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Social Science & Medicine 67 (2008) 606–617

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Social Science & Medicine

journal homepage: www.elsevier .com/locate/socscimed

Social capital and self-rated health: Results from the US 2006social capital survey of one community

Jennifer Schultz*, A. Maureen O’Brien, Bedassa TadesseDepartment of Economics, University of Minnesota, Duluth, MN 55812, United States

a r t i c l e i n f o

Article history:Available online 13 June 2008

Keywords:Individual social capitalSocial trustSocial interactionSocial networksSelf-rated healthUSA

* Corresponding author. Department of Economicnesota, 165 SBE, 412 Library Drive, Duluth, MN 5Tel.: þ1 218 726 6695.

E-mail address: jschultz@d.umn.edu (J. Schultz).

0277-9536/$ – see front matter � 2008 Elsevier Ltddoi:10.1016/j.socscimed.2008.05.002

a b s t r a c t

Using data from the 2006 Social Capital Community Survey in Duluth, Minnesota, andSuperior, Wisconsin, USA, we investigate associations between individual social capitalmeasures (attitudes on trust, formal group involvement, informal socializing, organizedgroup interaction, social support and volunteer activity) and self-rated health after control-ling for individual and economic characteristics. In particular, we address issues of socialcapital as an endogenous determinant of self-reported health using instrumental variablesprobit estimation. After accounting for the endogeneity of these various measures ofindividual social capital, we find that individual social capital is a significant predictor ofself-rated health.

� 2008 Elsevier Ltd. All rights reserved.

Introduction

Social capital, defined as ‘‘features of social organization,such as trust, norms and networks that can improve theefficiency of society by facilitating coordinated actions,’’(Putnam, 1993) has been identified as a componentexplaining variation in a wide range of public healthmeasures. An observed relationship between social capitaland health dates back to over 100 years when EmileDurkheim (1897), in his classic study of suicide, noted thatsocial integration can enhance well-being. Bourdieu (1980,1985) provides an early, contemporary analysis of socialcapital. He defines social capital as ‘‘the aggregate of theactual or potential resources which are linked to possessionof a durable network of more or less institutionalizedrelationships of mutual acquaintance or recognition.’’(Bourdieu, 1985, p. 248). He highlights the benefits to indi-viduals from participating in groups and the importance ofcreating these interactions and suggests that building social

s, University of Min-5812, United States.

. All rights reserved.

networks require investments in group activities. For anextensive review of other contemporary contributors tosocial capital refer to Portes (1998). More recently,a renewed interest in socioeconomic determinants ofhealth has also emerged resulting in numerous studieslinking social capital with various aspects of health andwell-being. Lomas (1998) highlights the importance offactors such as social cohesion resulting from high levelsof social capital by noting that individuals and their ill-health cannot be understood solely by looking inside theirbodies and brains; one must also look at their communities,their networks, their workplaces, their families and eventhe trajectories of their life.

Social capital affects health through a variety of path-ways. Kawachi, Kennedy, and Glass (1999) suggest threeways that social capital may influence individual health.First, the formal and informal social networks associatedwith high levels of social capital may help people to accesshealth education and information, address cultural normswhich may be detrimental to health (such as smoking)and advance prevention efforts. Second, social capitalmay influence health through collective action to designbetter health care delivery systems thereby increasingaccess to services. Finally, the support systems associated

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with social capital may act as a source of self-esteem andmutual respect. Brunner (1997) and Brunner and Marmot(1999) hypothesize a biological mechanism linking socialcapital and health noting that social isolation and lack ofsocial support may produce high levels of psychologicalstress. Chronic stress may be associated with conditionssuch as heart disease, depression, diabetes and high bloodpressure. Putnam (2000) claims that of all the domains inwhich the consequences of social capital have been traced,in none is the importance of social connectedness so wellestablished as in the case of health and well-being. Theissue, however, remains contentious in terms of definition,measurement, and interpretation. Kawachi, Kim, Coutts,and Subramanian (2004), for example, point out that oneimportant disagreement involves the distinction in defin-ing social capital as an individual attribute arising from so-cial networks and relationships or as a collective responsethat is located in the context of a country, region or state.This distinction is not inconsequential; it affects not onlyhow factors related to social capital are measured butalso influences the interpretation of the mechanisms link-ing social capital and health. Subramanian, Lochner, andKawachi (2000) also indicate that such a distinction is im-portant in guiding public policy on whether interventionsshould be targeted at ‘‘people’’ or ‘‘places’’.

Summarizing the results of 31 empirical studies investi-gating the relationship between social capital and healthconceptualized either at the community level or utilizinga multi-level framework to reflect both individual and com-munity responses, Kawachi et al. (2004) note that with fewexceptions, these studies consistently find an association be-tween social capital and health outcomes, seemingly provid-ing credence to the notion that social capital is a contextualvariable that benefits communities or societies. Relation-ships between measures of individual social capital andhealth outcomes have also been established by studies thatexamine the link at the individual level. Investigating the re-lationship between self-reported physical and emotionalhealth and social capital indicators defined at the individuallevel including measures of involvement in formal and infor-mal networks, trust, and a sense of being in control of one’sown life while controlling for the effects of age, education,gender, income and social status Rose (2000), for example,reports that individual social capital explains considerableamounts of variation in individual health in Russia. Hyyppaand Maki (2003) also report similar results. After adjustingfor demographic variables, their analysis shows that socialcapital indicators such as trust and participation are posi-tively related to good self-rated health in a bilingual commu-nity in Finland. In contrast, Veenstra (2000) finds thatindividual-level aspects of social capital such as social en-gagement in clubs and associations and trust are not stronglyrelated to self-rated health, and concludes that there is littleevidence of the compositional effects of social capital.

While the results from contextual approaches may beopen to individualistic interpretations, individualisticapproaches may also fail to recognize important contextualinfluences. To this end, Kawachi et al. (2004) argue thata multi-level framework in which individuals are ‘‘nested’’within areas is the most appropriate study design. Utilizingthis approach and the European Social Survey data,

Poortinga (2006) finds strong associations between indi-vidual levels of social trust, civic participation and self-rated health. However, after adjusting for compositionaldifferences in socio-demographics, the same study alsofinds that social trust and civic participation variables,when aggregated at the national level, are unrelated toself-rated health. Despite a lack of a direct contextual effect,Poortinga (2006) did find evidence of interactions betweensocial capital at the national level and social trust and civicparticipation at the individual level, suggesting a lack ofseemingly direct benefits from the available social (sup-port) networks to individuals who are less willing or ableto engage with others.

In summary, while there appears to be evidencesuggesting a strong relationship between social capitaland health outcomes, both at national and individual levels,how social capital should be conceptualized to understandits pathways to health is less clear. Indeed, not only arethere widespread calls for more research to investigatethe specific mechanisms that link social capital with health,but also, given the wide variety of constructs used inmeasuring social capital, a need to examine which compo-nents have substantive significance in health models. Wefocus our research on testing the effects of individual socialcapital on self-reported health. Our primary hypothesis isthat by providing support, encouraging social engagement,and facilitating interpersonal bonding, individual socialcapital enhances healthy behavior (e.g. smoking cessation,exercise, health care utilization, and drug use deterrence)through changes in attitudes, behavior and the supply ofinformation, which in turn leads to improvement in healthby enhancing individual’s responses to stressors and influ-encing self-esteem. In this link, while we acknowledge thepotential indirect effect of environment on both individualsocial capital and health, in this research we do not accountfor community or sub-community specific-factors that maypromote the health of the population.

We measure individual social capital using six differentbroad and multi-dimensional variables: social trust, orga-nized group interactions, informal social interactions,formal group involvement, social support and volunteeractivity. Previous studies have conceptualized that similarto producing human capital by investing in education, indi-viduals are able to build social capital by investing theirtime and financial resources in developing social relation-ships that may bring them a non-monetary benefit. Viewedfrom this perspective, social capital is a variable thatdepends on other individual and household characteristics,making it a potentially endogenous variable in models test-ing the effects of social capital on individual health. In thisstudy, we model health as a function of social capital whilesimultaneously evaluating the compositional effects ofindividual and household characteristics on both individualhealth and social capital. The results from our study suggestthat individuals with greater social trust and those who aremore socially engaged (through organized, informal andformal social interactions or through volunteer activities)have a higher probability of being healthy than those wholack social trust or those who are less socially engaged.

The remaining part of the paper is organized as follows.First, we present our theoretical framework. Next, we

J. Schultz et al. / Social Science & Medicine 67 (2008) 606–617608

introduce our methodology, and the data along with a de-scription of the variables and the methods used to constructthe individual social capital indexes. Finally, we present theresults from the empirical model, closing with a discussionof the study implications.

Theoretical framework

Several studies indicate that social capital is an invest-ment and consumption good that increases utility andimproves the welfare of a household (e.g. Bolin, Lindgren,Lindstrom, & Nystedt, 2003; Glaeser, Laibson, & Sacerdote,2002). We follow the theoretical model from Bolin et al.(2003) linking health and social capital by extending Jacob-son’s (2000) model that combines Becker’s (1965, 1974)theory of household production of goods and Grossman’s(1972) theory of health production. In Becker’s modelhouseholds produce commodities by using inputs of goodsand time according to the cost-minimization rules used intheories of firm behavior, and commodities are producedwith inputs of market goods and time. Consumers maxi-mize utility subject to a budget and resource constraintwhich determines the mix of commodities. Since marketgoods and services are inputs into the production of com-modities, in Becker’s (1965) model, the demand for com-modities is a derived demand. Becker (1974) alsoincorporates the idea of social interactions into standardeconomic theory. According to this theory, individualsengage in social interaction to increase their productivity.

Grossman (1972) constructs a theoretical model of thedemand for health and medical care (health investments)where individuals are producers of their own healththrough the selection of health behaviors (diet, exercise,smoking, drinking) and the consumption of medicalservices/goods that prevent illness and restore health. Themodel thus, leads to two interpretations of health capital:one, as a consumption good where individuals enjoy beinghealthy and the other as an investment good where healthmay influence the time available for productive use.

By including each family member as a producer of herown and other family members’ health, Jacobson (2000) ex-tends Grossman’s model to family units. Her model predictsthat families will invest in health until the rate of marginalconsumption benefits equals the rate of marginal net effec-tive costs of health capital. Combining Becker’s and Gross-man’s ideas and extending Jacobson’s model to socialcapital, Bolin et al. (2003) construct a theoretical model ofthe family as producer of health and social capital. In theirmodel, a household simultaneously invests in both healthand social capital. In this framework, the returns frominvesting in social capital, where families form and maintainconnections to other people, are direct, as interactions yieldutility, and indirect, as social capital stretches the house-hold’s resources. The factors that determine the family’sability to transform time and market goods into commodi-ties will enter the demand functions for market goods,health and social capital. We thus follow the theoreticalmodel derived by Bolin et al. (2003) which informs the de-velopment of our empirical model that allows examinationof whether social capital facilitates the production of healthfor individuals within households. In its simple form, Bolin

et al. (2003) present household utility from consumptionof commodities, health, and social capital as a function ofhealth (Ht) of each family member [each parent (P1, P2)and each child (Cj)], a commodity (Zt) produced by thehousehold and its social capital (St), described in Eq. (1):

U�

HP1

t ;HP2

t ;HCj

t ; Zt ; St

�(1)

The household allocates its resources over time to maxi-mize lifetime utility subject to a subjective rate of discount-ing and initial stocks of health and social capital for eachfamily member; the stocks of health and social capitalchange according to the gross level of investment in healthand social capital and rates of depreciation. Two conditionsrestrain the optimization of this utility: (i) the marginalcost of gross investments in health decreases with the stockof social capital and (ii) family wealth is a function ofmarket income, which is dependent on parental and childhealth, the price of the consumption commodity, the rateof interest and adjustment costs (i.e. the cost involved inadjusting from the actual to the desired stock of healthcapital). The first order conditions from the optimizationproblem imply that the family invests in health until themarginal utility of investment in health is equal to the mar-ginal cost for all household members simultaneously. Thismeans that the marginal rate of substitution between theparents’ consumption and investment benefits of healthis equal to the ratio of net marginal costs of health invest-ments. Likewise, the family invests in social capital untilthe marginal utility of an additional unit of social capitalis equal to the marginal cost of that unit. Furthermore,the family will allocate resources to equate the marginalutility of health that can be bought per dollar for each fam-ily member. Finally, the family invests in social capital untilthe marginal utility of an investment in social capital isequal to the marginal cost of the investment. The structuralequations of the model, constraints and first order condi-tions are discussed in Bolin et al. (2003).

The predictions derived from this model are that the netmarginal cost of health investments decreases with thelevel of social capital. This implies that the stock of healthcapital is larger in families where the stock of social capitalis high, all else constant. The model is restricted to evaluat-ing positive social capital. Nonetheless, in certain situa-tions, social capital derived from gang involvement ororganized crime could promote negative health behaviors(Paldam, 2000). Although, gang activity and organizedcrime in this community are rare to non-existent. Thesame variables that affect the optimal amounts of healthcapital may also determine the optimal amounts of socialcapital (e.g. age, education, family structure, and unem-ployment). The model predicts that the level of socialcapital will decrease with age, if the rate of depreciationof social capital increases with age. Investment in socialcapital may be lower for those who are married or cohab-itating, if the family is considered a substitute for socialcapital. Even though being married or cohabitation mayincrease the level of health, it could be muted once socialcapital is considered. Furthermore, the presence of childrenin the household may increase or decrease social capitaldepending on whether they are considered substitutes or

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complements to social capital. Employment effects may goin either direction as it depends on whether working timeis a complement to or substitute for social capital. Finally,being divorced may limit the size of one’s social networkand thus incorporating social capital could strengthen thenegative relationship between divorce and health.

