Fair Pay Act of 2009: Implications for Compensation Modeling - Part 2

In Part 1, I provided a summary of two compensation modeling approaches: (1) the human capital approach and (2) the direct determinants approach. To recap:
If one had access to all of the data on the direct determinants of compensation and ran an ideal model, one would find that compensation is perfectly preducted by these factors, and that no protected class status variable would enter the model. That is, the model would show no gender or race effects on compensation. Under these conditions, could one infer that no discrimination exists? No- discrimination may exist either as disparate impact or as disparate treatment.
We turn first to disparate impact- the case in which a neutrally applied practice differentially affects the protected and unprotected classes.

In the above scenario, we cannot infer that no discrimination exists. Each of the factors (direct determinants) in our model represent a compensation decision or "other practice". One could look at any of the factors in the model and may be able to show that the underlying decision or other practice had a disparate impact on the protected class.

Statistically, one would be able to determine whether or not disparate impact exists by simply removing one of the factors from the model and adding a variable for protected class status. The protected class coefficient would then measure the average effect of the removed factor on the compensation of protected class members. If the model shows that protected class status is statistically and practically significant, one can infer that the removed factor has a disparate impact.

This would shift the burden to the employer, requiring the employer to demonstrate that there is a valid business necessity for the use of the factor and that no equally valid alternative with less impact is available. Assuming that the employer could validate the use of such a factor, the analytical inquiry into disparate impact may continue.

Consider the case of "department". Assume that department is considered an "other factor" under the Fair Pay Act. There may be a valid business justification for considering department in compensation decisions, but assume that the plaintiff offers prima facie evidence that the decisions regarding department into which an employee is assigned considers factors that cause disparate impact on the protected class.

The practice being challenged as having a disparate impact would then shift from a direct determinant (department) to an indirect determinant (factors considered in assigning an employee to a department).

It would seem as though if an indirect determinant has a disparate impact on a direct determinant, and that direct determinant has a disparate impact on compensation, one could infer disparate impact. That is, if any direct determinant has a disparate impact, it would raise the question of whether any of its indirect determinants has a disparate impact.

An interesting question arises if the direct determinant of compensation does not have a disparate impact, but the indirect factor does have a disparate impact on the direct determinant (i.e., the impact of the indirect factor is offset by the effects of other indirect factors on the direct factor). The decision of whether this would constitute actionable disparate impact is one the Courts must eventually resolve. In the context of the classical theory of the disparate impact of "tests", one could consider the direct determinant to be the "test" and the indirect determinants to be the components of which the test is constituted. Under that concept, the components themselves would not be subject to challenge, unless the "test" (i.e., the direct determinant) has a disparate impact.

All of this, however, assumes that perfect data exists for all factors affecting compensation decisions and all other employment decisions. In reality, this is seldom the case; data on some factors simply may not exist, and data for other factors may not be found in any reasonably accessible form.

In the case in which we infer disparate impact, the protected class status variable will enter into the model along with all of the measurable direct determinants of compensation. The coefficient associated with the protected class variable will measure the disparate impact on the protected class as a result of all the unmeasured determinants (both direct and indirect) of compensation. The protected class coefficient expresses the "bottom line" impact of all of the determinants for which no data exists. The effects of each of the unmeasured determinants are not - and in fact cannot - be separated from one another. Under the 1991 Civil Right Act, this would be an acceptable statistical analysis if and only if data on these determinants is not available.

In Part 3, we will turn our attention to disparate treatment claims.