This guide describes how to work in the Analyze – Equal job view in Labs. Here you analyze all equal job groups to ensure there are no pay differences that can be attributed to gender, or to address pay differences that cannot be explained by objective reasons.

💡 Tip: You can analyze single-gender groups, even though these cannot be compared between women and men.

⚠️ Important: Under Swedish law, you must analyze pay differences between women and men in equal job and ensure there are no unjustified pay differences related to gender.

⚠️ Important: This feature requires that you have activated Analyse equal job under the Labs settings.

Before you start analysing, it is important to understand what the expected salary is and how it affects the analysis.

The predicted salary is the theoretical salary that an employee is expected to have according to the regression model, based on your pay structure and the pay factors you have chosen in the pay philosophy.

We use regression analysis to statistically determine whether pay differences can be explained by pay factors based on your pay philosophy. In a regression analysis, we try to predict salary using your pay factors. We examine whether there are relationships showing how the factors affect salary, and use these relationships to predict each employee's salary.

⚠️ Important: The predicted salary is an analytical tool – not a definitive answer. It helps you find outliers and potential pay discrimination, but reflects your current pay structure rather than what the "correct" salary is.

Predicted salary is rarely a perfect match for two reasons:

1. The pay philosophy does not cover all factors. There is rarely data for competence, even though it is one of the most desired factors in pay-setting. Factors missing from the model do not affect the calculation.

2. Your pay structure may not reflect the factors you desire. A common example is that performance rarely has a significant actual impact on salary in practice, even though it is a stated part of the pay philosophy.

The calculation is based on two parts:

1. Intercept – the theoretical base salary when all pay factors are zero.

2. Coefficients – each pay factor has its own coefficient indicating how much the expected salary increases per step in that factor. For example: per year for age or tenure, per level for position level.

These values apply to all employees in the organisation, but change if you edit employee data or change pay factors.

Formula:

Predicted salary = exp(intercept + factor₁ × coefficient₁ + factor₂ × coefficient₂ + ...)

Example – how is Bob's predicted salary calculated?

Regression results: Intercept 10.2431 · Age coefficient 0.0043 · Level coefficient 0.0603. Bob's data: age 33, level 7.

Expected salary (Bob) = exp(10.2431 + 0.0043 × 33 + 0.0603 × 7) = 49,370 SEK

The system calculates the difference between each employee's actual salary and predicted salary. Employees whose difference is 1.5 standard deviations or more are selected as outliers and prioritised for analysis.

💡 Tip: There may be equal job groups where all employees are outliers and groups where none are. This does not mean the model is wrong – it may be because a factor is missing, or that the group consistently sits above or below their predicted salary.

⚠️ Important: Outliers in the model are a starting point for analysis, not proof of discrimination. Always investigate whether there are objective explanations before drawing conclusions.

Inside each equal job group there are four analysis tabs:

The Analyse sidebar stays with you throughout all analysis steps. In the sidebar you can see employees divided into four groups:

Employees whose salary deviates by 1.5 standard deviations or more from the predicted salary. This may be due to a disproportionate salary or gender-neutral explanations – you need to analyse these individuals further.

Employees whose salary is higher or lower than the pay band for the work, even though the salary may be close to the predicted salary. Check whether the individual is at the right level or whether there is another explanation.

Employees whose actual salary is close to the predicted salary and can be explained by the pay factors. If you are short on time, you do not need to analyse these individuals further – but keep in mind that the model only sees the data that has been loaded.

Employees that you have marked as analysed. These are shown with a green circle in the chart.

Here you can filter to see the employees you want to view in the chart.

💡 Tip: Use the filter function if you are analysing a large group – it makes it easier to focus on a subset at a time.

⚠️ Important: Don't forget to clear the filter if you want to return to the original view.
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In the overview you can see all your equal job groups and their analysis status. You can filter by:

Use the search box to quickly find a specific job group, employee, or position. Click on Sort by in the top right corner to sort by Mean unadjusted pay gap (highest pay gap shown at the top) or Equal Job title (A to Z).

1. Click on an equal job group in the list to open the analysis.

In the Predicted salary tab, you should identify deviating salaries and pay differences between women and men. This could be men who stand out in salary relative to women, or women who stand out relative to men.

On the y-axis you see employees' actual salary and on the x-axis employees' predicted salary is shown.

The chart contains three lines:

In the example, the group HR assistants is analysed, consisting of one man and two women. The two women are within the dashed lines and have a salary that matches the model's prediction. The man is outside and is identified as an outlier – we now need to explain why his salary is higher with objective reasons.

1. Click on the individual in the chart that you want to explain.

2. The individual's employee card opens to the right.

3. In the employee card you can see information about the individual: age, base salary, salary components, total salary, expected salary, unique ID, tenure, and performance. Clicking on the Employment tab shows all information loaded for the individual.

4. Scroll down in the employee card to explain why the individual has a higher or lower salary.

5. Review historical analysis – if the individual was analysed in the previous year's pay equity analysis and received a comment or tag, that information is shown here. You can reuse previous comments by clicking Apply.

6. Explain current salary – click on one of the options under the heading Current salary to select an objective explanation.

7. You can also write your own explanation in the comment field (Other explanation).

8. When you have explained the individual, click the blue button "Mark employee as analyzed".z
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9. The individual now has a green circle around them in the chart and is marked as explained.

