Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion
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. The Bureau primarily imputes for missing values using mean-imputation methods which can reduce the true underlying variance of the imputed variables. For every variable entering TFP in 2002 and 2007 we show the dispersion is significantly smaller in the Census mean-imputed versus the Census non-imputed data. As an alternative to mean imputation we show how to use classification and regression trees (CART) to allow for a distribution of multiple possible impute values based on other plants that are CART-algorithmically determined to be similar based on other observed variables. For 90% of the 473 industries in 2002 and the 84% of the 471 industries in 2007 we find that TFP dispersion increases as we move from Census mean-imputed data to Census non-imputed data to the CART-imputed data.
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 or the National Bureau of Economic Research. All results have been reviewed to ensure that no confidential information is disclosed. Reiter gratefully acknowledges support from National Science Foundation grant SES 1131897.
T. Kirk White & Jerome P. Reiter & Amil Petrin, 2018. "Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion," The Review of Economics and Statistics, vol 100(3), pages 502-509. citation courtesy of