Welcome back to the latest of our newest blog series: the Unsung Women’s Project! We’ll be highlighting amazing women in STEM, by sharing the stories of all of the incredible, meaningful things women have done in their STEM careers that haven’t gotten the recognition they deserve.
Jessica Milli is an economist and study director at the Institute for Women’s Policy Research, the leading think tank in the United States that focuses primarily on policy research through a gendered lens. Once referred to as a “wonk star,” Dr. Milli leads the Institute’s work on women in business and STEM fields, with her research cited in national press, including Bloomberg, The Atlantic, and MarketWatch. She studied economics at the University of Wisconsin-Milwaukee, focusing on the economic causes and consequences of domestic violence, earning her Ph.D. at the age of 25. In her spare time, she is a master amateur baker, a fierce roller girl, and a globe trotter, who has danced with penguins in Antarctica.
I am a study director at the Institute for Women’s Policy Research, which encompasses a variety of different roles. Mainly, I do a lot of data analysis for various projects that we work on here at IWPR, which involves running tables and figures for all of our research reports. For some of our technical assistance projects like our paid sick days work and our paid family and medical leave work, I’m doing more complex modeling. I also run a lot of our research program portfolios, so I manage our paid sick leave work and our women in STEM, innovation, and entrepreneurship work, and I am frequently trying to develop new research areas. This involves a lot of proposal writing, talking with funders, and trying to get people excited about the work that we’re doing. Then, of course, the other part of things is talking with policymakers about our research and what our findings are, and helping to direct conversations at both the state and national level.
Not as bad as we might expect it to be! I think the thing that I’ve been noticing has been that the same issues that we’ve worked on in years past are still very important issues… it’s just the way that we’re tackling them is slightly different. We’ve been seeing a lot of this moving away from a national focus, and trying to get national legislation passed, to a more state or local level. The family and medical leave side of things is a very popular issue across the board– but even though legislators have proposed a number of different policies on both sides of the aisle, we can’t seem to agree on what that program might look like. We are, however, starting to see a lot more movement on that issue on the state and local level, and a lot of individual programs are being passed.
In terms of my background and how I got to where I am right now, I don’t think I can remember a time when I wasn’t a math nerd! I’ve always had a fascination with numbers, and I remember distinctly when I was in first or second grade, hearing about long division and thinking that it sounded like the coolest thing ever. I made my mom teach me how to do it, even though I wasn’t even supposed to learn it in school for a few more years. That love of math continued throughout my childhood, even as I got older and classes started getting harder. I was lucky to have some really fantastic math teachers along the way that really kept that love alive.
My teacher in high school, Mrs. Reynen, really comes to mind. I had her for three of my four years in high school, and she was so much fun. She was super high energy, and I always looked forward to going to her class even when we were learning less interesting things. I actually think I can credit her for my earliest interest in economics, too! When I was in high school I wasn’t sure exactly what I wanted to do career-wise, but I knew I wanted to do something that involved math.
What was really great about Mrs. Reynen and her classes was that she taught you beyond just getting the right answer. She spent a good amount of time actually showing you the theory behind it, and how you can use what you learned in the context of physics or engineering. Eventually, we got to the economics context. At the time, I happened to be taking an economics class as well, so when we learned that marginal revenue and marginal cost curves were just the derivatives of the total revenue and total cost curves, everything just made so much more sense in that econ class and things started clicking– which is how I decided that I wanted to go into economics. Eventually, I discovered that I really had a love of data-crunching statistics and working with numbers that way.
Definitely. I was always super intellectually curious when I was growing up, and my mom helped me a lot with math and with learning in general. Anything that I wanted to learn how to do, my mom would help me learn. I remember wanting to learn cursive before they taught it in class, and I asked her to teach me. When I was in seventh or eighth grade, I was taking an Algebra course and there was one section on weighted averages, and for whatever reason that homework assignment was not making sense to me at all. I was spending hours and hours and hours trying to figure it out.
I couldn’t do it, and I was so frustrated, but my mom sat down with me for a couple of hours and we worked through all of the problems. Eventually, we came up with answers for everything together. When I got to class the next day and we were going over the homework assignment, pretty much no one in my class had figured any of the problems out. Even the teacher couldn’t go through them when he went up to try and write the solutions on the overhead projector. So I raised my hand and was like, “Oh, don’t you want to do it this way?” And he just looks at me, and he gestures to the overhead projector, and he’s like, “Do you want to come up and show us how to do this?” So I went and taught my classmates how to do it.
I kind of consider myself a data whisperer, which is something that I discovered after high school, when I decided that I really loved math and economics. I had never taken a statistics course in my life, and I didn’t until I had already declared my major and was committed to econ. I remember going home a couple of times and telling some of our family friends what I was going to school for and they would ask me, “Oh, are you taking econometrics? I had a friend who took it, and he said it was the hardest thing ever.” And I would say, “Oh, I haven’t taken it yet but I’m going to have to!” That class and another forecasting class were the first times I’d ever worked with data before.
