- \n
- 62%<\/strong> said female dominated (>80% employees are women<\/em>) public sector (snapshot date 31st March<\/em>) employers. There were 420<\/strong> such employers in April 2021 who reported their UK gender pay gap.<\/li>\n
- 38%<\/strong> said male dominated (>80% employees are men<\/em>) private sector (snapshot date 5th April<\/em>) employers. There were 1584<\/strong> such employers in April 2021 who reported their UK gender pay gap.<\/li>\n<\/ul>\n
The majority are correct. In April 2021, for every \u00a31<\/strong> paid to the average man, the average woman was paid 90p<\/strong> at the average male dominated private sector employer and 77p<\/strong> at the average female dominated public sector employer. For context, the average woman at the average employer in the finance sector (who are often in the news for having large pay gaps) is paid 75p<\/strong>.<\/p>\n
<\/p>\n
Average Woman’s Pay by Gender Representation<\/strong><\/span><\/h5>\n
The two box plots below show a clear pattern. The black diamonds are the average hourly pay of women of the employers within each female representation band. The difference is even starker if I look at highly male dominated<\/strong> (>90% men) private sector employers where the average woman is paid 95p<\/strong> and there are no such public sector employers.<\/p>\n
![\"\"](\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2022\/05\/Gender-Dominance-Effect-2021.png\")
<\/p>\nFor those not familiar with box plots, the thin black lines show the range from the maximum <\/strong>(100th percentile<\/em>) to the minimum<\/strong> (0th percentile<\/em>), the thick black lines show the range from the upper decile<\/strong> (90th percentile<\/em>) to the lower decile <\/strong>(10th percentile<\/em>) and the green & purple boxes show the range from the upper quartile<\/strong> (75th percentile<\/em>) to the lower quartile<\/strong> (25th percentile)<\/em>.<\/p>\n
The chart shows private sector pay gaps are more variable than public sector pay gaps within each female representation band which is probably not a surprise given the public sector usually have national pay scales. The labels at the top of each representation band show the number of employers in that band and it is interesting to note only 1 in 9<\/strong> public sector employers have majority male workforces compared to 3 in 5 <\/strong>private sector employers.<\/p>\n
The same effect is seen if I use the median gender pay gap statistic instead. For every \u00a31<\/strong> paid to the median man, the median woman was paid 91p<\/strong> at the median male dominated private sector employer and 67p<\/strong> at the median female dominated public sector employer.<\/p>\n
The size of the employer does not affect the patterns seen. Gender pay gaps at female dominated public sector employers are 2 to 4<\/strong> times larger than male dominated private sector employers regardless of whether we are talking about small, medium or large employers.<\/p>\n
There are three reasons why this happens which are all connected<\/p>\n
- \n
- Public sector employers are much less diverse in terms of their gender breakdown by pay quarters<\/li>\n
- Women can thrive at extremely male dominated employers<\/li>\n
- Pay scales are not symmetric<\/li>\n<\/ol>\n
Public Sector Employers Lack Fingerprint Diversity<\/strong><\/span><\/h5>\n
Here are the pay quarter breakdowns by gender (or Gender Fingerprints<\/a>) of two employers who are Mirror Images<\/strong> of each other.\u00a0 The average woman is paid 87p<\/strong> at Western Power Distribution (South Wales)<\/a> and 79p<\/strong> at Bellevue Place Education Trust<\/a>.\u00a0 Clicking on their names will take you to their 2021 narratives.\u00a0 I chose these two because they are reasonably representative of the average male dominated private sector employer & the average female dominated public sector employer.<\/p>\n
![\"\"](\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2022\/05\/Gender-Dominance-Effect-2021-Examples.png\")
<\/p>\nI say these two employers are mirror images for these reasons.<\/p>\n
- \n
- Western Power are 87%<\/strong> men, Bellevue Place are 87%<\/strong> women.<\/li>\n
- Women are 2.5<\/strong> times more likely to be in the lower pay half at Western Power.<\/li>\n
- Men are 2.5<\/strong> times more likely to be in the upper pay half at Bellevue Place.<\/li>\n<\/ul>\n
75%<\/strong> of female dominated public sector employers are in the Education sector with the remainder in the NHS.\u00a0 Bellevue Place’s gender fingerprint is one I’ve seen time and time again in education especially among primary schools.\u00a0 \u00a090-95%<\/strong> of employees in the lower pay half are women with men clustered into the upper pay half or quarter.\u00a0 Even then the upper pay quarter is still 75%<\/strong> women so it is not the lack of women in high paying roles that causes the pay gap, it is the extreme lack of men in low paying roles and the reasons why are easy to figure out.\u00a0 Much harder to figure out is whether and how this will change in the future.\u00a0 Personally, I think such employers will struggle mightily to close their pay gaps and could be in the same place in 20 years time but if you think otherwise, please do comment on this article using the links at the end.<\/p>\n
