This dissertation examines how professional sports facility referenda compete for space on local government agendas. It uses newspaper articles from referenda campaigns to analyze how issue tones evolve over time and whether positive media coverage influences outcomes. The research applies theories of disproportionate information processing and population dynamics to model issue attention over the course of campaigns. It conducts case studies of stadium proposals in multiple cities to determine how framing strategies and media impacts affect the agenda setting process. The findings provide insights into dynamics of sports facility issues and lessons for how they compete against other policy concerns.

1. A GAME OF MILLIONS:
PROFESSIONAL SPORTS FACILITIES AND THE MEDIA’S INFLUENCE
ON THE AGENDA SETTING PROCESS
by
Michael Allen Greenberg
APPROVED BY SUPERVISORY COMMITTEE
Dr. Patrick T. Brandt, Chair
Dr. Euel Elliott
Dr. L. Douglas Kiel
Dr. Kurt Beron

2. Copyright 2009
Michael Allen Greenberg
All Rights Reserved

3. For my wife Katie and our daughter Isabelle

4. A GAME OF MILLIONS:
PROFESSIONAL SPORTS FACILITIES AND THE MEDIA’S INFLUENCE
ON THE AGENDA SETTING PROCESS
by
MICHAEL ALLEN GREENBERG, B.A., M.P.A., M.A.
DISSERTATION
Presented to the Faculty of
The University of Texas at Dallas
in Partial Fulfillment
of the Requirements
for the Degree of
DOCTOR OF PHILOSOPHY IN
POLITICAL SCIENCE
THE UNIVERSITY OF TEXAS AT DALLAS
December, 2009

5. ACKNOWLEDGMENTS
I am deeply indebted to many people that have provided unwavering support and patient
guidance. This dissertation would not have been possible without the generous assistance
of my committee chair, Dr. Patrick T. Brandt. Dr. Brandt provided valuable advice and
sustained me through each step of the process. His willingness to teach me the fundamentals
of EMACS, PERL, R, and LATEX helped me craft the dissertation that I so strongly wanted
to write. The technical skills I obtained throughout this dissertation process will enhance
my future research endeavours. I am thankful for the many hours of conversations about
sports facilities and the academic profession we have had over the years.
I also express my thanks to Dr. Euel Elliott for his instruction about the policy process.
I share Dr. Elliott’s passion for the policy and agenda setting processes. My research in
Dr. Elliott’s classes served as initial drafts of this dissertation. Early on, Dr. Elliott provided
valuable suggestions about the focus of this dissertation and introduced me to Jones and
Baumgartner’s Agendas and Instability in American Politics. A seminal moment for me
occurred when Dr. Elliott introduced me to punctuated equilibrium theory during my first
semester at UTD. I would also like to thank Dr. Doug Kiel for his willingness to serve as
a committee member as well as his suggestion to include a larger discussion of issue frames
in the dissertation. I also thank Dr. Kurt Beron who also serves as a valuable resource and
committee member.
I would be remiss if I did not recognize Dr. Scott Robinson of Texas A&M University for his
critical contribution. Dr. Robinson introduced me to Jones and Baumgartner’s Dissportion-
ate Information Processing Model as well as Boyce and DiPrima’s Population Dynamics
Model. Both models serve as the theoretical foundation for my dissertation. Thanks to
Dr. Robinson I was able to meet Frank Baumgartner and discuss the path of my disserta-
tion.
I am also indebted to the staff at McDermott Library on the UTD campus. Lastly, I would
v

6. like to thank Drs. Pamela Brandwein, Tom Brunell, Harold Clarke, Douglas Dow, Robert
Lowry, Clint Peinhardt and Marianne Stewart for their contribution to my growth as a
political scientist. My colleagues and professors in the School of Economic, Political and
Policy Sciences at the University of Texas at Dallas have had a profound impact upon my
life and will shape my future contributions as a scholar and a public servant.
November, 2009
vi

7. A GAME OF MILLIONS:
PROFESSIONAL SPORTS FACILITIES AND THE MEDIA’S INFLUENCE
ON THE AGENDA SETTING PROCESS
Publication No.
Michael Allen Greenberg, Ph.D.
The University of Texas at Dallas, 2009
Supervising Professor: Dr. Patrick T. Brandt
New multi-million dollar professional sports facilities are constructed at unprecedented rates
across the United States. This building boom continues into its fourth decade as billionaire
and multimillionaire franchise owners have found a willing financing partner in city and
state officials as well as taxpayers. All told, an estimated $20–$30 billion of public money
has been used to construct new professional sports facilities since the early 1990s (Siegfried
and Zimbalist, 2000, Delaney and Eckstein, 2003). A local government’s subsidy contribution
to a single facility can approach more than $500 million (Owen, 2003).
This dissertation uses a Bayesian Poisson changepoint model to indicate how professional
sports facility referenda successfully compete against other issues for space on the local
government agenda. The research includes a review of newspaper articles leading up to and
following a professional sports facility referendum. This review demonstrates the evolution
of issue tone over the course of a referendum election cycle. Also, the research determines if
positive newspaper articles serve as a powerful endorsement of professional sports financing
plans. In addition, the project reveals the emergence of new arguments and attributes
vii

8. from both sides of the referendum as the election cycle progresses (Jones and Baumgartner,
2005b).
There is no evidence to suggest that the sports facility construction boom is coming to an
end. In fact, with an average facility lifespan of 20 years or less; the building boom will
indeed continue. The broader impact of this research is to supply city and state leaders as
well as taxpayers and voters information beyond the economic benefit analysis of a stadium
proposal. Although it is important to understand the economic benefits of a publicly financed
sports facility, it is as important to understand how framing effects are used to define this
issue and how the media influences the agenda setting process.
viii

9. TABLE OF CONTENTS
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv
Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 The Policy Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 The Dynamics of the Agenda Setting Process . . . . . . . . . . . . . . . . . 6
1.3 Framing Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4 The Impact of Elite Influence . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.5 Conclusion on the Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
Chapter 2 Theoretical Framework and Hypotheses . . . . . . . . . . . . . . . . . . 14
2.1 The Disproportionate Information Processing Model . . . . . . . . . . . . . 15
2.1.1 A Critique of the Disproportionate Information Processing Model . . 21
2.2 Population Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.2.1 Capacity Threshold - The Upper Limit . . . . . . . . . . . . . . . . . 27
2.2.2 Signal Threshold - How Strong The Signal? . . . . . . . . . . . . . . 29
ix

10. 2.2.3 Issue Dynamics – The Variables . . . . . . . . . . . . . . . . . . . . . 30
2.3 The Issue Dynamics Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.5 Expectations of a Stereotypical Sports Facility Case . . . . . . . . . . . . . . 38
Chapter 3 Research Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.1 Case Selection and Definition . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.1.1 Comparative Cases Tied to Hypotheses . . . . . . . . . . . . . . . . . 41
3.2 Measurements of Signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.1 Keywords . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2.2 Article Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.3 Article Tone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
3.2.4 Article Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.3 Benefits of this Research Design . . . . . . . . . . . . . . . . . . . . . . . . . 47
Chapter 4 Methodology and Hypothesis Testing . . . . . . . . . . . . . . . . . . . . 48
4.1 Bayesian Multiple Changepoint Model Analysis . . . . . . . . . . . . . . . . 49
4.2 Hypothesis Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Chapter 5 Research Findings: Multiple Venue Cases . . . . . . . . . . . . . . . . . 53
5.1 Introduction to the Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
5.2 The Pennsylvania Cases – A Tale of Two Stadium Initiatives . . . . . . . . . 55
5.3 Pittsburgh – Heinz Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
x

11. 5.3.1 Pittsburgh – Plan A Model (Nov. 1, 1996 – Nov. 30, 1998) . . . . . . 57
5.3.2 Pittsburgh – Plan B Model (July 1, 1997 – July 31, 1999) . . . . . . 64
5.3.3 Pittsburgh – State Model (Feb. 1, 1998 – Feb. 29, 2000) . . . . . . . 68
5.3.4 Pittsburgh – State and Local Model (Nov. 1, 1996 – Feb 29, 2000) . . 73
5.4 Philadelphia – Lincoln Financial Field . . . . . . . . . . . . . . . . . . . . . 79
5.4.1 Philadelphia – State Model (Feb. 1, 1998 – Feb. 29, 2000) . . . . . . 80
5.4.2 Philadelphia – Local Model (Dec. 1, 1999 – Dec. 31, 2001) . . . . . . 86
5.4.3 Philadelphia – State and Local Model (Feb. 1, 1998 – Dec 31, 2001) . 89
5.5 What do we Learn from the Pennsylvania Case Results? . . . . . . . . . . . 97
Chapter 6 Research Findings: Single Venue Cases . . . . . . . . . . . . . . . . . . . 100
6.1 Arizona – University of Phoenix Stadium . . . . . . . . . . . . . . . . . . . . 100
6.2 Dallas – Cowboys Stadium . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
6.3 Houston – Reliant Stadium . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
6.4 Indianapolis – Lucas Oil Stadium . . . . . . . . . . . . . . . . . . . . . . . . 116
6.5 New York/New Jersey – New Meadowlands Stadium . . . . . . . . . . . . . 123
6.6 Seattle – Quest Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Chapter 7 Summary of Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.1 Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
7.1.1 Comparative Cases – Large Media Markets . . . . . . . . . . . . . . . 142
7.1.2 Comparative Cases – Small Media Markets . . . . . . . . . . . . . . . 143
7.1.3 Comparative Cases – Venue Differences . . . . . . . . . . . . . . . . . 144
xi

12. 7.1.4 Comparative Cases – Multiple Venue Dynamics . . . . . . . . . . . . 146
Chapter 8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Appendix A Daily Series: Word Counts, Tone and Word Counts × Tone . . . . . . 154
Appendix B Article Selection and Coding Procedures . . . . . . . . . . . . . . . . . 167
Appendix C Keyword Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Appendix D Correlation Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Vita
xii

