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The spatial economic impact of live music in Orange County, CA

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The spatial economic impact of live music in Orange County, CA

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THE SPATIAL ECONOMIC IMPACT OF LIVE MUSIC IN
ORANGE COUNTY, CALIFORNIA

by

Jeremy D. Olson-Shelton

__________________________________________________________

A Dissertation Presented to the
FACULTY OF THE USC SOL PRICE SCHOOL OF PUBLIC POLICY
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF POLICY, PLANNING, AND DEVELOPMENT

May 2013
Copyright 2013 Jeremy D. Olson-Shelton
ii
DEDICATION

To all those who fill the world with music and joy.

iii
ACKNOWLEDGEMENTS
This project was only possible due to the generous support of so many.
First and foremost, I would like to thank my family for their unwavering support
and understanding. I would also like to thank my chair Gary Painter, and
committee members, Tridib Banerjee, Jennifer Swift, Frederick Steinmann, and
Terry Cravens. This study has also had substantial guidance from Harry
Richardson and Deborah Natoli. In addition to those named above, there were
many other professors, classmates, and friends, who aided this study along the
way, teaching the skills necessary to perform these complex analyses and
appropriately synthesize the results. I would especially like to thank all those
who shared their opposing views so that I could better understand differing sides
of several issues. “From diversity comes strength” and that is exactly what they
have lent this study. I would also like to thank Bob Sanders, Tammy Noreyko,
and the Orange County Professional Musicians Union, local 7 for their support
with data for this research. Finally, I would like to thank the University of
Southern California for allowing me to take this wonderful journey and learn so
much along the way.

iv
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ................................................................... iii

LIST OF TABLES ................................................................................ v

LIST OF FIGURES ............................................................................. vii

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

CHAPTER 1: INTRODUCTION .......................................................... 1
Statement of the Problem ............................................................ 1
Contribution to the Literature ....................................................... 6
Research Question ...................................................................... 8

CHAPTER 2: A REVIEW OF THE LITERATURE ............................... 10
The Creative Economy ................................................................ 10
Music and the Arts in Society ...................................................... 16
Measuring Arts Impact ................................................................. 28
Spatial Analysis............................................................................ 36

CHAPTER 3: METHODOLOGY ......................................................... 43

CHAPTER 4: RESULTS ..................................................................... 56
Economic Analysis of Orange County, California ........................ 67
Spatial Analysis............................................................................ 79

CHAPTER 5: DISCUSSION AND CONCLUSIONS ........................... 107
Discussion .................................................................................... 108
Conclusions ................................................................................. 116
REFERENCES ................................................................................... 118

APPENDICES ..................................................................................... 132

v
LIST OF TABLES
Table 1. Location Quotients - Total Number of Employees 2010 ............. 70
Table 2. Location Quotients - 2006 - 2010 ............................................... 72
Table 3. Shift-Share Analysis ................................................................... 74
Table 4. Total multipliers by industry - Top 35 of 66 ................................ 77
Table 5. Top ten sectors impacted by the performing arts. ...................... 78
Table 6. Regression Output Models. ........................................................ 83
Table 7. Orange County Total Type Multipliers. ..................................... 132
Table 8. Orange County Industry Description Codes ............................. 134
Table 9. Orange County Total Requirements Table. .............................. 136

vi
LIST OF FIGURES
Figure 1. Arts, Entertainment, and Recreation Establishments .......... 56
Figure 2. Arts, Entertainment, and Recreation Total Payroll ............... 57
Figure 3. Map of California ................................................................. 58
Figure 4. Orange County, California and Neighbooring Counties ....... 59
Figure 5. Orange County, California Communities ............................. 60
Figure 6. Median Age Map .................................................................. 62
Figure 7. Percent Hispanic Map .......................................................... 63
Figure 8. Orange County Crime Distribution ....................................... 64
Figure 9. Orange County Music Venue Distribution ........................... 67
Figure 10.Orange County Music Venue Distribution and Land Use ... 68
Figure 11. Orange County Business Pattern Percentages ................. 69
Figure 12. Orange County Location Quotients ................................... 71
Figure 13. Music Venue Location Density Map ................................. 79
Figure 14. Orange County Music venue Hot Spots ........................... 81
Figure 15. Music Hot Spots with Union Event Locations ................... 82
Figure 16. Scatterplot of Venue Count and Annual Payroll ............... 84
Figure 17. Venue count and annual payroll residuals ......................... 85
Figure 18. Venue count and annual payroll residuals, Moran’s I ....... 86
Figure 19. Venue count and number of paid employees .................... 87
Figure 20. Venue count and number of employees, residuals ............ 88
Figure 21. Venue count and number of employees, Moran’s I .......... 89
vii
Figure 22. Scatterplot of venue count and average payroll ................ 91
Figure 23. Venue count and average payroll residuals ...................... 92
Figure 24. Venue count and average payroll residuals, Moran’s I ..... 93
Figure 25. Map of venue count and average payroll residuals .......... 94
Figure 26. Venue count and average payroll GWR coefficent map ... 95
Figure 27. Scatterplog of log-log re-expression ................................. 96
Figure 28. Log-log re-expression residuals ........................................ 97
Figure 29. Log-log residuals, Moran’s I .............................................. 98
Figure 30. Log-log re-expression local R
2
values .............................. 99
Figure 31. Scatterplot of venue counts and median home value ....... 100
Figure 32. Venue counts and median home value, Moran’s I ............ 101
Figure 33. Venue counts and median home value local R
2
............... 102
Figure 34. Venue counts and median home value local coefficients . 103

viii
ABSTRACT
This study explores the spatial-economic relationship between live music
events in Orange County, California and measures of economic strength. The
purpose of this study is to inform planners and funders of cultural activities about
the spatial-economic impacts their allocations have produced so that scarce
budgets can be targeted to maximize societal benefits. Spatial analysis
techniques, including cluster analysis and geographically weighted regression,
were utilized to explore the correlation between live music venues in Orange
County, California and economic indicators, including average per capita annual
income, number of jobs, and average real estate prices by zip code. Traditional
econometric techniques were also included for breadth and comparison
purposes. This study is an important step in understanding the effects of cultural
spending and adds to the economic impact literature.
1

CHAPTER 1 1
INTRODUCTION 2
“Music can change the world” - Ludwig Van Beethoven 3
4
Statement of the Problem 5
This study examines the relationship between the location of live music 6
events and the economic strength of the surrounding region. A multi-disciplinary 7
approach was utilized to investigate this relationship from multiple perspectives. 8
Using techniques from both Geographic Information Science (GIS) and 9
Economic Impact Analysis models, the connections between music event 10
locations and economic prosperity were explored, along with the mediating 11
impact of geography. Traditional economic analysis techniques were combined 12
with geographic analysis to provide a more comprehensive image of the 13
relationship between live music and the economy. 14
Need for the Study. Many studies have tried to measure the economic 15
impact of the arts, but these studies have severe limitations. For example, many 16
studies rely predominately on self-report, survey data and are subject to 17
nonresponse bias and under-coverage (see A. Gary Anderson Center for 18
Economic Research & The Orange County Business Committee for the Arts Inc., 19
2010). Others, based solely upon Input-Output analysis, address only regional 20
2

economic impacts (see Fowler & Andreoli, 2008; Rothfield, Coursey, Lee, Silver, 1
& Norris, 2006). Much of the desired application of these studies, however, is at 2
a finer scale. In order to revitalize communities at a local level, it may be 3
beneficial to target funding with greater precision. Previous studies have largely 4
ignored geography at the local level and may fall victim to the Modifiable Areal 5
Unit Problem, in which patterns vary according to the scale to which they are 6
aggregated (Fotheringham & Wong, 1991). Conversely, transportation networks 7
and other variables may obfuscate some of the finer scale variations at the local 8
level. Therefore, it is meaningful to incorporate multiple forms of analysis when 9
evaluating economic impacts. 10
There is a need for research that looks more comprehensively at the 11
impact of the arts at multiple scales. Regional economic impact analysis is an 12
important component of this research as it shows the large-scale impacts of the 13
industry. However, it is possible that real and measurable impacts of arts 14
organizations will be missed when data is aggregated to the regional or higher 15
scale. Thus, it is equally important to explore the smaller impact of arts locations 16
on the local economy. This study addresses this need by incorporating a mixed- 17
methods approach to studying the economic impact of live music; considering 18
both the large-scale impact of the arts industry and the local impact of live music 19
venue locations. 20
21
22
3

Background and Context of the Problem
The arts are an important part of the local community in that they can be 1
considered public goods (Guetzkow, 2002). Many local governments choose to 2
actively support arts and culture organizations as a way to enrich their 3
communities (City of Phoenix, 2013; City of San Diego Commission for Arts and 4
Culture, 2013; Seattle Office of Arts & Cultural Affairs, 2013). However, in times 5
of economic difficulty arts programs are frequently cut from public funding 6
(Pogrebin, 2011). When making these types of challenging budget decisions it is 7
important to clearly understand what impacts people’s lives and what that impact 8
looks like. 9
Citizens make difficult fiscal choices; prioritizing spending based on 10
assumptions about future returns on investments. While some expenditures and 11
results are easily quantifiable, others are not. The field of cultural policy has 12
been challenged for years to produce quantifiable figures to crunch and calculate 13
in cost benefit analysis charts (National Public Radio, 2010). Be it in zero based 14
budgeting reform (see Otten, 1978; Wetherbe & Montanari, 2006) or the new 15
public management (see Barzelay, 2001; Dunleavy & Hood, 1994; Pollitt & 16
Bouckaert, 2011), clear and quantifiable results are increasingly demanded of 17
public agencies. 18
Currently the United States allocates approximately $150 million dollars 19
annually to the National Endowment for the Arts (National Endowment for the 20
Arts, 2012). This amounts to 0.004 percent of the nation’s 3.7 trillion dollar 21
4

budget (United States Office of Management and Budget, 2012). The U.S. also 1
spends approximately $200 million dollars annually on military musicians 2
(Pincus, 2011). States spend a combined $263 million annually on the arts, 3
0.035 percent of state budgets (National Assembly of State Arts Agencies, 4
2012). Cities set policy requiring new developments to contribute a percentage 5
of their total cost to cultural activities (City of Brea, 2011; Sacramento 6
Metropolitan Arts Commission, 2011). The twenty-five largest arts funders 7
contribute more than $800 million in grants to arts organizations (Grantmakers in 8
the Arts, 2011). Cumulatively, this spending totals in the billions of dollars. While 9
this figure is only a fraction of the Gross Domestic Product of over $15 trillion 10
(Bureau of Economic Development, 2012) or even the total economic output of a 11
state, administrators struggle to balance budgets on borrowed dollars in any 12
municipality from federal to local school districts. Worthy endeavors that cannot 13
empirically demonstrate their worth are cut out of budgets. 14
Reflecting upon the common belief that the arts are an important part of 15
the fabric of humanity, deeper questions emerge: In what ways? How important 16
are they? What are their impacts? The literature is thick with studies on the 17
effects the arts have on the brain (George & Coch, 2011; Levitin, 2007; Moreno 18
et al., 2011) and how the arts reinvigorate cities (Currid, 2007; Florida, 2004). 19
This literature largely addresses the first two of these fundamental questions but 20
seems to be insufficient in addressing their impacts, especially with regards to 21
the economy. 22
5

While studies have been done attempting to put a number on the 1
economic impact of the arts, the literature consistently has been unable to 2
address this issue with sufficient precision to inform those who benefit most, or 3
least, from these programs. Even the most sophisticated economic modeling 4
techniques may fail to be adequate in assessing impacts where taste, 5
preference, mood and inspiration are causes of increased economic productivity. 6
Researchers can now utilize advances in the spatial sciences to help 7
capture this elusive impact, with such techniques as identifying residual patterns. 8
GIS can now harness tremendous computing power to analyze patterns in 9
disparate data sets that previously could not practically be compared. While this 10
science is still in its infancy and has limitations of its own, the insights it is now 11
able to provide are particularly beneficial in addressing the problem of how the 12
economy is impacted across space. Specifically, GIS technologies are well 13
suited towards identifying patterns that are spatial in nature and in measuring the 14
strength of observed spatial relationships. 15
16
Purpose of the Study
The purpose of this study is to measure the economic impact of live music 17
on the local economy. Traditional economic impact methodologies are used to 18
substantiate the regional economic impact of the industry. Spatial techniques are 19
utilized to explore the geographical relationship of live music venue locations and 20
6

economic strength at a finer scale. Many studies claim that arts events bring 1
multiple returns on investments (Americans for the Arts, 2007). If this is true, 2
there may be a geo-economic swell near areas with high concentrations of music 3
events. For example, the impact of the Music Center of Los Angeles County 4
would be expected to show up predominately in the Los Angeles Basin, rather 5
than in Las Vegas. 6
This study explores the extent to which live music events spatially covary 7
with measures of economic strength. In the presence of evidence that the arts 8
positively impact the economy at a regional scale, a correlation between specific 9
arts events locations and traditional economic metrics provides evidence about 10
how that impact is distributed across the local landscape. In other words, if the 11
regional economic impact analysis demonstrates that the arts have a positive 12
impact on the regional economy, then a positive correlation between live music 13
venue locations and economic measures may be considered evidence that live 14
music events positively impact the local economy. Spatial regression techniques 15
can be utilized to explore the relationship between live music locations and 16
measures of economic prosperity such as employment data and home values. 17
18
Contribution to the Literature 19
While several studies use traditional methods to estimate the economic 20
impact of the arts on a national or regional level, these techniques suffer from 21
7

many limitations when applied on a finer scale. Traditional economic analyses 1
utilize Location Quotients, Social Accounting Matrices, and/or Leontief multipliers 2
to estimate flows of goods within a region. Limitations of data availability and the 3
costliness of traditional survey methodologies mean that this type of analysis is 4
more practical for larger regions than small-scale areas. This is problematic for 5
researchers in public policy who may want to identify finer variations in economic 6
effects. Therefore it can be difficult to determine the economic impact of 7
programs in local communities. Incorporating GIS can help to mitigate some of 8
this difficulty. 9
This study applies spatial science techniques to compare events locations 10
and economic factors, in order to explore economic variations at the zip code and 11
finer scale. These variations are contrasted with traditional economic measures 12
at the county-wide level, demonstrating how regional impact analysis can be 13
augmented with techniques from geographic information sciences. This 14
technique can be extended to similar problems in public policy, where 15
researchers are interested in exploring the local impact of events or programs. 16
The advance in practice in this study is the use of multiple measures at 17
multiple scales to assess both the overall economic impact of an industry and its 18
spatial variations. Combining GIS techniques with traditional regional impact 19
analysis can provide a valuable additional measurement for program evaluations 20
and other policy analyses. It allows public officials to measure smaller-scale, 21
local variations as a part of their overall fact-finding process. The process of 22
8

combining these measures involves two main steps. First, the regional impact of 1
the encompassing industry can be explored using existing data and economic 2
impact modeling techniques. Second, this analysis can be extended to include 3
the local impact of the specific events or programs, using GIS techniques to map 4
the distribution and relationship of the events being studied and economic 5
measures. 6
7
Research Question 8
What is the spatial-economic impact of live music in Orange County, 9
California? 10
11
To answer this question two sub-questions must be addressed. First, what 12
do traditional measures of economic impact say about the relationship between 13
live music and the Orange County economy? Economic analyses were 14
performed using location quotients, shift share analysis, and regional Input- 15
Output analysis. Due to limitations in data availability, these analyses were 16
performed on pre-aggregated industry data that included the field of live music 17
performances. 18
Second, to what degree does a spatial relationship appear to exist 19
between live music and the economy? This is addressed by exploring the 20
degree to which live music venues correlate with economic indicators. This was 21
9

done by conducting a hot spot analysis, ordinary least squares regression, and 1
geographically weighted regression. 2
Collectively these analyses paint a picture of the degree to which areas in 3
Orange County with higher concentrations of live music events have higher 4
values on traditional economic indicators. The degree to which this pattern holds 5
true is assessed in answering the question: What is the spatial-economic impact 6
of live music in Orange County, California? 7
10

CHAPTER 2 1
A REVIEW OF THE LITERATURE 2
3
Isaac Newton once said, “If I have seen further it is only by standing on 4
the shoulders of giants.” This study draws from a variety of fields and thus 5
begins with a brief review of the theories and ideas that contribute to 6
understanding the spatial impact of live music on the economy. First, the 7
literature on the creative industries is explored broadly, including the impact of 8
the creative industries, the creative class, and creative places on the economy. 9
Second, the role the arts have in society is considered, including its role in social 10
capital and networking. Next, studies that evaluate the arts and their impact on 11
both people and the economy are reviewed, followed by a consideration of the 12
role of planning in arts and culture. Finally, different potential methods for 13
analyzing the spatial-economic impact of live music are explored, including 14
traditional economic tools and spatial analysis techniques. 15
16
The Creative Economy 17
The Creative Class
Creativity is what has traditionally placed the United States at the top of 18
the world when it comes to the economy. Creativity has historically allowed 19
11

people to dream the big dreams, adapt and overcome their problems, and 1
innovate in business. Richard Florida has spent his career researching the 2
creative economy and how these same creative abilities found in artists and 3
fostered by creative activities lead to innovations in functional fields like 4
engineering and medicine (Florida, 2004, 2008, 2010). Leonardo De Vinci 5
provides an exemplar, demonstrating how creative thinking can lead to world- 6
changing breakthroughs. The creative industries, including music, are an integral 7
part of the economy, particularly in large metropolitan areas such as New York 8
(Currid, 2007). 9
Richard Florida asserts that where individuals choose to live can have a 10
considerable impact their opportunities and level of productivity; that the 11
economic factors of “talent, innovation, and creativity” are keys to economic 12
success and clustered in particular regions across the United States (2008). He 13
goes on to say that when talented and creative people cluster together it 14
facilitates the generation of new ideas, making the region where this clustering 15
occurs more productive and promoting economic growth. Furthermore, the 16
global economy leads to more localized specialization, so place is becoming 17
more important rather than less relevant as many have been led to believe. 18
The creative class is an important component of both regional and 19
individual economic strength. Being part of the creative class can mean job 20
stability, for example a recent rise of unemployment to 10% nationally was 21
associated with an unemployment rate of less than 5% in the creative class 22
12

(Florida, 2012). Florida found that two main factors influenced housing values, 1
the wealth of the residents and the presence of a creative talent pool, which he 2
termed the Bohemian-Gay index; the second of which had a much larger impact 3
(Florida, 2008). In later writings, Florida utilizes a similar index, the creativity 4
index, emphasizing three components; technology, talent, and tolerance (Florida, 5
2012). In many respects, either index is as much a measure of local diversity 6
and tolerance as anything else. True diversity, of all types, creates an 7
environment that nurtures creativity and promotes successful problem solving 8
and is essential to building a climate that promotes prosperity (Florida, 2012; 9
Jacobs, 2002). Creative problem solving is in many respects one of synthesis, of 10
bringing diverse ideas together to create a comprehensive plan, a novel solution, 11
or even a beautiful piece of artwork built upon exposure to the work and ideas of 12
others (Florida, 2012). 13
Florida’s hypothesis is that locations that are more tolerant, more open- 14
minded, and more diverse draw more creative people, and that creative talent 15
drives economic growth. As areas increase in artistic concentration, they 16
become more desirable places to live and they draw other individuals to move 17
there as well (Currid, 2009). Forward thinking organizations follow the creative 18
talent that immigrates to these creative hubs, further stimulating this trend 19
(Florida, 2012). Recent research has found those areas with stronger 20
concentrations of members of the creative class have better weathered the 21
recent economic crises, having lower unemployment rates than the rest of the 22
13

nation (Stolarick & Currid-Halkett, 2012). To maintain continued growth, these 1
organizations and communities need economic and social investments for 2
continued creative thinking and collaboration (Florida, 2012). 3
4
The Role of Place in the Creative Economy
Place matters. It is becoming increasingly important both in the creative 5
economy and for the creative class. Therefore, it is important to consider the 6
importance of the built environment and its role in drawing individuals to the 7
region. What is it that draws the creative class? Is it only the people, or is there 8
something more? Can planners actively create an environment that creates this 9
desired effect? 10
The built environment. Public space is an important part of the creative 11
community. Jane Jacobs advocates for having diversity in buildings, different 12
uses, different ages, and places for people to meet and collaborate (Jacobs, 13
2002). Ray Oldenburg calls this the “Third Place” and argues that these Great 14
Good Places, including bars and coffee shops, are essential to the community, 15
sparking conversation and ad hoc collaboration (Oldenburg, 1999). Sadly, the 16
trends of increased privatization and globalization along with communications 17
technology are diminishing public space, leaving much of the job of maintaining 18
and promoting essential public spaces to planners and other public officials 19
(Banerjee, 2001). 20
14

The type of public space is important. Communities should support the 1
culture, diversity, and creativity of the people, not just address economic 2
concerns. For example, although tribal gaming casinos have been the most 3
successful pathway out of poverty for Native Americans, promoting a higher 4
standard of living, increased resources, and significantly more jobs, some 5
individuals are concerned that this may come at the cost of tribal culture and 6
identity (Gonzales, 2003). When planners go into a community to help enhance 7
public space, careful consideration should be taken to balance the preservation 8
of existing culture and uniqueness of the area, while also promoting the ever- 9
important components of tolerance and diversity. It is best for the culture to 10
come from the people, at a grass roots level, rather than having it introduced to 11
the community by corporate takeover (Jacobs, 2002). On the other hand, 12
inclusiveness is also a concern. All individuals should have access, from 13
disabled individuals needing wheelchair access to individuals from diverse ethnic 14
backgrounds, societies benefit most when all members have the opportunity to 15
leave their mark on the world (Jacobs, 2002; Jr, Polzer, Seyle, & Ko, 2004). 16
In creating the space that promotes the elements of creativity, particularly 17
talent and tolerance, urban designers need to pay special attention to the needs 18
of the specific community. Applying strategies from the social sciences, they 19
should research the unique needs, values, and interactions of their target setting 20
and apply design principles to support prosocial activities and inhibit negative 21
interactions (Banerjee & Loukaitou-Sideris, 2011). Whether planning new 22
15

communities, enhancing older neighborhoods, or conserving the best existing 1
urban spaces, urban planners can purposefully take steps to bring the 2
community together by providing the structure and opportunity, in terms of 3
informal meeting places, for interpersonal interactions, collaboration, and 4
relaxation (Banerjee, 2001; Banerjee & Loukaitou-Sideris, 2011; Jacobs, 2002; 5
Oldenburg, 1999). 6
The Bilbao effect. Bilbao, Spain provides one example of how targeted 7
urban redevelopment can transform a location and an economy. In the mid- 8
twentieth century Bilbao, Spain was a blighted area with very little to draw local 9
travel. Researchers proposed revitalization through “large scale urban 10
redevelopment” (Gomez, 1998; Richardson, 1975, p. 101). Bilbao’s economy 11
transformed with the creation of the Guggenheim museum, which attracted 12
approximately five million tourists and added more than $600 million to the local 13
economy in its first five years (Rybczynski, 2002). The effect has been marked 14
enough that it has even prompted policy tourists who travel to the region to learn 15
how they can similarly transform their own urban landscapes (Gonzalez, 2011). 16
This effect is not unique, nor is it solely viable in urban spaces. Branson, 17
Missouri experienced a similarly marked growth beginning in the 1980s when 18
country music celebrities opened music theatres along Highway 76 (American 19
Planning Association, 1993). Clearly the arts can have a transformative impact 20
on the economic and social landscape. In fact, one arts venue location, such as 21
the Guggenheim, can change the trajectory of an entire economy. This brings 22
16

new questions regarding the geographical effect of this impact. How far-reaching 1
is the impact? What would be the impact of multiple venue locations? 2
3
Music and the Arts in Society 4
Research on the effects of music and society are as varied as the types of 5
music in the world. Music occurs within a social context where the music and 6
social life are inseparably intertwined (Hargreaves & North, 1997; North & 7
Hargreaves, 2008; Supic̆ ić, 1987). Music events are used as icons of place and 8
time, from the legendary Woodstock concert to large and televised events like 9
the Rose Parade. These effects can cause an immediate, direct, positive 10
economic shock from related merchandising, as well as residual economic 11
benefit from future spending. Music also can help individuals move on in times 12
of sadness, when the solemn notes of a bugler aids in the grieving process for 13
family and community. This is not to suggest that music will definitively cause 14
the shift from mourning to productivity, but it can play an important role. 15
Music can bring people together in patriotism. There is a shared 16
emotional experience in listening to the President’s Own Band play the Stars and 17
Stripes Forever on the 4
th
of July or in bringing together opposing teams and fans 18
singing the National Anthem at a sporting event. The music unites people with a 19
shared sense of belonging, helping them to make connections across socio- 20
cultural boundaries (Leppert & McClary, 1989). This suggests that music can 21
17

increase productivity through the gentle, transformational reminder that, although 1
individuals may compete, they should also cooperate. These connections make 2
a personal economic impact as they allow individuals to forge new social links 3
and consequently increase their employability (Matarasso, 1997). 4
Music events can also be symbols of class and prestige. Individuals often 5
gain status based on “distinctions of taste” and may choose to attend certain 6
music events, such as the opera, to “see and be seen” (Bourdieu, 1984). They 7
make connections with others who hold similar values, increasing their cultural 8
capital. 9
10
Social Capital and Network Theory 11
Social Capital and Network theories, though separate, both share some 12
key issues relevant to this study. Individuals who support and attend music 13
events build social capital based upon “shared values and trust” (Field, 2003), 14
opening information channels (Coleman, 1988) that they can draw upon in 15
business ventures. 16
The stress free environment of musical events can also provide a semi- 17
regular opportunity to communicate with other important players in an informal 18
environment. American society has become disconnected, as society begun to 19
lose its sense of community (Putnam, 2001). Music events present an 20
opportunity for networking that often leads to connections and resources that can 21
inform decisions, open opportunities, and ultimately lead to more productivity. 22
18

