Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Use when appraising the credibility of the primary studies an Annual Review of Economics (ARE) review synthesizes, and when calibrating comprehensiveness, balance, and fair treatment of competing views. Weighs evidence and audits even-handedness; it does not design the spine (arecon-organizing-framework) or run your own identification.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 64% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 73% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 67% | 0% |
An ARE review runs no identification of its own — there is no design to defend, no replication package of your data. Instead you act as the field's referee-of-record for adjacent readers: you judge how much weight each primary study can bear so the review weighs the evidence correctly. Make the appraisal explicit, not implicit:
You are not re-running these — you are rating their credibility in the evidence matrix so the review's conclusions track the best evidence, not the loudest paper.
A few moments in an ARE review justify running something instead of only rating it: a survey table pools estimates and wants a formal meta-analytic average or publication-bias check; a pivotal magnitude predates the staggered-DiD corrections and its replication data are public, so you can report what callaway_santanna or bacon_decomposition actually does to it rather than speculate; or a weak-instrument worry could be settled by an effective_f_test on the archived first stage. For these, hand off to execution-with-mcp, which maps each design and reviewer objection to the callable StatsPAI / Stata MCP chain (detect_design → fit with as_handle=true → audit_result). Label any such figure as your re-analysis, and never report a number you did not compute.
Conflicting results are reconciled by credibility and by what each study estimates, never by tallying "7 studies positive, 4 negative." Two estimates that disagree often measure different objects (different populations, estimands, time horizons); say so, and let the framework's cells carry the distinction. A pooled "consensus" across non-comparable designs manufactures false agreement that ARE's methodologically literate readers will catch.
A review must be comprehensive in coverage yet selective in emphasis — and stay accessible in ~25–40 pages. Tier the corpus:
| Tier | Treatment | |------|-----------| | Foundational / field-defining | discussed in text, with what they established and their limits | | Important contributions | grouped and weighed within framework cells; cited with their finding | | Confirmatory / incremental | cited in clusters ("see also …") to show coverage without bloating prose | | Tangential | cited only where they bear on a specific claim |
Comprehensiveness is proven by the citation set + saturation log (arecon-literature-synthesis); selectivity is exercised in the prose.
ARE referees are frequently the surveyed authors themselves, so balance is strategic as well as ethical:
text【Credibility appraisal】pivotal studies rated (design/identification/robustness)? Y/N 【Conflict handling】reconciled by credibility + estimand (not vote-count)? Y/N 【Tiering】corpus split foundational/important/confirmatory/tangential? Y/N 【Comprehensiveness】saturation log supports "nothing important missing"? Y/N 【Steelman】each rival school stated at its strongest? Y/N 【Controversy】evidence-to-settle stated; author's read labelled? Y/N 【Self-citation audit】own work at warranted tier; emphasis identity-blind? Y/N 【Next step】→ arecon-tables-figures (who-found-what tables) → arecon-writing-style
Other measured skills in the registry, with their headline benchmark lift.