13. LIST OF TABLES
3.1 National Football League (NFL) sports facilities constructed via tax dollars
in the United States since 2002. . . . . . . . . . . . . . . . . . . . . . . . . . 42
5.1 Pittsburgh, Pennsylvania Changepoint Models. . . . . . . . . . . . . . . . . . 56
5.2 Pittsburgh Plan A changepoint dates and their 68% credible intervals, Novem-
ber 1996 – November 1998. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.3 Pittsburgh Plan B changepoint dates and their 68% credible intervals, July
1997 – July 1999. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.4 Pittsburgh State changepoint dates and their 68% credible intervals, February
1998 – February 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.5 Pittsburgh aggregate changepoint dates and their 68% credible intervals, Novem-
ber 1996 – February 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
5.6 Philadelphia, Pennsylvania Changepoint Models. . . . . . . . . . . . . . . . . 79
5.7 Philadelphia State changepoint dates and their 68% credible intervals, Febru-
ary 1998 - February 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.8 Philadelphia Local changepoint dates and their 68% credible intervals, De-
cember 1999 - December 2001. . . . . . . . . . . . . . . . . . . . . . . . . . . 88
5.9 Philadelphia combined changepoint dates and their 68% credible intervals,
February 1998 - December 2001. . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.1 Arizona changepoint dates and their 68% credible intervals, November 1999
– November 2001. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
xiii

14. 6.2 Dallas-Fort Worth changepoint dates and their 68% credible intervals, Novem-
ber 2003 - November 2005. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
6.3 Indianapolis changepoint dates and their 68% credible intervals, November
2003 – November 2005. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
6.4 New York changepoint dates and their 68% credible intervals, April 2004 -
April 2006. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
6.5 Seattle changepoint dates and their 68% credible intervals, June 1996 - June
1998. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
7.1 Total number of changepoints identified for each observation. . . . . . . . . . 137
7.2 Predominate tone preceding changepoints. . . . . . . . . . . . . . . . . . . . 138
D.1 Correlation Matrix–Large Media Markets. . . . . . . . . . . . . . . . . . . . 171
D.2 Correlation Matrix–Small Media Markets. . . . . . . . . . . . . . . . . . . . 171
D.3 Correlation Matrix–Local Government Venue. . . . . . . . . . . . . . . . . . 171
D.4 Correlation Matrix–PHL and PIT Comparison. . . . . . . . . . . . . . . . . 172
xiv

15. LIST OF FIGURES
2.1 Information-Processing Policy Systems with Institutional Costs . . . . . . . 20
2.2 Exponential growth: y vs. t for dy/dy = ry . . . . . . . . . . . . . . . . . . 26
2.3 Logistic growth: The phase line . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.4 f(y) vs. y for dy/dt = r(1 − y/K)y . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 f(y) vs. y for dy/dt = −r(1 − y/T)y . . . . . . . . . . . . . . . . . . . . . . 30
2.6 Growth with a critical threshold: The phase line . . . . . . . . . . . . . . . . 31
2.7 f(y) vs. y for dy/dt = −r(1 − y/T)(1 − y/K)y . . . . . . . . . . . . . . . . 32
2.8 Logistic growth with a threshold. The phase line . . . . . . . . . . . . . . . . 33
5.1 Cumulative number of Pittsburgh Post-Gazette articles related to Plan A and
the posterior arrival rate of articles, November 1996 – November 1998. . . . . 57
5.2 Article Tone in the Pittsburgh Post-Gazette related to Plan A, November 1996
- November 1998. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.3 Article Focus in the Pittsburgh Post-Gazette related to Plan A, November
1996 - November 1998. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
5.4 Cumulative number of Pittsburgh articles related to Plan B and the posterior
arrival rate of articles, July 1997 - July 1999. . . . . . . . . . . . . . . . . . . 66
5.5 Article Tone in the Pittsburgh Post Gazette related to Plan B, July 1997 -
July 1999. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
xv

16. 5.6 Article Focus in the Pittsburgh Post-Gazette related to Plan B, July 1997 -
July 1999. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.7 Cumulative number of Pittsburgh Post-Gazette articles related to State action
and the posterior arrival rate of articles, February 1998 - February 2000. . . 70
5.8 Article Tone in the Pittsburgh Post Gazette related to State action, February
1998 - February 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
5.9 Article Focus in the Pittsburgh Post Gazette related to State action, February
1998 - February 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.10 Cumulative number of Pittsburgh articles related to combined action and the
posterior arrival rate of articles . . . . . . . . . . . . . . . . . . . . . . . . . 76
5.11 Article Tone in Pittsburgh Post-Gazette related to the combined action . . . 77
5.12 Article Focus in Pittsburgh Post-Gazette related to combined action . . . . . 78
5.13 Cumulative number of Philadelphia articles related to the State action and
the posterior arrival rate of articles . . . . . . . . . . . . . . . . . . . . . . . 82
5.14 Article Tone in Philadelphia Area Newspapers related to State action . . . . 83
5.15 Article Focus in the Philadelphia Daily News related to State action. . . . . 84
5.16 Article Focus in the The Philadelphia Inquirer related to State action. . . . . 85
5.17 Cumulative number of Philadelphia articles related to the Local action and
the posterior arrival rate of articles . . . . . . . . . . . . . . . . . . . . . . . 86
5.18 Article Tone in Philadelphia Area Newspapers related to Local action . . . . 89
5.19 Article Focus in the Philadelphia Daily News related to Local action . . . . . 90
5.20 Article Focus in the The Philadelphia Inquirer related to Local action . . . . 91
xvi

17. 5.21 Cumulative number of Philadelphia articles related to combined action and
the posterior arrival rate of articles . . . . . . . . . . . . . . . . . . . . . . . 92
5.22 Article Tone in Philadelphia newspapers related to combined action . . . . . 94
5.23 Article focus in Philadelphia Daily News related to combined action . . . . . 95
5.24 Article focus in The Philadelphia Inquirer related to combined action . . . . 96
6.1 Cumulative number of Arizona articles and the arrival rate of articles. . . . . 101
6.2 Article Tone in Arizona Area Newspapers . . . . . . . . . . . . . . . . . . . 102
6.3 Article Focus in Arizona Republic . . . . . . . . . . . . . . . . . . . . . . . . 105
6.4 Cumulative number of Dallas articles and the arrival rate of articles . . . . . 108
6.5 Article Tone in Dallas Area Newspapers . . . . . . . . . . . . . . . . . . . . 110
6.6 Article Focus in Dallas Morning News . . . . . . . . . . . . . . . . . . . . . 111
6.7 Article Focus in Fort Worth Star-Telegram . . . . . . . . . . . . . . . . . . . 112
6.8 Cumulative number of Houston articles and the arrival rate of articles . . . . 115
6.9 Article Tone in the The Houston Chronicle . . . . . . . . . . . . . . . . . . . 116
6.10 Article Focus in The Houston Chronicle . . . . . . . . . . . . . . . . . . . . 117
6.11 Cumulative number of Indianapolis articles and the arrival rate of articles . . 118
6.12 Article Tone in Indianapolis Area Newspapers . . . . . . . . . . . . . . . . . 119
6.13 Article Focus in Indianapolis Star . . . . . . . . . . . . . . . . . . . . . . . . 121
6.14 Article Focus in Indianapolis Business Journal . . . . . . . . . . . . . . . . . 122
6.15 Cumulative number of New York articles and the arrival rate of articles . . . 124
6.16 Article Tone in New York/New Jersey Area Newspapers . . . . . . . . . . . 126
xvii

18. 6.17 Article Focus in New York Area Newspapers . . . . . . . . . . . . . . . . . . 127
6.18 Cumulative number of Seattle articles and the arrival rate of articles . . . . . 130
6.19 Article Tone in Seattle Area Newspapers . . . . . . . . . . . . . . . . . . . . 131
6.20 Article Tone in The Seattle Times . . . . . . . . . . . . . . . . . . . . . . . . 132
6.21 Article Tone in Seattle Post Intelligencer . . . . . . . . . . . . . . . . . . . . 133
6.22 Article Focus in The Seattle Times . . . . . . . . . . . . . . . . . . . . . . . 134
6.23 Article Focus in Seattle Post-Intelligencer . . . . . . . . . . . . . . . . . . . . 135
7.1 Arrival Rate Comparison of DFW, HOU, NY and PHL. . . . . . . . . . . . . 141
7.2 Arrival Rate Comparison of AZ, INDY, PIT and SEA. . . . . . . . . . . . . 143
7.3 Arrival Rate Comparison of Local Referendum cases. . . . . . . . . . . . . . 145
7.4 Arrival Rate Comparison of Philadelphia and Pittsburgh coverage of State
Action. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
7.5 Arrival Rate Comparison of PHL and PIT coverage of Local events. . . . . . 148
7.6 Arrival Rate Comparison of entire PHL and PIT time series. . . . . . . . . . 149
A.1 Word Counts, Tone and Word Counts × Tone for Pittsburgh Post-Gazette
related to Plan A, November 1996 - November 1998. . . . . . . . . . . . . . . 154
A.2 Word Counts, Tone and Word Counts × Tone for Pittsburgh newspapers
related to Plan B, July 1997 - July 1999. . . . . . . . . . . . . . . . . . . . . 155
A.3 Word Counts, Tone and Word Counts × Tone for Pittsburgh newspapers
related to state action, February 1998 - February 2000. . . . . . . . . . . . . 156
A.4 Word Counts, Tone and Word Counts × Tone for Pittsburgh newspapers
related to the combined action . . . . . . . . . . . . . . . . . . . . . . . . . . 157
xviii

19. A.5 Word Counts, Tone and Word Counts × Tone for Philadelphia newspapers
related to State action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
A.6 Word Counts, Tone and Word Counts × Tone for Philadelphia newspapers
related to Local action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
A.7 Word Counts, Tone and Word Counts × Tone for Philadelphia newspapers
related to the combined action . . . . . . . . . . . . . . . . . . . . . . . . . . 160
A.8 Word Counts, Tone and Word Counts × Tone for Arizona area newspapers . 161
A.9 Word Counts, Tone and Word Counts × Tone for Dallas-Fort Worth area
newspapers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
A.10 Word Counts, Tone and Word Counts × Tone for Houston Chronicle . . . . 163
A.11 Word Counts, Tone and Word Counts × Tone for Indianapolis area newspapers164
A.12 Word Counts, Tone and Word Counts × Tone for New York area newspapers 165
A.13 Word Counts, Tone and Word Counts × Tone for Seattle area newspapers . 166
xix

