Conference From GDP to Well-Being, Ancona, 3-5 December 2009

Integration in Social Networks as a form of Social Capital: Evidence from a Belgian survey on Social Cohesion

Bram Vanhoutte & Marc Hooghe

Centre for Political Science, KULeuven, Belgium

Introduction

• Social Capital on the individual level refers to network resources

• Networks are the focus of this paper

• Which people have what networks?• Are all network measures associated with generalized

trust & participation?• Are there context effects on network measures?

• Dataset is combination of survey (n=2080) and real life data on municipality level (n=40)

Social Capital

• Social Capital = Structure (networks) + Content (attitudes)(~De Toqueville, 1835; Durkheim, 1915)

• Not only beneficial for individuals, but also positive externalities on community level (Putnam, 1993)

• Many and diverse applications of social capital, but measurement of basic structural concept rather limited, e.g. participation in associations

• Wide range of informal relations have an impact on the individual, and networks produce different attitudes according to their composition, size and intensity

Bonding Social Ties

• Birds of a feather flock together (Lazarsfeld & Merton 1954)

• Bonding capital (~Social cohesion)– Strong ties between similar people– Emotional and social support networks– Thick trust generated by intensive regular contact

• Possible negative outcomes: exclusive groups, parochial, social control

Bridging Social Ties

• Connections between “different” people

• Bridging or Linking Capital– Weaker ties (Granovetter 1973)– Necessary for integration in diverse society of today– Mainly positive outcomes: lowers prejudice, widens perspective

• Identity Bridging: bridging culturally defined differences• Status Bridging: bridging socio-economical differences

Hypotheses

H1: Young, male, higher educated, having partner and religious attendance have a positive influence on size of close network and network diversity. Woman have more frequent contact with their close network

H2: Generalized trust and participation positively associated with all network measures. Ethnocentrism negatively associated with diverse networks.

H3: More diverse networks in larger cities, due to constraint of choice by context (Blau 1977)

Data and operationalisation

• Data: SCIF (Social Cohesion Indicators Flanders) – Survey, designed to allow multilevel research – Fieldwork April-July 2009, n=2080– Egocentric network measures

• Dependant: 4 network measures– Close network size (Bonding)– Frequency of close network contact (Bonding)– Identity diversity of wider network (Bridging)– Status diversity of wider network (Bridging)

Flemish region, Belgium (pop. 6,000,000)SCIF-survey: 2080 respondents in 40 municipalities

Close network size

• Total network size is unreliable, and less interesting for social capital

• With how many people do you talk about intimate matters?– In your family– In your friends-circle

• Indicator is sum of these two items, since both family and friends to whom one talks about intimate matters can be considered close ties

• Size of close network can be seen as a measure for social support

Close network size

0.0

5.1

.15

Pro

port

ion

0 10 20 30 40Close network size

observed proportion neg binom prob

poisson prob

mean = 7.276; overdispersion = .3367

Close network intensity

• Strong ties form through frequent contact, (Homans 1955) so frequency of contact is a good measure for the strength of bonding ties

• How often do you….?(never (0) – several times a week (5))– Visit family– Invite friends

• Indicator is sum of both item frequencies. Family you visit and friends you invite at home can be considered close ties

Close network intensity

Intensity of close network ties

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0 1 2 3 4 5 6 7 8 9 10

Identity diversity

• Do you have a friend …? (Yes/No)– With a different religious orientation

– With a different ethnic background

– With a different sexual orientation

– Of a different generation (at least 20 years of difference)

– With different political ideas

• Using item response theory (Mokken-scaling) these items prove to be one coherent scale (H=.40)

• Most common diversity by political ideas and generations• Difficult forms of diversity are religious orientation and ethnic

background

Identity diversity

Diversity friends network

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0 1 2 3 4 5

Status diversity

• Use of position generator (Lin & Dumin 1986)

• With which occupations do you have contact in daily life? Do you know a … in your family ? Or among your friends ? Or among your acquaintances? – These questions were asked for a list of 20

occupations, varying in socio-economic status.

• We use the number of occupations of these 20 that respondents could access, which is a very parsimonous and simple measure for status diversity in one’s network

Status diversity

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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Number of accessed positions

Individual level determinants

• Income was operationalised as the natural log of the estimated houshold income, standardized according to family composition

• Religious attendance was dichotomised with people attending service at least at religious holidays or more often coded 1.

• Generalized trust: factorscale ( 3 items)• Ethnocentrism: factorscale ( 3 items)

Community level determinants

• Factor analysis on 18 structural indicators on municipality level: demographics, socio-economics, criminological statistics and spatial planning

• 5 factors (more than 90% of variance)– Urban density– Population mobility– Population density– Economic wellbeing– Ageing of the population

Results 1: Close network size

Social Structure Higher education (+)

Student (+)

Religious attendance (+)

Social Capital Participation (+)

Generalised Trust (+)

Ethnocentrism (-)

Context No effects

R² .09

Results 1: Close network size

• Bonding ties in line with expectations– Higher educated, students and religious more social

support– Social capital indicators measure size of close

network quite well

• Explained variance relatively small, so possible influence

of other variables (psychological) in number of close ties

Results 2: Close network intensity (multilevel)Social Structure Female (++)

Age (-)

Religious attendance (++)

Social Capital Participation (++)

Generalised trust (+)

Ethnocentrism (-)

Context Urban density (-)

Economic well-being (--)

R² Total = .09

Between groups = .35

Results 2: Close network intensity• Frequency of contact with close network higher for

women and religious, lower for older people

• Again the indicators of social capital work well to predict close network intensity

• Although context effects are small, we see that network intensity is less in more urban and richer municipalities

Results 3: Identity diversity(multilevel)Social Structure Male (+)

Age (--)

Education (++)

Income (+)

Separation of partner (+)

Social Capital Participation (++)

Ethnocentrism (--)

Context Urban density (+)

Population density (+)

Ageing of the population (-)

R² Total = .22

Between groups = .62

Results 3: Identity diversity

• Social background predicts a diverse network quite well, and along expectations

• Although participation and ethnocentrism are related to diversity as expected, generalised trust does not have an influence

• Context effects rather small , the differences in diversity on municipality level mainly explained by effects of composition (45%)

Results 4: Status diversity(multilevel)Social Structure Male (++)

Age (-)

Education (++)

Living with partner (+)

Retired (--)

Religious attendance (+)

Social Capital Participation (++)

Ethnocentrism (-)

Context No effects

R² Total = .22

Between groups = .36

Results 4: Status diversity

• Strong influence of social background on socio-economic diversity of one’s network

• Participation associated with larger scope of network• Ethnocentrism lower with more status diversity

• No significant context effects, which is understandable since municipalities in Flanders have roughly similar compositions in terms of social status

Conclusions

• Social background has strong effects on bridging networks, not only directly but also in terms of community composition (cfr. choice-constraint approach)

• Social Capital indicators accurate for bonding networks, generalised trust seems less adequate for predicting bridging networks

• Participation seems to be both bridging and bonding – Further research with a more detailed typology of associations

• Modest direct context effects, urbanity associated with less bonding but more identitybridging networks