Insights on US Network Homophily and Strength by Gender, Race, and Community Income
Data Scientist at LinkedIn
Economic networks, such as LinkedIn connections, impact career opportunities, information exchange, and professional growth in today’s knowledge-based economy. However, significant disparities in network formation across demographic groups in the US imply that not all groups have equally strong networks.
In three new publications, we build on our prior research around network equity, digging deeper into how people are connected and what it might mean. First, a new research note investigates the extent of homophily — or the tendency to connect with similar people — in network formation. In an associated research note and in a working paper, we introduce a new model of network strength and use it to compare network features across race, gender, and community income groups.
Members tend to connect with others from similar backgrounds
As a global professional networking platform, LinkedIn allows members to grow their networks by sending and accepting connection invitations. To evaluate members’ propensity to connect with others from similar backgrounds, we analyze invitation and acceptance rates by the demographics of the connection invitation’s senders and receivers.
We find that:
Members tend to connect with individuals from similar racial/ethnic backgrounds. For instance, Black members are 88% more likely to send invitations to other Black members compared to the national average. Asian, Latino, and White members also show strong preferences, with 53%, 47%, and 23% higher likelihoods, respectively.
Women and men are 13% and 6% more likely to send invitations to their own gender, respectively.
Members in low-income ZIP Codes are 44% more likely to send invitations to others in low-income communities, while those in high-income communities are 19% more likely to send invitations to others in high-income communities.
Additionally, advantaged groups, including White members, men, and members in higher income communities, receive a disproportionate share of connection invitations. For example, residents in the highest income ZIP Codes receive 40.8% of all invitations, despite comprising 25% of the sample.
Network strength varies substantially across groups
To compare network strength across groups, we develop an economic network strength model that accounts for the information that a member can access through their connections to help advance their career. Specifically, we characterize an economic network’s strength based on four features:
Network size: given by a member’s total number of connections;
Information value: given by the share of a member’s connections in senior positions, with endorsed skills, who are not open to work, and work in related jobs or industries;
Information bandwidth: given by how frequently a member receives messages from their connections; and
Information non-redundancy: given by the share of a member’s connections that are not mutual with other connections, i.e. who are weak ties.
Networks that are relatively large, experienced, communicative, and non-concentrated are considered stronger within our framework.
Like our analysis of network formation disparities, our network strength research finds that men, Asian and White members, and members in high-income ZIP Codes have stronger networks compared to women, Black and Latino members, and members in low-income ZIP Codes. For example, our results show that:
Men’s average total network strength percentile is 6 points higher than women’s. In other words, if you selected 100 people and ranked them from the weakest to strongest economic networks, the average woman would be 6 people behind in the line than the average man.
On average, members in the highest income ZIP Codes’ total network strength percentile ranks 12.8 percentile points higher than members in the lowest income ZIP Codes.
Asian and White members’ average network strength percentiles are 56.4th and 49.8th, while Latino and Black members average in the 46.5th and 48.4th percentiles respectively.
Although women, Black and Latino members, and members in lower income communities have weaker network strength on average, their networks tend to be less redundant with more “weak ties.” This can be advantageous because weak tie connections are more likely to bridge them to other members, groups, and career opportunities outside of their immediate network.
Building strong, inclusive networks
The strength of an individual's economic network can significantly influence their career trajectory and economic outcomes. As such, the insular networking tendencies and network strength disparities that our research uncovers highlight the need for initiatives that promote inclusive networking, provide targeted support for underrepresented groups, and enhance information sharing.
To bridge these gaps, employers can facilitate cross-cultural and cross-income networking events as well as establish mentorship programs to encourage diverse interactions across demographic groups. Moreover, members can proactively strengthen their own networks by stepping outside of their comfort zones and sending out more connection invitations to members of diverse social and economic backgrounds and communicating more frequently with their existing connections.
Methodology
For all analyses, we limit attention to non-restricted, active accounts. For the race demographic analysis, we additionally limit to individuals who have self-identified their race and gender. Because self-identified members comprise a subset of total US members, results based on this sample may not be generalizable to the entire US LinkedIn membership. Moreover, the entire US LinkedIn membership may not be representative of the overall US population. This may be particularly true for the industries and occupations in which members work, with LinkedIn being overrepresented in certain professional fields such as engineering. Differences between groups may at least partially reflect differences in LinkedIn membership concentration between occupations and industries. Despite these limitations, our analysis can still provide valuable insights into how network trends influence economic opportunities.
This body of work represents the world seen through LinkedIn data, drawn from the anonymized and aggregated profile information of LinkedIn's 202+ million US members. As such, it is influenced by how members choose to use the platform, which can vary based on professional, social, and regional culture, as well as overall site availability and accessibility. In publishing these insights from LinkedIn's Economic Graph, we want to provide accurate statistics while ensuring our members' privacy. As a result, all data show aggregated information for the corresponding period following strict data quality thresholds that prevent disclosing any information about specific individuals.
Gender identity isn’t binary, and we recognize that some LinkedIn members identify beyond the traditional gender constructs of “man” and “woman.” If not explicitly self-identified, we have inferred the gender of members included in this analysis either by the pronouns used on their LinkedIn profiles or inferred based on first name. Members whose gender could not be inferred as either man or woman were excluded from this analysis.