Salary Research Tools: Where HR Teams Find Defensible Wage Data

What Makes a Compensation Data Source Defensible

It's a Tuesday afternoon and the VP of Engineering needs a job posted for a Network and Computer Systems Administrator — today, ideally, because the hiring manager already lost one candidate to a faster-moving competitor. The company has an office in Denver, which means Colorado's pay transparency law applies: the posting needs a real salary range, not a placeholder, and that range needs to hold up if anyone ever asks where the number came from.

So you open three tabs. Glassdoor shows one number, built from self-reported employee submissions. A free trial of a paid survey tool returns a second number, higher, with no visibility into the underlying sample. A quick search turns up a blog post citing a BLS figure that's two vintages old. None of these numbers agree, and only one of them was built to survive a records request.

This is the moment most HR teams realize that not all salary research tools are doing the same job. Some are built for job seekers browsing casually. Some are built to sell subscriptions. One type — government occupational wage data — was built from the start to be checked, cited, and defended. Here's how the major sources actually differ, and which one to reach for when the posting has to hold up.

"Defensible" doesn't mean expensive, and it doesn't mean precise to the dollar. It means you can answer three questions if someone — a board member, outside counsel, a state labor department — ever asks: Where did this number come from? How was it collected? When was it last updated? A source that can't answer all three isn't disqualified from informal use, but it shouldn't be the number sitting inside a compliance PDF or a board deck. That standard is what separates the sources below, more than any difference in sophistication or cost.

Mapping the Salary Research Tools Landscape: Government Data, Paid Surveys, and Crowd Reports

Most salary research tools fall into one of three buckets, and the bucket matters more than the brand. Government occupational wage programs — the U.S. Bureau of Labor Statistics' Occupational Employment and Wage Statistics (OEWS) program and Statistics Canada's Employee Wages by Occupation data — collect wages directly from employer-reported or survey-based establishment data, publish methodology documentation publicly, and are free to use because the underlying work is funded as a public good. Proprietary survey tools, like Payscale and Salary.com's CompAnalyst, aggregate compensation data from contributing employers or individuals under a private methodology, then sell access; the data can be richer in some dimensions, but the sampling and weighting aren't publicly auditable the way a federal statistical program's are. Crowd-sourced platforms, like Glassdoor, collect self-reported figures from site visitors with no employer verification step at all.

A fourth "tool" belongs in this list honestly: a spreadsheet and a manual download from the BLS website. It's still the most common salary research setup at small and mid-sized companies, and it isn't a bad one — the data underneath it is the same government dataset described below. What it's missing isn't accuracy; it's a timestamped record of when the download happened and what vintage it reflected, which is exactly what gets asked for later.

BLS OEWS: The Free Dataset Built for Compliance Documentation

The OEWS program is the closest thing to a universal baseline for U.S. salary research tools, and it's worth understanding what's actually inside it before using it. It covers approximately 830 detailed occupations, drawn from a rolling sample of about 1.1 million establishments collected over a three-year period. Estimates are published annually with a May reference date and cover the nation, every state, Washington D.C., the territories, and roughly 530 metropolitan and nonmetropolitan areas. The May 2024 estimates classify occupations using 22 of the 23 major groups in the 2018 Standard Occupational Classification system — military occupations are excluded. On response rates, the combined semiannual panels came in at 65.7% by establishment and 65.9% by weighted employment, covering roughly 55% of total national employment.

That's not a perfect census of every job in America, and OEWS doesn't claim to be — but it's a transparent, documented, government-run sampling process, which is precisely what proprietary survey tools generally don't disclose at the same level of detail. For the Network and Computer Systems Administrator role from the hook, OEWS (SOC 15-1244) put the median annual wage at $99,130 as of May 2025 — a single, sourced, dated number that can sit directly inside a compliance PDF without anyone needing to take your word for where it came from. For a full walkthrough of how to pull and read the underlying tables, see how to read BLS OEWS data.

Because OEWS is a public-domain federal dataset, there's no license fee attached to using it — a real structural difference from tools built on proprietary survey data, where access itself is the product.

