities. In

the first 5 years, NSF spent $2.4 million on three

centers, which was matched by about $3.0 million

from 24 private firms. By the end of fiscal year 1982

there were 13 centers, to which NSF had obligated

approximately $5.7 million, matched by approximately $16 million in research funding from industry.

State Science, Engineering, and Technology

Program (SSETP).NSF's Intergovernmental

Science and Technology Program (ISTP) was established in 1967, but through fiscal year 1972 it had

spent only $2.6 million on a number of small State

and local projects. President Nixon's Experimental

Research and Development Incentives Program increased ISTP's funding significantly in the early

1970's.28 One program, Technical Assistance Grants

"President's Message to Congress on Science and Technology, Mar.

16, 1972.

to State executive branches and legislatures, supported staff or consultants to provide technical assistance in State policy or research offices. It was

enhanced through the addition of SSETP in fiscal

year 1977.29

SSETP's goal was to improve the policy management process through studies of the present and

planned use of scientific and technical resources in

49 State executive branches and in 42 legislatures.

Eight executive branches and seven legislatures were

given follow-on awards to implement the recommended changes in their mechanisms for providing technical expertise. Seventeen similar awards

were planned but not made in fiscal year 1981 because of budget cutbacks. In many cases, however,

State governments implemented the recommended

changes without Federal funds, and many of the

State initiatives identified by OTA trace their origins

to this and other NSF programs. The total budget

for SSETP was $5.1 million for fiscal year 1977-81.m

Overall, 1ST? had awarded $41 million by fiscal year

1983, including $7.2 million to State executive

branches, $3.9 million to State legislative branches,

and $20.6 million to local governments.

"Legislative mandate for this program is found in the committee report on H.R. 12566, the NSF authorization for fiscal year 1977

vaSurvey of nine Stare science and technology foundations. Study

funded by NSF. "While these ecience foundations did not spark the

degree of economic development clorired or expected, the successful

->nes ..:Tracted at least four new de ent dollars for each foundation dollar. The additional taxes t xi from these new development dollars have at least equaleci .oundation's appropriations."
Appendixes

102
APPENDIX A

High-Technology Location and Regional

Development: The Theoretical Base*

Summary

Two major bodies of economic theory shed light on

the potential impacts of high-technology industrial development initiatives. Theories ofregional economic development provide a better understanding of the role of

high-technology complexes in the growth of regional

economies, but they do so on a macroeconomic level and

explain only partially or implicitly the factors that influence the creation of those centers. Industrial location

theories, on the other hand, identify the determinants

of business site selection and location decisionmaking on

a microeconomic level. To the extent that they address

the decisions of high-technology firms, therefore, they

can provide an understanding of what conditions or attributes of particular communities will make them most

attractive for high-technology development.

Regional economic development theories deal with

technological change and the role of high-technology industry in a variety of ways. Export base theory suse,ests

that the more successful interregional and intraregional

export industries are technology-intensive, and that

high-technology industries thus have higher multiplier

effects that hasten the process of regional economic

growth. Factor price equalization theories explain that

capital and labor flow between regions seeking their

highest return; this may have been a factor in high-technology Sunbelt growth over the past 20 years, but per

capita income convergence in the United States may

soon reverse this trend. Growth pole theory recognizes

the importance of propulsiv, high-technology sectors in

the urban growth process, asid explains how such centers

can perform as incubators or seedbeds for the birth of

new companies. Product lifecycle theories recognize that

products, firms, and industries have different locational

requirements at various stages of their technological development: while new product development tends to take

place in R&D-intensive locations like Boston or San

Francisco, mass production techniques allow production

to take place in more peripheral low-cost areas like the

Sunbelt or the East. Diffusion theory demonstrates that

the speed with which productivity-enhancing innovass tions spread between regions can play a critical role in

accelerating the economic growth process. When growth

The material in chi, appendix is based on the contractor report, "A Review

of Regional Growth and Industrial Location Theory: Towards Un&rstanding

the Development of High-Technology Complexes in the United States," pre,

pared fin OTA by John Rees (Syracuse University) And Howard Stafford (Univet- - say of Cincinnati), May 1983.

pole and product lifecycle theories are integrated into a

regional lifecycle theory, they provide a particularly

appropriate explanatory framework for understanding

the development and impacts of high-technology complexes: growth centers in the Sunbelt are seen to be new

economic structures that have bypassed theobsolescent

technologies of the old industrial heartland, but increasing inflation in these growth areas may result in a new

regional equilibrium and the reemergence of the indigenous technological potential of the older heartland.

Industrial location theory indicates that the executives

of high-technology companies make their locational decisions in much the same way as executives ofother companies, but that the factors that attract them to a community (or at least the priority given to various factors)

can be different from other types of industry. Appropriate labor, for example, is by far the most important single

factor in high-technology location decisions, but with the

skill level of the labor force replacing general availability and wage scales as the determining criterion. On the

other hand, high-technology firms put less weight on relative costs of transportation, since they are less closely tied

to the location of materials or markets than other industries.

Because of the increasing co ition for high-technology jobs, it has become even m re important recently

for communities to be aware of t location factors that

are important to corporate execu es. Community development officials need to assess eft- locational attributes in a realistic manner, matching their characteristics with the factors that are considered important by

particular industries and concentrating their development strategies on rectifying their deficiencies or building on their strengths, as appropriate. This does not mean

that a community has to simply look at a laundry list

of important factors and fill in the blanks in order to

attract high-technology development. In most cases, however, based on the needs of high - technology firms and

industries, communities would probably wish to investigate potential linkages with their existing base and to foster one or more of the following: manpower training or

retraining, technical and financial assistance, and improved access to cultural and recreational amenities.

Introduction

Many State and local governments, as well as universities and business groups, have established programs to

103

97
SV3 Technology, Innovation, and Regional Economic Development

stimulate the creation, attraction, and retention of hightechnology businesses. In general, these initiatives are intended to increase the level of technological innovation

in a specific region or community, or to make the area

more attractive to high-technology industries in order

to influence the location decisions of individual firms and

entrepreneurs. To be effective, such programs should be

based on an understanding of the factors that determine

the geographical distribution of high-technology industrial development, its potential role in regional economic

development, and the attributes that influence the business location decisions of high-technology firms and

entrepreneurs.

Two major bodies of economic theory shed light on

how and why high-technology industrial complexes develop around the country: 1) theories of regional economic growth, and 2) industrial location theory. This

appendix begins with a review of several partial theories

of regional economic growth, each of which deals at least

implicitly with the role and impacts of technological

change in regional economies. Because these theories deal

with regional development at a macroeconomic level,

their usefulness in understanding the patterns of high

technology depends on the cumulative effect of location

decision by individual firms. Theories of industrial location, in turn, shed light on what conditions or attributes

of particular communities are most likely to influence the

location decisions of high-technology companies.

Regional Growth Theories

and the Development of

High- Technology Complexes

Introduction

Most economic theories that purport to explain regional economic growth address the role of technological change, at least implicitly, and there is growing

evidence that the factors influencing technological

change may vary between regions in a systematic manner. A review of these theories to determine how they

deal with technological change and its role in the development of high-technology complexes suggests that there

is no single, acceptable, comprehensive regional growth

theory, bur rather a set of partial theories that explain

different aspects of the regional development process.'

There have been attempts to synthesize these partial

'John Rees, "Fegional Industrial Shifts in the U.S. and the Internal Genera.

non of Manuf ,Juring in Growth Centers of the Southwest," in Interregional

Movements and Regions/ Growth, W, Wheaton (ed.) (Washington, DC: Urban

Institute, 1979). Others noting this factor include Thomas and Le Heron (1975),

Oakey, Thwaites, and Nash (1980), joint Economic Committee (1982).

5P. Lloyd and P. Dicken, Location in Space: A Theoretical Approach to Er0-

r)0177iC Geography (New York: Harper & Row, 1977). Also, Weinstein and

Firestine (1978),

theories into a single regional growth theory.3 They

nevertheless remain difficult to operationalize for policy

guidance in designing high-technology industrial development initiatives. These partial theories involve:

the role of a region's export base,

regional income convergence or divergence over

time,

growth pole theory,

regional diffusion processes, and

product and regional lifecycles.

Export Base Theory

Several researchers have stressed the role of exports

as the initial trigger for regional growth.4 At its simplest,

export base theory states that a region's growth rate is

a function of interregional and international export performance:

This ability to export induces a flow of income into the

region which, through the familiar multiplier effect, tends

to expand the internal markets of the region for both national and region-serving goods and services. . . As the

regional market expands mid region-serving activities proliferate, conditions may develop for self reinforcing and

self sustaining regional growth, and new internal factors

may become important in determining the rates of regional growth, such aS external economies associated with

social overhead capital and the agglonaeration of industries, and internal economies of scale.'

The resource endow: bents of a region can therefore determine its competitive advantage over other regions, and

such endowments can be modified through technological change as well as changes in the labor force or capital pool. For example, Texas Instruments was originally

founded by three individuals who had relocated from the

Northeast in the 1930's in search of oil, and, due to the

lack of indigenous technology, began building their own

instruments and equipment.

Export-producing industries result in a regional

balance-of-payments surplus, and they also tend to have

strong forward and backward linkages with other industries in other regions. This in turn helps to integrate the

developing region into the national economy. Furthermore, "export industries tend to be technologically advanced and to operate at higher levels of productivity

. . . [generating income that] filters through the region

and helps to spur development of residentiary [nonexport] industries."6 Hence, export base theory recognizes

the higher multiplier potential of high-technology sec-

'H. Richardson, Regional Growth Theory (London: MadVlan, 1973).

'H. Perloff and L. Wingo, "Natural Resource Endowment and Regional Economic Growth," in Natural Resources and Economic Growth, ). Spengler (ed.)

(Washington, DC: Resources for the Future, 1964 Also, North (199).

tPertoff and Wing*, op. cit., p. 200.

'B. L Weinstein and R. Firestlite, Regional Growth and Decline in the United

States (New York: Praeger Publishers, 1978), P. 62,

104
App. AHigh-Technology Location and Regional Development: The Theoretical Base 99

tors, although the exact nature of such multipliers has

not been determined through empirical research.

Regional Income Inequality Theories

Two types of theories explain regir .Al growth in terms

of income inequality, usually for Jiing on developing

countries or growth regions in me .e advanced economies.

These theories suggest that tf.e regional development

process, one triggered by some initial stimulus, tends

to be cumulative in nature.

Factor Price Equalization Theories.The notion

of convergence in regional income; e erged from theories of international and interregio trade. These

models assume that factors of produ "oncapital and

labor in particularare "free" to move between regions

to seek their point of highest return. The flow of investment capital from Northern to Southern States in the

1970's, for instance, can be seen as movement from areas

of low return to areas of high return.?

Interregional flows eventually reach an equilibrium

where per-capita income is equalized between regions,

and evidence shows that a significant regional income

convergence took place in the United States over the last

50 years.8 In 1929, per-capita income in the Southwest

was only 53 percent of the U.S. average, but by 1976

this had reached 84 percent of the U.S. aver age. During

the same time period all but two of the industrial States

of the Northeast and Midwest showed relative declines

in per-capita income, with drastic declines in New York,

Connecticut, and Delaware. Since the Sunbelt States

were the largest recipients of both physical and human

capital over this period, the attempts of both industrial

companies and individuals to maximize income do seem

to be causing income convergence between regions.

Patterns of capital mobility within regions, however,

are more complex. "Income analysis of economic and

population trends during the seventies indicates that a

powerful decentralization of activity was occurring . . . [but] important qualifications need to be made about the

periphery, for it was not an economic monolith. "9 Unequal growth rates among the Sunbelt States may reflect

large differences in their industrial structure:

The dominant industries in the Carolinas, Tennessee,

and Texas have included textiles, apparel and fcx4

processingall comparatively labor intensive and ',,wwage industries at the mature end of their technology

cycles. Nearly 42 percent of the South's manufacturing

employment are in low-wage industries as compared to

only 20 percent for the U.S. as a whole. The South emTW. Wheaton (ed.), Interregional Movements and Regional Gra% th (Wash.

ington, DC: Urban institute, 1979).

Weinstein and Firesnne, op at.

°W. Keinath, "The Decentralisation of American Economic Lik: An Income

Evaluation," Economic Geogra,.ily, vol. 58, 1982, p. 356.

ploys only about 25 percent of its manufacturing workers in high -wage industries as compared to 37 percent for

the United States.e°

The regional income convergence between North and

South, in other words, appears to have been led by the

decentralization of relatively low-technology industries,

or by low-technology sectors of high-technology industries. This trend can also be explained as a regional manifestation of the product lifecycle model, discussed below.

Unbalanced Growth Theories.While factor equalization theories see regional convergence as the mechanism by which growth is transmitted throughout the national economic system, advocates of unbalanced growth

strongly dispute the effectiveness of these sprmtd effects."

One theory of unbalanced growth centers on the notion

of "cumulative causation," wherein market forces tend

to attract economic activity to areas that acquired an initial advantage through location, technology, or some

other factor. Peripheral regions will experience some

growth through spread effects, but lagging areas are debilitated by "backwash effects": labor and capital migrate

to the growth center, while low investment in public services inhibit the development of peripheral areas. Only

when spread effects are stronger than the backwash will

new economic centers emerge as the foundation for future

regional growth.

Growth Pole Theory

The notion that regional growth initially occurs around

one or more regional centers of economic strength can

be traced to the French' economist Francois Perroux,

whose original conception of growth poles referred to industrial sectors rather than their spatit, manifestation.

Growth poles were transformed into a spatial concept

by regional planners under the term "growth center."12

Polarizationgrowth of such polesdepends on propulsive institutions such as fast-growing industries, innovative companies and research universities. Such institutions drive the growth of their economic sector, but

"there [also) appear to be significant spatial polarizing

influznces present in the working of the multiplier.""

These geographical forces include the operation of scale

factors (specifically agglomeration economies), the spatial

clustering of innovations and the nature of i lclustrial

decisionmaking (discussed below).

Growth pole theory therefore recognizes the link between technology, innovation, and regional economic

growth more explicitly than the other theories reviewed

so far:

IDWeinsitein and Firestine, op. cit., p. 51.

"G. Myrclal, Rich Lands and Poor (New York: Harper & Row, 1957). Also,

Hirschman (1958).

'°D. F. Darwent, "Growth Poles and Growth Centers in Regional Planning;

A Review." Environment and Planning, vol. 1, 1969. Also, Hansen (1972).

"Lloyd and Dicker, op. cit., p. 406.

105
100 Technology, Innovation, and Regional Economic Development

Thus one may envisage the situation of a growing, successful economic system, say an industrial city, drawing

to it the ideas of spatially dispersed inventors searching

for sponsorship, pulling in the skills of migrants, investing

its own funds in the search for invention, and using its

accumulating capital and labor to convert this flood of

new technology into effective use."

This centripetal movement accelerates local growth by

enhancing the potential for invention and innovation."

The major advantages of large urban areas, in fact, may

not lie in their economic base, in the traditional sense,

but rather in their capacity to innovate, as reflected in

universities and research institutions with an explicit concern for creativity." This would help to explain the role

of the Massachusetts Institute of Technology and Stanford University in the creation of Route 128 and Silicon

Valley, respectively. And since most new businesses tend

to stay in areas where their founders were initially located, it is also likely that large urban areas will spawn

more new companies than small communites: their agglomerations serve as seedbeds or incubators for the creation of new firms."

There is little empirical evidence on how urban growth

poles act as industrial seedbeds, but there is evidence that

firm creation and innovation diffusion may be highly related to personnel movements between firms in the same

and related sectors. Research on the spinoff process in

the San Francisco area reveals that small firms have

higher spinoff rates than large firms, but this may not

hold true over time or over space.18 One key variable

that has received little attention is the role of organizational structure and corporate policy in the spinoff process. In this respect, one can classify spinoff firms according to how they came about:

Competitive spinoffs, where employees leave a

parent company and establish their own companies,

whose products compete directly with those of the

initial parent. Because many buyers require a "second source," the need for duplication and standardization of products can be a major stimulus for spinoff here.

Backward linked spinoffs, where employees set up

their own company to supply the parent with needed

materials or services. This may result from a conscious policy decision by the parent company to buy

rather than make a product it needs, i.e., where the

spinoff is directly encouraged by the parent.

p. 409.

A. Peed, "Ilffusion, Organuanonal Spatial Structure and City-System Development," Economic Geography, vol. 51, 1975.

''Wilbur Thompson, Internal and External Factors in the Development of

Urban Economies." in Issues in Urban Economies, H. Perloff and L. W.ngo (eds.)

(Bar more; Johns Hopkins Press, 1968),

"R. Struyk and F. lames, Intrametropolitan Industrial Location (Lexington,

MA, Lexington Rooks, 1975). Also in Cooper (1971) and Danilov (1972).

"A. C. Cooper. "St..n-Offs and Technical Entrepreneurship," IEEE Trans.

actions on Enkneering Management, EM.18, 1971.

106

Forward linked spinoffs, where employees set up a

company to market products on which they worked

for the parent. This may occur where an employee

identifies a potential use for a product and decides

to market the idea himself. This could have a major

effect on the diffusion and adoption of a particular

product.

Corporate policy can also influence regional rates of

firm creation, since large firms can limit the number of

external spinoffs by encouraging internal spinoffs for risky

R&D ventures. Texas Instruments, for example, finds

and keeps technical entrepreneurs through a small business development scheme within the company; this may

be one reason why the number of spinoffs in the Dallas

area (where Texas Instruments is the leading electronics

company) is low in comparison with the number of spinoffs from Fairchild in the San Francisco Bay area. One

would expect the larger urban growth poles to be the

most fertile spawning grounds for new high-technology

companies, but the faster growth of small and mediumsized growth centers in recent years suggests that agglomerating tendencies are also at work in these smaller corn.

munites. High-technology complexes can apparently develop in a wide variety of locations, and the next round

of high-technology development may a well be away from

large growth poles (e.g., Boston, San Francisco, New

York, Dallas, and Phoenix) and towards medium-sized

growth centersplaces like Austin, Albuquerque, Colorado Springs, and Portland, which are small enough to

offer a superior quality of life but still large enough to

provide necessary services and economic bases.

Diffusion Theory

Studies of technology transfer and the diffusion of industrial innovations often ignore the regional context of

innovations."' Conversely, geographers who study the

innovation diffusion process usually focus on consumer

rather than industrial innovations.2° There thus exists

a need to integrate appropriate elements of both economic and spatial models of innovation diffusion. Four

different approaches have been proposed:21

the adoption approach, which focuses on the process by which adoption occurs, mostly as a function

of the learning or communications process;

the market and infrastructure approach, focusing on

the ways in which adoption conditions Ire made

available via diffusion agencies and adoption

strategies;

1°E. Mansfield, et al., The Production and Application o f New Industrial Technology (New York: W. W. Norton. 1977). Also. Gold (1977).

2°T. Hagerstrand, Innovation Diffusion as a Spatial Awes, (Chicago: University of Chicago Press, 1967). Also in Brown (1980).

Brown, Innovation Diffusion (London: Methuen, 1980),
App. AHigh-Technology Location andllegionai Development: The Theoretical Base 101

the economic history perspective, which emphasizes

the dynamic, evolving nature of innovations; and

the development perspective, which focuses on the

impact of diffusion on employment and regional

disparities.

Based on these four approaches, four models of innovation diffusion models can be identified:

The epidemic diffusion model, which emphasizes

distance-decay factors and the logistics curve, where

diffusion is seen as a function of the contact system

of adopters. The "friction of distance" implies that

the diffusion or spread of innovations is most effective in areas close to the point of origin (see below

for more detail).

The hierarchical diffusion model, in which innovations filter down the urban size hierarchy; this does

not necessarily imply a rigid progression from larger

to smaller urban centers for all types of innovations,

since the organizational structure of multilocational

companies can cause diffusion to flow from small city

to large or from large city to even larger, or between

cities of approximately the same size."

The interindustry diffusion model, emphasizes the

sectoral environment of a firm and the importance

of variables such as market structure, profitability,

access to capital markets, and age of capital stock

in explaining the diffusion process."

The interorganizational diffusion model focuses on

the internal characteristics of firms as determinants

of diffusion, together with attitudinal and information variables.

These models have not yet been integrated into a comprehensive diffusion theory because they operate at different levels of analysis:

The epidemic and hierarchical diffusion models, strictly

viewed, deal with the question of how a phenomenon

develops in time and space, while only the industry-sped&

and firm-specific models attempt to answer the question

of why a particular diffusion pattern emerges. . . if one

thus questions the influence of space on the diffusion of

innovations one must proceed from both of the last-named

models and investigate how the validity of these models

is modified by the fact that the economic subjects are exposed to varying locational environments.24

Product and Regional Lifecycle Theories

Building on growth pole theory, and recognizing the

propulsive nature of technology in changing regional economic structure, regional researchers in the 1970's turned

to the product and technology lifecycle models for more

^"Fred. p .1Sti "Mansfirld, op. it

"H J. EWCTS and R W Wittman, "innovation-Oriented Regional

Rrgrotsal Studlr,, vol, 14, Ntstl, p. Ino

appropriate explanations of the changing locational re

quirements of firms whose products are at different stages

of maturity."

Briefly, the product cycle model is based on the premise that products evolve through three distinct stages:

an innovation stage, where a new product is developed and manufactured in the home region and introduced in a new marketiarea by exports;

a growth stage, in which external demand (interregional or international) expands to a point where

direct investment ir production facilities becomes

feasible and when process technology can be transferred; and

a standardization stage, when production may shift

to low-cob. locations.

This model has an explicit locational dimension, since

each stage of the product cycle has different locational

requirements. The innovation stage, which needs a high

input of R&D, is usually carried out in high-cost areas,

as in the case of mini- and microcomputers in California and Massachusetts. The standardization phase, on

the other hand, favors low-cost locations, typically peripi.eral areas where labor costs and the level of unionization are low. This part of the theory explains the early

loss of nearly 1 million production jobs from the Manufacturing Belt to the Sunbelt and foreign locations between 1947 and 1963.

As production operations accumulate in peripheral

growth centers, however, external economies will increase

in those locations, particularly agglomeration effects, and

service infrastructure. When demand in the receiving region grows to a critical threshold, industrial growth takes

off on its own through an indigenous seedbed effect

large branch plants begin spawning small new companies,

particularly in high-technology sectors.

Aiding this growth process in the new region is the

immigration of entrepreneurs. Evidence of such developments can be seen in the once-peripheral growth centers

or "sunspots" of the South and West, such as the DallasForth Worth area.26 This spatial manifestation of the

product cycle implies that, over time, regions can change

their roles from recipients of innovation via branch plants

to generators of innovation through indigenous growth.

The Manufacturing Belt has traditionally served as the

seedbed of innovation for the American industrial system, but the diffusion or technology-intensive growth sectors to peripheral ,Nth centers suggests that the innovation potential of the Manufacturing Belt has been

eroded and that of the periphery enhanced. Shift-share

analysis shows that the Manufacturing Belt specializes

in nationally declining industries, whereas the industrial

mix of peripheral areas showed a greater share of tech-

"Rees, op. cst. Also, Norton and Rees 09793, and Thomas 0980

107
702 Technology, innovation, and Regional Economic Development

nology-intensive growth industries.27 Cyclical changes

'resulting from the recessions of 1975 and 1982 may have

exacerbated this structural change, which could mark the

decline as the turning point for the Manufacturing Belt

as the dominant industrial core of the country. However,

the position of any region on its growth curve is the result of counterbalancing forces from the push of innovation adaptation, on the one hand, and the pull of inertia protecting existing structures, on the other. Recent

studies of the locational concentration of R&D and adoption rates for new production technologies suggest that

the Old industrial heartland still has more indigenous potential for economic revival than is generally accepted.'s

Evidence from the recent revival of New England is further testimony to this.

Recently, therefore, it has become popular to think in

terms of long cycles (or waves) of growth and decline,

but this time in a regional context." The notion of a

regional economic lifecycle has its antecedents in Kondratieff's long waves and Schumpeter's notion of "creative

destruction," in which new economic structures in new

regions bypass existing structures that have become functionally obsolete. Using this framework, New England

was the first Frostbelt area to enter a long economic

slump, and therefore would be expected to recover first.

But one has to treat such generalizations with care.

"There are two economies going on in the New England

states," according to one view, "the high-tech area but

aka the continuing struggle of the old mill towns."3° The

large number of part-time and low-wage jobs in the region has led some researchers to view New England as

a dual economy with a "missing middle . . . of skilled jobs

within par'cular industries which traditionally employed

the largest number of skilled and semi-skilled blue collar

workers.""

Others are skeptical that the industrial Midwest will

go through the same kind of economic transformation

as New England, due to inertial factors like high levels

of unionization and relatively high wages.

The future direction of the industrial heartland's lifecycle, and the reliance on high-technology industry as

the engine of revival, are clearly open to question. The

technological imperative that drove the revival of New

England may not be present in other areas, at least not

"R. D. Norton and 1. Rees. "The Po duct Cycle and the Spatial Decentralizanon of American Manufacturing" Regional Studies, vol. 13, 197g.

Unerback, "The Dynamics of Product and Process Innovation in Industry," in Technologkal Inrxwation far a Dynamic Economy, C. Hill and J. Ut

whack (eds.) (New York: Pergamon Press, 1970).

'O. Sternlieb and J. W. Hughes (eds.), Revitalising the Northeast (New

Brunswick. NJ: Rutgers University. Center for ' !than Policy Research, 1978).

nix National Journal, Feb. 26, 1983, p. 436.

+ifi. Harrison, "Rational"*Iition, Restructuring and Industrial Reorganisaion

in Older Regions; The Et onomic Transformation of New England Since World

War IL" Working Paper #72, Joint Center for Urban Studies, MITHarvard

University, 1982, p. 117.

to the same degree. However, the industrial,heritage of

the Manufacturing Belt, the quality of output associated

with its companies, and increasing wage inflation in Sunbelt regions may 'in time shift the comparative advantage back to America's older industrial regions.

Industrial Location Theory

and the Location Decisions :of

High-Technology Companies

Overview

The growth theories reviewed so fax deal with regional

economic development in a macro sense. Whether or not

they are applicable to understanding the location patterns of industry is dependent on'the cumulative effect

of individual investment decisions and how individual

decisionmakers react to their own perceptions of reality.

To date, industrial location theory can be divided Mto

two major schools of thought: least-cost theory and max-.

imum-demand theory.32 Dissatisfied with the unrealistic

assumptions of these theories, however, regional researchers have argued that a more appropriate understanding

of business location can only be achieved by examining

the decisionmaking process in its corporate context.33

Selecting the location for a new plant is typically a decision made by relatively few senior executives of a firm,

based on the objective and judgmental balancing of corporate goals and a variety of location factors. The location search typically proceeds sequentially: a region of

interest is delimited; subsections of the general region are

evaluated; towns that meet the minimum requirements

for the plant are identified; and firally a specific town

is selected and the building site is purchased. The location factors change in relative importance with each

Change in the geographical scale of the search.

Location faaora may be separated into two general

types: 1) those relating to the friction of distance, and 2)

those relating to the attributes of areas. Friction-of-distance variables are those which measure the costs of moving materials, products, peciple, or ideas across space.

The. L. costs may be measured in terms of miles, or monk,

or time, or even psychologically as ease or conveni*e.

The second category is concerned not with how far one

place is from another, but ratheewith the characteristics or attribute's of the area itself. Included are variables

such as labor, agglomeration and infrastructure, power,

water, and the quality of life. Specific factors vary in relative importance according to firm, place, and time; each

situation is unique. Location theory has traditionally em-

"Lloyd and Dkken, op. cif, Also Smith (1980).

"H. A. Stafford, Principles of Industrial Facility Loci/non (Atlanta: Conway

Publications, 1980).

108
App. A=High-Technology Location and Regional Development: The Theoretical ease 103

phasized friction-of-distance variables, but the attributesof-area variables are becoming more important for many

plant location decisions. The factors most often considered relevant, however, are access to markets, access to

materials, transportation facilities, labor (especially availability and productivity), utilities, business services, taxes,

and local "quality of life."

The Location Factors That influence

High-Technology industry

The popular concept of high - technology industry most

closely corresponds to firms or pants that produce hightechnology products. Compared with most manufacturing companies, these firms tend to be relatively small and

new. In conducting site sear( hes, they are likely to engage

in an informal, top-down style of decisionmaking (due

to the lack cf internal specialists) and have limited search

spaces, preferring to locate new activities close to existing

operations."

Friction-of-distance factors are relatively unimportant,

because they tranufacturP value-added products for

which unit transportatio EsIges are low, because their

input materials come from variety of sources and loca-

"R, ()akev. A. Thwaites, and P. Nash. The Regional Distribution of In.

novanve Manufacturing Establishments in Britain," Regional Studies, vol. 14,

1980.

rions, and because their markets also tend to be spatially

scattered.

Table A-1 indicates the relative significance of the 10

most important location variables according to various

ranking schemes, by high-technology and non-high-technology plants, and by location decisions at the regional

and within-region scales.

Labor.Labor stands out as the most important location determinant in the search for a new plant site. This

is especially so for high-technology plants a recent survey

found that 79 percent of high-technology decisionmakers

mentioned labor as an important factor in their selection of a branch plant location and this was the only

factor mentioned in more than half the location decisions." Ir an earlier survey conducted by the Joint Economic Committee (JEC) of Congress, fully 89 percent of

the respondents indicated that labor skills and availability

were "significant" or "very significant" at the regional

scale, with 96 percent the comparable figure at the withinregion scale. 36

While labor costs are of some importance, it appears

that the availability, attraction, and retention of skilled

technical and :ofessional personnel are the primary con-

"otafford, op. cit.

"Joint Economic Committee. U.S. Congress, Location of High Technology

Firms and Regional Economic Development (Washington. DC: U.S. Government Printing Office, June 1982).

Table A-1.Location Factors influencing New Manufacturing Plants

Determinants for high-technology v. non-high-technology plants

Rank High-technology plants Non-high-technology plants

I Labor Labor

2 Transportation availability Market access

3 Quality of life Transportation availability

4 Markets access Materials access

5 Utilities Utilities

6 Site characteristics Regulatory practice

7 Community characteristics Quality of life

8 Business climate Business climate

9 Taxes Site characteristics

10 Development organizations Taxes

SOURCE: H. A. Stafford, Survey of 104 Plants, 1983.

Determinants for high-technology plants between and within regions

Rank Selection of region Selection within region

1 Labor skilisiavallability

2 Labor costs

3 Tax climate within region

4 Acasemic institutions

5 Cost of living

6 Transportation

7 Markets access

8 Regional regulatory practices

9 Energy costs/availability

10 Cultural amenities

Labor availability

Statellocal tax structure

Business climate

Cost of property/construction

Trans..t availability for people

Ample area for expansion

Proximity to good schools

Proximity to amenities

Transport facilities for goods

Proximity to customers

SOURCE: Joint Economic Con/lifts., US. Congress, Location of Hlg, Tochnoiogy Ficnos and Raglan* Economic Development.

June 1. 1952, tables 1115 and 6, pp. 23 and 2f3. 109

36-737 0 84 8 s QL 3
104 Technology, Innovation, and Regional Economic Development

cerns when high-technology firms locate or expand production facilities. Even highly skilled labor tends to exhibit a significant degree of spatial inertia; in this sense,

high-technology industries are not truly 'footloose," because they are constrained by the uneven distribution

of relatively immobile labor. As a result, the R&D centers

of lace corporations are most often located in urban

areas that are rich in information, skills, and management."

Academic Institutions.Several studies have indicated the importance to high-technology industries of

;nearby colleges and universities, especially those that

focus on scientific and technical education. These educational institutions are directly influential because they

are repositories of technical information and they train

the needed engineers and technicians. They also are impor.ant in attracting and retaining skilled workers who

want continuing educational opportunities. Furthermore,

to the extent that new high-technology firms are spinoffs from existing enterprises, they are more likely to be

born and survive in the technology-rich environments

spawned by nearby universities. The importance of nearby academic institutions is consistent with the overwhelming locational importance of skilled labor, as are

the quality of life and cultural amenities variables.38

Quality of Life and Amenities.For all industries,

the human factor has become a more important locational variable in the past two decades. For some it has meant

a search for low-cost labor areas, but for high-technology

industry it means areas that, because they are attractive

to highly skilled workers, are thereby more productive

environments. Quality of life and the existence of sufficient amenities, both cultural and recreational, are difficult variables to measure, but there is little doubt that

they are critical in locational decisions." In table Al

these include not only "quality of life" and "proximity

to amenities," but also "academic institutions" and "proximity to good schools." A plant started in a community

which ranks low on the livability scale will soon have

difficulty in attracting, or even transferring, engineers and

managers.4°

Access to Markets, Materials, Transportation, and

Agglomeration. Industrial location theory has traditionally emphasized the costs of moving materials to the

plant and products to the consumers. These friction-ofdistance considerations are relatively unimportant for

high-technology firms, which produce items for which

transportation costs are a small proportion of delivered

"E. Malecki, "Corporate Organization of R z.nd D and the Location of Technological Activities." Rekonal Studies, vol. 14, 1950.

"E. P. Deuterman, "Seeding Science Rased Industry," New England Businru

Review, 19b6 Also Gibson (1970), and Joint Economic Committee (1982).

"Stafford, op. tit, p. 100,

"R. Schmenner, Making fitisirx.s., Location Decisions (Englewood Cliffs, NJ:

Prentice-Hall, 1(1S21, p 38.

price; transit time is more cscal than cost. They also

utilize a wide variety of inputs which are not conveniently

localized, so the advantages of locating near one supplier

are neutralized by the distances from others. Transportation is, however, a factor of some locational importance, but more in terms of the requisite modes and frequency: high-technology firms need easy access to highlevel, rapid transportation facilities (e.g., air travel) for

the movements of managerial and technical staff. Market access is a variable of moderate importance to hightechnology plants, but again the emphasis is on ease and

speed rather than cost. Access to customers is more important when the sale contract calls for service or when

there are significant reciprocal information transfers.

Taxes.No issue is more debated in the industrial

location literature than the influence of taxes on site

selection. Decisionmakers frequently mention the importance of regional and local tax differentials in their location decisions, but analysts usually conclude that taxes

are of relatively little importance, especially when regions

of interest are being determined. One consultant suggests

that industrialists often use taxes as rationalization for

opposing labor unions and other costs, real or imagined,

associated with an unsatisfactory regional image; based

on his company's studies, the consultant concludes that

"it is apparent that in every case State taxes are the least

significant of all factors."41 Other researchers have also

concluded that taxes are a relatively minor locational

variable, and that taxes are often as much an emotional

issue as a financial one.42 Low taxes may be somewhat

more valued by high-technology industries since they are

less locationally constrained by other factors.43 The JEC

survey indicates that taxes are the s'cond most important locational determinant for high-technology firms,

ranked "very significant" or "significant" by 67 percent

of respondents at the regional scale, at the within-region

scale rising to 85 percent." A more recent survey, however, places taxes as a minor locational variable: only 14

percent of the high-technology respondents even mentioned taxes as a location factor.45 The issue remains unresolved, but further complications are introduced when

it is noted that low taxes usually are negatively correlated

with several other attributes which high-technology firms

value, includ'ng public services, infrastructure, good

schools, and cultural amenities.

Financial Capital.Although access to financial capital is a key variable in R&D trends and innovation generation, very little is known about geographical differences in its availability. Industrial location literature

"H. L. Hunker, Industrial Development (Lexington, MA: Lexington Books,

1974). p, 139.

"Schm-enner, op. cit. Also, Stafford (1980), p. 109.

"Ibid., p, 50.

"JEC:, op. cit.

"Stafford, op. cit.

0
App. AHigh-Technology Location and Regional Development: The Theoretical Base 105

traditionally assumed uniform accessibility to financial

capital, and thin assumption has become part of the common wisdom without appropriate empirical testing.°

Given the different banking systems in the United States,

ranging from branch banking to unit banking as modified by multibank holding company acquisitions, the

assumption is probably faulty, particularly in the current

context of deregulation in the financial sector.

Because of the importance of venture capital in the generation and commercialization of innovations, particularly by small companies with higher risks attached to

them, regional and temporal variations in access to capital may be a more significant factor than has been

shown. For example, a recent study of the financing dif

faculties encountered by 2,000 companies found that

smaller firms have more difficulty than larger firms and

rural companies have greater difficulties than urban or

suburban firms; but when firms were aggregated by census region, there were few discernible differences in their

difficulties in obtaining capita1.47

Applying Theories to High-Technology

Development Programs

Because of the increasing involvement of States and

local communities in intense competition for high-technology plants and jobs, it has become even more important for them to be aware of location factors that industrial decisionmakers consider important. Above all, they

must assess their region's attributes in a realistic fashion

and then match them up with the factors valued by industry, as part of their economic strategy in their area

development programs.

The Need To Assess and Mobilize

Local Potential

There will be intense competition over the next few

years for a few selected high-technology industries, and

the job creation potential at the end may still be low.

