Your Local Media Arts Organization:  

The Intersection of Media Arts, Organizational Mission, and Community Development

Brettany Shannon

David C. Sloane

Price School of Public Policy

University of Southern California

(working paper)

This project was supported in part or in whole by an award from the Research: Art Works program

at the National Endowment for the Arts: Grant# 15‐3800‐7007.  

The opinions expressed in this paper are those of the author(s) and do not represent the views of

the Office of Research & Analysis or the National Endowment for the Arts. The NEA does not

guarantee the accuracy or completeness of the information included in this paper and is not

responsible for any consequence of its use.
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Your Local Media Arts Organization: 

The Intersection of Media Arts, Organizational Mission, and Community Development

Brettany Shannon

David C. Sloane

Price School of Public Policy, University of Southern California

This paper enters the increasingly complicated literature examining art’s relationship with 

neighborhood change, adding to it a specific look at the peculiarly information and communication 

technology-based artistic genre, media arts, vis-a-vis community media arts organizations (MAOs). Using 

the National Alliance for Media Arts and Culture’s (NAMAC) 2010 Mapping the Field Arts survey, we 

conduct a mixed methods research design, encompassing qualitative and quantitative analysis, with GISrecovered Census and American Community Survey data in support, to determine what relationship, if 

any, exists between an MAO and its surrounding community, and to what extent an MAO’s 

organizational mission affects this relation.

In service of these questions, we address two gaps we perceive in the literature. First, rather than rely 

on arbitrary and potentially misleading timeframes, we identified the exact move-in year for each of the 

project’s 114 arts organizations to calculate discrete neighborhood changes in the five years before and 

after an MAO’s arrival in a neighborhood. Second, we hypothesize that in addition to art form and 

industrial sector, organizational mission has directive and predictive power over an MAO’s community 

outreach and effect on/attitude towards neighborhood change.

Our evidence suggests media arts organizations do not directly contribute to neighborhood change; 

more likely they either follow or reinforce development processes already underway. However, we do 

find not all media arts organizations are the same: organizational missions often predict the distinctive

relations media arts organizations will share with their surrounding communities, reminding us of the 

great consequence initiating institutional agenda and convention can have on urban development.

Research support: This research was supported by grant number 13-3800-7007 from the National 

Endowment for the Arts.

Keywords: media arts, organizational mission, neighborhood change, community development
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INTRODUCTION

The 1990s were fraught with scholars lamenting capital’s successful articulation/capitulation of 

artistic and cultural integrity for urban development (Deutsche 1998; Miles 1997; Smith 1996; Zukin 

1995). Halfway through the decade artist and theorist Suzanne Lacy named media artists — individuals 

working in the wide range of media-based art practices including everything from film/video and 

photography to radio and digital art — as among the “vanguard groups” (1994, p. 25) capable of wresting 

back art from capital interests for the advancement of progressive politics and community advocacy. 

Nearly twenty years later and wholly embedded in the network society (Castells 1996, 1997, 1998), 

media artists dwell less on the fringe than they do in the fray. We live in an information society and 

whichever theory you hold truest to explain its social, economic, and political impacts (Webster 1992), 

these reverberations are nonetheless complete. This complete information and communication technology 

(ICT)-based digital-cultural transition begs the question: what of the media artist’s relationship with art 

and urban development? Have the once “vanguard” media artists entered the ranks of art’s “shock troops 

of gentrification” (Deutsche 1998) “urban pioneers” (Smith 1996), perfecting the “fine art of 

gentrification” (Deutsche and Ryan 1984)? Or have they stemmed the consumption-driven symbolic 

economy cum urban development tide and contributed to positive neighborhood change (Markusen and 

Johnson 2006; Seifert and Stern 2010; Wherry 2011)? 

In asking this question, this paper enters the increasingly complicated literature examining art’s 

relationship with neighborhood change, and adds to it the peculiarly ICT-based artistic genre vis-a-vis

media arts organizations (MAOs) and community development. That is, while ICT- (Apostol, Antoniadis, 

and Banerjee 2012; Castells 1989, 1996, 1997, 1998; Dourish and Bell 2007; Graham and Marvin 2001; 

Jenkins 2006; Mandarano, Meenar and Steins 2010; Mitchell 1999, 2003; Servon 2002) and cultural 

economy-concerned (Deutsche 1998; Deutsche and Ryan 1984; Grodach 2011; Grodach, Foster and 

Murdoch 2014; Ley 2003; Lloyd 2005; Markusen and Gadwa 2010; Markusen and Johnson 2006; Smith 

1996; Stern and Seifert 2010; Wherry 2011; Zukin 1989, 1995, 2010) scholars produce nuanced and 

complex research, these productive streams of literature have thus far remained separate despite their 
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consolidation in media arts organizations (MAOs). MAOs engage with any of the creative media 

processes, including film, video, and digital technology; can be for- or non-profit; and undertake 

everything from production to distribution to cultural policy advocacy. They are distinct in the arts for 

using all, airwaves, cyberspace, and the built environment to reach their audiences, and yet there is no 

literature about these organizations and how they might relate to urban planning.

Using a 2010 survey conducted by the United States’ premier media arts advocacy group, the 

National Alliance for Media Arts and Culture (NAMAC), this paper investigates what relationships exist 

between media arts organizations, their stated missions, and their surrounding communities. First in the 

paper, we provide a literature review briefly explaining media arts’ notable history, the current state of 

arts and gentrification scholarship, and how looking at organizational mission is a solid next step in the 

literature. Next, we explain the study’s data and research methods we use to better understand what 

relationships media arts organizations and their overarching missions have with their local communities. 

Finally, we consider what these findings means for urban planning practice and scholarship, as well as 

propose future research.

Our evidence suggests media arts organizations do not directly contribute to neighborhood change, 

and that it is more likely they either follow or reinforce development processes already underway. 

However, not all media arts organizations are the same. Their organizational missions often predict the 

distinctive relations media arts organizations will share with their surrounding communities, reminding us 

of the great consequence initiating institutional agenda and convention can have on urban development. 

In addition, we submit further qualitative research exploring organizations’ placement within institutional 

systems, among other things, will shed even more light on the critical issue of art’s intersection with 

neighborhood change.

LITERATURE REVIEW

Media arts emerge from the unification of two incongruous worlds, community-interested art and 

informationalism. Philosophically, they reflect the utopian tendencies of poststructuralism, feminism, the 
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happening, Duchampian conceptualism, Situationism, and performance art (Grau 2007; Lovejoy, Paul & 

Vesna 2011; Rush 2005; Tribe, Jana, & Grosenick 2006). With Nam June Paik's 1965 “discovery” of the 

consumer-grade Sony Portapak (Rush 2005), media arts have been among the more “democratic” art 

forms, often used to champion the causes of underserved groups and places. Given their relative ease of 

production and circulation, art theorists like Suzanne Lacy have historically held up media arts as the 

peaceful soldier’s creative weaponry in the war for social justice (Grau 2007; Rush 2005; Tribe, Jana, & 

Grosenick 2006). Practically, they articulate and impel advances in ICTs (Grau 2007; Lovejoy, Paul & 

Vesna 2011) and thus share a close relationship with informationalism and informational development 

(Castells 1989). Today new media provide the architecture for global communication networks (Galperin 

2007), as well as for our daily participatory (Jenkins 2006) and phenomenological (Manovich 2007) 

practices. The cultural producers responsible for new media’s design and user experience constitute the 

knowledge worker-rich forces of late capitalism (Currid 2009; Florida 2002; Lloyd 2005). Thus, perhaps 

more often representing early and mid-twentieth century utopianism, commercially employed digital 

artists embody the forces of gentrification (Lloyd 2005). 

At first glance, media arts’ primary function in the cultural economy undermines any media arts as 

paladin for justice narrative, but just as we know from the multivalent findings regarding art’s relation 

with gentrification, no story is so easily understood. Within the literature, there exist two strands of 

findings, critical and hopeful. The former implicates the cultural economy’s “artistic mode of production” 

(Zukin 1989) in wreaking late capitalism’s global havoc at the local level (Smith 1986; Deutsche and 

Ryan 1984; Ley 2003; Lloyd 2005; Mommaas 2004; Smith 1996; Zukin 1989, 1995, 2010). The latter by 

turns acknowledges the artistic dividend (Markusen and Schrock 2006) for neighborhood revitalization 

benefiting local constituencies and ethnic groups (Stern and Seifert 2010; Wherry 2011), shows art’s 

advantage to the local economy (Markusen and Schrock 2006; Wherry 2011), heralds local outreach 

efforts and social networking (Grodach 2011; Wherry 2011; Stern and Seifert 2010), and identifies spaces 

or “artists centers” (Markusen and Johnson 2006) for artists and the general public to build skills and 

engage in peer review (Grodach 2011; Markusen and Johnson 2006; Stern and Seifert 2010). 
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Of course both sides are right. So to better understand this complicated relationship between the arts 

and their local communities, recent research has sought to tease out specific conditions that correlate with 

positive and negative socioeconomic change in local communities. Grodach, Foster, and Murdoch (2014), 

for example, find fine arts associate more often with positive neighborhood revitalization, while 

commercial arts more often with gentrification. Here Grodach et al focus on industrial sector (namely, 

non- or for-profit) as the independent variable. However, in their piece repairing the lost historical 

connection between planning and community arts, urban planner Karen Chapple and performance studies 

Shannon Jackson (2010) introduce performance studies’ view of social instrumentalization to suggest 

another level of analysis beyond sector: intent. They ask, is art instrumentalized when it intensifies a retail 

district’s commercial viability? Or when its themes advocate a certain social program? And what about 

the artists’ own interests and intended outcomes? Even a benign project to elicit smiles from passersby 

takes on new significance whether that smile is meant to enhance a locality’s social use value or its 

valorization in the symbolic economy. In other words, interest and intended outcome signify a project’s 

meaning beyond what the art form or its sector alone conveys. 

All this recalls Suzanne Lacy’s 1994 exhortation of contemporary art critics to emphasize meaning 

over mechanism in their art analysis. Writing about new genre public art, she calls readers to distinguish 

between “interests” (ibid, p. 25) and “media-specific concerns” (ibid) when discussing artists and their 

public artworks, arguing contemporary art critics’ formalism precluded them from seeing “the broader 

implications for both art and society” (ibid). Had they considered artistic works through the artists’ 

interests — namely “leftist politics, social activism, redefined audiences, relevance for communities 

(particularly marginalized ones), and collaborative methodology” (ibid), art critics might have learned 

more about artistic practices of the time. Lacy, Chapple and Jackson, and Grodach et al each remind us 

that taking art at (literal) face value subverts opportunities to better comprehend contemporary art and 

urban development. Artists and their allied organizations inhabit myriad social fields (Bourdieu 1989) that 

inform a range of on-the-ground objectives — art itself, social work, social critique, even prestige and 

riches (Becker 2008; Thornton 2009).
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Returning to media arts, their aforementioned paradoxical role in our society brings into stark relief 

how meaning predominates over mechanism. The very fact that the same ICTs used for commercial 

purposes are also deployed for social movements (Castells 2009) indicates how important intent is, in 

addition to agent (Markusen and Schrock 2006; Zukin 1989) and industrial sector (Grodach, Foster and 

Murdoch 2014). So while media arts organizations may share artist and sector type as a group, their 

institutional agendas and consequent operations vary. From a neoinstitutionalist perspective, this makes 

sense. Institutions are not concretized superstructures, but constantly shifting social constructions 

(Jepperson 1991), “symbolically grounded, organizationally structured, politically defended, and 

technically and materially constrained” (Friedland and Alford 1991, pp. 248-9). Therefore, within the 

media arts organization universe, we should expect smaller galaxies to be governed by and expressive of 

different organizational mission types. However, no scholarship has addressed institutional intent to this 

point. This project aims to repair that omission to construe how organizations’ missions coincide with 

MAOs’ community outreach and neighborhood change.

A second gap in the research, a methodological one, relates to time-based analysis. In most cases, 

when research is longitudinal, it is generally a case study and thus contextual. In other cases with larger 

sample sizes, the before and after dates are arbitrary and unrelated to when arts organizations actually 

entered their respective communities. Introducing these two levels of analysis, organizational mission and 

relevant time analysis offers more detailed and salient information about art organizations’ relations with 

their local communities, necessary for planners and policymakers as they set their arts-based 

redevelopment agendas.

RESEARCH QUESTIONS

Well over a thousand independent MAOs operate throughout the country. Many of them function as 

private, inwardly focused organizations, without discrete links to their surrounding neighborhoods. Many 

others, however, operate expressly for and within their adult and youth communities, doing such things as 

opening their doors to provide production facilities, teaching classes and workshops, leasing equipment, 
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and exhibiting and screening amateur and professional works. These MAOs typify Markusen and 

Johnson’s (2006) “artists’ centers,” and are the subject of this research. This project seeks to understand 

the urban development connections these small, independent media arts organizations have with their 

local communities. To that end and using NAMAC’s 2010 “Mapping the Field” survey, we propose the 

following research questions and hypotheses: 

Research Q1:

Can we find a typology of overarching organizational missions within the NAMAC’s survey respondents’

answers? 

H1: Yes. Using the survey’s mission options1 as a guide and then conducting a qualitative analysis 

of the stated missions on each organization’s website, we posit the three main overarching goals are: Art, 

Art Community Building, and Community Building. 

Research Q2: 

What variations exist among these groups, controlling for targeted emphases (e.g. audience age, race 

ethnicity, scope of area targeted, whether rural or urban, etc.)? 

H2: Organizational missions do have specific relationships with targeted audiences and scope of 

area targeted. We hypothesize:

• Art organizations seek primarily to promote art and use the term “community” to refer to 

the art world (i.e. artists, art organizations, art events, etc.) at large.

• Art Community Building organizations also privilege the art world, but manifest this 

agenda at the geographically local level.

 1 The survey’s possible missions: (1) encouraging artistic expression, (2) nurturing independent artists, (3) nurturing 

emerging artists, (4) education, promoting media literacy, (5) media advocacy, (6) community building, serving as a 

meeting place for the media arts, (7) serving geographic communities and their issues, (8) serving social justice 

issues, (9) preserving cultural heritage, and (10) encouraging economic and workforce development.
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• Community Building organizations emphasize empowering local constituencies with the 

power of the media, one’s artist status/self-identification notwithstanding. 

Therefore, we argue, for example, that Art organizations will report seeking national and 

international audiences, whereas Community Building organizations will not, instead aiming their efforts 

towards local and marginalized audiences. Art Community Building organizations will emphasize 

localities but not target marginalized groups, focusing instead on the arts community.

Research Q3: 

What relationship can we find between an MAO’s presence at a particular location and the surrounding 

community? 

H3: We expect to find that MAO presence correlates with neighborhood change. We further argue 

that such change will look differently for each of the MAO goal types. That is, neighborhoods with Art 

Community Building MAOs will show sharper rates of population displacement along with improvement 

of place (gentrification), whereas Community Building organization-adjacent areas will observe more 

improvement of place and less population displacement (positive neighborhood change). 

Research Q4: 

The Mapping the Field survey had a 36% response rate. Is there a systematic difference between those 

organizations that did and did not respond to the survey that might complicate the findings in Research 

Questions 1 through 3? 

