Gender Bias in Hiring Report: Data-backed strategies for improving gender equality in the workplace

Joe Caccavale

22

January

2021

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25

min read

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DID YOU KNOW? According to a recent UN report, almost 90% of men and women hold some sort of bias against females.

Whilst some countries are doing better than others, there are no countries in the world with true gender equality, the study found.

Most hirers understand that gender equality is better for business as well as simply being the right thing to do…

But even the most well-intentioned advocates of diversity are prone to bias.

Unconscious bias training doesn’t work.

Simply being made aware of bias doesn’t work.

However, there are solid, evidence-based steps you can take to de-bias your hiring process to attract/ hire more women...



Gender bias in hiring: the report

  • The state of play
  • The science behind unconscious bias
  • Why bias training doesn’t work
  • Strategies for improving gender equality
  • Why is gender equality important?

Gender equality in the workplace - the state of play

Women have increased their presence in senior management and leadership roles over time but remain under-represented in top company positions.

Whilst society may seem to be getting more progressive, gender equality progress is actually slowing down over time.

Progress towards gender equality


Make no mistake - this is not a problem that is going to ‘sort itself out’ or gradually improve over time unless we take action.

Gender discrimination UK

A UK study (looking primarily at the effect of social class indicators) found that a candidate’s gender had a measurable impact on callback rates - albeit not an extreme one.

Job application callback rate by gender


Here you can see that men were called back in slightly higher numbers than women.

However, this doesn’t tell the full story.

To get the full picture, you have to drill down into individual roles.

Below are the results of a study carried out across the English labour market back in 2006.

Researchers sent carefully-matched applications to open job vacancies, specifically testing for gender discrimination in hiring. 

Leaving everything else the same, they changed only the sex on the application.

Here are the results:

Callback rated for different jobs


What does this tell us?

Well, it tells us that males tend to be favoured for typically male roles.

And females tend to be favoured for typically female roles.

If a job is dominated by the opposite sex, you’re likely to be penalised.

So, if top positions are generally male-dominated, females will find it harder to get into these positions.

And just a quick look at the FTSE 100 confirms this:

Gender diversity in the FTSE 100


In the FTSE 100, there are more CEOs/ chairman called John than there are women.


Gender discrimination USA

Despite efforts to hire more women, academic science suffers from a significant gender disparity.

Suspecting that this may be due to a gender bias in hiring, U.S researchers had the science faculties of research-intensive universities rate candidates' applications to a laboratory manager position.

Applications were randomly assigned either a male or female name.

Candidates were rated in terms of competence, hireability, and likelihood of receiving mentorship:

How science departments rated applications by gender


The study found that participants rated female candidates as being less competent and hireable than male candidates - despite their applications being identical.

Males were even offered higher starting salaries.

Although you might automatically assume that this is a result of a male bias against women, this wasn’t the case.

The gender of the hirer did not affect responses.

Female and male faculty were equally likely to be biased against the female candidate.

In another study, this time looking at class and gender bias in hiring, researchers sent applications to elite jobs at U.S law firms.

They created two fictitious candidates, matched on all characteristics except sex and class indicators.

Here’s what they found:

Class indicators' effect on interview callbacks


The study concluded that only men perceived as higher class had an advantage when applying to the most elite jobs. 

For women, a higher-class background actually led to a “commitment penalty.” 

Interviews with the employers found that they viewed higher-class female candidates as less committed to working and more likely to leave after having children.

This applied across all female candidates, regardless of their actual parental status.

Gender discrimination EU

A Eurobarometer survey released back in 2017, emphasised that gender equality has still not been achieved in the EU Member States. 

If we look at the research, this seems to ring true.

A recent study conducted in 2020 looked at gender bias in hiring for two male-dominated professions - mechanics and IT.

Recruiters for real-life open roles across Bulgaria, Greece, Norway and Switzerland were asked to rate hypothetical CVs.

The CVs were identical, expect for the sex of the applicant.

Below are the results - anything below the grey line represents discrimination.

Gender discrimination across EU


As you can see from the charts above, female candidates have a lower likelihood of being hired in…

  • Bulgaria (females mechanics were rated 43% less positively than males)
  • Greece (female mechanics were rated about 18% less positively than males)
  • Switzerland (female IT candidates were rated around 5% less positively than males)

Aside from just competence, females have also been found to suffer from parenthood-based discrimination.

Researchers investigating gender equality in Spain set out to determine how much of an affect being a parent had on hiring success.

