Abstract
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.
Highlights
Animal husbandry in India play an important role in national economy and socio-economic development
Expanding the availability of agricultural credit has been widely used as a policy to accelerate agricultural and rural development (World Bank, 2000)
The discriminant function was constructed by choosing the value of Ik in such a way that the ratio was equal to variation of 'Z' between groups of defaulters and non-defaulters divided by variation ofZ' within the groups of defaulters and non-defaulters, was the maximum
Summary
Animal husbandry in India play an important role in national economy and socio-economic development. Its contribution to agricultural gross domestic product (GDP) is 24.8% at current price (GOI, 2012-2013) and supports the livelihood of over 200 million rural poor (World Bank, 1999). It generates continuous stream of income and employment (Nargunde, 2013; Sinha et al, 2012; Enoma, 2010) and supports to reduce seasonality in livelihood patterns (Birthal and Ali, 2005) due to its more egalitarian distribution compared to land (Ahuja et al, 2000). Farm credit and sponsored programmes is an important intervention to address the issue of rural poverty among smallholder and landless farmers (Meyer and Nagarajan, 2000). Many efforts have been made and a continuous search for sustainable interventions through appropriate credit schemes is being conducted to improve the living conditions and quality of
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