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

An employment law attorney and data scientist says the scary truth most of the time is that managers use their discretion and subjectively value performance differently.

The secret reason you did (or did not) get a raise
[Photo: aydinozcanbaz/iStock]
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Think back to the last time you received an adjustment to your compensation. Were you told that it was because of your performance? Or that it was because you “exceeded expectations” in 360 peer reviews? Did you assume that HR applied deliberate math, sound methodologies, and calibrated results fairly and consistently across the organization to come to those conclusions?

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For many organizations, there’s a secret about this process. Your manager—or the HR department for that matter—probably can’t explain, let alone demonstrate with data and sound analysis, the factors that determined your change in pay. Here’s what’s likely really going on.

Most companies want to reward their better-performing employees with more compensation. Along with years of relevant experience, location, and tenure, performance is among the most common criteria companies use to determine who gets paid more than other comparable employees.

But the scary truth most of the time is this: Managers use their discretion and subjectively value performance differently. Why? Because they are human. And most companies lack the fundamental tools to know whether their pay policies are operating as intended. As a labor and employment law attorney and data scientist, I’ve seen firsthand how companies mostly operate in the dark when it comes to consistently and fairly applying pay policies.

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Good policies are foundational to fair pay

Effective pay policies should be fair, consistent, unbiased, and they should adequately incentivize intended behaviors and outcomes. They need to be defensible in a court of law. They should align with what companies have communicated to their employees, managers, and executives.

When pay policies are implemented inconsistently (like when some managers place more weight or not as much weight on educational attainment), or based on biased data (like policies that statistically inure a benefit to one group over another), they can inadvertently become one of the biggest drivers of pay disparities. That means that every time your organization changes compensation, it could be exacerbating the problems. And those problems are more than increased legal risk. They include hits to retention, engagement, productivity, morale, and overall brand.

As more companies focus on improving fairness in the workplace as a way to help address systemic inequality, employees deserve more transparency. They should be able to know that if their employer says they pay for performance, that they actually are doing so (consistently). And, companies deserve better tools to be able to quickly and dynamically analyze compensation data and know with certainty whether their strategy is working as intended or if not, be able to fix it.

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Data spotlights hidden gaps in pay policies

Many leaders will have theories on how their pay policies are working but few have the tools to know for sure. This is because traditionally, companies look to law firms and consultants to conduct pay equity analyses and few are able to meaningfully and dynamically examine pay policies because it is slow, static, and costly. So when leaders do get a chance to look under the hood, the data are illuminating.

When a major insurance company recently began its pay equity analysis, its leaders wanted to account for only one pay policy: performance rating. But as they looked at their data using the right tools, they realized performance ratings were not explaining variation in compensation much at all. This finding led the team to think very differently about their pay policies and apply a much more nuanced approach. Now, they are using nine policies to determine how employees get paid in a much more consistent and fair way.

Another company that held itself out as a pay-for-performance organization found out that it was anything but. Once their team examined their pay data using the right tools, they realized their performance ratings system favored men. Across the company, performance scores had little relationship to determining employee pay. And, in one group, they found employees were being paid less for higher performance ratings. By seeing the actual impact of their policies on compensation, they were able to address the root causes that were creating unfairness and focus on the factors that truly influenced pay. Again, the key to transformative change is having the right tools in place.

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As more companies turn to software that enables them to meaningfully, consistently, and dynamically evaluate pay and pay policies, leaders are finally gaining an always-on pulse of how decisions around pay impact fairness in the workplace. It’s no longer acceptable for employers to be in the dark when it comes to whether pay policies are working as designed in today’s culture of workplace transparency. By using the right technology, companies can finally hold a mirror up to their compensation strategies so both employees and employers can be confident that policies are driving valid differences in pay and incenting intended behaviors and outcomes, and are not biased or contributing to inequity in organizations.


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