I really admire academicians who don’t just confine themselves to the ivory tower, but regularly try and use their research findings and their prestige to influence discourse around policy, and are resources for policymakers.
Launched this year, the Brickell Metcalf internship aims to expand the opportunities for undergraduate students to participate in research examining liberty and freedom in the context of law, regulation, and policy. This year’s first interns—Jenny Wang and Elaine Yao—have already started their summer off working on research questions initiated by Jeffrey Grogger, the Irving Harris Professor in Urban Policy at the Harris School of Public Policy. We reached out to the students and their mentor to discuss the work they’ve done so far and how this summer could shape the students’ future careers in academia and policy.
Can you describe how the research question you’re working on came together? What makes it an important issue, both broadly and to you personally?
Elaine Yao: The current project I’m working on is an ongoing project of Professor Grogger's. Broadly speaking, we are attempting to find out if there is racial profiling by police in traffic stops. Professor Grogger did a version of this project in the past using data from Oakland, California. The question centers on how we can estimate the race distribution of the “risk population”—essentially, the people who are ‘at risk’ of being stopped? If it’s indeed different from the race distribution of drivers on the road as a whole, then perhaps there is evidence of racial profiling occurring. Right now, we have obtained access to five years of complete traffic stop for the state of Illinois, including Chicago, so we are attempting to answer this question using Chicago data. The approach we’re using is one that’s been termed the “veil of darkness” hypothesis, which rests on the basic claim that at night, police have more trouble seeing a driver’s race. So, we can answer the question of interest by comparing the race distribution of drivers stopped at day versus at night. I think it’s a pretty plausible approach, and given the amount of data we have access to, I think that it’s possible that we’ll come up with some reasonably interesting results.
Having moved to the Chicago area three years ago from a relatively homogenous, suburban setting, I’ve been keenly aware of racial dynamics in Chicago. Racial tension is unmistakable when you’re living in Chicago—it’s written throughout the city’s history and urban geography, and especially with the recent allegations of racial profiling by police departments across the country, issues of race and the police are especially urgent right now. In the past, my work with the Institute of Politics on campus has given me opportunities to interact with local politics and local politicians who have to deal with these interlinked issues of race, justice, and policing on a daily basis, and allowed me to work with people from different ethnic communities in Chicago. I’m constantly challenged by the question of how I can use my education and skills to do research and work that speaks to those experiences I’ve had, and can possibly help those communities, without attempting to impose my own viewpoints and conclusions. I think that this project is the perfect way to do that.
Jenny Wang: Much of what I want to do in the future, the reason I want to do research at all, is that I feel like there are a lot of things that I care about that have to do with urban development. And I think that where my skill sets lie, I think I could do the most and make the biggest difference by going to this field of work and doing research. The subject we’ve been approaching—income inequality—is inherently quite interesting to me. The perpetuation of income inequality, something I care about a lot, and just being able to talk about specific mechanisms, and about building the kinds of math that can be used to model it, is a very cool thing.
What work have you done on the project so far? Has anything about the project surprised you, or challenged your assumptions?
Elaine Yao: So far I’ve been doing a lot of data cleaning—processing the messy data sets from all these years (there are typos, missing information, inconsistencies), and constructing control variables. The project always surprises me; you’re always keeping a close eye on the data set and doing “sanity checks.” I suppose that since i’m in the midst of data-cleaning right now (actually, processing the data for non-Chicago police departments), my main puzzle is why there seem to be more traffic stops in the beginning of the year than later in the year. I don’t know if that’s maybe a problem with how I’m processing the data, or if it’s actually happening. We’ll have to see! There’s also an incredible amount of variation in how different police departments record data (both in the level of detail and consistency), which is inconvenient for me, but can also be telling.
Jenny Wang: I have very little practical knowledge [on this research topic] so far, since I’ve only just begun taking economics classes this year. So I’ve been doing a lot of literature review for him on the project that we’ve been working on, which has to do with estimating the coefficients for intergenerational transmission of income. Not looking just from parent to child, but from grandparent to child. And seeing how that changes the models that we’re—that have dominated the literature up until now. I did a lot of reading, found a lot of papers. And then after classes ended, I spent a week and a half just working on solving some—I guess I ended up just solving a system of four equations to see if that would get us anywhere. Professor Grogger did a really good job of explaining the stat to me, and then I just tried to figure out the algebra.
Is there a particular research skill or subject area that this project is helping you master?
Elaine Yao: Professor Grogger has been an incredibly kind mentor and a great example of how to execute research in a disciplined, replicable manner. He’s been patient with me (since I’m learning to use Stata, a programming language/software that I’ve had limited experience with in the past), but also open to my suggestions. Mostly he’s been great at teaching me about how to document your research, make everything clear and well-organized, and in general how to be a responsible, methodical, and deliberate researcher. It’s something I don’t think I could learn any other way.
Jeffrey Grogger: What we know about UChicago undergrads is that they’re very smart and they know how to work hard. That’s how they got here in the first place. Add a couple years of coursework and they have a set of broad skills that are just dying to be integrated, refined, and put to work on particular problems. With students like that, a little bit of input on my part can have a big impact.
I like students to understand not just the tasks I ask them to carry out, but the context, how they fit with the bigger question. That engages not only their skills, but their entire intellects. And intellectual engagement is a big part of the reason why student choose research careers.
Jenny Wang: I started working spring quarter, and didn’t really have technical skills, but now that it’s summer, I have time to build on those. So he gave me a mini-mini project in Stata to see if I could get familiar with the program. And hopefully I can do a little bit of reading and then start to — I don’t know, I feel like with those things, you really just need to learn by doing it.
There are things that don’t surprise me, like the importance of regression and stats. But it’s also nice [to see how things you learn can be applied]. I just finished the analysis sequence, and sometimes when you’re doing math, it’s like, when am I ever going to see this ever?
But it’s surprisingly relevant. We talked probability limits for stats, and we were talking about the continuous mapping theorems. It’s cool to see some of the math I’ve done so far show up. It can feel like economics and math are very disconnected, but they [really work hand-in-hand].
As undergraduates, you’re just getting your toes wet, research-wise. Does it feel like you’re interested in wrestling with these problems more as a researcher or are you more interested in kind of getting involved in bringing that evidence into the policy world, helping people kind of speak the language of research?
Jenny Wang: Yeah, I mean, research is a great—that’s just the way that I see my skill sets most utilized in the best way. As far as these issues that I care about. I’ve been thinking recently that I would love to work for the Fed or other very policy-relevant places. People don’t always think about the things that govern day to day decisions. But even looking at the prices at the grocery store—the Fed did that. It’s important.
Elaine Yao: At the moment it seems like graduate school and a research career might be in the works for me: I enjoy doing research and deeply investigating urgent questions with no obvious answers, and discovering things that might not be in line with conventional wisdom. At the same time, I really admire academicians who don’t just confine themselves to the ivory tower, but regularly try and use their research findings and their prestige to influence discourse around policy, and are resources for policymakers. I don’t think I could be satisfied with a life in which I simply conduct research and publish papers. I need to know that what I’m doing is having some kind of effect on the world.
People don’t always think about the things that govern day to day decisions. But even looking at the prices at the grocery store—the Fed did that. It’s important.