Paying people fairly is good for business. Underpay, and employees
will eventually look for a better offer. Overpay, and the payroll
budget and profitability will suffer. That's why companies use market
data to research the value of their jobs. But what is "market data"
anyway?
To determine the prevailing rate for a job, companies can "benchmark"
jobs against compensation surveys that are detailed and specific
to the companies' industries and regions. A good compensation survey
uses standard, proven methods of data gathering and statistical
analysis to determine how much companies pay for a specific job
in a specific industry. A number of types of organizations conduct
salary surveys, including compensation information businesses, compensation
consulting firms, industry associations, educational institutions,
and state and federal governments.
More than 80 percent of business managers and HR professionals
said their companies either participate in or purchase at least
one salary survey each year, according to a Salary.com poll. Companies
with fewer than 500 employees spend an average of $2,000 annually
on salary surveys, and companies with more than 5,000 employees
spend up to $15,000 or more each year on these important data sources.
Companies
pay for compensation data because the benefits exceed the costs.
The amount companies spend on surveys is just a fraction of a percent
of their total payroll costs. For example, although a company with
5,000 employees may spend $12,000 on compensation surveys, its total
payroll is probably at least $15 million - in which case, their
survey cost would be just eight one-hundredths of one percent of
payroll.
Companies
that participate in surveys (i.e., provide their own compensation
data) customarily receive a discount on the final report. Fees for
compensation surveys range considerably depending on the scope of
the survey (regional vs. national, number of jobs surveyed, etc.).
Participants could pay as little as a few hundred dollars for a
small regional survey, or a few thousand dollars for a comprehensive,
national survey. Perhaps the most expensive surveys are very specific
regional surveys - those that pinpoint a very particular segment
of the recruiting marketplace. Regardless of the survey, non-participants
typically pay more than participants.
How
do researchers conduct compensation surveys?
Surveys
are conducted on a semiannual, annual, or biennial basis. Surveys
normally fall into one of two categories: Custom and standard. Custom
surveys are ones that attempt to answer very specific questions
from a narrow selection of peer companies (e.g., What is the prevailing
pay rate for salespeople in the pharmaceuticals business in the
Northwest?). These custom surveys tend to be available to, and used
by, the participants only. Standard surveys, on the other hand,
are often published each year and attempt to cover the same range
of companies and jobs. These broad surveys are sometimes sold to
non-participants and made available to members or customers of the
survey sponsor/vendor. This focus of this article is primarily on
standard compensation surveys.
The
process of collecting data and producing a salary survey takes careful
planning and execution that requires economic investment, people
resources, and time. Some companies conduct surveys in-house using
their own staff and compensation experts. However, most companies
contract a third party to collect the data and do the number- crunching.
The third-party approach provides a level of independence that most
participants want. Some salary surveys are co-sponsored to attract
more participants and to add credibility to the numbers. An experienced
data provider in survey methods and statistical analysis is expected
to put out high-quality, reliable, accurate data.
Conducting
a salary survey is a time-consuming task. A traditional survey of
15 companies encompassing 20 positions can take between 6 and 12
weeks from the initial planning to the time the survey is distributed
to participants. For a survey that includes more participants and
more positions, it could take as long as four to six months. Survey
respondents then have two to six weeks to complete the questionnaire.
The length of time depends on the number of positions surveyed and
the amount of information requested for each incumbent (person in
a given job). After the data has been collected, it can take two
to three months to analyze the data and make the findings available
to the survey participants and others. Therefore the time from initiating
a survey and providing results can be up to 7 months or more.
Traditionally,
survey questionnaires are mailed out in paper form or on a diskette
to participants, namely company managers or executives and human
resources professionals, who will then complete and return the survey
before a predetermined closing date. When assessing the methodology
of a survey, it is important to look for the number of surveys mailed
out, the number of participants, and the number of employees in
the report summary. These numbers determine whether the survey is
representative of the jobs and the industry. Also, there are several
ways to collect and summarize data and it is critical that the user
understand the underlying assumptions of a particular survey to
assure its data is being used properly. It is even more critical
when using multiple surveys to be sure that comparisons or compilations
are done appropriately - on an "apples-to-apples" basis.
Compensation survey checklist Here are some considerations to weigh for a company who is deciding
whether to purchase a compensation survey.
The background of the survey research firm and cosponsors,
if any. Look for reputable firms that follow proven methods
to gather and analyze compensation data.
The scope of the survey. Look for studies that cover
industries, jobs, and regions that are most applicable to your
purposes; and that provide data on enough jobs to be cost-effective.
The survey methodology. Review the summary of the
methodology to make sure it's consistent with standards set forth
by reputable industry associations such as WorldatWork. Be especially
sure the research organization is surveying human resource professionals
or other people knowledgeable about compensation information within
a company, rather than individuals.
The number of participants in the survey. A good survey
should cover a representative number of companies for its target
population. A survey doesn't have to cover the entire industry
or region to be robust; even a few dozen responding employers
in some industries can provide enough data for a valid survey.
