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A cointegration analysis of dynamic externalities

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  • Zheng, Xiao-Ping

Abstract

This paper presents a cointegration analysis on the effects of dynamic externalities upon economic growth using time-series data from 1975 to 2003 on the one-digit industries of the Tokyo metropolitan area in Japan. Some new time-series econometric methods that have been recently developed to conduct unit root and cointegration tests are used in the analysis, allowing for an endogenously determined structural change in the time period studied. It also proposes a new type of dynamic externalities, called Network dynamic externalities, to represent knowledge spillovers resulting from the whole agglomerated area via transportation networks, and shows that they have cointegrated relations with the total factor productivity (TFP) of the manufacturing, finance, wholesale and retail trade, as well as the overall industries. In addition, evidence is also found that Marshall-Arrow-Romer (MAR) dynamic externalities, which are associated with own industrial production concentration, affect the TFP of most industries selected for estimation. However, Jacobs dynamic externalities, which are represented by the diversity of industrial production, only contribute to the TFP of the services industry, and Porter dynamic externalities, which are expressed by the competitiveness within industries, do not influence the selected industrial TFP.

Suggested Citation

  • Zheng, Xiao-Ping, 2010. "A cointegration analysis of dynamic externalities," Japan and the World Economy, Elsevier, vol. 22(2), pages 130-140, March.
  • Handle: RePEc:eee:japwor:v:22:y:2010:i:2:p:130-140
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    1. Timo Mitze & Teemu Makkonen, 2020. "When interaction matters: the contingent effects of spatial knowledge spillovers and internal R&I on firm productivity," The Journal of Technology Transfer, Springer, vol. 45(4), pages 1088-1120, August.
    2. Kejun Song & Gerald Simons & Wei Sun, 2019. "Knowledge Spillovers and Local Industry Growth: A Patent Citation Approach," The American Economist, Sage Publications, vol. 64(1), pages 60-72, March.
    3. Jian Wang & Junqian Xu, 2015. "Home market effect, spatial wages disparity: an empirical reinvestigation of China," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 55(2), pages 313-333, December.
    4. repec:got:cegedp:146 is not listed on IDEAS
    5. Wang, Jian & Sun, Furong & Lv, Kangjuan & Wang, Lisha, 2022. "Industrial agglomeration and firm energy intensity: How important is spatial proximity?," Energy Economics, Elsevier, vol. 112(C).
    6. ZHAO Ting & ZHAO Wei, 2012. "Dynamic Externalities and Manufacturing Productivity: An empirical comparison among China's top three municipalities," Discussion papers 12072, Research Institute of Economy, Trade and Industry (RIETI).
    7. Qing Liu & Jian Wang, 2022. "Spatial agglomeration and firm productivity: Does trade status matter?," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(S2), pages 5-18, November.
    8. Shruti Tripathi & Vikash Gautam, 2010. "Road Transport Infrastructure and Economic Growth in India," Journal of Infrastructure Development, India Development Foundation, vol. 2(2), pages 135-151, December.
    9. Krenz, Astrid, 2012. "A panel co-integration analysis of industrial and services sectors' agglomeration in the European Union," University of Göttingen Working Papers in Economics 146, University of Goettingen, Department of Economics.

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