Hi ​@jwilliams79! Excellent question. I’m going to tag some folks in case they can help 😄

​@LegoMD ​@SabbuSchreiber ​@brittbosma ​@MarinaS ​@rstambolieva ​@ruta.veite ​@Carly Murphy 

Hi ​@jwilliams79,

We actually haven’t looked at this yet but there are some analytics already available in the demographics section within Personio Analytics. But I’m sure others will be able to answer better 😊

Hi ​@jwilliams79  ,

I’m jumping in on this topic as it’s something we’re also working on in Portugal, and I’d love to hear about your experience as well as share ours.

On our side, the first step was to define job descriptions and the importance of each role within our organisation. Then, we linked this to specific categories, creating attributes to differentiate them. After that, we used the salary tab, where we keep records of all payments made to employees, including benefits, both under recurring compensations and one-time payments.

Unfortunately, I feel we haven’t yet mastered the analytics part – salary data isn’t easy to put into reports, especially when it comes to summing up different elements (or at least I haven’t figured out how to do it yet!).

Looking forward to hearing from other users and learning how they’re handling this. 

Regards,

P😊

Tagging in ​@andra.enache, ​@nina.johansson, ​@SalC, and ​@fmason as well! 

​@Moe It’s a data / maths thing and me and data don't mix well!  I had to get my daughter who was doing A’Level Maths last time i attempted this...my plan once we hit the threshold is to ask Co-Pilot to look at my data and do the heavy lifting for me. 

Making sure that your structure is well organised that job titles align is always a good start from my experience and remembering that it is pay gap reporting not equal pay analysis

Top tip - check to see if your payroll provider does it for you - it was one reason we chose ours - Payfit

​@fmason ditto on not being exactly a maths person 😅

Perhaps ​@brittbosma or ​@HRJoy could chime in as well? 

Hey ​@jwilliams79, whenever I do pay gap reporting, I pull the data I need from Personio and fiddle with it in excel. As far as I’m aware, there’s not enough capability in the Analytics section in Personio to do the conversions (annual salary to hourly salary) or calculations (mean and median for pay and bonus gaps) that you need for the report.

We’ve set up a “Gender Pay Gap” report in Personio with the attributes you need: FTE (if they’re full-time or part-time), gender, salary (fixed, hourly, effective), bonuses/one-time payments and benefits (if you have them in the system). You can also choose the date for the report so that you get the snapshot date and data that you need for the report.

In excel, I convert part-time to full-time so that you get equal FTE, and divide all into four equal groups from highest to lowest paid and identify the proportion of men and women in each quartile. Then, convert annual pay to hourly pay and identify the mean and median hourly pay for each quartile, and use the standard formula to get the pay gap in hourly pay and percentage. Standard formula is:

1. men’s mean/median hourly pay - women’s mean/median hourly pay = Z
2. (Z / men’s mean/median hourly pay) x 100 = percentage mean/median gender pay gap

You can use excel formulas to do this fairly quickly as it has mean and median built in. If you’re excel savvy, use VLOOKUP and FIND create an excel tool and dashboard where you just have to upload a new report or update the data to the sheet so that you don’t have to do the calculations over and over each time. 

We’ve found this to be the easiest, quickest and most accurate way for us to report on gender pay gaps. I hope it helps!

Hi ​@jwilliams79 

We haven't started started yet but if you are public sector the grades will make it easier to define. If private sector, ensuring that your roles are well defined will be of benefit when pulling your reports. The analytics section on Personio has headcount reports that will help, and then you can add your attributes (gender, FT or PT etc). I did a gender pay analysis a couple of years ago before in a different company, before we were actually in the scope for it. It is quite long winded and you have to do a lot of data manipulation but you’ll get there!