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Сетевой научно-практический журнал
серия Экономические исследования
Н
АУЧНЫИ
РЕЗУЛЬТАТ
STRATEGIC VECTORS OF THE GLOBAL ECONOMY DEVELOPMENT
UDC 336.71(510) DOI: 10.18413 / 2409-1634-2015-1-3-19-37
AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
Yongbin L.V., Associate Professor of School of Finance School of Finance, Zhongnan University of Economics and Law 182 Southlake Road, Wuhan City, Hubei Province, China [email protected] or [email protected] Xianping Zhou, Professor of School of Finance School of Finance, Zhongnan University of Economics and Law 182 Nanhu Avenue, East Lake High-tech Development Zone, Wuhan 430073, China Yingying Y.I., Postgraduate of School of Finance School of Finance, Zhongnan University of Economics and Law 182 Nanhu Avenue, East Lake High-tech Development Zone, Wuhan 430073, China
Yongbin L.V., Xianping ZHOU, Yingying Y.I.
Abstract
We make an empirical analysis of regional characteristics of influence factors on financial exclusion by using the data of types and levels of financial institutions in the nation from 1998 to 2012. The study shows the number of financial institution nodes continues to increase, but with uneven distributions; the density of financial institutions increases steadily and the gap between the eastern and the Midwest is huge; the commercial banks of the eastern region account for the highest but the rate of the commercial bank basic outlets is low and the rate of the rural institution bank basic outlets is relatively higher, in addition the proportion of the Midwest rural banks is high. Even though the financial exclusion eases, large differences still exist in different areas and the situation is rather critical in rural areas, especially the financial exclusion in western rural areas is the worst. The economic development level, the area scale, the government expenditure, the personal savings level, the per capita consumption and the educational level have different effects on financial exclusion. Accordingly, the author puts forward the advice of coordinating the financial resource from all regions and reducing the regional financial exclusion.
Keywords: Financial Exclusion, Types and Levels of Financial Institutions, Regional Disparity.
№3 2015
20 Ш
Сетевой научно-практический журнал
серия Экономические исследования
Н
АУЧНЫИ
РЕЗУЛЬТАТ
СТРАТЕГИЧЕСКИЕ ВЕКТОРЫ РАЗВИТИЯ ГЛОБАЛЬНОЙ ЭКОНОМИКИ
УДК 336.71(510) DOI: 10.18413 / 2409-1634-2015-1-3-19-37
Йонгбин Л.В, Хианпинь Чжоу, Йиньгинь Йи
эмпирический анализ регионального неравенства факторов, влияющих на
ФИНАНСОВОЕ НЕРАВНОПРАВИЕ
финансовых учреждений различных типов и уровней
В КИТАЕ
Йонгбин Л.В., доцент Школы Финансов Школа Финансов, Чжуннаньский Университет Экономики и Права Саутлэйк-Роуд 182, Ухань-Сити, провинция Хубэй, Китай [email protected] or [email protected] Хианпинь Чжоу, профессор Школы Финансов Школа Финансов, Чжуннаньский Университет Экономики и Права Нэнху-Авеню 182, Высокотехнологичная зона развития Восточного озера, Ухань 430073, Китай
Йиньгинь Йи, аспират Школы финансов Школа Финансов, Чжуннаньский Университет Экономики и Права Нэнху-Авеню 182, Высокотехнологичная зона развития Восточного озера, Ухань 430073, Китай
Аннотация
В работе проведен эмпирический анализ региональных особенностей факторов, влияющих на финансовое неравноправие, на основе данных о типах и уровнях финансовых учреждений в стране с 1998 по 2012. Исследование показывает, что число узловых финансовых учреждений с неравным финансовым распределением продолжает увеличиваться; плотность финансовых учреждений постоянно растет, и разница между восточным и западным регионами значительна; коммерческие банки восточного региона составляют самое высокое число. Уровень основных учреждений коммерческого банка низкий, а уровень основных учреждений сельского банка относительно выше. Кроме того, высока пропорция западных сельских банков. Даже при том, что финансовое неравноправие ослабляется, в различных областях все еще существуют значительные различия, а ситуация в сельских районах довольно критична, при наихудших показателях финансового неравноправия в западных сельских районах. Уровень экономического развития, масштаб региона, правительственные расходы, личный сберегательный уровень, потребление на душу населения и образовательный уровень оказывают различное влияние на финансовое неравноправие. В этой связи, автор предлагает координировать финансовые ресурсы из всех регионов и сокращать региональное финансовое неравноправие.
Ключевые слова: финансовое неравноправие, типы и уровни финансовых учреждений, региональное неравенство.
№3 2015
Yongbin L.V., Xianping Zhou, Yingying Y.I.
AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
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1. The literature review
l.i The research in foreign countries
The concept of financial exclusion is first proposed by the UK financial geographer Leyshon and Thrift (1993) and is gradually paid attention by scholars. Foreign scholars have tried to make definitions of financial exclusion (Kempson and Whyley, 1999; ANZ, 2004; Sherman Chan,2004;G loukoviezoff, 2006; European Commission, 2008), the more comprehensive one among them is put forward by the European Commission who argues if the content of financial exclusion should contains the bank exclusion, the savings exclusion, the loans exclusion and the insurance exclusion.
A lot of research is generated by scholars on the causes of financial exclusion which is composed of geography (Leyshon & Thrift, 1993; Kempson & Whyley,i999; Connoll & Hajaj, 2001), business strategy of financial institutions (Kempson & Whyley, 1999; Sinclair, 2001; Carbo et al., 2005; Kempson et al, 2005), IT requirements of financial products and services (Leyshon & Thrift, 1995; McDonnell, 2003), the self-exclusion of residents (Kempson & Jones, 2000; McDonnell, 2003; Chant Link, 2004; Corr, 2006; Beck et al., 2007), the lack of financial knowledge (RoyMorgan, 2003; McDonnell, 2003; Gibson, 2008), etc. The main causes acting on EU were summarized, which include social factors (the instability of financial markets and the reinforcement of anti-money laundering, demographic changes, the extent of income inequality), supply factors (strict risk assessment procedures, uncertainty of marketing methods, the geographical availability, unclarity of the product design, the public product selection difficulties), demand factors (public faith, concerns about the cost, fear of losing economic control, distrust of financial institutions).
