Habibollah Salami; Ebrahim Ensan
Abstract
Non-Performing Loans (NPL) including overdue, deferred, and doubtful debts, are serious challenges facing banking systems in Iran. These three categories are not the same in risk ordering. The presence of doubtful debts creates the highest risk for the banks. Thus, specifying factors that can reduce ...
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Non-Performing Loans (NPL) including overdue, deferred, and doubtful debts, are serious challenges facing banking systems in Iran. These three categories are not the same in risk ordering. The presence of doubtful debts creates the highest risk for the banks. Thus, specifying factors that can reduce the share of this category in total NPL is very crucial. The main objective of present study is to differentiate factors affecting different classes of NPL based on their differences in reducing each of the debt categories. To this end, data on debtors of the Iranian Agricultural Bank (Bank Keshavarzi) over 2009-2013 period in two provinces of Tehran and Kermanshah were analyzed. Since the dependent variable in this study is an ordered one, the Partial Proportional Odds model, a special form of the Generalized Ordered Logit model, is utilized to specify factors affecting NPL and to distinguish their impacts on different debt categories. Results indicate that the loan receivers with dishonored cheques in their profile, most likely would not be able to repay their loan and will fall to the doubtful debts group. On the other hand, loan paid to the services and agricultural production activities compared with loan allocated to the non-agricultural activities, loans paid for investment comparing to loans paid for current operational expenses, and payments financed from bank’s own resources comparing with payments accommodated from legally designated resources are important factors that can decrease the probability of turning loans to the doubtful debts. Consequently, to reduce the share of the doubtful debts in total non-performing loans, banks need to focus on managing and controlling the above mentioned influencing factors.
