presented by TAO LI. born in Yangling, Shaanxi Province, P.R.China

Transcription

1 EMPIRICIAL STUDIES ON LENDING VOLUME DECISIOINS, THE NUMBER OF LENDING APPROVALS, AND LENDING RATES ATTITUDES: ESTIMATION BASED ON HOUSEHOLD DATA FROM RURAL SHANDONG, CHINA Dssertaton to obtan the Ph. D. degree n the Internatonal Ph. D. Program for Agrcultural Scences n Goettngen (IPAG) at the Faculty of Agrcultural Scences, Georg-August-Unversty Göttngen, Germany presented by TAO LI. born n Yanglng, Shaanx Provnce, P.R.Chna Göttngen, December 2012

2 D7 1. Name of supervsor: Prof. Dr. Stephan von Cramon-Taubadel 2. Name of co-supervsor: Prof. Dr. Olver Mußhoff Date of dssertaton: 6. February 2013

3 ABSTRACT EMPRICIAL STUDIES ON LENDING VOLUME DECISOINS, THE NUMBER OF LENDING APPROVALS, AND LENDING RATES ATTITUDES: ESTIMATION BASED ON HOUSEHOLD DATA FROM RURAL SHANDONG, CHINA By Tao LI Ths study uses household level data collected n Shandong Provnce of Chna to study rural formal fnancal nsttutons lendng volume decsons, the number of lendng approvals, and respondents atttude towards nterest rates on formal loan n Chna s rural settng. The man body of the dssertaton conssts of three separate chapters. The frst chapter of the man body n the dssertaton examnes how rural households characterstcs affect one mportant dmenson of lendng decson-makng practces of fnancal nsttutons. Ths s the decson of how much to lend to a borrower (the volume decson). Ths paper relaxes the restrctve jont decson assumptons of the Tobt model and consder the volume decson of the loans gven by fnancal nsttutons condtonal on, rather than jontly wth, the decson whether to lend to a borrower n the frst place. Ths paper estmates ths model usng a two-step econometrc method and feld survey data from the provnce of Shandong n Chna. Fndngs show that households characterstcs whch affect loan approval dffer from those whch affect decson regardng loan volume. These fndngs thus broaden and deepen the extant studes on formal lendng decsons n rural settngs. The second chapter of the man body n the dssertaton attempts to dentfy sgnfcant household characterstcs that formal lender apply when they classfy formal loan lendng over a perod of years, ths study nterprets all loan rejecton(s) as a zero number and treats at least one tme loan approval as a postve ntegral ( 1). Thus, bnary outcomes are estmated related to lendng numbers over a fve-year perod usng a complementary log-log bnomal model and feld survey data from the provnce of Shandong, Chna. Fndngs mply that the Chnese government should strve to mprove educaton n rural areas and protect land rghts of all farm famles wth the am of ncreasng farmers chances for access to formal loans. These polcy mplcatons thus broaden and deepen the extant studes on formal lendng I

4 decsons n rural settngs. Fnally, the thrd chapter of man body n the dssertaton dentfes the determnants whch affect whether potental borrowers feel that nterest rates on formal loans are too hgh by examnng ther atttudes towards nterest rates on formal loans. The analyss s based on a sample of 290 respondents from rural Shandong n Chna; 22 percent of the respondents feel that nterest rates on formal loans are too hgh. A correctve method for sample selectvty, whch bulds on the work of van de Ven and van Praag (1981), has been appled n probt analyss. Emprcal analyss ndcates that respondents gender and man cash ncome source have mportant nfluences on ther atttudes towards hgh nterest rates recognton. Emprcal analyss ndcates that respondent s gender, man cash ncome source, and famly s total asset value have mportant nfluences on ther atttude towards hgh nterest rates recognton on formal loan. Emprcal fndngs mply that fnancal nsttutons should pay more attenton on borrowers who s female and whose man cash ncome source s farm work, and the government should change the monopolzaton of RCCs by promotng better competton n rural fnancal market. II

5 III Copyrght by TAO LI 2012

6 Ths Dssertaton s dedcated to My Parents Youqun LI & Xhu TANG, who remaned a source of Love, Support and Encouragement throughout! IV

7 ACKNOWLEDGEMENTS The completon of ths dssertaton and the entre graduate program would not have been possble wthout the support and encouragement from several people. I would lke to express my deepest apprecaton to my Major Advsor and Dssertaton Supervsor, Prof. Dr. Stephan von Cramon-Taubadel for hs total dedcaton, gudance and support throughout the PhD program. Many thanks for the detaled comments and suggestons that were nstrumental n completng ths fnal product. I am very grateful to my second supervsor, Prof. Dr. Olver Musshoff. Hs outstandng expertse motvated me very much. I would specally lke to thank Prof. Dr. Achm Spller for agreeng to read the manuscrpt and to partcpate n the defense of ths thess. I am also ndebted to Jun-Professor Xaohua Yu n Goettngen for hs methodologcal gudance, and Professor Rong Kong n Yanglng of Chna for her generous data sharng. Thank you all for your ndvdual contrbuton n brngng ths work to completon. My apprecaton to all former and current colleagues of Char of Agrcultural Polcy n the Department of Agrcultural Economcs and Rural Development. Many thanks for your kndness and help when I joned the kndly and lovely group n October of I am ndebted to the Chna Scholarshp Councl and Goettngen Graduate School of Socal Scences for ther separate 12 and 6 months fnancal assstance for my study. I am also grateful to all persons known and unknown n the department, the faculty, the unversty, the cty, and the country for ther varous knds of help throughout my lvng and study n Goettngen of Germany. My specal grattude goes to my famly, wthout whose love, support and encouragement, ths journey would not have been completed. Many thanks to my parents Youqun LI and Xhu TANG, on whose counsel my very foundaton s buld today. May both of you always stay n healthy and happy! The deepest apprecaton s expressed to my wfe Lhong CHEN for her love and companonshp. My apprecaton to my younger sster Sha LI for ever standng wth me. V

8 TABLE OF CONTENTS Page LIST OF TABLES...Ⅷ LIST OF FIGURES,..Ⅸ LIST OF ACRONYMS.Ⅹ CHAPTER ONE: INTRODUCTION 1 CHAPTER TWO: HOW DO RURAL HOUSEHOLD CHARACTERISTICS AFFECT FORMAL LOAN LENDING DECISIONS? TWO-STEP ECONOMETRIC ESTIMATION BASED ON HOUSEHOLD DATA FROM RURAL CHINA Introducton Background A Bref Introducton to Chna s Agrcultural Lendng Development Trends n Agrcultural Lendng n Recent Decades Theoretcal Framework One-Step Model Two-Step Model Econometrc Methods Probt Model for the Lendng Approval Decson Tobt and Truncated Regresson Models for the Lendng Volume Decson Choce of Econometrc Models Data Results and Dscusson Parameter Estmates of Probt Model Parameter Estmates of Truncated Regresson Model Summary, Conclusons, and Lmtatons References...27 CHAPTER THREE: ECONOMETRIC ESTIMATION OF RURAL HOUSEHOLD S CHARACTERISTICS AFFECTING THE NUMBER OF LENDING APPROVALS Introducton Background A Bref Introducton of Rural Formal Fnance n Chna Trends n Agrcultural Lendng n Recent Decades Theoretcal Framework Econometrc Methods Data Results and Dscusson Conclusons, Lmtatons, and Future Research Drecton..43 VI

9 References..46 CHAPTER FOUR: WHO FEELS THAT INTEREST RATES ON FORMAL LOANS ARE TOO HIGH IN RURAL FINANCIAL MARKETS? ESTIMATES FROM A SURVEY IN RURAL SHANDONG, CHINA Introducton Background Formal Fnancal Insttutons and Interest Rates n Rural Chna Trends n Agrcultural Lendng n Recent Decades Data Conceptual framework Emprcal Model Results and Dscusson Summary and Concluson 64 References...67 CHAPTER FIVE: SUMMARY AND OUTLOOK..70 APPENDIX..72 VII

10 LIST OF TABLES Table 1 (Chapter Two). Table 2 (Chapter Two). Table 1 (Chapter Three). Page Descrptve statstcs of dependent varables and covarates.21 Probt, Truncated regresson, and Tobt models analyss results for the lendng volume decsons 23 Descrptve statstcs of varables 41 Table 2 (Chapter Three). Complementary log-log Model analyss results for the number of lendng approvals..42 Table 1 (Chapter Four). Varable defnton and sample statstcs...55 Table 2 (Chapter Four). Maxmum lkelhood estmates of the parameters and margnal effects answerng the core queston ( δ ) and atttude towards nterest rates on formal loan ( γ ) equatons...63 VIII

11 LIST OF FIGURES Page Fgure 1 (Chapter Two) Agrcultural Loan Balance, the whole Country of Chna, yearly, Fgure 2 (Chapter Two) Agrcultural Loan Balance, Shandong Provnce of Chna, yearly, Fgure 3 (Chapter Two) Decsons of Formal Loan Lendng..12 Fgure 1 (Chapter Three) Agrcultural Loan Balance, the whole Country of Chna, yearly, Fgure 2 (Chapter Three) Agrcultural Loan Balance, Shandong Provnce of Chna, yearly, Fgure 3 (Chapter Three) The Number of Lendng Approvals..35 Fgure 1 (Chapter Four) Agrcultural Loan Balance, the whole Country of Chna, yearly, Fgure 2 (Chapter Four) Agrcultural Loan Balance, Shandong Provnce of Chna, yearly, IX

12 LIST OF ACRONYMS RCCs ABC ADBC RPS GDP RMB CBRC PBOC PSBC AME LR MLE SD OBS - Rural Credt Cooperatves - Agrcultural Bank of Chna - Agrcultural Development Bank of Chna - Rural Postal Savngs - Gross Domestc Product - Renmnb (Chnese money, Yuan) - Chnese Bankng Regulaton Commsson - People s Bank of Chna - Postal Savngs Bank of Chna - Average Margnal Effect - Log Lkelhood - Maxmum Lkelhood Estmaton - Standard Devaton - Amounts of Observatons X

13 CHAPTER ONE CHAPTER ONE INTRODUCTION Developng fnance n rural areas of many developng countres has the potental to expand users opportuntes for more effcent technology adopton and resource allocaton (World Bank, 2008). In Chna, a land that represents about a quarter of the global populaton, many people n rural areas also suffer from nadequate lqudty supples or fnancal servces. If rural resdents lack access to formal credt, then ther ablty to actvely partcpate n and beneft from the development process would be lmted (L et al., 2011). As a response to Chna s current rural fnancng stuaton, t s necessary to understand formal fnancal nsttutons lendng decsons and lendng numbers from specfc perspectves, such as loan volume and the number of loan approvals. Also, t s necessary to dentfy and target those who feel that nterest rates on formal loans are too hgh and dscover how to set them up wth proper fnancal servces. Aganst ths background, the current study uses household level data collected n rural Shandong, Chna. The study has three broad objectves, each of whch consttutes a core chapter topc. The frst objectve s to study lendng volume decsons of formal fnancal nsttutons n rural Shandong, Chna wth a vew to understand the lendng volume process, namely, whether lendng volume decsons are made jontly wth, or condtonal on, lendng approval decsons. The second objectve s to nvestgate the number of lendng approvals by formal fnancal nsttutons n rural Shandong, Chna wth a vew to dentfy sgnfcant household characterstcs that formal lenders apply when they classfy formal loan lendng over a perod of years. And thrd, the study explores rural resdents atttudes towards nterest rates on formal loan (lendng rates of formal loans), wth the purpose of lookng for those who feel that nterest rates on formal loan are too hgh. The Chapter Two ndcates that households characterstcs whch affect loan approval dffer from those whch affect decson regardng loan volume usng a two-step econometrc method. The Chapter Three mples that the Chnese government should strve to mprove educaton n rural areas and protect land rghts of all farm famles wth the am of ncreasng 1

14 CHAPTER ONE farmers chances for access to formal loans usng a complementary log-log bnomal model. The Chapter Four shows that respondent s gender, man cash ncome source, and famly s total asset value have mportant nfluences on ther atttude towards hgh nterest rates recognton on formal loans usng a correctve method for sample selectvty. These three core chapters related to studyng formal loans, to some extent, complement each other. Lendng volume decsons are comprsed of decdng whether to lend n stage one, and the decson of how much to lend (.e., volume) n stage two. Hence, studyng the number of lendng approvals s helpful to understand lendng volume, namely, what are the factors affectng the decson of lendng approval? Correspondngly, studyng lendng volume decsons s helpful to understand the mportance of lendng approval on the entre process. Studyng lendng rates also ndrectly helps to understand why fnancal nsttutons gve zero lendng approval and zero lendng volume to all rural farmers on the bass of ther censorng work. Ths s possbly because hgh lendng rates make lenders suspect the borrower s capablty of punctual loan repayment. Correspondngly, the study of lendng volume and the number of lendng approvals ndrectly shows that the lendng rate s an mportant determnant on lendng practce. Note, however, that the data used n the study needs to be vewed and acknowledged n lght of ts lmtatons. Frst, all respondent rural household characterstcs were gathered after the fve-year perod n whch loans were granted, but the ntal decson to approve the frst loan was made on the bass of the rural households characterstcs at the begnnng of the fve-year perod. Clearly, there s a potental endogenety problem assocated wth explanng past loan on the bass of current characterstcs. For example, a household s current asset stuaton s presumably a functon of how many loans that household has receved n the past, and the volumes of these loans. In other words, a household s assets wll tend to be hgher f a household has receved a loan. Hence, a covarate asset used n the thrd chapter, s suspected of endogenety, whch could make the fndngs n ths artcle based and nconsstent. Second, data lmtatons also lmts the number of household characterstcs that I can employ as covarates n my varous estmaton, and thus, some valuable fndngs mght be omtted. There are many varables that were collected n the survey that I would lke to use because they are clearly related to loans, such as the number of laborers for farm producton 2

15 CHAPTER ONE and the number of laborers for non-farm producton n survey perod, but that I cannot use because of the endogenety problem. 3

16 CHAPTER ONE References: World Bank (2008). World Development Report 2008: Agrculture for Development. The World Bank. k.pdf L, X., C. Gan, B. Hu (2011). Accessblty to Mcrocredt by Chnese Rural Households. Journal of Asan Economcs. 22(3):

17 CHAPTER TWO CHAPTER TWO HOW DO RURAL HOUSEHOLD CHARACTERISTICS AFFECT FORMAL LOAN LENDING DECISIONS? TWO-STEP ECONOMETRIC ESTIMATION BASED ON HOUSEHOLD DATA FROM RURAL CHINA 2.1. Introducton Formal loans from fnancal nsttutons are an mportant element whch serve agrcultural producton and mprove the lvng standards of rural households n extensve rural areas of developng countres. In Chna, a land whch has been undergong a seres of reforms of all segments and areas snce 1979, enormous changes have taken place n the rural fnancal market. In partcular, dfferences n regonal economc structures have led to changes n rural fnance demand n dfferent regons of the country (Xe et al., 2005). For example, n Shandong Provnce, whch s one of the developed coastal areas of Chna, rural households demand for formal loans for nonfarm actvtes s soarng. Many rural households, whch are manly smallholders, not only need lqudty for seasonal producton and current consumpton but also to fnance non-farm nvestments, constructon and ceremonal socal events (e.g., weddngs). To meet these dfferent knds of captal demand for both farm and nonfarm needs, Chna s rural fnancal nsttutons, whch are managed by the People s Bank of Chna (Central Bank of Chna), channel large volumes of fnancal captal to the country s agrcultural sector and rural areas as part of a comprehensve polcy amed at boostng the ncomes and welfares of rural households. Snce formal lendng n rural areas has changed over the last several decades from a demand perspectve, studes on formal loan lendng practces deserved more attenton n Chna and other developng countres. Prevous lterature manly sheds lght on agrcultural lendng because most formal loans were granted for agrcultural producton n rural areas 1. Past research has manly focused on 1 In fact, loans from formal fnancal nsttutons for farm and non-farm purposes n rural areas are often subsumed under agrcultural lendng n Chna. I dscuss ths n more detal later. To emphasze the source of loans, I use formal loan nstead of agrcultural lendng n ths artcle. 5

