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M arket opportunities identification m odel for retail and service
industries in selected M ichigan cities
Aworuwa, Olorundare Evaristus, Ph.D.
Michigan State University, 1991

Copyright ©1991 by Aworuwa, Olorundare Evaristus. All rights reserved.

UMI

300 N. Zeeb Rd.

Ann Arbor, MI 48106

MARKET OPPORTUNITIES IDENTIFICATION MODEL
FOR RETAIL AND SERVICE INDUSTRIES
IN SELECTED MICHIGAN CITIES

By

Olorundare Evaristus Aworuwa

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Resource Development

1990

ABSTRACT
MARKET OPPORTUNITIES IDENTIFICATION MODEL
FOR RETAIL AND SERVICE INDUSTRIES
IN SELECTED MICHIGAN CITIES
By
Olorundare Evaristus Aworuwa

This

study

is

concerned

with

developing

a

prediction model using six independent variables and the
multiple

regression

technique

business

opportunities

based

to

on

identify

the level

possible

of

supply

of

retail and service functions in selected Michigan cities.
Eighty

Michigan

cities

with

populations

of

10,000

to

100,000 were studied.
The

multiple

regression

technique

utilized

city

population, per capita income, unemployment, proximity to
a major
income,

city,

level

of distress,

and

county

per

capita

to predict level of supply of retail and service

functions per 10,000 persons for the 80 cities.
The dependent
retail
cities.

and

service
The

variable was
establishments

difference

between

the actual number
in
the

each

of

actual

the
and

of
80
the

predicted number of establishments was used as a measure
of level of supply.
the

actual

and

A high, negative difference between
predicted

number

of

establishments

Olorundare Evaristus Aworuwa

indicated

that a particular function was under supplied.

The acceptable level of statistical significance for all
tests was .05.
Major
predicted

findings were:

supply

levels

service functions;

(1) the model

for

the

ten

significantly

retail

and

ten

(2) the model results correlated with

estimates by local officials in 50 percent of retail and
70

percent

of

significant
levels.

service

in

County

functions;

and

(3)

predicting

retail

and

per

income

and

capita

proximity

service
city

was

supply

per

capita

were significant in predicting service supply levels.
Limitations
number of cities
the use
the

located

limitations,
served
for

as

(1)

a large

a measure of supply levels

of shopping

within

included:

is required to apply the model and

of units

effects

of the study

city

malls

limits

or

or

ignores

large multiple

periphery.

(2)

stores

Despite

the

the model is reliable for identifying under-

retail

economic

and

service

development

sectors,

and

professionals

a valuable
seeking

tool

economic

development based on home-grown businesses.
Recommendations:
determine
rates,
and

the

effects

economic

service

and

supply

Further
of

race,

fiscal
levels.

study
crime

policies
Only

on

is necessary
rate,

local

a city's

cities

to
tax

retail

similar

Michigan cities studied may be able to apply the model.

to

Copyrighted by
OLORUNDARE EVARISTUS AWORUWA
1991

ACKNOWLEDGMENTS
The
due

to

successful completion of this study has been

the

people— to

support

mention

and

them

cooperation
individually,

impossible.

However,

gratitude to

the following people

of

very

though,

I do wish to express

special
would

be

my profound

for their outstanding

support and dedication.
My

special

research director,
attention

to

gratitude

to Dr.

John Schweitzer,

my

for his enduring patience, painstaking

detail,

incisive

critiques,

valuable

/

suggestions,
every

step

Nickel,

professional
of

my

this

study.

major

committee,

for

unqualified

assistance,

advisor

his

support

Special
and

special
and

and

guidance

thanks

chair

of

interest,

to

Dr.

Paul

my

doctoral

guidance,

encouragement

at

and

throughout

my

doctoral program at Michigan State University.
I am also greatly indebted to Dr. Rex LaMore, co­
director
providing
topic.

of

my

the
I

encouragement

research,

for

opportunity

of

am

particularly

and

confidence

his

inspiration

choice

for

the

appreciative
in

me

and

and

for

research
of

for

his
being

instrumental to the partial funding of this study by the
Michigan

Partnership for Economic Development Assistance

(MP/EDA).
iv

My
Milton

special

thanks

Steinmuller

invaluable

for

to

Dr.

their

suggestions,

both

Frank

Fear

interest,

at

the

and

support,

proposal

stage

Dr.
and
and

the final stage of this study.

As a student of these two

outstanding

benefited

their

scholars,

wealth

of

I

have

academic

and

immensely

professional

from

teaching

experience.
I am also grateful to Dr. Ray vlasin for sparing
the

time

to

critique

the

constructive suggestions,

survey

instrument,

his

and the partial funding of this

study.
Special
and

special

thanks

friend,

to

for

Joshua

his

Bagaka's,

invaluable

a

colleague

suggestions

and

editing of the statistical aspects of the study.
Words
indebtedness
typing

most

unflinching

are inadequate to express my gratitude and
to
of

my

beloved

the

wife,

numerous

support

and

Oluremi

dissertation

understanding

Bosede,
drafts.

throughout

for
Her
the

trying and difficult periods of the doctoral study.
Above

all,

my unqualified

gratitude

to

Almighty

God, the source of all wisdom and knowledge to whom all
glory is due.
This

study

was

partially

funded

by

Dissertation

Fellowship Award from the Michigan State University Urban
Affairs

Program

and

by

the

Michigan

Economic Development Assistance (MP/EDA).
v

Partnership

for

TABLE OF CONTENTS
Page
LIST OF TABLES......................................... xi
LIST OF FIGURES....................................... xiv
LIST OF APPENDICES..................................... xv
Chapter
I.

INTRODUCTION....................................1
Overview........................................ 1
The National Economy......................... 1
Present Economic Trends......................3
The Decline of the Manufacturing Economy... 3
The Nonmanufacturing Industries............. 4
The Michigan Economy 1900-1970s............ 5
Decline of Manufacturing Sector and
Growth of Service Sector.................. 8
Michigan's Economy in 1980s.................. 10
Growth Trends in Michigan
Nonmanufacturing Industry.................. 12
Economies of Michigan Cities................. 20
public policy and structural Implications... 22
Socioeconomic Implications for
State and Local Government................. 24
Problem Statement............................. 26
The Purpose of This Study.....................28
Primary Objectives.......................... 28
Secondary Objective......................... 29
Assumptions of This Study.....................29
Definition of Terminology.....................30
Significance of Study......................... 32
Limitations.................................... 3 3
Methodology.................................... 34
Theoretical Framework......................... 37
Supply-Oriented Development Theory......... 38
Demand-Oriented Development Theory......... 38
Export-Based Theory......................... 39

vi

Page

Chapter

Location Theory............................... 40
Central Place Theory......................... 4 3
Dissertation Organization.................... 45
II.

REVIEW OF LITERATURE.......................... 46
Introduction...................................46
Organization of the Literature Review....... 47
Central Place Theory.......................... 48
Centrality of Place and Its Economic
Functions..................................48
Hierarchy of Central Places.................. 50
Central City Systems and Commercial
Hierarchies..................................52
Centrality and Location of Retail and
Service Functions........................... 54
Location Theory............................... 55
Location Theory and Retail and Service
Functions................................. 55
Location Approaches......................... 56
Location Decision Process.................. 58
Retail and Service Location Factors..........61
Retail and Service Market Structure..........64
Retail and Service Entrepreneurs............. 65
Determinant of Range of aProduct............ 67
Trade A r e a ...................................68
Market Development............................ 69
Retail and Service Development Factors.... 69
Consumer Behavior........................... 70
Market Structure............................ 71
Demographic Factors......................... 72
Age Structure.............................. 7 3
Consumer Income............................. 74
Cultural Factors............................ 75
Infrastructure (s)........................... 75
Competition..................................76
Market Organization........................... 76
Retail and Service Market Organization
77
Department Stores........................... 80
Independent Stores.......................... 80
Multiple Stores............................. 80
City Size, Structure, and Functions......... 81
Public Policy............................... 84

vii

Chapter

Page
Changing Patterns of Retail Market
Environment..................................85
Competition................................. 85
Consumer Mobility........................... 87
Application Models in Retail Markets.........87
Retail Gravitation Model................... 88
Laws of Retail Gravitation................. 90
The "Breaking Point" Formula............... 91
Physical Planning............................. 92
Shopping Centers............................ 92
Modification of Reilly's Mod e l
93
The Haydock Model........................... 95
Other Models...................................96
Market Potential Model......................96
Sales Potential Retail Model............... 97
Trade Area Capture (TAC) Mod e l............. 97
Pull Factor................................. 99
Location Quotient Model................... 100
Population-Employment Ratio Model..........102
Retail Compatibility Model................ 103
Regression Analysis Model................. 105
Recent Empirical Studies.................... 106
Summary....................................... 108

III.

RESEARCH METHODOLOGY AND THE DESIGN
OF THE STUDY................................. 112
The Model:
Market Opportunities
Identification Model in Retail
and Service Industries in Michigan....... 113
Multiple Regression Model................... 114
Hypotheses and Variables.................... 115
Measurement of Variables.................... 118
Level of Supply............................ 118
City Population............................ 119
City Per Capita Income.................... 119
Proximity to a Major City ................. 120
City Unemployment R a t e .................... 120
County Per Capita Income.................. 120
City's Level of Economic Distress.........120
Population and Sample......................121
Instrumentation.............................. 123
Data Collection.............................. 124
Data Analysis................................ 125
Analysis of Other Hypotheses................ 127

viii

Chapter

Page
Limitations of the Model.................... 128
Validity of the Model......................128
Summary....................................... 130

IV.

ANALYSIS OF D A T A ............................. 131
Phase One: The Model........................ 133
Phase Two: Analysis of City
Officials' Opinions........................ 142
Phase Three:
Relationship Between City
Government's Economic Development
Efforts and the City's Level
of Distress................................ 148
Phase Four: Relationship Between City
Government's Economic Development
Efforts with the Level of Tax
Revenues and Employment
Generated by the Sector................... 156
Phase Five:
Relationship Between
Classification of Economic Development
Mission and Ranking of Economic Sector
with the City's Level of
Distress.......162
Validation of the Model......................165
Comparative Analysis.......................167
Comparing the Model with TAC Scores
168
Comparing the Model with Pull Scores
171
Summary...................................... 17 3

tv7

r v c < v # w r 'j r i f i i i w / i i x u i n o • • • • • •

X / X.

Summary....................................... 172
Literature Review............................ 174
Methodology...................................175
Data Analysis and Results................... 176
Phase One— The Model.......................176
Phase Two:
City Officials' Perception
of Level of Retail and Service
Supply.................................... 177
Phase Three: Relationship Between
City Government's Development
Efforts and City's Level of
............................. 178
Distress
Phase Four:
City's Economic
Development Efforts and Level of
Tax Revenues, and Employment
Generated by Economic Sectors........... 179

ix

Page

Chapter

Phase Five:
City's Classification
of Economic Development Mission,
Ranking Importance of Economic
Sector and City's Level of Distress.... 181
Conclusion.................................... 181
Limitations of the Study.................... 185
Recommendations.............................. 186
The Model...................................186
Economic Sector and Economic
Development Mission......................187
APPENDICES............................................ 195
BIBLIOGRAPHY.......................................... 256

X

LIST OF TABLES
Table

Page

1.1.

Michigan Employment Trends Annual
Average Employment........................... 14

1.2

Michigan:
Average Annual Employment
Trends:
Manufacturing and Selected
Nonmanufacturing............................. 15

1.3

Selected Fast Growth Service Industries:
Michigan...................................... 17

1.4

Sample Cities by Population Size and
Economic Conditions.......................... 35

4.1.

Results of the Prediction of Levels of
Overall Supply of Retail Functions by
Six Predictors for the Four-Year Periods.. 135

4.2

Prediction of Level of Supply of Retail
Functions by Predictor Variables for the
Four-Year Periods.......................... 137

4.3.

Results of the Prediction of Levels of
Supply of Each of 10 Retail Functions by
the Six Predictor Regression Model
for 1987....................................

138

4.4.

Results
of the Prediction of Levels of
Overall Supply of Service Functions by
Six Predictors for the Four-Year Periods.. 140

4.5.

Prediction of Level of Supply of
Service Functions by Predictor Variables
for the Four-Year Periods.................. 141

4.6.

Results
of the Prediction of Levels of
Supply of Each of 10'Service Functions
by the Six Predictor Regression Model
for 1987....................................

xi

143

Page

Table
4.7.

Results of the Pearson Moment
Correlation Analysis of the
Relationships Between the Predicted
and Perceived Level of Supply of
Retail Functions............................ 146

4.8.

Results of the Pearson Moment
Correlation Analysis of the
Relationship Between the Predicted
and Perceived Level of Supply of
Service Functions........................... 147

4.9.

One-Way Analysis of Variance (ANOVA)
Results of the Differences in
Percentage of Staff Efforts
Allocation to Economic Sector Promotion
Among Cities with Different Levels of
Distress.....................................151

4.10 .

One-way Analysis of Variance (ANOVA)
Results of the Differences in
Percentage of Economic Development
Budget Allocated to the Four Economic
Sectors by Level of Distress............... 151

4.11 .

Results of Analysis of Variance
of the Differences in Levels of
Supply of Retail Functions by the
Cities' Level of Distress................. 15 3

4.12.

Results of Analysis of Variance
of the Differences in Levels of
Supply of Service.Functions by the
City's Level of Distress................... 155

4.13 .

Results of Pearson Moment Correlation
Analysis of the relationship Between
Staff Efforts Allocation in Each Sector
and Tax Revenues Generated by
the Sector...................................158
Results of Pearson Moment Correlation
Analysis of the Relationship Between
Development Budget Allocation in Each
Sector and Tax Revenues Generated by
The Sector...................................158

xii

Table

Page

4.15.

Results of Pearson Moment Correlation
Analysis of the Relationship Between
Level of Staff Efforts with Level of
Employment Generated by that Sector........ 160

4.16.

Results of Pearson Moment Correlation
Analysis of the Relationship Between
Levels of Budget Allocation and
Level of Employment Generated by
the Sector.................................. 161

4.17.

Chi-Square Results of the Relationship
Between Classification of Economic
Development Mission and City's Level
of Distress................................. 164

4.18.

One-Way Analysis of Variance (ANOVA)
Results of the Differences in Ranking
of Importance of Economic Sectors by
City's Level of Economic Distress..........166

4.19.

Results of Correlation Analysis Between
the Reported Levels of Supply of Retail
Function with the Supply Levels Generated
by the Model, TAC, and Pull................ 169

4.20.

Results of Correlation Analysis Between
the Reported Level of Supply of Service
Functions with the Supply Levels
Generated by the Model, TAC, andPull

4.21.

170

Number of the Most Significant
Correlations Between Reported Supply
Levels and Model, TAC, and Pull Scores.... 17 3

xiii

LIST OF FIGURES

Figure
1.

Map of Michigan

xiv

LIST OP APPENDICES
Appendix
A.

Page
Survey Instrument.............................196

B.

Retail Trade; Selected Service
Industries....................................209

C.

Measures of Economic Distress................212

D.

Multiple Regression Output................... 214

xv

CHAPTER I
INTRODUCTION

This

chapter

presents

an

overview

of historical

trends in the changing structure of the economic base of
the

United

States

and

the

state

of

Michigan

1900s to the later part of the 1980s.
major
and

public
their

Other

responses

economic

issues

purpose
the

policy

of

the study,

theoretical

in

for

this

the

It also examines

to the structural

consequences

addressed

from

changes

Michigan

chapter

cities.

include

the

conceptualization of the problem,

relevance

of

the

location

and

central

place theories,

and other related theories as a basis for

understanding

retail

activities

in

the

trade

economic

and

service

development

of

industry
cities

and

communities.
Overview
The National Economy
Prior to the mid-1900s, the economy of the United
States

was

However,
witnessed

primarily

by
the

the

end

rapid

agrarian
of
social

the

and

predominantly

1950s,

the

transformation

rural.

nation
of

a

had

rural,

agrarian society into a nation of rapidly expanding urban
1

2

centers.

The

progressively

economies

of

dependent

on

these
the

urban

centers

manufacture

of

became
durable

goods and service industries (Haber, 1959).
The
(1940

to

nation's

manufacturing
major

source

growth,
of

based

growth

World War II

of

was

economic

leadership and domination of global trade.
period

1970s)

of

technological

a

early

period

on

created

the

longest

economic
as

the

expansion

United

defense products

States

and

rapid

became

and supplies.

the

At the

end of the war (1945), the United States emerged with its
factories,

elaborate infrastructures

and

intact

ports

technology,
provided

and

skilled

motivation

economy.

(roads and railways)

operational.

labor,
for

and

Its

vast

globalization

sophisticated

natural
of

the

resources
nation's

The economy became the supplier of technology,

equipment,

and personnel

for the reconstruction of war-

ravaged Western Europe.

By the 1950s, the United States'

economy

a dominant

economy,

had

established

accounting

for 40

share

of the

global

percent of the world's goods

and services,

and controlling more than 70 percent of the

world's gold

reserves.

high-technology products,

It supplied most of the world's
and its industries generated 50

percent of the world's wealth (Kurtzman, 1988, p. 49).

3

Present Economic Trends
Unlike
economic

the

growth

post-World

and

War

prosperity

II

period,

were

when

spread

among

states/regions, the phenomenon since 1971 has been one of
uneven

and

fluctuating

pockets

of

economic

prosperity

across the country.
The

economic

decline

in one

state/region

(e.g.,

the Frostbelt) has been offset by the prosperity in other
states/regions

(e.g.,

Houston,

Texas,

and

the

Silicon

Valley, California). Similarly, gains made in one or more
sectors
been

(e.g.,

growth in service sector employment) have

offset

by

decline

production/manufacturing

in

other

and

sectors

petroleum

(e.g.,

industries)

(Kurtzman, 1988, pp. 102-104).
The Decline of Manufacturing
Economy
O

nation and

pi

^ ^
-L ll

4* U ^
UAHS

^
^ m ^
. «•» ^
id U I U W U U L C

^
4?
W A.

A* U ^
L a i'S

a m
4
C U W iiU IIIA C d

4« U a
U iiC

a

W i.

individual states were inevitable in view of:

(1) major changes in the foundation and structure of the
global

economy,

reduced

labor

(2)

costs,

increasing
(3)

greater

product
use

of

output

with

cost-efficient

technology and disengagement of a primary product economy
from an industrial economy,
U.S.

manufacturing

effectively

in

manufacturing

a

and

industries
global

sectors

(4) the inability of the
and

market.

products
The

was underscored

to

decline

by the

compete
in

erosion

the
in

4

its

share of total

employment.

The manufacturing share

of nonfarm employment in the private sector fell from 37
percent

in

1960

manufacturing
jobs,

but

to

24

generated

accounted

percent
one

out

in
of

1985.

every

for only one in five

In

three

1953,

nonfarm

in the

19 80s.

Manufacturing's share of the Gross National product (GNP)
also fell from 30 percent

in 1930 to 21 percent in 1985

(Kamer, 1988, pp. 3-4).
The Nonmanufacturinq Industries
Service-producing
moderate

employment

manufacturing,
become

areas

November,

accounted

rapid

employment

Apart

from

construction

and

industries

have

growth.

Nonfarm

by approximately 19 million jobs as

1988.

for

in

service-producing

employment increased
of

growth

the
of

industries.

The

service-producing

industries

89 percent of the total employment growth

or a growth of more than 42 million net jobs between 1960
and 1986;
one
63.5

of

the

service-producing

percent

(Kamer,
the

70 percent of it in private nonfarm jobs, with

1988,

President,

of

the

p.

4;

1989).

industries

private,
Kutscher,

nonfarm
1988;

However,

wage of the manufacturing sector,
fast-growing,

service-producing

accounting
personal

Economic

unlike

the

income

Report
high

for

of

labor

a great number of the

industries

require labor

with little or no job skills and offer low wages.

These

5

factors

(unskilled

labor

continue

to

have,

standard

of

living

and

serious
and

low

wages)

implications

equity

in

have,
for

income

and

the

may

future

distribution,

particularly in large population centers.
Perhaps

the

restructuring
manufacturing
U.S.

economy

sometimes
of

efforts,
jobs,
into

defined

"old

most

line

significant
and

has

been

legacy

consequent
the

subeconomies.
geographically

of

the

losses

in

fragmentation

of

the

These subeconomies

are

or

regionally

industry"— energy,

high

technology,

agriculture and services (Kamer, 1988, p. 2).
discussed

in

a

disparity

in

the

regions/cities

later

part

economic

is

closely

of

this

study,

conditions
linked to

of

in terms

As will be
the

current

the

nation's

the conditions

of

their subeconomies.
The Michigan Economy 1900-1970s
The
depended

economic

base

on agriculture,

transformation

of

manufacturing was,
the lumber

its

of

Michigan,

mining,

economic

prior

and lumber.
base

in a large part,

to

1900,

The rapid

from agriculture
due to

and transportation industries,

to

the success of

along with the

capacity of the state to successfully harness and channel
its

natural,

highly

human,

productive

lumber industry was

and

technological

economy.

The early

resources
impact

into
of

a

the

in the construction and expansion of

6

railroads

for

the

movement

of

agricultural

eastern and western regional markets.
success

achieved

by

the

railroad

products

Subsequently,

industry

led

to
the

to

the

diversification of its investments through the promotion
of

recreation

hotels)
along

and tourism

in the
the

Upper

inland

and

(e.g.,

financing large

Northern

coastal

lakes

Lower

of

resort

peninsulas

Michigan

and

(Jackson,

1988, pp. 91-92).
The
lumber

early

industry

production,

included

tourism

railroad cars,

transportation

to

the

and

manufacturing

industries,

.

x:

. u u ~

uiic

a

factors
and

management),

have

manufacturing economies
1896,

Michigan

1914,

the State'

of

the nation's

n

a

~ ~

produced

first

workforce
of

heavy,

development
i______ a . *

Lumuua uxun

heavy

dominant

its

In

contributed

skilled

for

of Michigan

salt

transportation

promotion

^aduiiiie

a

the

and carriages).

the

the- bedrock
had

and

a

the

and

-lhuciiiQi

formed

of

from

recreation,

industry

production

(manufacturing

growth

and

(ships,

significantly

These

industries

manufacturing,

the

w

in new

chemical

manufacturing
addition,

spin-offs

and
_• _____

eny.Lilts •

industrial

impact

on

the

and the nation.
gas-powered

cars;

In
by

automobile industry produced 78 percent
total

automobile

output

(Jackson,

1988,

p p . 92-96).
By

the end

transformation

of

of the

first half of the

Michigan's

economy

from

1900s,
agrarian

the
to

7

manufacturing
the

was

state's

household

complete.

industrial

appliances,

engines, etc.)

sector

force)

provided

activities

led

tertiary

the

incentives

the

state.

to

the

furniture,

machines,

of

for

rail

The

the

cars,

(elaborate

migration

The upsurge
growth

in the economy,

structure

(automobile,

of

highly productive skilled labor

industries.

industry

capacity

and its agglomeration economies

infrastructures,

to

expanding

industrial

network

industries

The

of

of

other

in manufacturing

other

secondary

automobile,

as

and

the

lead

became the major determinant of

state's

base

industry,

social

and

demographic development trends (Haber, 1959, pp. 82-83).
The

prosperity

and

economic

growth

in

Michigan

was epitomized by the lowest level of unemployment
than 3 percent)

in 1953.

(less

Economic prosperity attracted a

large influx of people from other parts of the nation who
were

in

improved

search

of

quality

of

employment
life.

opportunities

Between

1949

and

and

an

1957,

82

percent to 85 percent of the manufacturing production was
in durable goods.
decline

in

the

Ironically,

the

heavy

industries

economic
most

state's

which were

to

also

there was a corresponding

net

manufacturing

prosperity,

vulnerable

81-82) .

Conversely,

agricultural
sector,

the

"business

especially

"bedrock"

rendered

the

cycles"

employment.
those

of Michigan's

state's

(Haber,

economy

1959,

pp.

8

Decline of Manufacturing Sector
and Growth of Service Sector
Three
1945),
the

major

World

War

II

(1943-

(2) Korean war material production (1953), and (3)
automobile

periods
the

factors— (1)

of

war

an

industry

expanding

period

(1914-1955)— accounted

manufacturing

(1943-1945),

civilian population worked
of the Korean conflict

economy.

one-fifth

in

of

factories.

(1953),

for
During

the

state's

At the height

18 percent of the state's

population worked in manufacturing; similarly, 16 percent
of

the

peak

state's

population

auto years

stagnation
these

or

of

1955

decline

employment

had

factory

(Haber,

in

sources

the

1959).

growth

could

jobs

be

during

the

Understandably,

of

any

expected

or

all

of

to

have

a

significant implication for Michigan's economy.
The

1954 nationwide recession and the decline of

Korean

War

material

180,000

factory jobs,

second

national

percent

unemployment

state.

Hardest

industries

where

orders

an

in

the

loss

of

and about 150,000 defense jobs.

recession

hit

resulted

or

in

1958

a loss

were

the

of

resulted

406,000

automobile

additional

102,000

in

jobs
and

jobs

in

A

13.5
the

equipment
were

lost

(Haber, 1959).
The
the shift
Michigan's

loss

in

defense procurement,

in defense production
greatest

strength— to

largely due to

from wheeled
aircraft,

vehicles—

electronics,

9

and

missiles— Michigan's

in

existing

generation
growing

and

of

potential

defense

employment

800,000

new

weakest— led

jobs

to

defense

requirements

sector,

further

jobs.

The

became

accounting

in 1954 with less

the

for

than

losses
new

fastest

more

20,000

than

of such

jobs in Michigan (Haber, 1959, pp. 65-66).

An additional

factor

the

in

manufacturing

decentralization

of

the

decline

automobile

was

industry,

shifts in the geographical markets.
decentralization,
automobile
19 30s

to

employment
equipment)
1958 and

dropped

percent

in

reflecting

As a result of this

Michigan's share of the total national

employment
47

gradual

the

dropped

in

1958.

auto
from

from
At

industry
503,000

60

percent

the

state

(motor

in

1953

in

the

level,

vehicle
to

and

293,000

in

from 29.16 percent in 1979 to 22.91 percent in

1986 (Haber, 1959, pp. 87-98; Haas, 1988).
Of

great

significance

concerning

the

period

of

the 1950s was that, while it was the peak of the state's
economic
strong

prosperity

largely

manufacturing

industries,
decline

for

due

sector,

to

i.e.,

the

expansion

auto

and

a

this period also marked the beginning of the
that

decade

nonagricultural
receive

a

equipment

same

sector

and

the

"seed"

of

economic problems for state and local economies.
also

of

serious

when

sectors

the

attention

from

It was

nonmanufacturing

of the state's

economy

private

and

future

and

began to
public

10

sectors.

Between

1953

and 1957,

when manufacturing was

on the decline, the nonmanufacturing sectors
retail)

experienced

job gains

of

9.1

(service and

percent

or

49,000

jobs (Haber, 1959, pp. 87-96).
Most employment growth between 1950 and the 1970s
was

in

the

nonmanufacturing

sector.

Between

1965

and

1975, total nonmanufacturing jobs increased by 369,000 or
31 percent.
industry
retail

The

was

in

trade,

fastest growth in the nonmanufacturing
the

service

finance,

real

and personal

services),

between

and

1965

1976

industries
estate,

which

(wholesale

insurance,

increased

by

and

business

40.2

percent

(Michigan Economic Action Council

1976, p. 11).
The
plants

in

Rapids,

lopsided
a few

Lansing,

Ann Arbor,

concentration

areas,

primarily

Saginaw,

of

manufacturing

Detroit,

Jackson,

Flint,

Kalamazoo,

Grand

Muskegon,

and Bay City, not only skewed the bulk of the

state's employment opportunities to these areas, but also
laid

the

serious
state

foundation
economic

and

the

for

problems

nation

future
as

shifted

pockets

the

of

economic

from

poverty
base

manufacturing

of

and
the

to

a

nonmanufacturing economy (Haber, 1959, pp. 85-86).
Michigan's Economy in 1980s
The
although

a

decline

of

the

national

trend,

manufacturing
had

a

severe

industry,
impact

on

11

Michigan

whose

economy

had

depended

on

manufacturing for more than half a century.
of manufacturing decline,

heavy

The effects

such as a large loss in high-

salaried manufacturing jobs and a lowering of the quality
of life,

were particularly severe in the late 1970s

early 1980s.
percent

in

and

Manufacturing jobs had declined from 29.16
1979

to 22.91

percent

in

1986

(Haas,

1988).

This situation was aggravated by the nationwide recession
and

the

Today,

shock

of

high

energy

prices

in

early

1970s.

the prospects of future growth for the automobile

industry,

the

doubtful

"bedrock"

because

successful

inroads

(automobile,
foreign

of

strong
into

machine

Michigan

of

the

1988,

economy,

competition

manufacturing
and

along

Michigan's

(Kurtzman,

Michigan's

foreign

tools,

manufacturers,

perception

of

"poor
pp.

the

business

25-26;

and

the

industries

primary

with

is

metals)
widely

by
held

climate,"

Jackson,

in

19 88

pp.

91-93).
A major feature of this perception relates to the
high

cost

factors
and

as

high

worker

of

business

in

compensation,
costs.

with

unemployment

These

manufacturing

Michigan,

and

factors
other

such

insurance,

had

led

to

businesses

to

states and Third World Countries where business

conditions
are more

doing

energy

emigration
southern

of

(i.e.,

low

favorable.

labor
Most

and other production
significant

in

the

costs)

"wind"

of

12

economic

change

was

the

loss

of

advantage in basic industries.
growth

trends

in

industries

for

decline

growth

in

industries

the

of

in

13

were

the

of

of

traditional

A shift-share analysis of

the

period

in Michigan.

industries
vehicles,

each

Michigan's

nation's
1969

the

manufacturing

to

20

1979

core

showed

a

manufacturing

Most prominent of the declining
nonelectrical,

machinery,

motor

and fabricated and primary metal which had been

the mainstay of Michigan's economy (Jackson, 1988).
In the midst of general decline in manufacturing,
some industries
recovery
between
were

and
1978

restore

expansion
and

neither

largest

(e.g., machine tooling) did show signs of

1984.

enough

to

manufacturing
Michigan's

industries.

This

by

generating

However,
offset

the gains
the

industries

traditional

5,000

job
nor

advantage

development

new

jobs

in new jobs

losses

in

the

sufficient

to

in

the

(manufacturing

basic

decline)

across the country was evidence of a permanent change in
the

structure

of

the economic

base of Michigan

and

the

nation (Jackson, 1988, pp. 97-99).
Growth Trends in Michigan
Nonmanufacturing Industry
At the end of the national recession in December,
1982,

Michigan

unemployment
five

years

rate

unemployment
of

17.3

(1982-1987)

was

percent.

were

marked

at

750,000
However,
by

rapid

with
the

a

next

economic

13

recovery

and

a significant shift in sectoral

employment

from manufacturing to nonmanufacturing.

A total of more

than

by

4.2

economy

in

generated
these

million
1987.

Of

between

1982

later

(service,

people

jobs

were
this

and

were

employed
figure,

1987.

in

the

542,700

About

Michigan

jobs

90

were

percent

nonmanufacturing

of

industries

retail, and wholesale trade) as shown in Tables

1 and 2.
The
areas

state's

between

fastest

1982

and

growing

1986

were

in

service

industry

business

services,

such- as temporary help services (177.7 percent), computer
programming and software
(142.5
(67.6

percent),
percent),

service

and
all

sector

included

accounted
industry

of which

and

and

for

management

employment

research

engineering

(100.8 percent), data processing

0.7

employment.

public

accounted

in

1986.

development

architecture

only

and

percent

Ironically,

20.9

Other

(49.2

(36=9
of

for

relations
of the

services

percent)

percent),

and
which

the

state's

service

the

health

service

sector had the lowest growth rate (9.9 percent) from 1982
to

1986.

declined

Its
from

share

36.4

of

the

percent

in

state's

employment

1982

31.5

to

also

percent

in

occurred

in

1987 (Davis 1989, p. 4), as shown in Table 1.3.
While

the

fastest

employment

growth

business services and related areas of light and medium

14

Table 1.1.

Michigan Employment Trends Annual Average
Employment (0 0 0's )

Year

Annual Average

Percent Change

1972

3,438

—

1977

3,777

+ 9.86

1982

3,616

-4.26

1987

4,159

+15.02

Sources:

Michigan
Statistical
Abstract,
Bureau
of
Business
Research,
School
of
Business
Administration,
Wayne
State
University,
Detroit, Table v-I, 1986-87:
99.
Haas, Michigan Department of Commerce,

1988.

Table 1.2.

Michigan:
Average Annual Employment Trends:
Selected Nonmanufacturing (000s)

1972

1977

1982

1987

1972/77

Manufacturing and

1977/82

1982/87
•

Service
Manufacturing
Retail Trade

Sources:

455

581

650

826

27 .7

11.9

27.1

1,097

1,128

877

973

2.8

-22. 3

10.9

479

557

543

673

16.3

-2.5

23.9

Michigan Statistical Abstract, Bureau of Business Research, School
of Business Administration, Wayne State University, Detroit, Tables
XV-1 (368), XXI-1 (516) XXIII-1 (582) 1986-87, Michigan Department of
Commerce, 1989.

16

manufacturing
lumber,
were

industries

and wood

in

the

construction),

retail

manufacturing.
employment
(998,300)

(rubber and plastics,

trade

business, other

Retail

sector

the largest

trade

was the

(67 3,000 jobs)with

and services

(826,000 jobs)

furniture,

growth

areas

services,

and

third

largest

manufacturing
as first and second

largest employers, respectively, in 1987.

annual
1982

Rapid decline in the state's unemployment

from an

average

between

and

of

1987

of

to 8.2

largely

employment

percent

due
into

to taggressive

nonmanufacturing

(Haas, 1987; Giltman, 1987).
Although

declined
35

percent

hasbeen

diversification
sectors

15.5

dependency

on

auto

by less than 11 percent

percent

in

concentration
fastest

1978,

of

Michigan

auto

in 1982,
still

employment

manuf actur ing

growth

industry

in

area

employment

and less than

has
the

the

highest

nation.

was

The

manufacturing

construction.

Michigan manufacturers invested a total of

$3

the

billion

between

in

1983

California

in

(Haas,

1988).

growth

(8

construction

and

1987,

percent)between

accounted

for

employment

and

making

manufacturing
Despite

25.9
35.7

of manufacturing

the

construction
decline

1982

percent
percent of

Michigan

in

and
of

the

total

plants

second

to

investment
manufacturing

1987,

it

state's

(state)

still
total

earnings.

The service industry grew by 32.2 percent during the same

Table 1.3.

Selected Fast Growth Service Industries:

Michigan

Percent Change

Service Type

Temporary Help
Supply (Office and
nonoffice workers)

197 2

1977

1982

1986

1972/77

1977/82

1982/86

6,0 16

12,674

13,504

37,500

110.7

6.5

177.7

Computer
Programming and
Software

331

730

2,898

5,818

120.5

297.0

100.8

Data Processing
Service

3,457

5,890

8,861

21,486

70.4

50.4

142.5

9Ei6

2,168

3,049

4,549

119.9

40.6

49.2

Management and
Public Relation

3,451

5,812

9,974

16,715

68.4

71.6

67.6

Health Services

—

—

249,291

273,934

Research and
Development

Sources:

—

9.9

Michigan Statistical Abstract, Bureau of Business Research, School of
Business Administration, Wayne State Univesity, Detroit, Tables XXII2(974/75), 1976; XXII-3 (586/87), 1906-07.
Davis, Michigan Department of Commerce, 1909.

18

period,

but

state's
Retail

accounted

employment
trade

and

is the

for

only

20.9

percent

third

22.3

largest

after manufacturing and services.
retail

jobs

increased

from

percent

of

total

employer

of

the

earnings.

in Michigan

Between 1976 and 1986

531,000

to

637,000.

Annual

average earnings for retail trade in 1984 were $10,257 as
compared to $20,826 for overall state employment.
In 1987, retail trade accounted for 20 percent of
the state's employment,
income

(Giltman,

Commission,

but only 10 percent of its total

1987;

February

Michigan

1986).

Employment

The tourism and

industry

continues

to

experience

growth.

Travel-related

employment

recreation

strong
rose

Security

employment

to

184,000

in

1988.

This was an increase of 49,000 new jobs from 1982.

Travel

and tourism related expenditures

estimated

at

$14.1

billion

in

in Michigan were

1987.

This

was

a

percent growth between 13S2 and 1387 (Haas, 1355).
is

no

doubt

significant
economic

in

improved

Michigan's

growth

recovery

indicators,
growth

that

such
new

since
in

as

late

economy

the

start

1982.

declining

business

productivity,

has
of

If

sectoral

There

experienced
the

national

current

economic

unemployment,

incorporation

43.9

(24,882

growth,

and

continued
in

1988),

declining

inflation rates hold, Michigan may again reestablish its
national economic leadership lost in the late 1950.
strength

of

the

state's

rapid

economic

recovery

has

The
in

19

large part been due to its progressive diversification of
the

economy

from

single

industry

industry

economy

(e.g.,

services,

such as high-technology,

(auto)

business

to

services

a

mixed

and

other

retail trade,

tourism

and recreation, manufacturing construction, etc.).
Between
outpaced

1982

and

1987,

Michigan's

economy

the national averages in many economic sectors,

(e.g., 39 percent growth in per capita income compared to
35 percent nationally and a lower annual inflation rate).
Michigan businesses generated 86,000 more jobs than their
national

competitors and had a faster average employment

growth of 17 percent compared to the national average of
15.5

percent

performance

(Haas.

were

in

the

1988).

Areas

service

of

industries.

strongest
See

Table

1.3.
Current economic trends show the nonmanufacturing
(business

sector

services,

retail

construction,

transportation,

etc.)

direction

as

the

Michigan.

contribution

recovery underscores

an

utilities,
future

manufacturing
communication,

economic

growth

in

This sector's rate of consolidated growth and

significant

basic

for

trade,

the

to

the

state's

permanent

shift

rapid

economic

in the state's

economic structure from a manufacturing economy to

increasingly

steady,

upward

dominant
growth

nonmanufacturing
in

the

economy.

nonmanufacturing

The
sector

began in 1985 was expected to continue and was projected

20

as

the

main

source

for

the

state's

future

employment

(Davis, 1989).
Economies of Michigan Cities
The
however,

strong

may

performance- of

be misleading.

This

Michigan's

economy,

rosy picture

shrouds

the seriousness of deteriorating economic conditions that
a

great

While,

number

of

Michigan

in the aggregate,

cities

are

experiencing.

the state's economy has enjoyed

five years of strong economic recovery and growth and has
outperformed
economies

national

of

many

averages

Michigan

in

many

cities,

sectors,

the

particularly

old

industrial cities, are either stagnant or in distress.
The contradictions of Michigan's present economic
growth was best illustrated in a recent study of Michigan
metropolitan

areas'

economic

University of Michigan
of

Commerce

Business

performance

by

The

(cited in the Michigan Department
Report,

1989).

The

study

showed

employment growth in all Michigan's 12 major metropolitan
areas

between

ranged

from

Metropolitan

1982
a

low

and
4.5

Statistical

percent

(largest growth

All

metropolitan

12

personal
Battle

income

Creek

to

1988.
percent
Area

a high

of

to

Upper
a

high

growth

Peninsula
of

24.7

in Benton Harbor MSA.

also

from

employment

in

(MSA)

increase)

areas

ranging

The

a low

showed
of 21.9

38.9 percent

increases

in

percent

for

for Ann Arbor.

21

Detroit

MSA

However,

had

the

a

36.3

percent

economic

increase

reality

(Haas,

of

most

particularly the industrial ones is dismal.
the

cities

of

Kalamazoo,

Benton

Harbor,

Muskegon,

Michigan

Battle

cities

currently

by

State

distressed
Housing

and

the

and

Department

Urban

Detroit,

designated

of

For example,

are
as

Michigan

Development

cities,

Flint,

Creek

1988).

Jackson,
among

the

economically

and

by

the

Departments

U.S.
(U.S.

of Housing and Urban Development, October 14,

1987).
A logical inference from the above study is that
economic

growth

in

the

metropolitan

area

is

occurring

mostly in the suburban or peripheral areas of the cities,
and not
yet

to

in the inner cities.
find

employment
provided

stable

to
prior

Most of these cities have

economic

sources

replace

those

to

1970's.

the

which

income

and

manufacturing

had

It

of

may

be

accurate,,

however, to conclude that Michigan has, on the aggregate,
enjoyed
years,

unprecedented
and

has

the

economic
potential

growth
for

in

continued

current favorable conditions continue.
number

of

Michigan

manufacturing
significant
the

economic

experiencing

cities,

cities,

economic
recovery

have

recovery

the

and/or

to

five

growth

However,

especially
yet

last

a great

the

older

experience

growth.

if

any

Extending

and growth to all Michigan cities

stagnating

or

distressed

economies

is

a

22

major

task

confronting

policymakers

and

state

economic

and

local

development

government

professionals

in

Michigan.
Public Policy and Structural Implications .
In January,
arrived
throes

in
of

were

1981, a new Republican administration

Washington
economic

confronted

decline.

bleak— unemployment

with

Most

was

a

nation

economic

9.2

in

the

indicators

percent,

national

inflation was 9.4 percent, and the Gross National Product
(GNP)

and

manufacturing

productivity

were

at

an

annual

growth rate of 2.2 percent and 1.5 percent,

respectively

(Economic Report of the

Exports were

President,

1989).

also on the decline, and the unstable value of the dollar
in the

international market aggravated

a global economy

already in chaos (Kurtzman, 1988, pp. 56-57).
The U.S. had been outpaced by foreign competition
at

home

and

in

the

global

markets,

and

unemployment

skyrocketed as a great number of manufacturing plants and
nonfarm

businesses

where

business

etc.)

were

Reagan,

costs

more

came

either

favorable.

into

political mandate,

(low

office

to:

closed
wage,
The
with

or

migrated

minimal
new
the

overseas

regulations,

President,
resolve,

Ronald
and

a

(l) reduce the role and influence

of the federal government in state and local governments,
(2)

restore

economic

prosperity

by

ending

escalating

23

inflation and reducing absolute inflation,
international

peace

strengthening U.S.
Reagan

agenda

dedicated
the

and

influence

national

were not

by

security.

new,

except

scope

of

(Hutten and Sawhill,

their

The

and

items on the

for the

radical

and

implemented

and

socioeconomic

1984, pp.

(3) forge

improving

dispatch with which they were

wide

and

consequences

1-7; Mehtabdin,

1984, pp.

4, 15).
Measures
government

for

reducing

included

responsibilities
elementary
services),

major

transfer

in the

and

the

areas

high

role

of

and

influence of

federal

government

of domestic policy

school

education

income programs

and

(e.g.,
social

(Assistance to Families

with Dependent Children— AFD C ) , and other decision-making
authority

to

restoring

U.S.

] p 0 2 r s o n s i3 .

state

and

local

economic

prosperity

c c 2 rp o 2 r2 ts

c u ts

spread over three years.
encourage

personal

governments.

Efforts

included
sc irc ss

25

tiis

percent

b o srd

The tax cuts were designed

savings

and to

increase

at

2 nd!

to

the creation

of capital formation.
The

strengthening

of

national

security

was

characterized by a massive defense arms buildup, twice as
large
The

as the

arms

nation.
Total

tax cuts,

buildup was

which tripled the national debt.

the

largest

in the history

of the

The real defense budget rose 7 percent annually.

defense

program

budgets

rose

from

26

percent

in

24

1981

to

32

percent

in

1985

(Palmer

and

Sawmill,

1984;

Shafroth, 1989).
Socioeconomic Implications for the
State and Local Government
Reduction
programs

that

responsibility
programs
1983,

had

thus

continue

consolidation

were
of

not

state

been

to

Between

raised

in

from

the

1980

22

local

states

provide

of

the

few

to

social

became

the

governments.

