# Gender Gaps in Online Social Connectivity, Promotion and Relocation Reports
on LinkedIn

Ghazal Kalhor, 1 Hannah Gardner, 2 Ingmar Weber, 3 Ridhi Kashyap 2

###### Abstract

Online professional social networking platforms provide opportunities to
expand networks strategically for job opportunities and career advancement. A
large body of research shows that women’s offline networks are less
advantageous than men’s. How online platforms such as LinkedIn may reflect or
reproduce gendered networking behaviours, or how online social connectivity
may affect outcomes differentially by gender is not well understood. This
paper analyses aggregate, anonymised data from almost 10 million LinkedIn
users in the UK and US information technology (IT) sector collected from the
site’s advertising platform to explore how being connected to Big Tech
companies (‘social connectivity’) varies by gender, and how gender, age,
seniority and social connectivity shape the propensity to report job
promotions or relocations. Consistent with previous studies, we find there are
fewer women compared to men on LinkedIn in IT. Furthermore, female users are
less likely to be connected to Big Tech companies than men. However, when we
further analyse recent promotion or relocation reports, we find women are more
likely than men to have reported a recent promotion at work, suggesting high-
achieving women may be self-selecting onto LinkedIn. Even among this
positively selected group, though, we find men are more likely to report a
recent relocation. Social connectivity emerges as a significant predictor of
promotion and relocation reports, with an interaction effect between gender
and social connectivity indicating the payoffs to social connectivity for
promotion and relocation reports are larger for women. This suggests that
online networking has the potential for larger impacts for women, who
experience greater disadvantage in traditional networking contexts, and calls
for further research to understand differential impacts of online networking
for socially disadvantaged groups.

††footnotetext: This work is licensed under a Creative Commons Attribution 4.0
International License (CC BY 4.0).

## Introduction

Maintaining effective professional networks is important for career
progression. Within a broader landscape of the digitalisation of work, online
professional social networking sites (SNSs) offer widespread opportunities for
workers to manage their image and career, connect with professional networks,
and find new job roles. With over 875 million users, LinkedIn is the world’s
largest professional networking platform ††As of Jan 14, 2023:
https://about.linkedin.com.. Although the large user base engaging with
LinkedIn as part of their career strategy makes it an important platform to
study in its own right, recent work also shows that online networking
behaviour on LinkedIn influences job opportunities for users (Rajkumar et al.
2022; Wheeler et al. 2022).

Given the potentially significant benefits of online professional networking,
it is important to understand how different users, especially those who have
conventionally faced greater disadvantages in the labour market, such as
women, engage with online professional networks. A large body of research
shows that women’s offline networks are less advantageous than men’s (Wang
2009; Mengel 2020). How online platforms such as LinkedIn may reflect or
reproduce gendered networking behaviours (Bapna and Funk 2018; Greguletz,
Diehl, and Kreutzer 2018; Wang 2009; Kirton and Robertson 2018), or how online
social connectivity may affect outcomes differentially by gender is not well
understood. Although existing research highlights gender gaps in LinkedIn use
(Kashyap and Verkroost 2021), with larger gaps in certain industries such as
information technology (IT) (Haranko et al. 2018; Verkroost et al. 2020),
other dimensions such as gender gaps in social connectivity and its link with
job progression outcomes, such as promotion or relocation, have received
limited attention. This paper examines these dimensions using aggregated,
anonymised data from almost 10 million LinkedIn users from the site’s
advertising platform. Specifically, we examine how gender, age, seniority, and
connectedness to big companies (what we term social connectivity) are
associated with the propensity to report job promotions or relocations on the
LinkedIn population.

Our study focuses on LinkedIn users within the IT sector in the US and UK, a
male-dominated set of industries within which progression for women can be
made particularly difficult through pervasive gender stereotypes about
technical skills, ‘geek’ culture, ‘old boys’ clubs of informal networks, and
organisational factors such as long hours that increase the difficulty to
achieve work-family balance (Ahuja 2002; Armstrong, Riemenschneider, and
Giddens 2018). Socialising and networking events within IT companies are
commonly reported to be in male-oriented spaces – e.g., involving sports, pub
trips, and take place outside of already long industry working hours (Cross
and Linehan 2006; Kirton and Robertson 2018; McGee 2018; Earles 2020) – with
some women feeling uncomfortable or unable to attend as often as male
colleagues due to gendered family expectations, and others not receiving
invitations (Bjerk 2008; Kirton and Robertson 2018). These norms within the
industry may further reinforce disadvantages faced by women within it, and
limit their ability to expand their networks in beneficial ways for career
progression. In contrast, online networking theoretically offers greater
flexibility to participate in terms of time and location, with the potential
to bolster opportunities for those that face greater constraints, such as
women. It may provide opportunities to expand and build advantageous networks
beyond those encountered within one’s immediate work environment. However,
whether online connectivity provides these differential payoffs by gender has
received limited empirical attention, and is a question that our study
examines.