CVs were sent in response to 1,372 job ads for a variety of roles.

Take a look at the results below:

Gender and parenthood status effect on job applications


The researchers found that not only was there a general gender bias in hiring, but being a parent had a significantly more detrimental effect on female candidates’ chances. 

This isn’t just a Spanish phenomena, studies in the U.S have had similar findings...

  • 'Highly successful mothers tend to be discriminated against in hiring and promoting decisions because they are viewed as less warm, less likeable and more interpersonally hostile' (see study).
  • 'Mothers face penalties in hiring, starting salaries, and perceived competence while fathers can benefit from being a parent: for women, competency ratings were 10% lower for mothers compared to non-mothers among otherwise equal candidates' (see study).

What about AI?

In a recent Australian study, researchers gave 40 recruiters real-life CVs for jobs at UniBank.

The CVs were identical, except half had the candidate’s sex, and the other half were assigned either (traditionally) male or female names.

The recruiters were then asked to rank each candidate and collectively pick the top and bottom three CVs for each role.

Here’s how they ranked the resumes…

How recruiters rates candidates bases on gender and role


Recruiters preferred CVs from male candidates — even though they had the same education and experience as the female CVs. 

Both male and female panelists showed this bias.

The researchers used this data to create a hiring algorithm for ranking each candidate.

Unsurprisingly found that it reflected the recruiters' biases.

If the data being used to ‘train’ the AI was based on a biased data set, was it any wonder that the algorithm itself was also biased?

The results of this experiment can also be seen in real-life...

Amazon’s recruitment algorithm was found to display gender biases by downgrading CVs containing the word “women” or which contained reference to women’s colleges/ universities.

As was the case in the study above, the process of using ‘training data’ to build recommendations is often where the issue lies.

If the data is not objective, then the algorithm will follow suit.

If you wanted to build an AI that finds the best candidates, you’d first need to identify what current best candidates look like.

Let’s say you wanted to find a marketing or sales director...

Since around 66% of marketing and sales directors here in the UK are men, your algorithm would assume that a male name is a desirable quality for the position.

So, in a nutshell: AI uses human data sets. Humans are biased. As a result, the AI algorithms we build are also going to be biased.

Further reading around AI:


The science behind unconscious bias: a brief overview

We’re all prone to unconscious bias.

Bias - so long as it’s unconscious - doesn’t make you a bad person.

It just means that you’re human.

We naturally tend to put others into categories based on physical appearance and basic background information.

How does unconscious bias happen?

We make 1000’s of decisions every day.

Some big.

Some small.

To manage this demand, our brain has two ‘systems’ for decision-making (a theory popularised by Daniel Kahneman’s book, Thinking Fast and Slow).


  • System 1: for fast, intuitive thinking
  • System 2: for slower, more conscious thinking

System 1 is what we use to handle all of the micro-decisions that we make each day.

This is how you might find yourself walking to work on ‘auto-pilot.’

This system of thinking relies on mental-shortcuts and associations.

You see a red light, you instinctively stop.

You hear a familiar voice, you turn around.

Without System 1, we wouldn't be able to function on a daily basis.

We have some many micro-decisions to make that we can’t stop to ponder each one equally.

System 2, on the other hand, is for making those less frequent, more important decisions.

When planning a big project at work, you’d be using System 2 (you’d hope!).

If you were going to make a big purchase, you’d also be using System 2.

These are decisions that aren’t made using ‘gut instinct’ - they require a conscious effort.

Hiring bias occurs when we should be using System 2, but instead fall back on System 1.

This isn’t something intentional.

We do this without even realising.

Our brains will use shortcuts and general patterns to make an instant judgment.

We fall back on stereotypes, past experiences and what we’ve seen in the media - all of which prevent us from being objective.

Stereotypes dictate who we expect to see in a given role

Stereotypes act as a rule of thumb in terms of what is possible.

This is why women find it easier to get jobs as secretaries and men as engineers.

Generally, it’s not a case of males intentionally trying to keep women out of a given profession (although this may sometimes be the case), it’s more about the kind of person we expect to see in a role.

Whether you yourself are male or female, you’ll typically associate certain groups with certain characteristics and roles.

We might think of surgeons as being males aged 30-50.

We might think of nurses as being females aged 20-40.

These stereotypes are bred into us as children and continually perpetuated throughout our adult lives.

When it comes to hiring for these roles, we might subconsciously look for someone who ‘fits the bill.’

