Lindsay Cattell

Lindsay Cattell focuses on quantitative data analysis, predictive analytics, and impact evaluations in human services programs including job training programs, Temporary Assistance for Needy Families, and education.

She is currently the principal investigator for a quasi-experimental evaluation of the Kauffman Foundation’s entrepreneurial training program and an accuracy assessment of a machine-learning algorithm that predicts students’ risk of academic problems. Cattell has led or conducted dozens of quantitative data analyses for projects, including synthetic control evaluation, propensity-score evaluation, randomized controlled trial impact evaluations, cost-benefit analysis, and factor analysis. She has written research reports, design memos, and analysis memos; created table shells; written specifications; and written code in R, Stata, and Python.

Before joining Mathematica, Cattell conducted research on low-wage workers and minimum wage policies at the Institute for Research on Labor and Employment at the University of California, Berkeley. She also worked as a policy consultant for community-based organizations in New York City, where she completed more than a dozen participatory research projects on topics such as syringe-exchange programs, youth employment programs, low-wage industries, public housing conditions, and domestic workers’ rights. Cattell holds an M.P.P. from the University of California, Berkeley.