International Journal of Agriculture Sciences ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 7, Issue 2, 2015, pp.-450-453. Available online at http://www.bioinfopublication.org/jouarchive.php?opt=&jouid=BPJ0000217 FACTORS INFLUENCING OVERDUES OF TRIBAL BORROWERS IN JHARKHAND STATE OF INDIA: A POLICY PERSPECTIVE LAKRA K.1*, KUSHWAHA S.2 AND SINGH H.P.1 1Department of Agricultural Economics, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi - 221 005, UP, India. 2Lalit Narayan Mithila University, Kameshwar Nagar, Darbhanga - 846 004, Bihar, India. *Corresponding Author: Email- [email protected] Received: April 09, 2015; Revised: May 18, 2015; Accepted: May 21, 2015 Abstract- Utilization and repayment of borrowed agricultural loans has been one of the numerous of agricultural development in the develop- ing world and India is no exception. As such, this study focused on to identify the factors which influence overdues of tribal borrowers in Jhar- khand State of India. A sample of 240 borrowers was selected by multistage sampling technique for analyzing the problem of overdues. In estimating problem of overdues of the borrowers, the linear discriminant function analysis was employed and results showed that income and farm experience made the most significant contributions to the discrimination to the tune of 39.38 per cent and 15.10 per cent respectively. Other socio economic factors that contributed to the discrimination between defaulters and non-defaulters were age (11.95 per cent) land holding size (9.99 per cent), irrigation potential (9.48 per cent), amount borrowed (9.10 per cent) and family size (5.40 per cent). On the other hand education had a negative contribution of -0.40 per cent and cropping intensity has no contribution to discrimination between defaulters and non-defaulters. Keywords- Agriculture loan, Overdues, Tribal borrowers, Discriminant function analysis, Defaulter and non-defaulter Citation: Lakra K., Kushwaha S. and Singh H.P. (2015) Factors Influencing Overdues of Tribal Borrowers in Jharkhand State of India: a Policy Perspective. International Journal of Agriculture Sciences, ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 7, Issue 2, pp.-450-453. Copyright: Copyright©2015 Lakra K., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. Introduction lishment. During the year 2012-13, demand for agriculture credit It is an accepted fact that agricultural growth is required for overall was Rs. 16.66 billion and recovered only Rs. 10.32 billion means economic development in a country like India. Agricultural growth there are 32 per cent overdues in the state. Since the efficiency and depends on the growth of productivity that in turn requires sustained effectiveness of the flow of credit depends on the delivery mecha- infusion of capital. Adequate and timely availability of institutional nism at the beneficiaries level a sound rural credit system shall credit plays a pivotal role in agricultural development, particularly in advance the right quantum of credit to meet the working capital enhancing its productivity and improving the living standard of the requirements of the borrowers at right time, so that it is utilized peasant communities. It accelerates the pace of agricultural devel- properly in the production process so as to generate a surplus, opment if it is adequate in quantity, cheap, and timely made availa- which will be utilized to repay the principal plus interest amount. ble [1, 2, 3]. It enables the peasants to undertake new investments The study aims to identify the factors which influence overdues of and/or use of modern agricultural technologies to boost up agricul- the borrowers and suggest suitable policy measures. tural production [4]. The total agriculture credit of all banks together Methodology in the Jharkhand is Rs. 69.63 billion which constitutes 13.33 per cent of the gross credit during the year 2012-13 [5]. Agriculture Sampling Procedure credit in the state is lower than the national bench mark of 18 per A multistage sampling technique was used in the selection of dis- cent. However, it is showing an increasing trend over the years. tricts, blocks, villages and borrowers. The study was conducted in Although the growth in the flow of agricultural credit is increasing in Jharkhand state which was purposively selected because the re- Jharkhand, the state is also not free from the sicknesses that have searcher has knowledge and he is familiar with the locality. Jhar- afflicted the institutional credit agencies in the India as a whole. khand state consists of 24 districts out of which one-third were se- Agricultural credit is not only an issue of