Bellevue Place’s 2021 narrative describes all of this quite well<\/a> and even gives pay gaps within 4<\/strong> types of jobs.\u00a0 Interestingly, they have pay gaps favouring women in 3 of the 4<\/strong> job types but that doesn’t prevent them from having a large pay gap when combined.\u00a0 I have explained this effect before, which is known as Simpson’s Paradox,<\/strong>\u00a0in my article “What is the gender pay gap at Novartis<\/em><\/a><\/strong><\/span>“.<\/p>\n
Women can thrive in highly male dominated employers<\/strong><\/span><\/h5>\n
Whilst Western Power is close to the average gender fingerprint for male dominated private sector employers, there is much more variety in this group.\u00a0 If I focus on highly male dominated employers i.e. those where men make up more than 90% of employers, I find that 38%<\/strong> paid their average woman more than their average man, much higher than their counterpart of highly female dominated public sector employers where only 2%<\/strong> paid their average woman more than their average man.<\/p>\n
![\"\"](\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2022\/05\/Gender-Dominance-Effect-2021-female-pay-gap.png\")
<\/p>\nPart of the reason for this is sampling variation due to very small sample sizes.\u00a0 If an employer has only 400<\/strong> employees, of which 95%<\/strong> are men, that means they only have 20<\/strong> women.
![\"\"](\"https:\/\/marriott-stats.com\/nigels-blog\/wp-content\/uploads\/2022\/05\/Gender-Dominance-Effect-2021-extreme-male-dom.png\")
\u00a0 If the employer has no pay gap on average, we can expect 5<\/strong> women in each pay quarter.\u00a0 However, once people start joining and leaving, the laws of chance mean we can expect these to fluctuate between 0 & 10<\/strong> women per pay quarter and the result can be considerable changes in the pay gap.\u00a0 My article “Life on Mars<\/em><\/span><\/a><\/strong>” demonstrates this effect in more detail but what I see at Ferguson Marine<\/strong> here is completely consistent with this effect.<\/p>\nThe same effect can be expected to occur in the reverse situation when employers are highly female dominated.\u00a0 The chart above shows the % of employers with pay gaps favouring women rises as the employer approaches 100% female but the rise is nowhere near as fast as for highly male dominated employers in the private sector.\u00a0 Of course in the public sector as we saw above, the reverse happens with fewer and fewer employers having pay gaps favouring women as the employer approaches 100% female.\u00a0 So this is why I say that it appears that women can thrive in extremely male dominated places.<\/p>\n
Why this should be, I leave for you to think about but I am familiar with a number of claims on social media that women are increasingly using gender pay gaps to decide who they want to work for.\u00a0 If you are making such decision solely on the mean or median gender pay gap (and you should never be doing that!) then you should seek to work at a very male dominated employer with as few women as possible.<\/p>\n
If Ferguson Marine decided it wanted to increase the number of women working there, then all the evidence from the charts above show that in all probability the pay gap is going to widen as they add women to their workforce.\u00a0 The box plot at the beginning indicates that in the private sector, as you go from 0%<\/strong> women to 20%<\/strong> women, your pay gap goes from zero to large<\/strong> fairly quickly.\u00a0 A question that you should be asking yourself is would we see the same effect for other protected characteristics such as ethnicity, sexuality or disability.\u00a0 Ethnic minorities, LGBT and disabled people are all true minorities in the 0-20%<\/strong> range and I will be writing an article soon giving my thoughts on whether this is likely to be the case for disability.<\/p>\n
Update 1st August 2022 – Article has been now been published here<\/a>.<\/em><\/span><\/p>\n
Pay Scales are not symmetric<\/strong><\/span><\/h5>\n
Let’s return to our mirror image employers Bellevue Place (average woman paid 79p<\/strong>) & Western Power (average woman paid 87p<\/strong>).\u00a0 If they are mirror images, why are their pay gaps not the same?\u00a0 Why is it that men clustering in the upper pay quarter when they are a small minority has a larger effect on the pay gap than women clustering in the lower pay quarter when they are a small minority?<\/p>\n
