When was the Last Time You Adjusted Your Salary Structures for Compensation Planning?

Get ready for 2011 merit increases and compensation planning. Historically, at this time of the year, most compensation professionals on a fiscal calendar planning cycle begin “kicking off” their communications and planning calendars. Many anticipate this year to have the highest merit increases since the 2007 financial crisis, after “tightening the belts” the past few years.

Two things will be required to have an effective compensation planning cycle for 2011: Job Evaluations and Salary Structure Adjustments. Since workers are doing more with less, their jobs have likely changed, creating new hybrid jobs. Job evaluations to re-validate core benchmark jobs serve as a foundation for determining the salary structure adjustments. Unfortunately, there are no shortcuts here. Let’s take, for example, a retail organization in West Lafayette, Indiana, named Jenny Jeans. Two benchmark jobs, Warehouse Worker and Merchandise Marker, are representative of 15% of the company’s workforce and are in the same salary grade. The Compensation Analyst conducts a job analysis doing an on-site visit and concludes these two jobs have melded together to become a hybrid job subsequent to job losses experienced at the organization and is now titled Warehouse Merchandise Specialist. The new hybrid job responsibilities are weighted 40% Merchandise Marker and 60% Warehouse Worker.

Now, we want to market-price the core benchmark jobs (Cashier, Packer, and Warehouse Merchandise Specialist) in this salary grade and determine the adjustment to this grade within Jenny Jeans’ overall salary structure. To do this you will need to have access to recent salary survey data. Once you have the external data, market-price the benchmark jobs, calculate the differential between the market data and the actual internal pay rates and determine the adjustment to the midpoint or control point of the salary grade that is aligned with overall business objective and strategy. (Note, the market data has been adjusted to the January 2, 2012, effective planning date for the merit increases.) The minimum and maximum of the grade should be adjusted keeping the current spread (unless you are considering redesigning the overall structure, which is beyond the scope of this blog).

This process will need to be repeated for each salary grade in your salary structure. If you have any questions about the tools used in these explanations, please contact ERI at 800.627.3697.

New ERI White Paper

ERI Economic Research Institute’s recent white paper, “Five Technical Aspects of Compensation Data” by Jonas Johnson, Senior Researcher, is available for download via www.erieri.com.

In this white paper, Johnson explores the curvilinear nature of data, outliers, wage fluctuation, accurately aging data, and heteroscedasticity.

“Compensation professionals should consider these factors prior to setting pay practices,” writes Johnson. “Ignoring them could result in less than optimal data analyses, potentially resulting in a loss of talent or overpayment for labor.”

Technically Speaking… Does Excel Always Know What is Best For Your Compensation Data?

The use of Microsoft Excel in the business compensation environment for performing quick and easy calculations has become ubiquitous, making the need to cite usage statistics pretty much meaningless. I often cut and paste data into a spreadsheet for some quick compensation data analyses, even when I have much more powerful statistical software packages at my disposal.

It was during one of these exercises that something odd in my Excel spreadsheet jumped out at me. I was reviewing a long list of salaries for Non-Profit Executive Directors, focusing on the 10th percentile, and the values reported by Excel were, in my opinion, well, illogical.

So, I tried an experiment comparing the results from Excel 2007 to my “powerful” statistics software (SAS 9.2). I then discovered the SAS software has five different algorithms for calculating percentiles, while Excel presents a single result.

Rather than start with a long list of salaries, I started small. I went to the Internet and grabbed the first 35 numbers in the Fibonacci Sequence. I ranked them in order and just took a guess at what the 10th percentile might be.

With 35 numbers, N/10 = 3.5, and I came up with two guesses at the 10th percentile; either the 4th number in the sequence (which is 2) or, if interpolated, halfway between the 3rd and 4th numbers (which is 1.5). I decided my best answer would be 2.

Let’s take a look at the actual results.

All five SAS results fall in line with my original guesses (between 1.5 and 2). However, Excel reports the percentile falling nearly halfway between the 4th and 5th numbers – the difference is up to 60% in this example!

What is going on? Well, Wikipedia[1] notes that Excel uses an “alternate” method to calculate percentiles. While these numbers are small, just a 0.9 difference, think about it in terms of salary planning. Multiply these numbers by $10,000, and now that 10th percentile is off by an amazing $9,000!

Conclusions? Well, first, I will be very cautious using Excel to calculate percentiles. The fact that this discrepancy was obvious in a list of actual salaries does lead me to recommend when analyzing important data using Excel, take the time to ensure the results make sense. Attributing a $9,000 discrepancy to Excel’s quirky percentile algorithm may not get you very far.

Of broader concern is that Excel doesn’t provide any information in Help, or elsewhere, letting me know what choices it is making for me in regard to my data. I had assumed that it just knew what was best and, by not giving me any choices or even documenting the option it uses, I had no reason to question its authority. Now I do.

1 Wikipedia – Percentile. Retrieved 6 June, 2011 from http://en.wikipedia.org/wiki/Percentile