IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v603y2022ics0378437122004770.html
   My bibliography  Save this article

The growth path of high-tech industries: Statistical laws and evolution demands

Author

Listed:
  • Huang, Siyu
  • Shi, Yi
  • Chen, Qinghua
  • Li, Xiaomeng

Abstract

The development history of human society has always been accompanied by the continuous upgrading of industrial structures brought about by technological progress. Due to different production technologies, industries generally have different driving forces for their economic and social development, which can be described with scale characteristics. In recent years, studies have been conducted on the evolution path and growth conditions of high-tech industries; however, most of these studies have been limited by focusing on specific countries or cities. Based on gross domestic production (GDP) data and the urban scaling law for the counties of the United States, the current paper presents a universal path of industrial upgrading from a cross-regional perspective. The results show that while low-GDP counties have a comparative advantage in regard to low-tech industries, with the expansion of economic scale, high-GDP counties show a comparative advantage in regard to high-tech industries. This reversal phenomenon demonstrates the presence of a stable transition point at a GDP near $1010 during the period ranging from 2001 to 2019. The use of the scaling law and comparative advantage helps to capture the path of industrial upgrading using GDP empirical data. This information could help shed light on suggestions for the healthy and effective evolution of counties, both for governments and the research field.

Suggested Citation

  • Huang, Siyu & Shi, Yi & Chen, Qinghua & Li, Xiaomeng, 2022. "The growth path of high-tech industries: Statistical laws and evolution demands," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
  • Handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004770
    DOI: 10.1016/j.physa.2022.127719
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122004770
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2022.127719?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. , & Lorenz, Jan & ,, 2016. "Innovation vs. imitation and the evolution of productivity distributions," Theoretical Economics, Econometric Society, vol. 11(3), September.
    2. Borges, Ernesto P, 2004. "Empirical nonextensive laws for the county distribution of total personal income and gross domestic product," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 334(1), pages 255-266.
    3. D. Garlaschelli & T. Di Matteo & T. Aste & G. Caldarelli & M. I. Loffredo, 2007. "Interplay between topology and dynamics in the World Trade Web," Papers physics/0701030, arXiv.org.
    4. Jan Eeckhout, 2004. "Gibrat's Law for (All) Cities," American Economic Review, American Economic Association, vol. 94(5), pages 1429-1451, December.
    5. Patibandla, Murali & Petersen, Bent, 2002. "Role of Transnational Corporations in the Evolution of a High-Tech Industry: The Case of India's Software Industry," World Development, Elsevier, vol. 30(9), pages 1561-1577, September.
    6. Ernesto P. Borges, 2002. "Empirical nonextensive laws for the county distribution of total personal income and gross domestic product," Papers cond-mat/0205520, arXiv.org, revised Jan 2004.
    7. Acemoglu, Daron & Gancia, Gino & Zilibotti, Fabrizio, 2012. "Competing engines of growth: Innovation and standardization," Journal of Economic Theory, Elsevier, vol. 147(2), pages 570-601.3.
    8. Christian Richter Østergaard & Eunkyung Park, 2015. "What Makes Clusters Decline? A Study on Disruption and Evolution of a High-Tech Cluster in Denmark," Regional Studies, Taylor & Francis Journals, vol. 49(5), pages 834-849, May.
    9. Calvin Blackwell & Frank Hefner & Emily Lindberg, 2014. "Power Laws and Regional Convergence," The American Economist, Sage Publications, vol. 59(1), pages 70-75, May.
    10. Ufuk Akcigit & William R. Kerr, 2018. "Growth through Heterogeneous Innovations," Journal of Political Economy, University of Chicago Press, vol. 126(4), pages 1374-1443.
    11. Daron Acemoglu & Pascual Restrepo, 2018. "The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment," American Economic Review, American Economic Association, vol. 108(6), pages 1488-1542, June.
    12. Youngki Lee & Luis A. N. Amaral & David Canning & Martin Meyer & H. Eugene Stanley, 1998. "Universal features in the growth dynamics of complex organizations," Papers cond-mat/9804100, arXiv.org.
    13. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    14. Pierre-Alexandre Balland & Cristian Jara-Figueroa & Sergio G. Petralia & Mathieu P. A. Steijn & David L. Rigby & César A. Hidalgo, 2020. "Complex economic activities concentrate in large cities," Nature Human Behaviour, Nature, vol. 4(3), pages 248-254, March.
    15. Isaksson, Olov H.D. & Simeth, Markus & Seifert, Ralf W., 2016. "Knowledge spillovers in the supply chain: Evidence from the high tech sectors," Research Policy, Elsevier, vol. 45(3), pages 699-706.
    16. Marc Fleurbaey & Guillaume Gaulier, 2009. "International Comparisons of Living Standards by Equivalent Incomes," Scandinavian Journal of Economics, Wiley Blackwell, vol. 111(3), pages 597-624, September.
    17. Luis Bettencourt & Geoffrey West, 2010. "A unified theory of urban living," Nature, Nature, vol. 467(7318), pages 912-913, October.
    18. D. Garlaschelli & T. Di Matteo & T. Aste & G. Caldarelli & M. I. Loffredo, 2007. "Interplay between topology and dynamics in the World Trade Web," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 57(2), pages 159-164, May.