Because the rewards may be email and the game highly

competitive, each locality needs to assess its existing potential in order to establish realistic goals for attracting

high-technology industries. One of the most effective

tools for this purpose is the "target industry screening

method" developed by the Battelle Institute in I970.°

Developed as an alternative to the "shotgun approach"

of many community marketing efforts, the screening matrix method provides a more systematic way of match-

"D M. Smith, Industrial Location cLondon: John Wiles', 1Q80).

c11 Kat:man, "The Case Against Bailing Out Distressed Areas," in Public

Poltiiek /,r nAtre5wti Areas. S. Redburn and T. Buss (eds.) (Lexington, MA;

Lexington Rooks, 1952), p 33. "Battelle ln,titute, The Regional Potential Niodel (Columbus, OH. Battelle

Nicmondi Labs. 1470).

ing the attributes of a community with the needs of an

industry." This approach assumes that it is important

for future industry to be related to the existing economic

structure of an area in terms of industry linkages and

resource base. This implicitly recognizes the importance

of current attributes in attracting further development,

as explained by export base and growth center theories

described above.

After careful consideration of an area's comparative

advantages and current economic conditions, industries

whose location criteria most nearly match the community's attributes are identified as the highest order prospects for its recruitment efforts.

The types of locational criteria that should enter the

screening methodology in the context of high-technology

industries should include the following factors discussed

in earlier sections:

The existing economic base, particulady the presence of high-technology sectors or companies with

direct links to high-technology sectors. This approach could include input-output analysis and

would identify potential industries for import substitution::

The scientific and technical environment, including

access to major universities and research institutions.

Labor factors, including occupational mix (proportions of professional, skilled, and unskilled workers),

labor cost, and productivity as they relate to the labor intensity of existing industry.

Financial variables, including local property and income tax rates, the role of commercial banks and

savings and loan banks, and the presence of other

financial institutions with access to development

capital.

Amenities, particularly access to recreational and

cultural opportunities.

Access to local and national markets via different

forms of transportation.

Only through systematic assessment can the community assess its comparative advantage for attracting specific industries. A regional marketing plan should, however, look out for conflicting goals. For example, it is

conceivable that industries with a high propensity to attract suppliers (backward links) may result in the clustering of firms that could put excessive demands on certain

types of labor. ms in turn could result in higher rates

of wage inflation in the area, which could prove unattractive to other industries. An understanding of an

area's industrial base, plus an objective screening process, is one of the few sound ways of attracting future economic development, whether technology-intensive or

not. Without such systematic procedures, community

resources may be wasted.

4 . Sweet, "An Industrial Development Screening Matrix." Profmonal

Jeographer, May -1470.

11
106 Technology, innovation, and Regional Economic Development

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Rees, J., "Regional Industrial Shifts in the U.S. and the Internal Generation of Manufacturing in Growth Centers of the

Southwest," in Interregional Movements and Regional

Growth, W. Wheaton (ed.) (Washington, DC: Urban Institute, 1979).

Rees, J., and Briggs, R., "The Diffusion of New Technology

in American Manufacturing Industry," Working Paper,

Maxwell School, Syracase University, 1983.

112
App. AHigh-Technology Location and Regional Development: The Theoretical Base 107

Richardson, H., Regional Growth Theory (London: MacMillan, 1973).

Rosenberg, N., Technology and American Economic Growth

(New York: Harper & Row, 1972).

Schmenner, R., Making Business Location Decisions (Englewood Cliffs, NJ: Prentice-Hall, 1982).

Smith, D. M., Industrial Location (London: John Wiley, 1980).

Stafford, H. A., Principles of Industrial Facility Location

(Atlanta: Conway Publications, 1980.)

Stafford, H. A., "The Effects of Environmental Regulations on

Industrial Location," Working Paper, University of Cincinnati, project funded by the National Science Foundation,

1983.

Stanback, T., and Noyelle, T., Economic Transformation in

American Cities (in press).

Sternlieb, G., and Hughes, J. W. (eds.), Revitalizing the Northeast (New Brunswick, NJ: Rutgers University, Center for

Urban P ',licy Research, 1978).

Struyk, R., and James, F., Intrametropolitan Industrial Location (Lexington, MA: Lexington Books, 1975).

Sweet, D. C., "An Industrial Development Screening Matrix,"

Professional Geographer, May 1970, pp. 124-127.

Thomas, M., and LeHeron R., "Perspectives on Technological Change and the Process of Diffusion in the Manufacturing Sector," Economic Geography, vol. 51, 1975, pp.

231-251.

Thompson, W., "Internal and External Factors in the Development of Urban Economies," in Issues in Urban Economies, H. Perloff and L Wingo (eds.) (Baltimore: Johns

Hopkins Press, 1968).

Tiebout, C., "Location Theory, Empirical Evidence and Economic Evaluation," Papers and Proceedings of the Regional

Science Association, vol. III, 1957.

Townroe, P. M., "Industrial Location Decisions, A Study in

Management Behavior," University of Birmingham (U.K.)

Centre for Urban and Regional Studies, Occasional Paper

15, 1971, pp. 17-27.

Utterback, J., "The Dynamics of Product and Process Innovation in Industry," in Technological Innovation for a

Dynamic Economy, C. Hill and J. Litterback (eds.) (New

York: Pergamon Press, 1979.)

Vinson, IL, and Harrington, P., "Defining 'High Technology'

Industries in Massachusetts," Department of Manpower Development, Commonwealth of Massachusetts, 1979.

Weinstein, B. L, and Firestine, IL, Regional Growth and Decline in the United States (New York: Praeger Publishers,

1978).

Wheaton, W. (ed.), Interregional Movements and Regional

Growth (Washington, DC: Urban Institute, 1979).

Williamson, J. G., "Regional Inequality and the Process of National Development: A Description of the Patterns," Economic Development and Cultural Change, vol. 13, 1965,

pp. 3-45.

113
APPENDIX B

Formation and Growth in High-Technology

Firms: A Regional Assessment*

Summary

Recent publicity about rapid growth of employment

in high-technology industries has caught the attention

of policyrnakers at all levels of government. Many State

and local governments have already created programs

to encourage location of new high-technology businesses

in their regions. While there is no shortage of policies

promoting emphasis of local development efforts on encouragement of high-technology industries, there is a

dearth of empirical information on the efficacy of such

programs. This study attempts to provide an empirical

description of the high-technology sector, its regional

growth patterns, and the relationship of particular characteristics of metropolitan areas to high-technology

growth.

A large business microdata set, the U.S. Establishment

and Enterprise Microdata (USEEM) files has been developed at The Brookings Institution for the Small Business Administration with additional support from the

National Science Foundation (NSF). Several properties

of this new data set recommend its use for this type of

analysis. Most important is the facility to specify groups

of business establishments and examine details of their

behavior over time, differentiatinge.g., business formations, closures, expansions and contractions. The presence of information on organizational status of establishments (e.g., whether an establishment is a branch of a

la, ger business or an independent euterprise) permits the

analysis of the employment changes in establishments

within the context of their owning enterprises.

Adopting a broad, but rigorous, definition of high-technology industries, based on minimum levels of professional, scientific, and technical workers in each industry or of research and development (R&D) expenditures,

tabulations of matched records from the USEEM files

for 1976 and 1980 were prepared to provide data to analyze the size and distribution of the high-technology sector in 1976 and its growth from 1976 to 1980) Data for

This contractor report was prepared fee crrA by Catherine Arrnington, Can.

dee Harris, and Marjorie Odle of the Business Microdata Project, The Brook.

ings Institution, Washington, DC. The analysis and data were prepared for the

National Science Foundation under grant No. LSI 8212470 with additional analysts prepared for OTA under an interagency agreement with the Small Business

Administration. Original data development work was funded by SBA contract

No. 2641-0A-74.

'This definition of the hightechnology sector included 88 industries in manufacturing and business services, most of which rely heavily on evolving technologies.

108

thl. high-technology sector are contrasted with that for

the rest of the manufacturing and business service industries (called "low technology") and with that Tor

"other industries" (excluding manufacturing, business

services, and government). Employment growth and business formation data were extracted from these tabulations and integrated with other socioeconomic data for

a sample of 35 metropolitan areas. These aggregate data

were then used in a regression analysis examining the

relationships between the characteristics of the metropolitan areas in the sample and the formation and growth

of both high-technology and other business establishments.

The descriptive analysis of the tabulations and the regression analysis addresses the following questions. What

are the characteristics of high-technology establishments

and their patterns of employment growth? How does the

distribution and behavior of high - technology establishments differ from that of establishments in other industries? Are high-technology industries dominated by large

corporate enterprises, or do independent establishments

flourish in the atmosphere of innovation and growth?

Firms in these industries are subject to ever-shortening

technological lifecycles requiring them to innovate or die.

Which establishments are succeeding in their pursuit of

change and growth?

While this analysis is not designed to demonstrate cause

and effect relationships among these factors, it provides

important baseline data on the magnitude, location, and

growth of firms in the high-technology industries. In addition, the analysis advances the discussion of the sources

of employment growth by testing many popular hypotheses against actual data for the period 1976-80 and by

reformulating several hypotheses consistent with the

evidence.

The High-Technology Sector

Although the high-technology sector, as defined in this

etudy, included only 2 percent of the business establishments in the United States in 1976, it accounted for 7.4

percent of all private sector employment. High-technology industries comprised 11 percent of establishments,

with 21 percent of the employment in manufacturing and

business services in 1976. The average size of hightechnology establishments (69 employees) is more than

twice that of the low-technology manufacturing and busi114
App. 8Formation and Growth in High-Technology Firms: A Regional Assessment 109

ness services and five times the average establishment size

in "other industries."

Almost 90 percent of high-technology jobs are in firms

with at least 100 employees, compared to the national

average of 64 percent. Furthermore, the organizational

composition of the high-technology sector is heavily

skewed toward branches of large firms. More than .. percent of the jobs in high-technology industries are in mutt lest ablishment firms.

The distribution of high-technology jobs across four

broadly defined regions of the United States is generally

the same as that of employment in all industries. The

Northeast has 29 percent, the North Central has 28 percent, the South has 24 percent, and the West has 19 percent. Diverging from the other regions, the South's hightechnology employment is disproportionately in affiliates

of firms whose owners are located in different States.

In sum, the high-technology industries comprise a relatively small sector of the economy, concentrated in large

branch establishments of multiestablishment firms. Half

of the employment is in affiliated establishments which

are owned and operated by headquarters located in different States, which are frequently engaged in more than

one major industrial activity. The regional distribution

of high-technology employment is much the same as that

of employment in all industries.

Regional Growth Patterns

The previous section has provided a "snapshot" of the

current status of high-technology firms in the United

States. It is also important to identify trends or changes

in that status by evaluating the formation and growth

patterns in these industries. Are these reinforcing or

restructuring the established profile of the high-technology sector? High-technology industries exhibited two important characteristics in the broad regional assessment:

1) they have generally high employment growth rates,

and 2) differences in regional growth rates tend to offset

existing inequities in the distribution of employment in

high-technology and in other industries.

Employment in high-technology industries in the

United States grew a total of 19.5 percent between 1976

and 1980, an average annual growth rate of 4.5 percent

3mpared to the all industry rate of 3.6 percent. The percentage employment growth rate in high-technology industries was a third higher than in the low-technology

part of the manufacturing and business services. Of the

net increase in private sector employment between 1976

and 1980, high-technology industries contributed 9.5 percent, about 30 percent more than its 7.4 percent share

of employment in 1976.

Within each of the four regions, the growth rate of

high-technology employment exceeded that of the other

two industrial sectors. The discrepancy was greatest in

the South where high-technology employment increased

at a rate almost twice the overall average. However, the

interregional differences in high-technology growth rates

followed the same general pattern as growth rates for

other industrial sectors. In other words, the distribution

across regions of high-technology growth did not differ

markedly from that of other industries.

The regions which experienced the highest rates of

growth were those which previously had the smallest

shares of high-technology employment. Although the

South and the West held only 43 percent of the total

employment in high-technology in 1976, these two regions accounted for 70 percent of that sector's growth

between 1976 and 1980. This distribution of growth in

high-technology employment roughly paralleled that in

all industries, indicating a general trend towards equalizing the distribution of both high-technology and other

business activity across regions of the United States.

Formations of new business establishments are a major component of net employment change. Within each

region, the formation rates for the high-technology sector range from 30 to 50 percent higher than those for

the 'other industries" sector. Low-technology formation

rates were almost identical to those of the "other industries" sector in the Northeast and the North Central regions, but were stronger than "other industries" in the

South and West.

During the 1976-80 period, a region's shares of formations and its shares of associated new employment were

nearly the same for each of the three sectors. This share

was 19 percent for the Northeast, 23 percent for the

North Central, 34 percent for the South, and 24 percent for the West. Consistently, the Northeast and North

Central had formation rates below the U.S. average for

all three of these industry groupings while the South and

West had above average rates, of both business and job

creation.

Rates of job loss from closings and contractions were

similar across regions. Much of the decline in the older

industrial regions was attributable to lower rates of formation of new business establishments. Therefore, it is

not surprising that the regional differences in formation

rates are quite similar to differences in the rates of net

employment change. Again the tendency toward a redistribution of economic activity is reducing the skewed

nature of existing distributions.

A shift share analysis of manufacturing employment

growth, which separates employment growth into components attributable to national (average) growth, regional (competitive) differences, and industrial composition

(mix) differences, revealed some interesting aspects of regional growth patterns between 1976 and 1980. Looking first at the regional, competitive component of employment change, the neg: tive impact of the Northeast's

languishing economy was s large that its net growth was

115
110 Technology, innovidion, and Regional Economic Development

only one-third as high as it would have been if the regional economy had performed as well as the national

average. The region suffered below average growth rates

in every industrial sector. Both the South and the West

were strong in all industrial sectors, but the West was

almost twice as strong as the South.

Industrial composition effects were more startling, especially compared to those found in similar analyses of Manufacturing employment growth between 1963 and 1972

and between 1972 and 1976.1 While the Northeast used

to have a a large enough share of high-growth industries

to more than offset its very high share of low-growth

manufacturing, the mix in the 1976-80 period was just

barely balanced. This trend of decreasing advantage of

industrial mix is even stronger in the North Central region.' The South continues to have a relatively small

share of the high-growth industries, in spite of its overall excellent economic health. Restating the point made

above, although the South had by far the highest growth

rate for high-technology business, it had the smallest proportion of high-technology employment.

Regional differences in employment growth were attributable more to general regional economic performance than to specific differences in industrial composition in the shift .share analyses for each the periods

between 1963 and 1980. Consistent with these findings,

it has recently been suggested that the major problem

issuing from the economic changes of the 1970's is not

"one of declining industries or declining competitiveness

but one of declining places."' The decline of certain industries concentrated in the North and its relative loss

of population would not be so important if other industries were moving in to take up the slack. As pointed

out above, the Northeast had the lowest formation rate

in all three industrial sectors affording insufficient

counterweight to employment losses in its declining industries. The general weakness of the Northeast's economy has been exacerbated by an erosion, during the past

two decades, of its previously advantageous industrial

mix. The West, on the other hand, is developing an increasingly favorable industrial mix, with a disproportionately large share of high-growth, high-technology industries, buttressing its strong regional performance.

;Sec John Revs, "Regional Industrial Shifts in the U.S. and the Internal Generarion of Manufacturing in Growth Centers of the Southwest," in Interregional

lvfovernents and Regional Growth. William C. Wheaton led.) (Washington, DC:

The Urban Institute, 1981).

'The negative industrial mix components affecting the northern regions is poutsably understated due to the level of aggregation of the industrial sectors used

here. This analysis uses only three industrial sectors as milt components: I) highgrowth, high- technology industries; 2) low-growth, high-technology industries:

and 3) lowtechnology industries or "other manufacturing and business services."

*Shirley P. Burggraf, "Overview and Critique of Revitalization Issue," Polley

Studies Review, vol, 2. No. 4, May 1983, p. 674.

High-Technology Formations

in Metropolitan Areas

To better understand these regional patterns of hightechnology development a more detailed analysis of formation and employment growth in metropolitan areas

was conducted. Business formations constitute an important, if not determinant, force in net employment growth.

Through a regression analysis of business formation rates

on several metropolitan characteristics, it was hoped that

certain policy relevant relationships might be unearthed.'

:t was hypothesized that each of the dependent variables would be related to some or all of the following

factors:

the pool of potential entrepreneurs,

the relative costs of doing business,

the level of activity in that industrial sector,

regional economic conditions,

the quality of the labor force, and

general attractiveness of the city.

Business formations were expected to have strong positive associa-ions with the pool of potential entrepreneurs,

the economic health of the area, the general attractiveness of the area, the quality of the labor force, and the

level of extant activity in the same industrial sector. Only

the costs of doing business were expected to be negatively

related to formations.6 The relative attractiveness of the

city both to people and to business, as measured by the

rate of population growth in the first half of the 1970's,

was the factor most strongly associated with metropolitan

differences in formation rates. Each difference of 1 percent in the total population growth rate was associated

with close to a 4-percent difference in the rate of business formations in both high- and low-technology sectors. There was a corresponding 2.5-percent difference

in formations in the "other industries" sector.

Another proxy for attractiveness of the city and cultural amenities, city size (1975 population), was also associated positively with both high- and low-technology business formations, even after using total labor force as a

normalizing variable. Each 4-percent difference in city

size was associated with a 1-percent difference in the number of business formations in both the low- and high-technology sector.

Three measures of business costs were included: wage

rates, electricity costs, and local taxes. Each of these

variables was related negatively to formation rates in

every industrial sector, when differences in population

'For analyzing metropolitan differences in business (minions, the actual number of formations should be Nailed by some measure of the size of the area's economy to create easily comparable formation rates. Preliminary regressions on the

number of formations confirmed that the Id= of the total local labor force was

a more appropriate scale factor than the number of businesses.

'Description of the various indicators adopted as proxies for these factors is

provided below.

116
App. ftFormation and Growth in High-Technology Firms: A Regional Assessment 111

growth were not taken into consideration. Their explanatory power was strongest in the low-technology sector,

followed closely by the high-technology sector, and finally

by the "other industries" category.

As expected, differences in local economic conditions

were reflected roughly proportionately in differences in

formations in each industrial sector.? However, the hightechnology sector was somewhat less sensitive than the

other two sectors to local economic conditions.

The existing literature on high-technology firms postulates a more specific link between their formation and

growth and such variables as the availability of technical and scientific workers pr the presence of an extant,

healthy high-technology sector. As hypothesized, a significant positive relationship was found between technical skills and formation rates in high-technology industries. A 1-percent difference in the technical occupation

share of the labor force was associated with about 2-percent difference in formation rates. Though positive, the

relationship was only half as strong for the low-technology manufacturing and business services sector. For the

"other industries" sector, the relationship was negative

and insignificant.

It is frequently assumed that there is a large agglomeration effect in formations of high-technology businesses.

That is, new businesses will tend to locate near extant

successful firms in the same industry in order to take advantage of the established pools of support services and

of trained workers and managers. There was no measurable association of high-technology formations with the

share of local employment in high-technology industries.

Nor was there a discernible relationship with the absolute size of the high-technology sector, which would have

indicated that spinoffs from existing businesses contributed significantly to total formations. These two expected

relationships might be measurable with less aggregated

industrial groupings.

Finally, two firm size classes were considered in this

analysis: tiny firms (fewer than 20 employees) and large

firms (100 or more employees), representing divergent organizational types, as well as size classes. Tiny firms are

primarily independent businesses (single-establishment

firms) and are usually founded in the city where their

entrepreneur resides. Therefore, tiny high-technology formations were expected to be more sensitive than large

firm formations to intermetropolitan differences in the

supply of potential high-technology entrepreneurs (share

of work force in scientific and technical occupations) and

to differences in local economic conditions (employment

rate and overall growth rate). Large firm formations are

usually branches of existing enterprises, which are more

'Measures a current economk conditions used were the SMSA's employment

rate in 1976 (one minus the unemployment rate) and overall employment growth

(1976-80).

likely to base their location decisions on relative business costs (wage, tax, and utility rates), on availability

of an appropriate labor force, and on the general attractiveness of the area (previous period population growth).

For both size classes the relationships of formations to

each of the independent variables were generally of the

expected direction. Tiny firm formations were indeed

more sensitive to the measure of technical skills than were

large firm formations. They were also more sensitive to

variations in the measures of business costs, which were

statistically significant only fry.. formations of tiny firms.

Compared to large firm formations, a larger portion of

the total variation in tiny firm formatiOn rates was explained by the metropolitan characteristics. Contrary to

expectation, the strength of the local economy, as measured by overall employment growth, was not significantly

related to high-technology formations in either size class.

It was significantly related to formations in the low-technology sector.

Metropolitan Characteristics Associated

With Employment Growth

Net employment growth in a sector is a rather volatile

concept. It is usually a small number which represents

the balance between a large positive amount (new jobs

from business formations and expansions) and a large

negative amount (jobs lost due to closings and contractions). Rates of job loss are more similar across regions

than rates of job gains, so the analysis of net changes

is primarily the analysis of differences in formations and

expansions. Though influenced by many of the same factors, decisions to expand or contract employment in existing facilities generally reflect location decisions of

earlier periods and current fluctuations in economic conditions. In view of this, separate equations were formulated using the rates of net ernploymeat growth, for each

of the three industrial sector between 1976 and 1980 as

the dependent variable.8

Not surprisingly, many of the factors observed to be

important in explaining differences in business formation

rates are also significant in explaining differences in employment growth rates. In the high-technology sector,

growth rates were more sensitive to variations in metropolitan characteristics than in the low-technology or

"other industries" sectors. The level of education and

technical skills in a metropolitan area was closely associated with differences in high-technology growth rates,

but not with those of the low-technology nor the "other

industries" sectors.

An interesting small, but consistent and significant,

negative association was found between growth rates and

'Employment growth rates were measured as the ratio of 1980 sector employ.

ment to 1976 sector employment. or the difference between the logarithms of

1980 and 1978 employment.
112 Technology, Innovation, and Regional Economic Development

sector shares. In other words', in locales where the hightechnology sector had a relatively large share of total local

employment, its growth rate was somewhat lower than

average. For each 3-percent (not percentage points) increase in the employment share of the high-technology

sector, its growth rate was 1 percentage point lower. Lowtechnology and "other industries" were only half as sensitive to sector share size differences, but they were still

negatively related.

Tiny firm employment growth rates in both high- and

low-technology industries were generally much higher

than those of large firms. While the relationships between

large firm formations and the business cost variables were

not significant, large firm employment growth did exhibit

a significant negative relationship to the business cost

variables. Employment growth rates of existing tiny firms

are generally less sensitive than large firm rates to all significant metropolitan variables.

Employment growth in large high-technology firms is

particularly responsive to variations in the strength of

the local economy and inthe supply of technically skilled

labor. More notably, large firm growth rates in the hightechnology sector show a significant negative association

with sector share, while tiny firm growth was virtually

independent of that factor. Again a disaggregation to the

four-digit industry level might reveal an a lomeration

effect for either size class.

Introduction

Purpose

Concern engendered by the recent recession and persistent high levels of unemployment has produced a mass

of local, State, and Federal programs which attempt to

use "high technology" as a solution to many economic

ills.

These programs hope to provide a new era of growth

spawned by the emergence of new industries and the

transformation of traditional manufacturing industries

through technological innovation. Concurrent with the

popularization of this prescription for economic health,

almost every State government has created or considered creating programs to encourage innovation and the

formation of high-technology firms. Policies and programs with similar objectives at the Federal level have

also been proposed to Congress. The few highly publicized locales, such as "Silicon Valley" in California and

Route I28 in Massachusetts, which have benrfitecl from

the rapid development of the electronics industry in the

last decade, have become the paradigms for economic

growth or recovery for many cities in the Nation.

As is often the case, the advocacy of policy changes

has proceeded more quickly than the accumulation of

empirical information on which to base these policies.

Whether attempts to imitate known concentrations of

high technology are either feasible or desirable is a question that economists and policy planners are not yet prepared to answer. Too little is known about the innovation process, the formation decisions of new firms, the

plant location decisions of large firms, the dynamics of

product development, the connections between hightechnology firms and other firms which form complex

supplier-customer relationships at both the local and the

national level, and the effectiveness of alternative strategies for promoting such development.9

Most previous attempts to investigate the interaction

of innovation; formation of high-technology businesses

and regional economic development have been very limited in scopefocusing on single industries, specific communities or a single factor among the many which may

encourage innovation and growth. Neither the analysis

of the aggregate behavior of all businesses in an industry nor studies of single businesses can provide much useful guidance. The relevant unit of analysis is a group of

establishments or firms with similar characteristics of economic behavior. The missing body of knowledge might

be termed "industrial group dynamics"the mediumterm behavior of business establishments grouped by industry, region, size, firm structure, age, and other such

factors.

The objective of this current undertaking is to provide

a better foundation for the assessment of the potential

of high-technology firms to increase local employment

and for the design and implementation of policies to maximize that contribution. Through comparative analysis

of regional patterns of location and growth of firms in

high-technology industries contrasted with other industry sectors, this study attempts to identify special characteristics of high-technology businesses, of its new firms

and branch establishments, and of the communities in

which they choose to locate.

Data Resources

Until recently, policy analysts and scholarr who have

tackled questions in this area of industrial group dynamics have been severely limited by the lack of appropriate

data. Thus, their investigations must either start with

the collection of basic data by surveying a small sample

of businesses or be limited to the factors distinguishable

in cross-sectional aggregate data on businesses.

The recent development of a very broad microdata

base for U.S. business establishments now provides t' e

flexibility in aggregation which is needed to construct

appropriate statistical bases for analysis of the dynamics

of industrial groups. The USEEM files have been devel-

'Fos a review of the state of the art, we Louis 0, Tornatzky, et al., The Proc.

ess of Technokvkal Innovation: Reviewing the Literature (Washington, DC:

National Science Foundation, May 1983).

118
App. 8Formation and Growth in High-Technology Firms: A Regional Assessment 113

oped at The Brookings Institution for the Small Business Administration with additional support from NSF.

Each file represents a reasonably complete census of U.S.

business establishments with employees, with data on industrial activity, employment, location, age, and organizational status and on characteristics of its owning firm

if it is part of a multiestablishment enterprise. The

USEEM files now cover 1976, 1978, 1980, and 1 N2, but

weighted longitudinal data for analysis of changes in the

U.S. business population are available only for the 1978-

80 and the 1976-80 periods.

Several properties of this new data set recommend its

use for this type of analysis. In particular, the detailed

standard industrial classifications (four-digit SIC) in these

microdata will eliminate some of the more significant

problems of aggregation afflicting the definition and analysis of the high-technology sector.

A related major feature of this data base, in fact its

raison d'etre, is the facility to specify groups of business

establishments and examine their behavior over time.

The presence of information on organizational status of

establishments (e.g., whether' an establishment is a

branch of a larger business or an independem enterprise)

permits the analysis of the growth behavior of establishments within the context of their owning enterprises.°

Not only are the basic descriptive characteristics of the

owning enterprise available (e.g., employment size and

predominant industrial activity), but also the location

of the enterprise headquarters. This allows the employment growth resulting from local initiative to be distinguished from that attributable to tlr. location and

expansion decisions of larger, multiestablishment enterprises. A discussion of the basic data and its aggregation

into the high-technology growth tables and Metropolitan Area Aggregate Data Base is found below.

Scope of Analysis

In order to develop a comprehensive perspective on

the nature of high-technology firms and their role in regional economic development, certain questions must be

systematically addressed. First, what is a "high-technology" industry? To many State and local development offidais, it simply means any industry using electronics

which has high growth potential, preferably employment

growth. For the more rigorous demands of empirical research, the definition of high technology must encompass the notion of innovation in products and processes,

accompanied by relatively large expenditures on R&D

performed by a cadre of scientific and technical person

nel. Recognizing that any definition of high technology

1°Implicit in such an analytic breakdown is the need to distinguish between

enterprises (i.e., the legal and economic entity or "tints," including affiliates) and

mahlishrnents (i.e., geographically distinct operating units either independent

Or tied legally and financially to a larger entity).

based on groups of firms by industrial classification is

flawed, this study uses an explicit minimum on the proportion of scientific, engineering, and technical personnel relative to total staff or of R&D expenditures to sales

as the criteria for "high technology." This criterion identifies 29 of the 158 three -digit standard industrial classes

in manufacturing and business services as the "high-technology" sector. These are broken out into four-digit

classes, which are further discussed below.

Given this definition of high-technology industries,

what are the characteristics of high-technology establishments and their patterns of employment growth? How

does the distribution and behavior of high-technology

establishments differ from that of establishments in other

industries? Are high-technology industries dominated by

large corporate enterprises, or do independent establishments flourish in the atmosphere of innovation and

growth? Which kinds of establishments are succeeding

in the pursuit of growth?

Tabulations of the USEEM files provide important descriptive information on the magnitude, location, and

growth of firms in the high-technology industries, which

can be used to answer these basic questions. Changes

in establishment populations and employment and data

for business formations are detailed for high-technology

industries and for two comparison groups of non-hightechnology industries by various categories of business

characteristics.

For example, one popular assumption is that growth

within the high-technology sector is concentrated in

small, independent businesses, operated by dynamic entrepreneurs." In order to examine this aspect of hightechnology development, types of establishments were

distinguished in the tabulations as follows: independent

firms, owners of other establishments, affiliates (branches

and subsidiaries) of local (instate) firms, and affiliates of

national (out-of-State) firms. Establishments were also

classified by three enterprise (firm) employment size

classes: under 20 employaes, 20 to 99 employees, and 100

or more employees. In addition to totals for the United

States, information has been compiled for a sample of

35 Standard Metropolitan Statistical Areas (SMSAs) and

for the four major economic regions of the United

Statesthe Northeast, the North Central, the South,

and the West. Patterns of high-technology development

across the four broad regions are discussed in general and

with reference to these various categories of business.

In the next part of the analysis the information on business formations and employment growth in high-technology industries for each of the 35 SMSAs is analyzed

in relation to the socioeconomic characteristics of these

''David 1. Koch, William Con, Delores W. Sceinhauser, and Pamela V. Whig,

ham, "High Technology: The Southeast Reaches Out foe Growth Industry," Economic Re-wow, Federal Reserve of Atlanta, September 1983, p. 4.

119
114 Technology, Innovation, and Regional Economic Development

metropolitan areas. While this regression analysis is not

designed to demonstrate causal relationships, it advances

the discussion of the sources of employment growth by

providing accurate empirical data on high-technology development and noting the association or lack of association between that development and certain metropolitan

characteristics. Similarities and differences in the patterns

of high-technology development are again compared to

the patterns in the rest of manufacturing and business

services (low-technology") industries and in all other industries. Finally, this analysis considers two different

employment size classes and reveals some interesting differences in the behavior of small independent firms and

that of establishments which are part of larger firms.

Data Sources and Organization

Overview

Previous attempts to analyze the interaction of innovation, the formation of high-technology businesses and

regional economic development have been very limited

in scope. The most common units of analysis employed

in such studies have been either single industries, specific communities, or a single factor among the many

which may encourage innovation and growth. The relevant unit of analysis in this field is actually the particular sets of firms or establishments exhibiting similar economic behavior. To focus on the behavior of business

establishments classified by certain industrial, geographic,

or organizational characteristics, we must have access to

data with the appropriate flexibility and level of disaggregation. Microdata on the status and behavior of individual businesses, or very narrowly defined groups of

businesses, over time serves this purpose. In addition to

enabling us to identify the specific components of growth

which constitute the net changes in the business population and employment levels, only microdata permit the

accurate measure of change attributable to sets of businesses whose characteristics change over time. Comparisons of cross-sectional aggregate data nor only mask the

components of change, but seriously distort the apparent behavior of groups of businesses that shift from one

category to another between observations.

The remainder of this section describes our primary

data source, the U.S. Establishment and Enterprise

Microdata Base, and the means by which it was transformed and supplemented for this analysis. From the original data, a condensed longitudinal data base was first

constructed to permit efficient data processing of employment and population change data for the1976-80 period.

This condensed data base was then used to generate summary tables of descriptive data for metropolitan and regional areas. Finally, aggregate figures on employment,

employment change, and business formations were extracted from the summary tables and merged with various

indicators of socioeconomic characteristics of metropolitan areas derived from other statistical sources to form

a Metropolitan Area Aggregate Data Base. This Metropolitan Area Aggregate Data Base was used in conducting the regression analyses on factors associated with the

formation and growth of high-technology firms in particular areas of the United States.

U.S. Lstablishment and Enterprise

Microdata Base

Our capacity to explore the location and growth of

high-technology firms is dependent on our access to and

familiarity with the USEEM base, which was developed

at The Brookings Institution with support from the Small'

Business Administration and NSF." Derived from Dun

& Bradstreet's DUNS Market Identifiers files, the

USEEM have been restructured, edited, and supplemented with data from other sources. They now con

tain information for individual business establishments

(i.e., geographically distinct operating units), which are

linked to enterprise data for their owning firm (i.e., the

legal and economic entity, including branch and subsidiary establishments). Consequently, these microdata

allow us to analyze not only aggregate trends in industry groups, but to identify the characteristics and track

the behavior of particular firms or types of firms within

an industry (e.g., by employment size, ownership, etc.).

Providing a virtual census of U.S. businesses with employees, USEEM files currently span 6 years. They contain records for 4.2 million business establishments in

1976, increasing to 5.5 million in 1982. The data files

can be processed separately for cross-sectional analysis

or longitudinally, matching records for each establishment across files. In developing the USEEM files, apparent errors were corrected and missing variables were imputed where possible. For example, employment figures

for the 2 to 3 percent of establishments that did not report that data were estimated by calculating median employment levels by State and four-digit standard industrial classes using the Bureau of Census' County Business

Patterns data.

Perhaps most importantly, a major effort was undertaken to reconcile various indicators of organizational

status, relational pointers, and employment figures between member establishments of multiestablishment enterprises. In other words, a "family tree" was constructed

for each business enterprise, containing every branch and

subsidiary within the legal firm. These family trees were

then analyzed to assure completeness and consistency in

"For a cletaikci description of the USEEM data base see Candice S. Harris,

U.S. Establishment and Enterprise Microdata (USEEM): A Data Base Descripfirm, Business Microdata ProJect, The Brookings Itutitution, June 1983.

120
App. 8Formation and Growth In High-Technology Finns: A Regional Assessment 115

their hierarchical structures. Having ascertained the

proper structure of each complex business, the two employment figures provided (one for each establishment

and one for the enterprise as a whole) could then be

analyzed for consistency. Discrepancies between the aggregated establishment employment and the reported

firm employment were then corrected by: 1) increasing

the enterprise employment to reflect the total of all affiliated establishments; or 2) imputing proxy branch establishments to represent affiliates suggested by the total

enterprise employment, but not reported."

For longitudinal analysis, more extensive editing is applied. Deficiencies in the original data set recommend

caution in defining those records which represent accurate measures of employment change over time. The most

important problems affecting longitudinal analysis can

be categorized as follows: I) a large portion of the file

is not updated between observation years; 2) a few records exhibit unreasonable rates of employment change,

indicating possible coding errors; 3) there is a lag in registering business formations and closings; 4) coverage is

incomplete, but expanding in certain industries; and 5)

proxy records obviously cannot be tracked over time.

To compensate for these problems, records which are not

updated or which exhibit excessive employment change

are excluded from longitudinal analysis. Records for imputed proxy branches, those with estimated employment

figures, and those which indicate they are new coverage

of existing businesses are also excluded.

The remaining estal)iishment records with measurable

growth are consideree a nonrandom sample (about 60

to 70 percent of the on gloat population) whose population distribution is coml. 3red to that of the cross-sectional

files in the beginning and ending years of the analysis.

Accepting the cross-sectional USEEM file as representative of the universe of U.S. businesses with employees,

weighting factors are developed as a function of industry division (one-digit SIC), organizational status, establishment employment size, and enterprise employment

size. The formulae used to calculate the weighting factors explicitly consider the problems of reporting lags

related to formations and failures, and of expanding coverage. During longitudinal processing of the data, a

weight is applied to each record in the more reliable sample, bringing their aggregate employment up to the totals

in the cross-sectional data." It is this weighted subset of

the USEEM which is the basis for longitudinal analysis.

For any cross.section al file, these proxy branch establishments represent approximately 9 percent of all establishments and contain about 22 percent of total

employment.

"This weighting scheme assumes the use of large samples. Studies focused on

riartuwlY defined populations should bear this in mind. For an extensive discussion of the weighting techniques set C. Armington and M. Odle, "Weighting

the USEEM Files for Longitudinal Analysis of Employment Growth," Working

Paper No. 12, BUS.InftiMicroclats Project, The Brookings Institution, April 1983.

