The Location Patterns of Artistic Cultures: A Metro- and Neighborhood-Level Analysis

Carl Grodach1, Elizabeth Currid-Halkett2, Nicole Foster1, & James Murdoch III1

1University of Texas at Arlington

2University of Southern California

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# 12-3800-7004.

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 report and is not responsible for any consequence of its use.

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DOI: 10.1177/0042098013516523 published online 10 February 2014Urban Stud

Carl Grodach, Elizabeth Currid-Halkett, Nicole Foster and James Murdoch IIIThe location patterns of artistic clusters: A metro- and neighborhood-level analysis

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The location patterns of artisticclusters: A metro- andneighborhood-level analysis

Carl GrodachUniversity of Texas at Arlington, USA

Elizabeth Currid-HalkettUniversity of Southern California, USA

Nicole FosterUniversity of Texas at Arlington, USA

James Murdoch IIIUniversity of Texas at Arlington, USA

AbstractAnalysing census and industry data at the metro and neighbourhood levels, this paper seeks toidentify the location characteristics associated with artistic clusters and determine how thesecharacteristics vary across different places. We find that the arts cannot be taken overall as anurban panacea, but rather that their impact is place-specific and policy ought to reflect thesenuances. However, our work also finds that, paradoxically, the arts’ role in developing metroeconomies is as highly underestimated as it is overgeneralised. While arts clusters exhibit uniqueindustry, scale and place-specific attributes, we also find evidence that they cluster in ‘innovationdistricts’, suggesting they can play a larger role in economic development. To this end, our resultsraise important questions and point toward new approaches for arts-based urban developmentpolicy that look beyond a focus on the arts as amenities to consider the localised dynamicsbetween the arts and other industries.

Keywordsarts, arts policy, creative economy, economic development, industry cluster

Received June 2011; accepted November 2013

Introduction

Recent research asserts that the arts and art-ists provide important social and economic

Corresponding author:

Carl Grodach, University of Texas at Arlington, School of

Urban and Public Affairs, 601 S. Nedderman Dr., Arlington,

TX 76019-0588, USA.

Email: grodach@uta.edu

benefits to cities and neighbourhoods(Currid, 2007, 2009; Grodach, 2011;Markusen and Gadwa, 2010; Stern andSeifert, 2010). While such work documentsthe important role of artists in positiveneighbourhood change, much less is knownabout the larger patterns that artistic con-centrations exhibit in their location choicesand how these choices vary in differentplaces. This knowledge is essential todevelop a better understanding of where andunder what conditions artists and artisticbusinesses can contribute to place-basedimprovements.

In this paper, we analyse the widerpatterns of artistic location to identify thephysical, demographic and economic char-acteristics associated with arts industry clus-ters and determine how these characteristicsvary across different places. Analysing cen-sus and industry data at the metro andneighbourhood levels, we address the fol-lowing question: What types of attributesare associated with arts industry clusters andhow do these attributes vary in differentplaces? Our findings suggest that arts clus-ters exhibit unique scale and place-specificattributes. In this regard, we find that whilethe arts are likely to cluster in highly edu-cated, urbanized metros, there is high varia-tion in neighbourhood-level locationpatterns. We also uncover distinctionsbetween different arts clusters at the neigh-bourhood level suggesting that differentmodes of artistic production possess distinc-tive place-specific requirements. At the sametime, while we found surprisingly little asso-ciation between the arts and otherknowledge-based industries at the metrolevel, at the neighbourhood level there is evi-dence to suggest that arts industries clusterin ‘innovation districts’. These findingsshould inform a more locally focused policyand development approach.

The location characteristics ofartistic clusters

In considering the characteristics of neigh-bourhoods with a strong artistic presence,prior research has emphasised three broadfactors: affordable rents, neighbourhoodaesthetics, and characteristics of living andwork space. In this regard, numerous casestudies of gentrification demonstrate thatartists are not simply attracted by cheaprents alone, but by places that appeal to the‘artistic habitus’ or a lifestyle rooted in theaesthetic of older often industrial neighbour-hoods that contain buildings with historicarchitecture and adaptable, open floor plansand which are typically found in walkable,mixed use central city locations (Ley, 2003;Lloyd, 2010; Ryberg, 2012; Zukin, 1982).Additionally, some point toward the attrac-tion of social and economic diversity(Florida, 2002; Stern and Seifert, 2010).According to Richard Lloyd (2010), artistsare particularly attracted by the ‘street leveldiversity’ of neighbourhoods with significantminority populations and that tend to pos-sess above-average levels of poverty andcrime not simply because they are afford-able, but because such places serve as amark of social status and inspiration. Whilein-depth and helpful in identifying neigh-bourhood qualities that may attract artists,some of this work runs the risk of stereotyp-ing artists as a homogenous group that pri-marily seeks out troubled neighbourhoodsfor their aesthetic benefit, which inevitablyleads to gentrification.

Another significant stream of research toexplain the location characteristics of artistsand arts-related businesses comes from theeconomic geography literature on industrialdistricts. According to this work, comple-mentary firms concentrate or cluster to takeadvantage of specialised pools of labour and

2 Urban Studies

other industry-specific resources and efficien-cies (Scott, 2000; Storper, 1997). In relationto the arts and cultural sector, this work hasfocused on demonstrating the importance offeatures that enhance participants’ ability totap into supportive and collaborative socialnetworks and trade information on emergingtrends and styles (Currid, 2007, 2009;Grodach, 2011; Lloyd, 2010; Markusen andSchrock, 2006). Further, artists and artisticindustries are found to locate near concen-trations of artistic venues and specialisedinstitutions (e.g. nightclubs, art spaces,design schools) to gain access to their con-sumer base, industry gatekeepers, and poten-tial employment and contract opportunities(Currid and Williams, 2010; Lloyd, 2010).Finally, some research finds strong associa-tion between the arts and other creative orknowledge-rich industries at the regionallevel, particularly the employment of artistsand media and related industries (Florida,2002). However, other research finds that‘advanced services’ exhibit locally distinctclustering and concentration patterns or co-locate predominately in large cities (Curridand Connolly, 2008; Polese, 2012). As such,another potential factor influencing artisticlocation patterns could be attributed toaspects of the regional economic base.

