Economics + Data Thinking: A Practical Workflow

When I face a complex problem, I start by framing it as an economics question: what are the trade-offs, incentives, and constraints?

Then I move into a data workflow:

1. Define the question clearly.
2. Pick measurable indicators.
3. Collect and clean relevant data.
4. Visualize patterns before modeling.
5. Communicate findings with assumptions and limits.

Why this works

Economics gives structure. Data gives evidence.

Used together, they help me avoid vague conclusions and build decisions on a transparent process.

My default checklist

• Is the question specific enough?
• Are the metrics meaningful?
• Did I check alternative explanations?
• Can someone else reproduce the logic?

This approach keeps my work practical, explainable, and useful for real-world decisions.