Compete in your market - at a fair price

Job matching methodology

Global vendors often use the same job matching methodology and data collection process, making their surveys an attractive choice for firms operating worldwide. Local vendors may use benchmarking approaches tailored to national or regional needs and usually attract more domestic participants.

It therefore makes sense to assess the size of the respective database, its participants and the availability of job specific benchmark data for every country that you are interested in. There is no one-size-fits-all approach.

Collective bargaining agreements are an alternative source for salary comparisons since pay in some markets - especially Europe - is heavily regulated through labour agreements that cover a large employee population.

In a market like Germany, the strongest salary survey databases contain about 500,000 individual data points whereas the labour agreements for the metal and electronic industry alone cover more than 3,500,000 employees. In some cases, this data might be the better fit, at least for non-exempt jobs, and is often accessible at no cost.

gradar aligns strongly with collective bargaining agreements and we’ve captured the correlations in our own compensation survey rosetta stone documentation.

The reliability of the job matching process

Job matching is about identifying benchmark jobs from compensation surveys in order to assess the market price of a position. The validity and reliability of a market benchmark depends on the quality of the job matching between internal and external jobs.

There are four common types of job matching methodologies used today, though some elements may be combined into a unified approach:

• Matching by job title. This approach is most common for vendors that collect data directly from individuals and not from HR and Compensation professionals. As a result, it’s the most unreliable approach and is only feasible with a minimum level of accuracy for very generic jobs or professions such as plumbers or accountants. Some vendors even add personal characteristics such as job tenure or age to the analysis.
• Matching by benchmark job. Vendors use this approach by adding the core duties and responsibilities to a benchmark job profile, though it is fairly uncommon. Different ranks need separate descriptions, which makes this a fairly accurate approach. However, it’s difficult to select a better-fitting match if a higher or lower description does not exist.
• Capsule job descriptions. This is the enhanced version of matching by benchmark job, where a survey vendor would describe the main responsibilities and core duties in much more detail and outline modifiers for lower or higher levels of requirements. This approach is most common in club surveys.
• Job families with a non-analytical job leveling methodology. Most vendors today use this approach, with the quality of the generic job family descriptions satisfactory in most surveys. However, the more sub-families available, the more scattered the job matching and the less reliable the results. The quality of the generic job level descriptions varies a lot between and within surveys. The non-analytical approach is not as accurate as analytical job evaluation results and should not be used for pay equity decisions or as basis for compensation structuring.

Culpepper, our strategic partner, uses a combination of job classifications and levels (Executive, Management, Professional Individual Contributors and Support Individual Contributors) with comprehensive job family descriptions for Executives, Engineering, Health Care, Life Sciences, Operations, Sales and Technology roles.

Accurate job matching is the foundation of salary benchmarks and market comparisons. And matching an internal job to an external benchmark job of similar content is the key to accessing benchmark data from salary surveys. The benchmark data can be used to compare employees' salaries against market rates and may be included in the modelling of pay bands and in the determination of a competitive ratio of base and variable pay.

• gradar automatically suggests benchmark jobs that are likely to be a good fit to the position that you have evaluated.
• gradar is compatible with benchmark jobs from a number of locally and globally recognized publishers where the appropriate surveys for your organization cover relevant industries, jobs and markets.
• gradar enables you to store the job grading and job matching decision online.

Data in compensation surveys

Compensation survey market data is not a representative source of information. There can be no guarantee that conclusions drawn from a certain sample will extend to the population as a whole. Salary reports should always be used with care!

The population of a survey is made up from either the participating companies’ incumbent data, submitted by HR, or direct submissions from employees. It’s constantly changing, so vendors are faced with the challenge to maintain a certain level of consistency in their benchmark salaries over a period of time. To do this, the providers use different approaches to analyze and prepare their salary reports.

Market values from descriptive statistics

This approach represents salary information through percentiles:

• Based on pure data input
  Data is collected and described as is. There is no editing of the population or removal of data points. Vendors that follow this approach face volatile results in an annual comparison since the population rarely remains stable. This becomes more of a problem as the population size decreases.
• Based on edited data input
  Automated computer programs - or human analysts - edit the data by removing ‘outliers’ in order to keep median values relatively stable. A deviation of median values by +/- 5% may be shown over a given period of time.

The latter approach is likely to work well with submissions from HR or Compensation professionals as the vendor’s analyst will have someone to discuss the data with. The vendors who use direct inputs from employees will be fishing in the dark and may have to rely on the statistical analysis alone, without being able to validate the data input.

gradar and Culpepper collect compensation data directly from HR professionals in participating organizations. We combine the power of data analytics with the experience of their Compensation Specialists to ensure that data is both accurate and reliable.

Market values from statistical models

Statistical data is calculated through regression analysis and significance testing to show a possible market salary. This approach is only used in a few surveys.

Market values from experience

Data and experience from candidate interviews are combined to show market salary ranges. This approach is often used by local and global recruiting agencies.