Remote work salary adjustments link an employee's pay to where they live, not where the company is headquartered. The approach sounds straightforward until you try to implement it: two engineers doing identical work for the same company get paid differently based on their zip code. According to a 2025 LinkedIn Workforce Report, 68% of fully remote companies now apply some form of location-based pay adjustment — up from 41% in 2022. The policy is becoming standard. How companies apply it, however, varies widely and creates real differences in who they can hire.
This guide explains the two dominant models for remote work salary adjustments, how companies build geographic pay bands, and the practical trade-offs each approach creates for hiring.
What Are Remote Salary Adjustments?
Remote salary adjustments are systematic reductions (or increases) to a base compensation figure based on an employee's location. The premise: if you hire an engineer from Austin instead of San Francisco, and the local labor market for that role is 20% cheaper, you can pay 20% less and still be competitive in that market.
In practice, most adjustments are downward relative to a high-cost hub baseline. Companies headquartered in San Francisco or New York set their pay bands around those markets, then apply location multipliers that reduce pay for employees based elsewhere.
The key policy question is not whether to adjust — most companies with distributed teams do — but which data to adjust against:
- Cost of living — How much does it cost to maintain a comparable standard of living in this city?
- Cost of labor — What does the local market actually pay for this role and level?
These two questions produce different numbers, and the difference matters.
Cost-of-Labor vs Cost-of-Living: The Core Difference
| Dimension | Cost-of-Labor Model | Cost-of-Living Model |
|---|---|---|
| **Data source** | Local market salary surveys (Levels.fyi, Radford, LinkedIn Salary) | City cost indices (Numbeo, ERI, NerdWallet Cost of Living) |
| **What it measures** | What engineers in that city are paid by competing employers | How much it costs to maintain a standard of living (housing, food, transport) |
| **Typical adjustment range** | 5-20% below Tier 1 for mid-cost US cities | 15-35% below Tier 1 for mid-cost US cities |
| **Example: Austin vs SF** | Austin mid-level SDE: ~85% of SF rate | Austin CoL index: ~65% of SF, so pay is 65-70% |
| **Company examples** | Google, Meta, Apple | Many pre-IPO startups |
| **Candidate perception** | More defensible in negotiation | Often perceived as unfair; harder to justify to candidates |
Cost-of-labor models are more common among large public tech companies because they benchmark against actual competing offers. If a Stripe engineer in Denver is being recruited by Google's Denver office and other Denver tech employers, the relevant comparison is what those employers pay — not what a Denver apartment costs.
Cost-of-living models tend to produce steeper cuts. A candidate in Raleigh, NC could face a 30-35% reduction from a pure cost-of-living calculation, versus a 10-15% reduction under a cost-of-labor approach, because Raleigh's tech talent market has grown significantly even as housing remains cheaper than SF.
Key insight: The model you choose defines how you compete for talent. Cost-of-labor keeps you competitive against other tech employers in that city. Cost-of-living keeps your payroll budget down but may make your offers uncompetitive in growing tech hubs.
How Companies Set Geographic Pay Bands
Building a defensible geographic pay framework requires three inputs: a primary pay benchmark, a location adjustment methodology, and a tier classification system for your cities.
Step 1: Set your anchor market
Most companies anchor to their headquarters city or the US national median for remote roles. San Francisco is the most common anchor for tech companies; New York for finance and media roles.
Step 2: Choose your data sources
For tech salary benchmarks by role and level, industry-standard sources include:
- Levels.fyi — Crowdsourced total compensation data by company, role, level, and city. Most accurate for FAANG-adjacent benchmarks.
- Radford / Mercer — Enterprise compensation surveys, subscription-based, used by HR teams at public companies.
- LinkedIn Salary Insights — Broader industry data, useful for non-engineering roles.
- Glassdoor / Payscale — Public, less precise, useful as a sanity check.
For benchmarking methodologies in detail, see our guide to benchmarking tech salaries.
Step 3: Build your tier structure
Most companies define 3-5 geographic tiers. A practical three-tier US structure:
| Tier | Cities | Adjustment |
|---|---|---|
| Tier 1 | San Francisco Bay Area, New York City, Seattle, Boston | 100% of base |
| Tier 2 | Austin, Denver, Chicago, Miami, Los Angeles, Washington DC | 88-92% |
| Tier 3 | All other US locations | 75-82% |
Step 4: Define how and when adjustments apply
Critical questions to answer in writing before rolling out the policy:
- Does location affect base salary, total compensation, or both?
- When does an adjustment trigger? On hire? On relocation?
- What happens if an employee relocates without prior approval?
- Is the adjustment floor-limited (i.e., can pay only go down, not up, on relocation)?
Tier Structures: Real Examples from Tech
Publicly documented approaches from known companies:
Google uses a cost-of-labor model with city-specific pay bands. Engineers in the same level band earn different amounts based on their office location, with adjustments ranging from approximately 15% below the Bay Area rate for Austin to 5-10% below for New York. Remote employees are mapped to the pay zone of their primary work location.
GitLab (fully remote, public company) uses a location factor that combines cost-of-labor data from Radford with country-level adjustments. Their methodology is documented in their public handbook and uses a benchmark floor of 50% of the San Francisco rate as a global minimum.
Buffer applies a cost-of-living adjustment using Numbeo data, published openly. All employee salaries are public. Their location factor ranges from 1.0 (San Francisco) to as low as 0.45 for certain lower-cost countries.
