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Small and medium enterprise theory: evidence for Chinese TVEs

Author

Listed:
  • Mario Biggeri

    (Department of Economics, University of Florence, Italy)

  • Danilo Gambelli

    (DIBIAGA, Faculty of Agriculture, University of Ancona, Italy)

  • Christine Phillips

    (Department of Economics, University of Reading, UK)

Abstract

The research analyses small and medium enterprises (SMEs), i.e. township and village enterprises (TVEs), operating in rural China over the last decade. It focuses on factors that affect the growth of TVEs run by Township and Village (TV), defined as collective, at provincial level. The paper examines SME theory and the role of human capital in the SMEs development. Following this, it analyses the development of Chinese TVEs and their role in the socio-economic development of rural areas and of the national economy. A panel analysis is then performed, using provincial panel data of collective TVEs over the period 1986-93, in order to capture the major determinants of provincial growth. The results of the panel estimations highlight some important features of TVEs run by TV sector, and provide empirical evidence in support to the positive role of clustering (measured through a new index) and human capital in the provincial growth of this sector. Copyright © 1999 John Wiley & Sons, Ltd.

Suggested Citation

  • Mario Biggeri & Danilo Gambelli & Christine Phillips, 1999. "Small and medium enterprise theory: evidence for Chinese TVEs," Journal of International Development, John Wiley & Sons, Ltd., vol. 11(2), pages 197-219.
    • Handle: RePEc:wly:jintdv:v:11:y:1999:i:2:p:197-219
    DOI: 10.1002/(SICI)1099-1328(199903/04)11:2<197::AID-JID553>3.0.CO;2-U
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    References listed on IDEAS

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    Cited by:

    1. Kui-Wai Li, 2018. "Analyzing The Tfp Performance Of Chinese Industrial Enterprises," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 63(05), pages 1263-1284, December.
    2. Duczmal, Luiz & Assuncao, Renato, 2004. "A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 269-286, March.

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