Methodology

Data and the study design

We use data from the 2006 Social Capital CommunitySurvey (SCCS) of Duluth, Minnesota and Superior, Wiscon-sin (considered as one community as they are similar andclose to each other geographically). The data come froma survey sponsored by the Duluth/Superior Area Commu-nity Foundation and the Kennedy School of Government,directed by Dr. Robert Putnam, of Harvard University. The2006 Social Capital Community Survey was also conductedin partnership with seven other community foundationsand two national foundations (the Surdna Foundationand the Bernard and Audrey Rapoport Foundation).

Built on the 2000 Social Capital Community BenchmarkSurvey, developed by the Saguaro Seminar at Harvard Uni-versity, a random telephone survey of 500 households inDuluth/Superior was conducted by an international pollingfirm (TNS Intersearch) in mid-April 2006. Designed to mea-sure levels of individual and community social capital thetelephone survey queried these households about theirlevels of trust, civic engagement, and community connect-edness. Various measures of individual social capital wereconstructed based on the patterns of individual attitudes,behavior and activities in a community as revealed in thesurvey. The interviews further elicited information on indi-vidual characteristics (education, age, income, marital sta-tus, employment, children, and length of time inresidence in the community), a variety of behaviors andactivities (giving, volunteering, attending religious services,voting and other forms of political participation), attitudesand perceptions about the local community. The surveyresponse rate was 24.5%, which is higher than the nationalrate of the SCCS. The survey respondents are representativeof the target population, according to measures from the2000 US Census (e.g. similar age, gender, race, educationand income distributions).

The survey elicited information on individual andhousehold characteristics as well. These include age cate-gories (18–34, 35–49, 50–64, and 65þ), education level(high school or less, some college, and college graduate),marital status (married, living with partner, single, andwidowed), employment (employed, unemployed, retired,and disabled), household income (under $30,000, $30–$50,000, $50–75,000, and $75,000þ), any children underage 17 living in household, years of residence in thecommunity (living in community 5 years or less), race(white and non-white) and frequency of attending religiousactivities, excluding weddings and funerals [0 to a fewtimes per year versus more often (1–2 times per month,almost weekly, or weekly/more often)] and self-reportedhealth status (excellent, very good, good, fair or poorhealth). Self-reported health is the only health measure

available in the survey. Nonetheless, self-reported healthhas been shown to be a valid measure of general health(Gold, Franks, & Erickson, 1996; Idler & Benyamini, 1997;Lundberg & Manderbacka, 1996).

While testing the hypothesis using data from each of theeight communities covered in the overall survey mightprovide more insight, we restrict our analysis to data fromthe Duluth/Superior community because data from the sevenother communities were not available. Nonetheless, restrict-ing it to Duluth/Superior is helpful for several reasons. First,this community is perceived to have high levels of socialcapital [i.e. of eligible voters, participation rates in the 2004election were 75% compared to 55% nationally; volunteeractivity is higher in Duluth/Superior (13.77 h/year) comparedto the national average (9.69 h/year); labor union member-ship is 26% in Duluth/Superior versus 10% nationally]. Second,this community is older (according to 2000 Census, the per-cent of the population over 65 in Duluth is 15.1 versus 12.6nationally) and less mobile than other communities surveyed.Third, Duluth/Superior is fairly homogeneous in terms of race/ethnicity (according to the US Census, the percent of the pop-ulation in Duluth, MN and Superior, WI that is white is 92.7%and 94.3%, respectively).

Measures of social capital

In the 2000 SCCS study, researchers affiliated with theSaguaro Seminar constructed indexes of social capital basedon responses to clusters of questions.We replicated thismeth-odology to construct similar social capital indexes based onthe 2006 SCCS, using theoretical understandings of socialcapital. For example, the answers to five questions abouttrusting people in various contexts are combined to form a so-cial trust index. The Harvard team computed 10 indexes theyfelt were the most meaningful indicators of various aspects ofsocial capital (social trust; interracial trust; electoral politics;protest politics; civic leadership; associational involvement;informal socializing; diversity of friendships; giving and vol-unteering; and faith-based engagement). Concordantly, wecreate indexes that we believe are relevant in a health-behavior framework, namely social trust, associationalinvolvement, informal socializing, organized group interac-tion, and social support. With the exception of the social trustindex, these indexes measure whether a person is engaged insocial networks. The social trust index measures an individu-al’s trusting attitude (trustworthiness of others) and may bethought of as a result of social engagement or as social capitalrequired to participate in social engagement (Sobel, 2002).The responses from the questions used to construct the indi-vidualized indexes were created using national norms, bysubtracting the mean and then dividing by the standard devi-ation of the 2006 national sample to standardize eachresponse. We computed Cronbach’s coefficient (a) to checkthe reliability of the variables used for constructing each ofthe individual social capital indexes. Varying from 0.743 (forresponses to questions that enter into the construction ofthe social trust index), 0.6365 (for associational involvementindex), 0.657 (for informal social interaction index) and0.439(for organized interaction index) to 0.309 (for socialsupport index), the estimated coefficients show a moderateto high reliability coefficient. Each index is described below.

J. Schultz et al. / Social Science & Medicine 67 (2008) 606–617610

Social trustFive questions are used to construct the social trust

index. These questions include a question on general trust(yes/no): ‘‘Would you say that most people can be trusted,or that you can’t be too careful in dealing with people?’’ Thefour remaining questions ask participants whether theytrust ‘‘a lot, some, only a little, or not at all’’ the ‘‘peoplein your neighborhood;’’ ‘‘people you work with;’’ ‘‘peoplewho work in the stores where you shop;’’ and ‘‘police inyour local community.’’

Associational involvementThis counts the number of groups the respondent has

been involved with in the 12 months prior to the interview.The 17 questions included in the index cover the followingkinds of groups: an organization affiliated with religion; anadult sports club or league or outdoor activity club; youthorganizations such as youth sports leagues, scouts, and4H; a parents’ association like PTA or other school supportgroup; a veteran’s group; a neighborhood association;clubs or organizations for seniors; a charity or socialwelfare organization that provides services; a labor union;a professional, trade, farm or business association; serviceclubs or fraternal organizations such as Lions or Kiwanisor women’s club; ethnic, nationality, or civil rights groups;other public interest groups, PACs, political clubs or partycommittees; a literary, art, discussion or study group ormusical, dancing or singing group; any other hobby, invest-ment or garden clubs or societies; and support groups orself-help program for people with specific problems.

Organized group interactionsThis index is constructed from three questions: the

number of times in the past 12 months the respondent(1) attended any public meeting in which town or schoolaffairs were discussed, (2) attended a club meeting, and(3) attended a celebration, parade, or local sports or artevent.

Informal social interactionsThis index is based on the answers to five questions

about socializing over the past 12 months. Respondentsare asked how many times they had (1) friends over totheir home, (2) visited relatives in person or had themvisit their home, (3) socialized with coworkers outsideof work, (4) hung out with friends at park, shoppingmall or public place, and (5) played cards or board gameswith others.

Social supportThis index is based on answers to four questions about

social support. These include how often they communicatewith immediate neighbors, how many close friends theyhave, how many people they have to share confidencesand difficult decisions, and the number of times theyhave been in the home of a neighbor.