121

Condensed Longitudinal Data Base

As discussed above, the USEEM comprise an extremely large data set containing millions of records for individual business establishments. Processing the entire set

of files is exceedingly expensive and time consuming;

therefore, an extract of the data for both 1976 and 1980

was drawn from the USEEM to facilitate manipulation

of the data for this analysis. Containing only the sample of records with measurable growth, the records in

this extract were restricted to include a subset of the

variables from each establishment's USEEM record, an

appropriate weight, the location of the establishment

(county or SMSA) and the location of the establishment's

owner (State). The numeric representation of the SMSA

location of the establishment was derived from the

USEEM for the county and State, with reference to the

Bureau of Economic 4nalysis' definitions of the SMSAs

in the United States for 1980. The State location of the

owner was drawn from an auxiliary file containing information for multiestablishment firms. It was constructed

from the original DUNS Market Identifier data. The resulting data base is ordered by four-digit SIC codes, allowing efficient analysis at any level of industry aggregation.

Metropolitan and Regional Growth Tables

Descriptive tables were developed to provide aggregate

data on employment and employment change for the various industrial sectors under examination and for 35

selected SMSAs (see table B-1). Summary tables are also

provided for the four major geographic regions of the

United States and for the national totals. These tabulations are valuable sources of summary information on

the distribution of business establishments and employment and on changes in these measures between 1976

and 1980. Classified by several dimensions reflecting the

characteristics of the individual establishments in each

geographic area, these tables provide a rich source of

descriptive information in themselves, as well as input

data for the regression analyses described later in this

paper. The characteristics used for classifying establishments for aggregation are given in table B-2.

Metropolitan Area Aggregate Data Base

The USEEM along with auxiliary variables for 35

SMSAs were used in regression analyses to empirically

determine those factors most important in e; plaining the

growth of high-technology establishments. The general

rule used in the selection of these 35 SMSAs required

that a wide range of values for each of the auxiliary

variables be represented in our sample. For example,

metropolitan areas at both the high and low ends of the

average wage scale were selected. Also, SMSAs of differ-
116 Technology, Innovation, and Regional Economic Development

Table B1.-1976 Employment and1916-80 Employment Growth for Selected SPASAs Analyzed

SMSA listing

1975 total

employment

Fercent employment growth

High-technology

employment

All

industries

High-technology

, industries

Albuquerque, NM

Anaheim, CA

Atlanta, GA

133,949

815,167

- ,761

14,016

102,150

31,382

41.7

46.5

6.9

33.4

37.5

32.9

Austin, TX 135,811 7,788 33.5 182.4

Baltimore, MD 710,828 2;377 15.6 63.3

Baton Rouge, LA 139,816 7,094 41.3 48.9

Boise City, ID 43,203 679 33.2 135.3

Boston, MA-NH 1,682,753 208,388 10.5 26.9

Buffalo, NY 513,174 28,815 2.9 18.5

Chicago, IL 3,439,570 338,837 2.9 -5.6

Cincinnati, OH-KY-IN 584,522 41,371 9.3 15.5

Denver-Boulder, CO . , 682,577 41,522 34.7 64.4

Detroit, MI 1,499,381 73,297 6.9 15.7

Houston, TX 1,358,504 126,131 18.9 47.6

Kansas City, KA-MO 545,141 37,807 27.2 5.6

Louisville, KY 380,616 '21,664 39:2 -5.8

Miami, FL 702,456 15,222 5.0 38.2

Milwaukee, WI 629,013 70,44d 8.3 4.3

Minneapolis-St. Paul, MN-W1 871,042 84,489 15.5 -5.4

New Haven, CT 280,972 23,518 11.1 22.5

Omaha, NE-1A 253,527 11,345 11.8 10.4

Philadelphia, PA-NJ 1,777,038 215,875 1.4 -27.5

Phoenix, AZ 380,035 38,457 36.5 43.7

Pittsburgh, PA 7,290 44,983 4.7 10.7

Portland, ME 95,660 2,114 26.5 55.0

Portland, OR-WA 446,097 19,214 24.3 18.3

Raleigh-Durham, NC 198,139 17,813 19.5 55.1

Rochester, NY 342,548 73,755 -2.1 -38.3

St. Louis, MO-1L 881,855 59,828 14.9 22.9

Salt Lake City, Ogden, UT 252,392 21,233 44.5 30.4

San Diego, CA 461,782 62,334 25.3 8.9

San Jose, CA 521,405 154,909 23.6 28.7

Seattle, WA 585,397 48,286 41.7 160.1

Tampa-St. Petersburg, FL 432,427 17,729 27.6 72.9

Wilmington, DE-NJ-MD 183,634 31,257 1.4 -1.4

SOURCE: Mc* of TOchtVincy ASSISSMrtt.

ent population sizes were chosen carefully from each geographic region to ensure each region's fair representation

in the sample.

Five of the variables were extracted from the USEEM

Condensed Microdata Base. These were base year (1976)

employment and establishment counts, end-year employment and counts (1980), and the number of new business formations (1976-80). These four variables were aggregated for each of the four industry groupings: the

high-technology with high growth sector, the total

high-technology sector, the low-technology manufacturing and business services sector, and the "other industries" sector. Further, each of the four aggregates was

compiled for three employment size classes (see table description above). The remaining variables were obtained

from a variety of sources of data on SMSAs, as described

below. Transformation of these USEEM aggregates led

to the following additional variables for the regression

analyses: overall employment growth for 1976.80 for each

SMSA, the high-technology sector's share of this employ122

ment, the low-technology share of employment, and the

other industry share of employment.

The data for the variables describing socioeconomic

characteristics of the metropolitan areas were drawn9from

several sources including published data from the Census

Bureau, published data on wages and the labor forte from

the Bureau of Labor Statistics (BLS), and a recent study

published by the Advisory Commission on Intergovernmental Relations on local taxes by metropolitan area.

Table B-3 details the measure and source of each variable

in the data base.

High Technology: Definition

and Description

"High technology" has become a phrase frequently

used and ambiguously applied in both the professional

and popular literature. The term's possible interpretations range from a small grouping of "new age," research-
App. B-Formation and Growth in High-Technology Firms: A Regonal Assessment 117

Table 13-2.-USEEIA Establishment Characteristics

A. Organizational status:

1. independents - single - establishment firms

2. Tops-owners of multiestablishment firms

a Local affiliates- branches or subsidiaries whose

owning firm is located in in the same State

4, National affiliates-branciies subsidiaries whose

owning firm is not located in the same State

B. Finn (intiopriii) sispioymint silaaK

1. They establishments-in firms with fewer than 20

employees

2. Mid-small establishments-in firms with 20 to 99

employees

3. Large establishments -in firms with 100 or more

employees

C Primary industrial acililty:

1. High technology (see definition below)

2. Other manufacturing and business services

3. All other Industries-Le., not in 1 or 2

O. Type of longitudinal change:

1. Formation-initial startup of a new establishment

2. Closing-cessation of operations of existing

establishment

3. Expansion-continuing establishment with

increasing employing

4. Contraction-continuing establishment with

decreasing employment

5. Stable-continuing establishment with no

employment change E Biographic Watt=

1. SMSA-35 Standard Metropolitan Statistical Areas

2. Region -four regions (Northeast, North Central,

South, West)

3. National totals

SOURCE' Office of Tochnotooy Assessment.

intensive industries with sophisticated new products,

such as computer hardware and software, to a broader,

set of industries, including all manufacturing activities

which are implementing new methods or machinery.

Even the academic literature evidences little consistency

or rigor in defining "high-technology" industries. Obviously, establishing criteria for systematically identifying

those industries to be included in the "high-technology"

genre is a prerequisite to studying their characteristics

and growth patterns.

In order to assess the broad impact of high technology on business formation and growth, some precision

must be sacrificed in its definition. At best, businesses

can be classified by the four-digit SIC code indicating

their predominant industrial activity. Significant aggregation problems arise from three sources: I) within a given

class of industrial activity there are products and processes which are not in an innovative phase, 2) particular

fermi; engage in activities outside the primary industrial

activity by which they are classified, and 3) the period

of high growth associated with a specific innovation may

last much less than a year, or may continue, fueled by

related innovations, for many years.

Any analytic technique which attempts to categorize

industries into "high technology" and "low technology"

is bound to be flawed. All operational definitions currently in use employ data that are at least ordinal in

nature and often continuous. Schemes to reduce such

information to a nominal categorization, such as "high"

Table 13-3.-Metropolitan Variables

Type

Sector growth

Variable Measure

Sector jormation (1)

Sector formation rate (1)

Sector growth rate (1)

Strength of economy Employment growth (1)

Employment rate (3)

Business costs Electricity costs (2)

Wage rates (5)

Local tax per capita (4)

City characteristics Population (2)

Population density (2)

Percent manufacture (2)

Population growth (2)

Sector strength Sector size (1)

Sector share (1)

Labor quality Percent college (2)

Percent technical (2)

1976.80 new establishments in sector

1976.80 new establishments In sector11976 employment

1980 sector employment/1976 sector employment

1980 SMSA employment/1976 SMSA employment

1.00 minus 1976 SMSA unemployment rate

Average monthly commercial payments in 1976

1976 average wage for production workers

ACM capacity Index/effort index

1975 SMSA population

1975 SMSA population per square mile

1975 manufacturing employment/SMSA employment

1975 population/1970 population

1976 sector employment

1976 sector employment/1976 SMSA sector employment

1975 population with 4 years of college/adult population

1975 scientific, professional and technical employees/

total employed labor force

SOURCES

1. USEEM, Business Micro: late Project, The Brookings Institution, 1983.

2. Bureau of the Census, City and Coolly Data Book 1077, 1980.

3. Bureau of the Census, Metropolitan Area Data Book: 1984, 1983.

4. Advisory Commission on Intergovernmental Relations, Interstate Tax Computation," draft 1983.

5. Bureau of Labor Statistics, Employment and Earnings, fall 1977.

123
118 Technology, Innovation, and Regional Economic Development

and "low," are essentially arbitrary." Most serious is the

problem of aggregation in industry classifications mentioned above. Since each business establishment can produce numerous products and services, there is possible

pollution of its primary industrial classification due to

secondary products and activities. Furthermore, alternative production processes can span the range of available technologies associated with a particular product or

service, producing at best an agglomeration of somewhat

heterogeneous operations within a given industry

grouping.

Criteria for Selecting

High-Technology industries

Beating in mind these deficiencies inherent to the task,

previous efforts to identify those industries which can

most clearly be identified as high. technology were reviewed. An examination of the existing literature revealed two relatively rigorous approaches to defining

"high technology" based on available statistics. One approach draws upon data on the occupational composition of industry classes as described by the BLS industryoccupation matrix constructed for the standard industrial classifications (three-digit SIC) from the Occupational Employment Survey. The other utilizes information on expenditures for R&D activities collected and

published by NSF.

Researchers at Northeastern University, using the industry-occupation matrix from the 1980 Occupational

Employment Survey, defined as high technology those

industries with more than 8 percent of their employees

in scientific, engineering, and technical occupations and

at least 5 percent in the more narrow class of scientific

and engineering occupations." These breakpoints were

determined by the average proportion of such high-technology jobs in durable goods manufacturing, which ernploys the largest number and highest proportion of hightechnology workers of the major industrial groups in the

economy. The resulting list of 32 industries (three-digit

SIC) encompasses several service, transportation and

communication, and mining industries, as well as manufacturing industries. Utilization of this labor content

criteria focuses the analysis on the technology embodied

in the production process, rather than the characteristics of the material inputs or products.

"For 2 discussion of the imprecision afflkting definitions of high technology,

see Donald TomstskovicDevey and S. M. Miller, Can High-Tech Provide the

Jobs?" Challenge, May-June 1983; Fncourswing High-Technology Development

(Washington, DC: U.S. Congress, Office of Technology Assessment, OTAB.P.

S11.25, March 1984); and Amy Glasmeler, Ann Markusen, and Peter Hall. "Dec

fining High Technology Industries." Working Note No. 1, institute of Urban

and Regional Development, University of California, March 1983.

"Richard Greene, Paul Harrington, and Robert Vinson, "High Technology

Industry: Identifying and Tracking Emerging Sources of Employment Strength,"

New England Journal of Employment and Training, fall 1983.

The second approach to defining high technology has

relied on information on the expenditures for applied

R&D for particular product lines relative to some measure of their worth--e.g. value-added or value of shipments. Most analyses using this measure have limited

their consideration to direct technology inputs of thefinal

producer, excluding the technological composition of intermediate inputs. To circumvent the itrAtations of measures of direct R&D, Lester Davis developed an index

ranking industries on the basis of both direct and indirect

R&D expenditures as a portion of product sales for the

manufacturing product classes for which NSF collects

detailed R&D data." Estimates for the indirect R&D

input and the resultant total R&D for each product field

were generated by an input-output matrix for the U.S.

manufacturing sector. The use of total R&D, rather than

just direct, significantly changed the rankings of the 32

NSF product classes. To determine a breakpoint above

which product classes would be considered high technology, Davis assumed that there should be a discernible

point of discontinuity indicating substantially greater

technological in tensity in the next higher rank. Such

a break was present in a 30-percent jump between the

motor vehicle equipment class and the plastic materials

and synthetic resins class. Thus, Davis identified a total

of 10 NSF product classes, representing 18 three-digit SIC

classes, as high-technology industries.

Fourteen of Davis' 18 industries are also in the Northeastern University's listing of 20 manufacturing industries. However, the additional six in the Northeastern

list based on occupational composition represent almost

29 percent of Northeastern's total high-technology employment. Four of Davis' industries were missing from

the Northeastern list. These two groups are presented

below:

Low R&D, high - technology

occupation

Industrial organic chemicals

Miscellaneous chemical

products

Petroleum refining

Construction machinery

General industrial machinery

Electrical industrial apparatus

Accepting the necessarily arbitrary nature of defining

high-technology industries, the definition based on occupational composition was adopted, but supplemented

with the four additional categories with high R&D expenditures, but lower proportions of scientific and technical personnel than the established cutoff. The resulting

definition is more comprehensive, covering several nonmanufacturing industries whose products (and services)

High R&D, low-technology

occupation

Radio and TV receiving

equipment

Surgical, medical and dental

equipment

Ophthalmic goods

Watches and clocks

"Lester Davis, "New Definition of 'High Tech' Reveals That U.S. Competitiveness in This Area Has Been Declining," Business America, Oct. 18. 1982.

124
App. B-- Formation and Growth In High-Technology Firms: A Regional Assessment 119

are technologically intensive and whose potential contribution to employment is significant. Some further

modifications were made to the list of high-technology

industries produced by these criteria. The manufacturing and business services sectors contain most of the industries passing the high-technology criteria. A few high

technology components of the transportation and communication sector were excluded because much of their

industrial group dynamics between 1976 and 1980 was

influenced by Federal and local regulation and deregulation. The petroleum extraction industry from the mining sector was retained due to ambiguities in the assignment of firms between this and the petroleum refining

industry in manufacturing.is

The data set used in this analysis provides detailed industrial classifications, so all the high-technology industries identified at the three-digit level with the Northeastern University's criteria were expanded into their

four-digit SIC components. This procedure made the

problem of aggregation in industry classifications more

obvious. While few would argue the high-technology status of an industry like SIC 348: Ordinance and Accessories, one of its components is SIC 3482: Small Arms

Ammunition, which is not really within the concept of

high technology. In order to respect the rigorous definition, no four-digit manufacturing industries were eliminated from the list, so it remains comparable to most

based on three-digit classifications. However, the business services classifications comprised such a heterogeneous set of industries with such a large number of employees that it was determined necessary to further

modify the definition.

Exploratory tabulations indicated that those business

service industries which one would intuitively classify as

high technology accounted for only 10 percent of the

employment in the aggregated set of business service industries identified with the occupational composition criterion. By sheer volume, the other industries with 90 percent of the employees would have dominated the

behavior of the entire aggregate high-technology sector.

Retaining all the four -digit business service industries included in the three-digit classes identified with the occupational composition criterion would seriously distort

the aggregate data. Therefore, eight four-digit business

service industries were eliminated. Two of these eight encompass a highly diversified set of miscellaneous services"business services, not elsewhere classified" and

"services, not elsewhere classified." Two others employ

large proportions of engineering and professional personnel, but rely primarily on established techniquesengineering and architectural services and general management consulting. The remaining four provide narrowly

nhe classificanon problem afflictirg this industry is manifested in all Federal

and private data sources,

36-737 0 84 - 9 s QL 3

defined services, which in themselves do not embody

evolving technologiesdetective agencies, equipment

leasing, photofinishing labs, and trading stamp services.

The final list of the 88 four-digit industrial classes included as high-technology industries for this study, as

well as the qualifying industries that were excluded, are

listed in table B-4 along with their 1976 employment and

1976-80 growth rates.

High-Technology Industries in Perspective

Describing the high-technology sector and assessing its

potential contribution to regional development requires

a perspective on its economic role relative to other industrial sectors. In this study, two different broad classes

of industries are used as bases for examination of the relative importance and growth performance of high-technology industries. Since the definition of "high technology" has been confined primarily to manufacturing and

business services, excluded industries belonging to these

industry groups provide the best basis for comparison,

henceforth termed "low-technology manufacturing and

business services" or "low-technology industries." The

other reference population comprises all nongovernmental industries not classified in manufacturing and business services, termed "other industries," Attempts are

made to be as explicit as possible in referring to the appropriate base population, while avoiding tedious repetition.

Having now defined the industry sectors of interest, let

us examine their characteristics and distribution in the

economy.

Less than 2 percent of the business establishments in

the United States in 1976 fell under the heading "high

technology." Employment in high-technology establishments in 1976 was 5.6 million, or 7.4 percent of the private sector total.19 One-fifth of all employment in manufacturing and business services in 1976 was in high

technology. The attention received by the high-technology sector is merited by its potential for growth, not by

its current employment size. The high-technology sector's share of value-added is somewhat larger than its

share of manufacturing employment.20

The organizational composition of the high-technology

sector differs importantly from the rest of the business

population. Several aspects of this difference are apparent in table 8-5. Although 60 percent of all U.S. private

sector jobs are in multiestablishment firms, the high -tech-

"Whik excluding those establishments explicitly classified as "government,"

the USEEM base (and therefore the total employment figures) does include many

other public sector establishments, such as State universities and health service

establishments.

141111 a study, Robert Lawrence estimated that in 1980 high technology ac

counted for 33 percent of manufacturing employment and 38 percent of total

value added. See Robert Lawrence, "Is Trade Deindustrialiting America?" in

Brookings Papers on Economic Activity, 1:83, a Perry and 0, Runless(eds.)

(Washington, DC: The Broolongs Institution. 1983).

125
120 Technology, Innovation, and Regional Economic Development

Table 84.-Ifigh-Technology Industries

Industries

1976

national

employment

1976-80

national

growth rates

1976

national

industries employment

1976-80

national

growth rates

1311 Crude petroleum and 3574 Calculating and accounting

natural gas 289,618 -5.7 machines 17,234 8.4

1321 Natural gas liquids 6,018 8.8 3576 Scales and balances 9,731 13.0

2812 Alkalies and chlorine 27,974 -19.0 3579 Office machines 33,768 17.0

2813 Industrial gases

2816 inorganic pigments

21,800

28,405

7.9

2.9

3622 Industrial controls

3823 Welding apparatus.... .......

87,561

21,335

26.3

25.4

2821 Plastic materials, synthetic 3624 Carbon and graphite products... 14, 7 17.1

MOS. and nonvulcanizable 3629 Electrical industrial apparatus ... 33.1 11.4

elastomers 144,346 12.4 3651 Radio and IV receivers 115,758 -17.3

2822 Synthetic rubber 23,453 26.8 3652 Phonograph records and tapes .. 26,794 21.7

2823 Cellulosic manmade fibers .. 33,376 -35.3 3661 Telephone and telegraph

2824 Synthetic organic fibers 69.843 -4.1 apparatus 164,983 2.5

2831 Biological products ....... 14,828 -0.5 3862 Radlo-W transmitting 339,865 15.7

2833 Medicinal chemicals and 3671 Radio and IV electron tubas . 12,120 -10.9

botanical products 27,735 10.2 3672 Cathode ray TV picture tubes ... 13,279 -8.1

2834 Pharniamutical preparations 184,195 8.5 3673 Transmitting, industrial electron

2= 1 Gum and wood chemicals 10.853 13.1 tubes 19,158 2.0

2 5 Coal tar, crudes and cyclic 3674 Semiconductors 153.123 28.2

intermediates, dyes and 3675 Electronic capacitors 21,380 -55.2

organic pigments 16,285 3.7 3676 Resistors for electronic

2891 Adhesives and sealants 23.916 -2.6 apparatus 4,698 57.4

2892 Explosives 24,998 -24.8 3677 Electronic calls, transformers . 8.153 28.5

2893 Printing ink 14.080 14.2 3678 Connectors for electronics 4.415 74.5

2895 Carbon black 9,813 -62.4 3679 Electronic components, n.e.c. .. 262,215 . 33.0

2899 Chemicals and chemical 3721 Aircraft 243,436 35.3

preparation, n.e.c 64,841 10.2 3724 Aircraft engines and engine

2911 Petroleum refining . 128,751 39.5 parts 71,504 41.2

3482 Small arms ammunition 12,870 -0.6 3728 Aircraft parts and equipment,

3483 Ammunition 29,760 13.7 11.8.0 118,586 19.0

3486 Small arms 24,569 -16.2 3761 Guided missiles and space

3489 Ordinante and accessories 6,018 -0.8 vehicles 61,043 24.7

3511 Steam, gas, hydraulic turbines . 76.777 32.9 3764 Guided missiles and space

3519 Internal combustion engines . 90,639 13.8 propulsion units 10,043 -76.4

3531 Construction machinery and 3769 Guided missiles and space parts

equipment 159,515 19.2 and equipment, n.e.c. ... .. 645 75.7

3532 Mining machinery 30.663 24.3 3811 Engineering, lab, science

3533 011 machinery 81,185 27.5 research instruments 69,962 25.4

3534 Elevators and moving 3822 Automatic controls for regulating

stairways 20.634 -8.0 residential and commercial

3535 Conveyors 36,020 24.7 environment 69,806 8.0

3536 Hoists, industrial cranes 24,308 17.8 3823 Industrial instruments for

3537 industrial trucks, tractors,

trailers, stackers

3561 Pumps and pumping

45,043 19.4

measuring and control of

process variables

3824 Totalizing fluid meters and

45,850 43.8

equipment 65,685 48.3 counting devices 15,011 -9.4

3562 Ball and roller bearings 63,513 20.3 3829 Measuring and controlling

3563 Air and gas compressors 18,157 23.0 devices 17,384 27.3

3564 Blowers and exhaust and 3832 Optical instruments and lenses 23,909 23.5

ventilation tans 44,689 10.5 3841 Surgical and medical

3565 Industrial patterns 12,321 2.7 instruments 47,023 67.6

3586 Speed changers, industrial high- 3842 Orthopedic and surgical

speed gears 55,840 1.8 supplies 60,426

426

22.8

3567 Industrial process furnace and 3843 Dental equipment 19 17.6

ovens 3568 Mechanical power transmission

26,065 19.9 v 3851 Opaim goods hth ic d

3861 Photographic equipment .... 103,480

30.6

-19.8

equipment 9,555 25.6 3873 Watchas, clocks 34,235 11.8

3569 General industrial machinery ....

3572 Typewriters

76,552

18,378

20.8

-2.1 Business services

7372 Computer programming and

3573 Electronic computing

equipment 274,699 16.5

other services 65,870 98.9

126
App. B-Formation and Growth in High-Technology Firms: A Regional Assessment 121

Table 13-4.-HighTichnology industries (continued)

industries

1976

national

employment

1976-80

flational

growth rates Industries

1976

national

employment

1976-80.

national

growth rates

7374 Data processing equipment 162,753 54.4 7393 Detective and protection

7379 Computer related service,

n.e.c. 43,433 47.5

services

7394 Equipment rental and leasing . 299,226

204,469

25.0

32.8

7391 lb:search and development 1395 Photofinishing laboratories 84,092 18,3

labs 314,988 20.6 7396 Trading stamp seivices 6,230 -13.6

7397 Commercial testing labs 61,513 35.9 7399 Business services, n.e.c. 595,488 36.8

8922 Noncommercial educational and 8911 Engineering, architectural, and

science research survey SOVICOS 707,753 34.4

organizations 90,161 85.9 8999 Services, n.e.c 104,183 45.4

Total high technology 5,619,295 19.4

Qualifying Industries excluded from the high - technology sample

7392 Management,consultant, public

relations 611,697 45.7

SOURCE: Otila d Tadtaciagy ANIMISM.

Table 13.5.-1978 Shams of High-Tichnology Establishments and Employment by Affiliation and Firm Size (porcine

Multiestablishment firms Large firms' Tiny flrmsb

Establishment Employment Establishment Employment Establishment Employment

High technology 43.8 88.4 29.5 89.0 53.4 4.2

Low technology 28.9 72.7 16.9 75,0 68.9 10.6

Other industries 25.3 51.1 9.7 57.7 80.5 25.3

All Industries 26.2 60.0 12.4 64.0 76.0 20.0

kites firms ha* 100 or more amployass &mark* most an muttlastabitalunant.

btirry firms have Was than 20 amfooysis firmwicior, most are todapartriants.

SOURCE: Off foe of Tishri° logy ASSINSIIIMII.

nology sector is even more dominated by such firms (88

percent of jobs). Abut one-half of all high-technology

jobs are in national affiliates (branches and subsidiaries

of firms headquartered out-of-State), while the average

for all industries is only one-third.

The average number of employees in high-technology

establishments (69) is twice that of the low-technology

manufacturing and business services (32) and more than

five times the average in other industries (13). Almost

90 percent of high-technology jobs are in firms with more

than 100 employees compared to 75 percent for low-technology and 58 percent for all other industries. The hightechnology sector comprises 1.6 percent of all tiny firm

employment, 3.2 percent of mid-small firm employment,

and 10 percent of large firm employment.

While the distribution of high-technology employment

across the four regions of the United States roughly paralleis the distribution of overall employment, the regional

share data in table B-6 show that the Northeast and the

West have more than their proportional share of hightechnology employment, while the South is short. This

is more clearly evident in the variation in the sectoral

shares within each region. The employment share of the

high-technology sector varies from 5.8 percent in the

South to 8.9 percent of all jobs in the Northeast, The

West has a high-technology sector which is somewhat

above average, but it is considerably below average in

the size of its low-technology manufacturing and business services sector.

There is remarkable variation across regions in the organizational composition of the high-technology sector,

particularly with respect to multiestablishment firms;

which is shown in table B-7. Employment in high -technology affiliate establishments in the South exhibits the
122 Technology, Innovation, and Regional Economic Development

Table 9-8.-Dlatdbetlon of Total Private Sector Employment by

Repko and Industrial Sector* 1976 (poroont)

Sector within region Regional share

Relion High- technology Low-teolmology High-technology All industries

Northeast 33.6 29.3 24.8

North Central 7.4 30.8 27.6 27.6

South 5.8 27.3 23.8 30.6

West 8.4 22.9 192 17.0

U.S. total 7.4 28.2 100.0 100.0

SOURCE:0Mo of Toctrialoy Aossurforst

Table 117.Distribution of High-Technology Employment by Organizational Type,

1976 (pontoon)

Regian independents Tops

Local

affiliates

National

affiliates

Northeast 12.9 21.1 22.3 43.7

North Central 10.4 24.7 17.4 47.4 .

South 10.1 12.9 9.4 67.6

West 13.0 16.0 21.7 .49.3

U.S. total 11.6 19.2 17.8 51.5

SOURCE:0Mo of Taincgogy Annowartt.

greatest divergence from the mean. Whereas less than

50 percent of high-technology employment in the other

regions is in national affiliates, in the South out-of-State

headquarters control about 68 percent of all high-technology jobs. Though less exaggerated, affiliate employment in other industrial sectors in the South has a similar trend.

Regional Aspects of

High-Technology Development

Overview

Recent recession and record unemployment levels have

prompted policymakers to look increasingly toward the

fast-growing high-technology sector of the economy as

an important source of new jobs. In view of the remarkable growth in some, especially computer-related industries, many local planning and development agencies are

directing their resources toward attracting these Industries to their areas. Yet very little Is known at present

about the plant location decisions of high-technology

firms or even about the characteristics of growth in hightechnology industries. In the previous section the hightechnology sector and its distribution by region and

organizational status in 1976 was described. This section

focuses on the locus of growth in the high-technology

sector between 1976 and 1980.

Did the patterns of employment growth between 1976

and 1980 erode or reinforce the existing distribution and

composition of employment in the high-technology sector? Employment in high-technology industries in the

United States grew a total of 19.4 percent between 1976

and 1980, an average annual growth rate of 4.5 percent.

The growth of employment in high-technology industries was a third higher than the employment growth in

the low-technology part of the manufacturing and business services. Consequently, the high-technology sector

increased its 21-percent share of the manufacturing and

business sector service employment to 22 percent by 1980.

Within the high-technology sector, the subset of highgrowth industries grew from 65 percent of all high-technology employment to 71 percent due to their average

growth rate of 31 percent. Over this same period, the

low-technology manufacturing and business services sector grew almost 12 percent, while the "other industries"

sector grew 16 percent.

High-technology industries contributed 9.5 percent of

11 net PtW jobs between 1976 and 1980, of which 72.4

penzr.c were in large businesses. The low-technology sector's share of net new jobs created between 1976 and 1980

was 21.7 percent; "other industries" was 68.8 percent.

This growth in the low-technology manufacturing and

business services industries raises doubts concerning re,

cent claims that traditional basic manufacturing industries are dying out in the United States.

As shown in table B-8, the two regions with the lowest

shares of high-technology employment in 1976 experienced the highest rates of growth during the 1976-80'

period. Although they held only 43 percent of total employment in high technology in 1976, the South and the

West together accounted for 71 percent of that sector's

128
App. 9-Formation and Growth in HighTechnology Firms: A Regional Assessment 123

Table B-8.-Empioyment Growth Shares and Growth Rates by Region and by Sector, 197840 (percent)

High technology AU industries

Share of Share of Growth Share of Share of Growth

Region employment growth rate employment growth rate

Northeast 29.3 11.3 7.5 24.8 10.4 6.4

North Central 27.8 17.8 12.5 27.6 21.6 11.9

South 23.8 41.8 34.1 30.6 38.1 18.9

West 19.3 29.1 29.3 17.0 29.9 26.7

U.S. total 100.0 100.0 19.4 100.0 100.0 15.2

SOURCE: office of Technology Assossmotot

growth between 1976 and 1980. Thus regional shares of

high-technology employment are tending to converge,

balancing the distribution across the four regions. This

regional distribution of growth in high-technology em

ployment roughly paralleled that in la N-technology manufacturing and business services."

The Northeast and the West had relatively strong hightechnology sectors reflected in the larger shares of hightechnology employment relative to overall employment.

The North Central had proportional shares of national

employment in both sectors, while the South had a relatively small high-technology sector. Given the noted

tendency toward a more balanced distribution of hightechnology employment, it is not surprising that the

greatest excess in high-technology growth rate relative

to the region's overall growth rate was in the South (34

percent v. 19 percent). Obviously, the regions used in

this analysis are very broadly defined, and the hightechnology employment within those regions may be

highly concentrated in particular States or metropolitan

areas. Regional analysis at a more disaggregated level

would illuminate such intraregional discrepancies.22

Analysis of Regional Shifts

in Employment Shares

Employme 6rowth in the high-technology industries

for a given area comes from a variety of sources. Regional employment growth may result from a number of factOrS, in addition to simply reflecting national economic

trends. Particular regional characteristics might be attractive to businesses in general, regardless of their level of

technology or innovativeness. Such factors would include

abundant and inexpensive energy or labor resources.

Other regional characteristics might be of particular relevance to high-technology firms, such as a large pool of

"Another more detailed regional an for the periexi 1967-78 found a SI

star tendrils v toward convergence in regional shares of manufacturing employment. See Gregory Jackson, at al., Regional Diversity: Growth in the U.S., 1964-

1990 (Boston: Auburn Publishing House, 1981).

"See, for example, Andrew Wyckoff and Nancy CYConnor, "Patterns of

Growth and Structural Change in High Technology Industries in the New England States," Rumness Microdata Project, The Brookings Institution. August 1983,

(mimeo).

36- 737 0 - 04 - 10: QL 3

technical and scientific workers. Third, growth in a region's high-technology sector could result from expansion within existing high-technology firms or the formation of new high - technology establishments resulting

from a nationwide increased demand for their products.

What is the relative impact on regional employment

growth of their competitive advantages versus that of

their particular industrial structures? Shift share analysis is a useful technique for addressing this question."

This analysis divides growth of a region into a national

component (the national average growth rate), an industrial mix component (expected deviations from the

national average attributable to the composition of the

regional economy in terms of fast- and slow-growing industries), and a regional competitive component (differences in regional growth rates compared to national

growth rates for particular industry groups). This technique assumes homogeneity within regions and within

industry groups, to the level of aggregation used. Obviously industrial groupings are not homogeneous, as discussed earlier with respect to the problem of defining

high-technology industries.24 However, the areas comprising our regions and the four-digit industry classes

grouped in two of our three industrial categories were

chosen for their internal consistency in growth rates, so

the technique should still be useful at this crude level

of aggregation."

The first four columns of table B-9 summarize the findings of a more disaggregated shift share analysis of em-

"For a discussion of shift share analysis see Jackson, at al., op. cit.; or 1.. D.

Ashby, Growth hymns in Ernpiornene by County, 1540-50 and 195460 (Wash.

ingtors, DC: U.S. Government Printing Office, 1965).

"This is true of broad Liana such as the three employed here: high-technology/high-growth, high-technologyflow-gtowth, and low-technology tnanu.

facturing and business services. See M. Odle, "High Technology Winn High

Growth Industries," Small Business Research Conference Papers (Waltham, Mk

Bentley College, 1983).

3/711r annual percentage change in nonagricultural employment between 1970 -

and 1977 for the diviaions comprising our four regions are given below:

Northeast

New England

MidAtlantic

North Central

EAU North. Central .

West North Central

South

090 South Atlantic 287

0 15 East South Central 3 13

West South Central S 26

1.22 West

2 23 Mountain 5 26

Pacific 3 00

SOURCE, Adapted from table 4.! nt.

129
4

124 Technology, Innovation, and Regional Economic Development

Table B.S.Shift Shari Analysis of Changes in Manufacturing Employment

(estimated effects In thousands of employees)

1963 -72' 1972.781 1976-804 Total

Region Competitive Mix Competitive Mix Competitive Mix change

Northeast 954 93 398 9 615 2 369

North Central 178 141 76 60 302 24 744

South 884 29 502 78 486 52 1,457

West 35 7 158 5 431 75 1,034

'Adapted from John Rees, "Regional !Masan* Shifts," tabu, 3.

bills 1978 Nis, includeS manufacturing employees (r txt wawa), ptroleuirt cctrection employees (029 million) and bus:nose &entices employees (4.93 million).

SOURCE: Office of Technology Assessment.

ployment growth in manufacturing for two earlier periods." The relatively slow growth in the traditional

manufacturing regions, the Northeast and North Central, in contrast to the faster growth in the newer manufacturing centers in the South and West is apparent in

all three time periods shown in table B-8. The traditional

manufacturing regions have consistently large negative

competitive components. In the most recent period the

competitive component of employment loss attributable

to the Northeast's weak economy was nearly twice the

size of the actual net gain in employment. If the national

growth rates for the mix of industry sectors had prevailed

in the Northeasti.e., if the regional competitive effect

had been zero, rather than negativethen its employment growth would have been nearly three times as large

as it actually experienced.

The competitive regional advantages of the South and

the West appear to be in transition. Since the 1963-based

analysis, the South's competitive component has been

decreasing, while the West's has been increasing. In the

most current period the employment gains attributable

to differences in their regional economies were nearly the

same, although the South is almost twice as large (in employment) as the West.

Trends in the industrial mix effects dre more startling.

While the Northeast used to have a large enough share

of high-growth industries to more than offset its very high

share of low-growth manufacturing, the mix is now just

barely balanced: This trend of decreasing advantage of

industrial mix is even stronger in the North Central region, whose earlier advantageous industrial composition

effect was almost large enough to offset its disadvantageous competitive effect. In the recent period, both effects

were negative. Both of these regions' negative mix effects

are probably understated because the low-technology sector was not disaggregated. These regions would tend to

include the older, slower growing industries within that

industrial sector.

IThe figures in table B.8 represent the expected change in the region's employ

ment in thousands relative to the national growth rate for all industries and

given its particular industrial mix. The last column is the actual net change in

employment for the 1976.80 period.

The South continues to have a relatively small share

of the high-growth industries, in spite of its overall economic health. This is a restatement of the point made

above, that although the South had by far the highest

growth rate for high-technology business, it had the

smallest proportion of high-technology business. The

West, on the other hand, appears to be the paradigm

for every regional development council. In addition to

the substantial competitive advantage of its regional economy, the West shows n increasingly favorable industrial mix in the recent period. The industrial structure in

the West has shifted, so it has more than its expected

share of high-growth industries.

Two general conclusions from the shift share analysis

are particularly clear. First, the regional competitive effects overwhelm the industry mix effects in all these periods. Second, in spite of some shining examples of the

contrary, the older manufacturing areas are losing their

advantageous industrial mix in manufacturing. This

might be due to a shift in the locus of innovations and

development of new product lines, or it might reflect a

change in the pattern of location of branch plants in lower wage areas as high-technology products enter the stage

of larger scale, standardized production. Some evidence

for the latter effect was found in the organizational composition of high-technology growth, particularly in the

South. (See discussion below.)