The names of participants. Look for your competitors
and peers. For many jobs, you may be competing for candidates
with companies in different industries but the same geographic
area.
The number of incumbents covered by the survey; and the
sample size for each salary. A sample size of 30 or more is
more statistically significant than a sample size of 10, provided
the sample is representative of the statistical population.
The relevance of the job descriptions to the positions
being benchmarked. Look for a good match between the survey
and your company. Be sure to compare job descriptions, not just
job titles.
The effective date of the survey data. The date a
survey is published is always later than the effective date of
the data within the survey. If necessary, age the data from the
effective date to the current month.
Multiple survey sources. As with any form of research,
it is important to use multiple data sources to narrow in on the
"true" answer. Relying on a single source can be misleading if that
source doesn't perfectly reflect the market in question. WorldatWork
suggests that compensation analysts should use multiple data sources
wherever possible; consulting firms and academics agree. The exceptions
come when there is only one data source, or when there is a spot-on
data source, such as a custom survey, that truly describes a precise
market.
Number of participants. Make sure the participants are
a good sample of the recruiting market. Generally, eight to ten
participating companies is a good sample for positions below the
management level. The sample size should increase the more senior
the positions being surveyed, both to get a good representation
and to allow for more job matches, since each company is organized
differently. There could be limited pay data in some industries,
or the available data might not be representative of the industry
because of a low participation rate in the survey.
Some firms reveal a list of participants, or at least those
well known within the industry. The surveying company may disclose
big-name participants to draw more interest from smaller companies.
A list of major employers can also add credibility to the survey.
An important exception to note is that if a compensation analyst
or compensation consulting firm is using multiple surveys to produce
their own derivative market numbers, they will aggregate the data
by combining the surveys, placing differing weight on different
sources and sometimes even making a qualitative adjustment. When
the data has been aggregated in this manner, it is not customary
to report numbers or names of participants.
Participant profiles. The usefulness and relevance of
a salary survey depends largely on the survey participants. For
a small company, a salary survey of large corporations in the United
States will be less helpful in determining what to pay employees
than a survey of smaller organizations. Of course, a small company
in a "company town" may find itself in a position to have to pay
the same wages as the predominant employer in that town.
Survey participants can be quite different, depending on the
goal of the survey. If the survey covers pay in large companies
in different geographical locations, the surveying company has to
make sure that companies participating in the survey are of similar
size but from different locations.
To
participate or not to participate Here are some considerations for a company to weigh when deciding
whether to participate in a compensation survey
The prisoner's dilemma. Every participant improves
the quality and the validity of the results. Your company's participation
can stimulate others to participate. In other words, if nobody
puts data in, nobody gets data out.
Granularity. If your chief competitors are participating,
it may make sense for you to participate as well so that an industry
cut includes more statistically significant numbers.
Validity. If your company is one of a few players
in a region or an industry, your participation could make a significant
difference in the validity of the data.
Cost. Participants usually receive a significant discount
on the price of the survey.
Ease of use. Some surveys are easier and less time-consuming
to fill out than others.
Security. Gauge your comfort level with the means
of collecting the data. For instance, electronic mail is less
secure than some forms of Web-based transmission; paper and pencil
is less efficient but more comfortable to some respondents.
Added value. If you have participated in similar surveys
over the same period, or have purchased similar surveys, make
sure the survey in which you are considering participating adds
value, such as some key jobs that are difficult to benchmark.
Job
descriptions. Just as it is important to find surveys that compare
companies of a similar stature, it's also important that the jobs
being surveyed are comparable to the job being benchmarked. When
consulting a compensation survey, match the job descriptions rather
than the job titles, even if the survey uses generic or widely used
job titles. For example, an associate could be an entry-level position
at one consulting firm, or it could be the title for someone with
an MBA at another. Companies are structured differently, and different
companies use different names for the same jobs, so job descriptions
are the best way to match positions. Beware of surveys that use
only job titles, as it is unlikely the data will be a reasonable
representation of the jobs you're interested in.
A survey
job description should list the primary job function in one or two
sentences, followed by key responsibilities. While the descriptions
should be generic and not specific to any one company, they should
contain enough information for participants to match appropriately
to ensure the data is accurate. It is also important to match the
organizational level of the positions be surveyed. A position that
is at the group level at one company may be at the subgroup or the
sector level at another.
Job
titles are broken down differently in different surveys. Some surveys
break them down by levels within the organizations, i.e., senior
management, middle management, and entry level. Positions may also
be broken down by job families or the types of responsibilities,
i.e., business development, marketing, product management, and sales.
Compensation data. There are many things to consider
when analyzing the compensation components of a salary survey. Because
companies have different pay structures, compensation data is collected
in ranges as well as actual pay. Salary surveys can provide employers
more information on the marketplace and how to set competitive pay
without overpaying or underpaying employees. Surveys should ask
for the minimum, midpoint, and maximum for the surveyed positions,
in addition to the actual base salary paid.
Usually, the prevailing practice for any one job is to pay
a range of incomes. As a result, although the median pay for a job
is likely to be a definable number, the range is just as important.
