Analysing social attributes of loan default among small Indian Dairy farms: A discriminating approach
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.
|Date of creation:||04 Dec 2013|
|Date of revision:||20 Jan 2014|
|Publication status:||Published in Scientific Research and Essay 2.9(2014): pp. 2354-2358|
|Contact details of provider:|| Postal: Ludwigstraße 33, D-80539 Munich, Germany|
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- S. Gandhimathi, 2012. "Determinants of repayment and overdues in agricultural sector," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 4(5), pages 590-602.
- Arindam Bandyopadhyay, 2006. "Predicting probability of default of Indian corporate bonds: logistic and Z-score model approaches," Journal of Risk Finance, Emerald Group Publishing, vol. 7(3), pages 255-272, May.
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