Production Function Estimation and Capital Measurement Error

We look into the impact of measurement error in capital on the estimation of production functions. We introduce an identification scheme and an estimation procedure that jointly deals with measurement error in capital and the standard simultaneity bias due to unobserved productivity shocks. We use lagged investment to instrument for potentially mis-measured capital stock, while conditioning on the part of productivity that is persistent. Our estimation routine nests standard approaches in the literature, such as Ackerberg, Caves, and Frazer (2015). It requires no additional data as investment is usually collected with other producer level data. We show through Monte-Carlo experiments that a 40 percent measurement error in capital yields capital coefficients that are biased downward by a factor of two. We illustrate our approach using data for three distinct economies: China, India and Chile; which experienced radically different processes of capital and productivity dynamics. We find capital coefficients that are typically two times larger than those using standard approaches that only control for simultaneity