In a two-stage linear regression model with Normal noise, I consider James-Stein type shrinkage in the estimation of the first-stage instrumental variable coefficients. For at least four instrumental variables and a single endogenous regressor, I show that the standard two-stage least-squares estimator is dominated with respect to bias. I construct the dominating estimator by a variant of James-Stein shrinkage in a first-stage high-dimensional Normal-means problem followed by a control-function approach in the second stage; it preserves invariances of the structural instrumental variable equations.