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Regional Studies

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Rural banking in China: geographically accessiblebut still financially excluded?

Godfrey Yeung, Canfei He & Peng Zhang

To cite this article: Godfrey Yeung, Canfei He & Peng Zhang (2017) Rural banking in China:geographically accessible but still financially excluded?, Regional Studies, 51:2, 297-312, DOI:10.1080/00343404.2015.1100283

To link to this article: http://dx.doi.org/10.1080/00343404.2015.1100283

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Rural banking in China: geographically accessible but stillfinancially excluded?Godfrey Yeunga, Canfei Heb and Peng Zhangc

ABSTRACTRural banking in China: geographically accessible but still financially excluded? Regional Studies. Based on thedistribution patterns of rural credit cooperatives in about 2200 counties in 2009, this paper examines two aspects offinancial exclusion in rural China after the restructuring of the banking industry. Despite the state’s efforts to ensurefinancial inclusion in rural areas, poor farmers could be spatially included while still being denied loans due to theirinability to provide collateral, and the lack of formal credit records. The mismatch between the supply and demandof credit has led to informal loans substituting for formal loans and thus contributed to the proliferation of informalbanking in China.

KEYWORDSrural banking; geographical exclusion; financial exclusion; China

摘要

中国农村银行业：地理可达但依旧金融排斥？Regional Studies。本文以 2009 年中国两千两百多个县区农村信用合作

社的空间分布格局为基础，考察了中国农村银行业重组后，金融排除现象的两个方面。尽管国家确保农村地区金融包

容性的努力，减少了金融机构对贫困农民的地域排斥，贫困农民的贷款请求依旧会被拒绝。这是由于贫困农民无法提

供担保和正规信贷记录。这种信贷供给和需求之间的不匹配，使得非正规信贷逐渐替代了无法惠及农民的正规信贷，

进而促进了非正规金融机构在中国农村地区的扩展。

关键词

农村银行业; 地域排斥; 金融排斥; 中国

RÉSUMÉLes services bancaires en milieu rural chinois: sont-ils accessibles à l’échelle géographique mais aussi exclus sur le planfinancier? Regional Studies. À partir de la structure de la distribution des coopératives de crédit rurales situées dans àpeu près 2.200 comtés en 2009, cet article cherche à examiner deux aspects de l’exclusion financière dans la Chinerurale suite à la restructuration du secteur bancaire. En dépit des efforts de l’État visant à assurer l’inclusion financièredes zones rurales, les exploitants agricoles à faibles revenus pourraient s’avérer inclus à l’échelle géographique tout ense voyant toujours refuser des prêts parce qu’ils étaient incapables de fournir aucun nantissement, et à cause dumanque de dossiers de crédit officiels. L’inadéquation entre l’offre et la demande de crédit a entraîné la substitutiondes prêts informels aux prêts formels, ce qui a donc contribué à l’extension du secteur bancaire informel en Chine.

MOTS-CLÉSservices bancaires en milieu rural; exclusion géographique; exclusion financière; Chine

© 2015 Regional Studies Association

CONTACTa(Corresponding author) geoykyg@nus.edu.sgDepartment of Geography, National University of Singapore, Singapore.b hecanfei@urban.pku.edu.cnCollege of Urban and Environmental Sciences, Peking University, Beijing, China.c zhangpeng.amy@gmail.comFaculty of Economics, Centre for History and Economics, University of Cambridge, Cambridge, UK.

REGIONAL STUDIES, 2017VOL. 51, NO. 2, 297–312http://dx.doi.org/10.1080/00343404.2015.1100283

ZUSAMMENFASSUNGBankwesen in ländlichen Gebieten Chinas: geografisch zugänglich, aber weiterhin finanziell ausgegrenzt? Regional Studies.In diesem Beitrag untersuchen wir auf der Grundlage der Verteilungsmuster von ländlichen Darlehensgenossenschaften inungefähr 2200 Bezirken im Jahr 2009 zwei Aspekte der finanziellen Ausgrenzung im ländlichen China nach derUmstrukturierung des Bankwesens. Trotz der staatlichen Bemühungen um eine finanzielle Eingliederung in ländlichenGebieten kommt es vor, dass mittellose Landwirte zwar räumlich eingliedert sind, aber weiterhin keine Darlehenerhalten, da sie keine Sicherheiten oder formellen Bonitätsauskünfte vorweisen können. Die Diskrepanz zwischen demAngebot und der Nachfrage nach Darlehen hat zu informellen Darlehen als Ersatz für formelle Kredite geführt, was zurVerbreitung des informellen Bankwesens in China beigetragen hat.

SCHLÜSSELWÖRTERbankwesen in ländlichen gebieten; geografische ausgrenzung; finanzielle ausgrenzung; China

RESUMENBanca rural en China: ¿geográficamente accesible pero todavía económicamente excluida? Regional Studies. En esteartículo examinamos dos aspectos de la exclusión financiera en la China rural después de la reestructuración de laindustria bancaria a partir de los patrones de distribución de las cooperativas rurales de crédito en aproximadamente2200 distritos del año 2009. Pese a los esfuerzos estatales para asegurar la inclusión financiera en zonas rurales,aunque se incluya espacialmente a los campesinos pobres es posible que se les siga negando préstamos porque noofrecen garantías y carecen de historiales de crédito formales. La discrepancia entre la oferta y la demanda de créditoha creado préstamos informales que reemplazan a los préstamos formales, contribuyendo así a la proliferación de labanca informal en China.

PALABRAS CLAVESbanca rural; exclusión geográfica; exclusión financiera; China

JEL L89, P25, P30, R51HISTORY Received 13 March 2013; in revised form 4 August 2015

INTRODUCTION

Just before opening its banking market to foreign banks aspart of the World Trade Organization (WTO) accessiontreaty, the China Banking Regulatory Commission(CBRC) allowed private and foreign investors to enterthe rural banking market in 2006. HSBC was the first glo-bal banking giant to enter the Chinese rural banking mar-ket in 2007. Under the CBRC’s 2009 plan to encourageprivate entrepreneurs to invest in rural financial insti-tutions, foreign banks had more than 40 banking outletsin China by 2011.

Despite the increasing importance of rural banking,there has been no systematic investigation of the potentialinfluence of banking reforms on rural financial institutionsand their impact on the residents of rural China. Ruralfinancial institutions have been key vehicles for the deliveryof financial services to small-scale enterprises and farmersin rural China since their establishment during the ruralcooperative movement of the 1950s, when collective finan-cial units were spontaneously set up by rural communities.Rural credit cooperatives (RCCs), the successors to the col-lective financial units since the economic reforms of the1980s, along with the Agricultural Development Bank ofChina (ADBC), have had an instrumental role in financingrural development since the withdrawal of state-ownedcommercial banks (SOCBs) as part of the restructuringof the formal banking industry in China. Partly due to itsgeographical reach, RCCs have enjoyed a near monopoly

over deposits and lending in many areas and accountedfor 80% of agricultural loans in the mid-2000s (Ong,2011, p. 49; Park, Brandt, & Giles, 2003). As thesefinancial institutions aim to provide local banking servicesfor rural residents, 128 million (13.4%) of whom liveunder the 2011 poverty line of US$1 per capita per day,their existence and operational efficiency could impact onrural development in China by providing financial supportfor agricultural development or access to formal bankinginstitutions to facilitate participation in the formaleconomy.

The existing literature on financial geography focuseson major financial centres in developed countries (Grote,2007; Leyshon & Thrift, 1995) and emerging markets(Lai, 2012; Wójcik & Burger, 2010) rather than on ruralbanking in transitional economies where the state still hasa strong influence on socio-economic development.Much of the literature emphasizes the geographical aspectof financial exclusion, with exceptions such as Kempsonand Whyley (1999a, 1999b) and Kempson, Whyley, Cas-key, and Collard (2000). The consolidation of the financialmarkets (Avery, Bostic, Calem, & Canner, 1999) and sig-nificant structural changes in the financial sector have ledto the ‘desertification’ of banking services in low incomeareas (Kempson et al., 2000). Following the border debatesabout social exclusion, financial exclusion is generallydefined as ‘the inability, difficulty or reluctance of particulargroups to access mainstream financial services’ regardless ofthe reason (McKillop & Wilson, 2007, p. 9). In reality,

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financial exclusion has both geographical and non-geo-graphical dimensions and could be existed in differentforms (see the next section).

