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dissertation entitled

THE DETERMINANTS OF THE GEOGRAPHICAL DISTRIBUTION OF
THE FORMATION OF NEW AND SMALL TECHNOLOGY-BASED FIRMS

presented by

Stephen Geoffrey Graham

has been accepted towards fulfillment
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C>1982

STEPHEN GEOFFREY GRAHAM

All Rights Reserved

THE DETERMINANTS OF THE GEOGRAPHICAL DISTRIBUTION OF
THE FORMATION OF NEW AND SMALL TECHNOLOGY-BASED FIRMS

BY

Stephen Geoffrey Graham

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Finance and Insurance

1981

ABSTRACT

THE DETERMINANTS OF THE GEOGRAPHICAL DISTRIBUTION OF
THE FORMATION OF NEW AND SMALL TECHNOLOGY-BASED FIRMS

BY
Stephen Geoffrey Graham

The problem addressed by this research study is to
identify the factors or conditions which explain the pro-
nounced variation in the distribution among geographical
subdivisions of the U.S. of the formation of new and
small technology-based firms (NSTBF). In addition, this

study attempts to develop the policy implications of

 

these factors for guiding efforts to stimulate the
economic development of individual states of the U.S.
and/or their geographical subdivisions.

Step-wise multiple regression was used to select the

. “m-Mw— a.- ..

 

set of determinants or factors (independent variables)

which best explained the number_of new and small tech—

 

nology-based firms tNSTBF) formed in each standard Metro-
politan Statistical Area ofthe U.S. (dependent variable).
Data were obtained on the number of new firms formed.
Technology-based firms were selected from these data by
identifying those Standard Industrial Classifications
(SIC's) which were technology-intensive. Data were
developed (and arrayed) on the number of engineers and
scientists (in the life and physical sciences) employed

as a percentage of total employment for each SIC. Data

Stephen Geoffrey Graham

also were developed (and arrayed) on the ratio of re-
search and development expense to sales for the firms
in each SIC. For each array, the SIC's with data equal
to or greater than a level selected on the basis of
judgment were deemed technology-intensive, and the
selected SIC's from the two arrays were merged.

This list of technology-intensive SIC's was purged
of SIC's found to be capital intensive. The size of the
average investment made by all venture capitalists was
determined. Then all SIC's with firms whose net worth
exceeded that average investment size were removed from
the list of technology-intensive SIC's, leaving a list
of SIC's defining NSTBF.

The independent variables used in the multiple re-
gression were:

- The number of existing small and technology-

based firms.

- The number of technology-intensive universi-
ties, nonprofit research institutions, and
industrial firms.

- The research and development expenditures of
those establishments.

- The number of earned Ph.D. and Master's degrees
awarded in science and engineering.

- Federal obligations to universities and
colleges for technology-based fellowships,

traineeships, and training grants.

Stephen Geoffrey Graham

‘- The number of universities and colleges
receiving such grants.

- The number of patents issued.

- State taxes.

- Union strength.

- Strike severity.

- Labor cost.

Energy costs.

The results of this study show that the number of
technology-intensive universities, nonprofit research
institutions, and industrial firms is the principal
determinant of the geographical distribution of the
formation of NSTBF. A second determinant is the number
of earned Ph.D. and Master's degrees in science and
engineering conferred by universities and colleges.
These two factors explained 76% and 2%, respectively,
of the variability in the geographical distribution of
the formation of NSTBF. Each of the other factors ex-
plained 1% or less of that variability.

This Study has B9E.E¥.l.iezt}e-.l.}3s..t~iesn$- It Provides
policy guidance for the promotion of economic growth in

any geographical area of the U.S. It facilitates the
identification of those geographical areas of the U.S.
most conducive to economic stimulation. It demonstrates
the ineffectiveness of certain methods of increasing the

rate of formation of NSTBF presently in use. Efforts

Stephen Geoffrey Graham

to optimize the conditions traditionally considered when
attempting to attract industry to a geographical area
will not increase the rate of formation of NSTBF in that

area.

TABLE OF CONTENTS

List of Tables. . . . . . .

Chapter I - INTRODUCTION. .
A. Summary . . . . . . .

B. The Venture Capital Industry.

C. Grants Received . . .

Chapter II - REVIEW OF THE LITERATURE

Chapter III - METHOD OF RESEARCH.

A. Summary . . . . . . .

B. The Dependent Variable.
1. Number of Firms Formed in

2. Technology-Intensive Firms.

a. Data From the Census of Population.

1975.

b. Data From Standard and Poor' 8

Compustat Tape.

c. Completeness Tests.

3. Capital— —Intensive Firms
C. The Independent Variables
1. Number of Existing Small and Technology-

Based Firms . . .

2. Number of Technology-Intensive Universi-
ties, Nonprofit Research Institutions,

and Industrial Firms.

3. Research and Development Expenditures
of Those Establishments

4. Number of Earned Ph.D.
Degrees Awarded in Science and Engi-

neering . . . . .

and Master's

5. Federal Obligations to Universities

and Colleges for Technology-Based
Fellowships, Traineeships, and Training

Grants- . . . . .

6. Number of Universities and Colleges

Receiving Such Grants

Number of Patents
State Taxes . . .
Union Strength. .
10. Strike Severity .
11. Labor Cost. . . .
12. Energy Costs. . .

\Dmfl
o 0

ii

Issued.

page

iv

14

15
16
17
17

26
28

28
32

40

41
43

43

44

44
45
46

49
49
50

LL

iii

Chapter IV - FINDINGS AND CONCLUSIONS . .

. Observed Associations . . . . . .

. Step-Wise Multiple Regressions. .

. Multivariate t-Tests. . . . . . . .
Influence of Population - . - - -
No Shortage of Venture Capital. .

MUOUDKI’

Chapter V - POLICY IMPLICATIONS AND RECOMMENDATIONS

A. Summary . . . . . . . . . . . . . .
Jch. Policy Guidance for the Promotion of
Economic Growth - - - - - - . . . .
C. Geographical Areas Most Conducive to
Economic Stimulation- - - - . . . .
~39? State of Michigan - . - . .
2. Other Geographical Areas of the
D. Ineffectiveness of Present Methods.

APPENDIX. C O I O O O O C O O O O O O O 0
LIST OF REFERENCES

A. Chapter Endnotes. . . . . . . . . .
B. Bibliography. . . . . . . . . . . .

page

54
54
56
61
65
67

70
70

70
72
72

77
77

78

124
128

10.

ll.

12.

13.

LIST OF TABLES

Summary of Array of Number of Engineers and
Scientists As Percentage of Total Employment,
By SIC Category, 1970. . . . . . . . . . . . .

Calculation of Number of Engineers and
Scientists As Percentage of Total Employment,
By SIC Category, 1970. . . . . . . . . . . . .

Portion of Array of Technology-Intensive SIC
Categories by Number of Engineers and
Scientists as a Percentage of Total Employment,
1970, 6.56% and Higher . . . . . . . . . . . .

Summary of Array (R&D)/S Ratios, By SIC, 1980

Portion of Array of Technology-Intensive SIC's
by (R&D)/S Ratios, 1980, Above 9th Decile. . .

Technology-Intensive and Capital-Intensive
SIC's. I O O O O O O O O O O O O O O O I O O 0

List of SIC Codes Which Identify Firms as
Being NSTBF. O O O O O I I O I O O O O O O O 0

Number of NSTBF Formed in 1975, by SMSA/NECMA

Reconciliation of Number of NSTBF Formed with
Total Number of Firms Formed, 1975 . . . . . .

Extent of Available Data on Independent
variables 0 O O C I O O O O O O O I O O O I O 0

Correlation Coefficient Between Dependent
Variable (NSTBF) and Each Independent Variable

Independent Variables Which Explain Variability
of NSTBF and ESTBF, Separately . . . . . . . .

Correlation Coefficient Between Each Dependent

Variable (NSTBF and ESTBF) and Bach Independent
variable 0 O O O O O O O O O O O O O O O O O 0

iv

Page

18

19

25
26

27

29

33

35

39

53

55

57

58

rid

14.

15.

16.

17.

18.

19.

Page

Multivariate t-Test-eProbability (Two-Tailed)

of Equality of-Means of Populations Based on

All Independent Variables and on Single Inde-
pendent Variables and a Median Split of Number

of NSTBF . . . . . . . . . . . . . . . . . . . 63

Multivariate t-Test--Probability (Two-Tailed)

of Equality of Population Means Based on All
Independent Variables and on Single Independent
Variables and a Median Split of NSTBF as

Percentage of Total Firms Formed . . . . . . . 64

Correlation Coefficient Between Each Independent
Variable and Both Population and NSTBF . . . . 66

Number of Technology-Intensive Universities,
Nonprofit Research Institutions, and Industrial
Firms in Michigan, by SMSA, Circa 1977 . . . . 73

Research and Development Expenditures of Tech-
nology-Intensive Universities, Nonprofit

Research Institutions, and Industrial Firms

in Michigan, by SMSA, Circa 1977 . . . . . . . 73

Data for State of Michigan on Selected Factors
Highly Associated With the Geographical Distri-
bution of the Formation of NSTBF . . . . . . . 76

IN APPENDIX

Al.

A2.

A3.

A4.

A5.

Complete Array of Technology-Intensive SIC
Categories by Number of Engineers and Scientists
As Percentage of Total Employment, 1970. . . . 78

Complete Array of Technology-Intensive SIC
Codes by (R&D)/S Ratio, 1980 . . . . . . . . . 84

List of Science and Engineering Fields Used by
National Science Foundation In Its Annual Surveys
of Academic Science. . . . . . . . . . . . . . 90

Data by SMSA/NECMA on Factors Highly Associated
with the Geographical Distribution of the
Formation of NSTBF . . . . . . . . . . . . . . 93

Summary by State of Selected Taxes on Hypo-
thetical NSTBF and on Its Entrepreneur for
1979 O O O O O O O O O O O O O O O O O O O O 0 104

A6.

A7.

vi

Data by SMSA/NECMA on Selected Criteria Tra-
tionally Considered When Locating A New
BUSiness O O O O O O O O O O I O O O O O O O 0

Data by SMSA/NECMA on Number of Patents Issued
in 1975. O O O O O O O O O I O O O O O O O O O

Page

107

118

CHAPTER I

INTRODUCTION

A. Summary

The problem addressed by this research study is to
identify the factors or conditions which explain the
pronounced variation in the distribution among geo-
graphical subdivisions of the U.S. of the formation of
new and small technology-based firms (NSTBF). In
addition, this study attempts to develop the policy
implications of these factors for guiding efforts to
stimulate the economic development of individual states
of the U.S. and/or their geographical subdivisions.

Step-wise multiple regression was used to select
the set of determinants or factors (independent vari-
ables) which best explained the number of new and small
technology-based firms (NSTBF) formed in each Standard
Metropolitan Statistical Area of the U.S. (dependent
variable). Data were obtained on the number of new firms
formed. Technology-based firms were selected from these
data by identifying those Standard Industrial Classifi-
cations (SIC's) which were technology-intensive. Data

were developed (and arrayed) on the number of engineers

IF

and scientists (in the life and physical sciences) em-
ployed as a percentage of total employment for each
SIC. Data also were developed (and arrayed) on the
ratio of research and development expense to sales for
the firms in each SIC. For each array, the SIC's with
data equal to or greater than a level selected on the
basis of judgment were deemed technology-intensive, and
the selected SIC's from the two arrays were merged.

This list of technology-intensive SIC's was purged

of SIC's found to be capital intensive. The size of the
average investment made by all venture capitalists was
determined. Then all SIC's with firms whose net worth
exceeded that average investment size were removed from
the list of technology-intensive SIC's, leaving a list
of SIC's defining NSTBF.

The independent variables used in the multiple re-

gressions were:

- The number of existing small and technology-
based firms.

- The number of technology-intensive universi-
ties, nonprofit research institutions, and
industrial firms.

- The research and development expenditures of
those establishments.

- The number of earned Ph.D. and Master's degrees

awarded in science and engineering.

- Federal obligations to universities and
colleges for technology-based fellowships,
traineeships, and training grants.

- The number of universities and colleges
receiving such grants.

- The number of patents issued.

- State taxes.

- Union strength.

- Strike severity.

- Labor cost.

Energy costs.