Individuals who come together in music discover that through collaboration they 1
can achieve accomplishments that would be impossible to realize individually 2
(Halpern, 2005). Researchers found that participating in music is associated 3
with increases in cultural capital including educational attainment (Roose & 4
Stichele, 2011). Bygren, Konlann, and Johansson, after controlling for other 5
factors, found that individuals who attended cultural events had longer life 6
expectancies (Bygren, Konlaan, & Johansson, 1996), suggesting that music can 7
impact quality of life at the most fundamental level. 8
Music impacts society. It is associated with increases in social and 9
cultural capital. It helps to build networks, to help people work more effectively 10
and collaboratively. As mentioned in relation to individual productivity, if music 11
increases the productivity of groups there should be a measureable impact. 12
Business should be running more effectively and realizing increased revenues in 13
areas where these groups have more access to music events. 14
15
Arts Impact Studies
Claims have been made repeatedly and convincingly about the benefits of 16
arts and cultural events on society. Music has been shown to impact brain 17
development, and social wellbeing. It has been cited as aiding in the building of 18
networks, in fostering creativity, and in stimulating the economy. Music has been 19
likened to “exercise for higher brain function…[enhancing] our ability to think and 20
19

reason” (Shaw & Peterson, 2004). The studies that make these claims vary 1
dramatically in their robustness and their ability to demonstrate a clear 2
relationship between music and its impact. However, the evidence of impact is 3
growing, encouraging researchers to explore deeper and to apply new tools to 4
measure this impact. 5
Brain development and productivity. Brain research is going through a 6
spectacular boom right now, as technology has provided researchers with a 7
window into neural connections and the short and long term effects of exogenous 8
activities. Although it has long been suspected and asserted that activities can 9
play important roles in how brains develop and function, researchers now have 10
the ability to actually visualize the difference in live subjects (Andrade & 11
Bhattacharya, 2003; Knutson, Westdorp, Kaiser, & Hommer, 2000; Talan, 2009; 12
Zhang et al., 2012). Not only can the difference be seen in normally functioning 13
brains, this difference is also seen in abnormal brains such as those who suffer 14
from Autism (Whipple, 2004). These studies point to music causing increased 15
brain function and, consequently, the potential for greater productivity. 16
The Mozart Effect is one of the best known, and commonly 17
misunderstood, studies that link music with brain function. The authors, 18
recognizing that much of the previous research into music and the brain was 19
correlational or even anecdotal, set out to explore whether there was a causal 20
relation between musical experiences and abstract reasoning (Rauscher, Shaw, 21
& Ky, 1993). Using spatial tasks from an IQ test, they found that participants who 22
20

were exposed to Mozart immediately preceding the activity tested 8-9 points 1
higher than those exposed to relaxation tapes or silence. They have often been 2
criticized by researchers concerned that the results were not universally 3
replicable, and that the music had no lasting effect on the IQ of the participants, 4
and that the research does not actually prove that listening to music could 5
increase intelligence (e.g. see McKelvie & Low, 2002). These criticisms often 6
stem from the fact that popular media has misunderstood the study as proof that 7
listening to music can make you smarter. What the authors did prove, however, 8
is no less remarkable; musical experiences can impact performance on non- 9
musical tasks. 10
A large body of literature has explored the impact that musical 11
experiences have on performance and productivity in both humans and animals, 12
finding clear links between music and learning. For example, in a study 13
conducted on mice, subjects exposed to music in utero and after birth made less 14
mistakes as adults in a maze task than those exposed to white noise or only 15
silence. Furthermore, the brain chemistry of the experimental group was 16
markedly different than that of the two controls (Chikahisa et al., 2006). In 17
another study, researchers exploring the applications of music for neurological 18
rehabilitation found that stroke victims who listened to music had significantly 19
greater recovery in mood, verbal functioning, and focus compared to the control 20
group and those that listened to audio books (Särkämö et al., 2008). As 21
researchers explore further into why music can have both immediate and long 22
21

term learning impacts, there has been significant evidence that this learning may 1
be mediated by its impact on mood and arousal (Husain, Thompson, & 2
Schellenberg, 2002; Schellenberg, 2009; Witchel, 2010). 3
Listening to music can enhance non-cognitive tasks as well. For example, 4
studies have shown that background music can raise efficiency in the 5
performance of repetitive tasks, even in the presence of other distracting noises 6
(Flint, 2010). It improves the quality of work, including such complex tasks as 7
software design (Lesiuk, 2005). Outside of the workplace it can improve quality 8
of life in senior citizens and can lead to a more positive aging process (Laukka, 9
2007). In the Seven Habits of Highly Effective People, Stephen Covey 10
advocates having balance in your life, taking time to “Sharpen the Saw” by 11
participating in self-renewal activities (Covey, 2004). Participating in music 12
activities is a powerful way to renew ones spirit and increase productivity. 13
While listening to music has a real impact on the brain and productivity, 14
participating in music yields much more dramatic results. Childhood music 15
lessons are associated with lasting increases in cognitive ability across a variety 16
of disciplines (Moreno et al., 2011; Schellenberg, 2009; Shaw & Peterson, 2004). 17
Long-term training improves memory function (George & Coch, 2011). Musical 18
training impacts both brain chemistry and structure, increasing the quantity of 19
nerve tissues and improving the efficiency of brain connections (Altenmuller, 20
2003; Altenmüller, Kesselring, & Wiesendanger, 2006; Hughes & Franz, 2007; 21
Pascual-Leone, 2001; Patston, Kirk, Rolfe, Corballis, & Tippett, 2007). Musicians 22
22

truly process information differently than non-musicians (Schulz, Ross, & Pantev, 1
2003). These changes are dramatic enough to suggest that making music may 2
be an appropriate intervention for developmental and neurological disorders 3
(Wan & Schlaug, 2010). 4
The connections between music and the mind are clear, impacting the 5
mind both cognitively and psychologically (Levitin, 2007; Patel, 2008). It would 6
be reasonable to expect that areas with more music events provide more 7
opportunities for local residents to participate in music related activities. 8
Insomuch as this is true, and given the very real impact that music participation 9
has on productivity and thinking, this impact should be measureable. For 10
example, evidence of increased productivity should be seen in employment data 11
and local income patterns. 12
Arts and the economy. Research into the economic impact of the arts 13
varies widely in initial assumptions, methodological approaches, and conclusions 14
drawn. Some of these differences stem from differing definitions of such 15
fundamental constructs as the arts, impact, and communities, leading to a vast 16
array of differing claims (Guetzkow, 2002). Several themes reappear, implying 17
that arts positively impact economies through regenerating disadvantaged 18
communities, and by attracting visitors, residents, businesses, and investors 19
(Guetzkow, 2002; Kay, 2000). 20
An estimated 1.5 million people attend arts performances in the United 21
States each day, spending approximately $14.5 billion annually on admissions 22
23

alone, predominately on music performances (National Endowment for the Arts, 1
2011c). Even during a time of economic downturn, arts participation has 2
remained high in California, with over half of residents attending arts events 3
(Markusen, Gadwa, Barbour, & Beyers, 2011). Estimates of the impact of this 4
attendance vary widely. For example, the National Endowment of the Arts 5
estimates an additional $1.38 is added for each dollar spent in performing arts in 6
California (National Endowment for the Arts, 2011a). 7
Several studies have been conducted to try to estimate the impact of the 8
music industry at a regional level, most of which are at least partially based upon 9
economic modeling, such as Input-Output analysis. The primary focus has been 10
on value added to the Gross Domestic Product (GDP) and jobs created (Burgan, 11
2009). In Seattle, the music industry is estimated to bring nearly 40,000 jobs and 12
billions of dollars in sales and income to King County, Washington (Fowler & 13
Andreoli, 2008). Researchers in Chicago compared employment across a 14
variety of statistical areas and found that Chicago ranks third in the number of 15
jobs related to the music industry, after New York and Los Angeles, generating 16
revenues of approximately $80 million (Rothfield et al., 2006). The A. Gary 17
Anderson Center for Economic Research and The Orange County Business 18
Committee for the Arts performed an economic impact study of nonprofit 19
performing arts on Orange County, California that was primarily based on survey 20
data. They found the total economic impact to be increasing over time, with 21
current estimates at $483 million (A. Gary Anderson Center for Economic 22
24

Research & The Orange County Business Committee for the Arts Inc., 2010). 1
The Americans for the Arts conducted a research study that explored the 2
economic impact of the nonprofit arts in selected regions as well as in the nation 3
as a whole (Americans for the Arts, 2012). They found that the nonprofit arts 4
brings a total of $135.2 billion dollars to the economy in both organizational 5
revenue and audience expenditures, supporting more than 4 million jobs and 6
nearly $87 billion in income. They also found that governmental spending in the 7
arts is rewarded with a return on investment of more than five times the amount 8
spent in terms of local, state, and federal tax revenues. On average local 9
nonprofit arts attendees spend approximately $17 per person in the surrounding 10
communities, not including direct costs of the event. Non-local attendees spend 11
even more, averaging nearly $40 per person. They concluded by encouraging 12
planners to reevaluate how they view the arts in the community. Instead of 13
considering arts expenditures as fat to be cut out of the budget, community 14
leaders should support the arts as a means to ensure continued economic 15
prosperity. 16
This study brings several questions worthy of future research. First, if arts 17
attendees are spending large amounts of money in the local economy each time 18
they attend an event, how is that money distributed across the local landscape? 19
Second, do areas with more arts venues gain additional benefits, or is that 20
spending spread more thinly across the local landscape? Finally, since nonlocal 21
22
25

attendees spend almost twice the amount as local attendees, what is the most 1
efficient way to attract arts and cultural tourism to the local region? 2
3
Cultural Planning
Based on these varied claims, social and economic, society has heavily 4
invested in music and the arts. Cultural policy has been set, mandating inclusion 5
of arts in development and funding concerts for the public. Musicians are sent 6
abroad in military bands and diplomatic trips are sponsored, such as the New 7
York Philharmonic’s visit to Seoul, North Korea in 2008. Recognizing the value 8
of music and the arts, society spends a great deal of money funding the National 9
Endowment of the Arts, the National Endowment of the Humanities, and building 10
and sustaining venues like the Hollywood Bowl. Parades are held in local 11
communities and nationwide, including the Inaugural Parade, Macy’s 12
Thanksgiving Day Parade, and Pasadena’s Rose Parade. Governments make 13
direct and targeted plans to bring culture to their cities and foster the continued 14
growth of the arts. 15
Part of cultural planning includes the concept of creative placemaking, 16
creating a “creative” environment for local citizens to work, study, play, and just 17
be. Communities participate in creative placemaking, to animate “public and 18
private spaces, [rejuvenate] structures and streetscapes, [improve] local 19
business viability and public safety, and [bring] diverse people together to 20
26

celebrate, inspire, and be inspired” (Markusen & Gadwa, 2010). Cities have a 1
desire to be notable. They want to be “world class” with the best of something to 2
draw in tourists and inspire civic pride. This may be a championship basketball 3
team, iconic architecture such as the Eiffel Tower, or a world-class symphony 4
orchestra. According to Landry, planning creatively can draw people together 5
enabling innovation and allowing the city to become globally competitive (Landry, 6
2008). Cultural planning and placemaking allow locales to directly plan for and 7
create an environment that revitalizes the community and draws people to the 8
area. 9
Mission. Local governments participate in cultural planning because they 10
expect it to positively impact their communities. What that means for each 11
community is varied and unique, from enhancing public spaces to encouraging 12
civic dialogue (Becker & Americans for the Arts, 2004). In Brea, California, the 13
goal is to integrate development with public art in order to counterbalance the 14
negative impacts that development brings, such as noise, traffic, and “visual 15
blight” (City of Brea, 2011). In Long Beach they aim to “enhance the cultural 16
environment” of the city (Arts Council for Long Beach, 2011). 17
The California Arts Council aims to “advance California” by fostering 18
creativity (California Arts Council, 2011). The National Endowment of the Arts 19
(NEA) seeks to benefit “individuals and communities” by supporting state and 20
local arts agencies and philanthropy (Ball, 2011). The National Governors 21
Association encourages leaders to promote the arts to strengthen local 22
27

economies, recruit skilled workers, and attract tourism (National Governors 1
Association, 2009). Clearly there is an understanding that supporting the arts is 2
an important part of making the local community stronger and a better place to 3
be. 4
NEA. Unlike foreign countries that typically rely on centralized 5
government support, most nonprofit arts support in the U.S. comes from 6
nongovernmental sources, such as private contributions and earned income 7
(Mulcahy, 1999; National Endowment for the Arts, 2007). The NEA supports 8
state and local agencies and nonprofit organizations primarily with matching 9
grants to local agencies and nonprofits in the support of arts education, arts jobs, 10
and to some degree the arts institutions themselves (Ball, 2011; National 11
Endowment for the Arts, 2010, 2011b). They work on “enhancing the livability of 12
communities” by funding a variety of arts disciplines in a variety of geographical 13
areas (National Endowment for the Arts, 2010). 14
State and local arts councils. Historically, the purpose of state arts 15
agencies has been to stimulate arts activities and public appreciation of the arts, 16
while encouraging artistic freedom (Mulcahy, 2002). Lately meeting these goals 17
has necessitated some funding at the state and local level. State and local 18
councils combine the NEA grants with legislative appropriations, and some 19
funding from other sources, to foster arts education and accessibility, 20
participation, and infrastructure (National Assembly of State Arts Agencies, 21
2010). 22
28

Spending. The amount and quality of this funding varies largely by state 1
(Georgiou, 2008), with California ranking 50
th
in per capita spending (National 2
Assembly of State Arts Agencies, 2010). Between national, state, local, and 3
direct funding sources, the nation spends billions of dollars on the arts (see 4
National Endowment for the Arts, 2007). Therefore, it is important to evaluate 5
whether this funding is achieving its intended goal. 6
Expected benefits. With arts spending, funders expect a variety of returns 7
on their investments. The Americans for the Arts, using surveys of arts 8
participants, estimates a 7 to 1 return on investment for every dollar spent 9
(Americans for the Arts, 2007). Communities expect reduced crime because 10
people are engaged in positive activities. They expect to become more 11
competitive in the global market (Lin & Watada, 2010). They expect that 12
investment in the arts will bring about improvements in the community, the 13
economic outlook, and in individuals. However, it is important to base these 14
expectations on scientifically sound findings to avoid misappropriating the limited 15
funds available to support both the arts and community improvement (Sterngold, 16
2004). 17
Measuring Arts Impact 18
The most common estimates of economic impact, whether it be of the 19
arts, a terrorist attack, or a new shopping center are conducted using economic 20
modeling. The most common models used include Computable General 21
29

Equilibrium modeling (CGE), IMpact Analysis for Planning (IMPLAN), and other 1
input-output analysis models such as Regional Impact Analysis Inc. (RIMI). 2
However, researchers must be cautious when applying these strategies. They 3
should be cognizant of the underlying assumptions of the models along with the 4
resulting limitations of their output. Therefore, this review begins with a quick 5
overview of the principles of economic impact analysis. 6
Principles. Economic impact models are based upon common principles 7
and assumptions, including fixed prices, multiplier effects, no cross hauling, and 8
perfect substitutions (Partridge, 1998; Partridge & Rickman, 2008; Rickman & 9
Schwer, 1995). For example, prices are fixed and do not vary depending on 10
items being sold; a ticket to the Los Angeles Philharmonic would be expected to 11
have the same price as a ticket to a Bon Jovi concert. Multiplier effects are used 12
to capture both direct and indirect impacts. It is not only the value of the ticket 13
sales that impacts the economy; but, also the value of the other goods and 14
services that were provided to make the event possible, such as the electricity 15
used to light the event. The assumption of no cross hauling is the assumption 16
that goods will not be simultaneously imported and exported. In the labor pool 17
this would imply that all musicians in Orange County would be hired before any 18
musicians from Los Angeles or New York were imported. Similarly, consumers 19
would attend all available concerts in Orange County before traveling to any Los 20
Angeles venues. Finally, perfect substitution implies that similar goods are 21
readily substituted for one another; a Justin Bieber concert would be a perfect 22
30

substitution for a Prokofiev Symphony. As can be seen, some of these 1
assumptions typically used in standard economic analysis take unlikely leaps for 2
the purposes of simplification and measurability. 3
Currently Measured. Economic models are commonly applied for policy 4
analysis. These include cluster analysis, shift-share analysis, Input-Output 5
analysis, and Computable General Equilibrium modeling (CGE) to name a few. 6
(Partridge, 1998; Partridge & Rickman, 2008; Rickman & Schwer, 1995). 7
IMPLAN and RIMI are ready-made models based on basic Input-Output analysis, 8
providing cost and time savings. CGE offers a more dynamic and flexible 9
modeling framework, making it more appropriate for more complex policy 10
analyses. Much of the complexity of CGE for regional uses is in developing the 11
regional models, models that are already readily available for users of IMPLAN 12
and RIMI. Some compromises have been found by applying CGE modeling 13
techniques using existing IMPLAN data (Giesecke, 2011). 14
There is no consistent model that has been applied to measuring the 15
economic impact of music. For example, a study done to measure the impact of 16
the music industry in Nashville utilized RIMI (Raines & Brown, 2006), whereas 17
the previously mentioned Seattle study was based upon a locally developed 18
model (Fowler & Andreoli, 2008). This lack of consistency makes comparing 19
results across regions difficult, if not impossible. 20
Contributing Concepts. Economic impact modeling has been applied to 21
countless positive and negative impact studies, from studying the effects of 22
31

national disasters and terrorism to analyzing the impact of sports stadiums. For 1
example, Input-Output modeling has been successfully applied to measure the 2
impact of terrorist attacks on a sports stadium and the surrounding region (Lee, 3
Gordon, Moore, James, & Richardson, 2008). Baade et al. used taxable sales to 4
measure the impact of professional sports on the economy, but were unable to 5
detect any significant pattern (Baade, Baumann, & Matheson, 2008). Feng and 6
Humphries, however, were able to detect a spatial relationship between sports 7
facilities and residential home prices (Feng & Humphreys, 2008). 8
Traditional input-output models, such as IMPLAN, are conducted at a 9
regional or higher scale. As in the sports facilities and home prices study, the 10
real impact may be only seen at a finer scale. People may choose to eat and 11
shop close to a concert, rather than at a further location. The trend may be 12
relevant locally but not make a difference at a regional level. This spatial 13
difference may not be interesting to state lawmakers, but it would be invaluable 14
to local business owners, such as restaurateurs and shopkeepers. It would be 15
important to local government, in discovering how best to attract consumers to 16
spend money in their community. 17
18
Economic Base Theory 19
Location quotients. Input-output analysis is founded on the principles of 20
economic base theory, which can also be used in its own right to conduct an 21
32

economic-impact analysis. The simplest economic analysis is based on the 1
location quotient, and is commonly referred to as cluster analysis. Location 2
quotients are widely applied to problems of economic analysis, in part due to 3
their simplicity in calculation and the ease of understanding the results (M. M. 4
Miller, Gibson, & Wright, 1991; Warf, 2010). Locations quotients measure the 5
relative strength of the region within each sector in comparison to a target 6
economy, usually the national economy. Sectors with location quotients greater 7
than one indicate that the sector is export oriented within the region (Isserman, 8
1977). 9
Shift-Share analysis. Regional economies are not static entities and thus 10
location quotients in and of themselves do not provide a comprehensive picture 11
of the state of the local economy. A shift-share analysis uses the changing 12
location quotients over time to measure regional disparities in sectoral growth 13
(Ezcurra, Gil, Pascual, & Rapún, 2005; Ireland, Snead, & Miller, 2006). It can 14
help identify industries that the region is dependent upon for continued growth 15
and can highlight changes in the region to allow community leaders to plan for 16
the changing needs of the community (Quintero, 2007). In addition to traditional 17
shift-share techniques, many researchers incorporate probabilistic models into 18
shift-share analysis, allowing them to test hypotheses about regional employment 19
changes (Knudsen, 2000). 20
Shift-share analysis has successfully been applied to monitor the 21
changing economic climate of communities as they adapt to changes in their 22
33

economic production; for instance, as they move from primarily a manufacturing 1
dependent region to one predominately concerned with service (Wayne & John, 2
1989). It has also been utilized to measure the impact of relocating major league 3
sports franchises on regional employment, both in terms of losses and gains 4
(Tharp, 2004). In a mixed-method approach, shift-share analysis has been 5
augmented with GIS analysis to assess the spatial-economic impact of 6
development patterns in Hong Kong, finding in part that while the current housing 7
development strategies have had a positive impact on the local economy, 8
continued growth in the same vein would likely lead to overcrowding (Sui, 1995). 9
10
Fundamental Input-Output Concepts
Input-Output analysis is widely utilized in part because “for many purposes 11
they predict reasonably well” (Richardson, 1972, p. 9). Input-Output divides the 12
economy into sectors, with sales to other sectors and final demand of the sectors 13
product being used to estimate the economic effect that changes in output would 14
have upon the economy (Richardson, 1972). The difficulty and cost of 15
performing Input-Output analysis is primarily in the cost, time, and complexity in 16
creating the input-output tables, as they are primarily created using survey data. 17
Fortunately, the U.S. Department of Commerce regularly publishes national 18
Input-Output accounts and provides them to researchers free of charge (Bureau 19
of Economic Development, 2010). 20
34