In other words, we’ll look for someone who fits the stereotype of someone in that role.

Not conforming to a stereotype tends to result in penalisation

Looking at gender bias in hiring at a high level, it may seem that discrimination is minimal.

And in some studies, female candidates were even called back at higher rates than males.

However, you don't have to scratch very deeply beneath the surface to discover that such discrimination does in fact exist.

If females apply for ‘typically female’ roles, then their chances of a callback are fairly high.

So yes, females aren’t subject to hiring discrimination…

As long as they ‘stay in their lane’.

On the whole, senior positions pay more than junior/mid-level positions.

And according to Mercer’s 2020 report, the higher up the corporate ladder you go, the less women you’ll come across.

Female representation vs seniority


So whilst females may be able to get some jobs easily enough.

They can’t necessarily get the best paying ones.

Even when you remove seniority from the equation and just look at jobs functions, women are over-represented in support functions like administration, while men generally dominate operations, profit and loss, and research.

Note: these three male-dominated disciplines are viewed as ‘critical’ experiences for CEOs and board members to have.

This ‘subversion tax’ even applies to how females’ skills and general demeanour are perceived.

Attributes we may praise when exhibited by males tend to be seen as negative when exhibited by females.

The most famous example of this is the Heidi Roizen case study.

Heidi Roizen was a successful Silicon Valley venture capitalist.

At Columbia Business School, half of a class was presented with her case study with her real name on it (Heidi).

The other half had the same case study, except the name was changed to ‘Howard’.

The students rated ‘Howard’ and Heidi as equally competent…

But they would have preferred to work with Howard.

Heidi was also perceived as being significantly less likeable and worthy of being hired than Howard, as well as more selfish.

Further studies and surveys also confirm that we have set ideas around the roles males/females should and shouldn't be doing.

According to a European Commission survey, although 84% of participants believed that gender equality was important (including 80% of men), the survey also showed that more than one third of Europeans think that men are more ambitious than women (35%).

European attitudes towards gender stereotypes


Why training doesn’t work - a quick look at the evidence

Although the diversity training market is valued at around nearly $8 billion annually, research tells us that it’s simply not worth investing time and money into.

A meta-analysis of 426 studies found that while there was a reduction in bias immediately after training, this disappeared after about 8 weeks.

Training effect on implicit bias


When it comes to changing behaviour, and impacting real-world diversity, a study of 829 companies over 31 years showed that training had no positive effects in the average workplace. 

It's not that bias training is completely useless.

It does have some effect on bias.

It’s just that it’s extremely expensive and there are far cheaper means of removing bias that will have a greater, longer lasting impact. 

Bias training doesn’t work because it attempts to change what is essentially human nature.

Most of us are largely unaware of our biases, and so even when we’re made aware of them, we cannot rid ourselves of bias through simply trying to ‘do better.’

Although you can’t de-bias people, you can de-bias environments and processes.

Rather than changing how people themselves make choices, what behavioural scientists have found to be more effective in affecting real-world outcomes is redesigning the environment in which people make choices.

The most famous example of this involves orchestra auditions.

In an attempt to improve the gender diversity of orchestras, behavioural scientists set up ‘blind’ auditions - which had candidates perform behind a curtain.

As a result, double the number of women got through the auditions.

The scientist knew they couldn’t remove bias from the panel themselves, but they could remove bias from the audition process.

This same principle has been proven to work for de-biasing hiring too.

You can’t train hirers to be less biased.

But you can remove bias from your hiring process - without barely spending a penny - by simply designing an environment that makes bias impossible.



Gender bias in hiring: strategies for improving gender equality

Sourcing

Sourcing can be a red herring when it comes to improving diversity.

Organisations often assume their diversity issues can be solved simply by attracting a more diverse set of candidates at the very top of the funnel, when in reality, the rest of their process is also harming diversity.

However, if you’re struggling to source enough women, there are some practical, evidence-based steps you can take to address this.


Write inclusive job descriptions

The language you use in your job description will have a direct impact on who actually applies.

Certain words and phrases can convey subconscious meaning, that signals to candidates whether or not they’d belong in your workplace. 

Females are put off applying if the job description includes excessive ‘masculine language’ (or ‘masculine-coded’ language).

