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

Size: px
Start display at page:

Download "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

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

An Alternative Way to Measure Private Equity Performance

An Alternative Way to Measure Private Equity Performance An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate

More information

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy

Answer: A). There is a flatter IS curve in the high MPC economy. Original LM LM after increase in M. IS curve for low MPC economy 4.02 Quz Solutons Fall 2004 Multple-Choce Questons (30/00 ponts) Please, crcle the correct answer for each of the followng 0 multple-choce questons. For each queston, only one of the answers s correct.

More information

HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA*

HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* HOUSEHOLDS DEBT BURDEN: AN ANALYSIS BASED ON MICROECONOMIC DATA* Luísa Farnha** 1. INTRODUCTION The rapd growth n Portuguese households ndebtedness n the past few years ncreased the concerns that debt

More information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ). REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or

More information

! # %& ( ) +,../ 0 1 2 3 4 0 4 # 5##&.6 7% 8 # 0 4 2 #...

! # %& ( ) +,../ 0 1 2 3 4 0 4 # 5##&.6 7% 8 # 0 4 2 #... ! # %& ( ) +,../ 0 1 2 3 4 0 4 # 5##&.6 7% 8 # 0 4 2 #... 9 Sheffeld Economc Research Paper Seres SERP Number: 2011010 ISSN 1749-8368 Sarah Brown, Aurora Ortz-Núñez and Karl Taylor Educatonal loans and

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed

More information

Using Series to Analyze Financial Situations: Present Value

Using Series to Analyze Financial Situations: Present Value 2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated

More information

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION

PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIGIOUS AFFILIATION AND PARTICIPATION PRIVATE SCHOOL CHOICE: THE EFFECTS OF RELIIOUS AFFILIATION AND PARTICIPATION Danny Cohen-Zada Department of Economcs, Ben-uron Unversty, Beer-Sheva 84105, Israel Wllam Sander Department of Economcs, DePaul

More information

Statistical Methods to Develop Rating Models

Statistical Methods to Develop Rating Models Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and

More information

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall

Staff Paper. Farm Savings Accounts: Examining Income Variability, Eligibility, and Benefits. Brent Gloy, Eddy LaDue, and Charles Cuykendall SP 2005-02 August 2005 Staff Paper Department of Appled Economcs and Management Cornell Unversty, Ithaca, New York 14853-7801 USA Farm Savngs Accounts: Examnng Income Varablty, Elgblty, and Benefts Brent

More information

Analysis of Premium Liabilities for Australian Lines of Business

Analysis of Premium Liabilities for Australian Lines of Business Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton

More information

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic

Institute of Informatics, Faculty of Business and Management, Brno University of Technology,Czech Republic Lagrange Multplers as Quanttatve Indcators n Economcs Ivan Mezník Insttute of Informatcs, Faculty of Busness and Management, Brno Unversty of TechnologCzech Republc Abstract The quanttatve role of Lagrange

More information

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence 1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh

More information

Gender differences in revealed risk taking: evidence from mutual fund investors

Gender differences in revealed risk taking: evidence from mutual fund investors Economcs Letters 76 (2002) 151 158 www.elsever.com/ locate/ econbase Gender dfferences n revealed rsk takng: evdence from mutual fund nvestors a b c, * Peggy D. Dwyer, James H. Glkeson, John A. Lst a Unversty

More information

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently. Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract - Stock market s one of the most complcated systems

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..

More information

Kiel Institute for World Economics Duesternbrooker Weg 120 24105 Kiel (Germany) Kiel Working Paper No. 1120

Kiel Institute for World Economics Duesternbrooker Weg 120 24105 Kiel (Germany) Kiel Working Paper No. 1120 Kel Insttute for World Economcs Duesternbrooker Weg 45 Kel (Germany) Kel Workng Paper No. Path Dependences n enture Captal Markets by Andrea Schertler July The responsblty for the contents of the workng

More information

The OC Curve of Attribute Acceptance Plans

The OC Curve of Attribute Acceptance Plans The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4

