Five Standards for an OFCCP-Compliant Compensation Self-Evaluation: Standard #3

The third standard is an annual statistical analysis of the organization's compensation system. Here, I will discuss the actual modeling of the compensation system and the importance of "edge factors".

The official guidelines indicate that the statistical model used should incorporate legitimate factors that explain compensation differences within SSEGs. These explanatory factors typically include measures such as length of service, time in job, relevant experience in previous employment, education and certifications, and location. Collectively these factors are referred to by labor economists and statisticians as "edge factors".

Some edge factors will be readily measureable - for example, lenghth of service with the employer - while others are more difficult to quantify. If an factor believed to affect compensation is difficult to quantify a proxy variable can be used. A good proxy variable is one that is easily measurable and is highly correlated with the edge factor for which it is being substituted. Caution should be exercised in the selection and use of proxy variables, as they may not truly reflect what one is intending to measure.

For example, age at hire is sometimes used as a proxy for relevant prior experience if previous employment information is not available. Age at hire is easily measurable, since hire date and date of bith date are typically maintained in human resources databases. We would expect that age at hire would be somewhat correlated with prior experience; "older" workers typically have more prior experience than "younger" workers. However, age at hire may not reflect relevant prior work experience. Further, age at hire does not consider periods of absence from the labor market for such reasons as illness, education, personal reasons, etc., The use of age at hire may introduce a gender bias into the model, as women typically experience greater absense from the labor market than men due to childbearing and child rearing. Thus, using age at hire may overstate the true revelant prior experience for some individuals.

The statistical tool commonly selected for analysis is multiple regression analysis. Multiple regression analysis is one of the preferred statistical techniques because the calculations involved are relatively simple, the interpretation of estimated gender or race "effects" is straightforward, and the entire compensation structure can be expressed with one equation.

The beauty of multiple regression analysis is that this technique estimates the effects of each factor net of all the other factors in the model. In other words, it allows one to estimate how many more dollars of compensation an individual would be expected to receive if (s)he had one additional year of length of service, holding all other factors (such as time in job, education, etc.) constant. This allows the effects to be separated out and examined individually.

When reviewing and evaluating the results of multiple regression analysis, is it important top keep two issues in mind: (1) practical significance and (2) statistical significance. Practical significance refers to the size of the estimated effect to (in this case) compensation. An estimated effect is said to have practical significance if the effect is "big enough to matter". Statistical significance refers to whether the observed effect is the likely outcome of a gender- or race-neutral process. Unlike practical significance, there is a generally accepted "rule" for determining whether an effect is statistically significant. An observed outcome is said to be statstically significant if the probability (or likelihood) of that outcome is "sufficiently small" such that it is unlikely to occur under a gender- or race-neutral process. The commonly accepted definition of "sufficiently small" is 5%, which is equivalent to approximately 2 units of standard deviation.

The guidelines indicate that contractors with 500 or more employees must use multiple regression analysis. However, given the advantages of multiple regression analysis, employers with fewer than 500 employees should consider the use of this technique in their compensation self-evaluation.

A discussion of the fourth standard will be posted on October 26th.