Francisca García-Cobián Richter is a research associate professor at the Center on Poverty and Community Development. Prior to coming to Case Western Reserve, she was a research economist in community development at the Federal Reserve Bank of Cleveland.
Her research focuses on the analysis of social interventions and the environments in which they operate. Richter's recent work includes estimating the effects of housing and neighborhood quality on children’s academic outcomes, evaluating a pay-for-success intervention targeted to families in the child welfare system facing housing instability, and assessing the economic cost of childhood exposure to domestic violence.
Richter is also associate director of the Math Corps Cleveland, a community-oriented academic enrichment and mentoring program for local middle and high-school students.
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Why I Teach
Given the growing use of data technologies in social welfare decision-making, it’s crucial to empower students to influence their development for social good. This doesn’t require students to become data scientists, but to critically examine the assumptions and biases embedded in the (meta)data and models used to aid decision-making in social welfare, making sure technologies are informed by the experiences of those represented in the data. SASS 471 is offered as an elective for all post-baccalaureate students in collaboration with the Case School of Engineering, with the option to attain the Certificate in Data Science for Social Impact.
Why I Chose This Profession
Growing up in Lima, Peru, I saw grassroots organizations, like women-led soup kitchens, stand bravely against poverty, discrimination, terrorism and a weak social safety net. This sparked my interest in economics to fight poverty and challenge the narratives that fuel discrimination against the least powerful in seemingly all societies. At the Poverty Center, we work with colleagues across various colleges to leverage integrated administrative data systems (IDS) and community knowledge to inform social programs. Our ethical approach to data analytics aims to address discrimination bias in IDS and incorporate historical, contextual and community knowledge to promote social good and equity.