The literature on China has twomajor related but separ-ate strands: the expansion of foreign banks in China and thepotential impact of banking reforms on Chinese banks.Examples of the former approaches are Chen (2009) andHe and Yeung (2011). Other discussions in the literaturehave focused on the potential impact of banking reformson Chinese banks (e.g. Berger, Hasan, & Zhou, 2009; Jia,2009; Yeung, 2009b). Bonin and Huang (2002) examinedthe implications of China’s accession to theWTO for dom-estic banks, while Yeung (2009b) examined the lending cri-teria of SOCBs.

Wong (1997) conducted one of the earlier systematicinvestigations of rural public finance in four counties inShandong and Guizhou, while Ong (2006) examined thetriangular debts between township governments, collectiveenterprises and (informal) rural financial institutions. Xie(2003) and Ong (2011) examined the roles of RCCs inrural finance in China. All these studies are valuable foran understanding of rural finance, but they are largelybased on household surveys in selected regions in China.

This paper examines two particular aspects of financialexclusion in rural China under the restructuring of the bank-ing industry. Specifically, have RCCs included rural dwell-ers geographically by maintaining their outlets in remoteareas, and included low value-added customers by not pri-cing them out of financial products through harsh lendingconditions, presumably due to political considerations in atransitional economy where political stability is of para-mount importance to the central Chinese government?

In addition to demonstrate the spatial heterogeneity ofbanking reforms in the globalized world, this paper aimsto highlight the complex linkages between various formsof financial exclusion: one could be geographical included(in terms of physical accessibility of banking services) andyet excluded of credits due to conditions imposed by the for-mal lending policies, partly due to the political economy ofbanking reform in China. Distinct from the conventionalargument in financial geography that customers in Anglo-American banking industries are geographically excludedfrom accessing banking outlets and cannot meet bankingproduct access conditions, RCCs appear to demonstrate aunique pattern of exclusion partly as a consequence of theCBRC’s administrative directives for the blanket provisionof basic banking services to all at the same time as the intensefinancial pressure to reduce their non-performing loan(NPL) ratios. RCCs, on the one hand, have had to slowthe centralization drive of their banking operations, conse-quently their customers have experienced a lower level ofgeographical exclusion, but the lending conditions, on theother hand, could price some customers out of credit facili-ties. There is circumstantial evidence to suggest that poorfarmers, while spatially included, could still be deniedloans from RCCs due to their inability to provide collateraland their lack of (formal) credit records. The mismatchbetween the supply and demand of credit in improvised vil-lages could lead to informal loans replacing formal loans and

thus contributing to the proliferation of informal banking inChina through this form of substitution.

Before describing the methodology and data sources, abrief review of the literature, the background to the bankingreforms and the two research hypotheses are outlined in thenext section to highlight the specificities of the bankingreforms and their potential importance for financial exclu-sion in China. The fourth section describes and analyzesthe extent of geographical and conditional exclusion inthe rural banking industry in China. The findings of thispaper are then presented and analyzed before a discussionof the potential socio-economic implications for ruralbanking in China.

CONCEPTUAL FRAMEWORK ANDRESEARCH HYPOTHESES

Before outlining the two research hypotheses, this sectionreviews the relevant literature and provides a contextualoverview on the background of Chinese rural bankingindustry.

Convergence of the Anglo-American bankingindustry and its impactsThe new structural deregulation of Anglo-American com-mercial banks has prompted industrial consolidationthrough mergers and acquisitions (Berger, Demsetz, &Strahan, 1999; Dymski, 1999; Martin, 1999).1 To improveeconomic efficiency, banking operations are centralized andthe provision of banking services through bank brancheshas become marginalized. This is illustrated by the wide-spread use of automated telephone and electronic bankingrather than face-to-face interaction for the provision,assessment and processing of banking services (Leyshon& Pollard, 2000; Pollard, 1996; Wills, 1996). Market seg-mentation and financial exclusion are two specific featuresof the converged banking industry, whereby banks providetailor-made services for their high-value customers whilethey withdraw the full range of services to poorer custo-mers, charge fees to maintain low balance accounts underthe ‘user pays’ principle or even close branches in deprivedneighbourhoods to reduce costs and improve their overallcompetitiveness (Dymski & Veitch, 1996; Fuller, 1998;Gentle & Marshall, 1992). People without bank accountsare unable to access the wide range of services for whichbank accounts provide gateways, and are thus financiallyexcluded and have to settle all their transactions in cash.

Consequently, a particular group of people may experi-ence one or more forms of financial exclusion from the for-mal banking services (Kempson & Whyley, 1999a, 1999b;Kempson et al., 2000):

. Geographical exclusion: restriction of access due tobranch closures.

. Condition exclusion: inaccessible due to conditionsattached to financial products, e.g. the failure to qualifybecause of a minimum deposit, identity requirements ora poor credit history.

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REGIONAL STUDIES

. Price exclusion: inaccessible due to the unaffordableprices of financial products, e.g. the relative high costsof unauthorized overdrafts and insurance premiums.

. Marketing exclusion: lower value-added customers areunaware of the available financial services as they arenot targeted by banks in marketing and sales.

. Self-exclusion: people may not apply for a financial pro-duct because they believe they would be refused due toan unpleasant personal experience and cultural andpsychological barriers.

Although this paper focuses on the examination of geo-graphical and conditional exclusion in rural banking inChina, due to the limitations of the available data (seebelow), the findings also suggest circumstantial evidencefor other forms of exclusion. In reality, different forms offinancial exclusion may be interrelated, with one form ofexclusion possibly leading to another as both could be influ-enced by a complex set of interlinked determinants. Forinstance, people who self-exclude themselves from financialproducts may tend to be located in certain areas. Kempsonand Whyley (1999a) also point out that exclusion fromfinancial services is a dynamic process and has no singleexplanation as people can move in and out of financialexclusion temporarily or for the long-term.

Chinese banking industry in reformAlong with market-oriented reforms in the manufacturingsector, there has been a massive restructuring of the Chi-nese banking industry during the last few decades. Thissection provides a brief overview of the relevant issues tohelp contextualize the potential impact of banking reformson Chinese rural banking institutions.

China is an interesting case of banking reform. Theeconomy is moving toward (some form of) capitalismafter three decades of economic reform and rapid economicgrowth, while the state still plays an important role in theeconomy. The retail banking industry in China exhibitsfeatures typical of a transitional economy, with some ofthe largest publicly listed banks in the world coexistingwith thousands of small financial institutions. The presentChinese banking industry, regulated by the People’s Bankof China (PBoC, the central bank) and the CBRC,includes the SOCBs, joint stock commercial banks(JSCBs), city commercial banks, rural financial institutions,foreign banks and the three policy banks.2

The Chinese government is under tremendous pressureto reform the banking system. On the one hand, the state isunder internal pressure to improve governance and thusreduce the financial burden on the Ministry of Finance.Between 1998 and 2003, the SOCBs began to restructuretheir loan portfolios by adopting the strategy of commerciallending. In addition to an injection of 270 billion yuan(US$32.61 billion) into the banking system in 1998, thecentral government transferred 1.4 trillion yuan (US$169.1billion) of pre-1996 NPLs to the four newly created assetmanagement corporations (Yeung, 2009b). To reduce thefinancial burden of the state further, the State Councilallows a number of Chinese SOCBs to issue initial public

offerings (IPOs) of minority equities in local and overseasstock markets. SOCBs became, by definition, internationalholding financial institutions after their IPOs were floatedon the Stock Exchange of Hong Kong in 2005–06 and2010, in the case of ABC. The state is still the majorityequity holder in all SOCBs, with at least 54% of equityin the Bank of Communications (BOCOM). In 2013,the SOCBs controlled 43% of the 151.35 trillion yuan(US$24.44 trillion) of banking assets in China (CBRC,2013; Yeung, 2009a; Yeung, He, & Liu, 2012).