The scholars have also studied the influence factors of financial exclusion which can be summed up as income, social status, transaction cost, economic development level, social culture, characteristics of population, distract, race, the number of family population, the degree of optimism, liabilities, education level, housing property and the trust for financial institutions etc. (World Bank, 2008a. 2008b; FSA, 2000; Hogarth and O’Donnell, 2000;Jianakoplos and Bernasek, 1998; Christiansen et al., 2009; Ameriks
and Zeldes,2000; Puri and Robinson, 2007; Jerry Buckland and Wayne Simpson, 2008).
1.2 The research at home
Domestic research on financial exclusion started late, mainly involving the definition, status, performance, causes and so on. With more and more financial exclusion problems emerged, the research on it is also increasing. The financial exclusion had been defined by Lin Jin Xuejun and Tian Lin(2004a, 2004b, 2004c), Wu Wei et al. (2005), Wang Zhijun (2007), Xu Shaojun and Jin Xuejun (2009), Wang Xiuhua (2009), Zhu Yingli et al (2010), Suiyan Ying et al (2010), Li Tao, etc. (2010) , but with no unified conclusion.
Many studies have been made by domestic scholars about the status and performance of the financial exclusion. Xiu Hua and Qiu Zhaoxiang
(2010) analyzed the status of financial exclusion in urban and rural area of China. Xu Shengdao and Tian Lin (2008), Gao Peixing and Wang Xiuhua (2011) discussed the regional and the spatial difference of Financial exclusion in rural areas of our country. Wang Tianlin (2011) studied the urban and rural duality characteristic of the financial exclusion in China. Tian Lin (2007) analyzed the influence factors of spatial differences of financial exclusion in China. Tian Jie and Tao Jianping (2011), Wang Wei, etc. (2011) respectively established the Financial exclusion index and Financial Inclusion index to indicate the status of the Financial exclusion. Xu Shaojun, Jin Xuejun (2009) and Li Tao (2010) conducted research on the status of the financial exclusion on the basis of survey data. Lv Yongbin and Ji Qianqian (2014) obtained the comprehensive score and rank of China’s Rural Financial exclusion by making the use of the principal component analysis method.
For the causes of the Financial exclusion, He Dexu and Rao Ming (2008) carried on the analysis from the angel of the imbalance of supply and demand in financial market of China’s rural area; Gao Peixing and Wang Xiuhua (2011) emphasized income, financial efficiency, employment and the level of agricultural modernization; Tian Lin
(2011) focused on technology, income, education and other factors. Liu Junrong (2007) clarifies his opinion from the perspective of bank liquidity preference, the operation mode and organizational change in every stage during the development of banks. Zhu Yingli (2010) points out it was
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Yongbin L.V., Xianping Zhou, Yingying Y.I.
AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
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the weak economic foundation, the changes of banking system and the transformation of bank and enterprise system that cause the Financial exclusion in central region.
1.3 The perspective of this research
Overall, the overseas research on financial exclusion has formed a systematic system, but the domestic study of financial exclusion is not much and the research of which is also not systematic. Also, Domestic research is made mainly by using cross-section data or panel data of financial resources, which seems is not rich enough in empirical research. Since the different types of financial institutions provide different financial services and focusing on the different customer groups and regions, we need to distinguish between the types of financial institutions in examining the status of the financial exclusion. Moreover, even if it is the same type of financial institutions, the capacity of providing financial service and products varies much in different levels of branches, therefore, it is necessary to consider the levels of financial institutions in examining the financial exclusion.
The Financial exclusion is common in many countries and regions and will lead to serious economic and social problems. This paper tries to use the panel data of types and levels of the national financial institutions nodes to make a more in-depth analysis of the financial exclusion issue.
2. The description of the financial exclusion regional reality
2.1 The data description of types and levels of the branch nodes
Given assets and nodes of the banking financial institutions standing in the absolute leading position in the financial system, therefore using data of banking financial institution nodes to analyze financial exclusion issue is strongly representative. Since the commercial and the rural bank nodes (including rural credit cooperatives and rural cooperative banks) account for the most, we mainly discuss nodes of these two types of financial institutions. A large difference exists in the regions where the two kind of banks offering service as well as the financial services and products. As a result, by analyzing
the distribution, we can examine regional characteristic of financial exclusion.
The organization structure of China’s banking industry is usually «head office - branch - sub-branch - outlet». Branches and above level nodes can not only provide the general loan business, but also handle settlement business, credit card business, bill business, cash and the majority of foreign exchange business. Most demands of financial service from the public can be satisfied in the branch while sub-branches and savings offices, acting as simplified nodes, can only provide basic business and cannot meet the complete demand from the public. Thus, it may give rise to such situation: the types and number of nodes remaining unchanged, just the level declined, the recessive financial exclusion comes into being. So we need to bring the levels of financial institutions into consideration during the analysis of the financial exclusion problems.
We apply to the financial license information of the China Banking Regulatory Commission (CBRC) to gather data of types and levels of financial institutions nodes. The financial institutions contained in financial license information system includes: A- the policy bank, B - the commercial bank, C -the rural cooperative bank, D -the urban credit cooperative, E-the rural credit cooperative, F-the fund mutual cooperative, J - the asset-management company, K-the trust company, L-the financial company, M -the financial leasing company, N -the auto financial company, P -the currency brokerage company, Q - Loan Company, Z - the other financial institutions. We focus on the commercial bank (class B) and the rural bank (class C and class E).The data of financial license information by CBRC includes the aggregate information and statistics of the financial institutions classified by the nature, category, organization and region. A single financial institution includes the following financial elements: name, date of approval, address, organization code, the license issuing agency, serial number and issuing date.
According to the regulation of the financial license institution code compiling rule (trial), the organization category of commercial banks includes: H - head office, G -the business department of head office; B - the tier-one branch, K - the business department of the tier-
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Yongbin L.V., Xianping Zhou, Yingying Y.I.
AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
one branches, L -the second-level branch, M -the department directly under the branch, N -the business department of second-level branch, S - the sub-branch, U -the small local branch, V-business office, X - the other outlets. The organization categories of the rural cooperative bank include: H - head office, S - branch, U -the small local branch, V - savings office, X -the other outlets; The organization categories of the rural credit cooperative include: H -provincial rural credit cooperatives (autonomous regions and municipalities directly under the central government), B -the regional (city) association, S - the county (city) association, cooperative(county), T - the credit cooperative, U -the credit cooperative, V -the saving office, X -the other outlets. We classify the commercial banks and the rural banks into two types: the branch and above (the node that is about level U, V, X of the commercial bank, the rural credit cooperative and rural cooperative bank), the basic outlet (the node that is level U or V or X of the commercial bank, the rural credit cooperative, rural cooperative bank).
In order to investigate the region characteristic of financial exclusion, we divided the country into eastern, central and western areas. The eastern area includes 11 provinces or municipalities: Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong,
The Number of Financial Ins
Guangdong and Hainan; The central area includes 8 provinces: Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan; The western area includes 12 provinces or municipalities: the Inner Mongolia autonomous region, the Guangxi Zhuang autonomous region, Chongqing, Sichuan, Guizhou, Yunnan, the Tibet autonomous region, Shaanxi, Gansu, Qinghai, Ningxia Hui autonomous region, Xinjiang Uygur autonomous region.
2.2 The number of financial institution nodes & financial exclusion
2.2.1 The numb er of financial institution nodes is increasing on the whole, but the distribution is uneven
From 1998 to 2012, the number of financial institution nodes on average has been increasing year by year. From the angel of region, the eastern and western area have a faster growth level which is 163.81% and 135.32% respectively, while the central China is only 78.85%. A big difference exists among the eastern area, the central area and the western area. Overall, the number around the country grows rapidly. The situation of financial exclusion gets eased to some extent, but the regional difference of financial exclusion becomes more serious.
Table 1
ion Nodes from 1998 to 2012
Таблица 1
Количество узловых финансовых учреждений с 1998 по 2012 г.
Year National Eastern Central Western
Ave. Std. Ave. Std. Ave. Std. Ave. Std.
1998 2903.968 1950.126 3051.818 2449.734 4098.375 1367.639 1972.167 1311.222
1999 2972.548 1999.977 3130.182 2514.164 4178.25 1432.533 2024.25 1339.188
2000 3047.387 2032.359 3235.818 2546.175 4242.75 1476.292 2077.75 1372.596
2001 3172.419 2091.048 3416.182 2608.28 4373 1522.139 2148.583 1419.386
2002 3261.161 2129.198 3540.455 2635.284 4481.5 1562.853 2191.583 1444.735
2003 3359.581 2151.358 3729.818 2651.364 4543.75 1568.313 2230.75 1455.981
2004 3501.452 2226.362 3932.545 2717.362 4610 1575.331 2367.25 1656.285
2005 3863.129 2284.704 4441.364 2766.92 4826.625 1438.554 2690.75 1857.501
2006 4324.71 2572.243 5162.636 3213.038 5254.875 1486.365 2936.5 1954.514
2007 4741.645 2850.089 5658.818 3472.35 5625.375 1789.629 3311.75 2348.351
2008 5401.226 3171.725 6319.091 3865.428 6252.75 2018.208 3992.167 2779.38
2009 5633.161 3336.514 6652.636 4141.203 6511.25 2084.696 4113.25 2807.944
2010 5881.742 3456.868 7046.545 4185.297 6713.75 2148.109 4259.333 2999.719
2011 6194.323 3655.776 7504.545 4478.473 7030.375 2201.408 4435.917 3087.674
2012 6544.774 3932.421 8050.909 4862.819 7329.75 2170.958 4640.833 3317.644
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AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
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2.2.2 The density of financial institution nodes has a steady rise, and a large gap exists between the eastern area and the Midwest area
Here the ratio of the number of the financial institution nodes to the population and the area size will be taken into account, which is not in calculating the number of financial institution nodes, to arrive at the density of financial
institutions so as to further investigate the situation of financial exclusion in all regions. Table 2, from 1998 to 2012, shows that the growth of the density of financial institutions was steady on average, and it increased by 57.57% in 2012 than in 1998. However, different regional financial institutions density varies greatly. The average density of financial institutions in eastern is about seven times that of the Midwest where financial exclusion situation is more serious.
Table 2
Financial institution nodes density from 1998 to 2012
Таблица 2
Плотность узловых финансовых учреждений с 1998 по 2012 г.
Year National Eastern Central Western
Ave. Std. Ave. Std. Ave. Std. Ave. Std.
1998 0.162339 0.393071 0.383528 0.613145 0.04486 0.018581 0.037902 0.05944
1999 0.168958 0.411574 0.397777 0.644078 0.045263 0.018546 0.04167 0.058963
2000 0.162712 0.392373 0.383534 0.612113 0.045636 0.018414 0.038343 0.058661
2001 0.169385 0.414197 0.400375 0.647617 0.046722 0.01849 0.039421 0.060543
2002 0.174101 0.430435 0.41305 0.673985 0.047502 0.018418 0.039463 0.059913
2003 0.172463 0.420492 0.412237 0.655537 0.047955 0.018659 0.035676 0.046441
2004 0.176295 0.42271 0.421743 0.656267 0.048561 0.018995 0.036457 0.04598
2005 0.204152 0.477605 0.488971 0.736516 0.052272 0.018367 0.04432 0.054634
2006 0.210352 0.485259 0.501702 0.747138 0.056516 0.017895 0.045838 0.053989
2007 0.22351 0.510972 0.534046 0.784492 0.059639 0.017969 0.048098 0.053434
2008 0.242156 0.540547 0.565186 0.831797 0.066157 0.020302 0.063377 0.07676
2009 0.244203 0.54249 0.568062 0.83501 0.068511 0.020303 0.064461 0.076992
2010 0.246269 0.510774 0.564672 0.774725 0.070085 0.019717 0.071856 0.092166
2011 0.251514 0.511576 0.572297 0.77352 0.07333 0.020655 0.076252 0.100214
2012 0.255805 0.522064 0.586417 0.789344 0.076778 0.022357 0.072097 0.084556
2.3 The financial exclusion from different types of financial institutions
The commercial banks mainly serve the area that has a higher economic development while the rural banks are for the county economy and the below, taking more participation in the development of the rural finance. At the same time, the commercial bank is able to offer more comprehensive financial products and services for it is relatively rich in assets. Therefore, the area, especially the rural area that has less commercial bank nodes suffers a more serious problem of financial exclusion.
