social mobility and social capital in contemporary … mobility and social capital in contemporary...

Social mobility and social capital incontemporary Britain

Yaojun Li, Mike Savage and Alan Warde

Abstract

This paper seeks to contribute to social capital research by linking measures offormal and informal forms of social capital to social mobility trajectories andassessing their impact on social trust. Drawing on data from a recent nationalsurvey – Cultural Capital and Social Exclusion (2003/2004) – we analyse formalcivic engagement and informal social connections. The latter data are obtainedusing, for the first time in a study in Britain, Lin’s (2001) ‘Position Generator’approach as a means to identify the volume, range and position of individuals’informal social contacts. The pattern of contacts suggests that access to social tiesis strongly conditioned by mobility trajectory. We also show that civic engagementin formal associations is especially high among second-generation members of theservice class. It is also shown that both class trajectory and possession of two typesof social capital have significant impacts on trust. Among the social groups disad-vantaged in terms of bridging social ties are not only those in lower classes but alsowomen and members of minority ethnic groups.

Keywords: Civic engagement; position generator; social capital; social mobility;social networks; trust

Introduction

This paper contributes to research examining how social capital is related toprocesses of social stratification.We build on two important bodies of thinkingwhich have emerged in recent years. Firstly, following the lead of Putnam(2000), Lin (2001), Farr (2004) and Edwards, Franklin and Holland (2007), wefocus not only on formal engagements, notably through membership of vol-untary associations, but also on informal relationships and social networks, toallow a fuller and more sociologically sophisticated measure of social capital.Secondly, following from Hall (1999), Wuthnow (2002), Warde et al. (2003), Li,

Yaojun Li (Institute for Social Change, Manchester University), Savage and Warde (School of Social Sciences) (Correspondingauthor email: [email protected])© London School of Economics and Political Science 2008 ISSN 0007-1315 print/1468-4446 online.Published by Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden,MA 02148, USA on behalf of the LSE. DOI: 10.1111/j.1468-4446.2008.00200.x

The British Journal of Sociology 2008 Volume 59 Issue 3

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Savage and Pickles (2003) and Li, Pickles and Savage (2005), we relate socialcapital to processes of social stratification and inequality. It is now clear in theBritish context that there are deep, and growing, class inequalities in themobilization of social resources including social capital, which pit an appar-ently engaged and involved professional and managerial ‘service class’ againstan apparently increasingly disengaged working class (see Savage, Li andTampubolon 2007 for an overview). Given the importance of these issues, ourpaper analyses the relationship between social inequality and social capitalthrough the use of a measure of social network – the position generator –which has never been used before in Britain. We focus on how social mobilityinto and out of the privileged service class relates to the formation of socialcapital. Our analysis suggests that social capital is a key element in the con-solidation and reproduction of class advantage in Britain.

The next section explores theoretical and methodological issues involved indeveloping our approach to social capital and stratification. We show howdeploying Lin’s (2001) position generator approach allows us to measure howfar members of different social classes have exclusive social ties. In the thirdsection we introduce the Cultural Capital and Social Exclusion survey, our datasource, and describe our methods of analysis. The fourth section showshow upwardly and downwardly mobile, as well as intergenerationally stable,members of different social classes report very different kinds of social contactand associational memberships, and how class trajectory and formal and infor-mal social networks are related to generalized social trust. In the final part ofthe paper, we discuss the implications of our research findings.

Theoretical and methodological issues

The early work of Putnam (1993), which did so much to promote an interest insocial capital, focused on the role of associational membership as the keyindicator of social capital. Putnam’s more recent work, as well as that ofnumerous other commentators (Lin 2001; Burt 2005; Halpern 2005), empha-sizes the need to broaden our understanding to encompass more informalsettings in which social capital can be generated. This more balanced stance isclearly seen in Putnam’s statement that ‘when philosophers speak in exaltedtones of “civic engagement” and “democratic deliberation”, we are inclined tothink of community associations and public life as the higher form of socialinvolvement, but in everyday life, friendship and other informal types of socia-bility provide crucial social support’ (Putnam 2000: 95). Not only has Putnamcome to a greater appreciation of the importance of the informal forms ofsocial capital, he has also shown a growing interest in the different distributionof social capital. ‘Social capital’, he notes, ‘may conceivably be even less equi-tably distributed than financial and human capital’ (Putnam 2002: 415). This

392 Yaojun Li, Mike Savage and Alan Warde

© London School of Economics and Political Science 2008 British Journal of Sociology 59(3)

more sociological account of social capital rightly sees it as rooted in the socialstructure and related to the formation of group identities, akin to the argu-ments of Giddens (1973) and Goldthorpe (1987) on the ‘structuration’ of class,where interest lies in assessing the relationship between social position andforms of social interaction and cohesion.

Much excellent British research has examined informal networks usingqualitative/ethnographic methods (Willmott 1987; Spencer and Pahl 2006), butthere is limited work on informal social networks using national representativesurveys. This is largely because it is difficult to find good operational measuresrepresentative of the networks of the national population. Most relevant ques-tions from large-scale social surveys ask only about whether people have socialsupport in specific situations, which has the problem of conflating the effects ofsocial capital (social support) with the measure itself. Large-scale social surveysrarely have space to ask for sufficient information for studying the range and theintensity of social ties and their different roles. Some urban sociologists, likeWellman (1979) and Fischer (1982), have conducted surveys asking for detailedinformation on a large number of ties, but such extensive research has not beenattempted in the UK. The nearest equivalent is the Social Mobility Inquiry,conducted in 1972, which asked respondents to name up to three ‘spare-timeassociates’,1 their relationship to the respondent, and their occupation (Gold-thorpe 1987). More recent national surveys did not use equivalent or compa-rable measures. For instance, the British Election Study for 1992 asked therespondents to report the relationships and the occupations of three peoplewith whom the respondent most often talked about politics, and the variouswaves in the British Household Panel Survey have asked the respondents toreport on various attributes of three ‘best friends’. Questions such as these canonly be used to assess the ‘strong ties’ of best friendship, rather than the morediffuse ‘weak ties’ that bridge across different social sectors.

