--- name: aer-identification description: Use when selecting, implementing, or stress-testing the causal identification strategy for an empirical economics manuscript — difference-in-differences (including staggered designs), instrumental variables (including weak-IV-robust inference), regression discontinuity, synthetic control, or shift-share / Bartik. Apply before writing the introduction or results. --- # AER Identification ## Overview In modern AER-track empirical economics, **identification is the paper**. A weak design cannot be rescued by clever writing, more controls, or a larger sample. This skill walks through the five canonical design-based strategies, the modern defaults that have replaced naive textbook implementations, and the referee-anticipating tests each demands. If the identification strategy is fragile, return to `aer-topic-selection`. There is no point polishing an indefensible empirical strategy. ## When to Use - Designing the empirical strategy for a new project - The current strategy is TWFE / first-stage F / naive RDD and the referee will flag it - A prior submission was rejected on identification grounds and the design needs rebuilding - Choosing between two candidate identification strategies for the same question ## Master Decision Tree ``` Is treatment assignment plausibly random conditional on observables? ├── Yes, by design (RCT, lottery) → run the RCT analysis; register PAP via AEA RCT Registry └── No → identification must come from variation ├── Sharp threshold in a running variable → RDD (sharp or fuzzy) ├── Discrete policy change in some units, not others, over time → DiD │ ├── Single treatment date → canonical 2×2 DiD │ └── Staggered adoption → Callaway-Sant'Anna or Borusyak-Jaravel-Spiess ├── Endogenous regressor + plausibly exogenous shifter → IV │ ├── Shifter × pre-existing exposure shares → shift-share / Bartik │ └── Single instrument → weak-IV-robust inference if F < 50 ├── One treated unit / aggregate intervention → synthetic control └── None of the above → reconsider the question ``` ## Difference-in-Differences ### Canonical 2×2 (single treatment date, two groups) Use TWFE if and only if: - Treatment timing is **simultaneous** for all treated units - The control group is **never treated** - Treatment-effect heterogeneity is implausible Otherwise, TWFE produces biased and often sign-flipped estimates. ### Staggered Adoption (most modern applications) **Do not use TWFE.** Use one of: - **Callaway and Sant'Anna (2021)** — `csdid` (Stata), `did` (R). Identifies group-time average treatment effects (ATT(g,t)); estimands are doubly robust; supports event-study aggregation. - **Borusyak, Jaravel, and Spiess (2024)** — imputation estimator. - **de Chaisemartin and D'Haultfœuille (2020)** — `did_multiplegt`. - **Sun and Abraham (2021)** — interaction-weighted estimator for event studies. **Required diagnostics:** 1. Goodman-Bacon decomposition to show the share of weight from "forbidden" comparisons under TWFE 2. Event-study plot with the imputation or Callaway-Sant'Anna estimator 3. Pre-trends test reported as the joint test, not just the visual 4. Heterogeneity by treatment cohort ### Pre-Trends A flat pre-trend is necessary but not sufficient. Report: - Visual event-study plot with 95% confidence intervals - Formal joint test of pre-period coefficients (p-value) - Honest DiD (Rambachan-Roth 2023) sensitivity bounds for the post-period ## Instrumental Variables ### Weak Instruments The first-stage F > 10 rule is **obsolete**. Modern conventions: - For just-identified models: report **Anderson-Rubin (AR) confidence sets** as the primary inference. AR has correct size regardless of instrument strength. - For F < 50: 2SLS confidence intervals are unreliable; AR is required, not optional. - Stock-Yogo critical values for TSLS bias assume homoskedasticity and are rarely valid in modern clustered settings. Use `weakivtest` (Stata), `ivDiag` (R), or the Olea-Pflueger effective F statistic. ### Exclusion Restriction The IV's credibility depends on a story, not a test. State the exclusion restriction in **one sentence** in the introduction and defend it with: - Institutional narrative (one paragraph) - A placebo regression where the instrument predicts an outcome it should not affect - Sensitivity analysis: how much exclusion-restriction violation would overturn the result (Conley et al. 2012) ### Shift-Share / Bartik Two valid sources of identification, with very different implications: 1. **Exogenous shares (Goldsmith-Pinkham, Sorkin, Swift 2020)** — argue that pre-existing exposure shares are conditionally exogenous; report the Rotemberg weights and inspect the top-5 industries driving identification. 