Jue Identification

Spatial Identification (jue-identification)

When to trigger

• A causal claim rests on OLS + region controls, or TWFE on staggered place-based policy
• A shift-share/Bartik instrument's exogeneity (shares vs shocks) is asserted, not argued
• A boundary/border discontinuity lacks a continuity-of-confounders defense
• The estimate could be driven by spatial sorting/selection across locations rather than the treatment
• Control areas may be contaminated by spillovers/displacement (SUTVA failure)
• Inference ignores spatial autocorrelation (Conley/HAC) and overstates precision

The JUE identification bar

JUE referees are sophisticated about the failure modes that are specific to space. A clean national design is not enough; you must defend it against the three spatial confounds that recur in every urban paper: (1) sorting/selection — people, firms, and developers choose locations, so cross-location comparisons mix treatment with composition; (2) spillovers/SUTVA — treating one place moves activity to or from neighbors, contaminating controls and biasing reduced forms; (3) spatial autocorrelation — nearby units are correlated, so naive SEs are too small. State the estimand, name the identifying variation, show the diagnostic that could have failed, and address all three confounds explicitly.

Design paths

Path A: Boundary / spatial discontinuity (school zones, jurisdiction borders, corridors)

• Continuity defense: show pre-determined covariates are smooth across the boundary; the running variable is geographic distance.
• Local-linear with data-driven bandwidth; bias-corrected robust CIs; donut to drop units at the exact line; bandwidth sensitivity.
• Defend that the boundary is not also a discontinuity in something else (other jurisdiction services, zoning, natural features).
• The estimand is the local effect at the border — resist extrapolating to the whole city.

Path B: Shift-share / Bartik (local labor demand, immigration, trade exposure)

• State whether identification rests on exogenous shares (Goldsmith-Pinkham–Sorkin–Swift; report Rotemberg weights) or exogenous shocks (Borusyak–Hull–Jaravel).
• Show the implied just-identified instruments and which industries/shocks drive the estimate.
• Address that initial shares reflect prior sorting; defend pre-period balance and exclusion.

Path C: Historical / geographic IV (terrain, soil, historical infrastructure, lines on maps)

• Argue the instrument affects the outcome only through the spatial channel of interest — the exclusion restriction is where these papers live or die.
• Falsification on placebo outcomes and never-treated channels; control for the geography the instrument correlates with.

Path D: Place-based policy DiD / event study (zones, infrastructure, rezoning)

• With staggered rollout, move beyond TWFE (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille); clean event-study leads; Goodman-Bacon decomposition.
• Spillover-robust controls: ring/donut specifications around treated areas; estimate displacement to neighbors rather than assuming SUTVA.
• Rambachan–Roth honest-DID sensitivity to parallel-trend violations.

Path E: Mobility / neighborhood experiment (MTO-style, voucher lotteries)

• Lottery/randomization as the source of variation; ITT and LATE/TOT distinguished; non-compliance handled.
• Selection into take-up addressed; neighborhood exposure measured, not assumed.

Cross-cutting spatial inference

• Spatial autocorrelation: Conley spatial-HAC SEs (with a defended distance cutoff) or cluster at the spatial-market level; report how SEs change versus naive.
• Sorting/selection: show composition is balanced or model the sorting; never present a cross-location comparison as if locations were randomly assigned.
• SUTVA/spillovers: define the treated and control geography so that spillovers do not contaminate controls; estimate the spillover itself where possible.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. JUE is urban/spatial economics — sorting and spatial dependence; identification + Conley/spatial-robust inference.

• 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.