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Decomposing Aggregate Productivity

July 2022

Written by: Chen Yeh, N. Aaron Pancost

Working Paper Number:

CES-22-25

Abstract

In this note, we evaluate the sensitivity of commonly-used decompositions for aggregate productivity. Our analysis spans the universe of U.S. manufacturers from 1977 to 2012 and we find that, even holding the data and form of the production function fixed, results on aggregate productivity are extremely sensitive to how productivity at the firm level is measured. Even qualitative statements about the levels of aggregate productivity and the sign of the covariance between productivity and size are highly dependent on how production function parameters are estimated. Despite these difficulties, we uncover some consistent facts about productivity growth: (1) labor productivity is consistently higher and less error-prone than measures of multi-factor productivity; (2) most productivity growth comes from growth within firms, rather than from reallocation across firms; (3) what growth does come from reallocation appears to be driven by net entry, primarily from the exit of relatively less-productive firms.

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Keywords Keywords are automatically generated using KeyBERT, a powerful and innovative keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant keywords.

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:
production, productive, estimation, macroeconomic, report, manufacturing, aggregate, productivity growth, growth, manufacturer, labor, produce, measures productivity, productivity measures, factor productivity, productivity wage, accounting, sector, productivity analysis, productivity estimates, growth productivity, productivity size, revenue, productivity firms, firms productivity, aggregate productivity

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:
Census of Manufactures, National Bureau of Economic Research, Cobb-Douglas, Labor Productivity, Federal Reserve Bank, Longitudinal Business Database, Census of Manufacturing Firms, North American Industry Classification System, Census Bureau Disclosure Review Board, Federal Statistical Research Data Center

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