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

A-Tier
Journal: Review of Economics and Statistics
Year: 2018
Volume: 100
Issue: 3
Pages: 502-509

Authors (3)

T. Kirk White (Government of the United State...) Jerome P. Reiter (not in RePEc) Amil Petrin (not in RePEc)

Score contribution per author:

1.341 = (α=2.01 / 3 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:tpr:restat:v:100:y:2018:i:3:p:502-509
Journal Field
General
Author Count
3
Added to Database
2026-01-29