Pay Equity Analysis: Understanding the Entire Story

Statistical outcomes do not always tell the whole story, and this is particularly true for studies on pay equity.

A statistical finding of no difference in compensation based on gender or race often prematurely ends further discourse on the matter even when the results seem incorrect and inconsistent with many people’s lived experiences. Somehow, pay equity studies do not always capture the reality of what many people are feeling. While statistical analysis is supposed to reflect the true unseen nature of things, the math seems to fail in the instance of pay equity.

The numbers themselves are not to blame. Rather, pay equity studies can be restrictive in focus. They examine whether or not certain groups are equitably paid assuming all else is equal and groups have the same values on other important variables. But often “all else” is not equal and inequalities may not show up because the analyses presume no relevant differences between the groups being evaluated. Yet, important differences can exist in a number of ways.

SullivanCotter describes three areas where differences may exist, how they avoid detection in standard pay equity assessments, and what further analyses are required to help complete a story only partially told.

Selection Bias

Pay equity analyses are conducted on the organization’s current workforce. However, if members of certain groups leave the organization at differential rates and one of the reasons for this turnover is low wages, then the current workforce does not represent those who may be underpaid. By eliminating a group of low-paid employees, the comparison to the remaining members of the workforce will appear more favorable. To understand if members of certain groups are more apt to self-select out of the organization, studies that examine the incidence of turnover and compensation levels of employees who have left are necessary.

Performance Bias

Pay equity analyses that include performance as a factor will “correct” any differences between groups based on this variable. But that approach begs the important question: Why might groups differ on performance? As it happens, there are a number of well-documented reasons for these differences – and none concern actual ability. For example, women and people of color must perform at higher levels than white males to be seen as performatively equivalent. Additionally, other performance-enhancing opportunities such as access to training, developmental assignments, guided learning projects, and mentorship may not be the same for everyone. The only way to know if this is the case is to see if differences in access to these opportunities exist.

Career Bias

Generally, women and people of color do not occupy as high of rank as white males in organizations and, on average, receive lower compensation. It is not simply that there are fewer women and people of color in leadership positions, but that they are disproportionately absent. Again, the pay equity analyses will calibrate for hierarchical disparities when comparatively examining compensation. But what happens to women and people of color during their career trajectories? Do they make slower career progress or stall at certain organizational levels? Again, the literature is replete with studies on career-family imbalances and the sacrifices working women, for example, must make to sustain the viability of both their home and work lives. Only further analyses can say if there is a price that certain groups must pay.

If your organization is looking to conduct a meaningful pay equity analysis that gets to the root of the actual issue rather than remaining content with what is being outwardly projected, you’ll need to take the time to understand the entire story and assess whether or not the pay equity that may seem so apparent is truly equitable or not.