For these reasons, most literature hasemphasised that cultural industries display asignificant tendency to cluster in specificplaces. The few studies that attempt to dis-sect arts industries at the local level find thatcertain industries co-locate in distinct clus-ters rather than a single dense agglomerationof many industries (Currid and Williams,2010) and, at the regional level, that thereare distinct patterns between large and smallmetros (Denis-Jacob, 2012; Markusen andSchrock, 2006). We suspect that moredetailed patterns can be mined here.

While both streams of research help directus toward the attributes that influence thelocation preferences and clustering of artists

and artistic businesses, there has been noattempt to simultaneously explore thesestreams of literature by attempting to under-stand if and how the arts exhibit commonpatterns across places and geographic scale.For its part, the case study work is very loca-lised, not generalisable across many places,and primarily concentrates on ‘artists’loosely defined rather than articulating theirintegration in a larger artistic cluster orconcentration. In contrast, the economic geo-graphy literature tends to focus on theregional-level and often considers a verylarge group of creative industries or occupa-tions. Additionally, both streams of researchpredominately focus on large cities withestablished cultural industries (e.g. NewYork, Los Angeles, Chicago, San Francisco).We question the generalisability of such casesto the majority of the USA where differentprocesses and trends may be at work.

In sum, we have yet to truly explore ifthere are larger patterns in how the arts clus-ter generally and across a range of differentplaces, both within and outside the tradi-tional hubs of creative economy activity.This paper begins to address this gap in ourunderstanding of artistic location patternsby utilising data that enable us to considerthe aforementioned variables and their influ-ence on wider artistic location patternsacross a range of cities. We seek to under-stand the extent to which particular social,economic and demographic attributes mayshape or are shaped by the presence of artsclusters at both the metro and neighbour-hood levels. The ability to identify and ana-lyse the variables that influence locationpreferences of artistic businesses and howthese preferences vary in different places iscritical to advance our understanding of howthe arts are associated with place improve-ments. More broadly, we hope our findingshelp inform the implementation of targetedarts development policy for cities, neigh-bourhoods and the USA as a whole.

Grodach et al. 3

Data and methods

Data

We defined the arts cluster based on 22industries from the North AmericanIndustry Classification System (NAICS)(Table 1). We selected this group to capturethe range of industries directly involved inartistic production. We purposely excludedindustries related to the consumption of artsuch as book stores, recorded music stores,and radio and TV broadcasting. As such, weconcentrate on a fairly limited scope of activ-ity compared with many other studies of artsand creative economy clusters (Florida,

2002). While the argument can be made foran occupational approach (Markusen andSchrock, 2006), we focus on arts industriesin part because these are the only data avail-able at the neighbourhood-level nationwide.Additionally, not only are occupational dataan imperfect measure of artists, there is evi-dence that industry data stand as a reason-able proxy for arts clusters more broadly.Stern and Seifert (2010) find a high correla-tion between commercial arts firms, artsorganisations and artists.

We analysed the arts cluster at the regionaland neighbourhood levels. Regional-levelanalysis is based on a location quotient, orindustry concentration, in all 366 2010 metro-politan statistical areas in the USA. To proxyneighbourhood-level arts activity, we rely onZip Code Business Patterns data. While zipcodes – or any administratively defined geo-graphy such as census tracts – are imperfectdefinitions of a neighbourhood, they are themost consistent geography at which theCensus reports business patterns data at themicro-level. In total, we study 13,946 zips; allof those located in metro areas that containthe variables appropriate to our study.1 Thiscaptures 89% of US arts employment in 85%of the zips in all metro areas. We exploreneighbourhood-level arts concentrationsbased on two measures: location quotientand employment per capita.2 By employingthe two measures we get a better idea of howneighbourhood-level employment works inreference to both industry and population. Inaddition to analysing the data as a whole, webreak down our analysis based on four met-ropolitan area population size groups: largemetros (over 1,000,000), mid-sized metros(500,000 to 1,000,000), small metros (250,000to 500,000), and smallest metros (under250,000).

Using these data, in each populationgroup we examine the relationships betweenthe arts cluster and a set of 33 social, eco-nomic and housing variables derived from

Table 1. Industries in arts cluster.

NAICS code Industry

453920 Art dealers512110 Motion picture and video

production512191 Teleproduction and other

postproduction services512199 Other motion picture and video

industries512210 Record production512220 Integrated record production/

distribution512230 Music publishers512240 Sound recording studios541310 Architectural services541320 Landscape architectural services541410 Interior design services541420 Industrial design services541430 Graphic design services541490 Other specialised design services541922 Commercial photography611610 Fine arts schools711110 Theater companies and dinner

theaters711120 Dance companies711130 Musical groups and artists711190 Other performing arts companies711510 Independent artists, writers, and

performers712110 Museums

Source: 2010 US Census Bureau, North American

Industrial Classification System.

4 Urban Studies

the 2007–2011 American CommunitySurvey and 2010 NAICS business patterns(Table 2). At both the metro and neighbour-hood level, ACS data are based on the pro-portion or average of the total populationspecific to each variable. All industry mea-sures are based on a location quotient at themetro level and the LQ and per capitaemployment at the neighbourhood level. Wedivided these variables into a set of groupsthat represent assumptions about neighbour-hood- and metro-level features that accountfor arts cluster locations as discussed in theaforementioned literature. These include‘urban’ variables based on the widely heldassumption that artists and arts industriestend to agglomerate in the urban coreattracted by older housing stock, lowerrents, and a built environment that is com-paratively dense and more walkable than thesurrounding suburban communities. Weinclude a set of diversity variables given thatFlorida (2002), Stern and Seifert (2010) andothers claim that artists and others workingin creative occupations are attracted todiverse places. In addition to variables basedon race (black) and ethnicity (Hispanic), weinclude foreign-born, non-native Englishspeakers, and non-family households asproxies for diversity. We include variablesindicative of upward mobility (high educa-tion, income, rent and management occupa-tions) and disadvantage (poverty,unemployment, single-parent householdsand public assistance). We study variablesrelated to work at the neighbourhood levelbecause arts industries likely have a highproportion of people that work at home,given that a high share of people who tele-commute or freelance are in arts, sciencesand management industries and possess highlevels of education according to the Census(2012).

