The optimal fiscal expenditure scale of Yangtze River Delta on the perspective of financial agglomeration

Transcription

1 The optimal fiscal expenditure scale of Yangtze River Delta on the perspective of financial agglomeration LIU Jia-Cheng 1, CHEN Yu-Jie 2, LIU Nan 3 Abstract Taking Yangtze River Delta as an example, We use principal component analysis to study current situation of financial agglomeration by SPSS software in this three provinces (cities),while based on the time series of convergence theory, we analyzed the trend of financial agglomeration in the Yangtze River Delta region. The financial agglomeration in the Yangtze River Delta shows a trend of convergence. In addition, this paper constructs the measurement model of the optimal relative fiscal expenditure scale of three provinces (cities), and gives the optimal fiscal expenditure scale of Shanghai, Jiangsu province and Zhejiang province. Keywords fiscal expenditure; financial agglomeration; Yangtze River Delta 1 Introduction Financial agglomeration refers to the financial related enterprises gather in one area, with further cooperation and competition based on the enterprise division, using the similarity and complementary to form interdependent forms of industrial organization. Fiscal expenditure reflects the government's use of social resources at a given period. It is one of the main means that the government affects social and economic activities. Fiscal expenditure reflects the government's use of social resources at a given period. According to Keynes's fiscal theory, positive fiscal policy promotes economic growth through the multiplier effect. This effect is weakened after the increase of fiscal expenditure to a certain level because of the crowding out effect. Richard K.Vedder, Lowelle.Gallaway(1982)used the concave function model of fiscal expenditure and economic growth to find that the optimal scale of fiscal expenditure in the United States, Canada, Britain, Italy, Sweden and Denmark were 17.45%, 21.45%, 17.67%, 24.45%, 18.65%, 26.8%. Yang Youcai (2009) used the threshold regression model to draw a concave function relation between China's fiscal expenditure and economic growth, and calculated the optimal fiscal expenditure scale of China was 11.6%. 2 Status analysis of financial agglomeration trend This paper uses location entropy to measure the level of financial agglomeration cyjqpzm@163.com / ljc@hainu.edu.cn 1 Liu Jia-Cheng (1972- ): Hainan University, School of Economics and Management, postdoctoprofes sor, the main research areas are investment evaluation, regional economy and so on. 2 Correspondent:Chen Yu-Jie (1992- ): Hainan University, School of Economics and Management, gr aduate student, major research areas are economy and finance investment. 3 Liu Nan (1989- ): Hainan University, School of economics and management, graduate student, major research areas are economy and finance investment.

2 in three economic circles. Location entropy refers to the measurement of the distribution of specific factors in a region. It is the index that reflects the specialized level of a specific industry, which was first proposed and applied by P. Haggett. The formula is: LQ ij qij / qj qi / q qij is i industry s output value, quanty or other related indicators in j region, qi is the related indicators of all industries in j region, q is the related indicators of all sectors of the country. The greater entropy shows the higher level of agglomeration. Considering the data representativeness and availability, this paper selected relevant financial data in the statistical yearbook of three provinces (cities) in For the selection of explained variables, we consider the reality situation of the financial industry s development and the evaluation of industrial agglomeration model. Taking Ma Jun (2012) as a reference, we choose the location entropy of financial value(lqv), deposit balance(lqs), loan balance (LQl)and insurance premium income(lqr) to show the degree of financial agglomeration. In order to compare the degree of financial agglomeration more intuitively, we use SPSS software to do principal component analysis. In this paper, we take the location entropy of financial value, location entropy of financial value(lqv), deposit balance(lqs), loan balance (LQl)and insurance premium income(lqr ) as indicators to show the four-dimensional spatial variables of the financial agglomeration degree, then reduce the four-dimensional variables to one-dimensional variable as financial agglomeration index. We analyzes the financial development trend of the Yangtze River Delta region using time series based on convergence theory. Bernard and Durlarf (1996) define the common and convergent trend of per capita output. In short, if P sequences have K co-integration relationships, the P sequences are affected by P-k random factors. Therefore, it is crucial to determine the number of P sequences co-integration relationships to determine whether the P sequences are convergent. If there are P-1 co-integration relationships, it is shown that the economies of the P regions are affected by 1 stochastic trend and are convergent. If there are less than P-1 co-integration relationships, the economies of P regions are affected by P-k random factors and are not convergent. If the regional financial development is convergent, the financial development in the region shows a narrowing gap trend for a long time,.and plays a synergistic role in promoting the development of regional finance. Number of co-integration vectors Table 1. Results of Johansen co-integration test Trace Statistics 5% critical value Maximum characteristic root 5% critical value *** *** *** Note: * * * indicates rejection of the original hypothesis at the level of 1%