The use of social network analysis, a technique hitherto not widely adoptedin the British context, overcomes this limitation (see Scott 1992; Savage et al.2005). In particular, Lin’s (2001) ‘position generator’ approach provides anattractive solution. The basic idea is to ask respondents whether they knowpeople in different social locations. Such questions can be asked relativelyeconomically in a sample survey by asking whether a respondent knowspeople in a pre-specified, and relatively limited, number of contrasting occu-pational positions. The position generator is a useful device for estimating theextent and the structure of a web of contacts through providing summarymeasures of how many people in different kinds of occupational location areknown to the respondent. It can thus serve as a reasonable proxy for thevolume and the nature of informal social connection, hence the kind and theamount of social resources accessible to the respondent. By assessing whetherpeople know others in social positions similar to or different from their own,it also provides a way of measuring the stratified character of social networks,

Social mobility and social capital in contemporary Britain 393

British Journal of Sociology 59(3) © London School of Economics and Political Science 2008

and the extent to which respondents have contacts which span differentclasses, furnishing an important measure of ‘bridging’, or ‘linking’, socialcapital (Woolcock 1998, 2001; Szreter 2002).

In sum, the position generator approach has great potential for understand-ing the nature of informal ties within survey analysis methodology, enabling usto distinguish between people who have limited social connections and thosewho have a wide range of social ties and the character of those ties. Byreporting results of a new survey on Cultural Capital and Social Exclusion(CCSE) conducted in 2003/2004, which contains information in unprecedenteddetail on formal civic engagement and informal social ties, we can develop amore sophisticated understanding than hitherto available of the range andnature of formal and informal social involvement, and hence of the relation-ship between social capital and social stratification.

Besides demonstrating the value of the position generator, we also assess themore conventional measure of social capital, notably that of civic membership,which is given major emphasis in most previous studies (Hall 1999; Putnam2000; Warde et al. 2003; Li, Savage and Pickles 2003; Li, Pickles and Savage2005). Existing research has shown that it is important to distinguish carefullybetween types of organizations. Li, Savage and Pickles (2003) show, forinstance, that whereas some organizations, notably working-men’s social clubsand trade unions, have seen a significant decline in membership, this is not trueacross the board. It is also clear that organizations vary in their social compo-sition, which has a significant impact on the extent to which they are likely tobe conducive to the social mixing implied in the concept of ‘bridging’ socialcapital. We assess how far associational membership is likely to consolidateexclusive social connections.

It is well known that, cross-sectionally, there is an association between socialposition and access to social capital. However, it is difficult to disentangle thecausal processes involved. Is the ability to mobilize social capital the cause ofsocial advantage, or does privilege itself allow more social capital to beaccrued? In most previous studies it has not been possible to disentangle thesesystematically because current class position alone is used (though see Li,Savage and Pickles 2003 for some discussion). In this paper we separate outthose who are upwardly mobile, downwardly mobile, and socially stable, to seehow far mobility trajectory is associated with the possession of formal andinformal forms of social capital. In examining the relationship between mobil-ity trajectory and our measures of social capital, we will assess whether socialorigin has a powerful structuring effect, thereby to identify the embeddednessof social capital in relation to stratification and inequality (Bourdieu 1986).

Our aim, therefore, is to investigate how social interaction is conductedbetween different social classes in informal sociability and formal civic engage-ment, and whether there are class specific forms of sociability and involvementthat serve to reproduce social division.To put this in Putnam’s (2000) terms, do



we see significant amounts of bonding and bridging social capital and, if so,how is it socially distributed?

Data and methods

We use the data from the Cultural Capital and Social Exclusion2 (CCSE) survey,conducted in 2003/2004.This is a national representative sample for people aged18 or above and resident in private households in the four countries of the UK.It used a stratified, clustered random sample from 111 post code sectors, andachieved a response rate of 52 per cent with a final achieved sample size of 1,564(alongside an ethnic boost which we do not discuss here).The response rate wasrelatively low,3 but checks on the socio-demographic characteristics of thesample against other surveys indicate that the survey’s findings are comparablewith these, and weights have been applied throughout the analysis.

The CCSE is a unique data source because of the range and depth ofquestions it asks about cultural taste, participation and knowledge, and aboutsocial capital indicators (Bennett et al. 2005; Savage et al. 2005). It containsconventional measures of social capital, namely the membership of 17 typesof civic organizations the details of which are shown in Appendix Table I.Uniquely, it also contains a ‘position generator’ question, asking the respon-dents whether they knew socially anyone who had any of these jobs:4 (1)University/College lecturer, (2) Solicitor, (3) Bank or building society manager,(4) Secretary, (5) Clerical officer in national or local government, (6) Nurse, (7)Sales or shop assistant, (8) Electrician, (9) Postal worker (10) Factory worker,and (11) Bus or coach driver. These occupations were adapted for the Britishcontext by ensuring that they (i) all have a significant number of peopleworking in them, (ii) are from different social class locations, and (iii) havedifferent gender profiles.The respondents could nominate any of them as theirsocial contacts. From this we derived measures of volume, range and position ofrespondents’ contacts.Volume is the number of contacts reported, ranging from0 to 11, which gives an indication of how far individuals are connected withothers. It does not tell us, by itself, whether the contacts reported share similarsocial positions or whether they are from different sectors of the social hierar-chy. Range refers to the distance between the highest and the lowest position ofthe contacts’ status (we use the Cambridge Scale obtained from the BHPS 2004as an indicator of the respondents’ as well as their contacts’ status, see Stewart,Prandy and Blackburn 1980).The (status) position of contacts is the mean scoreon the Cambridge Scale for all the contacts reported by a respondent, indicatingthe social standing of a respondent’s network of informal contacts. By lookingat the range and the mean score we can see how far respondents’ contactsconcentrate within and span social groups, and thus the extent to which theyembody ‘bridging’, or ‘linking’ social capital.



