Four Steps to Use People Analytics to Understand the Gender Pay Gap
The Gender Pay Gap is an Onion
According to the World Economic Forum, on average women across the globe receive just 63% of their male counterpart’s wage, and this figure is even higher for BAME and other intersectional groups. Whilst this statistic is true in a broad sense, it is only through intelligent analytics, and building on your understanding of the true scale and specific drivers of workplace gender pay gaps, that it becomes a useful statistic.
We increasingly see leadership teams mark diversity as a priority issue, and we’ve heard about the business benefits of creating representative teams - see the McKinsey or Boston Consultancy Group reports, to name just a few. Companies are investing considerable sums to end pay inequality (see Salesforce’s recent actions). But how can we understand the core issues and drive change?
‘Discussing an organisations’ headline gender pay gap figure, whilst this may paint a general picture, can be a limiting and reductive approach to gender pay analysis.’
At Insight222’s peer meeting in Amsterdam, the Gapsquare team led the seminar ‘Gender Parity and Salary Equity: The Role of People Analytics’, an expansive session developing our understanding of figures like that 63% and looking at what they can teach us. Discussing an organisations’ headline gender pay gap figure, whilst this may paint a general picture, can be a limiting and reductive approach to gender pay analysis. For those of us with a genuine commitment to closing the gap, now is the time to step into analytics with your exploratory hat on.
Now for a metaphor that we have heard over the years which tells you a lot about the importance of breaking down data: Ladies and gentlemen, the gender pay gap, the equal pay gap, the ethnicity pay gap, all your basic pay parity issues - are onions. The best of us heading into the pay parity space, know that getting to the core of the issue requires understanding the layers that surround it.
Peeling back the layers
Some of these layers point to justifiable differences in pay, but some expose problematic practices where fixes in terms of policy and or behaviour are needed.
We’ve been privileged to work with proactive and engaged employers who have delved into expansive analytics and come up on top, making progress around gender and pay equity. The overarching figures tell a story, but a greater level of insight, ‘peeling back the layers’, gives companies access to actions which can be read in people-data and can ensure progress.
‘Using data to understand how and what makes their employees tick can be essential in retaining your workforce and rethinking your remuneration model with diversity and equality in mind.’
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To give an example Gapsquare recently worked with a company to help them understand the gendered nature of their pay and remuneration model. Particular allowances and benefits offered to staff had a skewed effect on their pay gaps. One individual is not necessarily going to utilise the same benefits as the next, so using data to understand how and what makes their employees tick can be essential in retaining your workforce and rethinking your remuneration model with equality and diversity in mind. This company found that doing so helped them to understand and minimise their pay gap.
Similarly another client entered into analysis under the impression that the key cause of gender pay inequality in their workplace was the lack of women in senior roles. They began an active strategy of hiring senior women only to discover this did not reduce their gap. With data-driven reflection, they discovered that it was a lack of women progressing through middle management (a leaky pipeline, or the missing middle) that was contributing far more heavily to their gap than a lack of senior women
‘Increased knowledge through layered analytics’
These are both examples of companies believing they have a clear picture of their salary equity data, but uncovering, through investigation, that their understanding and efforts toward change so far had been somewhat misdirected (and therefore less effective). In our experience, employers regularly implement more impactful workplaces policies when they have gained increased knowledge through layered analytics.
The four layered onion
In order to peel back the layers of pay gaps and understand how different comp and reward elements, company cultures and pay structures contribute to the overall picture, we recommend understanding and developing narrative around a few key areas summed up into four groups here.
Start by answering the crucial questions
A big question to ask yourself at the beginning is why are you doing this analysis? What are your company’s aims and what do they see as the emerging issues causing potential pay gaps and salary equity? At this point our clients often start with the questions that they think need answering but approach analysis with an open mind, accepting potential revelations in their data. Another crucial question to answer is around the national legislation that your company must adhere to. Knowing any applicable or relevant legislation before you start will help define some of your whys. We are living in a time of increasing employee & corporate leadership demands around pay and diversity. Companies often set goals that span far beyond the requirements of legislation, but knowing these is still necessary. In general, starting your analysis with a set of questions in mind is a great baseline for investigation.
‘The decision of who to include as your common population will have a lot of impact on the common reward elements you analyse.’
Break down your data based on common population
Thinking globally can be challenging when it comes to pay gap and pay equity legislation. Workforces in individual markets are made up of different types of employees - full and part time, temporary staff, contractors, secondments, zero-hour contracts, those on sick or parental leave, those who have different contractual requirements across different markets. Your first steps in measuring pay gaps is deciding how to define each employee and creating a model that can be applied across markets, allowing you to report on your workforce as a whole. The decision of who to include as your common population will have a lot of impact on the common reward elements you analyse.
Explore reward elements
Individual markets often have different models, as legislation differs and we know that global companies have complex pay and reward structures. When it comes to their analyses, companies need to make decisions on which reward elements to include and exclude. Doing so will allow employers to create a model that gives comparability and continuity.
Parental pay for example, could be different across markets, with some companies offering full pay in response to existing legislation, and therefore this is extrinsically linked to defining your common population for analysis.
‘Some elements reveal unconscious bias and understanding this is critical’
Organisations often start simply and look at the bare bones of pay and reward - annual compensation and variable bonuses. However, share-schemes, long-term incentives, flexible benefits and pension schemes can often be key to understanding the gendered nature of pay and reward. At Gapsquare, we have seen that some elements reveal unconscious bias and understanding this is critical to your business objectives around gender discrimination.
Contextualise based on location
Understand that a global approach is crucial, but that the local context continues to matter. Market rates for roles and national / local contexts influence and decide pay. If you are conducting analysis that is global, comparing rates of pay across contexts you may wish to work with compa ratios. Compa ratios are a normalised value taking into account local/national pay and compensation practices. Pay for a software engineer in San Francisco, when compared with a person in the same role in Delhi will be vastly different but not necessarily because there are equal pay issues.
‘Getting through this process has never been more exciting, more important, or more revealing.’
Consider the local context in order to compare effectively cross-country and cross-nation, is critical for a global pay analysis.
Starting with these four clear steps, embracing the exploratory nature of your analysis and basing your recommendations for change on multivariate analysis is a great way to start. In a survey we conducted at the recent Insight222 peer meeting, we found that 74% of People Analytics teams had either been tasked with gender pay analysis in the past year, currently, or are working on this within the next six months. Getting through this process has never been more exciting, more important, or more revealing. It is by starting with questions, exploring our populations, reward elements and accounting for differences in location that we can find that we emerge with unbeatable insights at the end of our analysis. Only after examining these variables can companies begin to quantify pay equity so that they can fully assess the influence of their policies and practices on their pay and compensation models, and restructure them to ensure the world of work is fair for everyone.
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ABOUT THE AUTHOR
Siân Webb is an expert in driving fair pay and inclusion through people analytics, intelligent data insights and innovative technology. She specialises in supporting companies understand the root causes of gender and ethnicity pay gaps, and building remuneration models that are fair and equitable. As VP Partnerships & Growth for Gapsquare, Siân boasts years of experience working closely with global companies to understand the HR challenges faced by multinationals, and how to solve them using technology. Though Siân has spoken across the globe, from London, Amsterdam, Berlin, Bologna to the hills of San Francisco, she remains most passionate about catalysing social change and fair pay within companies, one at a time.