Rankings based on sample survey estimates inherit sampling variability, and joint confidence regions for the overall ranking typically provide coverage well above the nominal 1 − α level. This paper compares individual-based and difference-based joint confidence regions for rankings through a Monte Carlo simulation using American Community Survey data for K = 9 states and K = 51 (all 50 states and DC) at α ∈ {0.01, 0.05, 0.10, 0.20}. The difference-based method yields tighter regions and a smaller coverage gap between ranking-level and parameter-level coverage at all α, though this advantage narrows substantially at K = 51. A pseudo-Z score analysis explains the gap as a structural feature of rank inference.