The Royal Statistical Society (RSS) and I have published two articles to help employers better calculate and interpret their gender pay gaps. The first article lists 10 recommendations to improve the quality of gender pay gap reporting, the second is an article in Significance magazine which explores in more detail, two of the recommendations concerning medians and quartiles.
After two years of mandatory gender pay gap reporting, there is increasing pressure to bring in pay gap reporting for other protected characteristics. At the moment, ethnicity is receiving the greatest attention and a number of politicians are calling for the introduction of mandatory ethnicity pay gap reporting.
In this post, I will explain why I am opposed to an ethnicity pay gap reporting process which simply replicates the gender pay gap reporting process. In a future post, I will explore what an ethnicity pay gap reporting process should look like if parliament decides it wants to make this law.
You have just started work for a new employer and with you joining, the company now has 25 employees. All are white including you. Would you raise your eyebrows at that?
So you’ve measured your gender pay gap (correctly I hope!) but you don’t know what to do next?
You are not alone, many employers are still getting their heads around how to interpret their pay gaps and are struggling to work out what it means for them. One outcome is that many consultants are out there waiting to advise you and among them are statisticians like me. But what exactly is it that statisticians bring to the party compared to other consultants? One answer is that statisticians use DMAIC to help organisations improve the quality of their products, services and processes.
On June 5th 2019, I had the privilege of being able to talk to the Treasury Select Committee about the “Effectiveness of Gender Pay Gap Reporting“. My name was put forward by the Royal Statistical Society and we spent an hour discussing a number of issues with a particular focus on the Finance sector.
Welcome to my next case study where I look at the pay gap figures of Unilever Ltd. Unilever turn out to be a very interesting case study for analysing year on year changes in their published statistics. In this case I will be looking at the changes between 2017 and 2018 for the two Unilever business units that have submitted GPG data which are:-
Clicking on those links will take you to the government’s gender pay gap website where you can see their published figures. For this post, I will be using my own spreadsheet which you can download for yourselves here.
With the publication of the 2018 gender pay gap data, many people want to know if the UK has made progress on closing its gender pay gap. The short answer is there was no change in 2018 from 2017.
April 2019 is bang in the middle of gender pay gap season as everyone digests the 2018 snapshot data uploaded by over 10,500 organisations in the UK employing more than 250 employees. Due to my work on pay gaps, I am being asked a number of questions and what is gender pay gap data and how it can be interpreted. As more questions come in, I will update this post so please bookmark this for future reference and follow me on Twitter for alerts as to when this has happened.
The government requires all organisations employing 250 or more employees to submit gender pay gap data. The latest set of submissions are supposed to be uploaded by 31st March 2019 but these figures refer to pay made in April 2018 i.e. a year ago. From the end of April 2019, organisations can submit their 2019 data and not wait for the deadline of March 2020. All data is available to the public and can be found on the government’s gender pay gap website. I have downloaded this data and created a spreadsheet tool to present the data in a more user-friendly and visual format.
If you are responsible for uploading your gender pay gap figures to the government’s portal, you may want to be sure that your figures are correct. I have analysed the 2017 data and I have identified that between 10-20% of organisations have submitted incorrect data. In most cases, this is because the existing government guidance for your calculations is poorly written and is open to misinterpretation.
Until the government rewrites its guidance to make it easier to understand, why not download my free spreadsheet which will do all the calculations for you?