To be published in the forthcoming issue of Area Development and Policy
See http://www.tandfonline.com/toc/rard20/current
Rural Banking in China: A Case of Centralization?
Godfrey YEUNG
Department of Geography
National University of Singapore
Singapore 117570
E-mail: [email protected]
Canfei HE
College of Urban and Environmental Sciences
Peking University
Beijing 100871
China
E-mail: [email protected]
Peng ZHANG
Faculty of Economics
Centre for History and Economics
University of Cambridge
UK
Email: [email protected]
Number of words (excluding references): 6,973
Acknowledgement: We would like to thank two anonymous reviewers and the
editors of Area Development and Policy for their invaluable comments. The first
author is grateful for the financial support of the NUS’s Academic Research Fund
(R-109-000-154-112) and the second of the National Natural Science Foundation
of China for Distinguished Young Scholars (No. 41425001). The authors are
responsible for any remaining errors and omissions.
2
Rural Banking in China: A Case of Centralization?
Abstract:
Four types of banking institutions responded differently to the drive to centralize Chinese
rural banking operations. In 2009 city commercial and market-oriented banks had a higher
density of outlets in economically developed than in less developed counties, as expected,
while rural banking institutions had an unexpectedly higher density in non-agricultural
than in agriculture-based counties. Circumstantial evidence suggests that the wholly state-
owned and policy-oriented Agricultural Development Bank of China has invested in
business-oriented activities in non-agricultural counties. These banking sector
developments could have long-term policy implications for rural development in China.
Keywords: rural banking, banking reform, centralization, China
3
1 INTRODUCTION
As part of the World Trade Organization’s (WTO) accession treaties, foreign
banking giants like HSBC and Citibank were allowed to enter the Chinese rural banking
market. A number of Sino-foreign joint ventures targeting the rural banking market were
formed after the China Banking Regulatory Commission (CBRC) issued a plan in 2009 to
encourage private investment in rural financial institutions. The opening up of rural
banking had the potential to facilitate a centralization of rural banking operations and
banking service provisions (hereinafter ‘centralization’) to cut costs, as Anglo-American
commercial banks do. Centralization is exemplified by the closure of sub-branches and
outlets (especially in less profitable remote locations) and the widespread use of
automated telephone and electronic banking rather than face-to-face interaction for the
provision, assessment and processing of banking services (Leyshon and Pollard, 2000). In
China this path of centralization could have long-term implications for the socio-economic
development of rural China, as the inclusion of the 70.17 million poor Chinese people in
rural areas living under the national poverty line of 2,300 yuan/year (US$376/year) in
formal banking institutions could facilitate their participation in the formal economy
(China Daily, 16 October 2015).
The relevant literature deals with banking and finance in general, and rural finance
in particular. Existing studies of the former mainly focus on foreign bank investment in
China (Lu and Dewhurst, 2007; He and Fu, 2008; He and Yeung, 2011) or discuss the
potential effects of banking reforms on Chinese banks (e.g., Jia, 2009). Yeung (2009a)
examined the lending criteria of state-owned commercial banks (SOCBs), while Liu and
Wu (2008) highlighted the spatial differentiation of bank branches and assets between
coastal and rural areas in the central and western provinces.
Studies of rural finance normally focus on the specific channels of formal and
informal finance for peasant households (e.g., Han, 2004; Ma, 2004). Han (2004) provided
a comprehensive overview of rural finance while Ma (2004) evaluated the difficulties of
reforming rural finance within the transitional Chinese economy. Watson (2003) provided
a historical account of the organizational transformation of the rural credit cooperatives
but did not provide an explanation of their lack of financial provision to farmers. This
shortcoming was addressed partly by Ong’s (2006, 2012) examination of the triangular
debts between township governments, collective enterprises, and (informal) rural financial
institutions, and the importance of political institutions in the development of rural credit
4
cooperatives. Yeung, He and Zhang (forthcoming) further highlight the importance of
collaterals and formal credit records in the financial exclusion of poor farmers in rural
China. Tsai (2002) provided a comprehensive account of informal means of finance, while
Park and Ren (2001) analyzed the difficulties of implementing micro-finance projects, and
Hu (2004) examined rotating savings and credit in a Chinese village. Despite the
importance of rural banking, there has been little systematic investigation of the effects of
banking reforms on rural financial institutions and any subsequent implications for rural
development in China.
Banks in China include a combination of institutions with various types of
ownership and size, from the gigantic SOCBs, the medium-sized joint-stock commercial
banks (JSCBs), city commercial banks, to numerous tiny rural financial institutions (see
the following section). As the local institutional environment may significantly differ
according to the type of banking institution and each area’s specific economic structure
and level of economic development, the impact of reforms on rural banking could differ
spatially in China. Although they perform an important role in local financing, as clearly
illustrated by the recent ‘lending companies’ crises in Zhejiang province, informal
(underground) and unregistered financial institutions are neither the focus nor considered
in this paper.
This paper examines whether different banking institutions respond differently to
the drive for the centralization of their Chinese rural banking operations. The four-tier
rural banking system in China – including the market-oriented SOCBs and JSCBs, city
commercial banks, rural financial institutions, and policy-oriented rural banks –
demonstrates various patterns of centralization. Under competitive pressure from some of
the global banking giants and with no residual financial burden from the planned
economy, city commercial banking institutions have a higher density of outlets in
economically developed counties. In spite of the CBRC’s claim that the provision of basic
banking services to everyone is guaranteed by the four-tier banking system, circumstantial
evidence suggests that the policy-oriented rural banking institution, the Agricultural
Development Bank of China (ADBC), has followed market-oriented banks in having a
higher density of outlets in non-agricultural counties. As rural banking institutions are
under pressure to reduce their non-performing loan (NPL) ratios, they also have a lower
density of outlets in agricultural counties while maintaining their banking operations in
5
non-agricultural counties. These developments in the rural banking industry could have
long-term implications for Chinese rural development.
A brief review of the literature, the background to the banking reforms and four
research hypotheses are outlined in the next section. The third and fourth sections describe
the methodology and data sources before analyzing the degree of centralization in the
banking industry in China. The paper concludes with a brief discussion of the socio-
economic implications of these findings for rural development.
2 CONCEPTUAL FRAMEWORK AND RESEARCH HYPOTHESES
2.1 Industrial consolidation in the Anglo-American banking industry
The industrial consolidation of Anglo-American commercial banks through
mergers and acquisitions was facilitated by the new deregulatory space (Dymski, 1999).
Leyshon and Pollard (2000) identify four characteristics of industrial convergence. The
widespread use of automated telephone and electronic banking for the provision,
assessment and processing of banking services illustrates the centralization of banking
operations and the marginalization of bank branches. To improve their overall
competitiveness, banks provide tailor-made services for their high value customers and
withdraw the full range of services from ordinary customers, charge fees to maintain low
balance accounts under the ‘user pays’ principle or even close branches in deprived
neighbourhoods. This results in market segmentation and financial exclusion. As banks
focus on investment-oriented products and fee-generating activities, the general public
without bank accounts have to settle all their transactions in cash and thus are unable to
access the wide range of services for which bank accounts provide the gateway. To create
greater workplace flexibility and competitiveness in the banking industry, full-time
employees may be replaced by part-time or temporary workers, possibly restructuring
established ‘paternalistic’ labour relations.
As the state is still the majority owner of all SOCBs in China, the state arguably
plays an even more important role in Chinese banking industry reforms than their Anglo-
American counterparts, making a brief review of Chinese reforms important.
2.2 Chinese banking industry reforms
The massive restructuring of the Chinese banking industry is mainly due to change
in the ownership structure of banks. The efficiency of formal banking institutions has a
6
direct impact on the operation of a market economy, from the provision of credits to the
transfer of funds, etc. China is an interesting case as the Chinese economy is moving
toward some form of capitalism while the state still plays a crucial regulatory, financial
and refinancing role in the banking and finance sectors. The retail banking industry in
China exhibits features typical of a transitional economy, with some of the largest publicly
listed banks in the world co-existing with hundreds of thousands of rural co-operative
institutions, which are much less regulated by the People’s Bank of China (PBoC, the
central bank) and the CBRC. The current Chinese banking industry incorporates the
SOCBs, JSCBs, city commercial banks, rural financial institutions, foreign banks, and the
three policy banks. The SOCBs include the ‘Big Four’ – namely the Industrial and
Commercial Bank of China (ICBC); Bank of China (BOC); China Construction Bank
(CCB) and the Agricultural Bank of China (ABC) – and the Bank of Communications
(BOCOM).
The Chinese government had to reform the banking system to maintain its
financial viability. On the one hand, the state is under internal economic pressure to
improve banking governance and reduce the financial burden on the Ministry of Finance.
SOCBs became responsible for NPLs after two rounds of de facto capital injection by the
state: 270 billion yuan (US$32.61 billion) in 1998, and the transfer of another 1.4 trillion
yuan (US$169.1 billion) of pre-1996 NPLs to four newly created Asset Management
Corporations (the China Orient; the China Huarong; the China Cinda; and the China Great
Wall). Between 1998 and 2003, the SOCBs began to restructure their loan portfolios by
adopting a commercial lending strategy (Yeung, 2009a; Dobson and Kashyap, 2006 on
NPLs; and Shih, 2004 on central government political considerations relating to the
management of NPLs).