H4: No, there is no systematic difference between these groups. 

ANALYTIC STRATEGY: USING GIS AND CENSUS INFORMATION FOR ORGANIZATIONNEIGHBORHOOD RELATIONSHIP

About the Dataset
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Since NAMAC’s founding in 1980, it has sought to increase and reinforce both the culture and 

industry of independent media arts. In June 2010, NAMAC surveyed 1,170 organizations via Survey 

Monkey (from its own member database and through snowball sampling). From 424 responses (a 36% 

response rate), NAMAC culled information about organizational types, primary media output, workforce 

demographics, revenue and expense streams, and so on. While the subsequent report provided 

constructive insight into the media arts sector, other questions within the 106-unit survey offer rich 

opportunity to learn how the arts organizations interrelate with their localities. (See Table 1.)

Table 1 Here

To develop the questionnaire, NAMAC collaborated with the Center for Survey Statistics and 

Methodology at Iowa State University and five intermediary organizations (i.e. University Film/Video 

Association [UFVA], CTCNet, Dance/USA, Grantmakers in Film and Electronic Communication 

[GFEM], and the National Federation of Community Broadcasters) to create a 106-question survey for its 

membership base. NAMAC member MAOs comprise: 

“...community-based media production centers and facilities, university-based programs, 

museums, media presenters and exhibitors, film festivals, distributors, film archives, youth 

media programs, community access television, digital arts and online groups, and policyrelated centers” (NAMAC 2009).

Given that the answers in the Mapping the Field survey emerge from objective inquiries, and that 

NAMAC did conduct a beta test with five organizations prior to full release, we feel comfortable 

characterizing them as valid. In addition, the majority of individuals answering the surveys reported 

holding senior positions on the executive, marketing, or development teams — positions of deep 

organizational knowledge — and so we take their answers as also reliable. The dataset, however, does 

contain some bias in that it derives from a membership and snowball sample. 

Methodology

The entire project called for a mixed methods research design, encompassing qualitative and 

quantitative analysis, with GIS-recovered data in support. In the following portion, we outline the 
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research steps, with particular attention to variables of interest, justifications for particular approaches, 

acknowledgements of limitations, and plans for addressing them.

Preparing the Datasets

Per above, the project has two lines of inquiry. The principal analysis, derived from the 424 

responses, investigates the relations independent MAOs share with their local communities. The 

secondary research shows to what extent the 36% responding organizations differ from the 64% that did 

not. To these ends, NAMAC provided us with the completed surveys and organizational information for 

the 424-organization “Respondent” list, as well as limited contact information for the 746-unit 

“Nonrespondent”2 list.

Our first step was to clean the Respondent and Nonrespondent lists. We removed from the project 

entirely of any MAOs either campus-located, civic/government-run, in-list duplicates, virtual addresses 

(e.g. organizations run from individuals’ homes), defunct organizations, or those too old (i.e. opened in 

1967) or too new (i.e. opened in 2008) for Census-based comparison analysis (see section “Quantitative 

Methods: Using GIS and the Census for the MAO Artists’ Center Research”). We eliminated educational 

and civic MAOs because their locations invariably already experience strong campus and city hall 

development effects that would muddy any research findings. From the Respondent list, we disqualified 

any organization we deemed to fall outside Markusen and Johnson’s Artists’ Centers: Evolution and 

Impact on Careers, Neighborhood and Economies (2006) report definition of “artists’ center.” The 

authors provide a slew of parameters, but name these two as essential for consideration as an artists’ 

center:

 2 NAMAC had only email addresses, contact names, and some organization names. To fill in our survey, we 

followed these steps for all organizations: (1) Checked domain name (@orgname.org) against its own and 

Respondent list for internal and external duplications, respectively. (2) Google searched full email name 

(contact@orgname.org) to find organization website and physical address. (In our experience, even defunct email 

addresses lead to correct MAO websites.) When organizations list PO Box addresses, we searched using Google 

Maps, by phone numbers, and/or social media sites. (3) As with the Respondent organizations, we removed all 

MAOs affiliated with or located on educational campuses, as well as civic/government entities. For the rest, we 

looked to website for organizational mission and age, generally on the same webpage.
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“...[having a] space dedicated to an artistic medium or a geographical or affinity community, 

accessible to all without a fee to walk in the door” and “[g]eneral membership at an 

affordable rate without screening requirements, though certain services may be restricted to 

those who meet criteria or successfully compete for them” (ibid, 11).

According to Markusen and Johnson, additional offerings include newsletters, classes for various 

experience levels, equipment for share or rental, meeting space for gatherings, competitions for 

grants/awards, opportunities for exhibitions/performances, training and feedback, connections to human 

and information resources, and teaching opportunities. Following these guidelines, we eliminated a total 

of 135 organizations without dedicated onsite programming (e.g. film festivals, mobile organizations, 

policy centers, etc.) to ensure we measured not administrative office presences by direct community 

engagement. 

Qualitative Methods: Finding Organizational Information

In the end we designated 114 MAO Artists’ Centers (ACs) from the Respondents list for the project’s 

principal research, and 249 Respondents and 281 Nonrespondents to statistically test against each other 

for systematic differences. We sought organizational mission and age of all Respondents and 

Nonrespondents for later comparison, as well as each AC's tenure at its respective location for the 

project’s neighborhood change evaluation. While NAMAC’s survey did ask for MAOs’ primary 

organizational missions, the specific survey design hampered useful cluster analysis3. Therefore, to 

answer Research Q1, we conducted a constant comparative method-driven content analysis pilot project 

with a randomly selected group of Respondents and Nonrespondents’ web-published mission statements

to discern three primary organizational goals. Conveniently, all MAOs have websites, where they feature 

 3 In the survey, Questions 8 and 9 both list possible organizational missions. Question 8 is inclusive, asking 

respondents to check “all that apply” (NAMAC 2010), and Question 9 asks respondents to “select the primary or 

one best descriptor of your organization’s overall mission (only one)” (ibid). While we hoped to run a cluster 

analysis, the inclusive-exclusive design resulted in statistical noise. We found running frequencies from the second 

question and comparing against their presence in the first did not condense the ten options into valid categories. 

Instead, only two relevant categories emerged, emphasizing the arts and community building, with a third much 

larger one comprising no real unifying characteristics. That is, the first two appeared cohesive and small, while the 

third was large and unwieldy. Members in this third category had just not exclusively selected the missions of the 

first two.
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their mission statements and founding dates. Less frequently, however, do MAOs include their tenure on 

their websites, therefore we called and/or emailed ACs directly for tenure information.4 From the number 

of years at a given location we can calculate values to make before and after move-in comparisons 

(detailed in next section). In some cases, ACs moved to their current locations after 2010, rendering a 

before and after Census comparison impossible (see next section). In these cases, we contacted the ACs 

for their previous addresses and tenures.

Quantitative Methods: Using GIS and the Census for the MAO Artists’ Center Research

To capture the 114 ACs’ potential neighborhood change effects, we chose the quarter-mile radial 

catchment surrounding each MAO AC as the unit of analysis. The benefit of the quarter-mile distance is 

that it abides by the widely accepted axiom that people will walk one quarter-mile from any initial 

distance, and so it allows us to test the neighborhood change hypothesis with more confidence. While the 

ideal unit of analysis is the census block, our oldest ACs date back to 1976, and the earlier Census years 

simply do not have that micro-geographic information. Also, and critically, collecting any other Census 

year information not directly related to an AC’s move-in date (i.e. comparing 1990 and 2000 Census data 

when an AC moved in in 1978) reveals correlative, at best, and spurious, at worst, relationships. In this 

design, we wanted to use census data to estimate the social and spatial characteristics of an AC at date of 

move-in, five years prior, and five years after to measure neighborhood change. The census tract level is 

the only and best way to capture such data, therefore we used it knowing it would give us consistent 

census-based dependent variables (see Table 2) from Census years 1970, 1980, 1990, 2000, and the 2008-

2012 ACS. (The diminution of the 2010 Census variable offerings required that we use the American 

Community Survey for more recently tenured MAOs).

Using U.S. Census TIGER/Line Shapefiles and GIS, we retrieved both census tract numbers (based 

on the 2010 Census tract boundaries) and the respective area ratio of each census tract within a given 

AC’s quarter-mile catchment. We first downloaded the 1970, 1980, 1990, and 2000 Census years’ data 

 4 We called/emailed all MAOs for which we had missing information, not just tenure data.
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from the Neighborhood Change Database (NCDB), a simple source for collecting and comparing census 

variables over time. Notably, as the redrawing of census tract boundaries has consistently proven a major 

barrier in census-based longitudinal analysis, we applied the Longitudinal Tract Database (LTDB)5 opensource crosswalk interpolation formula to create estimates within the 2010 tract boundaries for all Census 

years 1970, 1980, 1990, and 2000. We collected the 2008-2012 ACS variables through Social Explorer. 

Next, equipped with the appropriate, interpolated values for earlier Census years and raw ACS estimates, 

we multiplied them by each AC’s quarter-mile catchment area ratio, and used this formula to approximate 

values for move-in, five years before, and five years after:

Year Value Estimate Formula (for move-in year 1993):

Move-in est.: 1993 value = 1990 value + [(2000 value - 1990 value)/10*3]

5 years prior est.: 1988 value = 1980 value + [(1990 value - 1980 value)/10*8]

5 years after est.: 1998 value = 1990 value + [(2000 value - 1990 value)/10*8]

Though this formula works less perfectly for the 2008-2012 ACS data, we figure since the ACS is itself 

an estimate derived from smaller samples and the project formula assumes an amortized change in value 

between census years, we thought the resulting approximations would not be too far from reality. Finally, 

only after figuring the three relevant years’ estimates did we add up (or average, in the case of household 

income) each AC’s census tract values to shrink the list back to one value per MAO AC. 

Table 2 Here

Per Table 3, we selected the study’s dependent variables based on previous application in the 

gentrification and revitalization literature (Freeman 2005; Grodach, Foster and Murdoch 2014; Ley 1986; 

Sands and Reese 2013), as well as consistent availability across the Census years. We looked at: average 

household income, educational attainment (i.e. bachelor’s degree and higher), ethnicity (particularly 

presence of a White population), population density, and unemployment rate. Additional spatial 

characteristics include occupancy, owner-occupancy, and mode of transit to work. 

 5 This relatively new open-source programming code has quickly become the gold standard for valid longitudinal 

analysis.
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In addition to the Census variables, we devised a few project-specific variables in addition to 

organizational mission typology. To create a stratified sample, we looked up each AC’s home city’s 

population as of the 2010 Census and assigned it to one of three categories depending on its size:6 rurality 

(< 10,000 residents), CBSA (10,000 to 50,000 residents) MSA, CBSA, or rurality. We also divided the 

longest age and tenure ranges (47 and 34 years, respectively) by three each to transform the numeric 

variables into categorical ones for better analysis. Finally, since respondents were asked to “select all that 

apply,” we collapsed some of the original Mapping the Field (See Table 3).

Table 3 Here

To understand our current 114-MAO AC list and determine the effect of organizational mission, we 

split the list into organizational missions and looked for relationships with specific social groups and 

geographies the ACs claimed to target: ages, ethnicities, and marginalized groups, and rural/urban, 

neighborhoods/regions. Again using organizational mission, we looked for relationships with the 

following Census variables: race group, educational attainment, employment status, household income, 

housing occupancy, and owner-occupancy. We completed the same two analytical steps, still by 

organizational mission, after splitting in the MSA, CBSA, and rurality categories. 

In reality, there might be no statistically significant relationship between an AC and its local 

community. Therefore, before collecting a full 114-unit control group via GIS, we randomly selected a 

group of 24 from the AC treatment group for a neighborhood change pilot project. Using a random 

integer set generator,

7 we selected six each for the following variables: town type (i.e. MSA, CBSA, 

rurality), tenure, age, and organizational mission. With the resulting 24 ACs, we used GIS to select census 

tracts beyond each AC’s half-mile catchment and calculated move-in estimates for these categories 

frequently associated with gentrification (Grodach, Foster and Murdoch 2014): White population, 

bachelor’s degree and higher, unemployment, and occupancy rate. From the possible nearby census tracts, 

 6 We use the Federal Register Office of Management and Budget’s 2010 Standards for Delineating Metropolitan and 

Micropolitan Statistical Areas for these definitions. 

http://www.whitehouse.gov/sites/default/files/omb/assets/fedreg_2010/06282010_metro_standards-Complete.pdf 7 We used the random integer set generator from http://www.random.org/integer-sets/.
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we selected those that came within 5% of at least three of the four variables for a final difference-indifference analysis. A difference-in-difference test goes beyond simply determining whether 

neighborhood change has occurred and establishes if that change can be attributed specifically to a 

treatment group, here MAO Artists Centers. We compare the same variables as in the 114-unit Artists 

Center group but population density.

Quantitative Methods: Using GIS and the Census to Compare Respondent and Nonrespondent Lists

For this group, we followed largely the same process as with the ACs, only we used just the 2008-

2012 ACS values for current-day media art organization comparison. Our interest here was finding out 

whether and to what extent the Respondent organizations are statistically significantly different from the 

Nonrespondents. We use the same organizational mission, age, and Census variables as in the AC portion. 

FINDINGS

Organizational Mission Typology

For organizational mission, we found our three proposed types — Art, Art Community Building, and 

Community Building — covered the media arts organization landscape well. Per above, each MAO’s 

website contains the organization’s mission statement, which states plainly its primary focus and 

definition of community. Among the 114 MAOs, Community Building is the most common 

organizational goal, with 74 (65%) of the sample. Art Community Building is a distant second with 24 

(21%), followed by Art, which accounts for just 16 (14%) of the Artists Center organizations. Again, the 

distinguishing language is unambiguous. 

Founded in 1997, New York City-based Eyebeam typifies the Art MAO. Its mission refers only to art 

and art technology, intimates its “community” includes artists and other practitioners, welcomes but 

distinguishes a non-art “public,” and gives no geographic parameters: 
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“Eyebeam is an art and technology center that provides a fertile context and state-of-the art 

tools for digital research and experimentation. It is a lively incubator of creativity and though, 

where artists and technologists actively engage with culture, addressing the issues and 

concerns of our time. Eyebeam challenges conventions, celebrates the hack, educates the next 

generation, encourages collaboration, freely offers its contribution to the community, and 

invites the public to share in a spirit of openness: open source, open content and open 

distribution” (Eyebeam n.d.).

Other Art MAOs echo this overarching sentiment. Facets (2015), a film and new media organization 

operating since 1975 considers itself “a leading national media arts organization” (Facets 2015), with a 

“mission...to transform lives through the power of the world, classic, and independent film” (ibid). And 

while Aurora Picture Show hails its Houston provenance, it collaborates with arts institutions as far afield 

as Minneapolis’ Walker Art Center, and its Houston-ness is conspicuously absent from its self-definition. 

“Aurora Picture Show is a non-profit media arts center than presents artist-made, non-commercial film 

and video. We are dedicated to expanding the cinematic experience and promoting the understanding and 

appreciation of moving image art” (Aurora Picture Show 2015). For these and the other Art MAOs, art 

constitutes the organizational focal point and “community,” whether explicit or implicit, refers to an 

amorphous art-going world, rather than one geographically defined. That is, they prioritize the production 

and exhibition of art — or art for art’s sake (Bourdieu 1993) — and their conception of “community” is 

specific to the art world (Thornton 2009). 