Volunteer activityThis is not an index. It is measured as a continuous

variable. The question asked respondents how many timesthey volunteered in the 12 months prior to the survey.

The empirical model

As indicated earlier we investigate whether individualsocial capital affects self-rated health status. To do this wefirst must address the endogenous nature of social capital.Thus, we estimate social capital as a function of individualand household characteristics, and then estimate healthstatus as a function of predicted values of various individualsocial capital measures and individual and household char-acteristics. Given that we have a dichotomous health mea-sure and continuous social capital variables, we employ aninstrumental variable probit (IV probit) estimation tech-nique. In modeling measures of social capital as dependentvariables and treating them as consumption and invest-ment goods, we include explanatory measures of time costsand variables that proxy for the ability of individuals toproduce social capital. These include age, gender, married,living with a partner, divorced, any children under 17 yearsof age living in household, white, household income,education, employed and retired.

The instrumental variables we use are whether the indi-vidual resided in the community for less than 6 years anda religiosity variable (an indicator variable that equals oneif the individual reports attending religious service at leastonce per month, 0 if less than twice per year). DiPasqualeand Glaeser (1998) provide evidence that tenure in a com-munity explains variation in measures of social capital. Inparticular, they find a positive effect of homeownershipon social capital and attribute this to lower mobility ratesfor homeowners. Other studies find that religiosity influ-ences social capital consumption and investment. Ellisonand George (1994) and Glaeser, Laibson, Scheinkman, andSoutter (1999) find a positive correlation between socialtrust and religion/church attendance. Thus, the instrumen-tal variables are hypothesized to be highly correlated withsocial capital measures and uncorrelated with unobservedvariables that may influence health (i.e. independent ofthe error term in the health status equation).

Our empirical model is adapted from Bolin et al.’s (2003)theoretical model that links individual social capital withhealth. Unlike Bolin et al. (2003), however, we do notaccount for adjustment costs, and thus do not includelagged or future health status variables as our data do notextend beyond the specific reference period. The demandfor health equation that we estimate is, therefore:

Ht [ aXtD4StDyt (2)

where Ht is the stock of health capital in period t, Xt

includes demographic and family characteristics such asmarital status, number of children, age, education andemployment, and St is the level of social capital. We hy-pothesize that 4> 0. The demand for social capital equationis simply

St [ asXstDgIVtDyt (3)

where St is the stock of social capital in period t, Xst includesdemographic and family characteristics and IVt is instru-mental variable that identifies the system of equations sincethe stock of social capital in period t is an endogenous vari-able in Eq. (2). The instrumental variables we use include

J. Schultz et al. / Social Science & Medicine 67 (2008) 606–617 611

tenure in the community and religiosity. These are hypoth-esized to affect social capital at time t but are not correlatedwith unobserved factors that affect health capital at time t.The estimating equations for self-reported health status(1¼ healthy and 0 otherwise) of individual j, are therefore,

Pr�Yjs0

��Xj

�[ F

�Xjb�

(4)

where we treat self-reported health as a dichotomous out-come, and

Pr�Health Statusj [ i

�

[ Pr�kiL1<b1X1jDb2X2jD/DbkXkjDuj£ki

� (5)

where we treat self-reported health as a multi-outcomevariable using an ordered probit model (using all self-reported health responses). In the estimating equations, F

refers to the standard cumulative normal distribution, uj

is a normally distributed error term with k1, k2, . ki�1

cut-off points, where i is the number of possible healthoutcomes [which varies from the lowest outcome of 1(poor health) to the highest outcome of 5 (excellenthealth)] in our model.

We hypothesize that having children at home and beingemployed are likely to be complementary to social engage-ment. For instance, having children may increase member-ship in schools and youth groups increasing parentalinvolvement in school-related activities. Education mayincrease the marginal product of time and therefore mayhave a positive effect on the demand for social capital.Highly educated individuals are also more likely to beinformed and have the skills necessary to engage in socialactivities. Income is hypothesized to have a positive effecton individual social capital since those with higher incomesmay be able to demand more social capital assuming it isa normal good. Higher income households may also needto work less thus leaving more time to spend in producingsocial capital. Those who are retired will have more freetime to devote to producing social capital, thus we expectto see a positive effect. We expect those who live in thecommunity a short time to be less able to produce socialcapital as compared to those who have longer tenure inthe community. Finally, race and gender are used as controlvariables to capture the potential differences in culturalnorms and experiences (i.e. being discriminated against)and since social trust has been found to vary by race [seefor example Alesina and La Ferrara (2002), Glaeser, Laibson,Scheinkman, and Soutter (2000), and Toussaint, Kiecolt,and Morris (2000)].

We consider that those who attend religious servicesmore frequently are more likely to be engaged withmembers and thus have higher levels of individual socialcapital. A positive relationship between religiosity, usuallymeasured by the frequency of attending religious services,and health outcomes has been reported in the literature(e.g. Dupre, Franzese, & Parrado, 2006). For instance, a par-ticular religion or religious denomination might prohibitthe use of tobacco products or the consumption of alcohol(Gottlieb & Green, 1984) thus leading to better healthoutcomes. Levin and Vanderpool (1989), however, questionthe validity of the positive association between religion and

health. Accordingly, the effect of religion on health is envi-sioned as reflecting those of norms and networks, both ofwhich are elements of social capital. In this line of argu-ment, frequent religious involvement may lead to betterhealth outcomes not by and of itself but through the socialsupport or sense of belonging that individuals may receive(Strawbridge, Cohen, Shema, & Kaplan, 1997). Ellison andGeorge (1994) provide empirical evidence supporting thehypothesis that frequent religious attendance is positivelyassociated with larger and denser social networks. Theyargue that as shared values and expectations of congre-gants provide a basis for a common discourse of interper-sonal support, religious involvement improves the qualityof interpersonal relationships, and may make it last fora number of years, leading to the accumulation of mutuallysupportive ties between congregants. Examining the rela-tionship between spirituality, health beliefs and behaviorsamong college students, and accounting for other dimen-sions of religious practice, such as the frequency of churchattendance and prayer, Nagel and Sgoutas-Emch (2007)find that religious involvement, including church atten-dance, prayer, and spirituality, does not predict overallhealth or health behaviors. Yet, the authors suggest thatinstitutional expression of religiosity as measured bychurch attendance is more positively related to people’shealth than being intrinsically spiritual. This might suggestthat the relationship between religiosity and health mayinvolve the influence of social networks and norms. To in-form this debate, we investigate the relationship betweensocial capital and health conditional on the associationbetween religiosity and social capital.

Empirical results

Descriptive statistics for the individual social capitalmeasures and explanatory variables by self-reported healthstatus are reported in Table 1. The data in Table 1 indicatethat self-rated health is higher for individuals who areyounger, employed, better educated, have higher incomes,and have children. Joint F-tests conducted to determine ifthe means of the social capital measures differ significantlyacross health status indicate that social trust and organizedgroup interaction are higher for individuals who self-ratetheir health as very good or excellent compared to individ-uals in fair to poor health. The means for the organizedgroup interaction index and the informal socializing indexare higher for those reporting to be in poor or fair healthcompared to those in good health, however.