I remember getting so excited after learning my first very basic statistical model to forecast data into the future. After class that day, I immediately went home and downloaded some financial data, some stock market data from Yahoo Finance or something, and tried to see if I could predict the stock prices. I remember being so proud of myself for getting over 90 percent accuracy in that prediction. From there, my interest in data just grew.
Now, professionally, since I work a lot with data, being a “data whisperer” as a superpower kind of manifests in my ability to troubleshoot pretty much any data analysis coding problem just by looking at the massive spreadsheets of data that we’re working with. Oftentimes, it’s millions and millions of observations, and I can just look through it and see, “Oh, that doesn’t look quite right– so maybe this code needs to be modified in this way.”
I think that back when I was in school, the best way for me to study for exams and to really learn the material was through teaching it to my classmates that were having trouble. Even when I became a professor, I found that my understanding of the material really deepened when I had to explain the concepts to my students, often in multiple different ways. But, I also think that it depends on what it is that I’m learning as well. For music, it’s a combination of visual and auditory learning. When I first started out it was very visual– I had to see someone do it in order to replicate it– but now, when I’m trying to learn new pieces, it’s largely auditory. I need to hear it, and often hear it many times, in order to fully understand what I’m going for. With my data analysis, it has largely been visual.
When I have errors in my code or my results seem off, the way I learn how to fix the code and do it right the next time is to just pull up the massive spreadsheet, look at the data, and search for patterns that are correlated with the strange results I’m getting. Sure, you could look up the particular command that you’re trying to run in the help file or google it and probably find the answer that way, but actually looking at the data to try and figure it out myself has helped me gain a deeper understanding of the underlying data and how coding structures work generally. And if I didn’t do things that way I don’t think I’d be the “data whisperer” that I am today!
I don’t know if you could call it professionally, but generally people like Ruth Bader Ginsburg, Elizabeth Warren, and Serena Williams really inspire me, as really strong women that don’t put up with any bullshit and are fighting to change the status quo. Those are definitely people that I look up to, and I can only hope that someday I can become as badass as they are!
One of the biggest things that I’ve learned is not to second guess myself. I’ve struggled in the past with imposter syndrome and thinking, “While I do have a PhD, I’m young, and what do I know? Why does anyone want my opinion on XYZ topic?” I had to learn to have confidence in my abilities, and to recognize that I really do know what I am talking about. I’ve put in the work, and the fact that I am so young isn’t necessarily a factor that should count against me. In fact, that should actually give me more confidence in my abilities, because I have been able to accomplish so many things at such a young age.
Very early on, one of the biggest challenges that I had to overcome was my fear of public speaking. I always loved helping out my friends with their homework and thought being a teacher sounded really cool, but in speech class in high school I would always turn beet red. I would run out of breath, and generally felt terrified when I was up in front of my classmates– so I decided that anything that involved public speaking wasn’t for me! When I started my PhD program [I started grad school when I was 20, and I got my PhD when I was 25], what I didn’t really fully process at the time was that in exchange for the funding that they were going to give me to help put me through the program, I was going to be teaching classes at the university. It wasn’t even just leading discussion sections for the courses, they wanted you to put on a class with basically no support. So you were developing your own course content, your homework assignments, and exams, and on top of that you were talking for an hour and a half in front of 50 or so students. I was 21 or so when I started teaching at the college level, so at least half of my students at the time were my age or older, which was super intimidating.
I actually started [college] at the same time that pretty much everyone starts, at 18, but my high school had a really awesome program with the local community college where if you were a junior or a senior in high school and you met certain GPA requirements, you could take classes that would count for dual credit at both the college and high school level. I definitely took advantage of that. Since I took pretty much every single class I could for dual credit, and had AP credits on top of that, I was already a junior after my first semester in college.
I definitely think conquering that fear of public speaking was high on the list! I was so terrified my first day teaching a stats class for my university– I swear my students could smell the fear on me when I was handing out the syllabus. I was shaking, I was definitely beet red, and I was speaking so fast that I was running out of breath and hyperventilating in front of everybody! But it was so amazing how fast I got used to it. After the first week I just told myself I had to figure it out, and I got myself to calm down. I stopped having that visible reaction.
Luckily, I had a really great group of students that semester, and instead of looking at me like, “Who’s this young girl? What does she know? Why is she up here teaching?,” they were all really great, and they made that first semester really fun. Throughout my teaching career I got more and more comfortable being up in front of people, talking about complicated things, and trying to get them to learn something and take something away from it. Now, I think I finally hit that point in my career where I don’t have to write down every single thing that I want to say anymore, and I can go in with maybe three bullet points of the main topics I want to cover and have full confidence in my ability to talk about them.
