Abstract:
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We investigate the effects of editing and imputation in the U.S. Census Bureau’s Annual Survey of Manufactures (ASM) on measures of aggregate and plant-level productivity growth. Previous research found that the Census Bureau’s imputations in the ASM, particularly regression imputes, tend to decrease measured within-industry productivity dispersion (White, Reiter and Petrin 2018; Rotemberg and White 2021). We add longitudinal ratio-of-ratios edits to the simultaneous Bayesian edit-imputation method of Kim et al. (2015), applied to the 2009-2013 and 2014-2018 panels of the ASM. We estimate plant-level and aggregate productivity growth in the manufacturing sector in the raw reported data (i.e., before editing and imputation), the Census Bureau’s edited-imputed data, and the Bayesian edited-imputed data.
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