To improve governance, the State Council allows Chinese SOCBs to issue initial
public offerings (IPOs) of minority equities in local and/or overseas stock markets.
SOCBs became international financial holding institutions after their IPOs were floated on
the Stock Exchange of Hong Kong in 2005-06 and 2010, in the case of ABC. The state
still holds the majority equity of 54% or more in all SOCBs. In 2015, the SOCBs
controlled 39% of the 199.35 trillion yuan (US$32.45 trillion) of banking assets in China
(CBRC, 2015). Although partly owned by foreign investors, local governments are still
the majority shareholders of city commercial banks (see Yeung, 2009b; and Yeung, He
7
and Liu, 2012 on the impact of a ‘hybrid property’ (a mixture of public and private
property)).
Under the terms of the 2001 WTO accession treaty the state also had to open up
the Chinese banking market to foreign banks, which have been allowed to provide local
currency services to Chinese companies since 2003 and had full market access from 2006.
According to the CBRC, 153 foreign banks (40 are locally incorporated) operate in China
but they only accounted for 1.34% of the total banking assets in China in 2015 (CBRC,
2015, 47).
In addition to the SOCBs and JSCBs, the Chinese banking industry has three other
major types of banking institution, according to the CBRC’s classification. SOCBs and
JSCBs are grouped as market-oriented banks as they are under financial pressure to
improve their competitiveness and profitability, partly as a response to the deregulation of
the banking market. City commercial banks developed from city credit cooperatives,
which were established as independent credit cooperatives by residents (including local
city governments) in various Chinese cities in the 1980s, under the State Council’s
People’s Republic of China Temporary Regulations on Banking and Temporary
Regulations on City Credit Cooperatives in 1986. The original aims were to provide
financial services to residents and small- and medium-sized enterprises, and support local
economic development. To improve their financial viability, the minimum registration
requirement for city credit cooperatives was increased from 100,000 to 500,000 yuan
under the PBoC’s 1988 Regulations on City Credit Cooperatives. The number of city
credit cooperatives had reached 3,300 by the end of 1989.
Policy-oriented bank and rural financial institutions are particularly important for
Chinese rural development. The Agricultural Development Bank of China (ADBC) is a
policy-oriented bank and wholly owned by the state for the purpose of supporting
agricultural development. The rural financial institutions include rural cooperative banks,
the ubiquitous rural credit cooperatives, the postal savings bank, and other forms of rural
financial institutions that are de facto owned by local governments (Ong, 2006). If
banking operations in these two types of rural banking institutions are centralized, this
may not only have potential long-term implications for socio-economic development in
rural China, but could also affect the pace of poverty alleviation for tens of millions of
deprived Chinese in rural areas due either to unemployment and the associated economic
8
costs resulting from bank outlet closures, or financial exclusion resulting from the relative
inaccessibility of credit for agricultural development.
2.3 Restructuring of Chinese rural banking
All SOCBs, especially the ‘Big Four’, have tens of thousands of outlets all over
China: at their peak in 2000, SOCBs had more than 123,800 outlets (Yeung, 2009b). To
streamline their operations by outlets declined by 46% to 66,300 by 2008, SOCBs either
merged savings outlets into sub-branches or closed them in areas with small local banking
markets. Centralization is expected to be more evident in more economically developed
rural regions where operating costs are high.
Without the legacy of the blanket SOCB geographical coverage, the profit-oriented
JSCBs actually expanded their operations in the 2000s. As the extent of the centralization
of SOCBs (a reduction of more than 57,500 units) was larger than the expansion of JSCBs
(an expansion of more than 1,700 units) in the 2000s, a higher level of centralization of
banking operations in market-oriented SOCBs and JSCBs in rural areas is expected
(Yeung, 2009b), leading to the hypothesis that:
H1: The number of market-oriented banking units is positively related to the level of
economic development in Chinese rural administrative regions.
In contrast to the market-oriented banks, the ADBC is not expected to be under
political or financial pressure to streamline its operations. The ADBC and Export-Import
Bank of China (EXIM) are two wholly state-owned policy banks established in 1994
under the direct administration of the State Council of the Chinese central government.
Under the regulation and supervision of the PBoC and CBRC, the policy remit of the
ADBC is to promote development in agricultural and rural areas, specifically to raise
funds and provide credit for agriculture-related commercial businesses specified by the
central government, and to serve as an agent for the Ministry of Finance to allocate special
funds to support agricultural development (ADBC, 2010). Therefore, a lower level of
centralization of the banking operations of policy-oriented rural banks is expected:
H2: The number of policy-oriented banking units has no significant relationship to the
level of economic development in Chinese rural administrative regions.
Poor governance and credit risk controls have contributed to the high NPL ratios of
city credit cooperatives as a number of local governments have used them to finance their
enterprises. The numbers of city credit cooperatives increased significantly to more than
9
5,000 in the early 1990s, before merging into city commercial banks under the State
Council and PBoC’s Directives issued in 1995. The PBoC’s directive (Management of
City Credit Cooperatives) actually forbade the establishment of new city credit
cooperatives.1 City commercial banks are likely to centralize their banking operations
aggressively under the on-going restructuring. We thus expect the level of centralization
of banking operations in city commercial banks in rural areas to be the highest of all types
of banking institutions in China:
H3: The number of city commercial banking units is positively and significantly related to
the level of economic development in the rural administrative regions in China.
Compared with other banking institutions, rural financial institutions, including
rural cooperative banks, postal saving banks, and other forms of rural financial
institutions, are under less intense competitive pressure. Since their establishment during
the rural cooperative movement in the 1950s, the rural financial institutions have been key
vehicles for the delivery of financial services to farmers and small-scale family businesses
in rural China. Many of these institutions have poor corporate governance and are
financially unsound, partly due to a significant amount of loans to now-bankrupt township
and village enterprises and the need of rural county governments to bridge their fiscal gaps
(Park, Brandt & Giles, 2003; Ong, 2012).2 Since the policy guidelines for re-structuring
and strengthening corporate governance issued by the State Council in 2003, the number
of highly leveraged rural financial institutions has decreased significantly: rural financial
institutions currently have an NPL ratio of 2.5% (CBRC, 2015). Individually, each rural
financial institution is generally (much) smaller in scale and with a limited area of
coverage. Collectively, rural financial institutions have comprehensive geographical
coverage, especially in remote villages, in rural China. Other types of financial institution
are generally not keen to compete for low value-added businesses with high operating
costs in remote villages, leading to the hypothesis that there is a low level of centralization
of banking operations of rural financial institutions in rural areas:
1 Most city commercial banks are small to medium size in terms of equity. About 70% have registered
equity of less than 20 billion yuan. The NPL ratio fell from a peak of 34% to 12% in 2004, and 1.4% in
2015. 2 Local governments’ finances deteriorated after the implementation of the tax sharing system in 1994,
which significantly increased the central government’s share of tax revenues, while rural country
governments remained responsible for the provision of public services (Ong, 2006).
10
H4: The number of rural banking units has no significant relationship with the level of
economic development in Chinese rural administrative regions.
These hypotheses suggest that the four-tier rural banking system will reveal
differing patterns of centralization in China.
3 METHODS AND DATA SOURCES
3.1 Tendency of centralization
To highlight the potential difference in response of banking institutions in various
regions, the dataset was divided into agricultural and non-agricultural counties using the
median value of primary industry as a proportion of local GDP. Four sets of negative
binomial regressions were estimated to test the four hypotheses, with four types of
banking institutions as the dependent variables against seven explanatory variables. To
reveal the possible patterns of centralization in the four types of banking institutions, the
number of business outlets of market-oriented banks, policy-oriented banks, local
commercial banks, and local rural financial institutions in 2,576 county-level
administrative regions (hereinafter ‘county’) in 2009 were used as dependent variables
(see Supplementary note 1).
Seven explanatory variables were used with data in 2009 to reveal the possible
contributing factors to the centralization of operations of the four different types of
banking institutions. As the centralization of banking operations is more likely in areas
with fewer business opportunities, it is expected that the dependent variables will have a
negative relationship with GDP per capita in the primary (AGRGDPPC) sector but a
positive one with secondary (INDGDPPC) and tertiary (SERGDPPC) sector GDP per
capita (see Supplementary note 1). The number of employees in financial sectors as a
proportion of the local population (FINEMP) and rural residents as a proportion of the
local population (RURPOP) are two other indicators used to provide additional
information about the economic structure. The dependent variables is expected to have a
negative relationship with the indicator for the rural population but relate positively to
financial employment. The net income of rural residents as a proportion of the disposable
income of urban residents (RURINCOM), and government expenditure as a proportion of
revenue (GOVEXP) are indicators used to reveal the finances of rural residents and rural
governments respectively. A relatively stronger financial position of rural residents would
suggest a larger potential banking market and thus a lower the degree of centralization (the
11
expected positive sign) while the opposite would be the case for the weaker finance
situation of rural governments (hence, the expected negative sign).
