Paul Goldsmith-Pinkham joined Markus’ Academy for a mini-series on Claude Code for Applied Economists. Goldsmith-Pinkham is an Associate Professor of Finance at the Yale School of Management and a Faculty Research Fellow at NBER.
This post includes the first three (of seven) episodes. For additional information, visit our substack.
You can watch all Markus’ Academy webinars on the Markus’ Academy YouTube channel.
Paul introduced Claude Code as a terminal-based AI coding assistant that can read files, write and run code locally, and accelerate research workflows. He explained his key principles for using these tools optimally, for example the importance of the context window and compaction.
He contrasted Claude Code with the more sandboxed Cowork environment, and discussed other complementary tools like Ghostty, Zellij, and Oh My Zsh.
Paul’s detailed notes on this episode can be found here.
Paul showed how Claude Code dramatically shrinks the gap between a vague research idea and initial results. As an example, he used Claude Code to obtain and plot data on home ownership in the US. Claude Code found the relevant data from the Census, handled scraping issues, cleaned spreadsheets, generated the required scripts.
Paul’s detailed notes on this episode can be found here.
Paul used Claude Code to scrape SEC EDGAR filings. Claude Code extracted the risk-factors mentioned in firms’ 10-K reports and built a structured DuckDB database to show how tariff-related risk disclosure became more frequent and more specific in recent years.
Overall, this episode highlighted how to best work with Claude Code by planning and iterating on results. Paul turned messy filings into usable research data in just a few minutes.
Paul’s detailed notes on this episode can be found here.