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Technology Flows Matrix Estimation Revisited

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  • Frederic Scherer

Abstract

This paper revisits the methodological problems of estimating matrices showing how technological advances--measured by industry research and development outlays--flow from industries of origin to using industries. An early effort relied upon the analysis of 15 112 US patents. Several alternative methods are explored to address methodological questions concerning the choice of carrier matrices, the handling of diagonal elements, and the treatment of capital goods flows. Technology flow matrices estimated using diverse combinations of assumptions are tested for goodness-of-fit relative to the original patent-based matrix and for their ability to "predict' productivity growth in Solowian regression equations. Although some anomalies emerge, the best results are obtained using combined first-order transactions and capital flows matrices with diagonal elements adjusted to reflect the ratio of internal process to all R&D spending. However, flow data compiled using the Leontief inverse matrix add explanatory power in productivity growth regressions.

Suggested Citation

  • Frederic Scherer, 2003. "Technology Flows Matrix Estimation Revisited," Economic Systems Research, Taylor & Francis Journals, vol. 15(3), pages 327-358.
    • Handle: RePEc:taf:ecsysr:v:15:y:2003:i:3:p:327-358
    DOI: 10.1080/0953531032000111790
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    Cited by:

    1. Peilei Fan, 2011. "Innovation capacity and economic development: China and India," Economic Change and Restructuring, Springer, vol. 44(1), pages 49-73, April.
    2. López-Pueyo, Carmen & Barcenilla-Visús, Sara & Sanaú, Jaime, 2008. "International R&D spillovers and manufacturing productivity: A panel data analysis," Structural Change and Economic Dynamics, Elsevier, vol. 19(2), pages 152-172, June.
    3. Bennato, Anna Rita & Valletti, Tommaso, 2014. "Pharmaceutical innovation and parallel trade," International Journal of Industrial Organization, Elsevier, vol. 33(C), pages 83-92.
    4. Giovanni Cerulli & Bianca Poti`, 2009. "Measuring Intersectoral Knowledge Spillovers: An Application Of Sensitivity Analysis To Italy," Economic Systems Research, Taylor & Francis Journals, vol. 21(4), pages 409-436.
    5. David B. Audretsch & Albert N. Link & John T. Scott, 2018. "Frederic M. Scherer: Over a Half Century—and Counting—of Seminal Scholarly Contributions," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 52(4), pages 501-508, June.
    6. Barbara Diaz & Laura Moniche & Antonio Morillas, 2006. "A Fuzzy clustering approach to the key sectors of the Spanish economy," Economic Systems Research, Taylor & Francis Journals, vol. 18(3), pages 299-318.
    7. Mardones, Cristian & Velásquez, Andrés, 2021. "Macroeconomic, intersectoral, and environmental effects of R&D subsidies in Chile: An input-output approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    8. Francisco Mas-Verdú & Anthony Wensley & Martin Alba & José García Álvarez-Coque, 2011. "How much does KIBS contribute to the generation and diffusion of innovation?," Service Business, Springer;Pan-Pacific Business Association, vol. 5(3), pages 195-212, September.
    9. Mauro Caminati & Arsenio Stabile, 2010. "The Pattern Of Knowledge Flows Between Technology Fields," Metroeconomica, Wiley Blackwell, vol. 61(2), pages 364-397, May.

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