# Dramatic Implications of Humble Gender Pay Gap Reporting

## The UK has made a shaky start to tackling the gender pay gap. But they always had to start somewhere.

Starting from April this year, employers in the U.K with more than 250 employees have twelve months to publish overarching pay gap statistics. These statistics will have to be published every twelve months, both on the employer’s website and a government website. There are six statistics, and at first, I found them disturbing simple:

• mean gender pay gap
• median gender pay gap
• mean bonus gender pay gap
• median bonus gender pay gap
• proportion of males receiving a bonus and proportion of females receiving a bonus
• proportion of males and females when divided into four groups ordered from lowest to highest pay

Just six numbers. They tell us nothing about how similar employees are paid differently. These statistics are about all employees lumped together with no regard for position, experience, or success. These are high, high-level statistics. What can they really tell us?

Technical Interlude: Explaining the Averages
If you’ve forgotten the difference between mean and median, here’s a quick reminder. The mean is what we typically think of as the average: add all your values together and divide it by the the number of values in your list. In this case, if there are 250 employees, you add all their salaries together (or hourly pay or whatever you’ve decided to use) and then divide that number by 250. The mean difference would be the difference between the mean male and female compensation. Easy. Mean is the mean, clean, average that usually springs to mind.
Median is the middle value in an ordered list of values. So here you would order the salaries of the employees from least to most, and then use the salary right in the middle to calculate the difference. (If there are an even number of employees, take the mean of the two middle salaries.) The median is the middle.

The reason you might want to look at both of those numbers is if just a couple of employees have very high or low salaries, that can drag the mean difference higher or lower. The median gives a better sense of the difference between what most employees are making; the middle of the road difference.

A fun fact is that technically the word ‘average’ refers to any number that reflects the central or typical value in a set of data: mean, median, mode, or any other number you think is a good reflection of the data.

There are a whole host of reasons a company may have a gender pay gap when defined with these numbers, some of which may be out of their control. A company making a concerted effort to hire entry-level female engineers might see their pay gap increase even though they are working on a long-term strategy to reduce it. A company may compete to hire every female PhD graduate in a certain field — but if there are only a handful of them, it won’t do much for their reporting.

On the other hand, a low gender pay gap may hide other forms of discrimination. The pay gap between African men and women is often much higher than that of their Caucasian counterparts; similarly for those who are disabled. Or a low gender pay gap could result in more from strategically handling company data than a fair work environment. Big-budget organizations will certainly be hiring expert consultants to handle their data and generate numbers which look good.

But it is always easy to tear down an idea.

Though this type of reporting is brimming with problems, it will do at least two things. First, it will force companies to spend time with their pay data. Totaljobs’ survey of 145 employers indicated that 58% didn’t have the requisite information available upon request. It is incredibly hard to tackle a problem without describing it first. Even if a company frames and smudges their data to look better, they will not only see for themselves the raw indicators of gaps but they will inadvertently make that information more easily available. The data will have to be collected.

Second, the reporting and the report will get people thinking. Companies will get thinking about how to improve their numbers in order to improve their reputation. Potential recruits will start thinking about where they would rather work, further encouraging companies to work on the problem. And employees will begin thinking about the company they are in and maybe start demanding more detailed information from their company in order to do better from the inside out.

These six numbers are the tip of an iceberg. Companies will be forced to discover everything below in order to describe the part above. Information is the ultimate harbinger of power, which is why gender pay gap reporting has the potential to spur momentous change.

Katy Gero has been lucky enough to work at two tech startups with female CEOs. A year after finishing her undergraduate degree in mechanical engineering, she slyly transitioned into data and computer science roles. A long time writer and poet, she loves getting people interested in science and technology and bridging the gap between the arts and engineering.

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