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The Spatial Dimension of Trade- and FDI-driven Productivity Growth in Chinese Provinces: A Global Cointegration Approach

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  • Timo Mitze
  • Selin Özyurt

Abstract

This paper analyses the major determinants of long- and short-run labour productivity evolution for Chinese provinces between 1978 and 2010. The role played by openness to trade and foreign direct investment (FDI) constitutes the main focus of this analysis. From a methodological perspective, our main contribution is the inclusion of spatial effects into a dynamic error correction modelling framework. The results show that, in addition to domestic factors such as investment intensity and infrastructure use, trade openness and inward FDI also exert a direct impact on labour productivity. Furthermore, the geographical environment has a strong indirect influence on productivity: The more a region is surrounded by high-productive regions with good infrastructure and linkages to the world economy, the higher are its productivity level and growth rate. The magnitude of these impacts varies by spatial regime (coastal, interior provinces) and time period in focus. Especially in the recent past, trade and FDI activity appear to be increasingly important drivers of regional productivity evolution, both for coastal and interior regions. These findings have important policy implications: In order to fully exploit the benefits from such spillovers, coordinated industrial policies which foster regional complementarities and support the free movement of production factors across regional borders are crucial.

Suggested Citation

  • Timo Mitze & Selin Özyurt, 2014. "The Spatial Dimension of Trade- and FDI-driven Productivity Growth in Chinese Provinces: A Global Cointegration Approach," Growth and Change, Wiley Blackwell, vol. 45(2), pages 263-291, June.
  • Handle: RePEc:bla:growch:v:45:y:2014:i:2:p:263-291
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    2. Kosfeld, Reinhold & Dreger, Christian, 2019. "Towards an East German wage curve - NUTS boundaries, labour market regions and unemployment spillovers," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 115-124.
    3. Bernard Fingleton & Silvia Palombi, 2016. "Bootstrap J -Test for Panel Data Models with Spatially Dependent Error Components, a Spatial Lag and Additional Endogenous Variables," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(1), pages 7-26, March.
    4. Gutiérrez-Portilla, Paula & Maza, Adolfo & Villaverde, José, 2019. "A spatial approach to the FDI-growth nexus in Spain: Dealing with the headquarters effect," International Business Review, Elsevier, vol. 28(6), pages 1-1.
    5. Castellanos-Sosa, Francisco A. & Cabral, René & Mollick, André Varella, 2022. "Energy reform and energy consumption convergence in Mexico: A spatial approach," Structural Change and Economic Dynamics, Elsevier, vol. 61(C), pages 336-350.
    6. Tamás Krisztin & Philipp Piribauer, 2023. "A joint spatial econometric model for regional FDI and output growth," Papers in Regional Science, Wiley Blackwell, vol. 102(1), pages 87-106, February.

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    JEL classification:

    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • P20 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - General
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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