18 CHAPTER TWO fve aspects of agrcultural lendng. The frst aspect addresses the agrcultural lendng decson process. Stover et al. (1985), Featherstone et al. (2007) and Olagunju (2010) examne the agrcultural loan decson process and analyze the factors from the perspectve of the ndvdual loan offcer or fnancal nsttuton. The second aspect deals wth the testng of credt scorng or rsk models of the lendng decson. Hardy and Weed (1980), Barry et al. (1981), Thomas (2000), Zech and Pederson (2004), and Katchova and Barry (2005) examne such models whch are used to estmate loan requrements and potental rsks for lenders and help them decde whether or not to grant a loan to applcants. The thrd aspect addresses how nternal and external characterstcs of fnancal nsttutons themselves affect agrcultural lendng, Studes such as Mazzocco (1991), Bard et al. (2000), and Ahrendsen (2003) are representatve of ths branch of the lterature. The fourth aspect explores the relatonshp between the nformal and formal lendng sectors n developng countres. For example, Turvey and Kong (2010), Gurknger (2006), and Ghate (1992) examne the relatonshp between nformal and formal lendng n Chna, Peru, and Asan countres, respectvely. The ffth aspect models the role of the nformal and formal credt sectors (lenders) n developng countres. Boucher and Gurknger (2007) and Boucher et al. (2008) show that asymmetrc nformaton between lenders and borrowers can oblge formal lenders to rely on collateral to solve the moral hazard and adverse selecton problems nherent n credt transactons. Whle the lterature provdes many nsghts nto formal lendng, one mportant aspect of lendng decson-makng practces has receved very lttle attenton to date: Is the lendng volume decson condtonal on or taken jontly wth the lendng approval decson? In ths artcle, I attempt to answer the queston so as to broaden and deepen the prevous research on the lendng decsons. The lendng process generally consst of the followng stages: 1) Examnaton and approval of a loan applcaton. At ths stage the lender carefully examnes a loan applcant s qualfcaton so as to determne whether to lend to ths applcant or not. 2) Loan contractng and dstrbuton. At ths stage the lender determnes the volume of an approved loan and dstrbutes t to the applcant. Accordngly, the lendng decsons are made up of the decson of whether to lend (.e., the approval decson n stage 1) and the decson of how much to lend (.e., the volume decson n stage 2). 6

19 CHAPTER TWO These two lendng decsons are modeled jontly n most prevous analyses usng sngle equaton methods, such as the Tobt model, based on one-step emprcal estmaton (see, for example, Featherstone et al. (2007) and Olagunju (2010)). Ths assumes that the lender decdes whether and how much to lend smultaneously, and that both decsons are nfluenced by the same factors and n the same drecton (Ln and Schmdt 1984). In other words, a covarate that ncreases the probablty of loan approval also ncreases the loan volume. Ths hypothess may be restrctve. For example, women are often vewed as more rsk-averse than men n regards to economc actvtes (see, for example, Fletschner and Kenney, 2011; Gockel, 2009; etc.). For ths reason, the desred loan volume of female-headed households s possbly less than male-headed. In practce, each lendng nsttuton has ts own benchmark yeld, whch s the lowest proft from lendng and s determned by the loan s nterest proceeds and lendng costs. Under the premse of securng loan repayment capacty, the loan s nterest proceeds depend on the volume of loan, the level of nterest rate, and the terms of the loan. Obvously, f the volume of a loan that has been appled for s too small, t mght be dffcult for the fnancal nsttuton to reach the benchmark yeld and t mght therefore decde not to approve the loan. Therefore, f faced wth loan applcatons from both female-headed and male-headed households wth otherwse dentcal repayment capactes, nterest rates, loan terms, and lendng costs, but wth dfferent desred loan volumes, a lender wll be more lkely to lend to a male-headed household. In other words, all other thngs beng equal, t may be more dffcult for female-headed households to obtan loan approval from a fnancal nsttuton even f ther credtworthness s good. However, f a loan applcaton by a female-headed household s approved, the lendng nsttuton mght be more lkely to decde to provde the loan volume that she has appled for than n the case of a male-headed household. Ths s because emprcal research has shown that women play a more actve role regardng loan usages, whch has a postve effect on loan repayment (see, for example, Ptt and Khandker, 1998; D Espaller et al., 2011; etc.). Hence, t s possble that female-headed households wll be less lkely to receve loans, but more lkely to borrow larger volumes f ther loan applcatons are approved. If true, the covarate female-headed household (.e., the gender of household head) wll affect loan approval and loan volume n dfferent drectons. My contrbuton to the lterature, therefore, s that I model approval and volume 7

20 CHAPTER TWO decsons separately. I test the null hypothess of jont versus ndependent approval and volume decsons to determne whch hypothess s sutable for my dataset and econometrc analyss. To sum up, the prmary goal of ths artcle s to propose a two-step econometrc model to model how households characterstcs affect lenders decsons to approve loan applcatons, and determne the volumes of these loans. I test ths model usng feld survey data from the Provnce of Shandong n Chna. A two-step econometrc model captures the characterstcs whch nfluence lenders decsons to approve loans to rural households usng a bnary sub-model and, condtonal on the loan approval, the characterstcs that nfluence ther decsons on loan volume usng a condtonal truncated sub-model. The rest of ths paper s structured as follows. In the followng secton 2 I brefly ntroduce some background nformaton of formal loans n rural Chna. In secton 3 I develop a theoretcal framework, whch bulds on the work of Katchova and Mranda (2004), to llustrate decsons of formal loan lendng. In secton 4 I present and estmate the one-step and the proposed two-step models for the loan approval and volume decsons, and compare these two models. In secton 5 I descrbe the data. I present and dscuss the results n secton 6, and conclude n secton Background A Bref Introducton to Chna s Agrcultural Lendng Development Chna s formal fnancal system n rural areas can trace ts orgns back to the 1950s 2. At that tme, Rural Credt Cooperatves (RCCs), located n nearly every townshp (of whch there were over 30,000), were set up by the government to serve the credt needs of producers. In the centrally planned economc era (before 1978), these RCCs were desgned to channel 2 Offcally, rural fnancal nsttutons n Chna do not nclude nformal fnancal nsttutons, as these nsttutons are nether regulated nor supervsed by the People s Bank of Chna (PBC, Central Bank of Chna) and the deposts wth most of these nsttutons are not protected by the state (Shen and Cheng, 2004). Therefore, n ths paper, rural fnancal nsttutons refer to formal rural fnancal nsttutons only. I do not consder those sem-formal rural fnancal nsttutons, such as credt-only mcrocredt companes, whch are purely prvate fnancal nsttutons that fall n between formal and nformal fnancal nsttutons. 8

21 CHAPTER TWO necessary credt to the agrcultural sector so that t could provde cheaper gran and other raw nputs to support the development of the captal-ntensve ndustral sector (Shen and Cheng, 2004). In 1984, accompaned by economc reforms ntated n the late 1970s, the Chnese government started to reform ts bankng system. However, the reform process was very slow, especally as concerns the rural fnancal system. A comprehensve fnancal sector was not establshed untl the md-1990s. Then the formal fnancal sector n rural Chna was made up of the Agrcultural Bank of Chna (ABC), the ADBC, RCCs and Rural Postal Savngs (RPS), where ABC lent to agrcultural enterprses, rural cooperatves, and vllage organzatons, but usually not to ndvdual rural households, and RPS were the only depostory fnancal nsttutons. Moreover, durng the md-1990s, two man changes n agrcultural lendng were observed (Shen and Rozelle, 2004). Frst, banks began to depend more on collateral to secure loans. Second, there was a sharp shft n the lendng preferences of banks n favor of prvate frms ncludng almost all rural enterprses and many prvatzed small and medum state-owned enterprses. Durng ths perod, formal nsttutons (RCCs and banks) were reluctant to lend to small farmers due to the hgh admnstratve costs and rsks of such loans and a low celng on nterest rates set by the central bank (Cheng and Xu, 2003). Begnnng n 2001, Chna ntated a campagn to ncrease RCC lendng to small farmers, and lendng accelerated further followng the ssuance of a Number 1 Document n 2004 statng the government s ntent to rase rural ncomes and boost gran producton (Gale and Collender, 2006). Currently, RCCs play an overrdng role n the rural fnancal system n Chna (Guo and Ja, 2009), and more than 80 percent of formal agrcultural loans n Chna are made by the country s 30,000-plus Rural Credt Cooperatves (RCCs) (Gale and Collender, 2006). In addton, loans whch are classfed as agrcultural are ncreasngly used for nonagrcultural purposes such as house constructon, school fees, health care costs or nonfarm busness expenses, etc. Accordng to the People s Bank of Chnas gudelnes, mcro loans may be used for agrcultural producton, purchase of small farmng machnery; servces before, durng or after agrcultural producton; or housng, medcal servce, educaton and consumpton (Peoples Bank of Chna, 2001). 9

22 CHAPTER TWO To sum up, n recent decades the Chnese fnancal system has bult up a plentful supply of captal for agrcultural lendng. Chnese polcymakers are ncreasngly channelng lendng to smallholder farmers and agrbusness Trends n Agrcultural Lendng n Recent Decades Fgures 1 and 2 below respectvely show the balance of agrcultural loans for the whole country and the Shandong Provnce of Chna from 1979 to As shown n the Fgures, the balance of agrcultural loans n the whole country and the Provnce of Shandong ncreased very slowly and gently durng the frst 16 years from The relatve shares of agrcultural loans as a proporton of total loans fluctuated wthn a relatvely broad brand and dsplays a rough downtrend as agrculture s share of total GDP declned. However, the balance of agrcultural loans for the whole country and the Provnce of Shandong ncreased durng the perod. The agrcultural loan balance of the whole country and the Shandong Provnce respectvely reached the equvalent of 21,623 bllon Yuan (RMB) 3 and 296 bllon Yuan n 2009, up 19,704 bllon Yuan and 271 bllon Yuan snce 1996, respectvely. In 2009, the agrcultural share of all loans for the entre country and the Provnce of Shandong respectvely was 5% and 11%, a 2 and 4 percentage-pont ncrease from 1996, respectvely. Snce Shandong Provnce s one of Chna s most prosperous agrcultural regons, t s not unusual that the agrcultural share of all loans s hgher than the average level of the whole country snce the md-1990s, when the reform of the bankng system started to commercalze and modernze (see Fgures 1 and 2). Hence, Shandong provnce provdes an nterestng settng for studyng formal loan lendng practces n rural Chna. 3 1 Yuan s roughly equvalent to 0.15 $ USD. 10

23 CHAPTER TWO Agrcultural share of all loans (Chna,the whole country, left axs) Agrcultural Loan Balance (Chna,the whole country, rght axs) Percent Bllon Yuan (RMB) Year Fg.1. Agrcultural Loan Balance, the Whole Country of Chna, Yearly, Source: Chna Statstcal Yearbook (Varous Issues) Agrcultural share of all loans (Chna, Shandong Provnce, left axs) Agrcultural loan balance (Chna, Shandong Provnce,rght axs) Percent Bllon Yuan (RMB) Year Fg.2. Agrcultural Loan Balance, Shandong Provnce of Chna, Yearly, Source: Shandong Provnce Statstcal Yearbook, Chna (Varous Issues) 2.3. Theoretcal Framework Informaton and enforcement problems cause credt market mperfectons and even the complete falure of these markets (Gurknger and Boucher, 2008). Such loan constrants are especally lkely to arse under the condtons prevalng n developng countres (see, for example, Connng and Udry, 2005). I consder three lendng decsons for any loan applcaton: full approval, part approval, and rejecton. A household experences full approval f ts desred loan s approved n full by the lendng nsttuton. If ts desred loan s completely refused, the household suffered from loan constrants. In between these two stuatons, I refer to partal approval f t s desred loan s partally approved. In Fgure 3 I assume that fnancal nsttutons employ an underlyng ndcator to judge 11

24 CHAPTER TWO each formal loan applcant (rural household), and there s a dstrbuton of these ndcators across all applcants. I call ths ndcator the applcant s lqudty condton, whch s based on hs or her qualtatve and quanttatve characterstcs. The fnancal nsttutons have to measure ther expected gans and losses by carefully assessng each applcant s lqudty condton. Above an upper lqudty condton threshold, the fnancal nsttuton s expected utlty from complete full loan approval exceeds that from partal loan approval or rejecton. Below a lower lqudty condton threshold, rejecton takes place. In between these thresholds, the fnancal nsttuton s utlty from partal loan approval exceeds that from ether full approval or rejecton. Densty Lower threshold Upper threshold Lqudty condton rejecton part approval full approval Fg.3. Decsons of Formal Loan Lendng I propose a two-step theoretcal model of formal lendng decsons whch bulds on Katchova and Mranda (2004). Consder a fnancal nsttuton, ndexed by, that provdes a formal loan to a rural household j. If the rural household submts one loan applcaton, the fnancal nsttutons has to decde whether to approve ths applcaton, and how much of the desred loan to approve α. In ths artcle, I consder two estmaton models: a conventonal one-step model, whch assumes that the loan approval and lendng volume are decded smultaneously, and a two-step model, whch assumes that the lendng volume process s made condtonal on the process of loan approval. 12

25 CHAPTER TWO One-Step Model Assume a fnancal nsttuton chooses α to maxmze expected utlty of profts for one lendng decson: Max α 0 EU ( α R T b v b f ) (1) where α s the volume of granted loan, R s nterest rate for the granted loan, T s the term of the granted loan, α R T are the total expected proceeds for the lendng decson, v b s the possble expendture or loss for loan lendng such as the costs of screenng a loan applcaton, f b s the necessary cost for loan lendng such as the salary of loan offcers 4. Maxmzaton of expected fnancal nsttuton utlty yelds expressons related to the loan applcant s ( j ) household characterstcs, whch can embody the potental ablty of creatng gans for usng loans and guaranteeng loan repayment at a due date. Therefore, α s related to x j, a set of observable rural household s characterstcs such as the educaton level of the household head, land sze, number of laborers, etc. Formally, α = g β, ε ), ( α α α x j where β α s a vector of parameters, and ε α captures unmeasured factors related to formal loan lendng (Katchova and Mranda, 2004). In the one-step model, the fnancal nsttuton s decson to lend or not s equvalent to a soluton n whch at least one of the lower bounds s bndng: α = 0. The approval decson s modeled jontly wth the volume decson ( α ) n the sense that the same covarates (x j ) and the same coeffcents ( β ) affect both decsons (Katchova and Mranda, 2004). α 4 In practce, for the collateral whch s used for a loan applcaton, the market value of pledged asset P usually equals or exceeds the loan amount and ts nterest profts,.e., P α + αrt. However, for smplcty, I assume that P = α + αrt so that functon (1) s feasble. In addton, for parsmony, I omt the proceeds from the fees that all applcants must pay because such proceeds are usually very small. 13

26 CHAPTER TWO Two-Step Model In the two-step model, the volume decson s condtonal on the loan approval decson. The fnancal nsttuton frst decdes whether to approve a loan to applcant j, and f approval s chosen ( c = 1), then the optmal volume n the lendng practce ( a ) s chosen accordng to: v f v f Max EU (1 c )( ) + ( ) b b c Max αrt b b c = 0,1 α > 0 (2) In the two-step estmaton model, c = 0 ndcates loan rejecton and s assocated wth the frst term n the dscrete maxmzaton operaton, and c = 1 ndcates a lendng approval decson and s assocated wth the second term n the dscrete maxmzaton operaton. There s a trade-off between obtanng expected proceeds and coverng the costs assocated wth the lendng approval. As n the one-step model, maxmzaton of the expected utlty of proceeds n (2) yelds expressons that relate the lender s volume decson ( α ) to x j, a set of observable rural household s characterstcs. However, maxmzaton of expected utlty of proceeds also yelds expressons that relate the lender s approval decson ( c ) to a possbly dfferent set of observable rural household s characterstcs z j n the two-step model. Formally, c = h γ, ε ), where γ s a vector of parameters and ε c represents unmeasured ( c z j factors related to the decson of lendng approval (Katchova and Mranda, 2004). If the same covarates (z j and x j ) and the same coeffcents γ and β ) determne the ( α loan decson ( c ), and the volume decsons( α ), then the one-step and two-step models are dentcal. The two-step model allows the same covarate to affect lendng approval and the volume decsons n dfferent ways (va dfferent coeffcents) and dfferent covarates to affect the two decsons (Katchova and Mranda, 2004). 14

27 CHAPTER TWO 2.4. Econometrc Methods Before addressng estmaton and functonal form ssues I address the problem of data truncaton due to a large percentage zero lendng volumes (.e., households whose applcatons are rejected). Ths truncaton problem dffers from the sample selecton ncdental truncaton problem that s usually corrected usng Heckman s two-step procedure (Greene, 2000; Katchova and Mranda, 2004). Heckman s two-step model s not sutable here because the loan volumes for rural households whose applcatons are rejected are equal to zero rather than beng unobserved (Ln and Schmdt, 1984). Hence, followng the theoretcal framework, I outlne the emprcal models used to determne the households characterstcs whch nfluence lenders approval and volume decsons. The approval varable c s a dscrete two-choce varable, the volume of granted loan α s a nonnegatve varable. Ths leads to dfferent econometrc models for c and α. The loan approval decson c s modeled wth a probt model. The granted loan volume decson α s modeled jontly wth the approval decson usng Tobt model. The granted loan volume decson s modeled condtonal on the loan approval decson usng a truncated regresson. I assume that the dsturbances n the econometrc models of the decson of lendng approval, c and the lendng volume α are ndependent. Ths assumpton has been mplctly mantaned n the lterature when separate models are estmated wth any two varables ( c, α ) as a dependent varable (Katchova and Mranda, 2004). If a lender (formal fnancal nsttuton) decdes not to grant a loan to an applcant (rural household) ( c = 0 ) then automatcally α = 0, whereas f a lender decdes to grant a loan ( c = 1) then α > 0. Let Y equal α. For a lender wth c = 0, the lkelhood contrbuton s P ( = 0), whereas for a lender wth c = 1 c, the lkelhood contrbuton s P ( c 1) f ( Y c = 1) = P( c = 1) f ( Y Y > 0), where P( ) s the probablty and f ( ) s the = 15

