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jde-data-analysis

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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.

Journal-of-Development-Economics-Skills/skills/jde-data-analysis/SKILL.md

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# 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.
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