Below are a few examples of gender-coded language commonly used in job descriptions:

Masculine terms:

  • Analytical
  • Autonomous 
  • Decisive
  • Independent
  • Leader
  • Self-sufficient

Feminine terms:

  • Committed
  • Dependable
  • Honest
  • Loyal
  • Supportive
  • Trustworthy

By requiring characteristics typically attributed to males, you’re signalling that a male would be a better fit.

Whilst the effect of gendered language will vary from person to person, using masculine language will have an overall impact on the gender diversity of your candidate pool.

We looked at the effect of gender-coded language on gender diversity, using data from our Job Description Analysis Tool:

Gendered language effect on gender diversity


  • 54% female applicants when job description was strongly feminine
  • 51% female applicants when job description was feminine
  • 48% female applicants when job description was masculine
  • 44% female applicants when job description was strongly masculine
Key takeaway: feminine-coded job descriptions will increase the odds of women applying, while masculine coded job descriptions will discourage female applicants.

We then looked at the diversity of the people who were actually hired based on these job descriptions:

Gendered language effect on diversity of hires

Again, we can see that feminine-coded job descriptions increase the odds of hiring a female, and strongly masculine-coded job descriptions reduce those odds.

Aside from gendered language, how you list your requirements can also affect gender diversity.

Research has shown that generally speaking, women tend not to apply for roles unless they meet 100% of the criteria, whereas men will apply when they meet just 60% of the requirements.

Why does this happen?

It’s likely due to a combination of gendered differences in confidence.

Women are more socialised to follow the rules and men are socialised to believe rules are meant to be broken.

If you list too many ‘requirements’ - female applicants are more likely to perceive them as actual requirements they must fulfil in order to apply.

And males will be more likely to perceive them as mere desirables.

Note: This risk-aversion doesn’t make someone less capable of doing the given job. Being risk-averse has nothing to do with ability or competence.

Key takeaway: Only list requirements that are essential, no more than ten.
Job Description Template


Job boards

Not all job boards are going to bring you the same degree of diversity.

Some will bring more females than males and vice versa.

Given that a single job can cost £100 - £400, you’ll want to make sure your postings are attracting a diverse candidate pool.

The easiest (and cheapest) way to do this is by using UTM links.

By providing each job board you post to with a unique, trackable link, you can monitor where your candidates are coming from and distribute your budget accordingly.

To help get you started, we broke down the best job boards for gender diversity here.

If you’re still struggling to attract an even gender split (which may be the case for typically male-dominated roles), you can also try posting to specialist, female-specific job boards.

Specialist job boards:


Screening 

Screening is where most of the gender bias in hiring occurs, so if you only have the time/ resources to change one part of your process, make sure it's this stage.

You can source more females into your initial candidate pool but if your screening process isn’t fair and objective, your sourcing efforts will have been in vain.

Anonymise applications and ditch CVs

Most of the research around gender bias in hiring uses the names on CVs to determine the degree of bias.

So, the most logical step to prevent this bias is to simply remove names from applications together (as well as address, date of birth etc).

Anonymous applications with identifying information removed are undoubtedly a step in the right direction towards gender equality (at least in terms of hiring).

However, even with this information removed, what’s left still leads to bias.

CV bias breakdown


If we look at a standard CV, we see a candidate's work experience (usually with some details around what the candidate achieved in each role) and their academic background.

Although these may seem like fairly solid indicators of ability, the evidence suggests otherwise.

Firstly, even when we remove sex/ names that indicate sex from CVs, there’s the fact that women have a tendency to downplay their achievements.

On average, women rate their performance less favourably than equally performing men.

And secondly, they’re just not very good predictors of skill. 

According to the Schmidt-Hunter meta-analysis, education and experience don’t actually tell us much about a candidate's real-life ability.

Predictivity of assessment methods


Popular assessment methods were mapped against their predictive validity (how well they predict actual skills), and as you can see, years of experience and education - staples of the CV - were found to be the least predictive means of assessment.

So if CVs result in bias and aren’t predictive, what should you use instead?

Looking back at the chart above, we can see that ‘work sample tests’ are the most predictive assessment method.

What are work samples?

Work samples are interview-style questions designed to the specific skills required for the job.

They work by taking a realistic task or scenario that candidates would encounter in the role and asking candidates to either perform the task or explain how they would go about doing so.

The idea is to simulate the role as closely as possible by having candidates perform small parts of it.

Instead of candidates talking through their achievements or why they’d be a great hire, work samples directly test the relevant skills.

Given that females are less inclined to self-promote, work samples level the playing field by removing the need to ‘talk up’ skills/ achievements.

