ecj-identification

name	ecj-identification
description	Use when the empirical identification strategy is the bottleneck for a The Economic Journal (EJ) 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 ecj-theory-model).

Identification & Economic Interpretation (ecj-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, and its general relevance, are not pinned down

The EJ bar: credible identification AND broad economic meaning

EJ accepts both reduced-form and structural work across all fields, but the bar has two parts that must both clear:

1. Credible identification — the estimate isolates the causal/structural object you claim, to a standard a demanding referee accepts.
2. Economic meaning of broad interest — the estimate maps onto a parameter or margin that economists outside the subfield care about. A precisely identified but parochial effect is a field-journal paper here, because EJ's defining bar is broad relevance.

Reduced-form work should connect to a model or mechanism (see ecj-theory-model); structural work must make its identification transparent. Because EJ runs a reproducibility check via the EJ Data Editor before final acceptance (DCAS-endorsed; deposit to Zenodo — see ecj-replication-package), every identification claim must come from code that actually executes and reproduces. EJ's exposition premium also applies here: the identifying assumption must be stated in plain words a generalist can evaluate, not hidden in notation.

Design priority (strong → acceptable)

The right design is dictated by the economics, not by fashion. As a rough ordering of what travels well at EJ:

1. Quasi-experiment (DID, RDD, event study) mapped to a model prediction — reduced form whose coefficient has a stated, broadly interesting economic interpretation.
2. Structural estimation tied to a model — when the question is about a deep parameter, welfare, or counterfactuals; identification of parameters argued explicitly.
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, with external-validity discussion.
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 low-power joint pre-trend test — argue economically why pre-trends are flat.
• Map the coefficient to a model object: what does the ATT mean economically, and for whom does it generalize?
• 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. The Economic Journal is general-interest economics; the DiD/IV/RDD chain serves its broad applied lane.

• 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

• Identifying assumption stated in one plain 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 of broad interest
• Reduced-form work connects to a model or mechanism; structural work makes identification transparent
• Selection / general-equilibrium / external-validity threats to interpretation acknowledged
• The numbers come from code that runs (EJ Data Editor will rerun it)

Anti-patterns

• TWFE on staggered treatment with no discussion of heterogeneity bias
• A precisely identified effect with no statement of what it means, or of why a generalist should care
• 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
• An identification claim resting on numbers the deposited code cannot reproduce

Output format

【Design】structural / DID / IV / RDD / event study / other
【Identifying assumption】one plain sentence
【Economic interpretation of the estimate】... (and why it is of broad interest)
【Diagnostics done】[pre-trends, first-stage F, manipulation, param-ID, ...]
【Diagnostics missing】[...]
【Clustering level】... (justification)
【External-validity / GE caveats】...
【Next】ecj-theory-model (if mechanism not yet formalized) or ecj-robustness