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VOL. 10, NO. 2 
FALL 2021 

 

The Network Effect of Arts Incubators 
A Geospatial Perspective 

Stan Renard 
Oklahoma University 

Gianluca Zanella 
University of Texas at San 
Antonio 

 

ABSTRACT: Although there has been a proliferation of metrics to evaluate arts 
incubators, the academic field is still developing. Different models and methods of 
education are applied to the complex phenomena of arts incubators; therefore, it is 
crucial to measure the effectiveness of education programs from many different 
perspectives. Our aim is to propose a metric that can estimate the effect of each 
incubator activity based on the geospatial distribution of its participants. This GIS-
based metric will provide a descriptive measure for the quantity and density of the 
geographical communities affected by the incubator’s activity as well as 
socioeconomic and demographic benchmarks. Our study investigates fourteen US-
based arts incubators that offer entrepreneurial training to their associated 1,087 
incubatees. The goal of this study is to provide a metric that can assist arts incubators, 
program directors, arts administrators, and university programs in assessing program 
growth and funding and marketing efforts. KEYWORDS: Incubators, Arts, GIS, Non-
Conventional Entrepreneur, Artists, Entrepreneurship, Network Effects, Clusters. 
DOI: doi.org/10.34053/artivate.10.2.157 

Introduction 
Artist and entrepreneurs have common attributes that contrast them from other professionals 
(Daum, ; Poorsoltan, ). Furthermore, the entrepreneurial mindset of artists is well 
documented (Agrawal, Catalini, & Goldfarb, ; Barry, ; Lindqvist, ; Dworkin, 
). A recent study by Arenius et al. () highlights that “artists and entrepreneurs share 
three key features.” The authors explain that both “exhibit higher self-reported risk preference, 



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are more open to experience, and have greater intrinsic motivation” when benchmarked against 
a control group comprised of other professionals. Despite some artists’ reluctance to be 
represented as entrepreneurs (Hjorth, ; Haynes & Marshall, ), the practices associated 
with an entrepreneurial mindset have been recognized to increase the prospect of realizing an 
artistic career (Eikhof & Haunschild, ; Arenius et al., ). In addition, many of those 
artists who do not consider themselves entrepreneurs are at risk of losing the opportunity to 
exploit their economic potential, especially when the barrier of capitalizing on this economic 
potential is facilitated by a digital economy that enables artists to scale their work output readily. 
Indeed, the cultural-sector industries rely on live performances and content generation (Renard 
et al., ). 

However, content generation isn’t historically seen as a predictably scalable business 
model. Content remains a hit business, whether distributed in physical or digital form, and the 
ability to repeat a success can be illusive. Perhaps that is changing given the blurring between 
content, technology, social media, and audience. In some ways, innovating business practices 
within the cultural sector have been more off-putting for the traditional investor. Aspiring 
startups, artists, and creators must contend with entrenched, complex business relationships, 
rights, and long-term contracts. Incubator and accelerator programs are one innovative solution 
to support artists in becoming entrepreneurs and are committed to giving them the tools to do 
so, even within a volatile economic climate. Thus, relatively new programs are now finding an 
audience among artists and industry leaders alike. 

Incubators and accelerator programs supporting the cultural sector assist and develop 
creators across a wide range of art forms, including but not limited to music, film, theatrical 
arts, dance, creative writing, and video game design. These programs often combine a cash 
allowance, a co-working space, and a mentorship support system, and they are naturally very 
attractive for the participants involved. Importantly, they provide an option for artists and 
content creators wishing to go through a rigorous program of varying length and scope, and 
they treat them as a startup by fostering their entrepreneurial mindset and know-how to 
increase their chances of success in the marketplace (CCI, ; Essig, b, ). The oldest 
arts incubator was founded in  in Chicago (Kahn, ) a year after the formation of the 
National Business Incubator Association (NBIA), the trade association for business incubators. 
However, the information most pertinent to our study is the lack of metrics established to assess 
the education and incubation process, as well as the economic impact, of arts incubators in the 
United States. Indeed, several studies provide information about the services offered by arts 
incubators (Essig, b, ; Kahn, ), but none have evaluated the impact these services 
have on communities besides the artists themselves. The goal of this research is to explore a 
metric that can be useful in measuring the impact of an incubator’s activity. 

