Correlation Study On Tax And Economic Growth Of Chongqing

As China's political and economic reform and fiscal tax system continued to improve in recent years, tax revenue and economic output of Chongqing have achieved substantial growth. However, there is no accurate conclusion about correlation and interaction of tax and economic growth. Therefore, studies on tax and economic growth should be an important research topic. Its in-depth study, not only help improve tax theory, but also conducive to stability of the tax on the political, economy and promoting social harmony and are of great theoretical and practical significance.This article draws classical and neoclassical, Keynesian macroeconomic theory and the classical theory of the Laffer curve, but not rigidly adhere to them, use of qualitative analysis, empirical measurement and system analysis. First, basic concepts, measure and influencing factors, describe classic academic studies. Secondly, do empirical analysis on tax and economic growth 1994-2009, show that: tax and economic growth were highly linear correlation coefficient, 0.99; economic growth changes 100 units, tax changes in average 13 units; tax is through the substitution effect and income effects on the role of investment and consumption, tax change of 1 per million, changes in government investment to 273 million and private investment 398 million in the same direction, tax on consumption not significant. Again, based on system dynamics establish Chongqing Tax Economic System Dynamics Model, show that: Chongqing City, in 2011 GDP is expected to reach 886.57 billion, tax revenue 1094.7 billion, economic growth rate14.5%, tax rate 14.9%. Finally, promote harmonious development of policy recommendations of tax revenue and economic growth of Chongqing City.Innovation of this paper is use of system dynamics theory, focus of linear and nonlinear factors, equations, algorithms and data fitting in the model, and finally making economic forecasts and policy recommendations. Inadequacies of this article, including lack of data, factors not considered comprehensive, variable not enough good relations and algorithm description and limited academic level. Future studies can collect more panel data, analysis of the impact of additional factors, the application of more advanced software, to build more models of variables to carry out more in-depth.