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Industry Learning Environments and the Heterogeneity of Firm Performance

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Author Info

  • Natarajan Balasubramanian
  • Marvin Lieberman

Abstract

This paper characterizes inter-industry heterogeneity in rates of learning-by-doing and examines how industry learning rates are connected with firm performance. Using data from the Census Bureau and Compustat, we measure the industry learning rate as the coefficient on cumulative output in a production function. We find that learning rates vary considerably among industries and are higher in industries with greater R&D, advertising, and capital intensity. More importantly, we find that higher rates of learning are associated with wider dispersion of Tobin’s q and profitability among firms in the industry. Together, these findings suggest that learning intensity represents an important characteristic of the industry environment.

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File URL: ftp://ftp2.census.gov/ces/wp/2006/CES-WP-06-29.pdf
File Function: First version, 2006
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Bibliographic Info

Paper provided by Center for Economic Studies, U.S. Census Bureau in its series Working Papers with number 06-29.

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Length: 54 pages
Date of creation: Dec 2006
Date of revision:
Handle: RePEc:cen:wpaper:06-29

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Related research

Keywords: Learning; Firm Heterogeneity; RBV; Productivity;

This paper has been announced in the following NEP Reports:

References

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  1. Malerba, Franco, 1992. "Learning by Firms and Incremental Technical Change," Economic Journal, Royal Economic Society, vol. 102(413), pages 845-59, July.
  2. Gary P. Pisano & Richard M.J. Bohmer & Amy C. Edmondson, 2001. "Organizational Differences in Rates of Learning: Evidence from the Adoption of Minimally Invasive Cardiac Surgery," Management Science, INFORMS, vol. 47(6), pages 752-768, June.
  3. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
  4. Gruber, Harald, 2000. "The evolution of market structure in semiconductors: the role of product standards," Research Policy, Elsevier, vol. 29(6), pages 725-740, June.
  5. Gavin Sinclair & Steven Klepper & Wesley Cohen, 2000. "What's Experience Got to Do With It? Sources of Cost Reduction in a Large Specialty Chemicals Producer," Management Science, INFORMS, vol. 46(1), pages 28-45, January.
  6. Irwin, Douglas A & Klenow, Peter J, 1994. "Learning-by-Doing Spillovers in the Semiconductor Industry," Journal of Political Economy, University of Chicago Press, vol. 102(6), pages 1200-1227, December.
  7. Bahk, Byong-Hong & Gort, Michael, 1993. "Decomposing Learning by Doing in New Plants," Journal of Political Economy, University of Chicago Press, vol. 101(4), pages 561-83, August.
  8. S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
  9. John F. Muth, 1986. "Search Theory and the Manufacturing Progress Function," Management Science, INFORMS, vol. 32(8), pages 948-962, August.
  10. S.A. Lippman & R.P. Rumelt, 1982. "Uncertain Imitability: An Analysis of Interfirm Differences in Efficiency under Competition," Bell Journal of Economics, The RAND Corporation, vol. 13(2), pages 418-438, Autumn.
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Citations

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Cited by:
  1. Mulotte, L., 2013. "Do experience effects vary across governance modes? Evidence from new product introduction in the global aerospace industry, 1948–2000 (Online first)," Open Access publications from Tilburg University urn:nbn:nl:ui:12-5905829, Tilburg University.
  2. Martin Gervais & Nir Jaimovich & Henry E. Siu & Yaniv Yedid-Levi, 2013. "Technological Learning and Labor Market Dynamics," NBER Working Papers 19767, National Bureau of Economic Research, Inc.
  3. Osmundsen, Petter & Roll, Kristin Helen & Tveterås, Ragnar, 2010. "Faster Drilling with Expercience?," UiS Working Papers in Economics and Finance 2010/7, University of Stavanger.

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