Educating artists on entrepreneurial skills positively affects their community for two 
reasons. First, entrepreneurship is a means to support social mobility because entrepreneurs 
experience higher upward mobility than other workers, even in non-favorable economic 
environments (Quadrini, ; Sørensen & Sharkey, ). Second, artist entrepreneurs are role 
models who contribute to the transmitting of entrepreneurial culture over time, which affects 



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the economic development over long periods of time and after many structural changes (Fritsch 
& Wyrwich, ). However, it is also necessary to mention the documentation of the debate 
over the limiting factors of the benefits of entrepreneurship education in the arts when coping 
with a neoliberal agenda (Barbour, ; Alexander, ; Kandil & Bokkel, ). The 
argument favors fostering “environment acknowledgement and belonging” (i.e., identity and 
sense of self in relation to others in a community), which is “essential for community building 
and raising awareness” of marketable skills (Kandil & Bokkel, ). But arts incubators are in 
a unique position to approach and often reconcile community building, the development of 
artists’ identities, and entrepreneurial training (e.g., The Lark Theatre in New York City). It is 
for those reasons that  arts incubators affect communities through the entrepreneurial 
education they give to their incubatees. Our aim is to propose a metric that can estimate the 
effect of each incubator’s activity based on the geospatial distribution of their incubatees. This 
metric will provide a measure of quantity and density for the geographical communities affected 
by the incubator’s activity, including several key socio-economic and demographic benchmarks. 
The following section will introduce the literature review and the proposed research design. 

Literature Review 
Entrepreneurs that start a company create jobs into the local community and improve economic 
growth (Glaeser, Kerr, & Ponzetto, ). For this reason, policymakers and private 
organizations have been increasingly supporting entrepreneurial activity to fuel economic 
development (Schramm, ). The high risk of failure involved in new entrepreneurial 
businesses is balanced by the entrepreneur’s large returns and communities’ increasing wealth 
(De Nardi, Doctor, & Krane, ). Indeed, countries with the higher levels of entrepreneurial 
activity also enjoy higher average growth in gross domestic product and higher levels of 
employment (Hardy, ). Therefore, entrepreneurial activity is critical to economic progress 
because it creates new businesses, and, in turn, triggers the creation of new jobs (Nijkamp, ; 
Quadrini, ). 

To support and encourage early-stage entrepreneurial activity, an increasing number of 
business incubators provide physical workspace as well as technical, legal, managerial, and 
financial support to new startup companies. The National Business Incubation Association 
(NBIA) estimates that there were approximately  incubators in North America as of , but 
today , incubators are located in North America alone. Business incubator is a broad 
umbrella term referring to organizations that vary in their strategies. While some operate 
virtually, many are located in a physical space to foster networking among entrepreneurs and 
provide basic business support services. Some incubators focus their services on jumpstarting 
businesses that are more developed. In this case, they are defined as accelerators. The common 
factors across the different flavors of incubation are advice giving and coaching services, which 
includes social networking (Maia et al., ). These services help the firm’s survival after 
graduating from the incubation program (Ayatse, Kwahar, & Iyortsuun, ). In addition, 
participants in an incubation program benefit in the areas of revenue and firm growth, patents 



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application, obtaining finance or capital, and establishing alliances. Indeed, beside some 
contradictory findings (Tavoletti, ), there is empirical evidence that participants in an 
incubation program outperform nonparticipants in terms of firm survival and sales growth. To 
measure their performance, incubators have been using a wide range of metrics over the years. 
These metrics include tracking jobs, revenue, and, in some cases, return on investment and 
societal impact—this over a four to six-year period of the startup’s life. The emphasis on metrics 
of growth, job creation, and return on investment is a challenging fit for such arts incubators 
whose focus is on community wellbeing or image creation (Montgomery, ).  