361

in

1981

raise

259

in

taxes

to

needed

state and

27 major

Grant

to

local

desperately

and 1983

the

of

eliminated

and

reduced

compelling

programs.
were

and

social

local

cities

taxes

(Palmer

and

account,

due

Sawmill, 1984).
The

large

largely to
with

recession

of

interest

rates.

cities)

the

national

early

implications,

1980s

led

to

(the

largest

31.2 percent

single

large tax
and

cuts

the

rising

deep

debts

federal

and

government

revenue

source

for

in 1985 against 40 percent

Property taxes also declined to 20.5 percent in

fiscal

year

1984/85

in

structural

from

25.6

percent

in

1975.

the rapid change in the nation's economic base

manufacturing

resulted

the

Consequently,

fell to

Similarly,
from

revenue

the

funds

in 1975.

in

massive defense expenditures,

substantial

transfer

deficit

the

loss

to

service-producing

of manufacturing jobs

unemployment

(from

33.7

percent

industries
and
in

in huge
1950

to

25

19.9

percent

in

1985)

(The

Municipal

Yearbook,

1988;

Waite 1988, pp. 1-14).
Most affected by these developments are the older
industrial cities of the manufacturing belt, particularly
cities

in

sources

the

Midwest.

(user

fees

The

and

bulk

of

existing

miscellaneous

revenue

revenues)

are

unstable and often too inadequate to support the cities'
institutional
essential

infrastructures

for

sustained

(The Municipal
policy

of

immediate

boost

concentrations
Texas,
may

of

1988).

the

may

economies

defense

have

little

macro

further

or

no

of

industries

socioeconomic

succeeded
nation's

in

economy

OhUallachain,

1987).

the

have

defense

given

(e.g.,

an
with

California,

and Connecticut),
those

contracts.

From

Reagan

created
sector

benefiting

it

states/cities

fragmentation

the manufacturing

growth

states/cities

the

subeconomies

Those

and

by

agenda
of

the

earlier

(Kamer,

the

a

1988;

least

from

massive defense expenditures are a great number

of the small,

mid-sized,

economies

decline/distress.
economic

perspective,

into

of

whose

industry

consolidating

restructuring

Reagan's

impoverished

defense

conditions

While Reagan's

expenditures

to

other

development

Florida, New York, Missouri,

also

with

economic

Yearbook,

massive

and

recovery,

are
After
a

and large old industrial cities
either

stagnated

almost seven years

number

of

cities

in

or

in

of national
Michigan

are

26

still

struggling

change

in

with the costly

the

basic

realities

structure

of

the

of permanent

national/state

economy.
Problem Statement
The

primary

concerns

expressed

in

the

final

report of the National Governor's convention in 1986, and
reiterated
the

in their

serious

follow-up report in 1987,

condition

of their

states'

related to

economies.

The

governors warned that:
Thirty-seven of the nation's fifty states were
and continue to be in the middle of a recession,
with
food,
raw
materials,
and manufacturing
heartland of the country affected most severely.
This heartland is now suffering from unemployment
rates far in excess of the national average and
from declining urban and rural prosperity and
land values (Kurtzman, 1988, p. 102).
The governors'
and
that

other

manufacturing

have

lost

large

Federal

government

largest

single

payments

have

percent

concerns still prevail in Michigan

have

declined

1980

and

been

further

introduction

transfer

declined

at

1987.

eliminated

proportions

revenue

in 1985.

source

for

by the

in

transfer

cities

revenues.

represented

in

and

the
these

1975 to

31.2

(direct grant assistance)
5.3

position

of

percent
the

between

cities

has

1986 Tax Reform Act which

municipal

corporate

and

cities,

rate of

fiscal

deductible

of

from 40 percent

an annual
The

states

payments

The payments

weakened

of

heartland

taxes

sales
and

the

tax.
imposition

The
of

27

other conditions made a city's traditional source (public
purpose
The

exempt-bond)

reduction

of

of

raising

federal

funds

more

government's

expensive.

investment

in

discretionary local program by $124 billion between 1981
and

1988

has

government

adversely

related

training,

programs

medicaid,

sharing, etc.)

affected

the

most

needed

(education,

transportation,

health,

general

local
job

revenue

(Shafroth, 1989; Manson and Howland, 1984,

p. 111).
There has been reluctance on the part of the city
governments

to increase property tax,

source

city

of

political

revenues,

implications

because

even

though

also declined from 25.6 percent
in

the

1989).

fiscal

year

the second largest

1984/85

of

the

property

negative

taxes

have

in 1975 to 20.5 percent

(The

Municipal

Yearbook,

These developments had led to significant shifts

by city governments from dependence on federal government
transfer

payments

revenues

from

to

more

emphasis

nonproperty

on

generating

taxes

(user

fees

local
and

miscellaneous revenues).
However,
especially
continues

in
to

the

older,
be

current
industrial

fragile

because

fiscal
cities

environment,

of

existing

the
local

Midwest,
revenue

sources are unstable and often inadequate to support the
institutional

infrastructures

and

other

conditions

essential for sustained economic development and growth.

28

cities

The

major

task

and

economic

confronting

development

a

great

number

professionals

is

of
the

ongoing search for a comprehensive and effective economic
development
sources

strategy

essential

possible

economic

services)

that

that

for

a

can

city's

activities
have

identify:

the

(1)

income

(commerce,
potential

major

growth,

(2)

manufacturing,
for

employment

opportunities and profitable growth, and (3) a contingent
strategy for taking advantage of changes occurring in the
city's trade environment (Hustede et al., 1984).
The Purpose of This Study
The

purpose

of

this

study

is

to

analyze

the

economic conditions of selected Michigan cities based on
the

level

of

supply

of

retail

and

service

functions.

Specifically, this study cover the broad areas of inquiry
based on the following objectives:
Primary Objectives
Objective 1 :
To
explore
the
possibility
of
developing an appropriate model for identifying
potential business opportunities based on the
levels of supply of retail and service functions.
Objective 2 :
To determine whether supply levels of
retail and service functions can be predicted
using a set of socioeconomic variables
(city
population,
unemployment,
per
capita
income,
proximity to a major city, level of distress, and
county per capita income).
Objective 3 :
To
discover
if
there
are
any
significant variations in the levels of retail
and
service
functions
as
perceived
by
city
officials and the levels of supply of retail and

29

service functions generated by
model developed from this study.

the

statistical

Secondary Objective
Secondary Objective 1:
To determine if there were
any
statistically
significant
relationship
between level of resource
(staff and budget)
allocations
and
a
city's
economic
condition
(level of distress).
Assumptions of This Study
This
concerning
cities

study

is

similar

sharing

characteristics.

based

patterns
similar
In

the

on
of

a

set

of

economic

demographic
context

of

assumptions

growth
and

this

among

industry
study,

the

assumptions are:
Assumption 1 : That cities with similar socioeconomic
characteristics
are most
likely
to
manifest
similar
patterns of economic development
and
growth.
Assumption 2 : That given the rapid transformation of
the nation's economic base from manufacturing to
predominantly
retail
and
service
industries,
these industries are logical targets for economic
development and growth for cities, especially
small and midsize cities, and/or old industrial
cities either in the throes of economic distress
or economic stagnation.
Assumption 3 : That the level of supply of retail and
service functions may be reliable indicators of
business opportunities in the city.
Definition of Terminologies
The
used

in

following

this

consistency:

study

operational
for

the

definitions

purpose

of

have

clarity

been
and

30

Business

Opportunities:

(goods and/or services)

Consumer-business needs

that are to be met through market

mechanisms.
Economic
use

of

Development:

resources

productivity,
start-ups,

(local

creation

Activities

and

of

involving

nonlocal)

wealth

for

through

new

expansion of existing businesses,

employment

opportunities,

corporate incomes,

increase

in

the

greater
business

increase in

personal

and

increase in city's tax base, and other

activities that provide opportunity choices for consumers
and producers

(Shafer, 1989).

Economic

Development

economic development
targets
zone);

(e.g.,

efforts

central

entrepreneurial

enterprises);

Targets:
in terms

city

of:

development,

targets

occupational

Objects

(e.g.,

targets

geographical
enterprise

various
(e.g.,

for

start-up

employment

opportunities and labor); social targets (e.g., c o m m u n i t y
cooperative

unions);

and

retention of businesses)
Economic
jobs,

expansion

Growth:
of

old

business

targets

(flow

and

(Bowman, 1987).
A

continued

businesses,

increase

new

business

in

new

start­

ups, and increase in personal and corporate incomes.
Distressed
comparative

Ci t y :

A

disadvantage

in

city
terms

whose
of

economy

population

suffers
growth

(1960-1984), level of poverty,

age of housing, per capita

income

job

growth

(1969-1983),

lag

in

retail

and

31

manufacturing sectors
degree

of labor

(1977-1982), unemployment rate, and

surplus

(1984-1985)

(U.S.

Department

of

Housing and Urban Development, 1987).
A Central Function:
or

Any establishment of retail

service business that services

1989).

In

this

a population

study a function

and

(Shaffer,

central

functions

are used interchangeably.
Economic Function:

Any type of economic activity

within an industry that provides valuable information and
a

tool

for

commercial

city

policymakers,

developers

in

planners,

their

business

investment

and

decisions

(Shaffer, 1989).
Economic
regulation,

or

Development

Tool:

program/project

Any

public

employed

to

policy,

influence

types of economic activities (Bowman, 1987).
Undersupplied Function:
v

—»

w a.

A

r-s

»-*

A predicted higher need
m *-

w i.

ua.wuij.ui.

ul

«-■/—

d d

<■<

viuc

/

<~

^c •y • ,

prescription drug store, laundry) than actually exists.
Equilibrium Function:
actual

and

predicted

The nondifference between

number

of

goods

and

service

businesses available.
Oversupplied Function:
of

a

business

function

The excess in the number

(good

or

service)

over

the

number

of

expected/predicted market demand.
Trade
customers

Area

(local

Capture:

and nonlocal)

The

estimated

who buy from a community

32

(Shaffer,

1989).

The

Trade

Area

Capture

may

also

be

defined in terms of estimated number of people purchasing
a category of product (good or service) from a community.
Pull Factor:

The ratio of trade area capture to

community's population.
Significance of Study
The

significance

of

this

study

is

its

academic

and empirical contributions toward a better understanding
of economic development issues,

especially as manifested

by the levels of supply of retail and service functions.
Previous
cities were
322 U.S.

studies

on the

either macroscopic

economic

development

in their coverage

of

(e.g.,

cities of all sizes, Bowman, 1987) or restricted

to one or few cities
1987).

The

final

specific

in their

(e.g., Five City Studies,
data

focus,

results

from

the

and therefore,

Hausner,

study

are

are more likely

to be an accurate knowledge base in formulating effective
economic

development

policies

and

programs

relevant

to

these and similar cities.
The
model

information

developed

provided

thereof

are

by

uniquely

application in cities and places
the

United

States

of

this

America)

study

and

valuable

the
for

(other than Michigan or
which

have

similar

socioeconomic and demographic profiles.

In addition, the

final

are

data

and

model

from

this

study

expected

to

33

provide valuable

information as well as a tool for city

policymakers

planners

and

and

implementation.

in

target

Business

policy

formulation

investment

decisions,

especially in retail and service industries,
to

benefit

significantly

from

the

are expected

data

and

model

developed thereof.
Limitations
The data used in the study and the development of
the model were based on the years for which official data
were available.

The only official sources providing data

for major retail trade and selected service industries in
places

with

a

population

of

2,500

and

more

are

the

censuses for retail trade and selected service industries
which

are

focused

published

only

Industrial

on

every

double

five

digit

Classification

years.
levels

The
of

study

the

has

Standard

(SIC) code because this is the

only level for which corresponding data are available for
places
study

with

populations

of levels

two-digit

SIC gives

of functions.
on

the

supply

major
levels

predicted
reflect

of

supply

2,500
of

or

retail

information

more.
and

Basing
services

the
on

a

only on the major class

Predicted supply level of functions based
group
of

functions

the

supply level
an

of

oversupply,

subgroup

may

not

reflect

functions.

accurate

Thus,

while

of the major group functions may
functions

at

the

subgroup

may

34

actually be undersupply or at equilibrium level, e.g.,
predicted

level

automotive

as

supply

repair,

oversupply,
such

of

of

a major

services,

and

group

function—

parking— may

transmission

car

repairs,

rental,
body

parking

and

lots,

upholstery

automotive

repair

and paint shops could actually be undersupply.

and

the

be

even though any or more of the subfunctions,

passenger

limitation

a

(two digit

model

policymakers

SIC)

developed

and

economic

valuable

information

efficient

allocation of

The above

not withstanding,
from

this

study

development

base

in

local

policy
resources

shops

the

will

result
provide

professionals
formulation

a
and

in the economic

development and growth of a city.
Methodology
The study was conducted in two phases.
phase

covered

the

collection

and

The first

analysis

of

actual

number and sales of retail and service functions from 80
Michigan
The

cities

study

with

population

population
comprised

phases of economic development.

survey

The

second

of

economic

Michigan cities.
second

phase

The data

of

10,000
cities

to
at

100,000.
different

Please see Table 1.4.
the

development

phases were used

of the trends

of

of

study

was

official?

an

opinion

in

the

80

collected from the first and

to conduct

an in-depth analysis

(levels) of retail and service functions

Table 1.4.

Sample Cities by Population Size and Economic Conditions

Population Size

High % Distress
Points:
(7-5)

Moderate % Distress
(4-2)

Least % Distress
(-1, 0 , 1)

Total
•

10,000 -

49,000

50,000 - 100.000

17 (26.2)

25 (38.4)

23 (35.4)

65

4 (26.7)

6 (40.0)

5 (33.3)

15

21 (26.3)

31 (38.7)

28 (35.0)

80

36

and

to

develop

a

model

that

could

identify

market

opportunities based on these levels.
This
reliable

procedure

data

on:

has

(1)

allowed

the

actual

service industries in the cities,
of

economic

retail
(3)

and

development
service

the use
of

retail

and

(2) the expert opinions

officials

functions

size

of the most

about

the

levels

in the cities studied,

of
and

comparative analysis of data of actual state and city

official perceptions of retail and service industries and
the model developed thereof.
The finding and final conclusions from this study
were

based

Descriptive
analyze,

on

the

and

summary

of

inferential

interpret,

the

data

statistics

collected.

were

used

to

and summarize findings with regard to

the following areas of inquiry:
1.
(city

Whether

population,

a

set

capita

income)

socioeconomic

unemployment,

proximity to a major city,
per

of

can

per

variables

capita

level of distress,

be

used

to

income,

and county

predict

levels

of

retail and service central functions in the city.
2.
a city's

whether a model can be developed
business

opportunities

based

on

to identify

the

levels

of

supply of functions in retail and service industries.
3.
significant

Whether

there

relationships

were

between

any
the

statistically

levels

of

retail

and service functions as perceived by the city's economic

37

development

officials

and

the

levels

of

retail

and

service functions as generated by the prediction model.
Theoretical Framework
The
always

city

seeks,

resources.

as

in

an

economic

theory,

to

decision-making

maximize

returns

unit

on

its

Because such returns and expected growth are

contingent

on the

level

of market demand

and

supply of

local goods and services, economic decisions and actions
with
the

regard
two

to maximization

market

factors

of

of returns
demand

and

are dictated
supply

by

(Shaffer,

1989, pp. 12-13).
The

focus

development

of

(public

among
and

knowledge
general

theory

manifested

community

created

for

is
by

the
the

industries.

economic

has

essential
for

study

and service

private)

base

this

cities

function of retail
consensus

of

economic
levels

The lack of

development
a

developing

of

vacuum
a

actors
in

the

falsifiable

community economic development.

The

selection of a theoretical foundation for this study was
based on theories that are relevant in the development of
effective

local

policy

initiatives

development

(Shaffer, 1989, p. 41).

for

economic

Each of the economic

development theories briefly reviewed in the following is
a "building block"
study.

in the theoretical foundation of this

38

Supply-Oriented Development
Theory
Using the production function concept, the city's
economic

condition

evaluated

on

population

trends,

force

(output

and

growth

level

of

capital

its

(skilled

the

and

size

and

unskilled),

potentials)

accumulation,

quality
and

is

of

its

its

labor

technological

sophistication.
As

an economic

unit

operating

in a

free market

economy, the city is in competition with other cities and
communities
labor,

for

and

local

efficient

must not

to

(capital,

potential

given

the

business
competitive

only be able to put more of

efficient

bureaucratic

technologies,

environment

resources

a city wishing to maximize its output and/or

resources

eliminating

and

Therefore,

growth potential,
its

nonlocal

technology)

opportunities.
environment,

and

but

and

productive

"bottlenecks"
also must

and

create

use

by

adopting
a suitable

for the mobility of capital and/or

labor to

sectors of more productive use within the city's economy
(Shaffer, 1989, pp. 13-23).
Demand-Oriented Development
Theory
As previously discussed, the output of an economy
is

a

labor,

function

of

the

and technology.

level
Output,

of

capital

however,

accumulation,
is only one of

39

two

indispensable

economy.
are

The

the

functions

economic

"driving

(demand

activities

engine"

that

and

supply)

of demand

jointly

of

and

any

supply

determines

the

performance and level of economic condition.
The demand component of the economy comprises the
basic

(export)

community's
are

economic

dependent

demands

on

for

nonbasic

condition

the

level
and

sector.

and

of

A

potential

internal

services

city

for

and

produced

or

growth

external
locally.

the forces that influence the demands, and

can be

opportunities,
policies

and

goods

Understanding
how they

sector

transformed

is,

and

into

therefore,

programs

for

incomes and employment

essential

local

in

formulating

economic

development

(Shaffer, 1989) .
Export-Based Theory
This
export

theory

sector

as

examines
in-flow

the

critical

source

of

resources to boost the local economy.
is

particularly

economies
cities

vital

for

cities/

cash

role

of

and

other

The export market
communities

whose

are largely dependent on the export market,

previously

dependent

on

the

a nonexport

market,

or
but

whose economies are either in decline or distressed.
However,for
crucial factor
only the

level

for

a

nonself-sufficient
economic

of its

economy,

development/growth

resources

(capital,

the
is not

labor,

and

40

technology),

but

the

market.

is

also

that,

It
while

primary

scope

intensity

important

promotion

focus

and

of

for

an

of

cities

export

its

to

market

in economic development,

be
may

export
aware
be

a

attention to the

nonexport sector is also important.

This is particularly

true

allow the nonexport

when

sector

allocating

to

provide

resources that

those

support

services

the success of the export sector.
derive
sectors,

maximum

efficient

backward

within

channel

(Coffey

from

its

and

the

basic

city

accruing

and

are

returns

Polese,

for

For a local economy to

internal infrastructures

linkages)

efficiently
economy

returns

essential

1984,

(forward and
essential

into

pp.

nonbasic

the

1-11;

to

local

Shaffer,

1989, pp. 28-35).
Location Theory
The
where

city/community

economic

suppliers,
output,

and
and

participants
for

economic

a

participants
consumers)
raw

and

is

physical

environment

(investors,

producers,

exchange

materials.

their

economic

development/growth

capital,

labor,

Understanding

these

activities

is

important

of

or

community

a city

(Shaffer, 1989, pp. 46-47).
The

Location

understand

the

activities,

markets,

Theory,

spatial
and

as

a

relationship
the

concept,
between

infrastructures.

seeks

to

economic
Location

41

Theory not
location

only tries

decision

to provide an explanation for the

process

operate where they

are,

power

a

enjoyed

knowledge
according
to

by

and

reasons

city/community

location

provide

influence

theory,

valuable

business

businesses

but also examines the political

of business location
to

why

needs.
allows

location

location

that

has

Such

the

city's economic development agenda

good

knowledge,

city/community

information

decisions

a

in

that
favor

could
of

the

(Shaffer 1989, pp. 69-

70) .
The

theory

also

delineating

different

behavioral)

and

the

business

its

choice

in

synopses

of

provides

analytical

location
basic

factors

assumptions

of

a

theoretical

site.

for

(economic
that

The

location

methods

and

influence

following

tools

are

that

have

relevance for this study:
1

T>rrs ^

^

m

^ m A **»

^

markets,

for

principle

ignores

factors
relevance
as

raw

vigorously

pricing)

important

r>

w

W1VUO

dispersed
business

4* A

materials

engaged

greater

in

of

the

major

of demand

"Locational

flaw

and

maximization,

Interdependence,"

n

i u

^ 1 »»

c i j

with

each

competition

(low

market.

and

But

This

resources

as

ignoring these two

this

principle.

sometimes
is

outlining the spatial characteristics

t.t ^

labor

the

labor

of

«•»

Uxd^ Wi t i Wi d

of

in maximizing demand.
is

A 1 ^
A

price

control

location

^
.

its

The

referred

to

utility

in

(size and shape) of

42

a community/city

retail

trade

area

(Shaffer,

1989,

pp.

56-58).
2.

The least cost approach operates on the basic

assumption that market demand is not affected by business
location.

The

overriding

decision

is

(primary

concern),

allow

for

the

total

consideration

minimum

labor,

and

maximum profits.

cost

method,

cost

may

however,

not

(Shaffer,

is

1989,

p.

(minimization

of

A major

The

business

cost)

particularly

for communities/cities

development

policies,

packages as

and

costs

of the

with

profit

knowledge

location

transportation

flaw

location

guarantee

47).

of

agglomeration

that

necessarily

cost

in

the

that
least
least

maximization

of

this

is

factor

essential,

formulating economic

developing

cost

reduction

incentives for attracting new businesses and

retaining existing ones.
3•
focuses

The

on

behdviOLal

the

65p6Ct.

problem' of

of

obtaining

xOCauiOn
vital

uhcGry
location

information needed by business to select a site that will
maximize

its

decisions,

profits.

may

often

Business,

when

be handicapped

by

making
the

volume of available information on a location,
market potential,

level o f Jcompetition,

economic conditions in the city.
facilitate
location

its

economic

information

and

the

quality

its future

therefore,

by providing

conducive

and

and other future

A city can,

development

location

needed

environment

to

43

expedite the business location decision process

(Shaffer,

1989, p. 66; Nelson, 1958).
Central Place Theory
Market
and

may

opportunities are unserved consumer needs

also

environment

be
due

institutional
factors.
needs,

an
to

a

generating

economic

monopoly,

source

potential

businesses

of

imperfect

as

depends

other

unserved

economic

on

related
consumer

activity

and

market

market

information,

and

revenue

But converting

activities

an

market

opportunities

likely

opportunities.

of

limited

barriers,

Market
are

indication

employment

opportunities

the

for

into

cooperation

of

and their willingness to invest in the under­

served areas (Shaffer, 1989, pp. 125-126).
Central
seeks to address
for

Place

theory,

as

a

concept,

therefore,

(among others) these factors as a basis

understanding

uneven

distribution

of

goods

and

services among place. The theory is primarily a consumeroriented

concept

behavioral

operating

assumptions

of

under

two

basic

homogeneity

of

spatial

and

independent

businesses spread uniformly across the community with the
desire

to:

(1)

maximize

profits

(transportation of production costs)
greater

control

consumers

over

by

minimizing

costs

and (2) expansion of

the market they serve.

Similarly,

also try to minimize the distance traveled to

44

make purchases

(Christaller,

1966; Losch,

1954, pp.

105-

114; Berry, 1967, pp. 59-71).
The theory also provides greater understanding of
the

interdependence

among

communities/cities

and

their

hierarchies in terms of the rank-order goods and services
they

provide.

structure,

Based

on

the

concept

cities/communities

of

of

hierarchical

higher-order

provide

specialized goods and services, while those of the lowerorder

provide

services

generalized

(King,

1984,

or

pp.

convenient

28-43;

goods

and

Berry

and

Garrison,

is

analyzing

1958, pp. 107-121).
The

utility

of

economies of cities,
service
the

sectors.

this

and/or

relate

tc

It also provides greater

services

the

in

the

particularly the study of trade and

importance of the

goods

theory

insight into

relationship between

the range of

and the demand thresholds

economic

condition

of

an

area.

as they
ether

significant contributions of the theory include provision
of

valuable

Pull
Shift

analytical

Factor,

Location

Share,

employment
(domestic

etc.)

tools

Quotient,
for

potentials,
and

export)

(e.g.,

of

a

Model,

the

the Trade Area Capture,

market
and

Gravity

analyses,

area

sales

estimating
potentials

community/city• (Hustede

al., 1984; Shaffer, 1989, pp. 144-157).

et

45

Dissertation Organization
This

chapter

changing phases

has

provided

of economic

an

of

this

study.

of

the

structure of the nation and

the state of Michigan as a framework
problem

overview

Other

for addressing the

areas

covered

in

this

chapter included a statement of the research problem, set
of

assumptions,

theoretical

definition

and

significance

of

conceptual

and

terms,

establishing

foundations,

limitations

of

the

outlining

study,

and

the
the
the

research questions to be addressed by this study.
A

review

theoretical
Chapter

I,

of

and
is

relevant

literature,

conceptual
covered

in

framework
Chapter

procedures,
study.
of

and

of

statistical

the

established

in

Chapter

statement of the

research

tools

to

III

be

design

and

used

the

in

Chapter IV covers data analyses and presentation

findings

summary,

description

on

II.

discusses the model, selected variables,
hypotheses,

based

of

the

conclusion,

research findings.

research.
and

Chapter

recommendations

V

covers

based

on

the
the

CHAPTER II
REVIEW OF LITERATURE
Introduction
The

logic

of

economic

consumer and producer,
parties,

determine

relations

between

the

and the inherent interests of the

the

intensity

and

scope

of

economic

activity and the ultimate condition of the economy of the
community.
economic

Of

special

relations,

interest

are

the

in

spatial

consumer-producer
characteristics

of

economic activities, the behavior of the consumer and the
producer,
the

public

effects

structure,

policy

these
size,

and

government

factors
and

have

growth

on

of

regulations,
the

retail

and

organization,
and

service

industries in the local economy.
This
empirical

chapter
studies

understanding
trade

of

reviews
that

the

previous

have

geography

academic

contributed
and

economics

to
of

and
the

retail

and service industries in the economic development

of a community.
problem

area

author,

has

Namely,

a

The review also attempts to establish a

framework,
not

been

predictive

which

in

addressed
model

for

46

the
by

opinion

of

this

previous

studies.

identifying

business

47

opportunities

based on the

functions

community

in

the review
and

levels of retail and service

economic

development.

Although

is by no means exhaustive of all theoretical

empirical

studies

in

the

area

of

study,

it

has

attempted to ensure inclusion of the most current studies
on retail

and service

industries

in urban economics

and

community economic development.
Organization of the Literature Review
The
economic
three

sectors

of

knowledge

natural

previous

studies

(retail and service)

categories.

theoretical
and

review

The

first

base

on

resources,

infrastructural

systems,

interdependence

of

the

two

is organized

into

category

spatial

markets
etc.),

on

attributes
and

and

entrepreneur,

covers

the

(human

communication
the

consumer,

tripartite
and

the

community in the business location decision process.
The second category covers descriptive studies on
the

elements

socioeconomic
regulations).

and

influence

structure,

of

and

settlement
public

patterns, the

policy

(government

The third category covers empirical works

on the application of relevant location and central place
models

as

tools

of

analysis,

regional economic planning.

prediction,

business,

and

48

Central Places Theory
Centrality of Place and Its
Economic Functions
The

spatial

dimension

both

the activities

understanding
producer

and

the

trends

is

a

of

of

major
the

consumer

retail

distribution.

Critical to the development

these

activities

economic

centrality

of

distribution

the

(retail

location,

(settlements)

the

of

the

and

service

and scope of

service)
size

are

the

and

spatial

population

served,

of

accessibility,

and the impact of the other environmental

(competition,

of

and

in

availability

factors

variety

and

factor

functions,

transportation

consumer

system,

public

policy and governmental regulation, etc.).
Christaller's

(1966)

pioneering

work

in

the

theoretical understanding of centrality of place focussed
on three locational characteristics:
interdependence
areas,

between the

(1) the functional

central place

and

its trade

(2) the economics of demand and supply within the

subconcepts of "range" and
place functions,
population.

and

"threshold" values

of central

(3) density and distribution of the

The centrality of a location may, therefore,

be determined

by the economic interrelationships

(supply

of goods

and

services)between trade

maximum

distance

a

purchase

(range),

consumer

is

andthe

willing
minimum

areas, the

to travel to
level

of

make

a

demands

49

(threshold)
supply

required

goods

entrepreneur,

and
an

by

the

services

important

entrepreneur/producer
at

a

profit.

determinant

to

For

of the

the

threshold

is the size and distribution of the trade area population
(Christaller,

1966;

Dalrymple

and

Thompson,

1969;

King,

1984).
In other words the designation of the centrality
of a place,
providing
trade

be

needed

areas.

central

it a city or town,
goods

The

them

measurement

services

services

functions,

providing

and

while

are

and

the

to

goods

center

central

(qualitative

is the basic role in
the
so

provided

(towns

places.

and

surrounding

or

cities)

Among

quantitative)

are

various

which

have

been used to determine the centrality of a place/city are
a place's
the

size

predominant
of

the

type(s)

surrounding

merchandise v/holesale space,
its

economic

trade

(Dickinson,

1970,

pp.

the

institutions

1934; King,

155-159).

area,

activities,
amount

of

and the types and status of

and noneconomic

etc.)

of employment

1984,

However,

pp.

a

(i.e.,

Banks,

21-29;

Scott,

more

sophisticated

measure of the centrality of place is the use of indices
derived
sales)

from

wholesale-retail

trade

based on a study of 56 U.S.

ratios

(employmert,

standard metropolitan

areas (Siddnall, 1961, pp. 124-126).
Shaffer
concept

of

(1989)

centrality

applied
of place

the
to

basic

explain

theoretical
the

economic

50

activities
economic
and

within and among communities by analyzing the

interdependence

their

location
goods

communities,

factors

and

along

business
with

entrepreneurs

the

socioeconomic

that determine the types and

services

community.
broad

between

that

businesses

would

amount of

market

in

a

The concept of centrality also epitomizes the

concept

of city

classification

in terms

of

their

function and demographic importance.
Hierarchy of Central Places
The hierarchy concept is essentially based on the
premise
place

that most

areas

of

centrality

of

activities

towns
trade

village)

is

(shopping

behavior),

population
type

and

or

areas

of

a

within

1966,

center,

and

the

pp.

of

the

town,

or

orientation

distribution

trade

goods

in central

status

consumer

density

central

(Garner,

The

(urban

by

the

settlements

center offers

cities.

determined

variety

are carried out

area,

and/or

25-26;

of

and

the

services

the

Heilbrun,

1981,

p. 93; Davis, 1984, p. 26).
As
number

and

economic
places

the size
order

levels

functions

may

thus

and income of a place increase,

be

increase.
defined

functions they perform.
depth

study

of

(lower

The
in

Berry

consumers'

and

higher

hierarchy

terms
(1967)

of

orders)
of

the

its
of

central

specialty

embarked on an in-

behavioral

impact

on

the

51

classification

of

centrality

of

historical trends of farmers'
Iowa

(i.e.,

Council Bluffs,

Atlantic).
shopping

The

primary

behavior

of

Omaha,

was

the

based

on

the

Red Oak, Des Moines,

of

farmers

and hierarchy of a trade area.
methodology

place

shopping habits in parts of

focus

the

a

the

study

determined

was

how

centrality

A major part of the study

development

of

maps

for

the

succession of market areas whose status of centrality was
ranked from low to high.
based

on

volume

the

of

(resident)
result

type

of

The ranking of centrality was

goods

purchase,

and

or

services,

frequency

the

distance

a

consumer

was willing to travel to make purchases.

of

the

above

study

was

or

The

collaborated

by

Frankchowiak's (1978) study of consumers in Toledo, Ohio,
which

found

central

that

place

commodities,

consumers'

shopping

services

perception

was

based

provided,

and

of

on

hierarchy
the

the

of

type

of

uf

the

size

pla c e .
Christaller
study

to

determine

(1933),
the

cited

hierarchy

by King
of

in a separate

central

places

in

southern Germany, used a simple mathematical model (based
on

total

number of telephone connections

and population

in a region), to develop an index to measure the actual
and

potential

region.

The

importance

of

number

of

index was
a

central

telephone

then used
place

to

within

connections
identify
the

in

level

hierarchy

a
and
of

52

central

places.

Christaller

Based

concluded

on

that

the
the

results
spatial

of

the

study

distribution

of

central places was predominantly influenced by marketing
principles,

and

configuration

that

could

any
be

deviations
explained

from

by

the

expected

economic

and/or

noneconomic factors (King, 1984, pp. 29-49).
A similar
number

of

long

study

in the United States analyzed a

distance

telephone

calls

from

smaller

centers to the larger cities of Flint, Detroit, Lansing,
Saginaw,

and Bay City.

The study also found a positive

correlation between the size of a city and its sphere of
influence.

Other

studies

of

centrality

of

a

center

included measures of a town's newspaper area circulation,
extent of its

bus

service,

professional

services,

of

shopping

consumers

payroll addresses,

resident

population,

preferences

and

number of

and

surveys

the trading

of the centers (King, 1984, p. 52; Godlund,

areas

1956, p. 184;

Siddnall, 1961, pp. 124-32).
Central City Systems and
"Commercial Hierarchies
Cities
industry
markets

served

as

well

for

local

as

as

locations

other

and/or

for

economic

regional

business

activities.

goods

cities often share some degree of economic,

and

and
As

services,

cultural, and

social interdependence between and among other cities and
their peripheral places

(towns, village)

(King,

1984, pp.

53

20-21).

The

status

of

a

city within

the

hierarchy

of

cities is dependent on the number and types of goods and
services
areas.

available to its internal and surrounding trade
However, a clear delimitation of hierarchy within

the city becomes difficult as the city grows in size with
continuous

mobility

of

the

city's

population

and

dispersion of economic activities (Garner, 1966, p. 26).
viewed from the central systems perspectives, the
levels of these cities often manifest a hierarchy in the
central

cities

system.

The

level

of

cities

in

the

hierarchy may be determined by any, or a combination of,
the following factors:
order-level

of

goods

physical or population size, the
(lower-

level of economic condition
Central cities

or

higher-order),

and

the

(distress or sound economy).

at each level of the hierarchy also have

their defined market areas.
Shaffer's
study

of

(1969) analysis of Faust and ae Souza's

market

area

wholesale-retail

centers

in

Wisconsin showed that central cities at each level of the
hierarchy
areas

system

serve

(smaller

Similarly,

defined

towns,

central

cities

and

often

villages)
at

the

and
lower

smaller

trade

populations.
level

of

the

hierarchy also serve smaller trade areas.

However, while

lower

defined

level

central

cities

have

their

trade

areas, they also serve as market areas for central cities
at higher level of the hierarchy.

The larger the market

54

areas

and

population

goods

and

services

goods

and

services

explained

by

possible

the

the

provided.
by

the

by

served,

of

wider

the

order

of

Increases in higher order

central

degree

higher

cities

at

economies

market

each
of

areas

level

scale

and

is

made

density

of

population served.
In
central
their

other

place

hierarchy

economic

functions

are

population

words,

of

the
are

the

on

The

the

cities

of

measured

functions.
based

levels

in

the

by

the

number

of

number

and

order

of

and

density

size

and

cities

the

trade

of

areas

the

served

(Parr, 1987, pp. 222-23; Shaffer, 1989).
Centrality and Location of Retail
and Service Functions
Centrality

of

retail

and

service

functions

measured in relation to their spatial concentration.
higher

the

functions

center

level,

(Garner,

the more

1966, pp.

the

98-99).

is
The

concentration
Berry

(1967)

of

in a

major study of market centers and retail distributions in
the Midwest
only

found

a theory

that the

of location,

clusters

of activities,

patterns

of

spatial

but

central
size,

place

nature,

a theoretical

distributions

theory

is not

and spacing of
base for most

of urban

centers

and

retail and service business.
Retail

and

services

are

the

production and distribution network,

final

outcome

of

a

as well as the start

55

of

the

consumption process.

foundations

and service

locate

major

As central place goods, retail

It is logical that establishments engaging

and

at

possible

are

functions are very heavily consumer-oriented

(Parr, 1987).
retail

they

for the central place economic activities of

the central place system.

in

Therefore,

service

functions

convenient

population

centers

and

often
to

have

reach

potential

sought

the

customers

to

largest
(Johnson,

1964) .
The
spatial

application

distribution

historically,
reach

rural

of

of

evolved

retail

from

farming

and

and

political

provided entrepreneurs
variety

of

goods

place

service

entrepreneurs'

theory

to

functions,
attempts

who

converge

to
in

(e.g., local post office, rail

public administrative center,

cultural,

a

central

populations

convenient, central places
station,

the

etc.)

interactions.

for social,

These

places

the opportunity to sell consumers

and

services

(Berry,

1967;

Scott,

to

provide

1970, pp. 155-159).
Location Theory
Location Theory and Retail
and Service Functions
Location

theory

often

attempts

explanations for why economic activities occur where they
do.

Economic

largely

from

activities,
the

however,

attributes

of

a

are

known

location.

to

result

Location

56

theory

tends

to

be

seen

as

more

producer/supplier-

oriented because of its emphasis on such pull factors as
the markets,
etc.;

location

although

location

evolved

attributes
Retail

in

of

and

resources,

practice,

transportation systems,

the

choice

primarily

around

market

environment

the

service

activities

of

business

customers

are

and

(Shaffer,

strongly

the

1989).

consumer-

oriented in actuality, and their spatial distribution has
always

reflected

distribution

patterns

(Berry,

of

1965, pp.

population

and

150-54; Garner,

income

1966, pp.

98-99; Shaffer, 1989, p. 46-47).
Location Approaches
Retail
start
as

service

functions

of the consumption process,
the

crucial

consumption.
is,

and

The

therefore,

population

linkage

and

income

of

on

not

only

the

but continue to serve

between

location

dependent

are

production

retail/service

geographic

manifested

by

and

functions

distribution
the

patterns

of
of

consumer markets (Shaffer, 1989; Hoover, 1963, p. 4).
Shaffer's
and

community

classical
study

(1989)
economic

discussion

development

location approaches,

are

the

demand

dependence,

profit

which

provided

have

location

theory

examined

major

but more relevant to this

maximization/locational

maximization,
a

of

and

traditional

behavioral
guide

in

inter­
aspects
business

57

location

decisions.

However,

the

degree

of

emphasis

given to these approaches in location decision depends on
whether the target market is concentrated
or

dispersed

70).

(retail

business)

(Shaffer,

1989,

pp.

69-

The approaches included the following.
Demand maximization approach.

the

(manufacturing)

situation

spatially

of hexagonal

dispersed,

transportation
uniformly
business

cost

distributed
would

market,

with

This approach is

where

open

competition,

advantages,

and

spatially.

Under

traditionally

opt

the market

for

with

no

customers

this

a

is

approach,

location

that

would generate optimum value of sales and lower delivery
prices

than

the

competition

offers

(Shaffer,

1989,

pp.

56-57) .
Profit maximization.
maximization
focus

on

maximum

a

is

However, if profit

the

goal

of

location

that

has

return

(profit).

maximization)
maximization)

its

potentials

choice

for

the total

total
costs

would

generating

This decision approach

analyzes
and

business,

revenues

(profit
(demand

in relation to profit

potentials of the location (Shaffer, 1989, pp. 63-64).
Behavioral approach.

Since location decisions

are not based solely on profit maximization,
location

decision

approach

is

the

an emerging

behavioral

approach

58

which

essentially

(objective
location

focuses

functions)

as

decision.

consideration

is

nonmonetary

the primary

For

not

on

a

profit

factors

consideration

business

whose

maximization,

in

primary

any

location

which satisfies minimum profit potentials and the desired
objective criteria
market

(e.g., owner's hometown,

share/penetration,

etc.)

is

a

expansion of

likely

choice

(Shaffer, 1989, pp. 49-69).
Shaffer did, however, caution that the classical,
theoretical
situations
with

approaches
of

simple

were

"single-product and
organizations."

environment

become

product

multi-business

and

and

mass

more

production

approaches

originally

may

be

only

applied

single-plant companies

But

as

business

complex

in

terms

establishments,

operations,"
required

in

to

and

the

of

"multi­

large

scale,

a

combination

the

location

of

the

decision

process (1969, p. 66).
Location Decision Process
The choice of a business location is made within
the geographical context of a community.
as

the

decision-making

environment,

The community,
has

significant

influence on how and what location decision is made and
the

implication

consumer,
location

and

the

decision

for

other

local

government).
is

made

by

actors

Although
business,

(business,
the

ultimate

the

decision

59

process has to recognize the interdependence of the main
actors

(business,

The economic
these

need

and

government).

interests

and

behaviors

to

be

integrated

into

the

of

decision

(Shaffer, 1989).
Shaffer

discussion
steps

community,

and noneconomic

actors

process

consumer,

recognized

of five,

that

decision.

a

key,

business

According

these

conditions

input elements

goes
to

through

Shaffer,

in

the

in

the

and three major
making

location

location

decision

process of a business traditionally starts with trying to
identify,

evaluate,

communities

in

business

goals

Shaffer,

1989,

discussion
for

of

analysis

population

and

terms

of

(Blair
pp.

a

growth

potential
and

Premus,

70-72).

Gruen

location
of

compare

decision

a

site

short-

and

1989,
and

also

location's

economic

potentials,

income

and

long-term

pp.

Smith

stressed

74-75;
in

their

the

resources,
level,

its

need
i.e.,

consumer's

purchasing power, accessibility, and competitive factors,
which may determine the success or failure of locating a
business

(1965, pp. 30-37).
Hamilton

(1974),

motivation

for

businesses,

under

environmental
production
introduction,

in

assessing

new

increasing

forces,

such

obsolescence,

his

study

of

locations,

pressure
as
need

found

from

that

economic

resource
for

business

depletion,

new

and limited physical expansion

and

product

facilities,

60

are

often

compelled

locations.
nuclear
often
(1987)

Barring

accidents,

occurs

in

classified

three-phased
geographic

seek

any

etc.),
a

the

phased

strategy

disasters

process.

(earthquake,

Blair

and

identification

relation
the

business

Premus

of location decision as a

(1)

of

new

location decision process

the process

in

suitable,

serious

sequence:
area

management

to

to

the

business,

of

the

marketing
(2)

or

comparative

analysis of prospective locations and/or the communities
in

terms

functions
highway,
the

of

compatibility

with

business

objective

(e.g., a location's proximity to an interstate
railroad, or adequate public utilities), and (3)

selection of the appropriate location that satisfies

business requirement (Blair and Premus, 1987, pp. 72-85).
The
potential

economic

location

(profit

(economic
elicit
phases

complementary

and

benefits

of the

prospective

envisaged

maximization)

development/growth)

community

synergistic

input,

and

encourage

especially

by
the

during

that

By

of

business
community

cooperation

location decision process.
information

nature

the

and

early

providing

facilitates

location

decisions, a community has the opportunity to attract and
influence

new

businesses

community/city (Shaffer, 1989).

to

locate

in

their

61

Retail and Service Location Factors
Retail and service functions are dependent on the
two crucial factors of demand and supply.

It is only the

growth

guarantee

the

underscoring

the

and

survival

expansion

of

the

importance

of

economic

and

of

demand

functions,

the

thus

interdependence
psychological

interdependence

and

of

consumer-producer

behaviors.

Economic

operates within the conceptual

of demand threshold and the range.
threshold

that

range)

are

determining the type(s)
and the location

framework

Both concepts (demand

essential

of products

considerations

(goods

in

and services)

(where producers decide to market goods

and services), and consumer’s purchasing behavior.
Scott
threshold
support
MW*. «

(1970)

as

the

the
W

Shaffer

minimum

supply
a

wW

and

of

n l^ M
a r
o
WW

reasonable profits.

(1989)

purchasing
a

power

particular

an^

S4"«r»

MAAM

f
c
.WM

define

type

n n + ,on^4*
i al
WXWX

demand

necessary
of

good

to
or

4- o

h
*
W

This definition collaborates that of

Christaller

(range), Berry,

and King in different studies

of

activities

location

retail

central

place

theory.

and

The

patterns

concept

of

based

range

is

on
an

expression of the rational principle of consumer-producer
economic

behavior.

To

the

consumer,

the

range

is

the

maximum spatial distance a consumer is willing to travel
to

make

supplier,

a

particular
the

range

is

purchase.

But

to

the

producer/

the spatial distance that allows

62

efficient distribution costs and maximization of business
profits

(Griffith,

1982,

p.

