U.S. Census Bureau
Institutional Affiliation: U.S. Census Bureau
Information about this author at RePEc
NBER Working Papers and Publications
|August 2016||Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion|
with Jerome P. Reiter, Amil Petrin: w22569
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 in...
Published: 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
|February 2012||Plant-level Productivity and Imputation of Missing Data in U.S. Census Manufacturing Data|
with Jerome P. Reiter, Amil Petrin: w17816
Within-industry differences in measured plant-level productivity are large. A large literature has been devoted to explaining the causes and consequences of these differences. In the U.S. Census Bureau's manufacturing data, the Bureau imputes for missing values using methods known to result in underestimation of variability and potential bias in multivariate inferences. We present an alternative strategy for handling the missing data based on multiple imputation via sequences of classification and regression trees. We use our imputations and the Bureau's imputations to estimate within-industry productivity dispersions. The results suggest that there is more within-industry productivity dispersion than previous research has indicated. We also estimate relationships between productivity and ...
|January 2011||The Impact of Plant-level Resource Reallocations and Technical Progress on U.S. Macroeconomic Growth|
with Amil Petrin, Jerome P. Reiter: w16700
We build up from the plant level an "aggregate(d)" Solow residual by estimating every U.S. manufacturing plant's contribution to the change in aggregate final demand between 1976 and 1996. Our framework uses the Petrin and Levinsohn (2010) definition of aggregate productivity growth, which aggregates plant-level changes to changes in aggregate final demand in the presence of imperfect competition and other distortions/frictions. We decompose these contributions into plant-level resource reallocations and plant-level technical efficiency changes while allowing in the estimation for 459 different production technologies, one for each 4-digit SIC code. On average we find positive aggregate productivity growth of 2.2% in this sector during this period of declining share in U.S. GDP. We find th...
Published: Amil Petrin & Jerome Reiter & Kirk White, 2011. "The Impact of Plant-level Resource Reallocations and Technical Progress on U.S. Macroeconomic Growth," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 14(1), pages 3-26, January. citation courtesy of
|May 2008||Who Gentrifies Low-Income Neighborhoods?|
with Terra McKinnish, Randall Walsh: w14036
This paper uses confidential Census data, specifically the 1990 and 2000 Census Long Form data, to study the demographic processes underlying the gentrification of low-income urban neighborhoods during the 1990's. In contrast to previous studies, the analysis is conducted at the more refined census-tract level with a narrower definition of gentrification and more closely matched comparison neighborhoods. The analysis is also richly disaggregated by demographic characteristic, uncovering differential patterns by race, education, age and family structure that would not have emerged in the more aggregate analysis in previous studies. The results provide no evidence of displacement of low-income non-white households in gentrifying neighborhoods. The bulk of the increase in average family i...
Published: McKinnish, Terra & Walsh, Randall & Kirk White, T., 2010.
"Who gentrifies low-income neighborhoods?,"
Journal of Urban Economics,
Elsevier, vol. 67(2), pages 180-193, March.
citation courtesy of