Michigan Ross Professor Argues that the ‘E’ in DEI Requires Equity Analysts — And Launches a New Course to Help

In Rider’s course — called Equity Analytics, to be offered in both graduate and undergraduate versions in winter 2022 — students will learn how to analyze equity issues in real-world situations, augmented by new case studies and visits from industry experts and executives with relevant experience.

Long term, Rider hopes companies will start establishing a new role of equity analyst.

“It could be housed in HR, or it could be housed in other functions, but it would be someone who specializes in analyzing equality in opportunity,” Rider said in a recent interview. “To do that, our students need to learn analytic techniques and they need to work with data.”

Rider recently answered a few questions about equity analytics and his new course.

Do you think most companies recognize that they need to improve their culture in regard to diversity, equity, and inclusion?

Rider: I think that's broadly true. I think that most companies realize that disparities of many kinds — racial, gender, immigrant status, and so on — are prevalent, and they can and should be addressed. I think there's a lot of variation, however, in organizational commitment to addressing these disparities. Some of that is due to motivation, but we can address the part that is due to knowledge. 

What makes equity issues unique? Why can't a company just tell their analytics people, “You know numbers. Tell me how to fix this.”?

Rider: Equity issues are not straightforward. We think about equity as notions of fairness. But first of all, these notions vary across individuals. Second, fairness usually means equality in opportunity — but that doesn't necessarily mean equality in outcomes. 

Consider the distinction between differential treatment and disparate impact. Differential treatment is what we typically think of as discrimination; two people are treated differently based on their identity. But there's also disparate impact, when people are treated the same but doing so results in different outcomes because they differ in their access to resources or opportunities to demonstrate their capabilities.

To achieve equality in opportunity — equity — we need to use analytical frameworks that can isolate whether the disparity is caused by differential treatment or disparate impact and whether equivalent or differential treatment is needed to close gaps.

What advice would you have for companies looking to address these issues?

Rider: Hire a student who completed this course! In the short term, however, companies can start by documenting gaps generated by typical organizational processes. There are many steps in a hiring process, for example, that contribute to disparities in employment. The data will reveal those steps to us, if we look closely at how the data is generated. So I suggest not just looking at outcome data, like representation, but instead analyzing the entire process that produces those figures. 

That’s what students will learn to do. Follow the processes by which suppliers get contracts, employees get hired, or borrowers receive loans. My aspiration for this new course is to teach students these skills so that we can create a new role in organizations, the equity analyst.

Does that role exist at any companies that you're aware of today?

Rider: Not yet in name, but in practice. There are people doing this work, but their primary role is something different. I also work closely with large organizations that outsource these kinds of analytics to people like me, but I think that in five to 10 years, as more and more Ross students have these skills, organizations can just hire them.

The title of the new course is Equity Analytics. Why did you choose to focus specifically on the equity piece of the DEI equation?

Rider: It was intentional. Diversity is indexed by the representation of different groups, so the primary task is data collection and not data analytics. Inclusion is a topic that I think my Management and Organizations colleagues address well in their courses.

Equity is different because of this differential treatment / disparate impact lens. Equal representation is not equitable if the candidate pool or the applicant pool is not equivalent. You can still get equal representation even if some people are being mistreated or have a lower likelihood of advancing. If we want to study equity, then we can't be satisfied with representation. We can't be satisfied with feelings of inclusion. We have to analyze opportunities.

The course will take a very practical approach, right? Students will learn how to actually address these issues?

Rider: Yes. Students who finish the course will gain three basic analytical skills. The first is documenting gaps. Before we start closing gaps, we've got to know what the gaps are. Often it's not quite what we think it is or where in the process we think it is. Students will learn to diagnose and measure gaps.

The second is designing interventions to close those gaps. This may involve changes to personnel practices, supply chain policies, etc.

The third is implementing those designs and evaluating the results to see if they’re working.

Are you aware of any similar courses at other schools?

Rider: No. There are courses on people analytics, but those are largely focused on how to extract more productivity from labor. And then there are courses on DEI topics, but those are largely focused on managing diversity, to facilitate feelings of inclusion. 

I've had a lot of interest from colleagues and peers at other schools who heard about this course launch. They're asking for the syllabus and the materials.

Is there anything else students should know about the course? 

Rider: With the word “analytics” in the name, some students might think that it will require cutting-edge programming skills and statistics. That’s not how this course is designed.

Analytics does not have to be complex statistics, so we will use a lot of descriptive statistics to construct what I call “numerical narratives.” This is really a course on critical thinking — how to use data to tell stories, and not so much programming or advanced statistics.