For researchers intending to do regional economic analysis, there are 1
three choices for obtaining the necessary data. First, they may undergo the 2
traditional survey techniques and build the requisite Input-Output tables from 3
scratch. Unfortunately, this is time consuming and costly. Second, they may 4
employ non-survey and partial survey methods to estimate the regional tables 5
from existing tables. Finally, they may purchase the tables from commercial 6
providers, which predominately employ non-survey methodologies as well. For 7
those choosing to develop the tables using non-survey methods, the most robust 8
happens to be the simplest as well, a reduction technique based on regional 9
location quotients (Elliott, Flegg, & Webber, 1995; R. E. Miller & Blair, 1985). This 10
involves reducing the direct requirements table by multiplying the coefficients in 11
the table by the location quotients for the associated industry, but only when that 12
industry has a location quotient less than 1. The logic behind this is that where 13
the location quotients are greater than 1 the economic impact of regional 14
spending in the industry will stay within the region, while industries with location 15
quotients less than 1 will require some importations of goods and services. 16
Once the regional tables have been created, the multipliers are generated 17
by performing what is known as a Leontief transform (Leontief, 1986; R. E. Miller 18
& Blair, 1985; Richardson, 1972). The basic idea is that the economy can be 19
represented by a series of matrices. An equation can be created that represents 20
the output for each industry, or sector, as a sum of the demands for its goods or 21
services from each other sector in the matrix along with the demands from other 22
35

sources. For each sector !, the total output, !
!
, for that sector can be 1
represented by the equation: 2
!
!
=!
!!
!
!
+!
!!
!
!
+⋯+!
!"
!
!
+!
!
; with each coefficient, !
!"
, representing 3
demand of that sector’s goods from each of the other sectors and !
!
representing 4
the final demand of that sectors goods. Creating this equation for each of the 5
sectors creates the matrix equation, !=!"+!, where A is the coefficient matrix 6
and Y is final demand. In solving for the final demand matrix, the equation 7
becomes, != !−! !. Assuming that !−! is invertible, using techniques from 8
intermediate algebra, X can be found by taking the inverse of both sides. The 9
new equation becomes != !−!
!!
!, where the matrix !−!
!!
is called the 10
Leontif transform or total requirements matrix representing the direct, indirect, 11
and induced effects of the economy (see R. E. Miller & Blair, 1985; Richardson, 12
1972). 13
In simpler terms, the effects measured by the Leontif transform represent 14
the direct spending on each industry and the additional money spent in the 15
community by the industry and its employees. These effects, summed as 16
multipliers, show the total amount spent in the economy for each dollar invested 17
in each industry. As mentioned earlier, due to issues of data availability and 18
survey costs, the impact measured these models is more practical at the regional 19
and higher level. Therefore, these models are best suited to regional and national 20
analysis. Different strategies are needed to measure variations that occur at the 21
finer, local scale. 22
36

Spatial Analysis 1
Spatial analysis is a field with a history in warfare and health, but as 2
computer technology has increased, so too has its reach. GIS techniques are 3
increasingly being applied to policy decisions. It provides information beyond 4
merely identifying which sector overall is affected by policy or events, but also 5
helps to show where the sector is impacted at the finest scales. 6
Spatial analysis can provide valuable insights that traditional economic 7
analyses cannot. Concepts such as cluster analysis can show relative 8
importance of an industry to an area, as well as illuminate dispersion and analyze 9
regression with deference to gravity and spatial proximity. Cluster analysis can 10
be used to identify areas with higher and lower concentrations of the variable 11
being studied. Geographically weighted regression allows for the strength of an 12
association between variables to vary by location. These are important 13
considerations when measuring economic impact at a regional level. 14
Cluster Analysis. Cluster analysis in GIS is utilized to quantify variations 15
in spatial patterns (Jacquez, 2008). Clustering techniques have a rich history in 16
the mapping of epidemics. They have been utilized to identify the clustering of 17
West Nile Virus (Sugumaran, Larson, & DeGroote, 2009). Cluster analyses have 18
been used to identify geographic areas at higher risk for trauma and the need for 19
emergency services (Warden, Sahni, & Newgard, 2010). They have also been 20
utilized in identifying clusters of breast cancer patients, which has implications 21
both for services and support (Meliker, Jacquez, Goovaerts, Copeland, & 22
37

Yassine, 2009). GIS cluster analysis techniques can be utilized to identify areas 1
with higher concentrations of arts events, to analyze the shape of the events 2
distribution, and to monitor the changes in the distribution over time. 3
There are two basic objectives of cluster analysis, pattern identification 4
and hypothesis formation (Jacquez, 2008). Therefore, cluster analysis is usually 5
not merely an end unto itself, but a means of exploratory analysis. Particularly 6
since traditional forms of cluster analysis are highly impacted by edge effects, in 7
which values are impacted by their proximity to study boundaries (O'Sullivan & 8
Unwin, 2002). 9
Some forms of cluster analysis are useful in testing the appropriateness of 10
other statistical calculations. For example, cluster techniques can help to identify 11
whether there is a spatial pattern to the residuals calculated during regression 12
analysis. The Moran’s index can be calculated to test for this spatial 13
autocorrelation of residuals, where near values are more likely to be similar than 14
distant values (O'Sullivan & Unwin, 2002). Moran’s index values greater than 15
zero indicate a clustered pattern, less than zero indicates a dispersed pattern, 16
and values near zero indicate a random pattern (Mitchell, 2005). 17
Whether used in hypothesis formation, as in exploratory data analysis, or 18
in model evaluation, as in testing for spatial autocorrelation of residuals, GIS 19
based cluster analyses have been widely utilized to both visualize and measure 20
patterns in data at a variety of scales. Similarly, cluster analysis can provide 21
valuable insights for measuring economic impact at the local level. 22
38

Linear Regression Analysis. Ordinary Least Squares (OLS) Regression 1
while not a spatial analysis technique in and of itself can be applied to spatial 2
data. OLS is an elementary form of statistical analysis whose primary purpose is 3
to measure the strength of the linear association between the response variable 4
and one or more explanatory factors (Berman, 2002; Bock, Velleman, & De 5
Veaux, 2007; Starnes, Yates, & Moore, 2012). Statistical regression output 6
provides the researcher with many diagnostic values along with the calculated 7
regression coefficients. The p-value gives the probability that the observed 8
relationship was a product of chance variation, with low values giving evidence of 9
a statistically significant relationship. R
2
provides a measure of strength, with 10
values closer to 1 or -1 being evidence of a highly predictive regression equation. 11
It is important to note that the linear regression formula is only appropriate if the 12
relationship between the variables is truly linear. Researchers wanting to test 13
nonlinear relationships can transform the data to a new scale to straighten the 14
relationship, before performing the OLS analysis (Mitchell, 2005). 15
OLS analysis is also a first step in determining the relationship between 16
two or more variables before considering whether a relationship might be 17
mediated by spatial variations (Chainey, 2012). It can then be extended to the 18
local region, through the use of Geographically Weighted Regression (Brunsdon 19
et al., 1998). “OLS assumes relationships are consistent geographically 20
[whereas] … GWR recognizes that relationships between variables are likely to 21
vary across space” (Chainey, 2012). Therefore, in the presence of clustering of 22
39

the OLS residuals it is important to explore the observed relationship in a way 1
that takes into account geographic variations. 2
Geographically Weighted Regression. Geographically Weighted 3
Regression (GWR) is a powerful tool that can identify relationships between 4
variables, even when that relationship is not constant over space (Fotheringham, 5
Brunsdon, & Charlton, 2002). Unlike more traditional regression models, GWR 6
uses weighted measures to create estimates for each point in the dataset 7
(LeSage, 2004) and can capture changes that occur across space and time. For 8
example, housing prices are not constant (Bitter, Mulligan, & Dall‚Äôerba, 2007), 9
and those closest to the ocean cost significantly more than those further away. It 10
is likely then that housing closer to desirable locations such as concert venues 11
will be valued higher than those in areas with less cultural options. GWR can be 12
utilized to explore the extent to which the previously mentioned clustering 13
impacts local businesses. Finally, mapping the local R
2
values across space 14
shows you where the regression model is strongest, where interventions based 15
on the regression analysis are likely to have the biggest impact (Mitchell, 2005). 16
17
Reflection
Current studies attempting to measure arts impacts vary in both their 18
robustness and generalizability. Nevertheless, there is a clear pattern that 19
suggests that the arts have a strong, positive impact on both individuals and the 20
40

economy. Planners have been encouraged to consider the arts as a solution to 1
economic challenges, rather than excess expenditures to be cut. Through 2
cultural planning and creative placemaking, local governments seek to leverage 3
the arts to enhance their communities and attract talent and tourism. 4
The literature mentioned in this review has asserted that the creative 5
industries are an important part of a region’s economic growth, drawing a 6
creative talent poll and increasing housing values. They are an integral part of 7
urban redevelopment. Urban designers can leverage this by taking steps to 8
design spaces that support the collaboration of creative talent and provide a 9
venue for networking. In essence, planning for Great Good Places facilitates the 10
growth of social capital in the community. 11
Many live music venues are ideal locations for fostering creative 12
networking, including coffee houses, clubs, and performing arts centers. They 13
provide a location to relieve stress, network, and communicate with other 14
creative individuals – musicians and non-musicians alike. They provide a setting 15
for individuals to make connections, develop resources, and encounter new 16
opportunities. They provide a creative environment that is well suited for fostering 17
increased productivity and greater quality of life. The studies reviewed in this 18
section have shown that musical experiences make a difference in how people 19
think and perform their daily activities. Therefore, it is a reasonable expectation 20
that those areas with more musical spaces would show measurably increased 21
values on economic metrics. 22
41

In determining how to measure the impact of these spaces, there are a 1
variety of tools and methodologies available, from a variety of disciplines. The 2
arts impact literature is filled with studies that vary widely in purpose, 3
methodologies utilized, and sectors studies. Despite these differences, these 4
studies provide clear evidence that the arts are an important part of our 5
economy. They show this impact using differing tools such as surveys, traditional 6
economic impact analysis, or as in the case of Seattle, internally developed 7
models. Furthermore, the type of impact that is measurable varies by the scale at 8
which the study is being conducted. For example, the impact of one live music 9
venue location would be expected to show up better at the finer local scale than 10
at the larger regional scale. Conversely, the impact of the industry as whole, 11
relative to other industries, may be better measured at the regional or higher 12
scale. 13
There is no clear best practice in conducting arts impact studies; rather 14
the differing methods have unique strengths and weaknesses. Therefore, 15
planners wanting to evaluate arts programs in their community are left to choose 16
which of a variety of good but vastly different tools are best suited to their 17
situation. Another option is available, however, that of an interdisciplinary 18
approach. Interdisciplinary research has become increasingly common, 19
particularly when researchers are presented with complex problems (Rhoten and 20
Parker, 2004). It allows teams and individuals to combine the best tools from a 21
variety of fields and address issues that do not naturally combine themselves to a 22
42

single discipline. For example, Sui combined GIS and economics to evaluate the 1
spatial-economic impact of housing development strategies (1995). 2
Using a variety of tools to measure the same construct is not a new idea. 3
Educational researchers widely advocate for using multiple measures to evaluate 4
student and school performance (Fuller, Fitzgerald, and Lee, 2008; Henderson- 5
Montero, Julian, and Yen, 2003). Multiple measures can be similarly applied 6
here, using tools from GIS and economics to address the spatial-economic 7
impact of live-music in Orange County, California. The following chapter outlines 8
the methodology utilized in this study to perform this interdisciplinary analysis. 9
10
11
12
43

CHAPTER 3 1
METHODOLOGY 2
3
What is the spatial-economic impact of live music in Orange County, 4
California? In answering this research question, this study concentrated on two 5
subquestions. First, what do traditional measures of economic impact say about 6
the relationship between live music and the Orange County, California economy? 7
Second, to what degree does a spatial relationship appear to exist between live 8
music and the economy? The current study followed a mixed-methods approach, 9
incorporating some basic regional econometric strategies along with the spatial 10
analysis. An interdisciplinary approach was utilized to obtain a comprehensive 11
and thorough picture of the importance of the arts in the study region. 12
Traditional measures of economic impact were applied to explore the 13
relationship between live music and the regional economy. Orange County, 14
California is a large metropolitan area in Southern California. Individuals often 15
drive long distances to engage in a variety of activities. Many impacts of arts 16
events, such as job creation, and purchases made by the venue may only show 17
up at the coarser, regional scale. Economic cluster analysis, shift-share analysis, 18
and Input-Output analysis were performed on the region to capture these broader 19
impacts. 20
Spatial techniques were then applied to measure the spatial relationship 21
between live music and the economy. In general, spatial analysis has four main 22
44

components; preparing the data, exploring the data for trends and patterns, 1
analyzing the data, and interpreting the results (Charlton, 2008). In keeping with 2
this research model, several forms of spatial analysis were applied to music 3
event locations and economic indicators. Trends were identified utilizing GIS 4
based cluster analysis, identifying “hotspots”, areas with greater numbers of arts 5
events. The strength of observed relationships were measured using traditional 6
statistical regression techniques on spatial data. Additionally, geographically 7
weighted regression techniques were employed to determine the extent to which 8
any observed relationship between music event locations and economic strength 9
are mediated by geography. 10
11
Data preparation
Any quantitative analysis is only as strong as the data that it is based on. 12
Census data is one of the most widely used and trusted forms of conducting 13
spatial analysis on social data (Martin, 2008). In addition to the U.S. Census 14
Bureau’s collection of social data, the government regularly collects business and 15
economic data through departments such as the Bureau of Labor and Statistics 16
and the Bureau of Economic Analysis. A major concern of using publicly 17
available data is that the researcher is limited to levels of aggregation pre-defined 18
by the bureau that disseminates it. 19
20
45

Census data was utilized to provide census tract and zip code level 1
demographic data and information on economic indicators. For people and 2
households, the primary economic indicator utilized was median home value, 3
using the most recently available data from the United States Census (U.S. 4
Census Bureau, 2010a). For business and industry, data was also obtained from 5
the United States Economic Census (U.S. Census Bureau, 2007) and the United 6
States Bureau of Economic Analysis (U.S. Department of Commerce, 2012). 7
Outside of data collected and maintained by the government, public 8
information can be difficult to obtain. A great deal of data is collected by 9
organizations of all types and sizes. However, those organizations have both a 10
legal and ethical duty to protect the individuals about whom they collect 11
information. Therefore, the data is often not readily available to individuals 12
outside the organization (O'Sullivan & Unwin, 2002). As a result, there is a great 13
deal of data collected about the public that is not available for public use. 14
Furthermore, administrative data that is repurposed for research may be 15
incomplete, biased, or narrowly restricted for specific uses only (O'Sullivan & 16
Unwin, 2002). Researchers, therefore, must be cognizant of the limitations of the 17
types of data available to them as they seek to answer their questions and be 18
willing to pose the questions presented by the data available to them. 19
Economic Indicators. The Bureau of Labor and Statistics lists employment 20
data as one of its major economic indicators (U.S. Department of Labor, 2012); 21
and, it has therefore been an important component of the economic analysis in 22
46

this study. Input-Output accounts were obtained from the Bureau of Economic 1
Analysis (U.S. Department of Commerce, 2012). County Business Pattern data 2
and Zip Code Business Pattern data were obtained from the U.S. Census Bureau 3
(U.S. Census Bureau, 2010a). 4
As mentioned previously, much of the data available for this analysis is 5
limited to pre-defined aggregation levels, due to both the original purpose for 6
which data was collected and privacy concerns. For the economic datasets, this 7
provided a limitation in that data on the music industry alone was not readily 8
available. For this reason, much of the traditional economic analysis contained 9
herein is conducted on industries for which the music industry is a part, such as 10
arts, entertainment, and recreation, rather than on the music industry alone. 11
This limitation is addressed to some degree by utilizing multiple measures 12
and multiple analyses. While the traditional economic analyses focus on the 13
broader arts category, the local analyses focus specifically on music venue 14
locations. The real and positive impact of the arts in general is demonstrated at 15
the regional level, while the specific impact of music venue locations is explored 16
at a finer scale. Taken together, a picture emerges of the importance of music to 17
the Orange County region. 18
Events Locations. In order to compare the economic data with events 19
locations throughout the county, it was first necessary to identify the various 20
venue locations. A combined venue locations dataset was created using three 21
main sources. First, the Orange County Musician’s Union, Local 7, was 22
47

generous enough to provide an excel file listing events locations by venue name 1
and date. This dataset was limited to non-collective bargaining events only. 2
Second, events locations and dates for organizations with collective bargaining 3
agreements filed with the union were scraped from publicly available sources, 4
such as the Orange County Register or the organization’s website. Third, since 5
some music events contain non-union or out of area artists, a dataset was 6
scraped from the SongKick database, a database of current and historical 7
concerts from around the world. 8
These databases predominately listed the events locations by name, 9
although limited addresses were provided. To determine the remaining 10
addresses, multiple search engines were employed, including Google and Yelp. 11
In addition to determining the advertised address, the location was visually 12
verified using Google Maps and Bing Maps. Once the address had been verified 13
and added to the Excel spreadsheet, a database file was created in Access from 14
the completed Excel file. The database file was then imported into ArcGIS and 15
geocoded using the ArcGIS engine. 16
There were several limitations inherent in this process. All events were 17
treated as if they were the same value, with no relevance given to event size, 18
concert price, or artist reputation. Although the number of events at each 19
location was collected for union work, this was not the case for the locations 20
scraped from the SongKick database. Therefore, no differentiation was made for 21
venue locations that had frequent versus infrequent concerts. Finally, some 22
48

venues listed had multiple possible locations, or unclear explanations of 1
locations, and therefore could not be determined with precision or mapped. For 2
example, several entries listed events as occurring at “Various Orange County 3
Locations.” 4
Cartographic files. Geographic shapefiles for joining with economic data 5
were obtained from the US Census Bureau TIGER(Topologically Integrated 6
Geographic Encoding and Referencing) Products (U.S. Census Bureau, 2012). 7
Cartographic boundary files for display purposes were also downloaded from the 8
TIGER site. Shapefiles for the Southern California General Plan for land use 9
were provided by the Southern California Association of Governments (SCAG, 10
2011). Additionally, basemaps and reference layers freely available from the 11
ESRI website were utilized for display purposes (Esri, 2012b). 12
Preparing Data for Analysis. “Maps throughout this [dissertation] were 13
created using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the 14
intellectual property of Esri and are used herein under license. Copyright © Esri. 15
All rights reserved. For more information about Esri® software, please visit 16
www.esri.com” (Buckley, 2010). Shapefiles and economic tables were imported 17
into ArcGIS (Esri, 2012a). Census data and economic data were joined with the 18
relevant shapefiles. 19
For the purposes of this study, the definition of a music venue location is a 20
location that hosted at least one music event from the years 2006 to 2011. 21
These identifying events are primarily advertised in the SongKick database 22
49

and/or with the Local Musician’s Union. A limited number of locations are venues 1
that have a collective bargaining agreement with the musician’s union. 2
Due to the significant overlap of the three venue location datasets, they 3
were spatially joined into one large combined venue dataset using the collect 4
events function in ArcGIS. Locations with more than one event were collected as 5
a single point. A spatial join was then conducted to count the number of venues 6
within each geographic area. This added a venue count to each of the shapefiles 7
previously joined to economic data. This was done twice, once at the zip code 8
tabulation level and once at the census tract level. 9
Limiting the spatial analysis to venue locations rather than events 10
introduces challenges but addresses important limitations. For example, a venue 11
location that host events once a month is given equal weight as a venue location 12
that hosts multiple events per day. This may hide some of the very real variation 13
in economic impact between the locations. However, combining the datasets 14
addresses the limiting factor of having events counted twice, in two different 15
datasets; or, missed because they were less well advertised. Furthermore, 16
limiting the analysis to venue locations has a meaningful application to planning. 17
A positive relationship between live music locations and economic prosperity 18
would highlight the importance of providing musical spaces throughout the 19
community, as part of a comprehensive growth plan. 20
21
22
50

Economic Analyses
The economic analyses commenced by exploring the unique economic 1
position of Orange County, California relative to the United States as a whole. 2
The relative strength of the arts in Orange County was compared with the rest of 3
the U.S. This included examining location quotients, part of economic base 4
theory, and conducting a shift share analysis showing Orange County’s relative 5
position over time. 6
Location Quotients. Location quotients (LQ) are an important component 7
of economic base theory widely applied in part due its relative simplicity (M. M. 8
Miller et al., 1991), as compared to other forms of economic analysis. Data for 9
LQ analysis is readily available and calculations can easily be conducted by hand 10
(Isserman, 1977). Furthermore, in communicating the results of the analyses, this 11
reduced complexity means that the results can be more easily understood by the 12
“end-users”; policy makers, planners, and general population. 13
Many researchers use the term cluster analysis in describing analysis 14
conducted with location quotients. It should be noted that, while both are useful 15
and important in their own rights, LQs are a very different type of cluster analysis 16
than that conducted utilizing GIS software. Carroll, Reid, and Smith in comparing 17
LQs with GIS based cluster analysis recommended using both in identifying 18
clusters, as they paint slightly different pictures of the economy (2008). For 19
similar reasons, this study also employs a multi-disciplinary approach in exploring 20
the relative importance of live music in Orange County. 21
51

LQs are utilized to analyze regional growth, with the assumption that 1
growth will be highest in industries that are exporting their products beyond 2
regional boundaries (Krikelas, 1992). Location quotients are utilized to compare 3
an industry’s relative importance both regionally and nationally. Using 4
employment data the formula for a location quotient is 5
!"=
Regional Industrial Employment Total Regional Employment
National Industrial Employment Total National Employment

(Warf, 2010). Values greater than one indicate that the industry has a greater 6
concentration in the region’s economy. Spatial aggregation is an important 7
consideration in conducting cluster analysis, as LQ’s are more accurate at higher 8
levels of spatial and industrial aggregation (Billings & Johnson, 2012). 9
In calculating the LQs for this study, County Business Pattern data was 10
downloaded from the Census Bureau for Orange County, CA and the nation as a 11
whole (U.S. Census Bureau, 2010b). The proportion of the industries shares of 12
regional employment was compared to the industries share of national 13
employment. This was repeated for each of the years 2006 to 2010. 14
Shift-share Analysis. LQs can be informative in determining the relative 15
importance of an industry. However, the economy is not static. With that in mind 16
a shift-share analysis was conducted to determine the changing relative 17
importance of each of the industries under consideration. The primary purpose 18
of using a shift-share analysis is to investigate the temporal changes in 19
employment within the region (Hoppes, 1991). 20
21
52

