Hello! My name is Emily Cantrell, and I’m thrilled to join Scioto Analysis as a policy analyst. I am passionate about research on public policy that addresses poverty and inequality, with a focus on leveraging data science and computational methods. Alongside my work at Scioto Analysis, I am also completing a PhD in Sociology and Social Policy at Princeton University.
I was born and raised in Ohio, and my interest in social policy has deep personal roots. My brother has developmental disabilities, and growing up I experienced firsthand how public policies designed to support people with disabilities can shape a family’s daily life in profound ways. While programs and policies like Medicaid, the Individuals with Disabilities Education Act (IDEA), and the Americans with Disabilities Act (ADA) have been incredibly important for my brother’s care and education, shortcomings in such policies can also create barriers for those who rely on them. These experiences fueled my commitment to policy work that is not only data-driven, but also grounded in the realities of those affected.
As an undergraduate at Denison University, I designed my own major in Human Development and Social Policy, creating an interdisciplinary course of study to explore how economic inequality shapes children’s lives. Two courses that inspired me were an economics course on income inequality with Dr. Andrea Ziegert and a psychology seminar on child development and public policy with Dr. Gina Dow. Outside the classroom, I spent a summer working at a Cleveland-based non-profit that helps families navigate public benefits and community resources. There, I saw how gaps in child care access created barriers to parents' employment and economic stability. These experiences culminated in my senior thesis on the relationship between early childhood poverty and socioeconomic outcomes. For the analytical portion of my thesis, I assessed the relationship between early childhood care and education programs and kindergarten readiness using local data from Newark, Ohio. I loved the research process so much that I decided to pursue it as a career.
To build my skills in policy and program evaluation and gain experience in full-time research, I worked for two years at Child Trends, a child and family policy research organization based near D.C. In that role, I helped evaluate quality assurance programs for child care and contributed to a variety of other projects related to early childhood. This work affirmed my passion for social policy research, and led me to pursue a PhD at Princeton, where I have developed expertise in data science and computational methods. In my dissertation, I investigate the capabilities and limitations of machine learning models that predict life outcomes, such as "predictive risk models" used in child protective services. Drawing on U.S. and Dutch survey data as well as Dutch administrative registry data, I examine the (un)predictability of hundreds of life outcomes, evaluate the effectiveness of different data sources and modeling techniques for predictive performance, and explore what these findings reveal about limits to the predictability of the human life course.
Midway through my PhD, I worked as the committee assistant to the Health and Human Services Committee in the New Mexico House of Representatives during their 2022 legislative session. That experience deepened my interest in state-level policymaking and ultimately led me to connect with Rob Moore and the Scioto Analysis team. I am excited to apply my expertise in data science and social policy to support evidence-based policymaking in Ohio and beyond.