The results of this study show that the number of
technology-intensive universities, nonprofit research
institutions, and industrial firms is the principal
determinant of the geographical distribution of the
formation of NSTBF. A second determinant is the number
of earned Ph.D. and Master's degrees in science and
engineering conferred by universities and colleges.
These two factors explained 76% and 2%, respectively,
of the variability in the geographical distribution of
the formation of NSTBF. Each of the others factors ex-
plained 1% or less of that variability.

This study has policy implications. It provides
policy guidance for the promotion of economic growth in
any geographical area of the U.S. It facilitates the
identification of those geographical areas of the U.S.

:most conducive to economic stimulation. It demonstrates

the ineffectiveness of certain methods of increasing the
rate of formation of NSTBF presently in use. Efforts to
optimize the conditions traditionally considered when
attempting to attract industry to a geographical area
will 22E increase the rate of formation of NSTBF in that

area.

B. The Venture Capital Industry

Venture capital is the equity and long-term debt
financing of new and small enterprises organized to pro-
duce and to market new, unconventional, high-technology
products and services having high long-run growth potential.

Rubel1

has classified the types of financing offered
by venture capital firms.as follows:

Start-ups. Businesses that are still as the idea

 

stage. According to Deloitte, Haskins and Sells, the
prototype may have been developed, but operations have
not begun.2

First-stage financing. Companies up to one year

 

old, usually losing money, and for which profits could
be one to three years in the future.

Second-stage financing. Companies generally one to

 

three years old, which are either about to the break-
even stage or are projecting profits within one year.

Third-stage financing. This is usually considered

 

to be the last round of private financing, and at this

stage the company is likely to be in the black.

Buy-out or acquisition financing. Such projects

 

include financing the acquisition of a company to be
operated as a division of another company, or financing
the purchase of a business from a family that wishes to
sell that business, and so forth.

The firms making venture capital available are
organized in a variety of ways--as partnerships, cor-
porations, divisions of major corporations, publicly held
venture firms, venture capital funds formed within bank
trust departments, divisions of investment banking firms,
venture capital divisions of insurance companies, pension
funds or investment advisory firms, and a variety of
others.

According to Deloitte, Haskins, and Sells, the term
venture capital should exclude marginal loans.3 These
are loans made under various federal and state programs.
Such loans are either made directly by governmental units
or by private financial institutions with a full or
partial guarantee provided by a governmental unit. Many
of these programs are not designed to aid new and small
technology-based firms (NSTBF), and inclusion of these
programs would only serve to confuse the available data

on venture firms.

C. Grants Received
The cost of this research study was partially sup-

ported by two grants. One was a Faculty Research Grant

received from Ferris State College, Big Rapids, Michigan.
The other grant was received from the Office of Economic

Research of the Small Business Administration, Washington,

D.C.

CHAPTER II

REVIEW OF THE LITERATURE

Much of the research on the subject of venture
capital has concluded that the lack of venture capital
has been a major barrier to increased economic growth.

In 1976, the Governor of Michigan formed the
Governor's Advisory Commission on the Regulation of
Financial Institutions. One of the charges to the
Commission was to explore financing mechanisms that
might encourage the development of new high-growth enter-
prises in Michigan. According to the Commission, the
reason for that charge was as follows:

"That Michigan's economy could benefit from active
and successful entrepreneurs is not difficult to argue.
Our historical economic base, durable goods manufacturing,
has been moving out--moving to the "sun belt" states,
which appear to offer lower labor costs. For Michigan
this movement could lead to economic stagnation and in-
creased unemployment. New sources of growth are a pre-
requisite to the State's economic health."1

The Commission concluded that the two major needs

of entrepreneurs, management assistance and capital, are

not being met in Michigan. It recommended "...that the
state establish a new business development corporation
(NBDC) charged with the objective of developing new
enterprises with high growth potential which are or will
be located in Michigan, in order to increase employment
opportunities in the state in the long run."2 It sug-
gested that the NBDC's activities should include making

venture capital and managerial_assistange available to

“Whawfi w.-_...—-—-—-"

new enterprises in Michigan. These conclusions were
based on the results of a study performed for the Com-
mission by the accounting firm of Deloitte, Haskins,
and Sells.3

In lilf’ Brophygstudied the role of finance in the
development of new and small technology-based firms
(NSTBF) in Michigan. His purpose was to improve "the
availability of finance for technologically innovative

profit-oriented ventures in Michigan."4

Although his
conclusions and recommendations were directed toward
Michigan's unique problems, his general conclusions and
recommendations were (1) that the rate of formation of
NSTBF is greatest in places where interaction exists
between research universities, nonprofit research
laboratories, and private research-oriented firms;

(2) that the lack of adequate local access to venture

capital is one of the most serious barriers to the

successful development of NSTBF; and (3) that any state

can increase the rate of formation of NSTBF by creating
some form of business development corporation, one of
whose purposes would be to make venture capital available

to NSTBF.

 

_ TV“: :4 - _

 

Chasta‘inrkandpDeVries5 attempted to determine whether
firms whose business is based on substantial research and
development (not solely research and development) have
more difficulty than general manufacturing firms in
securing capital in Michigan. Specifically, the study
contrasted the problems of initial financing for new R
and D firms with those of new manufacturing firms and
with the problems of financing expansion by older manu-
facturing firms.

The authors found that new R and D firms went out
of state for most of their long-term debt and equity
capital, while most of the new and established manu-
facturing firms secured their capital in-state. The
study findings suggest that new R and D firms have had
difficulty in establishing an understanding with the
sources of capital in Michigan. The findings also
suggest that, since R and D firms are characterized by
high rates of growth, greater availability of capital to
R and D firms could provide substantial economic benefits
to Michigan.

It can be shown that NSTBF tend to be clustered in
a few urban areas of the U.S.: Los Angeles, San Francisco,

New York City-Northern New Jersey, Boston, Washington, D.C.,

10

and Ann Arbor, Michigan.6 A number of researchers have

7 and

suggested reasons for this clustering. Roberts
Cooper8 studied the Boston and San Francisco area
clusters, respectively, and concluded that NSTBF are
formed as "spin-offs" from technology-intensive uni-
versities, nonprofit research laboratories, and
industrial firms which, through interaction, provide a
local environment conducive to entrepreneurship and the
formation of NSTBF. "Roberts and others have made the
point that new and small technology-based firms usually
come into existence because of some deficiency in the
ability of large technology-based organizations to pro-
vide a sufficiently stimulating and rewarding entrepre-
neurial environment for its technologically innovative
personnel. This implies that large organizations (whether
industrial firms, universities, or nonprofit research
laboratories) have not been, as a rule, as aggressive as
possible in innovation."9
The interaction, referred to above by Brophy, Cooper,
and Roberts, among technology-intensive universities,
nonprofit research laboratories, and industrial firms has
been described by Mahar and Coddington. They found that
scientific complexes grow (1) from within via the "spin-
off" process and (2) by attracting branch plants and re-
search facilities. Internal growth by means of spin-offs

is accelerated when there is extensive interaction among

the elements of the scientific complex (universities,

11

nonprofit research institutions, and industrial firms).
"This interaction is achieved in various ways, such as
consulting relationships between industry and faculty
members, university-industry seminars, special courses
for practicing scientists and engineers, board of
directorships for faculty members, and adjunct professor-
ships for industry and government personnel."10
Lamont has identified other forms of interaction
among the three types of source organizations. Many
spin-off firms are formed by founders coming from more
than one source organization, e.g., university-industry
combinations.ll Moreover, "...university and primary
industry spin-offs have in turn seeded the scientific
complex by serving as source organizations for secondary
spin-offs...."12
Shimshoni is describing interaction when he refers
to the increased probability of diffusion of ideas and
techniques in scientific complexes through chance en—
counters with knowledgeable local individuals, often
through social and professional contacts.l3
A study by the U.S. Department of Commerce found
close, frequent consultations among technical people,
entrepreneurs, universities, venture capital sources, and
others essential to the innovative process, (i.e., inter-
action) to be a vitally important factor in the formation

of spin-off firms.14

12

Other researchers have concluded that a dominant factor
in the clustering of technology-based industry is the

distribution of federal government R and D funding.15

16 the factor which best explains

According to Clark,
this clustering is the cost of acquiring relevant tech-
nologies, which increases in direct proportion to the time
required to obtain the technologies. The time required,
in turn, is a function of geographical distance from the
relevant technological infrastructure.

17 surveyed 45 precision instru-

In 1963, Spiegelman
ment manufacturers in California, Oregon, New York,
Massachusetts, and Illinois to determine which of the
various possible determinants of plant location were the
most important to them. "It is clear from the pattern
of responses that, besides 'personal considerations,‘
the factors that are most important in determining location
are 'availability of professional staff' and 'availability:
of labor of required skill or ability;' of lesser impor-
tance are proximity to educational, testing, and research
facilities, and to suppliers and markets for reasons other
than transport cost.

"The three factors listed as unfavorable by largest
number of firms are: 'labor cost, other than professional
and managerial,‘ 'land availability and cost,’ and 'tax-
ation.’ Since most of the firms surveyed are in large

metropolitan areas, where wage rates, land costs, and

property taxes tend to be relatively high,...they may be

13

regarded as the costs for obtaining the advantages of
availability of labor and various services and insti-
tutions, of proximity to markets and suppliers, and of
amenities that attract professional personnel."

Since Spiegelman's survey was not conducted by
statistical sampling techniques, his results could not
be generalized to the entire industry. However, he be-
lieved that his survey data are strongly suggestive of

the location behavior of thenentire industry, since all

##— r- _ _ __... - cfl-flrvw ..,..,,_. '. I m-

 

sectors of the precision instruments industry were
represented. .

Cooper18 has also concluded that another essential
factor accounting for clusters of NSTBF in selected areas
is the existence within those clusters of pools of ex-
perienced and successful technological entrepreneurs.
Such pools reduce the perceived risk associated with
technological entrepreneurship.

Two directories of venture capital firms, prepared

19 20 are available. These

by Rubel and Dominguez,
directories provide such information as name, lOcation,
type of venture capital firm, preference as to investment

size, industry preference, stage of financing, etc.

CHAPTER III

METHOD OF RESEARCH

A. Summary
The principal hypothesis tested by this study is
that it is possible to identifyfithe determinants of the

geographical distribution of the formation of new and

- ‘- ..- , .

small technology-based firms (NSTBF) at the start:up
stage of financing. These firms need venture capital
both at the start-up stage of financing and at later
stages of financing prior to having access to the public
capital markets. In this study, however, the research
on the demand for venture capital is confined to the
venture capital needs of NSTBF at the start-up stage of
financing.

The literature on venture capital provides a rich
source of factors which could explain the geographical
distribution of the formation of NSTBF. A step-wise
multiple regression program was used to select the set
of those factors (independent variables) which best
explained the geographical distribution of the formation

—L.~o-u.w—.—.‘__.____ .4. -.

of NSTBF (dependent variable).

.W

W’ —.—~ ..—.. ---—.-a.-.—~ nw-'-— ”..-MDe-I

14

15

B. The Dependent Variable

The dependent variable was measured by the number
of NSTBF formed in 1975 in each Standard Metropolitan
Statistical Area or New England County Metropolitan
Area (SMSA/NECMA) of the U.S. A SMSA/NECMA is one or
more counties including a major city, as defined by the
Bureau of the Census of the U.S. Department of Commerce.

No ready source of the number of NSTBF formed annually

was (or is) available. Therefore, the data were obtained
in the following manner. First, the £2331 number of

firms formed, by SMSA/NECMA, was obtained. The technology-
based firms were selected from these data by identifying
those Standard Industrial Classifications (SIC's) which
were technology-intensive. Two methods were used to array
the SIC codes by technology-intensiveness.

Under one method, the number of engineers and scien-
tists (in the life and physical sciences) employed as a
percentage of total employment was calculated for each
SIC. The SIC's above the 9th decile of that array of
percentages were deemed technology-intensive.

The other method involved the calculation of the
ratio or research and development expense to sales ((R&D)/S)
for each firm in Standard and Poor's Compustat Tape. Those
data were then sorted by SIC code, and the (R&D)/S ratios

were calculated for each SIC code. These ratios, by SIC

16

code, were then arrayed. The SIC's above the 9th
decile of that array of ratios were deemed technology-
intensive.