2.3.1 The commercial bank in the eastern region accounts for the highest
As can be seen from table 3, from 1998 to 2012, the ratio of the commercial bank nodes has gradually declined from 1998 to 2012, with the ratio in 2012 lower by 10.13% than that in 1998. It reflects that the category of the national financial institutions is becoming richer. The proportion of the commercial bank nodes still account for a large part, in 2012, is 74.82%. The situation of financial exclusion turns to be more serious with the rate ranges from 83.31% in the eastern area, 71.39% in the central part to 68.53% in the western area.
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AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
The rate of commercial banks from 1998 to 2012
Table 3
Таблица 3
Доля коммерческих банков с 1998 по 2012 г.
Year National Eastern Central Western
Ave. Std. Ave. Std. Ave. Std. Ave. Std.
1998 83.25% 0.1182808 92.17% 0.0646216 77.70% 0.1102836 78.78% 0.1193605
1999 83.24% 0.1174172 92.02% 0.0635942 77.55% 0.1118021 78.98% 0.117984
2000 83.14% 0.1169901 91.79% 0.0606901 77.49% 0.1123344 78.98% 0.1193721
2001 83.36% 0.1162854 92.06% 0.0588281 77.78% 0.1108823 79.09% 0.1186595
2002 83.45% 0.115474 92.24% 0.0561487 77.79% 0.1096482 79.17% 0.117457
2003 83.58% 0.1158349 92.42% 0.0567395 77.98% 0.1102617 79.22% 0.1172885
2004 83.39% 0.1142531 92.37% 0.0609402 77.98% 0.1073828 78.77% 0.1111863
2005 80.68% 0.1321058 90.81% 0.0945953 75.51% 0.1022299 74.83% 0.1299224
2006 76.81% 0.1460924 84.98% 0.1358121 72.76% 0.1057319 72.02% 0.1547992
2007 73.86% 0.161387 83.04% 0.1595029 70.70% 0.1182174 67.55% 0.1597613
2008 74.69% 0.1577829 83.86% 0.1476847 71.39% 0.1193645 68.49% 0.1600381
2009 74.55% 0.1589823 84.18% 0.1432337 71.28% 0.1164908 67.91% 0.1636513
2010 73.81% 0.153399 82.23% 0.1399235 71.21% 0.1095651 67.84% 0.1650843
2011 74.37% 0.1515713 82.83% 0.1364265 71.90% 0.1067859 68.27% 0.1637614
2012 74.82% 0.149117 83.31% 0.1323053 72.58% 0.105656 68.53% 0.1604135
2.3.2 The high percentage of the rural banks in mid and west area
As can be seen from table 4, the percentage of the rural institutions has been high in the central and western area. In 2012, the ratio is 25.12% in the central region, 29.13% in the western region and 14.79% in the eastern
The rate of rura
region. The rural institutions still only act as the supplement of commercial banks, account for a quite small part, and offer the limited financial services. The situation of financial exclusion in the Midwest area is more serious as the rural banks accounting more.
Table 4
ks from 1998 to 2012
Таблица 4
Доля сельских банков с 1998 по 2012 г.
Year National Eastern Central Western
Ave. Std. Ave. Std. Ave. Std. Ave. Std.
1998 14.13% 0.116813 5.41% 0.0659653 19.92% 0.1150726 18.26% 0.1136851
1999 14.12% 0.1164367 5.47% 0.0653382 20.06% 0.1166497 18.08% 0.1125515
2000 14.21% 0.1162701 5.67% 0.0629653 20.10% 0.117307 18.12% 0.1141365
2001 14.09% 0.1156037 5.51% 0.0614756 19.88% 0.1156966 18.10% 0.1137242
2002 14.02% 0.1148671 5.40% 0.0590956 19.81% 0.1154262 18.06% 0.1125834
2003 13.99% 0.1148599 5.35% 0.0600894 19.74% 0.1147736 18.06% 0.1124441
2004 14.25% 0.1131201 5.47% 0.0645309 19.77% 0.1117439 18.61% 0.1056925
2005 17.21% 0.1305996 7.30% 0.0974944 22.40% 0.1043247 22.82% 0.1254673
2006 21.28% 0.1446487 13.36% 0.1373928 25.33% 0.1064049 25.83% 0.1515519
2007 24.33% 0.1601104 15.39% 0.161253 27.45% 0.119498 30.44% 0.1562904
2008 23.66% 0.1561695 14.69% 0.14921 26.92% 0.120677 29.72% 0.1554989
2009 23.77% 0.1572347 14.34% 0.1448726 27.01% 0.1174156 30.25% 0.1591504
2010 24.37% 0.1508175 16.18% 0.1394764 26.93% 0.1105435 30.18% 0.1603177
2011 23.64% 0.1488146 15.43% 0.1356931 26.01% 0.1079728 29.59% 0.1588481
2012 23.01% 0.1465419 14.79% 0.1324318 25.12% 0.1065459 29.13% 0.1556617
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AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
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2.4 The Financial exclusion of different levels of financial institutions
The financial institutions are profit-seeking and it needs to take the comprehensive factors into consideration in determining the level of financial institution nodes. Comparing with the branch and above, the basic outlets provide the limited category of services. The larger the basic outlets rate is, the more prominent the financial exclusion issue.
2.4.1 The low proportion of the
commercial bank basic outlets
As can be seen from table 5, the average proportion of the commercial banks basic outlets has declined, the financial exclusion eased just from the perspective of the commercial banks. The number of the commercial bank basic outlets in western region accounts for the highest, the central region follows and the eastern region is lowest with the proportion is respectively 15.47%, 16.38% and 21.05%, the situation of financial exclusion increases from the west to the east.
Table 5
The proportion of the commercial bank basic outlets from 1998 to 2012
Таблица 5
Соотношение основных учреждений коммерческих банков с 1998 по 2012 г.
Year National Eastern Central Western
Ave. Std. Ave. Std. Ave. Std. Ave. Std.
