What Intersectional Equality Really Looks Like

Monica G. Tibbits-Nutt, AICP, LEEP AP BD+C, an Executive Director at 128 Business Council, is a transportation planner and urban designer. She also serves as a Director on the MassDOT Board and on the Fiscal Management Control Board that oversees the MBTA. A graduate of the University of Southern Indiana and the Ohio State University, she previously worked in housing in Ohio. Her nonprofit work focuses on transportation planning in the 128 Corridor.

As a transportation planner, I think and talk constantly about intersectionality—that is, the interconnected nature of categories like race, class, gender, age, and ability. In planning contexts, we often end up discussing communities and individuals as if their experiences can be encapsulated by singular “boxes”: You are a woman, or you are Latina, or you are middle income. However, I want to take the opportunity of African American Women’s Equal Pay Day to discuss the ways in which the categories we use often hamper our efforts to create more diverse, more equitable workplaces. Both when we try to analyze the communities that we are serving and when we turn our analytical lens back on ourselves as institutions, we have little hope of successfully tackling the wage gap, or any of the other issues facing minority populations, without an intersectional approach.

Planners spend a lot of time grappling with demographic data. Unfortunately, most demographic data is presented in terms of broad, singular categories. For example, the median annual salary for an American planner in 2016 was $77,300. For planners who do not identify as white, that median salary drops to $75,900. For those who identify as female, the median salary is $72,600. But what about for planners who are simultaneously non-white and female? The simple answer is that we don’t know exactly how great the wage disparity is among planners when identity-markers intersect—even though pretty much all of us are more than one thing at once! 

Unfortunately, most salary surveys are not analyzed in an intersectional capacity and demographic data is infrequently collected in this manner either. The Treasurer’s Office of Economic Empowerment, however, has created a wage gap calculator where users can input their age and industry and view income data by race and gender. Tools like this one are a significant step in the right direction and a constant reminder of the pay inequities rampant throughout our society. In Massachusetts, the average woman makes 83 cents for every dollar earned by her white, male counterpart. This number is worse for Asian women at 80 cents, Native women at 63 cents, Black women at 61 cents, and Latina women at 50 cents. Furthermore, the wage gap is additionally exacerbated by other components of identity like motherhood, education, gender identity, disability, and sexual orientation.

Stepping back a bit, we can’t look at the issue of equal pay within our institutions without considering the issue of equal access to job opportunities. After all, before we can offer a diverse body of employees equal pay for commensurate work, we need our institutions to have a diverse body of employees to begin with. I am only a decade into my professional life, and yet I have already sat in countless conference rooms as high-level decisionmakers grapple with what exactly a diverse workforce should look like, and how they should go about measuring whether they have one. What exactly does “diverse” mean? The fact of the matter is that diversity is not a quantifiable benchmark. Therefore, I prefer to structure conversations around representation, since I very strongly believe that any institution that serves the public needs to be representative of the specific public that it serves.

So, are the planners working in the U.S. representative of the country as a whole? As of 2014, 38% of planners were female and 82% of planners were white. At that time, 51% of the U.S. population was female and 73% was white, and so we are obviously not quite hitting the mark—and this is only looking at national data via a single demographic variable at a time. Across industries, but especially for those industries that directly serve the public, we have to go beyond these broad, singular analyses and really immerse ourselves in the specificity and the complexity of the communities that we serve.

A community that your institution is meant to serve isn't defined merely by location, race, economic status, or age demographics. Understanding the specificity and complexity of a community means thinking about how that community's experiences have been shaped over decades by the intersection of, for example, being located in a transportation island created by two major highways without pedestrian bridges; with a 40% black-identifying and 55% latinx-identifying population; with primarily service-industry job options; and with an aging population. And you can’t truly understand how all of these intersecting factors affect that community without both knowing something about its past and presently having feet on the ground in that community. Thus, you need to be well-versed in the specific demographics of the communities you're serving, yes, but you also need to resist the urge to think that those statistics alone tell you everything you need to know.

So what does all of this mean in terms of institutional demographics? Having a more complex understanding of the population you serve in turn necessitates that you have a deeper understanding of your institution in order to know whether, as a team, you have the background, skills, and knowledgebase needed to serve that community. I bring all of this up because—while all industries should be held accountable for the diversity and equity of their workplaces—I very strongly believe that those of us in public-serving industries like planning literally cannot do our jobs properly if we aren’t holding ourselves accountable. Having a robustly representative workplace is not only a moral imperative, but also a deeply functional one.

When I think about the institutions that I am a part of, I try to go beyond the broad, singular kinds of demographics listed above and really analyze our workforce under a number of different rubrics:

1.  Departments and Hierarchies. Okay, so perhaps you have data that tells you that 15% of your institution is black-identifying and 50% of your institution is female-identifying, and perhaps that initially seems representative of the community you serve, but how do those numbers break down by department? Are all of your minority and female employees siloed into one or two departments? Are those percentages consistent across various levels of management?

2.  Processes for Hiring, Firing, and Promoting. You need to understand at what point in the institutional hierarchy folks are able to enter the workforce, and what the processes look like for internal advancement. It is not unusual for an organization to have community-representative demographics at the bottom of the hierarchy and at the top, but for there to still be a “representation hole” in the middle. This could mean that, despite what the overall numbers look like, minority and female candidates are subject to disproportionately low opportunities for internal advancement. In other words, minorities and women are being hired into entry-level positions within the institution at community-representative levels (perhaps even over-representative levels!), but rather than being promoted into managerial positions and then promoted again, an entirely different group of minorities and women are being introduced into the organization at the executive level. This rubric of analysis is often related to the first rubric: For example, perhaps many of your female and minority employees are siloed into departments that offer few opportunities for internal advancement.

3.  Intersectionality.  Bringing everything I’ve talked about full circle, once you’ve subjected your organization to these first two more granular rubrics for demographic analysis, you have to move beyond these traditional singular demographic categories and ask yourself whether your workforce reflects the composition and needs of the community when multiple categories of identity are considered simultaneously.

These are some of the tough questions that we must ask of the institutions in which we work, and of the institutions that work for us. If we shy away from these complex, systemic analyses in favor of “simpler” standards for equity and diversity, we are effectively trading away the future possibility of truly equitable workplaces—with the future promise of truly equal pay—in favor of fragmented advancements for a few that emerge incidentally from contexts in which different disenfranchised groups are pitted against one another to the detriment of the public as a whole.

*Planning and transportation data taken from the American Community Survey (www.census.gov/acs) and the APA/AICP Planners Salary Survey (www.planning.org/salary).