Shift share analysis decomposes these temporal changes into 3 main 1
components (Loveridge & Selting, 1998). Although the concept is relatively 2
consistent, the vocabulary used to describe these components varies widely from 3
author to author. This study follows a classical shift share formula (Hoppes, 4
1991; Knudsen, 2000; Loveridge & Selting, 1998; Stimson, Stough, & Roberts, 5
2006). The shift-share components are defined to be the National Share (NS), 6
Industry Mix (IM), and Regional Shift (RS). 7
The national share describes the change in jobs within the region and 8
industry that would be expected by the overall national change in employment. 9
For example, if the nation as a whole had a decrease in employment of 10%, the 10
national share would reflect a corresponding 10% reduction within the region for 11
each industry. The industry mix adds an adjustment to that number for the 12
differential behavior of the specific industry within the nation. For example, if the 13
national housing market was particularly hard hit and actually saw a decrease in 14
employment of 15%, the industry mix for housing would reflect a further reduction 15
of 5%. Finally, the regional shift adds a final adjustment to account for what 16
actually happened in the region. For example, if the regional housing market 17
realized only a 2% drop, the regional shift for housing would reflect a 13% 18
increase to compensate for the over-reduction. 19
Summing up the three components, NS + IM + RS, provides the actual 20
change in jobs. Supposing there were an initial 100 housing jobs in the 21
hypothetical region described above, NS = -10, IM = - 5, and RS = +13. The 22
53

actual job change would be -2, represented in the equation as (-10) + (-5) + 13. 1
Large positive regional shift values are of particular interest as they indicate that 2
the region is showing comparatively larger growth, or considerably slower 3
decline, in that industry than would be expected by the national and industry 4
changes in employment alone. 5
Chiang advocates for a dynamic form of shift-share analysis, in which the 6
shift-share components are calculated annually and them summed together over 7
time to control for year-to-year variations (Chiang, 2011). This is particularly 8
called for in rapidly changing economic periods. 9
Input-Output Tables. In addition to the economic cluster analysis and the 10
shift-share analysis, an input-output table was constructed for the Orange County 11
Economy. The first step was to download the U.S. Total Requirements Table 12
from the Bureau of Economic Development website (Bureau of Economic 13
Development, 2010). The Leontief Transform was reversed to create the original 14
direct requirements table. The table was reduced to a regional direct 15
requirements table using a traditional reduction technique, the Location Quotient 16
method, as described by Miller and Blair (R. E. Miller & Blair, 1985). For each 17
industry having a location quotient less than 1, the direct requirements row was 18
multiplied by the location quotient calculated earlier in the study. After the direct 19
requirements table was reduced, the Leontief Transform was re-applied to the 20
table creating the Orange County Total Requirements Table. The columns in this 21
regional table were summed to determine the total multipliers for each industry. 22
54

Spatial Analyses
Cluster Analysis. Understanding the data is an important component in 1
spatial analyses (Mitchell, 1999). Therefore, the first step in conducting spatial 2
analysis was to conduct some preliminary exploratory analyses. To that end, a 3
spatial version of cluster analysis was performed on the data. This allowed for 4
both a quantitative exploration of the degree of music venue location clustering 5
and an opportunity to display this clustering in the most natural of spatial 6
displays, a map. In addition to displaying differences in concentration, a hot-spot 7
analysis was performed to identify areas of clustering. 8
In creating the maps to display the results of the cluster analysis, careful 9
consideration was given to some important cartographic principles, particularly 10
with regards to color choice. First, in helping to highlight differences across 11
space it was necessary to consider that due to limitations in visual perception it is 12
best to use no more than five categories when displaying differences in values 13
using changes in color saturations (Peterson, 2009). Furthermore, differences in 14
color appear more extreme for objects nearer to one another than far away 15
(Monmonier, 1991). Second, many colors already have inherent meanings, such 16
red being hot and blue being cold (Monmonier, 1991; Peterson, 2009). To 17
leverage these perceptions, a hot/cold color pattern was chosen for most of the 18
analyses, with red indicating areas of higher concentration or higher values and 19
blue indicating areas with lower concentration or lower values. 20
21
55

Regression Analyses. To measure the association between the number 1
of venue locations within a region and economic indicators, an ordinary least 2
squares regression analysis was performed. The venue count in each zip code 3
was compared to employment number and annual salary data. Due to limitations 4
of ArcGIS with regards to traditionally statistical analyses, the spatial data tables 5
were imported into JMP, a statistical software package (SAS, 2012). Then, 6
returning to ArcGIS with the SAS results, the Moran’s Index was calculated to 7
identify any spatial clustering in the residuals. 8
Where there was evidence of spatial autocorrelation of the least squares 9
residuals, a geographically weighted regression was also performed. 10
Geographically weighted regression allows differential associations to be found 11
across space. Therefore, for each region the R
2
value and the regression 12
coefficients were calculated for each location across the study region. This is an 13
important analysis, since regional differences may impact the effect that live 14
music performances have on the local economy. 15
16
This study utilizes an interdisciplinary approach to explore the spatial- 17
economic impact of live music performances, incorporating the distinct 18
methodologies as described above. Using multiple perspectives provides a more 19
comprehensive and thorough picture of the importance live music and the arts to 20
Orange County, California. The results of this analysis are presented in the 21
following chapter. 22
56

CHAPTER 4 1
RESULTS 2
What is the spatial-economic impact of live music in Orange County, 3
California? To answer this question, it is important to understand the context of 4
music and the arts in the region. Orange County was chosen for this analysis for 5
several reasons. First, there is a large arts presence in the area, including 6
substantial representation by live music organizations. Orange County ranks 7
third in California for the number of arts, entertainment and recreation 8
establishments, behind Los Angeles and San Diego, see figure 1 (U.S. Census 9
Bureau, 2010b). Next, Orange County arts rank relatively high in annual payroll, 10
see figure 2, second only to Los Angeles (U.S. Census Bureau, 2010b). Finally, 11
the Orange County arts community is more oriented towards live performances. 12
13
11838
1072
954
488 487 487 478
378 334 334 319
0
2000
4000
6000
8000
10000
12000
Arts, Entertainment, and Recreation Establishments
Top 11 California Counties
Figure 1. Arts, Entertainment, and Recreation Establishments in California
57

1
2
Los Angeles has a significantly larger arts presence relative to the rest of 3
the state. However, Orange County was chosen over Los Angeles for this 4
analysis due to the fact that 42% of the creative economy in Los Angeles is 5
centered around the entertainment industries, including movies and sound 6
recording (Los Angeles County Economic Development Corporation, 2011). This 7
results in an unusual bias in the Los Angeles region that could confound much of 8
the exploration into performing arts, as artists often cross over into both fields. 9
Choosing Orange County instead, with only 1.9% of the creative economy 10
attributable to entertainment industries reduces most but not all of the bias 11
evident in the Los Angeles counter datasets. Therefore, this study focuses on 12
the impact of live music on the Orange County California economy. 13
7,125,000
1,065,000
1,013,000
563,000
537,000
431,000
398,000
243,000
201,000
153,000
0
2,000,000
4,000,000
6,000,000
8,000,000
Arts, Entertainment, and Recreation
Annual Payroll ($1,000)
Top 10California Counties
Figure 2. Arts, Entertainment, and Recreation Total Payroll
58

1

Figure 3. Map of California.
59

The Study Area – Orange County, California
Data is meaningless without context. Therefore, it is important to 1
understand the background of what makes Orange County unique before 2
proceeding with the numerical analyses. Orange County was formed in 1889 3
when the California Senate voted to annex a portion of Los Angeles County for 4
this purpose (Kling, Olin, & Poster, 1995). 5
6
Figure 4. Orange County, CA and Neighboring Counties.
60

Orange County is bordered in the north by Los Angeles County, in the 1
east by Riverside and San Bernardino counties, in the south by San Diego 2
County, and in the west by 42 miles of the Southern California Coast, as shown 3
in figure 4 (State of California Employment Development Department, 2012). In 4
its pre-colonial days it was the center of the Gabrielino tribe of Native Americans 5
and it is the home of Mission San Juan Capistrano (Starr, 2005). 6
7
Figure 5. Orange County, California Cities and Communities
61

There are 34 cities in Orange County: Aliso Viejo, Anaheim, Brea, Buena 1
Park, Costa Mesa, Cypress, Dana Point, Fountain Valley, Fullerton, Huntington 2
Beach, Irvine, La Habra, La Palma, Laguna Beach, Laguna Hills, Laguna Niguel, 3
Laguna Woods, Lake Forest, Los Alamitos, Mission Viejo, Newport Beach, 4
Orange, Placentia, Rancho Santa Margarita, San Clemente, San Juan 5
Capistrano, Santa Ana, Seal Beach, Stanton, Tustin, Villa Park, Westminster, 6
and Yorba Linda (County of Orange, 2013). There are also several important 7
unincorporated communities such as Anaheim Hills and Coto de Caza. 8
Community Profile. Orange County is relatively small, making up only 9
0.5% of California’s land mass, yet it houses fully 8% of California’s population; it 10
has a population density of more than 3,800 people per square mile, almost 16 11
times the California value (Census, 2012). While almost a quarter of the 12
population is under 18, the over 55 age group is growing rapidly and is an 13
important part of the demographic makeup of Orange County (Census, 2012; 14
Orange County Business Council & Orange County Workforce Investment Board, 15
2011). Figure 6 shows the distribution of median age across California. 16
Planners and policy makers should consider their community’s unique 17
needs, including projected demographic changes, for each new development 18
project. For example, as Orange County is home to 7% of California’s veteran 19
population (Census, 2012), residents may enjoy attending patriotic events that 20
often include live music. Similarly, the types of arts venues enjoyed by the youth 21
and aging population may differ markedly. 22
62

1
2
The Hispanic and Asian ethnic subgroups are also growing rapidly in 3
Orange County, increasing from 1990-2010 by 79.3% and 115.8% respectively 4
(Orange County Business Council & Orange County Workforce Investment 5
Board, 2011). Figure 7 shows the distribution of the Hispanic population across 6
Orange County, California. This is of particular interest in the current study, 7
Figure 6. Median Age Map.
63

given the importance of bandas to the Hispanic community “which traditionally 1
provide musical services for … festivals, parades, rituals, and ceremonies” 2
(Bergland, 2012). Large groups of individuals attend these events, enjoying the 3
music and spending money in the community. However, although they have a 4
potential impact on the local economy, these events may not be measured by 5
traditional venue locations. 6
7
Figure 7. Percent Hispanic Map.
64

1
2
Orange County is a particularly safe area to live. Upon examining data 3
readily available from the U.S. Department of Justice, it can be seen that Orange 4
Figure 8. Orange County Crime Distribution.
65

County has relatively few violent crimes (see U.S. Department of Justice, 2010). 1
Figure 8 shows a map of total number of crimes in terms of number of crimes per 2
1000 people. No Orange County city has more than five crimes per 1000, while 3
most cities have 3 or less. 4
The Orange County economy. A few simple calculations from readily 5
available 2010 Census data provide an introduction to the local Orange County 6
economy (see Census, 2012). According to the 2010 census, the median home 7
value in Orange County is $607,900, which is 32% higher than the California 8
median home value in general. With 45,022,513 in retail sales, Orange County 9
makes up nearly 10% of all California’s retail sales. Furthermore, with 10
$8,247,828 in accommodation and food service sales, Orange County makes up 11
more than 10% of sales in the accommodation and food services category. 12
There are 1,272,287 people employed in private nonfarm business, 13
making up only 1% of the private nonfarm employment, a low figure considering 14
that 8% of California’s population lives here. In fact, from 2000 to 2010 there has 15
been a 7.7% decrease in in private nonfarm employment, which is 2.8 times the 16
California decrease. Additionally, the Orange County unemployment rate was 17
7.7% as of August 2012, compared to California’s 10.4% (State of California 18
Employment Development Department, 2012). 19
A number of existing studies estimate that the arts are an important part of 20
the economy. According to County Business Pattern data, in 2010 there were 21
954 total businesses listed in the category of Arts, Entertainment, and Recreation 22
66

with 36,740 employees and a combined payroll of $1,064,668,000 (U.S. Census 1
Bureau, 2010b). From 2004 to 2009, there has been a 10% increase in 2
nonemployer visual & performing arts firms and a 2% increase in employment 3
within the industry (Los Angeles County Economic Development Corporation, 4
2011). The Otis College of Art and Design studied the creative economy in the 5
Los Angeles basin and reported the total direct and indirect impact of the visual 6
and performing arts in Orange County to be $800 million along with $7.6 million 7
in tax revenues (Los Angeles County Economic Development Corporation, 8
2011). The 11 performing arts survey participants for the Fall 2010 report, The 9
Economic Impact of the Nonprofit Arts on Orange County, had a combined 10
number of admissions of 1,398,599 and an estimated total economic impact of 11
$304,577,396, including direct spending, indirect spending and indirect audience 12
spending (A. Gary Anderson Center for Economic Research & The Orange 13
County Business Committee for the Arts Inc., 2010). Clearly the arts are an 14
important part of the economic health of Orange County. 15
The top nongovernment employer in Orange County is the Walt Disney 16
Company, an organization that also employs performing artists, whereas the 17
combined employees of all nonprofit arts organizations collectively rank 23
rd
(A. 18
Gary Anderson Center for Economic Research & The Orange County Business 19
Committee for the Arts Inc., 2010). While ranking 23
rd
is admirable, including 20
only nonprofit arts clearly underestimates the impact that performing arts in 21
general have in this county. To truly gain an understanding of the importance of 22
67

the arts in this region, for profit institutions like Disney should be combined with 1
the analysis of the nonprofit organizations. For this reason, this study explores 2
the collective economic impact of both for profit and non-profit live music events. 3
Economic Analysis of Orange County, California

Figure 9 above shows a map of the Orange County area, along with the 4
distribution of live music venue locations. Figure 10 shows the same venue 5
locations superimposed over a map of land use categories. In exploring the 6
impact that these locations have on the Orange County economy, this section 7
begins by establishing the relative importance of the arts industries in general. 8
Figure 9. Orange County Music Venue Distribution.

68

1
2
3
Figure 10. Orange County Music Venue Distribution and Land Use.
69

From examining Census county business patterns, from 2000 to 2012, it 1
can be determined that the leisure and hospitality industries have consistently 2
comprised approximately 10% of the Orange County labor market and its 3
subcategory arts, entertainment, & recreation has similarly been 2% of the total 4
labor market (U.S. Census Bureau, 2010b). This is illustrated in Figure 11. To 5
truly understand what this means, the significance of the arts being 2% of the 6
labor market in Orange County, it is important to compare these values with 7
typical expected values, in this case the United States. 8
Location Quotients. An economic cluster analysis was performed using 9
the number of employees in major industries (U.S. Census Bureau, 2010b). 10
Table 1 shows the location quotients (LQs) for the total number of employees in 11
2010 for Orange County, relative to the United States as a whole, with values 12
greater than or equal to 1 being highlighted in green, between 0.5 and 1 13
highlighted in orange, and below 0.5 highlighted in red. Areas with LQs higher 14
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
Jan-00
Jan-01
Jan-02
Jan-03
Jan-04
Jan-05
Jan-06
Jan-07
Jan-08
Jan-09
Jan-10
Jan-11
Jan-12
Percentage of the Orange County Labor Market
Leisure & Hospitality Arts, Entertainment & Recreation
Figure 11. Orange County Business Pattern Percentages
70

than 1 are of particular importance to the region, indicating relative strength for 1
those industries within the county. The utility industry was excluded due to data 2
being unavailable at the county level after 2006. 3
From table 1 it can be determined that Orange County’s most prevalent 4
industries include: construction, manufacturing, wholesale trade, finance and 5
insurance, real estate and rental and leasing, professional, scientific, and 6
technical services, management of companies and enterprises, administrative 7
and support and waste management and remediation services, arts, 8
entertainment, and recreation, and accommodation and food services. 9
Location Quotients - Total Number of Employees 2010
Industry LQ
Total for all sectors 1.00
Real estate and rental and leasing 1.66
Arts, entertainment, and recreation 1.61
Wholesale trade 1.57
Construction 1.26
Professional, scientific, and technical services 1.24
Manufacturing 1.15
Finance and insurance 1.12
Accommodation and food services 1.08
Management of companies and enterprises 1.06
Administrative and support and waste management and remediation
services
1.00
Information 0.94
Other services (except public administration) 0.85
Retail trade 0.83
Educational services 0.74
Health care and social assistance 0.69
Transportation and warehousing 0.54
Agriculture, forestry, fishing and hunting 0.14
Mining, quarrying, and oil and gas extraction 0.08
10
Table 1. Location Quotients - Total Number of Employees 2010
71

As can be seen from the chart in Figure 12, arts, entertainment, and 1
recreation, with a LQ of 1.61 is second only real estate, rental, and leasing, with 2
a slightly higher LQ of 1.66. This means that relative to the nation as a whole, 3
Orange County is relatively strong in employment in both the arts and real estate. 4
Conversely, it is relatively weak in mining, agriculture, and transportation. 5
Location Quotients were calculated for each year in the five-year period of 6
2006 to 2010. Table 2 shows a clear increasing trend of LQs for Arts, 7
entertainment and recreation over time. This means that in addition to being an 8
important part of the Orange County Economy, the relative importance of the 9
arts, entertainment, and recreation industry has been growing. To help quantify 10
this apparent increasing trend, and compare these changes with the nation as a 11
whole, a shift-share analysis was performed on the county business pattern data. 12
0.00

0.25

0.50

0.75

1.00

1.25

1.50

1.75

2.00

53-‐Real
estate

71-‐Entertainment

42-‐Wholesale
Trade

23-‐Construc>on

54-‐Scien>fic

31-‐Manufacturing

52-‐Finance

72-‐Hospitality

55-‐Management

56-‐Adminstra>ve

51-‐Informa>on

81-‐Other
Services

44-‐Retail
Trade

61-‐Educa>on

62-‐HealthCare

48-‐Transporta>on

11-‐Agriculture

21-‐Mining

Orange
County
LQ’s

Figure 12. Orange County Location Quotients
72

1
Table 2. Location Quotients for Total Number of Employees, 2006-2010
73

The shift share analysis, shown in table 3 , identified industries for which 1
the region is performing relatively better than the nation. The national share 2
shows the change in job numbers expected if the region followed only the 3
national trend in job changes. The industry mix adds an adjustment to this 4
number to allow for differences from industry to industry. The regional shift adds 5
an additional adjustment for what actually happened in the region, so that the 6
sum of the base year and the three shift-share components is the new total 7
number of jobs in the region for each industry. 8
The arts, entertainment, and recreation industry can be utilized to illustrate 9
this analysis. In 2009 there were 35,516 jobs in the sector. The national share 10
component shows how this number would have been impacted if the arts sector 11
had followed the national job trend for all sectors, a decrease of 2.23%. 12
Therefore the national share for arts, entertainment, and recreation was -792, we 13
would expect to lose 792 jobs in the sector. The industry mix adjusts this figure 14
for industry specific behavior within the nation, in this case a drop of only 0.34%. 15
In this case the industry mix is 673, because the arts are really only expected to 16
lose 119 jobs based on industry behavior, -792 + 673 = -119. The regional shift 17
adds a final adjustment for what actually happened in the region, in this case a 18
growth of 3.45%. The regional shift for arts, entertainment, and recreation in 19
Orange County is 1343 to reflect the -792 + 673 + 1343 = 1,224 additional jobs. 20
In comparison the county lost 4.82% of jobs overall during the same period. 21
22
74

1 Table 3. Shift-Share Analysis
75

Of particular interest are industries where the regional shifts are greater 1
than the industry mix, indicating that the industry is doing relatively better in the 2
local region than the nation as a whole, assuming regional shift is positive. The 3
analysis identifies three industries as increasing in relative importance to the 4
region. These are information, management of companies and enterprises, and 5
arts, entertainment, and recreation. The previously identified strength area, real 6
estate, on the other hand, had negative values in both the industry mix and 7
regional shift, indicating that job losses in real estate are worse than the pattern 8
of job losses in the nation. Also important to note, on closer inspection of the 9
tables, the information industry is also showing job losses overall, although those 10
losses are more serious for the nation as a whole than for Orange County. 11
Both the Cluster and Shift-Share analyses identify the industry of arts, 12
entertainment, and recreation as a growth industry for the Orange County region. 13
In fact, this sector is one of only two industries that had an increase in job 14
numbers during this time period. Arts, entertainment, and recreation saw an 15
increase of 1224 jobs while management of companies and enterprises saw an 16
increase of 1611. Every other sector saw job losses. Furthermore, with a LQ of 17
1.61, Orange County has a comparatively higher proportion of jobs in the arts 18
relative to the nation, compared to management with an LQ of 1.06. Additionally, 19
since the relative importance of this sector of the economy is increasing, the arts, 20
entertainment, and recreation industry should be an essential part of any 21
comprehensive community development plan in Orange County. 22
76

Input-Output. The U.S. industry by industry total requirements table was 1
converted to an industry by industry direct requirements table, by reversing the 2
Leontif transform. Subsequently, the location quotients calculated above were 3
utilized to reduce the direct requirements table to a regional version specific to 4
Orange County. Finally, a Leontief transform was conducted on the Orange 5
County direct requirements table to create the Orange County industry by 6
industry total requirements table. Appendix C contains a coded list of the Orange 7
County industry by industry total requirements table, and the listing of codes can 8
be found in appendix B. The sum of each column in the total requirements table 9
provides the total multiplier for that industry. 10
Of 66 studied industries, the multiplier for performing arts, spectator 11
sports, museums, and related activities ranks 24th, at approximately 1.60. Table 12
4 provides a listing of all the industries with multipliers greater than 1.5. The 13
multiplier of 1.6 indicates that for every $1 change in final demand of performing 14
arts, spectator sports, museums, and related activities in Orange County, CA, the 15
total output, including direct and indirect effects, is expected to change by 16
approximately $1.60. Approximately $0.47 of that benefit is spent on other 17
industries. See appendix A for a complete listing of total type multipliers. 18
The distribution of impacts is of particular interest. As typical for social 19
accounting matrices, performing arts, spectator sports, museums, and related 20
activities receives 70% of the impact of spending within the field. 21
22
77

Total multipliers by industry - Top 35 of 66
Rank Industries with regional multipliers greater than 1.5 Multiplier
1 Textile mills and textile product mills 2.05
2 Other transportation equipment 2.03
3 Plastics and rubber products 1.95
4 Chemical products 1.93
5 Funds, trusts, and other financial vehicles 1.86
6 Securities, commodity contracts, and investments 1.84
7 Printing and related support activities 1.81
8 Electrical equipment, appliances, and components 1.78
9 Motor vehicles, bodies and trailers, and parts 1.78
10 Paper products 1.77
11 Publishing industries (includes software) 1.73
12 Machinery 1.72
13 Primary metals 1.68
14 Support activities for mining 1.66
15 Fabricated metal products 1.65
16 Waste management and remediation services 1.65
17 Furniture and related products 1.63
18 Information and data processing services 1.63
19 State and local government enterprises 1.62
20 Federal general government 1.62
21 Nonmetallic mineral products 1.61
22 Accommodation 1.61
23 Broadcasting and telecommunications 1.60
24
Performing arts, spectator sports, museums, and
related activities 1.60
25 Food and beverage and tobacco products 1.58
26 Miscellaneous manufacturing 1.57
27 Hospitals and nursing and residential care facilities 1.57
28 Apparel and leather and allied products 1.55
29 Water transportation 1.55
30 Rail transportation 1.54
31 Construction 1.53
32 Wood products 1.53
33 Motion picture and sound recording industries 1.52
34 Air transportation 1.52
35 Insurance carriers and related activities 1.51
1
Table 4. Total multipliers by industry - Top 35 of 66
78

Conversely, 30% of the impact is on outside sectors. The most impacted 1
sectors include: miscellaneous professional, scientific, and technical services, 2
administrative and support services, real estate, and insurance carriers and 3
related activities. Table 5 shows the share of the total multiplier for each of the 4
top ten sectors impacted. 5
Top ten sectors impacted by the performing arts.