20. CHAPTER 1
INTRODUCTION
How does a professional stadium funding initiative successfully compete for scarce local
agenda space? What impact does the local media have upon this prevalent local government
issue? A majority of scholarly studies show that professional sports facility projects to be
economically irrational, yet they garner political and elite support. Do current models of
decision-making and agenda setting accurately describe the issue dynamics present when
a professional sports facility proposal is debated? Despite the numerous economic studies
on the subject, researchers have yet to explain why local politicians tend to make room on
the local agenda to address the use of public funds to build professional sports facilities.
In this project, I examine the media‘s impact upon the agenda setting process at the local
government level. I will use Jones and Baumgartner’s (1993) alternative approach to agenda
setting and will focus upon media coverage of these local policy debates.
Why focus on coverage of publicly funded professional sports facility projects? New
multi-million dollar professional sports facilities are constructed at unprecedented rates
across the United States. The sports facility construction boom continues into its fourth
decade as billionaire franchise owners have found a willing financing partner in city and
state officials as well as taxpayers. All told an estimated $20–$30 billion of public monies
have been used to construct new professional sports facilities since the early 1990s (Siegfried
and Zimbalist, 2000, Delaney and Eckstein, 2003). A local government’s subsidy contribution
to a single facility can approach more than $500 million (Owen, 2003).
Each major United States professional sports league, including the National Football
League (NFL), the National Basketball Association (NBA), the National Hockey League
1

21. 2
(NHL) and Major League Baseball (MLB) holds a virtual monopoly over the number of
teams allowed to participate and the franchise location. It is a monopoly in the classic and
locational sense. Professional sports leagues strategically place teams in geographic isolation
to prevent competition. The New York metropolitan area is the only United States city with
two NFL franchises (NFL Giants and Jets). New York’s large population can support two
NFL teams. David Swindell and Mark Rosentraub suggest that “because the four principal
leagues are able to constrain the supply of teams, a virtual bidding war between cities for
these scarce assets has broken out” (Swindell and Rosentraub, 1998, 12). This classic supply
vs. demand situation forces local governments and citizens to support a new publicly financed
venue, or face the threat that the team will move to another city that will ultimately support
the will of the sports franchise owner.
City and state leaders as well as economic sports consultants tout the various subsidies
as a means of increasing local economic development. However, there has been much debate
about the actual economic benefits of a new stadium, arena, or ballpark. Scholarly studies
have reached the near uniform conclusion that the financial benefits of a professional sports
facility do not materialize when measured by job gains (Baade and Dye, 1988, 1990, Baade,
1987, 1996, Blair, 1992) increases in personal income (Chema, 1996, Donnelly, 1988, Fort and
Quirk, 1995, Johnson, 1986, 1991, 1983, Ozanian et al., 1995) and increases in tax revenues
(Rosentraub and Swindell, 1991, Rosentraub, 1999, Zimmerman, 1996).
Voters do not seem to be overwhelmed by the economic development promises either
since 46 percent of referenda for tax-subsidized sports facilities failed over the past quarter
century (Rosentraub, 2009). Of the 54 percent of referenda that did pass, the margin of vic-
tory is typically very narrow (Rosentraub, 2009). As doubt has been cast on the proponents’
claims, alternative economic justifications have come to the fore. A new argument purports
that hosting one-time “mega–events” such as the Super Bowl (National Football League)
or the All Star Game (Major League Baseball) alone generate enough local economic activ-

22. 3
ity to warrant the multi-million dollar subsidies (Lertwachara and Cochran, 2007). Recent
economic studies question proponent’s new arguments that hosting these “mega–events”
does in fact generate enough local economic development to warrant million dollar subsidies
(Baade and Matheson, 2001, Coates and Humphreys, 2002, Matheson, 2005, Lertwachara
and Cochran, 2007). The claim that the investment of taxpayer dollars in a private sports
facility construction project generates additional economic gains for the local community
may be suspect at best and at worst wrong. However, this does not stop franchise owners
and local government leaders from touting such claims to garner necessary public support.
Scholarly research about professional sports facilities to this point has failed to reveal
why this issue receives the necessary attention to make it on the local government agenda
in the first place. The vast number of new, publicly funded professional sports facilities
throughout the United States indicates that this issue captures the attention of local leaders
as well as the voting public. This is not to say that some local governments have not
supported such endeavors. While an increasing number of local governments have refused to
set referenda elections, the fact remains that this particular issue accepts a prominent place
on local governments’ agendas.
I review two plausible decision-making models that serve as the theoretical foundation
for this project. I operationalize the models’ variables, thus making the models testable using
data gathered from counts of local newspaper articles. Academic research on the agenda
setting process has applied such models to federal policy processes (Baumgartner and Jones,
1993). My innovation is to modify the approach to provide analysis of the agenda setting
process at the local government level.
I use the empirical analysis to provide insight into current assumptions about the
agenda setting process at the local level. Few reliable local policy analysis tools are avail-
able. Most theoretical models of decision-making account for decisions and agenda setting

23. 4
processes at the federal government level. A goal of this project is to craft a testable theo-
retical model of decision-making that can be applied at state or local levels of government.
Chapter 1 begins with a literature review of agenda setting processes as a sub-function
of the policy process. In Chapter 2 I turn to an analysis of two plausible decision-making
models that serve as the foundation for a new issue dynamics model. Chapter 2 details the
theoretical framework and hypotheses for this research. In Chapter 3 I specify the research
design and the case selection criteria. The empirical model is presented in Chapter 4. In
Chapters 5 and 6 the results from each case study are presented. In addition, I identify
factors that enable this issue to receive a prominent place on the local agenda including
the impact of the local media. In Chapter 7 I summarize the key findings from the results.
Finally, in Chapter 8 I conclude with a discussion of the results and their relevance for
theoretical debates about the local government agenda setting process.
1.1 The Policy Process
Problem identification and agenda setting have a major impact upon the policy process.
Anderson (2003) identifies problem identification and agenda setting as the critical first
steps in the policy process. The process of characterizing problems in the political arena is
defined by policy researchers as problem definition (Rochefort and Cobb, 1994, 4–5). How
issues are defined is vital to the ultimate success or failure of a public policy. Perception
of an issue is just an important as objective facts when it comes to problem definition (see
Wood and Doan, 2003, 641). Successful problem definition attempts to aggregate individual
behaviors to produce collective behavior or action (see Wood and Doan, 2003, 645). Problem
definition attempts to influence individuals to join a particular side of the issue debate.
An example of successful problem definition is the negative perception of nuclear
energy in the late 1960s. Prior to this date, nuclear energy was defined as a source of

24. 5
technological advancement and American scientific prowess (Baumgartner and Jones, 1993).
In the late 1960s, an increase in media attention about the negative impacts of nuclear power
fostered by the nuclear disaster at Three Mile Island helped redefine the nuclear energy
debate. The number of federal nuclear regulations surged in the 1970s and 1980s. Also, the
number of United States Congressional hearings on the negative impact of nuclear energy
increased. The tone of these hearings as measured by Baumgartner and Jones (1993) was
overwhelmingly negative. No new nuclear power plants have been approved in the United
States since 1977 as a result of the positioning of nuclear power in the policy arena.
Only some problems become targets of public policy (see Anderson, 2003, 27). Those
problems tend to be the ones that are successfully defined. Successful problem definition
impacts local government officials and citizens to such a degree that they will be compelled to
act or voice support for a policy outcome. Individuals’ attitudes about policy are shaped and
framed by messages presented to groups by powerful issue proponents and opponents. The
side that maintains control of the message and the information flow is most often successful
(Anderson, 2003).
Anderson (2003) identifies the reason focus on the agenda setting process is relevant
to any public policy research. That is, “Why only some problems, out of all that exist,
receive consideration by policy-makers requires an examination of agenda setting; that is,
how governmental bodies decide what problems to address” (Anderson, 2003, 27). The goal
in defining any policy (whether pro or con) is to have more people support one side of the
debate over another. The side that is able to sway majority opinion to their view of the policy
debate is often more successful in defining the problem. One force that can make problem
definition successful is elite influence. The elite are typically public, social or political figures.
Elites influence problem definition often because it is their issue that is at stake. The second
force that can make problem definition successful are the media. The media shape public
views on a variety of public policies simply based on what is presented in addition to how

25. 6
the information is presented. Furthermore, the media are often controlled by the elite, thus
the elite often control the information flow about a topic.
1.2 The Dynamics of the Agenda Setting Process
The stages of the agenda setting process according to Jones and Baumgartner include prob-
lem definition, issue proposal and debate, and collective choice (Jones and Baumgartner,
2005b). Therefore, information, issue frames, and the decision-making process provide the
theoretical foundation for this research. These combine to form the foundation of the agenda
setting process. To develop multiple hypotheses related to a topic that involves an analysis
of human decision-making, it is critical to first understand the core merits of prior decision-
making concepts and theories.
The purpose of this section is two-fold. First, it assesses and illustrates the impact of
rational choice and in particular bounded rationality theory as the basis for understanding
how professional sports facility referenda succeed during the agenda setting process. Sec-
ond, this section addresses various decision-making concepts that might lead to a greater
appreciation of the dynamics involved during the agenda setting process at the local govern-
ment level. The purpose of mentioning multiple concepts (although each concept will have
its origins in rational choice theory) is to develop hypotheses that explain why the issue of
a publicly financed professional sports facility captures the interest of the media, political
leaders and the voting public.
The idea of a rational choice between or among different choices is a primary theme in
decision-making theory (Kramer, 1971). Shepsle and Bonchek (1997) make clear, in general
terms, that the decision-making process may be thought of in terms of (1) the inputs required,
(2) what the procedure does to those inputs, and (3) the output or outcome produced. The
policy process reflects proponents efforts to connect solutions and problems (Kingdon, 2003).

