DCI Consulting Blog

The California Fair Pay Act: Does $0 Really Mean $0?

Written by Michael Aamodt, Ph.D. | Nov 3, 2015 2:41:26 AM

Prior to the recently enacted California Fair Pay Act (CFPA), federal and state laws and regulations allowed employers to justify sex differences in salaries by using statistical methodologies to control for legitimate pay factors such as education, experience, and performance. Although the CFPA provides defenses for compensation differences based on seniority systems, merit systems, earnings systems based on quantity/quality of production, and bona fide occupational factors (other than sex), the law directs employers to explain the entire pay differential between men and women. This unprecedented requirement has already resulted in confusion within the employer community.

One of the most common questions we are already receiving from clients is, does the word “entire” actually mean a $0 average difference in salary between men and women?

The standard used by the courts (in cases tried under Title VII of the Civil Rights Act of 1964) has always been that the observed difference in salary must be statistically significant.  That is, the observed difference from $0 is not likely to be due to chance. For example, a contractor finds that there is a $1,500 average difference in salary between male and female administrative assistants and that difference is statistically significant (i.e., t ≥ 2.00).  After running a regression analysis that controls for years of experience and education, the difference drops to $400, which is not statistically significant (i.e., t < 2.00).  Years of case law are clear that the statistically non-significant difference would not be viewed as pay discrimination under Title VII guidelines for pay enforcement.  Would this statistically non-significant difference, however, be viewed by the CFPA as being illegal?  DCI has never seen a situation in which a regression analysis resulted in an average difference of exactly $0.

What about a job title in which there are too few employees to run a regression analysis?  Take, for example, a situation in which a male employee was paid $55,000 and a female employee $53,000. Both have the same amount of experience but the male employee has a master’s degree whereas the female employee only has a bachelor’s degree.  Does the master’s degree explain the “entire” $2,000 difference?  What if the master’s degree was actually valued at $5,000 per year? Does the male employee now have a case for pay discrimination?

We can’t wait for the first test of this concept to go to court – with a non-DCI client, of course.

By Mike Aamodt, Principal Consultant, and Jana Garman, Consultant at DCI Consulting Group