28 CHAPTER TWO probablty densty functon (Katchova and Mranda, 2004). Gven the ndependence of α (condtonal on c = 1), the jont densty f ( Y Y > 0) factors nto f ( α α > 0) (Katchova and Mranda, 2004) Probt Model for the Loan Approval Decson The dscrete choce of whether to approve or to reject a loan applcaton ( c ) can be estmated wth a probt model or a logt model. Followng Katchova and Mranda (2004), I employ the followng probt model: P c = 1) = Φ( γ ' z ) ( (3) where Φ( ) s the standard normal cumulatve probablty dstrbuton (cdf), z s an R 1 vector of household s characterstcs for rural household j, and γ s a vector of coeffcents Tobt and Truncated Regresson Models for the Lendng Volume Decson The dscrete decson of whether to approve a loan and the contnuous decson of the volume to lend s estmated usng a Tobt model (see, for example, Featherstone et al. (2007)). The Tobt model assumes that a latent varable α s generated by: α = β α + ε * x α (4) where x s an S 1vector of rural households characterstcs for lender, β α s vector of coeffcents, and εα are ndependently and normally dstrbuted wth mean zero and varance 2 σ (Katchova and Mranda, 2004). If α s negatve, the observed volume of lendng, α, s 16

29 CHAPTER TWO zero. When α s postve, α. = α The probablty that the volume of lendng s zero s: P( β x = 0) = Φ( ) σ α α (5) and the densty for the postve values of α s: f ( α α > 0) = 1 α β α x φ( ) f ( α) = σ ( 0) β σ P α > α x Φ( ) σ (6) where φ( ) s the standard normal probablty densty functon (pdf) (Katchova and Mranda, 2004). Equaton (5) represents the lendng approval decson, and s a vald probt model f consdered separately from equaton (6). Equaton (6) represents a truncated regresson for the postve values of the contnuous loan volume decson ( α > 0). The Tobt model arses when the approval decson n equaton (5) and the loan volume decson n equaton (6) have the same varables x j and the same parameter vector β α (Katchova and Mranda, 2004). In the Tobt model, a varable that ncreases the probablty of approval wll also ncrease the mean of volume granted (Ln and Schmdt, 1984). As shown n Katchova and Mranda (2004), the log-lkelhood for the Tobt model conssts of the probabltes for non-approval and a classcal regresson for the postve values of α : InL = X βα InΦ( ) + σ α = 0 α > 0 1 α X βα In φ σ σ (7) Cragg (1971) relaxed the assumpton that the same covarates and the same parameter vector affect both the loan approval decson and the loan volume decson. Followng Cragg, I 17

30 CHAPTER TWO consder a hurdle model n whch a lender makes a two-step decson. In the frst step, a probt model represents a lender s choce of whether to approve a loan ( c ): P c = 0) = Φ( γ ' z ) ( (8) If ths hurdle s crossed and c =1, a truncated regresson (equaton (6)) descrbes the choce of how much loan to lend ( α > 0). The log-lkelhood n Cragg s model s thus a sum of the log-lkelhood of the probt model (the frst two terms) and the log-lkelhood of the truncated regresson model (the second two terms) (Katchova and Mranda, 2004): InL = 1 ( ) α ( ) [ ( ) [ βα x βα x InΦ γ z + Φ In γ z + In φ InΦ ]] = 0 > 0 α σ σ σ c (9) Choce of Econometrc Models A choce between the Tobt specfcaton and Cragg alternatve s a test of the restrcton z j =x j and γ = β / α σ n equaton (9) (Ln and Schmdt, 1984; Green, 2000;Katchova and Mranda, 2004). Gven z j =x j as the frst condton, the second condton γ = β / α σ s a testable 18 restrcton. Thus, testng the Tobt model aganst the more general Cragg model nvolves the followng hypotheses: H 0 : Tobt, wth the lkelhood functon n equaton (7) H 1 : Cragg model (probt and a truncated regresson estmated separately), wth the lkelhood functon n equaton (9). Furthermore, the Tobt model can be tested aganst the Cragg model by estmatng a probt, a truncated regresson, and a Tobt model wth the same covarates (x j ), and by computng the followng lkelhood rato statstc:

31 CHAPTER TWO λ = 2( InLprobt + InLtruncated regresson InLTobt ) (10) where λ s ch-square dstrbuted under the null hypothess wth degrees of freedom equal to the number of covarates ncludng a constant (Green, 2000;Katchova and Mranda, 2004) Data The data used n ths paper were collected n Shandong Provnce (Chna) n July 2010 by face-to-face ntervews. 394 rural households were randomly selected from 5 vllages n one town (Man Zhuang) and one county (Nng Yang). The survey collected detaled nformaton on households formal borrowng actvtes over the last 5 years (.e., from July 2006 to July 2010) as well as households basc characterstcs. Durng the survey, respondents dentfed whether ther loan applcatons had been approved by fnancal nsttutons n the past fve years, and the loan volume they was last granted. The survey nformaton gathered also ncludes more general quanttatve and qualtatve rural households characterstcs whch are consdered mportant determnants of formal loan lendng. Overall, 204 households were elmnated because they dd not partcpate n my survey or dd not apply for a formal loan durng the past fve years. Ths left 190 households that had appled for at least one formal loan. Of these, a further 22 households had to be removed snce the nformaton they provded was ncomplete. Of the remanng 168 households that had appled for a formal loan n the past fve years, 85 were approved by fnancal nsttutons to receve a loan. Among the 85 households wth approved loans, 60% appled for a farm loan, for example for commercal greenhouse vegetable producton or purchasng a tractor, etc. The other 40% of these households appled for non-farm loans, such as for nonfarm busness actvtes, medcal care, chldren s educaton, house or car purchases, etc. Ths shows that formal loan lendng n the rural areas of Shandong Provnce of Chna s not confned to agrcultural lendng. The average volume of the dsbursed loan volume for the 85 households 19

32 CHAPTER TWO s thousands Yuan. Snce the perod span related to the formal loan survey was fve years, a loan for an ndvdual respondent household mght be dsbursed by a fnancal nsttuton to outsde of the perod studed. For the purpose of achevng accurate nformaton, the survey collected current respondent household nformaton wthn the survey perod, namely, a retrospectve survey related to respondent s hstorcal household nformaton was not conducted because many of those randomzed respondents, especally when they are not household heads, rarely know the full households nformaton hstory. For the sake of developng the emprcal analyss, however, t s thus very mportant to pck approprate covarates from the collected nformaton. A few of the covarates, whch not only represent socoeconomc characterstcs of rural households but also are exogenous, are hypotheszed to be relevant n the loan approval and loan volume decsons. These nclude zero-one ndcators for whether the household head s female, and has non-farm producton sklls. Also, n Chna, non-farm skll does not always refer to those professonal sklls that need professonal tranng and study, some smple physcal labor jobs, such as santaton worker, are vewed as non-farm skll as well. Some respondents mght say that the head of hs or her household has no non-farm skll, the plausble reason s ether that the household head s born wth a physcal dsablty or that the household head s too old for farm work (aged over 65 years old). Not many household heads n rural Chna absolutely dd not have non-farm skll, of course. Therefore, these two zero-one ndcators are approprate for the study and are exogenous for the number of lendng approvals analyss. The average age of a household head over a perod of years reflects that household s structure. Household structures was dvded nto four man types on the bass of age: (1) young household, defned as a household head s age between 18 and 35 years old; (2)mddle-aged household, defned as a household head age between 36 and 50 years old; (3)elderly household, defned as a household head s age between 51 and 65 years old; and (4)very elderly household, defned as a household head aged 66 years old or over. Hence, every respondng household can be easly determned on the bass of the dvson of household structure for the emprcal analyss. In Chna, farmland s stll owned and controlled by the state and leased to farmers. 20

33 CHAPTER TWO Farmers can t sell t, and they can t use t for collateral on a loan. In 1997, rural households were allowed to sgn a long-term lease of 30 years for the rght to work a plot, whch was allocated accordng to shares n vllage populatons. Hence, t s reasonable to beleve that the amount of farm land of every respondent household could not change from 2006 to 2010 due to undated sgned leases n the survey area. Therefore, the covarate land s exogenous and ntroduced nto the analyss. The household head s educaton level was also consdered exogenous because almost no household heads n rural Chna underwent educaton-change over the study perod, hence, t can also be ntroduced nto the emprcal analyss. Addtonally, the covarate number, whch s total number of loan approval except the last one n the fve-year perod, s also used n the analyss. Overall, all dependent varables and covarates are lsted and descrbed n Table 1 below. Table 1: Descrptve statstcs of dependent varables and covarates Varables Defnton Mean Std.Dev. Obs. Dependent Varables Approval Whether to approve to grant a loan to an applcant over the last fve years Volume The loan volume they were last granted over the last fve years n Yuan (thousands) Zero loan volume Postve loan volume Covarates Gender Whether the household head s female (female=1, male=0) YongHH 1 f household head s age s between 18 and years old; otherwse=0 MddleHH 1 f household head s age s between 36 and years old; otherwse=0 ElderHH 1 f household head s age s between 51 and years old; otherwse=0 Educaton Household head s educaton level n years Nonfarmskll Whether household head has non-farm skll (yes=1, non=0) Land Amount of farm land n Mu (1 Mu 0.16 Acre) Number The total number of lendng except the last one over the last fve years

34 CHAPTER TWO 2.6. Results and Dscusson All of the alternatve model specfcatons outlned above were estmated usng maxmum lkelhood methods. Intally, consderng that some rural households mght apply and be granted a loan only once over the last fve years, hence, the covarate Number s used on dsbursed loan volume decson rather than lendng approval decson analyss. Overall, the same set of covarates was used n all cases exceptng the aforementoned excepton. The Tobt model s frst tested aganst the more general Cragg specfcaton of a separate probt model and a truncated regresson model usng the test statstc n equaton (10). The lkelhood rato test statstcs of leads us to reject the Tobt model n favor of the Cragg model. Hence, the same household characterstcs do not nfluence both the lendng approval decson and the dsbursed loan volume decson n the same way va the restrcted coeffcents n the Tobt model. I therefore focus on the sgns and margnal effects of the covarates n the Cragg model n the followng. Results of the probt model for the loan approval decson, and the truncated regresson and Tobt models for the loan volume decson, are gven n Table 2. Both parameter estmates and average margnal effects (AMEs) are reported. In addton, the Cragg model can also be obtaned wth the craggt command n Stata, descrbed n Burk (2009). The process of craggt command ncorporates a probt model n the frst ter and a truncated regresson model n the second ter, hence, the emprcal results are same wth the result from the probt and truncated models. I report the values of Wald ch-square, Sgma, and Log Lkelhood from craggt command, whch ndcates Cragg s model fts my data as a whole, for emprcal results complement at the row bottom of Table 2. 22

35 CHAPTER TWO Table 2: Probt, truncated regresson, and Tobt model results for the loan approval and loan volume decsons Covarates Parameter Estmates Cragg Model Probt (loan approval) Truncated Regresso n (loan volume) Tobt Model (loan approval and volume) Average Margnal Effects (AMEs) Cragg Model Probt (loan approval) Truncated Regresso n (loan volume) N=168 N=85 N=168 N=168 N=85 Gender (0.98) (85.86) YoungHH (0.81) (104.95) MddleHH (0.69) (101.13) ElderHH (0.72) (103.52) Educaton 0.14 *** ** (0.01) (4.72) Nonfarmskll 0.82 ** * (0.35) (22.05) Land 0.41 *** (0.05) (2.85) Number 4.84 (11.69) Constant *** (0.84) (137.50) Sgma *** (21.27) (20.15) 6.83 (18.12) (18.67) 5.69 *** (1.20) *** (7.06) 4.35 *** (0.96) 3.41 (4.54) *** (22.35) (2.63) (10.34) Wald ch 2 (8) 6.81 LR ch 2 (7) *** LR ch 2 (8) *** Pseudo R Log Lkelhood Model Pearson Test Ch2(91)= * % correct predctons 59 Craggt: Sgma=44.82 *** (10.34) Wald ch 2 (7)=71.15 *** Log Lkelhood= (0.18) 0.13 (0.15) (0.13) (0.13) 0.03 *** (0.01) 0.15 ** (0.06) 0.08 *** (0.004) (85.86) (104.95) (101.13) (103.52) ** (4.72) * (22.05) (2.85) 4.84 (11.69) (137.50) Note: *, ** and *** ndcate sgnfcance at the 10%, 5%, and 1% levels, respectvely; standard errors n parentheses. coeffcents, AME values, and ther correspondng standard error obtaned by margns command n Stata. 23

36 CHAPTER TWO Parameter Estmates of Probt Model The results of the probt model ndcate that households wth a head who has a hgher educaton level and non-farm labor sklls are more lkely to be approved for a loan. Hgher educaton ncreases productvty so that more value can be created, and havng non-farm labor skll means more ncome sources. Thus, these two characterstcs have strong postve effects (.e., sgnfcance at 1% and 5% levels, respectvely) on the probablty of loan approval. In addton, as can be seen n column 2 of Table 2, the covarate land has a statstcally sgnfcant postve effect (sgnfcance at 1% level) on the loan approval decson. More land means that loan applcants potental productvty or rent proceeds can be ncreased; hence, t s one ndcator of repayment capacty Parameter Estmates of Truncated Regresson Model The parameter estmates for the truncated regresson model for the loan volume decson show that the household head s educaton level and whether or not he/she has non-farm producton sklls have sgnfcant postve effects (sgnfcance at 5% and 10% levels, respectvely) on the loan volume. Hence, havng a household head wth a hgher educaton and non-farm producton sklls ncreases the probablty of loan approval and the volume of the approved loan. Note that the coeffcents of the Tobt model n Table 2 usually dsplay about the bascally same sgn and sgnfcance as the coeffcents n the probt model. Ths ndcates that the jont decson of loan approval and loan volume n the Tobt model s heavly nfluenced by the lendng approval decson. These results hghlght the mportant advantage of the Cragg model that t allows (possbly dfferent) covarates to have dfferent effects on the loan approval and loan volume decsons. In addton, column 5 and 6 of Table 2 are the average margnal effects (AMEs) of the probablty of loan approval and loan volume decsons, respectvely. An average margnal effect s an estmate of a populaton-averaged margnal effect. When comparng the truncated regresson wth the probt models, not only does the sgnfcance of a few covarates AMEs n 24

37 CHAPTER TWO the truncated regresson model dsappear, but there are only two covarates nfluence the decson of loan volume n a same way from the decson of loan approval. Hence, these fndngs reconfrm the advantage of the Cragg model whch has already been establshed above, and such advantage s what was expected a pror and supports the dea that the assumpton of jont decsons on loan approval and loan volume mght be restrctve Summary, Conclusons, and Lmtatons In ths artcle I have examned how rural households characterstcs affect lendng decsons. I develop and estmate an econometrc model n whch a lender s decson about the volume of a loan s condtonal on the decson to approve ths loan. Feld survey data collected n the Provnce of Shandong n Chna s used as an example. Modelng the volume decson condtonal on, rather than jontly wth, the lendng approval decson leads to very dfferent results and mplcatons and thus complements and extends prevous research. My man emprcal fndngs are as follows. Frst, I fnd that rural household s characterstcs manly affect the loan approval decson rather than lendng volume decson. Second, I fnd that rural household s characterstcs that ncrease the probablty of loan approval do not necessarly ncrease the volume of approved loan or have any effect on the loan volume (see, for example, the covarate land n both decsons). If only the coeffcents whch have statstcal sgnfcance on both the loan approval and loan volume decsons are consdered, my emprcal results show that those characterstcs that ncrease the probablty of loan approval often ncrease the volume. For example, a rural household wth a well educated household head, whch s one of the two covarates whch have statstcally sgnfcant effects n all models, s not only benefcal for ganng loan approval but also benefcal for meetng the necesstes of loan requrements (.e., wthout sufferng from loan constrants). Two clear polcy mplcatons that emerge from the results are that educaton and land are hghly sgnfcant n the model estmates. Frstly, the government at all levels n Chna should strve to mprove educaton n rural areas wth the am of ncreasng farmers chances for access to formal loans whch can lead them to hgher productvty and household welfare. 25

38 CHAPTER TWO Although the educaton mportance has been dscussed frequently n the past, t s absolutely necessary to emphasze agan because of the poor state of educaton currently exstng n rural Chna. In fact, there s an ncreasngly wde gap of educaton development between urban and rural Chna due to both objectve and subjectve factors, such as management, urban-rural dualstc economc structure, mult-channel fnancng polces and development concepts behnd these. The unbalanced development of educaton serously threatens efforts to gve all people equal development opportunty n Chna, and the nequty leads to one of the mportant reasons that an undereducated farmer hardly has access to formal loans. Hence, all efforts for mprovng rural educaton are really deserved. Secondly, the central and local governments of Chna should also protect land rghts of all farm famles wth the am of ncreasng farmers chances for access to formal loans, whch s benefcal to promote farmers to make long-term nvestments on farm producton. These nvestments substantally boost farm household ncomes and wll be key to closng the ncome gap between Chnese ctes and rural areas. What s more, secure land rghts and long-term nvestments toward farm producton can guarantee Chna s gran securty. In fact, for the purpose of feedng ts 1.4 bllon people, the Chnese government should protect land rghts so as to ensure suffcent arable land, and also to prevent these lands from beng converted to nonagrcultural uses smultaneously. Note, however, that the data used n the study needs to be vewed and acknowledged n lght of ts lmtatons. All respondent rural household characterstcs were gathered after the fve-year perod n whch loans were granted, but the ntal decson to approve the frst loan was made on the bass of the rural households characterstcs at the begnnng of the fve-year perod. The fact that a rural household dd or dd not receve a loan lkely affected the development of at least some of the rural household s characterstcs. There are many varables that were collected n the survey that I would lke to use because they are clearly related to loans, such as the number of laborers for farm producton and the number of laborers for non-farm producton n survey perod, but that I cannot use because they are endogenous for my partal studes. Data lmtatons caused a lack of enough ndependent varables to use n the study, and thus, some valuable fndngs mght be omtted. 26