More information

Study on Model of Risks Assessment of Standard Operation in Rural Power Network

Study on Model of Risks Assessment of Standard Operation in Rural Power Network Study on Model of Rsks Assessment of Standard Operaton n Rural Power Network Qngj L 1, Tao Yang 2 1 Qngj L, College of Informaton and Electrcal Engneerng, Shenyang Agrculture Unversty, Shenyang 110866,

More information

THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE

THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE THE DETERMINANTS OF THE TUNISIAN BANKING INDUSTRY PROFITABILITY: PANEL EVIDENCE Samy Ben Naceur ERF Research Fellow Department of Fnance Unversté Lbre de Tuns Avenue Khéreddne Pacha, 002 Tuns Emal : sbennaceur@eudoramal.com

More information

Financial Instability and Life Insurance Demand + Mahito Okura *

Financial Instability and Life Insurance Demand + Mahito Okura * Fnancal Instablty and Lfe Insurance Demand + Mahto Okura * Norhro Kasuga ** Abstract Ths paper estmates prvate lfe nsurance and Kampo demand functons usng household-level data provded by the Postal Servces

More information

STATISTICAL DATA ANALYSIS IN EXCEL

STATISTICAL DATA ANALYSIS IN EXCEL Mcroarray Center STATISTICAL DATA ANALYSIS IN EXCEL Lecture 6 Some Advanced Topcs Dr. Petr Nazarov 14-01-013 petr.nazarov@crp-sante.lu Statstcal data analyss n Ecel. 6. Some advanced topcs Correcton for

More information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

Traditional versus Online Courses, Efforts, and Learning Performance

Traditional versus Online Courses, Efforts, and Learning Performance Tradtonal versus Onlne Courses, Efforts, and Learnng Performance Kuang-Cheng Tseng, Department of Internatonal Trade, Chung-Yuan Chrstan Unversty, Tawan Shan-Yng Chu, Department of Internatonal Trade,

More information

Criminal Justice System on Crime *

Criminal Justice System on Crime * On the Impact of the NSW Crmnal Justce System on Crme * Dr Vasls Sarafds, Dscplne of Operatons Management and Econometrcs Unversty of Sydney * Ths presentaton s based on jont work wth Rchard Kelaher 1

More information

Reporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide

Reporting Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (including SME Corporate), Sovereign and Bank Instruction Guide Reportng Forms ARF 113.0A, ARF 113.0B, ARF 113.0C and ARF 113.0D FIRB Corporate (ncludng SME Corporate), Soveregn and Bank Instructon Gude Ths nstructon gude s desgned to assst n the completon of the FIRB

More information

Factors Affecting Outsourcing for Information Technology Services in Rural Hospitals: Theory and Evidence

Factors Affecting Outsourcing for Information Technology Services in Rural Hospitals: Theory and Evidence Factors Affectng Outsourcng for Informaton Technology Servces n Rural Hosptals: Theory and Evdence Bran E. Whtacre Department of Agrcultural Economcs Oklahoma State Unversty bran.whtacre@okstate.edu J.

More information

The Application of Fractional Brownian Motion in Option Pricing

The Application of Fractional Brownian Motion in Option Pricing Vol. 0, No. (05), pp. 73-8 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qng-xn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com

More information

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1.

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1. HIGHER DOCTORATE DEGREES SUMMARY OF PRINCIPAL CHANGES General changes None Secton 3.2 Refer to text (Amendments to verson 03.0, UPR AS02 are shown n talcs.) 1 INTRODUCTION 1.1 The Unversty may award Hgher

More information

Management Quality, Financial and Investment Policies, and. Asymmetric Information

Management Quality, Financial and Investment Policies, and. Asymmetric Information Management Qualty, Fnancal and Investment Polces, and Asymmetrc Informaton Thomas J. Chemmanur * Imants Paegls ** and Karen Smonyan *** Current verson: December 2007 * Professor of Fnance, Carroll School

More information

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek

THE DISTRIBUTION OF LOAN PORTFOLIO VALUE * Oldrich Alfons Vasicek HE DISRIBUION OF LOAN PORFOLIO VALUE * Oldrch Alfons Vascek he amount of captal necessary to support a portfolo of debt securtes depends on the probablty dstrbuton of the portfolo loss. Consder a portfolo

