Salary Comparison by City: What Employers Pay Across U.S. Markets

Key Takeaways

• Salaries for the same role at the same level vary significantly across U.S. cities, driven by the cost of labor.
• A P4 Software Engineer in San Francisco earns 21% more than one in Salt Lake City at the median
• Blanket geographic differentials applied across all roles flatten variation that is meaningful and expensive to get wrong
• Location tiering is only effective when it reflects what employers are actually paying in each market

Most city-level salary comparisons are built on the wrong foundation.

Cost-of-living indexes are the default reference for many teams when setting location-based pay. But the cost of living and the cost of labor are not the same thing. Ranges built on living costs rather than labor market data produce salary compression, misaligned tiers, and pay structures that are hard to defend.

What employers pay in a given city is driven by local labor market demand, competition for talent, and candidate supply. Those forces do not move in line with rent or grocery prices. Pave data shows 88% of companies take location into account when determining compensation, but the methodology behind those location adjustments matters as much as the decision to make them.

Why Cost of Labor Is the Right Benchmark

Cost of living measures what an individual spends in a particular city. Cost of labor measures what employers pay to hire and retain talent in that market. For compensation benchmarking, only one of those inputs is relevant.

The two metrics frequently diverge in ways that matter. A Tier 1 city can have disproportionately high living costs without a matching labor cost premium across all functions. A Tier 2 market can command near-Tier-1 salaries for roles where local competition is intense. A uniform cost-of-living adjustment ignores this variation entirely.

Anchoring location pay to the cost of labor preserves pay range integrity, avoids premature compression, and keeps pay competitive where hiring pressure is highest. 

Salary Comparison by City: What the Data Shows

To understand how salaries vary by city, let’s explore benchmarks for three roles across three U.S. market tiers in Pave's compensation benchmarking platform. The tiers are designated based on several factors in Pave's market data, with more weight given to the cost of labor vs cost of living.

The tier gap is not uniform across roles. Software Engineers see a 21% difference between San Francisco and Salt Lake City. Account Executives see 35%. HR Generalists land at 31%. A blanket differential applied across all three would misprice at least two of them.

Boston sits closer to San Francisco than Salt Lake City for revenue roles. The Account Executive gap between Boston and San Francisco is roughly 2%. For Software Engineers, 13%. This aligns with broader geographic pay differential data from Pave, which shows sales salaries typically carry less geographic variance than other job families. Teams that treat Boston as a significantly lower-cost market than San Francisco will put sales offers at a competitive disadvantage.

Account Executive total cash shows the widest spread. The $76,100 gap between San Francisco and Salt Lake City reflects how sensitive sales compensation is to local market dynamics. Variable pay structures and quota design compound the base salary differential across markets.

Cash differentials are only part of the picture. For teams benchmarking total compensation, Pave's data on new hire equity shows geo discounts for equity run significantly wider than those for base salary. Tier 2 metros carry a -29% equity discount vs. Tier 1, while Tier 3 sits at -36%. Compensation teams that benchmark cash by city but apply national equity ranges are likely underpricing offers in Tier 1 and overpaying in Tier 3.

What This Means for Location-Based Pay Design

Tier designations need role-level data. Many teams group cities into tiers based on assumptions rather than function-specific benchmarks. If Boston is tiered significantly below San Francisco, Account Executive ranges will be uncompetitive where the data shows near-parity.

Uniform differentials do not work. Geographic adjustments applied across all roles flatten variation that is functionally meaningful. Benchmarking by role and level within each market produces tiers that are accurate by function. For teams working through how to structure those differentials in practice, Pave's framework for calculating geographic pay differentials covers the methodology in detail.

Tiers require regular maintenance. The competitive landscape for specific roles in Tier 2 and Tier 3 cities shifted materially over the past several years as distributed work expanded hiring pools and increased competition in markets that were previously lower-cost. Static tier structures drift faster than most teams realize.

In Pave, compensation teams can benchmark salaries by role, level, and city using real-time data from thousands of companies, making it possible to build and maintain location-based pay ranges that reflect current market conditions rather than last year's survey cycle.

Build Location Pay Ranges on Data, Not Assumptions

A P4 Software Engineer in San Francisco earns 21% more than one in Salt Lake City. A P4 Account Executive in Boston earns almost the same as one in San Francisco. Those differences are not visible in a cost-of-living index. They only show up in labor market data.

Pave's Market Data Pro gives compensation teams real-time salary benchmarks by role, level, and city, so location tier decisions are built on what employers are actually paying, not what a cost index suggests they should.

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