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Many candidates/professionals in accounting, business, finance, investment management, human resource management, project management, etc., use data provided by the professional body they hold a designation from or may aspire to, for example, the American Institute of Certified Public Accountants [AICPA], Chartered Professional Accountants [CPA Canada], The Association of Chartered Certified Accountants [ACCA], Chartered Institute of Management Accountants [CIMA], Project Management Institute [PMI], Global Association of Risk Professionals [GARP], Institute of Management accountants [IMA] CFA Institute, Association of Accounting Technicians (AAT), Institute of Chartered Accountants of England and Wales (ICAEW), & Association of International Certified Professional Accountants [AICPA], etc., to gain insights regarding salaries professionals command in the market (an example of reports provided by these institutes: salary-survey).
However, professionals should always evaluate figures provided by these bodies critically, as done in analysis-related work, utilizing judgment & common sense.
Data provided by the abovementioned bodies, more likely than not, can be biased upward (practically), not representing the whole picture with absolute clarity.
One factor commonly noticed is that salary data provided in official reports isn't always compatible with data available on other external sources such as government compiled statistics, payscale.com, industry reports, wages reported on indeed, etc.
This condition leads many to rely on the official reports produced by the Institutes that they are a member of, or aspire to be a member of, leading many to hold inaccurate estimates regarding the market value of their credentials or the credential(s)/Certification(s) they may be studying to acquire.
For example, suppose Isabella has heard from her friends that finance & investment analysts are paid very handsomely in her country. She may do some research and find out that the CFA designation is the most common designation held by financial analysts & is usually a requirement to enter the industry. To confirm whether financial and investment analysts are paid very handsomely, she may search salaries online for definitive answers.
However, anyone looking at a few wage-related resources would realize that the different sources present conflicting figures, i.e., PayScale may state an average salary for a designation as $75,000 p.a., while government statistics may state a mean of $65,000 p.a. & the Institute may present an average figure of $269,000 p.a. Thus, members/students, in such a situation, may rely more on figures presented by their professional body, rather than relying on other statistics.
Nonetheless, such an approach may not be ideal.
The reasons why salary figures in reports presented by professional bodies may not reflect real-world expectations candidates should hold:
One fundamental factor that most people should be aware of is data collected by the bodies discussed in this report having a longer right tail, i.e., being right-skewed; this is because the top earners usually earn substantially higher. One person from a group of 100 earning, say, $20 million, with the other 99 receiving a salary of $100,000, would push the average salary of the group up to $299,000; still, for 99% of group members, this figure would be 199% higher than their compensation, the compensation most receive.
Most individuals holding professional designations, of course, work in corporations, businesses, etc. These organizations are pyramid-like structures, where few people reach the top, commanding high salaries; nonetheless, the majority doesn't command nearly as much as those holding top, c-suit positions.
Therefore, if ten professionals who hold, say, the PMP designation, become CEOs, from a sample of 1000, the data of the whole group would be skewed right, increasing the mean substantially; if the 10 CEOs are omitted from the sample, and a trimmed mean is calculated, the results would present a more accurate figure, a value that the majority would actually be earning.
However, figures (positive outliers) considerably higher than what most designation-holders actually earn are included in the salary analyses reports presented by bodies discussed in this report, as a higher average salary attracts new candidates, and new candidates are the primary source of income for bodies issuing professional designations.
"Exaggerated figures motivate current students to complete the program and attract new candidates."
Those working at large organizations & earning handsomely are also more likely to respond to salaries surveys, whereas those that consider themselves to be in a transitional position, or those earning below the 25th percentile, are least likely to respond.
As we can see in figure 1, about 75% of the individuals included in the report earned lower than the mean of $296,000; 25% of participants that earned considerably more than the majority—some earning more than $10 million—skew the data rightwards. If a trimmed mean was calculated, i.e., excluding the top 5% of earners, as explained above, the mean value would be reduced considerably.
Hence, as explained above, the figure shared here shouldn't be quoted to a new student as compensation that they should expect once they complete their programs, nor should current charterholders expect to earn the mean value presented, as the majority, 75% of participants included in the compensation report, earned less than the mean.
Thus, charterholder should understand that the probability of earning lower than the mean value, as presented in the compensation report in figure 1, is about 75%; a supposition that charterholders, the majority of them, should earn a compensation close to the presented mean would be erroneous.
Furthermore, reported incomes that are considerably lower than what the professional bodies' internally estimate are likely to be omitted from the data analyzed, with the reason cited being 'underemployment or "poor fit" for the job position,' meaning that the institutes may omit low reported incomes, making an assumption that such respondents aren't employed in a position that utilizes their full skill set.
Thus, substantially lower wages (compared to the institutes' internal estimates) are omitted from wage reports, as institutes & bodies want the average compensation figures to represent incomes received by only those that are—as per their viewpoint—fully utilizing their skillset.
Similarly, data values considered 'outliers' or those not holding job titles considered suitable can also be omitted from the analysis to present favorable figures. For example, Project Management Institute's [PMI] Salary Survey (10th Edition) [publicly available] collected data from 45,346 respondents; however, 27% of collected figures were eliminated [p. 316, methodology], with reasons presented being similar to ones discussed in this report:
Arguably, such omission tactics are reasonably employed when a Certified/Chartered professional is employed as, say, a store cashier. Nonetheless, the same tactic can also be used to omit a percentile of income data that the subject body or Institute considers to be substantially lower than their estimates; they may thus—conveniently—omit lower reported incomes citing that such professionals are underemployed, job positions aren't the best fit, data collected is an outlier (positive outliers are usually not omitted), or 'omitted to maintain quality of the data collected,' whatever that might mean. . .
Such a tactic can skew the data to the right, and that, of course, increases the average salary value, which they then report publicly to attract new students to take up the examination & membership of their body.
Sample selection bias is another issue that can skew data when data collection is done through third parties or the collection process is delegated externally. For example, an institute may hire, say, an external HR consultancy to collect data on people who hold designations issued by the Institute.
However, if the external data collecting party has more available data points in a particular region (city, province, state, etc.), or a particular size of firms (more larger firms than midsized/smaller firms), etc., then the possibility of sample selection bias increases.
In conclusion, those interested in professional qualifications should understand that the average/mean figures presented by such bodies—with the obvious yet tacit signal being that those who get Certified/Chartered receive the mean figure—aren't what a new student or member should expect to earn in the market.
Due to a number of issues, as discussed in this work, candidates & new members should expect compensation packages considerably lower than the average figure publicly presented by bodies/institutions discussed in this report.
Candidates should look at all figures critically, not accepting material with a clear marketing agenda—not an epistemic motivation.