Importance weighting is a standard tool for correcting distribution shift, but its statistical behavior under target shift -- where the label distribution changes between training and testing while the conditional distribution of inputs given the label remains stable -- remains under-explored. We analyze importance-weighted kernel ridge regression under target shift and show that, because the weights depend only on the output variable, reweighting corrects the train-test mismatch without altering the input-space complexity that governs kernel generalization. Under standard RKHS regularity and capacity conditions and a mild Bernstein-type moment condition on the label weights, we obtain finite-sample guarantees showing that the estimator achieves the same convergence behavior as in the no-shift case, with shift severity affecting only the constants through weight moments. We complement these results with matching minimax lower bounds, establishing rate optimality and quantifying the unavoidable dependence on shift severity. We further study more general weighting schemes and prove that weight misspecification induces an irreducible bias: the estimator concentrates around an induced population regression function that generally differs from the desired test regression function unless the weights are accurate. Finally, we derive consequences for plug-in classification under target shift via standard calibration arguments.