Best Practices for Economic Data Research: From Question to Credible Insight
Today’s chosen theme: Best Practices for Economic Data Research. Welcome to a practical, story-driven guide to doing rigorous, ethical, and reproducible economic analysis—so your findings stand up to scrutiny and truly inform decisions. Subscribe for weekly, researcher-tested tips and real examples.
Finding and Vetting Data Sources
Record who produced the data, how often it is updated, and the collection methodology. Official statistical agencies, peer-reviewed repositories, and well-documented administrative sources generally inspire more confidence than ad hoc scraped compilations.
Finding and Vetting Data Sources
Compare key metrics across independent sources. If employment trends disagree, investigate definitions, seasonal adjustments, and sector coverage. Document differences openly; triangulation builds confidence and helps readers understand uncertainty without losing the signal.
Cleaning, Documentation, and Metadata Discipline
Build a Living Data Dictionary
Create and maintain a dictionary with variable names, definitions, units, transformations, and known caveats. Include source links and last updated dates. Readers appreciate clarity; future-you will be grateful when revisiting the project months later.
Scripted, Reproducible Pipelines
Avoid manual edits in spreadsheets. Use scripts to import, validate, transform, and export datasets. Log warnings for out-of-range values and failed merges. One command should rebuild the entire pipeline from raw to analysis-ready data reliably.
Outliers and Missingness with Intent
Decide, don’t improvise. Flag outliers, test winsorization thresholds, and explore multiple missing data strategies. Report your chosen approach and sensitivity results. A short note on why choices were made builds immense trust with skeptical readers.
Measurement and Construct Validity
Document the path from raw fields to final measures. For productivity, explain whether you used real output per hour or per worker, and why. Provide formulas in plain language alongside code to support reproducibility and comprehension.
Measurement and Construct Validity
Inflation and currency adjustments can change narratives. Choose consistent deflators, align calendar and fiscal years, and consider purchasing power parity when comparing across countries. Small timing shifts often explain big differences in preliminary results.
Transparent Methods and Robustness
State your baseline model plainly, including fixed effects, clustering choices, and identification strategy. If using differences-in-differences, discuss parallel trends checks and event-study diagnostics, not just coefficients and stars.
Ethics, Privacy, and Responsible Communication
Remove direct identifiers, but also consider linkage risks. Use differential privacy or aggregation where appropriate. Keep a clear data access policy and obtain approvals when handling sensitive administrative or proprietary records.
Ethics, Privacy, and Responsible Communication
Test whether results differ across demographic groups or regions in ways that reflect data bias rather than economics. Document limitations and invite community input on blind spots; transparency prevents overconfident policy recommendations.
Open with the policy or business question, present the data journey, then walk readers through results and limitations. A memorable anecdote—like a costly spreadsheet error avoided by scripting—keeps lessons grounded and practical.
Tell the Story: Visuals and Plain Language
