27 Theories of Ego Network Homogeneity
27.1 Blau’s Macrostructural Theory
Blau’s macrostructural theory provides a structural explanation for the composition of ego networks, specifically examining how sociodemographic characteristics influence the formation of ties and the overall homogeneity or heterogeneity of an individual’s connections. It posits that the larger-scale structure of social distinctions significantly impacts the smaller-scale composition of personal networks.
Blau defines social structure as the distribution of people across various social positions, and these distributions directly influence the opportunities individuals have to associate with one another. These social positions are based on distinctions that differentiate people, such as occupations, religious groups, communities, educational ranks, gender, and race. The dimensions along which these social distinctions are made are referred to as “parameters” in Blau’s framework. These can be categorical (nominal parameters), like gender, race, or religion, which have no inherent ordering, or rank-ordered (graduated parameters), such as education, age, or income. The degree of heterogeneity in a society is reflected by how equally or unequally people are distributed across these dimensions.
Blau’s theory includes several key principles, often referred to as Blau’s rules, which explain these macrostructural effects.
27.2 Core Tenets of Blau’s Macrostructural Theory
27.2.1 Group Size Effects
Blau’s theory highlights that the numerical size of social groups plays a crucial role in shaping interaction patterns, often leading to observed patterns of homogeneity or heterogeneity. These effects operate independently of individual preferences for similarity, meaning that people may form certain types of ties simply due to the demographic landscape rather than explicit choice.
Rule 1: Smaller Groups and Outgroup Ties Members of numerically smaller groups are proportionally more likely to form ties with individuals outside their own group (outgroup members). This means that individuals from minority groups might experience greater social diversity in their networks, not necessarily because they prefer it, but because the number of available ingroup members is limited, increasing the statistical probability of encountering and forming ties with outgroup individuals. Minorities, therefore, can benefit from outgroup ties by gaining social diversity and a diversity of intellectual or cultural outlook.
Rule 2: Larger Groups and Ingroup Ties Conversely, members of numerically larger groups are proportionally more likely to form ties with individuals within their own group (ingroup members). As the inequality in group sizes increases, members of the majority group tend to have almost exclusively ingroup relations.
Some group size effects examples include:
- Interracial Marriages: Data on interracial marriages in the U.S. illustrates these rules clearly. For instance, in 2013, a significantly higher percentage of individuals from numerically smaller racial or ethnic groups, such as American Indians (58%), were married to someone of a different race compared to White individuals (7%), who belong to a much larger group. This demonstrates how members of smaller groups are more likely to have outgroup relations, while members of larger groups are more likely to have ingroup relations.
- Interracial Friendships in Schools: Similarly, in schools, a larger proportion of students from smaller “Other-Race” categories engage in friendships with individuals from other racial groups. In contrast, Caucasians, typically a larger group in many schools, show a much higher percentage of ingroup friendships.
27.2.2 Multiform Heterogeneity Effects
This aspect of Blau’s theory addresses how multiple dimensions of social differentiation interact and correlate to influence network composition, leading to what is observed as heterogeneity.
Rule 3: Correlated Dimensions of Association Blau’s Rule 3 states that systematic correlations exist between different dimensions of association within a society, such as religion and race. For example, in the U.S., there are systematic correlations between religious preference and race, where a higher percentage of non-Hispanic whites are Protestant compared to Black or Hispanic individuals.
Blau’s Rule 3 implies that if an individual’s network exhibits a bias based on one characteristic (e.g., race), it is highly probable that it will also be biased by another correlated characteristic (e.g., religion). This can occur even if there is no explicit preference for homophily (the tendency to associate with similar others) along the second dimension. The observed homogeneity in one dimension might simply be an outcome of the underlying correlations between various social attributes at the societal level, rather than a direct individual preference.
27.4 Marsden’s Theory of Ego Network Diversity
Marsden’s (1987) work, particularly the one based on his analysis of the 1985 General Social Survey (GSS) data, contributes significantly to understanding the structural characteristics and diversity of ego networks. The 1985 GSS used a name generator approach to measure the ego networks of a representative sample of the American population (see Chapter 26), asking individuals to “Name all persons with whom you have discussed matters important to you,” thereby eliciting what are known as core discussion networks.
Key Propositions and Findings: Marsden’s propositions about ego networks provide insights into how their structural properties relate to diversity and homophily:
Network Size and Diversity:
- Larger ego networks tend to be more diverse, less homophilous, and less constrained (contacts are less likely to know one another). This suggests that as individuals expand their network size, they are more likely to encounter and form ties with people who are different from themselves.
- Smaller ego networks, conversely, tend to be more clustered and less diverse. This implies that smaller networks are often characterized by a higher concentration of similar individuals.
A general principle developed by Marsden is that greater network diversity corresponds to less homophily. Homophily refers to the tendency for individuals to associate with others who are similar to themselves based on sociodemographic characteristics like age, race, gender, education, or nationality. Diversity, in this context, measures the variation in alter attributes within the ego network.
Additionaly, Networks with a higher average tie strength tend to exhibit more clustering but less diversity. Strong ties often imply greater interdependence and shared contexts, leading to more interconnectedness among alters, but potentially limiting exposure to diverse perspectives. Similarly, when an ego network has a higher proportion of kin (relatives), it tends to show more homophily, less diversity, and more clustering. Kinship ties often represent strong, enduring, and homophilous relationships that contribute to network closure and homogeneity.
In the context of segregation, Marsden’s theory suggests that larger networks are less segregated than smaller networks, independently of individual preferences. This rule is often contrasted with Blau’s rule (which focuses on numerical preponderance) and can be combined with it to explain diversity patterns for minority groups.
Examples:
- Core Discussion Networks: Marsden’s study using 1987 GSS data on “core discussion networks” (people with whom one discusses important matters) revealed these patterns. For instance, data from the GSS 1985 suggests variations in network size, with some people having many contacts and others being “isolated”. These networks also tend to be relatively homogeneous with respect to important social attributes like age, race, gender, and education.
- Online Social Networks: Studies on online social networks, like Facebook, have also demonstrated Marsden’s rule. For example, analysis of Dutch, Turkish, and Moroccan Facebook users showed that the percentage of co-ethnic friends tends to decrease as the total number of friends increases but only for members of minority groups (Hofstra et al. 2017), reflecting that larger networks can be less segregated but depends on the group size distribution of alters. This illustrates that regardless of preferences for “choice homophily,” larger networks may inherently lead to more diverse contacts because individuals, particularly members of minority groups, eventually “run out of ingroup alters”.