2019 International Conference on Education, Management, Economics and Humanities (ICEMEH 2019) ISBN: 978-1-60595-616-9
Evaluation of the Competitiveness of Regional Financial Center in the Middle and Lower Reaches of the Yellow River—Based on the Perspective of Financial Subject
* Rui-bo LIU and Jun-qing CHEN No. 40, Shungeng Road, Shizhong District, Jinan City, Shandong Province, China *Corresponding author
Keywords: Regional financial centres, Financial subjects, Principal component analysis.
Abstract. The "13th Five-Year Plan" points out the role of the urban agglomeration radiation, optimizes the development of the three major urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta, and forms urban agglomerations such as the Northeast and Central Plains. In this paper, six principal cities of Hohhot, Taiyuan, Xi'an, Zhengzhou, Jinan and Qingdao are selected as the research objects to explore the influence of financial entities on the competitiveness of regional financial centers. Research shows that financial entities are an important aspect of the construction and development of regional financial centers. It reflects the current financial manpower situation and talent reserve of regional financial centers. The innovation of this paper is mainly reflected in the innovation of the selection region and the innovation of evaluation methods.
Introduction Internationally, in the 21st century, the international financial center has entered a new stage of development, and a diversified and multi-tiered international financial center pattern has been formed. From the domestic perspective, China's interior has a huge economic scale and a vast economic hinterland, and the domestic economic development presents obvious regional and hierarchical nature. The 13th five-year plan further pointed out that the development of a number of central cities, strengthening regional service functions, promoting the integrated development of key regions and cultivating and strengthening several key economic zones. Based on the overall strategy of regional development, with the “One Belt and One Road” construction, the coordinated development of Beijing-Tianjin-Hebei, and the construction of the Yangtze River Economic Belt, it will form a vertical and horizontal economic axis dominated by the economic belt along the coast along the Yangtze River. As the second largest river in China, the middle and lower reaches of the Yellow River basin covers an area of 750,000 square kilometers. Data from 2015 show that the Yellow River basin's GDP is about 5.5 trillion yuan, accounting for about 8 percent of the country's GDP.
Literature Review International theoretical research on financial centers mainly appeared in the late 1950s, 1970s and 1990s. The research on financial centers in China started relatively late. In the 1970s, Chinese academic circles began to pay attention to the research on financial centers, but it began to be widely studied after the 1980s. Overseas Research Foreign scholars have made a series of research achievements in the agglomeration effect and formation mechanism of regional financial centers. Davis(1990)[1] believed that the financial service industry has the tendency of agglomeration, which promotes the development of the financial service industry by attracting professional talents, reducing transaction costs and developing new 162
technologies. Thtift (1994)[2] found through research that the externality, asymmetry and information hinterland of information are the most important factors influencing the development of financial center, while information flow is the precondition for the development of financial center. Taylor (2010) [3] uses data from the London financial industry. He believes that interpersonal and geographical proximity plays an important role in maintaining the normal functioning of financial centers. High-quality financial talents and strong customer demand can greatly stimulate financial Industry innovation. Domestic Research Domestic scholars have also conducted research on many aspects of regional financial centers. Zhao Jing (2015)[4] held that the effect of financial agglomeration is reflected in the agglomeration effect, the effect of forming external economy and the effect of information spillover. In the process of promoting regional economic development, attention should be paid to the realization of these three effects. Tang Yifu and Yao Ling (2017)[5] put forward that the core functions of chongqing's construction of a functional financial center are five functions: convergence and radiation, settlement and settlement, economic structure adjustment, value discovery and risk management. The path of early government-led cultivation, medium market development and late mixed drive maturity should be selected. In the study of the comprehensive evaluation system of regional financial center competitiveness, Chinese scholars have also made a series of achievements.
Research Design Is the basic method of principal component analysis, by constructing the original variable linear combination of the appropriate, to produce a series of unrelated new variables, choose a few new variables When the problem of the study is determined, the size of information contained in the variable is usually measured by its variance or sample variance. Model of Principal Component Analysis The model of principal component analysis is as follows
F1 a11X1 a12X 2 a1 j X j F2 a21X1 a22X 2 a2 j X j (1) Fi ai1 X1 ai2 X 2 aij X j Ai1... Aij (j = 1, 2,... N) is the eigenvector X1,X2 corresponding to the eigenvalue of Fi Xj (j = 1,... ,n) is the value of the original data after standardized processing. In practice, the data used is often dimensionless, and the original data should be standardized before calculation. Calculation Steps of Principal Component Analysis The specific calculation steps are as follows (1) standard processing of raw data. The standardized formula adopted in this paper:
xij x j zij . (2) s j
2 1 n 1 n x j xij s j xij x j . (3) n i1 n i1 (2) find the correlation matrix R of standardized data. After normalization, the mean value is 0 and the variance is 1. The data covariance matrix is exactly the same as the correlation matrix R. 163 (3) calculate the eigenvalues and eigenvectors of R. (4) determine the number of principal components. (5) determine the principal components and the comprehensive evaluation function.
