Emma Huizenga

Hello, and welcome to my website! My name is Emma. I am a recent alum from Michigan State University with a Bachelor of Science in Sociology. I have two minors, Quantitative Data Analytics and Women’s and Gender Studies. I think my minors describe my research interests perfectly, as I am very passionate about issues related to reproductive justice, trans rights, and women’s institutional empowerment. I like to explore these topics through causal inference and and the curation of original data. More broadly, I am an aspiring political scientist/data scientist interested in how gendered health disparities and socioeconomic inequalities function both in the United States and globally.

My most recent project explores the impact of women’s legislative representation in the U.S. on gender-specific healthcare mandates. I will be presenting this project at Loyola’s State Politics and Policy Conference this year! I am currently working on getting this paper published soon, my working version can be viewed on my publications page.

I am currently employed at the Institute of Public Policy and Social Research, where I work under Matt Grossmann (and Timothy Hellwig + Christopher Wlezien) on a project examining cross-national midterm loss. Much of my work involves coding and cleaning election data, very tedious at times, but that’s what makes me such a good detail-oriented person. I also tutor kids at the Grandville Avenue Arts and Humanities Center, a position I have recently picked up and enjoy very much!

I have also built three original datasets. My first examines transgender nondiscrimination policy across the United States. My next two datasets support my current research: one compiles gender composition in state legislatures and insurance mandates from 2010–2024, and the other is a cosponsor dataset analyzing which demographics supported the passage of each bill. These datasets can be found on my github.

My previous research assistant experience includes qualitative analysis from questionnares (Sarah Prior), supporting text-analysis projects (Andy Halterman), and building datasets for a faculty sampling frame (Aaron McCright).

I am highly proficient in R, and moderately proficient in Python and LaTeX. I have been trained in advanced causal inference methods using observational data, as well as qualitative methods (e.g., survey and questionnaire design). I have received various grants, awards, and scholarships totaling $10,400.

Thank you for taking the time to read this and visit my website!