26. 7
Baumgartner and Jones (1993, 29) state “as governmental leaders shift their attention from
one problem to the next, policy entrepreneurs responsible for administering programs argue
that their program represents the best solution to the new problem, even though originally it
may have had no relationship to that problem.” In the context of professional sports facilities,
owners suggest that a new public sports facility will cure the city’s economic development
issues or will revive the city to “major league” status. The agenda setting process produces
a set of “winners” and “losers” (Baumgartner and Jones, 1993). Issue “winners” fuse policy
to strong symbols while issue “losers” attempt to redefine the debate to their advantage
(Baumgartner and Jones, 1993). Baumgartner and Jones (1993) assert that issue change
happens when attention to a policy issue is high. Public apathy is replaced by public calls
for action. They state that “through the mechanisms of agenda access and issue definition,
the broader forces of political control may intervene from time to time, changing policies
from what the self-interested might prefer” (Baumgartner and Jones, 1993).
To understand the agenda setting process it is critical to recognize how information is
presented, processed and ultimately acted upon. The ability to provide and process informa-
tion is a key tenet in understanding public policy outcomes (Jones and Baumgartner, 2005b).
During the agenda setting process information has many forms. Policy information can in-
clude media reports, economic studies, white papers, and testimony, among others. There
are also many “experts” or sources of information. During the policy cycle, information is
plentiful and it becomes the job of the agenda setter to decipher information in order to
make the best possible policy choice. This is especially true since bounded rationality schol-
ars have shown that individuals make decisions based upon heuristics. Said another way,
decision-makers make decisions based upon key pieces of information rather than evaluating
the totality of data. This is critical as only some information is considered during the policy
process. Jones and Baumgartner equate agenda setting in an organizational environment
to the attention allocation of individuals (Jones and Baumgartner, 2005b). Individuals can

27. 8
absorb only so much information and decision-makers can only focus on a few heuristics or
signals at a time. Jones and Baumgartner argue that “Human sensory systems are dispro-
portionately responsive to a signal in certain ranges of the signal. Threshold and habituation
effects cause humans to be exquisitely responsive to certain ranges of light intensity, sound,
electric shock, and so forth” (Jones and Baumgartner, 2005b, 84-85). The strength of signal,
therefore, becomes a key variable in the dynamics of the agenda setting process.
1.3 Framing Effects
Sophisticated research designs that attempt to understand the impact of framing effects
on voters have gained wide acceptance among political scientists and sociologists. Framing
“refers to alternative wordings of the same objective information that significantly alter the
model decision, though differences between frames should have no effect on the rational
decision” (Bazerman, 2006, 43). Bazerman (2006) suggests that framing effects impact how
individuals make decisions, even if the information they base the decision on is normatively
irrelevant. Nelson and Kinder (1996) stress the importance of frames and highlight the fact
that frames are in fact constructions of the issue. Frames highlight the crux of the problem
and even recommend an adequate response to the problem.
Frames have a major impact on public discourse and the agenda setting process. In an
article about large public works projects, Flyvbjerg et al. (2009) assert “politicians, planners,
or project champions deliberately and strategically overestimate benefits and underestimate
costs in order to increase the likelihood that their projects, and not their competition’s, gain
approval and funding. These actors purposely spin scenarios of success and gloss over the
potential for failure. This results in . . . ventures that are unlikely to come in on budget or on
time, or to deliver the promised benefits” (Flyvbjerg et al., 2009, 173). The issue of framing is
even more important when considering a professional sports facility referendum because the

28. 9
framing occurs outside typical political arenas. The topic moves from the metro section of a
newspaper to the sports section. The topic is discussed on sports talk radio programs and not
just news radio programs. Issue proponents likely shape the decision-maker’s perception of
the impact of a professional sports facility. In fact, an important political tool for supporters
of a new professional sports facility is a campaign that attempts to “warn local residents
about the danger of slipping to the depths of some nearby city, which has been socially
constructed as inferior. A community’s decline to minor league status . . . will surely be
exacerbated by not building a new stadium, which would precipitate a team’s decision to
leave the city” (Delaney and Eckstein, 2003, 39). The quest to be a “major league city” is a
constant theme used by sports facility referendum supporters. Officials in Cleveland stated
that the city would be just another Dayton, Ohio without professional sports franchises
(Delaney and Eckstein, 2003). Those cities with professional sports facilities tend to view
themselves as superior to those cities that do not. Professional sports franchises provide an
element of civic pride to citizens and voters.
Access to an engaged audience is a key factor that proponents of a professional sports
facility issue use to their advantage. The salient nature of professional sports referenda can
be effectively measured by voter turnout (VTO) data. Paul and Brown (2001) find that when
a professional sports facility issue was on the ballot, in a study that included sports facility
referenda from 1984 to August 2000, voter turnout averaged 41 percent and many sports
facility contests set local records for VTO. While issue salience is a factor, prior research has
shown that elite involvement only leads to greater participation and not to direct support
for the elite position. The issue of whether or not to build a professional sports facility via
taxpayer dollars is also interesting in that the typical citizen will have an opinion and will be
somewhat knowledgeable when compared to other civic issues (Paul and Brown, 2001). Issue
awareness necessitates cognitive ability, political motivation, and depth of media coverage
(Nicholson, 2003). Issue characteristics, media attention and campaign spending have been

29. 10
shown to have significant effects on voter awareness (Nicholson, 2003). A perfect storm is
created when a sports facility referendum is proposed. The confluence of high voter interest,
spending on advertising and high voter turnout contributes to a very active issue cycle.
How a sports stadium referendum issue is defined is vital to its ultimate success or
failure. This study reveals how political elites influence the media and ultimately define the
issue of a publicly financed professional sports facility. In addition, this research provides the
opportunity to examine the media’s interpretation of the debate about this issue. Rosentraub
(1999) suggests the media supports any effort to bring or team a team in a local media
market given the importance of sports to daily newspaper sales and local broadcast ratings.
Furthermore, Rosentraub (1999) argues that newspaper stories focus only on the positive
aspects of a new facility, rather than any potential negative implications. Noll and Zimbalist
(1997) posit as a result of their symbiotic relationship with sports, local media outlets are
likely to support a ballot initiative. Delaney and Eckstein suggest that stadium opponents
can be buoyed by local newspaper coverage of “the hidden processes leading to the building
of private stadiums with public dollars” (Delaney and Eckstein, 2003, 17). This study
provides a detailed analysis of local media’s role as proponent or opponent of major stadium
initiatives. Can proponents overwhelm the agenda setting process by simply providing more
information than the opponents or vice versa?
1.4 The Impact of Elite Influence
Paul and Brown (2001) investigate the limits of elite influence on public opinion toward the
subject of sports facility referenda. Elite influence is a key factor when analyzing professional
sports referenda because the issue of raising taxes is not an easy sell to taxpayers no matter
the problem that is being solved by the increase (Paul and Brown, 2001). Framing effects
are also important since public opinion about government policy is often group-centric.

30. 11
That is to say agenda setters must be aware of the beneficiaries (or victims) of a given
policy to protect their position as a member of the political elite (Nelson and Kinder, 1996).
Messages presented by the powerful leaders of referenda shape citizens’ attitudes about the
issue. Nelson and Kinder’s reference to groups in their 1996 study ties nicely to the issue of
sociotropic behavior, or group-based economic influences upon the decision-making process.
Paul and Brown (2001) posit that the media influence the issue debate more than
political elites. Paul and Brown (2001) draw extensively from bounded rationality models
proposed by Lippmann and Simon. Lippmann argues that a citizen’s political world is
compartmentalized by the mass media (Lippmann, 1991[1922]). This is because the real
world is much too complex to fully comprehend (Paul and Brown, 2001). Simon (1997)
maintains that the average citizen does not have the time or resources to fully engage an
issue. As sports have captured the public’s attention, the fact that this issue is on the front
page and on the sports page further amplifies the issue and signal. Thus, the public is apt to
comprehend the sports facility debate based on the way it is presented by the local media.
Downs (1957) asserts that most individuals choose to remain uninformed about gen-
eral political issues. The majority of individuals therefore rely on others (in particular the
media) to assist them in the formation of a political opinion (Paul and Brown, 2001). Paul
and Brown (2001) argue that social and political elites frame issues in such a manner that
might sway those citizens that have not formed an opinion. They conclude that elite opinion
impacts public opinion, even on a highly salient issue like a professional sports facility ballot
initiative. Furthermore, increased spending levels by either side of the argument do not
impact vote totals (Paul and Brown, 2001). If framing effects can have a tremendous im-
pact on decision-making behavior, opinion change on ballot propositions is also an essential
consideration.
Bowler and Donovan (1994) stress that “the mobilization of opinions associated with
the campaign is a crucial factor for understanding the dynamics of proposition contests. Ag-

31. 12
gregate shifts in opinion can be produced either by individual opinion switching (conversion),
by individuals forming firm opinions at different times (mobilization), or by both processes”
(Bowler and Donovan, 1994, 414). Opinion change is quite possible with a professional sports
facility referendum vote as economic impact information is made available, challenged and
defended throughout the election cycle.
How do local government leaders interpret how citizens react to media coverage about
a potential new football stadium or ballpark or arena? If the local editorial board and
sports columnists are supportive, do local government leaders assume that this will influence
potential voters? One could argue that if the media coverage is positive early in the process
that city leaders will push for a deal. On the other hand, if the media coverage is negative
early in the process, city leaders will instead focus on other issues.
1.5 Conclusion on the Literature
While the focus of this research is not an economic analysis of the impact of professional
sports facilities, the debate about the economics of such a proposal is a key attribute that
affects the decision to elevate the issue to the local agenda. In this case, the economic benefit
proposals are presented as information that is provided by the elite stakeholders through
the local media. The quest, therefore, is to understand how information is presented and
interpreted during the issue cycle. Paul and Brown (2001) conclude that elites’ framing,
including the media, have a tremendous impact on voters when faced with a vote choice for
or against a professional sports facility referendum. Aided by the results of the Paul and
Brown (2001) study, this analysis attempts to discover new attributes, or framing strategies
that transpire over the course of this issue cycle.
This literature review outlines the rational choice decision-making and bounded ratio-
nality theories as the foundation for this study. More precisely, the literature on the agenda

32. 13
setting process and issue framing effects promises to contribute to hypotheses that serve as
the basis for a deeper understanding of the local government agenda setting process.

