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Structural change and industrial classification

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  • Hicks, Diana

Abstract

Understanding of structural change is compromised because scholars do not clearly articulate the limits of the classification infrastructure (NAICS or GICS) that shapes empirical analysis. These limits are particularly salient in the study of innovation, an activity that by its nature challenges existing categories. Because innovative industries are often not part of the classification infrastructure, they are invisible in empirical analyses and in government statistics. This paper examines the classification of a population of highly innovative, often small, firms working in gaming devices, packaging, filtration, photonics, imaging, biomedical research and fabless semiconductor design. I find examples of knowledge integration, vertical disintegration and emerging industries that challenge both NAICS and GICS exposing their strengths and weaknesses.

Suggested Citation

  • Hicks, Diana, 2011. "Structural change and industrial classification," Structural Change and Economic Dynamics, Elsevier, vol. 22(2), pages 93-105, June.
    • Handle: RePEc:eee:streco:v:22:y:2011:i:2:p:93-105
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    References listed on IDEAS

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    Cited by:

    1. Christensen, Jesper Lindgaard, 2013. "The ability of current statistical classifications to separate services and manufacturing," Structural Change and Economic Dynamics, Elsevier, vol. 26(C), pages 47-60.
    2. Niembro, Andrés & Calá, Carla Daniela, 2024. "Regional structural change in Argentina (1996-2019): Concepts, measurements and unequal trajectories over the business cycle," Nülan. Deposited Documents 4106, Universidad Nacional de Mar del Plata, Facultad de Ciencias Económicas y Sociales, Centro de Documentación.
    3. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Papers 1706.04210, arXiv.org, revised Dec 2017.
    4. Jinho Choi & Yong Sik Chang, 2020. "Development of a New Methodology to Identity Promising Technology Areas Using M&A Information," Sustainability, MDPI, vol. 12(14), pages 1-25, July.
    5. François Lafond & Daniel Kim, 2019. "Long-run dynamics of the U.S. patent classification system," Journal of Evolutionary Economics, Springer, vol. 29(2), pages 631-664, April.
    6. Garbellini, Nadia & Wirkierman, Ariel Luis, 2014. "Blocks and circularity in labour requirements: An interplay between clusters and subsystems in the EU," Structural Change and Economic Dynamics, Elsevier, vol. 28(C), pages 60-74.
    7. Anders Broström & Eric Giertz, 2021. "Service development accounts for an even smaller share of European R&D investments than we may think," The Journal of Technology Transfer, Springer, vol. 46(4), pages 1256-1267, August.
    8. Niembro, Andrés & Calá, Carla Daniela, 2023. "Cambio estructural regional en Argentina: conceptualización y medición a nivel de áreas económicas locales (1996-2019)," Nülan. Deposited Documents 4051, Universidad Nacional de Mar del Plata, Facultad de Ciencias Económicas y Sociales, Centro de Documentación.
    9. Jinho Choi & Sunghun Chung & Yong Sik Chang, 2019. "Is M&A Information Useful for Exploring Promising Industries and Technologies?," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    10. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Data, MDPI, vol. 2(2), pages 1-77, June.

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