Pay explainability report
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This feature is available as part of the PET pro plan.
The pay explainability report is an essential part of the employee pay transparency report (see Employee pay transparency report). It helps HR teams hold insightful conversations with managers and employees related to the position of employee compensation compared to others performing work of equal value, in line with the EU Pay Transparency Directive (EUPTD). Article 7 of the EUPTD requires employers to share with their employees how their individual pay compares to similar colleagues (i.e. their "category of worker") upon exercise of their right to request pay information.
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The pay explainability report is not designed to be shared directly with employees; it provides insights to compensation teams, who can share the report with HRBPs and managers, to support their conversations with employees.
Viewing the report
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The pay explainability report can only be generated in conjunction with and navigator from the employee pay transparency report. For more information about the employee pay transparency report, see Employee pay transparency report.
To view the pay explainability report, proceed as follows:
Run pay equity analysis to arrive at the Analysis Overview page. For more information, see Interpreting pay gaps.
Scroll to the bottom of the page to view the Employee list data table.
In the table, locate the row for the relevant employee.
In the Selection column, click the three vertical dots icon to display the Employee actions contextual menu, as illustrated in the following figure:
Opening the pay transparency report
In the menu; click View employee pay transparency report. The employee pay transparency reports tab is automatically opened in the Pay Transparency Reports page.
Click the Pay explainability tab. The report opens and displays insights specific to the selected employee, as illustrated in the following figure:
Pay explainability report
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It is not possible to generate and view multiple employee reports at the same time. To view the report for a different employee, return to the employee list by clicking the arrow at the left top of the report.
Report breakdown
The pay explainability report is composed of five key sections:
Employee attributes
Report variables
Employee group positioning
Compensation highlights
Analytics deep dive per compensation driver
Employee attributes
The Employee attributes section is common to the pay explainability report and the employee pay transparency report. It contains the following information:
Employee ID: ID of the employee within the organization
Employee group: for EUPTD purposes, the group should follow the work of equal value principle as reflected by the worker category. By default, the main group defined in your dataset configuration is used. Alternatively, if you are using the Close by group configuration, the group corresponds to the one selected within the pay equity analysis.
Report variables
The report variables section of the report contains actionable elements that you can use to change the data displayed in the report, namely
The Compensation components drop-down list reflects the selected and displayed compensation components used throughout the report statistics. The compensation component selected when running the associated pay equity analysis is used by default. To use a different component, select any other compensation component from the drop-down list. The list includes compensation from your imported dataset.
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Alternative compensation elements can only be selected if any are configured in the original dataset configuration, using the Use additional compensation settings option. You cannot add other compensation components once you have configured a dataset and run an analysis. For more information about the configuration of the dataset and creation of the compensation model, refer to Uploading your data and Creating a compensation model.
The raise toggle: if raise suggestions were enabled in the original analysis, then this toggle enables you to view the data before and after raises. Keep in mind that for both the Before raises and After raises views, the data of the entire employee group is taken into account. Therefore, values such as the difference percentages, the averages and the percentile values are impacted. In the After raises you will see the positioning of the employee compared to the other employees in the group after compensation adjustments are applied to all the employees.
Summary and assumptions
The Summary and assumptions section offers a textual overview that summarizes the position of the employee's compensation in comparison to their group, highlighting the areas with the greatest differences and indicating any risk areas. In addition, if raise suggestions were included in the pay equity analysis, this section also indicates whether any raise was suggested for the employee.
The following figure illustrates the Summary and assumptions section and the information it can provide:
Summary and assumptions section
Employee group positioning
The Employee group positioning section offers a visual representation of the position of the selected employee within their pay range and their employee group. Here are the principles that govern the information displayed in this section:
The pay range position is only displayed if pay bands were configured with the pay equity analysis.
The pay range positioning rail displays the employee's current salary represented by a purple dot, and the employee's salary compared to the pay bands. The range is represented in light blue on the rail; with the minimum, mid-point and maximum values represented by blue dots on the rail. If the employee is positioned below or above the pay band, this may warrant an explanation and could indicate an area for remediation.
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The employee group position is always displayed on a rail, with the employee's current salary represented by a purple dot. It is compared to the employee group distribution with the following breakdown:
The mid range (second and third quartiles, i.e. 25th to 75th percentile points) is represented in dark blue on the rail.
The first and fourth quartiles (minimum to 25th percentile point and 75th percentile point to maximum) are represented in light blue on the rail.
The key points such as minimum, 25th percentile, 75th percentile and maximum and average represented by blue dots on the rail.