39 CHAPTER TWO References Ahrendsen, B.L., B.L. Dxon, and B. Luo (2003), The Effects of Bank Mergers on Commercal Bank Agrcultural Lendng, Paper presented at the Amercan Agrcultural Economcs Assocaton Annual Meetng, Montreal, Canada, July 27-30, Bard, S.K., P.J.Barry, and P.N.Ellnger (2000). Effects of Commercal Bank Structure on Agrcultural Lendng. Agrcultural Fnance Revew. 60: Barry, P.J., C. B. Baker, and L. R. Sannt (1981). Farmers' Credt Rsks and Lqudty Management. Amercan Journal of Agrcultural Economcs. 63(2): Boucher, S.R., and C. Gurknger (2007). Rsk, Wealth, and Sectoral Choce n Rural Credt Markets. Amercan Journal of Agrcultural Economcs. 89(4): Boucher, S.R., M.R. Carter, and C. Gurknger (2008). Rsk Ratonng and Wealth Effects n Credt Markets: Theory and Implcatons for Agrcultural Development. Amercan Journal of Agrcultural Economcs. 90(2): Burke, W.J. (2009). Fttng and Interprettng Cragg s Tobt Alternatve Usng Stata. Stata Journal 9(4): Cheng, E., and Z. Xu (2003). Rates of Interest, Credt Supply and Chna s Rural Development, Vctora Unversty of Technology. CSES Workng Paper. No. 20, May Chna Natonal Bureau of Statstcs. Chna Statstcal Yearbook. Bejng: Chna Statstcs Press, varous years. Connng, J., and C. Udry Rural Fnancal Markets n Developng Countres. In: Evenson R., Pngal P., Schultz T. (Eds.). The Handbook of Agrcultural Economcs. 3. Elsever, North-Holland. Cragg, J.G.(1971). Some Statstcal Models for Lmted Dependent Varables wth Applcatons to the Demand for Durable Goods. Econometrca. 39: D Espaller, B., Guérn, I. & Mersland, R. (2011). Women and repayment n mcrofnance: a global analyss. World Development. 39(5), Featherstone, A.M., C.A.Wlson, T.L.Kastens, and J.D. Jones (2007), Factors Affectng the Agrcultural Loan Decson-makng Process. Agrcultural Fnance Revew. 67(1):

40 CHAPTER TWO Fletschner, D., and L. Kenney (2011), Rural Women s Access to Fnancal Servces. Workng Paper, No Agrcultural Development Economcs Dvson, The Food and Agrculture Organzaton of the Unted Natons. March, 2011 Fredman, M.(1957). A Theory of the Consumpton Functon. Prnceton Unversty Press, Prnceton. Gale, F., and R. Collender (2006). New Drectons n Chna s Agrcultural Lendng. Workng Paper. WRS Economc Research Servce. Unted States Department of Agrculture. January, Ghate, P. (1992). Interacton between the Formal and Informal Fnancal Sectors: The Asan Experence. World Development. 20(6): Gockel, R.P. (2009), Credt and Rsk: Analyzng Determnants of Wllngness to Borrow More Credt n Rural Vetnam. Workng Paper, Henry M. Jackson School of Internatonal Student Degree Project, June 4, Greene, W.H. Econometrc Analyss, 4 th ed. New Jersey: Prentce-Hall, 2000 Gurknger, C. (2006). Understandng the Coexstence of Formal and Informal Credt Markets n Pura, Peru. Workng Paper. Center for Research n Economc Development. Unversty of Namur. Belgum. Gurknger, C., S. R. Boucher (2008). Credt Constrants and Productvty n Peruvan Agrculture. Agrculture Economcs. 39: Guo, P., and X. Ja (2009). The Structure and Reform of Rural Fnance n Chna. Chna Agrcultural Economc Revew. 1(2): Hardy, W.E., J.B. Weed (1980). Objectve Evaluaton for Agrcultural Lendng. Southern Journal of Agrcultural Economcs. July: Katchova, A.L., and P.J. Barry (2005), Credt Rsk Models and Agrcultural Lendng. Amercan Journal of Agrcultural Economcs. 87(1): Katchova, A.L., and Maro J. Mranda (2004). Two-step econometrc estmaton of farm characterstcs affectng marketng contract decsons. Amercan Journal of Agrcultural Economcs. 86(1): Ln, T.-F., and P. Schmdt (1984). A test of the Tobt specfcaton aganst an alternatve suggested by Cragg. Revew of Economcs and Statstcs. 66 (1):

41 CHAPTER TWO Mazzocco, M. (2004). Savngs, Rsk Sharng and Preferences for Rsk. Amercan Economc Revew. 94 (4): (September). Olagunju, F., and A Ajboye (2010), Agrcultural Lendng Decson: A Tobt Regresson Analyss. Afrcan Journal of Food Agrcultural Nutrton and Development. 10(5): Peoples Bank of Chna (2001). Gudelnes on Rural Household Mcro-Credt by Rural Credt Cooperatves, unpublshed document, December 10, Ptt, M.M., and S.R. Khandker (1998). The Impact of Group-Based Credt Programs on Poor Households n Bangladesh: Does the Gender of Partcpants Matter? Journal of Poltcal Economy. 106(5): Shandong Provnce Bureau of Statstcs. Shandong Provnce Statstcal Yearbook. Bejng: Chna Statstcs Press, varous years. Shen, M., and Cheng E.(2004). Restructurng Chna s Rural Fnancal System: Exstng Approaches, Challenges and The Future of Mcrofnance. Workng Paper. German Agency for Techncal Cooperaton (GTZ). December, Shen, M., and S. Rozelle (2004). Fnancal Intermedaton and Transton n Rural Chna: An Evolutonary Approach. Mmeo, Chna Center for Economc Research, Pekng Unversty. Stover, R.D., R.K. Teas, and R.J. Gardner (1985). Agrcultural Lendng Decson: A Multvarate Analyss. Amercan Journal of Agrcultural Economcs. 67(3): Thomas, L.C. (2000). A Survey of Credt and Behavoral Scorng: Forecastng Fnancal Rsk of Lendng to Consumers. Internatonal Journal of Forecastng. 16: Turvey, C.G. and R. Kong (2010). Informal Lendng among Frends and Relatves: Can Mcrocredt Compete n Rural Chna?. Chna Economc Revew. 21(4): Xe, P., Xu Z., Cheng E. and Shen M. (2005). Establshng a Framework for Sustanable Rural Fnance: Demand and Supply Analyss n Guzhou Provnce of the People s Republc of Chna. Workng Paper. Asan Development Bank. Aprl, Zech, L., and G. Pederson (2004). Applcaton of Credt Rsk Models to Agrcultural Lendng. Agrcultural Fnance Revew. 64(2):

42 CHAPTER THREE CHAPTER THREE ECONOMETRIC ESTIMATION OF RURAL HOUSEHOLD CHARACTERISTICS AFFECTING THE NUMBER OF LENDING APPROVALS 3.1. Introducton Formal loans from fnancal nsttutons contnue to play an mportant role n approaches to serve ncreased agrcultural producton and mprove the lvng standards of rural households around the developng world, both n polcy, academc dscussons, and n practce. However, wth economc development and contnuous efforts to mprove rural fnancal markets n developng countres, especally n rural Chna, loan demand has become ncreasngly complex and n many cases nvolves more frequent borrowng. For example, n the data analyzed for ths study, 32 percent of all households who were granted loans by fnancal nsttutons receved two or more loans over the fve-year perod. What s more, the number of lendng approvals over ths perod reflects cumulatve results of ndvdual lendng decson (approval or non-approval) for every borrower. In Chna, a varety of formal fnancal nsttutons can share potental and current clents credt nformaton wth each other due to the development of computers and dgtal data storage. If a borrower who has receved a loan before wants a new formal loan, then a lender can easly check hs/her credt record by usng shareware data among a varety of lenders to determne whether to grant hm/her a loan. If a borrower cannot repay hs/her last loan, then no lender wll approve hs/her new applcaton. Hence, for smplcty, the study does not dscrmnate among the varety types of lenders. Addtonally, the lendng number lnks a close relatonshp wth the borrower s credt scorng because a new loan approval depends on the borrower s credtworthness n the past. Consderng how mportant household characterstcs (.e., man components of the borrowers credt scorng) are for justfyng lendng approval, explorng whch household characterstcs affect the number of lendng approvals over a perod of years deserves more attenton n Chna and other developng countres. 30

43 CHAPTER THREE Past research has manly focused on fve aspects of agrcultural lendng. The frst aspect deals wth the testng of credt scorng or rsk models of the lendng decson. Bauer and Jordan(1971), Johnson and Hagan (1973), Dunn and Frey (1976), Hardy and Weed (1980), Barry et al. (1981), Thomas (2000), Zech and Pederson (2004), and Katchova and Barry (2005) devse and examne such models whch are used to evaluate relatve fnancal and personal characterstcs of borrowers and estmate loan requrements and potental rsks for lenders n order to help lenders decde whether or not to grant a loan to applcants. The second aspect addresses the agrcultural lendng decson process. Stover et al. (1985), Featherstone et al. (2007) and Olagunju (2010) examne the agrcultural loan decson process and analyze the factors from the perspectve of the ndvdual loan offcer or fnancal nsttuton. The thrd aspect addresses how nternal and external characterstcs of fnancal nsttutons themselves affect agrcultural lendng, Studes such as Mazzocco (1991), Bard et al. (2000), and Ahrendsen (2003) are representatve of ths branch of the lterature. The fourth aspect explores the relatonshp between the nformal and formal lendng sectors n developng countres. For example, Turvey and Kong (2010), Gurknger (2006), and Ghate (1992) examne the relatonshp between nformal and formal lendng n Chna, Peru, and Asan countres, respectvely. The ffth aspect models the role of the nformal and formal credt sectors (lenders) n developng countres. Boucher and Gurknger (2007) and Boucher et al. (2008) show that asymmetrc nformaton between lenders and borrowers can oblge formal lenders to rely on collateral to solve the moral hazard and adverse selecton problems nherent n credt transactons. Whle the past lterature provdes many nsghts nto formal lendng, one mportant aspect of lendng decson-makng practces has receved very lttle attenton to date: how do rural household characterstcs affect the number of loans over a perod of years? Ths artcle attempts to answer ths queston to broaden and deepen the prevous research on lendng decsons. In theory, a borrower could apply for a loan an unlmted number of tmes over a perod of years on the bass of protecton laws for consumers rghts and nterests. Correspondngly, a lender has to respond to every loan applcaton to determne whether to lend to a borrower or not. Hence, the total number of loans a borrower receves from a lender over a perod of years 31

44 CHAPTER THREE can be calculated by addng up every lendng decson related to whether a loan applcaton was approved. For example, assume a borrower submts three loan applcatons to a lender over a perod of years, f hs/her applcatons are all rejected, then the number of hs/her lendng approval s a zero outcome over the perod, whle f he/she gets lendng approval twce, then the outcome s two. Correspondngly, as far as all borrowers are concerned, the observed number of lendng approvals from all borrowers can be dvded nto two categores as a whole: zero lendng outcome and postve ntegral lendng outcome ( 1). The zero outcomes are nterpreted as a loan rejecton whch are all loan applcatons are rejected by (a) lender(s) and postve ntegral outcomes are treated as loan approval, even f there are partal loan applcatons approved durng the specfed perod. These bnary response values of lendng approval over a perod of years cannot be modeled usng logstc regresson or probt analyss, but they can be modeled usng complementary log-log bnomnal models. Ths study s contrbuton to the lterature, therefore, s modelng the number of lendng approvals that were sequentally made by lenders over a fve-year perod, and comparng specfc borrowers loan applcatons durng the same perod. To sum up, the prmary goal of ths artcle s to use a complementary log-log bnomnal econometrc model to nvestgate how household characterstcs affect the number of lendng approvals over a perod of fve years. The rest of ths paper s structured as follows. In the followng secton 2 I brefly ntroduce some background nformaton of formal loan n rural Chna. In secton 3 I develop a theoretcal framework to llustrate decsons of formal loan lendng over a perod of years. In secton 4 I present and estmate the complementary log-log bnomnal model. In secton 5 I descrbe the data. I present and dscuss the results n secton 6, and conclude n secton Background A Bref Introducton of Rural Formal Fnance n Chna Chna s fnancal system has undergone a seres of reforms snce the md-1990 s wth the am of buldng a modern and commercal fnancal system. The current rural formal fnancal 32

45 CHAPTER THREE system n Chna manly conssts of the Rural Credt Cooperatve (RCC), the Agrcultural Bank of Chna (ABC), the Agrcultural Development Bank of Chna (ADBC) and the Postal Savngs Bank of Chna (PSBC). The ABC generally loans to agrcultural enterprses, rural cooperatves, and vllage organzatons, but usually not to ndvdual rural households. The PSBC s the only depostory fnancal nsttuton. Among these four man nsttutons, more than 80 percent of formal agrcultural loans n Chna are made by the country s 30,000-plus Rural Credt Cooperatves (RCCs) (Gale and Collender, 2006). In other words, RCCs currently play an overrdng role n the rural fnancal system n Chna (Guo and Ja, 2009). Addtonally, the Chnese Bankng Regulatory Commsson (CBRC) and the People s Bank of Chna (PBOC) regulate all four of these fnancal nsttutons. Nowadays, wth the development of computer technology, all formal fnancal nsttutons have bult a clent credt hstory record network throughout all the nsttutons n the country. In other words, as long as any applcant starts hs/her loan applcaton, that nformaton wll be stored, traced, recorded, and retreved any tme by any formal fnancal nsttuton. Hence, dfferent lenders are able to exchange nformaton about the credtworthness of a borrower through such a network, and the same lender can learn about the borrower s performance over tme and develop trust n the future Trends n Agrcultural Lendng n Recent Decades Fgures 1 and 2 below respectvely show the balance of agrcultural loans for the whole country and the Shandong Provnce of Chna from 1979 to As shown n the Fgures, the balance of agrcultural loans n the whole country and the Provnce of Shandong ncreased very slowly and gently durng the frst 16 years from The relatve shares of agrcultural loans as a proporton of total loans fluctuated wthn a relatvely broad brand and dsplays a rough downtrend as agrculture s share of total GDP declned. However, the balance of agrcultural loans for the whole country and the Provnce of Shandong ncreased durng the perod. The agrcultural loan balance of the whole country and the Shandong Provnce respectvely reached the equvalent of 21,623 bllon Yuan and 296 bllon Yuan n 2009, up 19,704 bllon Yuan and 271 bllon 33

46 CHAPTER THREE Yuan snce 1996, respectvely. In 2009, the agrcultural share of all loans for the entre country and the Provnce of Shandong respectvely was 5% and 11%, a 2 and 4 percentage-pont ncrease from 1996, respectvely. Snce Shandong Provnce s one of Chna s most prosperous agrcultural regons, t s not unusual that the agrcultural share of all loans s hgher than the average level of the whole country snce the md-1990s, when the reform of the bankng system started to commercalze and modernze (see Fgures 1 and 2). Hence, Shandong provnce provdes an nterestng settng for studyng formal loan lendng practces n rural Chna. Agrcultural share of all loans (Chna,the whole country, left axs) Agrcultural Loan Balance (Chna,the whole country, rght axs) Percent Bllon Yuan (RMB) Year Fg.1. Agrcultural Loan Balance, the Whole Country of Chna, Yearly, Source: Chna Statstcal Yearbook (Varous Issues) Agrcultural share of all loans (Chna, Shandong Provnce, left axs) Agrcultural loan balance (Chna, Shandong Provnce,rght axs) Percent Bllon Yuan (RMB) Year Fg.2. Agrcultural Loan Balance, Shandong Provnce of Chna, Yearly, Source: Shandong Provnce Statstcal Yearbook, Chna (Varous Issues) 3.3. Theoretcal Framework Informaton and enforcement problems cause credt market mperfectons and even the 34

