A network approach to the puzzle ofwomen’s cultural participation

Danielle Kane*

Department of Sociology, University of Pennsylvania, Philadelphia, PA 19104-6299, USA

Abstract

This exploratory paper examines the interpenetration of gender and social networks inpredicting cultural consumption. The analyses use original data collected by the author on

421 students, American and foreign, at an elite university. In general, network density isassociated with participation in solidarity-producing activities and heterogeneity is associatedwith increased high culture participation. However, network variables, especially networkdiversity, emerge as much better predictors of women’s cultural participation than of men’s. I

conclude by suggesting that women may use cultural participation to draw symbolic bound-aries against men as well as against women from other network structures.# 2004 Published by Elsevier B.V.

1. Introduction

According to Bourdieu (1984), distinction is a key organizing principle of humanrelations; dominant groups seek to maintain and legitimate their privilege by dis-tinguishing themselves from lower-status groups. In democratic and capitalistsocieties, the boundaries are symbolic but have causal force; access to higher socialand professional circles is granted or restricted according to an individual’s culturalcapital, or knowledge of the culture and mores of the dominant class. A main focusof Bourdieu’s work is how cultural consumption practices reflect and uphold theseboundaries.An important critique of Bourdieu’s work is that he paid insufficient attention to

social connections (Erickson, 1996). Although individuals distinguish themselvesfrom others based on group membership (usually social classes for Bourdieu), he didnot address the role of personal networks, even though these networks are an

Poetics 32 (2004) 105–127

www.elsevier.com/locate/poetic

0304-422X/$ - see front matter # 2004 Published by Elsevier B.V.

doi:10.1016/j.poetic.2004.02.003

* Correspondence to: Department of Sociology, 113 McNeil, 3718 Locust Walk, Philadelphia, PA

19104-6299, USA.

E-mail address: dkane@ssc.upenn.edu (D. Kane).

important source of cultural resources (Erickson, 1996). A social network approachcan explore how the properties of a group affect members’ cultural resources andshifts attention to the dynamics of symbolic boundary formation. Focusing ondynamics could improve our ability to test Bourdieu’s theory outside its originalcontext. Currently these applications can become stymied by questions of theimportance of high culture per se for Bourdieu’s theory (e.g., Holt, 1997; Lamontand Lareau, 1988). An understanding of dynamics, even if developed in the contextof understanding high culture participation, could be generalized to examine strati-fication based on any cultural content and could lead to new questions about thesymbolic boundary formation process.Some work has begun to explore links between social network characteristics and

cultural preferences (Relish, 1997; Erickson, 1996). Although in its early stages andrelying on proxy network indicators, this work has shown promising connectionsbetween networks and culture that heighten the desire for conceptual refinement andgreater precision in measuring network characteristics.In this exploratory paper, I will use a social network approach to explore the

relatively well-established, yet poorly understood puzzle of women’s cultural parti-cipation. As distinct from dominated racial and class subgroups (Hall, 1992;Lamont and Lareau, 1988; Horowitz, 1983; Hebdige, 1979) women appear to assignmore legitimacy to high culture and consume greater levels than do men, the domi-nant group (Bryson, 1996; DiMaggio and Mohr, 1983; DiMaggio, 1982; Bihagenand Katz-Gerro, 2000); this trend was even noted occasionally by Bourdieu himself(1984, p.108). Yet some work suggests that women actually receive less return thanmen on their greater high culture investment (Robinson and Garnier, 1985). There-fore, women may be less able to transform cultural capital into economic capital,and women’s greater participation cannot be traced to class alone. How, then, dowomen use culture to draw symbolic boundaries?A social network approach could demonstrate its usefulness to our understanding

of symbolic boundaries by giving new insight into the gendered dynamics of culturalparticipation. To demonstrate the utility of a network approach we must firstestablish whether calculated network measures are associated with patterns in cul-tural participation. We can then consider whether these measures can shed light onwomen’s greater cultural participation.

2. Networks and cultural participation

Linking networks with cultural participation requires a consideration of thepossible mechanisms that would underlie this linkage. Research on networks andculture thus far has focused on network heterogeneity (Erickson, 1996; Relish,1997). This paper attempts to provide a more comprehensive picture of networkeffects on cultural participation by building on the heterogeneity research as well asoutlining the role of network density.Network heterogeneity measures the diversity of persons in a respondent’s

network; it ‘‘implies integration into several spheres of society’’ and is considered

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advantageous for information-gathering (Marsden, 1987: 124). Sociologists ofculture have linked network heterogeneity to ‘cultural omnivorousness,’ (Petersonand Simkus, 1992; Relish, 1997; Erickson, 1996), or the consumption of a widevariety of culture, both high and popular.This initial evidence for an effect of networks on culture heightens the desire for

greater precision in examining this linkage. Culture-network studies are limited by alack of calculated network heterogeneity scores, relying instead on proxy measures.1

Moreover, to my knowledge, no attention has been given to the relationshipbetween network density and cultural consumption. Finally, Erickson’s (1996)operationalization of cultural consumption has been challenged for its focus onsheer acquaintance with cultural genres. Something so easily acquired precludesacquaintance as a form of capital. What allows entrance into the social circles ofcultural elites is not simple recognition of specific restaurants, sports stars, or books,but the ability to sustain detailed conversations that are predicated upon the ‘‘abilityto enjoy (i.e. decode) ‘difficult’. . .cultural objects’’ (Holt, 1997: 106). A more rigor-ous test will therefore examine network effects on actual participation rather thancultural familiarity. In sum, a useful theory of networks and culture that can informour understanding of symbolic boundaries will use direct, calculated measures ofheterogeneity and density on cultural participation.

2.1. Heterogeneity

Heterogeneity has been valued in the business environment for channeling pro-fessionally strategic information to those in diverse networks (Granovetter, 1973;Campbell et al., 1986). The implication of this finding is that heterogeneity increasesexposure to less-readily accessible information; different types of people tap into agreater number and variety of networks and act as conduits of information fromsources otherwise removed from ego (Granovetter, 1973).In our case, we are interested in exposure to cultural information, and high culture

is generally less visible and less accessible than popular culture. For instance, themajority of television and radio stations broadcast popular culture material andvery little high culture programming. Like the Christmas music and displays thatbecome ubiquitous in December in the United States, popular culture may feelvirtually unavoidable. By contrast, one usually must make an effort to consume highculture—seek out programming on publicly-sponsored broadcasting or find liveperformances.This lower visibility and accessibility suggests three reasons why we might expect

high heterogeneity to be associated with increased high culture participation. First, anetwork with a substantial amount of heterogeneity in its members has increased

1 Because the GSS Network Module and the Culture Module do not appear in the same survey, Relish

(1997) relied on proxy measures of networks (number of association memberships, type of community

residence, and geographic mobility). Erickson’s work has perhaps been the most systematic and detailed

in her study of networks and culture: respondents indicated whether they knew someone in different

categories of occupations and whether their contact was a close friend or relative.

D. Kane / Poetics 32 (2004) 105–127 107

odds of including a member tapped into another network that circulates high cultureknowledge, who might then serve as a conduit of this taste to ego (Mark, 2003).Because homophily in friendship ties results both from the prior attributes thatattracted ego to alter and from alter’s influence in their continued association(Kandel, 1978), a friendship tie based on some other form of homophily maydevelop into shared participation in high culture activities if one person has a tastefor high culture.Second, the ability to sustain relationships with diverse others may indicate an

ability to appreciate culture that is not immediately accessible. In other words, whileindividuals in groups of similar others can derive comfort from a shared (and per-haps unspoken) understanding, associating with different others assumes that atleast in some matters this understanding and its attendant comfort will not be pre-sent. That this is true may be indexed by the high degree of, for instance, racialhomogeneity in the vast majority of close relationships (Marsden, 1987).Sustaining social ties with different others is probably facilitated by a willingness

to take a different perspective from one’s own and by a tolerance for sometimes notunderstanding or feeling understood. In short, a willingness to make an extra effort insocial relationships may indicate a willingness to make an extra effort in appreciatingculture. Alternatively, the necessity of making an effort in social relationships in diversenetworks may inculcate the willingness or even desire for culture that is not immediatelydecodable. (That is, the network structure may have a direct effect on personalityrather than act solely as an indicator of an underlying personal orientation.)By contrast, a desire for easy, automatic communication with others that requires

little effort and is more typical of a network in which all members are the samewould seem to mitigate against a taste for high culture, which often entails highlysymbolic modes of communication that are not so effortlessly enjoyed as popularculture. Finally, both high culture participation and diverse networks may indicatean underlying desire for a cosmopolitan identity, since both high culture and diversenetworks are associated with high status people (Bourdieu, 1984; Goldstein andWarren, 2000; Campbell et al., 1986).These three heterogeneity mechanisms—increasing odds of exposure, inculcating

the expectation of or even the desire for challenging cultural material, and indicatinga desire for a cosmopolitan identity—have different implications for both networktheory and the sociology of culture. Nonetheless, in all of these accounts, we wouldexpect network heterogeneity to be associated with high culture participation.

2.2. Density

Despite the intense interest in density in the network literature (e.g., Fischer, 1982;Wellman, 1979; Kadushin, 1982; Haynie, 2001), to my knowledge no work hasexamined the links between network density and cultural participation. In the net-work literature, density is associated with higher pressure to conform to groupexpectations but also with high social support and solidarity (e.g., Burt, 1992;Fischer, 1982). Common to these potential disadvantages and advantages of a densenetwork is an expectation of intense social engagement. Just as the habit of making

108 D. Kane / Poetics 32 (2004) 105–127

an effort in social relations may translate into the habit of making an effort todecode high culture, we might expect that individuals accustomed to intense socialengagement might also seek out a commensurate level of social intensity in theirleisure activities.Intensity in social relations often revolves around the problems and benefits

associated with social solidarity. Interaction ritual (IR) theory, a strand of Dur-kheimian theory, offers some indication of what types of cultural activities would belikely to generate feelings of solidarity. According to IR theory, three elements areneeded for a ritual or event to produce high levels of solidarity: (1) the physicalassembly of a group of people; (2) their common focus of attention and mutualawareness of it; and (3) a common emotional mood (Collins, 1988). We wouldtherefore expect the effect of density to vary with the number of solidarity ‘require-ments’ met by cultural activities.Cultural activities differ in the extent to which they fulfill these requirements.

Attending a sports event or a dance or musical performance, for instance, generallyfills all of the solidarity requirements and would therefore seem to generate greatersolidarity than visiting an art museum, where there is little common focus of atten-tion or shared mood (as visitors look at different works of art) and where anyphysical assembly is temporary (as visitors move from room to room on their own).We would therefore expect higher density to be associated with attending a sportsevent or performance than with visiting an art museum. Network density, therefore,has the added ability to generate specific predictions and can explain variations inparticipation among cultural activities, even among different high culture activities,a topic that has received little attention from sociologists of culture.

2.3. Gender

While Bourdieu (1984) conceived of symbolic boundary formation as a processengaged in by the dominant class, subsequent work has addressed the ways in whichnon-dominant classes also draw symbolic boundaries. For instance, Lamont (2000)found that black workers distinguished themselves from white workers who seemmotivated by middle class egotism. Waters (2001) has examined the ways in whichWest Indian immigrants use symbolic boundaries to distinguish themselves fromAfrican Americans.While boundary formation in the workplace based on gender has been discussed

at least implicitly (for a review, see Lamont and Molnar, 2002), women’s use ofculture in symbolic boundary formation has been relatively unexplored. Lamont(1992, 2000) is well-known for some of the most nuanced work on symbolicboundary formation, but her work has focused on men. Bourdieu remarked onwomen’s greater proclivity for artistic pursuits (1984: 105, 108) but directed littletheoretical attention to this phenomenon. He himself appeared ambivalent aboutthe role of gender vis-a-vis class. At times he writes of gender as a social divisionwithin a class (1984:107); elsewhere he conceives of gender as a stratifying mechan-ism that creates classes (Swartz, 1997, p. 155). (For a feminist critique of Bourdieu’sapproach to gender, see McCall, 1992).

D. Kane / Poetics 32 (2004) 105–127 109

In explaining the greater, more coherent high culture participation of adolescentgirls, DiMaggio (1982, p. 198) writes that ‘‘high cultural involvement may have beenpart of an identity kit that academically successful, high status girls, but not similarboys, possessed.’’ This suggests that cultural participation circulates through thenetworks of upper-class girls but not boys. Because status is conveyed by a socialnetwork (Fuchs, 2001; White, 1992), and because DiMaggio (1982) links status tocultural involvement for girls, we would expect to find a stronger connectionbetween network characteristics and cultural participation for females than formales.To sum up, following the lead of research that ties proxy indicators of network

heterogeneity to cultural omnivorousness, I posit a connection between networkcharacteristics and specific forms of cultural consumption. I suggest that high het-erogeneity will be related to greater high culture participation and that highdensity will be associated with activities that produce high group solidarity.Based on DiMaggio’s argument that cultural participation plays a key role ingirls’ but not boys’ identity kits, I suggest that women may draw symbolicboundaries differently from men and that women’s networks will be especially likelyto circulate cultural activities as a form of cultural capital. For this reason, I expectto find more evidence of network effects on women’s cultural participation than onmen’s.

3. Data and variables

3.1. Data

Because data including network and cultural measures are unavailable, originaldata were collected for this paper. In Bourdieu’s theory the education system plays akey role in elite students’ accumulation of cultural capital, so the university seemedto be a prime setting for exploring whether network dynamics are related to thisaccumulation. This paper reports data on 421 incoming undergraduate and gradu-ate students at an elite university, part of a larger longitudinal study of networks andculture.As an inducement to participate, each student received $5 and a chance to win

$1000 in a raffle. Surveys were distributed at required orientation and welcomeprograms for graduate students and in the undergraduate dormitory with the largestfreshmen population. Ninety percent of entering graduate students who attended therequired orientation or welcome program and 60 percent of incoming freshmen inthe targeted dormitory completed the survey. Students completed a backgroundquestionnaire, a battery of items from the World Values Survey (Inglehart andBaker, 2000), items from the General Social Survey(GSS) 1993 Culture Module, anda network survey. Students completed these materials within two weeks of theirarrival, and many respondents had arrived only days before taking the survey.(Results on networks and cultural participation therefore are based on the yearprevious to enrollment at this university.)

110 D. Kane / Poetics 32 (2004) 105–127

3.2. Network instrument

There has been little consistency in the network measures used by sociologists ofculture. Network variables have been represented in some quantitative studies byproxy indicators, such as geographic mobility since age 16, number of associationsto which respondent claims membership, and the type of community in which therespondent resides (Relish, 1997). Erickson (1996) used the most developed measureof network heterogeneity but did not examine ties among alters. To my knowledgeno study has examined the effects of density on cultural participation.To improve precision, I used an instrument similar to those used in large,

network-oriented datasets such as the National Longitudinal Survey of AdolescentHealth and the General Social Survey (GSS) Network Module. The network surveyin this study asked students to list up to six alters and to complete a profile for eachname listed, including the alter’s gender, race, religion, nationality, and source oftie (‘‘From where do you know this person?’’). A network matrix allowed therespondent to indicate whether each alter knew the other alters listed.Network research thus far has often emphasized friendship ties, perhaps at the

expense of other types of network ties that are salient for respondents. The GSSModule, for instance, asked respondents to list people with whom they discussedimportant matters, a strategy which ‘‘could be expected to elicit reasonably strongties, with prominent representation of kin among those cited’’ (Marsden, 1987). Theover-representation of strong ties in discussion networks may be particular to theUnited States. For instance, the GSS (discussion network) name generator used inan urban Chinese sample elicited networks that included intimates and non-intimates, including co-workers (Ruan, 1998).This study sought to capture a broader group of types of ties and to make

networks comparable across respondents’ cultural/national backgrounds. To thatend, each respondent was asked to list up to two people in three specific categories:(1) people with whom they studied or worked; (2) people with whom they spentleisure time; and (3) people with whom they discussed important matters.

3.3. Dependent variables

The 1993 GSS Culture Module acted as the model for the questions on this sectionof the survey. Respondents were given a list of activities and asked if they partici-pated in each within the last 12 months (that is, for the year preceding their arrivalat this university; for foreign students this usually means that participation tookplace in their home countries). The list and question wording were based on the GSSCulture Module. ‘High culture’ activities included: visiting an art museum or gallery,playing a musical instrument, attending a classical music performance (Western ornon-Western), and attending a ballet or other classical dance performance. Othercultural activities included: going to movies, attending a sports event, and reading apopular magazine.I predicted that network heterogeneity would be associated with increased parti-

cipation in high culture activities. I also predicted that high density would be

D. Kane / Poetics 32 (2004) 105–127 111

positively associated with participation in a high-solidarity-producing activity andwould be negatively related to low-solidarity-producing activities. Based on IR the-ory, we would predict that high density would be positively related to sports eventattendance, as well as attending dance and classical music performances. These cul-tural activities provide a physical assembly of people with a common focus ofattention and an awareness of that common focus, which in turn leads to a sharedemotional tone. In the case of sports events, there are also symbols of membershipthat are enacted through team mascots and team colors.By the same token, density should be negatively related to novel-reading, maga-

zine-reading, and museum attendance, all activities that tend to be pursued in amore solitary way and generally do not fulfill IR solidarity-generating criteria.Musical instruments can be played alone or in a group, so I make no predictionsregarding this activity. Because heterogeneity can increase odds of exposure to less-accessible high-culture activities, inculcate a preference for challenging culturalmaterial, or indicate a desire for a cosmopolitan identity, I expected that networkshigh in heterogeneity would be associated with increased high culture participation.Finally, I predicted that because of the intense nature of dense networks, the positiveeffects of heterogeneity on high culture participation would be amplified forrespondents in dense networks.Table 1 lists the frequencies for participation in all cultural activities. Consistent

with findings for the GSS 1993 Culture Module sample, popular culture activitiesgarnered the most support (Marsden and Swingle, 1994). Rates of participation forthis sample were far higher than for the GSS Culture Module sample for all culturalactivities. Ninety-eight percent of this sample had gone to a movie in the past 12months as compared to 70 percent in the GSS sample. Seventy-one percent of thissample had attended a sporting event during the past year as compared to 54 percentof the GSS sample.The sample for this study was nearly twice as likely to have participated in high-

culture activities: 79 percent visited a museum as compared to 41 percent of the GSS

Table 1

Percentage participation in popular and high culture activities during the past 12 months

Activity

Percentage of

sample participating

Percentage of GSS culture

module sample participatinga

High culture

Visiting Museum

79% (414) 41% (1593)

Playing instrument

46% (414) 23% (1590)

Attending dance performance

36% (413) 20% (1593)

Reading ‘great literature’

82% (413) NAb

Other cultural activities

Going to movie

98% (413) 70% (1594)

Reading popular magazine

92% (414) NAb

Attending sports event

71% (414) 54% (1594)

a Numbers are cited in Marsden and Swingle, 1994.b Respondents in the GSS sample were not asked these items (Davis et al., 2000).

112 D. Kane / Poetics 32 (2004) 105–127

sample; 46 percent had played a musical instrument as compared to the 23 percentof the GSS sample; and 36 percent of this sample had attended a dance perfor-mance, as compared to 20 percent of the GSS sample. Consistent with the culturalomnivore hypothesis, a comparison of the rates of participation from the nationallyrepresentative GSS sample and this sample, which is skewed toward higher educa-tion levels, indicates that higher education is related to greater participation in alltypes of cultural activities.Table 2 shows cultural participation by gender. Women’s participation is

generally higher than men’s. Consistently higher rates of cultural participation(usually high culture participation) generally connote a symbolic boundary.

Table 2

Gender differences in cultural participation during last 12 months

Male mean

Female mean Difference

N=209a

N=203

High Culture

Visiting museum

0.79 (0.03) 0.79 (0.03) 0.00

Playing Instrument

0.44 (0.03) 0.48 (0.04) �0.04

Attending classical concert

0.31 (0.03) 0.44 (0.03) �0.13

Attending Ballet

0.27 (0.03) 0.45 (0.04) �0.18

Reading a Novel

0.80 (0.03) 0.85 (0.02) �0.05

Other cultural activities

Going to movie

0.97 (0.01) 0.98 (0.01) �0.01

Reading popular magazine

0.88 (0.02) 0.95 (0.02) �0.07

Attending sports event

0.76 (0.30) 0.66 (0.03) 0.10

a N=208 for Movie Attendance. Standard errors in parentheses.

Table 3

Descriptive statistics for control variables and network variablesb

N

Descriptive stat.c

Father’s educationa

403 3.41 (0.80)

Proportion in academic program

405 0.60

Proportion female

412 0.48

Proportion of urban residence

409 0.51

Proportion graduate student

414 0.47

Proportion U.S. citizen

414 0.51

Mean network density

385 0.67 (0.29)

Mean network racial diversity

393 0.20 (0.26)

Mean network relig. diversity

362 0.37 (0.31)

Mean network gender diversity

398 0.71 (0.32)

a Father’s education is measured on a scale from 1 to 4.1=0–7 years, 2=8–12 years, 3=13–16 years,

and 4=>16 years.b The category of ‘Other’ includes students from: South America (3% of the total); the Middle East and

North Africa (1% of the total); Sub-Saharan Africa (1% of the total); and Carribbean (<1% of the total).c Numbers in parentheses are standard deviations.

D. Kane / Poetics 32 (2004) 105–127 113

Moreover, attending a sports event, an activity traditionally associated with a maleidentity, is the only activity for which women have a lower rate of participation.

3.4. Independent variables

Network density measures the proportion of possible ties in a respondent’snetwork that are actually present. The average density for the sample was 0.67 (seeTable 3). The mean for this sample is close to the 0.61 found by Marsden (1987) forthe GSS Network Module sample, which is higher than the 0.44 reported by Fischerfor his regional sample (1982). (Like Marsden (1987) and distinct from Fischer(1982), I include networks of size 2, which Marsden found to increase averagedensity.)Fig. 1 reveals that the density distribution for the sample is skewed left, with about

29 percent of the cases having perfectly dense networks. The density distribution issimilar to that found for the GSS Module (Marsden, 1987).

3.5. Heterogeneity

Because all information in the alters’ ‘profiles’ were nominal characteristics,heterogeneity was measured using the index of qualitative variation (IQV) (Agrestiand Agresti, 1977, p. 208), following Marsden’s example (1987). The IQV providesan intuitive metric for measuring diversity among qualitative variables. A standard-ized version of the diversity index is

I ¼ 1�� p2� �

= 1� 1=kð Þ

Network heterogeneity was calculated for race/ethnicity, religion, gender, andnationality. Nationality heterogeneity measures had no systematic effect on culturalconsumption and are omitted from the analyses.

Fig. 1. Network density histogram.

114 D. Kane / Poetics 32 (2004) 105–127

Ethnicity and race of alters were classified into nine categories of descent: Asian,African, European, Hispanic, and Other. In Fig. 2, scaling on the left axis applies tohomogeneous cases, while the scale on the right applies to all other cases. (Two scaleswere used to illustrate better the shape of the distribution of the non-homogeneouscases.) Fig. 2 reveals that racial heterogeneity is skewed right, with about 59 percentof the cases having completely homogeneous networks with respect to race/ethni-city. This is a much lower figure than that found in the GSS Module, where about 92percent of respondent’s networks are perfectly homogeneous (Marsden, 1987:125).The average racial heterogeneity was 0.19, as compared to the mean of 0.05 for

the GSS (Marsden, 1987:126). The greater racial heterogeneity may result from thegreater affluence of the sample, since a positive relationship has been found betweenSES and heterogeneity (e.g., Goldstein and Warren, 2000; Campbell, 1986). Withinthe sample, however, there was only a weak, nonsignificant correlation betweenracial heterogeneity and father’s education. Moreover, the broader range of tieselicited in this sample could increase the likelihood of racially or ethnically diversealters, as compared with the discussion networks examined in the GSS which drewheavily on kinship as a source of ties (a finding consistent with prior research,according to Marsden). The prevalence of kin-based ties is related to decreased race/ethnic heterogeneity (Marsden, 1987).Descriptions of the religious preferences of alters were grouped into six categories:

Agnostic/Atheist, Catholic, Protestant, Islamic, Jewish, and Other. The averagereligious heterogeneity score was 0.37, and the distribution is skewed right. About36 percent of the cases report networks that are completely homogeneous withrespect to religion (Fig. 3).Consistent with the pattern in the GSS Network Module, there was a higher

degree of gender heterogeneity in this sample than of any other type. The meangender heterogeneity in this sample was 0.71, roughly equivalent to the 0.68 meanfound in the GSS (Marsden, 1987). The data for this sample are more sharplyskewed, however. While for the GSS sample 37 percent of the respondents had

Fig. 2. Network ethnic heterogeneity histogram.

D. Kane / Poetics 32 (2004) 105–127 115

networks where the index of qualitative variation was 0.90 or greater (Marsden,1987), for this sample, about 56 percent had networks of 0.90 or greater. Whereas 22percent of GSS respondents had networks of only one sex, only 13 percent of thissample had gender-homogeneous networks (Fig. 4).Racial heterogeneity has a significant correlation of 0.31 with religious hetero-

geneity, suggesting that although the two are related, each measure taps a distinctaspect of network heterogeneity. Gender heterogeneity is not correlated with racialor religious heterogeneity. Interestingly, no measure of heterogeneity has asignificant correlation with density, suggesting that these two network characteristicsdo refer to autonomous phenomena. (For Correlation Matrix, see Appendix A.)2 Itis also possible, however, that this lack of correlation is related to the nature of thissample: perhaps young elites are sufficiently cosmopolitan to have a great deal ofheterogeneity in the types of people they know, regardless of how close-knit thegroup is. The interrelation of density and heterogeneity, therefore, would be afruitful topic for further research.Socioeconomic status can be challenging to measure for an international sample,

as there is no common metric for comparing income and occupation. This paperuses father’s level of education as proxy for socioeconomic status. Respondentscame from well-educated families; the mean category for father’s level of educationwas 13–16 years (at least some college), and more than half of the sample (55 per-cent) reported that their fathers had more than 16 years of education. Since experi-ence in the education system is central to Bourdieu’s argument, I include a variablemeasuring the amount of time spent in the education system: Because the sample is

Fig. 3. Network religious heterogeneity histogram.

2 A network size variable had no significant effects and was omitted from the models in the interests of

space. The networks reported here were much larger than those reported in the GSS Network Module.

Whereas nearly a quarter of the respondents for the GSS had networks of size 0 or 1, less than 2 percent

of this sample had networks of that size. Less than 6 percent of the GSS sample had networks of size 6 or

more, while half (51 percent) of this sample had networks of size 6. (Respondents could enter only up to

six alters.) The mean network size of the GSS sample was 3; for this sample the mean was 5.

116 D. Kane / Poetics 32 (2004) 105–127

drawn from a university population, the two categories are ‘undergraduate’ and‘graduate.’ Forty-seven percent of the sample were graduate students. Finally,I adopt Erickson’s (1996) native-born variable by creating a dummy for U.S.residence. Fifty-one percent of the sample were American citizens.Recent research that has attempted to refine and apply Bourdieu’s theory has iden-

tified new factors that can be used to explain cultural participation and stratification.Some research has suggested that Bourdieu’s findings in Distinction were particular toParis; more generally, urban residents may be unique in their cultural preferences (e.g.,Lamont, 1992; Crane, 1992). Fifty-one percent of the respondents described their homeresidence as urban. (The referent category was rural and suburban residence.) Otherresearch has raised the importance of variation in cultural participation by sub-group. Unfortunately, the data did not have sufficient intra-region variation on raceto use this as an independent variable. Forty-eight percent of the sample was female.

3.6. Statistical models

For the seven cultural activities, respondents were asked if they had participatedat least once during the last year. The dependent variables are dichotomous, so I uselogistic regression models to analyze the role of network variables and control vari-ables in predicting cultural participation. The rate of movie-going was so high (98percent of the sample had gone to a movie in a theater in the past twelve months)that models could not be fit, so this variable was dropped from the analyses. Inorder to examine the potential for different dynamics that may govern women’scultural participation, I use separate models for men and for women to test theeffects of density and diversity.3

Fig. 4. Network gender heterogeneity histogram.

3 In order to check for any potential bias resulting from missing data, a mean substitution procedure

with missing data dummies was employed. Missing data had a significant effect on having played an

instrument for certain model specifications, so findings for this variable should be interpreted with

caution. Because the missing data dummies in general were not significant and given that the mean

substitution procedure is known to artificially reduce standard errors (Allison, 2000), only cases with

complete data are used in the following models.

D. Kane / Poetics 32 (2004) 105–127 117

118

Table 4

Logistic regression coefficients of cultural activities on network characteristicsa

Sporting

event

Museum Instrument Dance

perfomance

Popular

magazine

Great

literature

Class. music

performance

Network density 1.11 (0.52)* �1.12 (0.52)* 0.32 (0.42) �0.31 (0.45) 1.54 (0.77)* 0.41 (0.56) �0.45 (0.44)

Network racial heterogeneity �0.31 (0.64) 0.31 (0.58) 0.76 (0.48) 1.15 (0.50)* 0.10 (0.88) 1.53 (0.79)* 0.63 (0.51)

Network religious heterogeneity 0.62 (0.51) 0.79 (0.50) 0.82 (0.42)* 0.39 (0.44) �1.45 (0.85)y 0.36 (0.57) 0.20 (0.44)

Network gender heterogeneity 0.01 (0.45) 0.37 (0.42) �00.05 (0.36) 0.91 (0.43)* 0.29 (0.64) 0.68 (0.46) 0.08 (0.39)

Intercept 0.94 0.83 �0.85 �2.32** 1.28 2.03* �1.19

N 324 324 324 323 324 324 323

Pseudo R square 0.29 0.06 0.08 0.12 0.08 0.14 0.09

Notes: ***<0.001, **<0.01, *<0.05, y<0.1.a Standard errors in parentheses. All models control for academic program, gender, father’s education level, urban residence, world region, and level at the

university (graduate or undergraduate). Results for control variables are reported in Appendix B.

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32(2004)105–127

4. Results and discussion

Models were fit for each of the seven cultural activities using logistic regression.Control variables generally were not significant in these models; possibly the vari-ables outlined in the culture literature as important may not be the best predictorsfor a student sample. For this reason and for ease of interpretation, the followingtables present only the coefficients of the network characteristics. Tables of coeffi-cients for control variables can be found in Appendix B.The findings reported in Table 4 provide consistent support for the hypothesized

link between network characteristics and cultural participation. Six of seven culturalactivities were significantly predicted by at least one network measure.

4.1. Density

Those with perfectly dense networks were three times more likely than those fromsparse networks to have attended a sports event in the past year. Moreover, therewas a negative relationship between network density and art museum attendance:students from dense networks were only a third as likely as students from sparsenetworks to have visited a museum in the past 12 months.These findings are consistent with the predictions of IR theory. Sports events

fulfill all of the solidarity-producing criteria outlined in IR theory: a physicalassembly of people, common focus of attention and mutual awareness of it, and ashared emotional tone. While art museums bring visitors into contact with oneanother, there is no physical assembly of the same group of people for any dura-tion. Visitors move through the rooms of a museum or gallery as they lookat different works of art with neither common focus of attention nor commonemotional mood.In addition, those in perfectly dense networks were more than four and a half

times more likely than those in sparse networks to have read a popular magazinein the last 12 months. I suggested earlier that popular music performances werelikely to generate a high degree of solidarity, and it is possible that popular maga-zines allow readers to participate vicariously in the high solidarity-producingpopular entertainment industry. Moreover, articles in popular magazines may beshared with and discussed among social networks, heightening the aspect of groupparticipation.Why is there no density effect on attending a classical music or dance perfor-

mance? These performances do feature the physical assembly of a stable group ofpeople with a common focus of attention and common mood (Collins, 1988). Thisemotional tone tends to be notably more subdued than at sports events, however.While audience members might become emotionally involved with a symphony, theetiquette of attending these performance precludes the sort of emotional outburstsassociated with attending sports events. From another perspective we might say thatat high culture performances emotional engagement is expected to be experienced as(and contained within) an individual, while sports events (and popular musicperformances) allow this engagement to be experienced as a group.

D. Kane / Poetics 32 (2004) 105–127 119

120

Table 5

Logistic regression coefficients of cultural activities on network characteristics: mena

Sporting

event

Museum Instrument Dance

performance

Popular

magazine

Great

literature

Class. music

performance

Network density 1.34 (0.73)y �0.43 (0.66) 0.14 (0.55) 0.04 (0.62) 1.17 (0.93) 1.53 (0.75) �0.52 (0.62)

Network racial heterogeneity �1.52 (1.15) 0.09 (0.89) 0.36 (0.74) 1.22 (0.84) 0.95 (1.29) 1.65 (1.37) 1.12 (0.88)

Network religious heterogeneity 0.62 (0.90) �0.01 (0.79) 1.00 (0.65) �0.13 (0.74) �1.74 (1.17) 0.10 (0.92) �0.85 (0.75)

Network gender heterogeneity �0.86 (0.73) 0.44 (0.59) �0.08 (0.52) 0.69 (0.66) 0.44 (0.81) 0.57 (0.55) �0.31 (0.58)

Intercept 0.99 0.36 �1.28 �2.15 2.60 1.44 �2.17

N 167 167 166 166 166 166 166

Pseudo R square 0.27 0.04 0.08 0.06 0.09 0.16 0.16

Notes: ***<0.001 **<0.01*<0.05 y<0.1.a Standard errors in parentheses. All models control for academic program, gender, father’s education level, urban residence, world region, and level at the

university (graduate or undergraduate). Results for control variables are reported in Appendix B.

Table 6

Logistic regression coefficients of cultural activities on network characteristics: womena

Sporting

Event

Museum Instrument Dance

Performance

Popular

Magazine

Great

Literature

Class. Music

Performance

Network density 1.16 (.82) �2.37 (0.90)* 0.64 (0.68) �0.91 (0.70) 3.09 (1.57) �1.78 (1.03) y �0.61 (0.68)

Network racial heterogeneity 0.44 (0.87) 0.36 (0.87) 0.97 (0.70) 1.55 (0.72) �0.65 (1.47) 1.08 (1.08) 0.31 (0.71)

Network religious heterogeneity 0.92 (0.68) 1.67 (0.71) 0.79 (0.58) 0.80 (0.59) �2.18 (1.45) 0.52 (0.80) 0.99 (0.60)y

Network gender heterogeneity 0.74 (0.64) 0.14 (0.64) 0.08 (0.54) 1.10 (0.59)y �0.98 (1.37) 0.35 (0.75) 0.32 (0.57)

Intercept �0.05 2.18 �0.64 �1.65 �0.60 4.22 1.10

N 158 158 158 157 157 158 157

Pseudo R square 0.36 0.16 0.09 0.13 0.16 0.14 0.10

Notes: ***<0.001, **<0.01, *<0.05, y<0.1.a Standard errors in parentheses. All models control for academic program, gender, father’s education level, urban residence, world region, and level at the

university (graduate or undergraduate). Results for control variables are reported in Appendix B.

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32(2004)105–127

4.2. Heterogeneity

As predicted, high heterogeneity was associated with increased high culture parti-cipation. Those with racially heterogeneous networks were about three times morelikely than those with homogeneous networks to have attended a classical danceperformance and were more than four and a half times as likely to have read a novelconsidered to be great literature. Students with networks high in religious hetero-geneity were nearly two and a half times more likely to have played an instrument inthe past 12 months; there was also a modest effect of religious heterogeneity onattending a classical dance performance.Overall, however, heterogeneity always had a positive effect on cultural partici-

pation, usually of a sizeable magnitude. Notably, where heterogeneity effects werestrongest (namely for playing an instrument, attending a dance performance, andreading great literature), density was not related to cultural participation. Con-versely, where density had an effect (sports, museum attendance, and reading apopular magazine), heterogeneity was unrelated.Significantly, network patterns cut across the standard categorization of cultural

activities (such as those used in the GSS Culture Module). Density measures cutacross the highbrow–lowbrow distinction by predicting both sports and art museumattendance. Heterogeneity measures predicted the passive activities of novel-readingand ballet attendance as well as the active participation in music, namely the playingof a musical instrument. The undermining of conventional dichotomies used tocategorize cultural participation suggests that social networks may give rise to theirown set of symbolic boundaries that reconfigure patterns of cultural participation.

4.3. Gender

Based on past research (DiMaggio, 1982) I hypothesized that different dynamicsmay govern women’s cultural participation. Tables 5 and 6 examine network effectson men’s and women’s cultural participation, respectively, and reveal that there aremany more network predictors of women’s participation than there are of men’s.Notably, network predictors are associated with activities that had the largestgender differences in rates of participation (classical music performance, classicaldance performance, reading a magazine, and attending a sports event).For men, there are only two, somewhat surprising, network effects: density

increases sports attendance—as predicted—but also novel-reading. This latter find-ing is an anomaly that is difficult to explain. For women, network effects are con-sistent with predictions. Density decreases high culture participation: densityactually decreases novel-reading (although this effect is significant only at the 0.1level) as well as museum attendance. On the other hand, density increases the like-lihood of having read a popular magazine. Overall, for women, density is associatedwith decreased high culture participation. By contrast, diversity was always asso-ciated with increased high culture participation for women, and the effect was oftensizeable. Women in networks high in religious heterogeneity were five times morelikely to have visited a museum than were women in homogeneous networks; high-

D. Kane / Poetics 32 (2004) 105–127 121

diversity women were more than two and a half times as likely to have gone to aclassical music concert. Women with networks high in racial diversity were almostfive times more likely than women in homogeneous networks to have attended aclassical dance performance. Finally, gender heterogeneity gains some significance(at the .1 level) in the models for women: women with networks high in genderdiversity were three times more likely to have gone to a classical dance performancethan were women with homogeneous networks.Tables 4–6 suggest some ways in which gender and networks may interpenetrate

in symbolic boundary formation. Erickson (1996) argues that the non-class-basedsports interest is one of the most important cultural activities for coordinating workrelations, and she speculates that women may be at a disadvantage relative to men inthis cultural regime. The findings from this paper are consistent with Erickson’sspeculation. Sports were the only activity where men’s participation was higher.More precisely, men in high-density networks have the greatest advantage becausethis capital circulates in their networks and leads to greater sports attendance,increasing further accumulation of this capital.Moreover, Table 6 reveals more information about gender differences in partici-

pation in high culture. With network predictors we see that it is not simply womenas a category who participate more in high culture, but specifically high diversitywomen and low density women. This suggests that women may be using culture todraw symbolic boundaries not only against men but also against women withdifferent network structures.How can one symbolically exclude a person with a particular network structure?

Some research has suggested that network structures give rise to particular person-ality structures and moral outlooks (Burt, 1998; Kane, 2003); negative reactionsagainst certain personalities or moral views may reflect different underlying networkstructures. Bryson (1996) has already demonstrated a link between political/socialtolerance and musical preferences; future work should explore the degree to whichthere is a coherent ‘syndrome’ of attitudes and preferences that can be associatedwith women in high-diversity, low-density networks. While the nature of the samplelimits generalization, the findings here suggest that incorporating calculated networkmeasures into analyses of cultural consumption should enrich our understanding ofsymbolic boundary formation.

5. Conclusion

Sociologists of culture made an important advance in connecting network structureto content when they included proxy measures of heterogeneity in studies of culturalknowledge and preferences. This study found an explicit link between calculateddensity and heterogeneity measures and participation in cultural activities. Networkcharacteristics were significantly related to every measure of cultural participation.As predicted, network density was positively associated with the highest solidarity-producing activity, sports event attendance, and was negatively associated withvisiting museums or art galleries.

122 D. Kane / Poetics 32 (2004) 105–127

Network heterogeneity increased the likelihood of participation for every highculture activity with which it was associated. Interestingly, religious heterogeneityhad an effect on some cultural activities while racial heterogeneity produced effectson others. Future research should explore different forms of heterogeneity and whythey appear to have different effects on cultural participation.Previous research has found that women are more likely than men to engage in

cultural activities, especially high culture activities, which suggests that women mayuse culture to draw symbolic boundaries against men. Women in this study gen-erally had higher rates of participation than men in high culture activities. Attendingsports events—the activity most associated with men—was the sole activity in whichwomen had a lower rate of participation.There was much more evidence for network effects on women’s participation

than on men’s, and the pattern of these effects provides a more nuanced under-standing of gender differences in cultural participation. While formerly women as agroup were considered to exhibit a stronger interest in high culture than were men,the findings in this paper reveal that it is specifically women with networks high indiversity and women with networks low in density who are the most likely toengage in high culture activities. This refinement suggests that women may drawboundaries not only against men but also against women of other networkstructures.

Acknowledgements

I thank Randall Collins, Paul DiMaggio, Grace Kao, Diana Crane, and ShawnBauldry. This research was supported by a generous grant from the University ofPennsylvania Department of Sociology Otto and Gertrude Pollack Grant forSummer Research and by a Doctoral Dissertation Improvement Grant from theNational Science Foundation.

Appendix A. Correlation matrix of network characteristics

Density

Racial het. Gender het. Relig. het.

Density

1.00

Racial het.

0.04 1.00

Gender Het.

�0.04 0.05 1.00

Relig. Het.

�0.01 0.31*** 0.05 1.00

D. Kane / Poetics 32 (2004) 105–127 123

124

Appendix B. Regression Results for Control Variables

Table B-1 Regression coefficients of cultural participation on control variablesa

Control

variable

Sporting

event

Museum Instrument Dance

performance

Popular

magazine

Great

literature

Class. music

Performance

Academic prog. �.05 (0.31) 0.07 (0.29) 0.33 (0.25) 0.19 (0.26) �0.29 (0.46) 0.42 (0.33) 0.20 (0.26)

Father’s ed. �0.02 (0.17) 0.23 (0.16) 0.10 (0.15) 0.02 (0.15) 0.05 (0.27) �0.40 (0.22)y 0.04 (0.15)

Graduate student �0.86 (0.34)** �0.08 (0.35) �0.41 (0.29) 0.14 (0.31) 0.59 (0.56) �0.50 (0.39) 0.15 (0.30)

Female �0.89 (0.31)*** �0.12 (0.29) �0.12 (0.25) 0.84 (0.26)*** 0.50 (0.47) 0.05 (0.34) 0.56 (0.26)*

US 1.11 (0.39)*** �0.27 (0.39) �0.25 (0.32) 0.11 (0.34) 0.45 (0.59) 0.15 (0.46) �0.49 (0.34)

Urban residence �0.52 (0.34) 0.05 (0.34) �0.30 (0.28) 0.21 (0.30) �0.06 (0.51) �0.40 (0.40) 0.52 (0.29)y

Notes: ***<0.001, **<0.01, *<0.05, y<0.1.aStandard errors in parentheses.

Table B-2 Regression coefficients of cultural participation on control variables: mena

Control

variable

Sporting

event

Museum Instrument Dance

performance

Popular

magazine

Great

literature

Class. music

performance

Academic prog. 0.26 (0.44) 0.22 (0.40) 0.44 (0.33) �0.01 (0.37) �0.61 (0.56) 0.46 (0.47 0.34 (0.38)

Father’s ed. �0.09 (0.23) 0.28 (0.22) 0.19 (0.20) �0.06 (0.21) �0.05 (0.33) �0.54 (0.29)y 0.18 (0.21)

Graduate student 0.06 (0.50) 0.32 (0.52) �0.23 (0.42) 0.63 (0.48) 0.20 (0.71) 0.16 (0.55) 0.85 (0.47)y

US 2.11 (0.71)*** �0.19 (0.58) 0.01 (0.48) 0.23 (0.57) �0.18 (0.79) 10.01 (0.71) 0.39 (0.58)

Urban residence �0.58 (0.52) �0.25 (0.49) �0.21 (0.40) 0.35 (0.46) �0.68 (0.64) �0.52 (0.57) 10.10 (0.46)*

Notes: ***<0.001, **<0.01, *<0.05, y<0.1.aStandard errors in parentheses.

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32(2004)105–127

Table B-3 Regression coefficients of cultural participation on control variables: womena

Control

variable

Sporting

event

Museum Instrument Dance

performance

Popular

magazine

Great

Literature

Class music

performance

Academic prog. �0.20 (0.46) �0.08 (0.47) 0.21 (0.38) 0.48 (0.40) 0.15 (0.80) 0.60 (0.52) 0.13 (0.39)

Father’s ed. �0.05 (0.28) 0.07 (0.27) 0.00 (�0.23) 0.09 (0.24) 0.29 (0.49) �0.34 (0.35) �0.28 (0.23)

Graduate student �1.54 (0.47)*** �0.59 (0.51) �0.57 (0.42) �0.20 (0.43) 1.47 (1.07) �1.29 (0.59)* �0.51 (0.44)

US 0.73 (0.50) �00.21 (0.53) �0.46 (0.45) 0.08 (0.46) 1.98 (1.09)y �0.48 (0.62) �1.14 (0.47)

Urban residence �0.42 (0.48) 0.46 (0.49) �0.38 (0.40) 0.06 (0.41) 1.42 (1.00) �0.23 (0.58) 0.13 (0.41)

Notes: ***<0.001, **<0.01, *<0.05, y<0.1.aStandard errors in parentheses.

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