jebo-robustness

Research

Use when a Journal of Economic Behavior & Organization (JEBO) result may be fragile to demand effects, multiple comparisons, specification, or tuning. Organizes robustness by the behavioral threat each check addresses — for experiments, observational designs, and simulations; it does not redesign the identification or write the prose.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Journal-of-Economic-Behavior-and-Organization-Skills/skills/jebo-robustness/SKILL.md

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Robustness Strategy (jebo-robustness)

When to trigger

An experiment has several treatment arms / outcomes and you have not corrected for multiplicity
A referee could attribute the effect to experimenter demand, confusion, or order effects
An observational result moves with controls, sample windows, or estimator choice
An agent-based result might be an artifact of grid, seed, or tuning choices
You have a long, unstructured "robustness" appendix and no map from check to threat

Organize robustness by behavioral threat, not by appendix list

At JEBO the right question is never "did we run enough checks?" but "for each way this could not be the behavioral mechanism, did we show it survives?" Build a threat → check map. The threats differ sharply across the four archetypes.

Experiments (lab/online/field)

Threat	Check JEBO referees expect
Experimenter demand	demand-treatment bounds (de Quidt-style), neutral framing, obfuscated objective
Multiple comparisons	pre-registered primary outcome; MHT correction (Romano–Wolf, List–Shaikh–Xu, BH) across arms/outcomes
Comprehension / confusion	results hold among subjects passing comprehension checks
Order / sequence effects	randomize order; show within-order stability
Subject pool / platform	replicate across pools (student vs. Prolific vs. field); attention screens on online samples
Bots / inattentive online subjects	attention checks, completion-time filters, duplicate-IP screening
Selection / attrition	balance among completers; Lee bounds if differential

Observational behavioral empirics

Specification curve / multiverse over reasonable controls and windows; show the headline is not a knife-edge.
Inference robustness: clustering level, wild-cluster bootstrap with few clusters, randomization inference where natural.
Placebo / falsification: effect absent where the mechanism predicts none; pre-trend tests for DID.
Sensitivity to unobservables (Oster δ; Rambachan–Roth honest-DID for parallel-trend violations).

Simulation / agent-based

Parameter sweeps over the behavioral-rule space; report the region where the result holds.
Seed sensitivity (many runs, report distribution not one path); grid/step-size invariance.
Sensitivity of emergent regularities to the behavioral rule chosen (e.g., reinforcement vs. EWA learning).

Distinguish "the effect is real" from "the mechanism is the claimed one"

JEBO's distinctive robustness demand is mechanism robustness: even a real, replicable effect can be driven by a different behavioral channel than claimed. Where possible, add a check that separates your mechanism from the leading alternative (a moderation test the rival channel does not predict, a mediation analysis with the caveats stated, or a treatment that shuts the rival channel off).

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JEBO spans behavioral/experimental and applied micro; randomization inference for experiments, DiD/IV for observational claims.

Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg.
OVB sensitivity: oster_delta / sensemakr.
Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley.
Re-fit off one handle: audit_result(result_id) lists missing checks + the exact suggest_function for each.
Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Decisive checks in the body, exhaustive battery in the appendix. JF execution walkthrough.

Checklist

Every robustness exhibit is labeled with the specific threat it neutralizes
Experiments: demand effects bounded; primary outcome pre-registered; MHT correction reported
Comprehension/order/subject-pool/attention threats addressed for the relevant design
Observational: spec-curve + inference robustness + placebo/falsification + unobservables sensitivity
Simulation: parameter sweeps + seed/grid sensitivity reported
At least one check separates the claimed mechanism from the leading alternative
The headline magnitude is reported with honest uncertainty across specifications

Anti-patterns

A robustness appendix that lists 20 regressions without saying what threat each rebuts
Reporting only the cell that survives MHT, omitting the corrected p-values across all arms
Treating "the effect replicates" as proof the mechanism is the claimed one
Online experiments with no attention/bot screening
An agent-based headline shown for a single seed and a single grid
Hand-picked control sets that quietly maximize the coefficient

Output format

【Archetype】experiment / observational / simulation
【Threat → check map】
  - <threat 1> → <check>
  - <threat 2> → <check>
【Multiplicity】primary outcome pre-registered? MHT method:
【Mechanism vs. alternative】<test separating claimed channel from rival>
【Headline stability】<range of estimate across specs + uncertainty>
【Next step】jebo-tables-figures