47 CHAPTER THREE complete falure of these markets (Gurknger and Boucher, 2008). Such loan constrants are especally lkely to arse under the condtons prevalng n developng countres (see, for example, Connng and Udry, 2005). I consder three stuatons for the number of lendng approval(s) for all loan applcaton(s) over a perod of years: full approval, part approval, and rejecton. A household experences full approval f ts desred number of loan applcaton(s) over a perod of years s/are approved n full by (a) lendng nsttuton(s). If ts desred number of loan applcaton(s) over a perod of years s/are completely refused, the household suffered from loan constrants. In between these two stuatons, I refer to partal approval f t s desred number of loan applcaton(s) over a perod of years s/are partally approved. In Fgure 3 I assume that there are some underlyng ndcators whch affect the number of lendng approvals over a perod of years as opposed to the number of loan applcatons n the same tme, and there s a dstrbuton of these ndcators across all applcants. I call ths ndcator the applcant s lqudty condton, whch s based on hs or her qualtatve and quanttatve characterstcs. Each applcant s lqudty condton s man determnant to affect hs or her fnal number of lendng approval over a perod of years. Above an upper lqudty condton threshold, the number of lendng approval from complete full loan approval exceeds that from partal loan approval or rejecton. Below a lower lqudty condton threshold, rejecton takes place. In between these thresholds, the number of lendng approval from partal loan approval exceeds that from ether full approval or rejecton. Densty Lower threshold Upper threshold Lqudty condton rejecton part approval full approval Fg.3. The Number of Lendng Approvals Ths study proposes a theoretcal model for lookng at formal lendng approvals over a perod of years. Consder a fnancal nsttuton, ndexed by, that lends a formal loan to a 35

48 CHAPTER THREE rural household j. If the rural household submts (a) loan applcaton(s) K ( K =1,2, N, and N s a postve nteger.) wthn a perod of years, fnancal nsttutons have to decde whether to approve all applcaton(s) or not, therefore K suffers ether non-lendng approval (all loan rejecton) or lendng approval (at least one tme loan approval). Assume a fnancal nsttuton maxmzes the expected utlty of profts durng a perod of years: N N v f v f [ ( 1 C j )( b j b j )] + [ C j ( j R j Tj b j b j )] (1) K K K K K K K K K Max EU α C j K 0,1 1 1 = K = K = Where C j K s a lender s lendng decson for a borrower j' s K th loan applcaton. If approval s chosen, then C j K =1; otherwse C j K =0. When the K th loan applcaton s approved, then the optmal volume n the lendng practce ( α j K ) s chosen, and the nterest rate ( R ) and the term of loan ( T j K j K ) are also determned. α R T s the expected jk jk jk proceeds for the K th loan approved by a lender, v b j K s the possble expendtures or losses for the K th lendng decson (non-lendng approval or lendng approval) such as the costs of screenng a loan applcaton, f b j K s the necessary cost of the lendng decson (non-lendng approval or lendng approval) such as the salary of loan offcers 1. Although any borrower can submt unlmted loan applcatons, the decson of lendng approval made by a lender s done sequentally, namely, f a borrower apples for a second loan, and f he/she performed well on the frst loan, then the lender wll be more lkely to approve the second and addtonal loans. In other words, only after repayng past loan n due tme wll a borrower submt hs/her new loan applcaton and have hgh possblty or probablty to get loan approval from the lender. Therefore, as far as a borrower ( j ) s 1 In practce, for the collateral whch s used for a loan applcaton, the market value of pledged asset P usually equals or exceeds the loan amount and ts nterest profts,.e., P α + αrt. However, for smplcty, I assume that P = α + αrt so that functon (1) s feasble. In addton, for parsmony, I omt the possble proceeds from the fees of a loan applcaton pad by applcants n all theoretcal models because such proceeds are usually very small even f they actually exst n lendng practce. 36

49 CHAPTER THREE concerned, hs/her total number of loan approvals tmes H ( H = 0,1,2, K ) over a perod of years does not always equal hs/her total number of loan applcatons tmes ( K ) over the same tme, namely, 0 H K. Snce we can observe the total number of lendng approval over a perod of years for any borrower j, hence, as to all borrowers ( j, j j ), we can 1 2 N nterpret H =0 as non-approval of lendng f all applcatons of a borrower ( = 1,2, N) over a perod of years are not approved by lenders, and treat H =1,2, K as lendng approval j f all of j s applcatons are partally or fully approved by a lender n the specfed perod. What s more, wthn a perod of years, whether a lender has lent to a specfc borrower ( C ), and f the lendng approval s chosen at least once ( C =1,.e., part or full loan applcaton(s) s/are approved ( 1 H K )), then functon (1) can be further descrbed for a bnary framework whch vews the ntertemporal nature of the lendng approval process as a whole and for the purpose of ths emprcal estmaton as follows: j j Max EU C j= 0,1 K v f v f ( 1 C j )( BK BK ) + C j Max ( α j R j Tj ) B K BK (2) H H H 1 H K H= 1 where B v K = b + b b s the possble total expendture or loss for all of a specfc v 1 v 2 v K borrower s ( j ) loan applcatons censorng work over a perod of years, B f K = b + b b, f 1 f 2 f K whch are the necessary total cost for all j s loan applcatons censorng work over a perod of years. α R T s the expected proceeds for the H th loan approved by a lender, heren, jh jh jh the proceeds from the loan applcaton fees that all applcants must pay s omtted. In functon (2), C j =0 ndcates non-lendng approval (.e., all of a borrower s loan applcatons are not approved by (a) lender(s) over a perod of years) and s assocated wth the frst term n the dscrete maxmzaton operaton. C j =1 ndcates lendng approval (.e., a borrower s part or full loan applcatons are approved by (a) lender(s)) and s assocated wth the second term n the dscrete maxmzaton operaton. There s a trade-off between obtanng 37

50 CHAPTER THREE expected proceeds and coverng the costs assocated wth the lendng approval. Maxmzaton of expected fnancal nsttuton utlty yelds expressons related to the loan applcant s ( j ) household characterstcs, whch can embody the potental ablty of creatng gans for usng loans and guaranteeng loan repayment at a due date. Therefore, H s related to x j, a set of observable rural household s characterstcs such as the educaton level of the household head, land sze, etc. Formally, H = g H ( β H x j, ε H ), where β H s a vector of parameters, and ε H captures unmeasured factors related to formal loan lendng Econometrc Methods In the emprcal models of the bnary outcome of lendng approval over a perod of years, the observed dependent varable, the total number of lendng approval for every borrower durng a tme nterval (e.g., fve years), s a non-negatve nteger. Therefore, the total number of lendng approval for every borrower as a whole takes on two values, zero or a postve nteger. The value of zero denotes a non-lendng approval ( C = 0), and the value of postve ntegers denotes a lendng approval (full or part) ( C = 1). The model follows: j j Pr exp( β j ) ( C = 1/ x ) = 1 e H x j j (3) Where the functon (3) s called a complementary log-log model (Green, 2011). The log-lkelhood n the complementary log-log model s thus gven by: InL = c = 1 InF( β x H ) + j c j j = 0 In [ 1 F( β x )] (4) H j where F( z) exp( z) = 1 e. 38

51 CHAPTER THREE 3.5. Data The data used n ths paper were collected n Shandong Provnce (Chna) n July 2010 by face-to-face ntervews. 394 rural households were randomly selected from 5 vllages n one town (Man Zhuang) and one county (Nng Yang). The survey collected detaled nformaton on households formal borrowng actvtes over the last 5 years (.e., from July 2006 to July 2010) as well as households basc characterstcs. Durng the survey, respondents dentfed whether loan applcatons were approved by fnancal nsttutons n a fve-year perod, and the number of loan granted by (a) lender(s). The survey nformaton gathered also ncludes more general quanttatve and qualtatve rural household characterstcs that are consdered mportant determnants of formal loan lendng. Overall, 204 households were elmnated because they dd not partcpate n the survey or dd not apply for a formal loan durng the past fve years. Ths left 190 households that had appled for at least one formal loan. Of these, a further 22 households had to be removed snce the nformaton they provded was ncomplete. Of the remanng 168 households that had appled for a formal loan n the past fve years, 85 were approved by fnancal nsttutons to receve a loan. On average, the number of formal loan granted by lenders for the 85 households over the last fve years s 1.49 tmes. Snce the perod span related to the formal loan survey was fve years, a loan for an ndvdual respondent household mght be dsbursed by a fnancal nsttuton to outsde of the perod studed. For the purpose of achevng accurate nformaton, the survey collected current respondent household nformaton wthn the survey perod, namely, a retrospectve survey related to respondent s hstorcal household nformaton was not conducted because many of those randomzed respondents, especally when they are not household heads, rarely know the full households nformaton hstory. For the sake of developng the emprcal analyss, however, t s thus very mportant to pck approprate covarates from the collected nformaton. A few of the covarates, whch not only represent socoeconomc characterstcs of rural households but also are exogenous, are hypotheszed to be relevant n the loan approval and loan volume decsons. These nclude zero-one ndcators for whether the household head s female, and has non-farm producton sklls. Also, n Chna, non-farm skll 39

52 CHAPTER THREE does not always refer to those professonal sklls that need professonal tranng and study, some smple physcal labor jobs, such as santaton worker, are vewed as non-farm skll as well. Some respondents mght say that the head of hs or her household has no non-farm skll, the plausble reason s ether that the household head s born wth a physcal dsablty or that the household head s too old for farm work (aged over 65 years old). Not many household heads n rural Chna absolutely dd not have non-farm skll, of course. Therefore, these two zero-one ndcators are approprate for the study and are exogenous for the number of lendng approvals analyss. The average age of a household head over a perod of years reflects that household s structure. Household structures was dvded nto four man types on the bass of age: (1) young household, defned as a household head s age between 18 and 35 years old; (2)mddle-aged household, defned as a household head age between 36 and 50 years old; (3)elderly household, defned as a household head s age between 51 and 65 years old; and (4)very elderly household, defned as a household head aged 66 years old or over. Hence, every respondng household can be easly determned on the bass of the dvson of household structure for the emprcal analyss. In Chna, farmland s stll owned and controlled by the state and leased to farmers. Farmers can t sell t, and they can t use t for collateral on a loan. In 1997, rural households were allowed to sgn a long-term lease of 30 years for the rght to work a plot, whch was allocated accordng to shares n vllage populatons. Hence, t s reasonable to beleve that the amount of farm land of every respondent household could not change from 2006 to 2010 due to undated sgned leases n the survey area. Therefore, the covarate land s exogenous and ntroduced nto the analyss. The household head s educaton level was also consdered exogenous because almost no household heads n rural Chna underwent educaton-change over the study perod, hence, t can also be ntroduced nto the emprcal analyss. Addtonally, the covarate asset, whch s household total asset value, s also used n the analyss. Overall, all dependent varables and covarates are lsted and descrbed n Table 1 below. 40

53 CHAPTER THREE Table 1: Descrptve statstcs of varables Varable Defnton Mean Std.Dev. Obs. Dependent Varable Number Number of lendng approvals tmes over the Covarates Gender last fve years Zero: Postve Integer: Whether the household head s female (female=1, male=0) YongHH 1 f household head s age s between 18 and years old; otherwse=0 MddleHH 1 f household head s age s between 36 and years old; otherwse=0 ElderHH 1 f household head s age s between 51 and years old; otherwse=0 Educaton Household head s educaton level n years Nonfarmskll Whether household head has non-farm skll (yes=1, non=0) Land Amount of farm land n Mu (1 Mu 0.16 Acre) Asset Household total asset value n Yuan (thousands) 2 ncludng house, land, farm machne, etc Results and Dscusson The model specfcaton outlned above was estmated usng maxmum lkelhood methods. Results of the complementary log-log model for the number of lendng approval over the fve-year perod are gven n Table 2. Both parameter estmates and average margnal effects (AMEs) are reported Yuan are roughly equvalent to 150$ USD. 41

54 CHAPTER THREE Table 2: Complementary log-log Bnomnal Model analyss results for the number of lendng approval Covarates Parameter Average Margnal Estmates Effects (AMEs) N=168 N=168 Gender (1.12) YoungHH 0.60 (0.93) MddleHH (0.82) ElderHH (0.84) Educaton 0.13 ** (0.05) Nonfarmskll 0.72 ** (0.34) Land 0.41 *** (0.06) Asset * (0.001) Constant *** LR ch 2 (7)= *** Log Lkelhood= (1.01) (0.20) 0.11 (0.16) (0.15) (0.15) 0.02 *** (0.01) 0.13 ** (0.06) 0.07 *** (0.01) * (0.0001) Note: *, ** and *** ndcate sgnfcance at the 10%, 5%, and 1% levels, respectvely; standard errors n parentheses. Coeffcents, AME values, and ther correspondng standard error obtaned by margns command n Stata. The results of the complementary log-log bnomnal model ndcate that households wth a head who has a hgher educaton level and non-farm labor sklls are more lkely to be approved for a loan. Hgher educaton ncreases productvty so that more value can be created, and havng non-farm labor skll means more ncome sources. Thus, these two characterstcs have strong postve effects on the probablty of loan approval (5% sgnfcance level). As can be seen n column 2 of Table 2, the covarate land has a statstcally sgnfcant postve effect (1% sgnfcance level) on the loan approval decson as well. More land means that loan applcants potental productvty or rent proceeds can be ncreased; hence, t s one ndcator of repayment capacty for lenders. 42

55 CHAPTER THREE A household s total asset value s often used by lenders to determne the ntal and/or ongong loan amount, and/or complance wth one or more debt covenants because they are often ncluded n the borrowng base 3. Household assets are often consdered as collateral, so t s not surprsng that they are assocated wth ncreased probablty of lendng approval. However, the sgnfcance level of varable asset s only 1%, and ts coeffcent s also very small. In addton, column 3 of Table 2 show the average margnal effects (AMEs) of the probablty of the number of lendng approvals. An average margnal effect s an estmate of a populaton-averaged margnal effect. The AMEs results of the complementary log-log model are unanmous to ts counterpart coeffcents wth the same sgn and statstcal sgnfcance, hence, for parsmony, they are not dscussed n the paper Conclusons, Lmtatons, and Future Research Drecton Ths artcle has examned how rural households characterstcs affect the number of lendng approvals over a perod of years. Feld survey data collected n the Provnce of Shandong, Chna s used as the sample. Modelng the number of lendng approvals over a fve-year perod leads to a few nterestng results and mplcatons and thus complements and extends prevous research. The man emprcal fndngs are that household head s educaton level and non-farm producton sklls, the amount of farm land,and the household s total asset value have statstcally sgnfcant effects n the model. These three factors are benefcal for borrowers to gan loan approval. Two clear polcy mplcatons that emerge from the results are that educaton and land are hghly sgnfcant n the model estmates. Frstly, the government at all levels n Chna should strve to mprove educaton n rural areas wth the am of ncreasng farmers chances for access to formal loans whch can lead them to hgher productvty and household welfare. Although the educaton mportance has been dscussed frequently n the past, t s absolutely 3 The borrowng base s the value assgned to a collecton of a borrower s assets, and ts calculaton s contractually specfed n an agreement between the lender and borrower. 43

56 CHAPTER THREE necessary to emphasze agan because of the poor state of educaton currently exstng n rural Chna. In fact, there s an ncreasngly wde gap of educaton development between urban and rural Chna due to both objectve and subjectve factors, such as management, urban-rural dualstc economc structure, mult-channel fnancng polces and development concepts behnd these. The unbalanced development of educaton serously threatens efforts to gve all people equal development opportunty n Chna, and the nequty leads to one of the mportant reasons that an undereducated farmer hardly has access to formal loans. Hence, all efforts for mprovng rural educaton are really deserved. Secondly, the central and local governments of Chna should also protect land rghts of all farm famles wth the am of ncreasng farmers chances for access to formal loans, whch s benefcal to promote farmers to make long-term nvestments on farm producton. These nvestments substantally boost farm household ncomes and wll be key to closng the ncome gap between Chnese ctes and rural areas. What s more, secure land rghts and long-term nvestments toward farm producton can guarantee Chna s gran securty. In fact, for the purpose of feedng ts 1.4 bllon people, the Chnese government should protect land rghts so as to ensure suffcent arable land, and also to prevent these lands from beng converted to nonagrcultural uses smultaneously. Although some contrbutons from ths study are partcularly applcable to rural fnance, the results of ths study also need to be vewed and acknowledged n lghts of ts lmtatons. All respondent rural households characterstcs were asked after the fve-year perod n whch loans were granted. But the ntal decson to approve the frst loan was made on the bass of the rural households characterstcs at the begnnng of the fve-year perod. The fact that a rural household dd or dd not receve a loan lkely has affected the development of that rural household s characterstcs. As far as a covarate asset s concerned, a household s assets wll tend to be hgher f a household has been able to borrow. Hence, the covarate asset used n ths artcle s suspected of endogenety, and the fndngs n ths artcle mght be based and nconsstent. Consderng the suspect covarate (.e., asset), future research should frst use a Hausman test to confrm whether the covarate s endogenous. If the test proves endogenety, then a proper proxy for the varable or a approprate nstrumental varable to deal wth endogenety 44

