Project title: Nature and Extent of Agricultural Indebtedness in Different States of India using

                       Data Mining Techniques

Project leader: Dr. Rajni Jain

Project team: Dr. S. S. Raju, Dr. P.  A. Lakshmi Prasanna

Background:

The agricultural credit delivery in India suffers from several vexing problems. On one hand, the heavy indebtedness is allegedly a cause of agrarian distress, on the other, the low recovery rates and mounting overdues of institutional credit have clogged the process of recycling credit. This calls for an in-depth study on nature and extent of indebtedness in the country, identifying the socio-economic conditions of the indebted farm households and implications of recent Government initiatives (debt waiver and debt relief scheme) on credit delivery system and rural economy.

Objectives:

1.    To assess the incidence and extent of agricultural indebtedness in the country

2.    To study the nature and structure of agricultural indebtedness in the country

3.    To study the implications of the Debt waiver and relief  scheme

4.    To identify socio-economic factors for agricultural indebtedness

5.    To study the extent of productive use of loan taken for farm business and its diversion to other activities

6.    To examine the trends of agricultural indebtedness based on different NSSO rounds

Study area:

All-India state level using NSSO 59^th round data

Methodology:

1.   Regression Analysis

2.   Decision tree  and clustering based data mining

3.   Probit/Logit

Key findings:

1.    The genuine farmers are not benefitted because of spurious land size records in societies and banks.

2.    Loan waiver scheme is dampening recovery system.

3.    Banks and societies are not interested to facilitate loan to beneficiary of debt waiver

4.    Very low utilisation of debt relief one time settlement scheme among farm households in Karnal and Hisar in anticipation of complete debt waiver scheme in future.

Funding agency: NCAP