Polish Art_Inkubator defines arts incubators as “an organization that supports future 
entrepreneurs, non-governmental organizations and artists by helping them to enter the 
creative industries sector. Arts incubators are platforms that empower artists and organizations 
to implement their business and artistic ideas” (Essig, ). In this sense, the arts incubator’s 
strategic role should be to lower the barriers that individual artists and small arts organizations 
face when they enter the cultural marketplace. Business survival and growth depends primarily 
on the capacity of each artist to develop entrepreneurial skills that enable them to exploit 
opportunities and overcome challenges. Arts incubator services aim to help artists identify the 
art concept or business model that best fulfils their creative aspirations and social aims. Such 
support can materialize through knowledge, space, services, and networking opportunities in 
order to successfully help new arts and culture enterprises (Essig, ). Another advantage is 
the connection to a network of talented artists who often share similar experiences, which 
facilitates peer-to-peer action learning. These partnerships among incubatees often promote 
collaborations with external artists, art organizations, or representatives of the community 
(Thom, ). This, in turn, has a positive impact on the artists and the economy (Cockpit, 
). Indeed, arts incubators are one of four types of programs that are used to support arts-
based community economic development along with artists’ cooperatives, development of 
tourism, and comprehensive approaches (Phillips, ).   

Because of the positive economic and social impact, arts incubators are used as policy tools 
to attain community economic and cultural development, community engagement, and the 
economic sustainability of individual artists. Indeed, the objective of arts incubators is often the 
local community and not the individual artist (Grodach, ). In this case, the community is 
the chief claimant stakeholder of the incubator, although individual artists or arts enterprises 
may be their clients. Arts incubation, in these instances, is a means toward community 
development rather than an end in itself (Essig, a). The incubator’s metrics reflect this fact 
as well. Evaluation metrics for the strategic priority of community development include growing 
attendance at community-wide events, increased foot traffic, and local media interest (Essig, 
). Such evaluation variables reveal that the incubator is often sustained by local donors and 
investors, thus their metrics are locally focused. However, there is an overlooked impact on 
communities: the indirect impact of the entrepreneurial education provided by the incubator to 
the artists. Though overlooked, such impact is significant because it is long lasting and crosses 
the spatial boundaries of the local communities. 

The entrepreneurial education that an artist receives from the arts incubator includes a set 



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of norms, skills, and values that can be transmitted among generations and, for this reason, can 
persist over a long period of time. In fact, entrepreneurs are role models that contribute to the 
transmission of entrepreneurship culture (Van Auken, Fry, & Stephens, ). On the other 
side, and after many structural changes, a strong local entrepreneurial culture affects the 
economic development over long periods of time (Fritsch & Wyrwich, ). On the long term, 
increased local entrepreneurship activity will support social mobility, even in non-favorable 
economic environments (Quadrini, ; Sørensen & Sharkey, ). Entrepreneurial 
education at the arts incubator triggers a cultural change that can have a pervasive, long-term 
influence on the regional economies (Hallam & Zanella, ; Williamson, ). For this 
reason, we propose to evaluate the impact of entrepreneurial education on artists by tracking 
the number of geographical communities that an incubator indirectly affects through their 
incubatees. This number, eventually weighted by the number of artists belonging to each 
community, will provide a geographical and quantitative map of the impact of each incubator’s 
activity. The following section will explain the methodology of our proposed geospatial metric. 

Methodology 
Our geospatial methodology is rooted in the form of an interactive mapping methodology and 
geographic information system (GIS) that can process location data (Renard, ). In other 
words, the methodology captures latitudes and longitudes, thus capturing specific location data 
(e.g., the address of an arts incubator or artist based on latitude and longitude). GIS is also 
designed to store, manipulate, analyze, manage, and represent spatial or geographic data. 
Finally, the methodology enables users to create map overlay (adding layers of data) and 
projections (cf. figure  and ).   