178;

Christaller,

1966;

Berry, 1967, p. 14; King, 1970, p. 24).
However,
Thomas

(1989)

travelling

there

and

is

technology,

facilities,
location

Parr

distance

producer/supplier
available

is

and

of

a consensus

and

(range)

for

influenced

by

modes

consumer

either

Denike

of

among Shepard

(1970)
the

or

or

frequency,

transportation,

service

maximum

consumer

purchase

socioeconomic

retail

that

and

shopping

profiles.

The

functions

must,

therefore, take into consideration, within the context of
range,

the characteristics and economic interests of the

business,

the consumer,

the nature of goods or services,

and the market competition.
Scott's
Britain,

the

retailers'
on:

(1)

profits

(1970)

United

States,

of

retail

and

the
and

potential
provide

physical

Australia,

for

the

access

to

to

in

Great

found

that

marketing
collaborate

and

in

location

with

to

greatest

maximize
number

(2) the structure of the market,

attractions,

engage

location

the

reputation,

nature of the market environment,
ability

sites

selection and value of location sites depended

possible customers,
the

study

and

successful
rent

Nelson's

and

(3)

competitive

(4) the retailer's

competition

bidding).
(1958,

of

pp.

These

(product
factors

45-55)

principles used in the selection of retail location.

eight

63

The

service

function,

like

the

retail

function,

is consumer oriented,

and thus seeks to locate in places

easily

to

sources

of

location

adheres

to the same

accessible

1982).

Its

central

place

retail
and

theory.

requirement

profit

Daniels

of

addition

minimum

maximization

also detailed

location

In

of

a

demand

to

consumer

for

other

service

final

the

(Daniels,

principles
satisfying

travel

the

distance

supplier/producer,

factors which influence

function

of

such

as

the

type

the
of

service function and the access cost and modes of travel.
However,

Daniels

distribution,
very
great

density,

important
deal

service

of

(skilled

and

that

while

population

and level of purchasing power are

influences.
influence

businesses,

transportation;

factors

cautioned

Other variables
on

the

including:

communication;
unskilled);

location
access

to

also have a

of

(government regulations,

of

city

or

information;

availability

type

retail

of

labor

institutional
etc.);

and

the behavior/decision of property owners, landowners,

and

development organizations
The

market

size

zoning laws,

(1982, pp. 30-32).
of

the

service

function

is

dependent on the level of economies of scale achieved in
the production
Similarly,
price,
of

and

in the volume of demand

the volume of demand is a function of not only

but more importantly,

income

for service.

(Heilbrun,

1981,

of population size and level
p.

93).

This

collaborates

64

earlier

findings

Hassinger

(1957)

of service

by

Hoffer

(19 35)

in Minnesota.

functions

in

Michigan

and

Both found that the type

in a place

reflects

its population

characteristics,

relationship between population changes,

changes

types

in

Heilbrun

the
(1981)

and

volume

pointed

out,

of

retail

however,

services.

that

while

increased economies of scale lead to a wider market area,
allowing large business to mass produce at lower prices,
the

situation

competitors

also

(Hoffer,

tends
1935,

to
p.

eliminate

12;

the

Hassinger,

small

1957,

pp.

235-40) .
Retail and Service Market Structure
A

retail

market

may

be

defined

in

terms

of

spatial distribution of supply and demand of a particular
good

or

service.

As

the

last

production/distribution/consumption
market

essentially

caters

link

process,

in
a

the
retail

to the final source of demand

by providing goods and services for personal or household
consumption.

Convenience

and

accessibility

are

major

attributes influencing the behavior of producer/suppliers
and consumers in the development and growth of the retail
market (Nelson, 1958, p. 3).
The
demand
product

heterogeneous

(i.e.,
types,

frequency,
etc.)

nature
size

along

of

the

level

of purchase,
with

the

of

final

convenience,

small

business

65

capital

outlay,

allows

suppliers/producers

easy

and

the

growth

competitive market environment.
multiplicity
sizes

of retail outlets

(Scott,

1970).

The

entry

of

potential

of

a

fierce

This ultimately leads to
and a wide range of shop

retail

market

is

a volatile

environment with as many business turnovers as there are
new

entrants.

influences
returns on

The

the

size

diversity
of

imperfect,

the

retail outlets

investments.

composition of the

of

Ironically,

retail

and the

environment,

level

of

the heterogeneous

retail and service markets

competitive

market

thus

create

an

constraining

most retail and service establishments from attaining the
optimum

size essential

for

maximizing

returns

on

investments (Scott, 1970, pp. 85-88; King, 1984, p. 59).
Retail and Service Entrepreneurs
Because
economic

the

focus here

exchange

between

is

on the

geography

producers/suppliers

of
and

consumers, the central place concepts of demand theshold,
range of product

(goods or services),

is

analyze

important

to

and

and trade area,

understand

the

types

it
and

scope of retail and service markets and related patterns
of population distribution.
The
goods

or

threshold

retail

services

entrepreneur's
is

contingent

decision
on

the

to

minimum

provide
demand

for the good or service and the potential

for

66

future

profits.

based on:

An

investment

decision

is,

therefore,

(1) evidence of a reliable, minimum market as

measured

by

potential,
internal

size

(2)

of

population

ability

economies

of

of

competitive

product

reliability

in

and

profit

producer/supplier
scale

pricing,

predicting

and
and

consumer

to

thus
(3)

a

growth
achieve

engage

in

measure

of

purchasing

behavior

(King, 1984, p. 22).
In estimating the minimum acceptable market level
for

a product,

such an estimate should be based

range of the products
In other words,

(good or service)

on the

to be supplied.

the supplier should estimate the maximum

distance the consumer will be willing to travel to make a
purchase,

since

hierarchy

and the number of various types of retail and

service

this

business

threshold

distance

functions

depends

on

provided.

the

level

Because

of

market

Is sensitive to Income and population chanQes,

size of population and income level are essential factors
in

estimating

the

size

Garrison, 1958, pp.

of

market

304-311; Kenyon,

threshold

(Berry

and

1967; Shaffer, 1989,

pp. 113 and 143).
Shaffer's
thresholds
based

for

on Faust

review
various

of .an earlier
retail

and Picket's

study

functions

study,

in

estimating
Wisconsin

showed variations

in

the minimum population size required to support each type
of

the

32

selected

retail

functions.

Results

showed,

67

among

other

required

to

store,

186

repair

shop,

each

things,

that

support

a tavern,

people
712

product

for
for

is

a population of

a gas
a shoe

528

people

station,
store,

different,

so

77 people was
for

375

for

etc.

also

is

a grocery
an

The
the

auto

range of
product's

market threshold, depending on the order (low or high) of
the product and the socioeconomic characteristics of the
population.

Berry's study in Iowa of trade area size and

population served also found strong positive correlation
(0.95)

between the number of businesses offered in trade

areas and the population they serve

(Berry,

1967, p.

35;

Shaffer, 1989, p. 137).
Determinant of Range of a Product
The range of a product is the geographical scope
of

the

demand

for

a product.

Christaller

defines

the

real range of a product as "the boundary which a consumer
would be supplied by a competitor or producer"
54).

Similarly,

supplier's
suppliers

in a free market economy, the individual

market
try

to

range

is

minimize

hexagonal

consumer

in

travel

shape
time,

ultimately tend to locate at a common center.
clustering

of

(1966, p.

suppliers

and

goods

and

as
they

Thus, the

services

in

one

location attracts population concentration and more often
leads

to

increased

efficiency

in the provision

of goods

68

and

services

(King,

1984,

pp.

29-31;

Parr

and

Denike,

1970; Shaffer, 1989, pp. 126-29).
Product

range

in terms of the geographic market

trade area is determined not only by distance, affordable
travel time and cost, but also by the intensity of market
competition,
available

quality

physical

frequency

of

of

modes

of

infrastructures,

product

profiles of consumers

purchase,
(Shaffer,

transportation,

level of technology,

and

the

socioeconomic

1989, p. 133; Shepard and

Thomas, 1989, pp. 44-45; Berry and Garrison, 1958).
Trade Area
The

trade

Areas,

as

a

geographical

expression,

integrate the concepts of demand threshold and the range
of the product.
from

where

sale

(Shaffer,

place

the

supplier
1989,

theoretical

supplier

from

environments,
high

order),

area.

decreases

or

producer

143).

the

primary

income

draws

Defined

perspective,

the

central
and

depending of

consume behavior.
trade

p.

represents

consumers

or

It represents a defined geographic area
most

from

location
locus

secondary

course on product

level,

size

a

of

of

the

central
of

the

attracting
(peripheral)
class

population,

(low
and

Each product (good or service) has its

Trade

areas

expand

as

population

(Tarver, 1957; Berry, 1967, p. 349).

density

69

Although trade area has been defined as:
center

of the

1967),

(2)

percent,
the

the

accessible point

percentage

(Gruen and Smith,

area

goods

most

most

important

and services,

concept

of

The

concepts

of range,

the

development,

customer

1965,
for

pp.

the

and

attraction— 85

30-37),

supply

Nader,

and

of

(3)

as

specialist

all of the definitions are based on

the

provide

of

(Thorpe

(1) the

hierarchy of

the

demand

essential

central

threshold,

basis

organization, and

for

place
and

system.

trade area

analyzing

structure

of

retail

the
and

service markets.
Market Development
Retail and Service
Development Factors
In the development of a retail or service market,
each

central

function has its

defined

market

area,

the growth of each market area is influenced by:
distance
frequency

consumers
of

concentration
available
(4)

types

are

purchases
of

modes

a
of

of market

willing
from

variety

of

to
the

central

transportation
area

(public

(urban or

(l) the

travel
trade

and

the

area,

(2)

functions,
and

rural),

considered

is

a

brief

essential

service functions?

overview
for

the

of

a

few

development

and

major
of

(3)

private),
(5)

distribution and density of a trade area population.
following

and

the
The

factors

retail

and

70

Consumer Behavior
The

consumer

is

the prime

His/her

target

of
is,

and

service

activities.

critical

to the rate and scope in development of retail

and service markets.

behavior

retail

therefore,

The size and number of retail

and

service central functions in any particular location are
dependent on the
income

level,

size

of the consumer population,

and the

level of competition

(Lakshmanan,

1965).

Given the importance of the consumer,

travel

behaviors are

expansion,

and

businesses.

crucial factors

innovation

of

Underscoring

in

choice and
the

retail

consumer

their

growth,

and

choice

service

and

travel

behavior are the consumer's perception of costs (product/
service prices),
pleasure

of

travel distance,

shopping

(Spohn

purchase time,

and

Allen,

1978,

and the
p.

106;

Ingene, 1984, p. 72).
5

T n

North

«»

Carolina,

distance
12.3

rs

u

Scott

customers

miles

furniture

to

a

(1970)

were

functions.

evidence

that the number

(1)

likely

concentration
functions,

(2)

of

to

There

to
retail

when

the

is

■! n

that

to

types

substantial

reduced

outlets
consumer

the

owns

miles

and

(from
to

order

a
of

collaborative
shopping trips

where

offering

greatest

varied

6.1

of multipurpose
be

v t

travel

store

depending on the

business

are:

found

willing

department

store),

4*

*> K ^

a

a car

there

is

variety

of

he/she

is

71

likely
trips,

to

make

more

especially,

make

fewer

area

with

frequent

buying

hierarchy

functions.

While

pattern

consumer

of

distance

for shopping goods,

multipurpose
a

long

(low-

these

and

trips

and

seem

to

shopping

(3) likely to
a

large

high-order)

to

behavior,

reflect

a

of

the

study

trade

of

trade

general
consumer

behaviors in nine Australian cities by Johnson and Rimmer
(1967) did not seem to find a strong relationship between
consumer

behavior

cities.

Johnson

generalization
sample

and

size

services

and

of

and

among

the hierarchical
Rimmer,

their
the

the

however,

finding

because

inconsistencies
nine

structure of the

cities

caution
of

in

the

studied

the

the

small

types

and

(Johnson

and

Rimmer, 1967, pp. 161-66; Scott, 1970, p. 60).
Market Structure/Socioeconomic
The

modern

been the

response

The

decades

in

last
the

increase
market,
suburban

structure
to changes

witnessed

socioeconomic
in

disposable

development

of

the

retail

market

has

in the market environment.

significant

structure

of

income,

more

of

interstate

settlements,

increase

the

positive
society

women

in the

highways,
in

changes
(e.g.,
labor

growth

of

urban-suburban

population migration, consumer lifestyles, etc.).
These
retail markets

developments

led

to

(goods and services)

the

restructuring

of

and their functional

72

and spatial hierarchies to satisfy changes in the channel
of

distribution

with

these

migration,

and

the

demand

socioeconomic

high

ratio

of

changes

car

1984).

Berry's

study

(Scott,

of

Associated

were

ownership,

changing image of retail stores
King,

structure.

population

and

customers'

1970, pp.

business

46-49;

patterns

in

metropolitan Chicago in 1958 found that suburban centers
where professionals

(lawyers, accountants, doctors, etc.)

lived had more diverse and speciality functions.
Demographic Factors
Pattern
and modes
the

types

an

and

and
area

the
of

sizes

of market

area,

outlets,

population

distribution

of transportation have had

development
of

of

the

the

faster

the

central

areas.

greater

greater

of

the

the

functions

and

the

The larger the population
rate

variety

The

density

great influence on

of growth
of

structure

in

central

attraction of larger

population.

and

of

business

functions,

stores
the

to the

market

influenced not only by levels of business functions,
also by ever-changing demographic characteristics

is
but

(Berry,

1967, pp. 90-93; Stabler, 1987, p. 227).
As

previous

income levels

studies

have

shown,

population

determine the size and location

and

service

functions.

and

income— individually

But

at a microlevel,

or

collectively— have

and

of retail
age,
also

sex,
had

73

a

significant

influence

on

the

type,

structure,

and

spatial distribution of retail and service activities.
Age Structure
Studies
Houston,

of

Seattle,

older age groups
shopping
was

in

consumer
and

Columbus

found

and

attitude

in

that

consumers

in

(50-64 years) were more oriented toward

center

attributed

behavior

to

city than younger
older

age

groups

age
being

groups.

traditionally

more loyal,

with little or no domestic commitment

nest),

possessing

1955,

and
p.

findings

82).

It

still

hold

validity.

is,

Scott's

and

income

however,
and

can

(1970)

behaviors in England
England

higher

not
be

levels

known

This

(empty

(Jonassen,

if Jonassen's

generalized

with

any

study of consumer demographic

found that older people in northern

younger

housewives

with

children

often

preferred to shop locally, while working women tended to
shop outside the community.
A market analysis study of the grocery sector by
Bird,

cited by Scott

share

of

client
status,
increase

cooperative

housewives
(2)
in

youthfulness

there
the
and

independent stores

(1970),
stores

found that:
increased

and

declined

was

a

market
middle

with

positive
share
class

of

(1) the market
with

the

lowering

correlation
multiple

housewives,

age

of

social
between

stores
and

and
(3)

appealed more to young and old upper-

74

income

groups

than

the middle

aged

group

(Scott,

1970,

pp. 64-66).
Consumer Income
while
have
the

been

found

retail

levels

population

of

to

and

types

influence

market,

later

spatial

income

of

central

the size

studies

and

also

functions

structure of

show

distribution

that

have

the

greater

influence on the types and structure of shopping centers
than population and types of functions (Jonassen, 1955).
Hayes

and Schul's

(1965)

study of the effects of

income on market structure in Greensboro, North Carolina,
cited

by

Scott

(1970),

found

that

shopping

centers

in

high income areas not only drew most of their sales from
these

areas,

symmetrical
U.S.

in

also

that

shape.

in most metropolitan

"center
or

of

gravity

markets
studies

(196 3)

rather

density,

thus

were
of

more

selected

found that retail

areas were

income"

population

the

Similarly,

cities by Boyce and Clark

sales

city

but

influenced

than

the

size

collaborating

by the
of the
similar

findings by Jonassen (1955).
A study of upscale department stores in Cheshire,
England,

by

Stone

(1964)

reported

by

Scott,

1970,

also

showed that even though the stores were located in a lowincome
wealthy

neighborhood,
areas

outside

most
of

of

its

clients

the

community.

.came
A

from

similar

75

pattern of
areas.

(low income area)

in England by Davis

shopping
more

found

for the lower-income

A study of consumer income on shopping centers in

Middleton
area)

income effect was

stores

and Street Lane
(1968)

in low-income

diversified

and

(high income

found that:

areas were

generally

(1) while

few,

offered

they were

lower-quality

merchandise than the stores in the high-income areas, and
(2)

more

speciality

located

in

and

high-priced

the high-income

area

establishments

than

in the

were

low-income

area.
Cultural Factors
There have been numerous studies on the influence
of

culture

studies
many

on

of

consumer

blacks

large

U.S.

in

shopping

the

cities

shoppers)
shoppers)

residential
and

habits between the older,

behaviors,

the

neighborhoods

contrasting

southwestern

Canada

in

shopping

conservative Mennonites

and the "modern" Mennonites
of

including

(local

(urban central city

(Murdie,

1965;

Hay,

1967; King, 1984).
Infrastructures
Market
where

areas

there

are

and

adequate

infrastructures,

public

and

of

greater

use

structure

services,

private

also

tend

facilities
social

transportation

to

expand

(physical

institutions)
than

public

76

transportation

(Godlund,

1956;

Stabler,

1987,

pp.

225-

241).
Competition
The

competitive

nature

of

retail

and

service

activities has influenced the rate of growth, dispersion,
and

sometimes

the

establishments.
influenced
of

the

the

failure

Competition
development,

retail

and

of

convenient

has

also

of

these

significantly
and

structure

However,

while

the consumer in terms of

selection,

shopping

number

markets.

benefited

product

a

organization,

service

competition may have
variety

of

competitive

facilities,

it has

prices,

also

led

and

to

the

contraction of trade areas and the failure of a number of
small retail and service businesses.
of

places,

compelled
with

created

imperfect

environment

which

a large number of retail businesses to operate

underutilized

maximize

an

It has, in a number

returns

capacity,
on

and

investments

were

thus

(Lewis,

unable

1945;

to

Scott,

1970, pp. 78-89; Heibrun, 1981, p. 107).
Market Organization
Emerging from the competitive environment is the
phenomenon

of

allocation

reorganization,
retail

and

restructuring

service

establishments.

of

greater
and

activities

Innovations

resources

to

the

rationalization

of

and
in

their

physical

management,

new

77

technology,
service

lower

and

expansion,

delivery

costs,

and

have

all

satisfaction

improved

improved economies of scale,

led

customer

to

market

and profit growth

for many of the large retail and service businesses.

An

analysis of economic activities of farming communities in
Wisconsin
could

by

Shaffer

achieve

showed

higher

efficiency,

size

could

constrained

be

transportation
market

price

competition

and

of

by

consumers

are

the

profit

by

creating

because

market

hexagonal market area"

and

willing

of

the market

what

of

the
to

it

system by

size,

level

pay.

of

Market

improves

mopping

Shaffer

farmers
through

the economies

important

of

some

output

monopoly

competition,

efficiency
area

although

agricultural

degree

costs,

is

that

the

up excess

termed

a

"regular

(1989, pp. 125-26).

Retail and Service Market Organization
Organization of the retail market has experienced
significant
century.

changes
Changes

consumer mobility,
of

trade

structure,
industries.
purpose

since
in

the

spatial

middle

of

the

population

twentieth

demographics,

and the socioeconomic characteristics

area

population,

and

organization

Changes

in

have
of

consumer

to multipurpose shopping,

all
the

influenced

retail

behavior,

and
from

size,
service
single

were also reflected by

78

the

hierarchical

changes

in

central

place

population

(Mulligan, 1984, pp. 53-54).
Retail
small,

single,

stores,

service

specialized

tailor,

dispersed
large

and

candle

rural

chain

have

function

units

makers,

population

multi-function

retail

outlets

stores

etc.)

to

from

(i.e.,

serving

concentrated

supermarkets,
in mid

evolved

discount

drug,

sparsely

medium
stores,

to
and

to large urban metropolitan

cities (Hartley, 1975, pp. 22-26).
Most

of

establishment
technology

the

has

in

occurred

base

consumer

disposable

improved
4“

the

of

the

^

O A W a

•

changing

restructuring

the

result

inter-

and

retail
improved

delivery,

changing

increase

in

growth

in

lifestyle,

■{ 1

^

retail

of

urban-suburban

opportunities
of

modern

economy,

and

in

Jm ***

of

service

income

of
±

the

national

concentration
modes

^

as

retailing,

economic

population

organization

axes,

and

intra-metropolitan
W W

4*

demanded

n

-i

U U

1

V

W

-C
X

organizational

establishments

into

larger

operations of optimum size to achieve economies of scale
and

maximize

market
centers

is

return

one

located

on

that
close

investment.

has
to

emphasized
highway

The

modern

planned

retail

shopping

intersections

with

adequate parking lots and also close to the urban market
(Berry, 1967, p. 56; Stabler, 1987).

79

There

seems

to be

a consensus

in the literature

that the broad classification of retail organizations can
be

broken

down

consumer

and

150-51;

retail
that

three

cooperative

stores,
pp.

into

stores,

categories:

(2)

the

Hartley,

1975,

p.

in Europe

retail

28).

(1)

(Dawson,

Scott's

1979,

study

and the United States

categories

were

defined

the

multiple/chain

(3) the interdependent stores

activities

the

major

of

found

differently

on

each continent.
Consumer
limited

in

cooperative

scope

in

the

stores

United

other industrialized nations.
of

many

retail

the

largest,

as

compared

a

common

to

(comprised

phenomena

of

the

The independent retail stores form

single

organizations,

States

relatively

Multiple stores

stores) are

capitalist economies.

are

often

establishment

small

in

scale,

of
and

retail

most

often

managed directly by the cvjneir (s ) of the establishment (s )
A

great

number

of

these

independents

are

specialized

goods stores (Dawson, 1979, p. 152).
Although
have

been

the

efforts

to

primary

motivation

(rationalization
establishments
in

these

Multiple

and

economies
for

integration)

into major categories,

categories
store

achieve

have

achieved

organizations,

of

scale

reorganization
of

retail

not all operations

economies

however,

have

of

scale.

been

most

80

successful in achieving economies of scale because of (l)
centralized,
standard

large-scale

selling

buying

systems,

and

and

(2)

decentralized,

stocking

specialized

functions based on vertical and horizontal integration of
retail

outlets

and

strategic

business

1970, p. 47; Hartley,

1975, pp. 28-32).

study

Garoian,

by

Mueller

and

locations

(Scott,

Ironically,

reported

by

Scott

the

(1970)

found that most of the growth by multiple stores has been
in the slowest-growing cities in the U.S.

(p. 82).

Department Stores
Most department stores have enjoyed economies of
scale due to horizontal and vertical integration of their
store's

operations,

thus

large-scale

selling

integration

measures

enabling

and

them

small-scale

have

helped

the

to

engage

buying.
growth

in

These
of

these

stores into department store chains (Scott, 1970).
Independent Stores
As

predominantly

had as much success
have

not

been

organizations
integration
relations
support

did

so

(engage

services

outlets, most

have

not

(as other categories) because members

very

that

grocery

homogeneous.

achieved
either
in

with

The

economies

of

by establishing

special

discount

wholesalers)

voluntary chains (Scott, 1970).

or

independent

scale

through

close network
purchases

by

merging

and
into

81

Multiple Stores
The organization and structure of multiple stores
vary depending on the location and level of the center in
the hierarchy of places
or

high),

it

and the order of functions

provides

its

customers.

For

(low

example,

in

large, urban centers, multiple stores are known primarily
for nonfood merchandise,
related

materials,

nonurban

areas,

as

main

they

shoes,

and

But

in

more

on

lines.

concentrate

Multiple stores also tend to locate

in areas with heavy

pedestrian traffic

46).

A Baltimore study,

while

multiple
dense

product

(multiple)

grocery merchandise.

moderately

especially clothing,

stores

(Scott,

1970,

p.

cited by Scott, also found that

more

often

population,

locate

independent

in

places

stores

tend

locate in both densely and sparsely populated areas.

of
to
In

spite of the diversity among these categories of stores,
tlisir

dBVBiGprriciit,

GiyaiiizstiGn,

aiiu

reflected spatial population distributions.
proximity

to

both the

city

and

suburban

location

liaV6

The need for
population

has

led to their location at urban peripheries or convenient
central

centers

that

provide

adequate

customer

shopping

convenience and facilities (Scott, 1970).
City Size, Structure, and Functions
A
and type

city's

size

of market

as

defined

demands

by

population

also influence

density

the structure

82

of

retail

businesses.

Retail,

as

a

service

function

dependent on the trade area population and income level,
does

not

served

tend

to

(Nelson,

grow

faster

1958, pp.

than

the

5 and 7).

area

A comparative study

of trade types and town size cited by Scott
Knapp,

and

Winsten,

correlations
retail
large

between

functions,
cities

highest

and

the

i.e.,

(500,000

ratio

stores

1961)

showed

size

an

correlations

medium-size

of

food

found

cities.

The

Hall,

varying

ratio

and

special

relationship

and

were

(ref.

cities

inverse

people)

population

between

trade— but

between

study

did

the

clothing
conclude

that speciality stores in the U.S. have a higher ratio in
places of greater distance, lower population density, and
high per capita income (Scott, 1970, pp. 47-49).
Traditionally,
the collection,
areas

regional

status

providing

retail

(Siadnall,
has
and

and local markets.

(1937)

regional
reviewing

purposes

in

and

nine

local

U.S.

Baltimore,

and
U.S.

p.

dual

service

have

included

and serving of internal or

I9bi,

the

studies

Atlanta,

major

functions

124).

A

city

responsibilities

functions to meet

of
of

regional

The structure of such markets tend to

Cleveland,

most

city's

distribution,

periphery

reflect

a

of urban
cities

Des

Moines,

Knoxville)
cities

market

in

came

demands.
land used

(Chicago,

general

the
have

for

retail

Philadelphia,

Washington,
to

Proudfoot

New

conclusion
five

York,
that

forms

of

83

retail

functional

district

(CBD)

structures:

(1)

the central business

characterized

by

a

shopping

goods

stores

network

which

make

accessible
suburbs,

and

center

principal

central
all

(2)

parts

the

similar

restricted

has

more

convenience

of

shopping

women's

which
and

clothing,

is

of

the

to

its

to

(3)

characterized
stores

furniture,

the

immediate

stores,

specialty

city,

business

character

more

street

district

outlying

in

the
by

neighborhood

oriented

toward

residential)
distance.

business
their

and

primary

attracting

f rermen-H v

markets,

stores

which

jewelry),

customers
customers

stores,

etc.,

and

(5)

more

more
(4)

often

(neighborhood
from

n r CCSrV

rnmnr<

fruit and vegetable stores,

shopping goods

are

a

(e.g.,

large department stores, and some convenience stores,
the

of

transportation

business

is

business

and

intra-city

from

although

and

concentration
men's

the

peripheries,

district,

areas

the

customers

which,

central
trade

to

and

concentration

walking

stores

JT.cat

convenience stores,
the isolated store

cluster which is similar in structure and characteristics
to the neighborhood stores in terms of product offerings,
but

is

often

(Proudfoot,

located

at

the

periphery

of

the

city

1937, pp. 425-42).

A similar study of business patterns focused on a
succession of land uses in Chicago as a regional center.
The

study

showed

how

various

land

uses

have

influenced

84

the structure of retail functions within the hierarchy of
Chicago's
major

metropolis.

spatial

The

features:

provided

the

highest

regional

and

local

offered
while
for

mixed

hierarchy
(1)

the

threshold

needs,

regional

core

central

(2)

and

manifested

the

of

three

the

city

functions

intermediate

community

level

for
areas

functions,

(3) the city fringe offered personal service stores
neighborhood

classification
retail

and

needs

(Berry,

collaborated

service

a

centers

1967,

p.

similar

51).

This

classification

in Calgary,

Canada,

of

by

Boal

in

the

and Johnson (1965, pp. 156-68).
Public Policy
Often

not

given

adequate

attention

discussion of spatial distribution patterns of retail and
service functions is the enormous influence of government
public

policy/regulation.

In

a

free

market

economy,

there is always the tendency to underestimate the direct
and

indirect

and

service

ways

in

which

inclusive)

economic

have

been

activities

shaped

by

nonlocal government policies and regulations.
regulations

such

as

zoning

laws,

building

local

capacity improvement,

etc.)

and

Government
permits,

fiscal and nonfiscal economic development pclicies
cost reductions,

(retail

and

(i.e.,

are some of

the public tools used by city government to regulate the

85

number,

size,

businesses

type(s)

and location of retail and service

(Scott, 1970, Bowman, 1987, pp. 54-55).

Changing Patterns of Retail Market Environment
Changes
with

the

state

in

postwar

and

government

a high

facilitated

pattern

of

developments.

highways,

housing,

the

the

shift

suburbs,
retail
Fisher,

of

loans

large,

markets

to

of

suburban

wealthy,

by

where

city level,

family

population

the

wealth

have
and
With

to

the

restructuring of

is

(Mitchelson

and

The changing market environment is

Berry's

which found that:

single

to the suburbs.

urban

came

inter­

settlements

also a movement and

1987, p. 51).

confirmed

for

of

ratio of automobile ownership,

growth

there was

markets

Construction

population movement from the city
the

retail

study

of

business

areas

in

Iowa

(1) as one moves from county level to

business structure becomes more complex,

and

(2) regional cities tend to have larger and more complex
highway-oriented

shops,

business

ribbon development,

and

other types of specialized consumer areas (Berry, 1967).
Competition
Competition
factor

influencing

Easy entry
of new

into the

has
the

perhaps
patterns

been
of

the

the

most

retail

potent
market.

retail market has led to the growth

retail and service

functions and the flurries of

innovation in retail and service technology.

The need to

86

stay ahead

of competition has resulted in the growth of

specialized,
stores,

mail

automatic
either

retail and service functions
order

vending

forced

profitable

services,

credit

machines,

etc.).

retail

trade

outlets

areas,

or

to
to

(e.g., discount

exclusion

services,

Competition

disperse
cluster

has

in search

into

of

locations

where they share the same trade areas and try to maximize
the

benefits

with

the

agglomeration economies provided by the environment.

But

the

of

ultimate

economies

choice

establishment

is

of

of scale along

location

contingent

of

a

on the

retail/service

type

of

trade,

ownership, and nature of the market (Scott, 1970; Dawson,
1979, p. 152; Applebaum and Cohen, 1961).
The
higher

order

resulting
them.
of
was

proximity
of

in

between

functions

the

loss

cities

intensifies

of

share of

of

the

same

competition,
sales

or
thus

by either

of

A study by Boyce and Clark found that the amount

sales

in

the

affected

Washington,

by

D.C.

central
its

business
proximity

(1963,

p.

district
to

of

Baltimore

Philadelphia

193). Proximity

and

influences

not only sales, but as Hodge's study of trade centers of
Saskatchewan

(the

Great

Plains) found,

the

population

density of small trade areas also tends to decrease with
increasing

proximity

to

larger

trade centers

1965, pp. 97-100; Boyce and Clark, 1963, p. 193).

(Hodge,

87

Consumer Mobility
As

a

retail

market

also consumer mobility

tends

to

influences

seek

consumers,

so

the type and patterns

of retail markets since consumer behavior varies with the
type

of

retail

hierarchy.
evident

function

From

that

these

and

discussions

the development

organizational
environments

structure
have

levels

of

been

of

and

and

disposable

retail

and

influenced

lifestyle,

automobile

income,

increasing

ownership

competition,

place

studies,

it

is

and distribution of modern

by

socioeconomic structure of the society,
personal,

central

changes

consumer

ratio,

service
changes

in

the

rising levels in

in consumer
mobility,

changing

market

age

tastes

increased
structure,

entry of more women in the labor force,

and

the outward shift of population from inner-urban centers
to the suburbs (Scott, 1970, pp. 80-83; Bowman, 1987).
Application Models in Retail Markets
Population,

income,

previously

discussed,

not

motivate

only

activities,
location,
central
Losch

but

pattern,
place

competition,

among the primary

development

also
and

theory

(1940), Galpin

Berry et al.

are

and

and

determine
scope

the

of retail

pioneered
(1915)

growth

by

as

was

forces

that

of

retail

function

type,

activities.

Christaller

The

(1966),

and subsequently advanced by

(1958) has provided an explanatory basis for

88

understanding

spatial

interdependence

and

patterns

of

analyses

of

consumer distribution and retail activities.
Application

models

associated

retail

and

service

markets

have

relied

on

central

place

and

with

directly

location

or

indirectly

theories

as

is

evident in the following key models.
Retail Gravitation Model
The

development

of early

gravitation models

in response to two areas of need:

was

(1) town planners who

were engaged in establishment of new shopping centers and
shopping
of

a

facilities,

verifiable

and

(2) social scientists in search

theoretical

base

for

understanding

fundamental relationships within urban structures

the

(Scott,

1970, p. 168).
Developing
market

and

its

a

model

spatial

for

analyzing

requirements

the

demands

knowledge of spatial distribution of consumers
shopping
models

behaviors.
were,

The

retail

therefore,

and

expected

service
to

retail
a

sound

and their

gravitation

provide

the

analytical tool for understanding the geography of retail
and

service

markets

and

The

gravitation models

consumer

factors

of population,

sales,

retail

space,

costs)

influence

patterns.

are essentially based on measures

of how

travel

distribution

and

employment,

distance

consumer

income,

(miles,

purchasing

total

time,
behavior

and
or

89

interactions

(Carruthers,

1962,

pp.

3-27;

Shepard

and

Thomas, 1980, pp. 20-30; Shaffer, 1989, pp. 143-46).
The
(1)

models

interactions

directly

as

operate

on

between

functions

between two centers,

the

two

of

broader

thesis

population

population

that:

centers

size

and

vary

distance

(2) there is a positive correlation

between large population centers,

(3) there is an inverse

relationship between distance and level of attraction by
centers,

and

competition

(4)

at

between

customers

by

both

reaction

customers

method
size,

of

distance
1970;

of

between

Wagner,

centers,

expected

gravitation
to

where

to

and

is

attractions

of

be

models

size

there

the

deal

same.

with

accessibility

the
of

The models also provide an inexpensive

determining

number

trade

is

the

shopping centers.

point

two

Essentially,
of

breaking

market

economic
centers

1974,

pp.

areas

functions
(Huff,
30-34;

based
of

1561,

on

the
pp.

Shaffer,

population

centers,
19— 25;

1989,

49) .
The gravitation formula is expressed as:

and

Seoul,

pp.

144-

90

Where:
I = expected interaction between places i and j
Ai and Aj = size of places i and j
Dij = distance between i and j
K = constant
a, b, and c = estimated parameters for the
gravity model and type of economic
activities.
Source:

Adopted from Shaffer, 1989, pp. 144-45.

Laws of Retail Gravitation
Although
originally used

the

law

of

retail

gravitation

was

for the study of population migration in

the 1800s, Reilly (1931) was the first to apply it to the
study

of

retail market

that

although

willingness
travel

people

are

attracted

to shop in such places

distance

willing

areas.Reilly's

to

population
(attractions)

(miles)

assume

to

size

and

in

each

and

the

law
by

large

(1)

cost consumers

are

to shop,

number

of central

and

places,

is influenced by

travel

place

postulates

the

and

(2)

the

functions

distance

between

p l aces.
Reilly's application of the model to study market
areas involved the analysis of 255 cases of various city
and

town

networks

in Texas.

A

review

of

these market

91

areas

by

Scott

(1970)

showed

that

population as the first power,
inverse distance
other power"

"the

exponent

and the

of the

exponent of the

is nearer the second power than to any

(p. 169).

Reilly's second study was developing a "Breaking
Point" equation to delineated total market areas for the
towns of Atlantic and Red Oak based on functions and city
hierarchy.

Reilly,

"Breaking
and

Point"

large

however,

equation

regional

cautioned that although the

is more

centers,

appropriate to cities

it may

also

be

applied

to

rural areas (Berry, 1967).
The "Breaking Point" Formula
Consumer's travel
distance between
shopping places D
and E
=

Distance (miles) between D and E
—
-----------------------------1+ /Population of D (large community
v Population of E (smaller community)

Sources: Berry, 1967, pp. 40-41; Hustede et al., 1984,
pp. 24-25.
Reilly's
because
exponent

of

its

model,
emphasis

values

when exponent

of

however,
on

exponent

population

to population size and distance.

seem

to

and

of distance may not

of two communities,
provide

has

the

and

criticized

restriction

distance,

to

especially

be positively related
It assumes homogeneity

except for size,
for

been

effects

and also does not
of

differences

in

92

patterns of consumer demands.
has

also

been

content

and

its

explanations
(1960),

providing
Scott,

to

provide ' persuasive

of

the

strong

valid

basis

pp.

169-71;

criticized
for

the

hierarchies

the

Reilly's

model

as

(Isard,

1989,

pp.

not
1956;

147-148).

(1970), using the gravity

areas

and

Isard

of

projection

Shaffer,

trade

theoretical

observed.

proponents

cited by Scott

study

different

limited

regularities

study,

to

its

the

also

1970,

Illeris'

inability

model,
a

for

for

one

gravitation

model

criticized

Reilly's gravitation model

of

central

distances

in

places

Denmark,

of

has,

however,

found that good or improved roads can reduce the

size

the

of

attempt

to

distance
validate

determine trade
studied
The

trade

study

delineated
point

Reilly's

areas

in

that

trade area
a

Wagner
law

(1974),

and

to

Springfield
Reilly's

and

law

in

an

effectively

areas using the Breaking Point

found

show

exponent.

formula,

Columbus,

neither

Ohio.

accurately

of both centers nor did the break

difference

in

the

number

attracted to both competing trade centers

of

customers

(Wagner,

1974,

pp. 32-33).
Physical Planning
Shopping Centers
Reilly's
establishment

of

model

has

planned

also

shopping

been

applied

centers

in

to

the

suburban

93

areas.

The development

centers

was

trends

based

and

on

estimates

profiles

structure,

of traditional

(i.e.,

expenditure

of

and new suburban
center

purchasing

by

population

power

central

income

functions),

accessibility to stores in terms of competition, customer
behavior, and quality of highway networks, store location
characteristics,

and

incorporation

these dimensions

centers

is

of

mixed

expected

to

stores

concentration.
into planned

maximize

external

The

shopping

economies

of

scale (Scott, 1970, p. 172).
Modification of Reilly's Model
In an attempt to bridge the gap between Reilly's
gravitation

model

developed

a

cognizance

that

by

the

consumer

modified
the

behavior,

gravitation

model

amount of increased

attractiveness

family size,
retail

and

income,

of

etc.,

a

place

or market

therefore,
share

that

based
sales

could

be

(1964)
on

the

generated

limited

by

even when there are increased

facilities or shorter distances.

model was,

Huff

Huff's modified

to estimate the fixed total sales
a place

can

control

based

on

the

probability of a consumer traveling from his/her place to
another location to shop given a number of other shopping
centers.
possible
customers

Establishing
to

generate
(sales)

such

a

probability

an estimate

from

a

has

made

it

for a fixed number of

defined

range

of

potential

94

customers.
distance

In

short,

rather

than

the model
the

gives

attraction

more emphasis

of

a

place

to

(Huff,

1964, pp. 36-37; Shaffer, 1989, pp. 145-46).

(a)

Estimating Market Share

s.
pab -

5- /

ab/

/n

(b) Estimating Fixed
Number of Customers

S®

.1 - f

1=1 d .

■ pi3ci

ab

Where:
(a)

P . = the probability of consumer at place a and
shopping in place b
Sfe

= size of shopping area and number of
available goods and services

D . = travel distance or time from place a to
aD
place b

(b)

n

= opportunities to alternative shopping
places

e

■ factor measuring the influence of distance/
travel time on different function (goods
and services) levels

Eij = expected number of potential customers from
place a to shopping center b
Ci

= number of potential customers

Sources:

(a) Berry, 1967, p. 42.
(b) Shaffer, 1989, pp. 146.

95

The Haydock Model
The Haydock model, developed at the University of
Manchester

in

1964,

used

a

complex

analysis

of

the

shopping center system and the experience of Reilly's law
to

develop

shopping
major

a

three

population
revealed
the

for

center in Haydock,

retail

into

proposal

and

service

groups
and

significant

three

hierarchy

groups
and

out-of-town

England.

functions

based

travel

an

on

cash

The

flow

and

of

to

classify
sales

result

instead

of

the

the

information

centers.

21

centers
of

study

expenditure patterns

provided

structure

The model used

retail

time.

regional

on

for
the

Although

the

Haydock model made major contributions to new approaches
in

retail location analysis along with the identification

of

shopping center systems, the

basic
center

assumptions
networks,

shopping

of:
(2 J

centers,

(l)

major weaknesses are its

closed

subj 6ctxVe

and

(3)

system

of

shopping

uo m y or i ^ d uion

strict

application

or

me

of

the

hierarchy principle and the use of inadequate statistical
data (Scott, 1970, pp. 174-77).
Other

studies

cited

gravitation model are:
(1967)

which

attraction,
allocation
goods;

(2)

used
travel

of

Scott which

(l) the

store
time

expenditure

Black's

by

model

a

by

on

the

South Bedfordshire study

floor-space
as

drew

as

distance

convenience

(1966)

was

factors
factor,
and

used

of
and

durable
to

study

96

shopping
total
use

systems

sales

and

around

and

Oxford

straight-line

traffic

flow.

by

using

distance

The basic

variables

to

predict

of
land

assumption of Black's

study was that the maximum distance consumers are willing
to

travel

miles);

to

a

(3)

shopping

Lewis

opportunities
facilities)

center

and Trail

is

(1968)

(e.g.,parking

as attraction

in a particular center.

25

and

factors

kilometers
also

looked

other

(15
at

shopping

for consumers

to shop

Lewis and Trial have argued that

the opportunities to attract customers will depend on the
volume of opportunities,

the distance of travel,

and the

intensity of competition among consumers;

(4) the Harris

Equilibrium

of

model

based

opportunities

was

transportation

project.

assumptions

that:

opportunities
factors,

and

used

are

the
to

The

(a)

concept

study the
model

shopping

influenced

by

was
trips

intervening
Penn-Jersey

based
and

different

on

the

shopping
behavioral

(b) that these factors also vary by spatial

distribution

of

opportunities,

population thresholds,
centers

on

consumer

behavior,

and economies of scale of shopping

(Scott, 1970, pp. 178-81).
Other Models

Market Potential Model
Drawing
Lakshmanan

and

on

the

Hansen

experience

of

(1965)developed

Huff's
the

model,

market

97

potential
The

model

model

was

metropolitan
zone

for

to

study

market

predicated

on

the

consumer

expenditures

in

thesis

region with many zones,

to the size of the center,
goods,

centers

Baltimore.
that

in

a

competition of each

is directly

proportional

amount of space for shopping

consumer travel time,

and the number of competing

amenities in each zone.
The utility of the model is its applicability to
situations

with

more

than

two market

centers,

adequacy

for analyzing overlapping market areas; and in evaluating
alternative strategies

(Scott, 1970, pp. 175-80).

Sales Potential Retail Model
The
potential

Sales

local

Potential

sales

based

Retail

on

Model

estimated

state

computes
average

and per capita sales adjusted by the ratio of local-state
per capital income (Shaffer, 1989).
Trade Area Capture (AC) Model
The
proportion

Trade
of

the

Area

Capture

population

Model

shopping

estimating the size of the retail market,
based on
made

the number of people
(e.g., a

household).
individually.

purchase

All

members

of
of

analyzes
locally,

the

in

calculation is

for whom the purchase
either

the

father/mother

household

are

is
of

a

counted

The TAC model is another way of estimating

98

potential

retail sales by measuring total purchases made

by local and nonlocal residents.
A basic assumption in the application of the TAC
model

is that local consumer tastes and preferences are

the same across the state.

The formula for applying the

trade area capture is:

ASik
TAC-ik =
J
Source:

(ASsj/Ps)

(Yc/Ys)

•

Shaffer, 1989, p. 152; Hustedde et al. 1984,
p. 56.

Where
TAC.k = Trade Area Capture for a central function
j measured in terms of customers in city
k
AS.,

= Annual retail sales for a central
function j in city k

AS

= Annual retail sales for a central
function j in the state

.
-*

P_
£>

= Total state ooDulation
~ ^

Yc

= County per capita income

Yg

= State per capita income

The TAC model
to

calculate

Trade

Area

population,

sales

uses

Reilly's

gravitation

area

capture.