In our analysis we use the usual socio-demographic control variables, such asage, sex, marital status and ethno-religious identity, which have been shown tohave an important impact on civic engagement (Warde et al. 2003; Modood2004; Li and Marsh 2008). Owing to the relatively small sample size, wecollapsed ethnicity into White and non-White groups. Similarly, we codedreligion into two groups: whether or not our respondents acknowledged anyreligious affiliation. In addition to these, we constructed a mobility trajectoryvariable by cross-tabulating the respondent’s and their parent’s5 class to yieldfour categories: (i) the stable service class, (ii) upwardly mobile into the serviceclass, (iii) downwardly mobile from the service class, and (iv) the stable non-service class (hereafter called the ‘stable working class’). Here we follow theestablished research positing the movement between the service and the otherclasses as the single most important transition in contemporary Britain (Gold-thorpe 1987; Erikson and Goldthorpe 1992; Goldthorpe and Mills 2004). Ourfour-fold classification as a measure of ‘social distance’ (Akerlof 1997) allows usto assess whether the stable service and the stable working classes stand moststarkly opposed in terms of access to different kinds of social capital, as onewould expect from the arguments of Hall (1999); Li, Savage and Pickles (2003);Li, Pickles and Savage (2005) and Warde et al. (2003). We can also considerwhether the two mobile groups are more similar to their class of origin ordestination, and whether they are intermediate between the stable classes.

Analysis

We begin by showing the proportions of our respondents who reported thatthey knew socially anyone with the 11 types of jobs or that they were in any ofthe 17 types of civic organizations.The data are presented in Figure I (note thatthe scales in the two panels of the figure are different).

Figure I: Number of social contacts and associational memberships

Panel 1: Social contact Panel 2: Associational membership

05

1015

Perc

ent

0 2 4 6 8 10Range of social networks

010

2030

40P

erc

en

t

0 2 4 6Number of memberships in civic organisations

Source: Cultural Capital and Social Exclusion Survey (2003/2004).



Figure I shows that around 11 per cent of the respondents did not have acontact in any of the listed occupations. Between 10 to 15 per cent reportedhaving each of 1 to 6 social contacts. The proportion of respondents havingmore than 7 contacts becomes increasingly small. Altogether, only about 15per cent of our respondents had 7 or more contacts. With regard to member-ships in civic organizations, 40 per cent of the respondents were not in any,around one third were in one, and another one fifth in two to four,organizations. A tiny proportion (0.2 per cent) of the respondents was in 7organizations, the largest number reported. Altogether, less than 1 per cent ofthe respondents were in more than 5 civic associations. Checking the resultsagainst another authoritative data set, the BHPS for 2004, we find similarpatterns. This gives us sufficient confidence in the reliability of our data(see Appendix Table I).

The structure of social contacts

Table I shows the Cambridge score for each of the 11 types of job (in brackets)and the probability of our respondents having such a contact in terms of his orher class trajectory. The contacts’ scores vary widely from 72 for University/College lecturer to 8 for bus driver. We can see striking differences in the wayin which our respondents from each of the class trajectories report knowingthe various named occupational groups.

Table I: Proportion and mean of social contacts by respondents in different class trajectories

Stable serviceclass

Upwardlymobile

Downwardlymobile

Stable workingclass

Contacts’ job (Cambridge score)University/College lecturer (72) 47.8 39.4 30.7 20.2Solicitor (64) 48.9 41.6 29.0 18.9Bank manager (53) 22.4 23.2 18.5 14.2Secretary (37) 53.8 51.2 41.8 33.8Clerical officer in govt. (35) 34.4 39.2 30.7 22.9Nurse (30) 62.8 64.6 51.4 46.5Shop assistant (23) 41.8 50.8 52.6 45.3Electrician (19) 34.2 49.0 37.2 41.8Postal worker (19) 24.0 31.1 18.5 26.6Factory worker (14) 27.9 40.0 44.0 43.5Bus driver (8) 12.6 23.4 18.5 25.7

Mean numbers of contacts 4.11 4.54 3.73 3.39Mean score of contacts’ status 38.1 34.0 33.0 28.8Distance of contacts’ scores 41.8 40.4 38.5 33.7

All (N) 208 296 180 880All (%) 13.3 18.9 11.5 56.3

Notes:1 The contacts’ Cambridge score was obtained from BHPS 2004 and was rounded here.2 For mean score, respondents who reported no contacts were dropped; for distance of scores,respondents who reported no contacts or 1 contact were dropped.3 Weighted data used in all analysis.



The stable service class is most likely to identify social contacts with higherstatus occupations. Nearly half identify a University lecturer and a Solicitor ascontacts, compared to only one in five amongst the stable working class. Bycontrast, only a quarter of the stable service class report knowing someonewho is a Factory worker or a Postal worker, and one in eight reports knowinga bus driver. These figures give a clear indication of how far social contact isaffected by social position and mobility trajectory. The upwardly mobile arenearly as likely to know a Factory worker or a Bus driver as the stable workingclass, and they are also nearly as likely to know a University lecturer or aSolicitor as a member of the stable service class. Their contacts are affectedboth by their current position and by their class of origin. The downwardlymobile, by contrast, are closer to the stable working class, and have markedlyfewer high status contacts than either group currently in the service class.Theythus appear unable to use their cultures of origin to maintain large numbers ofhigh status contacts.

The three lower rows in Table I give summary statistics demonstrating clearevidence of the homophilic principle. With the exception of the number ofcontacts, where the upwardly mobile tend, as might be expected from theirmobility trajectories, to have more contacts than the other three groups, it isthe stable service class who is most likely, and the stable working class leastlikely, to have contacts in higher social positions (as reflected in the meanstatus scores of contacts) and spanning a wider range of social hierarchy (asshown by the distance between contacts’ positions). If the volume, range andposition of contacts indicate resources accessible to the actor (Lin 2001),6 ourdata show that Britain remains strongly socially stratified in the new millen-nium, just as it was in the early 1970s (Goldthorpe 1981/2, 1987; Mitchell andCritchley 1985).

