Conference From GDP to Well-Being, Ancona, 3-5 December 2009 Integration in Social Networks as a...
-
Upload
tatiana-biller -
Category
Documents
-
view
214 -
download
0
Transcript of Conference From GDP to Well-Being, Ancona, 3-5 December 2009 Integration in Social Networks as a...
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
0
50
100
150
200
250
300
350
400
450
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
0
50
100
150
200
250
300
350
400
450
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
0
50
100
150
200
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