Composition of High-Technology

Employment Growth

Components of Employment Change.The data

used in this analysis allow us to differentiate employment

growth behavior for different types of firms within the

high-technology sector. These data track individual firms

over time, permitting classification of establishments

according to the their type of employment growth: formations, expansions, contractions, and closures. The

figures in table B-I0 show high-technology industries

have higher growth rates than low-technology industries

in all regions. This was true for both formations and expansions. The greatest contrast in the performance of

130
App. 8-Formation and Growth in High-Technology Finns: A Regional Assessment 125

Table 13.10.-Employment Change by Type and Region for

High. and Low-Technology Manufacturing and Business Services

(oweent of 1978 employment with **Won and sector, 1976.80)

Net Formations Expansions Contractions Closures

Northeast

High 7.5 16.3 21.3 -13.7 -16.4

Low 4.3 14.2 18.9 -11.6 -17.2

North Central

High `.2.5 19.6 19.2 -10.4 -159

Low 8.5 16.3 18.6 -10.5 -15.8

South

High 34.1 34.2 27.9 -9.6 -18.5

Low 15.8 22.5 22.3 -10.0 -19.0

West

High 29.3 28.4 33.5 -10.9 -21.7

Low 25.0 26.8 27.8 -9.6 -20.0

U.S. total

High 19.4 23.8 24.6 -11.3 -17.8

Low 11.7 19.0 21.0 -10.5 -17.7

SOURCE: Office of TectinclOillY ASSessment

high- and lqw-technology sectors was experienced in the

South. In that region, the rate of employment change

associated with formations in high-technology industries

was 50 percent higher than that in low-technology industries. This was the strongest determinant of the exceedingly high net employment growth rate for the hightechnology sector in the Scuth.27 Contraction and dosing rates were almost identical for both high- and lowtechnology industries in every region.

Organizational Aspects of Employment Growth. -

High- technology firms c,n also be classified by organizational structure. As pointed out in the section describing the high-technology sector, most of the employment

in these industries is in affiliates of large multiestablishment firms. Data in table B-11 are separated into four

types of business establishments based on their affiliations. Independents are establishments that are not

legally connected to other business establishments. Tops

are the owning establishments in multiestablishment enterprises. An interesting distinction can be made between

the two types of affiliated establishments of multiestablishment firms. Local affiliates are branch or subsidiary

establishments of firms whose owning company is located

in the same State; national affiliates belong to out-of-State

owners. 18

Although employment in independents in high-technology industries grew rapidly (36.9 percent between 1976

and 1980), their share of the high-technology labor force

zr These findings are consistent with earlier regional analyses that indicate the

growth component most responsible for regional differences in net job growth

was ft.7mations. See, for example. John Rees, "Regional Industrial Shifts," or

David Ei..ch, "Generating New Jobs, Are Government Incentives Effective"

Commentary (Washington, DC: Council for Urban Economic Development,

July 1979).

"This is the organkational status in 1976, except for formations which are

classified by their status in Iqs().

(11.6 percent) was relatively insignificant compared to

the share of employment in high-technology affiliates of

multiestablishment firms ( .4 percent). Of the employment growth that took place between 1976 and 1980,

about 22 percent was in independents, almost twice their

share of base year employment in 1976. Regionally, the

ratio of independents' growth shares to base employment

shares ranged from 1.6 in the South to 3.2 in the Northeast. For the low-technology sector, this ratio for independents hovered around 1.6 in all regions except the

Northeast. In that region, independents made a positive

contribution to a net loss of employment in the low-technology sector. Despite the predominance of large affiliates

in the high-technology sector, independents performed

better on average in high-technology industries than in

low-technology industries.

Affiliates of multiestablishment firms accounted for 69

percent of employment growth for the period, approximately equal to their share of high-technology employment in 1976. In the Northeast and the North Central

regions, high-technology affiliates' shares of employment

growth were higher than their shares of 1976 employment. The reverse was true in the South and the West.

For each type of establishment, regional growth rates and

growth shares were inversely related to the size of the

high-technology sector relative to the region's total

employment. Thus the overall pattern of convergence

in regional shares of high-technology employment is

manifested consistently in all types of establishments.

Local v. National Ownership.-In 1976 in the

United States, more than 60 percent of affiliated establishments in the high- and low-technology sectors of manufacturing and business services belonged to out-of-State

firms. These national affiliates had nearly three times the

employment of affiliates of local (instate) firms. The
Table al 1.Net Employment Growth Rates let High-Teelmology Establishments by

Organizational Type, iorepao (porcine

Region All types Independents Tops

Local

affiliates

National

affiliates

Northeast 8 24 4 11 6

North Central ... 13 22 2 30 9

South 34 53 25 32 33

West 29 57 26 43 17

U.S. total 19 37 8 26 18

agsbecia comanizabortei Odds in 1975, except for formations which are classified by status in 19110.

SOURCE: Office of Tedtinctogy ASINSiMftt.

employment growth between 1976 and 1980 diminished

these discrepancies. In the low-technology sector, local

affiliates grew 20 percent more than the national affiliates

during the period. In every region except the South local

high - technology affiliates were growing at two to three

times the national affiliates' rate. In the South, local and

national affiliates grew at roughly equal rates.

Translating the various employment shares (table B-7)

and growth rates (table B-11) into shares of net growth,

the relative importance of local v. national affiliates in

the job-generation process is more clearly evident. In the

Northeast, the North Central, and the West, the number of new jobs was approximately evenly split between

affiliates of local firms and affiliates of national firms. In

the South, however, about 300,000 of the 340,000 new

jobs generated in affiliated establishments were in affiliates of out-of-State firms. Much of this new employment

in national affiliates was due to formations of new business establishments." Given the importance of formations in determining net growth rates, let us look at this

component of employment change in more detail.

Regional Differences in Formations

For the 1976-80 period, a number of important regional

trends are evident in the pattern of hightechnology formations and their associated new employment. As with

net employment change, the rates of formations of new

high-technology establishments and their employment

contributions are inversely related to regional shares of

high technology in the base year. Again, since dosing

and contraction rates are fairly constant across regions,

formation rates account for much of the difference in

overall regional performance. Consistently, the Northeast and North Central had formation rates below the

U.S. average for all three of these industry groupings,

while the South and West had above average rates, both

of establishment formations and job creation (see table

B-12).

l'This probably untiettepeesents the strength of out-ofState firms because no

account has been taken of theft acquisition of local affiliates or independent establishments between 1976 and 1980.

As shown in table B-13, each region's share of formations and associated employment was relatively constant

across all sectors, indicating that there was little variation in the average size of new high-technology establishments across regions. Their constancy across Industrial sectors also suggests that location decisions of

high-technology firms are not significantly different from

those of firms in other industries at this broad regional

level. As noted above, this distribution tends to offset

existing inequalities in the regional shares of total national employment in the high-technology sector.

The intraregional variations in formation rates across

industrial sectors were quite similar. In each region, the

formation rate for the high-technology sector was about

30 percent higher than that for the "other industries"

sector, except in the South where high-technology formations were nearly 50 percent higher. Low-technology

formation rates were almost identical to those of the

"other industries" sector in the Northeast and the North

Central regions, but low-technology formations were

stronger than "other industries" in the South and West.

Turning to the impact on emplOyment, in each region

the rate of employment growth from formations was approximately the same in the high-technology as in the

low-technology sectors (see table B-12). The South exhibited a considerably higher rate of high-technology job

creation. However, recall that the South had the smallest

high-technology sector (5.8 percent) relative to its total

employment and a smaller share of national high-technology employment compared to its share of total national employment.

Organizational Aspects of Formations.Representing 56 percent of all high.technology establishments in

1976, independent establishments in all regions accounted for an average of 54 percent of formations in

the high-technology sector. Their share ranged from 51

percent in the Northeast to 59 percent in the West. The

role of independents in the high- technology sector is relatively weak compared to their 70 percent shares of population and formations in the two other industrial sectors. The distinctive position of independents in the highand low-technology sectors was more striking in forma-
App. B-Formation and Growth in HighTechnology Firms: A Regional Assessment 127

Table B-12.-E,,stablisitmant Formation Rates by Region and Industrial Sector, 197840 (percent)

U.S. total Northeast North Central South West

Rafe of establishment formations:

All industries 32.2 26.1 28.5 34.9 40.6

High technology 43.4 33.8 37.6 50.3 51.6

Low-technology manufacturing and

business services 33.9 25.4 28.7 39.8 43.3

Other industries 31.7 26.0 28.3 33.8 39.8

Rafe of employment change in formations:

All industries 21.9 16.0 19.2 25.3 28.7

High technology 23.8 16.3 19.6 34.2 28.4

Low-technology manufacturing and

business services 19.0 14.2 16.3 22.5 26.8

Other industries 22.9 16.9 28.3 25.8 29.3

SOURCE: Office of Technology Alialliment.

Table B-la- Regional Shares of Establishments and Employment

Nat Change and Formations by industrial Sector (percent)

Northeast North Central South West

Establishment Jobs Establishment Jobs Establishment Jobs Establishment Jobs

High technology:

Formations 19 20 21 23 34 34 28 23

Net change 14 11 19 18 38 42 28 29

Low technology:

Formations 20 20 22 26 32 35 26 19

Net change 6 10 19 22 39 40 38 29

Other industries:

Formations 19 17 24 24 34 36 23 23

Net change 4 11 17 22 36 37 43 30

SOURCE: Office of Technology Assess lent.

titan of new jobs. From their 11.6 percent share of hightechnology employment in 1976, independents contributed 11.9 percent of jobs in high-technology formations

between 1976 and 1980. The independents' share of

employment from formations in high-technology industries was consistently only one-third to one-half of that

of their counterparts in the "other industries" sector in

all regions.

The complementary shares of national affiliates in bight zhnology formations was two to three times their shares

of employment and establishments in formations in the

"other industries" sector. The South's formation rates

gave it a 39 percent share of the national affiliate formations, consistent with its 35 percent share of such hightechnology establishments in 1976.

Size Class Patterns in Employment

Growth and Formations

How did the employment growth and formation behavior compare for different firm size classes in the

various industries and regions? Size class distributions of

population and employment growth parallel to a large

extent the organizational distribution. That is, most

employment In affiliates is in large firms (100 or more

employees), while most tiny (fewer than 20 employees)

firms are independents (compare table 8-14 with table

/3-5.)

The average high-technology establishment (69 employees) is five times the average size in the "other industries" sector (13 employees). In 1976 less than 4 percent of all high technology jobs were in firms with fewer

than 20 employees (tiny firms); another 8 percent were

in firms with fewer than 100 employees (mid-small firms)

for a total of 12 percent in the smaller two size classes.

In contrast, these two size classes comprised 28 percent

of employment in the low-technology sector and 42 percent in the "other industries" sector.

Expectedly, tiny firms experienced the highest percentage growth between 1976 and 1980 of the three size

classes. This pattern and the generally higher growth

rates for the high-technology sector as a whole combined

to produce extremely high-growth rates for tiny hightechnology firms, ranging from 54 percent in the North

Central region to 91 percent in the West. High-technology growth rates for mid-small firms average about half

that of tiny firms, while growth rates of large firms were

about half that of the mid-small group: he relative mag133
128 Technology, Innovation. and Regional Economic Development

Table B-14.Employment Growth Rates and Shares by Firm Size and by Industrial Sector, 197640 (percent)

Number of employees in owning firm

All 0-19 20-99 100+ Ali 0-19 20-99 100+

Industry sector Growth rates Growth shares

High technology

Low technology

Other industries

19

12

16

70

44

23

35

14

15

16

7

14

100

100

100

15

40

37

12

17

16

73

43

47

SOURCE: Office of Technceogy Assessment.

nitude of growth rates in mid-small firms compared to

large firms in the other sectors did not exhibit a similar

pattern.

The highly concentrated organizational composition

of the high-technology industries was reflected in the distribution of net employment growth. Although the smallest firms contributed only 15.2 percent of net employment change in high-technology industries, this was

about four times their share of 1976 employment in that

sector. More than 72 percent of the new high-technology

jobs were in firms with 100 or more employees. This

sharply contrasts with the structure of employment and

growth in the other sectors, in which tiny firms contributed close to 40 percent of growth and large firms less

than 50 percent. Consistent with their dominant position in the high-technology sector (89 percent of all hightechnology employees), large firms accounted for 86 percent of the employment in high-technology formations.

In the low-technology sector, large firms contributed only

65 percent of formation employment, considerably less

than their 75 percent share of that industrial sector's

employment. The share of high-technology formation

employment was about 7.5 percent for tiny establishments, whose share of 1976 employment was only 4 percent. In low-technology industries, tiny firms contributed

almost twice as many jobs as would have been expected

from their share of 1976 employment.

A general pattern of a 20- to 30-percentage point spread

between the formation rates In the high- and low-technology sectors holds in each region for both the smallest

and largest size classes. The mid-small class (establishments in firms with 20 to 99 employees), showed little

variation in regional formation rates between the two

industry sectors. While the establishment formation rates

are similar for tiny and large firms, the rate of employment increase in formations in the smallest size class

ranges from 50 to 80 percent higher than in the largest

size class across regions. This differential in growth rates

largely reflects the small size of the tiny firms' employment base in 1976. The employment increases of the midsmall class compare more closely with those of the largest

class, generally about one-half the rates of the smallest

size class.

Summary

The high-technology industries comprise a small, relatively concentrated sector of the economy. Their 7.4 percent share of the Nation's private sector employment is

based in large affiliates cf large multiestablishment firms.

Only 11 percent of high-technology employment is in

firms with fewer than 100 employees. The distribution

of these jobs across the four broadly defined geographic

regions of the United States fits the same general pattern as that of employment in all industries.

Employment in the high-technology sector grew 19.4

percent between 1976 and 1980, about 66 percent faster

than the low-technology manufacturing and business

services sector. The regions which experienced the highest rates of growth were those which previously had the

smallest shares of high-technology employment. Although the South and the West held only 43 percent

of the total employment in high technology in 1976, these

two regions accounted for 70 percent of that sector's

growth between 1976 and 1980.

Since rates of job loss from closings and contractions

were similar across regions, net employment growth performance is largely a function of formation and expansion rates. Similar to net employment growth, the formation rates for the high-technology sector are

consistently about 30 to 50 percent higher than those

for the "other Industries" sector. The Northeast and

North Central had formation rates below the U.S. average for all three of these industry groupings while the

South and West had above average rates of both business and job creation. These distributions of net growth

and formations in high-technology employment roughly

paralleled those ire all industries, indicating a general

trend towards equalizing the distribution of both hightechnology and other business activity across regions of

the United States.

The shift share analysis of manufacturing employment

changes in four geographic regions confirmed the trends

apparent in the tabular data." In the analysis of data

*The three industrial sectors included low-technology manufacturing and business services and the high-technology sector, which was divided into "lowircnrth

rate" and Thigh-growth rate" subset-tors for this part of the analysis.

134
App. BFormation and Growth in HighTechnology Firms: A Regional Assessment a 129

for the 1976-80 period and in the findings of another

study for two earlier periods (1963-72 and 1972-76), the

most important observation is that the competitive effects of regional employment growth performance overwhelm the mix effects of regional industrial composition.

In all three periods competitive effects were negative for

the northern regions and positive fo: the South and

West, reflecting a general shift of new economic activity

to these more recently developed areas. Furthermore,

while the northern regions are losing their previous

advantageous mix of manufacturing industries, the West

is increasing its proportion of higher growth manufacturing industries.

Employment in the high-technology sector is predominantly in large establishments of large businesses. Nevertheless, the relative performance of small independent

firms compared to larger affiliates was much better in the

high-technology sector than in the other sectors. Despite

this very strong performance of small and independent

high-technology firms, their small share of high-technology employment prevents their making major inroads

into the dominant position of the larger firms. Consequently, the organizational and size distributions of the

high-technology sector in 1980 remained very much like

existing structure of the high-technology sector in 1976.

High Technology in

Metropolitan Areas

What regional or metropolitan characteristics foster the

formation and continued growth of high-technology businesses? Business formations and employment growth

were analyzed for a sample of 35 metropolitan areas to

identify characteristics associated with high-technology

development. The "low-technology" and "other industries" sectors were also studied in comparison to the hightechnology sector to identify any unique characteristics

in its growth behavior. Finally, differences in the patterns of formation and growth of very small high-technology businesses compared to high-technology affiliates

of large firms were also examined.

Many economic and demographic factors contribute

to differences in growth rates. A set of factors was chosen which would adequately explain a large portion of

the variation in growth across metropolitan areas. Factors which might be significant for one industry, but not

for the majority of industries sector level.

Decisions regarding the location of new business es- - tablishments are probably based on different criteria than

those determining expansion, contraction, or closings of

existing establishments, which together determine net

employment growth. Though associated with many of

the same factors as formation choices, decisions regarding existing facilities reflect location decisions of earlier

periods, current fluctuations in economic conditions, and

the dynamics of particular firms and industries. In view

of this, separate equations were formulated using as the

dependent variable either: 1) the rates of formation of

new business establishments, or 2) the rates of net employment growth, for the period 1976-80.3' Nonetheless,

business formations constitute an important, if not determinant, force in net employment growth which should

render the relationships qualitatively similar, but perhaps

quantitatively weaker for the net growth rates.

The relationship of the dependent variables to the

metropolitan characteristics was also expected to vary

across industrial sectors, so the two equations, were estimated separately for each of the three industrial sectors:

high-technology industries, "low-technology" (other manufacturing and business service) industries, and "other

industries." The subset of high-technology industries

which had above average growth was also analyzed separately. However, the results were so similar to those for

the whole high-technology sector that they are not explicitly presented in this appendix.

Variables and Their Relationships

to High-Technology Development

It was hypothesized that each of the dependent variables would be related multiplicatively to some or all of

the following factors: b2

the pool of potential entrepreneurs,

the relative costs of doing business,

the level of activity in that industrial sector,

regional economic conditions,

the quality of the labor force, and/or

general attractiveness of the city.

What were the expected relationships between these factors and the measures of growth and how were they operationalized?

In general, business formations should have a strong

positive relationship to both the pool of potential entrepreneurs and the economic health of the area. Other factors that should be positively related to formations are

the general attractiveness of the area, the quality of the

labor force, and the level of extant activity in the same

industrial sector. The only factor in this list that should

discourage formations (i.e., be negatively related to) is

the cost of doing business.

"Formation rates are calculated as the gross number of new establishments

opened per 1,000 workers in the metropolitan area in 1976. Employment growth

rates were cakulated as the ratio of employment in 1980 to employment itt 1976

for each mentipolitan area for each industrial sector.

"The multiplicative form assumes that the percent differences in the independ

ent variables are related to the percent differences in the dependent variables.

Thus, the coefficients may be interpreted as elasticities. A coefficient of two, fot

instance, on a variable measuring educational level in a formation equation means

that each one percent change in education kWl is associated with a 2 percent

change in formations, on average, allowing for the simultaneous effect of the

other independent variables.

135
130 Technology, innovation, and Regional Economic Development

Of the measures included in the Metropolitan Area

Aggregate Data Base described earlier, several can be considered indicators of the size of the pool of entrepreneurs.

For all industrial sectors, the proportion of the adult population with college degrees and city size should be relevant. The proportion of workers in scientific, technical,

and professional occupations is probably the best indicator of potential high-technology entrepreneurs.

The measures selected as independent variables are not

discrete proxies, but often overlap as indicators of more

than one of the metropolitan characteristics. Both the

educational variable and the technical skills variable also

provide a measure of the quality of the available labor

force, which should be important for both formations

and growth in high-technology industries. The proportion of the labor force in manufacturing and the share

in the relevant industrial sector have been suggested as

better indicators of the supply of appropriately skilled

labor.

Differences in the strength of the metropolitan economy would be reflected in the differences in growth in

each sector, ceteris paribus. Since the high-technology

sector is a very small portion of the total (7.4 percent

of total employment on average), it is reascinable to assume that the two different measures of the strength of

area economies used, area employment growth rates and

area employment rates are exogenous." This assumption

cannot be made for the "other industries" sector.

The association of high-technology growth and demographic characteristics of the metropolitan areas has been

the subject of much speculation and little testing. It has

been suggested that a higher population implies the availability of a broader range of goods and services which

are needed to support high-technology growth. The relationship of population density to high-technology growth

could be either positive, as an indicator of desirable concentration of support services, or negative, as a measure

of stagnation and lack of expansion potential of a city.

Differences in the proportion of employment in manufacturing have similarly dichotomized interpretations.

They might represent dependence on older, declining

Manufacturing industries with a large proportion of

unionized, high wage labor or, conversely, the greater

availability of skilled manufacturing workers, support

services, and other desirable infrastructure. In "other industries" Jiff' ences in population growth rates were expected to be proportional to differences in employment

growth rates since that sector provides consumer goods

and services for the expanding population.

Assuming businesses try to maximize profitability by

minimizing costs, the rates of formation should be inversely related to the indicators of business costs. Three

business cost measures were usedaverage hourly wages

"Employment rates were calculam.1 as one minus the unemployment rate.

for manufacturing production workers in 1976, average

monthly commercial electricity bills in 1976, and an index of per-capita local taxes in 1977. Wage rates were

expected to exhibit the strongest negative association because they represent a much larger portion of variable

costs than the other measures used here.

The level-ef existing activity in each industrial sector

(or sector strength) was expressed either in terms of size

(number of employees in the sector) or in terms of sector share (proportion of 1976 employment in that sector). A large agglomeration effect in high-technology business formations was anticipated in the form of a strong

relationship between sector size and formations. New

high-technology businesses were expected to favor locating near successful establishments in the same industries,

where they could take advantage of the concentration

of specialized support se ces, supply.arrangements, and

technically trained kers, as well as keep abreast of

the activities of their petitors.

The general formulation of the equations, which are

specified by the coefficients in tables B-15 and B-16, did

exhibit excellent statistical explanatory qualities. The

measures of relative business costs, however, were overwhelmed by the measures of regional economic strength.

Overall employment growth is related to the business cost

variables in much the same way as business formations

arei.e., the three business cost variables may be thought

of as partial predictors of employment growth rates. 34 Previous period population growth likewise eliminated the

significance of two of the three business ,!ost variables.

Consequently, alternative models were estimated in order to evaluate separately the relative importance of the

business cost differences. Version A of the equations

omits the employment growth variable and includes all

three of the business cost variables. Version B of the

equations includes the employment growth variable and

the population growth variable (for formations), but

drops the insignificant business cost variables'. Only the

electricity cost variable was retained in the formation

equations. Making explicit use of employment growth

alternately in place of or in addition to business cost

variables has the effect okxplaining sectoral deviations

from average growth rags.

Although the two measures of regional economic

health showed very similar elasticities, the employment

growth variable performed better. However, its usefulness

deteriorates with increases in the sector size. It has little

actual significance in the formation and growth equations for "other industries" because that sector is so large

that its growth is almost identical to the total growth.

"For all sectors together in out sample.

In growth 1.29 0.17 (In Actin( costs) 0.12 (In local tax) 0.22 (In

wages); but this only accounts for 40 percent of the ylriation in metropolitan

employment growth rates.

. 136
App. B-Formation and Growth in High-Technology Firms: A Regional Assessment 131

Table B-15.-Regression Coefficients for 197640 Business F unnationsi

High technology Low technology Other industries

Version A:

Technical skills 2.27 (5.86) 0.75 (2.08) 0.04 (0.14)

1976 wages -0.96 (-2.13) -1.10 (-2.62) -0.61 ( 1.74)

Local taxes -0.25 (-1.27) -0.50 (-2.64) -0.31 (-2.01)

Electric costs -0.46 (-2.09) -0.53 (-2.60) -0.29 (-1.72)

City size 0.25 (2.98) 0.19 (2.41) -0.07 (-1.09)

R squared 0.61 0.50 0.45

Variation 8:

Population growth 3.82 (6.19) 4.14 (7.80) 2.58 (5.54)

Technical skills. 1.88 (7.49) 0.34 (1.56) -0.31 (-1.65)

Employment growth 0.74 (1.08) 0.99 (2.61) 1.03 (3.08)

Electric costs -0.23 (-1.64) -0.29 (-2.38) -0.09( -0.82)

City size 0.28 (5.87) 0.21 (4.99) -0.04 (-1.00)

R squared 0.86 0.84 0.82

Log Omar regression was used on the ratio of businoss formations to local employment in 1976; therefore, the coefficientsWS eitatiOltiSS. The t statistics are shown

In parentheses.

SOURCE: Office of Technology Assessment.

Table 8-16.-Regression Coefficients for Employment Growths

High technology Low technology Other industries

Version A:

Technical skills 0.38 (1.37) 0.28 (2.57) 0.14 (1.17)

1976 wages -0.66 (-2.21) -0.13 (-1.05) -0.18 (-1.33)

Local taxes -028 (-1.92) -0.07 (-1.18) -0.08 (-1.26)

Electric costs -0.38 (-2.52) -0.16 (-2.68) -0.15 (-2.21)

R squared 0.45 0.41 0.31

Variation B:

Technical skills 1.07 (3.83) 0.03 (0.30) -0.11 (-2.07)

Employment growth 0.88 (2.89) 0.56 (4.60) 1.06 (14.46)

Sector share -0.34 (-5.36) -0.16 (-2.57) -0.12 (-1.59)

R squared 0.66 0.65 0.90

*Log linear regrossion was used on the ratio of 1980 to 1976 employment: tharsfOrs, the coefficients are MAIM-cities. The t statistics are shown in parentheses.

SOURCE: Office of Technology Assessment.

The principal two alternative measures of labor quality

also behaved similarly. The technical skills measure clearly explained more of the variation in high-technology

growth and formations. Further, it better differentiates

the behavior of the three industrial sectors, sc the alternative measure (proportion of adult population with at

least 4 years of college) was dropped from the equation.

Finally, most analyses of employment growth have used

sector size as the scale variable for growth. That implicit

assumption of proportionality was tested; sector size,

along with the other independent variables was used to

explain variance in the number of employees in 1980.

Indeed, sector size had an estimated multiplicative coefficient very close to one for all sectors. Consequently,

it was shifted to the left side of the equation, converting

growth to growth rates, with virtually no change ir. the

rest of the estimated coefficients.

Both versions of the formation and growth equations

estimated for three different industry sectors and for two

extreme firm size classes are presented in tables B-15

through B-18. Only for the high- and low-technology sectors were business size distinctions investigated. The two

firm size classes examined were "tiny" businesses (establishments in firms with less than 20 employees) distinguished from "large" businesses (establishments in firms

with at least 100 employees). These two size classes are

highly correlated with discrete business organization

types: most tiny businesses are independent single establishment firms, and most large business establishments

are affiliates of multiestablishment firms. Thus differences

between the size classes can also be interpreted as differences in behavior of these two organizational types.

Business Formations In Metropolitan Areas

The number of new business formations avid the net

increase in the number of employees in a metropolitan

area should both be proportional to the size of the metropolitan area. When estimating equations for the number of new business formations in an industrial sector,
132 Technology, Innovation, and Regional Economic Development

Table B-17.-Regression Coefficients for 197640 Business Formationsa

High-technology establishments Low-technology establishments

Tiny firm Large firm Tiny firm Large firm

Version A:

Technical skills 2.54 (5.27) 1.74 (3.96) 0.88 (2.24) 0.15 (0.48)

1976 wages -0.98 (-1.76) -0.76 (-1.48) -1.18 (-2.51) -0.61 (-1.68)

Local taxes -0.34 (-1.34) -0.10 (-0.44) -0.52 (-2.54) -0.40 (-2.45) Electric costs -0.61 (-2.25) -0.32 (-1.27) -0.59 (2.64) -0.26 (-1.45) City size 0.29 (2.60) 0.21 (2.23) 0.18 (2.17) 0.1341.90) R squared 0.57 0.40 0.50 0.32

Veriatkm B:

Population growth 4.66 (5.62) 2.86 (3.28) 4.43 (7.32) 2.75 (5.09) Technical skills 2.13 (6.33) 1.31 (3.70) 0.45 (1.82) -0.20 (-0.89)

Employment growth 0.59 (1.00) 1.07 (1.71) 0.99 (2.29) 1.04 (2.69) Electric costs -0.42 (-2.21) -0.04 (-0.19) -0.33 (-2.41) -0.07 (-0.55)

City size 0.33 (5.14) 0.26 (3.83) 0.20 (4.27) 0.16 (3.75)

R squared 0.81 0.66 0.83 0.71

aloft linear regression was used on the ratio of business formations to local employment in 1976; therefore, the coefficients are elasticities. The t statistics are shown

in parentheses.

SOURCE: Offl..te of Technolegy Assessment.

Table 8-18.-Regression Coefficients for Employment Growth'

High-technology establishments Low-technology establishments

Tiny firm Large firm Tiny firm Large firm

Version A:

Technical skills 0.58 (3.47) 0.39 (1.24) 0.21 (2.17) 0.28 (2122) 1976 wages -0.39 (-2.10) -0.68 (-1. ) -0.06 (-0.59) -0.08 ( -0.81) Local taxes 0.12 (1.38) -0.33 (-2.01) -0.08 (-1.45) -0.05 ( -0.70) Electric costs -0.23 (-2.51) -0.39 (-2.30) -0.10 (-1.91) -0.18 (-2.69) R squared 0.41 0.42 0.33 0.34

Variation B:

Technical skills 0.31 (1.43) 1.18 (3.71) 0.07 (0.75) -0.02 (-1.4)

Employment growth 0.71 (3.02) 0.90 (2.58) 0.47 (3.70) 0.81 (4.13) Sector share 0.03 (0.67) -0.39 ( -5.25) -0.05 (-0.76) -0.13 (-1.74) R squared 0.41 0.64 0.47 0.55

aLog linear regression was used on the ratio of 1980 to 1878 employment; therefore, the coefficients are elasticities. The t statistics We afiown in parentheses.

SOURCE: Office of Technology Assessment

the overall level of employment in 1976 had a coefficient

very close to one for all sectors." This was not true for

the number of establishments in the sector in 1976, which

is the customary scale variable used to create formation

rates. Therefore, the 1976 employment was moved to the

left side of the equation and formations per 1,000 workers were analyzed. Both versions of the equations accounting for metropolitan differences in formation rates

are shown in table B-12 for the industry sectors."

Previous period population growth was the factor most

strongly related to business formations in both the highand low-technology sectors. Representing an amalgam

of amenities-climate, perceived job opportunities, and

This measure of gross formations is quite different from the net increase in

number of establishments, which would be derived from comparing aggregate

data for two observation points.

"Regression results for formations in the high-growth, high-technology sector

were essentially identical to those for the whole: high.technology sector.

138

other attractions, population growth exhibited the highest coefficients (elasticities) in the regression equations.

Each 1-percent difference in this variable accounted for

a 4-percent difference in formation rates. The high-technology formation rates were slightly less sensitive than

the low-technology's to differences in previous period

population growth. Formations in the "other industries"

sector were not related to prek4is period population

growth. The consumer goods and service establishments

which dominate that sector might perhaps be more

strongly responsive to the increas,:d demand associated

with concurrent population ex. msion."

"A recent study of the relationship between population migration and job

migration found that during the 1960's, "jobs followed people quite strongly,

and people followed jobs less completely." See Katharine Bradbury, et al.,

ban Decline and the Future of American Cities (Washington, DC: The Brookings Institution, 1982), pp. 103.104.
App. BFormation and Growth in High-Technology Firms: A Regional Assessment 133

The quality of the labor supply and the pool of potential entrepreneurs, as measured by the proportion of

workers using scientific and technical skills, were expected

to be more important in explaining differences in hightechnology formations. The results strongly supported

this expectation; each percent (not percentage point) difference in the local share of technical occupations accounted for a 2-percent difference in formation rate in

high technology. /8 As anticipated, in the low-technology

sector the effect was much smaller, and in the "other industries" sector it was insignificant and dose to zero.

The three business cost variables used in Version A

all had the expected negative coefficients, and all were

significant in both the high- and low-technology sector

equations, except for impact of local taxes. The negative

impact of local taxes was significant in the low-technology and the "other industries" sectors, but not significant in the high-technology seetors. The relationship of

local taxes to business development is unclear both theoretically and empirically. It is possible that local taxes

have no measurable effect on high-technology formations

because their negative impact in terms of business costs

is offset by the symmetrical positive impact of local government expenditures. Nonetheless, this lack of a strong

relationship corroborates the findings of other analyses

concerning the relationship of local taxes and business

formation rates.39 Local differences in wage levels were

inversely proportional to differences in high- and lowtechnology establishment formations. Formation rates

twice as sensitive to wage rate differences as to the other

measures of business costs. For the "other industries" sector, the relationship of formations to these business cost

variables was similar, but of smaller magnitude. Again

this lower sensitivity to local differences is probably attributable to the large proportion of this sector which

serves local markets, making prir.ipally intrametropolitan location decisions.

When metropolitan employ; dent growth was added to

the equation to create Version B (see table B-15), it captured most of the business cost differences, leaving only

small coefficients for electric utility rates. In other words,

overall employment growth is strongly related to these

business cost variables in the same direction as are business formations. Utility rates did have an impact in excess of that astinied by employment growth differences

in the manufacturing and business service sectors, but

not in the "other industries" sector, which are less dependent on energy inputs.40

'For rsamply, there is a S,percem difference in technical shares between a

sill with per..cnr re, hnical and with 21 penr»i techrmal.

for csarnple. NAIL hacl Kinchnkc, Ta.xt, and Growth: Business 1m:enr, t, and a,ux,rnrc netr nt (W aShIngtOn , IX*, Council of State Planning

Agenk ic,, 11P11, and Dennis NV. Carlton, "The Location and Employment

hoic es of Nev. Firms. An Econometric Mcx.iel With Discrete and Continuous

F miovenous V at iables.' Review ,4 &or-tomb:5 and Statisrus, Septembfcr NS 3.

' Also. re, all that the "other industries" K.( tor is dominated by predominantly

small operati,inc which glee less «inSicicrdtion to AliernatiVc loclItions

New business formations as a whole were roughly proportional to employment growth, as expected, since they

are a major component of net employment growth. Hightechnology formation rates varied three-quarters of 1 percent for each 1- percent difference in the strength of the

local economy, measured by employment growth rates.41

While the regional analysis above indicated a strong and

close relationship between regional employment growth

rates and high-technology growth rates, that relationship

became less clear at the metropolitan level. The simple

correlation of high-technology formations with metropolitan employment growth was small and negative.

However, it is reassuring that the regressions, which

simultaneously take into account other important factors, reconfirm the close association between high-technology development and net employment growth rates.

City size had a small, but significant, positive association with formation rates in both the high- and low-technology sectors. The impact and significance of city size

increases greatly when previous period population growth

is also taken into consideration. The larger of the expanding metropolitan areas experienced more high- and lowtechnology formations. City size is highly correlated with

cultural and social amenities often cited as significant attractions to high-technology firms and their prospective

employees. The greater diversity of goods and services

available in larger cities apparently provides more advantages to new manufacturing and business service establishments than the offsetting small city advantages, such

as lower land costs, better city planning, and less congestion. City size had no measurable impact on formations in the "other industries." This divergence in the

behavior of the manufacturing and business service sectors from that of the "other industries" sector is noteworthy because of the dominant size of the "other industries." A recent study. of urban decline for a larger

sample of SMSAs found dry size to have a significant

negative impact on intermetropolitan business location

decisions.42

The expected agglomeration effect was not discernible

in terms of a relationship of differences in formations to

differences in relative size of the sector." Neither the

high-technology sector as a whole, nor the smaller subsector of high-growth/high-technology industries exhibited

a strong relationship to sector share. Nor was there the

expected association between differences in the absolute

level of employment in the high-technology sector and

the business formation rate, after taking into ,iccount the

other differences between the metropolitan areas. It is

"As mentioned earlier. the high sechnology sector employment is such a small

portion of the total labil force that net employment gtoss-th in a metropolitan

area can be considered ekostenous to it.

"Bradbury, et al., op. cit.. pp. 209.210

"Fhe employment in high technology establishments as a petcentage of the

total private sector employment in all rx,ngovernmental industries In the SMSA.

139
134 Technology, innovation, and Regional Economic Development

possible that the scientific and technical share of local

employment, which is already in the equation, captured

any agglomeration effect present. Relationships to relative sector size might surface in an analysis of growth

dynamics for more narrowly defined, individual industries.

The other two demographic variables, percent of employment in manufacturing and population density, were

not significant in any version of either the formation or

growth equations." Differences in city size might supplant the relevant aspects of these urban structure

variables.

The Version A equations, which exclude the variables

for metropolitan employment and population growth

rates, still explained well over half of the variation in

high-technology formation rates and in low-technology

formations. The proportion of scientific and technical

occupations and the average wage levels together accounted for 45 percent of the explained differences in

high-technology formations, but only 16 percent of the

explained differences in the other two industrial sectors.