Companies pay employees differently for various reasons. It could
be the company's pay philosophy; or it could be the geographic location
or the industry practice; or it could be the incumbent's length
of service or proficiency in the job. Whatever the reason, it is
unlikely that two companies will pay an employee doing the same
job exactly the same amount.
When reading the base pay figures, it's important to check
how the numbers are calculated. The surveying parties can dictate
to the participants how the numbers should be reported. Salaries
can be on an annual, monthly, or hourly basis. For example, if the
incumbent is a contract employee, hourly salaries are more relevant
than an annual figure. The survey may request pay data for individual
incumbents or averages for all incumbents matching a specific job
description, depending on the types of surveys and their objectives.
Incentives/bonuses. Look at both the actual annualized
payments and the target level expressed as a percentage of base
pay when evaluating incentives or bonuses. This allows for adjustments
for atypical incentives and bonuses. Be sure to understand what
is included in this figure and how it's collected. Although there
is not a right or wrong definition of what is included in this category,
it is important to understand how your numbers compare with those
reported. In that sense, you need to know what it represents.
Other payments. As compensation changes, salary surveys
are changing to include other forms of compensation such as profit
sharing and stock grants. For more senior-level positions, long-term
incentives are just as important as base salary. For example, an
executive's compensation package at a startup company can be made
up of mostly stock options rather than cash compensation. For a
survey to represent the total compensation, it needs to take into
account the cash valuation of stock options.
It is important to spend a little time learning how these stock
option numbers are reported. It is also important to note that with
stock options, the value may be a number, such as grant value, that
is presented as a dollar amount but is not a present value and therefore
cannot be added to base pay and incentive pay to provide a total
direct compensation number.
Effective date. For those surveys conducted on a regular
basis, such as annual surveys, the effective date will be until
the next survey is released in the following year. Otherwise, knowing
the effective date of the survey can prevent companies from using
outdated salary figures and causing error in pay budget forecasts.
If the survey is not current, the person using it should age
the salaries to the current date. If a survey was conducted in September,
the salaries are likely to be as of September or even August. If
you are using the survey in December to benchmark for a new position
in the company, you will have to age the number. A simple way to
do this is to take the annual rate at which salaries are moving
for this job and prorate it, salary increases overall this year
are around 3.5% but this may vary by job title.
A similar approach is used in setting pay levels across a company.
Sometimes these figures are set at the beginning, middle, or end
of the company's payroll year by aging the appropriate compensation
data to those dates.
What
about nontraditional sources of data, such as individuals?
Traditionally, pay data is collected by sending forms to human resources
professionals and sometimes business managers, those most knowledgeable
and authoritative when it comes to pay within their companies. Data
provided by corporate representatives is more accurate than data
reported by individual employees because employers have a strong
business incentive to report data accurately and consistently for
a wide range of jobs. They also have experience and understanding
of the process so that the data they provide tends to be relatively
"clean."
The growth
of Web-based data collection methods has made it technologically
feasible and cost-effective to gather compensation information from
individuals. These new sources often report data that is valid and
real. It is, of course, not the same as data collected and reported
by trained compensation professionals; and like traditional sources,
these alternative sources vary in depth, quality, relevance, and
other measures of integrity.
Conventional
wisdom has always dictated that compensation data from individuals
and recruiters is unavoidably, perpetually biased. Yet this hypothesis
has been difficult to test because the data has not been prevalent.
Although some see an incentive to exaggerate one's own salary or
that of one's most recently placed candidate, a significant misstatement
could backfire. Further, by assuring confidentiality and providing
additional information, individuals could be convinced to provide
very accurate data. Data from alternative sources, such as recruiters
or individuals, can be good or bad. When good, recruiter data can
be used as an accurate indication of what new-hires are being paid,
and individual data should approximate the general market. However,
both forms of data are still clearly different from company reported
information. Combining the different sets of numbers may be deceiving
but comparing them side-by-side can be revealing.
Why
pay for compensation data when it's available for free on the Web?
Compensation
information is becoming widely available on the Internet, either
for free or for a fee most individuals can afford. Many of these
sources provide accurate, timely information. Yet just as different
types of financial services firms fill different consumer needs
at different prices, not all data products are alike. A Web-based
brokerage business might provide the tools and information a user
needs to make investment decisions, but without the one-on-one,
custom consulting to shape a personal investment strategy. While
some investors are satisfied with data only, others are willing
to pay for the added value these consulting services provide.
Similarly,
companies are willing to pay more for compensation data that provides
the granularity of detail they need or the information that's hardest
to find. They look for the name and reputation of the organization
conducting the survey; the number of companies surveyed; the number
of "incumbents," or employees covered by the study; the names of
the companies that participated in the survey; and other measures
of data quality and relevance to their industry. A middle-market
professional compensation data tool might offer market-pricing information
on commonly priced jobs, providing aggregated information based
on primary and secondary research and analysis.
The top-of-the-line
product in the compensation data business is a custom study of what
each individual job within a company should pay based on very specific,
targeted market data. A large company could pay hundreds of thousands
of dollars, or more, for this type of in-depth companywide analysis.