Fixed-Effect Saturation Is Not Weak Identification: Certifying Inference under Measurement Error

Fixed-effect saturation alone is not weak identification: in the baseline model, fixed-effect--residualized OLS is unbiased and conventional inference is asymptotically exact for every residual treatment variance $\tau^2=nQ_K>0$. Classical measurement error in the treatment restores it, and we derive Stock--Yogo-style critical values for $\tau^2$. Under the local drift $\sigma_\nu^2 = c^2/n$, attenuation produces a non-central limit whose non-centrality $\eta$ decreases in $\tau^2$ and, under a treatment-balance condition, depends on the fixed-effect dimension $\rho$ only through an overall $\sqrt{1-\rho}$ scaling, leaving the within reliability $\rho$-free. Inverting the leading quadratic size distortion gives a closed-form threshold; the breakdown reliability has a fixed-point form in the reported $t$-statistic alone. The diagnostic needs only a lower bound on reliability, where bias correction needs a point estimate. We separate a descriptive \emph{point pass} from a \emph{formal certificate}, evaluated at an upper confidence bound and carrying false-certification probability at most $\gamma$. A cluster-level score CLT and Arellano-variance consistency under a checkable projection-compatibility condition yield $\eta_{CR}=\eta/\sqrt{\psi}$. Simulations confirm the threshold; in a saturated democracy--growth panel, aggregate V-Dem polyarchy is certified at $\gamma=0.05$ while its judicial-constraints sub-index is flagged under i.i.d.\ and clustered errors. The diagnostic covers classical error in a continuous regressor, \emph{not} binary-treatment misclassification.