Version B effectively addressed the deviation of some sectors' formation rates from the overall metropolitan

growth rate. Sector deviations resulted, principally, from

differences in sensitivity to previous period population

growth rates and the availability of technically skilled

labor. These two factors, along with city size and electricity costs, explained over 80 percent of the variation

in each sector's formation rates. In sum, high-technology

formations were distinguished from formations in the

other two sectors by a much greater sensitivity to the

share of scientific and technical occupations in the work

force and a somewhat greater sensitivity to city size.

Employment Growth in Metropolitan Areas

it is more difficult to explain the differences in employment growth rates, because they frequently represent a

rather small net difference between large positive flows

(of jobs from formations and expansions) and large negative flows (of job losses from closures and contractions).

The negative flows are less variable than the positive flows

so the intermetropolitan differences in net growth rates

may usefully be viewed as a function of their gross formation and expansion rates.45 Thus one would expect

many relationships to be similar to those found for the

formation rates, but generally weaker. Sector employment in the area was used to scale the growth numbers.

Growth rates are expressed as the ratio of 1980 employment to 1976 employment, centering the measure on 1.0,

"Population density was also found to be unimportant with respect to bun.-

ness location desision.s in Brsdburv, et al., op. cit., p. 103.

'Scs the regional analysis above and Catherine Armington, "Further Examination of the Sources of Employment Growth," Working Paper No. 12, Bunness Microciata Project, The Brookings Institution, March 1983.

so the percent changes in growth rates are similar to percentage point differences.

As with the formations equations, two versions of the

model explaining net employment growth are presented

in table B-15 for each industrial sector." Version A, excluding overall metropolitan employment growth rates,

shows the business cost variables are generally significant

with negative coefficients. Most of the coefficients are

smaller than their counterparts in the formation equations, indicating that net employment growth rates were

less sensitive to costs than were formation rates. Electricity costs had significant, negative coefficients'for all three

industrial sectors. Local tax rates were insignificant for

all three, and wage rates were significant only for the

high-technology sector.

The technical skills variable is much less important in

explaining net employment growth in the high-technology sector than it was for high-technology formations.

This probably results from the different characteristics

of the firms, or industries, dominating these two measures. Employment growth in the high-technology sector

would primarily reflect entry into or expansion of large

production facilities which require relatively smaller proportions of scientific and technical personnel. In contrast,

formations are numerically concentrated in smaller independent operations, which in the high-technology sector would rely heavily on such skilled employees.

Interpreting Version B as an explanation of the deviation of sector growth rates from the overall metropolitan

growth rate, it is not surprising that the technical skills

variable remains the most important distinguishing fact

or in explaining high-technology development.47

In the employment growth equations, sector share surprisingly had a negative coefficient. If a sector is 3 percent (not percentage points) larger than average, the net

employment growth rate would be expected to be 1 percent lower than otherwise. While it is tempting to label

this a negative agglomeration effect, the level of industry aggregation is possibly too broad to measure accurately agglomeration economies. Furthermore, the inclusion of the technical skills variable, measuring the

proportion of the total work force in scientific and technical occupations, may capture a significant portion of

such effects. This negative relationship to sector share

more likely reflects a tendency towards some optimal mix

of industrial sectors or shifts of certain high-technology

industries (or firms) across stages of the product lifecycle.

Those areas with large existing high-technology sectors

may suffer from a relative scarcity of technically skilled

"Again the regression results for the high-growth, high-technology subsector

were essentially the same as for the entire high-technology sector. The proper.

non of variation explained was somewhat lower. The low-growth, high-technology

subsector lost 1.3 percent of its employment between 1976 and 1980.

"Remember that differences in growth rates are a 'function of the posinve

growth components (formations and expansions/.

140
App. BFormation and Growth in High-Technology Firms: A Regional Assessment 135

labor to support further profitable development. Additionally, shifts into the mass production stage may elevate other considerations above the desirability of locating near an existing nexus of high-technology businesses.

Special Characteristics of

Small High-Technology Firms

Businesses with different organizational and managerial

structures might be expected to employ different sets of

criteria in deciding on locations for new operations. A

multiestablishment firm, with a scope of operations spanning several States and industries, may not be influenced

by the same factors as a small-scale entrepreneur seeking to set up a new independent business. Existing businesses frequently chose to expand their operations by

opening new branch establishments. Such formations

may supplenser_t or replace the firm's capacity in their

current business activity or extend their operations into

new industries r product lines. If a firm opens a new

branch establish! sent in the same State in which its operations are center ed, the location decision is likely a matter of production necessity or managerial convenience.

On the other hand, larger multiestablishment firms,

which choose to open branch establishments in distant

locations should be making such decisions on the basis

of profit-maximization. Metropolitan differences in these

formations of new establishments by large firms would

then be more closely associated with factors measuring

the relative busiitess costs and benefits.

For estaislithisient formations in large firms in the hightechnology sector, the most important associated variable

was the previous period population growth (see table

B-16.) This was followed by the technical skills variable

and the overall employment growth measure. The relationships between the rate of formations and these factors were strongly positive and significant. The remainder

of the explained variation was attributable to a small positive relationship with the city size (1976 population).

Contrary to expectation, formations in large firms were

not strongly related to variations in business costs, as

measured by lots1 taxes, wage rates, and electricity costs.

Though the relationships were all of the proper negative direction, only the wage-related costs approached

significance. For large firms in the low-technology sector, the pattern of association between establishment formations and the metropolitan characteristics was similar, with the notable exception of the technical skills

variable. Formations in large low-technology firms were

negatively related to the proportion of jobs in scientific,

technical, and professional occupations, but this was a

weak and insignificant relationship.

Smaller businesses, which include most independent

establishments, usually locate in the locale familiar to the

entrepreneur. Distant alternative sites are rarely considered for initial locations. The primary factors expected

to be associated with variations in formations of small,

independent businesses are those affecting the supply of

potential entrepreneurs and their financial ability to start

a business. Reflecting the pool of potential entrepreneurs,

a strong positive relationship was anticipated between

the formation rate acing high-technology firms and the

percent of the labor force in technical and scientific occupations and the previous period population growth.

Formations in tiny firms were further expected to exhibit

the relationships to business costs and other variables

demonstrated to be significant for the business population at large.

Indeed, the results of the regression analysis for the

population of establishments in tiny firms supported all

the expected relationships. New formations in tiny firms

in both high-technology industries and low-technology

manufacturing and business services had a strong positive

relationship to previous period population growth. Intermetropolitan differences in the formation rates of tiny

firms are also related to differences in the levels of technical shills. These relationships were significant and much

stronger for tiny firm formations than they were for large

firms. This supports earlier findings that indicate large

firms are less dependent nn the existing technical expertise in selecting their sites for new operations.

Formations in tiny firms were consistently negatively

associated with the variations in wage rates, electricity

costs, and local tax rates. High business costs may be

more constraining to small firms than to large firms.

Large firms often have a financial and managerial infrastructure that permits them to absorb higher costs or to

pass them through in the form of higher priced output.

Furthermore, their volume of business enables them to

obtain certain concessions which might not be available

to smaller firms.

Formations in tiny firms are more sensitive to differences in many of the metropolitan characteristics. These

factors accounted for 81 percent of the variation in tiny

firm formation rates, but only 66 percent of variation

in formations of large high-technology firms. The most

important predictor for both size classes in both the highand the low-technology sectors was previous period population growth. Although it had only a small effect, city

size was also significantly related to formations, more

strongly for large firms than for small. In the hightechnology sector, formations in both size 'classes were

very sensitive to the availability of scientific and technical workers. Business costs were much more important

in explaining variations informations in tiny firms than

they were for formations in large firms in both high- and

low-technology industries. Finally, high-technology forminion rates in large firms were twice as sensitive to our

141
136 Technology, Innovation, and Regional Economic Development

measure of local economic strengthoverall employment

growth.48

In both high- and low-technology industries, employment growth rates were generally much higher in tiny

firms than in large firms (see table 8-17). While the relationships between formations in large firms and the business cost variables were not significant, employment

growth did exhibit a significant negative relationship to

the business cost variables. Employment growth rates in

existing tiny firms are generally less sensitive than the

rates in large firm establishments to all significant metropolitan variables.

In high-technology establishments of large firms,

growth is particularly responsive to variations in the

strength of the local economy and in the supply of technically skilled labor. More notably, their growth rates

show. a significant negative association with sector share,

while tiny firm growth was virtually independent of that

factor. This discrepancy in the relationship of small and

large firm growth to the size of the high-technology sector is possibly a reflection of product cycle differences.

Employment growth in large high-technology firms is

most likely manifested in the establishment of large manufacturing plants, once production has become standardized, Such production facilities would likely reap more

benefits from inexpensive factor prices than from an established concentration of high-technology businesses

competing for the same manufacturing production workers. Again a more disaggregated analysis at the four-digit

industry level for more narrowly defined geographic areas

would help to clarify some of these issues.

Summary of Analysis

Recent publicity about rapid growth of employment

in high-technology industries has caught the attention

of policymakers at all levels of government. Many State

and local governments have already created programs

to encourage the formation and growth of new high-technology businesses in their regions. While calls abound

for policies promoting development of high-technology

industries, there is a dearth of empirical information on

the efficacy of such programs. This study has provided

an empirical description of the high-technology sector,

its employment growth patterns, and the association between the locus of high-technology growth and pctrticular characteristics of metropolitan areas, utilizing the U.S.

Establishment and Enterprise Microdata (USEEM) recently developed at The Brookings Institution,

"Other studies have found large firms to be more sensitive to cyclical fluctuations in general economic activity than srnall business. See, for example,Victor

7.arninvita, "Cyclical Aspects of Incorporations and the Formation of New Bust.

nets Enterprises," and Victor Zarnowita and Lionel Lerner, "Cyclical Changes

in (Rosiness Failures and Corporate Profits," in Business Cycle Inclistors: Vol.

tune 1. Geoffrey Moore (ed.) (Princeton, Nj: Princeton University Press, 1%1).

Adopting a broad definition of high-technology industries, based on minimum levels of professional, scientific,

and technical workers or of R&D expenditures relative

to total sales in each industry, a total of high-technology industries were identified in manufacturing and

business services. Longitudinal tabulations of the bUsiness microdata provided data for analysis of the hightechnology sector in 1976 and its growth from 1976 to

1980. Employment growth and business formation data

were extracted from these tabulations and integrated with

other socioeconomic data for a sample of 35 metropolitan

areas. These aggregate den were then used to examine

the relationships between the characteristics of the metropolitan areas in the sample and the formation and growth

of high-technology establishments. Data for the hightechnology sector were contrasted with those for the rest

'of the manufacturing and business service industries

(called "low technology") and with "other industries" (excluding manufacturing, business services and government).

Through descriptive analysis of the regional tabulations

and regression analysis of metropolitan data the following questions were addressed: What are the characteristics of high-technology establishments and their patterns

of employment growth? How does the distribution and

behavior of high-technology establishments differ from

that of establishments in other industries? Are there particular characteristics of metropolitan areas that, seem to

favor the formation and growth of high-technology industries?

Analytic Findings

The high-technology sector contains only a small part

of the Nation's jobs; it employed 5.6 million workers in

1976 and 6.7 million in 1980. Business services accounted

for around 15 percent of the high-technology employment. Aside from their shared dependence on evolving

technologies, the industries in the high-technology sector are far from homogeneous. As in nearly all industries, most high-technology businesses are very sinal!,

more than half of the high-technology establishments are

in firms with fewer than 20 employees. However, far more

than other industries, the high-technology sector's employment is concentrated in large firms, spanning both

producers and consumers of high-technology products

and processes/Statistically, the dynamic entrepreneurs

with small high-technology companies are overwhelmed

by large-scale, high-technology production in existing

businesses.

The sector includes industries and firms within industries in every phase of their product cycles, from

the innovation and testing phase to large-scale,

standardized production.
App. B7Formation and Growth in High-Technology Firms: A Regional Assessment 137

About 90 percent of high-technology employment

is in firms with at least 100 employees. High-technology establishments are on average five times the

size of establishments in-other industries.

More than half of the sector's emploNment is in

branch establishments of firms which are headquartered out-of-State.

The distribution of high-technology jobs across the

four regions of the United States fits the same general pattern as employment in all industries.

Within regions the importance of high-technology

industries in the local labor force ranged from 6 percent in the South to 9 percent in the Northeast.

HighTechnology Employment Growth.The interest in high-technology industries has centered largely

on their potential for generating new employment opportunities. Indeed, employment in that sector grew 19.4 percent between 1976 and 1980, about 66 percent faster than

the low-technology industries. However, patterns of recent employment change vary widely for industries within this sector. Some experienced mercurial employment

growth (e.g., computer programming grew more than 75

percent in the 4 years) and others dramatic shrinkage

(e.g., electronic capacitor manufacturing which reduced

employment by more than 50 percent).

Dividing the high-technology industries into high- and

low-growth subsets,. the 55 industries in the high-growth

subset grew 30.6 percent between 1976 and 1980." The

low-growth, high-technology subset lost employment, indicating that technological advance is not necessarily

associated with employment generation. This wide variation in growth rates across industries reflects changes in

labor requirements as firms within these industries move

through their different stages of their product lifecycle

and also the rapid processes of technological change

which can render certain product lines obsolete.5° In sum,

Growth rates in the high-technology sector were 66

percent higher than in low-technology industries,

but the small size of the sector limits its current contribution to net job creation (about 1.1 million of

the 11.5 million created between 1976 and 1980).

Within the sector, employment growth rates Vary

widely across industries. Despite high average growth

rates for the sector, almost one-fourth of the hightechnology industries experienced net losses of employment between 1976 and 1980.

Regional Aspects of Growth.Employment growth

rates vary more widely across regions than across industrial sectors. In other words, the performance of the region tends to overshadow differences in industry performIkfitird as idiocy or hriow thr all-industry average of 2 percent

'For c rhiir nigh disc ussion of carious employment elks( tc ASFAX'iated with trc h

nc iga c rigc, sec C Freeman, I Clark, rt al , 1.1r:employment and Tc% hno

,41 I441414%.441,41 (Neu Haven, C.3reenwood Press. 1'482)

ance. If a region is experiencing relatively high growth

in the high-technology sector compared to the national

average, it is likely to have higher growth rates in other

sectors.

The regional trends in the growth of high-technology

employment conformed with development theories of regional convergence due to factor price equalization. The

regions which experienced the highest rates of growth

were those which previously had the smallest shares of

high-technology employmenti.e., the South and the

West. Regional growth in the low - technology sector was

similarly distributed." The tendency toward equalizing

the regional shares of high-technology employment observed here is similar to findings of other studies of regional development in the 1970's.52

Since rates of employment loss due to business closings

and contractions are relatively constant for all regions,

regional differences in high-technology growth are largely

a function of formation rates. Each region's share of business formations was similar across industrial sectors. The

Northeast, for example, had 20 percent of all high-technology formations, 19 percent of all low-technology formations, and 20 percent of all "other industries" formations.

To reiterate, the major findings regarding the broad

regional distribution of high-technology growth are as

follows:

A region's high-technology growth is largely a reflection of its overall economic performance.

There appears to be a redistributive effect in that

those regions with the smallest shares of high-technology employment experienced higher growth rates.

Growth depends primarily on business formations;

a region tends to capture approximately the same

share of formations in each industry sector.

Organizational Aspects of Growth.A popular conception of the high-technology sector links its rapid expansion of employment to successful small independent

firms." In fact, independent firms in all industrial sectors did have much higher growth rates than affiliates

of larger firms. Moreover, the performance of highltechnology independents far outstripped their counterparts

in the other sectors. The evolutionary nature of this sector seems to favor independent firms more than the ma-

"It should also be noted that thew regions have a comparative advantage in

terms of energy supplies and costs. Some studies have found energy costs to he

significant in explaining the redistribution of population and economic activity

to the 1970s. See Shirley P. Burgmf, "Implications of Energy for Economic. De

velopment Planning," Monograph, National Councd fur Urban Economic, De

Yelopment. July PK.

"See jaason, et al., ,r,. or and John Rees and Howard Stafford, "A Review

of Regional Growth and Industrial Location Theory Towards Understanding

the Development of High:Test hnologv Complexes in the U.S.," paper prepared

under contract for the OfEce of Technology Assessment, May 1,433.

"See D. Koch, et al., "High Technology: The Southeast Reaches Out for

Growth Industry," Economic Review, Federal Reserve Bank of Atlanta, September 1983. pp. +16.

143
138 'Technology, Innovation, and Regional Economic Development

ture, stable structure of the industries in the low-technology sector.

Despite their dynamism, the independent firms still

contain only 12 percent of the high-technology sector's

employment. Though much of the innovation and the

competitive stimulus in high-technology industries is

probably in independent firms, the appreciable increments in employment derive from the implementation

of these innovations at later stages in the product takes

place in affiliates of large firms. Formations of national

affiliates accounted for more than 60 percent of the employment in new high - technology establishments. Affiliates of instate firms contributed another 25 percent of

such new high-technology jobs.

This study provides some evidence to support elements

of the product/regional lifecycle theories. These theories

suggest that certain stages of the production process have

different locationsl requirements.",,For instance, the

high-technology sectors in the Northeast and the West

have larger proportions of independents, and above average net formation rates for independents. The greater

presence of independents in these regions in which

high-technology industries were historically conceived

and concentrated could be evidence of the "seedbed" or

incubator effect often discussed in connection with the

high-technology sector. In the R&D phase of the product cycle, firms would be more likely to benefit from the

clustering of highly trained research and technical personnel and facilities that exist in these regions.55 On the

other hand, the South's high proportion of large production facilities owned by out-of-State firms and its

higher rates of growth in such establishments probably

reflects decisions to locate large, standardized production

facilities in areas with low labor costs and low unionization. Further research with more detailed industrial and

regional breakouts would be necessary to confirm this

hypothesis.

For the present, the findings regarding organizational

structure and high-technology growth can be summarized

as follows:

Independent firms have much higher growth rates

than affiliated establishments, but they hold less

than 12 percent of the high-technology sector's employment.

Affiliated establishments had similar growth rates in

both the high-technology and "other industries" sectors, but independent firms grew three times as fast

in high-technology compared to "other industries."

The South has an exceedingly high proportion of

employment in national affiliates in all industrial sectors, particularly in high-technology industries.

"Encouraging 1-14h-Tes.hnology Development, op. cit., app. 8,p. 8-I4,

"See C. Freeman. The Economics of Industrial Innovation (Cambridge, MA,

MIT NCB, 1982).

The high-technology employment growth rate of

local affiliates was more than 50 percent higher than

that of national affiliates. Only in the South were

national affiliates growing more rapidly than local

affiliates.

Formations and Metropolitan Characteristics.

An analysis of metropolitan business formation and

growth in the three sectors in relation to several

metropolitan characteristics was conducted to illuminate

the dynamics behind these regional trends and perhaps

to identify characteristics that communities might develop to foster high-technology development. As other

studies of high-technology have found, high-technology

businesses behave much the same as other businesses in

choosing sites for their operations. Businesses are attracted to areas with lower business costs, with healthy

local economies, and with a relatively attractive quality

of life.

What differentiates the location decisions of high-technology firms is the greater importance of an educated,

skilled labor force and local amenities. s6 This again coincides with numerous studies surveying high-technology

firms, in which they identified labor considerations as

their primary concern."

Each sector's formation rates were inversely related

to local business costswages, kcal taxes, and electricity costs.

Metropolitan differences in business formations in

all sectors were proportional to the size of the city's

labor force and to the strength of the local economy.

Previous period population growth, an indicator of

general attractiveness and growth potential, had the

strongest relationship to business formations, particularly in the high- and low-technology sectors.

The proportion of the labor force in technical occupations is strongly related to high-technology formations, but not to 'other sectors.

Both high- and low-technology formations are favored slightly by larger city population sizes. City

size is unrelated to formations in the "other industries" sector.

After accounting for differences in the technical occupation share of the local labor force, the relative

size of the extant high-technology sector was not

related to business formations.

Formations in the subset of high-growth, high-technology industries behaved virtually the same as the

entire high-technology population in relation to the

metropolitan variables.

"City size is used here as a proxy for local amenities.

"See joint Economic Committee. U.B. Congress, Location of High Technology Finns and Regional Economic Dm -lament egfashinigton, DC U.S. Govern.

ment Printing O(fice, June 1962). and Encouraging flish-Technology Develop

ment, op. cit., app. B.

144
App. 9Formation and Growth In High-Technology Firms: A Regional Assessment 139

Employment Growth and Metropolitan Characteristies.Since business formations play an important part

in employment growth it should not be surprising that

the results of the analysis of employment growth were

similar to the formation equations. In general, the

metropolitan characteristics explained less of the variation in net employment growth rates than of the variation in formation rates. Coefficients (elasticities) are

lower, and fewer variables are significant.

Business costs were negatively related to growth in

all sectors, but were significant only for the hightechnology sector.

The proportion of scientific and technical workers

was again most important and significant for the

high-technology sector.

Surprisingly, sector share had a significant negative

association with high- and low-technology growth.

This lends further support to regional convergence

tendencies mentioned above.

The high-growth, high-technology subsector behaved essentially the same as the entire high-technology sector, but less of the variation in its growth

was explained.

Business Size Considerations.Businesses with different organizational and managerial structures might be

expected to employ different sets of criteria in deciding

on locations for new operations. Therefore, relationships

were separately estimated for tiny firm formations and

for establishment formations in large firms in high technology." The direction of the relationships was generally the same, but the elasticities were consistently higher

for tiny firms than for large firms:

Formations of tiny high-technology firms were

almost twice as responsive as formations in large

firms to variations in previous period population

growth, the share of scientific and technical workers, and most business cost variables.

Formations in both size classes of high-technology

firms showed the expected strong relationship to

technical skills; that factor was insignificant for lowtechnology formations.

Overall employment growth, the exception, was

more strongly related to formations in large firms."

Employment growth rates in tiny firms were much

higher' than in large firms in both the high- and lowtechnology sectors, which is partly a function of their

smaller base employment. Contrary to the tendencies in

formations, establishment employment growth in tiny

"These two nu classes are approximately equivalent to the orgaruaational cat

egories of independents and affiliates, respectively.

"This might be expected since formation and failure behavior of Inge firms

tends to he much more sensitive to an term and to cyclkal fluctuations in the

economy. Sec Collate Harris. "Icebergs and Business Statistics: A Comparison

of Data fur Fai lute-, and Dissolutions," Business Microdoto Project, The Brook,

hugs Institut:on, ift. September 1981.

36-737 0 84 11 t QT, 3

firms was less sensitive than that in large firms to almost

all the significant metropolitan variables. Also, less of

the variation in net growth was explained for tiny firms.

Growth of high-technology employment in establishments in large firms was particularly responsive to variations in the strength of the local economy and in the

supply of technically skilled labor. Unexpectedly, large

firm establishment growth showed a small, but significant, negative relationship to relative sector size. As discussed above, this could reflect the stage of the product

lifecycle at which large employment increases occur.

Data Limitations and

Analytic Considerations

The initial difficulty encountered in studying high-technology development is the selection of a satisfactory definition of high technology and the criteria for choosing

the industries to be included in that category. Business

data are primarily available aggregated by industrial

classifications. Industry classes comprise groups of firms

producing a wide variety of products with production

processes of differing technological content, thus obscuring important differences among firms within each industrial grouping.

With this shortcoming in mind, a definition based on

occupational composition of industrial classes was

selectedone which in turn has its own limitations. The

occupational data are only available at the three-digit

level of standard industrial classification. Some of these

three-digit groupings contain four-digit component industries which would not qualify as high-technology industries. The absence of more detailed data on occupational

composition led to a subjective editing which eliminated

eight of the four-digit industry classes in the three-digit

business service groups identified with the established criterion. These subjective adjustments somewhat diminish

the rigor of the definitionof high technology used in this

analysis. No such adjustments were made to the systematic selection of manufacturing industries.

Other problems arise from industrial classification practices. In newly emerging industries, businesses may

assume indistinct classifications until revisions in the industry codes provide more appropriate alternatives. For

example, the two largest robotics manufacturers are in

different four -digit SIC groupings; one is in a general "not

elsewhere classified" category. In sum, industrial classes

are not the most desirable unit of analysis for the definition and study of innovation and development in hightechnology industries, but data availability currently dictates their use.

Further problems of aggregation arise in analyzing the

"high-technology sector." The common feature of the industries making up this sector is their utilization of evolving technologies and/or production of new products. The

145
140 Technology, Innovation: and Regional Economic Development

differences among these component industries are often

more striking than their similarities. The heterogeneity

of the sector is apparent in their differing rates of employment change during the 1976.80 period. Some high-tech-

.isology industries experienced employment growth in excess of 75 percent, while other contracted their work force

by as much as 50 percent. Clearly, this reflects a wide

range of changes in demand, product mix, and productivity. While businesses in some industries are rationalizing their production of goods facing slackening demand,

others are expanding employment rapidly in response to

consumer pressures. Disaggregating the high-technology

industries could reveal some important differences

masked by the broad grouping.

A partial step was taken in this direction by dividing

the sector into high- and law-growth industries based on

their divergence from average employment growth rates

between 1976 and 1980. The high-growth subset of the

high-technology sector accounted for 75 percent of the

business formations and all of the net employment

growth for the period studied. The 35. slower growing

industries as a group lost 13 percent of their employment. However, among the high-technology industries

experiencing low or negative employment growth are several industries with growing levels of output. For example, electronic capacitors increased output by 16 percent

between 1976 and 1979, despite reducing its employment

by more than 50 percent. Similarly, synthetic fibers

marked a 4-percent decline in employment, but an 18-

percent increase in output and a 6-percent increase in

productivity 60 Further analysis is needed of the dynamics

of component industries of the high-technology sector,

taking into account differences in their predominant state

of production.

Another limitation of this study derives from the procedures for classifying business establishments. Enterprise

employment size, geographic location, organization type,

and industry are determined by the 1976 data on establishments. New business formations were not in the 1976

file; therefore, the characteristics of formations and their

owning firms are drawn from the 1980 file. Consequently

newly formed affiliates of multiestablishment firms are

categorized under the size of their owning firm in 1980,

somewhat distorting the firm size distribution of growth.61

Consequently. this analysis did not address conversion

of -stablishments to business activities in different industrial sectors, relocation of plants, or changes in ownership structure. Probably the most important of these

omissions is the acquisition of high-technology businesses

by national conglomerates or by local entrepreneurs is

"U.S. Department of Labor, Bureau of Labor Statistics, Productivity Measures foe Selected Irxiustries, 195441 (Cilasbulgton, DC: U.S. Government Printing Office, December I982).

"Acquisition of the base year data for firsts size requires costly, complex proem:nu of the data files. Preliminary analysis indicated that the degree of distort= would be minimal given the broadly defined large firm size classa common means of expanding current activities or entering markets or industries. However, in themselves, such

acquisitions neither increase not decrease aggregate employment. Future studies of the dynamics of high-technology development should consider the role of such

changes in organizational structure.

The broadly defined regions used for comparative purposes may also be obscdring some important differences

in growth patterns. Intraregional variation is lost as the

characteristics of one subregion are offset by those of

another in the aggregate. Less broadly defined regional

groupings, such as the regional divisions defined by the

Bureau of the Census or States, would certainly reveal

more information on the patterns of high-technology formations and growth.

The sample of metropolitan areas used for the regret,

sion analysis was small and was not randomly selected.

Since the SMSAs were chosen on the basis of diversity

in the characteristics of interest, the "fit" of the regressions may be better than that produced with a random

sample. The 323 SMSAs defined in 1980 include many

rapidly developing areas whose populations have only

recently become large enough for inclusion as SMSAs.

Our sample probably underrepresents small and newer

metropolitan areas because comparable business cost data

were not available for many of these.

The variables used for the socioeconomic characteristics of the metropolitan areas are often averages for broad

classes. For example, the tax index is calculated from a

composite of all State and local taxes per capita, It is not

a precise measure of the actual tax burden faced by a

new business and does not explicitly take into consideration any special benefits offered by local governments

to induce businesses to locate there. The wage variable

is also an average fist all manufacturing production workers, which may not accurately reflect intermetropolitan

differences in wages for high-technology industries. As

more detailed data become available, further research

may clarify many of these ambiguities.

Finally, some mention must be made of the period of

observation used in this analysis, which encompasses

changes between December 1976 and December 1980.

The secular and cyclical trends prevailing during this

period include a retreat from record high inflation rates,

gradually increasing unemployment and rapid increases

in interest rates to record highs. Gross national product

grew 5 percent between 1976 and 1977, but suffered a

real decline of almost 2 percent during 1980. The rate

of business failures increased from 28 per 10,000 firms

from December 1976 to December 1977 to 42 per 10,000

for 1980, while the index of new firm formations declined

by 5.4 percentage points over the period."

"These are formations and failures of enterprises, not establishrnents which

are the primary unit of analysis in this study. Statistics are drawn from the Statistics/ Abstract of the United States 10243, p. 532.

146
App. 8Formation and Growth in High-Technology Firms: A Regional Assessment 141

It is often claimed that high-technology industries are

"recession-proof," but this is likely based on the simple

contrast to certain low-technology industries. such as

automobiles, which are extremely recession- sensitive. In

addition, the newness of products can generate demand

in spite of cyclical tendencies. One can only speculate

on the differing impact of cyclical conditions on and business formations in each of the three industrial sectors.

Data will soon be available for the analysis of each 2-

year period between 1976 and 1982, which will shed same

light on the limitations of the single, 4-year observation

period used in this study.

Policy Implications

Policymakers are anxious for assessments of the potential of high-technology development to ameliorate the

persisting problems of high unemployment, a declining

manufacturing sector and economic distress concentrated

in particular cities and regions of the country. The perceived qualities of high-technology industries which are

relevant to domestic employment problems sue:63

high rates of job generation (direct and indirect);

better quality jobs; and

independence of geographic constraints (i.e., hope

for distressed areas).

Before suggesting policy measures aimed at promoting

high-technology development, let us examine the evidence regarding these supposed benefits.

As a group, the high-technology industries have exhibited impressive growth rates in recent years. The hightechnology manufacturing and business service industries

grew 19.4 percent between 1976 and 19 , compared to

about 12 percent for the low-technology sector." Hightechnology manufacturing industries comprised about 20

percent of manufacturing employment in 1976 and more

than 40 percent of growth in that sector through 1980.

Though the smaller employment base of this sector requires the creation of relatively fewer jobs to achieve

high-growth rates, its very high share of the net employment increase validates its significance. Findings of

another study for a longer period confirm these trends.

Between 1970 and 1980 the employment growth rate in

high-technology manufacturing industries was more than

10 times that of the low-technology sector."

Despite the impressive growth performance of the hightechnology industries in the aggregate, the sector comprises a diverse set of industries undergoing rapid technological change with differing impacts on employment.

"For a discussion of the achrantagses ci high technology for manufacturing competitiveness and international trade, see Lawrence, op. cit.

"The lorechnology sector's growth Will considerably boisteteoin the induction of the non-high-technology businesses services. Employment growth in the

low.technology manufacturing industries was doses to 6 percent.

"Lawrence, op. cit,, p. 141.

In some industries the introduction and diffusion of new

products is rapidly expanding manufacturing and supporting service jobs. In others, reductions in demand

and/or increased productivity are resulting in net reductions of the work force." Of the high-technology industries anlyzed in this study, one-fifth actually reduced the

number of jobs they provided between 1976 and 1980.

A number of other factors can diminish the job creating potential of high-technology industries. Based on the

application devolving technologies, many high-technology products face high rates of obsolescence or failure.

Adoption of new processes can lead to productivity increases, and new product lines can capture markets for

existing goods, both kacling to displacement of workers

in other industries. Such offsetting tendencies must be

incorporated into assessments of the job-generating potential of high-technology industries.

Although this analysis did not directly address the

quality of jobs being generated by the high-technology

sector, a few words regarding this suggested benefit seem

appropriate. It is often assumed that the new employment opportunities created by the expansion of hightechnology industries dre better paying, less hazardous,

and more personally satisfying. Again in the aggregate,

the figures for high-technology industries are encouraging. In 1980 high technology's average rates of compensation of both manufacturing production workers and

of all employees were higher than those in low-technology manufacturing industries. In 1980 high-technology

production workers earned about 50 cents more per hour,

and the average high-technology employee made $3,500

more per year than their counterparts in the low-technology manufacturing sector.'?

As with most aggregate statistics, these figures disguise

enormous discrepancies in the occupational structure and

employee compensation within the industries. The proportion of scientific, engineering, and technical workers

to total employment in the high-technology industries

is about 18 percent." It has been argued that the scarce

human capital embodied in this 18 percent plus a relatively small number of other highly skilled workers, commands extremely high salaries which offset below average compensation paid a majority of production workers

in the high-technology sector. As high-technology industries (and firms within industries) move through the product cycle, their employment requirements vary radically.

In early stages, a small but highly trained body of scientific and engineering personnel is required. However,

large employment increases most likely occur in the mass

production stage, calling primarily for semiskilled work-

"See Freeman, Clark, et al., op cit.; and Doreen Massey and Richard Mergan,

The Anatomy of job Loss (London: Methuen, 1962).

"Lawrence, op. cit.

"Calculated for the threedigit SiCs identified in C3teene, et al.. op. cit.

147
142 Technology, innovation, and Regional Economic Development

ers for assembly or more skilled workers for equipment

maintenance. Furthermore, the demographic characteristics of the high-technology work force suggest that new

jobs created in these industries may not absorb the bluecollar production workers being displaced in declining

manufacturing industries.69 Although the evidence justifying this pessimism is by no means conclusive, such factors must be taken into acount when considering development programs targeted at high-technology industries,

particularly by regions with high concentrations of declining manufacturing industries.

Another benefit claimed for high-technology businesses

is their apparent independence of geographic constraints,

which renders almost any locale eligible for development.

High-technology industries serve national, rather than

local markets, so they are theoretically less vulnerable

to local economic conditions. The high value to volume

ratio of their products results in low transportation costs

per unit. Finally, as a group they are less energy-intensive than low-technology industries, freeing them in part

from regional constraints associated with the cost and

availability of energy resources.

Once again the empirical information confronts us with

the heterogeneity of the high-technology sector. The industries within this sector range from highly concentrated

(geographically) industries, which are resource-intensive

or efficient at very large scales of production, to industries whose establishments are scattered across the country.7° Furthermore, just as the employment requirements

of high-technology industries vary with the stages of the

product cycle, so can their locational requirements. As

pointed out earlier, some of the variation in these locational preference functions are reflected in the different

behavior of small, primarily independent firms and large

branch facilities. Formation rates of small, independent

firms appear to be higher where there are existing concenrratior.s of people employed in scientific and technical occupatior,. Given this diversity, generalizations

about the locational behavior and growth of high-technology businesses must be interpreted cautiously,

In spite of their footloose reputation and their significant presence in a multitude of communities, high-technology industries, as a group, did demonstrate some preferences in their locational behavior that might affect the

success of different areas in fostering its development. In

this analysis, forrriation rates of high-technology businesses were particularly sensitive to two factors which

"See Lawrence, op. cit., pp. 152,155; and Eileen Appelbaum. "'High Tech'

nd the Structural Unemployment of the Eighties," unpublished paper presented

at Annual Meeting of the American Economic Association, Washington,

Dec. 28, 1981.

70For a discussion of the geographic dispersion of high- technology manufac

turing production, set Amy Glasmeier, Peter Hall, and Ann Marlcusen, "Re.

cent Evidence on High Technology Industries' Spatial Tendencies: A Preliminary Investigation," Institute of Urban and Regional Development, University

of California, app. C to this report.

are not especially amenable to policy manipulationprevious period population growth and city size. Business

formations and groWth between 1976 and 19 in both

the high- and low-technology sectors were most strongly related to the rate of population expansion between

1970 and 1975. Evidently, the same metropolitan characteristics attracting new residents to these expanding

communities are attracting new businesses." People are

being drawn to areas with milder climates, lower energy

costs, and perceived greater economic opportunity, while

they are leaving areas with high population density, high

costs of living, and high unemployment. There has been

a clear redistributive element to the regional patterns of

the changes in population and economic activity. Those

regions with relative high incomes experienced lower

rates of per-capita income growth, those with large concentrations of employment experienced slower employment growth, and those with smaller high-technology sectors experienced more rapid growth in high-technology

industries."

In the initial formulation of the model, city size had

a small positive association with formation rates in the

high-technology sector, but its impact and significance

increased greatly when previous period population

growth was also taken into consideration. It is the larger

of the expanding metropolitan areas (i.e., of those with

the characteristics mentioned above) which experienced

higher rates of high-technology formations.

In many respects the high-technology sector behaved

like the low-technology and the "other industries" sectors. However, consistent with the findings of other studies, the one factor especially important to high-technology development was the proportion of employment in

scientific, engineering, and technical occupations." The

formation of small, independent firms was most sensitive

to this factor. Formation and expansion of high-technology businesses in an area can be constrained by the lack

of qualified entrepreneurs and technicians, The importance of this variable must be underlined since it is

somewhat amenable to public policy. Quality education

from the primary to thewstgraduate level can be an important factor in attracting prospective entrepreneurs,

firms and employees in high-technology fields. The gap

between the skills of the unemployed and of new entrants

to the work force and the demands of the labor market

will continue to grow unless substantial improvements

are made in the quality and distribution of educational

resources.