57 CHAPTER THREE problem s needed. 45

58 CHAPTER THREE References Ahrendsen, B.L., B.L. Dxon, and B. Luo (2003), The Effects of Bank Mergers on Commercal Bank Agrcultural Lendng, Paper presented at the Amercan Agrcultural Economcs Assocaton Annual Meetng, Montreal, Canada, July 27-30, ADBC (1994). The Agrcultural Development Bank of Chna Annual Report, publshed by ADBC, Bejng, Chna. Bard, S.K., P.J.Barry, and P.N.Ellnger (2000). Effects of Commercal Bank Structure on Agrcultural Lendng. Agrcultural Fnance Revew. 60: Barry, P.J., C. B. Baker, and L. R. Sannt (1981). Farmers' Credt Rsks and Lqudty Management. Amercan Journal of Agrcultural Economcs. 63(2): Bauer, Larry L. and John P. Jordan (1971). "A Statstcal Technque for Classfyng Loan Applcaton." Unversty of Tennessee Agr. Exp. Sta. Bull Boucher, S.R., and C. Gurknger (2007). Rsk, Wealth, and Sectoral Choce n Rural Credt Markets. Amercan Journal of Agrcultural Economcs. 89(4): Boucher, S.R., M.R. Carter, and C. Gurknger (2008). Rsk Ratonng and Wealth Effects n Credt Markets: Theory and Implcatons for Agrcultural Development. Amercan Journal of Agrcultural Economcs, 90(2): Chna Natonal Bureau of Statstcs. Chna Statstcal Yearbook. Bejng: Chna Statstcs Press, varous years. Connng, J., and C. Udry Rural Fnancal Markets n Developng Countres. In: Evenson R., Pngal P., Schultz T. (Eds.). The Handbook of Agrcultural Economcs. 3. Elsever, North-Holland. Dunn, D. J. and T. L. Frey (1976). "Dscrmnant Analyss of Loans for Cash Gran Farms." Agrcultural Fnance Revew. 36(Aprl). Featherstone, A.M., C.A.Wlson, T.L.Kastens, and J.D. Jones (2007), Factors Affectng the Agrcultural Loan Decson-makng Process. Agrcultural Fnance Revew. 67(1): Gale, F., and R. Collender (2006). New Drectons n Chna s Agrcultural Lendng. Workng Paper. WRS Economc Research Servce. Unted States Department of Agrculture. January,

59 CHAPTER THREE Ghate, P. (1992). Interacton between the Formal and Informal Fnancal Sectors: The Asan Experence. World Development, 20(6): Greene, W.H. Econometrc Analyss, 7 th ed. New Jersey: Prentce-Hall, 2011 Gurknger, C. (2006). Understandng the Coexstence of Formal and Informal Credt Markets n Pura, Peru. Workng Paper. Center for Research n Economc Development. Unversty of Namur. Belgum. Gurknger, C., S. R. Boucher (2008). Credt Constrants and Productvty n Peruvan Agrculture. Agrculture Economcs. 39: Guo, P., and X. Ja (2009). The Structure and Reform of Rural Fnance n Chna. Chna Agrcultural Economc Revew. 1(2): Hardy, W.E., J.B. Weed (1980). Objectve Evaluaton for Agrcultural Lendng. Southern Journal of Agrcultural Economcs. July: Johnson, R. B. and A. R. Hagan (1973). "Agrcultural Loan Evaluaton wth Dscrmnant analyss." Southern Journal of Agrcultural Economcs. 5: Katchova, A.L., and P.J. Barry (2005), Credt Rsk Models and Agrcultural Lendng. Amercan Journal of Agrcultural Economcs. 87(1): Mazzocco, M. (2004). Savngs, Rsk Sharng and Preferences for Rsk. Amercan Economc Revew. 94 (4): (September). McDowell, A. (2003). From the help desk: hurdle models. Stata Journal. 3(2): Shandong Provnce Bureau of Statstcs. Shandong Provnce Statstcal Yearbook. Bejng: Chna Statstcs Press, varous years. Stover, R.D., R.K. Teas, and R.J. Gardner (1985). Agrcultural Lendng Decson: A Multvarate Analyss. Amercan Journal of Agrcultural Economcs. 67(3): Thomas, L.C. (2000). A Survey of Credt and Behavoral Scorng: Forecastng Fnancal Rsk of Lendng to Consumers. Internatonal Journal of Forecastng. 16: Turvey, C.G. and R. Kong (2010). Informal Lendng Amongst Frends and Relatves: Can Mcrocredt Compete n Rural Chna?. Chna Economc Revew. 21(4): Zech, L., and G. Pederson (2004). Applcaton of Credt Rsk Models to Agrcultural Lendng. Agrcultural Fnance Revew. 64(2):

60 CHAPTER FOUR CHAPTER FOUR WHO FEELS THAT INTEREST RATES ON FORMAL LOANS ARE TOO HIGH IN RURAL FINANCIAL MARKETS? ESTIMATES FROM A SURVEY IN RURAL SHANDONG, CHINA 4.1. Introducton The provson of formal loans to potental borrowers n the rural areas of developng countres s hampered by hgh transacton costs that stem from small loan szes, lack of assets that are sutable as collateral, and covarate rsks (Petrck and Latruffe, 2005). These factors ncrease the costs of formal lendng and wll, all other thngs beng equal, result n ncreased nterest rates. In Chna s rural fnancal market, where the Rural Credt Cooperatves (RCCs) domnate the markets for lendng and depost servces (Guo and Ja, 2009), ther monopoly poston mght further nflate nterest rates. Hgh nterest rates wll lead to partal or full excluson of potental borrowers from formal fnancal systems, forcng them to rely on meager self-fnances, nterpersonal lendng or llegal usury and thus lmtng ther ablty to actvely partcpate n and beneft from the economc development process (L et al., 2011). The determnaton and mplcatons of nterest rates have been studed from many dfferent angles. Although most studes focus on macro fnance (e.g. Duffee, 2002; Jarrow et al., 2007), some studes address farm loans and the feld of mcro fnance. Featherstone et al. (1993) explore the relatonshp between nterest rates n the natonal money market and nterest rates on farm loans. Interest rate determnaton n underdeveloped rural areas has been studed by, for example, Bottomley (1975) and Petrck and Latruffe (2005). A large-scale feld experment carred out n South Afrca by Karlan and Znman (2008) n order to estmate nterest rate elastctes of demand for credt, shows that the demand curves are downward slopng and steeper for nterest rate ncreases relatve to the lender s standard rates. Other related studes nclude Salazar et al. (2010), Attanaso et al. (2008), Deheja et al. (2005), Alesse et al. (2005), Gross et al. (2002), etc. Castro and Texera (2006) study the effects of the Brazlan Interest Rate Equalzaton System (IRES) expendtures on Brazlan GDP, and 48

61 CHAPTER FOUR Mohane et al. (2002) analyze the effects of nterest rate celngs on the mcro lendng market n South Afrca. Factors affectng the nterest rate dstrbuton for the Farm Credt System s drect lendng assocatons n the US are studed by Dodson and Duncan (1998). One aspect of nterest rates n rural fnance that has not receved attenton to date concerns the atttudes of potental formal loan clents towards nterest rate levels. As we descrbe below, 22 percent of the respondents n the sample of Chnese farmers that we analyze beleve that nterest rates on formal loans are too hgh. If ths share s representatve of Chna as a whole, as many as 100 mllon rural farmers feel that nterest rates on formal loans are too hgh and are therefore partally or fully excluded from formal fnance. What are the typcal characterstcs of these potental rural borrowers and what dstngushes them from borrowers who are satsfed wth nterest rate levels? Economc modelng, polcy and practce suggest that nterest rate levels are crtcally related to the functonng of loan markets (Karlan and Znman, 2008). Understandng the atttude of potental formal borrowers n rural areas, on the level of lendng rates can thus respond to help polcymakers desgn optmal fnancal servces, mprove the rural fnancal structure, and thus to foster rural economc growth. The objectve of ths artcle s therefore to determne what soco-economc characterstcs nfluence some potental borrowers atttudes towards hgh nterest rates recognton on formal loans. To pursue the objectve we analyze feld survey data from Shandong Provnce n Chna usng a method proposed by van de Ven and van Praag that allows us to correct for systematc non-response n a probt-type equaton. The rest of ths paper s structured as follows. In secton 2 we provde some background nformaton on formal fnancal nsttutons and nterest rates n rural Chna. In secton 3 we descrbe the data we employ. In sectons 4 and 5, a conceptual framework and the proposed estmaton technque, respectvely, are presented. We present and dscuss the results n secton 6, and conclude n secton Background Although Chna s fnancal system has undergone a seres of reforms snce the md-1990 s wth the am of buldng a modern and commercal fnancal system, the basc structure of the 49

62 CHAPTER FOUR rural fnancal market of Chna s superfcally smlar to that of many developng countres. Formal credt programs are hghly centralzed, prvate lendng s strctly regulated and often consdered llegal, and credt ratonng s prevalent (Cheng and Xu, 2004; Ja et al., 2007; Guo and Ja, 2009). Chna s rural fnance conssts of formal, sem-formal, and nformal fnances (Guo and Ja, 2009). Rural sem-formal fnance refers to mcrocredt plots and projects ntated by government agences or NGOs whch just operate n small regons of ndvdual provnces n Chna. Rural nformal fnance refers to ether nterpersonal lendng, whch depends on personal relatonshp, or llegal usury, whch usually charges borrowers exorbtant nterest rates. Compared wth these alternatves, rural formal fnance n Chna operates wth the advantages of ample loan supply and legal status n Chna s rural fnancal market. Hence, sem-formal fnance and nformal fnance are poor substtutes for formal fnance n rural Chna Formal Fnancal Insttutons and Interest Rates n Rural Chna The current rural formal fnancal system n Chna manly conssts of the Rural Credt Cooperatve (RCC), the Agrcultural Bank of Chna (ABC), the Agrcultural Development Bank of Chna (ADBC) and the Postal Savngs Bank of Chna (PSBC). All four of these nsttutons are regulated by the Chnese Bankng Regulatory Commsson and the People's Bank of Chna (PBOC). The ABC lends to agrcultural enterprses, rural cooperatves, and vllage organzatons, but only rarely to ndvdual rural households. The PSBC s the depost-only fnancal nsttuton. Overall, more than 80 percent of formal agrcultural loans n Chna are made by the country s 30,000-plus Rural Credt Cooperatves (RCCs) (Gale and Collender, 2006). Thus, RCCs play an domnant role n the loan dsbursement and depost systems n rural Chna (Guo and Ja, 2009), and most formal borrowers n rural areas deal exclusvely wth ther local RCC. Pror to the ntaton of fnancal reforms n 1997, the Chnese government fxed nterest rates for both formal fnancal nsttuton deposts and loans. Followng 1997, nterest rates on 1 As ponted out by Feder et al. (1990,pp: ), nformal credt s not a good substtute for formal credt due to lmted fungblty (otherwse every borrower would exhaust hs or her nformal credt frst before gong to the RCC). 50

63 CHAPTER FOUR loans were gradually lberalzed and formal fnancal nsttutons were permtted to determne the nterest rate for a partcular loan wthn a gven range of a benchmark nterest rates set by the PBOC (Martn, 2012). Although the PBOC elmnated the celng on nterest rates n the course of the reform process, t contnued to set a benchmark nterest rate. Accordng to current Chnese law and regulaton, bank lendng rates can be no less than 90% of the benchmark rate set by the PBOC, wth no upward lmt (Martn, 2012). As a result, the formal fnancal nsttutons currently have consderable lattude to set nterest rates on loans, and the PBOC benchmark rates and permssble bands have only a lmted effect on nterest rates. Therefore, t s not surprsng that some potental formal borrowers n Chna s rural areas face nterest rates that they feel are too hgh. In the data that we analyze below, those potental borrowers who stated that the current nterest rate offered by formal nsttutons s too hgh also stated that the average maxmum annual nterest rate that they would accept was about 6 percent, whle the average annual lendng nterest rate offered by formal nsttutons was about 7 percent Trends n Agrcultural Lendng n Recent Decades Fgures 1 and 2 show the annual cumulatve loan dsbursement and the share of agrculture n all formal lendng for Chna as a whole and for Shandong Provnce, respectvely, from 1979 to As shown n the Fgures, the balance of agrcultural loans n Chna and Shandong Provnce ncreased slowly durng the frst 16 years from The relatve shares of agrcultural loans as a proporton of total loans fluctuated wthn a relatvely broad brand and dsplayed a slght downtrend as agrculture s share of total GDP declned. However, the balance of agrcultural loans ncreased strongly durng the perod both n Chna as a whole and n Shandong provnce, reachng 21,623 bllon Yuan (RMB) and 296 bllon Yuan respectvely n In respectvely, up 2 and 4 percentage ponts from Snce Shandong Provnce s one of Chna s most prosperous agrcultural regons, t s not unusual that the agrcultural share of all loans s hgher there than n Chna as a whole Yuan are approxmately equvalent to 150 US$. 51

64 CHAPTER FOUR Agrcultural share of all loans (Chna,the whole country, left axs) Agrcultural Loan Balance (Chna,the whole country, rght axs) Percent Bllon Yuan (RMB) Year Fg.1. Agrcultural Loan Balance, the Whole Country of Chna, Yearly, Source: Chna Statstcal Yearbook (Varous Issues) Percent Agrcultural share of all loans (Chna, Shandong Provnce, left axs) Agrcultural loan balance (Chna, Shandong Provnce,rght axs) Bllon Yuan (RMB) Year Fg.2. Agrcultural Loan Balance, Shandong Provnce of Chna, Yearly, Source: Shandong Provnce Statstcal Yearbook, Chna (Varous Issues) 4.3. Data The data used n ths paper were collected n Shandong Provnce (Chna) n July rural households were randomly selected from 5 vllages n one town (Man Zhuang) and one county (Nng Yang), but 104 selected respondents dd not wsh to partcpate, leavng a sample sze of 290. The survey collected detaled nformaton on households formal borrowng actvtes over the last 5 years (.e., from July 2006 to July 2010) as well as households basc demographc and economc characterstcs. For the purposes of ths study, the core queston n the survey questonnare reads as follows 3 : Gven that you wsh to borrow from a formal fnancal nsttuton, and comparng 3 Author s translaton from Chnese. The questonnare was desgned n Chnese n accordance wth Chnese readng habts and dom. 52

65 CHAPTER FOUR the nterest rate set by the formal nsttuton wth the maxmum nterest rate that you would accept, do you feel that the nterest rate set by the formal nsttuton s: A) too hgh; B) too low; C) far?. After complng the responses to ths queston, t was found that only 25 respondents who chose answer B. Thus, the responses can be manly dvded nto two categores: too hgh and far. In the paper, we manly shed lght on those respondents who answered A because they most lkely shy away from formal loan market because of nterest rates on formal loan set by the lenders. Snce other fnancng channels such as nterpersonal lendng are not good substtutons for formal fnancng under current stuaton of Chna rural fnancal market and consequently, some potental borrowers most lkely cannot meet ther fnancal needs f they are also excluded from formal loan. One of research goals on rural fnance n developng countres thus focuses on fndng out soco-economc characterstcs of some borrowers who suffer from formal loan constrants so as to desgn optmal polcy to help them to access to formal loan. Moreover, some respondents who answered B and C most lkely feel easy or free when they want to borrow from formal fnancal nsttutons so that we can pay lttle attenton to them. In consequence, n the followng we consder responses B and C together n a new category - not too hgh. Such a new dvson on respondents atttudes towards nterest rates on formal loan (.e., too hgh or not too hgh) allows us to clearly and better concentrate our core research, namely, who feels that nterest rates on formal loans are too hgh n rural fnancal markets?. Although there was no way to know exactly why 104 respondents abstaned from the survey, ther non-partcpaton resulted n non-responses of all survey questons related to rural fnance and consequently, we mss those respondents nformaton so that they cannot be selected to be n our sample. These non-responses, whch are called unt non-responses by Bernsky (2005), are random because these mssng observatons are random n the context of survey research. As a result, such dstortons are nconsequental n the aggregate (Bernsky, 2005), and can be excluded from the survey populaton wthout leadng to sample bas. Of the 290 respondents who partcpated, 184 respondents answered the core queston. Of these, 64 respondents, or 22 percent of the complete sample of 290, stated that the formal nterest rate s too hgh (response "A"). 106 respondents dd not answer the core queston, but dd answer other related questons. Heren, those 106 respondents solely dd not answer the core queston 53

66 CHAPTER FOUR n entre survey. As to such non-responses, there mght be a few reasons for explanaton such as these respondents mght lack suffcent nformaton on nterest rates on formal loan, however, whatever the reason may be, ther non-responses are vewed as ntentonal. Hence, these non-responses, whch are called tem non-responses by Bernsky (2005), are not random. We cannot exclude these non-responses from the sample wthout ncurrng the rsk of compostonal bases n potental borrowers atttudes towards nterest rates on formal loans (Bernsky, 2005). Other questons n the survey ncluded the followng: Is your man cash ncome from farm work? A) Yes; B) No Who s your preferred lender when you want to borrow money? A) relatves or frends; B) formal fnancal nsttutons; C) usury ; and Who dd you manly borrow from n the past? A) relatves or frends; B) formal fnancal nsttutons; C) usury. Only one respondent n our survey admtted that not only hs preferred lender but also hs past man source of loans was usury. One other respondent stated that usury was hs preferred source of loans, but not hs man source n the past. Usury rates wll presumably be much hgher than formal rates, so anyone accustomed to usury wll presumably fnd formal rates to be not too hgh. For brevty and convenence, we smply drop the two cases n whch usury was stated, and leavng these two observatons out of the study does not sgnfcantly alter the results. Hence, we fnally leave the sample sze of 288 for our analyss. In addton, the answers choces related to the questons of Who s your preferred lender when you want to borrow money? and Who dd you manly borrow from n the past? become two: A) relatves or frends and B) formal fnancal nsttutons. Responses to the questons dscussed above are coded nto the followng varables as follows: atttude equals 1 f the respondent states that nterest rates on formal loans are too hgh, and 0 otherwse; ncome equals 1 f the respondent s cash ncome s manly from farm work, and 0 otherwse; prefer equals 1 f the respondent s frst borrowng source s relatves or frends, and 0 otherwse; and source equals 1 f the respondent s man source of loans n the past was relatves or frends, and 0 otherwse. In addton, the varable response equals 1 f the respondent answered the core queston, and 0 otherwse. Personal characterstcs such as age and gender are also ncluded n our analyss, whereby gender equals zero for male respondents and one otherwse. Respondents educatonal attanment are coded nto seven 54