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Data Collection 
Geocoded datasets were necessary to proceed with GIS. Eight separate datasets were sourced. 
First, we identified  arts incubators that offer entrepreneurship/business training to artists in 
early stages of their career as well as mentorship and networking services (cf. table ). The 
collected information about each incubator was publicly available from their respective websites 
and includes the name of the incubator, location, year founded, form of incorporation, number 
of incubates, and number of geographical communities impacted. Second, we gathered the 
profile of , artists that are (or have participated in) those specific incubators/programs, 
which include names, art form practiced, date of participation, and current city and state of 
residence. This study uses addresses based on city and state geocoded data for both the arts 
incubators and the artist participants. Please note that the  incubator studies have collectively 
served tens of thousands of artists. However, this study only collected data from a subset of the 
programs offered by those incubators that focused on business and entrepreneurial skill training 
offered to artists over a certain length of time (i.e., ZooLabs in San Francisco offers a four-
months-long program that fosters the business acumen of musicians in the Bay area).  

Figure 1. Example of Map Overlay. Source: 
https://www.esri.com/news/arcnews/winter0203articles/introducing.html 



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In addition to the two layers of data described above, six socio-economic and demographic 
layers were used. That data was created by and sourced from () the American Community 
Survey (ACS) that is sourced from the US Census Bureau website1, () ESRI2, and () CBRE3. 
All three are reliable sources of information for population and housing information across the 
United States. Information used includes population density estimates (ESRI, ), poverty 
status variables measure (ACS, ), race and Hispanics origin status variables measure (ACS, 
), housing costs variables measure (ACS, ), US median household income (CBRE, 
), and US unemployment rate (CBRE, ). 

 
Table 1. List of Arts Incubators Studied. 

Arts Incubator Incubator 
Location by 
City and State 

Form of 
Incorporation 

Year 
Founded 

Number of 
Incubatees/ 
Participants 

Geographical 
Communities 
(Cities) 
Impacted 

Arts + Public 
Life at the 
University of 
Chicago4 

Chicago, IL (c)    

 Inc.5 Chicago, IL Limited 
Liability 
Company (LLC) 

   

Austin Music 
Foundation6 

Austin, TX (c)    

C Atlanta7 Atlanta, GA (c)    
Creative 
Capital8 

New York, NY (c)    

Creative Lab 
Hawaii9 

Honolulu, HI State Initiative    

Diaspora Vibe 
Cultural Arts 
Incubator10 

Miami, FL (c)    

First Peoples 
Fund11 

Rapid City, SD (c)    

National Arts Alexandria, (c)    

 
1 https://www.census.gov/programs-surveys/acs/data.html 
2 https://www.arcgis.com/home/group.html?id=1d1f24e8556642f49448f1c88b5a571b#overview 
3 https://www.cbre.us/real-estate-services/real-estate-industries/data-center-solutions 
4 https://arts.uchicago.edu/artsandpubliclife 
5 https://2112inc.com/ 
6 https://austinmusicfoundation.org/ 
7 https://c4atlanta.org/ 
8 https://creative-capital.org/ 
9 https://creativelab.hawaii.gov/ 
10 http://www.dvcai.org/ 
11 https://www.firstpeoplesfund.org/ 



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Strategies VA 
NEW INC12 New York, NY (c)    
The Arts and 
Business 
Council of 
Greater 
Boston13 

Boston, MA (c)    

The Corzo 
Center at 
UArts14 

Philadelphia, 
PA 

(c)  
(Defunct 
as of 
) 

  

The Lark15 New York, NY (c)    
Zoo Labs16 Oakland, CA (c)    
TOTAL  N/A Mean 

() 
  