Having looked at the general pattern of association between the respon-dent’s class trajectory and the profile of their social contacts, we report, inTable II, results from statistical modelling on the volume (number) of contacts,and on the mean and distance of contacts’ scores. To take into account theunobserved heterogeneity (selection bias) in the contacts reported,7 we use thenegative binomial regression model (NBRM) for the number of contacts (Longand Freese 2006) and the Heckman’s selection model for the mean and thedistance of contacts’ scores (Heckman 1976). As the selection model requiresidentifying variables present in the selection model but absent from the regres-sion model, we use dependent children in the household and the respondent’shealth status as such variables, which were shown in our prior analysis to besignificantly associated with observed outcomes (having contacts).8

Table II shows the results of our models. In all models, we include thesocio-demographic attributes as control variables and take the class trajectoryas the main variable of interest. With regard to the results in Model 19 on thenumber of reported contacts, we find that, other things being equal, women



and members of minority ethnic groups tend to have a significantly smallernumber of contacts, and those with religious affiliations tend to have a signifi-cantly larger number of contacts; and that there is a curvilinear relationshipbetween age and the number of contacts, with the middle aged having morecontacts.As regards the mobility trajectory effects, we find that, controlling forall other variables in the model, respondents intergenerationally stable in theservice class, and particularly those with an upward trajectory, were signifi-cantly more likely to have a larger social network. This confirms the impres-sions gained from Table I regarding the greater impact of destination thanorigin in this respect. The finding on the upwardly mobile is not surprising as

Table II: Regression models on the number of contacts, and the mean and distance of contacts’status scores

(1) (2) (3)

Negative binomialmodel on the

number of contacts

Heckman’s modelon the mean scoreof contacts’ status

Heckman’s modelon the rangeof contacts

β̂ s.e β̂ s.e β̂ s.e

RegressionFemale -0.101** (0.037) 1.347* (0.636) -0.795 (1.354)Partnered 0.037 (0.040) -0.925 (0.697) 0.048 (1.490)Age 0.528*** (0.071) 4.101*** (1.069) 18.920*** (2.327)Age squared -0.061*** (0.007) -0.401*** (0.105) -2.132*** (0.229)Having religion 0.083* (0.039) 1.815** (0.675) 2.746** (1.432)Ethnic minority -0.258* (0.103) -2.248† (1.268) -7.206** (2.756)Stable service class 0.111* (0.052) 10.700*** (0.957) 9.726*** (2.029)Upwardly mobile 0.236*** (0.045) 6.605*** (0.823) 9.544*** (1.747)Downwardly mobile 0.084 (0.059) 5.632*** (0.991) 6.713*** (2.115)Constant 0.275† (0.157) 15.993*** (2.439) -14.451** (5.292)Alpha 0.179 (0.020)SelectionFemale 0.093 (0.082) -0.002 (0.059)Partnered -0.013 (0.092) 0.005 (0.066)Age 0.235† (0.136) 0.757*** (0.101)Age squared -0.031* (0.013) -0.086*** (0.010)Having religion 0.033 (0.091) 0.095 (0.063)Ethnic minority -0.147 (0.155) -0.297** (0.116)Stable service class 0.605*** (0.149) 0.413*** (0.091)Upwardly mobile 0.455*** (0.119) 0.398*** (0.078)Downwardly mobile 0.240† (0.132) 0.184* (0.091)Poor health 0.044 (0.076) -0.002 (0.032)Having children -0.067 (0.097) -0.038 (0.038)Constant 0.732* (0.330) -0.721** (0.229)Rho 0.926 (0.019) 0.997 (0.002)Sigma 11.999 (0.259) 25.204 (0.564)

Log likelihood -3,498.37 -5,751.50 -5,984.04N 1,559 1,559 1,559

Note:1. The reference categories are: male, non-married, non-religious, White and stable working classin the regression models and, additionally,‘good health’ and no dependent children in the selectionmodels.2. † p < 0.10, * p < 0.05, ** p < 0.01 and *** p < 0.001 (the same below in modelling tables).



people from lower class backgrounds would have trekked longer journeys toreach the service-class destination, hence having more chances to meet peoplein different social locations. Further analysis shows that the upwardly mobilehad significantly more contacts than the stable service class.10

Models 2 and 3 in Table II give findings on the mean and the range of contacts’scores using Heckman’s selection models. The lower panels, under ‘selection’,show that the factors on chances of outcomes being observed (i.e. having anycontacts) have a fairly similar pattern to those in Model 1. Our main interesthere is in coefficients in the upper panels, under ‘regression’.With respect to themean status scores (Model 2), we note two features of immediate interest.Firstly, although women are found to have fewer contacts in Model 1, those whodo have contacts tend to‘contact up’,namely, to have contacts with higher statuspositions than their male counterparts. Secondly, although the upwardly mobileare found to have the largest social circles in Model 1,here in Model 2 we can seethat it is the stable service class whose contacts occupy by far the highest socialpositions. The two mobile groups have contacts situated in similar ranks of thesocial hierarchy, lower than those of the stable service class but significantlyhigher than those of the stable working class.11 We also note here the membersof minority ethnic groups tend to have contacts in lower social positions,reflecting patterns of residential segregation and labour market disadvantagesof minority ethnic groups in British society (Li and Heath 2008).12

With respect to status distance among contacts which may be taken as anindicator of ‘bridging’ social capital, we find, in the analysis of Model 3 ofTable II, that the middle aged and the religious tend to have contacts whosestatus position spans a wide spectrum of the social hierarchy and that membersof the minority ethnic groups tend to have contacts in close proximity. Holdingconstant these factors, we find that, regardless of mobility trajectory, peoplecurrently situated in the top social positions are much more likely to havebetter developed bridging ties. Whilst class of origin does not play a differen-tiating role among those currently in the service class, it has an impact upon therest. Thus, even though people downwardly mobile from service-class originsstill have significantly more bridging ties than did the stable working class, theyare clearly behind those currently incumbent in the service class.

The overall pattern of social contact thus suggests that in contemporaryBritish society sociability is neither individualistic with no class patterning, norentirely homophilic where people in higher positions only socialize with theirpeers. Rather, the findings suggest a two-tiered pattern. The advantaged tendto have larger social circles and higher-ranking contacts; at the same time,they are also more likely to have contacts more widely spread in the socialspectrum. Whilst continuing to enjoy bonding social capital built on social tieswith similar status positions, they also enjoy bridging social capital with peoplein different status positions, which is denied to those in the disadvantagedgroups.



