jfi-identification-strategy

name	jfi-identification-strategy
description	Use when auditing the core analytical engine of a Journal of Financial Intermediation (JFI) paper — for empirics, the causal design that separates credit supply from demand in banking data; for theory, the assumptions, equilibrium discipline, and proof exposition. It pressure-tests the design; it does not run the analysis.

Identification Strategy (jfi-identification-strategy)

When to trigger

• Setting up or defending the empirical design of a banking/intermediation paper
• Setting up or defending the assumptions and propositions of a theory paper

Empirical track (applied banking / credit)

JFI referees are unforgiving on identification in bank data. Build a credible causal design and defend it:

• Source of variation: a regulatory change, supervisory shock, branching deregulation, a discontinuity in capital/eligibility rules, or a plausibly exogenous credit-supply shifter.
• Modern estimators: staggered DID with heterogeneity-robust estimators (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille), IV with weak-IV-robust inference, or RDD with the rdrobust toolkit.
• Bank-data-specific threats: bank selection into treatment, borrower–firm sorting, balance-sheet timing and mechanical reverse causality, and the lending-channel separation of credit supply from demand (firm×time fixed effects in matched lender–borrower panels).
• Inference: cluster at the level of treatment assignment (often bank or market); wild-cluster bootstrap when clusters are few.

Theory track (intermediation models)

When the contribution is a model, identification means analytical discipline:

• State assumptions transparently and motivate each economically (what friction it encodes).
• Make results precise as propositions/lemmas; keep proof exposition readable — sketch the mechanism in the text, full proofs in an appendix.
• Argue generality: which results survive relaxed assumptions, and where the boundary lies.
• A numerical example (see jfi-data-analysis) can illustrate the mechanism without claiming empirical estimation.

The within-firm benchmark, and when it is not enough

The Khwaja–Mian within-firm estimator is this community's default answer to demand confounds: with multi-bank firms, firm×time fixed effects difference out borrower demand and isolate the credit-supply channel. A JFI referee then pushes past the default:

• Multi-bank firms are larger and less bank-dependent — show what the design's external margin (single-relationship firms) does, or bound how far the within-firm estimate travels.
• "Equal demand across a firm's lenders" is itself an assumption: a firm may cut demand for one bank's specialized product. Address with loan-purpose controls or product-level fixed effects.
• Firm-level real outcomes cannot carry firm×time FE; aggregate the bank shock to the firm with pre-period exposure shares, and defend share exogeneity as in shift-share designs.

Design selection for common intermediation shocks

Variation exploited	Default design	Venue-specific threat to pre-empt
Staggered regulation/deregulation across states or countries	Heterogeneity-robust staggered DID	Banks lobby for timing — show treatment is not predicted by pre-trend bank health
Capital- or size-threshold rule	RDD with density test	Banks bunch by managing the ratio; McCrary check is mandatory
Funding or deposit shock with differential exposure	Exposure (shift-share) design	Exposure shares correlate with local demand — balance on borrower observables
Run or crisis window	High-frequency event design	Mechanical balance-sheet timing; reverse causality from borrower distress

Worked contrast: one estimate, two readings (illustrative)

A 1pp funding shock reduces bank-level lending by 2.8pp (bank panel, OLS). At JFI that is not yet a result: the same number is consistent with shocked banks happening to serve shocked borrowers. The within-firm version at 1.6pp (firm×time FE) is the publishable object — and the 1.2pp gap becomes evidence on borrower–bank sorting worth its own paragraph, not a nuisance to hide. JFI referees read the movement of the coefficient across fixed-effect columns as a diagnostic in itself; design the identification section so that movement is interpreted, not merely displayed.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the identification claim, don't only argue it. Full map: execution-with-mcp. JFI is banking and financial intermediation — typically corporate / bank causal designs built around regulation and shocks.

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. If StatsPAI/Stata are not connected, adapt the vendored resources/code/ skeleton and flag any unverified number.

Anti-patterns

• OLS-plus-controls dressed up as identification on a bank panel
• Conflating credit supply and demand without firm×time absorption
• A theory whose key result silently depends on an unstated assumption
• Clustering at the wrong level, or ignoring few-cluster inference

Output format

【Track】empirical / theory
【Design or assumptions】<the variation, or the key assumptions>
【Top threat / boundary】<the main objection + answer>
【Inference / generality】<clustering, or which results survive>
【Next skill】jfi-data-analysis