## Related Work

Gender differences in building, maintaining, and using offline professional
networks have been widely investigated across different settings. Women’s
networks are generally characterised as smaller groups formed of stronger
ties, and more intimate connections than men’s (Wang 2009; Greguletz, Diehl,
and Kreutzer 2018). Within the IT sector, leveraging a natural experiment,
Bapna and Funk find gender differences in network formation, with women at an
IT conference meeting 42% fewer new contacts, spending 48% less time talking
with them, and adding 25% fewer LinkedIn connections than men (Bapna and Funk
2018). Mengel proposes that it is largely differences in network use across
genders that leads to higher payoffs for men (Mengel 2020). Their lab
experiments indicate that men reward their network neighbours more than women
do, and as men’s networks show greater gender homophily, these benefits are
disproportionately passed onto other men. Presenting yourself to potential
networking contacts, interviewing successfully, and negotiating promotions all
aid career advancement, but require a person to promote themselves to others.
In professional contexts, research has shown that women consistently
underperform when asked to self-promote, both in self-assessment and when
judged anonymously by others (Moss-Racusin and Rudman 2010; Smith and Huntoon
2013; Rudman 1998). The gendered expectation that women will act modestly has
been shown to drive this underperformance (Rudman 1998), with women altering
behaviour to avoid backlash from others who may derogate them for bragging
(Lindeman, Durik, and Dooley 2018). These studies suggest that women’s career
progression is disfavoured in traditional network-based professional contexts.

However, how these differences in networking and professional self-promotion
behaviours have been translated, reproduced or offset within the context of
new online professional spaces is not clear. LinkedIn, as the world’s largest
professional networking platform, is an important context in which to
understand how men and women use online spaces differentially, because recent
experimental work has shown that LinkedIn use has real job market implications
for users. Rajkumar and colleagues suggest there is a causal relationship
between LinkedIn’s creation of new ties in networks and their translation into
job opportunities (Rajkumar et al. 2022), particularly highlighting the
importance of moderately weak ties over strong ties in job transmission.
Wheeler et al. suggest links between training job-seekers to use LinkedIn, and
increased employment rates (Wheeler et al. 2022). Both suggest that
connectivity within online networks brings important benefits to users in
their working lives, although these studies do not examine heterogeneity of
these effects by gender.

A body of work is beginning to describe gender gaps found across LinkedIn,
showing how the platform is a gendered space for interaction, with differences
in how men and women use their profiles to self-present. Altenburger et al.
find that female MBA graduates are less likely to use free-form data fields on
their profiles (such as the Summary and Job Description fields) but are just
as likely to include structured fields (such as Honours and Skills)
(Altenburger et al. 2017). Similarly, another study suggested that men were
more likely to receive and give recommendations, and to display personal and
professional interests, but were unable to control for industry confounding in
these behaviours (Zide, Elman, and Shahani-Denning 2014). Aguado later drew on
this work but found conversely that women and more senior individuals showed
greater breadth of interaction on LinkedIn (encompassing recommendations
given, companies and people followed, and skills validated) (Aguado et al.
2019). Women were also more likely to have completed additional sections of
their profiles (such as interests identified, length of written text,
languages identified). However, less is known about how these online gender
differentials, particularly in connectivity to potentially advantageous users,
are related to professional outcomes.

To the best of our knowledge, only four previous studies have harnessed data
from LinkedIn’s advertising platform as we do to study gender gaps on
LinkedIn. Haranko et al. analyse gender gaps on LinkedIn across 20 US cities
and find that there is little variation across location, but larger variation
exists across industries, with the high-tech industry as among the most gender
imbalanced (Haranko et al. 2018). The authors also find technical, and
computing skills reported on LinkedIn to be highly male-dominated. Berte,
Kalimeri, and Paolotti (2023) examine subnational variations in gender gaps on
LinkedIn in Italy, and consistent with regional labour market gender gaps find
that women are also underrepresented on LinkedIn. Kashyap and Verkroost
analyse gender gaps on the platform across countries, ages, industries and
seniorities, and find women to be significantly underrepresented on LinkedIn
in Science, Engineering, Maths and Technology (STEM) fields, as well as in
higher-level managerial positions (Kashyap and Verkroost 2021). Our work
builds on these aforementioned studies, as well as Verkroost et al. (2020),
which computes gender gap indices (GGIs) using LinkedIn’s advertising platform
data, describing variation across countries and industries within the IT
sector specifically. We extend these studies by looking at gender differences
in LinkedIn across additional characteristics and behaviours, such as social
connectivity, promotion, and relocation reports.