On the other hand, the state had no choice but to openup the banking market to foreign banks as stipulated in theWTO accession treaty, signed in 2001. Foreign banks havebeen allowed to provide local currency services to Chinesecompanies since 11 December 2003, and had full marketaccess all over China from 11 December 2006, when allnon-prudential market access constraints against foreignfinancial institutions were removed. According to theCBRC, 92 foreign banks operate but they only accountedfor 1.73% of total banking assets in China in 2013(CBRC, 2013, pp. 17–18).

To improve its financial viability, the ABC adopted thecommercialization strategy of ‘large banks, large marketsand large industries’ from the mid-to-late 1990s by with-drawing from the agricultural banking market at countylevel to the more profitable businesses for commerce andindustries in cities. ABC delinked RCCs from its manage-ment in 1996 and subsequently reduced its outlets fromabout 60,000 to 24,000 between 1998 and 2008. Afterthe de facto withdrawal of ABC from rural banking,other rural financial institutions, including the ubiquitousRCCs, and those owned and controlled by local govern-ments became particularly important for rural finance inChina (Ong, 2006, 2011).

Restructuring and financial inclusion in Chineserural bankingTo maintain political stability, the CBRC has to implementpolicies that maintain the legitimacy of the Chinese Com-munist Party’s leadership in rural areas. The opening up ofthe Chinese banking sector could be interpreted as the roll-ing back of the state’s provision of banking services to thegeneral public, even the giving up of parts of the Chinesemarket to foreign banks. The state is surely concernedabout the rising rural–urban divide and the subsequentimplications for socio-economic and political stability. Aspolicy-makers are fixated on socio-economic and politicalstability (or ‘social stability’ – shehui wending – in the officialdocuments), domestic security takes high priority, resultingin state intervention in the banking industry to strengthenthe rural banking sector (Yeung, 2009a).

Many RCCs have suffered from financial losses, withhigh NPL ratios and poor corporate governance, partlydue to the significant loans granted to now-bankrupt town-ship and village enterprises (a proportion of such NPLs wastransferred from the ABC during their delinking in 1996)and partly due to rural county governments’ need to bridgetheir fiscal gaps. This was especially the case after theimplementation of the tax-sharing system in 1994,

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which, while increasing the central government share of taxrevenue significantly, rural county governments remainedresponsible for the provision of public services (Ma, 2004;Ong, 2006, 2011). By the end of 2000, RCCs’ NPLshad reached 517.3 billion yuan (US$62.49 billion),accounting for 49.9% of their total loan balances. The accu-mulated losses stood at 108.3 billion yuan, approximately10% of the total assets of RCCs. Closing down RCCs isnot an option for the state as they are the major holdersof rural household savings and providers of rural credit.Any large-scale shutdown of RCCs would exclude ruralresidents from their only access to formal banking servicesand could even result in social unrest in rural areas, partlybecause of the widespread misconception of rural dwellersthat their RCC savings are guaranteed by central govern-ment (Ong, 2011).

In 2003, the State Council issued policy guidelines torestructure and strengthen the corporate governance ofRCCs at township level by placing them under the super-vision of newly established RCC Unions at provincial level(Xie, 2003). In December 2006, the CBRC promulgatedGuidelines on Adjusting the Market-Entry Threshold forBanking Institutions in Rural Areas and lowered the entrycriteria for banks to establish new branches or subsidiariesin rural areas to improve the competition for RCCs(CBRC, 2006b). In addition to liberalizing the setting ofinterest rates, the PBoC also injected 166.1 billion yuan(US$21.84 billion) of capital into RCCs indirectly by issu-ing special treasury bills to purchase their NPLs by 2007.The number of high-risk rural financial institutions hassince been significantly reduced and the sector currentlyhas an average capital adequacy ratio of 8% and a NPLratio of about 10%. The reduction of NPLs, along withthe growth of deposits, the average profit per employeeand newly disbursed loans for agricultural purposes aresome of the evaluation criteria of RCCs (Ong, 2006),therefore the NPL ratios in RCCs (NPLRATE) is an effec-tive explanatory variable for potential risks in the localbanking market and the potential impact of governmentreforms on the banking industry.

In terms of rural lending, the PBoC implemented asmall-credit scheme in 1999 through a series of wholesaleloans with subsidized interest rates to RCCs. Under thisscheme, rural dwellers are able to secure loanswithout collat-eral or a guarantor as long as they have a good credit ratingwith the RCCs. The central bank provided a total of128.8 billion yuan (US$16.16 billion) of highly subsidizedloans to RCCs, with more than 90% disbursed to thoselocated in the western and central regions, between1999 and 2006 (Ong, 2011). This scheme is unsustain-able as its finance is largely dependent on new loans pro-vided by the PBoC. In addition, the CBRC implementedseveral policy guidelines in 2006 to facilitate rural lending,such as encouraging retail lending to farmers, cross-guar-antee lending between rural households, raised credit linesand syndicated loans (CBRC, 2006a, p. 52). The CBRCclaims that the provision of basic banking services toeveryone is guaranteed by the Chinese banking system(Yeung, 2009a). Given the fact that rural peasantry still

accounts for more than 57% of the total population andis where the political power base of the Chinese Commu-nist Party lies, it is a logical political decision to minimizethe extent of financial exclusion in rural areas.

Rural net income per capita (RURINCO), the ratio ofrural to urban households (RUHRATE), the number ofindustrial enterprises (LNIND), and gross domestic pro-duct (GDP) (LNGDP) are the four explanatory variablesused to reflect the potential size of local markets forRCCs. RURINCO reveals the potential power of con-sumption, and RUHRATE and LNIND are indirect indi-cators of the economic structure, i.e. a low value ofRUHRATE and high value of LNIND could suggest anarea with a higher level of industrialization. The value oflocal GDP is still an effective indicator of the size of thelocal economy, despite its well-known drawback of notmeasuring the contribution of the informal economy.

To comply with policy directives on blanket financialinclusion for the general public, this paper hypothesizesthat RCCs could slow the pace of the centralization ofbanking operations to achieve geographical inclusion andgrant loans to potentially higher-risk customers in orderto achieve conditional inclusion.

To reveal the trend towards geographical inclusion inrural banking, the spatial density of RCC outlets incounty-level administrative regions (hereafter ‘county’ forsimplicity) in 2009 is used as a dependent variable (Table 1).

Geographical inclusionThere is a significant relationship between geographicalinclusion, measured by the number of RCC outlets(NRCC) per 1000 residents, and the size and value of themarket in rural administrative regions in China:

NRCC = a+ b1 NPLRATE + b2 RURINCO

+ b3 RUHRATE + b4 LNIND

+ b5 LNGDP + b6 ABC

+ b7 POST + b8 ETHNIC + b9 REGION

+ b10 LNGOVEXP + 1

The RCC’s loan intensity to farmers in different countiesin 2009 is used as a dependent variable to test the con-ditional inclusion hypothesis (Table 1).

Conditional inclusionThere is a significant relationship between conditionalinclusion, measured by the ratio of loans value to farmersto the total value of loans dispensed by RCCs (LOANF),and the size and value of the market in rural administrativeregions in China:

LOANF = a+b1NPLRATE+b2RURINCO

+b3RUHRATE+b4LNIND

+b5LNGDP +b6ABC

+b7 POST +b8ETHNIC +b9REGION

+b10LNGOVEXP + 1

Rural banking in China: geographically accessible but still financially excluded? 301

REGIONAL STUDIES

The first explanatory variable reveals the potential risks forlocal banking markets and the potential impact of govern-ment reforms on the banking industry. If RCCs yield toCBRC pressure for financial inclusion, the NPL ratios inRCCs (NPLRATE) should have a positive relationshipwith the spatial density of RCC outlets (i.e., support forthe geographical inclusion hypothesis) and the loan inten-sity to farmers (i.e., support for the conditional inclusionhypothesis) (Table 1).