Art Community Building organizations, meanwhile, privilege art and the art world, but manifest this 

agenda at the local/regional level. In this way, they associate Bourdieu’s (1993) restricted production with 

a particular locale (Markusen 2006). For example, the Philadelphia Independent Film and Video 

Association (PIFVA) “is a membership organization started by filmmakers whose mission is to connect, 

support, sustain, and showcase Philadelphia regions’ independent media arts community” (n.d.). 

However, not every Art Community Building MAOs states its art- and geographic-emphasis together 

within the mission statement, but elsewhere on the same “About” webpage. The Southwest Alternate 

Media Project (SWAMP)’s mission statement explains it, “promotes the creation and appreciation of film, 

video, and new media as art forms of a multicultural community” (2014). This apparently art-only interest 

is refined under its “Philosophy” subheading: “SWAMP is a Texas-based media arts center committed to 
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the film and video art of this region, the artists producing independent images, and to the cultivation of an 

engaged audience” (ibid). Likewise, before resolving, “to foster the communication of arts, ideas, and 

information to diverse audiences through audio media” in its mission statement, Jack Straw Cultural 

Center identifies itself as, “[a] community-based resource...unlike any other in the [Northwest] region for 

local artists who work creatively with sound” (2014). Common to all is the sense that Art Community 

Building MAOs have a multivalent appreciation for the term “community.” They place their work within 

the larger media arts world (Thornton 2009), and at the same time verify their geographic milieus as both 

central to and the inspiration for said work (Lloyd 2005).

Community Building MAOs step back from “art” per se and instead report a desire to bring 

geographic communities together by imparting the critical (Buckingham 2000) and participatory (Jenkins 

2006; Jenkins et al 2009) literacies specific to media education. This group, more than Art or Art 

Community Building, has the most internal variation. Though all MAOs foreground a social purpose, 

some serve particular constituencies. Chicago’s Street-Level Youth Media teaches local “urban youth in 

media arts and emerging technologies for use in self-expression, communication, and social change” 

(2014). Its mission statement continues, stating Street-Level “programs build critical thinking skills in 

youth who have been historically neglected by policy-makers and mass media” (ibid). Other Community 

Building MAOs reach out broadly to foster community development. Michigan’s Greater Grand Rapids 

Community Media Center (CMC) aims “to effectively use technology and media to: 

• Tell share and preserve our own stories

• Better understand our differences

• Discuss our challenges and develop solutions

• Encourage and exercise the free exchange of ideas

• Practice inclusiveness and empowerment

• Promote and enable social change

• Build collaborations and partnerships in pursuit of a better community” (CMC 2015)

Finally, and not quite “artistic” are the Community Building community television stations. To curry 

favor for the history making Cable Communications Act of 1984, federal legislators included a provision 

that newly liberalized cable companies could — not must — set aside up to 5% of their profits for 

municipalities to institute autonomous public, education, and governmental (PEG) channels (Geller, 
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Ciamporcero & Lampert 1987). This legislation possibly explains Television’s primacy within the 

primary media types, with resultant organizations including such examples as Waycross in Forest Park, 

Ohio. Presiding over ten public, education, and government channels in the region, Waycross desires “[t]o 

bring local residents, schools, governments, churches, service groups, and businesses together with 

constructive, prudent uses of communication technologies to build and enhance Community” (2015). 

Waycross expands, defining “Community” in Aristotelian terms: “...the ideal of public life where 

residents respect and encourage one another to grow and develop as individuals, actively participating in 

their democratic government and share the sense of responsibility for the common good” (ibid). Waycross 

sets itself apart by using such frank language about the public good, but even with the comparative 

diversity within the Community Building group, the group members remain cohesive by using media 

communications for community empowerment and engagement. 

Media Art Organizations Artists Centers: “Mapping the Field” Survey Results

Not surprisingly, given the three organizational goal typologies and the large amount of internal 

difference within just the Community Building group, the 114-unit Artists Center group exhibits a great 

deal of variation. The MAOs are as young as four and old as 51 years, and their tenures range from three 

to 37 years. Per Table 4, the mean age and tenure of the ACs is 24.5 and 15.5 years, respectively. Per 

Table 3, we collapsed the numeric Age and Tenure variables into thirds to identify MAOs as 

“Mature/Long,” “Middle-aged/Medium,” or “New/Short.” The MAOs’ age distribution makes a nearperfect bell curve: 23% are New (15 years and younger), 53% are Middle-aged, and 25% qualify as 

Mature (31 to 47 years). The distribution of tenure, notably, indicates MAOs do relocate: 35% and 46% 

have Short and Medium tenures, respectively, whereas just 18% claim Long incumbencies. 

Table 4 Here

In terms of location, ACs are overwhelmingly urban: 65% are located in MSAs, followed by 19% in 

CBSAs and 16% in ruralities. Likewise, the majority (47%) name Television as their primary media type, 

with Film and Video as the second most common primary media type (29%). Together, the “newer” 
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digital media categories, Multimedia, and Digital and New Media together account for 18% of the Artists 

Centers. Radio and Sound Audio, generally regional radio stations, comprise 7% of the MAOs. Please 

note, no MAO chose either Web or Gaming as its primary media; therefore, these categories were 

eliminated altogether rather than bundled into another category.

Artists Center member organizations tend not to target particular age or race groups, or specific 

geographic areas, but do privilege urban areas and marginalized groups (44%), broadly defined. For 

example, a full 80% of ACs reported focusing on no age group, followed distantly by 12% who reported

to host youth and young adult programs. And while 23% of ACs report focusing on multiple minority 

races and ethnicities, 65% of ACs have no race/ethnicity focus. The geographic foci, meanwhile, signal 

the MAOs’ overarching urban-ness. While 49% of organizations report having no urban, suburban, or 

rural focus, 27% do seek out urban populations. Similarly, the 37% of MAOs naming “City to County” as 

their targeted geography ties with the proportion claiming to have no area emphasis at all. After this, 16% 

have regional scopes, serving multiple cities and states, and a full 10% serve their local neighborhoods. 

Just 1% seeks national and international audiences.

Organizational Missions and Mapping the Field Survey Relationships

In the Mapping the Field survey, NAMAC asked its respondents, “please indicate whether your 

organization’s mission targets or includes an emphasis on any of the following types of audiences or 

constituencies (all that apply)” (NAMAC 2010). We proposed that the three hypothesized organizational 

missions would have peculiar answers to NAMAC’s queries. Namely, Art organizations would report 

seeking national and international audiences, whereas Community Building organizations would not, 

instead directing their efforts towards local and marginalized audiences. Art Community Building 

organizations, meanwhile, would emphasize localities, but not necessarily marginalized or age groups. 

(See Table 5 for all row percentages.)

Town Type: We find that a statistically significant higher percentage of Art (4.91>|t|3.30 (p<.001)) 

and Art Community Building (3.2>|t|2.58 (p<.01)) MAOs are in large MSAs than Community Building 
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organizations. Conversely, a statistically significant higher percentage of Community Building 

organizations inhabit smaller CBSAs than both Art (-5.42>|t|3.30 (p<.001)) and Art Community Building 

(-3.64>|t|3.30 (p<.001)) MAOs. Interestingly, there exist no statistically significant relationships at the 

rural level. Rather, we find that the art-leaning MAOs locate in large urban centers, whereas community 

builders predominate in smaller, suburban cities. 

Table 5 Here

Age of Targeted Audience: In no instance is there a statistically significant relationship among goal 

typologies and their named targeted age groups. Instead, the three organizational missions share an 

overwhelming preference not to seek out specific age cohorts. 

Type of Media: NAMAC asked MAOs to identify their primary medium; it is here that we observe 

the perhaps the starkest differentiation between the art-leaning Art and Art Community Building, and the 

art-ambiguous Community Building mission types. That is, Community Building MAOs easily and 

significantly outweigh Art and Art Community Builders in the Television media category (-13.23>|t|3.30 

(p<.001), both). In fact, no arts-centric mission MAOs pointed to television as its primary medium. 

Further, while Community Building organizations do not have a statistically significant higher percentage 

of Radio ACs than do Art Community Building MAOs, the t-value of 1.85 suggests it is close to being so. 

Again, no Art organization claimed Radio as its primary medium, making Community Building 

organizations’ higher percentage of Radio outlets significantly higher (-2.55>|t|1.96 (p<.05). Meanwhile, 

Art (3.17>|t|2.58 (p<.01)) and Art Community Building (3.58>|t|3.30 (p<.001)) dominate the art form 

media, with statistically significant higher percentages of Film & Video ACs than Community Builders. 

Art and Art Community Building MAOs split the remaining arts-focused media types. For Multimedia 

ACs, organizations featuring two forms of media at the same strength, Art Community Building MAOs 

have a statistically significant higher percentage than do Community Builders (2.13>|t|1.96 (p<.05)). And 

Art MAOs outpace Community Building Digital/New Media organizations (2.16>|t|1.96 (p<.05)). In sum, 

Art and Art Community Building’s shared tendency toward the same sorts of media underscore how they 

privilege the art form and art production, whereas the Community Building MAOs’ preferences signal 
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their preoccupation with communication. 

Targeted Marginalized Groups: Both Art (2.5>|t|1.96 (p<.05)) and Community Building (3.82>|t|3.30 

(p<.001)) MAOs have statistically significant higher percentages of targeting marginalized groups than 

Art Community Building organizations. Intriguingly, while Community Building organizations do have a 

higher percentage of targeted marginalized groups than Art MAOs, this relationship is quite weak (tvalue, 0.18). We wonder if the Arts MAOs’ focus on marginalized groups is not bound up with 

fundraising obligations. Institutional arts donors often prioritize education and outreach efforts to 

underserved communities, and though we do not suggest contributed income is the reason for the Arts 

group’s tendency — socially engaged art is a rich and growing practice (Helguera 2011) — it may be a 

factor.

Race of audience: As with Age of Targeted Audience, we find no statistically significant relationship 

among goal typologies and particular racial or ethnic groups. Community Building MAOs have a stronger 

tendency away from seeking out specified minority groups that either the arts-centric organizations, but 

the largest t-value in the No Race/Ethnicity Target, describing the difference between Art Community 

Building and Community Building organizations, is just -1.52.

Location of audience: Location of Audience refers to whether and to what extent MAOs emphasize 

rural, urban, or suburban audiences. Within the projects Artists’ Center group, we find Art and Art 

Community Building organizations have equally statistically significant lower percentages of targeting 

Suburban audiences than Community Building organizations (-3.71>|t|3.30 (p<.001, both). Otherwise, 

however, Art MAOs lack significant relationships with any of the audience locations. By contrast, Art 

Community Building organizations do, with a have higher percentage seeking Urban audiences than 

Community Building MAOs (3.17>|t|3.30 (p<.001)), and a lower percentage than the same group in the 

None category (-2.22>|t|1.96 (p<.05)). In this question we find another dimension to the art-city 

connection. That is, while the arts-centric organizations lack significant relationships to Urban audiences, 

their perceived distance from strictly Suburban audiences suggests a pull toward the city. In addition, 

where Community Building MAOs have the largest proportion choosing No Audience Location, the Arts 
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Community Building organizations demonstrate a consistent disposition to specify, specifically toward 

the Urban and away from the Suburban. 

Scope of area targeted: Where Location of Audience parses out by urban- or rural-ness, Scope of 

Area conveys types of geographies, such as neighborhood, region, and nation. In this group, the Arts 

group has the most striking relationships to targeted geographies. Namely, these MAOs statistically 

significant lower percentages aiming for City/County than both Arts Community Building (-2.37>|t|1.96 

(p<.05)) and Community Building MAOs (-4.57>|t|3.30 (p<.001)). At the same time, they have 

significantly higher percentages naming No Scope than Arts Community Building (2.65>|t|2.58 (p<.01)) 

and Community Building (2.84>|t|2.58 (p<.01)) organizations. Otherwise, there are no statistically 

significant relationships in Scope of Area. These results underscore how Arts MAOs situate themselves 

and their work less in terms of their local communities than in terms of a larger, non-geographic arts 

community.

Overall, we find our hypotheses correctly predicted organizational goals’ relationships with the 

Mapping the Field questions, with the clear exception being that Art MAOs, like Community Building 

organizations, endeavor to reach marginalized communities. Art MAOs uphold art form and urban-ness

but target fewer Urban audiences because, as we see in Location of Audience and Scope of Area targeted, 

Art organizations express indifference to place-based considerations. They define “community” as social 

and creative, but not geographic. Art Community Building organizations, like Art MAOs, champion the 

arts, but are consistently and explicitly more grounded and urban (see Town Type, Location of Audience, 

and Scope of Area), likely because this is where their cultural producers live and work. Finally, 

Community Building MAOs disregard the “higher” art forms in favor of mainstream television and radio 

communication technologies that inhabit smaller (perhaps suburban) cities. Their preference for TV and 

radio, and affiliation toward Suburban audiences may reflect a majority share of PEG cable access 

channels in this particular category. Throughout, however, we find “art” and “community” share steady 

definitions — the former refers to media arts’ creative potential above and beyond conventional 

communication modes, and the latter affirms geography’s significance. These definitions accurately 
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predict the organizational missions’ relations with the Mapping the Field survey questions.

Media Arts Organizations and their Local Communities

Viewed as a group, there is no striking relationship between a media arts organization’s placement in 

a community and that community’s change over time. Per Table 6, we calculated the average values and 

percentages of select census variables within the study’s ACs’ quarter-mile catchments at three points in 

time: five years prior to move-in (hereafter “Minus 5”), move-in (hereafter “Move-In”), and five years 

after move-in (hereafter “Plus 5”). Please note we look at only 112 of the Artists Centers because two 

MAOs’ move-in dates predate available census tract information. Of all variables, only the Bachelor’s 

Degree and Higher and Household Income variables seem to experience jumps of any interest, but those 

jumps are uniform along the Minus 5 to Move-In to Plus 5 continuum, thereby making the connection 

between an MAO’s arrival to a neighborhood and that neighborhood’s change less clear.

Table 6 Here

Just comparing percentages, the study’s control group (see Table 7) indicates a flat relationship 

between MAO and neighborhood change. Comparing Table 6 and Table 7’s control group of 24 MAOs, 

we find that almost all the treatment and control groups’ corresponding variable values are and remain 

within five percentage points of one another over the ten-year period.

8 The exceptions here are the 

proportions of Whites and, echoing the 114-unit AC list, people with Bachelor’s Degrees and Higher, and 

average Household Incomes. For Whites, we find a larger than five percentage point gap between the 

groups through time. Both the treatment and control groups’ White proportions decrease from Minus 5 to 

Plus 5, but the control group lost the proportion of Whites at greater rate, resulting in an eight-point gap 

by the Plus 5 year. The control group starts with a lower Bachelor’s Degree and Higher percentage, a gap 

widening even more between Move-In and Plus 5. Finally, though the control group’s Household Income, 

which starts at approximately $4,000 fewer dollars per years, increases between Minus 5 and Move-In, it 

 8 NB: The population density measurement provides no insights. For one, we lacked land area measurements 

necessary to calculate the control group’s population density. For another, as the control groups comprise full census 

tracts, they invariably have larger populations than the treatment groups’ quarter-mile catchments.
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decreases again by Plus 5. Meanwhile, the treatment group experiences a steady upward trend over the ten 

year time period.