33. CHAPTER 2
THEORETICAL FRAMEWORK AND HYPOTHESES
The purpose of this research is to develop a model that provides a better understanding
of the local government agenda setting process. Current agenda setting models tend to
focus on the decision-making dynamics at the federal government level. In particular, recent
studies analyze federal budget changes over time (Baumgartner and Jones, 1993, Jones and
Baumgartner, 2005a,b). This section will define aspects of the decision-making process
about professional sports facility initiatives. Do current decision-making models explain
the dynamics of how a sports facility financing issue gets on the local ballot? In a larger
sense, can a revised decision-making model better explain what happens when multiple issues
compete for attention at the local government level? The following sections will explore the
Baumgartner and Jones (2005) disproportionate information processing model and the Boyce
and DiPrima (2009) population dynamics model.
The disproportionate information processing model is a logical launching off point
because the model serves as the foundation for punctuated equilibrium. Punctuated equi-
librium (see Baumgartner and Jones (1993)) predicts policy results will include both long
periods of policy stasis and inflections of rapid policy change. The disproportionate infor-
mation processing model attempts to provide a “model of choice that is consistent with
both incrementalism and punctuated equilibrium . . . ”(Jones and Baumgartner, 2005a, 329).
The implication of the disproportionate information processing model (that serves as the
foundation for punctuated equilibrium theory) is that policy outcomes will not be normally
14

34. 15
distributed, but rather policy outcomes will result in a leptokurtic distribution.1
Therefore,
policy outcomes include periods of stasis and punctuations. Jones and Baumgartner (2005a)
posit that the interaction of boundedly rational decision-makers and the institutions within
which they make choices leads to outputs or policy that show positive kurtosis. They term
this the general punctuation hypothesis. An example of a policy punctuation is a sudden
response to a military attack or a rapid change in government organization due to a terrorist
attack. At the local government level, a policy punctuation may include a large school bond
election or a significant shift in public funds to economic development projects. Professional
sports referenda are a good application of agenda setting models because they are good
cases of punctuated equilibrium concepts. A local government’s contribution to a profes-
sional sports facility represents a significant punctuation to the local government budget
cycle.
2.1 The Disproportionate Information Processing Model
Disproportionate information processing occurs when rational actors make decisions that
ignore important facts or information (Jones and Baumgartner, 2005a). The cognitive and
emotional limitations of decision-makers cause errors in choices that accrue over time. Often
“decision-makers recognize that previously ignored facets of the environment are relevant
and scramble to incorporate them into future decisions” (see Jones and Baumgartner, 2005b,
334). Incrementalism suggests that decisions at a particular time are a marginal adjustment
from a previous decision (Jones and Baumgartner, 2005a). The incrementalists’ key failure
was to not fully appreciate the implications of “error accumulation in incremental decision-
making and the consequent need to update episodically” (see Jones and Baumgartner, 2005b,
1
A leptokurtic distribution occurs when the distribution contains a high kurtosis value. That is, as
opposed to a normal distribution a leptokurtic distribution will have a higher peak around the mean and
fatter tails.

35. 16
334). Jones and Baumgartner’s disproportionate information processing model incorporates
the following four components:
1. A signal that is input into a policy-making system.
2. A friction mechanism that sets a threshold below which the system responds only
partially.
3. An error accumulation feature that builds up pressure in the environment that may
produce subsequent policy action.
4. A response that is dictated by the strength of the input signal and institutional friction
that has accumulated from previous periods.
The value of the disproportionate information processing model is that it results
in an empirical test of decision-making dynamics (Jones and Baumgartner, 2005a). The
disproportionate information processing model facilitates the first empirical, longitudinal
multi-issue evaluation of the agenda setting process (Mortensen, 2009). To make their case
for disproportionate information processing, Jones and Baumgartner (2005b) run multiple
simulations. In the simulations, input signals are drawn and run through a system that adds
friction (Jones and Baumgartner, 2005a). According to Jones and Baumgartner (2005a,
345), friction is “a parameter that operates as a threshold. Above the threshold, the signal
generates a response equivalent to the strength of the signal–the signal has overcome the
friction. Below the threshold, it generates a partial response. Friction is slowing down the
response. If the ’partial’ response is set to 0, then below the threshold we have ’gridlock’–
no response whatsoever.” Their model also has an error accumulation component that
simulates over corrections to ignored information. Finally, the strength of the input signal
and institutional friction impacts the response or output. The key variables in the model are
the information signal and the policy response (Jones and Baumgartner, 2005a). Variables

36. 17
in the disproportionate information processing model are R as the response (policy response)
and St as the input signal (information signal). The parameters include C as the friction
parameter, λ is the efficiency parameter, β is amplification parameter, Σ is the sum of the
input signal. The disproportionate information processing model is
If St + ΣS0<k > C, Rt = βSt
else, Rt = λβSt (2.1)
The author’s find that as friction increases, output distributions are leptokurtic (Jones and
Baumgartner, 2005a). According to Jones and Baumgartner, “Above the value of C, the
signal generates a response proportional to the strength of the signal. Below the value of C,
the signal generates only a partial response.” (Jones and Baumgartner, 2005a, 346). If the
signal does not generate a response, it is added to the next period’s signal strength. Over
time the buildup of prior signals along with a heightened amplification parameter leads to
a potential over correction, thus leading to a policy punctuation (Jones and Baumgartner,
2005a). Multiple simulations support their claim “that the interaction of cognitive factors
with institutional friction invariable leads to a pattern across time of general stability with
episodic punctuations” (Jones and Baumgartner, 2005a, 347).
According to Jones and Baumgartner (2005b), “leptokurtic distributions in policy
choice are prime indicators of disproportionality in the choice process” (see Jones and Baum-
gartner, 2005b, 336). The presence of a leptokurtic distribution supports Jones and Baum-
gartner’s assertion that policy is not incremental because incremental policy output produces
a normal distribution. In sum, leptokurtic distributions in policy outcomes indicate dispro-
portionality in the policy process. In sum, “a straightforward incremental policy process
will invariable lead to an outcome change distribution that is normal. And vice versa: any
normal distribution of policy outcome changes must have been generated by an incremental

37. 18
policy process” (Jones and Baumgartner, 2005a, 328). Jones and Baumgartner suggest that
if decision makers make decisions based upon “news,” instead of a set of indicators, they will
produce non-normal distributions of policy outputs (Jones and Baumgartner, 2005a).
Punctuated equilibrium theory provides a framework for analyzing changes and pat-
terns regarding matters of public policy and decision-making. Decision-making processes
and outcomes in conjunction with the complex interactions of policy groups, government
officials and the bureaucracy are more accurately characterized by the punctuated equilib-
rium theory model. Punctuated equilibrium theory reveals explanations of punctuations, or
public policy spikes in addition to incrementalism, or points of stasis within the public policy
arena. This theory is in contrast to incremental theory which only explains the plodding
nature of public policy. True describes the punctuated equilibrium pattern as “incremental
periods broken by major shifts or redirections” (see True, 2000, 3). That is, policy responses
and policy decisions are better characterized by dramatic responses to loud signals rather
than incremental responses to past decisions.
Jones and Baumgartner (2005b) favor the view of Simon (1996) that information is
plentiful and attention is in fact the limitation to the decision-making process. Douglas
Arnold’s (1990) implicit index model also adopts the notion that in an “information-rich”
environment, a model should explain, “how policy makers attend to and prioritize informa-
tion” (Jones and Baumgartner, 2005b, 330). The implicit index model suggests that various
sources of incoming data are indexed and weighted by the decision-maker. According to the
theory, a decision-maker examines an index that contains a weighted combination of indica-
tors and will update his or her beliefs based on the index (Jones and Baumgartner, 2005b).
While each indicator may in fact produce a non-normal frequency distribution, Jones and
Baumgartner (2005b) find that a weighted index of these indicators would in fact produce a
normal distribution. Jones and Baumgartner dismiss the implicit index model as an accurate
description of government policy behavior because their empirical simulations do not produce

38. 19
a normal distribution. According to Baumgartner and Jones (1993), punctuated equilibrium
theory proves that outputs of the decision-making process are not normally distributed, but
rather are characterized by a leptokurtic distribution (True et al., 1999).
Jones and Baumgartner (2005a) suggest that decision-makers are more likely to “lock
on” to one indicator, rather than build an elaborate index of weights and indicators. Indicator
lock differs from the implicit index model in that bounded rationality suggests that decision-
makers will hone in on one or two key aspects of a complex issue (Jones and Baumgartner,
2005a). Symbols, heuristics, and bias lead to a policy process that reveals a leptokurtic
distribution as decision-makers will likely focus on only one indicator. Jones and Baum-
gartner (2005b) argue that rather than an index construction strategy, decision-makers will
“lock on” to a single indicator that serves as a heuristic for future decisions. This theory
is supportive of bounded rationality and suggests that heuristics and symbols serve as the
key components to the decision-making processes. It is precisely this bounded rationality
component that foretells the presence of a leptokurtic distribution when empirically testing
policy decisions. That is, due to the “cognitive and emotional constitutions of decision mak-
ers, decision-making is cybernetic, continually under adjusting and then over correcting in
an erratic path” (see Jones and Baumgartner, 2005b, 334).
Jones and Baumgartner (2005a) also recognize the impact institutions can have upon
the decision-making process. Without the friction of institutional constraints, human factors
alone generally lead to disproportionate information processing (Jones and Baumgartner,
2005a). The imposition of institutional friction further amplifies the level of dispropor-
tionality (Jones and Baumgartner, 2005a). Decision-making systems impose decision costs,
transaction costs, information costs and cognitive costs (Jones and Baumgartner, 2005b).
Institutional friction is significant due to the impact outside information can have upon in-
stitutional systems. Institutional friction in the decision-making model leads to a discussion
of systems and the imposition of costs relative to the decision-making process.