    19. Moshe Levy, 2009. "Gibrat's Law for (All) Cities: Comment," American Economic Review, American Economic Association, vol. 99(4), pages 1672-1675, September.
    20. Sen, Hu & Chunxia, Yang & Xueshuai, Zhu & Zhilai, Zheng & Ya, Cao, 2015. "Distributions of region size and GDP and their relation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 46-56.
    21. Luís M A Bettencourt & José Lobo & Deborah Strumsky & Geoffrey B West, 2010. "Urban Scaling and Its Deviations: Revealing the Structure of Wealth, Innovation and Crime across Cities," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-9, November.
    22. D. Garlaschelli & M. I. Loffredo, 2004. "Fitness-dependent topological properties of the World Trade Web," Papers cond-mat/0403051, arXiv.org, revised Oct 2004.
    23. Enrico Moretti, 2021. "The Effect of High-Tech Clusters on the Productivity of Top Inventors," American Economic Review, American Economic Association, vol. 111(10), pages 3328-3375, October.
    24. Acemoglu, Daron, 2012. "Introduction to economic growth," Journal of Economic Theory, Elsevier, vol. 147(2), pages 545-550.
    25. Ivus, Olena, 2010. "Do stronger patent rights raise high-tech exports to the developing world?," Journal of International Economics, Elsevier, vol. 81(1), pages 38-47, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yiwen Liu & Jian Li & Yi Xu, 2022. "Quantitative Evaluation of High-Tech Industry Policies Based on the PMC-Index Model: A Case Study of China’s Beijing-Tianjin-Hebei Region," Sustainability, MDPI, vol. 14(15), pages 1-17, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Dueñas & Giorgio Fagiolo, 2013. "Modeling the International-Trade Network: a gravity approach," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 155-178, April.
    2. Christian Düben & Melanie Krause, 2021. "Population, light, and the size distribution of cities," Journal of Regional Science, Wiley Blackwell, vol. 61(1), pages 189-211, January.
    3. Mungo, Luca & Lafond, François & Astudillo-Estévez, Pablo & Farmer, J. Doyne, 2023. "Reconstructing production networks using machine learning," Journal of Economic Dynamics and Control, Elsevier, vol. 148(C).
    4. Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2010. "Complex stock trading network among investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4929-4941.
    5. Giorgio Fagiolo & Tiziano Squartini & Diego Garlaschelli, 2013. "Null models of economic networks: the case of the world trade web," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 8(1), pages 75-107, April.
    6. Dominik Hartmann & Flavio L. Pinheiro, 2022. "Economic complexity and inequality at the national and regional level," Papers 2206.00818, arXiv.org, revised Jun 2022.
    7. Barigozzi, Matteo & Fagiolo, Giorgio & Mangioni, Giuseppe, 2011. "Identifying the community structure of the international-trade multi-network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(11), pages 2051-2066.
    8. Assaf Almog & Rhys Bird & Diego Garlaschelli, 2015. "Enhanced Gravity Model of trade: reconciling macroeconomic and network models," Papers 1506.00348, arXiv.org, revised Feb 2019.
    9. Song, Dong-Ming & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2009. "Statistical properties of world investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2450-2460.
    10. Marco Dueñas & Giorgio Fagiolo, 2014. "Global Trade Imbalances: A Network Approach," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 17(03n04), pages 1-29.
    11. Vittorio Carlei & Francesca Affortunato & Alessandro Marra & Marco Brogi, 2019. "Does centrality of importing countries affect export prices in the global trade?," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(1), pages 529-551, January.
    12. Martin Arvidsson & Niclas Lovsjö & Marc Keuschnigg, 2023. "Urban scaling laws arise from within-city inequalities," Nature Human Behaviour, Nature, vol. 7(3), pages 365-374, March.
    13. Alves, L.G.A. & Ribeiro, H.V. & Lenzi, E.K. & Mendes, R.S., 2014. "Empirical analysis on the connection between power-law distributions and allometries for urban indicators," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 409(C), pages 175-182.
    14. Rosanna Grassi & Paolo Bartesaghi & Stefano Benati & Gian Paolo Clemente, 2021. "Multi-Attribute Community Detection in International Trade Network," Networks and Spatial Economics, Springer, vol. 21(3), pages 707-733, September.
    15. Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2020. "Community structure in the World Trade Network based on communicability distances," Papers 2001.06356, arXiv.org, revised Jul 2020.
    16. Tiziano Squartini & Diego Garlaschelli, 2012. "Triadic motifs and dyadic self-organization in the World Trade Network," Papers 1201.1215, arXiv.org, revised Jan 2012.
    17. Ramos, Arturo & Sanz-Gracia, Fernando, 2015. "US city size distribution revisited: Theory and empirical evidence," MPRA Paper 64051, University Library of Munich, Germany.
    18. Luu, Duc Thi & Lux, Thomas & Yanovski, Boyan, 2017. "Structural correlations in the Italian overnight money market: An analysis based on network configuration models," Economics Working Papers 2017-02, Christian-Albrechts-University of Kiel, Department of Economics.
    19. Wen-Jie Xie & Na Wei & Wei-Xing Zhou, 2020. "Evolving efficiency and robustness of global oil trade networks," Papers 2004.05325, arXiv.org.
    20. Giorgio Fagiolo, 2010. "The international-trade network: gravity equations and topological properties," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 5(1), pages 1-25, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:603:y:2022:i:c:s0378437122004770. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.