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Use when an American Economic Journal: Applied Economics (AEJ: Applied) manuscript needs a model to interpret, discipline, or structure its empirical estimates — not to lead the paper. Calibrates how much theory belongs in an empirical-first journal and where it goes; it does not design the identification (aeja-identification) or build a standalone structural estimation.
Passed 15 of 22 test cases with the skill, and 7 of 22 without it.
and 13 more in the full report
12 fixed5 passed either way2 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
We have a clean regression discontinuity design showing that local municipal subsidies raise heat pump adoption by 5 percentage points, but readers are asking whether this is driven by credit constraint relief or price responsiveness. We want to incorporate theoretical structure without letting a model upstage our main empirical results. Please analyze this setup and provide a breakdown covering: theory's job, tool chosen, key relation, validity assumptions + what it omits, magnitude delivered, and next step.
| Checked | Without skill | With skill |
|---|---|---|
| Uses the exact bracketed header '【Theory's job】'. | Missed | Passed |
| Uses the exact bracketed header '【Tool chosen】'. | Missed | Passed |
| Identifies the theory's primary job as mechanism. | Passed | Passed |
| Includes 'aeja-robustness' in the Next step tag. | Missed | Passed |
The instructions your model reads when the skill loads.
AEJ: Applied is empirical-first. Theory earns its place only when it interprets the estimate, sharpens the estimand, or unlocks a magnitude the design cannot deliver alone — never as the headline. Pick the lightest tool that does the job and keep the empirical estimate the star.
| Theory's job | Right amount of model | Where it goes |
|---|---|---|
| Name the mechanism | a few equations / a conceptual framework | short section before results |
| Map a reduced-form coefficient to a structural parameter | a sufficient-statistic / envelope argument | inline derivation + appendix |
| Deliver a welfare or counterfactual number | a calibrated or partially-structural model | a dedicated section, clearly bounded |
| Discipline heterogeneity / sign predictions | a simple model generating testable comparative statics | framework section, tested in results |
Where possible, express the welfare/policy object as a function of estimable elasticities (a Harberger/Chetty-style sufficient statistic) rather than estimating a full structural model. This keeps the credibility in the reduced-form design while delivering an economic magnitude. State the assumptions under which the sufficient statistic is valid and what it omits.
If the question genuinely requires out-of-sample counterfactuals or unobservable primitives, a small structural model is acceptable — but tie each parameter to a data feature, validate against an untargeted moment, and never let the model's assumptions silently replace the identification the design provided.
test cases passed, vs 7 / 22 without
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