39. 20
Figure 2.1. Information-Processing Policy Systems with Institutional Costs
Costs can be shown to act linearly on the system as shown in Equation 2.2. In
Equation 2.2, costs are subtracted from the amplification parameter and signal where
R = βS − C (2.2)
When costs are linear the reaction to the response remains proportionate to the signal
(Jones and Baumgartner, 2005a). However, Jones and Baumgartner suggest that signal and
institutional costs will interact with each other and magnify the signal’s effects (Jones and
Baumgartner, 2005a). If the costs are multiplied by the amplification parameter then the
reaction equation would be
R = βS × C (2.3)
The solid line in Figure 2.1 shows the impact interactive costs will have on the response as
the signal increases. Interactive costs reduce the impact of the signal.

40. 21
Based upon numerous simulations run by Jones and Baumgartner (2005b), their re-
sults indicate that an increase in friction will further punctuate the policy output. The
disproportionate information processing model finds “that the interaction of cognitive fac-
tors with institutional friction invariably leads to a pattern across time of general stability
with episodic punctuations” (see Jones and Baumgartner, 2005b, 347). As costs interact
with the strength of the signal we can see that “costs reduce action up to some threshold
and then gradually shift so that they amplify rather than reduce the reaction of the system
to larger inputs.” The solid line in Figure 2.1 shows such a model will produce virtually no
response when signals are low but massive reactions to strong signals; leptokurtosis results
from its disproportionality (see Jones and Baumgartner, 2005b, 340).
2.1.1 A Critique of the Disproportionate Information Processing Model
The Jones and Baumgartner disproportionate information processing model is a useful
decision-making tool that takes into account signal strength and the process by which the
decision-maker responds to the signal. The interaction of signal strength and the response
to the signal describes much of what occurs during the decision-making and agenda setting
processes. Decision-makers must process a myriad of data points by which a policy outcome
is chosen. In addition, the author’s account for institutional costs that further magnify dis-
proportionality in the decision-making process. The parabolic shape of the curves found in
Figure 2.1 indicates that the model does not account for institutional capacity limitations
or thresholds. That is, while costs are represented as institutional constraints, limitations of
the policy venue itself are not represented in the model. The disproportionate information
processing model implies an exponential response as signal strength rises. The only limit
to the response is the institutional friction that can impact the efficiency of the information
flow or signal. A more accurate characterization of an agenda setting model must account
for the dynamic flow of information and response. This necessitates a model that measures

41. 22
decision-making over time rather than static measures of decision-making at a given point
in time.
The disproportionate information processing model provides static measures of the
interaction of response and signal. The limitation of the disproportionate information process
model is due to the means of measurement. The model is non-linear, but non-dynamic and
does not describe the agenda setting process over time. A more accurate model of agenda
setting must adopt a difference equation approach in order to capture the dynamics across
the agenda setting time line. A well constructed difference equation approach measures
signal and response functions over time. A model based upon a difference equation will
show the dynamic interaction of signal and response that better explains the agenda setting
process at the local government level.
In the arena of local government agenda setting, it is unreasonable to think that a
policy response will rise in direct proportion with the signal as the linear equation approach
mandates. It is also unreasonable to expect that signals or information will flow into the pol-
icy venue consistently over time. Rather, information and signals will increase and decrease
at numerous points during the policy process. Said another way, an event like the release of
a major economic study will cause a spike or increase in attention to an issue. Conversely,
a week after the spike, attention will fall back to a previous level if no additional news or
signals are input into the system. The dynamic nature of information flows is missing from
the Jones and Baumgartner model. The flaw in this model is that information does not flow
into a policy arena in a smooth parabolic shape. Likewise, attention by decision-makers and
agenda setters on an issue is not consistent throughout the policy process.
Another criticism of the disproportionate information processing model is the assump-
tion that information or signals can grow exponentially regardless of the capacity considera-
tions of the agenda setting venue. The authors include institutional friction as a constraint,
but if the signal is strong enough in their model, it can overcome any institutional friction

42. 23
and ultimately produce a response. In fact, institutional friction will exaggerate the re-
sponse once the signal is strong enough (Jones and Baumgartner, 2005a). Figure 2.1 shows
that in a system with no costs or a system with linear costs, a strong signal will produce a
proportional response. Their model does not consider attention capacity constraints of the
decision-maker or the policy venue or institution. Institutional friction is not the same as in-
stitutional capacity. Capacity suggests a threshold or attention boundary that after a point,
a stronger signal does not mandate a more significant response. This is in direct contrast to
the results of interactive costs presented in Figure 2.1. Friction suggests that a strong signal
can overcome any institutional costs or constraints and there is no upper bound. A model
without an upper bound or capacity threshold does not seem apt to describe agenda setting
at the local government level.
A more accurate model of the local government agenda setting process must account
for two key components. First, the model should account for the dynamic flow of information
into the policy venue. Information flow into the policy venue is fluid, not static. This is
especially true at the lower levels of government because state and local priorities are subject
to more change than the federal agenda. There are exponentially more state, county and local
jurisdictions. The vast number of state and local venues implies that citizen and elite access
to these venues leads to greater and more constant change. Information flow in and out of
multiple policy venues is apt to change to reflect the current priority. In short, information
flow into the local agenda setting venue will rise and fall in a dynamic fashion throughout
an issue cycle. A local government agenda setting model must account for this ebb and flow
of information throughout the issue cycle.
Second, a government agenda setting model should account for capacity constraints
of the government venue. Bounded rationality suggests that decision-makers can process a
limited amount of information. Information flow or signal strength is not expected to grow
in an exponential manner. In a local government venue, an input signal related to an issue

43. 24
is expected to rise to an upper bound threshold parameter (if it rises at all) and then fall
back as attention to another issue becomes the focus of the agenda setters. The capacity of
the policy venue is the parameter or context that must be taken into account.
The rise and fall of attention in conjunction with a discussion of critical thresholds
is similar in construct to population dynamics models. Boyce and DiPrima (2009) state “A
model of this general sort apparently describes the population of the passenger pigeon, which
was present in the United States in vast numbers until late in the nineteenth century. It was
heavily hunted for food and for sport, and consequently its numbers were drastically reduced
by the late 1880s. Unfortunately, the passenger pigeon could apparently breed successfully
only when present in a large concentration, corresponding to a relatively high threshold.
Although a reasonably large number of individual birds remained alive in the late 1880s,
there were not enough in any one place to permit successful breeding, and the population
rapidly declined to extinction” (see Boyce and DiPrima, 2009, 87-88). As a species will rise
up to a peak critical point, level off and then potentially fall back to potential extinction,
attention to a local government issue will rise to a peak critical point and will level off, or
hit a constant equilibrium at the peak critical point. In addition, a species does not grow
in a non-linear manner. Species are limited by the very environment in which they live.
The next section explores a populations dynamics model and the potential agenda setting
ramifications of adapting this approach to the study of issue dynamics.
2.2 Population Dynamics
Population dynamics models based upon first order difference equations show the growth or
decline of a particular species in the context of the environment over time. As I show, a
model that specifies the rise and fall of a species population can be applied to policy issue
dynamics and the agenda setting process. Population dynamics models have been applied

44. 25
and adapted for use in a wide variety of fields including ecology and global economics (Boyce
and DiPrima, 2009).
This population dynamics model introduces here has two threshold points that corre-
spond to multiple equilibrium solutions. The equilibrium solutions of this model correspond
to no change or variation in the rise or fall of a species as time increases (Boyce and DiPrima,
2009). The first threshold is the point at which a species will either decline to extinction or
grow without bound. If the species does in fact grow, the second threshold marks the “satu-
ration level, or the environmental carrying capacity” of a species (Boyce and DiPrima, 2009,
81). This threshold marks the upper bound that cannot be exceeded due to environmental
constraints imposed upon a species.
What occurs if a policy attention is substituted for a species when the model is
applied? Or said another way, does the evolution of an agenda over time mirror the evolution
of a species over time? The level of attention can be equated to population dynamics. I posit
that if media coverage starts below a critical level, then the issue itself is not considered.
That is, if attention to an issue is unsustainable or unable to increase the issue will become
extinct. Conversely, if media coverage starts above a minimum critical level, then attention
paid to an issue eventually approaches the attention capacity threshold. As attention to an
issue approaches the attention capacity threshold, we can estimate that there would be a
leveling off of issue attention at the threshold.
The inclusion of threshold parameters provides an opportunity to evaluate issue atten-
tion where multiple issues compete. This is similar to the evaluation of population dynamics
when multiple species compete. Jones and Baumgartner (2005b) suggest that the level of the
amplification parameter combined with the input signal dictates the response to an issue.
The element of attention capacity within the context of the issue environment or venue is
missing from the Jones and Baumgartner model. An extrapolation of the Boyce and DiPrima
population dynamics model suggests that if little attention is applied to an issue, the issue

45. 26
Figure 2.2. Exponential growth: y vs. t for dy/dy = ry
(like a species) cannot propagate successfully and ultimately the issue (again like a species)
will become extinct. Additional attention applied at a point higher than a given threshold
level will result in increased issue attention. Finally, attention to an issue is not unbounded,
thus threshold levels must be incorporated into any robust issue dynamics model. The inclu-
sion of the upper bound threshold critical point will result in a more complete issue dynamics
model that represents the agenda setting process in the local government arena.
In their discussion of first order difference equations, Boyce and DiPrima (2009) show
under ideal conditions that the population of a species will grow exponentially for a period
of time. Such ideal conditions; however, do not exist, as limitations on space, food supply,
or other restrictions will reduce the population growth rate and end the exponential growth
(Boyce and DiPrima, 2009).
In the context of agenda setting studies, environmental limitations can be substituted
for attention capacity. According to Jones and Baumgartner’s disproportionate information
processing model, issue attention and attention capacity regarding a particular issue would
be seemingly unlimited if the signal strength is strong enough over a sustained time frame.
Boyce and DiPrima characterize this unlikely scenario as exponential growth as shown in
Figure 2.2. The decision-maker faces limitations in much the same manner as species. Species