Professionally, there are a couple of other things that really stand out. One has been the work that I’ve done on our paid sick days technical assistance projects. It was basically the first thing that I was tasked with working on when I started at IWPR, and I had to learn this really complicated statistical model that used sample predictions to estimate how many people in local areas didn’t have access to paid sick days at their jobs. Then, I needed to use the information from those models, and communicate with policymakers in those areas to help them make their decisions. Since then, things have gotten a little bit more complicated. We have to help them evaluate different proposals based on firm size, carve-outs, number of days offered, and so on. But since I’ve started working on this issue area, it’s really exploded in popularity, and there have been so many state and local laws that have passed. It’s been really amazing to see how influential my data has been in a lot of those debates, and to actually be able to see that the work that I’m doing is having an impact.
It’s really frustrating! All of the data sources that are collected by the government do not get down to levels below the census region level (the Northeast, South, the Midwest and the West), which means that if you have a state or a city that wants to know how many people have access to paid sick days, you can’t get that directly. What we have to do instead is to take microdata from the region level, and create a model that estimates the likelihood that someone has access to paid sick leave based on their personal demographic characteristics, their work characteristics and so on. Then, we take that model and run the population of people that are working in the area of interest from another data set, run them through that model, and calculate how many people are likely to have access based on the characteristics of that local workforce.
I used to keep track when I first started, and now there’s been so many! It’s moving so fast that I can’t even keep up anymore. When we were doing our status of women in the states report, we had to keep updating our work and family chapter because throughout our drafting process, the number of states and cities that had those laws kept changing!
The 81 or 82 cent number that the wage gap is at currently does not take into account anything. It’s just the ratio of women’s annual earnings to men’s annual earnings for full-time, year-round work– the only factors it assumes is that people are working at least 35 hours a week, for at least 50 weeks a year. It doesn’t take into account educational attainment, differences within that population in hours worked, their occupations, experience levels, and so on.
There is research that does account for those factors, though, and what that research has found is that while the wage gap shrinks substantially when those factors are accounted for, there’s still a solid chunk that can’t be explained, which means there’s still potentially discrimination taking place in the labor market, with those differences accounted for. Plus, differences in experience level– a factor that we can control for– is sometimes a factor of discrimination. The fact that we don’t have a federal paid family medical leave policy means that women typically take more time off of work around childbearing, for example, which hurts their experience levels and has an impact on their earnings potential and their ability to get promotions. Even though we can explain why we have a wage gap, it doesn’t necessarily mean that there’s nothing that we need to address.
For me, it means using my skills and my expertise to have some sort of a positive impact on the world. When I was teaching, for example, success to me was getting through to my students and having them learn something from me, or helping to inspire them to take more economics classes and learn more about the field. I remember when I started out teaching, I had a student in my class who was a basketball player, which was the big sport on campus. He wasn’t doing so great, and his homework assignments were not completed well. He was doing poorly on exams. He was coming to class, but he wasn’t really paying attention; he was on his phone or chatting with friends. So, when his coach emailed partway through the semester to check up on him and see how things were going, I definitely let him know that he wasn’t doing great, and that he wasn’t showing up for office hours, and the coach was like, “I will make sure that he’s in your office every single day during office hours and getting the help that he needs.” And this kid showed up every single time. He asked questions. He got help on his homework assignments. And I remember being so proud when I graded his final exam at the end of the semester and he had the second highest score on the exam. It was a really proud moment for me.
Sometimes I do get really into some of my data-heavy projects, and especially when I’m encountering challenges– like my code isn’t working, or I’m not really sure how to estimate something with the data that I have– I usually find myself thinking about it a lot, either consciously or subconsciously. I’ll think about it while I’m running, while I’m in the shower, and when I’m just staring at the ceiling at night. You’ve probably seen that math lady meme, where she’s just staring off into space, and there’s all of those calculations surrounding her– that’s been me a lot of the time! I’ll just be walking through the produce section in the grocery store and I’ll just stop dead in my tracks, like, “Yes! I’ve figured it out!”
I’m really physically active, so I’d probably spend more time doing those sorts of things. I’m toying around right now with the idea of getting certified as a personal trainer, so that’s one of the things I might do. I’ve also been doing Krav Maga lately, and one of my goals for my 30s is working towards a black belt. Plus, I’ve gotten really into roller derby lately, and I just joined a league in July! I’ve also been kicking around some independent research ideas that I could work on outside of work.
At one point, I was seriously considering dropping out of my PhD program and pursuing a career as a professional cage fighter– but maybe that was my desperation talking at the time. Those prelim exams are no joke.
Television! It’s actually a challenge at work when people are talking about whatever they’ve been watching, I totally can’t contribute.
Do you know someone (including yourself!) who has accomplished something incredible in STEM? Tell us more, we’d love to feature you!