FIGURE 2
National Civilian Labor Force
Sex
Female Male Race
American Indian or Alaskan Native
Asian
Black or African- American
Hispanic or Latino
Native Hawaiian or Pacific Islander
White Two or more races
Percentage 46.83 53.17
1.04 5.85 11.74
16.39 0.43
62.85 1.70
IDENTIFYING OPPORTUNITY
PEO C3T analyzed diversity profiles for the national civilian labor force, the federal workforce and the regional population from which it draws most of its employees. Identifying where the diversity of its workforce differs significantly from that of the region in which it operates can better shape an organization’s recruiting strategy. (Graphic courtesy of the author)
Federal Workforce
Percentage 43.23 56.77
1.65 5.58 18.07
8.39 0.45
64.66 1.20
Regionalized Population
Percentage 46.31 53.69
0.33 3.26 15.39
4.92 0.10
74.56 2.43
CONCLUSION Identifying where our organization’s diversity differs
significantly from our
region’s will allow us to develop an appro- priate recruiting strategy, like increasing our presence at predominantly female or African-American college and university job fairs, without chasing after unachiev- able metrics. In the end, we will have to move beyond objective EEOC metrics because we are seeking a workforce that possesses diverse backgrounds,
experi-
ences and ways of thinking, a workforce that will bring together varied perspec- tives to solve problems as a team.
Statistical analysis has moved us out of the funhouse and allowed us to create a mirror that captures an accurate image of our organization’s personnel appearance. With such a mirror in place, we can now see our community in the reflection of ourselves.
For more information, contact the author at
and red marbles and shifted to a more accessible position when I tilted the jar to reach in. Whether the biased selection was intentional or not, the fact that my selection was unlikely to be pulled at ran- dom from the larger population means that we should investigate the cause.
With employees, the bias could be that we hire a lot of engineers, and women are underrepresented in the field of engi- neering. Tis raises some questions we then need to consider: If we try for equal representation, is
that another form of
bias? Do those positions actually require an engineering degree, or are we hiring engineers out of habit? What can we do to effect change at the root of this prob- lem—that is, how can we ensure a more diverse field of candidates in the engi- neering discipline?
We used a two-proportion test to deter- mine whether the difference in proportions for a particular demographic was within the range of expected variability, the first proportion being the target demographic and the other proportion being the sum of all other demographics. Te result tells us how likely it is that we would have the demographic proportions that we do if we hired our workforce at random from the population in our region.
When the data indicated that a particular demographic was underrepresented in our organization, we considered the underly- ing causes or types of bias, and how the result would impact our recruitment strategy. In some cases (e.g., American Indian or Alaska Native, Native Hawai- ian or Pacific Islander), the proportions were too low for a valid statistical test; those we did not target for action.
jeffrey.t.hawkins10.civ@
mail.mil.
For more information about PEO C3T, go to
http://peoc3t.army.mil/c3t.
MR. THOM HAWKINS is the chief of
program analysis for PEO C3T. He holds an M.S. in library and information science from Drexel University and a B.A. in English from Washington College. He is Level III certified in program management and Level I certified in financial management, and is a member of the Army Acquisition Corps. He is an Army-certified Lean Six Sigma black belt and holds Project Management Professional and Risk Management Professional credentials from the Project Management Institute.
ASC.ARMY.MIL
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WORKFORCE
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