Industry Description
Share of
Total
Multiplier Rank
Performing arts, spectator sports, museums, and
related activities 70.89% 1
Miscellaneous professional, scientific, and technical
services 5.28% 2
Administrative and support services 3.46% 3
Real estate 3.35% 4
Insurance carriers and related activities 2.87% 5
Management of companies and enterprises 1.22% 6
Securities, commodity contracts, and investments 1.17% 7
Broadcasting and telecommunications 1.07% 8
Legal services 0.95% 9
Other services, except government 0.90% 10
All other industries 8.85% N/A
6
The LQ and shift-share analysis provide evidence that the arts are an 7
integral part of the Orange County economy. The input-output analysis provides 8
additional evidence, with a respectable multiplier value of 1.6, that spending in 9
the arts impacts industries across the economy, such as the real estate industry. 10
This indicates that the arts have a real, positive impact on Orange County. With 11
the impact of the general arts industry on the region thus established, the next 12
portion of the analysis is focused on the local impact of the music industry. 13
Table 5. Top ten sectors impacted by the performing arts.
79

Spatial Analysis
Spatial Cluster Analysis. The local impact analysis begins with an 1
exploration of the distribution of music venue locations across the Orange County 2
landscape. As a first step in the spatial analysis the music venue location 3
dataset was geocoded using Esri’s ArcGIS, verifying conflicts by inspection. For 4
the purposes of this study, a music venue location is defined as any location that 5
had at least one music event in the five-year period from 2006 to 2011. 6
7
Figure 13. Music Venue Location Density Map.
80

Upon preliminary inspection of Figures 9 and 10, there appear to be 1
several clusters of areas with higher numbers of venue locations. Several of 2
those visible clusters are in areas zoned for commercial purposes; however, this 3
is not exclusively the case. The mediating impact of zoning categories were not 4
included as part of this study; but would be an interesting source for further 5
analysis. 6
Venue counts were collected by zip code and tract level. The zip code 7
counts are displayed using a hot-cold pattern in figure 13. Zip codes with more 8
venues appear in orange and red, whereas zip codes with fewer venues appear 9
in green and blue. The Laguna Beach and Costa Mesa areas appear to have 10
relatively large numbers of music venue locations. Conversely, there are 11
relatively few events in the Santa Ana Mountains. 12
Another pattern appears upon further inspection of figure 13, the venue 13
locations seem to be clustered along major roadways. This is unsurprising; it is 14
logical to place venue locations in locations that are easy to access. However, 15
the distribution of these roadways was not included as a factor in this study. 16
Exploring the impact that these transportation networks have on the relationship 17
between live music venues and economic factors would be a worthy study for 18
further research. 19
A hot spot analysis was conducted on the Orange County venue counts at 20
the tract level. As can be seen in figure 14, the highest concentration of music 21
venue locations is in the west from the central coast to Santa Ana and Irvine. 22
81

The concentration is surprisingly sparse in the North Orange County region, near 1
Cypress and Anaheim. This result can be explained to some extent by the fact 2
that locations with more frequent events, such as Disneyland, are given equal 3
weight in this analysis as locations that have events only seasonally, such as the 4
Newport Jazz Festival. 5
6
Figure 14. Orange County Music Venue Hot Spots.
82

1
2
Figure 15 shows the same map overlaid with the location of union live 3
music events. These symbols are proportionally larger for locations with more 4
events. From this map it can be seen that while Anaheim may have relatively 5
fewer venue locations compared to some of the surrounding regions, it has 6
several venue locations that have a relatively large number of live music events, 7
Figure 15. Music Hot Spots with Union Event Locations.
83

for instance Disneyland. It should be noted, however, that these proportional 1
symbols do not take into account any nonunion performances that may have 2
occurred at these same locations or at locations where only nonunion musicians 3
performed. For this reason, all remaining analysis will use the combined venue 4
location dataset that does not take into account the number of events. 5
Ordinary Least Squares and Geographically Weighted Regression 6
To analyze the relationship between music venue locations and the local 7
economy, six different OLS regression analyses were performed. Three of these 8
analyses were followed by geographically weighted regression analysis due to 9
spatial clustering of the residuals. Table 6 provides a summary of the analyses. 10
Regression Output Models
# Equation
P-
Value R
2

GWR
?
1 !!"#$%% = 375,933 + 31,416∗!"#$" !"#$% <.001 0.17 No
2 PaidEmpoyees = 9,145+ 568∗!"#$" !"#$% <.001 0.20 No
3 Averagel!alary = 37,804+ 568∗!"#$" !"#$% 0.051 0.04 Yes
4 log(!alary)= 10.4+ 0.08∗ log (!"#$%) 0.007 0.07 Yes
5
log !alary = 10.4+ 0.08∗ log !"#$%
− 9∗10
!"
(!"#$ !"#$)
0.007
0.452 0.01 No
6 MedianHomeValue= 583,877+ 6,249∗!"#$" !"#$% 0.013 0.01 Yes
11
Model 1. Total annual payroll is a commonly used indicator to represent 12
the relative economic strength of a region. In order to determine whether areas 13
with more live music venues have higher total payroll as well, a regression 14
analysis was conducted by zip code to compare the number of venues to the 15
payroll as reported by the county business patterns dataset. A scatter plot of the 16
Table 6. Regression Output Models
84

relationship is shown in figure 16. It shows a moderate, linear relationship with a 1
clear pattern of heteroscedasticity, there is more variation in annual payroll as the 2
zip code venue count increases. The equation to predict payroll from the total 3
number of venues within a zip code is 4
! ̂!"#$%% = 375,933 + 31,416∗!"#$" !"#$%. A summary of the regression 5
analysis output for this relationship follows. 6
7
8
Summary of Fit 9

RSquare 0.174942
10
Parameter Estimates 11
Term Estimate Std Error t Ratio Prob>|t|
Intercept 375932.75 112726.7 3.33 0.0013*
VenueCount 31415.621 7272.773 4.32 <.0001*
12
Figure 16. Scatterplot of Venue Count and Annual Payroll

85

With a p-value less than .0001, there is a statistically significant 1
relationship between the number of venues in a zip code and total annual payroll 2
at any reasonable significance level. More specifically, zip codes with one 3
additional venue location are predicted to have an average increase in combined 4
annual salary of more than $31,000,000. With a R
2
value of 0.17, this model 5
accounts for approximately 17% of the variability in zip code annual payroll. 6
To assess the appropriateness of the model, a graph of the residuals 7
plotted against the predicted values and a histogram of the residuals are shown 8
below in figure 17. There is no evidence of a non-linear pattern and the 9
histogram is relatively unimodal and symmetric, indicating that a linear model is 10
appropriate in this context. 11
12
13
To determine if there was a spatial pattern to the residuals, the Moran’s-I 14
test for spatial autocorrelation was conducted. The Moran’s index was 15
approximately -.04 indicating that the residuals showed a random pattern. This 16
indicates that the regression model is an appropriate analysis tool in the context 17
of this study. The output for the Moran’s-I test can be seen in figure 18 below. 18
Figure 17. Venue count and annual payroll residuals.
86

1
Model 2. Another important indicator of economic strength is the total 2
number of jobs in an area. Therefore, a regression analysis was also conducted 3
to compare the number of venue locations against the total number of jobs in the 4
zip code. The scatterplot shows a moderate, positive, linear relationship with less 5
heteroscedasticity than the previous relationship. A scatterplot of this 6
relationship can be seen in figure 19. The resulting equation to predict the 7
number of jobs from the total number of venues within a zip code is 8
Figure 18. Venue count and annual payroll residuals, Moran’s I
87

PaidEmpoyees = 9,145+ 568∗!"#$" !"#$%. A summary of the regression 1
analysis output for this relationship follows. 2
3
4
Summary of Fit 5

RSquare 0.203098
6
Parameter Estimates 7
Term Estimate Std Error t Ratio Prob>|t|
Intercept 9144.9948 1860.638 4.91 <.0001*
VenueCount 568.49514 120.0425 4.74 <.0001*
8
With a p-value less than .0001, there is a statistically significant 9
relationship between the number of venues in a zip code and total number of 10
employees at any reasonable significance level. More specifically, zip codes with 11
Figure 19. Venue count and number of paid Employees.
88

one additional venue location are predicted to have an average increase of more 1
than 550 in total employees. With a R
2
value of 0.19, this model accounts for 2
approximately 19% of the variability in total number of employees. 3
To assess the appropriateness of the model, a graph of the residuals 4
plotted against the predicted values and a histogram of the residuals are shown 5
below in figure 20. There is no evidence of a non-linear pattern and the 6
histogram is relatively unimodal and symmetric, indicating that a linear model is 7
appropriate in this context. 8
9
10
To determine if there was a spatial pattern to the residuals, the Moran’s-I 11
test for spatial autocorrelation was conducted. The Moran’s index was 12
approximately -.002 indicating that the residuals showed a random pattern. This 13
indicates that the regression model is appropriate in this context. The output for 14
the Moran’s-I test can be seen in figure 21 below. 15
Figure 20. Venue count and number of employees residuals.
89

1
2
With a complete absence of spatial autocorrelation of the residuals for 3
both total number of employees and total annual payroll, there was no indication 4
that adding a spatial component to the regression analysis would provide 5
additional information. However, there is a chance that increasing numbers of 6
venues and total jobs or employees could be measuring the lurking factor of zip 7
code population or size. Therefore additional analyses were performed to allow 8
for differing zip code areas. 9
Figure 21. Venue count and number of employees, Moran’s I.
90

Model 3. Total payroll was divided by total number of employees to 1
calculate the variable of average employee income. The regression analysis on 2
this additional value indicated a possible spatial relationship in the results, 3
necessitating additional analyses. Geographically weighted regression is 4
generally performed in the presence of spatial autocorrelation of the residuals 5
from an ordinary least squares analysis. In situations where there is a spatial 6
pattern to the residuals, a geographically weighted regression can be performed 7
to identify both the pattern of the variation and the local regression coefficients. 8
A regression analysis was performed by zip code for Orange County, on 9
the number of venue locations versus the total annual payroll divided by the total 10
number of employees. A scatterplot of the relationship can be seen in figure 22 11
below. There is a moderate, increasing relationship, particularly for the interval 12
of 0 to 30 venue counts. However, this relationship has a potential curved 13
pattern and there is a clear outlier at zip code 92617, which is the zip code for the 14
University of California, Irvine. This indicates that university employees have 15
comparatively higher salaries than those working in areas with similar venue 16
counts. 17
The resulting equation is Average!alary = 37,804+ 568∗!"#$" !"#$%. 18
A summary of the regression analysis output for this relationship follows. 19
91

1
2
Summary of Fit 3

RSquare 0.042596
4
Parameter Estimates 5
Term Estimate Std Error t Ratio Prob>|t|
Intercept 37804.83 1972.546 19.17 <.0001*
VenueCount 251.81382 127.2625 1.98 0.0510
6
This suggests that for each additional venue location in the zip code 7
region, there is an average expected increase of $250.00 more per employee in 8
annual wages. The relationship between venue count and per employee annual 9
10
Figure 22. Scatterplot of venue count and average payroll.
92

wage, with a p-value of 0.051, is statistically significant at the α = 0.1 significance 1
level but not at α = 0.05 significance level. 2
To assess the appropriateness of the model, a graph of the residuals 3
plotted against the predicted values and a histogram of the residuals are shown 4
below in figure 23. There is clear evidence of a non-linear pattern in the 5
residuals plot, indicating that the data could benefit from re-expression. 6
7
8
Also, to determine if there was a spatial pattern to the residuals the 9
Moran’s-I test for spatial autocorrelation was conducted. The Moran’s index was 10
approximately 0.076 indicating that the residuals showed a significantly clustered 11
pattern. Therefore, a geographically weighted regression analysis would be 12
appropriate in this context. The output for the Moran’s-I test can be seen in 13
figure 24. 14
The pattern of the residuals can be visualized in the map in figure 25. 15
Areas in red indicate positive residuals meaning areas where per employee 16
Figure 23. Venue count and average payroll residuals.
93

annual wage is higher than predicted from the model, while areas in blue indicate 1
negative residuals, areas where per employee annual wage is lower than 2
predicted by the model. The zip code for the bright red area represents the 3
previously mentioned outlier of the University of California, Irvine. 4
5
6
7
Figure 24. Venue count and average payroll residuals, Moran’s I.
94

1
The local regression coefficients are displayed in figure 26. Looking at the 2
coefficients by geographic location, little variation is seen in the local slope 3
values. In fact, if rounded to the nearest whole number, each zip code location 4
has an identical coefficient of $252, indicating that for each additional venue 5
location the predicted average annual salary increase is $252 per employee in 6
Figure 25. Map of venue count and average payroll residuals.
95

every zip code. However, although the variation is small, looking at the 1
distribution it is clear that the coefficients become larger as they progress through 2
Orange County, California, indicating that a spatial relationship does exist, even if 3
the differences are negligible in practice. 4
5
6
Figure 26. Venue count and average payroll GWR coefficient map.
96

Model 4. Due to the potential curvilinear relationship in the ordinary least 1
squares regression, the previous analysis was repeated using the common 2
logarithm of both the count and the average salary. The scatterplot of the re- 3
expressed data can be seen in figure 27. Re-expressing the data helped to 4
improve the appropriateness of the linear analysis. 5
6
7
Summary of Fit 8

RSquare 0.079108
9
Parameter Estimates 10
Term Estimate Std Error t Ratio Prob>|t|
Intercept 10.402952 0.064489 161.31 <.0001*
Log_Count 0.080245 0.029186 2.75 0.0072*
Figure 27. Scatterplot of log-log re-expression.
97

A summary of the regression analysis output for this relationship can be 1
seen above. With an R
2
value of 0.08 the re-expressed model is not very strong. 2
However, the p-value of 0.0072 provides conclusive evidence of a statistically 3
significant nonlinear relationship between the number of venues in a zip code 4
and the average annual salary per employee. The residuals plot shown in figure 5
28 further supports the use of a nonlinear model for this relationship. There is no 6
clear pattern in the residuals plot and a histogram of the residuals is relatively 7
unimodal and symmetric. 8
9
10
The Moran’s Index of 0.059117, shown in figure 29, there is still significant 11
clustering in the results; therefore, a geographically weighted regression was 12
also performed on the re-expressed datasets. With an R
2
value of 0.23 this 13
model accounts for approximately 23% of the variation in average annual salary. 14
Figure 28. Log-log re-expression residuals.
98

1
2
Given the nature of the this study, an attempt to study factors that have an 3
indirect impact on the response variable, this R
2
value provides evidence of a 4
potential impact that has very real implications for practice. On mapping the local 5
R
2
values, a very real regional effect can be seen on the strength of the model. 6
Figure 30 illustrates this relationship, with areas in red showing where the 7
relationship is relatively strong and areas in blue indicating areas where the 8
relationship is relatively weak. Upon inspecting this map, it can be seen that the 9
model is most predictive along the central Orange County coast. 10
Figure 29. Log-log residuals, Moran’s I.
99

1
2
Model 5. The model was extended to include land area, on the 3
assumption that perhaps venue count locations might be impacted by the size of 4
the zip code. Although the log(count) coefficient was statistically significant, with 5
a p-value of 0.0072, land area was not, p-value 0.4521. A summary of the 6
regression analysis output for this relationship can be seen below. 7
Figure 30. Log-log re-expression local R
2
values.
100

Summary of Fit 1
2
RSquare 0.085107
3
Parameter Estimates 4
Term Estimate Std Error t Ratio Prob>|t|
Intercept 10.423348 0.070061 148.78 <.0001*
Log_Count 0.0805355 0.02926 2.75 0.0072*
ALAND10 -9.42e-10 1.247e-9 -0.76 0.4521
Model 6. Finally, median house value is an important economic indicator 5
available at the finer census tract level. Therefore, an ordinary least squares 6
analysis was conducted by tract, using the number of venue locations against the 7
median home value. Figure 31 shows the scatterplot for this relationship. The 8
equation to predict median house value from the total number of venues within 9
the census tract is MedianHomeValue= 583,877+ 6,249∗!"#$" !"#$%. A 10
summary of the regression analysis output for this relationship follows. 11
12
Figure 31. Scatterplot of venue counts and median home value.
101

Summary of Fit 1

RSquare 0.01056
2
Parameter Estimates 3
Term Estimate Std Error t Ratio Prob>|t|
Intercept 583877.49 9057.354 64.46 <.0001*
VenueCount 6248.8398 2520.289 2.48 0.0134*

The relationship between venue counts and median home value is 4
statistically significant, with a p-value of 0.0134. There is clear evidence of a 5
relationship between the number of live music venues and housing values. 6
However, with an R
2
value of only 0.01, this model is not very strong. This can 7
be partially explain by the Moran’s Index of 0.30, see figure 32. With a z-score of 8
46.65, this is an indication of a highly clustered pattern. 9
10
Figure 32. Venue counts and median home value, Moran’s I.
102

A geographically weighted regression was then performed on census tract 1
venue counts versus median home value. The R
2
value for this spatial 2
regression model was 0.4036 indicating that this model accounts for 40% of the 3
variability in median home value. However, when the local coefficients are 4
mapped it is clear that there is a considerable degree of variability in this 5
relationship. Figure 33 maps this model, which appears to be strongest in the 6
areas of Seal Beach and Huntington Beach in North Orange County and in 7
Laguna Beach towards the Southern portion of Orange County. 8
9
Figure 33. Venue counts and median home value local R
2
.
103

The values in these areas are also higher for the local coefficients, with 1
the highest predicted increase in median home value being in the northwest 2
corner of Orange County, Laguna Beach, and also in Trabuco Canyon in the 3
south, displayed in figure 34. There is a clear association between local music 4
venues and economic indicators, mediated to some degree by spatial factors. 5
Clusters in the map indicate that the influence of music is much more complex 6
than just adding one more venue location. 7
8
Figure 34. Venue counts and median home value local coefficients.
104

The models presented in this section and displayed for comparison in 1
table 6, provide conclusive evidence of a statistically significant relationship 2
between the number of live music venue locations and total payroll, number of 3
employees, average annual salary, and median home values. The strongest of 4
these models were those of total payroll and number of paid employees, with R
2
5
values of 0.17 and 0.20 respectively. When salary was averaged per capita, the 6
evidence was stronger for the non-linear model than the linear model. 7
8
Reflection 9
This study employed a multidisciplinary approach to provide a 10
comprehensive picture of the impact of the arts and live music on the Orange 11
County, California economy. Beginning with the macro scale, the larger industry 12
of the arts was evaluated within the broader scale of the entire Orange County 13
region. This was accomplished predominately through the use of Location 14
Quotients, Shift-Share Analysis, and Input-Output Analysis. 15
The LQ analysis demonstrated the relative importance of the arts to the 16
Orange County economy, particularly with reference to employment. The arts 17
LQ of 1.61 is the second highest in the region, superseded by only real estate 18
with a LQ of 1.66. Furthermore, this relative importance has been increasing 19
over time. The Shift-Share Analysis confirmed this growth trend. In fact, the arts 20
subsector is one of only two industries that experienced job growth within the 21
105

study time period. At a time when the United States experienced job losses at a 1
rate of 2.23% and the Orange County region as a whole experienced job losses 2
at a rate of 4.82%, jobs within the Orange County arts industry increased by 3
3.45%. 4
In addition to providing evidence of job growth in the arts, this study 5
affirms that spending within the arts realizes benefits throughout the regional 6
economy. The multiplier of 1.6 indicates that for every dollar spent on the arts in 7
Orange County, the total impact on the economy is $1.60. In contrast, real 8
estate, the only industry with a LQ higher than the arts, has a total multiplier 1.23. 9
This implies that investment in the arts has an impact of $0.37 more per dollar 10
than comparable investment in real estate. 11
Next, this study applied spatial analysis techniques to evaluate the impact 12
of live music venue locations at a finer scale. The cluster analysis revealed 13
significant clustering of arts events in the central, costal region of Orange County. 14
On inspecting figure 13, the map of the hotspots overlaid with proportional venue 15
counts, it can be seen that the major, aforementioned hotspots along the coast 16
have many venue event locations. Conversely, the area near Anaheim with less 17
venue locations has larger quantities of events per location. As of consequence 18
of this difference, and the fact that the investigation herein concentrated on 19
venue counts rather than event counts, it is likely that the regression analysis 20
underestimates the impact that live music venues have on the economy, 21
particularly in the Anaheim region. 22
106

This study established a statistically significant relationship between live 1
music venue locations and measures of economic strength, even in light of this 2
limitation. Areas with more venue locations had significantly more employees, 3
higher salaries per capita, and higher home values. It is likely, however, that an 4
event specific analysis would have achieved stronger models, in terms of larger 5
R
2
values. 6
Taken collectively, these results form a cohesive picture of the importance 7
of the arts and live music to the Orange County region. The live music results at 8
the local level are consistent with the broader arts results at the regional level. 9
This evidence suggests that live music positively impacts the local economy, with 10
far reaching implications for policy makers and planners. These implications are 11
discussed further in the following, concluding chapter of this study. 12
13
14
15
16
17
18
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CHAPTER 5 1
DISCUSSION AND CONCLUSIONS 2
3
Orange County is an area that is rich in the arts, from Laguna Beach’s 4
Pageant of the Masters (Infinity Research And Development Inc., 2011) to Brea’s 5
Art in Public Places program (City of Brea, 2011), the region is well known for 6
funding and promoting the arts. Furthermore, the creative industries are one of 7
the top four drivers of the Orange County economy (Orange County Business 8
Council & Orange County Workforce Investment Board, 2011). The primary goal 9
of this study was to quantify the impact of one component of these creative 10
industries, live music. 11
This study employed a multidisciplinary approach to investigate the 12
spatial-economic impact of live music at multiple scales. Regional economic 13
impact modeling techniques were applied to the entire Orange County region to 14
establish the general importance of the arts. GIS techniques were applied at the 15
finer zip code and census tract scales to evaluate the local impact of live music 16
venue locations. Combining the results from these two disciplines provided a 17
comprehensive conceptualization of the role that the arts play in in the Orange 18
County economy. 19
20
21
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Discussion 1
In reviewing both the literature and the results from the analysis, three 2
main themes occur. First, the arts are an important part of the Orange County 3
Community. Second, the arts have a positive impact on the economy. Finally, 4
the venues are important in and of themselves. 5
The arts community. The literature on the creative industries has 6
established the fundamental importance of the arts for local communities. They 7
are public goods that enrich the lives of the people as well as enriching the 8
region’s economic health. They draw creative people to the community, help 9
them establish social networks, and increase their personal effectiveness. 10
Likewise, the arts are an integral part of the Orange County culture. In addition 11
to benefiting local citizens, Orange County arts act as a draw, bringing people 12
from surrounding regions into the community. This importance was quantified in 13
the economic cluster analysis. 14
The LQ analysis demonstrated the relative importance of the arts to the 15
Orange County area, particularly with reference to employment. The LQ for arts, 16
entertainment, and recreation in Orange County, California is 1.61, higher than 17
every value except for real estate. Furthermore, the relative importance of the 18
arts to the region has been growing over time. For example, At a time when the 19
nation experienced job losses at a rate of 2.23% and the Orange County region 20
as a whole experienced job losses at a rate of 4.82%, jobs within the Orange 21
County arts industry increased by 3.45%. 22
109

