Are free salary benchmarking sources accurate?

You Google your job title to find out what it pays. Glassdoor says $145K. Robert Half says $175K. PayScale says $138K. LinkedIn Salary says "$130K–$170K." Levels.fyi (if your company's listed) shows $180K. Indeed says $152K average.

Six sources, five very different numbers, and a $50K spread. Which is "accurate"?

The honest answer is that none of them is — and all of them are. Each free source measures something real, but measures it imperfectly, and measures a different slice of the market. Understanding how each gets its data is the only way to read them critically and arrive at a number you can act on.

The two main categories of free salary data

Free salary data sources split into two families with very different methodologies:

1. Recruiter / agency salary guides — Robert Half, Hays, Michael Page, Frazer Jones. Annual or semi-annual publications, downloadable as PDFs.
2. Crowd-sourced platforms — Glassdoor, PayScale, LinkedIn Salary, Levels.fyi, Indeed Salary. Continuously updated databases of user-submitted reports.

There's a third quieter category — official data from ABS, the Fair Work Ombudsman, and industry bodies — which we'll get to.

Each category has a fundamentally different relationship with the data, and therefore fundamentally different biases.

How recruiter salary guides get their data

The big four AU recruitment firms — Robert Half, Hays, Michael Page, and Hudson — each publish annual salary guides. The methodology is broadly:

1. Survey thousands of professionals and employers each year (usually 8,000–15,000 respondents in AU)
2. Combine the survey responses with the firm's own placement data (actual offers extended to actual candidates)
3. Aggregate by role and metropolitan area
4. Publish low / median / high ranges

Strengths:

• Real placements. When Robert Half reports "Senior Software Engineer Sydney: $180K–$220K," at least some of that comes from offers they actually placed. That's stronger than self-reported data.
• Employer-side signal. Surveys include the buying side, not just the selling side. You see what employers say they're willing to pay, not just what employees claim to earn.
• Annual rhythm. Predictable update cycle; published reports become anchor reference points in industry.

Weaknesses:

• Selection bias toward agencies' clients. Recruitment firms place candidates at companies that pay agency fees. Those companies skew toward the upper end of the market (they're paying 15–25% of first-year salary to a recruiter). Direct-hire markets — startups hiring through their own networks, big tech with internal talent teams — are underrepresented.
• Survey self-selection. Respondents are usually senior, salary-conscious professionals connected to recruiters. The median worker who's never spoken to a recruiter doesn't appear.
• 6–18 month lag. Data collected in late 2025 reports on salaries set in earlier review cycles, published in early 2026, read through to early 2027. By then the market may have moved 10–15%. (More on this in salary benchmark vs salary survey.)
• Aggregated to broad categories. "Senior Software Engineer" is one row. It doesn't distinguish backend vs frontend vs ML, startup vs enterprise, fintech vs adtech.

Recruiter guides are best read as a ceiling estimate for the agency-placed market. If your offer matches, that's a good sign you're in the upper-quartile market. If it lags by 15–20%, that's defensible — you might be in a different (often direct-hire) submarket.

How crowd-sourced platforms get their data

Glassdoor, PayScale, LinkedIn Salary, Levels.fyi, and Indeed all rely on user-submitted salary reports, sometimes augmented by job-listing data.

The model is roughly:

1. Users sign up and submit their company, role, and pay
2. The platform aggregates, anonymises, and reports by role + company + location
3. Some platforms verify (Levels.fyi accepts offer-letter screenshots); most don't

Strengths:

• Coverage. Glassdoor has hundreds of millions of submissions globally. PayScale aggregates tens of millions. The raw data volume dwarfs what any agency could collect.
• Granularity (sometimes). Per-company data is the killer feature. Glassdoor can tell you "Senior PM at Canva" specifically, not just "Senior PM in Sydney." Recruiter guides can't.
• Currency. Continuous submission means recent data points are weeks old, not quarters.
• Free granular access. Most are free past a small contribution gate.

