What is a ‘compa-ratio’?

The Compa-ratio is a standard metric in compensation analysis, providing a quantitative means for organizations to ensure internal equity and market competitiveness in their salary structures. As a widely adopted metric, it facilitates data-driven decision-making regarding salary adjustments and helps organizations maintain fair and competitive compensation practices.

The term ‘compa-ratio,’ derived from "comparison ratio," reflects its fundamental purpose of evaluating how an individual's salary compares to a defined benchmark within an organization's compensation.

Compa-ratio has become a common metric used in compensation analysis: it’s a number that shows where a salary falls relative to the midpoint of a salary band.  

Let’s start with a basic example:

An employee makes $65,000. The midpoint of the salary band is $70,000.  The compa-ratio for that pay rate is: 

$65,000 (salary)
______________________ = 0.93
$70,000 (midpoint)

Here, the salary is 7% lower than the midpoint of the salary band. A compa-ratio of ‘1.00’ means that the employee is paid at the salary band's midpoint. A compa-ratio of 1.10 means the employee is paid 10% above the midpoint, and so on.  

Of note: some compensation teams use decimals to express compa-ratios, as I have here, but others like to express them as percentages (with “100%” being the same as “1.00,” 91% being the same as 0.91, etc.)

What is a midpoint?

Before we dive more into the usage of compa-ratios, it helps to have an understanding of the midpoint part of the equation. When salary ranges are built, they should have a minimum, midpoint, and maximum. When people talk about ‘salary ranges,’ they are usually talking about the minimum to the maximum (what you might see on a job description), but the midpoint is actually the foundation on which that range is constructed.

When compensation teams design salary ranges, they look at relevant market pay data, usually using large survey providers that aggregate pay information from thousands of anonymized employee records. Those datasets will have salary information for job functions at different levels and geographies.  

Let’s use an operations manager title as an example. In that data, the company may see that the median salary for an operations manager in their industry is $70,000. Let’s assume that the company wants to anchor its pay at the median of the market for their industry (the ‘median’ is the same as the 50th percentile).  

That salary range might look something like 

Min Midpoint Max
$56,000 $70,000     $84,000

This example's minimum and maximum are built by going 20% lower and 20% higher than the midpoint, respectively. This method has been a common rule of thumb for how salary ranges are built - but there are other approaches and variations that we’re not going to get into here.  The main takeaway of this salary range is that the company has decided to set its pay at the median (or 50th percentile), so their compa-ratios will be in comparison to the median of the market.

Alternatively, a company could decide they are going to pay a higher or lower percentile than the median.  Let’s say a company wanted to pay the 75th percentile as its pay philosophy, and the 75th percentile of the market for that same operations manager is $85,000.

The midpoint would be set to $85,000, and the minimum and maximum would be built off of that number.  A ‘compa-ratio’ of 1.0 would mean the operations manager is paid at the 75th percentile of the market.  

So, ‘midpoint’ doesn’t always mean the median of the market; it often means the midpoint of your company's salary bands, which are tied to the percentile of pay that the company sets.

Why is compa-ratio a powerful metric?

Compa-ratio is a single metric that controls for variables like job function, level, and geography, more easily allowing you to compare how the company is paying across groups where the actual salaries might differ. It allows you to answer questions like:

How are we paying relative to our compensation philosophy?

If the company wants to pay at the 75th percentile of the market (and sets the midpoints of their salary ranges to align to those numbers), you can look at the average or median of all the compa-ratios at the company.  If the median compa-ratio across the company is, say, 0.90, you know that the company is still about 10 points off from its stated goal. From here, you can do an analysis of which teams, levels, or job types are driving higher or lower compa-ratios.  

How are we doing on pay equity?
This is perhaps the most meaningful usage of compa-ratio.  You can look at the compa-ratios for different groups, whether that's across race/ethnicity, gender, or others, and see whether there are statistically significant differences in the average compa-ratios.  Are men at an average compa-ratio of 1.03, whereas women are at 0.97?  What groups are driving that, and where can we make fixes? 

Of note: It's essential to highlight that when conducting pay equity analysis using compa-ratios, it is crucial to complement the data with additional information about the organization, such as representation data or other contextual factors.

For example, I’ve found in doing pay equity studies that sometimes a minority group has a higher average compa-ratio than their majority counterparts (for example, Black employees coming in at higher compa-ratios than white employees).  At first glance, this may seem like good news - but sometimes it’s because those populations are being held in their roles longer before promotion, so they are getting higher in their ranges at the same level and, therefore, have higher ratios.

Other Metrics Used with Compa-Ratio

There are three metrics I use to look at employees against market salary ranges. Compa-ratio is the primary, but I always pair it with two others:

“Range Analysis”  

This is perhaps not a metric so much as a useful categorical variable.  Each employee is identified based on whether their salary is “Below Range,” “In Range,” or “Above Range” relative to the salary band.  This allows you to see the proportion of your population that might need a market adjustment (the subsequent cost would be to your organization), and alternatively, you can see how many people are your ‘exceptions’ who fall above your stated compensation philosophy.

“Position in Range” 

While compa-ratio is a metric that shows where a salary falls relative to the midpoint, position in range shows you how far a salary falls into a range.  

The equation for it is:

Position in Range =   (Employee’s Salary - Minimum of the Salary Band)
___________________________________________________
               (Maximum of the Salary Band - Minimum of the Salary Band)

Using the example from above, the ‘position in range’ of an employee making $65,000 would be:

$65,000 (salary)  - $56,000 (min)
____________________________________ = .321, or 32.1% into the salary range
$84,000 (max)  - $56,000 (min)  

This equation normalizes a salary into a number between 0 and 1 (or 0% and 100%).  An employee at 100% makes the maximum salary of the range.  Anything negative will be a ‘below-range’ salary, and anything above 100% will be an ‘above-range’ salary.

In conclusion, compa-ratio stands as a pivotal metric in compensation analysis; its ability to measure individual salaries against the midpoint of predefined salary bands provides organizations with a standardized and versatile tool for ensuring internal equity and market competitiveness. 

By offering insights into adherence to compensation philosophies, addressing pay equity concerns, and identifying areas for improvement, compa-ratio serves as a powerful instrument in strategic decision-making.

When coupled with complementary metrics like ‘range analysis’ and ‘position in range,’ it provides a comprehensive understanding of an organization's compensation landscape, allowing for nuanced adjustments and fostering fair and competitive compensation practices. However, the significance of contextual factors in pay equity analysis highlights the importance of a holistic approach to ensure a comprehensive understanding of compensation dynamics within the workforce.

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