In this episode we talk with Jonathan Colmer about working with research assistants and pre-docs: why hiring them is really a choice to mentor, how to build a team where early-career researchers thrive, and what it takes to fund and select them. Jonathan is an Associate Professor of Economics and Public Policy in the Department of Economics at the University of Virginia and the co-founder and director of the Environmental Inequality Lab. He works to understand how economic activity, policy and the environment interact to shape prosperity, opportunity and well-being. Sebastian Tello-Trillo is an Associate Professor of Public Policy and Economics at the Frank Batten School of Leadership and Public Policy at the University of Virginia. Alex Hollingsworth is an associate professor at Ohio State University. In this episode we discussed: - His new NBER paper with Patrick Baylis, Judson Boomhower and John Voorheis, **The Distribution and Consequences of Natural Disasters: Evidence from U.S. Wildfire Victims**, - A workflow built on a weekly and a daily plan, Todoist, Slack instead of email, regular meetings with his team, and deadlines he invents to trick himself into progress - Why working with RAs is **an investment in mentoring**, not outsourcing production, and why for most RAs it is a job rather than a lifestyle - Hiring undergrads for a single task first, encouraging AI as an assistant rather than a cognitive offload, and the verification app his team built with Claude to check OCR'd data - **Cohorts and community**: a summer internship program, two pre-docs hired a year, a weekly team meeting where someone pitches a research idea, and a no-agenda half hour with each pre-doc - How to select pre-docs in the age of AI: talk to people, add an oral walk-through of the technical task, and weigh communication, drive and motivation over skills that can be taught 📚 Resources mentioned: - Baylis, Boomhower, Colmer and Voorheis, "The Distribution and Consequences of Natural Disasters: Evidence from U.S. Wildfire Victims" (NBER Working Paper 35675): https://www.nber.org/papers/w35675 - The Environmental Inequality Lab: https://www.environmental-inequality-lab.org/ - Jonathan's website: https://sites.google.com/site/jonathancolmer/ - Todoist (Jonathan's to-do app): https://www.todoist.com/ - Eyal Frank and Anant Sudarshan, "The Social Costs of Keystone Species Collapse: Evidence from the Decline of Vultures in India" (AER 2024, Jonathan's current favorite paper): https://www.aeaweb.org/doi/10.1257/aer.20230016 - David Yeager, *10 to 25: The Science of Motivating Young People*: https://mitpressbookstore.mit.edu/book/9781668023884 - H. Spencer Banzhaf, *Pricing the Priceless*: https://ideas.repec.org/b/cup/cbooks/9781108491006.html - Samantha Harvey, *Orbital*: https://thebookerprizes.com/the-booker-library/books/orbital - Aristotle by Harmonic (Alex's recommendation): https://www.harmonic.fun/aristotle/ - Anil Ananthaswamy, *Why Machines Learn*: https://anilananthaswamy.com/why-machines-learn - Anil Ananthaswamy, *Through Two Doors at Once*: https://anilananthaswamy.com/through-two-doors-at-once - Consensus: https://consensus.app/ - LeapSpace (Elsevier): https://www.elsevier.com/products/leapspace 🎮 Recommendations of the week: - 📚 Jonathan's rec, a triplet of books: David Yeager's *10 to 25* for mentoring (and parenting), Spencer Banzhaf's *Pricing the Priceless* for anyone getting into environmental economics (he may be its modal buyer), and Samantha Harvey's Booker-winning *Orbital* for fun - 🧮 Alex's rec: Aristotle, Harmonic's AI built around formal verification in Lean 4, which is surprisingly fun for anything with proofs, econometrics class included - 🤖 Sebastian's rec: *Why Machines Learn* by Anil Ananthaswamy, a history of how we got to modern AI that explains the math simply, plus Consensus and LeapSpace for literature reviews with real citations