GIS Setup: ArcGIS Online 
For the purpose of this study, we used the online version of ArcGIS, which is powered by ESRI. 
ArcGIS is a powerful GIS platform and offers both desktop and online versions of the program. 
To generate our GIS maps, we imported the incubators, incubatees, and the six socio-economic 
and demographics datasets into ArcGIS as separate csv. files. Three types of visual 
representations have been generated. The first type is a cluster representation of the GIS data 
(cf. figures , , , and ). The cluster coefficient used is a measure of density or concentration 
of activity. For example, the  arts incubators are seen in figure  as wide blue circles setup to 
appear in a semi-transparent format to alleviate clutter. This enables the reader to view the 
incubatees, also shown in cluster format, and observe the sphere of influence of each incubator. 
We imported the incubatees’ data into ArcGIS as a separate layer and attributed color coating 
to connect incubatees to the arts incubator they participated in. Figures , , , and  are 
configurated this way. The second visual formatting does not use a cluster coefficient and shows 
all , participants associated with the  incubators and their arts entrepreneurship-focused 
programs as individual dots on the map (cf. figure ). This second visualization setup is 
projected with the six socioeconomic and demographic datasets (cf. figures -). Finally, 
several figures showcase one specific arts incubator and its associated incubatees/participants. 

 
12 https://www.newinc.org/ 
13 https://artsandbusinesscouncil.org/the-creative-entrepreneur-fellowship/ 
14 https://corzocenter.uarts.edu/ 
15 https://www.larktheatre.org/ 
16 https://www.zoolabs.org/ 



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This is a case study format (cf. figures , , and ). Those three types of visualizations helped 
to assist our analysis.  

 Observations & Analysis 
Table  summarizes the data connecting arts incubators, their 
associated incubatees, and the communities impacted. First, 
we observe that all but two of the arts incubators studied in our 
sample are incorporated as (c) nonprofit organizations. 
Of those, only two are university incubators, one being defunct 
(the Corzo Center at UArts). Please note that both arts 
incubators residing within a higher education institution serve 
community members and not students. For instance, 
Art+Public at the University of Chicago serves artists on the 
South Side of Chicago. We also see that the  arts incubators 

in our sample have existed for an average of  years. The oldest 
one is National Arts Strategies (), while  Inc. () is the most recently created one 
within our sample. We observe that two incubators are in Chicago, and three are in New York 
City. Regionally, eight are on the East Coast of the United States, three are in the Midwest, one 
is on the West Coast, one in the Southwest, and one in Hawaii. Those observations are 
important as we may assume that the highest concentration of incubatees will be located on the 
East Coast. However, our GIS maps tell us a more complete story and provide a powerful 
visualization tool that displays the prevalence of the Arts Incubators’ network effect (cf. figure 
). But the data in table  also informs us which incubators concentrate their efforts on their 
local communities based on how many geographical communities they impact. For instance, 

Figure 2. Geospatial Location of Arts Incubators (large blue circles) and Associated Incubatees from a 
Cluster Perspective 



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the Austin Music Foundation and C Atlanta both dedicate their efforts to only two adjacent 
geographical communities, thus their impact is more concentrated; whereas National Arts 
Strategies ( communities impacted) and Creative Capital ( communities impacted) have a 
broader reach. Our GIS representations further treat this information. 

Next, observe figure , which is setup in cluster format and presents the sphere of influence 
of the  arts incubators. Please note that the larger a colored filled node, the denser that cluster. 
The rationale in applying a clustering coefficient accounts for the potential of having more than 
one current or past incubatee living in a specific city and state. The same accounts for incubators 
located within the same city. Indeed, the highest concentration (density) of incubatees found in 
decreasing order are in New York City, Chicago, Austin, Los Angeles, the Bay Area, Miami, 
Honolulu, Boston, and Atlanta. Thus, New York City and Chicago are the two most dense 
clusters where current and past participants currently reside. This information, from a density 
perspective, shows us where the highest concentration of residencies of artists who are 
entrepreneurial minded, and it shows  the pervasive impact of entrepreneurial education of 
artists. We will fully address this issue later when we connect our socioeconomic data (cf. figures 
–). 