Stripe uses market-rate pay bands by city for on-site roles but requires remote employees to choose from a set of approved work locations, each with a defined pay rate. Employees who moved during COVID and chose lower-cost cities had their pay adjusted accordingly.
How Location-Based Pay Affects Offer Acceptance
Location-based adjustments have measurable effects on candidate behavior. LinkedIn data from 2025 shows fully remote roles with location-based pay adjustments receive 31% fewer applications than fully remote roles without them, all else equal.
For recruiters, the practical implications:
Transparency up front reduces friction. Companies that disclose their location pay methodology in job postings (now required by law in California, Colorado, and New York) report fewer late-stage negotiation breakdowns. Candidates self-select based on pay bands rather than discovering a gap at offer stage.
The gap between listed and delivered compensation matters. If a candidate in Denver sees a job listing with a $140,000-$180,000 range and receives a Tier 2-adjusted offer of $126,000, they feel misled — even if the math is disclosed. Either narrow the listed range or add explicit language: "Compensation is location-adjusted. For remote candidates, final offer will reflect your work location."
Senior and specialized candidates are most sensitive. Engineers at Staff+ and Principal levels, and specialists in ML, security, and distributed systems, have the most competing offers and the clearest sense of their market value. Aggressive location cuts on these roles produce high counter-offer rates.
For a complete view of what goes into a competitive offer, see how to write a job offer letter that accounts for all compensation components — including how to frame location-adjusted numbers clearly. And beyond salary, consider what engineers actually value beyond salary in your total package.
How Nextmantra AI Approaches This
Location scoring is one of the five parameters Nextmantra AI uses when evaluating candidates against job requirements. When a recruiter defines a role with a specific location or remote policy, the AI accounts for candidate location fit as part of the overall match score — not as a binary pass/fail, but as a weighted factor alongside skills, experience, and education alignment. This means a candidate in a Tier 3 location applying for a Tier 1-anchored role with location-adjusted pay shows up in results with their location explicitly noted, so recruiters can factor compensation expectations into their shortlisting before the interview stage. Fewer location-driven offer surprises means fewer late-stage withdrawals. See how Nextmantra AI handles this
Frequently Asked Questions
What is the difference between cost-of-living and cost-of-labor adjustments?
Cost-of-living adjustments reduce pay proportionally to how much cheaper it is to live in a given location relative to a high-cost hub. Cost-of-labor adjustments reduce pay based on what comparable talent commands in the local market. Cost-of-labor models are more defensible in negotiations because they reflect actual competing offers — not just housing prices. Cost-of-living models typically produce larger cuts.
Can a company legally pay remote employees less based on their location?
Yes, in most jurisdictions. In the US, pay differences based on geographic location are generally legal as long as they do not create disparate impact on protected classes. Some states (California, Colorado, New York) require pay transparency in job postings, which may expose differentials publicly. Always consult employment counsel when implementing geographic pay adjustments, particularly across multiple states.
How do I handle pay adjustments when an employee relocates?
Define your relocation policy before someone requests it. Standard practice is to apply the new location's pay band at the start of the next fiscal year or six months after relocation, whichever comes first. Retroactive immediate pay cuts are highly damaging to employee trust. Grandfathering current employees at their existing rate while applying location-adjusted rates to new hires in the same location is a common but temporary compromise.
Do remote salary adjustments apply to contractors?
They can, but the legal framework differs. Contractors (1099 in the US) typically negotiate rates independently, and geographic adjustments are a matter of commercial negotiation rather than company pay policy. However, misclassification risk increases when a company applies employment-like pay structures to contractors. For a full cost comparison, see the contractor vs. full-time cost guide.
What geographic tiers do most tech companies use?
Most large tech companies use three tiers: Tier 1 (SF Bay Area, NYC, Seattle, Boston — 100% of base), Tier 2 (Denver, Austin, Chicago, Miami — 85-92%), and Tier 3 (all other US locations — 75-82%). Companies with significant global remote teams, like GitLab, use country-level adjustments on top of city-level tiers, producing 10+ distinct pay zones worldwide.
How do fully remote companies publish their pay approach?
Transparent remote companies publish their compensation philosophy publicly. Buffer publishes every employee's salary formula. GitLab's handbook details its location factor methodology using Radford data combined with Numbeo. Basecamp eliminated location-based pay in 2021, paying all US employees at the 90th percentile of San Francisco rates regardless of location — a bold choice that simplifies administration but increases labor cost.
What is the impact of location-based pay on candidate pipelines?
Aggressive location-based cuts reduce candidate acceptance rates. LinkedIn's 2025 data shows fully remote roles with location-based pay adjustments receive 31% fewer applications than fully remote roles without them. The trade-off is cost efficiency vs. talent pool breadth. Companies competing for specialized engineering talent feel this most acutely.
Conclusion
Remote work salary adjustments are now standard practice, but how you implement them determines whether they save budget or cost you candidates. A cost-of-labor model anchored to real local benchmarks is more defensible and less damaging to offer acceptance rates than cost-of-living cuts. Define your tier structure, document the policy publicly, and disclose it early in the hiring process — late-stage compensation surprises are one of the most avoidable causes of candidate withdrawal.
Ready to evaluate candidates with location already factored in? [See Nextmantra AI in practice](https://nextmantra.ai/platform)
Sources: LinkedIn Workforce Report 2025; GitLab Handbook (compensation methodology); Buffer Salary Formula (buffer.com); Google pay band public disclosures; Numbeo Cost of Living Index 2026; Levels.fyi engineering compensation database 2026.