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Analysing social attributes of loan default among small Indian dairy farms: A discriminant approach

机译:分析印度小型奶牛场贷款违约的社会属性:一种判别方法

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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.
机译:该研究检查了社会经济因素,以区别信贷偿还的违约者和非违约者。采用多阶段抽样设计来选择农场受访者。数据采用个人访谈的方法通过结构化问卷收集。一种线性判别函数,用于检查在区分非违约者和违约者时不同因素的相对重要性。结果表明,来自农作物和牛奶生产的人均收入,支出占总收入,成年收入和非农业收入的原因,是将非违约者与违约者区分开的主要原因。发现非默认值(Z1)和默认值(Z2)的平均判别分数分别为0.316和-1.322。两组的临界平均判别分数(Z)为-0.503。 Z的高值对应于非默认值,而低值对应于默认值。后来发现派生的分类分析发现,在Z函数中正确地对83个违约者中的50个和37个非违约者中的32个进行了正确分类。因此,分组案例正确地作为违约因素分类为68.33%。因此,发现该模型可以有效地预测未知借款人更精确地是违约者还是非违约者。

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