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Airline hubs

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Title:
Airline hubs changes in urban employment structure and network connectivity
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Ivy, Russell L., 1962-
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English
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x, 186 leaves : ill. ; 29 cm.

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Subjects / Keywords:
Air carriers ( jstor )
Air transportation ( jstor )
Airports ( jstor )
Cities ( jstor )
Connectivity ( jstor )
Deregulation ( jstor )
Employment ( jstor )
Hubs ( jstor )
Passengers ( jstor )
Rates of change ( jstor )
Access to airports ( lcsh )
Dissertations, Academic -- Geography -- UF
Geography thesis Ph. D
Industrial location ( lcsh )
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bibliography ( marcgt )
non-fiction ( marcgt )

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Thesis:
Thesis (Ph. D.)--University of Florida, 1992.
Bibliography:
Includes bibliographical references (leaves 173-185).
General Note:
Typescript.
General Note:
Vita.
Statement of Responsibility:
by Russell L. Ivy.

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University of Florida
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Copyright [name of dissertation author]. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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AIRLINE HUBS: CHANGES IN URBAN EMPLOYMENT
STRUCTURE AND NETWORK CONNECTIVITY

















By

RUSSELL L. IVY














A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

1992














ACKNOWLEDGEMENTS


I would like to thank Dr. Edward Malecki for his support,

encouragement and guidance throughout my years at the

University of Florida. I also wish to thank the other members

of my supervisory committee, especially Dr. Timothy Fik, whose

quantitative assistance was invaluable. A great deal of

computer assistance, including all graphics, and patience were

given by Jan Coyne.

I would also like to thank Dr. Jesse Wheeler, Dr. J.

Trenton Kostbade and Ann Wright at the University of Missouri

for sparking my interest in geography.

Lastly, I would like to thank my mother, who has always

supported and encouraged without question anything and

everything I have attempted.















TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...................................... ii

LIST OF TABLES ................... ..................... vi

LIST OF FIGURES........................................ viii

ABSTRACT .................................... ........... ix


CHAPTERS

1 INTRODUCTION ................ ..................... 1

Hub-and-Spoke Structure....................... 1
Connectivity Change and the Location of
Economic Activities......................... 2
Objectives............ ......................... 3
Structure of Presentation...................... 3

2 INDUSTRIAL LOCATION DECISIONS .................... 5

Introduction................................... 5
Theory of Location of Organizations........... 5
Spatial Division of Labor..................... 7
Location Criteria of Various Parts of the
Firm ... ............... .................... 11
Regional Development and Industrial Location.. 18
Professional Workers.......................... 20
Labor Migration............ .................... 21
The Large Metropolitan Area.................... 21
Conclusion.................................... 24

3 THE DOMESTIC AIRLINE INDUSTRY.................... 25

Introduction..................................... 25
The Deregulated Airline Industry .............. 25
The Hub-and-Spoke System...................... 34
Conclusion.................................... 41

4 VARIATIONS IN HUB SERVICE IN THE DOMESTIC AIR
TRANSPORTATION NETWORK ......................... 42

Introduction................................... 42
The FAA Hub.................................... 43

iii









Deregulation of the U.S. Airline Industry..... 43
Hub-and-Spoke Growth and Development.......... 46
Service Variations............................ 47
Hub Connectivity................................ 51
Analyzing Hub Connectivity.................... 52
Results........................................ 55
Connectivity and Intensity Classification
Scheme .................... .................. 61
Conclusion.................................... 65

5 CHANGES IN CONNECTIVITY AND PROFESSIONAL EMPLOYMENT
LOCATION ........................................ 67

Introduction.................................. 67
Research Questions............................ 70
The Labor Data............. .................... 71
The Air Service Data.......................... 79
Research Methodology.......................... 85
Accessibility Indices......................... 86
Employment-Connectivity Relationships.......... 87
Regional Trends........ ........................ 96
Numerical Analysis............................ 107
Correlation Analysis....................... 110
Hierarchical Analysis of Hub Cities........ ll
Lag Structure............................... 115
Time Series.................... ............. 115
Conclusion.................................... 119

6 SUMMARY AND CONCLUSIONS.......................... 120

Introduction................................. .120
Summary of Results............................. 120
Guidelines for Future Research................ 122
Concluding Remarks and Contributions of the
Study ....................................... 125

APPENDICES

A CONNECTIVITY INDEX TOTALS........................ 126

B CMSA/MSA COUNTY COMPONENTS....................... 130

C ADMINISTRATIVE AND AUXILIARY EMPLOYMENT.......... 134

D TOTAL EMPLOYMENT BY MSA .......................... 138

E TOTAL ENPLANEMENTS BY MSA ........................ 143

F TOTAL NONSTOP DESTINATIONS REACHED FROM EACH
MSA........................................ .... 148

G ACCESSIBILITY INDICES: 1978-1988................ 152

iv









BIBLIOGRAPHY........................................ 173

BIOGRAPHICAL SKETCH.................................. 186














LIST OF TABLES


TABLE PAGE

2.1 Ten Most Important Factors in Selecting
Locations for Manufacturing/Processing
Plants................................ 12

2.2 Ten Most Important Factors in Selecting
Locations for Company Facilities...... 14

2.3 Five Most Important Factors in Selecting
Locations for Various Types of Company
Facilities............................. 16

2.4 Important Location Factors by Facility
Type................................... 19

3.1 Single Carrier Dominance at Hubs............ 40

4.1 Fall 1991 Hub Cities of the Major U.S.
Carriers............................... 45

4.2 Pre and Post-Deregulation Hub Cities
(1991)................................ 48

4.3 Nonstop Domestic Connections on all Carriers
by FAA Hub-Type (Fall 1991)............ 50

4.4 FAA Hubs ................................... 56

4.5 Accessibility Indices for Matrices Cl
and T................................. 58

4.6 Accessibility Indices of Matrix T With
Different Scalars..................... 60

4.7 Accessibility Indices of Individually
Weighted Nodes........................ 62

4.8 Hub Strength Classification Scheme.......... 63

5.1 60 Largest Metropolitan Areas of the U.S.
(1988-in thousands)................... 68








TABLE PAGE
5.2 Most Administrative/Auxiliary Employees:
1988................................... 73

5.3 Largest Growth in Administrative/Auxiliary
Employees: 1978-1988................. 74

5.4 Administrative/Auxiliary Employment
Loss: 1978-1988....................... 76

5.5 Administrative/Auxiliary Growth by Region:
1978-1988.............................. 76

5.6 Highest Percentage of Administrative/
Auxiliary as a Percentage of Total
Employment: 1988..................... 77

5.7 Multiple Airport Cities .................... 81

5.8 Leading Enplanement Cities of the U.S.:
1988................................... 82

5.9 Greatest Increases in Non-Stop Connections:
1978-1988.............................. 84

5.10 Study Set Cities With Highest Accessibility
Indices: 1988 ........................ 88

5.11 Mean Annual Rate of Change in Employment
and Connectivity: 1978-1988.......... 108

5.12 Hub Clusters Based on Population Totals.... 113

5.13 Mean Annual Rate of Change by Population
Cluster: 1978-1988................... 114

5.14 Time Series Analysis....................... 117















LIST OF FIGURES


FIGURE PAGE

3.1 Hub Cities of the Major U.S. Airlines:
1991................................... 36

5.1 Census Regions of the United States........ 78

5.2 Change in Professional Employment vs.
Connectivity.......................... 89

5.3 Nonhub Employment vs. Connectivity......... 91

5.4 Hub Employment vs. Connectivity............. 92

5.5 Employment Change: Hub vs. Nonhub.......... 93

5.6 Connectivity Change: Hub vs. Nonhub....... 95

5.7 Northeast Hub Rates of Change............... 97

5.8 Northeast Nonhub Rates of Change............ 98

5.9 North Central Hub Rates of Change......... 101

5.10 North Central Nonhub Rates of Change....... 102

5.11 Western Hub Rates of Change................ 103

5.12 Western Nonhub Rates of Change.............. 104

5.13 Southern Hub Rates of Change............... 105

5.14 Southern Nonhub Rates of Change............. 106


viii














Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree Doctor of Philosophy

AIRLINE HUBS: CHANGES IN URBAN EMPLOYMENT STRUCTURE
AND NETWORK CONNECTIVITY

By

Russell L. Ivy

August, 1992

Chairperson: Edward J. Malecki
Major Department: Geography

The deregulation of the domestic airline industry in the

U.S. brought about many changes in air transportation.

Carriers have come and gone, fares have fluctuated and

enplanement figures have risen tremendously. Fundamental

changes have also occurred in the geographic structure of the

air transport network. In particular, the hub-and-spoke

system has been adopted by major carriers and has led to

increases in flow efficiency and connectivity. One or more

cities are chosen by the airline as a regional collection

point for passengers. Passengers from many different origins

are funneled into the hub city to connect with flights to

their final destinations. Hub cities, therefore, are

characterized by having many nonstop destinations available

from their airports.








Living and working in hub cities can have tremendous

benefits for the time-sensitive traveler. Professional

employees such as research scientists and engineers, managers

and salesmen fall into this category. Face-to-face

communication is an important part of their job as they are

often required to travel quickly to a client, other facilities

of the firm or yet another city. Recent industrial location

literature and surveys of corporations imply that the high

quality air transportation that hub cities offer is extremely

desirable when locating or relocating these nonroutine

employees of the firm. The purpose of this study was to link

the changes in connectivity that occur as a city is chosen to

be an airline hub to changes in professional employment

growth.

The analysis of this study, however, fails to show a

significant relationship between changes in air service

connectivity and professional employment growth in the 60 MSAs

in the study set over the period from 1978 through 1988.














CHAPTER 1
INTRODUCTION


The deregulation of the domestic airline industry has

brought about many changes in air travel in the United States.

Both new and older, established carriers have disappeared in

bankruptcy courts or through mergers with other carriers.

Fares dropped tremendously at first, but have been on the rise

since the end of the 1980s, and passenger enplanements have

skyrocketed.

Another significant change has been in the geographic

structure of the air travel network itself. Fewer nonstop

flights are available to some cities than before, as service

is now fed into hub cities of specific carriers instead of

most or all larger cities within a region. This study will

examine changes in the professional employment structure of

those hub cities as their connectivity levels

rise.



Hub-and-Spoke Structure



Hub cities act as a collection point of air passengers in

a region and thus have a tremendous amount of flow in and out

of them. Passengers are flown into the city from many







2

different origins to connect with flights to their final

destinations. Hub cities, therefore, have a multitude of

nonstop alternatives available to local residents.



Connectivity Change and the Location of Economic Activities



It can be posited that the restructuring of air networks

can greatly influence the location of economic activities.

Because of the large number of nonstop flights and

destinations from hub airports, certain corporate activities

should find it advantageous to be located in a hub city.

Who is the corporate traveler? What types of positions

within the firm would require frequent air travel? Typically

such employees are those for whom a lot of face-to-face,

nonroutine communication both within and outside of the firm

is vital. Even though communication technology has made

phenomenal advancements in recent years, situations occur for

which there is no viable alternative or substitute for an on-

site visit. Firms desire close contact with their clients and

markets to respond quickly to their changing needs. Managers,

sales staff, scientists, engineers and other administrative

employees are often required to travel quickly to other parts

of the company, to clients, or to other locations. In order

to minimize travel time, it is preferable to fly nonstop to

one's final destination rather than to fly two or more flight

segments with layovers in between. The savings in total







3

travel time can be very significant to a profit-oriented

organization as well as the general public.



Objectives



How do hub cities measure up? Do they indeed have a

higher concentration of employees from management and research

and development labs? More importantly, has the change to a

hub-and-spoke network yet enhanced the attractiveness of newly

designated hub cities? Investigation of these issues is the

focus of this study.

Data will be collected and analyzed from various

publications of the Official Airline Guide, Inc., the U.S.

Census Bureau and the Federal Aviation Administration for each

year from 1978 through 1988 to examine the degree to which

changes in connectivity that occur when an airline chooses a

particular city as a transfer hub are related to changes in

employment growth of the above-mentioned type.



Structure of Presentation



Chapter 2 presents a review of the pertinent industrial

location literature, while Chapter 3 discusses the domestic

air transportation industry before and after deregulation.

These two chapters will build a framework within which to

place this dissertation in the broader perspective of







4

industrial location and air transportation research. In

Chapter 4, variations in hub service quality and connectivity

are discussed and a hub classification scheme (based on

connectivity) is derived using binary matrix analysis. The

main analysis (methodology and results) of this dissertation

is presented in Chapter 5. Chapter 6 is a summary of the

study and directions of future research.














CHAPTER 2
INDUSTRIAL LOCATION DECISIONS


Introduction


Different activities of the firm have different

locational requirements. Nonroutine activities, such as

management and research and development, are less influenced

by wage rates and site location costs than routine activities,

such as manufacturing. This chapter will look at the location

of organizations in the context of spatial division of labor,

and identify the specific locational needs of the various

activities of the firm. It also includes a discussion of

professional workers and the role they play in business

location decisions. Therefore, this chapter is an important

component of the study since it will help identify places

where professional workers are likely to be concentrated.



Theory of Location of Organizations



Traditionally, industrial location theory has been based

on profit maximization through the minimization of costs

(Alexander, Gibson, 1979; Boyce, 1978; Chapman, Walker, 1987;

De Souza, 1990; De Souza, Foust, 1979; Dicken, Lloyd, 1990;

Hoover, 1948; Wheeler, Muller, 1986). Transportation costs

5







6

and the locations of raw materials and markets were major

concerns. One of the main flaws in this approach, however, is

the assumption that labor quality, supply and cost are the

same at all locations (Moriarty, 1980; Watts, 1987).

Elaborate models to determine minimum-cost sites focused,

consequently, solely on transportation costs as spatially

variable (Dicken, Lloyd, 1990).

A broader view of industrial location theory, on the

other hand, holds that labor is the key to determining firm

location since, in reality, labor has a high degree of spatial

differentiation in terms of both supply and level of skill

and, therefore, cost (Storper, Walker, 1984). This

differentiation of labor also occurs according to both

industry (industries differ in their mix of labor) and

function within the firm, such as marketing, research and

development or routine manufacturing. Thus, the geographic

pattern of industry represents the spatial division of labor

as particular industries, as well as particular activities of

the firm, correspond to the geographic location of labor

(Blair, Premus, 1987; Czamanski, 1981; Massey, 1984; Storper,

Walker, 1983).

Spatial division of labor within the firm has become

quite commonplace and has been the subject of much academic

work (Schoenberger, 1986, 1987; Watts, 1987). Technological

improvements in communication and automation have increased

the spatial separability of parts of the production process







7

and of organizational functions (Storper, Walker, 1983). The

different parts of the organization can search for locations

that will more greatly benefit their specific tasks or roles

within the firm, without the necessity of being physically or

locationally tied to other functions of the organization that

might have entirely different locational requirements. As the

spatial variations in cost, quality and availability of

nonlabor inputs have largely diminished, the issue of labor

has risen greatly in importance (Moriarty, 1980; Storper,

Walker, 1984).



Spatial Division of Labor


Because a supply of unskilled labor for standardized,

routine production is virtually ubiquitous, labor-intensive

manufacturing and assembly operations can locate almost

anywhere (Blair, Premus, 1987; Clark, 1981; Czamanski, 1981;

Massey, 1984; Schmenner, 1982). When the manufacturing

procedure does become standardized or routine, firms will

establish branch plants--often in nonurban areas with

depressed local labor markets to reduce labor competition

(Erickson, Leinbach, 1979). Labor-intensive operations

primarily seek to reduce labor costs (to obtain higher profits

for the firm) and generally locate or relocate in low-wage

areas anywhere in the world. Such areas are typically located

outside of the traditional innovative centers of industry









since production need not be linked to innovative research and

development, which relies on highly skilled labor. Production

shifts to these peripheral regions from high-cost core regions

because the competition for labor in large urban areas, and

therefore the price paid for labor, has increased to the point

where profit margins are unacceptably low or nonexistent

(Barkley, 1988; Markusen, 1985). Thus, routine operations in

both manufacturing and services have largely decentralized to

peripheral areas within developed countries and to developing

countries (Clark, 1981; Schoenberger, 1986, 1987).

These industrial location shifts can be explained by

Vernon's product life cycle theory (1966). Industries, firms

and products move down the urban hierarchy as they go through

different stages (each with distinct locational requirements,

labor needs, growth rates and profitability) in their life

cycle (Barkley, 1988). Mobility of the various functions of

the firm is often a necessity for firm survival, influenced by

the potential for profit in each stage.

The profit cycle model complements the insights from the

project life cycle model. Markusen (1985) describes the long-

term profit cycle as having five characteristic stages. In

stage one (zero profit), output is very low (often done in

test runs) while a new product or new design is being

initiated. Costs per unit are high since the primary

workforce involved is composed of many nonroutine workers,

such as scientists, engineers and other technicians, who are







9

experimenting with the test runs. Production is usually

concentrated at or very near the firm's research and

development laboratory.

As development of the product or service becomes

successful, dramatic growth of production and profitability

occurs. The firm has a temporary monopoly in the market, and

demand-led prices for the new product or service will create

excess profits. In this stage (super profit), standardization

of production begins to take place, involving a greater number

of routine laborers than previously, and the average cost per

unit produced will begin to drop. As a result, production

begins to decentralize from the research and development

center.

In stage three (normal profit), other organizations enter

the market with similar products, and price competition can be

intense as the market approaches saturation. Mass

standardized production is now the norm as cost-cutting

becomes imperative. The trend toward decentralization becomes

accelerated and the role of the professional/technical worker

largely diminishes.

Stage four (normal-plus and normal-minus profit) and

stage five (negative profit) are both post-saturation stages.

In the former, profit levels can drop even further due to the

greatly increased competition or can rise slightly via

successful oligopolization. The latter stage is usually where







10
production will cease. The product becomes obsolete, and the

firm takes absolute losses on production.

Nonroutine and innovative activities of the firm, such as

research and development, corporate and regional headquarters

functions and marketing operations, need to be located in core

regions of developed nations as they have a high dependence on

skilled labor (Malecki, 1986). These divisions of the

organization cannot thrive in all locations in space as the

availability of labor skills varies by nation, region and city

(Kim, 1987; Walker, Storper, 1981).

These higher order functions often remain with or very

near the corporate headquarters in metropolitan areas or in

other metropolitan areas near other parts of the firm (Clark,

1981; Erickson and Leinbach, 1979). Due to rapid

technological and market changes, they require a high quality

and quantity of day-to-day information concerning competition,

suppliers and customer needs. While there is a trend toward

shorter production runs and more innovative products, which

requires interaction of research and development with

manufacturing, production and research activities remain on

the whole largely separate in purpose and location. This

"flexible" production, therefore, needs the same large-city

skilled labor as research and development (Arnold, Bernard,

1989; Malecki, 1991; Schoenberger, 1988). Thus, a

transportation and communication infrastructure is a

requirement for firms when locating nonroutine activities.









Location Criteria of Various Parts of the Firm


A recent survey of Fortune 500 firms clearly indicates

that the various parts of the firm have different locational

requirements (Corporate Site Selection for New Facilities,

1989). It examines location and relocation criteria of the

firms and separates the information into two general

categories: 1) manufacturing/processing plants and 2) company

facilities. The category of manufacturing/processing plants

includes assembly plants, processing plants, extraction plants

and warehouse/distribution centers, while the company

facilities category includes corporate headquarters, regional

headquarters, branch offices, sales offices and branch office

processing.

Table 2.1 shows the ten most important factors in

selecting locations for manufacturing/processing plants as

identified by the study. The criteria mostly relate to costs.

Reduction or minimization of labor costs, taxes,

transportation costs and land prices are high-priority

considerations for these facilities. It is evident that small

cities or, in some cases, rural areas in depressed economic

regions would be ideal choices (Goldfarb, Yezer, 1987; Heenan,

1991).

The study also found that the South Atlantic region of

the United States (North Carolina, South Carolina, Georgia and

Florida) was the most popular location choice for









TABLE 2.1
TEN MOST IMPORTANT FACTORS IN SELECTING LOCATIONS FOR
MANUFACTURING/PROCESSING PLANTS



Easy access to trucking services 79%

Easy access to domestic markets, customers
and clients 74%

Cost of labor 74%

Ample area for future expansion 73%

Easy availability of electricity 71%

Community receptivity to business and industry 70%

Reasonable government/state and or local
corporate tax structure 68%

Fair-market property costs 67%

Availability of skilled workers 64%

Extent of unionization 64%



Source: Corporate Site Selection For New Facilities, The
Time Inc. Magazine Company, 1989.







13

manufacturing/processing plants in the past five years and

will continue to be the favorite for the next five years.

Lower taxes, cost of living and relative absence of

unionization add to the attractiveness of this region to

firms. The South Atlantic was followed by the East South

Central region (Kentucky, Tennessee, Alabama and Mississippi)

and the West South Central region (Arkansas, Louisiana, Texas

and Oklahoma) in that order. Atlanta, Memphis and Dallas (in

rank order) were the most popular urban areas chosen for

location by those firms not desiring a small city or rural

location.

The attraction of the South has been evident for some

time. An earlier study of change in manufacturing employment

by state from 1967 to 1972 showed the greatest gains in

manufacturing employment in North Carolina (100,000), Texas

(78,000), Florida (58,000), Tennessee (49,000) and Georgia

(44,000) (Moriarty, 1980). Indeed the "Selling of the South"

for manufacturing operations began in the 1930s (Cobb, 1980;

1984).

The ten most important factors in selecting locations for

company facilities, a group that includes nonroutine corporate

functions (as determined by the Fortune 500 study), are listed

in Table 2.2. Here cost considerations largely give way to

access to markets and the supply of professional labor as well

as adequate transportation facilities.









TABLE 2.2
TEN MOST IMPORTANT FACTORS IN SELECTING LOCATIONS FOR
COMPANY FACILITIES



Easy access to domestic markets, customers
and clients 73%

Easy access to airport 62%

Efficient transportation facilities for
people/employees 59%

Availability of affordable housing 55%

Availability of technical or professional
workers 54%

Facilitates access to prospective clients 52%

Urban/metropolitan location 50%

Cost of living 48%

Reasonable government/state and/or local
corporate tax structure 48%

Fair-market property costs 48%




Source: Corporate Site Selection For New Facilities, The
Time Inc. Magazine Company, 1989.







15

Face-to-face, nonroutine communication both within and

outside of the firm is a vital part of the work of

professional employees. Even though communication technology

has made phenomenal advancements in recent years, there are

times when there is no viable alternative or substitute for an

on-site visit. Firms desire frequent contact with their

clients and markets to respond quickly to their changing

needs. Within the firm, management officials, research

scientists and engineers and other administrative service

professionals often need to interact with workers in other

facilities of the organization to deal with problems and new

innovations as quickly as possible. Consequently, frequent

air travel between the various facilities of the firm is quite

common for these employees (Coffey, Polese, 1989).

A comparison of the top location criteria determined by

the study for the various types of company facilities is

revealing (Table 2.3). Most of the lists are identical in

content with only slight changes in rank order of the

criteria.

The back office/processing category, however, is

noticeably different. This activity, traditionally associated

with the headquarters location, is now largely moving away

from central urban locations to, at the very least, suburban

locations where land costs are lower (Ady, 1986; Coffey,

Polese, 1987; Nelson, 1986). The nature of the work--

processing data and paperwork--can be classified as routine









TABLE 2.3
FIVE MOST IMPORTANT FACTORS IN SELECTING LOCATIONS FOR
VARIOUS TYPES OF COMPANY FACILITIES



Back Office/Processing Location

1) Availability of skilled workers
2) Availability of unskilled or semi-skilled workers
3) Efficient transportation facilities for people/employees
4) Easy access to domestic markets, customers and clients
5) Cost of labor


Branch Office/Recional Headauarters Location

1) Easy access to domestic markets, customers and clients
2) Facilitates access to prospective clients
3) Availability of technical or professional workers
4) Availability of skilled workers
5) Easy access to airport


Corporate Headquarters Location

1) Availability of technical or professional workers
2) Easy access to domestic markets, customers and clients
3) Easy access to airport
4) Availability of skilled workers
5) Efficient transportation facilities for people/employees


Sales Office Location

1) Easy access to domestic markets, customers and clients
2) Facilitates access to prospective clients
3) Easy access to airport
4) Efficient transportation facilities for people/employees
5) Availability of technical or professional workers




Source: Corporate Site Selection For New Facilities, The
Time Inc. Magazine Company, 1989.







17
even though it is unrelated to manufacturing in many cases,

such as in the finance and insurance sectors. A life cycle

from nonroutine to routine work also applies to service tasks.

The South Atlantic region of the nation proved to be the

most popular location choice for company facilities as well,

followed in rank order by the Pacific (California, Washington

and Oregon), the East North Central region (Ohio, Indiana,

Michigan, Illinois and Wisconsin) and the Middle Atlantic

region (New York, New Jersey and Pennsylvania). Because costs

are not the most important factor in locating these

facilities, the historically innovative industrial areas of

the United States still show up strongly. Atlanta, Chicago

and Dallas were the most popular urban areas chosen for

location or relocation of company facilities by the firms in

the survey.

One important firm facility not singled out by the

Fortune 500 study was research and development. Research and

development (R&D) employs mainly scientists, engineers and

other technicians who are involved in nonroutine, innovative

research and testing of new products or production processes

for the firm. R&D facilities also have their own set of

location requirements such as the availability of

professional/technical labor, good transportation

accessibility (particularly air) and access to scientific and

technical information (Ady, 1986; Browning, 1980; Lund, 1986;

Malecki, 1979a, 1979b, 1980a, 1980b, 1981). Most firms will







18
locate their R&D facility (or at least one such facility if

there are several) at or very near the corporate headquarters

(Malecki, 1980b).

An earlier study conducted by Dow Jones, Inc. (Browning,

1980) confirms that the important criteria found in the

Fortune 500 survey for the various firm facilities have been

important for at least a decade. The results of this study

are given in Table 2.4.



Regional Development and Industrial Location


Regional development is greatly dependent on the location

decisions of firms (Knapp, Graves, 1989). As the number of

acceptable sites for the various firm facilities has

increased, competition among cities, states and regions to

attract industry has become particularly fierce (Cobb, 1980,

1984; Johnson, 1989). Most nonurban areas have focused on

attracting manufacturing branch plants, as have some

metropolitan areas in low-wage parts of the nation. Most

urban areas, however, are searching for the nonroutine firm

facilities to locate in their city (Thompson, 1987).

According to Jacobs (1984), these nonroutine activities of the

firm are perceived to be more immune to cyclical and

structural changes in the economy and are less likely to

experience severe job loss or closure in bad economic times.









TABLE 2.4
IMPORTANT LOCATION FACTORS BY FACILITY TYPE



Manufacturing Plant

1) labor availability
2) energy availability
3) good highway infrastructure


Distribution Center

1) good highway infrastructure
2) market accessibility
3) labor availability


Corporate Headauarters

1) good air transportation service
2) good highway infrastructure
3) professional talent availability


Regional Office

1) good air transportation service
2) good highway infrastructure
3) professional talent availability


R&D Facility

1) professional talent availability
2) good air transportation service
3) good highway infrastructure


Source: Browning, 1980.









Professional Workers


The professional labor force is indeed highly mobile and

exerts a great deal of influence on corporate location

decisions (Buswell, 1983; Knapp, Graves, 1989). Rapid

technological change has created a greater need for highly

skilled professional workers, thus increasing the significance

of professional labor as a location factor (Weiss, 1985). As

they are highly educated and career-oriented, they have a

greater degree of choice over where they live and work than do

unskilled laborers (Cooper, Makin, 1985; Massey, 1984;

Storper, Walker, 1983).

Research has shown that professional workers have a

strong preference for large urban areas or at least areas

within commuting distance of one (Bradbury, 1988; Malecki,

1987). Cities provide a greater choice of jobs, the ability

to change jobs without changing residences, employment

opportunities for the spouse and greater cultural and

recreational amenities (Herzog, Schlottmann, Johnson, 1986;

Noyelle, Stanback, 1984). According to a recent study, the

dual-career couple has started to exert a great deal of

influence over the location decisions of some firms (Bradbury,

1989). Because of professional workers' narrow range of

preferred locational characteristics, Buswell (1983) and

others state that professional workers are at the same time

geographically immobile (Business Week, 1981).









Labor Migration


Studies have shown that migration of laborers is affected

by changing economic opportunities (Gentile, Stave, 1988;

Greenwood, 1988). Workers leave an area when they feel their

economic prospects are greater elsewhere (Goldfarb, Yezer,

1987). Long and Hansen (1979) found that about half of all

moves in the U.S. were to take a job or seek out new

employment.

Other factors affecting migration are education,

occupation, sex and marital and family status (De Jong,

Gardner, 1981; Kaufman, 1982). Young, well-educated

professional workers are prime candidates for migration

(Greenwood, 1981; Gentile, Stave, 1988). They have fewer ties

to specific areas and are often required to relocate within

their firm to further their careers. Men migrate more often

than women, and single workers more often than married

(especially those with children) (Gentile, Stave, 1988).



The Large Metropolitan Area


For nonroutine and innovative divisions of the firm, the

firm's locational needs and the locational preferences of its

professional workers must be considered together. These

different sets of priorities both restrict and reinforce one

another with the large metropolitan area being the common







22

solution. Despite the fact that there are negative attributes

associated with them (such as higher land costs, taxes and

competition for professional labor for the firm, and higher

housing costs, congestion, pollution and increasing crime

rates for the professional worker), large urban areas are

highly attractive to both firms and workers because of

agglomerative advantages and high quality of life (Dahmann,

1983).

Agglomeration economies are advantages associated with

being in close proximity to markets and services needed by the

firm. Sometimes these advantages arise from the organization

locating in a specific area, such as Silicon Valley, where

their industry or similar industries tend to cluster

(localization economies). These advantages can also occur,

however, to all firms in all industries in larger metropolitan

areas as firms, suppliers and clients interact with one

another (urbanization economies) (Scott, 1988a, 1988b; Watts,

1987).

Large urban areas provide the firm with an ample supply

of professional workers, suppliers, services, information and

important infrastructure (such as airports with frequent air

service to a variety of destinations) and facilitate face-to-

face contact when necessary (Andersson, 1985; Dorfman, 1983;

Malecki, 1987; Oakey, 1985; Scott, 1983). Being located in a

large metropolitan area maximizes the opportunities for a firm

to find the industrial linkages it needs while minimizing the







23

cost of seeking out those linkages (Coffey, Polese, 1989;

Hoare, 1985).

Large urban areas are attractive to professional workers

because they provide a high level of amenities (such as

cultural and recreational) which positively affect their

quality of life. Cutter (1985) defines quality of life as

one's happiness with the physical and social environment based

on how well that environment fits one's personal needs and

desires. This, of course, is a subjective concept (making it

difficult to measure) and can vary greatly from one individual

to another (Canter, 1983; Cutter, 1985). Affecting the

quality of life image for professional workers are such

factors as climate, cultural and recreational amenities,

transportation accessibility, higher educational

opportunities, quality of health care, crime rates, pollution

levels and cost of living (Boyer, Savageau, 1989; Glasmeier,

Hall, Markusen, 1984; Herzog, Schlottmann, Johnson, 1986;

Hsieh, Liu, 1983; Pennings, 1982).

Professional workers can be more selective about where

they live, and do indeed evaluate quality of life factors when

making locational decisions (Markusen, Hall, Glasmeier, 1986;

Power, 1980). Location is often the most important factor in

the rejection of job offers by job-hunting professionals and

transfers within the firm (Collie, 1986; Pinder, 1977). For

these workers, large urban areas offer greater employment

opportunities for themselves and their spouses, the







24
possibility for changing jobs without changing residences and

a generally more satisfying lifestyle.



Conclusion


Professional workers are engaged in nonroutine activities

of the firm. These employees have more control over where

they live and work than do employees of routine activities.

Location of nonroutine activities, therefore, must be a

combination of the residential desires of professional workers

and the specific needs of the firm. These workers seek places

that provide a high quality of life for them and their

families. The firm's needs largely relate to good air and

ground transportation, access to supplies and markets and, of

course, adequate supply of professional labor. Large urban

areas provide the solution, however, not all cities are equal.

This is particularly the case, for example, in air

transportation, which ranked high on the list of locational

needs for all nonroutine activities of the firm. Airline hub

cities have a much greater variety of nonstop destinations and

more frequent departures from their airports than nonhubs.

What follows is a discussion of the air transportation

industry and network in the United States.














CHAPTER 3
THE DOMESTIC AIRLINE INDUSTRY



Introduction


Since the deregulation of domestic airlines in 1978, a

tremendous degree of change has occurred in almost every

aspect of the air travel industry in the United States. As

the industry became deregulated, network structures, fares,

enplanement levels and service quality were greatly affected.

Many new carriers have come and gone (such as Air Florida,

Midway and People's Express), and several older, established

carriers (such as Eastern, Pan Am and Braniff) have

disappeared as well. The first part of this chapter deals

with the deregulated industry and its evolution thus far. The

second part describes the hub-and-spoke network structure,

which is largely a post-regulation phenomenon, and looks at

changes in the location and use of hub cities by the air

travel industry.



The Deregulated Airline Industry



For 40 years the domestic airline industry in the United

States was regulated by the federal government under the Civil







26

Aeronautics Board (CAB). Beginning in 1938, regulation by the

CAB was considered necessary in order to protect and ensure

the success of the industry. The CAB originally also set

safety standards, a task which was later (in 1958) delegated

to the Federal Aviation Administration (FAA) (Bailey, Graham,

Kaplan, 1985; Brown, 1987; Meyer, Oster, Morgan, Berman,

Strassman, 1981). The CAB had the power to decide if new

carriers could enter the air transportation business, to tell

carriers what particular routes they could fly, to regulate

fares, to award government subsidies to carriers who were

forced to fly nonprofitable routes to smaller cities, and to

control mergers and acquisitions (Bailey, Graham, Kaplan,

1985; Brenner, 1988; Brown, 1987; McIntosh, Goeldner, 1990).

For example, if a carrier wanted to increase its fare between

Newark and St. Louis, to begin scheduled service between

Memphis and Charlotte or to discontinue service to Tulsa, it

was required to have CAB approval.

During the 1970s, many economists and politicians began

to argue that regulation was no longer necessary. It was

increasingly felt that the U.S. airline industry was mature

and that government intervention was creating a very

inefficient system (Bailey, Graham, Kaplan, 1985; Brown, 1987;

Cates, 1978; Cooper, Maynard, 1972; Snow, 1977). Removing

the regulatory barriers, it was further argued, would force

the industry to become competitive, bringing about a more

efficient and affordable transportation system to the American









traveler (Meyer, Oster, 1987). Proponents of deregulation

agreed that many changes would occur early in the era of

deregulation that would resemble chaotic instability of the

industry but, with time, things would stabilize and the cost

and quality of air service would greatly improve in the

competitive environment (Goetz, Dempsey, 1989).

The Airline Deregulation Act of 1978 was signed by

President Carter and called for a gradual removal of

government control over domestic air transportation, with the

exception of safety standards (under FAA supervision) and

merger and acquisition approval (monitored by the Department

of Transportation). A gradual phaseout was recommended to

ease the transition to perfect competition in the industry

and, by the end of 1984 (when the CAB was dissolved), the

phaseout was completed. Following the lead of the U.S.,

deregulation of air transportation (or at least partial

deregulation) has been put in action in a few other western

countries, such as Canada and the United Kingdom (Graham,

1990).

A major goal and promise of deregulation was that the

cost of domestic air travel in the United States was to become

more affordable to the average American. This was supposed to

occur because carriers would now compete with one another for

business. Two or more carriers would be flying nearly

identical routes as they could now choose where they would

fly. Also, new airlines were allowed to form at will, which







28

would eliminate the near monopolistic situation of the larger

carriers with respect to total market share of U.S.

passengers. The contestable market theory of air

transportation held that the mere threat of a new entrant in

a market would keep fares down (Leigh, 1990).

During the early 1980s, it appeared that these

predictions were realized (Graham, Kaplan, Sibley, 1983;

Maraffa, Finnerty, 1988; Rose, 1981). Fares dropped

dramatically, allowing more people to afford air travel than

ever before, and over 100 new airlines, mostly small

commuters, began operation (Goetz, Dempsey, 1989). The latter

part of that decade, however, saw unexpected changes that have

caused some to question the wisdom of deregulation (Bauer,

Zlatoper, 1989; Business Week, 1986, 1988b, 1988c; Leigh,

1990; Morrison, Winston, 1989). In fact, discussions of at

least partial reregulation are starting to grow (Kuttner,

1989; Rose, Dahl, 1989).

Many of the new carriers that were supposed to challenge

the dominant market shares of the major carriers,

unfortunately, were short-lived. Despite the fact that new

carriers were successful in the early years of deregulation in

reducing the share of revenue passenger miles of the larger

airlines, in the long run most all were squeezed out (Goetz,

Dempsey, 1989). It is unlikely that there will be a lot of

new airlines entering the industry to replace them, as







29

investor confidence in new airline ventures has lessened

considerably (Dempsey, 1987; Rose, Dahl, 1989).

These new smaller carriers had several strikes against

them from the beginning. For example, the essential

infrastructure of gates, terminal facilities and landing slots

has been controlled by the major carriers, making it harder to

enter new markets. Almost 70% of all U.S. airports have no

gates at all that could be leased to new carriers--although

space may sometimes be sublet from other carriers (Hardaway,

1986). Because of their greater buildup of capital, the major

carriers have a higher chance of surviving fare wars, and they

have used this power to drive both new airlines and old,

nearly bankrupt carriers out of business. For those small

carriers that did not arrange marketing agreements with the

large carriers, much of their business was taken away by the

latter due to frequent flyer program loyalty (particularly by

business travelers) (Toh, Hu, 1988) and computerized

reservation systems made available to travel agencies (Davis,

1982; Oster, Pickrell, 1988).

