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Analysing social attributes of loan default among small Indian dairy farms: A discriminant approach
Author(s) -
Kumar Sinha Mukesh,
P. Dhaka J.,
Biswajit Mondal
Publication year - 2014
Publication title -
scientific research and essays
Language(s) - English
Resource type - Journals
ISSN - 1992-2248
DOI - 10.5897/sre2013.5796
Subject(s) - discriminant function analysis , linear discriminant analysis , loan , mathematics , multistage sampling , discriminant , agricultural science , statistics , business , computer science , artificial intelligence , environmental science , finance
The study examines the socio-economic factors discriminating defaulters and non-defaulters of credit repayment. Multi-stage sampling design was adopted for selection of farm respondents. The data were collected through structured questionnaire by personal interview method. A linear discriminant function considered to examine the relative importance of different factors in discriminating between non-defaulters and defaulters. The result revealed that per capita income from crop and milk production, expenditure to total income, earning adults and off-farm income explained major share in discriminating the non-defaulters from defaulters. The mean discriminant score for the non-defaulters (Z1) and defaulter (Z2) were found to be 0.316 and -1.322, respectively. The critical mean discriminant score (Z) for the two groups was found to be -0.503. The high value of Z corresponds to non-defaulter and low value to defaulter. Later the derived classification analysis was observed that 50 out of 83 defaulters and 32 out of 37 non-defaulters were rightly classified in Z function. Thus, grouped cases classified correctly as 68.33% as factors of default. Hence, the model is found to be valid to predict whether an unknown borrower is likely to be defaulter or non-defaulter more precisely. Key words: Discriminant function, credit, defaulter, dairy farmers.

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