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Technology Locks, Creative Destruction And Non-Convergence In Productivity Levels

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  • Douglas W Dwyer
Abstract
This paper presents a simple solution to a new model that seeks to explain the distribution of plants across productivity levels within an industry, and empirically confirms some key predictions using the U.S. textile industry. In the model, plants are locked into a given productivity level, until they exit or retool. Convex costs of adjustment captures the fact that more productive plants expand faster. Provided there is technical change, productivity levels do not converge; the model achieves persistent dispersion in productivity levels within the context of a distortion free competitive equilibrium. The equilibrium, however, is rather turbulent; plants continually come on line with the cutting edge technology, gradually expand and finally exit or retool when they cease to recover their variable costs. The more productive plants create jobs, while the less productive destroy them. The model establishes a close link between productivity growth and dispersion in productivity levels; more rapid productivity growth leads to more widespread dispersion. This prediction is empirically confirmed. Additionally, the model provides an explanation for S-shaped diffusion.

Suggested Citation

  • 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.
    • Handle: RePEc:cen:wpaper:95-6
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    File URL: https://www2.census.gov/ces/wp/1995/CES-WP-95-06.pdf
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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. Stuart Kauffman & Jose Lobo & William G. Macready, 1998. "Optimal Search on a Technology Landscape," Research in Economics 98-10-091e, Santa Fe Institute.
    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. Mahmut Yasar & Roderick M. Rejesus & Ilhami Mintemur, 2006. "Plant-level dynamics and aggregate productivity growth in the Turkish meat-processing industry: Evidence from longitudinal data," Agribusiness, John Wiley & Sons, Ltd., vol. 22(1), pages 91-107.
    5. Kauffman, Stuart & Lobo, Jose & Macready, William G., 2000. "Optimal search on a technology landscape," Journal of Economic Behavior & Organization, Elsevier, vol. 43(2), pages 141-166, October.
    6. Rodrigo A. Cerda & Diego Saravia, 2009. "Corporate Tax, Firm Destruction and Capital Stock Accumulation: Evidence From Chilean Plants, 1979-2004," Working Papers Central Bank of Chile 521, Central Bank of Chile.
    7. David Rigby & Jurgen Essletzbichler, 2000. "Impacts of Industry Mix, Technological Change, Selection and Plant Entry/Exit on Regional Productivity Growth," Regional Studies, Taylor & Francis Journals, vol. 34(4), pages 333-342.
    8. Douglas W Dwyer, 1997. "Productivity Races II: The Issue of Capital Measurement," Working Papers 97-3, Center for Economic Studies, U.S. Census Bureau.
    9. 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.
    10. 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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