More information

Financial Mathemetics

Financial Mathemetics Fnancal Mathemetcs 15 Mathematcs Grade 12 Teacher Gude Fnancal Maths Seres Overvew In ths seres we am to show how Mathematcs can be used to support personal fnancal decsons. In ths seres we jon Tebogo,

More information

Survive Then Thrive: Determinants of Success in the Economics Ph.D. Program. Wayne A. Grove Le Moyne College, Economics Department

Survive Then Thrive: Determinants of Success in the Economics Ph.D. Program. Wayne A. Grove Le Moyne College, Economics Department Survve Then Thrve: Determnants of Success n the Economcs Ph.D. Program Wayne A. Grove Le Moyne College, Economcs Department Donald H. Dutkowsky Syracuse Unversty, Economcs Department Andrew Grodner East

More information

Overview of monitoring and evaluation

Overview of monitoring and evaluation 540 Toolkt to Combat Traffckng n Persons Tool 10.1 Overvew of montorng and evaluaton Overvew Ths tool brefly descrbes both montorng and evaluaton, and the dstncton between the two. What s montorng? Montorng

More information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

LIFETIME INCOME OPTIONS

LIFETIME INCOME OPTIONS LIFETIME INCOME OPTIONS May 2011 by: Marca S. Wagner, Esq. The Wagner Law Group A Professonal Corporaton 99 Summer Street, 13 th Floor Boston, MA 02110 Tel: (617) 357-5200 Fax: (617) 357-5250 www.ersa-lawyers.com

More information

The Study on Farmers Debt, Loan Repayment and Guideline for Debt Settlement in the South of Thailand

The Study on Farmers Debt, Loan Repayment and Guideline for Debt Settlement in the South of Thailand Internatonal Journal of Agrculture and Food Scence Technology. ISSN 49-3050, Volume 4, Number 8 (03), pp. 835-840 Research Inda Publcatons http://www.rpublcaton.com/ jafst.htm The Study on Farmers Debt,

More information

Intra-year Cash Flow Patterns: A Simple Solution for an Unnecessary Appraisal Error

Intra-year Cash Flow Patterns: A Simple Solution for an Unnecessary Appraisal Error Intra-year Cash Flow Patterns: A Smple Soluton for an Unnecessary Apprasal Error By C. Donald Wggns (Professor of Accountng and Fnance, the Unversty of North Florda), B. Perry Woodsde (Assocate Professor

More information

Chapter 8 Group-based Lending and Adverse Selection: A Study on Risk Behavior and Group Formation 1

Chapter 8 Group-based Lending and Adverse Selection: A Study on Risk Behavior and Group Formation 1 Chapter 8 Group-based Lendng and Adverse Selecton: A Study on Rsk Behavor and Group Formaton 1 8.1 Introducton Ths chapter deals wth group formaton and the adverse selecton problem. In several theoretcal

More information

Efficient Project Portfolio as a tool for Enterprise Risk Management

Efficient Project Portfolio as a tool for Enterprise Risk Management Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse

More information

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000

Number of Levels Cumulative Annual operating Income per year construction costs costs ($) ($) ($) 1 600,000 35,000 100,000 2 2,200,000 60,000 350,000 Problem Set 5 Solutons 1 MIT s consderng buldng a new car park near Kendall Square. o unversty funds are avalable (overhead rates are under pressure and the new faclty would have to pay for tself from

More information

Covariate-based pricing of automobile insurance

Covariate-based pricing of automobile insurance Insurance Markets and Companes: Analyses and Actuaral Computatons, Volume 1, Issue 2, 2010 José Antono Ordaz (Span), María del Carmen Melgar (Span) Covarate-based prcng of automoble nsurance Abstract Ths

More information

CHAPTER 14 MORE ABOUT REGRESSION

CHAPTER 14 MORE ABOUT REGRESSION CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp

More information

Multiple-Period Attribution: Residuals and Compounding

Multiple-Period Attribution: Residuals and Compounding Multple-Perod Attrbuton: Resduals and Compoundng Our revewer gave these authors full marks for dealng wth an ssue that performance measurers and vendors often regard as propretary nformaton. In 1994, Dens