If

Capture

is

greater

than

either

the

city

is

the
the

value

formula
of the

trade

attracting

area

outside

clientele or the pattern of local residents' spending is,
on the average,

higher

than the state spending average.

99

Conversely,

if the trade area capture is less, either the

local residents'

spending levels are less than the state

average,

city

or

the

is losing

its potential

customers

(Shaffer, 1989; Hustedde et al., 56-57).
Among

the

appropriateness
retail

and

weakness

major

for

strengths

estimating

service

of

trade

functions.

of the TAC model,

the

model

area

is

its

capture

for

Considered

however,

a

major

is that unlike most

trade area models expressed as function of population and
distance,

the trade area capture incorporates

income and

expenditure but not distance factors (Shaffer, 1989).
Pull Factor
Augmenting the capability of the TAC model is the
pull

factor. The

pull

factor

calculates

the

proportion/ratio of the TAC to the city's population.
also

measures

the

nonlocal customers.
being able
city's

to

provide

1984) .

with

residents

which

a

TAC

and

shopping

of local demand

influence

regard

a valuable measure

nonlocal
trends

The

to

city

attracts

The pull factor has the advantage of

neutralize the

population

attraction.

degree

It

to

the

of

the

pull

changes

city's
factor,

in a

power

of

however,

for estimating the number of
locally,

(Shaffer,

1989;

and

the

different

Hustedde et al.,

100

The use of the trade area capture and pull factor
is,

however,

constrained

by:

(1)

Most

the

difficulty

obtaining

up-to-date data.

restricted

to the U.S. census of retail trade

industries

published

every

available

five years,

market data in most cases

(2)

data

of
are

and service
the relevant

are available for small,

mid-

and large-metropolitan places but not for populations of
less

than

2,500,

(3) the

basic

assumption

of

uniform

consumer

tastes and preferences and uniformity of buying

behavior

across

fixed

types

the

state,

of goods

and

(4)

the availability

of

and services with varying quantity

(Shaffer, 1989, pp. 152-56).
Location Quotient Model
While all of the previous models
on

internal markets

sales)
looks

in

the

that

location

currently
local

city

beyond

functions
the

the

(retained sales and potential retail
or

community,

city

trade

are patronized
quotient

purchased

focus primarily

from

the

area

location quotient
at

locally.

analyzes
outside

nonlocal

In other words,

goods
the

those

and

services

community/city

by

residents which could possibly be provided locally

(import substitution).
The

location

quotient

uses

local-national

employment ratios of an economic sector as indicators of
potential

for

import substitution.

However,

the use of

101

location

quotient

potential

must

to

examine

determine
local

import

and

substitution

accessible

sources

of

supply to ensure that there is viable market locally for
the particular good or service (Shaffer, 1989).
Two

different

(estimates

of

quotients
national

to

studies

regional
measure

by

economic

the

Isserman

impact)

ratio

of

(1977)

used

local

location

employment

to

employment in a particular sector.
Location

quotient

is expressed

in the

following

formula:
% Local employment in sector X
L Q

-------------------------------------------------------------------------------------------------------- •

=

% National employment X
Sources:

Shaffer, 1989, p. 154; Hustedde et al., 1984.
A

value

of

self-sufficiency
service.

But

1 is an

in

the

cities/communities

would

mean

particular

supply

a measure

close

that

a

sector

of

has

the

of

less

have

place
than

indication of

at

a community's

a particular
than

1,

least

less

a

and

good
if

national

other

value

employment

of

in

average,

or

1,

that

thus

an

indication of potential market for the particular good or
service.
In
model,

a

Murray

commercial

related
and

approach
Harris

development

of

to

(1978),
the

the
in

Turtle

location
their

quotient
study

Mountain

on

Indian

102

Reservation,
identify

used

population-employment

potential

import

substitution

ratios

trade

to

functions

for the Reservation.
Population-Employment
Ratio Model
Unlike

location

quotients,

population-employment

ratios for a city are interpreted in comparison to other
neighboring

cities/communities.

A

high

population-

employment ratio means that there are more people to each
worker in a particular industry than the average, thus an
indication

of

potential

opportunities.
employment
which

ratio

are more

avoids

the

quotient

employers,

are:

(1)

relevant

in

may
a

increased

advantages

often
city

its

of

employment

the

reliance

to the local

computational

which

especially

The

for

populationon

situation,

subjectivity

of

distort

actual

where

local

the

there

are

the

(2)

it

location
situation

few

dominant

(3) the use of. the entire population,

than only the employed,

data

rather

as more reliable, particularly in

a city that has a larger younger and/or older population
(Shaffer, 1989).
A
import
cities

comparative

substitution

estimate
for

by

,furniture

found that location quotient

employment
identify

ratio
import

(PE)

Shaffer

can

be

substitution

(1989)

retailing
(LQ)

used
for

in

of
five

and population-

independently
a

to

particular

103

good/service

in

a

city.

Both

can

also

be

used

to

reinforce import substitution estimates for goods and/or
services in a city.
less

than

particular
import

1

and

For example,

a location quotient of

high population-employment

good

or

substitution

service

in

potential

a

city

for

the

ratio

confirm
city

for

a

strong

(Shaffer,

1989, pp. 155-560.
Retail Compatibility Model
A major

contribution

to the theory

and practice

of scientific retail location was Nelson's
Compatibility

Model.

influence

retail

achieve
patterns

on

greater
of

The

location

volume

retail

model

of

has

business

had

decisions

business
that

and

(1958) Retail
significant

that

seek

develop

will

to

stable

benefit

the

entrepreneur and the community.
Nelson

defines

compatibility

as

the

degree

to

which two businesses interchange customers. His principle
of retail compatibility stipulates:
Two
compatible
businesses
located
in
close
proximity will show an increase in business
volume directly proportionate to the incidence of
total
customer
interchange
between
them,
inversely proportionate to the ratio of the
business volume of larger- stores to that of the
smaller store, and directly proportionate to the
sum of the ratios of purpose (visit to store as
major
purpose
of
shopping
trip)
to
total
purchasing in each of the two stores (1958, p.
6 6 ).

104

In

testing

the

compatibility

relationship,

he used

the

following formula:

V = I (VI + vs) x

X

Where
V

= increase in total volume of two stores

VI = volume of larger store (total purchasing)
PI = purposeful purchasing in large store
Vs = Volume of small store (total purchasing)
Ps = purposeful purchasing in smaller stores
1
Source:

= degree of interchange

Adopted form Nelson, 1958, p. 67.
Using this

business

formula to

more

than

10,000 individual shopping trips), Nelson found that:

(1)

there

districts

is

a

direct

interchange
business,

and

analyze a large number of

between
(2)

that

shopping

centers

relationship
two

businesses

the

high

(and

between
and

degree

the

their
of

rate

of

volume

of

compatibility

between two adjacent businesses leads to a greater volume
of

business

locations,
business,
carry

may

than
and

or

if

(3)

the

result

they

that

the

competitive
in

were

their

located

in

complementary
product

lines

cumulative

separate
nature

which

attraction

customers and thus account for the high compatibility.

of
they
to

105

In
pattern,

addition

to

the

advantages

(stable

retail

increased business volume, etc.), Nelson's model

facilitates
business

the

and

demonstrating

achievement

grouping
the

functions

together,

municipal

zoning

of

of

efficient

compatible

advantages

of

the model

has

of

retail

interchange

functions.

locating
become

districts

of
By

compatible

the

(Nelson,

basis
1958,

for
p.

vii).
Regression Analysis Model
The regression analysis model has been widely used
in

measuring

thresholds

and

relationships
identifying

and

estimating

hierarchical

population

levels

of urban

center and functions.
Berry and Garrison (1958) used regression analysis
to estimate population thresholds for retail functions and
established

hierarchies

of central places

and

functions.

In a study of establishments of central function in small
towns

of

Washington,

Berry

and

Garrison

analyzed

52

functions in six towns by first measuring the relationship
between population size of a central place and the number
of its functional units using the following equation:

106

N
P = A (B1 )
Where
P = population size of a place
N = number of establishments for a function
A and B = coefficients to be calculated from
available information
Source:

Adopted from King, 1984, p. 57.
Based

on

the

population

size

and

number

of

establishments for each of the 52 functions, this equation
was used

to calculate

the regression

and

Coefficients

with

B.

calculate
place).

the

expected

a

coefficients

value

value

for

of
P

1

were

for A

used

(population

of

to
a

The population estimates obtained were then taken

as the threshold or average level of population required
to

support

the

one

threshold

statistically

functional

establishment.

The

were

used

functions

then

determine

to

rank

hierarchical

levels

values

(King,

of
and

1984,

pp. 54-57, cited source, Berry and Garrison, 1958).
Recent Empirical Studies
Recent

empirical

studies

and

ongoing

economic

development efforts in some U.S. cities by the Council for
Economic Action

Inc.

development

of

a

Model

for identifying levels

(UBI)

under-supply)

of

(CEA), Boston,

method

business

(Urban

have

Business

focused

Identification

(equilibrium,

functions

in

on the

over- or

targeted

urban

107

areas.

The Urban Business Identification

developed

on

the

principle

of

central

(UBI) Model was

place

theory

and

predicated on the basic assumption that levels of retail,
wholesale and service activities are similar in cities of
similar characteristics.
Identification
selecting
study)

seven

Methodology

cities

(including

of similar profiles

usually
the

city

starts
of

by

specific

(population and income).

total number of establishments

in each business

for each of the cities is obtained.

The

industry

An average figure is

derived by dividing the total number of establishments in
each

industry

comparison

by the number

area

figure

of cities

obtained

is

(7).
then

The average
taken

as

the

standard or expected level of business function for each
of the cities.

If the actual number of business functions

(establishments) in a city is higher than the expected
average

comparison

oversupplied.
functions

area

figure),

then

the

(or

function

is

Similarly, if the actual number of business

is less than the expected number of functions,

then that function is considered to be undersupplied.
While

the

Identification

Model

(UBI)

appears

to

have significant success in identifying levels of business
functions

in

targeted

cities,

its

major

drawbacks

are:

(1) the selection of cities for comparative analysis lacks
a scientific base,

(2) the identification of the level of

business

based

function

on

simple

averages

could

be

108

misleading
effects

because

of

(method)

extremely

establishments
average),

it

on

(3)

does not

large

the

or

final

data

theoretically,

account

small

numbers

(average

UBI

for the

is

of

comparison

supposed

to

incorporate relevant endogenous and exogeneous factors in
the identification process,

but no information of what or

how exogeneous factors have been or should be incorporated
in the process was addressed, and (4) while population and
income

are

very

consideration
effects

of

government

important

should

be

function

threshold

given

type

regulations

to

(basic

(e.g.,

factors,

equal

characteristics
and

zoning

nonbasic)
laws,

and
and

building

permits).
However, while the UBI model may not have made any
significant
thus

far,

contribution
it

seems

to

to

the

succeed

advancement

in the

of

theory,

identification

of

small business development areas, generation of employment
opportunities,
areas

and wealth creation in the targeted urban

and where the model has

been applied

(Council

for

Economic Action, inc.).
Summary
In summary,

studies

reviewed in this chapter show

the importance of central place and location theories as a
primary
centers,

base

for

understanding

the

geography

of market

the distribution patterns of retail and service

109

activities,
suppliers

and

and

the

economic

consumers.

behaviors

The

central

of

producers/

place

theory

has

traditionally been used to classify a place in relation to
its

role

center

as

of

community

a

collection,

retail
and

its

and

production,

service

tributary

and

distribution

functions

areas.

within

Location

the

theory

is

essentially an integrative component of the central place
theory, given that centrality of a location in relation to
the market

is a primary consideration in the location of

any business.
Underscoring
theories

are

the

the

central

concepts

of

place

and

"threshold"

and

Both are determinant factors in the type(s)
supply

and

demand

community.

The

development
rural,

of

centers

review

towns,

consumer

have

highways,

of

intra

functions

the

shops

a

from

the

in

sparsely

multipurpose

shopping

influenced

cities,

by

shopping

infrastructures

interurban

in

evolutionary

industries

populated

and

"range."

and level of

and developments

largely

physical
and

to

densely

demographics

modernisation

examines

specialised

Trends

been

service

service

settlements,

metropolitan areas.
industries

and

also
and

purpose,

farm
in

retail

retail

single

populated

of

location

road

and

in the two
changes

in

behaviors,
(interstate

networks,

etc.),

improved retail and service technology, and convenient and
attractive shopping environments.

110

Most
positive
volume

of the

correlation

and

location

population

size,

distance.
the

It

estimating

of

Urban

sales

business

being

functions

as

used

part

approach to

order

service
and

showed

a

level,

functions

consumer

to

travel

not surprising that most of
primarily

(local

and

functions

determine
community

efforts in distressed cities.
a new

and

focused

export
and

and

the

Identification

to
of

reviewed

type(s),

have

Business

currently

the

level,

models

for

studies

retail

is, therefore,

potential

substitution)

between

income

theoretical

Boston's

empirical

Model

of

economic

import

community.

(UBI)

levels

on

is

business

development

Although the model provides

identifying business opportunities,

it

has neither been validated, nor has it gone far enough in
contributing

to

the

advancement

economic

development.

develop

a

reliable

There

of

is,

scientific

theory

in

therefore,
muuei.

mat

community
a

need

to

identiries

business opportunities by predicting levels of retail and
service functions in the city of community.
A major limitation of the above theories is that
none

of

race,

them

crime

provide
rate,

for

the

potential

discriminatory

influence

lending

which

practices,

excessive insurance coverage costs, or local property tax
may

have

on

local

investment

decision.

These

factors

could have significant negative effect on the patterns of
consumer

behavior,

business

location

decisions,

and

Ill

consequently, the level of retail and service functions in
a city or community.
Chapter
variables
hypotheses,
analysis.

III

focuses

(predictor
methodology

and
of

on the model,
response),
data

specifies

the

formulation

of

collection,

and

data

CHAPTER III
RESEARCH METHODOLOGY AND THE
DESIGN OF THE STUDY

In

this

chapter

Identification Model
presented.

the

Market

Opportunities

in retail and service industries is

The variables are defined, hypotheses stated,

and data collection and data analysis presented.
The model
Market
model

used

Opportunities
is

an

in this

study is

Identification

attempt

to use

a set

defined

Model
of

six

as the

(MOIM).

The

socioeconomic

measures as independent variables to predict the level of
supply

of

10

retail

Michigan

cities.

analysis

to

supply.
per

The

capita

city,

The

generate

10

model

service functions
uses

residuals

independent
income,

and

as measures

variables

unemployment,

level of economic distress,

income.

The

establishments

dependent
of

each

within the city limits.

multiple

are

80

regression
of level

city

of

population,

proximity

to

a major

and county per capital

variable
retail

in

and

is the

number

service

of

functions

The residual was computed as the

difference between actual number and the predicted number
of

retail

and

service

establishments in

112

each

of

the

113

retail and service functions in each city.

The residual

is used as a measure of the supply level of each of the
retail and service functions.
The Model: Market-Opportunities
Identification Model in Retailand Service Industries
in Michigan
The

model

has

attempted

to

predict

function supply for 80 cities in Michigan.
used

multiple

regression

to

generate

levels

of

The procedure

residuals

for

10

retail functions and 10 service functions for each of the
cities.
service
(below)

In

predicting

functions
the

regression.

levels

stated

method

of

of

supply

in Hypotheses
investigation

l,

of
2,
was

retail
3,

and

and

4

multiple

114

Multiple Regression Model
Yi " Y i “ B0 + Blxli + B2x 2i ■ * ■ • • • B 6x6i + e l
Where:
Y^

* actual level of retail or service function

Yj^

- predicted level of retail or service function
A

Yj-

= residual level of a function

IJ
o

- a constant common to all observations
(cities)

x i

= city per capita income

x 2i

= P°Pulation of the city

x 3^

= unemployment level for the city

x4i

= level of economic distress

x5i

- proximity of the city to a major city

x gi

- county per capita income for the county
where the city is located

Bj

= for j ■ 1, 2, 3, 4, 5, and 6 is the
regression coefficient for x.. . . . xc ,
1

D

respectively
e.

« the random error term, where E. N(0,~oj).

J

©

X

Attempts to validate the model included:
1.
and

the

service

estimates
functions

correlation of scores generated by the model
of

levels

given

development officials.

by

of

supply

local

of

retail

government

and

economic

115

2.

correlation of scores generated by the model

with scores of trade area capture and market area pull.
Hypotheses and Variables
The main objective of this study is to develop a
model using six independent variables to predict level of
supply

of

retail

and

service

functions

in

80

Michigan

fourteen

research

cities.
In

pursuit

hypotheses

of this

are tested.

objective,

The hypotheses

are intended to:

(1) determine the possibility of developing a predictive
model

and

validate

it;

relationship

between

distress

(i)

service
to

and

economic

mission;

level

(ii)

economic

of

in

of tax

to

level

(iii)

and

and

achieving
between

economic
retail

and

allocation

the

economic
city's

economic

employment

of

classification
(iv)

a

type

of

of supply of its

promoting

revenues

the

its staff and budget

relationship

allocation

city's

mission,

sectors

(b)

determine

sectors,

development

importance

budget

(a)

the level

functions,

promote

(2)

of

ranking

development
staff

sector

generated

and
by

and
the
that

sector.
Based
activities

are

city economy,
logically

on the
fast

assumption that
becoming

retail

and

service

the primary sectors of the

levels of retail and service functions will

have

direct

impact

(positive/negative)

on

the

116

economies
choice

of

of

literature,
Chapter

II.

cities.

variables

were

theories,
The

The

and

following
based

on

hypotheses
review

empirical

rationale

models

of

and

relevant

covered

for each hypothesis

the

in

is also

presented:
Hypothesis 1 .
The actual supply levels of retail
functions can be predicted significantly by city
population,
unemployment,
per
capita
income,
county per capita income, proximity to a major
city, and level of distress.
Hypothesis 2 :
The actual supply levels of service
functions can be predicted significantly by city
population,
unemployment,
per
capita
income,
county per capita income, proximity to a major
city, and level of distress.
The

above

patterns

of

functions.

hypotheses
growth

were

based

in the levels

The hypotheses were,

on
of

the
retail

therefore,

analysis
and

of

service

to determine

whether or not the levels of retail and service functions
can

be

significantly

variables

income,

(city

county

predicted

population,

per

capita

from

six

unemployment,

income,

proximity

independent
per
to

capita
a major

city, and level of distress).
Hypothesis
3 . There are statistically significant
relationships between levels of supply of retail
functions as perceived by the city's
economic
development officials and the residual levels of
supply
of retail
functions
generated
by the
model.
Hypothesis
4 . There are statistically significant
relationships between levels of supply of service
functions as
perceived by the cit y 's
economic
development officials and the residual levels of
supply
of service functions generated by the
model.

117

Hypothesis 5 .
There are statistically significant
relationship
between supply
levels of retail
functions and the level of economic distress of
the city.
Hypothesis 6 .
There are
statistically significant
relationships between supply levels of service
functions and the level of economic distress of
the city.
Hypothesis 7 .
There are
statistically significant
relationships between level of staff allocation
to economic sector promotions and a city's level
of economic distress.
Hypothesis 8 .
There are
statistically significant
relationships between level of budget allocations
to economic sector promotion and a city's level
of economic distress.
Hypothesis 9 .
There are
statistically significant
relationships
between
size of
sector
staff
allocation
and
the
level of
tax
revenues
generated by city's economic sectors.
Hypothesis 1 0 . There are
statistically significant
relationships between sector budget allocation
and the level of tax revenues generated by city's
economic sectors.
Hypothesis
1 1 . There are statistically significant
relationships between level of staff allocations
to promoting economic sectors and the levels of
employment generated by the sectors.
Hypothesis 12. There are
statistically significant
relationships between level of budget allocations
to promoting economic sectors and the levels of
employment generated by the sectors.
Hypothesis
.13. How a city classifies its economic
development mission is statistically related to
the city's level of economic distress.
Hypothesis
1 4 . The type of economic sector a city
ranks as important for achieving its economic
development mission is significantly related to a
city's level of economic distress.

118

The

above

hypotheses

were

based

on

the

assumption

the perception of what

retail

and service

over-

and

city's

or

undersupplied

a

that

functions are

level

of

economic

distress have significant influence on the allocation of
the

cities'

economic

development

resources

(staff

and

budget).
Measurement of Variables
The

primary

variables

used

in

the

study

were

defined as follows.
Level of Supply
The
population
functions
number

number
for

of

each

establishments

of

the

in a given city

of units,

10

cf

dollar

the

rather than

retail

values

were

and

and

10

10,000
service

is the level of supply.
the total

annual sales for each function,
each

retail

per

dollar value of

has been used to compute

service

generally

The

functions

not

available

because
for

to Lai

some

of

the retail and service functions in smaller cities.

The

level of supply for each of the 10 retail functions

and

10 service functions was measured in the following three
ways:
l.
number

of

retail

and

Actual
establishments
service

area

level
per

of

supply

10,000

operating

in

was

population
the

city.

defined
in

each
These

as

the

119

data were obtained from the Censuses of Retail Trade and
Selected Service Industries in Michigan (1987).
2.
estimate

Perceived

of

service

the

level of supply was defined as the

levels

of

supply

given

by

local

function

for

each

economic

retail

and

development

officials.
3.

Residual

level of

difference

between

per

population

10,000

predicted

for

the

each

supply was defined

actual
and

city

the

as the

number

of

establishments

number

of

establishments

using multiple

regression.

A

high positive difference between actual and the predicted
number

of establishments

oversupplied,

and

a

indicated that the function was

high

negative

difference

indicated

that the function was undersupplied.
City Population
The
number

of

population
people

living

of

a

city

within

was

the

measured

city

limits.

by

the
This

measure was obtained from the County and City Data Book,
1987.
City Per Capita Income
City per capita income was measured by the gross
income

of

the

city

divided

by

(County and City Data Book, 1987).

the

city's

population

120

Proximity to a Major City
A

dichotomous

variable

proximity to a major city.
any

city of

income.
was

was

created

to

indicate

A major city was defined as

equal or greater

size or higher per capita

Proximity was given a value of 1 if a major city

within

15 miles

and

0

if

there

was

no

major

city

within 15 miles.
City Unemployment Rate
The

city

unemployment

rate

was

measured

as

the

ratio of unemployed labor to total civilian labor force.
Since the periods

covered by this study were not census

years, calculation of the unemployment ratio was based on
the

Census-share method

and 1980 Censuses data

of disaggregation

based on

1970

(Bureau of Labor Statistics,

July

1979).
vm ^

^

4 1> ^

V t Q y i L .Q

County
average

*r m

per

income

« •• »»».

X J .IO U lllC

capita

per

county

income

was

resident.

measured
These

by

data

the
were

obtained from Michigan Statistical Abstract, 1987.
Ci t y 1s Level of Economic
Distress
Distress was based on a measure developed by the
U.S.

Department

measure
the

of Housing

assigned

following

distress

seven

and Urban Development.
points

factors:

to each
(1)

city

This

based

population

on

growth

121

(lag/decline)
poverty,

1960

and

(3) age of housing,

1969-1983,
sectors

between

(5)

job

1977-1982,

lag

(6)

1984,

(2)

amount

(4) per capita income growth

in

retail

and

manufacturing

rate of unemployment,

degree of labor surplus

of

(U.S.

and

(7) the

Department of Housing and

Urban Development, 1987; Bowman, 1987).
Population and Sample
The population for the study comprised
in Michigan
people.
cities

with populations

(See Figure
had

3.1

a population

deviation

of

study

the

for

21,074.

of Map
mean

The

following

between

10,000

80 cities

and

100,000

of Michigan).

of

31,602

cities

reasons:

and

were
(1)

a

chosen

The

80

standard
for

previous

the

studies

have shown that the number of cities within the range of
10,000
they

to

100,000

are

generally

fluctuations,
responsive

population

and

to

larger cities.

more

(3)

limited

tend

to

grow

sensitive

they

are

economic

rapidly,
to

often

development

(2)

economic

times

more

efforts

than

In view of the above considerations,

it

is, therefore, logical to expect that the number of cites
within

10,000

and

100,000

populations

will

surveyed

through

likely

continue to increase.
The
administered

80

cities

were

questionnaires.

self­

Of the 80 cities surveyed,

41 responded to the mailed survey.

122

Figure 3.1
M ichigan: S am p le cities by size.

CITY POPULATION
+ = 10,000 - 49,999
0 = 50,000 - 100,000.

+

0

123

Instrumentation
The data
collected

through

instrument
involved

was
in

survey was

a

mailed

designed

economic

to

survey

to find

between

functions

survey

development

primarily

relationship
service

for the second phase of the study were
instrument.
city
in

administrators

each

out whether

residual

levels

statistically

The

city.

The

there was

any

of

generated

retail
by

the

and
model

and the supply levels of retail and service functions as
perceived by the city officials surveyed.
The
questions

survey

covering

perceptions

the

of level

their cities,
city,

instrument

(3)

contained

following:

(1)

15

city

of retail and service

specific
officials'

functions

in

(2) the economic development mission of the

budget

allocations

for

promotion

of

local

industries, and (4) employment-tax shares of the economic
sectors of the local economy.
Respondents were asked to provide information on:
(1)

their

perceptions

functions,
economic

(2)
base,

of

levels

sectoral
and

(3)

of

retail

composition
general

of

and

service

the

city's

comments/opinions

on

levels of retail and service functions not covered by the
survey.

A

copy

of

the

instrument

is

presented

in

Appendix A.
To
the

ensure

information

that

the

survey

for

which

it

instrument
was

collected

designed,

the

124

organization,
survey

structure,

instrument

and clarity of contents of the

were

development officials,

pilot-tested

among

and survey experts

economic

at MSU

(Center

for Redeveloped Industrialized State, Technology Transfer
Center,

Resource

(Center

for

Commerce

Development),

Urban

Wayne

Affairs),

(Strategic

Fund

State

Michigan

Unit),

University

Department

Lansing

Chamber

of
of

Commerce, and the city of Chelsea (Community Education).
Data Collection
The data that were used in the investigation were
collected in two phases:
Phase
dependent
and

One

variables

dollar

values)

Commerce-Censuses
for

1987.

population,

Data

capita

(retail

retail

Data for
per

secondary
and

collected

of

from

U.S.

and

from

the

of

services

variables

unemployment,

collected

the

Department

proximity to a major city,

income) were

on

establishments

selected

predictor

income,

data

service

trade

the

capita

economic distress,
per

comprised

(city

level

of

and county
County-City

Book,1987; Michigan Employment Security Commission

(Labor

market

analysis,

research

and

statistics

division),

1987; Michigan Statistical Abstract 1987; U.S.

Department

of

Michigan

State

Housing

and

University

Urban
Center

Development,
for

1987;

Redevelopment

and
of

125

Industrialized States

(CRIS).

The data were gathered for

all of the 80 cities studied.
Phase Two:
government

data

were

gathered

from

local

economic development officials through mailed

survey instrument.
Data Analysis
The study is based on data for the six predictor
variables

for 1987,

economic
retail

surveyed opinion of local government

development
and

service

establishments

officials

on

functions,

in each of

the

level

and
10

of

actual

retail

and

supply

of

number

of

10 service

industries for 1987.
The
University
analyzed

data
IBM

using

Advanced

were
3090

into

mainframe,

programs

Statistics.

regression,

entered

from

model

the

Pearson

analysis of variance

the

Michigan
180,

SPSS-X,

State

and

were

version

3.1,

correlations,

multiple

(ANOVA), and Chi-Square

tests were used for analyzing and evaluating the data.
level

of

.05

was

the

acceptance

level

of

A

statistical

significance used in all tests.
For this study the Multiple Regression Model was
used.

Since

predictive
instead

the

power

of

statistically

primary
of

including

the
only

significant

concern
six
the
in

was

the

collective,

independent

variables,

variables
the

model,

that

were

all

six

126

predictors

were

inclusion

of

recognition

all
that

included

in

predictor
although

the

final

variables
all

the

was

model.
based

The
on

the

predictor

variables

might not have been statistically significant,

they were

substantively meaningful to the predicted outcome of the
model.

This approach also maintained consistency across

the twenty functions studied.
The dependent variable was the residual level of
supply

of

retail

and

service

functions.

However,

the

residual would have different meanings in different sizes
of

cities.

For

undersupply

example,

of five

in

a

very

large

city,

an

gasoline service stations would not

indicate a very significant undersupply, while in a small
city,

a deficit of five gasoline service stations would

be

very

a

supply,

significant

therefore,

supply.

was

The

adjusted

for

residual

level

differences

in

of
the

sizes of cities by computing the residuals based on per
10,000

population.

generate
measure

a

The multiple

residual

of

level

variable

of

regression was used to

which

function

has

supply.

been used
The

as

a

residuals

generated by the regression were scores indicating levels
of

function

actual number

supply

derived

as

the

and predicted number

difference

of establishments

each of the retail and service sectors.
indicated

an

oversupply

of

a

between

particular

in

Positive scores
function,

and

127

negative

scores indicated that a particular function was

undersupplied.
Analysis of Other Hypotheses
Correlation analyses have been used in Hypotheses
3

and

4

to

relationships
retail

and

between

service

development
of retail

determine

5,

(ANOVA)

used

was

significant
budget
and

6,

of

level

of

given

by

local

estimates

8,

to

between

any
levels

of

economic
of supply

by the model.

and 14 analysis

determine

relationships

supply

of levels

functions generated
7,

significant

of variance

statistically
of

staff

and

allocations for the promotion of economic sectors

(a)

supply

and

service

For .Hypotheses

statistically

estimates

functions

officials

and

any

the level of economic distress of the city,
levels

of

retail

and

service

functions,

and

(b)
(c)

the ranking of importance of economic sectors to achieve
the economic development mission.
11,

and

12

correlation

statistical

For Hypotheses 9, 10,

analyses were used

relationships between

staff

to determine
and

budget

allocations and tax revenues, and employment generated by
the city's economic sectors.
Square

test

significant
distress

and

was

used

For Hypothesis 13, the Chi-

to determine

relationships between
a

development mission.

city's

any

statistically

level

of

economic

classification

of

economic

128

Limitations of the Model
Validity of the Model
There

were

difficulties

in

trying

to

validate

this model because there was no tested method of directly
measuring
of

the

levels
retail

(equilibrium,
and

service

oversupply,

functions.

undersupply)
However,

two

methods were used to attempt to validate this model:
1.
the

The

residual levels

model were

supply

compared with

estimated

by

the

of

supply

the

generated

perceived

local

economic

levels

by
of

development

officials.

the

2.

The

model

were

residual levels
also

Trade Area Capture
for

each

customers

city.
buying

compared

(TAC)
TAC

of
with

supply
the

generated

results

of

by
the

and the market area PULL scores

is an

from local

estimate

of

businesses.

the

number

The Pull

of
is a

measure of the ability of a community to draw sales from
outside.

It

is the

ratio of the trade area capture to

the community population.
The major problems with the above methods are:
1.
service

functions

development
several

of

given

local

officials

reasons.

judgment,

Estimates

and

they

by

could

be

levels

of supply

government

somewhat

of

retail

economic

inaccurate

for

They were largely based on subjective
reflected

the

supply

levels

in

1990

and

129

instead of supply levels in 1987 for which the study data
were collected.
2.
Pull

The

factor

behavior

assume

Trade

uniform

throughout

the

Area

Capture

consumer

state.

(TAC)

taste

Neither

and

and

the

buying

control

for

effects of distance, but are adjusted only for income.
The
functions

level
was

of

based

classification code
major

groups

of

the "prediction

of

were

predicted

supply

reflected

the

automotive
not

had

combination

levels

on

same

of

of

retail

might

not

levels

and

service

groups,

have
of

Because

the

accurately

the

subgroup

a predicted oversupply level of

that

and
all

level

two

service

industrial

industries.

industry

service,

mean

and

standard

major

supply

For example,

the

service

levels

repairs,

retail

The two-digit SIC represents

or

supply

actual

of

two-digit

(SIC).

based

necessarily

group

on

retail

functions

functions.

supply

three

of

of

parking

supply.

the

functions

functions
Any

functions,

in

one,

may
the
or

could

a
be

oversupplied, while the third could be undersupplied.
The

model,

however,

effects

of

imports)

that may distort

levels

nonlocal

of supply

the city limits.

does

market
the

of retail and

not

demands

provide
(exports

for

the

and/or

accuracy of the predicted
service functions within

area

130

The use of unit instead of value ($) as a measure
of level

of supply of retail and

service establishments

does not compensate for the effects of establishment size
asmeasured

by

the

square

footage.

The

presence

of

shopping malls or large multiple stores could reduce the
expected number of retail and service establishments for
a given city

size while providing adequate services

for

the given market.
Summary

design
The

In

this chapter

and

methodology

data,their

discussed.
(predictor

of

sources,
The

and

the

model

the
and

research

described,
were

methodology

relevant

dependent)

was

hypotheses

were

presented.

were
and

identified

the

also

variables

and

defined.

The research instrument used in the second phase (survey)
of the study was also discussed.
of multiple

regression,

The statistical methods

Pearson correlation,

analysis of

variance, and chi-square that were used for data analysis
to

test

the

hypotheses

model were identified

and

to

develop

and discussed.

the

prediction

The limitations of

the model were also identified.
In

the next

Chapter

(Chapter

IV),

the

data

analyses will be covered and the findings of the research
presented.

CHAPTER IV

ANALYSIS OF DATA

In
studies,

the
and

preceding

method

chapters,

of

problems,

investigation

were

previous
discussed.

Chapter I identified the main problem and its importance
for

investigation.

literature
studies)

Chapter

of previous

on

the

works

geography,

II

reviewed

(theoretical
distribution

relevant

and

empirical

patterns,

related issues on retail and service industries.
III discussed

and

Chapter

the research methodology and the design of

the study.
In

this chapter,

the data analysis

is presented

in four phases:
Phase

one analyzes

supply levels

of retail

the model

and service

for predicting the
functions as stated

in Hypothesis 1 and 2.
Phase two analyzes the data of the opinion survey
of

city

government's

determine
significant
levels
city

of

economic

whether

there

relationships
retail

officials,

and
and

exists
between

service

the

development

of

to

statistically

estimates

functions

estimates

131

any

officials

as

supply

of

supply

reported
levels

by
for

132

retail

and

service

model.Analysis

of

functions

data

in

generated

Phase

by

the

is

guided

Two

study
by

Hypotheses 3 and 4.
Phase
survey

three

responses

analyzes

to

other components

determine

whether

of

there

the

existed

statistically significant relationships between levels of
allocation
for

the

of

city resources

promotion

retail

and

distress

of

of

economic

service supply
the

(staff efforts

city.

sectors,

and

functions and

Analysis

of

and budget)
levels

of

level

of

the

these

issues

was

significance

of

guided by Hypotheses 5, 6, 7, and 8.
Phase

four

relationships

between

allocations,
revenues

examines

levels

generated

(manufacturing,

the

staff

of

efforts

and

employment, and

size

budget
of

by the city's major economic

retail

trade,

services,

and

tax

sectors

wholesale).

The statistical tests for these questions were guided by
Hypotheses

9,

statistically
level

of

economic
sector

10,

11,

significant

economic

12.

addressed

In

addition,

relationships

distress

development

were

and

mission,

and
and

its

between

13

and

ranking
14

of

city's

classification

importance

in Hypotheses

tests

of
of

as Phase

Five of the study.
Besides

analysis

of data

and

research

findings,

this chapter also attempts to validate the study model by
comparing the model's

residual level of supply of retail

133

and

service

functions

with

the

levels

reported

by

the

city economic development officials.
Phase One:
In

addressing Hypothesis

regression

model

which

population,

city

proximity

The Model

to

was used

to

1

and

city,

the multiple

determine

unemployment,

a major

2,

level

the

per

extent

capita

of economic

to

income,

distress,

and county per capita income predict the supply levels of
retail

and

service

establishments
the

for

independent

functions.
each retail and

variable,

was used

to generate

between

the

measure

service

the multiple

and

These

of the

the

number

of

functions

as

regression model

residuals by taking the difference

actual

establishments.

Using

predicted

residuals

supply level

service functions.

the

In

were

adopted

for each of

this

case,

number

the

a

of

as

the

retail and

high

negative

residual indicates an undersupply of the function while a
high

positive

function.

residual

indicates

A low residual value

an

oversupply

(near 0.0)

of

the

indicates

an

optimum supply level of the retail or service function.
To
retail

and

ensure

that

service

the

residual level

functions

generated

regression

model

controlled

for

population

sizes

of the

80

cities

data

based

on

supply

were

the

the

by

of

supply

the

differences

studied,

levels

for

multiple
in

analyses
each

of

of

the
of
the

134

retail

and

service

functions

on per

10,000 persons.

The

finding for Hypothesis 1 and 2 are presented below.
Hypothesis 1 : The supply levels of retail functions
can
Be
predicted
significantly
by
city
population,
unemployment,
per
capita
income,
proximity to a major city, level of economic
distress, and county per capita income.
The

overall level

of supply of retail

functions

was obtained by computing the sum of the supply level of
all ten retail functions.
of

the

multiple

regression

supply of overall
for 1972,

1977,

Table 4.1 presents the results
in

predicting

1982,

of

and 1987.

From these results,

it

significantly predicted

level of supply of retail functions for all

the four time periods.
overall

level

retail functions by the six predictors

is shown that the six predictors
the overall

the

supply

The proportion of variance in the

level

of

retail

functions

that

is

explained by the six predictor model ranged from about 41
percent in 1972 to 29 percent in 1977.

The proportion of

variance in the overall level of supply accounted for by
the

six

predictor

model

was

about

38

percent

for

both

1982 and 1987.
Although

the

each of the four
was

rather

indicated

of

determination

for

fitted models for the four year periods

low,

that

coefficient

by

statistically
using

the

six

significant
predictors,

results
it

possible for the model to predict significantly the

was

135

Table 4.1.

Year

Results
of
the
Prediction
of
Levels
Overall Supply of Retail Functions by
Predictors for the Four-Year Periods.

Dependent
Variable

Multiple
R

R
Square

F-Value

of
Six

P-Value

•

1987

Retail
Units

.614

.377

7.049

.0000*

1982

Retail
Units

.615

.378

8.520

.0000*

1977

Retail
Units

.541

.293

5.707

.0002*

1972

Retail
Units

.644

.414

9.762

.0000*

*Significance at 0.05 level.

136

overall

levels

of

supply

of

retail

functions.

The

results were statistically significant at .05 level.

On

average the model accounted

for about 38 percent of the

proportion

the

of

variance

in

level

of

total

retail

functions.
Table
determining

4.2 presents

whether

or

the results of the t-test in

not

each

of

the

six

predictors

significantly predict the overall supply level of retail
functions.

From Table 4.2 it is shown that statistically

significant

results

were

observed

proximity to a major city
four-year periods.

for

the

predictor

( t^ -5, p < 0.05)

of

for all the

No other predictors were significant

at 0.05 level for any of the four-year periods.
In order to determine the extent to which the six
predictors

predicted

functions,

separate

retail

function.

determination,

each

Table

results

in Table 4.3,

retail

ten

4.3

shows

individual
was

the

used

retail

for

each

coefficient

of

multiple R, F-value and the corresponding

for

in

the

regression model

P-value

succeeded

of

each

of

the

(2)

of the supply level

functions.

it is shown that:

significantly

functions,

retail

predicting

From

(1)

nine

the

the model

of

the

ten

43 to 49 percent of the variances

in four of the ten retail functions

was explained by the model,

and

(3) less than 40 percent

of the variance in the supply level of other six retail
functions were explained by the model.

137

Table 4.2.

Prediction
of
Level
of
Supply
of
Retail
Functions by Predictor Variables for the FourYear Periods

Predictor Variable

Standardized
Regression
Coefficient

T-Value

Significance
Level

1987
County Per Capita
Income
Unemployment
Population
Proximity
Per Capita Income
Level of Distress

0.093
-0.002
-0.100
-0.650
0.169
0.012

0.751
-0.016
-0.929
-5.177
1.101
0.070

.4555
.9875
.3561
.0000*
.2749
.9443

-0.045
0.079
-0.103
-0.622
0.261

-0.452
0.631
-1.026
-5.752
1.904

.6524
.5302
.3086
.0000*
.0610

-0.031
0.076
-0.109
-0.467
0.030

-0.271
0.567
-1.009
-4.192
-0.227

.7874
.5727
.3167
.0001*
.8214

-0.019
0.142
-0.133
-0.570
0.090

-0.187
1.118
-1.357
-5.340
-0.685

.8520
.2674
.1791
.0000*
.4957

1982
County Per Capita
Income
Unemployment
Population
Proximity
Per Capita Income
1977
County Per Capita
Income
Unemployment
Population
Proximity
Per Capita Income
1972
County Per Capita
Income
Unemployment
Population
Proximity
Per Capita Income

*Significant at 0.05 level.

138

Table 4.3.

Results of the Prediction of Levels of Supply
of Each of 10 Retail Functions by the Six
Predictor Regression Model for 1987.

Retail
Function

Multiple
R

R
Square

F-Value

P-Value

•

Building material
and garden stores

.70

.49

11.21

.0000*

General
merchandise
stores

.67

.45

9.82

.0000*

Food stores

.55

.30

5.09

.0000*

Automotive
dealers

.66

.44

9.21

.0000*

Gasoline
service
stations

.59

.35

6.38

.0000*

Apparel and
accessory stores

.37

.14

1.91

.0914

sto r e s

.60

•w•>cw

c

Eating and
dining places

.66

.43

8.96

.0000*

Drug and
proprietary
store

.54

.30

4.97

.0003*

Miscellaneous
Retail Store

.58

.33

5.87

.0001*

Furniture and
house furnishing
\j

•n/o

A A A A X

•

SJUUU"

•
Note:

Significance at .05 level.

139

Hypothesis 2 ; The supply levels of service functions
can
Be
predicted
significantly
by
city
population,
unemployment,
per
capita
income,
proximity to a major city, level of economic
distress, and county per capita income.
As

in Hypothesis

1,

the overall level

of supply

of service functions was obtained by computing the sum of
the

supply

level

of

all

ten

service

functions.

The

results of the multiple regression analysis in predicting
the overall level of supply of service functions by the
six

predictors

for

1972,

1977,

1982,

and

1987

are

model

was

presented in Table 4.4.
Table
statistically

4.4

showed

that

significant

in

while

the

predicting

the

overall

supply level of service functions, only 40 percent of the
variance in the supply level of the service functions was
explained
the

by

three

the model.

previous

Comparing

periods

(1982,

indicated that although statistical
level

were

observed

for

the

the 1987
1977,

results to

and

1972)

it

significance at 0.05

three

year

periods,

the

proportion of variance explained was lower than 1987.
The results for the strength of prediction of the
overall level of supply by each of the six predictors are
presented in Table 4.5.
The

t-test

proximity to
capita

income

results

a major

city

(t .2

3.2,

show
(t^
p

that
-4.8,

<

0.05)

the

predictors

p < 0.05)
were

of

and per

significant

predictors of the overall supply of service functions for

140

Table 4.4.

Year

Results
of
the
Prediction
of
Levels
of
Overall Supply of Service Functions by Six
Predictors for the Four-Year Periods.

Dependent
Variable

Multiple
R

R
Square

F-Value

P-Value

•

1987

Service
(Units)

.633

.401

7.906

.0000*

1982

Service
(Units)

.563

.317

6.960

.0000*

1977

Service
(Units)

.545

.297

5.918

.0001*

1972

Service
(Units)

.572

.327

6.604

.0000*
•

*Significance at 0.05 level.

141

Table 4.5.

Prediction
of Level
of Supply
of Service
Functions by Predictor Variables for the FourYear Periods.

Predictor Variable

Standardized
Regression
Coefficient

T-Value

Significance
Level

1987
County Per Capita
Income
Unemployment
Population
Proximity
Per Capita Income
Level of Distress

0.263
-0.071
-0.011
-0.593
0.478
0.187

2.193
-0.461
-0.101
-4.843
3.188
1.095

.0316*
.6461
.9195
.0000*
.0021*
.2773

-0.060
0.188
0.058
-0.557
0.570

-0.572
1.437
0.560
-4.941
3.997

.5694
.1551
.5773
.0000*
.0002*

-0.075
0.092
-0.020
-0.543
0.338

-0.670
0.691
-0.184
-4.913
2.547

.5049
.4917
.8542
.0000*
.0131*

0.020
0.240
-0.063
-0.526
0.529

0.184
1.749
-0.592
-4.552
3.747

.8546
.0848
.5560
.0000*
.0004*

1982
County Per Capita
Income
Unemployment
Population
Proximity
Per Capita Income
1977
county Per Capita
Income
Unemployment
Population
Proximity
Per Capita Income
1972
County Per Capita
Income
Unemployment
Population
Proximity
Per Capita Income

*Significance at 0.05 level.

