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Industry Learning Environments and the Heterogeneity of Firm Performance

Author

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  • 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.

Suggested Citation

  • Natarajan Balasubramanian & Marvin Lieberman, 2006. "Industry Learning Environments and the Heterogeneity of Firm Performance," Working Papers 06-29, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:06-29
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    File URL: https://www2.census.gov/ces/wp/2006/CES-WP-06-29.pdf
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    References listed on IDEAS

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    1. Malerba, Franco, 1992. "Learning by Firms and Incremental Technical Change," Economic Journal, Royal Economic Society, vol. 102(413), pages 845-859, July.
    2. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    3. 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 561-583, August.
    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. 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.
    6. 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.
    7. S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Multiproduct Firms, Product Differentiation, and Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
    8. 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.
    9. John F. Muth, 1986. "Search Theory and the Manufacturing Progress Function," Management Science, INFORMS, vol. 32(8), pages 948-962, August.
    10. 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.
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    Citations

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    Cited by:

    1. Mulotte, L., 2014. "Do experience effects vary across governance modes? Evidence from new product introduction in the global aerospace industry, 1948–2000," Other publications TiSEM 2c79d4d6-2b71-4160-9781-f, Tilburg University, School of Economics and Management.
    2. Osmundsen, Petter & Roll, Kristin Helen & Tveterås, Ragnar, 2010. "Faster Drilling with Expercience?," UiS Working Papers in Economics and Finance 2010/7, University of Stavanger.
    3. Martin Gervais & Nir Jaimovich & Henry E. Siu & Yaniv Yedid‐Levi, 2015. "Technological Learning And Labor Market Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 27-53, February.
    4. Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2013. "Learning to learn and productivity growth: Evidence from a new car-assembly plant," Omega, Elsevier, vol. 41(2), pages 336-344.
    5. Marco Cucculelli & Lidia Mannarino & Valeria Pupo & Fernanda Ricotta, 2014. "Owner-management, firm age and productivity in Italian family firms," Mo.Fi.R. Working Papers 99, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.
    6. Kareem Haggag & Brian McManus & Giovanni Paci, 2017. "Learning by Driving: Productivity Improvements by New York City Taxi Drivers," American Economic Journal: Applied Economics, American Economic Association, vol. 9(1), pages 70-95, January.
    7. Funk, Jeffrey L. & Magee, Christopher L., 2015. "Rapid improvements with no commercial production: How do the improvements occur?," Research Policy, Elsevier, vol. 44(3), pages 777-788.
    8. Osmundsen, Petter & Roll, Kristin Helen & Tveteras, Ragnar, 2012. "Drilling speed—the relevance of experience," Energy Economics, Elsevier, vol. 34(3), pages 786-794.
    9. Natarajan Balasubramanian, 2011. "New Plant Venture Performance Differences Among Incumbent, Diversifying, and Entrepreneurial Firms: The Impact of Industry Learning Intensity," Management Science, INFORMS, vol. 57(3), pages 549-565, March.
    10. Marco Cucculelli, 2017. "Firm age and the probability of product innovation. Do CEO tenure and product tenure matter?," Mo.Fi.R. Working Papers 140, Money and Finance Research group (Mo.Fi.R.) - Univ. Politecnica Marche - Dept. Economic and Social Sciences.

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    Keywords

    Learning; Firm Heterogeneity; RBV; Productivity;

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