We estimate the compositional factors that determinelevels of social capital using a two stage IV probit modelfor each social capital measure (social trust, social support,associational involvement, organized group interactions,informal socializing and volunteer activity). The first stageresults analyzing the determinants of individual socialcapital measures are reported in Table 2. Each social capitalmeasure is an index except for associational involvement(number of associations affiliated with) and the volunteeractivity variable (a continuous measure of the number oftimes individuals volunteered in the last 12 months). Allmeasures are regressed on the same set of independentvariables in the equation.

Table 1Descriptive statistics: mean (standard deviation) of variables by self-rated health of respondents

Variables Health status(5¼ excellent)

Health status(4¼ very good)

Health status(3¼ good)

Health status(2¼ fair)

Health status(1¼ poor)

Total sample

Measures of individual social capitalSocial trust 0.3778 (0.6371) 0.2508 (0.6016) 0.0647 (0.6144) �0.0911 (0.6989) �0.0583 (0.8789) 0.1805 (0.6495)Social support 0.5262 (0.1098) 0.5345 (0.1118) 0.5131 (0.1135) 0.4910 (0.1277) 0.5182 (0.1149) 0.5214 (0.1142)Associational involvement 3.1154 (3.0539) 3.2000 (2.8999) 2.6929 (2.5371) 2.2963 (2.5963) 2.4615 (3.0170) 2.9188 (2.8174)Organized group interactions 0.4519 (1.0291) 0.2413 (0.8425) 0.0771 (0.8092) 0.1216 (0.7874) 0.1626 (0.7048) 0.2275 (0.8770)Informal socializing 0.2578 (0.6260) 0.1438 (0.7149) 0.0735 (0.6591) 0.1574 (0.7524) 0.3970 (0.6789) 0.1587 (0.6859)Volunteer activity 18.2308 (21.3697) 15.3353 (20.1474) 12.0945 (18.2693) 16.7593 (25.1298) 12.6154 (18.1408) 15.1880 (20.5654)

Other variablesAge 35–49 0.2692 (0.4457) 0.3000 (0.4596) 0.2598 (0.4403) 0.2222 (0.4196) 0.1538 (0.3755) 0.2692 (0.4440)Age 50–64 0.2885 (0.4552) 0.2882 (0.4543) 0.3071 (0.4631) 0.2593 (0.4423) 0.3846 (0.5064) 0.2927 (0.4555)Age 65þ 0.1923 (0.3960) 0.2941 (0.4570) 0.2992 (0.4597) 0.3519 (0.4820) 0.4615 (0.5189) 0.2842 (0.4515)Female 0.5577 (0.4991) 0.6118 (0.4888) 0.6457 (0.4802) 0.5556 (0.5016) 0.7692 (0.4385) 0.6068 (0.4890)Education some college 0.3077 (0.4638) 0.4059 (0.4925) 0.4252 (0.4963) 0.3704 (0.4874) 0.6923 (0.4804) 0.3932 (0.4890)Education college degree 0.5385 (0.5009) 0.3529 (0.4793) 0.2520 (0.4359) 0.1667 (0.3762) 0.0769 (0.2774) 0.3376 (0.4734)White 0.9712 (0.1682) 0.9647 (0.1851) 0.9843 (0.1250) 0.9259 (0.2644) 1.0000 (0.0000) 0.9679 (0.1763)Married 0.4712 (0.5016) 0.5235 (0.5009) 0.4961 (0.5020) 0.3148 (0.4688) 0.3077 (0.4804) 0.4744 (0.4999)Live w/partner 0.7404 (0.4405) 0.8353 (0.3720) 0.8110 (0.3930) 0.7593 (0.4315) 0.8462 (0.3755) 0.7991 (0.4011)Divorced 0.0865 (0.2825) 0.1176 (0.3231) 0.1496 (0.3581) 0.1852 (0.3921) 0.3077 (0.4804) 0.1325 (0.3394)Employed 0.6635 (0.4748) 0.6176 (0.4874) 0.5276 (0.5012) 0.3519 (0.4820) 0.0769 (0.2774) 0.5577 (0.4972)Retired 0.3077 (0.4638) 0.3353 (0.4735) 0.3937 (0.4905) 0.5370 (0.5033) 0.8462 (0.3755) 0.3825 (0.4865)Children in HH 0.3558 (0.4811) 0.2588 (0.4393) 0.3307 (0.4723) 0.2593 (0.4423) 0.1538 (0.3755) 0.2970 (0.4574)Income 30–50 0.2404 (0.4294) 0.2353 (0.4254) 0.2520 (0.4359) 0.1111 (0.3172) 0.1538 (0.3755) 0.2244 (0.4176)Income 50–75 0.2212 (0.4170) 0.2176 (0.4139) 0.1732 (0.3799) 0.0556 (0.2312) 0.0769 (0.2774) 0.1838 (0.3877)Income 75þ 0.3077 (0.4638) 0.2059 (0.4055) 0.1732 (0.3799) 0.0556 (0.2312) 0.0000 (0.0000) 0.1966 (0.3978)Missing income 0.0577 (0.2343) 0.0706 (0.2569) 0.0945 (0.2937) 0.0926 (0.2926) 0.0769 (0.2774) 0.0769 (0.2668)Living in community <6 years 0.1830 (0.3880) 0.1170 (0.3230) 0.1570 (0.3650) 0.1290 (0.3390) 0.0760 (0.2770) 0.1431 (0.3506)Religiosity 0.5570 (0.4990) 0.5880 (0.4930) 0.5030 (0.5010) 0.5180 (0.5040) 0.5380 (0.5180) 0.5490 (0.4980)

Number of observations 104 170 127 54 13 468

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Table 2Determinants of levels of individual social capital measures (OLS)

Variables Social trust Socialsupport

Associational involvement Organizedinteraction

Informalsocializing

Volunteeractivity

Age 35–49 0.0646 (0.097) �0.0405* (0.024) 0.860** (0.410) �0.0887 (0.130) �0.387*** (0.120) �3.497 (3.230)Age 50–64 0.239** (0.100) �0.0498* (0.027) 0.683 (0.450) �0.269* (0.140) �0.457*** (0.130) �3.618 (3.390)Age 65þ 0.338*** (0.130) �0.0321 (0.031) 1.438** (0.570) �0.255 (0.200) �0.484*** (0.160) �3.04 (4.420)Female 0.116** (0.056) 0.0200* (0.012) 0.245 (0.260) �0.158* (0.084) �0.0317 (0.063) 1.418 (1.940)Education some

college0.140* (0.072) �0.00133 (0.014) 0.485* (0.280) 0.301*** (0.079) 0.0657 (0.081) 4.746** (2.300)

Educationcollegedegree

0.321*** (0.071) 0.00923 (0.016) 1.015*** (0.330) 0.606*** (0.110) 0.0934 (0.086) 9.662*** (2.540)