67 CHAPTER FOUR categores from never went to school (the lowest level) to completed college or unversty (the hghest). To avod multcollnearty n the emprcal analyss, we use dummy varables for the frst sx of these attanment levels (edu1 through edu6). The respondent s famly s total assets (assets), the number of laborers (labor), and the number of household members under 12 years of age and aged 65 or over (oldchld) are also ncluded. The defntons, means and standard devatons of the varables used n the emprcal analyss are summarsed n Table 1. Table 1: Varable defnton and sample statstcs Varable Defnton Mean Std.Dev. Obs. Dependent Varable response Answerng the core queston (.e., Gven that you wsh to borrow from a formal fnancal nsttuton, and comparng the nterest rate set by the formal nsttuton wth the maxmum nterest rate that you would accept, do you feel that the nterest rate set by the formal nsttuton?) =1; otherwse=0 atttude Feel that nterest rates on formal loans are too hgh=1; otherwse= Independent Varable age Respondent s age n years gender Whether the respondent s female (male=0, female=1) educatonal Set of dummy varables wth the reference category attanment "completed college or unversty" 288 edu1 1 f respondent never went to school; otherwse= edu2 1 f respondent attaned some elementary school; otherwse= edu3 1 f respondent completed elementary school; otherwse= edu4 1 f respondent attaned some hgh school; otherwse= edu5 1 f respondent completed hgh school; otherwse= edu6 1 f respondent attended some unversty or college; otherwse= labor The number of famly laborers oldchld The number of famly members aged under 12 or over 65 years ncome 1 f man cash ncome comes from farm work; otherwse= prefer 1 f preferred source of loans s relatves or frends; otherwse= source 1 f man source of loans n past was relatves or frends; otherwse= asset Total asset value of household ncludng house, farm machne, other fxed assets, etc. (n thousand Yuan)

68 CHAPTER FOUR 4.4. Conceptual framework Our rural fnance survey was desgned to assess potental formal loan borrowers atttudes towards nterest rates. Atttude s descrbed or defned as a learned predsposton to respond n a consstently favorable or unfavorable manner wth respect to a gven object (Fshben and Ajzen, 1975). Consderng a truth that potental borrowers usually plan to use formal loans for a purpose of ether producton or consumpton and consequently, t s a underlyng dea that these potental borrowers each have a potental nvestment project n mnd, and that the rates of return of these nvestments follow some dstrbuton, wth some rates of return beng hgher than the current nterest rates, and others beng lower or same, and these potental borrowers formulatng ther atttudes correspondngly (.e. not too hgh or too hgh). Note, however, that consumpton cannot drectly create wealth, but potental borrowers who plan to borrow for consumpton should also look for a proper nvestment project because they have to repay the loan. Therefore, accordng to law and loan contract, whatever the purpose of borrowng may be, potental borrowers each should consder a nvestment project n mnd so as to repay loan n due tme. The use of rate of return of a nvestment project ( R atttude towards nterest rates on formal loan ( R Interest Invest ) as a way of relatng to an ) seems reasonable f potental formal loan borrowers who want to choose between two atttudes (.e., ether too hgh or not). Hence, n ths paper a respondent's atttude towards the nterest rate on formal loans s assumed to depend on hs or her personal rate dfference ( R Df ) between the nterest rate on formal loan and the rate of return of a potental nvestment project from borrowng, namely, R = R Interest - R Df Invest. As far as a respondent s Df R s concerned, Interest R can be vewed as constant term because t s set by the fnancal nsttutons, hence, the respondent s rate dfference s absolutely decded by R Invest soco-economc characterstcs as outlned n the followng equaton: R Df = R = R Interest Interest Invest R f ( age,gender,educaton ( edu1 edu6 = 1,2,,n respondents, whch n turn s nfluenced by hs or her ),labor,oldchld,ncome,asset ) (1) 56

69 CHAPTER FOUR further, consderng the constant term Interest R, equaton (1) can be smplfed below: R Df = F( age,gender,educaton ( edu1 edu6 ),labor,oldchld,ncome,asset ) = 1,2,,n respondents ( 2 ) Therefore, the rate dfference s nfluenced by the respondent's age, gender, and educatonal attanment, the number of laborers n hs or her famly, and the number of household members aged under 12 or over 65 years. Whether or not farmng generates the respondent's man cash ncome, and hs or her total famly s asset level are also hypotheszed to nfluence the rate dfference and, by extenson, atttude towards nterest rates on formal loans Emprcal Model In order to estmate the rate dfference, we specfy the conceptual model n equaton (2) as follows: R Df = αx + ε 1 ( 3 ) where once agan Df R represents the rate dfference to respondent of comng to hs or her atttude towards nterest rates on formal loan, above, and α s a vector of correspondng parameters. 2 dstrbuted, namely, ~ ( 0 σ ) ε. 1, We cannot actually observe the rate dfference 1 X s a vector of the covarates dscussed Df R ε 1 s assumed to be normally, but we do know whether or not the respondent states that the nterest rate on formal loans s too hgh. Thus we have an ndcator of whether or not R Df 0 s true (van de Ven and van Praag, 1981). In other words, all we observe s the dchotomous varable atttude, whch we label γ and whch equals 1 f the respondent states that nterest rates are too hgh, and 0 otherwse. Thus, γ s gven by: γ γ = 0 = 1 f f R R Df Df < 0 0 ( nterest rates are not too hgh ), ( nterest rates are too hgh ). ( 4 ) As mentoned above, a consderable percentage of the respondents dd not answer the 57

70 CHAPTER FOUR core queston but dd answer the other questons n the survey (.e., tem non-response). These observatons, f they are excluded from sample, could not only result n a compostonal bases (Bernsky, 2005), but also not be used to estmate the coeffcents α n equaton (3) (van de Ven and van Praag, 1981). Accordng to van de Ven and van Praag (1981), tem non-response behavor can also be analyzed by a means of probt analyss. For each respondent we defne the varable response δ = 1 f all questons related to rural fnance have been answered, and δ = 0 f all questons except the core queston were answered: δ = 0 f δ = 1 f Y = βz + ε Y < 0, Y 0, 2 ( 5a ) ( 5b ) where Y s an unobserved ndex of response propensty, β a vector of unknown parameters, Z a vector of exogenous varables, and ε 2, 2 ( 0 ) ~ σ 2 ε 2 s also assumed to be normally dstrbuted, namely,. Specfcally, we can vew tem non-response as respondents preferences for answerng the core queston. It s assumed that preferences represent the formaton of an ndvdual s state of mental awareness that prmarly reflects an ndvdual s belefs and perceptons about the objects, whch are dynamc n nature and can change sgnfcantly over tme (Huang, 1996). Thus, t s hypotheszed that a respondent s soco-economc characterstcs, for example, the varables age, gender, labor, oldchld, ncome, edu1,,edu6, prefer, source, and asset nfluence hs or her preferences for answerng the core queston (.e., Z=(age, gender, labor, oldchld, edu1,,edu6,ncome,prefer, source, asset, and constant term)). To sum up, snce we only observe atttudes towards nterest rates on formal loan for those respondents wllng to answer the core queston frst, equatons (5a) and (5b) model whether the respondent answered the core queston and consst of a probt model n the preference equaton. Moreover, equatons (3) and (4) are respondents atttudes towards nterest rates on formal loans and a probt model n the atttude equaton. In addton, δ n equaton (5a) s fully observed, but γ n equaton (4) s observed only for those respondents 58

71 CHAPTER FOUR who chose to answer the core queston. Although equatons (3), (4), (5a), and (5b) can be estmated ndependently of the probt procedure, there wll be a loss of effcency unless the error terms of the two probt equatons are ndependent (Meng and Schmdt, 1985). More mportantly, observatons for equatons (3) and (4) represent a choce-based or censored sample whch could be subjected to potental selectvty bas f estmated separately (Huang, 1996). Therefore, the approprate procedure s the jont estmaton of equatons (3), (4), (5a) and (5b) (Meng and Schmdt, 1985). Accordngly, followng van de Ven and van Pragg (1981), we assume that equaton (3) and ε 2 n equaton (5b) are bvarate standard normally dstrbuted 4 wth correlaton coeffcent ρ, the cumulatve bvarate normal dstrbuton functon Φ 2, and cumulatve standard normal dstrbuton functon Φ.Then, the lkelhood [based on eqs. (3), (4), (5a) and (5b)] s : ε 1 n N N 1 InL = Φ2( αx, βz; ρ ) Φ2( αx, βz; ρ ) Φ( βz ) = 1 = N + 1 = N M (6 ) where the frst N1observatons have γ = δ = 1( answered all questons and feel that nterest rates on formal loans are too hgh), the followng N N1 observatons have γ = 0 and δ =1 (answered all questons and feel that nterest rates on formal loans are not too hgh), and the last M N observatons have δ = 0 (answered all questons except the core queston on atttudes towards nterest rates) (van de Ven and van Praag, 1981) Results and Dscusson The probt wth sample selecton model was estmated usng the maxmum lkelhood method. The model specfcaton outlned above was ntally estmated usng the complete set of explanatory varables presented n Table 1. Lkelhood Rato tests were then used to exclude 4 Namely, σ 1 = σ 2 = 1. The assumpton s based on the well-known probt-normalzaton n order to get by maxmzng the nonlnear lkelhood functon (5) an asymptotcally effcent estmate of α and β. See, e.g. Goldberger (1964). 59

72 CHAPTER FOUR explanatory varables whch do not mprove the ft of the model. In the followng we report the results of the resultng parsmonous specfcatons, whch just focus on the sgns of statstcally sgnfcant fndngs for each explanatory varable. The maxmum lkelhood estmates of the model are presented n Table 2. The explanatory varables of dfferent educaton attanment levels (.e., edu1,,edu6 ) were excluded from the outcome functon wth respect to the respondents atttude towards hgh nterest rates recognton on formal loan because they dd not mprove the ft of the probt wth sample selecton model. A lkelhood-rato test ndcates that the null hypothess that the two error terms of probt model wth sample selecton are uncorrelated (.e., ρ = 0 ) can be rejected at the sgnfcance level, namely, the sum of the log lkelhoods from the two separate probt models do not equal the log lkelhood of the probt model wth sample selecton at the sgnfcance level. The results of the response equaton (5a and 5b) (see 60 δ n table 2) show that respondents whose famly member have more people aged over 65 and more chldren under 12 years of age, are less lkely to answer the core queston. A plausble explanaton s that more old and chldren people n a famly usually means more consumpton expendture than wealth creaton. Such famles may be generally less lkely to borrow and therefore wll lack the nformaton requred to answer the core queston. Respondents who have lower educaton attanment level (.e., never went to school or only attaned some elementary school) are also less lkely to answer the core queston. The strong negatve nfluence of edu1 and edu2, or ths phenomenon, mght partly be explaned by the reduced capablty of less educated people to fll n the questonnare. However, snce these same respondents dd answer the other questons n the survey, t may also be an ndcaton that those people rarely borrow money from formal fnancal nsttutons and were thus unable to answer core queston. Interestngly, as opposed to some respondents who completed elementary school (.e., varable edu3), respondents who attaned some hgh school (varable edu4) are less lkely to answer the core queston. The negatve nfluence of edu4, or ths phenomenon, mght partly be explaned by ncreased capablty of more educated people to manly rase funds through other fnancng channels such as nformal fnance so that they mght also have nsuffcent knowledge to

73 CHAPTER FOUR respond the core queston. As mght be expected, those respondents whose past borrowng source s relatves or frends are less lkely to answer the core queston. Fnally, the fndng of a negatve and sgnfcant coeffcent assocated wth the varable prefer s nterestng but makes sense because those respondents whose preferred source of loans s frends and relatves mght never access a formal loan, so they cannot gve ther answer wth respect to the core queston due to relatvely poor knowledge of nterest rates on formal loans. Wth respect to the atttude equaton (3 and 4), the results (see γ n table 2) show that beng female has a sgnfcant but negatve effect on the lkelhood that a respondent wll state that nterest rates on formal loans are too hgh. Ths fndng mght mply that female farmers are more confdent of usng formal loans than males. Ths phenomenon mght be explaned by the fact that females are more rsk-averse than males (see, for example, Fletschner and Kenney, 2011; Gockel, 2009; etc.), so that they mght effcently use formal loans f they can access the loan. Moreover, results ndcate that respondents whose man cash ncome s agrculture are less lkely to state that nterest rates on formal loans are too hgh. Ths fndng mght be explaned by the popular large scale fnancal support for agrcultural producton brought about by agrculture loans wth relatvely lower usng costs n Chna, for example, a well-known polcy that government purchases gran wthout lmtatons at protectve prces (.e., a knd of prce subsdy) not only compensates farmers all producton costs ncludng loan costs but also guarantees them a steady ncrease ncome. In addton, as mght be expected, the results suggest that total values about a famly s total asset exert sgnfcant but opposte nfluence on the lkelhood that a respondent wll state that nterest rates on formal loans are too hgh. A plausble explanaton s that more total assets are benefcal for smoothng any possble loan rsks and ncreasng the capablty of loan payment, so more assets reduce the respondents senstvty to nterest rates. In order to get some deas about the relatve nfluence of the atttude towards nterest rates on formal loans we respectvely calculated the margnal effects of the probablty Pr( δ 1), Pr( γ = 1), and Pr( γ 1 δ = 1) wth respect to explanatory varables at the = = average value n the sample (see Table 2). Consderng that the former and mddle margnal 61

74 CHAPTER FOUR effects of explanatory varables (columns 3 and 5 n Table 2) have the same sgn and sgnfcance as ther related coeffcents n the equatons (5a) and (3) (columns 2 and 4 n Table 2), for parsmony, we therefore just shed lght on the latter margnal effect (column 6 n Table 2). Column 6 represents the margnal effects for the probablty of an atttude towards hgh nterest rates on formal loan gven a response that a respondent answered the core queston beng observed (.e., Pr( γ 1 δ = 1) ). = From column 6 n Table 2, the varables gender, ncome, and asset have sgnfcant but negatve nfluence on the lkelhood that a respondent wll state that nterest rates on formal loan are too hgh, but the margnal effects wth respect to respondent s famly total asset values are neglgbly due to very small effect. Therefore, the remanng two varables (respondents gender and whether or not ther man cash ncome s from agrculture) are mportant for settng a proper loan program for fnancal nsttutons. Also the level of sgnfcance of the coeffcents of these two varables (statstcal sgnfcance at 1% level) shows ther mportance. We may conclude that respondents who are female and whose man cash ncome s from agrculture mght be top-prorty lendng objects of formal loans due to ther opposte atttude wth respect to the lkelhood that a respondent state that nterest rates on formal loan are too hgh. Snce these two types of respondents smultaneously ndcated responses of answerng the core queston and ther atttude towards hgh nterest rates on formal loan, thus, the rural formal fnancal nsttutons should pay more attenton to ther lqudty demands. Moreover, the condtonal margnal effects of varable edu1, edu2, prefer, and source have sgnfcant and postve nfluence on the lkelhood that a respondent wll state that nterest rates are too hgh. The postve margnal effects from the varable edu1 and edu2 can be explaned by the fact that relatvely lower educaton attanment levels largely lmt those respondents capablty of wealth creaton so that they hold an opnon that nterest rates on formal loans are too hgh. Combnng ths wth the fndngs of the response functon (column 2 and 3 n Table 2), t mght be concluded those respondents wth relatvely lower educaton level most lkely shed away the formal loan market because they cannot afford nterest rates on formal loans, and thus t s not dffcult to understand why they dd not respond the core 62

