As the world’s largest professional network with 930 million global members, LinkedIn connects talent to economic opportunity. Nonetheless, some members from underrepresented groups still face pre-existing labor market inequalities that can negatively impact their careers.  

With this in mind, our new White Paper analyzes disparities in network size and growth by gender, race, and local median income for LinkedIn members in the U.S. While our results reveal sizable gaps in average network sizes across these categories, they also show decreases in some gaps over time, and pinpoint networking behaviors that contribute most to these disparities for each group.  

There are significant gaps in total network size across groups  

Similar to existing disparities in the U.S. labor market, our analysis of networks finds that women, Black and Latino members, and members residing in low-income ZIP Codes have smaller networks compared to men, White and Asian members, and members residing in high-income ZIP Codes. For example, on average:  

• Women have 29.5% fewer connections than men.    

• Latino and Black members’ networks are 20.2% and 13.0% smaller than White members, respectively.    

Network gaps are most pronounced for Black women, Latinas, and members residing in the lowest-income ZIP Codes. Specifically:  

• Black women’s average networks are 28.6% smaller than the national average and 38.4% smaller than White men’s average networks.   

• Latinas have the smallest average network sizes of the groups we examined, 33.5% smaller than the national average and 42.6% smaller than the average network size of White men.   

• Members who reside in ZIP Codes in the lowest median income quartile have average networks that are 41.9% smaller than those of residents in the highest income ZIP Codes.  

 Most network gaps are narrowing over time  

We observe four major trends in network growth rates, which we define as the average number of new connections made each month, from 2018 to 2023.   

1. The gap between the rates at which women added new connections and men added new connections has declined by 47%.  

2. Latino members saw a 77% reduction in the gap in their new connection growth rate relative to White members. 

3. There’s been a shift in Black members experiencing faster-growing networks compared to White members. In April 2023, Black members added 8.7% more connections on average than White members, compared to 5.4% fewer connections five years prior. 

4. Unlike with gender and race, the gap between network growth rates for members residing in the lowest- and highest-income ZIP Codes isn’t narrowing. However, this disparity is smaller than the gaps in total network size by ZIP Code median income.  

Networking behaviors contribute differently to gaps across groups  

We deconstruct network growth rates to identify the primary source of observed disparities across four inputs, including the:   

1. Number of connection invitations members send  

2. Number of invitations they receive  

3. Rate at which members accept the invitations they receive  

4. Rate at which other members accept their invitations.  

Our analysis shows that most—although not all—of the disparities we observe are attributed to the number of invitations sent and received, rather than acceptance rates.  This means that for most groups, half or more disparities in network growth stem from factors members can control. These include the number of connection invitations they send and accept. 

In contrast, for two groups, more than half of observed network growth gaps result from factors beyond their control, this being the number of connection invitations they receive and the rate at which the invitations they send are accepted by others. This affects members residing in the lowest income ZIP Codes and for Latino men. For example:  

• Members living in the lowest-income ZIP Codes receive 43.0% fewer invitations and have a 4.9% lower probability of invitations they send being accepted than members living in the highest-income ZIP Codes.   

• Latinos receive 7.5% fewer invitations and have a 2.0% lower probability of invitations they send being accepted compared to White members.  

Although these external factors have less influence on the size of network disparities for other groups, they still have a noteworthy impact on the speed of network convergence (the rate at which networks gaps are closing). This is especially true for Black women and Latinas, who respectively receive 15.3% and 18.7% fewer invitations than the national average and have their sent invitations accepted at 3.0% below and 0.9% above the national average acceptance rate. Both groups would presumably experience a more rapid reduction of existing network size gaps if they received as many invitations and had their invitations accepted at the national average.   

In conclusion  

Economic networks play a crucial role in facilitating career opportunities, making it essential to understand the distribution of network resources among different demographic groups. Despite observing significant network disparities, we are encouraged to see many of these gaps narrowing over time.  

Furthermore, we believe that platforms like LinkedIn make it easier for professionals to connect to economic opportunity and each other by expanding access beyond traditional networks. In fact, we find that members from all groups can reduce network gaps through their engagement on the platform. On one hand, members from underrepresented groups can strengthen their networks by sending out more connection invitations, specifically to their  second- and third-degree connections. On the other hand, members with larger networks can send out and accept more connection invitations from members with smaller or emerging networks within their occupation, company, or industry.  

Methodology 

 

For all analysis, 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 approximately 5% of total U.S. members, results based on this subset may not be generalizable to the entire U.S. LinkedIn membership. Moreover, the entire US LinkedIn membership may not be representative of the overall U.S. 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 U.S. 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.