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Productivity Races I: Are Some Productivuty Measures Better Than Others?

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  • Douglas W Dwyer

Abstract

In this study we construct twelve different measures of productivity at the plant level and test which measures of productivity are most closely associated with direct measures of economic performance. We first examine how closely correlated these measures are with various measures of profits. We then evaluate the extent to which each productivity measure is associated with lower rates of plant closure and faster plant growth (growth in employment, output, and capital). All measures of productivity considered are credible in the sense that highly productive plants, regardless of measure, are clearly more profitable, less likely to close, and grow faster. Nevertheless, labor productivity and measures of total factor productivity that are based on regression estimates of production functions are better predictors of plant growth and survival than factor share-based measures of total factor productivity (TFP). Measures of productivity that are based on several years of data appear to outperform measures of productivity that are based solely on data from the most recent year.

Suggested Citation

  • Douglas W Dwyer, 1997. "Productivity Races I: Are Some Productivuty Measures Better Than Others?," Working Papers 97-2, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:97-2
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    File URL: https://www2.census.gov/ces/wp/1997/CES-WP-97-02.pdf
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    References listed on IDEAS

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    1. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    2. Douglas W Dwyer, 1995. "Technology Locks, Creative Destruction And Non-Convergence In Productivity Levels," Working Papers 95-6, Center for Economic Studies, U.S. Census Bureau.
    3. Olley, G Steven & Pakes, Ariel, 1996. "The Dynamics of Productivity in the Telecommunications Equipment Industry," Econometrica, Econometric Society, vol. 64(6), pages 1263-1297, November.
    4. Douglas W Dwyer, 1995. "Whittling Away At Productivity Dispersion," Working Papers 95-5, Center for Economic Studies, U.S. Census Bureau.
    5. Douglas W Dwyer, 1996. "ARE FIXED EFFECTS FIXED? Persistence in Plant Level Productivity," Working Papers 96-3, Center for Economic Studies, U.S. Census Bureau.
    6. Robert H Mcguckin & George A Pascoe, 1988. "The Longitudinal Research Database (LRD): Status And Research Possibilities," Working Papers 88-2, Center for Economic Studies, U.S. Census Bureau.
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    Cited by:

    1. Lucia Foster & John C. Haltiwanger & C. J. Krizan, 2001. "Aggregate Productivity Growth: Lessons from Microeconomic Evidence," NBER Chapters,in: New Developments in Productivity Analysis, pages 303-372 National Bureau of Economic Research, Inc.
    2. C.J. Krizan & John Haltiwanger & Lucia Foster, 2002. "The Link Between Aggregate and Micro Productivity Growth: Evidence from Retail Trade," Working Papers 02-18, Center for Economic Studies, U.S. Census Bureau.
    3. John Haltiwanger, 2002. "Understanding aggregate growth: The need for microeconomic evidence," New Zealand Economic Papers, Taylor & Francis Journals, vol. 36(1), pages 33-58.
    4. Raies, Asma & Ben Mimoun, Mohamed, 2009. "Le mécanisme de sélection des firmes est-il efficace? Une approche en termes de coût d’opportunité," L'Actualité Economique, Société Canadienne de Science Economique, vol. 85(2), pages 183-207, juin.

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    Keywords

    CES; economic; research; micro; data; microdata; chief; economist;

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