| name | jeem-identification |
| description | Use when the identification argument is the bottleneck for a Journal of Environmental Economics and Management (JEEM) manuscript — an environmental-policy causal design, a climate/weather IV, or a revealed- or stated-preference valuation. Stress-tests the data-to-welfare mapping before exhibits are finalized; it does not invent evidence or citations. |
Identification Strategy (jeem-identification)
When to trigger
- A regulation/permit-market effect rests on TWFE with staggered adoption, or on OLS + controls
- A hedonic or travel-cost estimate could be confounded by spatial sorting or omitted amenities
- A stated-preference WTP could be contaminated by hypothetical bias, scope insensitivity, or yea-saying
- A weather/pollution IV's exclusion restriction or its adaptation interpretation is challenged
- You are unsure the design recovers a welfare-relevant parameter, not just a reduced-form effect
The JEEM identification bar
JEEM identification is judged on two axes at once: the causal/preference-recovery argument must be credible, and the recovered object must be welfare-relevant — a marginal damage, a WTP, a pass-through, an abatement cost. Environmental data carry field-specific threats (spatial dependence, sorting, monitoring-station selection, weather endogeneity through adaptation) that generic applied-micro referees miss but JEEM referees will not. Pick the branch and make the mapping from data to the welfare object explicit.
Branch A — Environmental-policy causal design
- Regulation / standards DiD: with staggered rollout, abandon plain TWFE for heterogeneity-robust estimators (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille); show a clean event study with pre-period leads; report a Goodman-Bacon decomposition. Argue the regulation timing is not driven by prior pollution trends.
- Cap-and-trade / permit markets: the price and the cap are equilibrium objects — instrument or bound the endogeneity; watch for leakage to uncovered sources and reshuffling, which change the welfare sign.
- RD in standards / eligibility: sharp thresholds (an attainment cutoff, a plant-size or emissions threshold) — McCrary/Cattaneo–Jansson–Ma density test, covariate smoothness, bias-corrected CIs.
- Inference: cluster at the regulatory/assignment level; use spatial (Conley) SEs when units are geographic and shocks are correlated across space.
Branch B — Climate / weather IV and damages
- Argue weather realizations are as-good-as-random conditional on location and time fixed effects; defend exclusion (weather affects the outcome only through the modeled channel).
- Separate weather (short-run shock) from climate (long-run expectation) — the adaptation margin is the contribution; a panel weather coefficient is not a long-run climate-damage estimate without an adaptation argument.
- Handle spatial and serial correlation in errors; report the estimand (marginal damage at current vs. future climate).
Branch C — Revealed-preference valuation (hedonics, travel cost)
- Hedonics: the amenity coefficient is biased by sorting (Tiebout) and omitted correlated amenities. Use a quasi-experimental shock to the amenity (a plant opening/closing, a Superfund listing, a regulation), boundary discontinuities, or a sorting model; do not present a cross-sectional hedonic as causal WTP.
- Travel cost: address endogenous trip cost, multi-purpose trips, and the recreation-demand censoring (count models, Kuhn–Tucker demand systems).
- Be explicit about what welfare measure the capitalization or demand estimate recovers (marginal WTP vs. total amenity value) and its partial- vs. general-equilibrium scope.
Branch D — Stated-preference valuation (CV, discrete-choice experiments)
- Survey design is the identification: incentive compatibility, a credible payment vehicle, a consequential decision, and a scope test (WTP rises with the size of the good).
- Address hypothetical bias (cheap-talk, certainty calibration, inferred valuation), protest responses, and status-quo/yea-saying effects.
- Pre-specify the choice model (RUM / mixed logit / latent class) and report welfare (compensating variation) with its uncertainty, not just utility coefficients.
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe it. Full map:
execution-with-mcp. JEEM is environmental economics — policy/regulation designs and non-market valuation; the causal chain serves its program-evaluation 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
Anti-patterns
- A cross-sectional hedonic presented as causal amenity WTP (sorting ignored)
- Staggered TWFE on a regulation rollout with no heterogeneity-bias discussion
- Treating a panel weather coefficient as a long-run climate-damage estimate (no adaptation)
- A contingent-valuation WTP with no scope test and no hypothetical-bias correction
- Permit-market effects ignoring leakage/reshuffling, so the welfare sign is unproven
- Default (non-spatial) standard errors on spatially correlated environmental data
Worked vignette (illustrative)
A hedonic finds homes near a closed coal plant rose 6% in value and reports this as the WTP for cleaner air. A referee flags sorting: cleaner air may attract higher-income buyers. The JEEM fix is to exploit the closure timing in a DiD with parcel fixed effects, restrict to a narrow boundary band, and show pre-closure price trends were parallel; the spatial-clustered estimate settles at, say, 4.5% (illustrative), now defensible as a capitalization of the air-quality change rather than a sorting artifact.
Referee pushback mapped to the identification fix
- "This hedonic is sorting, not WTP." → Exploit a quasi-experimental amenity shock with parcel/boundary FE; show parallel pre-shock price trends.
- "Staggered TWFE is biased here." → Re-estimate with Callaway–Sant'Anna or Sun–Abraham; display flat event-study leads.
- "Your weather coefficient is not a climate-damage estimate." → Separate the short-run shock from the long-run expectation; model the adaptation margin and state the estimand.
- "The CV number could be hypothetical bias." → Report a scope test, an incentive-compatible payment vehicle, and a cheap-talk or certainty calibration.
- "Permit-market leakage flips your welfare sign." → Bound the response of uncovered sources and show the net welfare conclusion holds.
From identification to a welfare number
The JEEM-specific discipline is that identification is only half the job — the identified object must map to welfare. A clean DiD on emissions identifies an effect; the contribution is the implied marginal damage avoided or the cost per ton abated that a regulator can use. State, for your branch, the assumptions that license the welfare mapping (a VSL for mortality, a behavioral model for capitalization, a utility specification for choice WTP) and carry them into jeem-theory-model and jeem-tables-figures so the welfare claim is auditable rather than asserted.
Output format
【Journal】Journal of Environmental Economics and Management
【Skill】jeem-identification
【Branch】policy-causal / climate-IV / RP-valuation / SP-valuation
【Data-to-welfare mapping】one sentence
【Identification evidence】pre-trends+leads / weather-exogeneity / amenity-shock+boundary / scope-test
【Inference】clustering level + spatial SEs if geographic
【What it does NOT identify】marginal vs total; PE vs GE; weather vs climate
【Source status】verified URL / 待核实 / not asserted
【Next skill】jeem-theory-model
