Production function estimation using subjective expectations data

Abstract

Standard proxy methods for estimating production functions in the \Olley and Pakes (1996) tradition require assumptions on input choices. We introduce a new method that exploits (increasingly available) data on firms' expectations of their future output and inputs that allows us to obtain consistent production function parameter estimates while relaxing these input demand assumptions. In contrast to both proxy and dynamic panel methods like Blundell and Bond (2000), our proposed estimator can be implemented on a single cross-section of data and Monte Carlo simulations show it outperforms alternative estimators when firms' material input choices are subject to optimization error. Implementing a range of production function estimators on UK panel data, we find our proposed estimator yields results that are either similar to or more credible than commonly-used alternatives. These differences are larger in industries where material inputs appear harder to optimize. We show that the share of cross-firm TFP dispersion accounted for by persistent productivity differences is substantially larger when calculated using parameter estimates from our proposed estimator.