Inequality in civic membership

We now consider the relationship between social mobility and civicengagement. We distinguish between nine types of civic organization, andexplore how far mobility trajectories affect associational membership. Wereflect on the consistency of our results with previous research findings thatthere are powerful and increasing class differences in the propensities for civicengagement (Warde et al. 2003; Li, Savage and Pickles 2003; Li, Pickles andSavage 2005; Savage, Li and Tampubolon 2007; Li and Marsh 2008). Table IIIshows the association between class trajectory and the nine types of associa-tional membership that we have pooled together out of the original 17recorded in the survey.13 Looking at the bottom row, we see that the stableservice class is especially predisposed to join voluntary organizations: themean number of memberships is almost two. They are over-represented espe-cially in professional organizations, arts and culture organizations, politicalparties and environmental groups, trades unions and residents’ groups. Theupwardly mobile have markedly fewer memberships than the stable serviceclass, but nevertheless more than people subject to the two other trajectories.Notably the downwardly mobile exceed the upwardly mobile specifically inmembership of arts and religious organizations, suggesting that primary social-ization in cultural forms has persistent effects and possibly that religion pro-vides some compensation for a fall in social standing. The stable working classare infrequent joiners – they have less than one half as many memberships asthe stable service class – and only in one category, ‘social and working-men’sclubs’, do they outnumber any other group. The pattern here suggests that notonly is associational membership a feature of people in service class positions,but that associations are likely to be packed with the second-generationservice class.

To further explore the association between class and civic engagement,we first use latent class analysis (LCA)14 to try to establish patterns of civic

Table III: Percentage of respondents reporting membership in different civic organizations and themean number of memberships by class

Stable serviceclass

Upwardlymobile

Downwardlymobile

Stable workingclass

All

Sports 33.6 23.8 19.0 12.8 18.4Other 28.4 18.2 16.8 12.9 16.4Trade Union 19.7 14.6 9.9 13.0 13.8Religion/church 15.9 12.9 16.5 9.2 11.7Social/workingmen’s club 6.3 11.1 5.4 13.5 11.1Arts/culture 21.6 9.8 13.4 5.0 9.1Tenant’s group 13.1 7.7 8.2 5.2 7.1Professional organizations 22.4 10.9 5.9 1.7 6.7Party/environment 10.9 5.9 2.3 2.9 4.4

Mean membership 1.80 1.17 1.01 0.78 1.02



membership, and then use multinomial analysis to examine types of partici-pant.The responses to the questions on organizational membership as shown inthe last column of Table III can be taken as indicators of civic engagement, andcan be understood as forming a contingency table within which some degree ofassociation among responses is likely to exist.The LCA model aims to capturethis association by identifying a small number of discrete latent classes(categories) such that conditional on membership in a given latent class, theresponses to the items will be independent of each other.15

As shown in Table IV, a model postulating three latent classes appears to fitthe data adequately (Muthén 2001; McCutcheon 2002). Table IV shows theestimated relative size of the latent classes under this model and the estimatedprobabilities, conditional on latent class membership, of engagement in each ofthe nine civic groups. We find that the distribution to the three classes is veryuneven.The largest latent class (class 1), comprising 76 per cent of our sample,has a very low probability of membership, less than 5 per cent, in six civicgroups. The probability of their engagement in the other three groups is alsorather limited: 20 per cent in sports and 13 per cent in trade union andsocial/workingmen’s clubs. The second latent class, containing 18 per cent ofthe sample, also has a rather low probability (less than 10 per cent) of engage-ment in seven out of the nine groups, but a high probability of membership inreligion/church group (53 per cent) and in the ‘Other’ group (PTA, women’s

Table IV: Latent class measurement models fitted to data on civic membership

Model # classes df c2 p G2 p AIC BIC

1 1 492 1,108.96 0.00 402.59 0.99 9,342.53 9,390.732 2 492 714.16 0.00 336.44 1.00 9,172.88 9,274.633 3 482 506.41 0.21 269.76 1.00 9,126.21 9,281.504 4 472 523.84 0.05 254.18 1.00 9,130.64 9,339.47

Table V: Estimated size of the latent classes and the conditionalprobabilities of membership in civic organizations under a modelpostulating three latent classes

Latent class

1 2 3

Relative size (%) 0.760 0.180 0.060

Arts/culture 0.049 0.073 0.685Professional organizations 0.031 0.058 0.554Sports 0.199 0.019 0.488Other 0.016 0.708 0.417Trade Union 0.129 0.093 0.381Tenant’s group 0.049 0.075 0.339Party/environment 0.017 0.060 0.339Religion/church 0.013 0.529 0.196Social/workingmen’s club 0.129 0.010 0.167



groups, voluntary services, ethnic community group and other) at 71 per cent.By contrast, the numerically smallest third latent class, comprising only 6 percent of the sample, has a very active civic life, with over two thirds being inarts/culture groups, around one half in professional organizations and sportsclubs, around 40 per cent in ‘Other’ groups and trade unions, one third intenants’ and party/environment groups, and a non-negligible portion (justunder 20 per cent) in religious and social and workingmen’s associations aswell. The patterns here suggest that only a very small proportion of the popu-lation (6 per cent) are enthusiastic joiners, three quarters either belong to nocivic organization or only to leisure associations; and the remainder are mostobviously characterized by religious (and possibly parental) commitment,membership of which does not spill over into other forms of engagement. Wecall these three classes ‘inactive’, ‘religious’ and ‘civic engaged’ when examin-ing how far each of them has a distinctive social location.

Table VI reports how the three latent categories are distributed by mobilitytrajectory. Here we find quite clear class differences with regard to inactive andcivic engaged groups. In the former case, there is a notable increase of 57 percent in the stable service class to 83 per cent in the stable working class. In thelatter case there is a sharp decrease of 22 per cent to only 2 per cent. With thereligious affiliates, there are less pronounced class effects although the down-wardly mobile are relatively most likely to be found in the group (22 per cent,which is seven percentage points higher than the stable working class). Furtheranalysis (not shown in the table) also reveals that women, people with religiousaffiliations and members of minority ethnic groups are also more likely to befound in the religious latent class.