More information

Abstract. Keywords: Credit constraints, Double-hurdle, hybrid maize, adoption, Malawi

Abstract. Keywords: Credit constraints, Double-hurdle, hybrid maize, adoption, Malawi The Impact of Access to Credt on the Adopton of hybrd maze n Malaw: An Emprcal test of an Agrcultural Household Model under credt market falure Frankln Smtowe 1, Manfred Zeller 2 and Alexander Phr 3 Abstract

More information

Chapter 11 Practice Problems Answers

Chapter 11 Practice Problems Answers Chapter 11 Practce Problems Answers 1. Would you be more wllng to lend to a frend f she put all of her lfe savngs nto her busness than you would f she had not done so? Why? Ths problem s ntended to make

More information

CONSUMER LINES OF CREDIT: THE CHOICE BETWEEN CREDIT CARDS AND HELOCS. In the U.S. today consumers have a choice of two major types of lines of credit

CONSUMER LINES OF CREDIT: THE CHOICE BETWEEN CREDIT CARDS AND HELOCS. In the U.S. today consumers have a choice of two major types of lines of credit CONSUMER LINES OF CREDIT: THE CHOICE BETWEEN CREDIT CARDS AND HELOCS OSU Economcs Workng Paper WP04-04 I. INTRODUCTION In the U.S. today consumers have a choce of two major types of lnes of credt credt

More information

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET *

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET * Amy Fnkelsten Harvard Unversty and NBER James Poterba MIT and NBER * We are grateful to Jeffrey Brown, Perre-Andre

More information

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsn-yng Wu b a Professor (Management Scence), Natonal Chao

More information

Searching and Switching: Empirical estimates of consumer behaviour in regulated markets

Searching and Switching: Empirical estimates of consumer behaviour in regulated markets Searchng and Swtchng: Emprcal estmates of consumer behavour n regulated markets Catherne Waddams Prce Centre for Competton Polcy, Unversty of East Angla Catherne Webster Centre for Competton Polcy, Unversty

More information

The Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading

The Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading The Choce of Drect Dealng or Electronc Brokerage n Foregn Exchange Tradng Mchael Melvn & Ln Wen Arzona State Unversty Introducton Electronc Brokerage n Foregn Exchange Start from a base of zero n 1992

More information

How To Calculate The Accountng Perod Of Nequalty

How To Calculate The Accountng Perod Of Nequalty Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.

More information

Cahiers de la Chaire Santé

Cahiers de la Chaire Santé Cahers de la Chare Santé The nfluence of supplementary health nsurance on swtchng behavour: evdence from Swss data Auteurs : Brgtte Dormont, Perre-Yves Geoffard, Karne Lamraud N 4 - Janver 2010 1 The nfluence

More information

17 Capital tax competition

17 Capital tax competition 17 Captal tax competton 17.1 Introducton Governments would lke to tax a varety of transactons that ncreasngly appear to be moble across jursdctonal boundares. Ths creates one obvous problem: tax base flght.

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

Bargaining at Divorce: The Allocation of Custody

Bargaining at Divorce: The Allocation of Custody Barganng at Dvorce: The Allocaton of Custody Martn Halla Unversty of Lnz & IZA Chrstne Hölzl Unversty of Lnz December 2007 Abstract We model the barganng process of parents over custody at the tme of dvorce.

More information

HARVARD John M. Olin Center for Law, Economics, and Business

HARVARD John M. Olin Center for Law, Economics, and Business HARVARD John M. Oln Center for Law, Economcs, and Busness ISSN 1045-6333 ASYMMETRIC INFORMATION AND LEARNING IN THE AUTOMOBILE INSURANCE MARKET Alma Cohen Dscusson Paper No. 371 6/2002 Harvard Law School

More information

Health Insurance and Household Savings

Health Insurance and Household Savings Health Insurance and Household Savngs Mnchung Hsu Job Market Paper Last Updated: November, 2006 Abstract Recent emprcal studes have documented a puzzlng pattern of household savngs n the U.S.: households

More information

Section 5.4 Annuities, Present Value, and Amortization

Section 5.4 Annuities, Present Value, and Amortization Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today