The two lists of technology-intensive SIC's were
merged and subjected to two different completeness tests.
The technology-intensive SIC's were thus identified.

Finally, that list was purged of SIC's which repre-
sented capital-intensive industries, because the NSTBF
must be small enough so that the typical venture capitalist
will be able to finance them. The size of the average
investment made by all venture capitalists was determined.
Then all SIC's with firms whose net worth exceeded that
average investment size were identified. Those SIC's
were deemed capital-intensive and were removed from the
list of technology-intensive SIC's. The remaining list
of SIC's identified new and small technology-based firms

(NSTBF).

1. Number of Firms Formed in 1975

The gnly_source of data on the number of firms formed
in a given year and classified by Standard Industrial
Classification (SIC) and by geographical detail is the
Dun and Bradstreet Reference Book.1 That book contains
the following information for each of some 5 to 6 million
business establishments in the U.S.: state, county, city,
name of firm, credit rating, a four-digit SIC code, and

a one-digit code identifying the year the firm was formed,

17

provided that year occurred within the past ten years.
Dun's Marketing Services, a subsidiary of Dun and Brad-
street Corporation, sorted these 5 to 6 million firms to
select those firms formed in 1975, and tabulated the
number of such firms by SMSA/NECMA and by SIC.

This tabulation was designed to exclude all firms
engaged in retail and wholesale trade (SIC 5000-5999).
An assumption was made that, by definition, retail and
wholesale trade firms cannot be NSTBF. Since they handle
the more conventional products and services, they have
only normal growth potential. As noted earlier, NSTBF
are firms organized to produce and market new, unconven-
tional, high-technology products and services which are
expected to have relatively high growth potential.
Another reason for excluding retail and wholesale trade
firms was the high number of such firms--the handling of
data for 167,000 of such newly formed firms was avoided.
This is the difference between the 326,000 firms formed
in 1975 according to the Statistical Abstract of the U.S.2
and the 159,000 firms, exclusive of retail and wholesale
firms, formed in 1975 according to the Dun's Marketing

Services tabulation referred to above.

2. Technology-Intensive Firms
a. Data from the Census of Population
In addition to being new firms, NSTBF must also be

technology-intensive. It was possible to array the SIC

18

codes by technology-intensiveness by use of detailed
occupation and detailed industry data in the 1970 Census
of Population.3 For each SIC category, the number of
engineers and scientists (in the life and physical

sciences only) employed was expressed as a percentage

of the total employment (age 16 and over). See Table 2

for these data. These 155 SIC categories (groups of

SIC's) were arrayed on the basis of the number of scien-
tists and engineers as a percentage of total employment.

By judgment, the seventeen, SIC categories with percentages
of 6.56% and higher were deemed to be technology-intensive.
See Table 3 for an array of those percentages. See
Appendix Table A1 for a complete array of SIC categories
by number of engineers and scientists as a percentage of

total employment. Table 1 summarizes that complete array.

Table 1

Summary of Array of Number of Engineers and Scientists as
Percentage of Total Employment, by SIC Category, 1970

 

Percentage
Highest value 27.15
9th Decile 7.12
8th " 4.80
7th " 3.48
6th " 2.35
5th " 1.49
4th " 0.91
3rd " 0.46
2nd " 0.23
lst " 0.11

Lowest value 0.01

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m mHan

26

b. Data from Standard and Poor's Compustat Tape

Another means used to identify technology-intensive
firms was the ratio of research and development expense
to sales, (R&D)/S. These data were obtained from the
1977 Standard and Poor's Compustat Tape.4 This tape con-
tains financial statement data on all firms whose common
stock is traded on the New York and American Stock Ex-
changes plus about 300 additional firms of interest to
researchers, investors, etc. A computer program was

developed to calculate an (R&D)/S ratio for all firms

 

combined in each SIC code. These 179 ratios were then
arrayed. Again, by judgment, the seventeen SIC's with
(R&D)/S ratios of .029 and higher were deemed to be
technology-intensive. See Table 5 for an array of those
ratios. See Appendix Table A2 for a complete array of

SIC's by (R&D)/S ratios. Table 4 summarizes that array.

Table 4

Summary of Array of (R&D)/S Ratios, by SIC, 1980

Ratio
Highest value .084
9th Decile .029
8th " .020
7th " .013
6th " .008
5th " .005
4th " .003
3rd " .002
2nd " .000*
lst " .000*
Lowest value .000*

*Less than .0005

27

 

 

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n mHQMH

28

The list of SIC codes based on the number of engineers
and scientists as a percentage of total employment and
the list of SIC codes based on (R&D)/S ratios were

merged and are shown in Table 6.

c. Completeness Tests

Two means of testing the list of technology-intensive
SIC's for completeness were used. Each firm listed in
the 1979 Directory of the American Electronics Associations
was classified with a 4-digit SIC code. Those SIC codes
with five or more member firms accounted for two-thirds
of the membership. All of those SIC's appeared on the
list of technology-intensive SIC codes in Table 6.

In addition, the firms listed in the 1980 Directory

 

of Research, Development, and Testing Facilities in
Michigan prepared by the Industrial Development Division,
Institute of Science and Technology, The University of
Michigan,6 were classified by 4-digit SIC code on a test
basis (about one-half of the firms). Over one-half of
the SIC's of these tested firms were on the list of
technology-intensive SIC's. The remainder of the SIC's
appeared to be ESE relatively technology-intensive,

based on the nature of the industries involved.

3. Capital-Intensive Firms
Although they must be technology-intensive, NSTBF

must not be capital-intensive. In other words, they must

29

Table 6

Technology-Intensive and Capital-Intensive SIC's

 

 

Technology- No. of Technology- No. of
Intensive Small Intensive Small
SIC's Firms SIC's Firms
(1) (2) (1) (22
3641 4
1311 120 3643 7
1321 0 X 3644 3
1381 36 3645 10
1382 9 3646 5
1389 55 3647 1
2812 l 3648 0 X
2813 1 3651 5
2816 2 3652 4
2819 5 3661 5
2821 16 3662 38
2822 2 3671 0 X
2824 0 X 3672 0 X
2831 1 3673 1
2833 6 3674 17
2834 13 3675 0 X
2861 2 3676 0 X
2865 1 3677 4
2869 5 3678 0
2911 5 3679 60
3551 15 3691 3
3552 8 3692 1
3553 7 3693 2
3554 7 3694 2
3555 8 3699 6
3559 21 3721 2
3561 12 3724 4
3562 l 3728 14
3563 5 3811 14
3564 7 3822 6
3565 3 3823 7
3566 12 3824 3
3567 11 3825 10
3568 3 3829 4
3569 18 3832 6
3572 0 X 3841 5
3573 19 3843 4
3574 2 3861 10
3576 1 7372 12
3579 3 7374 20

3581
3582
3585
3586
3589
3592
3612
3613
3621
3622
3623
3624
3629

30

Table 6 (continued)

1 7379

l 7391
17 7392

O X 7397
12 8911

2 8999

5

8

5

7

5

2

9

X - Capital-Intensive industry

(1) - Merger of SIC's above 9th decile on the

(2) -

number of engineers and scientists as a
percentage of total employment (Table 3)
and on list of SIC's based on (R&D)/S
ratios (Table 5).

Number of firms classified in this SIC
only and with a net worth between $500,000
and $1,000,000 in 1980. Source: Dun &
Bradstreet, Inc., Million Dollar Directory,
Vol. II, 1980, New York, N.Y.

 

 

31

be small enough so that the typical venture capitalist
will be able to finance them. Data for fiscal year
1976 on average investment size was secured from Rubel's

Guide to Venture Capital Firms.7

Of the 352 venture
capital firms for which such data were available, about
90% of them invested less than $1 million per investment.

The average investment size was approximately $600,000.

Volume II of the Million Dollar Directory for 1980 pub-

 

lished by Dun and Bradstreet8 lists alphabetically all
firms with a net worth between $500,000 and $1,000,000.
This Directory is also arranged by 4-digit SIC code.
The number of firms in that Directory (limited by this
researcher to firms classified in one SIC code only) in
each SIC on the list of technology-intensive SIC's was
tabulated. These data are shown in Table 6. Each tech-
nology-intensive SIC having 29 firms with a net worth
between $500,000 and $1,000,000 was deemed to include 29
small firms. Based on the type of industry which each
such SIC represents, it was reasonable to conclude that
each such SIC consisted only of firms with a net worth
greater than $1,000,000. These SIC codes were removed
from the list of technology-intensive SIC codes. The
result was a list of SIC codes which identified NSTBF
(see Table 7).

The Dun's Marketing Services tabulation of all firms

formed in 1975, by SMSA/NECMA and by SIC was purged of

32

all firms in SIC's other than those which identified NSTBF.
The resulting number of NSTBF by SMSA/NECMA for 1975 is
shown in Table 8.

It should be noted that the 11,159 NSTBF formed in
1975 represent only about 3% of the 326,000 firms formed

in that year, as shown by the reconciliation in Table 9.

C. The Independent Variables

In multiple regression, the confidence interval about
the expected value of the projected dependent variable
must be reasonably narrow. To accomplish this, the number
of observations must be at least ten times the number of
independent variables, as a rule of thumb. Since I have
identified twelve independent variables (factors or
determinants of the geographical distribution of NSTBF),
the number of observations must be at least 120. There-
fore, data for each factor was obtained for each of the
266 Standard Metropolitan Statistical Areas or New England
County Metropolitan Areas (SMSA/NECMA's) in the U.S.

For each of the factors selected for use as an inde-
pendent variable in the test of the hypothesis there is
provided below: (1) the nature of that factor, (2) the
rationale for its selection, and (3) a description of

the type of data obtained to measure it.

*‘k‘k‘k‘k

33

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36

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38

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39

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40

FACTOR: ESTBF--Existence of a pool of experienced and
successful technological entrepreneurs, i.e., existing

small and technology-based firms.

RATIONALE: Lamont9 found that as an area develops a
successful cluster of NSTBF (primary spin-offs from

incubator organizations), secondary spin-offs occur from

 

F"
the NSTBF themselves, in effect accelerating the rate of
development. Cooper10 found that each successful new
firm provides an example for others who may follow. In
an environment including successful NSTBF as well as the 9;

incubator organizations, prospective entrepreneurs may
perceive the risks to be relatively low and the rewards
relatively high, because they find it relatively easy to

learn about what is involved in starting a NSTBF.

DATA: The number of small and technology-based firms in
existence in 1975 in each SMSA/NECMA of the U.S. was
obtained in the following manner. All of the data from

the 1975 County Business Patterns, published by the

 

Bureau of the Census of the U.S. Department of Commerce,11

was purchased in tape form from National Planning Data
Corporation, Ithica NY. These data provided the number
of existing establishments in 1975 by county and by SIC.
A computer program was developed to select from these

data the number of establishments in each SMSA/NECMA

41

having the same SIC codes as those used to define NSTBF
(see Table 7 in section B3 of Chapter III). These data
also are shown in Appendix Table A4.

*****
FACTOR: EEQEQf-The number of technology-intensive univer-
sities, nonprofit research laboratories, and industrial

firms.

RATIONALE: Roberts12 and Cooper13 studied the factors

which caused new and small technology-based firms (NSTBF)
to form in the Boston and San Francisco areas, respectively.
They both found that NSTBF are formed as "spin-offs" from
technology-intensive universities, nonprofit research
laboratories, and industrial firms. Such firms provide
an environment in which technically-trained employees
perceive entrepreneurial opportunities to transfer tech-
nology to NSTBF.

Clark14 concluded that NSTBF are formed near scien-
tific complexes in order to minimize the cost of acquiring

relevant technologies.

DATA: Data were gathered on the distribution by SMSA/NECMA
of the number of technology-intensive universities, non-
profit research institutions, and industrial firms.

Data on the number of technology-intensive universities

 

were obtained from the results of the National Science

Foundation's (NSF) Survey of Scientific and Engineering

 

42

Expenditures at Universities and Colleges for fiscal year

1978.15

The data were derived from 320 institutions with
doctorate programs in the sciences and/or engineering.
According to the NSF, doctorate-granting institutions are
an excellent indicator of research and development (R&D)
activities in academia as a whole, since 98 percent of
all R&D spending in academia has occurred in these insti-

tutions.16

A list of science and engineering fields
used by NSF in its annual surveys of academic science is
furnished as Appendix Table A3.

