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.

We propose a new measure of allocative efficiency based on unrealized increases in aggregate productivity growth. We show that the difference in the value of the marginal product of an input and its marginal cost at any plant—the plant-input gap—is exactly equal to the change in aggregate output that would occur if that plant changed that input's use by one unit. We show how to estimate this gap using plant-level data for 1982 to 1994 from Chilean manufacturing. We find the gaps for blue- and white-collar labor are quite large in absolute value, and these gaps (unlike for materials and electricity) are increasing over time. The timing of the sharpest increases in the labor gaps suggests that they may be related to increases in severance pay.