Two sensitivity tests were conducted. First, GDP per capita in the primary to
tertiary sectors was replaced by GDP growth rates (2008-2009) in the primary, secondary,
and tertiary sectors respectively (GDP per capita growth rate is not used as 2008 county-
level population data was not available). The local population as a ratio of the total
population was also been changed to the number of rural households as a proportion of the
total number of households. Second, two control variables were added to the model to
represent the size of the local economies: the number of enterprises, and the overall GDP
growth rate (2008-2009). The new results are largely consistent with the original ones (see
Supplementary note 1).
Observations with obvious reporting errors were dropped as where the explanatory
variable is equal to zero, or observations had abnormally high values (where RURINCOM
is larger than 100,000 and RURPOP is larger than 1). The cleaned data included 2,488
counties (2,439 if RURINCOM is included).
With the exception of ADBC and rural financial institutions (H2 & H4), the other
two dependent variables were expected to have a positive and significant relationship with
the level of economic development of the Chinese rural administrative regions (H1 & H3).
The robust estimator of variance is used to deal with the possible heteroskedasticity.
3.2 Data sources and limitations
Location data for banking business units at county level was obtained from the A
Collection of Rural Financing Atlas at the CBRC’s official website
(http://bankmap.cbrc.gov.cn/bank). The other economic and population data came from
provincial statistical yearbooks.
As data were only available for 2006 and 2009 time-series patterns of restructuring
of the rural banking industry could not be examined. The specific webpage on the CBRC
website containing this data is no longer available with no reason given (the raw data is
available on request from the authors). Nonetheless, the existing data permits examination
of the extent and determinants of centralization in the four major types of banking
institutions by the late 2000s.
12
4 CENTRALIZATION OF RURAL BANKING
All the coefficients have the expected signs and there is no sign of collinearity. The
highest correlation coefficient is 0.38 between GDP per capita in the secondary
(INDGDPPC) and tertiary sectors (SERGDPPC) (see supplementary note 1).
4.1 Centralization in market-oriented banks
There are signs of centralization among market-oriented banks at county level as
all the coefficients have the expected signs and are highly significant (at 0.01 level) in all
the models (Table 1).
<insert Table 1 about here>
The negative coefficients of GDP per capita in the primary sector (AGRGDPPC)
and the positive coefficients of GDP per capita in the tertiary sector (SERGDPPC) are
highly significant in all the models, in both agricultural and non-agricultural counties
(Table 1). Combined with the expected negative and highly significant coefficients for
rural residents in the local population ratio (RURPOP), and government expenditure as a
proportion of revenue (GOVEXP), these results are prima facie evidence for the
proposition that the market-oriented banks, including SOCBs and JSCBs, have fewer
outlets in areas with high agricultural output, and a higher density in areas with a higher
tertiary sector GDP per capita. This commercial decision made by market-oriented banks
was explained by a former senior banker who had worked for ICBC for more than two
decades: “Banks will only lend to customers if the potential return [based on the interest
rate] is larger than the potential costs [operating costs and the risk of default] of such
lending” (personal conversation, July 2014). Overall, the number of banking outlets
declined by 2.9%, from 61,346 in 2006 to 59,567 in 2009, possibly reducing the potential
access of ordinary clients to banking services. The coefficients have similar patterns in the
sensitivity test specifications (not included due to space constraints).
A possible explanation of these findings is the significant expansion of county area
banking businesses by market-oriented banks, especially the ABC. Although originally
established to support agricultural and rural development, the ABC had the highest NPL
ratios in the early 2000s and, under tremendous pressure from the Ministry of Finance,
had to reduce its financial losses. Moreover, the CBRC pushed the ABC and other SOCBs
to restructure their operations before the opening up of the local banking sectors as part of
the WTO accession treaty and to strengthen the capital adequacy ratio to prepare for the
13
implementation of Basel II and Basel III (Yeung, 2009b; He and Yeung, 2011). To
prepare for the IPO on the Stock Exchange of Hong Kong in 2010, the ABC underwent
massive restructuring to improve its balance sheet, including separation from the ADBC
operations and a massive transfer of NPLs to the China Great Wall Asset Management
Company (ABC, 2004:4). The former Executive Vice-President of ABC, Mr. Pan
Gongsheng, distinguished their mode of business from that of the rural financial
institutions by pointing out that large and medium-sized enterprises based in various
counties are their major customers. Through its extensive banking network, the balance of
loans in ABC’s County Area Banking Business unit reached 1,505 billion yuan in 2010.
(ABC is the only SOCB with service outlets and an electronic banking network reaching
every county in China.) Subsequently, the ABC rapidly reduced its NPLs from 30.62% in
2003 to 23.43% in 2006, 4.32% in 2008, and 2.39% in 2015 (ABC, 2004:2; 2006:2;
2010:10-11; 2015:6).
These results confirm hypothesis (H1) as there is a higher density of banking
outlets in market-oriented SOCBs and JSCBs in non-agricultural areas.
4.2 Policy-oriented bank jumps onto the bandwagon?
The pattern of regression coefficients for the policy-oriented bank in agricultural
counties largely replicates that of market-oriented banks. Most explanatory variables for
the ADBC have the same signs as in those of the market-oriented banks (Tables 1-2). The
number of ADBC outlets decreased at a much higher rate than those of market-oriented
banks at 5.4%, from 23,106 in 2006 to 21,853 in 2009. This result is unexpected as it was
hypothesized that the ADBC would maintain their banking operations in agricultural
counties.
<insert Table 2 about here>
The expected signs of coefficients in all the models for non-agricultural counties
were however as expected: the positive and significant coefficients for primary industry
GDP per capita (AGRGDPPC) and negative coefficients for secondary sector GDP per
capita (INDGSPPC) confirm that the ADBC has followed its policy remit to promote
agricultural development (Table 2). Interestingly, the ADBC appears to promote
agricultural development in non-agricultural counties! Moreover, the rural population
(RURPOP) and financial employment (FINEMP) coefficients negatively and positively
relate to the number of ADBC outlets respectively. Consistent results were obtained from
14
the sensitivity tests. In other words, the higher the proportion of rural residents in the local
population, the lower the number of ADBC’s outlets in those areas, and vice versa for
rural areas with a higher proportion of local employees in the financial sector.
A possible explanation of these unexpected findings is selective investment by the
ADBC in rural market districts. As providing credits to agriculture-related commercial
businesses is one of its specific policy remits, it is conceivable that the ADBC invests in
rural market districts. For instance, ADBC doubled its outlets to 10 in Jinming district, an
accessible market town with several industrial zones specializing in food processing and
other agribusinesses near Kaifeng municipality in Henan province. This increase contrasts
with the significant decrease of ADBC’s outlets, from 6 in 2006 to 2 in 2009, in the
Shizhu Tujiazu autonomous county, an impoverished county identified by the State
Council in eastern Chongqing municipality as having a GDP per capita in the primary to
tertiary sectors of only 1,100-1,413 yuan.
The above proposition is further supported by the near doubling of the ADBC loan
portfolios to agriculture-related businesses from 5.52% in 2006, to 9.83% in 2009, and
15.14% in 2014. Of the total outstanding ADBC loans of 884.4 billion yuan in 2006,
4.08% (36.1 billion yuan) was accounted for by grain and edible oil agribusinesses,
another 1.41% (12.48 billion yuan) by cotton agribusinesses, and 0.26 billion yuan by
ramie, silk and sugar processing enterprises. By the end of 2009, the ADBC’s loans had
increased to 1,451 billion yuan, with grain and edible oil agribusinesses accounting for
5.41% (78.52 billion yuan), cotton agribusinesses for 1.84% (26.69 billion yuan), and
sugar, silk, hemp and tobacco processing enterprises for 0.52% (7.6 billion yuan). The
ADBC’s loan portfolios also diversified into other agribusinesses sectors, with 19.68
billion yuan going to forestry, horticulture, fruit, tea, and medical herb processing firms
and another 10.14 billion yuan to 4,646 agricultural small enterprises (ADBC, 2006, 2009,
2014). To strengthen its mortgage business, the ADBC invested 1.5 billion yuan in 2012
in the Chongqing Xingnong Financing and Guarantee Co. Ltd., the first rural mortgage
guarantee company in China (China Daily, 5 March 2012). Subsequently, the net profits
of the ADBC increased more than five times, from 0.4 billion yuan in 2006 to 2.25 billion
yuan in 2009, and another six fold to 14.3 billion yuan in 2014 (ADBC, 2014). Apart from
fulfilling its rural development role, such as supporting water conservation and rural
construction projects, which still account for the lion’s share of its loan portfolios, the
15
ADBC obviously focuses more on the provision of credit to agriculture-related
commercial businesses.
There is some evidence of a lower density of outlets in remote villages and
investment in market districts in non-agricultural counties by the ADBC. This result is
unexpected, as the ADBC is not under the same financial pressure as other Chinese
banking institutions. This finding is, however, consistent with the weakening role of the
ADBC in supporting agriculture, partly due to the marketization of the procurement of
grain and cotton (Han, 2004). Hypothesis (H2) concerning policy-oriented banking cannot
therefore be accepted as the number of ADBC banking units has a significant and
unexpected relationship with the two explanatory variables relating to the level of
economic development in rural China.