In addition, GIS maps can provide us with significant data otherwise not available. For 
instance, GIS can showcase how incubators fair in terms of network influence within each 
cluster with a predominant value measure (cf. figures , , and ). We use ArcGIS to address 
any attributes that have been imported for each data layer. For instance, figure  indicates that 
Creative Capital is the incubator located in New York City with the strongest network effect and 
sphere of influence. It informs us that  of the incubatees in our sample reside in New York 
City and that Creative Capital has offered entrepreneurial training to more artists within this 

Figure 3. View of the Geographical Distribution of the 1087 Incubatees/Participants Studied 



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hub than other incubators within our sample. Note that the pop-up window only shows  out 
of  options. As the user browses through those options, the GIS platforms tell us which other 
incubators exert the most influence on artists in New York City. In order, those are: Creative 
Capital, National Arts Strategies, NEWINK, and the Lark. This is a very useful metric and 
benchmark for the staff of any arts incubator. It can assist them with competitive strategic 
planning for program development, funding strategies, and marketing efforts. 

Figure  shows the arts incubators’ network effect by year of participation within the San 
Francisco Bay Area cluster. It first informs us that there are  current and past incubatees 
residing within that cluster who have participated in entrepreneurial training with at least one 
of the  arts incubators in our sample. It also displays that most of the artists residing in the 
Bay Area took part in one of those programs in , thus being the predominant value. Please 
note that we only see two display options. It means that ArcGIS was only able to capture two 
aggregated measures that were predominant over the others. This is another crucial metric that 
can inform any program director when and where their programs were most effective. 

Figure 4. Example of Predominant Value of Network Effect within the New York Cluster 



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Figure  tells a different story. It showcases how a cluster can be treated with an art form as 
attribute. We observe in the combined New York and Philadelphia area that there are  

Figure 5. Example of Predominant Value of Network Effect by Year of Participation within the San 
Francisco Bay Area Cluster 

Figure 6. Example of Predominant Value of Network Effect by Art Form within the 
Combined New York/Philadelphia Cluster 



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current and past participants whose predominant value as art form is film. This means that 
artists who participated in the entrepreneurial training provided by the arts incubators were 
predominantly film makers. This is another poignant measure that can be used by incubators 
and program directors as they assess where their current and past participants reside and where 
discipline specific hubs are developing. 

The next two figures are examples of how two of the arts incubators within our sample have 

Figure 7. Participants and Grant Partners GIS Representation of First Peoples Fund (Note: Artist In 
Business Leadership Fellows are indicated in green on the map). 

Figure 8. Participants Clusters and Count for all participants of Creative Capital. Figure 8. Participants Clusters and Count for all participants of Creative Capital. 



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used GIS as a metric to assist their activities (cf. figures  and ). First, Peoples Fund, which is 
an arts incubator dedicated to “honoring and supporting Native artists and culture bearers for 
 years,” has employed GIS in their Indigenous Arts Ecology Report. It beautifully presents the 
organization’s network, their current and past participant to their programs, and where their 
various partner organizations and community partners are located (cf. figure ). On the other 
hand, Creative Capital, which prides itself on “funding the creation of groundbreaking art since 
,” chose to use a cluster coefficient and count with its GIS representation to show the reach 
the organization has had over the years and where their current and past participants reside (cf. 
figure ). Both are very effective ways of using GIS as a tool and metric and yet, both provided 
very different perspectives. 

Figure 9. Arts Incubators (black dots) and Population Density Estimate Map Projection 

Figure 10. Incubatees (red, blue, and green dots) and Population Density Projection Estimate Map 
Projection 



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The next section of our analysis aims to unpack the complexity of impact assessment based 
on the socioeconomic and demographic benchmarks to better understand the geographical 
communities of the , artists. First, figures , , and  are calibrated for population density. 
This is a useful and clear metric that informs us if the artists and incubators are in more dense 
or less dense areas. Figure  shows that all our arts incubators, except for one (First Peoples 
Fund), are in heavily populated areas. Figure  displays that the , artists also live, with 
some exceptions, in urban and very densely populated areas. Figure  isolates the participants 
to First Peoples Fund’s Artists in Business Leadership Fellows because it is the only arts 
incubator in our sample that caters to many artists residing in rural areas and shows the network 
of participants distributed across the United States. 
 

