Estimating production functions with robustness against errors in the proxy variables

This paper proposes a new approach to the identification and estimation of production functions. It extends the literature on the structural estimation of production functions, which dates back to the seminal work of Olley and Pakes (1996), by relaxing the scalar-unobservable assumption about the proxy variables. The key additional assumption needed in the identification argument is the existence of two conditionally independent proxy variables. The proposed generalized method of moment (GMM) estimator is flexible and straightforward to apply. The method is applied to study how rapidly firms in the Chilean food-product industry adjust their inputs in response to shocks to their productivity. (C) 2019 Elsevier B.V. All rights reserved.