## Data

Part of LinkedIn’s revenue is generated by offering an advertising platform,
allowing advertisers to reach over 875 million LinkedIn global
users††https://about.linkedin.com/. To maximise the effectiveness of
advertising campaigns, LinkedIn offers advertisers the possibility to
carefully target their audience based on a number of user attributes. The
available attributes are based on a combination of self-declared information,
and information inferred using machine learning from user activity and user
profiles. For example, users’ employment history or social network connections
are based on the information explicitly provided by them in their profiles. On
the other hand, age and gender are inferred from profile information,
including “the pronouns used when others recommend [them] for
skills††https://www.linkedin.com/help/linkedin/answer/a517610/inferred-age-or-
gender-on-linkedin. Similarly, information on the user’s job seniority is
inferred, most likely based on job titles. Information about a user’s location
is likely based on a combination of self-provided employment history and IP-
based geo-location††https://www.linkedin.com/help/lms/answer/a422631.
Advertisers can then target their advertisements to users with a desired
combination of attributes.

To launch and manage advertising campaigns, LinkedIn provides advertisers with
an online platform††https://www.linkedin.com/campaignmanager. As part of the
campaign and budget planning process, the advertising platform provides
advertisers with so-called “audience estimates”, estimating how many LinkedIn
users match the provided targeting criteria selected by the advertiser. These
aggregated estimates, which are provided free of cost before launching an
advertising campaign, create a kind of digital census: for any chosen set of
targeting attributes, potential advertisers can obtain a count estimate of how
many of its users match the targeting criteria. These estimates can be
collected programmatically through an
API††https://worldbank.github.io/connectivity˙mapping/linkedin˙nbs/interface.html.

For our analysis, we collected a large number of such individual audience
estimates by repeatedly modifying the targeting criteria provided to the
advertising platform. We decided to limit our data collection to cover
LinkedIn users in the US and the UK, two of the largest user populations on
LinkedIn (Kashyap and Verkroost 2021) that are also culturally similar. We
further narrowed our data population by only collecting audience estimates for
LinkedIn users currently employed in the information technology (IT) sector,
which we define by the company industry of users, covering the following 11
industries: Internet, Information Technology and Services, Computer Software,
Computer and Network Security, Computer Hardware, Computer Networking,
Wireless, Telecommunications, Semiconductors, Nanotechnology, and Consumer
Electronics. Aligned with previous work (Verkroost et al. 2020), our
definition of the IT sector relies on the OECD definition of the ICT industry
(Wunsch-Vincent and Vickery 2007), as defined according to International
Standard Industry Classification (ISIC) Revision 4. This definition includes
manufacturing-related industries, such as electronics and semiconductors, in
addition to service-related parts of the industry, like software and internet
services. It is worth noting that the names of some of these industries on
LinkedIn have been changed from 2021 to 2023. Therefore, we have provided the
mapping of their previous names to their current names in Table 1. For the
above-mentioned selection, we then looped over combinations of (i) a user’s
location, either the US or the UK, (ii) their inferred job seniority, (iii)
whether they recently reported a promotion or relocation, (iv) their gender,
(v) their age range (vi), and whether they have a social network connection to
an employee at (at least) one of several big companies, namely, Facebook,
Apple, Amazon Web Services, Microsoft, and Google. Together, these companies
are often referred to as the Big Five and are seen as the quintessential
representatives of Big Tech (Birch and Bronson 2022). As many connections to
employees at these companies are likely to come from colleagues at the same
companies, we excluded LinkedIn users who were at the time of data collection
working at any of the Big Five companies. Using this measure of social
connectivity captures the influence of external social networks at prestigious
companies. Prestigious external networks can have impacts on job prospects of
an individual through serving as better information access for the labour
market, through exposure to new ideas from established companies, and the
potential for external job offers from these companies. Together, each of
these can increase perceived desirability of employees, which may make an
individual’s existing company more likely to retain or promote them, or make
them competitive in the job market more generally.

Table 1: Mapping of previous names of industries to current names. Previous Name | Current Name
---|---
Internet | Technology, Information and Internet
Information Technology and Services | IT Services and IT Consulting
Computer Software | Software Development
Computer Hardware | Computer Hardware Manufacturing
Computer Networking | Computer Networking Products
Wireless | Wireless Services
Semiconductors | Semiconductor Manufacturing
Nanotechnology | Nanotechnology Research
Consumer Electronics | Computers and Electronics Manufacturing

Users are considered to have relocated if they have “recently relocated their
permanent location”, while LinkedIn infers recent promotions based on user-
provided profile updates to employment history. Table 2 provides an overview
of the features that we collected. This data was collected in June and July of
2021. Data were preprocessed and stored as a CSV file. We analysed the counts
of users for each combination of these variables (total of 192 unique
combinations, 2 (gender) $\times$ 4 (seniority) $\times$ 4 (age range)
$\times$ 3 (recent status) $\times$ 2 (social connectivity)) to make sure they
are not zero or, in other words, to remove sparsity from the dataset. Our
final dataset consisted of 156 combinations of the variables, as 36 were
dropped due to small audience counts. More specifically, to protect user
privacy, LinkedIn does not provide audience estimates when the targeted
audience is smaller than 300. Note that, apart from sparsity, our aggregate
data is conceptually _equivalent_ to individual-level data for the set of
covariates considered, as our data contains all possible cross-tabulations.
The dataset and analysis code supporting the conclusions are available at
https://github.com/kalhorghazal/icwsm-promotionrelocation-gendergaps.

