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H. Varian (1989)
MONITORING AGENTS WITH OTHER AGENTS
A. Hollis, Arthur Sweetman (1998)
Microcredit: What Can We Learn from the Past?World Development, 26
Maitreesh Ghatak (2000)
Screening by the Company You Keep: Joint Liability Lending and the Peer Selection EffectThe Economic Journal, 110
N. Hermes, R. Lensink, Habteab Teki (2003)
Peer monitoring, social ties and moral hazard in group lending programmes: evidence from EritreaThe annual research report
C. Turvey, R. Kong (2010)
Informal lending amongst friends and relatives: Can microcredit compete in rural China?China Economic Review, 21
Alessandra Cassar, Bruce Wydick (2010)
Does social capital matter? Evidence from a five-country group lending experiment, 62
E. Tassel (1999)
Group lending under asymmetric informationJournal of Development Economics, 60
Jaclyn Kropp, C. Turvey, D. Just, R. Kong, Pei Guo (2009)
Are the poor really more trustworthy? A micro‐lending experimentAgricultural Finance Review, 69
C. Turvey, R. Kong (2009)
Business and financial risks of small farm households in ChinaChina Agricultural Economic Review, 1
C. Turvey, R. Kong, Xuexi Huo (2010)
Borrowing amongst friends: the economics of informal credit in rural ChinaChina Agricultural Economic Review, 2
K. Hoff, J. Stiglitz (1990)
Introduction: imperfect information and rural credit markets - puzzles and policy perspectives.The World Bank Economic Review, 4
A. Mude (2006)
Making loans to make friends: explaining the dismal financial performance of financial service associationsAgricultural Finance Review, 66
Minggao Shen, Jikun Huang, Linxiu Zhang, S. Rozelle (2010)
Financial reform and transition in China: a study of the evolution of banks in rural ChinaAgricultural Finance Review, 70
Maitreesh Ghatak (1999)
Group lending, local information and peer selectionJournal of Development Economics, 60
Purpose – Based on a survey of 897 farm households, the purpose of this paper is to build a framework using cluster analysis to explain how farmers make decisions on joining group guarantee, and analyzes factors influencing their decisions using multinomial and binary Logit regressions. Design/methodology/approach – The approach of combining cluster analysis with Logit regression is an innovative approach to survey assessment. In addition, by design the authors have identified the four mutually exclusive groups of borrowers combining Group Guarantee membership and actual formal borrowing. Findings – An extremely important observation according to the data is that most farmers appear to be part of group guarantees only because they have to in order to get access to formal credit products. 87.21 percent of the people who belong to groups and utilize the formal credit products belong to this category because their lenders have made participation in groups compulsory for access to credit. This may ration farmers’ willingness to even apply for credit. It also indicates a preference on the part of older and more risk-averse respondents to avoid participation in group guarantees. Out of financial characteristics the total loan holdings appears to be the only significant indicator of participation in group guarantees. Furthermore the results indicate that informal and formal credit appear to be replaceable for farmers. Research limitations/implications – The survey is confined only to the counties investigated. China is very diverse in its agricultural economies and many RCCs operate under different guidance and rules from those investigated here. Hence, while the authors can claim that the results are indicative, the authors cannot claim that they will hold generally. Practical implications – Based on group guarantee loan mechanism and survey data analysis of 897 farm households, this paper analyzes influencing factors affecting farmers’ participation in group guarantees from microcosmic level, so as to provide some reference to further perfect micro credit operation mode and mechanism. Social implications – The results indicate that the Group Guarantee mechanism, while beneficial to some, may not hold global appeal for Chinese farmers. In the future RCCs may want to consider alternative approaches to loan security than placing the burden of guarantee on farmers’ family and friends. Originality/value – The approach of combining cluster analysis with Logit regression is an innovative approach to survey assessment. In addition, by design the authors have identified the four mutually exclusive groups of borrowers combining Group Guarantee membership and actual formal borrowing.
China Agricultural Economic Review – Emerald Publishing
Published: Feb 2, 2015
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