Positioning visualization rails
Compensation insights
The Compensation insights section is divided into two subsections:
The key highlights: cards and the summary table at the top of the Compensation insights section
The justification graph: graph and summary table in the bottom part of the Compensation insights section.
The key highlights provide insights from the compensation model derived from the selected pay equity analysis. The insights might help justify the position of the employee compared to their employee group, for employees whose compensation differs from their employee group peers, and in cases where this is driven by a difference in compensation driver values.
Consider the following example: assuming tenure and performance rating are significant compensation drivers within an organization, the suggested pay for an employee with a 2-year tenure and a low performance rating, is expected to be lower than an (average) employee with 5 year tenure and a medium performance rating. This is how the model may support justifying (part of) the pay difference between an employee and the average employee in their peer group.
First, the key statistics are summarized, including:
Current compensation of the employee: value of the selected compensation component for the employee, based on the dataset.
Suggested salary range: reflects the predicted pay of the employee based on the compensation model and the employee's values for the compensation drivers (variables) within a range of plus 5% and minus 5%. For more information about predicted pay, refer to Data column terminology & definitions.
Employee vs avg. male: compares the current compensation of the employee with the average compensation of male employees within the selected employee group. Here, the male group is considered the baseline and the percentage difference of the employee is displayed.
Employee vs avg. female: compares the employee current compensation with the average compensation of female employees within the selected employee group. Here the female group is considered the baseline and the percentage difference of the employee is displayed.
Employee vs average employee group: compares the current compensation of the employee with the average compensation of all employees within the selected employee group. Here, the employee group is considered the baseline and the percentage difference of the employee is displayed.
The following figure illustrates the justification key insights section:
Key explainability data
Because employees whose current compensation is below the average male, average female or average overall employee group are more likely to raise follow-up questions and request clarification on their compensation level, the justification graph helps you prepare for this conversation.
If the employee's current compensation is well within the suggested salary range, it is easier to justify any pay differences using the compensation drivers (see XREF).
If the employee's current compensation is below the suggested salary range, the system then cannot provide a justification and you may want to consider remediation through raise suggestions. For more information, refer to Getting suggested remediation actions.
The graph offers a visual representation of the employee's current compensation (purple vertical line) compared to:
The suggested pay range (in red)
The pay bands (light purple; visible only if pay bands were configured in the analysis)
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Pay bands will be included only if you have run a pay equity analysis including the raise suggestion model and have selected pay bands in the configuration of the analysis.
Male employees withing the employee group
Female employees within the employee group
All employees within the employee group.
In the graph, this reference data is represented b y the compensation distribution range and points as follows:
Group average (back vertical line) and group median (yellow vertical line)
Mid-range, second and third quartiles, 25th to 75th percentile points (in dark blue)
First and fourth quartile, minimum to 25th percentile point and 75th to maximum (in light blue)
Justification graph
Analytics deep dive per compensation driver
The Analytics breakdown by compensation driver section provides deeper insights into the specific compensation drivers (or variables), broken down by categorical and numerical, used in the compensation model and how the specific employees' values compare to those of peers in their employee group. In case pay differences between the employee and their peer group are driven by compensation drivers, these insights may help justify or explain these differences to the employee.
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In cases where the employees pay deviates strongly from the compensation model, the compensation drivers will not support any justification. In this scenario, either other relevant (perhaps exceptional) factors outside of the compensation model could be considered or pay gap remediation steps considered for the employee in question.
Based on the compensation model configuration in the pay equity analysis, variables (i.e. compensation drivers) will either be treated as categorical or numerical. A section of the report is dedicated to each type of compensation driver.
Categorical compensation driver
In the following example, education strongly impacts compensation within the organization. The specific employee in this example has a Masters degree (employee value is highlighted in light purple) which appears to be the highest level of education. A high percentage of employees within the category of worker have a masters degree (34%), however the greatest number of employees have a lower level of education (67%). Assuming that a higher level of education positively impacts compensation, this could not explain the employees lower compensation compared to their group. In fact, a higher level of compensation would be expected.
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It is important to review all compensation drivers in a similar manner and then take a holistic view based on the aggregate impact.
Categorical compensation drivers example
Numerical compensation drivers
In the following example, the level of responsibility moderately impacts compensation within the organization. The specific employee in this example has been defined as having level 2 responsibility, whereas the average male in their employee group has level 3 responsibility and the peer group as a whole has 2.3. Assuming that a higher level of responsibility positively impacts compensation, this could explain (in part) the lower level of compensation of the employee compared to the group and the even greater gap with male employees in the group.
Numerical compensation drivers example