The arts economy. The arts have a positive impact on the economy, 1
bringing millions of people to arts venues each day and billions of dollars to the 2
economy. The National Endowment of the Arts estimates an additional $1.38 is 3
added for each dollar spent in performing arts in the entire state of California 4
(National Endowment for the Arts, 2011a). The multiplier from the Input-Output 5
analysis conducted in this study estimates this figure to be $1.60 for just the 6
Orange County region. In addition to the impact of direct arts spending, people 7
spend money in the local community before and after attending arts events. For 8
instance, people often go to a nice dinner before attending a concert. The 9
American’s For the Arts estimated the amount spent in this manner and found 10
that local attendees to nonprofit arts events spend approximately $17 in the 11
community when they attend an event. Additionally, non-local arts attendees 12
average $40 per person. 13
The traditional economic analysis conducted in this study provides 14
significant evidence of a positive economic impact of the arts and related 15
industries. Clearly, this is a sector that should not be ignored in future planning. 16
However, the data was not disaggregated to include live music alone. 17
Nevertheless, the initial economic analysis on the economic impact of these 18
industries provides compelling evidence that live music performances, as a 19
subset of the studied industries, are an important part of the Orange County 20
regional economy. 21
22
110

The number of live music venue locations within a zip code or census tract 1
was shown to have a statistically significant, positive correlation with total annual 2
payroll, total number of paid employees, per capita annual income, and median 3
home value. This indicates that sub-regions with greater numbers of live music 4
venue locations also tend to have higher values on important economic 5
indicators. These important economic indicators are measures of local economic 6
strength. From this analysis alone it can be concluded that there is a positive 7
relationship between live music performances and the local economy. This 8
highlights the importance of continuing to support community arts programs as a 9
means to ensure continued economic prosperity. 10
Arts Venues. Place matters. Florida emphasizes that the communities 11
that people choose to live in have a marked impact on their ultimate life 12
trajectory. The clustering of talented individuals facilitates efficiency and creative 13
problem solving. This in turn facilitates the productivity and economic strength of 14
the community and region. Planners can facilitate these benefits by ensuring 15
that there are places for this collaborative networking to occur. Cultural planning 16
and placemaking allow policy makers to directly plan for and create an 17
environment that revitalizes the community and draws people to the area. 18
The current study provided additional evidence of the importance of 19
location. Rather than differentiating by the number of events or the number of 20
arts attendees, the analysis herein focused on individual venue locations. This 21
means that the observed significant relationship between live music and the 22
111

economy can be considered one of opportunity. The benefits are realized merely 1
by having a place within the community where live music events can occur. 2
Planners can leverage this by actively planning for and promoting arts venues 3
within the community, such as coffee houses, performing arts centers, and 4
outdoor bandstands. These third places become community-meeting places 5
where collaborative learning and networking can occur. 6
7
The economic and spatial models both provided evidence of a strong 8
association between live music and economic prosperity. The economic models 9
provided a broad picture of the overall impact the arts have on the Orange 10
County economy. The spatial models augmented this analysis by allowing the 11
local impact of live music events to be explored at a finer scale. Both are 12
valuable and important. This study has provided quantifiable evidence of a very 13
real relationship between the two factors at multiple scales and using multiple 14
metrics of economic strength. This suggests, therefore, that live music events 15
have a positive impact on the local economy. 16
17
Implications for Policy and Practice
The findings of this study have several important implications for policy 18
and practice. First, it demonstrates that in studying the impact of an industry at 19
the regional scale, it is important to consider potential spatial variation. Second, 20
112

interventions do not need to apply to an entire region to make a real difference 1
on the local economy. They can be targeted to identify regions where the 2
intervention is likely to make the biggest impact. In short, incorporating spatial 3
economic modeling in the fields of GIS, Economics, and Public Policy provides 4
important additional information that can better inform decision making and 5
further research. Finally, given the apparent importance of music, and the arts in 6
general, for economic growth, policy makers should be very cautious in their 7
attempts to balance the budget by making severe cuts to this sector. They may 8
very well be terminating the programs that are most likely to stimulate the 9
economic growth they seek. 10
11
Limitations of the Study
As with all studies, there are limitations in the scope and generalizability of 12
the findings of this study. First, the analyses in this study are based on models; 13
and, no models are perfect. For example, the economic models used throughout 14
this study all assume perfect substitution, where all products have equal utility 15
value. In practice this is not the case. In the context of this study, consumers 16
would find equal value in a live classical concert as they would find in a heavy 17
metal concert. Furthermore, given its dependence on the creative industries, 18
impact of live music that was observed in this study may be unique to the Orange 19
County region. Therefore, it would be useful and important to replicate this study 20
113

in disparate regions to identify whether the trend holds. 1
While this research is an informative study in the correlation between live 2
music events and how and where individuals spend their money, there are 3
countless other competing draws on society's attention and dollars that were not 4
captured here. This study largely excluded amateur performances, including 5
college and university music groups that sometimes can have a significant draw 6
and produce a similar economic effect. For example, high school band and 7
orchestra performances often draw local crowds who spend their dollars in the 8
community before and after performances. Private performances, such as large 9
parties, may also have a similar impact as individuals may stop by local stores for 10
host gifts, such as wine. 11
The spatial analysis had limitations in the precision of the geocoding. For 12
example an event at a conference center may be tagged at the centroid of the 13
building rather than precisely at the stage area. Furthermore, due to venue 14
locations being provided by name rather than exact addresses, there were some 15
venue locations that could not be mapped, and were therefore not included in the 16
dataset. Similarly, some of the events in the union dataset were listed as various 17
locations or various schools, which were similarly unable to be mapped. 18
Finally, the national economy had some extreme fluctuations during the 19
time period being studied. This may have had differential impacts on the zip 20
codes and census tracts used in this study, which was not accounted for in the 21
analyses. Additionally, some of the venue locations closed during the five year 22
114

period being studied, yet were included as they met the study definition of a 1
venue location. Finally, opportunity costs can obfuscate economic impact in an 2
area like Orange County that has a wide variety of entertainment options. 3
Also of concern, the relationship between venue locations and average 4
annual earnings had significant spatial variation. However, the practical 5
difference between the slope of the local regression lines was negligible, with the 6
lowest value at $251.66 per person for each additional venue location and the 7
highest value at $252.09 per person for each additional venue. More importantly 8
the strength of the model varied by location. Comparing the map of the local 9
coefficients in figure 9 to the cluster map with union performance frequencies 10
overlaid, figure 10, highlights an area needing further investigation. How much of 11
the geographic variation in model impact is due to the differential number of 12
events at each location? Repeating this study with the explanatory variable as 13
the total number of events, for instance by tracking all nonprofit and for profit 14
events over a period of time, would provide a great deal of additional insight into 15
the impact of the events themselves. Finally, although the model strength varied 16
widely in predicting the impact of the number of live music venues and median 17
home value, the important fact was that there is a highly significant relationship 18
between the two. 19
20
21
22
115

Suggestions for Future Research
The findings of this study have many implications for future research. 1
First, in order to generalize the findings in this research, similar analyses should 2
be conducted on alternate regions. In addition to union data and the SongKick 3
database, researchers may consider obtaining access to the Ticketmaster and 4
similar databases, as they are also able to track the total number of sales to each 5
event. 6
Second, a case study should be done on the role of the arts on university 7
campuses. UC Irvine showed up as an important point in this study, a point that 8
behaved markedly different from the surrounding regions. What impact do school 9
performances have on the economy? Additionally, how can universities use the 10
arts to support the economic growth of the communities in which they are 11
housed? 12
Third, as the type of music played at the concert was not considered in 13
this study it may be interesting to look at the differential impact of music genre on 14
the economy. Does the type of music matter? If so, what genres of music have 15
the largest impact? 16
Finally, are the right patrons supporting the arts? It would be interesting to 17
perform an alignment study between the corporate donations and advertising 18
funds paid to arts organizations and the industries that benefit most from arts 19
activities, as identified by the input-output analysis. Furthermore, it would be 20
interesting to explore the optimal funding levels for each group. For example, for 21
116

sectors other than performing arts, spectator sports, museums, and related 1
activities, real estate realizes nearly 12% of the impact of arts spending. Do they 2
in turn contribute a comparable proportion of arts funding? 3
4
Conclusions
The spatial economic impact of live music in Orange County, California is 5
extensive and positive. This study has shown that areas with higher numbers of 6
live music venues alone positively correlate with multiple measures of economic 7
strength, lending credibility to the multiple studies discussed in the literature 8
about how music and the arts in general impact peoples lives. While some 9
elements of this analysis are correlational and do not attempt to claim cause, 10
when taken together in context with the literature and multiple forms of analysis, 11
overwhelming evidence becomes apparent. 12
Although this study has limitations of its own, it has made a significant 13
contribution to both the cultural policy literature and the practice of economic 14
impact modeling. It has shown how by using a multidisciplinary approach of 15
regional input-output modeling, combined with Geographic Information Systems 16
analysis, researchers can deliver both the precision and inclusiveness desired in 17
economic impact analysis. It is no longer enough to settle for tradeoffs with 18
economic impact analysis. Multiple techniques can be employed to compensate 19
for traditional shortcomings and to illuminate hidden relationships. 20
117

The research performed herein further highlights the need for 1
policymakers and planners to make a space for cultural activities to occur. This 2
study did not attempt to measure the genre or quality of the live music being 3
performed; nor did it attempt to evaluate venue size. The fact that there was a 4
location where live music had occurred was enough to show statistically 5
significant results. 6
Music events and music venues are a part of the community and should 7
be included as a part of effective city planning. In difficult economic times it is 8
easy to be tempted to cut funding from important arts programs in the effort to 9
balance the budget. This is short sighted at best. Instead, communities should 10
leverage their local arts assets, promoting cultural tourism to their region along 11
with the associated economic growth. 12
13
118

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132

APPENDICES 1
Appendix A: Orange County Total Type Multipliers 2
Industry Description
Total
Multiplier
Farms 1.4285391
Forestry, fishing, and related activities 1.3826714
Oil and gas extraction 1.3425926
Mining, except oil and gas 1.3454713
Support activities for mining 1.6580997
Utilities 1.1078519
Construction 1.5270129
Wood products 1.5250314
Nonmetallic mineral products 1.6096290
Primary metals 1.6783707
Fabricated metal products 1.6514202
Machinery 1.7177113
Computer and electronic products 1.4258868
Electrical equipment, appliances, and components 1.7779328
Motor vehicles, bodies and trailers, and parts 1.7774699
Other transportation equipment 2.0349017
Furniture and related products 1.6337643
Miscellaneous manufacturing 1.5742493
Food and beverage and tobacco products 1.5799273
Textile mills and textile product mills 2.0507401
Apparel and leather and allied products 1.5487619
Paper products 1.7673291
Printing and related support activities 1.8084871
Petroleum and coal products 1.1700792
Chemical products 1.9308718
Plastics and rubber products 1.9498813
Wholesale trade 1.4050233
Retail trade 1.3692093
Air transportation 1.5212754
Rail transportation 1.5399027
Water transportation 1.5450649
Truck transportation 1.4457810
Table 7. Orange County Total Type Multipliers

133

Industry Description
Total
Multiplier
Transit and ground passenger transportation 1.1413507
Pipeline transportation 1.4061801
Other transportation and support activities 1.3441325
Warehousing and storage 1.3320930
Publishing industries (includes software) 1.7346872
Motion picture and sound recording industries 1.5220971
Broadcasting and telecommunications 1.6042859
Information and data processing services 1.6262531
Federal Reserve banks, credit intermediation, and related
activities 1.4913841
Securities, commodity contracts, and investments 1.8447325
Insurance carriers and related activities 1.5104499
Funds, trusts, and other financial vehicles 1.8618309
Real estate 1.2303979
Rental and leasing services and lessors of intangible assets 1.3162381
Legal services 1.2178247
Computer systems design and related services 1.3820155
Miscellaneous professional, scientific, and technical services 1.3946588
Management of companies and enterprises 1.4387682
Administrative and support services 1.3788609
Waste management and remediation services 1.6450613
Educational services 1.4140894
Ambulatory health care services 1.4268820
Hospitals and nursing and residential care facilities 1.5668364
Social assistance 1.4696186
Performing arts, spectator sports, museums, and related
activities 1.6033417
Amusements, gambling, and recreation industries 1.4821711
Accommodation 1.6087790
Food services and drinking places 1.4798823
Other services, except government 1.4397791
Federal general government 1.6195571
Federal government enterprises 1.2983498
State and local general government 1.4004832
State and local government enterprises 1.6202617
1
Table 7. Orange County Total Type Multipliers (Continued)

134

Appendix B: Orange County Industry Description Codes 1
Code Industry Description
111CA Farms
113FF Forestry, fishing, and related activities
211 Oil and gas extraction
212 Mining, except oil and gas
213 Support activities for mining
22 Utilities
23 Construction
321 Wood products
327 Nonmetallic mineral products
331 Primary metals
332 Fabricated metal products
333 Machinery
334 Computer and electronic products
335 Electrical equipment, appliances, and components
3361MV Motor vehicles, bodies and trailers, and parts
3364OT Other transportation equipment
337 Furniture and related products
339 Miscellaneous manufacturing
311FT Food and beverage and tobacco products
313TT Textile mills and textile product mills
315AL Apparel and leather and allied products
322 Paper products
323 Printing and related support activities
324 Petroleum and coal products
325 Chemical products
326 Plastics and rubber products
42 Wholesale trade
44RT Retail trade
481 Air transportation
482 Rail transportation
483 Water transportation
484 Truck transportation
485 Transit and ground passenger transportation
2
Table 8. Orange County Industry Description Codes

135

Code Industry Description
487OS Other transportation and support activities
493 Warehousing and storage
511 Publishing industries (includes software)
512 Motion picture and sound recording industries
513 Broadcasting and telecommunications
514 Information and data processing services
521CI
Federal Reserve banks, credit intermediation, and related
activities
523 Securities, commodity contracts, and investments
524 Insurance carriers and related activities
525 Funds, trusts, and other financial vehicles
531 Real estate
532RL Rental and leasing services and lessors of intangible assets
5411 Legal services
5415 Computer systems design and related services
5412OP Miscellaneous professional, scientific, and technical services
55 Management of companies and enterprises
561 Administrative and support services
562 Waste management and remediation services
61 Educational services
621 Ambulatory health care services
622HO Hospitals and nursing and residential care facilities
624 Social assistance
711AS
Performing arts, spectator sports, museums, and related
activities
713 Amusements, gambling, and recreation industries
721 Accommodation
722 Food services and drinking places
81 Other services, except government
GFG Federal general government
GFE Federal government enterprises
GSLG State and local general government
GSLE State and local government enterprises
1
2
Table 8. Orange County Industry Description Codes (Continued)

136

Appendix C: Orange County Total Requirements Table 1
Code 111CA 113FF 211 212 213 22
111CA 1.0279 0.0045 0.0001 0.0000 0.0001 0.0000
113FF 0.0024 1.0039 0.0000 0.0000 0.0000 0.0000
211 0.0010 0.0005 1.0109 0.0008 0.0007 0.0197
212 0.0002 0.0001 0.0002 1.0039 0.0005 0.0008
213 0.0000 0.0000 0.0013 0.0017 1.0006 0.0000
22 0.0118 0.0033 0.0090 0.0236 0.0124 1.0008
23 0.0075 0.0049 0.0486 0.0025 0.0040 0.0172
321 0.0004 0.0016 0.0003 0.0001 0.0008 0.0001
327 0.0008 0.0005 0.0023 0.0042 0.0114 0.0007
331 0.0016 0.0010 0.0062 0.0091 0.0299 0.0005
332 0.0088 0.0045 0.0225 0.0185 0.0375 0.0027
333 0.0080 0.0060 0.0085 0.0165 0.0240 0.0008
334 0.0038 0.0035 0.0037 0.0045 0.0093 0.0014
335 0.0020 0.0012 0.0023 0.0024 0.0076 0.0012
3361MV 0.0001 0.0001 0.0001 0.0001 0.0002 0.0000
3364OT 0.0005 0.0037 0.0005 0.0008 0.0012 0.0001
337 0.0004 0.0005 0.0005 0.0002 0.0005 0.0002
339 0.0015 0.0012 0.0013 0.0010 0.0058 0.0002
311FT 0.0363 0.0029 0.0004 0.0003 0.0013 0.0003
313TT 0.0013 0.0030 0.0004 0.0006 0.0011 0.0001
315AL 0.0003 0.0003 0.0001 0.0001 0.0002 0.0000
322 0.0027 0.0014 0.0019 0.0022 0.0082 0.0004
323 0.0012 0.0012 0.0013 0.0014 0.0036 0.0005
324 0.0051 0.0026 0.0011 0.0013 0.0027 0.0006
325 0.0949 0.0928 0.0253 0.0129 0.0673 0.0020
326 0.0067 0.0038 0.0054 0.0153 0.0139 0.0010
42 0.0708 0.0413 0.0156 0.0209 0.0451 0.0034
44RT 0.0028 0.0010 0.0035 0.0014 0.0021 0.0007
481 0.0002 0.0001 0.0001 0.0001 0.0003 0.0001
482 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000
483 0.0003 0.0000 0.0001 0.0001 0.0001 0.0000
484 0.0060 0.0021 0.0014 0.0085 0.0074 0.0007
485 0.0001 0.0001 0.0001 0.0001 0.0003 0.0001
486 0.0000 0.0000 0.0002 0.0000 0.0000 0.0010
487OS 0.0030 0.0026 0.0012 0.0061 0.0038 0.0013
493 0.0024 0.0005 0.0003 0.0004 0.0008 0.0001
511 0.0015 0.0020 0.0032 0.0037 0.0069 0.0011
512 0.0002 0.0003 0.0003 0.0003 0.0008 0.0001
2 Table 9. Orange County Total Requirements Table

137

Code 111CA 113FF 211 212 213 22
513 0.0037 0.0036 0.0062 0.0064 0.0152 0.0023
514 0.0006 0.0007 0.0008 0.0010 0.0023 0.0009
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0049 0.0022 0.0044 0.0111 0.0236 0.0013
524 0.0101 0.0065 0.0030 0.0047 0.0091 0.0011
525 0.0001 0.0001 0.0001 0.0001 0.0003 0.0000
531 0.0249 0.0842 0.0084 0.0074 0.0230 0.0027
532RL 0.0068 0.0032 0.0205 0.0159 0.0264 0.0014
5411 0.0033 0.0068 0.0066 0.0072 0.0253 0.0048
5415 0.0027 0.0041 0.0185 0.0297 0.0081 0.0019
5412OP 0.0177 0.0241 0.0318 0.0307 0.0948 0.0151
55 0.0083 0.0064 0.0375 0.0279 0.0510 0.0016
561 0.0076 0.0086 0.0080 0.0110 0.0229 0.0041
562 0.0010 0.0021 0.0012 0.0019 0.0050 0.0007
61 0.0008 0.0061 0.0000 0.0000 0.0001 0.0001
621 0.0000 0.0000 0.0000 0.0000 0.0001 0.0000
622HO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0008 0.0023 0.0008 0.0008 0.0020 0.0004
713 0.0002 0.0001 0.0001 0.0001 0.0004 0.0001
721 0.0007 0.0009 0.0008 0.0012 0.0039 0.0008
722 0.0017 0.0013 0.0017 0.0021 0.0060 0.0046
81 0.0037 0.0048 0.0032 0.0030 0.0085 0.0014
GFG 0.0030 0.0025 0.0032 0.0040 0.0074 0.0008
GFE 0.0018 0.0011 0.0010 0.0024 0.0022 0.0005
GSLG 0.0046 0.0088 0.0004 0.0006 0.0020 0.0002
GSLE 0.0053 0.0033 0.0039 0.0099 0.0060 0.0006
Total industry
output 1.4285 1.3827 1.3426 1.3455 1.6581 1.1079
1
2
3
4
Table 9. Orange County Total Requirements Table (Continued)

138

Code 23 321 327 331 332 333
111CA 0.0003 0.0033 0.0002 0.0001 0.0001 0.0001
113FF 0.0001 0.0067 0.0000 0.0000 0.0000 0.0000
211 0.0007 0.0006 0.0009 0.0009 0.0004 0.0003
212 0.0006 0.0001 0.0037 0.0043 0.0004 0.0002
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0050 0.0148 0.0288 0.0301 0.0113 0.0075
23 1.0037 0.0113 0.0129 0.0147 0.0084 0.0066
321 0.0050 1.0392 0.0013 0.0006 0.0004 0.0007
327 0.0239 0.0066 1.0636 0.0084 0.0031 0.0040
331 0.0090 0.0037 0.0082 1.1327 0.0791 0.0439
332 0.0574 0.0247 0.0304 0.0446 1.1202 0.1099
333 0.0181 0.0061 0.0050 0.0144 0.0125 1.0628
334 0.0112 0.0130 0.0237 0.0310 0.0229 0.0352
335 0.0239 0.0093 0.0034 0.0143 0.0094 0.0369
3361MV 0.0001 0.0001 0.0001 0.0001 0.0001 0.0004
3364OT 0.0010 0.0007 0.0008 0.0023 0.0013 0.0024
337 0.0066 0.0041 0.0007 0.0004 0.0005 0.0012
339 0.0021 0.0016 0.0038 0.0020 0.0028 0.0081
311FT 0.0007 0.0013 0.0015 0.0008 0.0009 0.0008
313TT 0.0021 0.0058 0.0044 0.0007 0.0007 0.0037
315AL 0.0003 0.0005 0.0004 0.0003 0.0002 0.0003
322 0.0038 0.0053 0.0172 0.0075 0.0057 0.0055
323 0.0027 0.0024 0.0023 0.0025 0.0024 0.0024
324 0.0041 0.0014 0.0017 0.0013 0.0008 0.0009
325 0.0271 0.0374 0.0576 0.0221 0.0375 0.0283
326 0.0185 0.0105 0.0139 0.0074 0.0090 0.0273
42 0.0395 0.0765 0.0519 0.0707 0.0556 0.0736
44RT 0.0330 0.0012 0.0015 0.0010 0.0010 0.0033
481 0.0002 0.0003 0.0004 0.0003 0.0003 0.0003
482 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000
483 0.0001 0.0000 0.0002 0.0002 0.0001 0.0001
484 0.0058 0.0094 0.0198 0.0083 0.0050 0.0051
485 0.0002 0.0003 0.0003 0.0002 0.0003 0.0002
486 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
487OS 0.0032 0.0132 0.0121 0.0050 0.0033 0.0033
493 0.0010 0.0027 0.0025 0.0022 0.0026 0.0026
511 0.0052 0.0036 0.0042 0.0037 0.0045 0.0044
512 0.0007 0.0005 0.0006 0.0005 0.0006 0.0006

Table 9. Orange County Total Requirements Table (Continued)

139

Code 23 321 327 331 332 333
513 0.0141 0.0101 0.0104 0.0086 0.0116 0.0106
514 0.0020 0.0033 0.0033 0.0025 0.0034 0.0032
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0075 0.0052 0.0068 0.0289 0.0356 0.0285
524 0.0046 0.0046 0.0051 0.0054 0.0055 0.0058
525 0.0002 0.0001 0.0002 0.0003 0.0003 0.0003
531 0.0136 0.0176 0.0132 0.0112 0.0171 0.0139
532RL 0.0134 0.0094 0.0120 0.0084 0.0123 0.0104
5411 0.0115 0.0066 0.0067 0.0069 0.0080 0.0079
5415 0.0063 0.0050 0.0053 0.0048 0.0098 0.0097
5412OP 0.0709 0.0447 0.0496 0.0404 0.0510 0.0471
55 0.0103 0.0306 0.0399 0.0294 0.0258 0.0385
561 0.0200 0.0200 0.0249 0.0301 0.0301 0.0237
562 0.0028 0.0036 0.0038 0.0083 0.0031 0.0021
61 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001
621 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000
622HO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0016 0.0023 0.0021 0.0016 0.0019 0.0018
713 0.0002 0.0004 0.0004 0.0002 0.0003 0.0003
721 0.0024 0.0038 0.0039 0.0023 0.0037 0.0030
722 0.0041 0.0067 0.0065 0.0049 0.0059 0.0050
81 0.0159 0.0099 0.0111 0.0111 0.0082 0.0067
GFG 0.0026 0.0028 0.0063 0.0042 0.0043 0.0083
GFE 0.0017 0.0024 0.0033 0.0032 0.0021 0.0028
GSLG 0.0011 0.0095 0.0014 0.0156 0.0019 0.0013
GSLE 0.0029 0.0080 0.0133 0.0137 0.0058 0.0040
Total industry
output
1.5270 1.5250 1.6096 1.6784 1.6514 1.7177
1
2
3
4
Table 9. Orange County Total Requirements Table (Continued)