A glance at the market-share data before and after

deregulation shows the trend in market dominance by the

largest carriers. At the end of regulation in 1978, the top

six airlines controlled 71% of the domestic air traffic. In

1983, that figure dropped to 65% due largely to heavy influx

of new carriers, but by the end of 1987 it had risen to 79%

(Brenner, 1988) and is rising going into the 1990s (Fortune,









1990). Contributing to this rise was the wave of acquisitions

and mergers that occurred during the mid 1980s. The larger

carriers began to realize that the best way to survive was to

increase their market shares and to neutralize their

competition by merging with other carriers (Carlton, Kaplan,

Sibley, 1983).

The main, and largely unforeseen, problem that has arisen

out of the merging has been the increased control gained by

individual airlines over travel in and out of certain cities.

The merger of Northwest and Republic Airlines, for example,

and TWA with Ozark created market shares of over 80% at

Minneapolis/St. Paul and at St. Louis for Northwest and TWA,

respectively (McGinley, 1989). Between 1985 and 1988, the

number of airports served by at least four airlines dropped by

52%, while the number of cities served by only one carrier

increased by 25% (Kuttner, 1989). Nearly two-thirds of all

route destinations are now near-monopolies (Traffic World,

1988). This dominance has permitted fare increases, rather

than the lower fares predicted by deregulation enthusiasts

(Bauer, Zlatoper, 1989; Business Week, 1988b). For example,

fares out of St. Louis rose twice as fast as the national

average after TWA purchased Ozark (McGinley, 1989).

Another change has been the dramatic decline in service

to some urban areas. Some cities are simply harder to get to

since deregulation, prompting passenger complaints but little

response from either airlines or the federal government.









Small cities have been most adversely affected, both in number

of nonstop destinations reached from their facility and in

seating capacity (Chan, 1982; Ivy, 1991; Kiel, 1989; Maraffa,

Kiel, 1985; Warren, 1984). Airlines have often replaced

larger jet aircraft with commuter turboprop planes with less

seating capacity on existing routes. Out of 522 small cities

that received scheduled commercial air service in 1978, 62%

experienced a decline in flight frequency, and nearly 30% of

those experienced complete loss of service (Goetz, Dempsey,

1989).

Perhaps the most important issue to passengers is safety

of air travel. This has become a major public issue because

of the seemingly high number of accidents and mechanical

failures of aircraft reported during the past decade.

Government statistics lead one to believe that air travel is

actually safer in the deregulated skies. Compared with the

decade prior to deregulation, jet accidents through 1987

declined by 36%, while the number of fatalities from air

crashes declined by 40% (Moses, Savage, 1988). What such data

do not show, however, is that the number of near-accidents has

been on the rise because of delayed mechanical attention of

aircraft and increased congestion, both in the air and along

runways due to the competitive nature of the deregulated

industry. The number of near-accidents has risen from 568 in

1980 to 1056 in 1987 (Moses, Savage, 1988).







32

According to the Department of Transportation, the amount

spent on aircraft maintenance dropped 30% during the first six

years of deregulation (Valente, McGinley, 1988). Moreover, a

survey of commercial airplane pilots revealed that almost half

believe that their companies defer maintenance of their fleets

too long. In addition, the average age of the industry's jets

increased by 21% since 1979, with more than half of the jets

in service being 16 years or more old in 1988 (Valente,

McGinley, 1988). Older fleets typically require more

maintenance and repairs than do newer aircraft.

The general consensus of the flying public is that the

quality of air service has greatly declined since

deregulation. A recent survey of consumers found that 50%

felt that such service had declined significantly, while under

20% expressed feelings of improvement. The FAA has reported

a soaring number of consumer complaints against the airlines

(Consumer Reports, 1988). Deregulation and the resulting

network geography of the major carriers have not delivered the

promised benefits to all cities and passengers alike

(Anderson, Kraus, 1981; Ippolito, 1981). As already

mentioned, small cities, on the whole, were adversely affected

in terms of seating capacity, fewer non-stop choices and less

frequent service. However, many large cities that were not

selected as hubs by major airlines (such as New Orleans,

Buffalo, and Louisville) have experienced service decline--

particularly in the number of non-stop destination choices







33

reached from each city. In terms of flight frequency,

destination choice and ticket prices, residents of cities

chosen as a hub by more than one carrier (such as Chicago,

Dallas/Ft. Worth, and Atlanta) have probably benefited most

from deregulation. Because they are hub cities, they offer a

multitude of non-stop destination choices at various times

during the day. Having more than one carrier using an airport

as a major transfer hub creates competition along many non-

stop routes, therefore, keeping fares competitively lower.

For example, while the national average annual fare change at

hub airports from 1985 to 1988 was a growth of 4%, Atlanta

(which served as a hub for Eastern and Delta during that

period) experienced a growth of only 1.5% (McGinley, 1989).

The 1980's saw great instability in the industry, and the

1990's (thusfar) are showing no signs of stabilization. Many

airlines are still in financial trouble (Continental, America

West and TWA are operating under bankruptcy protection as of

early 1992). Soaring oil prices, the current recession and

intensifying competition from the three giants of the industry

(American, United and Delta) are the main sources of the

problem (Business Week, 1991a, 1991b, 1991c, 1991d, 1991e,

1991f; Fortune, 1990). Now that over a decade has passed

since deregulation, it is increasingly clear that air

travelers will likely pay much more for lower quality, less

convenient and perhaps more risky transportation by the U.S.

airlines.









The Hub-and-Spoke System



The hub-and-spoke system currently in place in the U.S.

airline industry is largely a product of deregulation. It is

the new structure adopted by most airlines to compete more

effectively. The major carriers created hubs at strategic

regional points in their air networks so travelers from

numerous origins (spokes) could be routed into the hub city

and then connected with a flight to their final destinations

(Business Week, 1988a). The hub-and-spoke structure cuts

costs by creating greater overall efficiency with more

occupied seats. Route planners, therefore, approach monthly

scheduling differently today than prior to deregulation, as

such planning is now more important to the profitability of

the company than previously (Pollack 1977, 1982).

What few hubs that existed in the regulated era had a

slightly different function than hubs today. Chicago, Denver

and St. Louis, for example, were used to serve long-haul,

east-west markets which used aircraft that did not have

transcontinental capabilities (Lopuszynski, 1986). Thus,

these cities were set up as transfer points for coast-to-coast

traffic. Because of CAB control over routes flown, it would

have been difficult for an airline to set up a hub-and-spoke

network prior to deregulation.

The deregulated era saw many true hubs develop that acted

as collection points for travelers from many origins, sending









them off to many destinations. This new system, along with

increased cooperation and code-sharing between major carriers

and commuter carriers, made a much greater on-line (same

carrier) city-pair matching available to air travelers (Oster,

Pickrell, 1988).

The first true hub in this sense was developed by Delta

Airlines in Atlanta, and actually was in place before

deregulation (Lewis, Newton, 1979). Delta dominated traffic

in and out of Atlanta for decades and gradually added more and

more service as the years passed. It was the model system

copied by other airlines as deregulation forced them towards

greater efficiency in order to compete. Figure 3.1 shows the

major hub cities (in mid-1991) of the largest carriers.

Much has been written on the positioning of hubs in the

air transportation network (Bauer, 1987; Grove, O'Kelly, 1986;

Kanafani, Ghobrial, 1985; Lopuszynski, 1986; O'Kelly, 1986a,

1986b; Toh, Higgins, 1985). What makes one city likely to be

chosen as a hub over another city? In general, the very

largest cities have been favored over medium-sized cities, and

eastern cities over western cities. In fact, several cities

have been chosen as hubs by more than one carrier, such as New

York City, Chicago, Denver and Dallas.

The early trend of choosing the largest cities of the

nation as hub cities eventually died as these airports became

overly congested. Not only was safety an issue, but delayed

flights, overuse of infrastructure, little room for expansion





























































FIGURE 3.1
HUB CITIES OF THE MAJOR U.S. AIRLINES: 1991







37

and oversaturated markets plagued some of the hubs. Carriers

began to look for medium-sized cities as their transfer

points. Often these were strategically located near one of

the large, congested hubs to give travelers a pleasant

alternative to places like Chicago O'Hare. Piedmont Airlines,

later acquired by USAir, made an early success by choosing

Charlotte as its southeastern hub, allowing passengers to

bypass chronically congested Atlanta's Hartsfield (Davis,

1982). Many other carriers followed Piedmont's lead and

expanded their air networks by adding new hubs. This

explains, for example, American's new hubs at Nashville and

Raleigh/Durham. Competition among airports for hub selection

by a major carrier has become intense. The expanded service

creates big business for the chosen facility and city, and

airport planning boards are more aggressive now than in the

past (Barrett, 1987; Butler, Kiernan, 1987; Insight, 1988).

Lopuszynski (1986) has identified some common

characteristics of hub cities. It is an ex post facto list

that looked at hub cities already in use to find similarities

among them. Most hubs were found to contain several of the

following characteristics: 1) a sizeable population force

with strong business and commercial opportunities 2) a good

geographic location with respect to other population centers,

physical terrain and weather patterns 3) good airport

facilities with adequate room for expansion of gates and

runways 4) a strong economy and balanced workforce 5) air







38

service competition at a minimum acceptable level 6)

avoidance of existing major hubs.

While the hub-and-spoke strategy has created greater

overall efficiency and cost-cutting benefits for the airlines,

the very essence of the system creates problems of congestion,

environmental problems such as noise and air pollution, strain

on infrastructure and overworked air traffic controllers.

The hub-and-spoke system requires that airlines

concentrate many incoming and outbound flights during a narrow

time frame to maximize the number of city-pair combinations

that can be served. The resulting congestion has been too

much for most airports to handle. For example, Atlanta's

Hartsfield International Airport has an optimum capacity of 21

arrivals every 15 minutes, as determined by the FAA; however,

32 arrivals were recently scheduled by the airlines between

9:00 a.m. and 9:15 a.m. (Morganthau, 1987). At Chicago's

O'Hare, 42 departures were scheduled between 7:00 a.m. and

7:15 a.m., despite a capability to deal safely and efficiently

with only 23 (Morganthau, 1987). Increases in the frequency

of flights since 1978 at several hubs have been quite large.

Baltimore (+94.2%), Dallas/Ft. Worth (+52.2%), Houston

(+85.1%), Minneapolis/St. Paul (+60.1%) and Salt Lake City

(+51.9%) grew by more than half between 1978 and 1984, while

flight frequencies more than doubled at Charlotte (+134.2%)

and Newark (+114.2%) (Report on Airline Service, 1984).









Another problem of the hub-and-spoke strategy has been

the reduction in the number of nonstop flights from cities

other than hub cities. For many city pairs it now takes much

longer en route because virtually all flights are routed

through at least one hub (Goetz, Dempsey, 1989). The

opportunity cost of time is a factor that many travelers

consider important. In general, small cities and some of the

large spoke cities have often ended up worse off than they

were prior to deregulation as they have lost some (and in a

few cases all) nonstop service to cities that now have to be

reached through hubs (Bauer, 1987; Ivy, 1991).

The advantage for hub-city residents of a large number of

nonstop destinations is counterbalanced with some

disadvantages. These passengers generally pay much higher

fares and have less choice of carriers for most destinations

than was the case prior to deregulation (Bauer, 1987;

Borenstein, 1989; McGinley, 1989; Toh, Higgins, 1985). Single

carrier dominance (Table 3.1) and higher fares are the price

paid by residents for greater flight frequency and nonstop

destination choice. Hub cities have been hit the hardest with

fare increases since their residents are, in effect,

subsidizing longer haul passenger fares. Passengers flying

from Norfolk to Kansas City through USAir's Charlotte hub, for

example, would likely be charged the same fare as passengers

travelling on USAir from Charlotte to Kansas City only.

Today, flights originating at or departing for hub cities are








TABLE 3.1
SINGLE CARRIER DOMINANCE AT HUBS


city airport 1978 1990
code


Atlanta
Baltimore
Charlotte
Chicago
Cincinnati
Cleveland
Dallas
Dayton
Denver
Detroit
Honolulu
Houston
Las Vegas
Los Angeles
Memphis
Miami
Minneapolis
Nashville
New York
Newark
Orlando
Philadelphia
Phoenix
Pittsburgh
Raleigh/Durham
St. Louis
Salt Lake City
San Francisco
Seattle
Washington, D.C.


40.8%
26.2%
66.1%
27.6%
34.9%
57.6%
36.3%
35.3%
25.9%
23.6%
37.5%
24.6%
34.9%
26.2%
33.5%
36.4%
30.1%
27.1%
20.4%
28.2%
43.4%
28.0%
26.6%
51.3%
59.4%
34.6%
37.4%
42.4%
31.8%
24.3%


53.9%
71.5%
91.1%
35.5%
77.9%
38.7%
48.8%
74.8%
42.2%
65.4%
35.5%
50.4%
48.6%
17.8%
53.6%
18.3%
75.3%
57.5%
18.0%
48.9%
30.5%
46.3%
43.5%
83.0%
65.1%
74.3%
76.4%
29.1%
20.6%
23.4%


Source: Calculated from
Certified Route
Transportation,
1978, 1990.


Airport Activity


Statistics of


Air Carriers, U.S. Department of
Federal Aviation Administration,







priced up to 50% more than they would have been had

deregulation not occurred (Stockton, 1988). Comparison of

one-way fares on selected pairs indicate increases from 1978

to 1988 (in constant dollars) ranging from 28% to 495%, with

an average increase of well over 200% (Goetz, Dempsey, 1989).



Conclusion


Deregulation has had a much greater impact on the

domestic airline industry than anyone anticipated. Major

changes in service and connectivity have occurred in the U.S.

air transportation network. Small cities, and even large

cities not chosen as hubs, have experienced declines in flight

frequency and nonstop destination choice. Hubs have grown

tremendously in these areas, but fares have skyrocketed and

carrier choice along most routes has lessened. However, not

all hubs are alike. The next chapter will look at different

types of hubs and discuss hub intensity and connectivity.













CHAPTER 4
VARIATIONS IN HUB SERVICE IN THE DOMESTIC
AIR TRANSPORTATION NETWORK



Introduction



A hub is generally defined as a central collection point

or node in a transportation system or network. Usage of the

term, however, has become particularly applied in the air

transportation industry of the United States, largely since

deregulation and the advent of the hub-and-spoke system

discussed in the previous chapter. In reviewing air

transportation literature and data sources (both academic and

popular), one encounters the word frequently. Close scrutiny

of places labeled as air hubs, however, reveals that not all

hubs are equal in the service they offer, in either intensity

or connectivity. In fact, some hubs are vastly different from

others. Such variations can make the work of the air

transportation researcher problematic. This chapter will

identify different types of air hubs based on existing usage

of the term by the airlines and the Federal Aviation

Administration, and explore variations in hub intensity and

connectivity within the domestic air transportation network.









The FAA Hub



The initial usage of the word "hub" in the air

transportation industry was designated by the Civil

Aeronautics Board (now disbanded), and continued by the

Federal Aviation Administration (FAA). The FAA classifies all

communities with scheduled commercial air service as one of

four types of hub, and categorizes each hub based on its share

of the nation's annual enplanements. Data for multiple-

airport cities (like Chicago and Dallas) are usually summed to

represent a community total.

Large hubs, such as Atlanta and St. Louis, represent at

least 1.0% of the nation's total annual enplanements. New

Orleans and Norfolk, for example, are classified as medium

hubs since their share of total U.S. annual enplanements is

between .25% and .99%. Communities that represent between

.05% and .24% (like Richmond, VA) are classified as small

hubs, while non-hubs (such as Gainesville, FL) enplane less

than .05% of the nation's total passengers (Airport Activity

Statistics of Certified Route Air Carriers, 1990).



Deregulation of the U.S. Airline Industry


As discussed in the previous chapter, the Airline

Deregulation Act of 1978 forced the industry into a

competitive market situation. While airlines were adjusting







44

to the new environment, their network geography (node-linkage

association) was changing accordingly. Flow efficiency and

cost-reduction were made higher priorities. It became

increasingly clear that concentrating flights at one or more

key regional nodes in their air transportation networks would

raise the seat-occupancy levels, thus maximizing the usage of

aircraft. Such concentration would also maximize the number

of on-line (same carrier) city-pair matching available to

passengers. These central nodes typically offer non-stop

service to most every large and medium-sized city in the

nation and to smaller cities within the local region of the

node. Numerous arrivals and departures are scheduled within

a short time frame to allow the connections. This intense

type of system has become known as a "hub-and-spoke" network,

and now dominates U.S. air transportation. Table 4.1 lists

the U.S. hubs as designated by the major domestic carriers.

The list was compiled by consulting the fall 1991 schedules

and route maps of the carriers, and was confirmed by telephone

inquiry to each airline's public relations department. Not

included in Table 4.1 are a few cities labeled by some

carriers as mini-hubs. These are large cities that do not

serve as major transfer points within any airline's network,

but do offer some nonstop service (more than other spoke

cities) on an individual carrier (for example, Northwest

operates a mini-hub at Milwaukee and USAir uses Kansas City as










TABLE 4.1
FALL 1991 HUB CITIES OF THE MAJOR U.S. CARRIERS


city carriers) non-local
traffic


Atlanta
Baltimore
Charlotte
Chicago
Cincinnati
Cleveland
Dallas/Ft. Worth
Dayton
Denver
Detroit
Honolulu
Houston
Las Vegas
Los Angeles
Memphis
Miami
Minneapolis
Nashville
New York/Newark
Orlando
Philadelphia
Phoenix
Pittsburgh
Raleigh/Durham
St. Louis
Salt Lake City
San Francisco
Seattle
Washington


Delta
USAir
USAir
American, Midway, United
Delta
Continental
American, Southwest
USAir
Continental, United
Northwest
Continental, United
Continental, Southwest
America West
Delta
Northwest
American, Pan Am
Northwest
American
Continental, Delta, TWA
Delta, United
USAir, Midway
America West
USAir
American
TWA
Delta
United
Northwest
United


Note: Since the compilation of this list, Midway and Pan Am
Airlines have ceased operations, and USAir has planned the
closing of its Dayton hub in 1992.

Sources: Hub lists were obtained from the Fall 1991 schedules
and route maps of the major carriers. Transfer percentages
were supplied by the U.S. Department of Transportation for the
year ending 1990. Percentages were not separated in multiple-
airport communities.


67.60%
38.19%
74.19%
50.34%
53.86%
24.13%
60.04%
48.04%
50.19%
38.73%
31.72%
32.61%
22.27%
40.34%
62.92%
25.20%
47.76%
40.96%
21.87%
13.75%
22.04%
32.80%
60.64%
61.84%
55.33%
60.25%
24.40%
28.84%
20.03%







46

such). This service was built up to meet travel demand in

markets that some airlines considered to be underserved.



Hub-and-Spoke Growth and Development



The first domestic hub-and-spoke hubs chosen by

individual airlines were at airports that were already used by

carriers as connection or terminus points for long-haul, east-

west traffic using aircraft that did not have transcontinental

capabilities (Lopuszynski, 1986). These pre-deregulation

connecting airports were in the larger cities in the U.S., and

as such, generated high levels of local traffic. Individual

airlines tended to focus their early hub-and-spoke strategies

on those large-city connecting airports in which they already

controlled most of the scheduled departures. Most of these

airports were already well-connected (with non-stop service)

to other large cities in the nation. The hub-and-spoke hubs

were created as the carriers simply added many more flights to

many more destinations or spokes (particularly medium-sized

and small cities) at these facilities to build up connectivity

and create greater overall efficiency and market control at

the hub. In some cases, particular airports were selected by

more than one carrier as a hub-and-spoke hub. American and

United, for example, both created hubs at Chicago O'Hare,

while Atlanta became a major transfer city for Delta and the

now-defunct Eastern.







47

As these initial connecting hubs became saturated with

traffic, other (often medium-sized) cities were chosen as

transfer points, as new airlines developed and as older

airlines expanded their networks. Charlotte, Raleigh/Durham

and Nashville, for example, were developed as hubs around

Atlanta offering travelers a less-congested alternative in the

Southeast. Today, the largest carriers have as many as four

or five hubs scattered throughout the nation. One can divide

the list of hubs from Table 4.1 into two broad categories: 1)

hubs that were important connecting airports prior to

deregulation (although much less intensely connected than

today), and 2) hubs that achieved important transfer status

after deregulation (Table 4.2).



Service Variations



Closer examination of the cities on the hub list,

however, reveals vast differences among the airports' service

levels and functions. In fact, not all of the locations

listed in Table 4.1 really function as hub-and-spoke hubs. As

mentioned, hubs of this type are characterized by many non-

stop flights to cities of all sizes. Also, the very nature of

the network structure suggests that a good portion of the

traffic at these hubs should be nonlocal, if the hub is indeed

successful as a transfer point. Some of the airports labeled

by the major carriers as air hubs do not have true









TABLE 4.2
PRE AND POST-DEREGULATION HUB CITIES
(1991)


PRE-DEREGULATION HUBS:


Atlanta
Chicago
Dallas/Ft Worth
Denver
Honolulu
Houston
Los Angeles
Miami


Minneapolis
New York/Newark*
Philadelphia
Pittsburgh
St. Louis
San Francisco
Seattle


POST-DEREGULATION HUBS:


Baltimore (1983)
Charlotte (1981)
Cincinnati (1987)
Cleveland (1989)
Dayton (1982)
Detroit (1984)
Las Vegas (1985)


Memphis (1984)
Nashville (1986)
Orlando (1989)
Phoenix (1983)
Raleigh/Durham (1987)
Salt Lake City (1982)
Washington, D.C. (1986)


* Newark International Airport on its own would be classified
as a post- deregulation hub. People's Express (eventually
consumed by Continental Airlines) developed hub facilities
there in 1981.


Source: Hub information was obtained from a series of surveys
of airports and airlines by mail and telephone (in mid-1991).
The years next to the post-deregulation hubs indicate the year
of development as a major transfer hub as determined by the
airport in question.







49

hub-and-spoke functions, at least not at the same level of

intensity as others.

A few of the cities listed as hubs function as feeder

points for a specific carrier's international network. They

are usually well connected with the individual airline's other

domestic hub cities, and also offer nonstop service to and

from the largest cities (largest markets) in the nation.

Thus, they do act as transfer points for the carrier, but on

a less intense level. They are certainly not important

transfer points within the domestic air transportation

network. Los Angeles, Miami, New York (JFK), Seattle and San

Francisco are all international gateway hubs instead of hub-

and-spoke hubs for domestic air networks. These peripherally

located cities have a lower percentage of nonlocal traffic at

their airports than almost every other hub on the list (Table

4.1). This means that a smaller proportion of their traffic

is changing aircraft at their facility bound for a different

final destination. In addition, the particular carriers)

claiming hub status at each of the five facilities listed

above offer rather limited nonstop service from them,

especially in comparison to other domestic hub-and-spoke hubs.

Table 4.3 looks at nonstop connections between hubs listed in

Table 4.1 and FAA hub airports of various sizes. It is clear

that some hubs on the list (particularly the international

gateway hubs) are not as well connected to cities (spokes) of

all sizes as are many of the other hubs.










TABLE 4.3
NONSTOP DOMESTIC CONNECTIONS ON ALL CARRIERS BY FAA HUB-TYPE
(FALL 1991)



airports large medium small non total



ATL 27 23 33 24 107
BWI 22 16 10 19 67
CLT 24 18 27 25 94
CHI 27 29 35 27 119
CVG 27 17 19 7 70
CLE 22 13 7 3 45
DFW 27 26 25 23 102
DAY 17 6 7 6 36
DEN 25 19 16 24 84
DTW 25 16 14 14 70
HNL 11 2 3 5 21
HOU 26 16 10 9 61
LAS 22 12 3 1 38
LAX 27 17 5 14 63
MEM 23 10 14 20 67
MIA 20 10 4 4 38
MSP 26 13 11 30 81
BNA 24 14 18 14 70
NYC* 26 17 17 17 78
MCO 22 13 8 4 47
PHL 23 18 12 19 72
PHX 24 16 6 9 55
PIT 25 20 20 33 98
RDU 15 12 16 12 55
STL 27 22 16 19 83
SLC 18 12 7 17 55
SFO 26 14 8 14 62
SEA 22 7 4 14 47
DCA 25 17 23 19 84


*Includes Newark


Source: OAG Pocket Flight Guide, November 1991.







51

An emerging category is the destination hub. These are

cities that are popular travel destinations, and as such

offer great deal of nonstop service to and from large and

medium-sized cities around the nation to meet the high demand.

Orlando, Las Vegas and Honolulu fall into this classification

(although Honolulu also serves as an international connecting

point). All three generate comparatively low nonlocal traffic

rates (Table 4.1), and are not well connected to cities of

varying size (Table 4.3). Few passengers flying into these

cities are transferring to other domestic locations, and these

are mainly via commuter carriers from the local area. Some of

the international gateway hubs could be considered destination

hubs as well (like Los Angeles and New York) due to their

popularity as travel destinations.



Hub Connectivity


Because today's hubs (and the carriers operating them)

basically compete with one another for transfer traffic, the

success of a hub usually depends on how well connected it is

to other nodes in the U.S. air transportation network. The

hub airport (and carrier) that has a greater variety of

nonstop service to different sized cities in all parts of the

nation is in a better position to attract passengers and

control markets. Atlanta competes with Charlotte in the

Southeast, for example, and Dallas, Houston and Denver









basically compete in the West for the same transfer

passengers. What follows is a discussion of the measurement

of hub connectivity using a graph theoretical approach. Graph

theory allows one to view the network as a topological map

comprised of nodes (points of economic concentration) and

linkages (routes that connect two nodes). Thus, it shows

network connectivity in a relative sense. This technique

illustrates the strength (in terms of connectivity) and

attractive power of the hubs listed in Table 4.1.



Analyzing Hub Connectivity


One graph theoretic method of measuring the connectivity

or accessibility of a node begins with the construction of a

binary matrix that represents the network abstracted as a

graph (Lowe, Moryadas, 1975; Taaffe, Gauthier, 1973). It is

a square matrix in which the number of rows and columns each

represent the number of nodes in the transportation network.

The horizontal rows represent origin nodes, while the vertical

columns represent destination nodes. Both rows and columns,

of course, contain the same list of points. The cell entries

of the matrix are assigned a value of either one or zero. A

value of one shows the presence of a direct (nonstop) linkage

between specific nodal pairs, while a value of zero indicates

the absence of such a linkage. Nodes are not considered to be

connected to themselves; therefore, the principal diagonal of







53

the matrix (all i,i entries) contains zeroes as cell entries.

Thus, the connectivity matrix (Cl) shows first order

connections in a transportation network.

A vector of values that can be used as a crude measure of

nodal accessibility is obtained by summing the individual rows

or columns of the matrix. The higher the summed row or column

value of the node, the greater the accessibility of the point.

This accessibility index on its own is of limited usefulness,

however, because we are often interested in both direct and

indirect connections.

The number of indirect connections in a network can be

determined by powering the original connectivity matrix (Lowe,

Moryadas, 1975; Taaffe, Gauthier, 1973). The matrix (Cl) can

be multiplied by itself (resulting in matrix C2) to look at

second order or two-step connections (connections that pass

through an intermediate node) in the network. Likewise, the

third order connectivity matrix (C3) is obtained by

multiplying matrix Cl by matrix C2. To take all indirect

connections into account, the matrix should be powered to the

Nth order (CN), where N represents the diameter of the

transportation network. (The diameter is defined as the

shortest topologic distance between the two most distant nodes

in the network.) At this stage, all zero elements disappear

from the matrix indicating that all nodes are connected.

Summing the matrix Cl with the powered matrices shows the

total accessibility of each node within the network







54
(accessibility matrix). The individual rows or columns of

this accessibility matrix (T) can be summed to yield the gross

vertex connectivity for each node in the network.

Early researchers using the above-mentioned technique

(Garrison, 1960; Pitts, 1965), discovered that while powering

the matrix did give the maximum number of alternative paths in

a network, a number of redundancies (passing through the same

node more than once) were included in the final accessibility

matrix (T). This was particularly the case for nodes that

were directly connected in the original connectivity matrix

(Cl).

Another criticism by Garrison (1960) was that all

linkages should not be considered equal in importance. The

more indirect the linkage, the less it should add to the gross

vertex connectivity number. He introduced a procedure in

which a scalar number (s) that takes on a value between zero

and one is multiplied by the accessibility values in each

matrix powered according to the order of the matrix

[T=sC1+s2C2+s3C3+...+s"CN]. The real problem lies in assigning

the scalar value (Garrison's scalar of .3 was assigned

arbitrarily). The technique, however, does lessen the

importance of indirect connections relative to direct

connections and at the same time reducing the impact of

redundant paths. A new scalar method will be introduced in

the next section.









Results



The above-mentioned technique was applied to a study set

of 117 nodes to measure accessibility within the U.S. domestic

air transportation network. These nodes were all U.S. urban

areas (excluding 4 Hawaiian cities not well connected to the

U.S. mainland) that are classified by the FAA as a large

(total of 28), medium (total of 29) or small (total of 60) hub

(Table 4.4). The purpose was to find out how well connected

each of the airline hubs listed in Table 4.1 was to cities of

various sizes scattered around the nation. Due to the fact

that there are several hundred FAA non-hubs, they were

excluded to keep the size of the matrix manageable (117 x

117). The connectivity data for the original matrix (Cl) was

abstracted from the November 1991 issue of the OAG Pocket

Flight Guide.

The matrix was ordered to the third power (C3). At that

point, all of the non-zero elements disappeared from the

matrix cells. The diameter of this network is three because

one can fly between any domestic city pair in the study set in

three or fewer flight segments (due to the intense hub-and-

spoke structuring). The summed accessibility indices for each

metropolitan area for matrices Cl, C2, C3, and T are given in

Appendix A. The accessibility indices for each hub (from

matrix Cl and T) and their respective rankings are given in

Table 4.5. Note that the hub rankings for matrix Cl (direct









TABLE 4.4
FAA HUBS


Atlanta
Baltimore
Boston
Charlotte
Chicago
Dallas/Ft. Worth
Denver
Detroit
Honolulu
Houston
Kansas City
Las Vegas
Los Angeles
Memphis


Albuquerque
Austin
Buffalo
Cincinnati
Cleveland
Columbus
Dayton
El Paso
Ft. Myers
Hartford
Indianapolis
Jacksonville
Kahului
Lihue
Milwaukee
Nashville


FAA Large Hubs

Miami
Minneapolis/St. Paul
New York/Newark
Orlando
Philadelphia
Phoenix
Pittsburgh
St. Louis
Salt Lake City
San Diego
San Francisco
Seattle
Tampa
Washington, D.C.




FAA Medium Hubs

New Orleans
Norfolk
Oklahoma City
Ontario
Portland
Raleigh/Durham
Reno
Rochester
Sacramento
San Antonio
San Jose
Syracuse
Tucson
Tulsa
West Palm Beach










TABLE 4.4--continued



FAA Small Hubs


Akron/Canton
Albany
Allentown
Amarillo
Anchorage
Baton Rouge
Billings
Birmingham
Boise
Brownsville
Burlington
Cedar Rapids
Charleston (SC)
Charleston (WV)
Chattanooga
Colorado Springs
Columbia
Corpus Christi
Daytona Beach
Des Moines
Eugene
Fort Wayne
Fresno
Grand Rapids
Greensboro
Greenville
Harrisburg
Hilo
Huntsville
Islip
Jackson


Kailua-Kona
Knoxville
Lexington
Lincoln
Little Rock
Louisville
Lubbock
Madison
Melbourne
Midland
Mobile
Moline
Myrtle Beach
Omaha
Palm Springs
Pensacola
Portland
Providence
Richmond
Roanoke
Saginaw
Santa Barbara
Sarasota
Savannah
Shreveport
Sioux Falls
South Bend
Spokane
Tallahassee
Toledo
Witchita











ACCESSIBILITY


TABLE 4.5
INDICES FOR MATRICES C1 AND T


airports matrix Cl rank matrix T rank


ATL
BWI
CLT
CHI
CVG
CLE
DFW
DAY
DEN
DTW
HNL
HOU
LAS
LAX
MEM
MIA
MSP
BNA
NYC*
MCO
PHL
PHX
PIT
RDU
STL
SLC
SFO
SEA
DCA


93552
69753
83769
97950
82183
63749
88668
47457
71207
76515
20889
69597
53701
68678
63525
55436
70247
74299
81114
65272
74469
62542
83524
54992
81844
47692
64887
48591
82696


*Includes Newark







59
connections only) and matrix T (direct and indirect

connections) are slightly different (Spearman's rank order

coefficient of .979). Twelve of the cities (mostly ranked in

the top ten) remain at the same rank, but ten rise in the

rankings (Baltimore, Cincinnati, Cleveland, Detroit, Miami,

Minneapolis, Orlando, Philadelphia, San Francisco and Seattle)

while seven fall (Denver, Houston, Memphis, Phoenix,

Raleigh/Durham, St. Louis and Salt Lake City) when indirect as

well as direct connections are taken into account (matrix T).

The rankings show an eastern bias because more of the 117

nodes in the original matrix (Cl) were located in the East or

Midwest than in the western half of the United States.

Scalar multiplication was also performed on the original

and powered matrices. Table 4.6 shows the accessibility

matrix (T) indices using a variety of scalars. While the

magnitude of difference between the cities, of course, changes

as the scalar changes, the specific rank order of the cities

remains constant (concordant). It is also the exact rank

order of the unsealed accessibility matrix T (Table 4.5).

A more refined weighting technique assigns a different

scalar value to each of the 117 nodes in the network. This

weight is based on the individual node's share (from the last

column in Table 4.3) of total direct connections in the

network (1,969). One advantage of this weighting procedure is

that it allows the FAA nonhub connections to be taken into

account, albeit in an indirect way. The results of this











ACCESSIBILITY INDICES


TABLE 4.6
OF MATRIX T


WITH DIFFERENT SCALARS


airports s=.25 s=.3 s=.5 s=.75


CHI
ATL
DFW
CLT
PIT
DCA
CVG
STL
NYC*
DTW
PHL
BNA
DEN
MSP
BWI
HOU
LAX
MCO
SFO
CLE
MEM
PHX
MIA
RDU
LAS
SEA
SLC
DAY
HNL


1615
1542
1464
1380
1376
1362
1354
1350
1337
1260
1228
1225
1180
1161
1150
1149
1136
1077
1075
1051
1049
1036
916
906
889
807
791
783
347


2755
2630
2497
2354
2347
2324
2310
2303
2282
2150
2094
2090
2010
1980
1962
1961
1937
1838
1832
1793
1789
1765
1562
1546
1516
1374
1348
1336
592


12447
11886
11274
10642
10613
10508
10443
10405
10310
9723
9466
9444
9064
8938
8866
8854
8742
8302
8262
8104
8080
7963
7053
6989
6838
6192
6075
6035
2664


41543
39676
37614
35527
35424
35074
34856
34718
34407
32453
31589
31516
30219
29807
29588
29530
29146
27693
27539
27042
26951
26544
23523
23325
22793
20629
20244
20134
8871


*Includes Newark







61
weighting procedure are given in Table 4.7. The rankings are

slightly different from both the original connectivity matrix

Cl (Spearman's rank order coefficient of .979), and the

unweighted matrix T (Spearman's rank order coefficient of

.951). Cincinnati and St. Louis, for example, are closely

ranked in matrices Cl and T from Table 4.5, but the weighting

procedure requiring the calculation of a different scalar for

each individual node puts a greater difference in rankings

between these two cities (Table 4.7). This is because St.

Louis is connected to a greater total number of cities (once

nonhubs are included), and therefore, fares better in the

final ranking of accessibility numbers.



Connectivity and Intensity Classification Scheme


Using the accessibility information from Table 4.7 and

transfer traffic information from Table 4.1, the following

classification scheme measuring hub strength was derived.

Hubs are classified as super, maior--tyve A, maior--tpe B,

moderate--tye A, moderate--type B, minor--type A, minor--tye

B or non-hub (Table 4.8). The eight classifications were

made using a one-dimensional iterative partitioning clustering

method using the accessibility numbers given in Table 4.7

(Aldenderfer, Blashfield, 1984).

Chicago, Atlanta and Dallas are super hubs. They are the

top ranked for accessibility in all of the matrices that were










TABLE 4.7
ACCESSIBILITY INDICES OF INDIVIDUALLY


WEIGHTED NODES


airports weight accessibility rank
number


ATL
BWI
CLT
CHI
CVG
CLE
DFW
DAY
DEN
DTW
HNL
HOU
LAS
LAX
MEM
MIA
MSP
BNA
NYC*
MCO
PHL
PHX
PIT
RDU
STL
SLC
SFO
SEA
DCA


.054
.034
.048
.060
.036
.023
.052
.018
.043
.036
.011
.031
.019
.032
.034
.019
.041
.036
.040
.024
.037
.028
.050
.028
.042
.028
.031
.024
.043


22.76
5.53
15.13
31.24
7.57
2.24
19.87
1.05
10.23
6.96
0.25
4.69
1.39
4.89
5.18
1.34
8.70
6.84
9.45
2.50
7.18
3.44
16.49
3.02
10.78
2.72
4.36
1.94
11.44


*Includes Newark









TABLE 4.8
HUB STRENGTH CLASSIFICATION SCHEME


SUPER HUBS

MAJOR HUBS--TYPE A


MAJOR HUBS--TYPE B

MODERATE HUBS--TYPE A

MODERATE HUBS--TYPE B



MINOR HUBS--TYPE A

MINOR HUBS--TYPE B


NON-HUBS


Atlanta, Chicago, Dallas

Charlotte, Denver, Pittsburgh,
St. Louis

New York, Washington, D.C.

