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Journal of Chinese Political Science

, Volume 9, Issue 2, pp 1–26 | Cite as

Off balance: The unintended consequences of fiscal federalism in China

  • Kellee S. Tsai
Article

Abstract

This paper argues that neither the developmental state nor the marketpreserving federalism models are applicable to contemporary China. Despite superficial similarities with elements of each model, the political economy of reform in China violates key assumptions and expectations in both. In contrast to the expectations of the developmental state model, the center has not systematically allocated credit to the most productive sectors of the economy; instead the latter has had to rely on informal finance. And in contrast to the expectations of market-preserving federalism, fiscal decentralization has lead to a host of unintended consequences at the local level—namely, the hoarding of off-budget revenues for public goods provision, reliance on informal finance for private sector development, and local protectionism. These unintended and informal coping strategies depart substantially from the core components of both the developmental state and market-preserving federalism models.

At present, China’s financial order is very chaotic. For example, a lot of funds have been lost through off-budgetary channels and systems. It is quite common for [local governments and agencies] to wantonly collect fees and fines, to levy various charges, and to set up “little money lockers” without authorization... Such chaotic situations have not only seriously affected the government in exercising macro-economic regulations and control over fiscal revenues, and disrupted the unity in our national administrative order, but also provided an environment and conditions for corrupt behavior.

—President Jiang Zemin, January 20011

Keywords

Fiscal Decentralization CHINESE Political Science Fiscal Federalism Local Protectionism Budgetary Revenue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer 2004

Authors and Affiliations

  • Kellee S. Tsai
    • 1
  1. 1.Department of Political Science at Johns Hopkins UniversityUSA

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