Researching racial equity: Racial discrimination, choice constraints, and policy implications
In J-PAL North America’s researching racial equity blog series, we discuss how research plays a critical role in identifying structural inequities in systems and policies that disproportionately affect communities of color. A team of researchers, including J-PAL affiliated professors Peter Christensen (University of Illinois, Urbana-Champaign) and Christopher Timmins (Duke), are investigating the connections between racial discrimination in the housing market and environmental exposure risks. In part two, J-PAL staff interview Peter to discuss his ongoing series of evaluations, including a 2021 paper on housing discrimination, and the role randomized evaluations can play in addressing racial inequities.
Can you describe the motivation behind your research on housing discrimination and how your research seeks to address racial inequities?
Our research questions are driven by the experiences of people who are most impacted by this work. Engaging in public forums that bring together local housing and fair housing enforcement agencies, researchers, and representatives has helped us understand what is happening on the ground so that we can create our research designs to better identify and study these issues.
We know that there are racial and economic disparities in pollution exposure that are often tied to the neighborhoods where people live—these have long been documented by the environmental justice field. Bringing disparities to light is an important first step, but in and of itself might not lead to actionable policy change. Our research is really focused on disentangling the underlying mechanisms—what’s causing people to live in residential areas with higher pollution? And we know that neighborhoods impact more than pollution exposure, so we’re also interested in understanding the array of amenities and disamenities (e.g., schools, jobs, transportation) available in different areas.
There is a lot of other important research that aims to capture why people choose to live in different neighborhoods. We’re looking at a slightly different question, which is what factors constrain housing choices. One key piece of this work is to look at persistent income inequality, which one could easily assume is driving disparities in pollution exposures, because, of course, budgets affect who can live in which neighborhoods.
However, we also wanted to see if racial discrimination further constrains the choices of households of color, even with the same budget constraints as white households. That discrimination piece—where some groups have more choice constraints than others—is a very different policy question with different policy implications.
What do you see as the main policy implications of this research?
From a policy perspective, it’s important to understand the cause of a problem in order to best address it. For instance, if systematic differences in income are the primary cause of these disparities, then policy solutions should focus on addressing income inequality—a critical and challenging policy agenda in and of itself. Addressing discrimination that imposes constraints on choices in less polluted neighborhoods, on the other hand, requires enforcement of fair housing legislation and coordinated efforts between the Department of Housing and Urban Development (HUD) and the Environmental Protection Agency. These policy efforts are different from those that focus specifically on income mobility and have received less attention in recent years. So that’s why we’re motivated to understand the underlying mechanisms behind these disparities in neighborhood and pollution exposure, and this initial randomized evaluation on housing discrimination allowed us to do that.
That’s a great transition to looking at the role of randomized evaluations. What is the value of using a randomized evaluation—in this study and more generally—to address racial inequity, particularly systemically?
First, as I mentioned, is the ability to identify the underlying mechanisms. By manipulating one factor of a rental inquiry, the inquirer’s perceived race, we can meaningfully disentangle racial discrimination from other potential causes of disparate response rates. Understanding mechanisms can lead to changes in policy, and quantifying the scope of that mechanism can help justify spending public dollars to address the issue.
Second, the results of randomized evaluations are transparent. They do not require the same assumptions as other quasi-experimental methods. It’s helpful to be able to say to supporters and skeptics alike that “this is what we observed in a large-scale experiment using a familiar search platform.”
Finally, on the systemic piece, if studies like ours can help us understand patterns of behavior, they can help us begin to understand what guardrails to put in place. This study demonstrated that discrimination is occurring—whether people are cognizant of it or not—on digital housing platforms. So now we can ask: how can we reduce discrimination in the same digital markets?
We also have a new paper coming soon that evaluates the causal effects of historic and contemporary segregation on choices and choice constraints today. In this paper, we’re calculating the dollar estimates of the damages caused by discrimination, which could have not only policy but also legal implications. These estimates are only possible to obtain with an experiment.
A lot of your work seems driven by the potential policy implications of the research. What steps has your team taken to share your findings?
As one example, I was asked to speak about this work with MSNBC. We’ve also participated in HUD’s public forums to share our methodologies, discuss the results, and better understand what’s happening on the ground. That’s another way that we can make sure our research is consistent with what’s happening on the ground and is informing various efforts.
In these and other dissemination efforts, we try to help people understand the mechanisms of discrimination and also to explain the scale and heterogeneity of the problem—that discrimination facing renters from certain groups is stronger in some locations than others. So even on the national stage, we think it’s important to identify where households of color are facing the greatest constraints and begin to understand why.
In addition to coverage in national publications, and given that rates of discrimination varied by location, has your research been picked up at the local level as well?
Our hope is that through our dissemination at major national news outlets, we can provide evidence to support local agencies and community leaders in these areas with higher levels of discrimination.
That said, I have been interviewed by local news stations where reports of housing discrimination have increased recently and am contacted by individuals asking how to interpret the results in their contexts. And while there are some limitations of what I can say about specific neighborhoods, I’ll explain how they can accurately interpret the results to, say, a local representative. So in that sense there’s dissemination happening at kind of an individual level.
I also get emails from people challenging the results, saying that they follow HUD guidelines and fair housing laws. Since our method yields transparent results, I just say “this is what we found.” I try to explain the results in ways that help people understand the methodology and share related research that helps illustrate the nuances of discrimination and that it can happen subconsciously. I hope there’s some learning that can happen through that. And it also takes us back to some of the benefits of randomized evaluations: the results are the results.
The researching racial equity blog series features the contributions of researchers and partners in examining and addressing racial inequities and offers resources and tools for further learning. Part one shares an example of evaluating racial discrimination in employment. Part three gives an overview of stratification economics in the context of evaluations. Part four discusses how to center lived experiences throughout the research process and in impact evaluations. Part five shares guidance for incorporating inclusive and asset-based framing throughout the research cycle. Part six examines sources of bias in administrative data bias. Lastly, in part seven, Damon Jones and J-PAL staff share progress on researching racial equity and future areas of work.