Table 2: The dataset features and their possible values. Feature | Possible Values
---|---
Gender | Female, Male
Job Seniority | Entry, Senior, Manager, Director
Age Range | 18 to 24, 25 to 34, 35 to 54, 55+
Recent Status | Promoted, Relocated, Any
Social Connectivity | Connected to big companies, Any
Count | Integer Values $\geq 300$

## Results

### Gender Differences in LinkedIn Use

Our data show that there are fewer women than men in the IT sector on
LinkedIn, consistent with previous work on LinkedIn (Verkroost et al. 2020).
While this gender gap on LinkedIn may reflect there are fewer women working in
the IT sector (McKinney et al. 2008), it may also reflect how women working
within the IT sector select into being LinkedIn users. The distribution across
age groups differs between men and women, with Figure LABEL:fig:fig3 showing
that women on the platform and working in the IT sector are proportionately
younger than men. Women aged 25 to 34 make up half of the female professional
population in IT on LinkedIn, while the male distribution is flatter, with
similar proportions of workers in both the 25 to 34 and 35 to 54 age
categories. Lower proportions of women older than 35 perhaps reflect workforce
departure after family formation, or increasing gender balance in entry to the
sector over time. We also find that women in IT on LinkedIn are more junior
than their male counterparts. Figure LABEL:fig:fig3 shows distributions of
women and men across seniority levels to be largely similar at the senior and
manager levels, but that a higher proportion of all women are working in
entry-level positions than men, and a correspondingly lower proportion of
women are employed at the highest director rank. The results of a Kolmogorov-
Smirnov test (Hodges Jr 1958) indicate these differences between women’s and
men’s job seniority distributions is highly statistically significant
($p<10^{-16}$).

Our next analyses of the promotion and relocation reporting behaviours hence
normalises the male and female populations by age and seniority distribution
to make it possible to isolate differences attributable to gender using
demographic standardisation methods (Klein 2001). When applying adjustment to
our calculations, we consider gender-agnostic age or seniority distributions
on LinkedIn as the reference distribution.

### Promotion and Relocation Reports by Gender

Table LABEL:tab:tab3 displays promotion report rates by age- and seniority-
groups, and by gender. The age- and seniority-adjusted rates show that women
are between 6.5% and 18.8% more likely than men to recently report a
promotion. As shown in Table LABEL:tab:tab3, within each age category the
youngest LinkedIn users are most likely to report promotions, with
successively older age groups each reporting promotions at lower rates. Male
and female distributions track a similar trend with increasing age, but women
consistently have higher rates of promotion reports than men across all age
groups. After age-adjustment, per 100,000 women, 1,173 had recently been
promoted compared to 1,101 per 100,000 men, indicating a highly statistically
significant difference in promotion reports by gender of 72 per 100,000
($z=10.285$, $SE=7.000\times 10^{-5}$, $p<0.001$).

LinkedIn users at successively higher seniority levels are more likely to
report their recent promotions, except at the director level (Table
LABEL:tab:tab3). This is in line with existing work showing that senior
employees are more likely to be promoted than juniors (Mills 1985), although
our data cannot differentiate between actual promotion rates of users, and the
rate at which users report a promotion they have received to their online
network. Promotion report rates increase most between the manager and director
levels, which is also the seniority group among which there is the largest
difference between male and female reports. Our findings further indicate that
it is only at manager level that differences in promotion reports begin to
emerge strongly, with LinkedIn’s entry and senior groups showing similar rates
of promotion reports for both genders. Across all except entry-level users,
women have a higher promotion report rate than men. After seniority
adjustment, this higher rate of female promotion reports is maintained, with
1,259 per 100,000 female users and 1,060 per 100,000 male users reporting
promotion. This difference, of 199 per 100,000, is highly statistically
significant ($z=28.349$, $SE=7.020\times 10^{-5}$, $p<0.001$). Thus, overall,
even adjusting for gender differences in age and seniority composition among
LinkedIn users, we see women showing higher promotion reports.

Table LABEL:tab:tab4 displays age- and seniority- adjusted rates of male and
female relocation, according to LinkedIn’s categorisations, and suggests that
men are between 22.7% and 1.6% more likely to report relocation than women,
respectively. After age-adjustment, per 100,000 women, 1,140 had recently
relocated, compared to 1,399 per 100,000 men, indicating a difference of 259
per 100,000 fewer relocations for women ($z=34.430$, $SE=7.517\times 10^{-5}$,
$p<0.001$). After seniority adjustment, differences between male and female
relocation reports are smaller (-21.8 per 100,000) than after age-adjustment,
but differences are still statistically significant ($z=2.831$,
$SE=7.772\times 10^{-5}$, $p<0.01$). The increase in rates of female
relocation after seniority adjustment indicates that either mobility among
female-dominated entry positions is lower than average across ranks, and/or
that mobility among male-dominated director positions is higher. As shown in
Table LABEL:tab:tab4, entry-level women relocate at a higher rate than men,
but among senior employees and directors, males relocate significantly more,
in line with previous work showing that women are more mobile earlier in their
careers (Brandén and Ström 2011). For both genders, older users are more
likely to report their recent relocations except for the 55+ age range, where
women’s likelihood of reporting relocation decreases to similar levels as 25
to 34 year-olds, but men’s likelihood roughly stagnates. At all ages, women
are significantly less likely to report relocation.