The next four explanatory variables are used to reveal thepotential size of the local banking market. Following theexisting literature that concluded that the exclusion of finan-cial products is largely explained by factors relevant to income,such as low net household income and recipients of income-related benefits (Kempson & Whyley, 1999a), it is expectedthat rural net income per capita (RURINCO) to be negativelyrelated to the spatial density of RCC outlets and loan inten-sity to farmers. This paper included the ratio of rural to urbanhouseholds (RUHRATE), the number of industrial enter-prises (LNIND) and GDP (LNGDP) as other indicators ofthe potential size of local markets for RCCs.

Should the RCCs reveal various spatial patterns of geo-graphical and conditional inclusion in China, this scenariocould contrast with the financial exclusion experienced byAnglo-American low value-added customers during bank-ing reforms.

SOURCES OF DATA AND METHODS

Before presenting the results, a brief explanation of thesources of data, the methods used to make estimates andtheir potential limitations are presented in this section.

Location data on banking business units at county levelwere compiled from A Collection of Rural Financing Atlas atthe CBRC’s official website (see http://bankmap.cbrc.gov.cn/bank). Other economic and population data are drawnfrom various issues of provincial statistical yearbooks. It isunfortunate that the authors can only access data from2006 to 2009 and thus are unable to access data from the1990s or early 2000s to reveal the time-series patterns ofthe restructuring of the banking industry in rural China.For some unknown reason, this specific webpage is nolonger available from the CBRC website.

To reveal the trend towards geographical inclusion inrural banking, the spatial density of RCC outlets(NRCC) in 2191 counties in 2009 is used as a dependentvariable (Table 1). As a number of RCCs were transformedinto rural cooperative banks or rural commercial banksduring the banking reforms, the outlets of these rural finan-cial institutions is included as the dependent variable in thispaper.

The RCC’s loan intensity to farmers (LOANF) indifferent counties in 2009 is used as a dependent variableto test the conditional inclusion hypothesis (Table 1).This is not an ideal parameter for conditional inclusionbut it is better than other alternatives, e.g., lending for agri-cultural purposes includes loans to collectives, such as hugetobacco firms, rather than individuals. Other commonlyused proxies to measure the various aspects of conditionalexclusion in Anglo-American countries, such as the depos-its and loans ratio, accessibility of credit cards, pensions(Budd & Campbell, 1998) and life insurance (Whyley,McCormick, & Kempson, 1998), were not available tothe authors. The use of cross-sectional data allows the

Table 1. Variables and their expected signs.

Variables Explanations

Expected sign

NRCC LOANF

DependentNRCC Number of rural credit cooperative (RCC) outlets per 1000 residents for geographical inclusion

(spatial density of RCC outlets)

LOANF Ratio of loans value to farmers to the total value of loans dispensed by RCCs for conditional

inclusion (RCC loan intensity to farmers)

ExplanatoryNPLRATE Non-performing loan ratios in RCCs + +

RURINCO Rural net income per capita − −RUHRATE Ratio of rural to urban households + +

LNIND Logarithm of number of industrial enterprises − −LNGDP Logarithm of gross domestic product (GDP) − −

ControlABC Number of Agricultural Bank of China and Agricultural Development Bank of China (ADBC)

outlets

POST Number of postal saving institute outlets

ETHNIC ¼ 1 if it is an autonomous region for ethnic minority

LNGOVEXP Logarithm of local government’s expenditure

302 Godfrey Yeung et al.

REGIONAL STUDIES

examination of the possible scenarios where credit levels tofarmers are reduced while RCC geographical coverage ismaintained.

As bank lending could be geographically specific, thedata at provincial level were clustered to allow for withincorrelations. Clustered regressions were ran with NRCCand LOANF as dependent variables against five explanatoryvariables with data from 2009 to reveal the possible con-tributory factors to the geographical and conditionalinclusion of RCC customers (Table 1). The explanatoryvariable of NPLRATE could be both the cause and conse-quence of geographical and conditional exclusion.3 Ratherthan providing an unequivocal causal link between explana-tory and dependent variables, the main purpose of themodels is to establish the possible correlation betweenexplanatory variables and geographical and conditionalexclusion based on the available empirical data, then com-paring the results with the (personal) experience of intervie-wees. In addition to highlight the complex linkagesbetween various forms of financial exclusion, the experienceof Chinese rural dwellers presented in this paper demon-strates the spatial heterogeneity of banking reforms in theglobalized world.

To deal with possible non-linear relationships, thenumber of industrial enterprises and GDP were trans-formed into logarithm forms before entering them intothe model for calculation. All dependent and explanatoryvariables were also standardized to make it easy to comparedifferent coefficients in different models. The robust esti-mator of variance is used to deal with possibleheteroskedasticity.

To check the noise from the exogenous variables, fivecontrol variables were introduced in the model. The with-drawal of RCCs from rural areas could be the result ofcrowding-out by other financial institutions. Moreover,the low value of loans dispensed by RCCs to farmerscould be an indication of self-exclusion rather than con-ditional exclusion (Kempson & Whyley, 1999a). The lit-erature also revealed that region, ethnicity and maritalstatus are significant financial exclusion factors (Kempsonet al., 2000). Large-scale surveying with probabilitysampling is the only reliable and accurate means of exam-ining the potential impacts of these factors. In China, a sig-nificant proportion of ethnic minorities are located inautonomous regions in central and western China. Statisti-cal noise from exogenous variables would be indicated by anobvious change in the significance of the explanatory vari-ables when the number of ABC and ADBC outlets (ABC),the number of postal saving institute outlets (POST),autonomous regions for ethnic minorities (ETHNIC) andregion (REGION) are added into various specifications ofthe model.4 The logarithm of local government expendi-ture (LNGOVEXP) was also used to proxy the potentialfinancial impacts of local governments on two dimensionsof financial exclusion.

The Ramsey RESET test, the standardized test foromitted variables which examines the correlation betweenindependent variables and residuals, was conducted for allregressions. Although most of the tests rejected the

hypothesis that there are no omitted variables, the resultsthemselves do not invalidate the usefulness of the modelas there are always exogenous variables that could poten-tially improve the model’s fitness. Endogeneity is a moreimportant issue for the validity of the model.

Causality is always an issue in the analysis of cross-sec-tional data. In this paper, three methods were applied todeal with potential problems of endogeneity. First, robust-ness checks were conducted by excluding all autonomouscounties with ethnic minorities and the models werererun. Second, regressions with dependent variables in2009 and independent variables in 2006 were run as anotherrobustness check that RCC outlets or loan intensity in 2009could not influence the patterns of independent variables in2006. Third, instrumental variables (IV) regressions wereused further to deal with endogeneity with respect tocross-sectional data. Theoretically, causality is less of anissue in the geographical inclusion model as financial factorsare more important for the determination of RCC locations.

To complement the estimates from the statisticalmodels and provide further evidence for financial inclusion,the authors have conducted 64 semi-structured interviewsthrough accessibility sampling in eight provinces between2010 and 2014 (see Appendix A in the supplementaldata online for further methodological details).

INCLUSION OR EXCLUSION IN RURALBANKING?

Pearson’s correlation coefficients between the explanatoryvariables needed to be examined before the data could beanalyzed. There was no obvious sign of collinearity as thehighest correlation coefficient is 0.56 between the two con-trol variables: the number of ABC and ADBC outlets(ABC) and postal saving institute outlets (POST) (seeTable A2 in Appendix A in the supplemental data online).All other coefficients with a value below 0.5, and whichhave the expected signs, e.g., ABC negatively correlateswith the logarithm of the number of industrial enterprises(LNIND) and local government expenditure (LNGO-VEXP), while rural net income per capita (RURINCO)negatively correlates with NPL ratios (NPLRATE).

