Data Science Institute

Emily Rauscher is a Professor in the Sociology Department at Brown University and faculty affiliate of the Population Studies and Training Center, the Annenberg Institute for School Reform, and the Taubman Center for American Politics and Policy.

Rauscher studies inequality and education. Current work focuses on the effects of school spending to learn when and how spending can more effectively improve child well-being and increase equality. Returns to school investments go beyond the individual to benefit groups and societies. Her work documents these social benefits of educational investments, including more equal achievement and attainment, improved occupational opportunities, more equal marital patterns and timing, health benefits, and in some cases greater intergenerational equality. One project examines when and how school funding increases equality and child well-being. A second project measures hidden funds from non-profits (PTAs, PTOs, etc). Related work examines which types of reforms most effectively increase equality of infant health, prenatal care, and educational outcomes. Rauscher uses quantitative and mixed methods, causal inference techniques, and natural experiments to investigate these areas.

Rauscher's work has received support from the Spencer Foundation, the American Educational Research Association, the William T. Grant Foundation, the Gilead Foundation, the Russell Sage Foundation, and the National Academy of Education. Her research has received awards from the Federation of Associations in Behavioral and Brain Sciences (FABBS), the American Sociological Association (ASA) Section on Children and Youth, and Integrated Public Use Microdata Series (IPUMS). She received her BA from Wesleyan University, Master’s degrees from USC and Trinity College Dublin, and a PhD in Sociology from New York University.

How does your research, teaching, or other work relate to data or computational science?

My research uses national, administrative data and applies causal inference methods to estimate effects of school spending and other policy interventions on child well-being. Current work is using IRS non-profit data to identify all school-supporting non-profits in the US using Python, natural language processing, and manual coding to assess accuracy. The goal is to quantify the amount and inequality of additional "hidden" school revenue sources not recorded in district budgets. My teaching includes Introductory Statistics and advanced graduate statistics courses.