3 From Table 1, the results of the Johansen co-integration test and the maximum characteristic root test are consistent. The financial development in the Yangtze River Delta region has 2 co-integrating variables, which means 1 random factor. The financial development in the Yangtze River Delta region is convergence, with a narrowing long-term gap trend for the degree of regional financial agglomeration. 3 Empirical analysis of the optimal fiscal expenditure scale By analyzing hundreds of countries financial expenditure data, Ram (1986) found that there is a reasonable range of the ratio of fiscal expenditure to GDP. The fiscal expenditure has a positive effect on the economy in this range, otherwise, if beyond this range, it has a negative effect. Based on Ram's evaluation index, in this paper eg stands for the ratio of national fiscal expenditure to national GDP. Eg(sh), eg(js) and eg(zj) respectively stands for the ratio of local fiscal expenditure to local GDP divided by eg in Shanghai, Jiangsu and Zhejiang, which shows the scale of fiscal expenditure in the three provinces (cities), named as the scale of relative fiscal expenditure. There are two ways to calculate the scale of optimal fiscal expenditure: one is to establish the production function using Barro's natural efficiency conditions for government spending, the other is to construct equation using the theory that the scale of economic growth and fiscal expenditure has concave function proposed by VedderRK, Gallaway LE. Liu Huang (2011) use the method of constructor on the optimal scale of China's fiscal expenditure. Drawing lessons from the model of economic growth and fiscal expenditure, this paper calculates the optimal relative financial expenditure scale in the Yangtze River Delta region, which affect the financial agglomeration. The econometric model is as follows: Yt=a+β1Xt+β2Xt 2 +μt(t=2004,2005,,2015) Yt stands for the financial agglomeration index, and Xt represents the scale of fiscal expenditure. β1 measures the positive impact of the fiscal expenditure scale on the degree of financial agglomeration,β2 measures any negative impact on the scale of fiscal expenditure, whileβ1 andβ2 have the opposite sign. In order to overcome the sequence correlation and self-correlation, the AR term is added into the equation to correct the regression equation: Y(sh)= *eg(sh) *eg(sh) 2 +[AR(1)= ] R 2 = , F= (p= ), D.W= From the model coefficients, the fitting of the model is quite good. The P value of the F statistic is , less than 0.05, so the equation is significant at the 5% significance level. The residual is tested to see the validity of the model. Through the above tests, it is proved that the model s parameter estimation is effective and can accurately reflect the relationship between the scale of fiscal expenditure and the financial agglomeration. In addition, the sign of the coefficient is consistent with the previous prediction, which proves the previous conjecture that there is a concave function relationship between the scale of fiscal expenditure and

4 the financial agglomeration in Shanghai. Therefore, when finding the curve inflection point, we can find the relative fiscal expenditure scale when value of the financial agglomeration is largest. Taking derivative of the equation, dy/dx=β1+2β2, xoptimal=-(β1/2β2). According to the empirical results, the optimal fiscal expenditure scale in Shanghai is Similarly, we deduce the relative scale of optimal fiscal expenditure in Jiangsu province and Zhejiang province. Y(js)= *eg(js) *eg 2 (js)+ [AR(1)= ] R2= , F= (p= ), D.W= Y(zj)= *eg(zj) *eg 2 (zj)+ [AR(1)= ,MA(1)= ] R2= , F= (p= ), D.W= Through the test of Jiangsu and Zhejiang provinces, we prove that the model s parameter estimation is equally effective for Jiangsu and Zhejiang provinces, and can accurately reflect the relationship between the scale of financial expenditure and the financial agglomeration. In addition, the sign of the coefficient is consistent with the previous prediction, which proves the previous conjecture that there is a concave function relationship between the scale of fiscal expenditure and the financial agglomeration in Jiangsu and Zhejiang provinces. Therefore, when finding the curve inflection point, we can find the relative fiscal expenditure scale when value of the financial agglomeration is largest. Taking derivative of the equation, According to the empirical results, the optimal fiscal expenditure scale is in Jiangsu province, and is in Zhejiang province. In summary, the ratio of Shanghai's optimal fiscal expenditure /GDP and the national fiscal expenditure /GDP is , the same ratio in Jiangsu province is , and is in Zhejiang province. In this ratio, it can promote the integration of the financial industry and other industries, also effectively optimize the financial market. 4 Conclusions and suggestions In this paper, we use location entropy method to estimate the financial industry s location entropy of three provinces (cities) in the Yangtze River Delta, and use the factor analysis to obtain the financial agglomeration index by SPSS software. Based on the time series of convergence theory, we analysis the trend of financial agglomeration in the Yangtze River Delta region and find that the financial development is convergent, with the long-term gap of regional financial agglomeration narrowing. We find that the optimal fiscal expenditure scale is in Shanghai, in Jiangsu province, and in Zhejiang province. Here are a few suggestions for improvement. The government should improve the efficiency of fiscal expenditure. The scale of fiscal expenditure should be coordinated with economic development, and the proportion of fiscal expenditure and GDP should be kept within a reasonable range. The relative results show that 8 of 12 years of fiscal expenditure /GDP in Shanghai

5 are higher than the optimal scale of fiscal expenditure, we should limit the fiscal expenditure of Shanghai especially the administrative expenses and other non productive expenditure, reduce staff, streamline structure and improve the efficiency. 11 of 12 years of fiscal expenditure /GDP in Jiangsu Province are lower than the optimal scale of fiscal expenditure, the government should optimize the expenditure structure and increase the science and education expenditure. References Vedder R K, Gallaway L E. (1982). Productivity and Wages in the American Economy: A Tale of Two Centuries, Business & Economic History, vol.11.no Yang YC. (2009). Economic growth model and empirical research using institutional factors, Shandong University Ma J, Guan YF. (2015). The three major economic circles of financial industry agglomeration and influencing factors of,commercial Economic Research, no Bernard A B, Durlauf S N. (1996). Interpreting tests of the convergence hypothesis,journal of Econometrics, vol.71.no Romer PM. Increasing Returns and Long-Run Growth(1986),Journal of Political Economy, vol.94.no Liu H, Tian GX, Zheng JC. (2011). Empirical analysis of the scale of fiscal expenditure in China, Inquiry Into Economic issues, no