### Gender Differences in Social Connectivity

Women not working at a Big Tech company are less likely than men to be
connected to an employee of a Big Tech company, as shown in Figure 1, in line
with literature that shows that women’s networks are not as advantageous as
men’s (Greguletz, Diehl, and Kreutzer 2019). As shown in Figure 2, social
connectivity increases with seniority for both male and female users, with men
having higher or the same rates of connectivity at each rank, but with smaller
gender differentials at more senior Manager and Director ranks. This likely
reflects a type of survivorship bias (Fryer 2007; Smith and Huntoon 2013)
among the women at these higher ranks – although women are less likely to be
at these higher ranks, those who are present in them are positively selected,
and fairly equally socially connected to men at these ranks.

While social connectivity is similarly low for all the youngest users aged 18
to 24, a gender connectivity gap emerges and widens in the successive age
groups from the ages of 25 to 54, peaking among 35–54 year olds (Figure 3).
The gap in these age groups widens across the ages at which women are often
likely to experience interrupted labour market trajectories due to family
formation and care-giving roles, and when professional careers are likely to
become well established. Among the oldest employees, men over 55 maintain a
connectivity advantage over female colleagues, but both show social
connectivity levels comparable to the youngest (18–24) age group.

Figure 1: Bar blot of socially connected and unconnected users disaggregated
by gender. 95% confidence intervals shown, with standard errors computed via
bootstrapping. Figure 2: Gender-disaggregated bar plot of social connectivity
ratio (proportion connected to big company) at each seniority level. 95%
confidence intervals shown, with standard errors computed via bootstrapping.
Figure 3: Gender-disaggregated bar plot of social connectivity ratio
(proportion connected to big company) at each age group. 95% confidence
intervals shown, with standard errors computed via bootstrapping.

### Relationship between Social Connectivity and Status Reports

To assess the relationship between social connectivity and promotion and
relocation reports for LinkedIn users, we estimate different logistic
regression models (Kleinbaum et al. 2002), as promotion and relocation reports
are dichotomous (binary) outcomes. Through this analysis, we examine whether
potentially advantageous online connections are associated with job
progression outcomes, and whether this association differs by gender. We
estimate four models with different combinations of predictors, with social
connectivity being our key predictor of interest, and compare them using
Akaike Information Criterion (AIC) (Akaike 1974) to determine the model with
the best fit (smaller AIC value). The dataset on which these regressions are
estimated is 9,735,600 rows, each representing a specific user. These rows are
obtained by unrolling our aggregated dataset that contains counts of each
combination of targeting attributes. Table 5 shows an example of the initial
aggregated dataset. Table 5 and 5 present the unrolled (individual-level)
versions used for the promotion and relocation report models.

Table 3: An example of the initial dataset.

Gender | Job Seniority | Age Range | Social Connectivity | Recent Status | Count
---|---|---|---|---|---
Female | Senior | 35 to 54 | Connected to big companies | Any | 3
Female | Senior | 35 to 54 | Connected to big companies | Relocated | 1
Male | Manager | 25 to 34 | Not connected | Promoted | 3

Table 4: Unrolled version of the example in Table 5 for the promotion report
models.

Gender | Job Seniority | Age Range | Social Connectivity | Promotion Status
---|---|---|---|---
Female | Senior | 35 to 54 | Connected to big companies | Not promoted
Female | Senior | 35 to 54 | Connected to big companies | Not promoted
Male | Manager | 25 to 34 | Not connected | Promoted
Male | Manager | 25 to 34 | Not connected | Promoted
Male | Manager | 25 to 34 | Not connected | Promoted

Table 5: Unrolled version of the example in Table 5 for the relocation report
models.

Gender | Job Seniority | Age Range | Social Connectivity | Relocation Status
---|---|---|---|---
Female | Senior | 35 to 54 | Connected to big companies | Not relocated
Female | Senior | 35 to 54 | Connected to big companies | Not relocated
Female | Senior | 35 to 54 | Connected to big companies | Relocated