Statistics Canada NOC: The Northern Equivalent

For Canadian employers, or U.S. companies hiring across the border, the equivalent backbone is Statistics Canada's National Occupational Classification (NOC) system paired with its Employee Wages by Occupation dataset. The NOC comprises more than 40,000 job titles organized into 516 unit groups across six TEER (Training, Education, Experience, and Responsibilities) categories. Canada's Job Bank determines annual wage estimates for each of the 516 NOC (2021) occupations at the national, provincial, territorial, and economic-region levels, drawing primarily on Statistics Canada's Labour Force Survey. The underlying Employee Wages by Occupation dataset is published under the Open Government Licence – Canada and updated annually — the same public-domain, zero-licensing-cost structure as OEWS, just run by a different national statistical agency.

For a Canadian employer building a range for a role that touches Ontario's new posting requirements or British Columbia's existing ones, this is the dataset that mirrors what OEWS does for U.S. roles. A full breakdown of how the classification system and wage tables fit together is in the Statistics Canada NOC wage data guide.

Where Paid Survey Tools Like Payscale and Salary.com Fit

Payscale and Salary.com's CompAnalyst are both proprietary, survey-based tools, and they serve a real purpose: finer-grained cuts by company size, industry, or specific skill combinations than a government occupational table can offer, plus a planning layer built around that data. What they don't offer is a public, published sampling methodology you can hand to an auditor the way you can hand over an OEWS technical notes page.

Pricing is also genuinely murky, and worth naming honestly rather than guessing at. Payscale is sales-gated with no public list pricing; third-party buyer-transaction data gives a sense of the range rather than one number — Vendr reports a median buyer price of roughly $15,893–$15,947 per year, while SpendHound separately reports an SMB average (50–1,000 employees) of roughly $11,915–$13,888 per year versus an enterprise average (1,000+ employees) of roughly $100,236–$107,413 per year. Salary.com/CompAnalyst doesn't have a comparably verified public figure at all, so no number is cited here. Treat any of these as a starting point for your own sales conversation, not a stable published price. A direct side-by-side on methodology and fit is covered in BLS vs. Payscale data.

Why Glassdoor Numbers Don't Survive an Audit

Glassdoor occupies a different lane entirely. It's crowd-sourced, self-reported data aimed at job seekers comparing offers informally — not an audited or government dataset, and not built as a compliance-documentation tool. There's no establishment-level verification step, no disclosed sampling frame, and no way to confirm that a given submitted figure reflects base pay, total compensation, or something else the submitter interpreted loosely. That doesn't make it useless for a quick gut-check on market sentiment. It does make it a poor citation for a number that needs to hold up next to a state disclosure requirement or a board question about methodology. The fuller comparison is in Glassdoor vs. BLS salary data.

Building a Repeatable Research Process Instead of a One-Off Search

The real fix isn't picking one winning tool out of this list — it's being deliberate about which salary research tools answer which question. Government data (BLS OEWS for U.S. roles, Statistics Canada NOC for Canadian ones) is the right baseline for anything that needs to be defensible: a posted range, a compliance PDF, a board-facing pay-equity summary. Paid survey tools can add texture on top of that baseline for roles where market nuance matters more than documentation. Crowd-sourced data stays useful for informal sentiment-checking and nothing load-bearing.

This is also the structure the SalaryRange app is built around rather than trying to replace: occupation typeahead seeded from local BLS OEWS tables at every tier in the U.S., and from Statistics Canada NOC tables at the Professional tier and above in Canada, so the government baseline is already in the system rather than something pulled manually each time a role opens. For teams that want to run this process on a spreadsheet first, the Compensation Benchmarking Spreadsheet in the template store is a structured starting workbook built around the same government-data approach, meant to be filled in by hand or alongside a SalaryRange account.

For a broader look at how compensation tools and software fit together, the tools and software resource hub rounds out this comparison. And if you'd rather have new wage-data vintage releases and salary research breakdowns land in your inbox than go hunting for them, that's exactly what the newsletter is for.