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.
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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?
The core expertise that Statisticians offer to the world is drawing conclusions from small samples. Therefore, knowing how to design surveys, estimate the right sample size, decide on the right way to ask the question or measure a property are all essential skills for any statistical thinker. The skills you need to be competent in Sampling & Surveys are best captured by my Survey Wheel.
The city of Bath is among a number of cities in the UK tasked with reducing Nitrogen Oxide (NOx) emissions. NOx pollution is thought to contribute to poor health and the government has required clean air plans from the relevant local authorities to be in place before 2021. I had no idea that this would result in my statistical expertise being needed to answer a political row over the BathBreathes2021 plans to charge cars driving into Bath and you can read my report to see what my answer was!
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.
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.
Mention P-values and most people will probably shudder at some memory of an incomprehensible lecture or lesson on statistical tests. Words like null hypotheses, t-tests, statistical significance might pop into your mind with little understanding of what they are about. What you may know is that scientists have to report a p-value for any experiment they do or do they?
The area of Statistical Inference is a core area of study for any statistician. Put simply, Inference means to infer from the observations you’ve made about your data and to draw conclusions about what might be happening in real life. There are two parts to Inference.