White 0.598*** (0.210) �0.0376 (0.030) �2.455*** (0.670) �0.408 (0.360) 0.301 (0.240) �5.964 (7.260)Married 0.0962 (0.092) 0.012 (0.016) �0.0957 (0.400) 0.0334 (0.140) �0.323*** (0.110) �2.481 (3.220)Live w/partner �0.13 (0.110) 0.00539 (0.024) 0.105 (0.470) 0.233 (0.150) 0.152 (0.130) 3.404 (3.880)Divorced 0.0306 (0.110) 0.0106 (0.020) 0.38 (0.510) �0.117 (0.150) �0.132 (0.120) �2.819 (3.920)Employed 0.300** (0.150) 0.0638** (0.027) 0.144 (0.500) 0.0357 (0.140) 0.0749 (0.140) 4.157 (3.330)Retired �0.178 (0.160) 0.0377 (0.029) �0.222 (0.570) 0.0513 (0.160) 0.08 (0.150) 6.043 (3.680)Children 0.0277 (0.070) �0.0141 (0.015) 0.834** (0.360) �0.0788 (0.120) 0.0427 (0.083) 4.510* (2.390)Income 30–50 0.0504 (0.081) 0.0275* (0.016) 0.579* (0.340) 0.174 (0.120) 0.00718 (0.094) 0.434 (2.720)Income 50–75 0.145* (0.081) 0.00585 (0.019) 0.744** (0.370) 0.113 (0.110) 0.102 (0.099) �1.47 (2.800)Income 75þ 0.237*** (0.092) 0.0156 (0.020) 0.883** (0.410) 0.0956 (0.130) 0.0482 (0.110) �2.063 (3.030)Missing

income0.00648 (0.120) 0.0498*** (0.019) 0.676 (0.480) 0.085 (0.110) 0.17 (0.120) �1.235 (3.520)

Religiosity 0.117** (0.051) �0.0162 (0.011) 0.709*** (0.260) 0.160* (0.091) 0.0486 (0.052) 7.302*** (1.890)Live in

community<6 years

�0.117 (0.085) �0.0192 (0.036) �0.138 (0.320) 0.00192 (0.120) �0.0418 (0.073) �1.745 (2.490)

Constant �1.002*** (0.270) 0.518*** (0.044) 2.579*** (0.810) 0.194 (0.370) 0.105 (0.270) 7.611 (8.280)r �0.577 (0.480) 0.546 (1.580) �0.6 (0.470) �0.79 (0.730) �1.547 (1.230) �0.457 (0.330)ln s �0.589*** (0.036) �2.208*** (0.042) 0.967*** (0.035) �0.204*** (0.071) �0.425*** (0.032) 2.974*** (0.038)

Observations 468 468 468 468 468 468

Robust standard errors in parentheses; *p< 0.1, **p< 0.05, ***p< 0.01.

J. Schultz et al. / Social Science & Medicine 67 (2008) 606–617 613

Results for the social trust measure of individual socialcapital suggest that those who are 50 years of age or olderhave higher levels of social trust compared to those youn-ger than age 35. Individuals with higher levels of educationand income also have higher levels of social trust. Similarly,those who are employed or attend religious services alsohave higher levels of social trust compared to those whoare unemployed or those who do not frequent religiousservices. The effects for associational involvement are sim-ilar to the results for social trust. Individuals with childrenat home report higher levels of associational involvementas well. The results from the informal socializing modelsuggest that individuals older than 34 years of age areless engaged in these activities compared to individualsyounger than 35. Individuals with some college education,those who have children at home or those who attendreligious services more frequently, report more volunteeractivities compared to individuals with less education, nochildren residing at home or those who attend fewerthan two religious services per year.

The results from the second stage of the IV probit modelsappear inTable 3. The dependent variable in each equation isa dichotomous variable for self-rated health where 1 refersto being in excellent or very good health, and 0 indicatesbeing in good, fair or poor health. The Wald tests of exogene-ity for each measure of social capital included in the IV probitestimation fail to reject the hypothesis that each socialcapital measure is endogenous. Thus, we instrument thesocial capital measures using variables that refer to religious

attendance and tenure in the community. Table 3 data indi-cate that with the exception of the social support measure ofsocial capital, all other measures of social capital are signif-icantly positively correlated with self-rated health. In therestricted or unadjusted IV probit model with no other cova-riates, the social capital measures were also found to bepositive and significant predictors of being healthy.

To investigate the robustness of the relationshipbetween social trust and self-rated health, we also reportresults from an ordered probit model where self-ratedhealth is categorized into five categories: excellent, verygood, good, fair and poor health. Results from the orderedprobit model appear in Table 4. For the ordered probit esti-mation, we report results from models using the actualvalues of social trust rather than predicted values as the re-sults are similar for both specifications. Based on the resultsin the table, the statistically significant covariates of self-rated individual health status include social trust, income,age, education, marital status and disability.

Discussion

The results suggest that individuals with higher levels ofsocial trust, greater associational involvement, more partic-ipation in organized interactions, more informal socializingor those who volunteer perceive themselves to be healthiercompared to those with lower levels of these individualsocial capital measures. Accordingly, a 1% increase in thesocial trust index for an individual increases the probability

Table 3IV Probit estimation of individual social capital measures on self-reported health status

1 2 3 4 5 6

Social trust 1.171** [1.03] (0.580)Social support �3.794 [�9.58] (11.300)Associational

involvement0.227* [3.30] (0.120)

Organizedinteraction

0.851* [1.08] (0.490)

Informalsocializing

1.416*** [2.32] (0.290)

Volunteer activity 0.0234* [1.66] (0.013)Age 35–49 �0.380* (0.220) �0.407 (0.340) �0.472** (0.220) �0.165 (0.250) 0.404 (0.320) �0.217 (0.240)Age 50–64 �0.665*** (0.250) �0.5 (0.370) �0.497** (0.230) �0.066 (0.330) 0.466 (0.370) �0.284 (0.260)Age 65þ �0.784** (0.320) �0.422 (0.330) �0.678** (0.310) �0.0865 (0.340) 0.502 (0.420) �0.309 (0.290)Female �0.156 (0.140) 0.057 (0.250) �0.0781 (0.130) 0.112 (0.140) 0.035 (0.100) �0.0589 (0.130)Education some

college0.039 (0.210) 0.192 (0.240) 0.0823 (0.170) �0.0872 (0.270) 0.00138 (0.170) 0.0943 (0.170)

Education collegedegree

0.163 (0.360) 0.56 (0.380) 0.289 (0.300) �0.0567 (0.530) 0.122 (0.330) 0.327 (0.270)

