Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion

T. Kirk White, Jerome P. Reiter and Amil Petrin
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Jerome P. Reiter: Duke University
Amil Petrin: University of Minnesota, Twin Cities and NBER

The Review of Economics and Statistics, 2018, vol. 100, issue 3, 502-509

Abstract: 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.

Date: 2018
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Working Paper: Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion (2016)
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