1998 19.69% 0.115528 15.87% 0.075753 18.18% 0.051078 24.21% 0.160219
1999 19.53% 0.114882 15.69% 0.076076 18.13% 0.051595 23.99% 0.158895
2000 19.27% 0.115048 15.33% 0.075354 18.05% 0.051585 23.69% 0.159525
2001 19.46% 0.116757 15.91% 0.086521 17.94% 0.050191 23.72% 0.159236
2002 19.32% 0.116308 15.68% 0.085689 17.87% 0.049027 23.64% 0.158667
2003 19.12% 0.116718 15.28% 0.086028 17.70% 0.047811 23.59% 0.158664
2004 18.89% 0.115737 15.17% 0.085749 17.72% 0.047416 23.09% 0.158149
2005 19.22% 0.112063 16.97% 0.078287 17.53% 0.044718 22.41% 0.159948
2006 19.05% 0.111629 16.79% 0.07631 17.48% 0.046022 22.17% 0.159969
2007 18.76% 0.109422 16.42% 0.072996 17.51% 0.046058 21.74% 0.157582
2008 17.87% 0.116075 14.61% 0.063431 16.10% 0.041032 22.04% 0.169773
2009 17.71% 0.116184 14.51% 0.064413 15.87% 0.041088 21.87% 0.169741
2010 18.10% 0.112187 15.38% 0.060359 16.15% 0.041553 21.90% 0.16531
2011 17.86% 0.110297 15.37% 0.05633 16.15% 0.040989 21.27% 0.164689
2012 17.87% 0.107434 15.47% 0.055603 16.38% 0.039126 21.05% 0.160781
2.4.2 The high proportion of the rural bank basic outlets
As you can see in table 6, from 1998 to 2006, the proportion of rural institution basic outlets has been in a steady state especially after 2010. The number in 2012 on average is 89.46, while
the average rate in areas from east to west is 88.07%, 89.04% and 90.91%. No big difference exists. Seen from the high proportion, the institutions in rural areas mainly provide basic service, and the problem of financial exclusion is serious.
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Table 6
The proportion of the rural institution basic outlets from 1998 to 2012
Таблица 6
Соотношение основных учреждений сельских банков с 1998 по 2012 г.
Year National Eastern Central Western
Ave. Std. Ave. Std. Ave. Std. Ave. Std.
1998 92.73% 0.0569642 92.70% 0.0726424 91.76% 0.0707224 93.54% 0.0329816
1999 92.98% 0.0527011 93.49% 0.0592343 91.71% 0.0699138 93.62% 0.0325067
2000 93.06% 0.0519955 93.74% 0.0602971 91.62% 0.0675502 93.74% 0.0310521
2001 93.16% 0.0498522 93.92% 0.0551986 91.69% 0.0661445 93.80% 0.0301688
2002 9.3..30% 0.0478257 93.99% 0.054447 91.88% 0.0622211 93.94% 0.0291246
2003 93.21% 0.0501855 93.57% 0.062191 91.89% 0.0632039 94.01% 0.0281701
2004 92.93% 0.0524463 93.19% 0.0679598 91.59% 0.0644189 93.82% 0.0278408
2005 92.97% 0.0373645 92.47% 0.0543419 92.57% 0.0370662 93.65% 0.0245031
2006 93.32% 0.030973 93.10% 0.0441991 93.40% 0.025427 93.40% 0.0273451
2007 89.67% 0.1853685 82.11% 0.3343044 93.20% 0.0319035 92.89% 0.0196526
2008 92.94% 0.031773 93.29% 0.0453106 93.44% 0.0269502 92.27% 0.0236345
2009 92.07% 0.0537056 93.30% 0.0458227 91.41% 0.0796092 91.60% 0.0353625
2010 89.45% 0.1095518 87.81% 0.1633867 88.98% 0.1133765 91.14% 0.0447271
2011 89.39% 0.1088432 87.92% 0.1609597 89.01% 0.1136531 90.88% 0.047545
2012 89.46% 0.1075649 88.07% 0.1581012 89.04% 0.1135951 90.91% 0.0473664
As a whole, the number of financial institution nodes continues to increase, but with uneven distributions; the density of financial institutions increases steadily and the gap between the eastern and western regions is huge; the commercial banks of the eastern region account for the highest proportion but the rate of commercial basic outlets is low and the rate of the rural basic outlets is relatively higher, in addition the proportion of the Midwest rural banks is high. Even though financial exclusion eases, large differences still exist between different areas and the situation is rather critical in rural areas, especially the financial exclusion in western rural areas is the worst.
3. The analysis of the different features of financial exclusion in different areas
3.1 Sample selection
We will carry on the study of influence factors of financial exclusion from the types and the levels of the whole country’s financial institutions. The explained variables is composed of per financial institution outlets (ALL_POP), the rate of commercial bank outlets (B_RATIO), the rate of rural bank outlets (CE_RATIO), the rate of commercial bank basic outlets (BPRI_RA-TIO), the rate of rural bank basic outlets (CE-PRI_RATIO).
The explanatory variables are composed of three categories. The first category is the bank op-
portunity index which consists of the per capita GDP (GDP_POP), the per capita savings deposits (DEP_POP) and the per capita consumption (CONSUMER_POP). Goldberg (1990) pointed out that the banks decide whether to set up more branches on the possible opportunities in that area. The higher the POP is, the more prosperous the economy. In these districts, as the capital transfer, the bank can do more in both ends. Considering that the Chinese residents tend to save before consumption, the public may also hold a large amount of savings even in rural areas, which will provide enough sources of funding for the bank. With the scale of non-cash payment developing bigger and bigger, the per capita consumption of the larger areas may need more financial institutions to provide service outlets.
The second category is the government intervention index which includes the per capita expenditure (GOV_POP) and area scale (Area). Since the China’s government has great impact on economic activities, the rate of financial expenditure and gross domestic product measures the government economic intervention in the extent of each area. In addition, as the bank, offering products with public attributes, more or less with state-owned capital background, vulnerable to government intervention, would be required to complete the task of a complete coverage of ensuring the people’s basic financial demand. Therefore, in order to improve the coverage rate
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of the banking branch, the banks may increase its branches in the range of the vast area to ensure the geographic distance that the residents get services.
