Pay Equity Deep Dive Part VI: Trusaic’s Salary Range Finder
This is Part VI of our “Pay Equity Deep Dive Series.” Part I focused on Compensation Philosophy Review and Pay Analysis Group formation and testing . Part II focused on Wage Influencing Factors (WIFs) and Reliability and Robustness Testing . Part III focused on Tainted Variable Analysis and Root Cause Assessment . Part IV focused on Developing a Remediation Strategy and continued into Part V which focused on Key Considerations When Developing a Remediation Strategy . Part VI covers Trusaic’s Salary Range Finder.
A common question I hear is, how often should I conduct a pay equity analysis?
For most organizations, it is appropriate to conduct a pay equity analysis and remediate identified disparities on an annual basis. Remediating less often runs the risk of not addressing pay inequities in a timely manner and may lead to large remediation costs. Remediating more than once per year has its own drawbacks.
From a budgeting standpoint, most organizations are not in a position to make pay equity adjustments multiple times per year. Moreover, making pay equity adjustments outside of the merit process runs the risk of drawing unwanted attention to the adjustments.
That said, we recommend monitoring your pay equity situation throughout the year, either on a quarterly or semi-annual basis. Conducting a periodic pay equity audit provides feedback well in advance of your annual compensation cycle.
A periodic audit can help you understand how demographic changes within your workforce, changes in organizational structure, and changes in external market forces are affecting your pay equity outcomes.
Rather than remediate multiple times per year, an organization would be better served by proactively preventing pay inequities as pay decisions are made, and using an annual remediation process as a safeguard to address any pay inequities that remain.
Another common question I hear is, why do I need to conduct a pay equity analysis more than once? Why isn’t it “one and done?”
Even if an organization carries out a regular proactive pay equity analysis and addresses pay inequities, new pay disparities may emerge when carrying out the next pay equity analysis.
As your workforce and your organization evolve, your pay equity situation evolves with it.
Preventing Pay Inequities
Every compensation decision — whether it’s a new hire pay decision, a merit pay adjustment, a promotion decision, or an off-cycle pay adjustment — is an opportunity to help prevent, or potentially worsen, a pay inequity. For this reason, compensation decisions should be in alignment with internal equity at every stage of the employee lifecycle.
One way to do this is to take advantage of your pay equity analysis. As part of your pay equity analysis, you create a statistical model of pay for each of your Pay Analysis Groups (PAGs) . Each model includes Wage Influencing Factors (WIFs) , which are compensable factors that one would expect to influence employee pay, such as career level, job function/family, performance rating, company tenure, position tenure, line of business, educational attainment, and geographic location.
The regression weights associated with these WIFs can be used to compute an employee’s predicted pay. By design, this prediction is neutral to an individual’s demographic characteristics.
These pay models also can be used to compute predicted pay for individuals who are new to the organization, individuals who are being promoted, and individuals who are experiencing some other type of change (e.g., lateral move, geography change).
An Illustrative Example
As an illustrative example, let’s assume that the predicted pay for a typical incumbent in an Information Security Analyst II role in Chicago for Company XYZ is $125,000. The typical incumbent has six years of tenure, two years in the role, and three years of experience before joining the company.
The table below shows predicted pay for a new hire in the same role, someone promoted into the role internally, and someone in the role who is relocating to Chicago. In all cases, let’s assume that the individual has three years of experience before joining the company.
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For the new hire, tenure and time in job are both zero. This results in a predicted pay of $120,000. For the promotion, time in job is zero and let’s assume that the individual has six years of tenure. This results in a predicted pay of $122,000. Lastly, for the geography change, let’s assume that the individual has six years of tenure and two years in the role. This results in a predicted pay of $125,000 (same as the typical incumbent).
While a degree of flexibility in setting pay may be needed to attract and retain talent, using the models you create as part of your pay equity analysis to assist with pay decisions can help ensure that decisions are made in alignment with internal equity.
This approach to setting pay also ensures that an individual’s prior pay (internal or external) does not factor into a pay decision. This is particularly useful in the more than 20 U.S. states and 20 U.S. localities that have enacted salary history bans. These laws explicitly prohibit employers from requesting salary history information from job applicants. A salary history ban is also part of the EU Pay Transparency Directive .
Trusaic’s Salary Range Finder
Advances in technology have made the process of making fair pay decisions easier. Trusaic’s Salary Range Finder (SRF) is designed to help managers, recruiters, and HR practitioners make unbiased pay decisions aligned with internal equity.
The SRF uses the pay models developed as part of a pay equity analysis conducted using Trusaic’s PayParity platform and generates an equitable pay range that can inform pay decisions. The chart below shows an example from Trusaic’s SRF.
The example above is for an external hire into an Information Security Analyst II role in Chicago for Company XYZ. The “pay equity range” offers a pay range that will not worsen an existing pay disparity. In this example, the pay equity range is $118,800 to $126,800. This range is based on the neutral pay predictions discussed earlier (i.e., an employee’s or candidate’s demographic characteristics play no role in the creation of the range).
The SRF includes other information that might be useful in making pay decisions:
A couple of things to note from this example. Company XYZ is paying its current incumbents toward the top of its internal pre-approved range. Perhaps the company has struggled to attract and retain information security analysts and has needed to keep pay high relative to the internal range (and the market).
Because current employees are clustered around the top end of the internal range, the pay equity range is also clustered around the top end of the internal range.
One Last Question
Another question I hear is, why do pay predictions sometimes exceed or fall below our standard pay ranges?
While this can happen for several reasons, including flawed PAGs and unreliable models, in my experience the more common explanation is that organizations are paying people outside of their standard ranges.
A pay equity analysis is looking to see if people are paid fairly relative to each other (not relative to the standard range). It’s essentially an internal benchmark. If a company regularly pays people outside of the standard range for the role, then predictions outside of the standard range are to be expected.
Complementing your pay equity analysis with Salary Range Finder will provide you with a more comprehensive approach that will ensure your pay equity efforts stay on track with evolving organization and workforce dynamics.?
Stay tuned for Part VII of our “Pay Equity Deep-Dive” series, which will cover Pay Equity Methodology Myths & Misconceptions.