in these latter spheres individuals tend to meet more heterogeneous people in terms of social attributes. data were handled using social network analysis tools, resulting in 361 personal networks of individuals in poverty and 30 networks of middle class individuals. After running the statistical analyses, I intentionally chose a set of forty interviewees (twenty in each city) to comprise the qualitative part of the study in order to explore how the individuals use the networks in their daily lives. This article, however, is based mainly on the quantitative information of the research, and I refer the reader to other pieces about the qualitative results (Moya and Marques, 2013; Marques, 2012a). Several previous publications of this research explored different dimensions of the effects of the networks on poverty. The overall association between networks, segregation and poverty in São Paulo was analyzed in Marques (2012a). When compared to the middle-class networks, the personal networks of the poor were, on average, much smaller (53 in São Paulo and 41 in Salvador, against 93 nodes for the middle classes), more local (63.5% in Salvador and 60.5% in São Paulo against 20% local for middle class networks) and less varied in terms of sociability (3.8 different spheres in São Paulo and 3.5 in Salvador, against 5.5 for the middle classes). Inter-relations between different social and income groups were practically inexistent. The poorer city – Salvador - presented slightly smaller, more local and less varied networks, although these differences tended to disappear when compared with the São Paulo middle class. Physical proximity was relatively non important for the middle classes – only 5% of the of these networks lived in the same place of residence, resonating the community hypothesis stated by Wellman (1999). However, localism among the poor was much higher, reaching 32%, on average. Regardless of the differences between social groups, the networks varied substantially within each group (Bichir and Marques, 2012). To explore this variability, I developed a typology of networks and another one of sociability patterns. The combination of these typologies suggested the existence of four relational situations with different degrees of homophily. The typology also
varied sociability tend to be larger, as well as networks with several clustered subgroups. In the first case it is reasonable to interpret that when sociability becomes more varied, a larger number of alters is available for possible contacts. In the second case, however, it is more difficult to separate a causal direction and decide if it is the growth of the network which leads to the formation of more clusters, or the presence of dense sub-groups which leads to the growth of the network. In any case, size and dense subgroups reinforce each other. Finally, the model presents four variables associated with sociability. The presence of many countrymen in the networks tends to reduce the size of the network. A considerable amount of the Brazilian metropolitan poor are migrants from the poorer regions of the country. Previous results of this research showed that some individuals maintain, at their present location, a very intense sociability with persons who migrated from the same state, or sometimes from some small village. This is not a general phenomenon, but characterizes a subgroup of individuals, who live in almost ‘transplanted communities’ and for whom the integration process after migration happened in a very specific way. The model is informing us that this process tends to reduce the size of the networks, what can be interpreted as a creation of limits for growth. On the other hand, social interaction in the church sphere tends to enlarge the networks. The effect has also been described in the literature and in the Brazilian case involves the traditional catholic churches, but also the many neopentecostal temples which grew intensely in recent period. While the first dimension leads to homophilic ties, the church sphere tends to be more heterophilic, even when it is local (Marques, 2012a). The final two variables have a clear association with the main concern of this article. The presence of more intense sociability in the neighborhood tends to increase the networks and a higher proportion of ties between local and nodes reduces the size of the network. In both cases, this suggests that local sociability tends to increase the networks. And in fact the qualitative narratives of the research confirm this, by suggesting that developing activities locally is one of the ways of expanding the network. A substantial amount of
the local ties of the larger networks, however, tend to involve acquaintances tied only by interactions, instead of relations in Degenne (2009)’s sense. It is interesting to add that the above mentioned countrymen tend to be local (and have negative sign, as we saw), but the effect of local contacts is so strong that survives as a distinct and positive effect. Varied sociability Although varied sociability contributes to larger networks, it involves a different group of processes and is explained by different variables. I performed a similar test with linear regression of several variables to explain the total number of spheres as measure of varied sociability. The results are presented in the following table. As we can see, eight variables survived in the model. Table 2. Linear regression coefficients - # Spheres Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta (Constant) 2,940 ,213 13,822 ,000 São Paulo ,266 ,106 ,116 2,510 ,013 Years of schooling ,053 ,013 ,182 3,914 ,000 Nodes ,018 ,002 ,453 7,829 ,000 # 2-clans/total number of nodes -,910 ,307 -,173 -2,968 ,003 Sphere church (%) ,020 ,005 ,172 3,866 ,000 Sphere work (%) ,016 ,004 ,166 3,569 ,000 persons (%) -,005 ,002 -,112 -2,442 ,015 Segregated -,203 ,103 -,089 -1,976 ,049 Notes: Adj R2=0.323; N=358 cases (three outliers); Durbin Watson=2.16; Condition Index= 12.40. In this case, city differences did not disappear in the multivariate model and the São Paulo cases present systematically higher variability of sociability, even in face of the other variables. Years of schooling appear again with a positive and significant effect, increasing the probability of more varied networks for more schooled individuals. The number of nodes appears as positive and significant, what was expected considering our interpretation of the results of the previous regression. Another variable which also appears again is