Weaknesses:

• Self-selection bias is strong. People who report tend to be either proud (recently moved, got a strong offer) or aggrieved (feel underpaid, want validation). The contented median is rarely represented.
• No verification. Most platforms accept whatever you type. There's no check on accuracy. PayScale and Glassdoor estimate they include outliers and entry errors in their aggregate, but can't filter them all.
• Australian samples are often thin. Levels.fyi has millions of US data points but maybe a few hundred Australian senior engineers across all companies. Glassdoor's AU coverage outside Sydney/Melbourne thins out fast.
• Role-title noise. "Senior Product Manager" at one company is "Product Manager II" at another. Users self-classify; the platform doesn't normalise.
• Stale entries linger. A 2022 submission still appears in aggregates years later, weighting older market conditions equally with current ones unless the platform actively decays old data.

Crowd-sourced platforms are best read as a good indicator of direction and magnitude — and an excellent comparative tool between companies — but should not be used literally as your negotiation anchor.

The quieter category: official + industry data

Two often-overlooked sources:

• Australian Bureau of Statistics (ABS) — publishes Average Weekly Earnings, Earnings and Hours of Employees, and Characteristics of Employment. Authoritative methodology, broad coverage, but coarse: ANZSIC industry categories don't map cleanly to specific tech roles.
• Fair Work Ombudsman (FWO) — pay guides for award-covered roles. Less relevant for tech but critical for award workers.
• Industry-body data — TIA (Technology Industry Association), ACS (Australian Computer Society), and similar publish periodic compensation snapshots specific to tech.

These won't give you a Senior Engineer-at-Atlassian number, but they're an excellent sanity check: if every other source says $200K but ABS data for the broader industry centres at $130K, the gap is worth investigating.

A worked comparison

For the same role — say, Senior Software Engineer in Sydney — here's roughly what you'd see across sources in late 2026:

Source	Typical range	Anchored to
Robert Half guide	$180K–$220K (base)	Agency-placed market, 2025-cycle data
Hays guide	$170K–$210K (base)	Survey + placements, 2025-cycle
Levels.fyi (AU companies)	$165K–$250K (TC)	Self-reported total comp, public tech
Glassdoor	$135K–$185K (base)	Self-reported median, broad sample
LinkedIn Salary	$145K–$195K (base)	Opaque, LinkedIn user data
ABS (Programmer category)	$110K–$160K (median band)	All employer types, broader role

Notice: the highest and lowest numbers differ by ~$140K. None of them is wrong — they're answering different questions about different submarkets. The spread is the information.

What does "accurate" actually mean?

Accuracy depends on the question:

• "Is this role in the $150K or $250K ballpark?" Almost any source will get you a defensible answer within 20%. Free sources are highly accurate at this resolution.
• "What should I anchor my counter-offer to?" No single source is accurate enough. You need the intersection of multiple sources, weighted by what they each capture well.
• "What does my specific employer pay for this specific role?" Glassdoor's per-company data is your best free starting point, but verify against at least one other source — single submissions can be outliers.

A useful mental model: each free source has a confidence band that's wider than it admits. A Glassdoor number listed as "$150K" is really "$150K, plus or minus $20K, with 60% confidence." Three sources independently landing within $10K of each other gives you a much narrower combined confidence interval than any one of them alone.

The triangulation answer

Professional compensation teams don't rely on one source — they buy three to five paid surveys (Mercer, Aon Radford, WTW, etc.) and triangulate. The free-source equivalent is roughly:

1. One recruiter guide for the upper-quartile picture (Robert Half or Hays)
2. One crowd-sourced site for company-specific and currency (Glassdoor + Levels.fyi if your company's listed)
3. One official source for sanity-checking (ABS industry data)
4. One live signal — current job ads for the same role at similar employers; this is the most underused free source

If three of four sources agree within ~10%, you have a defensible anchor. If they disagree by 20%+, the role has high variance — usually because of unstated dimensions (specialisation, leadership scope, equity-heavy compensation).

This is exactly what a live salary benchmark does for you — pulls from multiple sources, weights them by recency and relevance, and gives you the intersection rather than asking you to do the cross-referencing by hand. EvenBetter Salary uses this approach by design; see our methodology for the full source list and weighting logic. (See salary benchmark vs salary survey for how this differs from reading a single guide.)

The honest summary

Are free salary benchmarking sources accurate? Yes, each one is — at the resolution it's designed for. None is accurate enough on its own to drive a real negotiation. The right way to use them is the way compensation professionals use paid surveys: as inputs to a triangulation, where the intersection is the answer.

A salary survey is not the ground truth. It's one more data point. The more data points you gather — and the more honestly you read each one — the closer you get to a number you can actually walk into a conversation with.