This paper contributes to the productivity literature by demonstrating novel econometric methods to estimate input-mix efficiency (IME) in a parametric framework. Input-mix efficiency is defined as the potential improvement in productivity with change in input mix. Any change in input-mix (e.g., land to labor ratio) will result in change in productivity. We minimize a nonlinear input-aggregator function (e.g., Constant Elasticity of Substitution) to derive an expression for input-mix efficiency. We estimate a Bayesian stochastic frontier for obtaining mix efficiency using US state-level agricultural data for the period 1960 – 2004. We note significant variation in input-mix efficiency across the states and regions, attributable to diverse topographic, geographic and infrastructure conditions. Furthermore, comparisons of allocative and mix efficiencies provide insightful policy implications. For example, the production incentives such as taxes and subsidies could help farmers in adjusting their input mix in response to changes in input prices, which can affect the US agricultural productivity significantly. We provide a simple way of estimating mix efficiency in an aggregate-input, aggregate-output framework. This framework can be extended by i) using flexible functional forms; ii) introducing various time- and region-varying input aggregators; and iii) defining more sophisticated weights for input aggregators.