Candidates aren’t required to talk about their skills, they’re instead asked to demonstrate their skills. 

Here’s an example of a work sample we used to hire a Community Lead:

You’ve been invited to be on a panel on hiring & recruitment. You’re the only D&I expert (possibly the only one that thinks it’s important there) in the room. What are your opening lines to the audience to convince and engage them on the subject?”

This work sample was crafted to test candidates’ communication skills, as well as their D&I knowledge.

The more closely your work samples mirror real-life, the more predictive they’ll be.

The scenario above is one that the candidate would undoubtedly have to tackle if they were to get the job.

Work Sample Cheatsheet

How to create your own work samples:

  • Decide on essential 6-8 skills necessary to succeed in the role.
  • Think of a job-specific scenario or task that would require 1-3 of these skills. This could be a day-to-day recurring task or a one-off issue. You could think back to a scenario that has already happened, or one that is likely to happen in the future.
  • Once you have 3-5 tasks/ scenarios, turn them into work samples by posing them hypothetically.
  • Give yourself scoring criteria - ideally a 1-5 star scale with a few bullet points for each star.

Work sample ideas:

  • Email to potential lead
  • Blog post draft
  • Customer complaint  to tackle
  • Prioritisation task
  • Analysis of favourite website/ product/ service etc
  • Logistical problem that needs to be solved
Key takeaway: Instead of CVs, have candidates answer 3-5 work sample questions anonymously.



Interviewing

When meeting candidates face-to-face, you’re inevitably going to be subject to some degree of unconscious bias.

According to one study, roughly 5% of interview decisions were made within the first minute of the interview, and nearly 30% within five minutes.

Another study found that candidates have around six minutes and 25 seconds to impress potential employers during their first meeting.

More often than not, hastily-made decisions based on a ‘feeling’ use the System 1 part of our brain.

Hirers might think their gut instinct is a genuine skill, honed over time, but this feeling is more likely to be their biases kicking in.

Switch to structured interviews 

Whilst you can’t interview blindfolded, there are measures you can take to make interviewing significantly more inclusive and impartial .

Your first step should be to ‘structure’ your interviews.

Structured interviews entail asking all candidates the same questions in the same order.

All candidates should have a similar interview experience, with as few tangents as possible.

Ask work sample-style questions 

Much like the details on a CV, traditional interview questions that probe into a candidate’s background and experience will trigger bias.

Instead, use work sample questions or case studies to further test skills.

The benefit of using these at the interview stage is that you get to see not just the answers candidates give, but how they approach the task and arrive at those answers.

Digital Marketer interview question:

*Candidate is shown some fake marketing data with some fake funnel metrics and Google Analytics data. They’re also shown our commercial targets.*

With a view of our commercial targets, talk through the above data and what it might mean.

Customer Success Manager interview question:

We would like to continuously improve our Customer Support. Our current system is to create monthly reports of user tickets. How would you categorise these tickets? And how would you make sure that we improve our support service?

Follow-up question: What kind of metrics would you use? And how would you define success?

The tasks above could just have easily been ‘tell me a time when…’ questions.

However, these backwards-looking questions require candidates to exaggerate their past achievements and in many cases, completely lie.

By asking how they would approach a given scenario, irrelevant factors such as confidence and one’s ability to embellish their past experience are removed from the equation.

Past the initial screening, gender bias in hiring is often due to how candidates ‘come across.’

We know from the Heidi Roizen case study that even when identical, a male and female’s past achievements and personality will be perceived differently based on gender alone.

Which is why we use forward-looking, hypothetical questions - to test skills over personality and accomplishments.

Key takeaway: ask forward-looking questions to remove biases around personality/ experience 

Have multiple interviewers and scoring criteria

Using data is the most effective way to eliminate gut instinct (bias) from hiring.

To use data to make decisions, you’ll first have to make sure you’re collecting it over the course of the process.

Every work sample and interview question should have scoring criteria - a 1-5 star scale that details what a good, bad and average answer would look like.

This will not only give you data to make decisions based on, but also negates bias, since hirers will have a set benchmark against which to judge candidates.

Here’s an example work sample with scoring criteria:

Question: 

We're in the process of reaching out to management consulting firms as part of this week's marketing campaign:

Draft an email to the Head of HR, introducing Applied, and trying to arrange an introductory call.