4.3 Centralization of city commercial banks
The banking operations of city commercial banks are centralized in both
agricultural and non-agricultural counties as most explanatory variables have the same
signs as those for market-oriented banks, e.g., significant and negative coefficients for
AGRGDPPC and positive coefficients for SERGDPPC (Tables 1 & 3). The negative and
highly significant coefficient for government expenditure as a proportion of revenue
(GOVEXP in model 8) also suggests that city commercial banks have a lower density of
outlets in less economically developed regions. For instance, all 15 city commercial banks
have closed down in Xuchang county of Henan province where 80% of the population are
in rural areas, the primary, secondary and tertiary GDP per capita was just 4,685, 5,606
and 3,282 yuan and local government’s expenditure was more than double its revenue.
<insert Table 3 about here>
As stated in section 2, city commercial banks are under tremendous competitive
pressure on three fronts. They have to compete first with the domestic banking giants,
which are much better funded and basically monopolize the profitable business with state-
owned enterprises, and second with the numerous rural financial institutions, which are
competitive after rounds of restructuring. Third, they have been under competitive
pressure from some of the global banking giants, who have extended their operations from
the major cities to second-tier cities and peripheral regions.
To remain competitive, city commercial banks have to maintain the momentum of
on-going restructuring and improve their governance. The numbers of city commercial
16
banks declined by 20% from 191 in 2006 to 154 in 2009, but the number of employees
increased from 133,000 to 180,000 at the same time, which suggests that the remaining
city credit cooperatives have either merged or been acquired by city commercial banks.
Thus the centralization of banking operations in city commercial banks has not resulted in
the same expected degree of marginalization in more economically developed areas. By
2015, there was a total of 133 city commercial banks registered with the CBRC. The
Beijing Bank and Shanghai Bank are among the most competitive (CBRC, 2015).
The above evidence suggests city commercial banks have reduced their banking
operations in both agricultural and non-agricultural counties. The third hypothesis (H3) is
thus accepted.
4.4 Selective centralization in rural financial institutions
The coefficients for rural financial institutions reveal an unexpected pattern of
distribution of its outlets and are a partial replication of the ADBC’s pattern. As in the
case of the ADBC, in agricultural counties the respective negative and positive
coefficients for primary and tertiary sector GDP per capita support the notion of a higher
concentration of rural financial institutions in areas with relatively higher tertiary sector
GDP per capita (Table 4). Importantly, in non-agricultural counties the positive
coefficients for primary and tertiary sector GDP per capita (Table 4) suggest a higher
concentration in economically more developed areas. Consistent sensitivity test results
provide additional support for this proposition. Instead of filling the possible gap in the
general public’s access to banking services as a result of the centralization of SOCBs
(especially the withdrawal of rural banking by the ABC), the above evidence suggest that
rural financial institutions may be following the ADBC by locating their outlets
selectively in rural market districts rather than remote areas dominated by primary
economies.3 This result is consistent with other researchers’ findings, that rural financial
institutions have reallocated their capital from agricultural development to finance the
development of local government-owned township and villages enterprises, partly due to
the influential roles of local secretaries of the Chinese Communist Party, i.e., the de facto
3 As rural financial institutions include eight types of banking operation, they were divided into two
categories according to the CBRC’s specific classification. Two types of cooperative, rural commercial
banks, and postal saving institutions were considered as ‘traditional rural financial institutions’. The other
four types of institution were considered as ‘new rural financial institutions’. This finding concerning the
reallocation of outlets is more a reflection of the traditional rural financial institutions.
17
leaders of local governments (Han, 2004; Ong, 2012:10-12; see also Park, Brandt, and
Giles, 2003).
<insert Table 4 about here>
In addition to the ADBC’s unexpected investment policy in market districts, the
low density of rural financial institutions in agricultural counties is another reason for the
possible financial exclusion of rural residents in poor regions. With a potentially higher
chance of default vis-à-vis other forms of lending, rural financial institutions are making
commercial decisions to focus their lending on customers with higher returns. No wonder
about 1,700 remote towns still had no access to banking services of any kind at the end of
2011, despite the CBRC initiating a nationwide programme to fill the “financial services
vacuum” in rural areas in 2009 (CBRC, 4 January 2012).
As part of the restructuring of the banking industry, the state injected US$4 billion
to recapitalize the technically insolvent rural credit co-operatives and separate them from
the ABC in 1996 (Ong, 2006).4 To improve their competitiveness, some of these rural
credit co-operatives merged to become rural cooperative or commercial banks. These
mergers contributed to the significant centralization of traditional rural financial
institutions. The number of rural credit cooperatives decreased massively, from 19,348 in
2006 to 3,056 in 2009 and to 2,649 in 2010, but there was a less aggressive decline in
employment from 634,659 in 2006 to 570,366 in 2009 and 550,859 in 2010. A successful
example is the Dongguan Rural Commercial Bank, a major banking institution in
Dongguan municipality. With the competitive advantage of geographical proximity to
potential clients and the availability of tacit knowledge (see Park, Brandt and Giles, 2003),
the rural financial institutions can secure competitive advantage by providing specific
services to local clients and a willingness to provide small loans to ‘sub-prime’ customers
without collateral and credit records, i.e., loans are granted based on personal or mutual
guarantees. This advantage explains why these rural financial institutions account for 78%
of total rural household loans and one-third of the local agriculture-related lending market
(China Daily, 3 August 2011), and could also explain the significant increase in the
number of rural cooperative or commercial banks, from 90 in 2006 to 239 in 2009 and 308
in 2010. Total employment in traditional rural financial institutions actually increased
from 691,850 in 2006 to 711,459 in 2009, and to 830,241 in 2014 (CBRC, 2006, 2009,
4 Rural credit cooperatives used to be subordinate to the ABC and functioned as branches of the ABC after
the collapsed of the commune system in 1979.
18
2014). In other words, there is only a decrease in the accessibility of these financial
institutions to the general public, especially in poorer agricultural counties, but without the
subsequent redundancies seen in developed countries. Presumably, this form of
centralization without massive redundancies in the rural banking industry is a consequence
of local government political intervention (see Yeung, 2009b).
Hypothesis (H4) concerning local rural financial institutions is therefore rejected as
there is evidence that rural financial institutions locate their outlets selectively in rural
market districts rather than in remote areas dominated by primary economies.
5 CONCLUSIONS AND IMPLICATIONS
This analysis of the distribution of four types of banking institution in about 2,500
counties in 2009 explored their varying responses to the drive for the centralization of
Chinese rural banking industry operations. In contrast to the experience of the Anglo-
American banking industries, where the centralization and marginalization of banking
operations are two sides of the same coin, the four-tier Chinese rural banking system
demonstrates empirically different patterns of centralization. Under competitive pressure
from global banking giants and the financial pressure to perform well, the low density of
SOCBs, JSCBs, and city commercial bank outlets in rural areas is to be expected. This
centralization of banking operations by these two types of profit-oriented banks is related
to the changing competitive environment in the banking market.
In spite of the CBRC claim that the provision of basic banking services to all
citizens is guaranteed by the four-tier banking system, circumstantial evidence suggests
that the policy-oriented ADBC has a strong presence in rural market districts. This result
is unexpected, as ADBC is not subject to the same financial pressure as other banking
institutions in China. This could well be a sign of the decentralization of the provision of
basic banking services and a switch from traditional policy-oriented rural banking
institutions to local rural banking institutions. However, rural financial institution outlets
are located in more developed rural market districts with more banking opportunities
rather than remote areas dominated by primary economies. Therefore, the general public
in remote agricultural counties may not be able to access formal banking services locally.
Despite the limited temporal coverage of data, these findings from examining
cross-sectional data could have significant policy implications for the banking industry
19
and rural development in China, in particular, the unexpected investment in market
districts by the ADBC and the centralization drive of rural financial institutions.
The investment by the entirely state-owned ADBC in business-oriented
agribusinesses in market districts is unexpected. The provision of credits to agriculture-
related commercial businesses raises a host of important issues about the role of the
ADBC in the political economy of China. The ADBC competes with venture capital and
private equity investment directly by investing in profitable but still highly fragmented
upstream (e.g., developers and suppliers of plant seeds) and downstream agribusinesses
(e.g., processing and distribution).5 Although the absolute amount of investment involved
is relatively modest, the 30 fold increase in ADBC loans to sugar, silk, hemp, and tobacco
processing enterprises raises an interesting question: to what extent is this massive
increase due to the drive for the long-term financial viability of the ADBC by its central
management (or even the State Council, to which the bank ultimately reports) or is it in
response to political pressure from local governments whose revenues are generated both
from the ownership of such enterprises (most major tobaccos firms are owned by local
governments) and taxes on profit?6 A careful reading of the latest CBRC Annual Report
implies that it could be the former, as ADBC is “optimizing its county level branch
network and service scope” (CBRC, 2010:33, emphasis added). Given the presumed
division of labour between the four types of banking institution, it is questionable whether
it is a logical (political) decision for the ADBC to redirect its resources to more
commercially oriented purposes.