2. **Exogenous shocks (Borusyak, Hull, Jaravel 2022; Adão, Kolesár, Morales 2019)** — argue that aggregate shocks are as-good-as-random; report shock-level inference. Pick one explicitly. Do not hand-wave between the two. ## Regression Discontinuity ### Modern Defaults - **Local linear regression with a triangular kernel.** Polynomials of order > 1 are discouraged (Gelman-Imbens 2019). - **MSE-optimal bandwidth (Calonico-Cattaneo-Titiunik 2014)** with the robust bias-corrected confidence interval. Use `rdrobust`. - **Donut RDD** if bunching near the cutoff is a concern. - **Covariate adjustment** for efficiency; main result must hold without it. ### Required Diagnostics 1. McCrary (2008) / Cattaneo-Jansson-Ma (2020) density test for manipulation of the running variable 2. Balance tests on predetermined covariates at the cutoff 3. Placebo cutoffs away from the true threshold 4. Bandwidth sensitivity — show the estimate across at least three bandwidths 5. Visual RD plot using `rdplot` with the binning method explicitly stated ## Synthetic Control ### When Appropriate - One (or few) treated units - Long pre-treatment outcome series (≥ 10 periods) - A large donor pool of plausibly comparable untreated units - Aggregate intervention (policy at the country, state, city level) ### Modern Extensions - **Generalized synthetic control (Xu 2017)** for multiple treated units - **Augmented synthetic control (Ben-Michael, Feller, Rothstein 2021)** for bias correction - **Synthetic DiD (Arkhangelsky et al. 2021)** combining SCM and DiD weighting ### Required Diagnostics 1. Placebo (in-time): apply SCM to pre-treatment fake intervention dates 2. Placebo (in-space): apply SCM to every donor as if it were treated; report the distribution of placebo effects 3. Permutation inference / Fisher exact p-value 4. Weight vector reported in the appendix; donors with > 10% weight discussed ## Field Experiments and RCTs If the paper uses a field experiment: - **Register with AEA RCT Registry** before the intervention begins. AEA journals require this prior to submission. - **Pre-analysis plan (PAP)** posted before unblinding. Per Olken and others, keep the PAP moderate in scope — pre-specify primary outcomes and the analysis specification, leave exploratory work clearly labeled as such. - **Power calculations** in the manuscript or appendix. - **Multiple-hypothesis correction** if more than one primary outcome. - **Attrition** documented and tested for differential attrition by treatment arm. ## Mechanism vs. Identification A common confusion: **identification answers whether X causes Y; mechanism answers why.** Mechanism evidence should not weaken the identification of the main effect. Run: - Subgroup heterogeneity (does the effect concentrate where theory predicts?) - Mediation analysis only if the mediator is itself plausibly exogenous (rare) - Auxiliary outcomes consistent with the proposed channel ## Red Flags for Referees - TWFE on staggered data with no Goodman-Bacon decomposition - First-stage F = 12 cited as evidence of instrument strength - RDD with a polynomial of order 4 - Synthetic control with no placebo inference - DiD with a "control group" of eventually-treated units - IV exclusion restriction defended only by "we control for X" - Quoting an Angrist-Pischke citation as a substitute for showing the diagnostic ## Repository Resources When working from the AER-skills repository or plugin bundle, load only the relevant resource: - Staggered DiD implementation: `templates/stata/03_main_did.do`, `templates/r/03_main_did.R`, or `templates/python/main_did.py` - Classic design examples: `examples/aer-exemplars.md` ## Handoff ```text STRATEGY: MODERN ESTIMATOR USED: REQUIRED DIAGNOSTICS REPORTED: INFERENCE METHOD: WEAK-IV / TWFE / POLY-ORDER RED FLAGS: NEXT SKILL: aer-robustness ``` ## Anti-Patterns - Defending an old design ("the prior literature used TWFE") when modern estimators exist - Reporting OLS-with-controls as the main specification and IV/RD as "robustness" - Using more than one identification strategy as if they were independent confirmations when they share identifying variation - Footnoting the identifying assumption instead of stating it in the introduction