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

T. Kirk White1; Jerome P. Reiter2; Amil Petrin3

1 Center for Economic Studies, U.S. Census Bureau · 2 Duke University · 3 University of Minnesota, Twin Cities and NBER

The Review of Economics and Statistics 2018 open access

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.

DOI
10.1162/rest_a_00678
Volume
100 (3)
Pages
502-509
Language
en
Export
BibTeX
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