Data on the number of technology-intensive nonprofit

 

research institutions were obtained from the results of

 

the NSF's Survey of Federal Support to Universities,
Colleges, and Selected Nonprofit Institutions for the

fiscal year 1977.17

The relevant portion of this survey
covered the 213 nonprofit institutions which received a
minimum of $300,000 in total federal obligations for
research and development during fiscal year 1977 or

$100,000 from any one agency.

Data on the number of technology-intensive industrial

 

firms was obtained from the 1977 Compustat Tape described
in section B2b of Chapter III above. Data on all of the
877 firms with R&D expenditures, in that tape, were used
in this study.

The above data were sorted by SMSA/NECMA, and are

shown in Appendix Table A4.

*****

43

FACTOR: R&DMIL--Expenditures on research and development
by technology-intensive universities, nonprofit insti-

tutions, and industrial firms.

RATIONALE: As noted above, NSTBF are likely to be "spin-
offs" from technology-intensive universities, nonprofit
research laboratories, and industrial firms. Therefore,
it seems reasonable that the rate of formation of NSTBF
may be related to the intensity of efforts to promote

and encourage technology-based activity. One means of
doing so is through expenditures for R&D at such incubator

organizations.

DATA: Data were gathered on the R&D expenditures of
universities, nonprofit research institutes, and industrial
firms for the same periods and from the same sources used
to develOp data for factor R&DNO above. These data were
sorted by SMSA/NECMA and are also shown in Appendix Table
A4.

*****
FACTOR: DEGREES--Production of technically trained uni-

versity graduates.

RATIONALE: Universities are one of the incubators of

"spin-off" NSTBF.18 It appears reasonable that a direct

44

relationship may exist between the number of technically
trained graduates of universities and the rate of for-

mation of NSTBF.

DATA: Data were obtained from the NSF on the number of
earned Ph.D. and Master's degrees in science and engineering
conferred by universities and colleges in 1974-1975.19
These data were sorted by SMSA/NECMA and are shown in
Appendix Table A4.

*****
FACTORS: FEDOBMIL and FEDOBNO--Intensity of promotion
and encouragement of technology-based activity by means
of federal obligations to universities and colleges for

fellowships, traineeships, and training grants. Factor

FEDOBMIL is measured in terms of the dollar amount of

 

federal support. Factor FEDOBNO is measured in terms of

the number of universities receiving these funds.

RATIONALE: The rationale for these factors is similar
to that for factor R&DMIL. Factors FEDOBMIL and FEDOBNO
provide data on additional means of promoting and en-

couraging technology-based activity.

DATA: Data were obtained from the National Science
Foundation on federal obligations to the 100 universities
and colleges which received the largest amounts of funds

for this purpose and which accounted for $180.8 million

45

of the $201.3 million total obligations for this purpose

20

in fiscal 1975. These data, sorted by SMSA/NECMA, are

also shown in Appendix Table A4.

*****

FACTOR: PATENTS--The rate of introduction of new products.

RATIONALE: This factor may provide an indication of the
technology-intensiveness of geographical areas. Tech—
nology-intensive geographical areas may be effective incu-

bators of primary "spin-offs" in the form of NSTBF.

DATA: The only available data on this factor is the

number of patents issued. Furthermore, these data are

only available by state. Where an SMSA/NECMA comprised
more than one state, the number of patents issued for each
of the states involved was averaged. The number of patents
issued by state in 1975 was obtained from the Patent and
Trademark Office of the U.S. Department of Commerce.21
These data, sorted by SMSA/NECMA, are shown in Appendix
Table A7.

*****

FACTORS: TAXES, STRSEV, UNSTR, LABCOST, and ENERGY--

 

 

Factors traditionally taken into consideration when
selecting the geographical area of the U.S. in which to

establish a new business.

46

RATIONALE: The costs of the factors of production and
the attractiveness of the business climate in general may
be significant explanatory factors in the location

decisions of NSTBF, just as they are for any new business.

DATA: Data were obtainable for a number of the factors
which should be considered by the entrepreneur when

selecting a location at which to form a new business.

Factor: TAXES--Data were obtained from the All State Tax
22

 

Handbook 1980 on the following taxes for each state:

 

unemployment insurance tax, business income tax, property
tax, capital values and franchise taxes, and the state
personal income tax. Using the DuPont system of financial
analysis, and reasonable assumptions, the financial state-
ments of a typical NTBF at December 31, 1979 and for the

year then ended were projected as follows:

 

 

($000)
Balance Sheet Income Statement
Assets $1,000 Debt $ 0 Sales $4,000
Net worth 1,000 Expenses 3,750
$1,000 $1,000 Taxable Income § 250

Assumptions:

(1) The following financial ratios were used:

 

Rate of Net Worth Rate of Profit Total

return on X Total Assets = return on = margin X asset

net worth investment on sales turnover
.25 X l = .25 = .0625 X 4

47

(2) A relatively high expected rate of return on net
worth and the inability to obtain debt financing are
characteristic of NSTBF. See section B of Chapter I

above.

(3) The net worth at formation of the NSTBF in 1975 was
assumed to be $600,000. As discussed in section A3 of
Chapter III above, the size of the average investment
made by venture capital firms in fiscal year 1976 was
$600,000. This investment was assumed to be equity

financing in view of the high risk level.

(4) The venture capitalist was assumed to have based
his estimates of taxes on projected financial statements
as of December 31, 1979 and for the year then ended and

on forecasts of tax rates as of December 31, 1979.

(5) The payroll of the NSTBF was assumed to be 70% of

sales, or $2,800,000.

The unemployment tax was assumed to have been levied
on the first $6,000 of wages per employee. Since average
annual earnings per employee was assumed to be $18,000,
the taxable payroll was one-third ($6,000/$18,000) of
$2,800,000, or about $1,000,000. The absolute high ex-
perience rates were applied to the taxable payroll.

State corporation income tax rates were applied to

the assumed taxable income of $250,000.

48

The first step in estimating the property tax was

 

to obtain the percentage at which the assumed $1,000,000
of property was assessed. The composite average rate of
assessment in each state was multiplied by the assumed
$1,000,000 of property value. That product was multiplied
by the composite average property tax rate in each state
to get the estimated property tax which would be paid by
this NSTBF in each state.

For capital values tax purposes the net worth of the
NSTBF was assumed to consist of the following at

December 31, 1979:

 

Common stock (par) 5 250,000
Paid-in capital 250,000
Retained earnings 500,000

$1,000,000

The rates for each state were applied to the appropriate
segments of net worth, as specified by law.

The state personal income tax was based on an assumed
eventual taxable income of $50,000 for an entrepreneur.
The rates for a joint return were used. For simplicity,
it was assumed that the entrepreneur had no capital gains
or losses and no interest or dividend income.

A summary by state of these five taxes on the hypo-
thetical NSTBF described above and on its entrepreneur
is provided in Appendix Table A5. The total taxes by

state were converted to a SMSA/NECMA basis. Where a

 

49

SMSA/NECMA was located in more than one state, a simple
average of the total taxes in each of the states involved

was used.

Factor: gNSTR--Union strength was measured in terms of
the 1974-1976 average of labor union membership as a per-
centage of nonagricultural employment. The data were
obtained from the Statistical Abstract of the U.S.23
State data were converted to SMSA/NECMA data. Where a

SMSA/NECMA was located in more than one state, a simple

 

average of the values for each state involved was obtained.

Factor: STRSEV—-Strike severity was measured in terms
of the average annual number of days idle per 100 union
members during the 1974-76 period. Data by state on the
number of days idle and on total union membership were
obtained from the Statistical Abstract of the U.S.24
State data were converted into SMSA/NECMA data. Where a

SMSA/NECMA was located in more than one state, a simple

average of the data for the states involved was obtained.

Factor: LABCOST--Labor cost. This factor was measured
by data on average weekly earnings by state for 1975.

The source was the Statistical Abstract of the U.S.25
State data were converted to SMSA/NECMA data. Where a
SMSA/NECMA was located in more than one state, the data

for each state involved were averaged.

50

Factor: ENERGY--Energy costs were measured by data by
state on cents per million BTU for each of the three
types of fuel used by electric utility plants in July,
1980: coal, oil, and gas. These data were obtained

26 An unweighted

from the U.S. Department of Energy.
average of the costs of these three fuels was calculated
for each state. However, one or two of the three forms
of energy (coal, oil, and gas) were not used by electric
utility plants in some states. Where cost data for a
particular fuel were available for at least 2/3 of the
states in a geographic region of the U.S., the regional
average cost of that fuel was used to estimate the cost
for that fuel in the states in that region with missing
data.

Appendix Table A6 presents a summary by SMSA/NECMA
of the data on the following factors: states taxes,
union strength, strike severity, labor cost, and energy
costs.

*****

Data on the number of NSTBF formed in 1975 (the

dependent variable) were available for 256 of the 266

SMSA/NECMA'S. No NSTBF were formed in 1975 in the following

SMSA/NECMA'S:

51

Anchorage AK
Clarksville-Hopkinsville TN-KY
Columbus GA-AL

Fargo-Moorhead ND-MN

Gadsden AL

Kankakee IL

Pine Bluff AR

Rochester MN

Wheeling WV-OH

Williamsport PA

Data for some SMSA/NECMA's were missing for most of
the independent variables. As shown in Table 10 below,
except for factors FEDOBMIL and FEDOBNO, data on each of
the independent variables were available for a high pro-
portion of the 256 SMSA/NECMA's covered by the dependent
variable. Although data for factors FEDOBMIL and FEDOBNO
were available for only 64 or 25% of these 256 SMSA/NECMA's
those data included 89.8% of the $201 million of federal
obligations for fellowships, traineeships, and training
grants incurred in fiscal year 1975.

In order to perform a step-wise multiple regression
using all 256 of the available observations on the dependent
variable, estimates of missing observations were made where
necessary for each of the independent variables. Each
of those variables was regressed on the dependent variable.
Each resulting regression equation was used to calculate
the missing observations for the particular independent
variable. The step-wise multiple regression was then run
using 256 observations on each variable.

A multiple t-test also was used to test the hypothesis

of this study. The continuous variable used to measure

52

the dependent variable in this study, the number of NSTBF
formed in 1975, was replaced by two different pairs of
categorical variables. One pair of categorical variables
was developed on the basis of the median number of NSTBF
formed in each SMSA/NECMA, being 11 firms. One category
comprised SMSA/NECMA's with 0 - ll NSTBF. The other
category comprised SMSA/NECMA'S with 12 or more NSTBF.
The several measures developed on each SMSA/NECMA were
the same factors used as independent variables in the
test of the hypothesis of this study by means of step-
wise multiple regression.

The other pair of categorical variables was developed

on the basis of the median value of the percentage of the
total number of firms formed in 1975 represented by NSTBF,
which was 6.02%. One category comprised SMSA/NECMA's for
which this percentage amounted to no more than 6.02%.
The other category comprised SMSA/NECMA's for which this
percentage was greater than 6.02%. The same independent
variables were used as for the first pair of categorical
variables.

Multiple t-tests were applied separately to each pair
of categorical variables. For each pair, the null hypothesis
tested was that the means of the two population categories,
using all of the independent variables, were equal. The
equality of the means of the two population categories

was also tested using single independent variables only.

 

53

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-H>munn<

CHAPTER IV

FINDINGS AND CONCLUSIONS

The hypothesis tested by this study is that some set
of factors (independent variables) described in Chapter
III-C above determines the number of new and small tech-
nology-based firms (NSTBF) formed in 1975 (dependent

variable).

A. Observed Associations

One by-product of the step-wise multiple regression
program used to test the hypothesis was a set of corre-
lation coefficients between the dependent variable and
each of the independent variables, as shown in Table 11.

A number of conclusions can be drawn from these data.
First, substantial positive correlation exists between
the dependent variable and measures of activities associated
with the process of technological innovation (ESTBF, R&DNO,
R&DMIL, DEGREES, FEDOBMIL, and FEDOBNO). The low corre-
lation between the dependent variable and the number of
patents issued is an exception and remains unexplained.

Second, little correlation exists between the dependent

variable and factors traditionally taken into consideration

54

55

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coHuMH>mHQQ<

56

when selecting the geographical area of the U.S. in which
to establish a new business (TAXES, UNSTR, STRSEV, LABCOST,
and ENERGY).