Figure 11. First Peoples Fund Artists in Business Leadership Fellows (red dots) and Population 
Density Estimate Map Projection 

On the other hand, figures  and  are calibrated to understand the percentage of the 
population by area whose income in the past twelve months is under the poverty level. Figure 
 is a zoomed-in version of figure . We observe that, with no exception, arts incubators 
studied are all located in areas with higher poverty levels.  Also, the artists in our sample who 
live in higher density areas also reside in areas with higher poverty levels. Figure  is calibrated 
for race and gives us a good understanding of where predominantly non-white areas intersect 
with our incubators and artists. However, when aggregating the whole dataset, most of the 
artists served by the arts incubators appear to reside in predominantly non-Hispanic white 
areas. Also, we learn that the majority of artists studied live in areas with a high cost of rent and 
utilities combined and which account for more than  percent of their household income (cf. 
figure  and ), but they also reside in areas where the US median household income is higher 
(cf. figure ). Finally, most reside in areas where unemployment rate is either low or moderate 



ARTIVATE 10.2 

16 

(cf. figure ). Socioeconomic and demographic datasets used in conjunction with our artists 
and arts incubators’ datasets are a powerful and essential tool for any arts administrator and 
incubator. They provide easily digestible information that can inform if the strategy of the 
organization is in line with its mission and serves its incubatees/participants in the most optimal 
way possible. 

 

 

Figure 13. Arts Incubators (black dots), Incubatees (red, blue, and green dots), and Poverty Status 
Measure Map Projection (Zoom in View) 

Figure 12. Arts Incubators (black dots), Incubatees (red, blue, and green dots), and Poverty 
Status Measure Map Projection 



ARTIVATE 10.2 

17 

 

Figure 14. Arts Incubators (black dots), Incubatees (red, blue, and green dots) and Race and Hispanic 
Variables Map Projection 

Figure 15. Arts Incubators (black dots), Incubatees (red, blue, and green dots), and Housing Costs Measure 
Map Projection 



ARTIVATE 10.2 

18 

 

Figure 16. Arts incubators (black dots), Incubatees (red, blue, and green dots), and Housing Costs 
Measure Map Projection (Zoom in and Pop-up window view) 

Figure 17. Arts incubators (black dots), Incubatees (red, blue, and green dots), and Median Household 
Income Measure Map Projection 



ARTIVATE 10.2 

19 

Discussion 
Though there is a proliferation of metrics to evaluate arts incubators, the academic field is still 
developing. Different models and methods of education are applied to the complex phenomena 
of arts incubators; therefore, it is crucial to measure the effectiveness of education programs 
from many different perspectives. Our research focuses on entrepreneurial education for artists. 
Our interest is fueled by the observation that entrepreneurs are a significant contributor to 
economic growth and job creation (Chatterji, Glaeser, & Kerr, ; Delgado, Porter, & Stern, 
; Glaeser et al., ; Schramm, ). In addition, entrepreneurs act as role models by 
influencing their communities to a much larger extent than non-business role models (Van 
Auken et al., ; Van Auken, Stephens, Fry, & Silva, ). Therefore, artist entrepreneurs 
that graduate from incubators are more likely to directly affect the economic well-being of their 
communities and indirectly affect the entrepreneurial culture across those communities. The 
indirect effect on the entrepreneurial culture is deep and can be transmitted over time (Van 
Auken et al., ).  Our proposed metric enables incubators to estimate the impact of their 
activities on the geographical communities affected by their operations. 

Study Limitations  
The study has several limitations. First, location data for the incubatees was limited to the city 
and state of residence, and it lacked the detail level that the street address could provide. That 
amount of detail would have yielded much more accurate results. Unfortunately, that 
information was not available for all artists. Second, the present study investigated  arts 
incubators and , associated incubatees. That is a limitation on its own as it prevented us 

Figure 18. Arts Incubators (black dots), Incubatees (red, blue, and green dots), and Unemployment 
Rate Measure Map Projection 



ARTIVATE 10.2 

20 

from going into the kind of details that a single case study would provide. Third, there are over 
 arts incubators across the United States. A similar study covering the fuller universe of arts 
incubators would yield more robust results. However, collecting the necessary data would take 
several years. Finally, the study wasn’t able to indicate the degree of impact by serving a larger 
number of communities. Indeed, the economic impact of serving a larger quantity of 
communities versus concentrating efforts on one more densely populated metroplex is beyond 
the purview of this study, but it could be addressed in further work. 

