Form EEO-1 Summary Compensation Data, Part III

On September 12, 2019, the EEOC announced its intention not to seek a renewal for the Component 2 data collection from the OMB. The notice explains that the original 2016 EEOC estimate of the burden of the collection was less than 1/20th (<5%) of the amount produced by following  GAO and OMB guidelines (recall that the burden estimate was questioned by and used as part of the OMB’s rationale for staying its approval for the collection, and the Court found that to be “arbitrary and capricious”). Using the required methodology, the notice estimates the burden on filers of Components 1 and 2 to be in excess of $1.2 billion for reporting years 2017 and 2018. The notice assigns approximately one-half (½) of the total burden in each reporting year to Components 1 and 2 respectively ($600+ million for each component, both years combined).  The Court’s required ratio (70+%) of actual to eligible filers for Component 2 has been met (76% have filed so far) meaning the Court’s Order to require Component 2, has (by the notice’s estimate) resulted in a $450+ million cost burden on filers. The events which led to the Component 2 collection are devastating examples of poor judgment in understanding the utility or effects of, executing (and preventing the execution of) a policy that might be well intentioned but over-reaches with an “at any cost” justification, and the active enforcement of that policy by a federal judge whose job is to interpret laws, not determine public policy. The current state of the EEO-1 Component 2 saga is that a collection is currently (and indefinitely, though perhaps closing on November 15) underway for reporting years 2017 and 2018 data; the Order requiring that has been appealed to the D.C. Circuit with briefs due in October, and no date set for argument (if any); the EEOC has not indicated whether it will release the data from the current collection; and, the EEOC believes that at this time the burden of the collection on filers greatly outweighs the likely utility of the data.