These two conclusions appear to provide statistical
support for the conclusions of an earlier researcher,
Spiegelman (see endnote 17 of Chapter II). He found that
the most important factors determining the location de-
cisions reported by 45 firms in the precision instruments
industry in five states across the U.S. were "availability
of professional staff," "availability of labor of required
skill or ability," and, of lesser importance, "proximity
to educational, testing and research facilities, and to
suppliers and markets for reasons other than transport
costs." He also found that most of these firms located
in large metrOpolitan areas in spite of the high wage
rates, land costs, and taxes in those areas, because the

favorable factors were present in those areas.

B. Step-Wise Multiple Regressions

The initial step-wise multiple regression of the data
of this study revealed that ESTBF explained 80% of the
variability of NSTBF. In other words, the coefficient
of determination (multiple R2) was .7997. The increase
in multiple R2 for each of the other independent variables
was insignificant, being less than .01 except for R&DNO
which was .0112 and R&DMIL which was .0123. This researcher

hypothesized that the reason for this finding is that all

57

small and technology-based firms, whether new ones or
existing ones, prosper under the same favorable conditions.
If this hypothesis is true, then separate step-wise
multiple regressions of NSTBF and ESTBF on the remaining
independent variables should produce similar results.

They did produce similar results, as shown in Tables 12

and 13.

Table 12

Independent Variables Which Explain Variability
of NSTBF and ESTBF, Separately

2

 

 

Increase in Multiple R If
Independent the Dependent Variable Is:
Variables NSTBF ESTBF
R&DNO .76 .
DEGREES .02 .02
TAXES .01 less than .01
OTHER less than less than
.01 each .01 each

These results show that the principal determinant of
the geographical distribution of the formation of NSTBF
is the number of technology-intensive universities, non-
profit research institutions, and industrial firms in a
SMSA/NECMA. This factor explained 76% and 86%, re—
spectively, of the variability in the dependent vari-
ables, NSTBF and ESTBF. A second determinant, which
explained an additional 2% in each case, is the number
of earned Ph.D. and Master's degrees in science and
engineering conferred by universities and colleges in a
SMSA/NECMA. Each of the other factors explained 1% or

less of the variability in the dependent variables.

58

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59

The resulting regression equations are given below.
Whether NSTBF or ESTBF is used as the dependent variable,
the coefficients of both dependent variables (R&DNO and
DEGREES) are significant. Multiple R is substantial and

positive for each regression equation.

NSTBF = 5.39 + 5.09 R&DNO + 0.11 DEGREES + e
(10.41) (to.o3)
Multiple R = .88

ESTBF = 17.55 + 22.16 R&DNO + 0.46 DEGREES + e

(11.22) (10.07)

Multiple R = .94

The present finding that the number of technology-
intensive universities, nonprofit research institutions,
and industrial firms (R&DNO) is the principal determinant
of the geographical distribution of the formation of NSTBF
supports the findings of certain earlier researchers.
Those findings are described in section C of Chapter III
as part of the rationale for using R&DNO as an independent
variable to test the major hypothesis of this study. In
their studies of the Boston and San Francisco areas, re-
spectively, Roberts and Cooper (see endnotes 12 and 13
to Chapter III, respectively) both found that NSTBF are
formed as "spin—offs" from technology-intensive universi-
ties, nonprofit research laboratories, and industrial
firms. Such firms provide an environment in which techni-

cally-trained employees perceive entrepreneurial

60

opportunities to transfer technology to NSTBF. The present
research study provides empirical evidence that the greater
the number of technology—intensive establishments of all
kinds in any given SMSA/NECMA the greater will be the ex-
pected number of entrepreneurial opportunities that will

be perceived and exploited.

An analysis of variance based on regressing the de-
pendent variable (NSTBF) on the two independent variables
which explain more than 1% of the variance in NSTBF (R&DNO
and DEGREES) resulted in an F ratio of 427.77 which was
greater than the table F ratio of 99.5 at the 1% signifi-
cance level. Therefore, there is a less than 1% proba-
bility that these results could have occurred by chance.
Thus the research hypothesis may be deemed to have been
supported.

An additional analysis of variance, this time based
on regressing the dependent variable, ESTBF, on the two
independent variables each of which explains more than
1% of the variance in ESTBF, R&DNO and DEGREES, results
in an F ratio of 918.14 which also is greater than the
table F ratio of 99.5 at the 1% significance level. Again,
there is a less than 1% probability that these results
could have Occurred by chance. These results provide
further evidence to support the research hypothesis.

The same conclusions can be drawn from the data in

Table 13 as from the data in Table 11. All small and

61

technology-based firms, whether new ones (NSTBF) or existing
ones (ESTBF), prosper under the same favorable conditions
(R&DNO, DEGREES, FEDOBMIL, FEDOBNO, AND R&DMIL). As be-
fore, none of the remaining variables show substantial

correlation with NSTBF or ESTBF.

C. Multivariate t-Tests F
An additional method used to test the major hypothesis

of this study was the multivariate t-test. The sample of .

 

256 observations was split into two samples by converting
the dependent variable (NSTBF) from a continuous variable
into a categorical variable based upon a median split of

the number of NSTBF. The two categories were 0-11 NSTBF

 

and 12 or more NSTBF.

The null hypothesis tested was that the means of the
two population categories, using all of the independent
variables, were equal. As in the earlier tests, the inde-
pendent variables were: R&DNO, DEGREES, FEDOBMIL, FEDOBNO,
R&DMIL, PATENTS, TAXES, UNSTR, STRSEV, LABCOST, and ENERGY.
The null hypothesis was rejected because the probability
(two-tailed) of equality of these means was less than
.005, as shown in Table 14. To interpret the results in
another way, the calculated F value was 4.8 versus a table
value of 2.2 at the .01 significance level. Therefore,
the two populations have different means, since the proba-
bility that this difference could occur by chance is

insignificant.

62

The next step was to test the difference between the
means of the two population categories on the basis of
each of the independent variables. Table 14 shows the
probability (two-tailed) of equality of these means. The
null hypothesis of equality of means of the two populations
based on single independent variables must be rejected
for all of those variables serving as measures of activities
associated with the process of technological innovation
(R&DNO, DEGREES, FEDOBMIL, FEDOBNO, and R&DMIL), since
there is less than a five hundreths of one percent chance
of equality of means. The null hypothesis must be accepted
for those variables measuring the factors traditionally
taken into account when selecting the location for a new
business (TAXES, UNSTR, STRSEV, LABCOST, and ENERGY) if
a significance level of one percent is used.

In summary, the results of these multivariate t-tests
reinforce the conclusions based upon observed associations
and step-wise multiple regression.

Another multivariate t-test was performed by con-
verting the dependent variable (NSTBF) from a continuous
variable into a categorical variable based upon a median

split of NSTBF as a percentage of total firms formed.

 

The two categories were 0 to 6.02%, and greater than 6.02%.
Table 15 shows the results of this test. They are almost
identical and thus provide further support for the major

hypothesis of this study.

63

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64

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65

D. Influence of Population

In drawing conclusions from the test of the hypothesis
of this study, consideration was given to the possibility
that the size of SMSA/NECMA's may have induced artificially
strong relationships between measures of the geographical
distribution of the formation of NSTBF and measures of its
determinants. To test this possibility it was hypothesized
(1) that the geographical distribution of the formation
of NSTBF is determined by factors other than population,
and (2) that those factors are highly correlated with the
size of geographical areas measured in terms of population.
If these hypotheses were supported by tests, then that
evidence would support the conclusion that the regression
relationship between the geographical distribution of the
formation of NSTBF and its determinants is not artificially
strong, but in fact is truly strong.

Table 16 shows the correlation coefficients between
population and each of the variables used to test the
hypothesis of this study. Population is highly correlated
with each of the factors which are highly correlated with
the geographical distribution of the formation of NSTBF:
R&DNO, DEGREES, FEDOBMIL, FEDOBNO, and R&DMIL. This
finding is reasonable, since it is reasonable to believe
that these types of innovative activity take place in urban
centers having relatively high population density, rather

than in remote areas.

 

66

 

 

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67

Population is not correlated with any of the factors
which are notcorrelated with the geographical distribution
of the formation of NSTBF. This finding is also reason-
able, since there is no reason to expect any systematic
relationship between population and the factors tradition-
ally considered when selecting a location for a new
business.

These results support the conclusion that the relation-

ship between the geographical distribution of the formation

 

of NSTBF and its determinants are not artificially strong,

but in fact are truly strong.

E. No Shortage of Venture Capital

Much of the research on the subject of venture capital
has concluded that the lack of venture capital has been
a major barrier to increased economic growth. The
(Michigan) Governor's Advisory Commission on the Regulation

2 and Chastain and

of Financial Institutions,1 Brophy,
DeVries3 all have reached that conclusion.

In theory, however, there can be no shortage of venture
capital. In a free market, at some price, capital flows
to where it is needed. Sharpe's Capital Asset Pricing
Model,4 a major contribution to the theory of finance,
holds that in equilibrium there is a direct and linear
relationship between the expected rate of return on any

asset and its systematic risk as measured by beta. What-

ever the level of risk associated with a new and small

68

technology-based firm (NSTBF), there is a corresponding
expected rate of return. That expected rate of return
will equal or exceed the rate of return required by some
venture capitalist somewhere in order to compensate him
for taking the risk involved in that NSTBF.

According to a recent article in the Grand Rapids
Press, based on a New York Times News Service report,
there is more venture capital money available now than

there are opportunities for investment. One factor has

 

been the reduction in the capital gains tax in 1978 to a
maximum rate of 28% from 49%. "Another factor...is that
the public's hunger for technology stocks has made it
easy for young companies to go public at high price-
earnings ratios, virtually insuring that a venture capital
company will recoup its investment quickly. Also, equity
in a young company is considered one of the few investments
that can outrun inflation."5
Evidence exists that there are plenty of risk-takers--
more than is generally suspected. As an example, consider
the history of Viatron Computer System Corp. common stock.
It initially sold at $15 per share in 1969. .The stock
soared to a high of $61. In 1971 it fell to $1 and the
company filed under the bankruptcy act. The company

raised the money it needed from the public (risk-takers),

but then made bad mistakes and went under.6

 

69

Another example is furnished by an article in the
September, 1975 issue of Fortune, "A Thinking Man's Guide

to Losing at the Track."'7

That article describes a study
which provides evidence that people are basically risk-
takers. By studying the odds of 1,000 races at the moment
the horses started off and the winnings of those horses
the article concludes that one can win by betting the
favorites, since so many people bet on the long shots.

Some entrepreneurs may face a self-imposed barrier
to available venture capital, because they may be reluctant
to share the equity in their firm with venture capitalists.

This hypothesis appears to be testable and should be the

subject of further research.

 

CHAPTER V

POLICY IMPLICATIONS AND RECOMMENDATIONS

A. Summary

This study has identified the determinants of the
geographical distribution of the formation of NSTBF. It
has the following implications. It provides policy
guidance for the promotion of economic growth in any
geographical area of the U.S. It facilitatesthe
identification of those geographical areas of the U.S.
most conducive to economic stimulation. It demonstrates

the ineffectiveness of certain methods of increasing the

rate of formation of NSTBF presently in use.

B. Policy Guidance for the Promotion of Economic Growth
This study has shown that 76: of the variability in
the geographical distribution of the formation of new and
small technology—based firms (NSTBF) is explained by the
number of technology-intensive universities, nonprofit
research institutions, and industrial firms (R&DNO) in
any given geographical area of the U.S. Therefore, to

improve the level of economic activity in any Standard

Metropolitan Statistical Area or New England County

70

71

Metropolitan Area (SMSA/NECMA) of the U.S. by increasing

the rate of formation of NSTBF, the most important policy

.

guideline is to maximize R&DNO. The interaction between

 

these different types of establishments in any given
SMSA/NECMA provides a local environment conducive to
entrepreneurship and the formation of NSTBF.

Another 2% of the variability in the geographical
distribution of the formation of NSTBF is explained by
the number of earned Ph.D. and Master's degrees in the
life and physical sciences and in engineering conferred
by universities and colleges (DEGREES). Accordingly,
the next most important policy guideline is to maximize
DEGREES.