To examine the differential impacts of social connectivity by gender, we
include a social connectivity $\times$ gender interaction term in our models.
We also check for potential multicollinearity of predictor variables in the
regression models by calculating the variance inflation factor (VIF) of each
variable. As all observed factors were smaller than 5.0 (the biggest factor
was 2.8 for the interaction of gender and social connectivity), we did not
exclude any predictors from the models, following standard guidelines
(Akinwande, Dikko, and Samson 2015). Table 6 shows the model estimates with
the outcome of whether a user has recently reported a promotion or not. Based
on the odds ratios of social connectivity, which are higher than one, we can
conclude that users connected to Big Tech companies, i.e. those with
potentially advantageous external networks, are more likely to report recent
promotions. Consistent with our age- and seniority-specific analyses shown
previously, these models also show that younger users, higher seniorities, as
well as women have higher odds of reporting promotions, and that these gender
differences in promotion reports persist even after we control for age and
seniority. Looking at the social connectivity $\times$ gender interaction, we
see a positive interaction (odds ratio higher than 1), which suggests that
social connectivity has higher payoffs (stronger positive association) for
women in predicting promotion. This differential impact by gender can be seen
in Figure 4 that shows the predicted probabilities of promotion by gender
(holding other covariates at their mean/mode values) by social connectivity
from the full model shown in Table 6. Although social connectivity boosts
promotion probability for both men and women, it bolsters women’s probability
of reporting a promotion comparatively more than men, approximately 0.046
percent, or 46 per 100,000 promotions more, which translates to a 3.86 percent
increase above the mean promotion report rate for women of 1191.2 per 100,000
(as shown in Table LABEL:tab:tab3).

Table 6: Estimates (odds ratios) from logistic regression models predicting promotion status by user characteristics. | Dependent variable = Recently promoted
---
(ref: Not promoted)
| Odds ratio
---
(standard error)
| Gender (ref: Male)
---
Female
| 1.032**
---
(0.010)
| 1.142***
---
(0.011)
| 1.142***
---
(0.007)
| 1.079***
---
(0.010)
| Job Seniority (ref: Entry)
---
Senior
- |  | 12.572***
---
(0.206)
| 14.974***
---
(0.246)
| 14.964***
---
(0.246)
Manager | - |  | 22.272***
---
(0.376)
| 28.147***
---
(0.477)
| 28.068***
---
(0.476)
Director | - |  | 18.119***
---
(0.314)
| 26.224***
---
(0.459)
| 26.179***
---
(0.458)
| Age Range (ref: 18 to 24)
---
25 to 34
| 0.709***
---
(0.007)
- |  | 0.567***
---
(0.006)
| 0.567***
---
(0.006)
35 to 54 |  | 0.518***
---
(0.005)
- |  | 0.294***
---
(0.003)
| 0.294***
---
(0.003)
55+ |  | 0.170***
---
(0.004)
- |  | 0.090***
---
(0.002)
| 0.091***
---
(0.002)
| Social Connectivity (ref: Not connected)
---
Connected to big companies
| 3.811***
---
(0.031)
| 2.462***
---
(0.020)
| 3.045***
---
(0.019)
| 2.922***
---
(0.024)
Social Connectivity $\times$ Gender |  | 1.227***
---
(0.015)
| 1.180***
---
(0.015)
- |  | 1.109***
---
(0.014)
Constant |  | 0.010***
---
(0.000)
| 0.001***
---
(0.000)
| 0.001***
---
(0.000)
| 0.001***
---
(0.000)
AIC | 1141919 | 1079141 | 1054174 | 1054109
N | 9,608,320
$*p<0.05,**p<0.01,***p<0.001$ |  |  |  |

Table 7 presents results from logistic regression models predicting users’
relocation status. Once again, the odds ratios of social connectivity are
higher than one, leading us to conclude that users connected to big companies
are more likely to report recent relocations. Similar to the previous analysis
of promotion reports, the best model (lowest AIC) is the one that includes all
predictors of user characteristics and the social connectivity $\times$ gender
interaction. Once again, we observe a positive interaction effect between
gender $\times$ social connectivity. However, as shown in Figure 4, the gender
gap in relocation among those that are socially connected to Big Tech firms on
LinkedIn is smaller – indicating a higher payoff to social connectivity for
women compared to men. To correct for multiple comparisons, such as multiple
variables potentially being statistically significant, all p-values in Table 6
and 7 have been adjusted using the Bonferroni correction (k = number of
variables of each model) (Rice, Schork, and Rao 2008).