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Grodach et al. 5

and services and are largely a part of the‘knowledge’ or ‘creative’ economy. Theseinclude composite measures of finance, hightechnology and media industries as well asthe presence of universities. We also create ameasure for local consumption amenities(See Appendix for list of composite indus-tries). In New York, Currid (2006) finds aclose association between the arts andfinance and Markusen and Schrock (2006)note that artists often work in media andrelated industries. Similarly, we suspect thatmany art industries could have close associa-tions with universities given the concentra-tion of faculty and students and theirpotential for supporting spin-off businesses.Finally, we include amenities variablesbecause some find that artists are associatedwith amenities such as bars, restaurants, cof-fee shops and juice bars (Clark, 2004; Silverand Clark, 2013).3

Methodology

To explore metro- and neighbourhood-levelarts industry cluster location characteristics,we first perform a bivariate correlation anal-ysis on the arts clusters and the census andindustry variables. Following this, we builda set of multivariate linear regression modelsusing ordinary least squares (OLS).Correlations enable us to identify the vari-ables with significant relationships to thearts industries and remove insignificant andhighly correlated variables from the regres-sion models.

Our dependent variable in both metroand neighbourhood models is the locationquotient of the total employment in the artsindustries listed in Table 1. Additionally, atthe zip code level, we perform a deeper anal-ysis using principal component factor analy-sis on two groupings of arts industries’ percapita employment based on correlationsand drop those arts industries with little tono relationship to other variables. As we

assume some correlation between arts fac-tors, we apply an Oblimin oblique rotation,which produces five distinct factors that welabel Cultural Product Services, MusicServices, Film Services, Arts Districts andArts Education Districts (see Appendix forfactor loadings). Cultural Product Servicesincludes a mix of arts industries related tofilm, music and design that commonly sup-port both the arts and other industries.Music Services includes industries related tothe production of music. Arts Districts rep-resent areas that contain a mix of museums,performing arts theatres and art galleries.Arts Education Districts are similar butcombine music groups (e.g. orchestras,operas and jazz ensembles) and fine artsschools. Both districts share a location witharchitects, all of which commonly seek acentral city location. We drop Film becauseonly two variables load on the factor.

Rather than summing the arts industriesinto a single cluster and assume that eachcontributes to the cluster equally, the princi-pal component factor analysis weights eachindustry according to its contribution in theoverall group. While we look broadly at thearts industry cluster, the factors allow us tomore precisely examine arts location charac-teristics at the smaller scale of the zip codewhere the literature finds varying co-locationpatterns among arts industries without pre-determining which arts industries clustertogether (Currid and Williams, 2010).

Correlations indicate a strong possibilityof multicollinearity among the industriesand amenities we include as independentvariables in our per capita model. To addressthis, we also conduct a principal componentsfactor analysis on per capita employmentvariables at the zip code level for all of theindustries listed in Table 2 above – finance,high technology, media industries and uni-versities – and the amenities – bars, coffeeshops and juice bars, and restaurants (seeAppendix for factor loadings). This produces

6 Urban Studies

a factor we label ‘Innovation Districts’ thatdisplays consistently high factor loadings forall of the variables and cumulatively explains56% of the variance. The industrial compo-sition of an innovation district varies, buttypically encompasses a range of industriesin technology, media and finance alongsidehigher-education institutions and amenitiescatering to this workforce in a defined geo-graphic area. Well-known examples arefound in Barcelona, Boston, Pittsburgh andToronto.

Following this, we build the OLS regres-sion models to identify the characteristicsthat best predict the location of arts activityoverall and by population size group. Themetro level model takes the form

y=X b+ e ð1Þ

where y is a vector (n3 1) of observationsof the dependent variable; X is a matrix(n3 p) of observations of the independentvariables; b is a vector (p3 1) of regressioncoefficients; and e is a vector (n3 1) of ran-dom error terms. The metro-level model alsoincludes a dummy variable to control forany locational effects specific to New YorkCity and Los Angeles.

For the zip code analysis, there is a con-cern that factors at the MSA correlated withour zip code level independent variables mayalso have an effect on the dependent vari-ables we are analysing. Thus, for the zip codelevel the model takes the form

y=X b+Dg + e ð2Þ

where, in addition to the elements previouslyexplained, D is a matrix (n3 j) of MSAdummy variables taking on a value of 1when the zip code is nested in the MSA and0 otherwise; and g is a vector (j3 1) ofregression coefficients for each of the MSAdummy variables. This vector is not reportedin the output as the coefficients are of littlesubstantive interest. Its main importance is

to control for metro-level fixed effects corre-lated with our independent variables thathave a significant relationship with ourdependent variables. Thus, results estimatedusing this equation account for the possibil-ity of omitted variable bias resulting frommetro-level fixed effects and should be con-sidered more conservative than runningmodels that do not consider metropolitaneffects.

We further adjust all models to accountfor possible multicollinearity by examiningthe bivariate correlation matrices for eachgeographic level as well as each populationgroup. In the final models presented, noindependent variable is correlated withanother at 0.7 or higher (or 20.7 or lower).We further adjust our models by removingvariables that produce high variance infla-tion factors (VIFs). Only one of our vari-ables in each of the final regression models(education at the metro level and non-familyhouseholds at the zip level) possess a VIFscore over 5 and all are below 10, indicatingthat multicollinearity should not have amajor impact on the results. Finally, weremove insignificant variables that do notcontribute to the adjusted R-squared for themetro-level population group models. Weperform this additional adjustment on thesemodels because they examine relatively smallsample sizes and thus require a smaller sub-set of variables for identification of signifi-cant relationships with arts clusters.

Analysis and results

Reinforcing expectations, metro data showthat arts clusters gravitate toward regionsthat are urbanized, relatively diverse andeconomically secure, and there is evidencethat the arts are associated with strongeconomies. Our local-level analysis, how-ever, reveals somewhat of a contrast. Here,the larger arts cluster splits into four clustersreflecting the distinct locational patterns of

Grodach et al. 7

the arts at the micro-level. Further, the pri-mary predictor of an arts cluster is the pres-ence of a larger innovation industry districtand, while we can identify the four distinctarts clusters, their social and physical char-acter is varied and difficult to pinpoint. Interms of metro size, we observe a distinct setof differences between large and mid tosmall metros. As we discuss later in thepaper, these findings provide an importantcorrective to common assumptions aboutartistic location patterns. In the first sectionwe present a brief descriptive analysis of thearts cluster geography, followed by theresults of bivariate correlations and the sta-tistical analysis.

Descriptive analysis: Arts cluster locations

Overall, as much of the extant literature sug-gests, very few metros possess strong artsconcentrations (LQ 1.2 or greater), just 28out of 366 metro areas, conforming to theoverarching ‘winner-take-all’ geography ofcultural industries (Currid, 2006; Scott,2000). Los Angeles, with its immense filmindustry, and Santa Fe, with its highly con-centrated arts and design sector, maintainby far the strongest concentrations in thearts cluster followed by a diverse set ofplaces including large metros such as NewYork and San Francisco, tourist destinationHonolulu, and music industry hub Nashville(Figure 1). Santa Fe’s considerably smalleremployment base amplifies its high concen-tration but other small metro college townsalso make a strong showing includingAshville, NC; Boulder, CO; and Ann Arbor,MI. In contrast, some large metros withestablished arts employment such as Bostonand Chicago do not hold a competitiveadvantage but still retain sizeable bases ofemployment in the arts.

The top neighbourhoods or localisedareas based on the arts district factors repre-sent a range of places. As anticipated, these

are predominately found in central city loca-tions and large metros, though not necessa-rily in the leading arts metros. CulturalProduct Services districts are led by LosAngeles (centred in Hollywood and SantaMonica) and, to a lesser extent, New York.Other strong areas include downtowns inBoston, Minneapolis, Pittsburgh and theDallas Design District. The majority of top20 Music Services areas are found in NewYork (including much of Midtown, Chelseaand the West Village) and Los Angeles. Thiscategory also includes Nashville’s MusicRow and downtown Portland, OR. In con-trast, Los Angeles and New York barelymake a showing in either of the top ArtsDistrict locations. Arts Districts represent amix of areas with established cultural facili-ties as in Chicago’s Loop area aroundMillennium Park and the Chicago SymphonyCenter, Minneapolis’s Hennepin Avenuetheaters, downtown San Jose, and theCanyon Road galleries of Santa Fe. Chicagoalso tops the Arts Education Districts withthe Arts Institute of Chicago and ColumbiaCollege. Other areas include downtown SanAntonio, downtown Philadelphia and UnionSquare in San Francisco, all of which containproximate art schools and cultural facilities.

Arts cluster correlations

Correlation analysis at the metro and neigh-bourhood levels suggests that arts clustersare attracted to urbanized areas regardlessof metro size (Tables 3–5). In fact, based onthe neighbourhood per capita measure, thesevariables become more significant as metrosize declines suggesting that, in line withexisting literature, arts clusters seek out‘urban’ neighbourhoods despite smaller citylocations. Beyond this, however, locationcharacteristics at the two scales part ways.At the metro level, variables representingupward mobility reveal the strongest positivecorrelations with the arts cluster and

8 Urban Studies

at UNIV OF TEXAS ARLINGTON on February 11, 2014usj.sagepub.comDownloaded from

associations are positive for diversity.Alongside this, the arts cluster holds a nega-tive relationship with the disadvantage vari-ables particularly in mid-sized metros. Incontrast, neighbourhood-level correlationsfor the census variables are weak or insignifi-cant but there is a close association with percapita amenities and industries (Table 5).Correlations with the amenity variables arestrongest for small metros (250,000–500,000)though all metro sizes hold moderate to verystrong associations. Arts clusters display thestrongest neighbourhood-level associationswith other industries, again most notable in

the small metros (250,000–500,000). Weaddress the high correlations in the regres-sion models by employing factor analysis asdescribed above.

In short, while there is evidence of a rela-tionship between arts clusters and indicatorsof economic health, the relationship is pre-dominately at the metro level. This couldindicate that while arts industries gravitatetoward and contribute to stronger metroeconomies, they do not necessarily driveneighbourhood-level growth or gentrifica-tion and that arts industries may depend on

Figure 1. 2010 metro arts cluster employment size and concentration.

Grodach et al. 9

different features depending on the scale inconsideration.

Predicting arts cluster locations: Regressionanalysis

Metro-level results. In this section, we aim todetermine the most influential variables inarts cluster locations looking at both the

wider urban region and local neighbourhoodlevels. Metro-level data confirm many of thebivariate correlation results and reinforcethe metro-/neighbourhood-level contrast.The models explain 59% of the variance inarts cluster location in all 366 metros andmore for three of the four population groups(Table 6). In line with the bivariate analysis,our remaining signifier of upward mobility,

Table 3. Significant metro correlations by metro size category.

10 Urban Studies

a highly educated metro population, is byfar the strongest predictor of the arts clus-ter.4 Alongside this, we find a negative asso-ciation with poverty. In the regressionmodels, the urban variables lose some oftheir explanatory power. Here, arts clustersare more likely found in areas with smaller,older housing and residents working athome, but they are negatively associatedwith indicators of density including multi-family units and walking to work. Foreign-born residents and non-family householdshold positive, significant associations. In

terms of amenities, only one of the variables,restaurants, holds a positive association andnone of the industries variables are signifi-cant. The Los Angeles/New York dummy ispositive and significant at 0.29.

Examining the results by population size,however, shows a clear distinction betweenthe large, mid, and small sized metros interms of arts location. In the large metros,the primary predictor of an arts cluster isessentially a location in Los Angles and NewYork. Mid-sized metros stand out for theirlack of diversity (high negative associations

Table 4. Neighbourhood LQ correlations by metro size category (significance \ 0.05 only).

Grodach et al. 11

with foreign-born residents and non-familyhouseholds) as well as significant and nega-tive associations with smaller, older housingand poverty and, unlike the large metros, astrong positive association with finance. Artsin the smaller metros are found in areas withhigh education levels, more people that workat home, and smaller, older housing units.Additionally, restaurants and a universitypresence define metros with arts industryclusters in the 250,000–500,000 range whilethe smallest metros show art cluster

associations with the diversity variables,lower poverty levels and finance.

Taken as a whole, our results suggest thatthere are few uniform predicators of an artspresence across the different metro sizegroups. While simple correlations pointtoward urbanized, diverse and upwardlymobile metro arts clusters, the regressionanalysis controls for strongly related variablesand, therefore, we see their weaker residualeffect. Ultimately, this indicates that there isconsiderable variation in the significance of

Table 5. Neighbourhood per capita correlations by metro size category (significance \ 0.05 only).

12 Urban Studies

the variable groupings by place and metropopulation size. In the discussion section wewill consider the implications.

Neighbourhood-level results. Neighbourhood-levelregression results provide an interestingcontrast to the metro level. Although theneighbourhood-level LQ measure registers aweak set of R2s, topping out at just 0.188 forneighbourhoods in large metros, there aresome significant differences from the metro-level analysis that deserve mention (Table 7).Overall, we continue to observe a strong butconsiderably weaker association with ahighly educated neighbourhood populationyet, in contrast to the metro level, there is no

significant relationship to poverty and weobserve conflicting associations with diver-sity (a negative association with foreign-bornresidents and a positive relationship withnon-family households). We continue to seesome signs of attraction to urban locations(positive associations with density and olderhousing). Finally, whereas metro industryassociations are uniformly insignificant,neighbourhood-level results register a posi-tive association with media industries andnegative links to other industries. These rela-tionships, moreover, are driven essentially bythe large metros where, unlike at the metrolevel, we get a number of significant, albeitweak, relationships.

Table 6. 2010 Metro-level arts cluster regression results.

All metros Large(over1,000,000)

Mid(500,000–1,000,000)

Small(250,000–500,000)

Smallest(under 250,000)

Pop. density 0.102 0.24 0.13Avg. rooms 20.138** 20.558*** 20.337*** 20.126Rental housing 0.046Multiunit housing 20.181*** 0.222 20.180** 20.209**Housing pre-1950 0.150** 20.394*** 0.342*** 0.199**Walk to work 20.178*** 20.167 0.229** 20.128 20.215**Black 20.004Foreign-born 0.156** 0.137 20.773*** 0.231***Non-family households 0.132* 0.143 20.488*** 0.269**BA degree 0.506*** 0.737*** 0.485***Unemployed 0.026 0.109Poverty 20.184*** 20.558*** 20.126 20.168**Public assistance 0.105** 0.153 0.107Vacant housing 0.011 20.257** 0.1Work at home 0.220*** 0.15 0.198** 0.285***NYC_LA 0.292*** 0.663***Media 0.038 0.109Financial 0.034 20.196* 0.546*** 0.157***High tech 20.024Universities 0.032 0.157**Coffee and juice Bars 20.123** 20.150*Bars 20.034 0.324***Restaurants 0.171*** 0.133 0.248*** 0.111R2 0.62 0.77 0.8 0.72 0.5Adjusted R2 0.59 0.72 0.75 0.68 0.46N 366 51 50 78 187

Note: *p \ 0.1; **p \ 0.05; ***p \ 0.01.

Grodach et al. 13

Based on the per capita measure, we getmore robust R2s in all but the music cluster,which defies a clear pattern based on ourdata and we exclude it for the most partfrom the discussion for this reason (Tables8–10).5 Bar the music cluster, there areimportant factors that can predict the loca-lised arts cluster locations. Above all, con-trasting with prior results, the innovationdistrict factor – a composite of knowledge-driven industries and amenities – is by farthe strongest predictor of all arts clusters.Unlike at the metro level, a combination ofhigh levels of education and low poverty arenot predictive of arts cluster locationpatterns.

Each cluster contains a number of signifi-cant variables indicative of place characterbut these predict a small amount of the

variance in arts cluster locations.6 Resultsindicate that the arts clusters are generallymore common in urbanized areas (density,walk to work) with fewer rental units andvarying associations with older housing.Non-family households are commonly pres-ent as well except in the arts education dis-tricts, which show no significant associationwith diversity. In short, the variables thatinfluence arts cluster locations appear to beboth place and neighbourhood specific andvary according to the type of arts cluster,whether arts district, arts education clusteror cultural product services (Tables 6–8).

Population groups show little variationfrom the broad finding that variables areidiosyncratic to place and arts cluster type.Overall, the innovation district factor doesremain a consistent, robust predictor of the

Table 7. 2010 Neighbourhood-level arts cluster regression results (LQ).

Variables All metros Large(over1,000,000)

Mid(500,000–1,000,000)

Small(250,000–500,000)

Smallest(under 250,000)

Pop. Density 0.043*** 0.051*** 20.039 20.016 0.045*Rental Housing 20.044** 20.059** 20.050 20.014 20.040Housing Pre-1950 0.116*** 0.129*** 0.089** 0.070* 0.066*Walk to work 0.003 20.019 0.050* 20.013 0.047*Black 20.008 20.008 0.053 20.032 0.020Foreign-born 20.056*** 20.034* 20.075* 20.086* 20.021Nonfamily Households 0.102*** 0.154*** 0.034 0.087* 0.023BA degree 0.227*** 0.232*** 0.179*** 0.120** 0.127***Unemployed 0.013 0.024 20.013 0.012 20.007Poverty 0.006 20.010 0.123** 0.016 20.038Vacant Housing 0.027** 0.015 0.022 0.002 0.093***Work at home 0.068*** 0.113*** 0.043* 0.025 0.005Media 0.021* 0.025* 0.017 0.017 0.007Financial 20.011 20.023* 0.026 20.012 0.021High-tech 20.024** 20.025* 20.012 20.032 20.037*Universities 20.017* 20.035** 0.026 20.008 0.000Coffee and Juice Bars 20.005 20.023* 0.020 0.005 20.010Bars 0.013 20.002 0.034 20.001 0.045*Restaurant 0.030*** 0.051*** 0.047* 0.002 20.011R2 0.163 0.196 0.106 0.095 0.150Adjusted R2 0.139 0.188 0.077 0.048 0.075N 13,946 7275 2161 1964 2546

Note: *p \ 0.1; **p \ 0.05; ***p\0.01.

14 Urban Studies

Table 8. Neighbourhood-level arts district regression results (per capita).

Variables All metros Large(over1,000,000)

Mid (500,000–1,000,000)

Small(250,000–500,000)

Smallest(under 250,000)

Pop. density 0.032*** 0.029** 20.083*** 20.014 20.077***Avg. HH Size 0.007 0.002 0.089*** 20.012 0.024Rental housing 20.035*** 20.063*** 20.039 0.016 20.056*Housing pre-1950 0.010 20.014 0.153*** 0.011 0.172***Walk to work 0.059*** 0.133*** 20.021 20.036** 0.081***Black 0.009 0.015 0.033 20.004 0.063**Foreign-born 20.015 0.002 20.076*** 20.008 20.037Non-familyhouseholds

0.032** 0.030 0.062* 20.018 0.087**

BA degree 0.001 0.005 0.017 0.000 0.111***Unemployed 20.006 20.016 0.004 0.009 0.044*Poverty 0.000 0.006 20.007 0.005 20.028Vacant housing 0.016** 0.029*** 20.002 20.002 0.025Work at home 20.016** 20.041*** 20.005 0.027* 0.007Innovation district 0.664*** 0.624*** 0.720*** 0.825*** 0.264***R2 0.49 0.48 0.52 0.68 0.22Adjusted R2 0.47 0.47 0.5 0.66 0.16N 13,946 7275 2161 1964 2546

Note: *p \ 0.1; **p \ 0.05; ***p \ 0.01.

Table 9. Neighbourhood-level arts education district regression results (per capita).

Variables Allmetros

Large(over1,000,000)

Mid(500,000–1,000,000)

Small (250,000–500,000)

Smallest(under250,000)

Pop. density 0.017* 0.032** 20.060*** 20.052** 0.084***Avg. household size 20.009 20.013 20.019 0.040 20.014Rental housing 20.031** 20.035* 20.003 0.031 0.096***Housing pre-1950 0.023** 0.035** 20.029 0.017 20.024Walk to work 20.036*** 20.073*** 20.005 0.021 20.054**Black 20.001 20.007 0.058** 20.006 0.031Foreign-born 0.009 0.019 20.031 20.028 0.032Non-familyhouseholds

20.017 20.022 0.090** 0.146*** 0.021

BA degree 20.003 20.014 20.064*** 0.034 0.048*Unemployment 0.031*** 0.046*** 20.010 0.040 20.014Poverty 0.007 20.004 0.048 20.016 20.009Vacant housing 20.009 20.004 20.081*** 20.023 20.048*Work at home 0.050*** 0.072*** 0.051*** 20.006 20.065***Innovation district 0.552*** 0.581*** 0.653*** 0.576*** 0.150***R2 0.29 0.31 0.49 0.42 0.13Adjusted R2 0.27 0.3 0.47 0.39 0.06N 13,946 7275 2161 1964 2546

Note: *p \ 0.1; **p \ 0.05; ***p\0.0.1

Grodach et al. 15

arts clusters, though it is considerably weakerin the smallest metros. In direct contrast tothe metro level results, high educationremains an insignificant predictor of the artsdistrict clusters in all but the smallest metros,except in the location of cultural product ser-vices in the mid-sized metros (but negative inlarge metros). The disadvantage variables,unemployment and poverty, are generallyinsignificant predictors of all arts clusters inall metros (unemployment is positive forlarge metro arts education districts and cul-tural product services and poverty is positivefor mid-sized metro cultural product ser-vices). Beyond these particular findings, cen-sus variable associations vary considerablybetween arts clusters and population groups.Large metro arts districts stand in contrast tothe other regions for their lack of older,rental properties but comparatively denseareas. Large metro arts education districtsare similar but found in older neighbour-hoods with low levels of walking to work.

Discussion and conclusions

We set out to identify the metro- andneighbourhood-level attributes that are asso-ciated with arts clusters and the extent towhich patterns in arts clustering mightemerge. In response to the lack of scalarcomparative work, we explore if and howthese attributes vary by place both undertak-ing a macro regional comparison and look-ing more closely at different measures ofneighbourhood dynamics. The use of thesetwo different geographies suggests differentramifications for development and policyinitiatives along with our understanding ofhow the arts spatially concentrate.

We find that the established hubs – LosAngeles, New York, Nashville and Santa Fe,for example – remain important art centres.However, we also find that other smallermetros and neighbourhoods outside thesehubs have notable employment concentra-tions. In short, while the arts industry geo-graphy is fairly dispersed, the major centres

Table 10. Neighbourhood-level cultural products services regression results (per capita).

Variables All metros Large(over1,000,000)

Mid(500,000–1,000,000)

Small (250,000–500,000)

Smallest(under250,000)

Pop. density 0.006 20.000 20.112*** 20.083*** 20.025Avg. household size 20.007 0.012 20.025 20.012 0.018Rental housing 20.039*** 20.039** 20.049 0.013 0.009Housing pre-1950 20.004 20.009 0.113*** 20.003 0.000Walk to work 0.027*** 0.010 0.009 0.053* 0.020Black 0.004 0.000 20.005 0.007 0.005Foreign-born 0.036*** 0.041** 20.032 0.020 20.028Non-familyhouseholds

0.082*** 0.133*** 0.057 0.165*** 0.050

BA degree 20.036*** 20.061*** 0.105*** 0.029 20.003Unemployment 0.033*** 0.057*** 0.035 20.032 0.030Poverty 20.029** 20.037** 0.098*** 0.006 20.004Vacant housing 20.052*** 20.078*** 20.069*** 20.005 20.020Work at home 0.056*** 0.094*** 20.045** 20.031 20.033Innovation district 0.518*** 0.546*** 0.496*** 0.249*** 0.158***R2 0.31 0.34 0.32 0.15 0.12Adjusted R2 0.29 0.33 0.3 0.11 0.04N 13,946 7275 2161 1964 2546

Note: *p \ 0.1; **p \ 0.05; ***p \ 0.01.

16 Urban Studies

remain the dominant economic capitals ofthe arts and culture industries.

With regard to policy and development,our statistical findings indicate that, on thewhole, arts industries exhibit distinct metro-and neighbourhood-level location patternswhich suggests that arts policy ought to betargeted through place-specific (rather thanoverarching) initiatives. There are distinc-tions across and between geographic scale,populations and the arts industries them-selves, more so than in high technology(Saxenian, 1996) or finance clusters (Sassen,1991). In other words, a comprehension ofarts clusters requires specificity and particu-lar attention to the uniqueness of the type ofart and the place itself. Targeted local devel-opment may be the most important meansby which to support the arts, rather thanbroader federal, state or regional efforts.Distinctions between arts clusters occur at alocalised level and thus ought to be sup-ported as such.

There are, however, some general findingsworth mentioning. Overall, the arts are morelikely to cluster in urbanized metros withindicators of economic health. This findingis not unexpected, given that places withstrong economies are more likely to provideemployment opportunities for an arts work-force. However, we found surprisingly littleassociation with the arts and other creativeor knowledge-intensive industries at themetro level as others have found for theleading centres of the global economy(Currid, 2006; Florida, 2002). At the sametime, Los Angeles and New York continueto explain the location of a considerableamount of metro-level arts employment,reaffirming their positions as the dominantlocations of commodified artistic productionin the USA.

Our results at the neighbourhood levelusing both measures report many oppositeresults from the metro level, suggesting thatarts clusters may seek out the broader

attributes of certain types of cities, butlocally require different attributes in theirproduction processes and ‘work life’, so tospeak. Our analysis of arts clusters tracksconcentration of arts industry employmentrather than where artists live. Thus, we findthat art clusters are tied less to conventionalsigns of gentrification and ‘urban’ character-istics but rather they may be found whereother related industries that rely on specia-lised expertise and knowledge abound, orwhat we call an ‘innovation district’. Whilewe do not challenge the general notion of anartistic enclave, our data underscore thatsuch areas are produced under stochasticconditions and that arts industries locate ina much wider variety of places but particu-larly in and around the innovation districts.

We also find previously overlooked seg-mentation between arts industries at theneighbourhood level. When measured basedon per capita employment, the larger artscluster rooted in prior literature splintersinto distinct clusters including two differenttypes of arts districts, cultural product ser-vices, music services and film services. Ourinitial regression models based on the fullcluster produced high multicollinearity withindustry variables despite nearly 14,000observations. As such, while this group ofindustries may make sense at a larger scaleor when studying the occupational skillsbase, in terms of neighbourhood-level indus-try clustering, we found more nuanced pat-terning to exist. In other words, thesedifferent arts clusters are drawn to neigh-bourhoods comprised of different character-istics. This suggests that various types of artproduction – whether design industries, filmor music – rely on different agglomerationeconomies and resources.

More generally, our research suggeststhat arts clusters do not have a definitivelocation pattern and that place-specificity –by size, scale and type of industry clusters –informs much of our findings. Local-level

Grodach et al. 17

arts industry clusters do not exhibit clearassociations with diversity or with urbancharacteristics except in larger metros. Thisconfirms our overall takeaway that we can-not generalise to the entire USA based onlarge metros alone, which often become theposter children for ubiquitous arts revitalisa-tion initiatives. Smaller metros tend to exhi-bit meaningfully different neighbourhoodcompositions associated with their arts clus-ters. Moreover, the location characteristicsof arts clusters are difficult to precisely iden-tify below the metro level because they arefound in such a wide variety of places. Artsindustries may locate in areas with particularindustry strengths or with other similarlyconcentrated labour markets in the CBD orsuburban business districts. They may befound in neighbourhoods with high concen-trations of specialised consumer amenities orin different types of arts-based entertain-ment districts and, therefore, comprise amuch more diverse set of places than thestereotype of a hip, bohemian neighbour-hood (Brennan, 2012).

Ultimately, these findings indicate thatarts scholars and urban policymakers needto rethink how we define, study and thinkabout artistic neighbourhoods as well as thecontext under which the arts are utilised tostimulate economic development or influ-ence gentrification. We caution that ourwork does not unearth the causal relation-ship between art and economic developmentand certainly not the direction of thisdynamic. Anecdotal and descriptive worksuggests an important link but the extent towhich there is a measurable, outcome-basedrelationship between art and developmentremains difficult to pin down. However, ourwork helps illuminate the distinctionbetween metro- and neighbourhood-leveldifferences in arts clusters. Our results sug-gest that arts industries and their labourpools seek out the larger urban milieu withdensity, diversity and amenities, but also

possess specific needs as related to their typeof cultural production, as the diverse localattributes by cluster imply.

From a policy perspective, our researchsuggests a very focused, localised and place-specific approach towards the arts as agentsin economic development, but also a broaderrole in wider regional growth efforts. Again,we caution that the extent to which the artsshape development is not entirely clear.However, our work does indicate means bywhich the relationship between the two couldbe nurtured across different clusters and geo-graphic scales.

Paradoxically, we find that the arts are notan urban panacea as recent literature andpolicy would suggest, but also their role indeveloping metro economies is highly under-estimated. While many of the variables linkedto arts clusters are incredibly place specific,the arts are linked to broad measures of inno-vation and development as captured by our‘innovation district’ factor, suggesting the artscan play a larger role in economic develop-ment irrespective of metro size or geographicboundaries of city and neighbourhood. Thisoverall finding would suggest that the currentinnovation district schemes as witnessed inBoston, New York City, and elsewhere oughtto incorporate arts industries.

To this end, these results raise importantquestions, but also new approaches for arts-based urban development policy and therole of the arts in creative and knowledge-based industries more widely. These areimportant issues because supporting indus-try clusters remains an important compo-nent of economic development planning andthe arts are increasingly on the policy radarsof many cities, but more often viewed asamenities than economically importantindustries. Arts-driven economic develop-ment is currently applied through a verycrude overarching arts district or ‘creativecity’ initiative rather than looking at the spe-cific relationship between local artistic

18 Urban Studies

clusters and their milieu. Such an approachalso ignores local dynamics between otherindustries and land use and zoning that mayinform and shape the economic geographyof firms and industrial activity.

Additionally, the amenity route may beshort-sighted in terms of economic develop-ment policy because it undervalues the possi-ble role of the arts in more robust andlong-term industrial development andbecause it ignores the agency of the peoplepowering artistic clusters. Our results sug-gest that the arts are a necessary componentof innovation districts and are associatedmore generally with economic growth.Stemming from the results of our analysis,the arts’ high association with innovationdistricts both reinforces popular notions ofcreative industries, but also points toward ashortcoming of common innovation districtpolicy, which tends to exclude arts industriesin the equation. Additionally, the role of awider urban dynamism should not be under-estimated as an attraction for artistic labourpools to local neighbourhoods. In short,while macro findings are consistent, the spe-cificity of place dynamics should motivatethose regions big and small – that are notnecessarily New York City or Los Angeles –to cultivate the types of localised attributesimportant to arts clusters. We hope thisresearch serves as a starting point forrethinking these dynamics and furtherresearch into the extent to which the artsmay be an important and underutilisedengine of economic growth and prosperity.

Funding

The research was supported by a grant from theNational Endowment for the Arts. Grant No. 12-3800-7004.

Notes

1. For this reason, we excluded zip codesthat consisted of single buildings and

self-contained sources of arts industryemployment such as film studios. We there-fore undercount but capture the vast majorityof metro area arts employment.

2. Both measures have their respective meritsand shortcomings for neighbourhood levelanalysis. The LQ is a problematic measure atthe micro level first and foremost because zipcodes do not contain the industry mix foundat the regional level. Additionally, thismethod includes a high proportion of zipswith low arts employment yet high LQs

because the zip itself had little employmentoverall, which may skew the results.Similarly, per capita employment has thedrawback of inflating places with very lowpopulation but high employment. This wasnot a major problem among the large numberof zips in the study, but it does understate thestrength of some zip codes in Los Angelesand particularly New York City. In short,while the per capita measure does not offerdirect comparability of neighbourhood- andmetro-level results, we feel it is the best optionfor this study. Nonetheless, we retain theneighbourhood LQ measure for comparabil-ity with the metro level where LQ is a stan-dard measure of concentration. Employingboth measures is useful given the exploratorynature of this study.

3. While the NAICS provide important industryemployment data, they are problematicbecause they do not allow us to differentiatebetween qualities of amenities – for examplethe difference between a chain and locallyowned restaurant. However, we also do notwant to make assumptions about the types ofamenities that may attract arts activity.Another shortcoming in our data is that weare not able to analyse local zoning and landuse, which are potentially important factorsin arts industry location at the neighbour-hood level.

4. We also ran the model replacing average house-hold income with education with virtually iden-

tical results, but a slightly lower association(0.19). These two variables are strongly relatedand so we do not include in the same model.

5. We ran per capita regressions on all artsindustries but results indicate high levels of

Grodach et al. 19

multicollinearity in the smaller populationgroups. This led us to discard these resultsand pursue the more nuanced arts factorapproach.

6. The adjusted R2 for regression models withcensus variables alone is 0.11, 0.02 and 0.06,for arts districts, arts education districts andcultural product services, respectively.Although we continue to see very weak rela-tionships with these variables in this modelthere are three notable differences. First, forall three arts clusters, density is positive

rather than negative though this variable stillshows a very weak association. Second, walk-ing to work becomes a more significant vari-able particularly for large metros (up to 0.445in large metro arts districts). Third, non-family households display a somewhatstronger association overall but particularlynotable in mid-sized metro arts districts(0.319) and arts education districts (0.323).

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Appendix

Variables for industry and amenities composite measures.

Industry NAICS Code NAICS Title

Media 511110 Newspaper publishers511120 Periodical publishers511130 Book publishers515111 Radio networks515112 Radio stations515120 Television broadcasting519110 News syndicates

Finance 521110 Monetary authorities522292 Real estate credit522310 Mortgage and non-mortgage loan brokers523110 Investment banking and securities dealing523120 Securities brokerage523130 Commodity contracts dealing523140 Commodity contracts brokerage523210 Securities and commodity exchanges523910 Miscellaneous intermediation523920 Portfolio management523930 Investment advice551111 Offices of bank holding cos.551112 Offices of other holding companies525990 REITs

High tech 511210 Software publishers541511 Custom computer programming services541512 Computer systems design services541513 Computer facilities management services541519 Other computer related services541710 R & D in the physical, engineering, and life sciences

Amenities 445110 Supermarkets445120 Convenience stores445210 Meat markets445220 Fish and seafood markets44523/ Fruit and vegetable markets445291 Baked goods stores311811 Retail bakeries722213 Snack and non-alcoholic beverage bars448110 Men’s clothing stores448120 Women’s clothing stores448130 Children’s and infants’ clothing stores448140 Family clothing stores448210 Shoe stores448310 Jewellery stores448320 Luggage and leather goods stores45121/ Book stores and news dealers453110 Florists722210 Full-service restaurants722410 Drinking places (alcoholic beverages)

Grodach et al. 21

Innovation district factor analysis

Variable Factor score

Media 0.764High tech 0.734Finance 0.814Universities 0.592Coffee & juice bars 0.867Bars 0.593Restaurants 0.821Variance explained 56%

Art industry group 1 factor analysis

Cultural products Music Film

Teleproduction 0.749Sound recording 0.761Graphic design 0.844Record production 0.762Integrated record prod. & distr. 0.860Music publishing 0.829Motion picture/video production 20.916Independent artists 20.937Variance explained 21% 33% 18%

Art industry group 2 factor analysis

Art district Art education

Music groups 0.969Architects 0.458 0.697Fine art schools 0.958Art dealers 0.617Museums 0.739Theatres 0.579Variance explained 21% 44%

22 Urban Studies