Three other factors, in addition to R&DNO and
DEGREES, were shown to have high positive correlation

with the geographical distribution of the formation of

NSTBF and therefore also should be maximized to the extent

practicable:

- Federal obligations to universities and
colleges for technology-based fellowships,
traineeships, and training grants (FEDOBMIL).

- The number of universities and colleges re-
ceiving FEDOBMIL (FEDOBNO).

- R&D expenditures of R&DNO (R&DMIL).

72

C. Geographical Areas Most Conducive to Economic
Stimulation

This study has shown that the existence of_technology-
intensive universities, nonprofit research organizations,
and industrial firms is by far the most important deter-
minant of the geographical distribution of the formation
of NSTBF in any given geographical area of the U.S. The
rate of formation of NSTBF is greatest in places where
interaction exists between these three types of establish-
ments. Therefore, those geographical areas most likely
to generate NSTBF would be those having the highest number

of all three types of establishments. The least promising

 

areas would be those with no establishments in any of the

three categories.

1. The State of Michigan

As of December 31, 1980, the state of Michigan had
the highest annual rate of unemployment in the country--
12.6%. (Indiana and Alaska tied for second place with
9.6%.)1 Fortunately, Michigan appears to contain at
least three SMSA's which are conducive to economic stimu-
lation, namely, Ann Arbor, Detroit, and Lansing-East
Lansing. Since each of these SMSA's has a large university
which is technology-intensive, one of the policy conditions
described in B above has been met. As shown in Tables
17 and 18, those three technology-intensive universities

are: University of Michigan (Ann Arbor), Wayne State

73

Table 17'

Number of Technology-Intensive Universities,

Nonprofit Research Institutions, and
Industrial Firms in Michigan,

by SMSA, Circa 1977

Industrial
Firms

 

 

 

Nonprofit

Research
SMSA Total Univs. Insts.
Ann Arbor 3 1 1
Detroit 22 l 1
Lansing-E. Lansing 1 1
Battle Creek 1
Bay City 1
Flint
Grand Rapids 3
Jackson
Kalamazoo-Portage '4 1
Muskegon
Saginaw

Table 18

 

1
20

l
1

R&D Expenditures of Technology-Intensive Universities,
Nonprofit Research Institutions, and Industrial Firms

in Michigan, by SMSA, Circa 1977 ($Mil.)

 

Nonprofit
Research
SMSA Total Univs. Insts.
Ann Arbor $ 95.1 $86.9 $7.4
Detroit 3,235.3 20.3 2.0
Lansing-E. Lansing 55.4 55.4
Battle Creek 7.7
Bay City 203.3
Flint
Grand Rapids 3.0
Jackson
Kalamazoo-Portage 103.0 *
Muskegon
Saginaw

 

* $18,000

 

Industrial
Firms

 

$ 8

3,213.0

 

74

University (Detroit), and Michigan State University
(Lansing-East Lansing). However, both the Ann Arbor and
Lansing SMSA's have virtually no technology-intensive
nonprofit research institutions and industrial firms.
Although the Detroit SMSA has a substantial number of
technology-intensive industrial firms (20 firms conducting
$3.2 billion of research and development), there is rela-
tively little R&D expenditure at Wayne State University,
and there are virtually no nonprofit research organizations.
In order to maximize R&DNO, which is the most important
policy guideline, a number of technology-intensive nonprofit
research institutions and industrial firms must be es-
tablished in each of the three SMSA's already having tech-
nology-intensive.universities: .Ann Arbor, Detroit, and
Lansing-East Lansing. One method for doing so is for'
those universities or the state of Michigan to establish
technology parks or centers adjacent to those campuses.
According to a recent Wall Street Journal article,
Rensselaer Polytechnic Institute, Yale University, and
State University of New York at Stony Brook are all plan-
ning to develop technology parks to attract high-technology
companies to their areas. They hope these companies will
provide both jobs for graduates and consulting for pro-
fessors.2 More importantly, however, the addition of
technology-intensive nonprofit research institutions and

industrial firms to the technology-intensive universities

75

in Ann Arbor, Detroit, and Lansing-East Lansing by this
means will create conditions conducive to the "spin-off"
of new and small technology-based firms (NSTBF). Once
these SMSA's develop NSTBF, secondary "spin-offs" of NSTBF
will occur, accelerating the rate of economic growth.

To implement the policy guideline DEGREES, effort
should be made to maximize the number of Ph.D. and
Master's degrees in the life and physical sciences and
in engineering conferred by the University of Michigan
(Ann Arbor), Wayne State University (Detroit),'and Michigan
State University (Lansing-East Lansing). Officials of
the state of Michigan and of these three universities
should determine the feasibility of increasing the
facilities at these universities for the purposes of
(l) enlarging their research and development capabilities,
and (2) increasing the number of Ph.D. and Master's degrees
granted annually in the life and physical sciences and in
engineering. The availability of state and/or federal
funding for these purposes also should be explored.

Table 19 shows data by SMSA for the state of Michigan
for the remaining factors shown by this study to be highly
associated with the geographical distribution of the
formation of NSTBF: the number of earned Ph.D. and Mas-
ter's degrees awarded in science and engineering (DEGREES);
federal obligations to universities and colleges for

technology—based fellowships, traineeships, and training

76

Table 19
Data for State of Michigan on Selected Factors Highly
Associated with the Geographical Distribution of the
Formation of NSTBF, Circa 1975

DEGREES FEDOBMIL FEDOBNO ESTBF

 

 

 

 

SMSA (No.) ($Mil.) (No.) (No.)
Ann Arbor 449 4.3 l 57
Detroit 215 .4 l 906
Lansing-E. Lansing 332 1.2 1 105
Battle Creek 42
Bay City 29
Flint 84 .
Grand Rapids 183 :
Jackson 37 .
Kalamazoo-Portage 76
Muskegon 33
Saginaw 56

grants (FEDOBMIL); the number of universities and colleges
receiving such grants (FEDOBNO); and the number of existing
small and technology-based firms (ESTBF).

Perhaps the most apparent deficiency revealed by these
data is the number and size of federal obligations to the
University of Michigan, Wayne State University, and
Michigan State University for technology-based fellowships,
traineeships, and training grants. The reason for this
deficiency should be found, and, if feasible, remedied.

Michigan has a trained industrial workforce plus
technological resources. These assets make the business
climate in the Ann Arbor, Detroit, and Lansing-East Lansing
SMSA's attractive, offsetting to some degree Michigan's

relatively high taxes.

77

2. Other Geographical Areas of the U.S.

The data for each SMSA/NECMA in the U.S. for each
of the determinants of the geographical distribution of
the formation of NSTBF identified by this study are
listed in Appendix Table A4. (The opportunities for in-
creasing the rate of economic growth in any other SMSA/
NECMA(s) may be identified by analyzing those data in
the same manner as the data for Michigan's SMSA's were

analyzed in the preceding section of this chapter.

D. Ineffectiveness of Present Methods

Another implication of this study is that efforts
to optimize the conditions traditionally considered
when attempting to attract industry to a geographical
area will not increase the rate of formation of NSTBF in
that area. The correlation between the geographical
distribution of the formation of NSTBF and the traditional
factors used to locate new businesses, such as state
taxes, union strength, strike severity, labor cost, and

energy costs, has been shown to be extremely low.

APPENDIX

78

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r—IHr—ir-IH v—lc—ir-lv—lu—i MHHHH r—{r—h—h-{H HHHHH

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m

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Ac.ucooc c< cance

a3-zz asmm .um-maaocmmacaz
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x9 camacaz

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cz-m<-zm cancamz

Hm mouooumHHH>mDuHHumcusonHmz
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mo MHumeuchuoq

x8 3mH>wa0H

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m2 cadns<uaoumH3mA
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mm moamh3mH

99

0H
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Nm
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mmm
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50H
me

mm

0mm
hmhv
mHm
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HNH

oHH
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mm

mm
mm
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Nb
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o O p o
WHOMO H HHHN HMI—{I—Gu—i

xomooooq m InI-HOO \OQ’MGM
r-{r-{r—iOr-l I—i r-lr-h—h—l HNHI—Iv—l

r-h—le—l v—l

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H
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0‘
m

d’lfir-fi

m

MHOHN \‘f \OMO‘N HNOOQ’
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mm
mmH
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don

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on
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QHQ’ON H Hmmnn HO‘r-i
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Hm huH0 wamcmm

<0 whamcm>JmmHHm> HSHmuvnmcxo
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Hm ovamHHo

<Humz mzmao

Mo caac maonmaxo

x9 mmmmwo

mHGm>Hhmcamm uwmmSuHoz
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<> Goumammumzmz unom3mz

cz xamzmz
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ahusnnmumzucm>mm ummzucm>mm 3mz

hz mHHH>mukmm
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100

mm
mHm

NmH
mv
vm

50
mm
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mm
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vm
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mm
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HvH
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HH©HH r-{r-{O Hu—C

HHMHH HHH HH

MOMON mnmmm mmmmm <fl-OON com
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Ac.uaooc << cance

moH
mm

mm
mm
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m¢¢
m
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HNH
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ow
mq
mm

\OOt—C Nmo (fir-l

N

OSLHNr-‘I ONHwI—i OH

HOWNN

<> mxocmom

<0 oaamucc
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<> vacanuwm
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cm muac cacmm

oz amnasc-nwamamm
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cc oaccsc

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AH mHHomm

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mOI>3 muumHumzuwusanmmem

101

mm
mm
vN
m¢v
mm

mm
50
H0
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mHm
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How
0mm
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mmm
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mv

mm
0m
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NmN

 

r—Ir—Ir-Ir-I HQHHv—I

Lfid’d'o dmmom
f—II—If—‘If—I HNHv—h—I

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NHt-lr-II—I r-Id'r-Ir-h—I r-h-IMLDQ'

mmmmm mmqu mmmmm
HHHHH HNHHH HHHMN

KIWI
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N

awcuaca «unuwxnn
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t—I

o.omN
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0‘
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I O
<-

NWx'fr-Id' wv—Ic—Itnr-I
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Mr—Ir-IH r—INOMO
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[\Nl—Ir—II—I ONr-Ir-lln OLn

Av.uaoov << mHnma

mz-<a muac xsoam
<4 unoam>munm

x8 GOchmaucmahmnm
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4m muOmmnmm
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umHHmz muammumumnumm muamm

<0 mach Gmm

<0 0cmem0uoomHocmum amm
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x9 cacoan< cam

xa oacwa< sum

as amcwo-cuac mama uamm

<0 mmumucozumvammm|mmcHHmm
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4Huoz deo4 .um

c: ccccoc .um

22 cDOHo .um
Hz 3mc4wmm

<0 ouamamnomm
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102

cm
¢m
mm
mm
mm

ch
hHH
vb
Ho
mmH

mm
00
Now
mm
50

HmH
mOH
mm
v¢
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00H
vb
MHH
m0
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r-lv-Ir-h—IO F‘II—II—IF'IO NOd't-Ic—I I-lv-Ir-ION v-Iv-lr-II-Iv-I

and-comm mmm<rm Hmomm WMOQN m<mmm
v—h-Ir—Iv-Ir—I r-II—Ir—Ir—II—I v—Ir-INr-Ir-I r-Ir-Ir-Iv—Iv—I I—lr-Ir-Iv-Iv-I

H

~o aarnoooa> r401FH3¢5 tnu1<N¢an
<3 Pharaoqol \oc>mh¢r~ I\r4<N¢<D
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(D

M <I'r-Ir-I

H

NmH
No
wq
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OH

me
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ac.uaooc c< magma

u—lv—II-nr-IN OOOF'ILn [\r-IMOO \Dr-INNx'I' Ov—Iv-Ir-Ir-I

cz aoumwcaum-uaaa>aaaz-ccmacaa>
<c mcmz-camamaamc-onmaam>

wz msomumoHuD

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wz mmdomH3m

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>3-mc aouaac3-maaa>cmnsmum
mc cacamwcaacm

c: camamwcaacm

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aa cacaawcaacm

<3 mcwxomm
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103

00H
mm
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omH

0N
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qu
N¢
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H0

 

r-Ir-Ir—Ir—lc—I Hr—h—I v—I Hr-Iv—I

MCDMNQ munm \‘I‘ \‘!‘\"I'\'I'
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wN
mo
Nu

mNH

wq
mm
mH

Nw
NOH
MN
mN¢
NH

Ac.uaoov << magma

OMOQ'Q' mmo \‘I‘ NMr—I

mo cmuum3nczoumwcsow

<m anew
<3 «aaxm»