In addition, we need to take into account of the hidden financial exclusion, that is, the residents can get the service of the bank nodes, but have no opportunity to enjoy the more advanced and complex services. People with higher education tend to require more professional services and the bank needs to establish institutions equipped with comprehensive functions to satisfy the public demand. Therefore we added the education level (EDUSTU_POP) variable, measured in the number of college students in per million of population
The samples range over China’s 31 provinces, cities and autonomous regions (except Hong Kong, Macao and Taiwan) and the time interval is from 1998 to 2012. The explained variable data is gathered from the website «the financial license information base» of China CBRC. We use dynamic panel model to make an analysis with the data of other variables comes from «China Statistical Yearbook», the National Bureau of Statistics website and CEInet Statistics Database.
3.2 The empirical results and analysis
3.2.1 The influence factor of per capita financial institution nodes
The regression results in Table 7 for per capita financial institutions nodes in different regions show that lagged explanatory variable Li
results are obvious and the current explanatory variable is significantly affected by the previous. The factors of per capita gross domestic product, area of different region, the per capita consumption, and the number of college students in per million populations impose impact on per capita financial institution outlets across the country. The area of the eastern, the per capita GDP and per capita consumption in the central, the per capita savings deposits in the western, all the factors influence respectively the per capita financial institutions outlets in different regions. Moreover, the number of college students in per million populations exerts more prominent effect among the factors above.
Resting on the geographical advantages and with the increasing numbers of branches, the coverage of it will be improved and more opportunities to get financial services will be available to the eastern residents. The level of economic development and consumption, which play great role in central area, enables the residents to get the financial services accordingly. On account of the backward financial development in the western region, financial institutions rely in great extent on savings, and more nodes will be set up because of the high savings demand. In addition, the local education level is an important cause of the recessive financial exclusion, and in the region with higher levels of per capita education, residents ask for more professional financial services, therefore financial institutions will estab-
Table 7
The estimation results of per capita financial institution nodes
Таблица 7
Результаты оценки показателей узловых финансовых учреждений в расчёте на
душу населения
Variable National Eastern Central Western
Coefficient P Coefficient P Coefficient P Coefficient P
all_pop Li. 0.82225 0.000 0.845359 0.000 0.950838 0.000 0.915258 0.000
gdp_p°p 0.05271 0.002 0.069899 0.017
Lnarea 0.029979 0.000 0.025821 0.013
dep_pop 0.092206 0.05
gov—pop
consumer_ pop 0.108 0.077 0.205316 0.000
edustu_pop 0.000795 0.000 0.000861 0.000 0.000479 0.000 0.001043 0.001
_cons -0.24193 0.000 -0.21559 0.085
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3.2.2 The influence factors of financial exclusion from different categories of financial institution outlets
Table 8 is the regression results on the commercial banks in different area. From a national point of view, all the explanatory variables except the education level exert significant effects on the number of commercial banks, among them, the per capita government spending and per capita consumption affect positively while the per capita GDP, the area of regions and per capita savings deposit affect negatively. From different regions, the area scale, per
capita government spending and the number of college students in per million population have obvious negative effect on the number of commercial banks in the eastern, the explanatory variables except the area scale have significant influence in central part, of which the per capita GDP, the per capita savings deposit and the number of college students in one million exert negative influence, an another hand the per capita government spending and per capita consumption affect in an opposite way, however, only the education level influence the number of commercial banks in the western region.
Table 8
rate of commercial banks
The estimation results of the
Таблица 8
Результаты оценки доли коммерческих банков
Variable National Eastern Central Western
Coefficient P Coefficient P Coefficient P Coefficient P
b_ratio L1. 0.92374 0.000 0.931442 0.000 0.924395 0.000 0.992735 0.000
gdp_p°p -0.06437 0.001 -0.08082 0.001
Lnarea -0.00905 0.019 -0.01293 0.01
dep_pop -0.02438 0.05 -0.01904 0.075
gov—pop 0.042002 0.064 -0.08405 0.063 0.213051 0.002
consumer_pop 0.219542 0.000 0.143166 0.000
edustu_pop -0.00013 0.061 -0.00034 0.01
_cons 0.169245 0.004 0.199029 0.015 0.141113 0.073
Table 9 shows the regression results of influence factors on the rural institutions in different parts. From a national point of view, the factor of per capita GDP, per capita government spending, per capita consumption and the number of college students in one million population affect the numbers of the rural institutions, among them, the per capita GDP and the number of college students in one million population have a positive effect while the other two have a negative one. From the angel of different regions, the per capita GDP and the
The estimation results
area scale produce a negative effect, while the per capita consumption affects in the opposite in the eastern, the influence of the per capita GDP, per capita savings deposits and the number of college students in one million population is positive and the function of the per capita government spending and per capita consumption is negative in the central part, however, in western part, just the number of college students in one million population affects positively.
Table 9
of the rural institutions
Таблица 9
Результаты оценки сельских учреждений
Variable National Eastern Central Western
Coefficient P Coefficient P P Coefficient P
ce ratio L1. 0.909659 0.000 0.92073 0.000 0.915144 0.000 0.973519 0.000
gdp pop 0.100094 0.000 0.092516 0.078 0.088922 0.001
Lnarea 0.011561 0.069
dep pop 0.02745 0.024
gov pop -0.07847 0.002 -0.25254 0.001
consumer pop -0.29223 0.000 -0.2643 0.04 -0.16366 0.000
edustu pop 0.000517 0.000 0.000161 0.022 0.000316 0.015
cons -0.12538 0.086
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3.2.3 The factor of financial exclusion from various levels
Table 10 shows the regression results of influence factors on commercial bank basic outlets in different parts of the provinces. From a national point of view, the factor of per capita consumption brings a positive impact on the numbers of the commercial bank basic outlets,
The estimation results of the
while, the other way around for the factor of per capita gross domestic product. Regionally, the impact of the explanatory variables of the eastern region on the commercial bank basic outlets is not so obvious, and per capita consumption of the central region produces a negative effect on them, while, that of the western affects the commercial banks positively.
