Fetch and analyze economic data for research
Introductions by Markus Brunnermeier Paul Goldsmith-Pinkham joined Markus’ Academy for a mini-series on Claude Code for Applied Economists. Task: Suggested implementation: Use Claude Code to fetch and analyze economic data for research. The workflow involves identifying data sources (e.g., Census, SEC EDGAR), handling scraping issues, cleaning spreadsheets, and generating scripts to create structured databases like DuckDB. The human role is to define the research question, review the generated code and data outputs, and validate the results before finalizing the analysis.; tools: Claude Code. Human role: The original post does not say how a person stayed involved.; limitations: Prerequisites include access to public data sources (Census, SEC EDGAR) and local execution environment for Claude Code. Integration uncertainty exists regarding the stability of scraping scripts against website changes. The source reliability is secondary (event description). Human approval is required to verify data accuracy and code logic, as automated extraction may miss context or introduce errors in complex filings..
Tools
Published on Agent Work Atlas:
Overview
Introductions by Markus Brunnermeier Paul Goldsmith-Pinkham joined Markus’ Academy for a mini-series on Claude Code for Applied Economists.
Workflow
Suggested implementation: Use Claude Code to fetch and analyze economic data for research. The workflow involves identifying data sources (e.g., Census, SEC EDGAR), handling scraping issues, cleaning spreadsheets, and generating scripts to create structured databases like DuckDB. The human role is to define the research question, review the generated code and data outputs, and validate the results before finalizing the analysis.
Reported result
The source describes the capability to shrink the gap between a vague research idea and initial results, citing examples like plotting home ownership data and extracting risk factors from 10-K reports. However, no specific quantitative metrics, time savings, or error rates are reported. A measurable acceptance check would be to time the process from raw data request to a validated plot or database schema and compare it to a manual baseline.
Human role
The original post does not say how a person stayed involved.
Limitations
Prerequisites include access to public data sources (Census, SEC EDGAR) and local execution environment for Claude Code. Integration uncertainty exists regarding the stability of scraping scripts against website changes. The source reliability is secondary (event description). Human approval is required to verify data accuracy and code logic, as automated extraction may miss context or introduce errors in complex filings.
Try it
Act as a research assistant using Claude Code. I need to analyze [replaceable: specific economic dataset, e.g., 'US home ownership rates' or 'SEC 10-K risk factors']. 1. Identify the official data source URL. 2. Write a Python script to fetch, clean, and structure the data into a local DuckDB database. 3. Generate a summary plot or table. 4. Stop and present the code and output for my review before saving any final files. 5. If the data fetch fails, report the error and suggest a fallback source. Do not publish or share any results without my explicit confirmation. Measure success by the accuracy of the extracted fields against the original source and the clarity of the generated visualization.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.