Calculating Adverse Impact

Adverse Impact Analysis is a quick and easy to use tool that can estimate adverse impact using a variety of both statistical and practical tests.  It includes tests that have been historically recommended by Federal regulators as well as cutting edge tests arising out of the latest research. 

Adverse impact is defined by the Uniform Guidelines as a substantially different rate of selection in hiring, promotion or other employment decision which works to the disadvantage of members of a race, sex or ethnic group (see Question & Answer #10).  Operationally, the Uniform Guidelines (see Section 4D) define adverse impact as:

A selection rate for any race, sex, or ethnic group which is less than four-fifths (4/5) (or eighty percent) of the rate for the group with the highest rate will generally be regarded by the Federal enforcement agencies as evidence of adverse impact, while a greater than four-fifths rate will generally not be regarded by Federal enforcement agencies as evidence of adverse impact. Smaller differences in selection rate may nevertheless constitute adverse impact, where they are significant in both statistical and practical terms or where a user's actions have discouraged applicants disproportionately on grounds of race, sex, or ethnic group. Greater differences in selection rate may not constitute adverse impact where the differences are based on small numbers and are not statistically significant, or where special recruiting or other programs cause the pool of minority or female candidates to be atypical of the normal pool of applicants from that group. Where the user's evidence concerning the impact of a selection procedure indicates adverse impact but is based upon numbers which are too small to be reliable, evidence concerning the impact of the procedure over a longer period of time and/or evidence concerning the impact which the selection procedure had when used in the same manner in similar circumstances elsewhere may be considered in determining adverse impact. Where the user has not maintained data on adverse impact as required by the documentation section of applicable guidelines, the Federal enforcement agencies may draw an inference of adverse impact of the selection process from the failure of the user to maintain such data, if the user has an underutilization of a group in the job category, as compared to the group's representation in the relevant labor market or, in the case of jobs filled from within, the applicable work force.

Four-Fifths Rule: The four-fifths rule (a.k.a. 4/5ths rule or 80% rule) is the simplest and most common way of estimating adverse impact.  It also appears to be the preferred method of enforcement agencies.  Unfortunately, it does not estimate whether or not adverse impact truly exists as accurately as one would like.  It tends to indicate adverse impact exists even when it does not (a Type I Error).

Statistical Tests: Statistical tests are designed to control Type I error (i.e., the error of determining adverse impact exists even when it does not).  However, statistical tests are most accurate when the sample is relatively large and balanced.  This is often not the case in adverse impact analyses since they are comparing majority vs. minority groups and the number of hires are typically not very large.  Although statistical test can control or avoid the error of indicating adverse impact exists even when it does not, statistical tests tend to make the opposite error - indicating adverse impact does not exist when, in fact, it does (a Type II error).

Practical Tests: These tests help guide decisions regarding the existence of adverse impact when samples are small.  They help balance the error associated with the 4/5ths rule and the limitations of statistical test as a result of small samples.

The four-fifths rule (or impact ratio) and statistical tests often do not indicate the same end result.  When sample sizes are smaller (which is often the case in the context of adverse impact calculations), the 4/5ths rule is more likely to indicate adverse impact exists than statistical tests are.  This often results in defendants arguing that statistical tests are more appropriate and that the impact ratio should be ignored because it is prone to Type I error whereas plaintiffs will argue that the 4/5ths rule should be used and that statistical tests should be ignored because they have low power and are prone to Type II error.  In situations such as this, it is important to consider the strengths and weakness of all methods as well as the probability of Type I and Type II error.

Adverse Impact Analysis is a very simple and easy to use tool that can calculate adverse impact using the four-fifths rule, statistical tests, and practical tests.

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