The Fairpay® Workforce Planner helps you analyse and prepare for scenarios that can impact pay equity. You can use this feature to plan the best case scenarios to meet your fair pay goals and define achievable targets for the future.
We’ve supported clients to use our Fairpay® Workforce Planner to plan for a fairer future. The University of Leicester, empowered by our workforce planning methodology, predicted how changes in representation across Job Levels would impact their pay gap, and how long it takes to make that impact.
Your questions, answered!
What is the workforce planner and why have we made it?
Our workforce planner is a tool used to model scenarios that will help manage your different employee groups – such as by Job Level or Department – with a diversity and inclusion perspective. You can plan actions to close your pay gap through employee recruitment, promotion and pay; you can increase women representation in Job Levels, increase the pay of specific employees, and more. We’re also planning to bring in data-driven attrition modelling.
We developed this functionality to help clients use their data to plan for the future, instead of only looking at their current pay gap. Companies can use the workforce planner results to inform steps going forward in creating a fairer workplace.
Why might I need the workforce planner?
The main advantages of Gapsquare’s workforce planner are that you’ll be able to set realistic diversity and inclusion goals in closing pay gaps, and you’ll be able to tailor scenarios that fit your company. It helps define achievable targets by showing how long it takes for your chosen scenarios to close your pay gap and how much investment they require. You can create specific strategies on closing the pay gap and see the estimated impact in real-time. Instead of pitching to your board or decision makers your current metrics, pitch your plans and predictions for future development and change. Communicate intention and tangible action plans based on evidence, rather than data without its broader context.
What data do I need to use the workforce planner?
As of July 2021, the functionality allows you to plan changes within your organisation based on Job Level, Department, or custom labels. To get the most out of workforce planning, these two labels should be mapped, although you can still use it with just one of these labels.
Additionally, since the results will tell you how long it will take to close your pay gap based on your current rate of improvement, we need at least 3 datasets from different periods of time to draw a trend. You can still plan scenarios without this, but you’ll get the most out of it if you can compare the scenario with your current rate of progress too.
What scenarios can I consider in the app?
As of July 2021, the functionality allows you to plan strategies within your organisation based on Job Level, Department and custom labels.
You can create multiple strategies per scenario, whereby you can increase representation of certain employees through progression or hiring, change the pay of either some of all employees within the group, or model cases where employees are leaving the group. You can also apply these strategies to individual employees; promote an employee, change their pay, or model them leaving the organisation. All these strategies are done based on either the Job Level or Department you choose.
What results will I get from this?
The results section shows the scenario impact on your mean and median unadjusted pay gaps, as well as changes in representation of women/BAME/disabled people in your company. By default, the results section shows the impact of the scenario on your entire company. You can further filter these results by Job Level or Department using the filter bars at the top.
You can also input FairPay Targets, which help contextualise your scenario within targets set within your company. Not all companies immediately aim for a 0% pay gap, and will set smaller targets year-on-year. By inputting specific targets, you’ll get to see if your scenario puts you on track to achieve them within your expected timeframe, and if it’s feasible in terms of cost.
What scenarios should I consider?
You can use your results in the Pay Analysis Groups (PAGs) section of the app to inform what scenarios fit your company best. By filtering PAGs by Job Level or Department, you’ll see which groups fall into the Red, Amber or Green categories. Those in the Red categories have the largest pay gaps, and should be prioritised in your scenarios.
To understand whether you should use a progression, hiring, or pay strategy in your scenario to tackle these groups’ pay gaps, you can click the “Explore” button in the PAGs page. This will help you understand whether the groups have issues with either representation, pay, or both.
Rewards elements will also play a role in your pay gap – for example, women generally take more salary sacrifice to access childcare vouchers than men. As of July 2021, rewards elements have not been integrated into Gapsquare’s workforce planner – but we are looking to include that functionality soon. If you are unsure of the app version you are using, speak to your account manager.
How are the results for the Fair Pay targets calculated?
For the current number of years you take to reach your targets:
Current number of years (without yet implementing the scenario) is calculated from historical data. Historical datasets must be finalised to be included in the calculation. For example, if the dataset you’re in is dated 1/5/2019, it finds datasets dated before that. It draws a trend to calculate the average rate of improvement. Based on this rate of improvement, the time taken to reach your target can be calculated. If you have a negative rate of improvement (i.e. your pay gap increases annually), it’s unlikely that you reach a target unless a scenario is implemented.
For the number of years you’ll take to reach your targets after implementing the scenario:
The trend is drawn from your historical data with the addition of your new scenario; the average rate of improvement now includes the pay gap after implementing the scenario. Historical datasets must be finalised to be included in the calculation. It can still take a long time to meet your target if the scenario doesn’t bring enough change.
There is an assumption that the entire scenario is implemented within a year, since the trend is drawn from your current dataset straight to your results after the scenario is implemented. For many companies, it is unrealistic to implement every strategy in a scenario within a year or less.
For the investment required:
This is the raw annual cost of progressing, hiring, and increasing the pay of employees. Only salary changes are accounted for. Recruitment, training, and administration costs are not included. The costs have been annualised by turning hourly salaries into annual salaries based on the average number of hours worked.
Under Fair Pay Targets, what does it mean if I get an error message saying I cannot reach my target?
If the rate of improvement of your pay gap is close to zero or even negative (i.e. your pay gaps grow annually), then you won’t reach a KPI to close your pay gap.
The FairPay Targets section also shows how long it takes to meet your KPI in terms of both the mean and median pay gaps. Adding a scenario will help you close your pay gap, but in some cases won’t close both the mean and median. This can happen if your scenario only addresses representation or pay in a small group, which can affect the mean but won’t affect the median as much. Therefore, you can get a good result on closing the mean pay gap, but still have an error for the median pay gap.