Geographical inclusion or exclusion?The estimates from the clustered regression models supportthe geographical inclusion hypothesis as most coefficientsare significant and have the expected signs (Table 2). Forinstance, there is a positive and highly significant relation-ship between NPL ratios in RCCs (NPLRATE) and theirspatial density in all the models. Other explanatory vari-ables – rural net income per capita (RURINCO) for thepotential rural banking market and the number of indus-trial enterprises (LNIND) and GDP (LNGDP) for thelevel of economic development – also show the expectednegative signs and are significant in most models. Thatis, RCCs are likely to maintain their physical presence ineconomically less developed areas, notwithstanding theirhigher level of NPL ratios. The authors conducted arobustness check by excluding all the autonomous counties

Rural banking in China: geographically accessible but still financially excluded? 303

REGIONAL STUDIES

of ethnic minorities from the dataset and reran theregressions. The results suggest that the above findingsare largely robust as all the coefficients have the samesigns and similar levels of significance (Table 3).

To examine whether the above patterns remainunchanged in counties with different income levels, thedataset is divided into two subgroups using the medianlevel of income as the benchmark and the clusteredregressions rerun. The findings could be a prima faciecase for some level of geographical exclusion in poor coun-ties as areas with higher income could have a higher chanceof getting credits (Dymski & Veitch, 1996; Gentle &Mar-shall, 1992) (Table 4). All the significant negative

coefficients of RURINCO are only associated with countieswhose income is below the median level, while all the sig-nificant positive coefficients of NPLRATE and RUH-RATE are associated with counties whose income isabove the median level. In other words, the spatial densityof RCCs is higher in rural areas with above-median incomelevel despite the higher level of NPL ratios. The negativerelationship between RURINCO and the spatial densityof RCC outlets could be due to the possible crowding-out effects of ABC and ADBC outlets in the samelocation, where its coefficient (ABC) is negatively and sig-nificantly (at 0.01 level) related to the dependent variable inall models. This could impose further pressure on the

Table 2. Clustered regression on spatial density of rural credit cooperative (RCC) outlets.Variables [1] [2] [3] [4] [5] [6]

RURINCO −0.114*** −0.0790*** −0.0579*** −0.0587*** −0.0552*** −0.0552***RUHRATE 0.0152 0.0103 0.0103 0.00778 0.00785

NPLRATE 0.132*** 0.112*** 0.109*** 0.109***

LNIND −0.0524** 0.00210 −0.0101 −0.0111LNGDP −0.219*** −0.120*** −0.159*** −0.158*** −0.156***ABC −0.283*** −0.320*** −0.311*** −0.309***POST 0.137*** 0.130*** 0.117*** 0.118***

LNGOVEXP 0.0825*** 0.142*** 0.137*** 0.138***

ETHNIC 0.174* 0.0755 0.0945 0.0938

Province fixed effect Yes Yes Yes Yes Yes Yes

Constant −0.302*** −0.209** −0.275*** −0.276*** −0.258*** −0.253***Ramsey RESET test 0.0025 0 0 0 0 0

(Prob> F )

Observations 2191 2189 2189 2189 2189 2189

R2 0.167 0.223 0.259 0.262 0.271 0.271

Note: ***p<0.01; **p<0.05; *p<0.10.Source: Authors’ calculation.

Table 3. Robustness test of the spatial density of rural credit cooperative (RCC) outlets: excludes autonomous counties of ethnicminorities.Variables [1] [2] [3] [4] [5] [6]

RURINCO −0.125*** −0.0871*** −0.0634*** −0.0643*** −0.0613*** −0.0612***RUHRATE 0.0151 0.0103 0.0103 0.00790 0.00797

NPLRATE 0.132*** 0.113*** 0.109*** 0.109***

LNIND −0.0526** 0.000334 −0.0105 −0.0113LNGDP −0.226*** −0.127*** −0.166*** −0.165*** −0.163***ABC −0.280*** −0.318*** −0.309*** −0.307***POST 0.136*** 0.130*** 0.116*** 0.118***

LNGOVEXP 0.0758*** 0.134*** 0.129*** 0.130***

Province fixed effect Yes Yes Yes Yes Yes Yes

Constant −0.303*** −0.208** −0.273*** −0.276*** −0.257*** −0.252***Ramsey RESET test 0 0 0 0 0 0

(Prob> F )

Observations 2079 2077 2077 2077 2077 2077

R2 0.174 0.230 0.269 0.271 0.279 0.279

Note: ***p<0.01; **p<0.05; *p<0.10.Source: Authors’ calculation.

304 Godfrey Yeung et al.

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Table 4. Clustered regression on spatial density of rural credit cooperative (RCC) outlets, with county in different income levels.

Variables

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]Income<median

Income>median

Income<median

Income>median

Income<median

Income>median

Income<median

Income>median

Income<median

Income>median

Income<median

Income>median

RURINCO −0.840*** 0.0223 −0.707*** 0.0182 −0.617*** 0.0348 −0.608*** 0.0290 −0.597*** 0.0287 −0.599*** 0.0291

RUHRATE −0.00663 0.636*** −0.00830 0.540*** −0.0105 0.552*** −0.0110 0.526*** −0.0110 0.531***

NPLRATE 0.0339 0.161*** 0.0435 0.126*** 0.0387 0.130*** 0.0386 0.130***

LNIND −0.0165 −0.0131 0.0247 0.0426 0.00465 0.0389 0.00423 0.0389

LNGDP −0.145*** −0.111*** −0.0671* −0.0385 −0.102*** −0.0665** −0.103*** −0.0573* −0.104*** −0.0624**ABC −0.300*** −0.230*** −0.357*** −0.256*** −0.358*** −0.234*** −0.359*** −0.241***POST 0.143*** 0.0892*** 0.125*** 0.0882*** 0.122*** 0.0822** 0.121*** 0.0779**

LNGOVEXP 0.103*** 0.0284 0.167*** 0.0811*** 0.166*** 0.0827*** 0.165*** 0.0803***

ETHNIC −0.0746 0.608*** −0.137 0.448** −0.133 0.489** −0.133 0.496**

Province

fixed effect

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Constant −0.158 −0.784*** −0.111 −0.597*** −0.115 −0.700*** −0.0895 −0.761*** −0.0897 −0.659*** −0.0920 −0.675***Ramsey

RESET test

0.1903 0.0809 0.3467 0 0.7069 0 0.4864 0 0.7215 0 0.7211 0

(Prob > F )

Observations 1096 1095 1094 1095 1094 1095 1094 1095 1094 1095 1094 1095

R2 0.172 0.242 0.190 0.291 0.208 0.325 0.219 0.323 0.220 0.333 0.220 0.334

Note: ***p<0.01; **p<0.05; *p<0.10.Source: Authors’ calculation.

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RCCs to improve their financial performance as the crowd-ing-out effect is apparently more severe in lower incomeareas where the corresponding coefficients have a highervalue in all the model specifications. There is, however,no obvious change in the significance of the explanatoryvariables when the control variables are introduced intothe model, so there could be no significant statisticalnoise from exogenous variables in the model.

The above evidence suggests that the geographicalinclusion of RCCs, presumably due to the CBRC directiveconcerning financial inclusion in rural China, does exist.Further examination suggests that there are some signs ofthe geographical exclusion of rural households in poorercounties. The geographical inclusion hypothesis is thusaccepted partially and this finding contrasts with the find-ings in developed countries (Leyshon & Thrift, 1993; Ley-shon & Pollard, 2000). Customers in rural China may notbe excluded geographically but are they excluded con-ditionally from the formal banking system?

Geographical access but financially excluded?The estimates from the models for conditional inclusionappear contrary to the experience of the Anglo-Americanbanking industry as most coefficients are significant andhave the signs consistent to the conditional inclusionhypothesis.

There is an expected positive and highly significant (at0.01 level) relationship between theNPL ratios (NPLRATE)and the loan intensity to farmers (Table 5).However, the loanintensity has an unexpected positive relationship with thenumber of industrial enterprises (LNIND). In other words,RCCs appear to lend to customers in areas with a higherlevel of industrialization despite their higher level of NPLs!As in the previous section, all autonomous ethnic minoritycounties were excluded from the data in the robustnesscheck. The results suggest the above findings are robust andconsistent with those from the main models (Table 6).

