Imputation in US Manufacturing Data and Its Implications for Productivity Dispersion

• 成果类型: Article
• 署名作者: White, T. Kirk; Reiter, Jerome P.; Petrin, Amil
• 署名单位: Duke University; University of Minnesota System; University of Minnesota Twin Cities; National Bureau of Economic Research
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00678
• 发表日期: 2018-07
• 页码: 502-509
• 关键词: multiple imputation

摘要

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.