Tracking Salary Trends Through Job Posting Data: A Competitive Intelligence Perspective

Introduction

A company recruiting senior data engineers in London, Singapore, and Bengaluru is competing in three different talent markets. The salary range it advertises in one city may be competitive, while the same compensation offer could be unremarkable elsewhere.

Now consider what happens when this information is examined across several competitors and tracked over multiple quarters. Differences in advertised salaries can reveal how employers position themselves in the talent market, which occupations are becoming more expensive to recruit for, and where compensation expectations are changing.

This makes salary information in job postings more than a recruitment detail. It can become a useful source of competitive intelligence. For market researchers, investment teams, and business intelligence platforms, the opportunity lies in understanding what compensation patterns reveal about competition between employers.

1. Advertised Salaries Are Signals of Employer Positioning

When companies advertise salary ranges, they communicate something about their compensation expectations for a particular role. Consider two technology companies recruiting for similar Senior Software Engineer positions in the same city.

Metric	Company A	Company B
Advertised minimum	$110,000	$125,000
Advertised maximum	$145,000	$160,000
Seniority	Senior	Senior
Location	New York	New York

Company B is advertising a higher compensation range.

This could indicate a different approach to attracting experienced engineers, a greater willingness to compete on pay, or differences in the responsibilities and skills associated with the position. The comparison becomes more useful when repeated across several roles and observation periods.

A company that consistently advertises higher ranges for comparable positions may be positioning itself aggressively in the talent market. Another company may maintain lower ranges or change its advertised compensation as recruitment conditions evolve.

However, advertised salaries represent employer-stated or estimated compensation, not confirmed employee earnings or accepted offers.

2. Three Salary Patterns That Reveal Competitive Intelligence

Salary data becomes particularly valuable when examined across three dimensions.

A. Employer pay positioning

Businesses can compare what competitors advertise for similar occupations and seniority levels.
For example, a financial services company may advertise higher compensation for cybersecurity specialists than other businesses in its sector.

This creates several research questions:

• Is the company consistently advertising higher pay for specialist positions?
• Does the difference exist across multiple departments?
• Is its compensation positioning changing over time?
• Are competitors increasing their ranges in response?

For sales intelligence platforms, this can provide useful context about companies competing for expensive specialist talent. For investment researchers, it can help examine changes in advertised labour costs.

B. Geographic compensation differences

The same occupation can command different advertised compensation across cities and countries. A company recruiting software engineers in London, New York, and Bengaluru may advertise substantially different salary ranges. Comparing these differences can help businesses understand:

• Where specialist talent is more expensive to attract.
• Which locations offer comparatively lower advertised compensation.
• How competitors position salaries across geographic markets.
• Whether compensation differences are changing over time.

This information is relevant to companies evaluating new hiring locations, consulting firms conducting market research, and investors examining changes in workforce costs.

C. Role and skills premiums

Not all capabilities carry the same compensation expectations. For example, two software engineering positions may have similar titles but different requirements. One may emphasize general application development, while another requires expertise in cloud security, distributed systems, or machine learning.

Examining compensation alongside advertised skills can reveal whether certain capabilities are associated with higher salary ranges. This is useful for understanding specialist talent competition and identifying areas where employers may be willing to advertise higher compensation.

3. Why Historical Salary Trends Matter

A single salary range provides a snapshot. A series of salary observations provides a much richer view of the market. Imagine that the advertised salary range for a particular occupation increases across several quarters.

That change could indicate:

• Increasing competition for experienced talent.
• Changing skill requirements.
• A shift in the seniority profile of vacancies.
• Different employers entering the same talent market.
• Changes in the geographic distribution of recruitment.

For investment and alternative data research teams, this creates an additional source of evidence. For example, a research team monitoring a group of technology companies could examine changes in advertised compensation for AI, cybersecurity, and cloud engineering positions.

The resulting trends could support hypotheses about changing talent costs or competition for specialist capabilities. They should not be interpreted as direct evidence of rising payroll expenses or financial performance. Advertised salaries are only one part of workforce economics.

