1. 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

2. 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)

3. 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

4. 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

6. 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

8. 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)

9. 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)

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

11. 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

12. Close network size

13. 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

14. Close network intensity

15. 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

16. Identity diversity

17. 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 ones network

18. Status diversity

19. 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)

20. 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

21. Results 1: Close network size Social StructureHigher education (+) Student (+) Religious attendance (+) Social CapitalParticipation (+) Generalised Trust (+) Ethnocentrism (-) ContextNo effects R².09

22. 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

23. Results 2: Close network intensity (multilevel) Social StructureFemale (++) Age (-) Religious attendance (++) Social CapitalParticipation (++) Generalised trust (+) Ethnocentrism (-) ContextUrban density (-) Economic well-being (--) R²Total =.09 Between groups =.35

24. 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

25. Results 3: Identity diversity (multilevel) Social StructureMale (+) Age (--) Education (++) Income (+) Separation of partner (+) Social CapitalParticipation (++) Ethnocentrism (--) ContextUrban density (+) Population density (+) Ageing of the population (-) R²Total =.22 Between groups =.62

26. 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%)

27. Results 4: Status diversity (multilevel) Social StructureMale (++) Age (-) Education (++) Living with partner (+) Retired (--) Religious attendance (+) Social CapitalParticipation (++) Ethnocentrism (-) ContextNo effects R²Total =.22 Between groups =.36

28. Results 4: Status diversity Strong influence of social background on socio- economic diversity of ones 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

29. 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