Table 7 Here

Accepting that looking at the group as a whole likely obscures nuances within overall change, we 

decided to dissect the 112-unit Artists’ Center dataset into parts for analysis. To that end, we created 

summary statistics tables for the following variables commonly used in gentrification analyses: White 

populations (since they have the strongest correlation with neighborhood change), educational attainment, 

unemployment, household income, and owner occupancy. We looked at all variables at the Minus 5, 

Move-In, and Plus 5 time periods, and in relation to the following conditions: length of tenure at a 

particular location, age, organizational mission, town type, and media type. In addition, we performed 

analyses of variance (ANOVA)9 to determine what, if any, variables10 within those conditions register 

statistically significant changes over time. 

Tenure: Research Question 3 asks, “What relationship can we find between an MAO’s tenure at a 

particular location and the surrounding community?” Based on our findings, the relationships are limited. 

Tables 8.1 through 8.5 show summary statistics for MAO tenures in the three time periods. All variables 

show change in some direction, but only the White populations and educational attainment exhibit any 

statistically significant differences. Per Table 8.1.1, there is a significantly different White population 

between the Move-In and Plus 5 years (f(2,109)=5.63, p<.005), which a Bonferroni post-hoc test narrows 

to the Long- and Short-tenured MAOs, specifically (f(2,109)=174.83, p<.005). Results from the same 

table also tell us there are statistically significant different rates of White population change between the 

Long and Short tenure, as well as Medium and Short tenure MAOs in all comparison timeframes, Minus 

5 to Move-In, Move-In to Plus 5, and Minus 5 to Plus 5. 

Table 8.1 Here

 9 We used Stata for the ANOVA and all other statistical analyses in this report.

10 We also created ANOVA tables for population density, but do not include them or their summary statistics tables 

because they were consistently insignificant.
Page 25 of 74

Table 8.1.1. Here

Table 8.2 Here 

Table 8.2.1 Here

Tenure also relates to educational attainment (see Table 8.2.1). In the rates of change for both Some 

College and Bachelor’s Degree and Higher, we see significant differences in all three timeframes and for 

nearly all tenure comparisons. In Some College’s population terms, only the difference between Medium 

and Short tenure MAOs is significant in the Minus 5 to Move-In (f(2,109)=23.03, p<.05) and Minus 5 to 

Plus 5 (f(2,109)=48.84, p<.05) time periods. Interestingly, there are no significant population changes in 

Bachelor’s Degree and Higher, though rates of change are significant throughout all timeframes. Like 

Some College, the post-hoc test places significance at the Minus 5 to Move-In and Minus 5 to Plus 5 time 

periods, and only for the differences between Long and Short (f(2,109)=0.026, p<.05, Minus 5 to MoveIn; f(2,109)=0.030, p<.005, Minus 5 to Plus 5) and Medium and Short (f(2,109)=0.045, p<.05, Minus 5 to 

Move-In; f(2,109)=0.047, p<.005, Minus 5 to Plus 5). 

Table 8.3. Here

Tables 8.4. Here

Table 8.4.1 Here 

Table 8.5 Here

Finally, we find average household incomes are statistically different only in the Minus 5 to Move-In 

timeframe, and between the Medium and Short tenure MAOs (f(2,109)=-6.288.96, p<.05; see Table 

8.4.1). In this case, the Medium tenure organizations observe the greatest increase in household income. 

Taking this with our previous findings regarding changes in White populations and educational 

attainment, we might assume that Artists Centers (re)locating in the last few years have moved into 

already “improving” neighborhoods, rather than influencing those changes. 

Age: Looking beyond tenure and into organizational age, we again found statistically significant 

differences for White population change and educational attainment. Per Table 9.1.1, Middle-aged and 

New MAOs have significantly different White population rate changes in the Move-In to Plus 5 
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(f(2,109)=0.031, p<.05) and Minus 5 to Plus 5 (f(2,109)=0.049, p<.05) timeframes. Per Table 9.1, 

Middle-aged MAOs observe a four percent decrease in their White populations, whereas the New MAOs 

exhibit a very small one percent increase over the ten-year period. 

Table 9.1 Here

Table 9.1.1. Here

Table 9.2 Here

Table 9.2.1 Here

Differences in educational attainment are more spread out. Per Table 9.2, percentages of people 

getting Some College and Bachelor’s Degree and Higher increase consistently over ten years. ANOVA 

analysis (see Table 9.2.1) shows the significant differences in advanced education exist between mostly 

between the Mature and other MAO ages. For example, where Mature MAOs show a relatively flat 

increase for Some College over the ten years, New MAOs jump nearly five percentage points 

(f(2,109)=0.041, p<.005). Meanwhile, Mature MAOs’ Bachelor’s Degree and Higher proportions rise 

significantly faster than Middle-aged MAOs’ (f(2,109)=-0.046, p<.01) in the same time period. That the 

majority of these shifts occur over the ten-year period, rather than between the Move-In to Plus 5 period, 

suggests these neighborhood changes were underway, and that MAO age has little direct relation to those 

demographic shifts. 

Table 9.3 Here

Table 9.4 Here

Table 9.5 Here 

Organizational Mission: The summary statistics for organizational mission (see Tables 10.1 through 

10.5) indicate that Community Building MAOs are less embedded in neighborhoods at risk of 

displacement then they are ensconced in already White (see Table 10.1), educated (see Table 10.2), and 

wealthy communities (see Table 10.4) with high owner occupancy (see Table 10.5). Statistically, any 

differences exist at the now-familiar educational attainment level, as well as owner occupancy. As a 

whole, the increase of populations with Bachelor’s Degree and Higher is significant by organizational 
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mission in all timeframes: Minus 5 and Move-In (f(2,109)=6.23, p<.005), Move-In and Plus 5 

(f(2,109)=5.08, p<.01), and Minus 5 and Plus 5 (f(2,109)=5.69, p<.005). A Bonferroni post-hoc test 

establishes these differences are consistent between Art and Community Building MAOs, such as the 

time period between Minus 5 and Plus 5 (f(2,109)=-299.63, p<.005). In addition, the significance for rate 

of change in educational attainment between the two organizational mission types is highest between the 

Minus 5 and Plus 5 years (f(2,109)=-0.60, p<.005). Overall, while all organizational missions observe 

upward trends in educational attainment, Art MAOs exhibit the sharpest incline, but evenly over the ten 

years. Therefore, we might infer Art MAOs do cater to better-educated populations, but we cannot 

assume those MAOs directly impact that aspect of neighborhood change. 

Table 10.1 Here

Table 10.2 Here

Table 10.2.1 Here

Results also tell us that some population changes in Owner Occupancy are statistically significant by 

organizational mission. However, relationships here share fewer consistencies than in educational 

attainment. That is, while the Move-In to Plus 5 (f(2,109)=0.032, p<.05) and Minus 5 to Plus 5 

(f(2,109)=0.048, p<.05) time periods have generally significant population shifts, post-hoc tests 

demonstrate the only significant difference exists between Art and Community Building organizations 

from Move-In to Plus 5 (f(2,109)=-62.24, p<.05). Per Table 10.5, Owner Occupancy among Art MAOs 

both trails the Community Building organizations, as well as suffers a percentage point loss while the 

latter group continues its incremental climb upward. From this, we might infer this reflects the Art and 

Community Building organizations’ urban and suburban (thus, probable home-owning) tendencies, 

respectively. 

Table 10.3 Here

Table 10.4 Here

Table 10.5 Here

Table 10.5.1 Here
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Town Type: Results tell us highly educated (Bachelor’s Degree and Higher) populations in all time 

periods, Minus 5 to Move-In (f(2,109)=0.0094, p<.01), Move-In to Plus 5 (f(2,109)=0.0051, p<.01), and 

Minus 5 to Plus 5 (f(2,109)=0.0064, p<.01), are significantly different by town type. In addition, rates of 

change are statistically significant by town type in the Move-In to Plus 5 (f(2,109)=0.0161, p<.05) and 

Minus 5 to Plus 5 (f(2,109)=0.0153, p<.05) timeframes. Bonferroni post-hoc tests show these differences 

are most likely to occur between the MSAs and CBSAs. For example, changes in population between 

Minus 5 and Plus 5 (f(2,109)=202.7, p<.05) and rates of change (f(2,109)=0.045, p<.05) are significantly 

different between the MSA and CBSA town types. Considering the strong link between the Art and 

Community Building organizational missions and MSA and CBSA town types, respectively, these 

findings possibly restate the aforementioned connection between Art MAOs and their localities, as well as 

hint at an overarching urban renaissance where NAMAC’s MSA-based Artists Centers become better 

educated, faster, than the other town types’ ACs. 

Table 11.1 Here

Table 11.2 Here

Table 11.2.1 Here

Table 11.3 Here

Table 11.4 Here

Table 11.5 Here

Type of Media: Finally, we analyzed type of media’s relation and again found statistically significant 

differences in the White and Bachelor’s Degree and Higher-attaining populations. For the former, only 

between the TV and Film & Video media types do we observe any significant changes, and primarily in 

rate of population change. For example, the rate of change between the exclusively Community Building 

(i.e. TV) and equally Art and Art Community Building-based (i.e. Film & Video) MAOs is significant 

within the Move-In to Plus 5 timeframe (f(2,109)=0.040, p<.01). In this case, we might construe the 

White population decrease in TV (i.e. Community Building) MAOs as typical of contemporary 

demographic shifts, and that the negligible change of the Film & Video MAOs intimates (assuming any 
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change exists) MAOs move into communities post-neighborhood change.

Table 12.1 Here

Table 12.1.1 Here

Table 12.2 Here

Table 12.2.1 Here

Table 12.3 Here

Table 12.4 Here

Table 12.5 Here

Difference-In-Difference Analysis

Not surprisingly, our difference-in-difference analyses showed no statistically significant variations 

between the treatment and control groups. Even looking at changes in White and Bachelor’s Degree and 

Higher-attaining populations, where we observed regular variation in the ANOVA analyses, we still find 

no significant relationships between the Move-In and Plus 5 time period (see Table 13). Therefore, based 

on these data, we cannot claim any causal relationship between media arts organizations and 

neighborhood change.

Table 13 Here 

Comparing the Respondent and Nonrespondent Lists

Lastly, we wanted to know whether to what extent the Respondent and Nonrespondent groups were 

different. For this, we looked at organizational mission, age of MAO, ethnicity, educational attainment, 

unemployment, vacancy, owner occupancy, and mode of transit to work (specifically, car, truck, and van 

usage against public transportation ridership). We found significant differences only in two areas. First, 

using a chi-square test, we discovered there are significantly more Community Building MAOs in the 

Nonrespondent group (see Table 14). While we did not track frequency, there did appear to be an even 

larger proportion of cable access channels (perhaps PEG-initiated) in the Nonrespondent group than in the 
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Respondent group, which might explain the difference. Second, we learned the Respondent group’s mean 

of Some High School was significantly higher than the Nonrespondent group’s mean (see Table 15). We 

propose this reflects the larger proportion of Community Building organizations within the 

Nonrespondent group. Per Table 10.2, Community Building MAOs consistently have the lowest 

proportion of Some High School of all organizational missions. 

Table 14 Here

Table 15 Here

Otherwise, there are no statistically meaningful differences between the two groups. In fact, in 

addition to having similar enough social characteristics, the Respondent and Nonrespondent MAOs also 

share like average ages (see Table 16). 

Table 16 Here

DISCUSSION AND IMPLICATIONS FOR FUTURE RESEARCH

The primary goal of this research project was to discover what connection(s) we might find between 

the presence of a media arts organization and its local community. Simply, we find the null hypothesis is 

true, and so we cannot say MAOs either contribute to or directly cause neighborhood change. We also 

sought to establish an organizational mission typology and test how the consequent missions intersected 

with various dimensions covered within the NAMAC Mapping the Field survey, as well as census-based 

social characteristics. It is from this portion of the research that we culled slightly unexpected results we 

perceive as useful for the framing and formation of both future research projects and arts-based policies. 

In this project, we addressed two gaps: one based in methodology and the other based in institutional 

agenda. For the former, we confirmed using tenure / move-in information and corresponding census 

information was an essential step toward capturing precise effect measurement, as opposed to some

researches that use census years convenient for analysis. As we saw in some of the ANOVA analyses’ 

Minus 5 to Plus 5 changes, such projects finding positive relationships between art organization and 
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neighborhood change conceivably reveal fewer truths about the cause of neighborhood change than they 

do the effects of the nationwide urban renaissance. The rare statistically significant relationship in our tenyear timeframes signals MAOs are neither neighborhood changers, nor even canaries in the coalmine. 

What if MAOs are latecomers and/or rote instruments for urban development, the sorts of institutions that 

punctuate local urban renaissance efforts, rather than driving them?

As Hwang and Sampson (2014) explain in their vital mixed-methods gentrification study, “Divergent 

Pathways of Gentrification: Racial Inequality and the Social Order of Renewal in Chicago 

Neighborhoods,” researchers’ varied neighborhood change results likely derive from still imperfect 

methodologies. On the one hand, they write, single neighborhood analyses impart rich, multi-dimensional 

qualitative stories about the effects on those certain locations, but cannot reasonably generalize. On the 

other hand, large dataset researches relying on census and administrative information, “cannot distinguish 

gentrification from other forms of neighborhood change and typically do not capture important qualitative 

or visible aspects of reinvestment, neighborhood upgrading, and hence renewal” (ibid, 6). For this report, 

the latter methodological misgiving is well taken. That is, while it seems unlikely the same resident 

groups in the MAO AC communities became that much better educated in the space of a decade, we 

cannot say for sure without residential tenure information, something unavailable at the census tract level. 

Not only do we lack residential tenure for the studied Artists Center neighborhoods, we have 

insufficient information about other factors Hwang and Sampson identify as external to changing 

communities, yet deeply implicated in their transformations. Namely, proximity to job opportunities in 

growing institutions, the presence of public physical amenities such as transit and parks, and the degree of 

state investment, direct or indirect. Unsurprisingly, qualitative analysis can capture all three external 

dimensions. Perhaps more surprising is how these topics jump out in the most initial web- and interviewbased research, and what they reveal about how and why MAOs move within their home cities. Eyebeam, 

for example, moved to an already gallery-rich Chelsea, New York City in 2001 (Cascone 2013), 

supporting Hwang and Sampson’s nod to job opportunities and public amenities, as well as Molotch and 

Treskon’s (2009) view that organizations do seek out, even endure rising rents in, “sticky” 
Page 32 of 74

neighborhoods. And in late 2013, it announced plans to relocate to the Brooklyn Academy of Music 

cultural district as a ground-floor anchor cultural institution in a new, upscale condominium development. 

This and several other adjacent long-term projects are direct beneficiaries of the City’s 2000 

determination and subsequent municipally supported campaign to transform downtown Brooklyn into a 

cultural destination (Cascone 2013). 

