rural banking in china: a case of...

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.

http://www.tandfonline.com/toc/rard20/current

mailto:[email protected]



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

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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

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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

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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

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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

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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

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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).

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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.

http://bankmap.cbrc.gov.cn/bank

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.


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.


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).


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



24

Table 3: Estimates for city commercial banks in non-agricultural and agricultural counties


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



25

Table 4: Estimates for rural financial institutions non-agricultural and agricultural counties


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



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

重大突破) China Banking Regulatory Commission’s press release on 4 January

2012. Accessed 3 May 2016.

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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


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


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


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


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


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


32


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



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



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



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



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



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



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