jde-data-analysis
Use when estimation, heterogeneity, attrition, measurement, or inference choices need to meet Journal of Development Economics (JDE) empirical norms — clustered field data, survey measurement error, and treatment-effect heterogeneity in low- and middle-income settings. Covers the analysis itself, not the identifying design.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jde-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Analysis (jde-data-analysis) ## When to trigger - The identification is settled but the estimation, inference, or heterogeneity analysis is unconvincing - A referee would question standard errors, attrition, measurement error, or sample construction - You need to decide how to present treatment-effect heterogeneity across subgroups - You are unsure the analysis would survive JDE's replication scrutiny ## JDE empirical norms JDE referees are experienced with the realities of **field and survey data in developing countries** — clustered sampling, panel attrition, noisy self-reports, seasonality, and small effective sample sizes. Analysis that ignores these reads as naive. Hold the work to these standards: - **Inference matched to the data structure.** Cluster at the level of treatment assignment or sampling (village, school, market); with few clusters use wild-cluster bootstrap or randomization inference rather than naive cluster-robust t-stats. - **Attrition and missing data.** Document panel attrition, test whether it is differential by treatment, and bound effects (Lee bounds) when it is. Survey non-response and refusal patterns belong in the appendix. - **Measurement.** Be
- When to trigger
- JDE empirical norms
- Robustness expected
- Worked analysis (illustrative)
- Empirical-credibility pushback and the fix
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Evidence pass for Journal of Development Economics
- Output format
What does the jde-data-analysis skill do?
Use when estimation, heterogeneity, attrition, measurement, or inference choices need to meet Journal of Development Economics (JDE) empirical norms — clustered field data, survey measurement error, and treatment-effect heterogeneity in low- and middle-income settings. Covers the analysis itself, not the identifying design.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill jde-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.