/aejmic-identification

name: aejmic-identification
description: Use when the question is what makes the result tight or what the data identify for an American Economic Journal: Microeconomics (AEJ: Micro) manuscript — covering both (a) structural/empirical-IO and experimental identification and (b) for pure theory, which assumptions drive the result and how robust the mechanism is. Stress-tests credibility; it does not build the model (see aejmic-theory-model).

Identification & What Makes the Result Tight (aejmic-identification)

AEJ: Micro is theory-first, so "identification" here is **two things**. For pure theory it means: **which assumptions are doing the work**, and how tight/robust the mechanism is. For structural and experimental work it means the standard **data-to-object mapping**. Pick the branch.

When to trigger

• (Theory) A referee asks whether the result is a knife-edge artifact of one assumption
• (Theory) You cannot say cleanly which primitive drives the comparative static
• (Structural) Parameters are estimated but it is unclear *what in the data* identifies them
• (Experimental) The estimand or the assumptions behind the treatment effect are not pinned down

Branch A: Pure theory — what makes the result tight

The AEJ: Micro bar is that the reader sees **exactly which assumption is load-bearing** and how far the mechanism extends.

• **Decompose the assumptions.** For each substantive assumption, ask: is the result *false* without it, *weaker* without it, or *unchanged* (then it was WLOG — say so)? The result is "tight" when you can name the assumption that breaks it.
• **Comparative statics as identification.** Show the sign/magnitude of the key comparative static and **what primitive drives it** (single-crossing? a supermodularity? a curvature condition?). Monotone-comparative-statics tools (Topkis, Milgrom–Shannon) make the driver explicit.
• **Necessity, not just sufficiency.** Where you can, show the assumption is *necessary* (a counterexample when it fails), not merely sufficient — this is what makes a characterization tight.
• **Robustness of the mechanism** (then hand to `aejmic-robustness` for full extensions): does the result survive a small perturbation of the information structure, the timing, or the type distribution?

Branch B: Structural / empirical IO

• **Name what identifies each parameter.** Tie parameters to specific data features / moments; argue identification from the model's structure, not "the estimator converged."
• **Targeted vs. untargeted moments;** report a sensitivity/informativeness measure so readers see which data move which parameters.
• **Estimation regularity:** objective (MLE/GMM/MSM), starting values, tolerances, multi-start; Monte Carlo recovery of known parameters.
• **Counterfactual validity:** argue the estimated parameters are policy-invariant enough for the counterfactual (Lucas critique).
• For reduced-form companions, use design-appropriate diagnostics (pre-trends, first-stage strength, density tests) and **report SEs, not asterisks**.

Branch C: Experimental (theory-grounded)

• **Design maps to the model:** each treatment isolates a model primitive or prediction; state the **estimand**.
• **Pre-registration** in a recognized registry where applicable; report deviations; include instructions/transcripts.
• Randomization balance; attrition (Lee bounds if differential); multiple-hypothesis adjustment; external-validity scope.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the identification claim, don't only argue it. Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). AEJ: Micro spans applied and structural micro; the chain below is for the reduced-form / causal lane — structural estimation uses the field's own solvers.

1. `detect_design` → `recommend` → fit with `as_handle=true` → `audit_result` to list the checks the design still owes. 2. **Staggered DiD:** `callaway_santanna` / `sun_abraham` + `bacon_decomposition` + `honest_did_from_result` (the pre-trend test is low-power, Roth 2022). 3. **IV:** `effective_f_test` + an `anderson_rubin_ci` (valid under weak instruments), not a 2SLS t-stat alone. 4. **RDD:** `rdrobust` (bias-corrected) + `rddensity` / `mccrary_test` for manipulation. 5. **OVB:** `oster_delta` / `sensemakr` — how strong a confounder would have to be.

Report the economic magnitude; route the full battery to the appendix; keep every number reproducible. A run end-to-end (synthetic data, real returns) is in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md). If StatsPAI/Stata are not connected, adapt the vendored `resources/code/` skeleton and flag any unverified number.

Checklist

• [ ] Branch chosen; the "what makes it tight / what identifies it" question answered in one sentence
• [ ] Theory: each substantive assumption classified (false/weaker/WLOG without it); the load-bearing one named
• [ ] Theory: key comparative static signed with its driving primitive; necessity shown where possible
• [ ] Structural: each parameter tied to identifying moments; sensitivity + Monte Carlo recovery
• [ ] Experimental: estimand stated; pre-registered; balance/attrition/MHT handled
• [ ] Inference (applied): SEs / coverage sets, never asterisks; clustering correct

Anti-patterns

• (Theory) A result whose driving assumption is never identified — "it just works"
• (Theory) Claiming a characterization is tight without a counterexample when the assumption fails
• (Structural) "The estimator converged" presented as identification
• (Structural) A counterfactual on calibrated parameters with no policy-invariance argument
• (Experimental) No pre-registration or no stated estimand; significance asterisks instead of SEs

Worked vignette (illustrative)

A matching paper proves stability is preserved under a new preference domain. A referee suspects it rides on a substitutabil