We found institutional investment’s effects at all scales. The Austin Film Society, spearheaded by 

director Richard Linklater, has benefited from major state support twice — first in 2000 to create 100,000 

square foot of production space, and again in 2012, via a Creative Placemaking bond package to support 

the conversation of the city’s erstwhile National Guard Building into a “Creative Media Hub that 

consolidates many of the small business tenant and AFS office spaces under one roof” (Austin Film 

Society, 2012). Buffalo’s Squeaky Wheel microcinema, a tool and eventual victim of the city’s first round 

of creative peacemaking initiatives (interview, 2012) has relocated a block away to be one developer’s 

ground floor tenants / amenities in a building dedicated to similar organizations. Per the local developer, 

“Squeaky Wheel moving in was a slam dunk on our part. It met our mission and fits the theme of the 

building that houses other non-profits and creative businesses” (Dabkowski, 2014). Critically, the media 

arts organizations here are not neighborhood changers, per se, but reinforcing instruments in urban 

development initiatives already underway. These examples underscore Hwang and Sampson’s point. Yes, 

researchers should widen their analytical scope and include even more exogenous variables to better 

appreciate how MAOs interrelate with their neighborhoods.

Our research finds the endogenous variable, organizational mission, also matters significantly. For 

research purposes, we can construe organizational mission to be at least correlated – and sometimes a 

proxy – for other variables. And for policymaking purposes, we see that even within the apparently niche

category, “nonprofit media arts organization,” we find considerable variation largely contingent on 

organizational mission/institutional agenda. While Art and Art Community Building MAOs are both 

more urban and likely to engage in avant-garde media practices, they differ in important respects. Art 

organizations locate in ruralities, in areas with higher wealth and lower unemployment, and seek out 
Page 33 of 74

marginalized communities more often than Art Community Building MAOs. We do not know the 

motivation for the Art organizations’ outreach to marginalized communities, but we see Art Community 

Building organizations tend to skip outreach altogether. This prompts the question whether they have any 

more non-artist interest for settling in a given neighborhood than the explicitly non-geographically bound 

Art MAOs, and to what extent creative placemaking initiatives supporting these organizations can assume 

non-mandated community outreach.

Our Community Building findings suggest yet another level for analysis. In our hypotheses, we 

anticipated this group would represent and serve underserved communities. Instead we find this group 

tends to be the most secure, comprising well educated, homeowning suburbanites. So perhaps 

Community Building MAOs do seek to empower local communities via the power of the media, artist 

status notwithstanding, their efforts are less on behalf of at-risk populations than their largely suburban, 

White, upper-middle class constituencies. But of course this group’s apparent stability might shift 

significantly were we to extract out the Television MAOs. If, as we suspect, the category constitutes 

suburban, politically empowered groups able to enact the socially weak Cable Communications Act of 

1984 policy, we recognize how an MAO’s initiating agency colors its day-to-day operations as much as 

by its organizational mission. 

We speculate we would find even more relevant variation among the three organizational missions

after parsing out exactly what or who founded the organization. Did the MAO emerge from legislated 

support? Or was it the project of independent actors? Was it a state- or developer-supported urban 

development effort (or a combination of both as we see in the Austin, New York, and Buffalo examples)?

Just as comprehending organizational mission helps us predict how an MAO will interrelate with its

surrounding neighborhood, so might closer inspection of its initiating agency’s institutional 

configurations and motivations. All of this advises we need to think more critically about what arts 

organizations are when we research them. Beyond their particular art forms and industrial sectors, arts 

organizations constitute operating organizational missions, possibly prefigured by their founding 
Page 34 of 74

initiators’ institutional agendas. Further research, therefore, is needed to explore these endogenous factors 

to create specific policies appropriate for the deeply variegated art world. 

ACKNOWLEDGEMENTS

Many thanks to the National Alliance for Media Arts and Culture (NAMAC) for sharing its 2010 

Mapping the Field Arts Field survey data. In particular, we extend grateful thanks to NAMAC’s former 

Executive Director Jack Walsh for his great stewardship and support in the shaping of this research 

project. In addition, we wish to recognize Sarah Mawhorter, Zhi Li, and Jihye Lee for their excellent 

research assistance.

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APPENDIX

Table 1. NAMAC “Mapping the Field” Organizational Indicators1

Variable Details

Year established Numeric 

Number of years at current location Numeric

Type of Media

Television

Radio

Film

Video

Sound/Audio

Multimedia

Web

Digital Arts

Gaming

New Media 

Other

Audience age emphasis

3-12

13-17

18-29

30-45

Over 65

No specific age

Audience race/ethnicity emphasis

Asian American/Pacific 

Islander

Black/African American

Latino/Hispanic

Native American

Multi-cultural groups

No specific racial or ethnic 

emphasis

Other (please specify)

Rural/urban emphasis Rural emphasis

Urban emphasis

Suburban emphasis

No specific rural or urban 

emphasis

Scope of area targeted 

Neighborhood

City/town

County

Multi-city

State

Multi-state

National

International

No specific area emphasis

Targeted marginalized groups

Women

Low income

People with disabilities

Immigrant populations

LGBT

No other targeted groups or 

emphases

Other (please specify) 1 Organizations were asked to select all that apply in multiple-choice questions.
Page 41 of 74

Table 2. Census Variables: Social and Spatial Indicators of Neighborhood Change:MAO AC List1

Variable Description

Population Density Number of people per square mile2

Average Household Income Average household income

Bachelor’s degree or higher Percentage of residents with bachelor’s or higher degrees

Unemployment Percentage of residents not working in civilian labor force

Black/African American Percentage of Black/African American residents

Latino Percentage of Latino residents

White Percentage of White residents

American Indian / AK Native Percentage of Native American, Alaskan Native residents

Asian Percentage of all Asian, Native Hawaiian, Pac. Islander residents

Other / Multiracial Percentage of Multiracial and “Other”-identified residents

Occupancy Percentage of occupied housing units

Drive to work Percentage of civilian labor force driving to work

Walk to work Percentage of civilian labor force walking to work

1 Sources: U.S. Census Years, 1970, 1980, 1990, 2000, and the 2008-2012 American Community Survey. 2 Population densities only for the 114-unit Artists Center group members.
Page 42 of 74

Table 3. NAMAC “Mapping the Field” Organizational Indicators, Collapsed for Analysis

Variable Details

Age

New (15 years and younger)

Middle-aged (16 to 30 years)

Mature (31 to 47 years)

Tenure

Short (10 years and under)

Medium (11 to 22 years)

Long (23 to 34 years)

Type of Media

TV

Radio and sound audio

Film and video

Multimedia

Digital and new media

Audience age emphasis

Youth and young adult

Adult and senior

Youth and senior

No age focus

Audience race/ethnicity emphasis

Asian American/Pacific 

Islander

Black/African American

Latino/Hispanic

Black and Latino

Multicultural groups

No specific racial or ethnic 

emphasis

Rural/urban emphasis Rural

Urban

Suburban

Urban / Suburban

No targeted audience location

Scope of area targeted 

Neighborhood

City to county

Regional: multicity to 

multistate

National to international

No specific area emphasis

Targeted marginalized groups1 Yes No

1 Marginalized groups include: women, low-income individuals, people with disabilities, immigrants, and LGBT 

populations.
Page 43 of 74

Table 4. Univariate Descriptive Statistics – “Mapping the Field” Survey: MAO ACs (N= 114)

%/Mean(SD) Range

Age of MAO 24.5 (10.6) 4, 51

Tenure 15.5(8.1) 3, 37

Population in 2010 948,628.3 (2115514) 1351, 8175133

Frequency(n) Proportion(%)

Age Missing – 0 0.0

New 26 22.8

Middle-aged 60 52.6

Mature 28 24.6

Tenure Missing – 0 0.0

Short 40 35.1

Medium 53 46.5

Long 21 18.4

Type of Town Missing – 0 0.0

MSA 74 64.9

CBSA 22 19.3

Rurality 18 15.8

Organizational Mission Missing – 0 0.0

Art 16 14.0

Art Community Building 24 21.1

Community Building 74 64.9

Type of Media Missing – 0 0.0

Television 53 46.5

Radio and Sound Audio 8 7.0

Film and Video 33 29.0

Multimedia 13 11.4

Digital and new media 7 6.1

Age of Audience Missing – 0 0.0

Youth and Young adult 14 12.3

Adult and Senior 5 4.4.

Youth and Senior 3 2.6

No age focus 92 80.7
Page 44 of 74

Table 4. Univariate Descriptive Statistics – “Mapping the Field” Survey: MAO ACs (cont.) 

Frequency(n) Proportion(%)

Race of Audience Missing – 0 0.0

Asian/Pacific Islander 3 2.6

Black/African American 3 2.6

Latino 4 3.5

Black and Latino 2 1.8

Multicultural groups 27 23.7

No race/ethnicity focus 75 65.8

Location of Audience Missing – 0 0.0

Rural 10 8.8

Urban 32 28.1

Suburban 9 7.9

Urban and Suburban 5 4.4

No Targeted Audience Location 58 50.9

Scope of Area Targeted Missing – 0 0.0

Neighborhood 11 9.7

City to County 42 36.8

Regional: Multicity to Multistate 18 15.8

National to International 1 0.9

No Area Emphasis 42 36.8

Targeted Marginalized Groups1 Missing – 0 0.0

Yes 50 43.9

No 64 56.1

1 Marginalized groups include: women, low-income individuals, people with disabilities, immigrants, and LGBT 

populations.
Page 45 of 74

Table 5. Summary Statistics – Mapping the Field Survey Answers by Organizational Mission (N=114)

Type of Town Age of Targeted Audience

Org. 

Mission MSA CBSA Rurality Total

Young/

Young 

Adult

Adult/

Senior

Youth/

Senior

No age 

target Total

Art

15 0 1 16 2 2 0 12 16

93.8% 0.0% 6.3% 100% 12.5% 12.5% 0.0% 75.0% 100%

Art 

Community 

Building

20 1 3 24 4 1 1 18 24

83.3% 4.2% 12.5% 100% 16.7% 4.2% 4.2% 75.0% 100%

Community 

Building

39 21 14 74 8 2 2 62 74

52.7% 28.4% 18.9% 100% 10.8% 2.7% 2.7% 83.8% 100%

Total 74 22 18 114 14 5 3 92 114

64.9% 19.3% 15.8% 100% 12.28% 4.4% 2.6% 80.7% 100%

Type of Media Targeted Marginalized Groups

Org. 

Mission TV

Radio/S

ound 

Audio

Film/

Video

Multimedia

Digital/

New 

Media

Total Yes No Total

Art

0 0 9 3 4 16 10 6 16

0.0% 0.0% 56.3% 18.8% 25.0% 100% 62.5% 37.5% 100%

Art 

Community 

Building

0 3 13 6 2 24 6 18 24

0.0% 12.5% 54.2% 25.0% 8.3% 100% 25.0% 75.0% 100%

Community 

Building

52 6 11 4 1 74 48 26 74

70.3% 8.1% 14.9% 5.4% 1.4% 100% 64.9% 35.1% 100%

Total 52 9 33 13 7 114 64 50 114

46.0% 7.9% 29.0% 11.4% 6.1% 100% 56.1% 43.9% 100%
Page 46 of 74

Table 5. Summary Statistics – Mapping the Field Survey Answers by Organizational Mission (cont.)

Org. 

Mission

Race of Audience

Asian/

Pacific 

Islander

Black/

African 

American

Latino Black and 

Latino

Multiple 

Ethnicities

No Race/

Ethnicity 

Target

Total

Art

1 0 1 0 5 9 16

6.3% 0.0% 6.3% 0.0% 31.3% 56.3% 100%

Art 

Community 

Building

1 1 0 2 7 13 24

4.2% 4.2% 0.0% 8.3% 29.2% 54.2% 100%

Community 

Building

1 2 3 0 15 53 74

1.4% 2.7% 4.1% 0.0% 20.3% 71.6% 100%

Total 3 3 4 2 27 75 114

2.6% 2.6% 3.5% 1.8% 23.7% 65.8% 100%

Org. 

Mission

Location of Audience Scope of Area

Rural Urban Suburban

Urban/

Suburban

No 

Audience 

Location

Total Neighborhood

City/

County Regional

National 

to International

No 

Scope Total

Art

1 5 0 3 7 16 1 1 2 1 11 16

6.3% 31.3% 0.0% 18.8% 43.8% 100% 6.3% 6.3% 12.5% 6.3% 68.8% 100%

Art 

Community 

Building

1 13 0 2 8 24 2 8 7 0 7 24

4.2% 54.2% 0.0% 8.3% 33.3% 100% 8.3% 33.3% 29.2% 0.0% 29.2% 100%

Community 

Building

8 14 9 0 43 74 8 33 9 0 24 74

10.8% 18.9% 12.2% 0.0% 58.11% 100% 10.8% 44.6% 12.2% 0.0% 32.4% 100%

Total 10 32 9 5 58 114 11 42 18 1 42 114

8.8% 28.1% 7.9% 4.4% 50.9% 100% 9.7% 36.8% 15.8% 0.9% 36.8% 100%
Page 47 of 74

Table 6. Univariate Descriptive Statistics – MAO Artists’ Center Group (N= 112)1

Mean(SD)

Minus 5 Move-In Plus 5

Total Population

Total population 1673.5(2505.3) 1691.0(2533.2) 1711.1(2571.6)

Population Density 33.3(49.9) 33.7(50.4) 34.1(51.2)

Age Group Population

15-17 223.9(396.0) 205.4(351.1) 181.7(281.9)

15-17% of Total 14.0% 13.2% 12.4%

18-24 364.7(500.9) 248.8(390.7) 259.8(429.3)

18-24 % of Total 15.9% 14.0% 14.1%

25-64 919.9(1521.5) 962.1(1605.3) 1004.7(1684.6)

25-64 % of Total 54.3% 55.6% 56.7%

65+ 190.6(274.9) 185.5(271.0) 181.5(269.9)

65+ % of Total 13.3% 13.1% 12.8%

Race Group Population

Black 263.9(612.1) 242.8(537.5) 222.3(458.3)

Black % of Total 11.0% 10.4% 9.7%

Latino 399.0(1151.4) 388.7(1046.7) 361.8(884.8)

Latino % of Total 9.0% 9.7% 10.3%

White 1072.6(1655.7) 1114.2(1716.7) 1135.7(1794.4)

White % of Total 68.1% 66.5% 65.5%

American Indian/AK Native 12.3(22.3) 13.3(20.7) 13.0(18.9)

American Indian/AK % of Total 0.9% 0.8% 0.8%

Asian 110.7(256.1) 136.8(313.8) 164.0(368.1)

Asian % of Total 5.5% 6.2% 7.0%

Other/Multiracial 239.1(751.2) 231.7(632.5) 205.3(456.3)

Other/Multiracial % of Total 5.5% 6.3% 6.8%
Page 48 of 74

Table 6. Univariate Descriptive Statistics – MAO Artists’ Center Group (cont.)1

Mean(SD)

Minus 5 Move-In Plus 5

Education Attainment

Some High School 158.4(255.4) 152.3(257.0) 153.8(287.8)

Some HS % of Total 13.1% 12.3% 11.9%

High School Diploma 226.9(305.6) 216.8(289.5) 209.0(280.3)

HS Diploma % of Total 24.5% 23.0% 21.6%

Some College 169.4(244.9) 185.2(257.8) 205.6(277.9)