In Table VII, we report results from multivariate analysis of the latentclasses in civic association. We first use multinomial logit models to showeffects on the chances of being religious and civic engaged in relation to thereference category of inactive, and then use binary logit models to show effectson the chances of being civic engaged relative to being religious. The controland explanatory variables are as in Table II.

With regard to the first contrast, we can see that women, the middle aged,people with religious affiliations, and minority ethnic groups are, as expected,significantly more likely to be in the religious than the inactive category. The

Table VI: Latent class membership by class trajectory (percentage by row)

Inactive Religious Civic engaged N

Stable service class 56.8 20.8 22.4 208Upward mobile 72.0 21.4 6.6 296Downward mobile 73.0 22.4 4.6 180Stable working class 82.7 15.1 2.2 880

All 76.0 18.0 6.0 1,564



class trajectory effects are also significant, with the downwardly mobile some-what more likely to be found in the religious associations than the upwardlymobile. Turning to the civic engaged versus inactive contrast, we find thatexcept for the age effects, the other demographic factors are not significant.However, the class effects are more pronounced than in the previous contrast.What is of particular note here is that even though all three classes aresignificantly more likely than the stable working class to be in the civic engagedrather than in the inactive latent class, the differences between the stableservice and the two mobile classes are marked and statistically significant.16

The predominance of the stable service class in civic associations is furtherconfirmed in the comparison between the civic engaged and religious catego-ries, thus showing the disproportionate role played by the stable service classin the civic life of contemporary Britain.

Trust and social capital

Finally in this section, we examine how social mobility and forms of socialcapital are related to generalized trust, widely accepted in the literature as acentral correlate of social capital and as a barometer of the health of democ-racy (Putnam 1993, 2000; Brehm and Rahn 1997; Paxton 1999; Robinson andJackson 2001; Li, Pickles and Savage 2005).17 No existing research has simul-taneously explored these relations.

Table VIII shows the effects of mobility trajectory, social connections(the latent class measures from civic engagement, and the mean and distanceof contacts’ status scores as well as number of contacts) and demographicattributes on generalized trust using ordinal logit regression models. The datashow that when only the mobility trajectory is used (Model 1), there is a clear

Table VII: Multinomial and binary logit models on latent class in associational membership

Religious vsinactive

Civic engagedvs inactive

Civic engagedvs religious

β̂ s.e β̂ s.e β̂ s.e

Female 0.688*** (0.166) -0.155 (0.236) -0.679* (0.293)Partnered 0.016 (0.163) 0.414 (0.274) 0.109 (0.324)Age 0.931*** (0.279) 1.395* (0.566) 0.660 (0.618)Age squared -0.072** (0.026) -0.119* (0.053) -0.070 (0.058)Having religion 0.981*** (0.185) 0.201 (0.239) -0.648* (0.309)Minority ethnic 0.783* (0.324) -0.857 (0.765) -1.707* (0.819)Stable service class 0.922*** (0.231) 2.763*** (0.311) 1.751*** (0.362)Upwardly mobile 0.555** (0.195) 1.445*** (0.356) 0.625 (0.411)Downwardly mobile 0.771** (0.244) 1.175** (0.442) 0.376 (0.469)

Constant -5.582*** (0.704) -7.619*** (1.465) -2.326 (1.566)Log likelihood -944.22 -171.56N 1,559 367



gradient. In Model 2 where the effects of civic membership are controlled for,the class effects are still significant, though they are reduced. We also findstrong net effects of civic engagement, especially for those who are ‘civicallyengaged’. Model 3 shows the effects of social contact net of class and civicengagement, and we find that it makes a significant contribution (see modelcomparison at the bottom of the table), especially for those with contacts inhigher status positions. We thus see that both formal and informal aspects ofsocial capital are conducive to generalized trust.When demographic attributesare taken into account (Model 4), the pattern associated with class and socialcapital is little changed, though women were more careful in dealing withothers (see also Robinson and Jackson 2001). Interestingly, in view of theargument on the generational decline in social capital, age makes no differenceto generalized trust when the more powerful socio-cultural factors are takeninto account.

Conclusion

In this paper, we have explored the link between class formation, social capitaland social trust.The first to use the ‘position generator’ approach in the UK, we

Table VIII: Ordinal logit regression models on social trust

Model 1 Model 2 Model 3 Model 4

β̂ s.e β̂ s.e β̂ s.e β̂ s.e

Stable service class 0.912*** (0.156) 0.726*** (0.166) 0.624*** (0.168) 0.595*** (0.174)Upwardly mobile 0.692*** (0.147) 0.640*** (0.148) 0.551*** (0.152) 0.519*** (0.153)Downwardly mobile 0.483* (0.193) 0.445* (0.193) 0.390* (0.197) 0.461* (0.202)Civic engaged 0.809*** (0.218) 0.693** (0.224) 0.637** (0.232)Religious 0.340* (0.147) 0.284† (0.151) 0.307* (0.154)Status of contacts 0.013** (0.005) 0.015** (0.005)Social distance -0.003 (0.005) -0.003 (0.005)No. of contacts 0.038 (0.039) 0.033 (0.039)Female -0.330** (0.122)Partnered 0.102 (0.121)Age 0.249 (0.204)Age squared -0.018 (0.019)Having religion -0.182 (0.128)Minority ethnic 0.462 (0.282)

Intercept 1 0.779 (0.083) 0.856 (0.087) 1.232 (0.152) 1.787 (0.499)Intercept 2 1.002 (0.086) 1.081 (0.091) 1.459 (0.155) 2.016 (0.501)(Pseudo) R2 0.020 0.027 0.032 0.040Model comparison c2 16.56a*** 9.80b* 17.42c**N 1,561 1,561 1,561 1,558

Notes:1 For the latent class variable on associational membership, inactive is the reference group. Forinformal social capital, ‘social distance’ refers to the difference between the highest and the lowestscores of the social contacts.2 In model comparison, a refers to terms in Model 2 that are additional to those in Model 1,b to terms in Model 3 that are additional to those in Model 2, and c to terms in Model 4 that areadditional to those in Model 3.



have shown that it gives a simple and yet robust measure of informal socialcapital. In so far as volume, range and position of social contacts have differenteffects on different aspects of the social process it is a tool which can effectivelydiscriminate between alternative ways in which social connections matter. Italso suggests that formal and informal forms of social capital are indepen-dently measurable and have independent effects.

Substantively, our analysis shows that both social contact and civic engage-ment are deeply rooted in the class structure, with the stable service class verydifferent from the stable working class in all of the aspects under study. Apartfrom the number of contacts, where the upwardly mobile show the highestcoefficients, attributable to their mobility trajectory, the stable service classreport contacts of higher status and spanning larger status distance. They aremore involved in associations than the other classes, have memberships inmost types of civic association, and have the highest levels of generalized trust.In some aspects examined, the downwardly mobile are more like the stableservice class than the upwardly mobile, such as in membership of religious andaffiliated associations. This suggests that even though the upwardly mobilehave larger social circles, they may not have such strong ties to high statuspersons as those born into the service class. Hence there is some evidence ofinter-generational processes of the reproduction of social capital (Bourdieu1986).

Secondly, our evidence shows a clear association between formal and infor-mal forms of social capital, and between them and generalized social trust. Forease of exposition, we present this evidence on the formal and the informalforms of social capital separately. Our findings on the overall patterns inTables II–VII suggest a close relationship between the two. Further analysisindeed shows that the civic activists not only have more social contacts thanthe inactive (4.85 vs 3.55), their contacts are also in higher positions (39.7 vs30.3) and contain more bridging ties (42.0 vs 29.9). Our data (Table VIII) alsoshow a clear association between social capital and social trust, even with classand demographic attributes controlled for. The strong mutual relationshipbetween these facets thus suggests that they are elements of a mutually rein-forcing web of advantage. In Putnam’s language,‘bridging’ and ‘bonding’ socialcapital tends to be found together in the hands of the service class. If we seebridging social capital as especially important in generating resources, as sug-gested by accounts of ‘the strength of weak ties’, it is those who are alreadyprivileged who are best placed to take advantage.

Thirdly we find that women and members of minority ethnic groups tend tohave smaller social circles of contacts and to have such contacts clusteredtogether, which may deny them some of the benefits associated with bridgingsocial ties. They are also less likely to be found in civic organizations. Here weconducted more refined analysis than Li, Savage and Pickles (2003) whichshowed the need to distinguish civic organizations from trade unions and



working-men’s and social clubs, by showing that there is also a distinctive formof involvement in religious and related groups which does not spill over intowider civic attachments and which disproportionately attracts women andethnic minorities.

It is for all these reasons, therefore, that we emphasize the need to recognizethe role of social capital within the context of processes of stratification andclass reproduction. In general, social capital in Britain is not distributed inways which would facilitate significant amounts of ‘bridging’ or ‘linking’between diverse groups. Those with most bridging capital are the privilegedservice class who also enjoy strong ‘bonding’ social capital.When we recognizethat those with more contacts tend to have contacts in higher status positions,we are forced to conclude that social capital primarily operates to entrenchprivilege, within and across generations (see also Merton 1957). Thus effortsaimed merely at increasing social capital by encouraging greater formal civicengagement without tackling the root causes of socio-economic disadvantagemay well aggravate rather than ameliorate social division.

(Date accepted: June 2008)

Appendix Table I: Civic membership in the CCSE as compared with the BHPS

Organizations Men Women Men + women

Sports clubs 24.6 (+1.3) 13.2 (+1.2) 18.4 (+1.0)Trade unions 15.3 (-1.2) 12.6 (-0.4) 13.8 (-0.9)Religious/church groups 7.3 (-0.8) 15.4 (+2.3) 11.7 (+0.8)Social/workingmen’s club 15.8 (+2.9)* 7.1 (+1.8)* 11.1 (+2.3)**Tenants/neighbourhood watch 7.0 (+1.9) 7.2 (+1.1) 7.1 (+1.4)*Professional/chambers 9.4 (-1.7) 4.5 (-2.6)** 6.7 (-2.4)**Artist/heritage organization 5.9 4.9 5.3Voluntary services 3.3 (+0.5) 5.6 (+1.1) 4.5 (+0.8)Parent–Teacher Association 3.2 (+1.4)* 4.9 (+0.8) 4.1 (+1.1)*Women’s group 0.5 (+0.5) 5.7 (+1.8) 3.3 (+1.2)Environmental group 3.2 (+0.1) 2.4 (-0.3) 2.7 (-0.2)Political party 2.1 (-0.1) 2.0 (+0.1) 2.1 (-0.1)Amateur music/drama group 1.6 2.4 2.0Fan club 2.5 1.6 2.0National/ethnic community 1.0 0.5 0.7Film society 0.9 0.3 0.5Other 6.3 (-1.4) 5.0 (-2.5) 5.6 (-2.2)

Note:1 The figures in brackets are data from the British Household Panel Survey (BHPS) in 2004 ascompared with the CCSE data. The + sign means that the figures in the CCSE are higher than inthe BHPS, and the - sign means that the figures in the CCSE are lower than in the BHPS. Somequestions are only asked in the CCSE but not in the BHPS, hence no comparison available.Weighted data are used.



Notes

1. In the Social Mobility Inquiry (1972),the instruction to the interviewer is: ‘Whenprobing, always refer to these people as“the people you most often spend yourspare time with,” not as friends’ (see Ques-tion 39 (f)). In the BHPS, such people aredirectly called ‘best friends’ (see Wave 2,Self-Completion Section, Questions 3 and4).

2. This paper draws on data produced bythe research team for the ESRC project Cul-tural Capital and Social Exclusion:A CriticalInvestigation (Award no R000239801). Theteam comprised Tony Bennett (PrincipalApplicant), Mike Savage, Elizabeth Silva,Alan Warde (Co-Applicants), David Wrightand Modesto Gayo-Cal (Research Fellows).The applicants were jointly responsible forthe design of the national survey, and thefocus groups and household interviews thatgenerated the quantitative and qualitativedate for the project. Elizabeth Silva, assistedby David Wright, co-ordinated the analysesof the qualitative data from the focus groupsand household interviews. Mike Savage andAlan Warde, assisted by Modesto Gayo-Cal,co-ordinated the analyses of the quantitativedata produced by the survey. Tony Bennettwas responsible for the overall direction andco-ordination of the project.

