Imputation in U . S . Manufacturing Data and Implications for Within-Industry Productivity Dispersion ∗

In the U.S. Census Bureau’s 2002 and 2007 manufacturing data, respectively, 79% and 73% of observa­ tions have imputed data for at least one variable used to compute total factor productivity. The Bureau imputes for missing values using methods known to result in underestimation of variability and potential bias in multivariate inferences. We present an alternative strategy for handling the missing data based on multiple imputation via sequences of classification and regression trees. We use our imputations and the Bureau’s imputations to estimate within-industry productivity dispersions for every manufacturing industry. The results suggest that there is even more within-industry productivity dispersion in U.S. man­ ufacturing than previous research has indicated. We also estimate relationships between plant exit, pro­ ductivity, prices, and demand shocks. For these estimands, we find the results are substantively robust to using an alternative imputation strategy. ∗Some of the research in this paper was conducted while the first author was a Census Bureau employee. Any opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. Corresponding author: T. Kirk White. Email: thomas.kirk.white@census.gov.