Some College % of Total 18.0% 19.4% 21.0%

Bachelor’s Degree or Higher 341.0(764.5) 417.7(913.3) 502.4(1061.3)

Bachelor’s or Higher % of Total 28.3% 32.7% 37.3%

Employment Status

Employed 787.2(1280.7) 836.0(1383.8) 893.5(1486.2)

Employed % of Total 91.8% 91.6% 91.2%

Unemployed 79.0(136.1) 81.0(132.2) 84.4(129.2)

Unemployed % of Total 8.2% 8.4% 8.8%

Income (inflated to $2013)

Average Household Income $68,721.8 $73,686.6 $76,797.0

Housing

# of Housing Unit 784.4(1254.9) 822.9(1320.4) 867.1(1386.3)

Occupied 718.2(1170.4) 747.6(1221.6) 778.9(1278.0)

Occupied % of Total 90.6% 89.9% 88.9%

Owner 155.7(161.9) 173.0(201.4) 192.9(239.8)

Owner % of Total 40.8% 40.7% 41.1%

Transportation

Car 317.9(291.4) 333.3(302.3) 348.8(316.6)

Car % of Total 61.2% 61.0% 60.5%

Transit 249.5(682.9) 264.8(740.3) 287.1(804.5)

Transit % of Total 12.6% 12.4% 12.3%

Walk 168.2(391.8) 178.3(428.5) 184.5(456.1)

Walk % of Total 13.1% 12.5% 11.7%

1 Owing to their pre-1970 move-in dates, census data was unavailable for two media arts organizations.
Page 49 of 74

Table 7. Univariate Descriptive Statistics – Control Group Census Tracts (N=24)

Mean(SD)

Minus 5 Move-In Plus 5

Total Population

Total population 4349.9(1553.2) 4495.1(1632.7) 4512.6(1750.0)

Age Group Population

15-17 666.5(397.1) 673.8(406.7) 637.3(397.3)

15-17% of Total 14.5% 14.1% 13.2%

18-24 485.7(230.0) 468.7(221.0) 433.2(219.5)

18-24 % of Total 11.2% 10.7% 10.0%

25-64 2378.8(924.1) 2518.9(1006.9) 2578.0(1089.8)

25-64 % of Total 55.4% 56.8% 58.2%

65+ 526.6(275.1) 553.3(307.5) 601.4(369.4)

65+ % of Total 12.9% 12.9% 13.3%

Race Group Population

Black 449.6(736.7) 469.9(783.9) 454.5(769.1)

Black % of Total 10.9% 10.8% 9.9%

Latino 694.8(1022.7) 794.1(1117.3) 795.8(1032.4)

Latino % of Total 13.3% 15.4% 14.9%

White 3045.8(1996.8) 3003.8(1940.7) 2976.8(1871.2)

White % of Total 62.0% 60.8% 57.5%

American Indian/AK Native 22.5(31.5) 22.9(29.8) 31.6(43.0)

American Indian/AK % of Total 0.18% 0.14% 0.23%

Asian 513.5(1085.1) 526.6(1145.7) 657.2(1336.0)

Asian % of Total 10.4% 10.2% 11.2%

Other/Multiracial 156.8(296.2) 104.3(111.4) 296.4(419.7)

Other/Multiracial % of Total 2.9% 2.5% 5.9%
Page 50 of 74

Table 7. Univariate Descriptive Statistics – Control Group Census Tracts (cont.)

Mean(SD)

Minus 5 Move-In Plus 5

Education Attainment

Some High School 391.1(217.5) 384.3(248.2) 419.6(323.2)

Some HS % of Total 14.5% 13.4% 14.2%

HS Diploma 704.2(335.7) 732.6(371.5) 745.0(432.0)

HS Diploma % of Total 24.2% 23.7% 23.1%

Some College 516.6(263.5) 583.5(284.4) 666.7(368.8)

Some College % of Total 17.8% 19.2% 20.8%

Bachelor’s Degree or Higher 758.2(787.2) 876.1(859.8) 1009.0(921.7)

Bachelor’s or Higher % of Total 24.3% 27.4% 31.2%

Employment Status

Employed 2138.8(939.8) 2198.6(979.2) 2197.4(1072.8)

Employed % of Total 92.1% 92.1% 92.0%

Unemployed 167.3(134.2) 189.2(221.4) 204.0(214.0)

Unemployed % of Total 8.0% 8.4% 9.3%

Income

Average Household Income $64,310.4 $69,507.7 $67,178.7

Housing

# of Housing Unit 1891.6(871.3) 1956.1(879.9) 2031.0(900.8)

Occupied 1729.3(819.6) 1783.3(834.0) 1825.9(856.8)

Occupied % of Total 91.6% 91.0% 89.4%

Owner 755.2(501.3) 805.1(516.6) 836.8(540.1)

Owner % of Total 43.4% 44.8% 45.3%

Transportation

Car 1532.4(878.2) 1559.5(876.7) 1524.2(899.4)

Car % of Total 60.3% 62.0% 63.2%

Transit 293.3(326.5) 308.9(345.2) 327.5(378.2)

Transit % of Total 13.7% 13.9% 14.7%

Walk 200.9(171.7) 194.0(190.8) 165.9(207.1)

Walk % of Total 10.0% 8.7% 6.8%
Page 51 of 74

Table 8.1. Summary Statistics – White Population Changes by Tenure (N=112)

Tenure of MAO Minus 5 Move-In Plus 5

Long

(>23 yrs)

2055.3(3058.5) 2091.9(3092.5) 2005.3(3179.7)

76.4% 73.7% 69.8%

Medium 

(11-22 yrs)

945.2(1240.8) 962.4(1298.3) 972.4(1352.2)

69.0% 66.4% 64.8%

Short

(<10 yrs)

774.6(921.4) 850.8(1076.2) 939.0(1271.0)

62.9% 63.1% 64.4%

Table 8.1.1. ANOVA Test – White Population Changes by Tenure (N=112)

Tenure of MAO Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In (% Δ)

Move-In to 

Plus 5 (% Δ)

Minus 5 to 

Plus 5 (% Δ)

Long vs. Medium -- 96.53(NS) 77.13(NS) 0.00079(NS) 0.023(NS) 0.024(NS)

Long vs. Short -- 174.83*** 214.38a 0.029* 0.052*** 0.081***

Medium vs. Short -- 78.30(NS) 137.25(NS) 0.028*** 0.028* 0.057***

F(2,109) 0.94 5.63 3.55 6.26 7.84 8.12

p value 0.39(NS) 0.0047*** 0.032* 0.0027*** 0.0007*** 0.0005***

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005; anot significant but p-value is close to .05; post-hoc test – Bonferroni
Page 52 of 74

Table 8.2. Summary Statistics – Educational Attainment by Tenure (N=112)

Tenure of MAO Some HS

Minus 5

Some HS 

Move-In

Some HS

Plus 5

HS Diploma

Minus 5

HS Diploma

Move-In

HS Diploma

Plus 5

Long

(>23 yrs)

209.5(302.2) 199.2(291.7) 187.4(275.3) 332.6(453.4) 309.8(412.5) 299.8(404.0)

14.8% 14.2% 13.1% 24.9% 23.6% 22.2%

Medium 

(11-22 yrs)

151.1(224.0) 139.6(208.1) 141.4(227.1) 222.0(263.4) 212.0(255.2) 203.7(254.3)

12.3% 11.4% 11.2% 26.0% 24.5% 23.1%

Short

(<10 yrs)

143.7(274.0) 147.0(299.5) 154.4(362.4) 183.2(266.6) 179.1(259.9) 172.8(238.2)

13.4% 12.7% 12.4% 22.3% 20.8% 19.3%

Tenure of MAO Some College

Minus 5

Some College

Move-In

Some College

Plus 5

Bachelor’s + 

Minus 5

Bachelor’s +

Move-In

Bachelor’s +

Plus 5

Long

(>23 yrs)

244.9(433.6) 259.9(447.3) 264.2(429.0) 631.8(1402.8) 746.9(1659.6) 864.2(1898.0)

15.3% 16.7% 17.3% 24.1% 27.7% 31.6%

Medium 

(11-22 yrs)

163.2(207.8) 169.3(209.5) 181.8(223.5) 309.7(655.1) 371.0(788.1) 436.3(909.9)

17.9% 18.4% 19.4% 28.0% 31.2% 35.3%

Short

(<10 yrs)

141.9(150.7) 170.9(187.2) 209.4(256.5) 244.3(361.4) 323.3(458.3) 418.2(601.7)

19.5% 22.1% 24.9% 30.7% 36.9% 42.7%

Table 8.2.1. ANOVA Test – Educational Attainment Changes by Tenure (N=112)

Tenure of MAO Some High School Some High School (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Long vs. Medium -- -- -- -- -- --

Long vs. Short -- -- -- -- -- --

Medium vs. Short -- -- -- -- -- --

F(2,109) 1.95 0.75 1.10 0.25 0.80 0.15

p value 0.15(NS) 0.48(NS) 0.34(NS) 0.78(NS) 0.45(NS) 0.86(NS)

n 112 112 112 112 112 112
Page 53 of 74

Tenure of MAO High School Diploma High School Diploma (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Long vs. Medium -- -- -- -- -- --

Long vs. Short -- -- -- -- -- --

Medium vs. Short -- -- -- -- -- --

F(2,109) 1.26 0.11 0.75 0.17 0.04 0.06

p value 0.29(NS) 0.89(NS) 0.47(NS) 0.85(NS) 0.96(NS) 0.94(NS)

n 112 112 112 112 112 112

Tenure of MAO Some College Some College (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Long vs. Medium -9.01(NS) 8.25(NS) -0.75(NS) -0.0095(NS) 0.0048(NS) -0.0047(NS)

Long vs. Short 14.03(NS) 34.14(NS) 48.19(NS) 0.012(NS) 0.022** 0.033*

Medium vs. Short 23.03* 25.91(NS) 48.94* 0.021*** 0.017** 0.038***

F(2,109) 4.38 3.35 3.75 11.63 6.56 10.06

p value 0.015* 0.039* 0.027* 0.00001*** 0.0020*** 0.0001***

n 112 112 112 112 112 112

Tenure of MAO Bachelor’s + Bachelor’s + (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Long vs. Medium -- -- -- -0.0034(NS) 0.0016(NS) -0.0018(NS)

Long vs. Short -- -- -- 0.026* 0.019(NS) 0.045*

Medium vs. Short -- -- -- 0.030*** 0.017(NS) 0.047***

F(2,109) 0.74 0.73 0.73 9.33 3.17 6.93

p value 0.48(NS) 0.48(NS) 0.48(NS) 0.0002*** 0.046* 0.0015***

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005, anot significant but p-value is close to .05; post-hoc test – Bonferroni 
Page 54 of 74

Table 8.3. Summary Statistics – Unemployment by Tenure (N=112)

Tenure of MAO Minus 5 Move-In Plus 5

Long 

(>23 yrs) 104.9(200.7) 110.5(198.0) 117.5(194.1)

7.4% 7.8% 8.3%

Medium 

(11-22 yrs) 78.1(118.7) 75.5(113.5) 75.6(114.8)

7.7% 7.5% 7.7%

Short (<10 yrs) 67.9(122.0) 74.4(118.1) 80.4(109.0)

9.2% 9.9% 10.5%

Table 8.4. Summary Statistics – Household Income Changes by Tenure (N=112)

Tenure of MAO Minus 5 Move-In Plus 5

Long

(>23 yrs) $49,499.9(21712.9) $51,748.9(20954.2) $57,406.2(20784.9)

Medium 

(11-22 yrs) $72,237.5(50290.2) $80,462.0(55969.5) $84,075.4(49749.7)

Short

(<10 yrs) $73,193.9(39681.3) $75,129.4(39599.6) $76,363.6(41108.9)

Table 8.4.1. ANOVA Test – Average Household Income Changes by Tenure (N=112)

Tenure of MAO Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Long vs. Medium 5975.58(NS) -- --

Long vs. Short -313.39(NS) -- --

Medium vs. Short -6288.96* -- --

F(2,109) 5.16 1.09 2.72

p value 0.0072** 0.34(NS) 0.071(NS)

n 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005; post-hoc test – Bonferroni
Page 55 of 74

Table 8.5. Summary Statistics – Owner Occupancy by Tenure (N=112)

Tenure of MAO Minus 5 Move-In Plus 5

Long

(>23 yrs)

191.4(175.1) 213.2(257.1) 237.8(322.2)

35.5% 33.5% 32.5%

Medium 

(11-22 yrs) 152.1(162.7) 166.0(194.1) 181.2(224.1)

45.3% 45.9% 46.7%

Short

(<10 yrs) 143.5(156.2) 163.2(183.6) 186.9(218.2)

37.4% 37.4% 37.8%

Table 9.1. Summary Statistics – White Population by Age (N=112)

Age of MAO Minus 5 Move-In Plus 5

Mature

(>31 yrs)

1690.0(2105.0) 1710.3(2250.1) 1719.4(2257.7)

62.5% 61.1% 60.0%

Middle-aged

(16-30 yrs)

936.0(1596.0) 972.0(1581.8) 971.4(1661.0)

71.5% 69.3% 67.4%

New (<15 yrs) 770.4(1092.6) 846.0(1274.5) 931.3(1491.6)

65.8% 65.4% 66.6%
Page 56 of 74

Table 9.1.1 ANOVA Test – White Population Changes by Age (N=112)

Age of MAO Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In (% Δ)

Move-In to 

Plus 5 (% Δ)

Minus 5 to 

Plus 5 (% Δ)

Mature vs. Middle-aged -- -- -- -- -0.008(NS) -0.016(NS)

Mature vs. New -- -- -- -- 0.023(NS) 0.033(NS)

Middle-aged vs. New -- -- -- -- 0.031* 0.049*

F(2,109) 0.51 1.80 1.54 1.69 3.39 2.29

p value 0.60(NS) 0.17(NS) 0.22(NS) 0.19(NS) 0.037* 0.055a

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005; anot significant but p-value is close to .05

Table 9.2. Summary Statistics – Educational Attainment by Age (N=112)

Age of MAO Some HS

Minus 5

Some HS MoveInSome HS

Plus 5

HS Diploma

Minus 5

HS Diploma

Move-In

HS Diploma

Plus 5

Mature

(>31 yrs)

231.3(211.9) 204.3(193.8) 187.6(179.0) 321.2(283.7) 294.5(279.8) 273.1(270.4)

14.8% 13.2% 12.4% 23.0% 20.9% 18.9%

Middle-aged

(16-30 yrs)

134.4(238.1) 129.7(224.6) 132.5(236.8) 206.4(303.3) 198.8(277.1) 195.0(274.9)

12.7% 12.0% 11.9% 26.2% 24.8% 23.7%

New

(<15 yrs)

140.8(322.0) 152.6(364.3) 169.3(448.3) 179.9(323.5) 180.9(323.1) 177.2(302.7)

12.5% 12.1% 11.7% 22.0% 20.9% 19.7%

Age of MAO Some College

Minus 5

Some College

Move-In

Some College

Plus 5

Bachelor’s + 

Minus 5

Bachelor’s +

Move-In

Bachelor’s +

Plus 5

Mature

(>31 yrs)

252.5(273.4) 257.5(272.7) 261.4(266.4) 551.3(1026.4) 664.0(1202.4) 798.3(1382.5)