Furthermore, rural financial institutions are still under pressure to restructure and
to improve their balance sheets in the increasingly competitive rural banking market. In
addition, to compete with their global banking giants after the opening up of rural banking
to foreign investors in 2006, they are under pressure to improve their liquidity after the
recent central government’s directive to improve debt management systems. The 2.5%
NPL ratio of rural commercial banks was the highest of all types of banking institution in
2015: as a comparison, the figures are 1.7% for SOCBs, 1.5 per cent for JSCBs, and 1.4%
for city commercial banks (CBRC, 2015). The Postal Savings Bank of China, which
5 Venture capital and private equity investment in agribusinesses increased significantly from US$48 million
in 2007 to US$444 million in the first quarter of 2011 (China Daily, 20 April 2012:14). 6 See Wong (2009) for an interesting discussion of the importance of the intergovernmental fiscal system for
the reform of the public sector.
20
operates more than 40,000 bank branches, followed in the footsteps of SOCBs by listing
on the Hong Kong Stock Exchange in 2016.
Under the CBRC’s directives for restructuring while maintaining local access to
banking facilities, a possible “compromise” for rural financial institutions is to expand
their operations in areas with higher potential market yields. Should this indeed be the
case, there could be fewer economic opportunities for residents in remote areas. In
addition to the stigmatized image of accepting social safety net benefits, including rural
minimum living stipends (nongcun dibao), poor Chinese living in remote areas may feel
excluded in that they are unable to access the formal banking system and their economic
opportunities are further limited. Coupled with the household registration system and an
inflexible labour market, the centralization of banking operations and subsequent financial
exclusion could further add to the already high level of social tension and associated social
and political problems, including the increasingly frequent popular protests and even riots
due to land expropriation and other perceived social injustices.
Strategically, the effectiveness of focusing on high yield markets by all types of
banking institutions is questionable. If rural financial institutions blindly follow the
centralization drive of SOCBs and city commercial banks, they will enter an already
crowded and highly competitive market and compete directly with these banking
institutions. Without the geographical coverage or access to a much bigger pool of low
cost financing of their heavy-weight counterparts, rural financial institutions could embark
on a vicious circle of restructuring-centralization-further restructuring, and thus move
further away from the CBRC’s goal of the guaranteed provision of basic banking services
to everyone, especially the impoverished residents in remote areas.
Obviously the magnitude of the re-orientation of resources by ADBC and other
types of rural banking institution must be further investigated along with other
complementary evidence to draw firmer conclusions about the extent of centralization and
its potential impact on agriculture-based economies. This issue is especially important
given President Xi Jinping’s pledge that the government will eradicate poverty in China
by 2020 (China Daily, 16 October 2015) and fact that yet there are 1,700 remote villages
that still have no access to banking services.
21
Acronyms:
ABC: Agricultural Bank of China
ADBC: Agricultural Development Bank of China
BOC: Bank of China
BOCOM: Bank of Communications
CBRC: China Banking Regulatory Commission
CCB: China Construction Bank
EXIM: Export-Import Bank of China
ICBC: Industrial and Commercial Bank of China
IPOs: initial public offerings
JSCBs: joint stock commercial banks
NBS: National Bureau of Statistics
NPLs: non-performing loans
PBoC: People’s Bank of China
SOCBs: state-owned commercial banks
WTO: World Trade Organization
22
Table 1: Estimates for market-oriented banks in non-agricultural and agricultural counties
Non-agricultural counties Agricultural counties VARIABLE
S [1] [2] [3] [4] [5] [6] [7] [8] [1] [2] [3] [4] [5] [6] [7] [8]
AGRGDPPC -6.246*** -3.951*** -3.620*** -0.304 -0.302 -0.468 -0.250** -0.708*** -0.756*** -0.753*** -0.772*** -0.753***
(0.413) (0.398) (1.222) (0.468) (0.468) (0.428) (0.0979) (0.135) (0.122) (0.125) (0.125) (0.113)
INDGDPPC
0.188**
* -0.0120 -0.0111 -0.0148 -0.0150 -
0.0206***
0.296**
* -0.0253 -0.0234 -0.0280 -0.0301 -0.0564*
(0.0622) (0.0160) (0.0183)
(0.00951
)
(0.00942
)
(0.00541
) (0.0279) (0.0368) (0.0358) (0.0339) (0.0338) (0.0312)
SERGDPPC
0.483***
0.421***
0.396*** 0.230*** 0.230*** 0.204***
0.694***
0.778***
0.640***
0.626***
0.624*** 0.563***
(0.0448) (0.0494) (0.115) (0.0401) (0.0400) (0.0360) (0.0478) (0.0820) (0.0909) (0.0879) (0.0873) (0.0807)
FINEMP 22.21 0.971 0.987 0.899
102.1*** 49.09* 49.88* 62.04**
(89.50) (1.343) (1.351) (1.229) (28.12) (26.96) (26.91) (24.55)
RURPOP
-
1.925***
-
1.924***
-1.655*** -0.751*** -0.747*** -0.610***
(0.143) (0.143) (0.134) (0.143) (0.142) (0.134)
RURINCOM 0.0310 0.00809 0.309* 0.179
(0.0376) (0.0264) (0.183) (0.131)
GOVEXP -
0.0597*** -0.0563***
(0.00703
)
(0.00665
)
Constant
4.061**
*
2.968**
*
2.610**
*
3.223**
*
3.118**
* 3.980*** 3.966*** 4.057***
2.910**
*
2.324**
*
2.127**
*
2.375**
*
2.287**
*
2.944**
*
2.813**
* 2.996***
(0.0680) (0.0893) (0.0556) (0.0869) (0.391) (0.103) (0.104) (0.0984) (0.0550) (0.0419) (0.0433) (0.0620) (0.0615) (0.141) (0.158) (0.141)
lnalpha 0.0117
0.127**
* -
0.121***
-
0.214***
-
0.220***
-
0.420***
-
0.421***
-
0.533*** -0.166*** -0.409*** -0.556*** -0.610*** -0.632*** -0.658*** -0.665*** -0.808***
(0.0379) (0.0486) (0.0441) (0.0437) (0.0446) (0.0422) (0.0422) (0.0439) (0.0490) (0.0490) (0.0487) (0.0498) (0.0476) (0.0474) (0.0473) (0.0498) Observations 1,224 1,224 1,224 1,224 1,224 1,224 1,224 1,224 1,225 1,225 1,225 1,225 1,225 1,225 1,225 1,225
Notes: *** p<0.01, ** p<0.05, * p<0.10
Standard errors in parentheses
Source: authors’ calculation.
23
Table 2: Estimates for the policy-oriented bank in non-agricultural and agricultural counties
Non-agricultural counties Agricultural counties VARIABLES [1] [2] [3] [4] [5] [6] [7] [8] [1] [2] [3] [4] [5] [6] [7] [8]
AGRGDPPC 0.881* 0.999* 1.043** 2.084*** 2.084*** 2.048*** -
0.333*** -0.362*** -0.409*** -0.412*** -0.415*** -0.400***
(0.500) (0.519) (0.522) (0.608) (0.608) (0.602) (0.126) (0.129) (0.135) (0.135) (0.135) (0.136)
INDGDPPC
-
0.0252*
-
0.0359* -0.0366** -0.0551** -0.0548**
-
0.0706***
-
0.00288 -0.0226 -0.0216 -0.0231 -0.0231 -0.0413*
(0.0148) (0.0183) (0.0185) (0.0219) (0.0218) (0.0231) (0.0120) (0.0223) (0.0217) (0.0217) (0.0218) (0.0227)
SERGDPPC -
0.000974 0.0346 0.0322 0.00634 0.00614 0.00549 0.0279 0.0904** 0.0170 0.0108 0.0100 -0.0137
(0.0244) (0.0274) (0.0274) (0.0300) (0.0299) (0.0306) (0.0228) (0.0435) (0.0464) (0.0466) (0.0467) (0.0480)
FINEMP 1.942*** 1.516** 1.507** 1.509** 59.31*** 49.13*** 48.89*** 58.25*** (0.712) (0.609) (0.608) (0.593) (17.23) (18.86) (18.92) (18.96)
RURPOP -0.560*** -0.562***
-
0.420*** -0.191 -0.192 -0.105 (0.150) (0.150) (0.155) (0.120) (0.121) (0.121)
RURINCOM -0.0215 -0.0308 0.0428
-
0.000374 (0.0424) (0.0461) (0.0919) (0.0894)
GOVEXP -
0.0317*** -0.0312***
(0.00706) (0.00637)
Constant -
0.475***
-
0.313***
-
0.342***
-
0.483*** -0.494*** -0.259** -0.249** -0.195
-
0.151***
-
0.283***
-
0.308*** -0.181*** -0.228*** -0.0624 -0.0784 0.00325
(0.0937) (0.0330) (0.0337) (0.105) (0.106) (0.121) (0.123) (0.124) (0.0513) (0.0245) (0.0259) (0.0520) (0.0527) (0.113) (0.120) (0.122)
lnalpha -20.69 -20.69 -20.69 -20.69 -20.69 -20.69 -20.69 -20.69 -24.05 -24.05 -24.05 -24.05 -24.05 -24.05 -24.05 -24.05 (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0) (0)
Observations 1,224 1,224 1,224 1,224 1,224 1,224 1,224 1,224 1,225 1,225 1,225 1,225 1,225 1,225 1,225 1,225
Notes: *** p<0.01, ** p<0.05, * p<0.10
Standard errors in parentheses
Source: authors’ calculation.