White �0.950** (0.480) �0.402 (0.330) 0.298 (0.480) 0.116 (0.510) �0.553 (0.340) �0.13 (0.380)Married �0.432** (0.200) �0.265 (0.430) �0.288 (0.210) �0.302 (0.220) 0.309 (0.320) �0.27 (0.210)Live w/partner 0.542** (0.240) 0.417 (0.380) 0.353 (0.260) 0.136 (0.370) �0.0341 (0.360) 0.32 (0.270)Divorced �0.554** (0.280) �0.485 (0.620) �0.592** (0.270) �0.348 (0.370) �0.0555 (0.390) �0.469* (0.270)Employed 0.0488 (0.440) 0.632 (0.450) 0.338 (0.290) 0.297 (0.300) 0.0779 (0.310) 0.297 (0.300)Retired 0.285 (0.310) 0.21 (0.440) 0.101 (0.270) 0.00121 (0.250) �0.0832 (0.260) �0.0862 (0.300)Children �0.293* (0.160) �0.308* (0.170) �0.448*** (0.170) �0.163 (0.220) �0.183 (0.190) �0.381** (0.170)Income 30–50 0.249 (0.200) 0.438** (0.180) 0.175 (0.220) 0.128 (0.260) 0.135 (0.230) 0.317* (0.180)Income 50–75 0.345 (0.290) 0.574 (0.410) 0.35 (0.290) 0.366 (0.350) 0.101 (0.360) 0.585*** (0.200)Income 75þ 0.145 (0.310) 0.503** (0.230) 0.219 (0.280) 0.298 (0.300) 0.13 (0.290) 0.500** (0.210)Missing income 0.148 (0.230) 0.367 (0.490) 0.00245 (0.260) 0.0716 (0.220) �0.168 (0.240) 0.196 (0.240)Constant 1.095 (0.860) 1.835 (5.830) �0.676 (0.490) �0.251 (0.400) �0.174 (0.390) �0.275 (0.460)

Observations 468 468 468 468 468 468Log likelihood �672.1 82.72 �1403 �855.5 �751.9 �2343Wald chi-square 109.00** 66.91*** 86.64*** 101.80*** 363.10*** 71.34***

Robust standard errors in ( ); elasticities in [ ].Dependent variable is healthy¼ 1 if self-reported health status¼ excellent or very good; 0 otherwise.

J. Schultz et al. / Social Science & Medicine 67 (2008) 606–617614

of perceiving to be in excellent/very good health by 1.03%.Similarly, a 1% increase in the associational involvementand informal socializing index of an individual is associatedwith a 3.30% and a 2.32% increase in the probability of anindividual reporting excellent/very good health, respec-tively. Summing over all significant social capital measuresresults in approximately a 10% increase in the probability ofbeing healthy when each index increases by 1%. Further-more, if social capital indexes increase by one standarddeviation this results in a 25% increase in the probabilityof being healthy. Our results also suggest that volunteeringan additional time each year would increase the probabilityof being healthy by almost 11%, all else constant. The direc-tions of these results are consistent with prior researchevaluating individual social capital and health. Based ona Swedish survey Bolin et al. (2003) found that havinga close friend was associated with a larger probabilitythat the respondent reported a high level of health. Hyyppaand Maki (2003) studied two communities in Finland withsimilar socioeconomic status but different morbidity,mortality and disability outcomes. They attribute thehealth inequality with an unequal distribution of socialcapital between the communities. Their results suggestthat associational activity and involvement in friendshipnetworks were significantly associated with good self-ratedhealth. Using cross-sectional data from rural communitiesin Madagascar, Sirven (2006) found that high levels of

social capital, as measured by collective action and socialnetworks, predict better self-rated health. He alsoaddressed the endogenous nature of social capital by mod-eling its composition and found marginal effects of socialcapital on health of approximately 0.42.

Many of our results from the demand for social capitalmodel are similar to prior studies. For instance, Bolinet al. (2003) found that social capital declined with ageand decreased for men and for those who were eithermarried or cohabitating. We found that women reportedhaving more social support and higher levels of social trust,and that organized interaction and informal socializingdeclined with age. Those who were married reported lessinformal socializing as well. As hypothesized higher levelsof education and income predicted higher levels of socialcapital. These latter results mimic those of Sirven (2006).

Our index measuring associational involvementincludes membership in youth or adult sports leagues.This likely involves some physical activity which mayimprove health directly. In light of the possible direct effectof sports league involvement and health, we re-estimatedthe associational involvement-health model after exclud-ing the sports league component and found similar results.The relationship between physical activity (as a way toimprove social capital) and health may be indirect aswell. For example, Lindstrom, Hanson, and Ostergren(2001) found that low social participation (e.g. activities

Table 4Ordered probit estimation of social trust on self-reported health status

Ordered probit(raw coefficients)

Marginal effects (ordered probit results)

1 2 3 4 5 6

Social trust 0.226** (0.089) �0.00612* (0.003) �0.0371** (0.015) �0.0449** (0.018) 0.0272** (0.012) 0.0610** (0.024)Age 35–49 �0.373** (0.180) 0.0126 (0.009) 0.0664* (0.035) 0.0677** (0.030) �0.0539* (0.031) �0.0927** (0.042)Age 50–64 �0.378* (0.200) 0.0125 (0.009) 0.0668* (0.038) 0.0691** (0.033) �0.0537 (0.033) �0.0946** (0.046)Age 65þ �0.726*** (0.240) 0.0302* (0.017) 0.136*** (0.051) 0.117*** (0.030) �0.114** (0.046) �0.168*** (0.048)Some college 0.0748 (0.130) �0.00199 (0.003) �0.0122 (0.021) �0.0149 (0.026) 0.00878 (0.015) 0.0203 (0.035)College degree 0.452*** (0.140) �0.0107** (0.005) �0.0685*** (0.021) �0.0922*** (0.031) 0.0422*** (0.013) 0.129*** (0.044)Female �0.0434 (0.110) 0.00116 (0.003) 0.00709 (0.018) 0.00864 (0.022) �0.00515 (0.013) �0.0117 (0.029)White �0.0672 (0.300) 0.00169 (0.007) 0.0107 (0.045) 0.0136 (0.061) �0.00737 (0.029) �0.0186 (0.084)Disabled �1.928*** (0.380) 0.321** (0.130) 0.323*** (0.048) �0.0863 (0.083) �0.359*** (0.041) �0.199*** (0.019)Live w/partner 0.342* (0.210) �0.0119 (0.010) �0.0617 (0.041) �0.0611* (0.033) 0.0511 (0.037) 0.0836* (0.045)Married �0.372** (0.170) 0.0106* (0.006) 0.0616** (0.029) 0.0723** (0.032) �0.0451** (0.022) �0.0993** (0.044)Divorced �0.505** (0.200) 0.0214 (0.014) 0.0974** (0.045) 0.0803*** (0.025) �0.0856** (0.043) �0.114*** (0.038)Employed 0.391* (0.220) �0.0114 (0.008) �0.0654* (0.038) �0.0750* (0.041) 0.0489* (0.030) 0.103* (0.057)Retired 0.41 (0.250) �0.0102 (0.007) �0.0639* (0.038) �0.0827 (0.051) 0.0419* (0.022) 0.115 (0.073)Children �0.106 (0.140) 0.00302 (0.004) 0.0178 (0.024) 0.0206 (0.026) �0.0134 (0.019) �0.028 (0.036)Income 30–50 0.505*** (0.150) �0.0103** (0.004) �0.0711*** (0.019) �0.106*** (0.032) 0.0355*** (0.010) 0.152*** (0.048)Income 50–75 0.501*** (0.140) �0.0107** (0.004) �0.0721*** (0.019) �0.104*** (0.031) 0.0386*** (0.011) 0.148*** (0.045)Income 75þ 0.527*** (0.180) �0.0103** (0.004) �0.0724*** (0.021) �0.111*** (0.039) 0.0331*** (0.010) 0.161*** (0.060)_Cut1 �1.935*** (0.40)_Cut2 �0.828** (0.38)_Cut3 0.166 (0.38)_Cut4 1.271*** (0.380)

Observations 468Log likelihood �595.10Wald chi-square 124.6***

Robust standard errors in parentheses.Dependent health variable is excellent health¼ 5; very good¼ 4; good¼ 3; fair¼ 2; poor¼ 1.

