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cover of episode S4E4: Maya Rossin-Slater, Health Economist, Stanford

S4E4: Maya Rossin-Slater, Health Economist, Stanford

2024/10/8
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The Mixtape with Scott

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Welcome to this week’s episode of the Mixtape with Scott! It’s a pleasure to introduce this week’s guest from Stanford University, Maya Rossin-Slater). Maya is a health economist who specializes in areas related to families in particular. Early work of hers focused on public policies aimed at labor markets as an avenue for helping families, notably paid leave). Her work has been unique for focusing on all parts of the family — mothers, fathers, as well as children. Her more recent work has moved into distressful events that affect all people in the family, and which most recently has moved into focusing on school shootings’ effects on the survivors). When you list all of Maya’s work, you can see patterns — spillovers within and across families, within schools, distressful events impact on the family, assistance in the labor market and its effect on families, and various topics in health. It’s a robust research agenda that seems to have over time shown real patterns of interest spanning all topics relevant to our understanding of the family as an important part of society, and policies that can help, including policies that encourage work.

But it is also fun to have Maya on the show because as longtime listeners know, I have an abiding interest in the “children and grandchildren of the revolution” — meaning those economists who can trace their lineage back to the Princeton Industrial Relations Section. And Maya is also a grandchild of the revolution. Her advisor was Janet Currie whose advisors were Orley Ashenfelter and David Card. So it is interesting to see the propagation of that department moving through the profession. May is only a few links removed from it, but you can see all the finger prints of the Section on her work — meticulous, observant, seeking credible answers to important policy questions regarding workers and their families using credible sources of variation that resemble an experimental design framework.

So thank you for all tuning in! And thank you for all your wonderful support.

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