46. 27
Figure 2.3. Logistic growth: The phase line
populations rise and fall based upon a variety of factors including food and water supply.
Likewise, attention to policy issues or agenda items are also impacted by a variety of factors.
The parabolic and exponential nature of Figure 2.2 is similar to what Baumgartner and
Jones conclude in Figure 2.1. A more accurate model of decision-making must account for
thresholds or constraints including issue attention and attention capacity. That is, these
curves cannot grow without bound; they will revert to some constant or stable level over
time in a population dynamics model.
2.2.1 Capacity Threshold - The Upper Limit
The Boyce and DiPrima (2009) population dynamics model identifies three equilibrium so-
lutions or thresholds that impact population growth. These thresholds are also referred to
as critical points. The first threshold or critical point is the environmental carrying capacity
or saturation level. The carrying capacity or saturation level measures the environmental
constraints that impact population growth. For a school of fish, the size of a pond or the

47. 28
amount of food in the pond measures the carrying capacity or saturation level that impacts
how many fish can flourish. At some point (threshold) the pond will become too full to
promote population growth. If the school of fish multiply and deplete the food supply faster
than the pond can respond, the school of fish is likely to face extinction (at least in that
pond). This concept can be adapted to represent the attention capacity of local government
leaders, community elites and the voting public. The local government venue can sustain
only so many issues at a given time. This is the key threshold point that is missing from the
disproportionate information processing model.
The phase line view of the capacity threshold in Figure 2.3 shows the upper movement
of y to K and the downward movement of y once K is approached. Since K is the upper
bound that is approached, but not exceeded, by growing populations starting below this
value, K is referred to as the saturation level, or as the attention capacity for a given issue.
Said another way, K as the threshold for attention capacity can serve as a measurement for
attention paid to competing agenda issues. This important threshold supports the notion
that there is a finite amount of attention that can be paid to any set of issues at any point
in time. Without threshold parameter K, attention capacity would be unbounded.
The value y in Figure 2.4 represents population growth. It can be substituted for
level of attention in the decision-making example. In Figure 2.4, if y is near zero or K,
then the slope of f(y) is near zero, so the solution curves are relatively flat. They become
steeper as the value of y leaves the neighborhood of zero or K. As the solutions approach
the equilibrium solution y = K as t approaches infinity, they do not attain this value at any
finite time. In sum, the presence of critical point K limits the amount of attention that can
be placed on any single issue.

48. 29
Figure 2.4. f(y) vs. y for dy/dt = r(1 − y/K)y
2.2.2 Signal Threshold - How Strong The Signal?
The second critical threshold represents the point a signal must reach to warrant political
elites’ attention. The identification of this critical threshold has important research impli-
cations. I have asserted that there is a fixed level of attention capacity for a decision-maker
or a given institution. This situation forces issues to compete against each other to gar-
ner some level of attention. By identifying the presence of a threshold level T that is the
critical point when elites’ attention turns to the issue, we can explore ways to measure sig-
nal strength. The disproportionate information processing model does recognize that some
threshold in signal strength must be reached before decision-makers respond, but does not
take into account attention capacity as measured separately by the threshold value K (Jones
and Baumgartner, 2005b).
Failure of the signal to reach threshold level T as shown in Figure 2.5, will result in
no attention allocated to an issue. In Figure 2.6, it becomes evident that as time increases,

49. 30
Figure 2.5. f(y) vs. y for dy/dt = −r(1 − y/T)y
y either approaches zero or grows without bound, depending on whether the initial value y0
is less than or greater than T (Boyce and DiPrima, 2009). While the value y in the Boyce
and DiPrima (2009) example represents population growth, it can be substituted for level
of attention in this decision-making example. This is to suggest that for an issue, the signal
strength must be strong enough to merit a critical level of attention. A critical point T
represents the moment when the issue merits attention from decision-makers.
2.2.3 Issue Dynamics – The Variables
If attention is allocated to a particular issue then the signal must reach a critical threshold.
Attention capacity is limited so unbounded attention cannot be applied. If we redefine the
inputs and variables of the Boyce and DiPrima population dynamics model, we can define
the following variables:

50. 31
Figure 2.6. Growth with a critical threshold: The phase line
1. y = Level of attention paid to an issue as measured by news article counts. The higher
the level of attention the more likely a response will result.
2. y1 = Level of attention paid to an issue as measured by the variable WORDCOUNTS
× TONE. This variable represents a slight modification in order to capture two ele-
ments of signal strength; length of newspaper article and tone. The higher the level of
attention the more likely a response will result. The purpose of y1 is to show article
density. Appendix A shows the y1 results for each case.
3. T = A threshold point that indicates signal strength is strong enough to merit a critical
level of attention by elites.
4. K = A threshold point that measures attention capacity of a decision-maker or insti-
tution.
5. r = A signal that is input into the policy-making system (y/y1 are a function of r).
Boyce and DiPrima (2009) modify the critical threshold model to account for the fact
that unbounded growth cannot occur when y is above critical threshold point T. Equation

51. 32
Figure 2.7. f(y) vs. y for dy/dt = −r(1 − y/T)(1 − y/K)y
2.4 represents this modification where r > 0 and 0 < T < K
dy
dt
= −r(1 −
y
T
)(1 −
y
K
)y (2.4)
If we graph f(y) versus y a parabola is created as shown in Figure 2.7. Figure 2.7 and
its phase plot found in Figure 2.8 shows that T is a threshold level, below which growth does
not occur and K is a threshold above which growth does not continue (Boyce and DiPrima,
2009). The intercepts on the y-axis are the critical points y = 0 and y = T, corresponding
to the equilibrium solutions φ1(t) = 0 and φ2(t) = T. If 0 < y < T, then dy/dt < 0, and y
decreases as t increases. In contrast, if y > T, then dy/dt > 0, and y grows as t increases.
Therefore, we can conclude that as time increases, y either approaches zero or grows to
threshold point K, depending on whether the initial value y0 is less or greater than T.

52. 33
Figure 2.8. Logistic growth with a threshold. The phase line
If we extend this population dynamics exercise to the world of policy choice and issue
attention, the value K represents the attention capacity of a decision-maker or decision-
making institution. The value of T represents the point a signal or input into the policy-
making system must reach before it receives some level of attention. Applying the threshold
parameters derived from the Boyce and DiPrima population dynamics model provides polit-
ical scientists an opportunity to explore new models of decision-making and agenda setting.
The three critical points include: y = 0, y = T, and y = K. If y or y1 start below the
threshold T, then y or y1 declines to ultimate issue extinction (Boyce and DiPrima, 2009).
Conversely, if y starts above T, then y or y1 eventually approaches the attention capacity
threshold K.

53. 34
2.3 The Issue Dynamics Model
The adaptation of the Boyce and DiPrima population dynamics model to evaluate local
government agenda setting results in the creation of a robust model. The issue dynamics
model addresses the primary criticisms of the disproportionate information processing model.
Rather than using a model that assumes a static relationship of signal and response at
a single point in time, the issue dynamics model shows the important relationship over
time. The issues dynamic model improves local agenda setting studies by showing dynamic
interactions of signal and response, rather than the quasi-linear approach adopted by Jones
and Baumgartner.
The important contribution of this research is to understand what would occur if the
Boyce and DiPrima model were applied to the problem of agenda setting. If y as a measure
of issue attention starts below critical threshold point T, then the issue is not considered.
Therefore, if attention to an issue is unsustainable or unable to increase the issue will become
extinct. Conversely, if y (a measure of issue attention) starts above T, then y eventually
approaches the attention capacity threshold parameter K. That is, as attention to an issue
approaches the capacity threshold, we can estimate that there would be a leveling off of issue
attention at the threshold.
The issue dynamics model leads to a critical innovation in the study of agenda set-
ting. Previous research focused on sub-function analysis. Most agenda setting studies group
issues at the function or the sub-function level. For example, issues concerning health care,
transportation or foreign policy can be grouped at a function level, but also broken into
subcategories. While issue specialization is certainly present, current models tend to de-
scribe issue conflict at the function level. An innovation of this research is to apply the issue
dynamics model across levels of government and across agendas in order to understand the
dynamic nature of the agenda setting process.

54. 35
2.4 Hypotheses
The issue dynamics model leads to testable hypotheses that explain the agenda setting
process when a professional sports referendum is debated. The hypotheses are organized
to best explain the issues dynamics of a professional sports facility over time. Once the
hypotheses are presented, I discuss the expectations of a stereotypical sports facility case if
the hypotheses are confirmed and another scenario if the hypotheses are not confirmed.
The properties of the issue dynamics model motivate the hypotheses. At the beginning
of an issue cycle, the media will focus on the issue. Initial media attention leads to increased
attention by the political and local elite. Hypothesis 1 draws support from research by Paul
and Brown (2001) that suggests the media can have a significant impact on the agenda
setting process. The signal in hypothesis 1 is based on the count of newspaper articles. The
number of newspaper articles is used as a proxy for the measure of media signal strength
and attention.
Hypothesis 1 (H1): (Signal) Strong media signals (based on article counts) will push a
professional sports facility proposal past the attention threshold parameter. Sustained
high values of y (level of attention based upon article counts) will push a professional
sports facility agenda item past critical threshold parameter T. Thus, the professional
sports facility issue is likely to appear on the local agenda. Conversely, sustained low
values of y will reveal a lack of attention needed to push the issue beyond critical
threshold parameter T. Thus, the professional sports facility issue is not likely to
appear on the local agenda.
The second hypothesis asserts that positive article tone is necessary to further increase
the signal strength. The logical consequence of the issues dynamics model and its proper-

55. 36
ties requires a reinforcing mechanism to increase the initial media signal. That reinforcing
mechanism is positive article tone.
Hypothesis 2 (H2): (Tone) Initial positive article tone will strengthen the signal (based
on article counts), thus elevating the issue to the local ballot in the form of a referen-
dum. Once past critical threshold value T, an increasing value of y will result. This
strengthened signal from the media will influence local leaders to place the professional
sports facility initiative on the local ballot in the form of a referendum.
Hypothesis 2 supports Rosentraub (1999) claim that the media will tend to support a new
professional sports facility. An alternative hypothesis would suggest that negative media
attention will not create a favorable environment for the political elite to support this issue,
thus the issue will not be placed on the local agenda. Regardless of tone, the article counts
will reflect the strength of signal thereby justifying attention of local government leaders.
Tone is important to consider because if government leaders sense that there is widespread
support for an issue then they will be more likely to support the sports facility referendum.
In contrast, the lack of perceived support in terms of negative media tone will not create the
sense of urgency necessary to promote the issue to the local agenda.
Downward pressure on media signal and elite attention is possible if negative articles
contradict previous positive articles. The response to a decrease in media signal will lead to
a framing change in order to regain positive media attention.
Hypothesis 3 (H3): (Changing Message) If article tone changes from positive to negative
during the election cycle, the political elite will change the message in order to reestab-
lish positive article tone. Once past critical threshold level T, if y decreases, that is
if the article tone changes from positive to negative, a distinct change in article focus
will occur. If article focus does indeed change, y will again increase toward critical
threshold point K.