Table 7: Estimates (odds ratios) from logistic regression models predicting relocation status by user characteristics. | Dependent variable = Recently relocated
---
(ref: Not relocated)
| Odds ratio
---
(standard error)
| Gender (ref: Male)
---
Female
| 0.787***
---
(0.006)
| 0.787***
---
(0.006)
| 0.838***
---
(0.005)
| 0.795***
---
(0.006)
| Job Seniority (ref: Entry)
---
Senior
- |  | 1.381***
---
(0.009)
| 1.324***
---
(0.009)
| 1.323***
---
(0.009)
Manager | - |  | 1.165***
---
(0.012)
| 1.110***
---
(0.011)
| 1.106***
---
(0.011)
Director | - |  | 2.147***
---
(0.019)
| 1.990***
---
(0.018)
| 1.987***
---
(0.018)
| Age Range (ref: 18 to 24)
---
25 to 34
| 1.158***
---
(0.014)
- |  | 1.123***
---
(0.013)
| 1.124***
---
(0.013)
35 to 54 |  | 1.555***
---
(0.018)
- |  | 1.381***
---
(0.016)
| 1.383***
---
(0.016)
55+ |  | 1.461***
---
(0.022)
- |  | 1.252***
---
(0.019)
| 1.255***
---
(0.019)
| Social Connectivity (ref: Not connected)
---
Connected to big companies
| 1.247***
---
(0.009)
| 1.191***
---
(0.009)
| 1.210***
---
(0.007)
| 1.150***
---
(0.008)
Social Connectivity $\times$ Gender |  | 1.190***
---
(0.015)
| 1.176***
---
(0.015)
- |  | 1.185***
---
(0.015)
Constant |  | 0.010***
---
(0.000)
| 0.011***
---
(0.000)
| 0.009***
---
(0.000)
| 0.009***
---
(0.000)
AIC | 1346296 | 1342040 | 1340790 | 1340623
N | 9,626,000
$*p<0.05,**p<0.01,***p<0.001$ |  |  |  |
Figure 4: Predicted probabilities of the interaction between gender and social
connectivity in promotion (left)/relocation (right) models. 95% confidence
intervals shown, derived from the combination of the z-scores and standard
errors (Diez, Barr, and Cetinkaya-Rundel 2012).

## Ethical Considerations

The data collected and used in this paper consist of aggregate and anonymous
user counts. As the smallest identifiable unit contains 300 users, we see
little to no reidentification risk of individual users. While such aggregate
data could, theoretically, still be used to map vulnerable groups, we do not
see the general data source – LinkedIn – nor the particular targeting
attributes posing a danger for this. The data used is also accessible free of
charge on LinkedIn’s advertising platform through APIs††See
https://worldbank.github.io/connectivity˙mapping/linkedin˙nbs/interface.html
for a helpful introduction to programmatically collecting such data. This type
of data access can be seen as a type of Data
Collaborative††https://datacollaboratives.org/, providing a non-standard way
of enabling partial auditing of large platforms.

In terms of the privacy _expectation_ of users, we believe that this type of
data is less problematic than public individual-level data consisting of
posts, comments, or pictures. At the same time, we acknowledge that LinkedIn
users might be unaware that their data can be accessed and analyzed in this
way, even if in anonymous and aggregate form (Anderson and Leigh Anderson
2020).

LinkedIn and all other online advertising platforms we are aware of only
support binary female-or-male gender for the ad targeting on the platform,
though it leaves some users unclassified. This could be viewed as a form of
exclusion, if not erasure, of gender minorities (Bivens and Haimson 2016). At
the same time, this design choice limits the potential use of the advertising
platform for targeted harassment of gender minorities. Furthermore, the binary
gender used by LinkedIn is automatically inferred, and also draws on pronouns
used by other users referring to the user, rather than based on self-
identified information. This will undoubtedly result in misclassifications of
some users. In a user-facing system, such misclassification has the risk of
causing psychological harm by misgendering the user without providing them
with an option to self-declare their gender identity (Hoffmann 2018; Keyes
2019). However, LinkedIn seems to only assign a gender to cases with
sufficient confidence. For example, for all the 6,500,000 LinkedIn users in
the US working in IT (using the same definition as in the paper), LinkedIn’s
advertising platform classifies 2,100,000 as female and 3,900,000 as male,
leaving 500,000 as unclassified††As of Jan 15, 2023, and per
https://www.linkedin.com/campaignmanager.. For cases that can be mapped with
sufficient confidence, openly accessible name-to-gender mappings achieve
accuracies of 95-97% (Santamaría and Mihaljević 2018). This suggests that for
those users for whom a (binary) gender is inferred, the precision is likely to
be high. For our population-level study on relative female-vs-male gender
gaps, we feel that these data are able to highlight important, aggregate
gender differences that outweigh the harm caused by potential (population-
level) gender misidentification.

## Discussion and Conclusion

Professional networking is important for career progression, yet research has
shown that women’s offline networks are less advantageous than men’s. How
these gender differences translate to online spaces, specifically the use of
online professional networking platforms, is not well understood. This study
examines gender differences in the information technology (IT) industry in two
of the largest LinkedIn user populations of the UK and US, leveraging
aggregated, anonymised data on the LinkedIn user population from its
advertising platform. Consistent with previous work using these data (Kashyap
and Verkroost 2021; Verkroost et al. 2020; Haranko et al. 2018; Berte,
Kalimeri, and Paolotti 2023), we find there are fewer women compared to men on
LinkedIn in IT. Female LinkedIn users are younger, less senior, and also less
likely to be connected to big companies compared with male LinkedIn users in
IT. Yet, they were more likely to report a recent promotion at work. Even in
this high-achieving sample, we nonetheless found women were less likely to
report a relocation, confirming previous research that highlights women’s
lower availability to relocate (Baldridge, Eddleston, and Veiga 2006;
Mansfield, Mutz, and Silver 2014).