"The wording here implies that it is primarily a function of migration pat.

terns; however, natural increase plays an important role in explaining differences

in growth rates.

7) See Jackson, et al., op. cit.; Bradbury, et al., op. cit., pp. 89.90; and Burg.

graf, "Implications of Energy for Economic Development Planning," op. cit.

"The proportion of the adult population with 4 or mare years of college was

also strongly related to high-technology development, but its effect was captured

by the technical occupations measure.

148
App. BFormation and Growth in High-Technology Firms: A Regional Assessment 143

In the wake of intense criticism of previous education

and training programs, researchers have recently completed several studies reviewing these earlier efforts and

suggesting effective avenues for pursuing educational

goals. With regard to secondary and postsecondary training and education, the programs deemed most effective

in achieving the desired match between individual skills

and economic needs are those which directly empower

the individual." It is argued that educational institutions,

which have been the primary recipients of Federal aid

in the past, are less responsive to the shifting demands

of the labor market than are the individuals seeking training. Empowering individuals, by directly subsidizing their

choice of apprOpriate educational and training investments, would be more effective."

A final comment on another factor with some policy

relevance for' high-technology development. In this analysis, local tax rates were found to have no significant effect either on high-technology formations or on net employment growth. This lends further support to a growing

body of evidence showing tax differences to be unimportant in explaining differing levels of business development." Furthermore, the provision of tax incentives entails sometimes significant opportunity costs. Since there

is little proof that such incentives lead to the creation

of business establishments that would not otherwise have

located in that locale, the community effectively subsidizes firms at the expense of foregone revenue.77

In conclusion, the efficacy of policies aimed only at influencing high-technology development is questionable.

There is no guarantee that the high- growth industries

of today will be those of the next decade. In fact, the

industries included under the rubric "high technology"

are an ever-changing set. Narrowly focused programs

which subsidize particular groups of businesses may actually result in losses to communities through distortion

of costs, foregone revenue, and other unanticipated effects. Undoubtedly, the high-technology sector is generating many employment opportunities, but the dynamics underlying this net effect are complex and must be

taken into consideration if employment objectives are

of tantamount concern. Policies encouraging economic

growth in all sectors might be preferable to "targeting"

development efforts on high technology or on any industries that are remarkable for their possibly ephemeral

growth. Communities should prepare their institutions

7See Bradbury, et al., op. cit., pp. 274.286,

"The educational program embodied in the C3.1. Bill is mg such a program,

and it cost effectiveness has been lauded by researchers and policymakers,

comprehensive proposal for creating such a program is presented in Roger

Vaughan and ,tune Sekera, "Investing in People," Policy ,Studies Review, vol.

2, No, 4, May 1983.

"See Kieschsuc, op, cit.

"See Bennet Harrison and Sandra Kanter, "The Political Economy of States'

job Creation Business Incentives," Journal of the American Institute of Plan.

nets, October 1978, p. 429

and residents to respond to the ever new social and economic contexts produced by rapid technological change.

The results of this analysis indicate that improving the

quality and access to education and training are a good

place to begin.

Clearly, the ability of communities to stimulate local

development depends in large part on the national economic climate. Macroeconomic policies which encourage

entrepreneurship and investment in all sectors of the

economy are a key ingredient for success in smaller geographic contexts. Policies aimed broadly at promoting

research, innovation, and investment in human capital

and at reducing impediments to economic growth would

increase total output and-thereby the potential for local

development.

Appendix B Bibliography

Appelbaum, Eileen, "'High Tech' and the Structut:al Unemployment of the Eighties," unpublished paper presented at

Annual Meeting of the American Economic Association,

Washington, DC: Dec. 28, 1981.

Bearse, Peter J., and Konopko, Deborah A., "A Comparative

Analysis of State Programs To Promote New Technology

Based Enterprise," in The New England Journal of Business and Economics, vol. 5, No. 2, spring 1979.

Bendick, Marc, Jr., and Ledebur, L C., "National Industrial

Policy and Economically Distressed Communities," in Public

Policies for Distressed Communities, F. S. Redburn and T.

F. Buss (eds.) (Lexington, MA: D. C. Heath, 1980).

Birch, David, "Generating New Job: Are Government Incentives Effective?" Commentary (Washington, DC: Council

for Urban Economic Development, July 1979).

Botkin, James, Dimancescu, Don, and Stata, Ray, "High Technology, Higher Education, and High Anxiety," Technology Review, October 1982.

Bradbury, K., Downs, A., and Small, K., Urban Decline and

the Future of American Cities (Washington, DC: The

Brookings Institution, 1982).

Browne, Lynn E., "High Technology and Business Services,"

New England Economic Review, July/August 1983.

Burggraf, Shirley P., "Overview and Critique of Revitalization

Issue," Policy Studies Review, vol. 2, No. 4, May 1983.

Burggraf, Shirley P.,4'Implications of Energy for Economic Development Planning," monograph, National Council for Urban Economic Development, July 1981.

Carlton, Dennis W., "The Location and Employment Choices

of New Firms: An Econometric. Model With Discrete and

Continuous Endogenous Variables," Review of Economics

and Statistics, September 1983.

Carlton, Dennis W., "Why New Firms Locate Where They Do:

An Econometric Model," interregional Movements and Regional Growth (Washington, DC: The Urban Institute,

1981). ...- Cooper, Arnold, and Bruno, Albert, "Success Among HighTechnology Firms," Business Horizons, April 1979.

Davis, Lester, "New Definition of 'High-Tech' Reveals' That

149
144 Technology, innovation, and Regional Economic Development

U.S. Competitiveness in This Area Has Been Declining,"

Business America, Oct. 18, 1982.

Freeman, C., et al., Unemployment and Technological Innovation (New Haven, CT: Greenwood Press, 1982).

Freeman, C., The Economics of Industrial Innovation (Cambridge, MA: MIT Press, 1982).

Glasmeier, Amy, Markusen, Ann, and Hall, Peter, "Defining

High Technology Industries," Working Note No. 1, Institute of Urban and Regional Development, University of California-Berkeley, March 1983.

Glasineier, Amy, Hall, Peter, and Markusen, Ann, "Recent

Evidence on High Technology.Industries' Spatial Tendencies: A Preliminary Investigation," Institute of Urban and

Regional Development, University of California-Berkeley,

unpublished draft, August 1983.

Greene, Richard, Harrington, Paul, and Vinson, Robert, "High

Technology Industry: Identifying and Tracking an Emerging Source of Employment Strength," New England Journal of Employment and Training, fall 1983.

Harrison, Bennett, and Kanter, Sandra, "The Political Economy of States' Job Creation Business Incentives," Journal

of the American Institute of Planners, October 1978, pp.

424-435.

Harrison, Bennett, and Bluestone, Barry, The Deindustrialization of America, (New York: Basic Books, 1982).

Hekman, John S., "The Future of High Technology Industry

in New England: A Case Study of Computers," New

England Economic Review, January/February 1980.

Hekman, John S., "Can New England Hold Onto Its High

Technology Industry?" New England Economic Review,

March/April 1980.

Hertsgaard, Mark, Nuclear Inc.: The Men and Money Behind

Nuclear Energy (New York: Pantheon, 1983).

Jackson, Gregory, et al., Regional Diversity: Growth in the

U.S., 1960-19X (Boston: Auburn Publishing House, 1981).

Joint Economic Committee, U. S. Congress, Location of High

Technology Firms and Regional Economic Development

(Washington, DC: U.S. Government Printing Office, June,

1982).

Kieschnick, Michael, Taxes and Growth: Business Incentives

and Economic Development (Washington, DC: Council of

State Planning Agencies, 1981),

Koch, D., et al., "High Technology: The Southeast Reaches

Out for Growth Industry," Economic Review, Federal Reserve Bank of Atlanta, September 1983, pp. 4-16.

150

Lawrence, Robert, Is Trade Deindustrialicing America ?" in

Brookings Papers on Economic Activity, 1:83, G. Perry and

G. Sunless (eds.) (Washington, DC: The Brookings Institution, 1983).

Lawson, Ann M., "Technological Growth and High Technology in U.S. Industries," Industrial Economics Review, vol.

1, No. 3, spring 1982.

Massachusetts Division of Employment Security: Job Market

Research, "High Technology Employment in Massachnsetts

and Selected States, 1975-79," Apr. 30,1981.

Massey, Doreen, and Meegan, Richard, The Anatomy of Job

Loss (London: Methuen, 1982).

Rees, John, "Regional Industrial Shifts in the U.S. and the Internal Generation of Manufacturing in Growth Centers of

the Southwest," in Interregional Movements and Regional

Growth, William C. Wheaton (ed.) (Washington, DC: The

Urban Institute, 1 1).

Pees, John, and Stafford, Howard, "A Review of Regional

Grow th and Industrial Location Theory: Towards Understanding the Development of High Technology Complexes

in the U.S.," paper prepared under contract for the Office

of Technology Assessment, May 1983.

Rostow, W. W., "Technology and Unemployment in the Western -World," Challenge, March-April 1981.

Rudman, B. J., "A Perspective on Manpower Issues in the High

Technology Industries of Massachusetts," New England

Journal of Business and Economics, winter 1981.

Teubal, M., "The R&D Performance Through Time of Young,

High-Technology Finns," Research Policy, vol. 11, No. 6,

December 1982,

Tomaskovic- Devey, Donald, and Miller, S. M., "Can HighTech Provide the Jobs?" Challenge, May-June 1983.

Tornatzky, Louis G., et al:, The Process of Technological Innovation: Reviewing the Literature (Washington, DC: National Science Foundation, May 1 3).

U.S. Congress, Office of Technology Assessment, Encouraging High-Technology Development, Background Paper No,

2, OT&BP-STI.25, March 1984.

Vaughan, Roger, and Sekera, June, "Investing in People," Policy

Studies Review, vol. 2, No. 4, May 1983.

Zarnowitz, Victor, and Lerner, Lionel, "Cyclical Changes in

Business Failures and Corporate Profits," in Business Cycle Indicators: Volume 1, Geoffrey Moore (ed.) (Princeton,

NJ: Princeton University Press, 1961).
APPENDIX C

Recent Evidence on High-Technology

Industries' Spatial Tendencies:

A Preliminary Investigation*

Introduction

In recent yearsamidst a serious recession, post-Depression levels of unemployment, and major plant closingslocal, State, and Federal policymakers have looked

to high-technology industries as the primary source of

new jobs. Efforts to attract high-technology employment

have resulted in a variety of job-creating strategies undertaken by all levels of government. As the Office of Technology Assessment's recent study shows,' 22 States have

some type of development program designed to create

and maintain new high-technology jobs. An interesting

finding of this study is that for a majority of States the

definition of high-technology industry remains easive,

resulting in incentive programs targeted toward any industry that creates employment.

The general assumptionthat a positive relationship

exists between high-technology industries and employment growthhas, with a few exceptions, gone untested

to date. In order to examine the incidence of hightechnology employment growth and locational tendencies, we developed a data base which is highly disaggregated on both a spatial and sectoral basis. Using the

1980 National Occupational Employment Statistics Matrix, which reports detailed industry occupational profiles, we selected a set of 29 three-digit industries which

had greater than the national manufacturing average of

scientific and technical occupations. These 29 industries

could be disaggregated into 100 four-digit sectors.

Using the 1972 and 1977 unpublished Census of Manufactures Plant Location tapes, we compiled county-level

four-digit industry tallies of manufacturing plants by

employment size category.' In conjunction with pe..blished employment data, we estimated industry employment levels from the tapes. Cross-checking with published national industry employment and plant totals

This report WAS prepared by Amy K. Glasmeier, Peter (.3. Hall, and Ann R.

Markusen, at the Institute for Urban and Regional Studies, University of

California-Berkeley under contract with ()TA. The development of the data

base and the majority of the descriptive analysis was done under contract with

the National Science Foundation, contract No. SES 82.08104.

'Teedwology Innovanba and Regional Economic Development Census of State

Government Inthati yes for High - Teanolngy Industrial DevelopmentBack

ground Paper (Washington, U.S. Congress. Office of Technology AssessMe nt , OTA. FiP-STI- 2 I, Ma v 1483).

'Employment site categories were used to estimate employment for individual

industries. No further analysis was ).--rfocnefl using the size categories,

confirmed the stability of our results: estimates across the

100 industries showed less than 1-percent variation between the two employment counts. Individual industry

employment estimates varied by less than 10 percent.

With this detailed data base we have been able to explore a number of commonly held views about hightechnology industries. Specifically, Nue have examined the

degree to which high-technology industries are homogeneous both in terms of their locational tendencies and

growth patterns. In this regard we have explored the degree to which these industries are in some way distinct

and tested the assumption that certain areas have garnered new high-technology employment growth. In addition we have examined the degree to which high-technology industry concentrations, and changes in them

over time, can be explained by characteristics of the labor force and metropolitan business climate.

In each phase of this research our basic hypothesis has

been that these industries exhibit much greater complexity, diversity, and ambiguity in their growth performance

and locational behavior, making generalizations more

hazardous than sanguine policymakers and the business

press would imply. For example, in simple growth terms,

one-third of the 277 metropolitan areas, as designated

in 1977, experienced net job loss in the 1972.7Z period.

In the sections that follow we take up each of these

issues in turn:

1. What is the nature and diversity among high-technology industries in both growth performance and

locational tendencies?

2. Which metropolitan areas exhibit the greatest hightechnology dependency and which have experienced

the gicatest rates of plant aad job change?

3. What spatial factors appear to be most closely associated with these industries?

The Growth and Locational Diversity

of High-Technology Industries

A major fault with the burgeoning literature on hightechnology industries is its tendency to treat them as a

rather homogeneous group. One of our fundamental hypotheses is that high-technology industries are exceptionally diverse in two ways. First, they vary widely in

plant and job creation potential. Second, they display

145

151
146 Technology, innovation, and Regional Economic Development

widely different locational patterns, both in degree of

dispersion and in tendencies toward increasing dispersion over time. A related hypothesisone that has prompted us to pursue a highly disaregated data analysisis that the constituent members of two- and three-digit

high-technology industries are dramatically different. The

documentation of these variations in growth and spatial

tendencies has crucial implications for economic development and planning. If substantial variation does exist, then high-technology job creati n strategies will have

to be finely tuned to individual sectors.

Growth Rates

High-technology industries produced widely divergent

employment growth rates over the mid- 1970's (see table

C-1). The best performers were sectors like Computers

and Petroleum Refining, which created total net new jobs

of 47,900 and 46,000 respectively. Smaller, innovative

sectors in the Scientific Instruments and Measuring and

Controlling Instruments fields posted even higher percentage job growth rates. For instance, the Fluid Meters

industry expanded at the phenomenal rate of 80.68 percent. These extraordinary performances are what both

policymakers and the press envision when they hanker

after high- technology economic development.

What is less popularly understood is the degree to

which growth in these sectors is in part countervailed

by job and plant losses in other high-technology sectors.

Among our set of 100,four-digit industries, a total of 34

actually declined in em; ioyment over the period, while

another 8 grew by less than the national manufacturing

average. Taken together, these relatively poor performers

constituted 42 percent of our industrial set, accounting

for 30 percent of total 1977 high-technoiogy employment.

Indeed, the better-than-average growth sectors had to

create 167,802 gross additional jobs to ensure a net gain

of 377,895 in all high-technology sectors.

Divergent growth rates are sometimes the product of

substitution of new commodities for older ones. In some

cases, these occur in quite different plants. For instance,

the deve!opment of robotics has resulted in some job

growth in computing equipment, electronics and even

service sectors like computer software displacing some

employment in machining sectors and other assemblytype production processes. In other cases, the substitutions are more closely related, even within three-digit categories. For instance, the growth of semiconductors is

directly linked to job loss in electronic tubes and transistors, as the product of the former displaces that of the

latter.'

`As we .hall see ix I; /w, these ,.fi +tint it, its arc ITTIF)Ortallt betause the ILK at

of sen,i,:uhluc tors is quite different from that of the produt it has repla;ed

interindustry Patterns of Dispersion

Similarly, the degree of geographical concentration is

quite distinct across high-technology sectors and varies

almost as much within industrial groupings as across the

entire set. In order to detect the variation among our

100 high-technology industries for both 1972 and 1977,4

the index provides a measure of the degree of dispersion

or concentration in each industry across all 3,140 counties in the United States for the given year. A value of

zero implies total spatial dispersion, while a value of

8.16 implies total concentration of the industry in one

county.' Entropy indexes were computed for both plants

and employment. Comparing the 2 years gives us a clear

indication of the tendencies toward further dispersion

or concentration among high-technology sectors.

Individual high-technology industries show extraordinary d,..grees of dispersion across U.S. counties. The

dispersio rankings differ slightly depending on whether

incidence of plants or employees by number of counties,

entropy index for plants, or entropy index for jobs, is

used (see table C-2). In 1977, the most highly concentrated industry in terms of employment was Miscellaneous Missile and Space Vehicle Parts (SIC 3769). Its nearly

10,000 jobs, concentrated in just 31 counties, gave it an

entropy index value of 6.22. A companion sector, Guided

Missiles and Space Vehicles (SIC 3761), posted the

highest concentration of plants, at 5.38, its 94,000 jobs

located in just 21 counties. On the basis of absolute

county incidence, the most highly concentrated industry was Tanks and Tank Components (SIC 3795), whose

12,000-plus jobs could be found in only 19, or less than

1 percent of all U.S. counties'

The industry with the most highly dispersed employment in 1977 was Fertilizers (SIC 2875). Its 12,000 jobs

were distributed across 469 counties. Its employment dispersion value was 2.65 and plant value was 2.08; both

were the lowest in the set. On a county-by-county basis,

Dies and Industrial Molds (SIC 3544) could be found in

he entropy index is calculated by the following formula:

where 1(0 is the overall entropy index of spatial ineyualitv fit

y is the share of the t°' county of all U.S. employment to industry i R is the

total number of counties in the United States = 1,140

Since the log of tern us undefined, this equation deals With the numerous , ases

of counties with no employment by setting the expression to the might of the

summation sign equal to:crn, since the hued portion will in each lase he mul

spited by a Sent, and an ondefmrd value times :en, is :etc,.

The entropy index was first developed by Theil (19t,7) and has been used 1s

lot ational researchers, primarily in Britain lKeehk, 1976: 25- Martin. 1872;

Chisholm and ()rpm). 1973; 34-N.

`The abs(illite values of the indexes are less interesting than their cuilnpiitatIvc

site of flis5 industries and c haniTs in them oer time 1.Ve would th,t xpe( I all

industries to be equally distributed across counties and not surprisingly.

the most ubiquitous of the set was to be found in only 74'4 out of the 3,140 wun,

ties in 1977, while secmid plae went cif an industry faund in 521 WU nix.

'An industry with the greatest ;mil-Ilse by number of cininties can be less

dispersed than others, as J the Lam- here, when its plants and or fobs remain

heavily ,onctuntratrci in oohs a few of those countic:
App. C-Recent Evidence on High-Technology Industries' Spatial Tendencies: A Preliminary Investigation 147

Table C1.High-Tsohnology industries Growth Performance, 1972.77

SIC industry name

1977

Number of

jobs

1977

Number of

plants

19" ...77

Net new

jobs

1972.77

Net new

plants

Percent job

change

Percent

plant

change

2812 Alkalies and chlorine 11,831 49 -1,500 1 -11.3 2.1

2813 Industrial gases 7,332 562 -2,100 59 -22.3 41.7

2816 Inorganic pigments 12,003 106 700 -8 -5.5 -7.0

2819 Industrial inorganic chemicals, n.e.c.a 78,192 564 15,000 180 23.3 46.9

2821 Plastic materials, syn. resins 57,111 397 2,400 74 4.4 22.9

2822 Synthetic rubber 11,538 63 -1,800 4 -13.5 6.8

2823 Cellulosic man-made fibers , 16,224 25 -1,100 7 -6.3 38.9

2824 Syn. organic fibers, ex. cellut 74,067 66 -4,200 5 -5.4 8.2

2831 Biological products 18,498 310 5,600 128 43.4 70.3

2833 Med. chem. botanical prod. 15,730 17? 6,600 37 72.3 26.4

2834 Pharmaceutical preparations 126,445 756 14,400 0 12.9 0.0

2841 Soap, other detergents 32,621 638 600 3 1.9 0.5

2842 Spec. cleaning, polishing prep 22,941 1,022 -3,000 -84 -11.6 -7.6

2843 Surface active finishing agents 6,851 175 -300 -3 -4.2 -1,7

2844 Perfumes, cosmetics, toilet prep. 50,775 693 2,7 0.48 5.6 7.4

2851 Paints, varnishes, lacquers, enamels 61,343 1,579 4, lit, -19 7.9 -1.2

2861 Gum, wood chemicals 4,717 119 - 1,1'.' -20 -18.9 -14.4

2865 Cyclic crudes, intermediates, dyes 35,499 191 7,500 17 26.7 9.8

2869 Industrial organic chemicals, n.e.c. 112,426 569 9,900 56 9.7 10.9

2873 Nitrogenous fertilizers 12,443 152 2,700 79 27.7 108.2

2874 Phosphatic fertilizers 15,704 91 -500 -54 -3.1 -37.2

2875 Fertilizers, mixing only 12,401 673 1,000 46 8.8 7.3

2879 Pesticides, agr. chem. n.e.c. 15,131 409 2,800 21 22.8 5.4

2891 Adhesives, sealants 16,647 573 1,800 110 12.1 23.8

2892 Explosives 11,546 97 -6,300 5 -35.3 5.4

2893 Printing ink 10,106 446 500 40 5.2 9.9

2895 Carbon black 2,601 31 -400 -6 -13.3 16.2

2899 Chem., chem. prep., n.e.c. 35,382 1,639 -1,800 34 -4.8 2.1

2911 Petroleum refining 102,398 349 46,000 26 80.1 8.0

3031 Reclaimed rubber 1,008 21 0 1 0.0 5.0

3482 Small arms ammunition 12,199 65 -3,600 3 -22.8 4.8

3483 Ammunition, except small arms, n.e.c. 20,589 81 -36,000 -14 -63.6 -14.7

3484 Small arms 17,495 112 1,400 30 8.7 38.6

3489 Ordnance, accessories, n.e.c. 19,042 89 -1,000 13 -4.9 17.1

3511 Steam, gas, hydraulic turbines 40,971 83 -5,400 8 -11.6 10.7

3519 internal combustion engines, n.e.c. 88,804 232 18,900 54 27.0 30.3

3531 Construction mach., equipt 155,129 922 21,500 175 16.0 23.4

3532 Mining mach., equipt 31,312 344 10,100 104 47.4 43.3

3533 Oil field mach., equipt.' 58,469 478 22,700 163 63.4 51.7

3534 Elevators, moving stairways 10,214 152 -4,800 -2 -32.0 -1.3

3535 Conveyors, conveying equipt 32,926 816 5,700 124 20.9 25.2

3536 Hoists, Ind. cranes, monorail syst 15,820 242 -500 54 -3.1 28.7

3537 Ind., trucks, tractors, trailers, stackers .. , . 28,383 475 3,000 95 11.7 25,0

3541 Mach. tools, metal cutting types 59,432 919 7,000 25 13.3 2.8

3542 Mach, tools, metal forming types 23,154 429 -400 46 -1.7 12,0

3544 Spec. dyes, die sets, jigs fix., Ind. molds . 106,175 7,152 7,800 536 7.9 8.1

3545 Mach. tool accessories, measur. devices . 54,177 1,412 7,400 181 15.8 14.7

3548 Power driven hand tools

,. 3547 Rolling mill machine equipment

27,667

8,529

124

83

.4,600

-2,500

36

16

19.9

-22.7

40.9

34.0

3549 Metalworking mach., n.e.c 19,086 534 5,800 141 43.7 35.9

3561 Pumps, pumping equipment 63,025 613 7,500 54 13.5 9.7

3562 Ball, roller bearings 50,286 149 -300 14 0.6 10.4

3563 Air, gas compressors 31,916 175 9,100 91 39.9 108.3

3564 Blowers, exhaust, ventil. fans 28,415 482 4,500 86 18.9 21.7

3565 industrial patterns 9,352 1,002 800 -19 9.3 -1,8

3566 Speed changers, ind., high drives, gears .. 24,572 327 -200 19 -0.8 -5,5

3567 Ind. process furnaces, ovens 16,260 327 1,600 61 10,9 22.9

3568 Mech. power transmission equip. n.e,c 32,564 226 4,800 71 17,3 45.8

3569 General Ind. mach. equipt. n.e.c 58,621 1,646 20,500 746 53.8 82.9

3573 Electronic computer equipment 192,510 932 47,900 332 33.1 55.3

3574 Cale. acc, mach. ex. elec. compt. equipt. . 15,474 64 -5,400 -15 -25.9 -19.0

fl

153
148 Technology, Innovation, and Regional Economic Development

Table C- 1.- High - Technology Ministries Growth Performance, 1972-77 (continued)

SIC industry name

1977

Number of

jobs

1977

Number of

plants

1972-77

Net new

jobs

1972-77

Net new

plants

Percent job

change

Percent

plant

change

3676 Scales, balances, ex. lab 6,738 103 400 6 6.3 6.2

3579 Office machines, n.e.c 42,398 218 7,900 3 22.9 1.4

3612 Power, distr. special transformers 43,360 279 -3,500 63 -7.5 29.2

3613 Switchgear, switchboard apparatus 72,211 668 2,800 100 4.0 17.6

3621 Motors, generators 96,951 447 6,600 22 7.3 5.2

3622 Industrial controls 56,408 728 4,100 143 7.8 24.5

3823 Welding apparatus, electric 17,409 176 2,000 10 13.0 8.0

3624 Carbon, graphite products 12,086 74 800 2 7.1 2.8

3629 Elec. Ind. apparatus, n.e.c. 16,490 223 4,300 35 35.3 18.6

3651 Radio, TV receiv. sets, ex. comm. types 74,639 581 -11,900 211 -13.8 570

3852 Phono records, pre-recorded MagTape 23,131 709 2,800 142 13.7 25.0

3661 Telephone, telegraph apparatus 124,345 264 -10,000 62 -7.4 30.7

3662 Radio TV transmit, signal, detect. equipt 333,006 2,121 14,900 350 4.7 19.8

3871 Cathode ray tubes, n.e.c. 36,808 146 -9,202 -8 -20.0 -5.2

3674 Semiconductors, related devices 114,011 545 16,400 219 16.8 67.2

3675 Electronic capacitors 28,647 118 1,300 5 4.8 4.4

3676 Resistors for electronic app. 24,918 101 800 5 3.3 5.2

3677 Resistors, electric apparatus 22,424 294 -3,500 54 -13.5 22.5

3678 Connectors, electronic apple. 26,020 133 7,900 42 43.6 46.2

3679 Electronic components, n.e.c. 125,998 3,118 25,400 1,276 25.2 69.3

3721 Aircraft 222,805 176 -9,100 8 -3.9 4.8

3724 Aircraft engines, parts 106,222 269 1,400 37 1.3 15.9

3728 Aircraft parts, auxiliary equipt., n.e.c 101,900 728 -200 34 -0.2 4.9

3743 Railroad equipt. 56,396 201 5,500 38 10.8 23.3

3781 Guided missiles, space vehicles 93,933 40 -24,400 -29 -20.6 -42.0

3764 Guided missiles, space veh. propulunits . 17,011 26 -2,200 -3 -11.4 -10.3

3769 Guided missiles, space veh. parts n.e.c. 10,189 42 -13,700 -6 -57.3 -12.5

3795 Tanks, tank components 12,122 24 6,500 2 115.6 9.1

3811 Eng., lab., scientific, research inst 42,178 786 5,697 47 15.6 8.4

3822 Auto., controls reg. resid., comm. env. apps 39,076 201 8,300 70 27.0 53.4

3823 ind. instr. measure, display 46,480 426 9,900 239 27.0 127.8

3824 Fluid meters, counting devices 18,032 111 7,100 50 79.5 82.0

3825 Instr. measuring, testing elec. elec. sigs 66,622 671 11,800 26 21.5 4.0

3829 Measuring, controlling devices, n.e.c 32,175 670 7,700 77 31.5 13.0

3832 Optical instru., lenses 29,883 545 11,200 51 59.5 10.3

3841 Surgical, medical inst. apparatus 43,206 651 8,700 145 25.2 28.7

3842 Orthopedic, prosthetic, surgical appl, 53,967 1,154 10,000 284 21.3 32.6

3843 Dental equipt., supplies 16,673 550 3,900 121 30.5 28.2

3861 Photographic equipt., supplies 111,568 780 15,700 156 16.4 25.0

afts c. -not eisewiefe classified

SOURCE: Office of Tectuf 4ogy Assessment

799 (or 25 percent) counties, and Miscellaneous Electronic Components (SIC 3679) in 521 (or 17 percent). However, in both these sectors the distributions of plants and

jobs were highly skewed among these few counties, so

that they exhibited less real dispersion with the entropy

index than did Fertilizers.

Several generalizations can be made about factors causing this wide range of spatial behavior. First, there appears to be little relationship between the size of an industry and its tendency to disperse. Some very large

employers are highly concentrated, while other sectors,

with only modest national levels of employment, are

quite dispersed. Scale economies within sectors probably

play a much greater role in the tendency for some to concentrate, irrespective of total industry size.

Second, industries appear to concentrate or disperse

by type of product, major customer and resource dependency. For instance, two types of industries are prominent

among the concentrators. Defense and space-oriented

high-technology manufacturers are extraordinarily concentrated. Of the 18 sectors with job entropy values

above 5.0 in 1977, eight produce weapons, tanks, spacecraft, airplanes and related parts. It is difficult not to draw

the conclusion that military and space spending patterns

and procurement practices have been a major contributor to the spatial concentration of an important segment

of high-technology sectors in a small number of counties.

A quite different set of industries which appear to be

highly resource oriented are also heavily concentrated

spatially. These sectors account for another seven of the
App. C-Recent Evidence on IlighTechnology Industries' Spatial Tendencies: A Preliminary Investigation 149

Table C- 2.- Dispersion indexes for High-Teehnology Industries

SIC Industry name

1972 dispersion

indexes for

1977 dispersion

Indexes for

Plants Employees Plants Employees

2812 Alkalies and chlorine 4.3 5.1 4.5 5.2

2813 Industrial gases 2.7 3.2 2.6 3.1

2818 Inorganic pigments 3.9 4.7 3.9 -4.5

2819 Industrial inorganic icals, n.e.c.* 2.8 3.7 2.6 3.7

2821 Plastic materials, tic resins 3.2 3.8 3.1 3.6

2822 Synthetic rubber 4.5 5.6 4.3 5.6

2823 Cellulosic manmade fibers 52 5.7 4.8 5.9

2824 Synthetic organic fibers, except cellulose 4.1 4.6 3.9 4.4

2831 Biological products 3.5 4.7 3.1 42

2833 -Medical chemical, botanical products 4.0 5.1 3.6 4.8

2834 Pharmaceutical preparations 3.2 4.0 3.1 3.9

2841 Soap, other detergents 3.3 4.1 3.3 4.0

2842 Special cleaning, polishing preparations 3.0 ' 3.8 3.0 3.7

2843 Surface active finishing agents 4.0 , 4.4 4.0 4.4

2844 Perfumes, cosmetics, toilet preparations 3.8 4.3 3.7 4.3

2851 Paints, varnishes, lacquers, enamels 3.1 3.6 2.9 3.4

2861 Gum, wood chemicals 3.5 4.8 3.7 4.5

2865 Cyclic crudes, intermediates, dyes 3.7 4.2 3.6 4.3

2869 industrial organic chemicals, n.e.c 3.0 3.9 3.0 3.8

2873 Nitrogenous fertilizers 3.9 4.3 3.3 4.0

2874 Phosphatic fertilizers 3.5 4.3 3.9 4.9

2875 Fertilizer's, mixing only 2.1, 2.7 2.1 2.7

2879 Pesticides, agricultural chemicals, n.e.c. 2.8\ 3.9 2.8 4.0

2891 Adhesives, sealants 3.5 3.9 3.3 3.7

2892 Explosives 3.9 5.0 3.8 4.7

2893 Printing ink 3.8 4.2 3.6 4.1.

2895 Carton black 4.8 5.2 4.9 5.3

2899 Chemicals, chemical preparations, n.e.c 2.8 3.3 2.8 3.1

2911 Petroleum refining 3.1 4.0 3.1 4.0

3031 Reclaimed rubber 5.3 8.1 5.1 6.0

3482 Small arms ammunition 4.2 5.7 4.2 5.8

3483 Ammunition, except small arms, n e c 4.0 4.6 3.9 4.8

3484 Small arms 4.3 5.5 '4.1 5.4

3489 Ordnance, accessories, n.e.c 4.0 5.4 4.1 5.3

3511 Steam, gas, hydraulic turbines 4.1 r - 4.0 5.2

3519 internal combustion engines, n.e.c. 3.7 3.5 4.5

3531 Construction machine equipment 2.5 3.%. 2.4 3.6

3532 Mining machinery, equipment 3.2 4.1 3.1 3.9

3533 Oil field machinery, equipment 4.5 5.5 4.2 5.3

3534 Elevatcgs, moving stairways 3.8 4.6 3.7 4.4

3535 Conveyors, conveying equipment 3.0 3.6 2.8 3.3

3536 Hoists, industrial cranes, monorail system 3.5 4.6 3.3 4.0

3537 industrial trucks, tractors, trailers., stackers 2.9 4.2 2.9 4.0

3541 Machine tools, metal cutting types 3.5 4.2 3.3 4.1

3542 Machine tools, metal forming types 3.8 4.3 3.3 4.2

3544 Special dyes, die sets, jigs fix., industrial molds 2.9 3.2 2.8 3.1

3545 Machine tool accessories, measuring devices 3.5 3.9 3.3 3.8

3546 Power driven ham tools 4.3 4.9 3.8 4.6

3547 Rolling mill machinery, equipment 4.6 5.6 4.5 5.4

3549 Metalworking machinery, n.e.c. 3.4 3.8 3.0 3.5

3561 Pumps, pumping equipment 3.0 3.5 2.9 3.5

3562 Bail, roller bearings 3.8 4.5 3.6 4.3

3563 Air, gas compressors 4.1 4.9 3.7 4.5

3564 Blowers, exhaust, ventilation fans 3.2 3.8 3.0 3.5

3565 Industrial patterns 3.1 3.5 3.0 3.4

3586 Speed changers, industrial high drives, gears 3.6 4.5 3.5 4.3

3567 Industrial process furnaces, ovens 3.7 4.3 3.8 4.1

3568 Machine power transmission equipment, n.e.c. 3.6 4.3 3.3 4.0

3569 General industrial machinery equipment, n.e.c. 3.0 3.4 2.7 3.0

3573 Electronic computing equipment 3.8 4.3 3.7 4.1

155'
150 Technology, Innovation, and Regional Economic Development

Table .-01sperelon Indexes for High-Teoluiolooy Industries (continued)

1972 dispersion

indexes_for

1977 dispersion

indexes for

SIC Industry name Plants Employees Plants Employees

-3574 Calculating, accounting machines except elec. computing equip. 4.2 5.4 4.4 5.3

3576 Scales, balances, except laboratory 4.1 5.1 3.9 4.7

3579 Office machines, n e.c. 3.7 4.8 3.7 4.9

3812 Power, distr. special transformers 3.3 4.2 3.2 4.0

3613 Switchgear, switchboard apparatus 3.2 4.0 3.0 3.7

3621 Motors, generators 3.1 3.4 3.0 3.3

3822 industrial controls 3.2 4.3 3.1 3,9 3823 Welding apparatus, electric 4.0 4.6 3.8 4.7

3624 Carbon, graphite products 4.3 5.4 4.4 5.3

3829 Electrical industrial apparatus, n.e.c. 3.8 4.4 3.6 4.3

3651 Radio, TV receiver sets, except comm. types 3.7 4.6 3.3 4.4

3652 Phono records, prerecorded MagTape 4.4 4.7 4.3 4.7

3661 Telephone, telegraph apparatus 3.6 4.4 3.6 4.4

3882 Radio/TV transmitting, signal detection equipment 3.1 3.7 3.0 3.6

3871 Cathode ray tubes, n.e c 4.9 6.0 3.8 4.9

3674 Semiconductors, related devices 3.9 4.7 3.7 4.8

3875 Electronic capacitors 3 4.0 4.2 4.2 4.4

3877 Resistors, electric apparatus 3.7 3.8 3.6 3.9

3878 Connectors, electronic appliances 4.5 5.0 4.2 4.7

3679 Electronic components, n.e.c. 3.1 3.4 3.0 3.4

3721 Aircraft 3.9 5.2 4.0 5.1

3724 Aircraft engines, engine parts 4.0 4.9 3.9 4.8

3728 Aircraft parts, auxiliary equipment, n.e.c 4.2 4.5 4.0 4.6

3743 Railroad equipment 3.8 4.7 3.5 4.7

3761 Guided missiles, space vehicles 4.9 5.6 5.4 5.7

3674 Guided missiles, space vehicles, propulsion units 5.0 5.5 5.2 5.6

3769 Guided missiles, space vehicles parts, n.e.c. 4.9 5.5 4.9 6.2

3795 Tanks, tank components 5.3 6.4 5.2 6.1

3811 Engineering, laboratory, scientific research Institutions 3.3 3.8 3.1 3.8

3822 Auto. controls for regulating residential commercial environment 3.8 4.9 3.5 4.7

3823 Industrial Instr., measure, display 4.0 4.9 3.5 4.4

3824 Fluid meters, counting devices 4.3 4.9 3.8 4.8

3825 Instr. measuring, testing elec. elec. sags 3.4 4.1 3.3 4.0

3829 Measuring, controlling devices, n.e.c 3.3 4.4 3.2 4.3

3832 Optical instruments, lenses 3.6 4.4' 3.4 4.2

3841 Surgical, medical instruments, apparatus 3.3 3.8 3.2 3.7

3842 Orthopedic, prosthetic, surgical appliances 2.9 3.6 2.7 3.5 3843 Dental equipment, suplies 3.6 4.2 3.3 4.1

3861 Photographic equipment, supplies 3.7 5.1 3.5 4.8

an.a.c.-not elsewhere classing/4.1,

NOTE: With this data, an Index of 8.2 implies total concentration, an Index of um Implies total cliocoefsion,

SOURCE. Oissmafor, et al.

top 18 highly concentrated industries and run the gamut

from oil field machinery to raw materials-related corn

modifies like carbon products, alkalies and chlorine, synthetic rubber, and manmade fibers., In some cases, such

as oil field machinery, it is demand from a resource sector that has drawn the industry to a small set of locations. In others, it is the supply of a raw material or a

primary processing sector input which seems to account

for its concentration.