Cincinnati, Memphis

Baltimore, Detroit, Houston,
Los Angeles, Minneapolis,
Nashville, Philadelphia

Raleigh/Durham, Salt Lake City

Cleveland, Orlando, Phoenix,
San Francisco

Dayton, Honolulu, Las Vegas,
Miami, Seattle


Note: Type A hubs have a transfer (non-local) passenger
percentage of 50% or greater, while the non-local percentage
for type B hubs is less than 50% (table 4.1).







64
constructed, and are indeed in a class by themselves. Because

they are large in population, more centrally located in the

nation than many of the other U.S. mega cities, and each an

important transfer point for more than one carrier for several

years (until early 1991, the now defunct Eastern Airlines had

hub operations at Atlanta's Hartsfield), these cities have

been the major pivot centers for air transportation for more

than a decade. Each has a non-local (transfer) traffic base

of over 50% of its total enplanements. They have a clear

advantage over peripheral hubs like Miami, New York and Los

Angeles in that they are more proximal to a greater number

ofnodes in all parts of the country. These hubs are also

operated by one or more of the financially strongest carriers

in the nation (American, Delta, United) helping to make

possible more flights to more destinations.

Maior hubs--type A are cities with high accessibility

numbers and transfer passenger percentages of over 50%

(excluding the super hubs). Charlotte, Pittsburgh, St. Louis

and Denver make up this category. While the major hubs--type

B have high accessibility numbers as well, their non-local

traffic base is less than 50% of the total traffic at their

facility. For these hubs (New York and Washington, D.C.),

this is more a reflection of the city in question's

popularity as a final destination (due to sheer population

size), and the fact that both are multiple airport cities,

than a reflection on the service offered at the airports.







65

The moderate hubs have medium-ranging accessibility

numbers and include almost one-third of the hubs from table

4.1. Only Memphis and Cincinnati have non-local passenger

percentages of over 50% (type A), although some of the tyDe B

moderate hubs are near that proportion.

Minor hubs--type A and minor hubs--type B have low

accessibility numbers. Again, tvye A hubs have non-local

passenger percentages of over 50% (Salt Lake City and

Raleigh/Durham, for example), while type B hubs (like San

Francisco and Orlando) do not.

Miami, Honolulu, Seattle, Las Vegas and Dayton make up

the non-hub category. These are cities (largely destination

or international gateway hubs) with very low accessibility

numbers and transfer percentages below (in most cases well

below) 50%. Dayton, a USAir hub, is being pulled down from

that status in early 1992. These cities are in no way

comparable to the higher domestic connectivity and major

transfer role played by the other hubs from Table 4.1.



Conclusion


This chapter brings to light possible confusion in the

usage of the term hub, from the FAA definition (which has

nothing whatsoever to do with transfer or connecting status)

to the various service levels of the airline definition. The

hub-and-spoke phenomenon of the post-regulation period has,







66

without a doubt, drastically changed air transportion in the

United States. The accessibility matrix (T) gives us a good

indication of which hubs are the most highly connected to more

of the nation, and therefore, which are more powerful in

controlling market shares. This might be used to help

understand why some carriers are more financially successful

than others in an industry that has been very unstable for

more than a decade. The next chapter will investigate the

relationship between changes in professional employment growth

rates and changes in air transportation connectivity in the 60

largest MSAs in the United States.














CHAPTER 5
CHANGES IN CONNECTIVITY AND
PROFESSIONAL EMPLOYMENT LOCATION



Introduction


This chapter investigates a number of research questions

concerning changes in air service connectivity, professional

employment and corporate location. Data were collected on the

60 largest metropolitan areas in the United States in 1988,

listed in Table 5.1. The MSAs (metropolitan statistical

areas) include a mix of hubs and nonhubs from every region of

the country, although there is a bias toward the east. The

total number of employees, number of administrative and

research and development employees, airport enplanements and

nonstop destinations available (of the 60-city study set) were

collected for these cities in each year from 1978 through

1988. The starting point of the analysis is 1978 since that

was the last fully regulated and fairly stable year for the

airline industry. The analysis continues through 1988 to show

progressive changes and help identify lag effects in the

relationship between changes in connectivity and professional

employment.









TABLE 5.1
60 LARGEST METROPOLITAN AREAS OF THE U.S.
(1988-in thousands)


New York/Newark CMSA (NYC) 18,120
Los Angeles CMSA (LAX) 13,770
Chicago CMSA (CHI) 8,181
San Francisco CMSA (SFO) 6,042
Philadelphia CMSA (PHL) 5,963
Detroit CMSA (DTW) 4,352
Boston CMSA (BOS) 4,110
Dallas/Ft. Worth CMSA (DFW) 3,766
Washington, D.C. MSA (WAS) 3,734
Houston CMSA (HOU) 3,641
Miami/Ft. Lauderdale CMSA (MIA) 3,001
Cleveland CMSA (CLE) 2,769
Atlanta MSA (ATL) 2,737
St. Louis MSA (STL) 2,467
Seattle CMSA (SEA) 2,421
Minneapolis/St. Paul CMSA (MSP) 2,388
Baltimore MSA (BWI) 2,342
San Diego MSA (SAN) 2,370
Pittsburgh CMSA (PIT) 2,284
Phoenix MSA (PHX) 2,030
Tampa/St. Petersburg MSA (TPA) 1,995
Denver CMSA (DEN) 1,858
Milwaukee CMSA (MKE) 1,562
Kansas City MSA (MCI) 1,575
Cincinnati CMSA (CVG) 1,449
Portland CMSA (PDX) 1,414
Sacramento MSA (SMF) 1,385
Norfolk MSA (ORF) 1,380
Columbus MSA (CMS) 1,344
San Antonio MSA (SAT) 1,323
New Orleans MSA (MSY) 1,307
Indianapolis MSA (IND) 1,237
Buffalo CMSA (BUF) 1,176
Providence CMSA (PVD) 1,125
Charlotte MSA (CLT) 1,112
Hartford CMSA (BDL) 1,068
Salt Lake City MSA (SLC) 1,065
Rochester MSA (ROC) 980
Memphis MSA (MEM) 979
Nashville MSA (BNA) 972
Orlando MSA (MCO) 971
Louisville MSA (SDF) 967
Oklahoma City MSA (OKC) 964
Dayton MSA (DAY) 948
Greensboro MSA (GSO) 925
Birmingham MSA (BHM) 923









TABLE 5.1--continued



Jacksonville MSA (JAX) 898
Albany MSA (ALB) 851
Richmond MSA (RIC) 844
Honolulu MSA (HNL) 838
West Palm Beach MSA (PBI) 818
Austin MSA (AUS) 748
Scranton MSA (AVP) 731
Tulsa MSA (TUL) 728
Raleigh/Durham MSA (RDU) 683
Allentown MSA (ABE) 677
Grand Rapids MSA (GRR) 665
Syracuse MSA (SYR) 650
Tucson MSA (TUS) 636
Las Vegas MSA (LAS) 631



Note: The three-letter code listed after each city is the
airport code for the city as assigned by the FAA. Some cities
on the list are served by more than one airport. In these
cases, one code is chosen to represent the urban area as a
whole, but includes total destinations and flows from all
airports in the metropolitan area.

Source: Statistical Abstract of the United States, 1990.









Research Questions



This chapter investigates how changes in connectivity are

related to changes in employment growth of those highly active

in nonroutine communication within and outside of the firm.

It is assumed that hub cities have a much greater selection of

nonstop destinations from their airports plus a higher rate of

connectivity change as the hub develops, and as such, have a

greater (and growing) concentration of scientists, engineers

and administrators in their workforce than nonhubs.

The study will also determine which change occurs first.

The potential demand for air service could attract one or more

airlines to set up hub operations with abundant nonstop

service in the city, thus making it an attractive choice for

companies to locate or relocate such activities.

Alternatively, the demand could have already existed, with an

airline merely stepping in to fill the void in service. The

changes could also occur simultaneously as new economic growth

regions are identified by the airline industry and other

corporations.

If an airline's choice of a hub does make that city

likely to draw more company headquarters, regional offices and

research and development labs and their workers to the city

(or induce such facilities to leave nonhub cities), the study

will determine how long it takes for the flow to begin. If

the effect is in the other direction, the study will determine









how long it takes the airline to respond (with increased

service levels) to the potential market growth brought on by

the new addition to the urban area's employment structure.

Finally, the study will look for regional and

hierarchical biases. Hubs in some areas may show a stronger

relationship between the two variables than hubs in other

parts of the nation, and hubs with higher population totals

could show a stronger or weaker relationship between

connectivity change and employment change than smaller-sized

hubs.



The Labor Data


The professional labor data were collected from the

County Business Patterns series published by the U.S. Census

Bureau. It is an annual publication with volumes for all 50

states, the District of Columbia and Puerto Rico.

Unfortunately, this labor information is not published on a

consistent basis for metropolitan areas as a whole. The

series records the number of employees per economic sector

(based on SIC codes) by county in the states. Within each

sector, a separate classification is reserved for professional

workers. The publication refers to these workers as

administrative and auxiliary employees, defined as personnel

working in central administration offices and auxiliary

establishments such as research labs and financial services.







72

While the category also includes some warehouse and

distribution employees (nonprofessional labor), County

Business Patterns was found to be the best source available

for obtaining raw numbers of professional workers in a

specific area.

The number of professional workers for each of the 60

cities in the study set was obtained by summing the

administrative and auxiliary employees for each sector in each

county included in the MSA (as determined by the U.S. Census

Bureau) (Appendix B). These data were collected for all 60

cities from 1978 (the beginning of the study period) through

1988 (the most current data available at the time of the

collection process). The results are given in Appendix C.

Table 5.2 shows the 20 MSA's (from the study set) with

the greatest number of professionals in their workforce at the

end of that period (1988). It is not surprising that the

largest cities in the nation in population rank high on this

list. Most of these 20 MSA's lie in the traditional

manufacturing belt, although a few southern and western urban

areas made the list as well.

Table 5.3 indicates the flow pattern of administrative

and auxiliary jobs and workers. This table shows the U.S.

cities that experienced the greatest increase in the number of

professional workers from 1978 to 1988. While a few of the

large metropolitan areas of the Northeast made this list (such

as Boston, New York/Newark and Philadelphia), the southern and









TABLE 5.2
MOST ADMINISTRATIVE/AUXILIARY EMPLOYEES: 1988


MSA number of population
professionals rank



1) New York/Newark 462,305 1
2) Los Angeles 193,284 2
3) Chicago 183,688 3
4) Boston 139,258 7
5) Detroit 136,782 6
6) Philadelphia 136,055 5
7) San Francisco 120,469 4
8) Dallas/Ft. Worth 109,966 8
9) Houston 95,362 10
10) Minneapolis/St. Paul 83,141 16
11) Atlanta 81,958 13
12) Washington, D.C. 64,363 9
13) Cleveland 63,586 12
14) St. Louis 58,350 14
15) Providence 55,189 34
16) Seattle 43,944 15
17) Cincinnati 40,221 23
18) Pittsburgh 39,973 19
19) Miami/Ft. Lauderdale 36,602 11
20) Columbus 33,304 29


Source: Calculated from County Busi
Census Bureau, 1990.


Patterns, U.S.


nw=. ~










TABLE 5.3
LARGEST GROWTH IN ADMINISTRATIVE/AUXILIARY EMPLOYEES:
1978-1988




MSA + change



1) Boston 68,528
2) New York/Newark 55,239
3) Dallas/Ft. Worth 49,976
4) Atlanta 39,931
5) San Francisco 31,222
6) Washington, D.C. 28,310
7) Providence 28,292
8) Philadelphia 22,989
9) Minneapolis/St. Paul 21,459
10) Houston 21,236
11) Seattle 18,496
12) Miami/Ft. Lauderdale 17,697
13) Los Angeles 16,575
14) Charlotte 13,967
15) Orlando 13,965
16) Jacksonville 12,273
17) San Diego 10,488
18) Richmond 10,315
19) Columbus 9,843
20) Phoenix 9,821


Patterns, U.S.


Source: Calculated from County Business
Census Bureau, 1980, 1990.


~







75

western parts of the United States are strongly represented,

including cities such as Charlotte, Richmond, Jacksonville,

Orlando and Phoenix which did not make the list in Table 5.2.

Some of the cities in Table 5.2 experienced a loss of

administrative and auxiliary workers during the period in

question. These are listed in Table 5.4. Almost all of these

cities are in the traditional manufacturing belt. Tulsa and

Oklahoma City, however, are oil-producing cities whose

fortunes changed during the early and middle 1980s as the

price for oil dropped significantly, and the economy of oil

states (particularly Oklahoma and Texas) suffered.

Table 5.5 gives a regional analysis for the 60

metropolitan areas in the study set. It shows the total and

average administrative and auxiliary employment growth of the

study set cities within each region. These regions are the

traditional divisions of the nation as defined by the U.S.

Census Bureau (Figure 5.1). The Northeast still shows up

strongly with the South being a near second.

Perhaps of greater interest to a discussion of

professional workers in urban locations is Table 5.6. It

shows the 20 cities with the highest percentage of

administrative and auxiliary workers as a proportion of their

total employment for 1988 (constructed from Appendices C and

D). Many of the larger cities on the list in Table 5.2 either

drop off this list or appear at a lower ranking on the list.

Some new cities appear that were absent from Table 5.2, such









TABLE 5.4
ADMINISTRATIVE/AUXILIARY EMPLOYMENT LOSS: 1978-1988



MSA change



Detroit -27,340
Pittsburgh -12,179
Albany -4,094
Indianapolis -2,216
Baltimore -1,999
Tulsa -1,674
Oklahoma City -1,258
Chicago -592
Buffalo -367


Source: Calculated from County Business Patterns, U.S.
Census Bureau 1980, 1990.





TABLE 5.5
ADMINISTRATIVE/AUXILIARY GROWTH BY REGION: 1978-1988



region number of employee MSA
MSA's growth average



Northeast 12 170,993 14,249

North Central 12 36,919 3,077

South 24 243,392 10,141

West 12 100,311 8,359



Source: Calculated from County Business Patterns, U.S.
Census Bureau, 1980, 1990.









TABLE 5.6
HIGHEST PERCENTAGE OF ADMINISTRATIVE/AUXILIARY AS A PORTION
OF TOTAL EMPLOYMENT: 1988



MSA percentage of
professionals


1) Tulsa 8.76
2) Detroit 7.75
3) Allentown/Bethlehem/Easton 7.38
4) Minneapolis/St. Paul 7.16
5) Greensboro/Winston/Salem 7.14
6) Houston 7.08
7) Dallas/Ft. Worth 6.58
8) Richmond 6.53
9) Atlanta 6.44
10) Rochester 6.07
11) Columbus 5.96
12) New York/Newark 5.86
13) St. Louis 5.83
14) Cleveland 5.74
15) Dayton 5.73
16) Philadelphia 5.72
17) Cincinnati 5.58
18) Chicago 5.43
19) Boston 5.20
20) Charlotte 5.18


Source: Calculated from County Business Patterns, U.S.
Census Bureau, 1990.































































FIGURE 5.1
CENSUS REGIONS OF THE UNITED STATES









as the Greensboro/Winston-Salem and Tulsa metropolitan areas.

Because it readily identifies cities with strong

administrative and auxiliary functions, using the percentage

of professional employment in the total labor force isprobably

a better indicator of the true administrative and auxiliary

cities in the United States.


The Air Service Data


The sources for the air service data were the Official

Airline Guide (OAG) and Airport Activity Statistics of

Certified Route Air Carriers. Enplanement figures (Appendix

E) were extracted from the latter source (published yearly by

the Federal Aviation Administration) which records a variety

of information about passenger and cargo flow at all domestic

airports with scheduled commercial service. The Official

Airline Guide is published monthly and gives route schedules

of the commercial carriers in the United States. It was the

source for nonstop (Appendix F) and other city-pair connection

data. Unfortunately, this publication is not widely available

because of its cost and the sheer size of each issue (creating

storage problems). Back issues of the publication were found

at a library (University of North Carolina at Chapel Hill)

which saves only one issue from every year (usually, but not

always, July). Therefore, connection and schedule data for

the various years are from summer schedules of the major







80

carriers. It should be noted that summer schedules are

usually expanded to accommodate extensive vacation travel, and

are not representative of the average service offered during

the year as a whole.

Data were collected on all 60 cities in the study set for

each year from 1978 through 1988. More current air data were

available, but the collection process was stopped at 1988 to

keep the air data consistent with the available labor data (as

discussed in the previous section of this chapter). In

communities with multiple airports (e.g. Dallas and Chicago),

the individual airport information was combined to obtain a

community total. Multiple airport communities are listed in

Table 5.7.

The U.S. cities with the highest enplanement (boarded

passenger) levels in 1988 are given in Table 5.8 by rank order

along with their average number of daily departures and the

number of cities in the study set to which they are connected

with nonstop air service (59 is the maximum number of

nonstops). The list consists of the largest cities in the

nation, plus a few popular travel destinations such as

Honolulu, Las Vegas and Orlando. It is not surprising that

almost all (except Boston) are airline hub cities (Table 4.1).

Every city (of the 60 in the study set) grew in enplanement

levels from 1978 to 1988 except Louisville (which dropped by

38,163 passengers) and Scranton/Wilkes/Barre (a decline of

16,240) (Appendix E).









TABLE 5.7
MULTIPLE AIRPORT CITIES


Chicago

Dallas

Detroit

Greensboro

Houston

Los Angeles


Miami

New York City

San Francisco

Washington, D.C.


Midway, O'Hare International

Love Field, DFW International

Detroit City, Detroit Metro-Wayne County

Greensboro, Smith-Reynolds

Hobby, Houston Intercontinental

L.A. International, Hollywood/Burbank,
Orange County, Long Beach

Miami International, Ft. Lauderdale

JFK, LaGuardia, Newark International

San Francisco International, Oakland

Washington National, Dulles International









TABLE 5.8
LEADING ENPLANEMENT CITIES OF THE U.S.: 1988


city enplanements non/stops* daily
departures



1) New York/Newark 32,820,184 48 957
2) Chicago 29,770,857 56 1045
3) Dallas 23,488,986 50 765
4) Los Angeles 22,836,344 37 743
5) Atlanta 21,824,125 56 756
6) San Francisco 15,173,602 28 542
7) Denver 14,441,817 40 514
8) Miami 13,360,799 27 399
9) Washington 11,586,627 49 482
10) Houston 10,712,269 38 432
11) Boston 10,141,298 40 316
12) St. Louis 9,554,454 46 384
13) Phoenix 9,455,324 31 364
14) Detroit 9,343,770 43 352
15) Honolulu 8,396,313 11 230
16) Pittsburgh 8,378,639 49 345
17) Minneapolis 8,170,952 40 301
18) Orlando 7,473,086 38 259
19) Las Vegas 6,864,803 28 220
20) Seattle 6,825,513 22 296


Sources: Official Airline Guide,
Activity Statistics of
Carriers, 1988.


July 1988 and Airport
Certified Route Air


* number of different non-stop connections of the 60 in the
study set


Certified Routg Air







83

The average number of daily departures (Table 5.8) were

calculated by dividing the number of commercial departures

scheduled by the airlines during the calendar year 1988 (as

recorded by the Federal Aviation Adminstration) by 365.

Again, most of the metropolitan areas in the study set saw an

increase in the number of daily airplane departures from 1978

to 1988. Los Angeles, Chicago, San Francisco, Houston,

Phoenix and Charlotte all saw increases of over 200 daily

departures. Eight cities, however, experienced declines.

Milwaukee, Grand Rapids, Albany, Scranton/Wilkes/Barre and

Birmingham all showed decreases of less than 3 daily

departures, while Louisville lost an average of 8 departures,

New Orleans an average of 13 and Buffalo an average of 16

daily departures from 1978 to 1988.

Some cities with a higher number of nonstop connections

than a few of those listed in Table 5.8 are Cincinnati (43),

Charlotte (40), Philadelphia (43), Cleveland (37) and

Baltimore (38). They are all hub cities, and as such, are

highly connected. Twelve cities in the study set experienced

significant increases (10 or more) in the number of nonstop

connections available from their airports between 1978 and

1988 (Table 5.9). They are largely post-deregulation hubs

(Table 4.2). Fifteen cities, most of them spoke or nonhub

cities from the study set, however, experienced reductions in

the number of nonstop connections from their facilities. The









TABLE 5.9
GREATEST INCREASES IN NONSTOP CONNECTIONS
1978-1988



city increase



1) Orlando 20

2) Charlotte 18

3) Cincinnati 17

4) Minneapolis 16

5) Raleigh/Durham 16

6) Nashville 15

7) Memphis 14

8) Phoenix 12

9) Pittsburgh 12

10) Salt Lake City 11

11) Atlanta 10

12) Baltimore 10



Source: Official Airline Guide, July, 1978 and 1988

Note: of the 60 cities in the study set







85
most severe decline was felt by Buffalo (7), a city whose

economy suffered during the period.



Research Methodology



The analysis in this chapter investigates whether or not

changes in connectivity are related to changes in

professionalemployment growth or vice versa. To measure the

impact of hub selection on this growth, a general hub/nonhub

comparison was carried out using all 60 cities in the study

set over the 11 year period.

Changes in connectivity were monitored using a derived

accessibility index. The yearly connectivity indices for each

metropolitan area were compared to changes in professional

employment by looking at the average rate of change of each

variable from one year to the next. Graphing both rate-of-

change values for each hub city over time should give an

indication of the relationship between the two variables. The

means of the yearly average rates of change for connectivity

and professional employment were calculated for each MSA, and

a simple correlation analysis was done to determine the

statistical relationship between the two. In addition, by

separating the data for the MSA's into different groups,

regional and hierarchical information was obtained.

To test for a lag structure in the relationship between

the variables, times series regression analysis was performed







86

using the average rates of change for each variable for all

years in the study period.



Accessibility Indices



To obtain accessibility measurements to be used in the

analysis, binary matrices were constructed for each of the 11

years in the study period (1978-1988). Each 60 X 60 matrix

had cell values of either one or zero. A value of one was

assigned to the cell if a direct air service connection

existed between the particular cities in question. If such a

connection did not exist, then the cell was assigned a value

of zero.

As discussed in Chapter 4, this simple connectivity

matrix (Cl), can be used to measure accessibility from a graph

theoretic approach. The row values are summed to give the

nodality index or gross vertex connectivity number for each

node. The larger the nodality index, the greater the relative

connectivity. Higher order connections can be taken into

account by powering the original connectivity matrix (C1)

until all of the zero elements disappear from the resultant

matrix. Summing the matrix Cl with the powered matrices

(yielding matrix T) allows one to measure total accessibility

within the network.

Each matrix was multiplied to the third power. At this

point, all zero elements disappeared from the resultant







87
matrices. Thus, the diameter of each network (from 1978

through 1988) is three. Appendix G gives the yearly

accessibility indices for each metropolitan area from matrices

Cl, C2, C3 and T from 1978 through 1988. It is particularly

important in this study to include indirect connectivity

effects since business travelers often visit multiple

destinations in one trip without necessarily going back

through the same hub city between flight segments. The cities

(airports) with the highest accessibility indices in 1988 are

ranked in Table 5.10.



Employment-Connectivity Relationships


Figure 5.2 compares the average rate of change from one

year to the next (from 1978 through 1988) in the number of

professional workers (administrative and auxiliary employees)

in the labor force in each of the 60 metropolitan areas to the

average rate of change in air service connectivity. The rates

were calculated from the raw numbers of administrative and

auxiliary employees and connectivity indices given in

appendices D and H, respectively. A quick glance reveals that

the two graphs have the same general trend in the first half

of the study period, but no apparent similarity in the latter

years. If the two variables are related to (affected by) one

another, a change in one seems to bring a near instantaneous

change in the other during the early years of the period, with









TABLE 5.10
STUDY SET CITIES WITH HIGHEST ACCESSIBILITY INDICES: 1988



city index
number



1) Chicago 53,747
2) Atlanta 53,386
3) Dallas 50,158
4) Washington, D.C. 49,999
5) New York/Newark 49,887
6) St. Louis 49,839
7) Pittsburgh 49,297
8) Cincinnati 46,562
9) Detroit 46,193
10) Charlotte 45,955
11) Philadelphia 45,403
12) Minneapolis 43,304
13) Boston 43,239
14) Denver 42,651
15) Cleveland 42,371
16) Houston 41,649
17) Orlando 41,623
18) Los Angeles 41,250
19) Baltimore 41,109
20) Memphis 40,628


Source: Appendix G












































Years


FIGURE 5.2
CHANGE IN PROFESSIONAL EMPLOYMENT VS. CONNECTIVITY







90

the employment change occurring slightly ahead of the

connectivity change. In the latter years, either there is no

relationship between the two or possibly a complex lag

structure exists.

The data were divided between hubs and nonhubs to compare

the different rates of change for both air connectivity and

professional employment for each group. Post-deregulation hub

cities were considered part of the nonhub group until the year

that hub status was achieved (Table 4.2). For example,

Baltimore was classified as a nonhub until 1983, but was

switched to the hub category after 1983.

The nonhub connectivity rate of change (Figure 5.3) stays

very close to the rate of change for professional employment

(practically the same graph), while the hub city connectivity

rate (Figure 5.4) stays below the employment rate of change

for much of the study period (until the latter years).

Indeed, the two lines in Figure 5.4 appear to have little in

common. The graphs (Figures 5.3 and 5.4) seem to suggest that

a stronger and virtually instantaneous relationship exists

between connectivity change and professional employment change

for nonhubs than for hub cities.

Figure 5.5 breaks the employment data between hubs and

nonhubs. Throughout a fair portion of the study period,

professional employment at nonhubs grew by a higher rate than

hub cities. The mean of the yearly average rates of

professional employment change during the study period for




Full Text
109
TABLE 5.11continued
MSA
employment
connectivity
Providence
8.81%
5.86%
Raleigh/Durham
7.89%
11.42%
Richmond
6.41%
4.61%
Rochester
6.78%
3.56%
Sacramento
7.49%
5.33%
St. Louis
1.74%
4.49%
Salt Lake City
3.30%
7.83%
San Antonio
7.54%
6.64%
San Diego
9.29%
1.70%
San Francisco
3.52%
2.32%
Scranton
0.84%
2.33%
Seattle
9.44%
5.73%
Syracuse
0.93%
5.75%
Tampa
7.02%
2.86%
Tucson
6.61%
6.47%
Tulsa
-0.29%
3.67%
Washington, D.C.
7.14%
2.97%
West Palm Beach
22.84%
8.74%


71
how long it takes the airline to respond (with increased
service levels) to the potential market growth brought on by
the new addition to the urban area's employment structure.
Finally, the study will look for regional and
hierarchical biases. Hubs in some areas may show a stronger
relationship between the two variables than hubs in other
parts of the nation, and hubs with higher population totals
could show a stronger or weaker relationship between
connectivity change and employment change than smaller-sized
hubs.
The Labor Data
The professional labor data were collected from the
County Business Patterns series published by the U.S. Census
Bureau. It is an annual publication with volumes for all 50
states, the District of Columbia and Puerto Rico.
Unfortunately, this labor information is not published on a
consistent basis for metropolitan areas as a whole. The
series records the number of employees per economic sector
(based on SIC codes) by county in the states. Within each
sector, a separate classification is reserved for professional
workers. The publication refers to these workers as
administrative and auxiliary employees, defined as personnel
working in central administration offices and auxiliary
establishments such as research labs and financial services.


86
using the average rates of change for each variable for all
years in the study period.
Accessibility Indices
To obtain accessibility measurements to be used in the
analysis, binary matrices were constructed for each of the 11
years in the study period (1978-1988). Each 60 X 60 matrix
had cell values of either one or zero. A value of one was
assigned to the cell if a direct air service connection
existed between the particular cities in question. If such a
connection did not exist, then the cell was assigned a value
of zero.
As discussed in Chapter 4, this simple connectivity
matrix (Cl) can be used to measure accessibility from a graph
theoretic approach. The row values are summed to give the
nodality index or gross vertex connectivity number for each
node. The larger the nodality index, the greater the relative
connectivity. Higher order connections can be taken into
account by powering the original connectivity matrix (Cl)
until all of the zero elements disappear from the resultant
matrix. Summing the matrix Cl with the powered matrices
(yielding matrix T) allows one to measure total accessibility
within the network.
Each matrix was multiplied to the third power. At this
point, all zero elements disappeared from the resultant


38
service competition at a minimum acceptable level 6)
avoidance of existing major hubs.
While the hub-and-spoke strategy has created greater
overall efficiency and cost-cutting benefits for the airlines,
the very essence of the system creates problems of congestion,
environmental problems such as noise and air pollution, strain
on infrastructure and overworked air traffic controllers.
The hub-and-spoke system requires that airlines
concentrate many incoming and outbound flights during a narrow
time frame to maximize the number of city-pair combinations
that can be served. The resulting congestion has been too
much for most airports to handle. For example, Atlanta's
Hartsfield International Airport has an optimum capacity of 21
arrivals every 15 minutes, as determined by the FAA; however,
32 arrivals were recently scheduled by the airlines between
9:00 a.m. and 9:15 a.m. (Morganthau, 1987). At Chicago's
O'Hare, 42 departures were scheduled between 7:00 a.m. and
7:15 a.m., despite a capability to deal safely and efficiently
with only 23 (Morganthau, 1987). Increases in the frequency
of flights since 1978 at several hubs have been quite large.
Baltimore (+94.2%), Dallas/Ft. Worth (+52.2%), Houston
(+85.1%), Minneapolis/St. Paul (+60.1%) and Salt Lake City
(+51.9%) grew by more than half between 1978 and 1984, while
flight frequencies more than doubled at Charlotte (+134.2%)
and Newark (+114.2%) (Report on Airline Service. 1984).


96
This suggests that as the air transportation network has
become saturated and more stable, any change in the network
now affects hubs and nonhubs fairly egually.
Regional Trends
To determine any regional variations in the growth rates,
the study set was separated into four different groups. These
groups are the traditional regional divisions of the
UnitedStates as defined by the Census Bureau (Figure 5.1).
The Northeast, North Central and West each have 12 of the 60
metropolitan areas of the study set within them. The
remaining 24 cities lie in the South.
Figures 5.7 and 5.8 compare the average rate of change of
the number of professional workers in the labor force in the
Northeast cities to their rate of change in air service
connectivity for hubs and nonhubs, respectively. The
connectivity rate of change remains fairly low for this group
of cities, with the hub graph never rising above .1 (or 10%
growth rate) and the nonhub graph only doing so near the end
of the study period. The urban areas of the Northeast are
among the largest in population and are major centers of
business. As such, they are (and always have been) major
market destinations for air travel. Also, the density of
major metropolitan areas in the Northeast has contributed to
high levels of air service connectivity before and after


UNIVERSITY OF FLORIDA
illlllillillllll
3 1262 08554 7700


TABLE 4.5
ACCESSIBILITY INDICES FOR MATRICES Cl AND T
58
airports
matrix Cl
rank
matrix T
rank
ATL
83
2
93552
2
BWI
48
16
69753
15
CLT
69
4
83769
4
CHI
92
i
97950
1
CVG
63
8
82183
7
CLE
42
23
63749
20
DFW
79
3
88668
3
DAY
30
28
47457
28
DEN
60
10
71207
13
DTW
56
11
76515
10
HNL
12
29
20889
29
HOU
52
14
69597
16
LAS
37
25
53701
25
LAX
48
17
68678
17
MEM
47
18
63525
21
MIA
34
26
55436
23
MSP
51
15
70247
14
BNA
56
12
74299
12
NYC*
61
9
81114
9
MCO
43
21
65272
18
PHL
53
13
74469
11
PHX
46
19
62542
22
PIT
65
5
83524
5
RDU
43
21
54992
24
STL
64
7
81844
8
SLC
38
24
47692
27
SFO
46
20
64887
19
SEA
33
27
48591
26
DCA
65
6
82696
6
Includes Newark


184
Schoenberger, Erica, 1987, Technological and
Organizational Change in Automobile Production:
Spatial Implications, Regional Studies. 21, pp.
199-214.
Schoenberger, Erica, 1988, From Fordism to Flexible
Accumulation: Technology, Competitive Strategies,
and International Location, Environment and
Planning D. 6, pp. 245-62.
Scott, A.J., 1983, Industrial Organization and the Logic
of Intra-Metropolitan Location: Theoretical
Considerations, Economic Geography 59, pp. 233-
249.
Scott, A.J., 1988a, New Industrial Spaces. London: Pion.
Scott, A.J., 1988b, Metropolis Los Angeles, CA:
University of California Press.
Snow, John W., 1977, The Problems of Airline Regulation
and the Ford Administration Proposal for Reform,
Regulation of Passenger Fares and Competition Among
the Airlines. Washington, DC: American Enterprise
Institute for Public Policy Research, pp. 3-37.
Statistical Abstract of the United States. annual,
Washington, DC: Government Printing Office, United
States Department of Commerce, Bureau of the
Census.
Stockton, C., 1988, When Eight Carriers Call the Shots,
New York Times. November 20, 3-1, pp. 3-6.
Storper, Michael and Walker, Richard, 1983, The Theory
of Labor and the Theory of Location, International
Journal of Urban and Regional Association, 7, pp.
1-41.
Storper, Michael and Walker, Richard, 1984, The Spatial
Division of Labor: Labor and the Location of
Industries, In L. Sawers and W. Tabb (Eds.),
Sunbelt/Snowbelt. New York: Oxford University
Press, pp. 19-47.
Taaffe, Edward and Gauthier, Howard L., 1973, Geography
of Transportation. Englewood Cliffs, NJ: Prentice-
Hall.
Thompson, Wilbur, 1987, Policy-based Analysis for Local
Economic Development, Economic Development
Quarterly. 3, pp. 203-13.


TABLE 5.2
MOST ADMINISTRATIVE/AUXILIARY EMPLOYEES: 1988
73
MSA
number of
professionals
population
rank
1)
New York/Newark
462,305
1
2)
Los Angeles
193,284
2
3)
Chicago
183,688
3
4)
Boston
139,258
7
5)
Detroit
136,782
6
6)
Philadelphia
136,055
5
7)
San Francisco
120,469
4
8)
Dallas/Ft. Worth
109,966
8
9)
Houston
95,362
10
10)
Minneapolis/St. Paul
83,141
16
ID
Atlanta
81,958
13
12)
Washington, D.C.
64,363
9
13)
Cleveland
63,586
12
14)
St. Louis
58,350
14
15)
Providence
55,189
34
16)
Seattle
43,944
15
17)
Cincinnati
40,221
23
18)
Pittsburgh
39,973
19
19)
Miami/Ft. Lauderdale
36,602
11
20)
Columbus
33,304
29
Source
Calculated from County Business Patterns. U.S
Census Bureau, 1990.


183
Pennings, J.M., 1982, The Urban Quality of Life and
Entrepreneurship, Academy of Management Journal.
25, pp. 63-79.
Pinder, C. 1977, Multiple Predictors of Post-Transfer
Satisfaction: The Role of Urban Factors, Personnel
Psychology. 30, pp. 543-566.
Pitts, F. 1965, A Graph-Theoretic Approach to
Historical Geography, The Professional Geographer.
17, pp. 15-20.
Pollack, Maurice, 1977, Some Elements of the Airline
Fleet Planning Problem, Transportation Research.
11, 301-310.
Pollack, Maurice, 1982, Airline Route-Frequency
Planning: Some Design Trade-Offs, Transportation
Research. 16A, pp. 149-159.
Power, T.M., 1980, The Economic Value of the Quality of
Life, Boulder, CO: Westview Press.
Report on Airline Service. 1984, Washington, DC:
Government Printing Office, United States
Department of Transportation, Civil Aeronautics
Board.
Rietveld, P., 1989, Infrastructure and Regional
Development: A Survey of Multi-Regional Economic
Models, The Annals of Regional Science. 23, pp.
255-274.
Rose, R. and Dahl, J., 1989, Aborted Takeoffs: Skies are
Deregulated, But Just Try Starting a Sizeable New
Airline, The Wall Street Journal July 19, pp. Al,
A16.
Rose, Warren, 1981, Three Years After Airline Passenger
Deregulation in the United States: A Report Card on
Trunkline Carriers, Transportation Journal. 21, pp.
51-58.
Schmenner, R.W., 1982, Making Business Location
Decisions, New York: Prentice-Hall.
Schoenberger, Erica, 1986, Competition, Competitive
Strategy, and Industrial Change: The Case of
Electronic Components, Economic Geography. 62, pp.
321-333.