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cz-cz-mc seawaaaaaz
29 mHHmm muHSUHB

mm «casoaz

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xa coma

104

<00.Hm
NOH.N©
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mmH.¢m
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wa.ww
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HmuOH

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me
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mumum

00m.H
005
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mN
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00m

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00H

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me

mmwnocmum
0am mmDHm>
amuacmc

mmmH How udmamammauam muH co 0am mmHmz
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me.0N
NHO.¢H
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m00.mm
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000.0H
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caa.ma
ccc.ma
cc<.ma
cacuaa
cam c

me

zuamaoum

m< mHQMH

Hmm.0H
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0mm.HN
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ccm.aa
c<<.<a
ccc.ma
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wa
mEoocH

mmmchDm

000.0m
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me

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105

 

cm<.ac
a<<.cm
ccc.ca
mam.aca
cam.ca

<<<.mc
aac.cca
cam.cc
acc.mc
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m00.0mH
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NmN.0NH

mmm.0NH
www.00
m00.Hm
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0N0.0N

mm0.0m
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0Hm.NNH
NON.mw

 

mML.. :uuur
cmc.m -
- cma.<
- cmm.a
cca.m ccm
cca.a mc
cca.a ccm.a
mcc.< mm
cca.a cca.a
cma.a -
caa.a -
cca.m ccm.a
ccm.m -
cca.a cmm
cma.a ccc.a
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ccm.c mca
ccm.< -
maa.a ccc.m
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cmc.c -
ccm.a -
cma.c -
c<<.a -

www.mH
NmH.0H
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me.wH

0N0.Hm
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mmm.0m
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mNm.HH
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wmm.0m
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5mm.mm
mHN.ww
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mmN.wH
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N00.NH

Aw.ucoov m< magma

000.0H
000.mH
000.mH

000.0N
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0mm.wH
000.0H
000.0H

000.NN
000.mH
000.mN
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0mm.mH

000.0N

moN.NH
mmw.0H
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0ma.m
000.0m
mnm.m
0mm.wm
0mm.mH

000.0m
000.00
000.00
000.mm
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000.00
000.00
000.00
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000.mm

000.00
000.Nm

000.Nm.

000.N0
000.N0

000.n0
000.0m
000.5m
000.00
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000.NN
000.mu
000.00
000.00
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22
>2
mz
H2
02

m2
HZ

106

mww.m0
mmw.0HH
000.00
0NH.n0
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mm0.NHH

0mm.0
NmN.N
mm0.N
mHm.N

 

0m

00H
0MH
0NH

mmw.wH
5H0.wN
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000.0H
m0N.MH
0m0.mm

Ac.ucouc m< cance

cmm.ca
ccc.ma

000.mH
omH.mH

000.NN
000.m0
000.mm
000.0m
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000.mm

§§§§§

.H.>

107

0N0
NHN
00N
000
00N

00N
mom
0HN
NON
00N

0N0
NHN
HmN
NmN
00N

NON
NON
00N
HON
NHN

I3
amuaccc

mumo0

anmcm

00N
00H
50H
00N
NmH

00H
NHN
NOH
Hmm
mHN

00N
00H
00H
50H
00N

00H
HmH
NmH
0NN
00H

A00

Amv
umo0
H0004

00H
0N0
00H
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HNN

on

H.020

huHum>mm
mxHHum

...awa...

Suwcmuum

I—INHv-IN

\Ot-h—Im <I'QO\U\M
WO‘QQ d‘wONh

(fit-IN

0.Nm

0.5N

0.0H

c

GOHGD

0.00
0.00
0.00
0.0NH
0.Nm

m.H0
0.0HH
0.00
0.0HH
H.0N

0.00
0.00
0.00H
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5.00
H.NOH
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A00

AooowV
mmme

muMum

<0 camammumxmm
xa cauca<

om-<c mucnms<

cz muac caucmau<
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oz caaa>mnc<
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<0 m>ou0 cmvum0umc<.mqum:BHm£ma<
x9 oaaauma<

<m mucoua<

cz-<c soucmm-acnmasumm-csouamaa<
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22 msvumswan<

wz houHuzwmuomcmnomuhcmnH<
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mo aoux<

xH mamH40<

 

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mmmfiwmnm 302 m waHumoo4 50:3 0mnm0Hmc00

0< mHan

maamcoauacmaa maamcauc cmuomamm no <2cmz\<mzm ma mumc

108

00N
00N
NON
N00
00H

HON
00N
50N
5HN
5HN

000
NmN

HOH
N00

00N
0HN
m5N
00N
0HH

5HN
50N

00N
50N

00H
NmH
HOH
0HN
0HN

0NN
00H
HmH
00H
00H

00H
00H
00H

00H
0HN

0HN
NOH
NmH
HOH
00H

00H
HON
HON
00N
50H

H5N

0HH
05H
0N0

00N
H5N

0N0
0N0

HMN
00N
0HH

000
05H

0HN
00m
00H
00N
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0N0
00H
00H
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0HN

m

r-IM

O‘N 00¢ O‘Nln NNNON 00500

NR
HMF‘IM OOH r-Iu-IN I-Ir-Im

0

0009-40 \OCD IDCDO VII-000% O‘GMQO‘

0
\‘I'
N

0.NH
0.00
0.00
0.0H
5.0N

m.Hm
5.00
0.05
0.00
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0.55
m.Hm
H.NOH
0.00
0.00

0.NNH
0.N0
5.00H

0.00
0.00

H.05
0.00
0.0NH
5.00
0.00H

0.00
0.0HH
0.0HH
N.50
N.00

02 mflcoummuucoummHumno

>3 coummHum£0

0m scummHumn0 suHOZucoummHumso
4H Hacucmmumcmnuaucwfimaam£0
<a ccacmm umcmc

mo Goucm0

0z couwaHHHDm

wz OHmmmsm

Xe coauMum omeHooncm5u0

NH ouHcmnammucmwaHHHmmanHH>mc30um

H0 munnamnumeBHOZuvuomBmumuuuommwkum
4m Coucmwmum

mZI<2 HHHQHm>mm
mocmu304ucouxuoumnHHmBO4ncoumom
0H mmHom
4H HmEMOZucouwaHEOOHm

zH GouwcHEOOHm

a< amnwaaauac
<m-wz couamsmcac
m2 “nonmanu-ax0aam
Hz cwaaaaam

2H mwcmHOuuDSuH< uuomuucoasmmm
az cuao 5mm

H2 xmmu0 mHuumm

<4 @0500 caumm

m2 mHoEHuHmm

A0.u:oov 0< mHan

109

NON
000
HmN
50N
5HN

000
00N
00H
50N
00H

NON
N00
00N
HON
00N

5HN
5HN
HON
NON
0NN

NON
HON
00N
N00
0HN

00N
0HN
00H

00H

NHN
00N
0HN

0HN

00H
0HN
00H
0NN
5HN

00H
00H
0NN
HOH
00H

00H
0NN
00N
0HN
00H

myaa:a

50N
00H
NmH
05

0N0

H00
00N
0N0
00H
0N0

H0

05H
00N
00N
HON

0N0
0N0
00N
0HH
000

H0

00N
00N
05H
00H

0.0N
0.0N
0.50
0.50
0.NH

H.0N
N.5N
0.0H
0.00
0.0H

N.0H
0.00
0.NH
0.N0
0.0N

0.NH
0.NH
0.N0
0.5

H.N0

N.0H
0.N0
0.0N
0.00
0.0H

0.HOH
0.00
0.00H
H.NOH
0.00

0.0HH
N.NOH
0.0HH
0.0HH
0.0HH

0.00
0.00
0.N0
0.55
0.00H

0.00
0.00
0.55
0.05
0.05

0.00
0.55
0.00
0.00
0.00

Av.ucoov 0<

manna

wMqu mHHH>mcm>m

mo 0H¢H00cwummamcmwsm
<m mwpm

wz mquHm

NH 0mmm Hm

H3 m.3330 dam

H3uz2 HOHHmmnmufiunHza
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H2 uHouumn

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00 HmeDomun>amn

4H “Sumomn

40 30000 mcoumma

$0 Gouhmo

4HI<H mcHHo2uvcmHmH MUOMnuHomcm>mo
xH nuho3 unomummHHmn

2H HumHH£0 mdauoo

mo manadHo0

0m awHAESHO0

02 mHnEDHo0

00 mmcHHam ovaoHo0
mo 0cmHm>mH0
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aa ammuanc

<0nza mwooamuum£0

110

5HN
00N
000
HON
HON

NON
00N

000
NON
0HH

50N
00N
00N
5HN
00N

0N0
00N
NNN
00N
00N

NON
0HN
50N
HON
00N

00H
00H
00H
00H
0NN

HOH
00H

NHN
00H
00H

HON

0N0
NOH
HON
NOH
00N

0HH
H5N

H00
50N

00H
NOH
0HN
0N0
00N

00H
0HN
0HN
00N
00N

H0

000
00H
50H
H5N

MMNMI—I

v—IMv-Im NHN

00000 wNI—I 00 00000
010100 <I‘CDO\ \ON NNWON

N
r-I

cnn
aux
FNV

0.0H

0.NH

00
N
H

N.0H
H.0H
0.00
N.0H
0.0

A0.ucoov 0< MHan

0.00
H.05

0.NNH
0.00H

0.55

0.05
0.H0

0.0HH

0.00

0.00H

0.0HH
0.HOH

H.05
0.00
0.N0

0.00
H.05
N.00
0.N0
0.N0

0.00
0.00

0.0HH

0.55
0.H0

29 coumdom
Hm aaHaaHoGom

H0 HoumHhmuchuHum BwZI0Homuumm
<0 wudanHHmm

m0 nBoumH00H2ucouHHEmm

0m whsncmuummmumHHH>cmmH0
oz “caoc swam

namHmmIcoumcfl3uouonmcmmn0

H3 000 ammu0

00 hmHmmH0

82 mHHmm ummuu

az _ccacmz camuo

22:02 mvuom 0cmH0

zH 0000030 ammununoafimmumnmu
4m 00H0 mmxmhncoumm>Hm0

4m mHHH>mmawm0

<0 cammum
zH maxw3 uuom

20um< nuwfim uuom

4m mumk2 uuom

4m 00030HHomanm0Hmvdm4 uuom

00 maa..mHHo0 uuom
4< monHon

H2 ucHHm
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oz maaa>muumcmz

111

5NN

00H

0NN
0N0
00H
NOH
00N

00N

00N
H00
0HN

05N

H00
00N
00H

NOH
00N
00H
H00
00N

00N
00H
0HN
000
500

0.0N
0.NH
0.00
0.50
0.NH

0.0H
0.N0
0.0H
H.0N
0.N0

0.0H
0.NH
H.0N
0.0N
0.00

0.50
H.0H
0.0N
H.0N
0.NH

0.NH
0.00
0.N0
H.0H
0.N0

0.00
0.00
0.0HH
0.00H
0.N0

N.50
H.05
N.50
0.0HH
H.05

0.05
0.00
0.0HH
N.00
0.0HH

0.00H
5.00
0.0NH
0.0HH
0.N0

5.00
0.0HH
H.05
0.00
0.00

>2 mmwm> mm4

xH owmum4

H2 0aflmcm4 ummmuwaHmcm4
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40 Gm>mm umucH3u0cmmem4

<4 mmHHm£0 me4

2H muumwmmm4 umm3umuumhmmm4
<4 muummwmm4

H3 wmmou0 m4

zH 080x02

. ze mHHH>xocM

x9 magEme-cmmaaam
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mm-cz zuao cmmcmm

H2 mwmuuo0uooumamme

<0 :3ouma500

<>uZH HoumHumuuHo0m0cH2nmuH0 cow:£00
hz kuH0 0mmum0

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40 mHHH>comxomh

02 Gemxomh

H2 nomxomh

za caaoamcmawca

4< mHHH>muadm

mOuan>3 0GMH£m<ucouwaHuaDm

Ac.uaoov c< magma

112

00N
000
5HN
00N
0HN

00N
5HN

50N
000

00N
50N
5HN
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0N0

HON
5HN
000
HON
00N

HON
05H
050
0HN
00H

00N

 

