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Innovation Strategies, External Knowledge and Productivity Growth

Author

Listed:
  • Christopher F Baum

    () (Boston College
    DIW Berlin)

  • Hans Lööf

    () (Royal Institute of Technology, Stockholm)

  • Pardis Nabavi

    () (Royal Institute of Technology, Stockholm)

Abstract

This paper studies firms’ capability to recombine internal and local knowledge. It measures the outcome in terms of total productivity growth. Using Swedish data on commuting time for face-to-face contacts across all 290 municipalities, we employ a time sensitive approach for calculating localized knowledge within a municipality and and its close neighbors. Internal knowledge is captured by register data on firms’ innovation intensity. The two sources of knowledge are modelled in a production function setting by discrete composite variables with different combinations of input factors. Applying the model on Swedish firm level panel data, we find strong evidence of differences in the capacity to benefit from external knowledge among persistent innovators, temporary innovators and noninnovators. The results are consistent regardless of whether innovation efforts are measured in terms of the frequency of patent applications or the level of R&D investment.

Suggested Citation

  • Christopher F Baum & Hans Lööf & Pardis Nabavi, 2015. "Innovation Strategies, External Knowledge and Productivity Growth," Boston College Working Papers in Economics 885, Boston College Department of Economics, revised 06 Dec 2017.
  • Handle: RePEc:boc:bocoec:885
    Note: Previously circulated as "Innovation, Spillovers and Productivity Growth: A Dynamic Panel Data Approach"
    as

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    References listed on IDEAS

    as
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    Cited by:

    1. Hans Lööf & Pardis Nabavi, 2015. "Knowledge spillovers, productivity and patent," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 55(1), pages 249-263, October.

    More about this item

    Keywords

    Innovation strategies; localized knowledge; patents; TFP growth; panel data;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D

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