Scoring criteria:

# 5 Stars 

  • Concise message, with a clear structure and well written. 
  • Skilfully and sensitively personalised to their particular needs.
  • Correct and clearly articulated understanding of what Applied is.
  • Some use of evidence or 3rd party comparison to demonstrate benefits/generate interest.
  • Not entirely focused on diversity benefits. Linking to the wider talent picture.

# 3 Stars

  • Good understanding of what Applied is.
  • Clearly written with good spelling/grammar and clarity/structure.
  • Personalised to the prospect.
  • Orientated around starting a conversation not "salesy"
  • A clear call to action.

# 1 Star

  • Not personalised to the business, or accusatory and insensitively handled
  • Lack of clarity of what Applied is.
  • Poorly written, in either spelling/grammar or clarity/structure.
  • Overly “Salesy”. Trying to sell rather than initiate a relationship.

For the most unbiased scores have three team members score work samples, and then another three score each interview round.

Crowd Wisdom’ is the general rule that collective judgment is more accurate than that of an individual.

An interviewer may have unconscious biases, but by having multiple interviewers much of this bias will be negated.

The more diverse your reviewers panel, the less biased the scores.

If you’re specifically aiming to increase gender diversity, you should assemble a gender diverse panel. 

Note: three reviewers per round is recommended since adding any additional reviewers will bring diminishing returns.

This may sound challenging with a small team, but the panel doesn’t need to consist of entirely fresh team members each time, so long as the combination is changing.

You can also have a single team member join every panel - this would usually be the hiring manager who originally created the role.

Here’s how you can use crowd wisdom with a small team:

Work sample screening: Reviewer A, Reviewer B, Reviewer C

Interview round 1: Reviewer A, Reviewer B, Reviewer D

Interview round 2: Reviewer A, Reviewer C, Reviewer E

At the end of the process, each candidate’s scores can be added up to form a leaderboard.

Final hiring decisions can be made on these scores alone.

Track gender diversity metrics

Tracking is essential to measuring how fair your process is.

If your goal is to attract and hire more women, you need to know how many women enter at the top of the funnel, and how they progress through the rest of the process.

Once you’re able to see how cohorts move through the various stages, you can identify any part of your process that may cause a disproportionate drop-off.

If you noticed that a woman scored particularly poorly on one particular work sample or interview question, you’d then be able to look at the question itself and ensure that it is inclusive.

It may be that the scoring criteria for that question favours a particular style of approach - corporate hiring processes in the U.S, for example, have been shown to prefer a masculine style of leadership

Even at the very beginning of the process, data will help ensure gender equality.

You should look to source at least a 50/50 gender split. Studies have shown that when there’s just one woman in the finalist pool, their chances of being hired are statistically zero.

Gender breakdown of finalist pool vs diversity of hires


Why? Because it highlights how different she is from the norm

Deviating from the norm is a risk for decision-makers, as people tend to alienate those who are different from the in-group.

So how should you go about collecting diversity data?

The easiest way to do this is by adding a basic form to your application process. 

You can then track the relevant diversity metrics you're looking to improve.

If your primary concern is gender bias, you have to know how females actually perform in order to concentrate your efforts in the right places.

Candidates may be skeptical about sharing this information and it should never be mandatory.

Be sure to explain that their data is being collected to ensure the fairness of the process and that it will only ever be used at an aggregate level.

Key takeaway: you can’t fix what you can’t measure, so start tracking diversity metrics.


Why is gender equality important?

We know that gender equality is the right thing to do morally. 

And this should be enough to inspire change.

Although making ‘the business case’ for diversity shouldn’t be necessary, the research still makes for interesting reading.

An Australian study claims to be the first to do real causal mapping that shows better gender diversity improves financial performance.

Whilst there’s no shortage of articles listing the benefits of gender diversity in the workplace (e.g. better reflection of customers, wider talent pool, improved collaboration), this research drives home the reality that gender equality is vital to a company’s success.

Researchers found that when there are women on Boards, companies are more likely to outperform their sectors.

Gender diversity of Boards vs performance


Companies that have no women on their Board are even three times more likely to be underperforming compared to those that have at least one-third female Boards.

If having more female Board members improves performance, then it’s safe to assume that this performance boost is true at both a company-wide and team level too.

So gender bias in hiring isn’t just an issue that women suffer from.

When their gender diversity is poor, entire organisations suffer too.



The Applied Platform was built to remove unconscious bias from hiring using behavioural science, so that every candidate gets a fair shot, and teams get to hire the best people.

Next steps: