---
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:

1. **Credible identification** — the estimate isolates the causal/structural object you claim.
2. **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:

1. **Structural estimation tied to a model** — when the question is about a deep parameter, welfare, or counterfactuals; identification of parameters argued explicitly.
2. **Quasi-experiment (DID, RDD, event study) that maps to a model prediction** — reduced form whose coefficient has a stated economic interpretation.
3. **Strong IV with a theory-grounded exclusion restriction** — first-stage strength plus an economic story for exogeneity and exclusion.
4. **RCT / lab evidence interpreted through a mechanism.**
5. **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.

## Checklist

- [ ] Identifying assumption stated in one sentence and defended economically
- [ ] Design-appropriate diagnostics done (pre-trends / first-stage F / manipulation test / parameter identification)
- [ ] Placebo or falsification test reported
- [ ] Standard errors clustered at the level of treatment assignment, justified
- [ ] The estimated object is given an explicit economic interpretation
- [ ] Reduced-form work connects to a model or mechanism; structural work makes identification transparent
- [ ] Selection / general-equilibrium threats to interpretation acknowledged

## 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
```