# Jeg Data Analysis > Use when building or auditing Journal of Economic Growth (JEG) empirical estimates, calibrated growth models, transition paths, cross-country and subnational panels, historical datasets, spatial (Conley) inference, robustness, and reproducibility for growth and comparative-development manuscripts. - Skill: `thedixitjain/jeg-data-analysis` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/jeg-data-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/jeg-data-analysis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/jeg-data-analysis --- # Data Analysis (jeg-data-analysis) ## When to trigger - You are estimating cross-country, panel, historical, or regional growth models - A theory paper includes calibration, simulation, or transition dynamics - Results need robustness, decomposition, or sensitivity checks for JEG ## Empirical growth checklist - Define the growth outcome: level, growth rate, convergence speed, productivity, human capital, fertility, technology, institutions, or development outcome. - Document the unit and horizon: country-year, region-decade, cohort, household, firm, or historical panel. - Separate long-run levels from short-run growth dynamics. - Show sample construction, merge rules, missingness, and influential observations. - Use specifications that match the question: convergence regressions, panel FE, IV, DID/RDD around reforms, synthetic controls, or structural estimates. ## Theory / calibration checklist - State calibrated parameters, data moments, and source for each moment. - Separate targeted from untargeted moments. - Report transition paths and steady states clearly. - Stress-test key elasticities, discount rates, depreciation, fertility, human capital, and technology parameters. - Make code reproducible enough to regenerate figures and tables. ## Growth-mechanism audit table Before drafting results, create a table with: - **Mechanism**: human capital, fertility, technology, institutions, trade, finance, migration, or OLG channel. - **Object**: growth rate, income level, TFP, convergence speed, transition path, or welfare. - **Discipline**: data moment, calibration target, theorem assumption, or identification source. - **Main sensitivity**: parameter or sample choice most likely to overturn the result. - **Replication artifact**: code or file that regenerates the exhibit. If an estimate or simulation does not map to a mechanism row, it is probably not central enough for JEG. ## Spatial and historical inference discipline Comparative-development empirics at JEG are usually geocoded, which changes the inference defaults: - Report Conley standard errors at multiple distance cutoffs (e.g., 100/250/500 km) for any gridded or regional outcome; clustered SEs at the modern administrative level are necessary but not sufficient. - When historical units do not coincide with modern ones, cluster at the historical unit — the level at which the treatment was assigned — and document the crosswalk. - Pre-empt the critique that persistence t-statistics can be inflated by smooth spatial trends: include flexible geographic controls (latitude-longitude polynomials or macro-region fixed effects) plus a spatial-noise placebo test. - For very long panels, keep measurement vintages separate: reconstructed pre-1950 series, modern national accounts, and nighttime lights are not interchangeable; show the result within each vintage where feasible. ## Worked vignette — auditing a comparative-development panel Illustrative setup: 2,400 grid cells in 41 countries; outcome is log light density in 2020; regressor is distance to a historical trade hub; candidate instrument is least-cost-path placement. - **Unit/horizon**: cell-level cross-section answering a long-run *level* question, so convergence-dynamics machinery is unnecessary; the persistence design applies. - **Inference**: coefficient 0.21; country-clustered SE 0.05, Conley 250 km SE 0.08, Conley 500 km SE 0.09 — report all three; the claim survives the widest cutoff. - **Mechanism row**: schooling in 1960 absorbs roughly 40% of the coefficient (illustrative), so human capital becomes a lead exhibit, not a robustness afterthought. - **Main sensitivity**: dropping cells within 50 km of modern capitals moves the estimate to 0.17; capital proximity goes into the audit table as the result's weakest joint. ## Estimator defaults by growth question - Long-run level question (deep determinants, persistence) → cross-sectional or grid design + Conley inference + mechanism decomposition. - Convergence-speed question → panel estimation alert to Nickell bias; system GMM only with instrument-count discipline and Hansen/AR(2) reporting. - Reform-timing question → modern staggered-adoption DID estimators with pre-trend evidence, never naive TWFE. - Theory-driven quantitative question → calibrated model with targeted and untargeted moments kept visibly separate. - Demographic or fertility question → cohort or census microdata aggregated to the mechanism's unit; verify the transition timing is identified by the data rather than assumed by the specification. ## Execution bridge (StatsPAI / Stata MCP) Run the battery, don't just enumerate it. Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). JEG (growth) uses cross-country and long-run panels with deep endogeneity; foreground identification and robustness to alternatives. - **Many outcomes / specifications:** `romano_wolf` (step-down FWER) or `benjamini_hochberg`. - **OVB sensitivity:** `oster_delta` / `sensemakr`. - **Inference:** `wild_cluster_bootstrap` (few clusters), `twoway_cluster` / `conley`. - **Re-fit off one handle:** `audit_result(result_id)` lists missing checks + the exact `suggest_function` for each. - **Exhibits:** `etable` / `did_summary_to_latex` from the handle — no retyped numbers. Decisive checks in the body, exhaustive battery in the appendix. [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md). ## Output format ```text [Paper type] empirical / theory / mixed [Data or model object] ... [Main estimator/calibration] ... [Robustness or sensitivity] ... [Reproducibility gaps] ... [Next step] jeg-tables-figures ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Economic-Growth-Skills/skills/jeg-data-analysis/SKILL.md`