In the Explore tab, you can analyse salaries from multiple data perspectives. We recommend this analysis as a good complement to the Expected salary tab. As mentioned earlier, the expected salary is an analytical tool – not a definitive answer – and Explore helps you find explanations that the model does not always capture.

Under the heading Chart type you can change how the chart is displayed.

Under the heading Group by you choose how employees should be grouped in the chart.

Under the heading Pay measure you choose which pay measure to display on the y-axis – for example base salary or total salary.

Under the heading Categories you choose which data to display on the x-axis. You can choose from:

💡 Tip: By changing the category on the x-axis, you can more easily find explanations for why the pay spread looks the way it does for the group.

Under Chart settings you can also choose to show:

⚠️ Important: To use the different data types in the chart settings, there must be data loaded in the salary file for each respective field.

1. Click on the individual in the chart that you want to explain, or who is shown as an outlier.

2. The individual's employee card opens to the right.

3. In the employee card you can see information about the individual: age, base salary, salary components, total salary, expected salary, unique ID, tenure, and performance. Clicking on the Employment tab shows all information loaded for the individual.

4. Scroll down in the employee card to explain why the individual has a higher or lower salary.

5. Review historical analysis – if the individual was analysed in the previous year's pay equity analysis and received a comment or tag, that information is shown here. You can reuse previous comments by clicking Apply.

6. Explain current salary – click on one of the options under the heading Current salary to select an objective explanation.

7. You can also write your own explanation under Other explanation in the comment field.

8. When you have explained the individual, click the blue button "Mark employee as analyzed".

9. The individual now has a green circle around them in the chart and is marked as explained.

💡 Tip: At the bottom of the chart there is a table showing key metrics for the entire group.

If you do not have an explanation for why an individual has a deviating salary and need to investigate further – for example with the individual's manager or another responsible person – you can flag the individual for analysis and return later.

1. Click on the individual in the chart that you want to flag.

2. The individual's employee card opens to the right.

3. Click on the target icon "flag for analysis" in the top right of the employee card.

4. The individual is now flagged and gets a red square around them in the chart.

5. You can also write a comment about why the individual was flagged under Other explanation in the comment field.

⚠️ Important: You can click on an individual to open the employee card regardless of whether you are in the Expected salary, Explore, or Structure tab.

💡 Tip: To easily get a list of all individuals you have flagged for analysis – go to the Report tab and export the Summary – employees report. Open the Excel file and filter on the Status column to show all with the value Flagged for analysis.
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If you cannot explain an individual's salary with legitimate and objective reasons, you need to either adjust the salary directly or include the individual in an action plan. Here is how:

1. Click on the individual in the chart whose salary you want to adjust.

2. The individual's employee card opens to the right.

3. Scroll down to the heading Adjust salary.

4. Enter how much you want to increase the salary under Year 1, Year 2, or Year 3. If you want to increase the salary all at once, use only Year 1.

5. Under Total salary increase you can see the total increase and under New total salary you can see what the new salary will be.

6. Click the blue button "Mark employee as analysed" when you are done.

💡 Tip: At the top of the chart you can click Simulate salaries. When you activate this function, you can see directly in the chart where the individual ends up after the salary adjustment you have entered. If you are unsure how much to adjust for an individual, you can enter 1 in the Year 1 field – the individual will still be included in the action plan and you can update the amount later.

⚠️ Important: All individuals for whom you have entered a salary adjustment are shown in the Actions report – you can find it under the tab Report → Actions.

In this tab you can identify gaps that reveal structural problems or identify patterns in how salaries are set within the group.

In Structure you will find a quick overview of the group you are analysing. The overview shows:

In the table below you can see key metrics for the group:

By switching the view under Group by, you can get an overview of how the salary distribution looks within the group.

Under Chart settings you can enable pay bands.

⚠️ Important: To be able to see pay bands within the group, there must be pay ranges entered in the Job Architecture under the Pay range tab.

💡 Tip: If you do not have pay ranges entered or do not use pay ranges in Sysarb, you can still use the chart to see what the pay structure looks like within the group.

This chart shows an overview of all employees' salaries in the group – from the lowest salary in the left corner up to the highest salary in the right corner. Scrolling down you will also find a table showing key metrics for the group.

In this tab you get a consolidated overview of all explanations you have written for individuals in the group, as well as the tags you have used to justify why an individual stands out in salary.

Under the heading Explanations you can see:

Example – In the group we have explained Anna Hansson with less experience (arrow down on experience) plus a comment (other explanation), and Johan Andersson with more experience (arrow up on experience) plus a custom comment. This gives a total of 3 explanations for the group.

Under the Action plan chart you can see:

In the table at the bottom you can see the names of the individuals for whom a salary adjustment has been created.

⚠️ Important: All individuals included under Action plan will also appear in the Actions report – you can find it under the tab Report → Actions.

The final step of the analysis is to write a summary using the tags you have used for individuals in the group. Here you document why the salary situation looks the way it does.

1. Open the Analyse sidebar.

2. Write your summary text in the Summary of your analysis box.

3. Click on Complete analysis in the top right corner.

4. The analysis is now marked as complete. You can choose to analyse a new work group by clicking on the name of the work group you are currently in at the top left, or click on the arrow that takes you back to the overview page with all your equal work groups.

⚠️ Important: The summary you write in the box is shown in the Summary report – you can find it under the tab Report → Summary.
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