TABLE 5.14
TIME SERIES ANALYSIS
117
Model 1A
Model IB
Y=connectivity rate of change for study set
X=professional employment rate of change for
study set
Y=professional employment rate of change for
study set
X=connectivity rate of change for study set
Model 2A
Model 2B
Model 3A
Model 3B
Y=hub connectivity rate of change
X=hub professional employment rate of change
Y=hub professional employment rate of change
X=hub connectivity rate of change
Y=nonhub connectivity rate of change
X=nonhub professional employment rate of
change
Y=nonhub professional employment rate of
change
X=nonhub connectivity rate of change
model
Rz
T sig
lag T
sig
F sig
D-W
1A
.16
.3933
.3801
.5909
1.69
IB
.04
.6887
.8404
.8782
2.09
2A
.02
.9106
.7829
.9453
1.69
2B
.23
.9384
.9384
.2366
2.26
3A
.29
.2669
. 1839
.3570
1.56
3B
.05
.8156
.7190
.8663
1.89


128
LIT
8
287
14916
LAX*
48
1112
67518
SDF
17
568
31890
LBB
5
78
4210
MSN
5
179
9636
MLB
9
244
14632
MEM*
47
970
62508
MIA*
34
889
54513
MAF
5
110
5894
MKE
28
809
47773
MSP*
51
1101
69095
MOB
8
244
12818
MLI
5
197
9886
MYR
5
174
9633
BNA*
56
1086
73157
MSY
26
783
45458
NYC*
61
1187
79866
ORF
17
578
32658
OKC
12
389
19952
OMA
15
493
26474
ONT
19
532
28412
MCO*
43
1013
64216
PSP
10
307
16160
PNS
11
298
16966
PHL*
53
1099
73317
PHX*
46
1021
61475
PIT*
65
1181
82278
PWM
7
221
13041
PDX
21
506
27394
PVD
13
433
25282
RDU*
43
791
54158
RNO
14
368
19864
RIC
15
481
27630
15211
68678
32475
4293
9820
14885
63525
55436
6009
48610
70247
13070
10088
9812
74299
46267
81114
33253
20353
26982
28963
65272
16477
17275
74469
62542
83524
13269
27921
25728
54992
20246
28126


65
The moderate hubs have medium-ranging accessibility
numbers and include almost one-third of the hubs from table
4.1. Only Memphis and Cincinnati have non-local passenger
percentages of over 50% I type A), although some of the type B
moderate hubs are near that proportion.
Minor hubstype A and minor hubstype B have low
accessibility numbers. Again, type A hubs have non-local
passenger percentages of over 50% (Salt Lake City and
Raleigh/Durham, for example), while type B hubs (like San
Francisco and Orlando) do not.
Miami, Honolulu, Seattle, Las Vegas and Dayton make up
the non-hub category. These are cities (largely destination
or international gateway hubs) with very low accessibility
numbers and transfer percentages below (in most cases well
below) 50%. Dayton, a USAir hub, is being pulled down from
that status in early 1992. These cities are in no way
comparable to the higher domestic connectivity and major
transfer role played by the other hubs from Table 4.1.
Conclusion
This chapter brings to light possible confusion in the
usage of the term hub, from the FAA definition (which has
nothing whatsoever to do with transfer or connecting status)
to the various service levels of the airline definition. The
hub-and-spoke phenomenon of the post-regulation period has,


135
PDX
19797
16322
16282
16704
17125
SMF
5242
4468
4747
5627
5339
ORF
7144
7458
8861
6907
7428
CMH
23461
24158
24669
25690
38681
SAT
7006
9644
8977
11251
9833
MSY
18354
17497
19181
21172
24632
IND
20949
18926
18895
17199
18312
BUF
9950
8596
7498
8356
11119
PVD
26897
32039
31878
36184
34087
CLT
14727
18985
20329
20091
20422
BDL
16216
20310
18504
17919
20511
SLC
10157
11107
12014
12028
12658
ROC
20983
11553
12202
13953
22409
MEM
11554
10030
12426
12817
16465
BNA
12215
13683
12170
13637
16299
MCO
5813
6218
6066
7283
7690
SDF
14291
15900
15244
15688
18090
OKC
11739
12356
12802
14365
16572
DAY
15478
19034
18058
16948
17477
GSO
22512
21053
20537
20709
25095
BHM
5331
5435
6257
5561
6403
JAX
934
9922
8723
9643
8898
ALB
12171
13096
14331
13833
14265
RIC
13811
14069
16940
16469
16389
HNL
6850
7622
8178
9203
8691
PBI
989
1698
1966
2109
3186
AUS
1397
2105
2310
1926
2079
AVP
4427
5165
4935
5048
4340
TUL
24862
24017
27011
27321
29902
RDU
7652
6672
7729
8429
8725
ABE
15362
14398
27159
16996
17400
GRR
5350
3595
7588
7434
8241
SYR
8995
8734
8910
9086
9198


APPENDIX A
CONNECTIVITY INDEX TOTALS
MATRIX1
MATRIX2
MATRIX3
TOTALS
CAK
8
279
15603
15890
ALB
16
390
24726
25132
ABQ
16
412
22144
22572
ABE
11
367
21136
21514
AMA
4
98
4890
4992
ANC
6
128
8003
8137
ATL*
83
1289
92180
93552
AUS
13
366
19652
20031
BWI*
48
1034
68671
69753
BTR
7
198
10624
10829
BIL
4
99
5381
5484
BHM
13
417
22789
23219
BOI
8
216
11698
11922
BOS
47
1058
68545
69650
BUF
18
513
31244
31775
BTV
6
213
12328
12547
CID
7
222
11758
11987
CHS
11
290
16646
16947
CRW
7
288
14896
15191
CLT*
69
1167
82533
83769
CHA
5
186
9995
10186
ORD*
92
1353
96505
97950
CVG*
63
1171
80949
82183
CLE*
42
953
62754
63749
126


4
industrial location and air transportation research. In
Chapter 4, variations in hub service quality and connectivity
are discussed and a hub classification scheme (based on
connectivity) is derived using binary matrix analysis. The
main analysis (methodology and results) of this dissertation
is presented in Chapter 5. Chapter 6 is a summary of the
study and directions of future research.


124
different units of measurement could yield higher significance
levels in the general correlation analysis and times series
models. Also, using median instead of mean values for the
accessibility indices could get rid of some of the problems
created by extreme outlier values found in the data set.
The unexpected failure of the time series analysis
suggests that a more complex relationship than was tested for
in the present study may exist between changes in air service
connectivity and professional employment. There are perhaps
other factors that influence connectivity and professional
employment change that should have been included in the study
and the time series model. The model should be expanded to
include some important geographic differences between the
cities in the study set. These might include population size
and hierarchical effects, regional effects, distance from one
hub to other hubs, economic health of the city, industrial mix
or occupational structure of the city, economic health of the
dominant carrier at each city, pre- vs. post-deregulation hub
status and connectivity strength. Some of the classification
schemes designed in the present study (Tables 4.8 and 5.12)
could be incorporated into the model as variables measuring
population (hierarchical effects) and connectivity (hub
strength). Regional effects appear to be somewhat important,
and could be tested explicitly through the usage of dummy
variables in a regression model. Future research will
directed towards the model specification.


39
Another problem of the hub-and-spoke strategy has been
the reduction in the number of nonstop flights from cities
other than hub cities. For many city pairs it now takes much
longer en route because virtually all flights are routed
through at least one hub (Goetz, Dempsey, 1989). The
opportunity cost of time is a factor that many travelers
consider important. In general, small cities and some of the
large spoke cities have often ended up worse off than they
were prior to deregulation as they have lost some (and in a
few cases all) nonstop service to cities that now have to be
reached through hubs (Bauer, 1987; Ivy, 1991).
The advantage for hub-city residents of a large number of
nonstop destinations is counterbalanced with some
disadvantages. These passengers generally pay much higher
fares and have less choice of carriers for most destinations
than was the case prior to deregulation (Bauer, 1987;
Borenstein, 1989; McGinley, 1989; Toh, Higgins, 1985). Single
carrier dominance (Table 3.1) and higher fares are the price
paid by residents for greater flight frequency and nonstop
destination choice. Hub cities have been hit the hardest with
fare increases since their residents are, in effect,
subsidizing longer haul passenger fares. Passengers flying
from Norfolk to Kansas City through USAir's Charlotte hub, for
example, would likely be charged the same fare as passengers
travelling on USAir from Charlotte to Kansas City only.
Today, flights originating at or departing for hub cities are


51
An emerging category is the destination hub. These are
cities that are popular travel destinations, and as such
offera great deal of nonstop service to and from large and
medium-sized cities around the nation to meet the high demand.
Orlando, Las Vegas and Honolulu fall into this classification
(although Honolulu also serves as an international connecting
point). All three generate comparatively low nonlocal traffic
rates (Table 4.1), and are not well connected to cities of
varying size (Table 4.3). Few passengers flying into these
cities are transferring to other domestic locations, and these
are mainly via commuter carriers from the local area. Some of
the international gateway hubs could be considered destination
hubs as well (like Los Angeles and New York) due to their
popularity as travel destinations.
Hub Connectivity
Because today's hubs (and the carriers operating them)
basically compete with one another for transfer traffic, the
success of a hub usually depends on how well connected it is
to other nodes in the .S. air transportation network. The
hub airport (and carrier) that has a greater variety of
nonstop service to different sized cities in all parts of the
nation is in a better position to attract passengers and
control markets. Atlanta competes with Charlotte in the
Southeast, for example, and Dallas, Houston and Denver


159
OKC
10
MCO
24
PHL
42
PHX
20
PIT
46
PDX
12
PVD
8
RDU
12
RIC
12
ROC
12
SMF
10
STL
42
SLC
15
SAT
8
SAN
17
SFO
29
AVP
5
SEA
20
SYR
11
TPA
32
TUS
10
TUL
12
DCA
45
PBI
12
1982 MATRIX1
ALB 11
ABE 8
ATL 54
AUS 5
10231
10622
22983
23821
33741
34911
17562
18220
35953
37192
10632
11024
9155
9520
10860
11266
11270
11704
11980
12428
8264
8569
34416
35615
13941
14459
8364
8696
15181
15741
25133
26045
5105
5303
17411
18054
10998
11421
28346
29348
9561
9923
11729
12167
35051
36263
12247
12712
MATRIX3
TOTAL
10712
11125
9289
9653
40512
41881
5110
5305
381
814
1128
638
1193
380
357
394
422
436
295
1157
503
324
543
883
193
623
412
970
352
426
1167
453
MATRIX2
402
356
1315
190


163
PDX
14
PVD
9
RDU
15
RIC
12
ROC
13
SMF
10
STL
43
SLC
20
SAT
10
SAN
17
SFO
26
AVP
6
SEA
19
SYR
13
TPA
30
TUS
8
TUL
12
DCA
46
PBI
15
1984
MATRIX1
ALB
13
ABE
8
ATL
54
AUS
8
BWI
34
BHM
9
BOS
37
BUF
16
CLT
29
CHI
56
CVG
30
CLE
31
12887
13363
9933
10318
13449
13925
11865
12310
12106
12551
8474
8785
36110
37351
16735
17341
10081
10467
15516
16085
22604
23418
5635
5845
17067
17696
12599
13067
27861
28834
7689
7977
12142
12592
36962
38223
15224
15782
MATRIX3
TOTAL
12853
13328
9565
9932
41525
42917
7999
8300
29586
30606
8867
9186
31669
32760
15170
15721
26543
27463
41908
43313
29406
30430
29713
30748
462
376
461
433
432
301
1198
586
376
552
788
204
610
455
943
280
438
1215
543
MATRIX2
462
359
1338
293
986
310
1054
535
891
1349
994
1004


64
constructed, and are indeed in a class by themselves. Because
they are large in population, more centrally located in the
nation than many of the other U.S. mega cities, and each an
important transfer point for more than one carrier for several
years (until early 1991, the now defunct Eastern Airlines had
hub operations at Atlanta's Hartsfield), these cities have
been the major pivot centers for air transportation for more
than a decade. Each has a non-local (transfer) traffic base
of over 50% of its total enplanements. They have a clear
advantage over peripheral hubs like Miami, New York and Los
Angeles in that they are more proximal to a greater number
ofnodes in all parts of the country. These hubs are also
operated by one or more of the financially strongest carriers
in the nation (American, Delta, United) helping to make
possible more flights to more destinations.
Manor hubstype A are cities with high accessibility
numbers and transfer passenger percentages of over 50%
(excluding the super hubs). Charlotte, Pittsburgh, St. Louis
and Denver make up this category. While the major hubstype
B have high accessibility numbers as well, their non-local
traffic base is less than 50% of the total traffic at their
facility. For these hubs (New York and Washington, D.C.),
this is more a reflection of the city in question's
popularity as a final destination (due to sheer population
size) and the fact that both are multiple airport cities,
than a reflection on the service offered at the airports.


rate of change
105
Years
FIGURE 5.13
SOUTHERN HUB RATES OF CHANGE


Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree Doctor of Philosophy
AIRLINE HUBS: CHANGES IN URBAN EMPLOYMENT STRUCTURE
AND NETWORK CONNECTIVITY
By
Russell L. Ivy
August, 1992
Chairperson: Edward J. Malecki
Major Department: Geography
The deregulation of the domestic airline industry in the
U.S. brought about many changes in air transportation.
Carriers have come and gone, fares have fluctuated and
enplanement figures have risen tremendously. Fundamental
changes have also occurred in the geographic structure of the
air transport network. In particular, the hub-and-spoke
system has been adopted by major carriers and has led to
increases in flow efficiency and connectivity. One or more
cities are chosen by the airline as a regional collection
point for passengers. Passengers from many different origins
are funneled into the hub city to connect with flights to
their final destinations. Hub cities, therefore, are
characterized by having many nonstop destinations available
from their airports.
ix


TABLE 5.12
HUB CLUSTERS BASED ON POPULATION TOTALS
113
GROUP 1
New York/Newark
Los Angeles
GROUP 2
Chicago
San Francisco
Philadelphia
GROUP 3
Detroit
Dallas/Ft. Worth
Washington, D.C.
Houston
GROUP 4
Miami
Atlanta
St. Louis
Seattle
Minneapolis/St. Paul
Baltimore
Pittsburgh
Phoenix
Denver
GROUP 5
Cincinnati
Charlotte
Salt Lake City
Memphis
Nashville
Dayton
Honolulu
Raleigh/Durham
Las Vegas


rate of
95
Years
FIGURE 5.6
CONNECTIVITY CHANGE: HUB VS.
NONHUB


CHAPTER 3
THE DOMESTIC AIRLINE INDUSTRY
Introduction
Since the deregulation of domestic airlines in 1978, a
tremendous degree of change has occurred in almost every
aspect of the air travel industry in the United States. As
the industry became deregulated, network structures, fares,
enplanement levels and service quality were greatly affected.
Many new carriers have come and gone (such as Air Florida,
Midway and People's Express), and several older, established
carriers (such as Eastern, Pan Am and Braniff) have
disappeared as well. The first part of this chapter deals
with the deregulated industry and its evolution thus far. The
second part describes the hub-and-spoke network structure,
which is largely a post-regulation phenomenon, and looks at
changes in the location and use of hub cities by the air
trave1 industry.
The Deregulated Airline Industry
For 40 years the domestic airline industry in the United
States was regulated by the federal government under the Civil
25


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156
1980
MATRIX1
ALB
11
ABE
8
ATL
52
AUS
3
BWI
26
BHM
12
BOS
36
BUF
19
CLT
24
CHI
57
CVG
25
CLE
34
CMH
20
DFW
46
DAY
16
DEN
38
DTW
35
GSO
15
BDL
21
HNL
6
HOU
36
IND
22
JAX
13
GRR
5
MKC
25
LAS
24
LAX
39
SDF
20
MEM
28
MIA
29
MKE
24
MSP
27
MATRIX3
TOTAL
10485
10893
9090
9452
38218
39533
2477
2570
22359
23176
10872
11270
29299
30338
16351
16951
20287
21018
39312
40662
23958
24829
29866
30933
19224
19931
34549
35757
15469
16028
30652
31742
30081
31153
13513
14029
19467
20189
5510
5718
28919
29953
20923
21683
12492
12967
5054
5236
22604
23433
21279
22065
31481
32600
18324
18994
24952
25848
26209
27159
22928
23772
24648
25546
MATRIX2
397
354
1263
90
791
386
1003
581
707
1293
846
1033
687
1162
543
1052
1037
501
701
202
998
738
462
177
804
762
1080
650
868
921
820
871


54
(accessibility matrix). The individual rows or columns of
this accessibility matrix (T) can be summed to yield the gross
vertex connectivity for each node in the network.
Early researchers using the above-mentioned technique
(Garrison, 1960; Pitts, 1965), discovered that while powering
the matrix did give the maximum number of alternative paths in
a network, a number of redundancies (passing through the same
node more than once) were included in the final accessibility
matrix (T) This was particularly the case for nodes that
were directly connected in the original connectivity matrix
(Cl) .
Another criticism by Garrison (1960) was that all
linkages should not be considered equal in importance. The
more indirect the linkage, the less it should add to the gross
vertex connectivity number. He introduced a procedure in
which a scalar number (s) that takes on a value between zero
and one is multiplied by the accessibility values in each
matrix powered according to the order of the matrix
[T=sC1+s2C2+s3C3+. .+snCN] The real problem lies in assigning
the scalar value (Garrison's scalar of .3 was assigned
arbitrarily). The technique, however, does lessen the
importance of indirect connections relative to direct
connections and at the same time reducing the impact of
redundant paths. A new scalar method will be introduced in
the next section.


24143
20305
8558
35112
20376
20828
11988
15928
15359
12322
10086
18121
15705
14938
26550
7669
9351
13786
18728
8165
3109
2594
3871
25767
7828
17914
7403
8009
2843
2246
20067
18733
9583
55189
28694
20467
13648
25239
17647
15407
19778
17861
10481
20826
31971
12588
13207
8077
24126
8826
6493
3653
4618
23188
13365
18671
7677
9573
3293
2147
20088
18974
19800
18981
8829
10248
36136
48512
23880
23003
22924
23408
11908
14109
13644
16470
15672
17378
14029
15291
12549
19610
18069
17315
12204
12223
15062
45560
35164
35859
11132
11125
9663
9985
12566
9182
22548
23934
8714
8423
3240
4169
2814
3189
3883
3884
27640
28002
7702
12316
15451
16996
7448
6727
8972
8712
2658
2754
2246
2305
20936
18587
22483
17911
10982
7964
49808
4562
24674
24745
22195
22024
13931
12510
14029
20781
17672
17061
16416
15246
17373
16156
17702
17578
13645
10588
18954
18827
42438
20717
12834
11654
10819
8902
14528
24211
21992
19504
7731
7960
4432
4499
3108
3440
3945
4546
27909
23304
9928
9893
20145
16400
5529
7383
7999
9150
3830
2820
2305
2449


TABLE 2.4
IMPORTANT LOCATION FACTORS BY FACILITY TYPE
19
Manufacturing Plant
1) labor availability
2) energy availability
3) good highway infrastructure
Distribution Center
1) good highway infrastructure
2) market accessibility
3) labor availability
Corporate Headquarters
1) good air transportation service
2) good highway infrastructure
3) professional talent availability
Regional Office
1) good air transportation service
2) good highway infrastructure
3) professional talent availability
R&D Facility
1) professional talent availability
2) good air transportation service
3) good highway infrastructure
Source:
Browning, 1980.


LIST OF TABLES
TABLE PAGE
2.1 Ten Most Important Factors in Selecting
Locations for Manufacturing/Processing
Plants 12
2.2 Ten Most Important Factors in Selecting
Locations for Company Facilities 14
2.3 Five Most Important Factors in Selecting
Locations for Various Types of Company
Facilities 16
2.4 Important Location Factors by Facility
Type 19
3.1 Single Carrier Dominance at Hubs 4 0
4.1 Fall 1991 Hub Cities of the Major U.S.
Carriers 45
4.2 Pre and Post-Deregulation Hub Cities
(1991) 48
4.3 Nonstop Domestic Connections on all Carriers
by FAA Hub-Type (Fall 1991) 50
4.4 FAA Hubs 56
4.5 Accessibility Indices for Matrices Cl
and T 58
4.6 Accessibility Indices of Matrix T With
Different Scalars 60
4.7 Accessibility Indices of Individually
Weighted Nodes 62
4.8 Hub Strength Classification Scheme 63
5.1 60 Largest Metropolitan Areas of the U.S.
(1988-in thousands) 68
vi


141
539555
578139
611904
596725
632017
644441
444553
474570
495637
278563
30943
334599
326420
357067
384426
430137
459213
483842
352966
370165
395023
427673
441956
437812
411429
444995
464438
373584
386528
404933
987667
1015888
1063227
422810
458046
476600
590400
624002
651498
294032
344613
338280
358419
372936
399554
307097
328268
346979
319980
355820
379899
294373
336816
366373
319111
337560
348638
328789
335128
334407
295569
314543
333606
356850
383842
404258
280779
302708
318477
258825
278288
311577
248603
264244
271937
285962
304643
321865
247118
256901
262794
211477
241476
260143
208412
242293
269135
212522
224016
230534
267219
271931
279052
226723
256288
276763
218363
233121
239810
628962
643688
649975
653775
672782
695059
508399
526786
555551
355369
386845
410931
402840
420298
433459
514973
539333
558867
399087
398159
392250
440179
422721
418412
488799
511802
537072
416053
418690
432552
1101661
1135481
1165177
496535
531224
553984
667169
705341
711714
344267
349265
354419
409307
397542
415692
354541
356225
388017
409957
422636
440110
388669
413135
441127
354550
367667
374907
324650
308785
388017
341134
354334
363362
418037
426809
447921
335682
338238
343970
314394
328335
343162
285152
299344
298092
338885
353996
369715
274517
284594
297324
279703
294969
311676
275244
262785
254614
237499
244980
256691
277215
261849
264621
292655
300628
324280
239652
247421
252978


75
western parts of the United States are strongly represented,
including cities such as Charlotte, Richmond, Jacksonville,
Orlando and Phoenix which did not make the list in Table 5.2.
Some of the cities in Table 5.2 experienced a loss of
administrative and auxiliary workers during the period in
guestion. These are listed in Table 5.4. Almost all of these
cities are in the traditional manufacturing belt. Tulsa and
Oklahoma City, however, are oil-producing cities whose
fortunes changed during the early and middle 1980s as the
price for oil dropped significantly, and the economy of oil
states (particularly Oklahoma and Texas) suffered.
Table 5.5 gives a regional analysis for the 60
metropolitan areas in the study set. It shows the total and
average administrative and auxiliary employment growth of the
study set cities within each region. These regions are the
traditional divisions of the nation as defined by the U.S.
Census Bureau (Figure 5.1). The Northeast still shows up
strongly with the South being a near second.
Perhaps of greater interest to a discussion of
professional workers in urban locations is Table 5.6. It
shows the 20 cities with the highest percentage of
administrative and auxiliary workers as a proportion of their
total employment for 1988 (constructed from Appendices C and
D). Many of the larger cities on the list in Table 5.2 either
drop off this list or appear at a lower ranking on the list.
Some new cities appear that were absent from Table 5.2, such


23
cost of seeking out those linkages (Coffey, Polese, 1989;
Hoare, 1985).
Large urban areas are attractive to professional workers
because they provide a high level of amenities (such as
cultural and recreational) which positively affect their
quality of life. Cutter (1985) defines quality of life as
one's happiness with the physical and social environment based
on how well that environment fits one's personal needs and
desires. This, of course, is a subjective concept (making it
difficult to measure) and can vary greatly from one individual
to another (Canter, 1983; Cutter, 1985). Affecting the
quality of life image for professional workers are such
factors as climate, cultural and recreational amenities,
transportation accessibility, higher educational
opportunities, quality of health care, crime rates, pollution
levels and cost of living (Boyer, Savageau, 1989; Glasmeier,
Hall, Markusen, 1984; Herzog, Schlottmann, Johnson, 1986;
Hsieh, Liu, 1983; Pennings, 1982).
Professional workers can be more selective about where
they live, and do indeed evaluate quality of life factors when
making locational decisions (Markusen, Hall, Glasmeier, 1986;
Power, 1980). Location is often the most important factor in
the rejection of job offers by job-hunting professionals and
transfers within the firm (Collie, 1986; Pinder, 1977). For
these workers, large urban areas offer greater employment
opportunities for themselves and their spouses, the


179
Herzog, H.W., Schlottmann, A.M. and Johnson, D.L., 1986,
High-Technology Jobs and Worker Mobility, Journal
of Regional Science. 26, pp. 445-459.
Hoare, A.G., 1985, Industrial Linkage Studies, In M.
Pacione (Ed.) Progress in Industrial Geography.
London: Croom Helm, pp. 40-81.
Hoover, Edgar M., 1948, The Location of Economic
Activity. New York: McGraw-Hill.
Hsieh, C.T. and Liu, B.C., 1983, Pursuance of Better
Quality of Life: In the Long Run, Better Quality
of Life is the Most Important Factor in Migration,
American Journal of Economics and Sociology. 42,
pp. 431-440.
Insight. 1988, For Airports Seeking Growth, Financiers
Offer a Rainbow, October24, pp. 40-42.
Ippolito, Richard, 1981, Estimating Airline Demand With
Quality of Service Variables, Journal of Transport
Economics and Policy. 15, pp. 7-16.
Ivy, Russell L. 1991, Trials of a Small City Airport:
A Case-study of Gainesville, The Florida
Geographer 25, pp. 41-53.
Jacobs, Jane, 1984, Cities and the Wealth of Nations.
New York: Random House.
Johnson, Merrill L., 1989, Industrial Transition and the
Location of High-technology Branch Plants in the
Nonmetropolitan Southeast, Economic Geography. 65,
pp. 33-47.
Kanafani, Adib and Ghobrial, Atef, A., 1985, Airline
Hubbing-Some Implications for Airport Economics,
Transportation Research 19A, pp. 15-27.
Kaufman, H.G., 1982, Professionals in Search of Work.
Toronto: Wiley and Sons.
Kiel, Don, 1989, The Effects of Airline Deregulation in
Northern New England: Changes in Route Structures
and Level of Service, 1970-1985, paper presented at
the annual meeting of the Association of American
Geographers, Baltimore, MD.
Kim, Sunwoong, 1987, Diversity in Urban Labor Markets
and Agglomeration Economies, Papers of the Regional
Science Association. 62, pp. 57-70.


118
The aim of the Durbin-Watson index is to make sure that no
autocorrelation exists among the residuals, which is one of
the assumptions of regression analysis. Autocorrelated
residuals often occur when important explanatory variables are
left out of the regression analysis. If autocorrelation is
detected among the residuals, the regression coefficients and
R2 statistic are not valid (Ostrom, 1990) .
Durbin and Watson designed a table that gives an upper
and lower numerical boundary that varies with sample size and
the number of regressors in the model. The generated Durbin-
Watson statistic is compared with that critical region. If
the value of the statistic is less than the lower boundary
listed in the table, then positive first-order autocorrelation
exists. If the statistic is greater than the upper boundary,
then we fail to reject the null hypothesis that
autocorrelation does not exist. When the Durbin-Watson
statistic falls within the specified region, the test is
considered inconclusive. For the models presented here, the
lower boundary for the statistic is given as .408 with the
upper boundary being 1.389 (Clark, Hosking, 1986). All of the
Durbin-Watson statistics from table 5.14 are above the upper
boundary. When a lag structure exists in the model, often the
residuals are autocorrelated. The fact that they are not
positively autocorrelated for these models indicates that if
a lag structure exists, it is rather weak one (Ostrom, 1990).


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TABLE 2.1
TEN MOST IMPORTANT FACTORS IN SELECTING LOCATIONS FOR
MANUFACTURING/PROCESSING PLANTS
12
Easy access to trucking services 79%
Easy access to domestic markets, customers
and clients 74%
Cost of labor 74%
Ample area for future expansion 73%
Easy availability of electricity 71%
Community receptivity to business and industry 70%
Reasonable government/state and or local
corporate tax structure 68%
Fair-market property costs 67%
Availability of skilled workers 64%
Extent of unionization 64%
Source:
Corporate Site Selection For New Facilities. The
Time Inc. Magazine Company, 1989.


70
Research Questions
This chapter investigates how changes in connectivity are
related to changes in employment growth of those highly active
in nonroutine communication within and outside of the firm.
It is assumed that hub cities have a much greater selection of
nonstop destinations from their airports plus a higher rate of
connectivity change as the hub develops, and as such, have a
greater (and growing) concentration of scientists, engineers
and administrators in their workforce than nonhubs.
The study will also determine which change occurs first.
The potential demand for air service could attract one or more
airlines to set up hub operations with abundant nonstop
service in the city, thus making it an attractive choice for
companies to locate or relocate such activities.
Alternatively, the demand could have already existed, with an
airline merely stepping in to fill the void in service. The
changes could also occur simultaneously as new economic growth
regions are identified by the airline industry and other
corporations.
If an airline's choice of a hub does make that city
likely to draw more company headquarters, regional offices and
research and development labs and their workers to the city
(or induce such facilities to leave nonhub cities), the study
will determine how long it takes for the flow to begin. If
the effect is in the other direction, the study will determine


31
Small cities have been most adversely affected, both in number
of nonstop destinations reached from their facility and in
seating capacity (Chan, 1982; Ivy, 1991; Kiel, 1989; Maraffa,
Kiel, 1985; Warren, 1984). Airlines have often replaced
larger jet aircraft with commuter turboprop planes with less
seating capacity on existing routes. Out of 522 small cities
that received scheduled commercial air service in 1978, 62%
experienced a decline in flight frequency, and nearly 30% of
those experienced complete loss of service (Goetz, Dempsey,
1989).
Perhaps the most important issue to passengers is safety
of air travel. This has become a major public issue because
of the seemingly high number of accidents and mechanical
failures of aircraft reported during the past decade.
Government statistics lead one to believe that air travel is
actually safer in the deregulated skies. Compared with the
decade prior to deregulation, jet accidents through 1987
declined by 36%, while the number of fatalities from air
crashes declined by 40% (Moses, Savage, 1988) What such data
do not show, however, is that the number of near-accidents has
been on the rise because of delayed mechanical attention of
aircraft and increased congestion, both in the air and along
runways due to the competitive nature of the deregulated
industry. The number of near-accidents has risen from 568 in
1980 to 1056 in 1987 (Moses, Savage, 1988).


TABLE 4.1
FALL 1991 HUB CITIES OF THE MAJOR U.S. CARRIERS
45
city
carrier(s)
non-local
traffic
Atlanta
Delta
67.60%
Baltimore
USAir
38.19%
Charlotte
USAir
74.19%
Chicago
American, Midway, United
50.34%
Cincinnati
Delta
53.86%
Cleveland
Continental
24.13%
Dallas/Ft. Worth
American, Southwest
60.04%
Dayton
USAir
48.04%
Denver
Continental, United
50.19%
Detroit
Northwest
38.73%
Honolulu
Continental, United
31.72%
Houston
Continental, Southwest
32.61%
Las Vegas
America West
22.27%
Los Angeles
Delta
40.34%
Memphis
Northwest
62.92%
Miami
American, Pan Am
25.20%
Minneapolis
Northwest
47.76%
Nashville
American
40.96%
New York/Newark
Continental, Delta, TWA
21.87%
Orlando
Delta, United
13.75%
Philadelphia
USAir, Midway
22.04%
Phoenix
America West
32.80%
Pittsburgh
USAir
60.64%
Raleigh/Durham
American
61.84%
St. Louis
TWA
55.33%
Salt Lake City
Delta
60.25%
San Francisco
United
24.40%
Seattle
Northwest
28.84%
Washington
United
20.03%
Note: Since the compilation of this list, Midway and Pan Am
Airlines have ceased operations, and USAir has planned the
closing of its Dayton hub in 1992.
Sources: Hub lists were obtained from the Fall 1991 schedules
and route maps of the major carriers. Transfer percentages
were supplied by the U.S. Department of Transportation for the
year ending 1990. Percentages were not separated in multiple-
airport communities.


145
TUS 837498 975735
LAS 3957489 4739343
1983
1984
1985
26880949
31752282
33938478
17368961
17713817
19532270
18933514
20026450
22748376
11650159
12239767
12955615
3980574
4365216
4760972
4888149
5357166
7163840
8044651
8702896
9112901
15682626
18482238
20935812
7885801
8191080
9015583
8504411
9198607
10017397
9942910
9949648
10669134
2626602
2751460
3023714
18648189
18920261
20678095
7815390
7946046
9555195
4954028
5060828
5709488
5781536
6127495
7250302
3142844
3505205
3930854
2296538
2876946
3408608
5544359
6259853
7002343
4800711
5750659
6713293
3830148
3962211
4240557
11404005
12812656
13862996
1769830
1703819
2014386
2399224
3100434
3424127
1352044
1115865
1350401
875881
821265
877377
4629185
4232324
4314916
1986
1987
1988
33958749
32755259
32820184
21648396
23219241
22836344
26492993
28663219
29770857
14186514
15034788
15173602
5423885
6602687
6633677
8206266
9254473
9343770
9695876
10255305
10141298
21822524
22340190
23488986
10890580
12030011
11586627
10281584
10858793
10712269
12166233
13271508
13360799
3092753
3102547
3547258
21376831
22649433
21824125
9825338
9727239
9554454
6651868
6825552
6825513
7981852
8310150
8170952
4558449
4901362
5180587
3847977
4009780
4369596
7469663
8156015
8378639
7719503
8784880
9455324
4675116
4798969
4538643
16087330
15593583
14441817
2136184
3264622
3542865
3911406
4481372
4469974
1514107
1619426
1779140


79
as the Greensboro/Winston-Salem and Tulsa metropolitan areas.
Because it readily identifies cities with strong
administrative and auxiliary functions, using the percentage
of professional employment in the total labor force isprobably
a better indicator of the true administrative and auxiliary
cities in the United States.
The Air Service Data
The sources for the air service data were the Official
Airline Guide (OAG) and Airport Activity Statistics of
Certified Route Air Carriers. Enplanement figures (Appendix
E) were extracted from the latter source (published yearly by
the Federal Aviation Administration) which records a variety
of information about passenger and cargo flow at all domestic
airports with scheduled commercial service. The Official
Airline Guide is published monthly and gives route schedules
of the commercial carriers in the United States. It was the
source for nonstop (Appendix F) and other city-pair connection
data. Unfortunately, this publication is not widely available
because of its cost and the sheer size of each issue (creating
storage problems). Back issues of the publication were found
at a library (University of North Carolina at Chapel Hill)
which saves only one issue from every year (usually, but not
always, July). Therefore, connection and schedule data for
the various years are from summer schedules of the major


66
without a doubt, drastically changed air transportion in the
United States. The accessibility matrix (T) gives us a good
indication of which hubs are the most highly connected to more
of the nation, and therefore, which are more powerful in
controlling market shares. This might be used to help
understand why some carriers are more financially successful
than others in an industry that has been very unstable for
more than a decade. The next chapter will investigate the
relationship between changes in professional employment growth
rates and changes in air transportation connectivity in the 60
largest MSAs in the United States.


125
Concluding Remarks and Contributions of the Study
One main contribution of this study is the hub
classification scheme designed in Chapter 4. The vast
differences in the service levels and connectivity of airports
labelled as hubs are something that has not yet been fully
addressed in the literature.
Another significant contribution is the realization that
the selection of a hub airport by an airline possibly has much
more to do with competition at the facility and other airports
near the facility than with the employment structure or even
economic health of the city in question. Clearly the
potential market size of the hub city is important (hence the
closing of USAir's Dayton hub), but as the main function of a
hub is as a transfer point, it is important to develop hubs
along strategic points in the air service network to
successfully compete with other hubs for the transfer traffic.
Another significant realization is that nonstop service
may not be as important to the business traveler or corporate
facility as was assumed. Many non-hubs are so well connected
today (albeit indirectly) that travel time and the quantity of
flight departures are not a significant problem in spoke
cities.


110
professional employment and connectivity indices (hence the
higher growth percentages).
Second, a regional analysis of the hub city mean growth
rates was conducted using the census regions from Figure 5.1.
Again, there does not appear to be a relationship between
connectivity and professional employment rates of change.
Southern hub cities experienced the highest rate of
professional employment change (4.85%), followed by the
Northeast (4.36%), the West (4.29%) and the North Central hubs
(3.52%). In connectivity change, however, it is the group of
hubs in the West that lead (6.39%), followed by the South
(6.22%), the North Central group (5.43%) and the Northeast
hubs (4.03%). The rank order correlation coefficient between
the two is exactly zero.
Correlation Analysis
The common method for measuring the degree of association
between two variables is the correlation coefficient
(Pearson's r). The value of the coefficient will always lie
between -1 and +1. A set of variables that are positively
correlated (an increase in one variable occurs with an
increase in the other) will have an (r) coefficient that lies
close to +1. A coefficient that lies close to -1 implies that
the variables are negatively correlated (an increase in one
variable occurs with a corresponding decrease in the other).


63
TABLE 4.8
HUB STRENGTH CLASSIFICATION SCHEME
SUPER HUBS
Atlanta, Chicago, Dallas
MAJOR HUBSTYPE A
Charlotte, Denver, Pittsburgh,
St. Louis
MAJOR HUBSTYPE B
New York, Washington, D.C.
MODERATE HUBSTYPE A
Cincinnati, Memphis
MODERATE HUBSTYPE B
Baltimore, Detroit, Houston,
Los Angeles, Minneapolis,
Nashville, Philadelphia
MINOR HUBSTYPE A
Raleigh/Durham, Salt Lake City
MINOR HUBSTYPE B
Cleveland, Orlando, Phoenix,
San Francisco
NON-HUBS
Dayton, Honolulu, Las Vegas,
Miami, Seattle
Note: Type A hubs have a transfer (non-local) passenger
percentage of 50% or greater, while the non-local percentage
for type B hubs is less than 50% (table 4.1).


49
hub-and-spoke functions, at least not at the same level of
intensity as others.
A few of the cities listed as hubs function as feeder
points for a specific carrier's international network. They
are usually well connected with the individual airline's other
domestic hub cities, and also offer nonstop service to and
from the largest cities (largest markets) in the nation.
Thus, they do act as transfer points for the carrier, but on
a less intense level. They are certainly not important
transfer points within the domestic air transportation
network. Los Angeles, Miami, New York (JFK), Seattle and San
Francisco are all internationa1 gateway hubs instead of hub-
and-spoke hubs for domestic air networks. These peripherally
located cities have a lower percentage of nonlocal traffic at
their airports than almost every other hub on the list (Table
4.1). This means that a smaller proportion of their traffic
is changing aircraft at their facility bound for a different
final destination. In addition, the particular carrier(s)
claiming hub status at each of the five facilities listed
above offer rather limited nonstop service from them,
especially in comparison to other domestic hub-and-spoke hubs.
Table 4.3 looks at nonstop connections between hubs listed in
Table 4.1 and FAA hub airports of various sizes. It is clear
that some hubs on the list (particularly the international
gateway hubs) are not as well connected to cities (spokes) of
all sizes as are many of the other hubs.


Deregulation of the U.S. Airline Industry 43
Hub-and-Spoke Growth and Development 4 6
Service Variations 47
Hub Connectivity 51
Analyzing Hub Connectivity 52
Results 55
Connectivity and Intensity Classification
Scheme 61
Conclusion 65
5 CHANGES IN CONNECTIVITY AND PROFESSIONAL EMPLOYMENT
LOCATION 67
Introduction 67
Research Questions 70
The Labor Data 71
The Air Service Data 79
Research Methodology 85
Accessibility Indices 86
Employment-Connectivity Relationships 87
Regional Trends 96
Numerical Analysis 107
Correlation Analysis 110
Hierarchical Analysis of Hub Cities Ill
Lag Structure 115
Time Series 115
Conclusion 119
6 SUMMARY AND CONCLUSIONS 120
Introduction 120
Summary of Results 12 0
Guidelines for Future Research 122
Concluding Remarks and Contributions of the
Study 125
APPENDICES
A CONNECTIVITY INDEX TOTALS 126
B CMSA/MSA COUNTY COMPONENTS 13 0
C ADMINISTRATIVE AND AUXILIARY EMPLOYMENT 134
D TOTAL EMPLOYMENT BY MSA 138
E TOTAL ENPLANEMENTS BY MSA 143
F TOTAL NONSTOP DESTINATIONS REACHED FROM EACH
MSA 14 8
G ACCESSIBILITY INDICES: 1978-1988 152
iv


26
Aeronautics Board (CAB). Beginning in 1938, regulation by the
CAB was considered necessary in order to protect and ensure
the success of the industry. The CAB originally also set
safety standards, a task which was later (in 1958) delegated
to the Federal Aviation Administration (FAA) (Bailey, Graham,
Kaplan, 1985; Brown, 1987; Meyer, Oster, Morgan, Berman,
Strassman, 1981) The CAB had the power to decide if new
carriers could enter the air transportation business, to tell
carriers what particular routes they could fly, to regulate
fares, to award government subsidies to carriers who were
forced to fly nonprofitable routes to smaller cities, and to
control mergers and acquisitions (Bailey, Graham, Kaplan,
1985; Brenner, 1988; Brown, 1987; McIntosh, Goeldner, 1990).
For example, if a carrier wanted to increase its fare between
Newark and St. Louis, to begin scheduled service between
Memphis and Charlotte or to discontinue service to Tulsa, it
was required to have CAB approval.
During the 1970s, many economists and politicians began
to argue that regulation was no longer necessary. It was
increasingly felt that the U.S. airline industry was mature
and that government intervention was creating a very
inefficient system (Bailey, Graham, Kaplan, 1985; Brown, 1987;
Cates, 1978; Cooper, Maynard, 1972; Snow, 1977). Removing
the regulatory barriers, it was further argued, would force
the industry to become competitive, bringing about a more
efficient and affordable transportation system to the American


88
TABLE 5.10
STUDY SET CITIES WITH HIGHEST ACCESSIBILITY INDICES: 1988
city
index
number
1)
Chicago
53,747
2)
Atlanta
53,386
3)
Dallas
50,158
4)
Washington, D.C.
49,999
5)
New York/Newark
49,887
6)
St. Louis
49,839
7)
Pittsburgh
49,297
8)
Cincinnati
46,562
9)
Detroit
46,193
10)
Charlotte
45,955
11)
Philadelphia
45,403
12)
Minneapolis
43,304
13)
Boston
43,239
14)
Denver
42,651
15)
Cleveland
42,371
16)
Houston
41,649
17)
Orlando
41,623
18)
Los Angeles
41,250
19)
Baltimore
41,109
20)
Memphis
40,628
Source: Appendix G


61
weighting procedure are given in Table 4.7. The rankings are
slightly different from both the original connectivity matrix
Cl (Spearman's rank order coefficient of .979), and the
unweighted matrix T (Spearman's rank order coefficient of
.951). Cincinnati and St. Louis, for example, are closely
ranked in matrices Cl and T from Table 4.5, but the weighting
procedure reguiring the calculation of a different scalar for
each individual node puts a greater difference in rankings
between these two cities (Table 4.7). This is because St.
Louis is connected to a greater total number of cities (once
nonhubs are included), and therefore, fares better in the
final ranking of accessibility numbers.
Connectivity and Intensity Classification Scheme
Using the accessibility information from Table 4.7 and
transfer traffic information from Table 4.1, the following
classification scheme measuring hub strength was derived.
Hubs are classified as super manortype A, majortype B,
moderatetype A, moderatetype B, minortype A, minortype
B or non-hub (Table 4.8). The eight classifications were
made using a one-dimensional iterative partitioning clustering
method using the accessibility numbers given in Table 4.7
(Aldenderfer, Blashfield, 1984).
Chicago, Atlanta and Dallas are super hubs. They are the
top ranked for accessibility in all of the matrices that were


APPENDIX B
CMSA/MSA COUNTY COMPONENTS
ALBANYAlbany, Greene, Montgomery, Rensselaer, Saratoga,
Schenectady
ALLENTOWNCarbon, Lehigh, Northampton, Warren (NJ)
ATLANTABarrow, Butts, Cherokee, Clayton, Cobb, Coweta, De
Kalb, Douglas, Fayette, Forsyth, Fulton, Gwinnett, Henry,
Newton, Paulding, Rockdale, Spalding, Walton
AUSTINHays, Travis, Williamson
BALTIMOREAnne Arundel, Baltimore, Carroll, Harford, Howard,
Queen Anne's, Baltimore city
BIRMINGHAMBlount, Jefferson, St. Clair, Shelby, Walker
BOSTONBriston, Essex, Middlesex, Norfolk, Plymouth, Suffolk,
Worcester, Hillsborough (NH), Rockingham (NH)
BUFFALOErie, Niagara
CHARLOTTECabarrus, Gaston, Lincoln, Mecklenburg, Rowan,
union, York (sc)
CHICAGOCook, Du Page, Grundy, Kane, Kendall, Lake, McHenry,
Will, Lake (IN), Porter (IN), Kenosha (WI)
CINCINNATIButler, Clermont, Hamilton, Warren, Dearborn (IN) ,
Boone (KY), Campbell (KY), Kenton (KY)
CLEVELANDCuyahoga, Geauga, Lake, Lorain, Medina, Portage,
Summit
COLUMBUSDelaware, Fairfield, Franklin, Licking, Madison,
Pickaway, Union
DALLASCollin, Dallas, Denton, Ellis, Johnson, Kaufman,
Parker, Rockwall, Tarrant
DAYTONClark, Greene, Miami, Montgomery
DENVERAdams, Arapahoe, Boulder, Denver, Douglas, Jefferson
130


94
hubs was 4.58%, while the nonhub mean was 10.38%. Since the
hub cities are mainly the largest metropolitan areas in the
study, and as such are entering the study period with a larger
base and proportion of professional employment in their
workforce, their rate of change would be expected to be lower
than that of the nonhubs. The graph also illustrates a
probable lag structure that exists between employment growth
in hub cities and nonhubs, with the hub growth occurring
earlier than the nonhub growth. The nonhub growth could be
spillover growth from hub cities.
Similarly, the connectivity data were divided between
hubs and nonhubs (Figure 5.6). The hub cities have somewhat
lower rates of change in connectivity than the nonhubs,
particularly in the beginning of the study period. For much
of the period, the hub graph lies below the nonhub graph.
This is due to the fact that many present-day hubs were also
pre-deregulation hubs (Table 4.2) and, therefore, already had
a high degree of connectivity compared to other cities.
Figure 5.6 suggests that, throughout most of the 11 year
period, slight changes in hub connectivity brought greater
rates of change (in both directions) in connectivity for the
nonhubs (spoke cities). The mean of the yearly average rates
of connectivity change for hub cities from 1978 through 1988
was 5.5%, while the non-hub mean was 6.8%.
Notice that towards the end of the study period, however,
the average rates of change for both groups are rather equal.


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31.
Bailey, E. Graham, D. and Kaplan, D., 1985,
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Barkley, David L., 1988, The Decentralization of High-
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173


24
possibility for changing jobs without changing residences and
a generally more satisfying lifestyle.
Conclusion
Professional workers are engaged in nonroutine activities
of the firm. These employees have more control over where
they live and work than do employees of routine activities.
Location of nonroutine activities, therefore, must be a
combination of the residential desires of professional workers
and the specific needs of the firm. These workers seek places
that provide a high guality of life for them and their
families. The firm's needs largely relate to good air and
ground transportation, access to supplies and markets and, of
course, adequate supply of professional labor. Large urban
areas provide the solution, however, not all cities are equal.
This is particularly the case, for example, in air
transportation, which ranked high on the list of locational
needs for all nonroutine activities of the firm. Airline hub
cities have a much greater variety of nonstop destinations and
more frequent departures from their airports than nonhubs.
What follows is a discussion of the air transportation
industry and network in the United States.


181
Malecki, Edward J., 1986, Technological Imperatives and
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Malecki, Edward J., 1987, The R&D Location Decision of
the Firm and Creative RegionsA Survey,
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Malecki, Edward J., 1991, Technology and Economic
Development London, UK: Longman Group.
Maraffa, Thomas A. and Finnerty, Thomas, 1988, Airline
Deregulation and Interurban Accessibility, paper
presented at the annual meeting of the Assocation
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Maraffa, Thomas A. and Kiel, Don, 1985, Air Service to
Cities Abandoned by Piedmont Aviation Since
Deregulation, Southeastern Geographer. 25, 16-29.
Markusen, A., 1985, Profit Cycles. Oligopoly and
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Markusen, A.R., Hall, P. and Glasmeier, A., 1986, High-
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Wall Street Journal. September 21, p. A24.
McIntosh, Robert W. and Goeldner, Charles R., 1990,
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Morganthau, A., 1987, Year of the Near Miss, Newsweek.
July 27, p. 24.


84
TABLE 5.9
GREATEST INCREASES IN NONSTOP CONNECTIONS
1978-1988
city
increase
1)
Orlando
20
2)
Charlotte
18
3)
Cincinnati
17
4)
Minneapolis
16
5)
Raleigh/Durham
16
6)
Nashville
15
V)
Memphis
14
8)
Phoenix
12
9)
Pittsburgh
12
10)
Salt Lake City
11
11)
Atlanta
10
12)
Baltimore
10
Source: Official Airline Guide. July, 1978 and 1988
Note: of the 60 cities in the study set


47
As these initial connecting hubs became saturated with
traffic, other (often medium-sized) cities were chosen as
transfer points, as new airlines developed and as older
airlines expanded their networks. Charlotte, Raleigh/Durham
and Nashville, for example, were developed as hubs around
Atlanta offering travelers a less-congested alternative in the
Southeast. Today, the largest carriers have as many as four
or five hubs scattered throughout the nation. One can divide
the list of hubs from Table 4.1 into two broad categories: 1)
hubs that were important connecting airports prior to
deregulation (although much less intensely connected than
today), and 2) hubs that achieved important transfer status
after deregulation (Table 4.2).
Service Variations
Closer examination of the cities on the hub list,
however, reveals vast differences among the airports' service
levels and functions. In fact, not all of the locations
listed in Table 4.1 really function as hub-and-spoke hubs. As
mentioned, hubs of this type are characterized by many non
stop flights to cities of all sizes. Also, the very nature of
the network structure suggests that a good portion of the
traffic at these hubs should be nonlocal, if the hub is indeed
successful as a transfer point. Some of the airports labeled
by the major carriers as air hubs do not have true


43
The FAA Hub
The initial usage of the word "hub" in the air
transportation industry was designated by the Civil
Aeronautics Board (now disbanded), and continued by the
Federal Aviation Administration (FAA). The FAA classifies all
communities with scheduled commercial air service as one of
four types of hub, and categorizes each hub based on its share
of the nation's annual enplanements. Data for multiple-
airport cities (like Chicago and Dallas) are usually summed to
represent a community total.
Large hubs, such as Atlanta and St. Louis, represent at
least 1.0% of the nation's total annual enplanements. New
Orleans and Norfolk, for example, are classified as medium
hubs since their share of total U.S. annual enplanements is
between .25% and .99%. Communities that represent between
.05% and .24% (like Richmond, VA) are classified as small
hubs. while non-hubs (such as Gainesville, FL) enplane less
than .05% of the nation's total passengers lAirport Activity
Statistics of Certified Route Air Carriers. 1990).
Deregulation of the U.S. Airline Industry
As discussed in the previous chapter, the Airline
Deregulation Act of 1978 forced the industry into a
competitive market situation. While airlines were adjusting


40
TABLE 3.1
SINGLE CARRIER DOMINANCE AT HUBS
city
airport
code
1978
1990
Atlanta
ATL
40.8%
53.9%
Baltimore
BWI
26.2%
71.5%
Charlotte
CLT
66.1%
91.1%
Chicago
CHI
27.6%
35.5%
Cincinnati
CVG
34.9%
77.9%
Cleveland
CLE
57.6%
38.7%
Dallas
DFW
36.3%
48.8%
Dayton
DAY
35.3%
74.8%
Denver
DEN
25.9%
42.2%
Detroit
DTW
23.6%
65.4%
Honolulu
HNL
37.5%
35.5%
Houston
HOU
24.6%
50.4%
Las Vegas
LAS
34.9%
48.6%
Los Angeles
LAX
26.2%
17.8%
Memphis
MEM
33.5%
53.6%
Miami
MIA
36.4%
18.3%
Minneapolis
MSP
30.1%
75.3%
Nashville
BNA
27.1%
57.5%
New York
NYC
20.4%
18.0%
Newark
EWR
28.2%
48.9%
Orlando
MCO
43.4%
30.5%
Philadelphia
PHL
28.0%
46.3%
Phoenix
PHX
26.6%
43.5%
Pittsburgh
PIT
51.3%
83.0%
Raleigh/Durham
RDU
59.4%
65.1%
St. Louis
STL
34.6%
74.3%
Salt Lake City
SLC
37.4%
76.4%
San Francisco
SFO
42.4%
29.1%
Seattle
SEA
31.8%
20.6%
Washington, D.C.
DCA
24.3%
23.4%
Calculated from Airport Activity Statistics of
Certified Route Air Carriers. U.S. Department of
Transportation, Federal Aviation Administration,
1978, 1990.
Source


132
Passaic (NJ), Somerset (NJ), Sussex (NJ), Union (NJ)
NORFOLKGloucester, James City, York, Chesapeake city,
Hampton city, Newport News city, Norfolk city, Poquoson
city, Portsmouth city, Suffolk city, Virginia Beach city,
Williamsburg city
OKLAHOMA CITYCanadian, Cleveland, Logan, McClain, Oklahoma,
Pottawatomie
ORLANDOOrange, Osceola, Seminole
PHILADELPHIABucks, Chester, Delaware, Montgomery,
Philadelphia, New Castle (DE), Cecil (MD), Burlington
(NJ) Camden (NJ), Cumberland (NJ), Gloucester (NJ) ,
Mercer (NJ), Salem (NJ)
PHOENIXMaricopa
PITTSBURGHAllegheny, Beaver, Fayette, Washington,
Westmoreland
PORTLANDClackamas, Multnomah, Washington, Yamhill, Clark
(WA)
PROVIDENCEBristol, Kent, Newport, Providence, Washington,
Bristol (MA), Norfolk (MA), Worcester (MA)
RALEIGH/DURHAMDurham, Franklin, Orange, Wake
RICHMONDCharles City, Chesterfield, Dinwiddie, Goochland,
Hanover, Henrico, New Kent, Powhatan, Prince George,
Colonial Heights city, Hopewell city, Petersburg city,
Richmond city
ROCHESTERLivingston, Monroe, Ontario, Orleans, Wayne
SACRAMENTOEl Dorado, Placer, Sacramento, Yolo
ST. LOUISFranklin, Jefferson, St. Charles, St. Louis, St.
Louis city, Clinton (IL), Jersey (IL), Madison (IL) ,
Monroe (IL), St. Clair (IL)
SALT LAKE CITYDavis, Salt Lake, Weber
SAN ANTONIOBexar, Comal, Guadalupe
SAN DIEGOSan Diego
SAN FRANCISCOAlameda, Contra Costa, Marin, Nappa, San
Francisco, San Mateo, Santa Clara, Santa Cruz, Solano,
Sonoma


165
ROC
13
SMF
10
STL
43
SLC
21
SAT
11
SAN
17
SFO
25
AVP
7
SEA
18
SYR
15
TPA
29
TUS
8
TUL
11
DCA
46
PBI
15
1985
MATRIX1
ALB
13
ABE
8
ATL
54
AUS
7
BWI
36
BHM
10
BOS
37
BUF
16
CLT
31
CHI
56
CVG
32
CLE
33
CMH
22
DFW
48
DAY
23
DEN
40
12382
12833
8470
8781
36298
37540
17814
18453
10936
11347
15428
15990
21812
22598
6945
7206
16328
16929
14696
15228
27811
28778
7648
7932
10956
11358
37266
38530
15234
15786
MATRIX3
TOTAL
13514
13998
10065
10439
44304
45747
8188
8489
33082
34190
10564
10929
33785
34917
16028
16593
29683
30675
44757
46214
32497
33593
33357
34480
23933
24758
40904
42252
24176
25000
35983
37191
438
301
1199
618
400
545
761
254
583
517
938
276
391
1218
537
MATRIX2
471
366
1389
294
1072
355
1095
549
961
1401
1064
1090
803
1300
801
1168


172
MEM
37
1196
39395
MIA
27
1046
33199
MKE
25
949
30109
MSP
39
1286
41979
BNA
31
1121
36088
MSY
23
903
28391
NYC
49
1438
48400
ORF
16
687
20797
OKC
12
474
14451
MCO
36
1232
40355
PHL
43
1320
44040
PHX
31
1012
32304
PIT
49
1421
47827
PDX
13
466
14406
PVD
12
503
15169
RDU
29
916
30197
RIC
13
526
16085
ROC
13
521
15838
SMF
11
363
11541
STL
49
1445
48345
SLC
25
788
25358
SAT
13
509
15465
SAN
21
737
23163
SFO
28
973
30818
AVP
5
246
7143
SEA
22
790
24659
SYR
18
669
20894
TPA
30
1084
35036
TUS
10
355
11177
TUL
12
456
14149
DCA
49
1441
48509
PBI
19
773
24028
40628
34272
31083
43304
37240
29317
49887
21500
14937
41623
45403
33347
49297
14885
15684
31142
16624
16372
11915
49839
26171
15987
23921
31819
7394
25471
21581
36150
11542
14617
49999
24820


TABLE 4.7
ACCESSIBILITY INDICES OF INDIVIDUALLY WEIGHTED NODES
62
airports
weight
accessibility
number
rank
ATL
.054
22.76
2
BWI
.034
5.53
15
CLT
.048
15.13
5
CHI
.060
31.24
1
CVG
.036
7.57
11
CLE
.023
2.24
24
DFW
. 052
19.87
3
DAY
.018
1.05
28
DEN
.043
10.23
8
DTW
.036
6.96
13
HNL
.011
0.25
29
HOU
.031
4.69
18
LAS
.019
1.39
26
LAX
. 032
4.89
17
MEM
.034
5.18
16
MIA
.019
1.34
27
MSP
. 041
8.70
10
BNA
.036
6.84
14
NYC*
.040
9.45
9
MCO
.024
2.50
23
PHL
.037
7.18
12
PHX
.028
3.44
20
PIT
.050
16.49
4
RDU
.028
3.02
21
STL
.042
10.78
7
SLC
. 028
2.72
22
SFO
.031
4.36
19
SEA
.024
1.94
25
DCA
. 043
11.44
6
Includes Newark


136
TUS
2013
2093
2291
2646
2297
LAS
1226
1472
1339
1530
1547
1983
1984
1985
1986
1987
1988
410012
438752
419826
393184
409800
462305
174085
190268
183975
204263
154243
193284
198148
194124
179601
207975
177831
183688
123500
118369
138236
112534
113071
120469
128266
123983
143438
132686
118283
136055
145021
145580
119960
123625
123612
136782
111235
123128
136076
326434
127195
139258
85915
73184
104327
112270
123612
109966
41458
44066
49516
54173
57874
64363
123159
123038
120691
115653
89684
95362
37063
43220
40413
34557
29737
36602
74263
70794
67672
60087
61799
63586
67460
70945
77279
80038
71902
81958
51886
50728
50399
52374
49668
58350
27753
46557
41800
46835
34835
43944
79018
83770
84420
86184
81342
83141
13535
13617
16555
16822
14452
20394
19915
20000
26452
26815
18978
23615
49268
60870
59391
35936
41670
39973
19675
19331
20699
20636
18189
26590
14876
19652
19028
19603
18633
17663
34267
36437
36860
37079
30229
28880
36552
37670
37819
39849
36373
40221
28157
24311
24183
25868
28287
28219
25530
29026
31917
33736
27150
26324
16454
14937
17963
16283
17220
21206
5695
6882
5175
6726
7439
9434
7178
8058
9291
9835
7481
7560
33834
38819
34468
38264
30347
33304
12071
12327
13793
15104
11949
12736


CHAPTER 6
SUMMARY AND CONCLUSIONS
Introduction
This study attempted to relate aspects of industrial
location theory to connectivity changes that have occurred in
the air transportation industry in the United States since
1978. As Chapter 2 stated, certain corporate facilities that
employ a great proportion of professional workers are often
interested in locating in or near cities that are easily
accessible to many other cities or markets. Because these
nonroutine employees must be able to quickly move around to
clients, suppliers and different parts of the firm, an airport
with high-quality service (well-connected and frequent
departures) is not only an advantage, but often a necessity
for these facilities. This final chapter summarizes the
findings of this study and identifies its important
contributions as well as its shortcomings.
Summary of Results
The analysis of this study fails to show a significant
relationship between changes in air service connectivity and
120


170
MKC
30
1075
33909
LAS
27
893
28053
LAX
37
1214
38936
SDF
17
680
21014
MEM
37
1181
38459
MIA
27
1027
32312
MKE
25
932
29307
MSP
39
1266
40899
BNA
30
1076
34314
MSY
23
887
27655
NYC
49
1413
47035
ORF
15
622
18790
OKC
12
470
14181
MCO
36
1213
39341
PHL
43
1297
42827
PHX
31
994
31477
PIT
48
1379
45818
PDX
13
460
14087
PVD
10
446
13099
RDU
27
868
28216
RIC
13
513
15587
ROC
13
511
15422
SMF
11
359
11284
STL
49
1420
47004
SLC
25
777
24764
SAT
13
501
15104
SAN
20
716
22201
SFO
27
921
28815
AVP
5
243
6990
SEA
22
776
24001
SYR
18
659
20387
TPA
30
1065
34115
TUS
9
323
9976
35014
28973
40187
21711
39677
33366
30264
42204
35420
28565
48497
19427
14663
40590
44167
32502
47245
14560
13555
29111
16113
15946
11654
48473
25566
15618
22937
29763
7238
24799
21064
35210
10308


LIST OF FIGURES
FIGURE PAGE
3.1 Hub Cities of the Major U.S. Airlines:
1991 36
5.1 Census Regions of the United States 78
5.2 Change in Professional Employment vs.
Connectivity 89
5.3 Nonhub Employment vs. Connectivity 91
5.4 Hub Employment vs. Connectivity 92
5.5 Employment Change: Hub vs. Nonhub 93
5.6 Connectivity Change: Hub vs. Nonhub 95
5.7 Northeast Hub Rates of Change 97
5.8 Northeast Nonhub Rates of Change 98
5.9 North Central Hub Rates of Change 101
5.10 North Central Nonhub Rates of Change 102
5.11 Western Hub Rates of Change 103
5.12 Western Nonhub Rates of Change 104
5.13 Southern Hub Rates of Change 105
5.14 Southern Nonhub Rates of Change 106
viii


162
CLT
28
861
25432
CHI
56
1343
41551
CVG
28
945
27709
CLE
31
1002
29470
CMH
22
762
22042
DFW
48
1250
38064
DAY
22
753
21787
DEN
41
1137
33969
DTW
33
1023
30266
GSO
15
553
15346
BDL
22
740
21179
HNL
6
196
5509
HOU
38
1075
32097
IND
22
762
22273
JAX
14
510
14316
GRR
7
222
6805
MKC
29
919
26828
LAS
21
697
19692
LAX
37
1079
31906
SDF
21
728
21038
MEM
27
849
25089
MIA
28
946
27512
MKE
22
770
22066
MSP
30
969
28237
BNA
21
717
20854
MSY
24
825
23758
NYC
51
1293
39677
ORF
14
518
14372
OKC
12
432
11915
MCO
31
981
28920
PHL
40
1115
33600
PHX
23
667
18887
PIT
46
1223
37249
26321
42950
28682
30503
22826
39362
22562
35147
31322
15914
21941
5711
33210
23057
14840
7034
27776
20410
33022
21787
25965
28486
22858
29236
21592
24607
41021
14904
12359
29932
34755
19577
38518


9
experimenting with the test runs. Production is usually
concentrated at or very near the firm's research and
development laboratory.
As development of the product or service becomes
successful, dramatic growth of production and profitability
occurs. The firm has a temporary monopoly in the market, and
demand-led prices for the new product or service will create
excess profits. In this stage (super profit), standardization
of production begins to take place, involving a greater number
of routine laborers than previously, and the average cost per
unit produced will begin to drop. As a result, production
begins to decentralize from the research and development
center.
In stage three (normal profit), other organizations enter
the market with similar products, and price competition can be
intense as the market approaches saturation. Mass
standardized production is now the norm as cost-cutting
becomes imperative. The trend toward decentralization becomes
accelerated and the role of the professional/technical worker
largely diminishes.
Stage four (normal-plus and normal-minus profit) and
stage five (negative profit) are both post-saturation stages.
In the former, profit levels can drop even further due to the
greatly increased competition or can rise slightly via
successful oligopolization. The latter stage is usually where


Living and working in hub cities can have tremendous
benefits for the time-sensitive traveler. Professional
employees such as research scientists and engineers, managers
and salesmen fall into this category. Face-to-face
communication is an important part of their job as they are
often required to travel quickly to a client, other facilities
of the firm or yet another city. Recent industrial location
literature and surveys of corporations imply that the high
quality air transportation that hub cities offer is extremely
desirable when locating or relocating these nonroutine
employees of the firm. The purpose of this study was to link
the changes in connectivity that occur as a city is chosen to
be an airline hub to changes in professional employment
growth.
The analysis of this study, however, fails to show a
significant relationship between changes in air service
connectivity and professional employment growth in the 60 MSAs
in the study set over the period from 1978 through 1988.
x


160
BWI
29
836
24385
BHM
10
347
9869
BOS
37
1034
30717
BUF
16
522
14547
CLT
27
824
24007
CHI
56
1325
40852
CVG
28
936
27242
CLE
30
973
28365
CMH
21
725
20778
DFW
47
1222
36992
DAY
17
586
16975
DEN
41
1132
33717
DTW
33
1012
29751
GSO
16
566
15703
BDL
21
716
20191
HNL
6
199
5562
HOU
38
1072
31929
IND
22
754
21890
JAX
14
501
13940
GRR
6
200
6013
MKC
29
916
26705
LAS
23
763
21614
LAX
38
1099
32461
SDF
21
722
20700
MEM
29
929
27070
MIA
28
933
26938
MKE
24
827
23567
MSP
29
940
27259
BNA
21
708
20446
MSY
24
822
23531
NYC
52
1287
39437
ORF
13
486
13155
OKC
12
429
11860
25250
10226
31788
15085
24858
42233
28206
29368
21524
38261
17578
34890
30796
16285
20928
5767
33039
22666
14455
6219
27650
22400
33598
21443
28028
27899
24418
28228
21175
24377
40776
13654
12301


97
Years
FIGURE 5.7
NORTHEAST HUB RATES OF CHANGE


185
Toh, Rex S. and Higgins, Richard G. 1985, The Impact of
Hub and Spoke Network Centralization and Route
Monopoly on Domestic Airline Profitability,
Transportation Journal. 24, pp. 16-27.
Toh, Rex S. and Hu, Michael Y. 1988, Frequent-Flier
Programs: Passenger Attributes and Attitudes,
Transportation Journal 28, pp. 11-22.
Traffic World. 1988, Supplement B, December 5.
Valente, R. and McGinley, L., 1988, Should Airlines
Scrap Their Oldest Planes for Safety's Sake?, The
Wall Street Journal. May 6, p. 1.
Vernon, R., 1966, International Investment and
International Trade in the Product Cycle, Quarterly
Journal of Economics. 80, pp. 190-207.
Vickerman, R.W., 1989, Measuring Changes in Regional
Competitiveness: The Effects of International
Infrastructure Investments, The Annals of Regional
Science. 23, pp. 275-286.
Walker, Richard and Storper, Michael, 1981, Capital and
Industrial Location, Progress in Human Geography.
5, pp. 473-509.
Warren, William D., 1984, Changing Air Transportation
Services for Smaller Metropolitan Regions: 1980-
1982, Transportation Quarterly. 38, pp. 245-266.
Watts, H.D., 1987, Industrial Geography. London, UK:
Longman Group.
Weiss, Marc A., 1985, High-Technology Industries and the
Future of Employment, In P. Hall and A. Markusen
(Eds.), Silicon Landscapes. Boston, MA: Allen and
Unwin, pp. 80-93.
Wheeler, James 0. and Muller, Peter, 1986, Economic
Geography New York: Wiley and Sons.


13
manufacturing/processing plants in the past five years and
will continue to be the favorite for the next five years.
Lower taxes, cost of living and relative absence of
unionization add to the attractiveness of this region to
firms. The South Atlantic was followed by the East South
Central region (Kentucky, Tennessee, Alabama and Mississippi)
and the West South Central region (Arkansas, Louisiana, Texas
and Oklahoma) in that order. Atlanta, Memphis and Dallas (in
rank order) were the most popular urban areas chosen for
location by those firms not desiring a small city or rural
location.
The attraction of the South has been evident for some
time. An earlier study of change in manufacturing employment
by state from 1967 to 1972 showed the greatest gains in
manufacturing employment in North Carolina (100,000), Texas
(78,000), Florida (58,000), Tennessee (49,000) and Georgia
(44,000) (Moriarty, 1980). Indeed the "Selling of the South"
for manufacturing operations began in the 1930s (Cobb, 1980;
1984) .
The ten most important factors in selecting locations for
company facilities, a group that includes nonroutine corporate
functions (as determined by the Fortune 500 study), are listed
in Table 2.2. Here cost considerations largely give way to
access to markets and the supply of professional labor as well
as adequate transportation facilities.


115
Lag Structure
Visual inspection of some of the graphs discussed earlier
in the chapter indicated the possibility of a lagged
relationship between the professional employment and
connectivity variables. Time series analysis was used to
further examine this issue.
Time Series
Time series is a regression technique that checks for a
relationship between variables over a fixed and constant
interval. There are two types of time series analysis. The
first looks at the relationship of variables observed at the
same point in time (nonlagged case), while the second relates
a current variable to past values of other variables in the
model (lagged case) Successfully generated models can be
used to explain the past behavior of variables and predict
future behavior as well (Ostrom, 1990) .
One problem with the lagged time series analysis is the
determination of the appropriate lag interval, in other words,
how long it may take the variable Xt to affect Yt. In this
case, however, the graphs suggest that if a lag exists, it
most likely occurs in one interval. Therefore, a lagged time
series analysis (multiple regression with a lagged variable)


33
reached from each city. In terms of flight frequency,
destination choice and ticket prices, residents of cities
chosen as a hub by more than one carrier (such as Chicago,
Dallas/Ft. Worth, and Atlanta) have probably benefited most
from deregulation. Because they are hub cities, they offer a
multitude of non-stop destination choices at various times
during the day. Having more than one carrier using an airport
as a major transfer hub creates competition along many non
stop routes, therefore, keeping fares competitively lower.
For example, while the national average annual fare change at
hub airports from 1985 to 1988 was a growth of 4%, Atlanta
(which served as a hub for Eastern and Delta during that
period) experienced a growth of only 1.5% (McGinley, 1989).
The 1980's saw great instability in the industry, and the
1990's (thusfar) are showing no signs of stabilization. Many
airlines are still in financial trouble (Continental, America
West and TWA are operating under bankruptcy protection as of
early 1992). Soaring oil prices, the current recession and
intensifying competition from the three giants of the industry
(American, United and Delta) are the main sources of the
problem (Business Week. 1991a, 1991b, 1991c, 1991d, 1991e,
1991f; Fortune, 1990). Now that over a decade has passed
since deregulation, it is increasingly clear that air
travelers will likely pay much more for lower quality, less
convenient and perhaps more risky transportation by the U.S.
airines.


167
SAT
11
SAN
19
SFO
26
AVP
6
SEA
20
SYR
15
TPA
30
TUS
8
TUL
11
DCA
47
PBI
15
1986
MATRIX1
ALB
13
ABE
8
ATL
54
AUS
7
BWI
37
BHM
12
BOS
39
BUF
16
CLT
31
CHI
56
CVG
34
CLE
33
CMH
22
DFW
48
DAY
25
DEN
40
DTW
40
GSO
15
BDL
22
HNL
9
11653
12075
18968
19630
24739
25597
6797
7047
19501
20187
15493
16037
30113
31129
8262
8556
11722
12137
40298
41626
16203
16773
MATRIX3
TOTAL
13984
14474
10448
10828
46581
48066
8622
8930
35260
36411
13526
13980
37099
38313
16560
17131
31189
32207
47057
48557
35714
36889
34976
36127
25181
26030
43233
44628
27426
28337
37912
39159
39139
40414
17076
17671
23684
24489
9842
10178
411
643
832
244
666
529
986
286
404
1281
555
MATRIX2
477
372
1431
301
1114
442
1175
555
987
1444
1141
1118
827
1347
886
1207
1235
580
783
327


3
travel time can be very significant to a profit-oriented
organization as well as the general public.
Objectives
How do hub cities measure up? Do they indeed have a
higher concentration of employees from management and research
and development labs? More importantly, has the change to a
hub-and-spoke network yet enhanced the attractiveness of newly
designated hub cities? Investigation of these issues is the
focus of this study.
Data will be collected and analyzed from various
publications of the Official Airline Guide, Inc., the U.S.
Census Bureau and the Federal Aviation Administration for each
year from 1978 through 1988 to examine the degree to which
changes in connectivity that occur when an airline chooses a
particular city as a transfer hub are related to changes in
employment growth of the above-mentioned type.
Structure of Presentation
Chapter 2 presents a review of the pertinent industrial
location literature, while Chapter 3 discusses the domestic
air transportation industry before and after deregulation.
These two chapters will build a framework within which to
place this dissertation in the broader perspective of


147
980374
4588640
1012189
4322838
1191205
4627078
1378830
5329158
1525859
6836053
1362402
6864803


BIBLIOGRAPHY 17 3
BIOGRAPHICAL SKETCH 18 6
v


77
TABLE 5.6
HIGHEST PERCENTAGE OF ADMINISTRATIVE/AUXILIARY AS A PORTION
OF TOTAL EMPLOYMENT: 1988
MSA
percentage of
professionals
1)
Tulsa
8.76
2)
Detroit
7.75
3)
A1lentown/Bethlehem/Easton
7.38
4)
Minneapolis/St. Paul
7.16
5)
Greensboro/Winston/Salem
7.14
6)
Houston
7.08
7)
Dallas/Ft. Worth
6.58
8)
Richmond
6.53
9)
Atlanta
6.44
10)
Rochester
6.07
11)
Columbus
5.96
12)
New York/Newark
5.86
13)
St. Louis
5.83
14)
Cleveland
5.74
15)
Dayton
5.73
16)
Philadelphia
5.72
17)
Cincinnati
5.58
18)
Chicago
5.43
19)
Boston
5.20
20)
Charlotte
5.18
Source:
Calculated from County Business Patterns. U.S.
Census Bureau, 1990.


11
Location Criteria of Various Parts of the Firm
A recent survey of Fortune 500 firms clearly indicates
that the various parts of the firm have different locational
requirements (Corporate Site Selection for New Facilities.
1989). It examines location and relocation criteria of the
firms and separates the information into two general
categories: 1) manufacturina/processina plants and 2) company
facilities. The category of manufacturing/processing plants
includes assembly plants, processing plants, extraction plants
and warehouse/distribution centers, while the company
facilities category includes corporate headquarters, regional
headquarters, branch offices, sales offices and branch office
processing.
Table 2.1 shows the ten most important factors in
selecting locations for manufacturing/processing plants as
identified by the study. The criteria mostly relate to costs.
Reduction or minimization of labor costs, taxes,
transportation costs and land prices are high-priority
considerations for these facilities. It is evident that small
cities or, in some cases, rural areas in depressed economic
regions would be ideal choices (Goldfarb, Yezer, 1987; Heenan,
1991) .
The study also found that the South Atlantic region of
the United States (North Carolina, South Carolina, Georgia and
Florida) was the most popular location choice for


129
ROA
9
312
17009
ROC
16
476
28539
SMF
18
449
24806
MBS
3
115
6016
STL*
64
1203
80577
SLC*
38
793
46861
SAT
17
516
27493
SAN
29
787
43392
SFO*
46
1071
63770
SJC
20
488
27068
SBA
7
191
10484
SRQ
14
447
25751
SAV
7
217
11570
SEA*
33
845
47713
SHV
7
184
10087
FSD
4
168
8187
SBN
9
264
15415
GEG
9
222
11859
SYR
20
555
33711
TLH
8
203
12496
TPA
32
856
52619
TOL
9
275
15804
TUS
11
369
19227
TUL
13
392
20417
DCA*
65
1174
81457
PBI
21
669
38946
ICT
8
274
14094
17330
29031
25273
6134
81844
47692
28026
44208
64887
27576
10682
26212
11794
48591
10278
8359
15688
12090
34286
12707
53507
16088
19607
20822
82696
39636
14376
* indicates hub city


161
MCO
27
PHL
40
PHX
21
PIT
46
PDX
13
PVD
9
RDU
14
RIC
12
ROC
13
SMF
10
STL
43
SLC
18
SAT
10
SAN
17
SFO
27
AVP
6
SEA
20
SYR
13
TPA
30
TUS
10
TUL
12
DCA
46
PBI
14
1983
MATRIX1
ALB
12
ABE
8
ATL
54
AUS
7
BWI
31
BHM
10
BOS
37
BUF
16
26071
26996
33023
34164
18210
18882
36747
38005
12163
12614
9741
10124
12257
12701
11617
12059
11869
12310
8440
8748
35687
36917
15968
16553
9873
10252
15579
16149
23594
24442
5585
5796
17824
18476
12352
12816
27276
28236
9729
10094
12098
12547
36411
37658
14073
14597
MATRIX3
TOTAL
11627
12064
9445
9810
41190
42576
7180
7457
26693
27625
9923
10280
31345
32432
14871
15415
898
1101
651
1212
438
374
430
430
428
298
1187
567
369
553
821
205
632
451
930
355
437
1201
510
MATRIX2
425
357
1332
270
901
347
1050
528


72
While the category also includes some warehouse and
distribution employees (nonprofessional labor), County
Business Patterns was found to be the best source available
for obtaining raw numbers of professional workers in a
specific area.
The number of professional workers for each of the 60
cities in the study set was obtained by summing the
administrative and auxiliary employees for each sector in each
county included in the MSA (as determined by the U.S. Census
Bureau) (Appendix B). These data were collected for all 60
cities from 1978 (the beginning of the study period) through
1988 (the most current data available at the time of the
collection process). The results are given in Appendix C.
Table 5.2 shows the 20 MSA's (from the study set) with
the greatest number of professionals in their workforce at the
end of that period (1988) It is not surprising that the
largest cities in the nation in population rank high on this
list. Most of these 20 MSA's lie in the traditional
manufacturing belt, although a few southern and western urban
areas made the list as well.
Table 5.3 indicates the flow pattern of administrative
and auxiliary jobs and workers. This table shows the U.S.
cities that experienced the greatest increase in the number of
professional workers from 1978 to 1988. While a few of the
large metropolitan areas of the Northeast made this list (such
as Boston, New York/Newark and Philadelphia), the southern and


68
TABLE 5.1
60 LARGEST METROPOLITAN AREAS OF THE U.S.
(1988-in thousands)
New York/Newark CMSA
Los Angeles CMSA
Chicago CMSA
San Francisco CMSA
Philadelphia CMSA
Detroit CMSA
Boston CMSA
Dallas/Ft. Worth CMSA
Washington, D.C. MSA
Houston CMSA
Miami/Ft. Lauderdale CMSA
Cleveland CMSA
Atlanta MSA
St. Louis MSA
Seattle CMSA
Minneapolis/St. Paul CMSA
Baltimore MSA
San Diego MSA
Pittsburgh CMSA
Phoenix MSA
Tampa/St. Petersburg MSA
Denver CMSA
Milwaukee CMSA
Kansas City MSA
Cincinnati CMSA
Portland CMSA
Sacramento MSA
Norfolk MSA
Columbus MSA
San Antonio MSA
New Orleans MSA
Indianapolis MSA
Buffalo CMSA
Providence CMSA
Charlotte MSA
Hartford CMSA
Salt Lake City MSA
Rochester MSA
Memphis MSA
Nashville MSA
Orlando MSA
Louisville MSA
Oklahoma City MSA
Dayton MSA
Greensboro MSA
Birmingham MSA
(NYC)
18,120
(LAX)
13,770
(CHI)
8,181
(SFO)
6,042
(PHL)
5,963
(DTW)
4,352
(BOS)
4,110
(DFW)
3,766
(WAS)
3,734
(HOU)
3,641
(MIA)
3,001
(CLE)
2,769
(ATL)
2,737
(STL)
2,467
(SEA)
2,421
(MSP)
2,388
(BWI)
2,342
(SAN)
2,370
(PIT)
2,284
(PHX)
2,030
(TPA)
1,995
(DEN)
1,858
(MKE)
1,562
(MCI)
1,575
(CVG)
1,449
(PDX)
1,414
(SMF)
1,385
(ORF)
1,380
(CMS)
1,344
(SAT)
1,323
(MSY)
1,307
(IND)
1,237
(BUF)
1,176
(PVD)
1,125
(CLT)
1,112
(BDL)
1,068
(SLC)
1,065
(ROC)
980
(MEM)
979
(BNA)
972
(MCO)
971
(SDF)
967
(OKC)
964
(DAY)
948
(GSO)
925
(BHM)
923


91
FIGURE 5.3
NONHUB EMPLOYMENT VS. CONNECTIVITY


8
since production need not be linked to innovative research and
development, which relies on highly skilled labor. Production
shifts to these peripheral regions from high-cost core regions
because the competition for labor in large urban areas, and
therefore the price paid for labor, has increased to the point
where profit margins are unacceptably low or nonexistent
(Barkley, 1988; Markusen, 1985). Thus, routine operations in
both manufacturing and services have largely decentralized to
peripheral areas within developed countries and to developing
countries (Clark, 1981; Schoenberger, 1986, 1987).
These industrial location shifts can be explained by
Vernon's product life cycle theory (1966). Industries, firms
and products move down the urban hierarchy as they go through
different stages (each with distinct locational requirements,
labor needs, growth rates and profitability) in their life
cycle (Barkley, 1988). Mobility of the various functions of
the firm is often a necessity for firm survival, influenced by
the potential for profit in each stage.
The profit cycle model complements the insights from the
project life cycle model. Markusen (1985) describes the long
term profit cycle as having five characteristic stages. In
stage one (zero profit), output is very low (often done in
test runs) while a new product or new design is being
initiated. Costs per unit are high since the primary
workforce involved is composed of many nonroutine workers,
such as scientists, engineers and other technicians, who are


108
TABLE 5.11
MEAN ANNUAL RATE OF CHANGE IN EMPLOYMENT AND CONNECTIVITY:
1978-1988
MSA
employment
connectivity
Albany
3.58%
5.34%
Allentown
6.03%
3.95%
Atlanta
7.78%
4.21%
Austin
11.27%
14.54%
Baltimore
0.66%
5.36%
Birmingham
10.01%
3.07%
Boston
16.75%
5.45%
Buffalo
1.43%
0.37%
Charlotte
7.33%
10.19%
Chicago
0.39%
3.24%
Cincinnati
2.54%
6.85%
Cleveland
2.38%
2.95%
Columbus
5.09%
4.44%
Dallas/Ft. Worth
7.40%
4.82%
Dayton
15.52%
6.65%
Denver
0.46%
4.68%
Detroit
-1.39%
4.25%
Grand Rapids
8.76%
9.06%
Greensboro
7.48%
5.05%
Hartford
2.84%
5.00%
Honolulu
2.85%
11.73%
Houston
3.30%
5.92%
Indianapolis
-0.54%
4.01%
Jacksonville
100.38%
6.52%
Kansas City
1.54%
3.99%
Las Vegas
6.81%
4.74%
Los Angeles
1.79%
2.79%
Louisville
2.42%
3.31%
Memphis
5.02%
6.79%
Miami
8.03%
2.50%
Milwaukee
2.18%
3.54%
Minneapolis
3.17%
7.11%
Nashville
-6.04%
8.00%
New Orleans
1.44%
3.19%
New York/Newark
1.47%
2.99%
Norfolk
1.59%
5.87%
Oklahoma City
-0.27%
1.29%
Orlando
14.60%
9.61%
Philadelphia
2.28%
3.36%
Phoenix
6.18%
7.40%
Pittsburgh
-0.93%
5.73%
Portland
1.39%
2.31%


CHAPTER 2
INDUSTRIAL LOCATION DECISIONS
Introduction
Different activities of the firm have different
locational requirements. Nonroutine activities, such as
management and research and development, are less influenced
by wage rates and site location costs than routine activities,
such as manufacturing. This chapter will look at the location
of organizations in the context of spatial division of labor,
and identify the specific locational needs of the various
activities of the firm. It also includes a discussion of
professional workers and the role they play in business
location decisions. Therefore, this chapter is an important
component of the study since it will help identify places
where professional workers are likely to be concentrated.
Theory of Location of Organizations
Traditionally, industrial location theory has been based
on profit maximization through the minimization of costs
(Alexander, Gibson, 1979; Boyce, 1978; Chapman, Walker, 1987;
De Souza, 1990; De Souza, Foust, 1979; Dicken, Lloyd, 1990;
Hoover, 1948; Wheeler, Muller, 1986). Transportation costs
5


BIOGRAPHICAL SKETCH
Russell L. Ivy was born in Missouri in 1962. He came to
the University of Florida after earning both bachelor's and
master's degrees in geography from the University of Missouri.
He is looking forward to a university teaching and research
career in geography, and will begin his career as a Visiting
Assistant Professor at Florida Atlantic University.
186


85
most severe decline was felt by Buffalo (7) a city whose
economy suffered during the period.
Research Methodology
The analysis in this chapter investigates whether or not
changes in connectivity are related to changes in
professionalemployment growth or vice versa. To measure the
impact of hub selection on this growth, a general hub/nonhub
comparison was carried out using all 60 cities in the study
set over the 11 year period.
Changes in connectivity were monitored using a derived
accessibility index. The yearly connectivity indices for each
metropolitan area were compared to changes in professional
employment by looking at the average rate of change of each
variable from one year to the next. Graphing both rate-of-
change values for each hub city over time should give an
indication of the relationship between the two variables. The
means of the yearly average rates of change for connectivity
and professional employment were calculated for each MSA, and
a simple correlation analysis was done to determine the
statistical relationship between the two. In addition, by
separating the data for the MSA's into different groups,
regional and hierarchical information was obtained.
To test for a lag structure in the relationship between
the variables, times series regression analysis was performed


CHAPTER 1
INTRODUCTION
The deregulation of the domestic airline industry has
brought about many changes in air travel in the United States.
Both new and older, established carriers have disappeared in
bankruptcy courts or through mergers with other carriers.
Fares dropped tremendously at first, but have been on the rise
since the end of the 1980s, and passenger enplanements have
skyrocketed.
Another significant change has been in the geographic
structure of the air travel network itself. Fewer nonstop
flights are available to some cities than before, as service
is now fed into hub cities of specific carriers instead of
most or all larger cities within a region. This study will
examine changes in the professional employment structure of
those hub cities as their connectivity levels
rise.
Hub-and-Sooke Structure
Hub cities act as a collection point of air passengers in
a region and thus have a tremendous amount of flow in and out
of them. Passengers are flown into the city from many
1


144
PDX
2058535
2154581
1804395
1731302
1850515
SMF
667303
1418324
1095186
1080347
1169559
ORF
887487
937337
951175
1099872
1186556
CMH
1290199
1383721
1219950
1121737
1234349
SAT
1111959
1336768
1533658
1626755
1667239
MSY
3018722
3137699
3107183
2928436
2852632
IND
1556591
1709074
1464569
1257385
1260612
BUF
1739843
1742871
1540313
1333165
1613151
PVD
500144
494879
459316
319354
305433
CLT
1456132
1559557
1480787
1894928
2768882
BDL
1476451
1604360
1401135
1162993
1144221
SLC
2022249
2173282
1996706
1902459
2680184
ROC
901125
946427
870480
736282
843811
MEM
2344531
2576902
2148730
1945933
2189650
BNA
1156836
1223219
1122084
1033206
1079076
MCO
2446984
3098437
3124568
2866389
3268933
SDF
1051933
1127869
993355
848184
874842
OKC
1042187
1125830
1076613
1118403
1261935
DAY
979209
999278
889035
707426
774638
GSO
668639
739975
731386
756800
707064
BHM
773652
797616
705297
643513
592253
JAX
820113
896136
872979
858902
982157
ALB
663818
718869
659135
414205
454903
RIC
567816
675934
619775
565832
461362
HNL
5864914
5874876
5654546
5442620
5664918
PBI
894810
1171058
1282940
1263192
1570159
AUS
517030
641368
887905
973399
1106150
AVP
167096
180210
151498
96811
81323
TUL
952622
1033231
1029951
1112368
1258053
RDU
792640
910504
866007
828176
911866
ABE
280296
309907
273839
152033
136490
GRR
421734
468289
429505
332870
370959
SYR
831029
892659
794244
717589
866290


18
locate their R&D facility (or at least one such facility if
there are several) at or very near the corporate headquarters
(Malecki, 1980b).
An earlier study conducted by Dow Jones, Inc. (Browning,
1980) confirms that the important criteria found in the
Fortune 500 survey for the various firm facilities have been
important for at least a decade. The results of this study
are given in Table 2.4.
Regional Development and Industrial Location
Regional development is greatly dependent on the location
decisions of firms (Knapp, Graves, 1989). As the number of
acceptable sites for the various firm facilities has
increased, competition among cities, states and regions to
attract industry has become particularly fierce (Cobb, 1980,
1984; Johnson, 1989). Most nonurban areas have focused on
attracting manufacturing branch plants, as have some
metropolitan areas in low-wage parts of the nation. Most
urban areas, however, are searching for the nonroutine firm
facilities to locate in their city (Thompson, 1987).
According to Jacobs (1984) these nonroutine activities of the
firm are perceived to be more immune to cyclical and
structural changes in the economy and are less likely to
experience severe job loss or closure in bad economic times.


123
and-spoke system are fairly recent events. Therefore, only 11
years were available for inspection. Eleven years is not
really long enough to do a meaningful time series analysis.
A minimum of 30 years is usually recommended (Ostrom, 1990).
Also, within those 11 years, the domestic airline industry,
and therefore air service connectivity, has been (and still
is) rather unstable as the network has adjusted to a vastly
different competitive route structure. As was discussed in
Chapter 3, many airlines have come and gone and the air
transportation network has been greatly affected. A longer,
more stable time period in domestic air transportation is
needed to do a meaningful analysis on connectivity change
brought on by deregulation.
Another problem has to do with the choice of rate of
change as the main unit of measurement. This created a
population bias. As has been stated, larger cities entered
the study period with higher connectivity indices and a
greater number of professional employees in their workforce
simply because of their population size and the agglomerative
advantages that exist in larger metropolitan areas. Because
those base numbers were already large, their rates of change
tended to be comparatively lower than the smaller cities in
the study set. A better choice of measurement might have been
something like per capita volume flow of air passengers or a
per capita accessibility index for connectivity along with a
per capita professional employment figure. The usage of


6
and the locations of raw materials and markets were major
concerns. One of the main flaws in this approach, however, is
the assumption that labor quality, supply and cost are the
same at all locations (Moriarty, 1980; Watts, 1987).
Elaborate models to determine minimum-cost sites focused,
consequently, solely on transportation costs as spatially
variable (Dicken, Lloyd, 1990).
A broader view of industrial location theory, on the
other hand, holds that labor is the key to determining firm
location since, in reality, labor has a high degree of spatial
differentiation in terms of both supply and level of skill
and, therefore, cost (Storper, Walker, 1984). This
differentiation of labor also occurs according to both
industry (industries differ in their mix of labor) and
function within the firm, such as marketing, research and
development or routine manufacturing. Thus, the geographic
pattern of industry represents the spatial division of labor
as particular industries, as well as particular activities of
the firm, correspond to the geographic location of labor
(Blair, Premus, 1987; Czamanski, 1981; Massey, 1984; Storper,
Walker, 1983).
Spatial division of labor within the firm has become
quite commonplace and has been the subject of much academic
work (Schoenberger, 1986, 1987; Watts, 1987). Technological
improvements in communication and automation have increased
the spatial separability of parts of the production process


APPENDIX C
ADMINISTRATIVE AND AUXILIARY EMPLOYMENT
1978
1979
1980
1981
1982
NYC
407066
411673
427080
422080
445398
LAX
176709
178964
156601
166875
182647
CHI
184280
181363
182405
187543
170690
SFO
89247
99587
106975
118890
125679
PHL
113066
104949
110831
111562
125857
DTW
164122
157019
149786
137221
126345
BOS
70730
103956
89057
108254
99341
DFW
59990
73337
73672
72094
85305
DCA
36053
26045
35660
38564
43253
HOU
74126
92386
97713
111682
126420
MIA
18905
21664
23691
33017
36799
CLE
54352
74749
65869
70633
77214
ATL
42027
50905
49540
58141
50394
STL
50023
46527
47083
49265
50625
SEA
25448
22619
25273
25025
37881
MSP
61682
66894
67393
74173
82285
SAN
9906
14602
16992
14876
14205
BWI
25614
25898
26146
27088
22332
PIT
52152
52970
54777
63219
58497
PHX
16769
20538
19786
16534
20591
TPA
9913
9821
10442
12006
16657
DEN
28440
29119
30456
31896
35353
CVG
37409
36867
39103
25542
37859
MCI
24726
25577
25164
25485
25597
MKE
22100
21513
22852
23398
23500
134


AIRLINE HUBS: CHANGES IN URBAN EMPLOYMENT
STRUCTURE AND NETWORK CONNECTIVITY
By
RUSSELL L. IVY
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
1992


22
solution. Despite the fact that there are negative attributes
associated with them (such as higher land costs, taxes and
competition for professional labor for the firm, and higher
housing costs, congestion, pollution and increasing crime
rates for the professional worker), large urban areas are
highly attractive to both firms and workers because of
agglomerative advantages and high quality of life (Dahmann,
1983).
Agglomeration economies are advantages associated with
being in close proximity to markets and services needed by the
firm. Sometimes these advantages arise from the organization
locating in a specific area, such as Silicon Valley, where
their industry or similar industries tend to cluster
(localization economies). These advantages can also occur,
however, to all firms in all industries in larger metropolitan
areas as firms, suppliers and clients interact with one
another (urbanization economies) (Scott, 1988a, 1988b; Watts,
1987).
Large urban areas provide the firm with an ample supply
of professional workers, suppliers, services, information and
important infrastructure (such as airports with frequent air
service to a variety of destinations) and facilitate face-to-
face contact when necessary (Andersson, 1985; Dorfman, 1983;
Malecki, 1987; Oakey, 1985; Scott, 1983). Being located in a
large metropolitan area maximizes the opportunities for a firm
to find the industrial linkages it needs while minimizing the


46
such). This service was built up to meet travel demand in
markets that some airlines considered to be underserved.
Hub-and-Spoke Growth and Development
The first domestic hub-and-spoke hubs chosen by
individual airlines were at airports that were already used by
carriers as connection or terminus points for long-haul, east-
west traffic using aircraft that did not have transcontinental
capabilities (Lopuszynski, 1986). These pre-deregulation
connecting airports were in the larger cities in the U.S., and
as such, generated high levels of local traffic. Individual
airlines tended to focus their early hub-and-spoke strategies
on those large-city connecting airports in which they already
controlled most of the scheduled departures. Most of these
airports were already well-connected (with non-stop service)
to other large cities in the nation. The hub-and-spoke hubs
were created as the carriers simply added many more flights to
many more destinations or spokes (particularly medium-sized
and small cities) at these facilities to build up connectivity
and create greater overall efficiency and market control at
the hub. In some cases, particular airports were selected by
more than one carrier as a hub-and-spoke hub. American and
United, for example, both created hubs at Chicago O'Hare,
while Atlanta became a major transfer city for Delta and the
now-defunct Eastern.


139
PDX
446085
486365
495895
485171
457312
SMF
242592
270577
278997
274995
278668
ORF
294766
304139
308058
313181
315668
CMH
392378
448235
448720
440159
429478
SAT
295450
307537
312329
332149
353287
MSY
404171
444950
441135
455014
454334
IND
427131
453702
443212
427776
417578
BUF
412805
429035
421905
408797
391392
PVD
918285
963926
963556
963783
959445
CLT
402120
424717
432470
434026
427325
BDL
542641
578878
595520
601861
592220
SLC
284647
298938
304604
302173
298737
ROC
344235
356396
353371
355403
359566
MEM
299038
311899
308988
304527
307781
BNA
308533
327928
335920
326784
320663
MCO
211048
234223
250287
263057
273502
SDF
334153
355425
346699
342644
327668
OKC
271703
296429
310452
330495
347526
DAY
331363
331266
326236
317269
300073
GSO
339475
359534
359332
363343
362602
BHM
294093
314685
309394
301496
295285
JAX
226746
240207
239554
244292
252405
ALB
231790
245962
247481
247828
246318
RIC
268360
284449
286375
283935
286838
HNL
229033
244952
252617
255656
247754
PBI
151626
172551
186358
201996
206263
AUS
142747
160257
169355
181284
195416
AVP
219313
225567
222482
218139
216577
TUL
237200
249295
263974
276214
287162
RDU
181491
202795
204046
210218
220379
ABE
230384
237920
238055
229775
228423
GRR
222660
241877
238609
235465
228371
SYR
211403
222404
217974
204957
211483


I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Edward J. Malecki, Chairman
Professor of Geography
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and qualtiy, as
a dissertation for the degree of Doctor of Philosophy.
Ary J. Lamme III
Associate Professor of Geography
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Assistant Professor of Geography
I certify that I have read this study and that in my
opinion it conforms to acceptable standards of scholarly
presentation and is fully adequate, in scope and quality, as
a dissertation for the degree of Doctor of Philosophy.
Robert M. Beland
Associate Professor of
Recreation, Parks,
and Tourism


44
to the new environment, their network geography (node-linkage
association) was changing accordingly. Flow efficiency and
cost-reduction were made higher priorities. It became
increasingly clear that concentrating flights at one or more
key regional nodes in their air transportation networks would
raise the seat-occupancy levels, thus maximizing the usage of
aircraft. Such concentration would also maximize the number
of on-line (same carrier) city-pair matchings available to
passengers. These central nodes typically offer non-stop
service to most every large and medium-sized city in the
nation and to smaller cities within the local region of the
node. Numerous arrivals and departures are scheduled within
a short time frame to allow the connections. This intense
type of system has become known as a "hub-and-spoke" network,
and now dominates U.S. air transportation. Table 4.1 lists
the U.S. hubs as designated by the major domestic carriers.
The list was compiled by consulting the fall 1991 schedules
and route maps of the carriers, and was confirmed by telephone
inguiry to each airline's public relations department. Not
included in Table 4.1 are a few cities labeled by some
carriers as mini-hubs. These are large cities that do not
serve as major transfer points within any airline's network,
but do offer some nonstop service (more than other spoke
cities) on an individual carrier (for example, Northwest
operates a mini-hub at Milwaukee and USAir uses Kansas City as


131
DETROITLapeer, Livingston, Macomb, Monroe, Oakland, St.
Clair, Washtenaw, Wayne
GRAND RAPIDSKent, Ottawa
GREENSBORODavidson, Davie, Forsyth, Guilford, Randolph,
Stokes, Yadkin
HARTFORDHartford, Litchfield, Middlesex, New London, Tolland
HONOLULUHonolulu
HOUSTONBrazoria, Fort Bend, Galveston, Harris, Liberty,
Montgomery, Waller
INDIANAPOLISBoone, Hamilton, Hancock, Hendricks, Johnson,
Marion, Morgan, Shelby
JACKSONVILLEClay, Duval, Nassau, St. Johns
KANSAS CITYCass, Clay, Jackson, Lafayette, Platte, Ray,
Johnson (KS), Leavenworth (KS), Miami (KS), Wyandotte
(KS)
LAS VEGASClark
LOS ANGELESLos Angeles, Orange, Riverside, San Bernardino,
Ventura
LOUISVILLEBullitt, Jefferson, Oldham, Shelby, Clark (IN) ,
Floyd (IN), Harrison (IN)
MEMPHISShelby, Tipton, Crittenden (AR), De Soto (MS)
MIAMIBroward, Dade
MILWAUKEEMilwaukee, Ozaukee, Racine, Washington, Waukesha
MINNEAPOLISAnoka, Carver, Chisago, Dakota, Hennepin, Isanti,
Ramsey, Scott, Washington, Wright, St. Croix (WI)
NASHVILLECheatham, Davidson, Dickson, Robertson, Rutherford,
Sumner, Williamson, Wilson
NEW ORLEANSJefferson, Orleans, St. Bernard, St. Charles, St.
John the Baptist, St. Tammany (parishes)
NEW YORK/NEWARKBronx, Kings, Nassau, New York, Orange,
Putnam, Queens, Richmond, Rockland, Suffolk, Westchester,
Fairfield (CT), Litchfield (CT), New Haven (CT), Bergen
(NJ), Essex (NJ), Hudson (NJ), Hunterdon (NJ) ,
Middlesex (NJ), Monmouth (NJ), Morris (NJ), Ocean (NJ),


146
2074741
2150617
2526852
1232658
1222395
1349631
1395088
1505242
1554449
1426267
1528154
1525507
1823971
2053088
2162423
2868966
3193181
2912675
1300098
1442102
1746752
1706336
1803770
1681206
314620
398756
553540
3763812
4226187
5102703
1420664
1535368
1705896
3237442
3477711
4137044
861319
895372
1229991
2359442
2283425
3469318
1108572
1216188
1395487
3721059
4108413
4848771
855970
845914
912181
1248378
1446365
1464565
1191509
1430970
1732155
740899
785241
1102525
612126
603114
714884
1044359
1056365
1160053
476936
498531
617558
476137
564687
703497
5375172
5978421
5979712
1776550
1823150
1795222
1248759
1643714
1828768
88746
97179
104022
1264143
1393037
1397434
1122732
1289108
1345077
144298
164576
167965
492820
510322
569248
1024852
1173429
1318086
2414960
2834327
2823311
1606346
1749987
1792347
1540653
1550095
1491928
1572763
1695005
1758894
2243454
2425259
2392332
3040026
3311172
3199970
2029928
2273057
2405638
1731363
1728690
1780070
715688
864078
944843
5687255
6021104
6619780
1998477
2267686
2321986
4650983
4728595
4729937
1241968
1254005
1241528
4177169
5023047
4532572
2165808
2987233
3244014
5946686
7074737
7473086
946140
1034162
1013770
1477962
1506126
1492526
2140242
2166547
2140470
1039838
1026113
993682
754100
912204
983167
1373191
1407222
1287939
733879
767609
817167
807801
873569
850593
7352027
7773253
8396313
1985478
2229254
2360993
1830831
1928535
1922447
114461
143513
150856
1382816
1388360
1362402
1441832
2316211
3517525
221559
263930
295168
577526
608976
596869
1381238
1499559
1473759


114
TABLE 5.13
MEAN ANNUAL RATE OF CHANGE BY POPULATION CLUSTER: 1978-1988
group number
mean employment
change
mean connectivity
change
one
1.63%
2.89%
two
2.06%
2.97%
three
4.11%
4.49%
four
5.11%
5.25%
five
5.02%
8.68%
Calculated from Table 5.11


83
The average number of daily departures (Table 5.8) were
calculated by dividing the number of commercial departures
scheduled by the airlines during the calendar year 1988 (as
recorded by the Federal Aviation Adminstration) by 365.
Again, most of the metropolitan areas in the study set saw an
increase in the number of daily airplane departures from 1978
to 1988. Los Angeles, Chicago, San Francisco, Houston,
Phoenix and Charlotte all saw increases of over 200 daily
departures. Eight cities, however, experienced declines.
Milwaukee, Grand Rapids, Albany, Scranton/Wilkes/Barre and
Birmingham all showed decreases of less than 3 daily
departures, while Louisville lost an average of 8 departures,
New Orleans an average of 13 and Buffalo an average of 16
daily departures from 1978 to 1988.
Some cities with a higher number of nonstop connections
than a few of those listed in Table 5.8 are Cincinnati (43),
Charlotte (40), Philadelphia (43), Cleveland (37) and
Baltimore (38) They are all hub cities, and as such, are
highly connected. Twelve cities in the study set experienced
significant increases (10 or more) in the number of nonstop
connections available from their airports between 1978 and
1988 (Table 5.9). They are largely post-deregulation hubs
(Table 4.2). Fifteen cities, most of them spoke or nonhub
cities from the study set, however, experienced reductions in
the number of nonstop connections from their facilities. The


166
DTW
38
1161
35936
GSO
15
571
16447
BDL
22
767
22758
HNL
7
270
7747
HOU
35
1097
33376
IND
24
859
25952
JAX
14
518
15170
GRR
7
240
7453
MKC
33
1060
32267
LAS
23
785
23262
LAX
37
1132
34632
SDF
19
683
20381
MEM
34
1040
32146
MIA
28
985
29616
MKE
22
792
23542
MSP
36
1144
35058
BNA
22
774
23307
MSY
24
855
25469
NYC
49
1324
41854
ORF
14
534
15377
OKC
12
452
12909
MCO
35
1122
34528
PHL
40
1163
36225
PHX
27
793
23910
PIT
46
1274
40117
PDX
13
440
12754
PVD
9
382
10540
RDU
17
541
16498
RIC
13
474
13670
ROC
15
509
14802
SMF
10
310
9103
STL
43
1248
38887
SLC
24
728
22017
37135
17033
23547
8024
34508
26835
15702
7700
33360
24070
35801
21083
33220
30629
24356
36238
24103
26348
43227
15925
13373
35685
37428
24730
41437
13207
10931
17056
14157
15326
9423
40178
22769


TABLE OF CONTENTS
ACKNOWLEDGEMENTS ii
LIST OF TABLES vi
LIST OF FIGURES viii
ABSTRACT ix
CHAPTERS
1 INTRODUCTION 1
Hub-and-Spoke Structure 1
Connectivity Change and the Location of
Economic Activities 2
Objectives 3
Structure of Presentation 3
2 INDUSTRIAL LOCATION DECISIONS 5
Introduction 5
Theory of Location of Organizations 5
Spatial Division of Labor 7
Location Criteria of Various Parts of the
Firm 11
Regional Development and Industrial Location.. 18
Professional Workers 20
Labor Migration 21
The Large Metropolitan Area 21
Conclusion 24
3 THE DOMESTIC AIRLINE INDUSTRY 25
Introduction 25
The Deregulated Airline Industry 25
The Hub-and-Spoke System 3 4
Conclusion 41
4 VARIATIONS IN HUB SERVICE IN THE DOMESTIC AIR
TRANSPORTATION NETWORK 42
Introduction 4 2
The FAA Hub 4 3
iii


99
deregulation and the hub-and-spoke intensification.
Therefore, the average rate of change in air service
connectivity in the Northeast is mainly below the other
regions of the U.S., as will be illustrated. Throughout a
fair portion of the 11 year period, the nonhub average rate of
change has been much more stable than the hub average for the
Northeast group of cities.
The average rate of change in professional employment for
the Northeast cities is similar to the connectivity rate of
change (fairly low) although both hubs and nonhubsexperienced
some negative rates of change (employment loss). The meager
rate of employment change that exists for most of the study
period is in response to a general U.S. trend of professional
employment growth away from the traditional manufacturing
areas (Northeast and Midwest) of the country as discussed
previously. Figure 5.8 shows that the nonhub rate of change
recently experienced a sharp rise in the Northeast. Perhaps
this contribution shows the importance of being located in the
Northeast for nonroutine parts of the firm, but out of
(although close to) the large, overly-congested, expensive,
crime-ridden cities.
The nonhub graph shows a stronger relationship between
employment change and connectivity change, being almost the
same graph for much of the study period. In the hub case,
there may be a complex lag structure between the two
variables.


APPENDIX F
TOTAL NONSTOP DESTINATIONS REACHED FROM EACH MSA
1978
1979
1980
1981
1982
NYC
51
51
52
52
52
LAX
40
39
39
38
38
CHI
57
57
57
57
56
SFO
31
30
30
29
27
PHL
42
42
42
42
40
DTW
37
40
35
33
33
BOS
31
35
36
38
37
DFW
41
46
46
48
48
DCA
50
49
46
45
46
HOU
30
33
36
37
40
MIA
30
29
29
28
28
CLE
39
39
34
33
30
ATL
46
50
52
52
54
STL
37
40
41
41
43
SEA
17
18
19
20
20
MSP
24
27
27
27
29
SAN
25
23
19
17
17
BWI
28
26
26
26
29
PIT
37
41
44
46
46
PHX
19
19
20
20
21
TPA
32
32
32
32
30
DEN
33
38
38
41
41
CVG
26
25
25
25
28
MCI
28
26
25
25
27
148


rate of change
101
Years
FIGURE 5.9
NORTH CENTRAL HUB RATES OF CHANGE


30
1990). Contributing to this rise was the wave of acquisitions
and mergers that occurred during the mid 1980s. The larger
carriers began to realize that the best way to survive was to
increase their market shares and to neutralize their
competition by merging with other carriers (Carlton, Kaplan,
Sibley, 1983).
The main, and largely unforeseen, problem that has arisen
out of the merging has been the increased control gained by
individual airlines over travel in and out of certain cities.
The merger of Northwest and Republic Airlines, for example,
and TWA with Ozark created market shares of over 80% at
Minneapolis/St. Paul and at St. Louis for Northwest and TWA,
respectively (McGinley, 1989). Between 1985 and 1988, the
number of airports served by at least four airlines dropped by
52%, while the number of cities served by only one carrier
increased by 25% (Kuttner, 1989). Nearly two-thirds of all
route destinations are now near-monopolies (Traffic World,
1988). This dominance has permitted fare increases, rather
than the lower fares predicted by deregulation enthusiasts
(Bauer, Zlatoper, 1989; Business Week. 1988b). For example,
fares out of St. Louis rose twice as fast as the national
average after TWA purchased Ozark (McGinley, 1989).
Another change has been the dramatic decline in service
to some urban areas. Some cities are simply harder to get to
since deregulation, prompting passenger complaints but little
response from either airlines or the federal government.


140
TUS
125193
143973
LAS
157138
186053
1983
1984
1985
6815433
7156862
7409762
4318343
4696663
4901049
2928981
3069308
3146315
2138089
2336252
2438952
1981639
2078043
2166079
1405134
1542405
1630732
2296299
2395737
2516006
1406102
1546900
1652650
1154179
1264847
1371145
1374819
1395559
1391437
967177
1037499
1059883
989526
1034343
1056224
899900
1004172
1106604
863525
915384
954702
747144
798017
831951
935472
1009026
1062196
548265
605535
655927
697253
756016
803542
747312
782185
789181
560401
643342
716445
529146
586464
636163
718671
768419
801120
570017
599952
623656
149545
156241
159234
196620
195011
193729
1986
1987
1988
7678372
7797530
7882767
5118092
5311411
5479860
3219927
3251211
3380339
2481297
2534682
2621281
2233870
2317414
2375108
1725069
1760620
1764577
2574481
2623145
2677890
1694038
1666154
1670699
1478163
1572264
1647312
1384926
1325571
1346228
1110397
1127963
1153702
1082964
1088266
1108405
1190816
1233837
1271674
986435
982533
1001090
874958
934612
979780
1093769
1131243
1161171
703813
734813
767646
842755
900361
903266
797865
813049
823432
755771
778148
802077
652305
684166
717829
806446
639132
767734
658268
688189
720738


100
The rate of change comparisons between professional
employment and air service connectivity for the cities in the
North Central region (Figures 5.9 and 5.10) are quite
different from the Northeast. Both graphs appear less stable
than the graphs of the Northeast. In particular, the
professional employment lines of both figures are very erratic
with wide jumps and drops throughout the study period,
indicating a high degree of vulnerability to changing economic
fortunes in the region. Both connectivity lines are much more
steady, upward-trending graphs with only a few drops in the
growth rates. In this region, however, it is the hub group
that is more stable and regular in its growth trends, and the
hub graph shows a lagged relationship between the two
variables, at least during the initial part of the time
period.
The graphs of the West (Figures 5.11 and 5.12) have
sporadic jumps and drops as well. In this region, however,
the nonhub group appears to have been more stable in its
growth pattern than the hub cities. Both graphs show apparent
lag structures, but on the nonhub graph (Figure 5.12), it
appears that the change in connectivity occurs slightly ahead
of the employment change (different from the other graphs that
indicated lag structures).
The graphs of the South show a great deal of fluctuation
in employment and connectivity rates of change (Figures 5.13
and 5.14). Most of the nonhub graph (Figure 5.14) seems to


37
56
26
40
33
37
48
46
40
28
31
54
43
19
30
17
31
46
23
30
41
28
27
22
14
10
14
150
13
10
23
)88
48
37
56
28
43
43
40
50
49
38
27
37
56
46
22
40
21
38
49
31
32
40
43
31
25
13
11
16
13
12
7
8
23
24
1984
1985
49
49
37
37
56
56
25
26
40
40
36
38
37
37
48
48
46
47
31
35
28
28
31
33
54
54
43
43
18
20
31
36
17
19
34
36
44
46
25
27
29
30
41
40
30
32
33
33
19
22
13
13
10
10
14
14
11
11
9
10
24
25
1986
1987
49
48
37
37
56
56
27
27
40
43
40
43
39
40
48
49
48
49
37
38
28
27
33
34
54
54
43
46
20
22
38
40
19
20
37
37
48
48
30
31
30
30
40
40
34
40
33
31
25
25
13
13
11
11
15
15


41
priced up to 50% more than they would have been had
deregulation not occurred (Stockton, 1988). Comparison of
one-way fares on selected pairs indicate increases from 1978
to 1988 (in constant dollars) ranging from 28% to 495%, with
an average increase of well over 200% (Goetz, Dempsey, 1989).
Conclusion
Deregulation has had a much greater impact on the
domestic airline industry than anyone anticipated. Major
changes in service and connectivity have occurred in the U.S.
air transportation network. Small cities, and even large
cities not chosen as hubs, have experienced declines in flight
frequency and nonstop destination choice. Hubs have grown
tremendously in these areas, but fares have skyrocketed and
carrier choice along most routes has lessened. However, not
all hubs are alike. The next chapter will look at different
types of hubs and discuss hub intensity and connectivity.