75 CHAPTER FOUR queston. The postve margnal effect from the varables prefer and source are as expected and can be explaned by the fact that most loans wth zero nterest from relatves or frends 5 are more lkely appealng so that respondents feel that nterest rates on formal loans are too hgh. Combnng ths wth the program wth respect to how to attract more farmers nto the formal loan market, we may conclude that popular agrcultural loans are very dffcult to attract those farmers who have cheap channels to rase funds, so the rural fnancal nsttutons may provde them other servces, such as fnancal plannng, so as to get servce yelds. Table 2: Maxmum lkelhood estmates of the parameters and margnal effects of response for answerng the core queston ( δ ) and atttude towards nterest rate on formal loan ( γ ) equatons Response ( ) Atttude Varable Condtonal Margnal Margnal Coeffcent Coeffcent Margnal Effects Effects Effects age 0.01(0.01) 0.005(0.003) -0.01(0.01) (0.002) (0.003) gender 0.02(0.19) 0.009(0.07) *** *** (0.05) *** (0.08) (0.19) labor -0.01(0.06) (0.02) -0.01(0.06) (0.02) (0.02) oldchld *** (0.07) *** (0.03) 0.02(0.07) (0.02) 0.04(0.03) ncome -0.20(0.18) -0.08(0.07) *** *** (0.05) *** (0.07) (0.19) asset (0.0006) (0.0002) * (0.001) * (0.0002) ** (0.0002) edu ** (0.66) *** (0.17) 0.69 *** (0.04) edu *** (0.60) *** (0.15) 0.70 *** (0.04) edu3-0.78(0.60) -0.30(0.23) 0.24(0.29) edu * (0.61) * (0.21) 0.38(0.43) edu5-0.71(0.58) -0.27(0.21) 0.15(0.15) edu6-0.77(0.60) -0.30(0.23) 0.24(0.30) prefer * (0.26) * (0.08) 0.07 ** (0.03) source *** (0.21) *** (0.07) 0.10 *** (0.03) constant 1.78 ** (0.73) 0.13(0.48) Log Lkelhood = LR test of ndep. Eqns ( ρ =0): =7.13 Wald (6) = ** Number of Observatons = 288 Note: *,**,***, represents 10%, 5%, and 1% sgnfcant level, respectvely; standard errors n parentheses. Margnal effects s at the same mean value. The approprate margnal effect for a bnary ndependent varable, say, d, would be calculated by takng the dfference of estmated probabltes when d ncreases from 0 to 1, namely, Margnal Effect=Pr(d=1)-Pr(d=0) (Greene, 2011). 5 Interest rates charged by relatves or frends are often zero or very low n the rural areas of Chna. Indeed, n our survey, 93% of the respondents, who answered the core queston, confrmed that the nterest rates charged by relatves or frends are zero. 63

76 CHAPTER FOUR 4.7. Summary and Concluson In ths artcle we studed respondents atttudes towards nterest rates on formal loan on the bass of ther responses of answerng all questons n the survey. For an estmaton method we used a probt model wth sample selecton whch was suggested by van de Ven and van Praag (1981). The maxmum lkelhood estmaton that accounts for sample selectvty bas was used to analyze the survey data collected for ths study. Our man concluson s that respondents gender, ther man cash ncome sources, and ther famly s total asset values have negatve nfluence on the lkelhood that a respondent wll state that nterest rates on formal loans are too hgh. The condtonal margnal effects, whch ndcate respondents atttudes towards hgh nterest rates on formal loans on the bass of ther response of answerng all survey questons, suggest that respondents gender, ther man cash ncome sources, relatvely lower educaton attanment levels, and past man borrowng source have mportant nfluence on the atttudes towards hgh nterest rates on formal loans. A clear mplcaton s that female clents or clents whose man cash ncome s from farm work deserve to be pad more attenton by formal loan lenders due to ther negatve atttudes towards hgh nterest rates recognton on formal loans. If Chna s rural formal fnancal nsttutons can closely combne wth the state s preferental polces whch support agrcultural producton and rural economc growth, then they can develop proper loan servces or programs whch are amed at these two types of clents n rural fnancal markets. In fact, from 2004 to 2009, each year s Chna s No. 1 documents released by the central government, elaboratng on the bggest economc concern of the state, clearly announced many polces to promote agrculture and rural development. Many polces from these documents encourage development of rural fnance and consequently, developng several polcy-led fnancal products, for example, agrcultural loan wth approprate dscount of lendng rate, can not only appeal to many potental clents but also promote formal loan s development. In addton, from the results of ths emprcal nvestgaton, we draw the followng polcy mplcaton. Snce 22 percent of the respondents felt that nterest rates on formal loans were 64

77 CHAPTER FOUR too hgh, t brngs evdence for the falure of rural formal loan markets to some extent. Consderng populaton sze n Chna s rural areas, we have enough reasons to beleve that there should be many potental formal loan borrowers who are sufferng loan constrants because they cannot afford nterest rates on formal loans. As we mentoned above, one obvous reason for those respondents predcaments s that RCCs currently have a monopoly n rural formal loan market so that borrowers almost have no multple optons and consequently, they mght be forced to leave the formal loan market. In order to redress the partally falure rural fnancal system, the Chnese government and POBC should change the monopolzaton of RCCs by allowng more other fnancal nsttutons to enter rural fnancal market for a compettve envronment. For example, the Chnese government and POBC should bend the rules for prvate or even foregn captal enterng the rural fnancal market so as to enlarge the rural fnancal supply to some extent. Pror to 2005 and 2006, although Chna s central government has opened up ts rural fnancal market to prvate or foregn mcrofnance nsttutons (MFI) (Guo and Ja, 2009; Turvey and Kong, 2010), these few lcensed nsttutons wth plot character operate n pretty small regons so that they cannot shake RCCs monopoly on formal loan dsbursement at all. In consequence, f a large number of prvate or foregn fnancal companes are lcensed by the government and POBC, then t should be benefcal to promote better competton n rural fnancal market. Another possble reason for those respondents predcaments s that the characterstcs of rural fnancal demands are usually small, scattered and seasonal as compared to urban fnancal demands so that tradtonal commercal banks would not lke to enter nto the rural fnancal market due to hgh-transacton costs and lmted collateral capacty. In Chna, for example, pror to 1995, ABC wthdrew from rural areas and focused prmarly n urban areas (Guo and Ja, 2009). And ABC s urbanzaton s also an mportant reason whch leads to RCCs current poston of monopoly n Chna s rural formal loan market, of course. In consequence, the Chnese government and POBC should lay out preferental polces for agrcultural or rural loan (e.g., exemptng fnancal nsttutons full sales tax and partal ncome tax f ther loans dsbursement are for agrcultural producton or rural development) so as to attract a great deal fnancal nsttutons to penetrate n rural areas. Ths s also a way to change the monopolzaton of exstng rural fnancal nsttutons and redress fnancal market 65

78 CHAPTER FOUR falure. Overall, as far as polcymakers (.e., the government and POBC) are concerned, the key pont of future work s to change the monopolzaton of current rural fnancal nsttutons and allow more fnancal nsttutons or captal to enter rural fnancal market so as to promote competton among dfferent nsttutonal lenders and thus potentally mprove the fnancal servces. 66

79 CHAPTER FOUR References: Alesse, Rob, S. Hochguertel, and G. Weber (2005). Consumer Credt: Evdence from Italan Mcro Data. Journal of the European Economc Assocaton. 3(1): Attanaso, O. P., P. K. Goldberg, and E. Kryazdou (2008). Credt Constrants n the Market for Consumer Durables: Evdence from Mcro Data on Car Loans. Internatonal Economc Revew. 49(2): Bernsky, A. J. (2005). Slent Voces: Publc Opnon and Poltcal Partcpaton n Amercan. Prnceton Unversty Press. Bottomley, A. (1975). Interest Rate Determnaton n Underdeveloped Rural Areas. Amercan Journal of Agrcultural Economcs. 57(2): Castro, E.R., and E. C. Texera (2006). Agrcultural Credt Interest Rate Equalzaton Polcy: A Growth Subsdy? Selected Paper. IAAE Conference, Gold Coast, Australa, August Cheng, E. and Xu, Z. (2004). Rates of Interest, Credt Supply and Chna s Rural Development. Savngs and Development Quarterly Revew. Vol (2): Deheja, R., H. Montgomery, and J. Morduch (2005). Do Interest Rates Matter? Credt Demand n the Dhaka Slums. ADB Insttute Dscusson Paper 37. Dodson, C.B., and M.R. Duncan (1998). The Determnants of Net Interest Rate Spreads for Farm Credt Drect Lendng Assocatons. Workng Paper. Proceedngs: 1998 Regonal Commttee NC-221, October 5-6, 1998, Lousvlle, Kentucky. Duffee, G.R. (2002). Term Prema and Interest Rate Forecasts n Affne Models. The Journal of Fnance. 57(1): Featherstone, A.M., B.K.Goddwn, and A. D. Barkema (1993). Dynamcs of Farm Interest Rates. Workng Paper. Proceedngs: 1993 Regonal Commttee NC-207, October 4-5, 1993, Chcago, Illnos Feder, G., L.J. Lau, J.Y. Ln, and X. Luo. (1990). The Relatonshp between Credt and Productvty n Chnese Agrculture: A Mcroeconomc Model of Dsequlbrum. Amercan Journal of Agrcultural Economcs. 72(5): Fshben, M. and Ajzen, I. (1975). Belef, Atttude, Intenton, and Behavor: An Introducton 67

80 CHAPTER FOUR to Theory and Research. Readng, MA: Addson-Wesley. Fletschner, D., and L. Kenney (2011), Rural Women s Access to Fnancal Servces. Workng Paper, No Agrcultural Development Economcs Dvson, The Food and Agrculture Organzaton of the Unted Natons. March, 2011 Gale, F., and R. Collender (2006). New Drectons n Chna s Agrcultural Lendng. Workng Paper. WRS Economc Research Servce. Unted States Department of Agrculture. January, Gockel, R.P. (2009), Credt and Rsk: Analyzng Determnants of Wllngness to Borrow More Credt n Rural Vetnam. Workng Paper, Henry M. Jackson School of Internatonal Student Degree Project, June 4, Goldberger, A.S. (1964). Econometrc Theory. New York: John Wley & Sons. Greene, W. (2011). Econometrc Analyss (7 th edton). Prentce Hall. Gross, D.B., and N.S. Souleles (2002). Do Lqudty Constrnts and Interest Rates Matter for Consumer Behavor? Evdence from Credt Card Data. Quarterly Journal of Economcs, 117(1): Guo, P., and X. Ja (2009). The Structure and Reform of Rural Fnance n Chna. Chna Agrcultural Economc Revew. 1(2): Huang, C.L. (1996). Consumer Preference and Atttudes towards Organcally Grown Produce. European Revew of Agrcultural Economcs. 23: Jarrow, R., H. L, and F. Zhao (2007). Interest Rate Caps Smle Too? But Can the LIBOR Market Models Capture the Smle? The Journal of Fnance. 62(1): Ja, X., Hedhues, F. and Zeller, M. (2007). Takng the Bands off Rural Credt Market: an Evdence from Chna. IAMO Forum 2007: Sustanable Rural Development: What s the Role of the Agr-food Sector? Halle (Saale). Karlan, D. S., and Joathan Znman (2008). Credt Elastctes n Less-Developed Economes: Implcatons for Mcrofnance, Amercan Economc Revew. 98(3): L, X., C. Gan, and B. Hu (2011). Accessblty to Mcrocredt by Chnese Rural Households. Journal of Asan Economcs. 22(3): Martn, M.F. (2012). Chna s Bankng System: Issues for Congress. CRS Report for Congress, Congressonal Research Servce R February 20,

81 CHAPTER FOUR Meng, C. L., and Schmdt, P. (1985). On the cost of Partal Observablty n the Bvarate Probt Model. Internatonal Economc Revew. 26(1): Mohane, H., G.K. Goetzee, and W. Grant (2002). The Effects of the Interest Rates Celngs on the Mcro Lendng Market n South Afrca. Workng Paper: Unversty of Pretora, Pretora South Afrca. btstream/18078/1/wp pdf Petrck, M., and L. Latruffe (2005). The Determnants of Polsh Farmers Credt Interest Rates: Hedonc Prce Analyss and Implcatons for Government Polcy. XI th Insernatonal Congress of the EAAE. Copenhagen, Denmark. August 24-27, Salazar, G., C. Turvey, V. Bogan, and L. Cubero (2010). How hgh s too hgh? Soarng Interest Rates and the Elastcty of Demand for Mcrocredt. Selected Paper. AAEA, CAES, & WAEA Jont Annual Meetng, Denver, Colorado, July 25-27, Turvey, C.G., and R. Kong (2010). Informal Lendng amongst Frends and Relatves: Can Mcrocredt Compete n Rural Chna? Chna Economc Revew. 21(4): van de Ven, W.P.M.M., and B.M.S. van Praag (1981). The Demand for Deductbles n Prvate Health Insurance: A Probt Model wth Sample Selecton. Journal of Econometrcs. 17:

82 CHAPTER FIVE CHAPTER FIVE SUMMARY AND OUTLOOK Ths dssertaton used randomzed feld survey data to estmate lendng volume decsons of lenders, the number of lendng approvals by lenders, and respondents atttudes about lendng rates of formal loans. The sample ncluded rural resdents collected from rural Shandong, Chna n July Ths dssertaton provded emprcal evdence for lendng volume decsons n Chapter Two, the number of lendng approvals over a perod of years n Chapter Three, and respondents atttudes towards nterest rates on formal loans n Chapter Four. The results manly show that rural households characterstcs were the man affect on the approval decson, rather than the lendng volume decson covered n Chapter Two. Chapter Three elaborates on the fndng that households where the head of the house has a hgher educaton level and has a large area of land were more lkely to be granted a loan by lenders over a perod of years. Addtonally, Chapter Four has a dscusson of the fndng that respondents gender and man cash ncome sources have mportant nfluences on the atttudes towards hgh nterest rate recognton on formal loan. These fndngs must be consdered when drawng polcy recommendatons, as the potental roles of educaton and amount of land owned ndcate not only ncreasng rural resdents welfare, but also the ablty to allow borrowers to gan a more advantaged poston n the fnancal market. Hence, the government at all levels n Chna should strve to mprove educaton n rural areas and protect land rghts of all farm famles wth the am of ncreasng farmers chances for access to formal loans. These fndngs also gve a clear mplcaton that female clents, or clents whose man cash ncome s from farm work, deserve to be pad more attenton by formal loan lenders, n part because they generally have postve atttudes about current lendng rates of formal loans. The fndngs brng to lght three possble dsequlbrum stuatons between lendng and borrowng practces, whch can all be summed up to the fact that loan demand does not equal loan supply n the rural fnancal market. The frst dsequlbrum s a volume dsequlbrum, 70

83 CHAPTER FIVE f a borrower s desred loan volume does not equal hs/her dsbursed loan volume. The second dsequlbrum s a number dsequlbrum, f a borrower receves a lower number of loan approvals tmes than a number of loan applcatons tmes. The thrd dsequlbrum s a loan rate dsequlbrum, f a borrower s expected nterest rate on formal loan does not equal the actual nterest rate set by the fnancal nsttutons. Ths leads to a queston that cannot be answered n the study but needs to be answered n the future: how do the household s characterstcs affect the dsequlbrum between lendng and borrowng practces? In addton, gven the results of ths study, two addtonal questons requre answers. Frst, how can the questonnare desgn be mproved to acheve hgher qualty data relatng households characterstcs? The data should be genune exogenous when I use them to be ndependent varables for my emprcal analyss n the dssertaton. If these data are genune exogenous, they can effectvely avod endogenety problems that arse n emprcal analyss. Second, what extent do the results apply to other settngs? Chna s a large country and suffers unbalanced development n varous rural areas and the results may vary across settngs. Therefore, further research needs to expand the survey area, whch can nclude a number of completely dfferent regons n Chna, to reach more general conclusons for the entre country. 71

84 APPENDIX APPENDIX SAMPLE QUESTION (ABRIDGED VERSION IN ENGLISH) NOTE: Author s translaton from Chnese. The questonnare was desgned n Chnese n accordance wth Chnese readng habts and dom. 1. Respondent s gender: male; female. Household head gender: male; female. 2.Responedent age: ; Household head s age:. 3. How many people do farm work n your famly? 4. How many people do off-farm work n your famly? 5. How many people are over 65 years old n your famly? 6. How many people are under 12 years of age n your famly? 7. What s the hghest level of educaton you have attaned? (Please ndcate only the last level of educaton completed or the level at whch you are currently studyng) 1uneducated (never went to school); 2completed some elementary; 3completed elementary; 4completed some mddle school; 5completed mddle school; 6completed some college or unversty; 7completed college and unversty. 8. How many years of the household head do he/she have? 9. How many Mu of land have you farmed? (Note: 1 Mu 0.16 Acre ). 10. Does the household head have skll(s) for workng non-farm job? Yes; No. 11. What s the total worth of all assets (ncludng house, land, fxed assets, etc.) n your famly (n thousands)? thousand Yuan. 12. Gven that you wsh to borrow from a formal fnancal nsttuton, and comparng the nterest rate set by the formal nsttuton wth the maxmum nterest rate that you would accept, do you feel that the nterest rate set by the formal nsttuton s: A) too hgh; B) too low; C) far 13. Is your man cash ncome from farm work? A) Yes; B) No 14. Who s your preferred lender when you want to borrow money? 72

85 APPENDIX A) relatves or frends; B) formal fnancal nsttutons; C) usury 15. Who dd you manly borrow from n the past? A) relatves or frends; B) formal fnancal nsttutons; C) usury 16. Dd you be granted loan(s) from formal fnancal nsttutons over the past fve years? A) Yes; B)No; C) Non-applcaton 17. If you receved formal loan (s), then how many tmes dd you be granted by formal fnancal nsttuton over the past fve years? And how much of the last loan dd you be granted by formal fnancal nsttuton over the past years? tme(s); Yuan. 73