Separating the dataset into two subgroups according toa county’s income level reveals that rural residents in poorercounties with lower levels of industrialization may have ahigher chance of suffering from conditional exclusion.This is illustrated by the expected and significant positivecoefficients of NPLRATE in all counties but the unex-pected positive coefficients of LNIND in counties abovethe median level of income, especially in model 4 (Table7). This suggests RCCs tend to have a higher loan intensityin industrial counties than agricultural areas with lowerincomes. This finding reflects the specificity of the Chinesebanking industry where RCCs still have a higher tendencyto lend to counties with high levels of NPLs, partly due totownship and village enterprises loans with the possibleinvolvement of local governments (see the next sectionand Ong, 2011). As with geographical exclusion, theauthors cannot rule out the possibility of a crowding-outeffect from the ABC and ADBC. There is, however, noobvious change in the significance of the explanatory vari-ables when control variables are entered into the model.

The above results suggest that the authors cannot drawa simple conclusion about conditional inclusion in terms of

loan intensity as RCCs do lend to areas with high levels ofNPLs (which supports the conditional inclusion prop-osition) but they also tend to lend to customers in areaswith higher levels of industrialization rather than agricul-tural counties (which supports the conditional exclusionproposition). How can such a phenomenon be explained?

Collateral lending and the informal bankingTo explain the intricate relationship between RCCs loanintensity and conditional inclusion, it is important tounderstand the processes of lending decisions and therole of informal banking.

Although RCCs should be the designated outlets forrural lending after the withdrawal of ABC, they may nothave a strong incentive to fulfil this role due to the increas-ingly competitive rural banking market. On the one hand,they are under intense pressure to improve their balancesheets after the recent central government directive toimprove local government debt management systems – itis estimated that debts amounted to 10.7 trillion yuan(US$1.7 trillion) at the end of 2010. Importantly, rural com-mercial banks still had the highest NPL ratio (1.9%) for alltypes of banking institutions in 2010 compared with 1.1%in SOCBs and 0.7% in JSCBs (CBRC, 2010, p. 140).About 10% (212) of RCCs are still regarded as high-riskinstitutions with an NPL of 700–800 billion yuan(US$111–127 billion). The CBRC urges those with anNPL ratio of higher than 30% to merge or be acquired byother financial institutions. On the other hand, RCCs havehad to compete with their much better funded local giantsand global banking rivals after the CBRC allowed theentrance of private and foreign investors into rural bankingin 2006. Under the CBRC’s 2009 plan to build 2000 villagebanks to compete with existing RCCs and encourage privateentrepreneurs to invest in rural financial institutions, theBOC Fullerton Community Bank aimed to establish 400rural outlets nationwide in the following five years.

In this keen competitive environment, RCC credit man-agers have to lend to make a profit as long as loans do nothave a high chance of defaulting. Economically developedareas with a higher level of industrialization and potentialdemand for high value loans are thus more profitable thanagricultural areas with lower levels of income. Lendingdecisions are based on the potential returns and costs of aloan where the returns are determined by the interest ratesand the costs are determined by the cost of banking plusthe risk of default. Based on the projection of revenues andother relevant documents, such as the market value of collat-erals (normally fixed assets), credit managers are able to esti-mate the risk of default and the possibility of recouping theprincipal of loans. Moreover, part of the risks of default inlending to townships and villages and other state-ownedenterprises is deflated by the guarantees of the corresponding(local) governmental institutions. Although these loans couldstill default and create so-called ‘triangle-debts’ where localgovernments have to bear the corresponding financialresponsibilities, this form of lending in the Chinese bankingindustry was not uncommon in the 2000s (Field Survey,April–July 2014; Yeung, 2009b).

306 Godfrey Yeung et al.

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This is, however, not the case in lending for agriculturalpurposes, which normally involves a (much) lower value ofcredit (i.e., a higher costs to benefits ratio) and higher levelsof risk (i.e., uncertainties concerning drought or stormdamage). Obviously, credit managers are normally reluc-tant to embrace such lending enthusiastically and thisexplains the main reason for the difficulties rural dwellershave in securing loans from the formal banking sector foragricultural purposes (Field Survey, April–July 2014).

The overwhelming importance of financial indicators inthe formal evaluation criteria of RCCs is an importantreason of the lack of incentive to support the policy onrural lending. Despite the policy directives to support the‘three nong’ (agriculture, countryside and farmers) develop-ment, included in Document No. 42 issued in February2014 (CBRC, 2014), all the interviewed senior or creditmanagers at RCCs explicitly pointed out the three prin-ciples of their managerial philosophy: (1) to minimize theNPL ratio, (2) to maintain profitability and (3) liquidity.RCC managers have been liable for NPLs since mid-2000, either through a reduction in their remuneration oreven dismissal in serious cases, and the NPL ratio is deter-mined by the lenders’ ability to repay their loans. Creditmanagers therefore have no choice but to protect theirown careers by adhering to the formal lending policy(Field Survey, April–July 2014). This is vividly illustratedby the words of a credit manager of an RCC in Dalian ofLiaoning (Field Survey, May 2014):

We are simply implementing the formal lending policy. […] I

personally will not disburse loans to a famer because of our

personal acquaintance [knowledge on his/her honesty and

the ability to repay the loan. […] After all, who would put

their own future career on the line for the sake of financial

inclusion under the personal responsibility system [where

I’m responsible for the NPLs]?

The importance of financial indicators in the evaluationof RCCs is consistent with the findings of other studies,e.g., RCCs ‘are primarily concerned with profit maximiza-tion and efficiency of capital’ (Ong, 2006, p. 191).

The above findings suggest that poor farmers could befinancially excluded, partly due to their lack of collateraland formal credit records. All the interviewed credit man-agers at RCCs emphasized the importance of upholdingthe current Chinese laws on lending (CBRC, 2012; alsoCBRC, 2014), which specify that collateral (normally infixed assets) and a good credit history (and rating) are twonecessary conditions for loans being offered (Field Survey,April–July 2014).

Various lending conditions are frequently cited byfarmers as the major barriers to securing loans fromRCCs. The proliferation of RCC lending conditions isvividly described by Mr Xiang, a farmer in Shanhe villagein Baojing county, Xiangxi Tujia and Miao autonomousprefecture of Hunan province in central China (Field Sur-vey, February 2011):

I remember clearly that RCCs used to provide large amounts

of loans with minimal lending conditions to farmers before

2001 as a response to government initiatives in agricultural

development. For example, a farmer used to be able to secure

a loan as long as another farmer acted as the guarantor of his

trustworthiness and no other collateral was needed. […]

RCCs started to gradually impose lending conditions (on

loans to farmers). […] You must now find a civil servant to

act as a guarantor and have fixed assets, like a shop, as collat-

eral to have any chance of securing a loan from RCCs. Do

you think that it is possible for an ordinary farmer in no

man’s land to have an acquaintance in local government

(and who is willing to act as a guarantor)? Do you think

that it is possible for a farmer working on the land all day

to own a shop or something similar? […] Should we need

Table 5. Clustered regression on rural credit cooperative (RCC) loan intensity to farmers.Variables [1] [2] [3] [4] [5] [6]

RURINCO 0.00664 0.00785 0.00356 −0.00159 0.00178 0.00138

RUHRATE −0.00535 −0.00529 −0.00123 −0.00295 −0.00328NPLRATE 0.0844*** 0.0871*** 0.0915*** 0.0910***

LNIND 0.0655** 0.0442* 0.0559** 0.0551**

LNGDP 0.0259 −0.0210 0.00689 0.0201 0.00930

ABC 0.0624** 0.0765** 0.0958*** 0.0862***

POST −0.0550* −0.0298 −0.0315 −0.0395LNGOVEXP −0.0951*** −0.106*** −0.103*** −0.109***ETHNIC 0.0527 0.0586 0.0707 0.0740

Province fixed effects Yes Yes Yes Yes Yes Yes

Constant −0.0175 −0.0396 −0.00856 −0.0455 −0.00351 −0.0261Ramsey RESET test 0.0025 0 0 0 0 0

(Prob> F )

Observations 2191 2189 2189 2189 2189 2189

R2 0.022 0.034 0.031 0.032 0.036 0.038

Note: ***p<0.01; **p<0.05; *p<0.10.Source: Authors’ calculation.