More information

Addendum to: Importing Skill-Biased Technology

Addendum to: Importing Skill-Biased Technology Addendum to: Importng Skll-Based Technology Arel Bursten UCLA and NBER Javer Cravno UCLA August 202 Jonathan Vogel Columba and NBER Abstract Ths Addendum derves the results dscussed n secton 3.3 of our

More information

Returns to Experience in Mozambique: A Nonparametric Regression Approach

Returns to Experience in Mozambique: A Nonparametric Regression Approach Returns to Experence n Mozambque: A Nonparametrc Regresson Approach Joel Muzma Conference Paper nº 27 Conferênca Inaugural do IESE Desafos para a nvestgação socal e económca em Moçambque 19 de Setembro

More information

The impact of bank capital requirements on bank risk: an econometric puzzle and a proposed solution

The impact of bank capital requirements on bank risk: an econometric puzzle and a proposed solution Banks and Bank Systems, Volume 4, Issue 1, 009 Robert L. Porter (USA) The mpact of bank captal requrements on bank rsk: an econometrc puzzle and a proposed soluton Abstract The relatonshp between bank

More information

Start me up: The Effectiveness of a Self-Employment Programme for Needy Unemployed People in Germany*

Start me up: The Effectiveness of a Self-Employment Programme for Needy Unemployed People in Germany* Start me up: The Effectveness of a Self-Employment Programme for Needy Unemployed People n Germany* Joachm Wolff Anton Nvorozhkn Date: 22/10/2008 Abstract In recent years actvaton of means-tested unemployment

More information

Calculation of Sampling Weights

Calculation of Sampling Weights Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

More information

Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001

Proceedings of the Annual Meeting of the American Statistical Association, August 5-9, 2001 Proceedngs of the Annual Meetng of the Amercan Statstcal Assocaton, August 5-9, 2001 LIST-ASSISTED SAMPLING: THE EFFECT OF TELEPHONE SYSTEM CHANGES ON DESIGN 1 Clyde Tucker, Bureau of Labor Statstcs James

More information

What is Candidate Sampling

What is Candidate Sampling What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble

More information

Binomial Link Functions. Lori Murray, Phil Munz

Binomial Link Functions. Lori Murray, Phil Munz Bnomal Lnk Functons Lor Murray, Phl Munz Bnomal Lnk Functons Logt Lnk functon: ( p) p ln 1 p Probt Lnk functon: ( p) 1 ( p) Complentary Log Log functon: ( p) ln( ln(1 p)) Motvatng Example A researcher

More information

Small pots lump sum payment instruction

Small pots lump sum payment instruction For customers Small pots lump sum payment nstructon Please read these notes before completng ths nstructon About ths nstructon Use ths nstructon f you re an ndvdual wth Aegon Retrement Choces Self Invested

More information

Is There A Tradeoff between Employer-Provided Health Insurance and Wages?

Is There A Tradeoff between Employer-Provided Health Insurance and Wages? Is There A Tradeoff between Employer-Provded Health Insurance and Wages? Lye Zhu, Southern Methodst Unversty October 2005 Abstract Though most of the lterature n health nsurance and the labor market assumes

More information

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds

The impact of hard discount control mechanism on the discount volatility of UK closed-end funds Investment Management and Fnancal Innovatons, Volume 10, Issue 3, 2013 Ahmed F. Salhn (Egypt) The mpact of hard dscount control mechansm on the dscount volatlty of UK closed-end funds Abstract The mpact

More information

Activity Scheduling for Cost-Time Investment Optimization in Project Management

Activity Scheduling for Cost-Time Investment Optimization in Project Management PROJECT MANAGEMENT 4 th Internatonal Conference on Industral Engneerng and Industral Management XIV Congreso de Ingenería de Organzacón Donosta- San Sebastán, September 8 th -10 th 010 Actvty Schedulng

More information

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network 700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School

More information

How To Study The Nfluence Of Health Insurance On Swtchng

How To Study The Nfluence Of Health Insurance On Swtchng Workng Paper n 07-02 The nfluence of supplementary health nsurance on swtchng behavour: evdence on Swss data Brgtte Dormont, Perre- Yves Geoffard, Karne Lamraud The nfluence of supplementary health nsurance

