Using Analytics to Achieve Pay Equity

One of the most challenging aspects of instituting pay equity in a firm is ensuring that like-for-like effort is rewarded equitably. Using analytics to achieve pay equity in an organization helps filter through disparities to arrive at a more accurate assessment of pay equity within a system. 

To that end, data-driven pay equity analysis, as practiced by Mercer, one of the globe’s leading HR consulting firms, consists of three constituent steps. These are data collection, running statistical models, and identifying areas of risk in order to institute remediation. 

Let’s take a look at each one in detail.

Step One – Data Collection/Prep

As the starting point for any thorough investigation, the data collection phase of a pay equity analysis first seeks to establish the parameters to be considered. 

In this instance, those consist of the gender, as well as the ethnic identity, of the sampled group. Pay, including base salary and overall compensation, is included as well. Other factors with the potential to drive pay differences are also incorporated. 

Employee characteristics considered include:

• Pay level

• Gender, race/ethnicity

• Previous experience (age)

• Amount of time on the job

• Performance history

• Education

Job-related factors include:

• Type of Industry

• Common duties

• Career level/salary guide

External factors include:

• Industry rate for the type of work

• Where the work is conducted

The overarching goal is to ensure that the data collection phase encapsulates the philosophies guiding compensation practices within the organization in question. 

Step Two – Generating Statistical Models

With baseline information gathered, it should next be segmented according to pay practices — excluding gender and ethnicity as factors. This will help ensure that the compensation norms in place are those you’d like to see proliferate to ensure that everyone is paid solely based upon experience, geographic location, and performance. 

The primary aim of this regression model is to determine the primary considerations that are driving the firm’s compensation practices. This will make it possible to review them and realign them if the need to do so is there. Once you have your results, confirm them with key members of the organization to ensure that they convey an accurate representation of the situation.

Step Three – Identifying and Remediating Issues (If They Exist)

Employing regression models in this fashion will reveal ingrained pay differences among various employee groups when gender and ethnicity are factored back in. When significant differences are illuminated for which no explanation exists, you will have identified the employees who should be compensated more in order to achieve pay equity. 

Working this way enables you to investigate pay gaps more thoroughly in order to investigate pay distributions on a group-by-group basis. This also gives you the ability to try a variety of adjustment approaches in order to tailor your strategies to specific situations. In addition, this method gives you the data you need to evaluate the efficacy of the approach you take to eliminate pay gaps.

Step Three Point One

It’s important to note that this is far from a “fix-it-and-forget-it” concern. Workplaces tend to be dynamic environments. People tend to regress to ingrained habits when solid enforcement is lacking. 

Thus, in addition to mandating a specific set of practices, you must remain vigilant to ensure that such practices are enacted and enforced. To this end, a thorough review of compensation models should be conducted at regular intervals. 

While we’re on the subject, the achievement of pay equity should be but one part of your goal here. This is a good opportunity to uncover the biases existing within your organization and determine their origin(s).  In other words, your true goal should be to get to the root causes of pay inequity in order to determine what needs to be done to eradicate them from the philosophies governing your business.