140

Code 334 335 3361MV 3364OT 337 339
111CA 0.0000 0.0001 0.0002 0.0001 0.0004 0.0004
113FF 0.0000 0.0000 0.0000 0.0000 0.0002 0.0001
211 0.0001 0.0004 0.0004 0.0003 0.0003 0.0003
212 0.0001 0.0004 0.0004 0.0003 0.0002 0.0002
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0027 0.0093 0.0092 0.0076 0.0078 0.0058
23 0.0029 0.0072 0.0064 0.0077 0.0074 0.0053
321 0.0001 0.0008 0.0014 0.0005 0.0166 0.0021
327 0.0009 0.0091 0.0090 0.0023 0.0025 0.0023
331 0.0062 0.0658 0.0406 0.0246 0.0170 0.0217
332 0.0169 0.0676 0.1039 0.0898 0.0448 0.0401
333 0.0027 0.0135 0.0424 0.0177 0.0038 0.0129
334 1.1210 0.0574 0.0677 0.1176 0.0183 0.0217
335 0.0087 1.0827 0.0164 0.0171 0.0031 0.0071
3361MV 0.0000 0.0001 1.0076 0.0003 0.0001 0.0001
3364OT 0.0011 0.0020 0.0035 1.1906 0.0009 0.0010
337 0.0005 0.0007 0.0012 0.0006 1.0394 0.0020
339 0.0009 0.0049 0.0060 0.0027 0.0022 1.0521
311FT 0.0004 0.0010 0.0010 0.0009 0.0011 0.0011
313TT 0.0004 0.0013 0.0081 0.0024 0.0348 0.0109
315AL 0.0001 0.0002 0.0052 0.0003 0.0019 0.0008
322 0.0020 0.0097 0.0076 0.0048 0.0106 0.0089
323 0.0023 0.0026 0.0025 0.0041 0.0025 0.0027
324 0.0003 0.0012 0.0008 0.0007 0.0009 0.0008
325 0.0148 0.0582 0.0488 0.0249 0.0518 0.0568
326 0.0059 0.0308 0.0501 0.0175 0.0522 0.0302
42 0.0483 0.0890 0.0884 0.0587 0.0605 0.0489
44RT 0.0006 0.0021 0.0039 0.0024 0.0063 0.0030
481 0.0002 0.0003 0.0003 0.0005 0.0003 0.0003
482 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
483 0.0000 0.0001 0.0001 0.0001 0.0000 0.0000
484 0.0017 0.0053 0.0067 0.0054 0.0073 0.0051
485 0.0001 0.0001 0.0002 0.0003 0.0003 0.0003
486 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
487OS 0.0019 0.0036 0.0039 0.0037 0.0089 0.0034
493 0.0016 0.0025 0.0023 0.0040 0.0026 0.0024
511 0.0162 0.0053 0.0055 0.0127 0.0042 0.0044
512 0.0004 0.0005 0.0005 0.0009 0.0006 0.0006

Table 9. Orange County Total Requirements Table (Continued)

141

Code 334 335 3361MV 3364OT 337 339
513 0.0071 0.0094 0.0086 0.0162 0.0120 0.0111
514 0.0021 0.0027 0.0024 0.0052 0.0039 0.0031
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0079 0.0218 0.0191 0.0428 0.0316 0.0287
524 0.0031 0.0059 0.0049 0.0072 0.0056 0.0048
525 0.0001 0.0002 0.0002 0.0004 0.0003 0.0003
531 0.0073 0.0225 0.0109 0.0174 0.0203 0.0148
532RL 0.0086 0.0108 0.0096 0.0196 0.0080 0.0119
5411 0.0048 0.0064 0.0054 0.0125 0.0061 0.0090
5415 0.0093 0.0055 0.0058 0.0208 0.0065 0.0059
5412OP 0.0450 0.0479 0.0462 0.0959 0.0521 0.0521
55 0.0341 0.0442 0.0517 0.0927 0.0224 0.0257
561 0.0125 0.0198 0.0186 0.0391 0.0180 0.0182
562 0.0009 0.0023 0.0026 0.0030 0.0037 0.0023
61 0.0000 0.0001 0.0001 0.0001 0.0001 0.0001
621 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000
622HO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0011 0.0016 0.0016 0.0028 0.0019 0.0017
713 0.0002 0.0002 0.0003 0.0004 0.0004 0.0003
721 0.0016 0.0021 0.0023 0.0041 0.0038 0.0036
722 0.0026 0.0038 0.0040 0.0071 0.0058 0.0055
81 0.0034 0.0068 0.0064 0.0095 0.0072 0.0058
GFG 0.0087 0.0180 0.0163 0.0058 0.0040 0.0076
GFE 0.0011 0.0032 0.0020 0.0022 0.0019 0.0017
GSLG 0.0004 0.0016 0.0013 0.0013 0.0014 0.0011
GSLE 0.0017 0.0048 0.0048 0.0045 0.0051 0.0035
Total industry
output 1.4259 1.7779 1.7775 2.0349 1.6338 1.5742
1
2
3
4
5
6
7
Table 9. Orange County Total Requirements Table (Continued)

142

Code 311FT 313TT 315AL 322 323 324
111CA 0.0528 0.0078 0.0011 0.0009 0.0004 0.0000
113FF 0.0005 0.0001 0.0001 0.0010 0.0001 0.0000
211 0.0005 0.0010 0.0003 0.0011 0.0007 0.0778
212 0.0002 0.0003 0.0001 0.0005 0.0001 0.0000
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001
22 0.0128 0.0200 0.0084 0.0294 0.0139 0.0028
23 0.0062 0.0088 0.0035 0.0131 0.0122 0.0050
321 0.0004 0.0008 0.0002 0.0055 0.0007 0.0000
327 0.0046 0.0020 0.0006 0.0016 0.0011 0.0006
331 0.0049 0.0033 0.0012 0.0043 0.0029 0.0007
332 0.0198 0.0163 0.0074 0.0278 0.0201 0.0031
333 0.0047 0.0040 0.0013 0.0087 0.0118 0.0010
334 0.0090 0.0235 0.0077 0.0253 0.0281 0.0014
335 0.0028 0.0031 0.0012 0.0043 0.0039 0.0005
3361MV 0.0001 0.0001 0.0000 0.0001 0.0001 0.0000
3364OT 0.0005 0.0016 0.0007 0.0012 0.0009 0.0001
337 0.0004 0.0010 0.0004 0.0011 0.0006 0.0001
339 0.0011 0.0067 0.0067 0.0022 0.0023 0.0003
311FT 1.0892 0.0048 0.0095 0.0039 0.0041 0.0002
313TT 0.0015 1.1728 0.0908 0.0217 0.0103 0.0002
315AL 0.0003 0.0394 1.0822 0.0013 0.0249 0.0001
322 0.0234 0.0138 0.0058 1.1436 0.1056 0.0006
323 0.0024 0.0034 0.0039 0.0037 1.0242 0.0004
324 0.0011 0.0027 0.0007 0.0024 0.0019 1.0048
325 0.0307 0.3333 0.0424 0.1150 0.0793 0.0135
326 0.0259 0.0154 0.0110 0.0244 0.0198 0.0012
42 0.0848 0.1033 0.0606 0.0852 0.0769 0.0206
44RT 0.0015 0.0022 0.0010 0.0014 0.0015 0.0010
481 0.0003 0.0005 0.0002 0.0003 0.0008 0.0000
482 0.0000 0.0000 0.0000 0.0001 0.0000 0.0000
483 0.0003 0.0001 0.0000 0.0001 0.0001 0.0000
484 0.0101 0.0100 0.0049 0.0091 0.0063 0.0009
485 0.0001 0.0002 0.0001 0.0002 0.0006 0.0000
486 0.0000 0.0001 0.0000 0.0000 0.0000 0.0003
487OS 0.0051 0.0061 0.0028 0.0069 0.0098 0.0010
493 0.0018 0.0027 0.0019 0.0024 0.0042 0.0002
511 0.0030 0.0048 0.0038 0.0036 0.0064 0.0006
512 0.0004 0.0006 0.0005 0.0005 0.0009 0.0001

1
Table 9. Orange County Total Requirements Table (Continued)

143

Code 311FT 313TT 315AL 322 323 324
513 0.0067 0.0109 0.0083 0.0087 0.0170 0.0012
514 0.0018 0.0030 0.0022 0.0028 0.0044 0.0002
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0048 0.0057 0.0038 0.0055 0.0082 0.0008
524 0.0046 0.0054 0.0039 0.0046 0.0091 0.0008
525 0.0001 0.0001 0.0001 0.0001 0.0002 0.0000
531 0.0119 0.0143 0.0117 0.0122 0.0268 0.0016
532RL 0.0091 0.0114 0.0099 0.0114 0.0192 0.0024
5411 0.0046 0.0070 0.0066 0.0057 0.0107 0.0010
5415 0.0037 0.0060 0.0039 0.0050 0.0116 0.0020
5412OP 0.0369 0.0600 0.0509 0.0425 0.0709 0.0065
55 0.0439 0.0404 0.0257 0.0408 0.0415 0.0056
561 0.0176 0.0225 0.0329 0.0209 0.0484 0.0024
562 0.0028 0.0037 0.0018 0.0049 0.0041 0.0006
61 0.0001 0.0001 0.0001 0.0001 0.0005 0.0000
621 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
622HO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0015 0.0022 0.0016 0.0020 0.0035 0.0002
713 0.0002 0.0003 0.0002 0.0003 0.0008 0.0000
721 0.0020 0.0029 0.0019 0.0030 0.0090 0.0003
722 0.0039 0.0059 0.0043 0.0058 0.0146 0.0007
81 0.0059 0.0088 0.0042 0.0105 0.0121 0.0010
GFG 0.0044 0.0098 0.0052 0.0047 0.0041 0.0014
GFE 0.0021 0.0030 0.0016 0.0032 0.0043 0.0006
GSLG 0.0018 0.0014 0.0007 0.0054 0.0019 0.0001
GSLE 0.0064 0.0095 0.0042 0.0133 0.0083 0.0012
Total industry
output 1.5799 2.0507 1.5488 1.7673 1.8085 1.1701
1
2
3
Table 9. Orange County Total Requirements Table (Continued)

144

Code 325 326 42 44RT 481 482
111CA 0.0011 0.0008 0.0001 0.0004 0.0002 0.0001
113FF 0.0001 0.0006 0.0000 0.0000 0.0000 0.0000
211 0.0023 0.0010 0.0003 0.0003 0.0023 0.0011
212 0.0007 0.0003 0.0000 0.0000 0.0001 0.0001
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0212 0.0171 0.0041 0.0065 0.0019 0.0024
23 0.0092 0.0107 0.0041 0.0066 0.0039 0.0629
321 0.0004 0.0014 0.0004 0.0004 0.0001 0.0027
327 0.0025 0.0053 0.0005 0.0009 0.0006 0.0020
331 0.0035 0.0061 0.0009 0.0006 0.0013 0.0104
332 0.0197 0.0326 0.0041 0.0042 0.0093 0.0114
333 0.0077 0.0100 0.0018 0.0014 0.0015 0.0049
334 0.0201 0.0288 0.0112 0.0061 0.0078 0.0064
335 0.0041 0.0094 0.0015 0.0015 0.0017 0.0057
3361MV 0.0001 0.0001 0.0001 0.0001 0.0000 0.0001
3364OT 0.0017 0.0015 0.0006 0.0003 0.0226 0.0205
337 0.0009 0.0020 0.0007 0.0009 0.0002 0.0007
339 0.0048 0.0051 0.0018 0.0014 0.0008 0.0009
311FT 0.0067 0.0047 0.0012 0.0008 0.0028 0.0004
313TT 0.0032 0.0157 0.0012 0.0032 0.0003 0.0004
315AL 0.0003 0.0010 0.0010 0.0012 0.0002 0.0002
322 0.0090 0.0192 0.0039 0.0029 0.0016 0.0021
323 0.0042 0.0030 0.0065 0.0071 0.0017 0.0027
324 0.0055 0.0030 0.0014 0.0007 0.0154 0.0074
325 1.3886 0.3571 0.0083 0.0080 0.0072 0.0115
326 0.0240 1.0725 0.0076 0.0069 0.0024 0.0033
42 0.0761 0.0698 1.0413 0.0196 0.0236 0.0281
44RT 0.0050 0.0023 0.0010 1.0021 0.0008 0.0031
481 0.0003 0.0004 0.0004 0.0002 1.0002 0.0002
482 0.0001 0.0000 0.0000 0.0000 0.0000 1.0000
483 0.0001 0.0001 0.0000 0.0000 0.0003 0.0001
484 0.0060 0.0058 0.0020 0.0034 0.0020 0.0030
485 0.0002 0.0003 0.0002 0.0001 0.0001 0.0008
486 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001
487OS 0.0042 0.0054 0.0186 0.0129 0.1115 0.0110
493 0.0016 0.0026 0.0073 0.0097 0.0010 0.0006
511 0.0065 0.0050 0.0051 0.0052 0.0027 0.0052

Table 9. Orange County Total Requirements Table (Continued)

145

Code 325 326 42 44RT 481 482
513 0.0116 0.0115 0.0140 0.0125 0.0131 0.0108
514 0.0026 0.0031 0.0023 0.0033 0.0022 0.0021
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0055 0.0069 0.0052 0.0045 0.0040 0.0531
524 0.0050 0.0050 0.0203 0.0209 0.0184 0.0069
525 0.0001 0.0001 0.0002 0.0002 0.0003 0.0004
531 0.0109 0.0156 0.0220 0.0498 0.0114 0.0128
532RL 0.0144 0.0121 0.0105 0.0093 0.0146 0.0614
5411 0.0063 0.0072 0.0082 0.0060 0.0053 0.0280
5415 0.0066 0.0065 0.0070 0.0063 0.0082 0.0219
5412OP 0.0890 0.0620 0.0648 0.0545 0.0282 0.0548
55 0.0595 0.0427 0.0276 0.0127 0.0094 0.0079
561 0.0197 0.0220 0.0373 0.0323 0.0264 0.0319
562 0.0030 0.0044 0.0018 0.0025 0.0019 0.0027
61 0.0001 0.0001 0.0004 0.0013 0.0002 0.0004
621 0.0001 0.0001 0.0000 0.0000 0.0002 0.0000
622HO 0.0000 0.0000 0.0000 0.0000 0.0002 0.0000
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0019 0.0024 0.0028 0.0031 0.0015 0.0068
713 0.0003 0.0004 0.0004 0.0004 0.0001 0.0002
721 0.0022 0.0043 0.0020 0.0013 0.0015 0.0018
722 0.0043 0.0072 0.0058 0.0059 0.0354 0.0032
81 0.0076 0.0096 0.0100 0.0088 0.0036 0.0090
GFG 0.0239 0.0121 0.0089 0.0018 0.0921 0.0050
GFE 0.0028 0.0024 0.0089 0.0086 0.0029 0.0015
GSLG 0.0011 0.0022 0.0008 0.0015 0.0003 0.0011
GSLE 0.0097 0.0086 0.0041 0.0049 0.0106 0.0030
Total industry
output 1.9309 1.9499 1.4050 1.3692 1.5213 1.5399
1
2
Table 9. Orange County Total Requirements Table (Continued)

146

Code 483 484 485 486 487OS 493
111CA 0.0001 0.0001 0.0000 0.0001 0.0001 0.0001
113FF 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
211 0.0024 0.0016 0.0007 0.0101 0.0013 0.0004
212 0.0001 0.0000 0.0000 0.0001 0.0000 0.0000
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0035 0.0029 0.0012 0.0053 0.0032 0.0084
23 0.0038 0.0037 0.0012 0.0644 0.0060 0.0067
321 0.0001 0.0003 0.0000 0.0004 0.0001 0.0003
327 0.0006 0.0006 0.0002 0.0019 0.0005 0.0007
331 0.0034 0.0014 0.0020 0.0030 0.0010 0.0011
332 0.0338 0.0117 0.0232 0.0242 0.0048 0.0064
333 0.0024 0.0020 0.0016 0.0207 0.0026 0.0061
334 0.0093 0.0045 0.0019 0.0048 0.0033 0.0036
335 0.0021 0.0030 0.0014 0.0036 0.0015 0.0048
3361MV 0.0000 0.0005 0.0008 0.0000 0.0001 0.0002
3364OT 0.0378 0.0008 0.0040 0.0007 0.0084 0.0004
337 0.0002 0.0002 0.0001 0.0006 0.0002 0.0002
339 0.0010 0.0006 0.0004 0.0010 0.0007 0.0016
311FT 0.0008 0.0006 0.0001 0.0003 0.0007 0.0004
313TT 0.0020 0.0005 0.0001 0.0009 0.0004 0.0003
315AL 0.0003 0.0002 0.0001 0.0002 0.0002 0.0001
322 0.0012 0.0020 0.0005 0.0022 0.0020 0.0024
323 0.0019 0.0022 0.0008 0.0024 0.0017 0.0014
324 0.0164 0.0109 0.0048 0.0022 0.0085 0.0012
325 0.0087 0.0101 0.0029 0.0071 0.0073 0.0063
326 0.0024 0.0097 0.0012 0.0059 0.0053 0.0042
42 0.0238 0.0248 0.0095 0.0162 0.0169 0.0109
44RT 0.0037 0.0115 0.0051 0.0038 0.0034 0.0011
481 0.0004 0.0007 0.0001 0.0001 0.0005 0.0003
482 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000
483 1.0003 0.0001 0.0000 0.0001 0.0001 0.0000
484 0.0034 1.0196 0.0010 0.0014 0.0028 0.0017
485 0.0001 0.0001 1.0000 0.0001 0.0001 0.0001
486 0.0001 0.0001 0.0000 1.0001 0.0000 0.0000
487OS 0.0843 0.0522 0.0020 0.0057 1.0543 0.0174
493 0.0037 0.0073 0.0006 0.0006 0.0053 1.0259
511 0.0029 0.0025 0.0010 0.0041 0.0022 0.0024
512 0.0006 0.0005 0.0002 0.0005 0.0005 0.0004

Table 9. Orange County Total Requirements Table (Continued)

147

Code 483 484 485 486 487OS 493
513 0.0088 0.0111 0.0034 0.0102 0.0090 0.0082
514 0.0021 0.0012 0.0006 0.0026 0.0010 0.0011
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0234 0.0052 0.0041 0.0116 0.0072 0.0036
524 0.0244 0.0530 0.0141 0.0207 0.0149 0.0168
525 0.0004 0.0004 0.0001 0.0003 0.0018 0.0002
531 0.0244 0.0170 0.0022 0.0148 0.0132 0.0520
532RL 0.0044 0.0047 0.0035 0.0029 0.0084 0.0052
5411 0.0047 0.0052 0.0025 0.0080 0.0048 0.0052
5415 0.0078 0.0060 0.0016 0.0044 0.0033 0.0052
5412OP 0.0314 0.0245 0.0124 0.0544 0.0218 0.0228
55 0.0122 0.0148 0.0047 0.0045 0.0162 0.0156
561 0.0219 0.0608 0.0095 0.0411 0.0346 0.0389
562 0.0132 0.0023 0.0047 0.0043 0.0090 0.0036
61 0.0001 0.0001 0.0000 0.0001 0.0001 0.0001
621 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
622HO 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0012 0.0013 0.0006 0.0011 0.0030 0.0032
713 0.0001 0.0001 0.0000 0.0001 0.0001 0.0001
721 0.0016 0.0011 0.0005 0.0014 0.0024 0.0021
722 0.0025 0.0032 0.0008 0.0025 0.0061 0.0029
81 0.0033 0.0099 0.0034 0.0139 0.0072 0.0090
GFG 0.0751 0.0021 0.0010 0.0037 0.0067 0.0016
GFE 0.0124 0.0246 0.0011 0.0043 0.0182 0.0096
GSLG 0.0026 0.0016 0.0009 0.0012 0.0032 0.0009
GSLE 0.0091 0.0060 0.0008 0.0030 0.0063 0.0065
Total industry
output 1.5451 1.4458 1.1414 1.4062 1.3441 1.3321
1
2
3
Table 9. Orange County Total Requirements Table (Continued)

148

Code 511 512 513 514 521CI 523
111CA 0.0002 0.0001 0.0001 0.0002 0.0001 0.0001
113FF 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
211 0.0003 0.0002 0.0002 0.0002 0.0001 0.0002
212 0.0001 0.0001 0.0001 0.0001 0.0000 0.0000
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0036 0.0041 0.0043 0.0033 0.0023 0.0036
23 0.0058 0.0054 0.0147 0.0071 0.0129 0.0080
321 0.0006 0.0010 0.0004 0.0015 0.0002 0.0002
327 0.0007 0.0005 0.0017 0.0030 0.0006 0.0007
331 0.0019 0.0009 0.0019 0.0020 0.0004 0.0007
332 0.0107 0.0029 0.0131 0.0113 0.0024 0.0036
333 0.0031 0.0022 0.0025 0.0028 0.0008 0.0012
334 0.0235 0.0108 0.0365 0.0319 0.0071 0.0110
335 0.0019 0.0011 0.0074 0.0046 0.0011 0.0018
3361MV 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000
3364OT 0.0008 0.0005 0.0012 0.0011 0.0004 0.0016
337 0.0003 0.0003 0.0005 0.0026 0.0002 0.0003
339 0.0015 0.0010 0.0013 0.0020 0.0010 0.0015
311FT 0.0017 0.0009 0.0008 0.0017 0.0013 0.0009
313TT 0.0009 0.0010 0.0005 0.0006 0.0002 0.0004
315AL 0.0012 0.0006 0.0011 0.0003 0.0002 0.0004
322 0.0090 0.0023 0.0021 0.0026 0.0016 0.0022
323 0.0449 0.0076 0.0040 0.0061 0.0040 0.0106
324 0.0009 0.0005 0.0006 0.0008 0.0004 0.0007
325 0.0146 0.0054 0.0067 0.0083 0.0030 0.0052
326 0.0048 0.0029 0.0056 0.0043 0.0014 0.0023
42 0.0355 0.0066 0.0176 0.0187 0.0057 0.0084
44RT 0.0008 0.0007 0.0009 0.0012 0.0011 0.0008
481 0.0008 0.0004 0.0003 0.0010 0.0005 0.0005
482 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
483 0.0001 0.0000 0.0001 0.0001 0.0000 0.0001
484 0.0021 0.0013 0.0013 0.0020 0.0005 0.0011
485 0.0008 0.0004 0.0004 0.0011 0.0007 0.0005
486 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
487OS 0.0149 0.0078 0.0022 0.0139 0.0024 0.0087
493 0.0038 0.0019 0.0009 0.0033 0.0004 0.0014
511 1.0337 0.0077 0.0083 0.0127 0.0058 0.0127
512 0.0017 1.0867 0.0422 0.0067 0.0010 0.0027

