We add to the methods for conditioning out serially correlated unobserved shocks to the production technology. We build on ideas first developed in They show how to use investment to control for correlation between input levels and the unobserved firm-specific productivity process. We show that intermediate inputs (those inputs which are typically subtracted out in a value-added production function) can also solve this simultaneity problem. We discuss some theoretical benefits of extending the proxy choice set in this direction and our empirical results suggest these benefits can be important.

In the U.S. Census Bureau’s 2002 and 2007 Censuses of Manufactures, 79% and 73% of observations, respectively, have imputed data for at least one variable used to compute total factor productivity (TFP). The bureau primarily imputes for missing values using mean-imputation methods, which can reduce the underlying variance of the imputed variables. For five variables entering TFP, we show that dispersion is significantly smaller in the Census mean-imputed versus the nonimputed data. We use classification and regression trees (CART) to produce multiple imputations with observed data for similar plants. For 90% of the 473 industries in 2002 and 84% of the 471 industries in 2007, we find that TFP dispersion increases as we move from Census mean-imputed data to nonimputed data to the CART-imputed data.