16.8% 17.7% 17.9% 28.8% 34.7% 40.7%

Middle-aged

(16-30 yrs)

149.8(252.1) 165.1(259.8) 183.1(259.7) 286.7(742.6) 353.5(898.3) 420.4(1039.2)

18.4% 19.6% 21.2% 26.5% 30.0% 33.8%

New

(<15 yrs)

131.7(178.4) 159.4(233.2) 201.8(329.1) 255.9(423.1) 319.5(520.3) 395.7(649.6)

18.4% 20.8% 23.6% 31.9% 36.8% 42.0%
Page 57 of 74

Table 9.2.1. ANOVA Test – Educational Attainment Changes by Age (N=112)

Age of MAO Some High School Some High School (% Δ)

Minus 5 to Move-In Move-In to Plus 5 Minus 5 to Plus 5 Minus 5 to Move-In Move-In to Plus 5 Minus 5 to Plus 5

Mature vs. Middle-aged 22.26* -- 41.88(NS) -- -- --

Mature vs. New 38.73*** -- 72.21*** -- -- --

Middle-aged vs. New 16.47(NS) -- 30.33(NS) -- -- --

F(2,109) 7.82 2.41 4.86 2.65 0.62 1.52

p value 0.0007*** 0.095(NS) 0.0095*** 0.075(NS) 0.54(NS) 0.22(NS)

n 112 112 112 112 112 112

Age of MAO High School Diploma High School Diploma (% Δ)

Minus 5 to Move-In Move-In to Plus 5 Minus 5 to Plus 5 Minus 5 to Move-In Move-In to Plus 5 Minus 5 to Plus 5

Mature vs. Middle-aged 19.17(NS) 17.56* 36.73* -- -- --

Mature vs. New 27.74a 17.67(NS) 45.41* -- -- --

Middle-aged vs. New 8.57(NS) 0.11(NS) 8.68(NS) -- -- --

F(2,109) 3.10 3.80 3.96 1.93 2.12 2.49

p value 0.049* 0.025* 0.022* 0.15(NS) 0.13(NS) 0.088(NS)

n 112 112 112 112 112 112

Age of MAO Some College Some College (% Δ)

Minus 5 to Move-In Move-In to Plus 5 Minus 5 to Plus 5 Minus 5 to Move-In Move-In to Plus 5 Minus 5 to Plus 5

Mature vs. Middle-aged -- 14.22(NS) 24.53(NS) 0.0038(NS) 0.014(NS) 0.018(NS)

Mature vs. New -- 38.61* 61.28* 0.016* 0.026** 0.041***

Middle-aged vs. New -- 24.39(NS) 36.75(NS) 0.012(NS) 0.012(NS) 0.024(NS)

F(2,109) 2.34 3.16 3.02 3.59 6.43 6.00

p value 0.10(NS) 0.046* 0.053a 0.030* 0.0023*** 0.0034***

n 112 112 112 112 112 112
Page 58 of 74

Age of MAO Bachelor’s + Bachelor’s + (% Δ)

Minus 5 to Move-In Move-In to Plus 5 Minus 5 to Plus 5 Minus 5 to Move-In Move-In to Plus 5 Minus 5 to Plus 5

Mature vs. Middle-aged -- -- -- -0.024* -0.023* -0.046**

Mature vs. New -- -- -- -0.0098(NS) -0.0082(NS) -0.018(NS)

Middle-aged vs. New -- -- -- 0.014(NS) 0.014(NS) 0.028(NS)

F(2,109) 0.80 1.43 1.10 4.50 4.03 5.17

p value 0.45(NS) 0.24(NS) 0.34(NS) 0.013* 0.021* 0.0071**

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005, anot significant but p-value is close to .05; post-hoc test – Bonferroni

Table 9.3. Summary Statistics – Unemployment by Age (N=112)

Age of MAO Minus 5 Move-In Plus 5

Mature

(>31 yrs)

114.4(120.2) 125.5(139.7) 134.0(143.1)

9.7% 10.2% 10.8%

Middle-aged

(16-30 yrs)

67.5(136.6) 66.2(123.2) 70.8(124.9)

7.7% 7.7% 8.1%

New

(<15 yrs)

70.2(148.4) 70.8(139.8) 66.3(115.8)

7.7% 8.2% 8.6%

Table 9.4. Summary Statistics – Household Income Changes by Age (N=112)

Age of 

MAO Minus 5 Move-In Plus 5

Mature

(>31 yrs) $54,032.7(20240.1) $57,813.9(22781.0) $63,548.0(26980.0)

Middle-aged

(16-30 yrs) $70,246.7(48354.3) $76,637.2(54135.4) $78,822.9(48935.4)

New

(<15 yrs)

$79,892.1(45912.4) $82,750.0(44056.6) $85,370.7(42960.5)
Page 59 of 74

Table 9.5. Summary Statistics – Owner Occupancy by Age (N=112)

Age of MAO Minus 5 Move-In Plus 5

Mature

(>31 yrs) 201.7(188.6) 227.4(270.0) 258.7(339.3)

29.0% 27.3% 27.2%

Middle-aged

(16-30 yrs) 141.6(151.4) 156.1(175.4) 171.3(199.5)

43.3% 43.7% 44.3%

New

(<15 yrs) 141.9(154.5) 157.6(174.7) 176.7(201.1)

47.1% 47.2% 47.5%

Table 10.1. Summary Statistics – White Population by Organizational Mission (N=112)

Org. 

Mission Minus 5 Move-In Plus 5

Art 2479.7(3402.0) 2493.2(3412.0) 2542.0(3471.3)

66.1% 65.9% 65.7%

Art Community 

Building

1243.9(1095.9) 1286.8(1260.6) 1358.3(1484.8)

53.4% 51.9% 52.9%

Community 

Building

710.2(930.5) 757.5(1035.1) 757.4(1086.0)

73.1% 71.2% 73.1%
Page 60 of 74

Table 10.2. Summary Statistics – Educational Attainment by Organizational Mission (N=114)

Org. 

Mission

Some HS

Minus 5

Some HS 

Move-In

Some HS

Plus 5

HS Diploma

Minus 5

HS Diploma

Move-In

HS Diploma

Plus 5

Art 247.5(332.4) 234.6(318.8) 221.2(297.7) 363.4(514.6) 339.5(457.6) 325.4(441.5)

13.1% 11.3% 10.3% 18.7% 17.6% 16.4%

Art Community 

Building

267.3(341.6) 262.7(377.4) 272.5(462.9) 350.8(326.4) 327.8(326.2) 305.3(306.6)

15.6% 14.7% 14.6% 24.3% 22.5% 20.7%

Community 

Building

104.5(182.8) 99.5(170.1) 101.7(188.5) 158.0(206.0) 155.0(204.8) 153.1(206.6)

12.4% 11.8% 11.5% 25.8% 24.4% 23.0%

Org. 

Mission

Some College

Minus 5

Some College

Move-In

Some College

Plus 5

Bachelor’s + 

Minus 5

Bachelor’s +

Move-In

Bachelor’s +

Plus 5

Art 334.3(490.4) 354.1(498.3) 362.8(473.0) 1031.6(1572.6) 1234.8(1835.2) 1434.7(2074.1)

16.4% 17.6% 18.0% 33.7% 41.0% 47.3%

Art Community 

Building

254.8(187.3) 283.1(234.8) 327.7(327.3) 320.0(230.5) 397.8(333.3) 497.4(480.1)

18.9% 20.4% 22.5% 25.4% 29.7% 35.3%

Community 

Building

106.4(138.6) 117.4(140.9) 132.7(155.2) 196.2(492.0) 244.9(609.7) 299.7(731.3)

18.1% 19.5% 21.2% 28.1% 31.8% 35.7%
Page 61 of 74

Table 10.2.1. ANOVA Test – Educational Attainment Changes by Goal Typology (N=112)

Goal Typology High School Diploma High School Diploma (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Art vs. Art Comm. Bldg. -- -- -- -- -- --

Art Comm. Bldg. vs. Comm. Bldg. -- -- -- -- -- --

Art vs. Comm. Bldg. -- -- -- -- -- --

F(2,109) 0.30 0.82 0.65 2.14 0.58 1.36

p value 0.74(NS) 0.44(NS) 0.52(NS) 0.12(NS) 0.56(NS) 0.26(NS)

n 112 112 112 112 112 112

Goal Typology High School Diploma High School Diploma (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Art vs. Art Comm. Bldg. -- -8.48(NS) -7.67(NS) -- -- --

Art Comm. Bldg. vs. Comm. Bldg. -- 20.6** 40.62* -- -- --

Art vs. Comm. Bldg. -- 12.1(NS) 32.95(NS) -- -- --

F(2,109) 3.02 5.11 4.49 0.84 0.62 0.91

p value 0.53a 0.008** 0.013* 0.43(NS) 0.54(NS) 0.41(NS)

n 112 112 112 112 112 112

Goal Typology Some College Some College (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Art vs. Art Comm. Bldg. -- -- -- -- -- --

Art Comm. Bldg. vs. Comm. Bldg. -- -- -- -- -- --

Art vs. Comm. Bldg. -- -- -- -- -- --

F(2,109) 1.92 2.74 2.34 0.11 2.25 1.09

p value 0.15(NS) 0.069a 0.10(NS) 0.89(NS) 0.11(NS) 0.34(NS)

n 112 112 112 112 112 112
Page 62 of 74

Goal Typology Bachelor’s + Bachelor’s + (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Art vs. Art Comm. Bldg. -125.4a -100.20(NS) -225.58(NS) -0.031* -0.0068(NS) -0.037(NS)

Art Comm. Bldg. vs. Comm. Bldg. -29.17(NS) -44.88(NS) -74.05(NS) -0.005(NS) -0.018(NS) -0.023(NS)

Art vs. Comm. Bldg. -154.56** -145.07** -299.63*** -0.036** -0.024* -0.60***

F(2,109) 6.23 5.08 5.69 6.95 4.28 6.05

p value 0.0028*** 0.0078** 0.0045*** 0.0014*** 0.016* 0.032***

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005, anot significant but p-value is close to .05; post-hoc test – Bonferroni

Table 10.3. Summary Statistics – Unemployment by Organizational Mission (N=112)

Org. 

Mission Minus 5 Move-In Plus 5

Art 138.9(219.7) 142.8(212.9) 149.7(206.9)

7.9% 7.9% 7.9%

Art Community Building 131.5(154.3) 137.7(152.3) 133.8(134.2)

11.6% 11.9% 11.6%

Community Building 49.3(93.0) 49.7(86.8) 54.6(93.2)

7.1% 7.4% 8.2%

Table 10.4. Summary Statistics – Household Income Changes by Organizational Mission (N=112)

Org. 

Mission Minus 5 Move-In Plus 5

Art $71,033.6(19041.3) $76,687.6(20000.2) $84,748.6(25188.3)

Art Community Building $52,970.2(25454.5) $55,857.4(27226.2) $58,701.9(29598.9)

Community Building $73,178.0(50408) $78,646.2(54269.9) $80,755.3(49141.0)
Page 63 of 74

Table 10.5. Summary Statistics – Owner Occupancy by Organizational Mission (N=112)

Org. 

Mission Minus 5 Move-In Plus 5

Art 265.5(262.7) 330.7(350.8) 391.5(421.8)

32.6% 30.5% 31.3%

Art Community Building 161.3(96.3) 165.6(100.8) 180.1(119.5)

24.7% 24.3% 24.6%

Community Building 129.8(140.9) 140.8(164.9) 153.3(191.9)

47.7% 48.1% 48.4%

Table 10.5.1. ANOVA Test – Owner Occupancy Changes by Goal Typology (N=112)

Goal Typology Owner Occupancy Owner Occupancy (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Art vs. Art Comm. Bldg. -- -63.21a -119.73(NS) -- -- --

Art Comm. Bldg. vs. Comm. Bldg. -- 0.97(NS) 5.35(NS) -- -- --

Art vs. Comm. Bldg. -- -62.24* -114.37a -- -- --

F(2,109) 2.54 3.57 3.11 0.25 1.95 0.92

p value 0.083a 0.032* 0.048* 0.78(NS) 0.15(NS) 0.40(NS)

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005; anot significant but p-value is close to .05
Page 64 of 74

Table 11.1. Summary Statistics – White Population by Town Type (N=112)

Town Type Minus 5 Move-In Plus 5

MSA 1450.3(1916.8) 1505.5(1985.7) 1545.5(2080.5)

59.1% 57.7% 57.3%

CBSA 471.7(399.6) 493.4(444.7) 468.6(391.5)

83.8% 81.7% 79.3%

Rurality 151.9(213.8) 157.6(214.6) 157.5(213.7)

87.9% 85.8% 84.1%

Table 11.2. Summary Statistics – Educational Attainment by Town Type (N=112)

Town Type Some HS

Minus 5

Some HS 

Move-In

Some HS

Plus 5

HS Diploma

Minus 5

HS Diploma

Move-In

HS Diploma

Plus 5

MSA 225.5(291.1) 215.9(295.1) 217.2(335.0) 307.8(344.0) 293.3(325.7) 282.1(315.8)

14.9% 14.0% 13.5% 22.6% 21.2% 20.0%

CBSA 41.0(54.0) 42.4(61.8) 45.9(74.4) 100.7(107.4) 97.6(104.7) 95.1(102.3)

10.0% 9.7% 9.8% 27.9% 26.4% 24.7%

Rurality 9.3(10.8) 9.3(10.7) 9.3(10.5) 26.5(28.6) 27.2(29.7) 27.2(30.3)

9.2% 8.3% 7.9% 28.4% 26.7% 20.0%

Town Type Some College

Minus 5

Some College

Move-In

Some College

Plus 5

Bachelor’s + 

Minus 5

Bachelor’s +

Move-In

Bachelor’s +

Plus 5

MSA 233.8(278.5) 254.3(291.8) 281.0(313.1) 475.1(910.0) 585.7(1084.3) 707.8(1255.4)

17.9% 19.2% 20.6% 27.1% 32.0% 37.3%

CBSA 61.9(55.9) 70.8(65.0) 81.9(79.6) 105.0(130.9) 118.6(148.8) 135.0(168.0)

17.6% 19.0% 20. % 31.8% 34.5% 37.4%

Rurality 19.5(21.1) 23.0(25.7) 27.1(32.5) 45.3(80.9) 52.1(86.6) 57.8(91.0)

19.4% 21.0% 23.4% 29.2% 33.2% 37.1%
Page 65 of 74

Table 11.2.1. ANOVA test – Educational Attainment Changes by Town Type (N=112)

Type of Town Some High School Some High School (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

MSA vs. CBSA -- -- -- -- -- --

CBSA vs. Rurality -- -- -- -- -- --

MSA vs. Rurality -- -- -- -- -- --

F(2,109) 0.96 0.02 0.22 0.82 0.48 0.77

p value 0.3874(NS) 0.9812(NS) 0.8062(NS) 0.4446(NS) 0.6176(NS) 0.4643(NS)

n 112 112 112 112 112 112

Type of Town High School Diploma High School Diploma (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