24
Table 3: Estimates for city commercial banks in non-agricultural and agricultural counties
Non-agricultural counties Agricultural counties VARIABLE
S [1] [2] [3] [4] [5] [6] [7] [8] [1] [2] [3] [4] [5] [6] [7] [8]
AGRGDPPC -10.36*** -8.192*** -6.329*** -0.700 -0.700 -0.276 -0.0986 -2.005*** -1.737*** -1.692*** -1.705*** -1.582***
(0.828) (0.865) (1.305) (1.119) (1.119) (1.139) (0.299) (0.414) (0.326) (0.324) (0.323) (0.324)
INDGDPPC
0.253**
* 0.00924 0.0466 -0.00858 -0.00861 -0.0276*
0.442**
* 0.0256 0.0244 0.0171 0.0144 -0.0166
(0.0896) (0.0829) (0.111) (0.0183) (0.0182) (0.0166) (0.0557) (0.0797) (0.0589) (0.0572) (0.0563) (0.0537)
SERGDPPC
0.712**
*
0.593**
* 0.350*
0.261**
*
0.261**
*
0.169**
*
1.068**
*
1.172**
*
0.635**
*
0.639**
*
0.638**
*
0.545**
* (0.0851) (0.126) (0.199) (0.0949) (0.0949) (0.0632) (0.109) (0.169) (0.159) (0.156) (0.152) (0.147)
FINEMP 184.7 8.622 8.631 2.728
384.2**
*
307.1**
*
302.8**
*
307.8**
* (125.1) (61.70) (61.75) (29.24) (76.60) (68.19) (67.65) (66.21)
RURPOP -3.724*** -3.724*** -3.495*** -0.783* -0.819* -0.641
(0.443) (0.444) (0.328) (0.431) (0.424) (0.400) RURINCOM 0.00829 -0.0396 0.583 0.399
(0.0962) (0.0786) (0.362) (0.266)
GOVEXP -0.172***
-
0.0973**
*
(0.0369) (0.0240)
Constant 2.409**
* 0.800**
* 0.209* 1.335**
* 0.526 2.524**
* 2.520**
* 2.943**
* 0.0847 -0.736*** -1.115*** -0.456*** -0.993*** -0.292 -0.513 -0.216
(0.101) (0.141) (0.125) (0.155) (0.485) (0.415) (0.421) (0.255) (0.151) (0.124) (0.128) (0.166) (0.156) (0.410) (0.432) (0.412)
lnalpha 1.519**
* 1.704**
* 1.524**
* 1.365**
* 1.308**
* 1.014**
* 1.014**
* 0.930**
* 1.618**
* 1.408**
* 1.238**
* 1.175**
* 1.072**
* 1.054**
* 1.048**
* 0.997**
*
(0.0658) (0.0582) (0.0613) (0.0737) (0.112) (0.0913) (0.0913) (0.0984) (0.0805) (0.0917) (0.0965) (0.0959) (0.0895) (0.0873) (0.0872) (0.0886)
Observations 1,224 1,224 1,224 1,224 1,224 1,224 1,224 1,224 1,225 1,225 1,225 1,225 1,225 1,225 1,225 1,225
Notes: *** p<0.01, ** p<0.05, * p<0.10
Standard errors in parentheses
Source: authors’ calculation.
25
Table 4: Estimates for rural financial institutions non-agricultural and agricultural counties
Non-agricultural counties Agricultural counties VARIABLE
S [1] [2] [3] [4] [5] [6] [7] [8] [1] [2] [3] [4] [5] [6] [7] [8]
AGRGDPP
C 0.835** 1.220*** 1.198*** 1.090*** 1.091***
1.109**
* -
0.285***
-
0.389***
-
0.315***
-
0.316***
-
0.331***
-0.328**
*
(0.412) (0.383) (0.387) (0.401) (0.401) (0.394) (0.0772) (0.104) (0.122) (0.123) (0.124) (0.117)
INDGDPP
C
-
0.00646 -0.0212*** -0.0212*** -0.0210*** -0.0212***
-
0.0251**
*
0.0692**
* -0.0129 -0.0143 -0.0143 -0.0170 -0.0322
(0.0189) (0.00736) (0.00739) (0.00755) (0.00744)
(0.00538
) (0.0184) (0.0270) (0.0265) (0.0265) (0.0263) (0.0253)
SERGDPPC 0.0352
0.0739***
0.0748***
0.0808***
0.0808***
0.0716**
0.177***
0.227***
0.372***
0.375***
0.373***
0.346***
(0.0229) (0.0270) (0.0269) (0.0296) (0.0295) (0.0279) (0.0351) (0.0615) (0.0698) (0.0704) (0.0698) (0.0677)
FINEMP -0.998 -0.922 -0.902 -0.898 -
119.2***
-
114.5***
-
113.6*** -105.9***
(0.792) (0.791) (0.788) (0.805) (20.41) (22.27) (22.20) (21.43)
RURPOP 0.0665 0.0706 0.215** 0.0750 0.0733 0.147 (0.0980) (0.0980) (0.0965) (0.128) (0.129) (0.129)
RURINCO
M 0.0435* 0.0299 0.248 0.144 (0.0239) (0.0185) (0.190) (0.161)
GOVEXP
-
0.0307**
* -
0.0261***
(0.00459
)
(0.00280
)
Constant
3.531**
*
3.665**
*
3.622**
* 3.428*** 3.434*** 3.402*** 3.382***
3.418**
*
3.832**
* 3.622***
3.572**
*
3.706**
*
3.799**
*
3.734**
*
3.635**
*
3.741**
*
(0.0743) (0.0307) (0.0257) (0.0707) (0.0718) (0.0894) (0.0900) (0.0888) (0.0370) (0.0287) (0.0322) (0.0444) (0.0550) (0.119) (0.146) (0.141)
lnalpha -
0.804***
-
0.797***
-
0.801***
-
0.819***
-
0.820***
-
0.820***
-
0.822*** -0.877***
-
0.718***
-
0.725*** -
0.739***
-
0.765***
-
0.794***
-
0.794***
-
0.799*** -0.864***
(0.0718) (0.0678) (0.0656) (0.0700) (0.0698) (0.0700) (0.0702) (0.0734) (0.0587) (0.0584) (0.0584) (0.0591) (0.0619) (0.0619) (0.0626) (0.0649) Observation
s 1,224 1,224 1,224 1,224 1,224 1,224 1,224 1,224 1,225 1,225 1,225 1,225 1,225 1,225 1,225 1,225
Notes: *** p<0.01, ** p<0.05, * p<0.10
Standard errors in parentheses
Source: authors’ calculation.
26
References:
Agricultural Bank of China (ABC) (various years) Annual Report of Agricultural Bank of
China. Accessed 3 May 2016. http://www.abchina.com/en/about-us/annual-report/
Agricultural Development Bank of China (ADBC) (various years) Annual Report of
Agricultural Development Bank of China. Accessed 3 May 2016.
http://www.adbc.com.cn:81/aboutus/report/
China Banking Regulatory Commission (CBRC) (various years) CBRC Annual Report.
Accessed 24 October 2016. http://www.cbrc.gov.cn/
China Banking Regulatory Commission (CBRC) (2012) “Significant breakthrough in
financial services vacuum in rural areas.” (金融机构空白乡镇网点覆盖工作取得
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28
Section A: Supplementary tables
Table A1: Variables and their expected signs
Variables Explanations Expected
sign
Dependent
MAKTBANK SOCBs & stock holding banks
POLBANK Agricultural Development Bank of China
COMBANK Local commercial banking institutes
RURBANK Local rural banking institutes
Explanatory
AGRGDPPC GDP per capita in primary industries -
INDGDPPC GDP per capita in secondary industries +
SERGDPPC GDP per capita in tertiary industries +
FINEMP Employees in financial sectors as a proportion of the
local population +
RURPOP Rural residents as a proportion of the local population -
RURINCOM Net income of rural residents as a proportion of the
disposable income of urban residents +
GOVEXP Government expenditure as a proportion of revenue -
Table A2: Descriptive statistics on variables
Variables N Mean Std. Dev. Min Max
Dependent
MAKTBANK 2488 21.19976 34.39519 0 784
POLBANK 2488 0.7290997 0.60337250 0 9
COMBANK 2488 2.063103 5.803895 0 107
RURBANK 2488 39.95579 29.65172 0 607
Independent
AGRGDPPC 2488 0.2866814 0.256822 6.08E-06 5.560305
INDGDPPC 2488 1.29971 3.173617 2.85E-06 134.0438
SERGDPPC 2488 0.8293278 1.082388 0.0000142 21.76654
FINEMP 2488 0.0025132 0.0072204 0 0.2608772
RURPOP 2488 0.6769688 0.2391439 0.0000507 1
RURINCOM 2439 0.4175778 0.2851488 0.0016328 7.175439
GOVEXP 2488 4.029699 6.976685 0.0034997 109.8204
Source: authors’ calculation.
