A growing issue today is identifying demographics, not of just applicants, but also employees. While typically 1% to 2% of each group may not identify, today anecdotally it is being found at 10% to 15%. There is a conflict today with 1960 and 1970’s regulations and today’s workforce that prefer no labels. Further, because of government reporting requirements and unknowns both by gender and ethnicity/race in the workforce, HR professionals are becoming more hesitant to override employee self-identifications.
On the gender side, it is becoming more problematic as Non-Binary and Transgender grow as a percent of the workforce. Only California requires a self-ID of nonbinary as well as male and female. A recent survey by the National Industry Liaison Group (NILG) shows that Non-Binary is not an issue yet for employers (likely due to the small population percentage of the total workforce). Doing visual observations and placing them in buckets is not respectful. Making this more complicated, unfortunately, is the fact that benefits are based on gender, and having no buckets makes it difficult for benefit providers.
On the ethnic/race side, it is becoming more difficult to differentiate by name or visual ID, and more minorities are self-identifying as White. Whether it is a way to gain more perceived value as an applicant or employee through presumably White advantage or it is a protest against self-identification is unclear, but it is a growing issue for employers.
The EEOC charged the National Academies of Sciences’ (NAS) to create a Panel to evaluate the quality of compensation data collected from U.S. Employers by the Equal Employment Opportunity Commission through the EEO-1 Form for the 2017 and 2018 reports. The data itself was problematic as the reporting was on W-2 Box 1 data. As described by Lynn Clements of Berkshire Associates during a public meeting on November 3rd, the EEOC wanted time in job data matched against point in time data. The incongruity of the data leads in part to the low quality of expected output. And as the other representatives pointed out about the data, W-2 data has issues from personal choice (e.g. 401K selection) to date of hire to leave of absence that impacts the amount found in Box 1.
As part of its presentation and discussion with the Panel, the NILG also discussed the issue of No Labels and the difficulty it is having on reporting and on the assumption that the Component II data actually provides accurate and meaningful data. Given the issues abound with identifying employees, using names and visual identifications led many times to a default gender and race (generally white). Although pay transparency is an admirable goal, the No Labels issue, among other things, lead to false positives. In other words, the Component 2 data reporting was an exercise in futility.
The NILG has advocated to the Office of Federal Contract Compliance Programs (OFCCP) and EEOC for over a year on the issue of Non-Binary and No Labels. The issue is even more precarious when creating affirmative action plans and identifying issues through statistical analysis. False positives may arise through the employment activity analyses giving an impression that a federal contractor’s outreach and population remains static and/or less diverse, when in fact it is the opposite.
More DE&I initiatives are based upon this problematic data. Goals are being established on archaic notions of ethnicity/race and gender. EEOC is looking at updating the EEO-1 report, but census data is far behind. Identifying goals, for example, for Non-Binary or Transexuals is virtually non-existent. There is a strong minority that states that goals for Non-Binary or the LGBTQ community should not be permitted, but for many DEI professionals, inclusiveness and overcoming social barriers is a primary goal.
The current problems of data collection need the government to lead different solution sets, but it’s not there yet. If no solution is found, census and other demographic data will be greatly flawed, and policy based on flawed data will likely be flawed. There is no perfect solution for these issues, but gradually, data collection will have to be more sensitive to the needs of the No-Label generation. In today’s world, employers should focus on creating a safe environment for their employees (and applicants) allowing them to feel secure when doing self-identification.