jpam-research-design

Research Design & Identification (jpam-research-design)

Credible identification is JPAM's core bar. The journal evaluates the effects of real policies and programs, so the design must connect the theory of change (jpam-theory-building) to evidence a policymaker can trust. State the estimand, the assumptions that license a causal reading, and how each is defended — then rule out the single strongest rival explanation. Selection-on- observables alone rarely clears the bar.

When to trigger

• Specifying or defending identification for a policy evaluation
• A reviewer questioned causal claims, parallel trends, the instrument, the discontinuity, or confounding
• Choosing among RCT / DiD / RD / IV / synthetic control for a given policy variation
• Preparing a pre-analysis plan for a prospective program evaluation

Design menu (match to the policy variation)

• RCT / field experiment. The gold standard where feasible. Report randomization unit, balance, power/MDE, take-up, attrition, and ITT vs. TOT/LATE. Pre-register primary outcomes and subgroups.
• Difference-in-differences / event study. For staggered policy adoption use heterogeneity-robust estimators (Callaway–Sant'Anna, Sun–Abraham, de Chaisemartin–D'Haultfœuille, BJS imputation) — not naive TWFE. Show pre-trends as an event study; test, don't assert, parallel trends.
• Regression discontinuity / kink. For eligibility thresholds and benefit formulas. Report bandwidth selection, local-polynomial robustness, density/manipulation tests (McCrary/rddensity), covariate continuity, and the local nature of the estimand.
• Instrumental variables. For policy-induced variation. Defend the exclusion restriction substantively, report first-stage strength (effective F / weak-IV-robust inference), and interpret the LATE — whose behavior the instrument shifts.
• Synthetic control. For a single treated unit (a state/country policy). Report donor pool, pre- period fit, placebo/permutation inference, and leave-one-out robustness.

Inference & policy-evaluation standards

• Cluster at the level of treatment assignment; with few clusters use wild-cluster bootstrap.
• Adjust for multiple outcomes/subgroups (and say which test is primary).
• Distinguish ITT vs. treatment-on-the-treated; report take-up for any offer-based program.
• Specify the estimand and target population — JPAM cares which population the policy decision is about.

The adjudication test (JPAM-specific)

For the single strongest rival explanation (selection, anticipation, concurrent policy, mean reversion), write one sentence: "If the rival were driving the result, the data would look like ___; instead they look like ___." If you cannot, the design does not yet identify the policy effect.

Checklist

• Estimand and target population stated explicitly
• Identifying assumption named and defended, not asserted
• Modern, heterogeneity-robust estimator for staggered DiD; pre-trends shown
• RD: bandwidth, density, and covariate-continuity tests reported
• IV: substantive exclusion argument + first-stage strength + LATE interpretation
• Clustering at the assignment level; multiple-testing handled
• Strongest rival explicitly ruled out (adjudication sentence)

Anti-patterns

• Naive TWFE on staggered policy adoption; clustering at the wrong level
• "Causal effect of the policy" language on a selection-on-observables design
• Asserting parallel trends without an event-study pre-trend test
• A weak or substantively implausible instrument waved through on a high F alone
• Ignoring take-up/attrition so ITT and TOT are conflated
• Over-claiming a local RD/LATE estimate as the average policy effect for the whole population

Calibration anchors (hedged)

• Credible identification is JPAM's price of entry; a real exogenous source of variation typically beats a richer set of controls on the same selection problem.
• The estimand JPAM cares about is the one the policy decision is about — be explicit when an RD/LATE is local and the decision concerns a broader population, and discuss external validity rather than papering over it.
• For staggered policy rollouts, default to a heterogeneity-robust estimator and show the underlying TWFE bias (e.g., a Goodman-Bacon decomposition) if a reviewer expects it.

Worked micro-example (illustrative)

A state raises a benefit eligibility threshold; the team uses an RD at the income cutoff. The design write-up states the estimand (effect at the threshold), defends continuity (covariates smooth across the cutoff, no manipulation by a density test), reports bandwidth and local-polynomial robustness, and adjudicates the strongest rival: "If families were sorting just under the cutoff to qualify, the running-variable density would spike there; it does not." It then flags that the estimate is local and discusses how it might differ away from the threshold. (Illustrative.)

Output format

【Design】RCT / DiD-event-study / RD-kink / IV / synthetic control
【Estimand + population】what is identified, for whom
【Key assumption(s)】and how each is defended
【Rival ruled out】the adjudication sentence
【Inference】clustering, multiple-testing, weak-IV plan
【Next】jpam-data-analysis

Supplementary resources

• ../../resources/code/ — runnable DiD / IV / RD / DML skeletons (Stata + Python)
• ../../../shared-resources/empirical-methods/reviewer-objection-checklist.md — objections by identification strategy, each with a preemption