quantity but of quality also. lected purposively on the basis of higher percentage of tribal popu- The increased availability of institutional credit has given rise to lation as per population census 2001 of India. These districts were: many related problems such as inadequacy, misutilization, lack of Simdega (72.45 per cent), Khunti (72.39 per cent), Gumla (69.75 timeliness in credit delivery etc. which directly or indirectly have a per cent), West Singhbhum (66.41 per cent), Lohardaga (56.41 per bearing on the repayment of the loan and therefore the overdues cent), Pakur (49.32 per cent), Dumka (46.62 per cent) and Latehar levels. The overdues of agriculture sector advances of institutional (45.30 per cent). One block was randomly selected from each dis- credit agencies have been mounting in Jharkhand from its estab- trict. Thus total numbers of 8 blocks i.e., Thethaitangar from Sim- International Journal of Agriculture Sciences ISSN: 0975-3710 & E-ISSN: 0975-9107, Volume 7, Issue 2, 2015 || Bioinfo Publications || 450 Factors Influencing Overdues of Tribal Borrowers in Jharkhand State of India: a Policy Perspective dega district, Khunti from Khunti district, Bharno from Gumla district, of the estimated Discriminant function was tested with the help of Sadar Chaibasa from West Singhbhum district, Bhandra from Lo- Fisher’s ‘F’ test is calculated as: hardaga district, Pakuria from Pakur district, Dumka from Dumka R 2 (n p 1) F district and Latehar from Latehar district were randomly selected. A (1 R 2 )( p) complete list of villages involved in bank borrowing in the sample (n n ) blocks was prepared and two villages were randomly selected from R 2 1 2 (n1 n2 ) each block and a total number of 16 villages namely Joram and S 1 Jampani from Thethaitangar block, Lamlum and Ramrong from X X X Khunti block, Bharno and Turiamba from Bharno block, Mesenda S 1 2 2 X X X and Tonda from Sadar Chaibasa, Bhandra and Masmano from 1 2 2 Bhandra block, Paliadaha and Khasa from Pakuria block, Jhitki and X 11 X 12 Sarsaria from Dumka block and Pasu and Arahara from Latehar X 21 X 22 block were selected. List of borrowers were obtained from banks where, and a total number of 15 borrowers were selected randomly from p = Number of variables considered in the function each village. Out of 15 borrowers, 10 borrowers were those who n1= Number of non-defaulters/non-willful defaulters were paying their loan and 5 borrowers were those who were in the category of defaulter. Thus, altogether 240 borrowers were finally n2= Number of defaulters/willful defaulters selected from 16 villages. The value of ‘F’ is tested at p and (n1+ n2- p-1) degree of freedom. Analytical Techniques Results and Discussion Discriminant Function Analysis Borrowers have been categorized as defaulters and non-defaulters In order to measure the net effect of each variable in this analysis in on the basis of loan repayment by the borrowers. The defaulters are all other variables were taken as constant by using discriminant those who have not repaid their loans and non-defaulters are those function approach. The relative importance of the study in regards who repaid their loan. Nine factors namely age, education, family to their power to discriminate between the groups of non-defaulters size, experience, land holding size, amount borrowed, cropping and defaulters and further between the non-willful and willful de- intensity and irrigation potential were taken to discriminate between faulters [6]. The general model used for the present study is as defaulters and non-defaulters. Out of 240 borrowers, 60 and 180 follows: borrowers were identified as defaulters and non-defaulters respec- tively. Z = y1X1 + y2X2 + y3X3 + y4X4 + y5X5 + y6X6 + y7X7 + y8X8 + y9X9 In estimating the problem of overdues of the borrowers, linear dis- where, criminant function analysis was employed and the result presented in [Table-1]. It could be observed that the variables made varied Z = Total discriminant score for defaulters and non-defaulters contribution to the loan repayment performance. Age, education, X1 = Age of the borrower in year family size, farming experience, income of the borrower and land X2 = Education (Scored 0 for illiterate, 1 for 1st– 5th class, 2 for 6th– holding size were statistically significant at 1 per cent level and 10th class, 3 for 11th– 12th class and 4 for graduation and above) amount borrowed and irrigation potential both were statistically significant at 10 per cent level. Cropping intensity showed insignifi- X3 = Family size in numbers cant result means there was no role of cropping intensity in the X4 = Farming experience in years problem of overdue. X5