Table 10
nmercial bank basic outlets
Таблица 10
Результаты оценки основных учреждений коммерческого банка
Variable National Eastern Central Western
Coefficient P Coefficient P Coefficient P Coefficient P
bpri_ratio L1. 0.972829 0.000 0.959064 0.000 0.943504 0.000 0.963355 0.000
gdp_p°p -0.0188 0.008 0.003736 0.581 -0.02461 0.151
Lnarea
dep_pop
gov—pop
consumer_pop 0.064083 0.004 -0.01946 0.077 0.104191 0.006
edustu_pop
_cons
Table 11 is the regression results on basis of the basic outlets in rural areas in different parts of the provinces. From a national perspective, the per capita gross domestic product, the area scale, the per capita government spending, and the number of college students in per million population account for the number of rural basic outlets significantly in eastern part, among the factors mentioned, the first two affect positively and the latter two negatively. From different regions, the explanatory variables except the constant term have significant influence in eastern, and the area scale, per capita consumption and the number
The estimation results of the
of college students in one million exert positive influence, in another hand the per capita GDP, per capita savings deposit and per capita government spending affect in a opposite way; however only the per capita government expenditure influences negatively on the rural basic outlets in central part; GDP per capita, the per capita deposits, per capita government spending, per capita consumption, the number of college students in per one million plays great role in the western region, and among these factors, the first two influence positively, nevertheless the latter three affect in a negative way on numbers of the rural basic outlets.
Table 11
ral institution basic outlets
Таблица 11
Результаты оценки основных учреждений сельского банка
Variable National Eastern Central Western
Coefficient P Coefficient P Coefficient P Coefficient P
cepri ratio L1. 0.518714 0.000 0.179777 0.000 0.972033 0.000 1.006195 0.000
gdp pop 0.07218 0.001 -0.08441 0.011 0.020575 0.002
Lnarea 0.010663 0.000 0.069711 0.000
dep pop -0.07746 0.000 0.027756 0.000
gov pop -0.14618 0.000 -0.18204 0.000 -0.1122 0.099 -0.03091 0.000
consumer pop 0.371756 0.000 -0.0879 0.000
edustu pop -0.00032 0.000 0.000625 0.000 -7.2E-05 0.043
cons °-318311 0.000
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31
4. The conclusions and outlooks
When comes to the issue of financial exclusion, types and levels of financial institutions should be considered. Of the same amount of institutions, different types of institution means different types of exclusion and different levels of institutions also means different levels of exclusion. China Banking Regulatory Commission releases financial license including the types and levels of financial institutions which could be a help for the analysis of financial exclusion from the perspective of different types and levels.
We found that, in recent years the overall numbers of nodes keep grow though, the severe financial exclusion still exists in different parts. Nationwide, a high level of economic development and per capita consumption, a large area and a large number of college students in per million populations, all of them mean more financial institution nodes. While the factors influence the number of the nodes in different regions vary, namely, area size is more important in the eastern , the economic development and the per capita consumption weight more in the central, the per capita deposits is more stressed in the western.
Secondly, the numbers of commercial bank nodes in different types of financial institutions still occupy a large proportion while the rural nodes take a small proportion. There is a big difference between commercial bank nodes and rural nodes, and different types of financial institutions result in the financial exclusion. For Commercial Bank, the higher degree of government involvement and the per capita consumption, the lower level of economic development and per capita savings rates will lead to increasing demand for commercial bank nodes and their services. For the rural institutions, the higher level of economic development, a broad area, a lower degree of government involvement and lower education
levels boost the number of rural nodes.
Finally, for the different levels of financial institutions, the proportion of commercial bank basic outlets and rural institution basic outlets are in a relatively stable state, although there are slightfluctuations,theoveralltrend isstillshowing that the coverage of the commercial banks is small and rural institution basic outlets account large. Then, the financial services provided by the business banks are more comprehensive, but the rural institutions provide the basic financial services only. The lower economic development and high per capita consumption levels lead to the increase of the proportion of commercial bank basic outlets. And higher level of economic development, a large area and a lower degree of government involvement and the per capita level of education in rural areas will increase the proportion of such institutions outlets. It is favored for the western to establish rural basic outlets, while the middle-eastern regions are inclined to set up fully functional branches.
The wide spread of Financial exclusion and the recessive Financial exclusion strengthened social unfair and restrained the total development of economy. Based on conclusions above, we think it is necessary to reduce the financial gap between the east, the west and central, to promote coordination of different types of financial institutions, to improve financial service level of the Midwest, and to publish financial knowledge.
This article applied dynamic panel data to execute regression analysis, and in the future spatial panel data can be used to make an analysis. In different regression equations, with same explanation variables, conclusions of different area varies a lot, some signs of ratio is opposite, which reminds us that factors that influence types and levels of financial institutions in different area can be different, policy for example. Further completions are needed in future researches.
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32
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Code Compiling Rules of Financial License Institution (Trial)
(Issued by CBRC No. [2007]i03)
To cope with the implementation of the measures for the administration of the financial license, these provisions are formulated.
I. The structure of institution code
The institution code consists of capital English letters and numbers, a total of 15, which are divided respectively into 6 parts , i.e., the institution category code, institution code, organization category code, the issuing authority code, address code, the order code, arranged in order from left to right.
The schematic sheet of institution code structure
No. 1 No. 2 No. 3 No. 4 No. 5 No. 6 No. 7 No. 8 No. 9 No. 10 No. 11 No. 12 No. 13 No. 14 No. 15
institution category code Institution code organization code the issuing authority code address code the order code
II. The meaning of institution code
The institution category code stands in the first, represented in a capital English letter -
A-the policy bank
B-the commercial bank
C-the rural cooperative bank
D-the urban credit cooperative
E-the rural credit cooperative
F-the mutual cooperative
J-the asset-management Company
K-the trust company
L-the financial company
M-the financial leasing company
N-the auto financing company
P-the money Brokerage Company
Q-the Loan Company
Z-the other financial institution
When a new institution type is built up, its institution category code needs to be made clear.
The institution code is from No.2 to No.5, represented by number.
Each legal institutions has a unique code generated by the computer system in accordance with the rules. The code is unified in the scope of nation except the rural credit cooperatives, the mutual cooperatives and the loan companies (E, F, Q types of institutions), a national coding. E, F, Q types of institutions are coded in the province (autonomous region, municipality directly under the central government) by the unified way.
