| name | jpe-identification |
| description | Use when the empirical identification strategy is the bottleneck for a Journal of Political Economy (JPE) manuscript — quasi-experimental designs (DID, IV, RDD, event study) or structural estimation. Stress-tests the design and its economic interpretation before drafting tables; it does not write the model from scratch (see jpe-theory-model). |
Identification & Economic Interpretation (jpe-identification)
When to trigger
- The empirical core is OLS + controls with no defended causal claim
- Staggered DID estimated with TWFE without addressing heterogeneity-bias critiques
- IV with a weak first stage or a thin exclusion argument
- Structural estimation where the source of parameter identification is not spelled out
- A clean causal effect exists but its economic interpretation is not pinned down
The JPE bar: credible identification AND economic meaning
JPE accepts both reduced-form and structural work, but the bar has two parts that must both clear:
- Credible identification — the estimate isolates the causal/structural object you claim.
- Economic interpretation — the estimate maps onto a parameter or margin that economic theory cares about. A credibly identified effect with no economic meaning is a half-paper here.
Reduced-form work should connect to a model or mechanism (see jpe-theory-model); structural work must make its identification transparent. Atheoretical correlation mining is the classic JPE desk-reject signal — and at JPE the desk screen is a co-editor (Chicago-centered board led by Esteban Rossi-Hansberg) and the submission fee is non-refundable, so an undisciplined design is a costly miss. JPE's price-theory heritage (e.g., Becker's "Crime and Punishment," JPE 1968) means the economic mechanism behind a clean estimate matters as much as the estimate. If the contribution is a deep quantitative-macro identification, consider whether JPE Macroeconomics is the better venue than the flagship.
Design priority (strong → acceptable)
The right design is dictated by the economics, not by fashion. As a rough ordering of what travels well at JPE:
- Structural estimation tied to a model — when the question is about a deep parameter, welfare, or counterfactuals; identification of parameters argued explicitly.
- Quasi-experiment (DID, RDD, event study) that maps to a model prediction — reduced form whose coefficient has a stated economic interpretation.
- Strong IV with a theory-grounded exclusion restriction — first-stage strength plus an economic story for exogeneity and exclusion.
- RCT / lab evidence interpreted through a mechanism.
- OLS with a serious endogeneity discussion — acceptable in theory-empirics or descriptive-with-model papers, not as the sole causal claim.
Branch paths
Branch A — DID / event study
- Staggered timing? Diagnose negative-weighting with Goodman-Bacon; estimate with a heterogeneity-robust estimator (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille, or Borusyak–Jaravel–Spiess).
- Pre-trends: show the event-study plot; do not lean only on a joint pre-trend test (low power) — argue economically why pre-trends are flat.
- Map the coefficient to a model object: what does the ATT mean economically?
- Placebo: randomize treatment timing/units; report the distribution.
Branch B — IV
- First-stage strength: report effective F (Montiel Olea–Pflueger); if weak, use Anderson–Rubin / weak-IV-robust CIs.
- Exclusion: defend in three registers — theory, institutional detail, and a placebo/over-identification check.
- Report the reduced form, not just 2SLS.
- State the LATE interpretation: whose behavior does the instrument move, and is that the population the economics is about?
Branch C — RDD
- McCrary / rddensity manipulation test.
- Optimal bandwidth (Calonico–Cattaneo–Titiunik) plus ≥3 bandwidth-robustness checks; bias-corrected CIs.
- Covariate smoothness at the cutoff; placebo cutoffs.
Branch D — Structural estimation
- State the model's microfoundations and the moments/variation that identify each parameter (a "what identifies what" paragraph is expected).
- External validation: do estimated parameters match independent evidence or untargeted moments?
- Provide counterfactuals and welfare, and show sensitivity to key assumptions.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. JPE is top-5 general-interest economics; a credible design is the entry ticket — modern DiD/IV/RDD and the magnitude for a broad readership.
detect_design → recommend → fit with as_handle=true → audit_result.- Observational causal claims: staggered DiD (
callaway_santanna / sun_abraham +
bacon_decomposition + honest_did_from_result); IV (effective_f_test +
anderson_rubin_ci); RDD (rdrobust + mccrary_test).
- Experiments: randomization-based inference +
romano_wolf for many-outcome control.
- Sensitivity:
oster_delta / sensemakr for observational claims.
Report the magnitude in interpretable units; route the full battery to the appendix. A
run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
Checklist
Anti-patterns
- TWFE on staggered treatment with no discussion of heterogeneity bias
- A precisely identified effect with no statement of what it means for economics
- IV exclusion asserted ("we argue the instrument is exogenous") without evidence
- Structural estimates with no "what identifies what" discussion — the model becomes a black box
- Clustering at the wrong level to manufacture significance
- Ignoring that the partial effect may be offset in general equilibrium
Output format
【Design】structural / DID / IV / RDD / event study / other
【Identifying assumption】one sentence
【Economic interpretation of the estimate】...
【Diagnostics done】[pre-trends, first-stage F, manipulation, param-ID, ...]
【Diagnostics missing】[...]
【Clustering level】... (justification)
【GE / selection caveats】...
【Next】jpe-theory-model (if mechanism not yet formalized) or jpe-robustness