MSA vs. CBSA -- -- -- -- -- --

CBSA vs. Rurality -- -- -- -- -- --

MSA vs. Rurality -- -- -- -- -- --

F(2,109) 1.23 1.43 1.58 0.17 0.57 0.42

p value 0.2960(NS) 0.2444(NS) 0.2117(NS) 0.8452(NS) 0.5669(NS) 0.6556(NS)

n 112 112 112 112 112 112

Type of Town Some College Some College (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

MSA vs. CBSA -- -- -- -- -- --

CBSA vs. Rurality -- -- -- -- -- --

MSA vs. Rurality -- -- -- -- -- --

F(2,109) 1.77 1.38 1.64 0.10 1.00 0.57

p value 0.1758(NS) 0.2559(NS) 0.1986(NS) 0.9094(NS) 0.3730(NS) 0.5682(NS)

n 112 112 112 112 112 112
Page 66 of 74

Type of Town Bachelor’s + Bachelor’s + (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

MSA vs. CBSA 96.9* 105.7* 202.7* 0.02a 0.024* 0.045*

CBSA vs. Rurality -6.9(NS) -10.7(NS) -17.6(NS) 0.01(NS) 0.0098(NS) 0.022(NS)

MSA vs. Rurality -103.9a -116.4* -220.3* -.009(NS) -0.014(NS) -0.023(NS)

F(2,109) 4.87 5.54 5.29 2.98 4.29 4.34

p value 0.0094** 0.0051** 0.0064** 0.0551a 0.0161* 0.0153*

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005, anot significant but p-value is close to .05; post-hoc test – Bonferroni

Table 11.3. Summary Statistics – Unemployment by Town Type (N=112)

Town Type Minus 5 Move-In Plus 5

MSA 113.7(156.3) 115.7(151.0) 119.6(146.4)

9.6% 9.7% 9.9%

CBSA 17.0(19.5) 20.1(22.8) 23.3(26.3)

5.4% 6.1% 7.1%

Rurality 3.7(5.3) 4.5(6.4) 5.9(8.9)

5.3% 5.5% 6.2%

Table 11.4. Summary Statistics – Household Income Changes by Town Type (N=112)

Town 

Type Minus 5 Move-In Plus 5

MSA $58,257.3(20526.0) $63,078.6(24319.7) $67,132.7(27936.2)

CBSA $89,021.8(77095.8) $95,551.3(84416.9) $99,300.1(73050.5)

Rurality $89,208.1(45037.0) $92,684.4(41768.9) $90,552.6(38497.3)
Page 67 of 74

Table 11.5. Summary Statistics – Owner Occupancy by Town Type (N=112)

Town 

Type Minus 5 Move-In Plus 5

MSA 193.8(176.2) 218.1(225.4) 246.1(271.6)

28.9% 28.7% 29.4%

CBSA 111.7(105.6) 116.1(110.0) 122.1(116.0)

61.9% 62.3% 62.5%

Rurality 39.9(54.7) 42.8(54.7) 43.9(52.9)

66.9% 66.5% 65.7%

Table 12.1. Summary Statistics – White Population by Type of Media (N=112)

Type of Media Minus 5 Move-In Plus 5

TV 613.1(951.0) 654.7(1068.3) 634.5(1093.2)

76.2% 73.7% 71.2%

Radio & Sound Audio 783.1(963.0) 831.0(998.8) 906.1(1123.7)

75.3% 74.4% 73.7%

Film & Video 1519.7(2129.8) 1583.5(2084.6) 1688.1(2257.8)

56.2% 56.0% 57.4%

Multimedia 1356.9(898.5) 1280.1(826.1) 1251.2(801.0)

58.9% 57.2% 57.1%

Digital & New Media 2245.3(3479.8) 2397.3(3840.2) 2381.3(3821.0)

70.8% 68.5% 66.3%
Page 68 of 74

Table 12.1.1. ANOVA test –for White Population Changes (N=112)

Type of Media White population White Population (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

TV vs. Radio & Sound Audio -- -- -- -- -- --

TV vs. Film & Video -- 124.79* -- -- 0.040** 0.062*

TV vs. Multimedia -- -- -- -- -- --

TV vs. Digital & New media -- -- -- -- -- --

Radio & Sound Audio vs. Film & Video -- -- -- -- -- --

Radio vs. Multimedia -- -- -- -- -- --

Radio vs. Digital and New Media -- -- -- -- -- --

Film & Video vs. Multimedia -- -- -- -- -- --

Film & Video vs. Digital & New media -- -- -- -- -- --

Multimedia vs. Digital & New media -- -- -- -- -- --

F(4,107) 1.72 2.54 2.16 1.58 3.16 2.80

p value 0.15(NS) 0.044* 0.79(NS) 0.18(NS) 0.017* 0.030*

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005
Page 69 of 74

Table 12.2. Summary Statistics – Educational Attainment by Type of Media (N=112)

Type of Media Some HS

Minus 5

Some HS 

Move-In

Some HS

Plus 5

HS Diploma

Minus 5

HS Diploma

Move-In

HS Diploma

Plus 5

TV 68.3(100.8) 65.8(93.8) 64.9(91.5) 128.1(165.9) 124.9(157.5) 121.8(149.6)

12.1% 11.5% 11.1% 26.5% 25.2% 23.8%

Radio & Sound Audio 92.8(163.1) 96.0(181.8) 113.6(233.2) 129.4(137.8) 125.6(148.5) 130.3(166.8)

10.7% 9.7% 9.5% 26.0% 24.5% 23.5%

Film & Video 286.8(387.6) 279.1(398.5) 288.9(464.7) 366.7(449.4) 350.9(416.5) 341.3(402.5)

14.9% 13.7% 13.3% 22.3% 21.0% 19.8%

Multimedia 241.9(173.7) 214.3(147.8) 195.1(132.3) 306.8(176.7) 273.4(153.1) 246.3(137.8)

17.8% 16.6% 15.6% 24.5% 22.4% 20.6%

Digital & New Media 159.9(271.4) 164.6(284.8) 166.6(274.3) 285.2(383.5) 286.4(422.1) 272.3(425.4)

7.1% 7.3% 8.1% 17.3% 15.4% 13.4%

Type of Media Some College

Minus 5

Some College

Move-In

Some College

Plus 5

Bachelor’s + 

Minus 5

Bachelor’s +

Move-In

Bachelor’s +

Plus 5

TV 84.6(121.8) 93.4(123.7) 105.0(132.9) 175.9(559.1) 212.6(682.6) 250.2(790.6)

18.3% 19.5% 20.9% 28.0% 31.4% 34.9%

Radio & Sound Audio 123.9(110.6) 138.3(116.9) 160.2(137.2) 150.9(243.1) 179.5(265.5) 217.0(294.0)

22.3% 24.7% 27.1% 24.5% 28.7% 32.5%

Film & Video 281.3(357.2) 307.8(378.0) 341.0(410.9) 533.5(868.1) 671.8(1032.2) 823.6(1207.0)

17.3% 18.6% 19.7% 29.3% 35.0% 41.4%

Multimedia 187.8(109.8) 205.6(105.7) 227.2(101.6) 231.4(171.5) 299.3(244.4) 395.2(353.8)

16.2% 18.6% 21.3% 20.2% 25.3% 31.2%

Digital & New Media 306.8(361.8) 322.9(379.2) 346.2(388.7) 1107.8(1714.4) 1272.2(2024.5) 1433.2(2323.7)

18.1% 18.8% 20.1% 46.1% 49.5% 53.1%
Page 70 of 74

Table 12.2.1. ANOVA test for – Educational Attainment Changes (N=112)

Type of Media Some High School Some High School (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

TV vs. Radio & Sound Audio -- -- -- -- -- --

TV vs. Film & Video -- -- -- -- -- --

TV vs. Multimedia -- -- -- -- -- --

TV vs. Digital & New media -- -- -- -- -- --

Radio & Sound Audio vs. Film & Video -- -- -- -- -- --

Radio vs. Multimedia -- -- -- -- -- --

Radio vs. Digital and New Media -- -- -- -- -- --

Film & Video vs. Multimedia -- -- -- -- -- --

Film & Video vs. Digital & New media -- -- -- -- -- --

Multimedia vs. Digital & New media -- -- -- -- -- --

F(4,107) 1.49 0.80 1.02 1.05 0.64 0.82

p value 0.21(NS) 0.53(NS) 0.40(NS) 0.38(NS) 0.63(NS) 0.51(NS)

n 112 112 112 112 112 112

Type of Media High School Diploma High School Diploma (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

TV vs. Radio & Sound Audio -- -- -- -- -- --

TV vs. Film & Video -- -- -- -- -- --

TV vs. Multimedia -- -- -- -- -- --

TV vs. Digital & New media -- -- -- -- -- --

Radio & Sound Audio vs. Film & Video -- -- -- -- -- --

Radio vs. Multimedia -- -- -- -- -- --

Radio vs. Digital and New Media -- -- -- -- -- --

Film & Video vs. Multimedia -- -- -- -- -- --

Film & Video vs. Digital & New media -- -- -- -- -- --

Multimedia vs. Digital & New media -- -- -- -- -- --
Page 71 of 74

F(4,107) 1.66 2.35 2.14 0.52 0.39 0.52

p value 0.16 0.59a 0.081(NS) 0.729(NS) 0.82(NS) 0.72(NS)

n 112 112 112 112 112 112

Type of Media Some College Some College (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

TV vs. Radio & Sound Audio -- -- -- -- -- --

TV vs. Film & Video -- -- -- -- -- --

TV vs. Multimedia -- -- -- -- -- --

TV vs. Digital & New media -- -- -- -- -- --

Radio & Sound Audio vs. Film & Video -- -- -- -- -- --

Radio vs. Multimedia -- -- -- -- -- --

Radio vs. Digital and New Media -- -- -- -- -- --

Film & Video vs. Multimedia -- -- -- -- -- --

Film & Video vs. Digital & New media -- -- -- -- -- --

Multimedia vs. Digital & New media -- -- -- -- -- --

F(4,107) 1.08 0.70 0.90 1.31 1.00 1.36

p value 0.37(NS) 0.59(NS) 0.47(NS) 0.27(NS) 0.41(NS) 0.25(NS)

n 112 112 112 112 112 112

Type of Media Bachelor’s + Bachelor’s + (% Δ)

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

Minus 5 to 

Move-In

Move-In to 

Plus 5

Minus 5 to 

Plus 5

TV vs. Radio & Sound Audio -- -- -- -- -- --

TV vs. Film & Video 101.75a 114.17* 215.92* 0.024* 0.028** 0.052**

TV vs. Multimedia -- -- -- -- -- --

TV vs. Digital & New media -- -- -- -- -- --

Radio & Sound Audio vs. Film & Video -- -- -- -- -- --

Radio vs. Multimedia -- -- -- -- -- --

Radio vs. Digital and New Media -- -- -- -- -- --

Film & Video vs. Multimedia -- -- -- -- -- --
Page 72 of 74

Film & Video vs. Digital & New media -- -- -- -- -- --

Multimedia vs. Digital & New media -- -- -- -- -- --

F(4,107) 2.68 2.85 2.79 2.52 3.89 3.85

p value 0.036* 0.027* 0.030* 0.046* 0.0055** 0.0058**

n 112 112 112 112 112 112

NS = Not Significant, *p <.05, **p <.01, ***p <.005, anot significant but p-value is close to .05; post-hoc test – Bonferroni

Table 12.3. Summary Statistics – Unemployment by Type of Media (N=112)

TV 31.1(51.5) 33.8(55.1) 37.3(60.1)

7.1% 7.2% 7.8%

Radio & Sound 78.0(131.2) 69.1(100.0) 79.9(105.3)

8.0% 8.5% 9.5%

Film & Video 145.3(206.0) 148.0(193.6) 150.5(182.0)

9.5% 10.0% 10.2%

Multimedia 97.4(71.3) 92.5(58.1) 86.9(45.9)

11.0% 10.4% 9.8%

Digital & New Media 98.4(161.4) 118.3(200.4) 132.7(210.4)

5.0% 6.1% 7.5%

Table 12.4. Summary Statistics – Household Income Changes by Type of Media (N=112)

Type of Media Minus 5 Move-In Plus 5

TV $75,568.4(57399.0) $80,774.6(61869.7) $82,394.9(54649.2)

Radio & Sound $61,114.4(14304.3) $64,251.9(16052.8) $65,629.8(17764.8)

Film & Video $63,532.9(27796.8) $67,362.7(30219.2) $71,812.6(34143.9)

Multimedia $54,389.0(12715.8) $59,998.6(15096.3) $64,456.4(21988.8)

Digital & New Media $76,895.0(35664.7) $86,144.9(35166.1) $93,678.4(37603.6)
Page 73 of 74

Table 12.5. Summary Statistics – Owner Occupancy by Type of Media (N=112)

Type of Media Minus 5 Move-In Plus 5

TV 107.7(131.7) 115.4(151.0) 124.4(170.2)

48.9% 49.3% 49.2%

Radio & Sound 104.3(96.5) 103.0(91.4) 105.8(92.8)

42.1% 41.4% 40.9%

Film & Video 207.0(181.3) 240.0(231.0) 276.6(280.8)

34.8% 33.6% 34.3%

Multimedia 175.1(124.7) 181.8(136.3) 202.9(159.1)

25.3% 25.8% 27.3%

Digital & New media 300.4(251.2) 358.1(366.9) 399.3(459.9)

35.7% 36.6% 37.2%

Table 13. Difference-in-Difference – White Populations and Bachelor’s Degree and Higher Population Change, Move-In to Plus 5

White Populations Bachelor’s +

No MAO MAO Impact Diff-in-Diff No MAO MAO Impact Diff-in-Diff

Move-In 3003.83 1114.15

48.59

876.11 417.71 -48.23

Plus 5 2976.79 1135.71 1009.04 502.42

t 0.09 -0.16

p 0.93(NS) 0.88(NS)

NS = Not Significant, *p <.05, **p <.01, ***p <.005, anot significant but p-value is close to .05
Page 74 of 74

Table 14. Respondent and Nonrespondent Groups Comparison –Goal Typology

Arts Arts Community Building Community Building Total

Nonrespondent 46 25 209 280

Respondent 63 37 147 247

Total 109 62 356 527

Chi-square 13.76

p 0.001***

NS = Not Significant, *p <.05, **p <.01, ***p <.005, anot significant but p-value is close to .05

Table 15. Respondent and Nonrespondent Groups Comparison –Educational Attainment

Some High School High School Diploma Some College Bachelor’s +

Mean(SE) Mean(SE) Mean(SE) Mean(SE)

Nonrespondent 78.59(27.11) 203.94(21.88) 238.73(20.80) 694.83(79.80)

Respondent 166.67(23.21) 193.11(18.41) 236.09(19.90) 660.51(89.43)

t -1.97 0.37 0.091 0.29

p 0.050* 0.71(NS) 0.93(NS) 0.77(NS)

Nonrespondent number = 280, Respondent = 247; NS = Not Significant, *p <.05, **p <.01, ***p <.005

Table 16. Respondent and Nonrespondent Groups Comparison – Age of Media Arts Organizations

Age of MAO

Mean(SE)

Nonrespondent 27.15(1.29)

Respondent 25.68(1.01)

t 0.88

p 0.38(NS)

Nonrespondent number = 280, Respondent = 247; NS = Not Significant, *p <.05, **p <.01, ***p <.005