00N
H00
0N0
00N
00N

00N
0N0
00N

H00

00

00N
0N0
50N
00H

00N
0N0

50H
00N

00N
050
00

00N
00N

H.0N

0.0H
5.0H
0.NH
0.0N
0.5N

0.N0
0.NH
0.0N
N.0H
N.0H

0.N0
0.0N
H.5H
0.0H
0.0H

Ac.uaooc o< magma

N.NOH
0.0HH
0.00
0.N0
0.H5

0.N0
0.00
0.55
0.0NH
0.0HH

0.N0
0.00
0.00
0.HOH
0.00

0.55
0.00
0.0NH
0.55
0.H0

0.55
5.0NH
0.H0
0.00
5.H0

a3-zz asmm .um-maaocmmcaaz
az mmxsmsaaz

xa camacaz

gm aamaz

mz-m<-zs canaemz

40 00000umHHH>wDuHHumsHsonHm2
x9 musncacm-uumnm-amaa<oz

mo camammcmz

mz mazmmZuHmummnocm2

az cowacmz

<0 Goom2

<> 0050:0000

NH x000054

anwM 0HHH>deo4

<0 £0000 0004-00H000< 004

mo mauzam-aamhoa

NH 30H>0c04

02 me0 0H5£m<nzoamum 0004

0< #000 0HuuH4 nuHOanoom mHuuH4
mz a50934

20 08H4
02 muumhm0ncouwame4

m2 CH305<uG0000304
Mo £00304
02 monan4

113

 

00N
0N0
05H
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50H

0HN
5HN
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50N

000
00N
00N
000
000

000

500
50N
50H
50N

00N
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0N0
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00H
00N
00H
00H
00H

05H
00H
00H
00H

00H

00N
00H
00N
00H
00H

00N

05H
HOH

HON

0HN
NOH
00N
00N
NOH

00N
00H
050
00N
00N

00N
0N0
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00N

00N
00H
NHH
00N
HON
HON
00H
00H
05N
00H
0HN
00N

00H
000

0.NH
0.5N
0.0N
0.NH
0.5H

0.0H
0.NH
0.50
0.0H

5.0H

0.0N
N.N0
0.0H
0.0N
0.0N

0.0N

0.0N
0.50
0.0H
0.00

0.N0
H.0H
0.0H
0.5N
H.0H

ac.ucooc c< magma

0.N0
0.00
5.0NH
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0.00
0.00
0.00H
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0.00

0.0NH
N.HOH
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0.NNH
0.NNH

0.0NH

0.N5H
H.NOH
0.05

0.0HH

H.05
0.00
N.50
0.00
0.00

40 2000 0E0G00

<0 0usuc0>u20HH0> HBH0u0H0Gx0
22 0H0000030

40 0000H90

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29 000000

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02 200302
02-22 2H0» 302
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90 £003u02u000004 302
90 C00HH02
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02
0HHH>0H20mu2008< nuu00nxUH3mafium 302
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22 2H000201500002
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114

50N
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50N
00N
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HON
00H
00N
000
NON

N5H
50N
50N
00N
050

500
HON
00N
50N
50N

N00
00N
000
00N
00N

00H
00N

00H
0NN
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00H
N5H
00H
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00H

00H
00H
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05H
00H
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00N
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00N

 

00N
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00N
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00H
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00

00H
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00N
05H
00N

05H
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000

5.0H
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5.0H
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0.50
0.0H
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H.0N
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N.0H
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0.50
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0.0N
0.50
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0.00
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20.00000 0< 04009

0.00
0.00

0.00
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0.00H
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0.N0
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0.N5H
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0.00

0.00
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<> 0000000
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115

50H
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mqm

mqm
dmm
0mm
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.0mm

«mm
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N5H
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mom
mmm
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qu
mom

00H

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00H
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mwN
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me
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mma
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0.NH
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m.n~
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N.0H
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m.m~
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a.mm

00.00000 00 00000

a.moa

N.00
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0.N0
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m.mw
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0.N0
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mz-<0 0000 00000
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116

mom
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mam
mom
mom

00N

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mqm
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new
qmm
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mmm
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00N
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mca
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mma
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mud
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00H
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mm

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00.00000 0< 00000

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0.0m
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5 H0
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0.50
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117

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00N
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mom
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118

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mo ‘coucmu

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119

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120

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121

 

 

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EN DNOTES

 

ENDNOTES

CHAPTER I - INTRODUCTION

lStanley Rubel, Guide to Venture Capital Sources,
4th ed. (Chicago: Capital Publishing Corp., 1977),

 

 

 

 

 

p. 171.
1
2Deloitte, Haskins and Sells, Report to Governor's .
Advisory Commission on the Regulation of Financial 3 ,4
Institutions (Detroit: Deloitte, HaskIns and_Sells, ; A}
1977), part IV, p. 2. §:_
3

Ibid., part IV, p. 7.

CHAPTER II - REVIEW OE THE LITERATURE

lGovernor's Advisory Commission on the Regulation
of Financial Institutions, Final Report (Lansing MI:
State of Michigan, 1977), p. 83.

2

 

Ibid., pp. 85-6.

3Deloitte, Haskins and Sells, op. cit.

4David J. Brophy, Finance, Entrepreneurship, and
Economic Development (Ann Arbor MI: Industrial Develop-
ment Division, Institute of Science and Technology,
University of Michigan, 1974), p. v.

5Clark Chastain and Marvin DeVries, Financing in
Michigan: R&D vs. Manufacturing Firms (Ann Arbor MI:
Industrial Development Division, Institute for Science
and Technology, University of Michigan, 1966).

6

 

 

Brophy, op. cit., p. 41.

7Edward Roberts and H. A. Wainer, Some Characteristics
of Technical Entrepreneurs (Cambridge MA: M.I.T. Sloan
School of Management, Working Paper 195, May, 1966).

 

 

124

125

8Arnold Cooper, "Small Companies Can Pioneer New
Products," Harvard Business Review, 44 (September-
October 1966): 1962479.

9

 

Brophy, op. cit., p. 45.

10James F. Mahar and Dean C. Coddington, "The Scien-
tific Complex-~Proceed with Caution," Harvard Business
Review, XLIII (January-February, 1965), 142-114.

 

llLawrence Lamont, "Technology Transfer, Innovation
and Marketing in Science-Oriented Spin—Off Firms" (Ph.D.
dissertation, University of Michigan, 1970), p. 35.

lzIbid., pp. 37-39.

13D. Shimshoni, "Aspects of Scientific Entrepreneur-
ship" (Ph.D. dissertation, Harvard University, 1966),
quoted in N. G. Clark, "Science, Technology and Regional
Economic Development," Research Policy (Amsterdam: North-
Holland Publishing Co.) 1 (1972): 299.

l4U.S., Department of Commerce, Technological Inno-
vation: Its Environment and Managgment, September 1967,
p. 14.

15

 

 

Brophy, op. cit., pp. 41-2.

15c1ark, op. cit., p. 308.

17R. G. Spiegelman, "A Method for Determining the
Location Characteristics of Footloose Industries: A Case
Study of the Precision Instrument Industry," Land
Economics, 40 (February 1964): 79-86.

18

 

Cooper, op. cit., p. 76.

Rubel, op. cit.

20John Dominguez, Venture Capital (Lexington MA:
D.C. Heath and Co., 1974):

19

 

CHAPTER III - METHOD OF RESEARCH

1Dun and Bradstreet, Inc., Reference Book (New York:
Dun and Bradstreet, Inc., 1980).

2U.S., Department of Commerce, Bureau of the Census,

Statistical Abstract of U.S., 1979.

 

 

126

3U.S., Department of Commerce, Bureau of the Census,
Census of Population, 1970, Subject Reports, Detailed '
Occupation by DetaiIed Industry, Series No. PC(2)-7C-
Table 8.

 

4Standard and Poor's Compustat Services, Inc.,
Compustat Tape (Denver: Standard and Poor's Compustat
Services, Inc., 1977).

 

5American Electronics Association, Directogy, Blst
ed. (Palo Alto: American Electronics Association, 1979).

6Industrial Development Division, Directory of Re-
search, Development, and Testing Facilities In Michigan
(Ann Arbor MI: Industrial Development Division, Institute
of Science and Technology, University of Michigan, 1980).

7

 

Rubel, op. cit.

8Dun and Bradstreet, Inc., Million Dollar Directory,
Vol. II (New York: Dun and Bradstreet, Inc.,‘1980).

9Lawrence Lamont, "Technology Transfer, Innovation
and Marketing in Science—Oriented Spin-Off Firms" (Ph.D.
dissertation, University of Michigan, 1970), p. 35.

10

 

Cooper, op. cit.

llU.S., Department of Commerce, Bureau of the Census,
County Business Patterns.

12

 

Roberts, op. cit.

l3Cooper, op. cit.

l4Clark, op. cit.

15U.S., National Science Foundation, Academic ScienceL
R&D Funds, Fiscal Year 1978, NSF 79-320, Table 10.

16

 

Ibid., p. l.

l7U.S., National Science Foundation, Federal Support
to Universities, Colleges, and Selected Nonprofit Insti-
tutions, Fiscal Year 1977, NSF 79-311, Table B-37.

18

 

 

 

Lamont, op. cit., p. 34.

19U.S., National Science Foundation, Division of
Information Systems, Earned Degrees Conferred, 1974-1975.

 

'H_;V

 

 

 

127

20U.S., National Science Foundation, Federal Support
to Universities, Colleges, and Selected Nonprofit Instl:
tutions, Fiscal Year 1975, NSF 77-303, Table B-lO.

21U.S., Department of Commerce, Patent and Trademark
Office, News, July 6, 1976, p. 4.

22Prentice-Hall, Inc., All States Tax Handbook 1980
(Englewood Cliffs, New Jersey: Prentice-Hall, Inc.,

23U.S., Department of Commerce, Bureau of the Census,
Statistical Abstract of U.S., 1979, Tables 699 and 705.

24

 

 

 

 

Ibid., Tables 685, 699, 703 and 705.

251bid., Table 687.

26U.S., Department of Energy, Energy Information
Administration, Cost and Quality of Fuels for Electric
Utility Plants-July 1980, DOE/EIA-0075(80/07).

 

 

CHAPTER IV - FINDINGS AND CONCLUSIONS

lGovernor's Advisory Commission on the Regulation
of Financial Institutions, op. cit., pp. 85-6.

2Brophy, 0p. cit., p. v.

3Clark Chastain and Marvin DeVries, op. cit.

4William F. Sharpe, "Capital Asset Prices: A Theory
of Market Equilibrium Under Conditions of Risk," The
Journal of Finance, 19 (September 1964): 439-440.

5

 

Grand Rapids Press, 28 June 1981, p. 7G.

 

6Wall Street Journal, 30 April 1971, p. l.

 

7Daniel Seligman, "A Thinking Man's Guide to Losing
at the Track," Fortune, 91 (September 1975): 81-87.

CHAPTER V - POLICY IMPLICATIONS AND RECOMMENDATIONS

1New York Times, 30 January 1981, p. 14.

 

2Wall Street Journal, 16 April 1981, p. l.

 

1..

 

 

 

BIBLIOGRAPHY

 

 

 

BIBLIOGRAPHY

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Brophy, David J. Finance, Entrepreneurship, apgiEconomic
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Clark, N. G. "Science, Technology and Regional Economic
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Lamont, Lawrence. "Technology Transfer, Innovation and
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Mahar, James F. and Coddington, Dean C. "The Scientific
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Hui?

 

 

 

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Oxenfeldt, Alfred R. New Firms and Free Enterprise: 'Pre-
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Prentice-Hall, Inc. All States Tax Handbook 1980. Engle-
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Roberts, Edward and Wainer, H. A. Some Chapacteristics
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Rubel, Stanley. Guide to Venture Capital Sources. 4th
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Seligman, Daniel. "A Thinking Man's Guide to Losing at
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Sharpe, William F. "Capital Asset Prices: A Theory of
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Shimshoni, D. "Aspects of Scientific Entrepreneurship."
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Spiegelman, R. G. "A Method for Determining the Location
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. Statistical Abstract of U.S., 1979.

U.S. Department of Commerce. Technological Innovation:
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U.S. Department of Commerce. Patent and Trademark Office.
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. Federal Support to Universities, Colleges, and
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