The secret reason you did (or did not) get a raise

p 1 the secret reason you did or did not get a raise

Suppose again to the final time you obtained an adjustment to your compensation. Had been you advised that it was due to your efficiency? Or that it was as a result of you “exceeded expectations” in 360 peer evaluations? Did you assume that HR utilized deliberate math, sound methodologies, and calibrated outcomes pretty and persistently throughout the group to return to these conclusions?

For a lot of organizations, there’s a secret about this course of. Your supervisor—or the HR division for that matter—in all probability can’t clarify, not to mention exhibit with information and sound evaluation, the components that decided your change in pay. Right here’s what’s possible actually occurring.

Most firms wish to reward their better-performing staff with extra compensation. Together with years of related expertise, location, and tenure, efficiency is among the many commonest standards firms use to find out who will get paid greater than different comparable staff.


However the scary reality more often than not is that this: Managers use their discretion and subjectively worth efficiency in a different way. Why? As a result of they’re human. And most firms lack the elemental instruments to know whether or not their pay insurance policies are working as supposed. As a labor and employment regulation lawyer and information scientist, I’ve seen firsthand how firms largely function in the dead of night in terms of persistently and pretty making use of pay insurance policies.

Good insurance policies are foundational to truthful pay

Efficient pay insurance policies ought to be truthful, constant, unbiased, and they need to adequately incentivize supposed behaviors and outcomes. They must be defensible in a courtroom of regulation. They need to align with what firms have communicated to their staff, managers, and executives.

When pay insurance policies are carried out inconsistently (like when some managers place extra weight or not as a lot weight on instructional attainment), or primarily based on biased information (like insurance policies that statistically inure a profit to 1 group over one other), they’ll inadvertently grow to be one of many largest drivers of pay disparities. That implies that each time your group modifications compensation, it could possibly be exacerbating the issues. And people issues are greater than elevated authorized threat. They embody hits to retention, engagement, productiveness, morale, and total model.

As extra firms deal with enhancing equity within the office as a means to assist handle systemic inequality, staff deserve extra transparency. They need to be capable of know that if their employer says they pay for efficiency, that they really are doing so (persistently). And, firms deserve higher instruments to have the ability to shortly and dynamically analyze compensation information and know with certainty whether or not their technique is working as supposed or if not, be capable of repair it.

Information spotlights hidden gaps in pay insurance policies

Many leaders may have theories on how their pay insurance policies are working however few have the instruments to know for certain. It’s because historically, firms look to regulation companies and consultants to conduct pay fairness analyses and few are capable of meaningfully and dynamically study pay insurance policies as a result of it’s sluggish, static, and expensive. So when leaders do get a probability to look below the hood, the information are illuminating.

When a main insurance coverage firm not too long ago started its pay fairness evaluation, its leaders needed to account for just one pay coverage: efficiency ranking. However as they checked out their information utilizing the precise instruments, they realized efficiency rankings weren’t explaining variation in compensation a lot in any respect. This discovering led the workforce to suppose very in a different way about their pay insurance policies and apply a way more nuanced method. Now, they’re utilizing 9 insurance policies to find out how staff get paid in a way more constant and truthful means.

One other firm that held itself out as a pay-for-performance group discovered that it was something however. As soon as their workforce examined their pay information utilizing the precise instruments, they realized their efficiency rankings system favored males. Throughout the corporate, efficiency scores had little relationship to figuring out worker pay. And, in a single group, they discovered staff had been being paid much less for greater efficiency rankings. By seeing the precise influence of their insurance policies on compensation, they had been capable of handle the basis causes that had been creating unfairness and deal with the components that really influenced pay. Once more, the important thing to transformative change is having the precise instruments in place.


As extra firms flip to software program that permits them to meaningfully, persistently, and dynamically consider pay and pay insurance policies, leaders are lastly gaining an always-on pulse of how selections round pay influence equity within the office. It’s not acceptable for employers to be in the dead of night in terms of whether or not pay insurance policies are working as designed in at this time’s tradition of office transparency. By utilizing the precise expertise, firms can lastly maintain a mirror as much as their compensation methods so each staff and employers may be assured that insurance policies are driving legitimate variations in pay and incenting supposed behaviors and outcomes, and aren’t biased or contributing to inequity in organizations.

Zev Eigen is the founder and chief science officer at Syndio, a SaaS pay analytics firm.