To close a pay gap you have to do three things:
- Measure where you are today.
- Specify where you want to be in the future.
- Identify the most effective way of getting there.
All 3 steps require the use of statistical thinking and statistical methods. Of course, other skills and processes are also needed but they cannot succeed on their own without the help of statistics
My 1-day training course “Introduction to Pay Gap Analytics” will take you through the different topics you need to master to become better at achieving these 3 things.
If you are attending the course, you will need to have this page open on your browser as many of the links listed here will be referred to during the course. You will also need to download the spreadsheet that is available via link A1 below.
A. Information on where to find data
You can use these links to find data for a particular employer and how to undertake the statutory calculations required by the government.
- Click here to download my spreadsheet of the latest gender pay gap data from over 10,000 employers
- Click here to visit the government portal for all gender pay gap statistics
- ACAS detailed guide to gender pay gap reporting
B. Introductions to gender pay gap data
You’ve done your gender pay gap calculations or retrieved data for a particular employer but what do the various numbers mean? What are they telling you and perhaps more importantly, what are they not telling you?
- 7 Ways to misuse gender pay gap data
- The difference between unequal pay and gender pay gaps
- Was there an improvement in 2018?
- What is the difference between the ONS ASHE figure and the GPG figure?
- How winning an equal pay case can widen the gender pay gap
- Why Gender Pay Fingerprints are superior to Gender Pay Gaps
- Did the pay gap narrow in 2019 even though half of employers failed to report?
- Why Novartis UK should have paid attention to a WW2 Bletchley Park codebreaker
C. Detecting errors in gender pay gap data
In most cases, the relevant calculations will have been undertaken by an HR professional. I know from my 30 years’ experience of working with non-statisticians that even though the calculations may appear to be simple to some people, errors will happen. The links given here tell you how you can spot errors in pay gap data, some of which are relatively simple, others more subtle.
- 1 in 10 employers have submitted incorrect data, are you one of them?
- Life on Mars and why small employer GPG data is so unreliable
- The good, bad & Unilever when looking at year on year trends in GPG
- How to spot an incorrect median gender pay gap (Sorry Cleveland Police!)
D. Improving pay gap reporting
The existing gender pay gap reporting process stems from the Equality Act 2010. It is very clear that whoever drafted that act was not a statistician and as a result, improvements will be needed going forward. Some of these improvements can be implemented by individual employers, others will need to be led by the Government Equalities Office who are the custodians of the relevant legislation.
- My 12 recommendations for improving GPG reporting
- The RSS’s 10 recommendations for improving GPG reporting
- Should the UK introduce Ethnicity Pay Gap reporting
- My evidence to the Treasury Select Committee on improving GPG reporting
- What is the best way to do Ethnicity Pay Gap reporting?
E. Other posts of interest
These links cover a wide range of topics related to pay gaps and diversity. I’ve also included a link D6 to a recommended text book on basic statistics.
- How to close a pay gap using DMAIC
- How to diagnose the causes of your GPG (produced by the Government Equalities Office)
- When is an all-white employer alright?
- 7 Articles on pay gap trends I wrote for Practical Law magazine
- Why closing a pay gap can take a generation or more unless you play Blackjack
- “Conned Again, Watson!” by Colin Bruce – Published in 2002, this is my all time favourite book on statistics. What Colin Bruce does is write Sherlock Holmes stories where Holmes is also a statistician as well as a detective. Some stories are little contrived but Bruce has done a great job of capturing the spirit of Conan Doyle’s books as well as illustrating a wide range of statistical and economic concepts. Indeed chapter 11 involves a payroll problem!
There may be other posts that I have not linked to in this post. Please visit my Diversity homepage to see a full list of my blog posts relating to pay gaps and diversity issues.
For more information about my other training courses in statistics, please visit my Statistical Training homepage.