29
Table A3: Pearson’s correlations between explanatory variables
(N=2439)
AGRGDPPC INDGDPPC SERGDPPC FINEMP RURPOP RURINCOM GOVEXP
AGRGDPPC 1
INDGDPPC 0.0766 1
SERGDPPC 0.0432 0.3875 1
FINEMP -0.0319 0.0858 0.2507 1
RURPOP 0.1057 -0.2054 -0.477 -0.2329 1
RURINCOM 0.0692 0.0656 0.0807 0.0048 -0.1158 1
GOVEXP -0.0053 -0.1279 -0.1931 -0.0564 0.2194 -0.1032 1
Source: authors’ calculation.
Section B: Sensitivity tests
Two sensitivity tests were conducted. First, GDP per capita in the primary to tertiary
sectors was replaced by GDP growth rates (2008-2009) in the primary (AGRGDPGW),
secondary (INDGDPGW), and tertiary sectors (SERGDPGW) respectively (GDP per
capita growth rate is not used as population data at county level in 2008 was not
available). The local population as a ratio of the total population was also changed to the
number of rural households as a proportion of the total number of households (also named
as RURPOP). Second, two control variables were added to the model (the number of
enterprises, ENTERP; and the overall GDP growth rate (2008-2009), GDPGROW; listed
under columns 3-6 in the tables at the end of this document).
Table B1: Descriptive statistics on variables
Variables N Mean Std. Dev. Min Max
AGRGDPGW 2478 7.0929 19.1007 -75.18 601
INDGDPGW 2478 17.8239 17.6487 -71.8 423.41
SERGDPGW 2478 15.6415 21.0753 -15.96 777
RURPOR 2478 0.6296 0.2473 0 1
ENTERP 2478 2,612.562 11,192.14 0 505521
GDPGROW 2478 14.0201 9.4520 -52.4 122.1
Source: authors’ calculation.
30
Table B2: Pearson’s correlations between explanatory variables
(N=2,478) AGRGDPGW INDGDPGW SERGDPGW FINEMP RURPOP RURINCOM GOVEXP ENTERP GDPGROW
AGRGDPGW 1
INDGDPGW 0.1299 1
SERGDPGW 0.147 0.0787 1
FINEMP -0.0173 0.0157 0.0054 1
RURPOP 0.0242 0.036 -0.0847 -0.1522 1
RURINCOM 0.0032 0.0079 -0.0027 -0.0055 -0.0066 1
GOVEXP 0.0067 0.0743 -0.0346 -0.0344 0.1773 -0.0724 1
ENTERP -0.0051 -0.0444 -0.0033 0.0207 -0.0613 0.026 -0.0639 1
GDPGROW 0.2145 0.6951 0.2563 0.0225 -0.0505 0.0144 -0.0044 -0.0202 1
Source: authors’ calculation.
31
Table B3a: Estimates for market-oriented banks in non-agricultural counties VARIABLES [1] [2] [3] [4] [5] [6]
AGRGDPGW -0.0298*** 0.00674 -0.0337*** 0.00389 -0.0379*** -0.000425
(0.00824) (0.00459) (0.00928) (0.00441) (0.00922) (0.00508)
INDGDPGW -0.0275*** -0.0124** -0.0198*** -0.00861** -0.0482*** -0.0234***
(0.00321) (0.00522) (0.00327) (0.00378) (0.00611) (0.00677)
SERGDPGW 0.0137* 0.000672 0.00884* 0.000507 -0.0107** -0.00772**
(0.00708) (0.00393) (0.00516) (0.00327) (0.00500) (0.00392)
FINEMP 48.67 11.78 9.059
(114.1) (96.18) (61.13)
RURPOP -1.563** -1.647*** -1.541***
(0.651) (0.576) (0.367)
RURINCOM 0.0866 0.0607 0.0474
(0.137) (0.0751) (0.0612)
GOVEXP -0.0230* -0.0194* -0.0182**
(0.0139) (0.0116) (0.00909)
ENTERP 9.87e-05*** 8.20e-05*** 9.42e-05*** 8.06e-05***
(1.08e-05) (1.42e-05) (1.04e-05) (1.18e-05)
GDPGROW 0.0839*** 0.0407***
(0.0126) (0.0142)
Constant 3.456*** 4.014*** 3.036*** 3.831*** 2.686*** 3.617***
(0.112) (0.798) (0.106) (0.646) (0.0995) (0.401)
lnalpha 0.00355 -0.345*** -0.190*** -0.520*** -0.262*** -0.537***
(0.0382) (0.0697) (0.0474) (0.0570) (0.0519) (0.0543)
Observations 1,266 1,266 1,266 1,266 1,266 1,266
Notes: *** p<0.01, ** p<0.05, * p<0.10; standard errors in parentheses
Source: authors’ calculation.
Table B3b: Estimates for market-oriented banks in agricultural counties
VARIABLES [1] [2] [3] [4] [5] [6]
AGRGDPGW 0.000366 -0.000549 -0.000166 -0.000917 -0.000944 -0.00151*
(0.00181) (0.000959) (0.00123) (0.000769) (0.00101) (0.000874)
INDGDPGW -0.00501** -0.00378** -0.00295** -0.00173 -0.00631* -0.00514**
(0.00195) (0.00169) (0.00148) (0.00120) (0.00332) (0.00208)
SERGDPGW 0.00194 -6.37e-05 0.00195 -8.93e-05 0.000311 -0.000994*
(0.00230) (0.000731) (0.00190) (0.000667) (0.000684) (0.000524)
FINEMP 89.56 66.03 61.80
(91.80) (86.42) (87.00)
RURPOP -1.126** -1.296*** -1.310***
(0.490) (0.471) (0.473)
RURINCOM 0.157* 0.0953 0.100
(0.0900) (0.0910) (0.0918)
GOVEXP -0.0689*** -0.0627*** -0.0631***
(0.00983) (0.00941) (0.00927)
ENTERP 6.23e-05* 3.65e-05 6.13e-05* 3.56e-05
(3.36e-05) (2.83e-05) (3.33e-05) (2.81e-05)
GDPGROW 0.0124** 0.0104***
(0.00528) (0.00386)
Constant 2.879*** 3.439*** 2.631*** 3.439*** 2.542*** 3.386***
(0.0672) (0.522) (0.103) (0.507) (0.102) (0.502)
lnalpha -0.00934 -0.426*** -0.121* -0.550*** -0.133** -0.561***
(0.0514) (0.0888) (0.0628) (0.0696) (0.0629) (0.0677)
Observations 1,212 1,212 1,212 1,212 1,212 1,212
Notes: *** p<0.01, ** p<0.05, * p<0.10; standard errors in parentheses
32
Source: authors’ calculation.
Table B4a: Estimates for the policy-oriented bank in non-agricultural counties
VARIABLES [1] [2] [3] [4] [5] [6]
AGRGDPGW 0.0287*** 0.0296*** 0.0285*** 0.0287*** 0.0280*** 0.0284***
(0.00744) (0.00748) (0.00746) (0.00746) (0.00749) (0.00755)
INDGDPGW 0.00167 0.00387* 0.00200 0.00397** 0.000442 0.00326
(0.00188) (0.00200) (0.00189) (0.00200) (0.00292) (0.00314)
SERGDPGW 0.0107*** 0.00878*** 0.0106*** 0.00887*** 0.00965*** 0.00849***
(0.00258) (0.00273) (0.00258) (0.00274) (0.00272) (0.00276)
FINEMP 1.519** 1.470** 1.477**
(0.617) (0.584) (0.583)
RURPOP 0.0699 0.114 0.120
(0.117) (0.117) (0.115)
RURINCOM -0.000928 -0.00161 -0.00194
(0.0443) (0.0445) (0.0445)
GOVEXP -0.0321*** -0.0304*** -0.0301***
(0.00744) (0.00730) (0.00738)
ENTERP 1.28e-05*** 1.03e-05** 1.27e-05*** 1.03e-05**
(4.28e-06) (4.05e-06) (4.25e-06) (4.03e-06)
GDPGROW 0.00472 0.00193
(0.00636) (0.00631)
Constant -0.622*** -0.572*** -0.664*** -0.635*** -0.684*** -0.647***
(0.0606) (0.0973) (0.0634) (0.102) (0.0693) (0.107)
lnalpha -22.54 -22.54 -22.54 -22.54 -22.54 -22.54
(0) (0) (0) (0) (0) (0)
Observations 1,266 1,266 1,266 1,266 1,266 1,266
Notes: *** p<0.01, ** p<0.05, * p<0.10; standard errors in parentheses
Source: authors’ calculation.