142

all the four-year periods.
2.19,

p

<

overall
other

0.05)

supply

was

a

significant

of services

predictor

overall

County per capita income (t =

supply

was
of

a

only

for the year

significant

services

predictor

for

any

of

the

1987.

No

predictor
of

the

of

the

four-year

periods.
To

determine

supply level

the

of the

the

six

for

each

are

presented

ten individual

predictors,
retail

strength

separate

function.

in Table

tests

showed

that

predicting

4.6.

the

service functions by

regression

The

value and the corresponding

of

results

Based
P-value,

model

was

used

for these tests

on the

observed

F-

the results of the

the six predictor model

significantly

predicted all the ten supply levels of service functions.
For this model,

the six predictors account for about 42

percent of the variance of the level of supply of health
services,
services,

40
and

engineering,

percent
42

of

the

percent

accounting,

supply
of

and

the
other

level

of

supply

personal
level

services.

of
The

proportion of variance in the supply level of the other
individual

services

that

is

explained

by

the

six

predictors was less than 40 percent.
Phase Two: Analysis of City
Officials' Opinions
Additional

data

for phase two of the study were

obtained through a survey instrument in the form of a

143

Table 4.6.

Results of the Prediction of Levels of Supply
of Each of 10 Service Functions by the Six
Predictor Regression Model for 1987

Service
Functions

Multiple
R

R
Square

F-Value

P-Value

•

Hotel, rooming
and lodging
places

.45

.25

3.92

.0019*

Automotive
repairs, service
and parking

.47

.22

3.40

.0053*

Miscellaneous
repair services

.49

.24

3.78

.0025*

Amusement and
recreation
services

.60

.36

6.51

.0000*

Health
services

.65

.42

8.50

.0000*

Legal
services

.58

.33

5.89

.0000*

Personal
services

.64

.40

8.03

.0000*

Business
services

.48

.23

3.50

.0043*

Social
services

.49

.24

3.65

.0033*

Engineering,
accounting and
other services

.65

.42

8.71

.0000*

*Significance at .05 level.

144

questionnaire mailed to city administrators involved with
economic development.
gather
level

information
of

supply

The questionnaire was designed to

on the officials'

of

retail

and

perceptions

service

functions,

economic development mission of the city,
and

budget

allocations

industries,
economy.

and

for

the

employment-tax

on the
the

staff efforts

promotion

shares

of

of

local

the

local

The main focus of phase two was to determine

whether

or

not

existed

between

statistically
the

levels

significant
of

supply

relationships

of

retail

and

service functions as perceived by the city officials and
the levels of supply of the same functions as predicted
by the model in phase one of the study.
Respondents
responded
guided

to

by

from

the

41

Hypotheses
Analysis

significance

of

the

of

city

functions

retail

the
and

officials
as

the

questionnaire.

Correlation

supply

of

3

and

was

and

predicted

the
by

cities

Analysis

surveyed

of

data

4.

The

Pearson

used

to

determine

relationship
service

80

between

functions
levels
the

of

model.

the

was

Moment
the

levels

of

as perceived

by

supply

of

the

Findings

for

Hypotheses 3 and 4 are presented below.
Hypothesis 3 :
There are statistically significant
relationships between levels of retail functions
as perceived by the city government's economic
development officials and the levels of retail
functions predicted by the model.

145

Table

4.7

presents

Correlation

Coefficient

relationship

between

the

the observed
and

the

Pearson

P-value

predicted

supply

Moment

for

level

of

the
the

retail functions and the supply levels of the same retail
functions as perceived by the city officials.
results,

it

relationships
of retail

is

shown

that

statistically

From these
significant

were observed between the levels of supply

functions

as perceived

by the c i t y 's economic

development officials, and the predicted levels of supply
of

retail

functions

following retail

generated by

functions:

the

model

General merchandise

<

station

Gas

service

the

Building material and garden

(r = 0.296, p < 0.05),
0.05),

for

(r

=

(r «= 0.375, p

0.506,

p

<

0.05),

Furniture and home furnishings (r - 0.352, p < 0.05), and
Eating

and

drinking

places

(r «= 0.379,

p

< 0.05).

No

statistically significant relationships were observed for
all other retail functions.
Hypothesis 4 :
There are statistically significant
relationships between levels of service functions
as perceived by the city government's economic
development officials and the levels of service
functions predicted by the model.
Table
Correlation

4.8

presents

Coefficient

the observed
and

the

Pearson

p-value

Moment

for

the

relationship between the predicted and perceived level of
supply of service functions.

From Table 4.8, it is shown

that statistically significant relationships were

146

Table 4.7.

Results of the Pearson Moment Correlation
Analysis of the Relationships Between the
Predicted and Perceived Level of Supply of
Retail Functions.

Retail Functions

r

p-v
•

Building material and garden

.296

.032*

General merchandise

.375

.009*

Food stores

.125

.223

Auto dealers

.214

.093

Gas service station

.506

.001*

Apparel and accessory

.205

.102

Furniture and home furnishings

.352

.013*

Eating and drinking places

.379

.008*

Drug and proprietary

.203

.108

Miscellaneous retail store

.103

.270

Total Retail

.245

.064
•

* = Significance at .05 level.

147

Table 4.8.

Results of the Pearson Moment Correlation
Analysis
of the Relationship Between the
Predicted and Perceived Level of Supply of
Service Functions.

Study Model
Service Functions
r

p-value
•

Hotel, room, lodging

.547

.000*

Auto repair service

.321

.023*

Misc. repair service

.486

.001*

Amusement and recreation

.015

.464

Health services

.240

.068

Legal services

.340

.017*

Personal services

.274

.048*

Business services

.222

.088

Social services

.624

.003*

Engineering accounting Etc.

.447

.002*

Total Service

.515

.000*
•

* = Significant at .05.

148

observed between the level of supply of service functions
as perceived by the city's economic development officials
and

the

predicted

service functions:
p

< 0.05),

Auto

levels

of

Hotel,

room, and lodging

repair

supply

service

for

the

following

(r = 0.547,

(r = 0.321,

p

< 0.05),

Miscellaneous repair service (r = 0.486, p < 0.05), Legal
services

(r = 0.340,

0.274, p < 0.05),
and Engineering,
Overall,

a

p

< 0.05),

Personal services

(r =

Social services

(r = 0.624, p < 0.05),

accounting, etc.

(r = 0.447, p < 0.05).

statistically

significant

positive

relation­

ship was observed between the predicted and the perceived
level

of

supply

of

service

functions

(r

=

0.515,

p

<

0.05).

Phase Three: Relationship Between City
Government's Economic Development
Efforts and the City's LeveT
of Distress
Lata

analysed

obtained

through

involved

in

In phase

responses

city

lhlcc

from

economic

ui

lius

city

sruoy were

administrators

development.

Several

questionnaire items were asked to determine the level of
importance of various sectors of the economy according to
the city officials.
survey

included,

wholesale
rating
sectors

trade,

was
in

Economic sectors

manufacturing,
and

obtained
the

order

other
by

sectors.

the
of

retail

identified in the
trade,

services,

The

importance

respondents

importance

with

ranking
1

=

the
most

149

important

and

about

economic

the

5

=

least

important.

sectors

Other

obtained

information

through

the

survey

included:
1.

Percentage

of

staff

efforts

allocated

to

economic sector promotion
2.

Percentage

of

economic

development

budget

dedicated to advancing the sector
3.

Percentage of c i t y 's tax revenue generated by
the sector

4.

Percentage

of

the

city's

employment

which

occur in the sector
In
the

U.S.

cities

addition,

based

Department

were

of

the measure

Housing

classified
(1)

on

into

three

distress:

with

distressed,

(3) highly distressed.

The main
whether
existed

or

not

least

focus

of

the

phase

moderately

to

the economic development efforts was guided

Variance

5,

(ANOVA)

statistically

6,

7,

was

significant

and
used

8.

city

economic

Analysis

Hypothesis

of

determine

relationships

government's
level

economic

efforts

by

the

of

(2)

significant

city

Development,

levels

three was

by

development
of

and

Urban

distressed,

statistically

between

and

developed

One-way
to

Analysis

determine

differences

distress.

existed

of

whether
in

the

economic development efforts among cities with different
levels of distress.

Specifically,

the statistical tests

150

in

phase

three

issues:

(1)

levels

of

of

the

retail

the

study

relationships
and

service

level of economic distress,

addressed
between

the

following

reported

functions

and

supply

the

city's

(2) the relationships between

reported supply levels of service functions and level of
economic distress,
staff
and

effort

the

between

(3) the relationship between levels of

allocations

level
level

of
of

city
budget

to

economic

distress,

sectors

(4)

allocations

the

to

promotions

relationship

economic

sector

promotion and the level of city distress.
Hypothesis 5 :
There are statistically significant
relationships between supply levels of retail
functions and the level of economic distress of
the city.
Table

4.9

(ANOVA)
data

shows

results

of

Analysis

of

Variance

for the responses from 41 city respondents.

showed

levels

the

of

no

statistically

supply

different

levels

T h u s , the

level

of
of

of

retail
distress

supply

of

significant
functions
(F

=

differences
to

0.983,

retail

The

cities
p

<

function

in

with
0.05).

does

not

vary with the city's level of distress.
Hypothesis 6 :
There are statistically significant
relationships between supply levels of service
functions and level of economic distress of the
city.
The results of Analysis of Variance of responses
from 41 cities are presented on Table 4.10.

From these

results, it is shown that no statistically significant

151

Table 4.9.

Results
of Analysis
of Variance of the
Differences in Levels of Supply of Retail
Functions by the Cities' Level of Distress.

C i t y 's Level of
Distress

N

Mean

S.D.

FValue

PValue
•

Least Distress

17

2.8026

.5079

Moderate Distress

14

2.5405

.6868

High Distress

10

2.5925

.3606

Total

41

2.6619

.5482

0.983

0.3836

•

Table 4.10.

Results of Analysis
of Variance of the
Differences in Levels of Supply of Service
Functions by the City' s Level of Distress.

City's Level of
Distress

N

Mean

S.D.

Least Distress

17

2.7608

.4271

Moderate Distress

14

2.8050

.8221

High Distress

10

2.6500

.2801

Total

41

2.7488

.5715

FValue

PValue

0.228

0.8029

152

differences

were

observed

in

the

level

of

supply

service functions among cities of different distress

of
(F =

0.228, p > 0.05).
Based

on

the

mean

level

of supply

of

service

functions by the level of distress, the data showed that
highly distressed
of

service

moderately
(2.76).

cities have the least level of supply

functions

(2.65)

distressed

(2.81)

At

the

0.05

compared
or

level,

the

to

either

least

however,

these

the

distressed
differences

were not statistically significant.
Hypothesis 7 :
There are statistically significant
relationships
between level of staff efforts
allocations to economic sector promotion and a
city's level of economic distress.
Table 4.11 shows the one-way Analysis of Variance
(ANOVA)

results

staff

efforts

among

cities

these

efforts

p
the

differences
to

different

it

is

to

economic
levels

shown

differences

in

in

the

sector

sector

of

that

percentage

no

for

all the
of

the

test

the data show
distressed

four

not

of

were

economic sectors.
were

From

statistically

percentage

promotion

of

promotion

distress.

cities with different levels of distress

results

highly

with

allocation

<0.05)

level,

the

allocation

results,

significance

among

for

staff

observed
(F

0.5,

Although

significant

at

0.05

that for the manufacturing sector,

cities

allocated

an

average

of

46

percent of staff efforts to manufacturing compared to an

153

Table 4.11.

One-Way Analysis of Variance (ANOVA) Results
of the Differences in Percentage of Staff
Efforts
Allocation
to
Economic
Sector
Promotion Among Cities with Different Levels
of Distress.

Economic Sector

Level of
Distress

n

Mean
Percentage

FValue

PValue
•

Manufacturing

Least

12

35.0

9

40.8

High

10

46.3

Least

12

20.8

9

30.1

High

10

31.3

Least

12

16.4

9

5.4

High

10

14.1

Least

12

3.1

9

1.7

10

3.8

Moderate

Retail Trade

Moderate

Services

Moderate

Wholesale
Trade

Moderate
High
NOTE:

0.304

0.740

0.559

0.578

0.883

0.425

0.369

0.695

N = 31.

*The sum of the percentages
of staff economic
development efforts allocated to the four sectors does
not equal 100 percent due to some efforts allocated to
other miscellaneous sectors

154

average
cities

of

41

and

percent

35

percent

for
for

the

the

moderately

least

distressed

distressed

cities.

On the other hand, least distressed and highly distressed
cities allocated an average of 16 percent and 14 percent
of

staff

compared

efforts,
to

5

respectively,

percent

for

the

to

service

moderately

sector

distressed

cities.
Hypothesis 8 ;
There are statistically significant
relationships between level of budget allocations
to economic sector promotion and a city's level
of economic distress.
Table
(ANOVA)

4.12

results

manufacturing,
Prom

these

of

retail,

in

is

Analysis

from

service,
it

exist
for

the

responses

results,

differences
allocations

presents

the

Variance

cities

covering

wholesale

sectors.

27

and

shown

of

that

level

of

no

significant

city's

budget

the promotion of economic sectors

among

cities with different levels of distress for any economic
sector.

However,

allocation

for

the

show

data

manufacturing

based on the mean percentages of budget

promotion
that
sector

distressed

cities

(47.8%)

least

or

services,

and

of

the

four

percentage
was

(52.9%)
distressed

wholesale

budget

highest
than

the

moderately

the

sectors,

allocation

among

(33.6%).

trade

economic

In

results

to

highly

distressed

retail

trade,

showed

moderately distressed cities allocate the least

that

155

Table 4.12.

One-way Analysis of Variance (ANOVA) Results
for
the
Differences
in
Percentage
of
Economic Development Budget Allocated to the
Four Economic Sectors by Level of Distress.

Economic Sector

Level of
Distress

n

FMean
PPercentage! Values Value
0

Manufacturing

Retail Trade

Services

Wholesale
Trade

NOTE:

Least

11

33.6

Moderate

8

47.8

High

8

52.9

11

29.5

Moderate

8

21.5

High

8

28.5

11

10.6

Moderate

8

6.0

High

8

10.4

11

3.4

Moderate

8

1.9

High

8

4.3

Least

Least

Least

1.028

0.373

0.228

0.798

0.335

0.718

0.342

0.714

N = 27.

*The sum of the percentages
of staff economic
development efforts allocated to the four sectors does
not equal 100 percent due to some efforts allocated to
other miscellaneous sectors.

156

percentage of

their budget than either least distressed

or highly distressed cities.
Phase Four:
Relationship Between City
Government's Economic Development
Efforts with the Level of Tax
Revenues and Employment
Generated by the Sectors
As

indicated

in phase

three

of

the

study,

city

government officials in charge of development were asked
to indicate the percentage of staff efforts allocated to
economic

sector

promotion,

percentage

of

economic

development budget dedicated to advancing the sector, and
their

perception

of

service functions.

the

level

of

supply

of

retail

and

All these were used as the indicators

of the city government's economic development efforts for
each

sector

city's

level

study

was

exists

a

various

and
of

whether
economic

designed

to

statistically

or

not

the

distress.

determine

effors
Phase

whether

significant

reflected

four
or

not

relationship

city government's economic efforts

of

the

there

between

and the level

of tax revenues and employment generated by each sector.
Research Hypotheses

9,

10,

11,

and

to guide the analysis of data in phase four.

12 were used
The Pearson

Moment Correlation Analysis was used to determine whether
or

not

between

statistically
city

significant

government's

economic

relationships
development

exists
efforts

157

and the

level of tax

revenues generated

by each of the

four economic sectors.
Hypothesis 9 :
There are statistically significant
relationships between the size of economic sector
staff allocation and the level of tax revenues
generated by city's economic sectors.
Table

4.13

presents

the

results

of

the

Pearson

Moment Correlation analysis for the relationship between
the

size

level

of

of

economic

sector

staff

tax

revenues

generated

by

allocation

and

the city's

the

economic

sector. The results showed that statistically significant
positive relationships

exist between allocation of staff

efforts and level of tax revenues generated by service (r
= 0.632,

p < 0.05)

sectors.
that

and wholesale

level

of

staff

sectors

which

However,

no statistically

observed

between

p

sectors

p < 0.05)

Both positive correlation coefficients indicate

high

0.09,

(r = 0.484,

> 0.05)
and

the

generate

staff
and

high

were

levels

of

allocated
tax

to

revenues.

significant relationships were

allocated

retail

level

efforts

trade

of tax

to

manufacturing

(r = 0.08,

revenues

p

generated

(r

=

> 0.05)
by the

sector.
Hypothesis 10:
There are statistically significant
relationships
between
the
sector
budget
allocation
and
the
level
of
tax
revenues
generated by the cit y 's economic sectors.
Table

4.14

presents

the

Pearson

Moment

Correlation analysis results for the relationship between
the size of budget allocation and tax revenues generated

158

Table 4.13.

Results
of
Pearson
Moment
Correlation
Analysis of the relationship Between Staff
Efforts Allocation in Each Sector and Tax
Revenues Generated by the Sector.

Economic Sector

r

P-Value
•

Manufacturing

0.093

0.310

Retail Trade

0.083

0.328

Services

0.632

0.000*

Wholesale Trade

0.484

0.013*
•

*Significance at the 0.05 level.

Table 4.14.

Results
of
Pearson
Moment
Correlation
Analysis
of
the
Relationship
Between
Development Budget Allocation in Each Sector
and Tax Revenues Generated by The Sector.

Economic Sector

r

P-Value
•

Manufacturing
Retail Trade

0.186

.177

-0.229

.125

Services

0.567

.001*

Wholesale Trade

0.415

.016*
•

*Significance at the 0.05 level.

159

by

the

four

economic

sectors.

The

results

showed

statistically significant relationships between the level
of tax revenues generated
0.05)

and wholesale trade

level

of budget

by the service
(r = 0.42,

allocation

p

(r = 0.57, p <
> 0.05)

and the

to the two economic sectors.

As in Hypothesis 9, the positive correlation coefficients
indicated
sectors

that

which

However,

higher
generated

budget

were

higher

levels

no statistically

allocated
of

correlation
indicated
tax

revenues

budget

allocation

relationship

was

observed

relationship

generated

by

to

not

revenues.

(r = 0.19, p >

(r = -0.23, p > 0.05).

coefficient

a negative

the

significant relationships were

observed for the sectors of manufacturing
0.05) and retail trade

tax

to

the

the

for

and

sector.

statistically

retail

between

sector

A negative
trade

the

level

of

the

level

of

However,

significant

at

this
0.05

level.
Hypothesis 1 1 :
There are statistically significant
relationships
between level
of staff efforts
allocation to promoting economic sectors and the
levels of employment generated by the sectors.
Table
Analysis

4.15

results

shows
for

the

the

Pearson Moment

relationships

Correlation

between

staff

efforts allocations and levels of employment generated by
the

four

shown

economic

that

sectors.

statistically

From

these

significant

results,

it

is

relationships

existed between staff effort allocation and level of

160

Table 4.15.

Results
of
Pearson
Moment
Correlation
Analysis of the Relationship Between Level
of Staff Efforts and Level of Employment
Generated by that Sector.

Economic Sector

r

P-Value
•

Manufacturing

0.398

.018*

Retail Trade

0.398

.398

Services

0.106

.296

Wholesale Trade

0.477

.005*
•

*Significance at the 0.05 level.

employment for the manufacturing (r = 0.40, p < 0.05) and
wholesale

trade

(r

=

0.48,

p

<

0.05)

sectors.

No

statistically significant relationships were observed for
the retail trade

(r = 0.40, p

0.11,

sectors.

p

> 0.05)

> 0.05)

However,

and services

for the

(r =

sectors

of

manufacturing and wholesale trade, the relationships were
positive
efforts

indicating
were

that

allocated

to

higher
the

more employment opportunities.

percentage

sectors

which

of

staff

generated

These relationships were

statistically significant at 0.05 level.
Hypothesis 12.
There are statistically significant
relationships between level of budget allocations
to
promoting
economic
sectors
and
level
of
employment generated by the sectors.

161

Table

4.16

presents

the

results

of

the

Pearson

Moment Correlation Analysis for the relationships between
budget allocations for economic development and level of
Table 4.16.

Results
Analysis
of Budget
Generated

of
Pearson
Moment
Correlation
of the Relationship Between Levels
Allocation and Level of Employment
by the Sector.

Economic Sector

r

Manufacturing
Retail Trade

P-Value

0.461

.012*

-0.501

.006*

Services

0.060

.391

Wholesale Trade

0.475

.009*
•

*Significance at the 0.05 level.

employment

generated

manufacturing,
sectors.

The

relationships
employment

trade,

results

showed

between

generated

retail trade

trade

(r

=

relationships
the

the

retail

0.05),

positive,

by

0.48,

budget
by

four

service,

allocation

manufacturing

<

(r

p < 0.05)

0.05)

sectors

and

statistically

(r = -0.50,
p

economic

of

wholesale
significant

and
=

level
0.46,

of
p

<

and wholesale

sectors.

While

the

for manufacturing and wholesale trade were
relationship

for

retail

was

negative

indicating that higher levels of budget were allocated to

162

a sector

which

generated low levels

statistically
between

significant

the

services

level

sector

of

(r =

of

employment.

relationship

budget
0.06,

p

No

was

observed

allocations

to

promoting

> 0.05)

the

and

level

of

employment generated by the same sector.
Phase Five: Relationship Between
Classification of Economic
Development Mission ancf
Ranking of Economic
Sector with the
City's Level of
Distress
Two

items

were

which

required

the

rank

in

order

the

manufacturing,

included

city

in

government

of

the

questionnaire

officials

importance

the

to:

(l)

sectors

of

retail trade, services and wholesale trade

in achieving the city's economic development mission, and
(2) classify their city government's economic development
mission in terms of whether it is a major objective, one
of

several

Phase

objectives,

five

used

relationship
development
economic

between
mission

sectors

distress.

a

minor

objective

these

responses

the

classification

and

with

Examination

the
the

to

city's

of

these

test

of

the

of

were

the

economic

ranking

level

issues

other.

examine

of

importance

and

of

the

economic
guided

by

Hypotheses 12 and 14.
The
was

used

chi-square
to

address

statistical

Hypothesis

13

while

significance
a

one-way

163

analysis of variance (ANOVA) was used to address research
Hypothesis 14.

The findings for Hypotheses 13 and 14 are

presented below.
Hypothesis 1 3 .
How a city classifies its economic
development mission is statistically related to
the city's level of distress.
The
respondent
mission.

data

on

cities

Table

4.17

classified

their

Classifications

summarized
economic

how

40

development

indicated economic development

as a major objective, one of several major objectives and
a minor

objective.

showed that:

The

breakdown

of cities'

responses

12.5 percent of respondent cities classified

economic development as their major objective; 80 percent
classified

economic development

as one of several major

objectives; and 7.5 percent as a minor objective.
Analysis

of the data

based on level

distress of respondent cities showed that:
(i.e.,

60 percent

and

over)

of cities

of economic
(l) majority

in all

levels

of

economic distress classified economic development as one
of several major objectives.
of

cities

objective
cities.

classifying
represented
This

However, the largest number

economic
40

percent

development

as

a

major

of the high distressed

relationship was tested

by the chi-square

test of statistical significance and was determined to be
2
significant (x
= 10.19, p < 0.05).
Thus the results
showed that more distressed cities were more likely to

164

Table 4.17.

Economic
Condition

Chi-Square Results of the Relationship
Between Classification of Economic
Development Mission and City's Level of
Distress.

N

Major
Obj ective

One of Several
Major Objectives

Minor
Objective
•

Least
Distressed

17

Moderate
Distressed

13

High
Distressed

10

Percent
Total

40

n

%

1

5.9

—

—

4

40.0
12.5

n

%

n

%

14

82.4

2

11.7

12

92.3

1

7.7

6

60.0
80.0

•

7.5
•

Chi square = 10.1914
df =

4

Significance level = .0370

165

have

economic development

as

a major objective in their

mission than less distressed cities.
Hypothesis 1 4 .
The type of economic sector a city
ranks as important for achieving its economic
development mission is significantly related to
the city's level of distress.
Table
the

4.18 show analysis of variance results for

differences

economic

in

sectors

the

by

importance

the

41

ranking

respondent

different levels of economic distress.

of

local

cities

with

Analysis of data

for each economic sector showed that while there were no
statistically

significant

0.05)

importance

cities'
of

in

the

ranking of

achieving

of manufacturing,

economic

distressed

development
cities.

(F

«

0.203,

economic

levels of economic distress,

importance

highly

differences

>
by

the highest ranking

retail,

and

mission

However,

sectors

p

service

for

given

by

trend was

not

was

this

statistically significant at the 0.05 level.
Validation of the Model
It was stated in the preceding chapter that there
was

no

tested

method

used

to

directly

measure

levels of retail and service functions.
developed

in this

levels

of

attempt

has

model.

study

selected
been

was

retail

made

in

designed
and

this

supply

Since the model
to predict

service
section

to

supply

functions,

an

validate

the

The validation was done by comparing the model's

166

Table 4.18.

One-Way Analysis of Variance (ANOVA) Results
of the Differences in Ranking of Importance
of Economic Sectors by Cit y ’s Level of
Economic Distress.

Economic Sector

Level of
Distress

n

Mean
Percentage

PFValuei Value
•

Manufacturing

Retail Trade

Services

Wholesale
Trade

NOTE:

Least

17

2.1

Moderate

14

2.0

High

10

1.8

Least

17

2.6

Moderate

14

2.1

High

10

1.7

Least

17

2.4

Moderate

14

2.5

High

10

2.3

Least

17

3.3

Moderate

14

3.6

High

10

3.4

N = 41.
*Signficiant at the .05 level.

0.203

0.817

1.95

0.156

0.102

0.904

0.435

0.651

167

residual supply levels with the supply levels reported by
local economic development officials.
Comparative Analysis
Comparative
retail

and

service

analysis of

the levels

functions

presented

functions:

There

of supply of

in Table 4.7

and

4.8 showed that:
1.
significant
supply

Retail

relationships

generated

by

the

between
model

were

statistically

residual

and

the

levels

supply

of

levels

reported by local government officials in five of the ten
retail

functions.

significant

There

relationship

was

however

between

total

no

statistically

level

of

retail

supply generated by the model and the total supply levels
reported

by

the local

government's

economic

development

officials.
2.

Service functions:

Statistically significant

relationships were observed between the residual level of
supply
the

of service

supply

local

functions

levels

of service

government's

economic

generated

by

functions

reported

development

seven of the ten service functions studied.
levels

resulting

levels

were

also

from the model
highly

the model

and

by

the

officials

in

The residual

and the reported supply

correlated

with

regard

total level of supply of all service functions.

to

the

168

The
supply

correlations

and

the

reported

between
supply

residual

levels

levels

provide

of

evidence

for the validity of the model.
Comparing the Model
with TAC Scores
In
resulting

comparing
from

the

the

Residual

model

and

the

Level
Trade

of

Supply

Area

Capture

Supply Level, the following results were observed:
1.
results

Retail

of

functions:

the

Pearson

Table

Moment

4.19

shows

correlation

the

Analysis

between the reported level of supply of retail functions
with

the

supply

Trade Area
that

Capture

supply

levels

significantly
supply

of

two

of

(TAC)

generated
and

However,

the

the
The

study

model,

results

showed

by the model

with

functions

by

Pull.

generated

correlated

retail

functions.
the model

levels

in

levels

and

the

reported

five

of

the

of supply

TAC were
level

ten

correlations

ten

retail

between

functions.

total

levels

There
of

retail

generated

and TAC were significantly correlated

the

of

supply

by

in only

were
of

high
total

retail functions reported by the city officials and TAC.
2.

Service

functions:

Table

Pearson Moment correlation analysis

4.20

presents

the

for the relationship

between the reported level of supply of service functions
and the supply level

of the same functions generated

the study model, Trade Area Capture (TAC).

The

by

169

Table 4.19.

Results of Correlation Analysis Between the
Reported
Levels
of
Supply
of
Retail
Function with the Supply Levels Generated
by the Model, TAC, and Pull.

Study Model

PULL

TAC

•

Retail
Function

r

P-V

r

P-V

r

P-V
•

Bldg material
and garden

.2960

.032*

.2683

.072

.0823

.330

General
merchandise

.3746

.009

.6693

.002*

.6746

.002*

Food stores

.1254

.223

.3314

.024*

.1421

.204

Automobile
dealers

.2139

.093

.5461

.001*

.3611

.023*

Gas service
station

.5055

.001*

.0541

.372

.2465

.065

Apparel and
accessory

.2052

.102

.4138

.006*

.2089

.111

home
furnishing

.3517

.013*

.2912

.050*

.2379

.091

Eating and
drinking

.3789

.008*

.2074

.103

.0243

.442

Drug and
proprietary

.2028

.108

.0860

.332

.1692

.195

Miscellaneous
retail stores

.1025

.270

.0336

.446

.1731

.239

Overall
Retail

.2446

.064

.3741

.009*

.1642

.159

TJH i * • « ^
v a
l»
W M L C

•■m J
CU1W

•

* Denotes most significant correlation.

170

Table 4.20.

Results of Correlation Analysis Between the
Reported
Level
of
Supply
of
Service
Functions with the Supply Levels Generated
by the Model, TAC, and Pull.

Study Model

PULL

TAC

•

Service
Function

r

P-V

r

P-V

r

P-V
•

H o t e l , room
lodging

.5465

.000*

.6231

.001*

.4678

.016

Auto repair
service

.3209

.023*

.0181

.456

.0151

.464

Miscellaneous
repair
service

.4857

.001*

.3217

.051

.4832

.005*

Amusement and
recreation

.0149

.464

-.0954

.293

-.1278

Health
services

.2397

.068

.2958

.034*

.4364

.003*

Legal
services

.3393

.017*

.1595

.188

.2875

.052

Personal
services

.2740

.045

.2645

.057

.4546

.002*

Business
services

.2216

.088

.2467

.077

.1733

.160

Social
services

.6239

.003*

.3569

.080

.5901

.006*

Engineering,
Accounting,
etc.

.4474

.002*

.3161

.034*

.4871

.002*

Overall
Service

.5147

.000*

.5382

.000*

.5801

.000*

* Denotes most significant correlation.

.232

171

correlation
service

results

showed

functions

significantly
service

that

generated

correlated

functions.

the

by

with

level

the

TAC

There were

of

study

in

two

significant

supply

Model
of

of

were

the

ten

correlations

in the levels of service supply generated by TAC and the
reported supply levels by local officials in three of the
ten service functions.

A significant correlation between

reported

and

Model,

supply

level

the

levels

generated

by

the

and TAC were also observed in regard to the total

supply level of service functions.
Comparing the Model
with Pull Scores
With

regard

to

the

relationship

between

the

Residual Supply Level and Pull Scores, the following were
observed:
1.
Table

Retail

4.15

showed

function;
high
TAC

Although

correlation

supply

levels

and

in

Supply

scores

correlated

two

the

between

retail

with

the

results

reported

functions,

residual

in

Pull

level

of

supply only in one retail function.
2.
(a)

Pull

Service

generated

functions:
supply

levels

levels generated by the model
ten

service

correlated

functions

and

Table

correlated

(residuals)

(b)

significantly with

4.20

Pull

six of the ten service functions.

that:

with supply

in five of the

Supply

reported

showed

levels

supply

also

levels

in

Similarly, Pull Supply

172

scores
TAC,

correlated

significantly

with

the

residual,

and

and reported levels of supply for the total service

functions.
The
were

Model,

evaluated

to

TAC

and

Pull

prediction

techniques

determine which one of the three was

the best tool for predicting

future levels of supply of

retail

for

and

service

function

similar

cities.

The

evaluation was based on the following criteria:
1.

The magnitude of the correlation coefficient
between the supply levels
generated

by

used

determine

to

the

model,

reported and those
TAC,

the

and

Pull

relative

level

were
of

efficiency.
2.

Any

of

the

above

tools

number

of

significant

cients

was

considered

Based

on the

summary

of

residual

levels,

with

the

highest

correlation

coeffi­

the

above conditions,

correlation
TAC and

data

best

prediction

the analysis

generated

by

Pull Supply scores

4.19 and 4.20 are presented in Table 4.21.

the

and

model's

from Tables

173

Number of the Most Significant Correlations
Between Reported Supply Levels and Model,
TAC, and Pull Scores.

Table 4.21.

Predicted
Function

Techniques and Number of Significant
Correlations
•

Model (Residual)

Pull

TAC

•

Retail Functions
Total Retail
Function
Service Functions
Total Service
Functions

4

3

—

1

4

1

—

Grand Total

1

—

3
1

—

5

8

5
t

Based
study

on

model

had

the

above

the

summary

highest

in Table

total

4.21,

number

of

the
most

significant correlation coefficients in predicting retail
and

service

functions.

The

model

may,

therefore,

be

considered the most efficient among the three techniques
for

predicting

levels

of

supply

for

retail

and

service

functions.
Summary
In
presented
validating

this
in

chapter,

five

the

phases,

study

the

research

together with

model.

The

findings

were

a procedure

multiple

of

regression

174

model

significantly

retail

and

income,

population,

city

and

predicted

service

county

predictors.

The

the

functions

using

unemployment,
per

capita

validation

level

of

city

proximity
income,

procedure

as

supply
per
to

of

capita
a

major

regression

determined

the

study model to be relatively more efficient in predicting
the level of supply of retail and service functions than
either

TAC

conclusions,

or

Pull.

The

summary

of

the

findings,

and recommendations are presented in Chapter

V of the dissertation.

CHAPTER V
SUMMARY, CONCLUSIONS, RECOMMENDATIONS
Summary
Introduction
This study has attempted to develop a prediction
model

using

city

unemployment,
distress,
business

population,

proximity

and

county

to

per

opportunities

per

a

major

capita

based

capita
city,

income,

on the

level

income,
level

to
of

of

identify
supply

of

retail and service functions in selected Michigan cities.
The sample used in the study comprised 80 Michigan cities
having

10,000

to

100,000

people.

The

cities

were

selected for study because previous studies showed that:
(l ) the

number

range

would

these

types

of

cities

continue
of

to

cities

within

experience
are

often

seasonal economic cycles,

and

respond

limited

positively

to

the

above
rapid

more

population
growth,

(2)

sensitive

to

(3) they are more likely to
economic

development

efforts than cities of larger-sizes.
The model developed in this study was expected to
provide
urban

valuable

planners,

information

for

entrepreneurs,

175

and

public

policymakers,

private

and

public

176

economic

development

professionals.

The study was

also

expected to provide answers to the following questions:
1.
that

What is the possibility of developing a model

could

identify

business

opportunities

based

on

levels of supply of retail and service functions?
2.

Can the level of supply of retail and service

functions

be

significantly

independent

variables

unemployment,

per

predicted
(i.e.,

capita

income,

using

city

a

set

of

population,

proximity

to

a major

city, level of distress, and county per capita income)?
3.

Are

there

any

significant

relationships

between levels of supply of retail and service functions
as perceived

by city economic development

the

supply

level

of

of retail

and service

officials

and

functions

as

generated by the model?
4.

Are

between levels

there

any

significant

relationships

of supply of retail and service f u n c t i o n s

and the city's level of economic distress?

levels

5.

Does a significant relationship exist between

of

staff

efforts

and

budget

allocation

and

a

city's level of economic distress?
6.

Are

there

any

significant

relationships

between the level of staff efforts and budget allocations
for

the promotion of economic

sectors and the levels of

tax revenues and employment generated by these sectors?

177

7.

Is

relationship
economic

there
between

a
a

development

statistically

city's

mission

significant

classification

and

the

city's

of
level

its
of

economic distress?
8.
sector

Does

have

the

any

ranking

significant

importance

relationship

of
to

economic

the

city's

level of economic distress?
Literature Review
The literature review focused on the theoretical
bases— Central

Place

understanding
distribution
and

the

the

Location

geography

patterns

economic

and

of

retail

activities

and

theories— for

of

market

centers,

and

service

industries

interests

of

producer/

supplier and consumer in business location decisions.
The
of

review examined

retail

and

service

industries,

hierarchy of market centers
population
behavior,

size,

consumer

competition,

the evolutionary development
structure,

as the result of changes
demographics

technology,

and

and
in

shopping

and consumer accessi­

bility to market centers.
Most
empirical

of

studies

the

prediction-related

reviewed

based

their

models

predictions

and
of

potential sales for market areas on the central place and
location
showed

theories.
a

positively

The

findings

significant

of

studies

correlation

reviewed
between

178

population
and

the

size;

income

level;

consumer

order

level,

volume,

type(s),

travel
and

distance

location

of

retail and service functions.
Methodology
Eighty

Michigan

cities

of

10,000

to

100,000

people were studied. The study model used six independent
variables— city

population,

unemployment,

proximity

distress,

county

and

to

per

per
a

major

capita

capita

income,

city,

income— to

level

of

predict

the

level of supply of retail and service functions for the
80 cities using the multiple regression technique.
The

dependent

establishments
functions

per

in

variable

each

of

10,000 people.

was

the

the retail

number
and

of

service

The residual generated by

the multiple regression was the difference between actual
number and the predicted number of establishments in each
function.

The residual was used as a measure of level of

supply in each of the retail and service functions within
the city limits.
The data for
reported
functions

estimates
by

the validation of the model
of

local

level

government

of retail
economic

officials collected through mailed survey.

and

were the
service

development
The secondary

data were actual number of establishments in each retail
and

service

functions

gathered

from

retail and selected service industries.

the

censuses

of

179

Data Analysis and Results
The
examined

data

in

generated

14

from

hypotheses

prediction model,

its

in

the

the

validation,

variables

were

development

mission,

and

of

a

resource

allocations to local economic development. The hypotheses
were tested at the .05 significance level.
Phase One— The Model
Hypotheses
levels

of

1

retail

and

and

2

stated

service

that

actual

functions

supply

could

be

significantly predicted by city population, unemployment,
per

capita

income,

proximity

to

a major

distress and county per capita income.

city,

level

of

Hypotheses 1 and

2 were tested using multiple regression.

For Hypothesis

1,

that

multiple

regression

predictors
supply
was

of

significantly
retail

found

prediction
percent

results

to

the

Overall

proportion

overall

Proximity

statistically

results.

of

predicted

functions.
be

showed

the
level

significant

variance

of

to a major city
in

the model accounted

of

six

in

the

the

for

38

level

of

supply of the retail functions.
For

Hypothesis

showed

that

the

supply

level

of

income,

proximity

2,

model
service
to

multiple

significantly

regression

results

predicted

overall

functions.

County

a major city,

and city

per

capita

per capita

income were significant predictors of the overall supply

180

level

of

service

functions.

Of

the

variance

in

the

supply level of the overall service functions 40 percent
was explained by the model.

Phase Two:
City Officials1
Perception of Level of Retail
and Sevice Supply
Hypotheses
of

retail

city's

and

of

generated
Positive

stores,

the

of

model

correlations

functions

(i.e.,

and

would
were

as

be

and

positively
in

5

the

residual

correlated.
10

retail

garden

supply

service

stores,

by

functions

of

and

Gasoline

furnishing

the

service

materials

of supply

perceived

and

found

merchandise,
home

that levels

officials

retail

Building

General

Furniture

functions

development

supply
by

4 stated

service

economic

levels

3 and

and

stations,

Eating

and

drinking places).
For the service functions, the reported estimates
and

the

residual

correlated

in

statistically

levels

seven

of

significant

of

supply

ten

were

functions

positive

significantly
studied.

A

relationship

was

observed between the predicted and the perceived level of
supply of the overall service functions.

181

Phase Three: Relationship Between
City Government's Development
Efforts and City's Level of
Distress
Hypotheses

5

and

6

tested

the

relationship

between supply levels of retail and service functions and
a city's level of economic distress.
were

no

level

statistically

of

retail

economic

significant

distress

supply.

Also

of

Using ANOVA there

relationships

a city

there

and

was

its

no

between
level

of

significant

relationship between the level of service supply and the
level of economic distress of the city.
Hypothesis

7,

an

analysis

of

variance

(ANOVA),

was used to test the relationship between level of staff
allocation
level

of

for

economic

relationships
economic

sector

distress.

between

sectors

wholesale)
not

economic

staff

promotion

There

were no

allocation

(manufacturing,

and

significant,

for promotion

retail,

highly

city's

significant

service,

and the level of economic distress.

statistically

a

of
and

Although

distressed

cities

allocated an average of 46 percent of their staff efforts
to promote manufacturing sectors

compared

to an average

of 41 percent and 31 percent by the moderately and least
distressed cities, respectively.
Hypothesis
relationship

8

between

(ANOVA)
budget

was

used

allocations

to
for.

test

the

economic

sector promotion and a city's level of economic distress.

182

Results

showed

significant

that

there

relationships

manufacturing,

retail,

were

between

service,

no

statistically

budget
and

allocations

wholesale,

to

and

a

city's level of economic distress.
Although not statistically significant,
level

of supply of service

distressed
service

cities

have

functions

moderately

functions showed

the

(2.65)

distressed

least

level

compared

(2.81)

or

that highly

of

with

the mean

supply
either

least distressed

of
the

(2.76)

cities.
Phase Four;
City's Economic
Development Efforts and Level
of Tax Revenues, and
Employment Generated
by Economic Sectors
Hypothesis

9

used

correlations

to

examine

the

relationships between size of sector staff allocation and
the level of tax revenues generated by a city's economic
sectors.

There were

correlations
revenues

between

statistically
staff

significant

allocation

and

for service and wholesale sectors,

positive

level

of

tax

but not

for

manufacturing and retail sectors.
Hypothesis
relationships
level

of

sectors.
budget

tax

10

between

used
sector

revenues

There

were

allocations

a

correlation
budget

generated
significant

and

tax

by

to

test

the

and

the

allocation
a

c i t y 's

relationships

revenues

generated

economic
between
by

the

183

service

and

wholesale

significant

sectors,

relationship

was

but

observed

no

statistically

for

manufacturing

and retail sectors.
Hypothesis
level of
and

concerned

staff allocation

level

of

Correlation
between

staff

by

However,

significant

between

the

and

there

were

relationships

the

manufacturing

sectors.

levels

and

no

of

wholesale

statistically

relationships observed in the case of retail

service,

efforts

by

significant

allocations

generated

sectors.

generated

indicated

effort

relationships

for economic sector promotion

employment

results

employment

and

11

thus

were

indicating higher percentage

allocated

to

sectors

that

of staff

generated

more

employment opportunities.
Hypothesis
relationship
economic

between

sector

generated

12

by

stated

level

promotion

the

that

of

budget

and

sector.

there

allocations

levels
Results

was

of
of

a
for

employment
correlation

computation showed satistically significant relationships
between

budget

generated

by

allocations

and

manufacturing,

the levels
retail,

of employment
and

wholesale

sectors; but the relationship for retail was negative.

184

Phase Five:
City's Classification
of Economic Development Mission,
Ranking Importance of Economic
Sector and City's Level
of Distress
In Hypothesis 13, the Chi-Square test was used to
determine any significant relationship between how a city
classifies

its

economic

city's

level

of

economic

showed

significant

development
distress.

relationships

mission
Results

and

the

not

only

between classifications

of a city's economic development mission and its level of
economic
more

distress,

likely

to

but

have

that more distressed
economic

development

cities were
as

a

major

objective in their mission than less distressed cities.
Hypothesis

14

sought

to

establish

whether

the

type of economic sector ranked as important for achieving
a city's

economic

development mission was

significantly

related to the city's level of economic distress.
results

showed

relationships

that

although

between

ranking

there
of

were

ANOVA

no

significant

importance

of economic

sector and achieving economic development mission, highly
distressed

cities

manufacturing,

retail,

seemed

more

likely

to

rank

and service as most important for

achieving their economic development mission.
Conclusion
Based

on

the

above

results

following conclusions were reached:

or

findings,

the

185

1.

