Plant-Level Productivity and Imputation of Missing Data in the Census of Manufactures
AbstractIn the U.S. Census of Manufactures, the Census Bureau imputes missing values using a combination of mean imputation, ratio imputation, and conditional mean imputation. It is wellknown that imputations based on these methods can result in underestimation of variability and potential bias in multivariate inferences. We show that this appears to be the case for the existing imputations in the Census of Manufactures. We then present an alternative strategy for handling the missing data based on multiple imputation. Specifically, we impute missing values via sequences of classification and regression trees, which offer a computationally straightforward and flexible approach for semi-automatic, large-scale multiple imputation. We also present an approach to evaluating these imputations based on posterior predictive checks. We use the multiple imputations, and the imputations currently employed by the Census Bureau, to estimate production function parameters and productivity dispersions. The results suggest that the two approaches provide quite different answers about productivity.
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Bibliographic InfoPaper provided by Center for Economic Studies, U.S. Census Bureau in its series Working Papers with number 11-02.
Length: 24 pages
Date of creation: Jan 2011
Date of revision:
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-02-19 (All new papers)
- NEP-BEC-2011-02-19 (Business Economics)
- NEP-CIS-2011-02-19 (Confederation of Independent States)
- NEP-ECM-2011-02-19 (Econometrics)
- NEP-EFF-2011-02-19 (Efficiency & Productivity)
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