What is best practice in workforce planning?
The CIPD and EHRC have guides on how to develop action plans in closing the gender pay gap. Best practice for workforce planning varies by company, and should be data-driven. You should be looking at your unadjusted and adjusted pay gaps, comparing the pay determining characteristics that affect your two pay gaps the most.
Who has access to workforce planning?
Talk to your account manager for app permissions. Workforce planning comes as an add-on for our FairPay app, so not all clients will have access to it.
How do I go into this functionality?
You can access the Workforce Planner by going into a finalised dataset, which will take you to your dashboard page. On the tabs on top, click on Workforce Planner. You can navigate out of the Workforce Planner using the tabs at the top too.
How do I delve more deeply into the data?
After getting your results, you can draw insights paired with other sections of the app.
If your pay gap is still high after implementing a scenario where women progress into upper quartiles, this could be due to the underrepresentation of men in the lower quartiles. We tend to see greater progress when both the upper and lower quartiles are addressed.
For example, your mean can decrease while your median stays the same. This may happen if you’ve added a few women in the upper quartile, as the mean is more susceptible to outliers and large changes. However, since there’s still an overrepresentation of women in the lower quartiles, the median won’t move as much.
Looking at your results alongside the detailed analysis of your pay gap can help highlight areas you may have left out when creating your scenario.
How does the workforce planner work?
You can create strategies on employee hiring, progression, pay or leaving. You’ll then be asked to choose a Job Level or Department. For example, you can add a strategy to promote employees from Job Level 2 to Job Level 1.
When representation is increased in a Job Level or Department, the new joiners enter at the mean salary. It is assumed that there are no pay variances for these new employees. Additionally, when employees are moved from one Job Level to another, the vacancies in the original Job Level are not immediately replaced. If you move 3 employees from Job Level 1 to Job Level 2, you then create 3 vacancies in Job Level 1 – you can fill these vacancies by creating a hiring strategy.
The strategies are implemented in chronological order. If you first increase the pay of an individual in Job Level 2, then increase the representation of women in Job Level 2, these women will join at a higher mean pay due to the increase in the individual’s pay. If you first increase the representation of women in Job Level 2, then increase the pay of the individual, then the women will be joining at the original mean pay in that Job Level.
There are some conditions that a scenario has to meet for a result to be valid. For example, let’s say Job Level 3 has 10 women in it. If you’ve modelled a scenario where 6 women from Job Level 3 are first moved into Job Level 1, and then you want an additional 6 women from Job Level 3 to move into Job Level 2, the second step would be invalid. This is because there are only 4 women left in Job Level 3 after moving 6 into Job Level 1.
Save the scenarios as you go along, to ensure any documentation you need won’t be lost.
Are all employees included in the analysis?
Employees that have their gender, ethnicity, or disability as N/A will not be included, as we won’t have data on the actual characteristics of the employee.
How does the app choose which departments/job level the new hires join at?
When you create a strategy to hire people into Job Level 2, the Job Level will be made up of different departments. The app then chooses which departments these new hires join based on your current employee split in Job Level 2. For example, if 25% of Job Level 2 is made up of employees in the Marketing department, then 25% of the new hires will join Marketing.
How do I know this is an accurate prediction?
Predictions won’t be fully accurate, because pay gaps are influenced by all of your Pay Determining Characteristics – at this moment, we are only able to predict based on Job Level and Department. If Job Level and Department are the main contributors in the contribution by variable graph on the Adjusted Pay Gap page, the workforce planner will be more accurate as we are modelling the factors that most impact your pay gap. If other factors such as Worked Hours or Tenure are the ones affecting your pay gap the most, the workforce planner results will not be as accurate. Due to our assumption that employees enter a Job Level or Department at the mean salary, the predictions are also susceptible to pay variances within a pay band.
Additionally, the annual investment required to implement the scenario is calculated based on the average number of hours worked by employees, and therefore doesn’t factor in part-time or overtime work.
How do I avoid errors in workforce planning?
When a scenario is invalid, the app will show an alert. You can then delete or edit the invalid part of the scenario, or enter an intermediate strategy to make the overall scenario valid.
For example, you’d like to promote 5 women in Job Level 2 to Job Level 1, but Job Level 2 only has 3 women. This scenario would then be invalid. But you can add an intermediate strategy, whereby you first hire an additional 2 women into Job Level 2, then promote 5 women into Job Level 1. Otherwise, you can hire 2 women into Job Level 1, then move the 3 women from Job Level 2 into Job Level 1.
Can you guarantee the outcome predicted in workforce planning?
We cannot guarantee that predictions will be 100% accurate. However, the workforce planner can still be used to analyse general trends and the best case scenario when you implement the scenarios. For example, you could model increasing representation of women by 50% in the top Job Level. The workforce planner will assume that all women joining this Job Level will join at the mean salary; but this isn’t necessarily the case, since employees progressing into a higher pay band may join at the lower end of that band. The results still show the change in your pay gap under the ideal case that there aren’t any pay variances for new employees in a Job Level, and therefore can still be used to estimate how much your pay gap might close.
Additionally, the FairPay Targets section carries a few assumptions. Since trends are drawn to calculate your average rate of improvement for your pay gap, it’s susceptible to outliers. Let’s say your pay gap trend across five years is 6%, 5%, 4%, 10%, 9%.
Rate of improvement between each year: 1%, 1%, -6%, 1%
Total rate of improvement: (1+1-6+1) = -3%
Average rate of improvement: -3%/5 = -0.6%
The sudden increase from 4% to 10% means that your average rate of improvement is negative, even if in most years your rate of improvement is 1%. So even if you’ve historically been able to decrease your pay gap, the sudden spike influences the calculation.