The Class Match Challenge: Cracking the Code on Government Compensation Benchmarking

Compensation benchmarking is one of the most powerful tools in public sector workforce planning, but it’s also one of the trickiest. At the heart of it lies a deceptively simple-sounding task: matching job classifications. For cities, counties, and other public agencies, this is anything but straightforward. When done poorly, it creates distorted comparisons, misleading insights, and ultimately poor decisions. But when done well, it becomes a strategic advantage.

This post breaks down the core challenges and solutions related to classification matching in public sector benchmark surveys, focusing on how to make the process more precise, fair, and useful. If you’re involved in compensation analysis, workforce planning, HR, or finance in a government setting, this is for you.

The Cost of Mismatched Classifications in Public Agencies

Imagine trying to compare salaries across different cities or counties, only to find that each one calls similar roles by different titles, bundles responsibilities differently, and sets varying requirements. Now imagine trying to make pay decisions or defend labor costs based on that data.

This is the central problem of the class match challenge in government. It undermines the very data that leaders rely on to plan and negotiate. Poor matches can lead to inaccurate market insights, erode trust with employees and unions, stall collective bargaining, and waste significant time and effort.

And yet, it’s a widespread issue. The root of the problem lies in how job classifications are defined and compared across jurisdictions.

Why Classification Matching is So Complex in Cities and Counties

The main complexity stems from the difference between classifications and jobs. A job is what a person actually does—it’s specific, often tailored to an individual. A classification, on the other hand, is a broader grouping that outlines the duties and expectations for a group of jobs within a government agency.

In the public sector, compensation structures are based on these classifications. But market data is based on actual jobs. This disconnect makes comparison difficult. One agency may label a role “Senior Analyst,” another may use “Management Analyst II,” while a third may divide the same scope of work into multiple more specific roles. On the surface, these don’t look alike—but functionally, they may be very similar.

A Better Framework for Matching Public Sector Roles

To bring clarity to classification matching, government organizations need a structured approach. A good class match framework examines three key aspects: job content (what the employee actually does), level of work (how complex or independent the work is), and minimum qualifications (required education, experience, or certifications).

Relying on job titles alone leads to faulty assumptions. Instead, public agencies must dive into class specs, postings, and actual duties. Matching becomes more effective when anchored in substance rather than surface-level comparisons.

Understanding Level of Work in Government Classifications

Among the three factors, level of work is the most misunderstood and often the most critical. For instance, a journey-level Accountant and a Senior Accountant may share tasks, but the decision-making authority, independence, and oversight responsibilities are different.

Level of work looks at how much judgment is required, whether the person supervises others, and whether the role involves routine duties or problem-solving. Misjudging this leads to skewed comparisons. For example, aligning a journey-level city role to a senior-level county role may inflate perceived market pay and cause long-term issues with internal equity and budget planning.

Matching Classifications More Effectively in Public Agencies

Better matching starts with using multiple sources of information—titles, specs, postings, and org charts. Then comes standardizing the comparison process. Many government organizations find success using templates or matrices to document and compare key features of each classification.

It’s also helpful to group classifications by function and level. Instead of finding one-to-one matches, you might cluster similar roles like “mid-level analysts” or “supervisory finance managers.” When matches are unclear, it’s more honest to flag them as partial or unmatched rather than forcing a comparison that doesn’t hold up.

Why Defensibility Matters in Government Compensation Work

Public sector compensation analysis must be defensible. That means you need to clearly document why a classification match was made, what criteria were used, and how it aligns with the agency’s compensation philosophy.

Defensibility is especially important in the public sector, where decisions face scrutiny from employees, unions, leadership, and sometimes the public. The ability to explain a match in simple terms—why a role in your county matches a role in another jurisdiction—is essential.

Consistency also supports defensibility. If your agency applies a certain standard for one department, that standard should be used across others unless a clear rationale justifies a difference.

Embracing Partial Matches in City and County Benchmarking

One of the most important shifts a public agency can make is to normalize the idea that partial matches are not just acceptable—they’re expected. Most government classifications won’t find a perfect mirror in the market. Cities and counties often build roles that blend duties found across multiple job types in other jurisdictions.

Labeling something a partial match signals maturity and sophistication. It shows that your analysis reflects reality. It also opens the door to thoughtful interpretation of the data, rather than pretending everything fits into a tidy box.

What to Do When There’s No Match in the Public Sector

If no direct match exists, there are still smart ways to move forward. You can look at adjacent roles with similar scope, analyze internal relationships between other classifications, or create composites from multiple external matches. What’s critical is documenting your approach clearly and being transparent about the limitations.

The temptation to force a bad match just to complete a spreadsheet undermines the credibility of the entire analysis. In the public sector, where transparency is key, it’s better to admit when no good comparison exists and explain your next best alternative.

Advancing Market-Based Pay Strategies in Government

Ultimately, accurate classification matching is about more than just surveys. It supports a shift toward a market-based pay philosophy in public service—where salaries are informed by credible market data rather than legacy structures.

For public agencies that want to align compensation with labor market realities, attract the right talent, and make data-driven, transparent decisions, investing in better matching isn’t optional. It’s foundational.

Broader Benefits for Cities and Counties

Better class matching does more than improve survey results. It helps governments plan their workforce more effectively, uncover inconsistencies in classification structures, and communicate more clearly with stakeholders. When classifications are well-matched and well-understood, pay equity becomes easier to manage, and long-term planning becomes more strategic.

Whether you’re part of a small city HR team or a large county finance department, the quality of your classification matching directly impacts your ability to make smart workforce and budget decisions.

Moving Forward: Building a Strategic Asset

Benchmarking is only as reliable as the class matches behind it. For cities, counties, and other public sector organizations, careful, thoughtful classification matching turns compensation benchmarking from a frustrating chore into a powerful planning tool.

Perfect matches will always be rare. But a consistent, transparent, and well-reasoned methodology is achievable—and essential. With that, governments can make smarter decisions, build trust, and gain a competitive edge in today’s tight labor market.

Getting class match right isn’t just a technical detail—it’s a strategic imperative for the public sector.