While the data preclude us from distinguishing whether the observed gender
differences in promotion rates reflect differences in propensity to report
promotions, or the actual prevalence of promotions among LinkedIn users, we
offer two plausible interpretations of these findings, which are not mutually
exclusive. First, aligned with prior studies that show positive selection
effects by gender on online platforms such as LinkedIn (Kashyap and Verkroost
2021; Verkroost et al. 2020) or Google+ (Magno and Weber 2014), women who are
on LinkedIn, especially in a highly unequal industry such as IT, may be high-
achieving, professionally driven, and positively selected. Second, women on
LinkedIn may choose to more actively share recent promotions to their online
networks, seeking visibility from the wider professional community at lower
costs afforded through online platforms. These benefits from online networking
may help women who face greater disadvantage in accessing offline networking
due to gendered family or caring responsibilities (e.g., attending conferences
or socialising after work), or have smaller offline professional networks. The
fact that women often face greater constraints, e.g., related to gendered
family expectations, in making job-related decisions is suggested by our
finding that even among this sample of professionally motivated women, women
are less likely to report relocations. Further suggestive of these constraints
is our finding that the social connectivity gap on LinkedIn between men and
women is greatest during the childbearing ages. While the lower relocation
rate may reflect a lower availability to relocate among women, the differences
in promotion versus relocations also suggest the pursuit of different career
progression strategies for men and women, which are likely to be shaped by
differing choice sets, norms and expectations.

Our findings add an important gendered nuance to recent research highlighting
the value of online professional networking via LinkedIn for job search and
mobility processes (Rajkumar et al. 2022; Wheeler et al. 2022). We find that
although women not working at Big Tech firms on average have lower social
connectivity to those at Big Tech companies than men, the payoffs to online
social connectivity for those with these networks are larger for women
compared with men. While Rajkumar et al. (2022) show the causal effects of
weak ties on LinkedIn for job search, our findings suggest returns to these
ties may be even larger for women, who have conventionally faced greater
disadvantages in accessing potentially advantageous network ties in the labour
market through traditional forms of networking. We acknowledge nonetheless
that the cross-sectional nature of the data implies that our findings cannot
be given a causal interpretation, and are susceptible to the potential for
reverse causality. Online social connectivity may not be the driver of higher
promotion rates, but women who are recently promoted may be seen as more
successful and attract more social connections. As such, the formation of
social links reflects a bidirectional process, which makes the interpretation
of social connectivity tricky. This process is also likely to be
algorithmically mediated, and the acceptance rate of such requests could also
correlate with other characteristics, which we are unable to control for in
our analyses. Longitudinal data are needed to better disentangle these
mechanisms underlying social connectivity and job progression.

We acknowledge that the data come with additional limitations. The audience
counts obtained from the marketing platform may include fake accounts, and
also include misrepresented or inaccurate affiliations. Moreover, there is a
sparsity-related limitation as LinkedIn’s advertising platform does not
provide counts below 300. Currently, our data has 36 out of 192 (19%) sparse
values. If we were to disaggregate by the two countries (US vs. UK), then we
would have 116 out of 384 (30%) sparse values. As we felt that this level of
sparsity with values missing not at random would be too high, we decided to
combine the two countries. Further, many of the targeting categories for which
counts are provided are algorithmically inferred, and are vulnerable to
biases. For example, people with non-standard careers who start university
later in life, might be misclassified as being younger than they are. Greater
transparency and documentation from platforms about the data-generating
process and algorithms underlying these data can be helpful to better
understand and address these biases. Even within our existing (binary)
analysis of gender, we recognise that men and women are not homogeneous
groups, and acknowledge that much heterogeneity exists in how workplace
structures may differentially affect the experiences and professional
trajectories of individuals – e.g., by race, immigration status, or sexual
orientation, and how these intersect.

Nonetheless, our findings expand on previous research about gender gaps on
LinkedIn, by exploring the additional dimensions of social connectivity and
how these are associated with job progression behaviours such as recent
promotions and relocations. They contribute to a growing body of work showing
the potential value of online professional networks for employment behaviours,
but highlight the need to integrate a gender perspective to understand the
differential impacts of online platforms on social and economic domains. With
the growing digitalisation of work, and also increasing levels of remote work
(Saad and Wigert 2021), online networking is likely to become even more
central to job progression and mobility processes. This increasing use of
online networking may help to mitigate gender gaps in the labour market. In
turn, policies that integrate online professional networking within
educational and job training programmes have the potential to help benefit
disadvantaged groups. Moreover, for employers, seeking potential candidates
through online platforms may serve to bring a broader pool of candidates to
their attention than those through traditional network-based contexts, e.g.
through conferences or events. For researchers, our study motivates further
studies of user behaviours on LinkedIn in its own right as the largest
professional networking platform, but also studies that examine how online
networking is experienced and used by different disadvantaged social groups,
and whether they reproduce or alter social inequalities experienced by them.

## Competing Interests

The authors declare that they have no competing interests.

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