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FDI与中国各城市的税收收入——基于221个城市数据的空间面板分析

逯建1 杨彬永1

(1.西南财经大学国际商学院)

【摘要】本文基于2003-2011年中国221个城市之间的地理距离,建立空间面板计量模型,测算FDI的流入究竟是否增加了中国各城市的税收收入。研究发现:FDI对税收增长具有负向作用,FDI每增加1%,各城市的税收收入将减少大约0.31%至0.37%,且城市的税收收入与周边城市呈现显著的正向相关关系。其原因可能在于各地方政府出于增加就业、促进地区GDP增长或提高政绩的需要,采用各种税收减免、税收奖励等吸引外资的经济政策。这种优惠政策一方面推动了GDP的增长,但另一方面却不可避免地造成地方政府的财政资源流失,使地方政府陷入“囚徒困境”。为此各地政府应协调FDI政策,尽量在促进经济发展的同时促进税收收入的增长。

【关键词】 FDI;税收收入;空间计量模型;政策竞争;

【DOI】

【基金资助】 ; ; ;

FDI and tax income of Chinese cities: a spatial panel analysis based on data of 221 cities

LU Jian1 YANG Binyong1

(1.International Business School, Southwest University of Finance and Economics (SWUFE))

【Abstract】By building a spatial econometric model based on the geographical distance among the 221 cities in China from 2003 to 2011, this paper examines whether FDI inflow increases the tax reveune of cities in China. It is found that FDI has a negative impact on tax increase, with 1% increase in FDI resulting in 0.31% to 0.37% decrease in tax income. The tax income of a city has a significantly positive corelation with that of the neighboring cities, possibly as a result of the economic policies for attracting foreign investment introduced by local governments such as various tax breaks and tax incentives to improve employment, promote regional GDP growth or boost administrative achievements. While these preferential policies can promote GDP growth, they could inevitably bring about the loss of financial resources of the local governments which may put the local government into “prisoner' s dilemma.” To address this, local governments should coordinate their policies on FDI and manage to promote the tax revenue growth while enhancing the economic development.

【Keywords】 FDI; tax income; spatial econometric model; policy competition;

【DOI】

【Funds】 ; ; ;

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    Footnote

    [1]. 1 See Eric W.Bond et al.(1986). [^Back]

    [2]. 2 Race to the bottom: a phrase to describe by depriving labour, consuming resources and damaging the environment to achieve growth. [^Back]

    [3]. 3 By introducing the (n-1) dummy variables in the original equation (n dummy variables will be introduced if there is no intersectional unit present) to represent different element. This is to fulfil the strong hypothesis of various disturbance terms are unrelated to the explanatory variables at different phases. Under this hypothesis, an estimation of individual heterogeneity ui can be reached. [^Back]

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This Article

ISSN:1002-4670

CN: 11-1692/F

Vol , No. 09, Pages 3-13

September 2015

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Article Outline

Abstract

  • Introduction
  • 1. Literature review
  • 2 Empirical methodology and data explanation
  • 3 The empirical results analysis
  • 4. Conclusion
  • Footnote

    References