The

study

showed

that

it

is

possible

to

develop a model that can significantly predict levels of
retail

and

service

variables— city

functions

population,

using

six

independent

unemployment,

per

capita

income, proximity to a major city, level of distress, and
county per capita income.
2.
reported

The

strong

estimates

correlations

found

of supply levels by local

between
government

economic development officials and the residual level of
supply
model
model

of

retail

and

have validated
as

a

service functions generated by the
and

reinforced

predictive

business opportunities

tool

the utility

capable

of

of the

identifying

in retail and service sectors for

economic planning, development, and growth.
3. The model was
predicting

levels

of

found to be more

supply

of

service

effective
functions

in
(70

percent in this study) than the levels of reLaii supply.
4.
the

six

The

significance

of

predictor/independent

performance
variables

of

in

each

of

predicting

level of supply seemed to depend on the economic sector.
5.

Proximity

significant

in

was

the

predicting

only

level

of

variable
supply

that
of

was

retail

functions, while county per capita income, proximity, and
city

per

capita

income

were

significant

level of supply of service functions.

in

predicting

186

6.

Irrespective

economic

development

economic

distress,

looked

upon

important

by

as

a

the

most

for

of the city's classification of

Although

primary

importance

of

the

all

their

to

goals/mission,

considered

manufacturing
achieving

highly

of

still

as

most

development

their

as

cities

sectors

economic

of

economic

distressed

retail

their

was

manufacturing

achieving

and

level

cities

economic

saw

the

its

sector

respondent

cities

development

to

and

manufacturing

achieving

mission.

important

mission

most

development

mission.
7.

The level of economic distress of a city had

significant

influence

on

how

economic development mission.
of

a

statistically

city's

level

economic

of

sector

ranked

city

classified

its

But there was no evidence

significant

economic
it

a

relationship

distress
most

between

and

the

important

for

type

a
of

achieving

its economic development mission.
8.
validated

The

model

with TAC

and

was
Pull

compared
scores

and

successfully

of level

of supply,

although the validation is not without its weakness.

The

model has used:

(2)

six

predictor

unemployment,
distress,
generate

and

(l) actual retail and service data,

variables

(population,

per

capita

income,

proximity to a major city, city's level of
county per

predicted

capita income),

residual

levels

to analyze and

of supply

for retail

187

and

service

functions

for

each

of

the

80

cities.

The

model has also provided for the effects of such important
factors as income and proximity.

Based on the advantages

of the model over the above two validating methods,

the

model might be considered as the most reliable method for
estimating

level

of

supply

for

retail

and

service

functions.
9.
data

may

Although
not

situation
predicting

be

in

relevant

1990,

future

the

supply

prediction

or

reflect

output

the

model

would

levels

of

based

actual

be

market

valuable

retail

and

in

service

functions using current available data.
Limitations of the Study
1.

Most of the data,

especially

the number of

retail and service establishments, used in the study were
based

on

the

censuses

of

retail

trade

and

service industries published every five years
with

population

of

2,500

and

more.

selected
for places

Thus,

current

projections or estimates of level of supply based on 1987
data may not reflect the actual supply situation.
2.
service

The predicted levels of supply of retail and

functions

(two-digit
depicted
functions.

SIC),
the

were

based

therefore,

actual

levels

on
might
of

major
not

industry
have

supply

of

groups

accurately
subgroup

on

1987

188

3.
local

Using

officials,

study model

estimates
TAC

could

and

of

levels

Pull

of

scores

to

be misleading because:

supply

from

validate

the

(1) estimates

of levels of supply by local officials were largely based
on subjective

judgment

reflecting supply levels

in 1990

and not in 1987 for which the study data were collected;
and

(b) TAC and Pull erroneously assume statewide uniform

consumer

taste

and

purchasing

behavior

and

ignore

the

for

application

important effects of travel distance.
4.
in

The model

regional markets

is only

or

suitable

places

with

a

large

number

of

cities with population of 10,000 and more, and similar in
characteristics to the Michigan cities studied.
5.
effects
which

The model does not provide for the potential

of race,
could

crime

rate,

influence

and cost of doing business

business

location

decisions

(investments or disinvestments),- and thus level of retail
and service supply in a city.
Recommendations
Based
following

on

the

findings

of

this

recommendations

are

proposed

While

the

model

study,

for

the

additional

study.
The Model
1.

has

been

successful

predicting supply levels for retail and service functions

in

189

at the group
model

to

function

predict

functions

at

level,

level

the

further study applying the

of supply

subgroup

level

of retail
relevant

and service

to

each

local

economy is essential.
2.
to

Increasing the number of predictor variables

include TAC,

crime

rate,

structure,
improve

import level,

property

tax

city's racial composition,

rate,

type

of

local

economic

and development policy tools and targets, may

the

predictive

also

provide

more

and

level

of

accuracy

valuable

of

the model.

information

relationships and

It

about

the

the

effects

may

types
these

variables have on the predicted level of supply of retail
and

service

functions

and

the

potential

business

opportunities in the city.
3.
the

use

of

A study is also essential to examine whether
the

establi shments

dollar
(units)

is

a

instead
more

the

accurate

measure

for

Dollar value as a measure of level of supply

nonexistence

determine

the

of shopping malls

effects

and

of

functions.

to

retail

number

level

important

supply of

of

predicting

is

of

value

the

service

existence/

or large multiple stores

may have on the actual rather than the theoretical number
of retail and service establishments in a city.
4.

Further

detailed

study

of

the

variable

of

proximity to a larger city is important to determine the
influence of this variable in predicting level of supply

190

of

retail

subgroup

and

service

level.

functions,

Knowing

more

especially

about

the

at

the

degree

of

influence of this factor may be important and valuable in
exploring
joint

intercity

venture

development

or

economic

shared

projects

development

equity

and/or

in

cooperation,

targeted

new

economic

business/commercial

planning.
Economic Sector and Economic
Development Mission
5.

Most

manufacturing
their

respondent

cities

still

considered

as the most important sector for achieving

economic

development

mission.

Manufacturing,

as

could be inferred, was not only viewed by respondents as
synonymous with success of economic development mission,
but with economic performance and economic welfare of the
city.

While manufacturing may be important in achieving

improved

economic

development

necessarily

achieve

economic

development

mission

for

traditional

manufacturing

cities

because

expensive

of

the

infrastructure

the

performance,
welfare

and/or

following

is

not

short-

it

long-term

improvements,

nol

economic

reasons:

accessible

and

may

large-

to

(1)
most

costs
or

of

long­

term tax abatements and potential environment pollution,
and

(2)

attracting advanced manufacturing businesses may

not only be too expensive, especially to the moderate and
highly

distressed

cities,

but

the

number

of

jobs

191

generated
justify

locally may

the

be too

few and

highly

skilled to

amount of resources expended to attract the

manufacturing business.
The

"foot

(traditional
guarantee

loose"

or

nature

advanced

that

they

will

of

these

businesses

manufacturing)
not

relocate

does

at

any

not
other

opportunity to maximize business profits or higher return
on

investments.

and/or

highly

achievement
primarily

For

distressed, to

of

on

cities,

their

nature)

mission/goal

and

base

economic

manufacturing

(unrealistic

especially

in

selection

the

of

moderate

success

development

indicates
the

the

a

major

economic

or

mission
weakness

development

development

target

for

achieving it.
Given
the

model

as

identifying
and

the above
an

integral

available

limitations

reasons

of

future study should use
part in the

resources,

the

city

as

process

economic
a

of

opportunities

basis

for

setting

realistic and attainable economic development mission and
adopting

appropriate

relevant,

economic

ultimate

goal

is

tools.
development
sustained

It is expected
mission

is

economic

that

one

a

whose

development

performance and economic welfare for the majority of the
city/community population.
6.

Development

tools

and

targets,

realistic analysis and identification of local resources,

while

192

opportunities,
establishing
goal,

and

limitations

relevant

equally

economic

important

to

are

critical

development
the

mission

success

of

in
or

mission

achievement are the type(s) and compatibility of economic
development
economic

tools

and

development

targets

employed

mission.

Future

in

pursuit

study

may

of
be

necessary to provide effective principles to ensure that
the identification and selection of economic development
tools and targets not only recognize,
with

local

distress,

economic

structures,

demographics,

and

but are compatible
level

the

of

economic

socio-political

environment.
Application of the Model
This

study

(predictive model)
for

identifying

is

a

basic

whose

outcome

is intended to provide a valuable tool
business

service sectors.

research

opportunities

in

retail

and

The model was based on the study of 80

Michigan cities; cities wishing to apply the model should
be

similar

in size

cities studied.
application

and

characteristics

to the Michigan

There is no guarantee, however, that its

will

always

be

successful.

But

for

cities

wishing to apply the model, the following steps should be
taken.
Step 1:
the

city's

A

economy

comprehensive
should

situation

include

the

analysis

of

collection

of

193

current data on:
and

(1) sources and level of local revenues

expenditures;

(3)

employment

structure

of

and

size

and quality

unemployment

the

nonmanufacturing
competition

(2)

local

from

levels;

economy

activities);

(5)

neighboring

of labor
(4)

based

on

basic

(manufacturing
areas

level

of

in

terms

of

communities

demographic

or

and

inflow and outflow of incomes and resources;
market

force;

(6) size of

the

local

characteristics;

and

(7) performance of local economic sectors in terms of

job opportunities and size of tax revenues generated.
Step 2:
is

The next phase of the economic analysis

to use the model to predict the number of retail and

service

businesses

that the community can support given

the city's profiles.
Step 3:
Steps

1

Based

on

the

information

and 2, current economic

provided

in

development mission/goal,

target, and policy tools should be evaluated to determine
their

impact,

opportunities,

implementation
and

and

relevance

limitations

of

to

local

economic
economy

identified in the previous steps.
Step 4:
steps

are

then

The data provided by the preceding three
used

to

economic

development

potential

economic

set

a

mission

development

sectors such as manufacturing,
for

promotion;

and

formulate

realistic
and

and

attainable

goal(s),

targets,

(i.e.,

identify
economic

retail, wholesale service)
appropriate

policy

tools

194

(e.g., cost reduction incentives for business development
and growth such as tax abatement), and promotion of local
market

expansion,

etc.),

for

achieving

sustained

community economic growth and welfare.
Step 5.
employed

by

The mission, targets, and policy tools

local

economies

for

should be monitored

regularly,

andperiodically evaluated

to

changes

incorporate

only

by

environment

a

any

pragmatic
that

success

in

economic

development

the environment.

response

to

in long-term,

changes
overall

It
in

is
the

economic

growth and economic welfare can be sustained by the local
economy.

APPENDICES

195

APPENDIX A
SURVEY INSTRUMENT

196

MICHIGAN PARTNERSHIP FOR ECONOMIC DEVELOPMENT
ASSISTANCE

A SURVEY
OF
RETAIL AND SERVICE OPPORTUNITIES IN MICHIGAN
CITIES

MICHIGAN STATE UNIVERSITY
WINTER 1990
197

198

INSTRUCTIONS FOR COMPLETING THIS QUESTIONNAIRE
The questionnaire is intended for the Director of Economic Development in the city or
the person who most directly serves this function. The person completing the Questionnaire
should be very knowledgeable of the city's overall economic development mission and
objectives, an d policies and programs. Information provided in the survey will remain
confidential. Only aggregate data will be disseminated on request to cities participating in
this survey.
We estimate 20 - 25 minutes will be required to complete the Questionnaire. Every
response is very important so we would appreciate it if you will answer all questions.
Please return the questionnaire, even if you are not able to answer all questions.
If you have questions about the survey or completing any o f the questions, please
contact:
Dare Aworuwa, Project Director,
Michigan State University,
Center for Urban Affairs,
Owen Graduate Center,
East Lansing, MI 48824.
Phone: (517) 353-9145 or (517) 355-8119
or
John Schweitzer, Research Director,
Michigan State University,
Center for Urban Affairs
Owen Graduate Center,
East Lansing, MI 48824.
Phone (517) 353-9144
Please return the completed survey questionnaire in the enclosed self-addressed
envelope by M ay 15, 1990.
Thank you for your cooperation and assistance.
This study is partially supported by the Michigan Partnership for Economic Development
Assistance. pursuant to the receipt of financial assistance from the Economic Development Administration,
Department of Commerce of the United States government, and the Michigan State University Urban Affairs
Programs.

199
Levels o f Retail and Service Functions
We want information about the status of selected retail and service businesses in your
city, and how well supplied your city is in various types of businesses. For this question
please use the following definitions:
VndersuppUed: The number of a particular type of business located within your
i

city limits is not adequate to meet the demands of your city.
Oversttpplied: The number of a particular type o f business within your city limits
is more than adequate to meet the demands of your city.
1.

Based on your personal opinion and experience, please rate each of the following

types of retail businesses in terms o f the level of supply within vourcitv limits using the
following scale:
DU = definitely undersupplied
SU = slightly undersupplied
AS = adequately supplied
SO = slightly oversupplied
DO = definitely oversupplied
DK = do not know
(Please CIRCLE one level of supply for each retail business sector)
Retail Business Sectors

Levels o f Supply W ithin City Limits

a) Apparel and accessory stores

DU

SU

AS

SO

DO

DK

b) Automotive dealers

DU

SU

AS

SO

DO

DK

c) Building materials, garden supplies stores

DU

SU

AS

SO

DO

DK

d) Drug and proprietary stores

DU

SU

AS

DO

DK

e) Eating and drinking places

DU

SU

AS

DO

DK

f) Food stores

DU

SU

AS

DO

DK

g) Furniture and home furnishing stores

DU

SU

AS

DO

DK

i) General merchandise stores

DU

SU

AS

DO

DK

j) Miscellaneous retail stores

DU

SU

AS

so
so
so
so
so
so

DO

DK

200
Levels o f Retail and Service Functions
We want information about the status of selected retail and service businesses in your
city, and how well supplied your city is in various types of businesses. For this question
please use the following definitions:
Undersupplied: The number of a particular type of business located within your
city limits is not adequate to meet the demands of your city.
Oversupplied: The number of a particular type of business within your city limits
is more than adequate to meet the demands of your city.
1.

Based on your personal opinion and experience, please rate each of the following

types of retail businesses in terms of the level of supply within vourcitv limits using the
following scale:
DU = definitely undersupplied
SU = slightly undersupplied
AS = adequately supplied
SO = slightly oversupplied
DO = definitely oversupplied
DK = do not know
(Please CIRCLE one level of supply for each retail business sector)
Retail Business Sectors

Levels o f Supply W ithin City Limits

a) Apparel and accessory stores

DU

SU

AS

SO

DO

DK

b) Automotive dealers

DU

SU

AS

SO

DO

DK

c) Building materials, garden supplies stores

DU

SU

AS

SO

DO

DK

d) Drug and proprietary stores

DU

SU

AS

SO

DO

DK

e) Eating and drinking places

DU

SU

AS

SO

DO

DK

f) Food stores

DU

SU

AS

SO

DO

DK

g) Furniture and home furnishing stores

DU

SU

AS

SO

DO

DK

i) General merchandise stores

DU

SU

AS

SO

DO

DK

j) Miscellaneous retail stores

DU

SU

AS

SO

DO

DK

201
2. If you rated any of the types of retail businesses as definitely undersupplied,
please give us reasons for the rating. Please be as specific as possible. (Use the letter
of the type of business e.g. a = Apparel and accessory stores, b = Automotive dealers, etc.)
Retail business________ Reason.

Retail business________ Reason.

Retail business________ Reason.

Retail business________ Reason.

Retail business________ Reason.

3. If you rated any of the types of retail businesses as definitely oversupplied, please
give us reasons for the rating. Please be as specific as possible. (Use the letter of the
type of business e.g. a = Apparel and accessory stores b = Automotive dealers, etc.)
Retail business__________ R eason________________________________________

Retail business

Reason

Retail business

Reason

Retail business

Reason

Retail business.

Reason

202

4. Do the establishments within your city limits in each of the following areas serve
primarily a local market, a regional market, or both? (check one for each sector)
M arket Area served
Retail business sectors

Local

a) Apparel and accessory stores

_____

Doth

D o n ’t know

_____

_____

_____

b) Automative dealers____________________ _____

_____

_____

_____

c) Building materials, garden supplies stores _____

_____

_____

_____

d) Drug and propriety stores

_____

_____

_____

_____

e) Earing and drinking places

_____

_____

_____

_____

f) Food stores________________________________

_____

_____

_____

g) Furniture and home furnishing stores

_____

_____

_____

_____

h) Gasoline service stations_______________ _____

_____

_____

_____

i) General merchandise stores_____________ _____

_____

_____

_____

j) Miscellaneous retail stores

_____

_____

_____

(please, continue on page 5)

_____

Regional

203
5. Please rate each of the following types of services in terms of the level of supply within
vonr citv limits using the following scale:
DU = definitely undersupplied
SU = slightly undersupplied
AS = adequately supplied
SO = slightly oversupplied
DO = definitely oversupplied
DK = do not know
(Please CIRCLE one level of supply for each service business sector)
Service Business Sectors

Levels o f Supply Within City Limits

a) Amusement and recreation services,
motion pictures, museums.

DU

SU

AS

SO

DO

DK

b) Automotive repair, services, and parking.

DU

SU

AS

SO

DO

DK

DU

SU

AS

SO

DO

DK

d) Engineering, accounting, other services

DU

SU

AS

SO

DO

DK

e) Health services.

DU

SU

AS

SO

DO

DK

f) Hotel, rooming and lodging places.

DU

SU

AS

SO

DO

DK

g) Legal services.

DU

SU

AS

SO

DO

DK

DU

SU

AS

SO

DO

DK

shops, phtographic studios, etc.)

DU

SU

AS

SO

DO

DK

j) Social services.

DU

SU

AS

SO

DO

DK

c) Business services (e.g. Advert, agencies,
Computer program, services, etc.)

h) Miscellaneous repair services (e.g. radio
& TV repain, refrig. & A/C services, etc.)
i) Personal services (e.g. barber & beauty

(please, continue on page 6)

204

6. If you rated any of the types of service businesses as definitely undersupplied,
please give us reasons for the rating. Please be as specific as possible. (Use the
letter of the type of business e.g. a = Amusement and recreation b = Automotive repairs, etc.)
Service business________ R e a so n _____________ ________________________

Service business

Reason

Service business

Reason

Service business

Reason

Service business

Reason

7. If you rated any of the types of service businesses as definitely oversupplied,
please give us reasons for the rating. Please be as specific as possible. (Use the letter
of the type of business e.g. a = Amusement and recreation b = Automotive repairs, etc.)

Service business________ Reason

Service business________ Reason

Service business________ Reason

Service business________ Reason

Service business.

Reason

205

8. Do the establishments within your city limits in each of the following areas serve
primarily a local market, a regional market, or both? (Check one for each sector).
M arket Area served
Service business sectors

Local

Regional

Doth

D on't know

_____

_____

_____

_____

_____

_____

_____

_____

_____

_____

d) Engineering, accounting, other services. _____

_____

_____

_____

e) Health services.______________________ _____

_____

_____

_____

f) Hotel, rooming and lodging places.

_____

_____

_____

_____

g) Legal services.____________________________

_____

_____

_____

_____

_____

_____

_____

_____

_____

_____

_____

_____

_____

_____

a) Amusement and recreation services,
motion pictures, museums.

_____

b) Automotive repair, services, and parking.
c) Business services (e.g. Advert. Agencies,
Computer Prog, services, etc.)

h) Miscellaneous repair services (e.g. radio
& T.V. repairs, refrig. & A/C services, etc.) _____
i) Personal services (e.g. barber & beauty
shops, photographic studios, etc)
j) Social services

9. Considering your city's overall economic development mission, please rank the
following sectors in order of their importance in achieving your city's economic
development goals in the last five years. Use 1 for the most important sector, 2 for the
second most important, etc.
Sector

R ank level

Manufacturing

_________

Retail Trade

_________

Services

_________

Wholesale Trade

_________

Other

_________

206
10.

The level of importance o f any sector to an economy is often measured by the
number of jobs created and/ or the size of tax revenues generated. Given your
knowledge of the city's economy, approximately what percentage of the city's
employment occurs in:
Sector

% Total Employment

Manufacturing

_____________

Retail Trade

_____________

Services

_____________

Wholesale Trade

_____________

Other

_____________
100%

Total
11.

Approximately what percentage of city’s tax revenues is generated by:
Sector

% Total Tax Revenues

Manufacturing

_____________

Retail Trade

_____________

Services

_____________

Wholesale Trade

_____________

Other__________________________________ _____________
Total

100%

Economic Development Mission, Objectives and Promotion
Budgei Allocation
The set of questions in this section are designed to help us understand your city's
economic development mission, objectives and promotion budget allocation.
12.

Please summarize the overall economic development mission of your city: (Please
ANSWER in space below).

207
13.

How would you classify your city government's economic development mission?
(please, CHECK your answer)
Economic development to us is:
the major city objective
one of the several major city objectives
a minor city objective
other (specify)__________________________________________________

14.

Given your personal knowledge of the city's economic development efforts, and its
economic development budget, what percentage of the economic development is
dedicated to advancing the following sectors?
% o f economic development
Sector

% o f sta ff effort

Manufacturing

________

________

Retail Trade

________

________

Services

________

________

Wholesale Trade

________

________

Other

________

________

Total

15.

budget allocation

100%

Total

100%

We would appreciated any additional information or comments you would like to
make about the city's economic opportunities.

(Please, continue on page 10)

208
16. Fill in name and address of person to whom the local analysis should be sent:
Name: ___________________________________________
Title:

___________________________________________

City:

___________________________________________

Address: _________________________________________
Tel:__ ____________________________________________

We thank you for your time and cooperation in completing this questionnaire. We
look forward to sharing the results of the study with you as soon as it is completed.

THANK YOU

MSI! is an affirmative action/equal opportunity institution

APPENDIX B
RETAIL TRADE
SELECTED SERVICE INDUSTRIES

209

210

DEPENDENT VARIABLES USED IN THE STUDY

A.

RETAIL TRADE (10)

SIC

BUSINESS

56

APPAREL AND ACCESSORY STORES

55

AUTOMOTIVE DEALERS

(EX 554)
52

BUILDING MATERIALS, GARDEN SUPPLY STORES

591

DRUG AND PROPRIETARY STORES

58

EATING AND DRINKING PLACES

54

FOOD STORES

57

FURNITURE AND HOME FURNISHING STORES

554

GASOLINE SERVICE STATIONS

53

GENERAL MERCHANDISE STORES

59

MISCELLANEOUS RETAIL STORES

(EX 591)

Source:

Census of Retail Trade, 1987.

211

B.

SELECTED SERVICE INDUSTRIES (10)

SIC

BUSINESS

78,79,84

AMUSEMENT AND RECREATION SERVICES, MOTION
PICTURES, MUSEUMS

75

AUTOMOTIVE REPAIR SERVICES AND PARKING

73

BUSINESS SERVICES (e.g., Advert, agencies,
computer programming services, etc.)

87
(ex 8733) ENGINEERING, ACCOUNTING, OTHER SERVICES
80

HEALTH SERVICES (e.g., medical, surgical, and
other health services)

70

HOTEL, ROOMING, AND LODGING PLACES

(ex 704)
81

LEGAL SERVICES

76

MISCELLANEOUS REPAIR SERVICES (e.g., radio & TV
repairs, refrig & a/c services etc.)

72

PERSONAL SERVICES (e.g. , uaLOeL St utiduuy snops,
photographic studios, etc.)

83

SOCIAL SERVICES (e.g., individual & family.
social, counseling, welfare, referral services,
job training & voc. rehab; child day care,
e t c .)

Source:

Census of Selected Service Industries, 1987

APPENDIX C
MEASURES OF ECONOMIC DISTRESS

212

FACTORS AS MEASURE OF LEVEL OF ECONOMIC
DISTRESS— 1987
(Categories of Distress:

1 to 7)

* POPULATION GROWTH LAG/DECLINE:
(25.3% or more— 19601984, 4.6% or less for large or s/cities, respectively.
* AMOUNT OF POVERTY (min. 12.3% below poverty level)
* AGE OF HOUSING (at least 20.2% constructed prior to
1940)
* PER CAPITA INCOME GROWTH 1969-1983 (increase— $6,203 or
less:
1969-1983).
* JOB LAG IN RETAIL AND MANUFACTURING SECTOR 1977 - 1982
(increase— 3.3% or less— 1977 - 1982)
* UNEMPLOYMENT (average rate:

6.5% or more)

* LABOR SURPLUS AREA (countries, including cities with
25,000 or more with unemployment rate of 9%— 19841985).

Source:

U. S. Department of Housing and Urban
Development for Urban Development Action Grant
(UDAG) Programs, 10/1987.

213

APPENDIX D
MULTIPLE REGRESSION OUTPUT

214

27-Dec-90
12:45:17

MARKET O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS
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27-Dec-90
12:45:18

C a s e x l s e
*:

MAR K E T O P P O R T U N I T Y IDENTIFICATION MODEL
P RELIMINARY ANALYSIS

P l o t

S e l e c t e d

C a s e

9

4 5
4 6
4 7
4 8
4 9

M T
P L E A S A N T
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6
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• R E S I D

27-Dec-90
12:45:24

MARKET OPPOR T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

• • • •
E q u a t i o n

N u m b e r

C a s e x l s e

P l o t

•:

S e l e c t e d

1

D e p e n d e n t

o f

S t a n d a r d i z e d

M :

M i s s i n g

V a r i e b l u .

M

B A T T L E
C R E E K
B A Y
C I T Y

•

B
9

B E N T O N
H A R B O R
B E R K L E Y

•

C A D I L L A C
C L A W S O N
D E A R B O R N

17

D E A R B O R N

IB
19
2 0
21
2 2
2 3
2 4

E
E
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E
E
F
F

2 5
2 6
2 7
2 8
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31

F
F
G
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3
3
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3
3
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•

11
•
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H T S

A
A
C
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R
A
R
R
R
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R
A
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K A L A M A Z O O
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C I T Y
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R

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A N D
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•

V E R L Y
H L S
G
R A P I D S
R M I N G H A M
R T O N

15
16

2
3
4
5
6
7
B

•

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14

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B
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11
12
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__

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3
4
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0 :

C I T Y
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B
L
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• R E S I D
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3
4
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9

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8
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1
1
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6
6
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• P R E D

. 2 5 8 7
. 4 2 9 0
• R E S I D

27-Dec-90
12:45:25

C a s a M l s a
*:

MARKET O PPORTUNITY IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

t

C a s e

41
4 2
4 3
4 4
4
4
4
4
4

5
6
7
8
9

5 0
51
5 2
5 3
5 4

Y
C
I
Q
V
L

S t a n d a r d i z e d
M i s s i n g

I
I
A
A
E
I

M
M
M
M
M

O N R O E
T
C L E M
T
P L E A
U S K E G O
U S K E G O

O
S
U
I
A

L
O
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N
N

R I V E R
R O U G E
R I V E R V I E W

6 5
6 6
6 7
6
6
7
7
7
7
7

8
9
0
1
2
3
4

E
L
V
L
N

S T E R
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A W

S A U L T

S T E

•

T
T
T
T

A
R
R
R

Y
A
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Y

•
•
•
•
•
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•
•
•
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•
•
•
•

C I T Y
•
•

W
W
V
C

Y
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T

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M I N G
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•

W A L K E R
W A Y N E
W E S T L A N D
W O O D H A V E N

•

•

S O U T H F I E L D
S O U T H G A T E
S T
C L A I R
S H O R E S

7 5
7 6
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2
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6
6
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6
1
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7
8
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9
6
0
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3
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6
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6
8
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4

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0
5
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6
2
6
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3
4
2
6
7

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• R E S I D

218

5
6
6
6
6
6

H
U
E
A
I

•

•

•

V I
K
P A R K
O S S O
N T I A C
R T
H U R O N

C
M
S
Y
G

E P 0 P 3
2 . 1 0
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2 . 3 4
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1 . 6 7

•

P O R T A G E

O
O
O
O
A

•
•

S H O R E S

5 8

R
R
R
R
S

3.
•

»

E N S
S A N T
N
N
H T S

5 6
5 7
9
0
1
2
3
4

0 0

N
P K
N
H T S
T T E
D A L E
D

N I L E S
N O R T O N
O
A
W
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O

R e s i d u a l

-3.0
0: ...

C
L
M
M
M
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N
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D

o f
M:

27-Dec-90
12:45:30

MARKET OPPOR T U N I T Y I D E N TIFICATION MODEL
PRELIMINARY ANALYSIS
•

E q u a t i o n

N u m b e r

C a s e w l s e

P l o t

*:

S e l e c t e d

#
l
2
3
4
5
6
7
8
9
10
1 1

15
16
17

S t a n d a r d i z e d
M i s s i n g

A
A
A
B
B
B
B
B

L
L
U
A
A
E
E
E

L
P
B
T
Y
N
R
V

E
E
U
T

N
N
R
L
C
T O
K L
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P A R
A
N
H L
E
C R
I T Y
N
H A
E Y
L Y
H

S
E E K

L

E

•
•

•
•

•
•
*

H T S

G
G
G
H
H
H
H

R
R
R
A
A
A
I

A
O
O
M
R
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G

N
S
S
T
P
E
H

D
S
S
R
E
L
L

V I L L E
E
P T
E
P T
A M C K
R
W O O
P A R K
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P

•
•
<

E A S T
L A N S I N G
E C O R S E
E S C A N A B A

•
•
•

T Y
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•
•

P K
W D S

•
•

D S
•
«

H O L L A N D
I N K S T E R
J A C K S O N

•
•
•
0:

.

o o

K A L A M A Z O O
K E N T W O O D
C I T Y
C I T Y

«

1 4 .
1 0 .
9 .
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1
4
7
7

7
2
1
4

3
1
2
4

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8
8
1 0
4

•

A R K

B . 5 1 4 8
9 . 0 9 6 1
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1 0 . 6 2 9 0

2 . 5 5
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3 . 7 4

8
8
5
7
8
1
1

5
B
8
8
4
9
3

2 . 8 1
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1 1 . 2 4
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•

8 . 0 9 8 1
1 4 . 7 7 7 2

1 0 . 6 0 0 2

1 2 .
B .
7 .
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5 .
1 6 .
7 .

H L S

• P R E D
1 5 . 3 2 4 5
1 6 . 0 4 0 4

1 1 . 3 5
1 1 . 3 4
6 . 7 9

9 . 9 6
1 7 . 9 9

•
•

N
N

2 8
7 7
6 0
6 0
5 0
5 8
1 0
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9 . 5 2
1 7 . 0 6

•

C A D I L L A C
C L A W S O N
D E A R B O R N

E P 0 P 5

2 0 . 6 1
1 . 8 4
1 1 . 0 4
1 4 . 1 2

L S

N G T O
N G T O
A L E
R
N
C I
H A V

R E G R E S S I O N

10.
6 .
1 0 .
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7 .
7 .
1 3 .
1 2 .

•

I
I
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t

P

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R B O R

M
M
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R
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5
6
7
8
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3
3
3
3
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L

Food Stores

0 . 0

K

F
F
F
F
G
G

3 0
31
3 2
3 3
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U

R e s i d u a l

B I G
R A P I O S
B I R M I N G H A M
B U R T O N

D E A R B O R N

M
E P 0 P 5

•

2 3
24
6
7
8
9

•

*

E
G R A N D
R A P I D S
E A S T
D E T R O I T

2
2
2
2

•

- 3 . 0
0-

C I T Y
A D R I A N
A L B I O N

18
19
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21
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C a s e

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V a r i a b l e . .

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9
9
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9

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7 . 8 8 1 9
9 . 8 8 4 2
1 4 . 5 2 5 4
1 0 . 6 5 0 6
9. 1 1 3 3
1 1 . 3 3 1 4
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9
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5
2
3
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8
7
7
7
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6
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3
2
3
9

• R E S I D
- 5 . 0 4 0 4
- 9 . 2 7 0 6
2 . 5 0 2 7
3 . 8 2 3 3
4
2
9
- 3
3
2

.
.
.
.

9
0
2
9

3
0
2
7

3
2
4
7

5
1
3
8

. 1
. 6
. 1
. 3
. 7

3
9
9
1
4

5
7
4
7
7

5
3
7
2
4

4 .
- .
- 5 .
1 .

4 8 6 4
5 8 0 1
9 0 7 4
2 9 0 4

- 4 . 1 4 1 2
. 0 7 7 4
3 . 4 6 0 2
2 . 1 9 5 3
- . 2 3 6 7
- 3 . 7
- 3 . 6
- 2 . 3
7 . 2
- . 9

5
9
6
4
7

2
5
8
9
9

6
6
7
4
1

- 5 .
- 1 .
1 .
3 .
- 2 .

7
8
6
6
6

0
9
0
4
5

5
5
6
4
4

5
3
6
9
4

- 1 . 2 2 0 5
- 4 . 1 8 4 9

1 4 . 8 9 6 8
9 . 0 9 8 1
1 5 . 1 1 4 0

- 4 . 7 0 9 4

8 . 1 0 8 9
7 . 8 0 6 4

. 5 6 6 5
- . 8 1 3 4

• P R E D

• R E S I D

- 6 . 1 8 7 8

219

12
13
14

D e p e n d e n t

O O

C e s e

1

27-Dee-90
12:45:30

C a s e M l s a
*:

MARKET O P P O R T U N I T Y IDENTI F I C A T I O N MODEL
PRELIMINARY ANAL Y S I S

P l o t

S e l e c t e d

R e s i d u a l

- 3 . 0
O : ...........

0 . 0
: ...........
.
•

C I T Y
L I N C O L N

4 2
4 3

M A D I S O N
H T S
M A R Q U E T T E

.
.

4 4
4 5

M E L V I N D A L E
M I D L A N D

.
.

4
4
4
4

6
7
B
9

M
M
M
M

O N R O E
T
C L E M E N S
T
P L E A S A N T
U S K E G O N

5
5
5
5
5

0
1
2
3
4

M
N
N
N
O

U
I
O
O
A

S
L
R
V
K

K E G O N
H T S
E S
T O N
S H O R E S
I
P A R K

5
5
5
5

5
6
7
8

O
P
P
P

W
O
O
O

O
N
R
R

S S O
T I A C
T
H U R O N
T A G E

5
6
6
6
6
6
6

9
0
1
2
3
4
5

R
R
R
R
R
R
S

I
I
O
O
O
O
A

V
V
C
M
S
Y
G

E
E
H
U
E
A
I

6 6
6 7
6 8
6 9
7 0
71
7 2
7 3

S
S
S
S
T
T
T
T

A
O
O
T
A
R
R
R

U L T
U T H F
U T H G
C L A
Y L O R
A V E R
E N T O
O Y

7
7
7
7
7
7
8

4
5
6
7
8
9
0

W
W
W
W
W
W
Y

A
A
E
O
Y
Y
P

L
Y
S
O
A
O
S

#

C I T Y ............................. ...........................................
- 3 . 0
0 . 0

K
N
T
O
N
M
I

R
R
E
L
V
L
N

E
E
L
H
D
I
L

V
S
U
I
A

P K

R
I
T
S
L
O
W

O U G E
E W
E R
H L S

S
I
A
I

T E
M A R I E
E L D
T E
R
S H O R E S

L E
A K

S E
N

N
V
T
G
N

D
E N
T E
T I

6 . 8 6
1 1 .23
•
.

.
.

8 . 9 7
1 1 .42
17 .87

•
.

•

•

.

1 6 . 0 6
1 0 . 5 1
1 1 .05
8 . 9 0
1 6 . 0 0
6 . 9 1

•
.

.

.
.
.
.

7 . 8 6
2 1 . 2 1

.

•

1 8 . 1 6
7 . 3 2

•
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•

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.

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1 4 . 8 1
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8
9
3
4
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5

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8 1
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.
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1 3 . 2 5
1 7 . 9 1

•

C I T Y

R
A
A
O
N
A

•
•

E P O P 5
1 1 .4 4

.•
.
.

3 2 . 2 6
7 . 5 6
8 . 6 2
6 . 7 8
1 0 . 9 5
6. 16
3 . 5 8
9 . 2 5
6 . 0 9
8 . 2 1
E P 0 P 5

• P R E D
8 . 5 4 6 5
1 0 . 6 8 4 5
1 4 . 5 0 8 2
9 . 1 0 1 1
8. 1 1 4 2
1 5 . 3 4 5 3
1 6 . 4 7 9 0
1 3 . 9 9 0 1
8 . 4 6 2 2
9 .
1 5 .
8 .
1 0 .
1 0 .

6
4
0
1
8

6
3
7
4
3

1
1
9
2
7

5
6
6
6
4

1 5 . 6 1 6 5
1 1 . 0 0 6 6
1 5 . 6 1 2 7
7
9
8
9

.
.
.
.

7
9
2
4

1
0
1
7

3
6
2
2

1
2
6
7

8 . 9 4 8 5
9 . 2 9 8 0
9 . 4 3 3 8
8 . 3 7 9 6
1 4 . 6 0 5 6
9 . 0 0 9 1
8 . 2 0 6 8
8. 1 3 3 3
7 . 6 6 3 6
1 4 . 9 8 8 5
8 . 1 8 4 4
9
8
8
7

.
.
.
.

1
3
8
1

3
7
2
2

2
9
1
0

9
1
7
7

8
8
7
9

. 6 2 9 3
. 7 7 6 1
. 5 3 4 4
. 8 3 9 1
• P R E D

• R E S I D
2 . 8 8 8 8
- 3 . 8 2 7 0
- 3 . 2 7 7 5
- . 1 3 2 5
3 . 3 0 9 6
2
- 3
2
-

. 5 2 0 0
. 4 168
. 4 8 3 0
. 5 9 0 3
. 7 6 3 5
. 5 6 8 4
- 1 . 1 7 0 3
- 2 .
1 0 .
2 .
- 3 .

2
3
5
6

8
7
4
8

2
0
1
5

6
8
7
7

- .
- .
- 1 .
1 .
- 6 .
- 3 .
- 3 .
- 3 .

8 0 6 6
0 4 5 5
0 9 5 6
0 7 3 1
2 5 1 5
9 6 9 3
1 192
9 9 5 0

4 . 8 6 7 3
3 . 3 0 2 7
7 . 4 4 9 5
1 . 3 3 5 8
. 4 6 5 9
1 7
- 1
2
-

.
.
.
.
.
.
.

4
2
6
5
5
1
9

8
6
2
1
9
3
6

5
9
6
1
7
0
2

2
6
5
0
3
6
3

- 5 . 0 4
. 4 7
- 1 . 4 4
- 1 . 6 2
• R E S

8
4
6
4
I

3
3
6
7
O

220

41

o o

C a s e

S t a n d a r d i z e d
M i s s i n g

O O

•

C a s e

o f
M:

27-Dec-90
12:45:35

MAR K E T O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALY S I S
• • • •

E q u a t i o n

N u m b e r

C a s e H l s e

P l o t

*:

S e l e c t e d

»

C a s e

Dependent Variable..

1

o f

S t a n d a r d i z e d

M:

M i s s i n g

1
2

C I T Y
A D R I A N
A L B I O N

3
4

A L L E N
P A R K
A L P E N A

5
6
7
8
9
10
11
12
13

A
B
B
B
B
B
B
B
B

14

C A D I L L A C

15
16
17
18
19
2 0
21
2 2

C
D
D
E
E
E
E
E

2 3
2 4

F A R M I N G T O N
F A R M I N G T O N

2
2
2
2
2

F
F
G
G
G

5
6
7
8
9

U
A
A
E
E
E
I
I
U

B
T
Y
N
R
V
G
R
R

U R N
T L E
C I
T O N
K L E
E R L
R A
M I N
T O N

L A W S
E A R B
E A R B
G R A
A S T
A S T
C O R S
S C A N

E
R
A
R
R

R
A
R
A
A

N
S
D
N
N

D
E
E
D
D

O
O
O
N
D
L
E
A

H
C
T Y
H
Y
Y
P I
G H

N
R
R
D
E
A

L S
R E E K
A R B O R
H L S
D S
A M

N
N

H
R A
T R O
N S I

3
3
3
3
3
3

H
H
H
H

3
4
5
6
7
8

3 9
4 0
C a s e
#

A
A
I
O

R
Z
G
L

P
E
H
L

T
P
I
N

S
I D S
T
G

Automotive Dealers

R e s i d u a l

E P 0 P 7
1 0 . 7 7

• P R E D
9 . 7 0 4 2

8 . 7 0
4 . 8 2
8 . 8 6

1 0 . 0 1 8 6
3 . 9 5 1 8
1 0 . 2 8 1 5

3 . 7 5
3 . 1 4
7 . 3 0
9 . 8 9
5 . 1 5
. 0 0
6 . 6 2
3 . 4 1

4 . 9 6
4 . 0 5
2 . 2 6
. 0 0
5 . 6 9
. 6 3
3 . 8 3
12. 2 3

H L S

A L E
R
N
C I T Y
H A V E N
V I L L E

G R O S S E
P T
G R O S S E
P T
H A M T R A M C K

EP0P7

R E G R E S S I O N

6 . 8 0
1 5 . 1 7

B A

3 0
31
3 2

M U L T I P L E

P K
W D S

E R
W O O D S
L
P A R K
L A N D
P A R K
A N D

INKSTER
J A C K S O N
K A L A M A Z O O
K E N T W O O D
C I T Y
C I T Y

6 . 9 2
3 . 8 3
4 . 3 9
3
4
1 2
1 0
2

. 6 2
. 3 0
. 1 5
. 7 0
. 1 1
. 0 0
3 . 2 1

1 . 3 6
4 . 9 5
3 . 1 2
1 0 . 0 2
2. 19
6 . 2 2
3 . 6 3
3 . 6 4
E P 0 P 7

3 . 6 3 7 6
4 . 4 8 2 5
7 . 4 9 2 4
4 . 9 9 5 6
9
2
5
9
4

.
.
.
.
.

4
0
1
4
6

8
8
1
0
5

2
1
2
3
2

6
7
3
1
1

3
3
2
4

.
.
.
.

1
8
3
6

0
5
8
6

7
4
1
1

5
0
2
4

4 . 6 0 2 6
5 . 5 2 6 6
9 . 6 6 3 0
3 . 6 0 B 7
3 . 0 3 0 1
4
4
4
4
4

.
.
.
.
.

7
9
6
6
8

6
2
9
7
6

1
5
2
7
4

2
9
8
3
2

2 . 5 1 3 0
2. 1 2 7 2
5 . 1 8 1 4
4 . 3 4 2 6
5 . 4 5 6 9
5 . 7 2 5 7
8 . 8 2 1 5
5 . 1 4 9 0
8 . 7 0 5
3 . 2 9 6
4 . 4 8 1
• P R E

0
8
1
D

• R E S I D
1 . 0 6 9 5
-1

. 3 1 4 5
. 8 6 6 7
- 1 . 4 2 4 1
- . 4 9 4 2
2 . 8 2 2 3
2 . 3 9 4 6
. 1 5 6 1
- 2 ! 8 6 0 1
1 . 3 2 6 3
1 . 6 6 5 8
5 . 7 6 2 7
. 3 1 2
. 9 4 2
- 1 . 5 9 0
- 2 . 3 8 1
1 . 0 3 3
- 3 . 7 7 1

5
5
4
2
4
4

- 1 . 6 9 5 2
2 . 5 6 7 2
3 . 3 0 8 3
. 7 9 6 1
- . 3 7 3 5
- 1 . 3 1 0 6
- . 3 8 9 1
7 . 4 6 8 5
5 . 8 3 4 8
- . 4 0 4 7
- 2 . 1 2 7 2
- 1 . 9 7 1 1
- 2 . 9 8 3 0
- . 5 0 6 4
- 2 . 6 0
1 . 1 9
- 2 . 9 5
- 2 . 4 8
. 3 2
- . 8 4
• R E S

3
9
4
3
8
4
I

2
2
6
7
7
7
D

C a s e e l s e

MARKET O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

o f

S t a n d a r d i z e d

M:

M i s s i n g
-

7 7
7 8
7 9

C a s e

3.0
0-

.

•
•
•
•

5 . 2 9
8 . 2 5
7 . 7 7
5 . 0 3
7 . 7 9
1 0 . 2 7
1 0 . 4 0
5 . 0 7
2 . 1 4
1 . 9 3
9 . 7 3

•
•
•

.•

•

•

•

•'
•

•

5 . 6 3
8 . 0 0

'•

it
•
•

4 . 9 5
. 8 8
2 . 8 6
2 . 4 2
1 . 6 6
4 . 2 5
3 . 4 7

•
*

‘

*

•

*•

•

"•
•
•

•
•
•

"•
•)

0

-3.0

E P 0 P 7
3 . 9 7
4 . 7 7
7 .0 2
1 . 7 9

•

woodhaven

WYANOOTTE
WYOMING
YPSILANTI
8 0
# CITY

0.0

3 . 3 1
6 . 4 5
3 . 5 7
6 . 2 5
4 . 0 2
4 . 4 2
1 8 . 3 4
2 . 3 6
4 . 1 6
6 . 7 B
9 . 0 5
2
3
5
7
3
E P

.
.
.
.
.
0

2
5
1
2
8
P

2
8
0
1
9
7

• P R E D
4 . 2 3 1 7
4 . 6 2 1 4
8
5
4
9
9
9

.
.
.
.
.
.

9
1
1
0
3
0

6
2
2
8
7
4

7
2
5
5
4
4

5
8
2
9
8
3

4 .
6 .
9 .
4 .
4 .
4 .
1 0 .
4 .
9 .

5
1
0
4
1
3
0
4
0

9
8
7
9
5
6
4
9
4

0
2
2
9
7
8
3
4
9

0
2
9
3
8
1
4
9
1

4
5
4
3
5
4
3
4

.
.
.
.
.
.
.
.

3
5
8
8
6
5
5
3

1
9
1
0
7
5
7
1

8
1
8
7
6
0
6
4

2
0
3
4
2
5
5
2

9
2
4
3
4
9
4
2

.
.
.
.
.
.
.
.

6
8
5
6
3
1
4
9

5
4
2
0
3
9
2
0

7
9
2
9
8
5
0
4

2
7
3
6
6
7
5
7

5
5
3
5
4
4

.
.
.
.
.
.

0 8 7 9
0 7 7 0
8 2 7 8
1 3 3 6
3 2 3 3
1 8 4 4

4 . 6 0 B B
• P R E D

• R E S I D
- . 2 6 4 3
. 1 4 9 0
-1 . 9
- 3 . 3
1 . 1
- . 8

4
2
6
4

8
9
8
0

3
0
8
4

- 1 . 6 0
- 4 . 0 1
3 . 1 9
4 . 0 8
1 . 3 2
. 5 6
- 2 . 0 1

2
9
7
4
7
7
4

8
2
0
7
1
5
2

- 2 . 4 4 0 1
- . 3 1 5 8
1 . 1 3 6 5
- 1 . 0 5 3 9
. 6 2 8 7
-

4
1
1
4
-

. 7 1
. 9 6
. 3 9
. 0 1
. 3 0
. 1 0

0
1
1
6
2
1

0
1
5
4
5
7

- 1 . 0 0 2
- 3 . 2 1 0
. 7 1 6
1 . 7 2 9
. 4 1 2
. 0 7 8
9. 1 4 7
- 2 . 0 5 8
1 . 2 5 7

5
2
4
7
6
9
2
7
7

1 . 6 9
3 . 9 7
- 1 . 6 1
- 1 . 5 5
. 7 8
3 . 0 2

8
6
8
6
4
9

3
0
0
2
0
4

- . 7 1 7 8
• R E S I D

222

Case « CITY
41
LINCOLN PK
MADISON HTS
4 2
4 3
MARQUETTE
4 4
MELVINDALE
MIOLANO
4 5
4 6
MONROE
4 7
MT CLEMENS
MT PLEASANT
4 8
4 9
MUSKEGON
MUSKEGON HTS
5 0
NILES
51
5 2
NORTON SHORES
NOVI
5 3
5 4
OAK PARK
OWOSSO
5 5
5 6
PONTIAC
5 7
PORT HURON
PORTAGE
5 8
RIVER ROUGE
5 9
RIVERVIEW
6 0
61
ROCHESTER HLS
6 2
ROMULUS
ROSEVILLE
6 3
6 4
ROYAL OAK
SAGINAW
6 5
SAULT STE MARIE
6 6
6 7
SOUTHFIELD
SOUTHGATE
6 8
6 9
ST CLAIR SHORES
TAYLOR
7 0
71
TRAVERSE CITY
TRENTON
7 2
TROY
7 3
7 4
WALKER
WAYNE
7 5
WESTLAND
7 6

R e s i d u a l

O O

27-Dec-90
12:45:35

M A R K E T OPPOR T U N I T Y IDENTIFICATION MODEL
P R ELIMINARY ANALYSIS

27-Dec-90
12:45:39

• • * *
E q u a t i o n

N u m b e r

C a s e w l s e

P l o t

*:

S e l e c t e d

3 3

34
35
36
37
38
39
40
Case #

Dependent Variable..

o f

S t a n d a r d i z e d

M :

M i s s i n g

CITY
ADRIAN
ALBION
ALLEN PARK
ALPENA
AUBURN HLS
BATTLE CREEK
BAY CITY
BENTON HARBOR
BERKLEY
BEVERLY HLS
BIG RAPIDS
BIRMINGHAM
BURTON
CADILLAC
CLAWSON
DEARBORN
DEARBORN HTS
E GRAND RAPIDS
EAST DETROIT
EAST LANSING
ECORSE
ESCANABA
FARMINGTON
FARMINGTON HLS
FERNDALE
FRASER
GARDEN CITY
GRAND HAVEN
GRANDVILLE
GROSSE PT PK
GROSSE PT WDS
HAMTRAMCK
HARPER WOODS
HAZEL PARK
HIGHLAND PARK
HOLLAND
INKSTER
JACKSON
KALAMAZOO
KENTWOOD CITY
CITY

• • • •

Gasoline Service Stations

EP0P9

R e s i d u a l

- 3 . 0
0 :

0.

__

0

3.0

......... :0

•
•
•

•
•
•
•

•
•

•

•
•

•
•

•
•

•
•

•

•

•

•
•

•|

•

•

•
•

•
•

•
•
•

•
0 :

R E G R E S S I O N

__

-3.0

0.0

3.0

EP0P9
8.81
7 .74
5 .46
15.06
5.63
4.25
4.03
10.59
6.87
.00
9.57
7.30
3.74
12.32
5.67
9.72
4.69
.00
4.56
2. OB
3.07
12.95
14.82
4.59
1 .99
7 .95
3.38
8.91
6.42
1 .41
4.26
1 .61
2.72
4 .46
2.73
B .64
1 .88
7 .84
4.01
3.64
EP0P9

•PRED
10.5473
9.9947
4.8610
10.8263

•RESID
-1 .7324
-2.2577
.6000
4.2313

2.8184
3.4829
6.0156
6.1700
9.2728
4.3623
5.1637
9.5668
5.8723
4.2039
4.930B
4.1068
5.2B94
4.7066
4.8152
10.1810
5.4586
5.7581
4.8567
5.5399
5.3365
4.5258
5.8062
4.1605
4. 3 164
4.4368
5.2561
5.6227
4.5665
10.0839
4.8782
8.7772
2.8392
5.3904
•PRED

1.4346
.5474
4.5776
.6989
. 2931
2.9406
-1 .4248
2.7555
-.1985
5.5161
-.2420
-4.1068
-.7.336
-2.6284
-1.7501
2.7686
9.3635
-1.1667
-2.8623
2.4138
-1 .9550
4.3811
.6132
-2.7551
-.0585
-2.8317
-2.5368
-1.1673
-1 .8342
-1.4454
-2.9974
-.9330
1.1748
-1.7540
•RESID

223

Case #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32

1

M U L T I P L E

27-0ec-90
12:45:40

C a s e w l s e
*:

MAR K E T O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

o f

S t a n d a r d i z e d

M:

M i s s i n g

R e s i d u a l

- 3 . 0
C a s e

#
41

C I T Y
L I N C O L N

P K

4 2

M A D I S O N

H T S

4 3

M A R Q U E T T E

4 4
4 5

M E L V I N D A L E
M I D L A N D

4
4
4
4
5

M
M
M
M
M

6
7
8
9
0

N I L E S

5 2
5 3
5 4

N O R T O N
S H O R E S
N O V I
O A K
P A R K
O
A C
H U R
G E
R O
V I E
S T E
U S
I L L
O A

O N
U G E
W
R
H L S
E
K

6 5

S A G I N A W

6 6
6 7

S A U L T
S T E
M A R I E
S O U T H F I E L D

6 8
6 9

S O U T H G A T E
S T
C L A I R
S H O R E S

7
7
7
7
7
7
7
7

T
T
T
T
W
W
W
W

0
1
2
3
4
5
6
7

7 8
7 9
8 0
#

A
R
R
R
A
A
E
O

Y
A
E
O
L
Y
S
O

L
V
N
Y
K
N
T
D

O R
E R S E
T O N

C I T Y

E R
E
L A N D
H A V E N

W Y A N D O T T E
W Y O M I N G
Y P S I L A N T I
C I T Y

-3.0

0.0

3.0

EP0P9
6.53
6.86
5.62
6.28
6.69
9.62
8.81
5.94
5.78
4.11
7 .20
4.15
3.22
8 .68
7 .78
3.94
6.22
5.44
1 .76
5.00
1.61
9.54
4.83
3.47
3.59
7.88
6.99
5.59
4.85
4.69
24.67
4.25
4.46
6.17
2.86
3.33
5.37
3.83
5.61
3.46
EP0P9

•PRED
4.1447
5.4640
8.B699
5.3567
5.3836
10.0617
10.6037
8.9890
3.2072
4.6164
9.2115
4.5004
6.2291
4.8550
10.9573
4. 1422
9.2B19
5.7079
4.7999
5.9566
5.9326
5.8265
4.6879
5.1357
3.4607
9.5335
5.1709
5. 1882
4.69 15
4.6544
9.9021
5.4089
5. 1537
5.6988
5.2987
4.6490
5.9428
4.0657
4.6180
4.2390
•PRED

•RESID
2.3897
1.3934
-3.2545
.9214
1.3035
-.4419
-1.7954
-3.0502
2.5703
-.5096
-2.0115
-.3549
-3.0137
3.8211
-3.1752
-.2003
-3.0633
-.2664
-3.0378
-.9566
-4.3220
3.7171
.1393
-1.6609
. 1270
-1.6539
1.8241
.4057
.1629
.0392
14.7659
-1.1576
-.694 1
.4664
-2.4416
-1 .3235
-.5713
-.2380
.9892
-.7803
•RESID

224

5
O W O S S
6
P O N T I
7
P O R T
8 * P O R T A
9
R I V E R
0
R I V E R
1
R O C H E
2
R O M U L
3
R O S E V
4
R O Y A L

3 . 0

E N S
S A N T
N
N
H T S

51

5
5
5
5
5
6
6
6
6
6

C a s e

O N R O E
T
C L E M
T
P L E A
U S K E G O
U S K E G O

0 . 0

27-0ec-90
12:45:44

MAR K E T O P P O R T U N I T Y IDENTIFICATION MODEL
P R ELIMINARY ANALYSIS

••••
E q u a t i o n

N u m b e r

C a s e w i s e

P l o t

*:

S e l e c t e d

0

1
2
3
4
5

6
7
8

18

S t a n d a r d i z e d

M:

M i s s i n g

C I T Y
A D R I A N

0:

B
B
B
B

E
I
I
U

V E R L Y
H L S
G
R A P I D S
R M I N G H A M
R T O N

C
C
D
D
E

A
L
E
E

D
A
A
A
G

34
35
3 6
37
3 8
3 9

40

R
R
A
A
A
I
O
N
A

O
O
M
R
Z
G
L
K
C

S
S
T
P
E
H
L
S
K

S
N
I
I
D
E
E

S
S
R
E
L
L
A
T
S

•

•
•
•
•
•

E
A
N
N
A
R
N

•

• |

•
•

C I T Y

T
T
K
O O
R K
P

•
•
•

P K
W D S

•
•

D S
•
• |
• |

A R K

K A L A M A Z O O
K E N T W O O D
C I T Y
C I T Y

•
•

0 . 0

3.

• R E S I D
- 7 . 2 4 6 6
- 8 . 8 8 8 4

6 . 2 5 3 8
5 . 9 3 6 8
4 . 4 4 0 6

1 5 . 3 3 3 5

9 . 6 5 8 0

- 3 . 9 3 3 9

2
2
8
4
4
6
6
5
5
4
7

5
6
8
5

#

11
1 7
8
1 3
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9
9

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5 ! 0 2 5 7
1 . 8 7 1 7

2'.
1 8 .
3 .
5 .
- 5 .
8 .
- 7 .

2
4
7
3
8
1
2

6
2
4
0
9
9
2

5
9
9
4
7
2
2

7
7
0
2
8
3
7

1 6 . 5 4 3 5
9 . 0 7 5 6

- 3 . 7 9 9 2
- 4 . 8 0 4 6

6. 1 8 3 3
6 . 4 8 1 0

- 2 . 2 3 4 8
- 4 . 9 4 8 4

1 4 . 4
1 4 . 0
1 4 . 9
7 . 3

3
3
6
1

3
7
5
8

2
2
5
0

9. 1 17 0
9 . 0 4 1 0

2 . 8 3
6 . 7 1
- 9 . 6 0
. 2 6
- 7 . 6 7

3
3
8
0
0

0
8
9
8
9

2 . 4 6
1 7 . 8 1
1 3 . 5 5
. 7 0
7 . 3 0

7
9
1 5
1 7

.
.
.
.

8
6
9
9

7
7
7
1

6
2
3
4

5
0
7
4

1 3 . 3 8
4 5 . 5 5
. 9 9
3 . 1 2
1 3 . 1 3
. 9 4
5 . 4 1

6
1 0
6
5
1 5
6
1 3

.
.
.
.
.
.
.

0
5
8
2
7
9
2

4
6
5
8
4
4
2

3
7
5
5
6
6
4

3
6
3
8
0
0
4

- 6 . 0 0 5 6
- 7 . 8 1 4 6

5 . 4 8 8 6
9 . 3 4 6 2
• P R E D

- . 0 5 0 3
1 3 . 3 1 1 1
• R E S I D

5 . 4 4
0 : ------ 3 . 0

D
0
8
4

- 8 . 1 4 2 5
7 . 4 2 5 4

2 0 . 7
5 . 3
7 . 5
1 . 4

*

E
2
6
2

1 4 . 7 1 8 1

1 . 5 3
1 7 . 2 7
•

•

H L S

R
8
5
1

14

3 . 9 5
•

A
T O N
T O N
E

• P
1 5 . 0
1 2 . 7
1 0 . 7

. 0 0
1 1 . 2 8
7 . 8 1
1 9 . 7 7

1
3
1
1

•

*

E
P
E
P
A M C
R
W
P A
A N D
N D
E R
O N

E P O P I 1
7 . 8 4

5 . 7
. 9
3 . 9
5 . 5
2 . 2
8 . 9
4 . 2
1 8 . 0
2 . 7
1 2 . 7
4 . 2

\•

D E T R O I T
L A N S I N G
B
G
G
L

)

o o

t

G
G
H
H
H
H
H
I
J

R
A
M
M
N
S
D

Accessory Stores

2 2 .

•

.•

A C
N
R N
R N
H T S
D
R A P I D S

G R A N D
H A V E N
G R A N D V I L L E

O
C
R
R
R
A
R

Apparel

3 . 8 7
2 . 5 7

U R N
H L S
T L E
C R E E K
C I T Y
T O N
H A R B O R
K L E Y

2 8
2 9

C
S
A
A
E
R
A

L

•

B
T
Y
N
R

E A S T
E A S T

P

•

U
A
A
E
E

L
O
O
O
N

I

•

A
B
B
B
B

L
S
B
B
A

T

3.

. . .

A L P E N A

I
W
R
R
R

L

R e s i d u a l

A L B I O N
A L L E N
P A R K

E
E
F
F
F
F
G

3 3

MU

E P 0 P 1 1

•

19
20
21
22
23
24
25
26
27
30
31
32

C a s e

o f

V a r i a b l e . .

2 2 . 6 6
E P O P I 1

- 6 . 5 8 1 8
9 . 9 3 7 3
3 . 8 8 0 1
- 1 5 . 2 7 0 9
- 1 0 . 6 1 5 1
3
-

7
4
5
2
2

.
.
.
.
.

3
9
8
1
6

3
7
6
6
1

2
9
5
3
5

8
7
2
2
4

225

9
10
1 1
12
13
14
15
16
17

D v p i n d e n t

o o

C a s e

1

27-Dec-90
12:45:44

C a s a w l s e
*:

MARKET O P P O R T U N I T Y I D E NTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

S t a n d a r d i z e d
M i s s i n g

- 3 . 0
0-

0 . 0

E P O P I 1
6 . 7 7

41

•

3 . 8
1 6 . 8
. 9
1 3 . 3
1 0 . 5

•
•
•
•
•

1 0 . 8 8
13. 2 5
9 . 8 0
5 . 4 8
8 . 0 0
2 . 7 6
41 . 0 9
9 . 0 0
1 2 . 9 7
4 . 5 1
9 . 1 8
1 6 . 8 2
2 . 6 4
4 . 2 9
3. 2 2

•

»
•
•
•

•

•
•

•

•

•
•

. 0
1 4 . 2
2 . 2
5 . 5

•

•
•

1 5 . 0 4

3 . 0 5
8 . 4 2

•

•

4 0
3
1 7
4

•
•
*
•
•
•
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0 : . .
- 3 . 0

0

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7
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2 8 . 3 9
7 . 2 4

•

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8
5
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7
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3
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3

8
1
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2

2 . 8 6
8. 2 5
5 . 3 7
4 . 1 5
4 . 0 1
2 . 1 6
E P 0 P 1 1

• P R E D
7 . 5 1 4 6
9 . 0 0 8 4
1 2 . 7 1 9 0
8 . 8 7 7 5
1 1 . 0 7 4 0
1 5 . 5
1 6 . 2
1 2 . 0
4 . 2

9
5
6
5

6 2
7 1
3 2
1 B

• R E S I D
- . 7 4 6 8
- 5 . 1 3 2 5
4. 1 2 71
- 7 . 9 8 0 7
2 .
- 5 .
- 5 .
1 .

3
0
3
1

0
6
7
8

0
0
6
4

2
2
3
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4
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8
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8
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5
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6
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.
.
.
.
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3 6 0 6
9 6 6 4
9 1 5 5
1 8 7 9
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3 6 9 6
0 5 0 1
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7 7 0 1

6 . 2 7 0 1
- 3 . 4 7 8 3
- 6 . 4 8 4 3

1 2
7
7
1 0
4
1 3
1 3

.
.
.
.
.
.
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3
6
4
3
8
2
3

3
0
4
5
6
4
9

- 9 . 1 3 2 0
- 7 . 6 1 2 0
6 . 8 2 0 1
- 8 . 0 8 9 3
. 6 5 5 9
1 . 7 9 0 5
1 5 . 0 6 3 3

9 . 3 0 9 6

- 2 . 0 7 0 3
- 6 . 6 8 3 7

5
1
6
5
6
5
2

3
2
8
5
3
2
7

9
7
1 4
1 0
1 3
8
8

.
.
.
.
.
.
.

7
1
8
6
2
6
2

3
0
2
1
2
6
7

5 0
0 1
7 3
5 6
8 3
3 3
6 3

8
1 0
7
7

.
.
.
.

5
0
1
3

0
5
8
0

1
4
4
2

2
2
3
9

6 . 1 2 3 6
• P R E D

1 . 1 1 5 1
- 5 . 9 6 6 4
- 6 . 1 5 1 8
2 7 . 8 9 8 2
. 7 8 2 3
- 2 . 7 4 5 9
- . 8 6 4 4
- 4 . 8 7 0 4

1 . 3 2 0 6
2 5 . 6 5 3 4
- 7 . 3 0 9 1
4 . 7 5 8 9
- 4 . 3 4 7 7
- 5
- 4
- 3

.
.
.
.

4
2
6
0

1
4
8
3

9
9
2
7

2
0
7
6

- 3 . 2 9 7 8
- 3 . 9 6 1 9
• R E S I D

226

CITY
LINCOLN PK
4 2
MADISON HTS
4 3
MAROUETTE
4 4
MELVINDALE
4 5
MIOLAND
MONROE
4 6
4 7
MT CLEMENS
MT PLEASANT
4 B
4 9
MUSKEGON
MUSKEGON HTS
5 0
51
NILES
NORTON SHORES
5 2
5 3
NOVI
5 4
OAK PARK
5 5
OWOSSO
5 6
PONTIAC
5 7
PORT HURON
5 8
PORTAGE
5 9
RIVER ROUGE
RIVERVIEW
6 0
61
ROCHESTER HLS
6 2
ROMULUS
6 3
ROSEVILLE
6 4
ROYAL OAK
SAGINAW
6 5
SAULT STE MARIE
6 6
6 7
SOUTHFIELD
SOUTHGATE
6 8
6 9
ST CLAIR SHORES
7 0
TAYLOR
71
TRAVERSE CITY
TRENTON
7 2
7 3
TROY
7 4
WALKER
7 5
WAYNE
WESTLAND
7 6
7 7
WOODHAVEN
7 8
WYANDOTTE
7 9
WYOMING
VPSILANTI
8 0
Case # CITY
*

R e s i d u a l

O O

Case

o f
M:

27-Dec-90
12:45:48

MAR K E T OPPOR T U N I T Y I D E N TIFICATION MODEL
PRELIMINARY ANALYSIS
• • • •

E q u a t i o n

N u m b e r

C a s e x l s e

P l o t

*:

S e l e c t e d

o f

S t a n d a r d i z e d

M:

M i s s i n g

EPOP13

2
3
4

A L B I O N
A L L E N
P A R K
A L P E N A

5
6
7

A U B U R N
H L S
B A T T L E
C R E E K
B A Y
C I T Y

8
9
10

B E N T O N
H A R B O R
B E R K L E Y
B E V E R L Y
H L S

11
12

B I G
R A P I D S
B I R M I N G H A M

13
14
15
16
17

B
C
C
D
O

U
A
L
E
E

R
D
A
A
A

T
I
W
R
R

O
L
S
B
B

N
L
O
O
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A C
N
R N
R N

E
G R A N D
R A P I D S
E A S T
D E T R O I T
E A S T
L A N S I N G
E C O R S E
E S C A N A B A

2 3

F A R M I N G T O N

2 4

F A R M I N G T O N

2
2
2
2
2

F
F
G
G
G

E
R
A
R
R

R
A
R
A
A

N
S
D
N
N

D
E
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D
D

G R O S S E
P T
G R O S S E
P T
H A M T R A M C K

3
3
3
3
3
3
3
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H A
H A
H I
H O
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J A
K A
K E
C I

3
4
5
6
7
8
9
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31
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6
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P K
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P A R K
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5.SB
5.80
4.50
15.94
3.13
5. 18
B.31
4 .94
4.58
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8.09
22.40
5.78
8.53
5.67
6.48
3.07
16.14
4.56
2.70
.77
12.95
12.85
4.90
5.58
1 .45
3.07
14.57
8.56
.70
6.08
5.35
6.12
1 .98
2.34
12.79
.63
8.39
4.66
12.59
EPOPI3

•PRED
10.0877
9.4130
6.4058
9.4346

•RESID
-4.2111
-3.6103
-1.90B6
6.5087

5.0246
4.2434
.3631
5.0724

. 1530
4.0690
4.5804
-.4932

9.5559
11.7738
4.3490
10.4608
5.8608
5.3419
5. 1385
11.3768
4.8113
3.3844
3.7608
10.1314
8.8321
7.8083
4.7027
5.1519
4.7201
5.2866
5. 1963
10.8999
1 1.9377
3.8948
6.3187
3.6354
2.7838
11.1825
3.6580
10.5416
4.3152
4.9134
•PRED

-1.4617
10.6216
1 .4294
-1.9300
-. 1871
1.1381
-2.0665
4.7659
-.2555
-.6828
-2.9945
2.8182
4.0138
-2.9109
.8817
-3.7057
-1.6460
9.2BB3
3.3629
-10.1972
-5.8550
1.4557
-.2004
-1.6552
-.4419
1.6026
-3.0311
-2.1564
.3462
7.6740
•RESID

227

C a s a

C I T Y
A D R I A N

• • • •

Furniture and Home Furnishing Stores

0.0

0:

#
1

R E G R E S S I O N

R e s i d u a l

- 3 . 0
C a s e

M U L T I P L E

Dependant Variable..

1

27-Dec-90
12:45:46

C a s e n l s e
*:

MARKET O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

o f

S t a n d a r d i z e d

M:

M i s s i n g

R e s i d u a l

O

C I T Y

1
2
3
4
5

L I N C O L N
P K
M A D I S O N
H T S
M A R Q U E T T E

4
4
4
4

6
7
8
9

6 7
6 8
6 9
7 0
71
7 2
7 3
7 4
7
7
7
7
7

5
6
7
8
9

8 0

9

•
•
•

E L V I N D A L E
I D L A N D
O N R O E
T
C L E M E N S
T
P L E A S A N T

M U S K E G O N
M U S K E G O N

[
•
•
•

I
O
O
A

L E S
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S H O R E S
V I
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O
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P
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R
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8
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6
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7
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.
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6
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7
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3
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8
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5 . 1 5 4 9
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4
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3

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.
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3
4
4
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6
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0
9
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• P R E D

- . 7 2 8 1
- . 2 2 8 5
- 3 . 0 0 7 4
- . 4 7 0 0
- 4 . 2 4 6 6
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1 . 1 3 8 0
- 1 . 0 6 5 5
• R E S I D

228

6 5
6 6

.

. , .

e . s . . .

5 0
51
5 2
5 3
5 4
5 5
5 6
5 7
5 8
5 9
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61
6 2
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6 4

M
M
M
M
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9
4
4
4
4
4

C a s e

27-Dec-90
12:45:52

M AR K E T O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS
•

E q u a t i o n

N u m b e r

C a s e w l s e

P l o t

*:

S e l e c t e d

C a s e

#
1
2

D e p e n d e n t

o f

S t a n d a r d i z e d

M:

M i s s i n g

N
R
V
G
R
R

T O N
H A R B O R
K L E Y
E R L Y
H L S
R A P I D S
M I N G H A M
T O N

C
C
D
D

A
L
E
E

D
A
A
A

I
W
R
R

F A R M I N G T O N
F A R M I N G T O N

2 5
2 6
27

F
F
G
G
G

E
R
A
R
R

R
A
R
A
A

N
S
D
N
N

O
E
E
D
D

A L E
R
N
C I T Y
H A V E N
V I L L E

G
G
H
H

R
R
A
A

O
O
M
R

S
S
T
P

S
S
R
E

E
P
E
P
A M C
R
W

3
3
3
3
3
4

5
6
7
8
9
0

0

P

L

E

•
•
•
•
•

R

E

S

S

I

O

N

•
•
•
•
*

H L S
•
•
•
•
•
•

T
P K
T
W D S
K
O O D S

•
•
•
•

H A Z E L
P A R K
H I G H L A N D
P A R K

•
•

H O L L A N D
I N K S T E R
J A C K S O N

•
•
•

K A L A M A Z O O
•

.

-3.0

.83

• P R E D
3 2 . 6 3 1 7

• R E S I D
- . 8 0 0 2

21 .28
2 8 . 9 1
3 7 . 2 0

3 3 . 2 5 5 9
1 8 . 8 0 0 1
3 2 . 3 8 1 1

- 1 1 . 9 7 9 3
1 0 . 1 1 0 9
4 . 8 2 0 0

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1 9 .
5 .
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1 2 .

•
•

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1 5 . 0 0
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2 4 . 4 3
31 .7 8
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5 . 5 1
2 0 . 6 0
2 3 . 8 6
1 5 . 3 0
3 2 . 2 3
1 9 . 8 6

•

C I T Y

E

31

•

E
G R A N D
R A P I D S
E A S T
D E T R O I T
E A S T
L A N S I N G

K E N T W O O D
C I T Y

R

E P O P I 5

•

2 3
2 4

3 2
3 3
3 4

I

Eating and Drinking Places

R e s i d u a l

H T S

E C O R S E
E S C A N A B A

3 0
31

T

•

A C
N
R N
R N

18
19
2 0
21
2 2

28
2 9

L

0.0

0
3 . 0

7
0
9
2
0

6
6
5
3
5

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3 2 . 6 0 8 4
2 1 . 5 1 8 3
1
3
2
1

8
3
0
6

.
.
.
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9
0
0
6

7
4
0
6

5
4
5
3

5
5
5
1

1
2
1
1

7
0
8
7

.
.
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3
8
7
6

0
3
0
1

7
5
8
7

6
5
8
7

2 6 . 8 2
3 8 . 8 5
3 8 . 5 4

1 9 . 4 8 8 0
3 2 . 5 7 8 2
2 0 . 9 1 9 0

1 8 . 2 1
1 5 . 9 6
2 2 . 4 2
1 5 . 0 6
41 . 3 0
1 7 . 1 2
1 7 . 5 7
1 4 . 6 0
2 0 . 8 7
21 .75
1 8 . 8 1
6. 2 5
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1
2
1
1
1
1

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0
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6
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2 9 . 4 8
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1 4 . 2 7
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6
0
1
1
3
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1
8
8
5
0
4

1 9 . 7 3 7 0
19. 1 1 2 8
3 2 . 6 2 8 1
I B . 5 7 6 3
3 2 . 7 3 7 7
1 7 . 6 8 8 2
1 8 . 0 5 2 3
• P R E D

5 . 5 2 2 5
1 3 . 2 6 5 4
- 4 . 1 9 2 9
- 1 2 . 0 0 5 0
2 . 3 3 7 6
- 3 . 6 7 9 8
- . 8 1 7 0
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8 . 0 9 9 7
1 . 7 7 0 8
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- 5 . 5 6 4 5
7 . 3 3 1 9
6 . 2 7 0 7
1 7
- 4
2

.
.
.
.

6
1
1
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1
4
0
9

8
3
5
3

5
7
5
3

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2 1 . 7 4 2 5
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- 3 . 0 4 2 6
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1 . 5 1 1 8
2 . 5 4 9 6
- . 9 2 5 1
- 1 2 . 8 6 7 7
- 3 . 6 0 2 5
- 1 0 . 7 3 9 3
- 3 . 2 5 4 3
3 . 4 1 7 6
- 3 . 7 8 6 6
• R E S I D

229

E
E
E
I
I
U

L
O
O
O

U

•

B
B
B
B
B
B

L
S
B
B

M

E P 0 P 1 5

•

B A T T L E
C R E E K
B A Y
C I T Y

IS
16
17

•

•

6
7

12
13
14

•

•

5

8
9
10
1 1

•

V a r i a b l e . .

- 3 . 0
0-

C I T Y
A D R I A N
A L B I O N
A L L E N
P A R K
A L P E N A
A U B U R N
H L S

3
4

C a s e

1

27-Dec-90
12:45:52

C a s e i ) 1 s e
•:

MARKET O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

*

C a s e
4
4
4
4

1
2
3
4

4 5
4 6
4 7
4 8
4 9
5 0
5
5
5
5

1
2
3
4

S t a n d a r d i z e d

M :

M i s s i n g

R
V
V
C

T
E
E
H

A G E
R
R O U G E
R V 1 E W
E S T E R
H L S

R
R
R
S

O
O
O
A

M
S
Y
G

U
E
A
I

L U S
V I L L E
L
O A K
N A W

•

3 8 . 3 4
2 9 . 2 4

•

S
O N

S H O R E S
•
•

P A R K
S O

•
•
•

T
C L
A Y L O
R A V E
R E N T

T
W
W
W
W
W
W
Y
C

R
A
A
E
O
Y
Y
P
I

O
L
Y
S
O
A
O
S
T

V
K
N
T
D
N
M
I
Y

A I R
R
R S E
O N

6
8
0
8
0
0
6
0

1
8
3
2
2
2
6
4

1 8 . 5 5 0 4
1 9 . 0 1 8 2

20.00

9
5
B
7
4
8
8
5
8

3
3
0
3
3
5
7
1
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5
5
4
7
9
6
1
9
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1 4 . 9
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1
1
1
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.
.
.
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6
3
2
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0
3
6
9

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3
8
5
2
0
2

5
6
0
9
5
4

1 4 . 9 8

•
•
•
•

S H O R E S
•
C I T Y
•

R
A
A
O
N
A

1
0
6
0
3
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9
9

.
.
.
.
.
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.
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2
5
9
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5
6
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3
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6
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1
6
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2
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3
9
6
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. 3 0 8 8
. 4 4 5 8
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1 . 1
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- 3 . 4
- . 6
- 5 . 5

7
4
0
8
0

9
7
1
9
4

3
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6
2
2

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1 3 .
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1 .

1 5 . 0 5
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1 4 . 6 4

•

1 7 . 0 1

0.0

7
9
7
4

- 1 . 2 3 9 2

1 8 . 5 8
2 4 . 6 4
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3
5
2
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2 0 . 10

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.
.
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3 2 . 7 4 6 8
18. 1 8 2 8
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1 6 . 6 5
1 7 . 6 2
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T I

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- 1
2
4

1 6 . 1 4

•
*
•

D
E N
T E

8
8
8
8

9
2
9
6
6
8
2
8
7

• R E S I D
-1 . 7 1 3 3
3 . 2 5 0 7

3 4 . 3 8
31 . 5 5
1 6 . 4 5
1 8 . 5 9

20.66
E
E
L
H
D
I
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5
4
8
3
4
0
3
4

1 8
1 9
1 9
3 2
1 8
3 2
1 7
1 9
1 8

1 9
1 7
1 3
2 5
1 6
2 7

21 .02
•

S A U L T
S T E
M A R I E
S O U T H F I E L D
S O U T H G A T E
S
T
T
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12

•

.
.
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1 7 .
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4
7
0
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4
9
1
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8
8
2
4

9
0
2
4

1 8 . 4 2 5 8
1 8 . 6 7 4 3
1
1
1
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8
6
8
9
7
9

.
.
.
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4
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1
9
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R

6 3
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1 1
7 3
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6
3
0
1

3
6
3
3

7
2
0
6

2
9
3
4

-1 . 7 5 1 1
2 3 . 2 8 1 3
- 4 . 2 1 1 0
2 . 2
- 2 . 0
- 1 . 1
- 2 . 4

3 7 2
2 8 2
9 7 3
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- 1
1
1
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5
8
0
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S

.
.
.
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9
0
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7
1
2
6
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3
3
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D

230

O
I
I
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7 8
7 9
8 0

1 7 . 8 3
3 5 . 2 7

1 8
1 9
3 2
1 9
1 8
3 3
3 3
3 2

3 3 . 5 5 1 8

P
R
R
R

7 5
7 6
7 7

L E
R T
V I
K
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3 2 . 7 6
1 7 . 9 4

2 5 .

5 8
5 9

6 9
7 0
71
7 2
7 3
7 4

•

H T S

P O N T I A C
P O R T
H U R O N

5
6
7
8

I
O
O
A
W

1 6 . 8 0

22.66

M O N R O E
M T
C L E M E N S
M T
P L E A S A N T
M U S K E G O N
M U S K E G O N

• P R E D

E P O P I 5
•

M A R Q U E T T E
M E L V 1 N D A L E
M I D L A N D

5 6
5 7

6
6
6
6

0.0

P K
H T S

5 5

6 2
6 3
6 4

Resldutil

- 3 . 0
0:

C I T Y
L I N C O L N
M A D I S O N

N
N
N
O
O

6 0
61

C a s a

o f

27-D0C-9O
12:45:56

MARKET OPPOR T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS
*

E q u a t i o n

N u m b e r

C a s e i t l s e

P l o t

*:

S e l e c t e d

C a s e

#
1
2
3
4
5
6
7
B
9
10
1 1
12
13
14
15
16
17

D e p e n d a n t

o f

S t a n d a r d i z e d

M:

M i s s i n g

A
B
B
B
B
B

U
A
A
E
E
E

B
T
Y
N
R
V

U R N
H L
T L E
C R
C I T Y
T O N
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H

I

P

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a n d

0.0

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•
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•
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•

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A
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T
P
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•
•
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A
•

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•

N
S
D
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C I T Y
D
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G R O S S E
G
H
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P
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•
*
•
•
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P K

•

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W D S
K
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P A R K

K A L A M A Z O O
K E N T W O O D
C I T Y
C I T Y

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4
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•

C A D I L L A C
C L A W S O N
B O R
B O R
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L A
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E

3 . 0

•

B I G
R A P I D S
B I R M I N G H A M
B U R T O N

E A R
E A R
G R
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R

R e s i d u a l

R B O R

F
F
G
G

6
7
8
9
0
#

T

Drug

e p o p i 7

S
E E K

2 5
2 6
27

3
3
3
3
4

L

A L L E N
P A R K
A L P E N A

F A R M I N G T O N
F A R M I N G T O N

3 5

U

•

2 3
2 4

3 2
3 3
3 4

M

•

18
19
2 0
21
2 2

31

V a r i a b l e .

- 3 . 0
0 : ........

C I T Y
A D R I A N
A L B I O N

D
D
E
E
E
E
E

2B
2 9
3 0

C a s e

1

3.

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• P R E D
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9
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3
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• P R E D

•
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5
5
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0
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- 1 . 0 3 9 1
. 5 4 8 2
-.

1 8 6 2

• R E S I D

M A R K E T O P PORTUNITY I D E NTIFICATION MODEL
PRELIMINARY ANALYSIS

i s e « 1 s e

P l o t

S e l e c t e d

C a s e

#
41
4 2
4 3

4 4
4 5
4 6
4 7
4 8
4 9
5 0
51
5 2
5 3
5 4
5
6
7
8
9
0
1
2
3
4

6 5
6 6
6 7
6 8

C a s e

6
7
7
7
7
7

9
0
1
2
3
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7
7
7
7
7

5
6
7
8
9

8 0
#

I
I
A
A

T
N
D
R

S t a n d a r d i z e d
M i s s i n g

Y
C O L N
P K
I S O N
H T S
Q U E T T E

R e s i d u a l

- 3 . 0
0 : . . ..

0. 0

•
•

T
C L
T
P L
U S K E
U S K E
I L E S

N
N
O
O
P
P
P
R
R

O R T
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W O S
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•
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4 . 0 0
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•

S H O R E S
•

P A R
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5 . 0 4

•
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•

M O N R O E

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•
•
•

C I T Y
•
*

R

•
A
A
O
N
A

N
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N

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•
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0 :

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-3.0

E P O P I 7
3 . 0 3
2 . 3 9
1 . 8 7

•

M E L V I N D A L E
M I D L A N D
M
M
M
M
N

3.
•

0.0

.....:

1 . 85
1 . 79
2 . 2 3
1 . 28
3 . 0 3
E P O P I 7

• P R E D
2 . 4 1 2 4
2 . 9 8 8 4
3 . 9 7 6 5
2 . 7 6 8 8
2 . 5 5 6 5
4 . 5 3 5 9
4
3
2
2

.
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5
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2 . 5 5 0 7
2 . 9 9 6 8
2
2
1
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. 5 3 5 9
. 8 7 6 8
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• R E S I D
. 6 2 1 4
- . 6
- 2 . 1
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0
0
7
8

3
4
1
7

2
7
9
1

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1 . 3 7
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9
6
7
9
0
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2
2
2
0
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6
2
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3
6
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2 . 4 6 9 8
• P R E D

. 5 5 6 6
• R E S I D

232

5
5
5
5
5
6
6
6
6
6

C
L
M
M

o f
M:

o o

12:45:57

OO

27-Dec-90

M A R K E T O P P O R T U N I T Y I D E NTIFICATION MODEL
PRELIMINARY ANALYSIS

27-0ec-90
12:46:01

• • • •
E q u a t i o n

N u m b e r

C a s e a l s e

P l o t

*:

S e l e c t e d

0

M i s s i n g

13

B U R T O N

14
15
16
17
18
19
2 0

C
C
D
D
E
E
E

•
•

L
S
B
B
A

L A
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O R
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N D
D E
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] •

•
*
•
•
•

H
R A
T R O
N S I

•
T
P
I
N

•

S
I D S
T
G

•
•
•

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E S C A N A B A
F A R M I N G T O N

2 4

F A R M I N G T O N

•

H L S

2 5

F E R N D A L E
F R A S E R
G A R D E N
C I T Y
G R A N D
H A V E N

2 9
3 0
31
3 2
3 3
3 4
3 5

G
G
G
H
H
H
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3 6
3 7
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3 9
4 0

K A L A M A Z O O
K E N T W O O D
C I T Y

0

N
S
S
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C I T Y

D
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27
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6 . 8 8

T O N
H A R B O R
K L E Y
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R A P I D S
M I N G H A M
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6
8
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• P R E

3
4
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6
3
6
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1
3
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1
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3
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5
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5 . 1 9 2 9
3 . 5 3 1 9
• R E S I D

233

B
B
B
B
B

E P 0 P 1 9
1 7 . 1 4

•

A N
O N
N
P A R K
N A

B
9
10
1 1
12

D
A
A
A
G
A S
A S

3 . 0

__

A U B U R N
H L S
B A T T L E
C R E E K
B A Y
C I T Y

A
L
E
E

R e s i d u a l

- 3 . 0
0 :

5
6
7

N
R
V
G
R

Y
I
I
E
E

M:

C
A
A
A
A

E
E
E
I
I

T
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B
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P

S t a n d a r d i z e d

1
2
3
4

21
2 2
2 3

C a s e

I
D
L
L
L

o f

R E G R E S S I O N

Miscellaneous Retail Stores

EPOP19

o
o

C a s e

M U L T I P L E

Dependent Variable..

1

27-Dec-90
12:46:02

C a s e w l s e
*:

MARKET OPPORT U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

C a s e
4
4
4
4

#
1
2
3
4

4 5
4 6
4 7
4 B
4 9
5
5
5
5
5

0
1
2
3
4

5 5
5 6
5 7

6 5
6 6
6 7
6 B
6 9
7 0
71
7 2
7 3
7
7
7
7
7
7
8
C a s e

4
5
6
7
8
9
0

*

S t a n d a r d i z e d
M i s s i n g

R e s i d u a l

- 3 . 0
0 :

•
e

O N R O E
T
C L E M E N S
T
P L E A S A N T
U S K E G O N

M
N
N
N
O
O

U
I
O
O
A
W

S K
L E
R T
V I
K
O S

E G O N
H T S
S
O N
S H O R E S

P
P
P
R
R

O
O
O
I
I

N
R
R
V
V

I A
H
A G
R
R V

R
R
R
S

O
O
O
A

M
S
Y
G

S
S
S
S
T
T
T
T

A
O
O
T
A
R
R
R

U L T
U T H F
U T H G
C L A
Y L O R
A V E R
E N T O
O Y

W
W
W
W
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27-Dee-90
12:46:06

MARK E T O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS
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12:46:06

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C a s e « 1 s e

MARK E T OPPORT U N I T Y IDENTIFICATION MODEL
PRELIM I N A R Y ANALYSIS

27-Dec-90
12:46:10

MARKET OPPORT U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS
• • • *

E q u a t i o n

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.
.
.
.
.
.
.
.
.

7
1
3
7
9
7
1
7
2
6

3
1
8
6
9
6
7
6
3
3

6
1
5
5
7
5
5
0
4
2

0
1
1
2
3
3
3
2
6
4

7
1 0
9
1 1
8
1 1
6
7

.
.
.
.
.
.
.
.
•

4 0 4 2
4 4 1 2
5 4 8 1
8 6 9 7
6 7 7 3
3 5 4 3
0 2 4 9
6 8 0 2
P R E D

- 4 . 4 2 4 9
2 . 2 8 2 2
2 . 9 5 5 3
4
- 9 . 6 1 7
3
- 4
3
- 6

.
3
.
.
.
.
.

1
E
6
5
5
2
9

6
0
3
7
1
7

4
0
3
4
9
1
7

0
3
6
0
0
2
3

- 1 . 5 2 4 7
- 1 . 5 8 9 8
6 . 0 9 8 0
. 6 9 4 2
- . 2 1 0 8
4 . 9 7 3 0
1 . 5 3 2 4
5. 1 9 0 2
3
1
2
5
3

.
.
.
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.