The arrangement of newly established institutions is in accordance with the institution type.
The examples of institution code compiling are as follows:
A-the policy bank 0001-National Development Bank
B-the commercial bank
0001-Industrial and Commercial Bank of China Limited ICBC
C- the rural cooperative bank 0001-Tianjin Dagang rural cooperative bank
D-the urban credit cooperative
0001-the urban credit cooperatives co., LTD of Handan
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E-the rural credit cooperative
0001-the rural credit cooperative in the Baozhi District of Tianjin The Xinjiang Uygur Autonomous Region
0001-the rural credit cooperative in Xinjiang Uygur Autonomous Region
F-the fund cooperative Jilin Province
0001-Baixin rural fund cooperatives in Village Runjia, County Lishu, Jilin Province Qinghai Province
0001--Xingle rural fund cooperatives in village Yurun, County Ledu, Qinghai Province
J-the asset-management company
0001—the Huarong asset-management company of China
K-the trust and investment company 0001-China Credit Trust Co
L-the financial company 0001-Cnooc Finance Corporation Ltd
M-the financial leasing company
0001-China National Foreign Trade Financial & Leasing CO., LTD
N-the automobile finance company 0001-Volkswagen automobile finance company
P-Money Brokerage Company 0001-Tullett Prebon SITICO (China) Limited
Q-Loan Company Sichuan Province
0001-Huimin Loan co., LTD in County Yilong, Sichuan Province.
The Xinjiang Uygur Autonomous Region 0001-
Z-the other financial company
0001-The central Treasury securities registration and settlement co., LTD
The organization category code stands in the sixth place, presented by the capital English letter. When the organization category code is different, the meaning of organization category code changes.
1. A-the policy bank H-the head office
G-the business department of head office
B-the tier-one branch
K-the tier-one branch of sales department
M-second-level branch
S-sub-branch
X-the other branches
2. B-the commercial bank
серия Экономические исследования
Yongbin L.V., Xianping Zhou, Yingying Y.I.
AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
H-the head office
G-the business department of head office
B-the tier-one branch
K-the tier-one branch of sales department
L-sub-branch
M-Direct Branch
N-the second-level branch of sales department
S-sub-branch
U-Small local branch,
V-savings agency
X-the other affiliated agency
3. C-the rural cooperative bank H-the head office S-sub-branch
U-Small local branch
V-savings agency
X-the other affiliated agency
4. D-the urban credit cooperative H-juridical person
S-branch office
X-the other affiliated agency
5. E-the rural credit cooperative
H—association of provinces, autonomous regions and municipalities B-association of the region (municipal)
S-association of county (city)
T-the credit cooperative U-the credit agency V-savings agency X-the other affiliated agency
6. F-the fund cooperative H-juridical person
7. J-asset-management company H-the headquarter
B-the office X-the other agencies
8. K-the trust company H-juridical person
9. N-the automobile finance company H-juridical person
10. L-the financial company H-the headquarter
B-the branch company X—the other branches
11. M-the financial leasing company H-the headquarter
B-the branch company X—the other branches
12. P-Money Brokerage Company H-the headquarter
B-the branch company X—the other branches
13. Q-Loan Company H-the headquarter
серия Экономические исследования
Yongbin L.V., Xianping Zhou, Yingying Y.I.
AN EMPIRICAL ANALYSIS OF REGIONAL DISPARITY OF INFLUENCE FACTORS ON FINANCIAL EXCLUSION BASED ON TYPES AND LEVELS OF FINANCIAL INSTITUTION IN CHINA
36
B-the branch company
X—the other branches
14. Z-the other financial company
The compiling rule of Z type institution of organization category code comply with the above.
When a new institution type is built up, its institution category code is defined in accordance with the above rule and the actual situation.
The issuing authority code is in the seventh place, represented by number.
1- the China Banking Regulatory Commission
2- the Banking Regulatory Bureau
3- the branch of the Banking Regulatory Bureau
The address code is from No. 8 to No.11, represented by number.
In accordance with the «codes for the administrative divisions of the people’s Republic of China» (GB/T2260), we take the former four code administratively divided of city (regions, autonomous, league and municipalities directly under the central government as the address code.
The example of the address code is following:
1100 stands for City Beijing, 1200 stands for City Tianjin, 1301 stands for City Shijiazhuang...
The order code is from No.12 to No.15, represented by number.
The institution is coded in the sequence of making and issuing of financial license if the institution category code, institution code, organization category code, the issuing authority code and address code are the same.
III.The encoding rules of institution code
The computer automatically generates the institution category code, the institution code, the organization category code, the license issuing author code, the address code and the order code in the order from left to right when compiling.
When a renewal financial license is needed for the administrative license changing, when the financial institution is terminated, and when the financial license is recovered and canceled, the institution code is no longer used in order to guarantee the uniqueness of the code and store the data for a long time for the computer.
In order to cope with the implementation of the administrative license law of the People’s Republic of China, the CBRC released all the financial institution information under the administrative permission list of the website on November 1, 2007 which is a new initiative to carry on open government and to safeguard the legitimate rights and interests of citizens, legal persons and other social public.
The financial license information contains the aggregate data and the statistics classified by the institution type, the organization category and the area, stressing on the single institution information which includes the name, the approval date, the address, the institution code, the issuing license authority, the serious number and the issuing date. In addition, for the purpose of facilitating the social from all walks of life to understand the dynamic information of CBRC administrative licensing, it also includes the annual number of newly established institutions, the permit lost data and the exit number, all of them are displayed on the website by 4 big modules: the holding list, the setting up list, the lost list and the exiting list which the social can click to make queries, validations, statistics and so on.
The financial license information provides the following functions: the first is the query function that allows the public to query and understand the institutions they pay attentions. The second is statistics that enables the public to make accurate statistics of certain kind of financial institution by the use of different conditions, such as the name, the address, the type, the regulator and the region. The third, validation function, in the understanding of the fuzzy name and the address, allows people to grasp the accurate information through an online authentication.
The social, by focusing on the license information the CRBC released, will play the role of social supervision and improve the capacity of discrimination of all kinds of illegal financial institutions.
серия Экономические исследования