34
The Hub-and-Spoke System
The hub-and-spoke system currently in place in the U.S.
airline industry is largely a product of deregulation. It is
the new structure adopted by most airlines to compete more
effectively. The major carriers created hubs at strategic
regional points in their air networks so travelers from
numerous origins (spokes) could be routed into the hub city
and then connected with a flight to their final destinations
(Business Week. 1988a). The hub-and-spoke structure cuts
costs by creating greater overall efficiency with more
occupied seats. Route planners, therefore, approach monthly
scheduling differently today than prior to deregulation, as
such planning is now more important to the profitability of
the company than previously (Pollack 1977, 1982).
What few hubs that existed in the regulated era had a
slightly different function than hubs today. Chicago, Denver
and St. Louis, for example, were used to serve long-haul,
east-west markets which used aircraft that did not have
transcontinental capabilities (Lopuszynski, 1986). Thus,
these cities were set up as transfer points for coast-to-coast
traffic. Because of CAB control over routes flown, it would
have been difficult for an airline to set up a hub-and-spoke
network prior to deregulation.
The deregulated era saw many true hubs develop that acted
as collection points for travelers from many origins, sending


rate of change
103
Years
FIGURE 5.11
WESTERN HUB RATES OF CHANGE


158
AUS
3
92
2562
BWI
26
792
22582
BHM
12
394
11282
BOS
38
1040
30806
BUF
16
514
14237
CLT
24
714
20700
CHI
56
1295
39695
CVG
26
894
25507
CLE
33
1023
29788
CMH
20
692
19560
DFW
47
1203
36088
DAY
16
548
15743
DEN
41
1114
32904
DTW
33
1005
29285
GSO
17
569
15729
BDL
21
702
19645
HNL
6
201
5540
HOU
37
1035
30427
IND
22
751
21529
JAX
13
463
12670
GRR
5
176
5088
MKC
25
815
23149
LAS
24
769
21641
LAX
38
1082
31702
SDF
23
769
21873
MEM
29
921
26611
MIA
28
913
26167
MKE
26
866
24592
MSP
28
899
25805
BNA
21
702
20069
MSY
23
806
22804
NYC
52
1265
38565
ORF
12
438
11708
2657
23400
11688
31884
14767
21438
41046
26427
30844
20272
37338
16307
34059
30323
16315
20368
5747
31499
22302
13146
5269
23989
22434
32822
22665
27561
27108
25484
26732
20792
23633
39882
12158


TABLE 4.4
FAA HUBS
56
FAA Larae Hubs
Atlanta
Miami
Baltimore
Minneapolis/St.
Boston
New York/Newark
Charlotte
Orlando
Chicago
Philadelphia
Dallas/Ft. Worth
Phoenix
Denver
Pittsburgh
Detroit
St. Louis
Honolulu
Salt Lake City
Houston
San Diego
Kansas City
San Francisco
Las Vegas
Seattle
Los Angeles
Tampa
Memphis
Washington, D.C
Albuquerque
FAA Medium Hubs
New Orleans
Austin
Norfolk
Buffalo
Oklahoma City
Cincinnati
Ontario
Cleveland
Portland
Columbus
Raleigh/Durham
Dayton
Reno
El Paso
Rochester
Ft. Myers
Sacramento
Hartford
San Antonio
Indianapolis
San Jose
Jacksonville
Syracuse
Kahului
Tucson
Lihue
Tulsa
Milwaukee
West Palm Beach
Nashville
Paul


142
227683
251607
269420
282216
301761
309410
210870
226612
236931
245382
244699
259554
157231
175446
187297
192734
204451
209786
187086
204475
215756
225403
253582
270247


180
Knapp, Thomas A. and Graves, Philip E. 1989, On the
Role of Amenities in Models of Migration and
Regional Development, Journal of Regional Science.
29, pp. 71-87.
Kuttner, Robert, 1989, Plane Truth, The New Republic.
July 17, pp. 21-23.
Leigh, Laurence E., 1990, Contestability in Deregulated
Airline Markets: Some Empirical Tests,
Transportation Journal. 30, pp. 49-57.
Lewis, W. David and Newton, Wesley P., 1979, Delta: The
History of an Airline. Athens, GA: University of
Georgia Press.
Long, L. and Hansen, K. 1979, Reasons for Interstate
Migration. Washington, D.C.: Government Printing
Office, United States Census Bureau.
Lopuszynski, Andrew, 1986, Perspectives on Airline
Hubbina in the U.S.. Washington, DC: Government
Printing Office, United States Department of
Transportation, Federal Aviation Administration.
Lowe, John C. and Moryadas, S., 1984, The Geography of
Movement Prospect Heights, IL: Waveland Press.
Lund, Leonard, 1986, Locating Corporate R&D Facilities.
Research Report No. 892, New York: The Conference
Board.
Malecki, Edward J., 1979a, Agglomeration and Intra-Firm
Linkages in R&D Location in the United States,
Tiidschrift voor Economishe en Sociale Geografie.
70, pp. 322-332.
Malecki, Edward J., 1979b, Locational Trends in R&D by
Large U.S. Corporations, 1965-1977, Economic
Geographer 55, pp. 309-323.
Malecki, Edward J., 1980a, Dimensions of R&D Locations
in the United States, Research Policy. 9, pp. 2-
22 .
Malecki, Edward J., 1980b, Corporate Organization of R&D
and the Location of Technological Activities,
Regional Studies. 14, pp. 219-234.
Malecki, Edward J., 1981, A Note on the Geographic
Concentration of Scientific Personnel in the USA,
Scientometrics. 3, pp. 107-114.


15
Face-to-face, nonroutine communication both within and
outside of the firm is a vital part of the work of
professional employees. Even though communication technology
has made phenomenal advancements in recent years, there are
times when there is no viable alternative or substitute for an
on-site visit. Firms desire frequent contact with their
clients and markets to respond quickly to their changing
needs. Within the firm, management officials, research
scientists and engineers and other administrative service
professionals often need to interact with workers in other
facilities of the organization to deal with problems and new
innovations as quickly as possible. Consequently, frequent
air travel between the various facilities of the firm is quite
common for these employees (Coffey, Polese, 1989) .
A comparison of the top location criteria determined by
the study for the various types of company facilities is
revealing (Table 2.3). Most of the lists are identical in
content with only slight changes in rank order of the
criteria.
The back office/processing category, however, is
noticeably different. This activity, traditionally associated
with the headquarters location, is now largely moving away
from central urban locations to, at the very least, suburban
locations where land costs are lower (Ady, 1986; Coffey,
Polese, 1987; Nelson, 1986). The nature of the work
processing data and paperworkcan be classified as routine


176
Clark, Gordon, 1981, The Employment Relation and the
Spatial Division of Labor: A Hypothesis, Annals of
the Association of American Geographers. 71, pp.
412-424.
Clark, W.A.V. and Hosking, P.L., 1986, Statistical
Methods for Geographers. New York: Wiley and Sons.
Cobb, James C., 1980, The Selling of the South: The
Southern Crusade for Industrial Development. Baton
Rouge, LA: Louisiana State University Press.
Cobb, James C. 1984, Industrialization and Southern
Society: 1877-1984. Lexington, KY: University Press
of Kentucky.
Coffey, William J. and Polese, Mario, 1987, Intrafirm
Trade in Business Services: Implications for the
Location of Office-based Activities, Papers of the
Regional Science Association. 62, pp. 71-80.
Coffey, William J. and Polese, Mario, 1989, Producer
Services and Regional Development: A Policy-
Oriented Perspective, Papers of the Regional
Science Association. 67, pp. 13-27.
Collie, H. Cris, 1986, Corporate Relocation: Changing
with the Times, Personnel Administrator. April, pp.
101-106.
Consumer Reports. 1988, The Trouble With Air Travel,
June, pp. 362-363.
Cooper, C.L. and Makin, P., 1985, The Mobile Managerial
Family, Journal of Management Development. 4, pp.
56-66.
Cooper, M.H. and Maynard, A., 1972, The Effect of
Regulated Competition on Scheduled Air Fares,
Journal of Transport Economics and Policy. 6, pp.
167-175.
Corporate Site Selection For New Facilities. 1989, New
York: The Time Incorporated Magazine Company.
County Business Patterns, annual by state, Washington,
DC: Government Printing Office, United States
Department of Commerce, Bureau of the Census.
Cutter, Susan, 1985, Rating Places: A Geographer's View
on Quality of Life. Washington, D.C.: Association
of American Geographers.


TABLE 2.3
FIVE MOST IMPORTANT FACTORS IN SELECTING LOCATIONS FOR
VARIOUS TYPES OF COMPANY FACILITIES
16
Back Office/Processina Location
1) Availability of skilled workers
2) Availability of unskilled or semi-skilled workers
3) Efficient transportation facilities for people/employees
4) Easy access to domestic markets, customers and clients
5) Cost of labor
Branch Office/Reaional Headquarters Location
1) Easy access to domestic markets, customers and clients
2) Facilitates access to prospective clients
3) Availability of technical or professional workers
4) Availability of skilled workers
5) Easy access to airport
Corporate Headquarters Location
1) Availability of technical or professional workers
2) Easy access to domestic markets, customers and clients
3) Easy access to airport
4) Availability of skilled workers
5) Efficient transportation facilities for people/employees
Sales Office Location
1) Easy access to domestic markets, customers and clients
2) Facilitates access to prospective clients
3) Easy access to airport
4) Efficient transportation facilities for people/employees
5) Availability of technical or professional workers
Source:
Corporate Site Selection For New Facilities. The
Time Inc. Magazine Company, 1989.


107
suggest a link between the two variables. The peak growth and
decline periods of the two lines occur at virtually the same
time during the first part of the period and slightly lagged
during the latter part of the period. The trend of the lag
suggests that fluctuations in the rate of change of
professional employment growth in the non-hubs of the South
has an impact on the average rate of change in air service
connectivity for the cities. The pattern for the hub group
(Figure 5.13) is more complex, but a general downward trend
for professional employment rate of change in the hub cities
is easily identifiable.
Numerical Analysis
Some simple numerical analysis was conducted using the
mean value of the year-to-year rates of change for each of the
MSA's in the study set (Table 5.11).
First, a general hub/non-hub comparison was made. Hub
cities experienced a mean rate of connectivity change of .0553
(5.53%), and a mean rate of professional employment change of
.0458 (4.58%). On the other hand, the nonhub group
experienced even higher growth rates of both connectivity
(6.79%) and professional employment (10.38%). Most all of the
smaller cities (in population size) from the study set fall
into the nonhub group. As smaller cities, they are generally
coming into the study period with lower base-levels of


29
investor confidence in new airline ventures has lessened
considerably (Dempsey, 1987; Rose, Dahl, 1989).
These new smaller carriers had several strikes against
them from the beginning. For example, the essential
infrastructure of gates, terminal facilities and landing slots
has been controlled by the major carriers, making it harder to
enter new markets. Almost 70% of all U.S. airports have no
gates at all that could be leased to new carriersalthough
space may sometimes be sublet from other carriers (Hardaway,
1986). Because of their greater buildup of capital, the major
carriers have a higher chance of surviving fare wars, and they
have used this power to drive both new airlines and old,
nearly bankrupt carriers out of business. For those small
carriers that did not arrange marketing agreements with the
large carriers, much of their business was taken away by the
latter due to frequent flyer program loyalty (particularly by
business travelers) (Toh, Hu, 1988) and computerized
reservation systems made available to travel agencies (Davis,
1982; Oster, Pickrell, 1988).
A glance at the market-share data before and after
deregulation shows the trend in market dominance by the
largest carriers. At the end of regulation in 1978, the top
six airlines controlled 71% of the domestic air traffic. In
1983, that figure dropped to 65% due largely to heavy influx
of new carriers, but by the end of 1987 it had risen to 79%
(Brenner, 1988) and is rising going into the 1990s (Fortune,


rate of change
98
1.4
EMPLOYMENT
CONNECTIVITY
/
y\ 1
1.2
0.8
0.6
0.4
0.2
-0.2
78-79 79-80 80-81 81 -82 82-83 83-84 84-85 85-86 86-87 87-88
Years
FIGURE 5.8
NORTHEAST NONHUB RATES OF CHANGE


50
TABLE 4.3
NONSTOP DOMESTIC
CONNECTIONS ON ALL CARRIERS BY FAA HUB-TYPE
(FALL 1991)
airports large
medium small non total
ATL
27
23
33
24
107
BWI
22
16
10
19
67
CLT
24
18
27
25
94
CHI
27
29
35
27
119
CVG
27
17
19
7
70
CLE
22
13
7
3
45
DFW
27
26
25
23
102
DAY
17
6
7
6
36
DEN
25
19
16
24
84
DTW
25
16
14
14
70
HNL
11
2
3
5
21
HOU
26
16
10
9
61
LAS
22
12
3
1
38
LAX
27
17
5
14
63
MEM
23
10
14
20
67
MIA
20
10
4
4
38
MSP
26
13
11
30
81
BNA
24
14
18
14
70
NYC*
26
17
17
17
78
MCO
22
13
8
4
47
PHL
23
18
12
19
72
PHX
24
16
6
9
55
PIT
25
20
20
33
98
RDU
15
12
16
12
55
STL
27
22
16
19
83
SLC
18
12
7
17
55
SFO
26
14
8
14
62
SEA
22
7
4
14
47
DCA
25
17
23
19
84
Source: PAG Pocket Flight Guide. November 1991.
Includes Newark


78
FIGURE 5.1
CENSUS REGIONS OF THE UNITED STATES


57
TABLE 4.4continued
FAA Small Hubs
Akron/Canton
Albany
Allentown
Amarillo
Anchorage
Baton Rouge
Billings
Birmingham
Boise
Brownsville
Burlington
Cedar Rapids
Charleston (SC)
Charleston (WV)
Chattanooga
Colorado Springs
Columbia
Corpus Christi
Daytona Beach
Des Moines
Eugene
Fort Wayne
Fresno
Grand Rapids
Greensboro
Greenville
Harrisburg
Hilo
Huntsville
Islip
Jackson
Kailua-Kona
Knoxville
Lexington
Lincoln
Little Rock
Louisville
Lubbock
Madison
Melbourne
Midland
Mobile
Moline
Myrtle Beach
Omaha
Palm Springs
Pensacola
Portland
Providence
Richmond
Roanoke
Saginaw
Santa Barbara
Sarasota
Savannah
Shreveport
Sioux Falls
South Bend
Spokane
Tallahassee
Toledo
Witchita


90
the employment change occurring slightly ahead of the
connectivity change. In the latter years, either there is no
relationship between the two or possibly a complex lag
structure exists.
The data were divided between hubs and nonhubs to compare
the different rates of change for both air connectivity and
professional employment for each group. Post-deregulation hub
cities were considered part of the nonhub group until the year
that hub status was achieved (Table 4.2). For example,
Baltimore was classifed as a nonhub until 1983, but was
switched to the hub category after 1983.
The nonhub connectivity rate of change (Figure 5.3) stays
very close to the rate of change for professional employment
(practically the same graph), while the hub city connectivity
rate (Figure 5.4) stays below the employment rate of change
for much of the study period (until the latter years).
Indeed, the two lines in Figure 5.4 appear to have little in
common. The graphs (Figures 5.3 and 5.4) seem to suggest that
a stronger and virtually instantaneous relationship exists
between connectivity change and professional employment change
for nonhubs than for hub cities.
Figure 5.5 breaks the employment data between hubs and
nonhubs. Throughout a fair portion of the study period,
professional employment at nonhubs grew by a higher rate than
hub cities. The mean of the yearly average rates of
professional employment change during the study period for


ACKNOWLEDGEMENTS
I would like to thank Dr. Edward Malecki for his support,
encouragement and guidance throughout my years at the
University of Florida. I also wish to thank the other members
of my supervisory committee, especially Dr. Timothy Fik, whose
quantitative assistance was invaluable. A great deal of
computer assistance, including all graphics, and patience were
given by Jan Coyne.
I would also like to thank Dr. Jesse Wheeler, Dr. J.
Trenton Kostbade and Ann Wright at the University of Missouri
for sparking my interest in geography.
Lastly, I would like to thank my mother, who has always
supported and encouraged without question anything and
everything I have attempted.
ii


157
BNA
19
MSY
23
NYC
52
ORF
12
OKC
11
MCO
23
PHL
42
PHX
20
PIT
44
PDX
12
PVD
8
RDU
12
RIC
13
ROC
12
SMF
9
STL
41
SLC
14
SAT
8
SAN
19
SFO
30
AVP
5
SEA
19
SYR
11
TPA
32
TUS
9
TUL
11
DCA
46
PBI
11
1981
MATRIX1
ALB
11
ABE
8
ATL
52
17974 18634
22240 23059
37821 39125
11627 12077
10761 11171
21415 22202
33178 34340
17207 17856
33794 34974
10408 10795
9056 9419
10764 11170
11677 12127
11846 12292
7676 7965
33486 34665
12514 12977
8132 8457
16920 17550
25067 25981
5060 5257
16257 16866
10977 11402
27763 28755
8961 9307
10742 11151
35437 36681
11102 11529
MATRIX3 TOTAL
10517 10924
9120 9480
38965 40292
641
796
1252
438
399
764
1120
629
1136
375
355
394
437
434
280
1138
449
317
611
884
192
590
414
960
337
398
1198
416
MATRIX2
396
352
1275


36
FIGURE 3.1
HUB CITIES OF THE MAJOR U.S.
AIRLINES
1991


116
was conducted using SPSS/PC+, a statistical package for the
IBM personal computer, with the following model formula:
Yt a + b0X{ + b1Xt.1 + et
The model was run for six different cases (Figures 5.2, 5.3
and 5.4), and the results are given in Table 5.14. The first
model examines the relationship between professional
employment and connectivity change for the entire study set,
while models two and three deal with the same relationship for
hubs and nonhubs, respectively. For each model, two different
cases were run switching the professional employment and
connectivity rates of change from independent to dependent
variables (Table 5.14).
In all six cases, the R2 value is closer to 0 than to 1,
indicating that no strong relationship exists between the
dependent and independent variables in the models (Table
5.14). Also, the estimated regressor coefficients (X, and Xt_
,)are not significant (T-test) at the .95 confidence level for
any case. The overall fit of the model (F-test) also fails to
be significant at the .95 confidence level in each case. This
last test is of great importance. Failure of the F-test
indicates that the R2 and T statistics are not valid (Clark,
Hosking, 1986).
Another statistic given in Table 5.14 is the Durbin-
Watson number. This statistic looks at the residuals
(estimated error terms) generated from regression analysis.


76
TABLE 5.4
ADMINISTRATIVE/AUXILIARY
EMPLOYMENT LOSS:
1978-1988
MSA
change
Detroit
Pittsburgh
Albany
Indianapolis
Baltimore
Tulsa
Oklahoma City
Chicago
Buffalo
-27,340
-12,179
-4,094
-2,216
-1,999
-1,674
-1,258
-592
-367
Source: Calculated from County Business
Patterns. U.S.
Census
Bureau 1980
, 1990.
TABLE 5.5
ADMINISTRATIVE/AUXILIARY GROWTH BY REGION
: 1978-1988
region
number of
MSA's
employee
growth
MSA
average
Northeast
12
170,993
14,249
North Central
12
36,919
3,077
South
24
243,392
10,141
West
12
100,311
8,359
Source: Calculated from County Business Patterns. U.S.
Census Bureau, 1980, 1990.


122
variables than hubs that belonged to the larger population
cluster groups (Table 5.13). Another interesting observation
from Tables 5.12 and 5.13 is that the population cluster
groups with the lowest rates of change in both professional
employment and air service connectivity are largely peripheral
cities. This would seem to indicate that more central
locations have been preferred both by airlines and by
corporations.
Guidelines for Future Research
One of the principal assumptions in the set of research
questions addressed in this study appears to have been
incorrect. It was assumed that hub cities would have
experienced substantially greater rates of connectivity change
than nonhubs. While it is true that hub cities have many more
flights to many more destinations, the connectivity indices of
most of the nonhubs have actually grown by higher percentages
as a result of the expansion of the hub-and-spoke system.
Nonhubs may be connected to fewer cities today than prior to
deregulation, but the cities they are now connected to (hub
cities) have much higher connectivity indices than before
deregulation. Therefore, the increased connectivity felt by
nonhubs is largely indirect.
The biggest shortcoming in the present study is the brief
time period. Deregulation and the intensification of the hub-


Ill
Statistical tests can be run to check that the strength of the
linear relationship is significant. Coefficient values that
lie close to zero are uncorrelated (no association) This
simple analysis, however, does not check for a lagged
relationship or association between the variables.
A correlation analysis was performed on the means (of
each of the 60 MSAs) of the annual rates of connectivity and
professional employment change (from Table 5.11). The
correlation coefficient (r) was found to be very close to zero
(.010678) and; therefore, uncorrelated. Breaking the data
between hubs and nonhubs, the (r) values were still closer to
zero than to +1 or -1 (.1371933 for the hub cities and
.2402332 for the nonhubs). Again, this test does not check
for a lagged relationship between the variables. A regional
correlation analysis was not done due to the small sample size
of each of the groups.
Hierarchical Analysis of Hub Cities
This subsection looks at the mean of the yearly rates of
change for the hub cities from Table 5.11 to see if a
hierarchical bias exists in the relationship between
connectivity change and professional employment growth. While
all of the hub cities in the table experienced positive mean
rates of connectivity change during the study period, two
cities (Detroit and Nashville) experienced a decline in the


rate of change
102
Years
FIGURE 5.10
NORTH CENTRAL NONHUB RATES OF CHANGE


27
traveler (Meyer, Oster, 1987) Proponents of deregulation
agreed that many changes would occur early in the era of
deregulation that would resemble chaotic instability of the
industry but, with time, things would stabilize and the cost
and quality of air service would greatly improve in the
competitive environment (Goetz, Dempsey, 1989).
The Airline Deregulation Act of 1978 was signed by
President Carter and called for a gradual removal of
government control over domestic air transportation, with the
exception of safety standards (under FAA supervision) and
merger and acquisition approval (monitored by the Department
of Transportation). A gradual phaseout was recommended to
ease the transition to perfect competition in the industry
and, by the end of 1984 (when the CAB was dissolved) the
phaseout was completed. Following the lead of the U.S.,
deregulation of air transportation (or at least partial
deregulation) has been put in action in a few other western
countries, such as Canada and the United Kingdom (Graham,
1990).
A major goal and promise of deregulation was that the
cost of domestic air travel in the United States was to become
more affordable to the average American. This was supposed to
occur because carriers would now compete with one another for
business. Two or more carriers would be flying nearly
identical routes as they could now choose where they would
fly. Also, new airlines were allowed to form at will, which


154
DCA
50
PBI
11
1979
MATRIX1
ALB
11
ABE
7
ATL
50
AUS
3
BWI
26
BHM
12
BOS
34
BUF
20
CLT
24
CHI
57
CVG
25
CLE
38
CMH
19
DFW
46
DAY
15
DEN
38
DTW
37
GSO
15
BDL
18
HNL
5
HOU
36
IND
22
JAX
12
GRR
5
MKC
26
LAS
24
LAX
39
SDF
20
MEM
26
36261 37553
10543 10955
MATRIX3 TOTAL
10407 10811
7784 8093
36896 38179
2414 2506
22069 22882
10698 11094
27889 28891
16910 17532
19981 20706
38854 40195
23625 24488
31392 32508
18443 19129
34095 35293
14604 15137
30390 31476
30657 31750
13344 13855
16763 17390
5006 5198
28581 29609
20655 21409
11388 11824
5104 5292
23580 24447
21279 22066
31224 32338
18150 18820
23172 24011
1242
401
MATRIX2
393
302
1233
89
787
384
968
602
701
1284
838
1078
667
1152
518
1048
1056
496
609
187
992
732
424
183
841
763
1075
650
813


35
them off to many destinations. This new system, along with
increased cooperation and code-sharing between major carriers
and commuter carriers, made a much greater on-line (same
carrier) city-pair matching available to air travelers (Oster,
Pickrell, 1988).
The first true hub in this sense was developed by Delta
Airlines in Atlanta, and actually was in place before
degregulation (Lewis, Newton, 1979). Delta dominated traffic
in and out of Atlanta for decades and gradually added more and
more service as the years passed. It was the model system
copied by other airlines as deregulation forced them towards
greater efficiency in order to compete. Figure 3.1 shows the
major hub cities (in mid-1991) of the largest carriers.
Much has been written on the positioning of hubs in the
air transportation network (Bauer, 1987; Grove, O'Kelly, 1986;
Kanafani, Ghobrial, 1985; Lopuszynski, 1986; O'Kelly, 1986a,
1986b; Toh, Higgins, 1985). What makes one city likely to be
chosen as a hub over another city? In general, the very
largest cities have been favored over medium-sized cities, and
eastern cities over western cities. In fact, several cities
have been chosen as hubs by more than one carrier, such as New
York City, Chicago, Denver and Dallas.
The early trend of choosing the largest cities of the
nation as hub cities eventually died as these airports became
overly congested. Not only was safety an issue, but delayed
flights, overuse of infrastructure, little room for expansion


119
Conclusion
The analysis of this chapter fails to show a relationship
between changes in air service connectivity and professional
employment. While some of the graphs seemed to indicate
visually that a lagged relationship existed between the two
variables, time series analysis failed to verify that
relationship statistically. However, a hierarchical bias was
shown to exist for hub clusters. Those clusters with smaller
average populations had higher average connectivity and
professional employment growth rates. The regional
information derived was inconclusive. The next chapter will
be a summary of the study and an identification of the
problems of the data set and the methods of analysis.


168
HOU
37
1156
36062
IND
26
957
29419
JAX
16
587
17665
GRR
7
251
7914
MKC
33
1091
33885
LAS
25
825
25143
LAX
37
1173
36586
SDF
18
680
20625
MEM
36
1113
35209
MIA
28
1012
31092
MKE
25
894
27350
MSP
38
1196
37498
BNA
26
918
28439
MSY
23
855
25938
NYC
49
1361
43915
ORF
15
582
17189
OKC
12
462
13543
MCO
36
1166
36713
PHL
40
1193
37978
PHX
30
924
28524
PIT
48
1326
42757
PDX
13
448
13348
PVD
9
388
10937
RDU
17
549
17124
RIC
13
483
14227
ROC
13
489
14301
SMF
11
353
10724
STL
43
1288
40935
SLC
25
758
23507
SAT
13
480
14107
SAN
19
661
19881
SFO
27
884
26947
AVP
6
247
7027
37255
30402
18268
8172
35009
25993
37796
21323
36358
32132
28269
38732
29383
26816
45325
17786
14017
37915
39211
29478
44131
13809
11334
17690
14723
14803
11088
42266
24290
14600
20561
27858
7280


178
Gentile, Ralph and Stave, Keith, 1988, Highly Trained
Workers and the Resurgence of New England:
Interregional Flows of Scientists, Engineers, and
Technicians, 1975-80, New England Economic
Indicators, 2, pp. iv-xi.
Glasmeier, A.K., Hall, P. and Markusen, A., 1984,
Evidence on High-Technology Industries' Spatial
Tendencies: A Preliminary Investigation, In
Technology, Innovation. and Regional Economic
Development. Washington, DC: U.S. Government
Printing Office, Office of Technology Assessment,
pp. 145-167.
Goetz, Andrew R. and Dempsey, Paul Stephen, 1989,
Airline Deregulation Ten Years After: Something
Foul in the Air, Journal of Air Law and Commerce.
54, pp. 927-963.
Goldfarb, Robert and Yezer, Anthony, 1987, Interregional
Wage Differential Dynamics, Papers of the Regional
Science Association. 62, pp. 45-56.
Graham, B.J., 1990, Deregulation of Domestic Passenger
Air Transport Services in the United Kingdom, 1980-
1989: A Case Study of Northern Ireland, Environment
and Planning C, 8, pp. 327-346.
Graham, D., Kaplan, D. and Sibley, D., 1983, Efficiency
and Competition in the Airline Industry, Bell
Journal of Economics. 14, pp. 118-138.
Greenwood, Michael J. 1981, Migration and Economic
Growth in the United States. New York: Academic
Press.
Greenwood, Michael J., 1988, Changing Patterns of
Migration and Regional Economic Growth in the U.S.:
A Demographic Perspective, Growth and Change. 19,
pp. 68-87.
Grove, Peter G. and O'Kelly, Morton E., 1986, Hub
Networks and Simulated Schedule Delay, Papers of
the Regional Science Association. 59, pp. 103-119.
Hardaway, J., 1986, The FAA Buy-Sell Slot Rule: Airline
Deregulation at the Crossroads, Journal of Air Law
and Commerce. 52, pp. 25-39.
Heenan, David A., 1991, The New Corporate Frontier. New
York: McGraw-Hill.


55
Results
The above-mentioned technique was applied to a study set
of 117 nodes to measure accessibility within the U.S. domestic
air transportation network. These nodes were all U.S. urban
areas (excluding 4 Hawaiian cities not well connected to the
U.S. mainland) that are classified by the FAA as a large
(total of 28), medium (total of 29) or small (total of 60) hub
(Table 4.4). The purpose was to find out how well connected
each of the airline hubs listed in Table 4.1 was to cities of
various sizes scattered around the nation. Due to the fact
that there are several hundred FAA non-hubs, they were
excluded to keep the size of the matrix manageable (117 x
117). The connectivity data for the original matrix (Cl) was
abstracted from the November 1991 issue of the PAG Pocket
Flight Guide.
The matrix was ordered to the third power (C3). At that
point, all of the non-zero elements disappeared from the
matrix cells. The diameter of this network is three because
one can fly between any domestic city pair in the study set in
three or fewer flight segments (due to the intense hub-and-
spoke structuring). The summed accessibility indices for each
metropolitan area for matrices Cl, C2, C3, and T are given in
Appendix A. The accessibility indices for each hub (from
matrix Cl and T) and their respective rankings are given in
Table 4.5. Note that the hub rankings for matrix Cl (direct


TABLE 4.2
PRE AND POST-DEREGULATION HUB CITIES
(1991)
48
PRE-DEREGULATION HUBS:
Atlanta
Chicago
Dalias/Ft Worth
Denver
Honolulu
Houston
Los Angeles
Miami
POST-DEREGULATION HUBS:
Baltimore (1983)
Charlotte (1981)
Cincinnati (1987)
Cleveland (1989)
Dayton (1982)
Detroit (1984)
Las Vegas (1985)
Minneapolis
New York/Newark*
Philadelphia
Pittsburgh
St. Louis
San Francisco
Seattle
Memphis (1984)
Nashville (1986)
Orlando (1989)
Phoenix (1983)
Raleigh/Durham (1987)
Salt Lake City (1982)
Washington, D.C. (1986)
* Newark International Airport on its own would be classified
as a post- deregulation hub. People's Express (eventually
consumed by Continental Airlines) developed hub facilities
there in 1981.
Source: Hub information was obtained from a series of surveys
of airports and airlines by mail and telephone (in mid-1991).
The years next to the post-deregulation hubs indicate the year
of development as a major transfer hub as determined by the
airport in question.


80
carriers. It should be noted that summer schedules are
usually expanded to accommodate extensive vacation travel, and
are not representative of the average service offered during
the year as a whole.
Data were collected on all 60 cities in the study set for
each year from 1978 through 1988. More current air data were
available, but the collection process was stopped at 1988 to
keep the air data consistent with the available labor data (as
discussed in the previous section of this chapter). In
communities with multiple airports (e.g. Dallas and Chicago),
the individual airport information was combined to obtain a
community total. Multiple airport communities are listed in
Table 5.7.
The U.S. cities with the highest enplanement (boarded
passenger) levels in 1988 are given in Table 5.8 by rank order
along with their average number of daily departures and the
number of cities in the study set to which they are connected
with nonstop air service (59 is the maximum number of
nonstops) The list consists of the largest cities in the
nation, plus a few popular travel destinations such as
Honolulu, Las Vegas and Orlando. It is not suprising that
almost all (except Boston) are airline hub cities (Table 4.1).
Every city (of the 60 in the study set) grew in enplanement
levels from 1978 to 1988 except Louisville (which dropped by
38,163 passengers) and Scranton/Wilkes/Barre (a decline of
16,240) (Appendix E) .


127
COS
8
293
14778
CAE
10
306
17362
CMH
22
712
41578
CRP
2
84
4065
DFW*
79
1290
87299
DAY*
30
735
46692
DAB
10
270
16396
DEN*
60
1127
70020
DSM
11
348
19534
DTW*
56
1099
75360
ELP
14
332
18811
EUG
4
99
5830
FMY
20
615
36303
FWA
9
245
14420
FAT
10
240
13188
GRR
10
301
18380
GSO
13
445
24729
GSP
10
308
17973
HRL
3
92
4908
MDT
12
388
22502
BDL
25
704
43004
HNL*
12
389
20488
HOU*
52
1077
68468
HSV
9
335
17355
IND
37
936
58409
ISP
11
318
19307
JAN
9
242
13329
JAX
23
592
34621
MKC
35
929
54985
TYS
11
430
22218
LAS*
37
891
52773
LEX
11
351
19567
LNK
5
190
9746
15079
17678
42312
4151
88668
47457
16676
71207
19893
76515
19157
5933
36938
14674
13438
18691
25187
18291
5003
22902
43733
20889
69597
17699
59382
19636
13580
35236
55949
22659
53701
19929
9941


69
TABLE 5.1continued
Jacksonville MSA
(JAX)
898
Albany MSA
(ALB)
851
Richmond MSA
(RIC)
844
Honolulu MSA
(HNL)
838
West Palm Beach MSA
(PBI)
818
Austin MSA
(AUS)
748
Scranton MSA
(AVP)
731
Tulsa MSA
(TUL)
728
Raleigh/Durham MSA
(RDU)
683
Allentown MSA
(ABE)
677
Grand Rapids MSA
(GRR)
665
Syracuse MSA
(SYR)
650
Tucson MSA
(TUS)
636
Las Vegas MSA
(LAS)
631
Note: The three-letter code listed after each city is the
airport code for the city as assigned by the FAA. Some cities
on the list are served by more than one airport. In these
cases, one code is chosen to represent the urban area as a
whole, but includes total destinations and flows from all
airports in the metropolitan area.
Source: Statistical Abstract of the United States. 1990.