3. For 28% of the unproductive sample,no contact could be made or no interviewcould be arranged because of illness, etc.Theresponse rates for academic surveys areusually lower than those for governmentsurveys. For example, the ESRC project on‘Telling the Future’ had a response rateof 53% (Li et al. 2002: 632). Governmentsurveys have also witnessed declining

response rates in recent years (see http://www.esds.ac.uk/government/faq/#nineteen),with the rate for the Home Office Citizen-ship Survey of 2003 (HOCS03) being only53% in the London area and 62% overall(HOCS03 Technical Report).

4. The question reads:‘On this card is a listof jobs. Please tell me whether you happen toknow anyone socially who has any of thesejobs? Please include friends and relatives.’

5. Parent is defined as the main house-hold earner when the respondent was agedbetween 14 and 16. The class trajectoryvariable in Table A is constructed usingparent’s and respondent’s class (which isslightly different from that in Li, Savage andPickles 2003: 523, note 6):

6. It is a pity that as the CCSE contains nodata on whether the contacts helped therespondent in getting his or her current job,we cannot replicate Lin’s (2001) findings inBritain.

7. As seen in Figure I, over 10% of ourrespondents did not report any contacts.Some of them may really have no socialcontacts. Others may have contacts, but theircontacts may have no jobs or have jobs thatare not listed in the survey.

8. The Heckman’s selection modelassumes the existence of an underlyingregression relationship: yj = xjb + u1j and thedependent variable for observation j isobserved if zjg + u2j > 0 where u1 ~ N(0, s),u2 ~ N(0, 1) and corr(u1, u2) = r. In our data,having dependent children is positivelyassociated with having social contacts andhaving poor health is negatively associatedwith having social contacts (p = 0.008 and0.034 respectively).

Table A

Respondent’s class

Service Non-service

Parental class Service Stable service Downwardly mobileNon-service Upwardly mobile Stable non-service



http://www.esds.ac.uk/government/faq/#nineteen

http://www.esds.ac.uk/government/faq/#nineteen

9. The significant evidence of overdisper-sion (a = 0.179, se = 0.020 as seen underModel I, and G2 = 204.36, p < 0.001) suggeststhat the negative binomial regression modelis preferred to the Poisson regression model.

The negative binomial distribution can bespecified by

Pr!

y xy

y aa

a a

a y

( ) =+( )( ) +

⎛⎝⎜

⎞⎠⎟ +

⎛⎝⎜

⎞⎠⎟

−

−

−

− −

−

ΓΓ

αμ

μμ

1

1

1

1 1

1

where y is the observed count, m is theexpected count, G() is the gamma functionand a determines the degree of dispersion inthe predictions.

10. Parameter test for the differencebetween the stable service class and theupwardly mobile yields a c2 = 4.8 at 1 df,p < 0.05.

11. Further analysis shows that the stableservice class are significantly more likelyto have higher status contacts than bothupwardly or downwardly mobile (c2 = 13.8and 16.9 respectively) but there is no signifi-cant difference between the latter twogroups.

12. We also conducted a random effectsmodel to assess the variances of the con-tacts’ scores by nesting them within eachrespondent and using the respondent’s ownstatus score (also derived from BHPS 2004)as a level 2 covariate, with variables inTable II and the number of contacts (inranking order) as level 1 covariates. Therespondent’s status score is highly significant(b = 0.082, p < 0.01). Full results are avail-able on request.

13. As shown in Appendix Table I, 9 outof 17 organizations have memberships withless than 5% of the sample, which will pose adifficulty in analysis and presentation. Giventhis, we have combined them into 9 groupsas follows: memberships in political parties(2.1%) and environmental groups (2.7%)are grouped together as they may be pursu-ing what Weber (1922/1968) calls the ‘idealinterests’. Only 2.3% of the respondents arein music/drama groups, 0.5% in film societ-ies, 0.2% in fan clubs, and 5.3% in art/heritage groups: these have been combinedas an ‘arts/culture’ group. Members inparent–teacher association (4.1%), women’s

groups (3.3%), voluntary services (4.5%),ethnic community group (0.7%), and other(5.6%) are combined as ‘Other’. It is alsonoted here that as only 0.8% of the respon-dents are in two or more arts/culture groups,0.4% in both party and environment groups,and 1.9% in 2 ‘Other’ organizations, wewould not make further differentiations.

14. The model can be specified by

Prexp

expy a c

Vs

tv

ij sijcs

iijct

=( ) = ( )

=( )( )∑ 1

where units are assumed to belong to one ofC discrete classes c = 1, . . . , C. The priorprobability that a unit j is in class c, pjc, is amodel parameter. If unit j is in class c, theconditional response probability that item itakes on the values as, s = 1, . . . , Si, is mod-elled as a multinomial logit.

15. Another way of analysing multipleresponses is to use multilevel models for cat-egorical indicator variables where each ofthe responses is nested within the respon-dent, and then use the item response theory(IRT) models to explore the overall associa-tion amongst the responses, and the multipleindicator and multiple cause (MIMIC)models to assess the direct and indirecteffects of explanatory variables on the civicmemberships (Skrondal and Rabe-Hesketh2004).We conducted analysis using both IRTand MIMIC models. The patterns suggestsimilar socio-economic underpinning of civicengagement to those reported in Table VII.Full results are available on request.

16. The differences between the stableservice and each of the two mobile groupsare significant at the 0.001 levels (c2 = 26.1and 14.2 respectively) but those between thelatter groups are not significant in the civicengaged versus inactive contrast.

17. The question reads: ‘Generally speak-ing, would you say that most people can betrusted, or that you can’t be too careful indealing with people?’ The response catego-ries are: (1) ‘Most people can be trusted’,(2) ‘Can’t be too careful’, and (3) ‘Other,depends’. In the analysis, the responses wererecoded so that 1 = cannot be too careful,2 = depends, and 3 = trusting.



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