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Productivity Dynamics Beyond-the-Mean in U.S. Manufacturing Industries

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  • Jens J. Krüger

    (University of Jena, Faculty of Economics)

Abstract

The approach of quantile regression is used to explore the fine details of the relation of current period and lagged productivity levels. Productivity is calculated as total factor productivity by a nonparametric approach using data for U.S. manufacturing industries on the three- and four-digit levels. Bootstrap-based confidence intervals and specification tests are reported. The results point to a first-order Markov process as a valid description of productivity transitions. Further conditioning variables show up insignificant in the majority of cases considered. The most notable exception is the variability of the growth process which increases explanatory power substantially.

Suggested Citation

  • Jens J. Krüger, 2003. "Productivity Dynamics Beyond-the-Mean in U.S. Manufacturing Industries," Jenaer Schriften zur Wirtschaftswissenschaft (Expired!) 09/2003, Friedrich-Schiller-Universität Jena, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:jen:jenasw:2003-09
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    More about this item

    Keywords

    productivity; persistence; quantile regression;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General
    • L16 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Industrial Organization and Macroeconomics; Macroeconomic Industrial Structure
    • N10 - Economic History - - Macroeconomics and Monetary Economics; Industrial Structure; Growth; Fluctuations - - - General, International, or Comparative
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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