56. 37
Hypothesis 3 suggests that once y is past critical threshold level T, if the article tone changes
from positive to negative a distinct change in article focus will occur. Article focus is the
primary subject matter of any article about the issue. The assumption is that local elites will
respond to negative media attention by changing the message. If article focus does indeed
change as the result of elite action, y will again increase toward critical threshold point K.
Positive media attention about an issue will reach its highest point at or near the elec-
tion date. As the issue model indicates, attention to an issue reaches a critical or maximum
point at the moment of a critical event like an election.
Hypothesis 4 (H4): (Critical Vote) Article tone and strength of signal, as measured by
article counts, will influence passage of the professional sports facility referendum, or
will support actions at a state or local government level. At the time of the referendum
election or other government action, y will approach or reach critical threshold level
K. Critical threshold level K can be viewed as the maximum level of attention that
can be given to an issue.
Hypothesis 4 suggests that the amount of positive media attention and framing effects will
impact voters opinions to such an extent that voters will be inclined to support the pro-
fessional sports facility referendum. This support will be reported by the media. If the
policy venue is a state legislature or local government board, positive media attention will
push local officials to act in support of a sports facility financing initiative. An alternative
hypothesis suggests that negative media attention would impact voter opinions to such an
extent that voters will be inclined to not support the professional sports facility referendum.
A more likely scenario holds that negative media attention will result in 1) decreased signal
strength (fewer articles) and 2) decreased attention allocation by local leaders.
Attention to an issue does not last forever. The media and local actors turn their
attention from issue to issue over the course of time. After an election, the amount of media

57. 38
attention will decrease as attention to another issue(s) begins to increase. Hypothesis 5
targets the post-election time period as the moment when attention to an issue begins to
decrease.
Hypothesis 5 (H5): (Post-Election) After an election (or action by local government agency
or board or action by a state legislature), the overall issue tone will turn negative and
the article counts will decrease below critical threshold level K and will fall toward
critical threshold level T. After the referendum election or action by state or local
officials, y values will decrease from critical threshold point K and will fall below crit-
ical threshold point T. The initial trend following the election will be moderately low
values of y as the impact of negative tone will be apparent. The more time that elapses
after the election, article counts as measured by y will decrease.
Hypothesis 5 suggests that the positive media attention leading up to an election will quickly
fade after the election. I posit that the tone will turn negative as many proponents’ economic
promises go unfulfilled and the realities of the project are revealed. Furthermore, as time
elapses after the election, fewer articles will be focused upon the professional sports facility
issue as attention will turn to other issues.
2.5 Expectations of a Stereotypical Sports Facility Case
In a stereotypical sports facility case, positive media attention is an important dynamic
driver of the ultimate success of the sports facility issue. In a hypothetical case, media
coverage of the professional sports referendum is expected to increase during the early phase
of the election cycle (H1–signal). If media coverage of the issue is insignificant early during
the agenda setting cycle, the issue will quickly fade as local government leaders determine
there is no sense of urgency from the public at large. A plethora of positive media coverage,

58. 39
however, serves as the primary indicator that local and state officials use as justification to
place the issue on the agenda (H2–tone). Newspaper articles highlight positive economic
studies commissioned by the professional sports franchise. Local leaders tout the economic
benefits presented in these biased reports. The media cover press conferences when stadium
designs and elaborate stadium models are made available to the public. Little if any negative
media coverage is present during the early portion of a professional sports facility issue cycle.
If media coverage turns negative, local elites will alter the strategy and message to ensure
that media coverage returns positive (H3–changing message). The amount of media coverage
increases up to the critical election or commission or board vote (H4–critical vote).
After the critical election or vote, media coverage of the sports facility issue decreases
as media focus turns to other issues. In a typical case, media coverage is also assumed to
turn negative as the realities of the massive public works project come to light. Issues such
as cost overruns, eminent domain conflicts and site selection just to name a few claim the
focus of the local media (H5–post-election). The critical election date or vote will serve as
a key moment when the expected shift in the amount of media coverage and change in tone
will be observed. The next chapter provides important details regarding the research design
including case selection and definition. In addition, a presentation of comparative cases is
provided.

59. CHAPTER 3
RESEARCH DESIGN
This chapter describes the components of the research design. First, I identify the case
selection parameters. Second, I describe the comparative cases tied to the hypotheses. Third,
I define the signal measurement, as well as the measurement of article tone and focus. I also
describe the process used to select the key words. Finally, I list the benefits of this research
design. A significant innovation in this research design is the use of newspaper coverage about
a local issue. Most agenda studies to date have focused on national issues rather than local
issues. A consequence of this design is an investigation of the media’s impact or influence
on the local government agenda setting process. The research design incorporates many
components of Baumgartner and Jones (1993) agenda setting research design. Baumgartner
and Jones (1993) innovative approach utilizes publicly available records as the primary data
source. The use of publicly available records allows for a greater collection of quantitative
indicators than could be gathered otherwise. The collection of data across multiple cases
yield cross-sectional comparisons over multiple time series. Additional detail regarding article
selection and coding procedures can be found in Appendix B.
3.1 Case Selection and Definition
This research focuses on eight National Football League (NFL) sports facility projects that
began construction after 2001. A multiple time series design is used to study the agenda
setting problem. NFL sports facility projects are selected for two key reasons. First, NFL
stadiums are significant public works projects. For example, the new home of the Dallas
Cowboys that opened in 2009 cost an estimated $1.2 billion. The large financial contribution
40

60. 41
by the local government represents a significant punctuation in the policy process. Second,
media coverage of NFL franchises is more prevalent than media coverage about any other
professional sports league in the United States. Professional football is the most popular
sport in the United States based upon attendance and television viewership (Zimbalist,
2006). This level of popularity is unmatched by any other professional sport and translates
into robust media coverage by the local newspaper(s). The time frame of facility construction
is also an important consideration for case selection. Construction typically begins well after
public financing is approved for a professional sports facility. The key referendum date
range of the NFL stadiums built since 2001, is from 1996 to 2005. While the amount of
information available from on-line databases has grown exponentially over the past decade,
there is limited access to such newspaper articles prior to 1995.
All eight NFL sports facilities are the result of an eventually successful action at the
state or local government level. The majority of the cases are the result of a successful
local referendum while others are the result of the actions of a local government commission
or board. Still other cases are the result of state legislative actions. The eight separate
initiatives are identified in Table 3.1. The media sources include the primary newspapers in
each city. The specific newspapers are identified in Table 3.1 as well as the year or years
of key decision dates. Key decision dates are defined as the date of a referendum election,
commission or board vote, or action taken at the state level.
3.1.1 Comparative Cases Tied to Hypotheses
Cross-case comparisons are used to discover key facts that can be generalized. In addition,
the size of media market and government venue provides two additional bases for comparison
and hypothesis testing. Those franchises that are located in large media markets include the
Dallas Cowboys, Houston Texans, New York Giants and New York Jets, and the Philadelphia

61. 42
Table 3.1. National Football League (NFL) sports facilities constructed via tax dollars in
the United States since 2002.
Team Name of Facility Primary Newspapers Key Date(s)
Arizona Cardinals University of
Phoenix Stadium
Arizona Republic and Tuc-
son Citizen
2000
Houston Texans Reliant Stadium The Houston Chronicle 1996
Dallas Cowboys Cowboys Stadium Dallas Morning News and
Fort Worth Star–Telegram
2004
Indianapolis Colts Lucas Oil Field The Indianapolis Star
and Indianapolis Business
Journal
2005
New York Giants
and New York Jets
New Meadowlands
Stadium
New York Times, Daily
News, Village Voice, The
Times Union, New York
Sun, New York Observer,
The Record, The Star-
Ledger, Herald News, Jer-
sey Journal
2005
Philadelphia Ea-
gles
Lincoln Financial
Field
Philadelphia Daily News
and The Philadelphia In-
quirer
1999, 2000
Pittsburgh Steelers Heinz Field Pittsburgh Post-Gazette 1997, 1998, 1999
Seattle Seahawks Quest Field Seattle Times and Seattle
Post-Intelligencer
1997
Eagles. Small media market franchises include the Arizona Cardinals, Indianapolis Colts,
Pittsburgh Steelers, and the Seattle Seahawks.
Facilities for the Arizona, Dallas, Houston and Seattle NFL franchises were the result
of successful referenda. The Indianapolis Colts’ facility was the result of state legislative
action. The stadium to be shared by the New York Giants and the New York Jets was the
result of local board approval. New NFL (and MLB) facilities in Philadelphia and Pittsburgh
required action at the state level and at the local level. The selection of cases in Table 3.1
are compared based on media market size, government venue that sponsored action, and
multiple venue dynamics.