Table B4b: Estimates for the policy-oriented bank in agricultural counties
VARIABLES [1] [2] [3] [4] [5] [6]
AGRGDPGW -0.00102 -0.00107 -0.00103 -0.00108 -0.00171 -0.00165
(0.00126) (0.00119) (0.00128) (0.00121) (0.00185) (0.00171)
INDGDPGW 0.000477 0.000965 0.000546 0.00101 -0.00253 -0.00117
(0.00128) (0.00134) (0.00129) (0.00135) (0.00187) (0.00176)
SERGDPGW 6.74e-05 -0.000208 7.06e-05 -0.000206 -0.000559 -0.000756
(0.000628) (0.000837) (0.000627) (0.000836) (0.000964) (0.00122)
FINEMP 0.554 0.531 0.367
(1.825) (1.843) (1.930)
RURPOP -0.204** -0.205** -0.200**
(0.101) (0.101) (0.101)
RURINCOM -0.0262 -0.0286 -0.0290
(0.0675) (0.0679) (0.0676)
GOVEXP -0.0362*** -0.0359*** -0.0355***
(0.00606) (0.00605) (0.00604)
ENTERP 1.72e-06** 1.34e-06* 1.71e-06** 1.35e-06**
(8.40e-07) (6.91e-07) (8.28e-07) (6.82e-07)
GDPGROW 0.00780** 0.00616*
(0.00332) (0.00315)
Constant -0.299*** -0.0287 -0.305*** -0.0327 -0.347*** -0.0741
(0.0353) (0.0815) (0.0356) (0.0816) (0.0367) (0.0814)
lnalpha -22.47 -22.47 -22.47 -22.47 -22.47 -22.47
(0) (0) (0) (0) (0) (0)
Observations 1,212 1,212 1,212 1,212 1,212 1,212
33
Notes: *** p<0.01, ** p<0.05, * p<0.10; standard errors in parentheses
Source: authors’ calculation.
Table B5a: Estimates for city commercial banks in non-agricultural counties
VARIABLES [1] [2] [3] [4] [5] [6]
AGRGDPGW -0.0706*** -0.00405 -0.0771*** -0.00717 -0.0848*** -0.0129
(0.0167) (0.00738) (0.0172) (0.00823) (0.0194) (0.00919)
INDGDPGW -0.0441*** -0.00235 -0.0324*** 0.00230 -0.103*** -0.0217
(0.00719) (0.00945) (0.00704) (0.00801) (0.0189) (0.0175)
SERGDPGW 0.0413*** 0.00855 0.0288** 0.00695 -0.0277** -0.00573
(0.0146) (0.0119) (0.0136) (0.0113) (0.0124) (0.0123)
FINEMP 164.7 126.8 116.6
(165.9) (167.7) (168.0)
RURPOP -2.247*** -2.344*** -2.198***
(0.702) (0.756) (0.708)
RURINCOM 0.0118 0.00887 0.00673
(0.130) (0.119) (0.122)
GOVEXP -0.159*** -0.151*** -0.147***
(0.0346) (0.0321) (0.0325)
ENTERP 0.000105*** 6.95e-05*** 0.000105*** 6.93e-05***
(1.59e-05) (2.35e-05) (1.69e-05) (2.37e-05)
GDPGROW 0.192*** 0.0571*
(0.0325) (0.0301)
Constant 1.088*** 1.563 0.685*** 1.426 0.0290 1.184
(0.196) (1.043) (0.204) (1.012) (0.220) (0.930)
lnalpha 1.528*** 1.002*** 1.427*** 0.949*** 1.313*** 0.933***
(0.0576) (0.128) (0.0611) (0.114) (0.0690) (0.111)
Observations 1,266 1,266 1,266 1,266 1,266 1,266
Notes: *** p<0.01, ** p<0.05, * p<0.10; standard errors in parentheses
Source: authors’ calculation.
Table B5b: Estimates for city commercial banks in agricultural counties
VARIABLES [1] [2] [3] [4] [5] [6]
AGRGDPGW -0.00316 -0.00367 -0.00372 -0.00511* -0.00327 -0.00663
(0.00327) (0.00235) (0.00304) (0.00278) (0.00287) (0.00458)
INDGDPGW -0.00906 -0.000266 -0.00124 0.0116 -0.0637*** -0.0303**
(0.00865) (0.0109) (0.00766) (0.00705) (0.0154) (0.0131)
SERGDPGW 0.0233 0.00897 0.0219 0.00634 -0.00131 -0.00152
(0.0149) (0.0148) (0.0155) (0.00937) (0.00267) (0.00224)
FINEMP 246.8 185.6 158.5
(158.2) (142.1) (142.4)
RURPOP -1.800** -2.510*** -2.607***
(0.829) (0.766) (0.802)
RURINCOM 0.193 0.147 0.169
(0.152) (0.142) (0.144)
GOVEXP -0.223*** -0.196*** -0.188***
(0.0494) (0.0449) (0.0431)
ENTERP 7.64e-05 2.34e-05 6.47e-05 1.90e-05
(5.48e-05) (3.81e-05) (5.38e-05) (3.17e-05)
GDPGROW 0.134*** 0.0890***
(0.0262) (0.0237)
Constant 0.220 0.889 -0.181 1.093 -0.726** 0.721
(0.215) (0.881) (0.276) (0.862) (0.297) (0.874)
lnalpha 2.084*** 1.545*** 2.023*** 1.423*** 1.954*** 1.375***
(0.0739) (0.127) (0.0706) (0.0977) (0.0739) (0.0955)
34
Observations 1,212 1,212 1,212 1,212 1,212 1,212
Notes: *** p<0.01, ** p<0.05, * p<0.10; standard errors in parentheses
Source: authors’ calculation.
Table B6a: Estimates for rural financial institutions in non-agricultural counties
VARIABLES [1] [2] [3] [4] [5] [6]
AGRGDPGW 0.0159*** 0.0156*** 0.0155*** 0.0135*** 0.0151*** 0.0133***
(0.00324) (0.00318) (0.00309) (0.00266) (0.00316) (0.00274)
INDGDPGW -0.00251 -0.000339 0.000292 0.00127 -0.00231 0.000259
(0.00229) (0.00206) (0.00203) (0.00187) (0.00585) (0.00514)
SERGDPGW 0.0100** 0.00689** 0.00900*** 0.00702** 0.00736* 0.00642
(0.00415) (0.00349) (0.00346) (0.00308) (0.00444) (0.00395)
FINEMP -0.459* -0.862** -0.853**
(0.246) (0.403) (0.413)
RURPOP 0.126 0.275*** 0.282***
(0.0897) (0.0798) (0.0848)
RURINCOM 0.0753 0.0635 0.0629
(0.0793) (0.0582) (0.0577)
GOVEXP -0.0279*** -0.0243*** -0.0242***
(0.00307) (0.00281) (0.00279)
ENTERP 5.23e-05*** 4.88e-05*** 5.18e-05*** 4.87e-05***
(7.21e-06) (6.98e-06) (7.34e-06) (7.03e-06)
GDPGROW 0.00654 0.00253
(0.0122) (0.0106)
Constant 3.590*** 3.585*** 3.387*** 3.302*** 3.371*** 3.291***
(0.0658) (0.0862) (0.0636) (0.0827) (0.0638) (0.0891)
lnalpha -0.578*** -0.654*** -0.690*** -0.760*** -0.691*** -0.760***
(0.0588) (0.0598) (0.0621) (0.0637) (0.0620) (0.0637)
Observations 1,266 1,266 1,266 1,266 1,266 1,266
Notes: *** p<0.01, ** p<0.05, * p<0.10; standard errors in parentheses
Source: authors’ calculation.
Table B6b: Estimates for rural financial institutions in agricultural counties
VARIABLES [1] [2] [3] [4] [5] [6]
AGRGDPGW -0.000582 -0.000702 -0.000678 -0.000773 -0.00101 -0.00102*
(0.000516) (0.000500) (0.000535) (0.000532) (0.000642) (0.000618)
INDGDPGW -0.00301* -0.00173 -0.00164 -0.000587 -0.00673*** -0.00494***
(0.00172) (0.00173) (0.00160) (0.00164) (0.00150) (0.00190)
SERGDPGW -0.000996 -0.000898* -0.00100* -0.000905* -0.00192*** -0.00159***
(0.000607) (0.000506) (0.000537) (0.000485) (0.000489) (0.000577)
FINEMP -5.947*** -6.466*** -6.725***
(0.990) (1.262) (1.414)
RURPOP 0.266*** 0.244*** 0.248***
(0.0976) (0.0898) (0.0903)
RURINCOM 0.0724 0.0295 0.0261
(0.0692) (0.0793) (0.0780)
GOVEXP -0.0354*** -0.0331*** -0.0321***
(0.00477) (0.00483) (0.00488)
ENTERP 2.26e-05 1.77e-05 2.17e-05 1.72e-05
(2.38e-05) (2.30e-05) (2.36e-05) (2.28e-05)
GDPGROW 0.0115*** 0.00943***
(0.00315) (0.00357)
Constant 3.684*** 3.604*** 3.585*** 3.546*** 3.528*** 3.496***
(0.0432) (0.0748) (0.0717) (0.101) (0.0679) (0.0995)
lnalpha -0.776*** -0.876*** -0.862*** -0.954*** -0.874*** -0.963***
35
(0.0709) (0.0733) (0.0650) (0.0651) (0.0645) (0.0648)
Observations 1,212 1,212 1,212 1,212 1,212 1,212
Notes: *** p<0.01, ** p<0.05, * p<0.10; standard errors in parentheses
Source: authors’ calculation.