Rural banking in China: geographically accessible but still financially excluded? 307

REGIONAL STUDIES

money urgently, we normally ask our relatives or close friends

to lend us 100 yuan or so, […] and they don’t charge us

interest.

This finding is consistent with the observation by Ma(2004, p. 92) of the importance of collateral in securingloans and the household survey conducted by Ong (2011,p. 60) in Shandong, Sichuan and Hebei in the mid-2000s, where 70% of respondents thought the ‘ability torepay’ was the main lending condition for securing loansfrom RCCs. Land can be used as collateral for a loan butmost poor farmers have no ownership rights over theland that they cultivate.

In addition to lending conditions, the finding maysuggest a mismatch between the demand and supply ofcredit in impoverished villages where farmers normallyneed just a few hundred to a thousand yuan: such smallvalue loans are better provided by micro-finance insti-tutions than RCCs (see below, and Park & Ren, 2001).This explains the highly unequal access to financial pro-ducts. According to a national survey of rural householdsconducted by the CBRC (2006a), fewer than 16% of farm-ers have access to formal credit and about 80% of loans topeasants are granted to those with high incomes. Due tothe risk of collateral-free lending, financially independentlocal governments are reluctant to fill the financing gap.Similar patterns of a mismatch between the supply anddemand for loans occur in other developed countries, butthis is clearly not what the CBRC planned.

The mismatch between the supply and demand forloans could lead to a substitution effect whereby informal

financial institute loans are a substitute for formal loans

provided by RCCs,5 which could partially explain the pro-

liferation of informal (including underground) banking in

China. There are 8591 non-bank small-loan providers

who raise their funds through issuing high yield equity

shares and bonds (they are not classified as banks because

they cannot accept deposits) (Wildau, 2014). Local econ-

omists at Wenzhou estimated that up to 95% of capital

flows were tied up with informal means of finance in the

mid-1980s (Tsai, 2002, p. 16). The International Monet-

ary Fund (IMF) (2014, p. 28) estimates that the informal

banking sector accounts for around 40% of GDP (18.64

trillion yuan /US$3 trillion) in China (Nomura Securities

estimated an even higher figure of 21.87 trillion yuan /

US$3.52 trillion based on the PBoC data; Hong, 2014).In addition to borrowing from friends and relatives, poor

farmers could access small loans through the involvement ofnon-governmental organizations (NGOs) or some special(experimental) lending schemes provided by a small numberof RCCs. To lower the financial gap produced by collaterallending, some bottom-up initiatives could cater for poorfarmers unable to access loans from the formal bankingsystem. For instance, a Co-operative Fund was establishedwith the assistance of the Kowk Foundation, a privatelyfinanced NGO in Shanhe village of Hunan province in2010. The local village government injected 150,000 yuan(US$22,158; 58% of the total equity) as the equity of thefund, and another 100,000 and 10,000 yuan (US$14,772and US$1477) were raised from the foundation and individ-ual farmers (as ‘shareholders’ of the foundation) respectively.The fund is managed by three elected representatives andoperates as a micro-finance institution by providing smalland collateral-free loans of up to 10,000 yuan to poor farmers(Field Survey, February 2011). The 5–6% interest ratescharged by this fund are below the commercial rate offeredby the ABC and much lower than the 15–20% per annumcharged by the 1300-plus registered micro-finance insti-tutions in China.6 To deal with the seasonal demand forcash when farmers sign off their contracted arable landwith land owners (i.e., local governments), some RCCs inLiaoning and Zhejiang provinces have provided a ‘collective

Table 6. Robustness test of rural credit cooperative (RCC) loan intensity to farmers: excludes autonomous counties of ethnicminorities.Variables [1] [2] [3] [4] [5] [6]

RURINCO 0.00854 0.00805 0.00312 −0.00152 0.00178 0.00118

RUHRATE −0.00520 −0.00534 −0.00127 −0.00296 −0.00327NPLRATE 0.0890*** 0.0920*** 0.0956*** 0.0952***

LNIND 0.0591** 0.0383 0.0493* 0.0486*

LNGDP 0.0428 −0.00507 0.0236 0.0359 0.0262

ABC 0.0587** 0.0722** 0.0905*** 0.0821***

POST −0.0505 −0.0239 −0.0272 −0.0342LNGOVEXP −0.101*** −0.112*** −0.110*** −0.115***Province fixed effects Yes Yes Yes Yes Yes Yes

Constant −0.0173 −0.0386 −0.00850 −0.0466 −0.00644 −0.0263Ramsey RESET test 0.0005 0 0 0 0 0

(Prob> F )

Observations 2079 2077 2077 2077 2077 2077

R2 0.022 0.036 0.031 0.033 0.038 0.039

Note: ***p<0.01; **p<0.05; *p<0.10.Source: Authors’ calculation.

308 Godfrey Yeung et al.

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Table 7. Clustered regression on rural credit cooperative (RCC) loan intensity to farmers, with county in different income levels.

Variables

[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12]Income<median

Income>median

Income<median

Income>median

Income<median

Income>median

Income<median

Income>median

Income<median

Income>median

Income<median

Income>median

RURINCO 0.147 0.00104 0.189 0.00500 0.211 −0.00146 0.181 0.000639 0.215 3.37e−05 0.199 0.000710

RUHRATE −0.00820 −0.0672 −0.0104 −0.00927 −0.00767 0.0214 −0.00852 −0.00641 −0.00865 0.00259

NPLRATE 0.0808*** 0.101** 0.0777*** 0.116*** 0.0810*** 0.116*** 0.0807*** 0.116***

LNIND 0.0306 0.0900** 0.0185 0.0674 0.0323 0.0738* 0.0314 0.0738*

LNGDP 0.00556 0.0227 −0.0141 −0.0251 0.0117 0.000581 0.0145 0.0139 0.00892 0.00427

ABC −0.0809 0.0912** −0.0399 0.0955** −0.0385 0.121*** −0.0444 0.109***

POST 0.0131 −0.0830* 0.0337 −0.0620 0.0322 −0.0629 0.0266 −0.0712*LNGOVEXP −0.109*** −0.0747** −0.0994** −0.0903** −0.0968** −0.0864** −0.104** −0.0911**ETHNIC 0.0858 −0.138 0.0733 −0.134 0.0816 −0.104 0.0801 −0.0904Province

fixed effect

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Constant 0.0867 −0.0941 0.0778 −0.0897 0.0905 −0.0257 0.0815 −0.128 0.0933 −0.0211 0.0761 −0.0512Ramsey

RESET test

0.0002 0.3574 0 0 0 0 0 0 0 0 0 0

(Prob > F )

Observations 1096 1095 1094 1095 1094 1095 1094 1095 1094 1095 1094 1095

R2 0.024 0.040 0.037 0.053 0.032 0.055 0.031 0.054 0.037 0.058 0.038 0.061

Note: ***p<0.01; **p<0.05; *p<0.10.Source: Authors’ calculation.

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guarantee’ lending scheme, which allows three or morehouseholds with local household registration (hokou) and agood credit history to guarantee each other’s borrowingwithout the need for collateral (Field Survey, April–May2014). The Postal Savings Bank also rolled out a micro-finance programme by allowing borrowers to use timedeposits as collateral in the mid-2000s (Ong, 2011, p. 53;see also Park & Ren, 2001). This and similar forms of bot-tom-up financing initiatives could generate more capital tofacilitate rural development. The sustainability of such lend-ing initiatives could be questionable due to the opaque gov-ernance structure of its supporting organizations as NGOscannot be recognized as financial institutions, which restrictstheir ability to secure sources of finance.

