BUILDING A BETTER MIRROR
FIGURE 1 THE VIEW FROM UP HERE
PEO C3T mapped and examined national and regional census data to determine whether the demographics of its workforce mirrored national and local demographics, in response to EEOC Management Directive 715. (Image courtesy of the author and
OpenHeatMap.com)
Harford, Cecil and Baltimore counties), we first looked at the location of our cur- rent workforce. Using the Manpower Information Retrieval and Reporting Sys- tem, our workforce accountability system, we identified how many employees lived in each ZIP code across the United States. We then summed up the employees by ZIP code into counties and generated a population map using Open Heat Map. (See Figure 1.)
In addition, we found that we had a num- ber of employees who commuted from central and northern New Jersey, at least partly because of base realignment and closure measures that moved command, control,
communications, computers,
intelligence, surveillance and reconnais- sance professionals from Fort Monmouth, New Jersey, in 2010. Because we are not actively recruiting residents from New Jersey for our current location in Mary- land, we also eliminated those data from the analysis. Te final result showed a concentration of employees around Har- ford County. (See Figure 2.)
Using the population map, we determined that an appropriate regional composite should include Harford County, any county sharing a border with Harford, and any county that shares a border with one that borders Harford County.
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To create the demographic makeup of the regional composite, we used data from the U.S. Census (2015 projec- tion), adjusted proportionally based on the percentage of our workforce cur- rently residing in each of those counties. Approximately 64 percent of our current population resides in Harford County; therefore, we multiplied each of Harford County’s demographics by 0.64 so that 64 percent of our regional demographic would “look like” Harford County.
For example, Harford County’s popula- tion is 4.3 percent Hispanic or Latino, so we multiplied 0.043 by 0.64 to calculate Harford County’s share of our region’s Hispanic
or Latino population. We
added that to the product of Cecil Coun- ty’s 17 percent of our workforce and that county’s 4.2 percent Hispanic or Latino population, etc.
Because census data are separate from labor data, we compared national census data to national labor participation rates for women and men and adjusted the ratio in our regional population accord- ingly, to account for different levels of labor participation by women and men. We assumed that there was no difference in labor participation based on race. Fig- ure 2 shows the different diversity profiles for the NCLF and the federal workforce,
and a demographic profile we developed based on the region from which we, PEO C3T, expect to recruit our workforce.
MIRROR, MIRROR, ON THE WALL … Te question, “How diverse are we?” can be partially answered with EEOC statis- tics. But it begs a second question: “How diverse should we be?” After all, “diverse” is a relative term, so it only makes sense in comparison. We could directly com- pare our organization’s diversity profile with the profile of our region, but if we were 1 percentage point below, does it mean we are falling short? How far off is it OK to be? For that matter, if we were 1 percentage point up somewhere, it would mean we were down somewhere else and would forever be chasing a perfect bal- ance. What is more important is that there is no evidence of bias—and to look for evidence of bias, we can use statistics.
If we had a jar with seven blue marbles, 10 red marbles and eight green marbles, and I reached in and pulled out only the eight green marbles, what is the prob- ability that this selection was made at random? It’s certainly unlikely, though possible. It is more likely that there was some type of bias involved. Maybe I like green marbles. Or maybe the green mar- bles were more lightweight than the blue
Army AL&T Magazine
July-September 2017
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