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Innovation Allocation, Knowledge Composition and Long-Run Growth

Author

Listed:
  • Nan Li

    (International Monetary Fund)

  • Jie Cai

    (University of New South Wales)

Abstract

Technologies differ in their scopes of applications. The types of knowledge a country possesses have important implications on its growth. This paper develop a multi-sector model of innovation, trade and growth, in which knowledge in one sector is applicable to innovation in another sector in various degrees and a country's composition of knowledge is endogenously determined. We find that lower trade costs and better institutions (that increase production productivity) improve aggregate innovation efficiency through the within-country allocation of R&D towards sectors with higher knowledge applicability. We construct measures quantifying the sectoral knowledge applicability using cross-sector patent citations. Based on this index, we present cross-country evidence that broadly supports the model's implications.

Suggested Citation

  • Nan Li & Jie Cai, 2015. "Innovation Allocation, Knowledge Composition and Long-Run Growth," 2015 Meeting Papers 1100, Society for Economic Dynamics.
    • Handle: RePEc:red:sed015:1100
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    References listed on IDEAS

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    Cited by:

    1. Bolhuis, Marijn, 2019. "Catch-Up Growth and Inter-Industry Productivity Spillovers," MPRA Paper 94730, University Library of Munich, Germany.

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