There is partial and circumstantial evidence for therejection of the conditional inclusion hypothesis – poorfarmers could be spatially included but still be deniedloans from RCCs due to their lack of collateral and formalcredit records. Consequently, poor farmers could beincluded geographically but could still financially excludedfrom the formal banking system and this financing gapcould be partially filled by informal banking institutions.

CONCLUSIONS

Based on the distribution patterns of RCCs in 2191 coun-ties in 2009, this paper examined the existence of twospecific aspects of financial exclusion in rural Chinaunder the restructuring of the banking industry.

In contrast to the conventional argument in financialgeography that low value-added bank customers in theAnglo-American banking industries are excluded frombanking operations geographically and banking productsconditionally, RCCs have demonstrated a unique patternof exclusion as a response to the CBRC’s directives forthe blanket provision of basic banking services for everyone,at the same time as the intense pressure on banks toimprove their competitiveness.

Although households in rural China may not beexcluded geographically, there is circumstantial evidencefor conditional exclusion according to the various specifica-tions of the clustered regression models and survey results:poor farmers could be spatially included but still be deniedloans from RCCs due to their lack of collateral and formalcredit records. As RCCs could less likely grant loans inareas with household incomes below the median leveland other areas with lower levels of industrialization, it isfurther argued that poor farmers living in economicallyless developed areas are more likely to be financiallyexcluded. This clearly illustrates the complex interlinkedrelationships between the different forms of financial exclu-sion and the deployment of policy responses.

The limited temporal coverage of data is the greatestdrawback of this study because there could be changes inthe coverage and provision of banking services over time.Nonetheless, some of the findings from examining thecross-sectional data could have profound policy impli-cations for the banking industry and rural development inChina – the intrinsic contradictory nature of the CBRC

directive on both financial inclusion and financial viability,and the lack of institutional regulations.

First, the CBRC’s directives on financial inclusion andfinancial viability have inherent contradictions. There is amismatch between the demand and supply of credit,because while farmers normally need only small loans ofjust a few hundred yuan, RCCs have little incentive to pro-vide such low profit-generating or even money-losing ser-vices. In addition, RCC managers have been liable for theNPLs since the mid-2000, so they have to adhere to theformal lending policy for the requirement of collateraland good credit history (Field Survey, April 2011 andApril–May 2014).

The financial capacity of RCCs to lend to farmers isfurther undermined by the restructuring of the rural bank-ing system when the ABC transferred a substantial value ofNPLs to RCCs after the division of its managementresponsibilities. Under these circumstances, RCCs have lit-tle choice but to compromise on the financial inclusiondirective and commercialize their lending criteria. Thisparticularly affects poor households in remote areas asmost low-income farmers have no access to agriculturalinsurance protection or credit guarantee systems.

The CBRC can easily scrutinize the number ofbranches and their exact locations in rural China andkeep a check on the pace of any potential geographicalexclusion of RCC customers. Although the CBRCimplemented several policy guidelines in 2006 to facilitaterural lending, such as encouraging retail lending to farmers,cross-guarantee lending between rural households, raisingcredit lines and syndicated loans (CBRC, 2006a), thesegeneral guidelines have no clauses specifically referring toRCCs and thus are ineffective in reducing the conditionalexclusion of needy rural dwellers. The transaction costs ofexamining the existence of conditional exclusion, whichis revealed by neither the location patterns of outlets northe macro-statistics on lending, are very high and this iswhere the RCCs can compromise on the CBRC’s policydirectives by paying only lip-service to the original principalof blanket access to financial services in remote rural China.

This illustrates how the twin goals of financially includ-ing the rural poor and maintaining the financial viability ofrural banking institutions under the post-WTO bankingreforms implemented by the CBRC are intrinsically contra-dictory. This explains the highly unequal access to credit byrural dwellers where fewer than 16% of farmers haveaccessed formal credits and 80% of loans are granted tohigher-income peasants (CBRC, 2006a). Different formsof financial exclusion are linked to a host of interlinkedsocio-economic factors.

Second, the substitution effect of the informal for for-mal loans has led to the proliferation of informal financialinstitutions in China, which could contribute to a signifi-cant increase in hidden NPLs. These informal financialinstitutions are not regulated by the CBRC or local govern-ments and the acceptance of mutual guarantees on a per-sonal basis (i.e., borrowers act as guarantors for eachother’s loans) could plant a time-bomb in rural financing.The recent high-profile bankruptcies of established

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companies involved with informal financial institutionswith hundreds of millions of yuan of loans in Wenzhouand Shanxi drew the attention of the central governmentto the importance of the financial gap in rural China(Wildau, 2014).

This paper has highlighted the complex interlinkedrelationships between different forms of financial exclusionthat could have significant policy implications, not just forthe development of rural banking systems and their impacton the already significant rural–urban divide in China, butalso for the longer-term socio-economic developmentand political stability of poor regions. It is debatablewhether the CBRC and provincial governments’ partialtransfer of responsibilities for financial inclusion andfinancial costs further down to county and local villagegovernments or even NGOs will be effective in thelong-term while the intrinsic conflict between financialviability and financial inclusion remains unresolved.Although microfinance can deal with information asym-metries in credit decisions, it may not be able to resolvethe financial needs of peasant households fully. Forinstance, borrowers could have difficulty making regularweekly repayments due to the seasonal nature of agricul-tural-based incomes, and the cost of attending regulargroup meetings for borrowers living in remote and moun-tainous regions is high (Park & Ren, 2001). The crux ofthe issue is how to close the official rural financing gap of5.4 trillion yuan (US$7977 billion) in 2010 (and 7.6 tril-lion yuan /US$1.24 trillion by 2015), according to theestimates of the China Development Bank, and yetmaintain the viability of the financial system. Thecomplex interlinked relationships between differentforms of financial exclusion demand a holistic policyresponse.

One obviously has to examine the extent of the reorien-tation of resources byRCCs and other types of rural bankinginstitution in greater detail to draw a more definitiveconclusion about the extent of financial exclusion and itspotential impact on the general public. This is especiallythe case when more than 1700 remote villages are still with-out access to banking services inChina after more than threedecades of economic reform and rapid reductions in povertyin general.

ACKNOWLEDGEMENT

The authors are grateful for the insightful comments madeby the reviewers and the editors on an earlier draft of thepaper. They are also grateful to a number of anonymousbankers and individuals who facilitated and participatedthe survey. The authors are responsible for the commentsand remaining omissions.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by theauthors.

FUNDING

The National University of Singapore’s Academic ResearchFund [grant number R-109-000-154-112] financed partsof the first author’s field survey. The second authoracknowledges the financial support from the NationalNatural Science of China [grant number 41425001].

SUPPLEMENTAL DATA

Supplemental data for this article can be accessed at http://10.1080/00343404.2015.1100283

NOTES

1. This and the following subsections rely heavily onYeung (2009a).2. The SOCBs include the ‘Big Four’: the Industrial andCommercial Bank of China (ICBC), the Bank of China(BOC), the China Construction Bank (CCB) and theAgricultural Bank of China (ABC) – and also the Bankof Communications (BOCOM).3. This point was raised by one of the reviewers.4. ETHNIC¼ 1 if it is an autonomous region for an ethnicminority, andREGION¼ 1 in easternChina;REGION¼ 2in central China; andREGION¼ 3 in western China. Mar-ital status is not included as a control variable as the corre-sponding data are not available.5. This important point was raised by one of thereviewers.6. Not confined to granting small loans, the Kwok Foun-dation negotiated a special credit line with the ABC bypaying it a special risk premium of 5% of the loan value(to offset the higher chance of it being NPL) in 2011. Inother words, the foundation acts as an ‘indirect guarantor’of these special loans – it borrows money from the formalbanking system and relends it to local farmers. Thisresulted in a total of 200,000 yuan loans granted to sevenwatermelon famers in 2011 (Field Survey, April 2011).

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