In both these two types of sectors, the "age" of the industry appears to have less to do with its locational concentration than its product type and client relationships.

In addition, plants in these industries are often quite large

scale in nature, either because their product is immense

(aircraft, missiles) or because continuous-process technology facilitates very large economies of scale. Therefore,

a few big plants serve most of the market.

At the other end of the spectrum, a set of relatively

mature, producer goods sectors dominate the rankings.

Many of these highly dispersed sectors produce heavy

or bulky material inputs for a relatively dispersed set of

industrial or agricultural consumers (fertilizers, paints,

chemicals). Others produce custom-made equipment for

relatively dispersed industrial users (dyes, motors, electronic components, conveyors). In both instances, market orientation seems to be drawing the industry.

In the middle range lie many of those high-technology

industries which are most innovative and fastest grow156
App. CRecent Evidence on High-Technology Industries' Spatial Tendencies: A Preliminary Investigation 151

ing. Computers, semiconductors, biological products,

measuring devices, industrial controls, optical instruments, and machine tools are all only moderately dispersed compared to the average for all high-technology

industries. This may be a function of youth and the need

to cluster in certain places in order to watch competitors,

draw upon secondary business services and a skilled labor pool, and be close to the centers of action. Yet it

is still true that sectors like semiconductors could be

found in a total of 182 counties in 1977, and computers

in 203, suggesting that decentralization of at least some

innovative high-technology production activities was

fairly advanced.

A final type of interindustry variation is the extent to

which the counties hosting high-technology plants and

jobs in a particular sector are closely associated

geographically. One measure of this is the degree to

which county incidence corresponds to the Standard

Metropolitan Statistical Area (SMSA) incidence. (Of

course, this comparison is complicated by the fact that

some SMSAs have more counties on average than

others.) The second column in table C-2 shows the number of SMSAs in which each industry is represented. The

lowest is for Carbon Black, which was produced in only

10 SMSAs in 1977. This same sector ranked 6th in

county concentrations; the implication is that the counties in which it is found are more apt to be included

within the same SMSAs in contrast to other highly concentrated sectors found in few counties but proportionately more SMSAs. The industry to be found in the

most SMSAs is Dyes and Industrial Molds (SIC 3544),

which occurred in 209 SMSAs in 1977. The defense and

space-related sectors discussed above showed a high degree of concentration in a few metropolitan areas: Guided

Missiles and Space Vehicles and Parts (SICs 3761 and

1764) were found in only 18 SMSAs, while Tanks (SIC

3795) are produced in only 12.

intraindustry Patterns of Dispersion

Sectors normally grouped together in two- and threedigit industries showed quite dramatic variation among

them in degrees and rates of dispersion. There were very

few three-digit industries whose constituent members had

similar entropy index values. The least variation was to

he found in two groups. First, relatively similar rates of

quite high concentration were found in the space and

defense-related sectors: Ordnance (SIC 348), Aircraft and

Parts (SIC 372), and Missiles (376). Second, the Measuring and Control Devices category (SIC 382) had relatively

similar dispersion patterns among its five component subsectors. However, other types of scientific instrument

subsectors had quite disparate patterns.

A few examples will illustrate the extent of these variations within broad groupings. Within Industrial Inorganic

Chemicals (SIC 281) subsectors, Alkalies and Chlorine

were highly concentrated while Industrial Gases were

quite dispersed; of our 100 sectors, the former ranked

84th in degree of job dispersion while the latter was 2d.

Within Metal Working Machinery (SIC 354), Dyes and

Industrial Molds was the third most dispersed hightechnology sector in our set, while Rolling Mill Equipment was 91st. Among Electronics sectors, Semiconductors ranked 78th while Miscellaneous Electronic Components ranked 6th. The point is that one four-digit

sector is often very unlike another sharing the same umbrella designation.

Changes in.Pattems of Dispersion, 1972-77

Over the period studied, there has been an overwhelming tendency for both plants and employment to disperse

across U.S. counties. In only 14 of our 100 sectors did

the plant entropy index increase, indicating cases of greater concentration. The job entropy index increased in 22, .

or about one in five, sectors. If anything, the degree of

differentiation among industries increased. Almost universally, dispersed sectors became even more dispersed,

while the highly concentrated ones were as apt to intensify their plant concentrations as they were to disperse.

More specifically, the vast majority of sectors which

did increase their plant concentrations fall into the two

groups mentioned abovemilitary-related sectors and

bulk materials processors. Here, the forces originally

working toward concentration seem to be continuing

that trend. This is particularly striking in the military

equipment and supplies case, where tanks, small arms

ammunition, guided missiles, space vehicles and parts,

aircraft, and aircraft parts were found among the minority of sectors that intensified either plant or job concentrations.

Product cycles theories suggest that the more innovative and fast-growing sectors tend to cluster initially

around a few sites of entrepreneurial initiative and specialized labor pools. Once the product is standardized

and market penetration becomes a dominant business

strategy, jobs will tend to disperse toward users. Once

markets are saturated and competition becomes intense,

production jobs may reconcentrate in the lowest cost

locations, far from the original centers of production.?

Although our data base does not permit us to test these

hypotheses in the appropriate longitudinal form, a number of cautious insights can be gleaned from the comparison of dispersion tendencies over this short period.

'For a full development of this evolutionary model and its spatial impliL glans,

see Ann R, Matkusen, "Profit Cytles, Oialigopoly and Regional Transformation."

Working raper No, 397, Institute of Urban and Regional !kyr lopment. Univer

arty of California, Berkeley, January 1983.

157
152 Technology, Innovation, and Regional Economic Development

Generally, the more innovative, growing hightechnology sectors tended to disperse along with other

sectors. However, variations within these groups appear

to be greater than for other groups. In a few cases,

employment continued to concentrate at existing sites,

supporting a product cycle interpretation which

hypothesizes concentrationin initial stages. Semiconductors is art outstanding example. While plants dispersed

in the 1970's, and the industry increased its incidence

from 120 to 182 counties, jobs actually became more concentrated spatially. Several other electronic aectors also

became more concentrated (resistors, telephone equipment). Most other rapid-growth sectors showed a modest tendency toward decentralization from original

growth centers.

The only other sectors which showed a tendency to

increase locational concentration rather than to disperse

were a set of relatively highly automated process sectors,

whose initial locations were already relatively dispersed.

Examples are industrial inorganic chemicals, pesticides

and agricultural chemicals, cosmetics, phosphatic fertilizers, and finishing agents. The tendency to reconcentrate here is often a result of rationalizationselective

plant closings and relatively larger scale plants in new

or existing locations.

A Summary of Industry Growth

and Locational Diversity Findings

The extraordinary degree of diversity across and among

industries in growth and locational tendencies vindicates

our hypotheses about large differences and supports our

choice of four-digit SIC categories as appropriate for spatial analysis. Growth rates range from the phenomenal

(110 percent) to the negative. While some high-technology sectors are found in as few as 10 metropolitan areas,

othersoften with lower numbers of total employees

can be found in more than I00even 200SMSAs.

Product type, client characteristics, and production process seem to he stronger determinants of comparative

dispersion patterns than maturity of the industry, national employment size, or three-digit SIC grouping. (The

lack of similar entropy values for industries in the given

years is not an adequate test of a product lifecycle

hypothesis, which argues that jobs and plants in any one

industry disperse over time; cross-sectional comparisons

of industries at different degrees of maturity are not a

good proxy for a longitudinal comparison.)

Most high-technology sectors did disperse over the

I 970's period studied, even when their growth rates were

modest. Exceptions (i.e., sectors which became more concentrated by 197? than in 1972) were in military and

space-related manufacturing, sectors with highly automated production processes, and occasionally, highly innovative sectors such as some types of electronics. In addition, a few declining high-technology sectors became

more spatially concentrated as a result of spatially selective rationalization.

The Range of High-Technology Presence

Across Metropolilan Areas

A central hypothesis of our work on high-technology

industries is thatwhether measured by plant and job

incidence, growth rates, or significance as a portion of

the economic base of individual regional or metropolitan

economiesthey are quite complex and diverse from

place to place, The most commonly cited example of a

fast-growing, "high-tech" center is Sili on Valley, corerminant with the San Jose SMSA. In fact, our research

indicates that Silicon Valley is an extraordinary place,

the exception rather than the rule by almost any measure. Generalizations about places that lack or have experienced negative growth rates in high-tesimology plants

and jobs are also difficult. New York did turn out to be

one of the most dramatic losers, but others in this position were surprises. But in both growth and decline cases,

some definite answers about the size, region, and type

of SMSA most likely to lead the group can be given.

To explore our contentions about complexity, we developed profiles of the 10 highest and 10 lowest metropolitan performers. We used the 264 SMSAs as defined

in 19778 for this analysis, because a county-by-county

analysis was beyond our present resources and because

metropolitan comparisons would convey more about the

patterns of high-technology spatial change to the lay

reader. Over 80 percent of all high-technology employment was located in these metros. Five measures of hightechnology industry incidence were computed: the ratio

of high-technology to areawide employment in 1977, the

percent change in plants, the percent change in employment, the net absolute change in numbers of plants from

1972 to 1977, and the net change in absolute numbers

of jobs for the same period. While we computed these

values for all SMSAs, we have chosen to analyze comparatively only those on the tail end of the distribution,

believing that the extremes give the best shorthand picture of the range of high-technology experience.

158

'Although there were 277 SMSA in 1977, six were not part of this analysts.

The SMSAs in Puerto Rico were not included as well as Burlington. VT, and

Cheyenne, WY. These latter two were excluded because metropolitan socioeconomic data Weft not available for them. The remaining six were New England

SMSAs which were collapsed into New England County Metropolitan Areas.

New England States present a particular problem when trying to aggregate

up from counties to SMSAs. in a number of cases, Northeastern SMSAs are

composed of noncontiguous counties, cities, and townships. To simplify spatial

analysis and aggregation of data on places, New England county metropolitan

areas were instituted. This research uses the Bureau of Economic Analysis 1977

definition of IsIECMAs. These include: Batton, MA, Bridgeport, CT. Hartford,

CT New London, CT, Worcester, MA, and Fail River, MA.
App. CRecent Evidence on High-Technology Industries' Spatial Tendencies: A Preliminary Investigation 153

Before looking at each set of leading gainers and losers,

it is worth summarizing briefly our findings on the basis

of size, region, and type of metropolitan area. For the

latter, we have grouped the leaders in three categories:

1) big-city SMSAs (those which focus on one of the traditional top 20 central cities); 2) adjacent, or suburban,

SMSAs (those which are contiguous to one of the previous class); and 3) independent SMSAs (those which fall

into neither of the previous two classes).

First of all, we found that big-city SMSAs dominate

the rankings of metros with the greatest absolute job loss.

Paradoxically, the same type of SMSA dominates the

rankings for absolute plant gain. As a result, we found

anomalies such as the fact that the Los Angeles SMSA

was ranked among the top 10 job losers but was also

found among the top 10 plant gainers. 1 Adjacent, or suburban, SMSAs along with t4e'newer

big-city SMSAs, are relatively more prominent fn the category of absolute job gain. This implies that these types

of SMSAs, like Anaheim and San Jose, CAaand Welleester, MA, are attracting bigger size plants thannider bigcity SMSAs. But in terms of absolute plafit losses, neither big-city nor suburban SMSAs make much of a showing among the leading ranks. Big numbers of plant closings are surprisingly concentrated in small and

medium-sized independent SMSAs like Lansing, MI,

Johnstown, PA, and Muncie, IN.

But if bigcity SMSAs are most prominent among the

places that account for the greatest absolute gains and

losses, they do not dominate the rankings of percentage

change. Here, small to medium-sized places are remarkably strong. For instance, in the percent gain in employment, not one big-city SMSA shows up in the top 10

and only one, Santa Rosa, CA, is suburban. Among

leading percent job losers, places like Eau Claire, WI,

Decatur, IL, and Gadsden, FL, crowd out larger places.

Only Miami among the big-city SMSAs shows up with

a leading net job decline rare. The implication is that

high-technology gains and losses are proportionately

much more dramatic and perhaps, traumatic, for these

medium-sized, mostly detached SMSAs.

What "., also quite clear is that the most high-technology dependent places, i.e. those with the greatest percent of their labor force in high-technology industries,

are also the smaller and medium-sized SMSAs. Only San

Jose, CA, among major metro areas shows up as having

a high ratio. The other top places are not apt to be those

people think of as high-technology dominated. Furthermore, the leading high-technology dependent centers are

more apt to be found in the Midwest and South than

in other parts of the country.

Regionally, other anomalies turn up. Texas, which is

often thought of as a high-technology State, has three

of the least high-technology dependent SMSAs in the

159

countryi.e., Killeen, McAllen, and Laredo. Frostbelt

locations do account for the worst absolute plant losses,

but Sunbelt SMSAs like Los Angeles and Miami are

prominent among the biggest job losers. Furthermore,

some Frostbelt big-city SMSAs are leading percent plant

gainers, like Minneapolis, Chicago, and Detroit. Southern and Midwestern SMSAs are more prominent among

percent job losers than Northeastern SMSAs. Nor did

States host high-technology growth evenly across their

metro areas. All of these points underscore the degree

to which spatial generalization is a hazardous task.

A final summary point is that the presence of high,

technology activity does not ensure an area an expansionary future. Over the 1972-77 period, fully one-third

of the 277 metro areas in the U.S. lost jobs. On average,

these job losses were larger in magnitude than were aver,

age job gains.

HighTechnology Employment Ratio

High-Technology Dependence: The Top Ten.

The high-technology employment ratio consists of total

high-technology employment divided by the metro labor force for each metro area. It is a measure of relative

high-technology industry dependence across all metropolitan areas. The higher the ratio, the more prominent

high-technology jobs are in the a real employment base.

Given popular impressions of high-technology leaders,

we expected to find places like Boston, San Jose, and several Texas SMSAs high on this list. And, we expected

most to be found in Sunbelt, particularly Southwest,

locations.

In fact, the places with highest and lowest high-technology dependence are a highly geographically diverse

group, as table C-3 shows. Among the top 10, only San

Jose fits the popular notion of a high-technology center.

Knowledge of the metropolitan industrial base and the

degree of military-government influence help explain this

diverse group of 10. For example, the extraordinary hightechnology dependence of Melbourne-Titusville, FL, is

largely due to Cape Canaveral and the NASA Kennedy

Space Center, as well as other nonspace, military-related

facilities located there. In another case, Wichita, KS, is

a small aircraft production center as well as the location

of Boeing and Lockheed's plane storage and maintenance

center. This diverse group of metro areas are not

predominantly concentrated in one region at the expense

of another. The Midwest and South account for six of

the top 10 locations. Nor were the least high-technology

dependent places a homogenous group.

High-Technology Dependence: The Bottom

Ten.The 10 metro areas with the lowest high-technology ratio were all Sunbelt and non-Northeastern locations. Several, like Grand Forks, ND, and Great Falls,
154 Technology, innovation, and Regional Economic Development

Table C- 3. -SMSAs With the Highest and Lowest HighTechnology Ratio, 1977

Highest percent Lowest percent Rockford, IL 0.1 17 Killeen, TX 0.00043 Melbourne-Titusville, FL 0.17956 Columbia, MO 0.00054 Wichita, KS 0.17870 Grand Folks, ND-MN 0.00081 San Jose, CA 0.17355 Pueblo, CO 0.00102 Binghamton, NY 0.15219 Anchorage AK 0.00118 Lake Charles, LA 0.14670 Clarksville. TN-KY 0.00173 Cedar Rapids, IA 0.14600 Honolulu, t. 0.00228 Bloomington, IN 0.12915 Great Falls, MT 0.00328 Johnson City, TN-WV 0.12817 McAllen Pharr, TX 0.00385 Longview, TX 0.12702 Laredo, TX 0.00398

Median 0.04364

SOURCE: Office of Technology Assessment.

MT, are predominantly agricultural centers. Anchorage's

economy is wrapped up with natural resource extractionrelated activities. Honolulu, HI, is a combined trade,

administration, and tourist center, while Columbia, MO,

is primarily a big university town. These latter two cases

demonstrate that educational and State government

centers do not necessarily ensure an even modest dose

of high-technology employment. Whereas no State was

represented more than once in the top 10 metro areas,

Texas has three of the 10 metro areas with the lowest

high-technology ratio.

Percentage Changes in Plants, 1972.77

Rankings by percentage change in plants also yield an

unexpected array of high-technology locations. Mediumsized noncentral city metros predominate in both top and

bottom rankings. Regional incidence is somewhat skewed

on a Sunbelt-Frostbelt basis. table C-4 presents the top

10 gainers and losers of high-technology plants in the

mid-1970's.

Percent Plant Gainers: The Top Ten. --A set of

non-Northeastern medium-sized metros dominates the

percent plant winner category. The use of percentage

changes highlight places with small high-technology bases

in the first period. With the exception of Oxnard, CA,

which had 55 plants in 1972 and added 63 more by 1977,

the top 10 locations all began with bases of less than 23.

As a group, these areas with the highest percentage increase in plants are nun- big -city SMSAs. Several, such

as Oxnard and Santa Cruz, CA, are adjacent to other

large metro areas such as Los Angeles and San Jose. Half

are in the Sunbelt, while the remainder are predominantly Midwestern.

Percent Plant Losers: The Bottom Ten.-The metro

areas with the greatest percentage loss in plants present

a striking contrast to the plant winners. Eight of the 10

are older Midwestern and Northeastern industrial metropolitan areas. Again, ar with the winners, the relatively

small economic base of these areas explains the significance of their losses. Four of the 10 locations had less

than 20 plants in 1972. A loss of two or three plants may

register as a 10- to 20-percent decline. All the top percent plant losers are thus medium-sized metro areas outside the urban core. As we shallsee below, none of these

percent plant losers was among the top 10 in percentage

employment loss. This implies that the average plant dosing was smaller than in other, larger, metropolitan areas.

Table C-4.-The Top Ten Gainers and Losers of HighTechnology Percent Plants Change, 1972.17

Winners

Percent

change

Number of plants

Losers

Percent

change

Number of plants

1972 1977 1972 1977

Lawton, OK 603.00 1 6 Elmira, NY -34.62 26 17 St. Cloud, MN 214.29 7 15 Anchorage, AK -20.00 10 8 Laredo, TX 150.00 2 3 Pensacola, FL -20.00 15 12 Santa Cruz, CA 137.50 16 22 Muncie, IN -18.00 50 41 Champaign-Urbana, IL 118.18 11 13 Kokomo, IN -17.86 28 23 Oxnard, CA 114.55 55 63 Johnstown, PA 16.13 31 26 Fort Myers, FL 110.00 10 11 Altoona, PA -14.29 14 12 Billings, MT 100.00 7 7 Terra Haute, IN 14.29 35 30 Cedar Rapids, IA 100.00 23 23 Williamsport, PA 14.29 21 18 Panama City, FL 100.00 3 3 Pine Bluff, AR -12.50 8 7

Median 18.18 11

SOURCE: Wilco of Technology Assessment
App. CRecent Evidence an High-Technology Industries' Spatial Tendencies:A Preliminary Investigation 155

Percentage Changes in Employment

Percent Job Growth: The Top Ten.SMSAs with

the greatest percentage growth in employment were generally suburban, medium-sized metros rather than either

smaller non-adjacent, non-big-city associated places or

large central city metros. All of the 10 areas with the

greatest percentage increase in high-technology employment are located outside the industrial Northeast (see

table C-5). This group encompasses sites of both new

high-technology growth, as well as expansion in existing

locations. But percent gain dois not necessarily imply

the greatest number of new jobs. As we shall see below,

with the exception of Lakeland, FL, none of the top percent gainers scored in the top 10 in terms of absolute

job gain. I4owever, unlike plant gainers, a metro did not

have to be small in order to post healthy high-technology

job growth rates. Santa Rosa, CA, Lakeland, FL, Lubbock, TX, and Savannah, 'GA, all had big hightechnology job bases tp the first period. The most significant local growth rates in high-technology employment

did not occur in the smaller locations but rather in suburban medium-size metro areas.

Percent job Loss: The Bottom Ten.Approximately one-third, or 86, metropolitan areas had net hightechnology employment losses between 1972 and 1977.

The net employment losers are relatively less geographically concentrated than job gainers: four of the 10 were

in the South and four were in the Midwest. As we shall

see below, three of the top 10 losers were also among the

top 10 losers in absolute employment: Decatur, IL, Miami, FL, and Parksburg, WV-OH. Rankings by percentage change underplay the extent ofabsolute job loss taking place in a number of locations. For example, Miami

lost almost half its high-technology employment base c.r

8,000 jobs, during the 5-year period. But its percent loss

was smaller than that of Brandenton, FL, which lost a

net 310 jobs in the same period.

Net Changes in

High-Technology Employment

Absolute Job Gain: The Top Ten.A total of 180

SMSAs, or about two-thirds, had absolute increases in

high-technology employment. The median increase was

248 employees. As seen in table C-6, the ensemble of the

top 10 metro areas with the greatest absolute change in

high-technology employment comes closest to resembling

the popular notion of high-technology centers. The top

five ranking is predictable with San Jose (Silicon Valley)

leading the group, followed by Anaheim, Houston, San

Diego, and Boston. The remaining locations present two

surprises: Oklahoma City, OK, and Lakeland, FL.

Regionally, two Boston-area SMSAs prevent the

Frostbelt from completely losing out. SMSAs in Texas,

California, Florida, and Massachusetts dominate net job

gain rankings.

Absolute Job Loss: The Bottom Ten. A total of 86

SMSAs, or one-third, lost high-technology employment

between 1972 and 1977. The median employment loss

was 741 employees. The State of New York was the biggest loser, with the metros of New York and Syracuse

posting a combined loss of 14,491 jobs. Two Sunbelt

metros, Miami and Los Angeles, also suffered significant

losses during the period. The group of losers is relatively

homogeneous when contrasted to the winners in that

they are predominantly older big-city industrial

metropolises. In three cases, Miami, FL, Decatur, IL, and

Parksburg, WV-OH, absolute losses in employnient

translated into large negative percent changes as well.

Eighty percent of the largest high-technology losers can

be loosely characterized as older Northeastern industrial

cities. But the inclusion of Los Angeles and Miami metros

in this group shows that adverse high-technology loss is

also a phenomenon in mature Sunbelt big-city SMSAs.

This is true despite big job gains in surrounding metro

areas in both California and Florida.

1

Table C5.The Top Ten Gainers and Losers of High-Technology Jobs, Percent Employment Change, 1872.71

Winners

Percent

change

Number of plants

Losers

Percent

change

Number of plants

1972 1977 1972 1977

Lawton, OK 2,266.97 5 130 Columbia, MO 82.48 181 28

St. Cloud, MN 1,265.66 85 1,173 Eau Claire, WI 67.29 2,728 889

Boise, ID 729.31 73 607 Newport News, VA 54.51 5,257 2,391

Santa Rosa, CA 360.58 834 3,842 Parkersburg, WV-OH 51.11 7,188 5,564

Lakeland, FL 266.69 3,095 11,228 DecatLi, IL 455.85 6,827 5,090

Lubbock, TX 237.44 1,294 4,366 Bradenton, FL 55.83 691 381

Topeka, KS 237.35 175 594 Clarksville, TNKY 41.09 141 83

Laredo, TX 220.84 36 117 Grand Forks, ND-MN 40. 62 37

Savannah, GA 204.78 2,783 8,482 Miami, FL 40.38 16,306 9,723

McAllen PharrEdinburg, TX , . 181.54 110 311 Gadsden, AL 39.87 1,225 78

SOURCE. Office of Technology Assessment.

161

36-737 O 84 12 Qt. 3
158 Technology, Innovation, and Rockmal Economic Deftly Mont

-A

Table CAThe Top Ten Net Employment Winners and LOWS, 1972.77

Winners Losers

San Jose, CA

Anaheim, CA

Houston, TX 4 I 31,909

30,612

C leseca

New York, NY

Philadelphia, PA

Cleveland, OH

-8,975

-8,588

-8,170 San Diego, CA 16,782 Miami, FL -6,584

Boston, MA 15,173 Syracuse, NY -5,521

Dallas, TX 12,067 Baltimore, MD -4,245

Worcester, MA 9,893 Jersey City, NJ - 4,082

Oklahoma City, OK 8,363 Parkersburg, WV-OH -3,684 Lakeland, FL 8,132 Los Angeles, CA -3,220

Phoenix, AZ 7,978 Decatur, IL -3,130

Median gain 248 Median loss 740

SOLACE: Office of Technology Assessment.

Net Changes in HighTechnology Plants

Net changes in high-technology plant location offer all

approximate measure of the location of new hightechnology growth. This group strongly resembles those

places with the most significant net employment changes,

though there are several anomalies. To begin, the median net change in plants across both gainers and losers

was nine additions. Most metros gained plants-214 out

of 264. Another 17 places experienced no plant change,

while 33 others had net plant losses.

Absolute Plant Gain: The Top Ten.Half of the top

10 plant gainers matched their prominence in job gain:

Anaheim, San Jose, Dallas, Houston, and Boston. However, another five did not gain employment in proportion to new plants. Los Angeles, for instance, was the

ninth largest job loser even though it was the second

largest plant gainer. This suggests that Los Angeles is still

hosting the growth of small, experimental or specialty

high-technology plants while losing out in the competition to maintain larger scale, more standardized operations.

Three new entrants to the top 10 high-technology

gainers are in this group: Detroit, Minneapolis-St. Paul,

and Chicago. Two of the three are particularly surprising: Detroit and Chicago. In all three cases, it appears

to be smaller plant growth that accounts for higher plant

than job gains. Detroit's performance must be weighed

against losses in the auto industry, which is not included

in the high-technology set. Furthermore, our findings are

limited to the mid-1970's period, prior to the worst

reverses in the auto industry.

Absolute Plant Losers: The Bottom Ten.The top

net plant losers, with the exception of Port Arthur, TX,

are predominantly Northeastern and Midwestern. As

seen in table C-7, New York shows the biggest plant loss

with three of the State's 10 metropolitan areasNew

York, Albany-Schenectady, and Elmiralosing a net

total of 175 plants. Among this group the magnitude of

loss differs dramatically: New York metro lost 159 plants,

almost 10 times as many as the next loser, Jersey City,

NJ, with 17. Thus within regions and across States, trends

may be of different orders of magnitude.

State and Regional Comparisons

of Net Employment Change

Popular discussions of high-technology growth often

treat certain States and regions as winners or losers, implying a high degree of homogeneity within them. Our

Table C-7.The Top Ten Net Maid Winners and Losers, _108

Winners Losers

Anaheim, CA 484 New York, NY -159

Los Angeles, CA 387 Jersey City, NJ -17

San Jose, CA 339 Elmira, NY -9

Dallas, TX 278 Muncie, IN -9

Chicago, IL 224 Albany-Schenectady, NY -7

Houston, TX 204 Port Arthur, TX -7

Boston, MA 191 East Lansing, MI -7

Minneapolis, MN 158 Wilmington, DE -6

San Francisco, CA 151 Johnstown, PA -5

Detroit, MI 145 Kokomo, IN -5

Median gain 9

SOURCE: Mlles of Technology Assessment.

'162
App. CRocant Evidence on High-Technology Industries' Spatial Tendencies: A Preliminary investigation dr r5?

data indicate that this generalization is not warranted, Table C.8.Ragkmal Comparison of Metro Area

particularly on a State-by-State level. A number of sup- Net Employment Change

posedly high-technology States, such as Texas, California, and Massachusetts, show extraordinary internal

variation in plant and job change across metropolitan

areas. Table C-8 ranks States on the basis of proportions

of SMSAs experiencing positive high-technology job

growth. In California, for instance, five of its 17 SMSAs

posted job losses in high-technology sectors between 1972

and 1977. Similarly, in Massachusetts, two of its five

SMSAs were high-technology losers.

A brief glance at the regional distribution of hightechnology metro employment loss underscores the distinct variability of high-technology employment change

described above. However, by imposing regional groupings there is a modest support for generalizations about

broad interregional divergence as seen in table C-8. Using

a loose regional breakdown which puts Texas in the

South and the Plains States in the Midwest, the four regiorts stack up as expected, with the largest number of

metro net employment losers concentrated in the Northeast, followed by the Midwest. Contrary to the literature

on regional antagonism, however, the West rather than

the South had the_fewest number of metropolitan losers,

though the difference is small. If we were to classify Texas

in the West rather than the South, the difference would

be considerably greater. Given these findings, perhaps

the war between the States breaks down on an East-West

rather than a North-South axis.

Why High-Technology industries

Are Where They Are

A third major question is, what explains the existing

array of high-technology economic activity and what

forces are redistributing it across the country? We have

demonstrated that there is indeed great diversity in hightechnology job and plant patterns and that dramatic

shifts in location have occurred in the 5-year period studdistributions and

metropolitan

areas

Employment

loss

Plant

loss

West

Arizona

5

0

Cor lo

2

Q

141110

lo

80 1 0

Idaho 1 0

Nebraska 1 0

,Nevada 2 0

South Dakota 1 0 .

Utah 2 0

Alaska 1 1 California 17 5

Montana 2 1 ew Mexico 1 1 Oregon 3 1 Washington 5 2

North Dakota 0 0

Wyoming 0

west totai 44 11

0

Percent metros that lost employment ..0.25

South:

Mississippi 3 0

Oklahoma 4 0

Alabama 8 2

Florida 16 5

Georgia 6 3

Kentucky 3 2

Louisiana 7 2

North Carolina 7 2

South Carolina 3 1 Tennessee , 5 3

Texas 25 3

Virginia 6 2

West Virginia 4 1 Arkansas 3 0

South tots! 97 26

Percent metros that lost employment - 0.26

Michroot

Illinois 9 3

Indiana 12 7

Iowa 7 1 Kenna 3 0

Pater:0211 12 4

Minnesota 8 1 Missouri 4 2

Olik, 14 6

Wisconsin 8 4

Midwest total 75 28

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

000

0

0

11

10

0

0

0

3

10

0

7

2

4

0

2

2

0

12

1 13

ied. Explaining these changes in them Percent metros that lost employment - 0.37

was the final task of our project.

The literature on high-technology industries suggests

that a set of locational characteristics are most apt to attract high-technology activity. Some of these factors are

common to all manufacturing; others are assumed to be

unique to this group of industries. In constructing a

model of high technology attractiveness and job/plant

shifts, we included five sets of variables intended to represent the major forces hypothesized in the literature.

First, we modeled in three features of labor supply;

wage rates, unionization rates, and area unemployment Percent metros that lost employment - 0 48

rates. We expected to find the first two negatively cone- SOURCE. Moo of Tochnology Assessment.

lated with high-technology job and plant shifts and the

latter positively correlated, on the supposition that high

Northoost

Connecticut 4 1 0

Delaware 1 0 1 Maine 2 1 0

Maryland 2 1 0

Massachusetts 5 2 1 New Hampshire

New Jersey

18

0

5

0

2

New York 10 6 4

Pennsylvania 12 7 3

Rhode island 1 0 0

111141110111 0 0 0

Northeast total 46 c3 11

163
158 Technology, Innovation, and Reg leinal Economic Development

wages, labor* militance, and tight labor markets. all

discourage high-technology job growth.

Second, we modeled in a set of three business climate

features: presence of specialized business services, presence of first-rate research facilities and talent, and incidence of defense spending. We expected that high leyels, of business services, research talent, and defense spending would all be positively related to net high-technology

job and plant gains as well as to high levels of hightechnology dominance in regional economies.

Third, we added a sit of basic infrastructure features:

transportation networks, both air and highway, and utility rates. We expected to And that access to superior freeway and airport facilities would positively contribute to

high-technology job and plant shifts, while relatively high utility rates would discourage them.

Fourth, we modeled in a set of amenities features which

are widely believed to be particularly attractive to a hightechnology labor force, particularly the professional/tech- nical personnel and entrepreneurs. These included avail- ability of a superior cultural environment, reasonable

housing prices, a relatively unpolluted atmosphere, a mild

climate, good schools for children and post-secondary

educational options. In each case, we expected hightechnology plant and job growth to be related to a

positive ranking.

Finally, we included a set of socioeconomic variables

which we suspected might be related to high-technology

growth and enclaves based on case study work in California. These included the percent of minority workers in

the population, to which we expected high-technology

performance to be negative!- related, and the proportion of relatively conservative voters in the population, which we expected to be positively related. A list of the

precise formulation of each of these variables, and their

data source, appears at the end cf this appendix.

In order to capture high-technology spatial patterns, which is what we wished to analyze with these metropolitan features, we formulated three different endogenous,

or dependent, variables. First, we constructed a ratio of

high-technology dependency (discussed in the previous

section) which captured the degree to which individual

SMSAs were highly or not at all dependent on hightechnology industries for their vitality. Second, we constructed an employment change variable by simply subtracting the total employment for each S?vISA for 1972

from that for 1977. Because we had a large number of

cases, especially at the disaggregated industry-by-industry

level, of no high-technology employment in the initial

period, we decided not to employ percent changes as a

method of measuring job shifts. (We considered throwing out all such cases, but thii would bias our sample; however, a preliminary run to compare the results indicated that we would not significantly increase the degree of explanation by using percentage changes.)

Third, we constructed a similar variable for analyzing

plant shifts. Because in these latter two cases we would

be regressing absolute job and plant shifts on a number

of relative (e.g., per-capita defense spending) measures,

we corrected for the influence of sheer size of SMSA by

adding in on the right-hand side, as an explanatory variable, total size of the labor force. This variable would

pick up the effects of differential sizei.e., it would account for the fact that a large SMSA like San Jose could

be expected to post more absolute job growth than a

small one tike Grand Forks, ND.

Methodology

Although we had a number of general hypotheses guiding the regression analysis, given the large number of pre- sumed explanatory variables, we chose to use a stepwise

regression procedure, rather than specifying a complete model before hand. Stepwise regression allows the re- searcher to explore the relationship between the independent and the dependent variables. In the context of

this project we used a forward stepwise inclusion procedure supported by the SPSS statistical package for the Social Sciences. Using this procedure, the computer selected variables in descending order based on preexisting statistical criteria which we specified. The order of inclusion using this procedure is determined by the

respective contribution of each variable to explained

variance. In other words, the variable that explains the

greatest amount of variance unexplained by the variables

already in the equation enters the equation at each step.

Three parameters were set to guide the inclusion of in-

, dependent variables in our regressions (N size, F statistics, and T tolerance). N size refersto the number of variables to be included in the equation. We specified that

any or all of the 19 variables could enter the equation

as long as they met the remaining criteria.

The second parameter, F, relates the F ratio computed

in a test for significance of a regression coefficient. In stepwise regression, at each step of the analysis, F ratios are

computed for variables not yet in the equation. This pa- rameter was set to the default as prespecified by the statistical program.

The third parameter, T, refers to user-specified toler- ance. The tolerance of an independent variable being

considered for inclusion is the proportion of the variance of that variable not explained by the independent variables already in the equation. This parameter was set to

allow all variables into the equation in which the proportion of variance not explained by other independent variables exceeded 0.1 percent. In this analysis, we chose

to apply rather liberal statistical c:iteria in order to ensure that the maximum number of variables would be

included in the individual industry and aggregate regres- sion results.