More information

7.5. Present Value of an Annuity. Investigate

7.5. Present Value of an Annuity. Investigate 7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on

More information

Data Mining from the Information Systems: Performance Indicators at Masaryk University in Brno

Data Mining from the Information Systems: Performance Indicators at Masaryk University in Brno Data Mnng from the Informaton Systems: Performance Indcators at Masaryk Unversty n Brno Mkuláš Bek EUA Workshop Strasbourg, 1-2 December 2006 1 Locaton of Brno Brno EUA Workshop Strasbourg, 1-2 December

More information

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET

ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET ADVERSE SELECTION IN INSURANCE MARKETS: POLICYHOLDER EVIDENCE FROM THE U.K. ANNUITY MARKET Amy Fnkelsten Harvard Unversty and NBER James Poterba MIT and NBER Revsed May 2003 ABSTRACT In ths paper, we nvestgate

More information

Transition Matrix Models of Consumer Credit Ratings

Transition Matrix Models of Consumer Credit Ratings Transton Matrx Models of Consumer Credt Ratngs Abstract Although the corporate credt rsk lterature has many studes modellng the change n the credt rsk of corporate bonds over tme, there s far less analyss

More information

An Interest-Oriented Network Evolution Mechanism for Online Communities

An Interest-Oriented Network Evolution Mechanism for Online Communities An Interest-Orented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne

More information

Do Banks Use Private Information from Consumer Accounts? Evidence of Relationship Lending in Credit Card Interest Rate Heterogeneity

Do Banks Use Private Information from Consumer Accounts? Evidence of Relationship Lending in Credit Card Interest Rate Heterogeneity Do Banks Use Prvate Informaton from Consumer Accounts? Evdence of Relatonshp Lendng n Credt Card Interest Rate Heterogenety Sougata Kerr, Stephen Cosslett, Luca Dunn December, 2004 Author nformaton: Kerr,

More information

ADVERTISEMENT FOR THE POST OF DIRECTOR, lim TIRUCHIRAPPALLI

ADVERTISEMENT FOR THE POST OF DIRECTOR, lim TIRUCHIRAPPALLI ADVERTSEMENT FOR THE POST OF DRECTOR, lm TRUCHRAPPALL The ndan nsttute of Management Truchrappall (MT), establshed n 2011 n the regon of Taml Nadu s a leadng management school n nda. ts vson s "Preparng

More information

Forecasting and Stress Testing Credit Card Default using Dynamic Models

Forecasting and Stress Testing Credit Card Default using Dynamic Models Forecastng and Stress Testng Credt Card Default usng Dynamc Models Tony Bellott and Jonathan Crook Credt Research Centre Unversty of Ednburgh Busness School Verson 4.5 Abstract Typcally models of credt

More information

Stress test for measuring insurance risks in non-life insurance

Stress test for measuring insurance risks in non-life insurance PROMEMORIA Datum June 01 Fnansnspektonen Författare Bengt von Bahr, Younes Elonq and Erk Elvers Stress test for measurng nsurance rsks n non-lfe nsurance Summary Ths memo descrbes stress testng of nsurance

More information

The Willingness to Pay for Job Amenities: Evidence from Mothers' Return to Work

The Willingness to Pay for Job Amenities: Evidence from Mothers' Return to Work ILRRevew Volume 65 Number 2 Artcle 10 2012 The Wllngness to Pay for Job Amentes: Evdence from Mothers' Return to Chrstna Felfe Unversty of St. Gallen, chrstna.felfe@unsg.ch The Wllngness to Pay for Job

More information

Risk Model of Long-Term Production Scheduling in Open Pit Gold Mining

Risk Model of Long-Term Production Scheduling in Open Pit Gold Mining Rsk Model of Long-Term Producton Schedulng n Open Pt Gold Mnng R Halatchev 1 and P Lever 2 ABSTRACT Open pt gold mnng s an mportant sector of the Australan mnng ndustry. It uses large amounts of nvestments,

More information

Marginal Benefit Incidence Analysis Using a Single Cross-section of Data. Mohamed Ihsan Ajwad and Quentin Wodon 1. World Bank.

Marginal Benefit Incidence Analysis Using a Single Cross-section of Data. Mohamed Ihsan Ajwad and Quentin Wodon 1. World Bank. Margnal Beneft Incdence Analyss Usng a Sngle Cross-secton of Data Mohamed Ihsan Ajwad and uentn Wodon World Bank August 200 Abstract In a recent paper, Lanjouw and Ravallon proposed an attractve and smple

More information

Evaluating credit risk models: A critique and a new proposal

Evaluating credit risk models: A critique and a new proposal Evaluatng credt rsk models: A crtque and a new proposal Hergen Frerchs* Gunter Löffler Unversty of Frankfurt (Man) February 14, 2001 Abstract Evaluatng the qualty of credt portfolo rsk models s an mportant

More information

A Multistage Model of Loans and the Role of Relationships

A Multistage Model of Loans and the Role of Relationships A Multstage Model of Loans and the Role of Relatonshps Sugato Chakravarty, Purdue Unversty, and Tansel Ylmazer, Purdue Unversty Abstract The goal of ths paper s to further our understandng of how relatonshps

More information

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING

ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 610-519-4390,

More information

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation

Exhaustive Regression. An Exploration of Regression-Based Data Mining Techniques Using Super Computation Exhaustve Regresson An Exploraton of Regresson-Based Data Mnng Technques Usng Super Computaton Antony Daves, Ph.D. Assocate Professor of Economcs Duquesne Unversty Pttsburgh, PA 58 Research Fellow The

More information

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia To appear n Journal o Appled Probablty June 2007 O-COSTAT SUM RED-AD-BLACK GAMES WITH BET-DEPEDET WI PROBABILITY FUCTIO LAURA POTIGGIA, Unversty o the Scences n Phladelpha Abstract In ths paper we nvestgate

More information

Traffic-light a stress test for life insurance provisions

Traffic-light a stress test for life insurance provisions MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax

More information

Simple Interest Loans (Section 5.1) :

Simple Interest Loans (Section 5.1) : Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part

More information

IMPACT ANALYSIS OF A CELLULAR PHONE

IMPACT ANALYSIS OF A CELLULAR PHONE 4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng

More information

TESTING FOR EVIDENCE OF ADVERSE SELECTION IN DEVELOPING AUTOMOBILE INSURANCE MARKET. Oksana Lyashuk

TESTING FOR EVIDENCE OF ADVERSE SELECTION IN DEVELOPING AUTOMOBILE INSURANCE MARKET. Oksana Lyashuk TESTING FOR EVIDENCE OF ADVERSE SELECTION IN DEVELOPING AUTOMOBILE INSURANCE MARKET by Oksana Lyashuk A thess submtted n partal fulfllment of the requrements for the degree of Master of Arts n Economcs

More information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

Efficiency Test on Taiwan s Life Insurance Industry- Using X-Efficiency Approach

Efficiency Test on Taiwan s Life Insurance Industry- Using X-Efficiency Approach Informaton and Management Scences Volume 18, Number 1, pp. 37-48, 2007 Effcency Test on Tawan s Lfe Insurance Industry- Usng X-Effcency Approach James C. Hao Tamkang Unversty R.O.C. Abstract Usng twenty-three

More information

Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School

Robust Design of Public Storage Warehouses. Yeming (Yale) Gong EMLYON Business School Robust Desgn of Publc Storage Warehouses Yemng (Yale) Gong EMLYON Busness School Rene de Koster Rotterdam school of management, Erasmus Unversty Abstract We apply robust optmzaton and revenue management

More information

Survive Then Thrive: Determining Success in the Economics Ph.D. Program. Wayne A. Grove Le Moyne College, Economics Department

Survive Then Thrive: Determining Success in the Economics Ph.D. Program. Wayne A. Grove Le Moyne College, Economics Department Survve Then Thrve: Determnng Success n the Economcs Ph.D. Program Wayne A. Grove Le Moyne College, Economcs Department Donald H. Dutkowsky Syracuse Unversty, Economcs Department Andrew Grodner East Carolna

More information