Table 9. Orange County Total Requirements Table (Continued)

149

Code
511 512 513 514 521CI 523
513 0.0293 0.0188 1.1369 0.0479 0.0190 0.0593
514 0.0089 0.0028 0.0045 1.0077 0.0054 0.0202
521CI 0.0000 0.0000 0.0000 0.0000 1.0000 0.0000
523 0.0103 0.0117 0.0074 0.0060 0.1249 1.1596
524 0.0198 0.0179 0.0059 0.0150 0.0516 0.0492
525 0.0004 0.0003 0.0002 0.0003 0.0010 0.0070
531 0.0366 0.0493 0.0224 0.0289 0.0241 0.0748
532RL 0.0206 0.0100 0.0108 0.0394 0.0048 0.0149
5411 0.0233 0.0156 0.0082 0.0149 0.0133 0.0283
5415 0.0253 0.0073 0.0115 0.0429 0.0100 0.0343
5412OP 0.1208 0.1014 0.0873 0.0855 0.0632 0.1272
55 0.0401 0.0125 0.0075 0.0156 0.0187 0.0222
561 0.1011 0.0429 0.0291 0.0632 0.0312 0.0653
562 0.0019 0.0016 0.0038 0.0022 0.0008 0.0019
61 0.0001 0.0001 0.0009 0.0003 0.0002 0.0002
621 0.0001 0.0001 0.0001 0.0001 0.0000 0.0001
622HO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0001
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0051 0.0265 0.0344 0.0051 0.0046 0.0053
713 0.0010 0.0004 0.0004 0.0014 0.0009 0.0006
721 0.0112 0.0048 0.0047 0.0161 0.0105 0.0069
722 0.0150 0.0069 0.0066 0.0196 0.0179 0.0089
81 0.0111 0.0077 0.0104 0.0229 0.0150 0.0141
GFG 0.0112 0.0074 0.0212 0.0173 0.0071 0.0382
GFE 0.0084 0.0050 0.0024 0.0089 0.0038 0.0064
GSLG 0.0012 0.0012 0.0021 0.0011 0.0007 0.0012
GSLE 0.0047 0.0038 0.0041 0.0049 0.0026 0.0042
Total industry
output 1.7347 1.5221 1.6043 1.6263 1.4914 1.8447
1
2
3
4
5
6
7
Table 9. Orange County Total Requirements Table (Continued)

150

Code 524 525 531 532RL 5411 5415
111CA 0.0000 0.0001 0.0001 0.0001 0.0001 0.0001
113FF 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
211 0.0001 0.0001 0.0001 0.0002 0.0001 0.0002
212 0.0000 0.0000 0.0001 0.0001 0.0000 0.0000
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0009 0.0021 0.0029 0.0044 0.0013 0.0039
23 0.0015 0.0053 0.0150 0.0047 0.0023 0.0031
321 0.0001 0.0001 0.0011 0.0006 0.0001 0.0001
327 0.0002 0.0004 0.0014 0.0007 0.0002 0.0004
331 0.0002 0.0004 0.0007 0.0008 0.0003 0.0006
332 0.0010 0.0020 0.0049 0.0039 0.0016 0.0037
333 0.0004 0.0007 0.0017 0.0018 0.0005 0.0014
334 0.0019 0.0058 0.0016 0.0034 0.0054 0.0205
335 0.0004 0.0010 0.0014 0.0017 0.0021 0.0022
3361MV 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
3364OT 0.0003 0.0007 0.0001 0.0004 0.0001 0.0006
337 0.0001 0.0002 0.0020 0.0005 0.0001 0.0002
339 0.0004 0.0008 0.0004 0.0008 0.0004 0.0007
311FT 0.0003 0.0007 0.0003 0.0006 0.0005 0.0012
313TT 0.0002 0.0002 0.0003 0.0006 0.0001 0.0002
315AL 0.0003 0.0002 0.0001 0.0004 0.0001 0.0002
322 0.0015 0.0011 0.0006 0.0020 0.0011 0.0015
323 0.0109 0.0052 0.0009 0.0025 0.0027 0.0029
324 0.0002 0.0003 0.0003 0.0004 0.0003 0.0004
325 0.0021 0.0031 0.0052 0.0043 0.0021 0.0033
326 0.0010 0.0014 0.0023 0.0036 0.0010 0.0019
42 0.0032 0.0046 0.0051 0.0312 0.0037 0.0087
44RT 0.0002 0.0005 0.0027 0.0009 0.0004 0.0005
481 0.0001 0.0003 0.0001 0.0003 0.0002 0.0006
482 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
483 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000
484 0.0003 0.0006 0.0007 0.0008 0.0004 0.0006
485 0.0001 0.0003 0.0001 0.0003 0.0003 0.0007
486 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
487OS 0.0018 0.0039 0.0006 0.0064 0.0022 0.0025
493 0.0003 0.0006 0.0002 0.0012 0.0004 0.0005
511 0.0035 0.0075 0.0012 0.0047 0.0037 0.0067
512 0.0006 0.0014 0.0002 0.0006 0.0006 0.0018

1
Table 9. Orange County Total Requirements Table (Continued)

151

Code
524 525 531 532RL 5411 5415
513 0.0122 0.0298 0.0035 0.0120 0.0136 0.0410
514 0.0019 0.0103 0.0005 0.0016 0.0019 0.0031
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0251 0.4753 0.0035 0.0054 0.0048 0.0058
524 1.3059 0.0376 0.0351 0.0231 0.0168 0.0109
525 0.0048 1.0030 0.0002 0.0002 0.0006 0.0005
531 0.0152 0.0390 1.0565 0.0248 0.0392 0.0495
532RL 0.0030 0.0068 0.0014 1.0117 0.0052 0.0088
5411 0.0160 0.0252 0.0112 0.0088 1.0072 0.0104
5415 0.0045 0.0200 0.0028 0.0059 0.0076 1.0169
5412OP 0.0347 0.0727 0.0124 0.0394 0.0255 0.0555
55 0.0052 0.0106 0.0033 0.0269 0.0069 0.0107
561 0.0275 0.0331 0.0214 0.0279 0.0306 0.0410
562 0.0010 0.0010 0.0058 0.0011 0.0007 0.0013
61 0.0000 0.0001 0.0000 0.0001 0.0000 0.0001
621 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000
622HO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0011 0.0031 0.0009 0.0024 0.0026 0.0043
713 0.0001 0.0003 0.0001 0.0003 0.0004 0.0010
721 0.0015 0.0044 0.0015 0.0038 0.0035 0.0105
722 0.0026 0.0080 0.0026 0.0068 0.0069 0.0159
81 0.0041 0.0079 0.0088 0.0135 0.0043 0.0058
GFG 0.0071 0.0163 0.0007 0.0077 0.0009 0.0102
GFE 0.0014 0.0029 0.0006 0.0040 0.0017 0.0021
GSLG 0.0004 0.0006 0.0011 -0.0005 0.0004 0.0007
GSLE 0.0010 0.0023 0.0023 0.0043 0.0021 0.0038
Total industry
output 1.5104 1.8618 1.2304 1.3162 1.2178 1.3820
1
2
3
Table 9. Orange County Total Requirements Table (Continued)

152

Code 5412OP 55 561 562 61 621
111CA 0.0002 0.0001 0.0004 0.0001 0.0008 0.0001
113FF 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
211 0.0002 0.0002 0.0006 0.0005 0.0006 0.0002
212 0.0001 0.0000 0.0000 0.0001 0.0001 0.0001
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0028 0.0036 0.0032 0.0069 0.0226 0.0033
23 0.0083 0.0048 0.0032 0.0042 0.0061 0.0044
321 0.0002 0.0001 0.0002 0.0003 0.0002 0.0002
327 0.0014 0.0005 0.0007 0.0015 0.0008 0.0014
331 0.0012 0.0008 0.0009 0.0036 0.0015 0.0010
332 0.0055 0.0041 0.0048 0.0238 0.0049 0.0039
333 0.0021 0.0013 0.0026 0.0134 0.0050 0.0015
334 0.0092 0.0200 0.0074 0.0088 0.0072 0.0076
335 0.0033 0.0014 0.0028 0.0075 0.0027 0.0011
3361MV 0.0000 0.0000 0.0001 0.0003 0.0000 0.0000
3364OT 0.0008 0.0005 0.0003 0.0009 0.0006 0.0003
337 0.0004 0.0002 0.0002 0.0003 0.0003 0.0004
339 0.0022 0.0011 0.0019 0.0080 0.0024 0.0207
311FT 0.0017 0.0008 0.0013 0.0015 0.0134 0.0013
313TT 0.0007 0.0003 0.0005 0.0017 0.0004 0.0008
315AL 0.0003 0.0003 0.0005 0.0026 0.0008 0.0007
322 0.0024 0.0018 0.0024 0.0033 0.0022 0.0024
323 0.0066 0.0068 0.0052 0.0035 0.0074 0.0036
324 0.0007 0.0004 0.0032 0.0018 0.0004 0.0006
325 0.0116 0.0068 0.0100 0.0143 0.0097 0.0410
326 0.0039 0.0018 0.0034 0.0055 0.0037 0.0068
42 0.0105 0.0111 0.0106 0.0190 0.0218 0.0153
44RT 0.0009 0.0005 0.0013 0.0009 0.0007 0.0013
481 0.0005 0.0002 0.0005 0.0009 0.0003 0.0003
482 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
483 0.0003 0.0001 0.0000 0.0000 0.0000 0.0000
484 0.0013 0.0027 0.0011 0.0029 0.0013 0.0017
485 0.0006 0.0002 0.0006 0.0008 0.0003 0.0003
486 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
487OS 0.0063 0.0023 0.0044 0.0123 0.0038 0.0033
493 0.0010 0.0004 0.0012 0.0018 0.0006 0.0008
511 0.0096 0.0115 0.0092 0.0069 0.0136 0.0044
512 0.0031 0.0013 0.0011 0.0008 0.0074 0.0007

Table 9. Orange County Total Requirements Table (Continued)

153

Code
5412OP 55 561 562 61 621
513 0.0122 0.0298 0.0035 0.0120 0.0136 0.0410
514 0.0019 0.0103 0.0005 0.0016 0.0019 0.0031
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0251 0.4753 0.0035 0.0054 0.0048 0.0058
524 1.3059 0.0376 0.0351 0.0231 0.0168 0.0109
525 0.0048 1.0030 0.0002 0.0002 0.0006 0.0005
531 0.0152 0.0390 1.0565 0.0248 0.0392 0.0495
532RL 0.0030 0.0068 0.0014 1.0117 0.0052 0.0088
5411 0.0160 0.0252 0.0112 0.0088 1.0072 0.0104
5415 0.0045 0.0200 0.0028 0.0059 0.0076 1.0169
5412OP 0.0347 0.0727 0.0124 0.0394 0.0255 0.0555
55 0.0052 0.0106 0.0033 0.0269 0.0069 0.0107
561 0.0275 0.0331 0.0214 0.0279 0.0306 0.0410
562 0.0010 0.0010 0.0058 0.0011 0.0007 0.0013
61 0.0000 0.0001 0.0000 0.0001 0.0000 0.0001
621 0.0000 0.0001 0.0000 0.0000 0.0000 0.0000
622HO 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
624 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0011 0.0031 0.0009 0.0024 0.0026 0.0043
713 0.0001 0.0003 0.0001 0.0003 0.0004 0.0010
721 0.0015 0.0044 0.0015 0.0038 0.0035 0.0105
722 0.0026 0.0080 0.0026 0.0068 0.0069 0.0159
81 0.0041 0.0079 0.0088 0.0135 0.0043 0.0058
GFG 0.0071 0.0163 0.0007 0.0077 0.0009 0.0102
GFE 0.0014 0.0029 0.0006 0.0040 0.0017 0.0021
GSLG 0.0004 0.0006 0.0011 -0.0005 0.0004 0.0007
GSLE 0.0010 0.0023 0.0023 0.0043 0.0021 0.0038
Total industry
output 1.5104 1.8618 1.2304 1.3162 1.2178 1.3820
1
2
Table 9. Orange County Total Requirements Table (Continued)

154

Code 622HO 624 711AS 713 721 722
111CA 0.0007 0.0005 0.0003 0.0007 0.0007 0.0042
113FF 0.0000 0.0000 0.0000 0.0001 0.0001 0.0003
211 0.0003 0.0003 0.0003 0.0004 0.0009 0.0004
212 0.0001 0.0001 0.0001 0.0002 0.0001 0.0001
213 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0076 0.0051 0.0054 0.0107 0.0220 0.0104
23 0.0070 0.0081 0.0058 0.0073 0.0177 0.0065
321 0.0003 0.0007 0.0002 0.0006 0.0006 0.0009
327 0.0014 0.0012 0.0005 0.0010 0.0014 0.0038
331 0.0010 0.0013 0.0005 0.0014 0.0013 0.0018
332 0.0045 0.0069 0.0024 0.0051 0.0092 0.0127
333 0.0017 0.0016 0.0010 0.0016 0.0019 0.0029
334 0.0077 0.0046 0.0034 0.0064 0.0089 0.0054
335 0.0015 0.0021 0.0010 0.0019 0.0030 0.0042
3361MV 0.0000 0.0001 0.0000 0.0000 0.0000 0.0001
3364OT 0.0003 0.0003 0.0002 0.0004 0.0004 0.0003
337 0.0005 0.0008 0.0033 0.0019 0.0004 0.0014
339 0.0146 0.0070 0.0015 0.0021 0.0031 0.0027
311FT 0.0131 0.0087 0.0043 0.0078 0.0111 0.0626
313TT 0.0033 0.0018 0.0006 0.0048 0.0029 0.0014
315AL 0.0023 0.0021 0.0017 0.0005 0.0006 0.0003
322 0.0058 0.0031 0.0019 0.0035 0.0108 0.0064
323 0.0039 0.0060 0.0089 0.0047 0.0099 0.0040
324 0.0008 0.0007 0.0006 0.0009 0.0015 0.0009
325 0.0396 0.0134 0.0059 0.0395 0.0191 0.0116
326 0.0109 0.0062 0.0019 0.0034 0.0047 0.0127
42 0.0214 0.0187 0.0083 0.0265 0.0214 0.0492
44RT 0.0021 0.0008 0.0030 0.0022 0.0014 0.0050
481 0.0002 0.0003 0.0003 0.0003 0.0004 0.0003
482 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
483 0.0000 0.0000 0.0000 0.0000 0.0001 0.0001
484 0.0022 0.0018 0.0011 0.0027 0.0019 0.0034
485 0.0002 0.0002 0.0015 0.0003 0.0003 0.0003
486 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
487OS 0.0028 0.0045 0.0065 0.0035 0.0038 0.0032
493 0.0013 0.0009 0.0015 0.0012 0.0013 0.0012
511 0.0040 0.0097 0.0076 0.0064 0.0097 0.0048
512 0.0017 0.0079 0.0025 0.0014 0.0051 0.0024

1
Table 9. Orange County Total Requirements Table (Continued)

155

Code
622HO 624 711AS 713 721 722
513 0.0123 0.0161 0.0172 0.0176 0.0282 0.0159
514 0.0027 0.0022 0.0033 0.0023 0.0031 0.0025
521CI 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0143 0.0409 0.0187 0.0061 0.0078 0.0047
524 0.0393 0.0255 0.0461 0.0277 0.0314 0.0203
525 0.0003 0.0004 0.0004 0.0002 0.0003 0.0002
531 0.1281 0.0704 0.0537 0.0435 0.0319 0.0404
532RL 0.0069 0.0058 0.0136 0.0080 0.0162 0.0111
5411 0.0105 0.0090 0.0152 0.0234 0.0128 0.0070
5415 0.0070 0.0078 0.0050 0.0069 0.0084 0.0045
5412OP 0.0438 0.0479 0.0846 0.0656 0.0953 0.0451
55 0.0347 0.0124 0.0195 0.0415 0.0352 0.0300
561 0.0446 0.0451 0.0555 0.0263 0.0525 0.0197
562 0.0031 0.0059 0.0023 0.0040 0.0119 0.0040
61 0.0002 0.0001 0.0036 0.0005 0.0001 0.0001
621 0.0133 0.0000 0.0012 0.0001 0.0001 0.0000
622HO 1.0004 0.0000 0.0000 0.0000 0.0000 0.0000
624 0.0000 1.0000 0.0000 0.0000 0.0000 0.0000
711AS 0.0019 0.0035 1.1366 0.0110 0.0051 0.0054
713 0.0002 0.0004 0.0005 1.0003 0.0011 0.0005
721 0.0020 0.0035 0.0033 0.0027 1.0049 0.0037
722 0.0088 0.0104 0.0075 0.0080 0.0304 1.0083
81 0.0136 0.0127 0.0144 0.0149 0.0178 0.0104
GFG 0.0023 0.0027 0.0027 0.0024 0.0033 0.0021
GFE 0.0048 0.0031 0.0033 0.0091 0.0169 0.0086
GSLG 0.0016 0.0014 0.0051 0.0022 0.0027 0.0015
GSLE 0.0054 0.0146 0.0061 0.0064 0.0136 0.0063
Total industry
output 1.5668 1.4696 1.6033 1.4822 1.6088 1.4799
1
Table 9. Orange County Total Requirements Table (Continued)

156

Code 81 GFG GFE GSLG GSLE
111CA 0.0001 0.0001 0.0005 0.0007 0.0001
113FF 0.0000 0.0000 0.0000 0.0000 0.0000
211 0.0002 0.0003 0.0012 0.0005 0.0057
212 0.0001 0.0001 0.0001 0.0001 0.0020
213 0.0000 0.0000 0.0000 0.0000 0.0000
22 0.0044 0.0046 0.0125 0.0064 0.0125
23 0.0088 0.0139 0.0308 0.0246 0.0899
321 0.0003 0.0003 0.0003 0.0007 0.0010
327 0.0016 0.0010 0.0010 0.0013 0.0094
331 0.0024 0.0023 0.0009 0.0011 0.0054
332 0.0108 0.0112 0.0063 0.0055 0.0326
333 0.0070 0.0033 0.0017 0.0035 0.0099
334 0.0167 0.0458 0.0031 0.0057 0.0135
335 0.0064 0.0051 0.0023 0.0026 0.0146
3361MV 0.0007 0.0001 0.0002 0.0001 0.0001
3364OT 0.0011 0.0380 0.0017 0.0003 0.0008
337 0.0006 0.0003 0.0003 0.0004 0.0017
339 0.0043 0.0037 0.0005 0.0048 0.0021
311FT 0.0013 0.0017 0.0094 0.0094 0.0007
313TT 0.0014 0.0012 0.0006 0.0018 0.0008
315AL 0.0018 0.0009 0.0015 0.0088 0.0004
322 0.0024 0.0033 0.0019 0.0053 0.0020
323 0.0064 0.0081 0.0044 0.0080 0.0035
324 0.0006 0.0013 0.0025 0.0022 0.0045
325 0.0130 0.0170 0.0058 0.0227 0.0265
326 0.0089 0.0055 0.0028 0.0058 0.0135
42 0.0186 0.0188 0.0138 0.0298 0.0254
44RT 0.0035 0.0009 0.0013 0.0013 0.0034
481 0.0003 0.0011 0.0015 0.0002 0.0002
482 0.0000 0.0000 0.0000 0.0000 0.0000
483 0.0000 0.0016 0.0004 0.0001 0.0001
484 0.0016 0.0036 0.0020 0.0025 0.0031
485 0.0003 0.0003 0.0002 0.0016 0.0003
486 0.0000 0.0000 0.0000 0.0000 0.0003
487OS 0.0062 0.0032 0.0041 0.0027 0.0074
493 0.0012 0.0011 0.0003 0.0020 0.0007
511 0.0062 0.0130 0.0028 0.0062 0.0079

1
Table 9. Orange County Total Requirements Table (Continued)

157

Code
81 GFG GFE GSLG GSLE
513 0.0152 0.0320 0.0171 0.0197 0.0121
514 0.0035 0.0130 0.0019 0.0052 0.0032
521CI 0.0000 0.0000 0.0000 0.0000 0.0000
523 0.0531 0.0045 0.0035 0.0077 0.0263
524 0.0163 0.0092 0.0031 0.0048 0.0075
525 0.0025 0.0002 0.0001 0.0001 0.0004
531 0.0507 0.0137 0.0167 0.0316 0.0283
532RL 0.0051 0.0052 0.0030 0.0048 0.0074
5411 0.0086 0.0072 0.0059 0.0082 0.0077
5415 0.0063 0.0651 0.0083 0.0088 0.0112
5412OP 0.0442 0.1326 0.0304 0.0409 0.0883
55 0.0098 0.0095 0.0032 0.0053 0.0076
561 0.0324 0.0453 0.0220 0.0307 0.0516
562 0.0031 0.0021 0.0017 0.0110 0.0192
61 0.0016 0.0011 0.0013 0.0029 0.0002
621 0.0002 0.0018 0.0000 0.0023 0.0000
622HO 0.0000 0.0018 0.0000 0.0005 0.0000
624 0.0000 0.0000 0.0000 0.0019 0.0000
711AS 0.0045 0.0037 0.0019 0.0019 0.0017
713 0.0003 0.0006 0.0002 0.0010 0.0003
721 0.0031 0.0079 0.0017 0.0026 0.0025
722 0.0063 0.0063 0.0135 0.0115 0.0049
81 1.0172 0.0076 0.0149 0.0117 0.0103
GFG 0.0032 1.0248 0.0165 0.0018 0.0027
GFE 0.0048 0.0019 1.0021 0.0026 0.0021
GSLG 0.0026 0.0022 0.0013 1.0041 0.0037
GSLE 0.0041 0.0045 0.0081 0.0072 1.0185
Total
industry
output 1.4398 1.6196 1.2983 1.4005 1.6203
1
2
3
Table 9. Orange County Total Requirements Table (Continued)

Abstract (if available)

Abstract This study explores the spatial-economic relationship between live music events in Orange County, California and measures of economic strength. The purpose of this study is to inform planners and funders of cultural activities about the spatial-economic impacts their allocations have produced so that scarce budgets can be targeted to maximize societal benefits. Spatial analysis techniques, including cluster analysis and geographically weighted regression, were utilized to explore the correlation between live music venues in Orange County, California and economic indicators, including average per capita annual income, number of jobs, and average real estate prices by zip code. Traditional econometric techniques were also included for breadth and comparison purposes. This study is an important step in understanding the effects of cultural spending and adds to the economic impact literature.

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Asset Metadata

Creator Olson-Shelton, Jeremy D. (author)

Core Title The spatial economic impact of live music in Orange County, CA

School School of Policy, Planning and Development

Degree Doctor of Policy, Planning & Development

Degree Program Policy, Planning, and Development

Publication Date 02/18/2013

Defense Date 12/19/2012

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag arts,arts impact,arts policy,Community development,economic impact,GIS,impact modeling,Input-Output,live music,music,OAI-PMH Harvest,Orange County,Planning,spatial econometrics

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Painter, Gary Dean (committee chair), Banerjee, Tridib K. (committee member), Cravens, Terry S. (committee member), Steinmann, Frederick (committee member), Swift, Jennifer N. (committee member)

Creator Email jeremy.olsonshelton@usc.edu,SCtuba@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-220431

Unique identifier UC11294442

Identifier usctheses-c3-220431 (legacy record id)

Legacy Identifier etd-OlsonShelt-1438.pdf

Dmrecord 220431

Document Type Dissertation

Rights Olson-Shelton, Jeremy D.

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA