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http://asr.sagepub.com/content/77/5/747The online version of this article can be found at:

DOI: 10.1177/0003122412457875

2012 77: 747American Sociological Review Chaeyoon Lim and Carol Ann MacGregor

Religion on Voluntary BehaviorReligion and Volunteering in Context: Disentangling the Contextual Effects of

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American Sociological Review77(5) 747 779 American SociologicalAssociation 2012DOI: 10.1177/0003122412457875http://asr.sagepub.com

Numerous studies have documented a positiverelationship between various measures of reli-gion as an independent variable and social andcivic outcomes such as philanthropic giving,community group membership, and volunteer-ing (e.g., Lam 2002, 2006; Wuthnow 1991).Although these studies disagree about the spe-cific mechanisms driving this relationship,most suggest it is religions community, notconviction, aspect that is most influential (e.g.,Putnam and Campbell 2010). More recently,scholars have built on this notion of religiouscommunity and asked whether religion mighthave spillover effects on the likelihood of vol-unteering among nonreligious people. These

studies ask whether secular people are morelikely to volunteer when they live in a highlyreligious environment, in that they are likely to

be connected to religious individuals and theircommunities through personal networks (Bor-gonovi 2008; Ruiter and De Graaf 2006; Traun-mller 2009). In addressing this question, a

457875 ASR XXX10.1177/0003122412457875American Sociological ReviewLim and MacGregor 2012

aUniversity of Wisconsin-Madison bLoyola University New Orleans

Corresponding Author:Chaeyoon Lim, University of Wisconsin-Madison, 2446 Sewell Social Science Building,1180 Observatory Drive, Madison, WI 53706E-mail: [email protected]

Religion and Volunteering inContext: Disentangling theContextual Effects of Religionon Voluntary Behavior

Chaeyoon Lim a and Carol Ann MacGregor b

AbstractThis study examines whether religions effect on volunteering spills over to nonreligiousindividuals through personal ties between religious and nonreligious individuals. We usethree different analytic strategies that focus on national, local, and personal network levelcontexts to identify the network spillover effect of religion on volunteering. We find thatif nonreligious people have close friends with religious affiliations, they are more likely tovolunteer for religious and nonreligious causes. However, this network spillover effect cannot

be inferred from the relationship between volunteering and national or local level religiouscontexta common approach in the literature. In fact, we find that the average level of localreligious participation is negatively associated with volunteering among the nonreligious inthe United States. This novel finding suggests that to fully understand religions civic rolein the wider community, we need to consider how religion might influence the civic life ofpeople outside religious communities, not just those within them. Our findings also suggestthat in spite of methodological advances, studies that purport to test mechanisms at one levelof analysis by using data at a larger level of aggregation run a high risk of committing anecological fallacy.

Keywordscontextual effects, network spillover, religion, volunteering

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748 American Sociological Review 77(5)

larger theoretical concern is at stake givencontinuing debates about religions civic role,especially whether religion is a dividing oruniting force in civil society (Lichterman 2005;

Putnam and Campbell 2010). If religionsinfluence on civic engagement indeed spillsover to nonreligious individuals and encour-ages their participation in communities, itwould suggest a mechanism through whichreligion could promote civic engagement andthus serve as a uniting force in civil society.This hypothesis is all the more relevant giventhe growing number of nonreligious people inthe United States and the growing gaps

between the religious and nonreligious in polit-ical and social attitudes (Hout and Fischer2002; Putnam and Campbell 2010).

Despite the significance of the issue, only afew studies have examined effects of religiouscontexts on nonreligious peoples civic behav-iors. Moreover, data limitations have preventeddirect examination of religions potential net-work spillover effect. As a result, previousstudies have used a more general, contextual

analysis strategy and considered the effect ofaverage religiosity in a country or region onthe likelihood of an individual volunteering.The result is a mismatch between the pro-

posed causal mechanism (influence through personal networks) and the empirical evidence(effect of collective religiosity at an aggre-gated level). In this study, we attempt toaddress this issue by looking at cross-national,local, and personal network level contexts. Indoing so, our article offers the most compre-hensive test of the network spillover effect todate and a more thorough examination ofreligions contextual effects on volunteering.

More specifically, our analysis consists ofthree separate studies that examine the net-work spillover hypothesis. First, we revisitRuiter and De Graafs pioneering 2006 studythat uses cross-national survey data to articu-late the mechanisms underlying religiouscontextual effects and documents the rela-tionship between religiosity in a country andan individuals volunteering behavior. We usea new comprehensive cross-national datasetthat includes almost 140 countries to examinethe relationship between national religious

contexts and volunteering. Contrary to Ruiterand De Graaf (2006), we find little evidencethat individual religiosity matters less indevout nations due to network spillover. Sec-

ond, we examine spillover effects by measur-ing contextual religiosity at the local level(i.e., county) in the United States. This is amore proximate measure of religiosity inindividuals environments than average churchattendance in a country. Using another newdataset that includes a sufficient number ofrespondents to represent most of the populouscounties in the United States, we show thatnonreligious people in counties with a higher

level of average religiosity are less, not more,likely to volunteer than their counterparts insecular areas. This is opposite what the net-work spillover hypothesis predicts. Finally,we examine the network spillover hypothesismore directly by measuring contextual religi-osity at the level of an individuals personalnetworks. We ask whether nonreligious peo-

ple are more likely to volunteer when theyreport having a friend with a religious affilia-

tion. Our findings suggest that people who arenot involved in religious congregations areindeed more likely to volunteer when theyhave religious friends.

Findings from these three studies offer notonly the most comprehensive test for religionsnetwork spillover effect on volunteering, butalso the first systematic examination of howdifferent levels of religious context might berelated to an individuals civic behavior. As aresult, this study contributes to our understand-ing of how religion can shape the civic life ofthe broader community. More generally, ourfindings provide important lessons for research-ers interested in the study of contextual effects.Results highlight that scholars should carefullyconsider how the level of aggregation mayinfluence the relationship between explanatoryand outcome variables.

RELIGION ANDVOLUNTEERINGConsiderable scholarly attention has been

paid to the relationship between religion andvarious pro-social and civic outcomes 1

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Lim and MacGregor 749

(Ellison 1992; Hall 2005; Regnerus, Smith,and Sikkink 1998). Evidence from a varietyof surveys conducted in different times and

places overwhelmingly suggests that reli-

gious individuals are more active volunteersand community participants than their secularcounterparts (e.g., Lam 2002, 2006; Lovelandet al. 2005; Park and Smith 2000; Putnam andCampbell 2010). Discussion of the mecha-nisms that link religion to volunteering high-lights two important components of religiosity,sometimes referred to as belief and belonging(Bellah 1991) or conviction and community(Wuthnow 1991). All major religions preach

some form of selflessness and place value onhelping others (Ellison 1992), but these con-victions are primarily fostered in the contextof religious communities. Some scholars evensuggest that beliefs hold no power if they arenot ratified by a community of fellow believ-ers (Stark and Bainbridge 1996; Wuthnow1991). Consequently, most scholars examin-ing this relationship focus on religious atten-dance as a key predictor. Congregations,

researchers suggest, are places where peoplemeet socially and with civically involvedfriends and, as a result, religious attendanceincreases the likelihood of learning aboutvolunteering opportunities or being asked tovolunteer (Musick, Wilson, and Bynum 2000;Ruiter and De Graaf 2006). Additionally,social ties, such as those established in faithcommunities, help to generate trust, and trustmakes it easier for people to step forward anddonate their time (Wilson 2000).

Evidence suggests that congregational net-works play a key role in recruiting religious

people into volunteerism (Cavendish 2000;Park and Smith 2000; Putnam and Campbell2010), but it is less well known whetherreligiosity in ones surrounding environmenthas an additional effect on volunteering overand above the effects of ones own commit-ment and involvement. Given that people aredrawn into volunteerism through personalnetworks, it seems logical to expect such con-textual effects from religion, but few studiesdirectly examine this question. A less obviousquestion, and one that has drawn even lessattention in the literature, is whether religious

contexts influence the volunteerism of peoplewho are not themselves involved in organizedreligion. Does religions effect spillover to

people outside religious communities? Is

there any evidence of such spillover effectsthrough personal networks? These are thequestions we turn to now.

CONTEXTUAL EFFECTS OFRELIGIONVarious structural and institutional factorscompose an individuals social environment,

but most contextual analyses in sociology

focus on effects of personal traits aggregatedover individuals in a geographic or organiza-tional unit (Books and Prysby 1988; Stipakand Hensler 1982). In the sociology of reli-gion, religious context is often operational-ized by aggregating personal religiouscharacteristics, most commonly religiousaffiliation or frequency of church attendance.Scholars have been interested in how reli-gious contexts influence outcomes like sui-

cide (Ellison, Burr, and McCall 1997),teenage delinquency (Regnerus 2003; Starkand Bainbridge 1996), and crime (Beyerleinand Hipp 2005).

Most of these studies employ RodneyStarks notion of moral communities (Starkand Bainbridge 1996)a recapitulation ofDurkheims conceptions of social integrationand social regulation. Noting that the relation-ship between personal religiosity and teenagedelinquency varies across regions in theUnited States, Stark and Bainbridge (1996:164) suggest that teenagers personal reli-giousness reduces delinquency only when themajority of their friends are religious and thusreligion enters freely into everyday interac-tions and becomes a valid part of the norma-tive system. In other words, personalreligiosity must be ratified by ones socialenvironment to affect behavior. In this moralcommunities thesis, Stark clearly invokes asocial interaction mechanism, which is themost commonly proposed explanation of con-textual analysis (Blalock 1984; Erbring andYoung 1979; Weatherford 1982). In this lineof argument, contextual effects are explained



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as a consequence of social interaction andinterpersonal influence among individualswithin a contextual unit. Aggregated personaltraits (e.g., average church attendance) serve

as proxies for personal networks and socialinteractions.Stark, however, also emphasizes that reli-

gion should be viewed as a group property,not a personal trait. Regnerus (2003:527)further develops this notion and argues thatreligious context is not simply a set of per-sonal relationships, but also an emergentgroup property that can affect individualsregardless of their status as immediate group

members. Viewed this way, contextualeffects might not require direct social interac-tions and instead might rely on indirect socialcontrol and support, enabled by structuralfeatures of social networks in a community,such as network density (Regnerus 2003) ornetwork closure (Smith 2003). From this per-spective, aggregated religiosity or affiliationwould represent structural features of net-works in a contextual unit rather than serving

as a proxy for individuals ego-centric net-works. It is also plausible that aggregatedreligiosity is a proxy for global or institu-tional contextual factors, such as legal andcultural institutions, rather than for socialinteraction or macro network structure, espe-cially when the contextual unit in question isa country or large region (Books and Prysby1988; Finke and Adamczyk 2008). In short,although most studies of the contextual effectsof religion employ the moral communitiesthesis, different theoretical mechanisms couldgenerate similar empirical contextual effectsof religion. It is difficult, however, to disen-tangle these mechanisms with a conventionalcontextual analysis that uses aggregated per-sonal religious characteristics, especiallywhen a large contextual unit is used.

Regardless of the mechanisms proposed, previous studies focus almost exclusively onhow religious contexts affect religious indi-viduals, what Regnerus (2003) calls the lightswitch portion of the moral communitiesthesis. Religious context matters, the lightswitch hypothesis argues, because it turnson the effect of personal religiosity. In other

words, it is only when personal religiosity iscombined with a high level of religiosity inones environment that religion influencesones behavior. Consequently, effects of reli-

gious contexts should be concentrated amongindividuals who are religious themselves, oreffects should be larger for religious than forsecular people. It remains unclear, however,how nonreligious individuals might beaffected by religiosity in their environments.In the next section, we discuss this question inthe context of civic engagement.

RELIGIOUS CONTEXTS ANDCIVIC ENGAGEMENT OF THENONRELIGIOUS

Only a few studies have examined contextualeffects of religion on civic engagement. Ofthese, some have focused on religious affilia-tion and shown that the proportion of Protestantsin a country or region is positively related tovoluntary organization membership (Lam

2006; Woodberry 2012) and social trust(Traunmller 2009). A few other studies featureorganizational context rather than national orregional context (Schwadel 2005; Serow andDreyden 1990). Schwadel (2005) finds thathigh levels of biblical literalism and friendshipswithin congregationscharacteristics oftenassociated with Evangelical Christianityarenegatively correlated with congregantsinvolvement in non-church organizations (seealso Beyerlein and Hipp 2005). Although thesestudies offer rare insights into how religiouscontexts influence voluntary behaviors, theyoffer little understanding regarding the signifi-cance of religious contexts for people who arenot involved in organized religion.

Ruiter and De Graaf (2006) explicitly con-sider how religious contextmeasured asaverage church attendance in a country might affect the volunteering behavior of thereligious and nonreligious differently. Theysuggest that devout countries have a higherlevel of volunteering than secular countries,not only because they have a larger number ofreligious individuals who are embedded inreligious networks and thus volunteer more,



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networks is on par with segregation by race(DiPrete et al. 2010). Levels of segregationmight be higher in devout areas than in secularones, and thus spillover may be less likely to

take place where there is a high level of con-textual religiosity. How the forces of baselineand inbreeding homophily net out is an empir-ical question beyond the scope of this article,

but for now, it is sufficient to point out thatsuch conflicting tendencies would make iteven more difficult to identify network effectswith highly aggregated measures of contex-tual factors.

The network spillover hypothesis also pre-

sumes that interpersonal influence and volun-teer recruitment through social networksoperate in much the same way between reli-gious and nonreligious individuals as they doamong religious people. The social movementliterature, however, suggests that recruitmentefforts through personal networks often strate-gically target people who are more reachableand have a higher propensity to participate(Brady, Schlozman, and Verba 1999; Lim

2010). Because regular churchgoers can berelatively easy to reach and tend to be activevolunteers, organizers may prioritize church-goers in their recruitment efforts. This might

be particularly true in more religious commu-nities where volunteering activities are likelyorganized by religious organizations andchurchgoing potential volunteers are abun-dant. As a result, secular people living indevout areas might be placed lower in organ-izers mobilization queues and are thereforeless likely to be asked to volunteer. Theseindividuals may also have a stronger incentiveto free-ride because they know their church-going neighbors will volunteer even if they donot (Oliver 1984). The social movement liter-ature also argues that interpersonal influencetends to hinge on a shared identity that is rel-evant to the activism in question (Lim 2008;McAdam and Paulsen 1993). This implies thatinterpersonal influence might be more effec-tive when individuals share a religious faith.As a result, even when secular people arecontacted by religious acquaintances, theymight be less likely to respond positively thanwould religious people.

Average church attendance of a country orlocal area could also relate to individual vol-unteering through mechanisms other than net-work spillover. For example, countries with a

higher religious concentration may simplyhave more organizations that provide volun-teering opportunities, and thus nonreligious

people may have more opportunities to volun-teer. Average church attendance could alsoserve as a proxy for national religious culture,which itself may influence individual behav-ior regardless of personal religiosity; thiscould occur through public discourse, media,and other social institutions, rather than

through interpersonal influence. Although thiscould be called a contextual effect, it would bea different mechanism than network spillover.

In short, Ruiter and De Graafs (2006)study is illuminating, but evidence for the net-work spillover hypothesis appears to be incon-clusive at best. Religiosity in highly aggregatedcontextual units could be related to individu-als volunteering behavior in ways that differfrom the mechanism suggested by the network

spillover hypothesis. Depending on whichmechanism prevails, it is plausible that reli-gious contexts matter differently for the reli-gious and the nonreligious. In certain contexts,like the United States, contextual religiositymay be unrelated or even negatively related tovolunteering among nonreligious individuals,especially if effects of contextual religiositythrough other mechanisms run counter to net-work spillover effects or are stronger thanspillover effects. Given these potential ten-sions, the network spillover hypothesis needsto be more carefully examined.

THE STUDYTo study whether there is a network spillovereffect of religion on volunteering, we com-

pare results from three different approachesthat measure individuals religious contextdifferently. We consider individuals religiouscontext at three different levels using threedifferent datasets. We use the Gallup WorldPoll data to revisit Ruiter and De Graafs(2006) key finding that nonreligious people indevout countries are more likely to volunteer



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than nonreligious people in secular countries,due to the spillover effect. Second, we use theGallup Daily Poll data to examine the rela-tionship between local religious contexts and

volunteering. If aggregated religiosity at thecountry level predicts individuals volunteer-ing due to the network spillover effect, wewould expect local level religiosity to have asimilar, or even stronger, relationship withvolunteering, because an individuals proba-

bility of having personal ties across religious boundaries should be more closely related tothe religious composition of a local area thanthat of an entire country. Finally, we use the

Faith Matters survey in the United States toexamine whether nonreligious people aremore likely to volunteer when they have closefriends who are religious.

Does Average Church Attendance ina Country Predict Volunteering?

First, we examine whether national religiouscontext, measured as average church atten-

dance in a country, predicts volunteering,especially among the nonreligious. We useGallup World Poll data (henceforth WorldPoll), a large survey series that Gallup hasconducted since 2005 in 156 countries (GallupInc. 2011). Designed to be representative of95 percent of the worlds adult population, theWorld Poll has interviewed, either by phoneor face-to-face (the latter mostly in develop-ing countries with telephone coverage lowerthan 80 percent), at least 1,000 individualsfrom each country, with a few exceptionswhere the sample size is between 500 and1,000 people. All interviews were conductedusing the major languages of each country,

based on nationally representative probabilitysamples (for more information on samplingmethods and other details of the surveys, seeGallup Inc. [2011]). In most of the 156 coun-tries surveyed, the survey has been conductedmore than once between 2005 and 2011 and,as a result, the pooled sample size for eachcountry is often substantially larger than1,000. Because of missing data, the final ana-lytic sample includes 373,604 respondentsfrom 138 countries. 3 The median sample size

per country in the analytic sample is 2,707.The most important advantage of the WorldPoll, for our purposes, is its extensive cover-agenot only in terms of geography, but also

culturally and politically. Because these datainclude a large number of countries, it isunlikely that an observed relationship isoverly sensitive to inclusion or exclusion of afew influential cases. In addition, as a resultof the large sample size in most countries,national religious context can be estimatedreliably by aggregating individual responsesin each country (for the list of countries andsample size in each country, see Part A, Table

S1 in the online supplement [http://asr.sage pub.com/supplemental]).The outcome variable in this study is

whether respondents volunteered their time toany organization. To measure religious serviceattendance, the World Poll asked whetherrespondents had attended a place of worshipor a religious service within the past sevendays. We coded the volunteering and religiousservice variables dichotomously. We adjust

for various individual- and country-level char-acteristics. At the individual level, we adjustfor age, gender, education, family status, andreligious preference. At the country level, con-trol variables include gross national income

per capita, life expectancy, mean years ofschooling, democracy score, and religious cul-ture (see Table A1 in the Appendix for addi-tional information on these variables).

The key question is whether averagechurch attendance in a country predicts vol-unteering more strongly for individuals whoare not themselves religious than for peoplewho regularly attend religious services. Weexamine this question using multilevel logis-tic regression, which allows individual- andcountry-level covariates to predict an indi-viduals volunteering. We start by fitting arandom-intercept model, which lets the inter-cept vary across countries. We then allow thecoefficient of individual religious serviceattendance to vary across countries and exam-ine the cross-level interaction between indi-vidual attendance and a countrys averageattendance to see whether average attendancein a country is more or less strongly related to



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volunteering for non-churchgoers than forchurchgoers. If the spillover effect proposed

by Ruiter and De Graaf exists, and if it isidentifiable using national religious context,

we should find that average attendance in acountry matters more for non-churchgoersthan for churchgoers. As a result, the gap involunteering between churchgoers and non-churchgoers should be smaller in countrieswith a higher level of average attendance.

Findings

Before looking at results of the multilevellogistic regressions, we examine the bivariaterelationship between the proportion of respon-dents in a country who volunteered and aver-age religious service attendance in that country(i.e., the proportion of a countrys respondentswho attended religious services last week)with a scatterplot (see Figure 1). Visuallyexamining the relationship is important

because Van der Meer and colleagues (2010)show that the relationship could be sensitive toa few influential cases. Figure 1 suggests thatalthough there are a few potential outliers, therelationship does not hinge on a small numberof influential cases. However, Figure 1 alsoindicates that the relationship between average

attendance and volunteering is curvilinear. Therate of volunteering is higher among both secu-lar and highly religious countries and loweramong moderately religious countries.

Table 1 presents results from multilevellogistic regressions. Model 1 shows howindividual- and country-level religious ser-vice attendance is related to volunteering, netof all control variables at both levels. Takinginto account the curvilinear relationship weobserve in Figure 1, we include a quadraticterm for the proportion of churchgoers ineach country. First, individual church attend-ance strongly predicts volunteering. Net ofindividual and country characteristics, theodds of volunteering for respondents whoattended religious services in the past weekare 1.87 times greater than the odds for thosewho did not. This is similar to the difference

between respondents with an elementary edu-cation or less and those with at least fouryears of education beyond high school.

In addition, Model 1 suggests that the proportion of churchgoers in a country is relatedto volunteering. Consistent with what weobserve in Figure 1, the level of volunteeringis higher in secular and devout countries andlower in moderately religious countries. InModel 2, we add the interaction between

Figure 1. National Religious Context and Volunteering across Countries



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Table 1 . Multilevel Logistic Regressions of Individual Volunteering on Individual- andCountry-Level Religious Service Attendance

Model 1 Model 2 Model 3 a Model 4 b

Individual-Level Covariates Age .001 **

(.000).001 *

(.000).001(.001)

.002 ***

(.000) Female .164 ***

(.009).165 ***

(.009).123 ***

(.013).198 ***

(.012) Elementary education or less

Secondary to three-year tertiaryeducation

.327 ***

(.011).326 ***

(.011).323 ***

(.018).336 ***

(.014) Four years of tertiary education

or more.634 ***

(.014).634 ***

(.014).641 ***

(.022).632 ***

(.020) Married/domestic partner

Single (never married) .043 ***

(.011).044 ***

(.011).012(.017)

.065 ***

(.015) Divorced/separated .023

(.018).020(.019)

.025(.025)

.004(.027)

Widowed .213 ***

(.019).215 ***

(.019).250 ***

(.030).184 ***

(.025) Has children age 15 years or

younger.008 *

(.004).008 *

(.004).013 *

(.006).005

(.005) Catholic

Protestant .114 ***

(.015).110 ***

(.015).105 ***

(.026).114 ***

(.019) Orthodox .109 ***

(.029)

.099 **

(.029)

.010

(.040)

.220 ***

(.045) Islam/Muslim .023(.022)

.016(.022)

.019(.041)

.030(.027)

Hinduism .085(.044)

.074(.045)

.044(.082)

.077(.053)

Buddhism .188 ***

(.034).204 ***

(.034).191 ***

(.048).221 ***

(.050) Judaism .118

(.085).150

(.085).185

(.108).111

(.138) Secular/atheist/agnostic/no religion .106 ***

(.024).070 **

(.025).044(.028)

.230 ***

(.065) Christian .094 ***

(.023).091 ***

(.023).068

(.036).113 ***

(.030) Other religion .054 *

(.024).077 **

(.025).061 *

(.031).134 **

(.045) Religious service attendance (A) .628 ***

(.010).570 ***

(.129)

Country-Level Covariates Religious diversity

Religious culture: ProtestantReligious culture: Catholic .179

(.172).166(.167)

.155(.166)

.237(.178)

Religious culture: Orthodox .791 ***

(.224).789 ***

(.217).856 ***

(.217).754 **

(.233) Religious culture: Muslim .373

(.214).438 *

(.208).498 *

(.208).400(.221)

Religious culture: Eastern .101(.232)

.136(.225)

.152(.224)

.200(.240)

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Model 1 Model 2 Model 3 a Model 4 b

Religious culture: other .312

(.288)

.295

(.280)

.303

(.278)

.373

(.298) Life expectancy at birth .011(.010)

.012(.010)

.012(.010)

.010(.010)

Average years of schooling .052(.034)

.054(.033)

.054(.032)

.041(.035)

Gross national income per capita .000(.000)

.000(.000)

.000(.000)

.000(.000)

Democracy score .021(.040)

.054(.039)

.065(.038)

.012(.041)

Proportion of churchgoers (B) 4.412 ***

(1.330)5.069 ***

(1.294)5.216 ***

(1.282)4.329 **

(1.376) Proportion of churchgoers

squared (C)4.281 **

(1.378)5.167 ***

(1.345)5.321 ***

(1.333)4.223 **

(1.423) A B .723

(.611)

A C 1.115(.625)

Intercept .294(.634)

.043(.617)

.139(.613)

.154(.654)

Level-2 var.: Intercept .348 .067 .315 .366Level-2 var.: (A) .322

N (individual) 373,604 373,604 187,161 186,443N (country) 138 138 138 138Log-likelihood 180073 179812 80625 99227

Note: Logit coefficients with standard errors in parentheses.aNon-churchgoers only.

bChurchgoers only.* p < .05; ** p < .01; *** p < .001 (two-tailed tests).

Table 1 . (continued)

individual attendance and proportion ofchurchgoers in a country to examine whetherthe gap between churchgoers and non-church-goers is smaller in devout nations becausenon-churchgoers in highly religious countriesvolunteer more than non-churchgoers in secu-lar countries. The interaction terms are notstatistically significant, suggesting that therelationship between national context and vol-unteering does not vary significantly by indi-vidual attendance status. To make interpretationof the interaction effect easier, we estimateModel 1 separately for churchgoers and non-churchgoers and calculate predicted probabili-ties of volunteering at varying levels ofaverage attendance in a country while fixingother variables at their mean values. Resultsare presented as Models 3 and 4 in Table 1 and

are also shown in Figure 1 as solid (churchgo-ers) and dashed (non-churchgoers) lines.Results suggest that the curvilinear patternapplies for churchgoers and non-churchgoers.The gap between churchgoers and non-churchgoers appears to become smaller amongthe most religious countries, but only slightly.

Summary

Our analyses suggest that national religiouscontexts do matter: there is a strong curvilinearrelationship between the proportion of church-goers in a country and the level of volunteering.The difference in volunteering between the mostsecular countries (or the most devout countries)and moderately religious countries, net ofindividual- and country-level characteristics, is



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larger than 10 percentage points. However, ourfindings also suggest that contrary to whatRuiter and De Graaf (2006) predict, nationalcontext matters almost equally for churchgoers

and non-churchgoers. To be fair, measures ofreligious service attendance and volunteering inthe World Poll do differ from those in the WorldValues Survey, which could be responsible fordiscrepancies between the two studies. However,given that their findings are based on a smallernumber of countries and are highly dependenton a handful of influential cases, we argue thatour findings, based on almost 140 countries,carry more weight than findings from the WVS,

which includes one third as many countries.4

Given that national religious context mat-ters regardless of personal religiosity, it seems

plausible that national religious culture, ratherthan the network spillover effect, is responsi-

ble for the relationship between national con-text and volunteering. In addition, countrieson the left-hand side of Figure 1 tend to bedeveloped nations with democratic politicalsystems, whereas countries on the right are

mostly developing nations with less demo-cratic or authoritarian political systems. Thissuggests that the relationship between reli-gion and volunteering may be conditioned bya nations political system and civic culture inaddition to its religious culture. To fullyunderstand why national religious contextsmatter, it is necessary to explore these poten-tial heterogeneities among countries. Thistask, however, is beyond the scope of thisarticle. For our purposes, it is sufficient tonote that cross-national data offer little sup-

port for the network spillover hypothesis.

Does Average Church Attendancein a Community Predict IndividualVolunteering?

Next, we turn to the religious context of localareas within the United States. Using datafrom a large survey collected by Gallup, weexamine whether average church attendancein U.S. counties predicts religious and nonre-ligious individuals volunteering. One of thedifficulties of studying the network spillovereffect using national context arises from the

fact that individual religious context is mea-sured at a highly aggregated level. To be sure,average attendance in a local area could be acoarse measure of an individuals religious

environment, and therefore analyses at thecounty level may have similar issues as those based on national context. Still, church atten-dance in a county should be a better approxi-mation of an individuals religiousenvironment than average attendance in anentire country. If a network spillover effectexists and is identifiable with an aggregatedmeasure of contextual religiosity, we shouldfind a stronger relationship, or at least a simi-

lar positive relationship, between averagechurch attendance in a county and volunteer-ing, especially for nonreligious individuals.

To examine how local religious contexts arerelated to volunteering, we use data from theGallup Daily Poll (henceforth Daily Poll). TheDaily Poll interviews daily at least 1,000 U.S.adults, age 18 years and older, using dualframe (including landline and cell-phone users)random-digit dial sampling (for more details of

the survey, see Gallup Inc. [2009]).5

BecauseGallup interviews 1,000 or more respondentsevery day except for major holidays, the cumu-lative data between 2008 and 2011 containinterviews from more than 1.2 million indi-viduals, representing almost all populous geo-graphical areas in the United States. This largesample size offers a unique opportunity tostudy effects of the local religious context onvolunteering. First, because the Daily Poll hasat least a few hundred respondents in most

populous counties, it can provide a relativelyreliable measure of religiosity for a large num-

ber of counties. For example, these data haveat least 200 respondents from 1,076 counties;with a higher threshold of 500 respondents,these data still cover more than 500 counties.This is an important advantage because most

previous studies use indirect indicators, suchas congregational adherence rates reported bydenominations, to measure local level religios-ity. Moreover, the Daily Poll includes a largenumber of geographic units, which have sub-stantial variations in terms of religiosity andthe level of volunteering. The large number ofareas represented in these data provides enough



13/34


statistical power to adjust for various commu-nity-level characteristics in addition to indi-vidual traits and to examine the cross-levelinteraction between individual- and county-

level attendance more reliablythe criticaltest for the network spillover hypothesis.Once again the key outcome variable is

volunteering. In 2008 and 2009, the Daily Pollasked whether respondents had volunteeredtime to an organization in the past month. Wecoded responses dichotomously. The DailyPoll also includes a measure of church attend-ance, which is measured in five categoriesranging from at least once a week to never.

We converted this to an interval variable byapproximating annual days of church attend-ance. Average church attendance in a county isthe mean of church attendance for all countyresidents. We calculated average churchattendance only for counties with at least 200respondents who answered the religious ser-vice attendance question. 6

To ensure that the relationship between reli-gious service attendance and volunteering is

not spurious, we adjust for various individual-and county-level characteristics. Individual-level controls include a number of demographicvariables and respondents religious prefer-ences. We also include county-level factorssuch as median household income, racial diver-sity, and poverty rate. Using the ReligiousCongregation Membership Study (Jones et al.2002), we include proportions of major reli-gious groups (i.e., Evangelical Protestant,Mainline Protestant, and Catholic) in a countyto examine whether religious compositions,rather than average attendance, influences vol-unteering of the nonreligious. More informa-tion on all variables in this section is availablein Table A2 in the Appendix. Similar to Study1, we use multilevel logistic regression toexamine the relationship between local reli-gious contexts and individuals volunteering.

FindingsModel 1 in Table 2 shows results from therandom-intercept multilevel logistic regression.Similar to what we find in Study 1, individuals

religious service attendance strongly predictsvolunteering. The coefficient suggests that netof all covariates in Model 1, one additionalinstance of church attendance per year is asso-

ciated with a 2.8 percent increase in the odds ofvolunteering. Average religious service atten-dance in a county also predicts volunteering,

but contrary to predictions of the network spill-over hypothesis, the coefficient is negative . Netof all individual and county characteristics inModel 1, respondents in more religious coun-ties are less , not more, likely to volunteer theirtime for an organization. 7 The negative rela-tionship is counterintuitive, especially given the

positive association between attendance andvolunteering at the individual level.Model 2 sheds some light on this puzzling

finding. Results suggest there is a significant positive cross-level interaction between indi-vidual attendance and average attendance in acounty. To make the interaction effect moreinterpretable, we calculate the predicted prob-ability of volunteering, separately for weeklychurchgoers and non-churchgoers, at three dif-

ferent levels of average attendance in a countywith all other covariates fixed at their full-sample means. Figure 2 depicts these probabil-ities with their confidence intervals. The solidline suggests there is no clear relationship

between local religious context and volunteer-ing among weekly churchgoers. In otherwords, the negative relationship we observe inModel 1 is largely driven by sporadic or occa-sional churchgoers, and non-churchgoers in

particular, for whom there is a strong negativerelationship between contextual religiosity andvolunteering. The latter finding about non-churchgoers directly contradicts the networkspillover hypothesis, which predicts a positiverelationship between contextual religiosity andvolunteering among the nonreligious. In addi-tion, our results for weekly churchgoers areinconsistent with the light-switch hypothesis.According to this hypothesis, we shouldobserve a higher level of volunteering amongregular churchgoers in highly religious areasthan among those in secular areas.

One possible explanation for the patternsin Figure 2 is that nonreligious people have



14/34

759

T a

b l e 2 . M u l

t i l e v e

l L o g

i s t i c

R e g r e s s

i o n s o f

V o l u n

t e e r

i n g o n

I n d i v i d u a l - a n d

C o u n t y -

L e v e

l R e l

i g i o u s

S e r v

i c e

A t t e n

d a n c e

i n t h e U n i

t e d S t a t e s

M o d e l

1

M o d e l

2

M o d e l

3

M o d e l

4

M o d e l

5 a

M o d e l

6 b

M o d e l

7 c

I n d i v i d u a l -

L e v e

l C o v a r

i a t e s

R e s p o n d e n

t s a g e

. 0 0 3 * * *

. 0 0 3 * * *

. 0 0 3 * * *

. 0 0 3 * * *

. 0 0 4 * * *

. 0 0 0

. 0 0 4 * * *

( . 0 0 0 )

( . 0 0 0 )

( . 0 0 0 )

( . 0 0 0 )

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

R e s p o n d e n

t i s

f e m a l e

. 2 1 4 * * *

. 2 1 4 * * *

. 2 1 4 * * *

. 2 1 4 * * *

. 2 9 5 * * *

. 2 1 3 *

* *

. 1 4 1 * * *

( . 0 1 0 )

( . 0 1 0 )

( . 0 1 0 )

( . 0 1 0 )

( . 0 1 6 )

( . 0 2 3 )

( . 0 1 7 )

R e s p o n d e n

t s r a c e

( r e f e r e n c e : W

h i t e )

B l a c k

. 1 8 0 * * *

. 1 8 1 * * *

. 1 8 0 * * *

. 1 8 1 * * *

. 1 7 2 * * *

. 1 8 7 * * *

. 1 6 5 * * *

( . 0 2 2 )

( . 0 2 2 )

( . 0 2 2 )

( . 0 2 2 )

( . 0 4 3 )

( . 0 4 4 )

( . 0 3 3 )

A s i a n

. 4 6 6 * * *

. 4 6 4 * * *

. 4 6 4 * * *

. 4 6 4 * * *

. 3 2 1 * * *

. 5 8 0 * * *

. 6 2 4 * * *

( . 0 4 4 )

( . 0 4 4 )

( . 0 4 4 )

( . 0 4 4 )

( . 0 6 2 )

( . 0 9 9 )

( . 0 7 9 )

H i s p a n

i c

. 3 9 4 * * *

. 3 9 2 * * *

. 3 9 2 * * *

. 3 9 3 * * *

. 2 0 5 * * *

. 4 1 1 * * *

. 5 0 3 * * *

( . 0 2 3 )

( . 0 2 3 )

( . 0 2 3 )

( . 0 2 3 )

( . 0 3 9 )

( . 0 5 0 )

( . 0 3 5 )

O t h e r s

. 0 3 6

. 0 3 7

. 0 3 7

. 0 3 8

. 1 2 0 * *

. 0 8 0

. 1 0 6 *

( . 0 2 9 )

( . 0 2 9 )

( . 0 2 9 )

( . 0 2 9 )

( . 0 4 3 )

( . 0 6 5 )

( . 0 4 9 )

M a r

i t a l s

t a t u s

( r e f e r e n c e : m a r r i e d

/ d o m e s

t i c p a r

t n e r

)

S i n g

l e ( n e v e r m a r r i e d

)

. 1 0 1 * * *

. 1 0 1 * * *

. 1 0 1 * * *

. 1 0 2 * * *

. 0 5 8 *

. 0 3 6

. 2 1 1 * * *

( . 0 1 6 )

( . 0 1 6 )

( . 0 1 6 )

( . 0 1 6 )

( . 0 2 4 )

( . 0 3 7 )

( . 0 2 9 )

S e p a r a

t e d / d i v o r c e d

. 1 4 2 * * *

. 1 4 1 * * *

. 1 4 1 * * *

. 1 4 1 * * *

. 1 2 3 * * *

. 0 9 2 * *

. 1 9 7 * * *

( . 0 1 6 )

( . 0 1 6 )

( . 0 1 6 )

( . 0 1 6 )

( . 0 2 3 )

( . 0 3 5 )

( . 0 2 8 )

W i d o w e d

. 1 7 8 * * *

. 1 7 7 * * *

. 1 7 7 * * *

. 1 7 7 * * *

. 1 3 4 * * *

. 2 1 7 * * *

. 1 9 6 * * *

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 3 2 )

( . 0 4 2 )

( . 0 2 7 )

N u m

b e r o f c h

i l d r e n u n

d e r a g e

1 8 ( r e f e r e n c e : n o n e

)

O n e

. 0 9 6 * * *

. 0 9 7 * * *

. 0 9 7 * * *

. 0 9 7 * * *

. 0 9 6 * * *

. 2 0 2 *

* *

. 0 3 1

( . 0 1 7 )

( . 0 1 7 )

( . 0 1 7 )

( . 0 1 7 )

( . 0 2 5 )

( . 0 3 5 )

( . 0 3 0 )

T w o

. 2 5 6 * * *

. 2 5 6 * * *

. 2 5 6 * * *

. 2 5 6 * * *

. 3 0 7 * * *

. 3 2 9 *

* *

. 1 6 8 * * *

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 2 7 )

( . 0 3 7 )

( . 0 3 1 )

T h r e e

. 3 8 1 * * *

. 3 8 2 * * *

. 3 8 2 * * *

. 3 8 1 * * *

. 4 3 2 * * *

. 4 7 9 *

* *

. 3 0 7 * * *

( . 0 2 3 )

( . 0 2 3 )

( . 0 2 3 )

( . 0 2 3 )

( . 0 3 8 )

( . 0 4 6 )

( . 0 3 6 )

E d u c a t i o n

( r e f e r e n c e : h

i g h s c

h o o l

d e g r e e

)

L e s s

t h a n

h i g h s c

h o o l

d e g r e e

. 3 7 6 * * *

. 3 7 6 * * *

. 3 7 5 * * *

. 3 7 4 * * *

. 2 7 1 * * *

. 3 3 3 * * *

. 4 4 0 * * *

( . 0 3 0 )

( . 0 3 0 )

( . 0 3 0 )

( . 0 3 0 )

( . 0 5 1 )

( . 0 6 6 )

( . 0 4 2 )

T e c h n i c a

l o r v o c a

t i o n a l s c

h o o l

/ s o m e c o

l l e g e

. 5 0 4 * * *

. 5 0 4 * * *

. 5 0 4 * * *

. 5 0 5 * * *

. 5 2 2 * * *

. 4 9 5 *

* *

. 4 9 9 * * *

( . 0 1 5 )

( . 0 1 5 )

( . 0 1 5 )

( . 0 1 5 )

( . 0 2 5 )

( . 0 3 4 )

( . 0 2 3 )

C o l

l e g e

d e g r e e

. 7 7 8 * * *

. 7 7 7 * * *

. 7 7 7 * * *

. 7 7 7 * * *

. 7 9 8 * * *

. 8 2 1 *

* *

. 7 4 9 * * *

( . 0 1 6 )

( . 0 1 6 )

( . 0 1 6 )

( . 0 1 6 )

( . 0 2 7 )

( . 0 3 6 )

( . 0 2 6 )

P o s t g r a

d u a t e e d u c a t

i o n

1 . 1 3 3 * * *

1 . 1 3 3 * * *

1 . 1 3 2 * * *

1 . 1 3 2 * * *

1 . 1 7 9 * * *

1 . 0 9 3 * * *

1 . 1 3 1 * * *

( . 0 1 7 )

( . 0 1 7 )

( . 0 1 7 )

( . 0 1 7 )

( . 0 2 8 )

( . 0 3 8 )

( . 0 2 8 )

( c o n

t i n u e d

)



15/34

760

M o d e l 1

M o d e l

2

M o d e l

3

M o d e l

4

M o d e l

5 a

M o d e l

6 b

M o d e l

7 c

M o n

t h l y h o u s e h o l

d i n c o m e

( r e f e r e n c e : l e s s

t h a n

$ 2 , 0

0 0 )

$ 2 , 0

0 0 t o $ 3

, 9 9 9

. 1 4 2 * * *

. 1 4 1 * * *

. 1 4 1 * * *

. 1 4 1 * * *

. 1 1 0 * * *

. 1 1 6 * *

. 1 8 1 * * *

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 2 8 )

( . 0 4 0 )

( . 0 2 8 )

$ 4 , 0

0 0 t o $ 7

, 4 9 9

. 2 6 7 * * *

. 2 6 6 * * *

. 2 6 5 * * *

. 2 6 5 * * *

. 2 5 9 * * *

. 2 4 8 * * *

. 2 8 6 * * *

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 2 8 )

( . 0 4 0 )

( . 0 2 9 )

$ 7 , 5

0 0 o r

h i g h e r

. 3 5 5 * * *

. 3 5 3 * * *

. 3 5 3 * * *

. 3 5 3 * * *

. 3 3 7 * * *

. 3 8 5 * * *

. 3 6 2 * * *

( . 0 1 9 )

( . 0 1 9 )

( . 0 1 9 )

( . 0 1 9 )

( . 0 3 0 )

( . 0 4 3 )

( . 0 3 3 )

D o n

t k n o w o r r e

f u s e

d t o a n s w e r

. 1 6 4 * * *

. 1 6 3 * * *

. 1 6 3 * * *

. 1 6 2 * * *

. 1 9 0 * * *

. 2 3 1 * * *

. 1 1 2 * * *

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 2 9 )

( . 0 4 0 )

( . 0 2 7 )

R e l

i g i o u s p r e f e r e n c e

( r e f e r e n c e : P r o

t e s t a n

t )

C a t

h o l i c

. 3 2 6 * * *

. 3 2 4 * * *

. 3 2 6 * * *

. 3 2 9 * * *

. 1 1 1 * * *

. 2 8 3 * * *

. 5 4 3 * * *

( . 0 1 4 )

( . 0 1 4 )

( . 0 1 4 )

( . 0 1 4 )

( . 0 2 3 )

( . 0 2 9 )

( . 0 2 1 )

J e w

i s h

. 1 2 1 * * *

. 1 1 4 * * *

. 1 1 4 * * *

. 1 1 3 * * *

. 1 5 0 * * *

. 2 5 3 * * *

. 0 8 4

( . 0 3 0 )

( . 0 3 0 )

( . 0 3 0 )

( . 0 3 0 )

( . 0 3 8 )

( . 0 6 5 )

( . 0 8 9 )

O t h e r C

h r i s t i a n

. 1 2 1 * * *

. 1 2 0 * * *

. 1 2 1 * * *

. 1 2 1 * * *

. 0 5 2 *

. 1 1 9 * * *

. 1 8 1 * * *

( . 0 1 5 )

( . 0 1 5 )

( . 0 1 5 )

( . 0 1 5 )

( . 0 2 6 )

( . 0 3 1 )

( . 0 2 3 )

O t h e r n o n - C

h r i s t i a n

. 0 3 2

. 0 2 9

. 0 2 8

. 0 2 7

. 1 4 9 * * *

. 0 0 1

. 2 2 7 * * *

( . 0 3 2 )

( . 0 3 2 )

( . 0 3 2 )

( . 0 3 2 )

( . 0 4 2 )

( . 0 7 3 )

( . 0 6 8 )

N o r e l i g

i o n

. 0 3 7 *

. 0 4 5 *

. 0 4 6 *

. 0 4 7 * *

. 0 3 6

. 1 0 1

. 5 2 8 * * *

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 1 8 )

( . 0 2 1 )

( . 0 8 5 )

( . 0 9 7 )

R e l

i g i o u s s e r v

i c e a t

t e n d a n c e

( A )

. 0 2 8 * * *

. 0 1 9 * * *

. 0 2 0 * * *

. 0 2 0 * * *

( . 0 0 0 )

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

C o u n

t y - L

e v e l

C o v a r

i a t e s

P o p u

l a t i o n

d e n s

i t y

i n c o u n

t y ( l o g g e

d )

. 0 2 7 * *

. 0 2 9 * * *

. 0 2 8 * *

. 0 2 8 * *

. 0 2 6 *

. 0 3 2

. 0 2 7 *

( . 0 0 9 )

( . 0 0 9 )

( . 0 0 9 )

( . 0 0 9 )

( . 0 1 2 )

( . 0 1 7 )

( . 0 1 3 )

%

o f p o p u l a t

i o n

i n u r

b a n a r e a

. 0 0 2 * *

. 0 0 1 *

. 0 0 2 * *

. 0 0 2 * *

. 0 0 2 * *

. 0 0 1

. 0 0 1

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

I n d e x o f r a c

i a l d i v e r s i

t y

. 0 3 2

. 0 3 1

. 0 2 7

. 0 2 1

. 0 1 5

. 0 5 0

. 1 6 3

( . 0 6 3 )

( . 0 6 3 )

( . 0 6 3 )

( . 0 6 2 )

( . 0 9 4 )

( . 1 2 0 )

( . 0 8 9 )

%

o f h o u s e

h o l d s

i n p o v e r t y

i n c o u n

t y

. 0 1 1 * *

. 0 1 1 * *

. 0 1 2 * * *

. 0 0 9 *

. 0 0 5

. 0 1 4

. 0 0 7

( . 0 0 4 )

( . 0 0 4 )

( . 0 0 4 )

( . 0 0 4 )

( . 0 0 6 )

( . 0 0 7 )

( . 0 0 5 )

%

o f r e s i d e n

t s w

i t h c o

l l e g e

d e g r e e

i n c o u n

t y

. 0 0 6 * * *

. 0 0 6 * * *

. 0 0 5 * * *

. 0 0 4 * * *

. 0 0 4 *

. 0 0 6 * *

. 0 0 4 *

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 2 )

( . 0 0 2 )

( . 0 0 2 )

M e d

i a n h o u s e

h o l d i n c o m e

i n c o u n

t y ( l o g g e

d )

. 2 6 5 * * *

. 2 6 6 * * *

. 2 4 4 * * *

. 2 1 3 * *

. 1 9 7

. 3 4 8 *

. 0 7 7

( . 0 6 5 )

( . 0 6 5 )

( . 0 7 2 )

( . 0 7 3 )

( . 1 0 8 )

( . 1 4 1 )

( . 1 0 8 )

T a

b l e 2 . ( c o n

t i n u e d

)

( c o n

t i n u e d

)



16/34

761

M o d e l 1

M o d e l

2

M o d e l

3

M o d e l

4

M o d e l

5 a

M o d e l

6 b

M o d e l

7 c

R e g

i o n ( r e

f e r e n c e :

S o u t

h )

E a s t

. 1 5 1 * * *

. 1 5 4 * * *

. 1 2 7 * * *

. 0 0 8

. 0 0 8

. 0 2 1

. 0 1 1

( . 0 2 2 )

( . 0 2 2 )

( . 0 2 3 )

( . 0 3 3 )

( . 0 4 9 )

( . 0 6 3 )

( . 0 4 8 )

M i d w e s t

. 1 5 0 * * *

. 1 5 7 * * *

. 1 3 1 * * *

. 0 8 3 * * *

. 0 1 5

. 1 5 1 * *

. 1 0 6 * *

( . 0 2 0 )

( . 0 2 0 )

( . 0 2 2 )

( . 0 2 4 )

( . 0 3 6 )

( . 0 4 6 )

( . 0 3 5 )

W e s

t

. 1 5 7 * * *

. 1 5 2 * * *

. 1 3 0 * * *

. 1 2 2 * * *

. 0 9 4 *

. 1 3 1 *

. 1 4 8 * * *

( . 0 2 6 )

( . 0 2 6 )

( . 0 2 7 )

( . 0 2 8 )

( . 0 3 9 )

( . 0 5 2 )

( . 0 4 1 )

A v e r a g e s e r v i c e a t

t e n d a n c e

i n c o u n

t y ( B )

. 0 1 4 * * *

. 0 2 2 * * *

. 0 2 0 * * *

. 0 1 8 * * *

. 0 2 0 * * *

. 0 1 4 * *

. 0 0 3

( . 0 0 2 )

( . 0 0 2 )

( . 0 0 2 )

( . 0 0 3 )

( . 0 0 4 )

( . 0 0 4 )

( . 0 0 3 )

A

B

. 0 0 4 * * *

. 0 0 4 * * *

. 0 0 4 * * *

( . 0 0 1 )

( . 0 0 1 )

( . 0 0 1 )

R e l

i g i o u s o r g a n i z a

t i o n

d e n s

i t y

( l o g g e

d )

. 0 7 8 *

. 0 2 9

. 0 1 4

. 0 6 8

. 1 0 9

( . 0 3 1 )

( . 0 4 4 )

( . 0 6 6 )

( . 0 8 6 )

( . 0 6 3 )

C i v i c o r g a n i z a

t i o n

d e n s

i t y

( l o g g e

d )

. 0 6 8 * *

. 0 4 6

. 0 4 3

. 0 1 7

. 0 6 1

( . 0 2 4 )

( . 0 2 5 )

( . 0 3 6 )

( . 0 4 8 )

( . 0 3 7 )

%

E v a n g e

l i c a

l P r o

t e s t a n

t ( l o g g e d

)

. 0 8 9 * * *

. 0 3 2

. 1 2 2 * *

. 1 1 1 * * *

( . 0 2 0 )

( . 0 3 0 )

( . 0 3 8 )

( . 0 2 9 )

%

M a i n l i n e

P r o t e s

t a n t

( l o g g e

d )

. 0 5 7 *

. 0 7 3 *

. 0 3 6

. 0 7 5 *

( . 0 2 3 )

( . 0 3 5 )

( . 0 4 4 )

( . 0 3 3 )

%

C a t

h o l i c ( l o g g e

d )

. 0 0 0

. 0 2 8

. 0 1 8

. 0 1 1

( . 0 1 3 )

( . 0 2 0 )

( . 0 2 5 )

( . 0 1 8 )

C o n s t a n

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Evangelical population. Similarly, public per-ception about the connection between Evan-gelical Protestants and right-wing politicsmay make secular peoplewho tend to be

socially and politically liberalmore reluc-tant to join activities organized by religiousorganizations and heavily attended by Evan-gelical Christians (Hout and Fischer 2002).

We test these possibilities by including thenumber of Evangelical Protestants, MainlineProtestants, and Catholics, each as a percent-age of a countys total population. 8 Consistentwith what previous studies suggest, a strong

presence of Evangelical Protestants is nega-

tively associated with volunteering, whereasthe percentage of Mainline Protestants is pos-itively related. Once these variables areincluded, neither of the organization densityvariables are statistically significant, indicat-ing that relationships in Model 3 are explained

by a countys religious composition. How-ever, adding these variables does not affectthe relationship between average attendance ina county and volunteering, or the interaction

between individual and average attendance.Although a countys religious composition isrelated to volunteering in ways predicted by

previous studies, this does not explain whynonreligious people in highly religious areasare less likely to volunteer than the nonreli-gious in more secular areas. 9

In Models 5 through 7, we estimate thespecification from Model 4 separately forweekly churchgoers, occasional churchgoers(i.e., attended services almost every weekor about once a month), and non-churchgo-ers (attended services seldom or never).In addition to checking the robustness of thecross-level interaction effect, this approachallows us to investigate whether effects ofother religion variables at the county levelalso vary by the level of individual attendancewithout including a number of cross-levelinteraction terms. As expected, averagechurch attendance in a county is negativelyrelated to volunteering among non-churchgo-ers (Model 5) 10 but there is no relationshipamong weekly churchgoers (Model 7). Thereis also a strong negative relationship between

average attendance and volunteering amongoccasional churchgoers, who attend religiousservices almost every week or about oncea month (Model 6). In other words, it is not

just secular people in highly religious areaswho are less likely to volunteer. Even moder-ately religious people who do not attend areligious service weekly are less likely tovolunteer if they live in a highly religiousarea. This finding is puzzling, because wewould not expect occasional churchgoers to

be turned away by a strong presence of reli-gion in the voluntary sector. We return to thisfinding later in the article.

In addition, the percentage of EvangelicalProtestants is negatively related to volunteer-ing among weekly and occasional churchgo-ers, but not among non-churchgoers. Thesefindings also seem strange, because we wouldexpect secular people, not regular churchgo-ers, to be more likely to turn away from vol-unteering activities when there is a strong

presence of Evangelical Protestants. We sus- pect these findings have to do with the com-

positional effect. Although we control forindividuals religious affiliation in the mod-els, we do not distinguish Evangelical Protes-tants from Mainline Protestants because theDaily Poll does not ask about specific denom-inations. As a result, the stronger negativerelationship between the percentage of Evan-gelical Protestants and volunteering forchurchgoers may capture cross county com-

positional differences among Protestants.

Summary

Using the Gallup Daily Poll data, we find thataverage religious service attendance in a localarea is either unrelated or negatively relatedto volunteering, depending on individualslevel of attendance. Contrary to what the net-work spillover hypothesis predicts, our find-ings suggest that nonreligious individualsliving in more religious areas are significantlyless likely to volunteer than are their counter-

parts in secular areas. Furthermore, occa-sional churchgoers are also less likely tovolunteer when they live in highly religious



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areas. Only weekly churchgoers volunteeringis unrelated to contextual religiosity. Althoughnot shown here, we find similar patterns withthe Social Capital Community Benchmark

Survey (SCCBS) data (Roper Center forPublic Opinion Research 2001). Our analysesof these data suggest that the negative rela-tionship between average attendance in alocal area and volunteering among the nonre-ligious applies to religious and nonreligioustypes of volunteering. 11

This study suggests that the negative rela-tionship is not driven by a strong presence ofEvangelical Protestantism in an area. Even

though the number of Evangelical Protestantsin a county is negatively related to volunteer-ing, it does not change the relationship betweenaverage attendance and volunteering. Indeed,the relationship between Evangelicalism andvolunteering is stronger among weekly church-goers than among non-churchgoers. We alsofind no evidence that the density of religious orcivic organizations in a county explains thenegative relationship between contextual relig-

iosity and volunteering.The findings in this section do, however,directly challenge the spillover hypothesis,which predicts that nonreligious people inreligious areas will volunteer at a higher ratethan their counterparts in secular communi-ties. Our findings are also inconsistent withthe light-switch hypothesis, in that contextualreligiosity is not related to volunteeringamong weekly churchgoers. We will return tothese findings about the role of local religiouscontext in our discussion. Next, however, weconsider the role of religious context meas-ured as religiosity among an individualsclose ties.

Does Having Religious FriendsPredict Individuals Volunteering?

In the previous two sections, we attempted toinfer the existence of a network spillovereffect from correlations between national orlocal religious contexts and individuals vol-unteering. This section tests the network spill-over hypothesis more directly by looking at

the relationship between religious composi-tions of intimate social networks and volun-teering. Using the Faith Matters surveys (FM),we examine whether nonreligious individuals

are more likely to volunteer when they havefriends who are religious. If having religiousfriends increases the likelihood that nonreli-gious people will volunteer, it will offer themost direct evidence for the spillover effect.

Faith Matters is a nationwide panel studyconducted between 2006 and 2011 to examinethe connection between religion and civic lifein the United States (Putnam and Campbell2010). The first wave of data was collected

through phone interviews with a large nationalrepresentative sample of respondents ( N =3,109). 12 Respondents were contacted for thesecond interview approximately a year laterand 61.6 percent ( N = 1,915) of the originalrespondents participated. In 2011, all originalrespondents were contacted again for the thirdinterview. A total of 1,810 respondents werereached and 1,685 of them (93 percent) par-ticipated in the survey, yielding a re-interview

rate of 54.2 percent.13

In addition, the 2011study interviewed a fresh sample of respond-ents; when combined with the panel respond-ents and properly weighted, the full sample ( N = 2,646) is representative of the U.S. popula-tion in 2011. In this study, we use the 2006 and2011 data for cross-sectional and panel analy-ses. 14 This dataset is useful for examining thenetwork spillover hypothesis for several rea-sons. First, these data include measures ofreligious compositions of respondents inti-mate social networks. Most relevant to thisstudy are questions that ask whether any of arespondents closest friends belong to one ofthe four major religious traditions: MainlineProtestant, Evangelical Protestant, Catholic,or other non-Christian religions. These ques-tions were repeated in 2011. In addition, the2011 survey asked whether any friends withreligious affiliations attended religious ser-vices regularly. As a result, we can examinewhether having churchgoing friends, ratherthan only nominally religious friends,increases the probability of volunteering forsecular people. Second, by using the panel



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data, we can attempt to address the issue ofcausality in a way that improves upon previ-ous studies based on cross-sectional data.Finally, a relatively large sample size, which

includes a substantial number of respondentswho are not religiously observant, is a greatadvantage because we focus on individualswho are not involved in organized religion.

The key outcome variable in this section iswhether respondents volunteered in the past12 months. Because we are interested inwhether having religious friends increases the

probability of volunteering among individu-als who are not regular members or partici-

pants of religious congregations, our analysisfocuses on respondents who report that theydo not attend religious services on a regular

basis. Ideally, our analysis would focus onindividuals who never attend religious ser-vices and claim no religious affiliation, butthis would limit the sample size to only acouple hundred respondents. Instead, we usea more generous threshold of about once ortwice a year to identify nonreligious respond-

ents.15

Given our interest in volunteering behaviors among people who are not regular participants in congregations, the threshold ofonce or twice a year seems reasonable. 16

We control for various personal character-istics that may influence both friendship withreligious people and volunteering. In additionto sociodemographic characteristics and per-sonal religiosity, we control for the number ofclose friends and the level of social engage-ment to ensure that the relationship betweenreligious friendship and volunteering is notdriven by a high level of social involvementamong nonreligious people with religiousfriends. For a similar reason, we control forthe number of racial groups in which respond-ents have close friends. For nonreligious peo-

ple, friendship with a religious person might be considered a bridging friendship; there-fore, the relationship between religiousfriendship and volunteering could be due to

personal characteristics (e.g., a high level oftolerance or civic mindedness) of people whohave more bridging friendship ties in general.This is particularly a concern because our

measure of religious friendship counts thenumber of religious traditions among whichrespondents have close friends rather than thetotal number of religious friends. In addition,

we use these panel data to examine whetherthe change in the number of religions amongclose friends between 2006 and 2011 predictsvolunteering in 2011, net of volunteering in2006 and other individual characteristics (seeTable A3 in the Appendix for a complete listof variables used in this section).

Findings

Table 3 presents results from logistic regres-sion analyses predicting the likelihood ofvolunteering in either 2006 or 2011. Model 1shows how the number of religious traditionsin which respondents have close friends isrelated to volunteering after controlling for

personal characteristics. 17 Controlling forrespondents own religiosity and levels ofsocial involvement, individuals who never orrarely attend a religious service but have

friends in more religious groups are signifi-cantly more likely to volunteer. When allvariables in Model 1 are set to their meanvalues, 35.5 percent of people who have noclose friends in any of the four religiousgroups are predicted to have volunteered inthe past year. In comparison, the model pre-dicts that 42.6 percent of people with closefriends in one religious group and 64.5 per-cent of respondents with friends in all fourreligious groups have volunteered. 18

Even though our analyses in Model 1focus on people who never or rarely attendreligious services, many of these respondentsdo have religious preferences. One may there-fore wonder whether having close friends inones own religious group is more influentialthan having friends only in other religions.One possibility is that bonding ties may bemore effective than bridging ties in recruiting

people for volunteer activities. Model 2examines this question by comparing respond-ents who have friends in their own religiousgroups with those who have friends only indifferent religious groups. Because most



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Table 3 . Logistic Regressions of Volunteering on Religious Friendship Networks (respond-ents who attend religious services once or twice a year or less)

Volunteering in 2006 Volunteering in 2011

Model 1 Model 2 Model 3 Model 4 Model 5Age .004 .002 .016

Religion and Volunteering in Conxtet

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