164
App. CRecent Evidence on High-Technology Industries' Spatial Tendencies: A Preliminary Investigation 159

Explaining the Geography of Aggregate

HighTechnology Industries

In order to test for association between aggregate hightechnology activity and the characteristics of metropolitan areas described above, we ran three regressions. First,

we looked for relationships between metro features and

the 1977 high-technology ratio. The results from this

regression tell us something about what features high

technology- dependent metros display. Second, we looked

at r employment change over the period 1972-77. This

regression tells us which features of metro areas are most

strongly associated with positive and negative job shifts.

Third, we regressed net plant change over the same

period on the same set of variables, to find out the direction and strength of relationships dominating plant shifts.

Factors As , ! With

High-Technoiugy Dependence

Only five metro features were found to be significantly

associated with high-technology jobs as a proportion of

area labor force. Together they explained only 18 percent of the variation across the 218 metros for which,we

had data for all 19 independent variables. The single

strongest factor was defense spending, which displayed

the expected r e correlation with heavy high-technology dent a.e. It accounted for one-third of the explained variaoon. Percent Hispanic was the second most

important factor; high-technology dependence was negarively related to the presence of this group in metro

areas. Percent black was similarly negatively correlated

with high-technology dependence, which also confirmed

our hypothesis. Both these findings suggest that heavy

reliance on high-technology is not associated spatially

with minority populations. This finding is interesting in

light of the fact that minority composition was not significant in either the aggregate or individual cases of

change in high-technology plants and jobs.

The final two factors which were significant in explaining high-technology dependence were industrial utility

rates and unemployment. In each case, the variable explained less than 2 percent of the total variation. However, both had signs opposite of what might be expected.

Strong high-technology dependence was positively related to high utility rates and low unemployment rates.

We think that in the case of this particular dependent

variable, that high-technology activity may he creating

jc.)bs, therefore lowering unemployment, rather than industries migrating to areas with surplus labor.

Explaining the Geography

of High-Technology Job Shifts

Our results for the regression in which employment

change over the period 1972-77 was regressed on the set

of independent variables were somewhat better than for

high-technology dependence in 1977. Overall, we were

able to explain 29 percent of the variation across 218

metro areas. Nine, 01 exactly half, of our variables contributed to this explanation, although sometimes the

direction of the relationship was unexpected.

Housing prices, freeway density, and per-capita defense

spending together accounted for half of the explained variation. Defense spending was positively related to job

shifts, as expected. However, the other two variables

showed the opposite of the predicted signs. Housing

prices were positively associated with high-technology job

shifts, rejecting the hypothesis that high housing prices

drove high-technology jobs away from certain areas in

the period studied. This finding might change if we used

a different year to compute housing price, although we

would have to assume that the relative differentials in

housing price across metros changed in order to make

this exercise meaningful. More likely, high-technology

job growth places upward pressures on housing prices,

reversing the causal relationship implied in the model.

The negative relationship found between freeway density and high-technology job growth may simply be the

result of a misspecification probiern, on which we speculate in the final section.

Six other factors each contributed less than 3 percent

apiece to explaining total variation. Several of these

unionization rates, percent black, educational options,

and 1977 labor forceall had the expected signs. High -

technology job shifts were negatively related to high levels

of unionization arid to large proportions of blacks, and

positively related to metro educational options and the

size of the 1977 labor force. The relatively low degree

of explanation added by this last variable, suggests that

a great deal of high-technol.ueob growth is not happening proportionately to sir of place. Some degree of

multicollinearity reduced the significance of the estimates

for the minority and educational options variables when

the full set of independent variables were included in the

regression.

Two additional variables, the arts index and the pollution index, both contributed tc explaining high-technology job shifts, but both had aexpected signs. Hightechnology job growth was negatively associated with a

high rating on the arts index, suggesting that the supermetros which have the greatest cultural advantages are

165
160 Technology, Innovation, and Regional Economic Development

not attracting net job growth in high-technology industries. A high pollution rating was positively correlated

with high-technology job growth, suggesting that poor

industrial air quality is not a significant deterrent to new

high-technology job growth in the aggregate. This may

be because many high-technology industries are "light"

in nature and do not add much to environmental degradation.

Explaining the Geography

of Aggregate Plant Shifts

The regression relating net change in high-technology

plants to the features of metro areas yielded much better results. Almost 68 percent of total variation was explained by nine of the place characteristics. Six of these

were the same ones which turned up in the job change

regression, with the same signs in every case. However,

their order of importance was quite different.

Size of the labor force in 1977 explained fully 26 percent of all net plant change. This simply means that absolute net gains in plants were greaftst in places where the

labor force was largest. This was to be expected, since

it was our way of dealing with the impossibility of using

percentage changes in plants as the dependent variable.

The second most important factor was presence of Fortune 500 headquarters, which explained an additional

24 percent of the variation, but in the opposite direction than that expected. Metropolitan areas which were

large net plant gainers are those which lack corporate

headquarters, while those metropolitan areas which do

host them have lost out in high-technology plant shifts.

This suggests that the bulk of high-technology manufacturing plants are not linked spatially to corporate headquarters functions. While this would not be surprising

for manufacturing in general, it is somewhat contrary to

popular impressions of the location of innovative sectors.

Of course, as we shall see below, this variable does indeed exert a strong attractive force on a minority of sectors taken individually.

The rest f the variables which were significant in this

regression all contributed only modest amounts tn explaining total variationin every case, less than 3 percent. The arts index was again negative, house prices

positive, and freeway density negativeall opposite to

the model's presuppositions, just as was true in the job

change regression. Defense spending, percent black, and

unionization rates were all significant and showed the

expected sign. Educational spending made its first showing in this regression, but it had an unexpected negative

sign. High-technology plants are not as a whole moving

toward places with relatively high per-capita school

spending.

Summarizing the Aggregate Findings

Overall, only two variables are consistently significantly

related to different measures of high-technology locational patterns. Per-capita defense spending and percent

black population contribute to explaining high-technology dependence as well as changes in jobs and plants over

time. In each case the characteristic yielded the expected

sign. Comparing job and plant shifts alone, labor force

(which was not included in the high-technology dependence equation) and unionization rates were also significant and in the expected direction. Three unexpected

influenceshousing prices positively, freeway density and

arts index negativelyalso turned up as significant for

explaining both plant and job shifts. Pollution and educational options helped to explain job shifts but not plant

shifts, while Fortune 500 headquarters and educational

spending per capita were significant for plant but not job

change.

However, the most important fact to underscore in

summarizing these results is that overall they are not very

impressive. Discounting the labor force variable, which

is really an attempt to convert absolute to relative

changes, in none of these cases did the total degree of

variation explained by significant variables exceed 37 percent (see above). While this is on average quite good for

cross-sectional analysis, we were unable to explain the

remaining variation in high-technology spatial patterns.

While our measures of the features of places may be imperfect, due to data limitations, the relatively poor results

must be taken seriously because the phenomenon we are

trying to explainhigh-technology spatial activityis

captured with extraordinary richness of detail on both

an industry and a place basis in our analysis. We believe

that these results offer strong support for our view that

individual high-technology industries are highly heterogeneous and display quite disparate spatial tendencies

which can only be understood by analyzing disaggregated

sectors.

Furthermore, great caution should be used in referring

to those characteristics which did turn out to be significant as "determinants" of high-technology location.

While per-capita defense spending did turn out to be

positive, significant, and present in all three regressions,

it is important to remember that it accounts for only 6,

4, and 2 percent of total variation respectively. The coefficients on unionization rates are similarly significant and

negative, but contribute less than 2 percent in each case.

Other than labor force, Fortune 500 headquarters is the

only variable which explains more than Id percent of

total variation and it appears only in the plant shift

results. Since it is unexpectedly negative, it tells us much

less than we would like. Knowing that corporate headquarters are more app to be negatively rather than positively associated with net plant change doesAaQt give us
App. C-- Recent Evidence on High-Technology Industries' Spatial TOMXICIOS: A Preliminary Investigation 161

a great deal of insight into why high-technology plants

locate where they do.

Explaining the Locations; Tendencies

of individual High-Technology Sectors

The results of the four-digit industry regressions show

a great deal of variationboth across industries and

across dependent variables. Using net employment

change as the dependent variable, only 29 of the 100 industries had explained variation greater than 20 percent.

Of these, labor force accounted for more than 10 percent of the explained variation in 12 of the 29. Since o"

results for net plant change were better than the rather

poor results using employment change, we focus in the

following discussion on the former. It portrays the model

in the best possible light.

Contrary to our original expectation, moving to a more

disaggregated sectoral level did not produce universally

better results with our independent variables. While regressions were computed for all 100 industries using plant

change as the dependent variable, 39 industries had less

than 20 percent explained variation and are consequently

not reported here. On the whole, the remaining industry regressions were less informative than 'expected; in

all but four cases less than 51 percent of the variation

was explained. These four exceptions were General Industrial Machinery, 3569 (R2 .7721), Instruments 3823

(R2 .719), Electronic Components, N.e.c. 3679 (R2 .700),*

and Industrial Chemicals 2899 (R2 .532). In all but Industrial Chemicals, 2899, labor force explained more

than 50 percent of the variation.

Among these four industries all were consistent across

significant independent variables. In the cases where labor force was highly significant, arts were negatively correlated. In three of the four industries, Fortune 500 headquarters was also negatively correlated, suggesting that

net plant change was away from large metro areas. House

spending was significant and positive in three of the four

industries. A third variable, major university, was also

unexpectedly negativeproviding yet more reinforcement for the negative major metro association with these

industries. .In two of the four industries, both climate and

percent black were significant and in the expected direction. On the whole, this group of four industries showed

surprising similarity in factors associated with spatial

location.

A second group of industries with between 40 and 50

percent explained variation lacked the degree of consistency found in the previous group. In three-quarters of

these 14 industries, more than half of the explained variation was accounted for by labor force. In the four indus-

*-"tti.e.k ." is an abbreviation for "not elsewhere classified."

tries where labor force accounted for less than 25 percent of the explained variation; the direction of the

variable loadings were consistent across three of the four.

Fortune 500 headquarters were strongly negative in all

'four industries.** In three of the four industries, 3841,

3675, and 2851, airport access was significant but in the

wrong direction in two of three cases. The same sign

reversal was found in the pollution varial,1Yone industry, Paints and Varnishes 2851, had the expected sign,

but Electrical Capacitors showed a negative relationship.

In two of the four cases, Surgical and Medical Instruments 3841 and Electronic Capacitors 3675, industrial

utility rates were negatively associated with plant difference.

The lack of explained industry variation was disappointing and suggests that high-technology industries

individually may be responsive to locationsl factors different from those commonly assumed. The lack of explanation led us to carefully examine those industries

popularly thought of as "high-tech" to see if the attributes

most commonly assumed to be associated with high-technology industry growth held for at least this set. By

"popular" we mean industries which have a shared set

of characteristics: high employment growth rates in the

current period; high proportions of professional and technical personnel in the labor force; and large R&D expenditures as a proportion of industry sales. Seven in-;

dustnes were chosen to report on hereComputers 3573,

Semiconductors 3674, Biological Products 3831, Telecommunications Equipment 3661, Missiles 3761, Aircraft Engines 3724, and Engineering Instruments 3811.

On the whole, the results for these high-technology

industries were less illuminating than expected. In only

one industry, Computers, was more than 40 percent of

the variation explained. In this industry, 23 percent of

the explained variation is accounted for by labor force.

In four of the seven industries, labor force was strongly

positive and significant; in the remaining three, labor

force was negatively related. This negative relationship

mar in part be explained by the relatively dispersed

nature of these industries as seen in the entropy index.

In this case, smaller as well as big metros have earned

large net plants additions.

Somewhat unexpectedly, more than five of the seven

"popular" high-technology industries appear repelled by

metropolitan forces. That is, these industries were not

attracted to places that had positive ratings on the Fortune 500, Arts, and Labor Force variables. As with the

aggregate industry regression, four of the seven industries were negatively related to Fortune 500 headquarAs suggested in the discussion of the aggregate industry regressions. this im.

plied that these four industries are not associated with super metropolitan areas.

This supposition is reinforced by the negative relation -ship between the arts in

dex in two of the four industries.

167
162 Technology, Innovation, and Regional Economic Development

ters. Only Missiles had a positive and significant relationship to this variable. A third variable accounting for

significant variation within industries was defense spending. Two of the three industries had the expected sign:

Telecommunications and Computers, with explained variation of 10 and 2 percent respectively. Missiles showed

defense to be negatively related to plant change. This

negative relationship can be explained in part by suggesting that missile employment, which declined overall

during the period ( 65 percent), has diminished precisely

in those areas where defense spending remains high.

While both the business press and selected State policy analysis suggest that high-technology industrial location is motivated by low housing prices, in general this

was not borne out in this analysis. In three of the seven

industries, house spending was positive and strongly significant. House spending was the single most important

variable in explaining Engineering Instruments, accounting for 16 percent of the explained variation.

Another popular notionthat there is a positive relationship between high-technology industry location and

education opportunitywas not found to be consistently

significant across the industries examined. As originally

hypothesized, educational options were positively associated with only three of the seven industries. And

whereas we expected that industries such as Semiconductors and Computers would be particularly sensitive

to the availability of educational resources, the individual

industry results imply that, relative to other variables,

educational options were not significant. They were significant and positive, however, in explaining Biological

Products, accounting for 16 percent of the variation.

These results suggest that popular notions of what influences high-technology location are not universally

borne out by individual industry regressions. Perhaps in

the case of house spending and educational options, high -

technology plant changes are independent of these influences because individual mobility compensates for

them.

At the outset we expected that detailed four-digit industry regressions would be particularly amenable to explanation using the place-based variables included here.

To our surprise the results, despite the detailed nature

of the data base, were not concluF:ve, and in a number

of cases the explained variation was in the opposite diree

don to that hypothesized. These results suggest that to

understand the location of this very heterogeneous group

of industries, disaggregated industry and place-based analysis is the more appropriate means,

For example, the model used here clearly ignores char -

acteristics associated with places such as geographically

concentrated entrepreneurship, which we know from

other eVident.c to explain the location of high-technology

industry in a number of popular high-technology centers

such as Phoenix, AZ, and San Jose, CA. Despite the lack

of explanation found in the industry regressions, the lack

of significance associated with variables such as unionism

and wage rates confirms our expectation that all industries face these factors and that it is only the marginal

case which uses such individual variables to make the

location decision.

A Review of the Performance

of the Explanatory Variables

The results of the individuallour-digit industry regressions offer us a chance to remark on the incidence an

direction of influence of our independent variables. Overall, we can throw out approximately half of our variables

as almost universally lacking in significance. Among the

rest, rankings can be constructed on the frecrency and

consistency of their influence on individual hightechnology industry shifts.

insignificant Factors

First of all, we can eliminate 10 of our 19 independent

variables from consideration at the disaggregated level.

In the 61 industries where we could explain more than

20 percent of total variation, these 10 variables were significantbut in only 5 percent or fewer cases did they

contribute more than 2 percent to the explanation. This

was true for both job and plant regressions. These variables are listed in table C-9.

Among these, several exhibited the expected sign quite

consistently in those few cases where they did show up.

Unionization was generally negatively related, while climate, percent Republican, and educational options were

almost universally positive when present.

The extremely poor showing in individual industry

cases of variables such as unionization rates, educational

options, educational spending, and percent black underscores the fact that the sum is often more than, or different from, its parts. Each of these appeared at least once

as significant in the regressions on aggregate job and plant

shifts. Educational options might be explained by its relatively high rate of correlation with two other variables

airports and arts both of which showed up frequently

Table C-9.Variables Insignificant in Explaining

High.Technology Industry Locations

Educational spending

Industrial utility rates

Manufacturing wage

Unionization rate

Climate index

Unemployment rate

Percent Republican

Percent Hispanic

Educational options

Percent black

SOURCE Office of Technology Assessernenl

168
App. CRekent Evidence on High-Technology Industries' Spatial Tendencies: A Preliminary Investigation 163

in the case of individual industries. However, in the cases

of the other three, we must assume that they often act

as relatively minor influences critical for only a very few

industries.

The composition of this set of 10 poor performers suggests some tentative speculations about the relative importance of our original groups of characteristics. The

labor force variables (wage rates, unionization rates, and

percent unemployed) have all been eliminated. The socioeconomic variables (percent black, percent Hispanic, and

percent Republican) have also been eliminated. Half of

the "amenities" variables also disappear. Overall, this suggests that labor-associated features are not important determinants of high-technology locational change, at least

not for explaining change in the period studied. Perhaps

they might be more important since 1977, but we will

have to await publication of the 1982 Census ofManufactures for confirmation. A possible interpretation of

this finding is that in this era of high labor mobility,

employers find it relatively easy to create a labor force

in locations to which they are drawn for other reasons.

People, in other words, readily follow jobs in high-technology sectors rather than vice versa.

On the other hand, several of these variables may be

better discriminators of high-technology changes within

metro areas and between metro and nonmetro sites. We

anticipate, for instance, that educational spending,

unionization rates, percent Republican and percent

minority are more important in explaining whether hightechnology industries locate in inner cities versus suburban or exurban areas than they are across metros. In

other words, they may determine intermetropolitan,

rather than intermetropolitan, shifts. Similarly, we

believe that educational options, wage rates, and industrial utility rates may explain more of the variation across

metropolitan, small town, and rural locations than they

do among metros. Confirming these hypotheses lay beyond our current resources and, in the latter case, available data.

The Most Common Factors Associated

With Metropolitan High-Technology Shifts

Nine of our independent variables turned up frequently

and contributed more than 2 percent to total explanation in the 61 plant shift regressions. These variables,

listed in descending order of frequency, are listed in table

C-10. This table also ranks the same variables in descending order on their support for the hypothesized sign of

the modeled relationship. Those at the top of this list

consistently confirmed the expected relationship, while.

those at the bottom consistently reversed the expected

direction of association. Those in the middle are variables

which, while often strong determinants, had contradictory effects on different industries.

Labor force, as expected, was the most commonly confirmed associate of absolute plant gain. Thus, in large

metropolitan economies plant gains were generally proportionately large, all else being equal. However, in a

minority of cases (21 percent), the opposite was true; in

these sectors, metros with large labor forces experienced

relatively smaller gains or negative plant shifts compared

to smaller economies. For the most part, these exceptions

occurred in heavy industries such as paints, carbon black,

ammunitions, motors, industrial patterns, and electrical

industry apparatus. However, several relatively new and

fast-growing sectors also fell into this group: Space Vehicle Parts 3769, Engineering 3811, Laboratory and Scientific Instruments, and Measuring Instruments 3823. Further qualitative research might be done to explain why

these sectors are attracted to metros with smaller labor

forces.

Fortune 500 was another frequent contributor to individual industry locational explanation. But as in the

aggregate case, the relationship was negative in the majority of cases (72 percent). It was the exceptional hightechnology industry that was attracted or maintained by

Fortune 500 headquarters metros. Several of these are

clearly business service-relatede.g., Photographic EquipTable C-10.Most Commonly Occurring and Consistency Ranking of Variables Explaining Net Plant Change

Most common rank°

Frequency

(number of Cases) Consistency onsistency rankingb

Percent cases

sign expected

Labor force 1977 48 Airports (+) 90%

Fortune 500 36 Defense spending (4-) 88

Arts index 22 Labor force (+) 79

Major universities 14 Major universities (4-) 57

Pollution index 13 Pollution () 38

Housing prices 13 Fortune 500 (+) 28

Freeway density 11 Arts (+) 27

Airports ranked 10 Freeway density (+) 0

Defense spending ) 8 House prices () 0

The frequency rating shows the number of successful Individual Industry regressions (N -131) In which this variable was a significant contributor to total explanation.

Variables which were significant at the 0.10 level.

bPercentages here show the percentages of cases in which the variable displayed the expected sign In the regression.

SOURCE Office of Technology Assessment.

169
164 Technology, Innovation, and Regional Economic Development

ment 3861, and Office Machines 3579. But machining

sectors, such as Industrial Patterns 3565 and Metalworking Machinery 3549, were also strongly oriented toward

Fortune 500 cities, perhaps because both produce relatively custom-made items that are often designed in corporate headquarters. Harder to explain are the orientations of the heavier industries of Petroleum Refining 2911,

Cyclic Crudes 3865 and Ammunition 3483 to Fortune

500 metros. Perhaps a few places like Houston, San Francisco, and Los Angeles explain the oil-related cases. But

more importantly, the rest of our high-technology sectors seem to be repelled by Fortune 500 headquarters

cities.

The third most frequently appearing factor was the arts

index. It was quite similar to the Fortune 500 variable

in having an unexpected negative loading in 73 percent

of the cases. Since the arts index is also an expression

of supermetro statusindeed, its correlation with Fortune 500 is the highest in the setwe speculate that it

is picking up the same spatial tendency. That is, a rich

cultural environment is not a significant pull factor for

high-technology plants and is more apt to be associated

with other features repelling (or inviting shutdowns of)

high-technology plants.

Two additional factorsmajor university and pollution

indexwere frequent contributors to explanation but

with contradictory effects on different sectors. Major universities had the expected positive effect on net plant

change in only 57 percent of significant regressions. These

were a heterogeneous group of industries which might

be associated in some way with university research labs.

They included synthetic fibers, industrial patterns, and

measuring and control devices, which might be spawned

by or drawn to the research labs of chemists, engineers,

and medical schools,respectively. The sectors which were

repelled by university atmospheres include special cleaning substances, explosives, miscellaneous machine tools,

and driving gears. The interpretation here might be that

these sectors require few innovations and no other characteristics associated with major universities.

Pollution is the other variable with strong dualistic patterns. It is a significant discourager of net plant change

in only 38 percent of the cases. These included what appear to be particularly obnoxious conversion processes

such as oil refining, ordnance production, special cleaning substances, and the production of industrial trucks

and tractors. On the other hand, high levels of industrial pollution were not a deterrent toin fact were positively associated withnet plant additions in calculators,

semiconductors, engineering lab instruments, fluid meters, and other measuring devices. These are all relatively

light manufacturing processes and thus would not be discouraged from locating in relatively poor industrial airquality areas. However, missiles and machine tools also

fall into this category. It may be the case that other factors associated with environmentally taxed industrial

metros are so critical to these industries that they will

locate there despite the potential increase in degradation

and despite high environmental cleanup compliance

costs.

Four other variables are relatively common contributors to explaining sector change, although they occur in

fewer cases than those described so far. Every one of

them, however, has a purer record for consistent sign

than do those occurring more frequently. Two, airport

access and defense spending, have a very consistent incidence of positive association with high-technology plant

growth-90 and 88 percent respectively. On the other

end of the spectrum, freeway density and housing prices

both had a universally perverse sign. Freeway density,

which was expected to be positively correlated with hightechnology growth, was, negative in every case where it

was a substantial contributor to an industry's explained

plant shift. As noted above, this may be a problem with

the variable's construction. Housing prices were universally positively associated with high-technology plant additions, a fact we have already attributed to a reversal

in the causal relationship.

In sum, the set of independent variables performed

quite disparately in the individual industry regressions.

Only three (airports, defense spending, and labor force)

were both frequent significant contributors and consistent in supporting the model's original suppositions. Two

others, freeway density and housing prices, were frequent

contributors, but consistently contradicted the model's

hypothesized direction of impact. Four othersmajor

university, pollution, Fortune 500, and the arts index

were often significant but operated quite variably in both

repelling and attracting high-technology plant shifts. The

performance of each in explaining job, rather than plant,

shifts was relatively similar although much smaller in

magnitude.

These more powerful factors in explaining hightechnology locational change fall much more frequently

into the broad categories of conditions of doing business

(Fortune 500, major university, and defense spending)

and transportation and communications (freeway density, airports ranked) than do those associated with the

labor force. The amenities variables that mattered (arts,

housing price, and pollution) were among the most highly

contradictory or consistently unexpected in sign; another

fact that reinforces the speculation that it is not employeerelated features that draw high-technology industry but

other factors that influence profitability, on both the

supply and demand side.

Although some high-technology industries are increasingly having a hard time surviving in places where housing prices are relatively high and transportation is con17'
App. C-- Accent Evidence on High-Technology industries' Spatial Tendencies: A Preliminary Investigation 165

gested, as is the case in Silicon Valley, this may be a

relatively freak or precocious case. Or, the agglomerative

needs of a new, highly innovative industry may keep it

in a supermetro even though some of its costs, direct or

indirect, are beginning to rise.

Conclusions

Our conclusions can be set out succinctly:

1. Homogeneity. High-technology industries are not

a homogeneous group; they are highly diverse in their

rates of growth (or decline), their potential for plant or

job growth, and their patterns of location.

2. Growth. While some high-technology industries

(Computers, Petroleum Refining) added large numbers

of jobs between 1972 &Id 1977 and others(Scientific Instruments, Measuring and Controlling Instruments)

achieved very high percentage growth, over one-third of

these industries lost jobs and more than two-fifths grew

at less than the manufacturing average.

3. Geographical concentration. While some hightechnology industries (Miscellaneous Missile and Space

Vehicle Parts; Guided Missiles and Space Vehicles;

Tanks and Tank Components) were highly geographically concentrated on one or another of the measures

used, others (Fertilizers; Dies and Industrial Molds;

Miscellaneous Electronic Components) were highly dispersed. The main explanation seems to be type of pr6duct, character of major consumer, and dependency on

natural resources. Thus, defense and space-oriented manufactures were highly concentrated, presumably because

of spending and procurement policies; resource-based industries (Carbon Products; Alkalies and Chlorine; Synthetic Rubber; Manmade Fibers) were also concentrated;

but industries producing heavy or bulky inputs for dispersed customers (Fertilizers, Paints, Chemicals) were

highly dispersed. Many fast-growing high-technology

industriesComputers, Semiconductors, Biological Products, Measuring Devices, Industrial Controls, Optical Instruments, Machine Toolswere moderately dispersed.

Constituent parts of major industrial groups tended to

have different degrees of concentration-dispersion than

the groups themselves. Between 1972-77, the great majority of industries tended to disperse; the sole exceptions

seemed to he military-related sectors and bulk materials

processing. Though most innovative, fast-growing hightechnology industries followed the general dispersion

trend, a fewSemiconductors, Resistors, Telephone

Equipmentfurther concentrated.

4. Hightechnology.dependent SMSAs. These are a

diverse group, some not well-known (Melbourne-Titusville around Cape Canaveral in Florida; Wichita, KS,

an important. base for aircraft maintenance and produc.

Lion). SMSAs with the lowest high-technology base are

ancontrary to mythin the Sunbelt; Texas has three.

5. Growth and change, 1972.77. This is very difficult to generalize because the patterns are differentfor

absolute and-for percentage growth, for plants and for

employment. Absolute plant growth was predictably

greatest in some bigger SMSAs, Lour of whichDetroit,

Minneapolis-St. Paul, Chicago, Bostonwere in the older

industrial belt. But percentage plant growth and percentage employment growth were fastest in medium-sized,

free-standing SMSAs outside the Northeast. Biggest absolute employment gains were recorded in SMSAs with a

high-technology reputation, led by San Jose (Silicon Valley), Anaheim, CA, Houston, San Diego, and Boston-plus two surprises, Lakeland, FL, and Oklahoma

City. Within supposedly high-technology States, there

is surprising variation: thus 5 out of 17 California SMSAs

posted high-technology job losses.

6. Explaining the aggregate patterns. A basic hypothesis underlying this studythat the distribution and

growth of high-technology industry could be statistically

explained in terms of a number of key locational factorshas been resoundingly disproved. Out of 19 factors

used for the analysis, only five ',defense spending, percent Hispanic, percent black, industrial utility rates, and

unemployment) were significantly associated with hightechnology concentration of employmentand even they

explained only 18 percent of the pattern. For employment change, the result was little better: nine variables

explained 29 percent of the pattern. Plant changes provided the best level of statistical explanation, with almost

68 percent of aggregate variation explained by nine of

the variables. But of this, size of labor force explained

26 percentand this could be somewhat discounted since

it must be related to absolute change.

7. Explaining the patterns: individual industries.

Repeating the analysis for individual industries' produced

no better result. Indeed it appeared to dispel certain

popular myths about high-technology industries: they do

nor appear attracted to areas with lower housing prices

or areas with good educational options, or to be sensitive

to trade union organization or wage rate differences. Nine

of the factorseducational spending, industrial utility

rates, manufacturing wage, unionization rate, climate,

unemployment rate, percent voting Republican, percent

Hispanic, and educational optionsappear to have little

or no relationship to growth of plants in individual high -

technology industries. Only fourlabor force, airports,

defense spending and, less strongly, major universitieswere significant contributors and also consistent in

supporting the hypothesized relationships. Others

presence of major corporate headquarters, pollution,

good arts availability, a well-developed freeway network,

and low house priceswere significant, but the relationship was the reverse of that postulated. Overall, the most

powerful factors seem to be business - related (lack, not

171
Technology, innovation, and Regional Economic Development

presence, of major industrial headquarters; presence of

major university; high defense spending) or transportation and communication-related (presence of major airport; lack, not presence, of highly developed freeway net)

rather than amenity-related (arts provision, housing

costs, pollution), which seem to be either contradictory

or perverse in their relationship to high-technology plant

growth: the general tendency is for high-technOlogy plant

growth to be related to lack, not presence, of arts provision; high, not low, housing costs; presence, not absence,

of pollution. This reinforces a speculation: that hightechnology industry is not drawnas often supposed

to locations attractive to employees, but rather is influenced by other factors that affect profitability. But this

is somewhat speculative.

8. Overall conclusion and policy implication. The

research has thus been rather less useful for what it has

proved, than for what it has evidently failed to prove.

Certainly, a whole host of factorsgenerally thought to

be important and even critical in attracting hightechnology industrydo not appear from this analysis

to have much, or indeed any, significance. This may be

surprising and even disappointing, but is clearly important in its negative way for policy formulation. Our most

important conclusion is that the location and growth of

high-technology industry is a very varied and disparate

process which will require highly disaggregated industry-by-industry analysis.

Independent Variable Description

Air Pollution Index. This index consists of a ranking

of metropolitan areas on the basis of measurable particulate pollution. Areas received a rating from 1 to

6 indicating insignificant to extreme levels of pollunon. The variable was resealed by reducing the increasing increments from 100, 200, etc., to 1, 2, 3, etc.

The data was taken from the Environmental Protection Agency, "Air Quality Data: 1976 Annual Statistics."

Airport Access. This index of accessibility allocates a rating from 0 to 4 to areas with airports having the

following designation: no airport, nonhub, small hub,

medium hub and large huh. The numerical values as- signed to the index are based on the percentage of

U.S. passenger traffic handled. The values for the

scaled increments are as follows: nonairport = 0; 1

= < 0.05 percent of total U.S. passengers served; 2

= 0.05 to 0.24 percent of total U.S. passengers served;

3 = 0.25 percent to 0.99 percent passengers served;

4 = 1 percent or more carried from the airport in

1978-79.

It is important to note that some areas, adjacent to

large metros such as New York, do not have their own

airport facilities but rather rely on the adjacent international airport services available. We considered

assigning the same designation to these places without airport service as their adjacent metros. Areas

which were less than 26 miles from the major airport

were considered for inclusion, but upon closer examination we chose to list them as having no airport.

A more complicated measure of transportation access

based on a gravity model could have resulted in better, or at least different, results.

Arts Index. This index ranks places on the basis of available arts and cultural resources. Areas are ranked on

the following characteristics: major university enrollment, symphony orchestras, opera companies, dance

companies, theatres, public television stations, fine arts

radio stations, museums, and public libraries. The index is additive, and was drawn from Rand McNally's

"Places Rated" Almanac. The data comprising the index were collected for the 1977-78 period.

Black population 1970. The 1970 black population raw

count was divided by the total population. The data

were taken from the U.S. Bureau of Census, "State

and Metropolitan Area Data Book, 1977."

Climate Index. Areas were rated on the following climatological characteristics: the numbc, of very hot

and very cold months; seasonal temperature variation;

the number of heating and cooling degree days; the

number of freezing days; the number of zero degree

days; the number of 90 degree days. The index was

constructed by initially assigning places 1,000 points

and then deducting points for negative attributes. The

data were published in "Places Rated" Almanac and

were collected for the year 1978.

Defense Spending Per Capita. Metropolitan defense

spending over $10,000 in contract value was divided

by the population. Data source was the State and M

ropolitan Area Data Book and was for 1977.

Educational Options Index. Metropolitan areas were

rated on the basis of available educational options.

Thi < index includes information on the following

items: the number of 4- and 2-year public and private

colleges and universities, the availability of evening

classes, the availability of professional schools. An index of educational options was constructed from a

scale of places rated AAA, AA, B, C, and nonavailable resource. Data source was the National Center

for Education Statistics and data were collected for

the year 1977-78.

Educational Spending. Dollars per student spent oreelementary education. Source was U.S. Department of

Health and Human Services; data were collected for

Fortune 500 Headquarters. Raw count of the number

of Fortune 500 headquarters located in metropolitan

172
App. CRecent Evidence on High-Technology industries' Spatial Tendencies: A Preliminary investigation 167

areas. Data were taken from "Fortune" magazine and

collected for 1977-78. Forty-five Fortune 500 companies were not included because their headquarters

are not located in major metropolitan areas.

Freeway Density. The number of freeway miles in metropolitan areas divided by the metropolitan land area.

Data were collected from the U.S. Department of

Transportation for the years 1977 and 1978.'

House Spending. The average sales price for a home in

metropolitan areas in 1976. Data were taken from the

annual U.S. Bureau of the Census' survey of average

house sales price in the U.S. metropolitan areas. Data

were collected for 1976.

Labor Force 1977. The raw count of metropolitan labor force, 1977. Data source was the State and Metropolitan Area Data Book, 1977.

Major University. Dummy variable constructed using

the highest ranked engineering and business schools

in the country. Statistics compiled by the the U.S.

Department of Education. Data were collected for

1975.

Spanish Population 1970. Raw count of Spanish-surnamed individuals in 1970 divided by 1970 population. Dar' 'ounce was the State and Metropolitan area

Data Book, 1977. This variable was particularly problematic because in 1970 there was no agreed-upon definition of "Spanish-speaking" person. 1980 Census

population counts are supposed to be significantly better than those reported in 1970.

1977 Unemployment Rate. Percent unemployment in

metropolitan areas. Data source was the State and

Metropolitan Area Data Book, 1977.

Percent of Unionized State Labor Force. Union membership as a percent of nonagricultural and military

employment for 1976. Data were only available at the

State level. Therefore, this measure is the percent of

unionized labor force for States. Data source was the

Handbook of Labor Statistics, 1979.

Industrial Utility Rates. Average State industrial utility rates, based on the average kilowatt -hour charge

for industrial electrical supply. Data were for 1980.

We used the data, despite the recent date of origin,

because we felt that the utility rate trends were nor

likely to deviate substantially between the earlier and

later periods. More importantly, we were interested

in marginal rather than absolute differences. Data

source was the State and Metropolitan Area Data

Book, 1977.

Percent Voted Republican. Percent of the voting population who voted for a Republican presidential candidate in 1976. Data source was the State and Metropolitan Area Data Book, 1977. In cases where

dominant party was Democratic, the difference was

calculated and then assigned to the area.

Average Manufacturing Wage, 1977. Average weekly number of hours worked divided by the average

weekly gross wages for manufacturing workers. Data

source was the Employment and Earnings State and

Area Series, U.S. Department of Labor, 1977. A second sourcei The Census of Manufacturers, table 4,

was consulted in those cases where the data were not

reported by the Department of Labor. The range

across areas varied from approximately $4 to $8 per

hour.

k not, letignwnts: The authors wish to thank the following individuals for their

acs,. :.like in this project: Carlos Davidson for programming and analytical

assistam c, Henry Ruderman for computer programming; Linda Wheaton

and Vijava Naranja for data entry assistance; the staff at the Institute of

Urban and Regional Development, University of California, Berkeley, and

the: staff at the Quantitative Anthropology Computer Lab, University of

California, Berkeley.

173
eOffice of Technology Assessment

The Office of Technology Assessment (OTA) was created in 1972 as an analytical arm of Congress. OTA's basic function is to help legislative policymakers

anticipate and plan for the consequences of technological changes and to examine the many ways, expected and unexpected, in which technology affects

people's lives. The assessment of technology calls for exploration of the physical,

biological, economic, social, and political impacts that can result from applications of scientific knowledge. OTA provides Congress wich independent and

timely information about the potential effectsboth beneficial and harmfulof

technological applications.

Requests for studies are made by chairmen of standing committees of the

House of Representatives or Senate; by the Technology Assessment Board,

the governing body of OTA; or by the Director of OTA in consultation with

the Board.

The Technology Assessment Board is composed of six members of the House,

six members of the Senate, and the OTA Director, who is a nonvoting member.

OTA has studies under way in nine program areas: energy and materials;

industry, technology, and employment; international security and commerce:

biological applications; food and renewable resources; health; communication

and information technologies; oceans and environment; and science, transportation, and innovation.

174