A 2025 NBER study examining US pay transparency requirements found that employers increased the share of job postings containing salary information by 30 percentage points in the affected settings. The researchers also reported wage increases of 1.3% to 3.6% across the datasets examined.

This illustrates why salary disclosure itself matters when analysing compensation trends: changes in what employers publish can influence both the availability of salary data and competitive behaviour.

4. What Data Do Companies Need to Analyse Salary Competition?

A salary range without context is difficult to compare. For meaningful competitive intelligence, buyers need compensation information connected to the underlying job record.

Data field	Intelligence value
Employer name and ID	Compare compensation across companies
Job title and occupation	Compare similar roles
Seniority and experience	Account for differences in responsibility
Salary minimum and maximum	Understand the advertised compensation range
Salary currency	Compare compensation in the correct monetary unit
Salary stated or estimated indicator	Distinguish employer disclosures from estimates
Location	Compare geographic talent markets
Skills	Identify capabilities associated with higher advertised pay
Employment type	Distinguish full-time and contract roles
First-seen and last-seen dates	Monitor the vacancy’s observed lifecycle
Historical snapshot date	Compare salary observations across periods

For example, an analyst comparing senior engineering salaries across London and New York needs to account for occupation, seniority, currency, and observation period. Without these attributes, differences in the dataset may reflect different roles or recruitment conditions rather than genuine compensation positioning.

The distinction between employer-disclosed and estimated salaries is particularly important. Both can add value, but they should remain separately identifiable in research and reporting.

5. How Propellum’s Salary Data Supports These Use Cases

Propellum provides job data infrastructure with salary information connected to employer, occupation, seniority, location, skills, and time.

Its Salary Intelligence API supports current compensation intelligence, including salary ranges, role-level compensation, geographic salary information, and market benchmarking.

The Job Records API provides the wider vacancy context, while the Historical Dataset supports comparisons across previous periods.

These products allow customers to work with salary observations as part of a broader job data dataset rather than treating compensation as an isolated number.

For a compensation benchmarking platform, this can support comparisons across employers and locations.

For a competitive intelligence company, it can help monitor how advertised compensation changes among selected competitors.

For an investment research firm, historical salary information can become an additional variable in research models examining workforce costs and competition for specialist talent.

Explore Propellum’s Salary and Compensation Intelligence to understand how salary data can be used across roles, industries, companies, and geographies.

6. Understanding the Limitations of Job Posting Salary Data

Salary information is valuable, but it should not be treated as a complete representation of the labour market.

Research by the Federal Reserve Bank of Minneapolis, analysing approximately 140 million US job postings from 2012 to 2018, found that only 13.5% contained wage information. More than half of the salary observations contained ranges, which were often relatively wide.

The researchers also found that salary disclosure varied systematically across employers and occupations.

This has several implications:

• Salary disclosure rates differ across markets and employer groups.
• Advertised ranges may cover a wide compensation interval.
• Salary information can be estimated rather than employer-disclosed.
• Bonuses, equity, benefits, and variable compensation may not be included.
• Different job descriptions may represent different levels of responsibility.

Therefore, salary intelligence should be evaluated using both compensation values and the context in which those values appear.

A reliable comparison distinguishes advertised pay from estimated values and avoids interpreting a small or unrepresentative sample as a complete market benchmark.

Conclusion

Salary information in job postings offers a useful perspective on how companies compete for talent. When examined across employers, occupations, locations, and time periods, it can reveal differences in compensation positioning, geographic pay patterns, and changing demand for specialist capabilities.

For competitive intelligence and alternative data users, the opportunity is to incorporate these observations into a broader understanding of company and market behaviour.

The value is not simply knowing what salary a company advertises today. It is understanding how that compensation compares with competitors, where the differences exist, and how the market is changing.

Advertised salaries are more than numbers attached to vacancies. They are observable signals of how employers position themselves in the competition for talent.

Frequently Asked Questions