Strategic Implications 
The impact of the incubators on various communities is heavily rooted in the mission statement 
of those organizations. In addition, most of those incubators have a small staff ( to  staff 
members on average), and they must focus their human resources, funds, and physical spaces, 
if any, on the target demographics that they serve. For instance, the Austin Music Foundation 
and C Atlanta are dedicating their resources to and concentrating their impact on artists that 
are residents of their respective cities/communities, whereas the First Peoples Fund’s goal is to 
assist Native artists in  communities across the United States, many living in rural areas. Some 
incubators have a dedicated arts incubation space that may include artist studios (C Atlanta), 
co-working spaces ( Inc.), and even a maker space (Art + Public Life), while others chose 
only to provide programs and assistance to artists (Creative Capital, National Arts Strategies). 
The later model of operation focuses its resources on reaching as many communities as possible, 
while incubators that invest in a facility are more likely to dedicate their resources to 
neighboring or local communities.  Each incubator has shown to be unique, and they don’t 
attempt to duplicate other programs but to provide specialized services based on the demands 
of the participants from the communities they serve. Building network capacity is cohort-
dependent, and participants that have attended programs within a cohort tend to develop a 
strong tie with lasting effects beyond the cohort’s program graduation. However, addressing the 
question of the economic value that arts incubators have on their communities is beyond the 
scope of this study. 

Implications for Future Research 
In expanding this work, three major research needs arise. First, this explorative study proposed 
a simple measure of quantity and density of community impact of the incubator activity. More 
complex measures may arise to account for the (local) population density and other contextual 
factors. Second, the effect of entrepreneurial culture needs to be evaluated to some extent. 
Identifying entrepreneurial activity through a longitudinal study on artists’ social media 
networks will estimate the effect of role models on their communities. Third, the economic 
impact of the value that arts incubators have on their communities could be quantified using a 
multiplier tool.  



ARTIVATE 10.2 

21 

Implication for Entrepreneurship Education 
Governments, academia, and private organizations are increasingly identifying entrepreneurial 
activity as a major contributor to job creation and long-term economic growth (Romer, ). 
Literature supports this line of thought by identifying an increase in employment and a growth 
in wealth creation associated with a region’s increase in entrepreneurial activity (Mitra, ). 
The contribution of arts incubators to local economic growth can be substantial, but it must be 
estimated. The results of this study indicate that the proposed geospatial metric measures the 
quantity and density of the communities impacted by the incubator’s entrepreneurial education. 
Such a metric has an impact on an educator’s activities in three ways. First, the measure is 
instrumental to educators to verify the effectiveness of their educational activities and programs. 
Combined with existing metrics, the use of the geospatial impact measure will enable the 
improvement of educational programs by understanding which non-local communities are 
affected the most by their operations. Second, leveraging the new metrics, incubators’ leadership 
will be able to extend their fundraising and marketing activities to the regions that are affected 
the most by their activities. Third, university incubators should consider adopting this 
assessment metric in evaluating their own programs, thereby shaping future research on the 
assessment of entrepreneurship training for years to come, particularly as university incubators 
deliver entrepreneurship education and expand their alumni network. That combination would 
accelerate the career progress of their students and could be quantifiably assessed with this 
metric. In sum, the new metric will improve the success rate of incubators and, at the same time, 
will enable the improvement of educational programs. 

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Cover Image Credit: Rights-cleared image to serve as a 
cover image for our contribution: "Art @ Incubate 
2014" by Incubate Festival is licensed with CC BY-
NC-ND 2.0: 
https://search.creativecommons.org/photos/203c676
c-bc51-4e58-abb9-16349f131c7d 

Funding Credit: This project is supported in part by an 
award from the National Endowment for the Arts. 

 