9
2
4
4
3

5
4
0
2
2

0
6
9
2
5

2
5
8
1
4

2
6
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1
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- 1
•

.
.
.
.
.
.
.
R

0
8
4
9
3
0
8
E

2
1
8
7
4
7
0
S

5
5
7
5
6
4
6
I

3
8
5
1
2
8
1
D

- 1
-

237

15
16
1 7
18
19
2 0
21
2 2

0.0
,
•

A L P E N A
A U B U R N

• • • •

R e s i d u e !

....

3
4
5

R E G R E S S I O N

Automotive, Repairs, Services, and Packing

EPOP23

- 3 . 0
0:

C I T Y
A D R I A N
A L B I O N

13
14

M U L T I P L E

Dependent Variable..

f
1
2

9
10
1 1
12

C a s e

1

27-Dec-90
12:46:11

C a s e w l s e
*:

MARKET OPPOR T U N I T Y IDENTI F I C A T I O N MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

C a s e
4
4
4
4

f
1
2
3
4

4 5
4 6
4 7

S t a n d a r d i z e d
M i s s i n g

M U S K E G O N
N I L E S
N O R T O N

N O V I
O A K
P A R K

5 5

O W O S S O

5 6
5 7

P
P
P
R
R

O
O
O
I
I

N
R
R
V
V

T
T
T
E
E

I A
H
A G
R
R V

R
R
R
R
S

O
O
O
O
A

C
M
S
Y
G

H
U
E
A
I

E
L
V
L
N

S
S
S
S

A U L T
O U T H F
O U T H G
T
C L A

6
7
7
7

9
0
1
2

7 3
7 4
7 5
7 6
7 7
7 8
7 9
8 0

t

•

•
•
•

S T E R
U S
I L L E
O A K
A W
S
I
A
I

•

•
•

H L S

**
•
•

|•
• ‘

•
* •

.•

N D O T T E
M I N G
I L A N T I
Y

.
.
.
.
.

0
4
8
5
3

0
5
6
0
B

4
3
7
3
1 5
9
5

.
.
.
.
.
.
.

7
5
8
0
3
2
4

0
2
6
2
5
7
4

6
7
7
7

.
.
.
.

2 1
16
5 4
2 4

5
6
2 6
3
7

.
.
.
.
.

5
9
5
7
4

5
0
7
8
3

9 . 8 6
1 1 . 9 0
5 . 5 4
5 . 3 7

• |

W E S T L A N D
W O O O H A V E N
A
O
S
T

"
• ’

C I T Y

T R O Y
W A L K E R
W A Y N E

Y
Y
P
I

.

•

•

3
0
4
7
5

9 . 1 5
1 1 . 8 4

•
•

T E
M A R I E
E L D
T E
R
S H O R E S

T A Y L O R
T R A V E R S E
T R E N T O N

W
W
V
C

1 2
6
2
4
1 0

.

•

C
U R O N
E
R O U G E
I E W

2
6
1
1
3

9 . 5 9
1 0 . 3 0
8 . 9 0

• ’

S H O R E S

P
.
.
.
.

8 . 0 8
1 0 . 9 9
2 6 . 4 2

• ‘

H T S

O
2
0
2
4

•

1 3 .
1 2 .
8 .
E P 0 P

•
0:

3.

0
0
6
2

8
2
5
3

• P R
7 . 2 7
9 . 2 5
1 1 . 2 4
8 . 5 9

E
8
5
6
6

D
5
5
4
3

7 . 3 6 8
1 2 . 2 1 8
1 3 . 2 4 7
1 1 . 2 1 5
7 . 2 0 7
9 . 6 6 4

3
0
3
2
2
7

• R E S
5 . 3 2
. 8 8
. 9 2
5 . 7 5

I
3
1
0
3

D
6
6
2
4

1 1 . 7 8 5 1
7 . 1 4 7 3

. 7 1 2 0
- 1 . 2 2 3 9
1 3 . 1 7 7 5
- 1 . 6 2 1 8
3 . 0 9 1 7
- . 7 6 6 7
. 2 1 4 9
- . 6 9 8 7

8 . 6 4 8 8
8 . 8 0 8 9
1 3 . 5 7 1 2

- 5 . 7 9 0 6
- 4 . 3 1 0 1
- 3 . 1 9 5 0

9 . 1 3 9 8
1 2 . 0 6 0 5
7 . 6 3 2 6
9 . 2 6 3 1
8 . 1 6 2 2

.01
- . 2 1
- 2 . 9 3
- 5 . 7 3
- . 3 0

12
5 6
3 1
8 9
5 1

8. 1 4 4 7
9 . 4 8 B 1
8 . 3 1 9 1

- 5 . 1 2 4 8
5 . 8 6 4 6
. 9 4 9 1

7 . 7 8 3 8
7 . 3 3 1 9

- 2 . 3 4 5 0
- 1 . 1 2 2 4

1 2 . 1 0 9 3
6 . 7 8 6 2

- 4 . 9 4 6 0
. 7 5 7 4

7 . 7 2 5 2
6 . 9 8 4 1

- . 4 8 6 0
- 1 . 4 3 6 2
- . 6 5 6 5

7
1 2
7
6
8
8

.
.
.
.
.
.

5
0
5
8
5
5

5
5
6
4
2
3

8
6
6
1
5
8

8
3
0
0
9
7

6
8
7
7
8

.
.
.
.
.
•

8
6
3
2
2
P

3
3
5
6
5
R

3
9
0
1
3
E

8
0
0
8
3
D

1 4 . 5 0 9 1
- 3 . 7 8 7 1
. 5 9 1 7
1 . 3 3 8 5
3 . 3 6 6 1
-1 . 2 9 1 2
- 3 . 2 6
5 . 7 2
4 . 7 5
. 3 9
• R E S

7
8
3
3
I

5
2
6
5
D

238

51
5 2

5
6
7
8

'•

\*

O N R O E
T
C L E M E N S
T
P L E A S A N T
U S K E G O N

5 3
5 4

E P
1
1
1
1

\*

M A R Q U E T T E
M E L V I N D A L E
M I D L A N D

5 0

6
6
6
6

0 . 0

P K
H T S

4 8
4 9

61
6 2
6 3
6 4

R e s i d u a l

- 3 . 0
0-

C I T Y
L I N C O L N
M A D I S O N

M
M
M
M

5 8
5 9
6 0

C a s e

o f
M:

27-Dec-90
12:46:15

MARKET O P P O R T U N I T Y IDENTI F I C A T I O N MODEL
P R ELIMINARY ANALYSIS
•

E q u a t i o n

N u m b e r

C a s e e l s e

P l o t

*:

S e l e c t e d

C a s e

S t a n d a r d i z e d
M i s s i n g

C I T Y
A D R I A N
A L B I O N

5
6
7

a
9

0

A L L E N
L
U
A
A
E
E

P
B
T
Y
N
R

N

H
R A
T R O
N S I

•
•
•

G R O S S E
P T
G R O S S E
P T
H A M T R A M C K

3 3
3 4

H A
H A
H I
H O
I N
J A

E
L
L
A
T
S

•
•
•

•

3 0
31
3 2

P
E
H
L
S
K

,e

S
I D S
T
G

H L S

G R A N D V I L L E

R
Z
G
L
K
C

•

•

2 8
2 9

0

•

•

F E R N D A L E
F R A S E R
G A R D E N
C I T Y

5
6
7
8
9
0

'•

•

A
T O N
T O N

A
N
E
O

•

.
•

•
•
•

L

E

R

E

G

R

E

S

S

I

O

N

•

•

•

•

.e
•
•
•
•

.•
0

-3.0

D
6
0
5
3

- 1 . 6 7 1 8
- 1 . 8 0 1 4

1 . 5 6 3 3
2 . 1 0 1 0
3 . 9 0 4 2

‘. 2 8 5 8
2 . 9 3 6 7
3 . 1 5 8 0

5
5
2
5

•
.
.
.
.

P
8
8
6
3

m

R
9
8
3
4

E
9
9
5
4

- 2 . 6 9 1 9
- 4 . 3 0 0 6
. 9 0 8 7

#

3 . 9 5 9 8
3 . 6 2 0 2
4 . 7 2 2 3

4 . 2 6
3 . 7 0

5 . 3 3 2 5
2 . 0 1 7 0

3 . 2 3
. 8 5
2 . 5 6
. 0 0
. 7 7
5 . 0 4
7 . 9 1

2 . 5 6
1 . 9 1
3 . 7 9
2 . 7 8
3 . 2 8

3 . 4 0
6 . 9 1
1 . 1 7
6 . 9 1
1 . 5 7
4 . 6 0
2 . 5 9
3 . 6 4
E P 0 P 2 5

- 2 . 9 8 7 7

5 . 5 5 3 9

2 . 8 4

7 . 2 3
1 . 5 4

• R E S I D
-1 . 4 9 2 2

4 . 3 0 0 6

4 . 8 7
3 . 7 4

. 7 0
.61
1 . 0 7

•

W O O D S
P A R K
N D
P A R K
D
R
N

2 . 9 0
. 9 6
3 . 5 4
4 . 3 8
1 . 6 5
5 . 0 4
7 . 0 6
2. 8 6
. 0 0
. 0 0

4 . 8 6
5 . 7 1

•
•

P K
W D S

K A L A M A Z O O
K E N T W O O D
C I T Y
C I T Y

P

4 . 5 9
4 . 7 9

H A V E N

R

I

E P O P 2 5
4 . 4 1

|

T
P
I
N

T

Miscellaneous Repair Services

•

2 5
2 6
2 7

3
3
3
3
3
4

L

4
8
5
5
9

8
8
2
2
4

4 . 9 6 8 8
4 . 8 6 0 5
4 . 6 4 9 5
5 . 0 6 9 8
3 . 9 8 7 4
3 . 0 5 4 0
2 . 7 0 9 2
3 . 2 6 4 6
1 . 9 5 4 7
1 . 8 6 2 5
3 . 0 1 0 5
2 . 9 1 6 0
5 . 5 7 3 3
3 . 2 9
5 . 7 0
3 . 1 1
4 . 7 8
1 . 9 1

6
3
9
6
3

5
5
0
3
4

2 . 9 8 4 2
• P R E D

. 1 1 8 8
- 1 . 8 7 8 7
- 1 . 0 7 7 1
1 . 6 8 5 9
. 6 6 8 8
- 1 . 0 6 9 2
- 1 . 2 3 2 6
- 2 . 7 8 5 2
- 2 . 5 2 3 1
. 0 6 7 1
3 . 0 4 4 7
- . 0 5 8 1
3
- 1
2
2

.
.
.
.
.

2
2
5
1
4

8
4
1
4
4

3 2
3 2
6 9
9 1
15

- 1 . 2 5 2 0
- 1 . 2 5 4 3
- 1 . 9 4 0 4
. 4
3 . 3
- 2 . 1
1 . 2

8
3
2
0

3 1
7 6
5 5
7 3

-1 . 5 5 1 6
- . 1 8 7 9
. 6 7 6 3
. 6 5 2 1
• R E S I D

239

B O R
A N O
D E
L A
S E
N A B
I N G
I N G

G R A N D

U

•
•

C A D I L L A C
C L A W S O N
D E A R B O R N
R
R
T
T
R
A
M
M

M

E P 0 P 2 5

.

P A R K

B I R M I N G H A M
B U R T O N

E A
G
A S
A S
C O
S C
A R
A R

•

R e s i d u a l

E N A
U R N
H L S
T L E
C R E E K
C I T Y
T O N
H A R B O R
K L E Y

12

D
E
E
E
E
E
F
F

•

•

B E V E R L Y
H L S
B I G
R A P I D S

15
16
17
16
19
2 0
21
2 2
2 3
2 4

•

V a r i a b l e . .

•

10
11
13
14

C a s e

o f

1

A
A
B
B
B
B

D e p e n d e n t

M:

0
2
3
4

i

27-Dac-90
12:46:15

C i s m l i *
*:

MAR K E T OPPOR T U N I T Y I D E N TIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

f
41
4 2
4 3
4 4

P K
H T S

4 6
4 7

M O N R O E
M T
C L E M E N S

4 8
4 9

M T
P L E A S A N T
M U S K E G O N
M U S K E G O N
H T S

5 3
5 4
5 5
5 6
5 7
8
9
0
1
2
3
4

6
6
6
6
6
7
7
7
7
7

5
6
7
8
9
0
1
2
3
4

7 5
7 6
7 7
7 8
7 9
8 0

5
6
3
5

*
*

I
O
O
A
W
O
O

L E
R T
V I
K
O S
N T
R T

S
O N

P
R
R
R

O
I
I
O

R
V
V
C

T
E
E
H

A G E
R
R O U G E
R V I E W
E S T E R
H L S

R
R
R
S

O
O
O
A

M
S
Y
G

U
E
A
I

L U S
V I L L E
L
O A K
N A W

•

T
T
T
T

A
R
R
R

Y
A
E
O

L O R
V E R S E
N T O N
Y

W
W
W
W
W

A
A
E
O
Y

L
Y
S
O
A

K
N
T
D
N

E
E
L
H
D

S
I
A
I

3 . 9 3
2 . 8 9
4 . 5 4

•

P A R K
S O
I A C
H U R O N

A U L T
O U T H F
O U T H G
T
C L A

•
•

1 .83
2 . 9 6
2 . 7 2
2 . 6 4
2 . 1 4
1 .21
5 . 8 1
3 . 4 8
4 . 2 3
1 . 9 3
2 . 8 7
4 . 8 0
2 . 6 3
4 . 0 2

•
•
*
•
•

•
•

T E
M A R I E
E L D
T E
R
S H O R E S

•
•
•

2 . 4 8
1 6 . 4 5
3 . 3 1
5 . 2 0
3 . 0 8
3 . 8 1
2 . 0 9
. 9 0
2 . 2 3

C I T Y
*
•

R

•

A N D
A V E N
O T T E

•

•

W Y O M I N G
V P S I L A N T I
C I T Y

15
2 8
6 2
5 0

5 . 0 3
3 . 7 7
2 . 7 4
3 . 2 0
1 .38

•

S H O R E S

S
S
S
S

.
.
.
.

1 2 . 9 5
•

•

N
N
N
O
O
P
P

E P 0 P 2 5
4 . 4 3
5 . 9 6

•
•

•
•
•

__

0 :
- 3 . 0

0. 0

oo

f

3.

3 . 6 8
3 . 4 6
E P O P 2 5

•
2 .
5.
4.

P R E D
5 0 7 1
1 8 7 1
2 7 8 0

• R E S I D

3. 2 8 2 0
3 . 1 3 3 3

2 . 9 9 6 1
. 4 8 8 9
- . 2 4 9 1

5 . 7 4 6 1
6 . 8 9 1 0
4 . 1 5 2 6
1 . 5 5 4 9
2 . 6 9 1 6
5 . 0 0 2 4
1 . 9 6 1 8
5 . 2 6 9 6
4 . 0 9 7 1
6 . 1 6 7 8
4 . 7 0 3 6
5
3
3
3
4
3
3
4
2

.
.
.
.
.
.
.
.
.

2
2
3
3
9
6
5
6
0

9
4
0
1
8
1
4
2
7

5
2
5
7
6
6
8
0
3

0
8
2
5
9
5
5
2
9

4
4
2
3

.
.
.
.

4
3
9
1

1
6
4
4

0
6
5
2

2
2
0
1

2 . 6 4 6 5
5
2
4
3
3
2
3
2

.
.
.
.
.
.
.
.

3
9
3
3
2
4
4
5

1
9
8
4
1
3
5
2

7
0
7
0
9
2
3
6

2
1
9
7
9
0
4
1

2 . 5 8 5 2
3 . 5 2 8 8
• P R E D

1 . 9 2 7 0
. 7 7 5 9
. 8 6 9 4

6 . 0 6 2 4
. 8 7 2
2 . 2 1 3
. 0 4 6
- 1 . 8 0 2

6
0
3
4

- . 5 8 0 0
-

1
2
1
2

. 3 3 9 6
. 0 0 5 1
. 6 2 8 2
. 8 7 3 3

- 2
-1

. 3 3 3 8
. 5 2 2 1
. 6 6 2 0
. 1 7 4 7

- 3
2
- 1

8 9
2 6
2 9
0 0
2 0
4 9
4 2
2 5
0 1
1 7

. 7 7
. 1 9
. 0 7
. 3 9
. 1 4
. 5 4
. 4 3
- . 3 1
. 8 8
- . 1 6

1 1 . 1 2 8 1
. 3 1 6 5
. 8 1 5 0
- . 2 5 8 1
. 5 8 9 7
- .
- 2 .
- .
1 .
- .
• R

3
5
2
0
0
E

3
5
9
9
7
S

8
8
3
9
0
I

1
1
2
5
0
D

240

5
5
6
6
6
6
6

0 . 0

...

L I N C O L N
M A D I S O N

M A R Q U E T T E
M E L V I N D A L E
M I D L A N D

51
5 2

R e s i d u a l

- 3 . 0
0 :

C I T Y

4 5

5 0

C a s e

S t a n d a r d i z e d
M i s s i n g

O O

C a s e

o f
M:

27-Dec-90
12:46:19

MARKET O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALY S I S
M U L T I P L E

Equation Numbar
C a a a w i a a
•:

P l o t

S e l e c t e d

1

Dapandant Varlabla..

o f

S t a n d a r d i z e d

M:

M i s s i n g

EPOP27

E

G

R

3.

O O

0 . 0

0: .
•
•

7 . 0 9
5 . 0 0
2 . 7 7

•
•

*

•
•

S

S

I

O

N

•

•

•

•

Amusement

3 . 7 8
5 . 6 5
5 . 1 5
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241

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ii

E P 0 P 2 7
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•

0:

E

R e s i d u a l

- 3 . 0

Case a CITY
l ADRIAN
2 ALBION
ALLEN PARK
3
4 ALPENA
5 AUBURN HLS
BATTLE CREEK
6
7 BAY CITY
B BENTON HARBOR
9 BERKLEY
10 BEVERLY HLS
11 BIG RAPIOS
12 BIRMINGHAM
13 BURTON
14 CADILLAC
15 CLAWSON
16 DEARBORN
17 DEARBORN HTS
IB E GRAND RAPIDS
19 EAST DETROIT
20 EAST LANSING
21 ECORSE
22 ESCANABA
23 FARMINGTON
24 FARMINGTON HLS
25 FERNDALE
26 FRASER
27 GARDEN CITY
2B GRAND HAVEN
29 GRANDVILLE
30 GROSSE PT PK
31 GROSSE PT WDS
32 HAMTRAMCK
HARPER WOODS
3 3
34 HAZEL PARK
35 HIGHLAND PARK
36 HOLLAND
37 INKSTER
JACKSON
3 8
39 KALAMAZOO
40 KENTWOOD CITY
CITY
C a s e
#

R

Amusement and Recreation Services, Motion Pictures

27-Dec-90
12:46:20

C a s e e l s e
*:

MARKET O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

C a s e

#
41
4 2
4 3
4 4
4 5
4 6
4 7
4 8
4 9
5 0
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#

S t a n d a r d i z e d
M i s s i n g

R a a l d u n l

- 3 . 0
0-

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M A D I S O N

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M A R Q U E T T E
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• R E S I D

242

5
5
6
6
6
6
6

o f
M :

27-Dec-90

MARK E T O P P O R T U N I T Y IDENTIFICATION MODEL
P R ELIMINARY ANALYSIS

12:46:24

►
E q u a t i o n

N u m b a r

C a s e w l s e

P l o t

*:

S e l e c t e d

1

O e p a n d a n t

o f

S t a n d a r d i z e d

M:

M i s s i n g

M U L T I P L E

EPOP29 H e a l t h

V a r l a b l o .

R E G R E S S I O N

• • • •

S erv ices

R e s i d u a l

-3 .0
C a s e

0

C I T Y

1

A O R I A N
A L B I O N

2
3
4
5
6
7
8
9
10
1 1

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A L L E N
P A R K
A L P E N A
A U B U R N
B
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N T O N
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-3.0

EP0P29
36.24
2 5 . 15
29.55
42.52
8.13
23.67
2 5 . 19
19.07
29.77
31 . 2 5
2 4 . 28
92.50
16.32
37.91
27.66
33.44
13.26
48.43
27 . 9 0
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57.31
36.42
1 1.97
15.91
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25.68
9.84
43.80
15.52
4 2 . 15
7.92
1 2 . 10
32.83
4.39
37.33
26.54
14.55
EP0P29

•PRED
40.0549
37.6225
26.5886
33.5441

•RESID
-3.8160
-12.4775
2.9649
8.9714

25.2450
20.5202
-.7047
24.3458

-1.5764
4.6687
19.7725
5.4195

33.3659
61.1843
20.4320
39.4462
28.1450
25.2814
21.8651
52.0856
21.9505
12.1873
15.7402
37.1259
43.9574
41.3531
26.5944
21.2460
17.8423
20.8512
17.0331
49.6099
54.4280
16.4532
24.0276
19.8564
13.5760
45.8658
15.3058
45.2743
23.2573
17.9517
•PRED

-9'.0833
31.3181
-4.1166
-1.5315
-.4854
8.1599
-8.6072
-3.6574
5.9538
1.7363
-8.8436
-8.3489
13.3548
-4.9283
-14.6279
-5.3386
7.6726
30.9707
8.6445
-39.7715
-10.6324
-.9369
18.1206
-1 1 .9 3 5 6
-1.4761
-13.0392
-10.9171
-7.9467
3.2868
-3.4062
•RESID

243

12
13
14

0 :

27-Dec-90
12:46:24

C a s e » 1 a e
*:

MAR K E T OPPORT U N I T Y IDENTIFICATION MODEL
PRELIM I N A R Y ANALYSIS

P l o t

S a l a c t a d

0 0

3
E P 0 P 2 9
1 3 . 5 4

•
•
•
•
•

•
•
•

•

•

•

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•

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1 3 . 2 2
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3
2
3
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4 5 . 4 2 8 6
2 0 . 8 3 6 9

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5 . 3 9
1 3 . 3 2
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21 . 2 5
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•
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4 3 . 0 5
7 . 1 7
31 . 7 6

•

0.0

0

3.0

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2 1 . 3 7 5 5
2 6 . 7 9 3 7
4 0 . 6 0 9 4
1 8 . 8 7 5 2
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• P R E D

• R E S I D
- 7 .
- 4 .
2 .
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6 .
1
2

6
7
9
7

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.
.
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3
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2
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7
5
3

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- 3 . 8 2 8 6
- 3 . 3 3 3 5
- 2 1
- 1 0
5
- 3
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- 5
- 6
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- 5
- 7
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5
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4 9
-

.
.
.
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.
.
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.
.
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3
1
7
7
2
7
8
9
5
4
1
8
5
4
3
7

5 0 5
3 3 7
4 8 9
7 4 6
8 2 6
9 7 2
2 2 0
6 2 5
5 8 6
3 3 8
8 3 3
5 6 3
6 2 3
9 5 1
4 6 8
4 8 7

- 3 . 0 0 7 5
- 3 . 5 4 8 9
6 4 . 5
1 9 . 2
- 4 . 8
2 . 4
- 3 . 3

4
4
3
5
5

9
1
7
1
3

5
7
6
4
7

- 4 . 4 9 1 8
- 4 . 5 8 3 5
- 8 . 7 2 2 3
- 7 . 5 9 0 6
1 5 . 1 8 4 2
• R E S I D

244

i

R e a l d u a l

0

C a s e

CITY
LINCOLN PK
MADISON HTS
MARQUETTE
MELVINDALE
MIDLAND
MONROE
MT CLEMENS
MT PLEASANT
MUSKEGON
MUSKEGON HTS
NILES
NORTON SHORES
NOVI
OAK PARK
OWOSSO
PONTIAC
PORT HURON
PORTAGE
RIVER ROUGE
RIVERVIEW
ROCHESTER HLS
ROMULUS
ROSEVILLE
ROYAL OAK
SAGINAW
SAULT STE MARIE
SOUTHFIELD
SOUTHGATE
ST CLAIR SHORES
TAYLOR
TRAVERSE CITY
TRENTON
TROY
WALKER
WAYNE
WESTLAND
WOODHAVEN
WYANDOTTE
WYOMING
YPSILANTI
CITY

1

$
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80

S t a n d a r d l z a d
M i s t i n g

COO

Case

o f
M:

27-Dec-90
12:46:26

M AR K E T OPPOR T U N I T Y IDENTI F I C A T I O N MODEL
PR ELIMINARY ANALYSIS
* • • •

E q u a t i o n

N u m b t r

C a s e w l s e

P l o t

*:

S e l e c t e d

Case #
1
2
3
4
5
6
7

8
9

15
16
17

IB
19

20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

Case #

Dependant Variable..

o f

S t a n d a r d l r e d

M:

M i s s i n g

CITY
ADRIAN
ALBION
ALLEN PARK
ALPENA
AUBURN HLS
BATTLE CREEK
BAY CITY
BENTON HARBOR
BERKLEY
BEVERLY HLS
BIG RAPIDS
BIRMINGHAM
BURTON
CADILLAC
CLAWSON
DEARBORN
DEARBORN HTS
E GRAND RAPIDS
EAST DETROIT
EAST LANSING
ECORSE
ESCANABA
FARMINGTON
FARMINGTON HLS
FERNDALE
FRASER
GARDEN CITY
GRAND HAVEN
GRANDVILLE
GROSSE PT PK
GROSSE PT WDS
HAMTRAMCK
HARPER WOODS
HAZEL PARK
HIGHLAND PARK
HOLLAND
INKSTER
JACKSON
KALAMAZOO
KENTWOOD CITY
CITY

M U L T I P L E
EPOP31

R E G R E S S I O N

• • e •

Legal Services

R e s i d u a l

- 3 . 0

.
• .
•
•
•

0: .. .

E P 0 P 3 1
9 . 3 0
5 . B O

• *

*
*

•'
,•
* ‘
•
•

•

•

•
•

'•
, •
. •
•#

•

•
•
0: . . .
-3.0

.9 2
9 . 7 2
2 . 8 5
3 . 5 1

e

•
•

7 . 1 5 0 8
5 . 0 5 4 0
- 3 . 0 3 9 7

1 .9 9
4 . 3 4

•
'•

.6 3
3 . 3 3
9 . 5 7
2 . 8 2

4 . 5 1
1 . 4 6
2 . 5 5
6 . 8 3
2 . 9 1
5 . 3 6
8 . 6 3
1 0 . 8 7
1 2 . 2 4

*e

•

4 . 5 0
9 . 7 4

1 2 . 5 1 2 1
5 . 2 2 9 8
9 . 4 4 6 6

5 . 7 2
2 . 7 6
5 . 8 9
5 1 . 6 1
2 . 7 2
1 2 . 3 2
4 . 2 6

*

•

\

•

• P R E D
1 3 . 5 1 8 8

7 . 3 0
3 . 2 1
7 . 4 8
1 .9 8
. 0 0
5 . 5 3
1 . 2 5
9 . 7 4
1 2 . 3 0
. 5 6
E P 0 P 3 1

• R E S I D
- 4 . 2 1 4 2
- 6 . 7 0 9 4
- . 7 3 2 5
. 2 9 6 5
- 3 18 2 2 4
4 . 5 1 7 8
5 . 8 6 4 5
- . 7 7 1 0

6 . 4 9 5 1
7
2 2
5
1 1
7

.
.
.
.
.

6
9
3
2
7

1
7
5
4
2

0
5
5
2
4

0
2
6
4
3

7 . 3 6 6 4
4
1 5
5
2
1 0

.
.
.
.
.
.

1 4 .
1 6 .
9.
3 .
1 .

6
7
3
4
0
4

7
1
0
5
1
0

0
5
0
3
9
7

2
2
5
6
9
9

7 4
1 5
1 8
3 6
7 0

6
0
1
2
5

7
8
3
3
7

2 . 3 1 2 2
- . 5 9 6 0
1 4 . 6 8 1 4

- 1 . 7 2 3 3
2 8 . 6 3 1 4
- 2 . 6 3 6 3
1 . 0 7 9 9
- 3 . 4 6 8 9
- 2 . 8 5 3 6
- 3 . 2 1 5 1
- 1 3 . 1 6 6 3
1 . 5 3 3 2
3 . 3 6 3 0
3 . 3 4 4 1
-

1
3
3
7

. 7 7 4
. 8 7 7
. 9 0 7
. 1 8 6
. 9 7 6

8
2
1
9
1

- . 7 8 3 5
7 . 4 0 4 4
3 . 4 4 9 1
- 1 1 . 1 6 7 7

1 6
2
2
5

.
.
.
.

6
0
8
9

4
6
6
7

8
7
7
0

1
3
0
4

- 9 . 3 4 8 8
1 . 1 4 2 9

2
1 5
2
1 6
7

.
.
.
.
.

8
6
1
4
1

6
0
2
8
1

7
3
0
7
7

6
8
2
2
4

- 2 . 8 6 7 6
- 1 0 . 0 7 5 1

1 . 2 2 0 1
• P R E D

4 . 6 1 0 9
- 3 . 9 9 0 2

- . 8 6 6 3
- 6 . 7 4 9 6
5 . 1 8 3 6
- . 6 6 0 6
• R E S I D

245

10
11
12
13
14

1

27-Dec-90
12:46:29

C a s e w l s e
*:

MARKET OPPOR T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS

P l o t

S e l e c t e d

Case #
41
42
43
44

S t a n d a r d i z e d
M i s s i n g

CITY
LINCOLN PK
MADISON HTS
MARQUETTE
MELVINDALE

45

M I D L A N D

46
47
48
49
50
51
52
53

MONROE
MT CLEMENS
MT PLEASANT
MUSKEGON
MUSKEGON HTS
NILES
NORTON SHORES
NOVI

54

O A K

55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80

OWOSSO
PONTIAC
PORT HURON
PORTAGE
RIVER ROUGE
RIVERVIEW
ROCHESTER HLS
ROMULUS
ROSEVILLE
ROYAL OAK
SAGINAW
SAULT STE MARIE
SOUTHFIELD
SOUTHGATE
ST CLAIR SHORES
TAYLOR
TRAVERSE CITY
TRENTON
TROY
WALKER
WAYNE
WESTLAND
WOODHAVEN
WYANDOTTE
WYOMING
YPSILANTI
CITY

•

R e s i d u a l

E P 0 P 3 1
2 . 8 0
1 . 19
1 0 . 7 6

•
1
7
1
1

R
.
.
.
.

E
7
9
3
0

S
6
0
1
0

I
0
3
1
5

D
4
4
4
4

1 . 9 0 2 3
4 . 9 1 3 7

- 4 . 5 3 3 1
2 6 . 3 4 3 4

9 . 2 6 2 3
1 . 2 6 3 0

- . 5 8 2 5
8 . 0 3 1 2
3 . 5 9 5 2

. 6
. 2
. 0
. 0
.9

8
9
5
0
2

2 . 2 3
3 . 5 2
2 . 8 6
1 .01
. 8 3
1 . 3 5
3 . 0 2
7 . 0 4
' 7 . 8 8
5 2 . 9 4
3 . 6 2
6 . 2 4
1 . 9 3
3 9 . 8 5
3 . 3 1
1 7 . 9 9
. 6 2
1 . 4 3
1 . 3 5
3 . 5 8
4 . 4 7
1 . 4 4
1 0 . 3 8
E P 0 P 3 1

3.0

-

1 5 . 9 8 5 2
1 8 . 7 3 4 3

3 . 5 2
1 0 . 0 7

0.0

D
9
0
1

. 9 0
6 . 1 3

1 . 4 3
1 . 2 9
1 0 . 3 8

-3.0

E
0
6
4

1 1 .4 5
4 5 . 0 8
8
9
2
1 2

PAR K

• P R
4 . 5 6
9 . 0 9
1 2 . 0 7

- 1 . 5 4 1 8
1 5 . 0 2 1 9
2 . 1 9 2 5
1 1 . 4 6 8 3
9 . 7 7 8 5
1 3 . 6 9 2 1
1 1 . 4 5 4 3
1 7 . 5 1 3 8
2 . 4 6 8 4
1 . 4 0 6 0
. 9 7 3 5
1 1 . 1 3 1 4
6
1 1
4

.
.
.
.

6
1
6
4

4
5
6
9

4
6
0
8

9
3
7
6

1 0 . 4 6 4 5
1 5 . 6 5 2 9
2 . 3 4 2 4
8 . 3 3 1 2
2 . 7 3 5 8
1 3 . 5 2 6 0
2 . 8 5 7 8
1 5 . 3 7 2 2
- . 9
1 . 2
4 . 2
. 7

2
5
4
8

2
3
3
4

8
8
0
9

3 . 8 5
1 . 8 9
4 . 2 3
• P R

1
2
7
E

1
4
4
D

1 . 2 1 6 1

- 3 . 0 2 1 9
- 1 . 2 7 1 3
- 1
-

0
8
3
7
7

.
.
.
.
.

0
4
3
9
4

3
9
1
3
4

9
3
6
4
5

3
1
0
7
7

- . 2 4 2 3
2. 1 1 8 2
1 . 8 8 3 7
- 1 0 . 1 2 4 8
1 . 4 7 4 7
- 4 . 8 0 4 7
- 8 . 6 3 9 1
2 . 5 3 8 8
- 2 . 5 8 4
3 7 . 2 8 9
1 . 2 7 7
- 2 . 0 8 9
- . 8 0 3
2 6 . 3 2 2
. 4 4 8
2 . 6 1 5

8
1
2
9
2
2
8
0

1 . 5 3 9 3
. 1 7 4 8
- 2 . 8 8 8 2
2 . 7 9 6 1
. 6 1 4 6
- . 4 5 0 5
6. 1 3 8 7
• R E S I D

246

Case

o f
M:

27-Dec-90
12:46:33

MARKET OPPORT U N I T V IDENTI F I C A T I O N MODEL
PRELIMINARY ANALYSIS
* • • •

E q u a t i o n

N u m b e r

C a s e w l s e

P l o t

*:

S e l e c t e d

1

M U L T I P L E

Dependent Variable..

o f

S t a n d a r d i z e d

M:

M i s s i n g

* * • *

R e s i d u a l

0.0

0 : ....

•
E P 0 P
1 2 .
1 2 .
14.

•
•
•
•

21 . 2 6
1 0 . 0 0
9 . 2 5
1 3 . 8 5
1 2 . 7 1

•
•
•

•

1 5 . 4 6
1 .8 4
8 .8 3
2 5 . 3 2

•
•

•

4 . 7 6
1 7 . 0 6
1 3 . 4 8
1 1 .22

*
•
•

•

•

6 . 6 3
3
1 0
4
3
1 6
1 8

•

•

•
•
•
•

•

•

•

9 .
1 7 .
1 2 .
4 .

•

•

4
8
3
8
5
7

0
2
6
3
5
7

5 .
8.
5 .
5 .
1 6 .
2 .
1 4 .

•
•
•

*
•
•
•

0 0

8
0
8
2

4
0
4
2

1 5 . 8 2

•

0 : ....
-3.0

.
.
.
.
.
.

1 1 . 48
1 0 . 7 7
9 . 4 0

•

•

3 3
2 4
5 7
13

.
3.

8 9
16
4 5
4 6
5 9
8 2
3 4

1 0 . 1 0
1 2 . 3 1
E P 0 P 3 3

• P R E D

• R E S I D

1 5 . 9 4 1 4

- 3 . 6 9 8 5

1 5 . 5 2 6 8
9 . 5 8 7 2
1 4 . 6 9 2 9

- 2 . 9 5 4 2
4 . 5 4 7 1

8 . 5 2 8 7

6 . 5 6 4 9

7 . 8 4 2 6
4 . 5 3 5 8
1 0 . 2 7 5 7

. 7 1 6
6 . 0 1 1
8 . 1 7 6
5. 1 7 9

1 4 ! 8 9 3 5

- 6 ! 0 6 3 5

1 5 .
8 .
1 5 .
1 0 .

1 0 . 2
- 3 . 5
1 . 3
2 . 6

0
3
7
8

3
3
0
5

9
7
0
8

0
4
7
8

8 . 9 8 1 0
8 .
1 3 .
9 .
7 .
7 .
1 5 .

7 4 1 3
0 4 9 2
0 3 0 6
5 9 2 6
6 3 1 1
3 6 8 9

1 2 . 9 1 2 2
1 2 . 2 3 9 0
1 0 . 0 9 6 2
9 . 2
8 . 3
8 . 6
8 . 6
1 2 . 7
1 3 . 4

5
7
9
8
0
1

0
2
9
9
5
3

7
2
5
6
5
6

7 . 7 9 9 5
9 . 4 9 5 2
9 . 2 8 1 4
6 . 9 3 4 6
1 7 . 1 0 1 4
7 . 6 0 7 1
1 6 . 3 3 1 6
8 . 5 8 2 7
8 . 5 5 9 2
• P R E D

7
7
6
1

7
8
0
6

9
3
0
3

5
8
9
4

2 . 2 4 3 3
- 2 . 1 1 2 4
- 9 . 6 5
1 . 7 8
- 3 . 2 2
- 3 . 7 9

0
9
8
9

7
4
5
7

1 . 1 7 7 9
5 . 8 6 2
- . 7 6 0
. 6 7 3
. 1 4 9

5
6
6
2

1 . 4 6 4 8
8 . 3 0 4 5
4 . 1 4 9 2
- 8 . 4 8 9 0
2 . 4 0 1 4
- 1 . 9 1 4 0
-1 . 3 3 7 5
- 3 . 8 3
-1 . 4 7
- . 5 1
- 4 . 7 8

5
0
5
5

8
1
4
8

- 1 . 9 9
1 . 5 1
3 . 7 4
• R E S

5
7
8
I

7
0
5
D

247

Case # CITY
1 ADRIAN
2 ALBION
ALLEN PARK
3
4 ALPENA
5 AUBURN HLS
6 BATTLE CREEK
7 BAY CITY
8 BENTON HARBOR
BERKLEY
9
10 BEVERLY HLS
1 1 BIG RAPIDS
12 BIRMINGHAM
13 BURTON
14 CADILLAC
15 CLAWSON
16 DEARBORN
17 DEARBORN HTS
18 E GRAND RAPIDS
19 EAST DETROIT
20 EAST LANSING
21 ECORSE
22 ESCANABA
23 FARMINGTON
24 FARMINGTON HLS
25 FERNDALE
26 FRASER
27 GARDEN CITY
28 GRAND HAVEN
29 GRANDVILLE
30 GROSSE PT PK
31 GROSSE PT WDS
32 HAMTRAMCK
33 HARPER WOODS
34 HAZEL PARK
35 HIGHLAND PARK
36 HOLLAND
37 INKSTER
38 JACKSON
39 KALAMAZOO
40 KENTWOOD CITY
CITY
C a s e
0

R E G R E S S I O N

Personal Services

EP0P33

27-Dec-90
12:46:33

C a s a w l s e
*:

MAR K E T OPPOR T U N I T Y IDENTI F I C A T I O N MODEL
P R ELIMINARY ANALYSIS

P l o t

S e l e c t e d

o f

S t a n d a r d i z e d

M:

M i s s i n g

R e s i d u a l

o

0

;

.•

•|

•

•

*
**

•

•

•
•

•
•
•
•

•
•

•

ii
•
•
*

.•
•
•

•
•
•

.•
’•

•
•

•
•*

.•
0:: ....
:...............:
u
. . . . ...........
..
- 3 . 0

0 . 0

3.

EP0P33
9. 10
9.24
13.10
3.59
11 .42
19.24
20.21
15.99
9.80
8.21
14 .40
5.07
7. 15
5.76
11 .67
5.91
14.81
10.64
3.52
8.57
3.62
2.07
8.11
9.06
10.07
15.76
15.77
10.20
11.51
4.56
26.57
10.39
13.23
11.71
8. 10
6.40
2.69
7.97
7.85
10.38
EP0P33

•PRED
8.4894
10.2558
15.7629
8.4598
9.5169
16.9294
17.4959
15.3539
7.0005
5.8414
16.5669
8.2773
11.5881
10.5133
15.7814
8.9560
16.3294
8.9954
7.5336
8.9510
11.2595
7.2315
8.5075
10.8062
7.0560
14.9332
1 1.9857
8.6783
9.3919
7.4490
16.4656
9.2413
12.0534
8.2454
8.1367
8.0261
8.5726
8.6273
7.9030
9.0307
•PRED

•RESID
.6121
-1.0131
-2.6605
-4.8724
1.9069
2.3101
2.7114
.6351
2.7960
2.3722
-2.1669
-3.2105
-4.4426
-4.7292
-4.1083
-3.0430
-1.5234
1.6403
-4.0094
-.3795
-7.6356
-5.1568
-.3979
-1 .7416
3.0171
.8261
3.7871
1.5224
2.1199
-2.8935
10.0999
1.1507
1.1768
3.4685
-.0414
-1 .6214
-5.8869
-.6528
-.0530
1.3454
•RESID

248

’ •

•

o o

CITY
LINCOLN PK
MADISON HTS
MARQUETTE
MELVINDALE
MIDLAND
MONROE
MT CLEMENS
MT PLEASANT
MUSKEGON
MUSKEGON HTS
NILES
NORTON SHORES
NOVI
OAK PARK
OWOSSO
PONTIAC
PORT HURON
PORTAGE
RIVER ROUGE
RIVERVIEW
ROCHESTER HLS
ROMULUS
ROSEVILLE
ROYAL OAK
SAGINAW
SAULT STE MARIE
SOUTHFIELD
SOUTHGATE
ST CLAIR SHORES
TAYLOR
TRAVERSE CITY
TRENTON
TROY
WALKER
WAYNE
WESTLAND
WOODHAVEN
WYANDOTTE
WYOMING
YPSILANTI
CITY

o

Case #
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
Case 0

27-Dec-90
12:46:37

MARKET O P P O R T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS
• • • •

E q u a t i o n

t s B H 1s e

N u m b e r

P l o t

S e l e c t e d

2
3
4
5
6
7
8
9

15
16
17
18
19
2 0
21
2 2
23
2 4
2 S
2 6
27
2 8
2 9
3 0
31
3 2
3 3
3 4
3
3
3
3
3
4
C a s e

5
6
7
8
9
0
#

S t a n d a r d i z e d
M i s s i n g

T
R
B
L
P

Y
I
I
E
E

A
B
B
B
B
B
B
B
B
C
C

U
A
A
E
E
E
I
I
U
A
L

B
T
Y
N
R
V
G
R
R
D
A

U R N
T L E
C I
T O N
K L E
E R L
R A
M I N
T O N
I L L
W S O

D
D
E
E
E

E A R B O R N
E A R B O R N
G R A N D
R
A S T
D E T R
A S T
L A N S

. . :0

•

A N
O N
N
P A R K
N A

•

0*
0

H L S
C R E E K
T Y
H A R B O R
Y
Y
H L S
P I D S
G H A M

0*

•
•

.
.
#
0
0
0

E C O R S E
E S C A N A B A

•
N
N

2 .
1 .
6 .
0 .
7 .
2 .
5 .
2 0 .
1 4 .

.

1
2
3
1
1

0

F
F
F
F
G
G

A
A
E
R
A
R

R
R
R
A
R
A

M
M
N
S
D
N

I
I
D
E
E
D

N G T O
N G T O
A L E
R
N
C I
H A V

G
G
G
H
H

R
R
R
A
A

A
O
O
M
R

N
S
S
T
P

D
S
S
R
E

V I L L E
E
P T
P K
E
P T
W D S
A M C K
R
W O O D S

H
H
H
I
J

A
I
O
N
A

Z
G
L
K
C

E
H
L
S
K

L
L
A
T
S

A
N
E
O

H L S

•e

0
0
T Y
E N

•e

#0
♦

K A L A M A Z O O
K E N T W O O D
C I T Y
C I T Y

•

.
.*

♦

.........

3
5
6
7
1
2
5
2
2

0
1
8
6
5
9
3
4
7

10.20

0

0 :

3
1
7
1

9 . 7 3
3 . 2 1

0
0

-3.0

7
3
4
3

7 . 7 3

0

P A R K
N D
P A R K
D
R
N

.
.
.
.

5 0 . 6 3
6 . 8 0
1 1 . 3 7
1 8 . 4 4
1 4 . 0 0
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252

C a s e

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M:

27-Dec-90
12:46:47

MARKET OPPOR T U N I T Y IDENTIFICATION MODEL
PRELIMINARY ANALYSIS
• • • •

E q u a t i o n

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41

BIBLIOGRAPHY

255

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