Empirical Analysis and Results In this paper, SPSS multivariate statistical analysis software is used and principal component analysis method is used to measure the financial competitiveness of each city. Sample Selection and Data Sources This study drew on the research results of China financial center index report (no. 9) and selected the representative xi 'an, zhengzhou, jinan and Qingdao in the middle and lower reaches of the Yellow River in combination with the division of the Yellow River basin in the level of economic significance. For Inner Mongolia and shanxi, the provincial capitals Hohhot and taiyuan are selected. In order to obtain the latest data and measure a city's development more accurately, this paper selects the data of 2017. The data used come from China's urban statistical yearbook, annual government work reports of various regions, bank and insurance association website, etc. Empirical Analysis Considering the availability and operability of data, this paper selects the evaluation index system of regional financial center competitiveness, including three primary indexes: financial subject, financial object and financial environment. Financial subjects include five three-level index systems, which are financial employees, the proportion of financial employees in total employment, the labor productivity of all employees, the number of college students and the number of colleges and universities. Table 1. Raw data for 2017.
City X1 X2 X3 X4 X5 Indicators Hohhot 1.9 23.9 24 186996 0.011 Taiyuan 3.8 44.0173 44 140916 0.015 Xi'an 10.3 72.68 63 138314 0.019
Zhengzhou 9.43 93.5 58 154223 0.016
Jinan 9.6 54.44 42 153212 0.02 Qingdao 5.9 34.0875 25 186751 0.01
Note: X1: number of financial employees; X2: college enrollment; X3: number of universities; X4: total labor productivity; X5: the proportion of financial employees in total employment After processing, the common factors are independent of each other, and the special factors are also independent of each other. The specific process is as follows: after standardization treatment, the standardized values of each indicator are as follows: Table 2. The standardized value of each indicator.
City ZX1 ZX2 ZX3 ZX4 ZX5 Hohhot -1.41111 -1.16074 -1.15264 1.23988 -1.0237 Taiyuan -0.86635 -0.37901 0.08233 -0.88189 -0.04095 Xi'an 0.99728 0.73479 1.25556 -1.0017 0.9418 Zhengzhou 0.74784 1.54383 0.94681 -0.26917 0.20474 Jinan 0.79658 0.026 -0.04117 -0.31572 1.18749 Qingdao -0.26425 -0.76487 -1.0909 1.2286 -1.26938
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Analyze the Common Degree of Variables. The following results were obtained by principal component analysis of standardized data using SPSS software:
Table 3. Common factor variance. Initial Extract
Number of financial employees 1.000 .750 Number of college students 1.000 .799 Number of colleges and universities 1.000 .924 Total labor productivity 1.000 .755 The proportion of financial employees in total 1.000 .802 employment The common degree of the variable X1 is 0.75, that is, the extracted common factor contributes 75% to the variance Var(X1) of the variable X1. As can be seen from the above table, all values are above 75%, indicating that more information is retained when the spatial variable is transformed into the factor space. Therefore, the effect of principal component analysis is significant. Use the Composition Matrix to Obtain the Eigenvectors. The data in the composition matrix (table 5) is divided by the square root of the eigenvalue corresponding to the principal component to find the feature vector muij. The specific results are shown in the following table: Table 4. Principal component eigenvector data.
Indicators µ1 Number of financial employees 0.431385 Number of college students 1.218839 Number of colleges and universities 1.622071 Total labor productivity -3.639840 The proportion of financial employees in total 5.908049 employment Calculation Results of Principal Component Analysis. multiply the obtained eigenvectors and the standardized data to obtain the expressions of the principal components:
F1 11 X1 21 X 2 51 X5 . (4) (Xiis the data after standardization of each indicator) (1) obtain the total score of financial subject competitiveness:
' ' ' 1 1 1 2 . (5) ’ ’ (λ1 , λ2 is the variance contribution rate of each principal component) Total score of financial subject competitiveness:
F 1 F1 . (6) The calculation results are as follows: Table 5. Each principal component score and composite score. City F1 F Ranking Hohhot -14.44431 -2.53469 6 Taiyuan 2.26586 0.39761 5 Xi'an 12.57263 2.20625 1 Zhengzhou 5.92942 1.04050 4 Jinan 8.47346 1.48692 3 Qingdao 9.78321 1.71677 2
165 Summary The comprehensive scores are in the order of xi 'an, Qingdao, Jinan, Zhengzhou, Taiyuan, Hohhot . The number of colleges and universities, the number of college students and the number of financial employees in Qingdao account for a relatively low proportion of total employment, which, to some extent, lowers the ranking of Qingdao in terms of financial subjects. If Qingdao wants to build a regional financial center, it needs to increase the number of local colleges and universities, increase the number of local colleges and universities, and improve the number of financial workers in the region. The ranking of Zhengzhou and Jinan is very close. From the specific indicators, the labor productivity of all employees, the proportion of financial employees in total employment and the number of financial employees in these two cities are very close. In recent years, Jinan has put forward the development strategy of "four centers" and the development policy of "new driving force and new economy". Therefore, Jinan needs to increase the number of financial employees in the region and further increase the number of universities and colleges in the region.
References [1] Davis E P. International Financial Centers: An Industrial Analysis[R]. London Bank of England Discussion Paper, 1990: 51-58. [2] Thrift N, Martin R. The Social and Cultural Determinants of International Financial Centers: The Case of the City of London [C]. Oxford: Basil Blackwell, 1994:325-355. [3] Taylor PJ.Financial services clustering and it’s significance for London [M]. London: Corporation of London, 2010:1-112 [4] Zhao Jing. Research on the interaction mechanism between financial development and technological innovation [D]. Hebei University, 2015. [5] YufuTang , Yao Ling. The framework design and path selection of building a functional financial center in Chongqing [J]. Exploration of Financial Theory, 2017-02.
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