Jru Identification

Identification Strategy (jru-identification)

When to trigger

• An experiment elicits a risk or ambiguity attitude but the mechanism may not be incentive-compatible (truthful revelation in doubt)
• A choice-list / BDM / matching-probability design is used and a referee questions whether it measures the parameter cleanly
• A structural model is estimated on field data and it is unclear what variation identifies the risk parameter (vs. beliefs, vs. constraints)
• A VSL or insurance-demand estimate rests on regressions whose exclusion or selection assumptions are not defended

The JRU identification bar

At JRU "identification" means the mapping from choices to the risk/uncertainty primitive must be explicit and defended — whether that primitive is elicited in the lab or estimated from the field. Because the journal spans theory, experiment, and empirics, identification splits by branch. The unifying demand: the procedure must reveal the intended parameter and not confound it with utility curvature, beliefs, or constraints.

Branch A: Experimental elicitation of risk / ambiguity preferences

• Incentive compatibility. State the mechanism and why it elicits truthfully: Becker–DeGroot–Marschak, multiple price lists / choice lists, the random-incentive (one-task-paid) system. Address the known threats — BDM is only IC under EU; the random-incentive system assumes isolation; multiple-switching in price lists signals confusion.
• Estimand before estimator. Name what the task is meant to recover — a switching point, a certainty equivalent, a matching probability — and the structural parameter it maps to (curvature, w(p), ambiguity index).
• Design that separates u from w. A single risk-attitude number cannot identify utility curvature and probability weighting jointly; use lottery menus designed to break that confound (e.g., varying probabilities at fixed outcomes).
• Stakes, hypothetical vs. real, order, and house-money effects stated and, where they matter, randomized.

Branch B: Structural / empirical estimation (risk preferences, VSL, insurance)

• Name the identifying variation. For VSL hedonic-wage: the wage–fatality-risk tradeoff, conditional on the compensating-differentials assumptions; defend why risk is not proxying for unobserved job disamenities. For insurance demand: the price/loss variation that moves takeup.
• Beliefs vs. preferences. Field choices reflect both; say how the design separates a risk attitude from a subjective belief (e.g., independent belief elicitation, or variation that moves one but not the other).
• Selection and measurement error in risk exposure addressed; report the estimating equation and the inference (clustered appropriately).
• Estimation regularity for structural models: objective (MLE/GMM/MSM), starting values, and recovery of known parameters in simulation.

The confounds JRU referees probe most

Three confounds recur across both branches; name how the design defeats each:

• Utility curvature vs. probability weighting. A single risk-attitude index cannot separate them; only a design that varies probabilities and outcomes independently can.
• Preferences vs. beliefs. Field and even lab choices reflect subjective probabilities; either elicit beliefs separately or use variation that moves price/cost while holding beliefs fixed.
• Preferences vs. constraints. Low takeup or conservative choices may reflect liquidity, not taste; control for or exploit variation in the constraint.

A clean identification section states, for each of these, whether the design breaks the confound or leaves it open — and the honest "leaves it open" entries belong in the limitations, not hidden.

Execution bridge (StatsPAI / Stata MCP)

Estimate and audit the design, don't only describe it. Full map: execution-with-mcp. JRU spans decision experiments and applied risk; randomization inference for experiments, DiD/IV for observational claims.

• 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

• Branch chosen; the choices-to-primitive mapping stated in one sentence
• Experimental: mechanism named and its incentive-compatibility (and its assumptions) defended
• Experimental: design separates utility curvature from probability weighting (or risk from ambiguity)
• Empirical: the identifying variation is named; preferences are separated from beliefs and constraints
• VSL: compensating-differentials / exclusion assumptions stated and probed
• Inference appropriate to the design (clustering at randomization or assignment level)
• The estimated parameter is not asked to carry interpretation the identification does not support