APPENDIX G
ACCESSIBILITY INDICES: 1978-1988
1978
MATRIX1
MATRIX2
MATRIX3
TOTAL
ALB
10
345
9031
9386
ABE
7
303
7657
7967
ATL
46
1177
34197
35420
AUS
4
121
3275
3400
BWI
29
844
23553
24426
BHM
14
429
11855
12298
BOS
31
873
24710
25614
BUF
23
650
18141
18814
CLT
22
616
17401
18039
CHI
57
1276
37839
39172
CVG
26
834
23389
24249
CLE
39
1081
30978
32098
CMH
19
660
18060
18739
DFW
41
1052
30381
31474
DAY
17
590
16343
16950
DEN
33
927
26260
27220
DTW
37
1037
29700
30774
GSO
14
441
11757
12212
BDL
18
599
16246
16863
HNL
5
186
4896
5087
HOU
30
844
23484
24358
IND
24
783
21790
22597
JAX
12
413
10983
11408
GRR
5
181
4957
5143
MKC
28
852
23739
24619
152


82
TABLE 5.8
LEADING ENPLANEMENT CITIES OF THE U.S.: 1988
city
enplanements
non/stops*
daily
departures
1)
New York/Newark
32,820,184
48
957
2)
Chicago
29,770,857
56
1045
3)
Dallas
23,488,986
50
765
4)
Los Angeles
22,836,344
37
743
5)
Atlanta
21,824,125
56
756
6)
San Francisco
15,173,602
28
542
7)
Denver
14,441,817
40
514
8)
Miami
13,360,799
27
399
9)
Washington
11,586,627
49
482
10)
Houston
10,712,269
38
432
11)
Boston
10,141,298
40
316
12)
St. Louis
9,554,454
46
384
13)
Phoenix
9,455,324
31
364
14)
Detroit
9,343,770
43
352
15)
Honolulu
8,396,313
11
230
16)
Pittsburgh
8,378,639
49
345
17)
Minneapolis
8,170,952
40
301
18)
Orlando
7,473,086
38
259
19)
Las Vegas
6,864,803
28
220
20)
Seattle
6,825,513
22
296
Sources: Official
Airline Guide.
July 1988
and Aimort
Activity
Statistics of
Certified
Route Air
Carriers,
1988.
* number of different non-stop connections of the 60 in the
study set


133
SCRANTONColumbia, Lackawanna, Luzerne, Monroe, Wyoming
SEATTLEKing, Pierce, Snohomish
SYRACUSEMadison, Onondaga, Oswego
TAMPAHernando, Hillsborough, Pasco, Pinellas
TUCSONPima
TULSACreek, Osage, Rogers, Tulsa, Wagoner
WASHINGTON, D.C.Washington, D.C., Calvert (MD), Charles
(MD) Frederick (MD), Montgomery (MD), Prince George's
(MD), Arlington (VA), Fairfax (VA), Loudoun (VA), Prince
William (VA), Stafford (VA), Alexandria city (VA),
Fairfax city (VA), Falls Church (VA), Manassas city (VA) ,
Manassas Park city (VA)
WEST PALM BEACHPalm Beach


APPENDIX D
TOTAL EMPLOYMENT BY MSA
1978
1979
1980
1981
1982
NYC
6589264
6722510
6762097
6783849
6790659
LAX
3956614
4356328
4444624
4441368
4427485
CHI
3106745
3294297
3217807
3132185
3003671
SFO
1887908
2047499
2109344
2148739
2150890
PHL
1931118
2001693
1990948
1989824
1966721
DTW
1628020
1736902
1604170
1522276
1436511
BOS
1960570
2100932
2150637
2190030
2196861
DFW
1153045
1240066
1292432
1337542
1373159
DCA
945750
1054015
1085407
1122460
1110240
HOU
1170627
1276306
1335236
1428129
1509961
MIA
854307
925996
965805
1009033
978951
CLE
1025546
1148918
1108309
1072664
1028846
ATL
771846
824298
842134
869085
878247
STL
872035
912192
899752
886694
873094
SEA
675791
756079
785910
788479
767922
MSP
892320
960289
974425
969049
950443
SAN
456649
506607
533027
538017
552699
BWI
660488
691489
702413
702572
697025
PIT
834680
880587
857639
848641
818590
PHX
450400
516567
532709
543605
547499
TPA
416217
454360
473540
496778
519245
DEN
607885
661734
681166
702802
732425
CVG
512753
613799
605422
592761
577492
MCI
552405
578567
574620
567149
553531
MKE
650217
677024
692466
645318
627339
138


74
TABLE 5.3
LARGEST GROWTH IN ADMINISTRATIVE/AUXILIARY EMPLOYEES:
1978-1988
MSA
+ change
1)
Boston
68,528
2)
New York/Newark
55,239
3)
Dallas/Ft. Worth
49,976
4)
Atlanta
39,931
5)
San Francisco
31,222
6)
Washington, D.C.
28,310
7)
Providence
28,292
8)
Philadelphia
22,989
9)
Minneapolis/St. Paul
21,459
10)
Houston
21,236
11)
Seattle
18,496
12)
Miami/Ft. Lauderdale
17,697
13)
Los Angeles
16,575
14)
Charlotte
13,967
15)
Orlando
13,965
16)
Jacksonville
12,273
17)
San Diego
10,488
18)
Richmond
10,315
19)
Columbus
9,843
20)
Phoenix
9,821
Source
Calculated from County Business Patterns. U.S
Census Bureau, 1980, 1990.


10
production will cease. The product becomes obsolete, and the
firm takes absolute losses on production.
Nonroutine and innovative activities of the firm, such as
research and development, corporate and regional headquarters
functions and marketing operations, need to be located in core
regions of developed nations as they have a high dependence on
skilled labor (Malecki, 1986). These divisions of the
organization cannot thrive in all locations in space as the
availability of labor skills varies by nation, region and city
(Kim, 1987; Walker, Storper, 1981).
These higher order functions often remain with or very
near the corporate headquarters in metropolitan areas or in
other metropolitan areas near other parts of the firm (Clark,
1981; Erickson and Leinbach, 1979) Due to rapid
technological and market changes, they require a high quality
and quantity of day-to-day information concerning competition,
suppliers and customer needs. While there is a trend toward
shorter production runs and more innovative products, which
requires interaction of research and development with
manufacturing, production and research activities remain on
the whole largely separate in purpose and location. This
"flexible" production, therefore, needs the same large-city
skilled labor as research and development (Arnold, Bernard,
1989; Malecki, 1991; Schoenberger, 1988). Thus, a
transportation and communication infrastructure is a
requirement for firms when locating nonroutine activities.


174
Bauer, Paul, 1987, Airline Hubs: A Study of Determining
Factors and Effects, Economic Review. Federal
Reserve Bank of Cleveland, 4, pp. 13-19.
Bauer, Paul W. and Zlatoper, Thomas J., 1989, The
Determinants of Direct Air Fares to Cleveland: How
Competitive?, Economic Review. Federal Reserve Bank
of Cleveland, 25, pp. 2-9.
Blair, John P. and Premus, Robert, 1987, Major Factors
in Industrial Location: A Review, Economic
Development Quarterly. 1, pp. 72-85.
Borenstein, Severin, 1989, Hubs and High Fares:
Dominance and Market Power in the U.S. Airline
Industry, Rand Journal of Economics. 20, pp. 344-
365.
Boyce, Ronald, 1978, The Bases of Economic Geography.
New York: Holt, Rinehart and Winston.
Boyer, R. and Savageau, D., 1989, Places Rated Almanac.
Chicago, IL: Rand McNally.
Bradbury, Susan L. 1988, R&D Facilities and
Professional Labor Location Preferences: A Case
Study in Tampa-St. Petersburg, The Florida
Geographer. 22, pp. 2-13.
Brenner, P., 1988, Airline Deregulation: A Case Study
in Public Policy Failure, Transportation Law
Journal 16, pp. 184-199.
Brown, Anthony, 1987, The Politics of Airline
Deregulation. Knoxville, TN: University of
Tennessee Press.
Browning, J. 1980, How to Select a New Business Site.
New York: McGraw-Hill.
Business Week. 1981, America's New Immobile Society,
July 27, pp. 58-62.
Business Week. 1986, Is Deregulation Working?, December
22, pp. 50-55.
Business Week. 1988a, Everything You Always Wanted to
Know About Airline Hubs, October 31, pp. 67-72.
Business Week. 1988b, Air Fares Have a Ticket to Rise,
December 12, 1988, pp. 30-31.


52
basically compete in the West for the same transfer
passengers. What follows is a discussion of the measurement
of hub connectivity using a graph theoretical approach. Graph
theory allows one to view the network as a topological map
comprised of nodes (points of economic concentration) and
linkages (routes that connect two nodes). Thus, it shows
network connectivity in a relative sense. This technique
illustrates the strength (in terms of connectivity) and
attractive power of the hubs listed in Table 4.1.
Analyzing Hub Connectivity
One graph theoretic method of measuring the connectivity
or accessibility of a node begins with the construction of a
binary matrix that represents the network abstracted as a
graph (Lowe, Moryadas, 1975; Taaffe, Gauthier, 1973). It is
a square matrix in which the number of rows and columns each
represent the number of nodes in the transportation network.
The horizontal rows represent origin nodes, while the vertical
columns represent destination nodes. Both rows and columns,
of course, contain the same list of points. The cell entries
of the matrix are assigned a value of either one or zero. A
value of one shows the presence of a direct (nonstop) linkage
between specific nodal pairs, while a value of zero indicates
the absence of such a linkage. Nodes are not considered to be
connected to themselves; therefore, the principal diagonal of


PAGE
TABLE
5.2 Most Administrative/Auxiliary Employees:
1988 73
5.3 Largest Growth in Administrative/Auxiliary
Employees: 1978-1988 74
5.4 Administrative/Auxiliary Employment
Loss: 1978-1988 76
5.5 Administrative/Auxiliary Growth by Region:
1978-1988 76
5.6 Highest Percentage of Administrative/
Auxiliary as a Percentage of Total
Employment: 1988 77
5.7 Multiple Airport Cities 81
5.8 Leading Enplanement Cities of the U.S.:
1988 82
5.9 Greatest Increases in Non-Stop Connections:
1978-1988 84
5.10 Study Set Cities With Highest Accessibility
Indices: 1988 88
5.11 Mean Annual Rate of Change in Employment
and Connectivity: 1978-1988 108
5.12 Hub Clusters Based on Population Totals.... 113
5.13 Mean Annual Rate of Change by Population
Cluster: 1978-1988 114
5.14 Time Series Analysis 117
vii


rate of change
92
FIGURE 5.4
HUB EMPLOYMENT VS. CONNECTIVITY


14
TABLE 2.2
TEN MOST IMPORTANT FACTORS IN SELECTING LOCATIONS FOR
COMPANY FACILITIES
Easy access to domestic markets, customers
and clients 73%
Easy access to airport 62%
Efficient transportation facilities for
people/employees 59%
Availability of affordable housing 55%
Availability of technical or professional
workers 54%
Facilitates access to prospective clients 52%
Urban/metropolitan location 50%
Cost of living 48%
Reasonable government/state and/or local
corporate tax structure 48%
Fair-market property costs 48%
Source:
Corporate Site Selection For New Facilities. The
Time Inc. Magazine Company, 1989.


155
MIA
29
916
25907
MKE
24
819
22805
MSP
27
867
24450
BNA
19
639
17728
MSY
21
710
19859
NYC
51
1236
37050
ORF
12
436
11527
OKC
13
464
12583
MCO
22
727
20223
PHL
42
1116
32876
PHX
19
615
16807
PIT
41
1081
31777
PDX
12
376
10396
PVD
8
349
8929
RDU
13
427
11679
RIC
12
392
10510
ROC
12
429
11660
SMF
8
263
7094
STL
40
1107
32402
SLC
14
449
12520
SAT
7
270
6905
SAN
23
714
19800
SFO
30
885
24991
AVP
5
189
4959
SEA
18
560
15327
SYR
12
447
11880
TPA
32
950
27307
TUS
8
288
7765
TUL
11
392
10630
DCA
48
1224
36143
PBI
11
413
10963
26852
23648
25344
18386
20590
38337
11975
13060
20972
34034
17441
32899
10784
9286
12119
10914
12101
7365
33549
12983
7182
20537
25906
5153
15905
12339
28289
8061
11033
37415
11387


149
MKE
24
24
24
26
25
PDX
14
12
12
12
13
SMF
8
8
9
10
10
ORF
13
12
11
11
13
CMH
19
19
20
20
21
SAT
10
7
8
8
10
MSY
25
21
23
23
24
IND
24
22
22
22
22
BUF
23
20
19
16
16
PVD
8
8
8
8
9
CLT
22
24
24
24
27
BDL
18
18
21
21
21
SLC
14
14
14
15
18
ROC
12
12
12
12
13
MEM
24
24
28
29
29
BNA
19
19
19
21
21
MCO
18
22
23
24
26
SDF
18
20
20
23
21
OKC
15
13
11
10
12
DAY
17
15
16
16
17
GSO
14
15
15
17
16
BHM
14
12
12
12
10
JAX
12
12
13
13
14
ALB
11
11
11
11
11
RIC
12
12
13
14
12
HNL
5
5
6
7
6
PBI
11
11
11
12
14
AUS
4
3
3
3
5
AVP
6
5
5
5
6
TUL
11
11
11
12
12
RDU
13
13
12
12
14
ABE
7
7
8
8
8
GRR
5
5
5
5
6


20
Professional Workers
The professional labor force is indeed highly mobile and
exerts a great deal of influence on corporate location
decisions (Buswell, 1983; Knapp, Graves, 1989). Rapid
technological change has created a greater need for highly
skilled professional workers, thus increasing the significance
of professional labor as a location factor (Weiss, 1985) As
they are highly educated and career-oriented, they have a
greater degree of choice over where they live and work than do
unskilled laborers (Cooper, Makin, 1985; Massey, 1984;
Storper, Walker, 1983) .
Research has shown that professional workers have a
strong preference for large urban areas or at least areas
within commuting distance of one (Bradbury, 1988; Malecki,
1987). Cities provide a greater choice of jobs, the ability
to change jobs without changing residences, employment
opportunities for the spouse and greater cultural and
recreational amenities (Herzog, Schlottmann, Johnson, 1986;
Noyelle, Stanback, 1984). According to a recent study, the
dual-career couple has started to exert a great deal of
influence over the location decisions of some firms (Bradbury,
1989). Because of professional workers' narrow range of
preferred locational characteristics, Buswell (1983) and
others state that professional workers are at the same time
geographically immobile lBusiness Week. 1981).


164
CMH
22
770
22355
DFW
48
1251
38229
DAY
23
778
22793
DEN
41
1138
34140
DTW
36
1082
32469
GSO
15
557
15578
BDL
22
744
21460
HNL
5
177
4912
HOU
31
948
27914
IND
24
829
24371
JAX
14
506
14366
GRR
7
226
6896
MKC
33
1013
29976
LAS
19
654
18357
LAX
37
1081
32113
SDF
20
699
20250
MEM
27
857
25341
MIA
28
952
27818
MKE
19
692
19765
MSP
31
997
29335
BNA
20
684
19976
MSY
25
850
24702
NYC
49
1275
39242
ORF
14
521
14554
OKC
13
461
12888
MCO
33
1038
30960
PHL
40
1124
34078
PHX
25
718
20676
PIT
44
1193
36426
PDX
13
426
11879
PVD
9
376
10032
RDU
16
495
14631
RIC
12
434
12001
23147
39528
23594
35319
33587
16150
22226
5094
28893
25224
14886
7129
31022
19030
33231
20969
26225
28798
20476
30363
20680
25577
40566
15089
13362
32031
35242
21419
37663
12318
10417
15142
12447


2
different origins to connect with flights to their final
destinations. Hub cities, therefore, have a multitude of
nonstop alternatives available to local residents.
Connectivity Change and the Location of Economic Activities
It can be posited that the restructuring of air networks
can greatly influence the location of economic activities.
Because of the large number of nonstop flights and
destinations from hub airports, certain corporate activities
should find it advantageous to be located in a hub city.
Who is the corporate traveler? What types of positions
within the firm would require frequent air travel? Typically
such employees are those for whom a lot of face-to-face,
nonroutine communication both within and outside of the firm
is vital. Even though communication technology has made
phenomenal advancements in recent years, situations occur for
which there is no viable alternative or substitute for an on
site visit. Firms desire close contact with their clients and
markets to respond quickly to their changing needs. Managers,
sales staff, scientists, engineers and other administrative
employees are often required to travel quickly to other parts
of the company, to clients, or to other locations. In order
to minimize travel time, it is preferable to fly nonstop to
one's final destination rather than to fly two or more flight
segments with layovers in between. The savings in total


87
matrices. Thus, the diameter of each network (from 1978
through 1988) is three. Appendix G gives the yearly
accessibility indices for each metropolitan area from matrices
Cl, C2, C3 and T from 1978 through 1988. It is particularly
important in this study to include indirect connectivity
effects since business travelers often visit multiple
destinations in one trip without necessarily going back
through the same hub city between flight segments. The cities
(airports) with the highest accessibility indices in 1988 are
ranked in Table 5.10.
Employment-Connectivity Relationships
Figure 5.2 compares the average rate of change from one
year to the next (from 1978 through 1988) in the number of
professional workers (administrative and auxiliary employees)
in the labor force in each of the 60 metropolitan areas to the
average rate of change in air service connectivity. The rates
were calculated from the raw numbers of administrative and
auxiliary employees and connectivity indices given in
appendices D and H, respectively. A quick glance reveals that
the two graphs have the same general trend in the first half
of the study period, but no apparent similarity in the latter
years. If the two variables are related to (affected by) one
another, a change in one seems to bring a near instantaneous
change in the other during the early years of the period, with


60
TABLE 4.6
ACCESSIBILITY INDICES OF MATRIX T WITH DIFFERENT SCALARS
airports
s=. 25
s=. 3
s=. 5
s= .75
CHI
1615
2755
12447
41543
ATL
1542
2630
11886
39676
DFW
1464
2497
11274
37614
CLT
1380
2354
10642
35527
PIT
1376
2347
10613
35424
DCA
1362
2324
10508
35074
CVG
1354
2310
10443
34856
STL
1350
2303
10405
34718
NYC*
1337
2282
10310
34407
DTW
1260
2150
9723
32453
PHL
1228
2094
9466
31589
BNA
1225
2090
9444
31516
DEN
1180
2010
9064
30219
MSP
1161
1980
8938
29807
BWI
1150
1962
8866
29588
HOU
1149
1961
8854
29530
LAX
1136
1937
8742
29146
MCO
1077
1838
8302
27693
SFO
1075
1832
8262
27539
CLE
1051
1793
8104
27042
MEM
1049
1789
8080
26951
PHX
1036
1765
7963
26544
MIA
916
1562
7053
23523
RDU
906
1546
6989
23325
LAS
889
1516
6838
22793
SEA
807
1374
6192
20629
SLC
791
1348
6075
20244
DAY
783
1336
6035
20134
HNL
347
592
2664
8871
Includes Newark


7
and of organizational functions (Storper, Walker, 1983). The
different parts of the organization can search for locations
that will more greatly benefit their specific tasks or roles
within the firm, without the necessity of being physically or
locationally tied to other functions of the organization that
might have entirely different locational requirements. As the
spatial variations in cost, quality and availability of
nonlabor inputs have largely diminished, the issue of labor
has risen greatly in importance (Moriarty, 1980; Storper,
Walker, 1984) .
Spatial Division of Labor
Because a supply of unskilled labor for standardized,
routine production is virtually ubiquitous, labor-intensive
manufacturing and assembly operations can locate almost
anywhere (Blair, Premus, 1987; Clark, 1981; Czamanski, 1981;
Massey, 1984; Schmenner, 1982). When the manufacturing
procedure does become standardized or routine, firms will
establish branch plantsoften in nonurban areas with
depressed local labor markets to reduce labor competition
(Erickson, Leinbach, 1979). Labor-intensive operations
primarily seek to reduce labor costs (to obtain higher profits
for the firm) and generally locate or relocate in low-wage
areas anywhere in the world. Such areas are typically located
outside of the traditional innovative centers of industry


This dissertation was submitted to the Graduate Faculty
of the Department of Geography in the College of Liberal Arts
and Sciences and to the Graduate School and was accepted as
partial fulfillment of the requirements for the degree of
Doctor of Philosophy.
August 1992
Dean, Graduate School


17
even though it is unrelated to manufacturing in many cases,
such as in the finance and insurance sectors. A life cycle
from nonroutine to routine work also applies to service tasks.
The South Atlantic region of the nation proved to be the
most popular location choice for company facilities as well,
followed in rank order by the Pacific (California, Washington
and Oregon), the East North Central region (Ohio, Indiana,
Michigan, Illinois and Wisconsin) and the Middle Atlantic
region (New York, New Jersey and Pennsylvania). Because costs
are not the most important factor in locating these
facilities, the historically innovative industrial areas of
the United States still show up strongly. Atlanta, Chicago
and Dallas were the most popular urban areas chosen for
location or relocation of company facilities by the firms in
the survey.
One important firm facility not singled out by the
Fortune 500 study was research and development. Research and
development (R&D) employs mainly scientists, engineers and
other technicians who are involved in nonroutine, innovative
research and testing of new products or production processes
for the firm. R&D facilities also have their own set of
location requirements such as the availability of
professional/technical labor, good transportation
accessibility (particularly air) and access to scientific and
technical information (Ady, 1986; Browning, 1980; Lund, 1986;
Malecki, 1979a, 1979b, 1980a, 1980b, 1981). Most firms will


21
Labor Migration
Studies have shown that migration of laborers is affected
by changing economic opportunities (Gentile, Stave, 1988;
Greenwood, 1988) Workers leave an area when they feel their
economic prospects are greater elsewhere (Goldfarb, Yezer,
1987). Long and Hansen (1979) found that about half of all
moves in the U.S. were to take a job or seek out new
employment.
Other factors affecting migration are education,
occupation, sex and marital and family status (De Jong,
Gardner, 1981; Kaufman, 1982). Young, well-educated
professional workers are prime candidates for migration
(Greenwood, 1981; Gentile, Stave, 1988). They have fewer ties
to specific areas and are often required to relocate within
their firm to further their careers. Men migrate more often
than women, and single workers more often than married
(especially those with children) (Gentile, Stave, 1988) .
The Large Metropolitan Area
For nonroutine and innovative divisions of the firm, the
firm's locational needs and the locational preferences of its
professional workers must be considered together. These
different sets of priorities both restrict and reinforce one
another with the large metropolitan area being the common



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According to the Department of Transportation, the amount
spent on aircraft maintenance dropped 30% during the first six
years of deregulation (Valente, McGinley, 1988). Moreover, a
survey of commercial airplane pilots revealed that almost half
believe that their companies defer maintenance of their fleets
too long. In addition, the average age of the industry's jets
increased by 21% since 1979, with more than half of the jets
in service being 16 years or more old in 1988 (Valente,
McGinley, 1988). Older fleets typically require more
maintenance and repairs than do newer aircraft.
The general consensus of the flying public is that the
quality of air service has greatly declined since
deregulation. A recent survey of consumers found that 50%
felt that such service had declined significantly, while under
20% expressed feelings of improvement. The FAA has reported
a soaring number of consumer complaints against the airlines
(Consumer Reports. 1988). Deregulation and the resulting
network geography of the major carriers have not delivered the
promised benefits to all cities and passengers alike
(Anderson, Kraus, 1981; Ippolito, 1981). As already
mentioned, small cities, on the whole, were adversely affected
in terms of seating capacity, fewer non-stop choices and less
frequent service. However, many large cities that were not
selected as hubs by major airlines (such as New Orleans,
Buffalo, and Louisville) have experienced service decline
particularly in the number of non-stop destination choices


112
mean rates of professional employment change. San Francisco,
Dallas/Ft. Worth, Washington, D.C., Miami, Atlanta, Seattle,
Pittsburgh and Dayton had higher mean annual rates of
employment change than connectivity change (unlike the other
19 cities).
To examine hierarchical differences in both rates of
change, the 27 hubs were placed into one of five groups based
on their 1988 population. An iterative clustering procedure
was used to identify the members of each group. Table 5.12
shows the five groups (clusters) that were derived, and the
mean of the yearly average rates of professional employment
and connectivity change are given in Table 5.13. As the
average population size of the MSA cluster decreases, the mean
annual rate of change tends to increase for both connectivity
and professional employment (rank order correlation
coefficient of .9). Smaller hub cities (in most cases
starting the study period with a lower base level of
professional employment and connectivity indices) have
experienced higher growth rates than the larger hubs.
However, individual hubs that rank high on the list of
professional employment growth rates do not necessarily have
correspondingly high-ranking rates of connectivity change (and
vice versa). The rank-order correlation coefficient is a
rather weak .1624, indicating that no statistically
significant relationship exists in the rankings of the two
mean growth rates for each hub city.


59
connections only) and matrix T (direct and indirect
connections) are slightly different (Spearman's rank order
coefficient of .979). Twelve of the cities (mostly ranked in
the top ten) remain at the same rank, but ten rise in the
rankings (Baltimore, Cincinnati, Cleveland, Detroit, Miami,
Minneapolis, Orlando, Philadelphia, San Francisco and Seattle)
while seven fall (Denver, Houston, Memphis, Phoenix,
Raleigh/Durham, St. Louis and Salt Lake City) when indirect as
well as direct connections are taken into account (matrix T).
The rankings show an eastern bias because more of the 117
nodes in the original matrix (Cl) were located in the East or
Midwest than in the western half of the United States.
Scalar multiplication was also performed on the original
and powered matrices. Table 4.6 shows the accessibility
matrix (T) indices using a variety of scalars. While the
magnitude of difference between the cities, of course, changes
as the scalar changes, the specific rank order of the cities
remains constant (concordant). It is also the exact rank
order of the unsealed accessibility matrix T (Table 4.5).
A more refined weighting technique assigns a different
scalar value to each of the 117 nodes in the network. This
weight is based on the individual node's share (from the last
column in Table 4.3) of total direct connections in the
network (1,969). One advantage of this weighting procedure is
that it allows the FAA nonhub connections to be taken into
account, albeit in an indirect way. The results of this


TABLE 5.7
MULTIPLE AIRPORT CITIES
81
Chicago
Midway, O'Hare International
Dallas
Love Field, DFW International
Detroit
Detroit City, Detroit Metro-Wayne County
Greensboro
Greensboro, Smith-Reynolds
Houston
Hobby, Houston Intercontinental
Los Angeles
L.A. International, Hollywood/Burbank,
Orange County, Long Beach
Miami
Miami International, Ft. Lauderdale
New York City
JFK, LaGuardia, Newark International
San Francisco
San Francisco International, Oakland
Washington, D.C.
Washington National, Dulles International


37
and oversaturated markets plagued some of the hubs. Carriers
began to look for medium-sized cities as their transfer
points. Often these were strategically located near one of
the large, congested hubs to give travelers a pleasant
alternative to places like Chicago O'Hare. Piedmont Airlines,
later acquired by USAir, made an early success by choosing
Charlotte as its southeastern hub, allowing passengers to
bypass chronically congested Atlanta's Hartsfield (Davis,
1982). Many other carriers followed Piedmont's lead and
expanded their air networks by adding new hubs. This
explains, for example, American's new hubs at Nashville and
Raleigh/Durham. Competition among airports for hub selection
by a major carrier has become intense. The expanded service
creates big business for the chosen facility and city, and
airport planning boards are more aggressive now than in the
past (Barrett, 1987; Butler, Kiernan, 1987; Insight. 1988).
Lopuszynski (1986) has identified some common
characteristics of hub cities. It is an ex post facto list
that looked at hub cities already in use to find similarities
among them. Most hubs were found to contain several of the
following characteristics: 1) a sizeable population force
with strong business and commercial opportunities 2) a good
geographic location with respect to other population centers,
physical terrain and weather patterns 3) good airport
facilities with adequate room for expansion of gates and
runways 4) a strong economy and balanced workforce 5) air


169
SEA
20
SYR
15
TPA
30
TUS
9
TUL
12
DCA
48
PBI
18
1987
MATRIX1
ALB
13
ABE
8
ATL
54
AUS
7
BWI
37
BHM
12
BOS
40
BUF
16
CLT
40
CHI
56
CVG
40
CLE
34
CMH
22
DFW
49
DAY
25
DEN
40
DTW
43
GSO
15
BDL
22
HNL
10
HOU
38
IND
26
JAX
16
GRR
8
20393
21095
15991
16540
31645
32690
9495
9821
13139
13591
43268
44660
20370
21064
MATRIX3
TOTAL
14816
15315
10978
11365
49782
51320
9041
9353
37890
39089
14657
15138
40743
42036
18051
18653
42312
43641
50337
51892
42286
43619
37961
39171
27231
28126
46700
48162
29333
30280
40285
41573
43567
44937
18558
19184
25706
26557
11423
11802
39321
40581
31308
32324
19122
19755
9828
10147
682
534
1015
317
440
1344
676
MATRIX2
486
379
1484
305
1162
469
1253
586
1289
1499
1293
1176
873
1413
922
1248
1327
611
829
369
1222
990
617
311


121
professional employment in the 60 MSAs in the study set over
the period from 1978 through 1988. As stated in Chapter 5,
the correlation coefficient between the two variables was far
from significant with the relationship for nonhub cities being
a little stronger than for hub locations (although still
insignificant).
The time series model also failed to show a significant
(lagged) tie between professional employment change and
connectivity change. This was unexpected because some of
graphs that were constructed seemed to indicate a fairly
strong lagged relationship of one time period between the two
variables.
The analysis failed to show a regional bias. The South
was the only region that showed any remote relationship
between the two rates of change. The southern group ranked
highest for connectivity change, and second highest for
professional employment change. The relationship in the South
would probably have been even stronger if the oil cities
(whose economies declined in the 1980s) in Texas, Oklahoma and
Louisiana had not been included in the group.
The tests for a hierarchical bias were clearly the
strongest and most interesting from a geographic perspective.
Many of the smaller cities in the study set (largely the
nonhubs) experienced greater rates of change for both
professional employment and connectivity. Even the smaller
hubs seemed to experience higher rates of change for both


28
would eliminate the near monopolistic situation of the larger
carriers with respect to total market share of U.S.
passengers. The contestable market theory of air
transportation held that the mere threat of a new entrant in
a market would keep fares down (Leigh, 1990).
During the early 1980s, it appeared that these
predictions were realized (Graham, Kaplan, Sibley, 1983;
Maraffa, Finnerty, 1988; Rose, 1981). Fares dropped
dramatically, allowing more people to afford air travel than
ever before, and over 100 new airlines, mostly small
commuters, began operation (Goetz, Dempsey, 1989). The latter
part of that decade, however, saw unexpected changes that have
caused some to question the wisdom of deregulation (Bauer,
Zlatoper, 1989; Business Week. 1986, 1988b, 1988c; Leigh,
1990; Morrison, Winston, 1989). In fact, discussions of at
least partial reregulation are starting to grow (Kuttner,
1989; Rose, Dahl, 1989).
Many of the new carriers that were supposed to challenge
the dominant market shares of the major carriers,
unfortunately, were short-lived. Despite the fact that new
carriers were successful in the early years of deregulation in
reducing the share of revenue passenger miles of the larger
airlines, in the long run most all were squeezed out (Goetz,
Dempsey, 1989). It is unlikely that there will be a lot of
new airlines entering the industry to replace them, as


rate of change
106
Years
FIGURE 5.14
SOUTHERN NONHUB RATES OF CHANGE


153
LAS
23
705
19497
LAX
40
1061
30342
SDF
18
574
15882
MEM
24
750
20936
MIA
30
926
25879
MKE
24
797
21944
MSP
24
766
21271
BNA
19
626
17143
MSY
25
745
20810
NYC
51
1221
35966
ORF
13
452
11832
OKC
15
479
13031
MCO
18
591
16235
PHL
42
1094
31716
PHX
19
590
15946
PIT
37
943
27522
PDX
14
423
11699
PVD
8
343
8670
RDU
13
424
11431
RIC
12
391
10310
ROC
12
421
11287
SMF
8
258
6903
STL
39
1088
31185
SLC
14
437
12081
SAT
10
318
8561
SAN
25
741
20419
SFO
31
891
24816
AVP
6
226
5884
SEA
17
532
14400
SYR
13
467
12372
TPA
32
935
26455
TUS
7
236
6321
TUL
11
369
9978
20225
31443
16474
21710
26835
22765
22061
17788
21580
37238
12297
13525
16844
32852
16555
28502
12136
9021
11868
10713
11720
7169
32312
12532
8889
21185
25738
6116
14949
12852
27422
6564
10358


53
the matrix (all i,i entries) contains zeroes as cell entries.
Thus, the connectivity matrix (Cl) shows first order
connections in a transportation network.
A vector of values that can be used as a crude measure of
nodal accessibility is obtained by summing the individual rows
or columns of the matrix. The higher the summed row or column
value of the node, the greater the accessibility of the point.
This accessibility index on its own is of limited usefulness,
however, because we are often interested in both direct and
indirect connections.
The number of indirect connections in a network can be
determined by powering the original connectivity matrix (Lowe,
Moryadas, 1975; Taaffe, Gauthier, 1973). The matrix (Cl) can
be multiplied by itself (resulting in matrix C2) to look at
second order or two-step connections (connections that pass
through an intermediate node) in the network. Likewise, the
third order connectivity matrix (C3) is obtained by
multiplying matrix Cl by matrix C2. To take all indirect
connections into account, the matrix should be powered to the
Nth order (CN), where N represents the diameter of the
transportation network. (The diameter is defined as the
shortest topologic distance between the two most distant nodes
in the network.) At this stage, all zero elements disappear
from the matrix indicating that all nodes are connected.
Summing the matrix Cl with the powered matrices shows the
total accessibility of each node within the network


0.5
Years
FIGURE 5.5
EMPLOYMENT CHANGE: HUB VS.
NONHUB


171
TUL
DCA
PBI
1988
ALB
ABE
ATL
AUS
BWI
BHM
BOS
BUF
CLT
CHI
CVG
CLE
CMH
DFW
DAY
DEN
DTW
GSO
BDL
HNL
HOU
IND
JAX
GRR
MKC
LAS
LAX
SDF
12
49
18
MATRIX1
13
8
56
7
38
12
40
16
42
57
43
37
22
50
25
40
43
15
22
11
38
26
17
9
30
28
37
17
451
1417
711
MATRIX2
493
384
1523
309
1206
479
1276
598
1338
1535
1360
1249
890
1452
938
1268
1351
624
845
429
1242
1005
651
367
1093
936
1234
694
13859
47189
22040
MATRIX3
15144
11235
51807
9228
39865
15041
41923
18542
44575
52155
45159
41085
27956
48656
30137
41343
44799
19068
26387
13211
40369
32076
20581
11527
34778
29877
39979
21578
14322
48655
22769
TOTAL
15650
11627
53386
9544
41109
15532
43239
19156
45955
53747
46562
42371
28868
50158
31100
42651
46193
19707
27254
13651
41649
33107
21249
11903
35901
30841
41250
22289


22
10
24
22
16
9
28
22
20
13
27
21
31
21
12
22
15
10
14
12
13
6
15
7
6
12
15
8
7
13
8
21
151
22
13
23
26
16
10
40
22
25
16
38
34
38
17
11
25
15
12
17
13
13
11
19
7
5
12
29
8
9
18
10
28
22
22
22
22
11
11
13
13
25
24
23
23
24
24
26
26
16
16
16
16
9
9
9
10
29
31
31
37
22
22
22
22
21
24
25
25
13
15
15
15
27
34
36
38
20
22
26
30
33
35
38
39
20
19
18
17
13
12
12
12
23
23
25
25
15
15
15
15
9
10
12
12
14
14
16
16
13
13
13
13
12
13
13
13
5
7
9
10
15
15
18
18
8
7
7
7
7
6
6
5
11
11
12
12
16
17
17
27
8
8
8
8
7
7
7
8
15
15
15
18
8
8
9
9
19
23
25
27


rate o change
104
Years
FIGURE 5.12
WESTERN NONHUB RATES OF CHANGE


rate of change
89
FIGURE 5.2
CHANGE IN PROFESSIONAL EMPLOYMENT VS. CONNECTIVITY


CHAPTER 4
VARIATIONS IN HUB SERVICE IN THE DOMESTIC
AIR TRANSPORTATION NETWORK
Introduction
A hub is generally defined as a central collection point
or node in a transportation system or network. Usage of the
term, however, has become particularly applied in the air
transportation industry of the United States, largely since
deregulation and the advent of the hub-and-spoke system
discussed in the previous chapter. In reviewing air
transportation literature and data sources (both academic and
popular), one encounters the word frequently. Close scrutiny
of places labeled as air hubs, however, reveals that not all
hubs are equal in the service they offer, in either intensity
or connectivity. In fact, some hubs are vastly different from
others. Such variations can make the work of the air
transportation researcher problematic. This chapter will
identify different types of air hubs based on existing usage
of the term by the airlines and the Federal Aviation
Administration, and explore variations in hub intensity and
connectivity within the domestic air transportation network.
42


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APPENDIX E
TOTAL ENPLANEMENTS BY MSA
NYC
1978
21289208
1979
22704666
1980
21726444
1981
21218167
1982
23077670
LAX
13479147
18543055
16341818
15768931
16058981
CHI
21575602
211178522
19417854
16906634
16699134
SFO
8598388
12111016
10330368
9624812
10385733
PHL
4138542
4528807
4058167
3581634
3844822
DTW
4842441
5524630
5050735
4749836
4790521
BOS
6286825
7093394
6844951
6622905
7111936
DFW
9638242
11419465
12775213
13923029
14685061
DCA
8036073
8585305
7756053
7282727
7132925
HOU
4632700
5617114
6806627
7515759
8472275
MIA
8993922
10476248
11037003
10018502
9833572
CLE
3541267
3693940
2989234
2619705
2521662
ATL
18226652
20797535
19994113
18702918
17322680
STL
4714822
5582691
5319480
5126290
5735255
SEA
4112657
4736290
4352439
4281971
4613282
MSP
3952367
4697295
4384643
4523898
5221988
SAN
2051226
3301759
2536337
2525428
2739957
BWI
1690098
1759614
1652494
1521330
1903299
PIT
4635645
5280953
5381659
4183339
4607761
PHX
3056169
3587529
3380121
3397867
3950386
TPA
3106218
3693478
3600730
3184121
3560548
DEN
8861423
9654132
9615785
10437142
11404157
CVG
1536142
1601087
1391638
1331791
1598641
MCI
2789820
3133092
2620100
2272343
2556815
MKE
1460755
1711072
1623318
1550847
1562381
143


CHAPTER 5
CHANGES IN CONNECTIVITY AND
PROFESSIONAL EMPLOYMENT LOCATION
Introduction
This chapter investigates a number of research questions
concerning changes in air service connectivity, professional
employment and corporate location. Data were collected on the
60 largest metropolitan areas in the United States in 1988,
listed in Table 5.1. The MSAs (metropolitan statistical
areas) include a mix of hubs and nonhubs from every region of
the country, although there is a bias toward the east. The
total number of employees, number of administrative and
research and development employees, airport enplanements and
nonstop destinations available (of the 60-city study set) were
collected for these cities in each year from 1978 through
1988. The starting point of the analysis is 1978 since that
was the last fully regulated and fairly stable year for the
airline industry. The analysis continues through 1988 to show
progressive changes and help identify lag effects in the
relationship between changes in connectivity and professional
employment.
67


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