The answer is because pay scales are not mirror images or symmetric.\u00a0 Let’s suppose an employer’s hourly pay scale ranges from \u00a310<\/strong> to \u00a350<\/strong> per hour and the average hourly pay is \u00a330<\/strong>.\u00a0 This pay scale will be symmetric<\/strong> or mirror image if the number of people earning \u00a350<\/strong> is equal to the number of people earning \u00a310<\/strong> or more generally if the number of people being paid \u00a330+X<\/strong> is equal to the number being paid \u00a330-X<\/strong>.\u00a0 If this was the case at both Bellevue Place and Western Power, then mathematically the pay gaps should be same because the average pay in the upper pay quarter should differ from the overall average of \u00a330<\/strong> by the same amount as the average pay in the lower pay quarter.<\/p>\n
This is not what happens in real life.\u00a0 It is much more likely that say 40%<\/strong> of employees are paid \u00a310<\/strong> per hour,<\/strong>\u00a030%<\/strong> are paid \u00a320<\/strong>, 20%<\/strong> are paid \u00a350<\/strong> and 10%<\/strong> are paid \u00a3100<\/strong>.\u00a0 If you do the maths you find the average pay is still \u00a330<\/strong> per hour but now it is clear that employees in the upper pay quarter will be paid \u00a370<\/strong> which is \u00a340<\/strong> above the average compared to only \u00a310<\/strong> (or \u00a320<\/strong> below average) for the lower pay quarter.\u00a0 If the minority gender is clustered in the upper pay quarter then what they earn differs by considerably more from what the majority gender is paid than if they were clustered in the lower pay quarter.<\/p>\n
Summary<\/span><\/strong><\/h5>\n
- \n
- Male dominated private sector employers have much smaller pay gaps and greater diversity of gender fingerprints than female dominated public sector employees which often have the same fingerprint.<\/li>\n
- A more in depth analysis suggests public sector pay gaps are deeply entrenched with little chance of progress.<\/li>\n
- On the flip side, pay gaps at highly male dominated private sector employers suggest women can thrive there.<\/li>\n
- To close their pay gaps, the public sector employer needs to recruit more men into lower paid roles rather than focus on more women in higher paid roles.<\/li>\n
- A highly male dominated employer that wishes to become less male dominated is very likely to see their pay gap widen.<\/li>\n<\/ol>\n
<\/p>\n
— Would you like to comment on this article? —-<\/span><\/strong><\/h5>\n
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— Want to know more about pay gaps?\u00a0 —<\/strong><\/span><\/h5>\n
You will find a full list of my pay gap & diversity related articles here which are grouped by theme<\/a><\/strong><\/span>.<\/p>\n
— Need help with understanding your pay gap? —<\/strong><\/span><\/h5>\n
I offer the following services.\u00a0 Please click on the headings for more details.<\/p>\n
- \n
- Analysis<\/span><\/strong><\/span> <\/a>– I can dig deep into your data to identify the key drivers of your pay gaps.\u00a0 I can build a model using a large number of variables such as pay band, seniority, job function, location, etc and use this to identify the priority areas for closing your gaps.<\/li>\n
- Training<\/span><\/strong><\/span><\/a> – I run training courses in basic statistics which are designed for non-statisticians such as people working in HR.\u00a0 The courses will show you how to perform the relevant calculations in Microsoft Excel, how to interpret what they mean for you and how to incorporate these in an action plan to close your gaps.<\/li>\n
- Expert Witness<\/span><\/strong><\/span><\/a> – Has your gender pay gap data uncovered an issue resulting in legal action?\u00a0 Need an expert independent statistician who can testify whether the data supports or contradicts a claim of discrimination?\u00a0 I have experience of acting as an expert witness for either plaintiff or defendant and I know how to testify and explain complex data in simple language that can be easily understood by non-statisticians.<\/li>\n<\/ol>\n
- Training<\/span><\/strong><\/span><\/a> – I run training courses in basic statistics which are designed for non-statisticians such as people working in HR.\u00a0 The courses will show you how to perform the relevant calculations in Microsoft Excel, how to interpret what they mean for you and how to incorporate these in an action plan to close your gaps.<\/li>\n
- Analysis<\/span><\/strong><\/span> <\/a>– I can dig deep into your data to identify the key drivers of your pay gaps.\u00a0 I can build a model using a large number of variables such as pay band, seniority, job function, location, etc and use this to identify the priority areas for closing your gaps.<\/li>\n
- Women are 2.5<\/strong> times more likely to be in the lower pay half at Western Power.<\/li>\n
- Western Power are 87%<\/strong> men, Bellevue Place are 87%<\/strong> women.<\/li>\n
- 38%<\/strong> said male dominated (>80% employees are men<\/em>) private sector (snapshot date 5th April<\/em>) employers. There were 1584<\/strong> such employers in April 2021 who reported their UK gender pay gap.<\/li>\n<\/ul>\n