| name | jpube-data-analysis |
| description | Use when handling the empirical analysis for a Journal of Public Economics (JPubE) manuscript — administrative tax/transfer/health/register data, elasticity and bunching estimation, sufficient-statistics and MVPF calculations, heterogeneity, and robustness. Executes the analysis plan; for the causal design itself use jpube-identification-strategy. |
Data Analysis (jpube-data-analysis)
When to trigger
- You are estimating a behavioral elasticity, take-up rate, or moral-hazard parameter
- The analysis uses administrative or register microdata with disclosure constraints
- You need to map estimates into welfare (DWL, MVPF, sufficient statistics)
- Robustness, heterogeneity, or measurement concerns are unresolved
What JPubE analysis looks like
Public economics at JPubE is typically built on large administrative or register data — tax records (IRS/SOI), social-insurance files (UI/DI/SSA), health-program data (Medicaid/CMS), or whole-population European registers — because credible policy elasticities need population-scale variation around kinks, notches, and reform dates. The analysis should convert clean identification into a policy-relevant quantity, not stop at a coefficient.
Analysis norms
- Estimate the policy parameter directly. Recover the taxable-income / labor-supply elasticity, the take-up or crowd-out rate, or the insurance-vs.-moral-hazard wedge that the welfare argument needs.
- Sufficient statistics & MVPF. Where you claim a welfare verdict, show the mapping from estimated responses to the formula explicitly, state the primitives held fixed, and propagate standard errors (delta method or bootstrap) into the welfare object.
- Respect disclosure and licensing. Restricted tax/health/register data require formal access and output clearance; document cell-size suppression and the access path, and supply programs even when microdata cannot be shared.
- Measurement honesty. Top-coding, income definitions, real-vs.-nominal, and program-rule coding drive public-finance results; document each choice.
- Heterogeneity that matters for policy. By income, eligibility margin, or jurisdiction — heterogeneity is the input to optimal nonlinear policy, not a fishing expedition.
- Robustness. Bin width and excluded region (bunching), bandwidth (RD/RKD), estimator choice (staggered DID), functional form, and sensitivity of the welfare conclusion to key elasticities.
Execution bridge (StatsPAI / Stata MCP)
Run the battery, don't just enumerate it. Full map:
execution-with-mcp. JPubE is public economics — tax/transfer/program designs; DiD/IV/RDD and bunching are central, magnitudes in policy units.
- 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.
Checklist
Anti-patterns
- Reporting a regression coefficient and never converting it to a welfare quantity
- An MVPF / sufficient-statistic number with no standard error or sensitivity analysis
- Ignoring disclosure rules when describing restricted administrative data
- Subgroup splits with no multiple-testing discipline presented as "heterogeneity"
Worked example: from elasticity to a welfare number (illustrative)
A DI-reform evaluation recovers a labor-supply response to a benefit cut, then builds the MVPF: the mechanical fiscal saving is the denominator; the behavioral fiscal externality (induced earnings → recovered taxes, minus crowd-out) adjusts the numerator, giving MVPF ≈ 0.8 (illustrative). The skill's norms then bind: state the primitives held fixed (no GE wage response, fixed program rules); propagate the elasticity's SE through the MVPF by the delta method, reporting a CI on the welfare object; and show how MVPF moves if the key elasticity sits at the high or low end of the literature. The welfare statistic with its uncertainty — not the bare elasticity — is the deliverable.
Calibration table: estimate → welfare object
| Estimated object | Welfare mapping | Watch for |
|---|
| Taxable-income elasticity | Marginal DWL / optimal top rate | Mean reversion, income shifting |
| Take-up / benefit response | MVPF numerator + denominator | Crowd-out onto other programs |
Evidence pass for Journal of Public Economics
Treat this skill as an executable review pass, not a prose hint. First lock the policy instrument, affected margin, identification design, and welfare or incidence interpretation; then judge whether the current manuscript answers the venue's real reader: public economists who ask whether policy design, fiscal incidence, or welfare interpretation is credible.
- Do the pass: Audit the research design before polishing prose: unit of analysis, comparison set, uncertainty, sensitivity, missingness, and reproducibility must be visible.
- Return a ledger: give
claim / evidence / risk / manuscript location rows, so the next agent can edit rather than rediscover the issue.
- Sibling guard: compare against JDE for development policy, JIE for cross-border policy, AEJ Economic Policy for broad policy readership; if a sibling owns the contribution, recommend re-routing before polishing format.
- Stop condition: do not give submission-ready advice until the pack's
resources/official-source-map.md has been checked for volatile rules and the manuscript has one concrete fix for the largest venue-specific risk.
Output format
【Data】source + restricted? + disclosure handled? [Y/N]
【Policy parameter】elasticity / take-up / crowd-out / moral-hazard wedge
【Welfare mapping】DWL / MVPF / sufficient stat — SEs propagated? [Y/N]
【Measurement choices】[income def, top-coding, rule coding, ...]
【Heterogeneity】policy margin: [...]
【Robustness done】[bin/bandwidth/estimator/sensitivity, ...]
【Next step】jpube-tables-figures