J. Schultz et al. / Social Science & Medicine 67 (2008) 606–617 615

in formal and informal groups) was a strong predictor oflow physical activity and of socioeconomic differences inlow leisure-time physical activity. They suggest that highlevels of social capital enhance an individual’s ability to in-fluence determinants relevant for future health. Therefore,access to high levels of social capital implies a strong senseof being able to influence one’s own health, thus increasesthe extent of leisure-time physical activity.

Although, the various social capital measures we employin our estimation capture different alternatives throughwhich individuals may produce and consume social capitaland health, it is very difficult to draw boundaries betweenthese various measures of social capital we employ. For ex-ample, there is a notion that those who have more socialtrust are more likely to be socially engaged. To investigatethis, we model social trust as a function of all the social cap-ital measures and find that with the exception of informalsocializing all social capital measures are positively corre-lated with social trust. This suggests that informal socializ-ing with friends, relatives or coworkers may notnecessarily help in building social trust. It has been shownthat family ties are strong in communities where social trustis not very developed (Banfield, 1967). However, socializingoutside informal networks (networks composed of individ-uals with different backgroundsdi.e. different education orincome levels, from other neighborhoods, different agegroups or non-native to community) may increase socialtrust. One may view informal socializing as bonding socialcapital and associational involvement and organized groupinteraction as bridging social capital measures. Bridging so-cial capital refers to interactions with individuals from

different demographic groups while bonding social capitaltypically is defined as interactions with individuals withsimilar backgrounds (family, friends). Our results suggestthat interacting with individuals from different demo-graphic groups and backgrounds may lead to greater socialtrust, rather than interacting with family members andfriends.

Hawe and Shiell (2000) suggest evaluating social capitalas a mediating variable of income effects on health status.To address this we conduct Sobel–Goodman tests to deter-mine whether each measure of individual social capital in-cluded in our model serves as mediators that carry theinfluence of income to health. In conducting the Sobel–Goodman tests, income is defined as a continuous variable.We find that income influences social trust, income affectshealth in the absence of social trust, social trust influenceshealth and the effect of income on health is reduced uponthe inclusion of social trust. Overall, social trust accountsfor 10.5% of the effect of income on self-rated health. Forthe other measures of social capital, the test results indicatethat while income has a significant positive effect onhealth, it does not have a proportionately observable effecton the remaining measures of social capital. Thus, the in-come mediating effects on all other social capital measuresremain small and insignificant. That is, social trust is theonly measure of social capital that has a statistically signif-icant role as a mediator of income.

Our results from the ordered probit model of the relation-ship between self-rated health and social trust are alsoconsistent with other studies which included similar socio-demographic variables (Hyyppa & Maki, 2003; Poortinga,

J. Schultz et al. / Social Science & Medicine 67 (2008) 606–617616

2006; Subramanian, Kim, & Kawachi, 2002). The resultssuggest that as the social trust index increases by one unit,the probability of being in very good health increases by2.7% and the probability of being in excellent health increasesby 6%. The probability of being in poor, fair or good healthdecreases by 0.6–4.4% with a one unit increase in social trust.Those with incomes above $30,000 have a 14–16% pointincrease in the probability of being in excellent health, a 1%point decrease in the probability of being in poor health anda 7% point decrease in the probability being in fair healthcompared to those with lower incomes. As age increases,the probability of being in very good or excellent healthdecreases.Furthermore, as education levels increasethe prob-ability of reporting very good or excellent health increases.

Finally, the significant and positive religiosity effect onhealth found in previous studies might be explained by thefailure to account for social capital measures. As such, weargue that religiosity in itself does not influence health. How-ever, one may argue that religiosity is potentially endogenous.Although there are no definitive studies showing that religios-ity affects health. Rather, religiosity is likely to be correlatedwith measures that do determine health. The influence likelyarises through social interaction that is a result of attendingreligious services with others of heterogeneous backgroundsand through promoting volunteerism.

One limitation of this research is that it does not ade-quately test the direction of causality between social capitaland health. Using cross-sectional data limits our ability todetermine whether poor health deters individuals fromparticipating in social interaction. However, we did testwhether disability status (provided in the survey as a reasonfor not being employed) was a significant predictor of socialinteraction, but found no effect. Also, individual levels ofsocial capital have been found to predict survival in longi-tudinal studies (Bygren, Benson, & Johansson, 1996; Dal-gard & Haheim, 1998; Eng, Rimm, Fitzmaurice, & Kawachi,2002; Glass, Mendes de Leon, Marottoli, & Berkman,1999; Konlaan, Theobald, & Bygren, 2002; Sundquist, Lin-strom, Malmstrom, Johansson, & Sundquist, 2004). Anotherlimitation is analyzing data from only one community, as itdoes not provide sufficient variation in environmental fac-tors that potentially influence health and social interaction,and results may not be generalized to communities thatdiffer significantly in socioeconomic and demographiccharacteristics. In future research we hope to investigatethe effects of environmental factors on health and socialcapital by studying multiple communities that are part ofthe larger SCCS. A final limitation is our inability to provethat our two chosen instrumental variables, tenure ina community and religiosity, are uncorrelated with theunobserved factors that influence health.

Conclusion

Our results suggest that the production and demand forsocial capital, through social engagement, are a function ofindividual characteristics. Higher levels of income and educa-tion are significant factors in predicting social engagementand social trust. Higher levels of social trust, associationalinvolvement, organized interaction, informal socializing andvolunteer activity predict higher self-rated health, after

accounting for endogeneity of these social capital measures.We argue that social trust is one potential outcome of socialengagement, particularly social interaction that bridges indi-viduals of different backgrounds. Finally, we find that religios-ity has a positive effect in determining levels of social trust andsocial interaction but is not a factor in explaining variation inperceived health status. The latter result is based on a regres-sion model where religiosityenters as a covariate in the healthequation without controlling for any measures of social capi-tal. These results can be used to support initiatives to increasesocial interaction and access to social institutions for particu-lar groups of individuals, namely those with lower levels ofincome and education and those who are unemployed.

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